pax_global_header00006660000000000000000000000064124123612520014510gustar00rootroot0000000000000052 comment=77168321c3396fe24f95630ec1b913fd5ab622e3 garli-2.1-release/000077500000000000000000000000001241236125200140665ustar00rootroot00000000000000garli-2.1-release/COPYING000066400000000000000000000773311241236125200151340ustar00rootroot00000000000000 GNU GENERAL PUBLIC LICENSE Version 3, 29 June 2007 Copyright (C) 2007 Free Software Foundation, Inc. Everyone is permitted to copy and distribute verbatim copies of this license document, but changing it is not allowed. Preamble The GNU General Public License is a free, copyleft license for software and other kinds of works. The licenses for most software and other practical works are designed to take away your freedom to share and change the works. By contrast, the GNU General Public License is intended to guarantee your freedom to share and change all versions of a program--to make sure it remains free software for all its users. 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If the disclaimer of warranty and limitation of liability provided above cannot be given local legal effect according to their terms, reviewing courts shall apply local law that most closely approximates an absolute waiver of all civil liability in connection with the Program, unless a warranty or assumption of liability accompanies a copy of the Program in return for a fee. END OF TERMS AND CONDITIONS garli-2.1-release/INSTALL000066400000000000000000000135271241236125200151270ustar00rootroot00000000000000GARLI COMPILATION/INSTALLATION INSTRUCTIONS: NOTE: For Windows and Mac OS X machines you should not need to compile the program manually, and it is recommended that you download one of the pre-compiled binaries. (If after reviewing this document you still have unsolved compilation problems, please email garli.support@gmail.com) ------ EXTRA EASY COMPILATION INSTRUCTIONS (USE OF THIS IS RECOMMENDED!): If this is a version with the NCL source code bundled (there will be an ncl-2.1.xx.tar.gz file in the same directory as this file), then you should be able to very easily do a default compilation and static linking to NCL by typing this in the same directory as this file: ./build_garli.sh This will do all of the necessary building of the NCL library, and you should eventually end up with a working Garli-#.# executable in the bin directory within the current directory. You are done. See the QuickStart.txt file, manual or support website for details on running the program. ->If you are doing an MPI build, use the Advanced instructions below. ->If you have a newer version of NCL that you want to use, just put the ncl-xxxx.tar.gz archive within this directory and the build_garli script will find and use it. ->If you want to check out the newest version of NCL (which could have bug fixes) from Sourceforge and build GARLI with it, do this: ./build_garli.sh --ncl-svn ->If you know what you are doing and want to use a different compiler (such as the intel icc compiler) instead of gcc you can invoke the build_garli.sh script like this: env CC=icc CXX=icpc ./build_garli.sh ->If you want to pass other arguments to GARLI's configure script, (you have no particular reason to do so) list them at the end, e.g. ./build_garli.sh [--ncl-svn] --enable-asserts ------ MANUAL/ADVANCED COMPILATION INSTRUCTIONS: (use this if the above simplified build script does work for you or doesn't meet your needs) ####BUILDING NCL#### To make and install GARLI, you first need to have a compiled copy of the Nexus Class Library version 2.1 by Paul Lewis and Mark Holder. The newest version of NCL 2.1 is available here: http://github.com/mtholder/ncl and can be checked out via anonymous svn or git: svn co https://github.com/mtholder/ncl or git clone https://github.com/mtholder/ncl Use of the trunk github version of NCL is recommended, but a slightly older version is also available here: http://sourceforge.net/projects/ncl/files/NCL/ VERY IMPORTANT: -NCL version 2.1 or better is required to compile GARLI. -You MUST run NCL's configure script with NCL_CONST_FUNCS defined. See below for how to do this ------ You can choose to compile GARLI with the NCL library either statically or dynamically linked in. Note that whether GARLI will link it statically or dynamically depends on how NCL itself is configured when it is built. TWO OPTIONS: -To STATICALLY link: Do this if you are compiling GARLI for your own use but are not a system administrator, or if want the GARLI executable to run on another machine of the same type. It will not require the NCL library to remain on the system after compilation of GARLI. Configure and build NCL like this (the shared NCL library will not be made): (from the NCL source root directory) env CPPFLAGS=-DNCL_CONST_FUNCS ./configure --prefix= --disable-shared make make install -To DYNAMICALLY link: You only really want to do this if you are an admin on the machine and are going to install NCL globally (e.g. in /usr/local/), or if you want to install it elsewhere and are the only one that will be using it on that machine. In those cases, configure and build as shown below (you can omit the prefix if you want to just install globally into the normal location at /usr/local/): (from the NCL source root directory) env CPPFLAGS=-DNCL_CONST_FUNCS ./configure --prefix= make make install ####BUILDING GARLI#### Now that NCL is built, it is time to configure and build GARLI . You will call GARLI's configure script as follows: (if you installed NCL globally at /usr/local, you can leave off the --with-ncl= argument): (from GARLI's source root directory) ./configure --prefix= --with-ncl= make make install To run the program once you've compiled the executable, you'll need to use a configuration file. Sample files and test datasets are provided in the example directory. ------ Other configuration notes: To use a compiler different from the default (for example the Intel icc compiler), call configure like this: env CC=icc CXX=icpc ./configure ... etc You can similarly pass extra CXXFLAGS or LDFLAGS: env CXXFLAGS= LDFLAGS= ./configure ... etc ------ Making an OS X Universal binary (this may not work with newer versions of OS X): To do this you first need to build the NCL static library with multiple architectures. Configure NCL like this (all on one line): env CXXFLAGS="-arch ppc -arch i386 -DNCL_CONST_FUNCS" LDFLAGS="-arch ppc -arch i386" \ ./configure --prefix= --disable-dependency-tracking --disable-shared Then configure GARLI like this (all on one line): env CXXFLAGS="-arch ppc -arch i386" LDFLAGS="-arch ppc -arch i386" \ ./configure --prefix= --disable-dependency-tracking \ --with-ncl= --- Other options that can be passed to configure: --enable-openmp (build the multithreaded openMP version - available with the proprietary Intel compiler, (CC=icc and CXX=icpc), or newer versions of gcc --enable-mpi (build the MPI run distributing version. It should automatically determine the correct MPI compiler script name (e.g. mpiCC) but if the underlying compiler is not gcc (try "mpiCC --version" to check) you should also set CC and CXX to the corresponding compiler type (e.g., CC=icc and CXX=icpc) to get the right compilation flags) --help (list other options of the configure script) garli-2.1-release/Makefile.am000066400000000000000000000010201241236125200161130ustar00rootroot00000000000000ACLOCAL_AMFLAGS = -I config/m4 EXTRA_DIST = \ build_garli.sh \ README.txt \ QuickStart.txt \ example \ project/standardGarliVC \ doc \ tests\ ncl-2.1.19.tar.gz SUBDIRS = src tests dist-hook: find "$(distdir)/doc" -depth -name .svn -and -type d -and -exec rm -rf {} \; find "$(distdir)/project" -depth -name .svn -and -type d -and -exec rm -rf {} \; find "$(distdir)/example" -depth -name .svn -and -type d -and -exec rm -rf {} \; find "$(distdir)/tests" -depth -name .svn -and -type d -and -exec rm -rf {} \; garli-2.1-release/QuickStart.txt000066400000000000000000000043251241236125200167250ustar00rootroot00000000000000 A very short guide to using non-graphical versions of GARLI: 1. Move or copy the GARLI executable (in the bin directory for precompiled versions) into the same directory as a GARLI configuration file named garli.conf and a dataset to be analyzed (in Nexus, Pylip or Fasta format). 2. Edit the garli.conf file in a text editor and enter the name of the dataset to be analyzed on the datafname line. The other lines in the configuration file can be ignored for now. 3. On Windows you can then double-click the executable. On other OS's you will need to start the program from the command line. In a terminal window, get to the directory with the files in it. Start the program with ./GarliXXX where the XXX will depend on the exact version of the program. 4. Look through the output files that have been created. They contain a variety of information about the search itself as well as the details of the inferred tree and model of substition. To try it out on the included sample dataset, just copy the program and execute it from the example/basic/ directory, without needing any other setup. This reads the garli.conf file in that directory, which is set up to analyze the included 64 taxon rana.nex dataset (this should take between 10 and 30 minutes, depending on the speed of your computer). You can also find sample configuration files that you can alter for your data in the example/basic directory. Configuration of partitioned models is necessarily more complex, but examples and template configuration files appear in the example/partition directory. See the support wiki listed below for lots more information. You can specify configuration files to use that are not named garli.conf by passing them on the command line: ./GarliXXX myconfigurationfilename To avoid having to copy the executable into each run directory you can either put it somewhere in your path (OS X or linux), make a shortcut in the directory (Windows) or make a symbolic to the executable in the directory (OS X or linux) For further details, see the GARLI Manual or visit the Garli Wiki support webpage: http://www.nescent.org/wg/garli Have fun, and let me know of any questions, problems or feedback: Derrick Zwickl garli.support@gmail.com garli-2.1-release/README.txt000066400000000000000000000227021241236125200155670ustar00rootroot00000000000000// GARLI Version 2.1 (September 2014) // Copyright 2005-2014 Derrick J. Zwickl // email: garli.support@gmail.com // // This program is free software: you can redistribute it and/or modify // it under the terms of the GNU General Public License as published by // the Free Software Foundation, either version 3 of the License, or // (at your option) any later version. // // This program is distributed in the hope that it will be useful, // but WITHOUT ANY WARRANTY; without even the implied warranty of // MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the // GNU General Public License for more details. // // You should have received a copy of the GNU General Public License // along with this program. If not, see . Please let me know of any problems, concerns or feedback (garli.support@gmail.com) GARLI version 2.1 is a minor update to version 2.0. Versions >= 2.0 include models for nucleotides, amino acids, odons, and morphology-like characters, any of which can be mixed together and applied to different subsets of data. Version 2.X should replace earlier versions, and should be backwards compatible with all configuration files and datasets that were used with the previous versions. ->See the support website (http://www.nescent.org/wg/garli) for detailed information on using the program. For very basic usage see QuickStart.txt. ->Example datasets and template configuration files files can be found in the example folder ->For compilation help see the INSTALL file. Versions >= 2.0 comes with an extremely easy build script, build_garli.sh, which should make compalation trivial on linux or OS X machines. ***New in version 2.1*** 1. MUCH faster parsing of very long alignments. e.g., alignments millions of nucleotides in length should be read thousands of times faster. 2. Lower memory usage with very large alignments. 3. Added ignorestopcodons entry to ignore stops rather than error out. 4. Better translation to amino acid characters in codon-aminoacid models. Codons containing ambiguity are no longer discarded if all resolutions of the ambiguity translate to the same amino acid. 5. Ability to read and use NEXUS wtsets to specify character counts. 6. Many changes in BOINC mode. 7. Fixes to minor/rare bugs. ***New in version 2.0*** 1. Ability to use partitioned models, giving the ability to divide up data and apply independent models to each. See this page for details on partitioned usage http://www.nescent.org/wg/garli/Using_partitioned_models 2. Ability to use the Mk/Mkv "morphology" models of Lewis, 2001. This can be applied to discrete data of any type with any number of states. http://www.nescent.org/wg/garli/Mkv_morphology_model 3. Significant improvement in parameter optimization 4. MANY minor improvements and new features. See the website. ***New in version 1.0*** 1. Ability to write sitewise log-likelihood values for all model types, in a format identical to PAUP*. This can be read directly into a program like CONSEL to perform statistical comparisons of topologies such as the SH or AU tests. (outputsitelikelihoods = 1) 2. Ability to collapse zero length branches at the end of any search, creating polytomies. This is now turned on by default. This setting can affect bootstrap values, since zero-length branches in a sense don't exist and probably shouldn't contribute to branch support values. It also tends to make trees inferred from multiple searches more similar, since trees that only differ in arbitrary resolutions of zero length branches are not truly different. (collapsebranches = 1) 3. Ability to infer full reversible amino acid rate matrices while doing a normal searching, adding 189 free parameters. This is probably not something of general utility unless you have a very large dataset. (datatype = aminoacid or codon-aminoacid, ratematrix = estimate) 4. Ability to use user-specified amino acid rate matrices. This allows the use of any existing amino acid matrix, regardless of whether GARLI implements them internally. Amino acid matrices estimated by GARLI can also have their parameter values fixed for use in other analyses. Note however that GARLI's matrix input format differs from other programs. (ratematrix = fixed, provide matrix in a Nexus GARLI block in the datafile or in a starting conditions file. See the file "examples/LGmodel.mod" for an example.) 5. Ability to infer internal state probabilities (ancestral states) for amino acid and codon models, in addition to the previously implemented nucleotide models. (inferinternalstateprobs = 1) 6. Substantial speed improvements for large constrained searches, especially backbone constraints 7. MPI parallel runs can now be checkpointed, allowing entire sets of runs to be restarted. Be sure to read the wiki page detailing the MPI version (http://www.nescent.org/wg/garli/MPI_version) to understand in what cases you might want to use this version. 8. More rigorous error checking of input trees, constraints and parameter values. 9. Significant improvements to the precision of parameter optimization. GARLI now puts significant effort into returning the very most optimal parameter values at the end of a search. These should be as accurate as values returned by other programs such as PAUP* or PAML. Previously the estimated parameter values were nearly optimal, but sometimes not quite there. 10. A "verification mode", which checks that a given configuration file and datafile are valid for use with GARLI, without starting an actual analysis. This can be useful, for example, in verifying that all configuration and input is proper while on your local machine before sending the input files to a computer cluster. The output will also tell you how much memory GARLI will be need to be allocated for the run, which might require adjustment of the "availablememory" setting in the configuration file (start GARLI with "-V"). 11. Much easier procedure for compiling of GARLI source code. 12. Fixes to numerous rare bugs in version 0.96 ***New in version 0.96*** 1.Rigorous reading of Nexus datasets using Paul Lewis and Mark Holder's Nexus Class Library (NCL). 2.Ability to read Nexus starting trees using NCL. 3.Ability to perform inference under amino acid and codon-based models of sequence evolution (datatype = aminoacid, datatype = codon). 4.Ability to specify multiple search replicates in a single config file (searchreps = #). 5.Ability to specify outgroups for orientation of inferred trees (outgroup = # # #). 6.Ability to use backbone as well as normal topological constraints. 7.Ability to create fast likelihood stepwise addition starting trees (streefname = stepwise). 8.MPI version that spreads a specified number of serial runs across processors using a single config file, writing output to different output files (for example, to do 25 bootstrap replicates simultaneously on each of 8 processors). 9.Ability to perform nucleotide inference using any sub-model of the General Time-Reversible model (GTR), in addition to all of the common "named" models (K2P, HKY, etc). 10.Speed increases for non-parametric bootstrapping Condensed summary of new model settings (for more detailed descriptions and for unchanged settings see the manual or support webpage): datatype = {nucleotide, aminoacid, codon, codon-aminoacid} - These set the type of model to be used. Note that aminoacid and codon models are MUCH slower than nucleotide models. -"aminoacid" is for datasets consisting of the 20 aminoacid single letter codes. -"codon" is for dna data (aligned in frame!) to be analyzed using a 60-62 state model that incorporates both the nucleotide substitution process and information on the genetic code. This involves the estimation of at least one dN/dS ratio (aka nonsynonymous/synonymous rate ratio, or omega or w). This is essentially the Goldman-Yang 1994 model and other related models. -"codon-aminoacid" is for aligned dna sequences that are translated to aminoacids and analyzed under an aminoacid model The different datatypes have different allowable model settings, listed here. For nucleotide data: ratematrix = {6rate, 2rate, 1rate, fixed, (a b c d e f) } statefrequencies = {estimate, empirical, equal, fixed} ratehetmodel = {none, gamma, gammafixed} numratecategories = {#} (not including invariant site class, must be 1 for ratehetmodel = none) invariantsites = {estimate, none} For aminoacid or codon-aminoacid data: ratematrix = {poisson, dayhoff, jones, wag, mtmam, mtrev} statefequencies = {equal, dayhoff, jones, wag, mtmam, mtrev, empirical, estimate} ratehetmodel = {none, gamma, gammafixed} numratecategories = {#} (not including invariant site class, must be 1 for ratehetmodel = none) invariantsites = {estimate, none, fixed} For codon data: ratematrix = {6rate, 2rate, 1rate, fixed, (a b c d e f) } (this is the nucleotide substitution process assumed by the codon model) statefrequencies = {empirical, equal, F1x4, F3x4} (F1x4 and F3x4 are PAML's terminology, and calculate the codon frequencies as the product of the total nucleotide frequencies or the nucleotide frequencies at each codon position, respectively) ratehetmodel = {none, nonsynonymous} {nonsynonymous estimates multiple dN/dS categories at the proportion of sites belonging to each. This is the M3 model of PAML) numratecategories = {#} (the number of dN/dS categories) invariantsites = {none} (not allowed in codon model) For codon or codon-aminoacid: Geneticcode = {standard, vertmito, invertmito} garli-2.1-release/bootstrap.sh000077500000000000000000000006511241236125200164440ustar00rootroot00000000000000#!/bin/sh # This script should be run whenever the inputs to autoconf/automake change # (namely the configure.ac file or the m4 macros in config/m4) # It appears that the autogenerated Makefiles enough contain dependency # information to know that it needs to run automake and config.status # whenever an Makefile.am changes. set -x aclocal -I config || exit autoheader || exit automake --add-missing || exit autoconf garli-2.1-release/build_garli.sh000077500000000000000000000066621241236125200167140ustar00rootroot00000000000000#!/bin/sh if [ $# -gt 0 ] then if [ "$1" = "--ncl-svn" -o "$1" = "--ncl-sourceforge" ] #if [ "$1" = "--ncl-svn" ] then if [ -d ncl-svn ] then echo "***NCL LIBRARY SOURCE FROM SUBVERSION ALREADY EXISTS***" echo "***CURRENT COPY WILL BE USED AS-IS. UPDATE IT MANUALLY OR***" echo "***DELETE THE ncl-svn DIRECTORY TO GET THE LATEST NCL SOURCE***" else echo "***CHECKING OUT NCL LIBRARY SOURCE VIA SUBVERSION***" if [ "$1" = "--ncl-svn" ];then svn co https://github.com/mtholder/ncl/trunk ncl-svn || exit else svn co http://svn.code.sf.net/p/ncl/code/branches/v2.1 ncl-svn || exit fi fi shift # this shifts the first cmd line argument out so that the rest can be passed to GARLI configure nclv="ncl-svn" cd ${nclv} || exit sh bootstrap.sh || exit cd .. elif [ "$1" = "-h" ] || [ "$1" = "--help" ] then echo "Usage ./$0 [--svn-ncl] [-h] [arguments to GALRI configure script]" echo " --ncl-svn Check out current NCL v2.1 source from github repo via anonymous svn, build NCL, then GARLI" echo " (prefer over ncl-sourceforge)" echo " --ncl-sourceforge Check out current NCL v2.1 source from sourceforge repo via anonymous svn," echo " build NCL, then GARLI" echo " --ncl-dist Automatically build NCL from a ncl-2.1.xx.tar.gz distribution" echo " in this directory, then build GARLI (default)" echo " -h --help Output this help and exit" echo " [other args] Other arguments are passed to GARLI's configure invocation" echo exit fi fi #if NCL wasn't checked out above if [ -z "${nclv}" ] then if [ "$1" = "--ncl-dist" ] then shift # this shifts the first cmd line argument out so that the rest can be passed to GARLI configure fi echo "***BUILDING NCL LIBRARY FROM SOURCE DISTRIBUTION***" nl=`ls -l ncl*.gz | wc -l` if [ $nl -eq 0 ] then echo "ERROR: No ncl-2.1.xx.tar.gz distributions found." echo " Provide one or try \"$0 --ncl-svn\" to checkout NCL via subversion and automatically build NCL and GARLI." exit elif [ ! $nl -eq 1 ] then echo "You have more than one NCL version..." nclv=`ls ncl*.gz | tail -n1 | sed 's/.tar.gz//'` echo "Using most recent: $nclv" else nclv=`ls ncl*.gz | sed 's/.tar.gz//'` fi if [ ! -d ${nclv} ] then tar xfvz ${nclv}.tar.gz || exit fi fi cd ${nclv} || exit echo "CONFIGURING NCL ..." env CXXFLAGS=-DNCL_CONST_FUNCS ./configure --prefix=`pwd`/installed --disable-shared --enable-static || exit make || exit echo "BUILDING NCL ..." make install || exit #make installcheck || exit cd .. echo "CONFIGURING GARLI ..." if [ ! -f configure ] then if [ -f bootstrap.sh ] then sh bootstrap.sh || exit else echo "Neither configure nor bootstrap.sh found. This is not a complete distribution." fi fi ./configure $@ --prefix=`pwd` --with-ncl=`pwd`/${nclv}/installed || exit echo "BUILDING GARLI ..." make || exit make install || exit cp ${nclv}/example/gapcode/NEXUSgapcode bin/ garli-2.1-release/config.h.in000066400000000000000000000062761241236125200161240ustar00rootroot00000000000000/* config.h.in. Generated from configure.ac by autoheader. */ /* profiler for assessing execution times */ #undef ENABLE_CUSTOM_PROFILER /* Define to 1 if you have the header file. */ #undef HAVE_FLOAT_H /* Define to 1 if you have the `floor' function. */ #undef HAVE_FLOOR /* Define to 1 if you have the header file. */ #undef HAVE_INTTYPES_H /* Define to 1 if your system has a GNU libc compatible `malloc' function, and to 0 otherwise. */ #undef HAVE_MALLOC /* Define to 1 if you have the header file. */ #undef HAVE_MALLOC_H /* Define to 1 if you have the `memmove' function. */ #undef HAVE_MEMMOVE /* Define to 1 if you have the header file. */ #undef HAVE_MEMORY_H /* Define to 1 if you have the `memset' function. */ #undef HAVE_MEMSET /* Define if you have the MPI library. */ #undef HAVE_MPI /* Define to 1 if you have the `pow' function. */ #undef HAVE_POW /* Define to 1 if you have the `sqrt' function. */ #undef HAVE_SQRT /* Define to 1 if stdbool.h conforms to C99. */ #undef HAVE_STDBOOL_H /* Define to 1 if you have the header file. */ #undef HAVE_STDDEF_H /* Define to 1 if you have the header file. */ #undef HAVE_STDINT_H /* Define to 1 if you have the header file. */ #undef HAVE_STDLIB_H /* Define to 1 if you have the `strchr' function. */ #undef HAVE_STRCHR /* Define to 1 if you have the `strdup' function. */ #undef HAVE_STRDUP /* Define to 1 if you have the header file. */ #undef HAVE_STRINGS_H /* Define to 1 if you have the header file. */ #undef HAVE_STRING_H /* Define to 1 if you have the `strtol' function. */ #undef HAVE_STRTOL /* Define to 1 if you have the header file. */ #undef HAVE_SYS_STAT_H /* Define to 1 if you have the header file. */ #undef HAVE_SYS_TIME_H /* Define to 1 if you have the header file. */ #undef HAVE_SYS_TYPES_H /* Define to 1 if you have the header file. */ #undef HAVE_UNISTD_H /* Define to 1 if the system has the type `_Bool'. */ #undef HAVE__BOOL /* monitors all allocation/deallocation, writes report */ #undef MONITORING_ALLOCATION /* Name of package */ #undef PACKAGE /* Define to the address where bug reports for this package should be sent. */ #undef PACKAGE_BUGREPORT /* Define to the full name of this package. */ #undef PACKAGE_NAME /* Define to the full name and version of this package. */ #undef PACKAGE_STRING /* Define to the one symbol short name of this package. */ #undef PACKAGE_TARNAME /* Define to the version of this package. */ #undef PACKAGE_VERSION /* Define to 1 if you have the ANSI C header files. */ #undef STDC_HEADERS /* Define to 1 if your declares `struct tm'. */ #undef TM_IN_SYS_TIME /* Version number of package */ #undef VERSION /* Define to empty if `const' does not conform to ANSI C. */ #undef const /* Define to `__inline__' or `__inline' if that's what the C compiler calls it, or to nothing if 'inline' is not supported under any name. */ #ifndef __cplusplus #undef inline #endif /* Define to rpl_malloc if the replacement function should be used. */ #undef malloc /* Define to `unsigned' if does not define. */ #undef size_t garli-2.1-release/config/000077500000000000000000000000001241236125200153335ustar00rootroot00000000000000garli-2.1-release/config/acx_mpi.m4000066400000000000000000000147041241236125200172230ustar00rootroot00000000000000# =========================================================================== # http://autoconf-archive.cryp.to/acx_mpi.html # =========================================================================== # # SYNOPSIS # # ACX_MPI([ACTION-IF-FOUND[, ACTION-IF-NOT-FOUND]]) # # DESCRIPTION # # This macro tries to find out how to compile programs that use MPI # (Message Passing Interface), a standard API for parallel process # communication (see http://www-unix.mcs.anl.gov/mpi/) # # On success, it sets the MPICC, MPICXX, MPIF77, or MPIFC output variable # to the name of the MPI compiler, depending upon the current language. # (This may just be $CC/$CXX/$F77/$FC, but is more often something like # mpicc/mpiCC/mpif77/mpif90.) It also sets MPILIBS to any libraries that # are needed for linking MPI (e.g. -lmpi or -lfmpi, if a special # MPICC/MPICXX/MPIF77/MPIFC was not found). # # If you want to compile everything with MPI, you should set: # # CC="MPICC" #OR# CXX="MPICXX" #OR# F77="MPIF77" #OR# FC="MPIFC" # LIBS="$MPILIBS $LIBS" # # NOTE: The above assumes that you will use $CC (or whatever) for linking # as well as for compiling. (This is the default for automake and most # Makefiles.) # # The user can force a particular library/compiler by setting the # MPICC/MPICXX/MPIF77/MPIFC and/or MPILIBS environment variables. # # ACTION-IF-FOUND is a list of shell commands to run if an MPI library is # found, and ACTION-IF-NOT-FOUND is a list of commands to run if it is not # found. If ACTION-IF-FOUND is not specified, the default action will # define HAVE_MPI. # # LAST MODIFICATION # # 2008-04-12 # # COPYLEFT # # Copyright (c) 2008 Steven G. Johnson # Copyright (c) 2008 Julian C. Cummings # # This program is free software: you can redistribute it and/or modify it # under the terms of the GNU General Public License as published by the # Free Software Foundation, either version 3 of the License, or (at your # option) any later version. # # This program is distributed in the hope that it will be useful, but # WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General # Public License for more details. # # You should have received a copy of the GNU General Public License along # with this program. If not, see . # # As a special exception, the respective Autoconf Macro's copyright owner # gives unlimited permission to copy, distribute and modify the configure # scripts that are the output of Autoconf when processing the Macro. You # need not follow the terms of the GNU General Public License when using # or distributing such scripts, even though portions of the text of the # Macro appear in them. The GNU General Public License (GPL) does govern # all other use of the material that constitutes the Autoconf Macro. # # This special exception to the GPL applies to versions of the Autoconf # Macro released by the Autoconf Macro Archive. When you make and # distribute a modified version of the Autoconf Macro, you may extend this # special exception to the GPL to apply to your modified version as well. AC_DEFUN([ACX_MPI], [ AC_PREREQ(2.50) dnl for AC_LANG_CASE AC_LANG_CASE([C], [ AC_REQUIRE([AC_PROG_CC]) AC_ARG_VAR(MPICC,[MPI C compiler command]) AC_CHECK_PROGS(MPICC, mpicc hcc mpxlc_r mpxlc mpcc cmpicc, $CC) acx_mpi_save_CC="$CC" CC="$MPICC" AC_SUBST(MPICC) ], [C++], [ AC_REQUIRE([AC_PROG_CXX]) AC_ARG_VAR(MPICXX,[MPI C++ compiler command]) AC_CHECK_PROGS(MPICXX, mpic++ mpicxx mpiCC hcp mpxlC_r mpxlC mpCC cmpic++, $CXX) acx_mpi_save_CXX="$CXX" CXX="$MPICXX" AC_SUBST(MPICXX) ], [Fortran 77], [ AC_REQUIRE([AC_PROG_F77]) AC_ARG_VAR(MPIF77,[MPI Fortran 77 compiler command]) AC_CHECK_PROGS(MPIF77, mpif77 hf77 mpxlf_r mpxlf mpf77 cmpifc, $F77) acx_mpi_save_F77="$F77" F77="$MPIF77" AC_SUBST(MPIF77) ], [Fortran], [ AC_REQUIRE([AC_PROG_FC]) AC_ARG_VAR(MPIFC,[MPI Fortran compiler command]) AC_CHECK_PROGS(MPIFC, mpif90 mpxlf95_r mpxlf90_r mpxlf95 mpxlf90 mpf90 cmpif90c, $FC) acx_mpi_save_FC="$FC" FC="$MPIFC" AC_SUBST(MPIFC) ]) if test x = x"$MPILIBS"; then AC_LANG_CASE([C], [AC_CHECK_FUNC(MPI_Init, [MPILIBS=" "])], [C++], [AC_CHECK_FUNC(MPI_Init, [MPILIBS=" "])], [Fortran 77], [AC_MSG_CHECKING([for MPI_Init]) AC_LINK_IFELSE([AC_LANG_PROGRAM([],[ call MPI_Init])],[MPILIBS=" " AC_MSG_RESULT(yes)], [AC_MSG_RESULT(no)])], [Fortran], [AC_MSG_CHECKING([for MPI_Init]) AC_LINK_IFELSE([AC_LANG_PROGRAM([],[ call MPI_Init])],[MPILIBS=" " AC_MSG_RESULT(yes)], [AC_MSG_RESULT(no)])]) fi AC_LANG_CASE([Fortran 77], [ if test x = x"$MPILIBS"; then AC_CHECK_LIB(fmpi, MPI_Init, [MPILIBS="-lfmpi"]) fi if test x = x"$MPILIBS"; then AC_CHECK_LIB(fmpich, MPI_Init, [MPILIBS="-lfmpich"]) fi ], [Fortran], [ if test x = x"$MPILIBS"; then AC_CHECK_LIB(fmpi, MPI_Init, [MPILIBS="-lfmpi"]) fi if test x = x"$MPILIBS"; then AC_CHECK_LIB(mpichf90, MPI_Init, [MPILIBS="-lmpichf90"]) fi ]) if test x = x"$MPILIBS"; then AC_CHECK_LIB(mpi, MPI_Init, [MPILIBS="-lmpi"]) fi if test x = x"$MPILIBS"; then AC_CHECK_LIB(mpich, MPI_Init, [MPILIBS="-lmpich"]) fi dnl We have to use AC_TRY_COMPILE and not AC_CHECK_HEADER because the dnl latter uses $CPP, not $CC (which may be mpicc). 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See the # GNU General Public License for more details. # # You should have received a copy of the GNU General Public License # along with this program. If not, see . # As a special exception to the GNU General Public License, if you # distribute this file as part of a program that contains a # configuration script generated by Autoconf, you may include it under # the same distribution terms that you use for the rest of that program. # This file is maintained in Automake, please report # bugs to or send patches to # . nl=' ' # We need space, tab and new line, in precisely that order. Quoting is # there to prevent tools from complaining about whitespace usage. IFS=" "" $nl" file_conv= # func_file_conv build_file lazy # Convert a $build file to $host form and store it in $file # Currently only supports Windows hosts. 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See the GNU # General Public License for more details. # # You should have received a copy of the GNU General Public License # along with this program; if not, write to the Free Software # Foundation, Inc., 51 Franklin Street - Fifth Floor, Boston, MA # 02110-1301, USA. # # As a special exception to the GNU General Public License, if you # distribute this file as part of a program that contains a # configuration script generated by Autoconf, you may include it under # the same distribution terms that you use for the rest of that program. # Originally written by Per Bothner. Please send patches (context # diff format) to and include a ChangeLog # entry. # # This script attempts to guess a canonical system name similar to # config.sub. If it succeeds, it prints the system name on stdout, and # exits with 0. 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But MiNT is downward compatible to TOS, so this should # be no problem. atarist[e]:*MiNT:*:* | atarist[e]:*mint:*:* | atarist[e]:*TOS:*:*) echo m68k-atari-mint${UNAME_RELEASE} exit ;; atari*:*MiNT:*:* | atari*:*mint:*:* | atarist[e]:*TOS:*:*) echo m68k-atari-mint${UNAME_RELEASE} exit ;; *falcon*:*MiNT:*:* | *falcon*:*mint:*:* | *falcon*:*TOS:*:*) echo m68k-atari-mint${UNAME_RELEASE} exit ;; milan*:*MiNT:*:* | milan*:*mint:*:* | *milan*:*TOS:*:*) echo m68k-milan-mint${UNAME_RELEASE} exit ;; hades*:*MiNT:*:* | hades*:*mint:*:* | *hades*:*TOS:*:*) echo m68k-hades-mint${UNAME_RELEASE} exit ;; *:*MiNT:*:* | *:*mint:*:* | *:*TOS:*:*) echo m68k-unknown-mint${UNAME_RELEASE} exit ;; m68k:machten:*:*) echo m68k-apple-machten${UNAME_RELEASE} exit ;; powerpc:machten:*:*) echo powerpc-apple-machten${UNAME_RELEASE} exit ;; RISC*:Mach:*:*) echo mips-dec-mach_bsd4.3 exit ;; RISC*:ULTRIX:*:*) echo mips-dec-ultrix${UNAME_RELEASE} exit ;; VAX*:ULTRIX*:*:*) echo vax-dec-ultrix${UNAME_RELEASE} exit ;; 2020:CLIX:*:* | 2430:CLIX:*:*) echo clipper-intergraph-clix${UNAME_RELEASE} exit ;; mips:*:*:UMIPS | mips:*:*:RISCos) eval $set_cc_for_build sed 's/^ //' << EOF >$dummy.c #ifdef __cplusplus #include /* for printf() prototype */ int main (int argc, char *argv[]) { #else int main (argc, argv) int argc; char *argv[]; { #endif #if defined (host_mips) && defined (MIPSEB) #if defined (SYSTYPE_SYSV) printf ("mips-mips-riscos%ssysv\n", argv[1]); exit (0); #endif #if defined (SYSTYPE_SVR4) printf ("mips-mips-riscos%ssvr4\n", argv[1]); exit (0); #endif #if defined (SYSTYPE_BSD43) || defined(SYSTYPE_BSD) printf ("mips-mips-riscos%sbsd\n", argv[1]); exit (0); #endif #endif exit (-1); } EOF $CC_FOR_BUILD -o $dummy $dummy.c && dummyarg=`echo "${UNAME_RELEASE}" | sed -n 's/\([0-9]*\).*/\1/p'` && SYSTEM_NAME=`$dummy $dummyarg` && { echo "$SYSTEM_NAME"; exit; } echo mips-mips-riscos${UNAME_RELEASE} exit ;; Motorola:PowerMAX_OS:*:*) echo powerpc-motorola-powermax exit ;; Motorola:*:4.3:PL8-*) echo powerpc-harris-powermax exit ;; Night_Hawk:*:*:PowerMAX_OS | Synergy:PowerMAX_OS:*:*) echo powerpc-harris-powermax exit ;; Night_Hawk:Power_UNIX:*:*) echo powerpc-harris-powerunix exit ;; m88k:CX/UX:7*:*) echo m88k-harris-cxux7 exit ;; m88k:*:4*:R4*) echo m88k-motorola-sysv4 exit ;; m88k:*:3*:R3*) echo m88k-motorola-sysv3 exit ;; AViiON:dgux:*:*) # DG/UX returns AViiON for all architectures UNAME_PROCESSOR=`/usr/bin/uname -p` if [ $UNAME_PROCESSOR = mc88100 ] || [ $UNAME_PROCESSOR = mc88110 ] then if [ ${TARGET_BINARY_INTERFACE}x = m88kdguxelfx ] || \ [ ${TARGET_BINARY_INTERFACE}x = x ] then echo m88k-dg-dgux${UNAME_RELEASE} else echo m88k-dg-dguxbcs${UNAME_RELEASE} fi else echo i586-dg-dgux${UNAME_RELEASE} fi exit ;; M88*:DolphinOS:*:*) # DolphinOS (SVR3) echo m88k-dolphin-sysv3 exit ;; M88*:*:R3*:*) # Delta 88k system running SVR3 echo m88k-motorola-sysv3 exit ;; XD88*:*:*:*) # Tektronix XD88 system running UTekV (SVR3) echo m88k-tektronix-sysv3 exit ;; Tek43[0-9][0-9]:UTek:*:*) # Tektronix 4300 system running UTek (BSD) echo m68k-tektronix-bsd exit ;; *:IRIX*:*:*) echo mips-sgi-irix`echo ${UNAME_RELEASE}|sed -e 's/-/_/g'` exit ;; ????????:AIX?:[12].1:2) # AIX 2.2.1 or AIX 2.1.1 is RT/PC AIX. echo romp-ibm-aix # uname -m gives an 8 hex-code CPU id exit ;; # Note that: echo "'`uname -s`'" gives 'AIX ' i*86:AIX:*:*) echo i386-ibm-aix exit ;; ia64:AIX:*:*) if [ -x /usr/bin/oslevel ] ; then IBM_REV=`/usr/bin/oslevel` else IBM_REV=${UNAME_VERSION}.${UNAME_RELEASE} fi echo ${UNAME_MACHINE}-ibm-aix${IBM_REV} exit ;; *:AIX:2:3) if grep bos325 /usr/include/stdio.h >/dev/null 2>&1; then eval $set_cc_for_build sed 's/^ //' << EOF >$dummy.c #include main() { if (!__power_pc()) exit(1); puts("powerpc-ibm-aix3.2.5"); exit(0); } EOF if $CC_FOR_BUILD -o $dummy $dummy.c && SYSTEM_NAME=`$dummy` then echo "$SYSTEM_NAME" else echo rs6000-ibm-aix3.2.5 fi elif grep bos324 /usr/include/stdio.h >/dev/null 2>&1; then echo rs6000-ibm-aix3.2.4 else echo rs6000-ibm-aix3.2 fi exit ;; *:AIX:*:[456]) IBM_CPU_ID=`/usr/sbin/lsdev -C -c processor -S available | sed 1q | awk '{ print $1 }'` if /usr/sbin/lsattr -El ${IBM_CPU_ID} | grep ' POWER' >/dev/null 2>&1; then IBM_ARCH=rs6000 else IBM_ARCH=powerpc fi if [ -x /usr/bin/oslevel ] ; then IBM_REV=`/usr/bin/oslevel` else IBM_REV=${UNAME_VERSION}.${UNAME_RELEASE} fi echo ${IBM_ARCH}-ibm-aix${IBM_REV} exit ;; *:AIX:*:*) echo rs6000-ibm-aix exit ;; ibmrt:4.4BSD:*|romp-ibm:BSD:*) echo romp-ibm-bsd4.4 exit ;; ibmrt:*BSD:*|romp-ibm:BSD:*) # covers RT/PC BSD and echo romp-ibm-bsd${UNAME_RELEASE} # 4.3 with uname added to exit ;; # report: romp-ibm BSD 4.3 *:BOSX:*:*) echo rs6000-bull-bosx exit ;; DPX/2?00:B.O.S.:*:*) echo m68k-bull-sysv3 exit ;; 9000/[34]??:4.3bsd:1.*:*) echo m68k-hp-bsd exit ;; hp300:4.4BSD:*:* | 9000/[34]??:4.3bsd:2.*:*) echo m68k-hp-bsd4.4 exit ;; 9000/[34678]??:HP-UX:*:*) HPUX_REV=`echo ${UNAME_RELEASE}|sed -e 's/[^.]*.[0B]*//'` case "${UNAME_MACHINE}" in 9000/31? ) HP_ARCH=m68000 ;; 9000/[34]?? ) HP_ARCH=m68k ;; 9000/[678][0-9][0-9]) if [ -x /usr/bin/getconf ]; then sc_cpu_version=`/usr/bin/getconf SC_CPU_VERSION 2>/dev/null` sc_kernel_bits=`/usr/bin/getconf SC_KERNEL_BITS 2>/dev/null` case "${sc_cpu_version}" in 523) HP_ARCH="hppa1.0" ;; # CPU_PA_RISC1_0 528) HP_ARCH="hppa1.1" ;; # CPU_PA_RISC1_1 532) # CPU_PA_RISC2_0 case "${sc_kernel_bits}" in 32) HP_ARCH="hppa2.0n" ;; 64) HP_ARCH="hppa2.0w" ;; '') HP_ARCH="hppa2.0" ;; # HP-UX 10.20 esac ;; esac fi if [ "${HP_ARCH}" = "" ]; then eval $set_cc_for_build sed 's/^ //' << EOF >$dummy.c #define _HPUX_SOURCE #include #include int main () { #if defined(_SC_KERNEL_BITS) long bits = sysconf(_SC_KERNEL_BITS); #endif long cpu = sysconf (_SC_CPU_VERSION); switch (cpu) { case CPU_PA_RISC1_0: puts ("hppa1.0"); break; case CPU_PA_RISC1_1: puts ("hppa1.1"); break; case CPU_PA_RISC2_0: #if defined(_SC_KERNEL_BITS) switch (bits) { case 64: puts ("hppa2.0w"); break; case 32: puts ("hppa2.0n"); break; default: puts ("hppa2.0"); break; } break; #else /* !defined(_SC_KERNEL_BITS) */ puts ("hppa2.0"); break; #endif default: puts ("hppa1.0"); break; } exit (0); } EOF (CCOPTS= $CC_FOR_BUILD -o $dummy $dummy.c 2>/dev/null) && HP_ARCH=`$dummy` test -z "$HP_ARCH" && HP_ARCH=hppa fi ;; esac if [ ${HP_ARCH} = "hppa2.0w" ] then eval $set_cc_for_build # hppa2.0w-hp-hpux* has a 64-bit kernel and a compiler generating # 32-bit code. hppa64-hp-hpux* has the same kernel and a compiler # generating 64-bit code. GNU and HP use different nomenclature: # # $ CC_FOR_BUILD=cc ./config.guess # => hppa2.0w-hp-hpux11.23 # $ CC_FOR_BUILD="cc +DA2.0w" ./config.guess # => hppa64-hp-hpux11.23 if echo __LP64__ | (CCOPTS= $CC_FOR_BUILD -E - 2>/dev/null) | grep -q __LP64__ then HP_ARCH="hppa2.0w" else HP_ARCH="hppa64" fi fi echo ${HP_ARCH}-hp-hpux${HPUX_REV} exit ;; ia64:HP-UX:*:*) HPUX_REV=`echo ${UNAME_RELEASE}|sed -e 's/[^.]*.[0B]*//'` echo ia64-hp-hpux${HPUX_REV} exit ;; 3050*:HI-UX:*:*) eval $set_cc_for_build sed 's/^ //' << EOF >$dummy.c #include int main () { long cpu = sysconf (_SC_CPU_VERSION); /* The order matters, because CPU_IS_HP_MC68K erroneously returns true for CPU_PA_RISC1_0. CPU_IS_PA_RISC returns correct results, however. */ if (CPU_IS_PA_RISC (cpu)) { switch (cpu) { case CPU_PA_RISC1_0: puts ("hppa1.0-hitachi-hiuxwe2"); break; case CPU_PA_RISC1_1: puts ("hppa1.1-hitachi-hiuxwe2"); break; case CPU_PA_RISC2_0: puts ("hppa2.0-hitachi-hiuxwe2"); break; default: puts ("hppa-hitachi-hiuxwe2"); break; } } else if (CPU_IS_HP_MC68K (cpu)) puts ("m68k-hitachi-hiuxwe2"); else puts ("unknown-hitachi-hiuxwe2"); exit (0); } EOF $CC_FOR_BUILD -o $dummy $dummy.c && SYSTEM_NAME=`$dummy` && { echo "$SYSTEM_NAME"; exit; } echo unknown-hitachi-hiuxwe2 exit ;; 9000/7??:4.3bsd:*:* | 9000/8?[79]:4.3bsd:*:* ) echo hppa1.1-hp-bsd exit ;; 9000/8??:4.3bsd:*:*) echo hppa1.0-hp-bsd exit ;; *9??*:MPE/iX:*:* | *3000*:MPE/iX:*:*) echo hppa1.0-hp-mpeix exit ;; hp7??:OSF1:*:* | hp8?[79]:OSF1:*:* ) echo hppa1.1-hp-osf exit ;; hp8??:OSF1:*:*) echo hppa1.0-hp-osf exit ;; i*86:OSF1:*:*) if [ -x /usr/sbin/sysversion ] ; then echo ${UNAME_MACHINE}-unknown-osf1mk else echo ${UNAME_MACHINE}-unknown-osf1 fi exit ;; parisc*:Lites*:*:*) echo hppa1.1-hp-lites exit ;; C1*:ConvexOS:*:* | convex:ConvexOS:C1*:*) echo c1-convex-bsd exit ;; C2*:ConvexOS:*:* | convex:ConvexOS:C2*:*) if getsysinfo -f scalar_acc then echo c32-convex-bsd else echo c2-convex-bsd fi exit ;; C34*:ConvexOS:*:* | convex:ConvexOS:C34*:*) echo c34-convex-bsd exit ;; C38*:ConvexOS:*:* | convex:ConvexOS:C38*:*) echo c38-convex-bsd exit ;; C4*:ConvexOS:*:* | convex:ConvexOS:C4*:*) echo c4-convex-bsd exit ;; CRAY*Y-MP:*:*:*) echo ymp-cray-unicos${UNAME_RELEASE} | sed -e 's/\.[^.]*$/.X/' exit ;; CRAY*[A-Z]90:*:*:*) echo ${UNAME_MACHINE}-cray-unicos${UNAME_RELEASE} \ | sed -e 's/CRAY.*\([A-Z]90\)/\1/' \ -e y/ABCDEFGHIJKLMNOPQRSTUVWXYZ/abcdefghijklmnopqrstuvwxyz/ \ -e 's/\.[^.]*$/.X/' exit ;; CRAY*TS:*:*:*) echo t90-cray-unicos${UNAME_RELEASE} | sed -e 's/\.[^.]*$/.X/' exit ;; CRAY*T3E:*:*:*) echo alphaev5-cray-unicosmk${UNAME_RELEASE} | sed -e 's/\.[^.]*$/.X/' exit ;; CRAY*SV1:*:*:*) echo sv1-cray-unicos${UNAME_RELEASE} | sed -e 's/\.[^.]*$/.X/' exit ;; *:UNICOS/mp:*:*) echo craynv-cray-unicosmp${UNAME_RELEASE} | sed -e 's/\.[^.]*$/.X/' exit ;; F30[01]:UNIX_System_V:*:* | F700:UNIX_System_V:*:*) FUJITSU_PROC=`uname -m | tr 'ABCDEFGHIJKLMNOPQRSTUVWXYZ' 'abcdefghijklmnopqrstuvwxyz'` FUJITSU_SYS=`uname -p | tr 'ABCDEFGHIJKLMNOPQRSTUVWXYZ' 'abcdefghijklmnopqrstuvwxyz' | sed -e 's/\///'` FUJITSU_REL=`echo ${UNAME_RELEASE} | sed -e 's/ /_/'` echo "${FUJITSU_PROC}-fujitsu-${FUJITSU_SYS}${FUJITSU_REL}" exit ;; 5000:UNIX_System_V:4.*:*) FUJITSU_SYS=`uname -p | tr 'ABCDEFGHIJKLMNOPQRSTUVWXYZ' 'abcdefghijklmnopqrstuvwxyz' | sed -e 's/\///'` FUJITSU_REL=`echo ${UNAME_RELEASE} | tr 'ABCDEFGHIJKLMNOPQRSTUVWXYZ' 'abcdefghijklmnopqrstuvwxyz' | sed -e 's/ /_/'` echo "sparc-fujitsu-${FUJITSU_SYS}${FUJITSU_REL}" exit ;; i*86:BSD/386:*:* | i*86:BSD/OS:*:* | *:Ascend\ Embedded/OS:*:*) echo ${UNAME_MACHINE}-pc-bsdi${UNAME_RELEASE} exit ;; sparc*:BSD/OS:*:*) echo sparc-unknown-bsdi${UNAME_RELEASE} exit ;; *:BSD/OS:*:*) echo ${UNAME_MACHINE}-unknown-bsdi${UNAME_RELEASE} exit ;; *:FreeBSD:*:*) case ${UNAME_MACHINE} in pc98) echo i386-unknown-freebsd`echo ${UNAME_RELEASE}|sed -e 's/[-(].*//'` ;; amd64) echo x86_64-unknown-freebsd`echo ${UNAME_RELEASE}|sed -e 's/[-(].*//'` ;; *) echo ${UNAME_MACHINE}-unknown-freebsd`echo ${UNAME_RELEASE}|sed -e 's/[-(].*//'` ;; esac exit ;; i*:CYGWIN*:*) echo ${UNAME_MACHINE}-pc-cygwin exit ;; *:MINGW*:*) echo ${UNAME_MACHINE}-pc-mingw32 exit ;; i*:windows32*:*) # uname -m includes "-pc" on this system. echo ${UNAME_MACHINE}-mingw32 exit ;; i*:PW*:*) echo ${UNAME_MACHINE}-pc-pw32 exit ;; *:Interix*:*) case ${UNAME_MACHINE} in x86) echo i586-pc-interix${UNAME_RELEASE} exit ;; authenticamd | genuineintel | EM64T) echo x86_64-unknown-interix${UNAME_RELEASE} exit ;; IA64) echo ia64-unknown-interix${UNAME_RELEASE} exit ;; esac ;; [345]86:Windows_95:* | [345]86:Windows_98:* | [345]86:Windows_NT:*) echo i${UNAME_MACHINE}-pc-mks exit ;; 8664:Windows_NT:*) echo x86_64-pc-mks exit ;; i*:Windows_NT*:* | Pentium*:Windows_NT*:*) # How do we know it's Interix rather than the generic POSIX subsystem? # It also conflicts with pre-2.0 versions of AT&T UWIN. Should we # UNAME_MACHINE based on the output of uname instead of i386? echo i586-pc-interix exit ;; i*:UWIN*:*) echo ${UNAME_MACHINE}-pc-uwin exit ;; amd64:CYGWIN*:*:* | x86_64:CYGWIN*:*:*) echo x86_64-unknown-cygwin exit ;; p*:CYGWIN*:*) echo powerpcle-unknown-cygwin exit ;; prep*:SunOS:5.*:*) echo powerpcle-unknown-solaris2`echo ${UNAME_RELEASE}|sed -e 's/[^.]*//'` exit ;; *:GNU:*:*) # the GNU system echo `echo ${UNAME_MACHINE}|sed -e 's,[-/].*$,,'`-unknown-gnu`echo ${UNAME_RELEASE}|sed -e 's,/.*$,,'` exit ;; *:GNU/*:*:*) # other systems with GNU libc and userland echo ${UNAME_MACHINE}-unknown-`echo ${UNAME_SYSTEM} | sed 's,^[^/]*/,,' | tr '[A-Z]' '[a-z]'``echo ${UNAME_RELEASE}|sed -e 's/[-(].*//'`-gnu exit ;; i*86:Minix:*:*) echo ${UNAME_MACHINE}-pc-minix exit ;; alpha:Linux:*:*) case `sed -n '/^cpu model/s/^.*: \(.*\)/\1/p' < /proc/cpuinfo` in EV5) UNAME_MACHINE=alphaev5 ;; EV56) UNAME_MACHINE=alphaev56 ;; PCA56) UNAME_MACHINE=alphapca56 ;; PCA57) UNAME_MACHINE=alphapca56 ;; EV6) UNAME_MACHINE=alphaev6 ;; EV67) UNAME_MACHINE=alphaev67 ;; EV68*) UNAME_MACHINE=alphaev68 ;; esac objdump --private-headers /bin/sh | grep -q ld.so.1 if test "$?" = 0 ; then LIBC="libc1" ; else LIBC="" ; fi echo ${UNAME_MACHINE}-unknown-linux-gnu${LIBC} exit ;; arm*:Linux:*:*) eval $set_cc_for_build if echo __ARM_EABI__ | $CC_FOR_BUILD -E - 2>/dev/null \ | grep -q __ARM_EABI__ then echo ${UNAME_MACHINE}-unknown-linux-gnu else echo ${UNAME_MACHINE}-unknown-linux-gnueabi fi exit ;; avr32*:Linux:*:*) echo ${UNAME_MACHINE}-unknown-linux-gnu exit ;; cris:Linux:*:*) echo cris-axis-linux-gnu exit ;; crisv32:Linux:*:*) echo crisv32-axis-linux-gnu exit ;; frv:Linux:*:*) echo frv-unknown-linux-gnu exit ;; i*86:Linux:*:*) LIBC=gnu eval $set_cc_for_build sed 's/^ //' << EOF >$dummy.c #ifdef __dietlibc__ LIBC=dietlibc #endif EOF eval `$CC_FOR_BUILD -E $dummy.c 2>/dev/null | grep '^LIBC'` echo "${UNAME_MACHINE}-pc-linux-${LIBC}" exit ;; ia64:Linux:*:*) echo ${UNAME_MACHINE}-unknown-linux-gnu exit ;; m32r*:Linux:*:*) echo ${UNAME_MACHINE}-unknown-linux-gnu exit ;; m68*:Linux:*:*) echo ${UNAME_MACHINE}-unknown-linux-gnu exit ;; mips:Linux:*:* | mips64:Linux:*:*) eval $set_cc_for_build sed 's/^ //' << EOF >$dummy.c #undef CPU #undef ${UNAME_MACHINE} #undef ${UNAME_MACHINE}el #if defined(__MIPSEL__) || defined(__MIPSEL) || defined(_MIPSEL) || defined(MIPSEL) CPU=${UNAME_MACHINE}el #else #if defined(__MIPSEB__) || defined(__MIPSEB) || defined(_MIPSEB) || defined(MIPSEB) CPU=${UNAME_MACHINE} #else CPU= #endif #endif EOF eval `$CC_FOR_BUILD -E $dummy.c 2>/dev/null | grep '^CPU'` test x"${CPU}" != x && { echo "${CPU}-unknown-linux-gnu"; exit; } ;; or32:Linux:*:*) echo or32-unknown-linux-gnu exit ;; padre:Linux:*:*) echo sparc-unknown-linux-gnu exit ;; parisc64:Linux:*:* | hppa64:Linux:*:*) echo hppa64-unknown-linux-gnu exit ;; parisc:Linux:*:* | hppa:Linux:*:*) # Look for CPU level case `grep '^cpu[^a-z]*:' /proc/cpuinfo 2>/dev/null | cut -d' ' -f2` in PA7*) echo hppa1.1-unknown-linux-gnu ;; PA8*) echo hppa2.0-unknown-linux-gnu ;; *) echo hppa-unknown-linux-gnu ;; esac exit ;; ppc64:Linux:*:*) echo powerpc64-unknown-linux-gnu exit ;; ppc:Linux:*:*) echo powerpc-unknown-linux-gnu exit ;; s390:Linux:*:* | s390x:Linux:*:*) echo ${UNAME_MACHINE}-ibm-linux exit ;; sh64*:Linux:*:*) echo ${UNAME_MACHINE}-unknown-linux-gnu exit ;; sh*:Linux:*:*) echo ${UNAME_MACHINE}-unknown-linux-gnu exit ;; sparc:Linux:*:* | sparc64:Linux:*:*) echo ${UNAME_MACHINE}-unknown-linux-gnu exit ;; vax:Linux:*:*) echo ${UNAME_MACHINE}-dec-linux-gnu exit ;; x86_64:Linux:*:*) echo x86_64-unknown-linux-gnu exit ;; xtensa*:Linux:*:*) echo ${UNAME_MACHINE}-unknown-linux-gnu exit ;; i*86:DYNIX/ptx:4*:*) # ptx 4.0 does uname -s correctly, with DYNIX/ptx in there. # earlier versions are messed up and put the nodename in both # sysname and nodename. echo i386-sequent-sysv4 exit ;; i*86:UNIX_SV:4.2MP:2.*) # Unixware is an offshoot of SVR4, but it has its own version # number series starting with 2... # I am not positive that other SVR4 systems won't match this, # I just have to hope. -- rms. # Use sysv4.2uw... so that sysv4* matches it. echo ${UNAME_MACHINE}-pc-sysv4.2uw${UNAME_VERSION} exit ;; i*86:OS/2:*:*) # If we were able to find `uname', then EMX Unix compatibility # is probably installed. echo ${UNAME_MACHINE}-pc-os2-emx exit ;; i*86:XTS-300:*:STOP) echo ${UNAME_MACHINE}-unknown-stop exit ;; i*86:atheos:*:*) echo ${UNAME_MACHINE}-unknown-atheos exit ;; i*86:syllable:*:*) echo ${UNAME_MACHINE}-pc-syllable exit ;; i*86:LynxOS:2.*:* | i*86:LynxOS:3.[01]*:* | i*86:LynxOS:4.[02]*:*) echo i386-unknown-lynxos${UNAME_RELEASE} exit ;; i*86:*DOS:*:*) echo ${UNAME_MACHINE}-pc-msdosdjgpp exit ;; i*86:*:4.*:* | i*86:SYSTEM_V:4.*:*) UNAME_REL=`echo ${UNAME_RELEASE} | sed 's/\/MP$//'` if grep Novell /usr/include/link.h >/dev/null 2>/dev/null; then echo ${UNAME_MACHINE}-univel-sysv${UNAME_REL} else echo ${UNAME_MACHINE}-pc-sysv${UNAME_REL} fi exit ;; i*86:*:5:[678]*) # UnixWare 7.x, OpenUNIX and OpenServer 6. case `/bin/uname -X | grep "^Machine"` in *486*) UNAME_MACHINE=i486 ;; *Pentium) UNAME_MACHINE=i586 ;; *Pent*|*Celeron) UNAME_MACHINE=i686 ;; esac echo ${UNAME_MACHINE}-unknown-sysv${UNAME_RELEASE}${UNAME_SYSTEM}${UNAME_VERSION} exit ;; i*86:*:3.2:*) if test -f /usr/options/cb.name; then UNAME_REL=`sed -n 's/.*Version //p' /dev/null >/dev/null ; then UNAME_REL=`(/bin/uname -X|grep Release|sed -e 's/.*= //')` (/bin/uname -X|grep i80486 >/dev/null) && UNAME_MACHINE=i486 (/bin/uname -X|grep '^Machine.*Pentium' >/dev/null) \ && UNAME_MACHINE=i586 (/bin/uname -X|grep '^Machine.*Pent *II' >/dev/null) \ && UNAME_MACHINE=i686 (/bin/uname -X|grep '^Machine.*Pentium Pro' >/dev/null) \ && UNAME_MACHINE=i686 echo ${UNAME_MACHINE}-pc-sco$UNAME_REL else echo ${UNAME_MACHINE}-pc-sysv32 fi exit ;; pc:*:*:*) # Left here for compatibility: # uname -m prints for DJGPP always 'pc', but it prints nothing about # the processor, so we play safe by assuming i586. # Note: whatever this is, it MUST be the same as what config.sub # prints for the "djgpp" host, or else GDB configury will decide that # this is a cross-build. echo i586-pc-msdosdjgpp exit ;; Intel:Mach:3*:*) echo i386-pc-mach3 exit ;; paragon:*:*:*) echo i860-intel-osf1 exit ;; i860:*:4.*:*) # i860-SVR4 if grep Stardent /usr/include/sys/uadmin.h >/dev/null 2>&1 ; then echo i860-stardent-sysv${UNAME_RELEASE} # Stardent Vistra i860-SVR4 else # Add other i860-SVR4 vendors below as they are discovered. echo i860-unknown-sysv${UNAME_RELEASE} # Unknown i860-SVR4 fi exit ;; mini*:CTIX:SYS*5:*) # "miniframe" echo m68010-convergent-sysv exit ;; mc68k:UNIX:SYSTEM5:3.51m) echo m68k-convergent-sysv exit ;; M680?0:D-NIX:5.3:*) echo m68k-diab-dnix exit ;; M68*:*:R3V[5678]*:*) test -r /sysV68 && { echo 'm68k-motorola-sysv'; exit; } ;; 3[345]??:*:4.0:3.0 | 3[34]??A:*:4.0:3.0 | 3[34]??,*:*:4.0:3.0 | 3[34]??/*:*:4.0:3.0 | 4400:*:4.0:3.0 | 4850:*:4.0:3.0 | SKA40:*:4.0:3.0 | SDS2:*:4.0:3.0 | SHG2:*:4.0:3.0 | S7501*:*:4.0:3.0) OS_REL='' test -r /etc/.relid \ && OS_REL=.`sed -n 's/[^ ]* [^ ]* \([0-9][0-9]\).*/\1/p' < /etc/.relid` /bin/uname -p 2>/dev/null | grep 86 >/dev/null \ && { echo i486-ncr-sysv4.3${OS_REL}; exit; } /bin/uname -p 2>/dev/null | /bin/grep entium >/dev/null \ && { echo i586-ncr-sysv4.3${OS_REL}; exit; } ;; 3[34]??:*:4.0:* | 3[34]??,*:*:4.0:*) /bin/uname -p 2>/dev/null | grep 86 >/dev/null \ && { echo i486-ncr-sysv4; exit; } ;; NCR*:*:4.2:* | MPRAS*:*:4.2:*) OS_REL='.3' test -r /etc/.relid \ && OS_REL=.`sed -n 's/[^ ]* [^ ]* \([0-9][0-9]\).*/\1/p' < /etc/.relid` /bin/uname -p 2>/dev/null | grep 86 >/dev/null \ && { echo i486-ncr-sysv4.3${OS_REL}; exit; } /bin/uname -p 2>/dev/null | /bin/grep entium >/dev/null \ && { echo i586-ncr-sysv4.3${OS_REL}; exit; } /bin/uname -p 2>/dev/null | /bin/grep pteron >/dev/null \ && { echo i586-ncr-sysv4.3${OS_REL}; exit; } ;; m68*:LynxOS:2.*:* | m68*:LynxOS:3.0*:*) echo m68k-unknown-lynxos${UNAME_RELEASE} exit ;; mc68030:UNIX_System_V:4.*:*) echo m68k-atari-sysv4 exit ;; TSUNAMI:LynxOS:2.*:*) echo sparc-unknown-lynxos${UNAME_RELEASE} exit ;; rs6000:LynxOS:2.*:*) echo rs6000-unknown-lynxos${UNAME_RELEASE} exit ;; PowerPC:LynxOS:2.*:* | PowerPC:LynxOS:3.[01]*:* | PowerPC:LynxOS:4.[02]*:*) echo powerpc-unknown-lynxos${UNAME_RELEASE} exit ;; SM[BE]S:UNIX_SV:*:*) echo mips-dde-sysv${UNAME_RELEASE} exit ;; RM*:ReliantUNIX-*:*:*) echo mips-sni-sysv4 exit ;; RM*:SINIX-*:*:*) echo mips-sni-sysv4 exit ;; *:SINIX-*:*:*) if uname -p 2>/dev/null >/dev/null ; then UNAME_MACHINE=`(uname -p) 2>/dev/null` echo ${UNAME_MACHINE}-sni-sysv4 else echo ns32k-sni-sysv fi exit ;; PENTIUM:*:4.0*:*) # Unisys `ClearPath HMP IX 4000' SVR4/MP effort # says echo i586-unisys-sysv4 exit ;; *:UNIX_System_V:4*:FTX*) # From Gerald Hewes . # How about differentiating between stratus architectures? -djm echo hppa1.1-stratus-sysv4 exit ;; *:*:*:FTX*) # From seanf@swdc.stratus.com. echo i860-stratus-sysv4 exit ;; i*86:VOS:*:*) # From Paul.Green@stratus.com. echo ${UNAME_MACHINE}-stratus-vos exit ;; *:VOS:*:*) # From Paul.Green@stratus.com. echo hppa1.1-stratus-vos exit ;; mc68*:A/UX:*:*) echo m68k-apple-aux${UNAME_RELEASE} exit ;; news*:NEWS-OS:6*:*) echo mips-sony-newsos6 exit ;; R[34]000:*System_V*:*:* | R4000:UNIX_SYSV:*:* | R*000:UNIX_SV:*:*) if [ -d /usr/nec ]; then echo mips-nec-sysv${UNAME_RELEASE} else echo mips-unknown-sysv${UNAME_RELEASE} fi exit ;; BeBox:BeOS:*:*) # BeOS running on hardware made by Be, PPC only. echo powerpc-be-beos exit ;; BeMac:BeOS:*:*) # BeOS running on Mac or Mac clone, PPC only. echo powerpc-apple-beos exit ;; BePC:BeOS:*:*) # BeOS running on Intel PC compatible. echo i586-pc-beos exit ;; BePC:Haiku:*:*) # Haiku running on Intel PC compatible. echo i586-pc-haiku exit ;; SX-4:SUPER-UX:*:*) echo sx4-nec-superux${UNAME_RELEASE} exit ;; SX-5:SUPER-UX:*:*) echo sx5-nec-superux${UNAME_RELEASE} exit ;; SX-6:SUPER-UX:*:*) echo sx6-nec-superux${UNAME_RELEASE} exit ;; SX-7:SUPER-UX:*:*) echo sx7-nec-superux${UNAME_RELEASE} exit ;; SX-8:SUPER-UX:*:*) echo sx8-nec-superux${UNAME_RELEASE} exit ;; SX-8R:SUPER-UX:*:*) echo sx8r-nec-superux${UNAME_RELEASE} exit ;; Power*:Rhapsody:*:*) echo powerpc-apple-rhapsody${UNAME_RELEASE} exit ;; *:Rhapsody:*:*) echo ${UNAME_MACHINE}-apple-rhapsody${UNAME_RELEASE} exit ;; *:Darwin:*:*) UNAME_PROCESSOR=`uname -p` || UNAME_PROCESSOR=unknown case $UNAME_PROCESSOR in i386) eval $set_cc_for_build if [ "$CC_FOR_BUILD" != 'no_compiler_found' ]; then if (echo '#ifdef __LP64__'; echo IS_64BIT_ARCH; echo '#endif') | \ (CCOPTS= $CC_FOR_BUILD -E - 2>/dev/null) | \ grep IS_64BIT_ARCH >/dev/null then UNAME_PROCESSOR="x86_64" fi fi ;; unknown) UNAME_PROCESSOR=powerpc ;; esac echo ${UNAME_PROCESSOR}-apple-darwin${UNAME_RELEASE} exit ;; *:procnto*:*:* | *:QNX:[0123456789]*:*) UNAME_PROCESSOR=`uname -p` if test "$UNAME_PROCESSOR" = "x86"; then UNAME_PROCESSOR=i386 UNAME_MACHINE=pc fi echo ${UNAME_PROCESSOR}-${UNAME_MACHINE}-nto-qnx${UNAME_RELEASE} exit ;; *:QNX:*:4*) echo i386-pc-qnx exit ;; NSE-?:NONSTOP_KERNEL:*:*) echo nse-tandem-nsk${UNAME_RELEASE} exit ;; NSR-?:NONSTOP_KERNEL:*:*) echo nsr-tandem-nsk${UNAME_RELEASE} exit ;; *:NonStop-UX:*:*) echo mips-compaq-nonstopux exit ;; BS2000:POSIX*:*:*) echo bs2000-siemens-sysv exit ;; DS/*:UNIX_System_V:*:*) echo ${UNAME_MACHINE}-${UNAME_SYSTEM}-${UNAME_RELEASE} exit ;; *:Plan9:*:*) # "uname -m" is not consistent, so use $cputype instead. 386 # is converted to i386 for consistency with other x86 # operating systems. if test "$cputype" = "386"; then UNAME_MACHINE=i386 else UNAME_MACHINE="$cputype" fi echo ${UNAME_MACHINE}-unknown-plan9 exit ;; *:TOPS-10:*:*) echo pdp10-unknown-tops10 exit ;; *:TENEX:*:*) echo pdp10-unknown-tenex exit ;; KS10:TOPS-20:*:* | KL10:TOPS-20:*:* | TYPE4:TOPS-20:*:*) echo pdp10-dec-tops20 exit ;; XKL-1:TOPS-20:*:* | TYPE5:TOPS-20:*:*) echo pdp10-xkl-tops20 exit ;; *:TOPS-20:*:*) echo pdp10-unknown-tops20 exit ;; *:ITS:*:*) echo pdp10-unknown-its exit ;; SEI:*:*:SEIUX) echo mips-sei-seiux${UNAME_RELEASE} exit ;; *:DragonFly:*:*) echo ${UNAME_MACHINE}-unknown-dragonfly`echo ${UNAME_RELEASE}|sed -e 's/[-(].*//'` exit ;; *:*VMS:*:*) UNAME_MACHINE=`(uname -p) 2>/dev/null` case "${UNAME_MACHINE}" in A*) echo alpha-dec-vms ; exit ;; I*) echo ia64-dec-vms ; exit ;; V*) echo vax-dec-vms ; exit ;; esac ;; *:XENIX:*:SysV) echo i386-pc-xenix exit ;; i*86:skyos:*:*) echo ${UNAME_MACHINE}-pc-skyos`echo ${UNAME_RELEASE}` | sed -e 's/ .*$//' exit ;; i*86:rdos:*:*) echo ${UNAME_MACHINE}-pc-rdos exit ;; i*86:AROS:*:*) echo ${UNAME_MACHINE}-pc-aros exit ;; esac #echo '(No uname command or uname output not recognized.)' 1>&2 #echo "${UNAME_MACHINE}:${UNAME_SYSTEM}:${UNAME_RELEASE}:${UNAME_VERSION}" 1>&2 eval $set_cc_for_build cat >$dummy.c < # include #endif main () { #if defined (sony) #if defined (MIPSEB) /* BFD wants "bsd" instead of "newsos". Perhaps BFD should be changed, I don't know.... */ printf ("mips-sony-bsd\n"); exit (0); #else #include printf ("m68k-sony-newsos%s\n", #ifdef NEWSOS4 "4" #else "" #endif ); exit (0); #endif #endif #if defined (__arm) && defined (__acorn) && defined (__unix) printf ("arm-acorn-riscix\n"); exit (0); #endif #if defined (hp300) && !defined (hpux) printf ("m68k-hp-bsd\n"); exit (0); #endif #if defined (NeXT) #if !defined (__ARCHITECTURE__) #define __ARCHITECTURE__ "m68k" #endif int version; version=`(hostinfo | sed -n 's/.*NeXT Mach \([0-9]*\).*/\1/p') 2>/dev/null`; if (version < 4) printf ("%s-next-nextstep%d\n", __ARCHITECTURE__, version); else printf ("%s-next-openstep%d\n", __ARCHITECTURE__, version); exit (0); #endif #if defined (MULTIMAX) || defined (n16) #if defined (UMAXV) printf ("ns32k-encore-sysv\n"); exit (0); #else #if defined (CMU) printf ("ns32k-encore-mach\n"); exit (0); #else printf ("ns32k-encore-bsd\n"); exit (0); #endif #endif #endif #if defined (__386BSD__) printf ("i386-pc-bsd\n"); exit (0); #endif #if defined (sequent) #if defined (i386) printf ("i386-sequent-dynix\n"); exit (0); #endif #if defined (ns32000) printf ("ns32k-sequent-dynix\n"); exit (0); #endif #endif #if defined (_SEQUENT_) struct utsname un; uname(&un); if (strncmp(un.version, "V2", 2) == 0) { printf ("i386-sequent-ptx2\n"); exit (0); } if (strncmp(un.version, "V1", 2) == 0) { /* XXX is V1 correct? */ printf ("i386-sequent-ptx1\n"); exit (0); } printf ("i386-sequent-ptx\n"); exit (0); #endif #if defined (vax) # if !defined (ultrix) # include # if defined (BSD) # if BSD == 43 printf ("vax-dec-bsd4.3\n"); exit (0); # else # if BSD == 199006 printf ("vax-dec-bsd4.3reno\n"); exit (0); # else printf ("vax-dec-bsd\n"); exit (0); # endif # endif # else printf ("vax-dec-bsd\n"); exit (0); # endif # else printf ("vax-dec-ultrix\n"); exit (0); # endif #endif #if defined (alliant) && defined (i860) printf ("i860-alliant-bsd\n"); exit (0); #endif exit (1); } EOF $CC_FOR_BUILD -o $dummy $dummy.c 2>/dev/null && SYSTEM_NAME=`$dummy` && { echo "$SYSTEM_NAME"; exit; } # Apollos put the system type in the environment. test -d /usr/apollo && { echo ${ISP}-apollo-${SYSTYPE}; exit; } # Convex versions that predate uname can use getsysinfo(1) if [ -x /usr/convex/getsysinfo ] then case `getsysinfo -f cpu_type` in c1*) echo c1-convex-bsd exit ;; c2*) if getsysinfo -f scalar_acc then echo c32-convex-bsd else echo c2-convex-bsd fi exit ;; c34*) echo c34-convex-bsd exit ;; c38*) echo c38-convex-bsd exit ;; c4*) echo c4-convex-bsd exit ;; esac fi cat >&2 < in order to provide the needed information to handle your system. config.guess timestamp = $timestamp uname -m = `(uname -m) 2>/dev/null || echo unknown` uname -r = `(uname -r) 2>/dev/null || echo unknown` uname -s = `(uname -s) 2>/dev/null || echo unknown` uname -v = `(uname -v) 2>/dev/null || echo unknown` /usr/bin/uname -p = `(/usr/bin/uname -p) 2>/dev/null` /bin/uname -X = `(/bin/uname -X) 2>/dev/null` hostinfo = `(hostinfo) 2>/dev/null` /bin/universe = `(/bin/universe) 2>/dev/null` /usr/bin/arch -k = `(/usr/bin/arch -k) 2>/dev/null` /bin/arch = `(/bin/arch) 2>/dev/null` /usr/bin/oslevel = `(/usr/bin/oslevel) 2>/dev/null` /usr/convex/getsysinfo = `(/usr/convex/getsysinfo) 2>/dev/null` UNAME_MACHINE = ${UNAME_MACHINE} UNAME_RELEASE = ${UNAME_RELEASE} UNAME_SYSTEM = ${UNAME_SYSTEM} UNAME_VERSION = ${UNAME_VERSION} EOF exit 1 # Local variables: # eval: (add-hook 'write-file-hooks 'time-stamp) # time-stamp-start: "timestamp='" # time-stamp-format: "%:y-%02m-%02d" # time-stamp-end: "'" # End: garli-2.1-release/config/config.sub000066400000000000000000000754071241236125200173300ustar00rootroot00000000000000#! /bin/sh # Configuration validation subroutine script. # Copyright (C) 1992, 1993, 1994, 1995, 1996, 1997, 1998, 1999, # 2000, 2001, 2002, 2003, 2004, 2005 Free Software Foundation, Inc. timestamp='2005-02-10' # This file is (in principle) common to ALL GNU software. # The presence of a machine in this file suggests that SOME GNU software # can handle that machine. It does not imply ALL GNU software can. # # This file is free software; you can redistribute it and/or modify # it under the terms of the GNU General Public License as published by # the Free Software Foundation; either version 2 of the License, or # (at your option) any later version. # # This program is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU General Public License for more details. # # You should have received a copy of the GNU General Public License # along with this program; if not, write to the Free Software # Foundation, Inc., 59 Temple Place - Suite 330, # Boston, MA 02111-1307, USA. # As a special exception to the GNU General Public License, if you # distribute this file as part of a program that contains a # configuration script generated by Autoconf, you may include it under # the same distribution terms that you use for the rest of that program. # Please send patches to . Submit a context # diff and a properly formatted ChangeLog entry. # # Configuration subroutine to validate and canonicalize a configuration type. # Supply the specified configuration type as an argument. # If it is invalid, we print an error message on stderr and exit with code 1. # Otherwise, we print the canonical config type on stdout and succeed. # This file is supposed to be the same for all GNU packages # and recognize all the CPU types, system types and aliases # that are meaningful with *any* GNU software. # Each package is responsible for reporting which valid configurations # it does not support. The user should be able to distinguish # a failure to support a valid configuration from a meaningless # configuration. # The goal of this file is to map all the various variations of a given # machine specification into a single specification in the form: # CPU_TYPE-MANUFACTURER-OPERATING_SYSTEM # or in some cases, the newer four-part form: # CPU_TYPE-MANUFACTURER-KERNEL-OPERATING_SYSTEM # It is wrong to echo any other type of specification. me=`echo "$0" | sed -e 's,.*/,,'` usage="\ Usage: $0 [OPTION] CPU-MFR-OPSYS $0 [OPTION] ALIAS Canonicalize a configuration name. Operation modes: -h, --help print this help, then exit -t, --time-stamp print date of last modification, then exit -v, --version print version number, then exit Report bugs and patches to ." version="\ GNU config.sub ($timestamp) Copyright (C) 1992, 1993, 1994, 1995, 1996, 1997, 1998, 1999, 2000, 2001, 2002, 2003, 2004, 2005 Free Software Foundation, Inc. This is free software; see the source for copying conditions. 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We also ### recognize some manufacturers as not being operating systems, so we ### can provide default operating systems below. case $os in -sun*os*) # Prevent following clause from handling this invalid input. ;; -dec* | -mips* | -sequent* | -encore* | -pc532* | -sgi* | -sony* | \ -att* | -7300* | -3300* | -delta* | -motorola* | -sun[234]* | \ -unicom* | -ibm* | -next | -hp | -isi* | -apollo | -altos* | \ -convergent* | -ncr* | -news | -32* | -3600* | -3100* | -hitachi* |\ -c[123]* | -convex* | -sun | -crds | -omron* | -dg | -ultra | -tti* | \ -harris | -dolphin | -highlevel | -gould | -cbm | -ns | -masscomp | \ -apple | -axis | -knuth | -cray) os= basic_machine=$1 ;; -sim | -cisco | -oki | -wec | -winbond) os= basic_machine=$1 ;; -scout) ;; -wrs) os=-vxworks basic_machine=$1 ;; -chorusos*) os=-chorusos basic_machine=$1 ;; -chorusrdb) os=-chorusrdb basic_machine=$1 ;; -hiux*) os=-hiuxwe2 ;; -sco5) os=-sco3.2v5 basic_machine=`echo $1 | sed -e 's/86-.*/86-pc/'` ;; -sco4) os=-sco3.2v4 basic_machine=`echo $1 | sed -e 's/86-.*/86-pc/'` ;; -sco3.2.[4-9]*) os=`echo $os | sed -e 's/sco3.2./sco3.2v/'` basic_machine=`echo $1 | sed -e 's/86-.*/86-pc/'` ;; -sco3.2v[4-9]*) # Don't forget version if it is 3.2v4 or newer. basic_machine=`echo $1 | sed -e 's/86-.*/86-pc/'` ;; -sco*) os=-sco3.2v2 basic_machine=`echo $1 | sed -e 's/86-.*/86-pc/'` ;; -udk*) basic_machine=`echo $1 | sed -e 's/86-.*/86-pc/'` ;; -isc) os=-isc2.2 basic_machine=`echo $1 | sed -e 's/86-.*/86-pc/'` ;; -clix*) basic_machine=clipper-intergraph ;; -isc*) basic_machine=`echo $1 | sed -e 's/86-.*/86-pc/'` ;; -lynx*) os=-lynxos ;; -ptx*) basic_machine=`echo $1 | sed -e 's/86-.*/86-sequent/'` ;; -windowsnt*) os=`echo $os | sed -e 's/windowsnt/winnt/'` ;; -psos*) os=-psos ;; -mint | -mint[0-9]*) basic_machine=m68k-atari os=-mint ;; esac # Decode aliases for certain CPU-COMPANY combinations. case $basic_machine in # Recognize the basic CPU types without company name. # Some are omitted here because they have special meanings below. 1750a | 580 \ | a29k \ | alpha | alphaev[4-8] | alphaev56 | alphaev6[78] | alphapca5[67] \ | alpha64 | alpha64ev[4-8] | alpha64ev56 | alpha64ev6[78] | alpha64pca5[67] \ | am33_2.0 \ | arc | arm | arm[bl]e | arme[lb] | armv[2345] | armv[345][lb] | avr \ | c4x | clipper \ | d10v | d30v | dlx | dsp16xx \ | fr30 | frv \ | h8300 | h8500 | hppa | hppa1.[01] | hppa2.0 | hppa2.0[nw] | hppa64 \ | i370 | i860 | i960 | ia64 \ | ip2k | iq2000 \ | m32r | m32rle | m68000 | m68k | m88k | maxq | mcore \ | mips | mipsbe | mipseb | mipsel | mipsle \ | mips16 \ | mips64 | mips64el \ | mips64vr | mips64vrel \ | mips64orion | mips64orionel \ | mips64vr4100 | mips64vr4100el \ | mips64vr4300 | mips64vr4300el \ | mips64vr5000 | mips64vr5000el \ | mipsisa32 | mipsisa32el \ | mipsisa32r2 | mipsisa32r2el \ | mipsisa64 | mipsisa64el \ | mipsisa64r2 | mipsisa64r2el \ | mipsisa64sb1 | mipsisa64sb1el \ | mipsisa64sr71k | mipsisa64sr71kel \ | mipstx39 | mipstx39el \ | mn10200 | mn10300 \ | msp430 \ | ns16k | ns32k \ | openrisc | or32 \ | pdp10 | pdp11 | pj | pjl \ | powerpc | powerpc64 | powerpc64le | powerpcle | ppcbe \ | pyramid \ | sh | sh[1234] | sh[23]e | sh[34]eb | shbe | shle | sh[1234]le | sh3ele \ | sh64 | sh64le \ | sparc | sparc64 | sparc86x | sparclet | sparclite | sparcv8 | sparcv9 | sparcv9b \ | strongarm \ | tahoe | thumb | tic4x | tic80 | tron \ | v850 | v850e \ | we32k \ | x86 | xscale | xscalee[bl] | xstormy16 | xtensa \ | z8k) basic_machine=$basic_machine-unknown ;; m6811 | m68hc11 | m6812 | m68hc12) # Motorola 68HC11/12. basic_machine=$basic_machine-unknown os=-none ;; m88110 | m680[12346]0 | m683?2 | m68360 | m5200 | v70 | w65 | z8k) ;; # We use `pc' rather than `unknown' # because (1) that's what they normally are, and # (2) the word "unknown" tends to confuse beginning users. i*86 | x86_64) basic_machine=$basic_machine-pc ;; # Object if more than one company name word. *-*-*) echo Invalid configuration \`$1\': machine \`$basic_machine\' not recognized 1>&2 exit 1 ;; # Recognize the basic CPU types with company name. 580-* \ | a29k-* \ | alpha-* | alphaev[4-8]-* | alphaev56-* | alphaev6[78]-* \ | alpha64-* | alpha64ev[4-8]-* | alpha64ev56-* | alpha64ev6[78]-* \ | alphapca5[67]-* | alpha64pca5[67]-* | arc-* \ | arm-* | armbe-* | armle-* | armeb-* | armv*-* \ | avr-* \ | bs2000-* \ | c[123]* | c30-* | [cjt]90-* | c4x-* | c54x-* | c55x-* | c6x-* \ | clipper-* | craynv-* | cydra-* \ | d10v-* | d30v-* | dlx-* \ | elxsi-* \ | f30[01]-* | f700-* | fr30-* | frv-* | fx80-* \ | h8300-* | h8500-* \ | hppa-* | hppa1.[01]-* | hppa2.0-* | hppa2.0[nw]-* | hppa64-* \ | i*86-* | i860-* | i960-* | ia64-* \ | ip2k-* | iq2000-* \ | m32r-* | m32rle-* \ | m68000-* | m680[012346]0-* | m68360-* | m683?2-* | m68k-* \ | m88110-* | m88k-* | maxq-* | mcore-* \ | mips-* | mipsbe-* | mipseb-* | mipsel-* | mipsle-* \ | mips16-* \ | mips64-* | mips64el-* \ | mips64vr-* | mips64vrel-* \ | mips64orion-* | mips64orionel-* \ | mips64vr4100-* | mips64vr4100el-* \ | mips64vr4300-* | mips64vr4300el-* \ | mips64vr5000-* | mips64vr5000el-* \ | mipsisa32-* | mipsisa32el-* \ | mipsisa32r2-* | mipsisa32r2el-* \ | mipsisa64-* | mipsisa64el-* \ | mipsisa64r2-* | mipsisa64r2el-* \ | mipsisa64sb1-* | mipsisa64sb1el-* \ | mipsisa64sr71k-* | mipsisa64sr71kel-* \ | mipstx39-* | mipstx39el-* \ | mmix-* \ | msp430-* \ | none-* | np1-* | ns16k-* | ns32k-* \ | orion-* \ | pdp10-* | pdp11-* | pj-* | pjl-* | pn-* | power-* \ | powerpc-* | powerpc64-* | powerpc64le-* | powerpcle-* | ppcbe-* \ | pyramid-* \ | romp-* | rs6000-* \ | sh-* | sh[1234]-* | sh[23]e-* | sh[34]eb-* | shbe-* \ | shle-* | sh[1234]le-* | sh3ele-* | sh64-* | sh64le-* \ | sparc-* | sparc64-* | sparc86x-* | sparclet-* | sparclite-* \ | sparcv8-* | sparcv9-* | sparcv9b-* | strongarm-* | sv1-* | sx?-* \ | tahoe-* | thumb-* \ | tic30-* | tic4x-* | tic54x-* | tic55x-* | tic6x-* | tic80-* \ | tron-* \ | v850-* | v850e-* | vax-* \ | we32k-* \ | x86-* | x86_64-* | xps100-* | xscale-* | xscalee[bl]-* \ | xstormy16-* | xtensa-* \ | ymp-* \ | z8k-*) ;; # Recognize the various machine names and aliases which stand # for a CPU type and a company and sometimes even an OS. 386bsd) basic_machine=i386-unknown os=-bsd ;; 3b1 | 7300 | 7300-att | att-7300 | pc7300 | safari | unixpc) basic_machine=m68000-att ;; 3b*) basic_machine=we32k-att ;; a29khif) basic_machine=a29k-amd os=-udi ;; abacus) basic_machine=abacus-unknown ;; adobe68k) basic_machine=m68010-adobe os=-scout ;; alliant | fx80) basic_machine=fx80-alliant ;; altos | altos3068) basic_machine=m68k-altos ;; am29k) basic_machine=a29k-none os=-bsd ;; amd64) basic_machine=x86_64-pc ;; amd64-*) basic_machine=x86_64-`echo $basic_machine | sed 's/^[^-]*-//'` ;; amdahl) basic_machine=580-amdahl os=-sysv ;; amiga | amiga-*) basic_machine=m68k-unknown ;; amigaos | amigados) basic_machine=m68k-unknown os=-amigaos ;; amigaunix | amix) basic_machine=m68k-unknown os=-sysv4 ;; apollo68) basic_machine=m68k-apollo os=-sysv ;; apollo68bsd) basic_machine=m68k-apollo os=-bsd ;; aux) basic_machine=m68k-apple os=-aux ;; balance) basic_machine=ns32k-sequent os=-dynix ;; c90) basic_machine=c90-cray os=-unicos ;; convex-c1) basic_machine=c1-convex os=-bsd ;; convex-c2) basic_machine=c2-convex os=-bsd ;; convex-c32) basic_machine=c32-convex os=-bsd ;; convex-c34) basic_machine=c34-convex os=-bsd ;; convex-c38) basic_machine=c38-convex os=-bsd ;; cray | j90) basic_machine=j90-cray os=-unicos ;; craynv) basic_machine=craynv-cray os=-unicosmp ;; cr16c) basic_machine=cr16c-unknown os=-elf ;; crds | unos) basic_machine=m68k-crds ;; crisv32 | crisv32-* | etraxfs*) basic_machine=crisv32-axis ;; cris | cris-* | etrax*) basic_machine=cris-axis ;; crx) basic_machine=crx-unknown os=-elf ;; da30 | da30-*) basic_machine=m68k-da30 ;; decstation | decstation-3100 | pmax | pmax-* | pmin | dec3100 | decstatn) basic_machine=mips-dec ;; decsystem10* | dec10*) basic_machine=pdp10-dec os=-tops10 ;; decsystem20* | dec20*) basic_machine=pdp10-dec os=-tops20 ;; delta | 3300 | motorola-3300 | motorola-delta \ | 3300-motorola | delta-motorola) basic_machine=m68k-motorola ;; delta88) basic_machine=m88k-motorola os=-sysv3 ;; djgpp) basic_machine=i586-pc os=-msdosdjgpp ;; dpx20 | dpx20-*) basic_machine=rs6000-bull os=-bosx ;; dpx2* | dpx2*-bull) basic_machine=m68k-bull os=-sysv3 ;; ebmon29k) basic_machine=a29k-amd os=-ebmon ;; elxsi) basic_machine=elxsi-elxsi os=-bsd ;; encore | umax | mmax) basic_machine=ns32k-encore ;; es1800 | OSE68k | ose68k | ose | OSE) basic_machine=m68k-ericsson os=-ose ;; fx2800) basic_machine=i860-alliant ;; genix) basic_machine=ns32k-ns ;; gmicro) basic_machine=tron-gmicro os=-sysv ;; go32) basic_machine=i386-pc os=-go32 ;; h3050r* | hiux*) basic_machine=hppa1.1-hitachi os=-hiuxwe2 ;; h8300hms) basic_machine=h8300-hitachi os=-hms ;; h8300xray) basic_machine=h8300-hitachi os=-xray ;; h8500hms) basic_machine=h8500-hitachi os=-hms ;; harris) basic_machine=m88k-harris os=-sysv3 ;; hp300-*) basic_machine=m68k-hp ;; hp300bsd) basic_machine=m68k-hp os=-bsd ;; hp300hpux) basic_machine=m68k-hp os=-hpux ;; hp3k9[0-9][0-9] | hp9[0-9][0-9]) basic_machine=hppa1.0-hp ;; hp9k2[0-9][0-9] | hp9k31[0-9]) basic_machine=m68000-hp ;; hp9k3[2-9][0-9]) basic_machine=m68k-hp ;; hp9k6[0-9][0-9] | hp6[0-9][0-9]) basic_machine=hppa1.0-hp ;; hp9k7[0-79][0-9] | hp7[0-79][0-9]) basic_machine=hppa1.1-hp ;; hp9k78[0-9] | hp78[0-9]) # FIXME: really hppa2.0-hp basic_machine=hppa1.1-hp ;; hp9k8[67]1 | hp8[67]1 | hp9k80[24] | hp80[24] | hp9k8[78]9 | hp8[78]9 | hp9k893 | hp893) # FIXME: really hppa2.0-hp basic_machine=hppa1.1-hp ;; hp9k8[0-9][13679] | hp8[0-9][13679]) basic_machine=hppa1.1-hp ;; hp9k8[0-9][0-9] | hp8[0-9][0-9]) basic_machine=hppa1.0-hp ;; hppa-next) os=-nextstep3 ;; hppaosf) basic_machine=hppa1.1-hp os=-osf ;; hppro) basic_machine=hppa1.1-hp os=-proelf ;; i370-ibm* | ibm*) basic_machine=i370-ibm ;; # I'm not sure what "Sysv32" means. Should this be sysv3.2? i*86v32) basic_machine=`echo $1 | sed -e 's/86.*/86-pc/'` os=-sysv32 ;; i*86v4*) basic_machine=`echo $1 | sed -e 's/86.*/86-pc/'` os=-sysv4 ;; i*86v) basic_machine=`echo $1 | sed -e 's/86.*/86-pc/'` os=-sysv ;; i*86sol2) basic_machine=`echo $1 | sed -e 's/86.*/86-pc/'` os=-solaris2 ;; i386mach) basic_machine=i386-mach os=-mach ;; i386-vsta | vsta) basic_machine=i386-unknown os=-vsta ;; iris | iris4d) basic_machine=mips-sgi case $os in -irix*) ;; *) os=-irix4 ;; esac ;; isi68 | isi) basic_machine=m68k-isi os=-sysv ;; m88k-omron*) basic_machine=m88k-omron ;; magnum | m3230) basic_machine=mips-mips os=-sysv ;; merlin) basic_machine=ns32k-utek os=-sysv ;; mingw32) basic_machine=i386-pc os=-mingw32 ;; miniframe) basic_machine=m68000-convergent ;; *mint | -mint[0-9]* | *MiNT | *MiNT[0-9]*) basic_machine=m68k-atari os=-mint ;; mips3*-*) basic_machine=`echo $basic_machine | sed -e 's/mips3/mips64/'` ;; mips3*) basic_machine=`echo $basic_machine | sed -e 's/mips3/mips64/'`-unknown ;; monitor) basic_machine=m68k-rom68k os=-coff ;; morphos) basic_machine=powerpc-unknown os=-morphos ;; msdos) basic_machine=i386-pc os=-msdos ;; mvs) basic_machine=i370-ibm os=-mvs ;; ncr3000) basic_machine=i486-ncr os=-sysv4 ;; netbsd386) basic_machine=i386-unknown os=-netbsd ;; netwinder) basic_machine=armv4l-rebel os=-linux ;; news | news700 | news800 | news900) basic_machine=m68k-sony os=-newsos ;; news1000) basic_machine=m68030-sony os=-newsos ;; news-3600 | risc-news) basic_machine=mips-sony os=-newsos ;; necv70) basic_machine=v70-nec os=-sysv ;; next | m*-next ) basic_machine=m68k-next case $os in -nextstep* ) ;; -ns2*) os=-nextstep2 ;; *) os=-nextstep3 ;; esac ;; nh3000) basic_machine=m68k-harris os=-cxux ;; nh[45]000) basic_machine=m88k-harris os=-cxux ;; nindy960) basic_machine=i960-intel os=-nindy ;; mon960) basic_machine=i960-intel os=-mon960 ;; nonstopux) basic_machine=mips-compaq os=-nonstopux ;; np1) basic_machine=np1-gould ;; nsr-tandem) basic_machine=nsr-tandem ;; op50n-* | op60c-*) basic_machine=hppa1.1-oki os=-proelf ;; or32 | or32-*) basic_machine=or32-unknown os=-coff ;; os400) basic_machine=powerpc-ibm os=-os400 ;; OSE68000 | ose68000) basic_machine=m68000-ericsson os=-ose ;; os68k) basic_machine=m68k-none os=-os68k ;; pa-hitachi) basic_machine=hppa1.1-hitachi os=-hiuxwe2 ;; paragon) basic_machine=i860-intel os=-osf ;; pbd) basic_machine=sparc-tti ;; pbb) basic_machine=m68k-tti ;; pc532 | pc532-*) basic_machine=ns32k-pc532 ;; pentium | p5 | k5 | k6 | nexgen | viac3) basic_machine=i586-pc ;; pentiumpro | p6 | 6x86 | athlon | athlon_*) basic_machine=i686-pc ;; pentiumii | pentium2 | pentiumiii | pentium3) basic_machine=i686-pc ;; pentium4) basic_machine=i786-pc ;; pentium-* | p5-* | k5-* | k6-* | nexgen-* | viac3-*) basic_machine=i586-`echo $basic_machine | sed 's/^[^-]*-//'` ;; pentiumpro-* | p6-* | 6x86-* | athlon-*) basic_machine=i686-`echo $basic_machine | sed 's/^[^-]*-//'` ;; pentiumii-* | pentium2-* | pentiumiii-* | pentium3-*) basic_machine=i686-`echo $basic_machine | sed 's/^[^-]*-//'` ;; pentium4-*) basic_machine=i786-`echo $basic_machine | sed 's/^[^-]*-//'` ;; pn) basic_machine=pn-gould ;; power) basic_machine=power-ibm ;; ppc) basic_machine=powerpc-unknown ;; ppc-*) basic_machine=powerpc-`echo $basic_machine | sed 's/^[^-]*-//'` ;; ppcle | powerpclittle | ppc-le | powerpc-little) basic_machine=powerpcle-unknown ;; ppcle-* | powerpclittle-*) basic_machine=powerpcle-`echo $basic_machine | sed 's/^[^-]*-//'` ;; ppc64) basic_machine=powerpc64-unknown ;; ppc64-*) basic_machine=powerpc64-`echo $basic_machine | sed 's/^[^-]*-//'` ;; ppc64le | powerpc64little | ppc64-le | powerpc64-little) basic_machine=powerpc64le-unknown ;; ppc64le-* | powerpc64little-*) basic_machine=powerpc64le-`echo $basic_machine | sed 's/^[^-]*-//'` ;; ps2) basic_machine=i386-ibm ;; pw32) basic_machine=i586-unknown os=-pw32 ;; rom68k) basic_machine=m68k-rom68k os=-coff ;; rm[46]00) basic_machine=mips-siemens ;; rtpc | rtpc-*) basic_machine=romp-ibm ;; s390 | s390-*) basic_machine=s390-ibm ;; s390x | s390x-*) basic_machine=s390x-ibm ;; sa29200) basic_machine=a29k-amd os=-udi ;; sb1) basic_machine=mipsisa64sb1-unknown ;; sb1el) basic_machine=mipsisa64sb1el-unknown ;; sei) basic_machine=mips-sei os=-seiux ;; sequent) basic_machine=i386-sequent ;; sh) basic_machine=sh-hitachi os=-hms ;; sh64) basic_machine=sh64-unknown ;; sparclite-wrs | simso-wrs) basic_machine=sparclite-wrs os=-vxworks ;; sps7) basic_machine=m68k-bull os=-sysv2 ;; spur) basic_machine=spur-unknown ;; st2000) basic_machine=m68k-tandem ;; stratus) basic_machine=i860-stratus os=-sysv4 ;; sun2) basic_machine=m68000-sun ;; sun2os3) basic_machine=m68000-sun os=-sunos3 ;; sun2os4) basic_machine=m68000-sun os=-sunos4 ;; sun3os3) basic_machine=m68k-sun os=-sunos3 ;; sun3os4) basic_machine=m68k-sun os=-sunos4 ;; sun4os3) basic_machine=sparc-sun os=-sunos3 ;; sun4os4) basic_machine=sparc-sun os=-sunos4 ;; sun4sol2) basic_machine=sparc-sun os=-solaris2 ;; sun3 | sun3-*) basic_machine=m68k-sun ;; sun4) basic_machine=sparc-sun ;; sun386 | sun386i | roadrunner) basic_machine=i386-sun ;; sv1) basic_machine=sv1-cray os=-unicos ;; symmetry) basic_machine=i386-sequent os=-dynix ;; t3e) basic_machine=alphaev5-cray os=-unicos ;; t90) basic_machine=t90-cray os=-unicos ;; tic54x | c54x*) basic_machine=tic54x-unknown os=-coff ;; tic55x | c55x*) basic_machine=tic55x-unknown os=-coff ;; tic6x | c6x*) basic_machine=tic6x-unknown os=-coff ;; tx39) basic_machine=mipstx39-unknown ;; tx39el) basic_machine=mipstx39el-unknown ;; toad1) basic_machine=pdp10-xkl os=-tops20 ;; tower | tower-32) basic_machine=m68k-ncr ;; tpf) basic_machine=s390x-ibm os=-tpf ;; udi29k) basic_machine=a29k-amd os=-udi ;; ultra3) basic_machine=a29k-nyu os=-sym1 ;; v810 | necv810) basic_machine=v810-nec os=-none ;; vaxv) basic_machine=vax-dec os=-sysv ;; vms) basic_machine=vax-dec os=-vms ;; vpp*|vx|vx-*) basic_machine=f301-fujitsu ;; vxworks960) basic_machine=i960-wrs os=-vxworks ;; vxworks68) basic_machine=m68k-wrs os=-vxworks ;; vxworks29k) basic_machine=a29k-wrs os=-vxworks ;; w65*) basic_machine=w65-wdc os=-none ;; w89k-*) basic_machine=hppa1.1-winbond os=-proelf ;; xbox) basic_machine=i686-pc os=-mingw32 ;; xps | xps100) basic_machine=xps100-honeywell ;; ymp) basic_machine=ymp-cray os=-unicos ;; z8k-*-coff) basic_machine=z8k-unknown os=-sim ;; none) basic_machine=none-none os=-none ;; # Here we handle the default manufacturer of certain CPU types. It is in # some cases the only manufacturer, in others, it is the most popular. w89k) basic_machine=hppa1.1-winbond ;; op50n) basic_machine=hppa1.1-oki ;; op60c) basic_machine=hppa1.1-oki ;; romp) basic_machine=romp-ibm ;; mmix) basic_machine=mmix-knuth ;; rs6000) basic_machine=rs6000-ibm ;; vax) basic_machine=vax-dec ;; pdp10) # there are many clones, so DEC is not a safe bet basic_machine=pdp10-unknown ;; pdp11) basic_machine=pdp11-dec ;; we32k) basic_machine=we32k-att ;; sh3 | sh4 | sh[34]eb | sh[1234]le | sh[23]ele) basic_machine=sh-unknown ;; sh64) basic_machine=sh64-unknown ;; sparc | sparcv8 | sparcv9 | sparcv9b) basic_machine=sparc-sun ;; cydra) basic_machine=cydra-cydrome ;; orion) basic_machine=orion-highlevel ;; orion105) basic_machine=clipper-highlevel ;; mac | mpw | mac-mpw) basic_machine=m68k-apple ;; pmac | pmac-mpw) basic_machine=powerpc-apple ;; *-unknown) # Make sure to match an already-canonicalized machine name. ;; *) echo Invalid configuration \`$1\': machine \`$basic_machine\' not recognized 1>&2 exit 1 ;; esac # Here we canonicalize certain aliases for manufacturers. case $basic_machine in *-digital*) basic_machine=`echo $basic_machine | sed 's/digital.*/dec/'` ;; *-commodore*) basic_machine=`echo $basic_machine | sed 's/commodore.*/cbm/'` ;; *) ;; esac # Decode manufacturer-specific aliases for certain operating systems. if [ x"$os" != x"" ] then case $os in # First match some system type aliases # that might get confused with valid system types. # -solaris* is a basic system type, with this one exception. -solaris1 | -solaris1.*) os=`echo $os | sed -e 's|solaris1|sunos4|'` ;; -solaris) os=-solaris2 ;; -svr4*) os=-sysv4 ;; -unixware*) os=-sysv4.2uw ;; -gnu/linux*) os=`echo $os | sed -e 's|gnu/linux|linux-gnu|'` ;; # First accept the basic system types. # The portable systems comes first. # Each alternative MUST END IN A *, to match a version number. # -sysv* is not here because it comes later, after sysvr4. -gnu* | -bsd* | -mach* | -minix* | -genix* | -ultrix* | -irix* \ | -*vms* | -sco* | -esix* | -isc* | -aix* | -sunos | -sunos[34]*\ | -hpux* | -unos* | -osf* | -luna* | -dgux* | -solaris* | -sym* \ | -amigaos* | -amigados* | -msdos* | -newsos* | -unicos* | -aof* \ | -aos* \ | -nindy* | -vxsim* | -vxworks* | -ebmon* | -hms* | -mvs* \ | -clix* | -riscos* | -uniplus* | -iris* | -rtu* | -xenix* \ | -hiux* | -386bsd* | -knetbsd* | -mirbsd* | -netbsd* | -openbsd* \ | -ekkobsd* | -kfreebsd* | -freebsd* | -riscix* | -lynxos* \ | -bosx* | -nextstep* | -cxux* | -aout* | -elf* | -oabi* \ | -ptx* | -coff* | -ecoff* | -winnt* | -domain* | -vsta* \ | -udi* | -eabi* | -lites* | -ieee* | -go32* | -aux* \ | -chorusos* | -chorusrdb* \ | -cygwin* | -pe* | -psos* | -moss* | -proelf* | -rtems* \ | -mingw32* | -linux-gnu* | -linux-uclibc* | -uxpv* | -beos* | -mpeix* | -udk* \ | -interix* | -uwin* | -mks* | -rhapsody* | -darwin* | -opened* \ | -openstep* | -oskit* | -conix* | -pw32* | -nonstopux* \ | -storm-chaos* | -tops10* | -tenex* | -tops20* | -its* \ | -os2* | -vos* | -palmos* | -uclinux* | -nucleus* \ | -morphos* | -superux* | -rtmk* | -rtmk-nova* | -windiss* \ | -powermax* | -dnix* | -nx6 | -nx7 | -sei* | -dragonfly*) # Remember, each alternative MUST END IN *, to match a version number. ;; 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We pick the logical manufacturer. vendor=unknown case $basic_machine in *-unknown) case $os in -riscix*) vendor=acorn ;; -sunos*) vendor=sun ;; -aix*) vendor=ibm ;; -beos*) vendor=be ;; -hpux*) vendor=hp ;; -mpeix*) vendor=hp ;; -hiux*) vendor=hitachi ;; -unos*) vendor=crds ;; -dgux*) vendor=dg ;; -luna*) vendor=omron ;; -genix*) vendor=ns ;; -mvs* | -opened*) vendor=ibm ;; -os400*) vendor=ibm ;; -ptx*) vendor=sequent ;; -tpf*) vendor=ibm ;; -vxsim* | -vxworks* | -windiss*) vendor=wrs ;; -aux*) vendor=apple ;; -hms*) vendor=hitachi ;; -mpw* | -macos*) vendor=apple ;; -*mint | -mint[0-9]* | -*MiNT | -MiNT[0-9]*) vendor=atari ;; -vos*) vendor=stratus ;; esac basic_machine=`echo $basic_machine | sed "s/unknown/$vendor/"` ;; esac echo $basic_machine$os exit 0 # Local variables: # eval: (add-hook 'write-file-hooks 'time-stamp) # time-stamp-start: "timestamp='" # time-stamp-format: "%:y-%02m-%02d" # time-stamp-end: "'" # End: garli-2.1-release/config/depcomp000066400000000000000000000275331241236125200167170ustar00rootroot00000000000000#! /bin/sh # depcomp - compile a program generating dependencies as side-effects # Copyright 1999, 2000 Free Software Foundation, Inc. # This program is free software; you can redistribute it and/or modify # it under the terms of the GNU General Public License as published by # the Free Software Foundation; either version 2, or (at your option) # any later version. # This program is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU General Public License for more details. # You should have received a copy of the GNU General Public License # along with this program; if not, write to the Free Software # Foundation, Inc., 59 Temple Place - Suite 330, Boston, MA # 02111-1307, USA. # As a special exception to the GNU General Public License, if you # distribute this file as part of a program that contains a # configuration script generated by Autoconf, you may include it under # the same distribution terms that you use for the rest of that program. # Originally written by Alexandre Oliva . if test -z "$depmode" || test -z "$source" || test -z "$object"; then echo "depcomp: Variables source, object and depmode must be set" 1>&2 exit 1 fi # `libtool' can also be set to `yes' or `no'. if test -z "$depfile"; then base=`echo "$object" | sed -e 's,^.*/,,' -e 's,\.\([^.]*\)$,.P\1,'` dir=`echo "$object" | sed 's,/.*$,/,'` if test "$dir" = "$object"; then dir= fi # FIXME: should be _deps on DOS. depfile="$dir.deps/$base" fi tmpdepfile=${tmpdepfile-`echo "$depfile" | sed 's/\.\([^.]*\)$/.T\1/'`} rm -f "$tmpdepfile" # Some modes work just like other modes, but use different flags. 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"$tmpdepfile" | sed 's% %\\ %g' | sed -n '/^\(.*\)$/ s::\1\::p' >> "$depfile" rm -f "$tmpdepfile" ;; none) exec "$@" ;; *) echo "Unknown depmode $depmode" 1>&2 exit 1 ;; esac exit 0 garli-2.1-release/config/install-sh000077500000000000000000000127011241236125200173400ustar00rootroot00000000000000#!/bin/sh # # install - install a program, script, or datafile # This comes from X11R5 (mit/util/scripts/install.sh). # # Copyright 1991 by the Massachusetts Institute of Technology # # Permission to use, copy, modify, distribute, and sell this software and its # documentation for any purpose is hereby granted without fee, provided that # the above copyright notice appear in all copies and that both that # copyright notice and this permission notice appear in supporting # documentation, and that the name of M.I.T. not be used in advertising or # publicity pertaining to distribution of the software without specific, # written prior permission. M.I.T. makes no representations about the # suitability of this software for any purpose. It is provided "as is" # without express or implied warranty. # # Calling this script install-sh is preferred over install.sh, to prevent # `make' implicit rules from creating a file called install from it # when there is no Makefile. # # This script is compatible with the BSD install script, but was written # from scratch. It can only install one file at a time, a restriction # shared with many OS's install programs. # set DOITPROG to echo to test this script # Don't use :- since 4.3BSD and earlier shells don't like it. doit="${DOITPROG-}" # put in absolute paths if you don't have them in your path; or use env. vars. mvprog="${MVPROG-mv}" cpprog="${CPPROG-cp}" chmodprog="${CHMODPROG-chmod}" chownprog="${CHOWNPROG-chown}" chgrpprog="${CHGRPPROG-chgrp}" stripprog="${STRIPPROG-strip}" rmprog="${RMPROG-rm}" mkdirprog="${MKDIRPROG-mkdir}" transformbasename="" transform_arg="" instcmd="$mvprog" chmodcmd="$chmodprog 0755" chowncmd="" chgrpcmd="" stripcmd="" rmcmd="$rmprog -f" mvcmd="$mvprog" src="" dst="" dir_arg="" while [ x"$1" != x ]; do case $1 in -c) instcmd="$cpprog" shift continue;; -d) dir_arg=true shift continue;; -m) chmodcmd="$chmodprog $2" shift shift continue;; -o) chowncmd="$chownprog $2" shift shift continue;; -g) chgrpcmd="$chgrpprog $2" shift shift continue;; -s) stripcmd="$stripprog" shift continue;; -t=*) transformarg=`echo $1 | sed 's/-t=//'` shift continue;; -b=*) transformbasename=`echo $1 | sed 's/-b=//'` shift continue;; *) if [ x"$src" = x ] then src=$1 else # this colon is to work around a 386BSD /bin/sh bug : dst=$1 fi shift continue;; esac done if [ x"$src" = x ] then echo "install: no input file specified" exit 1 else : fi if [ x"$dir_arg" != x ]; then dst=$src src="" if [ -d $dst ]; then instcmd=: chmodcmd="" else instcmd=$mkdirprog fi else # Waiting for this to be detected by the "$instcmd $src $dsttmp" command # might cause directories to be created, which would be especially bad # if $src (and thus $dsttmp) contains '*'. if [ -f "$src" ] || [ -d "$src" ] then : else echo "install: $src does not exist" exit 1 fi if [ x"$dst" = x ] then echo "install: no destination specified" exit 1 else : fi # If destination is a directory, append the input filename; if your system # does not like double slashes in filenames, you may need to add some logic if [ -d $dst ] then dst="$dst"/`basename $src` else : fi fi ## this sed command emulates the dirname command dstdir=`echo $dst | sed -e 's,[^/]*$,,;s,/$,,;s,^$,.,'` # Make sure that the destination directory exists. # this part is taken from Noah Friedman's mkinstalldirs script # Skip lots of stat calls in the usual case. if [ ! -d "$dstdir" ]; then defaultIFS=' ' IFS="${IFS-${defaultIFS}}" oIFS="${IFS}" # Some sh's can't handle IFS=/ for some reason. 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See the # GNU General Public License for more details. # # You should have received a copy of the GNU General Public License # along with GNU Libtool; see the file COPYING. If not, a copy # can be downloaded from http://www.gnu.org/licenses/gpl.html, or # obtained by writing to the Free Software Foundation, Inc., # 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301, USA. ]) # serial 57 LT_INIT # LT_PREREQ(VERSION) # ------------------ # Complain and exit if this libtool version is less that VERSION. m4_defun([LT_PREREQ], [m4_if(m4_version_compare(m4_defn([LT_PACKAGE_VERSION]), [$1]), -1, [m4_default([$3], [m4_fatal([Libtool version $1 or higher is required], 63)])], [$2])]) # _LT_CHECK_BUILDDIR # ------------------ # Complain if the absolute build directory name contains unusual characters m4_defun([_LT_CHECK_BUILDDIR], [case `pwd` in *\ * | *\ *) AC_MSG_WARN([Libtool does not cope well with whitespace in `pwd`]) ;; esac ]) # LT_INIT([OPTIONS]) # ------------------ AC_DEFUN([LT_INIT], [AC_PREREQ([2.58])dnl We use AC_INCLUDES_DEFAULT AC_REQUIRE([AC_CONFIG_AUX_DIR_DEFAULT])dnl AC_BEFORE([$0], [LT_LANG])dnl AC_BEFORE([$0], [LT_OUTPUT])dnl AC_BEFORE([$0], [LTDL_INIT])dnl m4_require([_LT_CHECK_BUILDDIR])dnl dnl Autoconf doesn't catch unexpanded LT_ macros by default: m4_pattern_forbid([^_?LT_[A-Z_]+$])dnl m4_pattern_allow([^(_LT_EOF|LT_DLGLOBAL|LT_DLLAZY_OR_NOW|LT_MULTI_MODULE)$])dnl dnl aclocal doesn't pull ltoptions.m4, ltsugar.m4, or ltversion.m4 dnl unless we require an AC_DEFUNed macro: AC_REQUIRE([LTOPTIONS_VERSION])dnl AC_REQUIRE([LTSUGAR_VERSION])dnl AC_REQUIRE([LTVERSION_VERSION])dnl AC_REQUIRE([LTOBSOLETE_VERSION])dnl m4_require([_LT_PROG_LTMAIN])dnl _LT_SHELL_INIT([SHELL=${CONFIG_SHELL-/bin/sh}]) dnl Parse OPTIONS _LT_SET_OPTIONS([$0], [$1]) # This can be used to rebuild libtool when needed LIBTOOL_DEPS="$ltmain" # Always use our own libtool. LIBTOOL='$(SHELL) $(top_builddir)/libtool' AC_SUBST(LIBTOOL)dnl _LT_SETUP # Only expand once: m4_define([LT_INIT]) ])# LT_INIT # Old names: AU_ALIAS([AC_PROG_LIBTOOL], [LT_INIT]) AU_ALIAS([AM_PROG_LIBTOOL], [LT_INIT]) dnl aclocal-1.4 backwards compatibility: dnl AC_DEFUN([AC_PROG_LIBTOOL], []) dnl AC_DEFUN([AM_PROG_LIBTOOL], []) # _LT_CC_BASENAME(CC) # ------------------- # Calculate cc_basename. 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In configure, this macro expands # each variable declared with _LT_DECL (and _LT_TAGDECL) into: # # ='`$ECHO "$" | $SED "$delay_single_quote_subst"`' m4_defun([_LT_CONFIG_STATUS_DECLARATIONS], [m4_foreach([_lt_var], m4_quote(lt_decl_all_varnames), [m4_n([_LT_CONFIG_STATUS_DECLARE(_lt_var)])])]) # _LT_LIBTOOL_TAGS # ---------------- # Output comment and list of tags supported by the script m4_defun([_LT_LIBTOOL_TAGS], [_LT_FORMAT_COMMENT([The names of the tagged configurations supported by this script])dnl available_tags="_LT_TAGS"dnl ]) # _LT_LIBTOOL_DECLARE(VARNAME, [TAG]) # ----------------------------------- # Extract the dictionary values for VARNAME (optionally with TAG) and # expand to a commented shell variable setting: # # # Some comment about what VAR is for. # visible_name=$lt_internal_name m4_define([_LT_LIBTOOL_DECLARE], [_LT_FORMAT_COMMENT(m4_quote(lt_dict_fetch([lt_decl_dict], [$1], [description])))[]dnl m4_pushdef([_libtool_name], m4_quote(lt_dict_fetch([lt_decl_dict], [$1], [libtool_name])))[]dnl m4_case(m4_quote(lt_dict_fetch([lt_decl_dict], [$1], [value])), [0], [_libtool_name=[$]$1], [1], [_libtool_name=$lt_[]$1], [2], [_libtool_name=$lt_[]$1], [_libtool_name=lt_dict_fetch([lt_decl_dict], [$1], [value])])[]dnl m4_ifval([$2], [_$2])[]m4_popdef([_libtool_name])[]dnl ]) # _LT_LIBTOOL_CONFIG_VARS # ----------------------- # Produce commented declarations of non-tagged libtool config variables # suitable for insertion in the LIBTOOL CONFIG section of the `libtool' # script. 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hppa*64*) [lt_cv_deplibs_check_method='file_magic (s[0-9][0-9][0-9]|ELF[ -][0-9][0-9])(-bit)?( [LM]SB)? shared object( file)?[, -]* PA-RISC [0-9]\.[0-9]'] lt_cv_file_magic_test_file=/usr/lib/pa20_64/libc.sl ;; *) lt_cv_deplibs_check_method='file_magic (s[[0-9]][[0-9]][[0-9]]|PA-RISC[[0-9]]\.[[0-9]]) shared library' lt_cv_file_magic_test_file=/usr/lib/libc.sl ;; esac ;; interix[[3-9]]*) # PIC code is broken on Interix 3.x, that's why |\.a not |_pic\.a here lt_cv_deplibs_check_method='match_pattern /lib[[^/]]+(\.so|\.a)$' ;; irix5* | irix6* | nonstopux*) case $LD in *-32|*"-32 ") libmagic=32-bit;; *-n32|*"-n32 ") libmagic=N32;; *-64|*"-64 ") libmagic=64-bit;; *) libmagic=never-match;; esac lt_cv_deplibs_check_method=pass_all ;; # This must be Linux ELF. linux* | k*bsd*-gnu | kopensolaris*-gnu) lt_cv_deplibs_check_method=pass_all ;; netbsd*) if echo __ELF__ | $CC -E - | $GREP __ELF__ > /dev/null; then lt_cv_deplibs_check_method='match_pattern /lib[[^/]]+(\.so\.[[0-9]]+\.[[0-9]]+|_pic\.a)$' else lt_cv_deplibs_check_method='match_pattern /lib[[^/]]+(\.so|_pic\.a)$' fi ;; 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then # Let the user override the test. lt_cv_path_NM="$NM" else lt_nm_to_check="${ac_tool_prefix}nm" if test -n "$ac_tool_prefix" && test "$build" = "$host"; then lt_nm_to_check="$lt_nm_to_check nm" fi for lt_tmp_nm in $lt_nm_to_check; do lt_save_ifs="$IFS"; IFS=$PATH_SEPARATOR for ac_dir in $PATH /usr/ccs/bin/elf /usr/ccs/bin /usr/ucb /bin; do IFS="$lt_save_ifs" test -z "$ac_dir" && ac_dir=. tmp_nm="$ac_dir/$lt_tmp_nm" if test -f "$tmp_nm" || test -f "$tmp_nm$ac_exeext" ; then # Check to see if the nm accepts a BSD-compat flag. # Adding the `sed 1q' prevents false positives on HP-UX, which says: # nm: unknown option "B" ignored # Tru64's nm complains that /dev/null is an invalid object file case `"$tmp_nm" -B /dev/null 2>&1 | sed '1q'` in */dev/null* | *'Invalid file or object type'*) lt_cv_path_NM="$tmp_nm -B" break ;; *) case `"$tmp_nm" -p /dev/null 2>&1 | sed '1q'` in */dev/null*) lt_cv_path_NM="$tmp_nm -p" break ;; *) lt_cv_path_NM=${lt_cv_path_NM="$tmp_nm"} # keep the first match, but continue # so that we can try to find one that supports BSD flags ;; 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then lt_cv_nm_interface="MS dumpbin" fi rm -f conftest*]) ])# LT_PATH_NM # Old names: AU_ALIAS([AM_PROG_NM], [LT_PATH_NM]) AU_ALIAS([AC_PROG_NM], [LT_PATH_NM]) dnl aclocal-1.4 backwards compatibility: dnl AC_DEFUN([AM_PROG_NM], []) dnl AC_DEFUN([AC_PROG_NM], []) # _LT_CHECK_SHAREDLIB_FROM_LINKLIB # -------------------------------- # how to determine the name of the shared library # associated with a specific link library. # -- PORTME fill in with the dynamic library characteristics m4_defun([_LT_CHECK_SHAREDLIB_FROM_LINKLIB], [m4_require([_LT_DECL_EGREP]) m4_require([_LT_DECL_OBJDUMP]) m4_require([_LT_DECL_DLLTOOL]) AC_CACHE_CHECK([how to associate runtime and link libraries], lt_cv_sharedlib_from_linklib_cmd, [lt_cv_sharedlib_from_linklib_cmd='unknown' case $host_os in cygwin* | mingw* | pw32* | cegcc*) # two different shell functions defined in ltmain.sh # decide which to use based on capabilities of $DLLTOOL case `$DLLTOOL --help 2>&1` in *--identify-strict*) lt_cv_sharedlib_from_linklib_cmd=func_cygming_dll_for_implib ;; *) lt_cv_sharedlib_from_linklib_cmd=func_cygming_dll_for_implib_fallback ;; esac ;; *) # fallback: assume linklib IS sharedlib lt_cv_sharedlib_from_linklib_cmd="$ECHO" ;; esac ]) sharedlib_from_linklib_cmd=$lt_cv_sharedlib_from_linklib_cmd test -z "$sharedlib_from_linklib_cmd" && sharedlib_from_linklib_cmd=$ECHO _LT_DECL([], [sharedlib_from_linklib_cmd], [1], [Command to associate shared and link libraries]) ])# _LT_CHECK_SHAREDLIB_FROM_LINKLIB # _LT_PATH_MANIFEST_TOOL # ---------------------- # locate the manifest tool m4_defun([_LT_PATH_MANIFEST_TOOL], [AC_CHECK_TOOL(MANIFEST_TOOL, mt, :) test -z "$MANIFEST_TOOL" && MANIFEST_TOOL=mt AC_CACHE_CHECK([if $MANIFEST_TOOL is a manifest tool], [lt_cv_path_mainfest_tool], [lt_cv_path_mainfest_tool=no echo "$as_me:$LINENO: $MANIFEST_TOOL '-?'" >&AS_MESSAGE_LOG_FD $MANIFEST_TOOL '-?' 2>conftest.err > conftest.out cat conftest.err >&AS_MESSAGE_LOG_FD if $GREP 'Manifest Tool' conftest.out > /dev/null; then lt_cv_path_mainfest_tool=yes fi rm -f conftest*]) if test "x$lt_cv_path_mainfest_tool" != xyes; then MANIFEST_TOOL=: fi _LT_DECL([], [MANIFEST_TOOL], [1], [Manifest tool])dnl ])# _LT_PATH_MANIFEST_TOOL # LT_LIB_M # -------- # check for math library AC_DEFUN([LT_LIB_M], [AC_REQUIRE([AC_CANONICAL_HOST])dnl LIBM= case $host in *-*-beos* | *-*-cegcc* | *-*-cygwin* | *-*-haiku* | *-*-pw32* | *-*-darwin*) # These system don't have libm, or don't need it ;; *-ncr-sysv4.3*) AC_CHECK_LIB(mw, _mwvalidcheckl, LIBM="-lmw") AC_CHECK_LIB(m, cos, LIBM="$LIBM -lm") ;; *) AC_CHECK_LIB(m, cos, LIBM="-lm") ;; esac AC_SUBST([LIBM]) ])# LT_LIB_M # Old name: AU_ALIAS([AC_CHECK_LIBM], [LT_LIB_M]) dnl aclocal-1.4 backwards compatibility: dnl AC_DEFUN([AC_CHECK_LIBM], []) # _LT_COMPILER_NO_RTTI([TAGNAME]) # ------------------------------- m4_defun([_LT_COMPILER_NO_RTTI], [m4_require([_LT_TAG_COMPILER])dnl _LT_TAGVAR(lt_prog_compiler_no_builtin_flag, $1)= if test "$GCC" = yes; then case $cc_basename in nvcc*) _LT_TAGVAR(lt_prog_compiler_no_builtin_flag, $1)=' -Xcompiler -fno-builtin' ;; *) _LT_TAGVAR(lt_prog_compiler_no_builtin_flag, $1)=' -fno-builtin' ;; esac _LT_COMPILER_OPTION([if $compiler supports -fno-rtti -fno-exceptions], lt_cv_prog_compiler_rtti_exceptions, [-fno-rtti -fno-exceptions], [], [_LT_TAGVAR(lt_prog_compiler_no_builtin_flag, $1)="$_LT_TAGVAR(lt_prog_compiler_no_builtin_flag, $1) -fno-rtti -fno-exceptions"]) fi _LT_TAGDECL([no_builtin_flag], [lt_prog_compiler_no_builtin_flag], [1], [Compiler flag to turn off builtin functions]) ])# _LT_COMPILER_NO_RTTI # _LT_CMD_GLOBAL_SYMBOLS # ---------------------- m4_defun([_LT_CMD_GLOBAL_SYMBOLS], [AC_REQUIRE([AC_CANONICAL_HOST])dnl AC_REQUIRE([AC_PROG_CC])dnl AC_REQUIRE([AC_PROG_AWK])dnl AC_REQUIRE([LT_PATH_NM])dnl AC_REQUIRE([LT_PATH_LD])dnl m4_require([_LT_DECL_SED])dnl m4_require([_LT_DECL_EGREP])dnl m4_require([_LT_TAG_COMPILER])dnl # Check for command to grab the raw symbol name followed by C symbol from nm. 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What could be older than Ultrix?!! ;)] # Character class describing NM global symbol codes. symcode='[[BCDEGRST]]' # Regexp to match symbols that can be accessed directly from C. sympat='\([[_A-Za-z]][[_A-Za-z0-9]]*\)' # Define system-specific variables. case $host_os in aix*) symcode='[[BCDT]]' ;; cygwin* | mingw* | pw32* | cegcc*) symcode='[[ABCDGISTW]]' ;; hpux*) if test "$host_cpu" = ia64; then symcode='[[ABCDEGRST]]' fi ;; irix* | nonstopux*) symcode='[[BCDEGRST]]' ;; osf*) symcode='[[BCDEGQRST]]' ;; solaris*) symcode='[[BDRT]]' ;; sco3.2v5*) symcode='[[DT]]' ;; sysv4.2uw2*) symcode='[[DT]]' ;; sysv5* | sco5v6* | unixware* | OpenUNIX*) symcode='[[ABDT]]' ;; sysv4) symcode='[[DFNSTU]]' ;; esac # If we're using GNU nm, then use its standard symbol codes. case `$NM -V 2>&1` in *GNU* | *'with BFD'*) symcode='[[ABCDGIRSTW]]' ;; esac # Transform an extracted symbol line into a proper C declaration. # Some systems (esp. on ia64) link data and code symbols differently, # so use this general approach. lt_cv_sys_global_symbol_to_cdecl="sed -n -e 's/^T .* \(.*\)$/extern int \1();/p' -e 's/^$symcode* .* \(.*\)$/extern char \1;/p'" # Transform an extracted symbol line into symbol name and symbol address lt_cv_sys_global_symbol_to_c_name_address="sed -n -e 's/^: \([[^ ]]*\)[[ ]]*$/ {\\\"\1\\\", (void *) 0},/p' -e 's/^$symcode* \([[^ ]]*\) \([[^ ]]*\)$/ {\"\2\", (void *) \&\2},/p'" lt_cv_sys_global_symbol_to_c_name_address_lib_prefix="sed -n -e 's/^: \([[^ ]]*\)[[ ]]*$/ {\\\"\1\\\", (void *) 0},/p' -e 's/^$symcode* \([[^ ]]*\) \(lib[[^ ]]*\)$/ {\"\2\", (void *) \&\2},/p' -e 's/^$symcode* \([[^ ]]*\) \([[^ ]]*\)$/ {\"lib\2\", (void *) \&\2},/p'" # Handle CRLF in mingw tool chain opt_cr= case $build_os in mingw*) opt_cr=`$ECHO 'x\{0,1\}' | tr x '\015'` # option cr in regexp ;; esac # Try without a prefix underscore, then with it. for ac_symprfx in "" "_"; do # Transform symcode, sympat, and symprfx into a raw symbol and a C symbol. symxfrm="\\1 $ac_symprfx\\2 \\2" # Write the raw and C identifiers. if test "$lt_cv_nm_interface" = "MS dumpbin"; then # Fake it for dumpbin and say T for any non-static function # and D for any global variable. # Also find C++ and __fastcall symbols from MSVC++, # which start with @ or ?. lt_cv_sys_global_symbol_pipe="$AWK ['"\ " {last_section=section; section=\$ 3};"\ " /Section length .*#relocs.*(pick any)/{hide[last_section]=1};"\ " \$ 0!~/External *\|/{next};"\ " / 0+ UNDEF /{next}; / UNDEF \([^|]\)*()/{next};"\ " {if(hide[section]) next};"\ " {f=0}; \$ 0~/\(\).*\|/{f=1}; {printf f ? \"T \" : \"D \"};"\ " {split(\$ 0, a, /\||\r/); split(a[2], s)};"\ " s[1]~/^[@?]/{print s[1], s[1]; next};"\ " s[1]~prfx {split(s[1],t,\"@\"); print t[1], substr(t[1],length(prfx))}"\ " ' prfx=^$ac_symprfx]" else lt_cv_sys_global_symbol_pipe="sed -n -e 's/^.*[[ ]]\($symcode$symcode*\)[[ ]][[ ]]*$ac_symprfx$sympat$opt_cr$/$symxfrm/p'" fi lt_cv_sys_global_symbol_pipe="$lt_cv_sys_global_symbol_pipe | sed '/ __gnu_lto/d'" # Check to see that the pipe works correctly. pipe_works=no rm -f conftest* cat > conftest.$ac_ext <<_LT_EOF #ifdef __cplusplus extern "C" { #endif char nm_test_var; void nm_test_func(void); void nm_test_func(void){} #ifdef __cplusplus } #endif int main(){nm_test_var='a';nm_test_func();return(0);} _LT_EOF if AC_TRY_EVAL(ac_compile); then # Now try to grab the symbols. nlist=conftest.nm if AC_TRY_EVAL(NM conftest.$ac_objext \| "$lt_cv_sys_global_symbol_pipe" \> $nlist) && test -s "$nlist"; then # Try sorting and uniquifying the output. if sort "$nlist" | uniq > "$nlist"T; then mv -f "$nlist"T "$nlist" else rm -f "$nlist"T fi # Make sure that we snagged all the symbols we need. if $GREP ' nm_test_var$' "$nlist" >/dev/null; then if $GREP ' nm_test_func$' "$nlist" >/dev/null; then cat <<_LT_EOF > conftest.$ac_ext /* Keep this code in sync between libtool.m4, ltmain, lt_system.h, and tests. */ #if defined(_WIN32) || defined(__CYGWIN__) || defined(_WIN32_WCE) /* DATA imports from DLLs on WIN32 con't be const, because runtime relocations are performed -- see ld's documentation on pseudo-relocs. */ # define LT@&t@_DLSYM_CONST #elif defined(__osf__) /* This system does not cope well with relocations in const data. */ # define LT@&t@_DLSYM_CONST #else # define LT@&t@_DLSYM_CONST const #endif #ifdef __cplusplus extern "C" { #endif _LT_EOF # Now generate the symbol file. eval "$lt_cv_sys_global_symbol_to_cdecl"' < "$nlist" | $GREP -v main >> conftest.$ac_ext' cat <<_LT_EOF >> conftest.$ac_ext /* The mapping between symbol names and symbols. */ LT@&t@_DLSYM_CONST struct { const char *name; void *address; } lt__PROGRAM__LTX_preloaded_symbols[[]] = { { "@PROGRAM@", (void *) 0 }, _LT_EOF $SED "s/^$symcode$symcode* \(.*\) \(.*\)$/ {\"\2\", (void *) \&\2},/" < "$nlist" | $GREP -v main >> conftest.$ac_ext cat <<\_LT_EOF >> conftest.$ac_ext {0, (void *) 0} }; /* This works around a problem in FreeBSD linker */ #ifdef FREEBSD_WORKAROUND static const void *lt_preloaded_setup() { return lt__PROGRAM__LTX_preloaded_symbols; } #endif #ifdef __cplusplus } #endif _LT_EOF # Now try linking the two files. mv conftest.$ac_objext conftstm.$ac_objext lt_globsym_save_LIBS=$LIBS lt_globsym_save_CFLAGS=$CFLAGS LIBS="conftstm.$ac_objext" CFLAGS="$CFLAGS$_LT_TAGVAR(lt_prog_compiler_no_builtin_flag, $1)" if AC_TRY_EVAL(ac_link) && test -s conftest${ac_exeext}; then pipe_works=yes fi LIBS=$lt_globsym_save_LIBS CFLAGS=$lt_globsym_save_CFLAGS else echo "cannot find nm_test_func in $nlist" >&AS_MESSAGE_LOG_FD fi else echo "cannot find nm_test_var in $nlist" >&AS_MESSAGE_LOG_FD fi else echo "cannot run $lt_cv_sys_global_symbol_pipe" >&AS_MESSAGE_LOG_FD fi else echo "$progname: failed program was:" >&AS_MESSAGE_LOG_FD cat conftest.$ac_ext >&5 fi rm -rf conftest* conftst* # Do not use the global_symbol_pipe unless it works. if test "$pipe_works" = yes; then break else lt_cv_sys_global_symbol_pipe= fi done ]) if test -z "$lt_cv_sys_global_symbol_pipe"; then lt_cv_sys_global_symbol_to_cdecl= fi if test -z "$lt_cv_sys_global_symbol_pipe$lt_cv_sys_global_symbol_to_cdecl"; then AC_MSG_RESULT(failed) else AC_MSG_RESULT(ok) fi # Response file support. if test "$lt_cv_nm_interface" = "MS dumpbin"; then nm_file_list_spec='@' elif $NM --help 2>/dev/null | grep '[[@]]FILE' >/dev/null; then nm_file_list_spec='@' fi _LT_DECL([global_symbol_pipe], [lt_cv_sys_global_symbol_pipe], [1], [Take the output of nm and produce a listing of raw symbols and C names]) _LT_DECL([global_symbol_to_cdecl], [lt_cv_sys_global_symbol_to_cdecl], [1], [Transform the output of nm in a proper C declaration]) _LT_DECL([global_symbol_to_c_name_address], [lt_cv_sys_global_symbol_to_c_name_address], [1], [Transform the output of nm in a C name address pair]) _LT_DECL([global_symbol_to_c_name_address_lib_prefix], [lt_cv_sys_global_symbol_to_c_name_address_lib_prefix], [1], [Transform the output of nm in a C name address pair when lib prefix is needed]) _LT_DECL([], [nm_file_list_spec], [1], [Specify filename containing input files for $NM]) ]) # _LT_CMD_GLOBAL_SYMBOLS # _LT_COMPILER_PIC([TAGNAME]) # --------------------------- m4_defun([_LT_COMPILER_PIC], [m4_require([_LT_TAG_COMPILER])dnl _LT_TAGVAR(lt_prog_compiler_wl, $1)= _LT_TAGVAR(lt_prog_compiler_pic, $1)= _LT_TAGVAR(lt_prog_compiler_static, $1)= m4_if([$1], [CXX], [ # C++ specific cases for pic, static, wl, etc. if test "$GXX" = yes; then _LT_TAGVAR(lt_prog_compiler_wl, $1)='-Wl,' _LT_TAGVAR(lt_prog_compiler_static, $1)='-static' case $host_os in aix*) # All AIX code is PIC. if test "$host_cpu" = ia64; then # AIX 5 now supports IA64 processor _LT_TAGVAR(lt_prog_compiler_static, $1)='-Bstatic' fi ;; amigaos*) case $host_cpu in powerpc) # see comment about AmigaOS4 .so support _LT_TAGVAR(lt_prog_compiler_pic, $1)='-fPIC' ;; m68k) # FIXME: we need at least 68020 code to build shared libraries, but # adding the `-m68020' flag to GCC prevents building anything better, # like `-m68040'. _LT_TAGVAR(lt_prog_compiler_pic, $1)='-m68020 -resident32 -malways-restore-a4' ;; esac ;; beos* | irix5* | irix6* | nonstopux* | osf3* | osf4* | osf5*) # PIC is the default for these OSes. ;; mingw* | cygwin* | os2* | pw32* | cegcc*) # This hack is so that the source file can tell whether it is being # built for inclusion in a dll (and should export symbols for example). # Although the cygwin gcc ignores -fPIC, still need this for old-style # (--disable-auto-import) libraries m4_if([$1], [GCJ], [], [_LT_TAGVAR(lt_prog_compiler_pic, $1)='-DDLL_EXPORT']) ;; darwin* | rhapsody*) # PIC is the default on this platform # Common symbols not allowed in MH_DYLIB files _LT_TAGVAR(lt_prog_compiler_pic, $1)='-fno-common' ;; *djgpp*) # DJGPP does not support shared libraries at all _LT_TAGVAR(lt_prog_compiler_pic, $1)= ;; haiku*) # PIC is the default for Haiku. # The "-static" flag exists, but is broken. _LT_TAGVAR(lt_prog_compiler_static, $1)= ;; interix[[3-9]]*) # Interix 3.x gcc -fpic/-fPIC options generate broken code. # Instead, we relocate shared libraries at runtime. ;; sysv4*MP*) if test -d /usr/nec; then _LT_TAGVAR(lt_prog_compiler_pic, $1)=-Kconform_pic fi ;; hpux*) # PIC is the default for 64-bit PA HP-UX, but not for 32-bit # PA HP-UX. On IA64 HP-UX, PIC is the default but the pic flag # sets the default TLS model and affects inlining. case $host_cpu in hppa*64*) ;; *) _LT_TAGVAR(lt_prog_compiler_pic, $1)='-fPIC' ;; esac ;; *qnx* | *nto*) # QNX uses GNU C++, but need to define -shared option too, otherwise # it will coredump. _LT_TAGVAR(lt_prog_compiler_pic, $1)='-fPIC -shared' ;; *) _LT_TAGVAR(lt_prog_compiler_pic, $1)='-fPIC' ;; esac else case $host_os in aix[[4-9]]*) # All AIX code is PIC. if test "$host_cpu" = ia64; then # AIX 5 now supports IA64 processor _LT_TAGVAR(lt_prog_compiler_static, $1)='-Bstatic' else _LT_TAGVAR(lt_prog_compiler_static, $1)='-bnso -bI:/lib/syscalls.exp' fi ;; chorus*) case $cc_basename in cxch68*) # Green Hills C++ Compiler # _LT_TAGVAR(lt_prog_compiler_static, $1)="--no_auto_instantiation -u __main -u __premain -u _abort -r $COOL_DIR/lib/libOrb.a $MVME_DIR/lib/CC/libC.a $MVME_DIR/lib/classix/libcx.s.a" ;; esac ;; mingw* | cygwin* | os2* | pw32* | cegcc*) # This hack is so that the source file can tell whether it is being # built for inclusion in a dll (and should export symbols for example). m4_if([$1], [GCJ], [], [_LT_TAGVAR(lt_prog_compiler_pic, $1)='-DDLL_EXPORT']) ;; dgux*) case $cc_basename in ec++*) _LT_TAGVAR(lt_prog_compiler_pic, $1)='-KPIC' ;; ghcx*) # Green Hills C++ Compiler _LT_TAGVAR(lt_prog_compiler_pic, $1)='-pic' ;; *) ;; esac ;; freebsd* | dragonfly*) # FreeBSD uses GNU C++ ;; hpux9* | hpux10* | hpux11*) case $cc_basename in CC*) _LT_TAGVAR(lt_prog_compiler_wl, $1)='-Wl,' _LT_TAGVAR(lt_prog_compiler_static, $1)='${wl}-a ${wl}archive' if test "$host_cpu" != ia64; then _LT_TAGVAR(lt_prog_compiler_pic, $1)='+Z' fi ;; aCC*) _LT_TAGVAR(lt_prog_compiler_wl, $1)='-Wl,' _LT_TAGVAR(lt_prog_compiler_static, $1)='${wl}-a ${wl}archive' case $host_cpu in hppa*64*|ia64*) # +Z the default ;; *) _LT_TAGVAR(lt_prog_compiler_pic, $1)='+Z' ;; esac ;; *) ;; esac ;; interix*) # This is c89, which is MS Visual C++ (no shared libs) # Anyone wants to do a port? ;; irix5* | irix6* | nonstopux*) case $cc_basename in CC*) _LT_TAGVAR(lt_prog_compiler_wl, $1)='-Wl,' _LT_TAGVAR(lt_prog_compiler_static, $1)='-non_shared' # CC pic flag -KPIC is the default. ;; *) ;; esac ;; linux* | k*bsd*-gnu | kopensolaris*-gnu) case $cc_basename in KCC*) # KAI C++ Compiler _LT_TAGVAR(lt_prog_compiler_wl, $1)='--backend -Wl,' _LT_TAGVAR(lt_prog_compiler_pic, $1)='-fPIC' ;; ecpc* ) # old Intel C++ for x86_64 which still supported -KPIC. _LT_TAGVAR(lt_prog_compiler_wl, $1)='-Wl,' _LT_TAGVAR(lt_prog_compiler_pic, $1)='-KPIC' _LT_TAGVAR(lt_prog_compiler_static, $1)='-static' ;; icpc* ) # Intel C++, used to be incompatible with GCC. # ICC 10 doesn't accept -KPIC any more. _LT_TAGVAR(lt_prog_compiler_wl, $1)='-Wl,' _LT_TAGVAR(lt_prog_compiler_pic, $1)='-fPIC' _LT_TAGVAR(lt_prog_compiler_static, $1)='-static' ;; pgCC* | pgcpp*) # Portland Group C++ compiler _LT_TAGVAR(lt_prog_compiler_wl, $1)='-Wl,' _LT_TAGVAR(lt_prog_compiler_pic, $1)='-fpic' _LT_TAGVAR(lt_prog_compiler_static, $1)='-Bstatic' ;; cxx*) # Compaq C++ # Make sure the PIC flag is empty. It appears that all Alpha # Linux and Compaq Tru64 Unix objects are PIC. _LT_TAGVAR(lt_prog_compiler_pic, $1)= _LT_TAGVAR(lt_prog_compiler_static, $1)='-non_shared' ;; xlc* | xlC* | bgxl[[cC]]* | mpixl[[cC]]*) # IBM XL 8.0, 9.0 on PPC and BlueGene _LT_TAGVAR(lt_prog_compiler_wl, $1)='-Wl,' _LT_TAGVAR(lt_prog_compiler_pic, $1)='-qpic' _LT_TAGVAR(lt_prog_compiler_static, $1)='-qstaticlink' ;; *) case `$CC -V 2>&1 | sed 5q` in *Sun\ C*) # Sun C++ 5.9 _LT_TAGVAR(lt_prog_compiler_pic, $1)='-KPIC' _LT_TAGVAR(lt_prog_compiler_static, $1)='-Bstatic' _LT_TAGVAR(lt_prog_compiler_wl, $1)='-Qoption ld ' ;; esac ;; esac ;; lynxos*) ;; m88k*) ;; mvs*) case $cc_basename in cxx*) _LT_TAGVAR(lt_prog_compiler_pic, $1)='-W c,exportall' ;; *) ;; esac ;; netbsd*) ;; *qnx* | *nto*) # QNX uses GNU C++, but need to define -shared option too, otherwise # it will coredump. _LT_TAGVAR(lt_prog_compiler_pic, $1)='-fPIC -shared' ;; osf3* | osf4* | osf5*) case $cc_basename in KCC*) _LT_TAGVAR(lt_prog_compiler_wl, $1)='--backend -Wl,' ;; RCC*) # Rational C++ 2.4.1 _LT_TAGVAR(lt_prog_compiler_pic, $1)='-pic' ;; cxx*) # Digital/Compaq C++ _LT_TAGVAR(lt_prog_compiler_wl, $1)='-Wl,' # Make sure the PIC flag is empty. It appears that all Alpha # Linux and Compaq Tru64 Unix objects are PIC. _LT_TAGVAR(lt_prog_compiler_pic, $1)= _LT_TAGVAR(lt_prog_compiler_static, $1)='-non_shared' ;; *) ;; esac ;; psos*) ;; solaris*) case $cc_basename in CC* | sunCC*) # Sun C++ 4.2, 5.x and Centerline C++ _LT_TAGVAR(lt_prog_compiler_pic, $1)='-KPIC' _LT_TAGVAR(lt_prog_compiler_static, $1)='-Bstatic' _LT_TAGVAR(lt_prog_compiler_wl, $1)='-Qoption ld ' ;; gcx*) # Green Hills C++ Compiler _LT_TAGVAR(lt_prog_compiler_pic, $1)='-PIC' ;; *) ;; esac ;; sunos4*) case $cc_basename in CC*) # Sun C++ 4.x _LT_TAGVAR(lt_prog_compiler_pic, $1)='-pic' _LT_TAGVAR(lt_prog_compiler_static, $1)='-Bstatic' ;; lcc*) # Lucid _LT_TAGVAR(lt_prog_compiler_pic, $1)='-pic' ;; *) ;; esac ;; sysv5* | unixware* | sco3.2v5* | sco5v6* | OpenUNIX*) case $cc_basename in CC*) _LT_TAGVAR(lt_prog_compiler_wl, $1)='-Wl,' _LT_TAGVAR(lt_prog_compiler_pic, $1)='-KPIC' _LT_TAGVAR(lt_prog_compiler_static, $1)='-Bstatic' ;; esac ;; tandem*) case $cc_basename in NCC*) # NonStop-UX NCC 3.20 _LT_TAGVAR(lt_prog_compiler_pic, $1)='-KPIC' ;; *) ;; esac ;; vxworks*) ;; *) _LT_TAGVAR(lt_prog_compiler_can_build_shared, $1)=no ;; esac fi ], [ if test "$GCC" = yes; then _LT_TAGVAR(lt_prog_compiler_wl, $1)='-Wl,' _LT_TAGVAR(lt_prog_compiler_static, $1)='-static' case $host_os in aix*) # All AIX code is PIC. if test "$host_cpu" = ia64; then # AIX 5 now supports IA64 processor _LT_TAGVAR(lt_prog_compiler_static, $1)='-Bstatic' fi ;; amigaos*) case $host_cpu in powerpc) # see comment about AmigaOS4 .so support _LT_TAGVAR(lt_prog_compiler_pic, $1)='-fPIC' ;; m68k) # FIXME: we need at least 68020 code to build shared libraries, but # adding the `-m68020' flag to GCC prevents building anything better, # like `-m68040'. _LT_TAGVAR(lt_prog_compiler_pic, $1)='-m68020 -resident32 -malways-restore-a4' ;; esac ;; beos* | irix5* | irix6* | nonstopux* | osf3* | osf4* | osf5*) # PIC is the default for these OSes. ;; mingw* | cygwin* | pw32* | os2* | cegcc*) # This hack is so that the source file can tell whether it is being # built for inclusion in a dll (and should export symbols for example). # Although the cygwin gcc ignores -fPIC, still need this for old-style # (--disable-auto-import) libraries m4_if([$1], [GCJ], [], [_LT_TAGVAR(lt_prog_compiler_pic, $1)='-DDLL_EXPORT']) ;; darwin* | rhapsody*) # PIC is the default on this platform # Common symbols not allowed in MH_DYLIB files _LT_TAGVAR(lt_prog_compiler_pic, $1)='-fno-common' ;; haiku*) # PIC is the default for Haiku. # The "-static" flag exists, but is broken. _LT_TAGVAR(lt_prog_compiler_static, $1)= ;; hpux*) # PIC is the default for 64-bit PA HP-UX, but not for 32-bit # PA HP-UX. On IA64 HP-UX, PIC is the default but the pic flag # sets the default TLS model and affects inlining. case $host_cpu in hppa*64*) # +Z the default ;; *) _LT_TAGVAR(lt_prog_compiler_pic, $1)='-fPIC' ;; esac ;; interix[[3-9]]*) # Interix 3.x gcc -fpic/-fPIC options generate broken code. # Instead, we relocate shared libraries at runtime. ;; msdosdjgpp*) # Just because we use GCC doesn't mean we suddenly get shared libraries # on systems that don't support them. _LT_TAGVAR(lt_prog_compiler_can_build_shared, $1)=no enable_shared=no ;; *nto* | *qnx*) # QNX uses GNU C++, but need to define -shared option too, otherwise # it will coredump. _LT_TAGVAR(lt_prog_compiler_pic, $1)='-fPIC -shared' ;; sysv4*MP*) if test -d /usr/nec; then _LT_TAGVAR(lt_prog_compiler_pic, $1)=-Kconform_pic fi ;; *) _LT_TAGVAR(lt_prog_compiler_pic, $1)='-fPIC' ;; esac case $cc_basename in nvcc*) # Cuda Compiler Driver 2.2 _LT_TAGVAR(lt_prog_compiler_wl, $1)='-Xlinker ' _LT_TAGVAR(lt_prog_compiler_pic, $1)='-Xcompiler -fPIC' ;; esac else # PORTME Check for flag to pass linker flags through the system compiler. case $host_os in aix*) _LT_TAGVAR(lt_prog_compiler_wl, $1)='-Wl,' if test "$host_cpu" = ia64; then # AIX 5 now supports IA64 processor _LT_TAGVAR(lt_prog_compiler_static, $1)='-Bstatic' else _LT_TAGVAR(lt_prog_compiler_static, $1)='-bnso -bI:/lib/syscalls.exp' fi ;; mingw* | cygwin* | pw32* | os2* | cegcc*) # This hack is so that the source file can tell whether it is being # built for inclusion in a dll (and should export symbols for example). m4_if([$1], [GCJ], [], [_LT_TAGVAR(lt_prog_compiler_pic, $1)='-DDLL_EXPORT']) ;; hpux9* | hpux10* | hpux11*) _LT_TAGVAR(lt_prog_compiler_wl, $1)='-Wl,' # PIC is the default for IA64 HP-UX and 64-bit HP-UX, but # not for PA HP-UX. case $host_cpu in hppa*64*|ia64*) # +Z the default ;; *) _LT_TAGVAR(lt_prog_compiler_pic, $1)='+Z' ;; esac # Is there a better lt_prog_compiler_static that works with the bundled CC? _LT_TAGVAR(lt_prog_compiler_static, $1)='${wl}-a ${wl}archive' ;; irix5* | irix6* | nonstopux*) _LT_TAGVAR(lt_prog_compiler_wl, $1)='-Wl,' # PIC (with -KPIC) is the default. _LT_TAGVAR(lt_prog_compiler_static, $1)='-non_shared' ;; linux* | k*bsd*-gnu | kopensolaris*-gnu) case $cc_basename in # old Intel for x86_64 which still supported -KPIC. ecc*) _LT_TAGVAR(lt_prog_compiler_wl, $1)='-Wl,' _LT_TAGVAR(lt_prog_compiler_pic, $1)='-KPIC' _LT_TAGVAR(lt_prog_compiler_static, $1)='-static' ;; # icc used to be incompatible with GCC. # ICC 10 doesn't accept -KPIC any more. icc* | ifort*) _LT_TAGVAR(lt_prog_compiler_wl, $1)='-Wl,' _LT_TAGVAR(lt_prog_compiler_pic, $1)='-fPIC' _LT_TAGVAR(lt_prog_compiler_static, $1)='-static' ;; # Lahey Fortran 8.1. lf95*) _LT_TAGVAR(lt_prog_compiler_wl, $1)='-Wl,' _LT_TAGVAR(lt_prog_compiler_pic, $1)='--shared' _LT_TAGVAR(lt_prog_compiler_static, $1)='--static' ;; nagfor*) # NAG Fortran compiler _LT_TAGVAR(lt_prog_compiler_wl, $1)='-Wl,-Wl,,' _LT_TAGVAR(lt_prog_compiler_pic, $1)='-PIC' _LT_TAGVAR(lt_prog_compiler_static, $1)='-Bstatic' ;; pgcc* | pgf77* | pgf90* | pgf95* | pgfortran*) # Portland Group compilers (*not* the Pentium gcc compiler, # which looks to be a dead project) _LT_TAGVAR(lt_prog_compiler_wl, $1)='-Wl,' _LT_TAGVAR(lt_prog_compiler_pic, $1)='-fpic' _LT_TAGVAR(lt_prog_compiler_static, $1)='-Bstatic' ;; ccc*) _LT_TAGVAR(lt_prog_compiler_wl, $1)='-Wl,' # All Alpha code is PIC. _LT_TAGVAR(lt_prog_compiler_static, $1)='-non_shared' ;; xl* | bgxl* | bgf* | mpixl*) # IBM XL C 8.0/Fortran 10.1, 11.1 on PPC and BlueGene _LT_TAGVAR(lt_prog_compiler_wl, $1)='-Wl,' _LT_TAGVAR(lt_prog_compiler_pic, $1)='-qpic' _LT_TAGVAR(lt_prog_compiler_static, $1)='-qstaticlink' ;; *) case `$CC -V 2>&1 | sed 5q` in *Sun\ F* | *Sun*Fortran*) # Sun Fortran 8.3 passes all unrecognized flags to the linker _LT_TAGVAR(lt_prog_compiler_pic, $1)='-KPIC' _LT_TAGVAR(lt_prog_compiler_static, $1)='-Bstatic' _LT_TAGVAR(lt_prog_compiler_wl, $1)='' ;; *Sun\ C*) # Sun C 5.9 _LT_TAGVAR(lt_prog_compiler_pic, $1)='-KPIC' _LT_TAGVAR(lt_prog_compiler_static, $1)='-Bstatic' _LT_TAGVAR(lt_prog_compiler_wl, $1)='-Wl,' ;; esac ;; esac ;; newsos6) _LT_TAGVAR(lt_prog_compiler_pic, $1)='-KPIC' _LT_TAGVAR(lt_prog_compiler_static, $1)='-Bstatic' ;; *nto* | *qnx*) # QNX uses GNU C++, but need to define -shared option too, otherwise # it will coredump. _LT_TAGVAR(lt_prog_compiler_pic, $1)='-fPIC -shared' ;; osf3* | osf4* | osf5*) _LT_TAGVAR(lt_prog_compiler_wl, $1)='-Wl,' # All OSF/1 code is PIC. _LT_TAGVAR(lt_prog_compiler_static, $1)='-non_shared' ;; rdos*) _LT_TAGVAR(lt_prog_compiler_static, $1)='-non_shared' ;; solaris*) _LT_TAGVAR(lt_prog_compiler_pic, $1)='-KPIC' _LT_TAGVAR(lt_prog_compiler_static, $1)='-Bstatic' case $cc_basename in f77* | f90* | f95* | sunf77* | sunf90* | sunf95*) _LT_TAGVAR(lt_prog_compiler_wl, $1)='-Qoption ld ';; *) _LT_TAGVAR(lt_prog_compiler_wl, $1)='-Wl,';; esac ;; sunos4*) _LT_TAGVAR(lt_prog_compiler_wl, $1)='-Qoption ld ' _LT_TAGVAR(lt_prog_compiler_pic, $1)='-PIC' _LT_TAGVAR(lt_prog_compiler_static, $1)='-Bstatic' ;; sysv4 | sysv4.2uw2* | sysv4.3*) _LT_TAGVAR(lt_prog_compiler_wl, $1)='-Wl,' _LT_TAGVAR(lt_prog_compiler_pic, $1)='-KPIC' _LT_TAGVAR(lt_prog_compiler_static, $1)='-Bstatic' ;; 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FIXME _LT_TAGVAR(archive_cmds, $1)='$CC -nostart $libobjs $deplibs $compiler_flags ${wl}-soname $wl$soname -o $lib' else _LT_TAGVAR(ld_shlibs, $1)=no fi ;; cygwin* | mingw* | pw32* | cegcc*) # _LT_TAGVAR(hardcode_libdir_flag_spec, $1) is actually meaningless, # as there is no search path for DLLs. _LT_TAGVAR(hardcode_libdir_flag_spec, $1)='-L$libdir' _LT_TAGVAR(export_dynamic_flag_spec, $1)='${wl}--export-all-symbols' _LT_TAGVAR(allow_undefined_flag, $1)=unsupported _LT_TAGVAR(always_export_symbols, $1)=no _LT_TAGVAR(enable_shared_with_static_runtimes, $1)=yes _LT_TAGVAR(export_symbols_cmds, $1)='$NM $libobjs $convenience | $global_symbol_pipe | $SED -e '\''/^[[BCDGRS]][[ ]]/s/.*[[ ]]\([[^ ]]*\)/\1 DATA/;s/^.*[[ ]]__nm__\([[^ ]]*\)[[ ]][[^ ]]*/\1 DATA/;/^I[[ ]]/d;/^[[AITW]][[ ]]/s/.* //'\'' | sort | uniq > $export_symbols' _LT_TAGVAR(exclude_expsyms, $1)=['[_]+GLOBAL_OFFSET_TABLE_|[_]+GLOBAL__[FID]_.*|[_]+head_[A-Za-z0-9_]+_dll|[A-Za-z0-9_]+_dll_iname'] if $LD --help 2>&1 | $GREP 'auto-import' > /dev/null; then _LT_TAGVAR(archive_cmds, $1)='$CC -shared $libobjs $deplibs $compiler_flags -o $output_objdir/$soname ${wl}--enable-auto-image-base -Xlinker --out-implib -Xlinker $lib' # If the export-symbols file already is a .def file (1st line # is EXPORTS), use it as is; otherwise, prepend... _LT_TAGVAR(archive_expsym_cmds, $1)='if test "x`$SED 1q $export_symbols`" = xEXPORTS; then cp $export_symbols $output_objdir/$soname.def; else echo EXPORTS > $output_objdir/$soname.def; cat $export_symbols >> $output_objdir/$soname.def; fi~ $CC -shared $output_objdir/$soname.def $libobjs $deplibs $compiler_flags -o $output_objdir/$soname ${wl}--enable-auto-image-base -Xlinker --out-implib -Xlinker $lib' else _LT_TAGVAR(ld_shlibs, $1)=no fi ;; haiku*) _LT_TAGVAR(archive_cmds, $1)='$CC -shared $libobjs $deplibs $compiler_flags ${wl}-soname $wl$soname -o $lib' _LT_TAGVAR(link_all_deplibs, $1)=yes ;; interix[[3-9]]*) _LT_TAGVAR(hardcode_direct, $1)=no _LT_TAGVAR(hardcode_shlibpath_var, $1)=no _LT_TAGVAR(hardcode_libdir_flag_spec, $1)='${wl}-rpath,$libdir' _LT_TAGVAR(export_dynamic_flag_spec, $1)='${wl}-E' # Hack: On Interix 3.x, we cannot compile PIC because of a broken gcc. # Instead, shared libraries are loaded at an image base (0x10000000 by # default) and relocated if they conflict, which is a slow very memory # consuming and fragmenting process. 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|| _lt_function_replace_fail=: ]) # _LT_PROG_REPLACE_SHELLFNS # ------------------------- # Replace existing portable implementations of several shell functions with # equivalent extended shell implementations where those features are available.. m4_defun([_LT_PROG_REPLACE_SHELLFNS], [if test x"$xsi_shell" = xyes; then _LT_PROG_FUNCTION_REPLACE([func_dirname], [dnl case ${1} in */*) func_dirname_result="${1%/*}${2}" ;; * ) func_dirname_result="${3}" ;; esac]) _LT_PROG_FUNCTION_REPLACE([func_basename], [dnl func_basename_result="${1##*/}"]) _LT_PROG_FUNCTION_REPLACE([func_dirname_and_basename], [dnl case ${1} in */*) func_dirname_result="${1%/*}${2}" ;; * ) func_dirname_result="${3}" ;; esac func_basename_result="${1##*/}"]) _LT_PROG_FUNCTION_REPLACE([func_stripname], [dnl # pdksh 5.2.14 does not do ${X%$Y} correctly if both X and Y are # positional parameters, so assign one to ordinary parameter first. func_stripname_result=${3} func_stripname_result=${func_stripname_result#"${1}"} func_stripname_result=${func_stripname_result%"${2}"}]) _LT_PROG_FUNCTION_REPLACE([func_split_long_opt], [dnl func_split_long_opt_name=${1%%=*} func_split_long_opt_arg=${1#*=}]) _LT_PROG_FUNCTION_REPLACE([func_split_short_opt], [dnl func_split_short_opt_arg=${1#??} func_split_short_opt_name=${1%"$func_split_short_opt_arg"}]) _LT_PROG_FUNCTION_REPLACE([func_lo2o], [dnl case ${1} in *.lo) func_lo2o_result=${1%.lo}.${objext} ;; *) func_lo2o_result=${1} ;; esac]) _LT_PROG_FUNCTION_REPLACE([func_xform], [ func_xform_result=${1%.*}.lo]) _LT_PROG_FUNCTION_REPLACE([func_arith], [ func_arith_result=$(( $[*] ))]) _LT_PROG_FUNCTION_REPLACE([func_len], [ func_len_result=${#1}]) fi if test x"$lt_shell_append" = xyes; then _LT_PROG_FUNCTION_REPLACE([func_append], [ eval "${1}+=\\${2}"]) _LT_PROG_FUNCTION_REPLACE([func_append_quoted], [dnl func_quote_for_eval "${2}" dnl m4 expansion turns \\\\ into \\, and then the shell eval turns that into \ eval "${1}+=\\\\ \\$func_quote_for_eval_result"]) # Save a `func_append' function call where possible by direct use of '+=' sed -e 's%func_append \([[a-zA-Z_]]\{1,\}\) "%\1+="%g' $cfgfile > $cfgfile.tmp \ && mv -f "$cfgfile.tmp" "$cfgfile" \ || (rm -f "$cfgfile" && cp "$cfgfile.tmp" "$cfgfile" && rm -f "$cfgfile.tmp") test 0 -eq $? || _lt_function_replace_fail=: else # Save a `func_append' function call even when '+=' is not available sed -e 's%func_append \([[a-zA-Z_]]\{1,\}\) "%\1="$\1%g' $cfgfile > $cfgfile.tmp \ && mv -f "$cfgfile.tmp" "$cfgfile" \ || (rm -f "$cfgfile" && cp "$cfgfile.tmp" "$cfgfile" && rm -f "$cfgfile.tmp") test 0 -eq $? || _lt_function_replace_fail=: fi if test x"$_lt_function_replace_fail" = x":"; then AC_MSG_WARN([Unable to substitute extended shell functions in $ofile]) fi ]) # _LT_PATH_CONVERSION_FUNCTIONS # ----------------------------- # Determine which file name conversion functions should be used by # func_to_host_file (and, implicitly, by func_to_host_path). These are needed # for certain cross-compile configurations and native mingw. m4_defun([_LT_PATH_CONVERSION_FUNCTIONS], [AC_REQUIRE([AC_CANONICAL_HOST])dnl AC_REQUIRE([AC_CANONICAL_BUILD])dnl AC_MSG_CHECKING([how to convert $build file names to $host format]) AC_CACHE_VAL(lt_cv_to_host_file_cmd, [case $host in *-*-mingw* ) case $build in *-*-mingw* ) # actually msys lt_cv_to_host_file_cmd=func_convert_file_msys_to_w32 ;; *-*-cygwin* ) lt_cv_to_host_file_cmd=func_convert_file_cygwin_to_w32 ;; * ) # otherwise, assume *nix lt_cv_to_host_file_cmd=func_convert_file_nix_to_w32 ;; esac ;; *-*-cygwin* ) case $build in *-*-mingw* ) # actually msys lt_cv_to_host_file_cmd=func_convert_file_msys_to_cygwin ;; *-*-cygwin* ) lt_cv_to_host_file_cmd=func_convert_file_noop ;; * ) # otherwise, assume *nix lt_cv_to_host_file_cmd=func_convert_file_nix_to_cygwin ;; esac ;; * ) # unhandled hosts (and "normal" native builds) lt_cv_to_host_file_cmd=func_convert_file_noop ;; esac ]) to_host_file_cmd=$lt_cv_to_host_file_cmd AC_MSG_RESULT([$lt_cv_to_host_file_cmd]) _LT_DECL([to_host_file_cmd], [lt_cv_to_host_file_cmd], [0], [convert $build file names to $host format])dnl AC_MSG_CHECKING([how to convert $build file names to toolchain format]) AC_CACHE_VAL(lt_cv_to_tool_file_cmd, [#assume ordinary cross tools, or native build. lt_cv_to_tool_file_cmd=func_convert_file_noop case $host in *-*-mingw* ) case $build in *-*-mingw* ) # actually msys lt_cv_to_tool_file_cmd=func_convert_file_msys_to_w32 ;; esac ;; esac ]) to_tool_file_cmd=$lt_cv_to_tool_file_cmd AC_MSG_RESULT([$lt_cv_to_tool_file_cmd]) _LT_DECL([to_tool_file_cmd], [lt_cv_to_tool_file_cmd], [0], [convert $build files to toolchain format])dnl ])# _LT_PATH_CONVERSION_FUNCTIONS garli-2.1-release/config/ltmain.sh000066400000000000000000010501711241236125200171600ustar00rootroot00000000000000 # libtool (GNU libtool) 2.4 # Written by Gordon Matzigkeit , 1996 # Copyright (C) 1996, 1997, 1998, 1999, 2000, 2001, 2003, 2004, 2005, 2006, # 2007, 2008, 2009, 2010 Free Software Foundation, Inc. # This is free software; see the source for copying conditions. There is NO # warranty; not even for MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. # GNU Libtool is free software; you can redistribute it and/or modify # it under the terms of the GNU General Public License as published by # the Free Software Foundation; either version 2 of the License, or # (at your option) any later version. # # As a special exception to the GNU General Public License, # if you distribute this file as part of a program or library that # is built using GNU Libtool, you may include this file under the # same distribution terms that you use for the rest of that program. # # GNU Libtool is distributed in the hope that it will be useful, but # WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU # General Public License for more details. # # You should have received a copy of the GNU General Public License # along with GNU Libtool; see the file COPYING. If not, a copy # can be downloaded from http://www.gnu.org/licenses/gpl.html, # or obtained by writing to the Free Software Foundation, Inc., # 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301, USA. # Usage: $progname [OPTION]... [MODE-ARG]... # # Provide generalized library-building support services. # # --config show all configuration variables # --debug enable verbose shell tracing # -n, --dry-run display commands without modifying any files # --features display basic configuration information and exit # --mode=MODE use operation mode MODE # --preserve-dup-deps don't remove duplicate dependency libraries # --quiet, --silent don't print informational messages # --no-quiet, --no-silent # print informational messages (default) # --tag=TAG use configuration variables from tag TAG # -v, --verbose print more informational messages than default # --no-verbose don't print the extra informational messages # --version print version information # -h, --help, --help-all print short, long, or detailed help message # # MODE must be one of the following: # # clean remove files from the build directory # compile compile a source file into a libtool object # execute automatically set library path, then run a program # finish complete the installation of libtool libraries # install install libraries or executables # link create a library or an executable # uninstall remove libraries from an installed directory # # MODE-ARGS vary depending on the MODE. When passed as first option, # `--mode=MODE' may be abbreviated as `MODE' or a unique abbreviation of that. # Try `$progname --help --mode=MODE' for a more detailed description of MODE. # # When reporting a bug, please describe a test case to reproduce it and # include the following information: # # host-triplet: $host # shell: $SHELL # compiler: $LTCC # compiler flags: $LTCFLAGS # linker: $LD (gnu? $with_gnu_ld) # $progname: (GNU libtool) 2.4 # automake: $automake_version # autoconf: $autoconf_version # # Report bugs to . # GNU libtool home page: . # General help using GNU software: . PROGRAM=libtool PACKAGE=libtool VERSION=2.4 TIMESTAMP="" package_revision=1.3293 # Be Bourne compatible if test -n "${ZSH_VERSION+set}" && (emulate sh) >/dev/null 2>&1; then emulate sh NULLCMD=: # Zsh 3.x and 4.x performs word splitting on ${1+"$@"}, which # is contrary to our usage. Disable this feature. alias -g '${1+"$@"}'='"$@"' setopt NO_GLOB_SUBST else case `(set -o) 2>/dev/null` in *posix*) set -o posix;; esac fi BIN_SH=xpg4; export BIN_SH # for Tru64 DUALCASE=1; export DUALCASE # for MKS sh # A function that is used when there is no print builtin or printf. func_fallback_echo () { eval 'cat <<_LTECHO_EOF $1 _LTECHO_EOF' } # NLS nuisances: We save the old values to restore during execute mode. lt_user_locale= lt_safe_locale= for lt_var in LANG LANGUAGE LC_ALL LC_CTYPE LC_COLLATE LC_MESSAGES do eval "if test \"\${$lt_var+set}\" = set; then save_$lt_var=\$$lt_var $lt_var=C export $lt_var lt_user_locale=\"$lt_var=\\\$save_\$lt_var; \$lt_user_locale\" lt_safe_locale=\"$lt_var=C; \$lt_safe_locale\" fi" done LC_ALL=C LANGUAGE=C export LANGUAGE LC_ALL $lt_unset CDPATH # Work around backward compatibility issue on IRIX 6.5. On IRIX 6.4+, sh # is ksh but when the shell is invoked as "sh" and the current value of # the _XPG environment variable is not equal to 1 (one), the special # positional parameter $0, within a function call, is the name of the # function. progpath="$0" : ${CP="cp -f"} test "${ECHO+set}" = set || ECHO=${as_echo-'printf %s\n'} : ${EGREP="grep -E"} : ${FGREP="grep -F"} : ${GREP="grep"} : ${LN_S="ln -s"} : ${MAKE="make"} : ${MKDIR="mkdir"} : ${MV="mv -f"} : ${RM="rm -f"} : ${SED="sed"} : ${SHELL="${CONFIG_SHELL-/bin/sh}"} : ${Xsed="$SED -e 1s/^X//"} # Global variables: EXIT_SUCCESS=0 EXIT_FAILURE=1 EXIT_MISMATCH=63 # $? = 63 is used to indicate version mismatch to missing. EXIT_SKIP=77 # $? = 77 is used to indicate a skipped test to automake. exit_status=$EXIT_SUCCESS # Make sure IFS has a sensible default lt_nl=' ' IFS=" $lt_nl" dirname="s,/[^/]*$,," basename="s,^.*/,," # func_dirname file append nondir_replacement # Compute the dirname of FILE. If nonempty, add APPEND to the result, # otherwise set result to NONDIR_REPLACEMENT. func_dirname () { func_dirname_result=`$ECHO "${1}" | $SED "$dirname"` if test "X$func_dirname_result" = "X${1}"; then func_dirname_result="${3}" else func_dirname_result="$func_dirname_result${2}" fi } # func_dirname may be replaced by extended shell implementation # func_basename file func_basename () { func_basename_result=`$ECHO "${1}" | $SED "$basename"` } # func_basename may be replaced by extended shell implementation # func_dirname_and_basename file append nondir_replacement # perform func_basename and func_dirname in a single function # call: # dirname: Compute the dirname of FILE. If nonempty, # add APPEND to the result, otherwise set result # to NONDIR_REPLACEMENT. # value returned in "$func_dirname_result" # basename: Compute filename of FILE. # value retuned in "$func_basename_result" # Implementation must be kept synchronized with func_dirname # and func_basename. For efficiency, we do not delegate to # those functions but instead duplicate the functionality here. func_dirname_and_basename () { # Extract subdirectory from the argument. func_dirname_result=`$ECHO "${1}" | $SED -e "$dirname"` if test "X$func_dirname_result" = "X${1}"; then func_dirname_result="${3}" else func_dirname_result="$func_dirname_result${2}" fi func_basename_result=`$ECHO "${1}" | $SED -e "$basename"` } # func_dirname_and_basename may be replaced by extended shell implementation # func_stripname prefix suffix name # strip PREFIX and SUFFIX off of NAME. # PREFIX and SUFFIX must not contain globbing or regex special # characters, hashes, percent signs, but SUFFIX may contain a leading # dot (in which case that matches only a dot). # func_strip_suffix prefix name func_stripname () { case ${2} in .*) func_stripname_result=`$ECHO "${3}" | $SED "s%^${1}%%; s%\\\\${2}\$%%"`;; *) func_stripname_result=`$ECHO "${3}" | $SED "s%^${1}%%; s%${2}\$%%"`;; esac } # func_stripname may be replaced by extended shell implementation # These SED scripts presuppose an absolute path with a trailing slash. pathcar='s,^/\([^/]*\).*$,\1,' pathcdr='s,^/[^/]*,,' removedotparts=':dotsl s@/\./@/@g t dotsl s,/\.$,/,' collapseslashes='s@/\{1,\}@/@g' finalslash='s,/*$,/,' # func_normal_abspath PATH # Remove doubled-up and trailing slashes, "." path components, # and cancel out any ".." path components in PATH after making # it an absolute path. # value returned in "$func_normal_abspath_result" func_normal_abspath () { # Start from root dir and reassemble the path. func_normal_abspath_result= func_normal_abspath_tpath=$1 func_normal_abspath_altnamespace= case $func_normal_abspath_tpath in "") # Empty path, that just means $cwd. func_stripname '' '/' "`pwd`" func_normal_abspath_result=$func_stripname_result return ;; # The next three entries are used to spot a run of precisely # two leading slashes without using negated character classes; # we take advantage of case's first-match behaviour. ///*) # Unusual form of absolute path, do nothing. ;; //*) # Not necessarily an ordinary path; POSIX reserves leading '//' # and for example Cygwin uses it to access remote file shares # over CIFS/SMB, so we conserve a leading double slash if found. func_normal_abspath_altnamespace=/ ;; /*) # Absolute path, do nothing. ;; *) # Relative path, prepend $cwd. func_normal_abspath_tpath=`pwd`/$func_normal_abspath_tpath ;; esac # Cancel out all the simple stuff to save iterations. We also want # the path to end with a slash for ease of parsing, so make sure # there is one (and only one) here. func_normal_abspath_tpath=`$ECHO "$func_normal_abspath_tpath" | $SED \ -e "$removedotparts" -e "$collapseslashes" -e "$finalslash"` while :; do # Processed it all yet? if test "$func_normal_abspath_tpath" = / ; then # If we ascended to the root using ".." the result may be empty now. if test -z "$func_normal_abspath_result" ; then func_normal_abspath_result=/ fi break fi func_normal_abspath_tcomponent=`$ECHO "$func_normal_abspath_tpath" | $SED \ -e "$pathcar"` func_normal_abspath_tpath=`$ECHO "$func_normal_abspath_tpath" | $SED \ -e "$pathcdr"` # Figure out what to do with it case $func_normal_abspath_tcomponent in "") # Trailing empty path component, ignore it. ;; ..) # Parent dir; strip last assembled component from result. func_dirname "$func_normal_abspath_result" func_normal_abspath_result=$func_dirname_result ;; *) # Actual path component, append it. func_normal_abspath_result=$func_normal_abspath_result/$func_normal_abspath_tcomponent ;; esac done # Restore leading double-slash if one was found on entry. func_normal_abspath_result=$func_normal_abspath_altnamespace$func_normal_abspath_result } # func_relative_path SRCDIR DSTDIR # generates a relative path from SRCDIR to DSTDIR, with a trailing # slash if non-empty, suitable for immediately appending a filename # without needing to append a separator. # value returned in "$func_relative_path_result" func_relative_path () { func_relative_path_result= func_normal_abspath "$1" func_relative_path_tlibdir=$func_normal_abspath_result func_normal_abspath "$2" func_relative_path_tbindir=$func_normal_abspath_result # Ascend the tree starting from libdir while :; do # check if we have found a prefix of bindir case $func_relative_path_tbindir in $func_relative_path_tlibdir) # found an exact match func_relative_path_tcancelled= break ;; $func_relative_path_tlibdir*) # found a matching prefix func_stripname "$func_relative_path_tlibdir" '' "$func_relative_path_tbindir" func_relative_path_tcancelled=$func_stripname_result if test -z "$func_relative_path_result"; then func_relative_path_result=. fi break ;; *) func_dirname $func_relative_path_tlibdir func_relative_path_tlibdir=${func_dirname_result} if test "x$func_relative_path_tlibdir" = x ; then # Have to descend all the way to the root! func_relative_path_result=../$func_relative_path_result func_relative_path_tcancelled=$func_relative_path_tbindir break fi func_relative_path_result=../$func_relative_path_result ;; esac done # Now calculate path; take care to avoid doubling-up slashes. func_stripname '' '/' "$func_relative_path_result" func_relative_path_result=$func_stripname_result func_stripname '/' '/' "$func_relative_path_tcancelled" if test "x$func_stripname_result" != x ; then func_relative_path_result=${func_relative_path_result}/${func_stripname_result} fi # Normalisation. If bindir is libdir, return empty string, # else relative path ending with a slash; either way, target # file name can be directly appended. if test ! -z "$func_relative_path_result"; then func_stripname './' '' "$func_relative_path_result/" func_relative_path_result=$func_stripname_result fi } # The name of this program: func_dirname_and_basename "$progpath" progname=$func_basename_result # Make sure we have an absolute path for reexecution: case $progpath in [\\/]*|[A-Za-z]:\\*) ;; *[\\/]*) progdir=$func_dirname_result progdir=`cd "$progdir" && pwd` progpath="$progdir/$progname" ;; *) save_IFS="$IFS" IFS=: for progdir in $PATH; do IFS="$save_IFS" test -x "$progdir/$progname" && break done IFS="$save_IFS" test -n "$progdir" || progdir=`pwd` progpath="$progdir/$progname" ;; esac # Sed substitution that helps us do robust quoting. It backslashifies # metacharacters that are still active within double-quoted strings. Xsed="${SED}"' -e 1s/^X//' sed_quote_subst='s/\([`"$\\]\)/\\\1/g' # Same as above, but do not quote variable references. double_quote_subst='s/\(["`\\]\)/\\\1/g' # Sed substitution that turns a string into a regex matching for the # string literally. sed_make_literal_regex='s,[].[^$\\*\/],\\&,g' # Sed substitution that converts a w32 file name or path # which contains forward slashes, into one that contains # (escaped) backslashes. A very naive implementation. lt_sed_naive_backslashify='s|\\\\*|\\|g;s|/|\\|g;s|\\|\\\\|g' # Re-`\' parameter expansions in output of double_quote_subst that were # `\'-ed in input to the same. If an odd number of `\' preceded a '$' # in input to double_quote_subst, that '$' was protected from expansion. # Since each input `\' is now two `\'s, look for any number of runs of # four `\'s followed by two `\'s and then a '$'. `\' that '$'. bs='\\' bs2='\\\\' bs4='\\\\\\\\' dollar='\$' sed_double_backslash="\ s/$bs4/&\\ /g s/^$bs2$dollar/$bs&/ s/\\([^$bs]\\)$bs2$dollar/\\1$bs2$bs$dollar/g s/\n//g" # Standard options: opt_dry_run=false opt_help=false opt_quiet=false opt_verbose=false opt_warning=: # func_echo arg... # Echo program name prefixed message, along with the current mode # name if it has been set yet. func_echo () { $ECHO "$progname: ${opt_mode+$opt_mode: }$*" } # func_verbose arg... # Echo program name prefixed message in verbose mode only. func_verbose () { $opt_verbose && func_echo ${1+"$@"} # A bug in bash halts the script if the last line of a function # fails when set -e is in force, so we need another command to # work around that: : } # func_echo_all arg... # Invoke $ECHO with all args, space-separated. func_echo_all () { $ECHO "$*" } # func_error arg... # Echo program name prefixed message to standard error. func_error () { $ECHO "$progname: ${opt_mode+$opt_mode: }"${1+"$@"} 1>&2 } # func_warning arg... # Echo program name prefixed warning message to standard error. func_warning () { $opt_warning && $ECHO "$progname: ${opt_mode+$opt_mode: }warning: "${1+"$@"} 1>&2 # bash bug again: : } # func_fatal_error arg... # Echo program name prefixed message to standard error, and exit. func_fatal_error () { func_error ${1+"$@"} exit $EXIT_FAILURE } # func_fatal_help arg... # Echo program name prefixed message to standard error, followed by # a help hint, and exit. func_fatal_help () { func_error ${1+"$@"} func_fatal_error "$help" } help="Try \`$progname --help' for more information." ## default # func_grep expression filename # Check whether EXPRESSION matches any line of FILENAME, without output. func_grep () { $GREP "$1" "$2" >/dev/null 2>&1 } # func_mkdir_p directory-path # Make sure the entire path to DIRECTORY-PATH is available. func_mkdir_p () { my_directory_path="$1" my_dir_list= if test -n "$my_directory_path" && test "$opt_dry_run" != ":"; then # Protect directory names starting with `-' case $my_directory_path in -*) my_directory_path="./$my_directory_path" ;; esac # While some portion of DIR does not yet exist... while test ! -d "$my_directory_path"; do # ...make a list in topmost first order. Use a colon delimited # list incase some portion of path contains whitespace. my_dir_list="$my_directory_path:$my_dir_list" # If the last portion added has no slash in it, the list is done case $my_directory_path in */*) ;; *) break ;; esac # ...otherwise throw away the child directory and loop my_directory_path=`$ECHO "$my_directory_path" | $SED -e "$dirname"` done my_dir_list=`$ECHO "$my_dir_list" | $SED 's,:*$,,'` save_mkdir_p_IFS="$IFS"; IFS=':' for my_dir in $my_dir_list; do IFS="$save_mkdir_p_IFS" # mkdir can fail with a `File exist' error if two processes # try to create one of the directories concurrently. Don't # stop in that case! $MKDIR "$my_dir" 2>/dev/null || : done IFS="$save_mkdir_p_IFS" # Bail out if we (or some other process) failed to create a directory. test -d "$my_directory_path" || \ func_fatal_error "Failed to create \`$1'" fi } # func_mktempdir [string] # Make a temporary directory that won't clash with other running # libtool processes, and avoids race conditions if possible. If # given, STRING is the basename for that directory. func_mktempdir () { my_template="${TMPDIR-/tmp}/${1-$progname}" if test "$opt_dry_run" = ":"; then # Return a directory name, but don't create it in dry-run mode my_tmpdir="${my_template}-$$" else # If mktemp works, use that first and foremost my_tmpdir=`mktemp -d "${my_template}-XXXXXXXX" 2>/dev/null` if test ! -d "$my_tmpdir"; then # Failing that, at least try and use $RANDOM to avoid a race my_tmpdir="${my_template}-${RANDOM-0}$$" save_mktempdir_umask=`umask` umask 0077 $MKDIR "$my_tmpdir" umask $save_mktempdir_umask fi # If we're not in dry-run mode, bomb out on failure test -d "$my_tmpdir" || \ func_fatal_error "cannot create temporary directory \`$my_tmpdir'" fi $ECHO "$my_tmpdir" } # func_quote_for_eval arg # Aesthetically quote ARG to be evaled later. # This function returns two values: FUNC_QUOTE_FOR_EVAL_RESULT # is double-quoted, suitable for a subsequent eval, whereas # FUNC_QUOTE_FOR_EVAL_UNQUOTED_RESULT has merely all characters # which are still active within double quotes backslashified. func_quote_for_eval () { case $1 in *[\\\`\"\$]*) func_quote_for_eval_unquoted_result=`$ECHO "$1" | $SED "$sed_quote_subst"` ;; *) func_quote_for_eval_unquoted_result="$1" ;; esac case $func_quote_for_eval_unquoted_result in # Double-quote args containing shell metacharacters to delay # word splitting, command substitution and and variable # expansion for a subsequent eval. # Many Bourne shells cannot handle close brackets correctly # in scan sets, so we specify it separately. *[\[\~\#\^\&\*\(\)\{\}\|\;\<\>\?\'\ \ ]*|*]*|"") func_quote_for_eval_result="\"$func_quote_for_eval_unquoted_result\"" ;; *) func_quote_for_eval_result="$func_quote_for_eval_unquoted_result" esac } # func_quote_for_expand arg # Aesthetically quote ARG to be evaled later; same as above, # but do not quote variable references. func_quote_for_expand () { case $1 in *[\\\`\"]*) my_arg=`$ECHO "$1" | $SED \ -e "$double_quote_subst" -e "$sed_double_backslash"` ;; *) my_arg="$1" ;; esac case $my_arg in # Double-quote args containing shell metacharacters to delay # word splitting and command substitution for a subsequent eval. # Many Bourne shells cannot handle close brackets correctly # in scan sets, so we specify it separately. *[\[\~\#\^\&\*\(\)\{\}\|\;\<\>\?\'\ \ ]*|*]*|"") my_arg="\"$my_arg\"" ;; esac func_quote_for_expand_result="$my_arg" } # func_show_eval cmd [fail_exp] # Unless opt_silent is true, then output CMD. Then, if opt_dryrun is # not true, evaluate CMD. If the evaluation of CMD fails, and FAIL_EXP # is given, then evaluate it. func_show_eval () { my_cmd="$1" my_fail_exp="${2-:}" ${opt_silent-false} || { func_quote_for_expand "$my_cmd" eval "func_echo $func_quote_for_expand_result" } if ${opt_dry_run-false}; then :; else eval "$my_cmd" my_status=$? if test "$my_status" -eq 0; then :; else eval "(exit $my_status); $my_fail_exp" fi fi } # func_show_eval_locale cmd [fail_exp] # Unless opt_silent is true, then output CMD. Then, if opt_dryrun is # not true, evaluate CMD. If the evaluation of CMD fails, and FAIL_EXP # is given, then evaluate it. Use the saved locale for evaluation. func_show_eval_locale () { my_cmd="$1" my_fail_exp="${2-:}" ${opt_silent-false} || { func_quote_for_expand "$my_cmd" eval "func_echo $func_quote_for_expand_result" } if ${opt_dry_run-false}; then :; else eval "$lt_user_locale $my_cmd" my_status=$? eval "$lt_safe_locale" if test "$my_status" -eq 0; then :; else eval "(exit $my_status); $my_fail_exp" fi fi } # func_tr_sh # Turn $1 into a string suitable for a shell variable name. # Result is stored in $func_tr_sh_result. All characters # not in the set a-zA-Z0-9_ are replaced with '_'. Further, # if $1 begins with a digit, a '_' is prepended as well. func_tr_sh () { case $1 in [0-9]* | *[!a-zA-Z0-9_]*) func_tr_sh_result=`$ECHO "$1" | $SED 's/^\([0-9]\)/_\1/; s/[^a-zA-Z0-9_]/_/g'` ;; * ) func_tr_sh_result=$1 ;; esac } # func_version # Echo version message to standard output and exit. func_version () { $opt_debug $SED -n '/(C)/!b go :more /\./!{ N s/\n# / / b more } :go /^# '$PROGRAM' (GNU /,/# warranty; / { s/^# // s/^# *$// s/\((C)\)[ 0-9,-]*\( [1-9][0-9]*\)/\1\2/ p }' < "$progpath" exit $? } # func_usage # Echo short help message to standard output and exit. func_usage () { $opt_debug $SED -n '/^# Usage:/,/^# *.*--help/ { s/^# // s/^# *$// s/\$progname/'$progname'/ p }' < "$progpath" echo $ECHO "run \`$progname --help | more' for full usage" exit $? } # func_help [NOEXIT] # Echo long help message to standard output and exit, # unless 'noexit' is passed as argument. func_help () { $opt_debug $SED -n '/^# Usage:/,/# Report bugs to/ { :print s/^# // s/^# *$// s*\$progname*'$progname'* s*\$host*'"$host"'* s*\$SHELL*'"$SHELL"'* s*\$LTCC*'"$LTCC"'* s*\$LTCFLAGS*'"$LTCFLAGS"'* s*\$LD*'"$LD"'* s/\$with_gnu_ld/'"$with_gnu_ld"'/ s/\$automake_version/'"`(automake --version) 2>/dev/null |$SED 1q`"'/ s/\$autoconf_version/'"`(autoconf --version) 2>/dev/null |$SED 1q`"'/ p d } /^# .* home page:/b print /^# General help using/b print ' < "$progpath" ret=$? if test -z "$1"; then exit $ret fi } # func_missing_arg argname # Echo program name prefixed message to standard error and set global # exit_cmd. func_missing_arg () { $opt_debug func_error "missing argument for $1." exit_cmd=exit } # func_split_short_opt shortopt # Set func_split_short_opt_name and func_split_short_opt_arg shell # variables after splitting SHORTOPT after the 2nd character. func_split_short_opt () { my_sed_short_opt='1s/^\(..\).*$/\1/;q' my_sed_short_rest='1s/^..\(.*\)$/\1/;q' func_split_short_opt_name=`$ECHO "$1" | $SED "$my_sed_short_opt"` func_split_short_opt_arg=`$ECHO "$1" | $SED "$my_sed_short_rest"` } # func_split_short_opt may be replaced by extended shell implementation # func_split_long_opt longopt # Set func_split_long_opt_name and func_split_long_opt_arg shell # variables after splitting LONGOPT at the `=' sign. func_split_long_opt () { my_sed_long_opt='1s/^\(--[^=]*\)=.*/\1/;q' my_sed_long_arg='1s/^--[^=]*=//' func_split_long_opt_name=`$ECHO "$1" | $SED "$my_sed_long_opt"` func_split_long_opt_arg=`$ECHO "$1" | $SED "$my_sed_long_arg"` } # func_split_long_opt may be replaced by extended shell implementation exit_cmd=: magic="%%%MAGIC variable%%%" magic_exe="%%%MAGIC EXE variable%%%" # Global variables. nonopt= preserve_args= lo2o="s/\\.lo\$/.${objext}/" o2lo="s/\\.${objext}\$/.lo/" extracted_archives= extracted_serial=0 # If this variable is set in any of the actions, the command in it # will be execed at the end. This prevents here-documents from being # left over by shells. exec_cmd= # func_append var value # Append VALUE to the end of shell variable VAR. func_append () { eval "${1}=\$${1}\${2}" } # func_append may be replaced by extended shell implementation # func_append_quoted var value # Quote VALUE and append to the end of shell variable VAR, separated # by a space. func_append_quoted () { func_quote_for_eval "${2}" eval "${1}=\$${1}\\ \$func_quote_for_eval_result" } # func_append_quoted may be replaced by extended shell implementation # func_arith arithmetic-term... func_arith () { func_arith_result=`expr "${@}"` } # func_arith may be replaced by extended shell implementation # func_len string # STRING may not start with a hyphen. func_len () { func_len_result=`expr "${1}" : ".*" 2>/dev/null || echo $max_cmd_len` } # func_len may be replaced by extended shell implementation # func_lo2o object func_lo2o () { func_lo2o_result=`$ECHO "${1}" | $SED "$lo2o"` } # func_lo2o may be replaced by extended shell implementation # func_xform libobj-or-source func_xform () { func_xform_result=`$ECHO "${1}" | $SED 's/\.[^.]*$/.lo/'` } # func_xform may be replaced by extended shell implementation # func_fatal_configuration arg... # Echo program name prefixed message to standard error, followed by # a configuration failure hint, and exit. func_fatal_configuration () { func_error ${1+"$@"} func_error "See the $PACKAGE documentation for more information." func_fatal_error "Fatal configuration error." } # func_config # Display the configuration for all the tags in this script. func_config () { re_begincf='^# ### BEGIN LIBTOOL' re_endcf='^# ### END LIBTOOL' # Default configuration. $SED "1,/$re_begincf CONFIG/d;/$re_endcf CONFIG/,\$d" < "$progpath" # Now print the configurations for the tags. for tagname in $taglist; do $SED -n "/$re_begincf TAG CONFIG: $tagname\$/,/$re_endcf TAG CONFIG: $tagname\$/p" < "$progpath" done exit $? } # func_features # Display the features supported by this script. func_features () { echo "host: $host" if test "$build_libtool_libs" = yes; then echo "enable shared libraries" else echo "disable shared libraries" fi if test "$build_old_libs" = yes; then echo "enable static libraries" else echo "disable static libraries" fi exit $? } # func_enable_tag tagname # Verify that TAGNAME is valid, and either flag an error and exit, or # enable the TAGNAME tag. We also add TAGNAME to the global $taglist # variable here. func_enable_tag () { # Global variable: tagname="$1" re_begincf="^# ### BEGIN LIBTOOL TAG CONFIG: $tagname\$" re_endcf="^# ### END LIBTOOL TAG CONFIG: $tagname\$" sed_extractcf="/$re_begincf/,/$re_endcf/p" # Validate tagname. case $tagname in *[!-_A-Za-z0-9,/]*) func_fatal_error "invalid tag name: $tagname" ;; esac # Don't test for the "default" C tag, as we know it's # there but not specially marked. case $tagname in CC) ;; *) if $GREP "$re_begincf" "$progpath" >/dev/null 2>&1; then taglist="$taglist $tagname" # Evaluate the configuration. Be careful to quote the path # and the sed script, to avoid splitting on whitespace, but # also don't use non-portable quotes within backquotes within # quotes we have to do it in 2 steps: extractedcf=`$SED -n -e "$sed_extractcf" < "$progpath"` eval "$extractedcf" else func_error "ignoring unknown tag $tagname" fi ;; esac } # func_check_version_match # Ensure that we are using m4 macros, and libtool script from the same # release of libtool. func_check_version_match () { if test "$package_revision" != "$macro_revision"; then if test "$VERSION" != "$macro_version"; then if test -z "$macro_version"; then cat >&2 <<_LT_EOF $progname: Version mismatch error. This is $PACKAGE $VERSION, but the $progname: definition of this LT_INIT comes from an older release. $progname: You should recreate aclocal.m4 with macros from $PACKAGE $VERSION $progname: and run autoconf again. _LT_EOF else cat >&2 <<_LT_EOF $progname: Version mismatch error. This is $PACKAGE $VERSION, but the $progname: definition of this LT_INIT comes from $PACKAGE $macro_version. $progname: You should recreate aclocal.m4 with macros from $PACKAGE $VERSION $progname: and run autoconf again. _LT_EOF fi else cat >&2 <<_LT_EOF $progname: Version mismatch error. This is $PACKAGE $VERSION, revision $package_revision, $progname: but the definition of this LT_INIT comes from revision $macro_revision. $progname: You should recreate aclocal.m4 with macros from revision $package_revision $progname: of $PACKAGE $VERSION and run autoconf again. _LT_EOF fi exit $EXIT_MISMATCH fi } # Shorthand for --mode=foo, only valid as the first argument case $1 in clean|clea|cle|cl) shift; set dummy --mode clean ${1+"$@"}; shift ;; compile|compil|compi|comp|com|co|c) shift; set dummy --mode compile ${1+"$@"}; shift ;; execute|execut|execu|exec|exe|ex|e) shift; set dummy --mode execute ${1+"$@"}; shift ;; finish|finis|fini|fin|fi|f) shift; set dummy --mode finish ${1+"$@"}; shift ;; install|instal|insta|inst|ins|in|i) shift; set dummy --mode install ${1+"$@"}; shift ;; link|lin|li|l) shift; set dummy --mode link ${1+"$@"}; shift ;; uninstall|uninstal|uninsta|uninst|unins|unin|uni|un|u) shift; set dummy --mode uninstall ${1+"$@"}; shift ;; esac # Option defaults: opt_debug=: opt_dry_run=false opt_config=false opt_preserve_dup_deps=false opt_features=false opt_finish=false opt_help=false opt_help_all=false opt_silent=: opt_verbose=: opt_silent=false opt_verbose=false # Parse options once, thoroughly. This comes as soon as possible in the # script to make things like `--version' happen as quickly as we can. { # this just eases exit handling while test $# -gt 0; do opt="$1" shift case $opt in --debug|-x) opt_debug='set -x' func_echo "enabling shell trace mode" $opt_debug ;; --dry-run|--dryrun|-n) opt_dry_run=: ;; --config) opt_config=: func_config ;; --dlopen|-dlopen) optarg="$1" opt_dlopen="${opt_dlopen+$opt_dlopen }$optarg" shift ;; --preserve-dup-deps) opt_preserve_dup_deps=: ;; --features) opt_features=: func_features ;; --finish) opt_finish=: set dummy --mode finish ${1+"$@"}; shift ;; --help) opt_help=: ;; --help-all) opt_help_all=: opt_help=': help-all' ;; --mode) test $# = 0 && func_missing_arg $opt && break optarg="$1" opt_mode="$optarg" case $optarg in # Valid mode arguments: clean|compile|execute|finish|install|link|relink|uninstall) ;; # Catch anything else as an error *) func_error "invalid argument for $opt" exit_cmd=exit break ;; esac shift ;; --no-silent|--no-quiet) opt_silent=false func_append preserve_args " $opt" ;; --no-verbose) opt_verbose=false func_append preserve_args " $opt" ;; --silent|--quiet) opt_silent=: func_append preserve_args " $opt" opt_verbose=false ;; --verbose|-v) opt_verbose=: func_append preserve_args " $opt" opt_silent=false ;; --tag) test $# = 0 && func_missing_arg $opt && break optarg="$1" opt_tag="$optarg" func_append preserve_args " $opt $optarg" func_enable_tag "$optarg" shift ;; -\?|-h) func_usage ;; --help) func_help ;; --version) func_version ;; # Separate optargs to long options: --*=*) func_split_long_opt "$opt" set dummy "$func_split_long_opt_name" "$func_split_long_opt_arg" ${1+"$@"} shift ;; # Separate non-argument short options: -\?*|-h*|-n*|-v*) func_split_short_opt "$opt" set dummy "$func_split_short_opt_name" "-$func_split_short_opt_arg" ${1+"$@"} shift ;; --) break ;; -*) func_fatal_help "unrecognized option \`$opt'" ;; *) set dummy "$opt" ${1+"$@"}; shift; break ;; esac done # Validate options: # save first non-option argument if test "$#" -gt 0; then nonopt="$opt" shift fi # preserve --debug test "$opt_debug" = : || func_append preserve_args " --debug" case $host in *cygwin* | *mingw* | *pw32* | *cegcc*) # don't eliminate duplications in $postdeps and $predeps opt_duplicate_compiler_generated_deps=: ;; *) opt_duplicate_compiler_generated_deps=$opt_preserve_dup_deps ;; esac $opt_help || { # Sanity checks first: func_check_version_match if test "$build_libtool_libs" != yes && test "$build_old_libs" != yes; then func_fatal_configuration "not configured to build any kind of library" fi # Darwin sucks eval std_shrext=\"$shrext_cmds\" # Only execute mode is allowed to have -dlopen flags. if test -n "$opt_dlopen" && test "$opt_mode" != execute; then func_error "unrecognized option \`-dlopen'" $ECHO "$help" 1>&2 exit $EXIT_FAILURE fi # Change the help message to a mode-specific one. generic_help="$help" help="Try \`$progname --help --mode=$opt_mode' for more information." } # Bail if the options were screwed $exit_cmd $EXIT_FAILURE } ## ----------- ## ## Main. ## ## ----------- ## # func_lalib_p file # True iff FILE is a libtool `.la' library or `.lo' object file. # This function is only a basic sanity check; it will hardly flush out # determined imposters. func_lalib_p () { test -f "$1" && $SED -e 4q "$1" 2>/dev/null \ | $GREP "^# Generated by .*$PACKAGE" > /dev/null 2>&1 } # func_lalib_unsafe_p file # True iff FILE is a libtool `.la' library or `.lo' object file. # This function implements the same check as func_lalib_p without # resorting to external programs. To this end, it redirects stdin and # closes it afterwards, without saving the original file descriptor. # As a safety measure, use it only where a negative result would be # fatal anyway. Works if `file' does not exist. func_lalib_unsafe_p () { lalib_p=no if test -f "$1" && test -r "$1" && exec 5<&0 <"$1"; then for lalib_p_l in 1 2 3 4 do read lalib_p_line case "$lalib_p_line" in \#\ Generated\ by\ *$PACKAGE* ) lalib_p=yes; break;; esac done exec 0<&5 5<&- fi test "$lalib_p" = yes } # func_ltwrapper_script_p file # True iff FILE is a libtool wrapper script # This function is only a basic sanity check; it will hardly flush out # determined imposters. func_ltwrapper_script_p () { func_lalib_p "$1" } # func_ltwrapper_executable_p file # True iff FILE is a libtool wrapper executable # This function is only a basic sanity check; it will hardly flush out # determined imposters. func_ltwrapper_executable_p () { func_ltwrapper_exec_suffix= case $1 in *.exe) ;; *) func_ltwrapper_exec_suffix=.exe ;; esac $GREP "$magic_exe" "$1$func_ltwrapper_exec_suffix" >/dev/null 2>&1 } # func_ltwrapper_scriptname file # Assumes file is an ltwrapper_executable # uses $file to determine the appropriate filename for a # temporary ltwrapper_script. func_ltwrapper_scriptname () { func_dirname_and_basename "$1" "" "." func_stripname '' '.exe' "$func_basename_result" func_ltwrapper_scriptname_result="$func_dirname_result/$objdir/${func_stripname_result}_ltshwrapper" } # func_ltwrapper_p file # True iff FILE is a libtool wrapper script or wrapper executable # This function is only a basic sanity check; it will hardly flush out # determined imposters. func_ltwrapper_p () { func_ltwrapper_script_p "$1" || func_ltwrapper_executable_p "$1" } # func_execute_cmds commands fail_cmd # Execute tilde-delimited COMMANDS. # If FAIL_CMD is given, eval that upon failure. # FAIL_CMD may read-access the current command in variable CMD! func_execute_cmds () { $opt_debug save_ifs=$IFS; IFS='~' for cmd in $1; do IFS=$save_ifs eval cmd=\"$cmd\" func_show_eval "$cmd" "${2-:}" done IFS=$save_ifs } # func_source file # Source FILE, adding directory component if necessary. # Note that it is not necessary on cygwin/mingw to append a dot to # FILE even if both FILE and FILE.exe exist: automatic-append-.exe # behavior happens only for exec(3), not for open(2)! Also, sourcing # `FILE.' does not work on cygwin managed mounts. func_source () { $opt_debug case $1 in */* | *\\*) . "$1" ;; *) . "./$1" ;; esac } # func_resolve_sysroot PATH # Replace a leading = in PATH with a sysroot. Store the result into # func_resolve_sysroot_result func_resolve_sysroot () { func_resolve_sysroot_result=$1 case $func_resolve_sysroot_result in =*) func_stripname '=' '' "$func_resolve_sysroot_result" func_resolve_sysroot_result=$lt_sysroot$func_stripname_result ;; esac } # func_replace_sysroot PATH # If PATH begins with the sysroot, replace it with = and # store the result into func_replace_sysroot_result. func_replace_sysroot () { case "$lt_sysroot:$1" in ?*:"$lt_sysroot"*) func_stripname "$lt_sysroot" '' "$1" func_replace_sysroot_result="=$func_stripname_result" ;; *) # Including no sysroot. func_replace_sysroot_result=$1 ;; esac } # func_infer_tag arg # Infer tagged configuration to use if any are available and # if one wasn't chosen via the "--tag" command line option. # Only attempt this if the compiler in the base compile # command doesn't match the default compiler. # arg is usually of the form 'gcc ...' func_infer_tag () { $opt_debug if test -n "$available_tags" && test -z "$tagname"; then CC_quoted= for arg in $CC; do func_append_quoted CC_quoted "$arg" done CC_expanded=`func_echo_all $CC` CC_quoted_expanded=`func_echo_all $CC_quoted` case $@ in # Blanks in the command may have been stripped by the calling shell, # but not from the CC environment variable when configure was run. " $CC "* | "$CC "* | " $CC_expanded "* | "$CC_expanded "* | \ " $CC_quoted"* | "$CC_quoted "* | " $CC_quoted_expanded "* | "$CC_quoted_expanded "*) ;; # Blanks at the start of $base_compile will cause this to fail # if we don't check for them as well. *) for z in $available_tags; do if $GREP "^# ### BEGIN LIBTOOL TAG CONFIG: $z$" < "$progpath" > /dev/null; then # Evaluate the configuration. eval "`${SED} -n -e '/^# ### BEGIN LIBTOOL TAG CONFIG: '$z'$/,/^# ### END LIBTOOL TAG CONFIG: '$z'$/p' < $progpath`" CC_quoted= for arg in $CC; do # Double-quote args containing other shell metacharacters. func_append_quoted CC_quoted "$arg" done CC_expanded=`func_echo_all $CC` CC_quoted_expanded=`func_echo_all $CC_quoted` case "$@ " in " $CC "* | "$CC "* | " $CC_expanded "* | "$CC_expanded "* | \ " $CC_quoted"* | "$CC_quoted "* | " $CC_quoted_expanded "* | "$CC_quoted_expanded "*) # The compiler in the base compile command matches # the one in the tagged configuration. # Assume this is the tagged configuration we want. tagname=$z break ;; esac fi done # If $tagname still isn't set, then no tagged configuration # was found and let the user know that the "--tag" command # line option must be used. if test -z "$tagname"; then func_echo "unable to infer tagged configuration" func_fatal_error "specify a tag with \`--tag'" # else # func_verbose "using $tagname tagged configuration" fi ;; esac fi } # func_write_libtool_object output_name pic_name nonpic_name # Create a libtool object file (analogous to a ".la" file), # but don't create it if we're doing a dry run. func_write_libtool_object () { write_libobj=${1} if test "$build_libtool_libs" = yes; then write_lobj=\'${2}\' else write_lobj=none fi if test "$build_old_libs" = yes; then write_oldobj=\'${3}\' else write_oldobj=none fi $opt_dry_run || { cat >${write_libobj}T </dev/null` if test "$?" -eq 0 && test -n "${func_convert_core_file_wine_to_w32_tmp}"; then func_convert_core_file_wine_to_w32_result=`$ECHO "$func_convert_core_file_wine_to_w32_tmp" | $SED -e "$lt_sed_naive_backslashify"` else func_convert_core_file_wine_to_w32_result= fi fi } # end: func_convert_core_file_wine_to_w32 # func_convert_core_path_wine_to_w32 ARG # Helper function used by path conversion functions when $build is *nix, and # $host is mingw, cygwin, or some other w32 environment. Relies on a correctly # configured wine environment available, with the winepath program in $build's # $PATH. Assumes ARG has no leading or trailing path separator characters. # # ARG is path to be converted from $build format to win32. # Result is available in $func_convert_core_path_wine_to_w32_result. # Unconvertible file (directory) names in ARG are skipped; if no directory names # are convertible, then the result may be empty. func_convert_core_path_wine_to_w32 () { $opt_debug # unfortunately, winepath doesn't convert paths, only file names func_convert_core_path_wine_to_w32_result="" if test -n "$1"; then oldIFS=$IFS IFS=: for func_convert_core_path_wine_to_w32_f in $1; do IFS=$oldIFS func_convert_core_file_wine_to_w32 "$func_convert_core_path_wine_to_w32_f" if test -n "$func_convert_core_file_wine_to_w32_result" ; then if test -z "$func_convert_core_path_wine_to_w32_result"; then func_convert_core_path_wine_to_w32_result="$func_convert_core_file_wine_to_w32_result" else func_append func_convert_core_path_wine_to_w32_result ";$func_convert_core_file_wine_to_w32_result" fi fi done IFS=$oldIFS fi } # end: func_convert_core_path_wine_to_w32 # func_cygpath ARGS... # Wrapper around calling the cygpath program via LT_CYGPATH. This is used when # when (1) $build is *nix and Cygwin is hosted via a wine environment; or (2) # $build is MSYS and $host is Cygwin, or (3) $build is Cygwin. In case (1) or # (2), returns the Cygwin file name or path in func_cygpath_result (input # file name or path is assumed to be in w32 format, as previously converted # from $build's *nix or MSYS format). In case (3), returns the w32 file name # or path in func_cygpath_result (input file name or path is assumed to be in # Cygwin format). Returns an empty string on error. # # ARGS are passed to cygpath, with the last one being the file name or path to # be converted. # # Specify the absolute *nix (or w32) name to cygpath in the LT_CYGPATH # environment variable; do not put it in $PATH. func_cygpath () { $opt_debug if test -n "$LT_CYGPATH" && test -f "$LT_CYGPATH"; then func_cygpath_result=`$LT_CYGPATH "$@" 2>/dev/null` if test "$?" -ne 0; then # on failure, ensure result is empty func_cygpath_result= fi else func_cygpath_result= func_error "LT_CYGPATH is empty or specifies non-existent file: \`$LT_CYGPATH'" fi } #end: func_cygpath # func_convert_core_msys_to_w32 ARG # Convert file name or path ARG from MSYS format to w32 format. Return # result in func_convert_core_msys_to_w32_result. func_convert_core_msys_to_w32 () { $opt_debug # awkward: cmd appends spaces to result func_convert_core_msys_to_w32_result=`( cmd //c echo "$1" ) 2>/dev/null | $SED -e 's/[ ]*$//' -e "$lt_sed_naive_backslashify"` } #end: func_convert_core_msys_to_w32 # func_convert_file_check ARG1 ARG2 # Verify that ARG1 (a file name in $build format) was converted to $host # format in ARG2. Otherwise, emit an error message, but continue (resetting # func_to_host_file_result to ARG1). func_convert_file_check () { $opt_debug if test -z "$2" && test -n "$1" ; then func_error "Could not determine host file name corresponding to" func_error " \`$1'" func_error "Continuing, but uninstalled executables may not work." # Fallback: func_to_host_file_result="$1" fi } # end func_convert_file_check # func_convert_path_check FROM_PATHSEP TO_PATHSEP FROM_PATH TO_PATH # Verify that FROM_PATH (a path in $build format) was converted to $host # format in TO_PATH. Otherwise, emit an error message, but continue, resetting # func_to_host_file_result to a simplistic fallback value (see below). func_convert_path_check () { $opt_debug if test -z "$4" && test -n "$3"; then func_error "Could not determine the host path corresponding to" func_error " \`$3'" func_error "Continuing, but uninstalled executables may not work." # Fallback. This is a deliberately simplistic "conversion" and # should not be "improved". See libtool.info. if test "x$1" != "x$2"; then lt_replace_pathsep_chars="s|$1|$2|g" func_to_host_path_result=`echo "$3" | $SED -e "$lt_replace_pathsep_chars"` else func_to_host_path_result="$3" fi fi } # end func_convert_path_check # func_convert_path_front_back_pathsep FRONTPAT BACKPAT REPL ORIG # Modifies func_to_host_path_result by prepending REPL if ORIG matches FRONTPAT # and appending REPL if ORIG matches BACKPAT. func_convert_path_front_back_pathsep () { $opt_debug case $4 in $1 ) func_to_host_path_result="$3$func_to_host_path_result" ;; esac case $4 in $2 ) func_append func_to_host_path_result "$3" ;; esac } # end func_convert_path_front_back_pathsep ################################################## # $build to $host FILE NAME CONVERSION FUNCTIONS # ################################################## # invoked via `$to_host_file_cmd ARG' # # In each case, ARG is the path to be converted from $build to $host format. # Result will be available in $func_to_host_file_result. # func_to_host_file ARG # Converts the file name ARG from $build format to $host format. Return result # in func_to_host_file_result. func_to_host_file () { $opt_debug $to_host_file_cmd "$1" } # end func_to_host_file # func_to_tool_file ARG LAZY # converts the file name ARG from $build format to toolchain format. Return # result in func_to_tool_file_result. If the conversion in use is listed # in (the comma separated) LAZY, no conversion takes place. func_to_tool_file () { $opt_debug case ,$2, in *,"$to_tool_file_cmd",*) func_to_tool_file_result=$1 ;; *) $to_tool_file_cmd "$1" func_to_tool_file_result=$func_to_host_file_result ;; esac } # end func_to_tool_file # func_convert_file_noop ARG # Copy ARG to func_to_host_file_result. func_convert_file_noop () { func_to_host_file_result="$1" } # end func_convert_file_noop # func_convert_file_msys_to_w32 ARG # Convert file name ARG from (mingw) MSYS to (mingw) w32 format; automatic # conversion to w32 is not available inside the cwrapper. Returns result in # func_to_host_file_result. func_convert_file_msys_to_w32 () { $opt_debug func_to_host_file_result="$1" if test -n "$1"; then func_convert_core_msys_to_w32 "$1" func_to_host_file_result="$func_convert_core_msys_to_w32_result" fi func_convert_file_check "$1" "$func_to_host_file_result" } # end func_convert_file_msys_to_w32 # func_convert_file_cygwin_to_w32 ARG # Convert file name ARG from Cygwin to w32 format. Returns result in # func_to_host_file_result. func_convert_file_cygwin_to_w32 () { $opt_debug func_to_host_file_result="$1" if test -n "$1"; then # because $build is cygwin, we call "the" cygpath in $PATH; no need to use # LT_CYGPATH in this case. func_to_host_file_result=`cygpath -m "$1"` fi func_convert_file_check "$1" "$func_to_host_file_result" } # end func_convert_file_cygwin_to_w32 # func_convert_file_nix_to_w32 ARG # Convert file name ARG from *nix to w32 format. Requires a wine environment # and a working winepath. Returns result in func_to_host_file_result. func_convert_file_nix_to_w32 () { $opt_debug func_to_host_file_result="$1" if test -n "$1"; then func_convert_core_file_wine_to_w32 "$1" func_to_host_file_result="$func_convert_core_file_wine_to_w32_result" fi func_convert_file_check "$1" "$func_to_host_file_result" } # end func_convert_file_nix_to_w32 # func_convert_file_msys_to_cygwin ARG # Convert file name ARG from MSYS to Cygwin format. Requires LT_CYGPATH set. # Returns result in func_to_host_file_result. func_convert_file_msys_to_cygwin () { $opt_debug func_to_host_file_result="$1" if test -n "$1"; then func_convert_core_msys_to_w32 "$1" func_cygpath -u "$func_convert_core_msys_to_w32_result" func_to_host_file_result="$func_cygpath_result" fi func_convert_file_check "$1" "$func_to_host_file_result" } # end func_convert_file_msys_to_cygwin # func_convert_file_nix_to_cygwin ARG # Convert file name ARG from *nix to Cygwin format. Requires Cygwin installed # in a wine environment, working winepath, and LT_CYGPATH set. Returns result # in func_to_host_file_result. func_convert_file_nix_to_cygwin () { $opt_debug func_to_host_file_result="$1" if test -n "$1"; then # convert from *nix to w32, then use cygpath to convert from w32 to cygwin. func_convert_core_file_wine_to_w32 "$1" func_cygpath -u "$func_convert_core_file_wine_to_w32_result" func_to_host_file_result="$func_cygpath_result" fi func_convert_file_check "$1" "$func_to_host_file_result" } # end func_convert_file_nix_to_cygwin ############################################# # $build to $host PATH CONVERSION FUNCTIONS # ############################################# # invoked via `$to_host_path_cmd ARG' # # In each case, ARG is the path to be converted from $build to $host format. # The result will be available in $func_to_host_path_result. # # Path separators are also converted from $build format to $host format. If # ARG begins or ends with a path separator character, it is preserved (but # converted to $host format) on output. # # All path conversion functions are named using the following convention: # file name conversion function : func_convert_file_X_to_Y () # path conversion function : func_convert_path_X_to_Y () # where, for any given $build/$host combination the 'X_to_Y' value is the # same. If conversion functions are added for new $build/$host combinations, # the two new functions must follow this pattern, or func_init_to_host_path_cmd # will break. # func_init_to_host_path_cmd # Ensures that function "pointer" variable $to_host_path_cmd is set to the # appropriate value, based on the value of $to_host_file_cmd. to_host_path_cmd= func_init_to_host_path_cmd () { $opt_debug if test -z "$to_host_path_cmd"; then func_stripname 'func_convert_file_' '' "$to_host_file_cmd" to_host_path_cmd="func_convert_path_${func_stripname_result}" fi } # func_to_host_path ARG # Converts the path ARG from $build format to $host format. Return result # in func_to_host_path_result. func_to_host_path () { $opt_debug func_init_to_host_path_cmd $to_host_path_cmd "$1" } # end func_to_host_path # func_convert_path_noop ARG # Copy ARG to func_to_host_path_result. func_convert_path_noop () { func_to_host_path_result="$1" } # end func_convert_path_noop # func_convert_path_msys_to_w32 ARG # Convert path ARG from (mingw) MSYS to (mingw) w32 format; automatic # conversion to w32 is not available inside the cwrapper. Returns result in # func_to_host_path_result. func_convert_path_msys_to_w32 () { $opt_debug func_to_host_path_result="$1" if test -n "$1"; then # Remove leading and trailing path separator characters from ARG. MSYS # behavior is inconsistent here; cygpath turns them into '.;' and ';.'; # and winepath ignores them completely. func_stripname : : "$1" func_to_host_path_tmp1=$func_stripname_result func_convert_core_msys_to_w32 "$func_to_host_path_tmp1" func_to_host_path_result="$func_convert_core_msys_to_w32_result" func_convert_path_check : ";" \ "$func_to_host_path_tmp1" "$func_to_host_path_result" func_convert_path_front_back_pathsep ":*" "*:" ";" "$1" fi } # end func_convert_path_msys_to_w32 # func_convert_path_cygwin_to_w32 ARG # Convert path ARG from Cygwin to w32 format. Returns result in # func_to_host_file_result. func_convert_path_cygwin_to_w32 () { $opt_debug func_to_host_path_result="$1" if test -n "$1"; then # See func_convert_path_msys_to_w32: func_stripname : : "$1" func_to_host_path_tmp1=$func_stripname_result func_to_host_path_result=`cygpath -m -p "$func_to_host_path_tmp1"` func_convert_path_check : ";" \ "$func_to_host_path_tmp1" "$func_to_host_path_result" func_convert_path_front_back_pathsep ":*" "*:" ";" "$1" fi } # end func_convert_path_cygwin_to_w32 # func_convert_path_nix_to_w32 ARG # Convert path ARG from *nix to w32 format. Requires a wine environment and # a working winepath. Returns result in func_to_host_file_result. func_convert_path_nix_to_w32 () { $opt_debug func_to_host_path_result="$1" if test -n "$1"; then # See func_convert_path_msys_to_w32: func_stripname : : "$1" func_to_host_path_tmp1=$func_stripname_result func_convert_core_path_wine_to_w32 "$func_to_host_path_tmp1" func_to_host_path_result="$func_convert_core_path_wine_to_w32_result" func_convert_path_check : ";" \ "$func_to_host_path_tmp1" "$func_to_host_path_result" func_convert_path_front_back_pathsep ":*" "*:" ";" "$1" fi } # end func_convert_path_nix_to_w32 # func_convert_path_msys_to_cygwin ARG # Convert path ARG from MSYS to Cygwin format. Requires LT_CYGPATH set. # Returns result in func_to_host_file_result. func_convert_path_msys_to_cygwin () { $opt_debug func_to_host_path_result="$1" if test -n "$1"; then # See func_convert_path_msys_to_w32: func_stripname : : "$1" func_to_host_path_tmp1=$func_stripname_result func_convert_core_msys_to_w32 "$func_to_host_path_tmp1" func_cygpath -u -p "$func_convert_core_msys_to_w32_result" func_to_host_path_result="$func_cygpath_result" func_convert_path_check : : \ "$func_to_host_path_tmp1" "$func_to_host_path_result" func_convert_path_front_back_pathsep ":*" "*:" : "$1" fi } # end func_convert_path_msys_to_cygwin # func_convert_path_nix_to_cygwin ARG # Convert path ARG from *nix to Cygwin format. Requires Cygwin installed in a # a wine environment, working winepath, and LT_CYGPATH set. Returns result in # func_to_host_file_result. func_convert_path_nix_to_cygwin () { $opt_debug func_to_host_path_result="$1" if test -n "$1"; then # Remove leading and trailing path separator characters from # ARG. msys behavior is inconsistent here, cygpath turns them # into '.;' and ';.', and winepath ignores them completely. func_stripname : : "$1" func_to_host_path_tmp1=$func_stripname_result func_convert_core_path_wine_to_w32 "$func_to_host_path_tmp1" func_cygpath -u -p "$func_convert_core_path_wine_to_w32_result" func_to_host_path_result="$func_cygpath_result" func_convert_path_check : : \ "$func_to_host_path_tmp1" "$func_to_host_path_result" func_convert_path_front_back_pathsep ":*" "*:" : "$1" fi } # end func_convert_path_nix_to_cygwin # func_mode_compile arg... func_mode_compile () { $opt_debug # Get the compilation command and the source file. base_compile= srcfile="$nonopt" # always keep a non-empty value in "srcfile" suppress_opt=yes suppress_output= arg_mode=normal libobj= later= pie_flag= for arg do case $arg_mode in arg ) # do not "continue". Instead, add this to base_compile lastarg="$arg" arg_mode=normal ;; target ) libobj="$arg" arg_mode=normal continue ;; normal ) # Accept any command-line options. case $arg in -o) test -n "$libobj" && \ func_fatal_error "you cannot specify \`-o' more than once" arg_mode=target continue ;; -pie | -fpie | -fPIE) func_append pie_flag " $arg" continue ;; -shared | -static | -prefer-pic | -prefer-non-pic) func_append later " $arg" continue ;; -no-suppress) suppress_opt=no continue ;; -Xcompiler) arg_mode=arg # the next one goes into the "base_compile" arg list continue # The current "srcfile" will either be retained or ;; # replaced later. I would guess that would be a bug. -Wc,*) func_stripname '-Wc,' '' "$arg" args=$func_stripname_result lastarg= save_ifs="$IFS"; IFS=',' for arg in $args; do IFS="$save_ifs" func_append_quoted lastarg "$arg" done IFS="$save_ifs" func_stripname ' ' '' "$lastarg" lastarg=$func_stripname_result # Add the arguments to base_compile. func_append base_compile " $lastarg" continue ;; *) # Accept the current argument as the source file. # The previous "srcfile" becomes the current argument. # lastarg="$srcfile" srcfile="$arg" ;; esac # case $arg ;; esac # case $arg_mode # Aesthetically quote the previous argument. func_append_quoted base_compile "$lastarg" done # for arg case $arg_mode in arg) func_fatal_error "you must specify an argument for -Xcompile" ;; target) func_fatal_error "you must specify a target with \`-o'" ;; *) # Get the name of the library object. test -z "$libobj" && { func_basename "$srcfile" libobj="$func_basename_result" } ;; esac # Recognize several different file suffixes. # If the user specifies -o file.o, it is replaced with file.lo case $libobj in *.[cCFSifmso] | \ *.ada | *.adb | *.ads | *.asm | \ *.c++ | *.cc | *.ii | *.class | *.cpp | *.cxx | \ *.[fF][09]? | *.for | *.java | *.obj | *.sx | *.cu | *.cup) func_xform "$libobj" libobj=$func_xform_result ;; esac case $libobj in *.lo) func_lo2o "$libobj"; obj=$func_lo2o_result ;; *) func_fatal_error "cannot determine name of library object from \`$libobj'" ;; esac func_infer_tag $base_compile for arg in $later; do case $arg in -shared) test "$build_libtool_libs" != yes && \ func_fatal_configuration "can not build a shared library" build_old_libs=no continue ;; -static) build_libtool_libs=no build_old_libs=yes continue ;; -prefer-pic) pic_mode=yes continue ;; -prefer-non-pic) pic_mode=no continue ;; esac done func_quote_for_eval "$libobj" test "X$libobj" != "X$func_quote_for_eval_result" \ && $ECHO "X$libobj" | $GREP '[]~#^*{};<>?"'"'"' &()|`$[]' \ && func_warning "libobj name \`$libobj' may not contain shell special characters." func_dirname_and_basename "$obj" "/" "" objname="$func_basename_result" xdir="$func_dirname_result" lobj=${xdir}$objdir/$objname test -z "$base_compile" && \ func_fatal_help "you must specify a compilation command" # Delete any leftover library objects. if test "$build_old_libs" = yes; then removelist="$obj $lobj $libobj ${libobj}T" else removelist="$lobj $libobj ${libobj}T" fi # On Cygwin there's no "real" PIC flag so we must build both object types case $host_os in cygwin* | mingw* | pw32* | os2* | cegcc*) pic_mode=default ;; esac if test "$pic_mode" = no && test "$deplibs_check_method" != pass_all; then # non-PIC code in shared libraries is not supported pic_mode=default fi # Calculate the filename of the output object if compiler does # not support -o with -c if test "$compiler_c_o" = no; then output_obj=`$ECHO "$srcfile" | $SED 's%^.*/%%; s%\.[^.]*$%%'`.${objext} lockfile="$output_obj.lock" else output_obj= need_locks=no lockfile= fi # Lock this critical section if it is needed # We use this script file to make the link, it avoids creating a new file if test "$need_locks" = yes; then until $opt_dry_run || ln "$progpath" "$lockfile" 2>/dev/null; do func_echo "Waiting for $lockfile to be removed" sleep 2 done elif test "$need_locks" = warn; then if test -f "$lockfile"; then $ECHO "\ *** ERROR, $lockfile exists and contains: `cat $lockfile 2>/dev/null` This indicates that another process is trying to use the same temporary object file, and libtool could not work around it because your compiler does not support \`-c' and \`-o' together. If you repeat this compilation, it may succeed, by chance, but you had better avoid parallel builds (make -j) in this platform, or get a better compiler." $opt_dry_run || $RM $removelist exit $EXIT_FAILURE fi func_append removelist " $output_obj" $ECHO "$srcfile" > "$lockfile" fi $opt_dry_run || $RM $removelist func_append removelist " $lockfile" trap '$opt_dry_run || $RM $removelist; exit $EXIT_FAILURE' 1 2 15 func_to_tool_file "$srcfile" func_convert_file_msys_to_w32 srcfile=$func_to_tool_file_result func_quote_for_eval "$srcfile" qsrcfile=$func_quote_for_eval_result # Only build a PIC object if we are building libtool libraries. if test "$build_libtool_libs" = yes; then # Without this assignment, base_compile gets emptied. fbsd_hideous_sh_bug=$base_compile if test "$pic_mode" != no; then command="$base_compile $qsrcfile $pic_flag" else # Don't build PIC code command="$base_compile $qsrcfile" fi func_mkdir_p "$xdir$objdir" if test -z "$output_obj"; then # Place PIC objects in $objdir func_append command " -o $lobj" fi func_show_eval_locale "$command" \ 'test -n "$output_obj" && $RM $removelist; exit $EXIT_FAILURE' if test "$need_locks" = warn && test "X`cat $lockfile 2>/dev/null`" != "X$srcfile"; then $ECHO "\ *** ERROR, $lockfile contains: `cat $lockfile 2>/dev/null` but it should contain: $srcfile This indicates that another process is trying to use the same temporary object file, and libtool could not work around it because your compiler does not support \`-c' and \`-o' together. If you repeat this compilation, it may succeed, by chance, but you had better avoid parallel builds (make -j) in this platform, or get a better compiler." $opt_dry_run || $RM $removelist exit $EXIT_FAILURE fi # Just move the object if needed, then go on to compile the next one if test -n "$output_obj" && test "X$output_obj" != "X$lobj"; then func_show_eval '$MV "$output_obj" "$lobj"' \ 'error=$?; $opt_dry_run || $RM $removelist; exit $error' fi # Allow error messages only from the first compilation. if test "$suppress_opt" = yes; then suppress_output=' >/dev/null 2>&1' fi fi # Only build a position-dependent object if we build old libraries. if test "$build_old_libs" = yes; then if test "$pic_mode" != yes; then # Don't build PIC code command="$base_compile $qsrcfile$pie_flag" else command="$base_compile $qsrcfile $pic_flag" fi if test "$compiler_c_o" = yes; then func_append command " -o $obj" fi # Suppress compiler output if we already did a PIC compilation. func_append command "$suppress_output" func_show_eval_locale "$command" \ '$opt_dry_run || $RM $removelist; exit $EXIT_FAILURE' if test "$need_locks" = warn && test "X`cat $lockfile 2>/dev/null`" != "X$srcfile"; then $ECHO "\ *** ERROR, $lockfile contains: `cat $lockfile 2>/dev/null` but it should contain: $srcfile This indicates that another process is trying to use the same temporary object file, and libtool could not work around it because your compiler does not support \`-c' and \`-o' together. If you repeat this compilation, it may succeed, by chance, but you had better avoid parallel builds (make -j) in this platform, or get a better compiler." $opt_dry_run || $RM $removelist exit $EXIT_FAILURE fi # Just move the object if needed if test -n "$output_obj" && test "X$output_obj" != "X$obj"; then func_show_eval '$MV "$output_obj" "$obj"' \ 'error=$?; $opt_dry_run || $RM $removelist; exit $error' fi fi $opt_dry_run || { func_write_libtool_object "$libobj" "$objdir/$objname" "$objname" # Unlock the critical section if it was locked if test "$need_locks" != no; then removelist=$lockfile $RM "$lockfile" fi } exit $EXIT_SUCCESS } $opt_help || { test "$opt_mode" = compile && func_mode_compile ${1+"$@"} } func_mode_help () { # We need to display help for each of the modes. case $opt_mode in "") # Generic help is extracted from the usage comments # at the start of this file. func_help ;; clean) $ECHO \ "Usage: $progname [OPTION]... --mode=clean RM [RM-OPTION]... FILE... Remove files from the build directory. RM is the name of the program to use to delete files associated with each FILE (typically \`/bin/rm'). RM-OPTIONS are options (such as \`-f') to be passed to RM. If FILE is a libtool library, object or program, all the files associated with it are deleted. Otherwise, only FILE itself is deleted using RM." ;; compile) $ECHO \ "Usage: $progname [OPTION]... --mode=compile COMPILE-COMMAND... SOURCEFILE Compile a source file into a libtool library object. This mode accepts the following additional options: -o OUTPUT-FILE set the output file name to OUTPUT-FILE -no-suppress do not suppress compiler output for multiple passes -prefer-pic try to build PIC objects only -prefer-non-pic try to build non-PIC objects only -shared do not build a \`.o' file suitable for static linking -static only build a \`.o' file suitable for static linking -Wc,FLAG pass FLAG directly to the compiler COMPILE-COMMAND is a command to be used in creating a \`standard' object file from the given SOURCEFILE. The output file name is determined by removing the directory component from SOURCEFILE, then substituting the C source code suffix \`.c' with the library object suffix, \`.lo'." ;; execute) $ECHO \ "Usage: $progname [OPTION]... --mode=execute COMMAND [ARGS]... Automatically set library path, then run a program. This mode accepts the following additional options: -dlopen FILE add the directory containing FILE to the library path This mode sets the library path environment variable according to \`-dlopen' flags. If any of the ARGS are libtool executable wrappers, then they are translated into their corresponding uninstalled binary, and any of their required library directories are added to the library path. Then, COMMAND is executed, with ARGS as arguments." ;; finish) $ECHO \ "Usage: $progname [OPTION]... --mode=finish [LIBDIR]... Complete the installation of libtool libraries. Each LIBDIR is a directory that contains libtool libraries. The commands that this mode executes may require superuser privileges. Use the \`--dry-run' option if you just want to see what would be executed." ;; install) $ECHO \ "Usage: $progname [OPTION]... --mode=install INSTALL-COMMAND... Install executables or libraries. INSTALL-COMMAND is the installation command. The first component should be either the \`install' or \`cp' program. The following components of INSTALL-COMMAND are treated specially: -inst-prefix-dir PREFIX-DIR Use PREFIX-DIR as a staging area for installation The rest of the components are interpreted as arguments to that command (only BSD-compatible install options are recognized)." ;; link) $ECHO \ "Usage: $progname [OPTION]... --mode=link LINK-COMMAND... Link object files or libraries together to form another library, or to create an executable program. LINK-COMMAND is a command using the C compiler that you would use to create a program from several object files. The following components of LINK-COMMAND are treated specially: -all-static do not do any dynamic linking at all -avoid-version do not add a version suffix if possible -bindir BINDIR specify path to binaries directory (for systems where libraries must be found in the PATH setting at runtime) -dlopen FILE \`-dlpreopen' FILE if it cannot be dlopened at runtime -dlpreopen FILE link in FILE and add its symbols to lt_preloaded_symbols -export-dynamic allow symbols from OUTPUT-FILE to be resolved with dlsym(3) -export-symbols SYMFILE try to export only the symbols listed in SYMFILE -export-symbols-regex REGEX try to export only the symbols matching REGEX -LLIBDIR search LIBDIR for required installed libraries -lNAME OUTPUT-FILE requires the installed library libNAME -module build a library that can dlopened -no-fast-install disable the fast-install mode -no-install link a not-installable executable -no-undefined declare that a library does not refer to external symbols -o OUTPUT-FILE create OUTPUT-FILE from the specified objects -objectlist FILE Use a list of object files found in FILE to specify objects -precious-files-regex REGEX don't remove output files matching REGEX -release RELEASE specify package release information -rpath LIBDIR the created library will eventually be installed in LIBDIR -R[ ]LIBDIR add LIBDIR to the runtime path of programs and libraries -shared only do dynamic linking of libtool libraries -shrext SUFFIX override the standard shared library file extension -static do not do any dynamic linking of uninstalled libtool libraries -static-libtool-libs do not do any dynamic linking of libtool libraries -version-info CURRENT[:REVISION[:AGE]] specify library version info [each variable defaults to 0] -weak LIBNAME declare that the target provides the LIBNAME interface -Wc,FLAG -Xcompiler FLAG pass linker-specific FLAG directly to the compiler -Wl,FLAG -Xlinker FLAG pass linker-specific FLAG directly to the linker -XCClinker FLAG pass link-specific FLAG to the compiler driver (CC) All other options (arguments beginning with \`-') are ignored. Every other argument is treated as a filename. Files ending in \`.la' are treated as uninstalled libtool libraries, other files are standard or library object files. If the OUTPUT-FILE ends in \`.la', then a libtool library is created, only library objects (\`.lo' files) may be specified, and \`-rpath' is required, except when creating a convenience library. If OUTPUT-FILE ends in \`.a' or \`.lib', then a standard library is created using \`ar' and \`ranlib', or on Windows using \`lib'. If OUTPUT-FILE ends in \`.lo' or \`.${objext}', then a reloadable object file is created, otherwise an executable program is created." ;; uninstall) $ECHO \ "Usage: $progname [OPTION]... --mode=uninstall RM [RM-OPTION]... FILE... Remove libraries from an installation directory. RM is the name of the program to use to delete files associated with each FILE (typically \`/bin/rm'). RM-OPTIONS are options (such as \`-f') to be passed to RM. If FILE is a libtool library, all the files associated with it are deleted. Otherwise, only FILE itself is deleted using RM." ;; *) func_fatal_help "invalid operation mode \`$opt_mode'" ;; esac echo $ECHO "Try \`$progname --help' for more information about other modes." } # Now that we've collected a possible --mode arg, show help if necessary if $opt_help; then if test "$opt_help" = :; then func_mode_help else { func_help noexit for opt_mode in compile link execute install finish uninstall clean; do func_mode_help done } | sed -n '1p; 2,$s/^Usage:/ or: /p' { func_help noexit for opt_mode in compile link execute install finish uninstall clean; do echo func_mode_help done } | sed '1d /^When reporting/,/^Report/{ H d } $x /information about other modes/d /more detailed .*MODE/d s/^Usage:.*--mode=\([^ ]*\) .*/Description of \1 mode:/' fi exit $? fi # func_mode_execute arg... func_mode_execute () { $opt_debug # The first argument is the command name. cmd="$nonopt" test -z "$cmd" && \ func_fatal_help "you must specify a COMMAND" # Handle -dlopen flags immediately. for file in $opt_dlopen; do test -f "$file" \ || func_fatal_help "\`$file' is not a file" dir= case $file in *.la) func_resolve_sysroot "$file" file=$func_resolve_sysroot_result # Check to see that this really is a libtool archive. func_lalib_unsafe_p "$file" \ || func_fatal_help "\`$lib' is not a valid libtool archive" # Read the libtool library. dlname= library_names= func_source "$file" # Skip this library if it cannot be dlopened. if test -z "$dlname"; then # Warn if it was a shared library. test -n "$library_names" && \ func_warning "\`$file' was not linked with \`-export-dynamic'" continue fi func_dirname "$file" "" "." dir="$func_dirname_result" if test -f "$dir/$objdir/$dlname"; then func_append dir "/$objdir" else if test ! -f "$dir/$dlname"; then func_fatal_error "cannot find \`$dlname' in \`$dir' or \`$dir/$objdir'" fi fi ;; *.lo) # Just add the directory containing the .lo file. func_dirname "$file" "" "." dir="$func_dirname_result" ;; *) func_warning "\`-dlopen' is ignored for non-libtool libraries and objects" continue ;; esac # Get the absolute pathname. absdir=`cd "$dir" && pwd` test -n "$absdir" && dir="$absdir" # Now add the directory to shlibpath_var. if eval "test -z \"\$$shlibpath_var\""; then eval "$shlibpath_var=\"\$dir\"" else eval "$shlibpath_var=\"\$dir:\$$shlibpath_var\"" fi done # This variable tells wrapper scripts just to set shlibpath_var # rather than running their programs. libtool_execute_magic="$magic" # Check if any of the arguments is a wrapper script. args= for file do case $file in -* | *.la | *.lo ) ;; *) # Do a test to see if this is really a libtool program. if func_ltwrapper_script_p "$file"; then func_source "$file" # Transform arg to wrapped name. file="$progdir/$program" elif func_ltwrapper_executable_p "$file"; then func_ltwrapper_scriptname "$file" func_source "$func_ltwrapper_scriptname_result" # Transform arg to wrapped name. file="$progdir/$program" fi ;; esac # Quote arguments (to preserve shell metacharacters). func_append_quoted args "$file" done if test "X$opt_dry_run" = Xfalse; then if test -n "$shlibpath_var"; then # Export the shlibpath_var. eval "export $shlibpath_var" fi # Restore saved environment variables for lt_var in LANG LANGUAGE LC_ALL LC_CTYPE LC_COLLATE LC_MESSAGES do eval "if test \"\${save_$lt_var+set}\" = set; then $lt_var=\$save_$lt_var; export $lt_var else $lt_unset $lt_var fi" done # Now prepare to actually exec the command. exec_cmd="\$cmd$args" else # Display what would be done. if test -n "$shlibpath_var"; then eval "\$ECHO \"\$shlibpath_var=\$$shlibpath_var\"" echo "export $shlibpath_var" fi $ECHO "$cmd$args" exit $EXIT_SUCCESS fi } test "$opt_mode" = execute && func_mode_execute ${1+"$@"} # func_mode_finish arg... func_mode_finish () { $opt_debug libs= libdirs= admincmds= for opt in "$nonopt" ${1+"$@"} do if test -d "$opt"; then func_append libdirs " $opt" elif test -f "$opt"; then if func_lalib_unsafe_p "$opt"; then func_append libs " $opt" else func_warning "\`$opt' is not a valid libtool archive" fi else func_fatal_error "invalid argument \`$opt'" fi done if test -n "$libs"; then if test -n "$lt_sysroot"; then sysroot_regex=`$ECHO "$lt_sysroot" | $SED "$sed_make_literal_regex"` sysroot_cmd="s/\([ ']\)$sysroot_regex/\1/g;" else sysroot_cmd= fi # Remove sysroot references if $opt_dry_run; then for lib in $libs; do echo "removing references to $lt_sysroot and \`=' prefixes from $lib" done else tmpdir=`func_mktempdir` for lib in $libs; do sed -e "${sysroot_cmd} s/\([ ']-[LR]\)=/\1/g; s/\([ ']\)=/\1/g" $lib \ > $tmpdir/tmp-la mv -f $tmpdir/tmp-la $lib done ${RM}r "$tmpdir" fi fi if test -n "$finish_cmds$finish_eval" && test -n "$libdirs"; then for libdir in $libdirs; do if test -n "$finish_cmds"; then # Do each command in the finish commands. func_execute_cmds "$finish_cmds" 'admincmds="$admincmds '"$cmd"'"' fi if test -n "$finish_eval"; then # Do the single finish_eval. eval cmds=\"$finish_eval\" $opt_dry_run || eval "$cmds" || func_append admincmds " $cmds" fi done fi # Exit here if they wanted silent mode. $opt_silent && exit $EXIT_SUCCESS if test -n "$finish_cmds$finish_eval" && test -n "$libdirs"; then echo "----------------------------------------------------------------------" echo "Libraries have been installed in:" for libdir in $libdirs; do $ECHO " $libdir" done echo echo "If you ever happen to want to link against installed libraries" echo "in a given directory, LIBDIR, you must either use libtool, and" echo "specify the full pathname of the library, or use the \`-LLIBDIR'" echo "flag during linking and do at least one of the following:" if test -n "$shlibpath_var"; then echo " - add LIBDIR to the \`$shlibpath_var' environment variable" echo " during execution" fi if test -n "$runpath_var"; then echo " - add LIBDIR to the \`$runpath_var' environment variable" echo " during linking" fi if test -n "$hardcode_libdir_flag_spec"; then libdir=LIBDIR eval flag=\"$hardcode_libdir_flag_spec\" $ECHO " - use the \`$flag' linker flag" fi if test -n "$admincmds"; then $ECHO " - have your system administrator run these commands:$admincmds" fi if test -f /etc/ld.so.conf; then echo " - have your system administrator add LIBDIR to \`/etc/ld.so.conf'" fi echo echo "See any operating system documentation about shared libraries for" case $host in solaris2.[6789]|solaris2.1[0-9]) echo "more information, such as the ld(1), crle(1) and ld.so(8) manual" echo "pages." ;; *) echo "more information, such as the ld(1) and ld.so(8) manual pages." ;; esac echo "----------------------------------------------------------------------" fi exit $EXIT_SUCCESS } test "$opt_mode" = finish && func_mode_finish ${1+"$@"} # func_mode_install arg... func_mode_install () { $opt_debug # There may be an optional sh(1) argument at the beginning of # install_prog (especially on Windows NT). if test "$nonopt" = "$SHELL" || test "$nonopt" = /bin/sh || # Allow the use of GNU shtool's install command. case $nonopt in *shtool*) :;; *) false;; esac; then # Aesthetically quote it. func_quote_for_eval "$nonopt" install_prog="$func_quote_for_eval_result " arg=$1 shift else install_prog= arg=$nonopt fi # The real first argument should be the name of the installation program. # Aesthetically quote it. func_quote_for_eval "$arg" func_append install_prog "$func_quote_for_eval_result" install_shared_prog=$install_prog case " $install_prog " in *[\\\ /]cp\ *) install_cp=: ;; *) install_cp=false ;; esac # We need to accept at least all the BSD install flags. dest= files= opts= prev= install_type= isdir=no stripme= no_mode=: for arg do arg2= if test -n "$dest"; then func_append files " $dest" dest=$arg continue fi case $arg in -d) isdir=yes ;; -f) if $install_cp; then :; else prev=$arg fi ;; -g | -m | -o) prev=$arg ;; -s) stripme=" -s" continue ;; -*) ;; *) # If the previous option needed an argument, then skip it. if test -n "$prev"; then if test "x$prev" = x-m && test -n "$install_override_mode"; then arg2=$install_override_mode no_mode=false fi prev= else dest=$arg continue fi ;; esac # Aesthetically quote the argument. func_quote_for_eval "$arg" func_append install_prog " $func_quote_for_eval_result" if test -n "$arg2"; then func_quote_for_eval "$arg2" fi func_append install_shared_prog " $func_quote_for_eval_result" done test -z "$install_prog" && \ func_fatal_help "you must specify an install program" test -n "$prev" && \ func_fatal_help "the \`$prev' option requires an argument" if test -n "$install_override_mode" && $no_mode; then if $install_cp; then :; else func_quote_for_eval "$install_override_mode" func_append install_shared_prog " -m $func_quote_for_eval_result" fi fi if test -z "$files"; then if test -z "$dest"; then func_fatal_help "no file or destination specified" else func_fatal_help "you must specify a destination" fi fi # Strip any trailing slash from the destination. func_stripname '' '/' "$dest" dest=$func_stripname_result # Check to see that the destination is a directory. test -d "$dest" && isdir=yes if test "$isdir" = yes; then destdir="$dest" destname= else func_dirname_and_basename "$dest" "" "." destdir="$func_dirname_result" destname="$func_basename_result" # Not a directory, so check to see that there is only one file specified. set dummy $files; shift test "$#" -gt 1 && \ func_fatal_help "\`$dest' is not a directory" fi case $destdir in [\\/]* | [A-Za-z]:[\\/]*) ;; *) for file in $files; do case $file in *.lo) ;; *) func_fatal_help "\`$destdir' must be an absolute directory name" ;; esac done ;; esac # This variable tells wrapper scripts just to set variables rather # than running their programs. libtool_install_magic="$magic" staticlibs= future_libdirs= current_libdirs= for file in $files; do # Do each installation. case $file in *.$libext) # Do the static libraries later. func_append staticlibs " $file" ;; *.la) func_resolve_sysroot "$file" file=$func_resolve_sysroot_result # Check to see that this really is a libtool archive. func_lalib_unsafe_p "$file" \ || func_fatal_help "\`$file' is not a valid libtool archive" library_names= old_library= relink_command= func_source "$file" # Add the libdir to current_libdirs if it is the destination. if test "X$destdir" = "X$libdir"; then case "$current_libdirs " in *" $libdir "*) ;; *) func_append current_libdirs " $libdir" ;; esac else # Note the libdir as a future libdir. case "$future_libdirs " in *" $libdir "*) ;; *) func_append future_libdirs " $libdir" ;; esac fi func_dirname "$file" "/" "" dir="$func_dirname_result" func_append dir "$objdir" if test -n "$relink_command"; then # Determine the prefix the user has applied to our future dir. inst_prefix_dir=`$ECHO "$destdir" | $SED -e "s%$libdir\$%%"` # Don't allow the user to place us outside of our expected # location b/c this prevents finding dependent libraries that # are installed to the same prefix. # At present, this check doesn't affect windows .dll's that # are installed into $libdir/../bin (currently, that works fine) # but it's something to keep an eye on. test "$inst_prefix_dir" = "$destdir" && \ func_fatal_error "error: cannot install \`$file' to a directory not ending in $libdir" if test -n "$inst_prefix_dir"; then # Stick the inst_prefix_dir data into the link command. relink_command=`$ECHO "$relink_command" | $SED "s%@inst_prefix_dir@%-inst-prefix-dir $inst_prefix_dir%"` else relink_command=`$ECHO "$relink_command" | $SED "s%@inst_prefix_dir@%%"` fi func_warning "relinking \`$file'" func_show_eval "$relink_command" \ 'func_fatal_error "error: relink \`$file'\'' with the above command before installing it"' fi # See the names of the shared library. set dummy $library_names; shift if test -n "$1"; then realname="$1" shift srcname="$realname" test -n "$relink_command" && srcname="$realname"T # Install the shared library and build the symlinks. func_show_eval "$install_shared_prog $dir/$srcname $destdir/$realname" \ 'exit $?' tstripme="$stripme" case $host_os in cygwin* | mingw* | pw32* | cegcc*) case $realname in *.dll.a) tstripme="" ;; esac ;; esac if test -n "$tstripme" && test -n "$striplib"; then func_show_eval "$striplib $destdir/$realname" 'exit $?' fi if test "$#" -gt 0; then # Delete the old symlinks, and create new ones. # Try `ln -sf' first, because the `ln' binary might depend on # the symlink we replace! Solaris /bin/ln does not understand -f, # so we also need to try rm && ln -s. for linkname do test "$linkname" != "$realname" \ && func_show_eval "(cd $destdir && { $LN_S -f $realname $linkname || { $RM $linkname && $LN_S $realname $linkname; }; })" done fi # Do each command in the postinstall commands. lib="$destdir/$realname" func_execute_cmds "$postinstall_cmds" 'exit $?' fi # Install the pseudo-library for information purposes. func_basename "$file" name="$func_basename_result" instname="$dir/$name"i func_show_eval "$install_prog $instname $destdir/$name" 'exit $?' # Maybe install the static library, too. test -n "$old_library" && func_append staticlibs " $dir/$old_library" ;; *.lo) # Install (i.e. copy) a libtool object. # Figure out destination file name, if it wasn't already specified. if test -n "$destname"; then destfile="$destdir/$destname" else func_basename "$file" destfile="$func_basename_result" destfile="$destdir/$destfile" fi # Deduce the name of the destination old-style object file. case $destfile in *.lo) func_lo2o "$destfile" staticdest=$func_lo2o_result ;; *.$objext) staticdest="$destfile" destfile= ;; *) func_fatal_help "cannot copy a libtool object to \`$destfile'" ;; esac # Install the libtool object if requested. test -n "$destfile" && \ func_show_eval "$install_prog $file $destfile" 'exit $?' # Install the old object if enabled. if test "$build_old_libs" = yes; then # Deduce the name of the old-style object file. func_lo2o "$file" staticobj=$func_lo2o_result func_show_eval "$install_prog \$staticobj \$staticdest" 'exit $?' fi exit $EXIT_SUCCESS ;; *) # Figure out destination file name, if it wasn't already specified. if test -n "$destname"; then destfile="$destdir/$destname" else func_basename "$file" destfile="$func_basename_result" destfile="$destdir/$destfile" fi # If the file is missing, and there is a .exe on the end, strip it # because it is most likely a libtool script we actually want to # install stripped_ext="" case $file in *.exe) if test ! -f "$file"; then func_stripname '' '.exe' "$file" file=$func_stripname_result stripped_ext=".exe" fi ;; esac # Do a test to see if this is really a libtool program. case $host in *cygwin* | *mingw*) if func_ltwrapper_executable_p "$file"; then func_ltwrapper_scriptname "$file" wrapper=$func_ltwrapper_scriptname_result else func_stripname '' '.exe' "$file" wrapper=$func_stripname_result fi ;; *) wrapper=$file ;; esac if func_ltwrapper_script_p "$wrapper"; then notinst_deplibs= relink_command= func_source "$wrapper" # Check the variables that should have been set. test -z "$generated_by_libtool_version" && \ func_fatal_error "invalid libtool wrapper script \`$wrapper'" finalize=yes for lib in $notinst_deplibs; do # Check to see that each library is installed. libdir= if test -f "$lib"; then func_source "$lib" fi libfile="$libdir/"`$ECHO "$lib" | $SED 's%^.*/%%g'` ### testsuite: skip nested quoting test if test -n "$libdir" && test ! -f "$libfile"; then func_warning "\`$lib' has not been installed in \`$libdir'" finalize=no fi done relink_command= func_source "$wrapper" outputname= if test "$fast_install" = no && test -n "$relink_command"; then $opt_dry_run || { if test "$finalize" = yes; then tmpdir=`func_mktempdir` func_basename "$file$stripped_ext" file="$func_basename_result" outputname="$tmpdir/$file" # Replace the output file specification. relink_command=`$ECHO "$relink_command" | $SED 's%@OUTPUT@%'"$outputname"'%g'` $opt_silent || { func_quote_for_expand "$relink_command" eval "func_echo $func_quote_for_expand_result" } if eval "$relink_command"; then : else func_error "error: relink \`$file' with the above command before installing it" $opt_dry_run || ${RM}r "$tmpdir" continue fi file="$outputname" else func_warning "cannot relink \`$file'" fi } else # Install the binary that we compiled earlier. file=`$ECHO "$file$stripped_ext" | $SED "s%\([^/]*\)$%$objdir/\1%"` fi fi # remove .exe since cygwin /usr/bin/install will append another # one anyway case $install_prog,$host in */usr/bin/install*,*cygwin*) case $file:$destfile in *.exe:*.exe) # this is ok ;; *.exe:*) destfile=$destfile.exe ;; *:*.exe) func_stripname '' '.exe' "$destfile" destfile=$func_stripname_result ;; esac ;; esac func_show_eval "$install_prog\$stripme \$file \$destfile" 'exit $?' $opt_dry_run || if test -n "$outputname"; then ${RM}r "$tmpdir" fi ;; esac done for file in $staticlibs; do func_basename "$file" name="$func_basename_result" # Set up the ranlib parameters. oldlib="$destdir/$name" func_show_eval "$install_prog \$file \$oldlib" 'exit $?' if test -n "$stripme" && test -n "$old_striplib"; then func_show_eval "$old_striplib $oldlib" 'exit $?' fi # Do each command in the postinstall commands. func_execute_cmds "$old_postinstall_cmds" 'exit $?' done test -n "$future_libdirs" && \ func_warning "remember to run \`$progname --finish$future_libdirs'" if test -n "$current_libdirs"; then # Maybe just do a dry run. $opt_dry_run && current_libdirs=" -n$current_libdirs" exec_cmd='$SHELL $progpath $preserve_args --finish$current_libdirs' else exit $EXIT_SUCCESS fi } test "$opt_mode" = install && func_mode_install ${1+"$@"} # func_generate_dlsyms outputname originator pic_p # Extract symbols from dlprefiles and create ${outputname}S.o with # a dlpreopen symbol table. func_generate_dlsyms () { $opt_debug my_outputname="$1" my_originator="$2" my_pic_p="${3-no}" my_prefix=`$ECHO "$my_originator" | sed 's%[^a-zA-Z0-9]%_%g'` my_dlsyms= if test -n "$dlfiles$dlprefiles" || test "$dlself" != no; then if test -n "$NM" && test -n "$global_symbol_pipe"; then my_dlsyms="${my_outputname}S.c" else func_error "not configured to extract global symbols from dlpreopened files" fi fi if test -n "$my_dlsyms"; then case $my_dlsyms in "") ;; *.c) # Discover the nlist of each of the dlfiles. nlist="$output_objdir/${my_outputname}.nm" func_show_eval "$RM $nlist ${nlist}S ${nlist}T" # Parse the name list into a source file. func_verbose "creating $output_objdir/$my_dlsyms" $opt_dry_run || $ECHO > "$output_objdir/$my_dlsyms" "\ /* $my_dlsyms - symbol resolution table for \`$my_outputname' dlsym emulation. */ /* Generated by $PROGRAM (GNU $PACKAGE$TIMESTAMP) $VERSION */ #ifdef __cplusplus extern \"C\" { #endif #if defined(__GNUC__) && (((__GNUC__ == 4) && (__GNUC_MINOR__ >= 4)) || (__GNUC__ > 4)) #pragma GCC diagnostic ignored \"-Wstrict-prototypes\" #endif /* Keep this code in sync between libtool.m4, ltmain, lt_system.h, and tests. */ #if defined(_WIN32) || defined(__CYGWIN__) || defined(_WIN32_WCE) /* DATA imports from DLLs on WIN32 con't be const, because runtime relocations are performed -- see ld's documentation on pseudo-relocs. */ # define LT_DLSYM_CONST #elif defined(__osf__) /* This system does not cope well with relocations in const data. */ # define LT_DLSYM_CONST #else # define LT_DLSYM_CONST const #endif /* External symbol declarations for the compiler. */\ " if test "$dlself" = yes; then func_verbose "generating symbol list for \`$output'" $opt_dry_run || echo ': @PROGRAM@ ' > "$nlist" # Add our own program objects to the symbol list. progfiles=`$ECHO "$objs$old_deplibs" | $SP2NL | $SED "$lo2o" | $NL2SP` for progfile in $progfiles; do func_to_tool_file "$progfile" func_convert_file_msys_to_w32 func_verbose "extracting global C symbols from \`$func_to_tool_file_result'" $opt_dry_run || eval "$NM $func_to_tool_file_result | $global_symbol_pipe >> '$nlist'" done if test -n "$exclude_expsyms"; then $opt_dry_run || { eval '$EGREP -v " ($exclude_expsyms)$" "$nlist" > "$nlist"T' eval '$MV "$nlist"T "$nlist"' } fi if test -n "$export_symbols_regex"; then $opt_dry_run || { eval '$EGREP -e "$export_symbols_regex" "$nlist" > "$nlist"T' eval '$MV "$nlist"T "$nlist"' } fi # Prepare the list of exported symbols if test -z "$export_symbols"; then export_symbols="$output_objdir/$outputname.exp" $opt_dry_run || { $RM $export_symbols eval "${SED} -n -e '/^: @PROGRAM@ $/d' -e 's/^.* \(.*\)$/\1/p' "'< "$nlist" > "$export_symbols"' case $host in *cygwin* | *mingw* | *cegcc* ) eval "echo EXPORTS "'> "$output_objdir/$outputname.def"' eval 'cat "$export_symbols" >> "$output_objdir/$outputname.def"' ;; esac } else $opt_dry_run || { eval "${SED} -e 's/\([].[*^$]\)/\\\\\1/g' -e 's/^/ /' -e 's/$/$/'"' < "$export_symbols" > "$output_objdir/$outputname.exp"' eval '$GREP -f "$output_objdir/$outputname.exp" < "$nlist" > "$nlist"T' eval '$MV "$nlist"T "$nlist"' case $host in *cygwin* | *mingw* | *cegcc* ) eval "echo EXPORTS "'> "$output_objdir/$outputname.def"' eval 'cat "$nlist" >> "$output_objdir/$outputname.def"' ;; esac } fi fi for dlprefile in $dlprefiles; do func_verbose "extracting global C symbols from \`$dlprefile'" func_basename "$dlprefile" name="$func_basename_result" case $host in *cygwin* | *mingw* | *cegcc* ) # if an import library, we need to obtain dlname if func_win32_import_lib_p "$dlprefile"; then func_tr_sh "$dlprefile" eval "curr_lafile=\$libfile_$func_tr_sh_result" dlprefile_dlbasename="" if test -n "$curr_lafile" && func_lalib_p "$curr_lafile"; then # Use subshell, to avoid clobbering current variable values dlprefile_dlname=`source "$curr_lafile" && echo "$dlname"` if test -n "$dlprefile_dlname" ; then func_basename "$dlprefile_dlname" dlprefile_dlbasename="$func_basename_result" else # no lafile. user explicitly requested -dlpreopen . $sharedlib_from_linklib_cmd "$dlprefile" dlprefile_dlbasename=$sharedlib_from_linklib_result fi fi $opt_dry_run || { if test -n "$dlprefile_dlbasename" ; then eval '$ECHO ": $dlprefile_dlbasename" >> "$nlist"' else func_warning "Could not compute DLL name from $name" eval '$ECHO ": $name " >> "$nlist"' fi func_to_tool_file "$dlprefile" func_convert_file_msys_to_w32 eval "$NM \"$func_to_tool_file_result\" 2>/dev/null | $global_symbol_pipe | $SED -e '/I __imp/d' -e 's/I __nm_/D /;s/_nm__//' >> '$nlist'" } else # not an import lib $opt_dry_run || { eval '$ECHO ": $name " >> "$nlist"' func_to_tool_file "$dlprefile" func_convert_file_msys_to_w32 eval "$NM \"$func_to_tool_file_result\" 2>/dev/null | $global_symbol_pipe >> '$nlist'" } fi ;; *) $opt_dry_run || { eval '$ECHO ": $name " >> "$nlist"' func_to_tool_file "$dlprefile" func_convert_file_msys_to_w32 eval "$NM \"$func_to_tool_file_result\" 2>/dev/null | $global_symbol_pipe >> '$nlist'" } ;; esac done $opt_dry_run || { # Make sure we have at least an empty file. test -f "$nlist" || : > "$nlist" if test -n "$exclude_expsyms"; then $EGREP -v " ($exclude_expsyms)$" "$nlist" > "$nlist"T $MV "$nlist"T "$nlist" fi # Try sorting and uniquifying the output. if $GREP -v "^: " < "$nlist" | if sort -k 3 /dev/null 2>&1; then sort -k 3 else sort +2 fi | uniq > "$nlist"S; then : else $GREP -v "^: " < "$nlist" > "$nlist"S fi if test -f "$nlist"S; then eval "$global_symbol_to_cdecl"' < "$nlist"S >> "$output_objdir/$my_dlsyms"' else echo '/* NONE */' >> "$output_objdir/$my_dlsyms" fi echo >> "$output_objdir/$my_dlsyms" "\ /* The mapping between symbol names and symbols. */ typedef struct { const char *name; void *address; } lt_dlsymlist; extern LT_DLSYM_CONST lt_dlsymlist lt_${my_prefix}_LTX_preloaded_symbols[]; LT_DLSYM_CONST lt_dlsymlist lt_${my_prefix}_LTX_preloaded_symbols[] = {\ { \"$my_originator\", (void *) 0 }," case $need_lib_prefix in no) eval "$global_symbol_to_c_name_address" < "$nlist" >> "$output_objdir/$my_dlsyms" ;; *) eval "$global_symbol_to_c_name_address_lib_prefix" < "$nlist" >> "$output_objdir/$my_dlsyms" ;; esac echo >> "$output_objdir/$my_dlsyms" "\ {0, (void *) 0} }; /* This works around a problem in FreeBSD linker */ #ifdef FREEBSD_WORKAROUND static const void *lt_preloaded_setup() { return lt_${my_prefix}_LTX_preloaded_symbols; } #endif #ifdef __cplusplus } #endif\ " } # !$opt_dry_run pic_flag_for_symtable= case "$compile_command " in *" -static "*) ;; *) case $host in # compiling the symbol table file with pic_flag works around # a FreeBSD bug that causes programs to crash when -lm is # linked before any other PIC object. But we must not use # pic_flag when linking with -static. The problem exists in # FreeBSD 2.2.6 and is fixed in FreeBSD 3.1. *-*-freebsd2*|*-*-freebsd3.0*|*-*-freebsdelf3.0*) pic_flag_for_symtable=" $pic_flag -DFREEBSD_WORKAROUND" ;; *-*-hpux*) pic_flag_for_symtable=" $pic_flag" ;; *) if test "X$my_pic_p" != Xno; then pic_flag_for_symtable=" $pic_flag" fi ;; esac ;; esac symtab_cflags= for arg in $LTCFLAGS; do case $arg in -pie | -fpie | -fPIE) ;; *) func_append symtab_cflags " $arg" ;; esac done # Now compile the dynamic symbol file. func_show_eval '(cd $output_objdir && $LTCC$symtab_cflags -c$no_builtin_flag$pic_flag_for_symtable "$my_dlsyms")' 'exit $?' # Clean up the generated files. func_show_eval '$RM "$output_objdir/$my_dlsyms" "$nlist" "${nlist}S" "${nlist}T"' # Transform the symbol file into the correct name. symfileobj="$output_objdir/${my_outputname}S.$objext" case $host in *cygwin* | *mingw* | *cegcc* ) if test -f "$output_objdir/$my_outputname.def"; then compile_command=`$ECHO "$compile_command" | $SED "s%@SYMFILE@%$output_objdir/$my_outputname.def $symfileobj%"` finalize_command=`$ECHO "$finalize_command" | $SED "s%@SYMFILE@%$output_objdir/$my_outputname.def $symfileobj%"` else compile_command=`$ECHO "$compile_command" | $SED "s%@SYMFILE@%$symfileobj%"` finalize_command=`$ECHO "$finalize_command" | $SED "s%@SYMFILE@%$symfileobj%"` fi ;; *) compile_command=`$ECHO "$compile_command" | $SED "s%@SYMFILE@%$symfileobj%"` finalize_command=`$ECHO "$finalize_command" | $SED "s%@SYMFILE@%$symfileobj%"` ;; esac ;; *) func_fatal_error "unknown suffix for \`$my_dlsyms'" ;; esac else # We keep going just in case the user didn't refer to # lt_preloaded_symbols. The linker will fail if global_symbol_pipe # really was required. # Nullify the symbol file. compile_command=`$ECHO "$compile_command" | $SED "s% @SYMFILE@%%"` finalize_command=`$ECHO "$finalize_command" | $SED "s% @SYMFILE@%%"` fi } # func_win32_libid arg # return the library type of file 'arg' # # Need a lot of goo to handle *both* DLLs and import libs # Has to be a shell function in order to 'eat' the argument # that is supplied when $file_magic_command is called. # Despite the name, also deal with 64 bit binaries. func_win32_libid () { $opt_debug win32_libid_type="unknown" win32_fileres=`file -L $1 2>/dev/null` case $win32_fileres in *ar\ archive\ import\ library*) # definitely import win32_libid_type="x86 archive import" ;; *ar\ archive*) # could be an import, or static # Keep the egrep pattern in sync with the one in _LT_CHECK_MAGIC_METHOD. if eval $OBJDUMP -f $1 | $SED -e '10q' 2>/dev/null | $EGREP 'file format (pei*-i386(.*architecture: i386)?|pe-arm-wince|pe-x86-64)' >/dev/null; then func_to_tool_file "$1" func_convert_file_msys_to_w32 win32_nmres=`eval $NM -f posix -A \"$func_to_tool_file_result\" | $SED -n -e ' 1,100{ / I /{ s,.*,import, p q } }'` case $win32_nmres in import*) win32_libid_type="x86 archive import";; *) win32_libid_type="x86 archive static";; esac fi ;; *DLL*) win32_libid_type="x86 DLL" ;; *executable*) # but shell scripts are "executable" too... case $win32_fileres in *MS\ Windows\ PE\ Intel*) win32_libid_type="x86 DLL" ;; esac ;; esac $ECHO "$win32_libid_type" } # func_cygming_dll_for_implib ARG # # Platform-specific function to extract the # name of the DLL associated with the specified # import library ARG. # Invoked by eval'ing the libtool variable # $sharedlib_from_linklib_cmd # Result is available in the variable # $sharedlib_from_linklib_result func_cygming_dll_for_implib () { $opt_debug sharedlib_from_linklib_result=`$DLLTOOL --identify-strict --identify "$1"` } # func_cygming_dll_for_implib_fallback_core SECTION_NAME LIBNAMEs # # The is the core of a fallback implementation of a # platform-specific function to extract the name of the # DLL associated with the specified import library LIBNAME. # # SECTION_NAME is either .idata$6 or .idata$7, depending # on the platform and compiler that created the implib. # # Echos the name of the DLL associated with the # specified import library. func_cygming_dll_for_implib_fallback_core () { $opt_debug match_literal=`$ECHO "$1" | $SED "$sed_make_literal_regex"` $OBJDUMP -s --section "$1" "$2" 2>/dev/null | $SED '/^Contents of section '"$match_literal"':/{ # Place marker at beginning of archive member dllname section s/.*/====MARK====/ p d } # These lines can sometimes be longer than 43 characters, but # are always uninteresting /:[ ]*file format pe[i]\{,1\}-/d /^In archive [^:]*:/d # Ensure marker is printed /^====MARK====/p # Remove all lines with less than 43 characters /^.\{43\}/!d # From remaining lines, remove first 43 characters s/^.\{43\}//' | $SED -n ' # Join marker and all lines until next marker into a single line /^====MARK====/ b para H $ b para b :para x s/\n//g # Remove the marker s/^====MARK====// # Remove trailing dots and whitespace s/[\. \t]*$// # Print /./p' | # we now have a list, one entry per line, of the stringified # contents of the appropriate section of all members of the # archive which possess that section. Heuristic: eliminate # all those which have a first or second character that is # a '.' (that is, objdump's representation of an unprintable # character.) This should work for all archives with less than # 0x302f exports -- but will fail for DLLs whose name actually # begins with a literal '.' or a single character followed by # a '.'. # # Of those that remain, print the first one. $SED -e '/^\./d;/^.\./d;q' } # func_cygming_gnu_implib_p ARG # This predicate returns with zero status (TRUE) if # ARG is a GNU/binutils-style import library. Returns # with nonzero status (FALSE) otherwise. func_cygming_gnu_implib_p () { $opt_debug func_to_tool_file "$1" func_convert_file_msys_to_w32 func_cygming_gnu_implib_tmp=`$NM "$func_to_tool_file_result" | eval "$global_symbol_pipe" | $EGREP ' (_head_[A-Za-z0-9_]+_[ad]l*|[A-Za-z0-9_]+_[ad]l*_iname)$'` test -n "$func_cygming_gnu_implib_tmp" } # func_cygming_ms_implib_p ARG # This predicate returns with zero status (TRUE) if # ARG is an MS-style import library. Returns # with nonzero status (FALSE) otherwise. func_cygming_ms_implib_p () { $opt_debug func_to_tool_file "$1" func_convert_file_msys_to_w32 func_cygming_ms_implib_tmp=`$NM "$func_to_tool_file_result" | eval "$global_symbol_pipe" | $GREP '_NULL_IMPORT_DESCRIPTOR'` test -n "$func_cygming_ms_implib_tmp" } # func_cygming_dll_for_implib_fallback ARG # Platform-specific function to extract the # name of the DLL associated with the specified # import library ARG. # # This fallback implementation is for use when $DLLTOOL # does not support the --identify-strict option. # Invoked by eval'ing the libtool variable # $sharedlib_from_linklib_cmd # Result is available in the variable # $sharedlib_from_linklib_result func_cygming_dll_for_implib_fallback () { $opt_debug if func_cygming_gnu_implib_p "$1" ; then # binutils import library sharedlib_from_linklib_result=`func_cygming_dll_for_implib_fallback_core '.idata$7' "$1"` elif func_cygming_ms_implib_p "$1" ; then # ms-generated import library sharedlib_from_linklib_result=`func_cygming_dll_for_implib_fallback_core '.idata$6' "$1"` else # unknown sharedlib_from_linklib_result="" fi } # func_extract_an_archive dir oldlib func_extract_an_archive () { $opt_debug f_ex_an_ar_dir="$1"; shift f_ex_an_ar_oldlib="$1" if test "$lock_old_archive_extraction" = yes; then lockfile=$f_ex_an_ar_oldlib.lock until $opt_dry_run || ln "$progpath" "$lockfile" 2>/dev/null; do func_echo "Waiting for $lockfile to be removed" sleep 2 done fi func_show_eval "(cd \$f_ex_an_ar_dir && $AR x \"\$f_ex_an_ar_oldlib\")" \ 'stat=$?; rm -f "$lockfile"; exit $stat' if test "$lock_old_archive_extraction" = yes; then $opt_dry_run || rm -f "$lockfile" fi if ($AR t "$f_ex_an_ar_oldlib" | sort | sort -uc >/dev/null 2>&1); then : else func_fatal_error "object name conflicts in archive: $f_ex_an_ar_dir/$f_ex_an_ar_oldlib" fi } # func_extract_archives gentop oldlib ... func_extract_archives () { $opt_debug my_gentop="$1"; shift my_oldlibs=${1+"$@"} my_oldobjs="" my_xlib="" my_xabs="" my_xdir="" for my_xlib in $my_oldlibs; do # Extract the objects. case $my_xlib in [\\/]* | [A-Za-z]:[\\/]*) my_xabs="$my_xlib" ;; *) my_xabs=`pwd`"/$my_xlib" ;; esac func_basename "$my_xlib" my_xlib="$func_basename_result" my_xlib_u=$my_xlib while :; do case " $extracted_archives " in *" $my_xlib_u "*) func_arith $extracted_serial + 1 extracted_serial=$func_arith_result my_xlib_u=lt$extracted_serial-$my_xlib ;; *) break ;; esac done extracted_archives="$extracted_archives $my_xlib_u" my_xdir="$my_gentop/$my_xlib_u" func_mkdir_p "$my_xdir" case $host in *-darwin*) func_verbose "Extracting $my_xabs" # Do not bother doing anything if just a dry run $opt_dry_run || { darwin_orig_dir=`pwd` cd $my_xdir || exit $? darwin_archive=$my_xabs darwin_curdir=`pwd` darwin_base_archive=`basename "$darwin_archive"` darwin_arches=`$LIPO -info "$darwin_archive" 2>/dev/null | $GREP Architectures 2>/dev/null || true` if test -n "$darwin_arches"; then darwin_arches=`$ECHO "$darwin_arches" | $SED -e 's/.*are://'` darwin_arch= func_verbose "$darwin_base_archive has multiple architectures $darwin_arches" for darwin_arch in $darwin_arches ; do func_mkdir_p "unfat-$$/${darwin_base_archive}-${darwin_arch}" $LIPO -thin $darwin_arch -output "unfat-$$/${darwin_base_archive}-${darwin_arch}/${darwin_base_archive}" "${darwin_archive}" cd "unfat-$$/${darwin_base_archive}-${darwin_arch}" func_extract_an_archive "`pwd`" "${darwin_base_archive}" cd "$darwin_curdir" $RM "unfat-$$/${darwin_base_archive}-${darwin_arch}/${darwin_base_archive}" done # $darwin_arches ## Okay now we've a bunch of thin objects, gotta fatten them up :) darwin_filelist=`find unfat-$$ -type f -name \*.o -print -o -name \*.lo -print | $SED -e "$basename" | sort -u` darwin_file= darwin_files= for darwin_file in $darwin_filelist; do darwin_files=`find unfat-$$ -name $darwin_file -print | sort | $NL2SP` $LIPO -create -output "$darwin_file" $darwin_files done # $darwin_filelist $RM -rf unfat-$$ cd "$darwin_orig_dir" else cd $darwin_orig_dir func_extract_an_archive "$my_xdir" "$my_xabs" fi # $darwin_arches } # !$opt_dry_run ;; *) func_extract_an_archive "$my_xdir" "$my_xabs" ;; esac my_oldobjs="$my_oldobjs "`find $my_xdir -name \*.$objext -print -o -name \*.lo -print | sort | $NL2SP` done func_extract_archives_result="$my_oldobjs" } # func_emit_wrapper [arg=no] # # Emit a libtool wrapper script on stdout. # Don't directly open a file because we may want to # incorporate the script contents within a cygwin/mingw # wrapper executable. Must ONLY be called from within # func_mode_link because it depends on a number of variables # set therein. # # ARG is the value that the WRAPPER_SCRIPT_BELONGS_IN_OBJDIR # variable will take. If 'yes', then the emitted script # will assume that the directory in which it is stored is # the $objdir directory. This is a cygwin/mingw-specific # behavior. func_emit_wrapper () { func_emit_wrapper_arg1=${1-no} $ECHO "\ #! $SHELL # $output - temporary wrapper script for $objdir/$outputname # Generated by $PROGRAM (GNU $PACKAGE$TIMESTAMP) $VERSION # # The $output program cannot be directly executed until all the libtool # libraries that it depends on are installed. # # This wrapper script should never be moved out of the build directory. # If it is, it will not operate correctly. # Sed substitution that helps us do robust quoting. It backslashifies # metacharacters that are still active within double-quoted strings. sed_quote_subst='$sed_quote_subst' # Be Bourne compatible if test -n \"\${ZSH_VERSION+set}\" && (emulate sh) >/dev/null 2>&1; then emulate sh NULLCMD=: # Zsh 3.x and 4.x performs word splitting on \${1+\"\$@\"}, which # is contrary to our usage. Disable this feature. alias -g '\${1+\"\$@\"}'='\"\$@\"' setopt NO_GLOB_SUBST else case \`(set -o) 2>/dev/null\` in *posix*) set -o posix;; esac fi BIN_SH=xpg4; export BIN_SH # for Tru64 DUALCASE=1; export DUALCASE # for MKS sh # The HP-UX ksh and POSIX shell print the target directory to stdout # if CDPATH is set. (unset CDPATH) >/dev/null 2>&1 && unset CDPATH relink_command=\"$relink_command\" # This environment variable determines our operation mode. if test \"\$libtool_install_magic\" = \"$magic\"; then # install mode needs the following variables: generated_by_libtool_version='$macro_version' notinst_deplibs='$notinst_deplibs' else # When we are sourced in execute mode, \$file and \$ECHO are already set. if test \"\$libtool_execute_magic\" != \"$magic\"; then file=\"\$0\"" qECHO=`$ECHO "$ECHO" | $SED "$sed_quote_subst"` $ECHO "\ # A function that is used when there is no print builtin or printf. func_fallback_echo () { eval 'cat <<_LTECHO_EOF \$1 _LTECHO_EOF' } ECHO=\"$qECHO\" fi # Very basic option parsing. These options are (a) specific to # the libtool wrapper, (b) are identical between the wrapper # /script/ and the wrapper /executable/ which is used only on # windows platforms, and (c) all begin with the string "--lt-" # (application programs are unlikely to have options which match # this pattern). # # There are only two supported options: --lt-debug and # --lt-dump-script. There is, deliberately, no --lt-help. # # The first argument to this parsing function should be the # script's $0 value, followed by "$@". lt_option_debug= func_parse_lt_options () { lt_script_arg0=\$0 shift for lt_opt do case \"\$lt_opt\" in --lt-debug) lt_option_debug=1 ;; --lt-dump-script) lt_dump_D=\`\$ECHO \"X\$lt_script_arg0\" | $SED -e 's/^X//' -e 's%/[^/]*$%%'\` test \"X\$lt_dump_D\" = \"X\$lt_script_arg0\" && lt_dump_D=. lt_dump_F=\`\$ECHO \"X\$lt_script_arg0\" | $SED -e 's/^X//' -e 's%^.*/%%'\` cat \"\$lt_dump_D/\$lt_dump_F\" exit 0 ;; --lt-*) \$ECHO \"Unrecognized --lt- option: '\$lt_opt'\" 1>&2 exit 1 ;; esac done # Print the debug banner immediately: if test -n \"\$lt_option_debug\"; then echo \"${outputname}:${output}:\${LINENO}: libtool wrapper (GNU $PACKAGE$TIMESTAMP) $VERSION\" 1>&2 fi } # Used when --lt-debug. Prints its arguments to stdout # (redirection is the responsibility of the caller) func_lt_dump_args () { lt_dump_args_N=1; for lt_arg do \$ECHO \"${outputname}:${output}:\${LINENO}: newargv[\$lt_dump_args_N]: \$lt_arg\" lt_dump_args_N=\`expr \$lt_dump_args_N + 1\` done } # Core function for launching the target application func_exec_program_core () { " case $host in # Backslashes separate directories on plain windows *-*-mingw | *-*-os2* | *-cegcc*) $ECHO "\ if test -n \"\$lt_option_debug\"; then \$ECHO \"${outputname}:${output}:\${LINENO}: newargv[0]: \$progdir\\\\\$program\" 1>&2 func_lt_dump_args \${1+\"\$@\"} 1>&2 fi exec \"\$progdir\\\\\$program\" \${1+\"\$@\"} " ;; *) $ECHO "\ if test -n \"\$lt_option_debug\"; then \$ECHO \"${outputname}:${output}:\${LINENO}: newargv[0]: \$progdir/\$program\" 1>&2 func_lt_dump_args \${1+\"\$@\"} 1>&2 fi exec \"\$progdir/\$program\" \${1+\"\$@\"} " ;; esac $ECHO "\ \$ECHO \"\$0: cannot exec \$program \$*\" 1>&2 exit 1 } # A function to encapsulate launching the target application # Strips options in the --lt-* namespace from \$@ and # launches target application with the remaining arguments. func_exec_program () { for lt_wr_arg do case \$lt_wr_arg in --lt-*) ;; *) set x \"\$@\" \"\$lt_wr_arg\"; shift;; esac shift done func_exec_program_core \${1+\"\$@\"} } # Parse options func_parse_lt_options \"\$0\" \${1+\"\$@\"} # Find the directory that this script lives in. thisdir=\`\$ECHO \"\$file\" | $SED 's%/[^/]*$%%'\` test \"x\$thisdir\" = \"x\$file\" && thisdir=. # Follow symbolic links until we get to the real thisdir. file=\`ls -ld \"\$file\" | $SED -n 's/.*-> //p'\` while test -n \"\$file\"; do destdir=\`\$ECHO \"\$file\" | $SED 's%/[^/]*\$%%'\` # If there was a directory component, then change thisdir. if test \"x\$destdir\" != \"x\$file\"; then case \"\$destdir\" in [\\\\/]* | [A-Za-z]:[\\\\/]*) thisdir=\"\$destdir\" ;; *) thisdir=\"\$thisdir/\$destdir\" ;; esac fi file=\`\$ECHO \"\$file\" | $SED 's%^.*/%%'\` file=\`ls -ld \"\$thisdir/\$file\" | $SED -n 's/.*-> //p'\` done # Usually 'no', except on cygwin/mingw when embedded into # the cwrapper. WRAPPER_SCRIPT_BELONGS_IN_OBJDIR=$func_emit_wrapper_arg1 if test \"\$WRAPPER_SCRIPT_BELONGS_IN_OBJDIR\" = \"yes\"; then # special case for '.' if test \"\$thisdir\" = \".\"; then thisdir=\`pwd\` fi # remove .libs from thisdir case \"\$thisdir\" in *[\\\\/]$objdir ) thisdir=\`\$ECHO \"\$thisdir\" | $SED 's%[\\\\/][^\\\\/]*$%%'\` ;; $objdir ) thisdir=. ;; esac fi # Try to get the absolute directory name. absdir=\`cd \"\$thisdir\" && pwd\` test -n \"\$absdir\" && thisdir=\"\$absdir\" " if test "$fast_install" = yes; then $ECHO "\ program=lt-'$outputname'$exeext progdir=\"\$thisdir/$objdir\" if test ! -f \"\$progdir/\$program\" || { file=\`ls -1dt \"\$progdir/\$program\" \"\$progdir/../\$program\" 2>/dev/null | ${SED} 1q\`; \\ test \"X\$file\" != \"X\$progdir/\$program\"; }; then file=\"\$\$-\$program\" if test ! -d \"\$progdir\"; then $MKDIR \"\$progdir\" else $RM \"\$progdir/\$file\" fi" $ECHO "\ # relink executable if necessary if test -n \"\$relink_command\"; then if relink_command_output=\`eval \$relink_command 2>&1\`; then : else $ECHO \"\$relink_command_output\" >&2 $RM \"\$progdir/\$file\" exit 1 fi fi $MV \"\$progdir/\$file\" \"\$progdir/\$program\" 2>/dev/null || { $RM \"\$progdir/\$program\"; $MV \"\$progdir/\$file\" \"\$progdir/\$program\"; } $RM \"\$progdir/\$file\" fi" else $ECHO "\ program='$outputname' progdir=\"\$thisdir/$objdir\" " fi $ECHO "\ if test -f \"\$progdir/\$program\"; then" # fixup the dll searchpath if we need to. # # Fix the DLL searchpath if we need to. Do this before prepending # to shlibpath, because on Windows, both are PATH and uninstalled # libraries must come first. if test -n "$dllsearchpath"; then $ECHO "\ # Add the dll search path components to the executable PATH PATH=$dllsearchpath:\$PATH " fi # Export our shlibpath_var if we have one. if test "$shlibpath_overrides_runpath" = yes && test -n "$shlibpath_var" && test -n "$temp_rpath"; then $ECHO "\ # Add our own library path to $shlibpath_var $shlibpath_var=\"$temp_rpath\$$shlibpath_var\" # Some systems cannot cope with colon-terminated $shlibpath_var # The second colon is a workaround for a bug in BeOS R4 sed $shlibpath_var=\`\$ECHO \"\$$shlibpath_var\" | $SED 's/::*\$//'\` export $shlibpath_var " fi $ECHO "\ if test \"\$libtool_execute_magic\" != \"$magic\"; then # Run the actual program with our arguments. func_exec_program \${1+\"\$@\"} fi else # The program doesn't exist. \$ECHO \"\$0: error: \\\`\$progdir/\$program' does not exist\" 1>&2 \$ECHO \"This script is just a wrapper for \$program.\" 1>&2 \$ECHO \"See the $PACKAGE documentation for more information.\" 1>&2 exit 1 fi fi\ " } # func_emit_cwrapperexe_src # emit the source code for a wrapper executable on stdout # Must ONLY be called from within func_mode_link because # it depends on a number of variable set therein. func_emit_cwrapperexe_src () { cat < #include #ifdef _MSC_VER # include # include # include #else # include # include # ifdef __CYGWIN__ # include # endif #endif #include #include #include #include #include #include #include #include /* declarations of non-ANSI functions */ #if defined(__MINGW32__) # ifdef __STRICT_ANSI__ int _putenv (const char *); # endif #elif defined(__CYGWIN__) # ifdef __STRICT_ANSI__ char *realpath (const char *, char *); int putenv (char *); int setenv (const char *, const char *, int); # endif /* #elif defined (other platforms) ... */ #endif /* portability defines, excluding path handling macros */ #if defined(_MSC_VER) # define setmode _setmode # define stat _stat # define chmod _chmod # define getcwd _getcwd # define putenv _putenv # define S_IXUSR _S_IEXEC # ifndef _INTPTR_T_DEFINED # define _INTPTR_T_DEFINED # define intptr_t int # endif #elif defined(__MINGW32__) # define setmode _setmode # define stat _stat # define chmod _chmod # define getcwd _getcwd # define putenv _putenv #elif defined(__CYGWIN__) # define HAVE_SETENV # define FOPEN_WB "wb" /* #elif defined (other platforms) ... */ #endif #if defined(PATH_MAX) # define LT_PATHMAX PATH_MAX #elif defined(MAXPATHLEN) # define LT_PATHMAX MAXPATHLEN #else # define LT_PATHMAX 1024 #endif #ifndef S_IXOTH # define S_IXOTH 0 #endif #ifndef S_IXGRP # define S_IXGRP 0 #endif /* path handling portability macros */ #ifndef DIR_SEPARATOR # define DIR_SEPARATOR '/' # define PATH_SEPARATOR ':' #endif #if defined (_WIN32) || defined (__MSDOS__) || defined (__DJGPP__) || \ defined (__OS2__) # define HAVE_DOS_BASED_FILE_SYSTEM # define FOPEN_WB "wb" # ifndef DIR_SEPARATOR_2 # define DIR_SEPARATOR_2 '\\' # endif # ifndef PATH_SEPARATOR_2 # define PATH_SEPARATOR_2 ';' # endif #endif #ifndef DIR_SEPARATOR_2 # define IS_DIR_SEPARATOR(ch) ((ch) == DIR_SEPARATOR) #else /* DIR_SEPARATOR_2 */ # define IS_DIR_SEPARATOR(ch) \ (((ch) == DIR_SEPARATOR) || ((ch) == DIR_SEPARATOR_2)) #endif /* DIR_SEPARATOR_2 */ #ifndef PATH_SEPARATOR_2 # define IS_PATH_SEPARATOR(ch) ((ch) == PATH_SEPARATOR) #else /* PATH_SEPARATOR_2 */ # define IS_PATH_SEPARATOR(ch) ((ch) == PATH_SEPARATOR_2) #endif /* PATH_SEPARATOR_2 */ #ifndef FOPEN_WB # define FOPEN_WB "w" #endif #ifndef _O_BINARY # define _O_BINARY 0 #endif #define XMALLOC(type, num) ((type *) xmalloc ((num) * sizeof(type))) #define XFREE(stale) do { \ if (stale) { free ((void *) stale); stale = 0; } \ } while (0) #if defined(LT_DEBUGWRAPPER) static int lt_debug = 1; #else static int lt_debug = 0; #endif const char *program_name = "libtool-wrapper"; /* in case xstrdup fails */ void *xmalloc (size_t num); char *xstrdup (const char *string); const char *base_name (const char *name); char *find_executable (const char *wrapper); char *chase_symlinks (const char *pathspec); int make_executable (const char *path); int check_executable (const char *path); char *strendzap (char *str, const char *pat); void lt_debugprintf (const char *file, int line, const char *fmt, ...); void lt_fatal (const char *file, int line, const char *message, ...); static const char *nonnull (const char *s); static const char *nonempty (const char *s); void lt_setenv (const char *name, const char *value); char *lt_extend_str (const char *orig_value, const char *add, int to_end); void lt_update_exe_path (const char *name, const char *value); void lt_update_lib_path (const char *name, const char *value); char **prepare_spawn (char **argv); void lt_dump_script (FILE *f); EOF cat <= 0) && (st.st_mode & (S_IXUSR | S_IXGRP | S_IXOTH))) return 1; else return 0; } int make_executable (const char *path) { int rval = 0; struct stat st; lt_debugprintf (__FILE__, __LINE__, "(make_executable): %s\n", nonempty (path)); if ((!path) || (!*path)) return 0; if (stat (path, &st) >= 0) { rval = chmod (path, st.st_mode | S_IXOTH | S_IXGRP | S_IXUSR); } return rval; } /* Searches for the full path of the wrapper. Returns newly allocated full path name if found, NULL otherwise Does not chase symlinks, even on platforms that support them. */ char * find_executable (const char *wrapper) { int has_slash = 0; const char *p; const char *p_next; /* static buffer for getcwd */ char tmp[LT_PATHMAX + 1]; int tmp_len; char *concat_name; lt_debugprintf (__FILE__, __LINE__, "(find_executable): %s\n", nonempty (wrapper)); if ((wrapper == NULL) || (*wrapper == '\0')) return NULL; /* Absolute path? */ #if defined (HAVE_DOS_BASED_FILE_SYSTEM) if (isalpha ((unsigned char) wrapper[0]) && wrapper[1] == ':') { concat_name = xstrdup (wrapper); if (check_executable (concat_name)) return concat_name; XFREE (concat_name); } else { #endif if (IS_DIR_SEPARATOR (wrapper[0])) { concat_name = xstrdup (wrapper); if (check_executable (concat_name)) return concat_name; XFREE (concat_name); } #if defined (HAVE_DOS_BASED_FILE_SYSTEM) } #endif for (p = wrapper; *p; p++) if (*p == '/') { has_slash = 1; break; } if (!has_slash) { /* no slashes; search PATH */ const char *path = getenv ("PATH"); if (path != NULL) { for (p = path; *p; p = p_next) { const char *q; size_t p_len; for (q = p; *q; q++) if (IS_PATH_SEPARATOR (*q)) break; p_len = q - p; p_next = (*q == '\0' ? q : q + 1); if (p_len == 0) { /* empty path: current directory */ if (getcwd (tmp, LT_PATHMAX) == NULL) lt_fatal (__FILE__, __LINE__, "getcwd failed: %s", nonnull (strerror (errno))); tmp_len = strlen (tmp); concat_name = XMALLOC (char, tmp_len + 1 + strlen (wrapper) + 1); memcpy (concat_name, tmp, tmp_len); concat_name[tmp_len] = '/'; strcpy (concat_name + tmp_len + 1, wrapper); } else { concat_name = XMALLOC (char, p_len + 1 + strlen (wrapper) + 1); memcpy (concat_name, p, p_len); concat_name[p_len] = '/'; strcpy (concat_name + p_len + 1, wrapper); } if (check_executable (concat_name)) return concat_name; XFREE (concat_name); } } /* not found in PATH; assume curdir */ } /* Relative path | not found in path: prepend cwd */ if (getcwd (tmp, LT_PATHMAX) == NULL) lt_fatal (__FILE__, __LINE__, "getcwd failed: %s", nonnull (strerror (errno))); tmp_len = strlen (tmp); concat_name = XMALLOC (char, tmp_len + 1 + strlen (wrapper) + 1); memcpy (concat_name, tmp, tmp_len); concat_name[tmp_len] = '/'; strcpy (concat_name + tmp_len + 1, wrapper); if (check_executable (concat_name)) return concat_name; XFREE (concat_name); return NULL; } char * chase_symlinks (const char *pathspec) { #ifndef S_ISLNK return xstrdup (pathspec); #else char buf[LT_PATHMAX]; struct stat s; char *tmp_pathspec = xstrdup (pathspec); char *p; int has_symlinks = 0; while (strlen (tmp_pathspec) && !has_symlinks) { lt_debugprintf (__FILE__, __LINE__, "checking path component for symlinks: %s\n", tmp_pathspec); if (lstat (tmp_pathspec, &s) == 0) { if (S_ISLNK (s.st_mode) != 0) { has_symlinks = 1; break; } /* search backwards for last DIR_SEPARATOR */ p = tmp_pathspec + strlen (tmp_pathspec) - 1; while ((p > tmp_pathspec) && (!IS_DIR_SEPARATOR (*p))) p--; if ((p == tmp_pathspec) && (!IS_DIR_SEPARATOR (*p))) { /* no more DIR_SEPARATORS left */ break; } *p = '\0'; } else { lt_fatal (__FILE__, __LINE__, "error accessing file \"%s\": %s", tmp_pathspec, nonnull (strerror (errno))); } } XFREE (tmp_pathspec); if (!has_symlinks) { return xstrdup (pathspec); } tmp_pathspec = realpath (pathspec, buf); if (tmp_pathspec == 0) { lt_fatal (__FILE__, __LINE__, "could not follow symlinks for %s", pathspec); } return xstrdup (tmp_pathspec); #endif } char * strendzap (char *str, const char *pat) { size_t len, patlen; assert (str != NULL); assert (pat != NULL); len = strlen (str); patlen = strlen (pat); if (patlen <= len) { str += len - patlen; if (strcmp (str, pat) == 0) *str = '\0'; } return str; } void lt_debugprintf (const char *file, int line, const char *fmt, ...) { va_list args; if (lt_debug) { (void) fprintf (stderr, "%s:%s:%d: ", program_name, file, line); va_start (args, fmt); (void) vfprintf (stderr, fmt, args); va_end (args); } } static void lt_error_core (int exit_status, const char *file, int line, const char *mode, const char *message, va_list ap) { fprintf (stderr, "%s:%s:%d: %s: ", program_name, file, line, mode); vfprintf (stderr, message, ap); fprintf (stderr, ".\n"); if (exit_status >= 0) exit (exit_status); } void lt_fatal (const char *file, int line, const char *message, ...) { va_list ap; va_start (ap, message); lt_error_core (EXIT_FAILURE, file, line, "FATAL", message, ap); va_end (ap); } static const char * nonnull (const char *s) { return s ? s : "(null)"; } static const char * nonempty (const char *s) { return (s && !*s) ? "(empty)" : nonnull (s); } void lt_setenv (const char *name, const char *value) { lt_debugprintf (__FILE__, __LINE__, "(lt_setenv) setting '%s' to '%s'\n", nonnull (name), nonnull (value)); { #ifdef HAVE_SETENV /* always make a copy, for consistency with !HAVE_SETENV */ char *str = xstrdup (value); setenv (name, str, 1); #else int len = strlen (name) + 1 + strlen (value) + 1; char *str = XMALLOC (char, len); sprintf (str, "%s=%s", name, value); if (putenv (str) != EXIT_SUCCESS) { XFREE (str); } #endif } } char * lt_extend_str (const char *orig_value, const char *add, int to_end) { char *new_value; if (orig_value && *orig_value) { int orig_value_len = strlen (orig_value); int add_len = strlen (add); new_value = XMALLOC (char, add_len + orig_value_len + 1); if (to_end) { strcpy (new_value, orig_value); strcpy (new_value + orig_value_len, add); } else { strcpy (new_value, add); strcpy (new_value + add_len, orig_value); } } else { new_value = xstrdup (add); } return new_value; } void lt_update_exe_path (const char *name, const char *value) { lt_debugprintf (__FILE__, __LINE__, "(lt_update_exe_path) modifying '%s' by prepending '%s'\n", nonnull (name), nonnull (value)); if (name && *name && value && *value) { char *new_value = lt_extend_str (getenv (name), value, 0); /* some systems can't cope with a ':'-terminated path #' */ int len = strlen (new_value); while (((len = strlen (new_value)) > 0) && IS_PATH_SEPARATOR (new_value[len-1])) { new_value[len-1] = '\0'; } lt_setenv (name, new_value); XFREE (new_value); } } void lt_update_lib_path (const char *name, const char *value) { lt_debugprintf (__FILE__, __LINE__, "(lt_update_lib_path) modifying '%s' by prepending '%s'\n", nonnull (name), nonnull (value)); if (name && *name && value && *value) { char *new_value = lt_extend_str (getenv (name), value, 0); lt_setenv (name, new_value); XFREE (new_value); } } EOF case $host_os in mingw*) cat <<"EOF" /* Prepares an argument vector before calling spawn(). Note that spawn() does not by itself call the command interpreter (getenv ("COMSPEC") != NULL ? getenv ("COMSPEC") : ({ OSVERSIONINFO v; v.dwOSVersionInfoSize = sizeof(OSVERSIONINFO); GetVersionEx(&v); v.dwPlatformId == VER_PLATFORM_WIN32_NT; }) ? "cmd.exe" : "command.com"). Instead it simply concatenates the arguments, separated by ' ', and calls CreateProcess(). We must quote the arguments since Win32 CreateProcess() interprets characters like ' ', '\t', '\\', '"' (but not '<' and '>') in a special way: - Space and tab are interpreted as delimiters. They are not treated as delimiters if they are surrounded by double quotes: "...". - Unescaped double quotes are removed from the input. Their only effect is that within double quotes, space and tab are treated like normal characters. - Backslashes not followed by double quotes are not special. - But 2*n+1 backslashes followed by a double quote become n backslashes followed by a double quote (n >= 0): \" -> " \\\" -> \" \\\\\" -> \\" */ #define SHELL_SPECIAL_CHARS "\"\\ \001\002\003\004\005\006\007\010\011\012\013\014\015\016\017\020\021\022\023\024\025\026\027\030\031\032\033\034\035\036\037" #define SHELL_SPACE_CHARS " \001\002\003\004\005\006\007\010\011\012\013\014\015\016\017\020\021\022\023\024\025\026\027\030\031\032\033\034\035\036\037" char ** prepare_spawn (char **argv) { size_t argc; char **new_argv; size_t i; /* Count number of arguments. */ for (argc = 0; argv[argc] != NULL; argc++) ; /* Allocate new argument vector. */ new_argv = XMALLOC (char *, argc + 1); /* Put quoted arguments into the new argument vector. */ for (i = 0; i < argc; i++) { const char *string = argv[i]; if (string[0] == '\0') new_argv[i] = xstrdup ("\"\""); else if (strpbrk (string, SHELL_SPECIAL_CHARS) != NULL) { int quote_around = (strpbrk (string, SHELL_SPACE_CHARS) != NULL); size_t length; unsigned int backslashes; const char *s; char *quoted_string; char *p; length = 0; backslashes = 0; if (quote_around) length++; for (s = string; *s != '\0'; s++) { char c = *s; if (c == '"') length += backslashes + 1; length++; if (c == '\\') backslashes++; else backslashes = 0; } if (quote_around) length += backslashes + 1; quoted_string = XMALLOC (char, length + 1); p = quoted_string; backslashes = 0; if (quote_around) *p++ = '"'; for (s = string; *s != '\0'; s++) { char c = *s; if (c == '"') { unsigned int j; for (j = backslashes + 1; j > 0; j--) *p++ = '\\'; } *p++ = c; if (c == '\\') backslashes++; else backslashes = 0; } if (quote_around) { unsigned int j; for (j = backslashes; j > 0; j--) *p++ = '\\'; *p++ = '"'; } *p = '\0'; new_argv[i] = quoted_string; } else new_argv[i] = (char *) string; } new_argv[argc] = NULL; return new_argv; } EOF ;; esac cat <<"EOF" void lt_dump_script (FILE* f) { EOF func_emit_wrapper yes | $SED -e 's/\([\\"]\)/\\\1/g' \ -e 's/^/ fputs ("/' -e 's/$/\\n", f);/' cat <<"EOF" } EOF } # end: func_emit_cwrapperexe_src # func_win32_import_lib_p ARG # True if ARG is an import lib, as indicated by $file_magic_cmd func_win32_import_lib_p () { $opt_debug case `eval $file_magic_cmd \"\$1\" 2>/dev/null | $SED -e 10q` in *import*) : ;; *) false ;; esac } # func_mode_link arg... func_mode_link () { $opt_debug case $host in *-*-cygwin* | *-*-mingw* | *-*-pw32* | *-*-os2* | *-cegcc*) # It is impossible to link a dll without this setting, and # we shouldn't force the makefile maintainer to figure out # which system we are compiling for in order to pass an extra # flag for every libtool invocation. # allow_undefined=no # FIXME: Unfortunately, there are problems with the above when trying # to make a dll which has undefined symbols, in which case not # even a static library is built. For now, we need to specify # -no-undefined on the libtool link line when we can be certain # that all symbols are satisfied, otherwise we get a static library. allow_undefined=yes ;; *) allow_undefined=yes ;; esac libtool_args=$nonopt base_compile="$nonopt $@" compile_command=$nonopt finalize_command=$nonopt compile_rpath= finalize_rpath= compile_shlibpath= finalize_shlibpath= convenience= old_convenience= deplibs= old_deplibs= compiler_flags= linker_flags= dllsearchpath= lib_search_path=`pwd` inst_prefix_dir= new_inherited_linker_flags= avoid_version=no bindir= dlfiles= dlprefiles= dlself=no export_dynamic=no export_symbols= export_symbols_regex= generated= libobjs= ltlibs= module=no no_install=no objs= non_pic_objects= precious_files_regex= prefer_static_libs=no preload=no prev= prevarg= release= rpath= xrpath= perm_rpath= temp_rpath= thread_safe=no vinfo= vinfo_number=no weak_libs= single_module="${wl}-single_module" func_infer_tag $base_compile # We need to know -static, to get the right output filenames. for arg do case $arg in -shared) test "$build_libtool_libs" != yes && \ func_fatal_configuration "can not build a shared library" build_old_libs=no break ;; -all-static | -static | -static-libtool-libs) case $arg in -all-static) if test "$build_libtool_libs" = yes && test -z "$link_static_flag"; then func_warning "complete static linking is impossible in this configuration" fi if test -n "$link_static_flag"; then dlopen_self=$dlopen_self_static fi prefer_static_libs=yes ;; -static) if test -z "$pic_flag" && test -n "$link_static_flag"; then dlopen_self=$dlopen_self_static fi prefer_static_libs=built ;; -static-libtool-libs) if test -z "$pic_flag" && test -n "$link_static_flag"; then dlopen_self=$dlopen_self_static fi prefer_static_libs=yes ;; esac build_libtool_libs=no build_old_libs=yes break ;; esac done # See if our shared archives depend on static archives. test -n "$old_archive_from_new_cmds" && build_old_libs=yes # Go through the arguments, transforming them on the way. while test "$#" -gt 0; do arg="$1" shift func_quote_for_eval "$arg" qarg=$func_quote_for_eval_unquoted_result func_append libtool_args " $func_quote_for_eval_result" # If the previous option needs an argument, assign it. if test -n "$prev"; then case $prev in output) func_append compile_command " @OUTPUT@" func_append finalize_command " @OUTPUT@" ;; esac case $prev in bindir) bindir="$arg" prev= continue ;; dlfiles|dlprefiles) if test "$preload" = no; then # Add the symbol object into the linking commands. func_append compile_command " @SYMFILE@" func_append finalize_command " @SYMFILE@" preload=yes fi case $arg in *.la | *.lo) ;; # We handle these cases below. force) if test "$dlself" = no; then dlself=needless export_dynamic=yes fi prev= continue ;; self) if test "$prev" = dlprefiles; then dlself=yes elif test "$prev" = dlfiles && test "$dlopen_self" != yes; then dlself=yes else dlself=needless export_dynamic=yes fi prev= continue ;; *) if test "$prev" = dlfiles; then func_append dlfiles " $arg" else func_append dlprefiles " $arg" fi prev= continue ;; esac ;; expsyms) export_symbols="$arg" test -f "$arg" \ || func_fatal_error "symbol file \`$arg' does not exist" prev= continue ;; expsyms_regex) export_symbols_regex="$arg" prev= continue ;; framework) case $host in *-*-darwin*) case "$deplibs " in *" $qarg.ltframework "*) ;; *) func_append deplibs " $qarg.ltframework" # this is fixed later ;; esac ;; esac prev= continue ;; inst_prefix) inst_prefix_dir="$arg" prev= continue ;; objectlist) if test -f "$arg"; then save_arg=$arg moreargs= for fil in `cat "$save_arg"` do # func_append moreargs " $fil" arg=$fil # A libtool-controlled object. # Check to see that this really is a libtool object. if func_lalib_unsafe_p "$arg"; then pic_object= non_pic_object= # Read the .lo file func_source "$arg" if test -z "$pic_object" || test -z "$non_pic_object" || test "$pic_object" = none && test "$non_pic_object" = none; then func_fatal_error "cannot find name of object for \`$arg'" fi # Extract subdirectory from the argument. func_dirname "$arg" "/" "" xdir="$func_dirname_result" if test "$pic_object" != none; then # Prepend the subdirectory the object is found in. pic_object="$xdir$pic_object" if test "$prev" = dlfiles; then if test "$build_libtool_libs" = yes && test "$dlopen_support" = yes; then func_append dlfiles " $pic_object" prev= continue else # If libtool objects are unsupported, then we need to preload. prev=dlprefiles fi fi # CHECK ME: I think I busted this. -Ossama if test "$prev" = dlprefiles; then # Preload the old-style object. func_append dlprefiles " $pic_object" prev= fi # A PIC object. func_append libobjs " $pic_object" arg="$pic_object" fi # Non-PIC object. if test "$non_pic_object" != none; then # Prepend the subdirectory the object is found in. non_pic_object="$xdir$non_pic_object" # A standard non-PIC object func_append non_pic_objects " $non_pic_object" if test -z "$pic_object" || test "$pic_object" = none ; then arg="$non_pic_object" fi else # If the PIC object exists, use it instead. # $xdir was prepended to $pic_object above. non_pic_object="$pic_object" func_append non_pic_objects " $non_pic_object" fi else # Only an error if not doing a dry-run. if $opt_dry_run; then # Extract subdirectory from the argument. func_dirname "$arg" "/" "" xdir="$func_dirname_result" func_lo2o "$arg" pic_object=$xdir$objdir/$func_lo2o_result non_pic_object=$xdir$func_lo2o_result func_append libobjs " $pic_object" func_append non_pic_objects " $non_pic_object" else func_fatal_error "\`$arg' is not a valid libtool object" fi fi done else func_fatal_error "link input file \`$arg' does not exist" fi arg=$save_arg prev= continue ;; precious_regex) precious_files_regex="$arg" prev= continue ;; release) release="-$arg" prev= continue ;; rpath | xrpath) # We need an absolute path. case $arg in [\\/]* | [A-Za-z]:[\\/]*) ;; *) func_fatal_error "only absolute run-paths are allowed" ;; esac if test "$prev" = rpath; then case "$rpath " in *" $arg "*) ;; *) func_append rpath " $arg" ;; esac else case "$xrpath " in *" $arg "*) ;; *) func_append xrpath " $arg" ;; esac fi prev= continue ;; shrext) shrext_cmds="$arg" prev= continue ;; weak) func_append weak_libs " $arg" prev= continue ;; xcclinker) func_append linker_flags " $qarg" func_append compiler_flags " $qarg" prev= func_append compile_command " $qarg" func_append finalize_command " $qarg" continue ;; xcompiler) func_append compiler_flags " $qarg" prev= func_append compile_command " $qarg" func_append finalize_command " $qarg" continue ;; xlinker) func_append linker_flags " $qarg" func_append compiler_flags " $wl$qarg" prev= func_append compile_command " $wl$qarg" func_append finalize_command " $wl$qarg" continue ;; *) eval "$prev=\"\$arg\"" prev= continue ;; esac fi # test -n "$prev" prevarg="$arg" case $arg in -all-static) if test -n "$link_static_flag"; then # See comment for -static flag below, for more details. func_append compile_command " $link_static_flag" func_append finalize_command " $link_static_flag" fi continue ;; -allow-undefined) # FIXME: remove this flag sometime in the future. func_fatal_error "\`-allow-undefined' must not be used because it is the default" ;; -avoid-version) avoid_version=yes continue ;; -bindir) prev=bindir continue ;; -dlopen) prev=dlfiles continue ;; -dlpreopen) prev=dlprefiles continue ;; -export-dynamic) export_dynamic=yes continue ;; -export-symbols | -export-symbols-regex) if test -n "$export_symbols" || test -n "$export_symbols_regex"; then func_fatal_error "more than one -exported-symbols argument is not allowed" fi if test "X$arg" = "X-export-symbols"; then prev=expsyms else prev=expsyms_regex fi continue ;; -framework) prev=framework continue ;; -inst-prefix-dir) prev=inst_prefix continue ;; # The native IRIX linker understands -LANG:*, -LIST:* and -LNO:* # so, if we see these flags be careful not to treat them like -L -L[A-Z][A-Z]*:*) case $with_gcc/$host in no/*-*-irix* | /*-*-irix*) func_append compile_command " $arg" func_append finalize_command " $arg" ;; esac continue ;; -L*) func_stripname "-L" '' "$arg" if test -z "$func_stripname_result"; then if test "$#" -gt 0; then func_fatal_error "require no space between \`-L' and \`$1'" else func_fatal_error "need path for \`-L' option" fi fi func_resolve_sysroot "$func_stripname_result" dir=$func_resolve_sysroot_result # We need an absolute path. case $dir in [\\/]* | [A-Za-z]:[\\/]*) ;; *) absdir=`cd "$dir" && pwd` test -z "$absdir" && \ func_fatal_error "cannot determine absolute directory name of \`$dir'" dir="$absdir" ;; esac case "$deplibs " in *" -L$dir "* | *" $arg "*) # Will only happen for absolute or sysroot arguments ;; *) # Preserve sysroot, but never include relative directories case $dir in [\\/]* | [A-Za-z]:[\\/]* | =*) func_append deplibs " $arg" ;; *) func_append deplibs " -L$dir" ;; esac func_append lib_search_path " $dir" ;; esac case $host in *-*-cygwin* | *-*-mingw* | *-*-pw32* | *-*-os2* | *-cegcc*) testbindir=`$ECHO "$dir" | $SED 's*/lib$*/bin*'` case :$dllsearchpath: in *":$dir:"*) ;; ::) dllsearchpath=$dir;; *) func_append dllsearchpath ":$dir";; esac case :$dllsearchpath: in *":$testbindir:"*) ;; ::) dllsearchpath=$testbindir;; *) func_append dllsearchpath ":$testbindir";; esac ;; esac continue ;; -l*) if test "X$arg" = "X-lc" || test "X$arg" = "X-lm"; then case $host in *-*-cygwin* | *-*-mingw* | *-*-pw32* | *-*-beos* | *-cegcc* | *-*-haiku*) # These systems don't actually have a C or math library (as such) continue ;; *-*-os2*) # These systems don't actually have a C library (as such) test "X$arg" = "X-lc" && continue ;; *-*-openbsd* | *-*-freebsd* | *-*-dragonfly*) # Do not include libc due to us having libc/libc_r. test "X$arg" = "X-lc" && continue ;; *-*-rhapsody* | *-*-darwin1.[012]) # Rhapsody C and math libraries are in the System framework func_append deplibs " System.ltframework" continue ;; *-*-sco3.2v5* | *-*-sco5v6*) # Causes problems with __ctype test "X$arg" = "X-lc" && continue ;; *-*-sysv4.2uw2* | *-*-sysv5* | *-*-unixware* | *-*-OpenUNIX*) # Compiler inserts libc in the correct place for threads to work test "X$arg" = "X-lc" && continue ;; esac elif test "X$arg" = "X-lc_r"; then case $host in *-*-openbsd* | *-*-freebsd* | *-*-dragonfly*) # Do not include libc_r directly, use -pthread flag. continue ;; esac fi func_append deplibs " $arg" continue ;; -module) module=yes continue ;; # Tru64 UNIX uses -model [arg] to determine the layout of C++ # classes, name mangling, and exception handling. # Darwin uses the -arch flag to determine output architecture. -model|-arch|-isysroot|--sysroot) func_append compiler_flags " $arg" func_append compile_command " $arg" func_append finalize_command " $arg" prev=xcompiler continue ;; -mt|-mthreads|-kthread|-Kthread|-pthread|-pthreads|--thread-safe|-threads) func_append compiler_flags " $arg" func_append compile_command " $arg" func_append finalize_command " $arg" case "$new_inherited_linker_flags " in *" $arg "*) ;; * ) func_append new_inherited_linker_flags " $arg" ;; esac continue ;; -multi_module) single_module="${wl}-multi_module" continue ;; -no-fast-install) fast_install=no continue ;; -no-install) case $host in *-*-cygwin* | *-*-mingw* | *-*-pw32* | *-*-os2* | *-*-darwin* | *-cegcc*) # The PATH hackery in wrapper scripts is required on Windows # and Darwin in order for the loader to find any dlls it needs. func_warning "\`-no-install' is ignored for $host" func_warning "assuming \`-no-fast-install' instead" fast_install=no ;; *) no_install=yes ;; esac continue ;; -no-undefined) allow_undefined=no continue ;; -objectlist) prev=objectlist continue ;; -o) prev=output ;; -precious-files-regex) prev=precious_regex continue ;; -release) prev=release continue ;; -rpath) prev=rpath continue ;; -R) prev=xrpath continue ;; -R*) func_stripname '-R' '' "$arg" dir=$func_stripname_result # We need an absolute path. case $dir in [\\/]* | [A-Za-z]:[\\/]*) ;; =*) func_stripname '=' '' "$dir" dir=$lt_sysroot$func_stripname_result ;; *) func_fatal_error "only absolute run-paths are allowed" ;; esac case "$xrpath " in *" $dir "*) ;; *) func_append xrpath " $dir" ;; esac continue ;; -shared) # The effects of -shared are defined in a previous loop. continue ;; -shrext) prev=shrext continue ;; -static | -static-libtool-libs) # The effects of -static are defined in a previous loop. # We used to do the same as -all-static on platforms that # didn't have a PIC flag, but the assumption that the effects # would be equivalent was wrong. It would break on at least # Digital Unix and AIX. continue ;; -thread-safe) thread_safe=yes continue ;; -version-info) prev=vinfo continue ;; -version-number) prev=vinfo vinfo_number=yes continue ;; -weak) prev=weak continue ;; -Wc,*) func_stripname '-Wc,' '' "$arg" args=$func_stripname_result arg= save_ifs="$IFS"; IFS=',' for flag in $args; do IFS="$save_ifs" func_quote_for_eval "$flag" func_append arg " $func_quote_for_eval_result" func_append compiler_flags " $func_quote_for_eval_result" done IFS="$save_ifs" func_stripname ' ' '' "$arg" arg=$func_stripname_result ;; -Wl,*) func_stripname '-Wl,' '' "$arg" args=$func_stripname_result arg= save_ifs="$IFS"; IFS=',' for flag in $args; do IFS="$save_ifs" func_quote_for_eval "$flag" func_append arg " $wl$func_quote_for_eval_result" func_append compiler_flags " $wl$func_quote_for_eval_result" func_append linker_flags " $func_quote_for_eval_result" done IFS="$save_ifs" func_stripname ' ' '' "$arg" arg=$func_stripname_result ;; -Xcompiler) prev=xcompiler continue ;; -Xlinker) prev=xlinker continue ;; -XCClinker) prev=xcclinker continue ;; # -msg_* for osf cc -msg_*) func_quote_for_eval "$arg" arg="$func_quote_for_eval_result" ;; # Flags to be passed through unchanged, with rationale: # -64, -mips[0-9] enable 64-bit mode for the SGI compiler # -r[0-9][0-9]* specify processor for the SGI compiler # -xarch=*, -xtarget=* enable 64-bit mode for the Sun compiler # +DA*, +DD* enable 64-bit mode for the HP compiler # -q* compiler args for the IBM compiler # -m*, -t[45]*, -txscale* architecture-specific flags for GCC # -F/path path to uninstalled frameworks, gcc on darwin # -p, -pg, --coverage, -fprofile-* profiling flags for GCC # @file GCC response files # -tp=* Portland pgcc target processor selection # --sysroot=* for sysroot support # -O*, -flto*, -fwhopr*, -fuse-linker-plugin GCC link-time optimization -64|-mips[0-9]|-r[0-9][0-9]*|-xarch=*|-xtarget=*|+DA*|+DD*|-q*|-m*| \ -t[45]*|-txscale*|-p|-pg|--coverage|-fprofile-*|-F*|@*|-tp=*|--sysroot=*| \ -O*|-flto*|-fwhopr*|-fuse-linker-plugin) func_quote_for_eval "$arg" arg="$func_quote_for_eval_result" func_append compile_command " $arg" func_append finalize_command " $arg" func_append compiler_flags " $arg" continue ;; # Some other compiler flag. -* | +*) func_quote_for_eval "$arg" arg="$func_quote_for_eval_result" ;; *.$objext) # A standard object. func_append objs " $arg" ;; *.lo) # A libtool-controlled object. # Check to see that this really is a libtool object. if func_lalib_unsafe_p "$arg"; then pic_object= non_pic_object= # Read the .lo file func_source "$arg" if test -z "$pic_object" || test -z "$non_pic_object" || test "$pic_object" = none && test "$non_pic_object" = none; then func_fatal_error "cannot find name of object for \`$arg'" fi # Extract subdirectory from the argument. func_dirname "$arg" "/" "" xdir="$func_dirname_result" if test "$pic_object" != none; then # Prepend the subdirectory the object is found in. pic_object="$xdir$pic_object" if test "$prev" = dlfiles; then if test "$build_libtool_libs" = yes && test "$dlopen_support" = yes; then func_append dlfiles " $pic_object" prev= continue else # If libtool objects are unsupported, then we need to preload. prev=dlprefiles fi fi # CHECK ME: I think I busted this. -Ossama if test "$prev" = dlprefiles; then # Preload the old-style object. func_append dlprefiles " $pic_object" prev= fi # A PIC object. func_append libobjs " $pic_object" arg="$pic_object" fi # Non-PIC object. if test "$non_pic_object" != none; then # Prepend the subdirectory the object is found in. non_pic_object="$xdir$non_pic_object" # A standard non-PIC object func_append non_pic_objects " $non_pic_object" if test -z "$pic_object" || test "$pic_object" = none ; then arg="$non_pic_object" fi else # If the PIC object exists, use it instead. # $xdir was prepended to $pic_object above. non_pic_object="$pic_object" func_append non_pic_objects " $non_pic_object" fi else # Only an error if not doing a dry-run. if $opt_dry_run; then # Extract subdirectory from the argument. func_dirname "$arg" "/" "" xdir="$func_dirname_result" func_lo2o "$arg" pic_object=$xdir$objdir/$func_lo2o_result non_pic_object=$xdir$func_lo2o_result func_append libobjs " $pic_object" func_append non_pic_objects " $non_pic_object" else func_fatal_error "\`$arg' is not a valid libtool object" fi fi ;; *.$libext) # An archive. func_append deplibs " $arg" func_append old_deplibs " $arg" continue ;; *.la) # A libtool-controlled library. func_resolve_sysroot "$arg" if test "$prev" = dlfiles; then # This library was specified with -dlopen. func_append dlfiles " $func_resolve_sysroot_result" prev= elif test "$prev" = dlprefiles; then # The library was specified with -dlpreopen. func_append dlprefiles " $func_resolve_sysroot_result" prev= else func_append deplibs " $func_resolve_sysroot_result" fi continue ;; # Some other compiler argument. *) # Unknown arguments in both finalize_command and compile_command need # to be aesthetically quoted because they are evaled later. func_quote_for_eval "$arg" arg="$func_quote_for_eval_result" ;; esac # arg # Now actually substitute the argument into the commands. if test -n "$arg"; then func_append compile_command " $arg" func_append finalize_command " $arg" fi done # argument parsing loop test -n "$prev" && \ func_fatal_help "the \`$prevarg' option requires an argument" if test "$export_dynamic" = yes && test -n "$export_dynamic_flag_spec"; then eval arg=\"$export_dynamic_flag_spec\" func_append compile_command " $arg" func_append finalize_command " $arg" fi oldlibs= # calculate the name of the file, without its directory func_basename "$output" outputname="$func_basename_result" libobjs_save="$libobjs" if test -n "$shlibpath_var"; then # get the directories listed in $shlibpath_var eval shlib_search_path=\`\$ECHO \"\${$shlibpath_var}\" \| \$SED \'s/:/ /g\'\` else shlib_search_path= fi eval sys_lib_search_path=\"$sys_lib_search_path_spec\" eval sys_lib_dlsearch_path=\"$sys_lib_dlsearch_path_spec\" func_dirname "$output" "/" "" output_objdir="$func_dirname_result$objdir" func_to_tool_file "$output_objdir/" tool_output_objdir=$func_to_tool_file_result # Create the object directory. func_mkdir_p "$output_objdir" # Determine the type of output case $output in "") func_fatal_help "you must specify an output file" ;; *.$libext) linkmode=oldlib ;; *.lo | *.$objext) linkmode=obj ;; *.la) linkmode=lib ;; *) linkmode=prog ;; # Anything else should be a program. esac specialdeplibs= libs= # Find all interdependent deplibs by searching for libraries # that are linked more than once (e.g. -la -lb -la) for deplib in $deplibs; do if $opt_preserve_dup_deps ; then case "$libs " in *" $deplib "*) func_append specialdeplibs " $deplib" ;; esac fi func_append libs " $deplib" done if test "$linkmode" = lib; then libs="$predeps $libs $compiler_lib_search_path $postdeps" # Compute libraries that are listed more than once in $predeps # $postdeps and mark them as special (i.e., whose duplicates are # not to be eliminated). pre_post_deps= if $opt_duplicate_compiler_generated_deps; then for pre_post_dep in $predeps $postdeps; do case "$pre_post_deps " in *" $pre_post_dep "*) func_append specialdeplibs " $pre_post_deps" ;; esac func_append pre_post_deps " $pre_post_dep" done fi pre_post_deps= fi deplibs= newdependency_libs= newlib_search_path= need_relink=no # whether we're linking any uninstalled libtool libraries notinst_deplibs= # not-installed libtool libraries notinst_path= # paths that contain not-installed libtool libraries case $linkmode in lib) passes="conv dlpreopen link" for file in $dlfiles $dlprefiles; do case $file in *.la) ;; *) func_fatal_help "libraries can \`-dlopen' only libtool libraries: $file" ;; esac done ;; prog) compile_deplibs= finalize_deplibs= alldeplibs=no newdlfiles= newdlprefiles= passes="conv scan dlopen dlpreopen link" ;; *) passes="conv" ;; esac for pass in $passes; do # The preopen pass in lib mode reverses $deplibs; put it back here # so that -L comes before libs that need it for instance... if test "$linkmode,$pass" = "lib,link"; then ## FIXME: Find the place where the list is rebuilt in the wrong ## order, and fix it there properly tmp_deplibs= for deplib in $deplibs; do tmp_deplibs="$deplib $tmp_deplibs" done deplibs="$tmp_deplibs" fi if test "$linkmode,$pass" = "lib,link" || test "$linkmode,$pass" = "prog,scan"; then libs="$deplibs" deplibs= fi if test "$linkmode" = prog; then case $pass in dlopen) libs="$dlfiles" ;; dlpreopen) libs="$dlprefiles" ;; link) libs="$deplibs %DEPLIBS% $dependency_libs" ;; esac fi if test "$linkmode,$pass" = "lib,dlpreopen"; then # Collect and forward deplibs of preopened libtool libs for lib in $dlprefiles; do # Ignore non-libtool-libs dependency_libs= func_resolve_sysroot "$lib" case $lib in *.la) func_source "$func_resolve_sysroot_result" ;; esac # Collect preopened libtool deplibs, except any this library # has declared as weak libs for deplib in $dependency_libs; do func_basename "$deplib" deplib_base=$func_basename_result case " $weak_libs " in *" $deplib_base "*) ;; *) func_append deplibs " $deplib" ;; esac done done libs="$dlprefiles" fi if test "$pass" = dlopen; then # Collect dlpreopened libraries save_deplibs="$deplibs" deplibs= fi for deplib in $libs; do lib= found=no case $deplib in -mt|-mthreads|-kthread|-Kthread|-pthread|-pthreads|--thread-safe|-threads) if test "$linkmode,$pass" = "prog,link"; then compile_deplibs="$deplib $compile_deplibs" finalize_deplibs="$deplib $finalize_deplibs" else func_append compiler_flags " $deplib" if test "$linkmode" = lib ; then case "$new_inherited_linker_flags " in *" $deplib "*) ;; * ) func_append new_inherited_linker_flags " $deplib" ;; esac fi fi continue ;; -l*) if test "$linkmode" != lib && test "$linkmode" != prog; then func_warning "\`-l' is ignored for archives/objects" continue fi func_stripname '-l' '' "$deplib" name=$func_stripname_result if test "$linkmode" = lib; then searchdirs="$newlib_search_path $lib_search_path $compiler_lib_search_dirs $sys_lib_search_path $shlib_search_path" else searchdirs="$newlib_search_path $lib_search_path $sys_lib_search_path $shlib_search_path" fi for searchdir in $searchdirs; do for search_ext in .la $std_shrext .so .a; do # Search the libtool library lib="$searchdir/lib${name}${search_ext}" if test -f "$lib"; then if test "$search_ext" = ".la"; then found=yes else found=no fi break 2 fi done done if test "$found" != yes; then # deplib doesn't seem to be a libtool library if test "$linkmode,$pass" = "prog,link"; then compile_deplibs="$deplib $compile_deplibs" finalize_deplibs="$deplib $finalize_deplibs" else deplibs="$deplib $deplibs" test "$linkmode" = lib && newdependency_libs="$deplib $newdependency_libs" fi continue else # deplib is a libtool library # If $allow_libtool_libs_with_static_runtimes && $deplib is a stdlib, # We need to do some special things here, and not later. if test "X$allow_libtool_libs_with_static_runtimes" = "Xyes" ; then case " $predeps $postdeps " in *" $deplib "*) if func_lalib_p "$lib"; then library_names= old_library= func_source "$lib" for l in $old_library $library_names; do ll="$l" done if test "X$ll" = "X$old_library" ; then # only static version available found=no func_dirname "$lib" "" "." ladir="$func_dirname_result" lib=$ladir/$old_library if test "$linkmode,$pass" = "prog,link"; then compile_deplibs="$deplib $compile_deplibs" finalize_deplibs="$deplib $finalize_deplibs" else deplibs="$deplib $deplibs" test "$linkmode" = lib && newdependency_libs="$deplib $newdependency_libs" fi continue fi fi ;; *) ;; esac fi fi ;; # -l *.ltframework) if test "$linkmode,$pass" = "prog,link"; then compile_deplibs="$deplib $compile_deplibs" finalize_deplibs="$deplib $finalize_deplibs" else deplibs="$deplib $deplibs" if test "$linkmode" = lib ; then case "$new_inherited_linker_flags " in *" $deplib "*) ;; * ) func_append new_inherited_linker_flags " $deplib" ;; esac fi fi continue ;; -L*) case $linkmode in lib) deplibs="$deplib $deplibs" test "$pass" = conv && continue newdependency_libs="$deplib $newdependency_libs" func_stripname '-L' '' "$deplib" func_resolve_sysroot "$func_stripname_result" func_append newlib_search_path " $func_resolve_sysroot_result" ;; prog) if test "$pass" = conv; then deplibs="$deplib $deplibs" continue fi if test "$pass" = scan; then deplibs="$deplib $deplibs" else compile_deplibs="$deplib $compile_deplibs" finalize_deplibs="$deplib $finalize_deplibs" fi func_stripname '-L' '' "$deplib" func_resolve_sysroot "$func_stripname_result" func_append newlib_search_path " $func_resolve_sysroot_result" ;; *) func_warning "\`-L' is ignored for archives/objects" ;; esac # linkmode continue ;; # -L -R*) if test "$pass" = link; then func_stripname '-R' '' "$deplib" func_resolve_sysroot "$func_stripname_result" dir=$func_resolve_sysroot_result # Make sure the xrpath contains only unique directories. case "$xrpath " in *" $dir "*) ;; *) func_append xrpath " $dir" ;; esac fi deplibs="$deplib $deplibs" continue ;; *.la) func_resolve_sysroot "$deplib" lib=$func_resolve_sysroot_result ;; *.$libext) if test "$pass" = conv; then deplibs="$deplib $deplibs" continue fi case $linkmode in lib) # Linking convenience modules into shared libraries is allowed, # but linking other static libraries is non-portable. case " $dlpreconveniencelibs " in *" $deplib "*) ;; *) valid_a_lib=no case $deplibs_check_method in match_pattern*) set dummy $deplibs_check_method; shift match_pattern_regex=`expr "$deplibs_check_method" : "$1 \(.*\)"` if eval "\$ECHO \"$deplib\"" 2>/dev/null | $SED 10q \ | $EGREP "$match_pattern_regex" > /dev/null; then valid_a_lib=yes fi ;; pass_all) valid_a_lib=yes ;; esac if test "$valid_a_lib" != yes; then echo $ECHO "*** Warning: Trying to link with static lib archive $deplib." echo "*** I have the capability to make that library automatically link in when" echo "*** you link to this library. But I can only do this if you have a" echo "*** shared version of the library, which you do not appear to have" echo "*** because the file extensions .$libext of this argument makes me believe" echo "*** that it is just a static archive that I should not use here." else echo $ECHO "*** Warning: Linking the shared library $output against the" $ECHO "*** static library $deplib is not portable!" deplibs="$deplib $deplibs" fi ;; esac continue ;; prog) if test "$pass" != link; then deplibs="$deplib $deplibs" else compile_deplibs="$deplib $compile_deplibs" finalize_deplibs="$deplib $finalize_deplibs" fi continue ;; esac # linkmode ;; # *.$libext *.lo | *.$objext) if test "$pass" = conv; then deplibs="$deplib $deplibs" elif test "$linkmode" = prog; then if test "$pass" = dlpreopen || test "$dlopen_support" != yes || test "$build_libtool_libs" = no; then # If there is no dlopen support or we're linking statically, # we need to preload. func_append newdlprefiles " $deplib" compile_deplibs="$deplib $compile_deplibs" finalize_deplibs="$deplib $finalize_deplibs" else func_append newdlfiles " $deplib" fi fi continue ;; %DEPLIBS%) alldeplibs=yes continue ;; esac # case $deplib if test "$found" = yes || test -f "$lib"; then : else func_fatal_error "cannot find the library \`$lib' or unhandled argument \`$deplib'" fi # Check to see that this really is a libtool archive. func_lalib_unsafe_p "$lib" \ || func_fatal_error "\`$lib' is not a valid libtool archive" func_dirname "$lib" "" "." ladir="$func_dirname_result" dlname= dlopen= dlpreopen= libdir= library_names= old_library= inherited_linker_flags= # If the library was installed with an old release of libtool, # it will not redefine variables installed, or shouldnotlink installed=yes shouldnotlink=no avoidtemprpath= # Read the .la file func_source "$lib" # Convert "-framework foo" to "foo.ltframework" if test -n "$inherited_linker_flags"; then tmp_inherited_linker_flags=`$ECHO "$inherited_linker_flags" | $SED 's/-framework \([^ $]*\)/\1.ltframework/g'` for tmp_inherited_linker_flag in $tmp_inherited_linker_flags; do case " $new_inherited_linker_flags " in *" $tmp_inherited_linker_flag "*) ;; *) func_append new_inherited_linker_flags " $tmp_inherited_linker_flag";; esac done fi dependency_libs=`$ECHO " $dependency_libs" | $SED 's% \([^ $]*\).ltframework% -framework \1%g'` if test "$linkmode,$pass" = "lib,link" || test "$linkmode,$pass" = "prog,scan" || { test "$linkmode" != prog && test "$linkmode" != lib; }; then test -n "$dlopen" && func_append dlfiles " $dlopen" test -n "$dlpreopen" && func_append dlprefiles " $dlpreopen" fi if test "$pass" = conv; then # Only check for convenience libraries deplibs="$lib $deplibs" if test -z "$libdir"; then if test -z "$old_library"; then func_fatal_error "cannot find name of link library for \`$lib'" fi # It is a libtool convenience library, so add in its objects. func_append convenience " $ladir/$objdir/$old_library" func_append old_convenience " $ladir/$objdir/$old_library" elif test "$linkmode" != prog && test "$linkmode" != lib; then func_fatal_error "\`$lib' is not a convenience library" fi tmp_libs= for deplib in $dependency_libs; do deplibs="$deplib $deplibs" if $opt_preserve_dup_deps ; then case "$tmp_libs " in *" $deplib "*) func_append specialdeplibs " $deplib" ;; esac fi func_append tmp_libs " $deplib" done continue fi # $pass = conv # Get the name of the library we link against. linklib= if test -n "$old_library" && { test "$prefer_static_libs" = yes || test "$prefer_static_libs,$installed" = "built,no"; }; then linklib=$old_library else for l in $old_library $library_names; do linklib="$l" done fi if test -z "$linklib"; then func_fatal_error "cannot find name of link library for \`$lib'" fi # This library was specified with -dlopen. if test "$pass" = dlopen; then if test -z "$libdir"; then func_fatal_error "cannot -dlopen a convenience library: \`$lib'" fi if test -z "$dlname" || test "$dlopen_support" != yes || test "$build_libtool_libs" = no; then # If there is no dlname, no dlopen support or we're linking # statically, we need to preload. We also need to preload any # dependent libraries so libltdl's deplib preloader doesn't # bomb out in the load deplibs phase. func_append dlprefiles " $lib $dependency_libs" else func_append newdlfiles " $lib" fi continue fi # $pass = dlopen # We need an absolute path. case $ladir in [\\/]* | [A-Za-z]:[\\/]*) abs_ladir="$ladir" ;; *) abs_ladir=`cd "$ladir" && pwd` if test -z "$abs_ladir"; then func_warning "cannot determine absolute directory name of \`$ladir'" func_warning "passing it literally to the linker, although it might fail" abs_ladir="$ladir" fi ;; esac func_basename "$lib" laname="$func_basename_result" # Find the relevant object directory and library name. if test "X$installed" = Xyes; then if test ! -f "$lt_sysroot$libdir/$linklib" && test -f "$abs_ladir/$linklib"; then func_warning "library \`$lib' was moved." dir="$ladir" absdir="$abs_ladir" libdir="$abs_ladir" else dir="$lt_sysroot$libdir" absdir="$lt_sysroot$libdir" fi test "X$hardcode_automatic" = Xyes && avoidtemprpath=yes else if test ! -f "$ladir/$objdir/$linklib" && test -f "$abs_ladir/$linklib"; then dir="$ladir" absdir="$abs_ladir" # Remove this search path later func_append notinst_path " $abs_ladir" else dir="$ladir/$objdir" absdir="$abs_ladir/$objdir" # Remove this search path later func_append notinst_path " $abs_ladir" fi fi # $installed = yes func_stripname 'lib' '.la' "$laname" name=$func_stripname_result # This library was specified with -dlpreopen. if test "$pass" = dlpreopen; then if test -z "$libdir" && test "$linkmode" = prog; then func_fatal_error "only libraries may -dlpreopen a convenience library: \`$lib'" fi case "$host" in # special handling for platforms with PE-DLLs. *cygwin* | *mingw* | *cegcc* ) # Linker will automatically link against shared library if both # static and shared are present. Therefore, ensure we extract # symbols from the import library if a shared library is present # (otherwise, the dlopen module name will be incorrect). We do # this by putting the import library name into $newdlprefiles. # We recover the dlopen module name by 'saving' the la file # name in a special purpose variable, and (later) extracting the # dlname from the la file. if test -n "$dlname"; then func_tr_sh "$dir/$linklib" eval "libfile_$func_tr_sh_result=\$abs_ladir/\$laname" func_append newdlprefiles " $dir/$linklib" else func_append newdlprefiles " $dir/$old_library" # Keep a list of preopened convenience libraries to check # that they are being used correctly in the link pass. test -z "$libdir" && \ func_append dlpreconveniencelibs " $dir/$old_library" fi ;; * ) # Prefer using a static library (so that no silly _DYNAMIC symbols # are required to link). if test -n "$old_library"; then func_append newdlprefiles " $dir/$old_library" # Keep a list of preopened convenience libraries to check # that they are being used correctly in the link pass. test -z "$libdir" && \ func_append dlpreconveniencelibs " $dir/$old_library" # Otherwise, use the dlname, so that lt_dlopen finds it. elif test -n "$dlname"; then func_append newdlprefiles " $dir/$dlname" else func_append newdlprefiles " $dir/$linklib" fi ;; esac fi # $pass = dlpreopen if test -z "$libdir"; then # Link the convenience library if test "$linkmode" = lib; then deplibs="$dir/$old_library $deplibs" elif test "$linkmode,$pass" = "prog,link"; then compile_deplibs="$dir/$old_library $compile_deplibs" finalize_deplibs="$dir/$old_library $finalize_deplibs" else deplibs="$lib $deplibs" # used for prog,scan pass fi continue fi if test "$linkmode" = prog && test "$pass" != link; then func_append newlib_search_path " $ladir" deplibs="$lib $deplibs" linkalldeplibs=no if test "$link_all_deplibs" != no || test -z "$library_names" || test "$build_libtool_libs" = no; then linkalldeplibs=yes fi tmp_libs= for deplib in $dependency_libs; do case $deplib in -L*) func_stripname '-L' '' "$deplib" func_resolve_sysroot "$func_stripname_result" func_append newlib_search_path " $func_resolve_sysroot_result" ;; esac # Need to link against all dependency_libs? if test "$linkalldeplibs" = yes; then deplibs="$deplib $deplibs" else # Need to hardcode shared library paths # or/and link against static libraries newdependency_libs="$deplib $newdependency_libs" fi if $opt_preserve_dup_deps ; then case "$tmp_libs " in *" $deplib "*) func_append specialdeplibs " $deplib" ;; esac fi func_append tmp_libs " $deplib" done # for deplib continue fi # $linkmode = prog... if test "$linkmode,$pass" = "prog,link"; then if test -n "$library_names" && { { test "$prefer_static_libs" = no || test "$prefer_static_libs,$installed" = "built,yes"; } || test -z "$old_library"; }; then # We need to hardcode the library path if test -n "$shlibpath_var" && test -z "$avoidtemprpath" ; then # Make sure the rpath contains only unique directories. case "$temp_rpath:" in *"$absdir:"*) ;; *) func_append temp_rpath "$absdir:" ;; esac fi # Hardcode the library path. # Skip directories that are in the system default run-time # search path. case " $sys_lib_dlsearch_path " in *" $absdir "*) ;; *) case "$compile_rpath " in *" $absdir "*) ;; *) func_append compile_rpath " $absdir" ;; esac ;; esac case " $sys_lib_dlsearch_path " in *" $libdir "*) ;; *) case "$finalize_rpath " in *" $libdir "*) ;; *) func_append finalize_rpath " $libdir" ;; esac ;; esac fi # $linkmode,$pass = prog,link... if test "$alldeplibs" = yes && { test "$deplibs_check_method" = pass_all || { test "$build_libtool_libs" = yes && test -n "$library_names"; }; }; then # We only need to search for static libraries continue fi fi link_static=no # Whether the deplib will be linked statically use_static_libs=$prefer_static_libs if test "$use_static_libs" = built && test "$installed" = yes; then use_static_libs=no fi if test -n "$library_names" && { test "$use_static_libs" = no || test -z "$old_library"; }; then case $host in *cygwin* | *mingw* | *cegcc*) # No point in relinking DLLs because paths are not encoded func_append notinst_deplibs " $lib" need_relink=no ;; *) if test "$installed" = no; then func_append notinst_deplibs " $lib" need_relink=yes fi ;; esac # This is a shared library # Warn about portability, can't link against -module's on some # systems (darwin). Don't bleat about dlopened modules though! dlopenmodule="" for dlpremoduletest in $dlprefiles; do if test "X$dlpremoduletest" = "X$lib"; then dlopenmodule="$dlpremoduletest" break fi done if test -z "$dlopenmodule" && test "$shouldnotlink" = yes && test "$pass" = link; then echo if test "$linkmode" = prog; then $ECHO "*** Warning: Linking the executable $output against the loadable module" else $ECHO "*** Warning: Linking the shared library $output against the loadable module" fi $ECHO "*** $linklib is not portable!" fi if test "$linkmode" = lib && test "$hardcode_into_libs" = yes; then # Hardcode the library path. # Skip directories that are in the system default run-time # search path. case " $sys_lib_dlsearch_path " in *" $absdir "*) ;; *) case "$compile_rpath " in *" $absdir "*) ;; *) func_append compile_rpath " $absdir" ;; esac ;; esac case " $sys_lib_dlsearch_path " in *" $libdir "*) ;; *) case "$finalize_rpath " in *" $libdir "*) ;; *) func_append finalize_rpath " $libdir" ;; esac ;; esac fi if test -n "$old_archive_from_expsyms_cmds"; then # figure out the soname set dummy $library_names shift realname="$1" shift libname=`eval "\\$ECHO \"$libname_spec\""` # use dlname if we got it. it's perfectly good, no? if test -n "$dlname"; then soname="$dlname" elif test -n "$soname_spec"; then # bleh windows case $host in *cygwin* | mingw* | *cegcc*) func_arith $current - $age major=$func_arith_result versuffix="-$major" ;; esac eval soname=\"$soname_spec\" else soname="$realname" fi # Make a new name for the extract_expsyms_cmds to use soroot="$soname" func_basename "$soroot" soname="$func_basename_result" func_stripname 'lib' '.dll' "$soname" newlib=libimp-$func_stripname_result.a # If the library has no export list, then create one now if test -f "$output_objdir/$soname-def"; then : else func_verbose "extracting exported symbol list from \`$soname'" func_execute_cmds "$extract_expsyms_cmds" 'exit $?' fi # Create $newlib if test -f "$output_objdir/$newlib"; then :; else func_verbose "generating import library for \`$soname'" func_execute_cmds "$old_archive_from_expsyms_cmds" 'exit $?' fi # make sure the library variables are pointing to the new library dir=$output_objdir linklib=$newlib fi # test -n "$old_archive_from_expsyms_cmds" if test "$linkmode" = prog || test "$opt_mode" != relink; then add_shlibpath= add_dir= add= lib_linked=yes case $hardcode_action in immediate | unsupported) if test "$hardcode_direct" = no; then add="$dir/$linklib" case $host in *-*-sco3.2v5.0.[024]*) add_dir="-L$dir" ;; *-*-sysv4*uw2*) add_dir="-L$dir" ;; *-*-sysv5OpenUNIX* | *-*-sysv5UnixWare7.[01].[10]* | \ *-*-unixware7*) add_dir="-L$dir" ;; *-*-darwin* ) # if the lib is a (non-dlopened) module then we can not # link against it, someone is ignoring the earlier warnings if /usr/bin/file -L $add 2> /dev/null | $GREP ": [^:]* bundle" >/dev/null ; then if test "X$dlopenmodule" != "X$lib"; then $ECHO "*** Warning: lib $linklib is a module, not a shared library" if test -z "$old_library" ; then echo echo "*** And there doesn't seem to be a static archive available" echo "*** The link will probably fail, sorry" else add="$dir/$old_library" fi elif test -n "$old_library"; then add="$dir/$old_library" fi fi esac elif test "$hardcode_minus_L" = no; then case $host in *-*-sunos*) add_shlibpath="$dir" ;; esac add_dir="-L$dir" add="-l$name" elif test "$hardcode_shlibpath_var" = no; then add_shlibpath="$dir" add="-l$name" else lib_linked=no fi ;; relink) if test "$hardcode_direct" = yes && test "$hardcode_direct_absolute" = no; then add="$dir/$linklib" elif test "$hardcode_minus_L" = yes; then add_dir="-L$dir" # Try looking first in the location we're being installed to. if test -n "$inst_prefix_dir"; then case $libdir in [\\/]*) func_append add_dir " -L$inst_prefix_dir$libdir" ;; esac fi add="-l$name" elif test "$hardcode_shlibpath_var" = yes; then add_shlibpath="$dir" add="-l$name" else lib_linked=no fi ;; *) lib_linked=no ;; esac if test "$lib_linked" != yes; then func_fatal_configuration "unsupported hardcode properties" fi if test -n "$add_shlibpath"; then case :$compile_shlibpath: in *":$add_shlibpath:"*) ;; *) func_append compile_shlibpath "$add_shlibpath:" ;; esac fi if test "$linkmode" = prog; then test -n "$add_dir" && compile_deplibs="$add_dir $compile_deplibs" test -n "$add" && compile_deplibs="$add $compile_deplibs" else test -n "$add_dir" && deplibs="$add_dir $deplibs" test -n "$add" && deplibs="$add $deplibs" if test "$hardcode_direct" != yes && test "$hardcode_minus_L" != yes && test "$hardcode_shlibpath_var" = yes; then case :$finalize_shlibpath: in *":$libdir:"*) ;; *) func_append finalize_shlibpath "$libdir:" ;; esac fi fi fi if test "$linkmode" = prog || test "$opt_mode" = relink; then add_shlibpath= add_dir= add= # Finalize command for both is simple: just hardcode it. if test "$hardcode_direct" = yes && test "$hardcode_direct_absolute" = no; then add="$libdir/$linklib" elif test "$hardcode_minus_L" = yes; then add_dir="-L$libdir" add="-l$name" elif test "$hardcode_shlibpath_var" = yes; then case :$finalize_shlibpath: in *":$libdir:"*) ;; *) func_append finalize_shlibpath "$libdir:" ;; esac add="-l$name" elif test "$hardcode_automatic" = yes; then if test -n "$inst_prefix_dir" && test -f "$inst_prefix_dir$libdir/$linklib" ; then add="$inst_prefix_dir$libdir/$linklib" else add="$libdir/$linklib" fi else # We cannot seem to hardcode it, guess we'll fake it. add_dir="-L$libdir" # Try looking first in the location we're being installed to. if test -n "$inst_prefix_dir"; then case $libdir in [\\/]*) func_append add_dir " -L$inst_prefix_dir$libdir" ;; esac fi add="-l$name" fi if test "$linkmode" = prog; then test -n "$add_dir" && finalize_deplibs="$add_dir $finalize_deplibs" test -n "$add" && finalize_deplibs="$add $finalize_deplibs" else test -n "$add_dir" && deplibs="$add_dir $deplibs" test -n "$add" && deplibs="$add $deplibs" fi fi elif test "$linkmode" = prog; then # Here we assume that one of hardcode_direct or hardcode_minus_L # is not unsupported. This is valid on all known static and # shared platforms. if test "$hardcode_direct" != unsupported; then test -n "$old_library" && linklib="$old_library" compile_deplibs="$dir/$linklib $compile_deplibs" finalize_deplibs="$dir/$linklib $finalize_deplibs" else compile_deplibs="-l$name -L$dir $compile_deplibs" finalize_deplibs="-l$name -L$dir $finalize_deplibs" fi elif test "$build_libtool_libs" = yes; then # Not a shared library if test "$deplibs_check_method" != pass_all; then # We're trying link a shared library against a static one # but the system doesn't support it. # Just print a warning and add the library to dependency_libs so # that the program can be linked against the static library. echo $ECHO "*** Warning: This system can not link to static lib archive $lib." echo "*** I have the capability to make that library automatically link in when" echo "*** you link to this library. But I can only do this if you have a" echo "*** shared version of the library, which you do not appear to have." if test "$module" = yes; then echo "*** But as you try to build a module library, libtool will still create " echo "*** a static module, that should work as long as the dlopening application" echo "*** is linked with the -dlopen flag to resolve symbols at runtime." if test -z "$global_symbol_pipe"; then echo echo "*** However, this would only work if libtool was able to extract symbol" echo "*** lists from a program, using \`nm' or equivalent, but libtool could" echo "*** not find such a program. So, this module is probably useless." echo "*** \`nm' from GNU binutils and a full rebuild may help." fi if test "$build_old_libs" = no; then build_libtool_libs=module build_old_libs=yes else build_libtool_libs=no fi fi else deplibs="$dir/$old_library $deplibs" link_static=yes fi fi # link shared/static library? if test "$linkmode" = lib; then if test -n "$dependency_libs" && { test "$hardcode_into_libs" != yes || test "$build_old_libs" = yes || test "$link_static" = yes; }; then # Extract -R from dependency_libs temp_deplibs= for libdir in $dependency_libs; do case $libdir in -R*) func_stripname '-R' '' "$libdir" temp_xrpath=$func_stripname_result case " $xrpath " in *" $temp_xrpath "*) ;; *) func_append xrpath " $temp_xrpath";; esac;; *) func_append temp_deplibs " $libdir";; esac done dependency_libs="$temp_deplibs" fi func_append newlib_search_path " $absdir" # Link against this library test "$link_static" = no && newdependency_libs="$abs_ladir/$laname $newdependency_libs" # ... and its dependency_libs tmp_libs= for deplib in $dependency_libs; do newdependency_libs="$deplib $newdependency_libs" case $deplib in -L*) func_stripname '-L' '' "$deplib" func_resolve_sysroot "$func_stripname_result";; *) func_resolve_sysroot "$deplib" ;; esac if $opt_preserve_dup_deps ; then case "$tmp_libs " in *" $func_resolve_sysroot_result "*) func_append specialdeplibs " $func_resolve_sysroot_result" ;; esac fi func_append tmp_libs " $func_resolve_sysroot_result" done if test "$link_all_deplibs" != no; then # Add the search paths of all dependency libraries for deplib in $dependency_libs; do path= case $deplib in -L*) path="$deplib" ;; *.la) func_resolve_sysroot "$deplib" deplib=$func_resolve_sysroot_result func_dirname "$deplib" "" "." dir=$func_dirname_result # We need an absolute path. case $dir in [\\/]* | [A-Za-z]:[\\/]*) absdir="$dir" ;; *) absdir=`cd "$dir" && pwd` if test -z "$absdir"; then func_warning "cannot determine absolute directory name of \`$dir'" absdir="$dir" fi ;; esac if $GREP "^installed=no" $deplib > /dev/null; then case $host in *-*-darwin*) depdepl= eval deplibrary_names=`${SED} -n -e 's/^library_names=\(.*\)$/\1/p' $deplib` if test -n "$deplibrary_names" ; then for tmp in $deplibrary_names ; do depdepl=$tmp done if test -f "$absdir/$objdir/$depdepl" ; then depdepl="$absdir/$objdir/$depdepl" darwin_install_name=`${OTOOL} -L $depdepl | awk '{if (NR == 2) {print $1;exit}}'` if test -z "$darwin_install_name"; then darwin_install_name=`${OTOOL64} -L $depdepl | awk '{if (NR == 2) {print $1;exit}}'` fi func_append compiler_flags " ${wl}-dylib_file ${wl}${darwin_install_name}:${depdepl}" func_append linker_flags " -dylib_file ${darwin_install_name}:${depdepl}" path= fi fi ;; *) path="-L$absdir/$objdir" ;; esac else eval libdir=`${SED} -n -e 's/^libdir=\(.*\)$/\1/p' $deplib` test -z "$libdir" && \ func_fatal_error "\`$deplib' is not a valid libtool archive" test "$absdir" != "$libdir" && \ func_warning "\`$deplib' seems to be moved" path="-L$absdir" fi ;; esac case " $deplibs " in *" $path "*) ;; *) deplibs="$path $deplibs" ;; esac done fi # link_all_deplibs != no fi # linkmode = lib done # for deplib in $libs if test "$pass" = link; then if test "$linkmode" = "prog"; then compile_deplibs="$new_inherited_linker_flags $compile_deplibs" finalize_deplibs="$new_inherited_linker_flags $finalize_deplibs" else compiler_flags="$compiler_flags "`$ECHO " $new_inherited_linker_flags" | $SED 's% \([^ $]*\).ltframework% -framework \1%g'` fi fi dependency_libs="$newdependency_libs" if test "$pass" = dlpreopen; then # Link the dlpreopened libraries before other libraries for deplib in $save_deplibs; do deplibs="$deplib $deplibs" done fi if test "$pass" != dlopen; then if test "$pass" != conv; then # Make sure lib_search_path contains only unique directories. lib_search_path= for dir in $newlib_search_path; do case "$lib_search_path " in *" $dir "*) ;; *) func_append lib_search_path " $dir" ;; esac done newlib_search_path= fi if test "$linkmode,$pass" != "prog,link"; then vars="deplibs" else vars="compile_deplibs finalize_deplibs" fi for var in $vars dependency_libs; do # Add libraries to $var in reverse order eval tmp_libs=\"\$$var\" new_libs= for deplib in $tmp_libs; do # FIXME: Pedantically, this is the right thing to do, so # that some nasty dependency loop isn't accidentally # broken: #new_libs="$deplib $new_libs" # Pragmatically, this seems to cause very few problems in # practice: case $deplib in -L*) new_libs="$deplib $new_libs" ;; -R*) ;; *) # And here is the reason: when a library appears more # than once as an explicit dependence of a library, or # is implicitly linked in more than once by the # compiler, it is considered special, and multiple # occurrences thereof are not removed. Compare this # with having the same library being listed as a # dependency of multiple other libraries: in this case, # we know (pedantically, we assume) the library does not # need to be listed more than once, so we keep only the # last copy. This is not always right, but it is rare # enough that we require users that really mean to play # such unportable linking tricks to link the library # using -Wl,-lname, so that libtool does not consider it # for duplicate removal. case " $specialdeplibs " in *" $deplib "*) new_libs="$deplib $new_libs" ;; *) case " $new_libs " in *" $deplib "*) ;; *) new_libs="$deplib $new_libs" ;; esac ;; esac ;; esac done tmp_libs= for deplib in $new_libs; do case $deplib in -L*) case " $tmp_libs " in *" $deplib "*) ;; *) func_append tmp_libs " $deplib" ;; esac ;; *) func_append tmp_libs " $deplib" ;; esac done eval $var=\"$tmp_libs\" done # for var fi # Last step: remove runtime libs from dependency_libs # (they stay in deplibs) tmp_libs= for i in $dependency_libs ; do case " $predeps $postdeps $compiler_lib_search_path " in *" $i "*) i="" ;; esac if test -n "$i" ; then func_append tmp_libs " $i" fi done dependency_libs=$tmp_libs done # for pass if test "$linkmode" = prog; then dlfiles="$newdlfiles" fi if test "$linkmode" = prog || test "$linkmode" = lib; then dlprefiles="$newdlprefiles" fi case $linkmode in oldlib) if test -n "$dlfiles$dlprefiles" || test "$dlself" != no; then func_warning "\`-dlopen' is ignored for archives" fi case " $deplibs" in *\ -l* | *\ -L*) func_warning "\`-l' and \`-L' are ignored for archives" ;; esac test -n "$rpath" && \ func_warning "\`-rpath' is ignored for archives" test -n "$xrpath" && \ func_warning "\`-R' is ignored for archives" test -n "$vinfo" && \ func_warning "\`-version-info/-version-number' is ignored for archives" test -n "$release" && \ func_warning "\`-release' is ignored for archives" test -n "$export_symbols$export_symbols_regex" && \ func_warning "\`-export-symbols' is ignored for archives" # Now set the variables for building old libraries. build_libtool_libs=no oldlibs="$output" func_append objs "$old_deplibs" ;; lib) # Make sure we only generate libraries of the form `libNAME.la'. case $outputname in lib*) func_stripname 'lib' '.la' "$outputname" name=$func_stripname_result eval shared_ext=\"$shrext_cmds\" eval libname=\"$libname_spec\" ;; *) test "$module" = no && \ func_fatal_help "libtool library \`$output' must begin with \`lib'" if test "$need_lib_prefix" != no; then # Add the "lib" prefix for modules if required func_stripname '' '.la' "$outputname" name=$func_stripname_result eval shared_ext=\"$shrext_cmds\" eval libname=\"$libname_spec\" else func_stripname '' '.la' "$outputname" libname=$func_stripname_result fi ;; esac if test -n "$objs"; then if test "$deplibs_check_method" != pass_all; then func_fatal_error "cannot build libtool library \`$output' from non-libtool objects on this host:$objs" else echo $ECHO "*** Warning: Linking the shared library $output against the non-libtool" $ECHO "*** objects $objs is not portable!" func_append libobjs " $objs" fi fi test "$dlself" != no && \ func_warning "\`-dlopen self' is ignored for libtool libraries" set dummy $rpath shift test "$#" -gt 1 && \ func_warning "ignoring multiple \`-rpath's for a libtool library" install_libdir="$1" oldlibs= if test -z "$rpath"; then if test "$build_libtool_libs" = yes; then # Building a libtool convenience library. # Some compilers have problems with a `.al' extension so # convenience libraries should have the same extension an # archive normally would. oldlibs="$output_objdir/$libname.$libext $oldlibs" build_libtool_libs=convenience build_old_libs=yes fi test -n "$vinfo" && \ func_warning "\`-version-info/-version-number' is ignored for convenience libraries" test -n "$release" && \ func_warning "\`-release' is ignored for convenience libraries" else # Parse the version information argument. save_ifs="$IFS"; IFS=':' set dummy $vinfo 0 0 0 shift IFS="$save_ifs" test -n "$7" && \ func_fatal_help "too many parameters to \`-version-info'" # convert absolute version numbers to libtool ages # this retains compatibility with .la files and attempts # to make the code below a bit more comprehensible case $vinfo_number in yes) number_major="$1" number_minor="$2" number_revision="$3" # # There are really only two kinds -- those that # use the current revision as the major version # and those that subtract age and use age as # a minor version. But, then there is irix # which has an extra 1 added just for fun # case $version_type in darwin|linux|osf|windows|none) func_arith $number_major + $number_minor current=$func_arith_result age="$number_minor" revision="$number_revision" ;; freebsd-aout|freebsd-elf|qnx|sunos) current="$number_major" revision="$number_minor" age="0" ;; irix|nonstopux) func_arith $number_major + $number_minor current=$func_arith_result age="$number_minor" revision="$number_minor" lt_irix_increment=no ;; esac ;; no) current="$1" revision="$2" age="$3" ;; esac # Check that each of the things are valid numbers. case $current in 0|[1-9]|[1-9][0-9]|[1-9][0-9][0-9]|[1-9][0-9][0-9][0-9]|[1-9][0-9][0-9][0-9][0-9]) ;; *) func_error "CURRENT \`$current' must be a nonnegative integer" func_fatal_error "\`$vinfo' is not valid version information" ;; esac case $revision in 0|[1-9]|[1-9][0-9]|[1-9][0-9][0-9]|[1-9][0-9][0-9][0-9]|[1-9][0-9][0-9][0-9][0-9]) ;; *) func_error "REVISION \`$revision' must be a nonnegative integer" func_fatal_error "\`$vinfo' is not valid version information" ;; esac case $age in 0|[1-9]|[1-9][0-9]|[1-9][0-9][0-9]|[1-9][0-9][0-9][0-9]|[1-9][0-9][0-9][0-9][0-9]) ;; *) func_error "AGE \`$age' must be a nonnegative integer" func_fatal_error "\`$vinfo' is not valid version information" ;; esac if test "$age" -gt "$current"; then func_error "AGE \`$age' is greater than the current interface number \`$current'" func_fatal_error "\`$vinfo' is not valid version information" fi # Calculate the version variables. major= versuffix= verstring= case $version_type in none) ;; darwin) # Like Linux, but with the current version available in # verstring for coding it into the library header func_arith $current - $age major=.$func_arith_result versuffix="$major.$age.$revision" # Darwin ld doesn't like 0 for these options... func_arith $current + 1 minor_current=$func_arith_result xlcverstring="${wl}-compatibility_version ${wl}$minor_current ${wl}-current_version ${wl}$minor_current.$revision" verstring="-compatibility_version $minor_current -current_version $minor_current.$revision" ;; freebsd-aout) major=".$current" versuffix=".$current.$revision"; ;; freebsd-elf) major=".$current" versuffix=".$current" ;; irix | nonstopux) if test "X$lt_irix_increment" = "Xno"; then func_arith $current - $age else func_arith $current - $age + 1 fi major=$func_arith_result case $version_type in nonstopux) verstring_prefix=nonstopux ;; *) verstring_prefix=sgi ;; esac verstring="$verstring_prefix$major.$revision" # Add in all the interfaces that we are compatible with. loop=$revision while test "$loop" -ne 0; do func_arith $revision - $loop iface=$func_arith_result func_arith $loop - 1 loop=$func_arith_result verstring="$verstring_prefix$major.$iface:$verstring" done # Before this point, $major must not contain `.'. major=.$major versuffix="$major.$revision" ;; linux) func_arith $current - $age major=.$func_arith_result versuffix="$major.$age.$revision" ;; osf) func_arith $current - $age major=.$func_arith_result versuffix=".$current.$age.$revision" verstring="$current.$age.$revision" # Add in all the interfaces that we are compatible with. loop=$age while test "$loop" -ne 0; do func_arith $current - $loop iface=$func_arith_result func_arith $loop - 1 loop=$func_arith_result verstring="$verstring:${iface}.0" done # Make executables depend on our current version. func_append verstring ":${current}.0" ;; qnx) major=".$current" versuffix=".$current" ;; sunos) major=".$current" versuffix=".$current.$revision" ;; windows) # Use '-' rather than '.', since we only want one # extension on DOS 8.3 filesystems. func_arith $current - $age major=$func_arith_result versuffix="-$major" ;; *) func_fatal_configuration "unknown library version type \`$version_type'" ;; esac # Clear the version info if we defaulted, and they specified a release. if test -z "$vinfo" && test -n "$release"; then major= case $version_type in darwin) # we can't check for "0.0" in archive_cmds due to quoting # problems, so we reset it completely verstring= ;; *) verstring="0.0" ;; esac if test "$need_version" = no; then versuffix= else versuffix=".0.0" fi fi # Remove version info from name if versioning should be avoided if test "$avoid_version" = yes && test "$need_version" = no; then major= versuffix= verstring="" fi # Check to see if the archive will have undefined symbols. if test "$allow_undefined" = yes; then if test "$allow_undefined_flag" = unsupported; then func_warning "undefined symbols not allowed in $host shared libraries" build_libtool_libs=no build_old_libs=yes fi else # Don't allow undefined symbols. allow_undefined_flag="$no_undefined_flag" fi fi func_generate_dlsyms "$libname" "$libname" "yes" func_append libobjs " $symfileobj" test "X$libobjs" = "X " && libobjs= if test "$opt_mode" != relink; then # Remove our outputs, but don't remove object files since they # may have been created when compiling PIC objects. removelist= tempremovelist=`$ECHO "$output_objdir/*"` for p in $tempremovelist; do case $p in *.$objext | *.gcno) ;; $output_objdir/$outputname | $output_objdir/$libname.* | $output_objdir/${libname}${release}.*) if test "X$precious_files_regex" != "X"; then if $ECHO "$p" | $EGREP -e "$precious_files_regex" >/dev/null 2>&1 then continue fi fi func_append removelist " $p" ;; *) ;; esac done test -n "$removelist" && \ func_show_eval "${RM}r \$removelist" fi # Now set the variables for building old libraries. if test "$build_old_libs" = yes && test "$build_libtool_libs" != convenience ; then func_append oldlibs " $output_objdir/$libname.$libext" # Transform .lo files to .o files. oldobjs="$objs "`$ECHO "$libobjs" | $SP2NL | $SED "/\.${libext}$/d; $lo2o" | $NL2SP` fi # Eliminate all temporary directories. #for path in $notinst_path; do # lib_search_path=`$ECHO "$lib_search_path " | $SED "s% $path % %g"` # deplibs=`$ECHO "$deplibs " | $SED "s% -L$path % %g"` # dependency_libs=`$ECHO "$dependency_libs " | $SED "s% -L$path % %g"` #done if test -n "$xrpath"; then # If the user specified any rpath flags, then add them. temp_xrpath= for libdir in $xrpath; do func_replace_sysroot "$libdir" func_append temp_xrpath " -R$func_replace_sysroot_result" case "$finalize_rpath " in *" $libdir "*) ;; *) func_append finalize_rpath " $libdir" ;; esac done if test "$hardcode_into_libs" != yes || test "$build_old_libs" = yes; then dependency_libs="$temp_xrpath $dependency_libs" fi fi # Make sure dlfiles contains only unique files that won't be dlpreopened old_dlfiles="$dlfiles" dlfiles= for lib in $old_dlfiles; do case " $dlprefiles $dlfiles " in *" $lib "*) ;; *) func_append dlfiles " $lib" ;; esac done # Make sure dlprefiles contains only unique files old_dlprefiles="$dlprefiles" dlprefiles= for lib in $old_dlprefiles; do case "$dlprefiles " in *" $lib "*) ;; *) func_append dlprefiles " $lib" ;; esac done if test "$build_libtool_libs" = yes; then if test -n "$rpath"; then case $host in *-*-cygwin* | *-*-mingw* | *-*-pw32* | *-*-os2* | *-*-beos* | *-cegcc* | *-*-haiku*) # these systems don't actually have a c library (as such)! ;; *-*-rhapsody* | *-*-darwin1.[012]) # Rhapsody C library is in the System framework func_append deplibs " System.ltframework" ;; *-*-netbsd*) # Don't link with libc until the a.out ld.so is fixed. ;; *-*-openbsd* | *-*-freebsd* | *-*-dragonfly*) # Do not include libc due to us having libc/libc_r. ;; *-*-sco3.2v5* | *-*-sco5v6*) # Causes problems with __ctype ;; *-*-sysv4.2uw2* | *-*-sysv5* | *-*-unixware* | *-*-OpenUNIX*) # Compiler inserts libc in the correct place for threads to work ;; *) # Add libc to deplibs on all other systems if necessary. if test "$build_libtool_need_lc" = "yes"; then func_append deplibs " -lc" fi ;; esac fi # Transform deplibs into only deplibs that can be linked in shared. name_save=$name libname_save=$libname release_save=$release versuffix_save=$versuffix major_save=$major # I'm not sure if I'm treating the release correctly. I think # release should show up in the -l (ie -lgmp5) so we don't want to # add it in twice. Is that correct? release="" versuffix="" major="" newdeplibs= droppeddeps=no case $deplibs_check_method in pass_all) # Don't check for shared/static. Everything works. # This might be a little naive. We might want to check # whether the library exists or not. But this is on # osf3 & osf4 and I'm not really sure... Just # implementing what was already the behavior. newdeplibs=$deplibs ;; test_compile) # This code stresses the "libraries are programs" paradigm to its # limits. Maybe even breaks it. We compile a program, linking it # against the deplibs as a proxy for the library. Then we can check # whether they linked in statically or dynamically with ldd. $opt_dry_run || $RM conftest.c cat > conftest.c </dev/null` $nocaseglob else potential_libs=`ls $i/$libnameglob[.-]* 2>/dev/null` fi for potent_lib in $potential_libs; do # Follow soft links. if ls -lLd "$potent_lib" 2>/dev/null | $GREP " -> " >/dev/null; then continue fi # The statement above tries to avoid entering an # endless loop below, in case of cyclic links. # We might still enter an endless loop, since a link # loop can be closed while we follow links, # but so what? potlib="$potent_lib" while test -h "$potlib" 2>/dev/null; do potliblink=`ls -ld $potlib | ${SED} 's/.* -> //'` case $potliblink in [\\/]* | [A-Za-z]:[\\/]*) potlib="$potliblink";; *) potlib=`$ECHO "$potlib" | $SED 's,[^/]*$,,'`"$potliblink";; esac done if eval $file_magic_cmd \"\$potlib\" 2>/dev/null | $SED -e 10q | $EGREP "$file_magic_regex" > /dev/null; then func_append newdeplibs " $a_deplib" a_deplib="" break 2 fi done done fi if test -n "$a_deplib" ; then droppeddeps=yes echo $ECHO "*** Warning: linker path does not have real file for library $a_deplib." echo "*** I have the capability to make that library automatically link in when" echo "*** you link to this library. But I can only do this if you have a" echo "*** shared version of the library, which you do not appear to have" echo "*** because I did check the linker path looking for a file starting" if test -z "$potlib" ; then $ECHO "*** with $libname but no candidates were found. (...for file magic test)" else $ECHO "*** with $libname and none of the candidates passed a file format test" $ECHO "*** using a file magic. Last file checked: $potlib" fi fi ;; *) # Add a -L argument. func_append newdeplibs " $a_deplib" ;; esac done # Gone through all deplibs. ;; match_pattern*) set dummy $deplibs_check_method; shift match_pattern_regex=`expr "$deplibs_check_method" : "$1 \(.*\)"` for a_deplib in $deplibs; do case $a_deplib in -l*) func_stripname -l '' "$a_deplib" name=$func_stripname_result if test "X$allow_libtool_libs_with_static_runtimes" = "Xyes" ; then case " $predeps $postdeps " in *" $a_deplib "*) func_append newdeplibs " $a_deplib" a_deplib="" ;; esac fi if test -n "$a_deplib" ; then libname=`eval "\\$ECHO \"$libname_spec\""` for i in $lib_search_path $sys_lib_search_path $shlib_search_path; do potential_libs=`ls $i/$libname[.-]* 2>/dev/null` for potent_lib in $potential_libs; do potlib="$potent_lib" # see symlink-check above in file_magic test if eval "\$ECHO \"$potent_lib\"" 2>/dev/null | $SED 10q | \ $EGREP "$match_pattern_regex" > /dev/null; then func_append newdeplibs " $a_deplib" a_deplib="" break 2 fi done done fi if test -n "$a_deplib" ; then droppeddeps=yes echo $ECHO "*** Warning: linker path does not have real file for library $a_deplib." echo "*** I have the capability to make that library automatically link in when" echo "*** you link to this library. But I can only do this if you have a" echo "*** shared version of the library, which you do not appear to have" echo "*** because I did check the linker path looking for a file starting" if test -z "$potlib" ; then $ECHO "*** with $libname but no candidates were found. (...for regex pattern test)" else $ECHO "*** with $libname and none of the candidates passed a file format test" $ECHO "*** using a regex pattern. Last file checked: $potlib" fi fi ;; *) # Add a -L argument. func_append newdeplibs " $a_deplib" ;; esac done # Gone through all deplibs. ;; none | unknown | *) newdeplibs="" tmp_deplibs=`$ECHO " $deplibs" | $SED 's/ -lc$//; s/ -[LR][^ ]*//g'` if test "X$allow_libtool_libs_with_static_runtimes" = "Xyes" ; then for i in $predeps $postdeps ; do # can't use Xsed below, because $i might contain '/' tmp_deplibs=`$ECHO " $tmp_deplibs" | $SED "s,$i,,"` done fi case $tmp_deplibs in *[!\ \ ]*) echo if test "X$deplibs_check_method" = "Xnone"; then echo "*** Warning: inter-library dependencies are not supported in this platform." else echo "*** Warning: inter-library dependencies are not known to be supported." fi echo "*** All declared inter-library dependencies are being dropped." droppeddeps=yes ;; esac ;; esac versuffix=$versuffix_save major=$major_save release=$release_save libname=$libname_save name=$name_save case $host in *-*-rhapsody* | *-*-darwin1.[012]) # On Rhapsody replace the C library with the System framework newdeplibs=`$ECHO " $newdeplibs" | $SED 's/ -lc / System.ltframework /'` ;; esac if test "$droppeddeps" = yes; then if test "$module" = yes; then echo echo "*** Warning: libtool could not satisfy all declared inter-library" $ECHO "*** dependencies of module $libname. Therefore, libtool will create" echo "*** a static module, that should work as long as the dlopening" echo "*** application is linked with the -dlopen flag." if test -z "$global_symbol_pipe"; then echo echo "*** However, this would only work if libtool was able to extract symbol" echo "*** lists from a program, using \`nm' or equivalent, but libtool could" echo "*** not find such a program. So, this module is probably useless." echo "*** \`nm' from GNU binutils and a full rebuild may help." fi if test "$build_old_libs" = no; then oldlibs="$output_objdir/$libname.$libext" build_libtool_libs=module build_old_libs=yes else build_libtool_libs=no fi else echo "*** The inter-library dependencies that have been dropped here will be" echo "*** automatically added whenever a program is linked with this library" echo "*** or is declared to -dlopen it." if test "$allow_undefined" = no; then echo echo "*** Since this library must not contain undefined symbols," echo "*** because either the platform does not support them or" echo "*** it was explicitly requested with -no-undefined," echo "*** libtool will only create a static version of it." if test "$build_old_libs" = no; then oldlibs="$output_objdir/$libname.$libext" build_libtool_libs=module build_old_libs=yes else build_libtool_libs=no fi fi fi fi # Done checking deplibs! deplibs=$newdeplibs fi # Time to change all our "foo.ltframework" stuff back to "-framework foo" case $host in *-*-darwin*) newdeplibs=`$ECHO " $newdeplibs" | $SED 's% \([^ $]*\).ltframework% -framework \1%g'` new_inherited_linker_flags=`$ECHO " $new_inherited_linker_flags" | $SED 's% \([^ $]*\).ltframework% -framework \1%g'` deplibs=`$ECHO " $deplibs" | $SED 's% \([^ $]*\).ltframework% -framework \1%g'` ;; esac # move library search paths that coincide with paths to not yet # installed libraries to the beginning of the library search list new_libs= for path in $notinst_path; do case " $new_libs " in *" -L$path/$objdir "*) ;; *) case " $deplibs " in *" -L$path/$objdir "*) func_append new_libs " -L$path/$objdir" ;; esac ;; esac done for deplib in $deplibs; do case $deplib in -L*) case " $new_libs " in *" $deplib "*) ;; *) func_append new_libs " $deplib" ;; esac ;; *) func_append new_libs " $deplib" ;; esac done deplibs="$new_libs" # All the library-specific variables (install_libdir is set above). library_names= old_library= dlname= # Test again, we may have decided not to build it any more if test "$build_libtool_libs" = yes; then if test "$hardcode_into_libs" = yes; then # Hardcode the library paths hardcode_libdirs= dep_rpath= rpath="$finalize_rpath" test "$opt_mode" != relink && rpath="$compile_rpath$rpath" for libdir in $rpath; do if test -n "$hardcode_libdir_flag_spec"; then if test -n "$hardcode_libdir_separator"; then func_replace_sysroot "$libdir" libdir=$func_replace_sysroot_result if test -z "$hardcode_libdirs"; then hardcode_libdirs="$libdir" else # Just accumulate the unique libdirs. case $hardcode_libdir_separator$hardcode_libdirs$hardcode_libdir_separator in *"$hardcode_libdir_separator$libdir$hardcode_libdir_separator"*) ;; *) func_append hardcode_libdirs "$hardcode_libdir_separator$libdir" ;; esac fi else eval flag=\"$hardcode_libdir_flag_spec\" func_append dep_rpath " $flag" fi elif test -n "$runpath_var"; then case "$perm_rpath " in *" $libdir "*) ;; *) func_apped perm_rpath " $libdir" ;; esac fi done # Substitute the hardcoded libdirs into the rpath. if test -n "$hardcode_libdir_separator" && test -n "$hardcode_libdirs"; then libdir="$hardcode_libdirs" if test -n "$hardcode_libdir_flag_spec_ld"; then eval dep_rpath=\"$hardcode_libdir_flag_spec_ld\" else eval dep_rpath=\"$hardcode_libdir_flag_spec\" fi fi if test -n "$runpath_var" && test -n "$perm_rpath"; then # We should set the runpath_var. rpath= for dir in $perm_rpath; do func_append rpath "$dir:" done eval "$runpath_var='$rpath\$$runpath_var'; export $runpath_var" fi test -n "$dep_rpath" && deplibs="$dep_rpath $deplibs" fi shlibpath="$finalize_shlibpath" test "$opt_mode" != relink && shlibpath="$compile_shlibpath$shlibpath" if test -n "$shlibpath"; then eval "$shlibpath_var='$shlibpath\$$shlibpath_var'; export $shlibpath_var" fi # Get the real and link names of the library. eval shared_ext=\"$shrext_cmds\" eval library_names=\"$library_names_spec\" set dummy $library_names shift realname="$1" shift if test -n "$soname_spec"; then eval soname=\"$soname_spec\" else soname="$realname" fi if test -z "$dlname"; then dlname=$soname fi lib="$output_objdir/$realname" linknames= for link do func_append linknames " $link" done # Use standard objects if they are pic test -z "$pic_flag" && libobjs=`$ECHO "$libobjs" | $SP2NL | $SED "$lo2o" | $NL2SP` test "X$libobjs" = "X " && libobjs= delfiles= if test -n "$export_symbols" && test -n "$include_expsyms"; then $opt_dry_run || cp "$export_symbols" "$output_objdir/$libname.uexp" export_symbols="$output_objdir/$libname.uexp" func_append delfiles " $export_symbols" fi orig_export_symbols= case $host_os in cygwin* | mingw* | cegcc*) if test -n "$export_symbols" && test -z "$export_symbols_regex"; then # exporting using user supplied symfile if test "x`$SED 1q $export_symbols`" != xEXPORTS; then # and it's NOT already a .def file. Must figure out # which of the given symbols are data symbols and tag # them as such. So, trigger use of export_symbols_cmds. # export_symbols gets reassigned inside the "prepare # the list of exported symbols" if statement, so the # include_expsyms logic still works. orig_export_symbols="$export_symbols" export_symbols= always_export_symbols=yes fi fi ;; esac # Prepare the list of exported symbols if test -z "$export_symbols"; then if test "$always_export_symbols" = yes || test -n "$export_symbols_regex"; then func_verbose "generating symbol list for \`$libname.la'" export_symbols="$output_objdir/$libname.exp" $opt_dry_run || $RM $export_symbols cmds=$export_symbols_cmds save_ifs="$IFS"; IFS='~' for cmd1 in $cmds; do IFS="$save_ifs" # Take the normal branch if the nm_file_list_spec branch # doesn't work or if tool conversion is not needed. case $nm_file_list_spec~$to_tool_file_cmd in *~func_convert_file_noop | *~func_convert_file_msys_to_w32 | ~*) try_normal_branch=yes eval cmd=\"$cmd1\" func_len " $cmd" len=$func_len_result ;; *) try_normal_branch=no ;; esac if test "$try_normal_branch" = yes \ && { test "$len" -lt "$max_cmd_len" \ || test "$max_cmd_len" -le -1; } then func_show_eval "$cmd" 'exit $?' skipped_export=false elif test -n "$nm_file_list_spec"; then func_basename "$output" output_la=$func_basename_result save_libobjs=$libobjs save_output=$output output=${output_objdir}/${output_la}.nm func_to_tool_file "$output" libobjs=$nm_file_list_spec$func_to_tool_file_result func_append delfiles " $output" func_verbose "creating $NM input file list: $output" for obj in $save_libobjs; do func_to_tool_file "$obj" $ECHO "$func_to_tool_file_result" done > "$output" eval cmd=\"$cmd1\" func_show_eval "$cmd" 'exit $?' output=$save_output libobjs=$save_libobjs skipped_export=false else # The command line is too long to execute in one step. func_verbose "using reloadable object file for export list..." skipped_export=: # Break out early, otherwise skipped_export may be # set to false by a later but shorter cmd. break fi done IFS="$save_ifs" if test -n "$export_symbols_regex" && test "X$skipped_export" != "X:"; then func_show_eval '$EGREP -e "$export_symbols_regex" "$export_symbols" > "${export_symbols}T"' func_show_eval '$MV "${export_symbols}T" "$export_symbols"' fi fi fi if test -n "$export_symbols" && test -n "$include_expsyms"; then tmp_export_symbols="$export_symbols" test -n "$orig_export_symbols" && tmp_export_symbols="$orig_export_symbols" $opt_dry_run || eval '$ECHO "$include_expsyms" | $SP2NL >> "$tmp_export_symbols"' fi if test "X$skipped_export" != "X:" && test -n "$orig_export_symbols"; then # The given exports_symbols file has to be filtered, so filter it. func_verbose "filter symbol list for \`$libname.la' to tag DATA exports" # FIXME: $output_objdir/$libname.filter potentially contains lots of # 's' commands which not all seds can handle. GNU sed should be fine # though. Also, the filter scales superlinearly with the number of # global variables. join(1) would be nice here, but unfortunately # isn't a blessed tool. $opt_dry_run || $SED -e '/[ ,]DATA/!d;s,\(.*\)\([ \,].*\),s|^\1$|\1\2|,' < $export_symbols > $output_objdir/$libname.filter func_append delfiles " $export_symbols $output_objdir/$libname.filter" export_symbols=$output_objdir/$libname.def $opt_dry_run || $SED -f $output_objdir/$libname.filter < $orig_export_symbols > $export_symbols fi tmp_deplibs= for test_deplib in $deplibs; do case " $convenience " in *" $test_deplib "*) ;; *) func_append tmp_deplibs " $test_deplib" ;; esac done deplibs="$tmp_deplibs" if test -n "$convenience"; then if test -n "$whole_archive_flag_spec" && test "$compiler_needs_object" = yes && test -z "$libobjs"; then # extract the archives, so we have objects to list. # TODO: could optimize this to just extract one archive. whole_archive_flag_spec= fi if test -n "$whole_archive_flag_spec"; then save_libobjs=$libobjs eval libobjs=\"\$libobjs $whole_archive_flag_spec\" test "X$libobjs" = "X " && libobjs= else gentop="$output_objdir/${outputname}x" func_append generated " $gentop" func_extract_archives $gentop $convenience func_append libobjs " $func_extract_archives_result" test "X$libobjs" = "X " && libobjs= fi fi if test "$thread_safe" = yes && test -n "$thread_safe_flag_spec"; then eval flag=\"$thread_safe_flag_spec\" func_append linker_flags " $flag" fi # Make a backup of the uninstalled library when relinking if test "$opt_mode" = relink; then $opt_dry_run || eval '(cd $output_objdir && $RM ${realname}U && $MV $realname ${realname}U)' || exit $? fi # Do each of the archive commands. if test "$module" = yes && test -n "$module_cmds" ; then if test -n "$export_symbols" && test -n "$module_expsym_cmds"; then eval test_cmds=\"$module_expsym_cmds\" cmds=$module_expsym_cmds else eval test_cmds=\"$module_cmds\" cmds=$module_cmds fi else if test -n "$export_symbols" && test -n "$archive_expsym_cmds"; then eval test_cmds=\"$archive_expsym_cmds\" cmds=$archive_expsym_cmds else eval test_cmds=\"$archive_cmds\" cmds=$archive_cmds fi fi if test "X$skipped_export" != "X:" && func_len " $test_cmds" && len=$func_len_result && test "$len" -lt "$max_cmd_len" || test "$max_cmd_len" -le -1; then : else # The command line is too long to link in one step, link piecewise # or, if using GNU ld and skipped_export is not :, use a linker # script. # Save the value of $output and $libobjs because we want to # use them later. If we have whole_archive_flag_spec, we # want to use save_libobjs as it was before # whole_archive_flag_spec was expanded, because we can't # assume the linker understands whole_archive_flag_spec. # This may have to be revisited, in case too many # convenience libraries get linked in and end up exceeding # the spec. if test -z "$convenience" || test -z "$whole_archive_flag_spec"; then save_libobjs=$libobjs fi save_output=$output func_basename "$output" output_la=$func_basename_result # Clear the reloadable object creation command queue and # initialize k to one. test_cmds= concat_cmds= objlist= last_robj= k=1 if test -n "$save_libobjs" && test "X$skipped_export" != "X:" && test "$with_gnu_ld" = yes; then output=${output_objdir}/${output_la}.lnkscript func_verbose "creating GNU ld script: $output" echo 'INPUT (' > $output for obj in $save_libobjs do func_to_tool_file "$obj" $ECHO "$func_to_tool_file_result" >> $output done echo ')' >> $output func_append delfiles " $output" func_to_tool_file "$output" output=$func_to_tool_file_result elif test -n "$save_libobjs" && test "X$skipped_export" != "X:" && test "X$file_list_spec" != X; then output=${output_objdir}/${output_la}.lnk func_verbose "creating linker input file list: $output" : > $output set x $save_libobjs shift firstobj= if test "$compiler_needs_object" = yes; then firstobj="$1 " shift fi for obj do func_to_tool_file "$obj" $ECHO "$func_to_tool_file_result" >> $output done func_append delfiles " $output" func_to_tool_file "$output" output=$firstobj\"$file_list_spec$func_to_tool_file_result\" else if test -n "$save_libobjs"; then func_verbose "creating reloadable object files..." output=$output_objdir/$output_la-${k}.$objext eval test_cmds=\"$reload_cmds\" func_len " $test_cmds" len0=$func_len_result len=$len0 # Loop over the list of objects to be linked. for obj in $save_libobjs do func_len " $obj" func_arith $len + $func_len_result len=$func_arith_result if test "X$objlist" = X || test "$len" -lt "$max_cmd_len"; then func_append objlist " $obj" else # The command $test_cmds is almost too long, add a # command to the queue. if test "$k" -eq 1 ; then # The first file doesn't have a previous command to add. reload_objs=$objlist eval concat_cmds=\"$reload_cmds\" else # All subsequent reloadable object files will link in # the last one created. reload_objs="$objlist $last_robj" eval concat_cmds=\"\$concat_cmds~$reload_cmds~\$RM $last_robj\" fi last_robj=$output_objdir/$output_la-${k}.$objext func_arith $k + 1 k=$func_arith_result output=$output_objdir/$output_la-${k}.$objext objlist=" $obj" func_len " $last_robj" func_arith $len0 + $func_len_result len=$func_arith_result fi done # Handle the remaining objects by creating one last # reloadable object file. All subsequent reloadable object # files will link in the last one created. test -z "$concat_cmds" || concat_cmds=$concat_cmds~ reload_objs="$objlist $last_robj" eval concat_cmds=\"\${concat_cmds}$reload_cmds\" if test -n "$last_robj"; then eval concat_cmds=\"\${concat_cmds}~\$RM $last_robj\" fi func_append delfiles " $output" else output= fi if ${skipped_export-false}; then func_verbose "generating symbol list for \`$libname.la'" export_symbols="$output_objdir/$libname.exp" $opt_dry_run || $RM $export_symbols libobjs=$output # Append the command to create the export file. test -z "$concat_cmds" || concat_cmds=$concat_cmds~ eval concat_cmds=\"\$concat_cmds$export_symbols_cmds\" if test -n "$last_robj"; then eval concat_cmds=\"\$concat_cmds~\$RM $last_robj\" fi fi test -n "$save_libobjs" && func_verbose "creating a temporary reloadable object file: $output" # Loop through the commands generated above and execute them. save_ifs="$IFS"; IFS='~' for cmd in $concat_cmds; do IFS="$save_ifs" $opt_silent || { func_quote_for_expand "$cmd" eval "func_echo $func_quote_for_expand_result" } $opt_dry_run || eval "$cmd" || { lt_exit=$? # Restore the uninstalled library and exit if test "$opt_mode" = relink; then ( cd "$output_objdir" && \ $RM "${realname}T" && \ $MV "${realname}U" "$realname" ) fi exit $lt_exit } done IFS="$save_ifs" if test -n "$export_symbols_regex" && ${skipped_export-false}; then func_show_eval '$EGREP -e "$export_symbols_regex" "$export_symbols" > "${export_symbols}T"' func_show_eval '$MV "${export_symbols}T" "$export_symbols"' fi fi if ${skipped_export-false}; then if test -n "$export_symbols" && test -n "$include_expsyms"; then tmp_export_symbols="$export_symbols" test -n "$orig_export_symbols" && tmp_export_symbols="$orig_export_symbols" $opt_dry_run || eval '$ECHO "$include_expsyms" | $SP2NL >> "$tmp_export_symbols"' fi if test -n "$orig_export_symbols"; then # The given exports_symbols file has to be filtered, so filter it. func_verbose "filter symbol list for \`$libname.la' to tag DATA exports" # FIXME: $output_objdir/$libname.filter potentially contains lots of # 's' commands which not all seds can handle. GNU sed should be fine # though. Also, the filter scales superlinearly with the number of # global variables. join(1) would be nice here, but unfortunately # isn't a blessed tool. $opt_dry_run || $SED -e '/[ ,]DATA/!d;s,\(.*\)\([ \,].*\),s|^\1$|\1\2|,' < $export_symbols > $output_objdir/$libname.filter func_append delfiles " $export_symbols $output_objdir/$libname.filter" export_symbols=$output_objdir/$libname.def $opt_dry_run || $SED -f $output_objdir/$libname.filter < $orig_export_symbols > $export_symbols fi fi libobjs=$output # Restore the value of output. output=$save_output if test -n "$convenience" && test -n "$whole_archive_flag_spec"; then eval libobjs=\"\$libobjs $whole_archive_flag_spec\" test "X$libobjs" = "X " && libobjs= fi # Expand the library linking commands again to reset the # value of $libobjs for piecewise linking. # Do each of the archive commands. if test "$module" = yes && test -n "$module_cmds" ; then if test -n "$export_symbols" && test -n "$module_expsym_cmds"; then cmds=$module_expsym_cmds else cmds=$module_cmds fi else if test -n "$export_symbols" && test -n "$archive_expsym_cmds"; then cmds=$archive_expsym_cmds else cmds=$archive_cmds fi fi fi if test -n "$delfiles"; then # Append the command to remove temporary files to $cmds. eval cmds=\"\$cmds~\$RM $delfiles\" fi # Add any objects from preloaded convenience libraries if test -n "$dlprefiles"; then gentop="$output_objdir/${outputname}x" func_append generated " $gentop" func_extract_archives $gentop $dlprefiles func_append libobjs " $func_extract_archives_result" test "X$libobjs" = "X " && libobjs= fi save_ifs="$IFS"; IFS='~' for cmd in $cmds; do IFS="$save_ifs" eval cmd=\"$cmd\" $opt_silent || { func_quote_for_expand "$cmd" eval "func_echo $func_quote_for_expand_result" } $opt_dry_run || eval "$cmd" || { lt_exit=$? # Restore the uninstalled library and exit if test "$opt_mode" = relink; then ( cd "$output_objdir" && \ $RM "${realname}T" && \ $MV "${realname}U" "$realname" ) fi exit $lt_exit } done IFS="$save_ifs" # Restore the uninstalled library and exit if test "$opt_mode" = relink; then $opt_dry_run || eval '(cd $output_objdir && $RM ${realname}T && $MV $realname ${realname}T && $MV ${realname}U $realname)' || exit $? if test -n "$convenience"; then if test -z "$whole_archive_flag_spec"; then func_show_eval '${RM}r "$gentop"' fi fi exit $EXIT_SUCCESS fi # Create links to the real library. for linkname in $linknames; do if test "$realname" != "$linkname"; then func_show_eval '(cd "$output_objdir" && $RM "$linkname" && $LN_S "$realname" "$linkname")' 'exit $?' fi done # If -module or -export-dynamic was specified, set the dlname. if test "$module" = yes || test "$export_dynamic" = yes; then # On all known operating systems, these are identical. dlname="$soname" fi fi ;; obj) if test -n "$dlfiles$dlprefiles" || test "$dlself" != no; then func_warning "\`-dlopen' is ignored for objects" fi case " $deplibs" in *\ -l* | *\ -L*) func_warning "\`-l' and \`-L' are ignored for objects" ;; esac test -n "$rpath" && \ func_warning "\`-rpath' is ignored for objects" test -n "$xrpath" && \ func_warning "\`-R' is ignored for objects" test -n "$vinfo" && \ func_warning "\`-version-info' is ignored for objects" test -n "$release" && \ func_warning "\`-release' is ignored for objects" case $output in *.lo) test -n "$objs$old_deplibs" && \ func_fatal_error "cannot build library object \`$output' from non-libtool objects" libobj=$output func_lo2o "$libobj" obj=$func_lo2o_result ;; *) libobj= obj="$output" ;; esac # Delete the old objects. $opt_dry_run || $RM $obj $libobj # Objects from convenience libraries. This assumes # single-version convenience libraries. Whenever we create # different ones for PIC/non-PIC, this we'll have to duplicate # the extraction. reload_conv_objs= gentop= # reload_cmds runs $LD directly, so let us get rid of # -Wl from whole_archive_flag_spec and hope we can get by with # turning comma into space.. wl= if test -n "$convenience"; then if test -n "$whole_archive_flag_spec"; then eval tmp_whole_archive_flags=\"$whole_archive_flag_spec\" reload_conv_objs=$reload_objs\ `$ECHO "$tmp_whole_archive_flags" | $SED 's|,| |g'` else gentop="$output_objdir/${obj}x" func_append generated " $gentop" func_extract_archives $gentop $convenience reload_conv_objs="$reload_objs $func_extract_archives_result" fi fi # If we're not building shared, we need to use non_pic_objs test "$build_libtool_libs" != yes && libobjs="$non_pic_objects" # Create the old-style object. reload_objs="$objs$old_deplibs "`$ECHO "$libobjs" | $SP2NL | $SED "/\.${libext}$/d; /\.lib$/d; $lo2o" | $NL2SP`" $reload_conv_objs" ### testsuite: skip nested quoting test output="$obj" func_execute_cmds "$reload_cmds" 'exit $?' # Exit if we aren't doing a library object file. if test -z "$libobj"; then if test -n "$gentop"; then func_show_eval '${RM}r "$gentop"' fi exit $EXIT_SUCCESS fi if test "$build_libtool_libs" != yes; then if test -n "$gentop"; then func_show_eval '${RM}r "$gentop"' fi # Create an invalid libtool object if no PIC, so that we don't # accidentally link it into a program. # $show "echo timestamp > $libobj" # $opt_dry_run || eval "echo timestamp > $libobj" || exit $? exit $EXIT_SUCCESS fi if test -n "$pic_flag" || test "$pic_mode" != default; then # Only do commands if we really have different PIC objects. reload_objs="$libobjs $reload_conv_objs" output="$libobj" func_execute_cmds "$reload_cmds" 'exit $?' fi if test -n "$gentop"; then func_show_eval '${RM}r "$gentop"' fi exit $EXIT_SUCCESS ;; prog) case $host in *cygwin*) func_stripname '' '.exe' "$output" output=$func_stripname_result.exe;; esac test -n "$vinfo" && \ func_warning "\`-version-info' is ignored for programs" test -n "$release" && \ func_warning "\`-release' is ignored for programs" test "$preload" = yes \ && test "$dlopen_support" = unknown \ && test "$dlopen_self" = unknown \ && test "$dlopen_self_static" = unknown && \ func_warning "\`LT_INIT([dlopen])' not used. Assuming no dlopen support." case $host in *-*-rhapsody* | *-*-darwin1.[012]) # On Rhapsody replace the C library is the System framework compile_deplibs=`$ECHO " $compile_deplibs" | $SED 's/ -lc / System.ltframework /'` finalize_deplibs=`$ECHO " $finalize_deplibs" | $SED 's/ -lc / System.ltframework /'` ;; esac case $host in *-*-darwin*) # Don't allow lazy linking, it breaks C++ global constructors # But is supposedly fixed on 10.4 or later (yay!). if test "$tagname" = CXX ; then case ${MACOSX_DEPLOYMENT_TARGET-10.0} in 10.[0123]) func_append compile_command " ${wl}-bind_at_load" func_append finalize_command " ${wl}-bind_at_load" ;; esac fi # Time to change all our "foo.ltframework" stuff back to "-framework foo" compile_deplibs=`$ECHO " $compile_deplibs" | $SED 's% \([^ $]*\).ltframework% -framework \1%g'` finalize_deplibs=`$ECHO " $finalize_deplibs" | $SED 's% \([^ $]*\).ltframework% -framework \1%g'` ;; esac # move library search paths that coincide with paths to not yet # installed libraries to the beginning of the library search list new_libs= for path in $notinst_path; do case " $new_libs " in *" -L$path/$objdir "*) ;; *) case " $compile_deplibs " in *" -L$path/$objdir "*) func_append new_libs " -L$path/$objdir" ;; esac ;; esac done for deplib in $compile_deplibs; do case $deplib in -L*) case " $new_libs " in *" $deplib "*) ;; *) func_append new_libs " $deplib" ;; esac ;; *) func_append new_libs " $deplib" ;; esac done compile_deplibs="$new_libs" func_append compile_command " $compile_deplibs" func_append finalize_command " $finalize_deplibs" if test -n "$rpath$xrpath"; then # If the user specified any rpath flags, then add them. for libdir in $rpath $xrpath; do # This is the magic to use -rpath. case "$finalize_rpath " in *" $libdir "*) ;; *) func_append finalize_rpath " $libdir" ;; esac done fi # Now hardcode the library paths rpath= hardcode_libdirs= for libdir in $compile_rpath $finalize_rpath; do if test -n "$hardcode_libdir_flag_spec"; then if test -n "$hardcode_libdir_separator"; then if test -z "$hardcode_libdirs"; then hardcode_libdirs="$libdir" else # Just accumulate the unique libdirs. case $hardcode_libdir_separator$hardcode_libdirs$hardcode_libdir_separator in *"$hardcode_libdir_separator$libdir$hardcode_libdir_separator"*) ;; *) func_append hardcode_libdirs "$hardcode_libdir_separator$libdir" ;; esac fi else eval flag=\"$hardcode_libdir_flag_spec\" func_append rpath " $flag" fi elif test -n "$runpath_var"; then case "$perm_rpath " in *" $libdir "*) ;; *) func_append perm_rpath " $libdir" ;; esac fi case $host in *-*-cygwin* | *-*-mingw* | *-*-pw32* | *-*-os2* | *-cegcc*) testbindir=`${ECHO} "$libdir" | ${SED} -e 's*/lib$*/bin*'` case :$dllsearchpath: in *":$libdir:"*) ;; ::) dllsearchpath=$libdir;; *) func_append dllsearchpath ":$libdir";; esac case :$dllsearchpath: in *":$testbindir:"*) ;; ::) dllsearchpath=$testbindir;; *) func_append dllsearchpath ":$testbindir";; esac ;; esac done # Substitute the hardcoded libdirs into the rpath. if test -n "$hardcode_libdir_separator" && test -n "$hardcode_libdirs"; then libdir="$hardcode_libdirs" eval rpath=\" $hardcode_libdir_flag_spec\" fi compile_rpath="$rpath" rpath= hardcode_libdirs= for libdir in $finalize_rpath; do if test -n "$hardcode_libdir_flag_spec"; then if test -n "$hardcode_libdir_separator"; then if test -z "$hardcode_libdirs"; then hardcode_libdirs="$libdir" else # Just accumulate the unique libdirs. case $hardcode_libdir_separator$hardcode_libdirs$hardcode_libdir_separator in *"$hardcode_libdir_separator$libdir$hardcode_libdir_separator"*) ;; *) func_append hardcode_libdirs "$hardcode_libdir_separator$libdir" ;; esac fi else eval flag=\"$hardcode_libdir_flag_spec\" func_append rpath " $flag" fi elif test -n "$runpath_var"; then case "$finalize_perm_rpath " in *" $libdir "*) ;; *) func_append finalize_perm_rpath " $libdir" ;; esac fi done # Substitute the hardcoded libdirs into the rpath. if test -n "$hardcode_libdir_separator" && test -n "$hardcode_libdirs"; then libdir="$hardcode_libdirs" eval rpath=\" $hardcode_libdir_flag_spec\" fi finalize_rpath="$rpath" if test -n "$libobjs" && test "$build_old_libs" = yes; then # Transform all the library objects into standard objects. compile_command=`$ECHO "$compile_command" | $SP2NL | $SED "$lo2o" | $NL2SP` finalize_command=`$ECHO "$finalize_command" | $SP2NL | $SED "$lo2o" | $NL2SP` fi func_generate_dlsyms "$outputname" "@PROGRAM@" "no" # template prelinking step if test -n "$prelink_cmds"; then func_execute_cmds "$prelink_cmds" 'exit $?' fi wrappers_required=yes case $host in *cegcc* | *mingw32ce*) # Disable wrappers for cegcc and mingw32ce hosts, we are cross compiling anyway. wrappers_required=no ;; *cygwin* | *mingw* ) if test "$build_libtool_libs" != yes; then wrappers_required=no fi ;; *) if test "$need_relink" = no || test "$build_libtool_libs" != yes; then wrappers_required=no fi ;; esac if test "$wrappers_required" = no; then # Replace the output file specification. compile_command=`$ECHO "$compile_command" | $SED 's%@OUTPUT@%'"$output"'%g'` link_command="$compile_command$compile_rpath" # We have no uninstalled library dependencies, so finalize right now. exit_status=0 func_show_eval "$link_command" 'exit_status=$?' if test -n "$postlink_cmds"; then func_to_tool_file "$output" postlink_cmds=`func_echo_all "$postlink_cmds" | $SED -e 's%@OUTPUT@%'"$output"'%g' -e 's%@TOOL_OUTPUT@%'"$func_to_tool_file_result"'%g'` func_execute_cmds "$postlink_cmds" 'exit $?' fi # Delete the generated files. if test -f "$output_objdir/${outputname}S.${objext}"; then func_show_eval '$RM "$output_objdir/${outputname}S.${objext}"' fi exit $exit_status fi if test -n "$compile_shlibpath$finalize_shlibpath"; then compile_command="$shlibpath_var=\"$compile_shlibpath$finalize_shlibpath\$$shlibpath_var\" $compile_command" fi if test -n "$finalize_shlibpath"; then finalize_command="$shlibpath_var=\"$finalize_shlibpath\$$shlibpath_var\" $finalize_command" fi compile_var= finalize_var= if test -n "$runpath_var"; then if test -n "$perm_rpath"; then # We should set the runpath_var. rpath= for dir in $perm_rpath; do func_append rpath "$dir:" done compile_var="$runpath_var=\"$rpath\$$runpath_var\" " fi if test -n "$finalize_perm_rpath"; then # We should set the runpath_var. rpath= for dir in $finalize_perm_rpath; do func_append rpath "$dir:" done finalize_var="$runpath_var=\"$rpath\$$runpath_var\" " fi fi if test "$no_install" = yes; then # We don't need to create a wrapper script. link_command="$compile_var$compile_command$compile_rpath" # Replace the output file specification. link_command=`$ECHO "$link_command" | $SED 's%@OUTPUT@%'"$output"'%g'` # Delete the old output file. $opt_dry_run || $RM $output # Link the executable and exit func_show_eval "$link_command" 'exit $?' if test -n "$postlink_cmds"; then func_to_tool_file "$output" postlink_cmds=`func_echo_all "$postlink_cmds" | $SED -e 's%@OUTPUT@%'"$output"'%g' -e 's%@TOOL_OUTPUT@%'"$func_to_tool_file_result"'%g'` func_execute_cmds "$postlink_cmds" 'exit $?' fi exit $EXIT_SUCCESS fi if test "$hardcode_action" = relink; then # Fast installation is not supported link_command="$compile_var$compile_command$compile_rpath" relink_command="$finalize_var$finalize_command$finalize_rpath" func_warning "this platform does not like uninstalled shared libraries" func_warning "\`$output' will be relinked during installation" else if test "$fast_install" != no; then link_command="$finalize_var$compile_command$finalize_rpath" if test "$fast_install" = yes; then relink_command=`$ECHO "$compile_var$compile_command$compile_rpath" | $SED 's%@OUTPUT@%\$progdir/\$file%g'` else # fast_install is set to needless relink_command= fi else link_command="$compile_var$compile_command$compile_rpath" relink_command="$finalize_var$finalize_command$finalize_rpath" fi fi # Replace the output file specification. link_command=`$ECHO "$link_command" | $SED 's%@OUTPUT@%'"$output_objdir/$outputname"'%g'` # Delete the old output files. $opt_dry_run || $RM $output $output_objdir/$outputname $output_objdir/lt-$outputname func_show_eval "$link_command" 'exit $?' if test -n "$postlink_cmds"; then func_to_tool_file "$output_objdir/$outputname" postlink_cmds=`func_echo_all "$postlink_cmds" | $SED -e 's%@OUTPUT@%'"$output_objdir/$outputname"'%g' -e 's%@TOOL_OUTPUT@%'"$func_to_tool_file_result"'%g'` func_execute_cmds "$postlink_cmds" 'exit $?' fi # Now create the wrapper script. func_verbose "creating $output" # Quote the relink command for shipping. if test -n "$relink_command"; then # Preserve any variables that may affect compiler behavior for var in $variables_saved_for_relink; do if eval test -z \"\${$var+set}\"; then relink_command="{ test -z \"\${$var+set}\" || $lt_unset $var || { $var=; export $var; }; }; $relink_command" elif eval var_value=\$$var; test -z "$var_value"; then relink_command="$var=; export $var; $relink_command" else func_quote_for_eval "$var_value" relink_command="$var=$func_quote_for_eval_result; export $var; $relink_command" fi done relink_command="(cd `pwd`; $relink_command)" relink_command=`$ECHO "$relink_command" | $SED "$sed_quote_subst"` fi # Only actually do things if not in dry run mode. $opt_dry_run || { # win32 will think the script is a binary if it has # a .exe suffix, so we strip it off here. case $output in *.exe) func_stripname '' '.exe' "$output" output=$func_stripname_result ;; esac # test for cygwin because mv fails w/o .exe extensions case $host in *cygwin*) exeext=.exe func_stripname '' '.exe' "$outputname" outputname=$func_stripname_result ;; *) exeext= ;; esac case $host in *cygwin* | *mingw* ) func_dirname_and_basename "$output" "" "." output_name=$func_basename_result output_path=$func_dirname_result cwrappersource="$output_path/$objdir/lt-$output_name.c" cwrapper="$output_path/$output_name.exe" $RM $cwrappersource $cwrapper trap "$RM $cwrappersource $cwrapper; exit $EXIT_FAILURE" 1 2 15 func_emit_cwrapperexe_src > $cwrappersource # The wrapper executable is built using the $host compiler, # because it contains $host paths and files. 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Grab either from any GNU archive site." file=`echo "$*" | sed -n 's/.*-o \([^ ]*\).*/\1/p'` if test -z "$file"; then file=`echo "$*" | sed 's/.* \([^ ]*\) *$/\1/'` file=`sed -n '/^@setfilename/ { s/.* \([^ ]*\) *$/\1/; p; q; }' $file` fi touch $file ;; tar) shift if test -n "$run"; then echo 1>&2 "ERROR: \`tar' requires --run" exit 1 fi # We have already tried tar in the generic part. # Look for gnutar/gtar before invocation to avoid ugly error # messages. if (gnutar --version > /dev/null 2>&1); then gnutar "$@" && exit 0 fi if (gtar --version > /dev/null 2>&1); then gtar "$@" && exit 0 fi firstarg="$1" if shift; then case "$firstarg" in *o*) firstarg=`echo "$firstarg" | sed s/o//` tar "$firstarg" "$@" && exit 0 ;; esac case "$firstarg" in *h*) firstarg=`echo "$firstarg" | sed s/h//` tar "$firstarg" "$@" && exit 0 ;; esac fi echo 1>&2 "\ WARNING: I can't seem to be able to run \`tar' with the given arguments. You may want to install GNU tar or Free paxutils, or check the command line arguments." exit 1 ;; *) echo 1>&2 "\ WARNING: \`$1' is needed, and you do not seem to have it handy on your system. You might have modified some files without having the proper tools for further handling them. Check the \`README' file, it often tells you about the needed prerequirements for installing this package. You may also peek at any GNU archive site, in case some other package would contain this missing \`$1' program." exit 1 ;; esac exit 0 garli-2.1-release/configure.ac000066400000000000000000000274361241236125200163700ustar00rootroot00000000000000# This file is processed by autoconf to create a Makefile for the GARLI # This file was written by hand (by mth using PAUP's configure.ac and http://autotoolset.sourceforge.net/tutorial.html as guides). AC_PREREQ(2.59) AC_INIT([Garli], [2.1], [garli.support@gmail.com]) AC_CONFIG_SRCDIR([src/garlimain.cpp]) AC_CONFIG_HEADER([config.h]) # Directory that contains install-sh and other auxiliary files AC_CONFIG_AUX_DIR([config]) ################################################################################ # Checks for build-platform and target info # this defines the "target" variable that is used later in this file ################################################################################ AC_CANONICAL_TARGET ################################################################################ # According to (http://www.mail-archive.com/autoconf@gnu.org/msg14232.html) # this macro should be after AC_INIT but before AM_INIT_AUTOMAKE ################################################################################ AC_CONFIG_MACRO_DIR(config) AM_INIT_AUTOMAKE([1.9 foreign dist-tarZ tar-ustar filename-length-max=299]) # Checks for programs. AC_PROG_LN_S AC_PROG_CC AC_PROG_CPP INP_CXXFLAGS="$CXXFLAGS" AC_PROG_CXX # Initialize CXXFLAGS to prevent it from defaulting to "-g -O2" CXXFLAGS="$INP_CXXFLAGS -DUNIX -DNCL_CONST_FUNCS" # required because we are building a library AC_PROG_RANLIB AC_PROG_INSTALL #AC_PROG_LIBTOOL # Checks for libraries. # Checks for header files. AC_HEADER_STDC AC_CHECK_HEADERS([float.h malloc.h stddef.h stdlib.h sys/time.h]) # Checks for typedefs, structures, and compiler characteristics. AC_HEADER_STDBOOL AC_C_CONST AC_C_INLINE AC_TYPE_SIZE_T AC_STRUCT_TM # Checks for library functions. AC_FUNC_ERROR_AT_LINE AC_FUNC_MALLOC AC_FUNC_STRTOD AC_CHECK_FUNCS([floor memmove memset pow sqrt strchr strdup strtol]) # A few miscelaneous features, not of general interest AC_ARG_ENABLE(profiler, AC_HELP_STRING([--enable-profiler], [built in runtime profiler]), [AC_DEFINE([ENABLE_CUSTOM_PROFILER], [1], [profiler for assessing execution times])], []) AC_ARG_ENABLE(leak_detection, AC_HELP_STRING([--enable-leak-detection], [memory leak detection is turned on]), [AC_DEFINE([MONITORING_ALLOCATION], [1], [monitors all allocation/deallocation, writes report])], []) #--------------------------------------------------------------------------------------------------# # Set CXXFLAGS # #--------------------------------------------------------------------------------------------------# # Check for debugging mode. AC_ARG_ENABLE(debugging, AC_HELP_STRING([--enable-debugging],[build for debugging]), , [enable_debugging=no]) if test "$enable_debugging" = yes; then AC_MSG_NOTICE([ *** NOTE: debugging is enabled; optimization is suppressed! ]) fi # we tell NCL that we will ignore asserts NCL_ASSERT_FLAG="-DIGNORE_NXS_ASSERT" # Check whether asserts should be allowed. AC_ARG_ENABLE(asserts, AC_HELP_STRING([--enable-asserts],[build with asserts on (NDEBUG not defined)]), , [enable_asserts=no]) if test "$enable_asserts" = yes; then AC_MSG_NOTICE([ *** NOTE: compiling with assertions on (NDEBUG not defined) ]) if test "$enable_debugging" = yes; then NCL_ASSERT_FLAG="" fi fi # Compile openMP multithreaded version AC_ARG_ENABLE(openmp, AC_HELP_STRING([--enable-openmp],[build OpenMP multithreaded version]), , [enable_openmp=no]) if test "$enable_openmp" = yes; then AC_MSG_NOTICE([ *** NOTE: compiling OpenMP multithreaded version ]) fi # Compile BOINC version AC_ARG_ENABLE(boinc, AC_HELP_STRING([--enable-boinc],[build BOINC version (requires BOINC library and api installation. Building may require much fiddling.)]), , [enable_boinc=no]) if test "$enable_boinc" = yes; then AC_MSG_NOTICE([ *** NOTE: compiling BOINC version ]) fi # Mpi run forking version AC_ARG_ENABLE(mpi, AC_HELP_STRING([--enable-mpi],[build MPI run distributing version]), , [enable_mpi=no]) if test "$enable_mpi" = yes; then AC_MSG_NOTICE([ *** NOTE: compiling MPI run distributing version ]) fi # old Mpi wrapper version - run series of configs named run0.conf, run1.conf, etc AC_ARG_ENABLE(oldmpi, AC_HELP_STRING([--enable-oldmpi],[build old MPI batch run version (YOU PROBABLY DON'T WANT THIS)]), , [enable_oldmpi=no]) if test "$enable_oldmpi" = yes; then AC_MSG_NOTICE([ *** NOTE: compiling old MPI batch run version ]) fi # single precision version (still being tested) AC_ARG_ENABLE(single-prec, AC_HELP_STRING([--enable-single-prec],[use single precision floating point variables (EXPERIMENTAL)]), , [enable_single_prec=no]) if test "$enable_single_prec" = yes; then AC_MSG_NOTICE([ *** NOTE: compiling single precision floating point version ]) fi # Initialize optimization flag in case it doesn't get set below. CXXFLAGS_OPTIM_SPEED="-O" # "-g" may not work with some compilers, but end users shouldn't be if test "$enable_debugging" = yes; then CXXFLAGS_OPTIM_SPEED="-O0" CXXFLAGS="$CXXFLAGS -Wall -g -Wreturn-type -Wunused -Wredundant-decls -Wcast-align -Wcomment -Wextra" fi #DJZ allow turning off NDEBUG only if test "$enable_asserts" = no; then CXXFLAGS="$CXXFLAGS -DNDEBUG" fi #DJZ OpenMP compilation if test "$enable_openmp" = yes; then if test "$CC" = "icc"; then CXXFLAGS="$CXXFLAGS -openmp" elif test "$CC" = "gcc"; then CXXFLAGS="$CXXFLAGS -fopenmp" else AC_MSG_ERROR([the --enable-openmp option can only be used with the intel compiler and newer versions of gcc (CC=icc or CC=gcc)]) fi fi #BOINC compilation if test "$enable_boinc" = yes; then CXXFLAGS="$CXXFLAGS -DBOINC" LIBS="$LIBS -lpthread -lboinc_api -lboinc" fi #Compiler choice if test "$CC" = "icc" -o "$CC" = "icc" ; then # Intel C compiler for Linux if test "$enable_debugging" = no; then CXXFLAGS_OPTIM_SPEED="-O2 -ip -funroll-loops -fno-alias" fi elif test "$CC" = "ccc"; then # Compaq C compiler for Linux if test "x$arch" = "x"; then arch="host" fi if test "$enable_debugging" = no; then CXXFLAGS_OPTIM_SPEED="-fast -inline speed -arch $arch" fi elif test "$CC" = "xlc"; then # IBM XL C compiler CCFLAGS="$CXXFLAGS -qsourcetype=c++ -qenablevmx -qchars=signed" if test "x$arch" = "x"; then arch="auto" fi if test "$enable_debugging" = no; then CXXFLAGS_OPTIM_SPEED="-O3 -qarch=$arch -qtune=$arch -qalias=ansi -qunroll=yes" fi elif test "x$GCC" = "xyes" ; then CXXFLAGS="$CXXFLAGS -Wno-uninitialized" if test "$enable_debugging" = yes; then CXXFLAGS_OPTIM_SPEED="-O0 -Wimplicit" else CXXFLAGS_OPTIM_SPEED="-O3 -ffast-math -funroll-loops -fstrict-aliasing" fi case "$build_os" in darwin*) CXXFLAGS="$CXXFLAGS" ;; *) CXXFLAGS="$CXXFLAGS -fsigned-char";; esac fi #single precision if test "$enable_single_prec" = yes; then CXXFLAGS="$CXXFLAGS -DSINGLE_PRECISION_FLOATS" if test "$CC" = "gcc" ; then CXXFLAGS="$CXXFLAGS -fno-caller-saves" fi fi AC_LANG(C++) ACX_MPI #MPI run forker if test "$enable_mpi" = yes; then CXXFLAGS="$CXXFLAGS -DSUBROUTINE_GARLI" CC="$MPICC" CXX="$MPICXX" LIBS="$MPILIBS $LIBS" fi #old MPI versionr if test "$enable_oldmpi" = yes; then CXXFLAGS="$CXXFLAGS -DOLD_SUBROUTINE_GARLI" CC="$MPICC" CXX="$MPICXX" LIBS="$MPILIBS $LIBS" fi CXXFLAGS="$CXXFLAGS $CXXFLAGS_OPTIM_SPEED" #Location of BOINC library and include files BOINC_INSTALL="/usr" AC_ARG_WITH( [boinc], AC_HELP_STRING( [--with-boinc=DIR], [Specify the root directory for the BOINC library (parent of the include/boinc and lib directories). Omit this flag to use a system level install of BOINC.] ), [ if ! test "$withval" = "yes" -o "$withval" = "no" ; then BOINC_INSTALL="$withval" fi ]) if test "$enable_boinc" = yes; then BOINC_INCL="$BOINC_INSTALL/include/boinc" BOINC_LIB_DIR="$BOINC_INSTALL/lib" if ! test -d "$BOINC_INSTALL" ; then AC_MSG_ERROR([A BOINC library and api installation is required for building the BOINC version of GARLI. Expecting a directory called $BOINC_INCL and libboinc library files in $BOINC_LIB_DIR. Use the --with-boinc flag with configure to specify a location other than /usr/local, or verify that the specified location is correct.]) fi CXXFLAGS="$CXXFLAGS -I$BOINC_INCL" LDFLAGS="$LDFLAGS -L$BOINC_LIB_DIR" fi ################################################################################ # Require the builder to provide the --with-ncl argument, # otherwise default to looking for a system level installation. # ideally people should be using the build_garli.sh install script ################################################################################ NCL_INC_DIR="/usr/local/include" NCL_LIB_DIR="/usr/local/lib" NCL_BIN_DIR="/usr/local/bin" AC_ARG_WITH( [ncl], AC_HELP_STRING( [--with-ncl=DIR], [Specify the root directory for the ncl version 2.1 or greater library (parent of the include/ncl and lib directories). Omit this flag and configure will look for a system level NCL installation. You might use the build_garli.sh script to automate the build process.] ), [ if ! test "$withval" = "yes" -o "$withval" = "no" ; then NCL_INC_DIR="$withval/include" NCL_LIB_DIR="$withval/lib" NCL_BIN_DIR="$withval/bin" fi ]) ####DJZ This was to look for an NCL install in other places, but #I gave up on it because the user should either use the script #or know what they are doing. #if ! test -n "$WNCL" ; then # echo WITH_NCL NOT SPECIFIED # if test -d "$srcdir/ncl/" ; then # echo FOUND NCL DIR # nl=`ls -ld $srcdir/ncl*/ | wc -l` # echo ### # echo $nl # echo ### # if ! test $nl = 1 ; then # AC_MSG_ERROR([Multiple NCL directories found.]) # fi # NCL_BASE=`ls -d $srcdir/ncl*/` # NCL_INC_DIR="$NCL_BASE/include" # NCL_LIB_DIR="$NCL_BASE/lib" # echo NCL_INC_DIR $NCL_INC_DIR # else # echo NO NCL DIR FOUND # fi #else # echo WITH_NCL SPECIFIED #fi ### if ! test -d "$NCL_INC_DIR/ncl" ; then AC_MSG_ERROR([NCL 2.1 or higher is a prerequisite for building Garli. 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0000215255 00000 n 0000215549 00000 n 0000215715 00000 n 0000215828 00000 n 0000220357 00000 n 0000220417 00000 n 0000220592 00000 n 0000220812 00000 n 0000221068 00000 n 0000221221 00000 n 0000221538 00000 n 0000221702 00000 n 0000221931 00000 n 0000222124 00000 n 0000222381 00000 n 0000222730 00000 n 0000222995 00000 n 0000223183 00000 n 0000224443 00000 n 0000224605 00000 n trailer < ] /DocChecksum /6F664ABC73EAF7149E2205A644283ADC >> startxref 224920 %%EOF garli-2.1-release/doc/NOTE.txt000066400000000000000000000001661241236125200161440ustar00rootroot00000000000000 DOCUMENTATION FOR THE PARTITIONED VERSION IS AVAILABLE AT http://www.nescent.org/wg_garli/Partition_testing_version garli-2.1-release/example/000077500000000000000000000000001241236125200155215ustar00rootroot00000000000000garli-2.1-release/example/basic/000077500000000000000000000000001241236125200166025ustar00rootroot00000000000000garli-2.1-release/example/basic/EXAMPLES.txt000066400000000000000000000065221241236125200205660ustar00rootroot00000000000000################# FOR PARTITIONED VERSION EXAMPLES SEE THE partition directory. For documentation of the partitioned version see: http://www.nescent.org/wg_garli/Using_partitioned_models The following may also be helpful if you aren't familar with general GARLI usage: https://www.nescent.org/wg_garli/Main_Page ################# GARLI version 2.0 examples - March 2011 SAMPLE DATASETS: Rana 64 taxon rRNA frog dataset: -This dataset is from Hillis and Wilcox. 2005. Phylogeny of the New World true frogs (Rana). Mol Phylogenet Evol. 34(2):299-314. -The 2 best trees for this dataset under the GTR+I+G model have log likelihood scores of -21812.66941 and -21812.64132. Individual search replicates on this dataset should take < 20 min. -A sample starting tree and model parameters in the legacy GARLI format is contained in ranastart.oldformat.tre -The same starting tree and model parameters in the new NEXUS format is contained in ranastart.nexus.tre -A sample constraint is contained in the files ranaconstraint.format1 and ranaconstraint.format2. The best tree with this constraint has an optimized log likelihood score of -21821.57688 Zakon 11 taxon Na+ channel gene fish dataset: -This dataset is from Zakon, Lu, Zwickl and Hillis. 2006. Sodium channel genes and the evolution of diversity in communication signals of electric fishes: Convergent molecular evolution. Proc. Natl. Acad. Sci. USA. 103(10):3675-80. -This is a protein coding gene, aligned in proper reading frame -This single dataset file can be analyzed using nucleotide, amino acid and codon models with the garli.conf.nuc.test, garli.conf.AA.test and garli.conf.codon.test configuration files, respectively. -There is an example of how to use a custom AA model (the "LG" model in this case) with this dataset in configuration file garli.conf.AA.LGmodel and model file LGmodel.mod. Configuration files: -The program will look for a configuration file named garli.conf in the current working directory, unless a different filename is passed to it from the command line -The included garli.conf.XXX.defaultSettings configuration files contain reasonable default search parameters. -HOWEVER, the substitution models specified therein are ARBITRARY, and careful thought should be put into choosing the appropriate model for other datasets. See the manual or support website for more information. -The garli.conf.XXX.test configuration files are meant for quick tests on the small 11 taxon dataset, and the settings contained are NOT good defaults for larger datasets. See above. -See the manual or website for a general tutorial on using the program and the meaning of the configuration file entries // Copyright 2005-2011 Derrick J. Zwickl // email: zwickl@nescent.org // // This program is free software: you can redistribute it and/or modify // it under the terms of the GNU General Public License as published by // the Free Software Foundation, either version 3 of the License, or // (at your option) any later version. // // This program is distributed in the hope that it will be useful, // but WITHOUT ANY WARRANTY; without even the implied warranty of // MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the // GNU General Public License for more details. // // You should have received a copy of the GNU General Public License // along with this program. If not, see . garli-2.1-release/example/basic/LGmodel.mod000066400000000000000000000164101241236125200206300ustar00rootroot00000000000000#NEXUS [see the "garli.conf.AA.LGmodel" configuration file for an example of how to use this model] [ Le, S.Q., and Gascuel, O. 2008. An improved general amino acid replacement matrix. Mol Biol Evol 25: 1307-1320. ] [this entire GALRI block can be copied into your NEXUS datafile to use the LG model. Also set ratematrix = fixed and statefrequencies = fixed in the configuration file. If you want to use the observed AA frequencies, remove the frequecy part of the block and set statefrequencies = empirical] begin garli; [this is the LG model rate matrix, in GARLI format (upper triangle, alphabetical by single letter codes)] [it is scaled such that the mean rate is 100, but GARLI does not require any particular scaling] r 243.500 38.656 101.598 24.819 202.114 35.106 14.657 52.486 38.675 109.961 27.080 115.206 94.882 41.586 462.446 209.301 249.250 17.679 21.420 6.120 0.342 108.123 55.689 62.662 31.366 1.298 58.110 87.426 51.728 7.374 8.297 52.294 272.397 111.863 191.672 65.557 114.020 512.992 1.704 82.657 90.697 1.046 27.681 1.475 2.499 496.584 38.588 51.201 12.126 121.332 41.661 3.714 2.924 13.217 1.840 34.127 41.467 4.330 176.791 6.816 16.996 52.994 41.030 403.888 35.606 59.867 59.141 23.971 7.616 11.743 8.764 66.732 108.855 2.340 253.635 175.976 8.758 9.241 3.508 5.158 35.396 16.142 64.046 240.373 763.432 30.472 0.852 29.019 4.330 13.651 140.640 19.268 26.214 38.171 170.218 12.701 7.503 26.266 5.349 10.652 68.211 35.836 43.286 441.125 49.779 470.891 237.387 96.850 57.157 11.643 58.408 519.152 15.561 405.499 418.074 18.734 7.658 7.127 12.423 6.271 101.128 1041.770 10.923 22.747 13.451 64.234 209.847 38.184 316.401 618.860 73.241 111.216 18.118 4.882 12.907 617.519 6.694 24.365 56.980 29.529 17.833 29.635 166.574 60.617 29.314 36.294 9.768 163.622 47.361 33.942 197.646 185.746 68.105 47.085 15.827 165.890 73.554 392.126 195.720 8.187 4.439 59.873 61.073 32.531 130.905 55.905 29.006 9.306 8.767 274.689 119.723 105.666 20.576 23.107 25.174 83.950 56.641 16.717 58.071 30.761 633.164 9.623 24.345 39.184 214.061 13.776 24.050 18.539 24.390 308.333 ; [these are the LG model amino acid frequencies, in GARLI order] e 0.079066 0.012937 0.053052 0.071586 0.042302 0.057337 0.022355 0.062157 0.0646 0.099081 0.022951 0.041977 0.04404 0.040767 0.055941 0.061197 0.053287 0.069147 0.012066 0.034155 ; end; [ MATRICES AND OTHER PROGRAMS Unfortunately, I beleive that GARLI, PAML, and MrBayes all have different orderings of the amino acids. PAML is alphabetical by three-letter code, MrBayes is alphabetical by full name (same as PAML, but swap Gln and Glu), GARLI is alphabetical by single letter code. Additionally, I believe that PAML takes the below diagonal matrix as input, while GARLI and MrBayes take the upper. Below is PAML's LG matrix, taken directly from PAML's distribution. Below that is my transformation of the matrix to what I think is correct for MrBayes. ] [ From PAML: (Equilibrium amino-acid frequencies and exchangeability matrix in PAML format). 0.425093 0.276818 0.751878 0.395144 0.123954 5.076149 2.489084 0.534551 0.528768 0.062556 0.969894 2.807908 1.695752 0.523386 0.084808 1.038545 0.363970 0.541712 5.243870 0.003499 4.128591 2.066040 0.390192 1.437645 0.844926 0.569265 0.267959 0.348847 0.358858 2.426601 4.509238 0.927114 0.640543 4.813505 0.423881 0.311484 0.149830 0.126991 0.191503 0.010690 0.320627 0.072854 0.044265 0.008705 0.108882 0.395337 0.301848 0.068427 0.015076 0.594007 0.582457 0.069673 0.044261 0.366317 4.145067 0.536518 6.326067 2.145078 0.282959 0.013266 3.234294 1.807177 0.296636 0.697264 0.159069 0.137500 1.124035 0.484133 0.371004 0.025548 0.893680 1.672569 0.173735 0.139538 0.442472 4.273607 6.312358 0.656604 0.253701 0.052722 0.089525 0.017416 1.105251 0.035855 0.018811 0.089586 0.682139 1.112727 2.592692 0.023918 1.798853 1.177651 0.332533 0.161787 0.394456 0.075382 0.624294 0.419409 0.196961 0.508851 0.078281 0.249060 0.390322 0.099849 0.094464 4.727182 0.858151 4.008358 1.240275 2.784478 1.223828 0.611973 1.739990 0.990012 0.064105 0.182287 0.748683 0.346960 0.361819 1.338132 2.139501 0.578987 2.000679 0.425860 1.143480 1.080136 0.604545 0.129836 0.584262 1.033739 0.302936 1.136863 2.020366 0.165001 0.571468 6.472279 0.180717 0.593607 0.045376 0.029890 0.670128 0.236199 0.077852 0.268491 0.597054 0.111660 0.619632 0.049906 0.696175 2.457121 0.095131 0.248862 0.140825 0.218959 0.314440 0.612025 0.135107 1.165532 0.257336 0.120037 0.054679 5.306834 0.232523 0.299648 0.131932 0.481306 7.803902 0.089613 0.400547 0.245841 3.151815 2.547870 0.170887 0.083688 0.037967 1.959291 0.210332 0.245034 0.076701 0.119013 10.649107 1.702745 0.185202 1.898718 0.654683 0.296501 0.098369 2.188158 0.189510 0.249313 0.079066 0.055941 0.041977 0.053052 0.012937 0.040767 0.071586 0.057337 0.022355 0.062157 0.099081 0.064600 0.022951 0.042302 0.044040 0.061197 0.053287 0.012066 0.034155 0.069147 A R N D C Q E G H I L K M F P S T W Y V Ala Arg Asn Asp Cys Gln Glu Gly His Ile Leu Lys Met Phe Pro Ser Thr Trp Tyr Val ] [ This is, I THINK, the MrBayes input order for the LG matrix (above diagonal, ordered alphabetically by full amino acid name. It is scaled such that the mean rate is 100, but relative rate matrices can in general be rescaled by any constant factor without changing the meaning. If you are using this as a Direchlet prior then the scaling DOES MATTER. If you are going to use this as MrBayes input YOU are responsible for understanding the implications of what you are doing (setting a prior) and for double checking that the ordering is correct. 41.586 27.080 38.656 243.500 101.598 94.882 202.114 35.106 14.657 38.675 52.486 109.961 24.819 115.206 462.446 209.301 17.679 21.420 249.250 73.554 12.126 52.294 35.606 274.689 38.171 237.387 12.423 29.529 618.860 47.361 5.158 32.531 83.950 56.641 58.071 30.761 16.717 496.584 51.728 52.994 165.890 140.640 441.125 18.734 6.694 209.847 36.294 8.758 15.827 392.126 195.720 4.439 59.873 8.187 6.120 512.992 51.201 82.657 90.697 1.046 1.475 27.681 2.499 1.704 38.588 121.332 41.661 2.924 13.217 3.714 0.342 8.297 55.689 62.662 31.366 58.110 1.298 87.426 108.123 7.374 272.397 111.863 65.557 114.020 191.672 403.888 34.127 41.467 4.330 6.816 176.791 16.996 1.840 41.030 59.867 59.141 7.616 11.743 23.971 26.214 470.891 7.127 56.980 316.401 163.622 3.508 61.073 119.723 105.666 23.107 25.174 20.576 30.472 0.852 4.330 29.019 13.651 8.764 19.268 170.218 12.701 26.266 5.349 7.503 10.652 35.836 68.211 43.286 66.732 49.779 96.850 57.157 58.408 519.152 11.643 405.499 15.561 418.074 108.855 7.658 6.271 101.128 10.923 22.747 1041.770 13.451 617.519 253.635 24.365 17.833 29.635 60.617 29.314 166.574 64.234 2.340 38.184 73.241 111.216 4.882 12.907 18.118 175.976 9.768 33.942 197.646 68.105 47.085 185.746 9.241 35.396 16.142 240.373 763.432 64.046 130.905 55.905 9.306 8.767 29.006 633.164 24.345 39.184 9.623 13.776 24.050 214.061 308.333 18.539 24.390 The amino acid frequencies, in MrBayes order: 0.079066 0.055941 0.041977 0.053052 0.012937 0.071586 0.040767 0.057337 0.022355 0.062157 0.099081 0.0646 0.022951 0.042302 0.04404 0.061197 0.053287 0.012066 0.034155 0.069147 ] garli-2.1-release/example/basic/garli.conf000066400000000000000000000023561241236125200205550ustar00rootroot00000000000000[general] datafname = rana.nex constraintfile = none streefname = stepwise attachmentspertaxon = 50 ofprefix = rana.nuc.GTRIG randseed = -1 availablememory = 512 logevery = 10 saveevery = 100 refinestart = 1 outputeachbettertopology = 0 outputcurrentbesttopology = 0 enforcetermconditions = 1 genthreshfortopoterm = 20000 scorethreshforterm = 0.05 significanttopochange = 0.01 outputphyliptree = 0 outputmostlyuselessfiles = 0 writecheckpoints = 0 restart = 0 outgroup = 1 resampleproportion = 1.0 inferinternalstateprobs = 0 outputsitelikelihoods = 0 optimizeinputonly = 0 collapsebranches = 1 searchreps = 2 bootstrapreps = 0 [model1] datatype = nucleotide ratematrix = 6rate statefrequencies = estimate ratehetmodel = gamma numratecats = 4 invariantsites = estimate [master] nindivs = 4 holdover = 1 selectionintensity = 0.5 holdoverpenalty = 0 stopgen = 5000000 stoptime = 5000000 startoptprec = 0.5 minoptprec = 0.01 numberofprecreductions = 10 treerejectionthreshold = 50.0 topoweight = 1.0 modweight = 0.05 brlenweight = 0.2 randnniweight = 0.1 randsprweight = 0.3 limsprweight = 0.6 intervallength = 100 intervalstostore = 5 limsprrange = 6 meanbrlenmuts = 5 gammashapebrlen = 1000 gammashapemodel = 1000 uniqueswapbias = 0.1 distanceswapbias = 1.0 garli-2.1-release/example/basic/garli.conf.AA.LGmodel000066400000000000000000000024251241236125200223540ustar00rootroot00000000000000[general] datafname = zakonEtAl2006.11tax.nex constraintfile = none streefname = LGmodel.mod attachmentspertaxon = 50 ofprefix = AA.LGmodel randseed = -1 availablememory = 512 logevery = 10 saveevery = 100 refinestart = 1 outputeachbettertopology = 0 outputcurrentbesttopology = 0 enforcetermconditions = 1 genthreshfortopoterm = 20000 scorethreshforterm = 0.05 significanttopochange = 0.01 outputphyliptree = 0 outputmostlyuselessfiles = 0 writecheckpoints = 0 restart = 0 outgroup = 1 resampleproportion = 1.0 inferinternalstateprobs = 0 outputsitelikelihoods = 0 optimizeinputonly = 0 collapsebranches = 1 searchreps = 2 bootstrapreps = 0 [model1] datatype = codon-aminoacid geneticcode = standard ratematrix = fixed statefrequencies = fixed ratehetmodel = gamma numratecats = 4 invariantsites = estimate [master] nindivs = 4 holdover = 1 selectionintensity = 0.5 holdoverpenalty = 0 stopgen = 5000000 stoptime = 5000000 startoptprec = 0.5 minoptprec = 0.01 numberofprecreductions = 10 treerejectionthreshold = 50.0 topoweight = 1.0 modweight = 0.05 brlenweight = 0.2 randnniweight = 0.1 randsprweight = 0.3 limsprweight = 0.6 intervallength = 100 intervalstostore = 5 limsprrange = 6 meanbrlenmuts = 5 gammashapebrlen = 1000 gammashapemodel = 1000 uniqueswapbias = 0.1 distanceswapbias = 1.0 garli-2.1-release/example/basic/garli.conf.AA.defaultSettings000066400000000000000000000023721241236125200241770ustar00rootroot00000000000000[general] datafname = zakonEtAl2006.11tax.nex constraintfile = none streefname = stepwise attachmentspertaxon = 50 ofprefix = AA.jonesFIG randseed = -1 availablememory = 512 logevery = 10 saveevery = 100 refinestart = 1 outputeachbettertopology = 0 outputcurrentbesttopology = 0 enforcetermconditions = 1 genthreshfortopoterm = 20000 scorethreshforterm = 0.05 significanttopochange = 0.01 outputphyliptree = 0 outputmostlyuselessfiles = 0 writecheckpoints = 0 restart = 0 outgroup = 1 resampleproportion = 1.0 inferinternalstateprobs = 0 outputsitelikelihoods = 0 optimizeinputonly = 0 collapsebranches = 1 searchreps = 2 bootstrapreps = 0 [model1] datatype = aminoacid ratematrix = jones statefrequencies = empirical ratehetmodel = gamma numratecats = 4 invariantsites = estimate [master] nindivs = 4 holdover = 1 selectionintensity = 0.5 holdoverpenalty = 0 stopgen = 5000000 stoptime = 5000000 startoptprec = 0.5 minoptprec = 0.01 numberofprecreductions = 10 treerejectionthreshold = 50.0 topoweight = 1.0 modweight = 0.05 brlenweight = 0.2 randnniweight = 0.1 randsprweight = 0.3 limsprweight = 0.6 intervallength = 100 intervalstostore = 5 limsprrange = 6 meanbrlenmuts = 5 gammashapebrlen = 1000 gammashapemodel = 1000 uniqueswapbias = 0.1 distanceswapbias = 1.0 garli-2.1-release/example/basic/garli.conf.AA.test000066400000000000000000000024251241236125200220100ustar00rootroot00000000000000[general] datafname = zakonEtAl2006.11tax.nex constraintfile = none streefname = stepwise attachmentspertaxon = 50 ofprefix = AA.jonesFIG randseed = -1 availablememory = 512 logevery = 10 saveevery = 100 refinestart = 1 outputeachbettertopology = 0 outputcurrentbesttopology = 0 enforcetermconditions = 1 genthreshfortopoterm = 2000 scorethreshforterm = 0.05 significanttopochange = 0.01 outputphyliptree = 0 outputmostlyuselessfiles = 0 writecheckpoints = 0 restart = 0 outgroup = 1 resampleproportion = 1.0 inferinternalstateprobs = 0 outputsitelikelihoods = 0 optimizeinputonly = 0 collapsebranches = 1 searchreps = 2 bootstrapreps = 0 [model1] datatype = codon-aminoacid geneticcode = standard ratematrix = jones statefrequencies = empirical ratehetmodel = gamma numratecats = 4 invariantsites = estimate [master] nindivs = 4 holdover = 1 selectionintensity = 0.5 holdoverpenalty = 0 stopgen = 5000000 stoptime = 5000000 startoptprec = 0.5 minoptprec = 0.01 numberofprecreductions = 1 treerejectionthreshold = 50.0 topoweight = 1.0 modweight = 0.05 brlenweight = 0.2 randnniweight = 0.1 randsprweight = 0.3 limsprweight = 0.6 intervallength = 100 intervalstostore = 5 limsprrange = 6 meanbrlenmuts = 5 gammashapebrlen = 1000 gammashapemodel = 1000 uniqueswapbias = 0.1 distanceswapbias = 1.0 garli-2.1-release/example/basic/garli.conf.codon.defaultSettings000066400000000000000000000024071241236125200250170ustar00rootroot00000000000000[general] datafname = zakonEtAl2006.11tax.nex constraintfile = none streefname = stepwise attachmentspertaxon = 50 ofprefix = codon.GY94.F3x4 randseed = -1 availablememory = 512 logevery = 10 saveevery = 100 refinestart = 1 outputeachbettertopology = 0 outputcurrentbesttopology = 0 enforcetermconditions = 1 genthreshfortopoterm = 20000 scorethreshforterm = 0.05 significanttopochange = 0.01 outputphyliptree = 0 outputmostlyuselessfiles = 0 writecheckpoints = 0 restart = 0 outgroup = 1 resampleproportion = 1.0 inferinternalstateprobs = 0 outputsitelikelihoods = 0 optimizeinputonly = 0 collapsebranches = 1 searchreps = 2 bootstrapreps = 0 [model1] datatype = codon geneticcode = standard ratematrix = 2rate statefrequencies = f3x4 ratehetmodel = none numratecats = 1 invariantsites = none [master] nindivs = 4 holdover = 1 selectionintensity = 0.5 holdoverpenalty = 0 stopgen = 5000000 stoptime = 5000000 startoptprec = 0.5 minoptprec = 0.01 numberofprecreductions = 10 treerejectionthreshold = 50.0 topoweight = 1.0 modweight = 0.05 brlenweight = 0.2 randnniweight = 0.1 randsprweight = 0.3 limsprweight = 0.6 intervallength = 100 intervalstostore = 5 limsprrange = 6 meanbrlenmuts = 5 gammashapebrlen = 1000 gammashapemodel = 1000 uniqueswapbias = 0.1 distanceswapbias = 1.0 garli-2.1-release/example/basic/garli.conf.codon.test000066400000000000000000000024051241236125200226270ustar00rootroot00000000000000[general] datafname = zakonEtAl2006.11tax.nex constraintfile = none streefname = stepwise attachmentspertaxon = 50 ofprefix = codon.GY94.F3x4 randseed = -1 availablememory = 512 logevery = 10 saveevery = 100 refinestart = 1 outputeachbettertopology = 0 outputcurrentbesttopology = 0 enforcetermconditions = 1 genthreshfortopoterm = 2000 scorethreshforterm = 0.05 significanttopochange = 0.01 outputphyliptree = 0 outputmostlyuselessfiles = 0 writecheckpoints = 0 restart = 0 outgroup = 1 resampleproportion = 1.0 inferinternalstateprobs = 0 outputsitelikelihoods = 0 optimizeinputonly = 0 collapsebranches = 1 searchreps = 2 bootstrapreps = 0 [model1] datatype = codon geneticcode = standard ratematrix = 2rate statefrequencies = f3x4 ratehetmodel = none numratecats = 1 invariantsites = none [master] nindivs = 4 holdover = 1 selectionintensity = 0.5 holdoverpenalty = 0 stopgen = 5000000 stoptime = 5000000 startoptprec = 0.5 minoptprec = 0.01 numberofprecreductions = 1 treerejectionthreshold = 50.0 topoweight = 1.0 modweight = 0.05 brlenweight = 0.2 randnniweight = 0.1 randsprweight = 0.3 limsprweight = 0.6 intervallength = 100 intervalstostore = 5 limsprrange = 6 meanbrlenmuts = 5 gammashapebrlen = 1000 gammashapemodel = 1000 uniqueswapbias = 0.1 distanceswapbias = 1.0 garli-2.1-release/example/basic/garli.conf.nuc.defaultSettings000066400000000000000000000023701241236125200245010ustar00rootroot00000000000000[general] datafname = zakonEtAl2006.11tax.nex constraintfile = none streefname = stepwise attachmentspertaxon = 50 ofprefix = nuc.GTRIG randseed = -1 availablememory = 512 logevery = 10 saveevery = 100 refinestart = 1 outputeachbettertopology = 0 outputcurrentbesttopology = 0 enforcetermconditions = 1 genthreshfortopoterm = 20000 scorethreshforterm = 0.05 significanttopochange = 0.01 outputphyliptree = 0 outputmostlyuselessfiles = 0 writecheckpoints = 0 restart = 0 outgroup = 1 resampleproportion = 1.0 inferinternalstateprobs = 0 outputsitelikelihoods = 0 optimizeinputonly = 0 collapsebranches = 1 searchreps = 2 bootstrapreps = 0 [model1] datatype = nucleotide ratematrix = 6rate statefrequencies = estimate ratehetmodel = gamma numratecats = 4 invariantsites = estimate [master] nindivs = 4 holdover = 1 selectionintensity = 0.5 holdoverpenalty = 0 stopgen = 5000000 stoptime = 5000000 startoptprec = 0.5 minoptprec = 0.01 numberofprecreductions = 10 treerejectionthreshold = 50.0 topoweight = 1.0 modweight = 0.05 brlenweight = 0.2 randnniweight = 0.1 randsprweight = 0.3 limsprweight = 0.6 intervallength = 100 intervalstostore = 5 limsprrange = 6 meanbrlenmuts = 5 gammashapebrlen = 1000 gammashapemodel = 1000 uniqueswapbias = 0.1 distanceswapbias = 1.0 garli-2.1-release/example/basic/garli.conf.nuc.test000066400000000000000000000023661241236125200223200ustar00rootroot00000000000000[general] datafname = zakonEtAl2006.11tax.nex constraintfile = none streefname = stepwise attachmentspertaxon = 50 ofprefix = nuc.GTRIG randseed = -1 availablememory = 512 logevery = 10 saveevery = 100 refinestart = 1 outputeachbettertopology = 0 outputcurrentbesttopology = 0 enforcetermconditions = 1 genthreshfortopoterm = 2000 scorethreshforterm = 0.05 significanttopochange = 0.01 outputphyliptree = 0 outputmostlyuselessfiles = 0 writecheckpoints = 0 restart = 0 outgroup = 1 resampleproportion = 1.0 inferinternalstateprobs = 0 outputsitelikelihoods = 0 optimizeinputonly = 0 collapsebranches = 1 searchreps = 2 bootstrapreps = 0 [model1] datatype = nucleotide ratematrix = 6rate statefrequencies = estimate ratehetmodel = gamma numratecats = 4 invariantsites = estimate [master] nindivs = 4 holdover = 1 selectionintensity = 0.5 holdoverpenalty = 0 stopgen = 5000000 stoptime = 5000000 startoptprec = 0.5 minoptprec = 0.01 numberofprecreductions = 1 treerejectionthreshold = 50.0 topoweight = 1.0 modweight = 0.05 brlenweight = 0.2 randnniweight = 0.1 randsprweight = 0.3 limsprweight = 0.6 intervallength = 100 intervalstostore = 5 limsprrange = 6 meanbrlenmuts = 5 gammashapebrlen = 1000 gammashapemodel = 1000 uniqueswapbias = 0.1 distanceswapbias = 1.0 garli-2.1-release/example/basic/rana.nex000066400000000000000000003726501241236125200202540ustar00rootroot00000000000000#NEXUS [this dataset is from: Hillis and Wilcox. 2005. Phylogeny of the New World true frogs (Rana). Mol Phylogenet Evol. 34(2):299-314. ] Begin data; Dimensions ntax=64 nchar=1976; Format datatype=dna gap=-; Matrix temporariaDMH84R1 GCCGTAAACAATTAACTCACATCCACA-CCGCCAGGGAATTACGAGCAATGCTTAAAACCCAAAGGATTTGACGGTGTCCCACCCCACTAGAGGAGCCTGTTCTATAATCGATGATCCCCGCTTCACCTAACCATCCCTCGCCTACCAGTCTGTATACCTCCGTCGAAAGCTTACCATGTGAACGCCC-GCAGTAAGCTTAATGATATCAACACGTCAGGTCAAGGTGCAGCTTAAGGGACGGAAAGTAATGGGCTACAATTTCTAATCTAGAACAAACGAAAGACTATGTGAAACCCTAGTCATGAAGGTGGATTTAGTAGTAAAAAAAAAATATAGTGTTCTTTTTAACCCGGCTCTGGGACGCGTACACACCGCCCGTCACCCTCTTCAATAGTA-C-CCCGTATGTTCCTAACCC-AACAC-CACGTTTT-AGAAGAAGCAAGTCGTAACATGGTGAGTGTACTGGAAAGTGCACTTGGTATA-CTACAAAATGTAGCTTAACAAAAGCCCCTCGCTTACACCTAGGAAATATCTGTTAAACCCGGATCATTTTGAGCCTAAAATCTAGCCCA-CTAATCCGTATGACCCCTCCAAAAAA-CAAAACATTTTAACATCTTAGTACAGGCGATCGAAAAA-TTTTTAAGCGCTTCAGATAAAGTACCGCAAGGGAAAAATGAAATAGAAATGAAATAACCCTAAAGCCTCAAATAGCAGAGATAATACCTCGTACCTTTTGCATCATGGTCTAGCCAGTCTACCC-AAGCAAAATGTAACTTTC-AGTTTGATACCCCGAAACCAAGCGAGCTACTTCAGAACAGCCAAAA-GGGCCAACCCGTCTCTGTTGCAAAAGAGTGGGAAGATTCTCAAGTAGAGGTGACAAGCCTACCGAGCTTGGAGATAGCTGGTTGTTCAGAAAAAGAGTTTTAGCTCTACCTTAAGTTTTTCC-ATTAAACTA-AACAAACCC-CAAGACTTAAGAGCTATTCAAATAAGGCACAGCTTATTTGAAATAGGGCACAGCCTACAACATAGGGT-AAACAAGA-GTAACTTAAA-TAAAGTAGGCCTAAAAGCAGCCACCTTTAGAAAAGCGTCAAAGCTTAACCACTCCCTACTCTCAA-TACCTTAAATTTTCACTAACCCCTCAC-TATTACTGAATAATTTTATA-ATTATATAAAAGCTATCATGCTAGAACTAGTAACAAGAAATTGCCT-TTCTCCTAAATGTAAACATAAACCAAAATAGACTATCTATTGGTTATTAACGTAAATGCCAAA-TTATAGCAACATCCTC------CAGAAAATCCTATAGCCCCC-AACGTTAACCTTACACTAGAACATTTCAGGAAAGATTTAAAGAAAAAGAAGGAACTCGGCAAATTTTAGCCTCGCCTGTTTACCAAAAACATCGCCTCTTGACAAAACATAAGAAGTCCAGCCTGCCCAGTGACAAAGTTCAACGGCCGCGGTACCCTAACCGTGCGAAGGTAGCATAATCACTTGTTCTTTAAATGAGGACTCGTATCAACGGCACTACGAAGGCTATACTGTCTCCTTTTTCTAATCAGTGAAACTGATCTCCCCGTGAAGAAGCGGGGATCAAAATATAAGACGAGAAGACCCCATGGAGCTTTAAACTCATCACGCACCTCT-GTGCCCTCATACCCTT-AAACAC-AAAAAATC-TACGTGCTAGTTTTAGGTTGGGGGGACCACGGATTATAACTTAACCTCCTTAACAAATGGGCTAACACCCTTATCCATGAGACACAGCTCTAAGAATTACTAAACTAATGCTT-ATGACCCGATA--TTCGATCAATGAACCAAGTTACCCTGGGGATAACAGCGCAATCTACTTCAAGAGCCCATATCGACAAGTAGGTTTACAACCTCCATGTTGGATCAGGGTATCCCGGTGGTGCAACCGCTACCGATG-GTTCGTTTGTTCAACG boyliiMVZ148929 GCCGTAAACAATTAACTCACACC-TCCAGCGCCAGGGGATTACGAGCAATGCTTAAAACCCAAAGGATTTGACGGTGTCCCACCCCACTAGAGGAGCCTGTTCTATAATCGATGATCCCCGCTTCACCCGACCACTCCTCGCTTATCAGTCTGTATACCTCCGTCGAAAGCTTACCATGTGAACGCCC-ACAGTAAGCCCAATGATGTCAACACGTCAGGTCAAGGTGCAGCTCAAGGAATGGGAAGTAATGGGCTACAATTTCTAATTTAGAACAAACGAAAGACTATGTGAAACCCTAGTCATGAAGGTGGATTTAGTAGTAAAAGGGAAATAGAGTGTTCCTTTTAACCCGGCTCTGGGACGCGTACACACCGCCCGTCACCCTCTTCAATAGTT-TTTC-TCATGTCCTTAACCC-CCGCG-CACATTTT-AGAAGAAGCAAGTCGTAACATGGTAAGTGTACTGGAAAGTGCACTTGGTATA-CTACAAAATGTAGCTTAACAAAAGCCCCTCGCTTACACCTAGGAAATATCTGTTAAACTCAGATCATTTTGAGCCTAAAATCTAGCCCA-CACACTCGCATGATTCCACTCTAAAA-CAAAACATTTTAACATTTTAGTACAGGCGATCGAAATA-TTTCTAAGCGCTTCAGATAAAGTACCGCAAGGGAAAAATGAAATAGAAATGAAATAACCCTAAAGCCCTAAACAGCAGAGATTATACCTCGTACCTTTTGCATCATGGTCTAGCCAGTCTACCC-AAGCAAAATGAAACTTTC-AGTTTGACACCCCGAAACTAAGCGAGCTACTTCAGAACAGCCTAAGAGGGCCAACCCGTCTCTGTTGCAAAAGAGTGGGAAGATTCTTAAGTAGAGGTGATAAGCCTACCGAGCTTAGAGATAGCTGGTTGTTCAGAAAAAGAGTTTTAGCTCTACCTTAAGCTTTCCC-ATTACATTA-AATAAATCT-CAAAACTTAAGAGCTATTCAAATAAGGCACAGCTTATTTGAAACAGGACACAACCTACACCACAGGGT-AAATTAGA-GTAAATCAAG-TAAAGTGGGCCTAAAAGCAGCCACCTTTTAAAAAGCGTTAAAGCTTAACTACTCCTTACTCGCAA-TTCCTTTAAGCCTCATTAACCCTTCAT-TATTACTGAACAATTTTATA-TTTTTATAAAAGCTATTATGCTAGAACTAGTAACAAGAAACTGCCT-TTCTCCTAAATGTAAACATAAGCCAAAATAGACCACCTATTGGTTATTAACGTAAATGCCTGAATCATACCAACAAAAAC------TAGAAAACCCTATGACCCCC-TACGTTAACCTTACACCAGAACATTCCAGGAAAGATTAAAAGAAAAAGAAGGAACTCGGCAAATTTTAGCCTCGCCTGTTTACCAAAAACATCGCCTCTTGACAAAACATAAGAGGTCCAGCCTGCCCAGTGACAAAGTTTAACGGCCGCGGTACCCTAACCGTGCGAAGGTAGCATAATCACTTGTTCTTTAAATGAAGACTCGTATCAACGGCACTACGAAGGCTATACTGTCTCCTTTTTCTAATCAGTGAAACTGATCTCCCCGTGAAGAAGCGGGGATTAAAATATAAGAC-A-AAGACCCCATGGAGCTTTAAACTCACCATACACCTCT-GTGTCCTCTCATCACC-CTACAC-AAAAAGCT-TGTATGCTAGTTTTAGGTTGGGGGGACCACGGATTAAAATATAACCTCCACAACAAATGGGCTAACACCCTAATCCACGAGATACAACTCTAAGAATTACTAAACTAATGTTT-ATGACCCGATAA-TTCGATCAATGAACCAAGTTACCCTGGGGATAACAGCGCAATCTACTTCAAGAACCCATATCGACAAGTATGTTTACAACCTCCATGTTGGATCAGGGTATCCCAGTGGTGCAGCCGCTACTAATG-GTTCGTTTGTTCAACG luteiventris_MT_MVZ191016 GCCGTAAACAATTAACTTACATT-TCCAGCGCCAGGGAATTACGAGCAATGCTTAAAACCCAAAGGATTTGACGGTGTCCCACCCCACTAGAGGAGCCTGTTCTATAATCGATGATCCCCGCTTCACCTGACCACTCCTCGCTTATCAGTCTGTATACCTCCGTCGAAAGCTTACCATGTGAACGCCC-GCAGTAAGCCTAATGATATCAACACGTCAGGTCAAGGTGCAGCTCAAGGAATGGGAAGTAATGGGCTACAATTTCTAACTTAGAACAAACGAAAGACTATGTGAAACCCTAGTCATGAAGGTGGATTTAGTAGTAAAAAGAAAATATAGTATTCTTTTTAACCCGGCTCTGGGACGCGTACACACCGCCCGTCACCCTCTTCAATAGTA--TTT-TTTTGTTCCTAACCC-CCTACACACGTCTT-AGAAGAAGCAAGTCGTAACATGGTAAGTGTACTGGAAAGTGCACTTGGTATA-CTACAAAATGTAGCTTAACAAAAGCCCCTCGCTTACACCTAGGAAATATCTGTTAAACCCAGATCATTTTGAGCCTAAAATCTAGCCCA-CACACTCGCATGATTCCACTTCAAAA-CAAAACATTTTAACATCTTAGTACAGGCGATCGAAAAA-TTTCTAAGCGCTTCAGATAAAGTACCGCAAGGGAAATATGAAATAGAAATGAAATAACCCTAAAGCCCTAAACAGCAGAGATAACACCTCGTACCTTTTGCATCATGGTCTAGCCAGTCTACCC-AAGCAAAATGAAACTTTC-AGTTTGACCCCCCGAAACTAAGCGAGCTACTTCAGAACAGCCCCAGGGGGCCAACCCGTCTCTGTTGCAAAAGAGTGGGAAGATTCTTAAGTAGAGGTGACAAGCCTACCGAGCTTAGAGATAGCTGGTTGTTCAGAAAAAGAGTTTTAGCTCTACCTTAAGCTTCCCG-ATTATATTA-AACAAACCC-TAAAGCTTAAGAGCTATTCAAATAAGGCACAGCTTATTTGAAACAGGGTACAACCTACACCACAGGGT-AAACACGA-GTAAACTAAA-TAAAGTGGGCCTAAAAGCAGCCACCTTCAAAAAAGCGTTAAAGCTTAACTACTATCTACTCATAA-TTCCTTTAAACCTCACTAACCCTTCAT-TATTACTGAACAATTTTATA-TTCATATAAAAGCTATCATGCTAGAACTAGTAACAAGAAATTGCCT-TTCTCCTAAATGTAAACGTAAACCAAGATAGACCATCTATTGGTTATTAACGCAAATGCTAAAATCATAGCAACATCTAC------TAGAAAATCCTATGACCTCCTAACGTTAACCTTACACTAGAACATTACAGGAAAGATTAAAAGAAAAAGAAGGAACTCGGCAAATTTTAGCCTCGCCTGTTTACCAAAAACATCGCCTCTTGACAAAACATAAGAAGTCCAGCCTGCCCAGTGACAAAGTTCAACGGCCGCGGTACCCTAACCGTGCGAAGGTAGCATAATCACTTGTTCTTTAAATGAGGACTAGTATCAACGGCACTACGAAGGCTATACTGTCTCCTTTTTCCAATCAGTGAAACTGATCTCCCCGTGAAGAAGCGAGGATAGACTTATAAGACGAGAAGACCCCATGGAGCTTTAAACTCACCATACACCTCT-GTGCCCCCTCACCATC-TGACAC-AAGAGATT-TGTATGCTAGTTTTAGGTTGGGGGGACCACGGAGTACAACTTAACCTCCACAACAAATGGGCTAACACCCTTATCCAAGAGATACAACTCTAAGAATTACTAAACTAATGTTT-ATGACCCGATAA-TTCGATCAATGAACCAAGTTACCCTGGGGATAACAGCGCAATCTACTTCAAGAGCCCATATCGACAAGTAGGTTTACGACCTCGATGTTGGATCAGGGTATCCCAGTGGTGCAGCCGCTACTAATG-GTTCGTTTGTTCAACG luteiventris_WA_MVZ225749 GCCGTAAACAATTAACTTACATT-TCCAGCGCCAGGGAATTACGAGCAATGCTTAAAACCCAAAGGATTTGACGGTGTCCCACCCCACTAGAGGAGCCTGTTCTATAATCGATGATCCCCGCTTCACCTGACCACTCCTCGCTTATCAGTCTGTATACCTCCGTCGAAAGCTTACCATGTGAACGCCC-GCAGTAAGCCTAATGATATCAACACGTCAGGTCAAGGTGCAGCTCAAGGAATGGGAAGTAATGGGCTACAATTTCTAATTTAGAACAAACGAAAGACTATGTGAAACCCTAGTCATGAAGGTGGATTTAGTAGTAAAAAGAAAATAGAGTATTCTTTTTAACCCGGCTCTGGGACGCGTACACACCGCCCGTCACCCTCTTCAATAGTA--TTT-TTTTGTTCCTAACCC-CCTACACACGTCTT-AGAAGAGGCAAGTCGTAACATGGTAAGTGTACTGGAAAGTGCACTTGGTATA-CTACAAAATGTAGCTTAACAAAAGCCCCTCGCTTACACCTAGGAAATATCTGTTAAACCCAGATCATTTTGAGCCTAAAATCTAGCCCA-CACACTCGCATGATTCCACTTCAAAA-CAAAACATTTTAACATCTTAGTACAGGCGATCGAAAAA-TTTCTAAGCGCTTCAGATAAAGTACCGCAAGGGAAATATGAAATAGAAATGAAATAACCCTAAAGCCCTAAACAGCAGAGATAACACCTCGTACCTTTTGCATCATGGTCTAGCCAGTCTACCC-AAGCAAAATGAAACTTTC-AGTTTGACCCCCCGAAACTAAGCGAGCTACTTCAGAACAGCCCCAAGGGGCCAACCCGTCTCTGTTGCAAAAGAGTGGGAAGATTCTTAAGTAGAGGTGACAAGCCTACCGAGCTTAGAGATAGCTGGTTGTTCAGAAAAAGAGTTTTAGCTCTACCTTAAGCTTCCCG-ATTATATTA-AACAAACCC-TAAAGCTTAAGAGCTATTCAAATAAGGCACAGCTTATTTGAAACAGGGTACAACCTACACCACAGGGT-AAACACGA-GTAAACTAAA-TAAAGTGGGCCTAAAAGCAGCCATCTTCAAAAAAGCGTTAAAGCTTAACTACTATCTACTCATAA-TTCCTTTAAACCTCACTAACCCTTCAT-TATTACTGAACAATTTTATA-TTCATATAAAAGCTATTATGCTAGAACTAGTAACAAGAAATTGCCT-TTCTCCTAAATGTAAACGTAAACCAAGATAGACCATCTATTGGTTATTAACGCAAATGCTAAAATCATAGCAACATCTAC------TAGAAAATCCTATGACCTCCTAACGTTAACCTTACACTAGAACATTACAGGAAAGATTAAAAGAAAAAGAAGGAACTCGGCAAATTTTAGCCTCGCCTGTTTACCAAAAACATCGCCTCTTGACAAAACATAAGAGGTCCAGCCTGCCCAGTGACAAAGTTCAACGGCCGCGGTACCCTAACCGTGCGAAGGTAGCATAATCACTTGTTCTTTAAATGAGGACTAGTATCAACGGCACTACGAGGGCTATACTGTCTCCTTTTTCCAATCAGTGAAACTGATCTCCCCGTGAAGAAGCGAGGATAGACTTATAAGACGAGAAGACCCCATGGAGCTTTAAACTCACCATACACCTCT-GTGCCCCCTCACCATC-TGACAC-AAGAGATT-TGTATGCTAGTTTTAGGTTGGGGGGACCACGGAGTACAACTTAACCTCCACAACAAATGGGCTAACACCCTTATCCAAGAGATACAACTCTAAGAATTACTAAACTAATGTTT-ATGACCCGATAA-TTCGATCAATGAACCAAGTTACCCTGGGGATAACAGCGCAATCTACTTCAAGAGCCCATATCGACAAGTAGGTTTACGACCTCGATGTTGGATCAGGGTATCCCAGTGGTGCAGCCGCTACTAATG-GTTCGTTTGTTCAACG muscosaMVZ149006 GCCGTAAACAATTAATTTACACC-TCCAACGCCAGGGAATTACGAGCAATGCTTAAAACCCAAAGGATTTGACGGTGTCCCACCCCACTAGAGGAGCCTGTTCTATAATCGATGATCCCCGCTTCACCCGACCGTTCCTCGCTCATCAGTCTGTATACCTCCGTCGAAAGCTTACCATGTGAACGCCA-ACAGTAAGCTTAATGATATCAACACGTCAGGTCAAGGTGCAGCTTAAGGAATGGGAAGTAATGGGCTACAATTTCTAATTTAGAACAAACGAAAGACTATGTGAAATCCTAGTCATGAAGGTGGATTTAGTAGTAAAAAGGAAATAGAGTGTCCTTTTTAACTCGGCTCTGGGACGCGTACACACCGCCCGTCACCCTCTTCAATAGTA-CTTT-TCCTGTTCCTAACCC-ACACG-CACATTTT-AGAAGAGGCAAGTCGTAACATGGTAAGTGTACTGGAAAGTGCACTTGGTATA-CTACAAAATGTAGCTTAACAAAAGCCCCTCGCTTACACCTAGGAAATATCTG-TAAACCCAGATCATTTTGAGCCTAAAATCTAGCCCA-CACACTCGCATGACCTTTTTACCAAA-CAAAACATTTTAACATTTTAGTACAGGCGATCGAAAAA-TTTCTAAGCGCTTCAGATAAAGTACCGCAAGGGAAAAATGAAATAGAAATGAAATAACCCTAAAGCCCCAGACAGCAGAGATTACACCTCGTACCTTTTGCATCATGGTCTAGCCAGTCTACCC-AAGCAAAATGAAACCTTC-AGTTTGACACCCCGAAACTAAGCGAGCTACTTTAGAACAGCC-TAATGGGCCAACCCGTCTCTGTTGCAAAAGAGTGGGAAGATTTTTAAGTAGAGGTGATAAGCCTACCGAGCTTAGAGATAGCTGGTTATTCAGGAAAAGAGTCTAAGCTCTACCTTAAGCTTCCTC-ATTATATTA-AGCAAGCCC-CAAAGCTTAAGAGCCATTCAAATAAGGCACAGCTTATTTGAAATAGGATACAACCTACAACACAGGGTTAAACAAGA-GTAAACTAAG-TAAAGTGGGCCTAAAAGCAGCCACCTTTTAAAAAGCGTTAAAGCTTAACTATTTTCTACTCATAA-TTCCTCTAACCCCCACTAACCCTTCAT-TATTACTGAATAACTTTATAATTCATATAAAAGCTATTATGCTAGAACTAGTAACAAGAAATTGCCT-TTCTCCTAAATGTAAACGTAAACCAAAATAGACCATCTATTGGTTATCAACGCAAATGCTAAAATCATAGCAACACTCAC------TAGAAAATCCTATGACCTCCCAACGTTAACCTTACACTAGAACATTCCAGGAAAGATTAAAAGAAAAAGAAGGAACTCGGCAAATCTTAGCCTCGCCTGTTTACCAAAAACATCGCCTCTTGACAAA-CATAAGAGGTCCAGCCTGCCCAGTGACAAAGTTCAACGGCCGCGGTACCCTAACCGTGCGAAGGTAGCATAATCACTTGTTCTTTAAATGAGGACTAGTATCAACGGCACTACGAGGGCTATACTGTCTCCTTTCTCCAATCAGTGAAACTGATCTCCCCGTGAAGAAGCGAGGATCAAAATATAAGACGAGAAGACCCCATGGAGCTTTAAACTCATCATACATCTCT-GTGCTCC-CCATCATC-ACACAC-AAGAAATC-TGTATGCTAGTTTTAGGTTGGGGGGACCACGGAGTAAAATTTAACCTCCACAACAAATGGGCTAACACCCTTATCTACGAGATACAACTCTAAGAATTACTAAACTAATGTTT-ATGACCCGATAA-TTCGATCAATGAACCAAGTTACCCTGGGGATAACAGCGCAATCTACTTCAAGAGCCCATATCGACAAGTAGGTTTACGACCTCGATGTTGGATCAGGGTATCCTAGTGGTGCAGCCGCTACTAACG-GTTCGTTTGTTCAACG auroraMVZ13957 GCCGTAAACAATTAACTTACACC-TCCAACGCCAGGGAATTACGAGCAATGCTTAAAACCCAAAGGATTTGACGGTGTCCCACCCCACTAGAGGAGCCTGTTCTATAATCGATGATCCCCGCTTCACCCGACCGTTCCTCGCCTATCAGTCTGTATACCTCCGTCGAAAGCTTACCATGTGAACGCCA-GCAGTAAGCTTAATGATATCAACACGTCAGGTCAAGGTGCAGCTTAAGGAACGGGAAGCAATGGGCTACAATTTCTAATCTAGAACAAACGAAAGATTATGTGAAATCCTAATCATGAAGGTGGATTTAGTAGTAAAAAGGAAATATAGTGTTCTTTTTAACTCGGCTCTGGGACGCGTACACACCGCCCGTCACCCTCTTCAATAGTA--TTT-TCCTGTCCCTAACCC-CCTTA-CACATTTT-AGAAGAAGCAAGTCGTAACATGGTAAGTGTACTGGAAAGTGCACTTGGAATA-TTACAAAATGTAGCTTAACAAAAGCCCCTCGCTTACACCTAGGAAATATCTGTTAAACCCAGATCATTTTGAGCCTAAAATCTAGCCCA-CACTCTCGCATGACTTCTCTTACAAA-CAAAACATTTTAACATTTTAGTACAGGCGATCGAAAAA-TTTCTAAGCGCTTCAGATAAAGTACCGCAAGGGAAAAATGAAATAGAAATGAAATAATCCTAAAGCCCCAGACAGCAGAGATTTTACCTCGTACCTTTTGCATCATGGTCTAGCCAGTCTCCCC-AAGCAAAATGAAACTTTT-AGTTTGACACCCCGAAACTAAGCGAGCTACTTCAGAACAGCC-TAATGGGCCAACCCGTCTCTGTTGCAAAAGAGTGGGAAGATTCTTAAGTAGAGGTGATAAGCCTACCGAGCTTAGAGATAGCTGGTTGTTCAGAAAAAGAGTTTTAGCTCTACCTTAAGCTTCCTC-ATTATATTA-AGCAAACCC-CAAAGCTTAAGAGCCATTCAAATAAGGCACAGCTTATTTGAAACAGGGTACAACCTACAATACAGGGT-AAACAAGA-ATAAACTAAG-TAAAGTGGGGCCAAAAGCAGCCATCTTTAGAAAAGCGTTAAAGCTTAACTATCTCCTACTCATAA-TTCCTCTAACCCCCTCTAACCCTTCAT-TACTACTGAACAATTTTATA-TCCATATAAAAGCTATTATGCTAGAACTAGTAACAAGAAATTGCCT-TTCTCCTAAATGTAAACGTAAACCAAGATAGACCATCTATTGGTTATAAACGCAAATGCCAAAATCATAATAACATTCAC------TAGAAAATCCTATGACTCCC-AGCGTTAACCTTACACTAGAACATTTCAGGAAAGATTAAAAGAAAAAGAAGGAACTCGGCAAATCTTAGCCTCGCCTGTTTACCAAAAACATCGCCTCTTGACAAA-CATAAGAAGTCCAGCCTGCCCAGTGACAAAGTTCAACGGCCGCGGTACCCTAACCGTGCGAAGGTAGCATAATCACTTGTTCTTTAAATGAGGACTCGTATCAACGGCACTACGAGGGCTATACTGTCTCCCTTTTCCAATCAGTGAAACTGATCTCCCCGTGAAGAAGCGGGAATCAAAATATAAGACGAGAAGACCCCATGGAGCTTTAAACTCATCATACACCTCT-GTGCTCTTCCATCATC-ACACAC-AAGAAATC-TGTATGCTAGTTTTAGGTTGGGGGGACCACGGAGTACAATTTAACCTCCACAACAAATGGGCTAACACCCTTATCCATGAGATACAACTCTAAGAATTACTAAACTAATGTTT-ATGACCCGATAA-TTCGATCAATGAACCAAGTTACCCTGGGGATAACAGCGCAATCTACTTCAAGAGCCCATATCGACAAGTAGGTTTACGACCTCGATGTTGGATCAGGGTATCCTAATGGTGCAGCCGCTACTAATG-GTTCGTTTGTTCAACG cascadaeMVZ148946 GCCGTTTACAATTAACTTACACC-TCCAACGCCAGGGGATTACGAGCAATGCTTAAAACCCAAAGGATTTGACGGTGTCCCACCCCACTAGAGGAGCCTGTTCTATAATCGATGATCCCCGCTTCACCCGACCGTTCCTCGCTTATCAGTCTGTATACCTCCGTCGAAAGCTTACCATGTGAACGCCA-ACAGTAAGCTTAATGATATCAACACGTCAGGTCAAGGTGCAGCTTAAGGAACGGGAAGCAATGGGCTACAATTTCTAATTTAGAACAAACGAAAGATTATGTGAAATCCTAATCATGAAGGTGGATTTAGTAGTAAAAAGGAAATAGAGTGTCCTTTTTAACTCGGCTCTGGGACGCGTACACACCGCCCGTCACCCTCTTCAATAGTA-TTTT-TTCTGTCCCTAACCC-CCCTCGCACATTTT-AGAAGAGGCAAGTCGTAACATGGTAAGTGTACTGGAAAGTGCACTTGGAATA-TTACAAAATGTAGCTTAACAAAAGCCCCTCGCTTACACCTAGGAAATATCTGTTAAACCCAGATCATTTTGAGCCTAAAATCTAGCCCG-CACACTCGCATGACTTCTCTCACAAA-CAAAACATTTTAACATTTTAGTACAGGCGATCGAAAAA-TTTCTAAGCGCTTCAGATAAAGTACCGCAAGGGAAAAATGAAATAGAAATGAAATAACCCTAAAGCCCCAGACAGCAGAGATTTTACCTCGTACCTTTTGCATCATGGTCTAGCCAGTCTACCC-AAGCAAAATGAAACTTTC-AGTTTGACACCCCGAAACTAAGCGAGCTACTTCAGAACAGCC-TAATGGGCCAACCCGTCTCTGTTGCAAAAGAGTGGGAAGATTCTCAAGTAGAGGTGATAAGCCTACCGAGCTTAGAGATAGCTGGTTGTTCAGAAAAAGAGTTTTAGCTCTACCTTAAGCTTCCTC-ATTACACTA-AGCAAACCC-CTAAGCTTAAGAGCCATTCAAATAAGGCACAGCTTATTTGAAACAGGGCACAACCTACAACACAGGGT-AAACAAGA-GTAAACTAAG-CAAAGTGGGCCCAAAAGCAGCCACCTTTAGAAAAGCGTTAAAGCTTAACTATCCTCTACTCATAA-TTCCTCTAACCCCCTCTAACCCTTCAT-TATTACTGAACAATTTTATA-TCCCTATAAAAGCTATTATGCTAGAACTAGTAACAAGAAATTGCTT-TTCTCCTAAATGTAAACGTAAACCAAGATAGACCATCTGTTGGTTATCAACGCAAATGCCAAAATCATAACAACACTTAC------TAGAAAACCCTATGACTCCC-AGCGTTAACCTTACACTAGAACATTTCAGGAAAGATTAAAAGAAAAAGAAGGAACTCGGCAAATCTTAGCCTCGCCTGTTTACCAAAAACATCGCCTCTTGACAAA-CATAAGAGGTCCAGCCTGCCCAGTGACAAAGTTCAACGGCCGCGGTACCCTAACCGTGCGAAGGTAGCATAATCACTTGTTCTTTAAATGAGGACTCGTATCAACGGCACTACGAGGGCTATACTGTCTCCCTTTTCCAATCAGTGAAACTGATCTCCCCGTGAAGAAGCGGGGATCAAGATATAAGACGAGAAGACCCCATGGAGCTTCAAACTCATCATACATCTCT-GTGCTCTCCCATCATT-ATACAC-AAGAAATC-TGTATGCTAGTTTTAGGTTGGGGGGACCACGGAGTACAATTTAACCTCCACAACAAATGGGCTAATACCCTTATCCATGAGATACAACTCTAAGAATTATTAAACTAATGTTT-ATGACCCGATAA-TTCGATCAATGAACCAAGTTACCCTGGGGATAACAGCGCAATCTACTTCAAGAGCCCGTATCGACAAGTAGGTTTACGACCTCGATGTTGGATCAGGGTATCCTAGTGGTGCAGCCGCTACTAATG-GTTCGTTTGTTCAACG sylvaticaMVZ137426 GCCGTAAACAATTAATTTACACCCACCAGCGCCAGGGAATTACGAGCAATGCTTAAAACCCAAAGGATTTGACGGTGTCCCACCCCACTAGAGGAGCCTGTTCTATAATCGATGATCCCCGCTTCACCCGACCACTCCTTG-CTATCAGTCTGTATACCTCCGTCGAAAGCTTACCATGTGAACGCCC-AAAGTAGGCTTAATGACGTCAACACGTCAGGTCAAGGTGCAGCTTAAGGGGTGGGAAGTAATGGGCTACAATTTCTAATCTAGAACAAACGAAAGACTATGTGAAATACTAGTCATGAAGGTGGATTTAGTAGTAAAAAGAAAGTAGAGTGTTCTTTTTAACCCGGCTCTGGGACGCGTACACACCGCCCGTCACCCTCTTCAATAGTA-ATTTATCCTGTTTCTAACCC-ACTAC-TACATTTT-AGAAGAGGCAAGTCGTAACATGGTAAGTGTACTGGAAAGTGCACTTGGAATA-C-ACAAAATGTAGCTTAATAAAAGCCCCTCGCTTACACCGAAGAGATACCCGTTTAATTCGGATCATTTTGAGCTTCAAATCTAGCCGCACACACTCGCATG-CCCTCTTTCCAAA-CAAAACATTTTAATATTATAGTACAGGCGATCGAAAAA-TT-CTTAGCGCTTCAGACAAAGTACCGCAAGGGAAAGATGAAATAGAAATGAAATAACCTTAAAGCCTTAAATAGCAGAGACACCCCCTCGTACCTTTTGCATCATGGTCTAGCTAGTCTACCC-AAGCAAAATGAAACTTTT-AGTTTGACATCCCGAAACTAAGTGAGCTACTTCAAAACAGCCCTAAGGGGCCAACCCGTCTCTGTTGCAAAAGAGTGGGAAGATTTTCAAGTAGAGGTAATAAGCCTACCGAACTTAGAGATAGCTGGTTGTTCAGGAAGAGAGTTTTAGCTCTACCTTAAGCTTCCCC-ATTTTACCA-AAAAATGCCCCAAAGCTTAAGAGCTATTCAAATAAGGCACAGCTTATTTGAAATAGGGTACAACCTCCAACATCGGGT-AAATTATA-GTAATTAAAA-TGAAGTGGGCCTAAAAGCAGCCATCTTTAAAAAAGCGTTAAAGCTTAATTATTCAT-ACTAAAAAATTCCTTAAATCCTAATTAACCCCTCAT-TTATACTGAACTGCTTTATA-T-TTTATAAAAGTAATAATGCTGGAACTAGTAACAAGAAATTGCCT-TTCTCCCAAATGTAAGCATAAACCAAAATGGACCATCTATTGGTAATTAACGTAAATGCAAAAACTATAGTAACACAAC-------TAGAAAACCCTATTATTATT-AGCGTTAACCTTACACTAGAACATTACAGGAAAGATTAAAAGAGAAAGAAGGAACTCGGCAAATTTTAGCCTCGCCTGTTTACCAAAAACACCGCTTCTTGA-AAAACATAAGAGGTCCAGCCTGCCCAGTGACAAAGTTCAACGGCCGCGGTACCCTAACCGTGCGAAGGTAGCATAATCACTTGTCCTTTAAATAGGGACTAGTATCAACGGCATCACGAGGGCTATACTGTCTCCTTTCTCCAATCAGTGAAACTGATCTCCCCGTGAAGAAGCGGGGATTAAACTATAAGACGAGAAGACCCCATGGAGCTTTAAACTCATCATACACCTTT-ATGACCTCACACCAAC-TGACCA-AAAAGACC-TGTATGCTAGTTTTAGGTTGGGGGGACCACGGAGTACAATTTAACCTCCACAACAAATGGGCTAATACCCTTATCCACGAGATACACCTCTAAGAATTACTAAACTAATGTTTAATGACCCAATAA-TTTGATCAATGAACCAAGTTACCCTGGGGATAACAGCGCAATCTACTTCAAGAGCCCCTATCGACAAGTAGGTTTACGACCTCGATGTTGGATCAGGGTATCCTAGTGGTGCAGCCGCTACTAACG-GTTCGTTTGTTCAACG sylvaticaDMH84R43 GCCGTAAACAATTAATTTACACCCACCAGCGCCAGGGAATTACGAGCAATGCTTAAAACCCAAAGGATTTGACGGTGTCCCACCCCACTAGAGGAGCCTGTTCTATAATCGATGATCCCCGCTTCACCCGACCACTCCTTG-CTATCAGTCTGTATACCTCCGTCGAAAGCTTACCATGTGAACGCCC-AAAGTAGGCTTAATGACGTCAACACGTCAGGTCAAGGTGCAGCTTAAGGGGTGGGAAGTAATGGGCTACAATTTCTAATCTAGAACAAACGAAAGACTATGTGAAATACTAGTCATGAAGGTGGATTTAGTAGTAAAAAGAAAGTAGAGTGTTCTTTTTAACTCGGCTCTGGGACGCGTACACACCGCCCGTCACCCTCTTCGATAGTA-ATTTATCCTGTTTCTAACCC-ACTAC-TACGTTTT-AAAAGAAGCAAGTCGTAACATGGTAAGTGTACTGGAAAGTGCACTTGGAATA-C-ACAAAATGTA-CTTAATAAAAGCCCCTCGCTTACACCGAAGAGATACCCGTTTAATTCGGATCATTTTGAGCTTCAAATCTAGCCGCACACACTCGCATG-CCCTCTTTCCAAA-CAAAACATTTTAATATTATAGTACAGGCGATCGAAAAA-TT-CTTAGCGCTTCAGACAAAGTACCGCAAGGGAAAGATGAAATAGAAATGAAATAACCTTAAAGCCTTAAATAGCAGAGACACCCCCTCGTACCTTTTGCATCATGGTCTAGCTAGTCTACCC-AAGCAAAATGAAACTTTT-AGTTTGACATCCCGAAACTAAGTGAGCTACTTCAAAACAGCCCTAAGGGGCCAACCCGTCTCTGTTGCAAAAGAGTGGGAAGATTTTCAAGTAGAGGTGATAAGCCTACCGAACTTAGAGATAGCTGGTTGTTCAGGAAGAGAGTTTTAGCTCTACCTTAAGCTTCCCC-ATTTTACCA-AAAA-TGCCCCAAAGCTTAAGAGCTATTCAAATAAGGCACAGCTTATTTGAAATAGGGTACAACCTCCAACATCGGGT-AAATTATA-GTAATTAAAA-TGAAGTGGGCCTAAAAGCAGCCACCTTTAAAAAAGCGTTAAAGCTTAATTATTTAT-ACTAAAAA-TTCCTTAAATCCTAATTAACCCCTCAT-TTATACTGAACTGCTTTATA-T-TTTATAAAAGTAATAATGCTGGAACTAGTAACAAGAAATTGCCT-TTCTCCCAAATGTAAGCATAAACCAAAATGGACCATCTATTGGTAATTAACGTAAATGCAAAAACTATAGTAACACAAC-------TAGAAAACCCTATTATTATT-AGCGTTAACCTTACACTAGAACATTACAGGAAAGATTAAAAGAGAAAGAAGGAACTCGGCAAATTTTAGCCTCGCCTGTTTACCAAAAACACCGCCTCTTGA-AAAACATAAGAGGTCCAGCCTGCCCAGTGACAAAGTTCAACGGCCGCGGTACCCTAACCGTGCGAAGGTAGCATAATCACTTGTCCTTTAAATAGGGACTAGTATCAACGGCATCACGAGGGCTATACTGTCTCCTTTCTCCAATCAGTGAAACTGATCTCCCCGTGAAGAAGCGGGGATTAAACTATAAGACGAGAAGACCCCATGGAGCTTTAAACTCATCATACACCTTT-ATGACCTCACACCAAC-TAACCA-AAAAGACC-TGTATGCTAGTTTTAGGTTGGGGGGACCACGGAGTACAATTTAACCTCCACAACAAATGGGCTAATACCCTTATCCACGAGATACACCTCTAAGAATTACTAAACTAATGTTTAATGACCCAATAA-TTTGATCAATGAACCAAGTTACCCTGGGGATAACAGCGCAATCTACTTCAAGAGCCCCTATCGACAAGTAGGTTTACGACCTCGATGTTGGATCAGGGTATCCTAGTGGTGCAGCCGCTACTAACG-GTTCGTTTGTTCAACG septentrionalesDCC3588 GCCGTAAACAATTAATTTACACCAATAAGCGCCAGGGAATTACGAGCAATGCTTAAAACCCAAAGGATTTGACGGTGTCCCACCCCACTAGAGGAGCCTGTTCTATAATCGATGATCCCCGATACACCCGACCATTTCTCGCTTATCAGTCTGTATACCTCCGTCGAAAGCTTACCATGTGAACGCCC-GCAGTAGGCTTAATGACGTCAGTACGTCAGGTCAAGGTGCAGCTTAAGAAATGGGAAGCAATGGGCTACAATTTCTAATTTAGAACAAACGAAAGACTGTATGAAATTACAATCATGAAGGTGGATTTAGTAGTAAAAAGAAAGTAGAGTGTTCTTTTTAACCCGGCTCTGGGACGCGTACACACCGCCCGTCACCCTCTTCGATAGTA-TCTTATCTAGTTCCTAACCT-ATTAC-TACATTTT-AGAAGAGGCAAGTCGTAACATGGTAAGTGTACTGGAAAGTGCACTTGGTTTA-T-ACAAGATGTAGCTTAATAAAAGCCTCTCGCTTACACCGAGAAAATACCCGTTTAAACCGGATCATTTTGAGCCTAAAATCTAGCCTAACACACTCGCATGACCCCCCCCCCTAA-ATAATCATTTTAATATTATAGTACAGGCGATCGAAAAA-TTTCTAAGCGCTTCAGACAAAGTACCGCAAGGGAAAGATGAAATAGAAATGAAATAATCTTAAAGCCCAAAACAGCAGAGATAATCTCTCGTACCTTTTGCATCATGGTCTAGCCAGTCTACCC-AAGCGAAATAAAACTTTT-AGTTTGCCATCCCGAAACTAAGTGAGCTACTTCAGAACAGTCCTACGGGACCAACCCATCTCTGTTGCAAAAGAGTGGGAAGATTTTTAAGTAGAGGTGATAAGCCTACCGAACTTAGAGATAGCTGGTTATTCAGGAAGAGAGTTTTAGCTCTACCTTAAGCTTTCTCTATCAAACTA-AAGAAACCC-CAAAGCTTAAGAGCTATTCAAATAAGGCACAGCTTATTTGAAATAGGAAACAACCTCCAACACTGGGT-AAATAATA-GTAATTTAAA-TAAAGTGGGCCTAAAAGCAGCCACCTTTTGAAAAGCGTTAAAGCTTAACTATAAACTACTAATAA-TGCCTTAAATATTAACTAACCCTTCAT-CCCTACTGAATCACTTTATA-TTTTTATAAAAGCTATTATGCTAGAACTAGTAACAAGAAATTGGCT-TTCTCCTAAATGTATGTATAAACCAAAATGGACCATCCACTGGTAATTAACGCAAATGCAAAATTTATAACAACACAAC-------TAGAAAACCCTATAACTACA-AACGTTAAACTTACACTAGAACATTCCAGGGAAGATTAAAAGAGAAAGAAGGAACTCGGCAAATTTTAGCCTCGCCTGTTTACCAAAAACATCGCCTCTTGATTAAACATAAGAGGTCCAGCCTGCCCAGTGACAAAGTTCAACGGCCGCGGTACCCTAACCGTGCGAAGGTAGCATAATCACTTGTCCTTTAAATAGGGACTTGTATCAACGGCACCACGAGGGCTATACTGTCTCCTTTCTCCAATCAGTGAAACTGATCTCCCCGTGAAGAAGCGGGGATTTTATTATAAGACGAGAAGACCCCATGGAGCTTTAAACTCATCATACATCTCT-ATGCACT-ACATCAAC-CCACAT-AAGAAATC-TGTATGCTAGTTTTAGGTTGGGGGGACCACGGAGTATAATTTAACCTCCACAACAAATGGGCTAACACCCTTATCTACGAGATACACCTCTAAGAATTACTAAACTAATGTTTAATGACCCAATAA-TTTGATCAATGAACCAAGTTACCCTGGGGATAACAGCGCAATCTACTTCAAGAGCCCCTATCGACAAGTAGGTTTACGACCTCGATGTTGGATCAGGGTATCCTAGTGGTGCAGCCGCTACTAATG-GTTCGTTTGTTCAACG grylioMVZ175945 ???????????????TTCACACCAATAAGCGCCAGGGAATTACGAGCAACGCTTAAAACCCAAAGGATTTGACGGTGTCCCACCCAACTAGAGGAGCCTGTTCTATAATCGATGATCCCCGATATACCCGACCATTTCTCGCTTATCAGTCTGTATACCTCCGTCGAAAGCTTACCATGTGAACGTCT-GCAGTAAGCTTAATGACGTCAACACGTCAGGTCAAGGTGCAGCTTAAGAAATGGGAAGTAATGGGCTACAATTTCTAACTTAGAACAAACGAAAGACTGTATGAAATTACAATCATGAAGGTGGATTTAGTAGTAAAAAAAAAGTATAGTGTTCTTTTTAACCCGGCTCTGGGACGCGTACACACCGCCCGTCACCCTCTTCAACAGTT-TACCATCCCGTTTCTAACCC-ATCAT-TACATTTT-AGAAGAAGCAAGTCGTAACATGGTAAGTGTACTGGAAAGTGCACTTGGTTTA-T-ACAAAATGTAGCTTAATAAAAGCCTCTCGCTTACACCGAAAAAATATCCGTTTAACCCGGATCATTTTGAGCCTAAAATCTAGCCTAACTCATTCGCATGACCCCCTCCACAAA-CAAATCATTTTAACATTATAGTACAGGCGATCGAAAAA-TTTCTAAGCGCTTCAGACAAAGTACCGCAAGGGAAAAATGAAATAGAAATGAAATAACTTTAAAGCCCTAAACAGCAGAGATAACCTCTCGTACCTTTTGCATCATGGTCTAGCTAGTCCACCC-AAGCAAAATAAAACCTTT-AGTTTGCCATCCCGAAACTAAGTGAGCTACTTCAGAACAGTCCTACAGGACCAACCCGTCTCTGTTGCAAAAGAGTGGGAAGATTCTTAAGTAGAGGTGATAAGCCTACCGAACTTAGAGATAGCTGGTTGTTCAGGAAATGAGTTTCAGCTCTACCTTAAGCTTTCTGTATCAAACTG-AAGAACCAC-CAAAGCTTAAGAGCTATTCAAATAAGGCACAGCTTATTTGAAACAGGAGACAACCTTTAATACCGGGT-AAACAACA-GTAACCCAAA-CAAAGTGGGCCTAAAAGCAGCCACCTTTTAAAAAGCGTTAAAGCTTAACTATTAACCACTAACAA-TACCCTAAATATTTATTAACCCTTCAT-CTCTACTGAACTACTTTATA-TTCTTATAAAGGCTATCATGCTAGAACTAGTAACAAGAAGTCGATT-TTCTCCTAAATGTAAGTATAAACCAAAACAGACCATCTATTGGTGATTAACGCAAATGCAAA-TTTATAGCAACACAAC-------TAGAAAACCCTATAACTATA-AGCGTTAACCTTACACCAGAACATTACAGGAAAGATTAAAAGAGAAAGAAGGAACTCGGCAAATTTTAGCCCCGCCTGTTTACCAAAAACATCGCCTCTTGATAAAACATAAGAGGTCCAGCCTGCCCAGTGACAAAGTTCAACGGCCGCGGTATCCTAACCGTGCGAAGGTAGCATAATCACTTGTCCTTTAAATAGGGACTTGTATCAACGGCACCAC?AGGGCTATACTGTCTCCTTTCTCCAATCAGTGAAACTGATCTCCCCGTGAAGAAGCGGGGATTCTACTATAAGACGAAAAAACCCCATGGAGCTTTAAACTCACCATACACCTTCTATGTTCT-GTATCAAC-TTACAC-AAAAAATC-TGTATGCTAGTTTTAGGTTGGGGGGACCACGGAGTATAATTTAACCTCCACAACAAATGGGCTAACACCCTTATCTACGAAACACACCTCTAAGAATTACTAAACTAATGTTTAATGACCCAATAA-TTTGATCAATGAACCAAGTTACCCTGGGGATAACAGCGCAATCTACTTCAAGAGCCCCTATCGACAAGTATGTTTACGACCTCCATGTTGGATCAGGGTATCCTAATGGTGCAACCGCTACTAATG-GTTCGTTTGTTCAACG virgatipesMVZ175944 ACCGTAAACAATTAATTTACACCAATAAGCGCCAGGGAATTACGAGCAACGCTTAAAACCCAAAGGATTTGACGGTGTCCCACCCCACTAGAGGAGCCTGTTCTATAATCGATGATCCCCGCTACACCCGACCATTTCTCGCTTATCAGTCTGTATACCTCCGTCGAAAGCTTACCATGTGAACGTCC-GCAGTAGGCTTAATGATGTCAATACGTCAGGTCAAGGTGCAGCTTAAGAAATGGGAAGTAATGGGCTACAATTTCTAACTTAGAACAAACGAAAGACTGTGTGAAATCACAATCATGAAGGTGGATTTAGTAGTAAAAAGAAAATAGAGTATTCTTTTTAACCCGGCTCTGGGACGCGTACACACCGCCCGTCACCCTCTTCGATAGTA-TCATATTCAGTTCTTAACCC-ATCAT-TACGTTTT-AGAAGAAGCAAGTCGTAACATGGTAAGTGTACTGGAAAGTGCACTTGGTTTA-T-ACAAGATGTAGCTTAATAAAAGCCTCTCGCTTACACCGAGAAAATGTCTGTTTAACCCAGATCATCTTGAGCCTAAAATCTAGCCTAATACATTCGCATGA-CCCCTTTACAAA-CAAATCATTTTACCATTCTAGTACAGGCGATCGAAAAA-TTTCTAAGCGCTTCAGATAGAGTACCGCAAGGGAAAGATGAAATAGAAATGAAATAATTTTAAAGCAATAAATAGCAGAGACACCCCCTCGTACCTTTTGCATCATGGTCTAGCCAGTCTACCC-AAGCAAAATAAAACTTTC-AGTTTGCCATCCCGAAACTAAGTGAGCTACTTCAGAACAGTCCTACGGGACCAACCCATCTCTGTTGCAAAAGAGTGGGAAGATTTTTAAGTAGAGGTGATAAGCCTACCGAACTTAGAGATAGCTGGTTATTCAGGAAAAGAGTTTTAGCTCTACCTTAAGTTTTTTCTATCAAGCTA-AAGAAACCC-CAAAACTTAAGAGCTATTCAAATAAGGCACAGCTTATTTGAAACAGGGAACAACCTCTAACACCGGGT-AAATAGTA-GTAATCTAAA-TAAAGTGGGCCTAAAAGCAGCCATCTTTTAAAAAGCGTTAAAGCTTAACTATAAATTACTAATAA-TGCCTTAAACATTAATAAACCCTTCAT-TCCTACTGAATTACTTTATA-TATCTATAAAAGCTATTATACTAGAACTAGTAATAAGAAATTGATT-TTCTCCTAAATGTAAATATAAACCAAAATGGACCATCCATTGGTAATTAACGCAAATGCAAAATTTATAGCAACACAAC-------TAGAAAACCCTATAATTATG-AACGTTAACCTTACACTAGAACATTCCAGGAAAGATTAAAAGAGAAAGAAGGAACTCGGCAAATTTTAGCCTCGCCTGTTTACCAAAAACATCGCCTCTTGATAAAACATAAGAAGTCCAGCCTGCCCAGTGACAAAGTTCAACGGCCGCGGTACCCTAACCGTGCGAAGGTAGCATAATCACTTGTCCTTTAAATAGGGACTCGTATCAACGGCACCACGAGGGCTATACTGTCTCCTTTCTCCAATCAATGAAACTGATCTTCCCGTGAAGAAGCGGGAATCCTAATATAAGACGAGAAGACCCCATGGAGCTTTAAACTCACCATACACCTCT-ATGCCCT-ACATCAAC-TTACCC-AAAAAACC-TGTATGCTAGTTTTAGGTTGGGGGGACCACGGATTATAATTCAACCTCCATAACAAATGGGCTAACACCCTTATCTACGAAATACACCTCTAAGAATTACTAAACTAATGTTTAATGACCCAATAA-TTTGATCAATGAACCAAGTTACCCTGGGGATAACAGCGCAATCTACTTCAAGAACCCCTATCGACAAGTAGGTTTACAACCTCCATGTTGGATCAGGGTATCCTAATGGTGCAGCCGCTACTAATG-GTTCGTTTGTTCAACG okaloosae GCCGTAAACAATTAATTTACACCAATAAGCGCCAGGGAATTACGAGCAATGCTTAAAACCCAAAGGATTTGACGGTGTCCCACCCCACTAGAGGAGCCTGTTCTATAATCGATGATCCCCGATATACCCGACCATTTCTCGCTTATCAGTCTGTATACCTCCGTCGAAAGCTTACCATGTGAACGCCT-GCAGTAGGCTTAATGACGTCAGTACGTCAGGTCAAGGTGCAGCTTAAGAAATGGGAAGTAATGGGCTACAATTTCTAACTTAGAACAAACGAAAGACTGTATGAAATTACAATCATGAAGGTGGATTTAGTAGTAAAAAGAAAGTAGAGTGTTCTTTTTAACCCGGCTCTGGGACGCGTACACACCGCCCGTCACCCTCTTCGATAGTA-TCTTATCCCGTTCCTAACCC-ACCAT-TACATTTT-AGAAGAGGCAAGTCGTAACATGGTAAGTGTACTGGAAAGTGCACTTGGTTTA-T-ACAAGATGTAGCTTAATAAAAGCCTCTCGCTTACACCGAGAAAATGTCCGTTTAATCCGGATCATCTTGAGCCTAAAATCTAGCCTAACACACTCGCATGTCCCCCTTACCAAA-CAAATCATTTTAACATTATAGTACAGGCGATCGAAAAA-TTTCTAAGCGCTTCAGATAAAGTACCGCAAGGGAAAGATGAAATAGAAATGAAATAATTTTAAAGCCTTAACCAGCAGAGACACCCTCTCGTACCTTTTGCATCATGGTCTAGCCAGTCTACTC-AAGCAAAATAAAACTTTC-AGTTTGCCATCCCGAAACTAAGTGAGCTACTTCAGAACAGTCCTATGGGACCAACCCATCTCTGTTGCAAAAGAGTGGGAAGATTCTTAAGTAGAGGTGATAAGCCTACCGAACTTAGAGATAGCTGGTTGTTCAGGAAAAGAGTTTTAGCTCTACCTTAAGCTCTCTCTATCAGACTA-AAGAAACCC-CAAAGCTTAAGAGCTATTCAAATAAGGCACAGCTTATTTGAAACAGGAAACAACCTCCAACACCGGGT-AAATAATA-GTAATCTAAA-TAAAGTGGGCCTAAAAGCAGCCACCTTTCAAAAAGCGTTAAAGCTTAACTATAAATTACTAATAA-TGCCTTAAATATCTATTAACCCTTCAT-TCCTACTGAACTATTTTATA--TTTTATAAAAGCAATCATGCTAGAACTAGTAACAAGAAATTGATT-TTCTCCTAAATGTAAGTATAAACCAAAATGGACCATCCATTGGTAATTAACGCAAATGCAAAATTTATAGTAACATATC-------TAGAAAACCCTATAAACACA-AACGTTAACCTTACACTAGAACATTCCAGGAAAGATTAAAAGAGAAAGAAGGAACTCGGCAAATTTTAGCCTCGCCTGTTTACCAAAAACATCGCCTCTTGATAAAACATAAGAGGTCCAGCCTGCCCAGTGACAAAGTTCAACGGCCGCGGTACCCTAACCGTGCGAAGGTAGCATAATCACTTGTCCTTTAAATAGGGACTTGTATCAACGGCACCACGAGGGCTATACTGTCTCCTTTCTCCAATCAGTGAAACTGATCTCCCCGTGAAGAAGCGGGGATTCTACTATAAGACGAGAAGACCCCATGGAGCTTTAAACTCACCATACACCTCT-ATGCCCC-ACATCAAC-TTACAC-AAGAAATC-TGTATGCTAGTTTTAGGTTGGGGGGACCACGGAGTATAATTGAACCTCCATAACAAATGGGCTAACACCCTTATCTACGAGATACACCTCTAAGAATTACTAAACTAATGTTTAATGACCCAATAA-TTTGATCAATGAACCAAGTTACCCTGGGGATAACAGCGCAATCTACTTCAAGAGCCCCTATCGACAAGTAGGTTTACGACCTCGATGTTGGATCAGGGTATCCTAGTGGTGCAGCCGCTACTAATG-GTTCGTTTGTTCAAC- clamitansJSF1118 GCCGTAAACAATTAATTTACACCAATAAGCGCCAGGGAATTACGAGCAATGCTTAAAACCCAAAGGATTTGACGGTGTCCCACCCCACTAGAGGAGCCTGTTCTATAATCGATGATCCCCGATATACCCGACCATTTCTCGCTTATCAGTCTGTATACCTCCGTCGAAAGCTTACCATGTGAACGCCT-GCAGTAGGCTTAATGACGTCAATACGTCAGGTCAAGGTGCAGCTTAAGAAATGGGAAGTAATGGGCTACAATTTCTAACTTAGAACAAACGAAAGACTGTATGAAATTACAATCATGAAGGTGGATTTAGTAGTAAAAAAAAAGTATAGTGTTCTTTTTAACCCGGCTCTGGGACGCGTACACACCGCCCGTCACCCTCTTCGATAGTA-TCTTATCCCGTTCCTAACCC-ATCAT-TACATTTT-AGAAGAGGCAAGTCGTAACATGGTAAGTGTACTGGAAAGTGCACTTGGTTTA-T-ACAAGATGTAGCTTAATAAAAGCCTCTCGCTTACACCGAGAAAATGTCCGTTTAATCCGGATCATCTTGAGCCTAAAATCTAGCCTAACACACTCGCATGTCCCCCTTACCAAA-CAAATCATTTTAACATTATAGTACAGGCGATCGAAAAA-TTTCTAAGCGCTTCAGACAAAGTACCGCAAGGGAAAGATGAAATAGAAATGAAATAATTTTAAAGCCCTAACCAGCAGAGACACCCTCTCGTACCTTTTGCATCATGGTCTAGCCAGTCTACTC-AAGCAAAATAAAACTTTC-AGTTTGCCATCCCGAAACTAAGTGAGCTACTTCAGAACAGTCCTATGGGACCAACCCATCTCTGTTGCAAAAGAGTGGGAAGATTCTTAAGTAGAGGTGATAAGCCTACCGAACTTAGAGATAGCTGGTTGTTCAGGAAAAGAGTTTTAGCTCTACCTTAAGCTCTCTCTATCAGACTA-AAGAAACCC--AAAGCTTAAGAGCTATTCAAATAAGGCACAGCTTATTTGAAACAGGAAACAACCTCTAACACCGGGT-AAATAATA-GTAATCTAAA-TAAAGTGGGCCTAAAAGCAGCCACCTTTTAAAAAGCGTTAAAGCTTAACTATAAATTACTAATAA-TGCCTTAAATATCCATTAACCCTTCAT-TCCTACTGAACTATTTTATA--TTTTATAAAAGCAATCATGCTAGAACTAGTAACAAGAAATTGATT-TTCTCCTAAATGTAAGTATAAACCAAAATGGACCATCTATTGGTAATTAACGCAAATGCAAAATTTATAGCAACATATC-------TAGAAAACCCTATAACCACA-AACGTTAACCTTACACTAGAACATTCCAGGAAAGATTAAAAGAGAAAGAAGGAACTCGGCAAATTTTAGCCTCGCCTGTTTACCAAAAACATCGCCTCTTGATAAAATATAAGAGGTCCAGCCTGCCCAGTGACAAAGTTCAACGGCCGCGGTACCCTAACCGTGCGAAGGTAGCATAATCACTTGTCCTTTAAATAGGGACTTGTATCAACGGCACCACGAGGGCTATACTGTCTCCTTTCTCCAATCAGTGAAACTGATCTCCCCGTGAAGAAGCGGGGATTCTACTATAAGACGAGAAGACCCCATGGAGCTTTAAACTCACCATACACCTCT-ATGCCCC-ACATCAAC-TTACAC-AAGAAATC-TGTATGCTAGTTTTAGGTTGGGGGGACCACGGAGTATAATTGAACCTCCATAACAAATGGGCTAACACCCTTATCTACGAAATACACCTCTAAGAATTACTAAACTAATGTTTAATGACCCAATAA-TTTGATCAATGAACCAAGTTACCCTGGGGATAACAGCGCAATCTACTTCAAGAGCCCCTATCGACAAGTAGGTTTACAACCTCCATGTTGGATCAGGGTATCCTAATGGTGCAGCCGCTACTAATG-GTTCGTTTGTTCAACG heckscheriMVZ164908 GCCGTAAACAATTAACTCACACCAATAAGCGCCAGGGAATTACGAGCAATGCTTAAAACCCAAAGGATTTGACGGTGTCCCACCCCACTAGAGGAGCCTGTTCTATAATCGATGATCCCCGATATACCTAACCATTTCTCGCTTATCAGTCTGTATACCTCCGTCGAAAGCTTACCATGTGAACGCCT-GCAGTAGGCTTAATGACGTCAATACGTCAGGTCAAGGTGCAACTTAAGAAGTGGAAAGTAATGGGCTACAATTTCTATCTTAGAACAAACGAAAGACTGTATGAAATTACAGTCATGAAGGTGGATTTAGCAGTAAAAAGAAAGTAGAGTGTTCTTTTTAACCCGGCTCTGGGACGCGTACACACCGCCCGTCACCCTCTTCGATAGTA-TCTTATCCCGTTCCTAACTC-ATTAT-TACATTTT-AGAAGAGGCAAGTCGTAACATGGTAAGTGTACTGGAAAGTGCACTTGGTTTA-T-ACAAAATGTAGCTTAATAAAAGCCTCTCGCTTACACCGAGAAAATGTCCGTTTAACCCGAATCATTTTGAGCCTAAAATCTAGCCTAACACATTCACATGACCCCCTTTTTAAA-CAAATCATTTTAACATTATAGTACAGGTGATCGAAAAA-TTTCTAAGCGCTTCAGACAAAGTACCGCAAGGGAAAGATGAAATAGAAATGAAATAATTTTCAAGCCCTAATCAGCAGAGATAATCTCTCGTACCTTTTGCATCATGGTCTAGCCAGTCTGCTC-AAGCAAAATAAAACTTTT-AGTTTGTCATCCCGAAACTAAGTGAGCTACTTCAGAACAGTCCAATGGGACCAACCCATCTCTGTTGCAAAAGAGTGGGAAGATTCTTAAGTAGAGGTGATAAGCCTACCGAACTTAGAGATAGCTGGTTGTTCAGGAAAAGAGTTTTAGCTCTACCTTAAGCTCTCTTTATCAAACTA-AAGAAACCC--GAAACTTAAGAGCTATTCAAATAAGGCACAGCTTATTTGAAACAGGAAACAACCTCCAACACCGGGT-AAATAATA-GTAATCTAAA-TAAAGTGGGCCTAAAAGCAGCCACCTTCTAAAAAGCGTTAAAGCTTAACTATATATTACTAATAA-TGCCTTAAATATTAATTAACCCTTCCC-TCCTACTGAACTATTTTATA-TCCCTATAAAAGCAATCATGCTAGAACTAGTAACAAGAAATTGATT-TTCTCCTAAATGTAAATATAAACCAAAATGGACTATCCACTGGTAATTAACGTAAATGCAGAATTTATAGCAACATAAC-------TAGAAAACCCTATAACTACA-AACGTTAACCTCACACTAGAACATTGCAGGAAAGATTAAAAGAGAAAGAAGGAACTCGGCAAATTTTAGCCTCGCCTGTTTACCAAAAACATCGCCTCTTGATGAAACATAAGAGGTCCAGCCTGCCCAGTGACAAAGTTCAACGGCCGCGGTACCCTAACCGTGCGAAGGTAGCATAATCACTTGTCCTTTAAATAGGGACTTGTATCAACGGCACCACGAGGGCTATACTGTCTCCTTTCTCCAATCAGTGAAACTGATCTCCCCGTGAAGAAGCGGGGATTCTATTATAAGACGAGAAGACCCCATGGAGCTTTAAACTCACCATACACCTCT-ACGCCCT-ACATCAAC-TTACAC-AAGAAATC-TGTATGCTAGTTTTAGGTTGGGGGGACCACGGAGTATAACTAAACCTCCGTAACAAATGGGCTAACACCCTTATCTACGAGATACACCTCTAAGAATTACTAAACTAATGTTTAATGACCCAATAA-TTTGATCAATGAACCAAGTTACCCTGGGGATAACAGCGCAATCTACTTCAAGAGCCCCTATCGACAAGTAGGTTTACGACCTCGATGTTGGATCAGGGTATCCTAATGGTGCAGCCGCTACTAATG-GTTCGTTTGTTCAACG catesbianaX12841 GCCGTAAACAATTAATTTACACCAATAAGCGCCAGGGAATTACGAGCAATGCTTAAAACCCAAAGGATTTGACGGTGTCCCACCCCACTAGAGGAGCCTGTTCTATAATCGATGATCCCCGATATACCCGACCATTTCTCGCTTATCAGTCTGTATACCTCCGTCGAAAGCTTACCATGTGAACGCTT-GCAGTAGGCTTAATGACGTCAATACGTCAGGTCAAGGTGCAGCTTAAGAAATGGGAAGTAATGGGCTACAATTTCTAATCTAGAACAAACGAAAGACTATATGAAATTATAATCATGAAGGTGGATTTAGTAGTAAAAAGAAAATAGAGTGTTCTTTTTAACCCGGCTCTGGGACGCGTACACACCGCCCGTCACCCTCTTCGATAGTA-TCTCACCCCGTTCCTAACCC-ACTAT-TACATTTT-AGAAGAGGCAAGTCGTAACATGGTAAGTGTACTGGAAAGTGCACTTGGTTTA-T-ACAAAATGTAGCTTAACAAAAGCCTCTCGCTTACACCGAGAAAATGTCCGTTTAACCCGAACCGTTTTGAGCCCAAAATCTAGCCTAACACATTCGCATGACCCCCTTACCAAA-CAAATCATTTTAACATTATAGTACAGGCGATCGAAAAA-TTTCTAAGCGCTTCAGACAAAGTACCGCAAGGGAAAGATGAAATAGAAATGAAATAATCTTAAAGCCCTAATCAGCAGAGATAACCTCTCGTACCTTTTGCATCATGGTCTAGCCAGTCTACTC-AAGCAAAATAAAACTTTTTAGTTTGCCCTCCCGAAACTAAGTGAGCTACTTCAGAACAGTCCTATGGGACCAACCCATCTCTGTTGCAAAAGAGTGGGAAGATTCTTAAGTAGAGGTGATAAGCCTACCGAACTTAGAGATAGCTGGTTGTTCAGGAAAAGAGTCTTAGCTCTACCTTAAGCTCTCTCTATTAAACTA-A-GAAATCC-CAAAGCTTAAGAGCTATTCAAATAAGGCACAGCTTATTTGAAACAGGAAACAACCTCTAACACCGGGT-GAATTATA-GTAATCTCAA-TAAAGTGGGCCTAAAAGCAGCCACCTTTCAAAAAGCGTTAAAGCTTAACTATAAATTACTAATAA-TACCTAAAATATTAATTAACCCTTCAT-TCCTACTGAACTATTTTATA-TCCCTATAAAAGCAATTATGCTAGAACTAGTAACAAGAAATTGATT-TTCTCCTAAATGTAAGTATAAACCAAAATGGACCATCTGTTGGTGATTAACGCAAATGCAAAATCTATAGCAACATAAC-------TAGAAAACCCTATAACTACA-AACGTTAACCTTACACTAGAACATTCCAGGAAAGATTAAAAGAGAAAGAAGGAACTCGGCAAATTTTAGCCTCGCCTGTTTACCAAAAACATCGCCTCTTGATAAAACATAAGAGGTCCAGCCTGCCCAGTGACAAAGTTCAACGGCCGCGGTACCCTAACCGTGCGAAGGTAGCATAATCACTTGTCCTTTAAATAGGGACTTGTATCAACGGCACCACGAGGGCTATACTGTCTCCTTTCTCCAATCAGTGAAACTGATCTCCCCGTGAAGAAGCGGGGATTATAATATAAGACGAGAAGACCCCATGGAGCTTTAAACTCATCATACACCTCT-ATGCCCT-ATATCAAC-TTACAC-AAGAAACC-TGTATGCTAGTTTTAGGTTGGGGGGACCACGGAGTATAATTAAACCTCCATAACAAATGGGCTAACACCCTTATCTACGAGATACACCTCTAAGAATTACTAAACTAATGTTTAATGACCCAATAA-TTTGATCAATGAACCAAGTTACCCTGGGGATAACAGCGCAATCTACTTCAAGAGCCCCTATCGACAAGTAGGTTTACGACCTCGATGTTGGATCAGGGTATCCTAGTGGTGCAGCCGCTACTAATG-GTTCGTTTGTTCAACG catesbianaDMH84R2 GCCGTAAACAATTAATTTACACCAATAAGCGCCAGGGAATTACGAGCAATGCTTAAAACCCAAAGGATTTGACGGTGTCCCACCCCACTAGAGGAGCCTGTTCTATAATCGATGATCCCCGATATACCCGACCATTTCTCGCTTATCAGTCTGTATACCTCCGTCGAAAGCTTACCATGTGAACGCTT-GCAGTAGGCTTAATGACGTCAATACGTCAGGTCAAGGTGCAGCTTAAGAAATGGGAAGTAATGGGCTACAATTTCTAATCTAGAACAAACGAAAGACTATATGAAATTATAATCATGAAGGTGGATTTAGTAGTAAAAAGAAAATAGAGTGTTCTTTTTAACCCGGCTCTGGGACGCGTACACACCGCCCGTCACCCTCTTCGATAGTA-TCTCACCCCGTTCCTAACCC-ACTAT-TACATTTT-AGAAGAGGCAAGTCGTAACATGGTAAGTGTACTGGAAAGTGCACTTGGTTTA-T-ACAAAATGTAGCTTAACAAAAGCCTCTCGCTTACACCGAGAAAATGTCCGTTTAACCCGAACCGTTTTGAGCCCAAAATCTAGCCTAACACATTCGCATGACCCCCTTACCAAA-CAAATCATTTTAACATTATAGTACAGGCGATCGAAAAA-TTTCTAAGCGCTTCAGACAAAGTACCGCAAGGGAAAGATGAAATAGAAATGAAATAATCTTAAAGCCCTAATCAGCAGAGATAACCTCTCGTACCTTTTGCATCATGGTCTAGCCAGTCTACTC-AAGCAAAATAAAACTTTTTAGTTTGCCCTCCCGAAACTAAGTGAGCTACTTCAGAACAGTCCTATGGGACCAACCCATCTCTGTTGCAAAAGAGTGGGAAGATTCTTAAGTAGAGGTGATAAGCCTACCGAACTTAGAGATAGCTGGTTGTTCAGGAAAAGAGTCTTAGCTCTACCTTAAGCTCTCTCTATTAAACTA-A-GAAATCC-CAAAGCTTAAGAGCTATTCAAATAAGGCACAGCTTATTTGAAACAGGAAACAACCTCTAACACCGGGT-GAATTATA-GTAATCTCAA-TAAAGTGGGCCTAAAAGCAGCCACCTTTCAAAAAGCGTTAAAGCTTAACTATAAATTACTAATAA-TACCTAAAATATTAATTAACCCTTCAT-TCCTACTGAACTATTTTATA-TCCCTATAAAAGCAATTATGCTAGAACTAGTAACAAGAAATTGATT-TTCTCCTAAATGTAAGTATAAACCAAAATGGACCATCTGTTGGTGATTAACGCAAATGCAAAATCTATAGCAACATAAC-------TAGAAAACCCTATAACTACA-AACGTTAACCTTACACTAGAACATTCCAGGAAAGATTAAAAGAGAAAGAAGGAACTCGGCAAATTTTAGCCTCGCCTGTTTACCAAAAACATCGCCTCTTGATAAAACATAAGAGGTCCAGCCTGCCCAGTGACAAAGTTCAACGGCCGCGGTACCCTAACCGTGCGAAGGTAGCATAATCACTTGTCCTTTAAATAGGGACTTGTATCAACGGCACCACGAGGGCTATACTGTCTCCTTTCTCCAATCAGTGAAACTGATCTCCCCGTGAAGAAGCGGGGATTATAATATAAGACGAGAAGACCCCATGGAGCTTTAAACTCATCATACACCTCT-ATGCCCT-ATATCAAC-TTACAC-AAGAAACC-TGTATGCTAGTTTTAGGTTGGGGGGACCACGGAGTATAATTAAACCTCCATAACAAATGGGCTAACACCCTTATCTACGAGATACACCTCTAAGAATTACTAAACTAATGTTTAATGACCCAATAA-TTTGATGAATGAACCAAGTTACCCTGGGGATAACAGGGCAATCTACTTCAAGAGCCCCTATCGACAAGTAGGTTTACGACCTCGATGTTGGATCAGGGTATCCTAGTGGTGCAGCCGCTACTAATG-GTTCGTTTGTTCAACG maculataKU195258 GCCGTAAACAATTAATTTACACCAATCACCGCCAGGGGACTACGAGCAATGCTTAAAACCCAAAGGATTTGACGGTGTCCCACCCCACTAGAGGAGCCTGTTCTATAATCGATGATCCCCGCTATACCCGACCATTCCTCGCTTATCAGTCTGTATACCTCCGTCGAAAGCTTACCATGTGAACGTTT-ACAGTATGCTTAATGACGTCAACACGTCAGGTCAAGGTGCAGCTTAAGGAATGGGAAGTGATGGGCTACAATTTCTAATCTAAAACAAACGGAAAGCTATGTGAAATCTTAGCCATGAAGGTGGATTTAGTAGTAAAAAGAAAGTATAGTGTTCTTTGTAACCCGGCTCTGGGACGCGTACACACCGCCCGTCACCCTCTTCAATAGTA-TCTT-CTATGTTCCTAACCT-ATTAT-TACACCTT-AAAAAAGGCAAGTCGTAACATGGTAAGTGTACTGGAAAGTGCACTTGGTAAA-TAACAAGATGTAGCTTAATAAAAGCCCCTCGCTTACACCGAAGAAATATCTGTTCAAATCAGATCATTTTGAGCCTAAAATCTAGCCCGACATTATCGCATAACACCCCCTTCAAA-CAAAACATTTTCTTATTATAGTACAGGTGATCGAAAAA-TTTCTAAGCGCTTCAGATAAAGTACCGCAAGGGAAAAATGAAATAAGATTGAAATAATTTTAAAGCCTTAAATAGCAGAGACACCCCCTCGTACCTTTTGCATCATGGTCTAGTTAGTCTACTC-AAGCAAAATGAAATTTT--AGTTAGACATCCCGAAACTAAGCGAGCTACTTCAAAACAGCCTAAA-GAGCTAACCCGTCTCTGTTGCAAAAGAGTGGGAAGATTTTCAAGTAGAAGTGAAAAGCCTACCGAGCTTAGAGATAGCTGGTTGTTCAGGAAAAGAGTCTTAGCTCTCCCTTAAGTTTCCCA-ATGACTTAA-AACAAACCT-TAAAACTTAAGAGCTATTCAGATAAGGCACAGCTTATTTGAAACAGGATACAACCTACAATAATGGGT-AAATTATA-TTAATTAAAT-TGAAGTAGGCCTAAAAGCGGCCACCTTTTAAAAAGCGTTAAAGCTTGATTAAAATT-ATTAATAA-TACCTAAAATTCTTATTAACCCTTTAT-TTCTACTGAACTATTTTATA-TTCTTATAAAAGTAATAATGCTAGAACTAGTAACAAGAAAAAGATTTTTCTCCTAAATGTAAATATATACCAAAATGGACTATCCGCTGGTAATCAACGCAAATGCAGAAATTATAGTAACCTTC--------TAGAAAACTCTATAATCCAT-AACGTTAACCTTACACTAGAACATTCCAGGAAAGATTAAAAAAGAAAGAAGGAACTCGGCAAATTTTAGCCTCGCCTGTTTACCAAAAACATCGCCTCTTGATAAAATATAAGAGGTCCAGCCTGCCCAGTGACAAAGTTCAACGGCCGCGGTATCCTAACCGTGCGAAGGTAGCATAATCACTTGTTCTTTAAATAGGGACTTGTATCAACGGCACCACGAAGGCCATACTGTCTCCTTTCTTCAATCAGTGAAACTGATCTCCCCGTGAAGAAGCGGGGATTTAAATATAAGACGAAAAGACCCCATGGAGCTTAAAACTCATCATACACCTCT-ATGCCCTTACATCAAC-TCACCT-AAGAAATT-TGTGTGATAGTTTTCGGTTGGGGGGACCTCGGAGTATAATATAACCTCCATAACAAATGGGCTAACACCCTTATCCACGAAAAACACCTCTAAGAATTATTAAATTAATGTTTAATGACCCAATAT--TTGATCAATGAACCAAGTTACCCTGGGGATAACAGCGCAATCTACTTCAAGAGTCCCTATCGACAAGTGGGTTTACGACCTCGATGTTGGATCAGGGTATCCTAATGGTGCAACCGCTACTAATG-GTTCGTTTGTTCAACG vibicariaMVZ11035 GCCGTAAACAATTAATTCACAACCAACAACGCCTGGGGACTACGAGCAATGCTTAAAACCCAAAGGATTTGACGGTGTCCCATCCTACTAGAGGAGCCTGTTCTATAATCGATAATCCCCGCTACACCTGACCACTCCTCGCTTTTCAGCCTGTATACCTCCGTCGAAAGCTTACCGCGTGAACGTTT-GCAGTGTGCTTAATGATATCAACACGTCAGGTCAAGGTGCAGCTTAAGGAATGGGAAGTAACTGGCTACAATCTCTAATTTAGAACAAACGAAAGACTGCATGAAATACAAGTCATGAAGGTGGATTTAGTAGTAAAAAGAAAATAGAGTGTTCTTTTTAACCCGGCTCTGGGACAAGTACACACCGCCCGTCACCCTCTTCGAAAATATTTTTTTTATGTTCCTAACCC-GTTAA-CACATTAT-AGAAGAAGCAAGTCGTAACATGGTAAGTGTACTGGAAAGTGCACTTGGCAAA-TAACAAGATGTAGCTTAATAAAAGCCCCTCGTTTACACCGAAGAAATGTCTGTTTAAGTCAGATCGTCTTGAGCCTAAAATCTAGCCCA-TATATTCGTATGACCCCCCTCCCAAA-CAAAACATTCTCTCATTATAGTACAGGAGATCGAAAAA-CTTCTAAGCGCTTCAGATAAAGTACCGCAAGGGAAAAATGAAATATAACTGAAATAATAATAAAGCCTTAAATAGCAGAGACACCCCCTCGTACCTTTTGCATCATGGTCTAGTTAGTCTACTC-AAGCAAAATGAAATTTTT-AGTTAGACATCCCGAAACTAAGCGAGCTACTTCAAAACAGCCCAAAGGGGCTAACCCGTCTCTGTTGCAAAAGAGTGGGAAGATTTTTAAGTAGAAGTGAAAAGCCTACCGAGCTTAGAGATAGCTGGTTGTTCAGGAAAAGAGTTTTAGCTCTACCTTAAGTTTATTT-ACGATTTTA-AACGAACCC-TAAAACTTAAGAGCTATTCAAATAAGGTA?AGCTTATTTGAAACAGGATACAACCTACATTAATGGGT-AAATAATAATGTATGGGG--TAAAGTTGGCCTAAAAGCAGCCACCCTT-AGAAAGCGTTAAAGCTCAACTTCTATC-ACTAATAA-TTTCCAAAATTCTAATTAACCCTTTAT-TTTTACTGAACTATTTTATA-ACCCTATAAAAGTAATAATGCTAGAACTAGTAACAAGAAATTGATT-TTCTCCTAAATGTAAATATACACCAAAATGGACCATCCATTGGTAATTAACGCAGATGCAAAAATTATAATAACCCCC--------TAGAAAAATTTATAGTTCTT-AACGTTAACCTTACACTAGAATATTACAGGAAAGATTTAAAGAAAAAGAAGGAACTCGGCAAATTTTAGCCTCGCCTGTTTACCAAAAACATCGCCTCTTGCCTAAATATAAGAGGTCCAGCCTGCCCAGTGACAAAGTTTAACGGCCGCGGTACCCTAACCGTGCGAAGGTAGCATAATCACTTGTTCTCTAAATAAGGACTTGTATCAACGGCATCACGAAGGCCATACTGTCTCCTTTCTCTAATCAGTGAAACTGATCTCTCCGTGAAGAAGCGGGGATTTTTATATTAGACGAGAAGACCCCATGGAGCTTAAAACTTATTATACACCTCT-TCTCTAT-ATATCATC-TTATTC-AAGAATACTTGTATGCCAGTTTTTGGTTGGGGAGACCTCGGAGTACAATATAACCTCCGCAATAAACGGACTAACACCCTTATCCATGAGAAACGTCTCTAAGAACTAATAAATTAATATTT-ATGATCCAATAG-TTTGATAAATGAACCAAGTTACCCTGGGGATAACAGCGCAATCTACTTCAAGAGCCCATATCGACAAGTAGGTTTACGACCTCGATGTTGGATCAGGGTCTCCTAGTGGTGCAACCGCTACTAATG-GTTCGTTTGTTCAACG warszewitshiiJSF1127 GCCGTAAACAATTAATTTACAACCAACAACGCCTGGGAACTACGAGCCATGCTTAAAACCCAAAGGATTTGACGGTGTCCCACCCCCCTAGAGGAGCCTGTTCTATAATCGATAACCCCCGCTACACCTTACCACTCCTCGCTTACCAGTCTGTATACCTCCGTCGAAAGCTTACCGTGTGAACGCCT-ACAGTATGCTTAATGATACCAATACGTCAGGTCAAGGTGCAACTTAAGGAGCGGAAAGCAATGGGCTACAATTTCTAACCTAGAACAAACGAAAGACTGCATGAAATACAAGTCATGAAGGTGGATCTAGTAGTAAAAAGAAAATAGAGTGTTCTTTTTAACCCGGCTCTGGGACAAGTACATACCGCCCGTCACCCTCTTCGACAGTATTTTTCCCTAGTCCTTAACCC-GCTAT-CACATTTT-AGAAGAAGCAAGTCGTAACATGGTAAGTGTACTGGAAAGTGTACTTGGTTAA-TAACAAGATGTAGCTTAATAAAAACTCCTCGTTTACACCGAGGAAATATCTGTTTAAACCAGATCATCTTGAGCCTAAAATCTAGCCGT--ATATTCACACGAACCCCCCCCCAAA-TAAAACATTTTCTCATTATAGTACAGGTGATCGAAAAA-CTTCTAAGCGCTTCAGAAACAGTACCGCAAGGGAAAAATGAAATATAATTGAAATAACCTTTAAGCCCTAAATAGCAGAGACACCCCCTCGTACCTTTTGCATCATGGTCTAGTTAGTCTACTC-AAGCAAAATGAAATTTTT-AGTTAGAAACCCCGAAACTAAGCGAGCTACTTCAAAACAGCCCGAA-GAGCTAACCCGTCTCTGTTGCAAAAGAGTGGGAAGATTTTTAAGTAGAGGTGAAAAGCCTACCGAGCTTAGAGACAGCTGGTTGTTCAGGAAAAGAGTTTTAGCTCTACCTTAAGTTTACTC-ACGATTTAA-AACTAACCT-TAAAACTTAAGAGCTATTCAAATAAGGTACAGCTTATTTGAAACAGGATACAACCTATAATAATGGGT-AAATAATA-GTGCTTAGAA-TAAAGTTGGCTTAAAAGCAGCCATCT--AAGAAAGCGTTAAAGCTCGACTCTGCC--ACTAGTAA-TTCCTAAAACCTTAATTAACCCTTTAT-TTTTACTGAACCATTTTATA-ATTTTATAAAAATGATAATGCTAGAACTAGTAACAAGAAATTGAT--TTCTCCTAAATATAAGCATAAACCAAAAAGGACTATCCATTGGTAATTAACGTGCATGAAAAAATTATATTAACCCCCCCCCCCCC--GAAAAATTTATAACCCCT-AACGTTAACCTTACATTAGAATATTACAGGAAAGATTTAAAGAAAAAGAAGGAACTCGGCAAATTTTAGTCTCGCCTGTTTACCAAAAACATCGCCTCTTGCCTGAATATAAGAGGTCCAGCCTGCCCAGTGACATAGTTTAACGGCCGCGGTAACCTAACCGTGCAAAGGTAGCATAATCACTTGTTCTCTAAATAGGGACTTGTATCAACGGCACCACGAAGATTATACTGTCTCCTTTTTCTAATCAGTGAAACTGATCTCCCCGTGAAGAAGCGGGGATTTTAATATTAGACGAGAAGACCCCATGGAGCTTAAAACTCGTCACTCACCTCT-TTACCAC-ACATCTAC-AAAGTT-AAGAGTTCTTGCGTGTCAGTTTTTGGTTGGGGAGACCTCGGAGTATAATAAAACCTCCGTAATAAATGGACTAGCACCCTTATCCACGAGAAACGGCTCTAAGAACTAATATATTAATATTT-ATGACCCAACAA-TTTGATAAATGAACCAAGTTACCCTGGGGATAACAGCGCAATCTACTTTTAGAGCCCCTATCGACAAGTAGGTTTACGACCTCGATGTTGGATCAGGGTCTCCTAGTGGTGCAACCGCTACTGATG-GTTCGTTTGTTCAACG palmipesVenAMNHA118801 ACCGTAAACAATTGATTTACACCTACAAACGCCAGGGAATTACGAGCAATGCTTAAAACCCAAAGGACTTGACGGTGTCCCACCCAACTAGAGGAGCCTGTTCTATAATCGATGATCCCCGCTACACCCGACCATTCCTTGCTCATCAGTCTGTATACCTCCGTCGAAAGCTTACCATGTGAACGTCT-GCAGTAAGCTCAATGACGTCAACACGTCAGGTCAAGGTGCAACTCAAGGACTGGGAAGTAATGGGCTACAATTTCTAATTTAGAACAAACGGAAGACTATGTGAAATCTTAGTCATGAAGGTGGATTTAGCAGTAAAAAGAAAATAGAGTGTTCTTTTTAACTTGGCTCTGGGATGCGTACACACCGCCCGTCACCCTCTTCAATAGTA--TACTATTAGTTCTTAACCC-ACAAT-CACGTTTT-AGAAGAAGCAAGTCGTAACATGGTAAGTGTACTGGAAAGTGCACTTGGTAAA-CAACAAAATGTAGCTTAACAAAAGCCCCTCGCTTACACCGAAGTAATATCTGTTAAAACCAGATCATTTTGAGCCTAAAATCTAGCCTATAACATTTAGATAACTCCATCCCCAAA-CAAAACATTTTCCTATTTTAGTACAGGCGATCGAAAAA-TTTCTAAGCGCTTCAGATATAGTACCGCAAGGGAAAAATGAAATAGAACTGAAATAACCTTAAAGCCCTAAATAGCAGAGATATAATCTTGTACCTTTTGCATCATGGTCTAGTAAGTTTACAC-AAGCAAAATGAAACTTTT-AGTTTGACATCCCGAAACTAAGCGAGCTACTTCGAAACAGCCCATA-GAGCTAACCCGTCTCTGTTGCAAAAGAGTGGGAAGATTTCTAAGTAGAAGTGATAAGCCTACCGAGATTAGAGATAGCTGGTTGTTCAGGAAAAGAGTTTTAGCTCTACCTTAAGTTTCTTT-ATGATTTTA-AACAGACCT-CAAGACTTAAGAGCTATTCAAATAAGGCACAGCTTATTTGAAGTAGGATACAACCTATTTTATAGGGT-AAATAATAATGTATTAAAA-TAAAGTGGGCCTAAAAGCAGCCATCTTTTAAAAAGCGTTAAAGCTTAACTTATATT-ACTAGTAA-TTTCTAAAATTTGAATTAACCCTTTAC-CCCTACTGAATTATTTTATA-TCTTTATAAAAGTAATAATGCTAGAACTAGTAACAAGAAACTGCCTGTTCTCCTAAATGCAAGCATAAACCAAAATAGACACCCTATTGGTAATTAACGTTAATGTAACAGCTGTAGCAACATAA--------TAGAAAACCCTACAACCTCC-AACGTTAACCTTACACTAGAGCATTCCAGGAAAGATTAAAAAAGAAAGAAGGAACTCGGCAAACTTCAGCCCCGCCTGTTTACCAAAAACATCGCCTCTTGAAAAATTATAAGAGGTCCAGCCTGCCCAGTGACTAAGTTCAACGGCCGCGGTACCCTAACCGTGCGAAGGTAGCATAATCACTTGTTCTTTAAATGAGGACTTGTATCAACGGCACCACGAAGACTGCACTGTCTCCTTTCTTCAATCAGTGAAACTGATCTCCCCGTGAAGAAGCGGGGATTAGACTATAAGACGAGAAGACCCCATGGAGCTTCAAACTCATTATACAACTCT-GTTCCCC-ATACCCCT-TGATAT-AAGAGACA-TGTATAATACCTTTGGGTTGGGGGGACCTCGGAGTACAACTTAACCTCCTAAGCAAATAGGTTAACACCTTTATCCATGAGAAACACCTCTAAGAATTATCAAATTAATGTCT-TTGACCCGATAA--TCGATCAATGAACCAAGTTACCCTGGGGATAACAGCGCAATCTACTTCAAGAGTTCCTATCGACAAGTGGGTTTACGACCTCGATGTTGGATCAGGGTATCCCAGTGGTGCAACCGCTACTAACG-GTTCGTTTGTTCAACG palmipesEcuKU204425 ACCGTAAACAATTGATTTACACCTACAAACGCCAGGGAATTACGAGCAATGCTTAAAACCCAAAGGACTTGACGGTGTCCCACCCAACTAGAGGAGCCTGTTCTATAATCGATGATCCCCGCTACACCCGACCATTCCTTGCTCATCAGTCTGTATACCTCCGTCGAAAGCTTACCATGTGAACGTCT-GCAGTAAGCTCAATGACGTCAACACGTCAGGTCAAGGTGCAACTCAAGGACTGGGAAGTAATGGGCTACAATTTCTAATTTAGAACAAACGGAAGACTATGTGAAATCTTAGTCATGAAGGTGGATTTAGCAGTAAAAAGAAAATAGAGTGTTCTTTTTAACTTGGCTCTGGGATGCGTACACACCGCCCGTCACCCTCTTCAATAGTA--TACTATTAGTTCTTAACCC-ATAAT-CACGTTTT-AGAAGAGGCAAGTCGTAACATGGTAAGTGTACTGGAAAGTGCACTTGGTAAA-CAACAAAATGTAGCTTAACAAAAGCCCCTCGCTTACACCGAAGTAATATCTGTTAAAACCAGATCATTTTGAGCCTAAAATCTAGCCTATAACATTTAGATAACTCCATCCCCAAA-CAAAACATTTTCCTATTTTAGTACAGGCGATCGAAAAA-TTTCTAAGCGCTTCAGATATAGTACCGCAAGGGAAAAATGAAATAGAACTGAAATAACCTTAAAGCCCTAAATAGCAGAGATATAATCTTGTACCTTTTGCATCATGGTCTAGTAAGTTTACAC-AAGCAAAATGAAACTTTT-AGTTTGACATCCCGAAACTAAGCGAGCTACTTCGAAACAGCCCATA-GAGCTAACCCGTCTCTGTTGCAAAAGAGTGGGAAGATTTCTAAGTAGAGGTGATAAGCCTACCGAGATTAGAGATAGCTGGTTGTTCAGGAAAAGAGTTTTAGCTCTACCTTAAGTTTCTTT-ATGATTTTA-AACAGACCT-CAAGACTTAAGAGCTATTCAAATAAGGCACAGCTTATTTGAAGTAGGATACAACCTATTTTATAGGGT-AAATAATAATGTATTAAAA-TAAAGTGGGCCTAAAAGCAGCCATCTTTTAAAAAGCGTTAAAGCTTAACTTATATT-ACTTACAA-TTCCGAATATTACAATTAACCCTTTAA-TTCTACTGAACTATTTTATA-TTTTTATAAAAGTAATTATGCTAGAACTAGTAACAAGAAATTGATT-TTCTCCTAAATGTAAGCTTACACCAAAATGGACCATCCATTGGTAATAAACGCAGATGAAAAAATTATAGCAACCTTC--------CAGAAAACCCTATATATCCACAGCGTTAATCTTACACTAGAACATTTCAGGAAAGATTTAAAGAGAAAGAAGGAACTCGGCAAATTTTAGCCCCGCCTGTTTACCAAAAACATCGCCTCTTGACAAAATATAAGAGGTCCAGCCTGCCCAGTGATAAA-TTTAACGGCCGCGGTATCCTAACCGTGCGAAGGTAGCATAATCATTTGTTCTTTAAATGGGGACTTGTATCAACGGCACCACGAGGGCCATACTGTCTCCTTTCTCTAGTCAGTGAAACTGATCTCCCCGTGAAGAAGCGGGGATTTAACTATTAGACGAGAAGACCCCATGGAGCTTAAAACTCACTATATAATTCT-GTACCTC-ATATCACC-TTAATT-CAGAATCC-TATATGCTAGTTTTAGGTTGGGGGGACCTCGGAGTATAATTTAACCTCCATAACAAATGGGCTAATACCCTTATCCAAGATAAACACCTCTAAGAATTATTAAATTAATGTTTAATGACCCGATATATTCGATCAATGAACCAAGTTACCCTGGGGATAACAGCGCAATCTACTTCAAGAGTCCATATCGACAAGTAGGTTTACGACCTCGATGTTGGATCAGGGTATCCTAATGGTGCAACCGCTACTAATG-GTTCGTTTGTTCAACG bwanaQCAZ13964 ACCGTAAACAATTGATTTACACCCACAAACGCCAGGGAATTACGAGCAATGCTTAAAACCCAAAGGATTTGACGGTGTCCCACCCAACTAGAGGAGCCTGTTCTATAATCGATGATCCCCGCTACACCCGACCACTCCTTGCTCATCAGTCTGTATACCTCCGTCGAAAGCTTACCATGTGAACGTCT-GCAGTAAGCTCAAGGACGTCAACACGTCAGGTCAAGGTGCAGCTTAAGGACTGGGAAGTAATGGGCTACAATTTCTAATCTAGAACAAACGGAAGACTATGTGAAATCTTAGTCATGAAGGTGGATTTAGTAGTAAAAAGAAAATAGAGTGTTCTTTTTAACTCGGCTCTGGGATGCGTACACACCGCCCGTCACCCTCTTCAATAGTA--TAATATCAGTTCTTAACCC-ACTAT-CACGCCTT-AGAAGAGGCAAGTCGTAACATGGTAAGTGTACTGGAAAGTGCACTTGGTAAA-TAACAAAATGTAGCTTAATAAAAGCCCTTCGCTTACACCGAAGAAATGTCTGTTAAAACCAGATCATTCTGAGCCTAAAATCTAGCCCATAATATTCAAATGGCCCCCTCTCCAAA-CAAAACATTTTCCTAGTTTAGTACAGGCGATCGAAAAA-TTTCTAAGCGCTTCAGATATAGTACCGCAAGGGAAAAATGAAATAGAACTGAAATAACCTTAAAGCCCCAAATAGCAGAGATATAATCTTGTACCTTTTGCATCATGGTCTAGTAAGTTTACAC-AAGCAAAATGAAACTTTC-AGTTTGACATCCCGAAACTAAGCGAGCTACTTCAAAACAGCCCAAA-GAGCTAACCCGTCTCTGTTGCAAAAGAGTGGGAAGATTTTTAAGTAGAGGTGATAAGCCTACCGAGCTTAGAGATAGCTGGTTGTTCAGGAAAAGAGTTTTAGCTCTACCTTAAGTTTCCCC-ATGATTCTTTAACAAACCT-TAAAACTTAAGAGCTATTCAAATAAGGCACAGCTTATTTGAAATAGGATACAACCTATTTTACTGGGT-AAGTAATAATGTATTAAAA-TAAAGTGGGCCTAAAAGCAGCCATCTTTTAAAAAGCGTTAAAGCTTAACTTATAAT-ACTTGTAA-TTCCAAATGCACCAACTAACCCTTTTA-CTATACTGAACTATTTTATA-TTCTTATAAAAGTAATTATGCTAGAACTAGTAACAAGAAATTGATT-TTCTCCTAAATGTAAGCTCACACCAAAATGGACCATCCGTTGGTAGTAAACGCAAATGAAAAAATTATAGCAACTCTC--------TAGAAAACCCTATATAACCCCAGCGTTAACCTTACACCAGAACATTTCAGGAAAGATTTAAAGAGAAAGAAGGAACTCGGCAAATTTTAGCCCCGCCTGTTTACCAAAAACATCGCCTCTTGACAAAATATAAGAGGTCCAGCCTGCCCAGTGATAAA-TTCAACGGCCGCGGTATCCTAACCGTGCGAAGGTAGCATAATCACTTGTTCTTTAAATGGGGACTTGTATCAACGGCATCACGAGGGCCATACTGTCTCCTTTCTCTAATCAGTGAAACTGATCTCCCCGTGAAGAAGCGGGGATTTAACTATTAGACGAGAAGACCCCATGGAGCTTAAAACTCAGTATATAATTTT-GTACCTT-ATACCACC-TTAACT-CAGAACCC-TATATACTAGTTTTAGGTTGGGGGGACCTCGGAGTATAATTTAACCTCCACAACAAATGGGCTAACACCCTTATCCAAGACAAACACCTCTAAGAATTATCAAATTAATGTTTAATGACCCGATATATTCGATCAATGGACCAAGTTACCCTGGGGATAACAGCGCAATCTACTTCAAGAGTCCCTATCGACAAGTAGGTTTACGACCTCGATGTTGGATCAGGGTATCCTAGTGGTGCAACCGCTACTAATG-GTTCGTTTGTTCAACG Sp_1_ecuadorQCAZ13219 ACCGTAAACAATTGACTTACACCCACAAACGCCAGGGAATTACGAGCAATGCTTAAAACCCAAAGGATTTGACGGTGTCCCACCCAACTAGAGGAGCCTGTTCTATAATCGATGATCCCCGCTACACCCGACCACTCCTTGCTCATCAGTCTGTATACCTCCGTCGAAAGCTTACCATGTGAACGTCT-GAAGTAAGCTCAATGACGTCAACACGTCAGGTCAAGGTGCAGCTTAAGGACTGGGAAGTAATGGGCTACAATTTCTAGTTTAGAACAAACGGAAGACTATGTGAAATCTTAGTCATGAAGGTGGATTTAGTAGTAAAAAGAAAATAGAGTGTTCTTTTTAACTCGGCTCTGGGATGCGTACACACCGCCCGTCACCCTCTTCAATAGTA--TATTACTAGTTCTTAACCC-ACTAT-CACGTCTT-AGAAGAGGCAAGTCGTA-CATGGTAAGTGTACTGGAAAGTGCACTTGGTAAA-CAACAAAATGTAGCTTAACAAAAGCCCCTCGCTTACACCGAAGAAATGTCTGTTAAAATCAGATCATTTTGAGCCTAAAATCTAGCCCCTAATATCCAAATGACTCCCT-CCCAAA-CAAAACATTTTCCTATTTTAGTACAGGCGATCGAAAAA-TTTCTAAGCGCTTCAGATATAGTACCGCAAGGGAAAAATGAAATAGAACTGAAATAACCTTAAAGCCCCAAATAGCAGAGACA-TATCTCGGACCTTTTGCATCATGGTCTAGTAAGTTTACAC-AAGCAAAATGAAACTTTTTAGTTTGACATCCCGAAACTAAGCGAGCTACTTCGAAACAGCCCAAA-GAGCTAACCCGTCTCTGTTGCAAAAGAGTGGGGAGATTTCTAAGTAGAGGTGATAAGCCTACCGAGATTAGAGATAGCTGGTTGTTCAGGAAAAGAGTTTTAGCTCTACCTTAAGTTTCCCC-ATGATTTTT-AACAGACCT-TAAAACTTAAGAGCTATTCAAATAAGGCACAGCTTATTTGAAATAGGATACAACCTATTTTATTGGGT-AAATAATAACGTATTAAAA-TAAAGTGGGCCTAAAAGCAGCCATCTTTTAAAAAGCGTTAAAGCTTAATTTATAAT-ACTTACAA-TTCCAAATATTCCAATTAACCCTTCTA-TTCTACTGAACTATTTTATA-TTTTTATAAAAGTAATTATGCTAGAACTAGTAACAAGAAATTGATT-TTCTCCTAAATGTAAGCTTACACCAAAATGGACCACCCATTGGTAATAAACGCAAATGAAAAAATTATAGCAACCTTC--------CAGAAAACCCTATATAACGCTAGCGTTAATCTTACACTAGAACATTTCAGGAAAGATTTAAAGAGAAAGAAGGAACTCGGCAAATTTTAGCC-CGCCTGTTTACCAAAAACATCGCCTCTTGACAAAATATAAGAGGTCCAGCCTGCCCAGTGATAAA-TTTAACGGCCGCGGTATCCTAACCGTGCGAAGGTAGCATAATCACTTGTTCTTTAAATGGGGACTCGTATCAACGGCATCACGAGGGCCATACTGTCTCCTTTCTCTAATCAGTGAAACTGATCTCCCCGTGAAGAAGCGGGGATATAACTATTAGACGAGAAGACCCCATGGAGCTTAAAACTCACTATATACTTCT-GTTCCTC-ATATCACC-ATAATT-CAGAATCT-TATATGCTAGTTTTAGGTTGGGGGGACCTCGGAGTATAATTTAACCTCCAAAACAAATGGGCTAATACCCTTATCCAAGATAAACACCTCTAAGAATTATTAAATTAATGTTCAATGACCCGACATATTCGATCAATGAACCAAGTTACCCTGGGGATAACAGCGCAATCTACTTCAAGAGTCCCTATCGACAAGTAGGTTTACGACCTCGATGTTGGATCAGGGTATCCTAGTGGTGCAACCGCTACTAATG-GTTCGTTTGTTCAACG vaillantiKU195299 ACCGTAAACAATTGATTTACACCCACAAACGCCAGGGAATTACGAGCAATGCTTAAAACCCAAAGGATTTGACGGTGTCCCACCCAACTAGAGGAGCCTGTTCTATAATCGATGATCCCCGCTACACCCGACCACTCCTTGCCCATCAGTCTGTTTACCTCCGTCGAAAGCTTACCATGTGAACGTCT-GCAGTAAGCCCAATGACGTCAACACGTCAGGTCAAGGTGCAGCTTAAGGACTGGGAAGTAATGGGCTACAATTTCTAATCTAGAACAAACGGAAGACTATGTGAAACCCTAGTCATGAAGGTGGATTTAGTAGTAAAAAGAAAATATAGTGTTCTTTTTAACTCGGCTCTGGGATGCGTACACACCGCCCGTCACCCTCTTCAATAGTA--TCTCCTCAGTTCCTAACCC-ACTAT-TACGTCTT-AAAAGAGGCAAGTCGTAACATGGTAAGTGTACTGGAAAGTGCACTTGGCAAA-CAACAAAATGTAGCTTAACAAAAGCCCTCCGCTTACACCGAAAAAATGTCTGTTAAACCCGGATCATTCTGAGCCTAAAATCTAGTCCTTAATATTCATATGACCCTCTCTTCAAA-TAAAACATTTTCCTATCTTAGTACAGGCGATCGAAAAA-TTTCTAAGCACTTCAGATAAAGTACCGCAAGGGAAAAATGAAATAGAAATGAAATAACCTTAAAGCCCCAAACAGCAGAGACACTATCTCGTACCTTTTGCATCATGGTCTAGTAAGTCTACCC-AAGCAAAACGAAACTTTC-AGTTTGACATCCCGAAACTAAGCGAGCTACTTCAAAACAGCCCAAA-GAGCTAACCCGTCTCTGTTGCAAAAGAGTGGGAAGATTTTCAAGTAGAGGTGACAAGCCTATCGAGCTTAGAGATAGCTGGTTGTTCAGGAAAAGAGTTTTAGCTCTACCTTAAGTTTATTT-GTGAATCTT-AACAAACCCTTAAAACTTAAGAGCTATTCAAATAAGGCACAGCTTATTTGAAATAGGATACAACCTATTTTACTGGGT-AAATAATAACGCACCAAAA-TAAAGTGGGCCTAAAAGCAGCCATCTTTTAAAAAGCGTTAAAGCTTAATTTATAAT-GCCAATAA-TCCCGAATATTTCAATTAACCCTTTTA-TTTTACTGAACTATTTTATA-CTCTTATAAAAGTAATTATGCTAGAACTAGTAACAAGAATTTGATT-TTCTCCTAAATGTAAGCTTACACCAAAATGGACCATCCATTGGTAATTAACGCTAATGCAAAA-CTATAACAACCTCC--------TAGAAAAACCTATATATCCACAGCGTTAACCTTACACTAGAACATTTCAGGAAAGATTTAAAGAGAAAGAAGGAACTCGGCAAATTTTAGCCTCGCCTGTTTACCAAAAACATCGCCTCTTGATTAAACATAAGAGGTCCAGCCTGCCCAGTGATAAT-TTCAACGGCCGCGGTATCCTAACCGTGCGAAGGTAGCATAATCACTTGTTCTTTAAATGGGGACTCGTATCAACGGCATCACGAGGGCCATACTGTCTCCTTTCTCTGATCAGTGAAACTGATCTCCCCGTGAAGAAGCGGGGATTTAACTATTAGACGAGAAGACCCCATGGAGCTTAAAACCCATCATATACTTCT-ATATCTT-ATATCACC-CCAATT-CAGAAACC-TATATGCTAATTTTAGGTTGGGGGGACCTCGGAATATAATTAAACCTCCATAACAAATGGGCTAACACCCTTATCCAAGAAAAACACCTCTAAGAATTATCAAATTAATGTTTAATGACCCGATAT-TTCCAACCATTAGACCAGTTACCCTGGGGAATACAACCCCATCTTCTTCCAAAATTCCTATCCAACAATTAGTTTACCAACTCCAATTTGGATCCGGGTTTCCCAATTGTTCCACCGCCA-TAATG-GTTCCTTTGTTCCACC julianiTNHC60324 ??CGTA?ACAATTGAT?TACACCCATA??CGCCA?GGAATTACGAGCAATGCTTAAAACCCAAAGGATTTGACGGTGTC?CACCCAACTATAGGAGCCTGTTCTATAATCGATGATCCCCGCTACACCCGACCACTCCTTGCCTAT?A?TCTGTATACCTCCGTCGAAAGCTTACCATGTGAACGCCT-ACAGTATGCCCAATGAC?TCAACACGTCAGGTCAAGGTGCAGCTTAAGGACTGGGAAGTAATGGGCTACAGTTTCTAATTTAGAACAAACGGAAGACTATGTGAAATCCTAGTCATGAAGGTGGATTTAGTAGTAAGAAGAAAATAGAGTGTTCTTTTTAACTCGGCTCTGGGATGCGTACACACCGCCCGTCACCCTCTTCAATAGTA--?CTTTTTAGTT?TTAACAC-ACTAC-CACGCCTT-AGAAGAGGCAAGTCGTAACATGGTAAGTGTACTGGAAAGTGCACTTGGTAAA-CAACAAAATATAGCTTAACAAAAGCCCTTCGCTTACACCGAAGAAATATCTGTTAAAACCAGATTATTTTG?GCCTAAAATCTAGCCCA-CTGATTCACATGCACCCCTCTTCTAA-TAAAACATTTTCCTATTTTAGTACAGGCGATCGAAAAA-TTTCTAAGCGCTTCAGATAAAGTACCGCAAGGGAAAAATGAAATAGAAATGAAATAACCTTAAAGCCTTAAACAGCAGAGATATTATCTCGTACCTTTTGCATCATGGTCTAGTAAGTCTATCC-AAGCAAAATGAAACTTTT-AGTTTGATACCCCGAAACTAAGCGAGCTACTTCAAAACAGCCCAAA-GAGCTAACCCGTCTCTGTTGCAAAAGAGTGGGAAGATTTTTAAGTAGAGGTGATAAGCCTACCGAGCTTAGAGATAGCTGGTTGTTCAGGAAAAGAGTTTTAGCTCTACCTTAAGTTTATTT-ATGACTTTC-AACAAACCT-TAAAACTTAAGAGCTATTCAAATAAGGCACAGCTTATTTGAAATAGGATACAACCTATTTTATTGGGT-AAATAGTA-GTATATTAAAATAAAGTGGGCCTAAAAGCAGCCACCTTTTAAAAAGCGTTTAAGCTTAATTTATAAT-ACTCATAA-TTCCAAATATTTTAACGAA?CCTTCTG-TTCTACTGAACTA?TTTA?A-C?TTTATAAAAGTAATTATGCTAGAACTAGTAACAAGAATATGATT-TCTCCCAAAATGTAAGTTTATACCAAAATGGACCATCCATTGGTAATCAACGCTAATGCAA-AATTATAGCAACCTTC--------TAGAAAACCCTATATACCCGCAGCGTTAATCTTACACTAGAACATTTCAGGAAAGATTTAAAGAGAAAGAAGGAACTCGGCAAATTTTAGCCTCGCCTGTTTACCAAAAACATCGCCTCTTGACAAAACATAAGAGGTCCAGCCTGCCCAGTGATAAA-TTTAACGGCCGCGGTATCCTAACCGTGCGAAGGTAGCATAATCACTTGTTCTTTAAATGGGGACTCGTATCAATGGCATCACGAGGGCCATACTGTCTCCTTTCTCCAATCAGTGAAACTGATCTCCCCGTGAAGAAGCGGGGATTTAACTATTAGACGAGAAGACCCCATGGAGCTTAAAACTCATCATATACCTCT-ATATATT-ATATCATC-CCAATT-AAGAAATT-TATATGTTAGTTTTAAGTTGGGGGGACCTCGGAGTACAATTTAACCTCCGTTACAAATGGGCTAATA?CCTTATCT?AGAAAAACA?CTCTAAGAATTACTAAATTAATGTTTAATGACCCGATTA-TTCGATCAATGAACCAAGTTACCCT?GGGATAACAACGCAATCTACTT?AAGAGTTCCTATCGACAAGTAGGTT-ACGAACTC?ATTTTGGATAAGGGTA?CC-AATTGTGCAACCGCTCCTAA?G-GTCCGTTGGTT?AACG sierramadrensisKU195181 GCCATAAACAATTAATTTACACTTATCAACGCCAGGGAATTACGAGCAATGCTTAAAACCCAAAGGATTTGACGGTGTCCCACCCAACTAGAGGAGCCTGTTCTATAATCGATAATCCCCGCTATACCTCACCATTCCTCGCTT-TCAGTCTGTATACCTCCGTCGAAAGCTTACCATGTGAACGTCT-ACAGTAGGCCCAATGATATCAACACGTCAGGTCAAGGTGCAGCTTAAGGAATAGGAAGAAATGGGCTACAATTTCTAATTTAGAACAAACGGAAGACTATGTGAAACC-TAGTCATGAAGGCGGATTTAGCAGTAAAAAGGAAATA?AGTGTTCTTTTTAATCTGGCTCTGGGACGCGTACACACCGCCCGTCACCCTCTTCAATAGTA-ACTTTATTTGTTTCTAACCT-ATTAT-TACATTTT-AGAAGAAGCAAGTCGTAACATGGTAAGTGTACTGGAAAGTGCACTTGG--TC-TATCAAAATGTACCTTAACTAAAGCCCTTTGTTTACACCGAAAACATAACTGTTAAAATCAGTTCATTTTGAGCCTAAAACCTAACCTAACATACTCGCATGAAC-TCTCTTCAAA-TAAAACATTTTATTATTATAGTACAGGTGATCGAAAAA-TTTCTAAGCGCTTCAGATAAAGTACCGCAAGGGAAAGATGAAATAGAATTGAAATAATCTTAAAGCCTTAAACAGTAGAGATATTCCCTCGTACCTTTTGCATCATGGTCTAGTTAGTCTATTC-AAGCAAAATGAAAATTTT-AGTTAGACACCCCGAAACTAAGGGAGCTACTTCAAAACAGCCTATT-GGGCCAACCCATCTCTGTTGCAAAAGAGTGGGAAGATTTTCAAGTAGAGGTAATAAGCCTACCGAACTTAGAGATAGCTGGTTGTTCAGGAAAAGAGTTTTAGCTCTACCTTAAGCTTCGTT-ATGATACTA-AACAAATACAAAAAGCTTAAGAGCTATTCAAATAAGGCACAGCTTATTTGAAATAGGATACAACCTATAATAATGGGT-AAGTAAAA-TTTAATAAAA-TGAAGTGGGCCTAAAAGCAGCCACCTTTTAAAAAGCGTTTAAGCTTAATTAGAATT-ATTAATAA-TTTCCACAATCTTTTCAAACCCTTTAT-TCTTACTGAACTACTTTATA-ATTTTATAAAAGCAATAATGCTAGAACTAGTAACAAGAAATTGATT-TTCTCCTAAATGTAAGCATAAACCAAAATGGACTCCCCATTGGTAATTAACGTCAATGCAAAA-TTATAACAACACAC--------TAGAAAACCTTATAACCGAT-AACGTTAACCTCACACTAGAACATTTCAGGAAAGATTAAAAGAGAAAGAAGGAACTCGGCAAAATTTAGCCTCGCCTGTTTACCAAAAACATCGCCTCTTGACAAAATATAAGAGGTCCAGCCTGCCCAGTGATATAATTCAACGGCCGCGGTACCCTAACCGTGCGAAGGTAGCATAATCACTTGTTCTTTAAATGGGGACTCGTATCAATGGCACCACGAAGGCTATACTGTCTCCTTTCTCCAATCAGTGAAACTGATCTTCCCGTGAAGAAGCGGGAATTTAAATATAAGACGAGAAGACCCCATGGAGCTTTAAACTCACTATACACCTCT-GCATCCT-TTAAATCCACCCACCCAAGAGTAT-TGTATACTAATTTTAGGCTGGGGGGGCCTCCGAATAAAATTTAACCTCCATAACAAATGGGCTAACACCCTTATCTACGAAAAACACCTCTAA?AATTATTAAAATAATGTTA-AAGACCCGATAA-TTCGATTAATGAACCAAGTTACCCTGGGGATAACAGCGCAATCTACTTCGAGAGTTCCTATCGACAAGTAGGTTTACGACCTCGATGTTGGATCAGGGTATCCCAGTGGTGCAACCGCTACTGATG-GTTCGTTTGTTCAACG psilonotaKU195119 ACCGTAAACAATTAATTTACACCAATCAGCGCCAGGGGATTACGAGCAATGCTTAAAACCCAAAGGATTTGACGGTGTCCCACCCAACTAGAGGAGCCTGTTCTATAATCGATGATCCCCGCTACACCCAACCATTTCTCGCTC-TCAGTCTGTATACCTCCGTCGAAAGCTTACCATGTGAACGTTT-ACAGTAGGCCCAAGGACGTCAATACGTCAGGTCAAGGTGCAGCTTAAGAAATGGGAAGCAATGGGCTACAATTTCTAATTTAGAACAAACGAAAGACTATGTGAAACCATAATC-TGAAGGTGGATTTAGCAGTAAAAAGGAAATATAGTGTTCTTTTTAACCTGGCTCTGGGACGCGTACACACCGCCCGTCACCCTCTTCGATAGTA-CTCCCATTTGTTCCTAACTT-ATTAT-TACGCCTT-AGAAGAAGCAAGTCGTAACATGGTAAGTGTACTGGAAAGTGCACTTGG--AC-TACCAAAATGTAGCTTAATTAAAGCCCCTCGCTTACACCGAAGATATGACTGTTAAAATCAGTTCATTTTGAGCCTAAAATCTAGCCCATATAT-TCATATGACTTTCTATCCAAA-CAAAACATTTTATTATTCTAGTATAGGTGATCGAAAGA-TTTCTAAGCGCTTCAGATAAAGTACCGTAAGGGAAAGATGAAATAGAATTGAAATAATCTTAAAGCCCTGTATAGCAGAGACACCCCCTCGTACCTTTTGCATCATGGTCTAGTTAGTTTATTC-AAGCAAAATGAAATTTTT-AGTTAGACACCCCGAAACTAAGGGAGCTACTTCAAAACAGCCTAAT-GGGCTAACCCGTCTCTGTTGCAAAAGAGTGGGAAGATTTTTAAGTAGAGGTGAAAAGCCTATCGAACTTAGAGATAGCTGGTTGTTCAGGAAAAGAGTTTTAGCTCTACCTTAAG-TTTACATATGATATAA-AACAAACTT-TGAAGCTTAAGAGCTATTCAAATAAGGCACAGCTTATTTGAAACAGGATACAACCTATAATATTGGGT-AAGTAATA-GTAGGATAAAATTAAGTGGGCCTAAAAGCAGCCACCTTCAAAAAAGCGTTAAAGCTTACCTAAAATT-ACTGATAA-TTTGTAAAATTATTATTAACCCTTTTT-TTGTACTGAACTATTTTATA-TATTTATAAAAGCAATAATGCTAGAACTAGTAACAAGAAATTGACT-TTCTCCTAAATGTAAGCTTAAACCAAAATTGACAACCCATTGGTAATTAACGTAAATGTAAAAACTATAATAAATTAC--------TAGAAAAACTTATAATCTAA-AACGTTAACCTAACACTAGAACATTATAGGAAAGATTAAAAGAGAAAGAAGGAACTCGGCAAAATTTAACCTCGCCTGTTTACCAAAAACATCGCCTCTTGATTAAATATAAGAGGTCCAGCCTGCCCAGTGACATAGTTTAACGGCCGCGGTACCCTAACCGTGCGAAGGTAGCATAATCAATTGTTCTATAAATAGGGACTAGTATCAACGGCACTACGAAGGTTATACTGTCTCCTTTCTCCAATCAGTGAAACTGATCTCCCCGTGAAGAAGCGGGGATATAACTATAAGACGAGAAGACCCCATGGAGCTTTAAACTCATTATACACCTCT-GTGATTTTTATAACCTGTTAATCCAAAAGACC-TGTATATTAGTTTTAGGTTGGGGGGACCCCGGAGTACAACTAAACCTCCGTAACAAATGGGCTAATACCCTTATCCACGAGAAACACCTCTAAGAATTAATAAATTAATGTTTAATGATCCGATTA-CTCGATCAATGAACCAAGTTACCCTGGGGATAACAGCGCAATCTATTTCCAGAGCTCCTATCGACAAGTAGGTTTACGACCTCGATGTTGGATCAGGGTATCCCAGTGGTGCAACCGCTACTAATGTGTTCGTTTGTTCAACG tarahumaraeKU194596 GCCGTAAACAATTAATTTACACCTACCAGCGCCAGGGAATTACGAGCAATGCTTAAAACCCAAAGGATTTGACGGTGTCCCACCCAACTAGAGGAGCCTGTTCTATAATCGATAATCCCCGCTACACCTAACCATTCCTTGCTT-TCAGTCTGTATACCTCCGTCGAAAGCTTACCATGTGAACGCCT-ACAGTAAGCCTAATGACGTCAACACGTCAGGTCAAGGTGCAGCTTAAGAAATGGAAAGCAATGGGCTACAATTTCTAATTTAGAATAAACGGAAGACTATGTGAAATCCTAGTCATGAAGGTGGATTTAGTAGTAAAAAGAAAATAGAGTGTTCTTTTTAACCCGGCTCTGGGACGCGTACACACCGCCCGTCACCCTCTTCAATAGTA-TTTTTCTCCGTTCCTAACTC-ACTAT-CACGTCTT-AGAAGAAGCAAGTCGTAACATGGTAAGTGTACTGGAAAGTGCACTTGG--TA-TAACAAAATGTAGCTTAACTAAAGCCCTCCGCTTACACCGAAGATATATCTGTTTAAACCAGTTCATTTTGAGCCTAAAATCTAGCCTACTACATCCACATGCCTTCCTATCCAGA-TAAAACATTTTATTATTTTAGTACAGGTGATCGAAAAA-TTTTTAAGCGCTTCAGATAAAGTACCGTAAGGGAAAGATGAAATAGAATTGAAATAACCTTAAAGCCTCAAATAGCAGAGACTCCCCCTCGTACCTTTTGCATCATGGTCTAGTTAGTTTATTC-AAGCAAAATGAAATTTTT-AGTTAGACACCCCGAAACTAAGGGAGCTACTTCAAAACAGCCTAAT-GGGCCAACCCGTCTCTGTTGCAAAAGAGTGGGAAGATTTTTAAGTAGAGGTGAAAAACCTACCGAACTTAGAGATAGCTGGTTGTTCAGGAAAAGAGTTTTAGCTCTACCTTAAACTTACTA-ATGATATAA-AACAAACTT-TGAAGCTTAAGAGCTATTCAAATAAGGCACAGCTTATTTGAAATAGGATACAACCTATTATACAGGGT-AAATAGTA-GTATTAAAAA-TTAAGTGGGCCTAAAAGCAGCCACCTTCAAAAAAGCGTTAAAGCTTAATTAAAATT-ACTTATAA-TTTCCAAAATTATTAATAACCCTTTAT-TTTTACTGAACTATTTTATA-TTCTTATAAAAGCAATGATGCTAGAACTAGTAACAAGAAATTGAC--TTCTCCTAAATGTAAGCATAAACCAAAATTGACATTCCATTGGTAATCAACGTAAATGCAGAAAATATAATAACCTAC--------TAGAAAAACCTATAATTTTT-TACGTTAACCTAACACTAGAATATTACAGGAAAGATTAAAAGAGAAAGAAGGAACTCGGCAAAATTTAGCCTCGCCTGTTTACCAAAAACATCGCCTCTTGATAAAACATAAGAGGTCCAGCCTGCCCAGTGACATAGTTCAACGGCCGCGGTACCCTAACCGTGCGAAGGTAGCATAATCAATTGTCCTCTAAATAGGGACTAGTATCAATGGCACCACGAAGGCTACACTGTCTCCTTTCTCTAATCAGTGAAACTGATCTCCCCGTGAAGAAGCGGGAATATAATTATAAGACGAGAAGACCCCATGGAGCTTTAAACTCATCATACACCTTC-ATATTAC-TTAAATCTACTAATCCTAAAGACC-TGTATGCTAATTTTAGGTTGGGGGGACCACGGAGTATAATTTAACCTCCACAACAAATGGGTTAATACCCTTATCCACGAGAAACACCTCTAAGAATTAATAAACTAATGTTTAATGATCCGATAA-CTCGATCAATGAACCAAGTTACCCTGGGGATAACAGCGCAATCTACTTCAAGAGCCCCTATCGACAAGTAGGTTTACGACCTCGATGTTGGATCAGGGTATCCTAGTGGTGCAACCGCTACTAATG-GTTCGTTTGTTCAACG zweifeliJAC7514 ACCGTAAACAATTAATTTACACCAATCAGCGCCAGGGAATTACGAGCAATGCTTAAAACCCAAAGGACTTGACGGTGTCCCACCCAACTAGAGGAGCCTGTTCTATAATCGATAATCCCCGCTACACCCGACCATTTCTTGCTT-TCAGTCTGTATACCTCCGTCGAAAGCTTACCATGTGAACGCCT-ACAGTAAGCCCAATGATATCAACACGTCAGGTCAAGGTGCAGCTTAAGAAGTGGGAAGCAATGGGCTACAATTTCTAATTTAGAACAAACGAAAGATTATGTGAAATCATATTC-TGAAGGTGGATTTAGCAGTAAAAAGAAAATAGAGTGTTCTTTTTAACCCGGCTCTGGGACGCGTACACACCGCCCGTCACCCTCTTCGATAGTA-AAATACTCTGTTCCTAACTC-ACTAT-TACGCCTT-AGAAGAAGCAAGTCGTAACATGGTAAGTGTACTGGAAAGTGCACTTGG--TA-TACCAAAATGTAGCTTAACCAAAGCCCTCCGCTTACACCGGAGATATATCTGTTCAAACCAGGTCATTTTGAGCCTAAAATCTAGCCTATATTGTTCATATGACTTTCTATCCAAA-TAAAACATTTTATTATTCTAGTACAGGCGATCGAAAAA-TTTCTAAGCGCTTCAGATAAAGTACCGTAAGGGAAAGATGAAATAGAATTGAAATAACCTTAAAGCCCAAAATAGTAGAGACACTCCCTCGTACCTTTTGCATCATGGTCTAGTTAGTTTACTC-AAGCAAAATGAAATTTT--AGTTAGACACCCCGAAACTGAGGGAGCTACTTCAAAACAGCCTAAT-GGGCCAACCCGTCTCTGTTGCAAAAGAGTGGGAAGATTTTTAAGTAGAGGTGAAAAACCTACCGAACTTAGAGATAGCTGGTTGTTCAGGAAAAGAGTCTTAGCTCTACCTTAAGCTTTACCTATGATACTC-AACAAATCT-TAAAACTTAAGAGCTATTCAAATAAGGCACAGCTTATTTGAAATAGGATACAACCTATTACATTGGGT-TAATCATA-GTGAATAAAG-CTAAGTGGGCCTAAAAGCAGCCACCTTCAAAAAAGCGTTAAAGCTTAATTAAAATT-ACTAATAA-TTTTCAAAACTACCATTAACCCTTTAT-CTATACTGAACTATTTTATA-TAATTATAAAAGCAATGATGCTAGAACTAGTAACAAGAAATTGACT-TTCTCCTAAATGTAAGTATAAACCAAAATTGACACTCTATTGGTAATTAACGTAAATGCAGAAACTATAGTAACACTAC-------TAGAAAAACCTATAACTCTT-AACGTTAACCTCACACTAGAACATTTCAGGAAAGATTAAAAGAAAAAGAAGGAACTCGGCAAAATTCAACCTCGCCTGTTTACCAAAAACATCGCCTCTTGATTAAACATAAGAGGTCCAGCCTGCCCAGTGACATAGTTCAACGGCCGCGGTACCCTAACCGTGCGAAGGTAGCATAATCAATTGTCCTCTAAATAGGGACTAGTATCAACGGCACCACGAGGGTTATACTGTCTCCTTTCTCCAATCAGTGAAACTGATCTCCCCGTGAAGAAGCGGGGATATTACTATAAGACGAGAAGACCCCATGGAGCTTTAAACTTATCATACACCTCT-ACGATCT-ATTAACCCACTCATCCCAAAAACC-TGTATGCTAATTTTAGGTTGGGGGGACCACGGAGTATAATTCAACCTCCACAACAAACGGGCTAATACCCTTATCCATGAAAAACACCTCTAAGAATTAATAAACTAATGTTTAATGATCCGATAA-CTCGATCAATGAACCAAGTTACCCTGGGGATAACAGCGCAATCTACTTCAAGAGCTCCTATCGACAAGTAGGTTTACGACCTCGATGTTGGATCAGGGTATCCTAATGGTGCAACCGCTACTAATG-GTTCGTTTGTTCAACG pustulosaJAC10555 ACCGTAAACAATTAATTTACACCAATCAGCGCCAGGGAATTACGAGCAATGCTTAAAACCCAAAGGATTTGACGGTGCCCCACCCAACTAGAGGAGCCTGTTCTATAATCGATAATCCCCGCTACACCTGACCATTTCTCGCCT-TCAGTCTGTATACCTCCGTCGAAAGCTTACCATGTGAACGTTT-ACAGTAAGCCTAATGACGTCAACACGTCAGGTCAAGGTGCAGCTTAAGAAATGGGAAGCAATGGGCTACAATTTCTAATTTAGAACAAACGGAAGACTATGTGAAACCCTAGTC-TCAAGGTGGATTTAGTAGTAAAAAGAAAATAAAGTGTTCTTTTTAACCTGGCTCTGGGACGCGTACACACCGCCCGTCACCCTCTTCGATAGTA-TATTCTTCTGTTCCTAACTA-ACTACTCACGTTTT-AGAAAAGGTAAGTCGTAACATGGTAAGTGTACTGGAAAGTGCACTTGG--TA--AACAAAATGTAACTTAAATAAAGTTCTCCGCTTACACCGTAGGTATATCTGTTAAAATCAGTTCATTTTGAGCCAAAAATCTAGCCTACTACA-TCATATGTCTCCCATCCCAAA-TAAAACATTTTATTATTTTAGTATAGGTGATCGAAAAA-TTTCTAAGCGCTTCAGATAAAGTACCGTAAGGGAAAGATGAAATAGAACTGAAATAACCTCAA-GCCTTAAATAGTAGAGATTACCCCTCGTACCTTTTGCATCATGGTCTAGTTAGTTTATTC-AAGCAAAATGAAATTTT--AGTTAGACACCCCGAAACTAAGGGAGCTACTTAAAAACAGCCTAAT-GGGCCAACCCGTCTCTGTTGCAAAAGAGTGGGAAGATTTTTTAGTAGAGGTGAAAAACCTACCGAACTTAGAGATAGCTGGTTGTTCAGGAAAAGAGTTTTAGCTCTACCTTAAGCCTTTTT-ATGTTATAA-AGCAAATTC-TAAAGCTTAAGAGTTATTCAAATAAGGCACAGCTTATTTGAAATAGGATACAACCTATATAATAGGGT-AAATTATAATGTAGAAAAA-TTAAGTGGGCCTGAAAGCAGCCACCTTCAAAAAAGCGTCAAAGCTTCATTTATATC-TTTAGTAA-TTTCTTAAATTATTAATAACCCTTTTT-TTCTACTGAACTATTTTATA-TTTTTATAAAAGTAATTATACTAGAACTAGTAACAAGAAATTGAAC-TTCTCCTAAATGTAAACATAAACCAAAATTGACACTCTATTGGTAATTAACGTAAATGCAGAAACTATAATAAAGTAC--------TAGAAAAACCTATAACTTCC-AACGTTAACCTTACACCAGTACATTTCAGGAAAGATTAAAAGAGAAAGAAGGAACTCGGCAAAATTTAGCCTCGCCTGTTTACCAAAAACATCGCCTTTCGATAAAATATGAGAGGTCCAGCCTGCCCAGTGACATAGTTCAACGGCCGCGGTACCCTAACCGTGCGAAGGTAGCATAATCAATTGTCCTCTAAATAGGGACTAGCATCAATGGCACCACGAAGGTTACACTGTCTCCTTTCTCTAATCAGTGAAACTGATCTCCCCGTGAAGAAGCGGGGATATAATTATAAGACGAGAAGACCCCATGGAGCTTTAAACTAATTATACACCTTT-TTAATAT--TAAATCCACTAAACCCAAAAACC-TGTATGCTAGTTTTAGGTTGGGGGGACCACGGAGTATAATATAACCTCCACAACAAATGGGTTAACACCCTTATCCACGAAAGACACCTCTAAGAATTAACAAACTAATGTTTAATGATCCGATAA-TTCGATTAATGAACCAAGTTACCCTGGGGATAACAGCGCAATCTACTTCAAGAGCTCCTATCGACAAGTAGGTTTACAACCTCGATGTTGGATCAGGGTATCCTAATGGTGCAACCGCTACTAATG-GTTCGTTTGTTCAACG pipiensJSF1119 ACCGTAAACAATTAATTTACACCCATCAGCGCCAGGGAAATACGAGCAATGCTTAAAACCCAAAGGATTTGACGGTGTCCCACCCAGCTAGAGGAGCCTGTTCTTTAATCGATGATCCCCGCTACACCTGACCATTTCTTGCTCATCAGTCTGTATACCTCCGTCGAAAGCTTACCATGTGAACGTCT-TCAGTAGGCTCAATGATATCAATACGTCAGGTCAAGGTGCAGCTTAAGAAATGGGAAGCAATTGGCTACCATTTCTAATATAGAACAAACGAAAGGCTATGTGAAATCATAGCAGCGAAGGTGGATTTAGTAATAAAAAAAAAATATAGTGTTCTTTTTAACCCGGCTCTGGGATGCGTACACACCGCCCGTCACCCTCTTCGATAGTA-AATAATATTGTCCCTAACCC-ATTAT-CACGTTTT-AGAAGAAGCAAGTCGAAACATGGTAAGTGTACTGGAAAGTGCACTTGGAAAA-TAACAAAATGTAGCTTAACAAAAGCCCTTCGCTTACACCGAAGAAATATCTGTTCAAATCAGATCATTTTGAGCCCAAAATCTAGCCTTCAAGACTCACATGAACCCCATTCCAAA-CAAAACATTCTCCCATTATAGTACAGGTGATAGAAAAAATTCTTAAGCGCTTTAGACAAAGTACCGCAAGGGAAAGATGAAATAGAACTGAAATAACCTTAAAGCCCTAAACAGCAGAGACACCCCCTCGTACCTTTTGCATCATGGTCTAGCTAGTCTACAC-AAGCAAAATGAAACTTTT-AGTTAGTCCTCCCGAAACTAAGCGAGCTACTTCAAAACAGCCCAATGGGGCTAACCCTTCTCTGTTGCAAAAGAGTGGGAAGATTTTCAAGTAGAAGTGAAAAGCCTACCGAGCTTAGAGATAGCTGGTTATTCAGGAAAAGAGTTTTAGCTCTACCTTAAGCTTCTTT-ATGACACCT-AACAAACCC-TAAAGCTTAAGAGCTATTCAAATAAGGCACAGCTTATTTGAAACAGGATACAACCTACAATACTGGGT-AAATAATAAGTGAATAAAA-TAAAGTTGGCCTAAAAGCAGCCACCTT-AAAAAAGCGTTAAAGCTTAGTTCTACAC-ACTTACAA-TTTCTAAAATTTTGATCAACCCTTTAT-CCCTACTGAATTATTTTATA-TCCTTATAAAAGTAATAATGCTAGAACTAGTAACAAGAAACTGCCCATTCTCCTAAATGCAAGCATAAACCAGAATAGACACCCTACTGGTAATCAACGTAAATGTCACTTTTATAGTAACATAG--------TAGAAAATCCTATAATCCCCTTACGTTAACCTTACACTAGAGCATTTCAGGAAAGATTAAAAGAGAAAGAAGGAACTCGGCAAACTTCAGCCTCGCCTGTTTACCAAAAACATCGCCTCTTGAAAAAATATAAGAAGTCCAGCCTGCCCAGTGACTAAGTTTAACGGCCGCGGTACCCTAACCGTGCGAAGGTAGCATAATCACTTGTTCTTTAATTGGGGACTCGTATCAACGGCATCACGAAGGCTGCACTGTCTCCTTTCTCTAATCAGTGAAACTGATCTCCCCGTGAAGAAGCGGGGATAAAATTATAAGACGAGAAGACCCCATGGAGCTTCAAACTCATCATGCAACTCT-GTCCTCC-ATATCCCT-TAATTC-AAGAGATG-TGCATGTTAGTTTTGGGTTGGGGGGACCTCGGAGTATAACTTAACCTCCAAAACAAATGGGTTAACACCTTTATCCGCGAGAAACACCTCTAAGAATTACTAAACTAATGCTTTTTGATCCGATAA--TCGATCAATGGACCAAGTTACCCTGGGGATAACAGCGCAATCTACTTCAAGAGTTCATATCGACAAGTGGGTTTACGACCTCCATGTTGGATCAGGGTATCCTGGTGGTGCAACCGCTACTAATG-GTTCGTTTGTTCAACG pipiensY10945 GCCGTAAACAATTAATTTACACCCATCAGCGCCAGGGAACTACGAGCAATGCTTAAAACCCAAAGGATTTGACGGTGTCCCACCCAGCTAGAGGAGCCTGTTCTTTAATCGATGATCCCCGCTACACCTGACCATTTCTTGCTCATCAGTCTGTATACCTCCGTCGAAAGCTTACCATGTGAACGTCT-TCAGTAGGCTCAATGATATCAATACGTCAGGTCAAGGTGCAGCTTAAGAAATGGGAAGCAATTGGCTACAATTTCTAATATAGAACAA-CGAAAGGCTATGTGAAATCATAGCAGCGAAGGTGGATTTAGTAGTAAAAAGAAAATAGAGTGTTCTTTTTAACCCGGCTCTGGGATGCGTACACACCGCCCGTCACCCTCTTCGATAGTA-AATAATATTATCCCTAACCC-ATTAT-CACGTTTT-AGAAGAGGCAAGTCGAAACATGGTAAGTGTACTGGAAAGTGCACTTGGAAAA-TAACAAAATGTAGCTTAACAAAAGCCCTTCGCTTACACCGAAGAAATATCTGTTCAAATCAGATCATTTTGAGCCCAAAATCTAGCCTTCAAAACTCACATGAACCCCCTTCCAAA-CAAAACATTCTCCCATTATAGTACAGGTGATAGAAAAAATTCTTAAGCGCTTTAGACAAAGTACCGCAAGG-AAAGATGAAATAGAACTGAAATAACCTTAAAGCCCTAAACAGCAGAGACACCCCCTCGTACCTTTTGCATCATGGTCTAGCTAGTCTACAC-AAGCAAAATGAAACTTTT-AGTTAGTCCTCCCGAAACTAAGCGAGCTACTTCAAAACAGCCCAATGGGCGTAACCCTTCTCTGTTGCAAAAGAGTGGGAAGATTTTCAAGTAGAAGTGAAAAGCCTACCGAGCTTAGAGATAGCTGGTTATTCAGGAAAAGAGTTTTAGCTCTACCTTAAGCTTCTTT-ATGACACCT-AACAAACCC-TAAAGCTTAAGAGCTATTCAAATAAGGCACAGCTTATTTGAAACAGGATACAACCTACAATACTGGGT-AAATAATAAGTGAATAAAA-TAAAGTTGGCCTAAAAGCAGCCACCTT-AAAAAAGCGTTAAAGCTTAGTTCTACAC-ACTTACAA-TTTCTAAAATTTTGATCAACCCTTTAT-CCCTACTGAATTATTTTATA-TCCTTATAAAAGTAATAATGCTAGAACTAGTAACAAGAAACTGCCCATTCTCCTAAATGCAAGCATAAACCAGAATAGACACCCTACTGGTAATCAACGTAAATGTCACTTTTATAGTAACATAG--------TAGAAAATCCTATAATCCCCTTACGTTAACCTTACACTAGAGCATTTCAGGAAAGATTAAAAGAGAAAGAAGGAACTCGGCAAACTTCAGCCTCGCCTGTTTACCAAAAACATCGCCTCTTGAAAAAATATAAGAGGTCCAGCCTGCCCAGTGACTAAGTTTAACGGCCGCGGTACCCTAACCGTGCGAAGGTAGCATAATCACTTGTTCTTTAATTGGGGACTCGTATCAACGGCATCACGAAGGCTGCACTGTCTCCTTTCTCTAATCAGTGAAACTGATCTCCCCGTGAAGAAGCGGGGATAAAATTATAAGACGAGAAGACCCCATGGAGCTTCAAACTCATCATGCAACTCT-GTCCTCC-ATATCCCT-TAATTC-AAGAGATG-TGCATGTTAGTTTTGGGTTGGGGGGACCTCGGAGTATAACTTAACCTCCAAAACAAATGGGTTAACACCTTTATCCGCGAGAAACACCTCTAAGAATTATTAAACTAATGCTTTTTGATCCGATAA--TCGATCAATGGACCAAGTTACCCTGGGGATAACAGCGCAATCTACTTCAAGAGTTCATATCGACAAGTGGGTTTACGACCTCGATGTTGGATCAGGGTATCCTGGTGGTGCAACCGCTACTAATG-GTTCGTTTGTTCAACG dunniJSF1017 GCCGTAAACAATTAATTTACACCCATCAGCGCCAGGGAACTACGAGCAATGCTTAAAACCCAAAGGATTTGACGGTGTCCCACCCAGCTAGAGGAGCCTGTTCTTTAATCGATGATCCCCGCTACACCTAACCATTTCTTGCTCATCAGTCTGTATACCTCCGTCGAAAGCTTACCATGTGAACGTCC-TCAGTAGGCTCAATGATATCAACACGTCAGGTCAAGGTGCAACTTAAGAAGTGGGAAGTAATTGGCTACAATTTCTAATCTAGAACAAACGAAAGGCTATGTGAAATCATAGCAGCGAAGGTGGATTTAGTAGTAAAAAGAAAATAGAGTGTTCTTTTTAACCCGGCTCTGGGATGCGTACACACCGCCCGTCACCCTCTTCGATAGTA-AATAATTCTGTCCCTAACCC-ATTAT-CACGTTTT-AGAAGAGGCAAGTCGAAACATGGTAAGTGTACTGGAAAGTGCGCTTGGAAAA-TAACAAAATGTAGCTTAACAAAAGCCCTTCGCTTACACCGAAGAAATATCTGTTAAAATCTGATCATTTTGAGCCCCAAATCTAGCCTTCAAAACTCGCATGAACCTCCTTCCTAA-CAAAACATTCTTCCATTATAGTACAGGTGATAGAAAAA-TTTTTAAGCGCTTTAGACATAGTACCGCAAGGGAAAGATGAAATAGAACTGAAATAACCTTAAAGCCCTAAACAGCAGAGACACCCCCTCGTACCTTTTGCATCATGGTCTAGCTAGTCTACCC-AAGCAAAATGAAACTTTT-AGTTAGCCCCCCCGAAACTAAGCGAGCTACTTCAAAACAGCCCCAAGGGGCTAACCCTTCTCTGTTGCAAAAGAGTGGGAAGATTTTCAAGTAGAAGTGAAAAGCCTACCGAGCTTAGAGATAGCTGGTTATTCAGGAAAAGAGTTTTAGCTCTACCTTAAGCTTCTTT-ATGACATCT-AACAAACCC-TAAAGCCTAAGAGCTATTCAAATAAGGCACAGCTTATTTGAAACAGGATACAACCTATTATATTGGGT-AAATTGCTAGTAGACAGAA-TAAAGTTGGCCTAAAAGCAGCCATCTT-AAAAAAGCGTTAAAGCTTAGTTTTACCT-ACTCCTAA-TTTCTTAAATTTTTATTAACCCTTTAC-CCCTACTGAATTATTTTATA-CCTCTATAAAAGCAATAATGCTAGAACTAGTAACAAGAAACTGCCTATTCTCCTAAATGCAAACATAAACCAGAATAGACACCCTACTGGTAATTAACGTAAATGTTACTTCTGTAGCAACACAA--------TAGAAAACCCTACATTACTC-CACGTTAACCTTACACCAGAGCATTTCAGGAAAGATTAAAAGAGAAAGAAGGAACTCGGCAAACCTCAGCCTCGCCTGTTTACCAAAAACATCGCCTCTTGAAAAAATATAAGAGGTCCAGCCTGCCCAGTGACTAAGTTTAACGGCCGCGGTACCCTAACCGTGCGAAGGTAGCATAATCACTTGTTCTTTAATTGGGGACTCGTATCAACGGCATCACGAAGGCTGCACTGTCTCCTTTCTCCAATCAGTGAAACTGATCTCCCCGTGAAGAAGCGGGGATATGATTATAAGACGAGAAGACCCCATGGAGCTTCAAACTCACCATACAACTCT-GTTCTAC-ACACCCTT-TAATTC-AAGAAATT-TGTATGTTAATTTTGGGTTGGGGGGAACTCGGAATATAACTTAACCTCCAAAACAAATAGGTTAACACCTTTATCCATGAAATACACCTCTAAGAATTACTAAACTAATGCTC-TTGATCCGATTA--TCGATCAATGAACCAAGTTACCCTGGGGATAACAGCGCAATCTACTCCAAGAGTTCATATCGACAAGTAGGTTTACAAACTCCATGTTGGATCAGGGTATCCTAATGGTGCAACCGCTACTAATG-GTTCGTTTGTTCAACG montezumaeJAC8836 GCCGTAAACAATTAATTTACACCCATCAGCGCCAGGGAAATACGAGCAATGCTTAAAACCCAAAGGATTTGACGGTGTCCCACCCAACTAGAGGAGCCTGTTCTTTAATCGATGATCCCCGCTACACCTAACCATTTCTTGCTCATCAGTCTGTATACCTCCGTCGAAAGCTTACCATGTGAACGTCC-TCAGTAGGCTCAATGATATCAACACGTCAGGTCAAGGTGCAACTTAAGAAGTGGGAAGTAATTGGCTACAATTTCTAATCTAAAACAAACGAAAGGCTATGTGAAATCATAGCAGCGAAGGTGGATTTAGTAGTAAAAAAAAAATATAGTGTTCTTTTTAACCCGGCTCTGGGATGCGTACACACCGCCCGTCACCCTCTTCGATAGTA-AATAATTCTGTTCCTAACCC-ATTAT-AGCGTTTT-AGAAGAAGCAAGTCGAAACATGGTAAGTGTACTGGAAAGTGCGCTTGGAAA--TAACAAAATGTAGCTTAACAAAAGCCCTTCGCTTACACCGAAGAAATATCTGTTAAAATCTGATCATTTTGAGCCCCAAATCTAGCCTTCAAAACTCACATGAACCTCCTTCCTAA-CAAAACATTCTCCCATTATAGTACAGGTGATAGAAAAA-TTTTTAAGCGCTTTAGACACAGTACCGCAAGGGAAAGATGAAATAGAACTGAAATAACCTTAAAGCCCTAAACAGCAGAGACACCCCCTCGTACCTTTTGCATCATGGTCTAGCTAGTCTACCC-AAGCAAAATGAAACTTTT-AGTTAGTCTCCCCGAAACTAAGCGAGCTACTTCAAAACAGCCCTATGGGGCTAACCCTTCTCTGTTGCAAAAGAGTGGGAAGATTTTCAAGTAGAAGTGAAAAGCCTACCGAGCTTAGAGATAGCTGGTTATTCAGGAAAAGAGTTTTAGCTCTACCTTAAGCTTCTTT-ATGACATCT-AACAAACCC-TAAAGCCTAAGAGCTATTCGAATAAGGCACAGCTTATTTGAAACAGGATACAACCTATTATACTGGGT-AAATTGCTAGTAGACAGAA-TAAGGTTGGCCTAAAAGCAGCCATCTT-AAAAAAGCGTTAAAGCTTAGTTTTACCT-ACTCCTAA-TTTCTTAAATTTTTATTAACCCTTTAT-CCCTACTGAATTATTTTATA-CCTCTATAAAAGCAATAATGCTAGAACTAGTAACAAGAAACTGCCCATTCTCCTAAATGTAAACATAAACCAGAATAGACACCCTACTGGTAATTAACGTTTTTGTTACTTCTGTAGCAACACAA--------TAGAAAACCCTACATCACTC-CACGTTAACCTTACACTAGAACATTTCAGGAAAGATTAAAAGAGAAAGAAGGAACTCGGCAAACCTCAGCCTCGCCTGTTTACCAAAAACATCGCCTCTTGAAAAAATATAAGAGGTCCAGCCTGCCCAGTGACTAAGTTTAACGGCCGCGGTACCCTAACCGTGCGAAGGTAGCATAATCACTTGTTCTTTAATTGGGGACTCGTATCAACGGCATCACGAAGGCTGCACTGTCTCCTTTCTCCAATCAGTGAAACTGATCTCCCCGTGAAGAAGCGGGGATATTTTTATAAGACGAAAAGACCCCATGGAGCTTCAAACTCACCATACAACTCT-GTTCTAC-ACACCCCC-TAATTC-AAAAAATA-TGTATGTTAGTTTTGGGTTGGGGGGACCTCGGAGTATAACTTAACCTCCAAAACAAATAGGTTAACACCTTTATCCATGAGATACACCTCTAAGAATTACTAAACTAATGCTC-TTGATCCGATTA--TCGATCAATGAACCAAGTTACCCTGGGGATAACAGCGCAATCTACTTCAAGAGTTCATATCGACAAGTAGGTTTACAACCTCGATGTTGGATCAGGGTATCCTAATGGTGCAACCGCTACTAATG-GTTCGTTTGTTCAACG sp_2_mex_JSF1106 GCCGTAAACAATTAACTTACACCCATCAGCGCCAGGGAACTACGAGCAATGCTTAAAACCCAAAGGATTTGACGGTGTCCCACCCAACTAGAGGAGCCTGTTCTTTAATCGATGATCCCCGCTACACCTGACCATTTCTTGCTCATCAGTCTGTATACCTCCGTCGAAAGCTTACCATGTGAACGTCC-TCAGTAGGCTCAATGATATCAACACGTCAGGTCAAGGTGCAACTTAAGAAATGGGAAGTAATTGGCTACAATTTCTAATCTAGAACAAACGAAAGGCTATGTGAAACCATAGCAGCGAAGGTGGATTTAGTAGTAAAAAGAAAACAGAGTGTTCTTTTTAACCCGGCTCTGGGATGCGTACACACCGCCCGTCACCCTCTTCGATAGTA-AATAATTCTGTCCCTAACCT-AATAT-CACGTCTT-AGAAGAGGCAAGTCGAAACATGGTAAGTGTACTGGAAAGTGCACTTGGTAAA-TAACAAAATGTAGCTTAACAAAAGCCCTTCGCTTACACCGAAGAAATATCTGTGAGAATCTGATCATTTTGAGCCTCAAATCTAGCCTTTAAAACTCTTATGAACCTCCTTCCAAA-CAAAACATTCTCCTATTATAGTACAGGTGATAGAAAAA-TTTTTAAGCGCTTTAGACAAAGTACCGCAAGGGAAAGATGAAATAGAACTGAAATAACCTTAAAGCCCTAAACAGCAGAGACACCCCCTCGTACCTTTTGCATCATGGTCTAGCTAGTCTACCC-AAGCAAAATGAAACTTTT-AGTTAGTCCTCCCGAAACTAAGCGAGCTACTTCAAAACAGCCCCATGGGGCTAACCCTTCTCTGTTGCAAAAGAGTGGGAAGATTTTCAAGTAGAAGTGAAAAGCCTACCGAGCTTAGAGATAGCTGGTTATTCAGGAAAAGAGTTTTAGCTCTACCTTAAGCTTCTTT-ATGACATCT-AACAAACCC-TAAAGCCTAAGAGCTATTCAAATTAGGGACAGCTTATTTGAAACAAGATACAACCTACTACACTGGGT-AA-TTGCT-GTAAATTAAA-TAAAGTTGGC-TAAAAGCAGC-ACCTT-AAAAAAGCGTTAAAGCTTAGTTTTATCT-ACTCCTAA-TTTCTTAAATTTTAACCAACCCTTTAC-CCCTACTGAATTATTTTATA-CCTCTATAAAAGCAATAATGCTAGAACTAGTAACAAGAAACTGCCTATTCTCCTAAATGCAAGTATAAACCAGAATAGACACTCTACTGGTAATTAACGTAAATGTCATTTATGTAGCAACACAA--------TAGAAAACCCTACATTACTC-CACGTTAACCTTACACCAGAGCATTTCAGGAAAGATTAAAAGAGAAAGAAGGAACTCGGCAAACCTCAGCCTCGCCTGTTTACCAAAAACATCGCCTCTTGAAAAAATATAAGAGGTCCAGCCTGCCCAGTGACTAAGTTTAACGGCCGCGGTACCCTAACCGTGCGAAGGTAGCATAATCACTTGTTCTTTAATTGGGGACTCGTATCAACGGCATCACGAAGGCTGCACTGTCTCCTTTCTCTAATCAGTGAAACTGATTTCCCCGTGAAGAAGCGGGGATAAAATTATAAGACGAGAAGACCCCATGGAGCTTCAAACTCACTATACAACTCT-GTTCTCC-ATACCCAT-TAATTC-AAGAGATA-TGTGTATTAGTTTTGGGTTGGGGGGACCTCGGAGTATAACTTAACCTCCGAAACAAATAGGTTGACACCTTTATCCATGAGAAACACCTCTAAGAATTACTAAACTAATGCTT-TTGATCCGATAA--TCGATCAATGAACCAAGTTACCCTGGGGATAACAGCGCAATCTACTTCAAGAGTTCATATCGACAAGTGGGTTTACGACCTCGATGTTGGATCAGGGTATCCTAGTGGTGCAACCGCTACTAATG-GTTCGTTTGTTCAACG chiricahuensisJSF1063 ????????????????????????ATCACCGCCAGGGAACTACGAGCAATGCTTAAAACCCAAAGGATTTGACGGTGTCCCACCCAACTAGAGGAGCCTGTTCTTTAATCGATGATCCCCGCTACACCTGACCATTTCTTGCTCATCAGTCTGTATACCTCCGTCGAAAGCTTACCATGTGAACGTCC-TCAGTAGGCTCAATGATATCAACACGTCAGGTCAAGGTGCAACTTAAGAAATGGGAAGTAATTGGCTACAATTTCTAATCTAGAACAAACGAAAGGCTATGTGAAATCATAGCAGCGAAGGTGGATTTAGTAGTAAAAAGAAAATAGAGTGTTCTTTTTAACCCGGCTCTGGGACGCGTACACACCGCCCGTCACCCTCTTCGATAGTA-AATAATTCTGTCCTTAACCT-AATAT-AACGTCTT-AGAAGAGGCAAGTCGAAACATGGTAAGTGTACTGGAAAGTGCACTTGGAAAA-TAACAAAATGTAGCTTAACAAAAGCCCTTCGCTTACACCGAAGAAATATCTGTTAAAATCTGATCATTTTGAGCCTCAAATCTAGCCTTCAAAACTCACATGAACCTCCTTCCAAA-CAAAACATTCTCCTATTATAGTACAGGCGATAGAAAAA-TTTTTAAGCGCTTTAGATAAAGTACCGCAAGGGAAAGATGAAATAGAACTGAAACAACCTTAAAGCCCTACACAGCAGAGACTCCCCCTCGTACCTTTTGCATCATGGTCTAGCTAGTCTACCC-AAGCAAAATGAAACTTTT-AGTTAGTCCCCCCGAAACTAAGCGAGCTACTTCAAAACAGCCCCATGGGGCTAACCCTTCTCTGTTGCAAAAGAGTGGGAAGATTTTCAAGTAGAAGTGAAAAGCCTACCGAGCTTAGAGATAGCTGGTTATTCAGGAAAAGAGTTTTAGCTCTACCTTAAGCTTCTTT-ATGACATCT-AACAAACCC-TAAAGCCTAAGAGCTATTCAAATAAGG?ACAGCTTATTTGAAACAGGATACAACCTATTATACTGGGT-AAATTGCCAGTAAATAAAA-TAAAGTTGGGCTAAAAGCAGCCATCTT-AAAAAAGCGTTAAAGCTTAGTTTTTTCT-ACTCATAA-TTCCTAAA-TTTTAACCAACCCTTTAC--CCTACTGAATTATTT-ATA-CCTCTATAAAAGTG-TAATGCTAGAACTAGTTACAAGAAAC?GCTAC--CTCCTAA-TGCAAGCATAAACCAGAATAGACACTCTACTGGTAATTAACGTAAATGTCATTTCTGTAGCAACACAA--------TAGAAAACCCTACATTACTC-CACGTTAACCTTACACCAGAGCATTTCAGGAAAGATTAGAAGAGAAAGAAGGAACTCGGCAAACCTCAGCCTCGCCTGTTTACCAAAAACATCGCCTCTTGACAAAATATAAGAGGTCCAGCCTGCCCAGTGACTAAGTTCAACGGCCGCGGTACCCTAACCGTGCGAAGGTAGCATAATCACTTGTTCTTTAATTGGGGACTCGTATCAACGGCATCACGAAGGCTGCACTGTCTCCTTTCTCCAATCAGTGAAACTGATCTCCCCGTGAAGAAGCGGGGATTAAATTAT?AGACGAGAAGACCC-ATGGAGCTTCAAACTCATTATACAACTCT-GTTCTCC-ACACCCCC-T?ATT?-AAGAGATA-TGTA-GTTAGTTTTGGGTTTGGGGGACCTCGGAGTATAACTTAACCTCCAAAACAAATTGGTTAACA?CTTTTTCCATGAGAAACA?CTCTTA?AA?TACTAAACTAATGCTT-TTGATCCGATTA--TCCAT?AATGAACCAAGTTACCCT?GGGATAACAACGCAATCTACTTTAAGAGTTAATATCGACCAGTGGGTTTACAACCTCCATTTTGGATAAGGGTACCC?AATGGTGCAACCGCTCCTAAAG-GTTCGTTTGTT?AAC? chiricahuensisJSF1092 GCCGTAAACAATTAACTTACATCCATCAGCGCCAGGGAACTACGAGCAATGCTTAAAACCCAAAGGATTTGACGGTGTCCCACCCAACTAGAGGAGCCTGTTCTTTAATCGATGATCCCCGCTACACCTGACCATTTCTTGCTCATCAGTCTGTATACCTCCGTCGAAAGCTTACCATGTGAACGTCC-TCAGTAGGCTCAATGATATCAACACGTCAGGTCAAGGTGCAACTTAAGAAATGGGAAGTAATTGGCTACAATTTCTAATCTAGAACAAACGAAAGGCTATGTGAAATCATAGCAGCGAAGGTGGATTTAGTAGTAAAAAGAAAATAGAGTGTTCTTTTTAACCCGGCTCTGGGATGCGTACACACCGCCCGTCACCCTCTTCGATAGTA-AATAATTCTGTCCTTAACCT-AATAT-AACGTCTT-AGAAGAAGCAAGTCGAAACATGGTAAGTGTATGGGAAAATGCACTTGGAAAA-TAACAAAATGTAGCTTAACAAAAGCCCTTCGCTTACACCGAAGAAATATCTGTTAAAATCTGATCATTTTGAGCCTCAAATCTAGCCTTCAAAACTCACATGAACCTCCTTCCAAA-CAAAACATTCTCCTATTATAGTACAGGCGATAGAAAAA-TTTTTAAGCGCTTTAGATAAAGTACCGCAAGGGAAAGATGAAATAGAACTGAAACAACCTTAAAGCCCTAGACAGCAGAGATTCCCCCTCGTACCTTTTGCATCATGGTCTAGCTAGTCTACCC-AAGCAAAATGAAACTTTT-AGTTAGTCCCCCCGAAACTAAGCGAGCTACTTCAAAACAGCCCCATGGGGCTAACCCTTCTCTGTTGCAAAAGAGTGGGAAGATTTTCAAGTAGAAGTGAAAAGCCTACCGAGCTTAGAGATAGCTGGTTATTCAGGAAAAGAGTTTTAGCTCTACCTTAAGCTTCTTT-ATGACATCT-AACAAACCC-TAAAGCCTAAGAGCTATTCAAATAAGGCACAGCTTATTTGAAACAGGACACAACCTATTATACTGGGT-AAATTGCCAGTAAATAAAA-TAAAGTTGGCCTAAAAGCAGCCATCTT-AAAAAAGCGTTAAAGCTTAGTTTTATCT-ACTTATAA-TTTCTTAAATTTTAACCAACCCTTTAC-CCCTACTGAATTATTTTATA-CCTCTATAAAAGTAATAATGCTAGAACTAGTAACAAGAAACTGCTTA-TCTCCTAAATGCAAGCATAAACCAGAATAGACACTCTGCTGGTAATTAACGTAAATGTCATTTCTGTAGCAACACAA--------TAGAAAACCCTACATCACTC-CACGTTAACCTTACACTAGAGCATTTCAGGAAAGATTAAAAGAGAAAGAAGGAACTCGGCAAACCTCAGCCTCGCCTGTTTACCAAAAACATCGCCTCTTGACAAAATATAAGAGGTCCAGCCTGCCCAGTGACTAAGTTCAACGGCCGCGGTACCCTAACCGTGCGAAGGTAGCATAATCACTTGTTCTTTAATTGGGGACTCGTATCAACGGCATCACGAAGGCTGCACTGTCTCCTTTCTCCAATCAGTGAAACTGATCTCCCCGTGAAGAAGCGGGGATAAAATTATAAGACGAGAAGACCCCATGGAGCTTCAAACTCATCATACAACTCT-GTTCTCC-ACACCCCC-TAATTC-AAGAAATA-TGTATGTTAATTTTGGGTTGGGGGGACCTCGGAGTATAACTTAACCTCCGAAACAAATAGGTTAACACCTTTATCCATGAGAAACACCTCTAAGAATTACTAAACTAATGCTT-TTGATCCGATAA--TCGATCAATGAACCAAGTTACCCTGGGGATAACAGCGCAATCTACTTCAAGAGTTCATATCGACAAGTGGGTTTACGACCTCGATGTTGGATCGGGGTATCCTAGTGGTGCAACCGCTACTAATG-GTTCGTTTGTTCAACG subaquavocalis GCCGTAAACAATTAACTTACATCCATCAGCGCCAGGGAACTACGAGCAATGCTTAAAACCCAAAGGATTTGACGGTGTCCCACCCAACTAGAGGAGCCTGTTCTTTAATCGATGATCCCCGCTACACCTGACCATTTCTTGCTCACCAGTCTGTATACCTCCGTCGAAAGCTTACCATGTGAACGTCC-TCAGTAGGCTCAATGATATCAACACGTCAGGTCAAGGTGCAACTTAAGAAATGGGAAGTAATTGGCTACAATTTCTAATCTAGAACAAACGAAAGGCTATGTGAAATCATAGCAGCGAAGGTGGATTTAGTAGTAAAAAGAAAATAGAGTGTTCTTTTTAACCCGGCTCTGGGACGCGTACACACCGCCCGTCACCCTCTTCAATAGTA-AATAATTCTGTCCTTAACCT-AATAT-AACGTCTT-AGAAGAGGCAAGTCGAAACATGGTAAGCGTACTGGAAAGTGCACTTGGAAAA-TAACAAAATGTAGCTTAACAAAAGCCCTTCGCTTACACCGAAGAAATATCTGTTAGAATCTGATCATTTTGAGCCTCAAATCTAGCCTTCAAAACTCACATGAACCTCCTTCCAAA-CAAAACATTCTCCTATTATAGTACAGGCGATAGAAAAA-TTTTTAAGCGCTTTAGATAAAGTACCGCAAGGGAAAGATGAAATAGAACTGAAACAACCTTAAAGCCCCAGACAGCAGAGATTCCCCATCGTACCTTTTGCATCATGGTCTAGCTAGTCTACCC-AAGCAAAATGAAACTTTT-AGTTAGTCCCCCCGAAACTAAGCGAGTTACTTCAAAACAGCCCCATGGGGCTAACCCTTCTCTGTTGCAAAAGAGTGGGAAGATTTTCAAGTAGAAGTGAAAAGCCTACCGAGCTTAGAGATAGCTGGTTATTCAGGAAAAGAGTTTTAGCTCTACCTTAAGCTTCTTT-ATGACATCT-AACAAACCC-TAAAGCCTAAGAGCTATTCAAATAAGGCACAGCTTATTTGAAACAGGATACAACCTATTATACTGGGT-AAACTGCCAGTAAATAAAA-TAAAGTTGGCCTAAAAGCAGCCATCTT-AAAAAAGCGTTAAAGCTTAGTTTTATCT-ACTCATAA-TTTCTTAAATTTTTACCAACCCTTTAC-CCCTACTGAATTATTTTATA-CCTCTATAAAAGTGATAATGCTAGAACTAGTAACAAGAAACTGCTTA-TCTCCTAAATGCAAGCATAAACCAGAATAGACACTCTACTGGTAATTAACGTAAATGTCATCTCTGTAGCAACACAA--------TAGAAAACCCTACATTACTC-CACGTTAACCTTACACCAGAGCATTTCAGGAAAGATTAAAAGAGAAAGAAGGAACTCGGCAAACCTCAGCCTCGCCTGTTTACCAAAAACATCGCCTCTTGAGAAAATATAAGAGGTCCAGCCTGCCCAGTGACTAAGTTCAACGGCCGCGGTACCCTAACCGTGCGAAGGTAGCATAATCACTTGTTCTTTAATTGGGGACTCGTATCAACGGCATCACGAAGGCTGCACTGTCTCCTTTCTCCAATCAGTGAAACTGATCTCCCCGTGAAGAAGCGGGGATAAAATTATAAGACGAGAAGACCCCATGGAGCTTCAAACTCATCATACAACTCT-GTTCTCC-ACACCCCC-TAATTC-AAGAGATG-TGTATGTTAGTTTTGGGTTGGGGGGACCTCGGAGTATAACTTAACCTCCAAAACAAATAGGTTAACACCTTTATCCATGAGAAACACCTCTAAGAATTACTAAACTAATGCTT-TTGATCCGATAA--TCGATCAATGAACCAAGTTACCCTGGGGATAACAGCGCAATCTACTTCAAGAGTTCATATCGACAAGTGGGTTTACGACCTCGATGTTGGATCAGGGTATCCTAGTGGTGCAACCGCTACTAATG-GTTCGTTTGTTCAACG palustrisJSF1110 ACCGTAAACAATTAATTTACACCTATCAGCGCCAGGGAATTACGAGCAATGCTTAAAACCCAAAGGATTTGACGGTGTCCCACCCAGCTAGAGGAGCCTGTTCTATAATCGATGATCCCCGCTACACCCGACCATTTTTCGCTCATCAGTCTGTATACCTCCGTCGAAAGCTTACCATGTGAACGTTCTTCAGTAGGCTCAATGATATCAACACGTCAGGTCAAGGTGCAGCTTAAGAAATGGGAAGTAATTGGCTACAATTTCTAATCTAGAACAAACGAAAGGCTATGTGAAATCATAACCACGAAGGTGGATTTAGTAGTAAAAAGAAAATAGAGTGTTCTTTTTAACCCGGCTCTGGGACGCGTACACACCGCCCGTCACCCTCTTCGATAGTA-CACAATTTTGTCCCTAACCA-ATTTT-CACGTTTT-AGAAGAAGCAAGTCGAAACACGGTAAGTGTACTGGAAAGTGCACTTGGAATA-TAACAAAATGTAGCTTAATAAAAGCCCTTCGCTTACACCGAAGAAATATCCGTTGAAATCTGATCATTTTGAGCCCTAAGTCTAGCCTTAAACCCTCGCATGAACCCCCTTCCAAA-CAAAACATTTTCCCATCATAGTACAGGCGATCGAAAAA-TTTCTAAGCGCTTCAGATAAAGTACCGCAAGGGAAAGATGAAATAGAACTGAAATAACCTTAAAGCCTCAAACAGCAGAGACATCTCCTCGTACCTTTTGCATCATGGTCTAGTTAGTCCACCC-AAGCAAAATGAAACTTTT-AGTTAGCCCTCCCGAAACTAAGCGAGCTACTTCAAAACAGCCCTATGGGGCCAACCCTTCTCTGTTGCAAAAGAGTGGGAAGATTTTCAAGTAGAAGTGAAAAGCCTACCGAGCTTAGAGATAGCTGGTTATTCAGGAAAAGAGTTTTAGCTCTACCTTAAGCTTCTTA-ATGAAACCT-AACAAACCT-TAAAGCTTAAGAGCTATTCAAATAAGGCACAGCTTATTTGAAATAGGATACAACCTAAAATACTGGGT-AAATACATAGTAATTAAAA-TAAAGTTGACCTAAGAGCAGCCATCTTCAAAAAAGCGTTAAAGCTTAGTTTTATCT-ACTAGTAA-TTTCTAAAATCTCAATCAACCCTTTAT-CCCTACTGAATTATTTTATA-TTTATATAAAAGTAATAATGCTAGAATTAGTAACAAGAAATTGTTTATTCTCCTAAATGCAAGCATAAACCAAAATAGACACCCTGTTGGTAATTAACGTAAATGTAACAAATATAGCAACACAA--------TAGAAAACCCTACAACCCTC-AACGTTAATCTTACACTAGAGCATTTCAGGAAAGATTTAAAGAGAAAGAAGGAACTCGGCAAACTTCAGCCTCGCCTGTTTACCAAAAACATCGCCTCTTGAAAAAATATAAGAGGTCCAGCCTGCCCAGTGACTAAGTTCAACGGCCGCGGTACCCTAACCGTGCGAAGGTAGCATAATCACTTGTTCTTTAAATGGGGACTTGTATCAACGGCACCACGAAGGCTGCACTGTCTCCTTTCTCCAATCAGTGAAACTGATCTCCCCGTGAAGAAGCGGGGATTAAACTATAAGACGAGAAGACCCCATGGAGCTTCAAACTCACCATACAACTCT-GTTCCCT-ACATCCCC-TGATTC-AAGAGATA-TGTATGTTAGTTTTGGGTTGGGGGGACCTCGGAGTACAACTTAACCTCCTAAACAAATAGGTTAACACCTTTATCCACGAGAAACACCTCTAAGAATTATTAAACTAATGTTT-TTGATCCGATAA--TCGATCAATGAACCAAGTTACCCTGGGGATAACAGCGCAATCTACTTCAAGAGTTCCTATCGACAAGTAGGTTTACGACCTCCATGTTGGATCAGGGTATCCTAATGGTGCAACCGCTACTAATG-GTTCGTTTGTTCAACG areolataJSF1111 GCCGTAAACAATTTATTTACACCTATAAGCGCCAGGGAATTACGAGCAATGCTTAAAACCCAAAGGATTTGACGGTGTCCCACCCAGCTAGAGGAGCCTGTTCTATAATCGATGATCCCCGCTACACCCGACCATTTTTCGCTCATCAGTCTGTATACCTCCGTCGAAAGCTTACCATGTGAACGTTCTTCAGTAGGCTCAATGATATCAACACGTCAGGTCAAGGTGCAACTTAAGAAATGGGAAGCAATTGGCTACAATTTCTAATCTAAAACAAACGAAAGGCTATGTGAAACCATAACCATGAAGGTGGATTTAGTAGTAAAAAAAAAATATAGTGTTCTTTTTAACCCGGCTCTGGGATGCGTACACACCGCCCGTCACCCTCTTCGATAGTA-CACAATATTGTCCGTAACCA-ATTTT-TACATTTTTAGAAGAAGCAAGTCGAAACACGGTAAGTGTACTGGAAAGTGCACTTGGAATA-TTACAAAATGTAGCTTAATAAAAGCACTTCGCTTACACCGAAGAAATATCCGTTCAAATCTGATCATTTTGAGCCCTAAATCTAGCCCCAAAAACTCGCATGAACCCCACCCCAAA-CAAAACATTTTCCCATCCTAGTACAGGCGATCGAAAAA-TTTCTAAGAGCTTCAGATAAAGTACCGCAAGGGAAAGATGAAATAGAACTGAAATAACCTTAAAGCCTTAAACAGCAGAGACACCCTCTCGTACCTTTTGCATCATGGTCTAGTTAGTCTACCC-AAGCAAAATGAAACTTTT-AGTTAGTCCCCCCGAAACTAAGCGAGCTACTTCAAAACAGCCCCATGGGGCCAACCCTTCTCTGTTGCAAAAGAGTGGGAAGATTTTCAAGTAGAAGTGAAAAGCCTACCGAGCTTAGAGATAGCTGGTTATTCAGGAAAAGAGTTTTAGCTCTACCTTAAGCTTCTTA-ATGAAACCC-AACAAGCCT-CAAAGCTTAAGAGCTATTCAAATAAGGCACAGCTTATTTGAAACAGGATACAACCTAAAATACAGGGT-AAATAAATAGTAATTTAAA-TGAAGTTGGCCTAAAAGCAGCCATCTTTAAAAAAGCGTTAAAGCTTAGTTTCACCT-ACTAGTAA-TCTCCAAAATTCTAATTAACCCTTTAT-CCCTACTGAATTATTTTATA-TTTTTATAAAAGCAATAATGCTAGAACTAGTAACAAGAAATTGCCTATTCTCCTAAATGCAAGCATAAACCAAAATAGACACCCTATTGGTAATCAACGTAAATGTAATAACTATAGCAACATAA--------TTGAAAACCCTACAACCCCT-AACGTTAACCTTACACTAGAGCATTTCAGGAAAGATTTAAAGAGAAAGAAGGAACTCGGCAAACTTCAGCCTCGCCTGTTTACCAAAAACATCGCCTCTTGAAAAAATATAAGAGGTCCAGCCTGCCCAGTGACTAAGTTCAACGGCCGCGGTACCCTAACCGTGCGAAGGTAGCATAATCACTTGTTCTTTAAATGGGGACTTGTATCAACGGCACCACGAAGGCTGCACTGTCTCCTTTCTCTAATCAGTGAAACTGATCTCCCCGTGAAGAAGCGGGGATTTGACTATAAGACGAGAAGACCCCATGGAGCTTCAAACTCACTATACAACTCT-GTTCTCT-ACATCTTA-TAATTC-AAGAGTTA-TGTATATTAGTTTTGGGTTGGGGGGACCTCGGAGTATAACTAAACCTCCTAAACAAATAGGTTAACACCTTTATCCACGAGAAACACCTCTAAGAATTATTAAATTAATGTTT-TTGATCCGATAA--TCGATCAATGAACCAAGTTACCCTGGGGATAACAGCGCAATCTACTTCAAGAGTTCCTATCGACAAGTAGGTTTACAACCTCCATGTTGGATCAGGGTATCCTAATGGTGCAACCGCTACTGATG-GTTCGTTTGTTCAACA sevosaUSC8236 GCCGTAAACAATTAATTTACACCCATCAGCGCCTGGGAATTACGAGCAATGCTTAAAACCCAAAGGATTTGACGGTGTCCCACCCAACTAGAGGAGCCTGTTCTATAATCGATGATCCCCGCTACACCCGACCATTTTTCGCTCATCAGTCTGTATACCTCCGTCGAAAGCTTACCATGTGAACGTTCTCTAGTAGGCTCAATGATATCAACACGTCAGGTCAAGGTGCAGCTTAAGAAATGGGAAGCAATTGGCTACAATTTCTAACTTAGAACAAACGAAAGGCTATGTGAAATCATAGCCATGAAGGTGGATTTAGTAGTAAAAAGAAAATAGAGTGTTCTTTTTAACCCGGCTCTGGGACGCGTACACACCGCCCGTCACCCTCTTCAATAGTA-CACAATATTGTCCATAACCA-ATTCT-TACGTTTT-AGAAGAGGCAAGTCGAAACACGGTAAGTGTACTGGAAAGTGCACTTGGAATA-TAACAAAATGTAGCTTAACAAAAGCCCTTCGCTTACACCGAAGAAATATCCGTTTAAATCTGATCATTTTGAGCCCTAAATCTAGCCCTAAAAACTCGCATGAACCCCACTCCAAA-CAAAACATTTTCCCATCATAGTACAGGCGATCGAAAAA-TTTCTAAGCGCTTCAGATAAAGTACCGCAAGGGAAAGATGAAATAGAACTGAAATAACCTTAAAGCTCTAAATAGCAGAGACACCCTCTCGTACCTTTTGCATCATGGTCTAGTTAGTCTACCC-AAGCAAAATGAAACTTTT-AGTTAGTCCTCCCGAAACTAAGCGAGCTACTTCAAAACAGCCCTATGGGGCCAACCCTTCTCTGTTGCAAAAGAGTGGGAAGATTTTCAAGTAGAAGTGAAAAGCCTACCGAGCTTAGAGATAGCTGGTTATTCAGGAAAAGAGTTTTAGCTCTACCTTAAGCTTCTTA-ATGAAACCT-AACAAACCT-TAAAGCTTAAGAGCTATTCAAATAAGGCACAGCTTATTTGAAATAGGATACAACCTAAAATACTGGGT-AAATAAGTAGTAATAAAAA-TGAAGTTGACCTAAGAGCAGCCATCTTTTAAAAAGCGTTAAAGCTTAGTTTTACCT-ACTAGTAA-TTTCCAAAATTCCAATCAACCCTTTAT-TCCTACTGAATTATTTTATA-CCTTTATAAAAGTAATAATGCTAGAACTAGTAACAAGAAATTGATTATTCTCCTAAATGCAAGCATAAACCAATATAGACATCCTATTGGTGATTAACGTAAATGTAACAACTGTAGCAACATAA--------TAGAAAACCCTGCAACTCCC-AACGTTAATCTTACACTAGAGCATTTCAGGAAAGATTTAAAGAGAAAGAAGGAACTCGGCAAACTTCAGCCTCGCCTGTTTACCAAAAACATCGCCTCTTGAAAAATTATAAGAGGTCCAGCCTGCCCAGTGACTAAGTTCAACGGCCGCGGTACCCTAACCGTGCGAAGGTAGCATAATCACTTGTTCTTTAAATGGGGACTTGTATCAACGGCACCACGAAGGCTGCACTGTCTCCTTTCTCCAATCAGTGAAACTGATCTCCCCGTGAAGAAGCGGGGATTTGATTATAAGACGAGAAGACCCCATGGAGCTTCAAACTCACCATACAACTCT-GCTCTCC-ACATCCCA-CAATTC-AAGAGACA-TGTATGTTAGTTTTGGGTTGGGGGGACCTCGGAGTACAACTTAACCTCCTAAACAAATAGGTTAACACCTTTATCCACGAGAAACACCTCTAAGAATTATTAAACTAATGTTT-TTGATCCGATAA--TCGATCAATGAACCAAGTTACCCTGGGGATAACAGCGCAATCTACTTCAAGAGTCCCTATCGACAAGTAGGTTTACGACCTCGATGTTGGATCAGGGTATCCTAGTGGTGCAACCGCTACTGA------------------- capitoSLU003 GCCGTAAACAATTAATTTACACCCATCAGCGCCAGGGAATTACGAGCAATGCTTAAAACCCAAAGGATTTGACGGTGTCCCACCCAACTAGAGGAGCCTGTTCTATAATCGATGATCCCCGCTACACCCGACCATTTTTCGCTCATCAGTCTGTATACCTCCGTCGAAAGCTTACCATGTGAACGTTCTCTAGTAGGCTCAATGATATCAACACGTCAGGTCAAGGTGCAGCTTAAGAAATGGGAAGCAATTGGCTACAATTTCTAACTTAGAACAAACGAAAGGCTATGTGAAATCATAGCCATGAAGGTGGATTTAGCAGTAAAAAGAAAATAGAGTGTTCTTTTTAACCCGGCTCTGGGACGTGTACACACCGCCCGTCACCCTCTTCAATAGTA-CACAATATTGTCCATAACCA-ATTCT-TACGTTTT-AGAAGAGGCAAGTCGAAACACGGTAAGTGTACTGGAAAGTGCACTTGGAATA-TAACAAAATGTAGCTTAACAAAAGCCCTTCGCTTACACCGAAGAAATATCCGTTAAA-TCTGATCATTTTGAGCCCTAAATCTAGCCCTAAAAACTCGCATGAACCCCACTCCAAA-CAAAACATTTTCCCATCATAGTACAGGCGATCGAAAAA-TTTCTAAGCGCTTCAGATAAAGTACCGCAAGGGAAAGATGAAATAGAACTGAAATAACCTTAAAGCTCTAAATAGCAGAGACACCCTCTCGTACCTTTTGCATCATGGTCTAGTTAGTCTACCC-AAGCAAAATGAAACTTTT-AGTTAGTCCTCCCGAAACTAAGCGAGCTACTTCAAAACAGCCCTATGGGGCCAACCCTTCTCTGTTGCAAAAGAGTGGGAAGATTTTCAAGTAGAAGTGAAAAGCCTACCGAGCTTAGAGATAGCTGGTTATTCAGGAAAAGAGTTTTAGCTCTACCTTAAGCTTCTTA-ATGAAACTT-AACAAACCT-TAAAGCTTAAGAGCTATTCAAATAAGGCACAGCTTATTTGAAATAGGATACAACCTAAAATACTGGGT-AAATAAATAGTAATAAAAA-TGAAGTTGACCTAAGAGCAGCCATCTTTTAAAAAGCGTTAAAGCTTAGTTTTTCAT-ACTAATAA-TTTCCAAAATTCCAATCAACCCTTTAT-TCCTACTGAATWWTTTTATA-CCTTTATAAAAGTAATAATGCTAGAACTAGTAACAAGAAATTGATTATTCTCCTAAATGCAAGCATAAACCAATATAGACATCCTATTGGTAATTAACGTAAGTGTAACAACTGAAGCAACATAA--------TAGAAAACCCTGCAACTCCC-AACGTTAATCTTACACTAGAGCATTTCAGGAAAGATTTAAAGAGAAAGAAGGAACTCGGCAAACTTCAGCCTCGCCTGTTTACCAAAAACATCGCCTCTTGAAAAATTATAAGAGGTCCAGCCTGCCCAGTGACTAAGTTCAACGGCCGCGGTACTCTAACCGTGCGAAGGTAGCATAATCACTTGTTCTTTAAATGGGGACTTGTATCAACGGCACCACGAAGGCTGCACTGTCTCCTTTCTCCAATCAGTGAAACTGATCTCCCCGTGAAGAAGCGGGGATTTGATTATAAGACGAGAAGACCCCATGGAGCTTCAAACTCACCATACAACTCT-GCTCTAT-ACATCCCA-CAATTC-AAGAGTCA-TGTATGTTAGTTTTGGGTTGGGGGGACCTCGGAGTACAACTTAACCTCCTAAACAAATAGGTTAACACCTTTATCCACGAGAAACACCTCTAAGAATTATCAAACTAATGTTT-TTGATCCGATAA--TCGATCAATGAACCAAGTTACCCTGGGGATAACAGCGCAATCTACTTCAAGAGCCCCTATCGACAAGTAGGTTAACGACCTCGATGTTGGATCAGGGTATCCTAGTGGTGCAACCG-------------------------- spectabilisJAC8622 GCCGTAAACAATTAATTTACATCTATCAGCGCCAGGGGACTACGAGCAATGCTTAAAACCCAAAGGATTTGACGGTGTCCCACCCAGCTAGAGGAGCCTGTTCTATAATCGATGATCCCCGCTATACCCCACCATTTTTAGCTTATCAGTCTGTATACCTCCGTCGAAAACTTACCATGTGAACGCCCTTCAGTAGGTTCAATGATATCAACACGTCAGGTCAAGGTGCAGCTTAAGAAATGGAAAGCAATTGGCTACAATTTCTAATTTAGAACAAACGAAAGGCTATGTGAAATCATAACCATGAAGGTGGATTTAGTAGTAAAAAGAAAATAGAGTGTTCTTTTTAACCCGGCTCTGGGACGTGTACACACCGCCCGTCACCCTCTTCGATAGTC-CATGATACTGTCCCTAACAA-ATTAT-TACGTTTT-AGAAGAAGCAAGTCGAAACACGGTAAGTGTACTGGAAAGTGCACTTGGAATA-TAACAAAATGTAGCTTAATAAAAGCCCTTCGCTTACACCGAAGAAACGTCTGTTTGAATCAGATCATTTTGAGCCCCAAATTTAGCCTTCAAAATTCGCATGAACCCTTCTCCAAA-CAAAACATTTTCTTACTCTATTACAGGTGATCAAAAAA-TTTCTAAGCGCTTCAGACAAAGTACCGCAAGGGAAAGATGAAATAGAATTGAAATAACCTTAAAGCCTTAAACAGCAGAGATACTACCTCGTACCTTTTGCATCATGGTCTAGTTAGTTTACCC-AAGCAAAATGAAACTTTT-AGTTAGCCTCCCCGAAACTAAGCGAGCTACTTCAAAACAGCCCTATGGGGCCAACCCTTCTCTGTTGCAAAAGAGTGGGAAGATTTTCAAGTAGAAGTGAAAAGCCTACCGAGCTTAGAGATAGCTGGTTATTCAGGAAAAGAGTTTTAGCTCTACCTTAAGCTTCTTC-ATGAAACTC-AACAAACCC-CAAAGCTTAAGAGCTATTCAAATAAGGCACAGCTTATTTGAAACAGGACACAACCTAAGATACTGGGT-AAATAACA-GTAATTAAAA-TTAAGTTGGCCTAAGAGCAGCCATCTTTAAAAAAGCGTTAAAGCTTAGTTTTATAC-ACTAGTAA-TTTCTAAAATTTTAATTAACCCTTTAC-CCCTACTGAATTATTTTATA-TCTTTATAAAAGTAATAATGCTAGAACTAGTAACAAGAAACTGCCTGTTCTCCTAAATGCAAGCATAAACCAAAATAGACACCCTATTGGTAATTAACGTTAATGTAACAGCTGTAGCAACATAA--------TAGAAAACCCTACAACCTCC-AACGTTAACCTTACACTAGAGCATTCCAGGAAAGATTAAAAAAGAAAGAAGGAACTCGGCAAACTTCAGCCCCGCCTGTTTACCAAAAACATCGCCTCTTGAAAAATTATAAGAGGTCCAGCCTGCCCAGTGACTAAGTTCAACGGCCGCGGTACCCTAACCGTGCGAAGGTAGCATAATCACTTGTTCTTTAAATGAGGACTTGTATCAACGGCACCAC?AGGACTGCACTGTCTCCTTTCTTCAATCAGTGAAACTGATCTCCCCGTGAAGAAGCGGGGATTAGACTATAAGACGAGAAGACCCCATGGAGCTTCAAACTCATTATACAACTCT-GTTCCCC-ATACCCCT-TGATAT-AAGAGACA-TGTATAATAGCTTTGGGTTGGGGGGACCTCGGAGTACAACTTAACCTCCTAAGCAAATAGGTTAACACCTTTATCCATGAGAAACACCTCTAAGAATTATCAAATTAATGTCT-TTGACCCGATAA--TCGATCAATGAACCAAGTTACCCTGGGGATAACAGCGCAATCTACTTCAAGAGTTCCTATCGACAAGTGGGTTTACGACCTCGATGTTGGATCAGGGTATCCCAGTGGTGCAACCGCTACTAACG-GTTCGTTTGTTCAACG forreriJSF1065 GCCGTAAACAATTAACTTACACCCATCAGCGCCAGGGAATTACGAGCAATGCTTAAAACCCAAAGGATTTGACGGTGTCCCAACCAGCTAGAGGAGCTTGTTCTATAATCGATGATCCCCGATACACCCGACCATTTTTAGCTTATCAGTCTGTATACCTCCGTCGAAAGCTTACCATGTGAACGTTTCTCAGTAGGCTCAATGATATCAACACGTCAGGTCAAGGTGCAGCTTAAGAAATGGGAAGCAATTTGCTACAATTTCTAATTTAGAACAAACGAAAGGCTATGTGAAATCATAACCACGAAGGTGGATTTAGTAGTAAAAAGAAAATAGAGTGTTCTTTTTAACCCGGCTCTGGGACGTGTACACACCGCCCGTCACCCTCTTCGATAGTC-CATAATACTGTCCCTAACCC-TTTAT-TACATTTT-AGAAGAGGCAAGTCGAAACATGGTAAGTGTACTGGAAAGTGCACTTGGAATA-TATCAAAATGTAGCTTAATAAAAGCCCTTCGCTTACACCGAAAGAATATCTGTTTAAATCAGGTCATTTTGAGCCTTAAATCTAGCCTTTAAAATTCGCATGAACCCCCTCCCAAA-CAAAACATTTTATCATTTTAGTACAGGCGATCGAAAAA-TTTCTAAGCGCTTCAGACATAGTACCGCAAGGGAAAGATGAAATAGAACTGAAATAACCTTAAAGCATCAAATAGCAGAGATTTCCCCTCGTACCTTTTGCATCATGGTCTAGTTAGTCTTCCCCAAGCAAAATGAAACTTTT-AGTTAGTCCCCCCGAAACTAAGCGAGCTACTTCAAAACAGCCCCACGGGGCCAACCCTTCTCTGTTGCAAAAGAGTGGGAAGATTTTTAAGTAGAAGTGAAAAGCCTATCGAGCTTAGAGATAGCTGGTTATTCAGGAAAAGAGTTTTAGCTCTACCTTAAGCTTCCTT-ATGAAACCC-AACAAACCT-TAAAGCTTAAGAGCTATTCAAATAAGGCACAGCTTATTTGAAACAGGATACAACCTAAAATAATGGGT-AAATTGAGAGTTATCAAAA-TTAAGTTGGCCTAAGAGCAGCCATCTTTAAAAAAGCGTTAAAGCTTAGTTTTATTT-ACTAATAA-TCCCTAAAATTCTAATCAACCCTTTAT-CCCTACTGAATTATTTTATA-CCCCTATAAAAATAATAATGCTAGAACTAGTAACAAGAAACTGCCCGTTCTCCTAAATGCAAGCATAAACCAAAATAGACACCCTGTTGGTAATTAACGTAAATGTAACAGCTATAGCAACATAA--------TAGAAAACCCTATAATCCCT-AACGTTAACCTTACACTAGAGCATTTCAGGAAAGATTAAAAAAGAAAGAAGGAACTCGGCAAACTTCAGCCCCGCCTGTTTACCAAAAACATCGCCTCTTGAAAAATTATAAGAGGTCCAGCCTGCCCAGTGACTAAGTTCAACGGCCGCGGTACCCTAACCGTGCGAAGGTAGCATAATCACTTGTTCTTTAAATGGGGACTCGTATCAACGGCACCACGAGGGCTGCACTGTCTCCTTTCTTCAATCAGTGAAACTGATCCCCCCGTGAAGAAGCGGGGATATAAATATAAGACGAGAAGACCCCATAGAGCTTCAAACTCATCATACAATTCT-GTGCTCT-AGACCCCA-TAACAC-AAGAAATG-TGTATGTTAGTTTTGGGTTGGGGGGACCTCGGAGTATAACTTAACCTCCTAAACGAACAGGTTAACACCTTTATCCATGAGAAACACCTCTAAGAATTATTAAATTAATGTAT-TTGACCCGATAG--TCGATCAATGAACCAAGTTACCTTGGGGATAACAGCGCAATCTACTTCAAGAGTTCCTATCGACAAGTAGGTTTACGACCTCCATGTTGGATCAGGGTATCCCGGTGGTGCAACCGCTACCAATG-GTTCGTTTGTTCAACG tlalociJSF1083 GCCGTAAACAATTAATTTACACCTATCAGCGCCAGGGAACTACGAGCAATGCTTAAAACCCAAAGGATTTGACGGTGTCCCACCCAGCTAGAGGAGCCTGTTCTATAATCGATGATCCCCGCTACACCCGACCATTTTTAGCTAATCAGTCTGTATACCTCCGTCGAAAGCTTACCATGTGAACGCTCTTCAGTAGGCTCAATGATATCAACACGTCAGGTCAAGGTGCAGCTTAAGAAATGGGAAGCTATTGGCTACAATTTCTAACTTAGAACAAACGAAAGGCTATGTGAAATCATAACCATGAAGGTGGATTTAGTAGTAAAAAGAAAATATAGTGTTCTTTTTAACCCGGCTCTGGGACGTGTACACACCGCCCGTCACCCTCTTCGATAGTT-CCTAATACTGTCCCTAACCC-ATTAT-TACATTTT-AGAAGAAGCAAGTCGAAACACGGTAAGTGTACTGGAAAGTGCACTTGGAAAA-TAACAAAATGTAGCTTAATAAAAGCCCTTCGCTTACACCGAAGAAATATCCGTTGAAATCAGATCATTTTGAGCCCTAAATCTAGCCTTCAAAATTCGCATGAACCTCTCCCCAAA-CAAAACATTTTCTTATCCTAGTACAGGCGATCGAAAAA-TTTCTAAGCGCTTCAGATAAAGTACCGCAAGGGAAAGATGAAATAGAATTGAAATAACCTTAAAGCCTTAAACAGCAGAGATATTACCTCGTACCTTTTGCATCATGGTCTAGTTAGTCTACCC-AAGCAAAATGAAACTTTT-AGTTAGTCCCCCCGAAACTAAGCGAGCTACTTCAAAACAGCCTTAT-GGGCCAACCCTTCTCTGTTGCAAAAGAGTGGGAAGATTTTCAAGTAGAAGTGAAAAGCCTACCGAGCTTAGAGATAGCTGGTTACTCAGGAAAAGAGTTTTAGCTCTACCTTAAGCTTCATT-ATGAAACCC-AACAAACCT-TAAAGCTTAAGAGCTATTCAAATAAGGCACAGCTTATTTGAAATAGGATACAACCTAGAATACTGGGT-TAATAGTA-GCAATTAAAA-TTAAGTTGGCCTAAGAGCAGTCACCTTTAAAAAAGCGTTAAAGCTTGATTTCAACC-GCTAATAA-TTTCTAAAATTTTTAATAACCCTTTAT-CCCTACTGAGTCATTTTATA-AATGTATAAAAGTGATAATGCTAGAACTAGTAACAAGAAACTGCTCATTCTCCTAAATGCAAGCATAAACCAAAATAGACACCCTATTGGTAATTAACGTAAATGTAACAGCTATAGTAACACAA--------TAGAAAACCCTATAACCTCT-AACGTTAACCTTACACTAGAGCATTTCAGGAAAGATTTAAAAAGAAAGAAGGAACTCGGCAAACTTCAGCCCCGCCTGTTTACCAAAAACATCGCCTCTTGAAAAATTATAAGAGGTCCAGCCTGCCCAGTGACTAAGTTCAACGGCCGCGGTACCCTAACCGTGCGAAGGTAGCATAATCACTTGTTCTTTAAATGGGGACTTGTATCAACGGCACCACGAAGGCTGCACTGTCTCCTTTCTTCAATCAGTGAAACTGATCTCCCCGTGAAGAAGCGGGGATTAAACTATAAGACGAGAAGACCCCATGGAGCTTCAAACTCACCATACAACCCT-GTTCTCC-ACATCCTTATAACAT-AAGAAACA-TGTATGATAGTTTTGGGTTGGGGGGACCTCGGAGTATAACTTAACCTCCTAAGCAAACAGGTTAACACCTTTATCCATGAGAAACACCTCTAAGAATTATTAAATTAATGTAT-TTGACCCGATAA--TCGATCAATGAACCAAGTTACCCTGGGGATAACAGCGCAATCTACTTCAAGAGTTCCTATCGACAAGTGGGTTTACGACCTCCATGTTGGATCAGGGTATCCTAGTGGTGCAACCGCTACTAACG-GTTCGTTTGTTCAACG berlandieriJSF1136 GCCGTAAACAATTAATTTACACCTATCAGCGCCAGGGAACTACGAGCAATGCTTAAAACCCAAAGGATTTGACGGTGTCCCACCCAGCTAGAGGAGCCTGTTCTATAATCGATGATCCCCGCTACACCCGACCATTTTTAGCTAATCAGTCTGTATACCTCCGTCGAAAGCTTACCATGTGAACGCTCTTCAGTAGGCTCAATGATATCAACACGTCAGGTCAAGGTGCAGCTTAAGAAATGGGAAGCTATTGGCTACAATTTCTAACTTAGAACAAACGAAAGGCTATGTGAAATCATAACCATGAAGGTGGATTTAGTAGTAAAAAGAAAATAGAGTGTTCTTTTTAACCCGGCTCTGGGACGTGTACACACCGCCCGTCACCCTCTTCGATAGTT-CCTAATACTGTCCCTAACCC-ATTAT-TACATTTT-AGAAGAAGCAAGTCGAAACACGGTAAGTGTACTGGAAAGTGCACTTGGAAAA-TAACAAAATGTAGCTTAATAAAAGCCCTTCGCTTACACCGAAGAAATATCCGTTGAAATCAGATCATTTTGAGCCCTAAATCTAGCCTTCAAAATTCGCATGAACCTCTCCCCAAA-CAAAACATTTTCTTATCCTAGTACAGGCGATCGAAAAA-TTTCTAAGCGCTTCAGATAAAGTACCGCAAGGGAAAGATGAAATAGAATTGAAATAACCTTAAAGCCTTAAACAGCAGAGATATTACCTCGTACCTTTTGCATCATGGTCTAGTTAGTCTACCC-AAGCAAAATGAAACTTTT-AGTTAGTCCCCCCGAAACTAAGCGAGCTACTTCAAAACAGCCTTAT-GGGCCAACCCTTCTCTGTTGCAAAAGAGTGGGAAGATTTTCAAGTAGAAGTGAAAAGCCTACCGAGCTTAGAGATAGCTGGTTACTCAGGAAAAGAGTTTTAGCTCTACCTTAAGCTTCATT-?TGAAACCC-AACAAACCT-TAAAGCTTAAGAGCTATTCAAATAAGGCACAGCTTATTTGAAATAGGATACAACCTAGAATACTGGGT-AAATAGTA-GCAATTAAAA-TTAAGTTGGCCTAAGAGCAGTC?CCTTTAAAAAAGCGTTAAAGCTTGATTTCAACT-GCTAATAA-TTTCTAAAATTTTTAATAACCCTTTAT-CCCTAATGAGTCATTTTATA-AATGTATAAAAGCGATAATGCTAGAATTAGTAACAAGAAACTGCTCATTTTCTTAAATGCAAGCATAAACCAAAATAGACACCCTATTGGTAATTAACGTAAATGTAACAGCTATAGTAACACAA--------TAGAAAACCCTATAACCTCT-AACGTTAACCTTACATTAGAGCATTTCAGGAAAGATTAAAAAAGAAAGAAGGAACTCGGCAAACTTCAGCCCCGCCTGTTTACCAAAAACATCGCCTCTTGAAAAATTATAAGAGGTCCAGCCTGCCCAGTGACTAAGTTCAACGGCCGCGGTACCCTAACCGTGCGAAGGTAGCATAATCACTTGTTCTTTAAATGGGGACTTGTATCAACGGCACCACGAGGGCTGCACTGTCTCCTTTCTTCAATCAGTGAAACTGATCTCCCCGTGAAGAAGCGGGGATTAAACTATAAGACGAGAAGACCCCATGGAGCTTCAAACTCATCATACAACCCT-GTTCTCC-ACATCCTCATAACAT-AAGAGATA-TGTATGATAGTTTTGGGTTGGGGGGACCTCGGAGTATAACTTAACCTCCTAAGCAAACAGGTTAACACCTTTATCCATGAGAAACACCTCTAAGAATTATTAAATTAATGTAT-TTGACCCGATAA--TCGATCAATGAACCAAGTTACCCTGGGGATAACAGCGCAATCTACTTCAAGAGTTCCTATCGACAAGTGGGTTTACGACCTCGATGTTGGATCAGGGTATCCTAATGGTGCAACCGCTACTAACG-GTTCGTTTGTTCAACG neovolcanicaJSF960 GCCGTAAACAATTAATTTACACCTATCAGCGCCAGGGAACTACGAGCAATGCTTAAAACCCAAAGGATTTGACGGTGTCCCACCCAGCTAGAGGAGCCTGTTCTATAATCGATGATCCCCGCTACACCCGACCATTTTTAGCTAATCAGTCTGTATACCTCCGTCGAAAGCTTACCATGTGAACGCTCTTCAGTAGGCTCAATGATATCAACACGTCAGGTCAAGGTGCAGCTTAAGAAATGGGAAGCTATTGGCTACAATTTCTAACTTAGAACAAACGAAAGGCTATGTGAAATCATAACCATGAAGGTGGATTTAGTAGTAAAAAGAAAATAGAGTGTTCTTTTTAACCCGGCTCTGGGACGTGTACACACCGCCCGTCACCCTCTTCGATAGTT-CCTAATACTGTCCCTAACCC-ATTAT-TACATTTT-AGAAGAGGCAAGTCGAAACACGGTAAGTGTACTGGAAAGTGCACTTGGAAAA-TAACAAAATGTAGCTTAATAAAAGCCCTTCGCTTACACCGAAGAAATATCCGTTGAAATCAGATCATTTTGAGCCCTAAATCTAGCCTTCAAAATTCGCATGAACCTCTCCCCAAA-CAAAACATTTTCTTATCCTAGTACAGGCGATCGAAAAA-TTTCTAAGCGCTTCAGATAAAGTACCGCAAGGGAAAGATGAAATAGAATTGAAATAACCTTAAAGCCTTAAACAGCAGAGATATTACCTCGTACCTTTTGCATCATGGTCTAGTTAGTCTACCC-AAGCAAAATGAAACTTTT-AGTTAGTCCCCCCGAAACTAAGCGAGCTACTTCAAAACAGCCTTAT-GGGCCAACCCTTCTCTGTTGCAAAAGAGTGGGAAGATTTTCAAGTAGAAGTGAAAAGCCTACCGAGCTTAGAGATAGCTGGTTACTCAGGAAAAGAGTTTTAGCTCTACCTTAAGCTTCATT-ATGAAACCC-AACAAACCT-TAAAGCTTAAGAGCTATTCAAATAAGGCACAGCTTATTTGAAATAGGATACAACCTAGAATACTGGGT-AAATAGTA-GCAATTAAAA-TTAAGTTGGCCTAAGAGCAGTCACCTTTAAAAAAGCGTTAAAGCTTGATTTCAACC-GCTAATAA-TTTCTAAAATTTTTAACAACCCTTTAT-CCCTACTGAGTCATTTTATA-AATGTATAAAAGTGATAATGCTAGAACTAGTAACAAGAAACTGCTCATTCTCCTAAATGCAAGCATAAACCAAAATAGACACCCTATTGGTAATTAACGTAAATGTAACAGCTATAGTAACACAA--------TAGAAAACCCTATAACCTCT-AACGTTAACCTTACACTAGAGCATTTCAGGAAAGATTTAAAAAGAAAGAAGGAACTCGGCAAACTTCAGCCCCGCCTGTTTACCAAAAACATCGCCTCTTGAAAAATTATAAGAGGTCCAGCCTGCCCAGTGACTAAGTTCAACGGCCGCGGTACCCTAACCGTGCGAAGGTAGCATAATCACTTGTTCTTTAAATGGGGACTTGTATCAACGGCACCACGAAGGCTGCACTGTCTCCTTTCTTCAATCAGTGAAACTGATCTCCCCGTGAAGAAGCGGGGATTAAACTATAAGACGAGAAGACCCCATGGAGCTTCAAACTCACCATACAACCCT-GTTCTCC-ACATCCTTATAACAT-AAGAGACA-TGTATGAT-GTTTTGGGTTGGGGGGACCTCGGAGTATAACTTAACCTCCTAAGCAAACAGGTTAACACCTTTATCCATGAGAAACACCTCTAAGAATTATTAAATTAATGTAT-TTGACCCGATAA--TCGATCAATGAACCAAGTTACCCTGGGGATAACAGCGCAATCTACTTCAAGAATTCCTATCGACAAGTGGGTTTACGACCTCGATGTTGGATCAGGGTATCCTAGTGGTGCAACCGCTACTAACG-GTTCGTTTGTTCAACG blairiJSF830 GCCGTAAACAATTAATTTACACCTATCAGCGCCAGGGAACTACGAGCAATGCTTAAAACCCAAAGGATTTGACGGTGTCCCACCCAGCTAGAGGAGCCTGTTCTATAATCGATGATCCCCGCTACACCCGACCATTTTTAGCTAATCAGTCTGTATACCTCCGTCGAAAGCTTACCATGTGAACGCTCTTCAGTAGGCTCAATGATATCAACACGTCAGGTCAAGGTGCAGCTTAAGAAATGGGAAGCTATTGGCTACAATTTCTAATTTAAAACAAACGAAAGGCTATGTGAAATCATAACCATGAAGGTGGATTTAGTAATAAAAAAAAAATATATTGTTCTTTTTAACCCGGCTCTGGGACGTGTACACACCGCCCGTCACCCTCTTCGATAGTT-CTTAATACTGTCCCTAACCC-ATTAT-TACATTTT-AGAAGAAGCAAGTCGAAACACGGTAAGTGTACTGGAAAGTGCACTTGGAAAA-TAACAAAATGTAGCTTAATAAAAGCCCTTCGCTTACACCGAAAAAATATCCGTTGAAATCAGATCATTTTGAGCCCTAAATCTAGCCTTCAAAATTCGCATGAACCTCTCCCCAAA-CAAAACATTTTCTTATCCTAGTACAGGCGATCGAAAAA-TTTCTAAGCGCTTCAGATAAAGTACCGCAAGGGAAAGATGAAATAGAACTGAAATAACCTTAAAGCCTTAAACAGCAGAGATACTACCTCGTACCTTTTGCATCATGGTCTAGTTAGTCTACCC-AAGCAAAATGAAACTTTT-AGTTAGTCCCCCCGAAACTAAGCGAGCTACTTCAAAACAGCCTTAT-GGGCCAACCCTTCTCTGTTGCAAAAGAGTGGGAAGATTTTCAAGTAGAAGTGAAAAGCCTACCGAGCTTAGAGATAGCTGGTTACTCAGGAAAAGAGTTTTAGCTCTACCTTAAGCTTCATT-ATGAAACCC-AACAAACCT-TAAAGCTTAAGAGCTATTCAAATAAGGCACAGCTTATTTGAAATAGGATACAACCTAGAATACTGGGT-AAATAACA-GCAATTAAAA-TTAAGTTGGCCTAAGAGCAGCCACCTTTAAAAAAGCGTTAAAGCTTGATTTCTACT-GCTAATAA-TTTCTAAAATTTTTAACAACCCTTTAT-CCCTACTGAGTCATTTTATA-TGCGTATAAAAGTGATAATGCTAGAACTAGTAACAAGAAACTGCTCATTCTCCTAAATGCAAGCATAAACCAAAATAGACACCCTATTGGTAATTAACGTAAATGTAACAGCTATAGTAACACAA--------TAGAAAACCCTATAACCTCT-AACGTTAACCTTACACTAGAGCATTTCAGGAAAGATTAAAAAAGAAAGAAGGAACTCGGCAAACTTCAGCCCCGCCTGTTTACCAAAAACATCGCCTCTTGAAAAATTATAAGAGGTCCAGCCTGCCCAGTGACTAAGTTCAACGGCCGCGGTACCCTAACCGTGCGAAGGTAGCATAATCACTTGTTCTTTAAATGGGGACTTGTATCAACGGCACCACGAGGGCTGCACTGTCTCCTTTCTTCAATCAGTGAAACTGATCTCCCCGTGAAGAAGCGGGGATTAAACTATAAGACGAGAAGACCCCATGGAGCTTCAAACTCATCATACAACCCT-GTTCTCC-ATATCCTCATAACAT-AAGAGACA-TGTGTGATAGTTTTGGGTTGGGGGGACCTCGGAGTATAACTTAACCTCCTAAGCAAACAGGTTAACACCTTTATCCATGAGAAACACCTCTAAGAATTATTAAATTAATGTAT-TTGACCCGATAA--TCGATCAATGAACCAAGTTACCCTGGGGATAACAGCGCAATCTACTTCAAGAGTTCCTATCGACAAGTGGGTTTACGACCTCGATGTTGGATCAGGGTATCCCAGTGGTGCAACCGCTACTAATG-GTTCGTTTGTTCAACG omiltemanaJAC7413 ACCGTAAACAATTAATTTACACCTATCAGCGCCCGGGGATTACGAGCAATGCTTAAAACCCAAAGGATTTGACGGTGTCCCAACCAGCTAGAGGAGCCTGTTCTATAATCGATGATCCCCGATACACCCGACCATTTTTAGCTAATCAGTCTGTATACCTCCGTCGAAAGCTTACCATGTGAACGCTCTTCAGTAGGCTCAAGGATATCAATACGTCAGGTCAAGGTGCAGCTTAAGAAATGGGAAGTAATTGGCTACAATTTCTAACTTAGAACAAACGAAAGGTTATGTGAAATCATAACCACGAAGGTGGATTTAGTAGTAAAAAGAAAATAGAGTGTTCTTTTTAACCCGGCTCTGGGACGTGTACACACCGCCCGTCACCCTCTTCGATAGTT-ATTAATATTGTTCCTAACCC-CCTAT-TACGTCTT-AGAAGAAGCAAGTCGAAACACGGTAAGTGTACTGGAAAGTGCACTTGGAAAA-TAACAAAATGTAGCTTAATAAAAGCCCTTCGCTTACACCGAAGAAATGTCTGTTTGAATCAGATCATTTTGAGCCCTAAATCTAGCCTTCAAAATTCGCATGACCCCCTCCCCAA--CAAAACATTTTCTTATCCTAGTACAGGCGATCGAAAAA-TTTCTAAGCGCTTCAGACAAAGTACCGTAAGGGAAAGATGAAATAGAACTGAAATAATCCTAAAGCCCTAAATAGCAGAGACACAACCTCGTACCTTTTGCATCATGGTCTAGTTAGTCTACCC-AAGCAAAATGAAACTTTT-AGTTAGTCCCCCCGAAACTAAGCGAGCTACTTCAAAACAGCCCTATGGGGCCAACCCTTCTCTGTTGCAAAAGAGTGGGAAGATTTTCAAGTAGAAGTGAAAAGCCTACCGAGCTTAGAGATAGCTGGTTATTCAGGAAAAGAGTTTTAGCTCTACCTTAAGCTTCTTT-ATGAGACCT-AACAAACCT-TAAAGCTTAAGAGCCATTCAAATAAGGCACAGCTTATTTGAAATAGGATACAACCTAGAATATTGGGT-AAATAACA-GTGATTAAAG-TTAAGTTGGCCTAAGAGCAGCCACCTTCAAAAAAGCGTTAAAGCTTAACTCTATCA-ACTAATAA-TCTCTAAAATTTTAATCAACCCTTTAT-CCTTACTGAATTATTT-ATA-CCTTTATAAAAGTAATAATGCTAGAACTAGTAACAAGAAACTGCCTGTTCTCCTAAATGCAAGCATAAACCAAAATAGACACCCTATTGGTAGTTAACGTAAATGTAATAACTATAGTAACGTAA--------TAGAAAACCCTATAACCTCT-AACGTTAATCTTACACCAGTGCATTCCAGGAAAGATTAAAAAAGAAAGAAGGAACTCGGCAAATTTTAGCCCCGCCTGTTTACCAAAAACATCGCCTCTTGAAAAGTTATAAGAGGTCCAGCCTGCCCAGTGACTAAGTTCAACGGCCGCGGTACCCTAACCGTGCGAAGGTAGCATAATCACTTGTTCTTTAAATGGGGACTTGTATCAACGGCACCACGAAGGCTGCACTGTCTCCTTTCTCCAATCAGTGAAACTGATCTCTCCGTGAAGAAGCGGGGATTAAATTATAAGACGAGAAGACCCCATGGAGCTTCAAACTCATTATACAACTCT-GTGCTCT-ATGCCCCA-CAACACATAGAAGCA-TGTATAGTAGTTTTGGGTTGGGGTGACCTCGGAGTATAACTCAGCCTCCAAAACAAACAGGTTAACACCTTTATCCATGAGAAACACCTCTAAGAATTATTAAACTAATGTAT-TTGACCCGATAG--TCGATCAATGAACCAAGTTACCCTGGGGATAACAGCGCAATCTACTTCAAGAGTTCCTATCGACAAGTGGGTTTACAACCTCGATGTTGGATCAGGGTATCCCAGTGGTGCAACCGCTACTAATG-GTTCGTTTGTTCAACG magnaocularisJSF1073 ACCGTAAACAATTAATTTACACCTATCAGCGCCAGGGAATTACGAGCAATGCTTAAAACCCAAAGGATTTGACGGTGTCCCACCCAGCTAGAGGAGCCTGTTCTATAATCGATGATCCCCGCTACACCCGACCATTTCTAGCTCATCATTCTGTATACCTCCGTCAAAAGCTTACCATGTGAACCCTCTTCAGTAGGCTCAAAGATATCATCACGTCAGGTCGAGGTGCAACTTAAGAGATGGGAAGTGATTGGCTACCTTTTCTAAATTAAAACAAACAAAAGGTTATGTCAAATCCTGGCCGTGAAGGTGGATTTAGTAATAAAAAAAAATTTTATTGTTCTTTTTAACCCGGGTCTGGGACGTGTACCCCCCGCCCCTCCCCCTCTTCGATTGTC-CATGATCCTGTCCCCAACCC-TTTAT-TATTTTTA-AGAAAATGCAAGTCGAAACACTGTAATTGTACTGGAAAGTGCTCTTGGATTA-TATCAAAGTGTAGCTTACTTAAAGCCCTTCGCTTACACCGAAGAAACATCTGTTTAAACCGTATCATTTAGAGCCCTAAATCTAGCCTTCATAATTCGCATGAACCCCCTCCCAA--CAAAACATTTTCTTATTTAAGTACAGGCGATCGAAAAA-TTTCTAAGCGCTTCAGACGAAGTACCGCGGGGGAAAGATGAAATAGAATTGAAATCCCCCTACCGCCTCAAACAGCAGAGATACCCCCTCGTACCTTTTGCATCATGGTCTAGTTAGTCCACTC-AAGCAAAATGAAACTTTT-AGTTAGTCCCCCCGAAACTGGACGAGCTACTTCAAAACAGCCCTGGGGGGCCAACCCTTCTCTGTTGCAAAAGAGTGGGAAGATTTTCAAGTGTAAGTGACCAGCCTACCGAGCTTAGAGATAGTTGGTTATTCAGGAAAAGAGTTTTAGTTTTACCTTAAGGTTCTTT-ATGAAACCC-AACAAACTT-TAAAGCTTAAGAGCTATTCAAATAAGGCACAGCTTATTTGAAACAGGATACAACTTAAAATACTGGGT-AAATAGCA-GTAATTAAAA-TTAAGTCGGCCTAAGAGCAGTCATTTTTAAAAAAGCGTTAAAGCTTGATTTTAAAA-ACTAATAA-TTTTTGAAATTTTTATCAACCCTTTAC-CCCTACTGAATTATTTTATA-CCCCTATAAAAACGATAATGCTAGAACTAGTAACAAGAAATTGCCTATTTTCCTAAATGCAAGTATAAGCCAAAATAGACACCCTATTGGTAGTTAACGTAAATGTAGCAGTTGTAGTAACTTAA--------TAGAAAACCCTACAGCCCCA-AACGTTAATCTTACATTAGAGCATTCCAGGAAAGATTAAAAGAGAAAGAAGGAATTCGGCAAACTTCAGCCCCGCCTGTTTACCAAAAACATCGCCTCTTGAAAAATTATAAGAGGTCCAGCCTGCCCAGTGATTAAGTTCAACGGCCGCGGTACCCTAACCGTGCGAAGGTAGCATAATCACTTGTTCTTTAAATGGGGACTTGTATCAACGGCACCACGAGGGCTGCACTGTTTCCTTTCTTCAATCAGTGAAACTGATCTCCCCGTGAAGAAGCGGGGATAAAATTATAAGACGAGAAGACCCCATGGAGCTTCAAACTCACTATACAACTCT-GTGCCTT-TCACCCC--TAACAC-AAGAGACA-TGTATACTAGTTTTGGGTTGGGGGGACCTCGGAGTATAACTTAACCTCCAAAGCAAACAGGTTAACACCTTTATCCAAGAGAAACACCTCTAAGAATTATTAAATTAATGTAT-TTGACCCGATAG--TCGATCAATGAACCAAGTTACCCTGGGGATAACAGCGCAATCTACTTCAAGAGTTCCTATCGACAAGTAGGTTTACGACCTCGATGTTGGATCAGGGCATCCCAGTGGTGCAACCGCTACTAATG-GTTCGTTTGTTCAACG yavapaiensisJSF1085 ACCGTAAACAATTAATTTACACCCATCAGCGCCAGGGAATTACGAGCAATGCTTAAAACCCAAAGGATTTGACGGTGTCCCACCCAGCTAGAGGAGCCTGTTCTATAATCGATGATCCCCGCTACACCCGACCATTTTTAGCTCATCAGTCTGTATACCTCCGTCGAAAGCTTACCATGTGAACGCTCTTCAGTAGGCCCAATGA-GTCAACACGTCAGGTCAAGGTGCAGCTTAAGAAATGGGAAGCAATTGGCTACAATTTCTAACTTAGAACAAACGAAAGGCTATGTGAAACCATAACCACGAAGGTGGATTTAGTAGTAAAAAGAAAATAGAGTGTTCTTTTTAACCCGGCTCTGGGACGTGTACACACCGCCCGTCACCCTCTTCGATAGTT-CAAAATACTGTCCCTAACCC-TTTAT-TACGTTTT-AGAAGAAGCAAGTCGAAACACGGTAAGTGTACTGGAAAGTGCACTTGGAAAA-TAGCAAAATGTAGCTTAATTAAAGCCCTTCGCTTACACCGAAAAAACATCTGTTTGGATTAGATTATTTTGAGCCCTAAATCTAGCCTTCTAAACTCGCATGAACCCCCCCCCAAA-CAAAACATTTTCTCATCTTAGTACAGGCGATCGAAAAA-TTTCTAAGCGCTTCAGACAAAGTACCGTAAGGGAAAGATGAAATAGAATTGAAACAACCCTAAAGCCTTAAATAGCAGAGATAACCCCTCGTACCTTTTGCATCATGGTCTAGTCAGTCTACCC-AAGCAAAATGAAACTTTT-AGTTAGTCCCCCCGAAACTAAGCGAGCTACTTCAAAACAGCCCTATGGGGCCAACCCTTCTCTGTTGCAAAAGAGTGGGAAGATTTTCAAGTAGAAGTGAAAAGCCTACCGAGCTTAGAGATAGCTGGTTATTCAGGAAAAGAGTTTTAGCTCTACCTTAAGCTTCTTT-ATGAAACCC-AACAAACCT-TAAAGCTTAAGAGCTATTCAAATAAGGCACAGCTTATTTGAAATAGGATACAACCTAAAATACTGGGT-AAATAATA-GTAATTAAAA-TTAAGTTGGCCTAAGAGCAGCCACCTTTAAAAAAGCGTTAAAGCTTGATTTTATAC-ACTAGTAA-TCCCTAAAATTTTTATTAACCCTTTAC-CCCTACTGAATTATTTTATA-CCCCTATAAAAATGATAATGCTAGAACTAGTAACAAGAAACTGCCTATTCTCCTAAATGCAAGTATAAACCAAAATAGACCCCCTATTGGTAATTAACGTAAATGTAACAGTTATAGTAACATAC--------TAGAAAACCCTATAGCCCCC-AACGTTAATCTTACACTAGAGCATTTCAGGAAAGATTAAAAGAGAAAGAAGGAACTCGGCAAACTTCAGCCCCGCCTGTTTACCAAAAACATCGCCTCTTGAAAAATTATAAGAGGTCCAGCCTGCCCAGTGACTAAGTTCAACGGCCGCGGTACCCTAACCGTGCGAAGGTAGCATAATCACTTGTTCTTTAAATGGGGACTTGTATCAACGGCACCACGAAGGCTGCACTGTCTCCTTTCTTCAATCAGTGAAACTGATCTCCCCGTGAAGAAGCGGGGATCAAATTATAAGACGAGAAGACCCCATGGAGCTTCAAACTCACCATACAACTCT-GTGCCCT-TCACC-TC-TAACAC-AAGAGTCA-TGTATGCTAGTTTTGGGTTGGGGGGACCTCGGAGTATAACTTAACCTCCTAAGCAAACAGGTTAACACCTTTATCTATGAGAAACACCTCTAAGAATTATTAAATTAATGTAT-TTGACCCGATAG--TCGATCAATGAACCAAGTTACCCTGGGGATAACAGCGCAATCTACTTCAAGAGTTCCTATCGACAAGTAGGTTTACGACCTCGATGTTGGATCAGGGCATCCCAGTGGTGCAACCGCTACTAATG-GTTCGTTTGTTCAACG sp_7_JaliscoJSF1000 GCCGTAAACAATTAATTTACACATATCAGCGCCAGGGAATTACGAGCGATGCTTAAAACCCAAAGGATTTGACGGTGTCCCAACCAGCTAGAGGAGCCTGTTCTATAATCGATGATCCCCGCTACACCCGACCATTTTTAGCTCATCAGTCTGTATACCTCCGTCGAAAGCTTACCATGTGAACGCTCTTCAGTAGGCTCAATGATATCAACACGTCAGGTCAAGGTGCAGCTTAAAAAATGGGAAGTAATTGGCTACAATTTCTAACTTAAAACAAACGAAAGGCTATGTGAAATCATGGCCACGAAGGTGGATTTAGTAATAAAAAGAAAATATAGTGTTCTTTTTAACCCGGCTCTGGGACGTGTACACACCGCCCGTCACCCTCTTCGATTGTC-CATGATACTGTCCCTAACCC-TTTAT-TATTTTTT-AGAAAAGGCAAGTCGAAACACGGTAAGTGTACTGGAAAGTGCACTTGGATTA-TATCAAAATGTAGCTTAGTTAAAGCCCTTCGCTTACACCGAAGAATCATCTGTTTAAACCAGATCATTTTGAGCCCTAAATCTAGCCTTCATAATTCGCATGAACCCCCTCCCAA--CAAAACATTTTCTTATTTAAGTACAGGCGATCGAAAAA-TTTCTAAGCGCTTCAGACGAAGTACCGCAAGGGAAAGATGAAATAGAATTGAAATAACCCTAAAGCCTCAAACAGCAGAGATACCCCCTCGTACCTTTTGCATCATGGTCTAGTTAGTCCACTC-AAGCAAAATGAAACTTTT-AGTTAGTCCCCCCGAAACTAAGCGAGCTACTTCAAAACAGCCCTATGGGGCCAACCCTTCTCTGTTGCAAAAGAGTGGGAAGATTTTCAAGTAGAAGTGAAAAGCCTACCGAGCTTAGAGATAGCTGGTTATTCAGGAAAAGAGTTTTAGCTCTACCTTAAGCTTCTTT-ATGAAACCT-AACAAACCC-CAAAGCTTAAGAGCTATTCAAATAAGGCACAGCTTATTTGAAACAGGGCACAACCTATAATACTGGGT-AAATAGTA-GTAACTAGAG-CTAAGTTGGCCTAAGAGCAGCCATCTTTAAAAAAGCGTTAAAGCTTAACTCCATTT-ACTAGCAA-TTTCTAAAATTTTTATTAACCCTTTAC-CCCTACTGAATTATTTTATA-CCCTTATAAAAACAATAATGCTAGAACTAGTAACAAGAAACTGCCAGTTCTCCTAAATGCAAGTATAAACCAAGATAGACATTCTGTTGGTAATTAACGTAAATGTAGAATCTATAGTAACATAT--------TAGAAAACTCTATAACCCCT-GACGTTAGCCTTACACTAGAGCATTTCAGGAAAGATTAAAAAAGAAAGAAGGAACTCGGCAAATTTCAGCCCCGCCTGTTTACCAAAAACATCGCCTCTTGAAAAATTATAAGAGGTCCAGCCTGCCCAGTGACTTAGTTTAACGGCCGCGGTACCCTAACCGTGCGAAGGTAGCATAATCACTTGTTCTTTAAATGGGGACTTGTATCAACGGCATCACGAAGGCTGCACTGTCTCCTTTCTTCAATCAGTGAAACTGATCTCCCCGTGAAGAAGCGGGGATTAACCTATAAGACGAAAAAACCCCATGGAGCTTCAAACTCATTATCCAACTCT-GTGCTCC-ACATCCTT-TAACAC-AAAAGATC-TGTATAGTATTTTTGGGTTGGGGGGACCTCGGATTACAACTTAACCTCCTAAGCAAACAGGTTAACACCTTTATCCATGAAAGACACCTCTAAAAATTATTAAATTGATGTAT-TTGACCCGATAG--TCGATCAATGAACCAATTTACCCTGGGGATAACAGCGCAATCTACTTCAAAATTTCTTATCGACAATTAGGTTTACAACCTCAATGTTGGATCAGGGTATCCCGGTGGTGCAACCGCTACTAACG-GTTCGTTTGTTCAACA macroglossaJAC10472 ACCGTAAACAATTTATTTACACCTACCAGCGCCAGGGAACTACGAGCAATGCTTAAAACCCAAAGGATTTGACGGTGTCCCACCCAGCTAGAGGAGCCTGTTCTATAATCGATGATCCCCGCTACACCCCACCATTTCTAGCTAATCAGTCTGTATACCTCCGTCGAAAGCTTACCATGTGAACGCTCTTCAGTAGGCTTAATGATATCAACACGTCAGGTCAAGGTGCAGCTTAAGAAATGGAAAGCAATTGGCTACAATTTCTAATTTAGAACAAACGAAAGGCTATGTGAAATCATAACCATGAAGGTGGATTTAGTAGTAAAAAGAAAATAGAGTGTTCTTTTTAACCCGGCTCTGGGACGTGTACACACCGCCCGTCACCCTCTTCGATAACT-CATGATACTGTCCCTAACCCCATTAT-TACCCTTT-AGAAGAAGCAAGTCGAAACACGGTAAGTGTACTGGAAAGTGCACTTGGAAAA-TAGCAAAATATAGCTTAATAAAAGCCCTTCGCTTACACCGAAGAAATATCTGTTTAAATCAGATTATTTTGAGCCCTAAATCTAGCCTTCAAAATTCGCATGAACCCCTTATAAAAACAAAACATTTTTTTATTTTAGTACAGGCGATCGAAAAA-TTTCTAAGCGCTTCAGACAAAGTACCGCAAGGGAAAGATGAAATAGAACTGAAATAACCCTAAAGCCTTAAACAGCAGAGATATCCCCTCGTACCTTTTGCATCATGGTCTAGTTAGTCTACCC-AAGCAAAACGAAACTTTT-AGTTAGTCCCCCCGAAACTAAGCGAGCTACTTCAAAACAGCCCTATGGGGCCAACCCTTCTCTGTTGCAAAAGAGTGGGAAGATTTTCAAGTAGAAGTGAAAAGCCTACCGAGCTTAGAGATAGCTGGTTATTCAGGAAAAGAGTTTTAGCTCTACCTTAAGCTTACTT-ATGAAACCC-AACAAACCT-TAAAGTTTAAGAGCTATTCAAATAAGGCACAGCTTATTTGAAACAGGATACAACCTAAAATACTGGGT-AAATAGTA-GTAATTGAAA-TTAAGTTGGCTTAAGAGCAGCCATCTTTAAAAAAGCGTTAAAGCTTAATTTTATGT-ACTAATAA-TTTCTAAAATTTTAATCAACCCTTTATTCCCCAGGGAATCATTTTATA-TCCTTATAAAAA-GATAATGCTAGAATTAGTAACAAGAAACTGCCCATTTTCTTAAATGCAAGCATAAACCAAAATAGACACCCTATTGGTAATTAACGTAAATGTAACAGCTATAGCAACATAA--------TAGAAAACCTTATAACCCCA-AACGTTAACCTTACACCAGAGCATTTCAGGAAAGATTAAAAAAGAAAGAAGGAACTCGGCAAACTTTAGCCCCGCCTGTTTACCAAAAACATCGCCTCTTGAAAAATTATAAGAGGTCCAGCCTGCCCAGTGACTAAGTTCAACGGCCGCGGTACCCTAACCGTGCGAAGGTAGCATAATCACTTGTTCTTTAAATGGGGACTTGTATCAACGGCACCACGAGGGCTGCACTGTCTCCTTTCTTCAATCAGTGAAACTGATCTCCCCGTGAAGAAGCGGGGATTAAACTATAAGACGAGAAGACCCCATGGAGCTTCAAACTCATTATGCAACTCT-GTGCTCC-ACACCCCC-TAACAC-AAGAGGCA-TGCATAACAGTTTTGGGTTGGGGGGACCTCGGAGTATAACTTAACCTCCTAAGCAAACAGGTTAGCACCTTTATCCATGAAAAACACCTCTAAGAATTATTAAATTAATGTAT-TTGACCCGATAA--TCGATCAATGAACCAAGTTACCCTGGGGATAACAGCGCAATCTACTTCAAGAGTTCCTATCGACAAGTGGGTTTACGACCTCCATGTTGGATCAGGGTATCCCAGTGGTGCAGCCGCTACTAATG-GTTCGTTTGTTCAACG macroglossaJSF7933 ACCGTAAACAATTTATTTACACCTACCAGCGCCAGGGAACTACGAGCAATGCTTAAAACCCAAAGGATTTGACGGTGTCCCACCCAGCTAGAGGAGCCTGTTCTATAATCGATGATCCCCGCTACACCCCACCATTTCTAGCTAATCAGTCTGTATACCTCCGTCGAAAGCTTACCATGTGAACGCTCTTCAGTAGGCTTAATGATATCAACACGTCAGGTCAAGGTGCAGCTTAAGAAATGGAAAGCAATTGGCTACAATTTCTAATTTAAAACAAACGAAAGGCTATGTGAAATCATAACCGTGAAGGTGGATTTAGTAGTAAAAAAAAAATATATTGTTCTTTTTAACCCGGCTCTGGGACGTGTACACACCGCCCGTCACCCTCTTCGATAACT-CATGATACTGTCCCTAACCCCATTAT-TACCTTTT-AGAAGAGGCAAGTCGAAACACGGTAAGTGTACTGGAAAGTGCACTTGGAAAA-TAGCAAAATATAGCTTAATAAAAGCCCTTCGCTTACACCGAAGAAATATCTGTTTAAATCAGATTATTTTGAGCCCTAAATCTAGCCTTCAAAATTCGCATGAACCCCTTATAAAAACAAAACATTTTTTTATATTAGTACAGGCGATCGAAAAA-TTTCTAAGCGCTTCAGACAAAGTACCGCAAGGGAAAGATGAAATAGAACTGAAATAACCCTAAAGCCTTAAACAGCAGAGATATCCCCTCGTACCTTTTGCATCATGGTCTAGTTAGTCTACCC-AAGCAAAATGAAACTTTT-AGTTAGTCCCCCCGAAACTAAGCGAGCTACTTCAAAACAGCCCTATGGGGCCAACCCTTCTCTGTTGCAAAAGAGTGGGAAGATTTTCAAGTAGAAGTGAAAAGCCTACCGAGCTTAGAGATAGCTGGTTATTCAGGAAAAGAGTTTTAGCTCTACCTTAAGCTTACTT-ATGAAACCC-AACAAACCT-TAAAGCTTAAGAGCTATTCAAATAAGGCACAGCTTATTTGAAACAGGATACAACCTAAAATACTGGGT-AAATAATA-GTAATTGAAA-TTAAGTTGGCCTAAGAGCAGCCATCTTTAAAAAAGCGTTAAAGCTTAATTTTATCT-ACTAATAA-TTTCTTAAATCTTAATCAACCCTTTATTCCCCACTGAATCATTTTATA-TCCTTATAAAAAAGATAATGCTAGAACTAGTAACAAGAAACTGCCCATTCTCCTAAATGCAAGCATAAACCAAAATAGACACCCTATTGGTAATTAACGTAAATGTAACAGCTATAGCAACATAA--------TAGAAAACCCTATAACCCCG-AACGTTAACCTTACACCAGAGCATTTCAGGAAAGATTAAAAAAGAAAGAAGGAACTCGGCAAACTTTAGCCCCGCCTGTTTACCAAAAACATCGCCTCTTGAAAAATTATAAGAGGTCCAGCCTGCCCAGTGACTAAGTTCAACGGCCGCGGTACCCTAACCGTGCGAAGGTAGCATAATCACTTGTTCTTTAAATGGGGACTTGTATCAACGGCACCACGAGGGCTGCACTGTCTCCTTTCTTCAATCAGTGAAACTGATCTCCCCGTGAAGAAGCGGGGATTAAACTATAAGACGAGAAGACCCCATGGAGCTTCAAACTCATTATGCAACTCT-GTGCTCC-ACATCCCC-TAACAC-AAGAGGCA-TGCATAACAGTTTTGGGTTGGGGGGACCTCGGAGTATAACTTAACCTCCTAAGCAAACAGGTTAGCACCTTTATCCATGAAAAACACCTCTAAGAATTATTAAATTAATGTAT-TTGACCCGATAA--TCGATCAATGAACCAAGTTACCCTGGGGATAACAGCGCAATCTACTTCAAGAGTTCCTATCGACAAGTGGGTTTACGACCTCGATGTTGGATCAGGGTATCCCAGTGGTGCAACCGCTACTAATG-GTTCGTTTGTTCAACG taylori286 ACCGTAAACAATTAATTTACACCTATCAGCGCCAGGGAACTACGAGCAATGCTTAAAACCCAAAGGATTTGACGGTGTCCCACCCAGCTAGAGGAGCCTGTTCTATAATCGATGATCCCCGCTATACCCAACCATTTCTAGCTAATCAGTCTGTATACCTCCGTCGAAAGCTTACCATGTGAACGTTTTTCAGTAGGCTTAATGATATCAACACGTCAGGTCAAGGTGCAGCTTAAGAAATGGAAAGCAATTGGCTACAATTTCTAACTTAGAACAAACGAAATGATATGTGAAATCATAATCATGAAGGTGGATTTAGTAGTAAAAAGAAAATAGAGTGTTCTTTTTAACCCGGCTCTGGGACGTGTACACACCGCCCGTCACCCTCTTCGATAACC-CATAATACTGTTCCTAACCCCATTAT-TTCATTTT-AGAAGAAGCAAGTCGAAACATGGTAAGTGTACTGGAAAGTGCACTTGGAAAA-TAACAAAATATAGCTTAATGAAAGCCCTTCGCTTACACCGAAGAAATGTCTGTTTAAATCAGATTATTTTGAGCCATAAATCTAGCCTTCAAAATTCGCATGAACCCCTCCCATAA-CAAAACATTTTCTTATCTTAGTACAGGCGATCGAAAAA-TTTCTAAGCGCTTCAGACAGAGTACCGCAAGGGAAAGATGATATAGAACTGAAATAACCCTAAAGCCTTAAACAGCAGAGATATTCCCTCGTACCTTTTGCATCATGGTCTAGTTAGTCCACCC-AAGCAAAATGAAACTTTT-AGTTAGTCCCCCCGAAACTAAGCGAGCTACTTCAAAACAGCCCTATGGGGCCAACCCTTCTCTGTTGCAAAAGAGTGGGAAGATTTTCAAGTAGAAGTGAAAAGCCTATCGAGCTTAGAGATAGCTGGTTATTCAGGATAAGAGTTTTAGCTCTACCTTAAGCTTCTTT-ATGAAACCC-AACAAATCT-TAAAGCTTAAGAGCTATTCAAATAAGGCACAGCTTATTTGAAATAGGATACAACCTAAAATATTGGGT-AAATAACA-GTGATTAAAA-TTAAGTTGGCCTAAGAGCAGTCATCTTTAAAAAAGCGTTAAAGCTTAGTTTTATAC-GCTAGTAA-TTTCTAAAATTTTTATTAACCCCTTAC-CCCTAGTGAATCATTTTATA-CCCCTATAAAAATGATAATGGTAGAACTAGTAACAAGAAACTGCCCATTTTCCTAAATGCAGGCATAAACCAAAATAGAAATCCTATTGGTAGTTAACGTAAGTGCAGCAGTTGTAGTAACGGAA--------TAGAAAACCCTACAGCCCTA-AACGTTAATCTTACACTAGAGCATTTCAGGAAAGATTAAAGAAGAAAGAAGGAACTCGGCAAATTTTAGCCCCGCCTGTTTACCAAAAACATCGCCTCTTGAAAAATTATAAGAGGTCCAGCCTGCCCAGTGACTAAGTTCAACGGCCGCGGTACCCTAACCGTGCGAAGGTAGCATAATCACTTGTTCTTTAAATGGGGACTCGTATCAACGGCATCACGAGGGCTGCACTGTCTCCTTTCTTAAATCAGTGAAACTGATCTCCCCGTGAAGAAGCGGGGATCAAACTATAAGACGAGAAGACCCCATGGAGCTTCAAACTCTTTATGCAACTCT-GTGCTCC-ACATCCCC-TAACAC-AAGAGACA-TGCATAATAGTTTTGGGTTGGGGGGACCTCGGAGTATAACTTAACCTCCTAAGCAAACAGGTTAGCACCTTTATCTACGAAAAACATCTCTAAGAATTATCAAACTAATGTATTTTGACCCGATAT--TCGATCAATGAACCAAGTTACCCTGGGGATAACAGCGCAATCTACTTCAAGAATTCCTATCGACAAGTGGGTTTACAACCTCCATGTTGGATCAGGGTGTCCCAGTGGTGCAACCGCTACTAATG-GTTCGTTTGTTCAACG sp_4_Panama ACCGTAAACAATTAATTTACACCTATCAGCGCCAGGGAATTACGAGCAATGCTTAAAACCCAAAGGATTTGACGGTGTCCCACCCAGCTAGAGGAGCCTGTTCTATAATCGATGATCCCCGTTACACCTAACCATTTTTAGCTAATCAGTCTGTATACCTCCGTCGAAAGCTTACCATGTGAACGTTTTTCAGTAGGCTTAATGATATCAACACGTCAGGTCAAGGTGCAGCTTAAGAAATGGGAAGCAATTGGCTACAATTTCTAATCTAGAACAAACGAAAGGTTATGTGAAATCATGACCACGAAGGTGGATTTAGTAGTAAAAAGAAAATAGAGTGTTCTTTTTAACCCGGCTCTGGGACGTGTACACACCGCCCGTCACCCTCTTCGATAGTT-CATGATACTGTCCCTAACCC-TTTAT-TACGTTTT-AGAAGAGGCAAGTCGAAACACGGTAAGTGTACTGGAAAGTGCACTTGGAAAA-TAACAAAATATAGCTTAATAAAAGCCCTTCGCTTACACCGAAGAAATATCTGTTTAAATCAGATTATTTTGAGCCCTAAATCTAGCCTTCAAAATTCGAATGAACCTCTCCCCAAA-TAAAACATTTTCTTATCCTAGTACAGGCGATCGAAAAA-TTTCTAAGCGCTTCAAATAAAGTACCGCAAGGGAAAGATGAAATAGAATTGAAATAACCCTAAAGCTTTAAACAGCAGAGATACTTCCTCGTACCTTTTGCATCATGGTCTAGTTAGTCTACCC-AAGCAAAATGAAACTTTT-AGTTAGTCCCCCCGAAACTAAGCGAGCTACTTCAAAACAGCCTTATGGGGCCAACCCTTCTCTGTTGCAAAAGAGTGGGAAGATTTTCAAGTAGAAGTGAAAAGCCTACCGAGCTTAGAGATAGCTGGTTATTCAGGAAAAGAGTTTTAGCTCTACCTTAAGCTTATTT-ATGAAACCC-AACAAACCT-TAAAGCTTAAGAGCTATTCAAATAAGGCACAGCTTATTTGAAACAGGATACAACCTAAAATACTGGGT-AAATAACA-GTAATTTAAA-TTAAGTTGGCCTAAGAGCAGCCATCTTTTAAAAAGCGTTAAAGCTTAATTTGATAT-ACTATTAA-TTTCTTAAATTTCAATTAACCCTTTAC-CCCTACTGAATTACTTTATA-TTCTTATAAAAATGATAATGCTAGAACTAGTAACAAGAAACTGCCCATTCTCCTAAATGCAAGTATAAACCAAAATAGACACCCTATTGGTAATTAACGTAAATGTAACAGCTATAGCAACATAA--------TAGAAAACCCTATAACCCCG-AACGTTAATCTTACACTAGAGCATTTCAGGAAAGATTAAAAAAGAAAGAAGGAACTCGGCAAATTTCAGCCCCGCCTGTTTACCAAAAACATCGCCTCTTGAAAAATTATAAGAGGTCCAGCCTGCCCAGTGACTAAGTTCAACGGCCGCGGTACCCTAACCGTGCGAAGGTAGCATAATCACTTGTTCTTTAAATGGGGACTTGTATCAACGGCACCACGAGGGCTGCACTGTCTCCTTTCTTCAATCAGTGAAACTGATCTCCCCGTGAAGAAGCGGGGATTAAACTATAAGACGAGAAGACCCCATGGAGCTTCAAACTCATTATACAACTCT-GTGCTCC-ACATCCCT-TAACAC-AAGAGGCA-TGCATAATAGTTTTGGGTTGGGGGGACCTCGGAGTATAACTTAACCTCCTAAGCAAACAGGTTACMACCTTTATCTATGAGRAACACCTCTAAGAATTATTAAATTAATGYAT-TTGACCCGATAG--TCGATCAATGAACCAAGTTACCCTGGGGATAACAGCGCAATCTACTTCAAGAGTTCCTATCGACAAGTAGGTTTACGACCTCGATGTTGGATCAGGGTATCCCAGTGGTGCAACCGCTACTAATG-GTTCGTTTGTTCAACG sp_5_CostaRichDMH86_210 ACCGTAAACAATTAATTTACACCTATCAGCGCCAGGGAAATACGAGCAATGCTTAAAACCCAAAGGATTTGACGGTGTCCCACCCAGCTAGAGGAGCCTGTTCTATAATCGATGATCCCCGTTACACCTAACCATTTTTAGCTAATCAGTCTGTATACCTCCGTCGAAAGCTTACCATGTGAACGTTCTTCAGTAGGCTTAATGATATCAACACGTCAGGTCAAGGTGCAGCTTAAGAAATGGGAAGCAATTGGCTACAATTTCTA-TTTAAAACAAACGAAAGGCTATGTGAAATCATAACCATGAAGGTGGATTTAGTAATAAAAAAAAAATATAGTGTTCTTTTTAACCCGGCTCTGGGACGTGTACACACCGCCCGTCACCCTCTTCGATAGTT-CATGATACTGTCCCTAACCC-TTTAT-TACGTTTT-AGAAAAAGCAAGTCGAAACACGGTAAGTGTACTGGAAAGTGCACTTGGAAAA-TAACAAAATATAGCTTAATAAAAGCCCTTCGCTTACACCGAAGAAATATCTGTTTAAATCAGATTATTTTGAGCCCTCAATCTAGCCTTCAAAATTCGCATGAA-CCCCTCTCAAA-CAAAACATTTTCTTATCTTAGTACAGGCGATCGAAAAA-TTTCTAAGCGCTTCAGACAAAGTACCGCAAGGGAAAGATGAAATAGAATTGAAATAACCCTAAAGCTTTAAACAGCAGAGATACTTCCTCGTACCTTTTGCATCATGGTCTAGTTAGTCTACCC-AAGCAAAATGAAACTTTT-AGTTAGTCCTCCCGAAACTAAGCGAGCTACTTCAAAACAGCCCTATGGGGCCAACCCTTCTCTGTTGCAAAAGAGTGGGAAGATTTTCAAGTAGAAGTGAAAAGCCTACCGAGCTTAGAGATAGCTGGTTGTTCAGGAAAAGAGTTTTAGCTCTACCTTAAGCTTCTTT-ATGAGACCC-AACAAACCT-TAAAGCTTAAGAGCTATTCAAATAAGGCACAGCTTATTTGAAACAGGATACAACCTAAAATACTGGGT-AAATTATA-GTAATTAAAA-TTAAGTTGGCCTAAGAGCAGCCATCTTTTAAAAAGCGTTAAAGCTTAGTTTTATAA-ACTAATAA-TTTCTTAAATT-CAATTAACCCTTTAC--CCTACTGAACTACTTTATA-TTCTTATAAAAATAATAATGCTAGAACTAGTAACAAGAAACTGCCCGTTCTCCTAAATGCAAGTATAAACCAAAATAGACACCCTATTGGTAATTAACGTAAATGTAGCAGTTGTAGTAACATTA-------GTAGAAAACTCTATAACCCCTAAACGTTAACCTTACACTAGAGCATTTCAGGAAAGATTAAAAAAGAAAGAAGGAACTCGGCAAACTTCAGCCCCGCCTGTTTACCAAAAACATCGCCTCTTGAAAAATTATAAGAGGTCCAGCCTGCCCAGTGACTAAGTTCAACGGCCGCGGTACCCTAACCGTGCGAAGGTAGCATAATCACTTGTTCTTTAAATGGGGACTTGTATCAACGGCACCACGAGGGCTGCACTGTCTCCTTTCTTTAATCAGTGAAACTGATCTCCCCGTGAAGAAGCGGGGATTAAGCTATAAGACGAGAAGACCCCATGGAGCTTCAAACTCATTATACAACTCT-GTGCCCC-ATATCCCT-TAACAC-AAGAAACA-TGTATAATAATTTTGGGTTGGGGGGACCTCGGAATATAACTTAACCTCCAAAGCAAACAGGTTAAAACCTTTATCCACGAAAAACACCTCTAAAAATTATTAAATTAATGTAT-TTGACCCGATAG--TCGATCAATGAACCAAGTTACCCTGGGGATAACAGCGCAATCTACTTCAAGAATTCCTATCGACAAGTAGGTTTACGACCTCCATGTTGGATCAGGGTATCCCAGTGGTGCAACCGCTACTAATG-GTTCGTTTGTTCAACG sp_6_CostaRicaDMH86_225 ACCGTAAACAATTAATTTACACCTATCAGCGCCAGGGAACTACGAGCAATGCTTAAAACCCAAAGGATTTGACGGTGTCCCACCCAGCTAGAGGAGCTTGTTCTATAATCGATGATCCCCGTTACACCTAACCATTTTTAGCTAATCAGTCTGTATACCTCCGTCGAAAGCTTACCATGTGAACGCTCTTCAGTAGGCTTAATGATATCAACACGTCAGGTCAAGGTGCAGCTTAAGAAATGGGAAGCAATTGGCTACAATTTCTAATCTAGAACAAACGAAAGGCTATGTGAAATCATAACCACGAAGGTGGATTTAGTAGTAAAAAGAAAATAGAGTGTTCTTTTTAACCCGGCTCTGGGACGTGTACACACCGCCCGTCACCCTCTTCGATAGTC-CATGATACTGTCCCTTACCC-TTTAT-TACATTTT-AGAAGAAGCAAGTCCAAACACGGTAAGCGTACTGGAAAGTGCGCTTGGAAAA-TAACAAAATATAGCTTAATAAAAGCCCTTCGCTTACACCGAAGAAATGTCTGTTTAAATCAGATTATTTTGAGCCCTAAATCTAGCCTTCAAAATTCGCATGAACCCCCTTCCAAG-CAAAACATTTTCTTATCTTAGTACAGGCGATCAAAAAA-TTTCTAAGCGCTTCAGACAAAGTACCGTAAGGGAAAGATGAAATAGAATTGAAATAACCTTAAAGCTTTAAACAGCAGAGATATTCCCTCGTACCTTTTGCATCATGGTCTAGTTAGTCTCCCC-AAGCAAAATGAAACTTTT-AGTTAGTCCCCCCGAAACTAAGCGAGCTACTTCAAAACAGCCCTATGGGGCCAACCCTTCTCTGTTGCAAAAGAGTGGGAAGATTTTCAAGTAGAAGTGAAAAGCCTATCGAGCTTAGAGATAGCTGGTTATTCAGGAAAAGAGTTTTAGCTCTACCTTAAGCTTCTTT-ATGAAATCC-AACAAACCT-TAAAGCTTAAGAGCTATTCAAATAAGGCACAGCTTATTTGAAACAGGATACAACCTAAAATATTGGGT-AAATAATA-GTAATTAAAA-TTAAGTCGGCCTAAGAGCAGCCACCTTTAAAAAAGCGTTAAAGCTTAGTCTTATGT-ACTAACAA-TTTCCAAAATTTCAATTAACCCTTACC-CCCTACTGAATTATTTTATA-TTCTTATAAAAGTGATAATGCTAGAACTAGTAACAAGAAACTGCCTATTCTCCTAAATGCAAGCATAAACCAAAATAGACACCCTGTTGGTAATTAACGTAAATGTAACAGCTATAGCAACACAA--------TAGAAAACCCTACAACCCTG-AACGTTAATCTTACACTAGAGCATTTCAGGAAAGATTAAAAAAGAAAGAAGGAACTCGGCAAACTTCAGCCCCGCCTGTTTACCAAAAACATCGCYTCTTGAAAAATGATAAGAGGTCCAGCCTGCCCAGTGACTAAGTTCAACGGCCGCGGTACCCTAACCGTGCGAAGGTAGCATAATCACTTGTTCTTTAAATGGGGACTTGTATCAACGGCACCACGAGGGCTGCACTGTCTCCTTTCTTCAATCAGTGAAACTGATCTCCCCGTGAAGAAGCGGGGATTAAATTATAAGACGAGAAGACCCCATGGAGCTTCAAACTCACCATACAACTCT-GTGCCCC-ATATCCCC-TAACAC-AAGAGACA-TGTACGGTGGTTTTGGGTTGGGGGGACCTCGGAGTATAACTTAACCTCCTAAGCAAACAGGTTAAAACCTTTATCCATGAGAAACACCTCTAAGAATTATTAAATTAATGTAT-TTGACCCGATAA--TCGATCAATGAACCAAGTTACCCTGGGGATAACAGCGCAATCTACTTCAAGAGTTCCTATCGACAGGTAGGTTTACGACCTCGATGTTGGATCAGGGTATCCCAGTGGTGCAACCGCTACTAATG-GTTCGTTTGTTCAACG sp_8_PueblaJAC9467 ACCGTAAACAATTAACTCACACCTATCAGCGCCAGGGAATTACGAGCAATGCTTAAAACCCAAAGGATTTGACGGTGTCCCAGCCAGCTAGAGGAGCCTGTTCTATAATCGATGATCCCCGCTACACCCGACCATTTTTAGCTCCGCAGTCTGTATACCTCCGTCGAAAGCTTACCATGTGAATGCCCTTCAGTAGGCTCAATGATATCG-TACGTCAGGTCAAGGTGCAGCTCAAGAAATGGAAAGTAATTGGCTACAATTTCTAGTTTAGAACAAACGAAAGGCTGTGTGAAATCACGGCCATGAAGGTGGATTTAGTAGTAAAAAGAAAATAGAGTGTTCTTTTTAACCCGGCTCTGAGACGTGTACACACCGCCCGTCACCCTCTTCGATAGTT-CATAATACTGTCCCTAACCC-TTTAT-TACATTTT-AGAAGAGGCAAGTCGAAACACGGTAAGTGTACTGGAAAGTGCACTTGGAAAA-TAACAAAATGTAGCTTAACTAAAGCCCTTCGCTTACACCGAAGAGATATCTGTTTAAACCAGATCATTTTGAGCCCTAAATCTAGCCTTCAAAATTCGCATGAAACACCCCCCAAA-AAAAACATTTCCTCATCTTAGTACAGGCGATCGAAAAA-TTTCTAAGCGCTTCAGATAAAGTACCGTGAGGGAAAGATGAAATAGAATTGAAATAACCATAAAGCCTCAAATAGCAGAGACA-CCCCTGGTACCTTTTGCATCATGGTCTAGTTAGTCTACCC-AAGCAAAATGAAACTTTT-AGTTAGCCTCCCCGAAACTAAGCGAGCTACTTCAAAACAGCCCTATGGGGCCAACCCTTCTCTGTTGCAAAAGAGTGGGAAGATTTTCAAGTAGAAGTGAAAAGCCTACCGAGCTTAGAGATAGCTGGTTATTCAGGAAAAGAGTTTTAGCTCTACCTTAAGCTCCTTT-ATGAAACCT-AACAAACCC-TGAAGCTTAAGAGCTATTCAAATAAGGCACAGCTTATTTGAAATAGGATACAACCTAATATACTGGGT-AACTAATA-GTAATTAAAA-CTAAGTTGGCCTAAGAGCAGCCATCTCTAAAAAAGCGTTAAAGCTTAATTTTTCAC-ACTGATAA-TCCCTAAAATCTTAATTAACCCTTCAC-CCCTACTGAATTATTTTATA-CCTTTATAAAAATGATAATGCTAGAACTAGTAACAAGAAACTGCCTGTTCTCCTAAATGCAAGCATAGACCAAAATAGACACCCTGTTGGTAATTAACGTAAATGAAGCAACTGTAGCAACATAA--------TAGAAAACCCTACAACCCCC-AACGTTAATCTTACACCAGAGCATTTCAGGAAAGATTCAAAGAGAAAGAAGGAACTCGGCAAACTTCAGCCCCGCCTGTTTACCAAAAACATCGCCTCTTGAAAAACTATAAGAGGTCCAGCCTGCCCAGTGACTAAGTTCAACGGCCGCGGTACCCTAACCGTGCGAA---AGCATAATCACTTGTTCTTTAAATGGGGACTTGTATCAACGGCATCACGAGGGCTGCACTGTCTCCTTTCTTCAATCAGTGAAACTGATCCCCCCGTGAAGAAGCGGGGATTAACCTATAAGACGAGAAGACCCCATGGAGCTTCAAACTCATTATGTAACTCT-GCGCTCT-ACACCCCT-TAACGC-AAGAATCC-TACATACTGGTTTTGGGTTGGGGGGACCTCGGAGTATAACTTAACCTCCTAAGCAAACAGGTTAGCACCTTTATCTATGAGAGACACCTCTAAGAATTATTAAATTAATGTAT-TTGACCCGATAG--TCGATCAATGAACCAAGTTACCCTGGGGATAACAGCGCAATCTACTTCAAGAGTTCTTATCGACAAGTAGGTTTACGACCTCGATGTTGGATCAGGGTATCCCGGTGGTGCAACCGCTACCAATG-GTTCGTTTGTTCAACG oncaLVT3542 ACCGTAAACAATTAATTTACACCCATCAGCGCCAGGGAATTACGAGCAATGCTTAAAACCCAAAGGATTTGACGGTGTCCCACCCAGCTAGAGGAGCCTGTTCTATAATCGATGATCCCCGCTACACCCGACCATTTTTAGCTCATCAGTCTGTATACCTCCGTCGAAAGCTTACCATGTGAACGCTCTTCAGTAGGCCCAATGA-GTCAACACGTCAGGTCAAGGTGCAGCTTAAGAAATGGGAAGCAATTGGCTACAATTTCTAACTTAGAACAAACGAAAGGCTATGTGAAACCATAACCACGAAGGTGGATTTAGTAGTAAAAAGAAAATAGAGTGTTCTTTTTAACCCGGCTCTGGGACGTGTACACACCGCCCGTCACCCTCTTCGATAGTT-CAAAATACTGTCCCTAACCC-TTTAT-CACGTTTT-AGAAGAGGCAAGTCGAAACACGGTAAGTGTACTGGAAAGTGCACTTGGAAAA-TAGCAAAATGTAGCTTAATTAAAGCCCTTCGCTTACACCGAAGAAACATCTGTTTGAATCAGATCATTTTGAGCCCTAAATCTAGCCTTCTAAACTCGCATGAACCCCCTCCCAAA-CAAAACATTTTCTCATCTTAGTACAGGCGATCGAAAAA-TTTCTAAGCGCTTCAGACAAAGTACCGTAAGGGAAAGATGAAATAGAATTGAAACAACCCTAAAGCCTTAAATAGCAGAGATAACCCCTCGTACCTTTTGCATCATGGTCTAGTCAGTCTACCC-AAGCAAAATGAAACTTTT-AGTTAGTCCCCCCGAAACTAAGCGAGCTACTTCAAAACAGCCCTATGGGGCCAACCCTTCTCTGTTGCAAAAGAGTGGGAAGATTTTCAAGTAGAAGTGAAAAGCCTACCGAGCTTAGAGATAGCTGGTTATTCAGGAAAAGAGTTTTAGCTCTACCTTAAGCTTCTTT-ATGAAACCC-AACAAACCT-TAAAGCTTAAGAGCTATTCAAATAAGGCACAGCTTATTTGAAATAGGACACAACCTAAAATACTGGGT-AAATAACA-GTAATTAAAA-TTAAGTTGGCCTAAGAGCAGCCACCTTTAAAAAAGCGTTAAAGCTTGATTTTATAC-ACTAGTAA-TTCCTAAAATTCTTATCAACCCTTTAT-CCCTACTGAATTATTTTATA--CCCTATAAAAACGATAATGCTAGAACTAGTAACAAGAAACTGCCTATTCTCCTAAATGCAAGTATAAGCCAAAATAGACACCCTATTGGTAATTAACGTAAATGTAACAGTTATAGTAACATAC--------TAGAAAACCCTATAGCCCCC-AACGTTAATCTTACACTAGAGCATTTCAGGAAAGATTAAAAGAGAAAGAAGGAACTCGGCAAACTTCAGCCCCGCCTGTTTACCAAAAACATCGCCTCTTGAAAAATTATAAGAGGTCCAGCCTGCCCAGTGACTAAGTTCAACGGCCGCGGTACCCTAACCGTGCGAAGGTAGCATAATCACTTGTTCTTTAAATGGGGACTTGTATCAACGGCACCACGAGGGCTGCACTGTCTCCTTTCTTCAATCAGTGAAACTGATCTCCCCGTGAAGAAGCGGGGATCAAATTATAAGACGAGAAGACCCCATGGAGCTTCAAACTCACCATGCAACTCC-GTGCCCT-TCA-CCTC-TAACAC-AAGAGTCA-TGTATGCTAGTTTTGGGTTGGGGGGACCTCGGAGTATAACTCAACCTCCTAAGCAAACAGGTTAACACCTTTATCTATGAAAAACACCTCTAAGAATTATTAAATTAATGTAT-TTGACCCGATAG--TCGATCAATGAACCAAGTTACCCTGGGGATAACAGCGCAATCTACTTCAAGAGTTCCTATCGACAAGTAGGTTTACGACCTCGATGTTGGATCAGGGCATCCCAGTGGTGCAACCGCTACTAATG-GTTCGTTTGTTCAACG sp_3_MichoacanJSF955 GCCGTAAACAATTAATTCACACCTATCAGCGCCAGGGGATTACGAGCAATGCTTAAAACCCAAAGGATTTGACGGTGTCCCACCCAGCTAGAGGAGCCTGTTCTATAATCGATGATCCCCGTTACACCCGACCATTTTTAGCTCATCAGTCTGTATACCTCCGTCGAAAGCTTACCATGTGAACGCTCTTCAGTAGGCTCAATGATGTCAACACGTCAGGTCAAGGTGCAGCTTAAGAAATGGGAAGCAATTGGCTACAATTTCTAATCTAGAACAAACGAAAGGCTATGTGAAATCATAACCTCGAAGGTGGATTTAGTAGTAAAAAGAAAGTAGAGTGTTCTTTTTAACCCGGCTCTGGGACGTGTACACACCGCCCGTCACCCTCTTCGATAGTC-CATGATACTGTTCCTAACCC-AATAT-TACATTTT-AGAAGAGGCAAGTCGAAACACGGTAAGTGTACTGGAAAGTGCACTTGGTAAA-TAACAAAATGTAGCTTAATAAAAGCCCTTCGCTTACACCGAAGAAATACCTGTTTGAATCAGATCATTTTGAGCCCTAAATCTAGCCTTCAAAATTCGCATGAACCCCCCCCCCTA-CAAAACATTTTCTTATCCTAGTACAGGCGATCGAAAAA-TTTCTAAGCGCTTCAGATAAAGTACCGCAAGGGAAAGATGAAATAGAATTGAAATAACTCTAAAGCCTTAAATAGCAGAGATTCTACCTCGTACCTTTTGCATCATGGTCTAGTTAGTCTACCC-AAGCAAAATGAAACTTTT-AGTTAGTCCCCCCGAAACTAAGCGAGCTACTTCAAAACAGCCCTATGGGGCCAACCCTTCTCTGTTGCAAAAGAGTGGGAAGATTTTCAAGTAGAAGTGAAAAGCCTACCGAGCTTAGAGATAGCTGGTTATTCAGGAAAAGAGTTTTAGCTCTACCTTAAGCTTCTTT-ATGAAACCC-AACAAACCT-TAAAGCTTAAGAGCTATTCAAATAAGGCACAGCTTATTTGAAATAGGACACAACCTAGAATAATGGGT-AAGTAACA-GCAATTAAAG-CTAAGTTGGCCTAAGAGCAGCCATCTTTAAAAAAGCGTTAAAGCTTAGTTTTGTAT-GCTAATAA-TTTCTAAAATTTTAATCAACCCTTTAT-CCCTACTGAATTATTTTATA-TTCCTATAAAAGTAATAATGCTAGAACTAGTAACAAGAAATTGCCTATTCTCCTAAATGCAAGCATAAACCAAAATAGACACCCTATTGGTAATTAACGTAAATGTGACAGCTATAGTAACATAA--------TAGAAAACCCTATAACCTCC-AACGTTAACCTTACACTAGAGCATTTCAGGAAAGATCTAAAAAGAAAGAAGGAACTCGGCAAACTTCAGCCCCGCCTGTTTACCAAAAACATCGCCTCTTGAAAAATTATAAGAGGTCCAGCCTGCCCAGTGACTAAGTTCAACGGCCGCGGTACCCTAACCGTGCGAAGGTAGCATAATCACTTGTTCTTTAAATGGGGACTTGTATCAACGGCACCACGAGGGCTGCACTGTCTCCTTTCTTCAATCAGTGAAACTGATCTCCCCGTGAAGAAGCGGGGATTAAACTATAAGACGAGAAGACCCCATGGAGCTTCAAACTCATTATACAACCCT-GTTCTTC-ATATCCTCCTAACAC-AAAGGACA-TGTATAATAGTTTTGGGTTGGGGGGACCTCGGAGTATAGCTTAACCTCCTAAACAAACAGGTTAACACCTTTATCCATGAGAAACACCTCTAAGAATTATTAAATTAATGTAT-TTGATCCGATTG--TCGATCAATGAACCAAGTTACCCTGGGGATAACAGCGCAATCTACTTCAAGAGTTCTTATCGACAAGTAGGTTTACGACCTCGATGTTGGATCAGGGTATCCCAGTGGTGCAACCGCTACTAATG-GTTCGTTTGTTCAACG sphenocephalaUSC7448 GCCGTAAACAATTAATTTACACCTATCAGCGCCAGGGAACTACGAGCAATGCTTAAAACCCAAAGGATTTGACGGTGTCCCACCCAGCTAGAGGAGCCTGTTCTATAATCGATGATCCCCGCTACACCCGACCATTTTTAGCTCATCAGTCTGTATACCTCCGTCGAAAGCTTACCATGTGAACGCCCTTCAGTAGGCTCAATGATATCAACACGTCAGGTCAAGGTGCAGCTTAAGAAATGGGAAGCAATTGGCTACAATTTCTAATCTAGAACAAACGAAAGGCTATGTGAAATCATAACCATGAAGGTGGATTTAGTAGTAAAAAGAAAATAGAGTGTTCTTTTTAACCCGGCTCTGGGACGTGTACACACCGCCCGTCACCCTCTTCGATAGTT-CATAATACTGTTCCTAACCC-ATTGT-TACATTTT-AGAAGAGGCAAGTCGAAACACGGTAAGTGTACTGGAAAGTGCACTTGGAAAAATAACAAAATGTAGCTTAATAAAAGCCCTTCGCTTACACCGAAGAAATATCTGTTTGAATCAGGTCATTTTGAGCCCTAAATCTAGCCTTCAAAATTCGCATGAACCCCATCCCAAA-CAAAACATTTTCTCATTCTAGTACAGGCGATCGAAAAA-TTTCTAAGCGCTTCAGATAAAGTACCGCAAGGGAAAGATGAAATAGAATTGAAATAACCTTAAAGCCTTAAACAGCAGAGATTCTACCTCGTACCTTTTGCATCATGGTCTAGTTAGTCTACCC-AAGCAAAATGAAACTTTT-AGTTAGTCCCCCCGAAACTAAGCGAGCTACTTCAAAACAGCCCAATGGGGCCAACCCTTCTCTGTTGCAAAAGAGTGGGAAGATTTTCAAGTAGAAGTGAAAAGCCTACCGAGCTTAGAGATAGCTGGTTACTCAGGAAAAGAGTTTTAGCTCTACCTTAAGCTTATTT-ATGAAACCC-AACAAACCC-TAAAGCTTAAGAGCTATTCAAATAAGGCACAGCTTATTTGAAATAGGATACAACCTAAAATACTGGGT-AAATAGCA-GTAACTAAAA-TTAAGTTGGCCTAAGAGCAGCCATCTTTTAAAAAGCGTTAAAGCTTAATTTTACCT-ACTGACAA-TTCCTAAAATTTTAATCAACCCTTTAT-TACTACTGAGTTACTTTATA-CCCCTATAAAAGTGATAATGCTAGAACTAGTAACAAGAAACTGCCCATTCTCCTAAATGCAAGTATAAGCCAAAATAGACTCCCTGTTGGTAATTAACGTAAATGTAACAGCTATAGCAACATAA--------TAGAAAACCCTATAACCTCC-AACGTTAATCTTACACCAGAGCATTTCAGGAAAGATTAAAAAAGAAAGAAGGAACTCGGCAAACTTCAGCCCCGCCTGTTTACCAAAAACATCGCCTCTTGAAAAATTATAAGAGGTCCAGCCTGCCCAGTGACTAAGTTCAACGGCCGCGGTACCCTAACCGTGCGAAGGTAGCATAATCACTTGTTCTTTAAATGGGGACTTGTATCAACGGCACCACGAGGGCTGCACTGTCTCCTTTCTTCAATCAGTGAAACTGATCTCCCCGTGAAGAAGCGGGGATTCATATATAAGACGAGAAGACCCCATGGAGCTTCAAACTCATTATACAACTCC-ATTCTCT-ACACCCTCCTAACAT-AAGAGTCA-TGTATAATGGTCTTGGGTTGGGGGGACCTCGGAGTATAACATAACCTCCTAAGCAAACAGGTTAACACCTTTATCCACGAGGAACACCTCTAAGAATTATTAAATTAATGCAC-CTGATCCGATAA--TCGATCAATGAACCAAGTTACCCTGGGGATAACAGCGCAATCTACTTCAAGAGTTCCTATCGACAAGTGGGTTTACGACCTCGATGTTGGATCAGGGTATCCCAGTGGTGCAACCGCTACTAATG-GTTCGTTTGTTCAACG utriculariaJSF845 GCCGTAAACAATTAATTTACACCTACCAGCGCCAGGGAACTACGAGCAATGCTTAAAACCCAAAGGATTTGACGGTGTCCCACCCAGCTAGAGGAGCCTGTTCTATAATCGATGATCCCCGCTACACCCGACCATTTTTAGCTCATCAGTCTGTATACCTCCGTCGAAAGCTTACCATGTGAACGCCCTTCAGTAGGCTCAATGATATCAACACGTCAGGTC?AGGTGCAGCTTAAGAAATGGGAAGCAATTGGCTACAATTTCTAATCTAAAACAAACGAAAGGCTATGTGAAATCATAACCATGAAGGTGGATTTAGTAATAAAAAGAAAATATAGTGTTCTTTTTAACCCGGCTCTGGGACGTGTACACACCGCCCGTCACCCTCTTCGATAGTT-CATAATACTGTTCCTAACCT-ATTAT-TACATTTT-AGAAGAGGCAAGTCGAAACACGGTAAGTGTACTGGAAAGTGCACTTGGAAAA-TAACAAAATGTAGCTTAATAAAAGCCCTCCGCTTACACCGAAGAAATATCTGTTTGAATCAGATCATTTTGAGCCCTAAATCTAGCCTTCAAAATTCGTATGAACCCTCTCCCAAA-CAAAACATTTTCTTATCCTAGTACAGGCGATCGAAAAA-TTTCTAAGCGCTTCAGATAAAGTACCGCAAGGGAAAGATGAAATAGAATTGAAATAACCTTAAAGCCTTAAACAGCAGAGATTATACCTCGTACCTTTTGCATCATGGTCTAGTTAGTCTACCC-AAGCAAAATGAAACTTTT-AGTTAGTCCCCCCGAAACTAAGCGAGCTACTTCAAAACAGCCCTATGGGGCCAACCCTTCTCTGTTGCAAAAGAGTGGGAAGATTTTCAAGTAGAAGTGAAAAGCCTATCGAGCTTAGAGATAGCTGGTTACTCAGGAAAAGAGTTTTAGCTCTACCTTAAGCTTCTTT-ATGAAACCC-AACAAACCT-TAAAGCTTAAGAACTATTCAAATAAGGCACAGCTTATTTGAAATAGGATACAACCTAAAATACTGGGT-AAATAACA-GTAATTAAAA-TTAAGTTGGCCTAAGAGCAGCCATCTTTAAAAAAGCGTTAAAGCTTAGTTTTACCT-ACTGACAA-T-CCTAAAATTTTAATCAACCCTTTAT-CACTACTGAGTTATTTTATA-CCCCTATAAAAGTGATAATGCTAGAACTAGTAACAAGAAACTGCCCATTCTCCTAAATGCAAGTATAAACCAAAATAGACACCCTGTTGGTAATTAACGTAAATGTAACAGCTATAGCAACATAA--------TAGAAAACCCTACAACCTTC-AACGTTAATCTTACACCAGAGCATTTCAGGAAAGATTAAAAAAGAAAGAAGGAACTCGGCAAACTTCAGCCCCGCCTGTTTACCAAAAACATCGCCTCTTGAAAAATTATAAGAGGTCCAGCCTGCCCAGTGACTAAGTTCAACGGCCGCGGTACCCTAACCGTGCGAAGGTAGCATAATCACTTGTTCTTTAAATGGGGACTTGTATCAACGGCACCACGAGGGCTGCACTGTCTCCTTTCTTCAATCAGTGAAACTGATCTCCCCGTGAAGAAGCGGGGATTCATATATAAGACGAGAAGACCCCATGGAGCTTCAAACTCATTATACAACTCT-ATTCTCC-ACACCCTCCTAACAC-AAGAGACA-TGTATGATGGTTTTGGGTTGGGGGGACCTCGGAGTATAACATATCCTCCTAAGCAAACAGGTTAACACCTTTATCCATGAAGAACACCTCTAAGAATTATTAAATTAATGCAT-CTGATCCGATAA--TCGATCAATGAACCAAGTTACCCTGGGGATAACAGCGCAATCTACTTCAAGAGTTCCTATCGACAAGTGGGTTTACGACCTCCATGTTGGATCAGGGTATCCCAGTGGTGCAACCGCTACTAATG-GTTCGTTTGTTCAACG ; End; garli-2.1-release/example/basic/rana.phy000066400000000000000000003723101241236125200202530ustar00rootroot0000000000000064 1976 temporariaDMH84R1 GCCGTAAACAATTAACTCACATCCACA-CCGCCAGGGAATTACGAGCAATGCTTAAAACCCAAAGGATTTGACGGTGTCCCACCCCACTAGAGGAGCCTGTTCTATAATCGATGATCCCCGCTTCACCTAACCATCCCTCGCCTACCAGTCTGTATACCTCCGTCGAAAGCTTACCATGTGAACGCCC-GCAGTAAGCTTAATGATATCAACACGTCAGGTCAAGGTGCAGCTTAAGGGACGGAAAGTAATGGGCTACAATTTCTAATCTAGAACAAACGAAAGACTATGTGAAACCCTAGTCATGAAGGTGGATTTAGTAGTAAAAAAAAAATATAGTGTTCTTTTTAACCCGGCTCTGGGACGCGTACACACCGCCCGTCACCCTCTTCAATAGTA-C-CCCGTATGTTCCTAACCC-AACAC-CACGTTTT-AGAAGAAGCAAGTCGTAACATGGTGAGTGTACTGGAAAGTGCACTTGGTATA-CTACAAAATGTAGCTTAACAAAAGCCCCTCGCTTACACCTAGGAAATATCTGTTAAACCCGGATCATTTTGAGCCTAAAATCTAGCCCA-CTAATCCGTATGACCCCTCCAAAAAA-CAAAACATTTTAACATCTTAGTACAGGCGATCGAAAAA-TTTTTAAGCGCTTCAGATAAAGTACCGCAAGGGAAAAATGAAATAGAAATGAAATAACCCTAAAGCCTCAAATAGCAGAGATAATACCTCGTACCTTTTGCATCATGGTCTAGCCAGTCTACCC-AAGCAAAATGTAACTTTC-AGTTTGATACCCCGAAACCAAGCGAGCTACTTCAGAACAGCCAAAA-GGGCCAACCCGTCTCTGTTGCAAAAGAGTGGGAAGATTCTCAAGTAGAGGTGACAAGCCTACCGAGCTTGGAGATAGCTGGTTGTTCAGAAAAAGAGTTTTAGCTCTACCTTAAGTTTTTCC-ATTAAACTA-AACAAACCC-CAAGACTTAAGAGCTATTCAAATAAGGCACAGCTTATTTGAAATAGGGCACAGCCTACAACATAGGGT-AAACAAGA-GTAACTTAAA-TAAAGTAGGCCTAAAAGCAGCCACCTTTAGAAAAGCGTCAAAGCTTAACCACTCCCTACTCTCAA-TACCTTAAATTTTCACTAACCCCTCAC-TATTACTGAATAATTTTATA-ATTATATAAAAGCTATCATGCTAGAACTAGTAACAAGAAATTGCCT-TTCTCCTAAATGTAAACATAAACCAAAATAGACTATCTATTGGTTATTAACGTAAATGCCAAA-TTATAGCAACATCCTC------CAGAAAATCCTATAGCCCCC-AACGTTAACCTTACACTAGAACATTTCAGGAAAGATTTAAAGAAAAAGAAGGAACTCGGCAAATTTTAGCCTCGCCTGTTTACCAAAAACATCGCCTCTTGACAAAACATAAGAAGTCCAGCCTGCCCAGTGACAAAGTTCAACGGCCGCGGTACCCTAACCGTGCGAAGGTAGCATAATCACTTGTTCTTTAAATGAGGACTCGTATCAACGGCACTACGAAGGCTATACTGTCTCCTTTTTCTAATCAGTGAAACTGATCTCCCCGTGAAGAAGCGGGGATCAAAATATAAGACGAGAAGACCCCATGGAGCTTTAAACTCATCACGCACCTCT-GTGCCCTCATACCCTT-AAACAC-AAAAAATC-TACGTGCTAGTTTTAGGTTGGGGGGACCACGGATTATAACTTAACCTCCTTAACAAATGGGCTAACACCCTTATCCATGAGACACAGCTCTAAGAATTACTAAACTAATGCTT-ATGACCCGATA--TTCGATCAATGAACCAAGTTACCCTGGGGATAACAGCGCAATCTACTTCAAGAGCCCATATCGACAAGTAGGTTTACAACCTCCATGTTGGATCAGGGTATCCCGGTGGTGCAACCGCTACCGATG-GTTCGTTTGTTCAACG boyliiMVZ148929 GCCGTAAACAATTAACTCACACC-TCCAGCGCCAGGGGATTACGAGCAATGCTTAAAACCCAAAGGATTTGACGGTGTCCCACCCCACTAGAGGAGCCTGTTCTATAATCGATGATCCCCGCTTCACCCGACCACTCCTCGCTTATCAGTCTGTATACCTCCGTCGAAAGCTTACCATGTGAACGCCC-ACAGTAAGCCCAATGATGTCAACACGTCAGGTCAAGGTGCAGCTCAAGGAATGGGAAGTAATGGGCTACAATTTCTAATTTAGAACAAACGAAAGACTATGTGAAACCCTAGTCATGAAGGTGGATTTAGTAGTAAAAGGGAAATAGAGTGTTCCTTTTAACCCGGCTCTGGGACGCGTACACACCGCCCGTCACCCTCTTCAATAGTT-TTTC-TCATGTCCTTAACCC-CCGCG-CACATTTT-AGAAGAAGCAAGTCGTAACATGGTAAGTGTACTGGAAAGTGCACTTGGTATA-CTACAAAATGTAGCTTAACAAAAGCCCCTCGCTTACACCTAGGAAATATCTGTTAAACTCAGATCATTTTGAGCCTAAAATCTAGCCCA-CACACTCGCATGATTCCACTCTAAAA-CAAAACATTTTAACATTTTAGTACAGGCGATCGAAATA-TTTCTAAGCGCTTCAGATAAAGTACCGCAAGGGAAAAATGAAATAGAAATGAAATAACCCTAAAGCCCTAAACAGCAGAGATTATACCTCGTACCTTTTGCATCATGGTCTAGCCAGTCTACCC-AAGCAAAATGAAACTTTC-AGTTTGACACCCCGAAACTAAGCGAGCTACTTCAGAACAGCCTAAGAGGGCCAACCCGTCTCTGTTGCAAAAGAGTGGGAAGATTCTTAAGTAGAGGTGATAAGCCTACCGAGCTTAGAGATAGCTGGTTGTTCAGAAAAAGAGTTTTAGCTCTACCTTAAGCTTTCCC-ATTACATTA-AATAAATCT-CAAAACTTAAGAGCTATTCAAATAAGGCACAGCTTATTTGAAACAGGACACAACCTACACCACAGGGT-AAATTAGA-GTAAATCAAG-TAAAGTGGGCCTAAAAGCAGCCACCTTTTAAAAAGCGTTAAAGCTTAACTACTCCTTACTCGCAA-TTCCTTTAAGCCTCATTAACCCTTCAT-TATTACTGAACAATTTTATA-TTTTTATAAAAGCTATTATGCTAGAACTAGTAACAAGAAACTGCCT-TTCTCCTAAATGTAAACATAAGCCAAAATAGACCACCTATTGGTTATTAACGTAAATGCCTGAATCATACCAACAAAAAC------TAGAAAACCCTATGACCCCC-TACGTTAACCTTACACCAGAACATTCCAGGAAAGATTAAAAGAAAAAGAAGGAACTCGGCAAATTTTAGCCTCGCCTGTTTACCAAAAACATCGCCTCTTGACAAAACATAAGAGGTCCAGCCTGCCCAGTGACAAAGTTTAACGGCCGCGGTACCCTAACCGTGCGAAGGTAGCATAATCACTTGTTCTTTAAATGAAGACTCGTATCAACGGCACTACGAAGGCTATACTGTCTCCTTTTTCTAATCAGTGAAACTGATCTCCCCGTGAAGAAGCGGGGATTAAAATATAAGAC-A-AAGACCCCATGGAGCTTTAAACTCACCATACACCTCT-GTGTCCTCTCATCACC-CTACAC-AAAAAGCT-TGTATGCTAGTTTTAGGTTGGGGGGACCACGGATTAAAATATAACCTCCACAACAAATGGGCTAACACCCTAATCCACGAGATACAACTCTAAGAATTACTAAACTAATGTTT-ATGACCCGATAA-TTCGATCAATGAACCAAGTTACCCTGGGGATAACAGCGCAATCTACTTCAAGAACCCATATCGACAAGTATGTTTACAACCTCCATGTTGGATCAGGGTATCCCAGTGGTGCAGCCGCTACTAATG-GTTCGTTTGTTCAACG luteiventris_MT_MVZ191016 GCCGTAAACAATTAACTTACATT-TCCAGCGCCAGGGAATTACGAGCAATGCTTAAAACCCAAAGGATTTGACGGTGTCCCACCCCACTAGAGGAGCCTGTTCTATAATCGATGATCCCCGCTTCACCTGACCACTCCTCGCTTATCAGTCTGTATACCTCCGTCGAAAGCTTACCATGTGAACGCCC-GCAGTAAGCCTAATGATATCAACACGTCAGGTCAAGGTGCAGCTCAAGGAATGGGAAGTAATGGGCTACAATTTCTAACTTAGAACAAACGAAAGACTATGTGAAACCCTAGTCATGAAGGTGGATTTAGTAGTAAAAAGAAAATATAGTATTCTTTTTAACCCGGCTCTGGGACGCGTACACACCGCCCGTCACCCTCTTCAATAGTA--TTT-TTTTGTTCCTAACCC-CCTACACACGTCTT-AGAAGAAGCAAGTCGTAACATGGTAAGTGTACTGGAAAGTGCACTTGGTATA-CTACAAAATGTAGCTTAACAAAAGCCCCTCGCTTACACCTAGGAAATATCTGTTAAACCCAGATCATTTTGAGCCTAAAATCTAGCCCA-CACACTCGCATGATTCCACTTCAAAA-CAAAACATTTTAACATCTTAGTACAGGCGATCGAAAAA-TTTCTAAGCGCTTCAGATAAAGTACCGCAAGGGAAATATGAAATAGAAATGAAATAACCCTAAAGCCCTAAACAGCAGAGATAACACCTCGTACCTTTTGCATCATGGTCTAGCCAGTCTACCC-AAGCAAAATGAAACTTTC-AGTTTGACCCCCCGAAACTAAGCGAGCTACTTCAGAACAGCCCCAGGGGGCCAACCCGTCTCTGTTGCAAAAGAGTGGGAAGATTCTTAAGTAGAGGTGACAAGCCTACCGAGCTTAGAGATAGCTGGTTGTTCAGAAAAAGAGTTTTAGCTCTACCTTAAGCTTCCCG-ATTATATTA-AACAAACCC-TAAAGCTTAAGAGCTATTCAAATAAGGCACAGCTTATTTGAAACAGGGTACAACCTACACCACAGGGT-AAACACGA-GTAAACTAAA-TAAAGTGGGCCTAAAAGCAGCCACCTTCAAAAAAGCGTTAAAGCTTAACTACTATCTACTCATAA-TTCCTTTAAACCTCACTAACCCTTCAT-TATTACTGAACAATTTTATA-TTCATATAAAAGCTATCATGCTAGAACTAGTAACAAGAAATTGCCT-TTCTCCTAAATGTAAACGTAAACCAAGATAGACCATCTATTGGTTATTAACGCAAATGCTAAAATCATAGCAACATCTAC------TAGAAAATCCTATGACCTCCTAACGTTAACCTTACACTAGAACATTACAGGAAAGATTAAAAGAAAAAGAAGGAACTCGGCAAATTTTAGCCTCGCCTGTTTACCAAAAACATCGCCTCTTGACAAAACATAAGAAGTCCAGCCTGCCCAGTGACAAAGTTCAACGGCCGCGGTACCCTAACCGTGCGAAGGTAGCATAATCACTTGTTCTTTAAATGAGGACTAGTATCAACGGCACTACGAAGGCTATACTGTCTCCTTTTTCCAATCAGTGAAACTGATCTCCCCGTGAAGAAGCGAGGATAGACTTATAAGACGAGAAGACCCCATGGAGCTTTAAACTCACCATACACCTCT-GTGCCCCCTCACCATC-TGACAC-AAGAGATT-TGTATGCTAGTTTTAGGTTGGGGGGACCACGGAGTACAACTTAACCTCCACAACAAATGGGCTAACACCCTTATCCAAGAGATACAACTCTAAGAATTACTAAACTAATGTTT-ATGACCCGATAA-TTCGATCAATGAACCAAGTTACCCTGGGGATAACAGCGCAATCTACTTCAAGAGCCCATATCGACAAGTAGGTTTACGACCTCGATGTTGGATCAGGGTATCCCAGTGGTGCAGCCGCTACTAATG-GTTCGTTTGTTCAACG luteiventris_WA_MVZ225749 GCCGTAAACAATTAACTTACATT-TCCAGCGCCAGGGAATTACGAGCAATGCTTAAAACCCAAAGGATTTGACGGTGTCCCACCCCACTAGAGGAGCCTGTTCTATAATCGATGATCCCCGCTTCACCTGACCACTCCTCGCTTATCAGTCTGTATACCTCCGTCGAAAGCTTACCATGTGAACGCCC-GCAGTAAGCCTAATGATATCAACACGTCAGGTCAAGGTGCAGCTCAAGGAATGGGAAGTAATGGGCTACAATTTCTAATTTAGAACAAACGAAAGACTATGTGAAACCCTAGTCATGAAGGTGGATTTAGTAGTAAAAAGAAAATAGAGTATTCTTTTTAACCCGGCTCTGGGACGCGTACACACCGCCCGTCACCCTCTTCAATAGTA--TTT-TTTTGTTCCTAACCC-CCTACACACGTCTT-AGAAGAGGCAAGTCGTAACATGGTAAGTGTACTGGAAAGTGCACTTGGTATA-CTACAAAATGTAGCTTAACAAAAGCCCCTCGCTTACACCTAGGAAATATCTGTTAAACCCAGATCATTTTGAGCCTAAAATCTAGCCCA-CACACTCGCATGATTCCACTTCAAAA-CAAAACATTTTAACATCTTAGTACAGGCGATCGAAAAA-TTTCTAAGCGCTTCAGATAAAGTACCGCAAGGGAAATATGAAATAGAAATGAAATAACCCTAAAGCCCTAAACAGCAGAGATAACACCTCGTACCTTTTGCATCATGGTCTAGCCAGTCTACCC-AAGCAAAATGAAACTTTC-AGTTTGACCCCCCGAAACTAAGCGAGCTACTTCAGAACAGCCCCAAGGGGCCAACCCGTCTCTGTTGCAAAAGAGTGGGAAGATTCTTAAGTAGAGGTGACAAGCCTACCGAGCTTAGAGATAGCTGGTTGTTCAGAAAAAGAGTTTTAGCTCTACCTTAAGCTTCCCG-ATTATATTA-AACAAACCC-TAAAGCTTAAGAGCTATTCAAATAAGGCACAGCTTATTTGAAACAGGGTACAACCTACACCACAGGGT-AAACACGA-GTAAACTAAA-TAAAGTGGGCCTAAAAGCAGCCATCTTCAAAAAAGCGTTAAAGCTTAACTACTATCTACTCATAA-TTCCTTTAAACCTCACTAACCCTTCAT-TATTACTGAACAATTTTATA-TTCATATAAAAGCTATTATGCTAGAACTAGTAACAAGAAATTGCCT-TTCTCCTAAATGTAAACGTAAACCAAGATAGACCATCTATTGGTTATTAACGCAAATGCTAAAATCATAGCAACATCTAC------TAGAAAATCCTATGACCTCCTAACGTTAACCTTACACTAGAACATTACAGGAAAGATTAAAAGAAAAAGAAGGAACTCGGCAAATTTTAGCCTCGCCTGTTTACCAAAAACATCGCCTCTTGACAAAACATAAGAGGTCCAGCCTGCCCAGTGACAAAGTTCAACGGCCGCGGTACCCTAACCGTGCGAAGGTAGCATAATCACTTGTTCTTTAAATGAGGACTAGTATCAACGGCACTACGAGGGCTATACTGTCTCCTTTTTCCAATCAGTGAAACTGATCTCCCCGTGAAGAAGCGAGGATAGACTTATAAGACGAGAAGACCCCATGGAGCTTTAAACTCACCATACACCTCT-GTGCCCCCTCACCATC-TGACAC-AAGAGATT-TGTATGCTAGTTTTAGGTTGGGGGGACCACGGAGTACAACTTAACCTCCACAACAAATGGGCTAACACCCTTATCCAAGAGATACAACTCTAAGAATTACTAAACTAATGTTT-ATGACCCGATAA-TTCGATCAATGAACCAAGTTACCCTGGGGATAACAGCGCAATCTACTTCAAGAGCCCATATCGACAAGTAGGTTTACGACCTCGATGTTGGATCAGGGTATCCCAGTGGTGCAGCCGCTACTAATG-GTTCGTTTGTTCAACG muscosaMVZ149006 GCCGTAAACAATTAATTTACACC-TCCAACGCCAGGGAATTACGAGCAATGCTTAAAACCCAAAGGATTTGACGGTGTCCCACCCCACTAGAGGAGCCTGTTCTATAATCGATGATCCCCGCTTCACCCGACCGTTCCTCGCTCATCAGTCTGTATACCTCCGTCGAAAGCTTACCATGTGAACGCCA-ACAGTAAGCTTAATGATATCAACACGTCAGGTCAAGGTGCAGCTTAAGGAATGGGAAGTAATGGGCTACAATTTCTAATTTAGAACAAACGAAAGACTATGTGAAATCCTAGTCATGAAGGTGGATTTAGTAGTAAAAAGGAAATAGAGTGTCCTTTTTAACTCGGCTCTGGGACGCGTACACACCGCCCGTCACCCTCTTCAATAGTA-CTTT-TCCTGTTCCTAACCC-ACACG-CACATTTT-AGAAGAGGCAAGTCGTAACATGGTAAGTGTACTGGAAAGTGCACTTGGTATA-CTACAAAATGTAGCTTAACAAAAGCCCCTCGCTTACACCTAGGAAATATCTG-TAAACCCAGATCATTTTGAGCCTAAAATCTAGCCCA-CACACTCGCATGACCTTTTTACCAAA-CAAAACATTTTAACATTTTAGTACAGGCGATCGAAAAA-TTTCTAAGCGCTTCAGATAAAGTACCGCAAGGGAAAAATGAAATAGAAATGAAATAACCCTAAAGCCCCAGACAGCAGAGATTACACCTCGTACCTTTTGCATCATGGTCTAGCCAGTCTACCC-AAGCAAAATGAAACCTTC-AGTTTGACACCCCGAAACTAAGCGAGCTACTTTAGAACAGCC-TAATGGGCCAACCCGTCTCTGTTGCAAAAGAGTGGGAAGATTTTTAAGTAGAGGTGATAAGCCTACCGAGCTTAGAGATAGCTGGTTATTCAGGAAAAGAGTCTAAGCTCTACCTTAAGCTTCCTC-ATTATATTA-AGCAAGCCC-CAAAGCTTAAGAGCCATTCAAATAAGGCACAGCTTATTTGAAATAGGATACAACCTACAACACAGGGTTAAACAAGA-GTAAACTAAG-TAAAGTGGGCCTAAAAGCAGCCACCTTTTAAAAAGCGTTAAAGCTTAACTATTTTCTACTCATAA-TTCCTCTAACCCCCACTAACCCTTCAT-TATTACTGAATAACTTTATAATTCATATAAAAGCTATTATGCTAGAACTAGTAACAAGAAATTGCCT-TTCTCCTAAATGTAAACGTAAACCAAAATAGACCATCTATTGGTTATCAACGCAAATGCTAAAATCATAGCAACACTCAC------TAGAAAATCCTATGACCTCCCAACGTTAACCTTACACTAGAACATTCCAGGAAAGATTAAAAGAAAAAGAAGGAACTCGGCAAATCTTAGCCTCGCCTGTTTACCAAAAACATCGCCTCTTGACAAA-CATAAGAGGTCCAGCCTGCCCAGTGACAAAGTTCAACGGCCGCGGTACCCTAACCGTGCGAAGGTAGCATAATCACTTGTTCTTTAAATGAGGACTAGTATCAACGGCACTACGAGGGCTATACTGTCTCCTTTCTCCAATCAGTGAAACTGATCTCCCCGTGAAGAAGCGAGGATCAAAATATAAGACGAGAAGACCCCATGGAGCTTTAAACTCATCATACATCTCT-GTGCTCC-CCATCATC-ACACAC-AAGAAATC-TGTATGCTAGTTTTAGGTTGGGGGGACCACGGAGTAAAATTTAACCTCCACAACAAATGGGCTAACACCCTTATCTACGAGATACAACTCTAAGAATTACTAAACTAATGTTT-ATGACCCGATAA-TTCGATCAATGAACCAAGTTACCCTGGGGATAACAGCGCAATCTACTTCAAGAGCCCATATCGACAAGTAGGTTTACGACCTCGATGTTGGATCAGGGTATCCTAGTGGTGCAGCCGCTACTAACG-GTTCGTTTGTTCAACG auroraMVZ13957 GCCGTAAACAATTAACTTACACC-TCCAACGCCAGGGAATTACGAGCAATGCTTAAAACCCAAAGGATTTGACGGTGTCCCACCCCACTAGAGGAGCCTGTTCTATAATCGATGATCCCCGCTTCACCCGACCGTTCCTCGCCTATCAGTCTGTATACCTCCGTCGAAAGCTTACCATGTGAACGCCA-GCAGTAAGCTTAATGATATCAACACGTCAGGTCAAGGTGCAGCTTAAGGAACGGGAAGCAATGGGCTACAATTTCTAATCTAGAACAAACGAAAGATTATGTGAAATCCTAATCATGAAGGTGGATTTAGTAGTAAAAAGGAAATATAGTGTTCTTTTTAACTCGGCTCTGGGACGCGTACACACCGCCCGTCACCCTCTTCAATAGTA--TTT-TCCTGTCCCTAACCC-CCTTA-CACATTTT-AGAAGAAGCAAGTCGTAACATGGTAAGTGTACTGGAAAGTGCACTTGGAATA-TTACAAAATGTAGCTTAACAAAAGCCCCTCGCTTACACCTAGGAAATATCTGTTAAACCCAGATCATTTTGAGCCTAAAATCTAGCCCA-CACTCTCGCATGACTTCTCTTACAAA-CAAAACATTTTAACATTTTAGTACAGGCGATCGAAAAA-TTTCTAAGCGCTTCAGATAAAGTACCGCAAGGGAAAAATGAAATAGAAATGAAATAATCCTAAAGCCCCAGACAGCAGAGATTTTACCTCGTACCTTTTGCATCATGGTCTAGCCAGTCTCCCC-AAGCAAAATGAAACTTTT-AGTTTGACACCCCGAAACTAAGCGAGCTACTTCAGAACAGCC-TAATGGGCCAACCCGTCTCTGTTGCAAAAGAGTGGGAAGATTCTTAAGTAGAGGTGATAAGCCTACCGAGCTTAGAGATAGCTGGTTGTTCAGAAAAAGAGTTTTAGCTCTACCTTAAGCTTCCTC-ATTATATTA-AGCAAACCC-CAAAGCTTAAGAGCCATTCAAATAAGGCACAGCTTATTTGAAACAGGGTACAACCTACAATACAGGGT-AAACAAGA-ATAAACTAAG-TAAAGTGGGGCCAAAAGCAGCCATCTTTAGAAAAGCGTTAAAGCTTAACTATCTCCTACTCATAA-TTCCTCTAACCCCCTCTAACCCTTCAT-TACTACTGAACAATTTTATA-TCCATATAAAAGCTATTATGCTAGAACTAGTAACAAGAAATTGCCT-TTCTCCTAAATGTAAACGTAAACCAAGATAGACCATCTATTGGTTATAAACGCAAATGCCAAAATCATAATAACATTCAC------TAGAAAATCCTATGACTCCC-AGCGTTAACCTTACACTAGAACATTTCAGGAAAGATTAAAAGAAAAAGAAGGAACTCGGCAAATCTTAGCCTCGCCTGTTTACCAAAAACATCGCCTCTTGACAAA-CATAAGAAGTCCAGCCTGCCCAGTGACAAAGTTCAACGGCCGCGGTACCCTAACCGTGCGAAGGTAGCATAATCACTTGTTCTTTAAATGAGGACTCGTATCAACGGCACTACGAGGGCTATACTGTCTCCCTTTTCCAATCAGTGAAACTGATCTCCCCGTGAAGAAGCGGGAATCAAAATATAAGACGAGAAGACCCCATGGAGCTTTAAACTCATCATACACCTCT-GTGCTCTTCCATCATC-ACACAC-AAGAAATC-TGTATGCTAGTTTTAGGTTGGGGGGACCACGGAGTACAATTTAACCTCCACAACAAATGGGCTAACACCCTTATCCATGAGATACAACTCTAAGAATTACTAAACTAATGTTT-ATGACCCGATAA-TTCGATCAATGAACCAAGTTACCCTGGGGATAACAGCGCAATCTACTTCAAGAGCCCATATCGACAAGTAGGTTTACGACCTCGATGTTGGATCAGGGTATCCTAATGGTGCAGCCGCTACTAATG-GTTCGTTTGTTCAACG cascadaeMVZ148946 GCCGTTTACAATTAACTTACACC-TCCAACGCCAGGGGATTACGAGCAATGCTTAAAACCCAAAGGATTTGACGGTGTCCCACCCCACTAGAGGAGCCTGTTCTATAATCGATGATCCCCGCTTCACCCGACCGTTCCTCGCTTATCAGTCTGTATACCTCCGTCGAAAGCTTACCATGTGAACGCCA-ACAGTAAGCTTAATGATATCAACACGTCAGGTCAAGGTGCAGCTTAAGGAACGGGAAGCAATGGGCTACAATTTCTAATTTAGAACAAACGAAAGATTATGTGAAATCCTAATCATGAAGGTGGATTTAGTAGTAAAAAGGAAATAGAGTGTCCTTTTTAACTCGGCTCTGGGACGCGTACACACCGCCCGTCACCCTCTTCAATAGTA-TTTT-TTCTGTCCCTAACCC-CCCTCGCACATTTT-AGAAGAGGCAAGTCGTAACATGGTAAGTGTACTGGAAAGTGCACTTGGAATA-TTACAAAATGTAGCTTAACAAAAGCCCCTCGCTTACACCTAGGAAATATCTGTTAAACCCAGATCATTTTGAGCCTAAAATCTAGCCCG-CACACTCGCATGACTTCTCTCACAAA-CAAAACATTTTAACATTTTAGTACAGGCGATCGAAAAA-TTTCTAAGCGCTTCAGATAAAGTACCGCAAGGGAAAAATGAAATAGAAATGAAATAACCCTAAAGCCCCAGACAGCAGAGATTTTACCTCGTACCTTTTGCATCATGGTCTAGCCAGTCTACCC-AAGCAAAATGAAACTTTC-AGTTTGACACCCCGAAACTAAGCGAGCTACTTCAGAACAGCC-TAATGGGCCAACCCGTCTCTGTTGCAAAAGAGTGGGAAGATTCTCAAGTAGAGGTGATAAGCCTACCGAGCTTAGAGATAGCTGGTTGTTCAGAAAAAGAGTTTTAGCTCTACCTTAAGCTTCCTC-ATTACACTA-AGCAAACCC-CTAAGCTTAAGAGCCATTCAAATAAGGCACAGCTTATTTGAAACAGGGCACAACCTACAACACAGGGT-AAACAAGA-GTAAACTAAG-CAAAGTGGGCCCAAAAGCAGCCACCTTTAGAAAAGCGTTAAAGCTTAACTATCCTCTACTCATAA-TTCCTCTAACCCCCTCTAACCCTTCAT-TATTACTGAACAATTTTATA-TCCCTATAAAAGCTATTATGCTAGAACTAGTAACAAGAAATTGCTT-TTCTCCTAAATGTAAACGTAAACCAAGATAGACCATCTGTTGGTTATCAACGCAAATGCCAAAATCATAACAACACTTAC------TAGAAAACCCTATGACTCCC-AGCGTTAACCTTACACTAGAACATTTCAGGAAAGATTAAAAGAAAAAGAAGGAACTCGGCAAATCTTAGCCTCGCCTGTTTACCAAAAACATCGCCTCTTGACAAA-CATAAGAGGTCCAGCCTGCCCAGTGACAAAGTTCAACGGCCGCGGTACCCTAACCGTGCGAAGGTAGCATAATCACTTGTTCTTTAAATGAGGACTCGTATCAACGGCACTACGAGGGCTATACTGTCTCCCTTTTCCAATCAGTGAAACTGATCTCCCCGTGAAGAAGCGGGGATCAAGATATAAGACGAGAAGACCCCATGGAGCTTCAAACTCATCATACATCTCT-GTGCTCTCCCATCATT-ATACAC-AAGAAATC-TGTATGCTAGTTTTAGGTTGGGGGGACCACGGAGTACAATTTAACCTCCACAACAAATGGGCTAATACCCTTATCCATGAGATACAACTCTAAGAATTATTAAACTAATGTTT-ATGACCCGATAA-TTCGATCAATGAACCAAGTTACCCTGGGGATAACAGCGCAATCTACTTCAAGAGCCCGTATCGACAAGTAGGTTTACGACCTCGATGTTGGATCAGGGTATCCTAGTGGTGCAGCCGCTACTAATG-GTTCGTTTGTTCAACG sylvaticaMVZ137426 GCCGTAAACAATTAATTTACACCCACCAGCGCCAGGGAATTACGAGCAATGCTTAAAACCCAAAGGATTTGACGGTGTCCCACCCCACTAGAGGAGCCTGTTCTATAATCGATGATCCCCGCTTCACCCGACCACTCCTTG-CTATCAGTCTGTATACCTCCGTCGAAAGCTTACCATGTGAACGCCC-AAAGTAGGCTTAATGACGTCAACACGTCAGGTCAAGGTGCAGCTTAAGGGGTGGGAAGTAATGGGCTACAATTTCTAATCTAGAACAAACGAAAGACTATGTGAAATACTAGTCATGAAGGTGGATTTAGTAGTAAAAAGAAAGTAGAGTGTTCTTTTTAACCCGGCTCTGGGACGCGTACACACCGCCCGTCACCCTCTTCAATAGTA-ATTTATCCTGTTTCTAACCC-ACTAC-TACATTTT-AGAAGAGGCAAGTCGTAACATGGTAAGTGTACTGGAAAGTGCACTTGGAATA-C-ACAAAATGTAGCTTAATAAAAGCCCCTCGCTTACACCGAAGAGATACCCGTTTAATTCGGATCATTTTGAGCTTCAAATCTAGCCGCACACACTCGCATG-CCCTCTTTCCAAA-CAAAACATTTTAATATTATAGTACAGGCGATCGAAAAA-TT-CTTAGCGCTTCAGACAAAGTACCGCAAGGGAAAGATGAAATAGAAATGAAATAACCTTAAAGCCTTAAATAGCAGAGACACCCCCTCGTACCTTTTGCATCATGGTCTAGCTAGTCTACCC-AAGCAAAATGAAACTTTT-AGTTTGACATCCCGAAACTAAGTGAGCTACTTCAAAACAGCCCTAAGGGGCCAACCCGTCTCTGTTGCAAAAGAGTGGGAAGATTTTCAAGTAGAGGTAATAAGCCTACCGAACTTAGAGATAGCTGGTTGTTCAGGAAGAGAGTTTTAGCTCTACCTTAAGCTTCCCC-ATTTTACCA-AAAAATGCCCCAAAGCTTAAGAGCTATTCAAATAAGGCACAGCTTATTTGAAATAGGGTACAACCTCCAACATCGGGT-AAATTATA-GTAATTAAAA-TGAAGTGGGCCTAAAAGCAGCCATCTTTAAAAAAGCGTTAAAGCTTAATTATTCAT-ACTAAAAAATTCCTTAAATCCTAATTAACCCCTCAT-TTATACTGAACTGCTTTATA-T-TTTATAAAAGTAATAATGCTGGAACTAGTAACAAGAAATTGCCT-TTCTCCCAAATGTAAGCATAAACCAAAATGGACCATCTATTGGTAATTAACGTAAATGCAAAAACTATAGTAACACAAC-------TAGAAAACCCTATTATTATT-AGCGTTAACCTTACACTAGAACATTACAGGAAAGATTAAAAGAGAAAGAAGGAACTCGGCAAATTTTAGCCTCGCCTGTTTACCAAAAACACCGCTTCTTGA-AAAACATAAGAGGTCCAGCCTGCCCAGTGACAAAGTTCAACGGCCGCGGTACCCTAACCGTGCGAAGGTAGCATAATCACTTGTCCTTTAAATAGGGACTAGTATCAACGGCATCACGAGGGCTATACTGTCTCCTTTCTCCAATCAGTGAAACTGATCTCCCCGTGAAGAAGCGGGGATTAAACTATAAGACGAGAAGACCCCATGGAGCTTTAAACTCATCATACACCTTT-ATGACCTCACACCAAC-TGACCA-AAAAGACC-TGTATGCTAGTTTTAGGTTGGGGGGACCACGGAGTACAATTTAACCTCCACAACAAATGGGCTAATACCCTTATCCACGAGATACACCTCTAAGAATTACTAAACTAATGTTTAATGACCCAATAA-TTTGATCAATGAACCAAGTTACCCTGGGGATAACAGCGCAATCTACTTCAAGAGCCCCTATCGACAAGTAGGTTTACGACCTCGATGTTGGATCAGGGTATCCTAGTGGTGCAGCCGCTACTAACG-GTTCGTTTGTTCAACG sylvaticaDMH84R43 GCCGTAAACAATTAATTTACACCCACCAGCGCCAGGGAATTACGAGCAATGCTTAAAACCCAAAGGATTTGACGGTGTCCCACCCCACTAGAGGAGCCTGTTCTATAATCGATGATCCCCGCTTCACCCGACCACTCCTTG-CTATCAGTCTGTATACCTCCGTCGAAAGCTTACCATGTGAACGCCC-AAAGTAGGCTTAATGACGTCAACACGTCAGGTCAAGGTGCAGCTTAAGGGGTGGGAAGTAATGGGCTACAATTTCTAATCTAGAACAAACGAAAGACTATGTGAAATACTAGTCATGAAGGTGGATTTAGTAGTAAAAAGAAAGTAGAGTGTTCTTTTTAACTCGGCTCTGGGACGCGTACACACCGCCCGTCACCCTCTTCGATAGTA-ATTTATCCTGTTTCTAACCC-ACTAC-TACGTTTT-AAAAGAAGCAAGTCGTAACATGGTAAGTGTACTGGAAAGTGCACTTGGAATA-C-ACAAAATGTA-CTTAATAAAAGCCCCTCGCTTACACCGAAGAGATACCCGTTTAATTCGGATCATTTTGAGCTTCAAATCTAGCCGCACACACTCGCATG-CCCTCTTTCCAAA-CAAAACATTTTAATATTATAGTACAGGCGATCGAAAAA-TT-CTTAGCGCTTCAGACAAAGTACCGCAAGGGAAAGATGAAATAGAAATGAAATAACCTTAAAGCCTTAAATAGCAGAGACACCCCCTCGTACCTTTTGCATCATGGTCTAGCTAGTCTACCC-AAGCAAAATGAAACTTTT-AGTTTGACATCCCGAAACTAAGTGAGCTACTTCAAAACAGCCCTAAGGGGCCAACCCGTCTCTGTTGCAAAAGAGTGGGAAGATTTTCAAGTAGAGGTGATAAGCCTACCGAACTTAGAGATAGCTGGTTGTTCAGGAAGAGAGTTTTAGCTCTACCTTAAGCTTCCCC-ATTTTACCA-AAAA-TGCCCCAAAGCTTAAGAGCTATTCAAATAAGGCACAGCTTATTTGAAATAGGGTACAACCTCCAACATCGGGT-AAATTATA-GTAATTAAAA-TGAAGTGGGCCTAAAAGCAGCCACCTTTAAAAAAGCGTTAAAGCTTAATTATTTAT-ACTAAAAA-TTCCTTAAATCCTAATTAACCCCTCAT-TTATACTGAACTGCTTTATA-T-TTTATAAAAGTAATAATGCTGGAACTAGTAACAAGAAATTGCCT-TTCTCCCAAATGTAAGCATAAACCAAAATGGACCATCTATTGGTAATTAACGTAAATGCAAAAACTATAGTAACACAAC-------TAGAAAACCCTATTATTATT-AGCGTTAACCTTACACTAGAACATTACAGGAAAGATTAAAAGAGAAAGAAGGAACTCGGCAAATTTTAGCCTCGCCTGTTTACCAAAAACACCGCCTCTTGA-AAAACATAAGAGGTCCAGCCTGCCCAGTGACAAAGTTCAACGGCCGCGGTACCCTAACCGTGCGAAGGTAGCATAATCACTTGTCCTTTAAATAGGGACTAGTATCAACGGCATCACGAGGGCTATACTGTCTCCTTTCTCCAATCAGTGAAACTGATCTCCCCGTGAAGAAGCGGGGATTAAACTATAAGACGAGAAGACCCCATGGAGCTTTAAACTCATCATACACCTTT-ATGACCTCACACCAAC-TAACCA-AAAAGACC-TGTATGCTAGTTTTAGGTTGGGGGGACCACGGAGTACAATTTAACCTCCACAACAAATGGGCTAATACCCTTATCCACGAGATACACCTCTAAGAATTACTAAACTAATGTTTAATGACCCAATAA-TTTGATCAATGAACCAAGTTACCCTGGGGATAACAGCGCAATCTACTTCAAGAGCCCCTATCGACAAGTAGGTTTACGACCTCGATGTTGGATCAGGGTATCCTAGTGGTGCAGCCGCTACTAACG-GTTCGTTTGTTCAACG septentrionalesDCC3588 GCCGTAAACAATTAATTTACACCAATAAGCGCCAGGGAATTACGAGCAATGCTTAAAACCCAAAGGATTTGACGGTGTCCCACCCCACTAGAGGAGCCTGTTCTATAATCGATGATCCCCGATACACCCGACCATTTCTCGCTTATCAGTCTGTATACCTCCGTCGAAAGCTTACCATGTGAACGCCC-GCAGTAGGCTTAATGACGTCAGTACGTCAGGTCAAGGTGCAGCTTAAGAAATGGGAAGCAATGGGCTACAATTTCTAATTTAGAACAAACGAAAGACTGTATGAAATTACAATCATGAAGGTGGATTTAGTAGTAAAAAGAAAGTAGAGTGTTCTTTTTAACCCGGCTCTGGGACGCGTACACACCGCCCGTCACCCTCTTCGATAGTA-TCTTATCTAGTTCCTAACCT-ATTAC-TACATTTT-AGAAGAGGCAAGTCGTAACATGGTAAGTGTACTGGAAAGTGCACTTGGTTTA-T-ACAAGATGTAGCTTAATAAAAGCCTCTCGCTTACACCGAGAAAATACCCGTTTAAACCGGATCATTTTGAGCCTAAAATCTAGCCTAACACACTCGCATGACCCCCCCCCCTAA-ATAATCATTTTAATATTATAGTACAGGCGATCGAAAAA-TTTCTAAGCGCTTCAGACAAAGTACCGCAAGGGAAAGATGAAATAGAAATGAAATAATCTTAAAGCCCAAAACAGCAGAGATAATCTCTCGTACCTTTTGCATCATGGTCTAGCCAGTCTACCC-AAGCGAAATAAAACTTTT-AGTTTGCCATCCCGAAACTAAGTGAGCTACTTCAGAACAGTCCTACGGGACCAACCCATCTCTGTTGCAAAAGAGTGGGAAGATTTTTAAGTAGAGGTGATAAGCCTACCGAACTTAGAGATAGCTGGTTATTCAGGAAGAGAGTTTTAGCTCTACCTTAAGCTTTCTCTATCAAACTA-AAGAAACCC-CAAAGCTTAAGAGCTATTCAAATAAGGCACAGCTTATTTGAAATAGGAAACAACCTCCAACACTGGGT-AAATAATA-GTAATTTAAA-TAAAGTGGGCCTAAAAGCAGCCACCTTTTGAAAAGCGTTAAAGCTTAACTATAAACTACTAATAA-TGCCTTAAATATTAACTAACCCTTCAT-CCCTACTGAATCACTTTATA-TTTTTATAAAAGCTATTATGCTAGAACTAGTAACAAGAAATTGGCT-TTCTCCTAAATGTATGTATAAACCAAAATGGACCATCCACTGGTAATTAACGCAAATGCAAAATTTATAACAACACAAC-------TAGAAAACCCTATAACTACA-AACGTTAAACTTACACTAGAACATTCCAGGGAAGATTAAAAGAGAAAGAAGGAACTCGGCAAATTTTAGCCTCGCCTGTTTACCAAAAACATCGCCTCTTGATTAAACATAAGAGGTCCAGCCTGCCCAGTGACAAAGTTCAACGGCCGCGGTACCCTAACCGTGCGAAGGTAGCATAATCACTTGTCCTTTAAATAGGGACTTGTATCAACGGCACCACGAGGGCTATACTGTCTCCTTTCTCCAATCAGTGAAACTGATCTCCCCGTGAAGAAGCGGGGATTTTATTATAAGACGAGAAGACCCCATGGAGCTTTAAACTCATCATACATCTCT-ATGCACT-ACATCAAC-CCACAT-AAGAAATC-TGTATGCTAGTTTTAGGTTGGGGGGACCACGGAGTATAATTTAACCTCCACAACAAATGGGCTAACACCCTTATCTACGAGATACACCTCTAAGAATTACTAAACTAATGTTTAATGACCCAATAA-TTTGATCAATGAACCAAGTTACCCTGGGGATAACAGCGCAATCTACTTCAAGAGCCCCTATCGACAAGTAGGTTTACGACCTCGATGTTGGATCAGGGTATCCTAGTGGTGCAGCCGCTACTAATG-GTTCGTTTGTTCAACG grylioMVZ175945 ???????????????TTCACACCAATAAGCGCCAGGGAATTACGAGCAACGCTTAAAACCCAAAGGATTTGACGGTGTCCCACCCAACTAGAGGAGCCTGTTCTATAATCGATGATCCCCGATATACCCGACCATTTCTCGCTTATCAGTCTGTATACCTCCGTCGAAAGCTTACCATGTGAACGTCT-GCAGTAAGCTTAATGACGTCAACACGTCAGGTCAAGGTGCAGCTTAAGAAATGGGAAGTAATGGGCTACAATTTCTAACTTAGAACAAACGAAAGACTGTATGAAATTACAATCATGAAGGTGGATTTAGTAGTAAAAAAAAAGTATAGTGTTCTTTTTAACCCGGCTCTGGGACGCGTACACACCGCCCGTCACCCTCTTCAACAGTT-TACCATCCCGTTTCTAACCC-ATCAT-TACATTTT-AGAAGAAGCAAGTCGTAACATGGTAAGTGTACTGGAAAGTGCACTTGGTTTA-T-ACAAAATGTAGCTTAATAAAAGCCTCTCGCTTACACCGAAAAAATATCCGTTTAACCCGGATCATTTTGAGCCTAAAATCTAGCCTAACTCATTCGCATGACCCCCTCCACAAA-CAAATCATTTTAACATTATAGTACAGGCGATCGAAAAA-TTTCTAAGCGCTTCAGACAAAGTACCGCAAGGGAAAAATGAAATAGAAATGAAATAACTTTAAAGCCCTAAACAGCAGAGATAACCTCTCGTACCTTTTGCATCATGGTCTAGCTAGTCCACCC-AAGCAAAATAAAACCTTT-AGTTTGCCATCCCGAAACTAAGTGAGCTACTTCAGAACAGTCCTACAGGACCAACCCGTCTCTGTTGCAAAAGAGTGGGAAGATTCTTAAGTAGAGGTGATAAGCCTACCGAACTTAGAGATAGCTGGTTGTTCAGGAAATGAGTTTCAGCTCTACCTTAAGCTTTCTGTATCAAACTG-AAGAACCAC-CAAAGCTTAAGAGCTATTCAAATAAGGCACAGCTTATTTGAAACAGGAGACAACCTTTAATACCGGGT-AAACAACA-GTAACCCAAA-CAAAGTGGGCCTAAAAGCAGCCACCTTTTAAAAAGCGTTAAAGCTTAACTATTAACCACTAACAA-TACCCTAAATATTTATTAACCCTTCAT-CTCTACTGAACTACTTTATA-TTCTTATAAAGGCTATCATGCTAGAACTAGTAACAAGAAGTCGATT-TTCTCCTAAATGTAAGTATAAACCAAAACAGACCATCTATTGGTGATTAACGCAAATGCAAA-TTTATAGCAACACAAC-------TAGAAAACCCTATAACTATA-AGCGTTAACCTTACACCAGAACATTACAGGAAAGATTAAAAGAGAAAGAAGGAACTCGGCAAATTTTAGCCCCGCCTGTTTACCAAAAACATCGCCTCTTGATAAAACATAAGAGGTCCAGCCTGCCCAGTGACAAAGTTCAACGGCCGCGGTATCCTAACCGTGCGAAGGTAGCATAATCACTTGTCCTTTAAATAGGGACTTGTATCAACGGCACCAC?AGGGCTATACTGTCTCCTTTCTCCAATCAGTGAAACTGATCTCCCCGTGAAGAAGCGGGGATTCTACTATAAGACGAAAAAACCCCATGGAGCTTTAAACTCACCATACACCTTCTATGTTCT-GTATCAAC-TTACAC-AAAAAATC-TGTATGCTAGTTTTAGGTTGGGGGGACCACGGAGTATAATTTAACCTCCACAACAAATGGGCTAACACCCTTATCTACGAAACACACCTCTAAGAATTACTAAACTAATGTTTAATGACCCAATAA-TTTGATCAATGAACCAAGTTACCCTGGGGATAACAGCGCAATCTACTTCAAGAGCCCCTATCGACAAGTATGTTTACGACCTCCATGTTGGATCAGGGTATCCTAATGGTGCAACCGCTACTAATG-GTTCGTTTGTTCAACG virgatipesMVZ175944 ACCGTAAACAATTAATTTACACCAATAAGCGCCAGGGAATTACGAGCAACGCTTAAAACCCAAAGGATTTGACGGTGTCCCACCCCACTAGAGGAGCCTGTTCTATAATCGATGATCCCCGCTACACCCGACCATTTCTCGCTTATCAGTCTGTATACCTCCGTCGAAAGCTTACCATGTGAACGTCC-GCAGTAGGCTTAATGATGTCAATACGTCAGGTCAAGGTGCAGCTTAAGAAATGGGAAGTAATGGGCTACAATTTCTAACTTAGAACAAACGAAAGACTGTGTGAAATCACAATCATGAAGGTGGATTTAGTAGTAAAAAGAAAATAGAGTATTCTTTTTAACCCGGCTCTGGGACGCGTACACACCGCCCGTCACCCTCTTCGATAGTA-TCATATTCAGTTCTTAACCC-ATCAT-TACGTTTT-AGAAGAAGCAAGTCGTAACATGGTAAGTGTACTGGAAAGTGCACTTGGTTTA-T-ACAAGATGTAGCTTAATAAAAGCCTCTCGCTTACACCGAGAAAATGTCTGTTTAACCCAGATCATCTTGAGCCTAAAATCTAGCCTAATACATTCGCATGA-CCCCTTTACAAA-CAAATCATTTTACCATTCTAGTACAGGCGATCGAAAAA-TTTCTAAGCGCTTCAGATAGAGTACCGCAAGGGAAAGATGAAATAGAAATGAAATAATTTTAAAGCAATAAATAGCAGAGACACCCCCTCGTACCTTTTGCATCATGGTCTAGCCAGTCTACCC-AAGCAAAATAAAACTTTC-AGTTTGCCATCCCGAAACTAAGTGAGCTACTTCAGAACAGTCCTACGGGACCAACCCATCTCTGTTGCAAAAGAGTGGGAAGATTTTTAAGTAGAGGTGATAAGCCTACCGAACTTAGAGATAGCTGGTTATTCAGGAAAAGAGTTTTAGCTCTACCTTAAGTTTTTTCTATCAAGCTA-AAGAAACCC-CAAAACTTAAGAGCTATTCAAATAAGGCACAGCTTATTTGAAACAGGGAACAACCTCTAACACCGGGT-AAATAGTA-GTAATCTAAA-TAAAGTGGGCCTAAAAGCAGCCATCTTTTAAAAAGCGTTAAAGCTTAACTATAAATTACTAATAA-TGCCTTAAACATTAATAAACCCTTCAT-TCCTACTGAATTACTTTATA-TATCTATAAAAGCTATTATACTAGAACTAGTAATAAGAAATTGATT-TTCTCCTAAATGTAAATATAAACCAAAATGGACCATCCATTGGTAATTAACGCAAATGCAAAATTTATAGCAACACAAC-------TAGAAAACCCTATAATTATG-AACGTTAACCTTACACTAGAACATTCCAGGAAAGATTAAAAGAGAAAGAAGGAACTCGGCAAATTTTAGCCTCGCCTGTTTACCAAAAACATCGCCTCTTGATAAAACATAAGAAGTCCAGCCTGCCCAGTGACAAAGTTCAACGGCCGCGGTACCCTAACCGTGCGAAGGTAGCATAATCACTTGTCCTTTAAATAGGGACTCGTATCAACGGCACCACGAGGGCTATACTGTCTCCTTTCTCCAATCAATGAAACTGATCTTCCCGTGAAGAAGCGGGAATCCTAATATAAGACGAGAAGACCCCATGGAGCTTTAAACTCACCATACACCTCT-ATGCCCT-ACATCAAC-TTACCC-AAAAAACC-TGTATGCTAGTTTTAGGTTGGGGGGACCACGGATTATAATTCAACCTCCATAACAAATGGGCTAACACCCTTATCTACGAAATACACCTCTAAGAATTACTAAACTAATGTTTAATGACCCAATAA-TTTGATCAATGAACCAAGTTACCCTGGGGATAACAGCGCAATCTACTTCAAGAACCCCTATCGACAAGTAGGTTTACAACCTCCATGTTGGATCAGGGTATCCTAATGGTGCAGCCGCTACTAATG-GTTCGTTTGTTCAACG okaloosae GCCGTAAACAATTAATTTACACCAATAAGCGCCAGGGAATTACGAGCAATGCTTAAAACCCAAAGGATTTGACGGTGTCCCACCCCACTAGAGGAGCCTGTTCTATAATCGATGATCCCCGATATACCCGACCATTTCTCGCTTATCAGTCTGTATACCTCCGTCGAAAGCTTACCATGTGAACGCCT-GCAGTAGGCTTAATGACGTCAGTACGTCAGGTCAAGGTGCAGCTTAAGAAATGGGAAGTAATGGGCTACAATTTCTAACTTAGAACAAACGAAAGACTGTATGAAATTACAATCATGAAGGTGGATTTAGTAGTAAAAAGAAAGTAGAGTGTTCTTTTTAACCCGGCTCTGGGACGCGTACACACCGCCCGTCACCCTCTTCGATAGTA-TCTTATCCCGTTCCTAACCC-ACCAT-TACATTTT-AGAAGAGGCAAGTCGTAACATGGTAAGTGTACTGGAAAGTGCACTTGGTTTA-T-ACAAGATGTAGCTTAATAAAAGCCTCTCGCTTACACCGAGAAAATGTCCGTTTAATCCGGATCATCTTGAGCCTAAAATCTAGCCTAACACACTCGCATGTCCCCCTTACCAAA-CAAATCATTTTAACATTATAGTACAGGCGATCGAAAAA-TTTCTAAGCGCTTCAGATAAAGTACCGCAAGGGAAAGATGAAATAGAAATGAAATAATTTTAAAGCCTTAACCAGCAGAGACACCCTCTCGTACCTTTTGCATCATGGTCTAGCCAGTCTACTC-AAGCAAAATAAAACTTTC-AGTTTGCCATCCCGAAACTAAGTGAGCTACTTCAGAACAGTCCTATGGGACCAACCCATCTCTGTTGCAAAAGAGTGGGAAGATTCTTAAGTAGAGGTGATAAGCCTACCGAACTTAGAGATAGCTGGTTGTTCAGGAAAAGAGTTTTAGCTCTACCTTAAGCTCTCTCTATCAGACTA-AAGAAACCC-CAAAGCTTAAGAGCTATTCAAATAAGGCACAGCTTATTTGAAACAGGAAACAACCTCCAACACCGGGT-AAATAATA-GTAATCTAAA-TAAAGTGGGCCTAAAAGCAGCCACCTTTCAAAAAGCGTTAAAGCTTAACTATAAATTACTAATAA-TGCCTTAAATATCTATTAACCCTTCAT-TCCTACTGAACTATTTTATA--TTTTATAAAAGCAATCATGCTAGAACTAGTAACAAGAAATTGATT-TTCTCCTAAATGTAAGTATAAACCAAAATGGACCATCCATTGGTAATTAACGCAAATGCAAAATTTATAGTAACATATC-------TAGAAAACCCTATAAACACA-AACGTTAACCTTACACTAGAACATTCCAGGAAAGATTAAAAGAGAAAGAAGGAACTCGGCAAATTTTAGCCTCGCCTGTTTACCAAAAACATCGCCTCTTGATAAAACATAAGAGGTCCAGCCTGCCCAGTGACAAAGTTCAACGGCCGCGGTACCCTAACCGTGCGAAGGTAGCATAATCACTTGTCCTTTAAATAGGGACTTGTATCAACGGCACCACGAGGGCTATACTGTCTCCTTTCTCCAATCAGTGAAACTGATCTCCCCGTGAAGAAGCGGGGATTCTACTATAAGACGAGAAGACCCCATGGAGCTTTAAACTCACCATACACCTCT-ATGCCCC-ACATCAAC-TTACAC-AAGAAATC-TGTATGCTAGTTTTAGGTTGGGGGGACCACGGAGTATAATTGAACCTCCATAACAAATGGGCTAACACCCTTATCTACGAGATACACCTCTAAGAATTACTAAACTAATGTTTAATGACCCAATAA-TTTGATCAATGAACCAAGTTACCCTGGGGATAACAGCGCAATCTACTTCAAGAGCCCCTATCGACAAGTAGGTTTACGACCTCGATGTTGGATCAGGGTATCCTAGTGGTGCAGCCGCTACTAATG-GTTCGTTTGTTCAAC- clamitansJSF1118 GCCGTAAACAATTAATTTACACCAATAAGCGCCAGGGAATTACGAGCAATGCTTAAAACCCAAAGGATTTGACGGTGTCCCACCCCACTAGAGGAGCCTGTTCTATAATCGATGATCCCCGATATACCCGACCATTTCTCGCTTATCAGTCTGTATACCTCCGTCGAAAGCTTACCATGTGAACGCCT-GCAGTAGGCTTAATGACGTCAATACGTCAGGTCAAGGTGCAGCTTAAGAAATGGGAAGTAATGGGCTACAATTTCTAACTTAGAACAAACGAAAGACTGTATGAAATTACAATCATGAAGGTGGATTTAGTAGTAAAAAAAAAGTATAGTGTTCTTTTTAACCCGGCTCTGGGACGCGTACACACCGCCCGTCACCCTCTTCGATAGTA-TCTTATCCCGTTCCTAACCC-ATCAT-TACATTTT-AGAAGAGGCAAGTCGTAACATGGTAAGTGTACTGGAAAGTGCACTTGGTTTA-T-ACAAGATGTAGCTTAATAAAAGCCTCTCGCTTACACCGAGAAAATGTCCGTTTAATCCGGATCATCTTGAGCCTAAAATCTAGCCTAACACACTCGCATGTCCCCCTTACCAAA-CAAATCATTTTAACATTATAGTACAGGCGATCGAAAAA-TTTCTAAGCGCTTCAGACAAAGTACCGCAAGGGAAAGATGAAATAGAAATGAAATAATTTTAAAGCCCTAACCAGCAGAGACACCCTCTCGTACCTTTTGCATCATGGTCTAGCCAGTCTACTC-AAGCAAAATAAAACTTTC-AGTTTGCCATCCCGAAACTAAGTGAGCTACTTCAGAACAGTCCTATGGGACCAACCCATCTCTGTTGCAAAAGAGTGGGAAGATTCTTAAGTAGAGGTGATAAGCCTACCGAACTTAGAGATAGCTGGTTGTTCAGGAAAAGAGTTTTAGCTCTACCTTAAGCTCTCTCTATCAGACTA-AAGAAACCC--AAAGCTTAAGAGCTATTCAAATAAGGCACAGCTTATTTGAAACAGGAAACAACCTCTAACACCGGGT-AAATAATA-GTAATCTAAA-TAAAGTGGGCCTAAAAGCAGCCACCTTTTAAAAAGCGTTAAAGCTTAACTATAAATTACTAATAA-TGCCTTAAATATCCATTAACCCTTCAT-TCCTACTGAACTATTTTATA--TTTTATAAAAGCAATCATGCTAGAACTAGTAACAAGAAATTGATT-TTCTCCTAAATGTAAGTATAAACCAAAATGGACCATCTATTGGTAATTAACGCAAATGCAAAATTTATAGCAACATATC-------TAGAAAACCCTATAACCACA-AACGTTAACCTTACACTAGAACATTCCAGGAAAGATTAAAAGAGAAAGAAGGAACTCGGCAAATTTTAGCCTCGCCTGTTTACCAAAAACATCGCCTCTTGATAAAATATAAGAGGTCCAGCCTGCCCAGTGACAAAGTTCAACGGCCGCGGTACCCTAACCGTGCGAAGGTAGCATAATCACTTGTCCTTTAAATAGGGACTTGTATCAACGGCACCACGAGGGCTATACTGTCTCCTTTCTCCAATCAGTGAAACTGATCTCCCCGTGAAGAAGCGGGGATTCTACTATAAGACGAGAAGACCCCATGGAGCTTTAAACTCACCATACACCTCT-ATGCCCC-ACATCAAC-TTACAC-AAGAAATC-TGTATGCTAGTTTTAGGTTGGGGGGACCACGGAGTATAATTGAACCTCCATAACAAATGGGCTAACACCCTTATCTACGAAATACACCTCTAAGAATTACTAAACTAATGTTTAATGACCCAATAA-TTTGATCAATGAACCAAGTTACCCTGGGGATAACAGCGCAATCTACTTCAAGAGCCCCTATCGACAAGTAGGTTTACAACCTCCATGTTGGATCAGGGTATCCTAATGGTGCAGCCGCTACTAATG-GTTCGTTTGTTCAACG heckscheriMVZ164908 GCCGTAAACAATTAACTCACACCAATAAGCGCCAGGGAATTACGAGCAATGCTTAAAACCCAAAGGATTTGACGGTGTCCCACCCCACTAGAGGAGCCTGTTCTATAATCGATGATCCCCGATATACCTAACCATTTCTCGCTTATCAGTCTGTATACCTCCGTCGAAAGCTTACCATGTGAACGCCT-GCAGTAGGCTTAATGACGTCAATACGTCAGGTCAAGGTGCAACTTAAGAAGTGGAAAGTAATGGGCTACAATTTCTATCTTAGAACAAACGAAAGACTGTATGAAATTACAGTCATGAAGGTGGATTTAGCAGTAAAAAGAAAGTAGAGTGTTCTTTTTAACCCGGCTCTGGGACGCGTACACACCGCCCGTCACCCTCTTCGATAGTA-TCTTATCCCGTTCCTAACTC-ATTAT-TACATTTT-AGAAGAGGCAAGTCGTAACATGGTAAGTGTACTGGAAAGTGCACTTGGTTTA-T-ACAAAATGTAGCTTAATAAAAGCCTCTCGCTTACACCGAGAAAATGTCCGTTTAACCCGAATCATTTTGAGCCTAAAATCTAGCCTAACACATTCACATGACCCCCTTTTTAAA-CAAATCATTTTAACATTATAGTACAGGTGATCGAAAAA-TTTCTAAGCGCTTCAGACAAAGTACCGCAAGGGAAAGATGAAATAGAAATGAAATAATTTTCAAGCCCTAATCAGCAGAGATAATCTCTCGTACCTTTTGCATCATGGTCTAGCCAGTCTGCTC-AAGCAAAATAAAACTTTT-AGTTTGTCATCCCGAAACTAAGTGAGCTACTTCAGAACAGTCCAATGGGACCAACCCATCTCTGTTGCAAAAGAGTGGGAAGATTCTTAAGTAGAGGTGATAAGCCTACCGAACTTAGAGATAGCTGGTTGTTCAGGAAAAGAGTTTTAGCTCTACCTTAAGCTCTCTTTATCAAACTA-AAGAAACCC--GAAACTTAAGAGCTATTCAAATAAGGCACAGCTTATTTGAAACAGGAAACAACCTCCAACACCGGGT-AAATAATA-GTAATCTAAA-TAAAGTGGGCCTAAAAGCAGCCACCTTCTAAAAAGCGTTAAAGCTTAACTATATATTACTAATAA-TGCCTTAAATATTAATTAACCCTTCCC-TCCTACTGAACTATTTTATA-TCCCTATAAAAGCAATCATGCTAGAACTAGTAACAAGAAATTGATT-TTCTCCTAAATGTAAATATAAACCAAAATGGACTATCCACTGGTAATTAACGTAAATGCAGAATTTATAGCAACATAAC-------TAGAAAACCCTATAACTACA-AACGTTAACCTCACACTAGAACATTGCAGGAAAGATTAAAAGAGAAAGAAGGAACTCGGCAAATTTTAGCCTCGCCTGTTTACCAAAAACATCGCCTCTTGATGAAACATAAGAGGTCCAGCCTGCCCAGTGACAAAGTTCAACGGCCGCGGTACCCTAACCGTGCGAAGGTAGCATAATCACTTGTCCTTTAAATAGGGACTTGTATCAACGGCACCACGAGGGCTATACTGTCTCCTTTCTCCAATCAGTGAAACTGATCTCCCCGTGAAGAAGCGGGGATTCTATTATAAGACGAGAAGACCCCATGGAGCTTTAAACTCACCATACACCTCT-ACGCCCT-ACATCAAC-TTACAC-AAGAAATC-TGTATGCTAGTTTTAGGTTGGGGGGACCACGGAGTATAACTAAACCTCCGTAACAAATGGGCTAACACCCTTATCTACGAGATACACCTCTAAGAATTACTAAACTAATGTTTAATGACCCAATAA-TTTGATCAATGAACCAAGTTACCCTGGGGATAACAGCGCAATCTACTTCAAGAGCCCCTATCGACAAGTAGGTTTACGACCTCGATGTTGGATCAGGGTATCCTAATGGTGCAGCCGCTACTAATG-GTTCGTTTGTTCAACG catesbianaX12841 GCCGTAAACAATTAATTTACACCAATAAGCGCCAGGGAATTACGAGCAATGCTTAAAACCCAAAGGATTTGACGGTGTCCCACCCCACTAGAGGAGCCTGTTCTATAATCGATGATCCCCGATATACCCGACCATTTCTCGCTTATCAGTCTGTATACCTCCGTCGAAAGCTTACCATGTGAACGCTT-GCAGTAGGCTTAATGACGTCAATACGTCAGGTCAAGGTGCAGCTTAAGAAATGGGAAGTAATGGGCTACAATTTCTAATCTAGAACAAACGAAAGACTATATGAAATTATAATCATGAAGGTGGATTTAGTAGTAAAAAGAAAATAGAGTGTTCTTTTTAACCCGGCTCTGGGACGCGTACACACCGCCCGTCACCCTCTTCGATAGTA-TCTCACCCCGTTCCTAACCC-ACTAT-TACATTTT-AGAAGAGGCAAGTCGTAACATGGTAAGTGTACTGGAAAGTGCACTTGGTTTA-T-ACAAAATGTAGCTTAACAAAAGCCTCTCGCTTACACCGAGAAAATGTCCGTTTAACCCGAACCGTTTTGAGCCCAAAATCTAGCCTAACACATTCGCATGACCCCCTTACCAAA-CAAATCATTTTAACATTATAGTACAGGCGATCGAAAAA-TTTCTAAGCGCTTCAGACAAAGTACCGCAAGGGAAAGATGAAATAGAAATGAAATAATCTTAAAGCCCTAATCAGCAGAGATAACCTCTCGTACCTTTTGCATCATGGTCTAGCCAGTCTACTC-AAGCAAAATAAAACTTTTTAGTTTGCCCTCCCGAAACTAAGTGAGCTACTTCAGAACAGTCCTATGGGACCAACCCATCTCTGTTGCAAAAGAGTGGGAAGATTCTTAAGTAGAGGTGATAAGCCTACCGAACTTAGAGATAGCTGGTTGTTCAGGAAAAGAGTCTTAGCTCTACCTTAAGCTCTCTCTATTAAACTA-A-GAAATCC-CAAAGCTTAAGAGCTATTCAAATAAGGCACAGCTTATTTGAAACAGGAAACAACCTCTAACACCGGGT-GAATTATA-GTAATCTCAA-TAAAGTGGGCCTAAAAGCAGCCACCTTTCAAAAAGCGTTAAAGCTTAACTATAAATTACTAATAA-TACCTAAAATATTAATTAACCCTTCAT-TCCTACTGAACTATTTTATA-TCCCTATAAAAGCAATTATGCTAGAACTAGTAACAAGAAATTGATT-TTCTCCTAAATGTAAGTATAAACCAAAATGGACCATCTGTTGGTGATTAACGCAAATGCAAAATCTATAGCAACATAAC-------TAGAAAACCCTATAACTACA-AACGTTAACCTTACACTAGAACATTCCAGGAAAGATTAAAAGAGAAAGAAGGAACTCGGCAAATTTTAGCCTCGCCTGTTTACCAAAAACATCGCCTCTTGATAAAACATAAGAGGTCCAGCCTGCCCAGTGACAAAGTTCAACGGCCGCGGTACCCTAACCGTGCGAAGGTAGCATAATCACTTGTCCTTTAAATAGGGACTTGTATCAACGGCACCACGAGGGCTATACTGTCTCCTTTCTCCAATCAGTGAAACTGATCTCCCCGTGAAGAAGCGGGGATTATAATATAAGACGAGAAGACCCCATGGAGCTTTAAACTCATCATACACCTCT-ATGCCCT-ATATCAAC-TTACAC-AAGAAACC-TGTATGCTAGTTTTAGGTTGGGGGGACCACGGAGTATAATTAAACCTCCATAACAAATGGGCTAACACCCTTATCTACGAGATACACCTCTAAGAATTACTAAACTAATGTTTAATGACCCAATAA-TTTGATCAATGAACCAAGTTACCCTGGGGATAACAGCGCAATCTACTTCAAGAGCCCCTATCGACAAGTAGGTTTACGACCTCGATGTTGGATCAGGGTATCCTAGTGGTGCAGCCGCTACTAATG-GTTCGTTTGTTCAACG catesbianaDMH84R2 GCCGTAAACAATTAATTTACACCAATAAGCGCCAGGGAATTACGAGCAATGCTTAAAACCCAAAGGATTTGACGGTGTCCCACCCCACTAGAGGAGCCTGTTCTATAATCGATGATCCCCGATATACCCGACCATTTCTCGCTTATCAGTCTGTATACCTCCGTCGAAAGCTTACCATGTGAACGCTT-GCAGTAGGCTTAATGACGTCAATACGTCAGGTCAAGGTGCAGCTTAAGAAATGGGAAGTAATGGGCTACAATTTCTAATCTAGAACAAACGAAAGACTATATGAAATTATAATCATGAAGGTGGATTTAGTAGTAAAAAGAAAATAGAGTGTTCTTTTTAACCCGGCTCTGGGACGCGTACACACCGCCCGTCACCCTCTTCGATAGTA-TCTCACCCCGTTCCTAACCC-ACTAT-TACATTTT-AGAAGAGGCAAGTCGTAACATGGTAAGTGTACTGGAAAGTGCACTTGGTTTA-T-ACAAAATGTAGCTTAACAAAAGCCTCTCGCTTACACCGAGAAAATGTCCGTTTAACCCGAACCGTTTTGAGCCCAAAATCTAGCCTAACACATTCGCATGACCCCCTTACCAAA-CAAATCATTTTAACATTATAGTACAGGCGATCGAAAAA-TTTCTAAGCGCTTCAGACAAAGTACCGCAAGGGAAAGATGAAATAGAAATGAAATAATCTTAAAGCCCTAATCAGCAGAGATAACCTCTCGTACCTTTTGCATCATGGTCTAGCCAGTCTACTC-AAGCAAAATAAAACTTTTTAGTTTGCCCTCCCGAAACTAAGTGAGCTACTTCAGAACAGTCCTATGGGACCAACCCATCTCTGTTGCAAAAGAGTGGGAAGATTCTTAAGTAGAGGTGATAAGCCTACCGAACTTAGAGATAGCTGGTTGTTCAGGAAAAGAGTCTTAGCTCTACCTTAAGCTCTCTCTATTAAACTA-A-GAAATCC-CAAAGCTTAAGAGCTATTCAAATAAGGCACAGCTTATTTGAAACAGGAAACAACCTCTAACACCGGGT-GAATTATA-GTAATCTCAA-TAAAGTGGGCCTAAAAGCAGCCACCTTTCAAAAAGCGTTAAAGCTTAACTATAAATTACTAATAA-TACCTAAAATATTAATTAACCCTTCAT-TCCTACTGAACTATTTTATA-TCCCTATAAAAGCAATTATGCTAGAACTAGTAACAAGAAATTGATT-TTCTCCTAAATGTAAGTATAAACCAAAATGGACCATCTGTTGGTGATTAACGCAAATGCAAAATCTATAGCAACATAAC-------TAGAAAACCCTATAACTACA-AACGTTAACCTTACACTAGAACATTCCAGGAAAGATTAAAAGAGAAAGAAGGAACTCGGCAAATTTTAGCCTCGCCTGTTTACCAAAAACATCGCCTCTTGATAAAACATAAGAGGTCCAGCCTGCCCAGTGACAAAGTTCAACGGCCGCGGTACCCTAACCGTGCGAAGGTAGCATAATCACTTGTCCTTTAAATAGGGACTTGTATCAACGGCACCACGAGGGCTATACTGTCTCCTTTCTCCAATCAGTGAAACTGATCTCCCCGTGAAGAAGCGGGGATTATAATATAAGACGAGAAGACCCCATGGAGCTTTAAACTCATCATACACCTCT-ATGCCCT-ATATCAAC-TTACAC-AAGAAACC-TGTATGCTAGTTTTAGGTTGGGGGGACCACGGAGTATAATTAAACCTCCATAACAAATGGGCTAACACCCTTATCTACGAGATACACCTCTAAGAATTACTAAACTAATGTTTAATGACCCAATAA-TTTGATGAATGAACCAAGTTACCCTGGGGATAACAGGGCAATCTACTTCAAGAGCCCCTATCGACAAGTAGGTTTACGACCTCGATGTTGGATCAGGGTATCCTAGTGGTGCAGCCGCTACTAATG-GTTCGTTTGTTCAACG maculataKU195258 GCCGTAAACAATTAATTTACACCAATCACCGCCAGGGGACTACGAGCAATGCTTAAAACCCAAAGGATTTGACGGTGTCCCACCCCACTAGAGGAGCCTGTTCTATAATCGATGATCCCCGCTATACCCGACCATTCCTCGCTTATCAGTCTGTATACCTCCGTCGAAAGCTTACCATGTGAACGTTT-ACAGTATGCTTAATGACGTCAACACGTCAGGTCAAGGTGCAGCTTAAGGAATGGGAAGTGATGGGCTACAATTTCTAATCTAAAACAAACGGAAAGCTATGTGAAATCTTAGCCATGAAGGTGGATTTAGTAGTAAAAAGAAAGTATAGTGTTCTTTGTAACCCGGCTCTGGGACGCGTACACACCGCCCGTCACCCTCTTCAATAGTA-TCTT-CTATGTTCCTAACCT-ATTAT-TACACCTT-AAAAAAGGCAAGTCGTAACATGGTAAGTGTACTGGAAAGTGCACTTGGTAAA-TAACAAGATGTAGCTTAATAAAAGCCCCTCGCTTACACCGAAGAAATATCTGTTCAAATCAGATCATTTTGAGCCTAAAATCTAGCCCGACATTATCGCATAACACCCCCTTCAAA-CAAAACATTTTCTTATTATAGTACAGGTGATCGAAAAA-TTTCTAAGCGCTTCAGATAAAGTACCGCAAGGGAAAAATGAAATAAGATTGAAATAATTTTAAAGCCTTAAATAGCAGAGACACCCCCTCGTACCTTTTGCATCATGGTCTAGTTAGTCTACTC-AAGCAAAATGAAATTTT--AGTTAGACATCCCGAAACTAAGCGAGCTACTTCAAAACAGCCTAAA-GAGCTAACCCGTCTCTGTTGCAAAAGAGTGGGAAGATTTTCAAGTAGAAGTGAAAAGCCTACCGAGCTTAGAGATAGCTGGTTGTTCAGGAAAAGAGTCTTAGCTCTCCCTTAAGTTTCCCA-ATGACTTAA-AACAAACCT-TAAAACTTAAGAGCTATTCAGATAAGGCACAGCTTATTTGAAACAGGATACAACCTACAATAATGGGT-AAATTATA-TTAATTAAAT-TGAAGTAGGCCTAAAAGCGGCCACCTTTTAAAAAGCGTTAAAGCTTGATTAAAATT-ATTAATAA-TACCTAAAATTCTTATTAACCCTTTAT-TTCTACTGAACTATTTTATA-TTCTTATAAAAGTAATAATGCTAGAACTAGTAACAAGAAAAAGATTTTTCTCCTAAATGTAAATATATACCAAAATGGACTATCCGCTGGTAATCAACGCAAATGCAGAAATTATAGTAACCTTC--------TAGAAAACTCTATAATCCAT-AACGTTAACCTTACACTAGAACATTCCAGGAAAGATTAAAAAAGAAAGAAGGAACTCGGCAAATTTTAGCCTCGCCTGTTTACCAAAAACATCGCCTCTTGATAAAATATAAGAGGTCCAGCCTGCCCAGTGACAAAGTTCAACGGCCGCGGTATCCTAACCGTGCGAAGGTAGCATAATCACTTGTTCTTTAAATAGGGACTTGTATCAACGGCACCACGAAGGCCATACTGTCTCCTTTCTTCAATCAGTGAAACTGATCTCCCCGTGAAGAAGCGGGGATTTAAATATAAGACGAAAAGACCCCATGGAGCTTAAAACTCATCATACACCTCT-ATGCCCTTACATCAAC-TCACCT-AAGAAATT-TGTGTGATAGTTTTCGGTTGGGGGGACCTCGGAGTATAATATAACCTCCATAACAAATGGGCTAACACCCTTATCCACGAAAAACACCTCTAAGAATTATTAAATTAATGTTTAATGACCCAATAT--TTGATCAATGAACCAAGTTACCCTGGGGATAACAGCGCAATCTACTTCAAGAGTCCCTATCGACAAGTGGGTTTACGACCTCGATGTTGGATCAGGGTATCCTAATGGTGCAACCGCTACTAATG-GTTCGTTTGTTCAACG vibicariaMVZ11035 GCCGTAAACAATTAATTCACAACCAACAACGCCTGGGGACTACGAGCAATGCTTAAAACCCAAAGGATTTGACGGTGTCCCATCCTACTAGAGGAGCCTGTTCTATAATCGATAATCCCCGCTACACCTGACCACTCCTCGCTTTTCAGCCTGTATACCTCCGTCGAAAGCTTACCGCGTGAACGTTT-GCAGTGTGCTTAATGATATCAACACGTCAGGTCAAGGTGCAGCTTAAGGAATGGGAAGTAACTGGCTACAATCTCTAATTTAGAACAAACGAAAGACTGCATGAAATACAAGTCATGAAGGTGGATTTAGTAGTAAAAAGAAAATAGAGTGTTCTTTTTAACCCGGCTCTGGGACAAGTACACACCGCCCGTCACCCTCTTCGAAAATATTTTTTTTATGTTCCTAACCC-GTTAA-CACATTAT-AGAAGAAGCAAGTCGTAACATGGTAAGTGTACTGGAAAGTGCACTTGGCAAA-TAACAAGATGTAGCTTAATAAAAGCCCCTCGTTTACACCGAAGAAATGTCTGTTTAAGTCAGATCGTCTTGAGCCTAAAATCTAGCCCA-TATATTCGTATGACCCCCCTCCCAAA-CAAAACATTCTCTCATTATAGTACAGGAGATCGAAAAA-CTTCTAAGCGCTTCAGATAAAGTACCGCAAGGGAAAAATGAAATATAACTGAAATAATAATAAAGCCTTAAATAGCAGAGACACCCCCTCGTACCTTTTGCATCATGGTCTAGTTAGTCTACTC-AAGCAAAATGAAATTTTT-AGTTAGACATCCCGAAACTAAGCGAGCTACTTCAAAACAGCCCAAAGGGGCTAACCCGTCTCTGTTGCAAAAGAGTGGGAAGATTTTTAAGTAGAAGTGAAAAGCCTACCGAGCTTAGAGATAGCTGGTTGTTCAGGAAAAGAGTTTTAGCTCTACCTTAAGTTTATTT-ACGATTTTA-AACGAACCC-TAAAACTTAAGAGCTATTCAAATAAGGTA?AGCTTATTTGAAACAGGATACAACCTACATTAATGGGT-AAATAATAATGTATGGGG--TAAAGTTGGCCTAAAAGCAGCCACCCTT-AGAAAGCGTTAAAGCTCAACTTCTATC-ACTAATAA-TTTCCAAAATTCTAATTAACCCTTTAT-TTTTACTGAACTATTTTATA-ACCCTATAAAAGTAATAATGCTAGAACTAGTAACAAGAAATTGATT-TTCTCCTAAATGTAAATATACACCAAAATGGACCATCCATTGGTAATTAACGCAGATGCAAAAATTATAATAACCCCC--------TAGAAAAATTTATAGTTCTT-AACGTTAACCTTACACTAGAATATTACAGGAAAGATTTAAAGAAAAAGAAGGAACTCGGCAAATTTTAGCCTCGCCTGTTTACCAAAAACATCGCCTCTTGCCTAAATATAAGAGGTCCAGCCTGCCCAGTGACAAAGTTTAACGGCCGCGGTACCCTAACCGTGCGAAGGTAGCATAATCACTTGTTCTCTAAATAAGGACTTGTATCAACGGCATCACGAAGGCCATACTGTCTCCTTTCTCTAATCAGTGAAACTGATCTCTCCGTGAAGAAGCGGGGATTTTTATATTAGACGAGAAGACCCCATGGAGCTTAAAACTTATTATACACCTCT-TCTCTAT-ATATCATC-TTATTC-AAGAATACTTGTATGCCAGTTTTTGGTTGGGGAGACCTCGGAGTACAATATAACCTCCGCAATAAACGGACTAACACCCTTATCCATGAGAAACGTCTCTAAGAACTAATAAATTAATATTT-ATGATCCAATAG-TTTGATAAATGAACCAAGTTACCCTGGGGATAACAGCGCAATCTACTTCAAGAGCCCATATCGACAAGTAGGTTTACGACCTCGATGTTGGATCAGGGTCTCCTAGTGGTGCAACCGCTACTAATG-GTTCGTTTGTTCAACG warszewitshiiJSF1127 GCCGTAAACAATTAATTTACAACCAACAACGCCTGGGAACTACGAGCCATGCTTAAAACCCAAAGGATTTGACGGTGTCCCACCCCCCTAGAGGAGCCTGTTCTATAATCGATAACCCCCGCTACACCTTACCACTCCTCGCTTACCAGTCTGTATACCTCCGTCGAAAGCTTACCGTGTGAACGCCT-ACAGTATGCTTAATGATACCAATACGTCAGGTCAAGGTGCAACTTAAGGAGCGGAAAGCAATGGGCTACAATTTCTAACCTAGAACAAACGAAAGACTGCATGAAATACAAGTCATGAAGGTGGATCTAGTAGTAAAAAGAAAATAGAGTGTTCTTTTTAACCCGGCTCTGGGACAAGTACATACCGCCCGTCACCCTCTTCGACAGTATTTTTCCCTAGTCCTTAACCC-GCTAT-CACATTTT-AGAAGAAGCAAGTCGTAACATGGTAAGTGTACTGGAAAGTGTACTTGGTTAA-TAACAAGATGTAGCTTAATAAAAACTCCTCGTTTACACCGAGGAAATATCTGTTTAAACCAGATCATCTTGAGCCTAAAATCTAGCCGT--ATATTCACACGAACCCCCCCCCAAA-TAAAACATTTTCTCATTATAGTACAGGTGATCGAAAAA-CTTCTAAGCGCTTCAGAAACAGTACCGCAAGGGAAAAATGAAATATAATTGAAATAACCTTTAAGCCCTAAATAGCAGAGACACCCCCTCGTACCTTTTGCATCATGGTCTAGTTAGTCTACTC-AAGCAAAATGAAATTTTT-AGTTAGAAACCCCGAAACTAAGCGAGCTACTTCAAAACAGCCCGAA-GAGCTAACCCGTCTCTGTTGCAAAAGAGTGGGAAGATTTTTAAGTAGAGGTGAAAAGCCTACCGAGCTTAGAGACAGCTGGTTGTTCAGGAAAAGAGTTTTAGCTCTACCTTAAGTTTACTC-ACGATTTAA-AACTAACCT-TAAAACTTAAGAGCTATTCAAATAAGGTACAGCTTATTTGAAACAGGATACAACCTATAATAATGGGT-AAATAATA-GTGCTTAGAA-TAAAGTTGGCTTAAAAGCAGCCATCT--AAGAAAGCGTTAAAGCTCGACTCTGCC--ACTAGTAA-TTCCTAAAACCTTAATTAACCCTTTAT-TTTTACTGAACCATTTTATA-ATTTTATAAAAATGATAATGCTAGAACTAGTAACAAGAAATTGAT--TTCTCCTAAATATAAGCATAAACCAAAAAGGACTATCCATTGGTAATTAACGTGCATGAAAAAATTATATTAACCCCCCCCCCCCC--GAAAAATTTATAACCCCT-AACGTTAACCTTACATTAGAATATTACAGGAAAGATTTAAAGAAAAAGAAGGAACTCGGCAAATTTTAGTCTCGCCTGTTTACCAAAAACATCGCCTCTTGCCTGAATATAAGAGGTCCAGCCTGCCCAGTGACATAGTTTAACGGCCGCGGTAACCTAACCGTGCAAAGGTAGCATAATCACTTGTTCTCTAAATAGGGACTTGTATCAACGGCACCACGAAGATTATACTGTCTCCTTTTTCTAATCAGTGAAACTGATCTCCCCGTGAAGAAGCGGGGATTTTAATATTAGACGAGAAGACCCCATGGAGCTTAAAACTCGTCACTCACCTCT-TTACCAC-ACATCTAC-AAAGTT-AAGAGTTCTTGCGTGTCAGTTTTTGGTTGGGGAGACCTCGGAGTATAATAAAACCTCCGTAATAAATGGACTAGCACCCTTATCCACGAGAAACGGCTCTAAGAACTAATATATTAATATTT-ATGACCCAACAA-TTTGATAAATGAACCAAGTTACCCTGGGGATAACAGCGCAATCTACTTTTAGAGCCCCTATCGACAAGTAGGTTTACGACCTCGATGTTGGATCAGGGTCTCCTAGTGGTGCAACCGCTACTGATG-GTTCGTTTGTTCAACG palmipesVenAMNHA118801 ACCGTAAACAATTGATTTACACCTACAAACGCCAGGGAATTACGAGCAATGCTTAAAACCCAAAGGACTTGACGGTGTCCCACCCAACTAGAGGAGCCTGTTCTATAATCGATGATCCCCGCTACACCCGACCATTCCTTGCTCATCAGTCTGTATACCTCCGTCGAAAGCTTACCATGTGAACGTCT-GCAGTAAGCTCAATGACGTCAACACGTCAGGTCAAGGTGCAACTCAAGGACTGGGAAGTAATGGGCTACAATTTCTAATTTAGAACAAACGGAAGACTATGTGAAATCTTAGTCATGAAGGTGGATTTAGCAGTAAAAAGAAAATAGAGTGTTCTTTTTAACTTGGCTCTGGGATGCGTACACACCGCCCGTCACCCTCTTCAATAGTA--TACTATTAGTTCTTAACCC-ACAAT-CACGTTTT-AGAAGAAGCAAGTCGTAACATGGTAAGTGTACTGGAAAGTGCACTTGGTAAA-CAACAAAATGTAGCTTAACAAAAGCCCCTCGCTTACACCGAAGTAATATCTGTTAAAACCAGATCATTTTGAGCCTAAAATCTAGCCTATAACATTTAGATAACTCCATCCCCAAA-CAAAACATTTTCCTATTTTAGTACAGGCGATCGAAAAA-TTTCTAAGCGCTTCAGATATAGTACCGCAAGGGAAAAATGAAATAGAACTGAAATAACCTTAAAGCCCTAAATAGCAGAGATATAATCTTGTACCTTTTGCATCATGGTCTAGTAAGTTTACAC-AAGCAAAATGAAACTTTT-AGTTTGACATCCCGAAACTAAGCGAGCTACTTCGAAACAGCCCATA-GAGCTAACCCGTCTCTGTTGCAAAAGAGTGGGAAGATTTCTAAGTAGAAGTGATAAGCCTACCGAGATTAGAGATAGCTGGTTGTTCAGGAAAAGAGTTTTAGCTCTACCTTAAGTTTCTTT-ATGATTTTA-AACAGACCT-CAAGACTTAAGAGCTATTCAAATAAGGCACAGCTTATTTGAAGTAGGATACAACCTATTTTATAGGGT-AAATAATAATGTATTAAAA-TAAAGTGGGCCTAAAAGCAGCCATCTTTTAAAAAGCGTTAAAGCTTAACTTATATT-ACTAGTAA-TTTCTAAAATTTGAATTAACCCTTTAC-CCCTACTGAATTATTTTATA-TCTTTATAAAAGTAATAATGCTAGAACTAGTAACAAGAAACTGCCTGTTCTCCTAAATGCAAGCATAAACCAAAATAGACACCCTATTGGTAATTAACGTTAATGTAACAGCTGTAGCAACATAA--------TAGAAAACCCTACAACCTCC-AACGTTAACCTTACACTAGAGCATTCCAGGAAAGATTAAAAAAGAAAGAAGGAACTCGGCAAACTTCAGCCCCGCCTGTTTACCAAAAACATCGCCTCTTGAAAAATTATAAGAGGTCCAGCCTGCCCAGTGACTAAGTTCAACGGCCGCGGTACCCTAACCGTGCGAAGGTAGCATAATCACTTGTTCTTTAAATGAGGACTTGTATCAACGGCACCACGAAGACTGCACTGTCTCCTTTCTTCAATCAGTGAAACTGATCTCCCCGTGAAGAAGCGGGGATTAGACTATAAGACGAGAAGACCCCATGGAGCTTCAAACTCATTATACAACTCT-GTTCCCC-ATACCCCT-TGATAT-AAGAGACA-TGTATAATACCTTTGGGTTGGGGGGACCTCGGAGTACAACTTAACCTCCTAAGCAAATAGGTTAACACCTTTATCCATGAGAAACACCTCTAAGAATTATCAAATTAATGTCT-TTGACCCGATAA--TCGATCAATGAACCAAGTTACCCTGGGGATAACAGCGCAATCTACTTCAAGAGTTCCTATCGACAAGTGGGTTTACGACCTCGATGTTGGATCAGGGTATCCCAGTGGTGCAACCGCTACTAACG-GTTCGTTTGTTCAACG palmipesEcuKU204425 ACCGTAAACAATTGATTTACACCTACAAACGCCAGGGAATTACGAGCAATGCTTAAAACCCAAAGGACTTGACGGTGTCCCACCCAACTAGAGGAGCCTGTTCTATAATCGATGATCCCCGCTACACCCGACCATTCCTTGCTCATCAGTCTGTATACCTCCGTCGAAAGCTTACCATGTGAACGTCT-GCAGTAAGCTCAATGACGTCAACACGTCAGGTCAAGGTGCAACTCAAGGACTGGGAAGTAATGGGCTACAATTTCTAATTTAGAACAAACGGAAGACTATGTGAAATCTTAGTCATGAAGGTGGATTTAGCAGTAAAAAGAAAATAGAGTGTTCTTTTTAACTTGGCTCTGGGATGCGTACACACCGCCCGTCACCCTCTTCAATAGTA--TACTATTAGTTCTTAACCC-ATAAT-CACGTTTT-AGAAGAGGCAAGTCGTAACATGGTAAGTGTACTGGAAAGTGCACTTGGTAAA-CAACAAAATGTAGCTTAACAAAAGCCCCTCGCTTACACCGAAGTAATATCTGTTAAAACCAGATCATTTTGAGCCTAAAATCTAGCCTATAACATTTAGATAACTCCATCCCCAAA-CAAAACATTTTCCTATTTTAGTACAGGCGATCGAAAAA-TTTCTAAGCGCTTCAGATATAGTACCGCAAGGGAAAAATGAAATAGAACTGAAATAACCTTAAAGCCCTAAATAGCAGAGATATAATCTTGTACCTTTTGCATCATGGTCTAGTAAGTTTACAC-AAGCAAAATGAAACTTTT-AGTTTGACATCCCGAAACTAAGCGAGCTACTTCGAAACAGCCCATA-GAGCTAACCCGTCTCTGTTGCAAAAGAGTGGGAAGATTTCTAAGTAGAGGTGATAAGCCTACCGAGATTAGAGATAGCTGGTTGTTCAGGAAAAGAGTTTTAGCTCTACCTTAAGTTTCTTT-ATGATTTTA-AACAGACCT-CAAGACTTAAGAGCTATTCAAATAAGGCACAGCTTATTTGAAGTAGGATACAACCTATTTTATAGGGT-AAATAATAATGTATTAAAA-TAAAGTGGGCCTAAAAGCAGCCATCTTTTAAAAAGCGTTAAAGCTTAACTTATATT-ACTTACAA-TTCCGAATATTACAATTAACCCTTTAA-TTCTACTGAACTATTTTATA-TTTTTATAAAAGTAATTATGCTAGAACTAGTAACAAGAAATTGATT-TTCTCCTAAATGTAAGCTTACACCAAAATGGACCATCCATTGGTAATAAACGCAGATGAAAAAATTATAGCAACCTTC--------CAGAAAACCCTATATATCCACAGCGTTAATCTTACACTAGAACATTTCAGGAAAGATTTAAAGAGAAAGAAGGAACTCGGCAAATTTTAGCCCCGCCTGTTTACCAAAAACATCGCCTCTTGACAAAATATAAGAGGTCCAGCCTGCCCAGTGATAAA-TTTAACGGCCGCGGTATCCTAACCGTGCGAAGGTAGCATAATCATTTGTTCTTTAAATGGGGACTTGTATCAACGGCACCACGAGGGCCATACTGTCTCCTTTCTCTAGTCAGTGAAACTGATCTCCCCGTGAAGAAGCGGGGATTTAACTATTAGACGAGAAGACCCCATGGAGCTTAAAACTCACTATATAATTCT-GTACCTC-ATATCACC-TTAATT-CAGAATCC-TATATGCTAGTTTTAGGTTGGGGGGACCTCGGAGTATAATTTAACCTCCATAACAAATGGGCTAATACCCTTATCCAAGATAAACACCTCTAAGAATTATTAAATTAATGTTTAATGACCCGATATATTCGATCAATGAACCAAGTTACCCTGGGGATAACAGCGCAATCTACTTCAAGAGTCCATATCGACAAGTAGGTTTACGACCTCGATGTTGGATCAGGGTATCCTAATGGTGCAACCGCTACTAATG-GTTCGTTTGTTCAACG bwanaQCAZ13964 ACCGTAAACAATTGATTTACACCCACAAACGCCAGGGAATTACGAGCAATGCTTAAAACCCAAAGGATTTGACGGTGTCCCACCCAACTAGAGGAGCCTGTTCTATAATCGATGATCCCCGCTACACCCGACCACTCCTTGCTCATCAGTCTGTATACCTCCGTCGAAAGCTTACCATGTGAACGTCT-GCAGTAAGCTCAAGGACGTCAACACGTCAGGTCAAGGTGCAGCTTAAGGACTGGGAAGTAATGGGCTACAATTTCTAATCTAGAACAAACGGAAGACTATGTGAAATCTTAGTCATGAAGGTGGATTTAGTAGTAAAAAGAAAATAGAGTGTTCTTTTTAACTCGGCTCTGGGATGCGTACACACCGCCCGTCACCCTCTTCAATAGTA--TAATATCAGTTCTTAACCC-ACTAT-CACGCCTT-AGAAGAGGCAAGTCGTAACATGGTAAGTGTACTGGAAAGTGCACTTGGTAAA-TAACAAAATGTAGCTTAATAAAAGCCCTTCGCTTACACCGAAGAAATGTCTGTTAAAACCAGATCATTCTGAGCCTAAAATCTAGCCCATAATATTCAAATGGCCCCCTCTCCAAA-CAAAACATTTTCCTAGTTTAGTACAGGCGATCGAAAAA-TTTCTAAGCGCTTCAGATATAGTACCGCAAGGGAAAAATGAAATAGAACTGAAATAACCTTAAAGCCCCAAATAGCAGAGATATAATCTTGTACCTTTTGCATCATGGTCTAGTAAGTTTACAC-AAGCAAAATGAAACTTTC-AGTTTGACATCCCGAAACTAAGCGAGCTACTTCAAAACAGCCCAAA-GAGCTAACCCGTCTCTGTTGCAAAAGAGTGGGAAGATTTTTAAGTAGAGGTGATAAGCCTACCGAGCTTAGAGATAGCTGGTTGTTCAGGAAAAGAGTTTTAGCTCTACCTTAAGTTTCCCC-ATGATTCTTTAACAAACCT-TAAAACTTAAGAGCTATTCAAATAAGGCACAGCTTATTTGAAATAGGATACAACCTATTTTACTGGGT-AAGTAATAATGTATTAAAA-TAAAGTGGGCCTAAAAGCAGCCATCTTTTAAAAAGCGTTAAAGCTTAACTTATAAT-ACTTGTAA-TTCCAAATGCACCAACTAACCCTTTTA-CTATACTGAACTATTTTATA-TTCTTATAAAAGTAATTATGCTAGAACTAGTAACAAGAAATTGATT-TTCTCCTAAATGTAAGCTCACACCAAAATGGACCATCCGTTGGTAGTAAACGCAAATGAAAAAATTATAGCAACTCTC--------TAGAAAACCCTATATAACCCCAGCGTTAACCTTACACCAGAACATTTCAGGAAAGATTTAAAGAGAAAGAAGGAACTCGGCAAATTTTAGCCCCGCCTGTTTACCAAAAACATCGCCTCTTGACAAAATATAAGAGGTCCAGCCTGCCCAGTGATAAA-TTCAACGGCCGCGGTATCCTAACCGTGCGAAGGTAGCATAATCACTTGTTCTTTAAATGGGGACTTGTATCAACGGCATCACGAGGGCCATACTGTCTCCTTTCTCTAATCAGTGAAACTGATCTCCCCGTGAAGAAGCGGGGATTTAACTATTAGACGAGAAGACCCCATGGAGCTTAAAACTCAGTATATAATTTT-GTACCTT-ATACCACC-TTAACT-CAGAACCC-TATATACTAGTTTTAGGTTGGGGGGACCTCGGAGTATAATTTAACCTCCACAACAAATGGGCTAACACCCTTATCCAAGACAAACACCTCTAAGAATTATCAAATTAATGTTTAATGACCCGATATATTCGATCAATGGACCAAGTTACCCTGGGGATAACAGCGCAATCTACTTCAAGAGTCCCTATCGACAAGTAGGTTTACGACCTCGATGTTGGATCAGGGTATCCTAGTGGTGCAACCGCTACTAATG-GTTCGTTTGTTCAACG Sp_1_ecuadorQCAZ13219 ACCGTAAACAATTGACTTACACCCACAAACGCCAGGGAATTACGAGCAATGCTTAAAACCCAAAGGATTTGACGGTGTCCCACCCAACTAGAGGAGCCTGTTCTATAATCGATGATCCCCGCTACACCCGACCACTCCTTGCTCATCAGTCTGTATACCTCCGTCGAAAGCTTACCATGTGAACGTCT-GAAGTAAGCTCAATGACGTCAACACGTCAGGTCAAGGTGCAGCTTAAGGACTGGGAAGTAATGGGCTACAATTTCTAGTTTAGAACAAACGGAAGACTATGTGAAATCTTAGTCATGAAGGTGGATTTAGTAGTAAAAAGAAAATAGAGTGTTCTTTTTAACTCGGCTCTGGGATGCGTACACACCGCCCGTCACCCTCTTCAATAGTA--TATTACTAGTTCTTAACCC-ACTAT-CACGTCTT-AGAAGAGGCAAGTCGTA-CATGGTAAGTGTACTGGAAAGTGCACTTGGTAAA-CAACAAAATGTAGCTTAACAAAAGCCCCTCGCTTACACCGAAGAAATGTCTGTTAAAATCAGATCATTTTGAGCCTAAAATCTAGCCCCTAATATCCAAATGACTCCCT-CCCAAA-CAAAACATTTTCCTATTTTAGTACAGGCGATCGAAAAA-TTTCTAAGCGCTTCAGATATAGTACCGCAAGGGAAAAATGAAATAGAACTGAAATAACCTTAAAGCCCCAAATAGCAGAGACA-TATCTCGGACCTTTTGCATCATGGTCTAGTAAGTTTACAC-AAGCAAAATGAAACTTTTTAGTTTGACATCCCGAAACTAAGCGAGCTACTTCGAAACAGCCCAAA-GAGCTAACCCGTCTCTGTTGCAAAAGAGTGGGGAGATTTCTAAGTAGAGGTGATAAGCCTACCGAGATTAGAGATAGCTGGTTGTTCAGGAAAAGAGTTTTAGCTCTACCTTAAGTTTCCCC-ATGATTTTT-AACAGACCT-TAAAACTTAAGAGCTATTCAAATAAGGCACAGCTTATTTGAAATAGGATACAACCTATTTTATTGGGT-AAATAATAACGTATTAAAA-TAAAGTGGGCCTAAAAGCAGCCATCTTTTAAAAAGCGTTAAAGCTTAATTTATAAT-ACTTACAA-TTCCAAATATTCCAATTAACCCTTCTA-TTCTACTGAACTATTTTATA-TTTTTATAAAAGTAATTATGCTAGAACTAGTAACAAGAAATTGATT-TTCTCCTAAATGTAAGCTTACACCAAAATGGACCACCCATTGGTAATAAACGCAAATGAAAAAATTATAGCAACCTTC--------CAGAAAACCCTATATAACGCTAGCGTTAATCTTACACTAGAACATTTCAGGAAAGATTTAAAGAGAAAGAAGGAACTCGGCAAATTTTAGCC-CGCCTGTTTACCAAAAACATCGCCTCTTGACAAAATATAAGAGGTCCAGCCTGCCCAGTGATAAA-TTTAACGGCCGCGGTATCCTAACCGTGCGAAGGTAGCATAATCACTTGTTCTTTAAATGGGGACTCGTATCAACGGCATCACGAGGGCCATACTGTCTCCTTTCTCTAATCAGTGAAACTGATCTCCCCGTGAAGAAGCGGGGATATAACTATTAGACGAGAAGACCCCATGGAGCTTAAAACTCACTATATACTTCT-GTTCCTC-ATATCACC-ATAATT-CAGAATCT-TATATGCTAGTTTTAGGTTGGGGGGACCTCGGAGTATAATTTAACCTCCAAAACAAATGGGCTAATACCCTTATCCAAGATAAACACCTCTAAGAATTATTAAATTAATGTTCAATGACCCGACATATTCGATCAATGAACCAAGTTACCCTGGGGATAACAGCGCAATCTACTTCAAGAGTCCCTATCGACAAGTAGGTTTACGACCTCGATGTTGGATCAGGGTATCCTAGTGGTGCAACCGCTACTAATG-GTTCGTTTGTTCAACG vaillantiKU195299 ACCGTAAACAATTGATTTACACCCACAAACGCCAGGGAATTACGAGCAATGCTTAAAACCCAAAGGATTTGACGGTGTCCCACCCAACTAGAGGAGCCTGTTCTATAATCGATGATCCCCGCTACACCCGACCACTCCTTGCCCATCAGTCTGTTTACCTCCGTCGAAAGCTTACCATGTGAACGTCT-GCAGTAAGCCCAATGACGTCAACACGTCAGGTCAAGGTGCAGCTTAAGGACTGGGAAGTAATGGGCTACAATTTCTAATCTAGAACAAACGGAAGACTATGTGAAACCCTAGTCATGAAGGTGGATTTAGTAGTAAAAAGAAAATATAGTGTTCTTTTTAACTCGGCTCTGGGATGCGTACACACCGCCCGTCACCCTCTTCAATAGTA--TCTCCTCAGTTCCTAACCC-ACTAT-TACGTCTT-AAAAGAGGCAAGTCGTAACATGGTAAGTGTACTGGAAAGTGCACTTGGCAAA-CAACAAAATGTAGCTTAACAAAAGCCCTCCGCTTACACCGAAAAAATGTCTGTTAAACCCGGATCATTCTGAGCCTAAAATCTAGTCCTTAATATTCATATGACCCTCTCTTCAAA-TAAAACATTTTCCTATCTTAGTACAGGCGATCGAAAAA-TTTCTAAGCACTTCAGATAAAGTACCGCAAGGGAAAAATGAAATAGAAATGAAATAACCTTAAAGCCCCAAACAGCAGAGACACTATCTCGTACCTTTTGCATCATGGTCTAGTAAGTCTACCC-AAGCAAAACGAAACTTTC-AGTTTGACATCCCGAAACTAAGCGAGCTACTTCAAAACAGCCCAAA-GAGCTAACCCGTCTCTGTTGCAAAAGAGTGGGAAGATTTTCAAGTAGAGGTGACAAGCCTATCGAGCTTAGAGATAGCTGGTTGTTCAGGAAAAGAGTTTTAGCTCTACCTTAAGTTTATTT-GTGAATCTT-AACAAACCCTTAAAACTTAAGAGCTATTCAAATAAGGCACAGCTTATTTGAAATAGGATACAACCTATTTTACTGGGT-AAATAATAACGCACCAAAA-TAAAGTGGGCCTAAAAGCAGCCATCTTTTAAAAAGCGTTAAAGCTTAATTTATAAT-GCCAATAA-TCCCGAATATTTCAATTAACCCTTTTA-TTTTACTGAACTATTTTATA-CTCTTATAAAAGTAATTATGCTAGAACTAGTAACAAGAATTTGATT-TTCTCCTAAATGTAAGCTTACACCAAAATGGACCATCCATTGGTAATTAACGCTAATGCAAAA-CTATAACAACCTCC--------TAGAAAAACCTATATATCCACAGCGTTAACCTTACACTAGAACATTTCAGGAAAGATTTAAAGAGAAAGAAGGAACTCGGCAAATTTTAGCCTCGCCTGTTTACCAAAAACATCGCCTCTTGATTAAACATAAGAGGTCCAGCCTGCCCAGTGATAAT-TTCAACGGCCGCGGTATCCTAACCGTGCGAAGGTAGCATAATCACTTGTTCTTTAAATGGGGACTCGTATCAACGGCATCACGAGGGCCATACTGTCTCCTTTCTCTGATCAGTGAAACTGATCTCCCCGTGAAGAAGCGGGGATTTAACTATTAGACGAGAAGACCCCATGGAGCTTAAAACCCATCATATACTTCT-ATATCTT-ATATCACC-CCAATT-CAGAAACC-TATATGCTAATTTTAGGTTGGGGGGACCTCGGAATATAATTAAACCTCCATAACAAATGGGCTAACACCCTTATCCAAGAAAAACACCTCTAAGAATTATCAAATTAATGTTTAATGACCCGATAT-TTCCAACCATTAGACCAGTTACCCTGGGGAATACAACCCCATCTTCTTCCAAAATTCCTATCCAACAATTAGTTTACCAACTCCAATTTGGATCCGGGTTTCCCAATTGTTCCACCGCCA-TAATG-GTTCCTTTGTTCCACC julianiTNHC60324 ??CGTA?ACAATTGAT?TACACCCATA??CGCCA?GGAATTACGAGCAATGCTTAAAACCCAAAGGATTTGACGGTGTC?CACCCAACTATAGGAGCCTGTTCTATAATCGATGATCCCCGCTACACCCGACCACTCCTTGCCTAT?A?TCTGTATACCTCCGTCGAAAGCTTACCATGTGAACGCCT-ACAGTATGCCCAATGAC?TCAACACGTCAGGTCAAGGTGCAGCTTAAGGACTGGGAAGTAATGGGCTACAGTTTCTAATTTAGAACAAACGGAAGACTATGTGAAATCCTAGTCATGAAGGTGGATTTAGTAGTAAGAAGAAAATAGAGTGTTCTTTTTAACTCGGCTCTGGGATGCGTACACACCGCCCGTCACCCTCTTCAATAGTA--?CTTTTTAGTT?TTAACAC-ACTAC-CACGCCTT-AGAAGAGGCAAGTCGTAACATGGTAAGTGTACTGGAAAGTGCACTTGGTAAA-CAACAAAATATAGCTTAACAAAAGCCCTTCGCTTACACCGAAGAAATATCTGTTAAAACCAGATTATTTTG?GCCTAAAATCTAGCCCA-CTGATTCACATGCACCCCTCTTCTAA-TAAAACATTTTCCTATTTTAGTACAGGCGATCGAAAAA-TTTCTAAGCGCTTCAGATAAAGTACCGCAAGGGAAAAATGAAATAGAAATGAAATAACCTTAAAGCCTTAAACAGCAGAGATATTATCTCGTACCTTTTGCATCATGGTCTAGTAAGTCTATCC-AAGCAAAATGAAACTTTT-AGTTTGATACCCCGAAACTAAGCGAGCTACTTCAAAACAGCCCAAA-GAGCTAACCCGTCTCTGTTGCAAAAGAGTGGGAAGATTTTTAAGTAGAGGTGATAAGCCTACCGAGCTTAGAGATAGCTGGTTGTTCAGGAAAAGAGTTTTAGCTCTACCTTAAGTTTATTT-ATGACTTTC-AACAAACCT-TAAAACTTAAGAGCTATTCAAATAAGGCACAGCTTATTTGAAATAGGATACAACCTATTTTATTGGGT-AAATAGTA-GTATATTAAAATAAAGTGGGCCTAAAAGCAGCCACCTTTTAAAAAGCGTTTAAGCTTAATTTATAAT-ACTCATAA-TTCCAAATATTTTAACGAA?CCTTCTG-TTCTACTGAACTA?TTTA?A-C?TTTATAAAAGTAATTATGCTAGAACTAGTAACAAGAATATGATT-TCTCCCAAAATGTAAGTTTATACCAAAATGGACCATCCATTGGTAATCAACGCTAATGCAA-AATTATAGCAACCTTC--------TAGAAAACCCTATATACCCGCAGCGTTAATCTTACACTAGAACATTTCAGGAAAGATTTAAAGAGAAAGAAGGAACTCGGCAAATTTTAGCCTCGCCTGTTTACCAAAAACATCGCCTCTTGACAAAACATAAGAGGTCCAGCCTGCCCAGTGATAAA-TTTAACGGCCGCGGTATCCTAACCGTGCGAAGGTAGCATAATCACTTGTTCTTTAAATGGGGACTCGTATCAATGGCATCACGAGGGCCATACTGTCTCCTTTCTCCAATCAGTGAAACTGATCTCCCCGTGAAGAAGCGGGGATTTAACTATTAGACGAGAAGACCCCATGGAGCTTAAAACTCATCATATACCTCT-ATATATT-ATATCATC-CCAATT-AAGAAATT-TATATGTTAGTTTTAAGTTGGGGGGACCTCGGAGTACAATTTAACCTCCGTTACAAATGGGCTAATA?CCTTATCT?AGAAAAACA?CTCTAAGAATTACTAAATTAATGTTTAATGACCCGATTA-TTCGATCAATGAACCAAGTTACCCT?GGGATAACAACGCAATCTACTT?AAGAGTTCCTATCGACAAGTAGGTT-ACGAACTC?ATTTTGGATAAGGGTA?CC-AATTGTGCAACCGCTCCTAA?G-GTCCGTTGGTT?AACG sierramadrensisKU195181 GCCATAAACAATTAATTTACACTTATCAACGCCAGGGAATTACGAGCAATGCTTAAAACCCAAAGGATTTGACGGTGTCCCACCCAACTAGAGGAGCCTGTTCTATAATCGATAATCCCCGCTATACCTCACCATTCCTCGCTT-TCAGTCTGTATACCTCCGTCGAAAGCTTACCATGTGAACGTCT-ACAGTAGGCCCAATGATATCAACACGTCAGGTCAAGGTGCAGCTTAAGGAATAGGAAGAAATGGGCTACAATTTCTAATTTAGAACAAACGGAAGACTATGTGAAACC-TAGTCATGAAGGCGGATTTAGCAGTAAAAAGGAAATA?AGTGTTCTTTTTAATCTGGCTCTGGGACGCGTACACACCGCCCGTCACCCTCTTCAATAGTA-ACTTTATTTGTTTCTAACCT-ATTAT-TACATTTT-AGAAGAAGCAAGTCGTAACATGGTAAGTGTACTGGAAAGTGCACTTGG--TC-TATCAAAATGTACCTTAACTAAAGCCCTTTGTTTACACCGAAAACATAACTGTTAAAATCAGTTCATTTTGAGCCTAAAACCTAACCTAACATACTCGCATGAAC-TCTCTTCAAA-TAAAACATTTTATTATTATAGTACAGGTGATCGAAAAA-TTTCTAAGCGCTTCAGATAAAGTACCGCAAGGGAAAGATGAAATAGAATTGAAATAATCTTAAAGCCTTAAACAGTAGAGATATTCCCTCGTACCTTTTGCATCATGGTCTAGTTAGTCTATTC-AAGCAAAATGAAAATTTT-AGTTAGACACCCCGAAACTAAGGGAGCTACTTCAAAACAGCCTATT-GGGCCAACCCATCTCTGTTGCAAAAGAGTGGGAAGATTTTCAAGTAGAGGTAATAAGCCTACCGAACTTAGAGATAGCTGGTTGTTCAGGAAAAGAGTTTTAGCTCTACCTTAAGCTTCGTT-ATGATACTA-AACAAATACAAAAAGCTTAAGAGCTATTCAAATAAGGCACAGCTTATTTGAAATAGGATACAACCTATAATAATGGGT-AAGTAAAA-TTTAATAAAA-TGAAGTGGGCCTAAAAGCAGCCACCTTTTAAAAAGCGTTTAAGCTTAATTAGAATT-ATTAATAA-TTTCCACAATCTTTTCAAACCCTTTAT-TCTTACTGAACTACTTTATA-ATTTTATAAAAGCAATAATGCTAGAACTAGTAACAAGAAATTGATT-TTCTCCTAAATGTAAGCATAAACCAAAATGGACTCCCCATTGGTAATTAACGTCAATGCAAAA-TTATAACAACACAC--------TAGAAAACCTTATAACCGAT-AACGTTAACCTCACACTAGAACATTTCAGGAAAGATTAAAAGAGAAAGAAGGAACTCGGCAAAATTTAGCCTCGCCTGTTTACCAAAAACATCGCCTCTTGACAAAATATAAGAGGTCCAGCCTGCCCAGTGATATAATTCAACGGCCGCGGTACCCTAACCGTGCGAAGGTAGCATAATCACTTGTTCTTTAAATGGGGACTCGTATCAATGGCACCACGAAGGCTATACTGTCTCCTTTCTCCAATCAGTGAAACTGATCTTCCCGTGAAGAAGCGGGAATTTAAATATAAGACGAGAAGACCCCATGGAGCTTTAAACTCACTATACACCTCT-GCATCCT-TTAAATCCACCCACCCAAGAGTAT-TGTATACTAATTTTAGGCTGGGGGGGCCTCCGAATAAAATTTAACCTCCATAACAAATGGGCTAACACCCTTATCTACGAAAAACACCTCTAA?AATTATTAAAATAATGTTA-AAGACCCGATAA-TTCGATTAATGAACCAAGTTACCCTGGGGATAACAGCGCAATCTACTTCGAGAGTTCCTATCGACAAGTAGGTTTACGACCTCGATGTTGGATCAGGGTATCCCAGTGGTGCAACCGCTACTGATG-GTTCGTTTGTTCAACG psilonotaKU195119 ACCGTAAACAATTAATTTACACCAATCAGCGCCAGGGGATTACGAGCAATGCTTAAAACCCAAAGGATTTGACGGTGTCCCACCCAACTAGAGGAGCCTGTTCTATAATCGATGATCCCCGCTACACCCAACCATTTCTCGCTC-TCAGTCTGTATACCTCCGTCGAAAGCTTACCATGTGAACGTTT-ACAGTAGGCCCAAGGACGTCAATACGTCAGGTCAAGGTGCAGCTTAAGAAATGGGAAGCAATGGGCTACAATTTCTAATTTAGAACAAACGAAAGACTATGTGAAACCATAATC-TGAAGGTGGATTTAGCAGTAAAAAGGAAATATAGTGTTCTTTTTAACCTGGCTCTGGGACGCGTACACACCGCCCGTCACCCTCTTCGATAGTA-CTCCCATTTGTTCCTAACTT-ATTAT-TACGCCTT-AGAAGAAGCAAGTCGTAACATGGTAAGTGTACTGGAAAGTGCACTTGG--AC-TACCAAAATGTAGCTTAATTAAAGCCCCTCGCTTACACCGAAGATATGACTGTTAAAATCAGTTCATTTTGAGCCTAAAATCTAGCCCATATAT-TCATATGACTTTCTATCCAAA-CAAAACATTTTATTATTCTAGTATAGGTGATCGAAAGA-TTTCTAAGCGCTTCAGATAAAGTACCGTAAGGGAAAGATGAAATAGAATTGAAATAATCTTAAAGCCCTGTATAGCAGAGACACCCCCTCGTACCTTTTGCATCATGGTCTAGTTAGTTTATTC-AAGCAAAATGAAATTTTT-AGTTAGACACCCCGAAACTAAGGGAGCTACTTCAAAACAGCCTAAT-GGGCTAACCCGTCTCTGTTGCAAAAGAGTGGGAAGATTTTTAAGTAGAGGTGAAAAGCCTATCGAACTTAGAGATAGCTGGTTGTTCAGGAAAAGAGTTTTAGCTCTACCTTAAG-TTTACATATGATATAA-AACAAACTT-TGAAGCTTAAGAGCTATTCAAATAAGGCACAGCTTATTTGAAACAGGATACAACCTATAATATTGGGT-AAGTAATA-GTAGGATAAAATTAAGTGGGCCTAAAAGCAGCCACCTTCAAAAAAGCGTTAAAGCTTACCTAAAATT-ACTGATAA-TTTGTAAAATTATTATTAACCCTTTTT-TTGTACTGAACTATTTTATA-TATTTATAAAAGCAATAATGCTAGAACTAGTAACAAGAAATTGACT-TTCTCCTAAATGTAAGCTTAAACCAAAATTGACAACCCATTGGTAATTAACGTAAATGTAAAAACTATAATAAATTAC--------TAGAAAAACTTATAATCTAA-AACGTTAACCTAACACTAGAACATTATAGGAAAGATTAAAAGAGAAAGAAGGAACTCGGCAAAATTTAACCTCGCCTGTTTACCAAAAACATCGCCTCTTGATTAAATATAAGAGGTCCAGCCTGCCCAGTGACATAGTTTAACGGCCGCGGTACCCTAACCGTGCGAAGGTAGCATAATCAATTGTTCTATAAATAGGGACTAGTATCAACGGCACTACGAAGGTTATACTGTCTCCTTTCTCCAATCAGTGAAACTGATCTCCCCGTGAAGAAGCGGGGATATAACTATAAGACGAGAAGACCCCATGGAGCTTTAAACTCATTATACACCTCT-GTGATTTTTATAACCTGTTAATCCAAAAGACC-TGTATATTAGTTTTAGGTTGGGGGGACCCCGGAGTACAACTAAACCTCCGTAACAAATGGGCTAATACCCTTATCCACGAGAAACACCTCTAAGAATTAATAAATTAATGTTTAATGATCCGATTA-CTCGATCAATGAACCAAGTTACCCTGGGGATAACAGCGCAATCTATTTCCAGAGCTCCTATCGACAAGTAGGTTTACGACCTCGATGTTGGATCAGGGTATCCCAGTGGTGCAACCGCTACTAATGTGTTCGTTTGTTCAACG tarahumaraeKU194596 GCCGTAAACAATTAATTTACACCTACCAGCGCCAGGGAATTACGAGCAATGCTTAAAACCCAAAGGATTTGACGGTGTCCCACCCAACTAGAGGAGCCTGTTCTATAATCGATAATCCCCGCTACACCTAACCATTCCTTGCTT-TCAGTCTGTATACCTCCGTCGAAAGCTTACCATGTGAACGCCT-ACAGTAAGCCTAATGACGTCAACACGTCAGGTCAAGGTGCAGCTTAAGAAATGGAAAGCAATGGGCTACAATTTCTAATTTAGAATAAACGGAAGACTATGTGAAATCCTAGTCATGAAGGTGGATTTAGTAGTAAAAAGAAAATAGAGTGTTCTTTTTAACCCGGCTCTGGGACGCGTACACACCGCCCGTCACCCTCTTCAATAGTA-TTTTTCTCCGTTCCTAACTC-ACTAT-CACGTCTT-AGAAGAAGCAAGTCGTAACATGGTAAGTGTACTGGAAAGTGCACTTGG--TA-TAACAAAATGTAGCTTAACTAAAGCCCTCCGCTTACACCGAAGATATATCTGTTTAAACCAGTTCATTTTGAGCCTAAAATCTAGCCTACTACATCCACATGCCTTCCTATCCAGA-TAAAACATTTTATTATTTTAGTACAGGTGATCGAAAAA-TTTTTAAGCGCTTCAGATAAAGTACCGTAAGGGAAAGATGAAATAGAATTGAAATAACCTTAAAGCCTCAAATAGCAGAGACTCCCCCTCGTACCTTTTGCATCATGGTCTAGTTAGTTTATTC-AAGCAAAATGAAATTTTT-AGTTAGACACCCCGAAACTAAGGGAGCTACTTCAAAACAGCCTAAT-GGGCCAACCCGTCTCTGTTGCAAAAGAGTGGGAAGATTTTTAAGTAGAGGTGAAAAACCTACCGAACTTAGAGATAGCTGGTTGTTCAGGAAAAGAGTTTTAGCTCTACCTTAAACTTACTA-ATGATATAA-AACAAACTT-TGAAGCTTAAGAGCTATTCAAATAAGGCACAGCTTATTTGAAATAGGATACAACCTATTATACAGGGT-AAATAGTA-GTATTAAAAA-TTAAGTGGGCCTAAAAGCAGCCACCTTCAAAAAAGCGTTAAAGCTTAATTAAAATT-ACTTATAA-TTTCCAAAATTATTAATAACCCTTTAT-TTTTACTGAACTATTTTATA-TTCTTATAAAAGCAATGATGCTAGAACTAGTAACAAGAAATTGAC--TTCTCCTAAATGTAAGCATAAACCAAAATTGACATTCCATTGGTAATCAACGTAAATGCAGAAAATATAATAACCTAC--------TAGAAAAACCTATAATTTTT-TACGTTAACCTAACACTAGAATATTACAGGAAAGATTAAAAGAGAAAGAAGGAACTCGGCAAAATTTAGCCTCGCCTGTTTACCAAAAACATCGCCTCTTGATAAAACATAAGAGGTCCAGCCTGCCCAGTGACATAGTTCAACGGCCGCGGTACCCTAACCGTGCGAAGGTAGCATAATCAATTGTCCTCTAAATAGGGACTAGTATCAATGGCACCACGAAGGCTACACTGTCTCCTTTCTCTAATCAGTGAAACTGATCTCCCCGTGAAGAAGCGGGAATATAATTATAAGACGAGAAGACCCCATGGAGCTTTAAACTCATCATACACCTTC-ATATTAC-TTAAATCTACTAATCCTAAAGACC-TGTATGCTAATTTTAGGTTGGGGGGACCACGGAGTATAATTTAACCTCCACAACAAATGGGTTAATACCCTTATCCACGAGAAACACCTCTAAGAATTAATAAACTAATGTTTAATGATCCGATAA-CTCGATCAATGAACCAAGTTACCCTGGGGATAACAGCGCAATCTACTTCAAGAGCCCCTATCGACAAGTAGGTTTACGACCTCGATGTTGGATCAGGGTATCCTAGTGGTGCAACCGCTACTAATG-GTTCGTTTGTTCAACG zweifeliJAC7514 ACCGTAAACAATTAATTTACACCAATCAGCGCCAGGGAATTACGAGCAATGCTTAAAACCCAAAGGACTTGACGGTGTCCCACCCAACTAGAGGAGCCTGTTCTATAATCGATAATCCCCGCTACACCCGACCATTTCTTGCTT-TCAGTCTGTATACCTCCGTCGAAAGCTTACCATGTGAACGCCT-ACAGTAAGCCCAATGATATCAACACGTCAGGTCAAGGTGCAGCTTAAGAAGTGGGAAGCAATGGGCTACAATTTCTAATTTAGAACAAACGAAAGATTATGTGAAATCATATTC-TGAAGGTGGATTTAGCAGTAAAAAGAAAATAGAGTGTTCTTTTTAACCCGGCTCTGGGACGCGTACACACCGCCCGTCACCCTCTTCGATAGTA-AAATACTCTGTTCCTAACTC-ACTAT-TACGCCTT-AGAAGAAGCAAGTCGTAACATGGTAAGTGTACTGGAAAGTGCACTTGG--TA-TACCAAAATGTAGCTTAACCAAAGCCCTCCGCTTACACCGGAGATATATCTGTTCAAACCAGGTCATTTTGAGCCTAAAATCTAGCCTATATTGTTCATATGACTTTCTATCCAAA-TAAAACATTTTATTATTCTAGTACAGGCGATCGAAAAA-TTTCTAAGCGCTTCAGATAAAGTACCGTAAGGGAAAGATGAAATAGAATTGAAATAACCTTAAAGCCCAAAATAGTAGAGACACTCCCTCGTACCTTTTGCATCATGGTCTAGTTAGTTTACTC-AAGCAAAATGAAATTTT--AGTTAGACACCCCGAAACTGAGGGAGCTACTTCAAAACAGCCTAAT-GGGCCAACCCGTCTCTGTTGCAAAAGAGTGGGAAGATTTTTAAGTAGAGGTGAAAAACCTACCGAACTTAGAGATAGCTGGTTGTTCAGGAAAAGAGTCTTAGCTCTACCTTAAGCTTTACCTATGATACTC-AACAAATCT-TAAAACTTAAGAGCTATTCAAATAAGGCACAGCTTATTTGAAATAGGATACAACCTATTACATTGGGT-TAATCATA-GTGAATAAAG-CTAAGTGGGCCTAAAAGCAGCCACCTTCAAAAAAGCGTTAAAGCTTAATTAAAATT-ACTAATAA-TTTTCAAAACTACCATTAACCCTTTAT-CTATACTGAACTATTTTATA-TAATTATAAAAGCAATGATGCTAGAACTAGTAACAAGAAATTGACT-TTCTCCTAAATGTAAGTATAAACCAAAATTGACACTCTATTGGTAATTAACGTAAATGCAGAAACTATAGTAACACTAC-------TAGAAAAACCTATAACTCTT-AACGTTAACCTCACACTAGAACATTTCAGGAAAGATTAAAAGAAAAAGAAGGAACTCGGCAAAATTCAACCTCGCCTGTTTACCAAAAACATCGCCTCTTGATTAAACATAAGAGGTCCAGCCTGCCCAGTGACATAGTTCAACGGCCGCGGTACCCTAACCGTGCGAAGGTAGCATAATCAATTGTCCTCTAAATAGGGACTAGTATCAACGGCACCACGAGGGTTATACTGTCTCCTTTCTCCAATCAGTGAAACTGATCTCCCCGTGAAGAAGCGGGGATATTACTATAAGACGAGAAGACCCCATGGAGCTTTAAACTTATCATACACCTCT-ACGATCT-ATTAACCCACTCATCCCAAAAACC-TGTATGCTAATTTTAGGTTGGGGGGACCACGGAGTATAATTCAACCTCCACAACAAACGGGCTAATACCCTTATCCATGAAAAACACCTCTAAGAATTAATAAACTAATGTTTAATGATCCGATAA-CTCGATCAATGAACCAAGTTACCCTGGGGATAACAGCGCAATCTACTTCAAGAGCTCCTATCGACAAGTAGGTTTACGACCTCGATGTTGGATCAGGGTATCCTAATGGTGCAACCGCTACTAATG-GTTCGTTTGTTCAACG pustulosaJAC10555 ACCGTAAACAATTAATTTACACCAATCAGCGCCAGGGAATTACGAGCAATGCTTAAAACCCAAAGGATTTGACGGTGCCCCACCCAACTAGAGGAGCCTGTTCTATAATCGATAATCCCCGCTACACCTGACCATTTCTCGCCT-TCAGTCTGTATACCTCCGTCGAAAGCTTACCATGTGAACGTTT-ACAGTAAGCCTAATGACGTCAACACGTCAGGTCAAGGTGCAGCTTAAGAAATGGGAAGCAATGGGCTACAATTTCTAATTTAGAACAAACGGAAGACTATGTGAAACCCTAGTC-TCAAGGTGGATTTAGTAGTAAAAAGAAAATAAAGTGTTCTTTTTAACCTGGCTCTGGGACGCGTACACACCGCCCGTCACCCTCTTCGATAGTA-TATTCTTCTGTTCCTAACTA-ACTACTCACGTTTT-AGAAAAGGTAAGTCGTAACATGGTAAGTGTACTGGAAAGTGCACTTGG--TA--AACAAAATGTAACTTAAATAAAGTTCTCCGCTTACACCGTAGGTATATCTGTTAAAATCAGTTCATTTTGAGCCAAAAATCTAGCCTACTACA-TCATATGTCTCCCATCCCAAA-TAAAACATTTTATTATTTTAGTATAGGTGATCGAAAAA-TTTCTAAGCGCTTCAGATAAAGTACCGTAAGGGAAAGATGAAATAGAACTGAAATAACCTCAA-GCCTTAAATAGTAGAGATTACCCCTCGTACCTTTTGCATCATGGTCTAGTTAGTTTATTC-AAGCAAAATGAAATTTT--AGTTAGACACCCCGAAACTAAGGGAGCTACTTAAAAACAGCCTAAT-GGGCCAACCCGTCTCTGTTGCAAAAGAGTGGGAAGATTTTTTAGTAGAGGTGAAAAACCTACCGAACTTAGAGATAGCTGGTTGTTCAGGAAAAGAGTTTTAGCTCTACCTTAAGCCTTTTT-ATGTTATAA-AGCAAATTC-TAAAGCTTAAGAGTTATTCAAATAAGGCACAGCTTATTTGAAATAGGATACAACCTATATAATAGGGT-AAATTATAATGTAGAAAAA-TTAAGTGGGCCTGAAAGCAGCCACCTTCAAAAAAGCGTCAAAGCTTCATTTATATC-TTTAGTAA-TTTCTTAAATTATTAATAACCCTTTTT-TTCTACTGAACTATTTTATA-TTTTTATAAAAGTAATTATACTAGAACTAGTAACAAGAAATTGAAC-TTCTCCTAAATGTAAACATAAACCAAAATTGACACTCTATTGGTAATTAACGTAAATGCAGAAACTATAATAAAGTAC--------TAGAAAAACCTATAACTTCC-AACGTTAACCTTACACCAGTACATTTCAGGAAAGATTAAAAGAGAAAGAAGGAACTCGGCAAAATTTAGCCTCGCCTGTTTACCAAAAACATCGCCTTTCGATAAAATATGAGAGGTCCAGCCTGCCCAGTGACATAGTTCAACGGCCGCGGTACCCTAACCGTGCGAAGGTAGCATAATCAATTGTCCTCTAAATAGGGACTAGCATCAATGGCACCACGAAGGTTACACTGTCTCCTTTCTCTAATCAGTGAAACTGATCTCCCCGTGAAGAAGCGGGGATATAATTATAAGACGAGAAGACCCCATGGAGCTTTAAACTAATTATACACCTTT-TTAATAT--TAAATCCACTAAACCCAAAAACC-TGTATGCTAGTTTTAGGTTGGGGGGACCACGGAGTATAATATAACCTCCACAACAAATGGGTTAACACCCTTATCCACGAAAGACACCTCTAAGAATTAACAAACTAATGTTTAATGATCCGATAA-TTCGATTAATGAACCAAGTTACCCTGGGGATAACAGCGCAATCTACTTCAAGAGCTCCTATCGACAAGTAGGTTTACAACCTCGATGTTGGATCAGGGTATCCTAATGGTGCAACCGCTACTAATG-GTTCGTTTGTTCAACG pipiensJSF1119 ACCGTAAACAATTAATTTACACCCATCAGCGCCAGGGAAATACGAGCAATGCTTAAAACCCAAAGGATTTGACGGTGTCCCACCCAGCTAGAGGAGCCTGTTCTTTAATCGATGATCCCCGCTACACCTGACCATTTCTTGCTCATCAGTCTGTATACCTCCGTCGAAAGCTTACCATGTGAACGTCT-TCAGTAGGCTCAATGATATCAATACGTCAGGTCAAGGTGCAGCTTAAGAAATGGGAAGCAATTGGCTACCATTTCTAATATAGAACAAACGAAAGGCTATGTGAAATCATAGCAGCGAAGGTGGATTTAGTAATAAAAAAAAAATATAGTGTTCTTTTTAACCCGGCTCTGGGATGCGTACACACCGCCCGTCACCCTCTTCGATAGTA-AATAATATTGTCCCTAACCC-ATTAT-CACGTTTT-AGAAGAAGCAAGTCGAAACATGGTAAGTGTACTGGAAAGTGCACTTGGAAAA-TAACAAAATGTAGCTTAACAAAAGCCCTTCGCTTACACCGAAGAAATATCTGTTCAAATCAGATCATTTTGAGCCCAAAATCTAGCCTTCAAGACTCACATGAACCCCATTCCAAA-CAAAACATTCTCCCATTATAGTACAGGTGATAGAAAAAATTCTTAAGCGCTTTAGACAAAGTACCGCAAGGGAAAGATGAAATAGAACTGAAATAACCTTAAAGCCCTAAACAGCAGAGACACCCCCTCGTACCTTTTGCATCATGGTCTAGCTAGTCTACAC-AAGCAAAATGAAACTTTT-AGTTAGTCCTCCCGAAACTAAGCGAGCTACTTCAAAACAGCCCAATGGGGCTAACCCTTCTCTGTTGCAAAAGAGTGGGAAGATTTTCAAGTAGAAGTGAAAAGCCTACCGAGCTTAGAGATAGCTGGTTATTCAGGAAAAGAGTTTTAGCTCTACCTTAAGCTTCTTT-ATGACACCT-AACAAACCC-TAAAGCTTAAGAGCTATTCAAATAAGGCACAGCTTATTTGAAACAGGATACAACCTACAATACTGGGT-AAATAATAAGTGAATAAAA-TAAAGTTGGCCTAAAAGCAGCCACCTT-AAAAAAGCGTTAAAGCTTAGTTCTACAC-ACTTACAA-TTTCTAAAATTTTGATCAACCCTTTAT-CCCTACTGAATTATTTTATA-TCCTTATAAAAGTAATAATGCTAGAACTAGTAACAAGAAACTGCCCATTCTCCTAAATGCAAGCATAAACCAGAATAGACACCCTACTGGTAATCAACGTAAATGTCACTTTTATAGTAACATAG--------TAGAAAATCCTATAATCCCCTTACGTTAACCTTACACTAGAGCATTTCAGGAAAGATTAAAAGAGAAAGAAGGAACTCGGCAAACTTCAGCCTCGCCTGTTTACCAAAAACATCGCCTCTTGAAAAAATATAAGAAGTCCAGCCTGCCCAGTGACTAAGTTTAACGGCCGCGGTACCCTAACCGTGCGAAGGTAGCATAATCACTTGTTCTTTAATTGGGGACTCGTATCAACGGCATCACGAAGGCTGCACTGTCTCCTTTCTCTAATCAGTGAAACTGATCTCCCCGTGAAGAAGCGGGGATAAAATTATAAGACGAGAAGACCCCATGGAGCTTCAAACTCATCATGCAACTCT-GTCCTCC-ATATCCCT-TAATTC-AAGAGATG-TGCATGTTAGTTTTGGGTTGGGGGGACCTCGGAGTATAACTTAACCTCCAAAACAAATGGGTTAACACCTTTATCCGCGAGAAACACCTCTAAGAATTACTAAACTAATGCTTTTTGATCCGATAA--TCGATCAATGGACCAAGTTACCCTGGGGATAACAGCGCAATCTACTTCAAGAGTTCATATCGACAAGTGGGTTTACGACCTCCATGTTGGATCAGGGTATCCTGGTGGTGCAACCGCTACTAATG-GTTCGTTTGTTCAACG pipiensY10945 GCCGTAAACAATTAATTTACACCCATCAGCGCCAGGGAACTACGAGCAATGCTTAAAACCCAAAGGATTTGACGGTGTCCCACCCAGCTAGAGGAGCCTGTTCTTTAATCGATGATCCCCGCTACACCTGACCATTTCTTGCTCATCAGTCTGTATACCTCCGTCGAAAGCTTACCATGTGAACGTCT-TCAGTAGGCTCAATGATATCAATACGTCAGGTCAAGGTGCAGCTTAAGAAATGGGAAGCAATTGGCTACAATTTCTAATATAGAACAA-CGAAAGGCTATGTGAAATCATAGCAGCGAAGGTGGATTTAGTAGTAAAAAGAAAATAGAGTGTTCTTTTTAACCCGGCTCTGGGATGCGTACACACCGCCCGTCACCCTCTTCGATAGTA-AATAATATTATCCCTAACCC-ATTAT-CACGTTTT-AGAAGAGGCAAGTCGAAACATGGTAAGTGTACTGGAAAGTGCACTTGGAAAA-TAACAAAATGTAGCTTAACAAAAGCCCTTCGCTTACACCGAAGAAATATCTGTTCAAATCAGATCATTTTGAGCCCAAAATCTAGCCTTCAAAACTCACATGAACCCCCTTCCAAA-CAAAACATTCTCCCATTATAGTACAGGTGATAGAAAAAATTCTTAAGCGCTTTAGACAAAGTACCGCAAGG-AAAGATGAAATAGAACTGAAATAACCTTAAAGCCCTAAACAGCAGAGACACCCCCTCGTACCTTTTGCATCATGGTCTAGCTAGTCTACAC-AAGCAAAATGAAACTTTT-AGTTAGTCCTCCCGAAACTAAGCGAGCTACTTCAAAACAGCCCAATGGGCGTAACCCTTCTCTGTTGCAAAAGAGTGGGAAGATTTTCAAGTAGAAGTGAAAAGCCTACCGAGCTTAGAGATAGCTGGTTATTCAGGAAAAGAGTTTTAGCTCTACCTTAAGCTTCTTT-ATGACACCT-AACAAACCC-TAAAGCTTAAGAGCTATTCAAATAAGGCACAGCTTATTTGAAACAGGATACAACCTACAATACTGGGT-AAATAATAAGTGAATAAAA-TAAAGTTGGCCTAAAAGCAGCCACCTT-AAAAAAGCGTTAAAGCTTAGTTCTACAC-ACTTACAA-TTTCTAAAATTTTGATCAACCCTTTAT-CCCTACTGAATTATTTTATA-TCCTTATAAAAGTAATAATGCTAGAACTAGTAACAAGAAACTGCCCATTCTCCTAAATGCAAGCATAAACCAGAATAGACACCCTACTGGTAATCAACGTAAATGTCACTTTTATAGTAACATAG--------TAGAAAATCCTATAATCCCCTTACGTTAACCTTACACTAGAGCATTTCAGGAAAGATTAAAAGAGAAAGAAGGAACTCGGCAAACTTCAGCCTCGCCTGTTTACCAAAAACATCGCCTCTTGAAAAAATATAAGAGGTCCAGCCTGCCCAGTGACTAAGTTTAACGGCCGCGGTACCCTAACCGTGCGAAGGTAGCATAATCACTTGTTCTTTAATTGGGGACTCGTATCAACGGCATCACGAAGGCTGCACTGTCTCCTTTCTCTAATCAGTGAAACTGATCTCCCCGTGAAGAAGCGGGGATAAAATTATAAGACGAGAAGACCCCATGGAGCTTCAAACTCATCATGCAACTCT-GTCCTCC-ATATCCCT-TAATTC-AAGAGATG-TGCATGTTAGTTTTGGGTTGGGGGGACCTCGGAGTATAACTTAACCTCCAAAACAAATGGGTTAACACCTTTATCCGCGAGAAACACCTCTAAGAATTATTAAACTAATGCTTTTTGATCCGATAA--TCGATCAATGGACCAAGTTACCCTGGGGATAACAGCGCAATCTACTTCAAGAGTTCATATCGACAAGTGGGTTTACGACCTCGATGTTGGATCAGGGTATCCTGGTGGTGCAACCGCTACTAATG-GTTCGTTTGTTCAACG dunniJSF1017 GCCGTAAACAATTAATTTACACCCATCAGCGCCAGGGAACTACGAGCAATGCTTAAAACCCAAAGGATTTGACGGTGTCCCACCCAGCTAGAGGAGCCTGTTCTTTAATCGATGATCCCCGCTACACCTAACCATTTCTTGCTCATCAGTCTGTATACCTCCGTCGAAAGCTTACCATGTGAACGTCC-TCAGTAGGCTCAATGATATCAACACGTCAGGTCAAGGTGCAACTTAAGAAGTGGGAAGTAATTGGCTACAATTTCTAATCTAGAACAAACGAAAGGCTATGTGAAATCATAGCAGCGAAGGTGGATTTAGTAGTAAAAAGAAAATAGAGTGTTCTTTTTAACCCGGCTCTGGGATGCGTACACACCGCCCGTCACCCTCTTCGATAGTA-AATAATTCTGTCCCTAACCC-ATTAT-CACGTTTT-AGAAGAGGCAAGTCGAAACATGGTAAGTGTACTGGAAAGTGCGCTTGGAAAA-TAACAAAATGTAGCTTAACAAAAGCCCTTCGCTTACACCGAAGAAATATCTGTTAAAATCTGATCATTTTGAGCCCCAAATCTAGCCTTCAAAACTCGCATGAACCTCCTTCCTAA-CAAAACATTCTTCCATTATAGTACAGGTGATAGAAAAA-TTTTTAAGCGCTTTAGACATAGTACCGCAAGGGAAAGATGAAATAGAACTGAAATAACCTTAAAGCCCTAAACAGCAGAGACACCCCCTCGTACCTTTTGCATCATGGTCTAGCTAGTCTACCC-AAGCAAAATGAAACTTTT-AGTTAGCCCCCCCGAAACTAAGCGAGCTACTTCAAAACAGCCCCAAGGGGCTAACCCTTCTCTGTTGCAAAAGAGTGGGAAGATTTTCAAGTAGAAGTGAAAAGCCTACCGAGCTTAGAGATAGCTGGTTATTCAGGAAAAGAGTTTTAGCTCTACCTTAAGCTTCTTT-ATGACATCT-AACAAACCC-TAAAGCCTAAGAGCTATTCAAATAAGGCACAGCTTATTTGAAACAGGATACAACCTATTATATTGGGT-AAATTGCTAGTAGACAGAA-TAAAGTTGGCCTAAAAGCAGCCATCTT-AAAAAAGCGTTAAAGCTTAGTTTTACCT-ACTCCTAA-TTTCTTAAATTTTTATTAACCCTTTAC-CCCTACTGAATTATTTTATA-CCTCTATAAAAGCAATAATGCTAGAACTAGTAACAAGAAACTGCCTATTCTCCTAAATGCAAACATAAACCAGAATAGACACCCTACTGGTAATTAACGTAAATGTTACTTCTGTAGCAACACAA--------TAGAAAACCCTACATTACTC-CACGTTAACCTTACACCAGAGCATTTCAGGAAAGATTAAAAGAGAAAGAAGGAACTCGGCAAACCTCAGCCTCGCCTGTTTACCAAAAACATCGCCTCTTGAAAAAATATAAGAGGTCCAGCCTGCCCAGTGACTAAGTTTAACGGCCGCGGTACCCTAACCGTGCGAAGGTAGCATAATCACTTGTTCTTTAATTGGGGACTCGTATCAACGGCATCACGAAGGCTGCACTGTCTCCTTTCTCCAATCAGTGAAACTGATCTCCCCGTGAAGAAGCGGGGATATGATTATAAGACGAGAAGACCCCATGGAGCTTCAAACTCACCATACAACTCT-GTTCTAC-ACACCCTT-TAATTC-AAGAAATT-TGTATGTTAATTTTGGGTTGGGGGGAACTCGGAATATAACTTAACCTCCAAAACAAATAGGTTAACACCTTTATCCATGAAATACACCTCTAAGAATTACTAAACTAATGCTC-TTGATCCGATTA--TCGATCAATGAACCAAGTTACCCTGGGGATAACAGCGCAATCTACTCCAAGAGTTCATATCGACAAGTAGGTTTACAAACTCCATGTTGGATCAGGGTATCCTAATGGTGCAACCGCTACTAATG-GTTCGTTTGTTCAACG montezumaeJAC8836 GCCGTAAACAATTAATTTACACCCATCAGCGCCAGGGAAATACGAGCAATGCTTAAAACCCAAAGGATTTGACGGTGTCCCACCCAACTAGAGGAGCCTGTTCTTTAATCGATGATCCCCGCTACACCTAACCATTTCTTGCTCATCAGTCTGTATACCTCCGTCGAAAGCTTACCATGTGAACGTCC-TCAGTAGGCTCAATGATATCAACACGTCAGGTCAAGGTGCAACTTAAGAAGTGGGAAGTAATTGGCTACAATTTCTAATCTAAAACAAACGAAAGGCTATGTGAAATCATAGCAGCGAAGGTGGATTTAGTAGTAAAAAAAAAATATAGTGTTCTTTTTAACCCGGCTCTGGGATGCGTACACACCGCCCGTCACCCTCTTCGATAGTA-AATAATTCTGTTCCTAACCC-ATTAT-AGCGTTTT-AGAAGAAGCAAGTCGAAACATGGTAAGTGTACTGGAAAGTGCGCTTGGAAA--TAACAAAATGTAGCTTAACAAAAGCCCTTCGCTTACACCGAAGAAATATCTGTTAAAATCTGATCATTTTGAGCCCCAAATCTAGCCTTCAAAACTCACATGAACCTCCTTCCTAA-CAAAACATTCTCCCATTATAGTACAGGTGATAGAAAAA-TTTTTAAGCGCTTTAGACACAGTACCGCAAGGGAAAGATGAAATAGAACTGAAATAACCTTAAAGCCCTAAACAGCAGAGACACCCCCTCGTACCTTTTGCATCATGGTCTAGCTAGTCTACCC-AAGCAAAATGAAACTTTT-AGTTAGTCTCCCCGAAACTAAGCGAGCTACTTCAAAACAGCCCTATGGGGCTAACCCTTCTCTGTTGCAAAAGAGTGGGAAGATTTTCAAGTAGAAGTGAAAAGCCTACCGAGCTTAGAGATAGCTGGTTATTCAGGAAAAGAGTTTTAGCTCTACCTTAAGCTTCTTT-ATGACATCT-AACAAACCC-TAAAGCCTAAGAGCTATTCGAATAAGGCACAGCTTATTTGAAACAGGATACAACCTATTATACTGGGT-AAATTGCTAGTAGACAGAA-TAAGGTTGGCCTAAAAGCAGCCATCTT-AAAAAAGCGTTAAAGCTTAGTTTTACCT-ACTCCTAA-TTTCTTAAATTTTTATTAACCCTTTAT-CCCTACTGAATTATTTTATA-CCTCTATAAAAGCAATAATGCTAGAACTAGTAACAAGAAACTGCCCATTCTCCTAAATGTAAACATAAACCAGAATAGACACCCTACTGGTAATTAACGTTTTTGTTACTTCTGTAGCAACACAA--------TAGAAAACCCTACATCACTC-CACGTTAACCTTACACTAGAACATTTCAGGAAAGATTAAAAGAGAAAGAAGGAACTCGGCAAACCTCAGCCTCGCCTGTTTACCAAAAACATCGCCTCTTGAAAAAATATAAGAGGTCCAGCCTGCCCAGTGACTAAGTTTAACGGCCGCGGTACCCTAACCGTGCGAAGGTAGCATAATCACTTGTTCTTTAATTGGGGACTCGTATCAACGGCATCACGAAGGCTGCACTGTCTCCTTTCTCCAATCAGTGAAACTGATCTCCCCGTGAAGAAGCGGGGATATTTTTATAAGACGAAAAGACCCCATGGAGCTTCAAACTCACCATACAACTCT-GTTCTAC-ACACCCCC-TAATTC-AAAAAATA-TGTATGTTAGTTTTGGGTTGGGGGGACCTCGGAGTATAACTTAACCTCCAAAACAAATAGGTTAACACCTTTATCCATGAGATACACCTCTAAGAATTACTAAACTAATGCTC-TTGATCCGATTA--TCGATCAATGAACCAAGTTACCCTGGGGATAACAGCGCAATCTACTTCAAGAGTTCATATCGACAAGTAGGTTTACAACCTCGATGTTGGATCAGGGTATCCTAATGGTGCAACCGCTACTAATG-GTTCGTTTGTTCAACG sp_2_mex_JSF1106 GCCGTAAACAATTAACTTACACCCATCAGCGCCAGGGAACTACGAGCAATGCTTAAAACCCAAAGGATTTGACGGTGTCCCACCCAACTAGAGGAGCCTGTTCTTTAATCGATGATCCCCGCTACACCTGACCATTTCTTGCTCATCAGTCTGTATACCTCCGTCGAAAGCTTACCATGTGAACGTCC-TCAGTAGGCTCAATGATATCAACACGTCAGGTCAAGGTGCAACTTAAGAAATGGGAAGTAATTGGCTACAATTTCTAATCTAGAACAAACGAAAGGCTATGTGAAACCATAGCAGCGAAGGTGGATTTAGTAGTAAAAAGAAAACAGAGTGTTCTTTTTAACCCGGCTCTGGGATGCGTACACACCGCCCGTCACCCTCTTCGATAGTA-AATAATTCTGTCCCTAACCT-AATAT-CACGTCTT-AGAAGAGGCAAGTCGAAACATGGTAAGTGTACTGGAAAGTGCACTTGGTAAA-TAACAAAATGTAGCTTAACAAAAGCCCTTCGCTTACACCGAAGAAATATCTGTGAGAATCTGATCATTTTGAGCCTCAAATCTAGCCTTTAAAACTCTTATGAACCTCCTTCCAAA-CAAAACATTCTCCTATTATAGTACAGGTGATAGAAAAA-TTTTTAAGCGCTTTAGACAAAGTACCGCAAGGGAAAGATGAAATAGAACTGAAATAACCTTAAAGCCCTAAACAGCAGAGACACCCCCTCGTACCTTTTGCATCATGGTCTAGCTAGTCTACCC-AAGCAAAATGAAACTTTT-AGTTAGTCCTCCCGAAACTAAGCGAGCTACTTCAAAACAGCCCCATGGGGCTAACCCTTCTCTGTTGCAAAAGAGTGGGAAGATTTTCAAGTAGAAGTGAAAAGCCTACCGAGCTTAGAGATAGCTGGTTATTCAGGAAAAGAGTTTTAGCTCTACCTTAAGCTTCTTT-ATGACATCT-AACAAACCC-TAAAGCCTAAGAGCTATTCAAATTAGGGACAGCTTATTTGAAACAAGATACAACCTACTACACTGGGT-AA-TTGCT-GTAAATTAAA-TAAAGTTGGC-TAAAAGCAGC-ACCTT-AAAAAAGCGTTAAAGCTTAGTTTTATCT-ACTCCTAA-TTTCTTAAATTTTAACCAACCCTTTAC-CCCTACTGAATTATTTTATA-CCTCTATAAAAGCAATAATGCTAGAACTAGTAACAAGAAACTGCCTATTCTCCTAAATGCAAGTATAAACCAGAATAGACACTCTACTGGTAATTAACGTAAATGTCATTTATGTAGCAACACAA--------TAGAAAACCCTACATTACTC-CACGTTAACCTTACACCAGAGCATTTCAGGAAAGATTAAAAGAGAAAGAAGGAACTCGGCAAACCTCAGCCTCGCCTGTTTACCAAAAACATCGCCTCTTGAAAAAATATAAGAGGTCCAGCCTGCCCAGTGACTAAGTTTAACGGCCGCGGTACCCTAACCGTGCGAAGGTAGCATAATCACTTGTTCTTTAATTGGGGACTCGTATCAACGGCATCACGAAGGCTGCACTGTCTCCTTTCTCTAATCAGTGAAACTGATTTCCCCGTGAAGAAGCGGGGATAAAATTATAAGACGAGAAGACCCCATGGAGCTTCAAACTCACTATACAACTCT-GTTCTCC-ATACCCAT-TAATTC-AAGAGATA-TGTGTATTAGTTTTGGGTTGGGGGGACCTCGGAGTATAACTTAACCTCCGAAACAAATAGGTTGACACCTTTATCCATGAGAAACACCTCTAAGAATTACTAAACTAATGCTT-TTGATCCGATAA--TCGATCAATGAACCAAGTTACCCTGGGGATAACAGCGCAATCTACTTCAAGAGTTCATATCGACAAGTGGGTTTACGACCTCGATGTTGGATCAGGGTATCCTAGTGGTGCAACCGCTACTAATG-GTTCGTTTGTTCAACG chiricahuensisJSF1063 ????????????????????????ATCACCGCCAGGGAACTACGAGCAATGCTTAAAACCCAAAGGATTTGACGGTGTCCCACCCAACTAGAGGAGCCTGTTCTTTAATCGATGATCCCCGCTACACCTGACCATTTCTTGCTCATCAGTCTGTATACCTCCGTCGAAAGCTTACCATGTGAACGTCC-TCAGTAGGCTCAATGATATCAACACGTCAGGTCAAGGTGCAACTTAAGAAATGGGAAGTAATTGGCTACAATTTCTAATCTAGAACAAACGAAAGGCTATGTGAAATCATAGCAGCGAAGGTGGATTTAGTAGTAAAAAGAAAATAGAGTGTTCTTTTTAACCCGGCTCTGGGACGCGTACACACCGCCCGTCACCCTCTTCGATAGTA-AATAATTCTGTCCTTAACCT-AATAT-AACGTCTT-AGAAGAGGCAAGTCGAAACATGGTAAGTGTACTGGAAAGTGCACTTGGAAAA-TAACAAAATGTAGCTTAACAAAAGCCCTTCGCTTACACCGAAGAAATATCTGTTAAAATCTGATCATTTTGAGCCTCAAATCTAGCCTTCAAAACTCACATGAACCTCCTTCCAAA-CAAAACATTCTCCTATTATAGTACAGGCGATAGAAAAA-TTTTTAAGCGCTTTAGATAAAGTACCGCAAGGGAAAGATGAAATAGAACTGAAACAACCTTAAAGCCCTACACAGCAGAGACTCCCCCTCGTACCTTTTGCATCATGGTCTAGCTAGTCTACCC-AAGCAAAATGAAACTTTT-AGTTAGTCCCCCCGAAACTAAGCGAGCTACTTCAAAACAGCCCCATGGGGCTAACCCTTCTCTGTTGCAAAAGAGTGGGAAGATTTTCAAGTAGAAGTGAAAAGCCTACCGAGCTTAGAGATAGCTGGTTATTCAGGAAAAGAGTTTTAGCTCTACCTTAAGCTTCTTT-ATGACATCT-AACAAACCC-TAAAGCCTAAGAGCTATTCAAATAAGG?ACAGCTTATTTGAAACAGGATACAACCTATTATACTGGGT-AAATTGCCAGTAAATAAAA-TAAAGTTGGGCTAAAAGCAGCCATCTT-AAAAAAGCGTTAAAGCTTAGTTTTTTCT-ACTCATAA-TTCCTAAA-TTTTAACCAACCCTTTAC--CCTACTGAATTATTT-ATA-CCTCTATAAAAGTG-TAATGCTAGAACTAGTTACAAGAAAC?GCTAC--CTCCTAA-TGCAAGCATAAACCAGAATAGACACTCTACTGGTAATTAACGTAAATGTCATTTCTGTAGCAACACAA--------TAGAAAACCCTACATTACTC-CACGTTAACCTTACACCAGAGCATTTCAGGAAAGATTAGAAGAGAAAGAAGGAACTCGGCAAACCTCAGCCTCGCCTGTTTACCAAAAACATCGCCTCTTGACAAAATATAAGAGGTCCAGCCTGCCCAGTGACTAAGTTCAACGGCCGCGGTACCCTAACCGTGCGAAGGTAGCATAATCACTTGTTCTTTAATTGGGGACTCGTATCAACGGCATCACGAAGGCTGCACTGTCTCCTTTCTCCAATCAGTGAAACTGATCTCCCCGTGAAGAAGCGGGGATTAAATTAT?AGACGAGAAGACCC-ATGGAGCTTCAAACTCATTATACAACTCT-GTTCTCC-ACACCCCC-T?ATT?-AAGAGATA-TGTA-GTTAGTTTTGGGTTTGGGGGACCTCGGAGTATAACTTAACCTCCAAAACAAATTGGTTAACA?CTTTTTCCATGAGAAACA?CTCTTA?AA?TACTAAACTAATGCTT-TTGATCCGATTA--TCCAT?AATGAACCAAGTTACCCT?GGGATAACAACGCAATCTACTTTAAGAGTTAATATCGACCAGTGGGTTTACAACCTCCATTTTGGATAAGGGTACCC?AATGGTGCAACCGCTCCTAAAG-GTTCGTTTGTT?AAC? chiricahuensisJSF1092 GCCGTAAACAATTAACTTACATCCATCAGCGCCAGGGAACTACGAGCAATGCTTAAAACCCAAAGGATTTGACGGTGTCCCACCCAACTAGAGGAGCCTGTTCTTTAATCGATGATCCCCGCTACACCTGACCATTTCTTGCTCATCAGTCTGTATACCTCCGTCGAAAGCTTACCATGTGAACGTCC-TCAGTAGGCTCAATGATATCAACACGTCAGGTCAAGGTGCAACTTAAGAAATGGGAAGTAATTGGCTACAATTTCTAATCTAGAACAAACGAAAGGCTATGTGAAATCATAGCAGCGAAGGTGGATTTAGTAGTAAAAAGAAAATAGAGTGTTCTTTTTAACCCGGCTCTGGGATGCGTACACACCGCCCGTCACCCTCTTCGATAGTA-AATAATTCTGTCCTTAACCT-AATAT-AACGTCTT-AGAAGAAGCAAGTCGAAACATGGTAAGTGTATGGGAAAATGCACTTGGAAAA-TAACAAAATGTAGCTTAACAAAAGCCCTTCGCTTACACCGAAGAAATATCTGTTAAAATCTGATCATTTTGAGCCTCAAATCTAGCCTTCAAAACTCACATGAACCTCCTTCCAAA-CAAAACATTCTCCTATTATAGTACAGGCGATAGAAAAA-TTTTTAAGCGCTTTAGATAAAGTACCGCAAGGGAAAGATGAAATAGAACTGAAACAACCTTAAAGCCCTAGACAGCAGAGATTCCCCCTCGTACCTTTTGCATCATGGTCTAGCTAGTCTACCC-AAGCAAAATGAAACTTTT-AGTTAGTCCCCCCGAAACTAAGCGAGCTACTTCAAAACAGCCCCATGGGGCTAACCCTTCTCTGTTGCAAAAGAGTGGGAAGATTTTCAAGTAGAAGTGAAAAGCCTACCGAGCTTAGAGATAGCTGGTTATTCAGGAAAAGAGTTTTAGCTCTACCTTAAGCTTCTTT-ATGACATCT-AACAAACCC-TAAAGCCTAAGAGCTATTCAAATAAGGCACAGCTTATTTGAAACAGGACACAACCTATTATACTGGGT-AAATTGCCAGTAAATAAAA-TAAAGTTGGCCTAAAAGCAGCCATCTT-AAAAAAGCGTTAAAGCTTAGTTTTATCT-ACTTATAA-TTTCTTAAATTTTAACCAACCCTTTAC-CCCTACTGAATTATTTTATA-CCTCTATAAAAGTAATAATGCTAGAACTAGTAACAAGAAACTGCTTA-TCTCCTAAATGCAAGCATAAACCAGAATAGACACTCTGCTGGTAATTAACGTAAATGTCATTTCTGTAGCAACACAA--------TAGAAAACCCTACATCACTC-CACGTTAACCTTACACTAGAGCATTTCAGGAAAGATTAAAAGAGAAAGAAGGAACTCGGCAAACCTCAGCCTCGCCTGTTTACCAAAAACATCGCCTCTTGACAAAATATAAGAGGTCCAGCCTGCCCAGTGACTAAGTTCAACGGCCGCGGTACCCTAACCGTGCGAAGGTAGCATAATCACTTGTTCTTTAATTGGGGACTCGTATCAACGGCATCACGAAGGCTGCACTGTCTCCTTTCTCCAATCAGTGAAACTGATCTCCCCGTGAAGAAGCGGGGATAAAATTATAAGACGAGAAGACCCCATGGAGCTTCAAACTCATCATACAACTCT-GTTCTCC-ACACCCCC-TAATTC-AAGAAATA-TGTATGTTAATTTTGGGTTGGGGGGACCTCGGAGTATAACTTAACCTCCGAAACAAATAGGTTAACACCTTTATCCATGAGAAACACCTCTAAGAATTACTAAACTAATGCTT-TTGATCCGATAA--TCGATCAATGAACCAAGTTACCCTGGGGATAACAGCGCAATCTACTTCAAGAGTTCATATCGACAAGTGGGTTTACGACCTCGATGTTGGATCGGGGTATCCTAGTGGTGCAACCGCTACTAATG-GTTCGTTTGTTCAACG subaquavocalis GCCGTAAACAATTAACTTACATCCATCAGCGCCAGGGAACTACGAGCAATGCTTAAAACCCAAAGGATTTGACGGTGTCCCACCCAACTAGAGGAGCCTGTTCTTTAATCGATGATCCCCGCTACACCTGACCATTTCTTGCTCACCAGTCTGTATACCTCCGTCGAAAGCTTACCATGTGAACGTCC-TCAGTAGGCTCAATGATATCAACACGTCAGGTCAAGGTGCAACTTAAGAAATGGGAAGTAATTGGCTACAATTTCTAATCTAGAACAAACGAAAGGCTATGTGAAATCATAGCAGCGAAGGTGGATTTAGTAGTAAAAAGAAAATAGAGTGTTCTTTTTAACCCGGCTCTGGGACGCGTACACACCGCCCGTCACCCTCTTCAATAGTA-AATAATTCTGTCCTTAACCT-AATAT-AACGTCTT-AGAAGAGGCAAGTCGAAACATGGTAAGCGTACTGGAAAGTGCACTTGGAAAA-TAACAAAATGTAGCTTAACAAAAGCCCTTCGCTTACACCGAAGAAATATCTGTTAGAATCTGATCATTTTGAGCCTCAAATCTAGCCTTCAAAACTCACATGAACCTCCTTCCAAA-CAAAACATTCTCCTATTATAGTACAGGCGATAGAAAAA-TTTTTAAGCGCTTTAGATAAAGTACCGCAAGGGAAAGATGAAATAGAACTGAAACAACCTTAAAGCCCCAGACAGCAGAGATTCCCCATCGTACCTTTTGCATCATGGTCTAGCTAGTCTACCC-AAGCAAAATGAAACTTTT-AGTTAGTCCCCCCGAAACTAAGCGAGTTACTTCAAAACAGCCCCATGGGGCTAACCCTTCTCTGTTGCAAAAGAGTGGGAAGATTTTCAAGTAGAAGTGAAAAGCCTACCGAGCTTAGAGATAGCTGGTTATTCAGGAAAAGAGTTTTAGCTCTACCTTAAGCTTCTTT-ATGACATCT-AACAAACCC-TAAAGCCTAAGAGCTATTCAAATAAGGCACAGCTTATTTGAAACAGGATACAACCTATTATACTGGGT-AAACTGCCAGTAAATAAAA-TAAAGTTGGCCTAAAAGCAGCCATCTT-AAAAAAGCGTTAAAGCTTAGTTTTATCT-ACTCATAA-TTTCTTAAATTTTTACCAACCCTTTAC-CCCTACTGAATTATTTTATA-CCTCTATAAAAGTGATAATGCTAGAACTAGTAACAAGAAACTGCTTA-TCTCCTAAATGCAAGCATAAACCAGAATAGACACTCTACTGGTAATTAACGTAAATGTCATCTCTGTAGCAACACAA--------TAGAAAACCCTACATTACTC-CACGTTAACCTTACACCAGAGCATTTCAGGAAAGATTAAAAGAGAAAGAAGGAACTCGGCAAACCTCAGCCTCGCCTGTTTACCAAAAACATCGCCTCTTGAGAAAATATAAGAGGTCCAGCCTGCCCAGTGACTAAGTTCAACGGCCGCGGTACCCTAACCGTGCGAAGGTAGCATAATCACTTGTTCTTTAATTGGGGACTCGTATCAACGGCATCACGAAGGCTGCACTGTCTCCTTTCTCCAATCAGTGAAACTGATCTCCCCGTGAAGAAGCGGGGATAAAATTATAAGACGAGAAGACCCCATGGAGCTTCAAACTCATCATACAACTCT-GTTCTCC-ACACCCCC-TAATTC-AAGAGATG-TGTATGTTAGTTTTGGGTTGGGGGGACCTCGGAGTATAACTTAACCTCCAAAACAAATAGGTTAACACCTTTATCCATGAGAAACACCTCTAAGAATTACTAAACTAATGCTT-TTGATCCGATAA--TCGATCAATGAACCAAGTTACCCTGGGGATAACAGCGCAATCTACTTCAAGAGTTCATATCGACAAGTGGGTTTACGACCTCGATGTTGGATCAGGGTATCCTAGTGGTGCAACCGCTACTAATG-GTTCGTTTGTTCAACG palustrisJSF1110 ACCGTAAACAATTAATTTACACCTATCAGCGCCAGGGAATTACGAGCAATGCTTAAAACCCAAAGGATTTGACGGTGTCCCACCCAGCTAGAGGAGCCTGTTCTATAATCGATGATCCCCGCTACACCCGACCATTTTTCGCTCATCAGTCTGTATACCTCCGTCGAAAGCTTACCATGTGAACGTTCTTCAGTAGGCTCAATGATATCAACACGTCAGGTCAAGGTGCAGCTTAAGAAATGGGAAGTAATTGGCTACAATTTCTAATCTAGAACAAACGAAAGGCTATGTGAAATCATAACCACGAAGGTGGATTTAGTAGTAAAAAGAAAATAGAGTGTTCTTTTTAACCCGGCTCTGGGACGCGTACACACCGCCCGTCACCCTCTTCGATAGTA-CACAATTTTGTCCCTAACCA-ATTTT-CACGTTTT-AGAAGAAGCAAGTCGAAACACGGTAAGTGTACTGGAAAGTGCACTTGGAATA-TAACAAAATGTAGCTTAATAAAAGCCCTTCGCTTACACCGAAGAAATATCCGTTGAAATCTGATCATTTTGAGCCCTAAGTCTAGCCTTAAACCCTCGCATGAACCCCCTTCCAAA-CAAAACATTTTCCCATCATAGTACAGGCGATCGAAAAA-TTTCTAAGCGCTTCAGATAAAGTACCGCAAGGGAAAGATGAAATAGAACTGAAATAACCTTAAAGCCTCAAACAGCAGAGACATCTCCTCGTACCTTTTGCATCATGGTCTAGTTAGTCCACCC-AAGCAAAATGAAACTTTT-AGTTAGCCCTCCCGAAACTAAGCGAGCTACTTCAAAACAGCCCTATGGGGCCAACCCTTCTCTGTTGCAAAAGAGTGGGAAGATTTTCAAGTAGAAGTGAAAAGCCTACCGAGCTTAGAGATAGCTGGTTATTCAGGAAAAGAGTTTTAGCTCTACCTTAAGCTTCTTA-ATGAAACCT-AACAAACCT-TAAAGCTTAAGAGCTATTCAAATAAGGCACAGCTTATTTGAAATAGGATACAACCTAAAATACTGGGT-AAATACATAGTAATTAAAA-TAAAGTTGACCTAAGAGCAGCCATCTTCAAAAAAGCGTTAAAGCTTAGTTTTATCT-ACTAGTAA-TTTCTAAAATCTCAATCAACCCTTTAT-CCCTACTGAATTATTTTATA-TTTATATAAAAGTAATAATGCTAGAATTAGTAACAAGAAATTGTTTATTCTCCTAAATGCAAGCATAAACCAAAATAGACACCCTGTTGGTAATTAACGTAAATGTAACAAATATAGCAACACAA--------TAGAAAACCCTACAACCCTC-AACGTTAATCTTACACTAGAGCATTTCAGGAAAGATTTAAAGAGAAAGAAGGAACTCGGCAAACTTCAGCCTCGCCTGTTTACCAAAAACATCGCCTCTTGAAAAAATATAAGAGGTCCAGCCTGCCCAGTGACTAAGTTCAACGGCCGCGGTACCCTAACCGTGCGAAGGTAGCATAATCACTTGTTCTTTAAATGGGGACTTGTATCAACGGCACCACGAAGGCTGCACTGTCTCCTTTCTCCAATCAGTGAAACTGATCTCCCCGTGAAGAAGCGGGGATTAAACTATAAGACGAGAAGACCCCATGGAGCTTCAAACTCACCATACAACTCT-GTTCCCT-ACATCCCC-TGATTC-AAGAGATA-TGTATGTTAGTTTTGGGTTGGGGGGACCTCGGAGTACAACTTAACCTCCTAAACAAATAGGTTAACACCTTTATCCACGAGAAACACCTCTAAGAATTATTAAACTAATGTTT-TTGATCCGATAA--TCGATCAATGAACCAAGTTACCCTGGGGATAACAGCGCAATCTACTTCAAGAGTTCCTATCGACAAGTAGGTTTACGACCTCCATGTTGGATCAGGGTATCCTAATGGTGCAACCGCTACTAATG-GTTCGTTTGTTCAACG areolataJSF1111 GCCGTAAACAATTTATTTACACCTATAAGCGCCAGGGAATTACGAGCAATGCTTAAAACCCAAAGGATTTGACGGTGTCCCACCCAGCTAGAGGAGCCTGTTCTATAATCGATGATCCCCGCTACACCCGACCATTTTTCGCTCATCAGTCTGTATACCTCCGTCGAAAGCTTACCATGTGAACGTTCTTCAGTAGGCTCAATGATATCAACACGTCAGGTCAAGGTGCAACTTAAGAAATGGGAAGCAATTGGCTACAATTTCTAATCTAAAACAAACGAAAGGCTATGTGAAACCATAACCATGAAGGTGGATTTAGTAGTAAAAAAAAAATATAGTGTTCTTTTTAACCCGGCTCTGGGATGCGTACACACCGCCCGTCACCCTCTTCGATAGTA-CACAATATTGTCCGTAACCA-ATTTT-TACATTTTTAGAAGAAGCAAGTCGAAACACGGTAAGTGTACTGGAAAGTGCACTTGGAATA-TTACAAAATGTAGCTTAATAAAAGCACTTCGCTTACACCGAAGAAATATCCGTTCAAATCTGATCATTTTGAGCCCTAAATCTAGCCCCAAAAACTCGCATGAACCCCACCCCAAA-CAAAACATTTTCCCATCCTAGTACAGGCGATCGAAAAA-TTTCTAAGAGCTTCAGATAAAGTACCGCAAGGGAAAGATGAAATAGAACTGAAATAACCTTAAAGCCTTAAACAGCAGAGACACCCTCTCGTACCTTTTGCATCATGGTCTAGTTAGTCTACCC-AAGCAAAATGAAACTTTT-AGTTAGTCCCCCCGAAACTAAGCGAGCTACTTCAAAACAGCCCCATGGGGCCAACCCTTCTCTGTTGCAAAAGAGTGGGAAGATTTTCAAGTAGAAGTGAAAAGCCTACCGAGCTTAGAGATAGCTGGTTATTCAGGAAAAGAGTTTTAGCTCTACCTTAAGCTTCTTA-ATGAAACCC-AACAAGCCT-CAAAGCTTAAGAGCTATTCAAATAAGGCACAGCTTATTTGAAACAGGATACAACCTAAAATACAGGGT-AAATAAATAGTAATTTAAA-TGAAGTTGGCCTAAAAGCAGCCATCTTTAAAAAAGCGTTAAAGCTTAGTTTCACCT-ACTAGTAA-TCTCCAAAATTCTAATTAACCCTTTAT-CCCTACTGAATTATTTTATA-TTTTTATAAAAGCAATAATGCTAGAACTAGTAACAAGAAATTGCCTATTCTCCTAAATGCAAGCATAAACCAAAATAGACACCCTATTGGTAATCAACGTAAATGTAATAACTATAGCAACATAA--------TTGAAAACCCTACAACCCCT-AACGTTAACCTTACACTAGAGCATTTCAGGAAAGATTTAAAGAGAAAGAAGGAACTCGGCAAACTTCAGCCTCGCCTGTTTACCAAAAACATCGCCTCTTGAAAAAATATAAGAGGTCCAGCCTGCCCAGTGACTAAGTTCAACGGCCGCGGTACCCTAACCGTGCGAAGGTAGCATAATCACTTGTTCTTTAAATGGGGACTTGTATCAACGGCACCACGAAGGCTGCACTGTCTCCTTTCTCTAATCAGTGAAACTGATCTCCCCGTGAAGAAGCGGGGATTTGACTATAAGACGAGAAGACCCCATGGAGCTTCAAACTCACTATACAACTCT-GTTCTCT-ACATCTTA-TAATTC-AAGAGTTA-TGTATATTAGTTTTGGGTTGGGGGGACCTCGGAGTATAACTAAACCTCCTAAACAAATAGGTTAACACCTTTATCCACGAGAAACACCTCTAAGAATTATTAAATTAATGTTT-TTGATCCGATAA--TCGATCAATGAACCAAGTTACCCTGGGGATAACAGCGCAATCTACTTCAAGAGTTCCTATCGACAAGTAGGTTTACAACCTCCATGTTGGATCAGGGTATCCTAATGGTGCAACCGCTACTGATG-GTTCGTTTGTTCAACA sevosaUSC8236 GCCGTAAACAATTAATTTACACCCATCAGCGCCTGGGAATTACGAGCAATGCTTAAAACCCAAAGGATTTGACGGTGTCCCACCCAACTAGAGGAGCCTGTTCTATAATCGATGATCCCCGCTACACCCGACCATTTTTCGCTCATCAGTCTGTATACCTCCGTCGAAAGCTTACCATGTGAACGTTCTCTAGTAGGCTCAATGATATCAACACGTCAGGTCAAGGTGCAGCTTAAGAAATGGGAAGCAATTGGCTACAATTTCTAACTTAGAACAAACGAAAGGCTATGTGAAATCATAGCCATGAAGGTGGATTTAGTAGTAAAAAGAAAATAGAGTGTTCTTTTTAACCCGGCTCTGGGACGCGTACACACCGCCCGTCACCCTCTTCAATAGTA-CACAATATTGTCCATAACCA-ATTCT-TACGTTTT-AGAAGAGGCAAGTCGAAACACGGTAAGTGTACTGGAAAGTGCACTTGGAATA-TAACAAAATGTAGCTTAACAAAAGCCCTTCGCTTACACCGAAGAAATATCCGTTTAAATCTGATCATTTTGAGCCCTAAATCTAGCCCTAAAAACTCGCATGAACCCCACTCCAAA-CAAAACATTTTCCCATCATAGTACAGGCGATCGAAAAA-TTTCTAAGCGCTTCAGATAAAGTACCGCAAGGGAAAGATGAAATAGAACTGAAATAACCTTAAAGCTCTAAATAGCAGAGACACCCTCTCGTACCTTTTGCATCATGGTCTAGTTAGTCTACCC-AAGCAAAATGAAACTTTT-AGTTAGTCCTCCCGAAACTAAGCGAGCTACTTCAAAACAGCCCTATGGGGCCAACCCTTCTCTGTTGCAAAAGAGTGGGAAGATTTTCAAGTAGAAGTGAAAAGCCTACCGAGCTTAGAGATAGCTGGTTATTCAGGAAAAGAGTTTTAGCTCTACCTTAAGCTTCTTA-ATGAAACCT-AACAAACCT-TAAAGCTTAAGAGCTATTCAAATAAGGCACAGCTTATTTGAAATAGGATACAACCTAAAATACTGGGT-AAATAAGTAGTAATAAAAA-TGAAGTTGACCTAAGAGCAGCCATCTTTTAAAAAGCGTTAAAGCTTAGTTTTACCT-ACTAGTAA-TTTCCAAAATTCCAATCAACCCTTTAT-TCCTACTGAATTATTTTATA-CCTTTATAAAAGTAATAATGCTAGAACTAGTAACAAGAAATTGATTATTCTCCTAAATGCAAGCATAAACCAATATAGACATCCTATTGGTGATTAACGTAAATGTAACAACTGTAGCAACATAA--------TAGAAAACCCTGCAACTCCC-AACGTTAATCTTACACTAGAGCATTTCAGGAAAGATTTAAAGAGAAAGAAGGAACTCGGCAAACTTCAGCCTCGCCTGTTTACCAAAAACATCGCCTCTTGAAAAATTATAAGAGGTCCAGCCTGCCCAGTGACTAAGTTCAACGGCCGCGGTACCCTAACCGTGCGAAGGTAGCATAATCACTTGTTCTTTAAATGGGGACTTGTATCAACGGCACCACGAAGGCTGCACTGTCTCCTTTCTCCAATCAGTGAAACTGATCTCCCCGTGAAGAAGCGGGGATTTGATTATAAGACGAGAAGACCCCATGGAGCTTCAAACTCACCATACAACTCT-GCTCTCC-ACATCCCA-CAATTC-AAGAGACA-TGTATGTTAGTTTTGGGTTGGGGGGACCTCGGAGTACAACTTAACCTCCTAAACAAATAGGTTAACACCTTTATCCACGAGAAACACCTCTAAGAATTATTAAACTAATGTTT-TTGATCCGATAA--TCGATCAATGAACCAAGTTACCCTGGGGATAACAGCGCAATCTACTTCAAGAGTCCCTATCGACAAGTAGGTTTACGACCTCGATGTTGGATCAGGGTATCCTAGTGGTGCAACCGCTACTGA------------------- capitoSLU003 GCCGTAAACAATTAATTTACACCCATCAGCGCCAGGGAATTACGAGCAATGCTTAAAACCCAAAGGATTTGACGGTGTCCCACCCAACTAGAGGAGCCTGTTCTATAATCGATGATCCCCGCTACACCCGACCATTTTTCGCTCATCAGTCTGTATACCTCCGTCGAAAGCTTACCATGTGAACGTTCTCTAGTAGGCTCAATGATATCAACACGTCAGGTCAAGGTGCAGCTTAAGAAATGGGAAGCAATTGGCTACAATTTCTAACTTAGAACAAACGAAAGGCTATGTGAAATCATAGCCATGAAGGTGGATTTAGCAGTAAAAAGAAAATAGAGTGTTCTTTTTAACCCGGCTCTGGGACGTGTACACACCGCCCGTCACCCTCTTCAATAGTA-CACAATATTGTCCATAACCA-ATTCT-TACGTTTT-AGAAGAGGCAAGTCGAAACACGGTAAGTGTACTGGAAAGTGCACTTGGAATA-TAACAAAATGTAGCTTAACAAAAGCCCTTCGCTTACACCGAAGAAATATCCGTTAAA-TCTGATCATTTTGAGCCCTAAATCTAGCCCTAAAAACTCGCATGAACCCCACTCCAAA-CAAAACATTTTCCCATCATAGTACAGGCGATCGAAAAA-TTTCTAAGCGCTTCAGATAAAGTACCGCAAGGGAAAGATGAAATAGAACTGAAATAACCTTAAAGCTCTAAATAGCAGAGACACCCTCTCGTACCTTTTGCATCATGGTCTAGTTAGTCTACCC-AAGCAAAATGAAACTTTT-AGTTAGTCCTCCCGAAACTAAGCGAGCTACTTCAAAACAGCCCTATGGGGCCAACCCTTCTCTGTTGCAAAAGAGTGGGAAGATTTTCAAGTAGAAGTGAAAAGCCTACCGAGCTTAGAGATAGCTGGTTATTCAGGAAAAGAGTTTTAGCTCTACCTTAAGCTTCTTA-ATGAAACTT-AACAAACCT-TAAAGCTTAAGAGCTATTCAAATAAGGCACAGCTTATTTGAAATAGGATACAACCTAAAATACTGGGT-AAATAAATAGTAATAAAAA-TGAAGTTGACCTAAGAGCAGCCATCTTTTAAAAAGCGTTAAAGCTTAGTTTTTCAT-ACTAATAA-TTTCCAAAATTCCAATCAACCCTTTAT-TCCTACTGAATWWTTTTATA-CCTTTATAAAAGTAATAATGCTAGAACTAGTAACAAGAAATTGATTATTCTCCTAAATGCAAGCATAAACCAATATAGACATCCTATTGGTAATTAACGTAAGTGTAACAACTGAAGCAACATAA--------TAGAAAACCCTGCAACTCCC-AACGTTAATCTTACACTAGAGCATTTCAGGAAAGATTTAAAGAGAAAGAAGGAACTCGGCAAACTTCAGCCTCGCCTGTTTACCAAAAACATCGCCTCTTGAAAAATTATAAGAGGTCCAGCCTGCCCAGTGACTAAGTTCAACGGCCGCGGTACTCTAACCGTGCGAAGGTAGCATAATCACTTGTTCTTTAAATGGGGACTTGTATCAACGGCACCACGAAGGCTGCACTGTCTCCTTTCTCCAATCAGTGAAACTGATCTCCCCGTGAAGAAGCGGGGATTTGATTATAAGACGAGAAGACCCCATGGAGCTTCAAACTCACCATACAACTCT-GCTCTAT-ACATCCCA-CAATTC-AAGAGTCA-TGTATGTTAGTTTTGGGTTGGGGGGACCTCGGAGTACAACTTAACCTCCTAAACAAATAGGTTAACACCTTTATCCACGAGAAACACCTCTAAGAATTATCAAACTAATGTTT-TTGATCCGATAA--TCGATCAATGAACCAAGTTACCCTGGGGATAACAGCGCAATCTACTTCAAGAGCCCCTATCGACAAGTAGGTTAACGACCTCGATGTTGGATCAGGGTATCCTAGTGGTGCAACCG-------------------------- spectabilisJAC8622 GCCGTAAACAATTAATTTACATCTATCAGCGCCAGGGGACTACGAGCAATGCTTAAAACCCAAAGGATTTGACGGTGTCCCACCCAGCTAGAGGAGCCTGTTCTATAATCGATGATCCCCGCTATACCCCACCATTTTTAGCTTATCAGTCTGTATACCTCCGTCGAAAACTTACCATGTGAACGCCCTTCAGTAGGTTCAATGATATCAACACGTCAGGTCAAGGTGCAGCTTAAGAAATGGAAAGCAATTGGCTACAATTTCTAATTTAGAACAAACGAAAGGCTATGTGAAATCATAACCATGAAGGTGGATTTAGTAGTAAAAAGAAAATAGAGTGTTCTTTTTAACCCGGCTCTGGGACGTGTACACACCGCCCGTCACCCTCTTCGATAGTC-CATGATACTGTCCCTAACAA-ATTAT-TACGTTTT-AGAAGAAGCAAGTCGAAACACGGTAAGTGTACTGGAAAGTGCACTTGGAATA-TAACAAAATGTAGCTTAATAAAAGCCCTTCGCTTACACCGAAGAAACGTCTGTTTGAATCAGATCATTTTGAGCCCCAAATTTAGCCTTCAAAATTCGCATGAACCCTTCTCCAAA-CAAAACATTTTCTTACTCTATTACAGGTGATCAAAAAA-TTTCTAAGCGCTTCAGACAAAGTACCGCAAGGGAAAGATGAAATAGAATTGAAATAACCTTAAAGCCTTAAACAGCAGAGATACTACCTCGTACCTTTTGCATCATGGTCTAGTTAGTTTACCC-AAGCAAAATGAAACTTTT-AGTTAGCCTCCCCGAAACTAAGCGAGCTACTTCAAAACAGCCCTATGGGGCCAACCCTTCTCTGTTGCAAAAGAGTGGGAAGATTTTCAAGTAGAAGTGAAAAGCCTACCGAGCTTAGAGATAGCTGGTTATTCAGGAAAAGAGTTTTAGCTCTACCTTAAGCTTCTTC-ATGAAACTC-AACAAACCC-CAAAGCTTAAGAGCTATTCAAATAAGGCACAGCTTATTTGAAACAGGACACAACCTAAGATACTGGGT-AAATAACA-GTAATTAAAA-TTAAGTTGGCCTAAGAGCAGCCATCTTTAAAAAAGCGTTAAAGCTTAGTTTTATAC-ACTAGTAA-TTTCTAAAATTTTAATTAACCCTTTAC-CCCTACTGAATTATTTTATA-TCTTTATAAAAGTAATAATGCTAGAACTAGTAACAAGAAACTGCCTGTTCTCCTAAATGCAAGCATAAACCAAAATAGACACCCTATTGGTAATTAACGTTAATGTAACAGCTGTAGCAACATAA--------TAGAAAACCCTACAACCTCC-AACGTTAACCTTACACTAGAGCATTCCAGGAAAGATTAAAAAAGAAAGAAGGAACTCGGCAAACTTCAGCCCCGCCTGTTTACCAAAAACATCGCCTCTTGAAAAATTATAAGAGGTCCAGCCTGCCCAGTGACTAAGTTCAACGGCCGCGGTACCCTAACCGTGCGAAGGTAGCATAATCACTTGTTCTTTAAATGAGGACTTGTATCAACGGCACCAC?AGGACTGCACTGTCTCCTTTCTTCAATCAGTGAAACTGATCTCCCCGTGAAGAAGCGGGGATTAGACTATAAGACGAGAAGACCCCATGGAGCTTCAAACTCATTATACAACTCT-GTTCCCC-ATACCCCT-TGATAT-AAGAGACA-TGTATAATAGCTTTGGGTTGGGGGGACCTCGGAGTACAACTTAACCTCCTAAGCAAATAGGTTAACACCTTTATCCATGAGAAACACCTCTAAGAATTATCAAATTAATGTCT-TTGACCCGATAA--TCGATCAATGAACCAAGTTACCCTGGGGATAACAGCGCAATCTACTTCAAGAGTTCCTATCGACAAGTGGGTTTACGACCTCGATGTTGGATCAGGGTATCCCAGTGGTGCAACCGCTACTAACG-GTTCGTTTGTTCAACG forreriJSF1065 GCCGTAAACAATTAACTTACACCCATCAGCGCCAGGGAATTACGAGCAATGCTTAAAACCCAAAGGATTTGACGGTGTCCCAACCAGCTAGAGGAGCTTGTTCTATAATCGATGATCCCCGATACACCCGACCATTTTTAGCTTATCAGTCTGTATACCTCCGTCGAAAGCTTACCATGTGAACGTTTCTCAGTAGGCTCAATGATATCAACACGTCAGGTCAAGGTGCAGCTTAAGAAATGGGAAGCAATTTGCTACAATTTCTAATTTAGAACAAACGAAAGGCTATGTGAAATCATAACCACGAAGGTGGATTTAGTAGTAAAAAGAAAATAGAGTGTTCTTTTTAACCCGGCTCTGGGACGTGTACACACCGCCCGTCACCCTCTTCGATAGTC-CATAATACTGTCCCTAACCC-TTTAT-TACATTTT-AGAAGAGGCAAGTCGAAACATGGTAAGTGTACTGGAAAGTGCACTTGGAATA-TATCAAAATGTAGCTTAATAAAAGCCCTTCGCTTACACCGAAAGAATATCTGTTTAAATCAGGTCATTTTGAGCCTTAAATCTAGCCTTTAAAATTCGCATGAACCCCCTCCCAAA-CAAAACATTTTATCATTTTAGTACAGGCGATCGAAAAA-TTTCTAAGCGCTTCAGACATAGTACCGCAAGGGAAAGATGAAATAGAACTGAAATAACCTTAAAGCATCAAATAGCAGAGATTTCCCCTCGTACCTTTTGCATCATGGTCTAGTTAGTCTTCCCCAAGCAAAATGAAACTTTT-AGTTAGTCCCCCCGAAACTAAGCGAGCTACTTCAAAACAGCCCCACGGGGCCAACCCTTCTCTGTTGCAAAAGAGTGGGAAGATTTTTAAGTAGAAGTGAAAAGCCTATCGAGCTTAGAGATAGCTGGTTATTCAGGAAAAGAGTTTTAGCTCTACCTTAAGCTTCCTT-ATGAAACCC-AACAAACCT-TAAAGCTTAAGAGCTATTCAAATAAGGCACAGCTTATTTGAAACAGGATACAACCTAAAATAATGGGT-AAATTGAGAGTTATCAAAA-TTAAGTTGGCCTAAGAGCAGCCATCTTTAAAAAAGCGTTAAAGCTTAGTTTTATTT-ACTAATAA-TCCCTAAAATTCTAATCAACCCTTTAT-CCCTACTGAATTATTTTATA-CCCCTATAAAAATAATAATGCTAGAACTAGTAACAAGAAACTGCCCGTTCTCCTAAATGCAAGCATAAACCAAAATAGACACCCTGTTGGTAATTAACGTAAATGTAACAGCTATAGCAACATAA--------TAGAAAACCCTATAATCCCT-AACGTTAACCTTACACTAGAGCATTTCAGGAAAGATTAAAAAAGAAAGAAGGAACTCGGCAAACTTCAGCCCCGCCTGTTTACCAAAAACATCGCCTCTTGAAAAATTATAAGAGGTCCAGCCTGCCCAGTGACTAAGTTCAACGGCCGCGGTACCCTAACCGTGCGAAGGTAGCATAATCACTTGTTCTTTAAATGGGGACTCGTATCAACGGCACCACGAGGGCTGCACTGTCTCCTTTCTTCAATCAGTGAAACTGATCCCCCCGTGAAGAAGCGGGGATATAAATATAAGACGAGAAGACCCCATAGAGCTTCAAACTCATCATACAATTCT-GTGCTCT-AGACCCCA-TAACAC-AAGAAATG-TGTATGTTAGTTTTGGGTTGGGGGGACCTCGGAGTATAACTTAACCTCCTAAACGAACAGGTTAACACCTTTATCCATGAGAAACACCTCTAAGAATTATTAAATTAATGTAT-TTGACCCGATAG--TCGATCAATGAACCAAGTTACCTTGGGGATAACAGCGCAATCTACTTCAAGAGTTCCTATCGACAAGTAGGTTTACGACCTCCATGTTGGATCAGGGTATCCCGGTGGTGCAACCGCTACCAATG-GTTCGTTTGTTCAACG tlalociJSF1083 GCCGTAAACAATTAATTTACACCTATCAGCGCCAGGGAACTACGAGCAATGCTTAAAACCCAAAGGATTTGACGGTGTCCCACCCAGCTAGAGGAGCCTGTTCTATAATCGATGATCCCCGCTACACCCGACCATTTTTAGCTAATCAGTCTGTATACCTCCGTCGAAAGCTTACCATGTGAACGCTCTTCAGTAGGCTCAATGATATCAACACGTCAGGTCAAGGTGCAGCTTAAGAAATGGGAAGCTATTGGCTACAATTTCTAACTTAGAACAAACGAAAGGCTATGTGAAATCATAACCATGAAGGTGGATTTAGTAGTAAAAAGAAAATATAGTGTTCTTTTTAACCCGGCTCTGGGACGTGTACACACCGCCCGTCACCCTCTTCGATAGTT-CCTAATACTGTCCCTAACCC-ATTAT-TACATTTT-AGAAGAAGCAAGTCGAAACACGGTAAGTGTACTGGAAAGTGCACTTGGAAAA-TAACAAAATGTAGCTTAATAAAAGCCCTTCGCTTACACCGAAGAAATATCCGTTGAAATCAGATCATTTTGAGCCCTAAATCTAGCCTTCAAAATTCGCATGAACCTCTCCCCAAA-CAAAACATTTTCTTATCCTAGTACAGGCGATCGAAAAA-TTTCTAAGCGCTTCAGATAAAGTACCGCAAGGGAAAGATGAAATAGAATTGAAATAACCTTAAAGCCTTAAACAGCAGAGATATTACCTCGTACCTTTTGCATCATGGTCTAGTTAGTCTACCC-AAGCAAAATGAAACTTTT-AGTTAGTCCCCCCGAAACTAAGCGAGCTACTTCAAAACAGCCTTAT-GGGCCAACCCTTCTCTGTTGCAAAAGAGTGGGAAGATTTTCAAGTAGAAGTGAAAAGCCTACCGAGCTTAGAGATAGCTGGTTACTCAGGAAAAGAGTTTTAGCTCTACCTTAAGCTTCATT-ATGAAACCC-AACAAACCT-TAAAGCTTAAGAGCTATTCAAATAAGGCACAGCTTATTTGAAATAGGATACAACCTAGAATACTGGGT-TAATAGTA-GCAATTAAAA-TTAAGTTGGCCTAAGAGCAGTCACCTTTAAAAAAGCGTTAAAGCTTGATTTCAACC-GCTAATAA-TTTCTAAAATTTTTAATAACCCTTTAT-CCCTACTGAGTCATTTTATA-AATGTATAAAAGTGATAATGCTAGAACTAGTAACAAGAAACTGCTCATTCTCCTAAATGCAAGCATAAACCAAAATAGACACCCTATTGGTAATTAACGTAAATGTAACAGCTATAGTAACACAA--------TAGAAAACCCTATAACCTCT-AACGTTAACCTTACACTAGAGCATTTCAGGAAAGATTTAAAAAGAAAGAAGGAACTCGGCAAACTTCAGCCCCGCCTGTTTACCAAAAACATCGCCTCTTGAAAAATTATAAGAGGTCCAGCCTGCCCAGTGACTAAGTTCAACGGCCGCGGTACCCTAACCGTGCGAAGGTAGCATAATCACTTGTTCTTTAAATGGGGACTTGTATCAACGGCACCACGAAGGCTGCACTGTCTCCTTTCTTCAATCAGTGAAACTGATCTCCCCGTGAAGAAGCGGGGATTAAACTATAAGACGAGAAGACCCCATGGAGCTTCAAACTCACCATACAACCCT-GTTCTCC-ACATCCTTATAACAT-AAGAAACA-TGTATGATAGTTTTGGGTTGGGGGGACCTCGGAGTATAACTTAACCTCCTAAGCAAACAGGTTAACACCTTTATCCATGAGAAACACCTCTAAGAATTATTAAATTAATGTAT-TTGACCCGATAA--TCGATCAATGAACCAAGTTACCCTGGGGATAACAGCGCAATCTACTTCAAGAGTTCCTATCGACAAGTGGGTTTACGACCTCCATGTTGGATCAGGGTATCCTAGTGGTGCAACCGCTACTAACG-GTTCGTTTGTTCAACG berlandieriJSF1136 GCCGTAAACAATTAATTTACACCTATCAGCGCCAGGGAACTACGAGCAATGCTTAAAACCCAAAGGATTTGACGGTGTCCCACCCAGCTAGAGGAGCCTGTTCTATAATCGATGATCCCCGCTACACCCGACCATTTTTAGCTAATCAGTCTGTATACCTCCGTCGAAAGCTTACCATGTGAACGCTCTTCAGTAGGCTCAATGATATCAACACGTCAGGTCAAGGTGCAGCTTAAGAAATGGGAAGCTATTGGCTACAATTTCTAACTTAGAACAAACGAAAGGCTATGTGAAATCATAACCATGAAGGTGGATTTAGTAGTAAAAAGAAAATAGAGTGTTCTTTTTAACCCGGCTCTGGGACGTGTACACACCGCCCGTCACCCTCTTCGATAGTT-CCTAATACTGTCCCTAACCC-ATTAT-TACATTTT-AGAAGAAGCAAGTCGAAACACGGTAAGTGTACTGGAAAGTGCACTTGGAAAA-TAACAAAATGTAGCTTAATAAAAGCCCTTCGCTTACACCGAAGAAATATCCGTTGAAATCAGATCATTTTGAGCCCTAAATCTAGCCTTCAAAATTCGCATGAACCTCTCCCCAAA-CAAAACATTTTCTTATCCTAGTACAGGCGATCGAAAAA-TTTCTAAGCGCTTCAGATAAAGTACCGCAAGGGAAAGATGAAATAGAATTGAAATAACCTTAAAGCCTTAAACAGCAGAGATATTACCTCGTACCTTTTGCATCATGGTCTAGTTAGTCTACCC-AAGCAAAATGAAACTTTT-AGTTAGTCCCCCCGAAACTAAGCGAGCTACTTCAAAACAGCCTTAT-GGGCCAACCCTTCTCTGTTGCAAAAGAGTGGGAAGATTTTCAAGTAGAAGTGAAAAGCCTACCGAGCTTAGAGATAGCTGGTTACTCAGGAAAAGAGTTTTAGCTCTACCTTAAGCTTCATT-?TGAAACCC-AACAAACCT-TAAAGCTTAAGAGCTATTCAAATAAGGCACAGCTTATTTGAAATAGGATACAACCTAGAATACTGGGT-AAATAGTA-GCAATTAAAA-TTAAGTTGGCCTAAGAGCAGTC?CCTTTAAAAAAGCGTTAAAGCTTGATTTCAACT-GCTAATAA-TTTCTAAAATTTTTAATAACCCTTTAT-CCCTAATGAGTCATTTTATA-AATGTATAAAAGCGATAATGCTAGAATTAGTAACAAGAAACTGCTCATTTTCTTAAATGCAAGCATAAACCAAAATAGACACCCTATTGGTAATTAACGTAAATGTAACAGCTATAGTAACACAA--------TAGAAAACCCTATAACCTCT-AACGTTAACCTTACATTAGAGCATTTCAGGAAAGATTAAAAAAGAAAGAAGGAACTCGGCAAACTTCAGCCCCGCCTGTTTACCAAAAACATCGCCTCTTGAAAAATTATAAGAGGTCCAGCCTGCCCAGTGACTAAGTTCAACGGCCGCGGTACCCTAACCGTGCGAAGGTAGCATAATCACTTGTTCTTTAAATGGGGACTTGTATCAACGGCACCACGAGGGCTGCACTGTCTCCTTTCTTCAATCAGTGAAACTGATCTCCCCGTGAAGAAGCGGGGATTAAACTATAAGACGAGAAGACCCCATGGAGCTTCAAACTCATCATACAACCCT-GTTCTCC-ACATCCTCATAACAT-AAGAGATA-TGTATGATAGTTTTGGGTTGGGGGGACCTCGGAGTATAACTTAACCTCCTAAGCAAACAGGTTAACACCTTTATCCATGAGAAACACCTCTAAGAATTATTAAATTAATGTAT-TTGACCCGATAA--TCGATCAATGAACCAAGTTACCCTGGGGATAACAGCGCAATCTACTTCAAGAGTTCCTATCGACAAGTGGGTTTACGACCTCGATGTTGGATCAGGGTATCCTAATGGTGCAACCGCTACTAACG-GTTCGTTTGTTCAACG neovolcanicaJSF960 GCCGTAAACAATTAATTTACACCTATCAGCGCCAGGGAACTACGAGCAATGCTTAAAACCCAAAGGATTTGACGGTGTCCCACCCAGCTAGAGGAGCCTGTTCTATAATCGATGATCCCCGCTACACCCGACCATTTTTAGCTAATCAGTCTGTATACCTCCGTCGAAAGCTTACCATGTGAACGCTCTTCAGTAGGCTCAATGATATCAACACGTCAGGTCAAGGTGCAGCTTAAGAAATGGGAAGCTATTGGCTACAATTTCTAACTTAGAACAAACGAAAGGCTATGTGAAATCATAACCATGAAGGTGGATTTAGTAGTAAAAAGAAAATAGAGTGTTCTTTTTAACCCGGCTCTGGGACGTGTACACACCGCCCGTCACCCTCTTCGATAGTT-CCTAATACTGTCCCTAACCC-ATTAT-TACATTTT-AGAAGAGGCAAGTCGAAACACGGTAAGTGTACTGGAAAGTGCACTTGGAAAA-TAACAAAATGTAGCTTAATAAAAGCCCTTCGCTTACACCGAAGAAATATCCGTTGAAATCAGATCATTTTGAGCCCTAAATCTAGCCTTCAAAATTCGCATGAACCTCTCCCCAAA-CAAAACATTTTCTTATCCTAGTACAGGCGATCGAAAAA-TTTCTAAGCGCTTCAGATAAAGTACCGCAAGGGAAAGATGAAATAGAATTGAAATAACCTTAAAGCCTTAAACAGCAGAGATATTACCTCGTACCTTTTGCATCATGGTCTAGTTAGTCTACCC-AAGCAAAATGAAACTTTT-AGTTAGTCCCCCCGAAACTAAGCGAGCTACTTCAAAACAGCCTTAT-GGGCCAACCCTTCTCTGTTGCAAAAGAGTGGGAAGATTTTCAAGTAGAAGTGAAAAGCCTACCGAGCTTAGAGATAGCTGGTTACTCAGGAAAAGAGTTTTAGCTCTACCTTAAGCTTCATT-ATGAAACCC-AACAAACCT-TAAAGCTTAAGAGCTATTCAAATAAGGCACAGCTTATTTGAAATAGGATACAACCTAGAATACTGGGT-AAATAGTA-GCAATTAAAA-TTAAGTTGGCCTAAGAGCAGTCACCTTTAAAAAAGCGTTAAAGCTTGATTTCAACC-GCTAATAA-TTTCTAAAATTTTTAACAACCCTTTAT-CCCTACTGAGTCATTTTATA-AATGTATAAAAGTGATAATGCTAGAACTAGTAACAAGAAACTGCTCATTCTCCTAAATGCAAGCATAAACCAAAATAGACACCCTATTGGTAATTAACGTAAATGTAACAGCTATAGTAACACAA--------TAGAAAACCCTATAACCTCT-AACGTTAACCTTACACTAGAGCATTTCAGGAAAGATTTAAAAAGAAAGAAGGAACTCGGCAAACTTCAGCCCCGCCTGTTTACCAAAAACATCGCCTCTTGAAAAATTATAAGAGGTCCAGCCTGCCCAGTGACTAAGTTCAACGGCCGCGGTACCCTAACCGTGCGAAGGTAGCATAATCACTTGTTCTTTAAATGGGGACTTGTATCAACGGCACCACGAAGGCTGCACTGTCTCCTTTCTTCAATCAGTGAAACTGATCTCCCCGTGAAGAAGCGGGGATTAAACTATAAGACGAGAAGACCCCATGGAGCTTCAAACTCACCATACAACCCT-GTTCTCC-ACATCCTTATAACAT-AAGAGACA-TGTATGAT-GTTTTGGGTTGGGGGGACCTCGGAGTATAACTTAACCTCCTAAGCAAACAGGTTAACACCTTTATCCATGAGAAACACCTCTAAGAATTATTAAATTAATGTAT-TTGACCCGATAA--TCGATCAATGAACCAAGTTACCCTGGGGATAACAGCGCAATCTACTTCAAGAATTCCTATCGACAAGTGGGTTTACGACCTCGATGTTGGATCAGGGTATCCTAGTGGTGCAACCGCTACTAACG-GTTCGTTTGTTCAACG blairiJSF830 GCCGTAAACAATTAATTTACACCTATCAGCGCCAGGGAACTACGAGCAATGCTTAAAACCCAAAGGATTTGACGGTGTCCCACCCAGCTAGAGGAGCCTGTTCTATAATCGATGATCCCCGCTACACCCGACCATTTTTAGCTAATCAGTCTGTATACCTCCGTCGAAAGCTTACCATGTGAACGCTCTTCAGTAGGCTCAATGATATCAACACGTCAGGTCAAGGTGCAGCTTAAGAAATGGGAAGCTATTGGCTACAATTTCTAATTTAAAACAAACGAAAGGCTATGTGAAATCATAACCATGAAGGTGGATTTAGTAATAAAAAAAAAATATATTGTTCTTTTTAACCCGGCTCTGGGACGTGTACACACCGCCCGTCACCCTCTTCGATAGTT-CTTAATACTGTCCCTAACCC-ATTAT-TACATTTT-AGAAGAAGCAAGTCGAAACACGGTAAGTGTACTGGAAAGTGCACTTGGAAAA-TAACAAAATGTAGCTTAATAAAAGCCCTTCGCTTACACCGAAAAAATATCCGTTGAAATCAGATCATTTTGAGCCCTAAATCTAGCCTTCAAAATTCGCATGAACCTCTCCCCAAA-CAAAACATTTTCTTATCCTAGTACAGGCGATCGAAAAA-TTTCTAAGCGCTTCAGATAAAGTACCGCAAGGGAAAGATGAAATAGAACTGAAATAACCTTAAAGCCTTAAACAGCAGAGATACTACCTCGTACCTTTTGCATCATGGTCTAGTTAGTCTACCC-AAGCAAAATGAAACTTTT-AGTTAGTCCCCCCGAAACTAAGCGAGCTACTTCAAAACAGCCTTAT-GGGCCAACCCTTCTCTGTTGCAAAAGAGTGGGAAGATTTTCAAGTAGAAGTGAAAAGCCTACCGAGCTTAGAGATAGCTGGTTACTCAGGAAAAGAGTTTTAGCTCTACCTTAAGCTTCATT-ATGAAACCC-AACAAACCT-TAAAGCTTAAGAGCTATTCAAATAAGGCACAGCTTATTTGAAATAGGATACAACCTAGAATACTGGGT-AAATAACA-GCAATTAAAA-TTAAGTTGGCCTAAGAGCAGCCACCTTTAAAAAAGCGTTAAAGCTTGATTTCTACT-GCTAATAA-TTTCTAAAATTTTTAACAACCCTTTAT-CCCTACTGAGTCATTTTATA-TGCGTATAAAAGTGATAATGCTAGAACTAGTAACAAGAAACTGCTCATTCTCCTAAATGCAAGCATAAACCAAAATAGACACCCTATTGGTAATTAACGTAAATGTAACAGCTATAGTAACACAA--------TAGAAAACCCTATAACCTCT-AACGTTAACCTTACACTAGAGCATTTCAGGAAAGATTAAAAAAGAAAGAAGGAACTCGGCAAACTTCAGCCCCGCCTGTTTACCAAAAACATCGCCTCTTGAAAAATTATAAGAGGTCCAGCCTGCCCAGTGACTAAGTTCAACGGCCGCGGTACCCTAACCGTGCGAAGGTAGCATAATCACTTGTTCTTTAAATGGGGACTTGTATCAACGGCACCACGAGGGCTGCACTGTCTCCTTTCTTCAATCAGTGAAACTGATCTCCCCGTGAAGAAGCGGGGATTAAACTATAAGACGAGAAGACCCCATGGAGCTTCAAACTCATCATACAACCCT-GTTCTCC-ATATCCTCATAACAT-AAGAGACA-TGTGTGATAGTTTTGGGTTGGGGGGACCTCGGAGTATAACTTAACCTCCTAAGCAAACAGGTTAACACCTTTATCCATGAGAAACACCTCTAAGAATTATTAAATTAATGTAT-TTGACCCGATAA--TCGATCAATGAACCAAGTTACCCTGGGGATAACAGCGCAATCTACTTCAAGAGTTCCTATCGACAAGTGGGTTTACGACCTCGATGTTGGATCAGGGTATCCCAGTGGTGCAACCGCTACTAATG-GTTCGTTTGTTCAACG omiltemanaJAC7413 ACCGTAAACAATTAATTTACACCTATCAGCGCCCGGGGATTACGAGCAATGCTTAAAACCCAAAGGATTTGACGGTGTCCCAACCAGCTAGAGGAGCCTGTTCTATAATCGATGATCCCCGATACACCCGACCATTTTTAGCTAATCAGTCTGTATACCTCCGTCGAAAGCTTACCATGTGAACGCTCTTCAGTAGGCTCAAGGATATCAATACGTCAGGTCAAGGTGCAGCTTAAGAAATGGGAAGTAATTGGCTACAATTTCTAACTTAGAACAAACGAAAGGTTATGTGAAATCATAACCACGAAGGTGGATTTAGTAGTAAAAAGAAAATAGAGTGTTCTTTTTAACCCGGCTCTGGGACGTGTACACACCGCCCGTCACCCTCTTCGATAGTT-ATTAATATTGTTCCTAACCC-CCTAT-TACGTCTT-AGAAGAAGCAAGTCGAAACACGGTAAGTGTACTGGAAAGTGCACTTGGAAAA-TAACAAAATGTAGCTTAATAAAAGCCCTTCGCTTACACCGAAGAAATGTCTGTTTGAATCAGATCATTTTGAGCCCTAAATCTAGCCTTCAAAATTCGCATGACCCCCTCCCCAA--CAAAACATTTTCTTATCCTAGTACAGGCGATCGAAAAA-TTTCTAAGCGCTTCAGACAAAGTACCGTAAGGGAAAGATGAAATAGAACTGAAATAATCCTAAAGCCCTAAATAGCAGAGACACAACCTCGTACCTTTTGCATCATGGTCTAGTTAGTCTACCC-AAGCAAAATGAAACTTTT-AGTTAGTCCCCCCGAAACTAAGCGAGCTACTTCAAAACAGCCCTATGGGGCCAACCCTTCTCTGTTGCAAAAGAGTGGGAAGATTTTCAAGTAGAAGTGAAAAGCCTACCGAGCTTAGAGATAGCTGGTTATTCAGGAAAAGAGTTTTAGCTCTACCTTAAGCTTCTTT-ATGAGACCT-AACAAACCT-TAAAGCTTAAGAGCCATTCAAATAAGGCACAGCTTATTTGAAATAGGATACAACCTAGAATATTGGGT-AAATAACA-GTGATTAAAG-TTAAGTTGGCCTAAGAGCAGCCACCTTCAAAAAAGCGTTAAAGCTTAACTCTATCA-ACTAATAA-TCTCTAAAATTTTAATCAACCCTTTAT-CCTTACTGAATTATTT-ATA-CCTTTATAAAAGTAATAATGCTAGAACTAGTAACAAGAAACTGCCTGTTCTCCTAAATGCAAGCATAAACCAAAATAGACACCCTATTGGTAGTTAACGTAAATGTAATAACTATAGTAACGTAA--------TAGAAAACCCTATAACCTCT-AACGTTAATCTTACACCAGTGCATTCCAGGAAAGATTAAAAAAGAAAGAAGGAACTCGGCAAATTTTAGCCCCGCCTGTTTACCAAAAACATCGCCTCTTGAAAAGTTATAAGAGGTCCAGCCTGCCCAGTGACTAAGTTCAACGGCCGCGGTACCCTAACCGTGCGAAGGTAGCATAATCACTTGTTCTTTAAATGGGGACTTGTATCAACGGCACCACGAAGGCTGCACTGTCTCCTTTCTCCAATCAGTGAAACTGATCTCTCCGTGAAGAAGCGGGGATTAAATTATAAGACGAGAAGACCCCATGGAGCTTCAAACTCATTATACAACTCT-GTGCTCT-ATGCCCCA-CAACACATAGAAGCA-TGTATAGTAGTTTTGGGTTGGGGTGACCTCGGAGTATAACTCAGCCTCCAAAACAAACAGGTTAACACCTTTATCCATGAGAAACACCTCTAAGAATTATTAAACTAATGTAT-TTGACCCGATAG--TCGATCAATGAACCAAGTTACCCTGGGGATAACAGCGCAATCTACTTCAAGAGTTCCTATCGACAAGTGGGTTTACAACCTCGATGTTGGATCAGGGTATCCCAGTGGTGCAACCGCTACTAATG-GTTCGTTTGTTCAACG magnaocularisJSF1073 ACCGTAAACAATTAATTTACACCTATCAGCGCCAGGGAATTACGAGCAATGCTTAAAACCCAAAGGATTTGACGGTGTCCCACCCAGCTAGAGGAGCCTGTTCTATAATCGATGATCCCCGCTACACCCGACCATTTCTAGCTCATCATTCTGTATACCTCCGTCAAAAGCTTACCATGTGAACCCTCTTCAGTAGGCTCAAAGATATCATCACGTCAGGTCGAGGTGCAACTTAAGAGATGGGAAGTGATTGGCTACCTTTTCTAAATTAAAACAAACAAAAGGTTATGTCAAATCCTGGCCGTGAAGGTGGATTTAGTAATAAAAAAAAATTTTATTGTTCTTTTTAACCCGGGTCTGGGACGTGTACCCCCCGCCCCTCCCCCTCTTCGATTGTC-CATGATCCTGTCCCCAACCC-TTTAT-TATTTTTA-AGAAAATGCAAGTCGAAACACTGTAATTGTACTGGAAAGTGCTCTTGGATTA-TATCAAAGTGTAGCTTACTTAAAGCCCTTCGCTTACACCGAAGAAACATCTGTTTAAACCGTATCATTTAGAGCCCTAAATCTAGCCTTCATAATTCGCATGAACCCCCTCCCAA--CAAAACATTTTCTTATTTAAGTACAGGCGATCGAAAAA-TTTCTAAGCGCTTCAGACGAAGTACCGCGGGGGAAAGATGAAATAGAATTGAAATCCCCCTACCGCCTCAAACAGCAGAGATACCCCCTCGTACCTTTTGCATCATGGTCTAGTTAGTCCACTC-AAGCAAAATGAAACTTTT-AGTTAGTCCCCCCGAAACTGGACGAGCTACTTCAAAACAGCCCTGGGGGGCCAACCCTTCTCTGTTGCAAAAGAGTGGGAAGATTTTCAAGTGTAAGTGACCAGCCTACCGAGCTTAGAGATAGTTGGTTATTCAGGAAAAGAGTTTTAGTTTTACCTTAAGGTTCTTT-ATGAAACCC-AACAAACTT-TAAAGCTTAAGAGCTATTCAAATAAGGCACAGCTTATTTGAAACAGGATACAACTTAAAATACTGGGT-AAATAGCA-GTAATTAAAA-TTAAGTCGGCCTAAGAGCAGTCATTTTTAAAAAAGCGTTAAAGCTTGATTTTAAAA-ACTAATAA-TTTTTGAAATTTTTATCAACCCTTTAC-CCCTACTGAATTATTTTATA-CCCCTATAAAAACGATAATGCTAGAACTAGTAACAAGAAATTGCCTATTTTCCTAAATGCAAGTATAAGCCAAAATAGACACCCTATTGGTAGTTAACGTAAATGTAGCAGTTGTAGTAACTTAA--------TAGAAAACCCTACAGCCCCA-AACGTTAATCTTACATTAGAGCATTCCAGGAAAGATTAAAAGAGAAAGAAGGAATTCGGCAAACTTCAGCCCCGCCTGTTTACCAAAAACATCGCCTCTTGAAAAATTATAAGAGGTCCAGCCTGCCCAGTGATTAAGTTCAACGGCCGCGGTACCCTAACCGTGCGAAGGTAGCATAATCACTTGTTCTTTAAATGGGGACTTGTATCAACGGCACCACGAGGGCTGCACTGTTTCCTTTCTTCAATCAGTGAAACTGATCTCCCCGTGAAGAAGCGGGGATAAAATTATAAGACGAGAAGACCCCATGGAGCTTCAAACTCACTATACAACTCT-GTGCCTT-TCACCCC--TAACAC-AAGAGACA-TGTATACTAGTTTTGGGTTGGGGGGACCTCGGAGTATAACTTAACCTCCAAAGCAAACAGGTTAACACCTTTATCCAAGAGAAACACCTCTAAGAATTATTAAATTAATGTAT-TTGACCCGATAG--TCGATCAATGAACCAAGTTACCCTGGGGATAACAGCGCAATCTACTTCAAGAGTTCCTATCGACAAGTAGGTTTACGACCTCGATGTTGGATCAGGGCATCCCAGTGGTGCAACCGCTACTAATG-GTTCGTTTGTTCAACG yavapaiensisJSF1085 ACCGTAAACAATTAATTTACACCCATCAGCGCCAGGGAATTACGAGCAATGCTTAAAACCCAAAGGATTTGACGGTGTCCCACCCAGCTAGAGGAGCCTGTTCTATAATCGATGATCCCCGCTACACCCGACCATTTTTAGCTCATCAGTCTGTATACCTCCGTCGAAAGCTTACCATGTGAACGCTCTTCAGTAGGCCCAATGA-GTCAACACGTCAGGTCAAGGTGCAGCTTAAGAAATGGGAAGCAATTGGCTACAATTTCTAACTTAGAACAAACGAAAGGCTATGTGAAACCATAACCACGAAGGTGGATTTAGTAGTAAAAAGAAAATAGAGTGTTCTTTTTAACCCGGCTCTGGGACGTGTACACACCGCCCGTCACCCTCTTCGATAGTT-CAAAATACTGTCCCTAACCC-TTTAT-TACGTTTT-AGAAGAAGCAAGTCGAAACACGGTAAGTGTACTGGAAAGTGCACTTGGAAAA-TAGCAAAATGTAGCTTAATTAAAGCCCTTCGCTTACACCGAAAAAACATCTGTTTGGATTAGATTATTTTGAGCCCTAAATCTAGCCTTCTAAACTCGCATGAACCCCCCCCCAAA-CAAAACATTTTCTCATCTTAGTACAGGCGATCGAAAAA-TTTCTAAGCGCTTCAGACAAAGTACCGTAAGGGAAAGATGAAATAGAATTGAAACAACCCTAAAGCCTTAAATAGCAGAGATAACCCCTCGTACCTTTTGCATCATGGTCTAGTCAGTCTACCC-AAGCAAAATGAAACTTTT-AGTTAGTCCCCCCGAAACTAAGCGAGCTACTTCAAAACAGCCCTATGGGGCCAACCCTTCTCTGTTGCAAAAGAGTGGGAAGATTTTCAAGTAGAAGTGAAAAGCCTACCGAGCTTAGAGATAGCTGGTTATTCAGGAAAAGAGTTTTAGCTCTACCTTAAGCTTCTTT-ATGAAACCC-AACAAACCT-TAAAGCTTAAGAGCTATTCAAATAAGGCACAGCTTATTTGAAATAGGATACAACCTAAAATACTGGGT-AAATAATA-GTAATTAAAA-TTAAGTTGGCCTAAGAGCAGCCACCTTTAAAAAAGCGTTAAAGCTTGATTTTATAC-ACTAGTAA-TCCCTAAAATTTTTATTAACCCTTTAC-CCCTACTGAATTATTTTATA-CCCCTATAAAAATGATAATGCTAGAACTAGTAACAAGAAACTGCCTATTCTCCTAAATGCAAGTATAAACCAAAATAGACCCCCTATTGGTAATTAACGTAAATGTAACAGTTATAGTAACATAC--------TAGAAAACCCTATAGCCCCC-AACGTTAATCTTACACTAGAGCATTTCAGGAAAGATTAAAAGAGAAAGAAGGAACTCGGCAAACTTCAGCCCCGCCTGTTTACCAAAAACATCGCCTCTTGAAAAATTATAAGAGGTCCAGCCTGCCCAGTGACTAAGTTCAACGGCCGCGGTACCCTAACCGTGCGAAGGTAGCATAATCACTTGTTCTTTAAATGGGGACTTGTATCAACGGCACCACGAAGGCTGCACTGTCTCCTTTCTTCAATCAGTGAAACTGATCTCCCCGTGAAGAAGCGGGGATCAAATTATAAGACGAGAAGACCCCATGGAGCTTCAAACTCACCATACAACTCT-GTGCCCT-TCACC-TC-TAACAC-AAGAGTCA-TGTATGCTAGTTTTGGGTTGGGGGGACCTCGGAGTATAACTTAACCTCCTAAGCAAACAGGTTAACACCTTTATCTATGAGAAACACCTCTAAGAATTATTAAATTAATGTAT-TTGACCCGATAG--TCGATCAATGAACCAAGTTACCCTGGGGATAACAGCGCAATCTACTTCAAGAGTTCCTATCGACAAGTAGGTTTACGACCTCGATGTTGGATCAGGGCATCCCAGTGGTGCAACCGCTACTAATG-GTTCGTTTGTTCAACG sp_7_JaliscoJSF1000 GCCGTAAACAATTAATTTACACATATCAGCGCCAGGGAATTACGAGCGATGCTTAAAACCCAAAGGATTTGACGGTGTCCCAACCAGCTAGAGGAGCCTGTTCTATAATCGATGATCCCCGCTACACCCGACCATTTTTAGCTCATCAGTCTGTATACCTCCGTCGAAAGCTTACCATGTGAACGCTCTTCAGTAGGCTCAATGATATCAACACGTCAGGTCAAGGTGCAGCTTAAAAAATGGGAAGTAATTGGCTACAATTTCTAACTTAAAACAAACGAAAGGCTATGTGAAATCATGGCCACGAAGGTGGATTTAGTAATAAAAAGAAAATATAGTGTTCTTTTTAACCCGGCTCTGGGACGTGTACACACCGCCCGTCACCCTCTTCGATTGTC-CATGATACTGTCCCTAACCC-TTTAT-TATTTTTT-AGAAAAGGCAAGTCGAAACACGGTAAGTGTACTGGAAAGTGCACTTGGATTA-TATCAAAATGTAGCTTAGTTAAAGCCCTTCGCTTACACCGAAGAATCATCTGTTTAAACCAGATCATTTTGAGCCCTAAATCTAGCCTTCATAATTCGCATGAACCCCCTCCCAA--CAAAACATTTTCTTATTTAAGTACAGGCGATCGAAAAA-TTTCTAAGCGCTTCAGACGAAGTACCGCAAGGGAAAGATGAAATAGAATTGAAATAACCCTAAAGCCTCAAACAGCAGAGATACCCCCTCGTACCTTTTGCATCATGGTCTAGTTAGTCCACTC-AAGCAAAATGAAACTTTT-AGTTAGTCCCCCCGAAACTAAGCGAGCTACTTCAAAACAGCCCTATGGGGCCAACCCTTCTCTGTTGCAAAAGAGTGGGAAGATTTTCAAGTAGAAGTGAAAAGCCTACCGAGCTTAGAGATAGCTGGTTATTCAGGAAAAGAGTTTTAGCTCTACCTTAAGCTTCTTT-ATGAAACCT-AACAAACCC-CAAAGCTTAAGAGCTATTCAAATAAGGCACAGCTTATTTGAAACAGGGCACAACCTATAATACTGGGT-AAATAGTA-GTAACTAGAG-CTAAGTTGGCCTAAGAGCAGCCATCTTTAAAAAAGCGTTAAAGCTTAACTCCATTT-ACTAGCAA-TTTCTAAAATTTTTATTAACCCTTTAC-CCCTACTGAATTATTTTATA-CCCTTATAAAAACAATAATGCTAGAACTAGTAACAAGAAACTGCCAGTTCTCCTAAATGCAAGTATAAACCAAGATAGACATTCTGTTGGTAATTAACGTAAATGTAGAATCTATAGTAACATAT--------TAGAAAACTCTATAACCCCT-GACGTTAGCCTTACACTAGAGCATTTCAGGAAAGATTAAAAAAGAAAGAAGGAACTCGGCAAATTTCAGCCCCGCCTGTTTACCAAAAACATCGCCTCTTGAAAAATTATAAGAGGTCCAGCCTGCCCAGTGACTTAGTTTAACGGCCGCGGTACCCTAACCGTGCGAAGGTAGCATAATCACTTGTTCTTTAAATGGGGACTTGTATCAACGGCATCACGAAGGCTGCACTGTCTCCTTTCTTCAATCAGTGAAACTGATCTCCCCGTGAAGAAGCGGGGATTAACCTATAAGACGAAAAAACCCCATGGAGCTTCAAACTCATTATCCAACTCT-GTGCTCC-ACATCCTT-TAACAC-AAAAGATC-TGTATAGTATTTTTGGGTTGGGGGGACCTCGGATTACAACTTAACCTCCTAAGCAAACAGGTTAACACCTTTATCCATGAAAGACACCTCTAAAAATTATTAAATTGATGTAT-TTGACCCGATAG--TCGATCAATGAACCAATTTACCCTGGGGATAACAGCGCAATCTACTTCAAAATTTCTTATCGACAATTAGGTTTACAACCTCAATGTTGGATCAGGGTATCCCGGTGGTGCAACCGCTACTAACG-GTTCGTTTGTTCAACA macroglossaJAC10472 ACCGTAAACAATTTATTTACACCTACCAGCGCCAGGGAACTACGAGCAATGCTTAAAACCCAAAGGATTTGACGGTGTCCCACCCAGCTAGAGGAGCCTGTTCTATAATCGATGATCCCCGCTACACCCCACCATTTCTAGCTAATCAGTCTGTATACCTCCGTCGAAAGCTTACCATGTGAACGCTCTTCAGTAGGCTTAATGATATCAACACGTCAGGTCAAGGTGCAGCTTAAGAAATGGAAAGCAATTGGCTACAATTTCTAATTTAGAACAAACGAAAGGCTATGTGAAATCATAACCATGAAGGTGGATTTAGTAGTAAAAAGAAAATAGAGTGTTCTTTTTAACCCGGCTCTGGGACGTGTACACACCGCCCGTCACCCTCTTCGATAACT-CATGATACTGTCCCTAACCCCATTAT-TACCCTTT-AGAAGAAGCAAGTCGAAACACGGTAAGTGTACTGGAAAGTGCACTTGGAAAA-TAGCAAAATATAGCTTAATAAAAGCCCTTCGCTTACACCGAAGAAATATCTGTTTAAATCAGATTATTTTGAGCCCTAAATCTAGCCTTCAAAATTCGCATGAACCCCTTATAAAAACAAAACATTTTTTTATTTTAGTACAGGCGATCGAAAAA-TTTCTAAGCGCTTCAGACAAAGTACCGCAAGGGAAAGATGAAATAGAACTGAAATAACCCTAAAGCCTTAAACAGCAGAGATATCCCCTCGTACCTTTTGCATCATGGTCTAGTTAGTCTACCC-AAGCAAAACGAAACTTTT-AGTTAGTCCCCCCGAAACTAAGCGAGCTACTTCAAAACAGCCCTATGGGGCCAACCCTTCTCTGTTGCAAAAGAGTGGGAAGATTTTCAAGTAGAAGTGAAAAGCCTACCGAGCTTAGAGATAGCTGGTTATTCAGGAAAAGAGTTTTAGCTCTACCTTAAGCTTACTT-ATGAAACCC-AACAAACCT-TAAAGTTTAAGAGCTATTCAAATAAGGCACAGCTTATTTGAAACAGGATACAACCTAAAATACTGGGT-AAATAGTA-GTAATTGAAA-TTAAGTTGGCTTAAGAGCAGCCATCTTTAAAAAAGCGTTAAAGCTTAATTTTATGT-ACTAATAA-TTTCTAAAATTTTAATCAACCCTTTATTCCCCAGGGAATCATTTTATA-TCCTTATAAAAA-GATAATGCTAGAATTAGTAACAAGAAACTGCCCATTTTCTTAAATGCAAGCATAAACCAAAATAGACACCCTATTGGTAATTAACGTAAATGTAACAGCTATAGCAACATAA--------TAGAAAACCTTATAACCCCA-AACGTTAACCTTACACCAGAGCATTTCAGGAAAGATTAAAAAAGAAAGAAGGAACTCGGCAAACTTTAGCCCCGCCTGTTTACCAAAAACATCGCCTCTTGAAAAATTATAAGAGGTCCAGCCTGCCCAGTGACTAAGTTCAACGGCCGCGGTACCCTAACCGTGCGAAGGTAGCATAATCACTTGTTCTTTAAATGGGGACTTGTATCAACGGCACCACGAGGGCTGCACTGTCTCCTTTCTTCAATCAGTGAAACTGATCTCCCCGTGAAGAAGCGGGGATTAAACTATAAGACGAGAAGACCCCATGGAGCTTCAAACTCATTATGCAACTCT-GTGCTCC-ACACCCCC-TAACAC-AAGAGGCA-TGCATAACAGTTTTGGGTTGGGGGGACCTCGGAGTATAACTTAACCTCCTAAGCAAACAGGTTAGCACCTTTATCCATGAAAAACACCTCTAAGAATTATTAAATTAATGTAT-TTGACCCGATAA--TCGATCAATGAACCAAGTTACCCTGGGGATAACAGCGCAATCTACTTCAAGAGTTCCTATCGACAAGTGGGTTTACGACCTCCATGTTGGATCAGGGTATCCCAGTGGTGCAGCCGCTACTAATG-GTTCGTTTGTTCAACG macroglossaJSF7933 ACCGTAAACAATTTATTTACACCTACCAGCGCCAGGGAACTACGAGCAATGCTTAAAACCCAAAGGATTTGACGGTGTCCCACCCAGCTAGAGGAGCCTGTTCTATAATCGATGATCCCCGCTACACCCCACCATTTCTAGCTAATCAGTCTGTATACCTCCGTCGAAAGCTTACCATGTGAACGCTCTTCAGTAGGCTTAATGATATCAACACGTCAGGTCAAGGTGCAGCTTAAGAAATGGAAAGCAATTGGCTACAATTTCTAATTTAAAACAAACGAAAGGCTATGTGAAATCATAACCGTGAAGGTGGATTTAGTAGTAAAAAAAAAATATATTGTTCTTTTTAACCCGGCTCTGGGACGTGTACACACCGCCCGTCACCCTCTTCGATAACT-CATGATACTGTCCCTAACCCCATTAT-TACCTTTT-AGAAGAGGCAAGTCGAAACACGGTAAGTGTACTGGAAAGTGCACTTGGAAAA-TAGCAAAATATAGCTTAATAAAAGCCCTTCGCTTACACCGAAGAAATATCTGTTTAAATCAGATTATTTTGAGCCCTAAATCTAGCCTTCAAAATTCGCATGAACCCCTTATAAAAACAAAACATTTTTTTATATTAGTACAGGCGATCGAAAAA-TTTCTAAGCGCTTCAGACAAAGTACCGCAAGGGAAAGATGAAATAGAACTGAAATAACCCTAAAGCCTTAAACAGCAGAGATATCCCCTCGTACCTTTTGCATCATGGTCTAGTTAGTCTACCC-AAGCAAAATGAAACTTTT-AGTTAGTCCCCCCGAAACTAAGCGAGCTACTTCAAAACAGCCCTATGGGGCCAACCCTTCTCTGTTGCAAAAGAGTGGGAAGATTTTCAAGTAGAAGTGAAAAGCCTACCGAGCTTAGAGATAGCTGGTTATTCAGGAAAAGAGTTTTAGCTCTACCTTAAGCTTACTT-ATGAAACCC-AACAAACCT-TAAAGCTTAAGAGCTATTCAAATAAGGCACAGCTTATTTGAAACAGGATACAACCTAAAATACTGGGT-AAATAATA-GTAATTGAAA-TTAAGTTGGCCTAAGAGCAGCCATCTTTAAAAAAGCGTTAAAGCTTAATTTTATCT-ACTAATAA-TTTCTTAAATCTTAATCAACCCTTTATTCCCCACTGAATCATTTTATA-TCCTTATAAAAAAGATAATGCTAGAACTAGTAACAAGAAACTGCCCATTCTCCTAAATGCAAGCATAAACCAAAATAGACACCCTATTGGTAATTAACGTAAATGTAACAGCTATAGCAACATAA--------TAGAAAACCCTATAACCCCG-AACGTTAACCTTACACCAGAGCATTTCAGGAAAGATTAAAAAAGAAAGAAGGAACTCGGCAAACTTTAGCCCCGCCTGTTTACCAAAAACATCGCCTCTTGAAAAATTATAAGAGGTCCAGCCTGCCCAGTGACTAAGTTCAACGGCCGCGGTACCCTAACCGTGCGAAGGTAGCATAATCACTTGTTCTTTAAATGGGGACTTGTATCAACGGCACCACGAGGGCTGCACTGTCTCCTTTCTTCAATCAGTGAAACTGATCTCCCCGTGAAGAAGCGGGGATTAAACTATAAGACGAGAAGACCCCATGGAGCTTCAAACTCATTATGCAACTCT-GTGCTCC-ACATCCCC-TAACAC-AAGAGGCA-TGCATAACAGTTTTGGGTTGGGGGGACCTCGGAGTATAACTTAACCTCCTAAGCAAACAGGTTAGCACCTTTATCCATGAAAAACACCTCTAAGAATTATTAAATTAATGTAT-TTGACCCGATAA--TCGATCAATGAACCAAGTTACCCTGGGGATAACAGCGCAATCTACTTCAAGAGTTCCTATCGACAAGTGGGTTTACGACCTCGATGTTGGATCAGGGTATCCCAGTGGTGCAACCGCTACTAATG-GTTCGTTTGTTCAACG taylori286 ACCGTAAACAATTAATTTACACCTATCAGCGCCAGGGAACTACGAGCAATGCTTAAAACCCAAAGGATTTGACGGTGTCCCACCCAGCTAGAGGAGCCTGTTCTATAATCGATGATCCCCGCTATACCCAACCATTTCTAGCTAATCAGTCTGTATACCTCCGTCGAAAGCTTACCATGTGAACGTTTTTCAGTAGGCTTAATGATATCAACACGTCAGGTCAAGGTGCAGCTTAAGAAATGGAAAGCAATTGGCTACAATTTCTAACTTAGAACAAACGAAATGATATGTGAAATCATAATCATGAAGGTGGATTTAGTAGTAAAAAGAAAATAGAGTGTTCTTTTTAACCCGGCTCTGGGACGTGTACACACCGCCCGTCACCCTCTTCGATAACC-CATAATACTGTTCCTAACCCCATTAT-TTCATTTT-AGAAGAAGCAAGTCGAAACATGGTAAGTGTACTGGAAAGTGCACTTGGAAAA-TAACAAAATATAGCTTAATGAAAGCCCTTCGCTTACACCGAAGAAATGTCTGTTTAAATCAGATTATTTTGAGCCATAAATCTAGCCTTCAAAATTCGCATGAACCCCTCCCATAA-CAAAACATTTTCTTATCTTAGTACAGGCGATCGAAAAA-TTTCTAAGCGCTTCAGACAGAGTACCGCAAGGGAAAGATGATATAGAACTGAAATAACCCTAAAGCCTTAAACAGCAGAGATATTCCCTCGTACCTTTTGCATCATGGTCTAGTTAGTCCACCC-AAGCAAAATGAAACTTTT-AGTTAGTCCCCCCGAAACTAAGCGAGCTACTTCAAAACAGCCCTATGGGGCCAACCCTTCTCTGTTGCAAAAGAGTGGGAAGATTTTCAAGTAGAAGTGAAAAGCCTATCGAGCTTAGAGATAGCTGGTTATTCAGGATAAGAGTTTTAGCTCTACCTTAAGCTTCTTT-ATGAAACCC-AACAAATCT-TAAAGCTTAAGAGCTATTCAAATAAGGCACAGCTTATTTGAAATAGGATACAACCTAAAATATTGGGT-AAATAACA-GTGATTAAAA-TTAAGTTGGCCTAAGAGCAGTCATCTTTAAAAAAGCGTTAAAGCTTAGTTTTATAC-GCTAGTAA-TTTCTAAAATTTTTATTAACCCCTTAC-CCCTAGTGAATCATTTTATA-CCCCTATAAAAATGATAATGGTAGAACTAGTAACAAGAAACTGCCCATTTTCCTAAATGCAGGCATAAACCAAAATAGAAATCCTATTGGTAGTTAACGTAAGTGCAGCAGTTGTAGTAACGGAA--------TAGAAAACCCTACAGCCCTA-AACGTTAATCTTACACTAGAGCATTTCAGGAAAGATTAAAGAAGAAAGAAGGAACTCGGCAAATTTTAGCCCCGCCTGTTTACCAAAAACATCGCCTCTTGAAAAATTATAAGAGGTCCAGCCTGCCCAGTGACTAAGTTCAACGGCCGCGGTACCCTAACCGTGCGAAGGTAGCATAATCACTTGTTCTTTAAATGGGGACTCGTATCAACGGCATCACGAGGGCTGCACTGTCTCCTTTCTTAAATCAGTGAAACTGATCTCCCCGTGAAGAAGCGGGGATCAAACTATAAGACGAGAAGACCCCATGGAGCTTCAAACTCTTTATGCAACTCT-GTGCTCC-ACATCCCC-TAACAC-AAGAGACA-TGCATAATAGTTTTGGGTTGGGGGGACCTCGGAGTATAACTTAACCTCCTAAGCAAACAGGTTAGCACCTTTATCTACGAAAAACATCTCTAAGAATTATCAAACTAATGTATTTTGACCCGATAT--TCGATCAATGAACCAAGTTACCCTGGGGATAACAGCGCAATCTACTTCAAGAATTCCTATCGACAAGTGGGTTTACAACCTCCATGTTGGATCAGGGTGTCCCAGTGGTGCAACCGCTACTAATG-GTTCGTTTGTTCAACG sp_4_Panama ACCGTAAACAATTAATTTACACCTATCAGCGCCAGGGAATTACGAGCAATGCTTAAAACCCAAAGGATTTGACGGTGTCCCACCCAGCTAGAGGAGCCTGTTCTATAATCGATGATCCCCGTTACACCTAACCATTTTTAGCTAATCAGTCTGTATACCTCCGTCGAAAGCTTACCATGTGAACGTTTTTCAGTAGGCTTAATGATATCAACACGTCAGGTCAAGGTGCAGCTTAAGAAATGGGAAGCAATTGGCTACAATTTCTAATCTAGAACAAACGAAAGGTTATGTGAAATCATGACCACGAAGGTGGATTTAGTAGTAAAAAGAAAATAGAGTGTTCTTTTTAACCCGGCTCTGGGACGTGTACACACCGCCCGTCACCCTCTTCGATAGTT-CATGATACTGTCCCTAACCC-TTTAT-TACGTTTT-AGAAGAGGCAAGTCGAAACACGGTAAGTGTACTGGAAAGTGCACTTGGAAAA-TAACAAAATATAGCTTAATAAAAGCCCTTCGCTTACACCGAAGAAATATCTGTTTAAATCAGATTATTTTGAGCCCTAAATCTAGCCTTCAAAATTCGAATGAACCTCTCCCCAAA-TAAAACATTTTCTTATCCTAGTACAGGCGATCGAAAAA-TTTCTAAGCGCTTCAAATAAAGTACCGCAAGGGAAAGATGAAATAGAATTGAAATAACCCTAAAGCTTTAAACAGCAGAGATACTTCCTCGTACCTTTTGCATCATGGTCTAGTTAGTCTACCC-AAGCAAAATGAAACTTTT-AGTTAGTCCCCCCGAAACTAAGCGAGCTACTTCAAAACAGCCTTATGGGGCCAACCCTTCTCTGTTGCAAAAGAGTGGGAAGATTTTCAAGTAGAAGTGAAAAGCCTACCGAGCTTAGAGATAGCTGGTTATTCAGGAAAAGAGTTTTAGCTCTACCTTAAGCTTATTT-ATGAAACCC-AACAAACCT-TAAAGCTTAAGAGCTATTCAAATAAGGCACAGCTTATTTGAAACAGGATACAACCTAAAATACTGGGT-AAATAACA-GTAATTTAAA-TTAAGTTGGCCTAAGAGCAGCCATCTTTTAAAAAGCGTTAAAGCTTAATTTGATAT-ACTATTAA-TTTCTTAAATTTCAATTAACCCTTTAC-CCCTACTGAATTACTTTATA-TTCTTATAAAAATGATAATGCTAGAACTAGTAACAAGAAACTGCCCATTCTCCTAAATGCAAGTATAAACCAAAATAGACACCCTATTGGTAATTAACGTAAATGTAACAGCTATAGCAACATAA--------TAGAAAACCCTATAACCCCG-AACGTTAATCTTACACTAGAGCATTTCAGGAAAGATTAAAAAAGAAAGAAGGAACTCGGCAAATTTCAGCCCCGCCTGTTTACCAAAAACATCGCCTCTTGAAAAATTATAAGAGGTCCAGCCTGCCCAGTGACTAAGTTCAACGGCCGCGGTACCCTAACCGTGCGAAGGTAGCATAATCACTTGTTCTTTAAATGGGGACTTGTATCAACGGCACCACGAGGGCTGCACTGTCTCCTTTCTTCAATCAGTGAAACTGATCTCCCCGTGAAGAAGCGGGGATTAAACTATAAGACGAGAAGACCCCATGGAGCTTCAAACTCATTATACAACTCT-GTGCTCC-ACATCCCT-TAACAC-AAGAGGCA-TGCATAATAGTTTTGGGTTGGGGGGACCTCGGAGTATAACTTAACCTCCTAAGCAAACAGGTTACMACCTTTATCTATGAGRAACACCTCTAAGAATTATTAAATTAATGYAT-TTGACCCGATAG--TCGATCAATGAACCAAGTTACCCTGGGGATAACAGCGCAATCTACTTCAAGAGTTCCTATCGACAAGTAGGTTTACGACCTCGATGTTGGATCAGGGTATCCCAGTGGTGCAACCGCTACTAATG-GTTCGTTTGTTCAACG sp_5_CostaRichDMH86_210 ACCGTAAACAATTAATTTACACCTATCAGCGCCAGGGAAATACGAGCAATGCTTAAAACCCAAAGGATTTGACGGTGTCCCACCCAGCTAGAGGAGCCTGTTCTATAATCGATGATCCCCGTTACACCTAACCATTTTTAGCTAATCAGTCTGTATACCTCCGTCGAAAGCTTACCATGTGAACGTTCTTCAGTAGGCTTAATGATATCAACACGTCAGGTCAAGGTGCAGCTTAAGAAATGGGAAGCAATTGGCTACAATTTCTA-TTTAAAACAAACGAAAGGCTATGTGAAATCATAACCATGAAGGTGGATTTAGTAATAAAAAAAAAATATAGTGTTCTTTTTAACCCGGCTCTGGGACGTGTACACACCGCCCGTCACCCTCTTCGATAGTT-CATGATACTGTCCCTAACCC-TTTAT-TACGTTTT-AGAAAAAGCAAGTCGAAACACGGTAAGTGTACTGGAAAGTGCACTTGGAAAA-TAACAAAATATAGCTTAATAAAAGCCCTTCGCTTACACCGAAGAAATATCTGTTTAAATCAGATTATTTTGAGCCCTCAATCTAGCCTTCAAAATTCGCATGAA-CCCCTCTCAAA-CAAAACATTTTCTTATCTTAGTACAGGCGATCGAAAAA-TTTCTAAGCGCTTCAGACAAAGTACCGCAAGGGAAAGATGAAATAGAATTGAAATAACCCTAAAGCTTTAAACAGCAGAGATACTTCCTCGTACCTTTTGCATCATGGTCTAGTTAGTCTACCC-AAGCAAAATGAAACTTTT-AGTTAGTCCTCCCGAAACTAAGCGAGCTACTTCAAAACAGCCCTATGGGGCCAACCCTTCTCTGTTGCAAAAGAGTGGGAAGATTTTCAAGTAGAAGTGAAAAGCCTACCGAGCTTAGAGATAGCTGGTTGTTCAGGAAAAGAGTTTTAGCTCTACCTTAAGCTTCTTT-ATGAGACCC-AACAAACCT-TAAAGCTTAAGAGCTATTCAAATAAGGCACAGCTTATTTGAAACAGGATACAACCTAAAATACTGGGT-AAATTATA-GTAATTAAAA-TTAAGTTGGCCTAAGAGCAGCCATCTTTTAAAAAGCGTTAAAGCTTAGTTTTATAA-ACTAATAA-TTTCTTAAATT-CAATTAACCCTTTAC--CCTACTGAACTACTTTATA-TTCTTATAAAAATAATAATGCTAGAACTAGTAACAAGAAACTGCCCGTTCTCCTAAATGCAAGTATAAACCAAAATAGACACCCTATTGGTAATTAACGTAAATGTAGCAGTTGTAGTAACATTA-------GTAGAAAACTCTATAACCCCTAAACGTTAACCTTACACTAGAGCATTTCAGGAAAGATTAAAAAAGAAAGAAGGAACTCGGCAAACTTCAGCCCCGCCTGTTTACCAAAAACATCGCCTCTTGAAAAATTATAAGAGGTCCAGCCTGCCCAGTGACTAAGTTCAACGGCCGCGGTACCCTAACCGTGCGAAGGTAGCATAATCACTTGTTCTTTAAATGGGGACTTGTATCAACGGCACCACGAGGGCTGCACTGTCTCCTTTCTTTAATCAGTGAAACTGATCTCCCCGTGAAGAAGCGGGGATTAAGCTATAAGACGAGAAGACCCCATGGAGCTTCAAACTCATTATACAACTCT-GTGCCCC-ATATCCCT-TAACAC-AAGAAACA-TGTATAATAATTTTGGGTTGGGGGGACCTCGGAATATAACTTAACCTCCAAAGCAAACAGGTTAAAACCTTTATCCACGAAAAACACCTCTAAAAATTATTAAATTAATGTAT-TTGACCCGATAG--TCGATCAATGAACCAAGTTACCCTGGGGATAACAGCGCAATCTACTTCAAGAATTCCTATCGACAAGTAGGTTTACGACCTCCATGTTGGATCAGGGTATCCCAGTGGTGCAACCGCTACTAATG-GTTCGTTTGTTCAACG sp_6_CostaRicaDMH86_225 ACCGTAAACAATTAATTTACACCTATCAGCGCCAGGGAACTACGAGCAATGCTTAAAACCCAAAGGATTTGACGGTGTCCCACCCAGCTAGAGGAGCTTGTTCTATAATCGATGATCCCCGTTACACCTAACCATTTTTAGCTAATCAGTCTGTATACCTCCGTCGAAAGCTTACCATGTGAACGCTCTTCAGTAGGCTTAATGATATCAACACGTCAGGTCAAGGTGCAGCTTAAGAAATGGGAAGCAATTGGCTACAATTTCTAATCTAGAACAAACGAAAGGCTATGTGAAATCATAACCACGAAGGTGGATTTAGTAGTAAAAAGAAAATAGAGTGTTCTTTTTAACCCGGCTCTGGGACGTGTACACACCGCCCGTCACCCTCTTCGATAGTC-CATGATACTGTCCCTTACCC-TTTAT-TACATTTT-AGAAGAAGCAAGTCCAAACACGGTAAGCGTACTGGAAAGTGCGCTTGGAAAA-TAACAAAATATAGCTTAATAAAAGCCCTTCGCTTACACCGAAGAAATGTCTGTTTAAATCAGATTATTTTGAGCCCTAAATCTAGCCTTCAAAATTCGCATGAACCCCCTTCCAAG-CAAAACATTTTCTTATCTTAGTACAGGCGATCAAAAAA-TTTCTAAGCGCTTCAGACAAAGTACCGTAAGGGAAAGATGAAATAGAATTGAAATAACCTTAAAGCTTTAAACAGCAGAGATATTCCCTCGTACCTTTTGCATCATGGTCTAGTTAGTCTCCCC-AAGCAAAATGAAACTTTT-AGTTAGTCCCCCCGAAACTAAGCGAGCTACTTCAAAACAGCCCTATGGGGCCAACCCTTCTCTGTTGCAAAAGAGTGGGAAGATTTTCAAGTAGAAGTGAAAAGCCTATCGAGCTTAGAGATAGCTGGTTATTCAGGAAAAGAGTTTTAGCTCTACCTTAAGCTTCTTT-ATGAAATCC-AACAAACCT-TAAAGCTTAAGAGCTATTCAAATAAGGCACAGCTTATTTGAAACAGGATACAACCTAAAATATTGGGT-AAATAATA-GTAATTAAAA-TTAAGTCGGCCTAAGAGCAGCCACCTTTAAAAAAGCGTTAAAGCTTAGTCTTATGT-ACTAACAA-TTTCCAAAATTTCAATTAACCCTTACC-CCCTACTGAATTATTTTATA-TTCTTATAAAAGTGATAATGCTAGAACTAGTAACAAGAAACTGCCTATTCTCCTAAATGCAAGCATAAACCAAAATAGACACCCTGTTGGTAATTAACGTAAATGTAACAGCTATAGCAACACAA--------TAGAAAACCCTACAACCCTG-AACGTTAATCTTACACTAGAGCATTTCAGGAAAGATTAAAAAAGAAAGAAGGAACTCGGCAAACTTCAGCCCCGCCTGTTTACCAAAAACATCGCYTCTTGAAAAATGATAAGAGGTCCAGCCTGCCCAGTGACTAAGTTCAACGGCCGCGGTACCCTAACCGTGCGAAGGTAGCATAATCACTTGTTCTTTAAATGGGGACTTGTATCAACGGCACCACGAGGGCTGCACTGTCTCCTTTCTTCAATCAGTGAAACTGATCTCCCCGTGAAGAAGCGGGGATTAAATTATAAGACGAGAAGACCCCATGGAGCTTCAAACTCACCATACAACTCT-GTGCCCC-ATATCCCC-TAACAC-AAGAGACA-TGTACGGTGGTTTTGGGTTGGGGGGACCTCGGAGTATAACTTAACCTCCTAAGCAAACAGGTTAAAACCTTTATCCATGAGAAACACCTCTAAGAATTATTAAATTAATGTAT-TTGACCCGATAA--TCGATCAATGAACCAAGTTACCCTGGGGATAACAGCGCAATCTACTTCAAGAGTTCCTATCGACAGGTAGGTTTACGACCTCGATGTTGGATCAGGGTATCCCAGTGGTGCAACCGCTACTAATG-GTTCGTTTGTTCAACG sp_8_PueblaJAC9467 ACCGTAAACAATTAACTCACACCTATCAGCGCCAGGGAATTACGAGCAATGCTTAAAACCCAAAGGATTTGACGGTGTCCCAGCCAGCTAGAGGAGCCTGTTCTATAATCGATGATCCCCGCTACACCCGACCATTTTTAGCTCCGCAGTCTGTATACCTCCGTCGAAAGCTTACCATGTGAATGCCCTTCAGTAGGCTCAATGATATCG-TACGTCAGGTCAAGGTGCAGCTCAAGAAATGGAAAGTAATTGGCTACAATTTCTAGTTTAGAACAAACGAAAGGCTGTGTGAAATCACGGCCATGAAGGTGGATTTAGTAGTAAAAAGAAAATAGAGTGTTCTTTTTAACCCGGCTCTGAGACGTGTACACACCGCCCGTCACCCTCTTCGATAGTT-CATAATACTGTCCCTAACCC-TTTAT-TACATTTT-AGAAGAGGCAAGTCGAAACACGGTAAGTGTACTGGAAAGTGCACTTGGAAAA-TAACAAAATGTAGCTTAACTAAAGCCCTTCGCTTACACCGAAGAGATATCTGTTTAAACCAGATCATTTTGAGCCCTAAATCTAGCCTTCAAAATTCGCATGAAACACCCCCCAAA-AAAAACATTTCCTCATCTTAGTACAGGCGATCGAAAAA-TTTCTAAGCGCTTCAGATAAAGTACCGTGAGGGAAAGATGAAATAGAATTGAAATAACCATAAAGCCTCAAATAGCAGAGACA-CCCCTGGTACCTTTTGCATCATGGTCTAGTTAGTCTACCC-AAGCAAAATGAAACTTTT-AGTTAGCCTCCCCGAAACTAAGCGAGCTACTTCAAAACAGCCCTATGGGGCCAACCCTTCTCTGTTGCAAAAGAGTGGGAAGATTTTCAAGTAGAAGTGAAAAGCCTACCGAGCTTAGAGATAGCTGGTTATTCAGGAAAAGAGTTTTAGCTCTACCTTAAGCTCCTTT-ATGAAACCT-AACAAACCC-TGAAGCTTAAGAGCTATTCAAATAAGGCACAGCTTATTTGAAATAGGATACAACCTAATATACTGGGT-AACTAATA-GTAATTAAAA-CTAAGTTGGCCTAAGAGCAGCCATCTCTAAAAAAGCGTTAAAGCTTAATTTTTCAC-ACTGATAA-TCCCTAAAATCTTAATTAACCCTTCAC-CCCTACTGAATTATTTTATA-CCTTTATAAAAATGATAATGCTAGAACTAGTAACAAGAAACTGCCTGTTCTCCTAAATGCAAGCATAGACCAAAATAGACACCCTGTTGGTAATTAACGTAAATGAAGCAACTGTAGCAACATAA--------TAGAAAACCCTACAACCCCC-AACGTTAATCTTACACCAGAGCATTTCAGGAAAGATTCAAAGAGAAAGAAGGAACTCGGCAAACTTCAGCCCCGCCTGTTTACCAAAAACATCGCCTCTTGAAAAACTATAAGAGGTCCAGCCTGCCCAGTGACTAAGTTCAACGGCCGCGGTACCCTAACCGTGCGAA---AGCATAATCACTTGTTCTTTAAATGGGGACTTGTATCAACGGCATCACGAGGGCTGCACTGTCTCCTTTCTTCAATCAGTGAAACTGATCCCCCCGTGAAGAAGCGGGGATTAACCTATAAGACGAGAAGACCCCATGGAGCTTCAAACTCATTATGTAACTCT-GCGCTCT-ACACCCCT-TAACGC-AAGAATCC-TACATACTGGTTTTGGGTTGGGGGGACCTCGGAGTATAACTTAACCTCCTAAGCAAACAGGTTAGCACCTTTATCTATGAGAGACACCTCTAAGAATTATTAAATTAATGTAT-TTGACCCGATAG--TCGATCAATGAACCAAGTTACCCTGGGGATAACAGCGCAATCTACTTCAAGAGTTCTTATCGACAAGTAGGTTTACGACCTCGATGTTGGATCAGGGTATCCCGGTGGTGCAACCGCTACCAATG-GTTCGTTTGTTCAACG oncaLVT3542 ACCGTAAACAATTAATTTACACCCATCAGCGCCAGGGAATTACGAGCAATGCTTAAAACCCAAAGGATTTGACGGTGTCCCACCCAGCTAGAGGAGCCTGTTCTATAATCGATGATCCCCGCTACACCCGACCATTTTTAGCTCATCAGTCTGTATACCTCCGTCGAAAGCTTACCATGTGAACGCTCTTCAGTAGGCCCAATGA-GTCAACACGTCAGGTCAAGGTGCAGCTTAAGAAATGGGAAGCAATTGGCTACAATTTCTAACTTAGAACAAACGAAAGGCTATGTGAAACCATAACCACGAAGGTGGATTTAGTAGTAAAAAGAAAATAGAGTGTTCTTTTTAACCCGGCTCTGGGACGTGTACACACCGCCCGTCACCCTCTTCGATAGTT-CAAAATACTGTCCCTAACCC-TTTAT-CACGTTTT-AGAAGAGGCAAGTCGAAACACGGTAAGTGTACTGGAAAGTGCACTTGGAAAA-TAGCAAAATGTAGCTTAATTAAAGCCCTTCGCTTACACCGAAGAAACATCTGTTTGAATCAGATCATTTTGAGCCCTAAATCTAGCCTTCTAAACTCGCATGAACCCCCTCCCAAA-CAAAACATTTTCTCATCTTAGTACAGGCGATCGAAAAA-TTTCTAAGCGCTTCAGACAAAGTACCGTAAGGGAAAGATGAAATAGAATTGAAACAACCCTAAAGCCTTAAATAGCAGAGATAACCCCTCGTACCTTTTGCATCATGGTCTAGTCAGTCTACCC-AAGCAAAATGAAACTTTT-AGTTAGTCCCCCCGAAACTAAGCGAGCTACTTCAAAACAGCCCTATGGGGCCAACCCTTCTCTGTTGCAAAAGAGTGGGAAGATTTTCAAGTAGAAGTGAAAAGCCTACCGAGCTTAGAGATAGCTGGTTATTCAGGAAAAGAGTTTTAGCTCTACCTTAAGCTTCTTT-ATGAAACCC-AACAAACCT-TAAAGCTTAAGAGCTATTCAAATAAGGCACAGCTTATTTGAAATAGGACACAACCTAAAATACTGGGT-AAATAACA-GTAATTAAAA-TTAAGTTGGCCTAAGAGCAGCCACCTTTAAAAAAGCGTTAAAGCTTGATTTTATAC-ACTAGTAA-TTCCTAAAATTCTTATCAACCCTTTAT-CCCTACTGAATTATTTTATA--CCCTATAAAAACGATAATGCTAGAACTAGTAACAAGAAACTGCCTATTCTCCTAAATGCAAGTATAAGCCAAAATAGACACCCTATTGGTAATTAACGTAAATGTAACAGTTATAGTAACATAC--------TAGAAAACCCTATAGCCCCC-AACGTTAATCTTACACTAGAGCATTTCAGGAAAGATTAAAAGAGAAAGAAGGAACTCGGCAAACTTCAGCCCCGCCTGTTTACCAAAAACATCGCCTCTTGAAAAATTATAAGAGGTCCAGCCTGCCCAGTGACTAAGTTCAACGGCCGCGGTACCCTAACCGTGCGAAGGTAGCATAATCACTTGTTCTTTAAATGGGGACTTGTATCAACGGCACCACGAGGGCTGCACTGTCTCCTTTCTTCAATCAGTGAAACTGATCTCCCCGTGAAGAAGCGGGGATCAAATTATAAGACGAGAAGACCCCATGGAGCTTCAAACTCACCATGCAACTCC-GTGCCCT-TCA-CCTC-TAACAC-AAGAGTCA-TGTATGCTAGTTTTGGGTTGGGGGGACCTCGGAGTATAACTCAACCTCCTAAGCAAACAGGTTAACACCTTTATCTATGAAAAACACCTCTAAGAATTATTAAATTAATGTAT-TTGACCCGATAG--TCGATCAATGAACCAAGTTACCCTGGGGATAACAGCGCAATCTACTTCAAGAGTTCCTATCGACAAGTAGGTTTACGACCTCGATGTTGGATCAGGGCATCCCAGTGGTGCAACCGCTACTAATG-GTTCGTTTGTTCAACG sp_3_MichoacanJSF955 GCCGTAAACAATTAATTCACACCTATCAGCGCCAGGGGATTACGAGCAATGCTTAAAACCCAAAGGATTTGACGGTGTCCCACCCAGCTAGAGGAGCCTGTTCTATAATCGATGATCCCCGTTACACCCGACCATTTTTAGCTCATCAGTCTGTATACCTCCGTCGAAAGCTTACCATGTGAACGCTCTTCAGTAGGCTCAATGATGTCAACACGTCAGGTCAAGGTGCAGCTTAAGAAATGGGAAGCAATTGGCTACAATTTCTAATCTAGAACAAACGAAAGGCTATGTGAAATCATAACCTCGAAGGTGGATTTAGTAGTAAAAAGAAAGTAGAGTGTTCTTTTTAACCCGGCTCTGGGACGTGTACACACCGCCCGTCACCCTCTTCGATAGTC-CATGATACTGTTCCTAACCC-AATAT-TACATTTT-AGAAGAGGCAAGTCGAAACACGGTAAGTGTACTGGAAAGTGCACTTGGTAAA-TAACAAAATGTAGCTTAATAAAAGCCCTTCGCTTACACCGAAGAAATACCTGTTTGAATCAGATCATTTTGAGCCCTAAATCTAGCCTTCAAAATTCGCATGAACCCCCCCCCCTA-CAAAACATTTTCTTATCCTAGTACAGGCGATCGAAAAA-TTTCTAAGCGCTTCAGATAAAGTACCGCAAGGGAAAGATGAAATAGAATTGAAATAACTCTAAAGCCTTAAATAGCAGAGATTCTACCTCGTACCTTTTGCATCATGGTCTAGTTAGTCTACCC-AAGCAAAATGAAACTTTT-AGTTAGTCCCCCCGAAACTAAGCGAGCTACTTCAAAACAGCCCTATGGGGCCAACCCTTCTCTGTTGCAAAAGAGTGGGAAGATTTTCAAGTAGAAGTGAAAAGCCTACCGAGCTTAGAGATAGCTGGTTATTCAGGAAAAGAGTTTTAGCTCTACCTTAAGCTTCTTT-ATGAAACCC-AACAAACCT-TAAAGCTTAAGAGCTATTCAAATAAGGCACAGCTTATTTGAAATAGGACACAACCTAGAATAATGGGT-AAGTAACA-GCAATTAAAG-CTAAGTTGGCCTAAGAGCAGCCATCTTTAAAAAAGCGTTAAAGCTTAGTTTTGTAT-GCTAATAA-TTTCTAAAATTTTAATCAACCCTTTAT-CCCTACTGAATTATTTTATA-TTCCTATAAAAGTAATAATGCTAGAACTAGTAACAAGAAATTGCCTATTCTCCTAAATGCAAGCATAAACCAAAATAGACACCCTATTGGTAATTAACGTAAATGTGACAGCTATAGTAACATAA--------TAGAAAACCCTATAACCTCC-AACGTTAACCTTACACTAGAGCATTTCAGGAAAGATCTAAAAAGAAAGAAGGAACTCGGCAAACTTCAGCCCCGCCTGTTTACCAAAAACATCGCCTCTTGAAAAATTATAAGAGGTCCAGCCTGCCCAGTGACTAAGTTCAACGGCCGCGGTACCCTAACCGTGCGAAGGTAGCATAATCACTTGTTCTTTAAATGGGGACTTGTATCAACGGCACCACGAGGGCTGCACTGTCTCCTTTCTTCAATCAGTGAAACTGATCTCCCCGTGAAGAAGCGGGGATTAAACTATAAGACGAGAAGACCCCATGGAGCTTCAAACTCATTATACAACCCT-GTTCTTC-ATATCCTCCTAACAC-AAAGGACA-TGTATAATAGTTTTGGGTTGGGGGGACCTCGGAGTATAGCTTAACCTCCTAAACAAACAGGTTAACACCTTTATCCATGAGAAACACCTCTAAGAATTATTAAATTAATGTAT-TTGATCCGATTG--TCGATCAATGAACCAAGTTACCCTGGGGATAACAGCGCAATCTACTTCAAGAGTTCTTATCGACAAGTAGGTTTACGACCTCGATGTTGGATCAGGGTATCCCAGTGGTGCAACCGCTACTAATG-GTTCGTTTGTTCAACG sphenocephalaUSC7448 GCCGTAAACAATTAATTTACACCTATCAGCGCCAGGGAACTACGAGCAATGCTTAAAACCCAAAGGATTTGACGGTGTCCCACCCAGCTAGAGGAGCCTGTTCTATAATCGATGATCCCCGCTACACCCGACCATTTTTAGCTCATCAGTCTGTATACCTCCGTCGAAAGCTTACCATGTGAACGCCCTTCAGTAGGCTCAATGATATCAACACGTCAGGTCAAGGTGCAGCTTAAGAAATGGGAAGCAATTGGCTACAATTTCTAATCTAGAACAAACGAAAGGCTATGTGAAATCATAACCATGAAGGTGGATTTAGTAGTAAAAAGAAAATAGAGTGTTCTTTTTAACCCGGCTCTGGGACGTGTACACACCGCCCGTCACCCTCTTCGATAGTT-CATAATACTGTTCCTAACCC-ATTGT-TACATTTT-AGAAGAGGCAAGTCGAAACACGGTAAGTGTACTGGAAAGTGCACTTGGAAAAATAACAAAATGTAGCTTAATAAAAGCCCTTCGCTTACACCGAAGAAATATCTGTTTGAATCAGGTCATTTTGAGCCCTAAATCTAGCCTTCAAAATTCGCATGAACCCCATCCCAAA-CAAAACATTTTCTCATTCTAGTACAGGCGATCGAAAAA-TTTCTAAGCGCTTCAGATAAAGTACCGCAAGGGAAAGATGAAATAGAATTGAAATAACCTTAAAGCCTTAAACAGCAGAGATTCTACCTCGTACCTTTTGCATCATGGTCTAGTTAGTCTACCC-AAGCAAAATGAAACTTTT-AGTTAGTCCCCCCGAAACTAAGCGAGCTACTTCAAAACAGCCCAATGGGGCCAACCCTTCTCTGTTGCAAAAGAGTGGGAAGATTTTCAAGTAGAAGTGAAAAGCCTACCGAGCTTAGAGATAGCTGGTTACTCAGGAAAAGAGTTTTAGCTCTACCTTAAGCTTATTT-ATGAAACCC-AACAAACCC-TAAAGCTTAAGAGCTATTCAAATAAGGCACAGCTTATTTGAAATAGGATACAACCTAAAATACTGGGT-AAATAGCA-GTAACTAAAA-TTAAGTTGGCCTAAGAGCAGCCATCTTTTAAAAAGCGTTAAAGCTTAATTTTACCT-ACTGACAA-TTCCTAAAATTTTAATCAACCCTTTAT-TACTACTGAGTTACTTTATA-CCCCTATAAAAGTGATAATGCTAGAACTAGTAACAAGAAACTGCCCATTCTCCTAAATGCAAGTATAAGCCAAAATAGACTCCCTGTTGGTAATTAACGTAAATGTAACAGCTATAGCAACATAA--------TAGAAAACCCTATAACCTCC-AACGTTAATCTTACACCAGAGCATTTCAGGAAAGATTAAAAAAGAAAGAAGGAACTCGGCAAACTTCAGCCCCGCCTGTTTACCAAAAACATCGCCTCTTGAAAAATTATAAGAGGTCCAGCCTGCCCAGTGACTAAGTTCAACGGCCGCGGTACCCTAACCGTGCGAAGGTAGCATAATCACTTGTTCTTTAAATGGGGACTTGTATCAACGGCACCACGAGGGCTGCACTGTCTCCTTTCTTCAATCAGTGAAACTGATCTCCCCGTGAAGAAGCGGGGATTCATATATAAGACGAGAAGACCCCATGGAGCTTCAAACTCATTATACAACTCC-ATTCTCT-ACACCCTCCTAACAT-AAGAGTCA-TGTATAATGGTCTTGGGTTGGGGGGACCTCGGAGTATAACATAACCTCCTAAGCAAACAGGTTAACACCTTTATCCACGAGGAACACCTCTAAGAATTATTAAATTAATGCAC-CTGATCCGATAA--TCGATCAATGAACCAAGTTACCCTGGGGATAACAGCGCAATCTACTTCAAGAGTTCCTATCGACAAGTGGGTTTACGACCTCGATGTTGGATCAGGGTATCCCAGTGGTGCAACCGCTACTAATG-GTTCGTTTGTTCAACG utriculariaJSF845 GCCGTAAACAATTAATTTACACCTACCAGCGCCAGGGAACTACGAGCAATGCTTAAAACCCAAAGGATTTGACGGTGTCCCACCCAGCTAGAGGAGCCTGTTCTATAATCGATGATCCCCGCTACACCCGACCATTTTTAGCTCATCAGTCTGTATACCTCCGTCGAAAGCTTACCATGTGAACGCCCTTCAGTAGGCTCAATGATATCAACACGTCAGGTC?AGGTGCAGCTTAAGAAATGGGAAGCAATTGGCTACAATTTCTAATCTAAAACAAACGAAAGGCTATGTGAAATCATAACCATGAAGGTGGATTTAGTAATAAAAAGAAAATATAGTGTTCTTTTTAACCCGGCTCTGGGACGTGTACACACCGCCCGTCACCCTCTTCGATAGTT-CATAATACTGTTCCTAACCT-ATTAT-TACATTTT-AGAAGAGGCAAGTCGAAACACGGTAAGTGTACTGGAAAGTGCACTTGGAAAA-TAACAAAATGTAGCTTAATAAAAGCCCTCCGCTTACACCGAAGAAATATCTGTTTGAATCAGATCATTTTGAGCCCTAAATCTAGCCTTCAAAATTCGTATGAACCCTCTCCCAAA-CAAAACATTTTCTTATCCTAGTACAGGCGATCGAAAAA-TTTCTAAGCGCTTCAGATAAAGTACCGCAAGGGAAAGATGAAATAGAATTGAAATAACCTTAAAGCCTTAAACAGCAGAGATTATACCTCGTACCTTTTGCATCATGGTCTAGTTAGTCTACCC-AAGCAAAATGAAACTTTT-AGTTAGTCCCCCCGAAACTAAGCGAGCTACTTCAAAACAGCCCTATGGGGCCAACCCTTCTCTGTTGCAAAAGAGTGGGAAGATTTTCAAGTAGAAGTGAAAAGCCTATCGAGCTTAGAGATAGCTGGTTACTCAGGAAAAGAGTTTTAGCTCTACCTTAAGCTTCTTT-ATGAAACCC-AACAAACCT-TAAAGCTTAAGAACTATTCAAATAAGGCACAGCTTATTTGAAATAGGATACAACCTAAAATACTGGGT-AAATAACA-GTAATTAAAA-TTAAGTTGGCCTAAGAGCAGCCATCTTTAAAAAAGCGTTAAAGCTTAGTTTTACCT-ACTGACAA-T-CCTAAAATTTTAATCAACCCTTTAT-CACTACTGAGTTATTTTATA-CCCCTATAAAAGTGATAATGCTAGAACTAGTAACAAGAAACTGCCCATTCTCCTAAATGCAAGTATAAACCAAAATAGACACCCTGTTGGTAATTAACGTAAATGTAACAGCTATAGCAACATAA--------TAGAAAACCCTACAACCTTC-AACGTTAATCTTACACCAGAGCATTTCAGGAAAGATTAAAAAAGAAAGAAGGAACTCGGCAAACTTCAGCCCCGCCTGTTTACCAAAAACATCGCCTCTTGAAAAATTATAAGAGGTCCAGCCTGCCCAGTGACTAAGTTCAACGGCCGCGGTACCCTAACCGTGCGAAGGTAGCATAATCACTTGTTCTTTAAATGGGGACTTGTATCAACGGCACCACGAGGGCTGCACTGTCTCCTTTCTTCAATCAGTGAAACTGATCTCCCCGTGAAGAAGCGGGGATTCATATATAAGACGAGAAGACCCCATGGAGCTTCAAACTCATTATACAACTCT-ATTCTCC-ACACCCTCCTAACAC-AAGAGACA-TGTATGATGGTTTTGGGTTGGGGGGACCTCGGAGTATAACATATCCTCCTAAGCAAACAGGTTAACACCTTTATCCATGAAGAACACCTCTAAGAATTATTAAATTAATGCAT-CTGATCCGATAA--TCGATCAATGAACCAAGTTACCCTGGGGATAACAGCGCAATCTACTTCAAGAGTTCCTATCGACAAGTGGGTTTACGACCTCCATGTTGGATCAGGGTATCCCAGTGGTGCAACCGCTACTAATG-GTTCGTTTGTTCAACG garli-2.1-release/example/basic/ranaconstraint.format1000066400000000000000000000002761241236125200231300ustar00rootroot00000000000000+((1,2,3,4,5,6,7,8,9),10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64); garli-2.1-release/example/basic/ranaconstraint.format2000066400000000000000000000001031241236125200231160ustar00rootroot00000000000000+*********....................................................... garli-2.1-release/example/basic/ranastart.nexus.tre000066400000000000000000000064721241236125200224670ustar00rootroot00000000000000#NEXUS begin garli; r 2.3632 8.3104 2.3675 0.97512 19.142 b 0.34589 0.20981 0.14884 0.29546 a 0.58462 p 0.29489; end; Begin trees; Translate 1 temporariaDMH84R1, 2 boyliiMVZ148929, 3 luteiventris_MT_MVZ191016, 4 luteiventris_WA_MVZ225749, 5 muscosaMVZ149006, 6 auroraMVZ13957, 7 cascadaeMVZ148946, 8 sylvaticaMVZ137426, 9 sylvaticaDMH84R43, 10 septentrionalesDCC3588, 11 grylioMVZ175945, 12 virgatipesMVZ175944, 13 okaloosae, 14 clamitansJSF1118, 15 heckscheriMVZ164908, 16 catesbianaX12841, 17 catesbianaDMH84R2, 18 maculataKU195258, 19 vibicariaMVZ11035, 20 warszewitshiiJSF1127, 21 palmipesVenAMNHA118801, 22 palmipesEcuKU204425, 23 bwanaQCAZ13964, 24 Sp_1_ecuadorQCAZ13219, 25 vaillantiKU195299, 26 julianiTNHC60324, 27 sierramadrensisKU195181, 28 psilonotaKU195119, 29 tarahumaraeKU194596, 30 zweifeliJAC7514, 31 pustulosaJAC10555, 32 pipiensJSF1119, 33 pipiensY10945, 34 dunniJSF1017, 35 montezumaeJAC8836, 36 sp_2_mex_JSF1106, 37 chiricahuensisJSF1063, 38 chiricahuensisJSF1092, 39 subaquavocalis, 40 palustrisJSF1110, 41 areolataJSF1111, 42 sevosaUSC8236, 43 capitoSLU003, 44 spectabilisJAC8622, 45 forreriJSF1065, 46 tlalociJSF1083, 47 berlandieriJSF1136, 48 neovolcanicaJSF960, 49 blairiJSF830, 50 omiltemanaJAC7413, 51 magnaocularisJSF1073, 52 yavapaiensisJSF1085, 53 sp_7_JaliscoJSF1000, 54 macroglossaJAC10472, 55 macroglossaJSF7933, 56 taylori286, 57 sp_4_Panama, 58 sp_5_CostaRichDMH86_210, 59 sp_6_CostaRicaDMH86_225, 60 sp_8_PueblaJAC9467, 61 oncaLVT3542, 62 sp_3_MichoacanJSF955, 63 sphenocephalaUSC7448, 64 utriculariaJSF845 ; tree start = [&U] ((40:0.02390280,((43:0.01019800,42:0.00212128):0.02307062,41:0.03616946):0.00883063):0.01463739,(((32:0.00722334,33:0.00204765):0.02609594,((34:0.01049106,35:0.01695165):0.01589729,((38:0.00849269,(37:0.02125788,39:0.00797791):0.00155937):0.00951851,36:0.01916016):0.00603120):0.02141512):0.03480802,(((((31:0.08886663,29:0.03220190):0.01558966,(30:0.05714606,28:0.08150107):0.01778533):0.04266049,27:0.11352818):0.03367044,(((8:0.00402014,9:0.00245546):0.05750656,(12:0.04433748,(10:0.03404319,(11:0.06329406,((14:0.00621398,13:0.00462441):0.01054165,((16:0.00000002,17:0.00128513):0.02427753,15:0.02934699):0.00315810):0.00650169):0.00268996):0.00620524):0.04351198):0.01538881,(((2:0.06128546,(3:0.00492180,4:0.00000001):0.02562456):0.00784094,((6:0.01592388,7:0.01787847):0.01532535,5:0.02397119):0.01999234):0.00840586,1:0.08912154):0.05666457):0.03151628):0.00238185,(((25:0.07601420,26:0.06292996):0.01559673,((24:0.01684210,(21:0.09110211,22:0.00492954):0.02655718):0.00931973,23:0.02792710):0.01636045):0.06445593,((19:0.05710583,20:0.10305727):0.07557000,18:0.07438950):0.01908065):0.02000394):0.09096969):0.00661514,(((61:0.00581276,52:0.00701734):0.02800765,(((((55:0.00641422,54:0.01067980):0.01441481,56:0.05390237):0.01282941,(59:0.03260024,(57:0.01704422,58:0.02943971):0.00437423):0.00583290):0.00631916,((62:0.03172957,(44:0.04389019,(((47:0.00560171,(48:0.00157822,46:0.00307091):0.00321953):0.00712665,49:0.00782905):0.02298766,(64:0.01083078,63:0.01513543):0.01745903):0.00581102):0.00240902):0.00220052,50:0.06017234):0.00588537):0.00386259,(60:0.06247630,(51:0.08236732,53:0.05718048):0.01554180):0.00666918):0.00331383):0.00423051,45:0.04910331):0.02668421); end; garli-2.1-release/example/basic/ranastart.oldformat.tre000066400000000000000000000033571241236125200233130ustar00rootroot00000000000000r 2.3632 8.3104 2.3675 0.97512 19.142 b 0.34589 0.20981 0.14884 0.29546 a 0.58462 p 0.29489 ((40:0.02390280,((43:0.01019800,42:0.00212128):0.02307062,41:0.03616946):0.00883063):0.01463739,(((32:0.00722334,33:0.00204765):0.02609594,((34:0.01049106,35:0.01695165):0.01589729,((38:0.00849269,(37:0.02125788,39:0.00797791):0.00155937):0.00951851,36:0.01916016):0.00603120):0.02141512):0.03480802,(((((31:0.08886663,29:0.03220190):0.01558966,(30:0.05714606,28:0.08150107):0.01778533):0.04266049,27:0.11352818):0.03367044,(((8:0.00402014,9:0.00245546):0.05750656,(12:0.04433748,(10:0.03404319,(11:0.06329406,((14:0.00621398,13:0.00462441):0.01054165,((16:0.00000002,17:0.00128513):0.02427753,15:0.02934699):0.00315810):0.00650169):0.00268996):0.00620524):0.04351198):0.01538881,(((2:0.06128546,(3:0.00492180,4:0.00000001):0.02562456):0.00784094,((6:0.01592388,7:0.01787847):0.01532535,5:0.02397119):0.01999234):0.00840586,1:0.08912154):0.05666457):0.03151628):0.00238185,(((25:0.07601420,26:0.06292996):0.01559673,((24:0.01684210,(21:0.09110211,22:0.00492954):0.02655718):0.00931973,23:0.02792710):0.01636045):0.06445593,((19:0.05710583,20:0.10305727):0.07557000,18:0.07438950):0.01908065):0.02000394):0.09096969):0.00661514,(((61:0.00581276,52:0.00701734):0.02800765,(((((55:0.00641422,54:0.01067980):0.01441481,56:0.05390237):0.01282941,(59:0.03260024,(57:0.01704422,58:0.02943971):0.00437423):0.00583290):0.00631916,((62:0.03172957,(44:0.04389019,(((47:0.00560171,(48:0.00157822,46:0.00307091):0.00321953):0.00712665,49:0.00782905):0.02298766,(64:0.01083078,63:0.01513543):0.01745903):0.00581102):0.00240902):0.00220052,50:0.06017234):0.00588537):0.00386259,(60:0.06247630,(51:0.08236732,53:0.05718048):0.01554180):0.00666918):0.00331383):0.00423051,45:0.04910331):0.02668421); garli-2.1-release/example/basic/zakonEtAl2006.11tax.nex000066400000000000000000000575431241236125200224710ustar00rootroot00000000000000#NEXUS [ This dataset is from: Zakon, Lu, Zwickl and Hillis. 2006. Sodium channel genes and the evolution of diversity in communication signals of electric fishes: Convergent molecular evolution. Proc. Natl. Acad. Sci. USA. 103(10):3675-80. ] begin data; dimensions ntax=11 nchar=2178; format datatype=dna missing=? gap=-; matrix MorNa6 CCTGTGACTCCACATTTTGAGCACGTACTCAGTGTGGGAAACCTGGTTTTCTCAGGGATATTTGCTGGTGAAATGGTCTTGAAAATTATTGCTATGGACCCCTACTACTACTTCCAGGTTGGATGGAACGTGTTTGACAGCATCATTGTTACCATGAGTATGGTGGAGATGGTACTGGCTGATGTAGAGGGTCTGTCGGTTCTGCGGTCCTTTCGTTTGCTACGTGTCTTCAAGCTTGCCAAATCATGGCCTACCCTCAACATGCTGCTAACGATCATCGGAAACTCAGTGGGTGCTCTGGGGAACCTCACCGTGGTGCTGGCCATCATCGTTTTCATCTTCGCTGTGGTTGGAATGCAGCTGTTTGCCAAAAACTACAAGGACTGCGTCTGCAAGATCGCCGAGGATTGTGAGCTGCCCCGGTGGCACATGCATGACTTCTTCCACTCTTTCCTCATCGTGTTCCGCATCCTCTGTGGAGAGTGGATTGAGACCATGTGGGACTGCATGGAAGTGGCCAACAGAAACATGTGTTTGGTCCTCTTCTTAATGGTCATGATAATTGGGAACCTGGTGGTTCTGAACCTTTTCCTGGCCTTGCTGCTTAGCTCATTCAGCGGGGACAATCTGCAAATGGCAGATGACGACGGCGAGCTGAACAATCTGCAGCTTTCCGCACTCAGGATCACCAGAGCCATTGATTGGGTGAAGGCCTACGTTAGAGGGCTGATCTGGAAGATCCTGGGCAAGCAGCCAAGAGTGCTGGATGGTTTATCTCACTGGGCAACCTTCACCGTACCCATTGCCCAGGAAGAGTCTGATTTAGAAGATGGTGTGTCTGAGTGCAGCACAGTGGACTACGTGCCCCCTCCGCCGGATGAAGTGGAGGAACCGGAGCCTGTGGAACCTGAGGCCTGTTACACTGACAACTGCCTTAGACGGTGTCCTTGTCTGGTGCTGGACACCTCAGAGGGCAGAGGGAAGACCTGGTGGAACCTCAGGAGAACCTGCTACACCATTGTGGAGCATGACTACTTTGAGTCCTCCATAATCTTCATGATCCTTCTCAGCAGTGGTGCCTTGGCCTTTGAAGACATATATCTTGAAAGACGCAGAACGATAAAAATCCTGCTGGAATATGCAGATAAAGTCTTCAGCTATGTATTTGTTATTGAGATGCTCCTTAAGTGGGTGGCTTATGGTTACAAAGTATACTTTACCAATGCCTGGTGCTGGCTGGACTTCTTGATTGTTGATGTTTCCTTGGTCAGTTTGGCAGCAAGCATAATGGGCTATTCTGAACTAGGACCCATAAAGTCTTTGAGAACTCTTAGGGCTCTGAGGCCTCTAAGAGCCCTTTCCAGGTTTGAGGGGATGCGGGTTGTGGTGAACGCCCTTGTGGGGGCCGTCCCCGCCATCTTCAATGTGATGCTGGTCTGTCTCATCTTCTGGCTCATCTTCAGCATCATGGGGGTTAACCTGTTTGCCGGGACATTCTACCACTGCCTCAACACCACAACTGGGGAGATGTTTACCATTGATGTTGTAAACAACTATAGTGAGTGTTTGGCCCTCATGCACACAAACGAGGTGCGCTGGGCCAACGTCAGGGTCAACTATGACAACGTTGGGATGGGTTACCTGTCTCTGTTGCAAGTGTCAACATTCAAAGGCTGGATGGAAATTATGTATGCGGCTGTCGACTCACGTAAGGTGGGTCAACAGCCCTCATATGAGGCCAACCTTTACATGTACGTGTACTTTGTCATCTTCATCATCTTTGGGTCCTTCTTTACACTCAACCTCTTCATTGGTGTCATCATTGACAACTTCAATCAACAAAAGAATAAGATGGGAGGA---GATTGCTTTATGACTGAGGAGCAGAAGAAATATTACGACGCTATGAAAAAGCTAGGCAACAAGAAGCCAGCGAAGCCCATTCCAAGACCAACGGGCAAAATACCAGGCCTAGTATATGACTTCATCAGTCAGCAGGCCTTTGACATCTTTATCATGGTACTGATTTGCCTGAACATGGTGACCATGATGGTGGAGGAAGATGACCAAAGTGAACAGAAGACAGACATGCTGGGCAAAATCAATGCAGTCTTCATTGTGGTCTTCAGCAGTGAATGTTTGCTGAAGATGATTGCACTGAGACAATACTTCTTTACC ClownNa6 CCCATGAGCCCTGAGTTTGACCACATGCTCTCTGTGGGAAACCTGGTTTTCACTGGAATCTTCACAGCTGAAATGGTCCTAAAACTCATTGCTATGGACCCCTACTACTACTTCCAGGTTGGATGGAACATATTTGACAGCATCATTGTCACTCTAAGCCTAGTGGAACTGGGGCTCGCTAATGTTCAGGGTCTGTCAGTCCTGCGATCCTTTCGTTTGTTGCGAGTGTTCAAGCTGGCAAAGTCTTGGCCCACCCTCAACATGCTGATCAAGATCATCGGGAATTCCGTGGGCGCCCTGGGCAACCTGACCCTGGTGCTGGCCATCATCGTCTTCATCTTCGCCGTGGTGGGCATGCAGCTCTTTGGGAAGACCTACAAGGACTGCGTGTGCAAGATTGCCAGTGACTGCGAGCTTCCCCGCTGGCACATGAATGACTTCTTCCACTCGTTCCTTATCGTGTTCCGCATCCTCTGCGGGGAGTGGATCGAGACCATGTGGGACTGCATGGAGGTGGCTGGTGCAGGCATGTGCCTCGTGGTCTTCATGATGGTCATGGTCATTGGGAACCTAGTGGTGCTGAATCTCTTCCTGGCTTTGCTGCTCAGTTCATTCAGTGGAGACAACCTAGCAGGCGGTGATGAGGATGGCGAGATGAACAACTTGCAGATTGCTATCGGAAGGATCACCCGAGGCATTGACTGGGTGAAGGCATTTGTCATGGGACTGGTGTGGCGGGTGATGGGCAAAAAGCCTAAAATGCTGGATGGTTTATCTCACTGGGTAACCCTCAGTGTGCCCATGGCACAGGAGGAATCCGACTTAGAAGACGACTCCTCTGAATGCAGCACTGTGGACTATAGGCCTCCAGAGCCAGTGGAGGAGGAAGAACCAGAACAGGTGGAGCCTGTGGAGTGTTTTACTGATGACTGTGTCAGACGTTGCCCTTGTCTGACGGTGGACATCACGCAGGGCAAAGGAAGGACCTGGTGGAATCTCAGGAAAACATGTTACACCATCGTGGAGCATGACTACTTTGAGACCTTCATCATCTTCATGATCCTGCTTAGCAGTGGGGCCTTGGCCTTTGAAGATATATACATTGAAAGGCGCAGAACAATAAAAATCATTCTGGAATATGCAGACAAAGTATTTACATACGTATTTGTTGTTGAAATGCTCTTGAAGTGGGTTGCTTATGGTTTCAAGACATACTTCACTAATGCCTGGTGCTGGCTGGACTTTTTAATTGTGGATGTGTCCTTGATCAGTTTGACAGCAAACCTCATGGGCTACTCAGAGCTGGGGCCTATCAAATCCCTGAGAACCCTGAGGGCCCTGAGGCCACTACGAGCCCTGTCTAGGTTTGAGGGCATGAGAGTGGTGGTAAATGCATTGGTAGGGGCCATCCTTTCCATCTTCAACGTACTGCTGGTCTGTCTCATTTTCTGGCTTATCTTCAGCATTATGGGTGTCAACCTTTTTGCTGGAAAGTTCTACCGCTGTATCAACACCACCACAGAGGAGCTATTACCTGTCGAGATTGTGAACAATAAGAGTGACTGCTTGAATCTCATGCACACAAATGAAGTGCGCTGGGTCAATGTGAAGGTCAACTATGACAACGTTGGCCTTGGTTACCTCTCTCTACTCCAAGTTGCAACATTTAAAGGGTGGATGGACATTATGTATGCAGCTGTGGACTCTCGTGAGGTGGAAGAGCAGCCCTTGTATGAGGAAAACCTCTATATGTACTTATACTTCGTCATCTTCATCATTTTTGGGTCATTCTTTACACTCAACCTTTTCATTGGTGCCATCATCGACAACTTTAATCAGCAAAAGAAAAAGCTTGGTGGGAAGGATATCTTCATGACCGAGGAGCAAAAGAAGTACTACAATGCCATGAAAAAGCTTGGTTCCAAAAAGCCAGTGAAGCCTATTCCAAGACCTACGAACAAAATACAAGGTGTGGTATTTGACTTCATCAGTCAGCAGTTCTTTGACATCTTTATCATGGTATTGATCTGCCTCAACATGGTGACCATGATGGTGGAGACAGATGACCAAAGTCAGGAAAAAGAGAATATACTGAACCAAATCAATCTGGTATTCATTGTGATCTTCACCAGCGAATGCGTCTTGAAGATGTTTGCACTTAGACATTATTTCTTCACC AraNa6 CCAATGAGTCCCGCGTTTGACCATATGCTGACCGTGGGAAACCTCGTTTTTACGGGGATCTTTACAGCTGAGATGGTATTCAAGCTCATCGCCATGGATCCATACCACTACTTCCAGGTTGGATGGAACATTTTTGACAGCATCATTGTCACACTTAGCCTGGTGGAGCTGGGTCTCGCGAATGTTCAGGGCCTTTCGGTCTTGCGCTCCTTCCGCTTGCTGCGGGTCTTCAAGCTGGCCAAGTCTTGGCCTACCCTGAACATGCTCATCAAGATCATTGGAAACTCAGTGGGTGCCCTAGGGAACCTCACACTGGTGCTGGCCATCATCGTCTTCATCTTCGCTGTCGTGGGCATGCAGCTGTTCGGTAAGAGCTACAAGGACTGTGTGTGTAAGATTGCAGAGGACTGTGAGCTACCCCGCTGGCACATGAATGACTTCTTCCACTCCTTCCTCATCGTGTTCCGCATCTTGTGTGGCGAGTGGATCGAGACCATGTGGGACTGCATGGAGGTGGCGGGCGCTGGCATGTGTCTCGTTGTCTTCATGATGGTCATGGTCATCGGCAACCTGGTGGTCCTGAACCTCTTCCTGGCTTTGCTGCTGAGCTCGTTCAGTGGAGACAACCTGGCTGGAGGAGACGATGATGGCGAGATGAACAACCTGCAGATTGCCATTGGCAGGATCACCAGAGGCATTGACTGGATAAAAGCCTTTGCCATGGGCTTCATATGGAAGTTACTTGGAAAGAAGGCCAAGATGCTGGATGGTTTATCCCACTGGGTGACCCTGAGTGTTCCCATTGCCCAGGGAGAGTCTGATTTGGAGGATGACTCCTCTGAATGCAGCACGGTGGACTACAGACCCCCAGAACCAGAGGAGGAGGAGGAGCCTGAGCAGCAGGAGCCTGAGGCCTGTTTTACTGAGGATTGCTTCCGGCGTATGCCATGTTTGATGGTGGACATCACGCAGGGGAAGGGCAAGACCTGGTGGAAACTACGGAAAACCTGTTTTACCATTGTGGAGCATGGCTATTTTGAGACCTTCATCATTTTCATGATCCTTCTCAGCAGTGGAGCTCTGGCTTTTGAAGACATATACATTGAAAAGCGCAGAGTTATCAAAATCATCCTGGAATATGCGGACAAAGTCTTCACCTATGTATTTGTTATTGAAATGGTCCTCAAGTGGGTGGCTTATGGGTTCAAAGTATACTTCACAAACGCCTGGTGCTGGCTGGACTTCCTCATCGTTGATGTGTCCTTGATCAGTCTGACCGCTAACCTCATGGGCTACTCTGAGCTGGGGCCCATTAAGTCTCTGAGAACACTTAGGGCCCTTAGGCCCCTGAGGGCCCTCTCCAGGTTTGAGGGGATGAGGGTGGTGGTAAATGCGCTTGTGGGAGCCATCCTCTCCATTTTCAACGTTCTGCTCGTGTGCCTCATCTTCTGGCTCATCTTCAGCATCATGGGCGTTAACCTGTTTGCTGGGAAGTTCTACTACTGCATTAACACCACCTCAGAGGAGCGCTTACCCATTGATGTTGTGAATAACAAGAGCGACTGCATGGCCCTAATGCACACCAATGAGGTGCGCTGGGTCAACGTCAAGGTGAACTATGACAATGTCGGCTTGGGCTATCTCTCTCTGCTGCAGGTGGCTACTTTTAAAGGTTGGATGGATATAATGTATGCTGCCGTGGACTCACGGGAGGTGGGGGAGCAACCCTCCTATGAGGTCAACATCTACATGTACTTGTACTTTGTCATCTTCATCATCTTCGGGTCCTTCTTCACGCTCAACCTCTTCATTGGTGTCATCATTGACAACTTCAATCAGCAAAAGAAAAAGTTAGGAGGAAAAGACATATTCATGACTGAGGAACAGAAGAAGTATTACAATGCCATGAAGAAACTTGGCTCCAAGAAGCCAGTGAAGCCCATCCCACGACCTTCGAATAAAATTCAAGGCATGGTGTTTGACTTCATTACGCAGCAGTTTTTTGATATTTTCATCATGGTACTGATCTGCCTCAACATGGTGACCATGATGGTGGAGACGGATGATCAAAGCGAGGACAAAGAAAATGTCCTCTACCAGATTAACCTGGTCTTCATTGTGATCTTCACCTGCGAGTGCGTCCTCAAAATGTTTGCGCTTAGACAGTACTTCTTCACC puffNa6 CCCATGACCGAAGAGTTCGACTACATGCTTTCAGTGGGAAATCTGGTTTTCACAGGAATCTTCGCGGCGGAAATGTTCTTCAAATTGATCGCCATGGATCCGTACTACTATTTCCAAGTTGGCTGGAACATTTTTGACAGCATCATCGTCACGCTCAGTCTGGTGGAGTTAGGGCTTGCAAACGTCCAGGGGCTGTCCGTCCTCAGGTCCTTCCGTCTGCTTCGGGTCTTCAAACTTGCCAAGTCCTGGCCCACGCTCAACATGCTGATCAAGATTATCGGTAATTCAGTTGGAGCTTTAGGGAATCTGACTTTGGTGCTGGCCATCATCGTCTTCATCTTCGCTGTGGTGGGGATGCAGCTCTTCGGCAAAAGCTACAAGGACTGTGTGTGCAAGATTTCCTCCGACTGCGAGCTGCCACGCTGGCACATGAATGACTTCTTCCACTCCTTCCTCATCGTGTTCCGCATCCTGTGCGGCGAGTGGATCGAGACCATGTGGGACTGCATGGAGGTGGCTGGAGCCGGGATGTGCTTGGTTGTCTTCATGATGGTCATGGTCATCGGGAACCTCGTGGTGTTGAATCTCTTCCTGGCCCTGCTGCTCAGCTCATTCAGCGGAGACAACCTCTCGGTCGGAGACGACGATGGAGAGCTGAACAATCTTCAGATCGCCATCGGAAGGATCACACGAGGCGGCAACTGGCTCAAAGCCTTCTTCATCGGAACGCTTCAACGGGTTCTTGGAAGGGAACCAAAATTGGCAGACGGGATCGCCAACTGTCTTAGTATCACCGTCCCCATCGCCCTGGGAGAGTCGGACTCTGAAGGCGATTCTTCAGTGTGCAGCACAGTGGACTATCAGCCCCCAGAGCCTGAGGAAGAGGAAGAGCCGGACCTGGTGGAGCCAGAGGCCTGCTTCACTGACAACTGTGTGAAGCGCTGGCCTTGTCTGAACGTGGACATCAGCCAGGGGAAAGGAAAGAAGTGGTGGAACCTCCGCAAGACCTGCTTCACTATTGTGGAGCATGACTGGTTTGAGACCTTCATCATTTTCATGATCCTCCTCAGCAGCGGAGCTCTGGCCTTTGAAGACATATACATCGAAAGACGAAGAACCGTGAAAATTGTCCTGGAGTTTGCTGACAAAGTTTTCACCTTCATCTTTGTCATCGAGATGCTCCTGAAATGGGTCGCCTATGGCTTCAAGACCTACTTCACCAATGCCTGGTGCTGGCTGGACTTTTTCATCGTGGACATTTCCCTGATCAGTCTATCTGCCAACTTGATGGGCTTCTCTGACCTCGGACCAATCAAATCGCTCAGAACTCTCAGGGCTCTGCGGCCTCTTCGGGCGCTGTCCAGATTTGAAGGGATGAGGGTGGTGGTGAACGCTCTCATCGGAGCCATTCCCTCCATCTTCAACGTGCTCCTGGTGTGCCTGATCTTCTGGCTCATCTTCAGCATCATGGGAGTGAACCTGTTTGCGGGGAAGTTCTACCGCTGCATCAACACCACCACGGCGGAGCTCTTCCCCATCTCTGTGGTCAACAACAAGAGCGACTGCGTGGCGCTGCAGGCCACGCAGGAGGCCCGCTGGGTCAACGTCAAAGTCAACTACGACAACGTGGCAAAAGGCTACCTGTCGCTGCTTCAAATCGCAACTTTTAAAGGCTGGATGGATATTATGTATCCTGCGGTTGACTCAAGAGAGGTGGAAGAGCAACCTTCTTATGAGATCAACCTCTACATGTACATCTACTTTGTCATCTTTATCATCTTTGGCTCTTTCTTCACGCTGAACCTCTTCATCGGCGTCATCATCGACAATTTCAACCAGCAGAAGAAAAAGTTAGGAGATAAAGACATCTTCATGACAGAGGAGCAGAAAAAGTACTACGAAGCCATGAAGAAACTCGGCTCAAAGAAGCCGCAGAAGCCGATCCCACGTCCAGCTAACCTAATCCAGGGGCTAGTGTTTGACTTCATCAGTCAGCAGTTCTTTGACATCTTCATCATGGTGCTCATCTGTCTCAACATGGTCACCATGATGGTGGAGACGGACGACCAGAGTCCGGCGAAGGAGGACTTCCTCTTCAAAGTGAACGTGGCTTTTATTGTGGTCTTCACCGGGGAGTGCACGTTAAAGCTCATCGCCCTGCGACATTACTTCTTCACC NewZebra CCTATGAGTCCACATTTTGAACATGTCCTCTCAGTGGGCAACTTGGTGTTCACAGGAATCTTCACAGCTGAAATGGTGTTCAAGCTTATAGCTATGGACCCTTACTACTACTTCCAGGTGGGCTGGAACATTTTTGACAGCATCATTGTCACACTCAGCCTGGTGGAGTTGGGACTGGCCAACGTTCAGGGATTGTCCGTTCTAAGGTCCTTTCGTTTGCTACGTGTCTTCAAACTGGCTAAATCTTGGCCCACCCTTAACATGCTGATCAAGATCATCGGCAACTCAGTGGGTGCTCTAGGGAACCTAACACTTGTTCTGGCCATCATTGTCTTCATCTTTGCCGTGGTGGGCATGCAGCTTTTTGGAAAAAGCTACAAGGACTGCGTTTGTAAGATCTCTGAGGATTGCGAGCTGCCCCGCTGGCACATGAACGACTTCTTCCACTCATTCCTCATCGTCTTTCGGATCTTATGTGGAGAGTGGATTGAGACCATGTGGGACTGCATGGAGGTGGCAGGAGCTAGCATGTGTTTGATAGTCTTCATGATGGTCATGGTCATCGGAAACCTTGTGGTGCTGAATCTGTTTCTGGCCCTGCTGCTTAGCTCCTTCAGTGGAGATAACCTGTCTGGAGGTGATGATGATGGAGAGATGAACAACCTTCAGATTGCCATTGGCCGCATCACCAGGGGTATCGATTGGGTTAAAGCCTTAGTTGCCAGTATGGTGCAACGGATTCTGGGAAAGAAACCTAAAATGGCAGATGGTCTGACCAACTGTTTGACATTGACTGTACCTATTGCTCGTTGTGAGTCTGATGTGGAGGGTGACTCTTCGGTTTGTAGCACAGTGGACTACCAGCCTCCAGAACCTGTAGAAGAAGAGGAACCAGAACCTGAAGAACCAGAGGCCTGTTTCACAGAGGGCTGTATTAGGCGATGTGCATGTTTGAGTGTTGACATCACAGAAGGATGGGGTAAAAAATGGTGGAACCTCAGAAGGACATGCTTCACCATCGTTGAGCATGATTACTTTGAGACCTTCATCATCTTTATGATCCTCCTTAGCAGTGGAGCACTGGCTTTTGAGGATATCAACATTGAGAGGCGCAGAGTGATCAAGATCATTCTGGAGTATGCTGATAAAGTCTTTACATATATTTTTATAGTGGAGATGTTACTGAAGTGGGTGGCATATGGCTTCAAGACCTACTTCACTAATGCATGGTGCTGGCTGGACTTCCTCATTGTGGATGTGTCTCTGGTCAGTTTAACGGCTAATTTAATGGGCTATTCTGAGCTGGGGGCAATCAAATCTCTCAGGACACTTAGAGCTCTTCGTCCACTTCGAGCCCTATCCAGATTTGAAGGAATGAGGGTGGTAGTGAATGCACTTGTAGGTGCCATTCCCTCTATTTTTAACGTGCTCCTGGTGTGTCTGATATTCTGGCTCATCTTCAGCATTATGGGGGTCAATCTGTTTGCCGGAAAATTCTACCACTGCATCAACACCACCACAGAGGAACGGATCCCCATGGATGTAGTCAACAACAAGAGTGACTGCATGGCACTGATGTACACCAACGAGGTGCGATGGGTCAATGTCAAGGTCAACTACGACAACGTGGGACTCGGCTACCTCTCTCTGCTGCAGATTGCCACATTCAAAGGCTGGATGGATATCATGTATGCTGCAGTGGATTCTAGAGAGGTGGATGAGCAGCCATCATATGAAATCAACCTTTACATGTACCTTTATTTTGTTATTTTCATCATTTTTGGCTCCTTTTTTACTCTCAACCTCTTTATTGGTGTCATCATTGACAACTTCAATCAGCAAAAATCAAAGTTTGGAGGGAAAGACATTTTCATGACTGAGGAACAGAAAAAGTACTACAATGCCATGAAGAAGCTGGGTGCAAAGAAACGTCCAAAACCTATACCTCGACCATCAAATATTATCCAGGGTTTGGTGTTTGACTTCATATCAAAACAGTTCTTTGACATTTTTATCATGGTGCTAATCTGCCTCAACATGGTGACCATGATGATAGAGACGGATGATCAGAGTGCTGAGAAAGAATATGTCCTGTACCAGATCAATCTGGTCTTCATCGTCGTCTTCACAAGCGAATGTGTACTTAAATTATTTGCACTCAGACAGTACTTTTTCACT SterNa6 CCCATGAGCGAAACCTTTCAACACGTGCTCACCATAGGGAACCTGGTGTTTACTACCATCTTTACGGCTGAAATGGTGTCGAAGATCATCGCCCTGGACCCTTACTACTACTTCCAGGTGGGCTGGAACATCTTCGACTGCATCATCGTCACTCTCAGTCTGGTGGAGCTAAGCCTATCCAACATGCCGGGCCTGTCTGTGCTCAGATCCTTTCGTTTGATGCGTATTTTCAAGCTGGCCAAGTCCTGGCCCACGCTCAACATGCTGATCAAGATCATCGGCAACTCAATGGGCGCCCTGGGGAACCTGACCTTCGTGTTGGCCATCGTCATCTTCATCTTCGCCGTGGTGGGCTTCCAGCTGTTCGGGAAGAGCTACAAGGACAACGTGTGCAAGGTCAGCGCGGACTGCACGCTGCCTCGCTGGCACATGAACGACTTCTTCCACTCCTTCCTGATCGTGTTTCGCATCCTGTGCGGCGAGTGGATCGAGACCATGTGGGACTGCATGGAGGTGGACGGAGTGCCCATGTGCCTCACCGTCTTCATGATGGTCATGGTCATCGGAAACCTGGTGATGCTGAACCTGTTCCTTGCCTTGCTTCTCAGCTCATTCAGCTGCGACAATCTTGCCGCGCCAGACGATGACAGTGAAGTTACCAACATCCAGATCTCCATTGTGCGCATCAGCAGAGGGATAAGCTGGGTGAAGAAATTCATTGTAGGCACAGCCTGGTGGATCATGGGCAGGAAGCCCAAGATTGTAGATGGGATTACCAACTATGTTGTTCTGAATGTGCCTATTGCCAAGGGGGAGTCTGAGGTTGAGGATGACTCTTCGATTTGCAGTTCAGTGGACTACGAGCTTCTACAACCCGAGGAGGAAAAGGAA---GAGCCTGTTGATCCAGAAGCCTGTTTTACAGAAAACTGTGTGAGGTACTTTCCATGTCTGGATGTGGACATCACACAGGGGAAAGGGAAGATCTGGTGGAACCTCCGCTGCACCTGCTACAACATCGTGGAACATCACTATTTTGAAAACTTTCTCATCTTCATGATTCTCCTCAGTAGTGGAGTACTGGCATTCGAGGATGTTAATATCGAACGCCGCAGGGTCATTAAGACCATGTTGGAGTATGCAGACATAGTCTTCACATATATTTTCGTGGTGGAGATGTTTCTGAAGTGGACTGCATATGGGTTTAAAGCGTACTTCACCAGTGCCTGGTGCTGGCTGGATTTTTTTATTGTTGATGTGTCAGTTATTAGCTTAGTAGCCAATGTGTTGGGCTATGCAGAGCTGGGACCAGTCAGATCGCTCAGAACTTTCAGGGCTCTTCGACCTTTACGTGCCCTTTCCAGATTTGAAGGAATGAGGGTGGTAGTGAATGCATTGCTCGGTGCCATCCCCTCCATCATGAACGTCCTATTGGTGTGTCTGATCTTCTGGCTGATCTTCAGCATCATGGGGGTCAACTTGTTTGCGGGAAAGTTCTACCGCTGCATTAACACCACCACAGATGAGGTTCTGTCCACAGAGCAAGTGAACAACAGGAGTGAATGCATGGCACTAATGCACACTAATGAGGTGCGTTGGGTCAACCTTAAGGTCAACTACGACAATGTGGGCCAGGGATATCTCTCCTTGCTTCAAGTGGCCACATTTAAAGGGTGGATGGGCATCATGTATGGTGCAGTGGACTCTAGAGAGGTAGAGGATCAGCCATCATATGAGATTAACCTCTACATGTACCTGTACTTTGTCATCTTCATCACATTTGGATCCTTTTTTATCCTCAACCTTTTCATTGGTGTCATCATTGACAATTTTAACCGGCAAAAACAAAAGTTAGGAGGAGATGACCTCTTTATGACAGATGAACAAAAAAAGTATTATGCTGCCATGAAGAAGCTGGGTTCCAAGAAACCACTCAAACCTATACCCCGTCCTTCGAATATGGTTCAAGGGGTGGTGTTCGACTTCATCTCCCAAAAGTTCTTTGACATTTCCATCATGGTTCTCATCTGCCTCAACATGGTGATCATGATGGTGGAGGCGGACGACCAGAGTGAAGAGAAAGAGAATGTCCTCTATCAGATCAATATCATATTTATTGTCNTCTTCACCGGAGAGAGTTTACTCAAGTTGTTTGGACTTAGACATTACTTCTTCACT eelNa6 CCCATGAACGAAAGCTTTCAGAGTCTGCTCAGTGCAGGAAACCTGGTGTTTACCACTATCTTTGCGGCTGAAATGGTGTTGAAGATCATTGCCTTGGACCCCTACTACTACTTCCAGCAGACGTGGAACATATTTGACAGCATCATTGTCAGTCTCAGTCTGTTGGAGCTTGGACTATCCAATATGCAAGGAATGTCTGTGCTCAGATCCTTACGTTTGCTGCGTATCTTCAAATTGGCCAAGTCCTGGCCCACGCTCAACATTCTGATCAAGATAATCTGCAACTCGGTGGGCGCTCTGGGCAACCTGACCATTGTGCTGGCCATTATCGTCTTCATCTTCGCCTTGGTGGGCTTTCAGCTGTTCGGAAAGAACTACAAGGAGTACGTGTGCAAGATCTCTGATGACTGTGAGCTGCCCCGCTGGCACATGAATGACTTCTTCCACTCCTTCCTGATTGTGTTCCGTGCCTTGTGTGGCGAGTGGATTGAGACCATGTGGGACTGCATGGAGGTGGGCGGAGTTCCTATGTGCCTCGCCGTCTACATGATGGTCATAATCATTGGGAACCTGGTGATGCTGAACCTTTTCCTTGCCTTGCTTCTAAGCTCATTCAGCAGCGACAATCTCAGTTCAATTGAAGAAGATGATGAAGTTAACAGCCTCCAGGTTGCCTCTGAGCGCATTAGTAGGGCAAAAAACTGGGTGAAGATCTTCATCACTGGCACAGTCCTGTGGATCCAGGGCAAGAAGCCCAAGATTGTAGATGGGATAACCAACTGTGTAACTCTGAATCTACCCATTGTAAAGGGGGAGTCAGAGATCGAAGAAGACTCTTCAGTTTGTAGTACAGTGGACTATAGTCCTTCAGAACAAGAGGAGCCAGAGGAACTAGAGTCCAAAGATCCAGAAGCATGTTTTACAGAAAAATGTATATGGCGATTTCCTTTTCTGGATGTGGACATCACACAGGGGAAAGGGAAGATCTGGTGGAACCTACGTAGGACCTGCTACACCATCGTGGAGCATGACTACTTTGAAACCTTCATCATATTCATGATTCTCCTCAGTAGTGGAGTTCTGGCCTTTGAGGACATTTATATTTGGCGTCGCAGGGTGATTAAGGTCATCTTGGAGTATGCAGACAAAGTCTTCACATATGTCTTCATAGTAGAGATGTTACTTAAGTGGGTTGCATATGGGTTTAAAAGATATTTCACTGATGCCTGGTGCTGGCTCGACTTTGTAATTGTTGGTGCATCAATAATGGGCATAACATCCAGTTTGTTGGGCTATGAAGAGCTGGGAGCAATCAAAAATCTCAGAACTATCAGGGCTCTTCGCCCTTTACGTGCCCTTTCCAGATTTGAAGGAATGAAGGTGGTAGTGAGAGCATTGCTTGGTGCCATCCCCTCCATCATGAACGTGCTGCTGGTGTGTCTGATGTTCTGGCTCATCTTCAGCATTATGGGGGTCAATTTGTTTGCTGGAAAGTTCTACCGCTGCATTAACACCACCACAGATGAGATTCTGCCCGTGGAGGAAGTGAACAACCGGAGTGACTGCATGGCACTAATGTACACTAACGAGGTGCGCTGGGTCAACCTTAAGGTCAACTATGACAATGCGGGCATGGGATACCTCTCCCTGCTACAAGTGTCTACATTTAAAGGCTGGATGGACATCATGTATGCTGCAGTGGACTCCAGAGAGGTAGAGGATCAGCCAATCTATGAGATTAATGTCTACATGTACCTGTATTTTGTCATCTTCATCGTATTTGGAGCCTTCTTTACCCTCAACCTTTTCATTGGTGTCATCATAGACAACTTCAACCGTCAAAAGCAAAAGTTAGGAGGAGAAGATCTCTTTATGACAGAAGAGCAGAAGAAGTACTACAATGCCATGAAGAAGCTGGGTTCGAAGAAAGCTGCCAAATGTATACCCCGCCCTTCGAATGTGGTTCAAGGTGTGGTGTACGACATAGTCACCCAACCATTCACTGATATTTTCATCATGGCTCTCATTTGCATCAACATGGTGGCTATGATGGTCGAGTCGGAGGACCAGAGTCAAGTGAAGAAGGACATTCTCTCTCAGATCAATGTCATATTCGTTATCATCTTCACTGTAGAGTGCTTGTTAAAGCTACTTGCACTTAGACAGTACTTCTTCACT catNa6 CCCATGAGTTCGAACTTTGAACACGTGCTCAGTGTTGGCAATTTGGTGTTCACTGGTATTTTCACGGCTGAAATGGTGTTCAAGCTCATTGCCTTGGACCCCTTCTACTACTTCCAGGTTGGCTGGAACATATTTGACAGCATCATCGTCACTCTTAGCCTGGTGGAGTTAGGCCTGGCCAATGTGCAGGGTCTGTCTGTACTCAGATCCTTTCGTTTGCTGCGAGTCTTTAAGCTGGCTAAATCCTGGCCCACGCTCAACATGCTGATCAAAATCATTGGAAACTCTGTGGGTGCTCTGGGGAACCTGACTCTGGTGCTGGCCATCGTCGTCTTCATCTTCGCCGTCGTAGGCATGCAACTTTTTGGCAAGAGCTACAAGGACTGCGTGTGTAAGATTGCAGAGGACTGCGAACTGCCCCGCTGGCACATGAACGATTTTTTCCATTCGTTTCTCATTGTCTTCCGCATCCTTTGTGGTGAATGGATTGAAACCATGTGGGACTGCATGGAGGTGGCTGGAGCAGGCATGTGCCTTGTGGTTTTCCTTATGGTCATGGTCATAGGAAACCTGGTGGTGCTGAACCTGTTCCTTGCCTTGTTGCTCAGCTCTTTCAGCGGGGACAATCTCTCAGCAGGTGATGAAGATGGTGAAATGAACAATCTCCAGATTGCCATCGGCCGCATCACCAGGGGCATTGACTGGGTCAAATCCTTCATCATTGGCCTTGTACAGCAGATACTTTGCAGGAAGCCTAAGATGGCAGATAGGTTGACCAACTGTCTGACCCTGAATGTACCAATTGCCAAAGCTGAGTCTGATGTTGAAGAAGACTCTTCAATGTGTAGCACAGTGGACTATAGACCTCCAGAATCCGAGGAGGAAGAGGAACCAGAACCTGTTGAGCCAGAAGCCTGTTTTACTGAAAACTGTGTGAGACGATGTCCATGTCTGAATTTGGACATCACTCAGGGGAGGGGAAAGAGTTGGTGGAATCTGCGCAGAACTTGCTACACCATAGTGGAGCATGATTACTTTGAAACCTTCATCATCTTCATGATTCTCCTCAGTAGTGGTGCACTGGCCTTTGACGACATTTACATTGAGCGTCGCAGGGTGATTAAGATTATCTTGGAATATGCAGACCAAGTCTTCACATATATTTTTGTCATAGAGATGTTACTGAAATGGGTTGCGTATGGCTTCAAGACATACTTCACCAATGCCTGGTGCTGGCTGGACTTTTTCATCGTTGATGTGTCACTTATCGGTTTAACGGCAAATCTGTTGGGCTATTCAGAGCTGGGACCAATAAAATCTCTCAGAACTCTTAGGGCGCTTCGACCTTTACGTGCCCTGTCCAGATTTGAAGGAATGAGGGTGGTAGTGAACGCATTGCTGGGTGCCATTCCTTCCATCATGAATGTACTCCTGGTGTGTCTAATATTCTGGCTGATCTTCAGTATTATGGGGGTCAACCTGTTTGCTGGGAAATACTACCGCTGCATTAATACCACCACAGAAGAACTTTTACCCATCGAGCAAGTGAACAACATGAGTGATTGCATAGCACTAATGCACACTAAAGAAGCACGCTGGGTCAATGTCAAGGTCAACTTTGACAATGTGGGCTTGGGTTACCTTTCCCTGCTACAAGAGGCTACATTTAAAGGCTGGATGGACATTATGTATGCTGCAGTGGATTCCAGAGAGGTGGAAGAACAGCCATCATATGAGATTAACATATATATGTATCTGTATTTTGTCATCTTCATCATCTTTGGCTCCTCCTTCACCCTCAACCTCTTCATTGGTGTCATCATTGACAACTTTAATCAGCAAAAGCAAAAGTTTGGTGGGGAAGATCTCTTCATGACAGAGGAGCAGAAAAAGTACTACAATGCCATGAAAAAGCTTGGTTCCAAGAAGCCCGTCAAACCCATACCTCGCCCTGCGAATATGATCCAGGGCATAGTGTTTGACTTCATCTCTCAGCAGTTCTTTGACATTTTCATCATGGTGCTCATTTGCCTCAACAAGGTTACCATGATGATTGAGACAGATGACCAAAGTGCAGAGAAAGAATATGTTCTCTATCAGATCAACTTAATCTTCATTGTTGTCTTCACTGGGGAGTGCATCCTCAAAATGTTTGCACTGAGACAATACTTTTTCACT AptNa6 ---------------------------CTCACTGTGGGGAACCTGGTGTTTACTGGCATCTTTACGGCTGAAATGGTGTTTAAGCTCATTGCCATGGACCCCTACTACTACTTCCAGGTGGGCTGGAACATCTTCGACAGCATCATCGTCACCCTCAGTCTGGTGGAGCTGGGGCTAGCCAACGTGCAGGGTCTGTCTGTGCTCAGGTCCTTCCGTTTGCTGCGTGTCTTCAAGTTGGCCAAGTCCTGGCCAACGCTCAATATGCTCATCAAGATCATTGGCAACTCGGTGGGAGCCCTGGGCAACCTGACACTGGTGCTGGCCATTATTGTCTTCATCTTTGCCGTGGTGGGCATGCAGCTATTTGGGAAGAGCTACAAGGACTGCGTGTGCAAGATTGCGCTGGACTGCGAGCTTCCCCGCTGGCACATGACGGACTTCTTCCACTCCTTCCTGATCGTGTTCCGCATCCTATGCGGCGAGTGGATTGAGACCATGTGGGACTGCATGGAGGTGGCTGGACCGTCCATGTGCCTCATCGTCTTCATGTTGGTCATGGTCATTGGGAACCTGGTGGTGCTGAACCTGTTCCTTGCATTGCTTCTCAGCTCATTCAGCGGTGACAATCTCTCGGCAAGCGACGATGACAGTGAGATTAACAACCTCCAGATCGCCACAGGGCGCATCAGCAGAGCGATTGGCTGGGTGAAGAACTTTATCATCAGCACAGTCCAGTGGGTTCTGGGCAGAAAGCCCAAGATGGTGGATGGCATGACCAACTGCGTAGTCCTGAATGTGCCCATTGCCAAGGGGGAATCTGAGATTGAAGGAGACTATTCAGTTTGCAGTACAGCAGACTACAGACCTCCAGAACCCGAGGAGGAAAAGGTACCAGAGACCAATGATCCAGAAGCCTGCTTTACAGAAAATTGTGTGAGGCGATTTCCTTGTCTCAATGTGGACATCACCCAGGGGAAAGGGAAGAGCTGGTGGAACCTACGCAGAACCTGCTACATCATCGTGGAGCATGACTACTTTGAGACCTTCATCATCTTCATGATTCTCCTCAGTAGCGGAGCACTGGCTTTCGAGGACATTTATATAGAGCGTCGCAAGATGATTAAGATCATCTTGGAGTACGCAGACAAAATCTTCACCTATGTTTTCATAATGGAGATGTTACTGAAGTGGGTTGCTTATGGGTTTAAAACGTACTTCACCAATGCCTGGTGCTGGCTGGACTTTCTTATTGTTGATGTGTCAATTATTAGCTTAACAGCCAATCTGTTGGGCTATTCAGAGCTGGGACCAATCAAATCTCTCAGAACACTCAGGGCTCTTCGACCGCTACGTGCCCTTTCCAGATTTGAAGGAATGAGGGTGGTAGTGAATGCGTTGGTTGGCGCCATCCCCTCCATCATGAACGTGCTGCTGGTTTGTCTGATCTTCTGGCTCATCTTCAGTATCATGGGGGTCAACTTGTTTGCTGGAAAGTTCTACCGCTGCATTAACACTACCACCGAGGAGCTTCTGCCCATGGAGGAAGTGAACAACAGGAGTGATTGCATGGCGCTAATGCACACTAATGAGGTGCGCTGGGTCAATGTCAAGGTGAACTACGACAACGTCGCCCTGGGATACCTTTCCCTGCTGCAAGTGGCTACATTTAAAGGCTGGATGGACATCATGTATGCTGCAGTGGACTCCAGAGAGGTAGAGGAGCAGCCATCATATGAGATTAACCTCTACATGTACCTGTACTTTGTCATCTTCATCATATTGGGATCCTTTTTTACCCTCAACCTTTTCATTGGTGTCATCATAGACAACTTCAACAGGCAGAAGCAAAAGTTTGGAGGAGAAGATCTCTTTATGACGGAGGAGCAGAAGAAGTACTACAATGCCATGAAGAAGCTGGGATCCAAGAAGCCTGTCAAACCTATACCCCGTCCTACGAATGTTATTCAAGGTGTGGTGTTCGACCTCATTTCCCAGCAGTTCTTTGATATTTTCATCATGGTTCTCATTTGCCTCAACATGGTGACCATGATGGTGGAGACTGATGACCAGAGCAAAGAGAAAGAGCACATCCTCTATCAAATCAACGTCATATTCATTGTCGTCTTCACTGGAGAGTGTTTGCTCAAGATGTTTGCACTGAGGCAGTACTTCTTCACT PinniNa6 CCCATGAGTGAAACGTTTGATTACGTCCTCAGCACAGGGAACCTGGTGTTTACCATCATCTTTGCAGCTGAAATGGTCTTGAAGCTCATTGCCATGGACCCCTACTACTACTTCCAGCAGACGTGGAACATCTTTGACTTTTTCATTGTCTCACTCAGTCTGGTGGAGATGGGACTGGCTAACATGCAGGGGCTGTCAGTGCTTAGGTCCTTTCGACTGCTGCGTATCTTTAAGTTGGCCAAGTCCTGGCCCACGCTCAATATTCTGATCAAGATCATCTGCAACTCGGTGGGCGCCCTGGGAAACCTGACCATCGTGCTGGCCATTATCGTCTTCATCTTCGCCTTGGTGGGCATGCAGCTGTTCGGGAAGAATTACAAAGAGTTTGTGTGCAAGATCAGTGCAGACTGTACGCTGCCTCGCTGGCATATGAATGACTTCTTCCATTCCTTCCTGATTGTGTTCCGCTGCCTGTGCGGCGAGTGGATTGAGACTATGTGGGACTGTATGGAGGTGGGCGGTGTGCCCATGTGCCTCAGCGTTTACATGATGGTCATAATCATCGGGAACCTGGTGGTGCTGAACCTGTTCCTTGCCTTACTGCTAAGCTCATTCAGTGGTGACAATCTCACTGCAAACGATGATGACCAAGAGGATAACAACATCCTGATTGCAGCTGAGCGGATCAGCAGGGCAAAACTCTGGGTGAAGGGGTTCATAATACGGACGGTCTTGGGGATGCTGGGCAAGGAGCCAAAGATTGTGAATGGGCTAGCCAACGGTGTAGTTCTGAATGTGCCCATTGCCAAGGGCGAGTCTGAGACTGAAGATGACTCTTCAGTCTGCAGTACAGTGGACTACAGTCCTCCAAATCCAGAGGAACCCGAGGAACCAGAACCCGATAATCCAGAAGATTGTTTAACGGAAGAATGTGTGTCACGATTTCCTTGGCTGAATGTGGACATAACACAGCCAAAAGGGAAGAGTTGGTGGAACCTTCGTAGGACATGCTACGTCATCGTAGAGCATGACTACTTTGAGACTTTCATCATCTTCATGATTCTCCTCAGTAGTGGAGCACTGGCTTTCGAGGACATTTATATTGAGCGTCGCAGGGTGATTAAGATCATCTTGGAGTATGCGGACAAAGTCTTCACATATATTTTCATAGCAGAGATGTTACTGAAGTGGGTTGCATATGGGTTTAAAAAGTACTTCTCCGACGCCTGGTGCTGGTTAGACTTTCTAATTGTTGATGTGTCAATAATTAGCTTAACAGCCAATTTGTTGGGCTATTCAGAGTTGGGACCAATCAAATCTCTCAGAACTCTCAGGGCTCTTCGACCTTTACGTGCACTTTCCAGATTTGAAGGAATGAGGGTGGTAGTCAAAGCATTGGTTGGCGCCATCCCCTCCATCGTGAACGTGCTGCTGGTATGTCTCATGTTCTGGCTCATCTTCAGCATTATGGGAGTCAACTTGTTTGCTGGAAAGTTCTACCGCTGCATTAACACTACCACAGAAGAGACCATGCCCYTGGAAGAAGTCAACAACCGCAGTGACTGCAATGCACTTATGTACACTAATGAGGTGCGATGGGTCAACCTTAAGGTCAACTATGACAATGCAGGCATGGGATACCTCTCCCTGCTACAAGTGGCAACATTTAAAGGTTGGATGGACATCATGTATGCTGCAGTGGACTCCAGAGGGGTAGAGGATCAGCCGATATACGAGATTAACGTCTACATGTACCTGTATTTTGTCATCTTCATCGTATTTGGATCCTTTTTCACCCTAAACCTCTTCATTGGTGTCATCATAGACAACTTCAACCGTCAAAAGCAAAAGTTAGGAGGAGATGATCTCTTTATGACAGAAGAACAGAAAAAGTATTATGATGCCATGAAGAAGCTGGGTTCCAAGAAACCTGTCAARGTTATACCACGCCCTTCGAACAAGATTCTGGGTGTGTTGTATGACATAGTCAACCAACGGGTCACTGATATTTTCATCATGTCTCTCATTTGGCTAAACATGGTTACCATGATGGTGGAGACAGATGACCAGAGCGAAGAAAAGAAGAATGTTCTCTATCAGATCAATTTAATATTCATTATCATCTTCACTGGAGAATGTCTGCTCAAGTTGCTTGCACTAAGACATTACTTCTTCACT tetra CCCATGACCCAGGAGTTCGACTACATGCTTTCAGTGGGAAATCTGGTTTTCACAGGAATTTTTGCAGCAGAAATGTTCTTCAAGCTGATCGCCATGGATCCGTACTACTATTTCCAAGTTGGCTGGAACATTTTTGACAGCATCATTGTCACCCTCAGCCTGGTAGAGTTGGGGCTTGCGAACGTCCAGGGCCTGTCTGTCCTCAGGTCCTTCCGCCTGCTCCGTGTCTTCAAACTTGCCAAATCCTGGCCCACACTCAACATGCTGATCAAGATTATTGGGAGCTCAGTTGGAGCGCTAGGGAATCTGACGTTGGTGCTGGCCATCATCGTCTTCATCTTCGCTGTGGTGGGGATGCAGCTCTTTGGCAAAAGCTACAAGGACTGCGTGTGCAAGATTTCCACGGAGTGCGAGCTGCCGCGCTGGCACATGAATGACTTCTTCCACTCCTTCCTCATCGTCTTCCGCATCCTGTGTGGCGAATGGATCGAGAACATGTGGGCCTGCATGGAAGTGGCTGGAGCTGGGATGTGCTTAGTTGTCTTCATGATGGTCATGGTGATTGGAAACCTCGTGGTGTTGAACCTCTTCCTGGCCCTGCTGCTCAGCTCGTTCAGCGGGGACAATCTGTCCATCGGAGAGGACGATGGAGAGATGAACAATCTTCAGATTGCCATCGGCAGAATCACACGAGGTGGAAACTGGCTCAAGACCCTTGTCATCAGAACGGTCCTGCAGCTTCTCGGTAGGGAGCAGAAAACGGCAGATGGGATAGCTAACTGTCTTGTTATCAACGTCCCCATCGCCTTGGGGGAGTCAGACTCTGAAGGCGAGTCTTCAGTGTGCAGCACAGCAGACTATCGGCCCCCCGAGCCTGAGGAAGAGGAAGAGCCGGAACCACTGGAGCCAGAGGCCTGCTTTACTGACAACTGCGTCAAACACTGGCCTTGTCTGAACGTGGACGTCACCCAAGGTCAAGGGAAGAAGTGGTGGAACCTCCGCAAGACCTGCTTCACAATCGTAGAGCATGACTGGTTTGAGACCTTCATCATCTTCATGATCCTCCTCAGCAGCGGAGCCCTGGCCTTTGAAGATATATACATCGAAAGACGAAGAACCGTCAAAATTATCCTGGAGTTTGCCGACAAAGTTTTCACCTTCATCTTTGTCCTTGAGATGGTGCTGAAATGGGTGGCCTATGGCTTCAAGACCTACTTCACCAACGCCTGGTGCTGGTTGGACTTTTTCATTGTAGACATTTCCCTGATCAGTTTATCGGCCAACCTGATGGGCCTCTCTGACCTGGGACCAATCAAATCTCTCAGAACACTCCGGGCACTGAGGCCTCTTCGAGCTCTGTCCAGATTTGAAGGGATGAGGGTGGTGGTGAACGCTCTTATCGGAGCCATTCCCTCCATCTTCAACGTGCTGCTGGTGTGCCTGATCTTCTGGCTCATCTTCAGCATCATGGGAGTGAACCTGTTTGCGGGGAAGTTCTACCACTGCATCAACACCACCACACAGGAGCTCTTCCCCATCTCTGTGGTCAACAACAAGAGCGACTGCATGGCCGTCCAGGCCACGCAGGAGGCCCGCTGGGTCAACGTCAAGGTCAACTACGACAACGTGGGAAAAGGCTACCTGTCGCTGCTTCAAATCGCCACTTTTAAAGGCTGGACGGCCATTATGTATGCTGCAGTAGATTCAAGAGAGGTGGAAGAGCAACCTTCCTATGAGATCAACCTGTACATGTACATCTACTTTGTCATCTTCATCATCTTTGGCGCTTTCTTCACGCTCAACCTGTTCATCGGCGTCATCATCGATAACTTCAACCAGCAGAAGAGAAAGATA---AACAAAGACATCTTCATGACGGAGGAGCAGAAAAAGTACTACGAAGCCATGAAGAAACTCGGCTCCAAGAAGCCGCAGAAGCCGATCCCACGTCCGACCAACCTCATCCAGGGAATGGTGTTTGACTTCATCAGTCAGCAGTTCTTTGACATCTTCATCATGGTGCTCATCTGCCTCAACATGGTGACCATGATGGTGGAGACGGACGACCAGAGCCCCGAGAAGGAGGATTTCCTCTTCAAAGTGAACGTGGCTTTTATCGTGGTCTTCACGGGGGAGTGCATGCTGAAGCTCATCGCCCTGCGACAGTACTTCTTCACC ; end; garli-2.1-release/example/partition/000077500000000000000000000000001241236125200175325ustar00rootroot00000000000000garli-2.1-release/example/partition/EXAMPLES.txt000066400000000000000000000013361241236125200215140ustar00rootroot00000000000000 Included here are template config files and example analysis results for partitioned analyses. Looking at the .screen.log files in the example runs (especially the end) will give you a feeling for what the program is estimating and what the results will consist of. For the template config files, "large data" generally refers to alignments with more than about 50 sequences. Note that to use the template configs you'd still need to specify the partitioning of your data, which is done in the nexus file containing your alignment (sorry, nexus format required for this). See this page for help on doing this: http://www.nescent.org/wg_garli/Using_partitioned_models or look at the datafiles in the example runs directory. garli-2.1-release/example/partition/exampleRuns/000077500000000000000000000000001241236125200220355ustar00rootroot00000000000000garli-2.1-release/example/partition/exampleRuns/3parts.diffModelTypes/000077500000000000000000000000001241236125200261665ustar00rootroot000000000000003diffModels.byCodonPos.best.all.tre000066400000000000000000000103061241236125200345430ustar00rootroot00000000000000garli-2.1-release/example/partition/exampleRuns/3parts.diffModelTypes#NEXUS begin trees; translate 1 MorNa6, 2 ClownNa6, 3 AraNa6, 4 puffNa6, 5 NewZebra, 6 SterNa6, 7 eelNa6, 8 catNa6, 9 AptNa6, 10 PinniNa6, 11 tetra; tree rep1BEST = [&U][!GarliScore -13315.772][!GarliModel S 0.541083 0.300389 2.158528 M1 r 1.96588 2.57892 1.41068 1.41068 3.72213 1.00000 e 0.31012 0.17667 0.29716 0.21605 a 0.40969 M2 r 4.38001 7.08451 1.60936 7.08451 4.38001 1.00000 e 0.26922 0.16358 0.16049 0.40670 a 0.36093 M3 r 1.00000 4.93590 3.39070 0.45684 4.93590 1.00000 e 0.15654 0.35368 0.28766 0.20212 a 4.01423 p 0.03350 ](1:0.21759518,(3:0.12609984,((4:0.05332794,11:0.06160710):0.18946162,(5:0.14365194,(8:0.09122513,(9:0.05810216,(6:0.11278706,(7:0.09080192,10:0.10109035):0.05163352):0.02472848):0.07917932):0.05294966):0.04642114):0.06297315):0.02293784,2:0.10628007); tree rep2 = [&U][!GarliScore -13315.77229666][!GarliModel S 0.541345 0.300381 2.158273 M1 r 1.96172 2.57460 1.40818 1.40818 3.71484 1.00000 e 0.31015 0.17669 0.29714 0.21602 a 0.40961 M2 r 4.40047 7.11569 1.61706 7.11569 4.40047 1.00000 e 0.26920 0.16358 0.16053 0.40669 a 0.36105 M3 r 1.00000 4.93800 3.39310 0.45719 4.93800 1.00000 e 0.15654 0.35368 0.28766 0.20212 a 4.01602 p 0.03354 ](2:0.10626455,(((11:0.06161183,4:0.05330808):0.18948346,(5:0.14366753,((9:0.05810558,(6:0.11278082,(7:0.09078922,10:0.10108717):0.05164893):0.02472738):0.07918203,8:0.09121671):0.05293968):0.04640997):0.06297128,3:0.12608006):0.02294454,1:0.21758982); tree rep3 = [&U][!GarliScore -13315.77224255][!GarliModel S 0.540836 0.300086 2.159077 M1 r 1.96446 2.57738 1.40996 1.40996 3.71994 1.00000 e 0.31014 0.17667 0.29716 0.21603 a 0.40977 M2 r 4.38218 7.08739 1.61042 7.08739 4.38218 1.00000 e 0.26921 0.16357 0.16049 0.40673 a 0.36094 M3 r 1.00000 4.93832 3.39276 0.45687 4.93832 1.00000 e 0.15654 0.35368 0.28765 0.20213 a 4.00776 p 0.03342 ](2:0.10629075,(((4:0.05333074,11:0.06162012):0.18950693,(5:0.14367501,(8:0.09123564,(9:0.05810956,((7:0.09080952,10:0.10110478):0.05165007,6:0.11280368):0.02472727):0.07919223):0.05296064):0.04642498):0.06299376,3:0.12611788):0.02294343,1:0.21765011); tree rep4 = [&U][!GarliScore -13315.77217788][!GarliModel S 0.540963 0.300207 2.158830 M1 r 1.96456 2.57739 1.40997 1.40997 3.72040 1.00000 e 0.31015 0.17665 0.29715 0.21604 a 0.40965 M2 r 4.38364 7.09014 1.61093 7.09014 4.38364 1.00000 e 0.26922 0.16357 0.16049 0.40673 a 0.36086 M3 r 1.00000 4.93944 3.39448 0.45719 4.93944 1.00000 e 0.15653 0.35369 0.28766 0.20212 a 4.01150 p 0.03349 ](1:0.21760998,(((11:0.06163237,4:0.05331949):0.18948609,((8:0.09121350,(9:0.05809253,((7:0.09080617,10:0.10108228):0.05163812,6:0.11278987):0.02474678):0.07918892):0.05295520,5:0.14366297):0.04643880):0.06295288,3:0.12610141):0.02294421,2:0.10625545); tree rep5 = [&U][!GarliScore -13315.77223006][!GarliModel S 0.541067 0.300689 2.158244 M1 r 1.96243 2.57513 1.40854 1.40854 3.71682 1.00000 e 0.31016 0.17668 0.29715 0.21601 a 0.40971 M2 r 4.38087 7.08608 1.60937 7.08608 4.38087 1.00000 e 0.26923 0.16359 0.16050 0.40668 a 0.36115 M3 r 1.00000 4.93780 3.39315 0.45723 4.93780 1.00000 e 0.15654 0.35368 0.28767 0.20211 a 4.01707 p 0.03353 ](1:0.21757792,(((5:0.14364584,(8:0.09121837,(9:0.05810218,(6:0.11278051,(10:0.10108817,7:0.09079318):0.05163517):0.02472655):0.07917323):0.05294520):0.04642154,(4:0.05332531,11:0.06160436):0.18944432):0.06296281,3:0.12609271):0.02293983,2:0.10627390); end; [M1 begin paup; clear; gett file=3diffModels.byCodonPos.best.all.tre storebr; lset userbr nst=6 rclass=( 0 1 2 2 3 4 ) rmat=(1.96587554 2.57891825 1.41068018 1.41068018 3.72213378) base=(0.31012353 0.17666541 0.29716350) rates=gamma shape= 0.40969102 ncat=4 pinv= 0.00000000; end; ] [M2 begin paup; clear; gett file=3diffModels.byCodonPos.best.all.tre storebr; lset userbr nst=6 rclass=( 0 1 2 1 0 3 ) rmat=(4.38001120 7.08450573 1.60935984 7.08450573 4.38001120) base=(0.26922421 0.16357952 0.16049220) rates=gamma shape= 0.36092727 ncat=4 pinv= 0.00000000; end; ] [M3 begin paup; clear; gett file=3diffModels.byCodonPos.best.all.tre storebr; lset userbr nst=6 rclass=( 0 1 2 3 1 0 ) rmat=(1.00000000 4.93590137 3.39069798 0.45684430 4.93590137) base=(0.15654232 0.35367871 0.28765655) rates=gamma shape= 4.01423139 ncat=4 pinv= 0.03350150; end; ] 3diffModels.byCodonPos.best.tre000066400000000000000000000022141241236125200337730ustar00rootroot00000000000000garli-2.1-release/example/partition/exampleRuns/3parts.diffModelTypes#NEXUS begin trees; translate 1 MorNa6, 2 ClownNa6, 3 AraNa6, 4 puffNa6, 5 NewZebra, 6 SterNa6, 7 eelNa6, 8 catNa6, 9 AptNa6, 10 PinniNa6, 11 tetra; tree bestREP1 = [&U][!GarliScore -13315.772132][!GarliModel S 0.541083 0.300389 2.158528 M1 r 1.96588 2.57892 1.41068 1.41068 3.72213 1.00000 e 0.31012 0.17667 0.29716 0.21605 a 0.40969 M2 r 4.38001 7.08451 1.60936 7.08451 4.38001 1.00000 e 0.26922 0.16358 0.16049 0.40670 a 0.36093 M3 r 1.00000 4.93590 3.39070 0.45684 4.93590 1.00000 e 0.15654 0.35368 0.28766 0.20212 a 4.01423 p 0.03350 ](1:0.21759518,(3:0.12609984,((4:0.05332794,11:0.06160710):0.18946162,(5:0.14365194,(8:0.09122513,(9:0.05810216,(6:0.11278706,(7:0.09080192,10:0.10109035):0.05163352):0.02472848):0.07917932):0.05294966):0.04642114):0.06297315):0.02293784,2:0.10628007); end; [ S 0.541083 0.300389 2.158528 M1 r 1.96588 2.57892 1.41068 1.41068 3.72213 1.00000 e 0.31012 0.17667 0.29716 0.21605 a 0.40969 M2 r 4.38001 7.08451 1.60936 7.08451 4.38001 1.00000 e 0.26922 0.16358 0.16049 0.40670 a 0.36093 M3 r 1.00000 4.93590 3.39070 0.45684 4.93590 1.00000 e 0.15654 0.35368 0.28766 0.20212 a 4.01423 p 0.03350 ] 3diffModels.byCodonPos.log00.log000066400000000000000000003176661241236125200337720ustar00rootroot00000000000000garli-2.1-release/example/partition/exampleRuns/3parts.diffModelTypesSearch rep 1 (of 5) random seed = 852004 gen best_like time optPrecision 0 -13370.00554 1 0.5 10 -13331.52657 2 0.5 20 -13331.25468 2 0.5 30 -13319.95803 2 0.5 40 -13319.53005 2 0.5 50 -13319.53005 2 0.5 60 -13319.42267 2 0.5 70 -13319.20903 2 0.5 80 -13319.02858 2 0.5 90 -13319.02858 2 0.5 100 -13318.75875 2 0.5 110 -13318.75875 2 0.5 120 -13318.6918 3 0.5 130 -13318.6918 3 0.5 140 -13318.67738 3 0.5 150 -13318.03798 3 0.5 160 -13318.03798 3 0.5 170 -13318.00773 3 0.5 180 -13317.88994 3 0.5 190 -13317.88346 3 0.5 200 -13317.86398 3 0.5 210 -13317.86244 3 0.5 220 -13317.75838 4 0.5 230 -13317.68772 4 0.5 240 -13317.68772 4 0.5 250 -13317.68772 4 0.5 260 -13317.68772 4 0.5 270 -13317.68772 4 0.5 280 -13317.66103 4 0.5 290 -13317.52471 4 0.5 300 -13317.35386 4 0.5 310 -13317.35386 4 0.5 320 -13317.31751 5 0.5 330 -13317.31751 5 0.5 340 -13317.31751 5 0.5 350 -13317.31751 5 0.5 360 -13317.23419 5 0.5 370 -13317.21712 5 0.5 380 -13317.21712 5 0.5 390 -13317.11526 5 0.5 400 -13317.11237 5 0.5 410 -13317.11066 6 0.5 420 -13317.05928 6 0.5 430 -13317.04815 6 0.5 440 -13317.04586 6 0.5 450 -13317.01279 6 0.5 460 -13317.01279 6 0.5 470 -13317.00404 6 0.5 480 -13317.00352 6 0.5 490 -13317.00352 6 0.5 500 -13316.99313 7 0.5 510 -13316.98931 7 0.5 520 -13316.98931 7 0.5 530 -13316.95647 7 0.5 540 -13316.8836 7 0.5 550 -13316.8836 7 0.5 560 -13316.81087 7 0.5 570 -13316.81087 7 0.5 580 -13316.79839 7 0.5 590 -13316.75335 7 0.5 600 -13316.75335 8 0.5 610 -13316.65766 8 0.402 620 -13316.65766 8 0.402 630 -13316.62249 8 0.402 640 -13316.61034 8 0.402 650 -13316.55455 8 0.402 660 -13316.5506 8 0.402 670 -13316.5482 8 0.402 680 -13316.5482 8 0.402 690 -13316.54015 8 0.402 700 -13316.53197 8 0.402 710 -13316.51527 9 0.402 720 -13316.51527 9 0.402 730 -13316.51527 9 0.402 740 -13316.51527 9 0.402 750 -13316.51071 9 0.402 760 -13316.50156 9 0.402 770 -13316.49125 9 0.402 780 -13316.49125 9 0.402 790 -13316.44795 9 0.402 800 -13316.44795 9 0.402 810 -13316.43994 9 0.402 820 -13316.42609 9 0.402 830 -13316.42609 10 0.402 840 -13316.42609 10 0.402 850 -13316.42609 10 0.402 860 -13316.42507 10 0.402 870 -13316.4201 10 0.402 880 -13316.4201 10 0.402 890 -13316.4201 10 0.402 900 -13316.4201 10 0.402 910 -13316.4201 10 0.402 920 -13316.40262 10 0.402 930 -13316.40262 10 0.402 940 -13316.38502 10 0.402 950 -13316.38395 11 0.402 960 -13316.38395 11 0.402 970 -13316.38395 11 0.402 980 -13316.38395 11 0.402 990 -13316.35742 11 0.402 1000 -13316.35742 11 0.402 1010 -13316.35327 11 0.402 1020 -13316.35327 11 0.402 1030 -13316.34275 11 0.402 1040 -13316.27344 12 0.402 1050 -13316.27344 12 0.402 1060 -13316.27136 12 0.402 1070 -13316.24888 12 0.402 1080 -13316.24882 12 0.402 1090 -13316.24882 12 0.402 1100 -13316.24882 12 0.402 1110 -13316.22498 12 0.304 1120 -13316.22014 12 0.304 1130 -13316.22014 12 0.304 1140 -13316.21597 13 0.304 1150 -13316.21597 13 0.304 1160 -13316.21597 13 0.304 1170 -13316.19966 13 0.304 1180 -13316.18592 13 0.304 1190 -13316.18592 13 0.304 1200 -13316.17579 13 0.304 1210 -13316.17579 13 0.304 1220 -13316.17579 13 0.304 1230 -13316.17539 13 0.304 1240 -13316.17539 14 0.304 1250 -13316.17539 14 0.304 1260 -13316.17539 14 0.304 1270 -13316.17539 14 0.304 1280 -13316.17505 14 0.304 1290 -13316.1715 14 0.304 1300 -13316.17144 14 0.304 1310 -13316.17144 14 0.304 1320 -13316.16515 14 0.304 1330 -13316.16489 14 0.304 1340 -13316.12089 14 0.304 1350 -13316.12089 15 0.304 1360 -13316.12089 15 0.304 1370 -13316.12089 15 0.304 1380 -13316.09721 15 0.304 1390 -13316.09721 15 0.304 1400 -13316.09377 15 0.304 1410 -13316.09127 15 0.304 1420 -13316.09127 15 0.304 1430 -13316.09127 15 0.304 1440 -13316.08964 15 0.304 1450 -13316.06292 16 0.304 1460 -13316.06292 16 0.304 1470 -13316.06292 16 0.304 1480 -13316.06292 16 0.304 1490 -13316.06292 16 0.304 1500 -13316.05179 16 0.304 1510 -13316.04494 16 0.304 1520 -13316.04391 16 0.304 1530 -13316.0305 16 0.304 1540 -13316.0305 16 0.304 1550 -13316.0305 17 0.304 1560 -13316.0268 17 0.304 1570 -13316.0268 17 0.304 1580 -13316.00242 17 0.304 1590 -13316.00242 17 0.304 1600 -13315.99578 17 0.304 1610 -13315.97958 17 0.206 1620 -13315.97958 17 0.206 1630 -13315.96539 17 0.206 1640 -13315.96408 18 0.206 1650 -13315.9544 18 0.206 1660 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-13315.79273 579 0.01 7630 -13315.79273 580 0.01 7640 -13315.79273 580 0.01 7650 -13315.79273 580 0.01 7660 -13315.79235 580 0.01 7670 -13315.79235 580 0.01 7680 -13315.79235 580 0.01 7690 -13315.79234 580 0.01 7700 -13315.79234 580 0.01 7710 -13315.79234 581 0.01 7720 -13315.79229 581 0.01 7730 -13315.79229 581 0.01 7740 -13315.79229 581 0.01 7750 -13315.79229 581 0.01 7760 -13315.79229 581 0.01 7770 -13315.79229 581 0.01 7780 -13315.79229 582 0.01 7790 -13315.79229 582 0.01 7800 -13315.79229 582 0.01 7810 -13315.79229 582 0.01 7820 -13315.79229 582 0.01 7830 -13315.79227 582 0.01 7840 -13315.79227 582 0.01 7850 -13315.79227 583 0.01 7860 -13315.79227 583 0.01 7870 -13315.79212 583 0.01 7880 -13315.79212 583 0.01 7890 -13315.79212 583 0.01 7900 -13315.79212 583 0.01 7910 -13315.79212 583 0.01 7920 -13315.79212 583 0.01 7930 -13315.79212 584 0.01 7940 -13315.79212 584 0.01 7950 -13315.79212 584 0.01 7960 -13315.79212 584 0.01 7970 -13315.79212 584 0.01 7980 -13315.79212 584 0.01 7990 -13315.79212 584 0.01 8000 -13315.79212 585 0.01 8010 -13315.79212 585 0.01 8020 -13315.79212 585 0.01 8030 -13315.79212 585 0.01 8040 -13315.79212 585 0.01 8050 -13315.79212 585 0.01 8060 -13315.79212 586 0.01 8070 -13315.79212 586 0.01 8080 -13315.79212 586 0.01 8090 -13315.79212 586 0.01 8100 -13315.79212 586 0.01 Score after final optimization: -13315.77223 Final -13315.77223 590 0.01 3diffModels.byCodonPos.screen.log000066400000000000000000001772541241236125200343250ustar00rootroot00000000000000garli-2.1-release/example/partition/exampleRuns/3parts.diffModelTypesRunning GARLI-PART Version 2.0.1008 (17 Mar 2011) ->Single processor version<- ############################################################## This is GARLI 2.0, the first "official" release including partitioned models. It is a merging of official release 1.0 and beta version GARLI-PART 0.97 Briefly, it includes models for nucleotides, amino acids, codons, and morphology-like characters, any of which can be mixed together and applied to different subsets of data. General program usage is extensively documented here: http://www.nescent.org/wg_garli/ see this page for details on partitioned usage: http://www.nescent.org/wg_garli/Partition_testing_version and this page for details on Mkv mophology model usage: http://www.nescent.org/wg_garli/Mkv_morphology_model PLEASE LET ME KNOW OF ANY PROBLEMS AT: garli.support@gmail.com ############################################################## This version has undergone much testing, but is still a BETA VERSION. - Please check results carefully! - Compiled Mar 21 2011 13:13:18 using Intel icc compiler version 9.10 Using NCL version 2.1.10 ####################################################### Reading config file garli.conf ################################################### READING OF DATA Attempting to read data file in Nexus format (using NCL): zakonEtAl2006.11tax.nex ... Reading DATA block... successful Reading SETS block... successful ################################################### PARTITIONING OF DATA AND MODELS CHECK: DIFFERENT MODEL TYPES AND MODEL PARAMETERS APPLY TO EACH DATA SUBSET (no linkage) GARLI data subset 1 CHARACTERS block #1 ("Untitled DATA Block 1") CHARPARTITION subset #1 ("1stpos") Data read as Nucleotide data, modeled as Nucleotide data Summary of data: 11 sequences. 441 constant characters. 171 parsimony-informative characters. 114 uninformative variable characters. 726 total characters. 238 unique patterns in compressed data matrix. Pattern processing required < 1 second GARLI data subset 2 CHARACTERS block #1 ("Untitled DATA Block 1") CHARPARTITION subset #2 ("2ndpos") Data read as Nucleotide data, modeled as Nucleotide data Summary of data: 11 sequences. 528 constant characters. 90 parsimony-informative characters. 108 uninformative variable characters. 726 total characters. 158 unique patterns in compressed data matrix. Pattern processing required < 1 second GARLI data subset 3 CHARACTERS block #1 ("Untitled DATA Block 1") CHARPARTITION subset #3 ("3rdpos") Data read as Nucleotide data, modeled as Nucleotide data Summary of data: 11 sequences. 103 constant characters. 507 parsimony-informative characters. 116 uninformative variable characters. 726 total characters. 549 unique patterns in compressed data matrix. Pattern processing required < 1 second ################################################### NOTE: Unlike many programs, the amount of system memory that Garli will use can be controlled by the user. (This comes from the availablememory setting in the configuration file. Availablememory should NOT be set to more than the actual amount of physical memory that your computer has installed) For this dataset: Mem level availablememory setting great >= 11 MB good approx 10 MB to 9 MB low approx 8 MB to 5 MB very low approx 4 MB to 4 MB the minimum required availablememory is 4 MB You specified that Garli should use at most 512.0 MB of memory. Garli will actually use approx. 16.1 MB of memory **Your memory level is: great (you don't need to change anything)** ####################################################### Found outgroup specification: 1 ####################################################### STARTING RUN >>>Search rep 1 (of 5)<<< MODEL REPORT - Parameters are at their INITIAL values (not yet optimized) Model 1 Number of states = 4 (nucleotide data) Nucleotide Relative Rate Matrix: User specified matrix type: ( 0 1 2 2 3 4 ) AC = 1.000, AG = 4.000, AT = 1.000, CG = 1.000, CT = 4.000, GT = 1.000 Equilibrium State Frequencies: estimated (ACGT) 0.3157 0.1746 0.3004 0.2093 Rate Heterogeneity Model: 4 discrete gamma distributed rate categories, alpha param estimated 0.5000 Substitution rate categories under this model: rate proportion 0.0334 0.2500 0.2519 0.2500 0.8203 0.2500 2.8944 0.2500 Model 2 Number of states = 4 (nucleotide data) Nucleotide Relative Rate Matrix: User specified matrix type: ( 0 1 2 1 0 3 ) AC = 4.000, AG = 4.000, AT = 1.000, CG = 4.000, CT = 4.000, GT = 1.000 Equilibrium State Frequencies: estimated (ACGT) 0.2703 0.1566 0.1628 0.4103 Rate Heterogeneity Model: 4 discrete gamma distributed rate categories, alpha param estimated 0.5000 Substitution rate categories under this model: rate proportion 0.0334 0.2500 0.2519 0.2500 0.8203 0.2500 2.8944 0.2500 Model 3 Number of states = 4 (nucleotide data) Nucleotide Relative Rate Matrix: User specified matrix type: ( 0 1 2 3 1 0 ) AC = 1.000, AG = 4.000, AT = 1.000, CG = 1.000, CT = 4.000, GT = 1.000 Equilibrium State Frequencies: estimated (ACGT) 0.1460 0.3609 0.2915 0.2015 Rate Heterogeneity Model: 4 discrete gamma distributed rate categories, alpha param estimated 0.5000 with an invariant (invariable) site category, proportion estimated 0.0355 Substitution rate categories under this model: rate proportion 0.0000 0.0355 0.0334 0.2411 0.2519 0.2411 0.8203 0.2411 2.8944 0.2411 Subset rate multipliers: 1.00 1.00 1.00 Starting with seed=852004 creating likelihood stepwise addition starting tree... number of taxa added: 4 5 6 Optimizing parameters... improved 284.867 lnL Optimizing branchlengths... improved 109.865 lnL 7 8 9 10 11 Initial ln Likelihood: -13739.2722 optimizing: starting branch lengths, alpha shape, prop. invar, rel rates, eq freqs, subset rates... pass 1:+ 197.592 (branch= 5.99 scale= 0.00 alpha= 39.14 freqs= 25.16 rel rates= 71.09 pinv= 0.00 subset rates= 56.21) pass 2:+ 93.534 (branch= 11.02 scale= 1.42 alpha= 7.20 freqs= 12.45 rel rates= 4.35 pinv= 0.79 subset rates= 56.30) pass 3:+ 52.621 (branch= 5.01 scale= 1.30 alpha= 7.59 freqs= 1.02 rel rates= 1.79 pinv= 0.01 subset rates= 35.90) pass 4:+ 16.507 (branch= 0.00 scale= 0.81 alpha= 0.95 freqs= 0.16 rel rates= 1.58 pinv= 0.01 subset rates= 13.00) pass 5:+ 8.128 (branch= 0.68 scale= 0.00 alpha= 0.01 freqs= 0.20 rel rates= 1.99 pinv= 0.01 subset rates= 5.23) pass 6:+ 0.727 (branch= 0.00 scale= 0.00 alpha= 0.01 freqs= 0.15 rel rates= 0.01 pinv= 0.01 subset rates= 0.54) pass 7:+ 0.157 (branch= 0.00 scale= 0.00 alpha= 0.00 freqs= 0.13 rel rates= 0.01 pinv= 0.01 subset rates= 0.00) lnL after optimization: -13370.0055 gen current_lnL precision last_tree_imp 0 -13370.0055 0.500 0 100 -13318.7588 0.500 30 200 -13317.8640 0.500 30 300 -13317.3539 0.500 30 400 -13317.1124 0.500 30 500 -13316.9931 0.500 30 600 -13316.7533 0.500 30 Optimization precision reduced Optimizing parameters... improved 0.052 lnL Optimizing branchlengths... improved 0.000 lnL 700 -13316.5320 0.402 30 800 -13316.4480 0.402 30 900 -13316.4201 0.402 30 1000 -13316.3574 0.402 30 1100 -13316.2488 0.402 30 Optimization precision reduced Optimizing parameters... improved 0.024 lnL Optimizing branchlengths... improved 0.000 lnL 1200 -13316.1758 0.304 30 1300 -13316.1714 0.304 30 1400 -13316.0938 0.304 30 1500 -13316.0518 0.304 30 1600 -13315.9958 0.304 30 Optimization precision reduced Optimizing parameters... improved 0.013 lnL Optimizing branchlengths... improved 0.000 lnL 1700 -13315.9424 0.206 30 1800 -13315.9278 0.206 30 1900 -13315.8903 0.206 30 2000 -13315.8642 0.206 30 2100 -13315.8580 0.206 30 Optimization precision reduced Optimizing parameters... improved 0.003 lnL Optimizing branchlengths... improved 0.000 lnL 2200 -13315.8540 0.108 30 2300 -13315.8529 0.108 30 2400 -13315.8461 0.108 30 2500 -13315.8458 0.108 30 2600 -13315.8453 0.108 30 Optimization precision reduced Optimizing parameters... improved 0.001 lnL Optimizing branchlengths... improved 0.017 lnL 2700 -13315.8279 0.010 30 2800 -13315.8275 0.010 30 2900 -13315.8257 0.010 30 3000 -13315.8242 0.010 30 3100 -13315.8240 0.010 30 3200 -13315.8237 0.010 30 3300 -13315.8130 0.010 30 3400 -13315.8130 0.010 30 3500 -13315.8093 0.010 30 3600 -13315.8087 0.010 30 3700 -13315.8086 0.010 30 3800 -13315.8078 0.010 30 3900 -13315.8072 0.010 30 4000 -13315.8072 0.010 30 4100 -13315.8016 0.010 30 4200 -13315.8007 0.010 30 4300 -13315.7978 0.010 30 4400 -13315.7973 0.010 30 4500 -13315.7970 0.010 30 4600 -13315.7967 0.010 30 4700 -13315.7949 0.010 30 4800 -13315.7948 0.010 30 4900 -13315.7948 0.010 30 5000 -13315.7919 0.010 30 5100 -13315.7911 0.010 30 5200 -13315.7911 0.010 30 5300 -13315.7906 0.010 30 5400 -13315.7906 0.010 30 5500 -13315.7903 0.010 30 5600 -13315.7899 0.010 30 5700 -13315.7883 0.010 30 5800 -13315.7880 0.010 30 5900 -13315.7876 0.010 30 6000 -13315.7873 0.010 30 6100 -13315.7873 0.010 30 6200 -13315.7870 0.010 30 6300 -13315.7863 0.010 30 6400 -13315.7851 0.010 30 6500 -13315.7846 0.010 30 6600 -13315.7846 0.010 30 6700 -13315.7846 0.010 30 6800 -13315.7846 0.010 30 6900 -13315.7844 0.010 30 7000 -13315.7838 0.010 30 7100 -13315.7837 0.010 30 7200 -13315.7837 0.010 30 7300 -13315.7837 0.010 30 7400 -13315.7837 0.010 30 7500 -13315.7837 0.010 30 7600 -13315.7837 0.010 30 7700 -13315.7833 0.010 30 Reached termination condition! last topological improvement at gen 30 Improvement over last 500 gen = 0.00047 Current score = -13315.7833 Performing final optimizations... pass 1 : -13315.7830 (branch= 0.0000 alpha= 0.0000 pinv= 0.0000 eq freqs= 0.0001 rel rates= 0.0001 subset rates= 0.0000) pass 2 : -13315.7827 (branch= 0.0000 alpha= 0.0001 pinv= 0.0000 eq freqs= 0.0001 rel rates= 0.0001 subset rates= 0.0000) pass 3 : -13315.7772 (branch= 0.0054 alpha= 0.0000 pinv= 0.0000 eq freqs= 0.0000 rel rates= 0.0001 subset rates= 0.0000) pass 4 : -13315.7748 (branch= 0.0024 alpha= 0.0000 pinv= 0.0000 eq freqs= 0.0000 rel rates= 0.0000 subset rates= 0.0000) pass 5 : -13315.7740 (branch= 0.0007 alpha= 0.0000 pinv= 0.0000 eq freqs= 0.0000 rel rates= 0.0001 subset rates= 0.0000) pass 6 : -13315.7729 (branch= 0.0011 alpha= 0.0000 pinv= 0.0000 eq freqs= 0.0000 rel rates= 0.0000 subset rates= 0.0000) pass 7 : -13315.7726 (branch= 0.0002 alpha= 0.0000 pinv= 0.0000 eq freqs= 0.0000 rel rates= 0.0001 subset rates= 0.0000) pass 8 : -13315.7725 (branch= 0.0000 alpha= 0.0000 pinv= 0.0000 eq freqs= 0.0000 rel rates= 0.0000 subset rates= 0.0000) pass 9 : -13315.7723 (branch= 0.0001 alpha= 0.0000 pinv= 0.0000 eq freqs= 0.0000 rel rates= 0.0000 subset rates= 0.0000) pass 10: -13315.7722 (branch= 0.0001 alpha= 0.0000 pinv= 0.0000 eq freqs= 0.0000 rel rates= 0.0000 subset rates= 0.0000) pass 11: -13315.7722 (branch= 0.0000 alpha= 0.0000 pinv= 0.0000 eq freqs= 0.0000 rel rates= 0.0000 subset rates= 0.0000) pass 12: -13315.7722 (branch= 0.0000 alpha= 0.0000 pinv= 0.0000 eq freqs= 0.0000 rel rates= 0.0000 subset rates= 0.0000) pass 13: -13315.7722 (branch= 0.0000 alpha= 0.0000 pinv= 0.0000 eq freqs= 0.0000 rel rates= 0.0000 subset rates= 0.0000) pass 14: -13315.7721 (branch= 0.0000 alpha= 0.0000 pinv= 0.0000 eq freqs= 0.0000 rel rates= 0.0000 subset rates= 0.0000) pass 15: -13315.7721 (branch= 0.0000 alpha= 0.0000 pinv= 0.0000 eq freqs= 0.0000 rel rates= 0.0000 subset rates= 0.0000) pass 16: -13315.7721 (branch= 0.0000 alpha= 0.0000 pinv= 0.0000 eq freqs= 0.0000 rel rates= 0.0000 subset rates= 0.0000) Looking for minimum length branches... Final score = -13315.7721 Time used so far = 0 hours, 1 minutes and 42 seconds MODEL REPORT - Parameter values are FINAL Model 1 Number of states = 4 (nucleotide data) Nucleotide Relative Rate Matrix: User specified matrix type: ( 0 1 2 2 3 4 ) AC = 1.966, AG = 2.579, AT = 1.411, CG = 1.411, CT = 3.722, GT = 1.000 Equilibrium State Frequencies: estimated (ACGT) 0.3101 0.1767 0.2972 0.2160 Rate Heterogeneity Model: 4 discrete gamma distributed rate categories, alpha param estimated 0.4097 Substitution rate categories under this model: rate proportion 0.0181 0.2500 0.1890 0.2500 0.7416 0.2500 3.0513 0.2500 Model 2 Number of states = 4 (nucleotide data) Nucleotide Relative Rate Matrix: User specified matrix type: ( 0 1 2 1 0 3 ) AC = 4.380, AG = 7.085, AT = 1.609, CG = 7.085, CT = 4.380, GT = 1.000 Equilibrium State Frequencies: estimated (ACGT) 0.2692 0.1636 0.1605 0.4067 Rate Heterogeneity Model: 4 discrete gamma distributed rate categories, alpha param estimated 0.3609 Substitution rate categories under this model: rate proportion 0.0115 0.2500 0.1520 0.2500 0.6853 0.2500 3.1512 0.2500 Model 3 Number of states = 4 (nucleotide data) Nucleotide Relative Rate Matrix: User specified matrix type: ( 0 1 2 3 1 0 ) AC = 1.000, AG = 4.936, AT = 3.391, CG = 0.457, CT = 4.936, GT = 1.000 Equilibrium State Frequencies: estimated (ACGT) 0.1565 0.3537 0.2877 0.2021 Rate Heterogeneity Model: 4 discrete gamma distributed rate categories, alpha param estimated 4.0142 with an invariant (invariable) site category, proportion estimated 0.0335 Substitution rate categories under this model: rate proportion 0.0000 0.0335 0.4551 0.2416 0.7757 0.2416 1.0844 0.2416 1.6848 0.2416 Subset rate multipliers: 0.54 0.30 2.16 NOTE: Collapsing of minimum length branches was requested (collapsebranches = 1) No branches were short enough to be collapsed. >>>Completed Search rep 1 (of 5)<<< >>>Search rep 2 (of 5)<<< MODEL REPORT - Parameters are at their INITIAL values (not yet optimized) Model 1 Number of states = 4 (nucleotide data) Nucleotide Relative Rate Matrix: User specified matrix type: ( 0 1 2 2 3 4 ) AC = 1.000, AG = 4.000, AT = 1.000, CG = 1.000, CT = 4.000, GT = 1.000 Equilibrium State Frequencies: estimated (ACGT) 0.3157 0.1746 0.3004 0.2093 Rate Heterogeneity Model: 4 discrete gamma distributed rate categories, alpha param estimated 0.5000 Substitution rate categories under this model: rate proportion 0.0334 0.2500 0.2519 0.2500 0.8203 0.2500 2.8944 0.2500 Model 2 Number of states = 4 (nucleotide data) Nucleotide Relative Rate Matrix: User specified matrix type: ( 0 1 2 1 0 3 ) AC = 4.000, AG = 4.000, AT = 1.000, CG = 4.000, CT = 4.000, GT = 1.000 Equilibrium State Frequencies: estimated (ACGT) 0.2703 0.1566 0.1628 0.4103 Rate Heterogeneity Model: 4 discrete gamma distributed rate categories, alpha param estimated 0.5000 Substitution rate categories under this model: rate proportion 0.0334 0.2500 0.2519 0.2500 0.8203 0.2500 2.8944 0.2500 Model 3 Number of states = 4 (nucleotide data) Nucleotide Relative Rate Matrix: User specified matrix type: ( 0 1 2 3 1 0 ) AC = 1.000, AG = 4.000, AT = 1.000, CG = 1.000, CT = 4.000, GT = 1.000 Equilibrium State Frequencies: estimated (ACGT) 0.1460 0.3609 0.2915 0.2015 Rate Heterogeneity Model: 4 discrete gamma distributed rate categories, alpha param estimated 0.5000 with an invariant (invariable) site category, proportion estimated 0.0355 Substitution rate categories under this model: rate proportion 0.0000 0.0355 0.0334 0.2411 0.2519 0.2411 0.8203 0.2411 2.8944 0.2411 Subset rate multipliers: 1.00 1.00 1.00 Starting with seed=1163395386 creating likelihood stepwise addition starting tree... number of taxa added: 4 5 6 Optimizing parameters... improved 251.052 lnL Optimizing branchlengths... improved 42.822 lnL 7 8 9 10 11 Initial ln Likelihood: -13708.2945 optimizing: starting branch lengths, alpha shape, prop. invar, rel rates, eq freqs, subset rates... pass 1:+ 195.142 (branch= 3.84 scale= 0.00 alpha= 39.93 freqs= 28.21 rel rates= 67.92 pinv= 0.00 subset rates= 55.24) pass 2:+ 90.129 (branch= 9.79 scale= 1.73 alpha= 6.49 freqs= 11.51 rel rates= 4.22 pinv= 1.00 subset rates= 55.39) pass 3:+ 50.181 (branch= 8.72 scale= 0.00 alpha= 4.95 freqs= 1.58 rel rates= 1.37 pinv= 0.01 subset rates= 33.53) pass 4:+ 18.019 (branch= 1.73 scale= 0.71 alpha= 0.96 freqs= 0.15 rel rates= 2.28 pinv= 0.01 subset rates= 12.18) pass 5:+ 5.247 (branch= 0.00 scale= 0.00 alpha= 0.01 freqs= 0.14 rel rates= 0.54 pinv= 0.01 subset rates= 4.54) pass 6:+ 0.677 (branch= 0.00 scale= 0.00 alpha= 0.01 freqs= 0.64 rel rates= 0.01 pinv= 0.01 subset rates= 0.00) pass 7:+ 0.077 (branch= 0.00 scale= 0.00 alpha= 0.01 freqs= 0.04 rel rates= 0.01 pinv= 0.01 subset rates= 0.00) lnL after optimization: -13348.8218 gen current_lnL precision last_tree_imp 0 -13348.8218 0.500 0 100 -13319.1023 0.500 3 200 -13318.5364 0.500 3 300 -13317.6152 0.500 3 400 -13317.5790 0.500 3 500 -13317.4175 0.500 3 600 -13317.3894 0.500 3 Optimization precision reduced Optimizing parameters... improved 0.627 lnL Optimizing branchlengths... improved 0.000 lnL 700 -13316.5392 0.402 3 800 -13316.3891 0.402 3 900 -13316.3181 0.402 3 1000 -13316.1841 0.402 3 1100 -13316.1042 0.402 3 Optimization precision reduced Optimizing parameters... improved 0.014 lnL Optimizing branchlengths... improved 0.000 lnL 1200 -13316.0886 0.304 3 1300 -13316.0664 0.304 3 1400 -13316.0481 0.304 3 1500 -13316.0285 0.304 3 1600 -13315.9524 0.304 3 Optimization precision reduced Optimizing parameters... improved 0.004 lnL Optimizing branchlengths... improved 0.000 lnL 1700 -13315.9479 0.206 3 1800 -13315.9284 0.206 3 1900 -13315.8732 0.206 3 2000 -13315.8515 0.206 3 2100 -13315.8477 0.206 3 Optimization precision reduced Optimizing parameters... improved 0.002 lnL Optimizing branchlengths... improved 0.000 lnL 2200 -13315.8461 0.108 3 2300 -13315.8265 0.108 3 2400 -13315.8118 0.108 3 2500 -13315.8107 0.108 3 2600 -13315.8043 0.108 3 Optimization precision reduced Optimizing parameters... improved 0.001 lnL Optimizing branchlengths... improved 0.000 lnL 2700 -13315.8013 0.010 3 2800 -13315.8000 0.010 3 2900 -13315.7998 0.010 3 3000 -13315.7995 0.010 3 3100 -13315.7995 0.010 3 3200 -13315.7993 0.010 3 3300 -13315.7975 0.010 3 3400 -13315.7975 0.010 3 3500 -13315.7973 0.010 3 3600 -13315.7973 0.010 3 3700 -13315.7972 0.010 3 3800 -13315.7972 0.010 3 3900 -13315.7972 0.010 3 4000 -13315.7906 0.010 3 4100 -13315.7905 0.010 3 4200 -13315.7903 0.010 3 4300 -13315.7901 0.010 3 4400 -13315.7901 0.010 3 4500 -13315.7901 0.010 3 4600 -13315.7901 0.010 3 4700 -13315.7901 0.010 3 4800 -13315.7901 0.010 3 4900 -13315.7900 0.010 3 5000 -13315.7900 0.010 3 5100 -13315.7899 0.010 3 5200 -13315.7899 0.010 3 5300 -13315.7899 0.010 3 5400 -13315.7899 0.010 3 5500 -13315.7899 0.010 3 5600 -13315.7899 0.010 3 5700 -13315.7899 0.010 3 5800 -13315.7889 0.010 3 5900 -13315.7889 0.010 3 6000 -13315.7889 0.010 3 6100 -13315.7889 0.010 3 6200 -13315.7889 0.010 3 6300 -13315.7884 0.010 3 6400 -13315.7884 0.010 3 6500 -13315.7884 0.010 3 6600 -13315.7873 0.010 3 6700 -13315.7873 0.010 3 6800 -13315.7873 0.010 3 6900 -13315.7870 0.010 3 7000 -13315.7870 0.010 3 7100 -13315.7870 0.010 3 7200 -13315.7869 0.010 3 7300 -13315.7866 0.010 3 7400 -13315.7836 0.010 3 7500 -13315.7836 0.010 3 7600 -13315.7836 0.010 3 7700 -13315.7836 0.010 3 7800 -13315.7836 0.010 3 7900 -13315.7836 0.010 3 Reached termination condition! last topological improvement at gen 3 Improvement over last 500 gen = 0.00001 Current score = -13315.7836 Performing final optimizations... pass 1 : -13315.7832 (branch= 0.0000 alpha= 0.0001 pinv= 0.0000 eq freqs= 0.0002 rel rates= 0.0001 subset rates= 0.0000) pass 2 : -13315.7829 (branch= 0.0000 alpha= 0.0001 pinv= 0.0000 eq freqs= 0.0001 rel rates= 0.0001 subset rates= 0.0000) pass 3 : -13315.7783 (branch= 0.0043 alpha= 0.0000 pinv= 0.0000 eq freqs= 0.0001 rel rates= 0.0001 subset rates= 0.0000) pass 4 : -13315.7757 (branch= 0.0025 alpha= 0.0000 pinv= 0.0000 eq freqs= 0.0000 rel rates= 0.0001 subset rates= 0.0000) pass 5 : -13315.7732 (branch= 0.0024 alpha= 0.0000 pinv= 0.0000 eq freqs= 0.0000 rel rates= 0.0001 subset rates= 0.0000) pass 6 : -13315.7728 (branch= 0.0003 alpha= 0.0000 pinv= 0.0000 eq freqs= 0.0000 rel rates= 0.0001 subset rates= 0.0000) pass 7 : -13315.7726 (branch= 0.0002 alpha= 0.0000 pinv= 0.0000 eq freqs= 0.0000 rel rates= 0.0000 subset rates= 0.0000) pass 8 : -13315.7726 (branch= 0.0000 alpha= 0.0000 pinv= 0.0000 eq freqs= 0.0000 rel rates= 0.0000 subset rates= 0.0000) pass 9 : -13315.7725 (branch= 0.0000 alpha= 0.0000 pinv= 0.0000 eq freqs= 0.0000 rel rates= 0.0000 subset rates= 0.0000) pass 10: -13315.7725 (branch= 0.0000 alpha= 0.0000 pinv= 0.0000 eq freqs= 0.0000 rel rates= 0.0000 subset rates= 0.0000) pass 11: -13315.7724 (branch= 0.0000 alpha= 0.0000 pinv= 0.0000 eq freqs= 0.0000 rel rates= 0.0000 subset rates= 0.0000) pass 12: -13315.7723 (branch= 0.0000 alpha= 0.0000 pinv= 0.0000 eq freqs= 0.0000 rel rates= 0.0000 subset rates= 0.0001) Looking for minimum length branches... Final score = -13315.7723 Time used so far = 0 hours, 3 minutes and 27 seconds MODEL REPORT - Parameter values are FINAL Model 1 Number of states = 4 (nucleotide data) Nucleotide Relative Rate Matrix: User specified matrix type: ( 0 1 2 2 3 4 ) AC = 1.962, AG = 2.575, AT = 1.408, CG = 1.408, CT = 3.715, GT = 1.000 Equilibrium State Frequencies: estimated (ACGT) 0.3102 0.1767 0.2971 0.2160 Rate Heterogeneity Model: 4 discrete gamma distributed rate categories, alpha param estimated 0.4096 Substitution rate categories under this model: rate proportion 0.0181 0.2500 0.1889 0.2500 0.7415 0.2500 3.0515 0.2500 Model 2 Number of states = 4 (nucleotide data) Nucleotide Relative Rate Matrix: User specified matrix type: ( 0 1 2 1 0 3 ) AC = 4.400, AG = 7.116, AT = 1.617, CG = 7.116, CT = 4.400, GT = 1.000 Equilibrium State Frequencies: estimated (ACGT) 0.2692 0.1636 0.1605 0.4067 Rate Heterogeneity Model: 4 discrete gamma distributed rate categories, alpha param estimated 0.3611 Substitution rate categories under this model: rate proportion 0.0115 0.2500 0.1521 0.2500 0.6854 0.2500 3.1509 0.2500 Model 3 Number of states = 4 (nucleotide data) Nucleotide Relative Rate Matrix: User specified matrix type: ( 0 1 2 3 1 0 ) AC = 1.000, AG = 4.938, AT = 3.393, CG = 0.457, CT = 4.938, GT = 1.000 Equilibrium State Frequencies: estimated (ACGT) 0.1565 0.3537 0.2877 0.2021 Rate Heterogeneity Model: 4 discrete gamma distributed rate categories, alpha param estimated 4.0160 with an invariant (invariable) site category, proportion estimated 0.0335 Substitution rate categories under this model: rate proportion 0.0000 0.0335 0.4552 0.2416 0.7758 0.2416 1.0844 0.2416 1.6846 0.2416 Subset rate multipliers: 0.54 0.30 2.16 NOTE: Collapsing of minimum length branches was requested (collapsebranches = 1) No branches were short enough to be collapsed. >>>Completed Search rep 2 (of 5)<<< >>>Search rep 3 (of 5)<<< MODEL REPORT - Parameters are at their INITIAL values (not yet optimized) Model 1 Number of states = 4 (nucleotide data) Nucleotide Relative Rate Matrix: User specified matrix type: ( 0 1 2 2 3 4 ) AC = 1.000, AG = 4.000, AT = 1.000, CG = 1.000, CT = 4.000, GT = 1.000 Equilibrium State Frequencies: estimated (ACGT) 0.3157 0.1746 0.3004 0.2093 Rate Heterogeneity Model: 4 discrete gamma distributed rate categories, alpha param estimated 0.5000 Substitution rate categories under this model: rate proportion 0.0334 0.2500 0.2519 0.2500 0.8203 0.2500 2.8944 0.2500 Model 2 Number of states = 4 (nucleotide data) Nucleotide Relative Rate Matrix: User specified matrix type: ( 0 1 2 1 0 3 ) AC = 4.000, AG = 4.000, AT = 1.000, CG = 4.000, CT = 4.000, GT = 1.000 Equilibrium State Frequencies: estimated (ACGT) 0.2703 0.1566 0.1628 0.4103 Rate Heterogeneity Model: 4 discrete gamma distributed rate categories, alpha param estimated 0.5000 Substitution rate categories under this model: rate proportion 0.0334 0.2500 0.2519 0.2500 0.8203 0.2500 2.8944 0.2500 Model 3 Number of states = 4 (nucleotide data) Nucleotide Relative Rate Matrix: User specified matrix type: ( 0 1 2 3 1 0 ) AC = 1.000, AG = 4.000, AT = 1.000, CG = 1.000, CT = 4.000, GT = 1.000 Equilibrium State Frequencies: estimated (ACGT) 0.1460 0.3609 0.2915 0.2015 Rate Heterogeneity Model: 4 discrete gamma distributed rate categories, alpha param estimated 0.5000 with an invariant (invariable) site category, proportion estimated 0.0355 Substitution rate categories under this model: rate proportion 0.0000 0.0355 0.0334 0.2411 0.2519 0.2411 0.8203 0.2411 2.8944 0.2411 Subset rate multipliers: 1.00 1.00 1.00 Starting with seed=1320446270 creating likelihood stepwise addition starting tree... number of taxa added: 4 5 6 Optimizing parameters... improved 182.213 lnL Optimizing branchlengths... improved 23.611 lnL 7 8 9 10 11 Initial ln Likelihood: -13653.6032 optimizing: starting branch lengths, alpha shape, prop. invar, rel rates, eq freqs, subset rates... pass 1:+ 178.674 (branch= 11.12 scale= 0.00 alpha= 11.90 freqs= 26.62 rel rates= 64.15 pinv= 0.37 subset rates= 64.52) pass 2:+ 86.362 (branch= 4.68 scale= 1.93 alpha= 5.43 freqs= 12.73 rel rates= 3.97 pinv= 0.01 subset rates= 57.60) pass 3:+ 44.763 (branch= 3.77 scale= 0.92 alpha= 3.02 freqs= 0.93 rel rates= 2.17 pinv= 0.01 subset rates= 33.93) pass 4:+ 17.413 (branch= 1.07 scale= 0.00 alpha= 0.66 freqs= 0.18 rel rates= 3.09 pinv= 0.01 subset rates= 12.39) pass 5:+ 5.472 (branch= 0.00 scale= 0.00 alpha= 0.01 freqs= 0.17 rel rates= 0.63 pinv= 0.01 subset rates= 4.66) pass 6:+ 0.158 (branch= 0.00 scale= 0.00 alpha= 0.00 freqs= 0.13 rel rates= 0.01 pinv= 0.01 subset rates= 0.00) lnL after optimization: -13320.7617 gen current_lnL precision last_tree_imp 0 -13320.7617 0.500 0 100 -13318.9005 0.500 0 200 -13318.2255 0.500 0 300 -13318.0181 0.500 0 400 -13317.3604 0.500 0 500 -13316.9407 0.500 0 Optimization precision reduced Optimizing parameters... improved 0.061 lnL Optimizing branchlengths... improved 0.000 lnL 600 -13316.6639 0.402 0 700 -13316.5914 0.402 0 800 -13316.5551 0.402 0 900 -13316.4227 0.402 0 1000 -13316.4019 0.402 0 Optimization precision reduced Optimizing parameters... improved 0.025 lnL Optimizing branchlengths... improved 0.000 lnL 1100 -13316.3551 0.304 0 1200 -13316.3373 0.304 0 1300 -13316.2828 0.304 0 1400 -13316.2796 0.304 0 1500 -13316.2045 0.304 0 Optimization precision reduced Optimizing parameters... improved 0.014 lnL Optimizing branchlengths... improved 0.000 lnL 1600 -13316.1466 0.206 0 1700 -13316.1059 0.206 0 1800 -13316.0999 0.206 0 1900 -13316.0997 0.206 0 2000 -13316.0620 0.206 0 Optimization precision reduced Optimizing parameters... improved 0.121 lnL Optimizing branchlengths... improved 0.000 lnL 2100 -13315.9412 0.108 0 2200 -13315.9384 0.108 0 2300 -13315.9384 0.108 0 2400 -13315.9007 0.108 0 2500 -13315.8810 0.108 0 Optimization precision reduced Optimizing parameters... improved 0.034 lnL Optimizing branchlengths... improved 0.000 lnL 2600 -13315.8424 0.010 0 2700 -13315.8416 0.010 0 2800 -13315.8416 0.010 0 2900 -13315.8294 0.010 0 3000 -13315.8285 0.010 0 3100 -13315.8282 0.010 0 3200 -13315.8272 0.010 0 3300 -13315.8240 0.010 0 3400 -13315.8209 0.010 0 3500 -13315.8195 0.010 0 3600 -13315.8173 0.010 0 3700 -13315.8173 0.010 0 3800 -13315.8158 0.010 0 3900 -13315.8157 0.010 0 4000 -13315.8122 0.010 0 4100 -13315.8112 0.010 0 4200 -13315.8112 0.010 0 4300 -13315.8112 0.010 0 4400 -13315.8112 0.010 0 4500 -13315.8112 0.010 0 4600 -13315.8083 0.010 0 4700 -13315.8075 0.010 0 4800 -13315.8070 0.010 0 4900 -13315.8070 0.010 0 5000 -13315.8070 0.010 0 5100 -13315.8070 0.010 0 5200 -13315.8067 0.010 0 5300 -13315.8067 0.010 0 5400 -13315.8059 0.010 0 5500 -13315.8058 0.010 0 5600 -13315.8040 0.010 0 5700 -13315.8026 0.010 0 5800 -13315.8026 0.010 0 5900 -13315.8026 0.010 0 6000 -13315.8024 0.010 0 6100 -13315.8015 0.010 0 6200 -13315.8010 0.010 0 6300 -13315.8010 0.010 0 6400 -13315.7996 0.010 0 6500 -13315.7993 0.010 0 6600 -13315.7993 0.010 0 6700 -13315.7964 0.010 0 6800 -13315.7926 0.010 0 6900 -13315.7926 0.010 0 7000 -13315.7925 0.010 0 7100 -13315.7925 0.010 0 7200 -13315.7925 0.010 0 7300 -13315.7925 0.010 0 7400 -13315.7925 0.010 0 7500 -13315.7924 0.010 0 7600 -13315.7922 0.010 0 Reached termination condition! last topological improvement at gen 0 Improvement over last 500 gen = 0.00030 Current score = -13315.7922 Performing final optimizations... pass 1 : -13315.7848 (branch= 0.0069 alpha= 0.0000 pinv= 0.0000 eq freqs= 0.0003 rel rates= 0.0002 subset rates= 0.0000) pass 2 : -13315.7809 (branch= 0.0032 alpha= 0.0000 pinv= 0.0000 eq freqs= 0.0002 rel rates= 0.0001 subset rates= 0.0004) pass 3 : -13315.7772 (branch= 0.0034 alpha= 0.0000 pinv= 0.0000 eq freqs= 0.0002 rel rates= 0.0001 subset rates= 0.0000) pass 4 : -13315.7770 (branch= 0.0000 alpha= 0.0000 pinv= 0.0000 eq freqs= 0.0001 rel rates= 0.0001 subset rates= 0.0000) pass 5 : -13315.7750 (branch= 0.0017 alpha= 0.0000 pinv= 0.0000 eq freqs= 0.0002 rel rates= 0.0001 subset rates= 0.0000) pass 6 : -13315.7737 (branch= 0.0009 alpha= 0.0000 pinv= 0.0000 eq freqs= 0.0001 rel rates= 0.0001 subset rates= 0.0002) pass 7 : -13315.7730 (branch= 0.0006 alpha= 0.0000 pinv= 0.0000 eq freqs= 0.0001 rel rates= 0.0001 subset rates= 0.0000) pass 8 : -13315.7727 (branch= 0.0001 alpha= 0.0000 pinv= 0.0000 eq freqs= 0.0001 rel rates= 0.0001 subset rates= 0.0000) pass 9 : -13315.7724 (branch= 0.0002 alpha= 0.0000 pinv= 0.0000 eq freqs= 0.0000 rel rates= 0.0000 subset rates= 0.0000) pass 10: -13315.7724 (branch= 0.0000 alpha= 0.0000 pinv= 0.0000 eq freqs= 0.0000 rel rates= 0.0000 subset rates= 0.0000) pass 11: -13315.7723 (branch= 0.0000 alpha= 0.0000 pinv= 0.0000 eq freqs= 0.0000 rel rates= 0.0000 subset rates= 0.0000) pass 12: -13315.7723 (branch= 0.0000 alpha= 0.0000 pinv= 0.0000 eq freqs= 0.0000 rel rates= 0.0000 subset rates= 0.0000) pass 13: -13315.7723 (branch= 0.0000 alpha= 0.0000 pinv= 0.0000 eq freqs= 0.0000 rel rates= 0.0000 subset rates= 0.0000) pass 14: -13315.7723 (branch= 0.0000 alpha= 0.0000 pinv= 0.0000 eq freqs= 0.0000 rel rates= 0.0000 subset rates= 0.0000) pass 15: -13315.7722 (branch= 0.0000 alpha= 0.0000 pinv= 0.0000 eq freqs= 0.0000 rel rates= 0.0000 subset rates= 0.0000) pass 16: -13315.7722 (branch= 0.0000 alpha= 0.0000 pinv= 0.0000 eq freqs= 0.0000 rel rates= 0.0000 subset rates= 0.0000) Looking for minimum length branches... Final score = -13315.7722 Time used so far = 0 hours, 5 minutes and 27 seconds MODEL REPORT - Parameter values are FINAL Model 1 Number of states = 4 (nucleotide data) Nucleotide Relative Rate Matrix: User specified matrix type: ( 0 1 2 2 3 4 ) AC = 1.964, AG = 2.577, AT = 1.410, CG = 1.410, CT = 3.720, GT = 1.000 Equilibrium State Frequencies: estimated (ACGT) 0.3101 0.1767 0.2972 0.2160 Rate Heterogeneity Model: 4 discrete gamma distributed rate categories, alpha param estimated 0.4098 Substitution rate categories under this model: rate proportion 0.0181 0.2500 0.1890 0.2500 0.7416 0.2500 3.0512 0.2500 Model 2 Number of states = 4 (nucleotide data) Nucleotide Relative Rate Matrix: User specified matrix type: ( 0 1 2 1 0 3 ) AC = 4.382, AG = 7.087, AT = 1.610, CG = 7.087, CT = 4.382, GT = 1.000 Equilibrium State Frequencies: estimated (ACGT) 0.2692 0.1636 0.1605 0.4067 Rate Heterogeneity Model: 4 discrete gamma distributed rate categories, alpha param estimated 0.3609 Substitution rate categories under this model: rate proportion 0.0115 0.2500 0.1520 0.2500 0.6853 0.2500 3.1512 0.2500 Model 3 Number of states = 4 (nucleotide data) Nucleotide Relative Rate Matrix: User specified matrix type: ( 0 1 2 3 1 0 ) AC = 1.000, AG = 4.938, AT = 3.393, CG = 0.457, CT = 4.938, GT = 1.000 Equilibrium State Frequencies: estimated (ACGT) 0.1565 0.3537 0.2877 0.2021 Rate Heterogeneity Model: 4 discrete gamma distributed rate categories, alpha param estimated 4.0078 with an invariant (invariable) site category, proportion estimated 0.0334 Substitution rate categories under this model: rate proportion 0.0000 0.0334 0.4548 0.2416 0.7755 0.2416 1.0844 0.2416 1.6853 0.2416 Subset rate multipliers: 0.54 0.30 2.16 NOTE: Collapsing of minimum length branches was requested (collapsebranches = 1) No branches were short enough to be collapsed. >>>Completed Search rep 3 (of 5)<<< >>>Search rep 4 (of 5)<<< MODEL REPORT - Parameters are at their INITIAL values (not yet optimized) Model 1 Number of states = 4 (nucleotide data) Nucleotide Relative Rate Matrix: User specified matrix type: ( 0 1 2 2 3 4 ) AC = 1.000, AG = 4.000, AT = 1.000, CG = 1.000, CT = 4.000, GT = 1.000 Equilibrium State Frequencies: estimated (ACGT) 0.3157 0.1746 0.3004 0.2093 Rate Heterogeneity Model: 4 discrete gamma distributed rate categories, alpha param estimated 0.5000 Substitution rate categories under this model: rate proportion 0.0334 0.2500 0.2519 0.2500 0.8203 0.2500 2.8944 0.2500 Model 2 Number of states = 4 (nucleotide data) Nucleotide Relative Rate Matrix: User specified matrix type: ( 0 1 2 1 0 3 ) AC = 4.000, AG = 4.000, AT = 1.000, CG = 4.000, CT = 4.000, GT = 1.000 Equilibrium State Frequencies: estimated (ACGT) 0.2703 0.1566 0.1628 0.4103 Rate Heterogeneity Model: 4 discrete gamma distributed rate categories, alpha param estimated 0.5000 Substitution rate categories under this model: rate proportion 0.0334 0.2500 0.2519 0.2500 0.8203 0.2500 2.8944 0.2500 Model 3 Number of states = 4 (nucleotide data) Nucleotide Relative Rate Matrix: User specified matrix type: ( 0 1 2 3 1 0 ) AC = 1.000, AG = 4.000, AT = 1.000, CG = 1.000, CT = 4.000, GT = 1.000 Equilibrium State Frequencies: estimated (ACGT) 0.1460 0.3609 0.2915 0.2015 Rate Heterogeneity Model: 4 discrete gamma distributed rate categories, alpha param estimated 0.5000 with an invariant (invariable) site category, proportion estimated 0.0355 Substitution rate categories under this model: rate proportion 0.0000 0.0355 0.0334 0.2411 0.2519 0.2411 0.8203 0.2411 2.8944 0.2411 Subset rate multipliers: 1.00 1.00 1.00 Starting with seed=490393635 creating likelihood stepwise addition starting tree... number of taxa added: 4 5 6 Optimizing parameters... improved 254.487 lnL Optimizing branchlengths... improved 50.375 lnL 7 8 9 10 11 Initial ln Likelihood: -13796.1049 optimizing: starting branch lengths, alpha shape, prop. invar, rel rates, eq freqs, subset rates... pass 1:+ 200.360 (branch= 15.47 scale= 0.00 alpha= 26.67 freqs= 29.27 rel rates= 69.20 pinv= 0.96 subset rates= 58.78) pass 2:+ 84.224 (branch= 7.28 scale= 1.63 alpha= 5.32 freqs= 13.47 rel rates= 3.51 pinv= 1.20 subset rates= 51.81) pass 3:+ 47.400 (branch= 4.57 scale= 1.26 alpha= 5.73 freqs= 1.41 rel rates= 2.51 pinv= 0.01 subset rates= 31.92) pass 4:+ 16.242 (branch= 1.20 scale= 0.50 alpha= 0.72 freqs= 0.16 rel rates= 1.54 pinv= 0.00 subset rates= 12.11) pass 5:+ 5.048 (branch= 0.00 scale= 0.00 alpha= 0.01 freqs= 0.13 rel rates= 0.01 pinv= 0.00 subset rates= 4.90) pass 6:+ 1.167 (branch= 0.00 scale= 0.00 alpha= 0.00 freqs= 0.11 rel rates= 0.58 pinv= 0.00 subset rates= 0.47) pass 7:+ 0.096 (branch= 0.00 scale= 0.00 alpha= 0.00 freqs= 0.09 rel rates= 0.01 pinv= 0.00 subset rates= 0.00) lnL after optimization: -13441.5669 gen current_lnL precision last_tree_imp 0 -13441.5669 0.500 0 100 -13319.1751 0.500 27 200 -13318.6657 0.500 27 300 -13318.5942 0.500 27 400 -13318.5663 0.500 27 500 -13318.4781 0.500 27 600 -13318.2512 0.500 27 Optimization precision reduced Optimizing parameters... improved 0.082 lnL Optimizing branchlengths... improved 0.000 lnL 700 -13317.5316 0.402 27 800 -13317.2393 0.402 27 900 -13316.9226 0.402 27 1000 -13316.6411 0.402 27 1100 -13316.3656 0.402 27 Optimization precision reduced Optimizing parameters... improved 0.045 lnL Optimizing branchlengths... improved 0.000 lnL 1200 -13316.2876 0.304 27 1300 -13316.2215 0.304 27 1400 -13316.1849 0.304 27 1500 -13316.1600 0.304 27 1600 -13316.1287 0.304 27 Optimization precision reduced Optimizing parameters... improved 0.027 lnL Optimizing branchlengths... improved 0.000 lnL 1700 -13315.9846 0.206 27 1800 -13315.9797 0.206 27 1900 -13315.9603 0.206 27 2000 -13315.9509 0.206 27 2100 -13315.9348 0.206 27 Optimization precision reduced Optimizing parameters... improved 0.009 lnL Optimizing branchlengths... improved 0.000 lnL 2200 -13315.9119 0.108 27 2300 -13315.9083 0.108 27 2400 -13315.9054 0.108 27 2500 -13315.8826 0.108 27 2600 -13315.8815 0.108 27 Optimization precision reduced Optimizing parameters... improved 0.014 lnL Optimizing branchlengths... improved 0.021 lnL 2700 -13315.8419 0.010 27 2800 -13315.8414 0.010 27 2900 -13315.8414 0.010 27 3000 -13315.8345 0.010 27 3100 -13315.8324 0.010 27 3200 -13315.8270 0.010 27 3300 -13315.8117 0.010 27 3400 -13315.8091 0.010 27 3500 -13315.8058 0.010 27 3600 -13315.8048 0.010 27 3700 -13315.8048 0.010 27 3800 -13315.8017 0.010 27 3900 -13315.8016 0.010 27 4000 -13315.8016 0.010 27 4100 -13315.8011 0.010 27 4200 -13315.8011 0.010 27 4300 -13315.8011 0.010 27 4400 -13315.8011 0.010 27 4500 -13315.7962 0.010 27 4600 -13315.7962 0.010 27 4700 -13315.7962 0.010 27 4800 -13315.7953 0.010 27 4900 -13315.7953 0.010 27 5000 -13315.7953 0.010 27 5100 -13315.7953 0.010 27 5200 -13315.7953 0.010 27 5300 -13315.7936 0.010 27 5400 -13315.7935 0.010 27 5500 -13315.7933 0.010 27 5600 -13315.7931 0.010 27 5700 -13315.7928 0.010 27 5800 -13315.7914 0.010 27 5900 -13315.7914 0.010 27 6000 -13315.7914 0.010 27 6100 -13315.7912 0.010 27 6200 -13315.7910 0.010 27 6300 -13315.7896 0.010 27 6400 -13315.7876 0.010 27 6500 -13315.7875 0.010 27 6600 -13315.7868 0.010 27 6700 -13315.7867 0.010 27 6800 -13315.7867 0.010 27 6900 -13315.7867 0.010 27 7000 -13315.7866 0.010 27 7100 -13315.7865 0.010 27 7200 -13315.7865 0.010 27 7300 -13315.7865 0.010 27 7400 -13315.7865 0.010 27 7500 -13315.7865 0.010 27 7600 -13315.7859 0.010 27 7700 -13315.7848 0.010 27 7800 -13315.7848 0.010 27 7900 -13315.7842 0.010 27 8000 -13315.7842 0.010 27 8100 -13315.7842 0.010 27 8200 -13315.7842 0.010 27 Reached termination condition! last topological improvement at gen 27 Improvement over last 500 gen = 0.00063 Current score = -13315.7842 Performing final optimizations... pass 1 : -13315.7835 (branch= 0.0000 alpha= 0.0001 pinv= 0.0000 eq freqs= 0.0003 rel rates= 0.0002 subset rates= 0.0002) pass 2 : -13315.7829 (branch= 0.0000 alpha= 0.0001 pinv= 0.0000 eq freqs= 0.0003 rel rates= 0.0001 subset rates= 0.0001) pass 3 : -13315.7779 (branch= 0.0035 alpha= 0.0000 pinv= 0.0000 eq freqs= 0.0015 rel rates= 0.0000 subset rates= 0.0000) pass 4 : -13315.7760 (branch= 0.0014 alpha= 0.0001 pinv= 0.0000 eq freqs= 0.0002 rel rates= 0.0001 subset rates= 0.0000) pass 5 : -13315.7734 (branch= 0.0023 alpha= 0.0001 pinv= 0.0000 eq freqs= 0.0002 rel rates= 0.0000 subset rates= 0.0000) pass 6 : -13315.7730 (branch= 0.0000 alpha= 0.0001 pinv= 0.0000 eq freqs= 0.0003 rel rates= 0.0000 subset rates= 0.0000) pass 7 : -13315.7727 (branch= 0.0002 alpha= 0.0001 pinv= 0.0000 eq freqs= 0.0001 rel rates= 0.0000 subset rates= 0.0000) pass 8 : -13315.7724 (branch= 0.0003 alpha= 0.0000 pinv= 0.0000 eq freqs= 0.0000 rel rates= 0.0000 subset rates= 0.0000) pass 9 : -13315.7722 (branch= 0.0001 alpha= 0.0000 pinv= 0.0000 eq freqs= 0.0000 rel rates= 0.0000 subset rates= 0.0000) pass 10: -13315.7722 (branch= 0.0000 alpha= 0.0000 pinv= 0.0000 eq freqs= 0.0000 rel rates= 0.0000 subset rates= 0.0000) pass 11: -13315.7722 (branch= 0.0000 alpha= 0.0000 pinv= 0.0000 eq freqs= 0.0000 rel rates= 0.0000 subset rates= 0.0000) Looking for minimum length branches... Final score = -13315.7722 Time used so far = 0 hours, 7 minutes and 46 seconds MODEL REPORT - Parameter values are FINAL Model 1 Number of states = 4 (nucleotide data) Nucleotide Relative Rate Matrix: User specified matrix type: ( 0 1 2 2 3 4 ) AC = 1.965, AG = 2.577, AT = 1.410, CG = 1.410, CT = 3.720, GT = 1.000 Equilibrium State Frequencies: estimated (ACGT) 0.3102 0.1767 0.2972 0.2160 Rate Heterogeneity Model: 4 discrete gamma distributed rate categories, alpha param estimated 0.4097 Substitution rate categories under this model: rate proportion 0.0181 0.2500 0.1889 0.2500 0.7415 0.2500 3.0514 0.2500 Model 2 Number of states = 4 (nucleotide data) Nucleotide Relative Rate Matrix: User specified matrix type: ( 0 1 2 1 0 3 ) AC = 4.384, AG = 7.090, AT = 1.611, CG = 7.090, CT = 4.384, GT = 1.000 Equilibrium State Frequencies: estimated (ACGT) 0.2692 0.1636 0.1605 0.4067 Rate Heterogeneity Model: 4 discrete gamma distributed rate categories, alpha param estimated 0.3609 Substitution rate categories under this model: rate proportion 0.0115 0.2500 0.1520 0.2500 0.6852 0.2500 3.1513 0.2500 Model 3 Number of states = 4 (nucleotide data) Nucleotide Relative Rate Matrix: User specified matrix type: ( 0 1 2 3 1 0 ) AC = 1.000, AG = 4.939, AT = 3.394, CG = 0.457, CT = 4.939, GT = 1.000 Equilibrium State Frequencies: estimated (ACGT) 0.1565 0.3537 0.2877 0.2021 Rate Heterogeneity Model: 4 discrete gamma distributed rate categories, alpha param estimated 4.0115 with an invariant (invariable) site category, proportion estimated 0.0335 Substitution rate categories under this model: rate proportion 0.0000 0.0335 0.4550 0.2416 0.7756 0.2416 1.0844 0.2416 1.6850 0.2416 Subset rate multipliers: 0.54 0.30 2.16 NOTE: Collapsing of minimum length branches was requested (collapsebranches = 1) No branches were short enough to be collapsed. >>>Completed Search rep 4 (of 5)<<< >>>Search rep 5 (of 5)<<< MODEL REPORT - Parameters are at their INITIAL values (not yet optimized) Model 1 Number of states = 4 (nucleotide data) Nucleotide Relative Rate Matrix: User specified matrix type: ( 0 1 2 2 3 4 ) AC = 1.000, AG = 4.000, AT = 1.000, CG = 1.000, CT = 4.000, GT = 1.000 Equilibrium State Frequencies: estimated (ACGT) 0.3157 0.1746 0.3004 0.2093 Rate Heterogeneity Model: 4 discrete gamma distributed rate categories, alpha param estimated 0.5000 Substitution rate categories under this model: rate proportion 0.0334 0.2500 0.2519 0.2500 0.8203 0.2500 2.8944 0.2500 Model 2 Number of states = 4 (nucleotide data) Nucleotide Relative Rate Matrix: User specified matrix type: ( 0 1 2 1 0 3 ) AC = 4.000, AG = 4.000, AT = 1.000, CG = 4.000, CT = 4.000, GT = 1.000 Equilibrium State Frequencies: estimated (ACGT) 0.2703 0.1566 0.1628 0.4103 Rate Heterogeneity Model: 4 discrete gamma distributed rate categories, alpha param estimated 0.5000 Substitution rate categories under this model: rate proportion 0.0334 0.2500 0.2519 0.2500 0.8203 0.2500 2.8944 0.2500 Model 3 Number of states = 4 (nucleotide data) Nucleotide Relative Rate Matrix: User specified matrix type: ( 0 1 2 3 1 0 ) AC = 1.000, AG = 4.000, AT = 1.000, CG = 1.000, CT = 4.000, GT = 1.000 Equilibrium State Frequencies: estimated (ACGT) 0.1460 0.3609 0.2915 0.2015 Rate Heterogeneity Model: 4 discrete gamma distributed rate categories, alpha param estimated 0.5000 with an invariant (invariable) site category, proportion estimated 0.0355 Substitution rate categories under this model: rate proportion 0.0000 0.0355 0.0334 0.2411 0.2519 0.2411 0.8203 0.2411 2.8944 0.2411 Subset rate multipliers: 1.00 1.00 1.00 Starting with seed=1922346580 creating likelihood stepwise addition starting tree... number of taxa added: 4 5 6 Optimizing parameters... improved 198.984 lnL Optimizing branchlengths... improved 64.290 lnL 7 8 9 10 11 Initial ln Likelihood: -13643.0835 optimizing: starting branch lengths, alpha shape, prop. invar, rel rates, eq freqs, subset rates... pass 1:+ 166.300 (branch= 6.38 scale= 1.32 alpha= 5.62 freqs= 26.48 rel rates= 65.82 pinv= 0.81 subset rates= 59.87) pass 2:+ 86.601 (branch= 7.41 scale= 1.89 alpha= 5.35 freqs= 12.24 rel rates= 3.04 pinv= 0.66 subset rates= 56.01) pass 3:+ 47.155 (branch= 5.78 scale= 0.66 alpha= 3.94 freqs= 0.98 rel rates= 2.37 pinv= 0.01 subset rates= 33.42) pass 4:+ 17.442 (branch= 0.49 scale= 0.57 alpha= 0.74 freqs= 0.18 rel rates= 3.03 pinv= 0.02 subset rates= 12.41) pass 5:+ 5.438 (branch= 0.00 scale= 0.00 alpha= 0.01 freqs= 0.17 rel rates= 0.56 pinv= 0.02 subset rates= 4.69) pass 6:+ 0.155 (branch= 0.00 scale= 0.00 alpha= 0.00 freqs= 0.13 rel rates= 0.01 pinv= 0.01 subset rates= 0.00) lnL after optimization: -13319.9928 gen current_lnL precision last_tree_imp 0 -13319.9928 0.500 0 100 -13318.8931 0.500 0 200 -13318.1402 0.500 0 300 -13317.7489 0.500 0 400 -13317.4083 0.500 0 500 -13316.8158 0.500 0 Optimization precision reduced Optimizing parameters... improved 0.067 lnL Optimizing branchlengths... improved 0.000 lnL 600 -13316.5461 0.402 0 700 -13316.4355 0.402 0 800 -13316.3446 0.402 0 900 -13316.3035 0.402 0 1000 -13316.2369 0.402 0 Optimization precision reduced Optimizing parameters... improved 0.035 lnL Optimizing branchlengths... improved 0.000 lnL 1100 -13316.1538 0.304 0 1200 -13316.0789 0.304 0 1300 -13316.0516 0.304 0 1400 -13316.0267 0.304 0 1500 -13316.0232 0.304 0 Optimization precision reduced Optimizing parameters... improved 0.020 lnL Optimizing branchlengths... improved 0.000 lnL 1600 -13315.9865 0.206 0 1700 -13315.9675 0.206 0 1800 -13315.9224 0.206 0 1900 -13315.9215 0.206 0 2000 -13315.9196 0.206 0 Optimization precision reduced Optimizing parameters... improved 0.006 lnL Optimizing branchlengths... improved 0.000 lnL 2100 -13315.9095 0.108 0 2200 -13315.9037 0.108 0 2300 -13315.8985 0.108 0 2400 -13315.8890 0.108 0 2500 -13315.8796 0.108 0 Optimization precision reduced Optimizing parameters... improved 0.013 lnL Optimizing branchlengths... improved 0.011 lnL 2600 -13315.8524 0.010 0 2700 -13315.8419 0.010 0 2800 -13315.8399 0.010 0 2900 -13315.8380 0.010 0 3000 -13315.8310 0.010 0 3100 -13315.8292 0.010 0 3200 -13315.8292 0.010 0 3300 -13315.8290 0.010 0 3400 -13315.8288 0.010 0 3500 -13315.8260 0.010 0 3600 -13315.8255 0.010 0 3700 -13315.8253 0.010 0 3800 -13315.8209 0.010 0 3900 -13315.8209 0.010 0 4000 -13315.8207 0.010 0 4100 -13315.8207 0.010 0 4200 -13315.8176 0.010 0 4300 -13315.8130 0.010 0 4400 -13315.8127 0.010 0 4500 -13315.8122 0.010 0 4600 -13315.8122 0.010 0 4700 -13315.8089 0.010 0 4800 -13315.8080 0.010 0 4900 -13315.8080 0.010 0 5000 -13315.8080 0.010 0 5100 -13315.8080 0.010 0 5200 -13315.8080 0.010 0 5300 -13315.8080 0.010 0 5400 -13315.8076 0.010 0 5500 -13315.8076 0.010 0 5600 -13315.8071 0.010 0 5700 -13315.8071 0.010 0 5800 -13315.8071 0.010 0 5900 -13315.8071 0.010 0 6000 -13315.8066 0.010 0 6100 -13315.8063 0.010 0 6200 -13315.8063 0.010 0 6300 -13315.8049 0.010 0 6400 -13315.8048 0.010 0 6500 -13315.8048 0.010 0 6600 -13315.8046 0.010 0 6700 -13315.8023 0.010 0 6800 -13315.8017 0.010 0 6900 -13315.8017 0.010 0 7000 -13315.8016 0.010 0 7100 -13315.7993 0.010 0 7200 -13315.7991 0.010 0 7300 -13315.7991 0.010 0 7400 -13315.7941 0.010 0 7500 -13315.7939 0.010 0 7600 -13315.7927 0.010 0 7700 -13315.7923 0.010 0 7800 -13315.7923 0.010 0 7900 -13315.7921 0.010 0 8000 -13315.7921 0.010 0 8100 -13315.7921 0.010 0 Reached termination condition! last topological improvement at gen 0 Improvement over last 500 gen = 0.00061 Current score = -13315.7921 Performing final optimizations... pass 1 : -13315.7914 (branch= 0.0000 alpha= 0.0001 pinv= 0.0000 eq freqs= 0.0003 rel rates= 0.0004 subset rates= 0.0000) pass 2 : -13315.7882 (branch= 0.0000 alpha= 0.0026 pinv= 0.0000 eq freqs= 0.0003 rel rates= 0.0002 subset rates= 0.0000) pass 3 : -13315.7851 (branch= 0.0013 alpha= 0.0000 pinv= 0.0000 eq freqs= 0.0017 rel rates= 0.0002 subset rates= 0.0000) pass 4 : -13315.7799 (branch= 0.0041 alpha= 0.0000 pinv= 0.0000 eq freqs= 0.0002 rel rates= 0.0008 subset rates= 0.0000) pass 5 : -13315.7758 (branch= 0.0020 alpha= 0.0004 pinv= 0.0000 eq freqs= 0.0010 rel rates= 0.0006 subset rates= 0.0001) pass 6 : -13315.7743 (branch= 0.0009 alpha= 0.0000 pinv= 0.0002 eq freqs= 0.0001 rel rates= 0.0001 subset rates= 0.0001) pass 7 : -13315.7735 (branch= 0.0003 alpha= 0.0001 pinv= 0.0000 eq freqs= 0.0001 rel rates= 0.0002 subset rates= 0.0000) pass 8 : -13315.7733 (branch= 0.0001 alpha= 0.0000 pinv= 0.0000 eq freqs= 0.0000 rel rates= 0.0002 subset rates= 0.0000) pass 9 : -13315.7727 (branch= 0.0002 alpha= 0.0000 pinv= 0.0000 eq freqs= 0.0000 rel rates= 0.0003 subset rates= 0.0000) pass 10: -13315.7726 (branch= 0.0001 alpha= 0.0000 pinv= 0.0000 eq freqs= 0.0000 rel rates= 0.0001 subset rates= 0.0000) pass 11: -13315.7725 (branch= 0.0000 alpha= 0.0000 pinv= 0.0000 eq freqs= 0.0000 rel rates= 0.0001 subset rates= 0.0000) pass 12: -13315.7724 (branch= 0.0000 alpha= 0.0000 pinv= 0.0000 eq freqs= 0.0000 rel rates= 0.0000 subset rates= 0.0000) pass 13: -13315.7724 (branch= 0.0000 alpha= 0.0000 pinv= 0.0000 eq freqs= 0.0000 rel rates= 0.0000 subset rates= 0.0000) pass 14: -13315.7724 (branch= 0.0000 alpha= 0.0000 pinv= 0.0000 eq freqs= 0.0000 rel rates= 0.0000 subset rates= 0.0000) pass 15: -13315.7723 (branch= 0.0000 alpha= 0.0000 pinv= 0.0000 eq freqs= 0.0000 rel rates= 0.0000 subset rates= 0.0000) pass 16: -13315.7723 (branch= 0.0000 alpha= 0.0000 pinv= 0.0000 eq freqs= 0.0000 rel rates= 0.0000 subset rates= 0.0000) pass 17: -13315.7723 (branch= 0.0000 alpha= 0.0000 pinv= 0.0000 eq freqs= 0.0000 rel rates= 0.0000 subset rates= 0.0000) pass 18: -13315.7723 (branch= 0.0000 alpha= 0.0000 pinv= 0.0000 eq freqs= 0.0000 rel rates= 0.0000 subset rates= 0.0000) pass 19: -13315.7722 (branch= 0.0000 alpha= 0.0000 pinv= 0.0000 eq freqs= 0.0000 rel rates= 0.0000 subset rates= 0.0000) pass 20: -13315.7722 (branch= 0.0000 alpha= 0.0000 pinv= 0.0000 eq freqs= 0.0000 rel rates= 0.0000 subset rates= 0.0000) optimization up to ... pass 21: -13315.7722 (branch= 0.0000 alpha= 0.0000 pinv= 0.0000 eq freqs= 0.0000 rel rates= 0.0000 subset rates= 0.0000) Looking for minimum length branches... Final score = -13315.7722 Time used = 0 hours, 9 minutes and 50 seconds MODEL REPORT - Parameter values are FINAL Model 1 Number of states = 4 (nucleotide data) Nucleotide Relative Rate Matrix: User specified matrix type: ( 0 1 2 2 3 4 ) AC = 1.962, AG = 2.575, AT = 1.409, CG = 1.409, CT = 3.717, GT = 1.000 Equilibrium State Frequencies: estimated (ACGT) 0.3102 0.1767 0.2971 0.2160 Rate Heterogeneity Model: 4 discrete gamma distributed rate categories, alpha param estimated 0.4097 Substitution rate categories under this model: rate proportion 0.0181 0.2500 0.1890 0.2500 0.7416 0.2500 3.0513 0.2500 Model 2 Number of states = 4 (nucleotide data) Nucleotide Relative Rate Matrix: User specified matrix type: ( 0 1 2 1 0 3 ) AC = 4.381, AG = 7.086, AT = 1.609, CG = 7.086, CT = 4.381, GT = 1.000 Equilibrium State Frequencies: estimated (ACGT) 0.2692 0.1636 0.1605 0.4067 Rate Heterogeneity Model: 4 discrete gamma distributed rate categories, alpha param estimated 0.3612 Substitution rate categories under this model: rate proportion 0.0115 0.2500 0.1522 0.2500 0.6856 0.2500 3.1507 0.2500 Model 3 Number of states = 4 (nucleotide data) Nucleotide Relative Rate Matrix: User specified matrix type: ( 0 1 2 3 1 0 ) AC = 1.000, AG = 4.938, AT = 3.393, CG = 0.457, CT = 4.938, GT = 1.000 Equilibrium State Frequencies: estimated (ACGT) 0.1565 0.3537 0.2877 0.2021 Rate Heterogeneity Model: 4 discrete gamma distributed rate categories, alpha param estimated 4.0171 with an invariant (invariable) site category, proportion estimated 0.0335 Substitution rate categories under this model: rate proportion 0.0000 0.0335 0.4553 0.2416 0.7758 0.2416 1.0844 0.2416 1.6845 0.2416 Subset rate multipliers: 0.54 0.30 2.16 NOTE: Collapsing of minimum length branches was requested (collapsebranches = 1) No branches were short enough to be collapsed. >>>Completed Search rep 5 (of 5)<<< ####################################################### Completed 5 replicate search(es) (of 5). NOTE: Unless the following output indicates that search replicates found the same topology, you should assume that they found different topologies. Results: Replicate 1 : -13315.7721 (best) Replicate 2 : -13315.7723 (same topology as 1) Replicate 3 : -13315.7722 (same topology as 1) Replicate 4 : -13315.7722 (same topology as 1) Replicate 5 : -13315.7722 (same topology as 1) Parameter estimates across search replicates: Partition model subset 1: r(AC) r(AG) r(AT) r(CG) r(CT) r(GT) pi(A) pi(C) pi(G) pi(T) alpha rep 1: 1.966 2.579 1.411 1.411 3.722 1 0.310 0.177 0.297 0.216 0.410 rep 2: 1.962 2.575 1.408 1.408 3.715 1 0.310 0.177 0.297 0.216 0.410 rep 3: 1.964 2.577 1.41 1.41 3.72 1 0.310 0.177 0.297 0.216 0.410 rep 4: 1.965 2.577 1.41 1.41 3.72 1 0.310 0.177 0.297 0.216 0.410 rep 5: 1.962 2.575 1.409 1.409 3.717 1 0.310 0.177 0.297 0.216 0.410 Partition model subset 2: r(AC) r(AG) r(AT) r(CG) r(CT) r(GT) pi(A) pi(C) pi(G) pi(T) alpha rep 1: 4.38 7.085 1.609 7.085 4.38 1 0.269 0.164 0.160 0.407 0.361 rep 2: 4.4 7.116 1.617 7.116 4.4 1 0.269 0.164 0.161 0.407 0.361 rep 3: 4.382 7.087 1.61 7.087 4.382 1 0.269 0.164 0.160 0.407 0.361 rep 4: 4.384 7.09 1.611 7.09 4.384 1 0.269 0.164 0.160 0.407 0.361 rep 5: 4.381 7.086 1.609 7.086 4.381 1 0.269 0.164 0.161 0.407 0.361 Partition model subset 3: r(AC) r(AG) r(AT) r(CG) r(CT) r(GT) pi(A) pi(C) pi(G) pi(T) alpha pinv rep 1: 1 4.936 3.391 0.4568 4.936 1 0.157 0.354 0.288 0.202 4.014 0.034 rep 2: 1 4.938 3.393 0.4572 4.938 1 0.157 0.354 0.288 0.202 4.016 0.034 rep 3: 1 4.938 3.393 0.4569 4.938 1 0.157 0.354 0.288 0.202 4.008 0.033 rep 4: 1 4.939 3.394 0.4572 4.939 1 0.157 0.354 0.288 0.202 4.011 0.033 rep 5: 1 4.938 3.393 0.4572 4.938 1 0.157 0.354 0.288 0.202 4.017 0.034 Treelengths and subset rate multipliers: TL R(1) R(2) R(3) rep 1: 1.693 0.541 0.300 2.159 rep 2: 1.693 0.541 0.300 2.158 rep 3: 1.693 0.541 0.300 2.159 rep 4: 1.693 0.541 0.300 2.159 rep 5: 1.693 0.541 0.301 2.158 Saving final trees from all search reps to 3diffModels.byCodonPos.best.all.tre Saving final tree from best search rep (#1) to 3diffModels.byCodonPos.best.tre ####################################################### garli-2.1-release/example/partition/exampleRuns/3parts.diffModelTypes/garli.conf000066400000000000000000000031411241236125200301320ustar00rootroot00000000000000[general] datafname = zakonEtAl2006.11tax.nex constraintfile = none streefname = stepwise attachmentspertaxon = 50 ofprefix = 3diffModels.byCodonPos randseed = -1 availablememory = 512 logevery = 10 saveevery = 100 refinestart = 1 outputeachbettertopology = 0 outputcurrentbesttopology = 0 enforcetermconditions = 1 genthreshfortopoterm = 5000 scorethreshforterm = 0.001 significanttopochange = 0.01 outputphyliptree = 0 outputmostlyuselessfiles = 0 writecheckpoints = 0 restart = 0 outgroup = 1 resampleproportion = 1.0 inferinternalstateprobs = 0 outputsitelikelihoods = 0 optimizeinputonly = 0 collapsebranches = 1 searchreps = 5 bootstrapreps = 0 linkmodels = 0 subsetspecificrates = 1 [model1] datatype = nucleotide ratematrix = ( 0 1 2 2 3 4 ) statefrequencies = estimate ratehetmodel = gamma numratecats = 4 invariantsites = none [model2] datatype = nucleotide ratematrix = ( 0 1 2 1 0 3 ) statefrequencies = estimate ratehetmodel = gamma numratecats = 4 invariantsites = none [model3] datatype = nucleotide ratematrix = ( 0 1 2 3 1 0 ) statefrequencies = estimate ratehetmodel = gamma numratecats = 4 invariantsites = estimate [master] nindivs = 4 holdover = 1 selectionintensity = 0.5 holdoverpenalty = 0 stopgen = 5000000 stoptime = 5000000 startoptprec = 0.5 minoptprec = 0.01 numberofprecreductions = 5 treerejectionthreshold = 50.0 topoweight = 0.01 modweight = 0.002 brlenweight = 0.002 randnniweight = 0.1 randsprweight = 0.3 limsprweight = 0.6 intervallength = 100 intervalstostore = 5 limsprrange = 6 meanbrlenmuts = 5 gammashapebrlen = 1000 gammashapemodel = 1000 uniqueswapbias = 0.1 distanceswapbias = 1.0 garli-2.1-release/example/partition/exampleRuns/3parts.diffModelTypes/zakonEtAl2006.11tax.nex000066400000000000000000000577671241236125200320650ustar00rootroot00000000000000#NEXUS [ This dataset is from: Zakon, Lu, Zwickl and Hillis. 2006. Sodium channel genes and the evolution of diversity in communication signals of electric fishes: Convergent molecular evolution. Proc. Natl. Acad. Sci. USA. 103(10):3675-80. ] begin data; dimensions ntax=11 nchar=2178; format datatype=dna missing=? gap=-; matrix MorNa6 CCTGTGACTCCACATTTTGAGCACGTACTCAGTGTGGGAAACCTGGTTTTCTCAGGGATATTTGCTGGTGAAATGGTCTTGAAAATTATTGCTATGGACCCCTACTACTACTTCCAGGTTGGATGGAACGTGTTTGACAGCATCATTGTTACCATGAGTATGGTGGAGATGGTACTGGCTGATGTAGAGGGTCTGTCGGTTCTGCGGTCCTTTCGTTTGCTACGTGTCTTCAAGCTTGCCAAATCATGGCCTACCCTCAACATGCTGCTAACGATCATCGGAAACTCAGTGGGTGCTCTGGGGAACCTCACCGTGGTGCTGGCCATCATCGTTTTCATCTTCGCTGTGGTTGGAATGCAGCTGTTTGCCAAAAACTACAAGGACTGCGTCTGCAAGATCGCCGAGGATTGTGAGCTGCCCCGGTGGCACATGCATGACTTCTTCCACTCTTTCCTCATCGTGTTCCGCATCCTCTGTGGAGAGTGGATTGAGACCATGTGGGACTGCATGGAAGTGGCCAACAGAAACATGTGTTTGGTCCTCTTCTTAATGGTCATGATAATTGGGAACCTGGTGGTTCTGAACCTTTTCCTGGCCTTGCTGCTTAGCTCATTCAGCGGGGACAATCTGCAAATGGCAGATGACGACGGCGAGCTGAACAATCTGCAGCTTTCCGCACTCAGGATCACCAGAGCCATTGATTGGGTGAAGGCCTACGTTAGAGGGCTGATCTGGAAGATCCTGGGCAAGCAGCCAAGAGTGCTGGATGGTTTATCTCACTGGGCAACCTTCACCGTACCCATTGCCCAGGAAGAGTCTGATTTAGAAGATGGTGTGTCTGAGTGCAGCACAGTGGACTACGTGCCCCCTCCGCCGGATGAAGTGGAGGAACCGGAGCCTGTGGAACCTGAGGCCTGTTACACTGACAACTGCCTTAGACGGTGTCCTTGTCTGGTGCTGGACACCTCAGAGGGCAGAGGGAAGACCTGGTGGAACCTCAGGAGAACCTGCTACACCATTGTGGAGCATGACTACTTTGAGTCCTCCATAATCTTCATGATCCTTCTCAGCAGTGGTGCCTTGGCCTTTGAAGACATATATCTTGAAAGACGCAGAACGATAAAAATCCTGCTGGAATATGCAGATAAAGTCTTCAGCTATGTATTTGTTATTGAGATGCTCCTTAAGTGGGTGGCTTATGGTTACAAAGTATACTTTACCAATGCCTGGTGCTGGCTGGACTTCTTGATTGTTGATGTTTCCTTGGTCAGTTTGGCAGCAAGCATAATGGGCTATTCTGAACTAGGACCCATAAAGTCTTTGAGAACTCTTAGGGCTCTGAGGCCTCTAAGAGCCCTTTCCAGGTTTGAGGGGATGCGGGTTGTGGTGAACGCCCTTGTGGGGGCCGTCCCCGCCATCTTCAATGTGATGCTGGTCTGTCTCATCTTCTGGCTCATCTTCAGCATCATGGGGGTTAACCTGTTTGCCGGGACATTCTACCACTGCCTCAACACCACAACTGGGGAGATGTTTACCATTGATGTTGTAAACAACTATAGTGAGTGTTTGGCCCTCATGCACACAAACGAGGTGCGCTGGGCCAACGTCAGGGTCAACTATGACAACGTTGGGATGGGTTACCTGTCTCTGTTGCAAGTGTCAACATTCAAAGGCTGGATGGAAATTATGTATGCGGCTGTCGACTCACGTAAGGTGGGTCAACAGCCCTCATATGAGGCCAACCTTTACATGTACGTGTACTTTGTCATCTTCATCATCTTTGGGTCCTTCTTTACACTCAACCTCTTCATTGGTGTCATCATTGACAACTTCAATCAACAAAAGAATAAGATGGGAGGA---GATTGCTTTATGACTGAGGAGCAGAAGAAATATTACGACGCTATGAAAAAGCTAGGCAACAAGAAGCCAGCGAAGCCCATTCCAAGACCAACGGGCAAAATACCAGGCCTAGTATATGACTTCATCAGTCAGCAGGCCTTTGACATCTTTATCATGGTACTGATTTGCCTGAACATGGTGACCATGATGGTGGAGGAAGATGACCAAAGTGAACAGAAGACAGACATGCTGGGCAAAATCAATGCAGTCTTCATTGTGGTCTTCAGCAGTGAATGTTTGCTGAAGATGATTGCACTGAGACAATACTTCTTTACC ClownNa6 CCCATGAGCCCTGAGTTTGACCACATGCTCTCTGTGGGAAACCTGGTTTTCACTGGAATCTTCACAGCTGAAATGGTCCTAAAACTCATTGCTATGGACCCCTACTACTACTTCCAGGTTGGATGGAACATATTTGACAGCATCATTGTCACTCTAAGCCTAGTGGAACTGGGGCTCGCTAATGTTCAGGGTCTGTCAGTCCTGCGATCCTTTCGTTTGTTGCGAGTGTTCAAGCTGGCAAAGTCTTGGCCCACCCTCAACATGCTGATCAAGATCATCGGGAATTCCGTGGGCGCCCTGGGCAACCTGACCCTGGTGCTGGCCATCATCGTCTTCATCTTCGCCGTGGTGGGCATGCAGCTCTTTGGGAAGACCTACAAGGACTGCGTGTGCAAGATTGCCAGTGACTGCGAGCTTCCCCGCTGGCACATGAATGACTTCTTCCACTCGTTCCTTATCGTGTTCCGCATCCTCTGCGGGGAGTGGATCGAGACCATGTGGGACTGCATGGAGGTGGCTGGTGCAGGCATGTGCCTCGTGGTCTTCATGATGGTCATGGTCATTGGGAACCTAGTGGTGCTGAATCTCTTCCTGGCTTTGCTGCTCAGTTCATTCAGTGGAGACAACCTAGCAGGCGGTGATGAGGATGGCGAGATGAACAACTTGCAGATTGCTATCGGAAGGATCACCCGAGGCATTGACTGGGTGAAGGCATTTGTCATGGGACTGGTGTGGCGGGTGATGGGCAAAAAGCCTAAAATGCTGGATGGTTTATCTCACTGGGTAACCCTCAGTGTGCCCATGGCACAGGAGGAATCCGACTTAGAAGACGACTCCTCTGAATGCAGCACTGTGGACTATAGGCCTCCAGAGCCAGTGGAGGAGGAAGAACCAGAACAGGTGGAGCCTGTGGAGTGTTTTACTGATGACTGTGTCAGACGTTGCCCTTGTCTGACGGTGGACATCACGCAGGGCAAAGGAAGGACCTGGTGGAATCTCAGGAAAACATGTTACACCATCGTGGAGCATGACTACTTTGAGACCTTCATCATCTTCATGATCCTGCTTAGCAGTGGGGCCTTGGCCTTTGAAGATATATACATTGAAAGGCGCAGAACAATAAAAATCATTCTGGAATATGCAGACAAAGTATTTACATACGTATTTGTTGTTGAAATGCTCTTGAAGTGGGTTGCTTATGGTTTCAAGACATACTTCACTAATGCCTGGTGCTGGCTGGACTTTTTAATTGTGGATGTGTCCTTGATCAGTTTGACAGCAAACCTCATGGGCTACTCAGAGCTGGGGCCTATCAAATCCCTGAGAACCCTGAGGGCCCTGAGGCCACTACGAGCCCTGTCTAGGTTTGAGGGCATGAGAGTGGTGGTAAATGCATTGGTAGGGGCCATCCTTTCCATCTTCAACGTACTGCTGGTCTGTCTCATTTTCTGGCTTATCTTCAGCATTATGGGTGTCAACCTTTTTGCTGGAAAGTTCTACCGCTGTATCAACACCACCACAGAGGAGCTATTACCTGTCGAGATTGTGAACAATAAGAGTGACTGCTTGAATCTCATGCACACAAATGAAGTGCGCTGGGTCAATGTGAAGGTCAACTATGACAACGTTGGCCTTGGTTACCTCTCTCTACTCCAAGTTGCAACATTTAAAGGGTGGATGGACATTATGTATGCAGCTGTGGACTCTCGTGAGGTGGAAGAGCAGCCCTTGTATGAGGAAAACCTCTATATGTACTTATACTTCGTCATCTTCATCATTTTTGGGTCATTCTTTACACTCAACCTTTTCATTGGTGCCATCATCGACAACTTTAATCAGCAAAAGAAAAAGCTTGGTGGGAAGGATATCTTCATGACCGAGGAGCAAAAGAAGTACTACAATGCCATGAAAAAGCTTGGTTCCAAAAAGCCAGTGAAGCCTATTCCAAGACCTACGAACAAAATACAAGGTGTGGTATTTGACTTCATCAGTCAGCAGTTCTTTGACATCTTTATCATGGTATTGATCTGCCTCAACATGGTGACCATGATGGTGGAGACAGATGACCAAAGTCAGGAAAAAGAGAATATACTGAACCAAATCAATCTGGTATTCATTGTGATCTTCACCAGCGAATGCGTCTTGAAGATGTTTGCACTTAGACATTATTTCTTCACC AraNa6 CCAATGAGTCCCGCGTTTGACCATATGCTGACCGTGGGAAACCTCGTTTTTACGGGGATCTTTACAGCTGAGATGGTATTCAAGCTCATCGCCATGGATCCATACCACTACTTCCAGGTTGGATGGAACATTTTTGACAGCATCATTGTCACACTTAGCCTGGTGGAGCTGGGTCTCGCGAATGTTCAGGGCCTTTCGGTCTTGCGCTCCTTCCGCTTGCTGCGGGTCTTCAAGCTGGCCAAGTCTTGGCCTACCCTGAACATGCTCATCAAGATCATTGGAAACTCAGTGGGTGCCCTAGGGAACCTCACACTGGTGCTGGCCATCATCGTCTTCATCTTCGCTGTCGTGGGCATGCAGCTGTTCGGTAAGAGCTACAAGGACTGTGTGTGTAAGATTGCAGAGGACTGTGAGCTACCCCGCTGGCACATGAATGACTTCTTCCACTCCTTCCTCATCGTGTTCCGCATCTTGTGTGGCGAGTGGATCGAGACCATGTGGGACTGCATGGAGGTGGCGGGCGCTGGCATGTGTCTCGTTGTCTTCATGATGGTCATGGTCATCGGCAACCTGGTGGTCCTGAACCTCTTCCTGGCTTTGCTGCTGAGCTCGTTCAGTGGAGACAACCTGGCTGGAGGAGACGATGATGGCGAGATGAACAACCTGCAGATTGCCATTGGCAGGATCACCAGAGGCATTGACTGGATAAAAGCCTTTGCCATGGGCTTCATATGGAAGTTACTTGGAAAGAAGGCCAAGATGCTGGATGGTTTATCCCACTGGGTGACCCTGAGTGTTCCCATTGCCCAGGGAGAGTCTGATTTGGAGGATGACTCCTCTGAATGCAGCACGGTGGACTACAGACCCCCAGAACCAGAGGAGGAGGAGGAGCCTGAGCAGCAGGAGCCTGAGGCCTGTTTTACTGAGGATTGCTTCCGGCGTATGCCATGTTTGATGGTGGACATCACGCAGGGGAAGGGCAAGACCTGGTGGAAACTACGGAAAACCTGTTTTACCATTGTGGAGCATGGCTATTTTGAGACCTTCATCATTTTCATGATCCTTCTCAGCAGTGGAGCTCTGGCTTTTGAAGACATATACATTGAAAAGCGCAGAGTTATCAAAATCATCCTGGAATATGCGGACAAAGTCTTCACCTATGTATTTGTTATTGAAATGGTCCTCAAGTGGGTGGCTTATGGGTTCAAAGTATACTTCACAAACGCCTGGTGCTGGCTGGACTTCCTCATCGTTGATGTGTCCTTGATCAGTCTGACCGCTAACCTCATGGGCTACTCTGAGCTGGGGCCCATTAAGTCTCTGAGAACACTTAGGGCCCTTAGGCCCCTGAGGGCCCTCTCCAGGTTTGAGGGGATGAGGGTGGTGGTAAATGCGCTTGTGGGAGCCATCCTCTCCATTTTCAACGTTCTGCTCGTGTGCCTCATCTTCTGGCTCATCTTCAGCATCATGGGCGTTAACCTGTTTGCTGGGAAGTTCTACTACTGCATTAACACCACCTCAGAGGAGCGCTTACCCATTGATGTTGTGAATAACAAGAGCGACTGCATGGCCCTAATGCACACCAATGAGGTGCGCTGGGTCAACGTCAAGGTGAACTATGACAATGTCGGCTTGGGCTATCTCTCTCTGCTGCAGGTGGCTACTTTTAAAGGTTGGATGGATATAATGTATGCTGCCGTGGACTCACGGGAGGTGGGGGAGCAACCCTCCTATGAGGTCAACATCTACATGTACTTGTACTTTGTCATCTTCATCATCTTCGGGTCCTTCTTCACGCTCAACCTCTTCATTGGTGTCATCATTGACAACTTCAATCAGCAAAAGAAAAAGTTAGGAGGAAAAGACATATTCATGACTGAGGAACAGAAGAAGTATTACAATGCCATGAAGAAACTTGGCTCCAAGAAGCCAGTGAAGCCCATCCCACGACCTTCGAATAAAATTCAAGGCATGGTGTTTGACTTCATTACGCAGCAGTTTTTTGATATTTTCATCATGGTACTGATCTGCCTCAACATGGTGACCATGATGGTGGAGACGGATGATCAAAGCGAGGACAAAGAAAATGTCCTCTACCAGATTAACCTGGTCTTCATTGTGATCTTCACCTGCGAGTGCGTCCTCAAAATGTTTGCGCTTAGACAGTACTTCTTCACC puffNa6 CCCATGACCGAAGAGTTCGACTACATGCTTTCAGTGGGAAATCTGGTTTTCACAGGAATCTTCGCGGCGGAAATGTTCTTCAAATTGATCGCCATGGATCCGTACTACTATTTCCAAGTTGGCTGGAACATTTTTGACAGCATCATCGTCACGCTCAGTCTGGTGGAGTTAGGGCTTGCAAACGTCCAGGGGCTGTCCGTCCTCAGGTCCTTCCGTCTGCTTCGGGTCTTCAAACTTGCCAAGTCCTGGCCCACGCTCAACATGCTGATCAAGATTATCGGTAATTCAGTTGGAGCTTTAGGGAATCTGACTTTGGTGCTGGCCATCATCGTCTTCATCTTCGCTGTGGTGGGGATGCAGCTCTTCGGCAAAAGCTACAAGGACTGTGTGTGCAAGATTTCCTCCGACTGCGAGCTGCCACGCTGGCACATGAATGACTTCTTCCACTCCTTCCTCATCGTGTTCCGCATCCTGTGCGGCGAGTGGATCGAGACCATGTGGGACTGCATGGAGGTGGCTGGAGCCGGGATGTGCTTGGTTGTCTTCATGATGGTCATGGTCATCGGGAACCTCGTGGTGTTGAATCTCTTCCTGGCCCTGCTGCTCAGCTCATTCAGCGGAGACAACCTCTCGGTCGGAGACGACGATGGAGAGCTGAACAATCTTCAGATCGCCATCGGAAGGATCACACGAGGCGGCAACTGGCTCAAAGCCTTCTTCATCGGAACGCTTCAACGGGTTCTTGGAAGGGAACCAAAATTGGCAGACGGGATCGCCAACTGTCTTAGTATCACCGTCCCCATCGCCCTGGGAGAGTCGGACTCTGAAGGCGATTCTTCAGTGTGCAGCACAGTGGACTATCAGCCCCCAGAGCCTGAGGAAGAGGAAGAGCCGGACCTGGTGGAGCCAGAGGCCTGCTTCACTGACAACTGTGTGAAGCGCTGGCCTTGTCTGAACGTGGACATCAGCCAGGGGAAAGGAAAGAAGTGGTGGAACCTCCGCAAGACCTGCTTCACTATTGTGGAGCATGACTGGTTTGAGACCTTCATCATTTTCATGATCCTCCTCAGCAGCGGAGCTCTGGCCTTTGAAGACATATACATCGAAAGACGAAGAACCGTGAAAATTGTCCTGGAGTTTGCTGACAAAGTTTTCACCTTCATCTTTGTCATCGAGATGCTCCTGAAATGGGTCGCCTATGGCTTCAAGACCTACTTCACCAATGCCTGGTGCTGGCTGGACTTTTTCATCGTGGACATTTCCCTGATCAGTCTATCTGCCAACTTGATGGGCTTCTCTGACCTCGGACCAATCAAATCGCTCAGAACTCTCAGGGCTCTGCGGCCTCTTCGGGCGCTGTCCAGATTTGAAGGGATGAGGGTGGTGGTGAACGCTCTCATCGGAGCCATTCCCTCCATCTTCAACGTGCTCCTGGTGTGCCTGATCTTCTGGCTCATCTTCAGCATCATGGGAGTGAACCTGTTTGCGGGGAAGTTCTACCGCTGCATCAACACCACCACGGCGGAGCTCTTCCCCATCTCTGTGGTCAACAACAAGAGCGACTGCGTGGCGCTGCAGGCCACGCAGGAGGCCCGCTGGGTCAACGTCAAAGTCAACTACGACAACGTGGCAAAAGGCTACCTGTCGCTGCTTCAAATCGCAACTTTTAAAGGCTGGATGGATATTATGTATCCTGCGGTTGACTCAAGAGAGGTGGAAGAGCAACCTTCTTATGAGATCAACCTCTACATGTACATCTACTTTGTCATCTTTATCATCTTTGGCTCTTTCTTCACGCTGAACCTCTTCATCGGCGTCATCATCGACAATTTCAACCAGCAGAAGAAAAAGTTAGGAGATAAAGACATCTTCATGACAGAGGAGCAGAAAAAGTACTACGAAGCCATGAAGAAACTCGGCTCAAAGAAGCCGCAGAAGCCGATCCCACGTCCAGCTAACCTAATCCAGGGGCTAGTGTTTGACTTCATCAGTCAGCAGTTCTTTGACATCTTCATCATGGTGCTCATCTGTCTCAACATGGTCACCATGATGGTGGAGACGGACGACCAGAGTCCGGCGAAGGAGGACTTCCTCTTCAAAGTGAACGTGGCTTTTATTGTGGTCTTCACCGGGGAGTGCACGTTAAAGCTCATCGCCCTGCGACATTACTTCTTCACC NewZebra CCTATGAGTCCACATTTTGAACATGTCCTCTCAGTGGGCAACTTGGTGTTCACAGGAATCTTCACAGCTGAAATGGTGTTCAAGCTTATAGCTATGGACCCTTACTACTACTTCCAGGTGGGCTGGAACATTTTTGACAGCATCATTGTCACACTCAGCCTGGTGGAGTTGGGACTGGCCAACGTTCAGGGATTGTCCGTTCTAAGGTCCTTTCGTTTGCTACGTGTCTTCAAACTGGCTAAATCTTGGCCCACCCTTAACATGCTGATCAAGATCATCGGCAACTCAGTGGGTGCTCTAGGGAACCTAACACTTGTTCTGGCCATCATTGTCTTCATCTTTGCCGTGGTGGGCATGCAGCTTTTTGGAAAAAGCTACAAGGACTGCGTTTGTAAGATCTCTGAGGATTGCGAGCTGCCCCGCTGGCACATGAACGACTTCTTCCACTCATTCCTCATCGTCTTTCGGATCTTATGTGGAGAGTGGATTGAGACCATGTGGGACTGCATGGAGGTGGCAGGAGCTAGCATGTGTTTGATAGTCTTCATGATGGTCATGGTCATCGGAAACCTTGTGGTGCTGAATCTGTTTCTGGCCCTGCTGCTTAGCTCCTTCAGTGGAGATAACCTGTCTGGAGGTGATGATGATGGAGAGATGAACAACCTTCAGATTGCCATTGGCCGCATCACCAGGGGTATCGATTGGGTTAAAGCCTTAGTTGCCAGTATGGTGCAACGGATTCTGGGAAAGAAACCTAAAATGGCAGATGGTCTGACCAACTGTTTGACATTGACTGTACCTATTGCTCGTTGTGAGTCTGATGTGGAGGGTGACTCTTCGGTTTGTAGCACAGTGGACTACCAGCCTCCAGAACCTGTAGAAGAAGAGGAACCAGAACCTGAAGAACCAGAGGCCTGTTTCACAGAGGGCTGTATTAGGCGATGTGCATGTTTGAGTGTTGACATCACAGAAGGATGGGGTAAAAAATGGTGGAACCTCAGAAGGACATGCTTCACCATCGTTGAGCATGATTACTTTGAGACCTTCATCATCTTTATGATCCTCCTTAGCAGTGGAGCACTGGCTTTTGAGGATATCAACATTGAGAGGCGCAGAGTGATCAAGATCATTCTGGAGTATGCTGATAAAGTCTTTACATATATTTTTATAGTGGAGATGTTACTGAAGTGGGTGGCATATGGCTTCAAGACCTACTTCACTAATGCATGGTGCTGGCTGGACTTCCTCATTGTGGATGTGTCTCTGGTCAGTTTAACGGCTAATTTAATGGGCTATTCTGAGCTGGGGGCAATCAAATCTCTCAGGACACTTAGAGCTCTTCGTCCACTTCGAGCCCTATCCAGATTTGAAGGAATGAGGGTGGTAGTGAATGCACTTGTAGGTGCCATTCCCTCTATTTTTAACGTGCTCCTGGTGTGTCTGATATTCTGGCTCATCTTCAGCATTATGGGGGTCAATCTGTTTGCCGGAAAATTCTACCACTGCATCAACACCACCACAGAGGAACGGATCCCCATGGATGTAGTCAACAACAAGAGTGACTGCATGGCACTGATGTACACCAACGAGGTGCGATGGGTCAATGTCAAGGTCAACTACGACAACGTGGGACTCGGCTACCTCTCTCTGCTGCAGATTGCCACATTCAAAGGCTGGATGGATATCATGTATGCTGCAGTGGATTCTAGAGAGGTGGATGAGCAGCCATCATATGAAATCAACCTTTACATGTACCTTTATTTTGTTATTTTCATCATTTTTGGCTCCTTTTTTACTCTCAACCTCTTTATTGGTGTCATCATTGACAACTTCAATCAGCAAAAATCAAAGTTTGGAGGGAAAGACATTTTCATGACTGAGGAACAGAAAAAGTACTACAATGCCATGAAGAAGCTGGGTGCAAAGAAACGTCCAAAACCTATACCTCGACCATCAAATATTATCCAGGGTTTGGTGTTTGACTTCATATCAAAACAGTTCTTTGACATTTTTATCATGGTGCTAATCTGCCTCAACATGGTGACCATGATGATAGAGACGGATGATCAGAGTGCTGAGAAAGAATATGTCCTGTACCAGATCAATCTGGTCTTCATCGTCGTCTTCACAAGCGAATGTGTACTTAAATTATTTGCACTCAGACAGTACTTTTTCACT SterNa6 CCCATGAGCGAAACCTTTCAACACGTGCTCACCATAGGGAACCTGGTGTTTACTACCATCTTTACGGCTGAAATGGTGTCGAAGATCATCGCCCTGGACCCTTACTACTACTTCCAGGTGGGCTGGAACATCTTCGACTGCATCATCGTCACTCTCAGTCTGGTGGAGCTAAGCCTATCCAACATGCCGGGCCTGTCTGTGCTCAGATCCTTTCGTTTGATGCGTATTTTCAAGCTGGCCAAGTCCTGGCCCACGCTCAACATGCTGATCAAGATCATCGGCAACTCAATGGGCGCCCTGGGGAACCTGACCTTCGTGTTGGCCATCGTCATCTTCATCTTCGCCGTGGTGGGCTTCCAGCTGTTCGGGAAGAGCTACAAGGACAACGTGTGCAAGGTCAGCGCGGACTGCACGCTGCCTCGCTGGCACATGAACGACTTCTTCCACTCCTTCCTGATCGTGTTTCGCATCCTGTGCGGCGAGTGGATCGAGACCATGTGGGACTGCATGGAGGTGGACGGAGTGCCCATGTGCCTCACCGTCTTCATGATGGTCATGGTCATCGGAAACCTGGTGATGCTGAACCTGTTCCTTGCCTTGCTTCTCAGCTCATTCAGCTGCGACAATCTTGCCGCGCCAGACGATGACAGTGAAGTTACCAACATCCAGATCTCCATTGTGCGCATCAGCAGAGGGATAAGCTGGGTGAAGAAATTCATTGTAGGCACAGCCTGGTGGATCATGGGCAGGAAGCCCAAGATTGTAGATGGGATTACCAACTATGTTGTTCTGAATGTGCCTATTGCCAAGGGGGAGTCTGAGGTTGAGGATGACTCTTCGATTTGCAGTTCAGTGGACTACGAGCTTCTACAACCCGAGGAGGAAAAGGAA---GAGCCTGTTGATCCAGAAGCCTGTTTTACAGAAAACTGTGTGAGGTACTTTCCATGTCTGGATGTGGACATCACACAGGGGAAAGGGAAGATCTGGTGGAACCTCCGCTGCACCTGCTACAACATCGTGGAACATCACTATTTTGAAAACTTTCTCATCTTCATGATTCTCCTCAGTAGTGGAGTACTGGCATTCGAGGATGTTAATATCGAACGCCGCAGGGTCATTAAGACCATGTTGGAGTATGCAGACATAGTCTTCACATATATTTTCGTGGTGGAGATGTTTCTGAAGTGGACTGCATATGGGTTTAAAGCGTACTTCACCAGTGCCTGGTGCTGGCTGGATTTTTTTATTGTTGATGTGTCAGTTATTAGCTTAGTAGCCAATGTGTTGGGCTATGCAGAGCTGGGACCAGTCAGATCGCTCAGAACTTTCAGGGCTCTTCGACCTTTACGTGCCCTTTCCAGATTTGAAGGAATGAGGGTGGTAGTGAATGCATTGCTCGGTGCCATCCCCTCCATCATGAACGTCCTATTGGTGTGTCTGATCTTCTGGCTGATCTTCAGCATCATGGGGGTCAACTTGTTTGCGGGAAAGTTCTACCGCTGCATTAACACCACCACAGATGAGGTTCTGTCCACAGAGCAAGTGAACAACAGGAGTGAATGCATGGCACTAATGCACACTAATGAGGTGCGTTGGGTCAACCTTAAGGTCAACTACGACAATGTGGGCCAGGGATATCTCTCCTTGCTTCAAGTGGCCACATTTAAAGGGTGGATGGGCATCATGTATGGTGCAGTGGACTCTAGAGAGGTAGAGGATCAGCCATCATATGAGATTAACCTCTACATGTACCTGTACTTTGTCATCTTCATCACATTTGGATCCTTTTTTATCCTCAACCTTTTCATTGGTGTCATCATTGACAATTTTAACCGGCAAAAACAAAAGTTAGGAGGAGATGACCTCTTTATGACAGATGAACAAAAAAAGTATTATGCTGCCATGAAGAAGCTGGGTTCCAAGAAACCACTCAAACCTATACCCCGTCCTTCGAATATGGTTCAAGGGGTGGTGTTCGACTTCATCTCCCAAAAGTTCTTTGACATTTCCATCATGGTTCTCATCTGCCTCAACATGGTGATCATGATGGTGGAGGCGGACGACCAGAGTGAAGAGAAAGAGAATGTCCTCTATCAGATCAATATCATATTTATTGTCNTCTTCACCGGAGAGAGTTTACTCAAGTTGTTTGGACTTAGACATTACTTCTTCACT eelNa6 CCCATGAACGAAAGCTTTCAGAGTCTGCTCAGTGCAGGAAACCTGGTGTTTACCACTATCTTTGCGGCTGAAATGGTGTTGAAGATCATTGCCTTGGACCCCTACTACTACTTCCAGCAGACGTGGAACATATTTGACAGCATCATTGTCAGTCTCAGTCTGTTGGAGCTTGGACTATCCAATATGCAAGGAATGTCTGTGCTCAGATCCTTACGTTTGCTGCGTATCTTCAAATTGGCCAAGTCCTGGCCCACGCTCAACATTCTGATCAAGATAATCTGCAACTCGGTGGGCGCTCTGGGCAACCTGACCATTGTGCTGGCCATTATCGTCTTCATCTTCGCCTTGGTGGGCTTTCAGCTGTTCGGAAAGAACTACAAGGAGTACGTGTGCAAGATCTCTGATGACTGTGAGCTGCCCCGCTGGCACATGAATGACTTCTTCCACTCCTTCCTGATTGTGTTCCGTGCCTTGTGTGGCGAGTGGATTGAGACCATGTGGGACTGCATGGAGGTGGGCGGAGTTCCTATGTGCCTCGCCGTCTACATGATGGTCATAATCATTGGGAACCTGGTGATGCTGAACCTTTTCCTTGCCTTGCTTCTAAGCTCATTCAGCAGCGACAATCTCAGTTCAATTGAAGAAGATGATGAAGTTAACAGCCTCCAGGTTGCCTCTGAGCGCATTAGTAGGGCAAAAAACTGGGTGAAGATCTTCATCACTGGCACAGTCCTGTGGATCCAGGGCAAGAAGCCCAAGATTGTAGATGGGATAACCAACTGTGTAACTCTGAATCTACCCATTGTAAAGGGGGAGTCAGAGATCGAAGAAGACTCTTCAGTTTGTAGTACAGTGGACTATAGTCCTTCAGAACAAGAGGAGCCAGAGGAACTAGAGTCCAAAGATCCAGAAGCATGTTTTACAGAAAAATGTATATGGCGATTTCCTTTTCTGGATGTGGACATCACACAGGGGAAAGGGAAGATCTGGTGGAACCTACGTAGGACCTGCTACACCATCGTGGAGCATGACTACTTTGAAACCTTCATCATATTCATGATTCTCCTCAGTAGTGGAGTTCTGGCCTTTGAGGACATTTATATTTGGCGTCGCAGGGTGATTAAGGTCATCTTGGAGTATGCAGACAAAGTCTTCACATATGTCTTCATAGTAGAGATGTTACTTAAGTGGGTTGCATATGGGTTTAAAAGATATTTCACTGATGCCTGGTGCTGGCTCGACTTTGTAATTGTTGGTGCATCAATAATGGGCATAACATCCAGTTTGTTGGGCTATGAAGAGCTGGGAGCAATCAAAAATCTCAGAACTATCAGGGCTCTTCGCCCTTTACGTGCCCTTTCCAGATTTGAAGGAATGAAGGTGGTAGTGAGAGCATTGCTTGGTGCCATCCCCTCCATCATGAACGTGCTGCTGGTGTGTCTGATGTTCTGGCTCATCTTCAGCATTATGGGGGTCAATTTGTTTGCTGGAAAGTTCTACCGCTGCATTAACACCACCACAGATGAGATTCTGCCCGTGGAGGAAGTGAACAACCGGAGTGACTGCATGGCACTAATGTACACTAACGAGGTGCGCTGGGTCAACCTTAAGGTCAACTATGACAATGCGGGCATGGGATACCTCTCCCTGCTACAAGTGTCTACATTTAAAGGCTGGATGGACATCATGTATGCTGCAGTGGACTCCAGAGAGGTAGAGGATCAGCCAATCTATGAGATTAATGTCTACATGTACCTGTATTTTGTCATCTTCATCGTATTTGGAGCCTTCTTTACCCTCAACCTTTTCATTGGTGTCATCATAGACAACTTCAACCGTCAAAAGCAAAAGTTAGGAGGAGAAGATCTCTTTATGACAGAAGAGCAGAAGAAGTACTACAATGCCATGAAGAAGCTGGGTTCGAAGAAAGCTGCCAAATGTATACCCCGCCCTTCGAATGTGGTTCAAGGTGTGGTGTACGACATAGTCACCCAACCATTCACTGATATTTTCATCATGGCTCTCATTTGCATCAACATGGTGGCTATGATGGTCGAGTCGGAGGACCAGAGTCAAGTGAAGAAGGACATTCTCTCTCAGATCAATGTCATATTCGTTATCATCTTCACTGTAGAGTGCTTGTTAAAGCTACTTGCACTTAGACAGTACTTCTTCACT catNa6 CCCATGAGTTCGAACTTTGAACACGTGCTCAGTGTTGGCAATTTGGTGTTCACTGGTATTTTCACGGCTGAAATGGTGTTCAAGCTCATTGCCTTGGACCCCTTCTACTACTTCCAGGTTGGCTGGAACATATTTGACAGCATCATCGTCACTCTTAGCCTGGTGGAGTTAGGCCTGGCCAATGTGCAGGGTCTGTCTGTACTCAGATCCTTTCGTTTGCTGCGAGTCTTTAAGCTGGCTAAATCCTGGCCCACGCTCAACATGCTGATCAAAATCATTGGAAACTCTGTGGGTGCTCTGGGGAACCTGACTCTGGTGCTGGCCATCGTCGTCTTCATCTTCGCCGTCGTAGGCATGCAACTTTTTGGCAAGAGCTACAAGGACTGCGTGTGTAAGATTGCAGAGGACTGCGAACTGCCCCGCTGGCACATGAACGATTTTTTCCATTCGTTTCTCATTGTCTTCCGCATCCTTTGTGGTGAATGGATTGAAACCATGTGGGACTGCATGGAGGTGGCTGGAGCAGGCATGTGCCTTGTGGTTTTCCTTATGGTCATGGTCATAGGAAACCTGGTGGTGCTGAACCTGTTCCTTGCCTTGTTGCTCAGCTCTTTCAGCGGGGACAATCTCTCAGCAGGTGATGAAGATGGTGAAATGAACAATCTCCAGATTGCCATCGGCCGCATCACCAGGGGCATTGACTGGGTCAAATCCTTCATCATTGGCCTTGTACAGCAGATACTTTGCAGGAAGCCTAAGATGGCAGATAGGTTGACCAACTGTCTGACCCTGAATGTACCAATTGCCAAAGCTGAGTCTGATGTTGAAGAAGACTCTTCAATGTGTAGCACAGTGGACTATAGACCTCCAGAATCCGAGGAGGAAGAGGAACCAGAACCTGTTGAGCCAGAAGCCTGTTTTACTGAAAACTGTGTGAGACGATGTCCATGTCTGAATTTGGACATCACTCAGGGGAGGGGAAAGAGTTGGTGGAATCTGCGCAGAACTTGCTACACCATAGTGGAGCATGATTACTTTGAAACCTTCATCATCTTCATGATTCTCCTCAGTAGTGGTGCACTGGCCTTTGACGACATTTACATTGAGCGTCGCAGGGTGATTAAGATTATCTTGGAATATGCAGACCAAGTCTTCACATATATTTTTGTCATAGAGATGTTACTGAAATGGGTTGCGTATGGCTTCAAGACATACTTCACCAATGCCTGGTGCTGGCTGGACTTTTTCATCGTTGATGTGTCACTTATCGGTTTAACGGCAAATCTGTTGGGCTATTCAGAGCTGGGACCAATAAAATCTCTCAGAACTCTTAGGGCGCTTCGACCTTTACGTGCCCTGTCCAGATTTGAAGGAATGAGGGTGGTAGTGAACGCATTGCTGGGTGCCATTCCTTCCATCATGAATGTACTCCTGGTGTGTCTAATATTCTGGCTGATCTTCAGTATTATGGGGGTCAACCTGTTTGCTGGGAAATACTACCGCTGCATTAATACCACCACAGAAGAACTTTTACCCATCGAGCAAGTGAACAACATGAGTGATTGCATAGCACTAATGCACACTAAAGAAGCACGCTGGGTCAATGTCAAGGTCAACTTTGACAATGTGGGCTTGGGTTACCTTTCCCTGCTACAAGAGGCTACATTTAAAGGCTGGATGGACATTATGTATGCTGCAGTGGATTCCAGAGAGGTGGAAGAACAGCCATCATATGAGATTAACATATATATGTATCTGTATTTTGTCATCTTCATCATCTTTGGCTCCTCCTTCACCCTCAACCTCTTCATTGGTGTCATCATTGACAACTTTAATCAGCAAAAGCAAAAGTTTGGTGGGGAAGATCTCTTCATGACAGAGGAGCAGAAAAAGTACTACAATGCCATGAAAAAGCTTGGTTCCAAGAAGCCCGTCAAACCCATACCTCGCCCTGCGAATATGATCCAGGGCATAGTGTTTGACTTCATCTCTCAGCAGTTCTTTGACATTTTCATCATGGTGCTCATTTGCCTCAACAAGGTTACCATGATGATTGAGACAGATGACCAAAGTGCAGAGAAAGAATATGTTCTCTATCAGATCAACTTAATCTTCATTGTTGTCTTCACTGGGGAGTGCATCCTCAAAATGTTTGCACTGAGACAATACTTTTTCACT AptNa6 ---------------------------CTCACTGTGGGGAACCTGGTGTTTACTGGCATCTTTACGGCTGAAATGGTGTTTAAGCTCATTGCCATGGACCCCTACTACTACTTCCAGGTGGGCTGGAACATCTTCGACAGCATCATCGTCACCCTCAGTCTGGTGGAGCTGGGGCTAGCCAACGTGCAGGGTCTGTCTGTGCTCAGGTCCTTCCGTTTGCTGCGTGTCTTCAAGTTGGCCAAGTCCTGGCCAACGCTCAATATGCTCATCAAGATCATTGGCAACTCGGTGGGAGCCCTGGGCAACCTGACACTGGTGCTGGCCATTATTGTCTTCATCTTTGCCGTGGTGGGCATGCAGCTATTTGGGAAGAGCTACAAGGACTGCGTGTGCAAGATTGCGCTGGACTGCGAGCTTCCCCGCTGGCACATGACGGACTTCTTCCACTCCTTCCTGATCGTGTTCCGCATCCTATGCGGCGAGTGGATTGAGACCATGTGGGACTGCATGGAGGTGGCTGGACCGTCCATGTGCCTCATCGTCTTCATGTTGGTCATGGTCATTGGGAACCTGGTGGTGCTGAACCTGTTCCTTGCATTGCTTCTCAGCTCATTCAGCGGTGACAATCTCTCGGCAAGCGACGATGACAGTGAGATTAACAACCTCCAGATCGCCACAGGGCGCATCAGCAGAGCGATTGGCTGGGTGAAGAACTTTATCATCAGCACAGTCCAGTGGGTTCTGGGCAGAAAGCCCAAGATGGTGGATGGCATGACCAACTGCGTAGTCCTGAATGTGCCCATTGCCAAGGGGGAATCTGAGATTGAAGGAGACTATTCAGTTTGCAGTACAGCAGACTACAGACCTCCAGAACCCGAGGAGGAAAAGGTACCAGAGACCAATGATCCAGAAGCCTGCTTTACAGAAAATTGTGTGAGGCGATTTCCTTGTCTCAATGTGGACATCACCCAGGGGAAAGGGAAGAGCTGGTGGAACCTACGCAGAACCTGCTACATCATCGTGGAGCATGACTACTTTGAGACCTTCATCATCTTCATGATTCTCCTCAGTAGCGGAGCACTGGCTTTCGAGGACATTTATATAGAGCGTCGCAAGATGATTAAGATCATCTTGGAGTACGCAGACAAAATCTTCACCTATGTTTTCATAATGGAGATGTTACTGAAGTGGGTTGCTTATGGGTTTAAAACGTACTTCACCAATGCCTGGTGCTGGCTGGACTTTCTTATTGTTGATGTGTCAATTATTAGCTTAACAGCCAATCTGTTGGGCTATTCAGAGCTGGGACCAATCAAATCTCTCAGAACACTCAGGGCTCTTCGACCGCTACGTGCCCTTTCCAGATTTGAAGGAATGAGGGTGGTAGTGAATGCGTTGGTTGGCGCCATCCCCTCCATCATGAACGTGCTGCTGGTTTGTCTGATCTTCTGGCTCATCTTCAGTATCATGGGGGTCAACTTGTTTGCTGGAAAGTTCTACCGCTGCATTAACACTACCACCGAGGAGCTTCTGCCCATGGAGGAAGTGAACAACAGGAGTGATTGCATGGCGCTAATGCACACTAATGAGGTGCGCTGGGTCAATGTCAAGGTGAACTACGACAACGTCGCCCTGGGATACCTTTCCCTGCTGCAAGTGGCTACATTTAAAGGCTGGATGGACATCATGTATGCTGCAGTGGACTCCAGAGAGGTAGAGGAGCAGCCATCATATGAGATTAACCTCTACATGTACCTGTACTTTGTCATCTTCATCATATTGGGATCCTTTTTTACCCTCAACCTTTTCATTGGTGTCATCATAGACAACTTCAACAGGCAGAAGCAAAAGTTTGGAGGAGAAGATCTCTTTATGACGGAGGAGCAGAAGAAGTACTACAATGCCATGAAGAAGCTGGGATCCAAGAAGCCTGTCAAACCTATACCCCGTCCTACGAATGTTATTCAAGGTGTGGTGTTCGACCTCATTTCCCAGCAGTTCTTTGATATTTTCATCATGGTTCTCATTTGCCTCAACATGGTGACCATGATGGTGGAGACTGATGACCAGAGCAAAGAGAAAGAGCACATCCTCTATCAAATCAACGTCATATTCATTGTCGTCTTCACTGGAGAGTGTTTGCTCAAGATGTTTGCACTGAGGCAGTACTTCTTCACT PinniNa6 CCCATGAGTGAAACGTTTGATTACGTCCTCAGCACAGGGAACCTGGTGTTTACCATCATCTTTGCAGCTGAAATGGTCTTGAAGCTCATTGCCATGGACCCCTACTACTACTTCCAGCAGACGTGGAACATCTTTGACTTTTTCATTGTCTCACTCAGTCTGGTGGAGATGGGACTGGCTAACATGCAGGGGCTGTCAGTGCTTAGGTCCTTTCGACTGCTGCGTATCTTTAAGTTGGCCAAGTCCTGGCCCACGCTCAATATTCTGATCAAGATCATCTGCAACTCGGTGGGCGCCCTGGGAAACCTGACCATCGTGCTGGCCATTATCGTCTTCATCTTCGCCTTGGTGGGCATGCAGCTGTTCGGGAAGAATTACAAAGAGTTTGTGTGCAAGATCAGTGCAGACTGTACGCTGCCTCGCTGGCATATGAATGACTTCTTCCATTCCTTCCTGATTGTGTTCCGCTGCCTGTGCGGCGAGTGGATTGAGACTATGTGGGACTGTATGGAGGTGGGCGGTGTGCCCATGTGCCTCAGCGTTTACATGATGGTCATAATCATCGGGAACCTGGTGGTGCTGAACCTGTTCCTTGCCTTACTGCTAAGCTCATTCAGTGGTGACAATCTCACTGCAAACGATGATGACCAAGAGGATAACAACATCCTGATTGCAGCTGAGCGGATCAGCAGGGCAAAACTCTGGGTGAAGGGGTTCATAATACGGACGGTCTTGGGGATGCTGGGCAAGGAGCCAAAGATTGTGAATGGGCTAGCCAACGGTGTAGTTCTGAATGTGCCCATTGCCAAGGGCGAGTCTGAGACTGAAGATGACTCTTCAGTCTGCAGTACAGTGGACTACAGTCCTCCAAATCCAGAGGAACCCGAGGAACCAGAACCCGATAATCCAGAAGATTGTTTAACGGAAGAATGTGTGTCACGATTTCCTTGGCTGAATGTGGACATAACACAGCCAAAAGGGAAGAGTTGGTGGAACCTTCGTAGGACATGCTACGTCATCGTAGAGCATGACTACTTTGAGACTTTCATCATCTTCATGATTCTCCTCAGTAGTGGAGCACTGGCTTTCGAGGACATTTATATTGAGCGTCGCAGGGTGATTAAGATCATCTTGGAGTATGCGGACAAAGTCTTCACATATATTTTCATAGCAGAGATGTTACTGAAGTGGGTTGCATATGGGTTTAAAAAGTACTTCTCCGACGCCTGGTGCTGGTTAGACTTTCTAATTGTTGATGTGTCAATAATTAGCTTAACAGCCAATTTGTTGGGCTATTCAGAGTTGGGACCAATCAAATCTCTCAGAACTCTCAGGGCTCTTCGACCTTTACGTGCACTTTCCAGATTTGAAGGAATGAGGGTGGTAGTCAAAGCATTGGTTGGCGCCATCCCCTCCATCGTGAACGTGCTGCTGGTATGTCTCATGTTCTGGCTCATCTTCAGCATTATGGGAGTCAACTTGTTTGCTGGAAAGTTCTACCGCTGCATTAACACTACCACAGAAGAGACCATGCCCYTGGAAGAAGTCAACAACCGCAGTGACTGCAATGCACTTATGTACACTAATGAGGTGCGATGGGTCAACCTTAAGGTCAACTATGACAATGCAGGCATGGGATACCTCTCCCTGCTACAAGTGGCAACATTTAAAGGTTGGATGGACATCATGTATGCTGCAGTGGACTCCAGAGGGGTAGAGGATCAGCCGATATACGAGATTAACGTCTACATGTACCTGTATTTTGTCATCTTCATCGTATTTGGATCCTTTTTCACCCTAAACCTCTTCATTGGTGTCATCATAGACAACTTCAACCGTCAAAAGCAAAAGTTAGGAGGAGATGATCTCTTTATGACAGAAGAACAGAAAAAGTATTATGATGCCATGAAGAAGCTGGGTTCCAAGAAACCTGTCAARGTTATACCACGCCCTTCGAACAAGATTCTGGGTGTGTTGTATGACATAGTCAACCAACGGGTCACTGATATTTTCATCATGTCTCTCATTTGGCTAAACATGGTTACCATGATGGTGGAGACAGATGACCAGAGCGAAGAAAAGAAGAATGTTCTCTATCAGATCAATTTAATATTCATTATCATCTTCACTGGAGAATGTCTGCTCAAGTTGCTTGCACTAAGACATTACTTCTTCACT tetra CCCATGACCCAGGAGTTCGACTACATGCTTTCAGTGGGAAATCTGGTTTTCACAGGAATTTTTGCAGCAGAAATGTTCTTCAAGCTGATCGCCATGGATCCGTACTACTATTTCCAAGTTGGCTGGAACATTTTTGACAGCATCATTGTCACCCTCAGCCTGGTAGAGTTGGGGCTTGCGAACGTCCAGGGCCTGTCTGTCCTCAGGTCCTTCCGCCTGCTCCGTGTCTTCAAACTTGCCAAATCCTGGCCCACACTCAACATGCTGATCAAGATTATTGGGAGCTCAGTTGGAGCGCTAGGGAATCTGACGTTGGTGCTGGCCATCATCGTCTTCATCTTCGCTGTGGTGGGGATGCAGCTCTTTGGCAAAAGCTACAAGGACTGCGTGTGCAAGATTTCCACGGAGTGCGAGCTGCCGCGCTGGCACATGAATGACTTCTTCCACTCCTTCCTCATCGTCTTCCGCATCCTGTGTGGCGAATGGATCGAGAACATGTGGGCCTGCATGGAAGTGGCTGGAGCTGGGATGTGCTTAGTTGTCTTCATGATGGTCATGGTGATTGGAAACCTCGTGGTGTTGAACCTCTTCCTGGCCCTGCTGCTCAGCTCGTTCAGCGGGGACAATCTGTCCATCGGAGAGGACGATGGAGAGATGAACAATCTTCAGATTGCCATCGGCAGAATCACACGAGGTGGAAACTGGCTCAAGACCCTTGTCATCAGAACGGTCCTGCAGCTTCTCGGTAGGGAGCAGAAAACGGCAGATGGGATAGCTAACTGTCTTGTTATCAACGTCCCCATCGCCTTGGGGGAGTCAGACTCTGAAGGCGAGTCTTCAGTGTGCAGCACAGCAGACTATCGGCCCCCCGAGCCTGAGGAAGAGGAAGAGCCGGAACCACTGGAGCCAGAGGCCTGCTTTACTGACAACTGCGTCAAACACTGGCCTTGTCTGAACGTGGACGTCACCCAAGGTCAAGGGAAGAAGTGGTGGAACCTCCGCAAGACCTGCTTCACAATCGTAGAGCATGACTGGTTTGAGACCTTCATCATCTTCATGATCCTCCTCAGCAGCGGAGCCCTGGCCTTTGAAGATATATACATCGAAAGACGAAGAACCGTCAAAATTATCCTGGAGTTTGCCGACAAAGTTTTCACCTTCATCTTTGTCCTTGAGATGGTGCTGAAATGGGTGGCCTATGGCTTCAAGACCTACTTCACCAACGCCTGGTGCTGGTTGGACTTTTTCATTGTAGACATTTCCCTGATCAGTTTATCGGCCAACCTGATGGGCCTCTCTGACCTGGGACCAATCAAATCTCTCAGAACACTCCGGGCACTGAGGCCTCTTCGAGCTCTGTCCAGATTTGAAGGGATGAGGGTGGTGGTGAACGCTCTTATCGGAGCCATTCCCTCCATCTTCAACGTGCTGCTGGTGTGCCTGATCTTCTGGCTCATCTTCAGCATCATGGGAGTGAACCTGTTTGCGGGGAAGTTCTACCACTGCATCAACACCACCACACAGGAGCTCTTCCCCATCTCTGTGGTCAACAACAAGAGCGACTGCATGGCCGTCCAGGCCACGCAGGAGGCCCGCTGGGTCAACGTCAAGGTCAACTACGACAACGTGGGAAAAGGCTACCTGTCGCTGCTTCAAATCGCCACTTTTAAAGGCTGGACGGCCATTATGTATGCTGCAGTAGATTCAAGAGAGGTGGAAGAGCAACCTTCCTATGAGATCAACCTGTACATGTACATCTACTTTGTCATCTTCATCATCTTTGGCGCTTTCTTCACGCTCAACCTGTTCATCGGCGTCATCATCGATAACTTCAACCAGCAGAAGAGAAAGATA---AACAAAGACATCTTCATGACGGAGGAGCAGAAAAAGTACTACGAAGCCATGAAGAAACTCGGCTCCAAGAAGCCGCAGAAGCCGATCCCACGTCCGACCAACCTCATCCAGGGAATGGTGTTTGACTTCATCAGTCAGCAGTTCTTTGACATCTTCATCATGGTGCTCATCTGCCTCAACATGGTGACCATGATGGTGGAGACGGACGACCAGAGCCCCGAGAAGGAGGATTTCCTCTTCAAAGTGAACGTGGCTTTTATCGTGGTCTTCACGGGGGAGTGCATGCTGAAGCTCATCGCCCTGCGACAGTACTTCTTCACC ; end; begin sets; charset 1st = 1-2178\3; charset 2nd = 2-2178\3; charset 3rd = 3-2178\3; charpartition byPos = 1stpos:1st, 2ndpos:2nd, 3rdpos:3rd; end; garli-2.1-release/example/partition/exampleRuns/3parts.sameModelType/000077500000000000000000000000001241236125200260205ustar00rootroot00000000000000garli-2.1-release/example/partition/exampleRuns/3parts.sameModelType/GTRG.byCodonPos.best.all.tre000066400000000000000000000100721241236125200330600ustar00rootroot00000000000000#NEXUS begin trees; translate 1 MorNa6, 2 ClownNa6, 3 AraNa6, 4 puffNa6, 5 NewZebra, 6 SterNa6, 7 eelNa6, 8 catNa6, 9 AptNa6, 10 PinniNa6, 11 tetra; tree rep1 = [&U][!GarliScore -13317.476][!GarliModel S 0.537510 0.298334 2.164156 M1 r 1.96303 2.57644 1.41588 1.40309 3.71934 1.00000 e 0.31003 0.17680 0.29731 0.21587 a 0.40948 M2 r 4.35487 7.08884 1.61154 7.10637 4.40765 1.00000 e 0.26945 0.16356 0.16047 0.40652 a 0.36068 M3 r 1.05753 5.26691 3.56999 0.45425 5.00066 1.00000 e 0.15376 0.35598 0.28703 0.20323 a 2.98105 ](2:0.10703165,(((4:0.05364663,11:0.06184584):0.19108047,(5:0.14417819,((9:0.05850189,(6:0.11371497,(7:0.09145933,10:0.10150461):0.05203645):0.02486157):0.07979798,8:0.09166433):0.05351795):0.04674942):0.06361033,3:0.12725331):0.02261605,1:0.21923902); tree rep2 = [&U][!GarliScore -13317.47562068][!GarliModel S 0.537815 0.298299 2.163886 M1 r 1.96121 2.57482 1.41467 1.40183 3.71590 1.00000 e 0.31004 0.17684 0.29727 0.21585 a 0.40945 M2 r 4.34466 7.07387 1.60798 7.08746 4.39742 1.00000 e 0.26945 0.16357 0.16046 0.40651 a 0.36064 M3 r 1.05779 5.26662 3.56935 0.45432 4.99980 1.00000 e 0.15376 0.35598 0.28703 0.20323 a 2.98250 ](1:0.21918938,(3:0.12723378,((11:0.06183524,4:0.05365126):0.19104889,(5:0.14416302,(((6:0.11370992,(7:0.09143862,10:0.10149577):0.05203654):0.02486205,9:0.05848888):0.07981018,8:0.09166066):0.05350567):0.04676098):0.06360036):0.02261541,2:0.10702161); tree rep3BEST = [&U][!GarliScore -13317.47553635][!GarliModel S 0.537685 0.298385 2.163931 M1 r 1.96467 2.57867 1.41721 1.40421 3.72196 1.00000 e 0.31001 0.17680 0.29731 0.21588 a 0.40946 M2 r 4.35109 7.08420 1.61029 7.09735 4.40365 1.00000 e 0.26944 0.16357 0.16047 0.40651 a 0.36069 M3 r 1.05688 5.26146 3.56445 0.45377 4.99483 1.00000 e 0.15376 0.35599 0.28702 0.20324 a 2.98242 ](2:0.10704821,(3:0.12724057,((((9:0.05848787,(6:0.11368725,(10:0.10150703,7:0.09144655):0.05202563):0.02486140):0.07978134,8:0.09165788):0.05350505,5:0.14419138):0.04673026,(4:0.05366940,11:0.06182619):0.19104871):0.06360954):0.02259423,1:0.21918319); tree rep4 = [&U][!GarliScore -13317.47562688][!GarliModel S 0.537329 0.298606 2.164065 M1 r 1.96515 2.57963 1.41791 1.40466 3.72296 1.00000 e 0.31000 0.17681 0.29731 0.21588 a 0.40937 M2 r 4.35036 7.08252 1.60977 7.09693 4.40309 1.00000 e 0.26945 0.16358 0.16048 0.40649 a 0.36085 M3 r 1.05782 5.26791 3.56940 0.45444 4.99990 1.00000 e 0.15375 0.35598 0.28701 0.20325 a 2.98191 ](1:0.21921103,(((5:0.14417250,(((6:0.11369300,(10:0.10148761,7:0.09145309):0.05203563):0.02485464,9:0.05849728):0.07980049,8:0.09165214):0.05351734):0.04674431,(11:0.06183956,4:0.05364225):0.19106125):0.06361709,3:0.12723095):0.02260540,2:0.10704353); tree rep5 = [&U][!GarliScore -13317.47556216][!GarliModel S 0.537809 0.298334 2.163856 M1 r 1.96255 2.57667 1.41592 1.40291 3.71885 1.00000 e 0.31002 0.17683 0.29729 0.21586 a 0.40947 M2 r 4.35420 7.08806 1.61141 7.10209 4.40718 1.00000 e 0.26944 0.16356 0.16048 0.40652 a 0.36067 M3 r 1.05664 5.26048 3.56398 0.45380 4.99439 1.00000 e 0.15377 0.35599 0.28701 0.20323 a 2.98294 ](2:0.10701725,(((4:0.05365011,11:0.06184420):0.19105362,(5:0.14416439,(8:0.09164590,(9:0.05848480,(6:0.11368858,(10:0.10149155,7:0.09145777):0.05203313):0.02485827):0.07980515):0.05350612):0.04675175):0.06359820,3:0.12722448):0.02261579,1:0.21919746); end; [M1 begin paup; clear; gett file=GTRG.byCodonPos.best.all.tre storebr; lset userbr nst=6 rmat=(1.96466607 2.57866828 1.41720682 1.40421118 3.72196291) base=(0.31001147 0.17679842 0.29731015) rates=gamma shape= 0.40945560 ncat=4 pinv= 0.00000000; end; ] [M2 begin paup; clear; gett file=GTRG.byCodonPos.best.all.tre storebr; lset userbr nst=6 rmat=(4.35109191 7.08420034 1.61028932 7.09735225 4.40364615) base=(0.26944152 0.16357276 0.16047328) rates=gamma shape= 0.36068741 ncat=4 pinv= 0.00000000; end; ] [M3 begin paup; clear; gett file=GTRG.byCodonPos.best.all.tre storebr; lset userbr nst=6 rmat=(1.05688356 5.26146270 3.56444849 0.45377036 4.99483076) base=(0.15376193 0.35598688 0.28701584) rates=gamma shape= 2.98242438 ncat=4 pinv= 0.00000000; end; ] garli-2.1-release/example/partition/exampleRuns/3parts.sameModelType/GTRG.byCodonPos.best.tre000066400000000000000000000021701241236125200323110ustar00rootroot00000000000000#NEXUS begin trees; translate 1 MorNa6, 2 ClownNa6, 3 AraNa6, 4 puffNa6, 5 NewZebra, 6 SterNa6, 7 eelNa6, 8 catNa6, 9 AptNa6, 10 PinniNa6, 11 tetra; tree bestREP3 = [&U][!GarliScore -13317.475536][!GarliModel S 0.537685 0.298385 2.163931 M1 r 1.96467 2.57867 1.41721 1.40421 3.72196 1.00000 e 0.31001 0.17680 0.29731 0.21588 a 0.40946 M2 r 4.35109 7.08420 1.61029 7.09735 4.40365 1.00000 e 0.26944 0.16357 0.16047 0.40651 a 0.36069 M3 r 1.05688 5.26146 3.56445 0.45377 4.99483 1.00000 e 0.15376 0.35599 0.28702 0.20324 a 2.98242 ](2:0.10704821,(3:0.12724057,((((9:0.05848787,(6:0.11368725,(10:0.10150703,7:0.09144655):0.05202563):0.02486140):0.07978134,8:0.09165788):0.05350505,5:0.14419138):0.04673026,(4:0.05366940,11:0.06182619):0.19104871):0.06360954):0.02259423,1:0.21918319); end; [ S 0.537685 0.298385 2.163931 M1 r 1.96467 2.57867 1.41721 1.40421 3.72196 1.00000 e 0.31001 0.17680 0.29731 0.21588 a 0.40946 M2 r 4.35109 7.08420 1.61029 7.09735 4.40365 1.00000 e 0.26944 0.16357 0.16047 0.40651 a 0.36069 M3 r 1.05688 5.26146 3.56445 0.45377 4.99483 1.00000 e 0.15376 0.35599 0.28702 0.20324 a 2.98242 ] garli-2.1-release/example/partition/exampleRuns/3parts.sameModelType/GTRG.byCodonPos.log00.log000066400000000000000000003175601241236125200323000ustar00rootroot00000000000000Search rep 1 (of 5) random seed = 406932 gen best_like time optPrecision 0 -13490.0811 1 0.5 10 -13374.29999 1 0.5 20 -13374.23319 1 0.5 30 -13374.20572 1 0.5 40 -13336.92602 1 0.5 50 -13336.92566 1 0.5 60 -13325.49332 1 0.5 70 -13324.96079 2 0.5 80 -13324.67627 2 0.5 90 -13324.51124 2 0.5 100 -13324.39124 2 0.5 110 -13324.20155 2 0.5 120 -13324.06185 2 0.5 130 -13323.89357 2 0.5 140 -13323.89276 2 0.5 150 -13323.49276 2 0.5 160 -13323.42757 3 0.5 170 -13323.37692 3 0.5 180 -13323.37692 3 0.5 190 -13323.3742 3 0.5 200 -13323.13567 3 0.5 210 -13323.03843 3 0.5 220 -13322.98322 3 0.5 230 -13322.97665 3 0.5 240 -13322.97246 3 0.5 250 -13322.92396 3 0.5 260 -13322.91993 4 0.5 270 -13322.85394 4 0.5 280 -13322.62924 4 0.5 290 -13322.62605 4 0.5 300 -13322.60346 4 0.5 310 -13322.60346 4 0.5 320 -13322.57552 4 0.5 330 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->Single processor version<- ############################################################## This is GARLI 2.0, the first "official" release including partitioned models. It is a merging of official release 1.0 and beta version GARLI-PART 0.97 Briefly, it includes models for nucleotides, amino acids, codons, and morphology-like characters, any of which can be mixed together and applied to different subsets of data. General program usage is extensively documented here: http://www.nescent.org/wg_garli/ see this page for details on partitioned usage: http://www.nescent.org/wg_garli/Partition_testing_version and this page for details on Mkv mophology model usage: http://www.nescent.org/wg_garli/Mkv_morphology_model PLEASE LET ME KNOW OF ANY PROBLEMS AT: garli.support@gmail.com ############################################################## This version has undergone much testing, but is still a BETA VERSION. - Please check results carefully! - Compiled Mar 21 2011 13:13:18 using Intel icc compiler version 9.10 Using NCL version 2.1.10 ####################################################### Reading config file garli.conf ################################################### READING OF DATA Attempting to read data file in Nexus format (using NCL): zakonEtAl2006.11tax.nex ... Reading DATA block... successful Reading SETS block... successful ################################################### PARTITIONING OF DATA AND MODELS CHECK: ONE MODEL TYPE APPLIES TO ALL DATA SUBSETS, BUT WITH INDEPENDENT MODEL PARAMETERS (no linkage) GARLI data subset 1 CHARACTERS block #1 ("Untitled DATA Block 1") CHARPARTITION subset #1 ("1stpos") Data read as Nucleotide data, modeled as Nucleotide data Summary of data: 11 sequences. 441 constant characters. 171 parsimony-informative characters. 114 uninformative variable characters. 726 total characters. 238 unique patterns in compressed data matrix. Pattern processing required < 1 second GARLI data subset 2 CHARACTERS block #1 ("Untitled DATA Block 1") CHARPARTITION subset #2 ("2ndpos") Data read as Nucleotide data, modeled as Nucleotide data Summary of data: 11 sequences. 528 constant characters. 90 parsimony-informative characters. 108 uninformative variable characters. 726 total characters. 158 unique patterns in compressed data matrix. Pattern processing required < 1 second GARLI data subset 3 CHARACTERS block #1 ("Untitled DATA Block 1") CHARPARTITION subset #3 ("3rdpos") Data read as Nucleotide data, modeled as Nucleotide data Summary of data: 11 sequences. 103 constant characters. 507 parsimony-informative characters. 116 uninformative variable characters. 726 total characters. 549 unique patterns in compressed data matrix. Pattern processing required < 1 second ################################################### NOTE: Unlike many programs, the amount of system memory that Garli will use can be controlled by the user. (This comes from the availablememory setting in the configuration file. Availablememory should NOT be set to more than the actual amount of physical memory that your computer has installed) For this dataset: Mem level availablememory setting great >= 11 MB good approx 10 MB to 9 MB low approx 8 MB to 5 MB very low approx 4 MB to 4 MB the minimum required availablememory is 4 MB You specified that Garli should use at most 512.0 MB of memory. Garli will actually use approx. 16.1 MB of memory **Your memory level is: great (you don't need to change anything)** ####################################################### Found outgroup specification: 1 ####################################################### STARTING RUN >>>Search rep 1 (of 5)<<< MODEL REPORT - Parameters are at their INITIAL values (not yet optimized) Model 1 Number of states = 4 (nucleotide data) Nucleotide Relative Rate Matrix: 6 rates AC = 1.000, AG = 4.000, AT = 1.000, CG = 1.000, CT = 4.000, GT = 1.000 Equilibrium State Frequencies: estimated (ACGT) 0.3157 0.1746 0.3004 0.2093 Rate Heterogeneity Model: 4 discrete gamma distributed rate categories, alpha param estimated 0.5000 Substitution rate categories under this model: rate proportion 0.0334 0.2500 0.2519 0.2500 0.8203 0.2500 2.8944 0.2500 Model 2 Number of states = 4 (nucleotide data) Nucleotide Relative Rate Matrix: 6 rates AC = 1.000, AG = 4.000, AT = 1.000, CG = 1.000, CT = 4.000, GT = 1.000 Equilibrium State Frequencies: estimated (ACGT) 0.2703 0.1566 0.1628 0.4103 Rate Heterogeneity Model: 4 discrete gamma distributed rate categories, alpha param estimated 0.5000 Substitution rate categories under this model: rate proportion 0.0334 0.2500 0.2519 0.2500 0.8203 0.2500 2.8944 0.2500 Model 3 Number of states = 4 (nucleotide data) Nucleotide Relative Rate Matrix: 6 rates AC = 1.000, AG = 4.000, AT = 1.000, CG = 1.000, CT = 4.000, GT = 1.000 Equilibrium State Frequencies: estimated (ACGT) 0.1460 0.3609 0.2915 0.2015 Rate Heterogeneity Model: 4 discrete gamma distributed rate categories, alpha param estimated 0.5000 Substitution rate categories under this model: rate proportion 0.0334 0.2500 0.2519 0.2500 0.8203 0.2500 2.8944 0.2500 Subset rate multipliers: 1.00 1.00 1.00 Starting with seed=406932 creating likelihood stepwise addition starting tree... number of taxa added: 4 5 6 Optimizing parameters... improved 210.740 lnL Optimizing branchlengths... improved 33.602 lnL 7 8 9 10 11 Initial ln Likelihood: -13863.3954 optimizing: starting branch lengths, alpha shape, rel rates, eq freqs, subset rates... pass 1:+ 205.400 (branch= 9.43 scale= 1.70 alpha= 5.05 freqs= 34.11 rel rates= 96.25 subset rates= 58.87) pass 2:+ 100.305 (branch= 8.26 scale= 1.50 alpha= 6.51 freqs= 16.99 rel rates= 8.80 subset rates= 58.25) pass 3:+ 45.314 (branch= 2.13 scale= 2.25 alpha= 3.89 freqs= 1.27 rel rates= 0.79 subset rates= 34.98) pass 4:+ 16.505 (branch= 0.00 scale= 0.00 alpha= 0.78 freqs= 0.61 rel rates= 2.25 subset rates= 12.86) pass 5:+ 4.860 (branch= 0.00 scale= 0.00 alpha= 0.01 freqs= 0.14 rel rates= 0.01 subset rates= 4.70) pass 6:+ 0.817 (branch= 0.00 scale= 0.00 alpha= 0.01 freqs= 0.12 rel rates= 0.69 subset rates= 0.00) pass 7:+ 0.114 (branch= 0.00 scale= 0.00 alpha= 0.01 freqs= 0.09 rel rates= 0.01 subset rates= 0.00) lnL after optimization: -13490.0811 gen current_lnL precision last_tree_imp 0 -13490.0811 0.500 0 100 -13324.3912 0.500 55 200 -13323.1357 0.500 55 300 -13322.6035 0.500 55 400 -13322.4782 0.500 55 500 -13321.8755 0.500 55 600 -13321.4740 0.500 55 Optimization precision reduced Optimizing parameters... improved 1.779 lnL Optimizing branchlengths... improved 0.000 lnL 700 -13318.7011 0.402 55 800 -13318.4686 0.402 55 900 -13318.2340 0.402 55 1000 -13318.0609 0.402 55 1100 -13317.9954 0.402 55 Optimization precision reduced Optimizing parameters... improved 0.036 lnL Optimizing branchlengths... improved 0.000 lnL 1200 -13317.8825 0.304 55 1300 -13317.8431 0.304 55 1400 -13317.7822 0.304 55 1500 -13317.7491 0.304 55 1600 -13317.7135 0.304 55 Optimization precision reduced Optimizing parameters... improved 0.009 lnL Optimizing branchlengths... improved 0.000 lnL 1700 -13317.6211 0.206 55 1800 -13317.5931 0.206 55 1900 -13317.5875 0.206 55 2000 -13317.5820 0.206 55 2100 -13317.5775 0.206 55 Optimization precision reduced Optimizing parameters... improved 0.004 lnL Optimizing branchlengths... improved 0.000 lnL 2200 -13317.5723 0.108 55 2300 -13317.5679 0.108 55 2400 -13317.5652 0.108 55 2500 -13317.5605 0.108 55 2600 -13317.5541 0.108 55 Optimization precision reduced Optimizing parameters... improved 0.001 lnL Optimizing branchlengths... improved 0.000 lnL 2700 -13317.5471 0.010 55 2800 -13317.5389 0.010 55 2900 -13317.5358 0.010 55 3000 -13317.5275 0.010 55 3100 -13317.5268 0.010 55 3200 -13317.5259 0.010 55 3300 -13317.5245 0.010 55 3400 -13317.5228 0.010 55 3500 -13317.5221 0.010 55 3600 -13317.5218 0.010 55 3700 -13317.5218 0.010 55 3800 -13317.5192 0.010 55 3900 -13317.5162 0.010 55 4000 -13317.5142 0.010 55 4100 -13317.5139 0.010 55 4200 -13317.5138 0.010 55 4300 -13317.5138 0.010 55 4400 -13317.5137 0.010 55 4500 -13317.5132 0.010 55 4600 -13317.5107 0.010 55 4700 -13317.5107 0.010 55 4800 -13317.5084 0.010 55 4900 -13317.5074 0.010 55 5000 -13317.5070 0.010 55 5100 -13317.5059 0.010 55 5200 -13317.5059 0.010 55 5300 -13317.5043 0.010 55 5400 -13317.5034 0.010 55 5500 -13317.5034 0.010 55 5600 -13317.5031 0.010 55 5700 -13317.5028 0.010 55 5800 -13317.5028 0.010 55 5900 -13317.5028 0.010 55 6000 -13317.5022 0.010 55 6100 -13317.5022 0.010 55 6200 -13317.5003 0.010 55 6300 -13317.5002 0.010 55 6400 -13317.5002 0.010 55 6500 -13317.5001 0.010 55 6600 -13317.4997 0.010 55 6700 -13317.4997 0.010 55 6800 -13317.4995 0.010 55 6900 -13317.4995 0.010 55 7000 -13317.4995 0.010 55 7100 -13317.4985 0.010 55 7200 -13317.4983 0.010 55 7300 -13317.4965 0.010 55 7400 -13317.4965 0.010 55 7500 -13317.4965 0.010 55 7600 -13317.4965 0.010 55 7700 -13317.4964 0.010 55 7800 -13317.4964 0.010 55 Reached termination condition! last topological improvement at gen 55 Improvement over last 500 gen = 0.00013 Current score = -13317.4964 Performing final optimizations... pass 1 : -13317.4958 (branch= 0.0000 alpha= 0.0000 eq freqs= 0.0003 rel rates= 0.0003 subset rates= 0.0000) pass 2 : -13317.4913 (branch= 0.0040 alpha= 0.0000 eq freqs= 0.0002 rel rates= 0.0002 subset rates= 0.0000) pass 3 : -13317.4845 (branch= 0.0050 alpha= 0.0000 eq freqs= 0.0002 rel rates= 0.0015 subset rates= 0.0000) pass 4 : -13317.4803 (branch= 0.0037 alpha= 0.0000 eq freqs= 0.0002 rel rates= 0.0001 subset rates= 0.0000) pass 5 : -13317.4783 (branch= 0.0013 alpha= 0.0000 eq freqs= 0.0006 rel rates= 0.0001 subset rates= 0.0000) pass 6 : -13317.4768 (branch= 0.0004 alpha= 0.0002 eq freqs= 0.0002 rel rates= 0.0007 subset rates= 0.0000) pass 7 : -13317.4761 (branch= 0.0003 alpha= 0.0000 eq freqs= 0.0002 rel rates= 0.0003 subset rates= 0.0000) pass 8 : -13317.4759 (branch= 0.0000 alpha= 0.0000 eq freqs= 0.0001 rel rates= 0.0001 subset rates= 0.0000) pass 9 : -13317.4757 (branch= 0.0001 alpha= 0.0000 eq freqs= 0.0001 rel rates= 0.0000 subset rates= 0.0000) pass 10: -13317.4756 (branch= 0.0000 alpha= 0.0000 eq freqs= 0.0001 rel rates= 0.0000 subset rates= 0.0000) pass 11: -13317.4756 (branch= 0.0000 alpha= 0.0000 eq freqs= 0.0000 rel rates= 0.0000 subset rates= 0.0000) pass 12: -13317.4756 (branch= 0.0000 alpha= 0.0000 eq freqs= 0.0000 rel rates= 0.0000 subset rates= 0.0000) pass 13: -13317.4756 (branch= 0.0000 alpha= 0.0000 eq freqs= 0.0000 rel rates= 0.0000 subset rates= 0.0000) Looking for minimum length branches... Final score = -13317.4756 Time used so far = 0 hours, 1 minutes and 42 seconds MODEL REPORT - Parameter values are FINAL Model 1 Number of states = 4 (nucleotide data) Nucleotide Relative Rate Matrix: 6 rates AC = 1.963, AG = 2.576, AT = 1.416, CG = 1.403, CT = 3.719, GT = 1.000 Equilibrium State Frequencies: estimated (ACGT) 0.3100 0.1768 0.2973 0.2159 Rate Heterogeneity Model: 4 discrete gamma distributed rate categories, alpha param estimated 0.4095 Substitution rate categories under this model: rate proportion 0.0181 0.2500 0.1888 0.2500 0.7413 0.2500 3.0518 0.2500 Model 2 Number of states = 4 (nucleotide data) Nucleotide Relative Rate Matrix: 6 rates AC = 4.355, AG = 7.089, AT = 1.612, CG = 7.106, CT = 4.408, GT = 1.000 Equilibrium State Frequencies: estimated (ACGT) 0.2694 0.1636 0.1605 0.4065 Rate Heterogeneity Model: 4 discrete gamma distributed rate categories, alpha param estimated 0.3607 Substitution rate categories under this model: rate proportion 0.0115 0.2500 0.1518 0.2500 0.6850 0.2500 3.1517 0.2500 Model 3 Number of states = 4 (nucleotide data) Nucleotide Relative Rate Matrix: 6 rates AC = 1.058, AG = 5.267, AT = 3.570, CG = 0.454, CT = 5.001, GT = 1.000 Equilibrium State Frequencies: estimated (ACGT) 0.1538 0.3560 0.2870 0.2032 Rate Heterogeneity Model: 4 discrete gamma distributed rate categories, alpha param estimated 2.9811 Substitution rate categories under this model: rate proportion 0.3878 0.2500 0.7305 0.2500 1.0824 0.2500 1.7993 0.2500 Subset rate multipliers: 0.54 0.30 2.16 NOTE: Collapsing of minimum length branches was requested (collapsebranches = 1) No branches were short enough to be collapsed. >>>Completed Search rep 1 (of 5)<<< >>>Search rep 2 (of 5)<<< MODEL REPORT - Parameters are at their INITIAL values (not yet optimized) Model 1 Number of states = 4 (nucleotide data) Nucleotide Relative Rate Matrix: 6 rates AC = 1.000, AG = 4.000, AT = 1.000, CG = 1.000, CT = 4.000, GT = 1.000 Equilibrium State Frequencies: estimated (ACGT) 0.3157 0.1746 0.3004 0.2093 Rate Heterogeneity Model: 4 discrete gamma distributed rate categories, alpha param estimated 0.5000 Substitution rate categories under this model: rate proportion 0.0334 0.2500 0.2519 0.2500 0.8203 0.2500 2.8944 0.2500 Model 2 Number of states = 4 (nucleotide data) Nucleotide Relative Rate Matrix: 6 rates AC = 1.000, AG = 4.000, AT = 1.000, CG = 1.000, CT = 4.000, GT = 1.000 Equilibrium State Frequencies: estimated (ACGT) 0.2703 0.1566 0.1628 0.4103 Rate Heterogeneity Model: 4 discrete gamma distributed rate categories, alpha param estimated 0.5000 Substitution rate categories under this model: rate proportion 0.0334 0.2500 0.2519 0.2500 0.8203 0.2500 2.8944 0.2500 Model 3 Number of states = 4 (nucleotide data) Nucleotide Relative Rate Matrix: 6 rates AC = 1.000, AG = 4.000, AT = 1.000, CG = 1.000, CT = 4.000, GT = 1.000 Equilibrium State Frequencies: estimated (ACGT) 0.1460 0.3609 0.2915 0.2015 Rate Heterogeneity Model: 4 discrete gamma distributed rate categories, alpha param estimated 0.5000 Substitution rate categories under this model: rate proportion 0.0334 0.2500 0.2519 0.2500 0.8203 0.2500 2.8944 0.2500 Subset rate multipliers: 1.00 1.00 1.00 Starting with seed=459000111 creating likelihood stepwise addition starting tree... number of taxa added: 4 5 6 Optimizing parameters... improved 201.185 lnL Optimizing branchlengths... improved 31.443 lnL 7 8 9 10 11 Initial ln Likelihood: -13690.6959 optimizing: starting branch lengths, alpha shape, rel rates, eq freqs, subset rates... pass 1:+ 202.627 (branch= 5.99 scale= 1.70 alpha= 13.57 freqs= 30.18 rel rates= 91.73 subset rates= 59.46) pass 2:+ 98.686 (branch= 9.86 scale= 1.14 alpha= 6.13 freqs= 15.70 rel rates= 7.50 subset rates= 58.36) pass 3:+ 43.190 (branch= 4.46 scale= 1.45 alpha= 2.52 freqs= 0.86 rel rates= 0.01 subset rates= 33.89) pass 4:+ 15.348 (branch= 0.47 scale= 0.00 alpha= 0.73 freqs= 0.17 rel rates= 1.82 subset rates= 12.15) pass 5:+ 5.652 (branch= 0.00 scale= 0.00 alpha= 0.01 freqs= 0.18 rel rates= 0.68 subset rates= 4.78) pass 6:+ 0.170 (branch= 0.00 scale= 0.00 alpha= 0.01 freqs= 0.15 rel rates= 0.01 subset rates= 0.00) lnL after optimization: -13325.0231 gen current_lnL precision last_tree_imp 0 -13325.0231 0.500 0 100 -13322.0616 0.500 0 200 -13320.9899 0.500 0 300 -13320.2533 0.500 0 400 -13319.6815 0.500 0 500 -13319.4627 0.500 0 Optimization precision reduced Optimizing parameters... improved 0.075 lnL Optimizing branchlengths... improved 0.000 lnL 600 -13319.2426 0.402 0 700 -13318.8351 0.402 0 800 -13318.6816 0.402 0 900 -13318.6055 0.402 0 1000 -13318.4483 0.402 0 Optimization precision reduced Optimizing parameters... improved 0.022 lnL Optimizing branchlengths... improved 0.000 lnL 1100 -13318.3744 0.304 0 1200 -13318.0244 0.304 0 1300 -13317.9528 0.304 0 1400 -13317.9440 0.304 0 1500 -13317.8239 0.304 0 Optimization precision reduced Optimizing parameters... improved 0.017 lnL Optimizing branchlengths... improved 0.000 lnL 1600 -13317.7723 0.206 0 1700 -13317.7206 0.206 0 1800 -13317.7058 0.206 0 1900 -13317.6828 0.206 0 2000 -13317.6463 0.206 0 Optimization precision reduced Optimizing parameters... improved 0.007 lnL Optimizing branchlengths... improved 0.000 lnL 2100 -13317.6293 0.108 0 2200 -13317.6100 0.108 0 2300 -13317.6100 0.108 0 2400 -13317.5512 0.108 0 2500 -13317.5457 0.108 0 Optimization precision reduced Optimizing parameters... improved 0.012 lnL Optimizing branchlengths... improved 0.017 lnL 2600 -13317.5109 0.010 0 2700 -13317.5104 0.010 0 2800 -13317.5057 0.010 0 2900 -13317.5023 0.010 0 3000 -13317.4979 0.010 0 3100 -13317.4977 0.010 0 3200 -13317.4901 0.010 0 3300 -13317.4901 0.010 0 3400 -13317.4901 0.010 0 3500 -13317.4896 0.010 0 3600 -13317.4894 0.010 0 3700 -13317.4894 0.010 0 3800 -13317.4894 0.010 0 3900 -13317.4883 0.010 0 4000 -13317.4877 0.010 0 4100 -13317.4871 0.010 0 4200 -13317.4871 0.010 0 4300 -13317.4870 0.010 0 4400 -13317.4870 0.010 0 4500 -13317.4870 0.010 0 4600 -13317.4869 0.010 0 4700 -13317.4869 0.010 0 4800 -13317.4864 0.010 0 4900 -13317.4859 0.010 0 5000 -13317.4855 0.010 0 5100 -13317.4848 0.010 0 5200 -13317.4848 0.010 0 5300 -13317.4839 0.010 0 5400 -13317.4839 0.010 0 5500 -13317.4839 0.010 0 5600 -13317.4839 0.010 0 5700 -13317.4839 0.010 0 5800 -13317.4832 0.010 0 5900 -13317.4832 0.010 0 6000 -13317.4830 0.010 0 6100 -13317.4829 0.010 0 6200 -13317.4824 0.010 0 6300 -13317.4823 0.010 0 6400 -13317.4816 0.010 0 6500 -13317.4813 0.010 0 6600 -13317.4812 0.010 0 6700 -13317.4812 0.010 0 6800 -13317.4811 0.010 0 6900 -13317.4811 0.010 0 7000 -13317.4811 0.010 0 7100 -13317.4810 0.010 0 7200 -13317.4805 0.010 0 7300 -13317.4805 0.010 0 7400 -13317.4804 0.010 0 7500 -13317.4804 0.010 0 7600 -13317.4803 0.010 0 Reached termination condition! last topological improvement at gen 0 Improvement over last 500 gen = 0.00072 Current score = -13317.4803 Performing final optimizations... pass 1 : -13317.4799 (branch= 0.0000 alpha= 0.0000 eq freqs= 0.0002 rel rates= 0.0001 subset rates= 0.0000) pass 2 : -13317.4797 (branch= 0.0000 alpha= 0.0000 eq freqs= 0.0001 rel rates= 0.0001 subset rates= 0.0000) pass 3 : -13317.4795 (branch= 0.0000 alpha= 0.0000 eq freqs= 0.0001 rel rates= 0.0001 subset rates= 0.0000) pass 4 : -13317.4794 (branch= 0.0000 alpha= 0.0000 eq freqs= 0.0001 rel rates= 0.0000 subset rates= 0.0000) pass 5 : -13317.4771 (branch= 0.0021 alpha= 0.0000 eq freqs= 0.0001 rel rates= 0.0001 subset rates= 0.0000) pass 6 : -13317.4766 (branch= 0.0004 alpha= 0.0000 eq freqs= 0.0000 rel rates= 0.0001 subset rates= 0.0000) pass 7 : -13317.4763 (branch= 0.0003 alpha= 0.0000 eq freqs= 0.0000 rel rates= 0.0000 subset rates= 0.0000) pass 8 : -13317.4760 (branch= 0.0003 alpha= 0.0000 eq freqs= 0.0000 rel rates= 0.0000 subset rates= 0.0000) pass 9 : -13317.4758 (branch= 0.0002 alpha= 0.0000 eq freqs= 0.0000 rel rates= 0.0001 subset rates= 0.0000) pass 10: -13317.4757 (branch= 0.0000 alpha= 0.0000 eq freqs= 0.0000 rel rates= 0.0000 subset rates= 0.0000) pass 11: -13317.4757 (branch= 0.0000 alpha= 0.0000 eq freqs= 0.0000 rel rates= 0.0000 subset rates= 0.0000) pass 12: -13317.4756 (branch= 0.0000 alpha= 0.0000 eq freqs= 0.0000 rel rates= 0.0000 subset rates= 0.0000) pass 13: -13317.4756 (branch= 0.0000 alpha= 0.0000 eq freqs= 0.0000 rel rates= 0.0000 subset rates= 0.0000) pass 14: -13317.4756 (branch= 0.0000 alpha= 0.0000 eq freqs= 0.0000 rel rates= 0.0000 subset rates= 0.0000) Looking for minimum length branches... Final score = -13317.4756 Time used so far = 0 hours, 3 minutes and 23 seconds MODEL REPORT - Parameter values are FINAL Model 1 Number of states = 4 (nucleotide data) Nucleotide Relative Rate Matrix: 6 rates AC = 1.961, AG = 2.575, AT = 1.415, CG = 1.402, CT = 3.716, GT = 1.000 Equilibrium State Frequencies: estimated (ACGT) 0.3100 0.1768 0.2973 0.2159 Rate Heterogeneity Model: 4 discrete gamma distributed rate categories, alpha param estimated 0.4094 Substitution rate categories under this model: rate proportion 0.0181 0.2500 0.1888 0.2500 0.7413 0.2500 3.0518 0.2500 Model 2 Number of states = 4 (nucleotide data) Nucleotide Relative Rate Matrix: 6 rates AC = 4.345, AG = 7.074, AT = 1.608, CG = 7.087, CT = 4.397, GT = 1.000 Equilibrium State Frequencies: estimated (ACGT) 0.2695 0.1636 0.1605 0.4065 Rate Heterogeneity Model: 4 discrete gamma distributed rate categories, alpha param estimated 0.3606 Substitution rate categories under this model: rate proportion 0.0115 0.2500 0.1518 0.2500 0.6849 0.2500 3.1518 0.2500 Model 3 Number of states = 4 (nucleotide data) Nucleotide Relative Rate Matrix: 6 rates AC = 1.058, AG = 5.267, AT = 3.569, CG = 0.454, CT = 5.000, GT = 1.000 Equilibrium State Frequencies: estimated (ACGT) 0.1538 0.3560 0.2870 0.2032 Rate Heterogeneity Model: 4 discrete gamma distributed rate categories, alpha param estimated 2.9825 Substitution rate categories under this model: rate proportion 0.3879 0.2500 0.7306 0.2500 1.0824 0.2500 1.7991 0.2500 Subset rate multipliers: 0.54 0.30 2.16 NOTE: Collapsing of minimum length branches was requested (collapsebranches = 1) No branches were short enough to be collapsed. >>>Completed Search rep 2 (of 5)<<< >>>Search rep 3 (of 5)<<< MODEL REPORT - Parameters are at their INITIAL values (not yet optimized) Model 1 Number of states = 4 (nucleotide data) Nucleotide Relative Rate Matrix: 6 rates AC = 1.000, AG = 4.000, AT = 1.000, CG = 1.000, CT = 4.000, GT = 1.000 Equilibrium State Frequencies: estimated (ACGT) 0.3157 0.1746 0.3004 0.2093 Rate Heterogeneity Model: 4 discrete gamma distributed rate categories, alpha param estimated 0.5000 Substitution rate categories under this model: rate proportion 0.0334 0.2500 0.2519 0.2500 0.8203 0.2500 2.8944 0.2500 Model 2 Number of states = 4 (nucleotide data) Nucleotide Relative Rate Matrix: 6 rates AC = 1.000, AG = 4.000, AT = 1.000, CG = 1.000, CT = 4.000, GT = 1.000 Equilibrium State Frequencies: estimated (ACGT) 0.2703 0.1566 0.1628 0.4103 Rate Heterogeneity Model: 4 discrete gamma distributed rate categories, alpha param estimated 0.5000 Substitution rate categories under this model: rate proportion 0.0334 0.2500 0.2519 0.2500 0.8203 0.2500 2.8944 0.2500 Model 3 Number of states = 4 (nucleotide data) Nucleotide Relative Rate Matrix: 6 rates AC = 1.000, AG = 4.000, AT = 1.000, CG = 1.000, CT = 4.000, GT = 1.000 Equilibrium State Frequencies: estimated (ACGT) 0.1460 0.3609 0.2915 0.2015 Rate Heterogeneity Model: 4 discrete gamma distributed rate categories, alpha param estimated 0.5000 Substitution rate categories under this model: rate proportion 0.0334 0.2500 0.2519 0.2500 0.8203 0.2500 2.8944 0.2500 Subset rate multipliers: 1.00 1.00 1.00 Starting with seed=401425806 creating likelihood stepwise addition starting tree... number of taxa added: 4 5 6 Optimizing parameters... improved 179.383 lnL Optimizing branchlengths... improved 52.565 lnL 7 8 9 10 11 Initial ln Likelihood: -13715.9016 optimizing: starting branch lengths, alpha shape, rel rates, eq freqs, subset rates... pass 1:+ 198.274 (branch= 5.93 scale= 1.29 alpha= 6.51 freqs= 33.68 rel rates= 89.23 subset rates= 61.63) pass 2:+ 98.585 (branch= 9.39 scale= 0.81 alpha= 6.30 freqs= 15.61 rel rates= 7.49 subset rates= 58.98) pass 3:+ 43.758 (branch= 2.89 scale= 1.86 alpha= 3.08 freqs= 1.01 rel rates= 0.58 subset rates= 34.34) pass 4:+ 15.850 (branch= 0.00 scale= 0.00 alpha= 0.80 freqs= 0.69 rel rates= 1.97 subset rates= 12.39) pass 5:+ 5.880 (branch= 0.00 scale= 0.00 alpha= 0.01 freqs= 0.13 rel rates= 0.85 subset rates= 4.89) pass 6:+ 0.109 (branch= 0.00 scale= 0.00 alpha= 0.01 freqs= 0.09 rel rates= 0.01 subset rates= 0.00) lnL after optimization: -13353.4450 gen current_lnL precision last_tree_imp 0 -13353.4450 0.500 0 100 -13322.7799 0.500 10 200 -13321.2908 0.500 10 300 -13320.4759 0.500 10 400 -13319.9059 0.500 10 500 -13319.7203 0.500 10 600 -13319.0563 0.500 10 Optimization precision reduced Optimizing parameters... improved 0.032 lnL Optimizing branchlengths... improved 0.000 lnL 700 -13318.2812 0.402 10 800 -13318.0210 0.402 10 900 -13317.9775 0.402 10 1000 -13317.8428 0.402 10 1100 -13317.7889 0.402 10 Optimization precision reduced Optimizing parameters... improved 0.013 lnL Optimizing branchlengths... improved 0.000 lnL 1200 -13317.6984 0.304 10 1300 -13317.6918 0.304 10 1400 -13317.6882 0.304 10 1500 -13317.6602 0.304 10 1600 -13317.6430 0.304 10 Optimization precision reduced Optimizing parameters... improved 0.006 lnL Optimizing branchlengths... improved 0.000 lnL 1700 -13317.6200 0.206 10 1800 -13317.6164 0.206 10 1900 -13317.6138 0.206 10 2000 -13317.5718 0.206 10 2100 -13317.5611 0.206 10 Optimization precision reduced Optimizing parameters... improved 0.003 lnL Optimizing branchlengths... improved 0.000 lnL 2200 -13317.5572 0.108 10 2300 -13317.5302 0.108 10 2400 -13317.5302 0.108 10 2500 -13317.5288 0.108 10 2600 -13317.5158 0.108 10 Optimization precision reduced Optimizing parameters... improved 0.001 lnL Optimizing branchlengths... improved 0.000 lnL 2700 -13317.5085 0.010 10 2800 -13317.5085 0.010 10 2900 -13317.5075 0.010 10 3000 -13317.5069 0.010 10 3100 -13317.5067 0.010 10 3200 -13317.5067 0.010 10 3300 -13317.5067 0.010 10 3400 -13317.5067 0.010 10 3500 -13317.5067 0.010 10 3600 -13317.5056 0.010 10 3700 -13317.5039 0.010 10 3800 -13317.5011 0.010 10 3900 -13317.5008 0.010 10 4000 -13317.5008 0.010 10 4100 -13317.5008 0.010 10 4200 -13317.5008 0.010 10 4300 -13317.5008 0.010 10 4400 -13317.5007 0.010 10 4500 -13317.5007 0.010 10 4600 -13317.5006 0.010 10 4700 -13317.5001 0.010 10 4800 -13317.5001 0.010 10 4900 -13317.5001 0.010 10 5000 -13317.5000 0.010 10 5100 -13317.5000 0.010 10 5200 -13317.4968 0.010 10 5300 -13317.4968 0.010 10 5400 -13317.4933 0.010 10 5500 -13317.4933 0.010 10 5600 -13317.4933 0.010 10 5700 -13317.4933 0.010 10 5800 -13317.4933 0.010 10 5900 -13317.4933 0.010 10 6000 -13317.4933 0.010 10 6100 -13317.4933 0.010 10 6200 -13317.4933 0.010 10 6300 -13317.4933 0.010 10 6400 -13317.4932 0.010 10 6500 -13317.4924 0.010 10 6600 -13317.4909 0.010 10 6700 -13317.4909 0.010 10 6800 -13317.4907 0.010 10 6900 -13317.4907 0.010 10 7000 -13317.4905 0.010 10 7100 -13317.4905 0.010 10 7200 -13317.4902 0.010 10 7300 -13317.4902 0.010 10 7400 -13317.4902 0.010 10 7500 -13317.4902 0.010 10 7600 -13317.4874 0.010 10 7700 -13317.4873 0.010 10 7800 -13317.4873 0.010 10 7900 -13317.4873 0.010 10 8000 -13317.4873 0.010 10 8100 -13317.4873 0.010 10 Reached termination condition! last topological improvement at gen 10 Improvement over last 500 gen = 0.00010 Current score = -13317.4873 Performing final optimizations... pass 1 : -13317.4869 (branch= 0.0000 alpha= 0.0000 eq freqs= 0.0001 rel rates= 0.0002 subset rates= 0.0000) pass 2 : -13317.4838 (branch= 0.0028 alpha= 0.0000 eq freqs= 0.0001 rel rates= 0.0002 subset rates= 0.0000) pass 3 : -13317.4806 (branch= 0.0031 alpha= 0.0000 eq freqs= 0.0001 rel rates= 0.0001 subset rates= 0.0000) pass 4 : -13317.4784 (branch= 0.0019 alpha= 0.0000 eq freqs= 0.0001 rel rates= 0.0000 subset rates= 0.0000) pass 5 : -13317.4773 (branch= 0.0010 alpha= 0.0000 eq freqs= 0.0001 rel rates= 0.0000 subset rates= 0.0000) pass 6 : -13317.4765 (branch= 0.0005 alpha= 0.0000 eq freqs= 0.0003 rel rates= 0.0001 subset rates= 0.0000) pass 7 : -13317.4759 (branch= 0.0005 alpha= 0.0000 eq freqs= 0.0000 rel rates= 0.0000 subset rates= 0.0000) pass 8 : -13317.4757 (branch= 0.0001 alpha= 0.0000 eq freqs= 0.0001 rel rates= 0.0000 subset rates= 0.0000) pass 9 : -13317.4756 (branch= 0.0000 alpha= 0.0000 eq freqs= 0.0000 rel rates= 0.0000 subset rates= 0.0000) pass 10: -13317.4756 (branch= 0.0000 alpha= 0.0000 eq freqs= 0.0000 rel rates= 0.0000 subset rates= 0.0000) pass 11: -13317.4755 (branch= 0.0000 alpha= 0.0000 eq freqs= 0.0000 rel rates= 0.0000 subset rates= 0.0000) pass 12: -13317.4755 (branch= 0.0000 alpha= 0.0000 eq freqs= 0.0000 rel rates= 0.0000 subset rates= 0.0000) Looking for minimum length branches... Final score = -13317.4755 Time used so far = 0 hours, 5 minutes and 12 seconds MODEL REPORT - Parameter values are FINAL Model 1 Number of states = 4 (nucleotide data) Nucleotide Relative Rate Matrix: 6 rates AC = 1.965, AG = 2.579, AT = 1.417, CG = 1.404, CT = 3.722, GT = 1.000 Equilibrium State Frequencies: estimated (ACGT) 0.3100 0.1768 0.2973 0.2159 Rate Heterogeneity Model: 4 discrete gamma distributed rate categories, alpha param estimated 0.4095 Substitution rate categories under this model: rate proportion 0.0181 0.2500 0.1888 0.2500 0.7413 0.2500 3.0518 0.2500 Model 2 Number of states = 4 (nucleotide data) Nucleotide Relative Rate Matrix: 6 rates AC = 4.351, AG = 7.084, AT = 1.610, CG = 7.097, CT = 4.404, GT = 1.000 Equilibrium State Frequencies: estimated (ACGT) 0.2694 0.1636 0.1605 0.4065 Rate Heterogeneity Model: 4 discrete gamma distributed rate categories, alpha param estimated 0.3607 Substitution rate categories under this model: rate proportion 0.0115 0.2500 0.1518 0.2500 0.6850 0.2500 3.1517 0.2500 Model 3 Number of states = 4 (nucleotide data) Nucleotide Relative Rate Matrix: 6 rates AC = 1.057, AG = 5.261, AT = 3.564, CG = 0.454, CT = 4.995, GT = 1.000 Equilibrium State Frequencies: estimated (ACGT) 0.1538 0.3560 0.2870 0.2032 Rate Heterogeneity Model: 4 discrete gamma distributed rate categories, alpha param estimated 2.9824 Substitution rate categories under this model: rate proportion 0.3879 0.2500 0.7306 0.2500 1.0824 0.2500 1.7991 0.2500 Subset rate multipliers: 0.54 0.30 2.16 NOTE: Collapsing of minimum length branches was requested (collapsebranches = 1) No branches were short enough to be collapsed. >>>Completed Search rep 3 (of 5)<<< >>>Search rep 4 (of 5)<<< MODEL REPORT - Parameters are at their INITIAL values (not yet optimized) Model 1 Number of states = 4 (nucleotide data) Nucleotide Relative Rate Matrix: 6 rates AC = 1.000, AG = 4.000, AT = 1.000, CG = 1.000, CT = 4.000, GT = 1.000 Equilibrium State Frequencies: estimated (ACGT) 0.3157 0.1746 0.3004 0.2093 Rate Heterogeneity Model: 4 discrete gamma distributed rate categories, alpha param estimated 0.5000 Substitution rate categories under this model: rate proportion 0.0334 0.2500 0.2519 0.2500 0.8203 0.2500 2.8944 0.2500 Model 2 Number of states = 4 (nucleotide data) Nucleotide Relative Rate Matrix: 6 rates AC = 1.000, AG = 4.000, AT = 1.000, CG = 1.000, CT = 4.000, GT = 1.000 Equilibrium State Frequencies: estimated (ACGT) 0.2703 0.1566 0.1628 0.4103 Rate Heterogeneity Model: 4 discrete gamma distributed rate categories, alpha param estimated 0.5000 Substitution rate categories under this model: rate proportion 0.0334 0.2500 0.2519 0.2500 0.8203 0.2500 2.8944 0.2500 Model 3 Number of states = 4 (nucleotide data) Nucleotide Relative Rate Matrix: 6 rates AC = 1.000, AG = 4.000, AT = 1.000, CG = 1.000, CT = 4.000, GT = 1.000 Equilibrium State Frequencies: estimated (ACGT) 0.1460 0.3609 0.2915 0.2015 Rate Heterogeneity Model: 4 discrete gamma distributed rate categories, alpha param estimated 0.5000 Substitution rate categories under this model: rate proportion 0.0334 0.2500 0.2519 0.2500 0.8203 0.2500 2.8944 0.2500 Subset rate multipliers: 1.00 1.00 1.00 Starting with seed=110869126 creating likelihood stepwise addition starting tree... number of taxa added: 4 5 6 Optimizing parameters... improved 222.535 lnL Optimizing branchlengths... improved 12.552 lnL 7 8 9 10 11 Initial ln Likelihood: -13686.2656 optimizing: starting branch lengths, alpha shape, rel rates, eq freqs, subset rates... pass 1:+ 197.479 (branch= 6.03 scale= 1.63 alpha= 8.07 freqs= 28.85 rel rates= 93.04 subset rates= 59.85) pass 2:+ 96.629 (branch= 12.32 scale= 0.50 alpha= 5.59 freqs= 14.32 rel rates= 6.45 subset rates= 57.45) pass 3:+ 46.043 (branch= 7.56 scale= 0.62 alpha= 2.44 freqs= 1.83 rel rates= 0.01 subset rates= 33.58) pass 4:+ 17.375 (branch= 1.70 scale= 0.00 alpha= 0.69 freqs= 0.15 rel rates= 2.55 subset rates= 12.29) pass 5:+ 5.749 (branch= 0.00 scale= 0.00 alpha= 0.29 freqs= 0.69 rel rates= 0.01 subset rates= 4.75) pass 6:+ 1.159 (branch= 0.00 scale= 0.00 alpha= 0.01 freqs= 0.10 rel rates= 0.47 subset rates= 0.59) pass 7:+ 0.086 (branch= 0.00 scale= 0.00 alpha= 0.00 freqs= 0.07 rel rates= 0.01 subset rates= 0.00) lnL after optimization: -13321.7448 gen current_lnL precision last_tree_imp 0 -13321.7448 0.500 0 100 -13320.2658 0.500 0 200 -13320.0646 0.500 0 300 -13319.3706 0.500 0 400 -13318.6406 0.500 0 500 -13318.2430 0.500 0 Optimization precision reduced Optimizing parameters... improved 0.042 lnL Optimizing branchlengths... improved 0.000 lnL 600 -13318.0693 0.402 0 700 -13317.8631 0.402 0 800 -13317.7904 0.402 0 900 -13317.7352 0.402 0 1000 -13317.6734 0.402 0 Optimization precision reduced Optimizing parameters... improved 0.014 lnL Optimizing branchlengths... improved 0.000 lnL 1100 -13317.6275 0.304 0 1200 -13317.6191 0.304 0 1300 -13317.6089 0.304 0 1400 -13317.5867 0.304 0 1500 -13317.5818 0.304 0 Optimization precision reduced Optimizing parameters... improved 0.006 lnL Optimizing branchlengths... improved 0.000 lnL 1600 -13317.5683 0.206 0 1700 -13317.5612 0.206 0 1800 -13317.5607 0.206 0 1900 -13317.5592 0.206 0 2000 -13317.5559 0.206 0 Optimization precision reduced Optimizing parameters... improved 0.004 lnL Optimizing branchlengths... improved 0.000 lnL 2100 -13317.5491 0.108 0 2200 -13317.5483 0.108 0 2300 -13317.5483 0.108 0 2400 -13317.5478 0.108 0 2500 -13317.5468 0.108 0 Optimization precision reduced Optimizing parameters... improved 0.001 lnL Optimizing branchlengths... improved 0.023 lnL 2600 -13317.5204 0.010 0 2700 -13317.5187 0.010 0 2800 -13317.5187 0.010 0 2900 -13317.5180 0.010 0 3000 -13317.5113 0.010 0 3100 -13317.5058 0.010 0 3200 -13317.5058 0.010 0 3300 -13317.5044 0.010 0 3400 -13317.5042 0.010 0 3500 -13317.5042 0.010 0 3600 -13317.5042 0.010 0 3700 -13317.5041 0.010 0 3800 -13317.4994 0.010 0 3900 -13317.4994 0.010 0 4000 -13317.4991 0.010 0 4100 -13317.4991 0.010 0 4200 -13317.4982 0.010 0 4300 -13317.4979 0.010 0 4400 -13317.4979 0.010 0 4500 -13317.4964 0.010 0 4600 -13317.4964 0.010 0 4700 -13317.4962 0.010 0 4800 -13317.4959 0.010 0 4900 -13317.4954 0.010 0 5000 -13317.4953 0.010 0 5100 -13317.4949 0.010 0 5200 -13317.4932 0.010 0 5300 -13317.4931 0.010 0 5400 -13317.4931 0.010 0 5500 -13317.4931 0.010 0 5600 -13317.4930 0.010 0 5700 -13317.4928 0.010 0 5800 -13317.4926 0.010 0 5900 -13317.4926 0.010 0 6000 -13317.4926 0.010 0 6100 -13317.4926 0.010 0 6200 -13317.4923 0.010 0 6300 -13317.4923 0.010 0 6400 -13317.4920 0.010 0 6500 -13317.4920 0.010 0 6600 -13317.4920 0.010 0 6700 -13317.4920 0.010 0 6800 -13317.4920 0.010 0 6900 -13317.4920 0.010 0 7000 -13317.4920 0.010 0 7100 -13317.4920 0.010 0 7200 -13317.4920 0.010 0 7300 -13317.4920 0.010 0 7400 -13317.4920 0.010 0 7500 -13317.4920 0.010 0 7600 -13317.4919 0.010 0 Reached termination condition! last topological improvement at gen 0 Improvement over last 500 gen = 0.00011 Current score = -13317.4919 Performing final optimizations... pass 1 : -13317.4915 (branch= 0.0000 alpha= 0.0001 eq freqs= 0.0003 rel rates= 0.0001 subset rates= 0.0000) pass 2 : -13317.4913 (branch= 0.0000 alpha= 0.0000 eq freqs= 0.0002 rel rates= 0.0001 subset rates= 0.0000) pass 3 : -13317.4809 (branch= 0.0101 alpha= 0.0000 eq freqs= 0.0002 rel rates= 0.0000 subset rates= 0.0000) pass 4 : -13317.4791 (branch= 0.0016 alpha= 0.0000 eq freqs= 0.0001 rel rates= 0.0000 subset rates= 0.0000) pass 5 : -13317.4775 (branch= 0.0014 alpha= 0.0000 eq freqs= 0.0001 rel rates= 0.0000 subset rates= 0.0000) pass 6 : -13317.4765 (branch= 0.0009 alpha= 0.0000 eq freqs= 0.0001 rel rates= 0.0000 subset rates= 0.0000) pass 7 : -13317.4759 (branch= 0.0003 alpha= 0.0000 eq freqs= 0.0001 rel rates= 0.0000 subset rates= 0.0000) pass 8 : -13317.4758 (branch= 0.0001 alpha= 0.0000 eq freqs= 0.0000 rel rates= 0.0000 subset rates= 0.0000) pass 9 : -13317.4757 (branch= 0.0001 alpha= 0.0000 eq freqs= 0.0000 rel rates= 0.0000 subset rates= 0.0000) pass 10: -13317.4757 (branch= 0.0000 alpha= 0.0000 eq freqs= 0.0000 rel rates= 0.0000 subset rates= 0.0000) pass 11: -13317.4756 (branch= 0.0000 alpha= 0.0000 eq freqs= 0.0000 rel rates= 0.0000 subset rates= 0.0000) pass 12: -13317.4756 (branch= 0.0000 alpha= 0.0000 eq freqs= 0.0000 rel rates= 0.0000 subset rates= 0.0000) Looking for minimum length branches... Final score = -13317.4756 Time used so far = 0 hours, 6 minutes and 51 seconds MODEL REPORT - Parameter values are FINAL Model 1 Number of states = 4 (nucleotide data) Nucleotide Relative Rate Matrix: 6 rates AC = 1.965, AG = 2.580, AT = 1.418, CG = 1.405, CT = 3.723, GT = 1.000 Equilibrium State Frequencies: estimated (ACGT) 0.3100 0.1768 0.2973 0.2159 Rate Heterogeneity Model: 4 discrete gamma distributed rate categories, alpha param estimated 0.4094 Substitution rate categories under this model: rate proportion 0.0181 0.2500 0.1887 0.2500 0.7412 0.2500 3.0520 0.2500 Model 2 Number of states = 4 (nucleotide data) Nucleotide Relative Rate Matrix: 6 rates AC = 4.350, AG = 7.083, AT = 1.610, CG = 7.097, CT = 4.403, GT = 1.000 Equilibrium State Frequencies: estimated (ACGT) 0.2695 0.1636 0.1605 0.4065 Rate Heterogeneity Model: 4 discrete gamma distributed rate categories, alpha param estimated 0.3609 Substitution rate categories under this model: rate proportion 0.0115 0.2500 0.1520 0.2500 0.6852 0.2500 3.1513 0.2500 Model 3 Number of states = 4 (nucleotide data) Nucleotide Relative Rate Matrix: 6 rates AC = 1.058, AG = 5.268, AT = 3.569, CG = 0.454, CT = 5.000, GT = 1.000 Equilibrium State Frequencies: estimated (ACGT) 0.1538 0.3560 0.2870 0.2032 Rate Heterogeneity Model: 4 discrete gamma distributed rate categories, alpha param estimated 2.9819 Substitution rate categories under this model: rate proportion 0.3879 0.2500 0.7305 0.2500 1.0824 0.2500 1.7992 0.2500 Subset rate multipliers: 0.54 0.30 2.16 NOTE: Collapsing of minimum length branches was requested (collapsebranches = 1) No branches were short enough to be collapsed. >>>Completed Search rep 4 (of 5)<<< >>>Search rep 5 (of 5)<<< MODEL REPORT - Parameters are at their INITIAL values (not yet optimized) Model 1 Number of states = 4 (nucleotide data) Nucleotide Relative Rate Matrix: 6 rates AC = 1.000, AG = 4.000, AT = 1.000, CG = 1.000, CT = 4.000, GT = 1.000 Equilibrium State Frequencies: estimated (ACGT) 0.3157 0.1746 0.3004 0.2093 Rate Heterogeneity Model: 4 discrete gamma distributed rate categories, alpha param estimated 0.5000 Substitution rate categories under this model: rate proportion 0.0334 0.2500 0.2519 0.2500 0.8203 0.2500 2.8944 0.2500 Model 2 Number of states = 4 (nucleotide data) Nucleotide Relative Rate Matrix: 6 rates AC = 1.000, AG = 4.000, AT = 1.000, CG = 1.000, CT = 4.000, GT = 1.000 Equilibrium State Frequencies: estimated (ACGT) 0.2703 0.1566 0.1628 0.4103 Rate Heterogeneity Model: 4 discrete gamma distributed rate categories, alpha param estimated 0.5000 Substitution rate categories under this model: rate proportion 0.0334 0.2500 0.2519 0.2500 0.8203 0.2500 2.8944 0.2500 Model 3 Number of states = 4 (nucleotide data) Nucleotide Relative Rate Matrix: 6 rates AC = 1.000, AG = 4.000, AT = 1.000, CG = 1.000, CT = 4.000, GT = 1.000 Equilibrium State Frequencies: estimated (ACGT) 0.1460 0.3609 0.2915 0.2015 Rate Heterogeneity Model: 4 discrete gamma distributed rate categories, alpha param estimated 0.5000 Substitution rate categories under this model: rate proportion 0.0334 0.2500 0.2519 0.2500 0.8203 0.2500 2.8944 0.2500 Subset rate multipliers: 1.00 1.00 1.00 Starting with seed=1432730019 creating likelihood stepwise addition starting tree... number of taxa added: 4 5 6 Optimizing parameters... improved 204.175 lnL Optimizing branchlengths... improved 54.430 lnL 7 8 9 10 11 Initial ln Likelihood: -13850.6663 optimizing: starting branch lengths, alpha shape, rel rates, eq freqs, subset rates... pass 1:+ 206.901 (branch= 10.49 scale= 0.59 alpha= 2.66 freqs= 34.28 rel rates= 99.72 subset rates= 59.16) pass 2:+ 96.794 (branch= 7.46 scale= 2.85 alpha= 6.26 freqs= 16.52 rel rates= 6.62 subset rates= 57.09) pass 3:+ 45.055 (branch= 5.78 scale= 0.00 alpha= 3.33 freqs= 1.52 rel rates= 1.31 subset rates= 33.11) pass 4:+ 20.882 (branch= 3.40 scale= 0.00 alpha= 0.78 freqs= 0.71 rel rates= 3.02 subset rates= 12.98) pass 5:+ 6.430 (branch= 0.00 scale= 0.00 alpha= 0.01 freqs= 0.07 rel rates= 1.39 subset rates= 4.96) pass 6:+ 0.079 (branch= 0.00 scale= 0.00 alpha= 0.01 freqs= 0.06 rel rates= 0.01 subset rates= 0.00) lnL after optimization: -13474.5256 gen current_lnL precision last_tree_imp 0 -13474.5256 0.500 0 100 -13324.0391 0.500 40 200 -13323.4406 0.500 40 300 -13322.7068 0.500 40 400 -13322.3876 0.500 40 500 -13321.4452 0.500 40 600 -13320.9924 0.500 40 Optimization precision reduced Optimizing parameters... improved 0.541 lnL Optimizing branchlengths... improved 0.000 lnL 700 -13319.6463 0.402 40 800 -13319.1584 0.402 40 900 -13318.8903 0.402 40 1000 -13318.5134 0.402 40 1100 -13318.2613 0.402 40 Optimization precision reduced Optimizing parameters... improved 0.027 lnL Optimizing branchlengths... improved 0.000 lnL 1200 -13318.1072 0.304 40 1300 -13318.0828 0.304 40 1400 -13317.9779 0.304 40 1500 -13317.9633 0.304 40 1600 -13317.9586 0.304 40 Optimization precision reduced Optimizing parameters... improved 0.015 lnL Optimizing branchlengths... improved 0.000 lnL 1700 -13317.8625 0.206 40 1800 -13317.7611 0.206 40 1900 -13317.6621 0.206 40 2000 -13317.6395 0.206 40 2100 -13317.6333 0.206 40 Optimization precision reduced Optimizing parameters... improved 0.005 lnL Optimizing branchlengths... improved 0.000 lnL 2200 -13317.6222 0.108 40 2300 -13317.6178 0.108 40 2400 -13317.6061 0.108 40 2500 -13317.6061 0.108 40 2600 -13317.6048 0.108 40 Optimization precision reduced Optimizing parameters... improved 0.001 lnL Optimizing branchlengths... improved 0.049 lnL 2700 -13317.5462 0.010 40 2800 -13317.5293 0.010 40 2900 -13317.5292 0.010 40 3000 -13317.5266 0.010 40 3100 -13317.5249 0.010 40 3200 -13317.5230 0.010 40 3300 -13317.5205 0.010 40 3400 -13317.5202 0.010 40 3500 -13317.5202 0.010 40 3600 -13317.5202 0.010 40 3700 -13317.5200 0.010 40 3800 -13317.5094 0.010 40 3900 -13317.4994 0.010 40 4000 -13317.4985 0.010 40 4100 -13317.4984 0.010 40 4200 -13317.4984 0.010 40 4300 -13317.4980 0.010 40 4400 -13317.4947 0.010 40 4500 -13317.4942 0.010 40 4600 -13317.4942 0.010 40 4700 -13317.4927 0.010 40 4800 -13317.4927 0.010 40 4900 -13317.4921 0.010 40 5000 -13317.4912 0.010 40 5100 -13317.4907 0.010 40 5200 -13317.4906 0.010 40 5300 -13317.4906 0.010 40 5400 -13317.4906 0.010 40 5500 -13317.4903 0.010 40 5600 -13317.4896 0.010 40 5700 -13317.4895 0.010 40 5800 -13317.4894 0.010 40 5900 -13317.4894 0.010 40 6000 -13317.4881 0.010 40 6100 -13317.4880 0.010 40 6200 -13317.4876 0.010 40 6300 -13317.4875 0.010 40 6400 -13317.4875 0.010 40 6500 -13317.4874 0.010 40 6600 -13317.4874 0.010 40 6700 -13317.4873 0.010 40 6800 -13317.4873 0.010 40 6900 -13317.4873 0.010 40 7000 -13317.4872 0.010 40 7100 -13317.4872 0.010 40 7200 -13317.4871 0.010 40 7300 -13317.4868 0.010 40 7400 -13317.4862 0.010 40 7500 -13317.4860 0.010 40 7600 -13317.4857 0.010 40 7700 -13317.4857 0.010 40 7800 -13317.4857 0.010 40 7900 -13317.4826 0.010 40 8000 -13317.4826 0.010 40 8100 -13317.4825 0.010 40 8200 -13317.4821 0.010 40 8300 -13317.4821 0.010 40 8400 -13317.4819 0.010 40 Reached termination condition! last topological improvement at gen 40 Improvement over last 500 gen = 0.00076 Current score = -13317.4819 Performing final optimizations... pass 1 : -13317.4814 (branch= 0.0000 alpha= 0.0000 eq freqs= 0.0003 rel rates= 0.0001 subset rates= 0.0000) pass 2 : -13317.4810 (branch= 0.0000 alpha= 0.0001 eq freqs= 0.0002 rel rates= 0.0002 subset rates= 0.0000) pass 3 : -13317.4807 (branch= 0.0000 alpha= 0.0000 eq freqs= 0.0002 rel rates= 0.0001 subset rates= 0.0000) pass 4 : -13317.4805 (branch= 0.0000 alpha= 0.0000 eq freqs= 0.0001 rel rates= 0.0001 subset rates= 0.0000) pass 5 : -13317.4768 (branch= 0.0031 alpha= 0.0000 eq freqs= 0.0001 rel rates= 0.0004 subset rates= 0.0000) pass 6 : -13317.4763 (branch= 0.0004 alpha= 0.0000 eq freqs= 0.0001 rel rates= 0.0000 subset rates= 0.0000) pass 7 : -13317.4759 (branch= 0.0002 alpha= 0.0000 eq freqs= 0.0001 rel rates= 0.0000 subset rates= 0.0000) pass 8 : -13317.4758 (branch= 0.0001 alpha= 0.0000 eq freqs= 0.0000 rel rates= 0.0000 subset rates= 0.0000) pass 9 : -13317.4756 (branch= 0.0002 alpha= 0.0000 eq freqs= 0.0000 rel rates= 0.0000 subset rates= 0.0000) pass 10: -13317.4756 (branch= 0.0000 alpha= 0.0000 eq freqs= 0.0000 rel rates= 0.0000 subset rates= 0.0000) pass 11: -13317.4756 (branch= 0.0000 alpha= 0.0000 eq freqs= 0.0000 rel rates= 0.0000 subset rates= 0.0000) pass 12: -13317.4756 (branch= 0.0000 alpha= 0.0000 eq freqs= 0.0000 rel rates= 0.0000 subset rates= 0.0000) pass 13: -13317.4756 (branch= 0.0000 alpha= 0.0000 eq freqs= 0.0000 rel rates= 0.0000 subset rates= 0.0000) Looking for minimum length branches... Final score = -13317.4756 Time used = 0 hours, 8 minutes and 41 seconds MODEL REPORT - Parameter values are FINAL Model 1 Number of states = 4 (nucleotide data) Nucleotide Relative Rate Matrix: 6 rates AC = 1.963, AG = 2.577, AT = 1.416, CG = 1.403, CT = 3.719, GT = 1.000 Equilibrium State Frequencies: estimated (ACGT) 0.3100 0.1768 0.2973 0.2159 Rate Heterogeneity Model: 4 discrete gamma distributed rate categories, alpha param estimated 0.4095 Substitution rate categories under this model: rate proportion 0.0181 0.2500 0.1888 0.2500 0.7413 0.2500 3.0518 0.2500 Model 2 Number of states = 4 (nucleotide data) Nucleotide Relative Rate Matrix: 6 rates AC = 4.354, AG = 7.088, AT = 1.611, CG = 7.102, CT = 4.407, GT = 1.000 Equilibrium State Frequencies: estimated (ACGT) 0.2694 0.1636 0.1605 0.4065 Rate Heterogeneity Model: 4 discrete gamma distributed rate categories, alpha param estimated 0.3607 Substitution rate categories under this model: rate proportion 0.0115 0.2500 0.1518 0.2500 0.6850 0.2500 3.1517 0.2500 Model 3 Number of states = 4 (nucleotide data) Nucleotide Relative Rate Matrix: 6 rates AC = 1.057, AG = 5.260, AT = 3.564, CG = 0.454, CT = 4.994, GT = 1.000 Equilibrium State Frequencies: estimated (ACGT) 0.1538 0.3560 0.2870 0.2032 Rate Heterogeneity Model: 4 discrete gamma distributed rate categories, alpha param estimated 2.9829 Substitution rate categories under this model: rate proportion 0.3879 0.2500 0.7306 0.2500 1.0824 0.2500 1.7991 0.2500 Subset rate multipliers: 0.54 0.30 2.16 NOTE: Collapsing of minimum length branches was requested (collapsebranches = 1) No branches were short enough to be collapsed. >>>Completed Search rep 5 (of 5)<<< ####################################################### Completed 5 replicate search(es) (of 5). NOTE: Unless the following output indicates that search replicates found the same topology, you should assume that they found different topologies. Results: Replicate 1 : -13317.4756 Replicate 2 : -13317.4756 (same topology as 1) Replicate 3 : -13317.4755 (best) (same topology as 1) Replicate 4 : -13317.4756 (same topology as 1) Replicate 5 : -13317.4756 (same topology as 1) Parameter estimates across search replicates: Partition model subset 1: r(AC) r(AG) r(AT) r(CG) r(CT) r(GT) pi(A) pi(C) pi(G) pi(T) alpha rep 1: 1.963 2.576 1.416 1.403 3.719 1 0.310 0.177 0.297 0.216 0.409 rep 2: 1.961 2.575 1.415 1.402 3.716 1 0.310 0.177 0.297 0.216 0.409 rep 3: 1.965 2.579 1.417 1.404 3.722 1 0.310 0.177 0.297 0.216 0.409 rep 4: 1.965 2.58 1.418 1.405 3.723 1 0.310 0.177 0.297 0.216 0.409 rep 5: 1.963 2.577 1.416 1.403 3.719 1 0.310 0.177 0.297 0.216 0.409 Partition model subset 2: r(AC) r(AG) r(AT) r(CG) r(CT) r(GT) pi(A) pi(C) pi(G) pi(T) alpha rep 1: 4.355 7.089 1.612 7.106 4.408 1 0.269 0.164 0.160 0.407 0.361 rep 2: 4.345 7.074 1.608 7.087 4.397 1 0.269 0.164 0.160 0.407 0.361 rep 3: 4.351 7.084 1.61 7.097 4.404 1 0.269 0.164 0.160 0.407 0.361 rep 4: 4.35 7.083 1.61 7.097 4.403 1 0.269 0.164 0.160 0.406 0.361 rep 5: 4.354 7.088 1.611 7.102 4.407 1 0.269 0.164 0.160 0.407 0.361 Partition model subset 3: r(AC) r(AG) r(AT) r(CG) r(CT) r(GT) pi(A) pi(C) pi(G) pi(T) alpha rep 1: 1.058 5.267 3.57 0.4542 5.001 1 0.154 0.356 0.287 0.203 2.981 rep 2: 1.058 5.267 3.569 0.4543 5 1 0.154 0.356 0.287 0.203 2.982 rep 3: 1.057 5.261 3.564 0.4538 4.995 1 0.154 0.356 0.287 0.203 2.982 rep 4: 1.058 5.268 3.569 0.4544 5 1 0.154 0.356 0.287 0.203 2.982 rep 5: 1.057 5.26 3.564 0.4538 4.994 1 0.154 0.356 0.287 0.203 2.983 Treelengths and subset rate multipliers: TL R(1) R(2) R(3) rep 1: 1.704 0.538 0.298 2.164 rep 2: 1.704 0.538 0.298 2.164 rep 3: 1.704 0.538 0.298 2.164 rep 4: 1.704 0.537 0.299 2.164 rep 5: 1.704 0.538 0.298 2.164 Saving final trees from all search reps to GTRG.byCodonPos.best.all.tre Saving final tree from best search rep (#3) to GTRG.byCodonPos.best.tre ####################################################### garli-2.1-release/example/partition/exampleRuns/3parts.sameModelType/garli.conf000066400000000000000000000024441241236125200277710ustar00rootroot00000000000000[general] datafname = zakonEtAl2006.11tax.nex constraintfile = none streefname = stepwise attachmentspertaxon = 50 ofprefix = GTRG.byCodonPos randseed = -1 availablememory = 512 logevery = 10 saveevery = 100 refinestart = 1 outputeachbettertopology = 0 outputcurrentbesttopology = 0 enforcetermconditions = 1 genthreshfortopoterm = 5000 scorethreshforterm = 0.001 significanttopochange = 0.01 outputphyliptree = 0 outputmostlyuselessfiles = 0 writecheckpoints = 0 restart = 0 outgroup = 1 resampleproportion = 1.0 inferinternalstateprobs = 0 outputsitelikelihoods = 0 optimizeinputonly = 0 collapsebranches = 1 searchreps = 5 bootstrapreps = 0 linkmodels = 0 subsetspecificrates = 1 [model1] datatype = nucleotide ratematrix = 6rate statefrequencies = estimate ratehetmodel = gamma numratecats = 4 invariantsites = none [master] nindivs = 4 holdover = 1 selectionintensity = 0.5 holdoverpenalty = 0 stopgen = 5000000 stoptime = 5000000 startoptprec = 0.5 minoptprec = 0.01 numberofprecreductions = 5 treerejectionthreshold = 50.0 topoweight = 0.01 modweight = 0.002 brlenweight = 0.002 randnniweight = 0.1 randsprweight = 0.3 limsprweight = 0.6 intervallength = 100 intervalstostore = 5 limsprrange = 6 meanbrlenmuts = 5 gammashapebrlen = 1000 gammashapemodel = 1000 uniqueswapbias = 0.1 distanceswapbias = 1.0 garli-2.1-release/example/partition/exampleRuns/3parts.sameModelType/zakonEtAl2006.11tax.nex000066400000000000000000000577671241236125200317170ustar00rootroot00000000000000#NEXUS [ This dataset is from: Zakon, Lu, Zwickl and Hillis. 2006. Sodium channel genes and the evolution of diversity in communication signals of electric fishes: Convergent molecular evolution. Proc. Natl. Acad. Sci. USA. 103(10):3675-80. ] begin data; dimensions ntax=11 nchar=2178; format datatype=dna missing=? gap=-; matrix MorNa6 CCTGTGACTCCACATTTTGAGCACGTACTCAGTGTGGGAAACCTGGTTTTCTCAGGGATATTTGCTGGTGAAATGGTCTTGAAAATTATTGCTATGGACCCCTACTACTACTTCCAGGTTGGATGGAACGTGTTTGACAGCATCATTGTTACCATGAGTATGGTGGAGATGGTACTGGCTGATGTAGAGGGTCTGTCGGTTCTGCGGTCCTTTCGTTTGCTACGTGTCTTCAAGCTTGCCAAATCATGGCCTACCCTCAACATGCTGCTAACGATCATCGGAAACTCAGTGGGTGCTCTGGGGAACCTCACCGTGGTGCTGGCCATCATCGTTTTCATCTTCGCTGTGGTTGGAATGCAGCTGTTTGCCAAAAACTACAAGGACTGCGTCTGCAAGATCGCCGAGGATTGTGAGCTGCCCCGGTGGCACATGCATGACTTCTTCCACTCTTTCCTCATCGTGTTCCGCATCCTCTGTGGAGAGTGGATTGAGACCATGTGGGACTGCATGGAAGTGGCCAACAGAAACATGTGTTTGGTCCTCTTCTTAATGGTCATGATAATTGGGAACCTGGTGGTTCTGAACCTTTTCCTGGCCTTGCTGCTTAGCTCATTCAGCGGGGACAATCTGCAAATGGCAGATGACGACGGCGAGCTGAACAATCTGCAGCTTTCCGCACTCAGGATCACCAGAGCCATTGATTGGGTGAAGGCCTACGTTAGAGGGCTGATCTGGAAGATCCTGGGCAAGCAGCCAAGAGTGCTGGATGGTTTATCTCACTGGGCAACCTTCACCGTACCCATTGCCCAGGAAGAGTCTGATTTAGAAGATGGTGTGTCTGAGTGCAGCACAGTGGACTACGTGCCCCCTCCGCCGGATGAAGTGGAGGAACCGGAGCCTGTGGAACCTGAGGCCTGTTACACTGACAACTGCCTTAGACGGTGTCCTTGTCTGGTGCTGGACACCTCAGAGGGCAGAGGGAAGACCTGGTGGAACCTCAGGAGAACCTGCTACACCATTGTGGAGCATGACTACTTTGAGTCCTCCATAATCTTCATGATCCTTCTCAGCAGTGGTGCCTTGGCCTTTGAAGACATATATCTTGAAAGACGCAGAACGATAAAAATCCTGCTGGAATATGCAGATAAAGTCTTCAGCTATGTATTTGTTATTGAGATGCTCCTTAAGTGGGTGGCTTATGGTTACAAAGTATACTTTACCAATGCCTGGTGCTGGCTGGACTTCTTGATTGTTGATGTTTCCTTGGTCAGTTTGGCAGCAAGCATAATGGGCTATTCTGAACTAGGACCCATAAAGTCTTTGAGAACTCTTAGGGCTCTGAGGCCTCTAAGAGCCCTTTCCAGGTTTGAGGGGATGCGGGTTGTGGTGAACGCCCTTGTGGGGGCCGTCCCCGCCATCTTCAATGTGATGCTGGTCTGTCTCATCTTCTGGCTCATCTTCAGCATCATGGGGGTTAACCTGTTTGCCGGGACATTCTACCACTGCCTCAACACCACAACTGGGGAGATGTTTACCATTGATGTTGTAAACAACTATAGTGAGTGTTTGGCCCTCATGCACACAAACGAGGTGCGCTGGGCCAACGTCAGGGTCAACTATGACAACGTTGGGATGGGTTACCTGTCTCTGTTGCAAGTGTCAACATTCAAAGGCTGGATGGAAATTATGTATGCGGCTGTCGACTCACGTAAGGTGGGTCAACAGCCCTCATATGAGGCCAACCTTTACATGTACGTGTACTTTGTCATCTTCATCATCTTTGGGTCCTTCTTTACACTCAACCTCTTCATTGGTGTCATCATTGACAACTTCAATCAACAAAAGAATAAGATGGGAGGA---GATTGCTTTATGACTGAGGAGCAGAAGAAATATTACGACGCTATGAAAAAGCTAGGCAACAAGAAGCCAGCGAAGCCCATTCCAAGACCAACGGGCAAAATACCAGGCCTAGTATATGACTTCATCAGTCAGCAGGCCTTTGACATCTTTATCATGGTACTGATTTGCCTGAACATGGTGACCATGATGGTGGAGGAAGATGACCAAAGTGAACAGAAGACAGACATGCTGGGCAAAATCAATGCAGTCTTCATTGTGGTCTTCAGCAGTGAATGTTTGCTGAAGATGATTGCACTGAGACAATACTTCTTTACC ClownNa6 CCCATGAGCCCTGAGTTTGACCACATGCTCTCTGTGGGAAACCTGGTTTTCACTGGAATCTTCACAGCTGAAATGGTCCTAAAACTCATTGCTATGGACCCCTACTACTACTTCCAGGTTGGATGGAACATATTTGACAGCATCATTGTCACTCTAAGCCTAGTGGAACTGGGGCTCGCTAATGTTCAGGGTCTGTCAGTCCTGCGATCCTTTCGTTTGTTGCGAGTGTTCAAGCTGGCAAAGTCTTGGCCCACCCTCAACATGCTGATCAAGATCATCGGGAATTCCGTGGGCGCCCTGGGCAACCTGACCCTGGTGCTGGCCATCATCGTCTTCATCTTCGCCGTGGTGGGCATGCAGCTCTTTGGGAAGACCTACAAGGACTGCGTGTGCAAGATTGCCAGTGACTGCGAGCTTCCCCGCTGGCACATGAATGACTTCTTCCACTCGTTCCTTATCGTGTTCCGCATCCTCTGCGGGGAGTGGATCGAGACCATGTGGGACTGCATGGAGGTGGCTGGTGCAGGCATGTGCCTCGTGGTCTTCATGATGGTCATGGTCATTGGGAACCTAGTGGTGCTGAATCTCTTCCTGGCTTTGCTGCTCAGTTCATTCAGTGGAGACAACCTAGCAGGCGGTGATGAGGATGGCGAGATGAACAACTTGCAGATTGCTATCGGAAGGATCACCCGAGGCATTGACTGGGTGAAGGCATTTGTCATGGGACTGGTGTGGCGGGTGATGGGCAAAAAGCCTAAAATGCTGGATGGTTTATCTCACTGGGTAACCCTCAGTGTGCCCATGGCACAGGAGGAATCCGACTTAGAAGACGACTCCTCTGAATGCAGCACTGTGGACTATAGGCCTCCAGAGCCAGTGGAGGAGGAAGAACCAGAACAGGTGGAGCCTGTGGAGTGTTTTACTGATGACTGTGTCAGACGTTGCCCTTGTCTGACGGTGGACATCACGCAGGGCAAAGGAAGGACCTGGTGGAATCTCAGGAAAACATGTTACACCATCGTGGAGCATGACTACTTTGAGACCTTCATCATCTTCATGATCCTGCTTAGCAGTGGGGCCTTGGCCTTTGAAGATATATACATTGAAAGGCGCAGAACAATAAAAATCATTCTGGAATATGCAGACAAAGTATTTACATACGTATTTGTTGTTGAAATGCTCTTGAAGTGGGTTGCTTATGGTTTCAAGACATACTTCACTAATGCCTGGTGCTGGCTGGACTTTTTAATTGTGGATGTGTCCTTGATCAGTTTGACAGCAAACCTCATGGGCTACTCAGAGCTGGGGCCTATCAAATCCCTGAGAACCCTGAGGGCCCTGAGGCCACTACGAGCCCTGTCTAGGTTTGAGGGCATGAGAGTGGTGGTAAATGCATTGGTAGGGGCCATCCTTTCCATCTTCAACGTACTGCTGGTCTGTCTCATTTTCTGGCTTATCTTCAGCATTATGGGTGTCAACCTTTTTGCTGGAAAGTTCTACCGCTGTATCAACACCACCACAGAGGAGCTATTACCTGTCGAGATTGTGAACAATAAGAGTGACTGCTTGAATCTCATGCACACAAATGAAGTGCGCTGGGTCAATGTGAAGGTCAACTATGACAACGTTGGCCTTGGTTACCTCTCTCTACTCCAAGTTGCAACATTTAAAGGGTGGATGGACATTATGTATGCAGCTGTGGACTCTCGTGAGGTGGAAGAGCAGCCCTTGTATGAGGAAAACCTCTATATGTACTTATACTTCGTCATCTTCATCATTTTTGGGTCATTCTTTACACTCAACCTTTTCATTGGTGCCATCATCGACAACTTTAATCAGCAAAAGAAAAAGCTTGGTGGGAAGGATATCTTCATGACCGAGGAGCAAAAGAAGTACTACAATGCCATGAAAAAGCTTGGTTCCAAAAAGCCAGTGAAGCCTATTCCAAGACCTACGAACAAAATACAAGGTGTGGTATTTGACTTCATCAGTCAGCAGTTCTTTGACATCTTTATCATGGTATTGATCTGCCTCAACATGGTGACCATGATGGTGGAGACAGATGACCAAAGTCAGGAAAAAGAGAATATACTGAACCAAATCAATCTGGTATTCATTGTGATCTTCACCAGCGAATGCGTCTTGAAGATGTTTGCACTTAGACATTATTTCTTCACC AraNa6 CCAATGAGTCCCGCGTTTGACCATATGCTGACCGTGGGAAACCTCGTTTTTACGGGGATCTTTACAGCTGAGATGGTATTCAAGCTCATCGCCATGGATCCATACCACTACTTCCAGGTTGGATGGAACATTTTTGACAGCATCATTGTCACACTTAGCCTGGTGGAGCTGGGTCTCGCGAATGTTCAGGGCCTTTCGGTCTTGCGCTCCTTCCGCTTGCTGCGGGTCTTCAAGCTGGCCAAGTCTTGGCCTACCCTGAACATGCTCATCAAGATCATTGGAAACTCAGTGGGTGCCCTAGGGAACCTCACACTGGTGCTGGCCATCATCGTCTTCATCTTCGCTGTCGTGGGCATGCAGCTGTTCGGTAAGAGCTACAAGGACTGTGTGTGTAAGATTGCAGAGGACTGTGAGCTACCCCGCTGGCACATGAATGACTTCTTCCACTCCTTCCTCATCGTGTTCCGCATCTTGTGTGGCGAGTGGATCGAGACCATGTGGGACTGCATGGAGGTGGCGGGCGCTGGCATGTGTCTCGTTGTCTTCATGATGGTCATGGTCATCGGCAACCTGGTGGTCCTGAACCTCTTCCTGGCTTTGCTGCTGAGCTCGTTCAGTGGAGACAACCTGGCTGGAGGAGACGATGATGGCGAGATGAACAACCTGCAGATTGCCATTGGCAGGATCACCAGAGGCATTGACTGGATAAAAGCCTTTGCCATGGGCTTCATATGGAAGTTACTTGGAAAGAAGGCCAAGATGCTGGATGGTTTATCCCACTGGGTGACCCTGAGTGTTCCCATTGCCCAGGGAGAGTCTGATTTGGAGGATGACTCCTCTGAATGCAGCACGGTGGACTACAGACCCCCAGAACCAGAGGAGGAGGAGGAGCCTGAGCAGCAGGAGCCTGAGGCCTGTTTTACTGAGGATTGCTTCCGGCGTATGCCATGTTTGATGGTGGACATCACGCAGGGGAAGGGCAAGACCTGGTGGAAACTACGGAAAACCTGTTTTACCATTGTGGAGCATGGCTATTTTGAGACCTTCATCATTTTCATGATCCTTCTCAGCAGTGGAGCTCTGGCTTTTGAAGACATATACATTGAAAAGCGCAGAGTTATCAAAATCATCCTGGAATATGCGGACAAAGTCTTCACCTATGTATTTGTTATTGAAATGGTCCTCAAGTGGGTGGCTTATGGGTTCAAAGTATACTTCACAAACGCCTGGTGCTGGCTGGACTTCCTCATCGTTGATGTGTCCTTGATCAGTCTGACCGCTAACCTCATGGGCTACTCTGAGCTGGGGCCCATTAAGTCTCTGAGAACACTTAGGGCCCTTAGGCCCCTGAGGGCCCTCTCCAGGTTTGAGGGGATGAGGGTGGTGGTAAATGCGCTTGTGGGAGCCATCCTCTCCATTTTCAACGTTCTGCTCGTGTGCCTCATCTTCTGGCTCATCTTCAGCATCATGGGCGTTAACCTGTTTGCTGGGAAGTTCTACTACTGCATTAACACCACCTCAGAGGAGCGCTTACCCATTGATGTTGTGAATAACAAGAGCGACTGCATGGCCCTAATGCACACCAATGAGGTGCGCTGGGTCAACGTCAAGGTGAACTATGACAATGTCGGCTTGGGCTATCTCTCTCTGCTGCAGGTGGCTACTTTTAAAGGTTGGATGGATATAATGTATGCTGCCGTGGACTCACGGGAGGTGGGGGAGCAACCCTCCTATGAGGTCAACATCTACATGTACTTGTACTTTGTCATCTTCATCATCTTCGGGTCCTTCTTCACGCTCAACCTCTTCATTGGTGTCATCATTGACAACTTCAATCAGCAAAAGAAAAAGTTAGGAGGAAAAGACATATTCATGACTGAGGAACAGAAGAAGTATTACAATGCCATGAAGAAACTTGGCTCCAAGAAGCCAGTGAAGCCCATCCCACGACCTTCGAATAAAATTCAAGGCATGGTGTTTGACTTCATTACGCAGCAGTTTTTTGATATTTTCATCATGGTACTGATCTGCCTCAACATGGTGACCATGATGGTGGAGACGGATGATCAAAGCGAGGACAAAGAAAATGTCCTCTACCAGATTAACCTGGTCTTCATTGTGATCTTCACCTGCGAGTGCGTCCTCAAAATGTTTGCGCTTAGACAGTACTTCTTCACC puffNa6 CCCATGACCGAAGAGTTCGACTACATGCTTTCAGTGGGAAATCTGGTTTTCACAGGAATCTTCGCGGCGGAAATGTTCTTCAAATTGATCGCCATGGATCCGTACTACTATTTCCAAGTTGGCTGGAACATTTTTGACAGCATCATCGTCACGCTCAGTCTGGTGGAGTTAGGGCTTGCAAACGTCCAGGGGCTGTCCGTCCTCAGGTCCTTCCGTCTGCTTCGGGTCTTCAAACTTGCCAAGTCCTGGCCCACGCTCAACATGCTGATCAAGATTATCGGTAATTCAGTTGGAGCTTTAGGGAATCTGACTTTGGTGCTGGCCATCATCGTCTTCATCTTCGCTGTGGTGGGGATGCAGCTCTTCGGCAAAAGCTACAAGGACTGTGTGTGCAAGATTTCCTCCGACTGCGAGCTGCCACGCTGGCACATGAATGACTTCTTCCACTCCTTCCTCATCGTGTTCCGCATCCTGTGCGGCGAGTGGATCGAGACCATGTGGGACTGCATGGAGGTGGCTGGAGCCGGGATGTGCTTGGTTGTCTTCATGATGGTCATGGTCATCGGGAACCTCGTGGTGTTGAATCTCTTCCTGGCCCTGCTGCTCAGCTCATTCAGCGGAGACAACCTCTCGGTCGGAGACGACGATGGAGAGCTGAACAATCTTCAGATCGCCATCGGAAGGATCACACGAGGCGGCAACTGGCTCAAAGCCTTCTTCATCGGAACGCTTCAACGGGTTCTTGGAAGGGAACCAAAATTGGCAGACGGGATCGCCAACTGTCTTAGTATCACCGTCCCCATCGCCCTGGGAGAGTCGGACTCTGAAGGCGATTCTTCAGTGTGCAGCACAGTGGACTATCAGCCCCCAGAGCCTGAGGAAGAGGAAGAGCCGGACCTGGTGGAGCCAGAGGCCTGCTTCACTGACAACTGTGTGAAGCGCTGGCCTTGTCTGAACGTGGACATCAGCCAGGGGAAAGGAAAGAAGTGGTGGAACCTCCGCAAGACCTGCTTCACTATTGTGGAGCATGACTGGTTTGAGACCTTCATCATTTTCATGATCCTCCTCAGCAGCGGAGCTCTGGCCTTTGAAGACATATACATCGAAAGACGAAGAACCGTGAAAATTGTCCTGGAGTTTGCTGACAAAGTTTTCACCTTCATCTTTGTCATCGAGATGCTCCTGAAATGGGTCGCCTATGGCTTCAAGACCTACTTCACCAATGCCTGGTGCTGGCTGGACTTTTTCATCGTGGACATTTCCCTGATCAGTCTATCTGCCAACTTGATGGGCTTCTCTGACCTCGGACCAATCAAATCGCTCAGAACTCTCAGGGCTCTGCGGCCTCTTCGGGCGCTGTCCAGATTTGAAGGGATGAGGGTGGTGGTGAACGCTCTCATCGGAGCCATTCCCTCCATCTTCAACGTGCTCCTGGTGTGCCTGATCTTCTGGCTCATCTTCAGCATCATGGGAGTGAACCTGTTTGCGGGGAAGTTCTACCGCTGCATCAACACCACCACGGCGGAGCTCTTCCCCATCTCTGTGGTCAACAACAAGAGCGACTGCGTGGCGCTGCAGGCCACGCAGGAGGCCCGCTGGGTCAACGTCAAAGTCAACTACGACAACGTGGCAAAAGGCTACCTGTCGCTGCTTCAAATCGCAACTTTTAAAGGCTGGATGGATATTATGTATCCTGCGGTTGACTCAAGAGAGGTGGAAGAGCAACCTTCTTATGAGATCAACCTCTACATGTACATCTACTTTGTCATCTTTATCATCTTTGGCTCTTTCTTCACGCTGAACCTCTTCATCGGCGTCATCATCGACAATTTCAACCAGCAGAAGAAAAAGTTAGGAGATAAAGACATCTTCATGACAGAGGAGCAGAAAAAGTACTACGAAGCCATGAAGAAACTCGGCTCAAAGAAGCCGCAGAAGCCGATCCCACGTCCAGCTAACCTAATCCAGGGGCTAGTGTTTGACTTCATCAGTCAGCAGTTCTTTGACATCTTCATCATGGTGCTCATCTGTCTCAACATGGTCACCATGATGGTGGAGACGGACGACCAGAGTCCGGCGAAGGAGGACTTCCTCTTCAAAGTGAACGTGGCTTTTATTGTGGTCTTCACCGGGGAGTGCACGTTAAAGCTCATCGCCCTGCGACATTACTTCTTCACC NewZebra CCTATGAGTCCACATTTTGAACATGTCCTCTCAGTGGGCAACTTGGTGTTCACAGGAATCTTCACAGCTGAAATGGTGTTCAAGCTTATAGCTATGGACCCTTACTACTACTTCCAGGTGGGCTGGAACATTTTTGACAGCATCATTGTCACACTCAGCCTGGTGGAGTTGGGACTGGCCAACGTTCAGGGATTGTCCGTTCTAAGGTCCTTTCGTTTGCTACGTGTCTTCAAACTGGCTAAATCTTGGCCCACCCTTAACATGCTGATCAAGATCATCGGCAACTCAGTGGGTGCTCTAGGGAACCTAACACTTGTTCTGGCCATCATTGTCTTCATCTTTGCCGTGGTGGGCATGCAGCTTTTTGGAAAAAGCTACAAGGACTGCGTTTGTAAGATCTCTGAGGATTGCGAGCTGCCCCGCTGGCACATGAACGACTTCTTCCACTCATTCCTCATCGTCTTTCGGATCTTATGTGGAGAGTGGATTGAGACCATGTGGGACTGCATGGAGGTGGCAGGAGCTAGCATGTGTTTGATAGTCTTCATGATGGTCATGGTCATCGGAAACCTTGTGGTGCTGAATCTGTTTCTGGCCCTGCTGCTTAGCTCCTTCAGTGGAGATAACCTGTCTGGAGGTGATGATGATGGAGAGATGAACAACCTTCAGATTGCCATTGGCCGCATCACCAGGGGTATCGATTGGGTTAAAGCCTTAGTTGCCAGTATGGTGCAACGGATTCTGGGAAAGAAACCTAAAATGGCAGATGGTCTGACCAACTGTTTGACATTGACTGTACCTATTGCTCGTTGTGAGTCTGATGTGGAGGGTGACTCTTCGGTTTGTAGCACAGTGGACTACCAGCCTCCAGAACCTGTAGAAGAAGAGGAACCAGAACCTGAAGAACCAGAGGCCTGTTTCACAGAGGGCTGTATTAGGCGATGTGCATGTTTGAGTGTTGACATCACAGAAGGATGGGGTAAAAAATGGTGGAACCTCAGAAGGACATGCTTCACCATCGTTGAGCATGATTACTTTGAGACCTTCATCATCTTTATGATCCTCCTTAGCAGTGGAGCACTGGCTTTTGAGGATATCAACATTGAGAGGCGCAGAGTGATCAAGATCATTCTGGAGTATGCTGATAAAGTCTTTACATATATTTTTATAGTGGAGATGTTACTGAAGTGGGTGGCATATGGCTTCAAGACCTACTTCACTAATGCATGGTGCTGGCTGGACTTCCTCATTGTGGATGTGTCTCTGGTCAGTTTAACGGCTAATTTAATGGGCTATTCTGAGCTGGGGGCAATCAAATCTCTCAGGACACTTAGAGCTCTTCGTCCACTTCGAGCCCTATCCAGATTTGAAGGAATGAGGGTGGTAGTGAATGCACTTGTAGGTGCCATTCCCTCTATTTTTAACGTGCTCCTGGTGTGTCTGATATTCTGGCTCATCTTCAGCATTATGGGGGTCAATCTGTTTGCCGGAAAATTCTACCACTGCATCAACACCACCACAGAGGAACGGATCCCCATGGATGTAGTCAACAACAAGAGTGACTGCATGGCACTGATGTACACCAACGAGGTGCGATGGGTCAATGTCAAGGTCAACTACGACAACGTGGGACTCGGCTACCTCTCTCTGCTGCAGATTGCCACATTCAAAGGCTGGATGGATATCATGTATGCTGCAGTGGATTCTAGAGAGGTGGATGAGCAGCCATCATATGAAATCAACCTTTACATGTACCTTTATTTTGTTATTTTCATCATTTTTGGCTCCTTTTTTACTCTCAACCTCTTTATTGGTGTCATCATTGACAACTTCAATCAGCAAAAATCAAAGTTTGGAGGGAAAGACATTTTCATGACTGAGGAACAGAAAAAGTACTACAATGCCATGAAGAAGCTGGGTGCAAAGAAACGTCCAAAACCTATACCTCGACCATCAAATATTATCCAGGGTTTGGTGTTTGACTTCATATCAAAACAGTTCTTTGACATTTTTATCATGGTGCTAATCTGCCTCAACATGGTGACCATGATGATAGAGACGGATGATCAGAGTGCTGAGAAAGAATATGTCCTGTACCAGATCAATCTGGTCTTCATCGTCGTCTTCACAAGCGAATGTGTACTTAAATTATTTGCACTCAGACAGTACTTTTTCACT SterNa6 CCCATGAGCGAAACCTTTCAACACGTGCTCACCATAGGGAACCTGGTGTTTACTACCATCTTTACGGCTGAAATGGTGTCGAAGATCATCGCCCTGGACCCTTACTACTACTTCCAGGTGGGCTGGAACATCTTCGACTGCATCATCGTCACTCTCAGTCTGGTGGAGCTAAGCCTATCCAACATGCCGGGCCTGTCTGTGCTCAGATCCTTTCGTTTGATGCGTATTTTCAAGCTGGCCAAGTCCTGGCCCACGCTCAACATGCTGATCAAGATCATCGGCAACTCAATGGGCGCCCTGGGGAACCTGACCTTCGTGTTGGCCATCGTCATCTTCATCTTCGCCGTGGTGGGCTTCCAGCTGTTCGGGAAGAGCTACAAGGACAACGTGTGCAAGGTCAGCGCGGACTGCACGCTGCCTCGCTGGCACATGAACGACTTCTTCCACTCCTTCCTGATCGTGTTTCGCATCCTGTGCGGCGAGTGGATCGAGACCATGTGGGACTGCATGGAGGTGGACGGAGTGCCCATGTGCCTCACCGTCTTCATGATGGTCATGGTCATCGGAAACCTGGTGATGCTGAACCTGTTCCTTGCCTTGCTTCTCAGCTCATTCAGCTGCGACAATCTTGCCGCGCCAGACGATGACAGTGAAGTTACCAACATCCAGATCTCCATTGTGCGCATCAGCAGAGGGATAAGCTGGGTGAAGAAATTCATTGTAGGCACAGCCTGGTGGATCATGGGCAGGAAGCCCAAGATTGTAGATGGGATTACCAACTATGTTGTTCTGAATGTGCCTATTGCCAAGGGGGAGTCTGAGGTTGAGGATGACTCTTCGATTTGCAGTTCAGTGGACTACGAGCTTCTACAACCCGAGGAGGAAAAGGAA---GAGCCTGTTGATCCAGAAGCCTGTTTTACAGAAAACTGTGTGAGGTACTTTCCATGTCTGGATGTGGACATCACACAGGGGAAAGGGAAGATCTGGTGGAACCTCCGCTGCACCTGCTACAACATCGTGGAACATCACTATTTTGAAAACTTTCTCATCTTCATGATTCTCCTCAGTAGTGGAGTACTGGCATTCGAGGATGTTAATATCGAACGCCGCAGGGTCATTAAGACCATGTTGGAGTATGCAGACATAGTCTTCACATATATTTTCGTGGTGGAGATGTTTCTGAAGTGGACTGCATATGGGTTTAAAGCGTACTTCACCAGTGCCTGGTGCTGGCTGGATTTTTTTATTGTTGATGTGTCAGTTATTAGCTTAGTAGCCAATGTGTTGGGCTATGCAGAGCTGGGACCAGTCAGATCGCTCAGAACTTTCAGGGCTCTTCGACCTTTACGTGCCCTTTCCAGATTTGAAGGAATGAGGGTGGTAGTGAATGCATTGCTCGGTGCCATCCCCTCCATCATGAACGTCCTATTGGTGTGTCTGATCTTCTGGCTGATCTTCAGCATCATGGGGGTCAACTTGTTTGCGGGAAAGTTCTACCGCTGCATTAACACCACCACAGATGAGGTTCTGTCCACAGAGCAAGTGAACAACAGGAGTGAATGCATGGCACTAATGCACACTAATGAGGTGCGTTGGGTCAACCTTAAGGTCAACTACGACAATGTGGGCCAGGGATATCTCTCCTTGCTTCAAGTGGCCACATTTAAAGGGTGGATGGGCATCATGTATGGTGCAGTGGACTCTAGAGAGGTAGAGGATCAGCCATCATATGAGATTAACCTCTACATGTACCTGTACTTTGTCATCTTCATCACATTTGGATCCTTTTTTATCCTCAACCTTTTCATTGGTGTCATCATTGACAATTTTAACCGGCAAAAACAAAAGTTAGGAGGAGATGACCTCTTTATGACAGATGAACAAAAAAAGTATTATGCTGCCATGAAGAAGCTGGGTTCCAAGAAACCACTCAAACCTATACCCCGTCCTTCGAATATGGTTCAAGGGGTGGTGTTCGACTTCATCTCCCAAAAGTTCTTTGACATTTCCATCATGGTTCTCATCTGCCTCAACATGGTGATCATGATGGTGGAGGCGGACGACCAGAGTGAAGAGAAAGAGAATGTCCTCTATCAGATCAATATCATATTTATTGTCNTCTTCACCGGAGAGAGTTTACTCAAGTTGTTTGGACTTAGACATTACTTCTTCACT eelNa6 CCCATGAACGAAAGCTTTCAGAGTCTGCTCAGTGCAGGAAACCTGGTGTTTACCACTATCTTTGCGGCTGAAATGGTGTTGAAGATCATTGCCTTGGACCCCTACTACTACTTCCAGCAGACGTGGAACATATTTGACAGCATCATTGTCAGTCTCAGTCTGTTGGAGCTTGGACTATCCAATATGCAAGGAATGTCTGTGCTCAGATCCTTACGTTTGCTGCGTATCTTCAAATTGGCCAAGTCCTGGCCCACGCTCAACATTCTGATCAAGATAATCTGCAACTCGGTGGGCGCTCTGGGCAACCTGACCATTGTGCTGGCCATTATCGTCTTCATCTTCGCCTTGGTGGGCTTTCAGCTGTTCGGAAAGAACTACAAGGAGTACGTGTGCAAGATCTCTGATGACTGTGAGCTGCCCCGCTGGCACATGAATGACTTCTTCCACTCCTTCCTGATTGTGTTCCGTGCCTTGTGTGGCGAGTGGATTGAGACCATGTGGGACTGCATGGAGGTGGGCGGAGTTCCTATGTGCCTCGCCGTCTACATGATGGTCATAATCATTGGGAACCTGGTGATGCTGAACCTTTTCCTTGCCTTGCTTCTAAGCTCATTCAGCAGCGACAATCTCAGTTCAATTGAAGAAGATGATGAAGTTAACAGCCTCCAGGTTGCCTCTGAGCGCATTAGTAGGGCAAAAAACTGGGTGAAGATCTTCATCACTGGCACAGTCCTGTGGATCCAGGGCAAGAAGCCCAAGATTGTAGATGGGATAACCAACTGTGTAACTCTGAATCTACCCATTGTAAAGGGGGAGTCAGAGATCGAAGAAGACTCTTCAGTTTGTAGTACAGTGGACTATAGTCCTTCAGAACAAGAGGAGCCAGAGGAACTAGAGTCCAAAGATCCAGAAGCATGTTTTACAGAAAAATGTATATGGCGATTTCCTTTTCTGGATGTGGACATCACACAGGGGAAAGGGAAGATCTGGTGGAACCTACGTAGGACCTGCTACACCATCGTGGAGCATGACTACTTTGAAACCTTCATCATATTCATGATTCTCCTCAGTAGTGGAGTTCTGGCCTTTGAGGACATTTATATTTGGCGTCGCAGGGTGATTAAGGTCATCTTGGAGTATGCAGACAAAGTCTTCACATATGTCTTCATAGTAGAGATGTTACTTAAGTGGGTTGCATATGGGTTTAAAAGATATTTCACTGATGCCTGGTGCTGGCTCGACTTTGTAATTGTTGGTGCATCAATAATGGGCATAACATCCAGTTTGTTGGGCTATGAAGAGCTGGGAGCAATCAAAAATCTCAGAACTATCAGGGCTCTTCGCCCTTTACGTGCCCTTTCCAGATTTGAAGGAATGAAGGTGGTAGTGAGAGCATTGCTTGGTGCCATCCCCTCCATCATGAACGTGCTGCTGGTGTGTCTGATGTTCTGGCTCATCTTCAGCATTATGGGGGTCAATTTGTTTGCTGGAAAGTTCTACCGCTGCATTAACACCACCACAGATGAGATTCTGCCCGTGGAGGAAGTGAACAACCGGAGTGACTGCATGGCACTAATGTACACTAACGAGGTGCGCTGGGTCAACCTTAAGGTCAACTATGACAATGCGGGCATGGGATACCTCTCCCTGCTACAAGTGTCTACATTTAAAGGCTGGATGGACATCATGTATGCTGCAGTGGACTCCAGAGAGGTAGAGGATCAGCCAATCTATGAGATTAATGTCTACATGTACCTGTATTTTGTCATCTTCATCGTATTTGGAGCCTTCTTTACCCTCAACCTTTTCATTGGTGTCATCATAGACAACTTCAACCGTCAAAAGCAAAAGTTAGGAGGAGAAGATCTCTTTATGACAGAAGAGCAGAAGAAGTACTACAATGCCATGAAGAAGCTGGGTTCGAAGAAAGCTGCCAAATGTATACCCCGCCCTTCGAATGTGGTTCAAGGTGTGGTGTACGACATAGTCACCCAACCATTCACTGATATTTTCATCATGGCTCTCATTTGCATCAACATGGTGGCTATGATGGTCGAGTCGGAGGACCAGAGTCAAGTGAAGAAGGACATTCTCTCTCAGATCAATGTCATATTCGTTATCATCTTCACTGTAGAGTGCTTGTTAAAGCTACTTGCACTTAGACAGTACTTCTTCACT catNa6 CCCATGAGTTCGAACTTTGAACACGTGCTCAGTGTTGGCAATTTGGTGTTCACTGGTATTTTCACGGCTGAAATGGTGTTCAAGCTCATTGCCTTGGACCCCTTCTACTACTTCCAGGTTGGCTGGAACATATTTGACAGCATCATCGTCACTCTTAGCCTGGTGGAGTTAGGCCTGGCCAATGTGCAGGGTCTGTCTGTACTCAGATCCTTTCGTTTGCTGCGAGTCTTTAAGCTGGCTAAATCCTGGCCCACGCTCAACATGCTGATCAAAATCATTGGAAACTCTGTGGGTGCTCTGGGGAACCTGACTCTGGTGCTGGCCATCGTCGTCTTCATCTTCGCCGTCGTAGGCATGCAACTTTTTGGCAAGAGCTACAAGGACTGCGTGTGTAAGATTGCAGAGGACTGCGAACTGCCCCGCTGGCACATGAACGATTTTTTCCATTCGTTTCTCATTGTCTTCCGCATCCTTTGTGGTGAATGGATTGAAACCATGTGGGACTGCATGGAGGTGGCTGGAGCAGGCATGTGCCTTGTGGTTTTCCTTATGGTCATGGTCATAGGAAACCTGGTGGTGCTGAACCTGTTCCTTGCCTTGTTGCTCAGCTCTTTCAGCGGGGACAATCTCTCAGCAGGTGATGAAGATGGTGAAATGAACAATCTCCAGATTGCCATCGGCCGCATCACCAGGGGCATTGACTGGGTCAAATCCTTCATCATTGGCCTTGTACAGCAGATACTTTGCAGGAAGCCTAAGATGGCAGATAGGTTGACCAACTGTCTGACCCTGAATGTACCAATTGCCAAAGCTGAGTCTGATGTTGAAGAAGACTCTTCAATGTGTAGCACAGTGGACTATAGACCTCCAGAATCCGAGGAGGAAGAGGAACCAGAACCTGTTGAGCCAGAAGCCTGTTTTACTGAAAACTGTGTGAGACGATGTCCATGTCTGAATTTGGACATCACTCAGGGGAGGGGAAAGAGTTGGTGGAATCTGCGCAGAACTTGCTACACCATAGTGGAGCATGATTACTTTGAAACCTTCATCATCTTCATGATTCTCCTCAGTAGTGGTGCACTGGCCTTTGACGACATTTACATTGAGCGTCGCAGGGTGATTAAGATTATCTTGGAATATGCAGACCAAGTCTTCACATATATTTTTGTCATAGAGATGTTACTGAAATGGGTTGCGTATGGCTTCAAGACATACTTCACCAATGCCTGGTGCTGGCTGGACTTTTTCATCGTTGATGTGTCACTTATCGGTTTAACGGCAAATCTGTTGGGCTATTCAGAGCTGGGACCAATAAAATCTCTCAGAACTCTTAGGGCGCTTCGACCTTTACGTGCCCTGTCCAGATTTGAAGGAATGAGGGTGGTAGTGAACGCATTGCTGGGTGCCATTCCTTCCATCATGAATGTACTCCTGGTGTGTCTAATATTCTGGCTGATCTTCAGTATTATGGGGGTCAACCTGTTTGCTGGGAAATACTACCGCTGCATTAATACCACCACAGAAGAACTTTTACCCATCGAGCAAGTGAACAACATGAGTGATTGCATAGCACTAATGCACACTAAAGAAGCACGCTGGGTCAATGTCAAGGTCAACTTTGACAATGTGGGCTTGGGTTACCTTTCCCTGCTACAAGAGGCTACATTTAAAGGCTGGATGGACATTATGTATGCTGCAGTGGATTCCAGAGAGGTGGAAGAACAGCCATCATATGAGATTAACATATATATGTATCTGTATTTTGTCATCTTCATCATCTTTGGCTCCTCCTTCACCCTCAACCTCTTCATTGGTGTCATCATTGACAACTTTAATCAGCAAAAGCAAAAGTTTGGTGGGGAAGATCTCTTCATGACAGAGGAGCAGAAAAAGTACTACAATGCCATGAAAAAGCTTGGTTCCAAGAAGCCCGTCAAACCCATACCTCGCCCTGCGAATATGATCCAGGGCATAGTGTTTGACTTCATCTCTCAGCAGTTCTTTGACATTTTCATCATGGTGCTCATTTGCCTCAACAAGGTTACCATGATGATTGAGACAGATGACCAAAGTGCAGAGAAAGAATATGTTCTCTATCAGATCAACTTAATCTTCATTGTTGTCTTCACTGGGGAGTGCATCCTCAAAATGTTTGCACTGAGACAATACTTTTTCACT AptNa6 ---------------------------CTCACTGTGGGGAACCTGGTGTTTACTGGCATCTTTACGGCTGAAATGGTGTTTAAGCTCATTGCCATGGACCCCTACTACTACTTCCAGGTGGGCTGGAACATCTTCGACAGCATCATCGTCACCCTCAGTCTGGTGGAGCTGGGGCTAGCCAACGTGCAGGGTCTGTCTGTGCTCAGGTCCTTCCGTTTGCTGCGTGTCTTCAAGTTGGCCAAGTCCTGGCCAACGCTCAATATGCTCATCAAGATCATTGGCAACTCGGTGGGAGCCCTGGGCAACCTGACACTGGTGCTGGCCATTATTGTCTTCATCTTTGCCGTGGTGGGCATGCAGCTATTTGGGAAGAGCTACAAGGACTGCGTGTGCAAGATTGCGCTGGACTGCGAGCTTCCCCGCTGGCACATGACGGACTTCTTCCACTCCTTCCTGATCGTGTTCCGCATCCTATGCGGCGAGTGGATTGAGACCATGTGGGACTGCATGGAGGTGGCTGGACCGTCCATGTGCCTCATCGTCTTCATGTTGGTCATGGTCATTGGGAACCTGGTGGTGCTGAACCTGTTCCTTGCATTGCTTCTCAGCTCATTCAGCGGTGACAATCTCTCGGCAAGCGACGATGACAGTGAGATTAACAACCTCCAGATCGCCACAGGGCGCATCAGCAGAGCGATTGGCTGGGTGAAGAACTTTATCATCAGCACAGTCCAGTGGGTTCTGGGCAGAAAGCCCAAGATGGTGGATGGCATGACCAACTGCGTAGTCCTGAATGTGCCCATTGCCAAGGGGGAATCTGAGATTGAAGGAGACTATTCAGTTTGCAGTACAGCAGACTACAGACCTCCAGAACCCGAGGAGGAAAAGGTACCAGAGACCAATGATCCAGAAGCCTGCTTTACAGAAAATTGTGTGAGGCGATTTCCTTGTCTCAATGTGGACATCACCCAGGGGAAAGGGAAGAGCTGGTGGAACCTACGCAGAACCTGCTACATCATCGTGGAGCATGACTACTTTGAGACCTTCATCATCTTCATGATTCTCCTCAGTAGCGGAGCACTGGCTTTCGAGGACATTTATATAGAGCGTCGCAAGATGATTAAGATCATCTTGGAGTACGCAGACAAAATCTTCACCTATGTTTTCATAATGGAGATGTTACTGAAGTGGGTTGCTTATGGGTTTAAAACGTACTTCACCAATGCCTGGTGCTGGCTGGACTTTCTTATTGTTGATGTGTCAATTATTAGCTTAACAGCCAATCTGTTGGGCTATTCAGAGCTGGGACCAATCAAATCTCTCAGAACACTCAGGGCTCTTCGACCGCTACGTGCCCTTTCCAGATTTGAAGGAATGAGGGTGGTAGTGAATGCGTTGGTTGGCGCCATCCCCTCCATCATGAACGTGCTGCTGGTTTGTCTGATCTTCTGGCTCATCTTCAGTATCATGGGGGTCAACTTGTTTGCTGGAAAGTTCTACCGCTGCATTAACACTACCACCGAGGAGCTTCTGCCCATGGAGGAAGTGAACAACAGGAGTGATTGCATGGCGCTAATGCACACTAATGAGGTGCGCTGGGTCAATGTCAAGGTGAACTACGACAACGTCGCCCTGGGATACCTTTCCCTGCTGCAAGTGGCTACATTTAAAGGCTGGATGGACATCATGTATGCTGCAGTGGACTCCAGAGAGGTAGAGGAGCAGCCATCATATGAGATTAACCTCTACATGTACCTGTACTTTGTCATCTTCATCATATTGGGATCCTTTTTTACCCTCAACCTTTTCATTGGTGTCATCATAGACAACTTCAACAGGCAGAAGCAAAAGTTTGGAGGAGAAGATCTCTTTATGACGGAGGAGCAGAAGAAGTACTACAATGCCATGAAGAAGCTGGGATCCAAGAAGCCTGTCAAACCTATACCCCGTCCTACGAATGTTATTCAAGGTGTGGTGTTCGACCTCATTTCCCAGCAGTTCTTTGATATTTTCATCATGGTTCTCATTTGCCTCAACATGGTGACCATGATGGTGGAGACTGATGACCAGAGCAAAGAGAAAGAGCACATCCTCTATCAAATCAACGTCATATTCATTGTCGTCTTCACTGGAGAGTGTTTGCTCAAGATGTTTGCACTGAGGCAGTACTTCTTCACT PinniNa6 CCCATGAGTGAAACGTTTGATTACGTCCTCAGCACAGGGAACCTGGTGTTTACCATCATCTTTGCAGCTGAAATGGTCTTGAAGCTCATTGCCATGGACCCCTACTACTACTTCCAGCAGACGTGGAACATCTTTGACTTTTTCATTGTCTCACTCAGTCTGGTGGAGATGGGACTGGCTAACATGCAGGGGCTGTCAGTGCTTAGGTCCTTTCGACTGCTGCGTATCTTTAAGTTGGCCAAGTCCTGGCCCACGCTCAATATTCTGATCAAGATCATCTGCAACTCGGTGGGCGCCCTGGGAAACCTGACCATCGTGCTGGCCATTATCGTCTTCATCTTCGCCTTGGTGGGCATGCAGCTGTTCGGGAAGAATTACAAAGAGTTTGTGTGCAAGATCAGTGCAGACTGTACGCTGCCTCGCTGGCATATGAATGACTTCTTCCATTCCTTCCTGATTGTGTTCCGCTGCCTGTGCGGCGAGTGGATTGAGACTATGTGGGACTGTATGGAGGTGGGCGGTGTGCCCATGTGCCTCAGCGTTTACATGATGGTCATAATCATCGGGAACCTGGTGGTGCTGAACCTGTTCCTTGCCTTACTGCTAAGCTCATTCAGTGGTGACAATCTCACTGCAAACGATGATGACCAAGAGGATAACAACATCCTGATTGCAGCTGAGCGGATCAGCAGGGCAAAACTCTGGGTGAAGGGGTTCATAATACGGACGGTCTTGGGGATGCTGGGCAAGGAGCCAAAGATTGTGAATGGGCTAGCCAACGGTGTAGTTCTGAATGTGCCCATTGCCAAGGGCGAGTCTGAGACTGAAGATGACTCTTCAGTCTGCAGTACAGTGGACTACAGTCCTCCAAATCCAGAGGAACCCGAGGAACCAGAACCCGATAATCCAGAAGATTGTTTAACGGAAGAATGTGTGTCACGATTTCCTTGGCTGAATGTGGACATAACACAGCCAAAAGGGAAGAGTTGGTGGAACCTTCGTAGGACATGCTACGTCATCGTAGAGCATGACTACTTTGAGACTTTCATCATCTTCATGATTCTCCTCAGTAGTGGAGCACTGGCTTTCGAGGACATTTATATTGAGCGTCGCAGGGTGATTAAGATCATCTTGGAGTATGCGGACAAAGTCTTCACATATATTTTCATAGCAGAGATGTTACTGAAGTGGGTTGCATATGGGTTTAAAAAGTACTTCTCCGACGCCTGGTGCTGGTTAGACTTTCTAATTGTTGATGTGTCAATAATTAGCTTAACAGCCAATTTGTTGGGCTATTCAGAGTTGGGACCAATCAAATCTCTCAGAACTCTCAGGGCTCTTCGACCTTTACGTGCACTTTCCAGATTTGAAGGAATGAGGGTGGTAGTCAAAGCATTGGTTGGCGCCATCCCCTCCATCGTGAACGTGCTGCTGGTATGTCTCATGTTCTGGCTCATCTTCAGCATTATGGGAGTCAACTTGTTTGCTGGAAAGTTCTACCGCTGCATTAACACTACCACAGAAGAGACCATGCCCYTGGAAGAAGTCAACAACCGCAGTGACTGCAATGCACTTATGTACACTAATGAGGTGCGATGGGTCAACCTTAAGGTCAACTATGACAATGCAGGCATGGGATACCTCTCCCTGCTACAAGTGGCAACATTTAAAGGTTGGATGGACATCATGTATGCTGCAGTGGACTCCAGAGGGGTAGAGGATCAGCCGATATACGAGATTAACGTCTACATGTACCTGTATTTTGTCATCTTCATCGTATTTGGATCCTTTTTCACCCTAAACCTCTTCATTGGTGTCATCATAGACAACTTCAACCGTCAAAAGCAAAAGTTAGGAGGAGATGATCTCTTTATGACAGAAGAACAGAAAAAGTATTATGATGCCATGAAGAAGCTGGGTTCCAAGAAACCTGTCAARGTTATACCACGCCCTTCGAACAAGATTCTGGGTGTGTTGTATGACATAGTCAACCAACGGGTCACTGATATTTTCATCATGTCTCTCATTTGGCTAAACATGGTTACCATGATGGTGGAGACAGATGACCAGAGCGAAGAAAAGAAGAATGTTCTCTATCAGATCAATTTAATATTCATTATCATCTTCACTGGAGAATGTCTGCTCAAGTTGCTTGCACTAAGACATTACTTCTTCACT tetra CCCATGACCCAGGAGTTCGACTACATGCTTTCAGTGGGAAATCTGGTTTTCACAGGAATTTTTGCAGCAGAAATGTTCTTCAAGCTGATCGCCATGGATCCGTACTACTATTTCCAAGTTGGCTGGAACATTTTTGACAGCATCATTGTCACCCTCAGCCTGGTAGAGTTGGGGCTTGCGAACGTCCAGGGCCTGTCTGTCCTCAGGTCCTTCCGCCTGCTCCGTGTCTTCAAACTTGCCAAATCCTGGCCCACACTCAACATGCTGATCAAGATTATTGGGAGCTCAGTTGGAGCGCTAGGGAATCTGACGTTGGTGCTGGCCATCATCGTCTTCATCTTCGCTGTGGTGGGGATGCAGCTCTTTGGCAAAAGCTACAAGGACTGCGTGTGCAAGATTTCCACGGAGTGCGAGCTGCCGCGCTGGCACATGAATGACTTCTTCCACTCCTTCCTCATCGTCTTCCGCATCCTGTGTGGCGAATGGATCGAGAACATGTGGGCCTGCATGGAAGTGGCTGGAGCTGGGATGTGCTTAGTTGTCTTCATGATGGTCATGGTGATTGGAAACCTCGTGGTGTTGAACCTCTTCCTGGCCCTGCTGCTCAGCTCGTTCAGCGGGGACAATCTGTCCATCGGAGAGGACGATGGAGAGATGAACAATCTTCAGATTGCCATCGGCAGAATCACACGAGGTGGAAACTGGCTCAAGACCCTTGTCATCAGAACGGTCCTGCAGCTTCTCGGTAGGGAGCAGAAAACGGCAGATGGGATAGCTAACTGTCTTGTTATCAACGTCCCCATCGCCTTGGGGGAGTCAGACTCTGAAGGCGAGTCTTCAGTGTGCAGCACAGCAGACTATCGGCCCCCCGAGCCTGAGGAAGAGGAAGAGCCGGAACCACTGGAGCCAGAGGCCTGCTTTACTGACAACTGCGTCAAACACTGGCCTTGTCTGAACGTGGACGTCACCCAAGGTCAAGGGAAGAAGTGGTGGAACCTCCGCAAGACCTGCTTCACAATCGTAGAGCATGACTGGTTTGAGACCTTCATCATCTTCATGATCCTCCTCAGCAGCGGAGCCCTGGCCTTTGAAGATATATACATCGAAAGACGAAGAACCGTCAAAATTATCCTGGAGTTTGCCGACAAAGTTTTCACCTTCATCTTTGTCCTTGAGATGGTGCTGAAATGGGTGGCCTATGGCTTCAAGACCTACTTCACCAACGCCTGGTGCTGGTTGGACTTTTTCATTGTAGACATTTCCCTGATCAGTTTATCGGCCAACCTGATGGGCCTCTCTGACCTGGGACCAATCAAATCTCTCAGAACACTCCGGGCACTGAGGCCTCTTCGAGCTCTGTCCAGATTTGAAGGGATGAGGGTGGTGGTGAACGCTCTTATCGGAGCCATTCCCTCCATCTTCAACGTGCTGCTGGTGTGCCTGATCTTCTGGCTCATCTTCAGCATCATGGGAGTGAACCTGTTTGCGGGGAAGTTCTACCACTGCATCAACACCACCACACAGGAGCTCTTCCCCATCTCTGTGGTCAACAACAAGAGCGACTGCATGGCCGTCCAGGCCACGCAGGAGGCCCGCTGGGTCAACGTCAAGGTCAACTACGACAACGTGGGAAAAGGCTACCTGTCGCTGCTTCAAATCGCCACTTTTAAAGGCTGGACGGCCATTATGTATGCTGCAGTAGATTCAAGAGAGGTGGAAGAGCAACCTTCCTATGAGATCAACCTGTACATGTACATCTACTTTGTCATCTTCATCATCTTTGGCGCTTTCTTCACGCTCAACCTGTTCATCGGCGTCATCATCGATAACTTCAACCAGCAGAAGAGAAAGATA---AACAAAGACATCTTCATGACGGAGGAGCAGAAAAAGTACTACGAAGCCATGAAGAAACTCGGCTCCAAGAAGCCGCAGAAGCCGATCCCACGTCCGACCAACCTCATCCAGGGAATGGTGTTTGACTTCATCAGTCAGCAGTTCTTTGACATCTTCATCATGGTGCTCATCTGCCTCAACATGGTGACCATGATGGTGGAGACGGACGACCAGAGCCCCGAGAAGGAGGATTTCCTCTTCAAAGTGAACGTGGCTTTTATCGTGGTCTTCACGGGGGAGTGCATGCTGAAGCTCATCGCCCTGCGACAGTACTTCTTCACC ; end; begin sets; charset 1st = 1-2178\3; charset 2nd = 2-2178\3; charset 3rd = 3-2178\3; charpartition byPos = 1stpos:1st, 2ndpos:2nd, 3rdpos:3rd; end; garli-2.1-release/example/partition/exampleRuns/dna+Mkv/000077500000000000000000000000001241236125200233305ustar00rootroot00000000000000garli-2.1-release/example/partition/exampleRuns/dna+Mkv/dnaPlusGapCoding.nex000066400000000000000000015215661241236125200272460ustar00rootroot00000000000000#NEXUS BEGIN TAXA; TITLE Untitled_TAXA_Block_1; DIMENSIONS NTax = 64; TAXLABELS temporariaDMH84R1 boyliiMVZ148929 luteiventris_MT_MVZ191016 luteiventris_WA_MVZ225749 muscosaMVZ149006 auroraMVZ13957 cascadaeMVZ148946 sylvaticaMVZ137426 sylvaticaDMH84R43 septentrionalesDCC3588 grylioMVZ175945 okaloosae clamitansJSF1118 heckscheriMVZ164908 catesbianaX12841 catesbianaDMH84R2 virgatipesMVZ175944 maculataKU195258 vibicariaMVZ11035 warszewitshiiJSF1127 palmipesVenAMNHA118801 palmipesEcuKU204425 Sp_1_ecuadorQCAZ13219 bwanaQCAZ13964 vaillantiKU195299 julianiTNHC60324 sierramadrensisKU195181 psilonotaKU195119 zweifeliJAC7514 tarahumaraeKU194596 pustulosaJAC10555 pipiensJSF1119 pipiensY10945 dunniJSF1017 montezumaeJAC8836 sp_2_mex_JSF1106 chiricahuensisJSF1063 subaquavocalis chiricahuensisJSF1092 palustrisJSF1110 areolataJSF1111 sevosaUSC8236 capitoSLU003 spectabilisJAC8622 omiltemanaJAC7413 sp_3_MichoacanJSF955 tlalociJSF1083 neovolcanicaJSF960 berlandieriJSF1136 blairiJSF830 sphenocephalaUSC7448 utriculariaJSF845 forreriJSF1065 magnaocularisJSF1073 sp_7_JaliscoJSF1000 yavapaiensisJSF1085 oncaLVT3542 sp_8_PueblaJAC9467 macroglossaJAC10472 macroglossaJSF7933 taylori286 sp_4_Panama sp_5_CostaRichDMH86_210 sp_6_CostaRicaDMH86_225; END; BEGIN CHARACTERS; TITLE Untitled_DATA_Block_1GapsAsMissing; LINK TAXA = Untitled_TAXA_Block_1; DIMENSIONS NChar=3211; FORMAT Datatype=DNA; Matrix temporariaDMH84R1 ACA?CTTGT?A??AGTG?GC?T????????GACCTGTAG??TT???T?TT?AACAAACTA????GCG??????????GGTG?ACAAACAT??GGT?TTTAATCT??TG?TGTT?GA??TT???TT?A???????T?AC???????C?AA?????????CCAACT?ACAA??CCAGTAACGAC????G??CCCGAATATG??C?TA?AT?TAT?AT??CGG???AT?ACT?????????T??ACACTGTCGTTG?CTATCGTTAT???CTTGGTTGTA?TCT??????????A?CGATATA?ATGGAAT?CTGAGAT???AC????CTCC???TCAACTCGC??CT????????CTCTAAAT?????TG?T??C?AATG??TAGATACTA???????ATAAAACTTTC??C????????GCCATTAC??T??????????AAAAATTGACAGTA??TACA?ACATCACAGG?AT??T??????????????????TC?CTGCAATGTC?ACATTCCTG????C?TCCC?CGCAGCAA???C??CGC?TTATGAAGTCTCTCTCATCAT?ATAT???C????TA?????AGGAC?????CA???T?TG?TGAA?TTATTT???T????CAT?C???CC?TGCCCCA??A????TTAAACGATCTTAA??????T?CTC?TAG???TAGTT??ACAA???????ATAGT?ACGAATCTAGTTCT?CT?????????TGGA????TT?CA???AAA?AC???T??????T?????AGCTCG????AA???????CCCTT?TCCA????CCAGTGATC??G?CTTACCTTAACA??CCTAG?GTAA?????AAAATC?TAG?GCA??TA?TATACCTAGAA?TGA??????CTG?CGATCTATTGCAAGTATTTAA???TTCA??????ACCT?TAGT??????AT?GCATTAA?T?????GTCTACATAAAAAT????AA?CC?ACATAAT??ATATT??TCTAGAGAATT???GTAGCACC???GGGATCACT?C??AACCCA????CA?CAAA?ATT?????A?AACCCC??GT?????TAA???C??CAT???GGG???TA??????ACTG??TTCAA?CATCGA?ACGGCTTCGAGT?AATTATAAAAAGAAA?AT??GCATTAGA??TG?????????TAAAGA??????A??GA?????????ACTATT??TCCAATTAT??GCCCTGTAT??TTA?CGGTAAATGTAAT?C?TT?ATA?TATCATGG??A?ATC?AATTCCTATA????????CA??A???????????????????????C?GA?AA??????GATC??????AA?TAT?TGG??????CCAAAC??CTAGCT??TCAAA???GAAAAGC??TTCC?T????A?GC??????AATGCAAA?TAACCA?????TTAAG?CCAA???????TGT??ATACGTTCACAA????TGGCAAACTC?C???????TGAG??????TTTAAATCT??G?CC?TCGTG??GAAGATTCTTG????GTC?T?A???AAT??CGACTAC???????A?CACAG?TTA?A?TCTCTCC?TTTTATA???T??AAA???????????AGCATATTGCAT??????TAATTA?T?ATAAATTGAAGTAA?GCTCTTACTGAATGACTAGG?GA?CTC??TGAACTGATTA???GGAACA?TACGTTTGGTATA?ACCGTAT?????????GTT?T??T??TCTAGCTGAAGCGCTGACACTACCT?AAT????????????TATCATACGCT????GGACCCAAAACAA?CAAAA???AT????CG?ACT???A????A?????AACTATC?????T??T?????AATC????GCAGTATTAC?????ACAAGAATT??????GAAA?AGGAC??CAAA????GAAAACGAGCAAA?????????ACGA????CTACTGTCAG?GCT?GATAGTGATTAACT??GCT?ATAA?AACACTG?????CTAAGG?CCGAAATTA?AAAGTTGTGAAG???TTA?A?C?CTATGGACAT??A?AGAGT??A?GT?TCAA?AAC?????????G?????TTC?GTACTAAT????TTCAGT???AAA?T??????A????GT???????CTA????????????TCACGC??TCATAGTC??????????ACCATA?GT?TGT?A??TAATC???ATATTC?ACTGCTCT?ATA???GT?????AATACACG????????TTTGA?A??A?????TTTCC??????TAT?GTT?T?TTT????TC?TA?CGATA?TCA??AG??????????TT?T?TAGGA?TCTA????AAAAA??????????TGTGGATAGCTTATCGCT?TGAGATGA?ATAATAACCAAGTACGTACCA???TTTG?TC?TA?CTC??????AAA?ACT??????ACTCA?GC?????????AC?????G?????CT?????ATCCGG?TTTTTT????????A???TC?G?C??GT?A???ACTCCTGTATAA????????ACC?TT?CGA?ATGA????TACT?GATCC??????????TCA??CC????????A?TCTTGG??AGAATG?A???AATAGCG?GCGC????CCATTT????????TTAACTC???TA??GAT?AC??AT???????C????CACCCGATAG?GGTAAGGAAGTGTGTAGAGATAATA?GT?A?????????A???TACGGCATCCAGTCTACG?AATC?AC?TGCTAGT?GAA???????AACAATTC?A?TCGCAAACC?G?AT??CTT?CTATGTC??TTTA?CGTAC?CA?CTACAATTGA???AG????C?GA?CATTCTGAAA??A?GTA???TCATAT?GT?CTGGA??GCCTTAATCC????TTCTAGTA???TTTAA??CT?AACTTA????CAATGTTGTTA????AGATTC?CG??C??CATAGATCATATCATC?AATAT??????ATATA???????TTAGA???????TATTAATT????CGCTTA?????A?CTACTTCA???????T???TTG?ATA?TGA??AGCAGCCAAA????A??AGA??A?T?????????ATA?CCTGTAAAT??TGA?C????????CTAGC?CT?CTTAGG?A?A?C?????TTGATACGAGTATTA?G???GGTC?TTAACA boyliiMVZ148929 ACT?CCCGC?A??AGTG?GC?T????????GACCTGTAG??T?????????AACCAACTA????GTG???????????GTG?ACAAACCC??GGT?TTTAATCT??CG?TAAT?GA??TTGA?TT?A???????C?AC???????G?AA?????????CCCATT?ACAG??CCAGTAACGAC????G??CTCGAATATA??C?TA?GT?TAT?AT??CGG???ATAACC?????????AG?ACACCG??GCTG?ATAGC????????TTTAGTTGTA?TCT??????????A?CGGTATACATGGAA??CTGAGG????AC????CTCC???TTTACTCGC??CT????????CTTTAAAT?????TG?T??C?AATG??TTGATACTA???????CTGATATTTTC??C????????GCCATTAC??A???????????AAAGTTGACAAAA??CACA?ACATTACAGG?AT??T??????????????????TT?CTTAAAT?TC?ACATTCCTG????T?TTCC?TGCCTTAA???A??CGC?TTAAGAAGACTCACTAATCCT?A?AT???CC???TT?????AGCAC?????CA???T?TA?AGCG?TCATTT???T????CTT?C???TT?AATCCTA??AGAACTTAAA???TCTTCA??????TTCCC?TAG???TTG????TTAT???????ATAGT?ACGAATATAG?CCA?GT?????????TGTA????TC?CA???AAA?AC???T??????C?????AGCTTG????AA???????TTTAT?TTCA????CC??GGT?C??A?C????????ACA??CCTGG?GTAG?????AAAATC?CAA?GCA??TA?TATAATTAGAA?T?T??????CTG?CGATATACAGTCCGTACTCAT???TTTG??????ACCT?TAGT??????A??ACATT?A?T?????GTC????AAAAAAT????AA?CC?A?ACATT??ATAAT??TCTCGAGGACT???GAAGGAAT???GGGACGAAT?C??A??CTA????CA?TAAT?ATT?????AATACCCC??AT?????TAA???CTACAG???AGG??ATA??????ACTG??TTCAA?CATCAA?TCTG?TTTGA???AA?TATAATAAGCAC?AT??AGGTTAGA?CAG?????????CAAAGA??????A??GA?????????ACTATT??TCCAATTTT??GCCTTCTATTATTA?C?GTAAATGGAAT?C?TT?AAA?TACCATGG??C?ATC?AATTCCTATA????????CG??A???????????????????????C?GA?AA??????GATC??????AA?CAT?CGGTTCCCACTAGAC??GTAGAT??TTAAA???GAAAATT??TTCA?T????ATGC??????AATGGGCA?TCAACA?????TTAGG?CCAC???????CGT??ATACGTCCACAT????AGACAAACTT?C???????TGTG??????TATACATCC??G?CC?TAGTA??G?TAATTCTTA????GTT?A?A???AAT??CAACTGC???????A?CACAG?GTAGATTCTCTCC?TTTTATA???C??GAA???????????AACATATTGCAT??????T????A?C?ATAAATTGAAACAA?GCTCTTACTGAACGATTAAG?GATCTC??TGAACTGATTA???GGGACG?TACG?CTGGAATA?ACCGTAT?????????GTT?A??T??TCCAGACGAAGCACAGACAATTCTT?ACT????????????TATCATCTGCC????AGACTCAACAAAAGTAGAAGACAT????CG?GAT???ATTTAA?GC??AACTATC?????T??T?????AACA????GCAGTACTGC?????ACGAGAACT??????GAAAA?TGAC??CAAAC???AAAAACAAG?AAA?????????TTGC????ATACTGCCAA?GCT?GATAGAAATTAACT??GCT?ATAG?AATACTGG??C?CTAAGG?TCGAAGCTA?TAAATT??GGAG???TCA?A?C?TTAAAAACAT??A?AGGGC??A?GT?CAAT?AGGAA?C??A??C?????GTT?GTACTAAG????ATCAGT???AAA?A????????????TGAAATCCCCC????????????TCACGC??TCATTGTA??????????ACTATA?GC?TGT?A??TA???????????C?GCTACTGT?ATA???GTCAAGGAGTATACG????????ATT?A?A??T?????TTTCC??????TAT?TTT?T?TTT????TC?CA?CGATA?CCA??AG??????????TT?T?TAGGA?TCTA????AAAAA??????????TGTA?ATAGGTTATCGCT?TAAGATGA?ATAATAGCTAAGTACGTACCA???TCTG?TA?TATCCC??????AAA?TTT??????ACTTA?GC?????????AT?????G????TCT?????ACCCGG?TCTTTT????????A????T?G?T??GT?A???AT?CCTGTACGAT?A?AT????C?TA?CGC?ACGC????TACA?GAATC???????????????CT????????A?TTTTGG??AAAATA?A???AACAGTG?GAGC????CCAATT????????CTAATAT???TA??GTC?AC??AT???????C????CACTTGATAG?GGTGAGGAAATGCGTGGAGTTTATG?GT?A?????????A???CATAGTAG?????CCGTC?GAAC?AT?TTCCAGG?CAA???????AACAATTC?A?TTGTGA????????????T?GTAC??C??TTTA?CGTAC?CA?CTACTATTGA???AG????C?AA?CATTCTTAAA??A?GTA???TCCAAT?GT?CTGGA??GCTGTATCCC????CTCTTGTA???TTTTC??CT?AACTTA????GAATGTTGATA????AGCCCC?CA??C??CATCGATTATATCGTC?AATAA??????ATGTA???????TTAGG???????TGGTAATT????CACTAA?????A?TTACTTCA???????T???CCGTATA?T?A??AGCA???AAA????A??TGA??T?A?????????ATA?CCAGTAAAT??CGAAC????????CCTGT?CT?CTTAGAAA?A?TCAGAATTGATACAAGTATTA?G???GGTC?TTAACA luteiventris_MT_MVZ191016 ACC?CTCGT?A??AGTG?GC?T????????GACCTGTAG??T?????????AACAAACTA????GTG??????????GGTG?ACAAACCT??GGT?TTTGACCT??CG?TATT?GA??TTGA?TT?A???????C?AC???????T?AA?????????CCAATT?ACAA??CCCGTAACGAC????G??CTTGAATA?A??A?TA?AT?TAT?AT??CGG???ACAACC?????????AG?ACATCGTCGCTG?TCATC????????TTTAGTTGCA?T?T???????????????TATA?ATGGAAT?CTGAGGT???AC????CTCC???TTCACTCGC??CT????????CTCTAAAT?????TG?T??T?AATG??TTGATACTA???????ATGAGGCTTTC??C????????GCCATTAC??A??????????AGAAGTTGACAAAA??TACA?ACATCACAGG?AC??T??????????????????TT?CCAAAATGTC?ACAGTCCTG????T?TACC?CGCTGTAA???C??TGC?TTAGGAAGCCTCACTCGTCAT?T?AT???CC???TA?????AGT?C?????CC???T?TG?GATA?TCACTT???T????CTT?C???AC??GCCCTA??AGAACTTAGA???TCTTAA??????TTCCC?TAG???CAG????TCAA???????AAAGT?ACGAATGTAGTTCA?AT?????????TGTA????TT?CA???AAA?AC???T??????C?????AGCTCG????AA???????TCAAT?TCCA????CC??AATTC??A?C????????GCA??CCTGG?GTAA?????AAAATC?TAA?GCA??TA?GATGAATAGAATTGT??????CTG?CGATATATAGCATGTATTCAT???TTTA??????ACCT?TAGT??????A??AGATTAA?T?????GTCAACACAAAAAT????AA?CC?A?ACATT??ATAAT??TCTAGAGAACT???GAAGCAAT???GGGACAATT?C??A??????????A?CAAA?ATT?????ATTACCCC??AT?????CAA???C??CAC???GGG??ATA??????ACTG??TTCAA?CATCAA?TCAG?TTCGA???AA?TATAATAAGTAT?AT??GCATTAGA?CCG?????????CAAAGA??????A??GA?????????ACTATT??TCCAATTTT??GCCTTATCTTATTA?CAATAAATGAAAC?C?TT?ACA?TACCATGG??CTATC?AATTCCTATA????????CA??A???????????????????????C?GA?AA??????GATC??????AA?TAT?CGG??????CTAGAC??CTAGAT??TTAAA???GAAAAGT??TCTA?T????ACGC??????AATGGGTA?TCAACA?????TTAAG?TCAA???????CGT??ATACGTCCACAA????AGACAGACTA?C???????TGAG??????TGTATATCT??G?CC?TTGTA??G?TAATTGCTG????GTC?C?A???AAT??CAACT?????????????CAG?TTAGATTCACTCC?TTTTATA???C??GAT???????????ATCATATTGCAT??????TAAA?A?C?ATAAATTGAAATAA?GCTCTTACTGAATGACTAGG?GA?CTC??TGAACTGATCA???GGAACA?TACG?CTGGTATA?ACCGTAT?????????GTT?A??C??TCTAGACGAAGCGCCGACAATGCTT?ACT????????????TATCATTTGCT????AGACTCAATAAAAGTACAAGACAA????CG?GAT???A????A?TC??AACCATC?????T??T?????AATA????GCAGTACTGC?????ACAAGATCT??????AAAAT?TGAC??CAAAT???AAAAACTAGCAAA?????????CCGA????ATACTGCCAT?GCT?GATAG???TTAACT??GCT?ATAA???????GG??C?CTAAGA?CCGAAGCTA?TAATTT??GGAG???TTA?A?C?TTATAAACAT??A?AGGGT??A?GT?TAAT?AGAAA?T??A??G?????ATT?GTACTAAA????ACCAAT???AAA?A????????????TGAAGTCCTTC????????????TTACGC??TC????TA??????????ACTATA?GT?TGT?A??TAATC???ACATTC?GCTCCTGT?ATA???GT?????AGTACACG????????TTTAATA??T?????CTTAC??????TAT?TTT?T?TTT????CC?CA?CGATA?CCA??AG??????????CT?T?TAGGA?ACTA????AAAAA??????????CGTA?ATAGGTTATCGAT?TAAGATAA?ATAATAACCAAGTACGTACCA???TTTG?TA?TA?CCC??????AAA?ATT??????ACTGA?GC?????????AT?????G????TCT?????ACCTGG?G????T????????A????A?G?T??GC?A???AC?CCTGTACAAT?G?AA??ACC?CA?CGT?ACGT????TACA?GATCC??????????TCA??CT????????A?TTTTGG??AAAATA?A???AACAGTG?GCGC????CCA?TT????????CTAACAT???TA??GAT?AC??AT???????T????CACTTGATAG?GGTGAGGAATTGTGTTGAGCTAATG?GT?A?????????A???CACGACAT?????C???T?TATC?AC?TCTCA?G?GAA???????AACAATTT?A?TTGTAAATCAG?AT??CTT?CTATGCC??TTTA?CGTAC?CA?CTACAATTGG???AG????A?AA?CATTTCAAAA??A?GTA???TTTTAT?GT?CTAGA??GCCCTATGCT????CTCTAGTA???TATCC??CT?AACTTA????CAATGTCGATA????AGGTCC?CA??C??CATCGATAATATCGTC?GTTAA??????ATATA???????TCAGG???????TAATAATT????CACTTA?????A?CTACTTCG???????T???ACG?ATA?T?A??AGCA???AAA????T??CGA??C?C?????????ATA?CCTGTAAAT??TGAAC????????CCAAC?CA?CTTAGAAA?A?TCAAAATTGATATGAGTATTA?C???GGTC?ATAACA luteiventris_WA_MVZ225749 ACC?CTCGT?A??AGTG?GC?T????????GACCTGTAG??T?????????AACAAACTA????GTG??????????GGTG?ACAAACCT??GGT?TTTGACCT??CG?TATT?GA??TTGA?TT?A???????C?AC???????T?AA?????????CCAATT?ACAA??CCCGTAACGAC????G??CTTGAATA?A??T?TA?AT?TAT?AT??CGG???ACAACC?????????AG?ACATCGTCGCTG?TTATC????????TTTAGTTGCA?T?T???????????????TATA?ATGGAAT?CTGAGGT???AC????CTCC???TTCACTCGC??CT????????CTCTAAAT?????TG?T??T?AATG??TTGATACTA???????ATGAGGCTTTC??C????????GCCATTAC??A??????????AGAAGTTGACAAAA??TACA?ACATCACAGG?AC??T??????????????????TT?CCAAAATGTC?ACAGTCCTG????T?TACC?CGCTGTAA???C??TGC?TTAGGAAGTCTCACTCGTCAT?T?AT???CC???TA?????AGT?C?????CC???T?TG?GATA?TCACTT???T????CTT?C???AC??GCCCTA??AGAACTTAGA???TCTTAA??????TTCCC?TAG???CAG????TCAA???????ATAGT?ACGAATGTAGTTCA?AT?????????TGTA????TT?CA???AAA?AC???T??????C?????AGCTCG????AA???????TCAAT?TCCA????CC??AATTC??A?C????????GCA??CCTGG?GTAA?????AAAATC?TAA?GCA??TA?AATAAATAGAATTGT??????CTG?CGATATATAGCATGTATTCAT???TTTA??????ACCT?TAGT??????A??AGATTAA?T?????GTCAACACAAAAAT????AA?CC?A?ACATT??ATAAT??TCTAGAGAACT???GAAGCAAT???GGGACAATT?C??A??????????A?CAAA?ATT?????ATTACCCC??AT?????CAA???C??CAC???GGGA?ATA??????ACTG??TTCAA?CATCAA?TCAG?TTCGA???AA?TATAATAAGTAT?AT??GCATTAGA?CTG?????????CAAAGA??????A??GA?????????ACTATT??TCCAATTTT??GCCTTATCTTATTA?CAATAAATGAAAC?C?TT?ACA?TACCATGG??CTATC?AATTCCTATA????????CA??A???????????????????????C?GA?AA??????GATC??????AA?TAT?CGG??????CTAGAC??CTAGAT??TTAAA???GAAAAGT??TCTA?T????ACGC??????AATGGGTA?TCAACA?????TTAAG?TCAA???????CGT??ATACGTCCACAA????AGACAGACTA?C???????TGAG??????TGTATATCT??G?CC?TTGTA??G?TAATTGCTG????GTC?C?A???AAT??CAACT?????????????CAG?TTAGATTCACTCC?TTTTATA???C??GAT???????????ATCATATTGCAT??????TAAT?A?C?ATAAATTGAAACAA?GCTCTTACTGAATGACTAGG?GA?CTC??TGAACTGATCA???GGAACA?TACG?CTGGTATA?ACCGTAT?????????GTT?A??C??TCTAGACGAAGCGCCGACAATTCTT?ACT????????????TATCATTTGCT????AGACTCAATAAAAGTACAAGACAT????CG?GAT???A????A?TC??AACCATC?????T??T?????AATA????GCAGTACTGC?????ACAAGATCT??????AAAAC?TGAC??CAAAT???AAAAACTAGCAAA?????????CCGA????ATACTGCCAT?GCT?GATAG???TTAACT??GCT?ATAA???????GG??C?CTAAGA?CCGAAGCTA?TAATTT??GGAG???TTA?A?C?TTATAAACAT??A?AGGGT??A?GT?TAAT?AGAAA?T??A??G?????ATT?GTACTAAA????ACCAAT???AAA?A????????????TGAAGTCCTTC????????????TTACGC??TC????TA??????????ACTATA?GT?TGT?A??TAATC???ACATTC?GCTCCTAT?ATA???GT?????AGTACACG????????TTTAATA??T?????CTTAC??????TAT?TTT?T?TTT????CC?CA?CGATA?TCA??AG??????????CT?T?TAGGA?ACTA????AAAAA??????????CGTA?ATAGGTTATCGAT?TAAGATAA?ATAATAACCAAGTACGTACCA???TTTG?TA?TA?CCC??????AAA?ATT??????ACTGA?GC?????????AT?????G????TCT?????ACCTGG?G????T????????A????A?G?T??GC?A???AC?CCTGTACAAT?G?AA??ACC?CA?CGC?ACGC????TACA?GATCC??????????TCC??CT????????A?TTTTGG??AAAATA?A???AACAGTG?GCGC????CCACTT????????CTAACAT???TA??GAC?AC??AT???????C????CACTTGATAG?GGTGAGGAATTGTGTAGAGCTAATG?GT?A?????????A???CACGACAT?????C???T?TATC?AC?TCTCA?A?GAA???????AACAATTT?A?TTGTAAATCAG?AT??CTT?CTATGCC??TTTA?CGTAC?CA?CTACAATTGG???AG????T?AA?CATTCCAAAA??A?GTA???TTTTAT?GT?CTAGA??GCCATATGCT????CTCTAGTA???TATCC??CT?AACTTA????CAATGTCGATA????AGGTTC?CA??C??CATCGATAATATCGTC?GTTAA??????ATATA???????TCAGG???????TAATAATT????CACTTA?????A?CTACTTCG???????T???TCG?ATA?T?A??AGCA???AAA????A??CGA??C?C?????????ATA?CCTGTAAAT??TGAAC????????CCAAC?CA?CTTAGAAA?A?TCAAAATTGATATGAGTATTA?T???GGTC?ATAACA muscosaMVZ149006 ACT?CCCGT?A??AGTG?GC?T????????GACCTGTAG??TT???T?TT?AACAAACTA????GTG??????????GGTG?ACAAACCT??GGC?TTTGACCT??CG?TCTT?GA??TTGA?TT?A???????C?AC??????????A?????????CCAATT?ACAA??TCCGTAACGAC????A??CTCGAATATA??C?TA?AT?TAT?AT??CGG???ATAACC?????????AG?ACACCGTCGCTG?TTATC????????TTTAGTTGTA?TCT??????????A?CGGTATA?ATGGAAT?CTGAGGT???AC????CTCC???TATACTCGC??CT????????CTTTAAAT?????TG?T??T?TATG??TAGATACTA???????CTAAAACTTTC??C????????GCCATTAC??A??????????AGAAGTTGACACAA????CG?ACATCACGGG?AT??A??????????????????TC?CCAAAATGTC?ACATTCCTG????T?TACT?CGCTGTAA???G??AGC????TGAAGCTTCCCTTCTTGT?TTAT???CC???TA?????AGTAC?????CG???T?TA?AGAA?TCACTT???T????CTT?C???CA?AATCCTA??AGAACTTAAACGATCTTAA??????TTCCC?TAG???TAG????CCAA???????ATAGT?ACGAATTTAGTTCT?CT?????????TGAA????TC?CA???AAA?AC???T??????T?????AGCTCG????AA???????TGAAT?TTCA????CC??AGTTC??A?C????????ACA??CCTGG?GTAA?????AAAATC?CAA?GCA??TA?TATAGATAGAA?TGT??????CTG?CGATTTATAGCCTGTATTCAT???TTTA??????ACCT?TAGT??????A??ACATTAA?T?????GTCACCAAAAAAAT????AA?TC?A?ACAAT??ATAAT??TCTAGAGAATT???GAAGCAAT???GGGATCATT?C??A?????????CA?TAAG?ATT?????ATTACCCA??AT?????TAA???C??CAA???GGG??ATA??????ACTG??TTCAA?CAACAG?TCAG?TTGGA???TA?AGTAATAAGAAA?AT??GTATTAAA?CTG?????????CAAAG???????A??GA?????????ATTATT??TCCAATTCT??GCCTTATATTATCA??TATAAATGTAAT?C?TT?ATA?TGCCATGG??C?ATC?AATTCCTATA????????CA??A???????????????????????C?GA?AA??????GATC??????AA?TAT?CGG??????CAACAC??TTAGAT??TTAAA???GAAAAGC??TTCA?T????AAGC??????AATGAGAA?TCAATA?????TTAGG?CCTT???????TGT??ATACGTTCACAA????AGGCAAACTC?C???????TGAG??????TACATATGT??G?CC?TTGTA??GATAATTCTTG????GTC?C?A???AAT??CAACTAC???????A?CTCAG?TTAGATTCGCCCC?TTTTATA???C??GATGTGTTGT????AACATATTGCGT??????TAAT?A?C?ATAAATTGAAATAA?GCTCTTACTGAATGACTAGG?GA?CTC??TGAACTGATCA???GGACCA?TACG?CTGGTATA?ACCGTA????????????T?A??T??TCTAGAAGAAGCG??GACAATCCTT?ACT????????????TATCTTTTGCT????AGACTCAAAAAAAGTAAAAAACAT????CG?AAT???A????A?CC??AACCATC?????T??T?????AACA????GCAGTACTAC?????ACGAGAACT??????GAATC?TGAC??CAAAC???AAAAACTAGCAAA?????????TCGA????TTACTGCCAA?GCT?GATAGCAATTAACT??GCT?ATAG?AACACTGG??C?CTAAGT?CCGAAGCTA?TAAATT??GGAG???TTA?A?C?CT?T???????????GGGC??A?GT?AAAT?AGAAACT??G??G?????ATT?GTACTAAA????ACTAAT???ATA?A????????????TGAAGTTCTCC????????????TCACGC??TCATAGTA??????????ACAATA?GT?TGT?A??TAACC???ACATTC?TCTACTTT?ATA???GC?????AGTATACA????????CTTGA?A??C?????CTTAC??????TAT?TTT?T?TCT????TC?CA?CGATA?GCA??AG??????????TT?T?TAGGA?TCTA????AAAGA??????????CGTA?ATAGTACACCGCT?TAAGATAA?ATTATAA???????CGTACCA???TTCG?TA?CA?CCC??????AAA?ATT??????ACTCA?GC?????????AT?????G????TCT?????ACACGG?TCTTTT????????A????T?G?T??GT?A???AC?CCTGT????T?G?AA??ACC?CC?CGC?ATGT????TACA?GATTC??????????TCT??CT????????A?TCTCGG??AAAACA?A???AACAGTG?GCGC????CCATTT????????GTAATAT???TA??GTC?AC??AT???????C????CACTTGATAG?GGTAAGGAATTGTGTAGAGCTAATG?GT?A?????????A???CATGACAT?????CTCTC?AATC?AC?TATCAGA?GAA???????AGCAATTC?A?T?????????G?AT??CTT?CTATGCC??TTTA?TGTAC?CA?CTACAATTGG???AG????C?AA?CATTTCAAAA??A?GTA???TCTAAT?GT?CTGGA??GCCATATGCC????TTCTAGTA???TTTCC??CT?AACTTA????AAATGTCGA?A?????GGCTC?CA??C??CATCGATAATATCATC?GATAA??????ATTTA???????TCAGG???????CGATAATT????CACTTA?????A?CTACTTCA???????C???TCG?ATA?T?A??AGCT???AAA????A??CGA??T?A?????????ATA?CCTGTAAAT?????????????????AGT?CT?CTTAGAAA?A?TCAAAATTGATATGAGTATTA?A???GGTC?CTGACA auroraMVZ13957 ?TC?CCCAT?A??AGTG?GC?T????????GACCTGTAG??TT???T?TT?AACAAACTA????GTG??????????GGTG?ACAAACTC??GGC?TTTTACCT??TG?TCAT?GA??TCGA?TT?A???????C?AT???????A?AA?????????CCAATT?ACAA??CCCGTAACGAC????A??CTCGTGTATA??T?TA?AT?TAT?AT??CGG???ATAACC?????????AG?ACACTGTCGCTG?TTATC????????TTTAGTTGTA?TCT??????????A?CGGTATA?ATGGAAT?CTGAGAT???AC????CACC???TTTACTCGC??CT????????TTCTAAAT?????TG?T??T?AATA??TTGATACTA???????ATAAAACTTTC??C????????GCCATTAC??A??????????AGAAGTTGACAAAA????CA?ACATCAC?GG?AC??A??????????????????TC?CCTAAATGTT?ACATTCCTG????T?TGCC?TGCTGTAA???T??AGC????AGAAGTTTCTCTTTTCAT?TTAT???CC???TG?????AGTGC?????CG???T?TA?GAAA?TCACTT???T????CTT?C???CA?AATCCTA??AGAACTTAAACGATCTTGA??????TTCCC?TAG???CTG??TCCCAA???????ACAGT?ACGAATATAGTCTA?TT?????????TGCA????TC?CA???AAA?AC???T??????T?????AGCTTG????AA???????TTTAT?TTCA????CC??ACCTC??A?C????????ACA??CCTGG?GTAA?????AAAATC?CAA?GCA??TA?CATCGATAGAA?TGT??????CTGGCGATAAATAGTATGTA?TCTT???TTTA??????ACCT?TAGT??????A??ATACTAA?T?????GTCATCAAAAAGAT????AA?TC?A?ATAAT??ATAAT??TCTAGAGAACTA??GAAGCAAT???GGGATTATT?C??A??CAT????CA?CAAG?ATT?????ATTACCCC??AT?????TAA???C??CAA???GGG??ATA??????ACTG??TTCAATCAACAA?TCAG?TTCGA???AA?AATAACAAGGAC?AT??GTATTAGA?CTG?????????CAAAGA??????A??GA?????????ATTACT??TCCAATTTT??GCCTTAT?TTATCA?CTGTAAATGAA???C?TT?AAA?TGCCATGG??T?ATC?AATTCCTATA????????CA??A???????????????????????C?GA?AA??????GAT???????AG?TAT?CGG??????CCATAC??T?AGAT??TTAAA???GAAAAGT??TTCC?T????ATGC??????AATGAAAA?TCATCA?????TTAGG?TCAT???????TGT??ATACGTTCACAA????AGGCAAACTC?C???????TGAG??????T?CATATTT??G?CC?TTGTA??GATAATTCTTG????ATC?C?A???AAT??CAACTGCGCGAAAAA?CACAG?TTAGATTCGCCCC?TTTTATA???T??GATGTATTGT????AACATATTGCAT??????TAAT?A?T?ATAAATTGAAGTAA?GCTCTTACCGAATGACTACG?GA?CTC??TGAAC???TCA???GGATCA?TACG?CTGGTATA?ACCGTA????????????T?A??T??TCCAGACGAAGCG??GACAATCCTT?ATT????????????TATCATTTACT????AGACTCAATAAAAGTAAAAGACAT????CG?AAT???A????A?TC??AACCATC?????T??T?????AATA????GCAGTAATAC?????ACAAGAACT??????GAATC?TGAC??CAAAC???AAAAACTAGCAAA?????????TCAA????TTACTGCCAG?GCT?GATAGC?ATTAACT??GCT?ATAA?AACACTGG??C?CTAAGT?CCGAAGTTT?AAAATT??GGAG???TTA?A?C?TT?TAAACAT??G?AGGGT??A?GT?CTAT?AGAATCT??G??A?????ATC?GTACTAAG????ACAATT???ATA?A????????????TGAAGTTCTTC????????????TCTCGC??TCATAGTA??????????ACAATA?GT?CGT?A??TAATC???ATATTC?GCTGCTGT?ATA???GC?????AATATACG????????CTTAA?A??C?????CTTAC??????TGT?TTT?T?TTT????TC?CA?CGATA?TC???AG??????????TT?T?TAGGA?TCTA????AAAGA??????????CGTA?ATAGCCTATCGCT?TG?GATAA?ATTATAGCCAAGTACGTATCA???TTCG?TG?CA?CCC??????AAA?ATT??????ACTCA?GC?????????AT?????G????CCT?????ACCCGG?CCTTTT????????A????T?GTT??GT?A???AT?C??GT????T?G?AG??ACC?CT?CGG?ATGT????TACA?GACTC??????????TCA??CT????????A?TCTTGG??AAAACA?A???AACAGTG?GCGC????CCATTT????????TTAATAT???TA??GTT?AC??AT???????C????CACTTGATAG?GGTCAGGAATTGTGTGGAGTTAATG?GT?A?????????A???CAGGACAT?????CTTTT?GATC?AC?TTTCACG?GAA???????AGCAATTT?A?TAGCAAATCAG?AT??CTT?TTATGCC??TTTA?CGTGC?CAACTACATTTGG???AG????T?GA?CATTTCTAAA??A?GTA???TCTTAT?GT?CTGGA??GCCATATGCC????TTCTAGTA???TTTCC??CT?AACTTA????AAATGTCGATATATTAGGTTC?CA??C??CATCGA?AATATCGTC?AAT?A??????ATTTA???????TCAGG???????TAAT??TT????CCCTAA?????A?CTACTTCA???????C???TCG?ATA?T?A??AGCA???AAA????A??TGA??T?A?????????ATA?CCTGTGAAC??TGAAC????????CTAGC?CT?CTTAGAAA?A?TCAAAATTGATATGAGTATTA?G???GGTC?ATGACA cascadaeMVZ148946 ATC?CCCAC?A??AGTG?GC?T????????GACCTGTAG??TT???T?TT?AACGAACTA????GTG??????????GGTG?ACAAACTT??GGC?TTTAATCT??TG?TCAT?CA??TCGA?TT?A???????T?AT???????A?AA?????????CCCATT?ACAA??CCCGTAACGAC????A??CTCGAGTATA??T?TA?AT?TAT?AT??CGG???ATAACC?????????GG?ACACCGACGCTG?TCACC????????TTTTGTTGTA?TCT??????????A?CGATATA?ATGGAAT?CTGAGAT???AC????CTCC???T????TCGC??CT????????CTCTAAAT?????TG?T??C?TATA??TTGATACTA???????TTAAAACTTTC??C????????GCCATTAC??A??????????AGAAATTGACAAAA????CG?ACACCAC?GG?AC??A??????????????????CC?CATTAATGTT?ACATTCCTG????T?TGCC?CGCTGTAA???T??AGC????AGAGGACTCTCTTCTTAT?TTAT???CC???TA?????AGTGC?????CG???T?TG?GGAG?TCACTT???T????CTT?C???TC?AATCCTA??AGAGCTTAAACGATCTTAA??????TTCCC?TAG???CAG????CTAA???????ACAAT?ACGAATCTAGTCTA?TT?????????CGCA????TT?CA???AAA?AC???T??????T?????AGCTCG????AA???????CTTAT?TCCA????CC??AACTC??A?C????????ACA??CCTGG?GTAA?????AAAATC?CCA?GCA??TA?TATAGATAGAA?TGC??????CTGGCGATATATAGTATGTA?TCCT???TTTA??????ACCT?TAGT??????A??ACATTAA?T?????GTCACCAGAAAGAT????AA?TC?A?ACAGT??ATAAT??TCTAGAGAACTA??GAAGCAAC???GGGATCATT?C??A??CAT????CA?CAAA?ATT?????ATTACCCC??AT?????TAA???C??CAA???GGG??ATA??????ACTG??TTCAA?CAACAA?TCAG?TTTGA???AA?AATAACAAGAAC?AT??GTATTAGA?CCG?????????TAAAGA??????A??GA?????????ATTACT??TCCAATTTT??GCCTTATTTTATC???CGTAAATGGAAT?C?TT?ATA?TACCATGG??C?ATC?AATTCCTATA????????CA??A???????????????????????C?GA?AA??????GATC??????AA?TAT?CGG??????CCACAC??TTAGAT??TTAAA???GAAAAGT??TTCC?T????ATGC??????AATGAGAT?TCAATA?????TTAGG?CCAT???????AGT??ATACGTACACAA????AAGCAAACTC?C???????TGAG??????TACATATTT??G?CC?TTGTA??GATAATTCTTA????ATC?C?A???AAT??CAACTACACGAAGAA?CGCAG?TTAGATTCGCCCC?TTTTATA???C??GATGTATTGT????AACATATTGCAT??????TAAT?A?C?ATAAATTGAAGTAA?GCTCTTACCGAATGACTATG?GA?CTC??TGCACTGATCA???GGATCA?TACG?CTGGCATA?ACCGTA????????????T?A??T??TCCAGACGAAGCG??GACAATCCCT?AAT????????????TATCATCTGCT????AGACTCAACAAAAGTACAGGACAT????CG?AAT???A????A?TC??AACCATC?????T??T?????AATA????GCAGTATTAC?????ACAAGTGCT??????GAATC?TGAC??CAAAC???AAAAACTAGTAAA?????????TCGA????TTACTGCCAG?GCT?GTTAGC?ATTAATT??GCT?ATAA?AACACTGG??C?CTAAGT?TCGAAGCTA?TAAATT??GGAG???TCA?A?C?TT?TAAACAT??A?AGGGT??A?GT?CAAC?AGAATCT??G??G?????ATC?GTACTAAG????ACTACT???ATA?A????????????TGAAGTTCCTC????????????TCTCGC??TCATAGTA??????????ACAATA?GT?C?T?A??TAATC???ATATTC?GCTGCTAT?ATA???GC?????AATATACA????????CTTGA?A??C?????TTTAC??????TAT?TTT?C?TTT????TC?C??CGATA?TCA??AG??????????TT?T?TAGGA?TCTA????AAAGA??????????CGTA?ATAGTCTCTCGTT?TG?GATAA?ATAATAGCCAAGTACGTATCA???TTCG?TG?CA?CCC??????AAA?ACT??????ACTAA?GC?????????AA?????G????CCT?????ACTCGG?CCTTTT????????A????T?GTC??GT?A???AC?C??GT????T?A?AA??ACC?CT?CGG?AAGT????TACA?GACTC??????????TCA??CT????????A?TCTTGG??AAAACA?A???AACAGTG?GCGC????CCATTT????????TTAATAT???TA??GTT?AC??AT???????C????CACATGATAG?GGTGAGGAATTATGTGGAGTTAATG?GT?A?????????A???CAGGACAT?????CTCTT?GATC?AC?TACCACA?GAA???????AGCAATTT?A?TCGCAAATCAG?AT??CTT?TTACGCC??TTTA?CGTTC?CAACTACTTTTGA???AG????T?AA??TTTTCTAAA??A???A???TTCTAT?GT?CTGGA??GCCATATGCC????TTCTAGTA???TTTCC??CT?AACTTA????AAATGTCGATATACTAGGTCC?CA??C??CATCGA?AATATCGTC?GGTAA??????ATTTA???????TCAGG???????TTATGATT????CCCTCA?????A?CCACTTCA???????C???TTG?ATA?T?A??AGCA???AAA????A??TGA??T?A?????????ATA?CCTGTGAAC??CGAAC????????CTAGC?CT?CTTAGAAA?A?TCAAAATTGATATGAGTATTA?A???GGTC?CTTACA sylvaticaMVZ137426 ACC?CCTGT?A??AGTG?GC?T????????GTCCTGTTG??TT???T?TT?AACTAA??A????GTG??????????GGTG?ACA????????GT?TTTGACCT???????????????TGA?TT?A???????C?AC???????A?AA?????????CCGATT?ACAA??CCCGTAATGTC????A????????TATA??C?TA??T?TAT?AT??CAG???ACAACCTTCAT??A?AG?ACAAAGTCGCTG?TTAAC????????CATAGTTGTTCTCT??????????A?CGCTATA?ATGGAAT?CTGAGGT???AC????CTCC???TCTACTCGC??CT????????CCCTAAAT?????TG?T??T?AATG??TAGATACTA???????ATGAAACTTTC??C????????GCCTTTAC??A??????????ACAAGTTGACACAA??TACG??CGCTACTGG?AT??CGT??????????A?TAC?TT?CACAAA????????AT?CTG????T?TATT?CGCCGTAAA?CTGTTGC?T??CGAA?ATTCTCTCACCAT?TTAT???CA???TA?????AGTAC?????CT???T?TA?AGAA?TCACTT???TCTGGCGTGC???TC?AGACCTA??AGAACTTAAACGA?CTTAA??????TTCTC?TGG???CAG?????CAC????????TAGT?ACAAATTTAGTCCC?AT?????????AGAA????TTTCA???AAA?AC???T??????C?????AGCTAA????AA???????TAGTT?TTCA????CC??ATATC??A?C????????GCA??GTCGG?ATAT?????GAAATC?G???????????GATAGATAG????GC??????CTG?AGA???ATAGTCTATATTCGT???TTTT??????ACCT?TAGT??????A??AAAATAA?T?????GTCAA????????T????AA?CA?A?AT??T??ATAAT??TCTAGGGAATT???GAAGCAAA???GGGACTATT?C??A??CTA????CA?CTCT?ATT?????ACTACCCT??AT?????CAAT??C??CAA???GGG??ATAT?????ACCG??TTCAA?CATCAA?TTAG?TTCGA???AA?C?TA?AAAGGAA?AT??ATATTAAA?CCG?????????TAGAGA??????A??GATC??ACCCAATTATT??CACA?TTTT??GCT???TATTATTA?CAGTAAACGAAAG?C?TT?GTA?CACCATGG??C?ATC?????CCTACA????????CA??A???????????????????????C?GA?AA??????GCTC??????A??????TGG??????CCAGAC??CTAGCT??????????GAAAAGC??TCTA?T????ATAC??????AATGACAA?TCATCA?????TTAGG?ACAA???????AGT??ATACGTTCACAA????TGGCAGACCC?C???????TGCG???????????ATTC??G?AC?TCGTG??GATAAATATTA????GTT?C?AATTAAT???GACTGC???????A?CCCAG?ATAGATTCCCTCC?TTTTATA???C??AAG???????????A?CATATTGC?TTACGT?CAAC?A?C????AGTTGGAGAAA?GCTCCTGCTGAACGATTAAG?GA?CTC??TCATTTGATTG???GGTTCA?TACG?TTGGTATA?TCCGCAA?????????GTT?A??C??T????AGGAAGCGCCAACTATCCCT?ACT????????????CATCT???????????GACTTATTACAAGAA?AAAACAT????CG?GAT???A????A?TT??AACCTCCCGCATT??T?????AATA??CCGCAGTAATAC?????ACAAGAACT??????GAAATATGAC??CAAAT???AAAAACTCGCAAA?????????TCCA????TTA?TGACAAAGCT?GGTAGCAACTGACT??GCT?ATAA?A?TATTGG??C?CTACGA?CCTAAGTAT?AAAATT?TGG??????TA?A?C?TTACAAACAT??G?AGAGA??A?GT?GAAC?GGAAA?T??G?CA?????CTG?GTACTAAG????ATAACT???AAA?A??????A????GTGAAGTTCTAT????????????TCACGC??TCATAGTA??????????ACAATA?GT?TGT?G??TAATC???ATATTC?G?????TT?ATA???GT?????GTTATACG????????TTTGA?A??A?????TTTAC??????TAT?TTC?T?TTT????TC?CA?CAATA??CG??AG??????????TT?C?TAGGA?TATA????AAAGA??????????AGGA?ATAGCACACCGCT?TGAGATAA?ATCA??ACCAAGT????ATCA???TTTG?TC?AA?CCC??????AAA?ATT??????ACTTA?GT?????????????????????TTT?????ATCCGG?CTTTTT????????A???TT?G?C??GT?A???AT?CCTGTATAAT?C?AA??ACC?AT?CGC?AAGC????TACA?GATTT??????????T?A??CT????????A?TCTTGG??ACAACG?A???AACAGTG?GTGC????CCATTTATATATGGATAATAT???TA??GCT?AC??AT???????C????CACTTGATAG?GGTCAGGAAATATGTGGAGCTAATG?GT?A?????????A???TATGCTAA?????CTGTC?GACC?AT?TCCTAGC?AAA???????AGCAATCG?C?TTGCAAATCAG?GT??CTT?TTGCGTC??TTTA?TGTGC?CA?CTACAGTTGA???AG????C?AA?CGTTCTCAAA??AAGTA???CCACAT?GT?CTCGC??GCCGTATGCC???????T?GTA???TGTTC??CT?AACTTA????TAATATTGGTA????AGAGTC?CG??C??CATCGATCATAT?GTC?CATAT??????ATGTA???????TCAGA???????GGGTA?????????CTTA?????A?CCACTTCA???????C???TTA?ATA?T?A??A?CA???AAA????T??AGG??C?A?????????AAG?CCAGTAAAT??CGAACA???????CTCGC?CA?CTT?GC?????TCATAATTGATATGGGTATTA?T???GGTC?ATAACA sylvaticaDMH84R43 ACC?CCTGT?A??AGTG?GC?T????????GTCCTGTTG??TT???T?TT?AACTAA??A????GTG??????????GGTG?ACA????????GT?TTTGACCT???????????????TGA?TT?A???????C?AC???????A?AA?????????CCGATT?ACAA??CCCGTAATGTC????A????????TATA??C?TA??T?TAT?AT??CAG???ACGACCTTCAT??A?AG?ACAATGTCGCTG?TTAAC????????CATAGTTGTTCTCT??????????A?CGCTATA?ATGGAAT?CTGAGAT???AC????CTCC???TCTGCTCGC??CT????????CCCTAAAT?????TG?T??T?AATG??TAGATACTA???????ATGAAACTTTC??C????????GCCTTTAC??A??????????ACAAGTTGACACAA??TACG??CGCTACTGG?AT??TGT??????????A?TAC?TT?CAAAAA????????AT?CTG????T?TCTT?CGCCGTAAA?CTGCTGC?T??CGAA?ATTCTCTCACCAT?TTAT???CA???TA?????AGTAC?????CT???T?TA?AGAA?TCACTT???TCTGGCGTGC???TC?AGTCCTA??AGAACTTAAACGA?CTTAA??????TTCTC?TGG???CAG?????CAC????????AAGT?ACAAATTTAGTCCT?AT?????????AGAA????TCTCA???AAA?AC???T??????A?????AGCTAA????AA???????TAGTT?TTCA????CC??ATATC??A?C????????GCA??GTCGG?GTAT?????GAAATC?G???????????GATAGATAG????GC??????CTG?AGA???ATAGTCTATATTCGT???TTTC??????ACCT?TAGT??????A??AAAATAA?T?????GTCAA????????T????AA?CA?A?ATAAT??ATAAT??TCTGGGGAATT???GAAGCAAT???GGGACCATT?C??A??CTA????CA?CTCC?ATT?????ACTACCCT??AT?????CAAT??C??CAA???GGG??ATAT?????ACCG??TTCAA?CATCAA?TTAG?TTCGA???AA?C?TA?AAAGGAA?AT??ATATTAAA?CCG?????????TAGAGA??????A??GATC??ACCCAATTATT??CACAATTTT??GCT???TATTATTA?CAGTAAACGAAAG?C?TT?GTA?CACCATGG??C?ATC?????CCTACA????????CA??A???????????????????????C?GA?AA??????GCTC??????A??????TGG??????CTAGAC??CTAGCT??????????GAAAAGC??TCTA?T????ATAC??????AATGACAA?TCATCA?????TTAGG?ACAA???????CGT??ATACGTTCACAA????TGGCAGACCC?C???????TGCG???????????ATTC??G?AC?TCGTA??GATAAATATTA????GTT?C?AATTAAT???GACTGC???????A?CCCAG?ATAGATTCCCTCC?TTTTATA???C??AAG???????????A?CATATTGC?TTACGT?CAAC?A?C????AGTTGGAGAAA?GCTCCTGCTGAACGATTAAG?GA?CTC??TCATTTGATTG???GGTTCA?TACG?TTGGTATA?TCCGCAA?????????GTT?A??C??T????AGGAAGCGCCAACTATTCGT?ACT????????????CATCT???????????GACTTATTACAAGAA?AAAACAT????CG?GAT???A????A?TT??AACCTCCCGCATT??T?????AATA??CCGCAGTAATAC?????ACAAGAACT??????GAAATATGAC??CAAAT???AAAAACTTGCAAA?????????TCGA????TTA?TGACAAAGCT?GGTAGCAAATGACT??GCT?ATAA?A?TATTGG??C?CTACGA?CCTAAGTAT?AAAATT?TGG??????TA?A?C?TTACAAACAT??G?AGAGA??A?GT?GAAC?GGAAA?T??G?CA?????CTG?GTACTAAG????ATAAGT???AAA?A??????A????GTGAAGTTCTAT????????????TCACGC??TCATAGTA??????????ACAATA?GT?TGT?G??TAATC???ATATTC?G?????TT?ATA???GT?????GTTATACG????????TTTGA?A??A?????TTTAC??????TAT?TTC?T?TTT????TC?CA?CAATA??CG??AG??????????TT?C?TAGGA?TATA????AAAGA??????????AGGA?ATAGCACACCGCT?TGAGATAA?ATCA??ACCAAGT????ATCA???TCTG?TC?AA?CCC??????AAA?ATT??????ACTTA?GT?????????????????????TTT?????ATCCGG?CTTTTT????????A???TT?G?T??GT?A???AT?CCTGTATAAT?C?AA??ACC?AT?CGC?ATGC????TACA?GATTT??????????T?A??CT????????A?TCTTGG??ACAATG?A???AACAGTG?GTGC????CCATTTATATATGGCTAATAC???TA??GCT?AC??AT???????C????CACTTGATAG?GGTGAGGAAATATGTGGAGCTAATG?GT?A?????????A???TATGCCAA?????CTGTC?GACC?AT?TCCTAGC?AAA???????AGCAATCG?C?TTGCAAATCAG?GT??CTT?TTGCGTC??TTTA?TGTGC?CA?CTACAGTTGA???AG????C?AA?CGTTCTCAAA??AAGTA???TCACAT?GT?CTCGC??GCCGTATGCC???????T?GTA???TGTTC??CT?AACTTA????CAATATTGATA????AGAGTC?CG??C??CATCGATCATAT?GTC?CATAT??????ATGTA???????TCAGA???????GAGTA?????????CTTA?????A?CCACTTCA???????C???TTA?ATA?T?A??A?CA???AAA????T??AGG??C?A?????????AAG?CCAGTAAAT??CGAACA???????CTTGC?CA?CTT?GC?????TCAAAATTGATATGGGTATTA?T???GGTC?ATAACA septentrionalesDCC3588 ACTTCTC???AT?AGTG?GC?T????????GTCCTGTCG??TT???T?TT?AAGTAACTG????GTG??????????GGTG?ACA????????GT?TTTAATTT??TAGTATCAAAAGTTGA?TT?A???????C?AC???????C?AA?????????CCTATT?ATAG??CCCGTAATGAC????A????????TATA??T?TA?GT?TAT?????CAG???ATGACCTTCAC??A?CC?ACCGTGTCGCTG?CCATT????????TCTAGTTGTA?TCT??????????A?CGTTATA?ATGGAAT?CTGAGAT???AC????CTCC???TTTACTCGC??CT????????TTTTAAAT?????TG?T??T?GATG??TGGATACCA???????ATGAAACTTTC??C????????GCCATTAC??A??????????AAAAATTGACAGAA??CACA??CGCCACCGG?GT??GGT??????????A?TAC?CA?CAAAAA?GTA?ACACT?CTG????TCTGCC?CACCGCAAA?CT??TGC?CTAAGAA?CTTCTCTCATCAT?TTAT???CC???TA?????AGTAC?????CT???TTTG?TGGA?TCACTT???T????CATGC????A?GGTCCTA??AGATCTTAAACGA?CTTAA??????TTCTC?TGG???CCG????CCAC???????CAAGT?ACAA?TATAGTTCT?AT?????????AGT?????TC?CA???AAA?AC???T??????A?????AGCTAG????AA???????ATGTT?TTCA????CC??ACATC??C?C????????CCA??ATTGG?ATAT?????AAAATC?TAA?GCA??TA?GATATA?AG???TGA??????CTG?CGATCCATAGCTCGTATTCGT???TTTT??????A?????ATT??????A??CTATTAA?T?????GTCCC????????T????AA?TA?A?ATACT??ATAAT??TCTCGAGAACT???GAAGCAAC???GGGATTATT?C??A??CTG????CA?CATA?ATT?????ACTACCCT??AT?????CAAT??C??CAA???AGG??ATAT?????ACCG??TT???????CAA?TCTG?TTTGA???AA?C?TA?TAAGCAG?AT??ACCTTAGA?CCG?????????CAAAGA??????A??GATC??ACTCTATTAAT??TCCAATTCT??GCTTTGTATTATTA?CAGTAAACGCAAG?C?TT?ACA?CGCCATGG??T?ATC?GATCCCTATA????????CA??G???????????????????????C?GA?AA??????GATC??????TA?AGT?CGG??????CCAGAC??CTAGTT??TTAAA???GAAAAAT??TCCA?T????A?AC??????AATGAAAA?TCAACAC????TTAGG?ACAG???????AGT??ATACGTGCACAA????CGACAGACCC?C???????TGTG???????????ATTA??G?CC?TCGTA??GACAAATCTTA????GTT?C?A???AAT??CAACTGC???????A?CACAG?TTAGATTCTCTTC?TTTTACA???C??AAG???????????A?CATATTGCAT??????CAAT?A?C????AGTTGAAGAAC?GCTCTTGCCGAATGATTA?G?GA?CTC??TTATTTGATTG???GGAACA?TAC?????GTATA?TACGCAA?????????GTT?T??C??T????GAGAAGCTC??ACACTGCTT?ACT????????????CATCA???????????GACCCAACACAAGCA?AAAACAT????CG?GAT???A????A?AT??AACCATCCACATT??T?????AAGA??CCGCAGTAATCC?????ACGAGAACTAACAAAGAAAAATGAC??CAAAC???AAA?ACTTGCAAA?????????CCGA?????TACTGACAGAGCT?GGTAGTA?TTCACT??GCT?ATAA?A?TTC??G??C?CTACGA?CCCAAGCAG?AAAATT?TGATG???TTA?A?C?CTATAAACAT??A??AAGC??A?GT?TAATGGGAAA?T????????????TA?GCACTAAAATTGATAATT???A??????????A????GTGAAATGCTGC????????????TCACGC??TCATGGTG??????????ACAA???GC?TGT?A??TAATC???ATATTC?G?????AT???A???GT?????AGTATACG????????CTTGA?A??A?????TTTAC??????TAT?TTC?C?TCT????TC?CA?CAATA??CA??AG??????????CT?C?TAGGA?CATA????AAAAA??????????AGG??????AACATCGTT?CGAGATTG?ATTA??ACCAAGTCCGTACCA???TATG?TT?AA?CCC??????AAAGCTT??????TCTTA?GT?????????CC?????G????CCT?????ATCCGG?TGTTTT????????A???TC?G?T??GTGA???AT?TCTGTATAAT?G?AA??ACC?AC?CGT?ATGT????TACA?GATTT??????????TCC??CT????????A?TATTGG??AAAATT?A???AACAGTG?TCGC?????????????????????ACAT???TA??GCA?AC??TT???????C????CACTCGATAG?GGTAAGGAACTATGTGGAG??AACA?GT?ACCATTAAATT???TACGTTAA?????CTACC?GGCC?AT?TGTCAGT?AGA???????AGCAATCT?C?TTGCCAATCAG?GT??CTT?TTGCGTC??TTTT?CGTAC?CA?CTACTATTGA???AG????C?AA?CGTTATAAAA??AAGTA???CTGTAT?GT?CTAGC??GCTGTATACC????CC?T?GTA???TATTC??CT?AACTTA????CAATGTTGAT?????AGACAC?CG??C??CATTG???????CGTC?AATAT??????ATGTACAAGACCTCTGG???????GAATAATT????CCCTCA?????A?TTAATTCA???????G???TCA?ATA?T?A??A?CA???AAA????A??AGG??C?C?????????ATA?CCCGTAAAT??CGA???????????AAGC?CA?CTT?GTAA?A?TCACAATTGATATAAGTATTA?T???GGTC?ATCACA grylioMVZ175945 ACTCCTC???AT?AGTG?AC?T????????GTCCTTTTG??TT???T?CT?AAGTAACTG????GTG??????????GGTG?ACA????????GT?TTTAACTT??TAGTATCACA??TTGA?CT?A???????C?AC???????A?AA?????????CCAATT?GCAA??CCCGTAATGACT???A????????TATA??C?TA?TT?TAT?????CAG???ATGACCTTCAT??A?CA?ACTGTGTCGTTG?CTAAC????????CTTAGTTGTC?TCT??????????A?CGCTATA?ATGGAAT?CTGAGAT???AC????CTCC???TTTACTCGC??CT????????ACTTAAAT?????TG?T??T?GATG??TCGATACCA???????ATGATACTTTC??T????????GCCATTAC??A??????????AAAAATTGTCAGGA??CACA??CGTCACTGG?AT??GGT??????????A?TAC?TC?CAAGAA?GTC?ACATT?CTG????T?TGTC?CACCGCAAA?CT??TGC?CTAAGAA?ACTCCCTCCTCAT?CTAT???CC???TA?????GGCAC?????CC???T?TT?TGGC?TCATTT???T????CATGC????A?GGTCCTA??AGAACTTAGGCGA?CTTGA??????TTCCC?TAG???CAG????TCAC???????CTAAT?ACAA?TATAGTTCA?TT?????????AGCA????TC?CA???AAA?AC???T??????C?????AGCTAG????AA???????AAACT?TTCA????CC??ATATC??G?C????????CCA??ATCGG?ATAC?????ACAACC?CGA?GCA??TA?TATAAGTCG???TAA??????CTG?TGATCTACCGCTTGTAATCGT???TTTC??????A?????ATC???????????ACTAA?T?????GTCAT????????T????AA?TA?A?ACAAT??ATAAT??TCTTGAGAACT???AAAGCAAC???GGGACTATT?CTCA??CCG????CA?CACG?ATT?????ATTACCCT??AT?????CAAT??C??CAA???GGG??ATAT?????ACCG??TTCGA?CACCAA?TCTG?TTAGA???AA?C?TA?TAAGAAG?AT??AACTTAAA?CTG?????????CAAAGA??????A??GATC??ACTCTATTACT??TCCACTTTT??GTTTTGTACTATTA?CAGTAAACGAAAG?C?TT?ATA?CCCCA?????????????TCCCTATA????????CA??A???????????????????????C?GA?AA??????GGTC??????CT?AGT?CGG??????TTAAA??????GTT??TTAAA???GAAAAGT??TTCA?T????A?CC??????AATGGGAA?TCAACA?????TTAGG?ACAG???????CGT??ATACGTGCACAA????CGACAGACCA?C???????TGAG???????????ATCT??G?AC?TCGTA???ACAAATCTTC????GTT?C?A???AAT??CAACTGC???????A?CTCAG?CTAAATTCTCTAC?TTTTATA???A??AAG???????????A?CATATTGCCT???????AAT?A?CC???AGCTGAAGGAA?GCTCTTGCTGAAT?ATTATG?GA?CTC??TTAATTGAT????????????TAC?????GTATA?AATGCAA?????????GTT?A??T??T????ACGAAGCCC??ACAATGCCC?A??????????????CATCT???????????GACCCAACACAAGAT?AAAACAT????CG?GAT???A????AATT??AACTATCCGCATT??T?????AAAA??CCGCAGTATTCC?????ACGAGAAA?CACAAAGAAATATGAC??CAAAT???CAA?ACCTGCAAA?????????CCGA????TTACTGACACAGCTAGGTAGTA?TTTACT??GCT?ATAA?A?CTC??G??C?CTAAG??ACTAAGCAG?AAAA????????????TA?A?C?CTATAAACAT??A??GAGT??A?GT?CGACGGAGAA?T??GTCA?????ATC?GTACTAAGATTGACTACT????AA?A??????A????GTGAAATTCCAT????????????TCACGC??TCATGGTG??????????ACAATA?GC?TGT?A??TAACC???AGATTC?A?????TT?ATA???GT?????ACTATACG????????TTTGA?A??A?????ATTAC??????TAT?ATC?T?TTT????CC?CA?CAATA??CA??AG??????????TT?T?TAGGA?CATA????A??TA??????????AGG??????AACGTCGTT?TGAGATCG?ATAA??ACCAAGTACGTACTA???TATG?TCTAA?CCC??????AAAGATT??????ACTCA?GC?????????TA?????G????TTT?????ATTAGG?CCTTTTAAGATCCTA???TC?G?T??GTGA???AT?TCTGTATAAT?G?AA??ACC?AT?CGC?ATGC????TATC?G???T??????????TCA??CTAGACAC?CA?TATTGG??AAAAAT?A???AACAGTG?TTGC?????????????????????ATAT???TA??GCT?AC??AT???????T????CACTT?ATAG?GGTTAGGAACTATGTGGAG??GATA?GT?ACTAT??????????ATATTAA?????CTAGA?GATC?CT?TGTCAGA?AGA???????AACAATCT?T?TTACCAATCAG?AT??CTT?TTGTGT???TTTG?TGTCC??A?CTATTATTGA???AG????T?AA?CATTTTCAAA??AAGTA???CTGCAT?GT?CTTGC??GCCGTATACC????TC?T?GTA???T?TCC??CT?AACTTA????TAATGTTGCT?????AGACAC?CG??C??CATTG???????CGTC?AATAT??????ATATA???????TTTGG???????AAATAACT????CACTTA?????A?TTAAT?CA???????A???TTA?ATA?T?A??A?CA???AAA????T??AGA??T?C?????????ATA?CCTGTAAAT??CGA???????????CAGC?CA?CTT?GCAA?A?TCAGAATTGATACATGTATAC?T???GGTC?TTTACA okaloosae ACTTCTT???AT?AGTG?GC?T????????GTCCTGTTG??CT???T?TT?AAGTAGCTA????ATG??????????GGTG?ACA????????GT?TTTAATTT??CAGTATCAAA??TTGA?TT?A???????C?AC???????A?AA?????????CCAATT?ACAA??CCCGTAATGAC????A????????TATA??C?TA?AT?TAT?????CAG???ATGACCTTCTT??A?TA?ACCGAGTCGCTG?CTAAC????????TATAGTTGCA?TCT??????????A?CGCTATA?ATGGAAT?CTGAGAT???AC????CTCC???TTTTCTCGC??CT????????AATTAAAT?????T?????C?GATG??TAGATACCA???????ATGAAACTTTC??C????????GCCGTTAC??A??????????ACAAATTGTCAGTA??CACA??CGTCACTGG?AT??GGT??????????A?TAC?TT?CACGAA?GTA?ACACT?CTG????T?TATC?CACCGCAAA?CC??AGC?CTAAGAA?CCTCTCTCTTCAT?TTAT???CC???TA?????AGCAC?????CT???T?TG?TGGA?TCACTT???T????CATGC????G?AGCCCTA??AGAACTTAAACGA?CTTAA??????TTCTC?TAG???CTG????TCAC???????CAAGT?ACAA?TATAGTTCT?GT?????????AGTA????TC?CA???AAA?AC???T??????A?????AGCTAG????AA???????AAGGT?TTCA????CC??ACGTC??A?C????????CCA??ATCGG?ATAT?????AAAATC?TAACGCA??TA?TATAAATAG???TAG??????CTG?CGATCCATAGCCTGTATTCAT???TTTC??????A?????ATT??????A??CTATTAA?T?????GTCAC????????T????AA?TA?A?ACAGT??ATAAT??TCTAGCGAATT???AAAGCAAT???GGGACTATT?C??A??CTG????CA?CATA?ATT?????ACTACC???????????CAAT??C??CAA???GGG??ATAT?????ACCG??TTCGA?CATCAA?TCTG?TTCGA???AA?C?TA?TAAGAAT?AT??ATATTAGA?CCG?????????CAAAGA??????A??GATCTCACCCTATTACT??ACCAATTCT??GCTTTGTACTATTA?CAGTAAATGCAAA?C?TT?GTA?CACCATGG??T?ATC?GATTCCTATA????????TG??A???????????????????????C?GA?AA??????GATC??????TA?AGT?CGG??????TTAGAC??CTAGTT??TTAAA???GAAAAGC??TCCA?T????A?AC??????AATGAGAA?TCAACA?A???TTAGG?GCAG???????TGT??ATACGTCCACAA????TGACAGACCT?C???????TGTG???????????ATTG??G?CC?TTGTA??GATAAATCTTA????GTT?T?A???AAT??CGACTGC???????A?CACAG?CTAGATTCACTCC?TTTTATA???C??GAA???????????A?CATATTGCAT??????CAAT?A?C????AGCTGAAGAAG?GCTCTTGCCGAGTGATTATG?GA?CTC??TTAATTGATTG???GGAACA?TAC?????GTATA?TATGCAA?????????GTT?A??C??T????GGGAAGCTC??AC?????TT?A??????????????CATCC???????????GACCCAATACAAGAA?AAAACAT????CG?AAT???A????A?CT??AACCATCCGCATT??T?????AAA???CCGCAGTAATTC?????ACAAGAACTGACAAAGAAATATGAC??CAAAC???GAA?ACTCGCAAA?????????TCGA????TTACTGACAAAGCT?GGTAGAA?TTAACT??GCT?ATAC?A?TCT??G??C?CTAC?T?CCCAAGCAG?AAAATT?TGGAG???TTA?A?C?CTATAAACAT??A??GTGA??A?GT?CGACGGAAAA?T??G?CA?????ATA?GTACTAAGATTGATAATT???ATA?A??????A????GTGAAATTCTAC????????????TCACGC??TCATGGTA??????????ACAATA?GC?CGT?A??TAATC???AGATTC?G?????CT?ACA???GT?????ATTATACA????????TTTGA?A??A?????TTTAC??????TAT?TTC?T?TCT????TC?CA?C???????A??AG??????????CT?C?TAGGA?CATA????AAAAA??????????AGG??????AATGTCGCT?TGAGATTG?ATCA??GCCAAGTACGTACCA???TATG?TA?AA?CCC??????AAAGCTT??????ACTTA?GT?????????AT?????G????TCT?????ATCCGG?TCTTTT????????A???TT?G?T??GTGA???AT?TCTGTATAAT?G?AA??ACC?AT?CGT?ATGT????TACA?GATTT??????????TCA??CT????????A?TCTTGG??AAAATT?A???AACAGTG?TCGC?????????????????????ATAT???TA??GTT?AC??AT???????C????CACCCGATAG?GGTCAGGAACTATGTGGAG??AATA?GT?ACTATTAAATT???TACGCTAA?????CTACT?GATC?AT?TACCAGA?AGA???????AACAATTTCT?TTGCCAATCAG?AT??CTT?TTGTGTC??TTTA?TGTAC?CA?CTACTATTGG???AG????T?AA?CATTTTAAAAAAAAGTA???CCGTAT?GT?CTAAC??GCCGTATTCC????TC?T?GTA???TGTTC??CC?AACTTA????CAATGTTGGT?????AGCTGC?CG??C??CATCG???????CGTC?AATAT??????ATGTA???????TCTGG???????GAATAATT????CACTCA?????A?TTAATTCG???????A???TCA?ATA?T?A??A?CA???AAG????A??AGG??C?G?????????ATA?CCTTTAAAT??CGA???????????TAGC?CA?CTT?GTAA?A?CCGCAATTGATATAAGTATTAAT???GGTC?CTAACA clamitansJSF1118 ACTTCTC???AT?AGTG?GC?T????????GTCCTGTTG??TT???T?TT?AAGTAGCTG????ATG??????????GGTG?ACA????????GT?TTTAATTT??TAGTATCAAA??TTGA?TT?A???????C?AC???????A?AA?????????CCAATT?ACAA??CCTGTAATGAC????A????????TATA??C?TA?AT?TAT?????CAG???ATGACCTTCAT??A?TA?ACCGAGTCGCTG?CTAAC????????TATAGTTGCA?TCT??????????A?CGCTATA?ATGGAAT?CTGAGAT???AC????CTCC???TTTTCTCGC??CT????????AATTAAAT?????T?????T?GATG??TTGATACCA???????TTGAAACTTTC??C????????GCCGTTAC??A??????????ATAAATTGTCAGTA??CACA??CGTCACTGG?AT??GGT??????????A?TAC?TT?CACGAA?GTA?ACACT?CTG????T?TATC?CACCGCAAA?CC??TGC?CTAAGAA?CCTCTCTCTTCAT?TTAT???CC???TA?????TGAAC?????CT???T?TG?CGGA?TCACTT???T????CATGC????G?AGCCCTA??AGACCTTAAACGA?CTTAA??????TTCTC?TAG???CAG????TCAC???????CAAGT?ACAA?TATAGTTCT?GT?????????AGTA????TA?CA???AAA?AC???T??????A?????AGCTAG????AA???????AAGGT?TTCA????CC??ATGTC??A?C????????CCA??ATCGG?ATAT?????AAAATC?TAACGCA??TA?GATAAATAG???TGG??????CTG?CGATCCATAGCCTGTATTCAT???TTTC??????A?????ATT??????A??CTATTAA?T?????GTCAC????????T????AA?TA?A?ACAGT??ATAAT??TCTAGAGAAAT???AAAGCAAC???GGGACTATT?C??A??CCG????CA?CATA?ATT?????ACTACC???????????CAAT??C??CAA???GGG??ATAT?????ACCG??TTCGA?CATCAA?TCTG?TTCGA???AA?C?TA?TAAGAAT?AT??ATCTTAGA?CCG?????????CAAAGA??????A??GATCTCACCCTATTACT??ACCAATTCT??GCTTTGTACTATTA?CAGTAAATGCAAG?C?TT?GCA?CACCATGG??T?ATC?TATCTCTATA????????TG??A???????????????????????C?GA?AA??????GATC??????CA?AGT?CGG??????TTAGAC??CTAGTT??TTAAA???GAAAAGC??TCCA?T????A?AC??????AATGAGAA?TCAACA?G???TTAGG?GCAG???????TGT??ATACGTTCACAA????TGACATACCT?C???????TGTG???????????ATTG??G?CC?TTGTA??GACAAATCTTA????GTT?C?A???AAT??CGACTGC???????A?CACAG?CTAGATTCACTCC?TTTTATA???C??GAA???????????A?CATATTGCAT??????CAAT?A?C????AGCTGAAGAAG?GCTCTTGCCGAATGATTATG?GA?CTC??TTAATTGATTG???GGAACA?TAC?????GTATA?TACGCAA?????????GTT?A??C??T????GGGAAGCTC??AC?????TT?A??????????????CATCC???????????GACCCAACACAAGAA?AAAACAT????CG?AAT???A????A?CT??AACCATCCGCATT??T?????AAA???CCGCAGTAATTC?????ACAAGAACTAACAAAGAAATATGAC??CAAAC???AAA?ACTCGCAAA?????????TCGA????TTACTGACAAAGCT?GGTAGAA?TTAACT??GCT?ATAC?A?TCT??G??C?CTAC?G?CTCAAGC??????ATT?TGGCG???TTA?A?C?CTATAAACTT??A??GTGA??A?GT?CGACGGAAAA?T??G?CA?????ATA?GTACTAAGATTGATAACT???ATA?A??????A????GTGAAATTCTAC????????????TCACGC??TCATGGTA??????????ACAATA?GC?TGT?A??TAATC???AGATTC?A?????TT?ACA???GT?????ATTATACA????????TTTGA?A??A?????TTTAC??????TAT?TTC?T?TCT????TC?CA?C???????A??AG??????????CT?C?TAGGA?CATA????AAAAA??????????AGG??????AATGTCGTT?TGAGATTG?ATCA??GCCAAGTACGTACCA???TATG?TA?AA?CCC??????AAAGCTT??????ACTTA?GT?????????GT?????G????TCT?????ATCCGG?TCTTTT????????A???TT?G?T??GTGA???AT?TCTGTATAAT?A?AA??ACC?AT?CGT?ATGT????TACA?GATTT???????????CA??CT????????A?TCTTGG??AAAATT?A???AACAGTG?TCGC?????????????????????ATAT???TA??GTT?AC??CT???????C????CACCCGATAG?GGTCAGGAATTATGTGGAG??AATA?GT?ACTATTAAATT???TACGCTAA?????CTACT?GATC?AT?TACCAGA?AGA???????AACAATTCCT?TTGCCAATCAG?AT??CTT?TTGCGTC??TTTA?TGTAC?CA?CTACTATTGG???AG????T?AA?CATTTTAAAAAAAAGTA???CCGTAT?GT?CTAAC??GCCGTATTCC????TC?T?GTA???TATTC??CT?AACTTACTCACAATGTTGAT?????AGCTAC?CG??C??CATCG???????CGTC?AATAT??????ATGTA???????TTTGG???????GAATAATT????CACTCA?????A?TTAATTCA???????A???TCA?ATA?T?A??A?CA???AAG????A??AGG??C?C?????????ATA?CCTTTAAAA??CGA???????????CAGC?CA?CTT?GTAA?A?CCGCAATTGATATAAGTATCAAT???GGTC?ATAACA heckscheriMVZ164908 ACTTCTC???AT?AGTG?GC?T????????GTCCTGTTG??TT???T?CT?AAGCAGCTG????GTT??????????GGTG?ACA????????GC?TTTAATTT??TAGTGTCAAA??TTGA?TT?A???????C?AC???????A?AA?????????CCAATT?ACAA??CCCGTAATGAC????A????????TATA??C?TA?AT?TAT?????CAG???ATGACCTTCAT??A?TA?ACCGGGTCGCTG?CCAAC????????TAGAGTTGCA?TCT??????????A?CGCTATA?ATGGAAT?C??AGAT???AC????CTCC???TCTGCTCGC??CT????????AATTAAAT?????T?????A?GATG??TAGATACCA???????GTAATACTTTC??T????????GCCGTTAC??A??????????ACAAATTGCCAATA??CACA??CGTCACTGG?AC??TGT??????????A?TAC?CT?CAAGAA?GTA?ACATT?CTG????T?TATC?CACCGCAAA?CC??TGC?CTAGGAA?TCTCTCTCT???????AT???CC???TA?????AGCAC?????CT???T?TC?TGAA?TCACTT???T????CATGC????G?AGCCCTA??AGAACTTAAACGA?CTTAA??????T?CTC?TGG???AAG????TCAC???????CAAGT?ACAA?TATAGTTCT?GTCCACTATTTAGTA????TC?CA????????C???T??????T?????AGCTAG????AA???????AAGAT?TTGA????CC??AAATC??A?C????????CCA??ATCGG?ATAC?????AAAATC?TAA?GCA??TA?GATAAATAG???TGG??????CTG?CGATAAATAGCCTGTATTCAC???TTTC??????A?????ATT??????A??ATATTAA?T?????GTCAC????????T????AA?TA?A?ACAGT??ATAAT??TCTAGAGAATT???AAAGCAAA???GGGACTATT?C??A??CCGATAGCA?CACA?ATT?????ACTACCCT??AT?????TAAT??C??CAA???GGG??ATAT?????ACTG??TTCGA?CATCAA?TCTG?TTCGA???AA?C?TA?TAAGAAA?AT??ATATTAGA?CCG?????????CAAAGA??????A??GACCTCACCCTATTACT??CCCAATTTT??GCTTTATTCTATTA?CAGTAAACGGAAG?C?TT?GCA?CACCA??G??T?ATC?GATACCTATA????????CA??A???????????????????????C?GA?AATAAACAGATCTATAACTT?AGT?CGG??????CTAGAC??TTAGTT??TTAAA???GAAAAGT??TTCA?T????A?AC??????AATGAAAA?TCAATA?T???TTAGG?GCAG???????TGT??ATACGTGCACAAACAGCGACAGACCT?C???????TGTG???????????ATAG??G?TC?TCGTA??GACAAATTTTA????GTT?C?A???AAT??CTACTGC???????A?CACAG?CTAGATTCCCTCC?TTTTACA???C??AAA???????????A?CATATTGCAT??????TAAT?A?C????AGCTGAAGAAA?GCTCTTACAGAATGATTAAG?GA?CTC??TTAATTGA??G???GGAACA?TAC?????GTATA?TACGCAT?????????GTT?A??A??T????GAGAAGCAC??AC?????TT?A??????????????CATCT???????????GACCCAACACAAGAA?AAAACAT????CG?AAT???A????A?TT??AACCATCCGCATT??T?????AAAA??CCGCAGTAATCC?????ACGAGAACTAACAAAGAAAAATGAC??CAAAC???GAA?ACTCGCAAA?????????TCTA????TTACTGACAAAGCT?GGTAGTA?TTAACT??GCT?ATAA?A?TAC??G??C?CTACGA?TCGAAGCAG?AAAATT?TGGAG???TTA?A?C?CTATAAACAT??A??CAGT??A?GT?TGACGAAAAA?T??G?CA?????ATG?GCACTACGATTGATAACT???AAA?A??????A????GTGAAATTCCAT????????????TCACGC??TCATGGTA??????????ACAATA?GT?TGT?A??TAATC???AGATTC?G?????CT?ACA???GT?????AATATACA????????TTTGA?A??A?????TTTAC??????TAT?TTC?C?TCT????TC?CA?C???????A??AG??????????CT?C?TAGGA?TATA????AAAAA??????????AGG??????GATATCGCT?TGAGATTG?ATTA??ACCAAGTA?GTACCA???TATG?TA?AA?CCC??????AAAGCTT??????ACTTA?GT?????????CT?????G????TCT?????ATCCGG?CCTTTT????????A???TT?G?T??GTGA???AT?TCTGTATAAT?G?AA??ACC?AC?CGC?ATGT????TACA?GATTT??????????TCG??CT????????A????????????ATT?A???AACAGTG?TCGC?????????????????????ATAC???TA??GTT?AC??AT???????C????CACCCGATAG?GGTTAGGAACTATGTCGAA??A????GT?ACTATTAAATT???TATGCTAA?????CTACT?CATC?AT?TATCAGC?AGA???????AACAATTCCC?TTGCCAATCAG?AT??CTT?TTGCGTC??TTCA?TGTAC?CA?CTACTATTGA???AG????A?AA?CATTTTGAAA??AAGTA???CCATAT?GT?CTAGC??GCCTTATTCC????TC?T?GTA???TATTC??CT?AACTTA????CAATGTTGAT?????AGTCAC?CG??C??CATTG???????CGTC?AATAA??????ATGTA???????TTTGA???????GAATAATT????CACTCA?????A?TTAATTCT???????A???CCA?ATA?T?A??A?CA???AAA????A??AGG??C?C?????????ATA?CCTTTAAAC??TGA???????????TAAC?CA?CTT?GTAA?ACCCATAATTGATATAAGTATATAA???GGTC?AT?ACA catesbianaX12841 ACTTCTC???AT?AGTG?GC?T????????GTCCTGTTG??TT???T?TT?AAGG?ACTG????GTG??????????GGTG?ACA????????GT?TTTAATTT??CAGTATCAAA??TTGA?TT?A???????C?AC???????A?AA?????????CCAATT?ACAA??CCCGTAACGAC????T????????TATA??A?TA?GT?TAT?????CAG???ATGACCTTCAT??A?TA?ACCAGGTCGCTG?CCAAC????????TACAGTTGTG?TCT??????????A?CGTTATA?ATGGAAT?CTGAGAT???AC????CTCC???TTTGCTCGC??CT????????AATTAAAT?????T?????C?GATG??TTGATACCA???????ATGATACTTTC??C????????GCCGTTAC??A??????????ATAAGTTGTCAATA??CACA??CGTCACTGG?AT??TGC??????????A?TAC?TT?CACGAA?GTA?ACATT?CTG????C?TATT?CACCGCAAA?CC??CGC?CTAAGAA?CTTCTCTCTTTAT?TTAT???TC???TA?????AGTAC?????CC???T?TG?TGGA?TCACTT???T????CATGC????G?GGTCCTA??GGAACTTACACGA?CTTAA????????????????????G????TCAC???????CAAGT?ACAA?TATAGTTCT?GT?????????GGTA????TT?CA???AAA?AC???T??????A?????AGCTAG????AA???????AAGTT?TTCA????CC??AAATC??A?C????????TCA??TTAGG?ATAT?????AAAATC?TAA?GCA??TA?GATAGATAG???TGG??????CTG?TGATACATTGCTTGTATTCAT???TTTC??????A?????ATT??????A?ACAACTAA?T?????GTCAT????????T????AA?CAAA?ACAGT??ATAAT??TCTAGAGAATT???AAAGCAAC???GGGATTATT?C??A??CTG????CA?CATG?ATT?????ACTACCCT??AT?????CAAT??C??CGA???GGG??ATAT?????ACCG??TTCGA?CATCAA?CCCG?TTTGA???AA?T?TA?AAAGAAC?AA??ATTTT?GA?CCG?????????CAAAGA??????A??GACCTCACCCTATTACT??TCCAATTCT??GCTTTGTACTATTA?CAGTAAACGCAAG?C?TT?GCA?CACCATGG??A?ATC?GATCCCTATA????????CA??A???????????????????????C?GA?AA??????GATC??????CA?AGT?CGG??????CAAGAC??CTAGTT??TTAAA???GAAAAGC??TTCA?T????A?AC??????AATGAAAA?TTAACA?A???TTAGGGACAG???????CGT??ATACGTCCACAA????AGACAGACCG?C???????TGTG???????????ATCG??G?CC?TTGTA??GACAAATCTTA????GTT?C?A???AAT??CAACTGC???????A?CACAG?ATAGATTCTCTCC?TTTTACA???C??CAA???????????A?CATATTGCAT??????CAAT?A?C????AGCTGAAGA?A?GCTCTTGCCGAATGATTACG?GA?CTC??TTAATTGA??G???GGAACA?TAC?????GTATA?TACGCAA?????????GTT?A??C??T????G?GAAGCCC??AC?????TT?A??????????????CATCA???????????GACCCAACACAAGAA?AAAATAT????CG?AAT???A????A?CT??AACCATCCGCATT??T?????AAAA??TCGCAGTATTCC?????ACGAGAACTAACAA?GAAATATGAC??CAAAC???GAA?ACTCGCAAA?????????TCGA????TTACTGACAAAGCT?GGTAGTA?TTAACT??GCT?ATAA?A?TCC??G??C?CTACGA?CCCAATCAG?AAAATT?TGGAG???TTA?A?C?TTATAAACAT??A??CTGC??A?GT?CGACGGAAAA?A??G?CA?????TTA?GTACTAAGATTGATAACC???AAA?A??????A????GTAAAATACTAT????????????TCACGC??TCATGGTA??????????ACAATA?GC?CGT?A??TAATC???AGATTC?G?????TT?ACA???GT?????GTTATACA????????TTTAA?A??A?????TTTAC??????TTT?TTA?C?TTT????TC?TA?C???????A??AG??????????TT?C?TAGGA?CGTA????AAAAA??????????AGG??????AACATCGTT??GAGATTG?ATCA??ACCAAGTACGTACCA???TAAG?TA?AA?CCC??????AAAGATT??????ACTGA?GC?????????CT?????G????TCT?????ATCCGG?TCTTTT????????A???TT?G?T??GTGA???AT?TCTGTATAAT?A?AA??ACC?AC?CGT?ATGT????TACA?GACTT??????????TCA??CT????????A?TGTTGG??AAAATT?A???AACAGTG?TCGC?????????????????????ACAT???TA??GTG?AC??AT???????C????CACCCGATAG?GGTTAGGAACTATGTTGAG??AATA?GT?ACTATTAAATT???TATGCTAA?????CTACC?AATC?AT?TGTCAAA?AGA???????AACAATTCCT?TTGTCAATCAG?AA??CTT?TTGCGTC??TTTAGTGTAC?CA?CTACTATTGG???AG????A?AA?CGTTTTGAAA??AAGTA???CCGAAT?GT?CTAGC??GCCGTATTCC????TC?T?GTA???TGTTC??CT?AACTTA????TAATGTTGAT?????AGCTTC?CG??C??CATTG???????CGTC?AATAT??????ATGTA???????TTTGA???????GAATAATT????CTCTAA?????A?TTAATTCA???????A???GCA?ATA?T?A??A?CA???AAA????A??AGG??T?T?????????ATA?TCCCTAAAT??CGA???????????TAGC?CA?CTT?GTAA?A?CCATAATTGATATAGGTATTAAT???GGTC?ATAACA catesbianaDMH84R2 ACTTCTC???AT?AGTG?GC?T????????GTCCTGTTG??TT???T?TT?AAGG?ACTG????GTG??????????GGTG?ACA????????GT?TTTAATTT??CAGTATCAAA??TTGA?TT?A???????C?AC???????A?AA?????????CCAATT?ACAA??CCCGTAACGAC????T????????TATA??A?TA?GT?TAT?????CAG???ATGACCTTCAT??A?TA?ACCAGGTCGCTG?CCAAC????????TACAGTTGTG?TCT??????????A?CGTTATA?ATGGAAT?CTGAGAT???AC????CTCC???TTTGCTCGC??CT????????AACTAAAT?????T?????C?GATG??TTGATACCA???????ATGATACTTTC??C????????GCCGTTAC??A??????????ATAAGTTGTCAATA??CACA??CGTCACTGG?AT??TGC??????????A?TAC?TT?CACGAA?GTA?ACATT?CTG????C?TATT?CACCGCAAA?CC??CGC?CTAAGAA?CTTCTCTCTTTAT?TTAT???TC???TA?????AGTAC?????CC???T?TG?TGGA?TCACTT???T????CATGC????G?GGTCCTA??GGAACTTACACGA?CTTAA????????????????????G????TCAC???????CAAGT?ACAA?TATAGTTCT?GT?????????GGTA????TT?CA???AAA?AC???T??????A?????AGCTAG????AA???????AAGTT?TTCA????CC??GAATC??A?C????????TCA??TTAGG?ATAT?????AAAATC?TAA?GCA??TA?GATAGATAG???TGG??????CTG?TGATACATTGCTTGTATTCAT???TTTC??????A?????ATT??????A?ACAACTAA?T?????GTCAT????????T????AA?CAAA?ACAGT??ATAAT??TCTAGAGAATT???AAAGCAAC???GGGATTATT?C??A??CTG????CA?CATG?ATT?????ACTACCCT??AT?????CAAT??C??CGA???GGG??ATAT?????ACCG??TTCGA?CATCAA?CCCG?TTTGA???AA?T?TA?AAAGAAC?AA??ATTTT?GA?CCG?????????CAAAGA??????A??GACCTCACCCTATTACT??TCCAATTCT??GCTTTGTACTATTA?CAGTAAACGCAAG?C?TT?GCA?CACCATGG??A?ATC?GATCCCTATA????????CC??A???????????????????????C?GA?AA??????GATC??????CA?AGT?CGG??????CAAAAC??CTAGTT??TTAAA???GAAAAGT??TTCA?T????A?AC??????AATGAAAA?TTAACA?A???TTAGGGACAG???????CGT??ATACGTCCACAA????AGACAGACCG?C???????TGTG???????????ATCG??G?TC?TTGTA??GACAAATCTTA????GTT?C?A???AAT??CAACTGC???????A?CACAG?ATAGATTCTCTCC?TTTTACA???C??CAA???????????A?CATATTGCAT??????CAAT?A?C????AGCTGAAGA?A?GCTCTTGCCGAATGATTACG?GA?CTC??TTAATTGA??G???GGAACA?TAC?????GTATA?TACGCAA?????????GTT?A??C??T????G?GAAGCCC??AC?????TT?A??????????????CATCA???????????GACCCAACACAAGAA?AAAATAT????CG?AAT???A????A?CT??AACCATCCGCATT??T?????AAAA??TCGCAGTATTCC?????ACGAGAACTAACAA?GAAATATGAC??CAAAC???GAA?ACTCGCAAA?????????TCGA????TTACTGACAAAGCT?GGTAGTA?TTAACT??GCT?ATAA?A?TCC??G??C?CTACGA?CCCAATCAG?AAAATT?TGGAG???TTA?A?C?TTATAAACAT??A??CAGC??A?GT?CGACGGAAAA?G??G?CA?????TTA?GTACTAAGATTGATAACC???AAA?A??????A????GTAAAATACTAT????????????TCACGC??TCATGGTA??????????ACAATA?GC?CGT?A??TAATC???AGATTC?G?????TT?ACA???GT?????GTTATACA????????TTTAA?A??A?????TTTAC??????TTT?TTA?C?TTT????TC?TA?C???????A??AG??????????TT?C?TAGGA?CGTA????AAAAA??????????AGG??????AACATCGTT??GAGATTG?ATCA??ACCAAGTACGTACCA???TAAG?TA?AA?CCC??????AAAGATT??????ACTGA?GC?????????CT?????G????TCT?????ATCCGG?TCTTTT????????A???TT?G?T??GTGA???AT?TCTGTATAAT?A?AA??ACC?AC?CGT?ATGT????TACA?GACTT??????????TCA??CT????????A?TGTTGG??AAAATT?A???AACAGTG?TCGC?????????????????????ACAT???TA??GTG?AC??AT???????C????CACCCGATAG?GGTTAGGAACTATGTTGAG??AATA?GT?ACTATTAAATT???TATGCTAA?????CTACC?AATC?AT?TGTCAAA?AGA???????AACAATTCCT?TTGTCAATCAG?AA??CTT?TTGCGTC??TTTAGTGTAC?CA?CTACTATTGG???AA????A?AA?CGTTTTGAAA??AAGTA???CCGAAT?GT?CTAGC??GCCGTATTCC????TC?T?GTA???TGTTC??CT?AACTTA????TAATGTTGAT?????AGCGTC?CG??C??CATTG???????CGTC?AATAT??????ATGTA???????TTTGA???????GAATAATT????CTCTAA?????A?TTAATTCA???????A???GCA?ATA?T?A??A?CA???AAA????A??AGG??T?T?????????ATA?TCCCTAAAT??CGA???????????TAGC?CA?CTT?GTAA?A?CCATAATTGATATAGGTATTAAT???GGTC?ATAACA virgatipesMVZ175944 ACCTCTTAT?AT?AGTG?GC?T????????GTCCTG?CG??TT???T?TT?GAGTAACTG????GTG??????????AGTG?ACA????????GC?TTTAATTT??CAGTAACAAA??TTGA?TT?A???????C?AC???????A?AA?????????CCAATT?ACAA??CCCGTAAAGAC????A????????TATA??C?TAAAT?TAT?????CAA???ATGACCTTCAT??A?AA?ACCAAGTCGCTG?CT??????????????AGTTGTG?TCT??????????A?CGCTATA?ATGGAAT?CTGAGGT???AC????CTCC???TTCACTCGC??CT????????ACTTAAAT?????TG?T??G?GATG??TAGATACCA???????ATTAAATTTTC??C????????GCCGTTAC??C??????????ATAAGTTGGCAGAA??CACA??CGCCACCGG?AT??ACT??????????A?TAC?TT?CAGGAA?GTA?ACATT?CTG????T?TATC?CACCGCAAA?CC??TGC?CTAAGAA?TCTCTCTCCTTAC?TTAT???TC???TA?????AGCAC?????CT???T?TA?TGGA?TCACTT???T????CATGC????A?AAACCTA??AGAACT?AAACGA?CTTAA??????TTCCC?TGG???CAG????ACAC???????CAAGT?ACAA?TCTAGTTCT?AT?????????AGTA????TT?CA???AAA?AC???T??????A?????AGCTAG????AA???????AGGTT?TTCA????CC??ACATC??A?C????????CCA??ACCGG?ATAT?????AAAACC?TCA?GCA??TA?AATAGGTAG???TGA??????CTG?CGATTCATAGTTTGTATTCGT???TTTG??????A?????ATT??????A??ATATTAA?T?????GTCAT????????T????AA?TA?A?ACAGT??ATAAT??TCTAGAGAACT???AAAGCAAT???GGGTCTAAT?C??A??CTG????CA?CATTTATT?????ACTACCCT??AT?????CAAT??C??CAA???GGG??ATAT?????ACCG??TTCGA?CATCAA?TCTG?TTCGA???AA?A?TA?AAA?AAG?AT??TTTTTAGA?CTG?????????CAAAGA??????A??TATC??ACCCTATTATT??CCCAATTCT??GCTTTGTGCTATTA?CTGTAAATGCAAG?C?TT?GCA?CATCATGG??A?ATC?GATCCCTATA????????CA??A???????????????????????C?GA?AA??????GACC??????TA?AGT?CGG??????CTAGAC??CTAGTT??TTAAA???GAAAAGC??TTCA?T????A?GC??????AATGAGAA?TTAACA?????TTAGG?ACAC???????CGT??ATACGTTCACAC????C???AGACCT?C???????TGTG???????????ATAG??G?TC?TCGTA??GAAAAATCTTC????GTT?C?A???AAA??CGACTAC???????A?CTCAG?CTAGATTCCCTCC?TTTTAT????C??CAT???????????A?CATATTG?GT??????CAAT?A?C????AATTGAAGAAA?GCTCTTGCCGAATGATTACG?GA?CTC??TCAATTGATTG???GGAACT?TACG?TTG?TATA?TA?GCAA?????????GTT?T??T??T????GAGAAGCCC??ACAATACTT?ACT????????????CATCC???????????GACCTAATACAAGAA?AAGACAT????CG?GAT???A????A?CT??AACTATCCGCATT??T?????AAAA??CCGCAGTATTCC?????ACCAGAGATAACAAAGAAATATGAC??CAAAC???AAAAACTTGCAAA?????????TTGA????TTACTGACAGAGCT?GGTAGTA?CTGACT??GCT?ATAA?A?TCT??G??C?CTATGT?CCTAAGCAG?AAAATT?TGATG???TGA?A?C?TTATAAACAT??A??GGGA??A?GT?CTAC?TGAAC?T??G?CA?????ATT?GTACTAAAATTGATTACT???AAA?A??????A????GTAAAATCCCAT????????????TCACGC??TCATAGTA??????????ACAATA?GC?TGT?C??TAATC???ATATTC?G?????CT?ATA???GT?????AGTATACG????????TTTAA?A??A?????TTTAC??????TAT?TTC?T?TTT????TC?CA?AAATA??CA??AG??????????CT?C?TAGGA?CATA????AAAAA??????????AGG??????AGCGTCGCT?TGAGATTG?ATTA??GCCAAGTACGTACCA????ATG?TC?AA?CCC??????AAAGTTT??????ACTCA?GT?????????GC?????G????CCT?????ATCTGG?TCTTTT????????A???TC?G?T??GTGA???AT?TCTGTATAAT?G?AA??ACC?TT?CGT?ATGT????TACA?GATTT??????????TCA??CT????????A?TATTGG??ATAATT?A???AACAGTG?CCGC?????????????????????ATAT???TA??GTT?AC??AT???????C????C?CCCGATAG?GGTTAGGAACTATGTGGAGATAATA?GT?ACAATTAAATT???TATGCCAA?????CTTTC?AATC?AT?TTTCAGC?AAA???????AACAATCT?C?TTGCCAATCAG?AT??CTT?TTGCGTC??TTAA?CGTAC?TA?CTACTT??GG???AG????T?AA?CGTTTTCAAA??AAGTA???CCATAT?GT?CTTGC??GCCTTATACC????AC?T?GTA???TATCC??CC?AACTTA????AAATGTTGCT?????AGATAC?CG??C??CATTG???????CGTC?AATAT??????ATGTA???????TCTGG???????GAGTAATT????CTCTCA?????A?TCA?TTCA???????A???TTG?ATA?T?A??A?CA???AAA????A??AGGTCC?A?????????ATA?CCTATAAAC??CGA???????????CAGC?CA?CTT?GTAA?A?TCATAATTGATATATGTATTA?T???GGTC?ATAACA maculataKU195258 ACC?CCCGT?A??AGTG?GC?T????????GTCCTGTCG??TT???T?CT?AACTAACTA????GTA???TA?AC??GGTG?ACAATCTT??GGT?TTT??CCT??TA?TATT?TA??TTAA?TT?A???????C?AC???????G?AA?????????CCAATT?ACAA??TCCGTAATGAC????AA???????TGTATAC?TA?AT???T?AT??CAT???ATGACTTTCAT??A?AG?ACATAGACGTTG?CCAAC????????TTTAGTTGTC?TCC??????????A?CGATTTA?ATGGAAT?CTGAGGTAT?AC????CTCC???TTTACTCGC??CT????????CCGTAAA??????TG?T??T?CATT??TCGATACTA???????ATGAAATTTTC??C????????GCCCTTAC??A??????????A??AGTTGGCACAAA?CACA??CAATA?TGG?AC??TGA??????????A?CAC?GC?CTGAAATGTA?ACATT?CTG????T?TATA?AGCCATAAA?CA??AGC?TTTAGAA?TTTCTCTCATTTT?TTAT???CC???TA?????AGTAC?????CG???T?TG?GGAG?TCACTT???T????CGTGC???GT?CACCCCA??AGAACTTAAGCGATCTTAA??????TTCTC?TAG???TAG????ACAT???????CTAAT?ACAAATA?AGTTCC?GT?????????AGGACTT?TC?CA???AAA?AC???T??????A?????AGCTCG????AA?TATGCTTTGTT?TCTA????CC??ACGTC??A????????????A??AACGGTATAC?????AAAATC?AAA?A?A??TA?AA????????????C??????CTT?TGATAAAT??????????TAT???TTTA??????ACCT??AGT??????A??CT??TAA?T?????GCCGC????????T????TA?CC?A?ACACT??ATAA???TCTCGTGAACT???AAAGCAAA???GG?????TT?C??A??CGA????CA?TACA?ATT?????ACTACCCC??AT?????TAAT?????CAG???GGG??ATAT?????ACTG??TTCAA?CATCAA?CCAG?TTAGA???AA?C?TAATAAGGAT?AT??CACT??GA?CAG?????????CAGAGA??????A??GA?????????GTTAAT??TCCAATTCT??GCTTTTTGTTATTA?CGGTAAATGCAAT?C?TT?AAA?TACCATGG??T?????AATCCCT?T?????????CA??A???????????????????????C?GA?AA????????AC??????CA?AATACGG??????TTACAC??CTAGT???TTAAA???GAAAAGT??TTCA?T????AGGT???????ATGGAGG?TCAATA?????TTAGG?ACGT???????TGT??ATACGTCCACCG????AGAC??ACTT?C???????TGAGG??????????ATT?????AC?TCGTA??GAAAATTCCTT????GTG?C?A???AAT??TAACTAC???????AACACAG?CTAGATTCTCTCC?TTTTATA???C??AAA???????????A?CATATTGCAT??????TAGT?A?C?ATAAGTTGAACTAG?GCTCT???AGAATGATTATG?GA?CTC??TTCATTGATCG???GGAACA?TACG?TAGGTATA?ACCGTACCTTCACCAGGTT?C??A??T????AAGAAGCACCGACCT??CCT?ATTTC??????????CATCTTCAGCC????AGACTCAACACAAGAATAACACAT????CG?GAT???A????A?CA??ACCCAACCCC?TT??T?????AAAC??GTGCAGTTTTAC?????ACGAGAACT??????AGAGAATGAC??CAAAC???TAAAACTTGCAAA?????????TCAA????TTACTGACAGAGCT?GGTAGTAATTTACT??GCT?ATAA?A?TGATGG??C?C??AGCTTCAAAACAG?AAAATT?TGGAG???TAA?A?C?TTACACGCAT??A?GGCGA??G?GT?CGAT?ATAAT?T??G?CA?????GTA?GTACTA??????ACCAAT???AAA?G??????A????GTGAACTTCAAC????????????TCGCGC??TCA??GTC??????????ACAATA?GT?AGT?A??AAATC???A??TTC?G?????TT?ATT???G????????TCTACG????????CTTAA?A??T?????TTTAC??????TGT?ATC?A?TAT????CC?CA?CAATA??CG??AG??????????CT?T?TA?GA?TATA????AAAAA??????????AGTA?ACTGTCCCTCGAT?TGAG?TAG?A??A??ACCAAGTACGTACCA???TCTG?TG?AA?CTC??????AAA?AAT??????GCTAA?GA?????????AT?????G????ACT?????ATTTGG?TATTT?????????A???CC?G?C??GT?A???CC?TCTGTATAAC?TGAA??ACC?AT?CG??A????????ACA?GATCT??????????TCC??CA????????A?TCTTGG??ACAATA?A???AA??????????????CCATTT????????GTAA??????TA??GAC?AC??AT???????C????CACTCGATAG?GGATAGGAAATCTGTGAAA??AATG?GT?A?????????A???CATAGCAT?????CTACC?AATC?GT?TTCCAGG?AAA???????ATCAA?CT?C?TCGCAAGCC?G?AC??CTT?TTGAGCC??TTTT?AGTTC?CA?CTACAGTTGG???AG????C?AA??ATTTTT?AA??A?GTA???CT?CAT?GT?CTCGC??GCTTTAAGCT????TT?T?GTA???TATTC??CA?AACTTA????TAATGTTGGTA????AGTTAT?CG??C??CATCGAT???ATCATC?TATAC??????ATATA???????TCAGG???????AGATGATT????CCCTTA?????A?CGACTTCA???????C???CCG?ATA?T?A??A?CG???AAA????T??AGG??T?A?????????ATA?CCCGTAAAT??TTAAC????????CAAGC?CA?CTT?GTAA?T?TAGGAATTG?TACGAGTATCA?T???GGTC?GTGACA vibicariaMVZ11035 ACT?C?????????????GC?T????????GACCTGTGG??TT???T?TT?CATT????A????ATG???TA?GC??GGTG?ACAACCAA??GGT?TTT??TCTGACCGTAGC?CA??TTGA?TT?A???????CGAT???????G?GA?????????CCAA?????????????TAACGAC????G?T??????TATA??T?TA?TT???T?AT??CAG???ATAACCCTCAT??ACGT?ACAAAGGCGCTG?CTATC????????TTC?GTTGGT?TCT??????????A?CGTTCTA?ATGGAAT?CT?????????C????CTCC???TTCC?TCGC??CT????????CTCTAAA??????TG?T??T?CATG??TAGATACTA???????CTGAAACTTTC??C????????GCCATTAC??ATTTTTTAC??A??AGTTGACAAAA??CACCT?CATTGCCGG?AC??TTA??????????A?TAC?TC?CTAAAATGTA?ACACT?CTG????T?TATA?A?????????C???TGC?TTTAGGA?TTTCTCCCGACCT?ACAA???CC???TA?????AGTAC?????CA???T?TTTAGAG?TCATTT???T????CATGC???TC?CGCCCCA??GGAACTTAGACGATCTTAA??????TT?GCCTAG???CAG????TCAC???????GAAT??ACAAATGTAGTTCC?TT?????????AGAACGTATT?CA???AAA?AC???T??????G?????AGCTCG????AA?TACGCTTAAAT?TCTA????CC??TATTC??A????????????A??AACGGTTTAC?????AA??????????TA??TA?CA????????????T??????CTT?CG????????????????TTTGCATTTG??????ACCT?TAAC??????A??GG??TAA?T?????GCCGT????????T????AA?CT?A?ACAAT??ATAA???T??AGAGAATT???AAAGCAGA???GG?????TT?C??A??CTA????CACTAGT?ATT?????ACTACCCC??AT?????TAAT??C??CAC???GGG??ATAT?????ACCG??TTCAA?CAACAT?TCTG?TTAGA???TA?T??AACAAGCGC?ATCA?ACT??GA?CTA?????????TAAAGT??????A?????????????GCTAC???TCCAATT?T??GCTTTATCCTATAA?CAGTAAATGAAAA?C?TC?GAA?TACCATGG??T?ATC?GATCCCT?TA????????CA??A???????????????????????C?GATAA????????AC??????TA?AATATGG??????TTAGAC??CTAGTT??TTAAA???AAAAAGC??TTCA?T????AGGT??????AATGATAACTCAATA?????TCA?G?AAGA???????CGT??ATACGTACACAA????AG?C??ACCG?C???????TGAG??????????????TCCG??C?TTGTC??GAGAAT?CTTG????GTG?C?A???AAT??TAACTAC???????A?CTCAG?TTAGATTCACTGC?TTTTATATTAT??GAA???????????A?CATATTGCAT??????TAAC?A?C?ATAAGCTGAATTAG?GC?CA???AGAATGACTATG?GA?CTC??TCCGTTGATTG???GGGACA?TACG?CTGGTATA?ACTGTAC?????????ATT?A??C??T????GCGAAGCGCTGACCATACTT?ATTTC??????????CA?CCTATGCT????AAACCCAACAAAAGAAAAAAAAAT????CG?GATGTAA????A?TA??ATCCA??CGC?TT??T?????AATC??CTG???????AC?????ACAAGA?????????AAAAAATGAC??CAAAAAATCAAAACTCGAA?A?????????TCTAAAC?TTACTGACAAAGCT?GGTAGTTATTCA?T??GCT?ATAG?A?CGATGG??C?CTAGGT?TCAAAACAG??AAATT?TGAGG???TAA?AT?????????CAT??A?AGCGA??A?GT?TAAC?AATAA?A??G?CA?????TTA?GTACTA???????AGAAT???AAA?G??????A????GTAAA???CA?C????????????TCACGC??ACA??ATA??????????ACAATA?GT?CGT????TAACC???AAATTC?G?????AT?ATA???GT?????ACTGTACG????????TTTGA?A??CACGAACTTAC??????TAT?CTC?G?TTT????CC?CA?CAGTA??CG??AG??????????CT?T?TAGG????TG????AAAGA??????????GGAA?ACAG?TAATCGAT?TAAGATAG?A??A??GCCAAGTACGTACCAAATTTTG?TA?AA?CCC??????AAA?GCT??????ACTAA?GA?????????CA?????G????TTT?????ATCTGG?CATTT?????????A???CT?G?C??AT?A???AT?CCTGTATAAT?CGAT??ACC?TA??GG?A????????TTA?GATCT??????????TCC??CA????????A?TATTGG??ACAACG?A???AATA?TG?TCGC????CCATTT????????ATAATGT???TA??GCC?AC??AT????????????CACTTGATAG?GGATAGGAAATTTGTCAAG??AATG?GT?A?????????A???TACGGTAT?????CAGTC??????????TGCAGC?AAA???????ATCAATCA?T?TTGCAAATCAA?TC??CTT?TTGAGCC??TTTT?CGTAC?CA?CTACAGTTGG??????????????????TTA?AA??A?GTA???CTTCAT?GT?CTTGT??GCTATAAACT????CC?T?GTA???TATAT??CA?AACTTA????TAATGTTGGTA????AGGTCC?CA??C??CATTGTTTATATCGTC?GATAC??????ATATA???????TCAGGTCCCATAAGATGATT????CTCTAA?????ATAA?CTTCA???????ACGATCG???????????????????G????AAGAGA??A?T?????????ATA?CCCGTAAAA??TGAAC????????CTAGC??????T?GTAA?C?TCAAA?TTG?TATGAGTATCC?A???GGTC?TTGACA warszewitshiiJSF1127 ACA?CCTGT????AGTG?GC?T????????GT?CTGTGG??TT???T?CT?CAAA???TA????ATG???TA?G????GTG?AC?ATCGC??GGC?TTT??CCT??CAGCAAC?TA??TTAA?GT?A???????C?AC???????T?AA?????????TC???????????????????GAC????G?A??????CATA??T?TA?CT???T?AG??CAA???ATAAACCTCAC??ACGC?ACAAAGACGCTG?ACAAC?????GTCTTC?????TC?TCT??????????A?C?GTCTA?ATGGAAT?CTGA????????ACAGCTCC???TTT??????????????????CTGTAAA??????TG?T??T?CATG??TGGATACTACATTTCCCTAAACT?TTC??C????????GCCATTA???A??????????A??AGTTGACAAAA??CACA??CATTACTGG?AC??TTC??????????A?TAC?TA?CCCGAATGTGCACACT?CTG????C?TATA?A?????????CT??TGC?CTTAGAA?CCTCT?????AAT?ACAT???CC???TA?????GGTAC?????CA???T?TCTA?AG?TCACTT???T????CATGC???GA?GATCCCA??GGAACTTAGACGATCTT?A??????TT?GCTTAGATATAG????ATAC???????CAAT??ACAAATCTAGTACA?TT?????????AGAACATCTT?CA???AAA?A??????????????????????G????AA?TACGCCATGTT?TTCA????CC??AATTC??A????????????A??AAAAGTAT?G?????CA??????????TA??TA?AA????????????T??????TTT?CGATCTAC??????????TTC???TTTG??????ACCT?TAAT??????A??AG??TAA?T?????GCCGT????????T????AA?CT?A?ATAAT??ATAA???T??AGAGAATT???AGAGCACA???GG?????TT?C??A??CAG????CACCAAT?AAT?????ATTACCCC??ATTAGAACAAT??C??CAA???GGG??ATAT?????ACCG??TTCAA?CATCGG?ACAG?TTCGA???TA?C??AATAAGCGT?ATTA?CTT??GA?CCA?????????CAAAGT??????A?????????????GTTAT???CCCAATT?T??GCTTTC?TCTATAA?CTCTAAACGACAA?C?TC?GAA?GTCCATGG??C?ATC?GATCCCT?CA????????CC??A???????????????????????C?GATAA????????AC??????AA?AATATGG??????CTAGAC??CTAGTT??TGAAA???AAAAAGC??CTTA?T????ACAC??????AATGACAA?TAAATA?????TCAGG?TAGG???????GGT??ATACGTGCACAG????AG?C??ACCA?C???????TGAG???????????ATTA??A??CGTTGTT??GAAAAT?ATTG????GTG?T?A???AAT??TAACTAC???????A?CTCAA?TTAAATTCACTCCCTT?T?TACTGT??AAA???????????A?CATATTGCAT??????CAGC?A?C?ATAAGCTGAACAAA?GC?CA???AGATCGATTATG?GA?CTC??TCCGTTGATTG???GGAACC?TACG?CTGG??TA?CTCGTAT?????????ATT?G??C??T????GCGAAGCGCCGACCTTACATCATTTC??????????CA?CATTCGCC????GAACTCAATAAAAGCAGAATATAC????CG?GTT???A????A?AA??ATCAA??CGC?TC??T?????AACG???TG???????AC?????ATAAGAATT??????AGAGAATGAC??CAAAGAATCAAAACTTGAAAA?????????TCCA????TTACTGACATAGCT?GGTAGTCATTTATT??GCC?ATAA?A?CCATGA??C?CTAGGA?CCAAAACAA?AAAATT?TGATG???TTA?ATC?TTATAAACATA?AAAGTGC??A?GT?ATAC?GAAAA?C??G?CA?????TTA?GTACTA??????GCAAAT???AAA?G??????A????GTCATCTTCT?T????????????TCCCGC??ACA??ATA??????????ACAATA?GT?TGT?C??TAATC???AAATTC?G?????AT?ATC???GT?????AATCTACATATGAAATCTT???A??A?????TATTCTTCCTCTAT?CTC?C?TCT????TC?CA?CTGTA??CT??AG??????????CT?T?TAGGA?CATG????AAAAA??????????GGAT?ATGG?CTATCGTT?TAA?ATAGAA??A??ACC?AGTACGTACCAAACTTTG?TA?TA?CCC??????AAA?AAT??????ACTA???G?????????TT?????G????TAT?????AAATGG?TATTT?????????A???CT?G?T??AT?A???A??CCTGTATAAT?AGAC??ACC?AA?CGTGA????????ACA?GATCT??????????TCT??CA????????A?TGTTGG??ACAACG?A???AATA?TG?TCGC????C??????????????????GT???TA??GCT?AC??AT????????????CACATGATAG?GGATAGGAAGTTTGTCAAG??TATG?GT?A?????????A???TACGGCAT?????CAACT??????????CATAGC?AAA???????ACC?ATCT?TGT?ACAAATCAA?CC??CTT?TTGCGAC??TTCC?TGTAC?CA?CTACAATTGT???AG????T?GA??ATTTCA?AA??A?GTA???TACAA??GT?ATAGTA?GCTGTAACCT????AC?T?GTA???TGTCT??CA?AACCTA????T??TGTTGATA????AGGCTCGCG??C??CATCGATAGTAT??????ATAG??????ATATA???????TTAGGTTCCATAGAATGATT????CTCTAA?????A?AG?CTTCA???????A???TTG???????????????????G????TAGGGG??A?AATTCACCACATA?CCCGTAAAT??CGA?C????????CTGGT?CA?CTT?GAAA?C?TCGAA?TTG?TATGCGTATCC?GAG?GGTC?TTGACA palmipesVenAMNHA118801 TCT?CCCGG?A??AGTG?GC?T????????GCCCTATCG??TT???T?AT?AATAACCTA????GTGAGATAAAA??GGTG?ACAATCTT??GGC?TTTGATCT??TAGTGTT?TA??TTAG?TT?A???????C?AC???????T?AA?????????CCTATT?GCAA??CCTGTAACGAC????A????????CATA??G?TA?AT???T?AT??CAA???AATACCTTCAT??A?CT?ACAGGGACGTTG?TAATC????????TGGAGTTGTT?TCT??????????A?CG?TTTA?ATGGAAG?CTGAAAT???AC????CTCC????CTACTCGC???T????????CAATACAT?????TG?T??A?C??????CGATACTA???????TTAAAAATTTC??T????????GCCTTTAC???????????????????TGCCAAAA??CACA??CCGTACTGG?AG??ATG??????????A?TAC?CT?CAAT?ATGCG?ACAGT?CTG????T?TGTA?TGCTACAAA?CC??GGC?CTTTGAA?CTTCTCCCGTCTTCCCAT???TC???TA?????TGTAC?????CC???T?TC?TGAG?TCAATT???T????CGTGC???CC?AATCCC???????CTTAGACGATCTTAA??????TTC?C?TAG???AAG????TTAT???????CCACTCACAAATTTAGTCTC?AT?????????TG??????TC????????A?AC???T??????A?????AGCTCG????AA?TACGCCAC????CGTACAT?CC??GATTC??A????????????A??AAAGGCATAC?????ACCATC?TGG?GCG??TA?GA????????????T??????CTT?AGATCTATCACTCG?ATTCAA???TTTT????????????????????????AT??TAA?T?????GACAA????????T????AA?CG????????????AA???TCTTGAGAAAT???GAAGCAAC???GA?????TT?C??A??TAA????CA?TACG?ATT?????AGCACCCA??AT????????T??C??CAG???GGG??ATAA?????ACCG??TTCAA?CACCTA?CAAG?TTGGA???TA?C?TAATAAGTAT?AT??TTTTCAAA?CTG?????????CA?????????????AA?????????ACTACC??ACCAATTCT??GCCTTATTTTATTA?CGGTAAATG??????????ACA?TTCCATGG??T?ATC?AATCTCT?TACCCATACTCA??A???????????????????????C?GA?AA????????TC??????TC?AAT?CGA??????TTAAAC??CTAGTT??TTAAA???GAAAAAC??TTCA?T????AACT??????AATGGTAG?TTAACA?????TTAGG?GCGG???????TGT??ATACGTCCACAT????CGAC??ACCT?C???????TGAG???????????ATTG??G?AC?TTGTC??GATAAATCTTG????GTG?C?A???AAC??TAACTAC???????A?CTCAA?ATAGATTCCCACC?TTTTATA???T??CAT???????????A?CATATTGCAT????????AT?A?C?ATAAGTTGAACAAA?GCTCT???AGGGTGAT???GAGA?CTC???????CGATTG???A?GACA?TACG?CCGGCATA?ATCGTAC?????????GTT?T??C??T????AAGAAGCCC??ACCGTCCCT?ATT????????????CATCATCTACTCT??AGACCCAAAAC?AGAAAAATACAC????CG?AAT???A????A?ACCCAACCAACCAC?TT??T?????AATA??TTGCAGTACTTC?????ATTAGGAAT??????AGAC??TGAC??CAAAT???TAAAACTTGCAA??????????TCAA???GTTACTGACACAGCT?GATAGTAATTTACT??GCT?ATAC?A?CTCT??????????AA?TCTAAGTATACAAATT??GTTG???TAA?A???CTATAGACAT??T??GAGT??A?GT?AGAC?CAGAA?C??A?CT?????CT??GTACTA??????ATCAGT???AAA?G??????A????GTA?ATTTCGAA????????????TT???C??TCA??GTA??????????ACTATA?GG?TGT?A??TAACC???ACATTC?T?????TT??TT???GT?????AATGCACG????????TTTAA?ATAC?????TTTCC??????TAT?TTC???TAT????CC?CA?TAGTA??CG??CG??????????TT?T?TAGGA?CATA????AAACA??????????AGAA?ATAGCACTTCAGTCTAAGACAG?ATTA??ACCAAGTACGTACC????TTTGATT?TA?CCC??????AAA?GT???????TCTCA?GA?????????AT?????G????TTT?????ACCTGG?TATTT?????????A???TC?G?TG?GT?A???AC?CCT?CATAAT?G?A???ACCAGT?CGT?TAGTCATATATC?GATTT??????????TCT??CT????????A?TTTTGG??AAAACA?ACATAAG?????ACGC????CCAATT????????ATAATAC???TC??GTT?AC??CT???????C????CACCTGATAG?GG?AAGG?????TG???????AATA?GT?A?????????G???AACGGCAT?????CATCA?GAAC?TT?TGTCAAC?AA??????????????AT?C?TTGTAAATCAG?AA??CT???????????T????TGT???CA?CTACACTTGAGT?AG????T?AA??ATTCT????????GTA???CTACAT?GT?CTAGC??GCATTAAACC????A????GTA???TGTTT??CA?AACTTA????TAATGTTGGTA????AGGGAC?CA??C??CATAGATCATATCACC?AATAT??????ATATA???????TTAGG???????AAGTAACTTTATCCCTTA?????A?CCACTTCA???????A???ATG?ATA?T?A??A?C????AAA????A??TGA??G?T?????????ATA?CCAG???A????GAAC????????CGAGC?CA?CTT?GTAA?G?TCATAATTGATACGGGTATAA?A??TGGTC?CTGATA palmipesEcuKU204425 ACT?CCCGG?A??AGCG?GC?T????????GCCCTGTTG??TT???T?TT?AATTACCTA????GTGAGTTACAT??GGTG?ACAATCCA??GGC?TTTGATCT??CAGTACT?TA??TTAG?TT?A???????C?AT???????A?AA?????????CCAATT?ACAA??CCCGTAACGAC????A????????TATA??G?TA?GT???T?AT??CAG???ATTAACTTCAT??A?AT?ACAGAGACGTTG?TTAAC????????TGGAGTTGTC?TCC??????????A?CG?TGTA?ATGGAAA?CTGAGCT???AC????CTCC????CTACTCGC??CT????????CCATACAT?????TG?T??C?C??????TGATACTA???????CTAAAAATTTC??T????????GCCATTAC??A??????????A??AGTTGACAAAA??CACA??CAGTACCGG?AC??TTA??????????A?TAC?CT?CAAA?ATGCG?ACAGT?CTG????C?TATA?TGCTACAAA?CC??GGC?TTTCGAA?TTTCTCCCATCCTCTTAT???CC???TA?????GGTAC?????CT???T?TC?AGAG?TCAATT???T????CGTGC???CT?AATCCC???????CTTAAACGATCTTAA??????TTCCC?TAG???AAG????TCAT???????CGACT?ACAAATATAGTTCC?CT?????????CGAA????TA????????A?AC???T??????A?????AGCTTG????AA?TATGCCCTGAT?CTTA????CC??GAATC??C????????????A??ACAGGTATAC?????ACCATC?TGA?GCG??TA?AA????????????T??????CTT?CGATCTATAGTTTG?ATTCAA???TTTA??????ACCT?TAGT??????A??AT??TAA?T?????GCCGA????????T????AA?CG????????????AA???TCTCGAGAAAT???GAAGCAAT???GG?????TT?C??A??TAA????CA?TATT?ATT?????ACTACCCT??AT????????T??C??CAG???GGG??ATAA?????ACTG??TTCAA?CACCAA?CAAG?TTGGA???TA?C?TAATAAGAAC?AT??CTCTCAAA?CTG?????????CAAAGA??????A??AA?????????GCTATT??ACCAATTTT??GCTTTATATTATTA?CAGTAAATG??????????ACA?TACCATGG??T?ATC?AATCTCT?TA????????CA??A???????????????????????C?GA?AA????????TC??????CA?AAT?CGA??????CCAGAC??CTAGTT??TTAAA???GAAAAGA??TACA?T????AGTT??????AATGACAG?TTATCA?????TTAGG?ACGA???????TGT??ATACGTCCACAT????AGGC??ACCC?C???????TGAG???????????ATCA??G?AC?TTGTC??GAGAAATCTTA????GTG?T?A???AAC??TAACTGC???????A?CCCAG?TTAGATTCCCACC?TTTTATA???C??CAA???????????A?CATATTGCAT??????TAGT?A?C?ATAAGTTGAATCAA?GCTCT???AGAGTGAT???GAGA?CTT???????AGATTG???GGAACT?TACG?CTGGTATA?ATCGTAC?????????GTT?T??T??T????GGGAAGCGCCAACCTTACCT?AAT????????????CATCGTTCGCT????AGACCCAACAC?AGAAAAATACAT????CG?AAT???A????A?ATGCATCTAACCAC?TT??T?????AATG??TCGCAGTACTGC?????ATCAGGACT??????AGACCATGAC??CAAAC???TAAAACTCGCAA??????????TCAA???GTTACTGACAAAGCT?GATAGTAATTAACT??GCT?ATAC?A?CTCT??????????GA?TCAAAGTAGAAAAATT?TGGAG???TTA?A???TTACAGACAT??T??AAGG??A?GT?CGAT?GAGAA?T??A?CA?????TT??GTACTA??????AGGAGT???AAA?G??????A????GTG?ACTTCGAC????????????TCACGC??TCA??GTG??????????ACCATA?GG?TGT?A??TAATC???ACATTC?T?????CT??TT???GT?????AATGCACG????????TTTAA?A??C?????TTTCC??????TAT?TTC?T?TAT????TC?CA?CAATA??CG??TG??????????TT?T?TAGGA?TATA????AAATA??????????AGAA?ATAGTACTTCACTCTGAGACAG?ATAA??ACCAAGTACGTACC????TTTGATT?TA?CCC??????AAA?GTT??????ACTGA?GA?????????AT?????G????TTT?????ACTTGG?CATTT?????????A???TC?G?T??GT?A???AC?CCT?CATAAT?A?AG??ACC?CC?CGT?CCGC????TATA?GACCT??????????TCT??CT????????A?TATTGG??AAAACG?A???AAGGGTG?ACGC????CCATTT????????GTAATAC???TC??GTT?AC??AT???????C????CACATGATAG?GGGCAGG?????TGTGAAG??AATG?GT?A?????????G???CATGGCAT?????CTCTT?AAAC?AT?TACCAGC?AAA???????ATCAATAT?C?TCGCAAGTCAG?AA??CT???????????T????TGT???CA?CTACAGTTGG???AG????T?AA??ATTTTC?AA??A?GTA???CTATAT?GT?CTTGC??GCTTTAAGCC????CC?T?GTA???TGTTT??CA?AACTTA????AAATGTTGATA????AGACAC?CA??C??CATAGATAATATCGCC?AATAC??????ATATA???????TTAGG???????AAGTAATT????CTCTCA?????A?CAACTTCA???????T???TTG?ATA?T?A??A?CA???AAG????G??CGA??A?A?????????ATA?CCAGTAAA????GAAC????????CAAGC?CA?CTT?GTAA?A?TCGAAATTGATATGAGTATTA?T??TGGTC?ATGATA Sp_1_ecuadorQCAZ13219 ACT?CTTGA?A??AGCG?GC?T????????GCCCTGTCG??TT???T?TT?AATAACCTA????GTG???TACAC??GGTG?ACAATCTA??GGT?TTTAATCT??TAGTACT?TA??TTAG?TT?A???????C?AT???????G?AA?????????CCAATT?ACAA??CCTGTAACGAC????A????????TATA??G?TA?CT???T?ATTTCAA???ATTACCTTCGT??A?AC?ACAAAGACGTTG?TAATC????????TAGAGTTGTT?TCC??????????A?CG?TGTA?ATGGAAT?CTGAGAT???AC????CTCC????CTACTCGC??CT????????TCATACAT?????TG?T??C?C??????CGATACTA???????CTAAAACTTTC??T????????GCCATTAC??A??????????A??AGTTGACAAAA??CACT??CAGTACCGG?AA??CGA??????????A?TAC?AT?CAGAAATGCG?ACAGT?CTG????T?TATA?CGCTACAA??CT??AGC?TTTTGAA?TTTCTCCCATTTTCTTAT???CC???TA?????GGTAC?????CC???T?TG?AGAG?TCAATT???T????CGTGC???CT?AACCCCA??AGAACTTAAGCGATCTTAA??????TTCTC?TAG???AAG????TCAT???????CGATT?ACAAATCTAGTTCC?CT?????????CGAA????TA????????A?AC???T??????A?????AGCTCG????AA?TATGCTCTGAT?CCTA????CC??GATTC??G????????????A??ACAGGTATAT?????ACCATC?AGA?GCG??TA?AA????????????T??????CTT?TGATCTATAGCTTG?ATTCGT???TTTA??????ACCT?TAGT??????A??AT??TAA?T?????GCCAT????????T????AA?TG????????????AA???TCTAGCGAACT???GAAGCAAC???GG?????TT?C??A??TAA????CA?TATA?AAT?????ACTACCCT??AT?????TAAT??C??CAG???GGG??ATAA?????ACTG??TTCAA?CACCAA?CAAG?TTGGA???AA?C?TAATAAGAAC?AT??CTTT??AA?CCG?????????CAAAGA??????A??AA?????????GTTATT??CCCAATTAT??GCTTTTTATTATTA?CAGTAAATGTAAA?C?TT?GCA?TGCCATGG??T?ATC?AATCTCT?TA????????CA??A???????????????????????C?GA?AA????????TC??????CA?AAT?CGA??????CTAGAC??CTAGCT??TCAAA???GAAAAGT??TACA?TACAAAGTT??????AATGATAG?TCATCA?????TTAGG?ACGA???????CGT??ATACGTCCACAT????AGGC??ACCC?C???????TGAG???????????ATTT??G?AC?TCATA??GAGAAATTTTG????GTG?T?A???AAC??TCACTAC???????A?CCCAG?TTAGATTCTCACC?TTTTATA???CTCTAA???????????A?CATATTGCAT??????TAGT?A?C?ATAAGTTGAATCAA?GCTCT???AGAATGATCAGGAGA?CTT???????CGATTG???GGAACA?TACG?CTGGTATA?ATCGTAC?????????GTT?A??T??T????GTGAAGCTCTAACCCTACCT?AAT????????????CATCGTTTGCT????AGACTCAACAA?AGAATAATACAT????CG?AAT???A????A?ATGCATCTAACCAC?TT??T?????AATG??TCGCAGTATTTC?????ATTAGTACT??????AGACCATGAC??CAAAT???AAAAACTCGCAA??????????TCAA???GTTACTGACAAAGCT?GATAGGAATTGACT??GCT?ATAT?A?CTTT??????????GA?TCAAAGCATATAAATT?TGGAG???TTA?A?C?CTATAAACAT??T??GAGG??A?GT?TTAT?GTGAA?T??A?CG?????CT??GTACTA??????AGAAGT???ATA?G??????A????GTG?ACTTCGAC????????????TCACGC??TCA??GTG??????????ACAATA?GG?TGT?A??TAACC???ACATTC?T?????AT??TT???GT?????AATGTACG????????TTTAA?A??C?????TTTTC??????TAT?T??????AT????TC?CA?CAATA??CG??TG??????????TT?T?TAGGA?TATA????AAATA??????????AGAA?ACAGTACCTCGGTCTGAGACAG?ATTA??ACCAAGTACGTACC????TTTGATT?CA?CCC??????AAA?TTT??????ACTGA?GA?????????TT?????G????TCT?????ATTCGG?CATTT?????????A???TC?G?T??GT?A???AC?CCT?CATAAA?A?AG??ACC?CC?CGT?CCGC????TATA?GACCT??????????TCT??CT????????A?TATTGG??AAAACG?A???AAGAGTG?ATGC????CCATTT????????GTAATAC???TC??GTC?AC??AT???????C????CACATGATAG?GGGTAGG?????TGTGAAG??AATG?GT?A?????????G???CATGATAT?????CTTCT?GACC?AT?TATCAGC?AAA???????ACCAATAT?C?TCGCAAGTCAG?AT??CT???????????T????TGT???CA?CTACCGTTGG???AG????A?AA??ATTGTT?AA??A?GTA???CCACAT?GT?CTTGC??GCTTTAAGCT????TC?T?GTA???TGTTT??CA?AACTTA????TAATGTTGATA????AGGTAC?CA??C??CATAGATAATATCGCC?AATAC??????ATATA???????TCAGG???????AAGTAATT????CCCTTA?????A?CGACTTCA???????T???CTG?ATA?T?A??A?CA???AAG????A??GGA??A?C?????????ATA?CCAGTAAA????GAAC????????CCAGT?CA?CTT?GTAA?A?TCGAAATTGATACGAGTATTA?T???GGTC?GTGATA bwanaQCAZ13964 ACT?CCCGC?A??AGTG?GC?T????????GCCCTGTTG??TT???T?TT?AACTATCTA????GTG???TACAT??GGTG?ACA???????GGT?TTTGATCT??TAGTATC?TA??TTAG?TT?A???????C?AT???????A?AA?????????ACAATT?ACAA??CC??TAATGAC????T????????TATA??GCTA?AT???T?AT??CAG???ATAAACTACAT??A?AC?ACAAGGACGTTG?TGAAC????????TTGAGTTGTC?TCC??????????A?CG?TGTA?ATGGAAT?CTGAGAT???AC????CTCC????CCACTCGC??CT????????CTATACAT?????TG?T??C?C??????TGA???TA???????ATAA?ATTTTC??C????????GCCATTAC??A??????????A??AGTTGACAAAA??CACA??CAGCACCGG?AG??AAA??????????A?TAC?CC?CCGAAATGCG?ACAGT?CTG????C?TATA?CGCTACAAA?CT??AGC?TTTCGAA?CTTCTCCTATTCTTTTAT???CC???TA?????GGCAC?????CC???T?TT?AGAG?TCAATT???T????CGTGC???CTCTGTCCTA??GGAACTTAAACGATCTTAA??????TTCTC?TAG???TGG????TCACACC????CGAAT?ACAAATCTAGTTCC?CT?????????CGAA????TG????????A?AC???T??????ACGTCTAGCTTG????AA?TATGCCCTGAT?CCTA????CC??GAGTC??G????????????A??ACAGGTATAA?????ACCATC?AGA?GCG??TA?AA????????????A??????CTT?CGATCTATAGCGTG?ATTCTT???TTAA??????ACCT?TAGT??????A??GT??TAA?T?????GCCGA????????T????AA?TG????????????AA???TCTAGAGAACT???GTAGCACT???GG?????TT?C??A??TAA????CA?TACC?ACT?????ACTACCCT??AT?????TAAT??C??CAG???GGG??ATAA?????ACTG??TTCAA?CACCAA?CAAG?TTAGA???AA?C?TAATAAGGAT?AT??ACTTTAAA?CTG?????????CAAAGA??????A??GA?????????GTTATT??TCCAATTTT??GCTTTGTATTATTA?CAGTAAATGTGAC?C?TT?GCA?TACCATGG??T?ATC?AATCTCT?TA????????CA??A???????????????????????C?GA?AA????????TC??????CA?AAT?TGA??????CTAGAC??CTAGTT??TTAAA???GAAAAGC??TACA?T????AGTT??????AATGATAG?TCACCA?????TTAGG?CCGA???????AGT??ATACGTCCACAT????AAAC??ACCC?C???????TGCG???????????ATTT??G?AC?TCGTA??GAGAAATTTTG????GTG?T?A???AAC??TAACTAC???????A?CCCAG?ATAGATTCACACC?TTTTATA???C??TAA???????????A?CATATTGCAT??????TAGT?A?C?ATAAGTTGAACTAA?GCTCT???AGATTGATCACGAGA?CTT???CCATCGATTG???AGAACA?TACG?CTGGTATA?AACGTAC?????????GTT?T??A??T????GTGAAGCGCCGACCTTACCT?AA?????????????CATCATTTGCT????AGACCCAATAC?AGAATAATACAT????CG?AAT???A????A?ACGCATCCAACCAC?TTCTT?????AATG??TCGCAGTACTTC?????ATTAGTACT??????AGATCATGA?????AAT???AAAAACTCGCAA??????????GCAT???GTTACTGACAAAGCT?GATAGTAATTAACT??GCT?ATAT?A?TTTTGG??CACTAAGA?TCAAAGCATATAAATT?TGGTG???TTA?A?C?TTAAAAACAT??T??GAGA??A?GT?TTAC?GTGAA?T??A?CG?????TT??GTACTA??????AAAAGT???AAA?G??????A????GTG?ACTTCGAT????????????TCACGC??TCA??GTG??????????ACAATA?GG?TGT?A??TAACC???ACATTC?T?????AT??TA???GT?????AATGCACG????????TTTAA?A??C?????TCTTC??????TAT?TTT?A?TAT????TC?CA?CAATA??CA???????????????T?T?TAGGA?TATA????AAATA??????????AGTA?A????ATATCGATCTGAGATAG?ATCA??ACCAAGTACGTACC????TTCGATC?TA?CCC??????AAA?CTT??????ACTTA?GA?????????AT?????G????ACT?????ATTTGG?CATTT?????????A???TC?G?T??GT????????CCT?CATAAT?A?AG??ACC?CT?CGC?CTGC????TATA?GACCT??????????TCT??CT????????A?TATTGG??AAAAAC?A???AAGAGTG?ACGC????CCATTT????????GTAATAC???TC??GCC?AC??AT???????C????CACCTGATAG?GGATAGG?????TGTGAAG??AATG?GT?A?????????A???CATGATAT?????CTACC?CACC?AC?TCATAGC?AAA???????ACCAATAC?C?TTGCAAGTCAG?AT??CT???????????T????TGT???CA?CTACAATTGG???AG????C?AA??ATTTTA?AA??A?GTA???TCATAT?GT?CTTGC??GCTCTAAACT????CA?T?GTA???TGTTT??CA?AAC?????????ATGTTGATA????AGACAC?CA??C??CATAGATAATATCGCC?AATAC??????ATATA???????TTAGG???????AAATGATT????CTCTCA?????A?TGACTTCA???????A???CTG?ATA?T?A??A?CA???AAG????A??AGG??A?T?????????ATA?CCAATAAA????GAAC????????CTAGT?CA?CTT?GTAA?A?TCGGAATTGATACGAGTATTA?A???GGTC?TTGATA vaillantiKU195299 ????????C?A??AGTG?GC?T????????GCCCT?T?G??TT???T?TT?CACCACCTA????GTG???TA?AC??GGTG?GCAATCCA??GGC?TTTGATCT??TAGTTGC?T????TAG?CT?A???????C?AT???????ATAA?????????CCGATT?ACAA??CCCGTAACGAC????A????????AATA??G?TA?TT???T?AT??CAG???ATAACTTTCAT??A?AT?ACATAGACGTTG?TAACC????????CGTAGTTGTC?TC???????????A?CGCTGTA?ATGGAAT?CTGAGAT???AC????CTCC????TTACTCGC??CT????????CTATACAT?????TG?T??C?C??????CGATACTA???????TTAAAATTTTC???????????GCCATTAC??A??????????A??AGTTGACATAA??TACA??CAACACCGG?AG??TGG??????????A?TAC?TC?CCTAAATGCG?ACAAT?CTG????T????A?CGCCATAAA?CC??AGC?TTTAGAA?CTTCCCCCATTCTTATAT???CC???????????GTACACTACCA???T?TT?AGAA?TCAATT???T????CGTGC???CA?GATCCCA??GGAACTTAAACGATCT??????????TCCC?TAG????GG????TCAT???????CAAAT?ACAAATGTAGTTCG?CT?????????GGGA????TT????????A?ACTTGT????????????AGCTAG????AA?TATGCCCTGGT?CCTA????CC??GCTTC??A????????????A??ACTGGTATAA???????CATC?TGA?GCA??TA?TA????????????C??????CTC?TGATGTATGGTACG?ACTCGA???T?TA??????ACCT?TAGT??????A??AT??TAA?TT????GCCAA????????T????AA???????CAAT??ATAA???TCTAGAGAACT???GAAGCAAA???GC?????TT?C??A??TAA????CA?TATA?ATT?????ACTACC?T??AT?????TAAT??C??CAA???GGG??ATAA?????ACTG??TTCAA?CACCAA?TATG?TTAGA???AA?C?TAGTAAGAAC?AT??CCCT??TA?CGGCTAG?????CAAAGA??????A??GA?????????GC??TT??CCCAATTTT??GCTTTCTATTATTA?CAATAAAAGAGAT?C?TT?GCA?CACCATGG??A?ATCGAATTTCT?TA????????CT??T??????????????????????????A?AA????????TC??????CA?AAT?TGG??????TTATAC??ATAGTT??TTAAA???GAAAAGC??TACA?T????AGCT??????AATAAAAG?TTATCA?????TTAGG?ACGA???????GGT??ATACGTTCACAT????CTTC??A???????????????G???????????ATTT??G?GC?TAGTG??GAGAAATATTA????GTT?T?A???AAT??TAACTTC???????A?CTCAG?CTAGATTCACAC???TTTACA???T??AAA???????????A?CATATTGCAT??????TAGT?A?C?ATAAGTTGAATTAA?GCTCT???AGAATGATCACGAGA?CTC??TCTATCGATT????GGATCT?TACG?TTGGTATA?ACCGTAT?????????GTT?A??A??T????GAGAAGCTCCAACCATACCT?ATT????????????CATCGTTTACT????AAACACATTAT?AGAAGAAAACAT????CG?AAT???A????A?AC?CATCCAACCGC?TT??T?????AACG??TCGCA??ATTGC?????A?CAGTACT??????AAAACATGAC??CAAAC???AAAAACCCGCAA??????????TCAA???GCTACTGGCAACGCT?GATAGTAATTGACT??GCT?ATATGA?CTCTGA??C?CTAAGA?TCA?AGCAG?CAAATT?TGATG???T?A?A?C?ATATAAACAT??A?CGTGA??A?GT?TCAC?ATAAA?T???????????CT??GTACTA??????AGCAGT???AAA?G??????A????GTTAACTACTGC????????????TCACGC??TCA??GTG??????????ACTATA?GG?TGT?G??TAACC???AAATGC?G?????GT?ATA???GT?????CATGG?CG????????ATTGA?A????????????C??????TAT?TTT?G?TTT????TC?CA?CAATA??CG??TG??????????CT?C?TAGGA?CATA????AAATA??????????AGAA?ACAGCTCCTCGATCTGAGACAG?ATTA??ACCAATTA?GTATC????TTTGCTT?TA?CCT??????AAA?A????????GCTCA?GA?????????AT?????G????TC????????T??G?CATTT?????????A???CC?G?G??GT?A???AT?CCTGTATAAT?A?AT??ACC?TT?CGA?ATGT????TATA?GATTC??????????TCTAACT????????ATTCTTGG??AAAATA?A???AATAGTG?ATGC????CCATTT????????GTAATAT???TA??GTC?AC??AT???????C????CACATGATA??GGAAAGG?????TGTGTAG??AATA?GT?A?????????A???CATGGTAT?????CATAGCAACCAAC?TGCCA????AA???????ATCAATTC?T?TTGCAACTCAG?AC??CT???????????T????CGT???CA?CTACGGTTGA???AG????T?AA??ATTTTT?AA??A?GTA???CCACAT?GT?CTTGC??GCTCCAAGCC????TC?T?G?A???TGTTC??CA?AACTTA????GAATGTTGATA????AGAGAT?CG??C??CATCGATTATATCCC??AATAC??????ATATA???????TTAGA???????AAGTAATT????CTC????????????????CA???????A???TTC?ATA?T?A??A?CA???AAT????G??TGG??C?T?????????ATA?CCGGTAAA????GAAC????????CCGGT?CA?CTT?GTAA?G?TCTTAATTGATACGAGTATCA?C???GGTC?GTGATA julianiTNHC60324 ????????T?A??AGTG?GC?T????????GCCCT?TCG??TT???T?AT?AATAACCTA????GTG???TA?AT??GGTG?ACAATCCA??GGC?TTTGATCT??TAGTCCC?A????TAG?TT?C???????C?AT???????G?AA?????????CCAATT???????CCCGTAACGGC????A????????TATA??G?TA?ATG??T?AT??C??????TAACCTTCGT??A?AC?ACAAA?GCGTTG????CC????????CTTAGTTGAT?TC???????????A?CGTTGTA?ATGGAAT?CTGAAAT???AC????CTCC????CTACTCGCAACT????????TAATACAT?????TG?T??C?C??????AGATACTA???????TTAATACTTTC??C????????GCCATTAC??A??????????A??AATTGACAAAA??CACC??CAATACCGG?AA??TGT??????????A?TAC?TT?CATAAATGCG?ACACT?CTG????G????G?AGCCATAAA?CG??AGC?TTTTGAA?CCTCTCTCATACTTTCAT???CC???????????GTAC?????CC???T?TC?AGAA?TCGATT???T????CGTGC???TT?ATACCCA??AGACCTTAAACGATCTTAA??????TTCTC?TAG???AAG????TCAT???????CAAAT?ACAAATATAGTTTA?CT?????????GGGA????TT????????A?AC???T??????A?????AGCTCG????AA?TATGCTC?AAT?CCTA????CC??AAAT???A????????????A??ACTGGTATAG?????ACCATC?CGA?ACA??TA?AA????????????C??????CTT?TGATTTATGGTGCG?ACTCGT???TTTA??????ACCT?TAGC??????A??AC??TAA?T?????GCCAA????????T????AA?TG?A?ATAGT??ATAA???TCTTGAGAACT???GAAGCAAA???GT?????TT?C??A??TAT????CA?TACA?ATT?????ACTACC?T??AT?????TAAT??C??CGA???GGG??ATAA?????ACTG??TTCAA?CATCAA?AAAG?TCAGA???AA?C?TAGTAAGAAT?AT??TTATTAAA?CCG?TTG?????CAGAGA??????A??GA?????????GT?ATT??TCCAATTCT??GCTTTGTATTATTA?CAATAAAAGTGAT?C?TT?GTA?AACCATGG??T?ATCGAATTTCT?TA????????CA??A???????????????????????C?GA?AA????????TC??????AA?AAT?CG???????TTAGAC??TTAGTT??TTAAA???GAAAATG??TTTG?T????AGCT??????AATG?AGG?CCATCA?????TTAGG?ACGA???????TGT??ATACGTTCACAT????ATGC??ACCA?C???????TGCG???????????ATTC??G?CC?TCGTATCGAAAAATTTTG????GTT?T?A???AAT??TCACTAC???????A?CTC???ATAGATTCACGCC?TTTTATA???T??CAC???????????A?CATATTGCAT??????TAGT?A?T?ATAAGATGAATTAA?GCTCT???AGAATGATCAAGAGA?CTC??TCCTTCGATCG???GGAACT?TACG?TTGGTATA?GTCGTAC?????????GTT?A??A??T????GTGAAGCACCGACCTTTCAT?ATT????????????CATCTTTTGCT????AGACCCAGTAT????AAAAAATAT????CG?AAT???A????A?ACACATCCAACCAC?TT??T?????AATG??TCGCAGTAATAC?????ATTAGTCCT??????GGAGCATGAC??CAAAT???GAAAACTTGCAA??????????TCTA???GTTACTGACAGCGCT?GATAGTAATTGTCT??GCT?ATAT?A?ATCTG??????????T?TCA?AGCAC?TAAATT?TGAAG???T?A?A?C?ATACAAACAT??A?ACCGT??A?GT?CCAC?GTAAG?T???????????AT??GTACTA??????ATGAAT???AAA?G??????A????GTTAGCTCCTAC????????????TCACGC??TCA??GTA??????????ACCATA?GG?TGT?C??TAACCATAATATTC?G?????GT?ATA???GT?????TATATACG????????TTTAA?A??C?????TTTAC??????TAT?TTC?T?TTT????TC?CA?CAATA??CG??TG??????????AT?T?T?GGACCATA????AAATA??????????AGAA?ACAGCTCGTCGGTCTGAGACAG?ATTA??ACCAAATACGTACC????TTCGATA?CA?CCC??????AAA?CCT??????GCTGA?GA?????????ACAGGA?G????TTT?????ATACGG?TGTTT?????????A???CT?G?C??GT?A???TT?CCTGTACATT?T?AG??ACC?CT?CGC?AGGC????TATC?GACCT??????????TCT??CT????????A?TCTTGG??AAAA????????AGAGTG?GTGCCCCGCCATTT????????GTA???????????????AC??TT???????C????CACGTGATAG?GGACAGG?????TGTGAAG??AATA?GT?A?????????A???C??GAAAT?????CTCACCTATC?AC?TACCAAA?AGA???????ATCAATTC?C?TCGCAAGCCAG?CC??CT???????????T????TGT???CA?CTACGATTGA???AG????T?AA??ATTA???AA??A?GTA???CCAT?T?GT?CTTGC?TGCCT??AACCTCA?TC?T?GTA???TTTTC??CA?AACTTA????TAA??TCGATA????AGAAAC?CG??C??CATTGATTATATCGCC?CGTAT??????ATATA???????TCAGA???????AAATAATT????CACTTA?????A?AAGCTTCA???????T???CTG?ATA?T?A??A?CA???AAA????A??AGG??C?T?????????ATA?TCCGTAAA????GAAC????????CTAGT?CG?CTT?GCAA?A?TCGAAATTGATATGAGTATCG?A???GGTC?GTGATA sierramadrensisKU195181 ACA?CCAGT?A??AGTG?GC?T????????GTCCTGTCG??CT???T?CT?AACCAACTA????GCG??????????GGTG?TCAAACGT??GG??TTTGAACT??CAGCATC?AA??TTGATTT?A???????C?AT???????A?AA?????????TCGATT?ACAA??ACTGTAATGAC????C????????TATA??T?TA?TT???T?AT??CAG???ATAACCTTCAT??A?AG????ATGGCGATG?TGGGC????????TCTAGTTGTT?TCA??????????A?CGTTCTA?ATGGAAT?CTGAGTT???AC????CTCC???TCTGCTCGC??CT????????CCATAAAT?????AG?T??A?AATG??TAGATACTA???????ATAAAACTTTC??C????????GCCATTAC??A??????????A??AATTGACAAAA??CACG??CACCACCGGTAC??GGT??????????A?TAC?ATACAAAAATGTA?ACATT?CTG????G?T?????GCTATAAA?C????????TAGG????ATCTCTCTTCCT?CTAT???CC???TA?????AGTAC?????CA???T?TG?GACT?TCAC?T???T????CGTGC???TG?ATACCAG??GGAACTTAAACGA????????????TTCTC?TAG???GTG????CTAT???????TAAAT?ACGAAT???????T?GT?????????CGTA????TC?CA???AAA?AC???T??????C?????AGCTTG????AAATATGCGTAAGT?TTCA????CC??GTGTCC?A????????????A??AAAGGTATAC?????AACATC?TGA?ACA??TA?TA????????????AT?????CTA?GGATATATAGCGAG?ACTTAT???TTTT??????ACCT?TAGT??????A??A??????????????TCAG????????TTTTTAA?TT?A?ATACT??ATAAT??TCTTGAGAACT???AAAGCAG????GG?????TT?C??A??CTA????CA?TGCT?ATT?????ATTACCCT??AT?????CAAT??C??CAA???AGG??ATAC?????ACTG??TTCAA?CACCAA?CCAG?TTAGA???AA???TAAGAAGAA??AT??ATATTAGA?CCG?????????CAAAGA??????A??GA?????????AATATT??TCCATT?AT??GCTCTCTCTTATTA?CAGTAAATGCAAT?C?TT?GCATTACCATG?TTT?ATC?AATACCTATA????????CT??A???????????????????????C????AA????????AC??????AA?AAT?GGA??????TCAAAC??CTAGT??????????????AAGC??TTCA?T????AAAT??????A??GGTAA?TTAGCA?????TTAGG?GCAC???????CGT??ATACGTCCACAA????GGCC??ACCT?CCGGAATATGGG???????????AT????G?T?????????GAGGATTATTACC??GTT?C?A???AAA??TAACTCC???????A?CTCAG??TAGATTCCCCC????TTACA???T??CAT???????????A?CATATTGCAT??????TAAT?ACC?ATAAGTTGAAGCAG?GCTCT???GGAATGATTAAG??A?CAT??TCCATTGATTGA??GGGGCG?TACG?TTGGTA?A?CTCGTAT?????????GTTCT??T??T????GAGAAGCGCAGACTCTACTT?AGA????????????CATCTTCTGCT????TGACCTAACACAA??AAAAAAAGT????CG?CGT???A????A?TC??ATCCATCCACATC??T?????AACA??AAGCAGTAATTC?????ACAAGGCTT??????ATAATATGACAACAAAT???CAA?ACATGCAAA?????????TCGC????TTA?TGACAAAGTT?GGTAGAAAGTTACT??GCT?ATAG?A?TACTGT??C?CTAAGC?CC????????GAAATT?T?ACGGT?TCA?A?C?TTACAAGCAT??A?GGTGG??ATGT?TAAT?GAGAA?T??G?CA?????GTT?ATACTA??????AATAGC???AAA?A??????A????GTAAATTCCATC????????????TCACGC??ACATTGTG??????????ACAATAAGT?TGT?A??TAAT????ACATTC?G?????AT?ATG???GT?????CCTATACG????????CTTAA?A??C?????TTTAC??????TCT?TTT???TCT????TT?CA?CGATA??CG??AG??????????TT?T?TAGGA??????????AAAA??????????AG?A?ACGGACTATCGTT?TGAGACAG?ATCA??CCCAAGTACGTATCA???TTCG?TA?AA?CCC??????AAA?ATT??????ACTAA????????????TC?????G????CCTA????ATTCGG?CCTTT?????????A???CT?G?T??GT?A???AC?CCTGTATAAT?A?AC??ACC?TC?CGC?ATGT????TAC???AACT??????????TCC??CT????????A?TCTTGG??ATAATT?A???AATAGTG?ACGC????CCATTT????????CTTACAT???TA??GCC?AC??AT???????C????CACCCG???????TTAGGAAGTTTGTGGAAG????G?GTTA??????????????ATGGTAT?????CTGGA???CC?AA?TC??AGA??TA???????AAC?ATCA?C?TTGAAACTCAG?CC??C????????????TTTG?GGTCC?CA?CTACGATTGA???AG????T?GA??ATTTTCAAA??A?GTA???CTGTAT?GT?CTCGC??GCCAT?TACA????CC?T?GTA???TTTTC??CAGAACTTA????AAATGTAG?????????CTAC?CG??C??CATCGCTTAT?TCGCC?TATAC??????ATATA???????TGAGG???????AGATAATT????CTCTTA?????A?CGATTTCA???????T????TG?ATA?T?A??A?CA???AAA????A??AGG??A?T?????????ATA?CCGGTAA?A??TGAAC????????C?GAC?CC?CTT?GTAG?A?TCTAAATTGATACGGGTATCA?G???GGTC?TTAACA psilonotaKU195119 ACC?CA??C??????CG?GC?T???????????CTGTAG??TT???TTTT?AACTACCTAA?TCGCG??????????GGTG?GCAATCCC???GT?TTTCA?CT??CAGCATC?CA??T?????TTAAC?????T?ATCCTCTT?T?AA?????????TCAATT?AT????CCTGTAACGAC?CCCG????????TATA??C?TA?AT???T?AT??CCG???ATGACTTTCAT??A?AG????TA??CGTTG?TAGAC????????TATAGTTGTA?TCC?????????????GCTTTA?ATGGAAT?CTGAACT???AC????CTCC???TCTACTCGC??CT????????CCCTAAAT?????TG?T??C?AATG??TCGATACCA???????TTAAAAATTT???T????????GCCCTTAC??A??????????A??AGTTGCCATAA??CACG??CCTCGCCGGTAA??ATG??????????A?TAC?CC?CGA??ATGTA?ACATT?CTG????C?TATT?AGCTATAAA?C????????TACGAA?ACTC?ATCTCATT?CTGC???CCC??TA?????AGTAC?????CG???T?TT?AGTAATCATTT???T????CATGC???TA?AATCCTA??GGACCTTACACGATCTTAAAATCG?TTCTC?TGG???TTG????TCAT???????TAAAT?ACGAATATAGTCCA?CT?????????AGAA????TG?CA???AAA?AC???T??????T?????A?CTCG????AA?TA?GCTTAGTT?TTCA????CC??ACATCC?A????????????A??AATGGTATAT?????GTAATC?CCA?GCA??TA?AA????????????T??????CTA?CGATCCATTTCGTG?ACTCAC???TTTA??????ACCT?TAGA??????A??AA??TAA?T?????GCCAG????????T???????TA?A?ATA????ATAAT??TCTAGTGAATT???GAAGCAAT???G????????????????CA????CA?CACT?ATTTCACTATTACCCC??AT?????TAATTCC??CAA???AGG??ATAT?????ACCG??TTCAA?CATC?A?CCAG?T?AGA???AA???TAAAAAGAAA?AT??CTGTTAGA?CAA?????????CAAAGA??????A??GA?????????TATATT??TCCATTTCT??GTTCTATTCTATTATCGGTAAAAGGAAG?CGTT?ACATCTCCATGGTCT?ATC?AATTACTATA????????CA??G???????????????????????C????AA????????AC??????TA?TAT?CGG??????CTAGAC??TTAGC??????????????AAGT??TATG???????AGC??????AATGCAAA?TCACCA?????TTA?????AAC????????T??ATTCGTCCACAA????TGGC??ACTA?C???????TGCG???????????ATCG??GTTC?TTGTT??G??AAA???????????????????AAA??TTACTGC???????A?CTCAG?ATACATTCTCCCC?TTTTATA???C??G?A???????G???A?CATATTGCGT??????TAAT?A?C?ATAAGCTGAAGGAG?GCTCT???AGAAAGA???CG?GA?CTT??TCTATTGATTG???GGATCA?TACG?TTGGCA?G?ATTGTAC?????????GT??A??G??T?????AGAAGCGCTAACCATACCT?ATT????????????CATCCTCTGCT????CGACCCAGCACAAGAAAAAAAAGT????CG?AAT???A????A?TA??ACCTATCCACATC??T?????AACG??CTGCAGTACTTC?????ACAAGAA?????????TAATACGACAACAAAC???AAA?ACCCGC??A?????????CCTC????TTA?TGACAAAGCT?GATAGCAACTAACT??G?T????C?A?C??TGG??C?CTAAGA?CC????????TAAATT?TGATGGT?TGACA?CCATACA????T??A?GGCGC??A?GT?TAGT?TGGAT?T??G?CA?????GTA?GTACTA??????AAAAGT???AAA?A??????A????GTTAACTACACA????????????TCACGC???CATGGTG???????????CGATA?GCCCGTAA??TAAC????ATATTC?G?????TT?ATA???GT?????CATATACA????????TTTGA?A??C?????CTTAC??????TCT?TCC???TTTTACCGC?CA?CAATA???G??AG??????????TT?T?TAGGA?CGTC???CAAAAA??????????AGCA?ACAGACCATCGAT?TAAGACAG?ATTA??CCCAAGTACAT?CCA???TCAG?TG?AA?CCC??????AAA?ATT??????ACTCA?GT?????????GT?????G????TATGATAGATTCGG?CTTTT?????????A???CT?G?T??GT?A???AT?CCTGTATAAT?A?ACT?ACC?TT?CGT?ATGT????TTCA?GACAT??????????TCG????????????A?TTTTGG??ATAACT?A???AACAGTG?ATGC????CCATTT????????TTAAT?????????????????GT???????C????C??????????????AGA????TTGTCGAATCGATG?GT?A?????????A????ATGGTAT?????CTAC????????A?TTCCAAT??TA???????AAC?ATAA?T?TTGTAAATCAG?AT??CTT?TTAGGAC??T????TGTCC?CA?CTACACTTGC???AG????GAAA??ATTACAAAA??A?GTAT??CC???T?GT?CTAGC??GCAATACACA????TT?T?GTA???TGTTT??CT?AACTTA????GAATGTAGTTA????AGGTAC?CG??C??CATAGATC??ATCG??????AT??????ATATA???????TCAGG???????AGGTAATT????CACTTA?????A?CGTTTTCA???????C???ATG?ATA?T?A??A?CT???AAA????A??GGG??A?G??????????TA?CCTGTACAT??CGAAC????????CGGGC?CA?CTT?GCAATA?TCTTAATTGATAT?AGTATTA???????TC?GT?ACA zweifeliJAC7514 ????CACGT?A??AGTG?GC?T????????GTCCTGTCG??TT???T?TT?AACCAACTAATGCGCG??????????GGTG?TCAATCCC??GGT?TTTGATCT??AAGCATC?CA??T?????T?AAA?????C?AT???????C?AA?????????TCAATT?AT????CCTGTAATGAC?CCCG????????TATA??C?TA?AT???T?AT??CAA???ATAACGTTCAT??A?AA????CGGACGTTG?TTGAC????????TTTAGTTGTT?TCC??????????A?CGCTTTA?A?GGAAT?CTGAAGT???AC????CTCC???TATACTCGC???T????????CCGTAAAT?????TG?T??C?AATA??TAGATACTA???????GTAAAATTTT???T????????GCCATTAC??A????????TAA??ACTTGTCATAA??CACA??CATCACCGGTAA??CTA??????????A?CAC?CC?CTA??ATGTA?ACATT?CTG????T?TTTT?TGCTATAAA?C????????TATGGA?ATTCTATCTTTTT?TGAC???CCC??TA?????CGTAC?????CA???T?TG?AGTAATCATTT???T????CGTGC???CA?TTCCCTG??TGAACTTAAACGATCTTAA??????TTCGC?TAG???ATG????ACAT???????TAAGT?ACGAATGTAGTCCT?AT?????????GGTA????TT?CA???AAA?AC???T??????C?????AGCTCG????AA?TATGCTTGGCT?TCCA????CC??GTTTCC?A????????????A??AACGGTATAT?????AAAATC?AAA?ACA??TA?TA????????????C???????TG?TGATACATGATGTG?ATTCTC???TTTA??????ACCT?TAGG??????A??AA??TAA?T?????GCCGA????????T????AA?TA?A?ATACT??ATAAT??TCTAGTGAATT???G?AGCAACTATG????????????????CA????CA?CAAT?ATT?????ATTACCCC??AT?????TAAT??C??CAA???AGG??ATAT?????ACTG??TTCAA?CACCAA?TCAG?T?TGA???AA???TAACAAGAAA?AT??CCGTTAGA?CGA?????????CAAAGA??????A??GA?????????AGTATT??TCCACTTCT??GCTTTGTTCTATTATCTGTAAATGTAAA?C?TT?GTATGCCCATGGTCT?ATC?GATCACTATA????????CT??A???????????????????????C????AA????????AC??????AG?AAT?CGG??????CTAGAC??CTAGC??????????????AAGG??TACG???????AGC??????AATGTCAA?TCAACA?????TTAGG?ACA????????????????CGTTCACGA????AGGC??ACTC?C???????TGTG???????????ATTT??GCTC?TTGTT??GAGAAATATTC????GTC?C?A???AAT??TTACTAC???????A?CACAA?TTAGATTCACCCC?TTTTACA???C??GAA???????G???A?CATATTGCAT??????TAAT?A?C?ATAAGCTGAAGTAA?GCTCT???AGAATGA???CG?GA?CTC??TCTATTGATTG?CGGGGCCA?TA?G?TTGGTA?A?ACTGTAT?????????GT??A??T??T????ATGAAGCGCTAACCGTTCAT?ACT????????????CATCTTTTGCC????AGACTTAATAAAAGAACAGAAAGC????CG?TAT???A????A?TA??ACCTAACCACATT??T?????AACA??TTGCAGTATTCC?????ACAAGAATT??????GCACTACGACAACAAAT???AAA?ACTCGCAAA?????????CCAT????TTA?TGACAGAGCT?GGTAGCAACTAACT??GCC?ATAA?A?T??TGG??C?CTAAGC?CC????????CAAATT?TGATGGT?TCACA?CGTTACA????T?TA?GGCGA??A?GTCTAAC?AAGAT?T??A?CG?????GTA?GTACTA??????ATAAAT???AAA?A????????????????TTTCACG????????????TCACGC??TCATAATA??????????ACTATA?GTATGT?A??TAAT????ATATCC?G?????TC?ATG???GT?????CATTTACA????????TTTAA?A??C?????CTTAC??????TCT?ACC???TTT????TC?CA?CAGTA??CG??AG??????????CT?T?TAGGA?TGTCTATCAAAAA??????????AGCA?ACTGTCTGTCTTT?TGAGACTG?ATTA??ACCAAGTACGTACCA???TTAG?TA?AA?CCC??????AAA?GTT??????ACTTA?GT?????????CT?????G????TTTG????ATTCGG?CTTTT?????????A???CT?G?C??GT?A???AC?CCTGTATAAT?A?AC??ACC?CT?CGT?ATGT????TACA?GATAT??????????TCC????????????A?TTTCGG??ATAACA?A???AATAGTG?AAGC????CCATTT????????CTAATAC???TA??GTT?AT??GTCACACTTC????C??????????????AGG?????TGTAGAATTGATA?GT?A?????????A????ACGGAAT?????CCAA????????A?TCCCAAA??TA???????AAC?ATCA?T?TTAAAAGTCAG?AC??CTT?ATAAGAC??T?TA?CGTAC?CA?CTACACTTGT???AG????CACA??ATTATAAAA??A?GTA???CT???T????CTAGC??GCACTACACA????TC?T?GTA???TTTTC??CT?AACTTA????AAATGTTGGTA????AG?AGC?CG??C??CATAGATA??ATCG??????AC??????ATCTA???????TCAGG???????AAATAATT????CACTTA?????A?TGATTTCA???????T???ATG?ATA?T?A??A?CA???AAAGAACC??AGA??T?T?????????ATA?CCCGTAAAT??TGAAC????????CCGAC?CG?CTT?GAAG?A?T???AATTGATAT?AGTATTA???????TC?GTGACA tarahumaraeKU194596 A?C?CCC???A??AGTG?GC?T????????GTCCTGTTG??TTTTGT?CT?GACCAACTAATGTGCG??????????GGTG?ACAATCTT??GGC?TTTGATCT??CAGTGTC?AA??TTGA?CT?AAC?????C?AT???????C?AA?????????TCTATT?AT????CCTGTAAAGAC?CCCG????????TATA??C?TA?AT???T?AT??CAG???ACAACCTTCAT??A?AAG???TGGACGTTG?TTGAC????????TCCAGTTGTT?TCC??????????A?CGC?CTA?ATGGAAC?CTGACAT??TAC????CTCC???TTTACTCGC??CT????????CCTTAAAT?????TG?T??T?AATA??TAGATACCA???????GTAAAATTTTC??T????????GCCATTAC??G??????????A??AGTTGACAAAA??CACA??CATCACCGGTAT??CAA??????????A?TAC?TT?CAG??ATGTA?ACACT?CTG????C?TATT?AGCTATAAA?C????????TACGAA?TTTCTACCTTTTT?TCAC???CCC??TAGCATGAG?AC?????CC???T?TA?AGTAATCACTTACTT????CGTGC???TA?TGGCCCA??GGAACTTAAACGATCTTGA??????TTCTC?TAG???TGG????TCAT???????TGAGT?ACGAATATAGTCCC?TT?????????GG???????????????AA?AC???T??????C?????AGCTTG????AA?TATGCCTAGTT?TCCA????CC??AAATCC?A????????????A??AACAGTATAA?????GAAATC?TAA?GCA??TA?GA????????????T??????CTG?CGATACATA?TACG?ACTCAC???TTTA??????ACCT?TAGA??????A??AT??TAA?T?????GCCGC????????T????AA??G?A?ATACT??ATAAT??TCTAGAAAAAT???GAAGCAAT???G????????????????TA????CA?TACC?ATT?????ATTACCCT??AT?????TAAT??C??CAA???GGG??ATAT?????ACTG??TTCAA?CACCAA?TCAG?T?TGA???AA???TAACAAGAAC?AT??CCGTTAGACCAG?????????CAAAGA??????A??GA?????????AGTACT??CCCACTTCT??GCTTTATTCTATTATCGGTAAACGCAAG?C?TT?GCATTCCCATGGTTT?ATC?AATGACTATA????????CA??A???????????????????????C????AA????????AC??????AT?ATT?CGG??????CTAAAC??TTAGT??????????????AAGA??TATA???????AGC??????AATGAGGA?TCAACA?????TTAGG?TCAAC??????TGT??ATACGTCCACAT????AGGC??ACTC?C???????TGAG???????????ATCG??GTGC?TTGTC??GAGAAATTTTA????GTA?C?A???AAC??TAACTAC???????A?CGCAG?ATAGATTCTCTCC?TTTTATA???C??AAA???????G???A?CATATTGCAT??????GAAT?G?C?ATAAGCTGAAGTAG?GCTCT???GGAATGATTACG?GA?CTC??TCTATTGATTG???AGGTCA?TACG?TTGGTA?A?ATCGTAT?????????GT??A??C??T????ATGAAGCGCTAACCATACTT?ACC????????????CATCTTCCGCT????AGACTTAATATAAGAAGAGAAAGT????CG?AAT???A????A?CC??ATCTACCCACATT??T????????A??CTGCAGTATTTC?????ACAAGGAAT??????AAAATACGACAACAAAT???CAA?ACTTGCAAA?????????CCTT????TTA?TGACAGAGCT?GGTAGCCATTAACT??GCT?ATAA?A?T??TGG??C?CTAAGA?CC????????CAAATT?TGCTGAT?TTA?A?CATTACA????T??A?GGCGC??A?GT?TGAC?AAGAA?T??G?CC?????GTT?GTACTA??????ACAATT???AAA?A??????A????GTAAATTTCATA????????????TCACGC??TCATAGTG??????????ACAATA?GTATGT?A??TAAT????ATATCC?T?????CT?ATG???GT?????CATATACA????????CTTAA?A??T?????CTTCC??????TCT?TCC???T???????CCCA?C?ATA??CG??AG??????????TT?T?TAGGA?TATC???TAATAA??????????AGCA?ACAGACTTCCGCT?TGAGACAG?ATCA??TCCA?GTACGTATC????TTAG?TT?AA?CCC??????AAA?ATC??????ACTTA?GT?????????GT?????GTTTATCTA????AT?CGG?TTTTT?????????A???TC?G?T??GT?A???AC?CCTGTACAAT?G?AT??ACC?TC?CGT?ATGA????TACA?GATAT??????????TCG????????????A?TTTTGG??ATAAAT?A???AATAGTG?ACGC????CCATTT????????TTAATCC???TA??GCT?AT??GT???????C????C??????????????AGG????CTGTAGAATTGATG?GT?A?????????A????ATGGCAT?????CTATT?AATC?AAATCCCAGA??CA???????AAC?ATCT?C?TCGTAATTCAA?AC??ATT?ATAGGTC??T?TA?TGTAC?CA?CTACAATTGT???AG????CAAA??ATTCTAAAA??A?GTA???CC???T?GT?CTAAC??GCATTATACA????CC?T?GT????TTTTC??CA?AACCTA????AAATGTAGATA????AGGTTC?CG??C??CATCGATTGTATC???????AC??????ATATA???????TCAGG???????AGATAATT????CACTTA?????A?CGATTTCA???????A???TTG?ATA?T?A??A?CA???AAA????A??TGG??C?C?????????ATA?CCTATAAAT??TGAAC????????CTAGC?CG?CTT?GCAA?A?TCTAAATTGATAT?AGTATCA?A???GGTC?GTGACA pustulosaJAC10555 AAT?CCCGA?A??AGTGTGC?T????????GTCCTGTAG??TTTTGT?CT?GACTACCTATTGTGCG??????????GGTG?ACAATCCC??GGC?TTTAACCT??GCGCAAC?AA??T?????T?CAT?????C?AT???????C?AA?????????TCAATT?AT????TCTGTAATGAC?CCCG????????TGTA??A?TA?AT???T?AT??CAA???ACAACGTTCGT??A?AA????TTGACGTTG?TAGTC????????TTCAGTTGAA?TCC??????????A?CGT?CTA?ATGGAAT?CTGAAAT???AC????CTCC???TTTCCTCGC??CT????????ACTCAAAT?????TG?T??C?AATA??TAGATACTA???????A??AAAATTTC??T????????GCCATTAC??A??????????A??AATTGACAAA????????????CACCGGTAT??AGA??????????G?TAC?CC?CAG??ATGTA?ACAAT?CTG????C?TGTT?AGCTGTAAATC????????TAAGAA?TTTCTATTCTTTT?CTAC???CCC??TA?????AGCGC?????CT???T?TA?TGTAATCAC?????T????CATGC???TT?TACCCTA??TGAGCTTACACGATCTTGA??????TTCGC?TAG???CAG????ACAC???C???TAAAT?ACGAATATAGTCTC?CT?????????GG???????????????AA?AC???T??????A?????AGCTAG????AA?T?????TTATT?TCCA????CC??ACATCC?C????????????A??AAAGGTATATAACGTGAAATC?TAA?GCA??TA?AA????????????T??????CTG?CGATTTATG?CTCG?ATTCAC???TTTA??????ACCT?TAGA??????A??AA??TAA?T?????GCCAT????????T????AA?TA?A?ATATT??ATAAT??TCCAGAGAATT???GAAGCAGT???G????????????????CA????CA?CACT?ATT?????ACCACCCT??AT?????TAAT??C??CAA???AGG??ATAT?????ACTG??TTCAA?CATCAA????????CGA???AA???TAAC?AGAAA?AT??TCGTTAAGCCAG?????????CAAA????????A??GA?????????AGTAA??????ACTTTT??GCTTTATCATATTATCTGTAAATGAAAT?C?TT?GTATTTCCATGGTCT?ATC?GATTACTATA????????CC??A???????????????????????C????AA????????AC??????CC?GAT?CGG??????CTAAAC??TTAGT??????????????AAGC??TGCA???????GGTCTATTAAATGAAGT?TAAACA?????TTAGG?CCCAC??????TGTATATACGTTCACGT????AGAC??ACAC?C???????TGGG???????????ATTA??GATC?TTATC??GATAAATATTA????GTT?A?A???AAC??TGACTAC???????A?CTCAG?TTAGATTCCCACC?TTTTACA???T??GAA???????G???A?CA???T?CAT??????TAAT?A?C?ATAAGCTGAAACAG?GCTCT???GGAATGATTACG?GA?CCC??TCTATTGATTG????AAACA?TACG?TTGATA?A?ATAGTAT?????????GT??A??G??T????AGGAAGCGCCAACCATGC???ATC????????????CATCATTCGCC??ATAGACCTAATATAAGAAAAGAAAGT????CG?GAT???A????A?TC??ATCTACCCACATC??T????????A??CCGCAGTAC?CC?????ACTAGGAAT??????AAAATACGACAACAAAT???TAA?ACTTGCAAA?????????CCAC????TTA?TGACAAAGCT?GGTAGAAATTATCT??GCTCATAG?A?G??TGA??C?CTAAAA?CC????????CAAATT?AGATGATCTCA?A?CATTATA????T??A?GGCGG??A?GT?TGAT?GAAAA?C??G?CC?????ATT?GTACTA??????AAAAAT???ATA?A??????A????GTAAATTCCCCA????????????TCACGC??TCATGGTA??????????ACGATA?GTTCGT?GCTTAAC????ACATCC?G?????AT?ATG???GT?????CATATACA????????TTTAA?A??T?????CTTAC??????TCT?TCCA??TTT????TT?CA?C?ATAG?CT??AG??????????TT?C?TA?GA?TATC???CAAAAA??????????GGAA?ACAGTACATCGAT?TGAGACAG?ATCA??TCCA?GTTCGTATCA???TTAG?TG?AA?CCC??????AAA?AGC??????ACTTA?GT?????????AA?????G????ACTG????ACTCGG?TTTTT?????????A???TT?G?T??GT?A???AC?CCTGTATAAT?A?AC??ACC?TA?CAG?CTGT????TGTA?GATAC??????????TCT????????????A?TCTCGG??ACAATG?A???AATAGTG?ATGC????CCATTT????????CTGACCC???TA??GCC?ATGT?T???????T????C??????????????AGG????CTGTAGAATTAATA?GT?A?????????A????AGGGAAT?????CGGTT?TATC?AA?TCTCAAC??TA???????AAC?ATCT?T?TCGTAAACCAA?AC??CTT?TTAAGTC??T?TA?CGTGC?CA?CTACAATTGT???AG????TAGA??TTTTTCAAA??A?GTA???CC???T?GT?CTTAC??GCATTA?TCA????TC?T?GTA???????????????CTTA????GAATGTAGATA????AGGTAC?CG??T??CATC?ATTCTATC???????AC??????ATGTA???????TTTGG???????AGATAATT????CA????????????????TCA???????C???TTG?ATA?T?A??A?CA???AAT????C??TGG??????????????ATATCCTGTAAAACCTGAAC????????CCTAC?CG?CTT?ATA??A?T?CGAATTGATAT?AG?ATTA?A???GGTC?TTGACA pipiensJSF1119 ACC?CCCGC?A??AGCG?GC?T????????GTCCTGTTG??CT???T?TT?CATAAACTA????GTG??????????GGTG?ACAAACTC??GGTTTTTAATCT??GGGTACC?TA??TTAG?AT?A???????C?AA???????G?TA?????????CCAATT?ACAAAACCTGTAATGCC????T????????GATA??C?TA?TT???T?AT??CAG???ATAACCTT??T??A?TT????TAGACGTTG?TTATC????????TACAGTTGCA???CCCTGTCCTACA?CGATCTA?ATGGAAC?CTGAGCT????C????CTCCCTATTCGCTCGC???C????????CTTTAAAT?????TG?T??A?AATG??TCGATACTA???????ATGAAAATTTC??T????????GCCATTAC??A??????????A??ATTTGACATAA?ACACT???A??AC?GG?AT??TAAACAAACTCTTCATAC?A??CCCGAATGTG?ACACT?CTG????C?TTTA?CGCTATAAA?CA??GGCCTTAAGCA?AA??????ATTT??GTAT???CC???TA?????GGTAC????????????TG?AGAT?TCGTTT???T????CATGC???GC?TATCCAA??GGATCTTAAACGATCTTAA??????TTCTC?TAG???ATG????ACAC???????CGAAT?ACAAATATAGTTCT?AT?????????TGAG????TC?CA???AAA?AC???T??????G?????AGCTAG????AA?TATGCTTAATT?TACA????CC??TAATC??A????????????A??GTTGGCATAT?????GAAATC?TAT?ACAAGTA?GA????????????A??????TTG?TGATATATCGGATG?ACTCAT???TTTACGTCACACCT?TCGT??????A??AT???AA?T?????GACGC????????T????AT?TA????TACT??ATAAT??TCTGGAGAACT?CCGAAGCAGC???GG?????TT?C??A??CTA????CA?CAAT?AAT?????ATTACCCT??AT?????TAAT??C??CAG???AGG??ATATCTCCGACTG??TTCAA?CATCAA?TCGG?TTTG????AA?C?TAACAAGAAA?AT??GTATTAGA?CTG?????????CAAAGG??????A??GA?????????GATATT??TCCAATTTT??GCTTTATACTATTA?CTGTAAATGAAGG?C?TT?ATA?GACCATGG??C?ATC?AAT??CTCTA????????CA??GCAAATTT????????????AAT?C????A?????????GC??????CG?TAT?CGG??????CAATAC??CTAGTT??TTAAATTTGA?AAGA??TACAAT????AAGC??????AATAACAA?TCAATA??TTCTTAGG?GCAA???????TGT??ATACGTACACAG????AGAC??ACCC?C???????TGTG???????????CTAA??G?CC?TCCTA??GATAACTATTG??TTGTA?C?A???AAT??TATCTCC???????A?CCCAG?CTATATTCTCCCC?TTTTATA???T??CAG???????????A????ATTGCAT??????TAAT?A?C?ATAAGCTGAAGCAG?GCTCT???A??ATGATTAAG?GA?ACC??TCTATTGATTG???AGGACA?TAC???????ATAAGTTGCAC??????????TT?G??T??T????????????CTGACTATACCT?ACT????????????CATCTTTCGCT????TGACTCAAAATAAGAATATAAAAC????CA?AGT???A????A?AC??ATCCATCCACATT??T?????AATA??CTGCAGTATCCC??????CCAG?ACT??????AAATCATGAC??CAAAT?????AAACTCGCAAATGTTGTG??TTAA????CTA?TGACATAGCT?GGTAGTAATTATCT??GCT?ATAA?A?AACTGG??C?CTAAGA?CTCAATCAC?TAAATT?TGAAG???TAA?A?C?CTATAC????????AGAGG??A?GT?TGTC?GGAAG?T??G?CA?????GTA?GTACTA??????A?AAA??????A?A??????A????GTGAACTACCCT?AGGATAGAC?TTCACGC??TCATAATA??????????ACCATA?GT?TGT?A??TAATC?????????CG?????TT??TACTTAT?????ACTACACT????????TTTGA?A??T?????CCTAC??????TTT?CTT?G?TAT????CC?GA?CAATA??????????????????????????GA?TTTA????AAAGA??????????AGGA?ACAGCTCATCATT?TGAGACGG?ATCA??AC??AGTACGTACCA???TCTG?TT?GA?CCC???????AA?ATT??????ACTTA?GT?????????GA?????G????TAT?????ATCC?G?ATTTT?????????A???CC?G?C??GT?ATGCAT?CCTGTACAATCA?A???ACC?GG?CGT?A??T????TTCA?GACTT??????????TCC??CT????????A?TATTGG??ATAATT?A???AATAGTG?TC???????CATTT????????TTAATATTTCTAACGCT?AC??CT???????T??????????AT?G?GGTCAGGAAGTTTGTGGAGTTAATG?GT?A?????????AACATATGACAT?????CTGTC?AAAC?AA?TTCCAGC?GTA???????AGCAATCA????TGTAATTCAC?AT??CTTGTTATGCC??TTCA?GGTCC?TA?CTACCGTTGG???AG????G?GA??A??????????A?GTA???CCGTAT?GT?CTTGT??GCTATATACA????CT?T?GTA???TCTTT??CA?AACTTA????TAATGTTGTTA????????AC?CG??T??CAT?GGTA???????C?AATAT??????ATATA???????TTAGG???????AGATAATT????CTCTTA?????????????CATTACTACT???TTA?ATA?T?A??A?CA???AAA????A??AGA??T?G?????????ATA?CCA?TAAAT??CGAAC????????TCAGA?CT?CTT?GAAA?T?TCGAAATTGATACGAGTATTA?G???GGTC?TTAACA pipiensY10945 ACC?CCCGC?A??AGCG?GC?T????????GTCCTGTCG??CT???T?TT?CATAAACTA????GTG??????????GGTG?ACAAACTC??GGTTTTTAATCT??GGGTACC?TA??TTAG?AT?A???????C?AA???????G?TA?????????CCAATT?ACAAAACCTGTAATGCC????T????????GATA??C?TA?AT???T?AT??CAG???ATAACCTT??TACA?TT????TAGACGTTG?TTATC????????TACAGTTGCA???CCCTGTCCTACA?CGATCTA?ATGGAAC?CTGAGCT????C????CTCCCTATTCACTCGC???C????????CTTTAAAT?????TG?T??A?AATG??TCGATACT????????????GAATTTC??T????????GCCATTAC??A??????????A??ATTTGACATAA?ACACC???ACCAC?GG?AT??TAAACAAACTCTTCATAC?A??CCCGAATGTG?ACATT?CTG????C?TATA?AGCTATAAA?CA??GGCCTTAAGAA?AA??????ATTT??GTAT???CC???TA?????GGTAC????????????TG?AGAT?TCGTTT???T????CATGC???GC?TATCCAA??GGATCTTAAACGATCTTAA??????TTCTC?TAG???ATG????ACAC???????CGAAT?ACAAATATAGTTCT?AT?????????TGAG????TC?CA???AAA?AC???T??????G?????AGCTAG????AA?TATGCTTAATT?TACA????CC??TGATC??A????????????A??GTTGGCATAT?????GAAATC?TAT?ACAAGTA?GA????????????A??????TTG?TGATATATCGGATG?ACTCAT???TTTACGTCACACCT?TCGT??????A??AT???AA?T?????GCCGC????????T????TT?TA????TACT??ATAAT??TCTGGAGAACT?CCGAAGCAGC???GG?????TT?C??A??CTA????CA?CAAT?AAT?????ACTACCCT??AT?????TAAT??C??CAG???AGG??ATATCTCCGACTG??TTCAA?CATCAA?TCGG?TTTG????AA?C?TAACAAGAAA?AT??GTATTAGA?CTG?????????CAAAGG??????A??GA?????????GATATT??TCCAATTTT??GCTTTATACTATTA?CTGTAAATGAAGG?C?TT?ATA?GACCATGG??C?ATC?AAT??CTCTA????????CA??GCAAATTT????????????AAT?C????A?????????GC??????CG?CAT?CGG??????CAATAC??CTAGTT??TTAAATTTGA?AAGA??TACAGT????AAGC??????AATAACAA?TCAATA??TTCTTAGG?GCAG???????TGT??ATACGTACACAA????AGAC??ACCC?C???????TGTG???????????CTAA??G?CC?TCCTA??GATAACTATTG????G??????????????TATCTCC???????A?CCCAG?CTATATTCTCCCC?TTTTATA???T??CAA???????????A????ATTGCAT??????TAAT?A?C?ATAAGCTGAAGCAG?GCTCT???A??ATGATTAAG?GA?ACC??TCTATTGATTG???AGGACA?TAC???????ATAAGTTGCAC??????????TT?A??C??T????????????CTGACTATACCT?ACT????????????CATCTTTCGCT????TGACTCAAGATAAGAATATAAAAC????CA?AGT???A????A?AC??ATCCATCCACATT??T?????AATA??CTGCAGTATCCC??????CCAG?ACT??????AAATCATGAC??CAAAT?????AAACTCGCAAATGTTGTG??TTAA????CTA?TGACATAGCT?GGTAGTAATTTTCT??GCT?ATAA?A?AACTGG??C?CTAAGG?CTCAACCAC?TAAATT?TGAAG???TAA?A?C?CTATAC????????AGAGG??A?GT?TGTC?GGAAG?T??G?CA?????GTA?GTACTA??????A??????????A?G??????A????GTGAATTACCCT?AGGATAGAC?TTCACGC??TCATAATA??????????ACCATA?GT?TGT?A??TAATC?????????CG?????TT??TACTTAT?????AATACACT????????TTTGA?A??C?????CCTAC??????TTT?CTT?G?TAT????CC?GA?CAATA??????????????????????????GA?TTTA????AAAGA??????????AGAA?ACAGCTCATCATT?TGAGACGG?ATCA??AC??AGTACGTACCA???TCTG?TA?GA?CCC???????AA?ATT??????ACTCA?GT?????????GA?????G????TAT?????ATCC?G?ATTTT?????????A???CC?G?C??GT?ATGCAC?CCTGTACAATCA?A???ACC?GG?CGT?A??T????TACA?GACTT??????????TCT??CT????????A?TATTGG??ATAACT?A???AATAGTG?AC???????CATTT????????TTAATATCTCTAACGCT?AC??CT???????T??????????AT?G?GGTTAGGAAGTTTGTGGAGTTAATG?GT?A?????????AACATATGGCAT?????CTGTT?AAAC?AA?TTTCAGT?GTA???????AGCAATCT????TGTAATCCAC?AT??CTTGTTACGCC??TTCA?GGTCC?TA?CTACAGTTGG???AG????G?GA??A??????????A?GTA???CCGTAT?GT?CTTGT??GCTATATACA????CT?T?GTA???TCTTT??CA?AACTTA????TAATGTTGTTA????????AC?CG??T??CAT?GGTA???????C?AATAT??????ATATA???????TTAGG???????AGATAATT????CTCTTA?????????????CATTACTACT???TTA?ATA?T?A??A?CA???AAA????A??AGA??T?G?????????ATA?CCAGTAAAT??CGAAC????????TCAGA?CT?CTT?GAAA?T?TCGAAATTGATACGAGTATTA?G???GGTC?TTAACA dunniJSF1017 ACC?CCCGT?A??AGCG????T????????GTCCTGTTG??TT???T?TT?CACAAACTA????GTG??????????GGTG?GCAAACTT??GGTCTTTAATCT??TGGTACC?TA??TTAG?AT?A???????T?AA???????G?TA?????????TCAATT?ACAAAACCTGTAATGTC????C????????TATA??T?TA?AT?????AT??CAG???ATAACCTT??T??A?AT????TCGACGTTG?TT??????????????AGTTGCA???C??????????A?CGATCTA?ATGGAAC?CTGAGCT????C????CTCCCTATTTACTCGC???T????????CTTTAAAT?????TG?T??C?AATG??TCGATACTA???????ATCAAACTTTC??G????????GCCATTAC?????????????A??ACTTGACATAA??CACC???ACCAC?GG?AA??CAG??????????CATAC?A??CGTGAATGTG?ACACT?CTG????C?TATG?AGCTATAAA?CC??AGCCTTAAGAA?GA??????ATTT??GTAT???CC???TA?????CGTAC????????????TT?AAAT?TCGTTT???T????CATGC???GG?CAACCTA??GGATCTTAAACGATCTTAA??????TTCTC?TAG???TTG????ACAT???????CGAAT?ACAAATTTAGTTCA?CT?????????TGTG????TT?CA???AAA?AC???T??????G?????AGCTAG????AA?TATGCTTTATT?TACA????CC??TACTC??A????????????A??GACGGCATAT?????GAAATC?CAT?ACAAATA?TA????????????A??????CTG?TGATATATTTACTG?ACTCAT???TTTT??????ACCT?TCGT??????A??AC??TAA?T?????GCCGT????????T????AA?CA????TACT??ATAAT??TCTGGAGAACT?TCGAAGCA????????????TT?C??A??CTT?????A?GATT?AAT?????ATTACCCT??AT?????TAAT??C??CAG???AGG??ATATATCCGACTG??TTCAA?CACCAA?TCGG?TTCGA???AA?C?TAATAAGAAA?AT??ACATTAGA?CCG?????????CAAAGG??????A??GA?????????GA?ATT??TCCAATTTT??GCTTTGTGCTATTA?CTGTAAA??AAAA?C?TT?GTA?GTCCATGG??C?ATC?AATT?CTATG????????CA??GCAAATCT????????????TAT?C????A?????????GC??????AG?CAT?CGG??????TTAAAC??CTAGTT??TTAAATTTAA?AAGG??TATAGT????AAGC??????AATAATAA?TCAACA??TTCTTAGG?TCAG???????GGT??ATACGTTCACAG????CGTC??ACCT?C???????TGAG???????????CTTA??G?CC?TTCTA??GATAAATATTA????GTAGC?A???AAT??TGTCTAC???????A?CCCAG?TTACATTCCCCCC?TTTTACA???C??TAA???????????A?CATATTGCAT??????CACT?A?C?ATAAGATGAAGTAG?GCTCT???A??ATGATTACG?GA?ACT??TCTATTGATCG???AGAACA??????????????AGTTGTAC??????????TT?A??A??T????????????CTGACCATGCCT?ACC????????????CA????CTGCT????AGACTCAAAATATGAAAACAAAAT????CA?AGT???A????A?CC??ATCCAACCACATT??T?????AATA??CTGCAGTATTCC??????CTAG?ACT??????AAAAAATGAC??CAAAT???TAAAACCTGCAAACGTTGTG??TTAA????TTA?TGACATAGCT?GGTAGTAACTTTCT??GCT?ATAA?A?AACTGG??C?CTAAGA?CCTAACCAA?AAAATT?TGACG???TAA?A?C?TTATAA????????AGTGT??A?GT?TTTA?AAAAA?C??G?CA?????ATA?GTACTA??????A?CAGT???AAA?G??????A????GTGAATTACTCG?AGGATAGAC?TTCACGC??TCATGATG??????????ACCATA?GC?TGT?G??TAAAC?????????CA?????TT??TATTTGT?????ACTACACT????????TTTGA?A??T?????TCTTC???????CT?CTT?G?TAT????TC?GA?CAATA??????????????????????????GA?TTTA????AAAGA??????????AGTA?ACAGTCTATCGCT?TGAGACAG?ATCA??AC??AGTACGTACCA???TCTG?TA?GA?CCC???????AA?GCTGTAAAGACTTA?GT?????????AC????CG????TAT?????ATCTGG?ATTTT?????????A???CC?G?T??GT?ATGTAT?CCTGTACAAT?A?A???ACC?GG?CGT?A??C????TCTA?GACTT??????????TCT??CT????????A?TGTCGG??ATAACT?A???AATAGTG?AC???????CATTT????????TTAATATCTCTA??GCT?AC??CT???????C??????????AT?G?GGTTAGGAAGTTTG?GGAGCTAATA?GT?A?????????C???TAAGATAT?????CTGCT?AAAC?AA?TTACAGT?GTA???????AGCAATCC????AGTAACCCAC?GT??CTTGTTACG??????CA?AGTCC?TA?CTATAATTGA???AG????G?GA??A??????????A?GTA???CCATAT?GT?CTTGT??GCTTTATACA????CT?T?GTA???TCTTT??CA?TACTTA????TAATGTTGCTA????????AT?CG??T??CAT?GATA???????C?AATAA??????ATATA???????TAAGG???????GGTTAATT????CCCTCA?????????????CA???????T???TTA?ATA?T?A??A?CA???AAT????C??AGG??T?G?????????ATA?CCAGTAAAT??TGAAC????????TTAGA?CT?CTT?GAAA?A?TCGAAATTGATACGAGTATCA?G???GGTC?TTGACA montezumaeJAC8836 ACC?CCAGT?A??AGCG????T????????GTCCTGTCG??TT???T?CT?CACCAACTA????GTG??????????GGTG?GCAAACTT??GGTCTTTAATCT??TGGTACC?TA??TTAG?AT?A???????T?AA???????G?TA?????????CCGATT?ATAAGACCTGTAATGCC????T????????TATA??T?TA?GT?????AT??AAG???ATAACCTT??T??A?AT????TCGACGTTG?TT??????????????AGT???A???C??????????A?CGATTTA?ATGGAAC?CTGAGCT????C????CTCCCTATATACTCGC???T????????CGTTAAAT?????TG?T??T?AATG??TCGATACTA???????TTCAAATTTTC??G????????GCCATTAC?????????????A??ATTTGACATAA??CACC???ACCAC?GG?AA??CAG??????????CATAC?A??CGAGAATGTG?ACACT?CTG????T?TATG?AGCTATAAA?CC??AGCCTTAAGAA?GA??????ATTT??ATAT???CC???TA?????AGTAC????????????TA?AAAT?TCGTTT???T????CATGC???AG?CATCCTA??GGATCTTAAACGATCTTAA??????TTCTC?TAG???TAG????ACAT???????CGAAT??CAAATTTAGTTCA?TT?????????TGCG????TT?CA???AAA?AC???T??????G?????AGCTAG????AA?TATGCTTAATT?TACA????CC??TACTC??A????????????A??GACAGCATAT?????GAAATC?CAC?ACAAGTA?TA????????????A??????CTG?TGATGTATTTACTG?ATTTAT???TTTT??????ACCT?TCGT??????A??AT??TAA?T?????GCCGT????????T????AA?CA????TATC??ATAAT??TCTGGAGAACT?TCGAAGCA????????????TT?C??A??CTT?????A?CATA?ACT?????ATTACCCC??AT?????TAAT??C??CAG???AGG??ATATATCCAACTG??TTCAA?CAACAA?TCGG?TTCGA???AA?C?TAATAAGAAA?AT??ACATTAGA?CCG?????????CAAAGG??????A??GA?????????GA?ATT??TCCAATTTT??GCTTTGTACTATTA?CCGTAAA??GAAG?C?TT?TTA?GTCCATGG??T?ATC?AATT?CTATG????????CA??GCAAATCT????????????TAT?C????A?????????GC??????AA?CAT?CGG??????TTAGAC??CTAGTT??TTAAATTTAA?AAGG??TATAGT????AAAC??????AATAATAA?TTAACA??TTCTTAGG?TCAG???????AGT??ATACGTTCACAG????CGTC??ACCT?C???????TGAG???????????CTTG??G?AC?TTCTA??GATAAATTTTA????GTAGC?A???AAC??TATCTCC???????A?CTCAG?TTACATTCCCCCC?TTTTACA???C??TAA???????????A?CATATTGCAT??????CACT?A?C?ATAAGATGAAGAAG?GCTCT???A??ACGATTATG?GA?ACC??TCTATTGATCG???AGAACC??????????????AGTTGTAC??????????TT?A??A??T????????????CTAACCATGCGT?ACC????????????CA????CTGCT????AGACCCAAGATATGAAAATAAAAC????CA?GGT???A????A?TC??ATCCAACCACATT??T?????AACA??TTGCAGTACTACGCCAG?CTAG?ACT??????AAAAAATGAC??CAAAT???TAAAACTTGCAAACGTTGTG??TTAT????TTA?TGACATAGCT?GGTAGTAATTTTCT??GCT?ATAA?A?AACTGG??C?CTAAGG?CCTAACCAA?AAAATT?TGAAG???TAA?A?C?TTATAA????????AGAGT??A?GT?TTTA?AAAAG?C??G?CA?????ATA?GTACTA??????A?CAGA???AAA?G??????A????GTGAATTACCCG?AGGATAGAC?TTCACGC??TCATGATA??????????ACCATA?GT?TGT?G??TAACC?????????CA?????TT??TAATTGT?????ACTACACC????????TTTGA?A??T?????TCTCC???????TT?TTT?G?TAT????TC?GA?CAATA??????????????????????????GA?CTTA????AAAGA??????????CGTA?ACAGTCTATCGCT?TGAGACGG?ATCA??AC??AGTACGTACCA???TCTG?TA?GA?CCC???????AA?GCTGTGAAGACTTA?GT?????????AC????CG????TAT?????ATCCGG?ATTTT?????????A???CC?G?T??GT?ATGTAT?CCTGTACAAT?A?A???ACC?GG?CGT?A??C????TCTA?GACAT??????????TCG??CT????????A?TGTCGG??ATAACT?A???AATAGTG?AC???????CATTT????????CTAATATTTCTA??GCC?AC??CT???????C??????????AT?G?GGTTAGGAAGTTTG?GGAGTTAATA?GT?A?????????A???TATGATAT?????CTGCT?AAAC?AA?TTACAGT?GTA???????AGCAATCC????TGTAACACAC?GTAACTTGTTAAG??????TA?AGTCC?TA?CTACAATTGA???AA????G?GA??A??????????A?GTA???CCATAT?GT?CTCGT??GCTCTACACA????CT?T?GTA???TCTTT??C??TACTTA????TAATGTTGCTA????????AC?CG??T??CAT?GATA???????C?AATAA??????ATATA???????TAAGG???????GGTTAATT????CTCTCA?????????????CA???????T???TTA?ATA?T?A??A?CA???AAC????C??AGG??T?G?????????ATA?CCAGTAAAT??TGAAC????????TTAGA?CT?CTT?GAAA?A?TCGAAATTGATACAAGTATGA?G???GGTC?TTAACA sp_2_mex_JSF1106 ACC?CTCGT?A??AGCG????T????????GTCCTGTTG??TT???T?TT?CATAAACTA????GTG??????????GGTG?GCAAACTT??GGTCTTTAACCT??CGGTACC?TA??TTAG?AT?A???????T?AA???????G?TA?????????CCAATT?ATAAGACCTGTAATGCC????T????????TATA??C?TA?GT???T?AT??CAG???ATAACCTT??T??A?AT????CCGACGCTG?TT??????????????AGTTGCA???C??????????A?CGATTTA?ATGGAAC?CTGAGTT????C????CTCCCTATTTACTCGC???C????????ACTTAAATGCATCTG?T??G?AATG??TCGATACTA???????ATCAAGTTTTC??C????????GCCATTACCT???????????A??ACTTGACATAA??CACT???ATCAC?GG?AA??TAA??????????CATAC?A??CAAGAATGTG?ACACT?CTG????C?TGTG?AGCTATAAA?CC??AGCCTTAAGAA?G??????????????TAC???CC???TA?????GGTAC????????????TA?AAAT?TCGTTT???T????CATGC???G??????CCA??GGATCTTAAACGATCTTAA??????TTCTC?TAG???TCG????AC?????????CGAAT?ACAAATATAGTTCA?TT?????????TGCG????TT?CA???AAA?AC???T??????G?????AGCTGA????AA?TATGCTTAATT?TACA????CC??TATTC??A????????????A??GTTGGCATAC?????GAAATC?TAT?ACAAGTA?CA????????????A??????C?G?T?ATGTATTTGTAG?ACTCAT???TTTC??????ACCT?TCGT??????A??AC??TAA?T?????GCCGA????????T????AA?CA????TACT??ATAAT??TCAGGAGAACT?CCGAAGCA????????????TT?C??A??CCT?????A?TATT?AAT?????ACTACCCT??AT?????TAAT??C??CAG???AGG??ATATTTCCAACTG??TTCAA?CACCAA?TTGG?TTCGA???AA?C?TAATAAGAAA?AT??GCATTAGA?CTG?????????CAAAGG??????A??GA?????????GA?ATT??TCCAATTAT??GCTTTGTACTATTA?CTGTAAA??AAGG?C?TT?GTA?GACCATGG??T?ATC?TATT?CTTTA????????CA??GCAAATCT????????????CAT?C????A?????????GC??????AT?CAT?CGG??????TTAGAC??CTAGTT??TTAAATTTAA?AAGG??TATAGT????AAGC??????AATAATAA?TAAACA??TTCTTAGG?CCAA???????AGT??ATACGTCCACAG????AGAC??ACCT?C???????TGAG???????????C?AA??G?TC?T???????ATAAATATTA????GTAGC?A???AAT??TATCTAC???????A?CTCAG?ATATATTCCCTAC?TTTTACA???C??TAA???????????A?CATATTGCAT??????CAAT?A?C?ATAAGCTGAAGTAG?GCTCT???A??ATGACTATG?GA?ACT??TCTATTGATCG???AGGACT??????????????AGCTGTAC??????????TT?A??A??T????????????CTGACCTTACAT?ACC????????????CA????TTGCT????AGACCCAAAATAAGAATATAAAAT????CA?AGT???A????A?CC??ATCTAACCACATT??T?????AATA??CTGCAGTACTTC??????CTAG?ACT??????AAAATATGAC??CAAAT???CAAAACTTGCAAATGTTATA??TTAA????TTA?TGACATAGCT?GGTAGCAATTTTCT??GCT?ATAA?A?AACTGG??C?CTAAGG?CCCAACCAA?TAAATT?TGAAG???TGA?A???TTATAC????????AGGGC??A?GT?TACA?AAAAG?A??G?CA?????ATG?GTACTA??????A?AAGT???AAA?G??????A????GTAAACTACTCG?AGGATAGAC?TTCACGC??TCATGATA??????????ACCATA?GC?TGT?G??TAATC?????????CA?????TT??TATCTGT?????AATACACT????????TTTAA?A??T?????TCTCC???????TT?CTA?G?TAT????TC?GA?AAATA??????????????????????????GA?TTTA????AAAGA??????????AGAA?ACAGCCCATCGCT?TGAGATGG?ATCA??AC??AGTACGTACCA???TTTG?TA?GA?CCC???????AA?ACT??????ACTTA?GT?????????AC????CG????TAT?????ATCCGG?ACTTT?????????A???CT?G?C??GT?ATGTAT?CCTGTACAAT?A?A???ACC?GG?CGT?A??C????TTTC??ACCT??????????TCT??CT????????A?TGTCGG??ATAATT?A???AATAGTG?AC???????CATTT????????CTAATATTTCTA??GCT?AC??CT???????C??????????AT?G?GGTAAGGAAGTCTG?GGAGTTAATA?GT?A?????????A???TATGATAT?????CCGCT?AAAC?AA?TTACAAA?GTA???????AGCAATCC????TGTAACCCAC?GT??CTTGTTATGCC??TTTA?CG?????A?CTACAATTGA???AA????G?GA??T??????????A?GTA???TGATAT?GT?CTCGT??GCTCTATTCA????TT?T?GTA???TCTCT??CA?AACTTA????CAATGTCGTTA????????AC?CG??T??CAT?GATA???????C?AATAA??????ATATA???????TAAGG???????GGTTAATT????CCCTCA?????????????CA???????C???TTA?ATA?T?A??A?CA???AAC????A??AGA??C?G?????????ATA?CCAGTAAAT??TGAAC????????TAAGA?CT?CTT?GGAA?T?TCGGAATTGATACGGGTATCA?G???GGTC?TTAACA chiricahuensisJSF1063 ACT?CTTGT?A??AGCG????T????????GTCCTGTTG??TT???T?CT?CAAAAACTA????GTG??????????GGTG?CCAAACCT??GGTCTTTAATCT??TGGTACC?TA??TTAG?AT?A???????T?AA???????G?TA?????????CCAATT?ATAAGACCTGTAATGCC????A????????TATA??C?TA?GT???T?AT??CAG???ATTACCTT??T??A?AT????CAGACGTTG?TT??????????????AGTTGTT???C??????????A?CGATTTA?ATGGAAC?CTGAGTT????C????CTCCCTATTTACTCGC???T????????CTTTAAAT?????TG?T??T?A?????TCGATACTA???????GTCAGATTTTC??C????????GCCATTAC?????????????A??ACTTGACATAA??CACT???ACCAC?GG?AA??TAA??????????CATACTA??CGTGAATGTG?ACACT?CTG????T?TGTG?AGCCATAAA?CC??AGCCTTAGGAA?AA??????ATAT??GTCT???CC???TA?????GGTAC????????????TA?AGAA?TCGTTT???T????CATGC???GG?CATCCTA??GGATCTTAAACGATCTTAA??????TTCAC?TAG???ACG????ACAT???????CAAAT?ACAAATATAGTCCA?CT?????????CGCG????TC?CA???AAA?AC???T??????G?????AGCTGG????AA?TATGCTTAATT?TACA????CC??TATTC??A????????????A??ATAGGCATAT?????GAAATC?TAT?ACAAGTA?CA????????????A??????CTG?TGATGTATTTGTAG?ATTCAT???TTTT??????ACCT?TTGT??????A??AT??TAA?T?????GCCTT????????T????AG?CA????TACT??ATAAT??TCTGGAGAACT?CCGAAGCA????????????TT?C??A??CTT?????A?TATT?AAT?????ACTACCCT??AT?????TAAT??C??CAG???GGG??ATATTTCCCACTG??TTCAA?CATC???TCGG?TTTGA???AA?C?TAACAAGAAA?AT??GTATTAGA?CTG?????????CAAAGG??????A??GA?????????TA?ATT??TCCAATTAT??GCTTTGTACTATTA?CCGTAAA??AGGG?C?TT?GTA?GACCATGG??G?ATC?AATT?CTTTA????????????????ATTT????????????AAT?C??????????????GC??????TA?CAT?AGG??????TTAGAC??CTAGTT??TAAAATTTAA?AAGA??TATAGT????AAGC??????AATATTAA?TTAACA??TTCTTAGG?TCAA???????AGT??ATACGTCCACAG????CGAC??ACCT?C???????TGCG???????????CTCG??G?AC?TCCTA??GATAAATATTC????GTA???A???AAT??TATCTGC???????A?CTCAG?ACATATTCTCTCC?TTTTACA???C??TAA???????????A?CATATTGCAT??????CAAT?A?C?ATAAGCTGAAGTAG?GCTCT???A??ATGAC?ATG?GA?ACT??TCTATTGATAG???AGGACT??????????????AGTTGTAA??????????TT?A??A??T????????????CTTACCGTACAT?ACT????????????CA????TCGCT????AGACTAAAAATAAGAAAATAAAAC????CA?AGT???A????A?CC??ATCTACCCACATT??T?????AATA??CTGCAGTAATTC??????CCAG?CCT??????AAAATATGAC??CAAAT???GAAAACTCGCAAATGTTATG??TTGT????TTA?TGACATAGCT?GGTAGTAATTCTCT??GCT?ATAA?A?AACTGG??C?CTAGGG?ACTAACCAA?GAAATT?TGAAG???TGA?A???TTAAAT????????AGAGT??G?GT?TGCA?TAAAG?A??G?CA?????CTG?GTACTA??????A?TATC???AAA?G??????A????GTGAACTACTCACAGGATAGAC?TTCACGC??TCATGATCTAATACGTCTACCATA?GC?TGT?G??TAACC?????????CA?????CT??TATTTGT?????AATCCACT????????TTTAA?A??T?????CCTCC???????TT?TTT?A?TAT????TC?GA?CAATA??????????????????????????GA?CTTA????AAAGA??????????A?????????CCATCACT?TGAGACGG?ATCA??AC????TACGTACCA???TCTG?TC?GA?CCC???????AA?ACT??????GCTCA?GT?????????AC????CG????TAT?????ATTCGG?ATTTT?????????A???CT?G?T??GT?ATGTAC?CCTGTACAAT?C?A???ACC?GA?CGT?A??C????TTTA??ATCT??????????TCC??CT????????A?TATCGG??ATAATT?A???AATAGTG?AC???????CATTT????????CTAATATCTCTA??GCT?AC??CT???????C??????????AT?G?GGTAAGGAAGTTTG?GGAGCTAATAAGT?A?????????G???CATGACAT?????CTGTT?AAGC?AA?TTACAGG?GCA???????AGCAATCT????TGTAAATCAC?GT??TTTGTTATGAC??TTTA?CGTCC?TA?CTACAATTGA??CAA????A?GA??T??????????A?GTA???CAATAT?GT?CTCAT??GCCCTATACA????GT?T?GTA???T?????????????TA????TAATGTTGCTA????????AC?CG??T??CA??GATT???????C?AATAA??????ATATA???????TCAGG???????GACTAATT????CCCTTA?????????????CA???????T???TTA?ATAAT?A??A?CA???AAT????G??AGA??C?C?????????ATA?CCAGTAAAT??TGAAC????????TAAGA?CT?CTT?GAAA?T?TCGAAATTGATATGGGTATCA?A???GGTC?ATCACA subaquavocalis ACC?CCAGT?A?AAGCG????T????????GCCCTGTTG??TT???T?CT?AATAAACTA????GTG??????????GGTG?GCAAACCT??GGTCTTTAACCT??CGGTACC?CA??TTAG?AT?A???????T?AA???????G?TA?????????CCGATT?ATAAGACCTGTAATGCC????A????????TATA??C?TA?GT???T?AT??CAG???ATAACCTT??T??A?AT????TAGACGTTG?TT??????????????AGTTGCG???C??????????A?CGATTTA?ATGGAAC?CTGAGTT????C????CTCCCTATTTACTCGC???T????????CCTTAAAT?????TG?T??G?AATG??TTGATACTA???????ATCAAATTTTC??C????????GCCATTAC?????????????A??ACTTGACATAA??CACC???ACCAC?GG?AA??TAA??????????CATAC?G??CATGAATGTG?ACACT?CTG????A?TATG?TGCTATAAA?CC??AGCCTTAAGAC?AA??????ATTT??GTCT???CC???TA?????GGTAC????????????TA?AAAT?TCGTTT???T????CATGC???GG?CATCCTA??GGATCTTAAACGATCTTAA??????TTCTC?TAG???TCG????ACAC???????CAAAT?ACAAATATAGTCCA?CT?????????TGCG????TT?CA???AAA?AC???T??????G?????AGCTAG????AA?TATGCTTAATT?TGCA????CC??TGTTC??A????????????A??GTAGGCATAT?????GAAATC?TAT?ACAAGTA?TA????????????T??????CTG?TGATATATATGTAG?ACTCAT???TTTT??????ACCT?TCGT??????A??AT??TAA?T?????GCCTT????????T????AG?CA????TACT??ATAAT??TCTGGAAAACT?ACGAAGCA????????????TT?C??A??CCT?????A?TAGC?AAT?????ACTACCCT??AT?????TAAT??C??CAG???AGG??ATATTTCCCACTG??TTCAA?CATCAA?TCGG?TTCGA???AA?C?TAAAAAGAAA?AT??GCATTAGA?CTG?????????CAAAGG??????AC?GA?????????GA?ATT??TCCAATTGT??GCTTTGTACTATTA?CTGTAAA??AAGG?C?TT?ATA?AACCATGG??T?ATC?AATT?CTATA????????????????ATCT????????????TAT?C????A?????????GC??????AA?CAT?AGG??????TTAGAC??CTAGTT??TTAAATTTAA?AAGG??TATAGT????AAGC??????AATAAGAA?TAAACA??TTCTTAGG?TCAA???????AGT??ATACGTCCACAG????AGAC??ACCT?C???????TGCG???????????CTCG??G?AC?TCCTA??GATAAATATTA????GTAGT?A???AAT??TATCTTC???????A?CACAG?ATACATTCCCTCC?TTTTACA???C??TAA???????????A?CATATTGCAT??????CAAT?A?C?ATAAGTTGAAGTAG?GCTCT???A??ATGACTATG?GA?ACT??TCTATTGATCG???AGGACT??????????????AGTTGTAC??????????TT?A??A??T????????????CTTACCATACAT?ACC????????????CA????TCGCT????AGACTCAAAATAAGAAAATAAAAC????CA?AGT???A????A?CC??ATCCACCCGCATT??T?????AATA??CTGCAGTATTCC??????CTAG?ACT??????AAAATATGAC??CAAAT???GAAAACTCGCAAATGTTATG??TTGA????TTA?TGACATAGCT?GGTAGTAATTTTCT??GCT?ATAA?A?AATTGG??C?CTAGGA?CCCAACCAA?AAAATT?TGAAG???TCA?A???TTAAAT????????GGAGC??G?GT?TACA?AAAAG?A??G?CA?????TTG?GCACTA??????A?CAAC???AAA?G??????A????GTAAACTACCCGTAGGATAAAC?TTCACGC??TCATGATT??????????ACCATA?GC?TGT?G??TAACC?????????CA?????TT??TATTTGT?????AGTACACT????????CTTAA?A??T?????CCTCC???????TT?CTT?G?TAT????TC?GA?CAATA??????????????????????????GA?CTTA????AAAGA??????????AGAA?ACAGCCCATCGTT?TGAGACGG?ATCA??AC??AGTACGTACCA???TCTG?TA?GA?CCC???????AA?GCT??????ACTTA?GT?????????TC????CG????CAT?????ATCCGG?ATTTT?????????A???CT?G?T??GT?ATGTAC?CCTGTACAAT?A?A???ACC?GG?CGT??????????CTA??ACTT??????????TCC??CT????????A?TATCGG??ATAATT?A???AATAGTG?AC???????CATTT????????TTAATATCTCTA??GCT?AC??CT???????C??????????AT?G?GGTAAGGAAGTTTG?GGAGCTAATAAGT?A?????????A???CATGATAT?????CTGCT?AAAC?AA?TCACAGA?GTA???????AGCAATCC????TGTGATCCAC?GT??CTTGTTATGAC??TTTA?TGTTC?TA?CTACAATTGA???AA????G?GA??T??????????A?GTA?GTCAATAT?GT?CTCGT??GCTCTATGCA????TT?T?GTA???T?????????????TA????TAATGTTGTTA????????AC?CG??T??CAT?GATT???????C?AATAA??????ATATA???????TCAGG???????GGTTAATT????CCCTTA?????????????CC???????T???TTA?ATA?T?A??A?CA???AAT????G??AGA??C?C?????????ATA?CCAGTAAAT??TGAAC????????TAAGA?CT?CTT?GAAA?T?TCGAAATTGATACGGGTATCA?A???GGTC?ATAATA chiricahuensisJSF1092 ACC?CCCGT?A??AGCG????T????????GTCCTGTTG??TT???T?T??AATAAACTA????GTG??????????GGTG?GCAAACTT??GGTCTTTAACCT??CGGTACC?AA??TTAG?AT?A???????T?AA???????G?TA?????????CCCATT?ATAAGACCTGTAATGCC????T????????TATA??C?TA?GT???T?AT??CAG???ATAACCTT??T??A?AT????TAGACGTTG?TT??????????????AGTTGCG???C??????????A?CGATTTA?ATGGAAC?CTGAGTT????C????CTCCCTATTTTCTCGC???T????????CCTTAAAT?????TG?T??G?AATG??TTGATACTA???????ACCAAATTTTC??C????????GCCATTAC?????????????A??ACTTGACATAA??CACC???ACCAC?GG?AA??TAA??????????CATAC?G??CGTGAATGTG?ACACT?CTG????T?TATG?AGCTATAAA?CC??AGCCTTACGAA?AA??????TTTC??G????????????A?????GGTAC????????????TA?AAAT?TCGTTT???T????CATGC???GG?TACCCTA??GGATCTTAAACGATCTTAA??????TTCTC?TAG???TCG????ACAC???????CAAAT?ACAAATATAGTCCA?CT?????????TG?????????CA???AAA?AC???T??????G?????AGCTAG????AA?TATGCTTAATT?TACA????CC??CAGTC??G????????????A??GTAGGCATAT?????AAAATC?TAT?ACAAGTA?TA????????????A??????CTG?TGATATATTTGTAG?ACTCAT???TTTT??????ACCT?TCGT??????A??AC??TAA?T?????GCCTC????????T????AG?CA????AATT??ATAAT??TCTAGAGAATT?CCGAAGCA????????????CT?C??A??CTT?????A?TATC?AAT?????ATTACCCT??AT?????CAAT??C??CAG???AGG??ATATTTCCTACTG??TTCAA?CATCCA?TCGG?TTCGA???AA?C?TAATAAGAAA?AT??ACATTAGA?CTG?????????CAAAGG??????A??GA?????????GA?ATT??TCCAATTGT??GCTTTGTACTATTA?CTGTAAA??AAGG?C?TT?GTA?GACCATGG??T?ATC?AATT?CTATA????????????????ATCT????????????TAT?C????A?????????GC??????AA?CAT?AGG??????TTAGAC??CTAGTT??TTAAATTTAA?AAGG??TACAGT????AAGC??????AATAATAA?TAAACA??TTCTTAGG?CCAA???????AGT??ATACGTTCACAG????AGAC??ACCT?C???????TGCG???????????CTAG??G?AC?TCCTA??GATAAATATTA????GTAGC?A???AAT??TATCTGC???????A?CCCAG?ATACATTCCCTCC?TTTTACA???C??CAA???????????A?CATATTGCAT??????CAAT?A?C?ATAAGTTGAAATAG?GCTCT???A??ATGACTATG?GA?ACT??TCTATTGATCG???AGGACT??????????????AGTTGTAC??????????TT?A??A??T????????????CTTACCATGCAT?ACC????????????CA????TCGCT????AGA???AAAATAAGAATATAAAAC????CA?AGT???A????A?CC??ATCCACCCACATT??T?????AAAA??CTGCAGTACTCC??????CTAG?ACT??????ACAATATGAC??CAAAT???GAAAACTCGTAAATGTTATG??TTGA????TTA?TGACATAGCT?GGTAGTAATTTTCT??GCT?ATAA?A?AACTGG??C?CTAGGG?CCCAACCAA?GAAATT?TGAAG???TTA?A???TTATAT????????AGAGA??G?GT?TACA?AAAAG?A??G?CA?????ATG?GTACTA??????A?CAAC???AAA?G??????A????GTGAACTACTCGTAGGATAGAC?TTCACGC??TCATGATA??????????ACCATA?GC?TGT?G??TAACC?????????CA?????CT??TATTTGT?????AATACACT????????TTTAA?A??T?????CCTCC???????TT?CTC?G?TAT????TC?GA?CAATA??????????????????????????GA?CTTA????AAAGA??????????AGAA?ACAGCCCATCGTT?TGAGACGG?ATCA??AC??AGTACGTACCA???TCTG?TG?GA?CCC???????AA?GCT??????ACTTA?GT?????????GC????CG????TAT?????ATCCGG?ATTTT?????????A???CC?G?T??GT?ATGTAC?CCTGTACAAT?T?A???ACC?GA?CGT?A??C????TCTA??ACTT??????????TCC??CT????????A?TATCGG??ATAATT?A???AATAGTG?AC???????CATTT????????CTAAGATCTCTA??GCT?AC??CT???????C??????????AT?G?GGTCAGGAAATTTG?GGAGCTAATAAGT?A?????????A???CATGATAT?????CTGCT?AAAC?AA?TTACAGG?GTA???????AGCAATCC????CGTAACCCAC?GT??CTTGTTATGAC??TTTA?TGTCC?TA?CTACAATTGA???AA????G?GA??T??????????A?GTA???CAATAT?GT?CTCGT??GCTTTATGCA????TT?T?GTA???TCTC???CA?AACTTA????TAATGTTGTTA????????AC?CG??T??CAT?GATT???????C?AATAA??????ATATA???????TTAGG???????GGTTAATT????CCCTTA?????????????CA???????T???TTA?ATA?T?A??A?CA???AAT????G??AGA??T?C?????????ATA?CCAGTAAAT??TGAAC????????TAAGA?CT?CTT?GAAA?T?TCGAAATTGATATGGGTATCA?A???GGTC?TTAACA palustrisJSF1110 ACT?CTCGC?A??AGCG?GC?T????????GTCCTGTAG??TT???T?TT?AATTAACTA????GTG??????????GGTG?GCAAACTT??GGCCTTTAAACT??AGGTATT?CA??TTAA?AT?A???????C?AC???????A?TA?????????CCGATT?ACAAAATCTGTAATGGC????T????????AATA??C?TA?AT???A?AT??CAG???ATGACCTT??T??A?CC????GGGACGATG?TCACC????????TTTAGTTGCA?TCC??????????A?CGGTTTA?ATGGAAC?CTGAGCT????C????CTCC???TTCACTCGC???TA???ATTACCTTAACT?????TG?T??A?AATGTGTAGATACTA???????ATGAAGCTTTC??C????????GCCATTAC??A??????????A??ATTTGTCACAA??CACT???GTCAC?GG?AC??TAT??????????AATAC?A??CCTAAATGTA?ACACT?CTG????T?TTT??A????TAAA?CT??GGC?TTACGAA?A?TCACTCATTTT?GGAC???CC???TA?????AGTAC?????CA???T?TA?AGAT?TCGTTT???T????CGTGC???GC?GACCCGT??GGAACTTAGACGATCTTAA??????TTCCC?TAG???TCG????ACAA???????CTAAT?ACAAATATAGTCCA?T??????????TGAG????TT?CA???AAA?AC???T??????G?????AGCTTG????AA?TACGCATAGCT?TACA????CC??TAGTC??A????????????A??GTCGGCGTAC?????AAAATC?TAA?GCAATTA?AA????????????A??????CTG?CGATGTATCGTTTG?ACTCAT???TTTA??????ACCT?TCGTATCC??A??AT??TAA?T?????GCCGA????????T????AA?TA?A?ATACT??ATAAT??TCTGGAGAATT???GAAGCAGC???GG?????TTGC??A??CTA????CA?CATC?AAT?????ATTACCCC??AT?????CAAT??C??CAA???AGG??ATATCTCCAACTG??TTCAA?CATCAA?TCCG?TTTGA???AA?T?TAACAAGAAA?AT??CTATTAGA?CCG?????????CAAAGG?????????GA?????????AATATT??TCCAATTCT??GCTTTATACTATTA?CTGTAAATGGAAA?C?TT?GTA?GACCATGG??C?ATC?AATTTCTATA????????CA??GCAAATCTGTT??ATCTTTACAT?C????A?????????GC??????AA?AAT?AGG??????CTAGAC??TTAGTT??TTAAA???GA?AAGG??TACA?T????AAGC??????AATA???A?TCAGTA?????T?AGG?CCAA???????TGT??ATACGTACACAA????TGGC??ACCT?C???????TGAG???????????ATTC??G?TC?TTTTA??GATAAATATTA????GTA?C?A???AAT??TAACTAC???????A?CCCAG?TTACATTCACCCC?TTTTATA???C??TAG???????????A?CATATTGCTT??????CAAT?A?C?ATAAGTTGAAGCAG?GCTCT???A??TTGATTATG?GA?ACC??TCTATTGATTG???AG?ACA?TAC???????ATA?ACTGTAC??????????TT?A??C??T????????????CCGACC???????ACC????????????CATCATTTGCT????AGACTCAATACAAGAAAAACAAAT????CA?AAT???A????A?CC??ATCCATCCGCATT??T?????AACA??C???AGTATTTC?????ACTAG?ACT??????AAAATATGAC??CAAAA???GAAAACTTGTAAAAGTTATG??TTAA????TTA?TGACATAGCT?GGTAGTAACTCACT??GCT?ATAA?A?AACTGG??C?CTAAAA?CCCAAATAA?TAAATT?TGGCG???TTA?A?C?CTATAA????????AGAGG??A?GT?AATC?AGAAA?T??G?CT?????CTG?GTACTA??????A?CAAT???AGA?A??????A????GTTAATTACTAT?AGGGTAGAC?TTCACGC??TCATGGTT??????????ACAATA?GT?TGT?A??TAACC?????????CG?????TT??TAGCTGT?????ATTCCACT????????TTTGA?A??T?????CCTAC??????TAT?TTT?C?TA?????CC?GA?CAATA??CG??AG??????????CT?T?TAGGA?TTTA????AAACA??????????AGCA?ATAGCACCTCATT?CGAGACCG?ATCA??AC??AGTACGTACCA???TCTG?TA?GA?CCC??????AAA?GTT??????ACTTA?GC?????????AC?????G????AAT?????ATTCGGAATTTT?????????A???CT?G?T??GT?A???AC?CCTGTACCAT?A?AT??A?????????????T????TACA?GACTT??????????TCA??CT????????A?TCTTGG??ACGATT?A???AAAAGTG?ACGC????CCATTT????????TTAACATCTCTA??GCT?AC??AT???????C????CACCCGAT?G?GGTAAGGAAGTTTGTAGAGTTAATG?GT?A?????????C???TATGGCAT?????CTCTC?GAAC?AG?TCTCAGT?ATA???????AGCAATCA????TACAAGCCAT?AT??CTTGATA?GCC??TTTA?AGTC??TA?CTACAGTTGA???AG????G?CA??A??????????A?GTA???CCACAT?GT?CTTGC??GCTATATCCA????TT?T?GTA???TCTTT??CA?AACTTA????TAATGTTGCTA????????AC?CG??T??CA??GATA???????C?A?TAA??????ATATA??????????????????????TAATT????CTCTCA?????????????CA???????T???TTA?ATA?T?A??A?CA???AAA????G??AGG??T?C?????????ATA?CCGGTAAAT??TGAAC????????CCAGA?CC?CTT?GCAA?T?CCGCAATTGATACGAGTATTA?T???GGTC?CTAACA areolataJSF1111 ACT?CTCGC?A??AGCG?GC?T????????GTCCTGTAG??TT???T?TT?AATTACCTA????GTG??????????G?TG?GCAAACTCGCGGCCTTTCAACT??AGGCATG?CA??TTAA?AT?A???????C?AC???????G?TA?????????CCAATT?ATAAAGCCTGTAATGTC????T????????AATA??C?TA?TT???T?AT??CAG???ATAACTTT??C??A?AC????GGGACGCTG?TCATC????????TTTAGTTGCA?TCC??????????A?CGATTTA?ATGGAAC?CTGAGCT????C????CTCC???TTCACTCGC???TA???ATTACCCTAAAT?????TG?T??A?AATGTATAGATACTA???????ATGAAG?????????????????CCATTAC??A??????????A??ATTTGCCACAA??CACT???ATCAC?GG?AC??CAC??????????AAAAC?A??CATAAATGTA?ACATT?C??????C?TCT??A????TAAA?CT??GGC?TTATGAA?A?TCACCCATTTT?GGAC???CC???TA?????AGTAC?????CA???T?TA?AAAT?TCGTTT???T????CATGC???GT?GTTCCAT??GGAACTTAGACGATCTTAA??????TTCCC?TAG???CTG????ACAA???????TTATT?ACAAATATAGTCCA?TT?????????TGAG????TT?CA???AAA?AC???TCTAATAG?????AGCTCG????AA?TACGCTTAATT?TACA????CC??AACTC??A????????????A??GTTGGCATAC?????AAAATC?TAG?GCAATTA?AA????????????T??????CTG?TGATGTATTGTCTG?ATTCTC???TTTA??????ACCT?TCGT??????A??AT??TAA?T?????GCCGG????????T????AA?TA?A?ATACT??ATAATAATCTTGAGAATT???GAAGCAGG???GG?????TT?C??A??CTA????CA?CATT?AGT?????ACTACCCC??AT?????CAAT??C??CAA???AGG??ATATTCCCGACTG??T?CAA?CATCAACTCGG?TTTGA???AA?T?TAAGAAGAAA?AT??TTATTAAA?CGG?????????CAAAGG??????A??GA?????????AATATT??CCCAATTTT??GCTTTTTACTATTA?CTGTAAACGGAAA?C?TT?GCA?GACCATGG??C?ATC?AATTTCTGTA????????CA??GCAAATCTATT??ATCTTT?CATTC????A?????????GC??????GA?AAT?TGG??????TTAGAC??CTAGAT??TTAAA???GA?AAGA??TACA?T????AAGC??????AATA???A?TTAATA?????T?AGG?CCCA???????TGT??ATACGTGCACAA????CGGC??ACCT?C???????TGCG?GC????????ATTA??G?TC?TCTTA??GATAAATCTTA????GTA?T?A???AAT??TAACTAC???????A?CTCAG?ATACATTCACCCC?TTTTATA???C??TAG???????????A?CATATTGCTT??????TAAT?T?C?ATAAGCTGAAGCAA?GCTCT???A??GTGATTATG?GA?ACC??TCTATTGATCG???AG?ACA?TAC???????ATA?ACTGTAC??????????TT?A??C??T????????????CCGACC???????ACC??CAACTCGCGACATCATTTGCC????AGACTCAATATAAGAATAACAAAC????CA?AAT???A????A?TC??ACCCATCCGCATT??T?????AATA??C???AGTACTTC?????ACTAG?GCT??????AAAACATGAC??CAAAA???AAAAAC???TAA?????ATG??TTAA????TTA?TGACATAGCT?GGTAGCAACTGACT??GCT?ATAA?A?AACTGG??C?CTAAAG?TCCAAATAA?TAAATT?TGGTG???TGA?A?C?CTATAA????????AGAGA??A?GT?AATT?AGCAA?T??G?CA?????CTG?GTACTA??????A?AAGT???AGA?ACCTTGTA????GTTAATTACTAA?AGGATACAC?TTCACGC??TCATGGTT??????????ACTATG?GC?CGT?G??TAACC?????????CG?????AT??TAGCTGT?????AATACACT????????TTTGA?A??T?????TTTAC??????TAT?TTT?G?TA?????CC?GA?CAATA??CG??AG??????????CT?T?TAGGA?TTTA????AAATA??????????AGTA?ATAGCTCATCAAT?CGAGACGG?ATTA??AC??AGTACATACCA???TCTG?TA?GA?CCC??????AAA?ATT??????ACTTA?GTCCTATTTCTTC?????G????TAT?????ATCCGG?TTTTT?????????A???CA?G?T??GT?A???AT?CCTGTACCAC?A?AT??A?????????????T????TACA?GAAGT??????????TCT??CT????????A?TTTTGGGAATGATT?A???AAAAGTG?ACGC????CCATTT????????TTAACATCTCTA??GCT?AC??AT???????CGTTACACACGAT?G?GGTCAGGAGGTTTGTAGAGTTAATG?GT?A?????????C???TAAGGCAT?????CTCCC?GAAC?AG?TTTCAGG?AAA???????AGCAATCA????TGAAAACCAC?AT??TTTGATA?GCC??TTCA?AGTCC?TA?CTACAGTTGG???AG????G?TA??A??????????A?GTA???TAACAT?GT?CTTGC??GCTCTATTCA????CT?T?GTA???TTTTT??CA?AACTTA????CAATG?TGATA????????AC?CG??TATCAT?GATA???????C?AAT????????ATATA??????????????????????TAATT????CCCTCA?????????????CA???????T???TCA?ATA?T?A??A?CA???AAA????C??AGG??T?T?????????ATA?CCAGTAAAA??TGAAC????????CCAGA?CC?CTT?GTAG?C?TC?TAATTGATACGAGTATTA?A???GGTC?TTAACA sevosaUSC8236 ACT?CTTGT?A??AGCG?GC?T????????GTCCTGTAG??TT???T?TT?AACCAACTA????GTG??????????GGTG?TCAAACTT??GGTCTTTTAACT??GGGTATC?CA??TTAA?AT?A???????C?AT???????A?TA?????????CCGATT?ACAAAGTCTGTAATGCC????T????????AATA??C?TA?CT???T?AT??CAG???ATAACTTT??T??A?AC????GGGACGTTG?TAACC????????TCTAGTTGCA?TCC??????????A?CGATTTA?ATGGAAC?CTGAGTT????C????CTCC???TTCACTCGC???TA???ATTACCTTAAAT?????TG?T??A?AATGTATAGATACTA???????ATGAAG?????????????????CCATTAC??A??????????A??ATTTGTCACAA??CACT???ATCAC?GG?AC??TAC??????????AATAC?G??CATAAATGTA?ACACT?CTGTATAT?TTT??A????TAAA?CC??GGC?TTACGAA?AATCACTCATTTT?GGAC???CC???TA?????AGTAC?????CA???T?TA?AGAT?TCGTTT???T????CATGC???GA?GTCCCAT??GGAACTTAGACGATCTTAA??????TTCCC?TGG???TTG????ACAA???????CTAAT?ACAAATATAGTCCA?CT?????????TGAG????TT?CA???AAA?AC???T??????G?????AGCTTG????AA?TACGCCTAACT?TACA????CC??TA??????????????????A??GATGGCATAC?????AAAATC?TAA?GCAAC?A?AA????????????A??????CTG?TGATATATTGTCTG?ACTCTT???TTTG??????ACCT?TCGT??????A??AT??TAA?T?????GCCGA????????T????AA?TA?A?ATACT??ATAAT??TCTCGCGAATT???GAAGCAAC???GG?????TT?C??A??CTA????CA?CATT?AAT?????ATTACCCC??AT?????CAAT??C??CAA???AGG??ATATTTCCGACTG??TTCAA?CATCAACTCGG?TTTGA???GA?T?TAAAAAGAAA?AT??TAGTTAGA?CGG?????????CAAAGG??????A??GA?????????AATATT??TCCAATTTT??GCTCTATACTATTA?CCGTAAACGAAAA?C?TT?GTA?GACCATGG??C?ATC?AATTTCTATA????????CA??GCAAATTTGTC??ATCTTTATAT?C????A????????????????????????TGG??????TTAGAC??CTAGTT??TTAAA???GA?AAG???TACA?T????AAGC??????AATA???A?TCAATA?????T?AGG?CCCC???????CGT??ATACGTACACAA????TGGC??ACCT?C???????TGTG???????????ATCA??G?TC?TTTTG??GATAAATATTA????GTA?T?A???AAT??TAACTAC???????A?CCCAG?CTACATTCACACC?TTTTATA???C??TAA???????????A?CATATTGCTT??????CAAT?A?C?ATAAGTTGAAGCAG?GCTCT???A??TTGATTACG?GA?ACC??TCTATTGATTG???A??ACA?TAC???????ATA?ACTGTAC??????????TT?A??T??T????????????CCGACC???????ATC????????????CA?CATTTGCC????AGACTCAATATAAGAATAATAAAT????CA?AAT???A????A?TC??ACCCATCCGCATT??T?????AATA??C???AGTATTTC?????ACCAG?ACT??????AAAACATGAC??CAAAA???GAAAACTTGTAAAAGTTATA??TTAA????TTA?TGACATAGCT?GGTAGTAATAGACT??GCT?ATAA?A?AACTGG??C?CTA?AG?CCTAAATAA?TAAATT?TGGCG???TGA?A?C?CTATAA????????AGAGT??A?GT?AATC?AGTCT?T??G?CA?????CTG?GTACTA??????A?AAAT???AGA?A??????A????GTCAATTACTTG?AGGATAGAC?TTCACGC??TCATGGTT??????????ACAATA?GT?TGT?G??TAATC?????????CG?????TT??TAGCTGT?????AATCCACT????????TTTAA?A??G?????CCTAC??????TAT?TTC?A?TA?????CC?GA?CAATA??CA??AG??????????CT?T?TAGGA?CTTA????AAATA??????????AGCA?ATAGCTCGTCAAT?CGAGACGG?ATCA??AC??AGTACATACCA???TCTG?TC?AA?CCC??????AAA?ATT??????ACTTA?GTCCTATTTCTTC?????G????TAT?????ATCTGG?ATTTT?????????A???GT?G?T??GT?A???AC?CCTGTACCAT?A?AC??A?????????????T????TACA?GACAT??????????TCC??CT????????A?TATTGG??ACGAAT?A???AAAAGTG?ACGC????CCATTT????????TTAACATTTCTA??GCT?AC??AT???????C????CACCCGAT?G?GGTTAGGAAGTTTGTGGAGTTTATG?GT?A?????????T???TATGGCAT?????CTACC?GTGC?AG?TTCCAGT?ATA???????ACCAATCA????TGTAAACCAC?GT??CTTGATA?GCC??TTTA?GGTTC?TA?CTACAGTTGA???AG????G?TA??A??????????A?GTA???CTATATAGT?CTTGC??GCTTTATTCA????TT?T?GTA???TGTTT??CA?AACTTA????TAATGTTGTTA????????AC?CG??C??CAT?GATA???????C?AAT????????ATATA??????????????????????TAATT????CCCTCA?????????????CA???????C???TTA?ATA?T?A??A?CA???AAG????A??AGG??T?T?????????ATA?CCAGTAAAT??TGAAC????????TCGGA?CT?CTT?GCAA?T?TCGTAATTGATACGAGTATTA?T???GGTC?CTAACA capitoSLU003 ACC?CTTGC?A??AGCG?GC?T????????GTCCTGTA???TT???T?TT?AACCAACTA????GTG??????????GGTG?TCAAACTT??GGCCTTTTAACT??AGGTATC?CA??TTAA?AT?A???????C?AT???????A?TA?????????CCGATT?ACAAAGTCTGTAATGCC????T????????AATA??C?TA?CT???T?AT??CAG???ATAACTTT??T??A?AC????AGGACGTTG?TTACC????????TTTAGTTGCA?TCC??????????A?CGATTTA?ATGGAAC?CTGAGTT????C????CTCC????TCACTCGC???TA???ATTACCCTAAAT?????TG?T??A?AATGTATAGATACTA???????ATGAAG?????????????????CCATTAC??A????????????????TGTCACAA??CACT???ATCAC?GG?AG??CAC??????????AATAC?G??CATAAATGTA?ACACT?CTGTATAT?TTT??A????TAAA?CT??GGC?TTACGAA?AATCACTCATTTC?GGAC???CC???TA?????AGTAC?????CA???T?TA?CGAT?TCGTTT???T????CATGC???GC?GTCCCAT??GGACCTTAGACGATCTTAA??????TTCCC?TGG???TAG????ACAA???????CTAAT?ACAAATATAGTCCA?CT?????????TGAG????TT?CA???AAA?AC???T??????G?????AGCTTG????AA?TACGCCTAGCT?TACA????CC??TA??????????????????A??GATGGCATAT?????AAAATC?TAA?GCAAC?A?AA????????????A??????CTG?TGATATATTGTCTG?ATTCTT???TTTG??????ACCT?TCGT??????A??AT??TAA?T?????GCCGA????????T????AA?TA?A?ATACT??ATAAT??TCTCGCGAATT???GAAGCAAT???GG?????TT?C??A??CTA????CA?CACT?AAT?????ATTACCCC??AT?????CAAT??C??CAA???AGG??ATATTTCCGACTG??TTCAA?CATCAACTCGG?TTTGA???GA?T?TAAAAAGAAA?AT??TATTTAGA?CAG?????????CAAAGG??????A??GA?????????AATATT??CCCAATTTT??GCTTTATACTATTA?CCGTAAACGAAAA?C?TT?GTA?GACCATGG??T?ATC?GATTTCTATA????????CA??GCAAATCTGTC??ATCTTTATAT?C????A????????????????????????TGG??????TTAGAC??CTAGTT??TTAAA???GA?AAG???TACA?T????AAGC??????AATA???A?TCAATA?????T?AGG?CCTC???????TGT??ATACGTACACAA????CGAC??ACCT?C???????TGCG???????????ATTA??G?TC?TTTTG??GATAAATATTA????ATA?T?A???AAT??TAACTAC???????A?CCCAG?CTACATTCACACC?TTTTATA???C??TAA???????????A?CATATTGCTT??????CAAT?A?C?ATAAGTTGAAGCAG?GCTCT???A??TTGATTACG?GA?ACC??TCTATTGATTG???AG?ACA?TAC???????ATA?ACTGTAC??????????TT?A??C??T????????????CCGACC???????ATC????????????CA?CATTTGCC????AGACTCAACAGAAGAATAAAAAAT????CA?AAT???A????A?TC??ACCTATCCGCATT??T?????AATA??C???AGTATTTC?????ACTAG?ACT??????AAAATATGAC??CAAAG???GAAAACTTGTAAAAGTTATA??TTAA????TTA?TGACATAGCT?GGTAGTAACAGACT??GCT?ATAA?A?AACTGG??C?CTA?AG?CCTAAATAA?TAAATT?TGGTG???TGA?A?C?CTATAA????????AGAGT??A?GT?AATC?AGTCT?T??G?CA?????CTG?GTACTA??????A?AATT???AGA?A??????A????GTCAATTACTCG?AGGATAGAC?TTCACGC??TCATGGTT??????????ACAATA?GT?TGT?G??TAATC?????????CG?????AT??TAGCTGT?????AATTCACT????????TTTAA?A??T?????CCTAC??????TAT?TTC?A?TA?????TC?GA?CAATA??CA??AG??????????CT?T?TAGGA?CTTA????AAATA??????????AGCA?ATAGCTTGTCATT?CGAGACGG?ATCA??AC??AGTACATACCA???TATG?TC?GA?CCC??????AAA?ATT??????ACTTA?GTCCTATTTCTTT?????G????TAT?????ATCTGG?ATTTT?????????A???GT?G?T??GT?A???AC?CCTGTACCAT?A?AC??A?????????????T????TACA?GACAT??????????TCC??CT????????A?TATTGG??ACGAAT?A???AAAAGTG?ATGC????CCATTT????????TTAATATTTCTA??GCT?AC??AT???????C????CACCCGAT?G?GGTTAGGAAGTCTGTGGAGTTTATG?GT?A?????????T???TATGGCAT?????CTACC?GCGC?AG?TCCCAGC?ATA???????ACCAATCA????TGTAAACCAC?GT??CTTGATA?GCC??TTTA?GGTCC?TA?CTACAGTTGA???AG????G?TA??G??????????A?GTA???CTATATAGT?CTTGC??GCTTTATTCA????TT?T?GTA???TATTT??CA?AACTTA????TAATGTTGTTA????????AC?CG??C??CAT?GATA???????C?CAT????????A?ATA??????????????????????TAATT????CCCTCA?????????????CA???????C???TTA?ATA?T?A??A?CA???AAG????G??AGG??T?T?????????ATA?CCAGTGAAT??TGAAC????????TTGGA?CT?CTT?GTAA?C?TCGCAATTGATACGAGTATTA?T???GGTC?CTAACA spectabilisJAC8622 ATT?CCCGC?A??AGCG?GC?T????????G?CCTGTCG??TT???T?GT?AACAAACTA????ATG??????????GGTG?GCAAACTT??GGCCTTTGATCT??AGGTACC?TA??TTAA?TT?A???????C?AC???????C?TA?????????CCCATT?ACAAAGCCTGTAATGCC????C????????AATA??T?TA?AT???T?AT??CAG???ACAACCTT??T??A?AT????TAGACGTTG?ATACC????????TATAGTTGTA?TCC??????????A?CGATTTA?ATGGAAC?CTGAATT????C????CTCC???TTCACTCGC???TA???ACTGCTTTAAAT?????TG?T??T?AATGTATAGATACTA???????CTGAAACTTTCAAC????????GCCATTAC??A??????????A??ATTTGCCATAA??CACT???ACCTC?GG?AT??CAG??????????AATAC?T??CATGAATGT??ACATT?CTG????T?TCCGGAGCCATA?A?CT??CGC?TTAGGAA?GGTCACTCATTCT?GAAT???CC???TA?????AGTAC?????CA???T?TG?AGGT?T?GTTT???T????CTTGC???GT?CATCCAT??AGAACTTAGACGATCTTAA?????CTTCCC?TAG???CCG????TTAG???????CGAAT?ACAAATTTAGTTCG?AT?????????TGCG????TC?CATTGAAA?AC???T??????T?????AG?TTG????AA?TATGCCTGGTTCTAAA????CC??CAGTC??A????????????A??GTCAGTATAA?????GAAATC?CCA?GCAATTA?TA????????????A?CACCACTG?TGA????????CAG?ACTCTT???TTTA??????ACCT???GT??????A??AT??TAA?T??????CCAA????????T????AA?TA?A?ACACT??ATAAT??TCTGGTGAATT???CAAGCAGG???GG?????CT?C??A??CTA????CA?TATT?AAT?????ATTACCCT??AT?????C?AT??C??CGGTGGAGG??ATATTTCCAACTG??TTCAA?CACCAA?TCGG?TTTGA???AA?T?TAAAAAGAAA?AC??TCTTTATA?CAG?????????AAAAGG??????A??GA?????????AATATTGATTCAAT?TT??GCTTTGTACTATTA?CGGTAAACGGAAG?C?TT?GTA?GCCCATGG??A?ATC?AATCTCTACA????????CA??ACAAATCTATC??ATCTTTATAC?C????A?????????GC??????GATTAT?TGG??????CTA?AC??TTAGTT??TCAAA???GA?AAAA??TAAA?T????AAGC??????AATA???A?TCAATA?????TTAGG?GCAA???????CGT??ATTCGTACACAA????TGGC??ACCA?C???????TGTG???????????ATTA??G?TC?TCCTA??GAGAAATCCTT????GTA?T?A???AAT??TAACTAC???????A?CTCAG?ATACATTCTCTCC?TTT????????????C???????????A?CATATTGCTT??????CAAT?T?C?ATAAGTTGAAGCAG?GCTCT???A??CTGATTATG?GA?CCT??TCCATTGA??????AGAACA?TAC???????AT??GTTGTAC??????????TT?A??A??T????????????CCGACCATTCCT?ACC????????????CATCTTCTACT????AGACCCAACACAAGAA??ATATAT?????????????A????A?CC??ATCCATCCGCATT??T?????AAAA??C???AGTATTCC?????ATCAG?ACT??????AAATTATGAC??CAAAT???CAAAACTTGTAAAAGATATG???TAA????T????GACATAGCT?GGTAGTAACTAGCT??GCT?ATAA?A?AACTGAAAC?CTAAGA?CCTAAATAA?TAAATT?TGGCG???TAA?A?C?TTATAC????????AGAGA??A?GT?AATC?AGAAA?T??G?CT?????TTA?GCACTA??????A?AACC???AAA?G??????A????GTCAATTACTTT?AGGATAGAC?TTCACGC??TCATGGTG??????????ACCATA?GC?TGT?C??TAACC?????????CG?????AT??TATCTGT?????AATCCACC????????ATTGA?A??T?????ACTAC??????TTT?CTT?A?TCT????CC?GA?CAATA??CG??AG??????????GT?C?TAGGA?CTTA????AATGA??????????AGAA?ATGGCTCATCA???TTAG?CGT?ATTA??AC??AGTACGAATCA???TCTG?TG?AA?CCC??????AAA?AT???????ACTCA?GT?????????CA?????G????TAT?????ATCTGG?TCTTT?????????AAAACT?G?T??GT?A???AC?CCTGT?CAAT?A?AC?TACC?AA?CGT?G??T????TGCA?GACTT??????????TCA??CT????????A?TTTTGG??ACGATATA???AATAGTG?ACGC????CCATTT????????TTGATATCTCTA??GTA?AC??AT???????C????CACCCGAT?G?GGTAAGGAAGTTTGTGGAATTAATG?GT?A?????????G???TATGGAAT?????CTATC?GAGC?AA?TGC?AGC?ATA???????ATCAATCA????AGCAAACCAT?A???CTTGATA?GCC??TTTC?AGTT??TA?CTACCGTTGA???AGATCAG?GA??A??????????A?GTA???CTGTAT?GT?CTTGC??GCTTTACGCA????TT?T?GTA???TATATTCCA?AACTTA????TAATGTTGA??????????AC?CG??T??CAT?GATG???????C?AATAA??????ATATA??????????????????????TAATT????CCCTCA?????????????CA???????A???TTA?ATA?T?A??A?CA???AAA????C??AGG??C?C?????????ATA??CAGTAAAT??CGAAC?CCTATCCCAAGA?CT?CTT?GCAA?T?TCGGAATTGATACGGGTATTA?A???GGTC?TTAACA omiltemanaJAC7413 ATC?CTCGC?A??AGCG?GC?T????????GTCCTGACG??TT???T?AT?CACGATCTA????GCG??????????GGTG?GCA???????GGACTTTGATCT??AGGTACT?CA??TTAA?AT?A???????C?AC???????A?TA?????????CCCATT?ACAAAGCCTGTAATGTC????T????????GATA??T?TA?AT???T?AT??CAG???ATAACTTT??T??A?AT????TTGACGTTG?ATATC????????TCAAGTTGTT?TCC??????????A?CGATCTA?ATGGAAC?CTGAATT????C????CTCC???TTCACTCGC???TAAAGGCTATTCTAAAT?????TG?T??A?AATGTATAGATACTA???????GTGAAACTTTCTAT????????GCCATTAC??A??????????A??AATTGTCACAA??CACT???CCCAC?GG?AT??TAA??????????AATAC?T??CATGAATGTA??CATT?CTG????T?TCTG?AGCTATA?A?CC??AGC?TTAGGAA?GGTCACCCACATT?AGAT???C????TA?????AG?AC?????CT???T?TC?GGGT?TCGCTT???T????CCTGC???GC?AGTCCGT??AGAACTTAGGCGATCTTG??????CTTCTC?TAG???TCG????TTAC????ACCCAAAT?ACAAATATAGTTCG?CT?????????CGCG????TT?CATTGAAA?AC???T??????G?????AGCTCG????AA?TATGCTTGGTT?TAAA????CC??AAGTC??A????????????A??????GCATAA?????AAAATC?CAA?ACAATTA?AA????????????G??????CTG?TGA????????CAG?ATTCAT???TTTA??????ACCT???GT??????A??AT??TAA?T??????CCAA????????T????AA?TA?A?ACACT??ATAAT??TCTAGTGAATT???GAAGCAGG???GG?????CT?C??A??CCA????CA?CACT?AAT?????ATTACCCC??AT??????AAT??C??CAG???AGG??ATATATTTTACTG??TTCAA?CACCAA?TCGG?TTATA???AA?T?TAATAAGGAA?AT??ACTTTACG?CCG?????????CATAG???????A??GA?????????AATATC??TTCAATTTT??GCTTTATTCTATTA?CTGTAAATGGAAG?C?TT?ATA?GACCATGG??A?ATC?AATATCTATA????????CA??GCAAATTTATT??ATCTTTACAC?C????A??????????C??????AA?CAT?TGG??????CAA?AC??CTAGTT??TTAAA???G????????TATA?T????ATGC??????AATA???A?TCAACA?????TTAAG?ACAA?AATTGCTGT??ATTCGTACACAA????CAGC??ACCA?C???????TGCG???????????ATCA??G?TC?TCCTA??GACAAATTTTG????GTG?T?A???AAT??TAACTGC???????A?CTCAG?CTACATTCCCTCT?TTT????????????T???????????A?CATA?TGCTT???????AAT?A?C?ATAAGATGAAATAA?GCTCT???A??TTGATTACG?GA?ACC??TCCATTGATTG???AGGACG?TAC???????ATA?GCTGTAC??????????TT?GCCT??T????????????CCGACCGTTCTT?AGC????????????CATCTTATACA????AGACTCAGCACAAG?A??ATAAAC????CA?AAT???A????A?CC??ACCCATCCGCATT??T?????AATA??C???AGTAATCC?????A?AAG?ACT??????AAAATATGAC??CAAAT???AAAAACTTGCAAAAGATATG??TTAA????TTA?TGAC????CT?GGTAGTAACTAACT??GCT?ATAA?A?AACTGT??C?CTACGG?CCTAAACAA?CAAATT?TGGGG???TAA?A?C?TTAC???????????????????GT?GATC?ACTAA?C??G?CT?????CTA?GCACTA??????A?AAGC???AAA?G??????A????GTTAATTTCATT?AGGATAGACTTTCACGC??TCATAGTA??????????ACTATA?GT?TGT?G??TAATC?????????CG?????GT??TACCTGT?????AATAAACC????????ATTAA?A??T?????TCTAC??????TTT??TT?A?TCT????TC?GATCAATA??CG??AG??????????TT?CCTAGG?????A????AAAGATATTT???AAAGAA?ACAGTTTATCA???TGAGACGC?ATCA??AC??AGTACGTATCA???TCTG?TT?GA?CTC??????AAA?ATC??????GCTCA?GC?????????GA?????G????TAT?????ATCCGG?CCTTT?????????A???CT?G?C??GT?A???AC?CCTGT?CAAT?A?AT?TACC?AC?CGT?A??T????TACA?GATCT??????????TCT??CT????????A?TATTGG??ACGAATTA???AATAGTG?ACGC????CCATTT????????TTAATGTCTCTG??GCAGAC??A????????C????CACTCGAT?G?GGTAAGGAAGTTTGTGGAAATAATG?GT?A?????????A???AATGGGAT?????CTACC?GAAC?AA?TAA?AGA?ATA???????AACAATCA????TGTAAACCAT?A?????TGATA?GCC??TTTA?AGTCC?TA?CTACAGTTGA???AGAACGA?GA??G??????????A?GTA???CCATAT?GT?CTTGC??GCTTTACGCA????AT?T?GTA???TCTTT??CA?AACTTA????TAATGTTGGTA????????AC?CA??T??CAT?GATG???????C?AATAT??????ATTTA??????????????????????TAATT????CTCTCA?????????????CA???????T???TTA?ATA?T?A??A?CA???AAA????A??AGG??T?T?????????ATA??CGATAAAT??TAA???CCTATCCCAAG?????CTT?GCAA?T?TCGGAATTGATACGTGTATCA?G???GGTC?CTAACA sp_3_MichoacanJSF955 ACA?CTCGC?A??AGCG?GC?T????????GTCCTGTCG??TT???T?GT?GACAAACTA????GTG???????????GTG?GCAAACTT??GGCCTTTGATCT??AGGTACC?TA??TTAA?AT?A???????C?AC???????G?TA?????????CCCATT?ACAAAGCCTGTAACGAC????T????????AATA??T?TA?AT???T?AT??CAG???ATAACTTT??T??A?AC????TAGACGTTG?ATACC????????TTTAGTTGTA?TCC??????????A?CGA?TTA?ATGGAAC?CTGAATT????C????CTCC???TTCGCTCGC???TA???GTTACTTTAAAT?????TG?T??T?AATGTATAGATACTA???????ATGAAATTCTCTAC????????GCCATTAC??A??????????A??AGTTGTCATAA??CACT???ACCAC?GG?AT??CAA??????????AATAC?A??CATGAATGTA?ACAGT?CTG????T?TTTG?AGCTATA?A?CT??GGC?TTAGGAA?AGTCACTAATTCT?GGAT???CC???TA?????AGCAC?????CT???T?TC?GGGC?TCGTTT???T????CCTGC???GC?AGCC??T??AG??CTTAAACGATCTTA??????CTTCCC?TAG???GAG????TTA???????????AT?ACAAATATAGTTCA?TT?????????CGCG????TT?CATTGAAA?AC???T??????A?????AGCTTG????AA?TATGCCTAGCT?TAAA????CC??TAGTC??A????????????A??GTTGGCATAA?????AAAATC?CAA?GCAACTA?TA????????????A??????CTG?TGA????????TAG?ACTCAT???TTTA??????ACCT???GT??????A??AT??TAA?T??????CCGA????????T????AA?TA?A?ACATT??ATAAT??TCTGGTGAAAT???GAAGCAGA???GG?????CT?C??A??CCA????CA?CATC?AAT?????ATTACCCC??AT?????CAAT??C??CAG???AGG??ATATTTCCGACTG??TTCAA?CACCAA?TTGG?TTCGA???AA?T?TAATAAGCAA?AT??CCATTACA?CTG?????????CAAAGG??????A??GA?????????AATATT??TTCAATTTT??GCTTTGTTCTATTA?CGGTAAATGGAAA?C?TT?GTA?GACCATGG??A?ATC?AATCTCTATA????????CA??ACAAATCTATC??ATCTTTATAC?C????A?????????GC??????GT?TAT?TGG??????CTA?AC??TTAGTT??TTAAA???GA?AAAA??TACA?T????AAGT??????AATA???A?TCAATA?????TTAGG?CCAA???????TGT??ATTCGTACACAA????TGGC??ACCT?C???????TGTG???????????ATCA??G?TC?TGCTG??GACAAATACTA????GTG?T?A???AAT??TAACTAC???????A?CTCAG?TTACATTCGCCCC?TTT????????????A???????????A?CATATTGCTT??????CAAT?A?C?ATAAGCTGAAGTAG?GCTCT???A??CTGATTATG?GA?ACC??TTCATTGATCG???AGAACATTAC???????ATA?ATTGTAT??????????TT?A??C??T????????????CCAACAGTTCTT?ACC????????????CATCATCTACC????AGACACAATATAAGAA??AAAAAT????CATAAT???A????A?TT??ATCCATCCGCATT??T?????AGCA??C???AGTAGTTC???????AAG?ACT??????CAAATATGAC??CAAAT???AAAAACTTGCAAA??????G??TTAA????TTA?TGACATAGCT?GGTAGTAATTACCT??GCT?ATAA?A?AGCTGG??C?CTAAGG?CCTAAATA??TAAATT?TGGGG???TAA?A???CTATGA????????AAAGGCCA?GT?AATT?AGAAA?T??G?CA?????CTT?GCACTA??????A?AAAT???AAA?G??????A????GTTAATTACTTC?GGGATAGAC?CTCGCGCCTTCATGGTA??????????ACCATA?GT?TGT?G??TAAAC?????????CA?????AT??TACATGT?????AGTACACT????????TTTAA?A??T?????TCTAC??????TCT?CTA?A?TTT????CC?GA?CGATA??CG??AG??????????TTAT?TAGGA?TTTA????AAAAA??????????AGAA?ACAGCTTATCA???TGAGACGT?ATCA??AC??AGTACGTATCA???TTTG?TA?GA?CCC??????AA??GTC??????ACTTA?GT?????????TA?????G????CAT????????????CCTTT?????????A???CC?G?T??GT?A???AT?CCTGT?CAAT?A?AT?TACC?AG?CGT?A??T????TACA?GAACT??????????TCC??CT????????A?TATTGG??ACGACTTA???AACAGTG?ACGC????CCATTT????????TTAACATTTCTA??GCA?AC??GT???????C????CACCCGAT?G?GGTGAGGAAGTTTGTGGAATTAATG?GT?A?????????T???TATGGAAT?????CTACC?GAAC?AA?TAC?AAT?ATA???????AGCAATCA????AGCAAACCAT?A?????TGATA?GTC??TTTT?AGTTC?TA?CTACAGTTGG???AGACCAG?GA??A??????????A?GTA???CCATAT?GT?CTTGC??GCTTAACGCA????TT?T?GTA???TTTTT??CA?AACTTA????TAATGTTGATA????????AC?CG??C??CAT?GATA???????C?AATAA??????ATATA??????????????????????TAATT????CCCTCA?????????????CA???????T???TCA?ATA?T?A????CG???AAA????A??AGG??T?T?????????ATA?CCAGTAAAT??CGAAC?CCTATCCCCAAA?CC?CTT?GTAA?T?TCGTAATTGATAAGAGTATTA?A???GGTC?TTAACA tlalociJSF1083 ACT?CTCGC?A??AGCG?GC?T????????GTCCTGTCG??TT???T?TT?AACG?ACTA????GTG??????????GGTG?GCAAACAC??GGCTTTTAAGCT??AGGTACC?CA??TTAA?CT?A???????C?AT???????A?TA?????????CCCATT?ACAAAGCCTGTAATGCC????T????????AATA??T?TA?AT???T????????????TAACTTT??C??A?AT????TTGACGTTG?ACACC????????CCTAGTTGTT?TCC??????????A?CGATTTA?ATGGAAC?CTG?ATT????C????CTCC???TCCC???GC???TA???GCTTTTTTAAAT?????TG?T??T?AATGTATTGATACTA???????CTGAAACTTTCTAT????????GCCTTTAC??A??????????A??AATTGTCACAA??TACC????????????AC??TAA??????????AATAC?A??CATGAATGTA?ACATT?CTG????T?TCTA?AGCTATA?A?CT??GGC?TTAGGAA?GTTCACTCATATT?GGTT???CC???TA?????AGTAC?????CT???T?TG?TGAT?TCGTTT???T????CCTGC???GC?AAACCAC??AGAACTTAGACGATCTTAA?????TTTCTC?TTG???TTG????TTAC???????CAAAT?ACAAATGTAGTTTA?TT?????????TGTG????TT?CATTGAAA?AC???T??????G?????AGCTTG????AA?TATGCCTAATT?TAAA????CC??CAGTC?TA????????????A??GTCTGTCTAA?????AAAACC?CAA?GCAACTA?TA????????????G??????CTG?TGA????????TAG?ATTCAT???TTTA??????ACCT???GT??????A??AC??TAA?T??????CCAC????????T????AATTA?A?ATATT??ATAAT??TCTCGCGAATT???GAAGCAGT???GG?????CT?C??A??CCC????CA?TATC?AAT?????ATTACCCT??AT?????CAAT??C??CAG???AGG??ATATTTCCAACTG??TTCAA?CACCAA?TCGG?TTCGA???AA?T?TAATAAGAAA?AT??CCTTTAAA?CTG?????????CAAAGGCTAATCA??GA?????????CATATT??TTCAATTTT??GCCTTGTACTATTA?CAGTAAATGGAAA?C?TT?TTA?GACCATGG??A?ATC?AATTTCTATA????????CA??ACAAATTTTTT??ATCTCTATAC?C????A?????????GC??????AA?CAT?TGG??????CTA?ACTACTAGTT????AAA???GA?AAAA??TACA?T????AAGC??????AATA???A?TCAATA?????TCAGG?CCAA???????TGT??ATTCGTACACAA????TGTC??ACTT?C???????T?????????????????T??G?CC?TCTTA??GACAAATCCTA????GTG?C?A???AAT??TAACTAC?????????CTCAG?TTACATTCGCCCC?TTT????????????G???????????A?CATATTGCTT??????CAAT?A?T?ATAAGTTGAAGAAG?GCTCT???A??TTGATTAGG?GA?ACC??TCCACTGATTG???AGAACG?TAC???????ATA?GTTGTAC??????????TT?A??C??T????????????CCGACCGTACTT?ACC????????????TATCCTCCACT????GGACCCAATACAAGAA??ATAAAT????CA?AAT???A????A?AC??ACCCAACCACATT??T?????AACA??C???AGTATTTC?????ACAAG?ACT??????AAACTATGAC??CAAAT???GAA??CTTGCAAAAGTTTTG??TTAA????TTA?TGACACAGCT?GGTAGTAATTAACT??GCT?ATAA?A?AGCTGG??C?CTAAGA?TTAAAGTAT?TAAATT?TGACG???TGA?A?C?CTATAA????????AGAGT??A?GT?TATT?AGCAA?T??G?CGAACGATTA?GCACTA??????A?CAGT???AAA?G??????A????GTTAAGTCCTTC?AGGATAGAC?TTCACGC??TCATGGTA??????????ACCATA?GT?TGT?G??TAATC?????????CG?????GTC?TATATGT?????AATACACT????????TTTAA?A??T?????CCTAC??????TTT?CTC?A?TTT????AC?GA?CAATA??CG??AG??????????CT?C?T?????TTTA????AAAGA??????????AGAA?ACAGCCCGTCC???TGAGACGT?ATCA??AC??AGTACGTATCA???TTTG?TG?AA?CCC??????AAA?GTC??????ACTTA?GC?????????GA?????G????TCT?????ATTCGG?TCTTT?????????A???CA?G?T??GT?A???AC?CCTGT?CAAT?A?AC?TACC?ATACGC?A??T????TACA?GATAT??????????TCG??CT????????A?TATTGG??ATGATCTA???AATAGTG???????????????????????TTAACACTTCTA??GTA?AC??AT???????C????CACCCGAT?G?GGTAAGGAAGTTTGTGGAATTGATG?GT?A?????????T???TATGGAAT?????CTGTT?AAAC?AA?TAT?AGT?ATAATTTCATAGCAATCA????CGCAAACCAT?A???CTTGATA?GCC??TTTA?AGTCC?TA?CTACAGTTGA???AGTTCAA?GA??A??????????A?GCA???CCATAT?GT?CTTGC??GCTGTACACT???TTT?T?GTA???TCTCT??CA?AACTTA????CAATGTTGATA????????AC?CA??T??CAT?GATG???????C?AATAGACTTT?ATATA??????????????????????TAATT????CTCTCA?????????????CA???????C???TTA?ATA?T?A??A?CA???AAA????A??TGG??T?T?????????ATA?CCAGTAAAT??CGAAC?CCTACCCCCAGC?CC?CTT?GCAA?T?TCGGAATTGATACGAGTATTA?A???GGTC?CTAACA neovolcanicaJSF960 ACT?CTTGC?A??AGCG?GC?T????????GTCCTGTCG??TT???T?TT?GACG?ACTA????GTG??????????GGTG?GCAAACAC??GGCTTTTAAGCT??AGGTACC?CA??TTAA?CT?A???????C?AT???????A?TA?????????CCCATT?ACAAAGCCTGTAATGCC????T????????AATA??T?TA?AT???T?AT??CAG???ATAACTTT??C??A?AT????TTGACGTTG?ACACC????????CCTAGTTGTT?TCC??????????A?CGATTTA?ATGGAAC?CTG?ACT????C????CTCC???TCCC???GC???TA???GCTTTTTTAAAT?????TG?T??T?AATGTATAGATACTA???????TTGAAACTTTCTAT????????GCCTTTAC??A??????????A??AATTGTCACAA??TACC????????????AC??TAA??????????AATAC?A??CATGAATGTA?ACATT?CTG????T?TCTG?AGCTATA?A?CT??GGC?TTAGGAA?GTTCACTCATTTT?GGTT???CC???TA?????AGTAC?????CT???T?TG?TGAT?TCGTTT???T????CCTGC???GC?AAACCAC??AGAACTTAGACGATCTTAA?????TTTCTC?TTG???TTG????TTAC???????CAAAT?ACAAATGTAGTTTA?TT?????????TGCG????TT?CATTGAAA?AC???T??????G?????AGCTTG????AA?TATGCCTAATT?TAAA????CC??CAGTC?TA????????????A??GTCTGTCTAA?????AAAACC?CAA?GCAACTA?TA????????????G??????CTG?TGA????????TAG?ATTCAT???TTTA??????ACCT???GT??????A??AC??TAA?T??????CCAC????????T????AATTA?A?ATATT??ATAAT??TCTCGCGAATT???GAAGCAGT???GG?????CT?C??A??CCC????CA?TATC?AAT?????ATTACCCT??AT?????CAAT??C??CAG???AGG??ATATTTCCAACTG??TTCAA?CACCAA?TCGG?TTTGA???AA?T?TAATAAGAAA?AT??TCTTTAAA?CTG?????????CAAAGGCTAATCA??GA?????????CATACT??TTCAATTTT??GCCTTGTACTATTA?CAGTAAATGGAAA?C?TT?TTA?GACCATGG??A?ATC?AATTTCTATA????????CA??ACAAATTTTTT??ATCTCTATAC?C????A?????????GC??????AA?CAT?TGG??????CTA?AC??CTAGTT????AAA???GA?AAAA??TACA?T????AAGC??????AATA???A?TCAATA?????TCAGG?TCAA???????TGT??ATTCGTACACAA????TGGC??ACTT?C???????T?????????????????T??G?CC?TCTTA??GACAAATCCTA????GTG?C?A???AAT??TGACTAC?????????CTCAG?TTACATTCGCCCC?TTT????????????G???????????A?CATATTGCTT??????CAAT?A?T?ATAAGTTGAAGAAG?GCTCT???A??TTGATTAGG?GA?ACC??TCCACTGATTG???AGAACG?TAC???????ATA?GTTGTAC??????????TT?T??C??T????????????CCGACCGTACTT?ACC????????????TATCCTCCACT????GGACCCAATACAAGAA??ATAAAT????CA?AAT???A????A?AC??ACCCAACCACATT??T?????AACA??C???AGTATTTC?????ACAAG?ACT??????AAACTATGAC??CAAAT???GAA??CTTGCAAAAGTTTTG??TTAA????TTA?TGACATAGCT?GGTAGTAATTAACT??GCT?ATAA?A?AGCTGG??C?CTAAGA?TTAAAGTAT?TAAATT?TGACG???TGA?A?C?CTATAA????????AGAGT??A?GT?AATT?AGCAA?T??G?CAAACGATTA?GCACTA??????A?CAGT???AAA?G??????A????GTTAAGTACTTC?AGGATAGAC?TTCACGC??TCATGGTA??????????ACCATA?GT?TGT?G??TAATC?????????CG?????GTC?TATATGT?????AATACACT????????TTTAA?A??T?????CCTAC??????TTT?CTC?A?TTT????AC?GA?CAATA??CG??AG??????????CT?C?T?????TTTA????AAAGA??????????AGAA?ACAGCCCGTCC???TGAGACGT?ATCA??A???AGTACGTATCA???TTTG?TG?AA?CCC??????AAA?GTC??????ACTTA?GC?????????GA?????G????CCT?????ATTCGG?TCTTT?????????A???CA?G?T??GT?A???GC?CCTGT?CAAT?A?AC?TACC?AT?CGC?A??T????TACA?GATAT??????????TCG??CT????????A?TATTGG??ATGATCTA???AATAGTG???????????????????????TTAATACTTCTA??GTA?AC??AT???????C????CACTCGAT?G?GGTAAGGAAGTTTGTGGAACTGATG?GT?A?????????T???TATGGAAT?????CTGTT?GAAC?AA?TAT?AGT?ATAATTTCATAGCAATCA????CGCAAACCAT?A???CTTGATA?GCC??TTTA?AGTCC?TA?CTACAGTTGA???AGTCCAA?GA??A??????????A?GCA???CCATAT?GT?CTTGC??GCTGTACACT???TTT?T?GTA???TCTCT??CA?AACTTA????CAATGTTGATA????????AC?CA??T??CAT?GATG???????C?AATAAACTTT?ATATA??????????????????????TAATT????CTCTCA?????????????CA???????C???TTA?ATA?T?A??A?CA???AAA????A??TGG??T?T?????????ATA?CCAGTAAAT??CGAAC?CCTACCCCCAGC?CC?CTT?GCAA?T?TCGGAATTGATACGAGTATTA?A???GGTC?CTAACA berlandieriJSF1136 ACT?CTTGC?A??AGCG?GC?T????????GTCCTGTCG??TT???T?GT?AACA?ACTA????GTG??????????GGTG?GCAAACAC??GGCCTTTAAGCT??AGGTACC?CA??TTAA?CT?A???????C?AT???????A?TA?????????CCCATT?ACAAAACCTGTAATGCC????T????????AATA??T?TA?AT???T?AT??CAG???ATAACTTT??C??A?AT????TTGACGTTG?ACACC????????CCTAGTTGTT?TCC??????????A?CGATTTA?ATGGAAC?CTG?ATT????C????CTCC???TCGC???GC???TA???GCTTTTTTAAAT?????TG?T??T?AATGTATAGATACTA???????ATGAAACTTTCTAT????????GCCTTTAC??A??????????A??AATTGTCACAA??TACC????????????AC??TAA??????????AATAC?A??CACGAATGTA?ACATT?CTG????T?TCTG?AGCTATA?A?CT??GGC?TTAGGAA?GGTCACTCATTTT?AGTT???CC???TA?????AGCAC?????CT???T?TA?TGAT?TCGTTT???T????CCTGC???GC?AACCCAC??AGAGCTTAGACGATCTTAA?????TTTCTC?TAG???TTG????TTAC???????CAAAT?ACAAATGTAGTTCA?TT?????????TGCG????TT?CATTGAAA?AC???T??????G?????AGCTTG????AA?TATGCCTAATT?TAAA????CC??CCGTC?TA????????????A??GTCTGTCTAA?????GAAACC?CAA?GCAATTA?TA????????????G??????CTG?TGA????????TAG?ATTCCT???TTTA??????ACCT???GT??????A??AC??TAA?T??????CCAT????????T????AATTA?A?AAATT??ATAAT??TCTCGCGAATT???GAAGCAGC???GG?????CT?C??A??CCC????CA?TTCC?AAT?????ATTACCCT??AT?????CAAT??C??CAG???AGG??ATATTTCCAACTG??TTCAA?CACCAA?TCGG?TTTGA???AA?T?TAAAAAGAAA?AT??CCCTTAAA?CTG?????????CAAAGGCTAATCA??GA?????????TATATT??TTCAATTTT??GCCTTGTACTATTA?CAGTAAATGGAAA?C?TT?TTA?GACCATGG??A?ATC?AATTTCTCTA????????CA??ACAAATTTATT??ATTTCTATAC?C????A?????????GC??????AA?CAT?TGG??????CTA?AC??CTAGTT????AAA???GA?AAAA??TACA?T????AAGC??????AA????????CAATA?????TCAGG?TCAA???????CGT??ATTCGTACACAA????TGAC??ACTT?C???????TGTG???????????ATTA??G?CC?TCTTA??GACAAATCCTA????GTG?C?A???AAT??TAACTAC?????????CTCAG?CTACATTCGCCCC?TTT????????????G???????????A?CATATTGCTT??????CAAT?A?T?ATAAGTTGAAGAAG?GCTCT???A??TTGATTAGG?GA?ACC??TCCACTGATTG???AGAACG?TAC???????ATA?GTTGTAC??????????TT?A??C??T????????????CCGACCGTACAT?ACC????????????TATCATCCACT????GGACCCAATACAAGAA??ATAAAT????CA?AAT???A????A?AC??ACCCAACCACATT??T?????AACA??C???AGTATTTC?????ACAAG?ACT??????AGACTATGAC??CAAAT???GAA??CCTGCAAAAGTTATG??TTAA????ATA?TGACATAGCT?GGTAGTAATTAACT??GCT?ATAA?A?AGCTGG??C?CTAAGG?TTAAAGTAT?TAAATT?TGATG???TGA?A?C?CTATAA????????AGAGG??A?GT?AATT?AGCAA?T??G?CGAACGATTA?GCACTA??????A?TAGT???AAA?G??????A????GTTAAATACTTC?AGGATAGAC?TTCACGC??TCATGGTA??????????ACCATA?GT?TGT?G??TAATC?????????CG?????GTC?TATGTGT?????AATACACT????????TTTAA?A??T?????CCTAC??????TTT?CTC?A?TT???????????????A??CG??AG??????????CT?C?T?????TTTA????AAAGA??????????AGAA?ACAGCCCGTCC???TGAGACGT?ATCA??AC??AGTACGTATCA???TTTG?TG?AA?CCC??????AAA?GTC??????ACTTA?GC?????????GA?????G????CCT?????ATTCGG?TCTTT?????????A???CA?G?T??GT?A???AC?CCTGT?CAAT?A?AC?TACC?AT?CGC?A??T????TACA?GATAT??????????TCG??CT????????A?TTTTGG??ATGATCTA???AATAGTG???????????????????????TTAATACTTCTA??GTA?AC??AT???????C????CACTCGAT?G?GGTAAGGAAGTTTGTGGAATTGATG?GT?A?????????T???TATGGAAT?????CTGTT?AAAC?AA?TAT?AGT?ATAATTTCATAGCAATCA????CGCAAACCAT?A???CTTGATA?GTC??TTTA?AGTCC?TA?CTACAGTTGA???AGTTCCA?GA??A??????????A?GCA???CCATAT?GT?CTTGC??GCTGTACACT???TAT?T?GTA???TCTCT??CA?AACTTA????CAATGTTGATA????????AC?CA??T??CAT?GATG???????C?GATAAACTTT?ATATA??????????????????????TAATT????CTCTCA?????????????CA???????C???TTA?ATA?T?A??A?CA???AAA????A??TGG??C?T?????????ATA?CCAGTAAAT??CGAAC?CCTATCCCCAGC?CC?CTT?GCAA?T?TCGGAATTGATACGAGTATTA?A???GGTC?CTAACA blairiJSF830 ACT?CTTGC?A??AGCG?GC?T????????GTCCTGTCG??TT???T?GT?AACA?ACTA????GTG??????????GGTG?GCAAACAC??GGCCTTTAATCT??AGGTACC?CA??TTAA?CT?A??CGATAC?AT???????A?TA?????????CCCATT?ACAAAGCCTGTAATGCC????T????????AATA??T?TA?AT???T?AT??CAG???ATAACTTT??C??A?AT????TTGACGTTG?ACACC????????CCTAGTTGTT?TCC??????????A?CGATTTA?ATGGAAC?CTG?ATT????C????CTCC???TTCC???GC???TA???GTTTCTTTAAAT?????TG?T??T?A?TGTATAGATACTA???????ATGAAACTTTCTAC????????GCCTTTAC??T??????????A??AATTGACACAA??TACC????????????AC??CAA??????????AATAC?T??CATGAATGTA?ACATT?CTG????T?TCTG?AGCTATA?A?CT??CGC?TTAGGAA?GGTCACTCATTTT?AGTT???CC???TA?????AGTAC?????CT???T?TC?TGAT?TCGTTT???T????CCTGC???GT?AAACCAT??AGAACTTAGACGATCTTAA?????TTTCTC?TAG???TTG????TTAC???????CAAAT?ACAAATGTAGTTCA?TT?????????TGCG????TT?CATTGAAA?AC???T??????G?????AGCTAG????AA?TATGCCTAATT?TAAA????CC??CCGTC?TT????????????A??GTTTGTATAA?????AAAACC?CAA?GCAACTA?CA????????????G??????CTG?TGA????????TAG?ATTCAC???TTTA??????ACCT???GT??????A??AT??TAA?T??????CCAC????????T????AATTA?A?ACATT??ATAAT??TCTGGCGAATT???G?AGCAGT???GG?????CT?C??A??CCA????CA?TATT?AAT?????ACTACCCT??AT?????CAAT??C??CAG???AGG??ATATATCCTACTG??TTCAA?CACCAA?TCGG?TTTGA???AA?T?TAACAAGAAA?AT??TCTTTAAA?CTG?????????CAAAGG??????A??GA?????????AATATT??TTCAATTTT??GCCTTGTACTATTA?CAGTAAATGGAAA?C?TT?TTA?GACCATGG??A?ATC?AATTTCTATA????????CA??ACAAATTTGTT??ATCTTTATAC?C????A?????????GC??????GA?CAT?TGG??????CTA?AC??CTAGTT????AAA???GA?AAAA??TACA?T????AAGC??????AATA???A?TCAATA?????TCAGG?TCAA???????CGT??ATTCGTACACAA????TGGC??ACTT?C???????TGTG???????????ATTA??G?CC?TATTA??GACAAATGCTA????GTG?C?A???AAT??TAACTAC?????????CCCAG?CTACATTCGCCCC?TTT????????????G???????????A?CATATTGCCT??????CAAT?A?C?ATAAGTTGAAGAAG?GCTCT???A??TTGATTAGG?GA?ACC??TCCACTGATTG???AGAACG?TAC???????ATA?GTTGTAT??????????TT?A??C??T????????????CCGACCGTACTT?ACC????????????CATCCTCTACT????AGACCCAATATAAGAA??ATAAAT????CA?AAT???A????A?AC??ATCCATCCACATT??T?????AACA??C???AGTATTTC?????ACAAG?ACT??????ATAATATGAC??CAAAT???AAA??CTTGCAAAAGTTATG??TTAA????TTA?TGACATAGCT?GGTAGTAATTAACT??GCT?ATAA?A?AGCTGG??C?CTACGG?TTAAAATAC?TAAATT?TG???????????????TATAA????????AGAGG??A?GT?GATT?AGCAA?T??G?CGAACGATTA?GCACTA??????A?CAGT???AAA?G??????A????GTTAAATACTAC?AGGATAGAC?TTCACGC??TCATGGTA??????????ACAATA?GT?TGT?G??TAATC?????????CG?????GTC?TATATGT?????AGTACACT????????TTTAA?A??T?????CCTAC??????TAT?CTT?A?TTT????AC?GA?CAATA??CG??AG??????????CT?C?T?????CTTA????AAAGA??????????AGAA?ACAGCCCATCC???TGAGACGT?ATCA??AC??AGTACGTATCA???TTTG?TG?AA?CCC??????AAA?GTC??????ACTTA?GC?????????GA?????G????CTT?????ATCCGG?TCTTT?????????A???CA?G?T?TGT?A???AC?CCTGT?CAAT?A?AC?TACC?AT?CGC?A??T????TACA?GATAT??????????TCA??CT????????A?TTTTGG??ATGATCCA???AATAGTG???????????????????????TTAATATCTCTA??GCC?AC??AT???????C????CACACGAT?G?GGTAAGGAAGTTTGTGGAACTGATG?GT?A?????????T???TATGGAAT?????CTGCC?GAAC?AA?TAT?AGT?ATAATTTCATAGCAATCA????TGCAAACCAT?A???CTTGATA?GCC??TTTA?AGTCC?TA?CTACGGTTGA???AGTTCAG?GA??A??????????A?GT?????CATAT?GT?CTTGC??GCTGTACTCT???TTT?T?GTAATATCTTT??CA?AACTTA????CAATGTTGTTA????????AC?CA??T??CAT?GATG???????C?AATAAACTTT?ATATA??????????????????????TAATT????CCCTCA?????????????CA???????C???TTA?ATA?T?A??A?CA???AAA????A??CGG??C?T?????????ATA?CCAGTAAAT??CGAAC?CCTATCCCCAGT?CC?CTT?GCAA?T?TCGGAATTGATACGAGTATTA?G???GGTC?CTAACA sphenocephalaUSC7448 ACT?CTCGT?A??AGCG?GC?T????????GTCCTGTTG??TT???T?GT?AACC?ACTA????GTG??????????GGTG?GCAAACTT??GGCTTTTGACCT??TAGTGCC?AA??TTAA?AT?A???????C?AC???????G?TA?????????CCTATT?ACAAAGCCTGTAATGCC????T????????AATA??T?TA?AT???T?AT??CAG???ATAACTTT??C??A?AC????ACGACGTTG?ACAAC????????TCTAGTTGCA?TCT??????????A?CGGTTTA?ATGGAAC?CTGAATT????C????CTCC???TCCGCTCGC???TA???ATTACTTTAAAT?????TG?T??T?AATGTATAGATACTA???????TTGAAATTTTCTAC????????GCCTTTAC??A??????????A??AATTGTCACAA??TACT???AACAC??G?AT??TAA??????????AATAC?T??CAAGAATGTA?ACATT?CTG????T?TTTG?AGCTATA?A?CC??GGC?TTAGGAA?GGTCACTCATTTT?AGAT???CC???TA?????GGTAC?????CT???T?TA?GGAC?TCGTTT???T????CCTGC???AA?AGGCCAG??AGAACTTAGACGATCTTAA?????TTTCCC?TAG???GTG????TCAC???????CAAAT?ACAAATATAGTTCT?TT?????????CGTG????TT?CATTGAAA?AC???T??????G?????AGCTCGAT?TAA?TATGCATAGCT?TAAA????CC??TCGTC??A????????????A??GTCGGCATAA?????AAAATC?CAA?GCAACTA?AA????????????C??????CTA?TGA????????TAG?ACTCTC???TTTA??????ACCT???GT??????A??AA??TAA?T??????CCAT????????T????AA?TA?A?ATACT??ATAAT??TCTAGTGAATT???GAAGCAGC???GG?????CT?C??A??CCA????CA?TATT?AAT?????ATTACCCC??AT?????CAAT??C??CAG???AGG??ATATTTTCAACTG??TTCAA?CACCAA?CCGG?TTCGA???AA?T?TAATAAGAAT?AT??TTTTTAAA?CCG?????????CAAAGG??????A??GA?????????AATATT??ATCAATTTT??GCTTTGTTCTATTA?CGGTAAAAGGAAA?C?TT?GTA?GACCATGG??A?ATC?AATTTCTACA????????CA??ACAAATCTTTA??ATCTTTATAC?C????A?????????GC??????AA?CAT?TGG??????CTA?AC??CTAGTT??TTAAA???GA?AAAC??TGCA?T????AAGC??????AATA???A?TCAATA?????TTAGG?TCAA???????TGT??ATTCGTACACAA????TGGC??ACCT?C???????TGAG???????????ATTC??G?CC?TCCTA??GACAAATCCTT????GTG?C?A???AAT??TAACTAC?????????CCCAG?CTACATTCGCTCC?TTT??????????????????????????????TTGCTT??????CAAT?A?C?ATAAGTTGGAGAAG?GCTCT???A??CTGATTAGG?GA?ACC??TCCATTGATTG???AGAACG?TAC???????ATA?CTTGTAT??????????TT?A??C?????????????????GACCGTACCT?ATC????????????CA????????????????CCCAATACAAGAA??ATAAAT????CA?GAT???A????A?TT??ATCCATCCGCATT??T?????AACT??C???AGTATTAC?????ACAAG?ACT??????AAAATATGAC??CAAAT???AAAAACTCGCAAAAGTTATG??TTCA????TTA?TGACATAGCT?GGTAGTAATT?ACT??GCT?ATAA?A?AATTGG??C?CTAAGG?CCTAAATAT?CAAATT?TGGAG???TGA?A?C?ATACGA????????AGAGA??A?GT?GATC?AGAAA?T??G?CG?????CTT?GTACTA??????A?CATT???AAA?G??????A????GTTAATTACTTT?AGGATAAAC?TTCACGC??TCATGGTT??????????ACCATA?GT?TGT?G??TAATC?????????CG?????ATC?TCCCTGT?????AGTACACT????????TTTAA?A??T?????CCTAC??????TCT?CTT?A?TTT????TC?GA?CAATA??CG??AG??????????CT?C?TAGGA?TTTA????AAAGA??????????AGAA?ACAGCTTATCA???TGAGACGT?ATTA??AC??AGTACGTATCA???TTTG?TT?GA?CCC??????AAA?GTC??????ACTTA?GC?????????AA?????G????AAT?????ATCCGG?TTTTT?????????A???CT?G?T??GT?A???AT?CCTGT?CAAT?A?AC?TACC?AT?CGC?A??T????TACA?GATTTGCTCGGTACATCT??CT????????A?TCTTGG??ACGACCTA???AATAGCG?ACGC????CCATTT????????TTAATATTTCTA??GCT?AC??AT???????C????CACCCGAT?G?GGTAAGGAAGTTTGTGGAACTAATG?GT?A?????????T???TACGTAAT?????CTGCC?GAAC?AA?TAT?AGA?ATA???????AGCAATCA????TGAAAACCAT?A???CTTGATA?GCC??TTTA?AGTCC?TA?CTACAGTTGA???AGTACAG?GA??A??????????A?GTA???C??TAT?GT?CTTGC??GCTTTACGCA????TT?T?GTA???TCTTT??CA?AACTTA????CAATGTTGATA????????AC?CG??T??CAT?GATC???????CCAATAC??????ATATA??????????????????????TAATT????CCCTCA?????????????CA???????C???TCA?ATA?T?A??A?CA???AAA????A??TGG??T?T?????????ATA?CCAGTAAAT??CGAAC?CCTATCTCCAGA?CC?CTT?GCAA?T?TCGGAATTGATACGAGTATTA?A???GGTC?TTAACA utriculariaJSF845 ACT?CTTGC?A??AGCG?GC?T????????GTCCTGTTG??TT???T?GT?AACC?ACTA????GTG??????????GGTG?GCAAACTC??GGCTTTTGAACT??AAGTATC?CA??TTAA?AT?A???????C?AC???????G?TA?????????CCCATT?ACAAAGCCTGTAATGCC????T????????AATA??T?TA?AT???T?AT??CAG???ATAACTTT??C??A?AT????TCGACGTTG?ATAAC????????TCTAGTTGTA?TCC??????????A?CGGTTTA?ATGGAAC?CTGAATT????C????CTCC???TCCACTCGC???TA???GTTACTTTAAAT?????TG?T??T?AATGTATAGATACTA???????ATGAAATTTTCTAC????????GCCTTTAC??A??????????A??AATTGTCACAA??TACT???AACAC??G?AC??TAA??????????AATAC?T??CATGAATGTA?ACATT?CTG????T?TATG?AGCTATA?A?CC??GGC?TT?GGAA?TGTCACTCATCGT?AGAT???CC???TA?????AGTAC?????CT???T?TA?GGAT?TCGTTT???T????CCTGC???GT?AGGCCAG??AGAACTTAGACGATCTTAA?????CTTCCC?TAG???TTG????TCAC???????CAGAT?ACAAATATAGTTCT?TT?????????CGTG????TT?CATTGAAA?AC???T??????G?????AGCTTGATATAA?TATGCTTAGTT?TAAA????CC??TCGTC??A????????????A??GTTGGCATAA?????AAAATC?CAA?GCAATTA?AA????????????A??????CTA?TGA????????TAG?ACTCAC???TTTA??????ACCT???GT??????A??AA??TAA?T??????CCAC????????T????AA?TA?A?ATACT??ATAAT??TCTAGTGAATT???GAAGCAGA???GG?????CT?C??A??CCA????CA?CATA?AAT?????ATTACCCT??AT?????TAAT??C??CAG???AGG??ATATTTTCAACTG??TTCAA?CACCAA?CCGG?TTTGA???AA?T?TAACAAGAAA?AT??TCTTTAAA?CTG?????????CAAAGG??????A??GA?????????AATATT??ATCAATTTT??GCTTTGTTCTATTA?CGGTAAATGGAAA?C?TT?GTA?GACCATGG??A?ATC?AATTTCTACA????????CA??ACAAATTTATC??ATCTTTATAC?C????A?????????AC??????AA?CAT?TGG??????CTA?AC??CTAGTT??TTAAA???GA?AAAA??TGCG?T????AAGC??????AATA???A?TCAATA?????TTAGG?ACAG???????TGT??ATTCGTACACAA????TGGC??ACCT?C???????TGAG???????????ATCC??G?TC?TCCTA??GACAAATCCTT????GTG?TAA???AAT??TAACTAC?????????CCCAG?CTACATTCGCTCC?TTT??????????????????????????????TTGCTT??????CAAT?A?C?ATAAGTTGGAGAAG?GCTCT???A??CTGATTAGG?GA?ACT??TCCATTGATTG???AGAACG?TAC???????ATA?GCTGTAT??????????TT?A??T??T????????????CCGACCGTACCT?ACC????????????CATCATCTACT????AGACCCACTACAAGAA??ATAAAT????CA?GAT???A????A?CC??AGCCATCCGCATT??T?????AACT??C???AGTATTAC?????ACTAG?ATT??????AAAATATGAC??CAAAC???AAAAACTCGCAAAGGTTGTG??TTAA????TTA?TGACACAGCT?GGTAGTGATT?ACT??GCT?ATAA?A?AACTGG??C?CTAAGG?CCTAAATAC?CAAATT?TGGAG???TAA?A?C?ATATTA????????AGAGA??A?GT?GATC?ATAAA?T??G?CG?????CTT?GTACTA??????A?CATT???AAA?G??????A????GTTAACTACTTT?AGGATAGAC?TTCACGC??TCATGGTG??????????ACCATA?GT?TGT?G??TAATC?????????CG?????TTC?TACCTGT?????AATGCACT????????TTTGA?A??T?????CCTAC??????TCT?CTT?A?TTT????TC?GA?CAATA??CG??AG??????????CT?C?TAGGA?TTTA????AAAGA??????????AGAA?ACAGCTTA??A???TGAGACGT?ATCA??AC??AGTACGTATCA???TTTG?TA?GA?CCC??????AAA?GTC??????GCTTA?GC?????????AA?????G????AAT?????ATCCGG?TCTTT?????????A???CT?G?T??GT?A???AC?CCTGT?CAAT?A?AT?TACC?AT?CGC?A??T????TATA?GACCTGCTCGTCACATCT??CT????????A?TGTTGG??ACGACCTA???AATAGTG?ACGC????CCATTT????????TTAATATTTCTA??GCA?AC??AT???????C????CACTCGAT?G?GGTAAGGATGTTTGTGGAACTAATG?GT?A?????????T???TATGTAAT?????CCGTC?CAAC?AA?TAC?AGT?ATA???????AGCAATCA????TGAAAACCAT?A???CTTGATA?GCC??TTTA?AGTCC?TA?CTACAGTTGA???AGTCCAG?GA??A??????????A?GTA???CCATAT?GT?CTTGC??GCTTTACGCA????TT?T?GTA???TCTTT??CA?AACTTA????CAAT????????????????C?CG??T??CAT?GATA???????CCAATAC??????ATATA??????????????????????TTATT????CCCTCA?????????????CA???????C???TTA?ATA?T?A??A?CA???AAA????A??AGG??T?T?????????ATA?CCAATAAAT??CGAAC?CCTATCTCAAGA?CC?CTT?GCAA?T?TCGAAATTGATACGAGTATTA?G???GGTC?CTAACA forreriJSF1065 AC???ACAT?A??AGCG?GC?T????????GTCCTGTAG??TT???T?CT?AATGAACTA????GCG??????????GGTG?GCAAACCT??GGCCTTTGACCT??AGGTACC?TA??TTAA?AT?A???????C?AC???????A?TA?????????CCCACT?ACAAAACCTGTAACGTC????T????????AATA??C?TA?TT???T?AT??CAG???ATAACCTT??T??A?AC????TAGACGCTG?ATATT????????TTAAGTTGTA?TCT??????????A?CGATTTA?ATGGAAACCTGAACT????C????CTCC???TTCACTCGC????????G?TGCTCTAAAT?????TG?TGAT?AATGTATAGATAC????????????AAACTTTCTAC????????GCCATTAC??A??????????A??ACTTGTCACAA??CACT???ATCAC?GG?AT??TTA??????????AATAC?A??CATGAATGTA?ACATT?CTG????T?TTTG?CGCCATA?A?CC??GGC?TTA?GAA?GGTCACTCATTTT?AGAT???CC???TA?????TGCCC?????CT???T?TA?AGGT?TCGTTT???T????CCTTC???TC?GACCCATAGCGAACTTAGACGATCTTAA?????CTTCCC?TAG???CTG????TCAT???????CGAAT?ACAAATATAGTTCA?TT?????????CGCG????TT?CA???AAA?AC???T??????G?????AGCTTG????????ATGCTCGGTT?TGAA???ACC??CAATC??A????????????A??GTCGGCATAA?????AAAATC?AAA?ACAAATA?AA????????????G??????CTG?TGA????????TAC?AATCAC???TTTA??????ACCT???GA??????A??GA??TAA?T??????CCAA????????T????AA?TA?A?ATACC??ATAAT??TCGAGCAAACT???GAAGCAGT???GG?????CT?C??A??CAG????CA?AACT?AAT?????ATTACCCC??AT?????CAAT??C??CAG???AGG??ATATCCCTTACTG??TTCAA?CAACAA?TCGG?TTCGA???AA?T?TAACAAGAACAAT??ACCTTAAA?CCG?????????CAAAGG??????A??GA?????????AATAT???????????T??GCTTTGC??TATTA?CGGTAAATGCAAA?C?TT?GTA?GACC?TGG??A?ATC?AATTTCTATA????????CA??ACAAATCTTTC??ATCTTTACAC?C????A?????????GC??????GA?CAT?TGG??????CTA?AC??CTAGTT??TTAAA???GA?AAGA??TACG?T????AAAC??????AATA???A?TCAGTA?????TTAGG?GCGA???????TGT??ATTCGTGCACAA????TC?C??ACTT?C???????TGTG???????????ATCA??G?TC?TCTTA??GATATATTCTT????GTA?T?A???AATATTGACTAC???????A?CCCAG?CTACATTCGCCCC?TTT????????????T????????CGCA?CATATTGCTT?????CTAAT?A?C?ATAAGTTGAAGGAA?GCTCT???A??ATGATTACG?GA?ACTTTTCCGTCGATTG???AGAACA?TAC????????????ATGTAC??????????TT?A??C??T????????????CTGACCATACTT?ATC????????????CATCATTCACT????AGATTCAAAACAAGAACAACAAAT????CA?AAT???A????A?TC??ATCTATCCGCATT??T?????AATG??C???AGTATTTC?????ACGAG?ACT??????GAATTACG?C??CAAAT???AAAAACTTGCAAAAGCTGTG??CTAA????TTA?TGACATAGCT?GGTAGTAATTCAAT??GCT?ATAT?A?AACTGG??C?CTAAGG?CCTAAATAG?GAAATT?TGGGG???TAA?A?C?TTACAT????????AAAGA??A?GT?TATC?AAAAA?T??G?CA?????GTT?GCACTA??????A?AAAT???AAA?G??????A????GTTAACTACTTT?AGGATAGAC?TTCACGC??TCATGGTA??????????ACTATA?GT?TGT?G??TAATC?????????CG?????AT??TACCTGT?????ACTACACC????????CTTGA?A??A?????CTTAC??????TCT?CCT?G?TCT????TC?GA?CAATA??CGTCAG??????????TT?T?TAGGA?TTTA????AAAGA??????????AGCA?ACAGTTCGTCA???TGAGACGT?ATCA??AC??AGTTCGTATCA???TCTG?TA?GA?CCC??????AAA?ATC??????ACTTATGC?????????CA?????G????TTT?????ACCCGG?TCTTT?????????A???CT?G?T??GT?A???AC?CCTGT?CAAT?T?AT?TACC?AA?CGC?A??T????TATA?GATCC??????????TCA??CT????????A?TA??GG??ACGATCTA???AACGGTG?ACGC????CCATTT????????CTAATAT?TCTA??GCA?AC??AT???????C????CACCCGAT?G?GGTAAGGAAGTATGTGGAATTA?TG?GT?A?????????G???TATGGAAT?????????T?AATC?AA?TAT?AAC?ATA???????AACAATCT????TGCAAACCAT?A???CTTGATA?G????????????CC?TA?CTACAGTTGA???AGCCCGA?GA??A??????????A?GTA???CTATAT?GT?CTCGG??GCTTTATACA????CT?T?GTA???TCTTC??CA?AACTTA????TAATG?TGATA????????GC?CG??T??CAT?GATG???????C?AATAA??????ATCTA??????????????????????TAATT????CACTCA?????????????CA???????C???TTA?ATA?T?A??A?CA???AAA????A??GGA??T?T?????????AT??CCAGTAAAT??CGAAC?CCAATCCCAAGA?CTTCTT?GTAT?T?TTGGAATTGATACATGTATTA?G???GGTCTTTAACA magnaocularisJSF1073 GCG?CTCGC????AGCG?GCAT????????GTCCTGTTG??TT???T?TT?CATGAGCTA????ACG????????GCGGTG?????ACCT??GGCTTTTAACCT??A?GTAAC?TA??TTAA?AT?T???????C?AC???????A?TA?????????CCTATT?ACAAAGCCTGTAACGTC????A????????AATA??T?TA?A?????CAT??CA???????ACTTT??C??A?CT????TAG????TG?ACATC????????CTATGTTGTG?TCC??????????A?CGGTCTA?ATGGAAC????AATT????C????CTCC???TCCACTCGC????????GTTACCCTAGAT?????TG????ATAATGTATCGATACTA???????GTAAAACTTTCTAC????????GCCGTTAC??A??????????A??ATTTGGCATAA??CACT???AACAC?GG??CACTTA??????????ATTAC?A??CTAAAATGTA?ACACT?CTG????T?TACG?AGCTATA?A?CA??AGC?TTAAGAA?GATCACTCGCCTT?TAATTCACC???TA?????CGTAC?????CT???T?TA?AGGT?TCATTT???T????CTTGC???GG?GAGCCAT??AGAGTTTAAACGATCTTTA?????CTTCCC?TAG???TTG????ACAC???????CAAAT?ACAAATTTAGTTCG?TT?????????TGAG????TC?CA???AAA?AC???T??????T?????AGCTC?????AA?TATGCCTAGTT?TAAA????CC??TTTTC??A????????????A??GTTAGTATAA?????AAAATC?TAA?GCAACTA?AA????????????A??????CTG?CGA????????GAG?ATTCAC???TTCA??????ACCT???GT????CTA??GA??TAA?T??????CCAG????????T????AA?TA?A?ATACT??ATAAT??TCGTGCGAACT???G?????AT????????????????A??CTA????CA?AATG?AAT?????ATTACCCTGAAT?????TAAT??C??CAA???GGG??ATATTAC?????GCGTTCAA?CAACAA?CCGG?TTCGA??TAA?A?TAA?AAGAAA?AT??CT?TTAAT?CCG????TTATTCAAAGG??????A?TGA?????????AATATT??TTCAATTTT??GAATTTTTTTATTA?CAGTAAATGGA?A?C?TTAGTA?GACCATGG??A?ATC?AATTTCTATA????????CAACGCAAATATTTC??ATCTTTACAT?C????A?????????AC??????AA?TAT?CGG??????TTA?AC??GTAGCT??TTAAA???GA?AAGAA?TACA?T????AAGC??????AATA???A?TTATTA?????TTAGG?GCG????????AGT??ATCCGTGCACTC????AGCC??ACCC?C???????TGCG???CAT?????ATTA??G?TC?TCCTA??GACAAATCCTC????GTA?C?A???AAA??TA???GC???????A?CCCAG?CTACATTCACA?C?TTT????????????A????????CGCA?CATATTGCTT??????CAAT?A?T?ATAAGTTGAAAAAG?GCTCT???A??GTGATTATG?GA?ACC??TCGGTAGATTG???AGAGCG?TAC???????ATA?ACTGTAC??????????TT?T??C??T????????????CCGACAATTC???ATC????????????CATCCTTTGCG????AGACCTAAAAAAAGAATAACAAAT????CA?AGT???A????A?TC??ATCTACCCACTTT??T?????A?CG??C???AGTACTCC?????ACAA??ACT??????AAAGCA?G?C??CAAAT???TAAAACTTGCAAACGTTATG??TTTC????TTA?TGACA???CT?GATAGTAAATAATTTAGCC?AT???A?AACTGA??C?CTAAGC?TCTAAATAG?TAAATT?TGTAG???TCA?A?C?CTATAA????????AGTGA??A?GT?GATC?CCAAG?A??G?CC?????GTG?GTACTA??????A?GAGC???ACA?G??????A????GTCAATTGCCCT?AGGTTAGAC?TTCACGC??TTATGGTA??????????ACCATA?GT?TGT?G??TAACC?????????CG?????AT??TACCTGT?????TCTTCACT????????ATTAA?A??T?????CCTAC????????TATTC?A?TCT????TC?GA?CGATA??CG??GG???????????T?T?TAGGA??TTA????AAATA??????????AGGA?ACAGTCAGTC?????GAGATGT?ATCA??AC??AGTACGTGTCA?????TG?TT?GA?CCCTGATAAAAA?GCT??????GCTTA?GC?????????AA?????G????TAT?????ATGCGG?TATTT?????????G???CC?G?T??GT?A???AA?CCTGT?C???????????CC?AT?CAC?G??C????TTCT?GAATT??????????TCA??CT????????A?TCTTGG??ACGATCCA???AATAGTG?ACGC????CCATTT????????TTAAAATTTCTA??GCA?AC??AT???????C????CACATGAT?G?GGTCA??AAATCTGTAGAATTAATG?GT?A?????????A???TAGGGAAT?????CTGCT?AACC?AA?TGA?ACT?ATA???????ATCAATCA????TGCAAGCCAC?A???ATTGATA?GCC??TTCA?AGTCC?TA?CTACAATTGA???AGCACGA?GAA?A??????????A?GTA???ACAAAT?GT?CTC?C??GCTTTAACCA????CC?T?GTA???TCTTT??CA?GACTTA????TAATTTTGGTA????????AC?CG??T??CAT?GATG???????C?AGTAA??????ATGTA??????????????????????TAATT????CTCTCA?????????????CA???????C???TTA?ATA?T????A?CA???AAA????A??AGG??T?C?????????ATA?CCCGTAAAT??TGAAC?CCTATCCTTAGA?CT????????A?T?TCGAAATTGATACATGTATAA?A???GGTC?TTAATA sp_7_JaliscoJSF1000 ACA?CTTGT?A??AGCG?GCATTAAAA???GTCCTGTTG??TT???T?CT?AATAAACTA????GCG??????????GGTG?TCAAAC???????CTTTGATCT??A?GTGCC?AA??TTAA?AT?A???????C?AT???????A?TA?????????CCAATT?ATAAAGCCTGTAATGCC????C????????AATA??T?TA?AT???CAAT??CAG???ATAACTTT??C??A?AT????TCG?????G?GCATC????????TTTAGTTGTA?TCC??????????A?CGATCTA?ATGGAAC?CTGAATT????C????CTCC???TCTCCTCGC????????GTTGCGTTAAAT?????TGCT??T?AATGTATTGATACTA???????ATAAAACTTTCTAC????????GCCGTTAC??A??????????A??ATTTGTCACAA??CACC???ACCAC?GG?ATACACA??????????AATAC?A??C?AGAATGTA?ACACT?CTG????G?TACG?AGCCATA?A?CC??AGC?TTAGGAA?AGTCACTCATCTT?AAATTCACC???TA?????TGTAC?????CTAGCT?TC?GGGC?TCGTTT???T????CCTGC???GC?AACCCAT??AGAGCTTAGACGATCTTAA?????CTTCCC?TAG????TG????TCAC???????TGAAT?ACAAATCTAGTTCA?TT?????????CGTG????TA?CA???AAA?AC???T??????T?????AGCTC?????AA?TATGCCCAGTT?TAAA????CC??TTATC??AC???????????A??GTTGGTATAA?????AAAATCAGAA?GCAACTA?TA????????????G??????CTG?TGA????????AAA?ACTCTT???TTCA??????ACCT???GT??????A??GA??TAA?T?CCAT?CCGA????????T????AA?TA?A?ACATT??ATAAT??TCGGGCGAATT???GAAGCAGC???AG?????CT?C??A??CTA????CA?CAAC?AAT?????ATTACCCCGAAT?????CAAT??C??CA????CGG??ATATCTCCAACTG??TTCAA?CACCAA?TCGG?TTTGA??TAA?C?TAAAAAGAAG?AT??TC?TTAAT?CTG?????????CAAAGG??????A?CGA?????????AATACT??TTC?ATTCT??GAATTCTATTATTA?CAGTAAATGCAAA?C?TT?ATA?GACCATGG??A?ATC?AATATCTATA????????CA??ACAAATTTATT??ATCTA????T?C????A?????????CC??????AA???T?TGG??????CTA?AC??TTAGCT??TAAAA???TA?AAGA??TACA?T????AAAC??????AATA???A?TCAATA?????TAAGG?ACGA???????AGT??ATTCGTGCACAA????CGGC??ACCT?C???????TGTG???????????TTCC??G?TC?TTCTA??GATAAATGCTC????GTA?T?A???AAC??TAACTGC???????A?CCCAG?CTACATTCGCA?C?TTT????????????A????????CGCA?CATATTGCTT??????CAAT?G?T?ATAAGTTGAAGTAT?GCTCT???A??ATGATTACG?GA?ATC??TCCGTAGATTG???AGGACA?TAC???????ATA?ATTGTAC??????????TT?C??C??T????????????CCGACCGTACTT?ATC????????????CATCTTTTACG????AGACCCAAAAAAAGAACAATAAAT????CA?ACT???A????A?CC??ACCTAACCACATA??T?????A?GA??C???AGTGTTCC?????ACAA??TTT??????TAAGCA?G?C??CAAAT???TAAAACTCGCAAACGTTATG??TTAA????TTA?TGACAGAGCT?GGTAGTCACTAATTTAGCC?ATAA?A?AGCTGA??C?CTAAGC?ACTAAATAA?CAAATT?TGGGG???TCA?A?C?CTATAC????????AGAGA??A?GT?CATC?TTAAA?C??G?CG?????TTG?GAACTA??????A?AATT???AAA?G??????A????GTTAACTACTTT?AGGATAGAC?TTCACGC??TTATAGTA??????????ACCATA?GT?CGT?A??TAATC?????????CG?????GT??TAACTGT?????AATTTACT????????ATTCA?A??T?????CCTAC??????TATAGTT?A?TCT????AC?GA?CAATA??CG??AG??????????CT?T?TAGGA??TTA????AAACA???????????GAA?ACCGCTCGCCA???TGAGACAT?ATTA??AC??AGTACGTATCA???TT?G?TT?AA?CCC??????AAA?TCC??????ACTTA?GC?????????CA?????G????TTT?????ATCTGG?CTTTT?????????A???CT?G?T??GT?A???GC?CCTGT?CACT?A?AT?TACC?AG?CGT?A??A????TCCA?GATCC??????????TCT??CT????????A?TATTGG??ACGATGTA???AACAGTG?ACGC????CCATTT????????TT????????????GCA?AC??AT???????C????CACCC?AT?G?GGCAA??AACTTT??GGAAATAATG?GT?A?????????T???TATGACAT?????CTTTT?AAAC?AA?TAC?AGA?ACA???????AGCAATCG????TGTAACCCAC?A???CTTGATA?GCC??TTTA?AGTTC?TA?CTAAAGTTGA???AGTTCAG?AA??A??????????A?GTA???CTATAT?GT?CTTGC??GCTTTACACA????TT?T?GTA???TCTTT??CG?AACATA????AAATTTTGATA????????AC?CG??T??CAT?G?TA???????C?AATAA??????ATATA??????????????????????TAATT????CTCTTA?????????????CA???????T???TTT?ATA?T?A??A?CA???AAA????G??GGG??C?C?????????ATA?CCGGTAAAC??CGAAC?CTTATCCCACGAACC????????A?T?TCGAAATTGATACAGGTATTG?G????GTC??TGACA yavapaiensisJSF1085 ACA?CTCGC?A??AGCG?GC?T????????GTCCTGTCGTGTT???T?TT?AACGAACTA????GCG??????????GGTGTGCAAACTT??GGCCTTTCACCT??AGGTACC?TA??TTAA?AT?A???????C?AC???????A?TA?????????CCTATT?ACAAAACCTGTAATGCC????T????????AATA??T?TA?AT???TAAT??C??????TAACTTT??T??A?AT????CCGACGTTG?GCAAC????????AATA??TGTA?TCC??????????A?CGATTTA?A?GGAAT?CTGAATT????C????CTCC???TTCACTCGC????????GTTCCTCTAAAT?????TG?T??A?AATATATAGATACTA???????CTGA?ATTTTCTATCACCTTATGCCATTAC??A??????????A??ATTTGCCACAA??CACT???ACCAC?GG?ATACAAA??????????AGCAC?A??CACGAATGTA?ACATT?CTG????T?TTCA?AGCCATA?A?CC??GGC?TTAGGAA?AGTCACTTATCTT?GAATTCACC?GCTA?????AGTAC?????CA???T?TG?AGGC?TCGTTT???T????CCTGC???AA?AAACCAT??AGAACTTAGACGATCTTAA?????CTTCAC?TAG???CCG????TCAT???????TAAAT?ACAAATCTAGTTCA?AT?????????CGTG????TT?CA???AAA?AC???T??????G?????AGCTT?????AA?TATGCCTAGTT?TAAA????CC??TTATC??A????????????A??GTTGGCATAA?????GAAACC?CAA?GCAA?TA?GA????????????G??????CTG?TGA????????AAG?ATTCAT???TTCA??????ACCT???GT??????A??GT??TAA?T??????CCAA????????T????AA?TC?A?ACATT??ATAAT??TCGAGTGAATT???GAAGCAGC???GG?????TT?C??A??CAA????CA?CATG?AAT?????ATTACCCC??AT?????CAAT??C??CAG???AGG??ATATTCCCCACAG??TTCAA?CAACAA?TCAG?TTTGA???AA?C?TAACAAGGAA?AT??TCTTTATG?CCG?????????CAAAGG??????A?CGA?????????AATACT??TTCAATTTT??GCATTATATTATTA?CGGTAAATGGAGA?C?TT?GCA?GACCATGG??G?ATC?AATTTCTTTA????????CA??ACAAATCTATCTCATCTTTATAC?C????A?????????GC??????GA?TAT?CGG??????CCA?AC??TTAGCT??TTAAA???GA?AAGA??TACA?T????ATGC??????AATA???A?TCAATA?????TTAGG?ACAT???????TGT??ATCCGTACACAA????TGGC??ACCT?C???????TGCG???????????ATTA??G?TC?TTTTA??GATAAATATTT????GTA?T?A???AAT??TAACTAC???????A?CTCAG?CTATATTCGCC?C?TTT????????????A????????CGCA?CATATTGCTT??????CAAT?A?C?ATAAGATGAAGTTG?GCTCT???A??ATGATTAAG?GA?ACC??TCCGTTGATTG???AGAACA?TAC???????ATA?ACTGTAC??????????TT?A??C??T????????????CCGACCATACTT?ACC????????????CATCTTCAACC????AGACCTAACAAAAGAATAATAAAT????CA?AAT???A????A?AC??ACCTACCCGCATT??TTTAATA?TA??C???AGTACTCC?????ATAA??ACT??????AAAGTA?G?C??CAAAT???AAAAACTTGCAAAAGTTATG??TTAA????TTA?TGCCAAAGCT?GGTAGTGATCAACT??GCT?ATAG?A?T?CTGA??C?CTAAGG?CCTAAATAC?TAAATT?TGGAG???TGA?A?C?TTATAT????????AGATG??A?GT?AATC?AAAAA?T??G?CC?????CTA?GCACTA??????A?AAGT???AAA?G??????A????GTTAACTACATC?ATGATAGAC?TTCACGC??TCATGGTA??????????ACTATA?GT?TGT?G??TAATC?????????CG?????AT??????????????AATTTACT????????CTTGA?A??T?????CCTCC??????TCT?CTT?A?TAT????TC?GA?CAATA??CG??AG??????????TT?T?TAGGA?TCTA????AAAGA??????????AGGG?ACAGATTGTCA???TGAGACGT?ATCA??AC??AGTACGTAACA???TTTG?TG?GA?CCC??????AAA?GTC??????ACTCA?GC?????????TA?????G????CAT?????ATCCGG?TCTTT?????????A???CT?G?T??GT?A???AC?CCTGT?CAAT?G?AT?TACC?AT?CGC?G??C????TACA?GATCT??????????TCA??CT????????A?TATTGG??ATGATCTA???AAGAGTG?ACGC????CCATTT????????TTAATATTTCTA??GCA?AC??AT???????C????CACTCGAT?G?GGTAAGAAAGTTTGTGGAATTAATG?GT?A?????????A???TACGATAT?????CTATC?TAAC?AA?TAG?AGA?TTA???????AGCAATCA????CGCAAACCATTA???CTTGGTA?GCC??T??A?AGTAC?TA?CTACAATTGA???AGTTCAG?AA??A??????????A?GTA???CCATAT?GT?CTTGC??GCCCTACGCA????TT?T?GTA???TCTTT??CA?AACTTA????TAATGTTGGTA????????AC?CG??T??CAT?GATA???????C?AATAA??????ATATA??????????????????????TTATT????CTCTAA?????????????CA???????C???TTA?ATA?T?A??A?CA???AAA????G??AGG??T?C?????????ATA?CCGGTAAAT??TGAAC?CCTATCCCCGGA?CG?CTT?GTAA?T?TCGGAATTGATATGAGTATTA?G???GGTC?GTAACA oncaLVT3542 ACA?CTCGC?A??AGCG?GC?T????????GTCCTGTCGTGTT???T?TT?AACGAACTA????GCG??????????GGTG?GCAAACAT??GGCCTTTTACCT??AGGTATC?TA??TTAA?AT?A???????C?AC???????A?TA?????????CCCATT?ACAAACCCTGTAATGCC????T????????AATA??T?TA?AT???TAAT??C??????TAACTTT??T??A?AT????TCGACGTTG?GCAAC????????TATA??TGTG?TCC??????????A?CGATTTA?A?GGAAT?CTGAATT????C????CTCC???TTCACTCGC????????GTTCCTTTAAAT?????TG?T??A?AATGTATAGATACTA???????CTGA?ACTTTCTATCACCTTATGCCATTAC??A??????????A??ACTTGTCACAA??CACT???ACCAC?GG?ATACGAA??????????AACAC?A??CGCGAATGTA?ACATT?CTG????T?TTCA?AGCCATA?A?CC??GGC?TTAGGAA?AATCACTCATCTT?GAATTCACC?GCTA?????AGTAC?????CA???T?TG?AGGT?TCGTTT???T????CCTGC???AA?AAACCAT??AGAACTTAGACGATCTTAA?????CTTCTC?TAG???CCG????TCAT???????TAAGT?ACAAATCTAGTTCA?AT?????????CGCG????TT?CA???AAA?AC???T??????G?????AGCTT?????AA?TATGCCTAGTT?TAAA????CC??TTATC??A????????????A??GTTGGCATAA?????GAAACC?CAA?GCAA?TA?GA????????????G??????CTG?TGA????????AAG?ATTCAT???TTCA??????ACCT???GT??????A??GT??TAA?T??????CCAA????????T????AA?TC?A?ACATT??ATAAT??TCGAGTGAATT???GAAGCAGC???GG?????TT?C??A??CAA????CA?CATG?AAT?????ATTACCCC??AT?????CAAT??C??CAG???AGG??ATATTCTCCACAG??TTCAA?CAACAA?TCGG?TTTGA???AA?C?TAAAAAGGAA?AT??TCTTTAAG?CCG?????????CAAAGG??????A?CGA?????????AATACT??TTCAATTTT??GCATTATACTATTA?CGGTAAATGGAGA?C?TT?GGA?GACCATGG??G?ATC?AATTTCTTTA????????CA??ACAAATCTATCTCATCTTTATAC?C????A?????????GC??????GA?TAT?CGG??????CTA?AC??TTAGCT??TTAAA???GA?AAGA??TACA?T????ATGC??????AATA???A?TCAATA?????TTAGG?ACAT???????TGT??ATCCGTGCACAA????TGGC??ACCT?C???????TGCG???????????ATTA??G?CC?TTTTA??GATAAATATTT????GTA?T?A???AAT??TAACTAC???????A?CTCAG?CTATATTCGCC?C?TTT????????????A????????CGCA?CATATTGCTT??????CAAT?A?C?ATAAGATGAAGTAG?GCTCT???A??ATGATTAAG?GA?ACC??TCCGTTGATTG???AGAACA?TAC???????ATA?ACTGTAC??????????TT?G??A??T????????????CCGACCATACTT?ATC????????????CATCTTCAACC????AGACCTAAAAAAAGAACAATAAAT????CA?AAT???A????A?AC??ACCTACCCGCATT??T?????A?TA??C???AGTATTTC?????ACAA??ACT??????AAAGTA?G?C??CAAAT???AAAAACTTGCAAAAGTTATG??TTAA????TTA?TGCCAAAGCT?GGTAGTGATCGACT??GCT?ATAG?A?TGCTGA??C?CTAAGG?CCTAAATAC?AAAATT?TGGGG???TGA?A?C?TTATAT????????AGATG??A?GT?AATC?AAAAA?T??G?CC?????CTA?GCACTA??????A?AAGT???AAA?G??????A????GTTAACTACATC?ATGATAGAC?TTCACGC??TCATGGTG??????????ACTATA?GT?TGT?G??TAATC?????????CG?????AT??????????????AATTTACT????????CTTGA?A??T?????CCTCC??????TCT?CTT?A?TAT????TC?GA?CAATA??CG??AG??????????TT?T?TAGGA?TTTA????AAAGA??????????AGGG?ACAGCTTGTCA???TGAGACGT?ATCA??AC??AGTACGTAACA???TTTG?TG?GA?CCC??????AAA?GTC??????ACTCA?GC?????????TA?????G????CAT?????ATCCGG?TCTTT?????????A???CT?G?T??GT?A???AC?CCTGT?CAAT?G?AT?TACC?AA?CGC?G??C????TACA?GATCT??????????TCA??CT????????A?TATTGG??ATGATCTA???AATAGTG?ACGC????CCATTT????????TTAATATTTCTA??GCA?AC??AT???????C????CACTTGAT?G?GGTAAGAAAGTTTGTGGAATTAATG?GT?A?????????A???TACGATAT?????CTATC?TAAC?AA?TAG?AGA?TTA???????AGCAATCA????AGCAAACCATTA???CTTGGTA?GCC??TTTA?AGTAC?TA?CTACAATTGA???AGTTCAG?AA??A??????????A?GTA???CCATAT?GT?CTTGC??GCTCTACGCA????TT?T?GTA???TCTTT??CA?AACTTA????TAATGTTGGTA????????AC?CG??T??CAT?GATA???????C?AATAA??????ATATA??????????????????????TTATT????CACTCA?????????????CA???????C???TTA?ATA?T?A??A?CA???AAA????G??AGG??T?C?????????ATA?CCAGTAAAT??TGAAC?CCTATCCCTGGA?CC?CTT?GTAA?T?TCGGAATTGATACGAGTATTA?A???GGTC?GTAACA sp_8_PueblaJAC9467 ACA?CTTGC?A??AGTG?GC?T????????GTCCTGTAGTGTT???T?TTAAATCAACTA????GCG??????????GGTG?GCAAACTT??GGCCTTTAATCT??AGGAACT?CA??TTAA?TT?A???????C?AC???????G?TA?????????CCCATT?ACAAAACCTGTAATGCC????T????????AATA??C?TA?AT???TAAA??C??????TAACTTT??T??A?AC????CTG???TTG?GTACT????????TCTAGTTGTG?TCC??????????A?CGATTTA?ATGGAA??CTGAAAT????C????CTCC???TCCACTCGC????????ATTTCGTTAAAT?????TG?T??T?AATGTATTGATACCA???????GTAAAATTTTCTACCACCTCACGCCATTAC??A??????????A??ATTTGCCACAA??CACT???AT????????TACGGA??????????AATAC?G??CGTAAAT?TA?ACAGT?CTG????C?TCTG?AGCTATA?A?CT??AGC?TTATGAA?AGTCACTCATTTT?GAACCCACC???TA?????AGAAC?????CT???T?TA?TGGC?TCGTTT???T????CCTGC???GA?CAACCAT??AGAACTTAAACGATCTTAA?????CTTCAC?TAG???ATG????TCAC???????TGAAT?ACAA???TAGTTCA?AT?????????CGCG????TC?CA???AAA?AC???T??????G?????AGCTT?????AA?TATGCCTAATT?CCAA????CC??ACATC??A????????????ACTTTCGGGCTAC?????GAAAT???????????????????????????????????CTG?CGA????????TAT?AATCAT???TTCA??????ACCT???GT??????A??AC??TAA?T??????CC?A????????T????AA?CA?A?ATACT??ATAAT??TCGCGTGAATT???GAAGCAGC???GA?????CT?C??A??CCA????CA?CATT?AAT?????ATTACCCC??AT?????CAAT??C??CAG???AGG??ATATCTCCAACTG??TTCAA?CAGCAA?TCGG?TTA?A???AA?T?TAACAAGAAA?AT??ATTTTAAG?CGG?????????CACAGG??????A?TGA?????????AATAAT??TTCAATTTTATGCATTATCT??????CTGTAAATGTAAG?C?TT?ATA?GACCATGG??A?ATC?AATTTCTATA????????CG??GCAAATCTATC??ATTTTTACAT?C????A?????????AC??????AA?TAT?GGG??????CAA?AC??CTAGCT??TT?AA???GA?AAGA??TCTA?T????AAAC??????AATA???A?TAAATA?????TTAGG?GCAT???????GGT??ATTCGTACACAA????TGGC??ACCT?C???????TGCG???????????ATCG??G?GC?TCCTG??GAAA?ATTCTT????GTA?C?A???AAT??TAACTA????????A?CTCAG?TTACATTCGC??C?TTT????????????C????????CGCA?CATATTGCTT??????CAGT?A?T?ATAAGTTGAAGCAG?GCTCC???A??ATGATTATG?GA?ACC??TCCAT?GATTC???AGAACG?TAC???????ATA?ACTGTAT??????????TT?A??C??T????????????CCGACCATACCT?ACC????????????CATCCTT????????????CCAAAAAAAGAATAACAGAT????CA?AAT???A????A?TC??ATCTATCCG?ATG??T?????A?TA??C???AGTACTTC?????ATAA??A????????AAAGTA?G?C??CAAAT???AAAAACTCGCAAAAGTTATG??TTAA????TTA?TGACAAAGCT?GGTAGTAACTAATT??GC??ATAG?A?TCCTGG??C?CTAGGG?CCTAAATAC?TAAATT?TGGCG???TCA?A?C?TTATAT????????AGAGG??A?GT?TCCC?ACAAA?C??G?CA?????GTTAGCACTA??????A?AAGT???AAA?G??????A????GTAAACTACATT?ATGATAAAC?TTCACGC??TCATGGTA??????????ACAATA?GT?TGT?G??TAATC?????????CG?????GT??TACTTGT?????ACTTCACT????????CTTGA?A??T?????CCTCC??????TTT?TTT?A?TTT????CC?AA?CAATA??CG??AG??????????CT?T?TAGGA?TTTA????AAAGA??????????TGAA?ACAGCCCG?CA???TGAGACGA?ATTA??AC??AGTACGTAACA???CTTG?TA?GA?CCC??????AAA?ATC??????ACTTA?GC?????????TG?????G????CAT?????ATTCGG?ACTTT?????????A???CCAG?C??GT?A???AG?CCTGT?CAAT?A?AT?TACC?AA?CGC?G??T????TGTA?GACCT??????????TCC??CT????????A?TTTAGG??AAGACTTA???AATAGTGTACGC????CCATTT????????TTAACATCTCTA????A?AC??AT???????C????CACACGAT?G?GGTAAGAAAGTTTGTGGACTTAATG?GT?A?????????G???CACGGCAT?????CTTTC?AAAC?AA?TGT?AGT?CTA???????AGCAATCG????TGCAAGA??G?A???CTTGATA?GCC??TTTA?TGTTCCTA?CTACAATTGA???ATTCCAA?AA??A??????????A?GTA???CCTCAT?GT?????C??GCTCTACTCA????TT?T??TA???TTTTT??CA?AACTTA????CAATGTCG?TA????????AC?CGTAT??CAT?GATA???????C?AATAA?????AATATA??????????????????????TAATT????CTCTCACATGT????????CA???????C???TTA?ATA?T?AAAA?CA???AAA????A??GGG??T?T?????????ATA?CCAGTAAAC??TGAAC?TCTATCCCAGGA?CC?CTT?GCAA?T?TCGAA?TTGATACGAGTATGT?A???GGTC?TTTACA macroglossaJAC10472 ACC?CTCGC?A??AGCG?GC?T????????GTCCTGTTG??TT???T?TT?AA????CTA????GCG??????????GGTG?GCAAGCCT??GGCCTTTGACCT??AGGTACC?AA??TTAA?AT?A???????C?AC???????A?TA?????????CCTATTTACAAAACCTGTAATGTC????T????????A??A??T?TA?AT???TAAT??CAG???ATAACTTT??T??A?TC????TTGACGTTG?ACATC????????CATAGTTGTA?TCC??????????A?CGATTTA?ATGGAAC?CTGAACT????C????CTCC???TTCACTCGC????????GCTGCTCTAAAT?????TG?T??T?AATGTATGGATACTA???????CTGAAATTTTCTAC????????GCCATTAC??A??????????A??ATTTGCCACAA??CACA???TCCAC?GG?AT??AAA??????????AATAC?A??CTAGAATGTA?ACATT?CTG????T?TACA?CGCTATA?A?CC??GGC?TTAGGAA?AGTCACTCATTAT?GAAT???CC???TA?????AGTAC?????CT???T?TT?GGGT?TCGTTT???T????CCTGC???GC?AGTCCAC??AGAACTTAAACGATCTTAA?????CTTCTC?TAG???TCG????TCAC???????TGAAT?ACAAATTTAGTTCAATT?????????CGTG????TC?CA???AAA?AC???T??????A?????AGCTC?????AA?TATGCCTAATT?TAAA????CC??TAGTC??A????????????A??GTCGGCATAA?????AAGACC?CAA?ACAACTA?CA????????????A??????CTG?TGA????????AAG?ACTCCT???TTCA??????ACCT???GT??????A??AT??TAA?T??????CCAG????????T????AA?TA?A?ATACT??ATAAT??TCGGGCGGAAT???AAAGCAGG???GG?????CT?C??A??CCA????CA?CACT?AAT?????ATTACCCC??AT?????CAAT??C??CAC???AGG?GATATTCTCCACTG??TTCAA?CAACAA?GCGG?TTTAA???AA?T?TAATAAGAAA?AT??ACTTTAAG?CTG?????????CAAAGG??????A??GA?????????ACTATT??CTCACTTTT??GCATTGTCCTATTA?CAGTAAATGGAAA?C?TT?TTA?GACCATGG??A?ATC?AATTTCTATA????????CA??GCAAATTTGTT??ATCTTTACAC?CT???A?????????GC??????GA?TAT?TGG??????TCA?AC??CTAGCTCTTTAAA???GA?AAGA??TGTA?T????AGGC??????AATA???A?TTACTA?????TTAGG?ATAT???????TGT??ATTCGTGCACAA????CGGC??ACTT?C???????TGTG???????????ATCA??G?AC?TCCTA??GACAAATTCTA????ATG?T?A???AAT??TTACTGC???????A?CCCAGTCTACATTCTCA?C?TTT????????????A????????CGCA?CATAT???TT???????AAT?A?C?ATAAGTTGGAATAG?GCTCT???G??ATGATTAAG?GA?ACC??TATGTTGATAG???AGATCA?TAC???????ATA?ACTGTAC??????????TT?A??CCGT????????????CTGACCGTACCT?ACC????????????CATCTTCTACC????AGACTCAAAATAAGAATAATAGAT????CA?AAT???A????A?TC??ATCCATCCGCATT??T?????A?CA??C???AGTAC?TC?????ACAAG?ACT??????TAAATATG?C??CAAAA???AAAAACTTGTAAAGGTTATG??TTAA????TTA?TGACAAAGCT?GGTAGTAATTGATT??GCT?ATAA?A?AACTGG??C?CTAAGG?CCTAAATAC?AAAATT?TGGGG???TGA?A?C?CTATAT????????AGAGG??A?GT?GATC?AATAA?T??G?CG?????TTG?GCACTA??????A?TAAT???AAA?G??????A????GTTAACTACTCT?AGGATAAAC?TTCACGC??TCATGATA??????????ACAATA?GT?TGT?G??TAATC?????????CG?????TT??TACTTGT?????ATTACACT????????TTTAA?A??C?????CCTAC??????TCT?CTC?T?TTT????TC?GA?CGATA??CG??A????????????T?C?TAGGA?TCTA????AAAGA??????????AGAA?ACAGTTCATCA???TGAGACGT?ATCA??AC??AGTACGTATCA???TTTG?TT?GA?CCC??????AAA?ATC??????ACTAA?GT?????????AA?????G????CAT?????ATTCGG?CATTT?????????A???CT?G?T??GT?A???AC?CCTGT?CAAT?A?AG?TACC?AG?CGT?A??A????TACA?GACTT??????????TCT??CT????????A?TGT?GG??ACGATCTA???AATAGTG?ACGC????CCATTT????????CTAATATCTCTA??GCA?AC??AT???????A????CACACGAT?G??GTAAGGAAGTTTGTGGAATTAATG?GT?A?????????A???TATGGAAT?????CCTCT?GACC?AA?TGG?AGA?ACA???????AGCAATCA????TGCAAGCCAA?A???CTTGCTA?GCC??TTTA?AGTTC?TA?CTACAGTTGA???AGTT?????A??A??????????A?GTA???CCCAAT?GT?CTCGC??GCT????GCA????TT?T?GTA???TCTCT??CA?AACTTA????TAATGTTGATA????????GC?CA??T??CAT?GGTG???????C?AATAA??????ATATA??????????????????????TAATT????CTCTTA?????????????CA???????C???TTA?ATA?T?A??A?CA???AAT????A??AGG??T?G?????????ATA?CCAGTAAAT??CGAAC?CCTATCCCCAGA?CT?CT??GCAA?T?TCGAAATTGATAAGAGTATTA?G???GGTC?TTGACA macroglossaJSF7933 ACC?CTCGC?A??AGCG?GC?T????????GTCCTGTCG??TT???T?TT?AA????CTA????GCG??????????GGTG?GCAAACTT??GGCCTTTGATCT??AGGTACC?TA??TTAA?AT?A???????C?AC???????A?TA?????????CCTATTTACAAAGCCTGTAATGTC????T????????A??A??T?TA?AT???TAAT??CAA???ATAACTTT??T??A?TC????TAGACGTTG?ACATC????????CATAGTTGTA?TCC??????????A?CGATTTA?ATGGAAC?CTGAACT????C????CTCC???TTCGCTCGC????????GCTGCACTAAAT?????TG?T??T?AATGTATAGATACTA???????TTGAAATTTTCTAC????????GCCATTAC??A??????????A??ATTTGCCACAA??CACA???TCCAC?GG?AT??AAA??????????AATAC?G??CTAAAATGTA?ACATT?CTG????T?TACA?CGCAATA?A?CT??GGC?TTAGGAA?AGTCACTCATTAT?AAAT???CC???TA?????AGTAC?????CT???T?TG?GGGT?TCGTTT???T????CCTGC???GC?GGTCCAC??AGAACTTAGACGATCTTAA?????CTTCTC?TAG???TCG????TCAC???????TGAAT?ACAAATTTAGTTCA?TT?????????CGCG????TC?CA???AAA?AC???T??????A?????AGCTA?????AA?TATGCCTAACT?TAAA????CC??TAGTC??A????????????A??GTCGGCATAA?????AAGATC?CAA?ACAACTA?CA????????????A??????CTG?TGA????????AAG?ACTCCT???TTCA??????ACCT???GT??????A??AT??TAA?T??????CCAG????????T????AA?TA?A?ATACT??ATAAT??TCGGGCGAAAT???AAAGCAGG???GG?????CT?C??A??CCA????CA?CACT?AAT?????ATTACCCC??AT?????CAAT??C??CAG???AGG?GATATTTTCTACTG??TTCAA?CAACAA?TCGG?TTTGA???AA?T?TAAAAAGAAA?AT??ACTTTAAG?CTG?????????CAAAGG??????A??GA?????????ACTACT??CTCACTTAT??GCATTGTCCTATTA?CAGTAAATGGAAG?C?TT?TTA?ACCCATGG??A?ATC?AATTTCTATA????????CA??ACAAATTTGTT??ATCTTTATAC?CT???A?????????GC??????GA?TAT?TGG??????TCA?AC??CTAGCTCTTTAAA???GA?AAGA??TATA?T????AAGC??????AATA???A?TTACTA?????TTAGG?ATAT???????TGT??ATTCGTGCACAA????CGGC??ACTT?C???????TGTG???????????ATCA??G?AC?TCCTA??GACAAATCCTA????GTG?T?A???AAT??TTACTGC???????A?CCCAGTCTACATTCTCC?C?TTT????????????A????????CGCA?CATAT???TT??????CAAT?A?C?ATAAGTTGGAATAG?GCTCT???G??ATGATTAAG?GA?ACC??TCCGTTGATTG???AGAACA?TAC???????ATA?ACCGTAC??????????TT?A??CCGT????????????CCGACCGTACCT?ACC????????????CATCCTCTACC????AGACTCAAAACAAGAACAATAGAT????CA?AGT???A????A?TC??ATCCATCCGCATT??T?????A?TA??C???AGTAC?CC?????ACAAG?ACT??????AAAATATG?C??CAAAA???AAAAACTTGTAAAGGTTATG??TTAA????TTA?TGACAAAGCT?GGTAGTAATTGATT??GCT?ATAA?A?AACTGC??C?CTAAGG?CCTAAATAC?AAAATT?TGAGG???TGA?A?C?CTATAT????????AGAGG??A?GT?GATC?AATAA?T??G?CG?????TTG?GCACTA??????A?CAAT???AAA?G??????A????GTTAACTACTTT?AGGATAAAC?TTCACGC??TCATGATA??????????ACAATA?GT?TGT?G??TAATC?????????CG?????CT??TACTTGT?????AGTACACT????????TTTAA?A??C?????CCTGC??????TCT?CTT?T?TGT????TC?GA?CGATA??CG??AG??????????TT?C?TAGGA?TCTA????AAAGA??????????AGAA?ACAGCTCATCA???TGAGACGT?ATCA??AC??AGTACGTATCA???TTTG?TT?GA?CCC??????AAA?ATC??????ACTCA?GT?????????AA?????G????CAT?????ATTCGG?TATTT?????????A???CT?G?C??GT?A???AC?CCTGT?CAAT?A?AG?TACC?AG?CGT?A??A????TACA?GACGT??????????TCC??CT????????A?TGTTGG??ACGATCTA???AATAGTG?ACGC????CCATTT????????CTAATATCTCTA??GCA?AC??CT???????A????CACACGAT?G??GTAAGGAAGTTTGTGGAATTAATG?GT?A?????????A???TATGGAAT?????CCTCT?GACC?AA?TAG?AGT?ACA???????AGCAATCA????TGCAAGCCAA?A???CTTGCTA?GCC??TTTA?AGTTC?TA?CTACAGTTGA???AGTC?????A??A??????????A?GTA???CCCAAT?GT?CTCGC??GCT????GCA????CT?T?GTA???TCTTT??CA?AACTTA????TAATGTTGATA????????AC?CA??T??CAT?GGTG???????C?AATAA??????ATATA??????????????????????TAATT????CTCTTA?????????????CA???????C???TTA?ATA?T?A??A?CA???AAT????C??AGG??T?G?????????ATA?CCAGTAAAT??CGAAC?CCTATCCCTAGA?CT?CTT?GCAA?T?TCGAAATTGATAAGAGTATTA?G???GGTC?TTAACA taylori286 ATA?CTCGC?A??AGCG?GC?T?????TCAGTCCTGTCG??TT???T?TT?AACGAACTA????GCG??????????GGTG?GCAAACCT??GGCCTTTGACCT??AGGAAAC?TA??TCAA?AT?A???????C?AC???????A?TA?????????ACC?TTTACAAAGCCTGTAATGCC????T????????GATA??T?TA?GT???TAAT??CAG???ATAACCTT??T??A?AC????CCGACGTTG?ATATC????????CACAGTTGTA?TCC??????????A?CGTTTTA?ATGGAAC?CTGAACT????C????CTCC???TCCACTCGC????????GTTGCCCTAAAT?????TG?T??G?AATGTATAGATACTA???????ATAAGACTTTCTAC????????GCCATTAC??A??????????A??AGTTGTCACAA??CACA???TCCAC?GG?AA??AGA??????????AATAC?G??CAGGAATGTA?ACATT?CTG????T?TTAA?C??TATA?A?CA??GGC?TTAGGAA?AATCACTCATTTT?AAAT???CC???TA?????TGTAC?????CT???T?TC?GGGT?TCGTTT???T????CCTGCAAAGA?G?CCCAT??AGAACTTAGACGATCTTAA?????CTTCCC?CAG???TTG????ACA????????CGAAT?ACAAATCTAGTCT?????????????CGTG????TA?CA???AAA?AC???T??????A?????AGCTC?????AA?TATGCCTAATT?CAAA????CC??ATATC??A????????????A??GTCAGCATAG?????AGGATC?CAA?ACAATTA?GA????????????G??????CTA?TGA????????TTG?ACTCAT???TTTA??????ACCT???GT??????A??AT??TAA?T??????TCAC????????T????AA?TC?A?ATACTGCATAAT??TCGGGTGAATT???AAAGCAGT???GG?????CT?C??A??CTA????CA?CATT?AAT?????ATTACCCC??AT?????CAAT??C??CAG???AGG?GATATTTACTACTG??TTCAA?CAACAA?CCGG?TCTGA????A?T?TA??AAGAAG?AT??GTTTTAAG?CTG?????????CAAAGG??????A??GA?????????ACTATT??TTCACTTTT??ATA??GTGTTATTA?CAGTAAATGCACATC?TT?GTA?ATCCATGG??A?ATC?AATTTCTATA????????CA??ACAAATCTATG??ATTTTTATAC?C????A?????????GC??????AA?AAT?TGA??????TCA?AC??CTAGCT??TTAAA???GA?AAGA?CTATA?T????ATAC??????AATA???A?TCATTA?????TTAGG?ATAA???????TGT??ATTCGTGCACAA????AAGC??ACCT?C????????GTG???????????ATTT??G?AC?TTCTA??GAAAAATTCTA????GTG?A?A???AAC??TCACTGC???????A?CTCAA?TTACATTCGCT?C?TTT????????????T????????CGCA?CATATTGCTT??????TAAT?A?C?ATAAGCTGAA??AG?GCTCT???A??ATGATTATG?GA?ACT??TCCATTGATTG???GGAACA?TAC???????ATA?TCCGTAC??????????TT?A??CCGT????????????CCGACAGTATCT?ACC????????????CATCAT??ACC????AGACCCAAAAAAAGAACAAGAGAT????CA?TGT???A????A?TC??ATC??TCCACATT??T?????A?CG??C???AGTAA?CC?????ACAAG?ACT??????AAAACATG?C??CAAAA???GAAAACTTGTAAAAGTTATG??TTAA????TTA?TGACATAGCT?GGTAGTAATTTATT??GCT?ATAA?A?ACTTGG??C?CTAAGG?CCCAAATAC?AAATTT?TGTAG???TGA?A?C?TTATAT????????AGA????A?GT?CATC?AACAA?T??G?CG?????TTG?GCACTA??????A?AAAT???AAA?G??????ATGTCGTTAACTACTGC?GTGATAAAC?TTCACGC??TCATGATT??????????ACAATA?GT?TGT?G??TAATC?????????CG?????TT??TATTTGT?????ATTGCACT????????TTTAA?A??C?????CCTAC??????TCT?TTT?CTTTT????TC?GA?CGAT???CG??AG??????????TT?T?TAGGA?CTTA????AAAGA??????????AGGA?ACAGCTACTCA???TGAGACGT?ATCA??AC??AGTACGTACGA???TGTG?TA?GA?C????????AAA?ATC??????GCTGA?GC?????????AA?????G????CAT?????ATTCGG?CTTTT?????????A???CT?G?T??GT?A???AC?CCTGT?CAAT?A?A??TACC?AA?CGC?A??A????TCTC?GATTT??????????TCT??CC????????A?TATTGG??ATGATTTA???AACAGTG?ACGC????CCATTT????????TTAATATTTCTT??GCA?AC??AT???????A????CACACGAT?GA?GCAAGGAAGTTTGTGGAT??AATG?GT?A?????????A???TATGAAAT?????CCTCG?AATC?AA?TTT?AGATACA???????AGCAATCA????TGCAAAACAT?G???CTTGATA?GTC??TTTA?AGTAC?TA?CTACAGTT?A???AGTT?????A??A??????????A?GTA???TTATAT?GT?TTCGT??GCT????GCA????CT?T?GTA???TCTTT??CA?AACTTA????AAATGTTGTTA????????AC?CA??T??CAT?GGTA???????C?AATAA??????ATATA??????????????????????TAACT????CTCTTA?????????????CA???????C???CTA?ATA?T?A??A?CA???AAT????A??AGA??T?G?????????ATA?CCAGTAAAT??TGAAC?CCTATCCCTAGA?CC???????AA?T?TCGGAATTGATATGAGTATCG?G???GGTC?CTAACA sp_4_Panama ACC?CTCGC?A??AGCG?GC?T????????GTCCTATCG??TT???T?TT?AATGAACTA????GCG??????????GGTG?GCAAACTT??GGCTTTTGACCT??AGGTACC?TA??TTAA??T?A???????C?AC???????A?TA?????????CCCATT?ACAAAGCCTGTAATGTC????T????????AATA??T?TA?AT???CAAT??CCG???ATAACTTT??T??A?AC????TAGACGTTG?ACAAC????????TATAGTTGTA?TCC??????????A?CGATTTA?ATGGAAC?CTGAATT????C????CTCC???TCCACTCGC????????CTTACCCTAAAT?????TG?T??T?AATGTATAGATACTA???????ATGAAACTTTCTAC????????GCCATTAC??A??????????A??ATTTGCCACAA??CACT???ACCAC?GG?AT??GAG??????????AATAC?G??CGTAAATGTA?ACATT?CTG????T?TTCG?AGCTATA?A?CC??GGC?TTAGGAA?GGTCACTCTTTCT?GGAT???CC???TA?????AGTAC?????CT???T?TA?GGGT?TCGTTT???T????CCTGC???AC?AGCCCAT??GGAACTTAGACGATCTTAA?????TTTCTC?TAG???TAG????TCAC???????TAAAT?ACAAATTTAGTTCA?TT?????????CGCG????TC?CA???AAACAC???T??????G?????AGCTT?????AA?TATGCTTTGTT?TGAA????CC??TAGTC??A????????????A??GTCGGCATAA?????AAGATC?CGA?TCAATTA?AA????????????A??????CTG?TGA????????CAG?ATTCAT???TTCA??????ACCT???GT??????A??AT??TAA?T??????CCAG????????T????AA?TA?A?ACAGT??ATAAT??TCGGGCGAATT???GAAGTAAG???GG?????CT?C??A??CTA????CA?CGTA?AAT?????ATTACCCC??AT?????CAAT??C??CAG???AGG?GATATTCCCAACTG??TTCAA?CAACAA?TCAG?TTTGA???AA?T?TAACAAGAAC?AT??ACTTTAAG?CTG?????????CAAAGG??????A??GA?????????AATATT??TTCAATTTT??GCATTGTACTATTA?CAGTAAATGGAAA?C?TT?GTA?GACCATGG??A?ATC?AATTACTATA????????CA??ACAAATCTATT??ATCTTTACAC?C????A?????????GC??????GC?TAT?TAG??????CCA?AC??TTAGCT??TTAAA???GA?AAGC??TACA?T????AAGC??????AATA???A?TTAATA?????TTAGG?GCAA???????TGT??ATTCGTGCACAA????TAGC??ACCT?C???????TGTG???????????ATTA??G?CC?TTCTA??GACAAATCCTA????GTA?T?A???AAA??TCACTGC???????A?CCCAG?TTACATTCGCC?C?TTT????????????A????????CGCA?CATATTGCTT??????CAAT?A?C?ATAAGTTGAAGCAG?GCTCT???A??ATGATTATG?GA?ACC??TCCGTTGATTG???AGAACA?TAC???????ATA?ACTGTAC??????????TT?A??CCGT????????????CTGACCGTTCTT?ACC????????????CATCTTCTACC????AGACCCAAGAAAAGAAGAATAAATTATACA?AAT???A????A?TC??ATCCAACCGCATT??T?????A?AA??C???AGTGA?TC?????ACAAG?ACT??????AAAAT????????????????AAAACTTGCAAAAGTTATG??TTCA????TTA?TGACATAGCT?GGTAGTCATTAATT??GCT?ATAA?A?AACTGG??C?CTAAGG?CCTAAATAT?CAAATT?TGGAG???TGA?A?C?CT?TAT????????AGAGG??A?GT?TGTC?AAAAA?T??G?CG?????CTA?GTACTA??????A?A?ATATGAAA?G??????A????GTTAACTACT???AGGATAAAC?TTTACGC??TCATGGTA??????????ACAATA?GT?AGT?A??TAATC?????????CG?????TT??TACTTGT?????AGTACACT????????TTTAA?A??T?????CCTAC??????TTT?CTT?A?TCT????CC?GA?CGATA??CG??AGTGACACAGTTTT?A?TAGGA?CTTA????AAAGA??????????AGAA?ACAGTTTATCA???TGAGACAT?ATCA??AC??AGTACGTATCA???TCTG?TA?GA?CCC??????AAA?GTC??????GCTTA?GC?????????GA?????G????CAT?????ATCCGG?CCTCT?????????A???CT?G?T??GT?A???AC?CCTGT?CAAC?A?AT?TACC?AG?CGA?A??A????TACA?GATTT??????????TCA??CT????????A?TGTTGG??ATGATCTA???AAC???G?ACGC????CCATTT????????TTAATATCTCTA??GCA?AC??AT???????C????CACCCGAT?G??GTAAGGAAATTTGTGGAACTAATG?GT?A?????????A???TATGGAAT?????CCTTT?GAGC?AA?TAC?AGG?ATA???????AGCAATCA????CGCAAGCCAT?A???CTTGATA?GCC??TTTA?AGTTC?TA?CTACAGTTGA???AGTCCAA?GA??A??????????A?GTA???CCATAT?GTCCTCGC??GCCTTATGCA????CT?T?GTA???ACTTT??CA?AACTTA????CAATGTTGATA????????AC?CG??T??CAT?GGTA???????C?CATAA??????ATATA??????????????????????TAATT????CTCTTA?????????????CA???????C???TTA?ATA?T?A??A?CA???AAA????A??AGG??T?A?????????ATA?CCAGTAAAT??CGAAC?CCTATCCCAAGG?CT?CTT?GCAA?T?TCGCAATTGATACAAGTATTA?C???GGTC?ATAACA sp_5_CostaRichDMH86_210 ACA?CTCGTTA??AGCG?GC?T????????GTCCTGTCG??TT???T?TT?AATAAACTA????GCG??????????GGTG?GCAAACCT??GGCCTTTG?TCT??AGGCACC?CA??TTAA?GT?A???????C?AC???????A?CA?????????CCTATT?AAAAAGCCTGTAACGCC????C????????AATA??T?TA?GT???CAAT??CAGTTTATAACTTT??T??A?AC????AAGACGTTG?ATAAC????????TATCGTTGTT?TCC??????????ATCGATTTA?ATGGAAC?CTGAACT????C????CTCC???TCCACTCGC????????GTTGTTTTAAAT?????TG?T??C?AATGTATAGATACTA???????ATGAGATTTTCTAC????????GCCATTAC??A??????????A??ATTTGCCACAA??CACT???ACCAC?GG?AA??TAA??????????AATAC?A??CAAGAATGTA?ACACT?CTG????T?TTCG?AGCTATA?A?CC??AGC?TTAGGAA?AGTCACTCATTTT?GATT???CC???TA?????AGTAC?????CC???T?TT?GGGT?TCGTTT???T????CCTGC???AC?AGG?CAT??AGAGCTTAAGCGATCTTAA?????TTTCTC?TAG???TAG????TCAT???????TGAAT?ACAAATTTAGTTCA?TT?????????CGCG????TC?CA???AAA?AC???T??????G?????AGCTT?????AA?TATGCCTAGTT?TAAA????CC??TAGTC??A????????????A??GTTAGAATAA?????AAGATC?CGA?ACAATTA?AA????????????G??????CTG?TGA????????CAG?ACTCAT???TTCA??????ACCT???GT??????A??AC??TAA?T??????CCGG????????T????AA?TA?A?ATAAT??ATAAT??TCGTGCGAATT???GAAGTAAA???GG?????CT?C??A??CTA????CA?CATA?AAT?????ATTACCCT??AT?????CAAT??C??CAG???AGG?GA???TCCCAACTG??TTCAA?CAACAA?CCAG?TTTGA???AA?T?TAAGAAGAAA?AT??ACCTTAAG?CTG?????????CAAAGG??????A??GA?????????AATATT??CTCAATTCT??GCATTGTACTA??A?CAGTAAATGAAAA?C?TT?GTA?GACCATGG??A?ATC?AATTTCTATA????????CA??ACAAATCTATC??ATCTTTAAAC?C????A?????????GC??????GA?CAT?TGG??????CCA?AC??CTAGCT??TTAAA???GA?AAGG???ACA?T????AAGC??????AATA???A?TCAATA?????TTAGG?GCAA???????CGT??ATTCGTGCACAA????TGGC??ACCT?C???????TGCG???????????ATTA??G?TC?TCCTA??GACAAAT??TA????GTA?T?A???AAG??TCACTGC???????A?CCCAG?TTACATTCGCA?C?TTT????????????A????????CGCA?CATATTGCTT??????CAAT?A?T?ATAAGCTGAAGCAATGCTCT???T??TTGATTACG?GA?ATC??TTCGTTGATTG???AGAACA?TAC???????ATA?GCTGTAC??????????TT?A??TCGT????????????CTGACCATCCTT?AAC????????????CATCCTCTACC????AGACCCAAAACAAGAAGAATAAAT????CA?AAT???A????A?TA??ATCCAACCACATT??T?????A?AGTCC???AGTAA?TC?????ACAAG?ACT??????AAAGT????????????????AAAACTTGCAAAGGTTATG??TTAA????TTA?TGACACAGCT?GGTA??TATTGATT??GCT?ATAA?A?AACTGG??C?CTAAGA?CCTAAACAT?CAAATT?TGGGG???TGA?A?C?CT?TAT????????AGAGG??A?GT?CG?????AAA?CGTA?CG?????ATT?GCACTA??????A?AAGT???AAAAG??????A????GTCAACTGCCTC?AGGATAAAC?TTTACGC??TCATGGTA??????????ACAATA?GT?TGT?G??TAATC?????????CG?????CT??TACTTGC?????AATACACT????????TTTAA?A??C?????CCTAC??????TTT?TTT?C?TTT????TT?GA?CGATA??CG??CG??????????TT?T?TAGGA?TTTA????AAAGA??????????AGAA?ATAGCTTATCA???TGAGACAT?ATCA??AC??AGTACGTATCA???TCTG?TA?GA?CCC??????AAA?ATC??????CCTTA?GC?????????GA?????G????CAT?????ATCCGG?TCTTT?????????A???CT?G?T??GT?A???AC?CCTGT?CAAT?A?AT?TACC?AG?CGT?A??A????TACACGACCT??????????TCC??CT????????A?TGTTGG??ATGATCTA???AATAGTG?ACGC????CCATTT????????TTAATATCTCTA??GCA?AC??AT???????C????CACCCGAT?G??GTTAGGAAG??????GAACTGATG?GT?A?????????A???TATGGAAT???????TTT?GAAC?AA?TTT?AGA?ATA???????AACAATCA????TGCAAACCAT?A???CTTGATA?GTCTATTTA?AGTAC?TA??TACAGTTGA???AGTCCAC?GA??A??????????A?GTA???CCATAT?GTCCTCGC??GCTATATGCA????CT?T?GTA???TCTTT??CA?AACTTA????CAATGTTGATA????????AC?CG??T??CAT?GGTA???????C?AATAA??????ATATA??????????????????????TAATT????CTCTAA?????????????CA???????C???TTA?ATA?T?A??A?CA???AAA????A??AGG??T?A?????????ATA?CCAGTAAAT??CGAAC?CCTATCCCCAGA?CT?CTT?GCAA?T?TCGAAATTGATACGAGTATTA?G???GGTC?TTAACA sp_6_CostaRicaDMH86_225 AAC?CTCGC?A??AGCG?GC?T????????GTCCTGTAG??TT???T?TT?AATGAACTA????GCG??????????GGTG?GCAAACCC??GGCCTTTGATCT??AGGTATC?TA??TTAA?AT?T???????C?AC??????CA?TATAGTCGGAACCCATT?ACAAATCCTGTAATGCC????C????????AATA??T?TA?AT???TAAT??CAG???ATAACTTT??T??A?GC????TAGACGTTGAATAAC????????TGTAGTTGTA?TCC??????????A?CGATTTA?ATGGAAC?CTGAATT????C????CTCC???TTCACTCGC????????GCTGCTCTAAAT?????TG?T??T?GATATAT?GATACCA???????ATGAAACTTTCTAC????????GCCATTAC??A??????????A??ACTTGTCACAA??CACA???AGTAC?GG?AC??GAA??????????AATAC?A??CATGAATGTA?ACACT?CTG????A?TTCG?AACTATA?A??A??CGC?TTAGGAA?AGTCACTCATTTT?GAAT???CC???TA?????AGTAC?????CC???T?TT?GGGT?TCGCTT???T????CCTGC???AT?GGTCCAT??AGATCTTAGACGATCTTAA?????CTTCTC?TAG???CAG????CCAC???????AGAAT?ACAAATTTAGTTCA?AT?????????AGTG????TC?CA???AAA?AC???T??????G?????AGCTT?????AA?TATGCTTTGTT?TAGA????CC??TCATC??A????????????A??GTT??????A?????AAGATC?CA??ACAACTA?CA????????????G??????CTC?CGA????????CAG?ACTCCA???TTCA??????ACCT???AT??????A??AC??TAAGT??????CCAG????????T????AA?TA?A?ACAAT??ATAAT??TCGGGAGAATT???GGAGTAAG???GG?????CT?C??A??CCA????CA?GATA?AAT?????ATTACCCC??AT?????CAAT??C??CAG???AGG?GATATTCCCTACTG??TTCAA?CACCAA?TCAG?TTTGA???AA?T?TAAAAAGAAA?AT??ACTTTAAG?CTG?????????CAAAGG??????A??GA?????????AATATT??TTCAATTTT??GCATTGTACTATTA?CAGTAAATGAAAA?C?TT?GTA?GACCATGG??A?ATC?AATTTCTACA????????CA??ACAAATTTATC??ATCTTTCTAC?C????A?????????GC??????AA?TAT?TGG??????CCA?AC??CTAGCT??TTAAA???GA?AAGA??TC?????????GGC??????AATA???A?CTAATA?????TTAGG?ACAA???????TGT??ATTCGTGCACAA????TGGC??ACCT?C???????TGTG???????????ATTA??G?TC?TTCTT??GACAAATGATG????GTA?T?A???AAA??TCACTGC???????A?CCCAG?CTACATTCACC?C?TTT????????????T????????CGCA?CATATTGCTT??????CAAT?A?C?ATAAGTTGAAGAAG?GCTCT???A??TTGATTACG?GA?ACC??TTCGTTGATTG???AGAACA?TAC???????ATA?ACTGTAT??????????TT?A??CCGT????????????CTGACCGTCCTT?ACC????????????CATCCTCCACC????AGACCCAAAACAAGAAGAACAAAT????CA?AAT???A????A?CC??ATCTAACCGCATT??T?????A?CG??C???AGTAA?TC?????ACAAG?CCT??????AAATTATG?C??CAAAT???AA???CTTGCAAAAGTTATG??ATAA????TTA?TGACACAGCT?GGTAGTCATTGATT??GCT?ATGA?A?AACTGG??C?CTAAGA?TCTAAATAC?TAAATT?TGTAG???TGA?A?C?TT?TAT????????AGGGA??A?GT?TGTC?AAAAA?T??G?CG?????CTC?GGACTA??????A?CAAT???AAA?G??????A????GTTAACTACTTT?AGGATAAAC?TTCACGC??TCATAGTA??????????ACAATG?GT?TGT?G??TAATC?????????CG?????CT??TACATGT?????AATACACT????????TTTGA?A??A?????CCTTC???????TT?CTT?A?TCT????CC?GA?TGATA??CG??AG??????????TT?T?TAGGA?TGTA????AAAGA??????????AGAA?ACAGCTTGTCG???TGAGACGT?ATTA??AC??AGTACGTATCA???TCTG?TT?GA?CCC??????AAA?GTC??????GCTCA?GT?????????TA?????G????CAT?????ATTAGG?CTTTT?????????A???CT?G?C??GT?A???AC?CCTGT?CAAT?A?AT?TACC?AT?CGT?A??A????TACA?GATTT??????????TCC??CT????????A?TGTTGG??ATGATTTA???AATAGCG?ACGC????CCATTT????????CTAATATTTCTA??GGA?AC??AT???????C????CACCTGAT?G??GTAAGGAAGTCTGTGG???TAATG?GT?A?????????A???TAAGGTAT?????CCTCA?GA?C?AG?TAT?AGT?ATA???????AACAATCA????TGC?ATCCAT?A???CTTGATA?GCC??TTTA?AGTCC?TA?CTACAGTTGC???AGTTCAG?GA??A??????????A?GTA???CCATAT?GTCCTCGT??GCCTTATGCA????CT?T?GTA???TCTCT??CT?AACTTA????TAATGTTGGTA????????AC?CG??T??CAT?GGTT???????C?AATAA??????ATATA??????????????????????TAATT????CCCTTA?????????????CA???????A???TCA?ATA?T?A??A?CA???AAA????C??GGG??GAA?????????ATA?CCAGTAAAT??TGAAC?CCTATCCTCAGA?CC?CTT?GCAA?T?TCGGAATTGATACGAGTATTA?A???GGTC?CTAACA ; END; BEGIN CHARACTERS; TITLE Untitled_DATA_Block_1GapsAsBinary; LINK TAXA = Untitled_TAXA_Block_1; DIMENSIONS NChar=2723; CharStateLabels 1 col_1, 2 col_2, 3 col_3, 4 col_4, 5 col_5, 6 col_6, 7 col_7, 8 col_8, 9 col_9, 10 col_10, 11 col_11, 12 col_12, 13 col_13, 14 col_14, 15 col_15, 16 col_16, 17 col_17, 18 col_18, 19 col_19, 20 col_20, 21 col_21, 22 col_23, 23 col_24, 24 col_25, 25 col_26, 26 col_27, 27 col_28, 28 col_29, 29 col_30, 30 col_31, 31 col_32, 32 col_33, 33 col_36, 34 col_37, 35 col_38, 36 col_39, 37 col_40, 38 col_41, 39 col_43, 40 col_44, 41 col_45, 42 col_46, 43 col_47, 44 col_48, 45 col_49, 46 col_50, 47 col_51, 48 col_54, 49 col_55, 50 col_56, 51 col_57, 52 col_58, 53 col_59, 54 col_61, 55 col_62, 56 col_63, 57 col_64, 58 col_68, 59 col_69, 60 col_70, 61 col_71, 62 col_72, 63 col_73, 64 col_74, 65 col_75, 66 col_76, 67 col_77, 68 col_78, 69 col_79, 70 col_82, 71 col_83, 72 col_84, 73 col_85, 74 col_86, 75 col_87, 76 col_88, 77 col_89, 78 col_90, 79 col_91, 80 col_92, 81 col_93, 82 col_94, 83 col_95, 84 col_96, 85 col_100, 86 col_101, 87 col_102, 88 col_105, 89 col_106, 90 col_107, 91 col_108, 92 col_109, 93 col_110, 94 col_111, 95 col_112, 96 col_113, 97 col_114, 98 col_115, 99 col_116, 100 col_117, 101 col_118, 102 col_119, 103 col_120, 104 col_121, 105 col_122, 106 col_123, 107 col_124, 108 col_126, 109 col_128, 110 col_129, 111 col_130, 112 col_131, 113 col_132, 114 col_133, 115 col_134, 116 col_136, 117 col_139, 118 col_140, 119 col_141, 120 col_142, 121 col_143, 122 col_144, 123 col_145, 124 col_146, 125 col_147, 126 col_148, 127 col_150, 128 col_151, 129 col_152, 130 col_153, 131 col_154, 132 col_155, 133 col_156, 134 col_157, 135 col_158, 136 col_161, 137 col_162, 138 col_163, 139 col_164, 140 col_165, 141 col_166, 142 col_167, 143 col_168, 144 col_169, 145 col_170, 146 col_171, 147 col_172, 148 col_173, 149 col_174, 150 col_175, 151 col_176, 152 col_177, 153 col_178, 154 col_179, 155 col_183, 156 col_184, 157 col_185, 158 col_186, 159 col_188, 160 col_189, 161 col_190, 162 col_191, 163 col_192, 164 col_193, 165 col_194, 166 col_195, 167 col_197, 168 col_198, 169 col_200, 170 col_201, 171 col_203, 172 col_206, 173 col_207, 174 col_208, 175 col_209, 176 col_210, 177 col_211, 178 col_212, 179 col_213, 180 col_214, 181 col_215, 182 col_216, 183 col_217, 184 col_218, 185 col_219, 186 col_220, 187 col_221, 188 col_222, 189 col_223, 190 col_224, 191 col_225, 192 col_226, 193 col_230, 194 col_231, 195 col_232, 196 col_233, 197 col_234, 198 col_235, 199 col_236, 200 col_237, 201 col_238, 202 col_240, 203 col_241, 204 col_242, 205 col_243, 206 col_244, 207 col_247, 208 col_248, 209 col_249, 210 col_250, 211 col_251, 212 col_252, 213 col_254, 214 col_255, 215 col_256, 216 col_257, 217 col_258, 218 col_259, 219 col_260, 220 col_261, 221 col_262, 222 col_263, 223 col_264, 224 col_265, 225 col_266, 226 col_267, 227 col_268, 228 col_269, 229 col_270, 230 col_271, 231 col_272, 232 col_273, 233 col_274, 234 col_275, 235 col_276, 236 col_278, 237 col_279, 238 col_280, 239 col_281, 240 col_282, 241 col_283, 242 col_284, 243 col_285, 244 col_286, 245 col_287, 246 col_288, 247 col_289, 248 col_290, 249 col_291, 250 col_292, 251 col_293, 252 col_294, 253 col_295, 254 col_296, 255 col_297, 256 col_301, 257 col_303, 258 col_308, 259 col_309, 260 col_310, 261 col_311, 262 col_312, 263 col_313, 264 col_314, 265 col_315, 266 col_316, 267 col_317, 268 col_318, 269 col_319, 270 col_320, 271 col_321, 272 col_322, 273 col_323, 274 col_324, 275 col_325, 276 col_330, 277 col_331, 278 col_332, 279 col_333, 280 col_334, 281 col_335, 282 col_336, 283 col_337, 284 col_338, 285 col_339, 286 col_340, 287 col_341, 288 col_342, 289 col_343, 290 col_344, 291 col_345, 292 col_346, 293 col_347, 294 col_348, 295 col_349, 296 col_350, 297 col_351, 298 col_352, 299 col_353, 300 col_361, 301 col_362, 302 col_363, 303 col_364, 304 col_365, 305 col_366, 306 col_368, 307 col_369, 308 col_370, 309 col_371, 310 col_372, 311 col_374, 312 col_376, 313 col_377, 314 col_378, 315 col_379, 316 col_380, 317 col_381, 318 col_382, 319 col_385, 320 col_386, 321 col_387, 322 col_388, 323 col_389, 324 col_390, 325 col_391, 326 col_392, 327 col_393, 328 col_394, 329 col_395, 330 col_396, 331 col_397, 332 col_398, 333 col_399, 334 col_400, 335 col_401, 336 col_403, 337 col_404, 338 col_405, 339 col_406, 340 col_407, 341 col_408, 342 col_409, 343 col_410, 344 col_411, 345 col_412, 346 col_413, 347 col_414, 348 col_415, 349 col_416, 350 col_417, 351 col_418, 352 col_419, 353 col_426, 354 col_427, 355 col_428, 356 col_429, 357 col_430, 358 col_431, 359 col_432, 360 col_433, 361 col_434, 362 col_435, 363 col_436, 364 col_437, 365 col_438, 366 col_439, 367 col_440, 368 col_441, 369 col_442, 370 col_443, 371 col_444, 372 col_445, 373 col_453, 374 col_454, 375 col_455, 376 col_456, 377 col_457, 378 col_458, 379 col_459, 380 col_460, 381 col_461, 382 col_462, 383 col_463, 384 col_464, 385 col_465, 386 col_466, 387 col_467, 388 col_468, 389 col_469, 390 col_470, 391 col_471, 392 col_472, 393 col_474, 394 col_475, 395 col_477, 396 col_478, 397 col_479, 398 col_480, 399 col_481, 400 col_482, 401 col_483, 402 col_484, 403 col_485, 404 col_486, 405 col_487, 406 col_488, 407 col_489, 408 col_490, 409 col_491, 410 col_492, 411 col_493, 412 col_494, 413 col_496, 414 col_497, 415 col_499, 416 col_501, 417 col_502, 418 col_504, 419 col_505, 420 col_506, 421 col_507, 422 col_508, 423 col_509, 424 col_510, 425 col_511, 426 col_514, 427 col_516, 428 col_517, 429 col_518, 430 col_519, 431 col_520, 432 col_521, 433 col_523, 434 col_524, 435 col_525, 436 col_526, 437 col_527, 438 col_528, 439 col_529, 440 col_530, 441 col_531, 442 col_532, 443 col_533, 444 col_534, 445 col_535, 446 col_536, 447 col_537, 448 col_538, 449 col_539, 450 col_540, 451 col_541, 452 col_542, 453 col_543, 454 col_544, 455 col_545, 456 col_546, 457 col_547, 458 col_548, 459 col_549, 460 col_550, 461 col_552, 462 col_553, 463 col_554, 464 col_555, 465 col_556, 466 col_557, 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1527 col_1819, 1528 col_1820, 1529 col_1821, 1530 col_1822, 1531 col_1824, 1532 col_1825, 1533 col_1827, 1534 col_1828, 1535 col_1829, 1536 col_1832, 1537 col_1833, 1538 col_1834, 1539 col_1835, 1540 col_1836, 1541 col_1837, 1542 col_1838, 1543 col_1839, 1544 col_1840, 1545 col_1841, 1546 col_1842, 1547 col_1843, 1548 col_1844, 1549 col_1846, 1550 col_1847, 1551 col_1848, 1552 col_1849, 1553 col_1851, 1554 col_1852, 1555 col_1853, 1556 col_1854, 1557 col_1855, 1558 col_1859, 1559 col_1860, 1560 col_1861, 1561 col_1862, 1562 col_1863, 1563 col_1864, 1564 col_1865, 1565 col_1866, 1566 col_1867, 1567 col_1869, 1568 col_1870, 1569 col_1872, 1570 col_1873, 1571 col_1874, 1572 col_1875, 1573 col_1876, 1574 col_1877, 1575 col_1878, 1576 col_1879, 1577 col_1880, 1578 col_1881, 1579 col_1882, 1580 col_1883, 1581 col_1884, 1582 col_1885, 1583 col_1886, 1584 col_1887, 1585 col_1888, 1586 col_1889, 1587 col_1890, 1588 col_1891, 1589 col_1892, 1590 col_1895, 1591 col_1896, 1592 col_1897, 1593 col_1898, 1594 col_1899, 1595 col_1900, 1596 col_1901, 1597 col_1904, 1598 col_1905, 1599 col_1906, 1600 col_1907, 1601 col_1908, 1602 col_1909, 1603 col_1910, 1604 col_1911, 1605 col_1912, 1606 col_1913, 1607 col_1914, 1608 col_1915, 1609 col_1919, 1610 col_1920, 1611 col_1921, 1612 col_1922, 1613 col_1923, 1614 col_1924, 1615 col_1925, 1616 col_1926, 1617 col_1927, 1618 col_1928, 1619 col_1929, 1620 col_1930, 1621 col_1931, 1622 col_1932, 1623 col_1933, 1624 col_1934, 1625 col_1935, 1626 col_1936, 1627 col_1937, 1628 col_1938, 1629 col_1939, 1630 col_1941, 1631 col_1942, 1632 col_1943, 1633 col_1944, 1634 col_1945, 1635 col_1946, 1636 col_1947, 1637 col_1948, 1638 col_1949, 1639 col_1950, 1640 col_1951, 1641 col_1952, 1642 col_1953, 1643 col_1954, 1644 col_1955, 1645 col_1956, 1646 col_1957, 1647 col_1961, 1648 col_1962, 1649 col_1963, 1650 col_1964, 1651 col_1965, 1652 col_1966, 1653 col_1967, 1654 col_1968, 1655 col_1969, 1656 col_1973, 1657 col_1974, 1658 col_1975, 1659 col_1976, 1660 col_1979, 1661 col_1984, 1662 col_1985, 1663 col_1986, 1664 col_1987, 1665 col_1990, 1666 col_1992, 1667 col_1994, 1668 col_1995, 1669 col_1997, 1670 col_1998, 1671 col_1999, 1672 col_2000, 1673 col_2001, 1674 col_2002, 1675 col_2003, 1676 col_2004, 1677 col_2005, 1678 col_2006, 1679 col_2007, 1680 col_2008, 1681 col_2009, 1682 col_2010, 1683 col_2011, 1684 col_2012, 1685 col_2013, 1686 col_2014, 1687 col_2015, 1688 col_2016, 1689 col_2017, 1690 col_2018, 1691 col_2019, 1692 col_2020, 1693 col_2021, 1694 col_2022, 1695 col_2023, 1696 col_2026, 1697 col_2027, 1698 col_2028, 1699 col_2029, 1700 col_2030, 1701 col_2031, 1702 col_2032, 1703 col_2033, 1704 col_2034, 1705 col_2035, 1706 col_2036, 1707 col_2038, 1708 col_2039, 1709 col_2040, 1710 col_2041, 1711 col_2042, 1712 col_2043, 1713 col_2044, 1714 col_2045, 1715 col_2046, 1716 col_2047, 1717 col_2048, 1718 col_2049, 1719 col_2050, 1720 col_2051, 1721 col_2052, 1722 col_2053, 1723 col_2054, 1724 col_2055, 1725 col_2056, 1726 col_2057, 1727 col_2058, 1728 col_2059, 1729 col_2060, 1730 col_2061, 1731 col_2062, 1732 col_2063, 1733 col_2064, 1734 col_2065, 1735 col_2066, 1736 col_2067, 1737 col_2068, 1738 col_2069, 1739 col_2070, 1740 col_2071, 1741 col_2072, 1742 col_2073, 1743 col_2074, 1744 col_2075, 1745 col_2076, 1746 col_2077, 1747 col_2078, 1748 col_2079, 1749 col_2082, 1750 col_2085, 1751 col_2086, 1752 col_2087, 1753 col_2088, 1754 col_2089, 1755 col_2091, 1756 col_2092, 1757 col_2093, 1758 col_2094, 1759 col_2095, 1760 col_2096, 1761 col_2097, 1762 col_2098, 1763 col_2099, 1764 col_2100, 1765 col_2101, 1766 col_2102, 1767 col_2103, 1768 col_2104, 1769 col_2105, 1770 col_2106, 1771 col_2108, 1772 col_2109, 1773 col_2116, 1774 col_2117, 1775 col_2118, 1776 col_2119, 1777 col_2120, 1778 col_2121, 1779 col_2122, 1780 col_2123, 1781 col_2124, 1782 col_2125, 1783 col_2126, 1784 col_2127, 1785 col_2128, 1786 col_2129, 1787 col_2130, 1788 col_2131, 1789 col_2132, 1790 col_2133, 1791 col_2134, 1792 col_2135, 1793 col_2136, 1794 col_2137, 1795 col_2138, 1796 col_2139, 1797 col_2140, 1798 col_2141, 1799 col_2142, 1800 col_2143, 1801 col_2144, 1802 col_2145, 1803 col_2146, 1804 col_2147, 1805 col_2148, 1806 col_2149, 1807 col_2150, 1808 col_2151, 1809 col_2152, 1810 col_2153, 1811 col_2154, 1812 col_2155, 1813 col_2157, 1814 col_2158, 1815 col_2159, 1816 col_2160, 1817 col_2161, 1818 col_2162, 1819 col_2163, 1820 col_2164, 1821 col_2165, 1822 col_2166, 1823 col_2167, 1824 col_2168, 1825 col_2169, 1826 col_2170, 1827 col_2173, 1828 col_2174, 1829 col_2175, 1830 col_2177, 1831 col_2178, 1832 col_2179, 1833 col_2181, 1834 col_2182, 1835 col_2183, 1836 col_2184, 1837 col_2187, 1838 col_2188, 1839 col_2189, 1840 col_2190, 1841 col_2191, 1842 col_2192, 1843 col_2193, 1844 col_2194, 1845 col_2195, 1846 col_2196, 1847 col_2197, 1848 col_2201, 1849 col_2202, 1850 col_2203, 1851 col_2206, 1852 col_2208, 1853 col_2210, 1854 col_2211, 1855 col_2212, 1856 col_2213, 1857 col_2216, 1858 col_2217, 1859 col_2218, 1860 col_2219, 1861 col_2220, 1862 col_2221, 1863 col_2222, 1864 col_2223, 1865 col_2224, 1866 col_2225, 1867 col_2226, 1868 col_2227, 1869 col_2228, 1870 col_2230, 1871 col_2231, 1872 col_2232, 1873 col_2233, 1874 col_2234, 1875 col_2237, 1876 col_2238, 1877 col_2239, 1878 col_2240, 1879 col_2241, 1880 col_2242, 1881 col_2243, 1882 col_2244, 1883 col_2245, 1884 col_2246, 1885 col_2247, 1886 col_2248, 1887 col_2249, 1888 col_2250, 1889 col_2251, 1890 col_2252, 1891 col_2256, 1892 col_2259, 1893 col_2260, 1894 col_2261, 1895 col_2262, 1896 col_2263, 1897 col_2264, 1898 col_2265, 1899 col_2266, 1900 col_2270, 1901 col_2271, 1902 col_2272, 1903 col_2274, 1904 col_2275, 1905 col_2276, 1906 col_2277, 1907 col_2278, 1908 col_2279, 1909 col_2280, 1910 col_2281, 1911 col_2282, 1912 col_2283, 1913 col_2284, 1914 col_2285, 1915 col_2287, 1916 col_2288, 1917 col_2289, 1918 col_2290, 1919 col_2291, 1920 col_2292, 1921 col_2293, 1922 col_2294, 1923 col_2296, 1924 col_2297, 1925 col_2298, 1926 col_2299, 1927 col_2300, 1928 col_2301, 1929 col_2302, 1930 col_2303, 1931 col_2304, 1932 col_2305, 1933 col_2306, 1934 col_2307, 1935 col_2308, 1936 col_2309, 1937 col_2310, 1938 col_2311, 1939 col_2312, 1940 col_2313, 1941 col_2314, 1942 col_2315, 1943 col_2316, 1944 col_2317, 1945 col_2318, 1946 col_2319, 1947 col_2320, 1948 col_2321, 1949 col_2322, 1950 col_2323, 1951 col_2324, 1952 col_2325, 1953 col_2326, 1954 col_2327, 1955 col_2328, 1956 col_2329, 1957 col_2330, 1958 col_2331, 1959 col_2332, 1960 col_2333, 1961 col_2334, 1962 col_2335, 1963 col_2336, 1964 col_2337, 1965 col_2338, 1966 col_2339, 1967 col_2340, 1968 col_2341, 1969 col_2342, 1970 col_2343, 1971 col_2344, 1972 col_2345, 1973 col_2346, 1974 col_2347, 1975 col_2348, 1976 col_2349, 1977 col_2350, 1978 col_2351, 1979 col_2352, 1980 col_2353, 1981 col_2354, 1982 col_2355, 1983 col_2356, 1984 col_2357, 1985 col_2358, 1986 col_2359, 1987 col_2360, 1988 col_2363, 1989 col_2364, 1990 col_2365, 1991 col_2366, 1992 col_2367, 1993 col_2368, 1994 col_2369, 1995 col_2370, 1996 col_2371, 1997 col_2372, 1998 col_2373, 1999 col_2374, 2000 col_2375, 2001 col_2376, 2002 col_2377, 2003 col_2378, 2004 col_2379, 2005 col_2380, 2006 col_2381, 2007 col_2382, 2008 col_2386, 2009 col_2387, 2010 col_2388, 2011 col_2389, 2012 col_2390, 2013 col_2391, 2014 col_2392, 2015 col_2394, 2016 col_2395, 2017 col_2396, 2018 col_2400, 2019 col_2402, 2020 col_2403, 2021 col_2405, 2022 col_2406, 2023 col_2408, 2024 col_2409, 2025 col_2410, 2026 col_2411, 2027 col_2412, 2028 col_2413, 2029 col_2414, 2030 col_2415, 2031 col_2416, 2032 col_2417, 2033 col_2418, 2034 col_2421, 2035 col_2422, 2036 col_2423, 2037 col_2424, 2038 col_2425, 2039 col_2426, 2040 col_2427, 2041 col_2429, 2042 col_2432, 2043 col_2435, 2044 col_2437, 2045 col_2438, 2046 col_2439, 2047 col_2440, 2048 col_2441, 2049 col_2442, 2050 col_2443, 2051 col_2444, 2052 col_2445, 2053 col_2447, 2054 col_2448, 2055 col_2450, 2056 col_2451, 2057 col_2452, 2058 col_2453, 2059 col_2454, 2060 col_2455, 2061 col_2456, 2062 col_2457, 2063 col_2462, 2064 col_2463, 2065 col_2464, 2066 col_2465, 2067 col_2466, 2068 col_2467, 2069 col_2468, 2070 col_2469, 2071 col_2470, 2072 col_2471, 2073 col_2472, 2074 col_2473, 2075 col_2474, 2076 col_2475, 2077 col_2476, 2078 col_2477, 2079 col_2478, 2080 col_2479, 2081 col_2480, 2082 col_2481, 2083 col_2482, 2084 col_2483, 2085 col_2484, 2086 col_2485, 2087 col_2486, 2088 col_2487, 2089 col_2489, 2090 col_2490, 2091 col_2491, 2092 col_2492, 2093 col_2493, 2094 col_2494, 2095 col_2495, 2096 col_2496, 2097 col_2497, 2098 col_2498, 2099 col_2499, 2100 col_2500, 2101 col_2501, 2102 col_2503, 2103 col_2504, 2104 col_2505, 2105 col_2506, 2106 col_2507, 2107 col_2508, 2108 col_2509, 2109 col_2510, 2110 col_2511, 2111 col_2512, 2112 col_2513, 2113 col_2514, 2114 col_2515, 2115 col_2517, 2116 col_2518, 2117 col_2519, 2118 col_2520, 2119 col_2522, 2120 col_2524, 2121 col_2526, 2122 col_2527, 2123 col_2530, 2124 col_2531, 2125 col_2532, 2126 col_2533, 2127 col_2534, 2128 col_2535, 2129 col_2536, 2130 col_2537, 2131 col_2539, 2132 col_2540, 2133 col_2541, 2134 col_2543, 2135 col_2544, 2136 col_2545, 2137 col_2546, 2138 col_2547, 2139 col_2548, 2140 col_2549, 2141 col_2550, 2142 col_2551, 2143 col_2552, 2144 col_2553, 2145 col_2554, 2146 col_2555, 2147 col_2556, 2148 col_2557, 2149 col_2558, 2150 col_2559, 2151 col_2560, 2152 col_2561, 2153 col_2562, 2154 col_2563, 2155 col_2564, 2156 col_2565, 2157 col_2566, 2158 col_2567, 2159 col_2568, 2160 col_2569, 2161 col_2570, 2162 col_2571, 2163 col_2572, 2164 col_2573, 2165 col_2574, 2166 col_2577, 2167 col_2578, 2168 col_2579, 2169 col_2580, 2170 col_2581, 2171 col_2582, 2172 col_2584, 2173 col_2585, 2174 col_2586, 2175 col_2587, 2176 col_2588, 2177 col_2589, 2178 col_2590, 2179 col_2591, 2180 col_2592, 2181 col_2593, 2182 col_2594, 2183 col_2595, 2184 col_2596, 2185 col_2597, 2186 col_2598, 2187 col_2599, 2188 col_2600, 2189 col_2601, 2190 col_2602, 2191 col_2603, 2192 col_2604, 2193 col_2605, 2194 col_2606, 2195 col_2607, 2196 col_2608, 2197 col_2610, 2198 col_2611, 2199 col_2612, 2200 col_2613, 2201 col_2614, 2202 col_2615, 2203 col_2616, 2204 col_2617, 2205 col_2618, 2206 col_2619, 2207 col_2620, 2208 col_2621, 2209 col_2623, 2210 col_2624, 2211 col_2625, 2212 col_2626, 2213 col_2627, 2214 col_2628, 2215 col_2629, 2216 col_2630, 2217 col_2632, 2218 col_2633, 2219 col_2634, 2220 col_2635, 2221 col_2636, 2222 col_2637, 2223 col_2638, 2224 col_2639, 2225 col_2640, 2226 col_2641, 2227 col_2642, 2228 col_2643, 2229 col_2644, 2230 col_2645, 2231 col_2646, 2232 col_2647, 2233 col_2648, 2234 col_2649, 2235 col_2650, 2236 col_2651, 2237 col_2652, 2238 col_2653, 2239 col_2654, 2240 col_2655, 2241 col_2656, 2242 col_2657, 2243 col_2658, 2244 col_2659, 2245 col_2660, 2246 col_2661, 2247 col_2662, 2248 col_2663, 2249 col_2664, 2250 col_2665, 2251 col_2666, 2252 col_2667, 2253 col_2668, 2254 col_2669, 2255 col_2670, 2256 col_2671, 2257 col_2672, 2258 col_2673, 2259 col_2674, 2260 col_2675, 2261 col_2676, 2262 col_2677, 2263 col_2678, 2264 col_2679, 2265 col_2680, 2266 col_2681, 2267 col_2682, 2268 col_2683, 2269 col_2684, 2270 col_2685, 2271 col_2686, 2272 col_2687, 2273 col_2688, 2274 col_2689, 2275 col_2690, 2276 col_2691, 2277 col_2692, 2278 col_2693, 2279 col_2694, 2280 col_2695, 2281 col_2696, 2282 col_2697, 2283 col_2698, 2284 col_2699, 2285 col_2700, 2286 col_2701, 2287 col_2702, 2288 col_2703, 2289 col_2704, 2290 col_2705, 2291 col_2706, 2292 col_2707, 2293 col_2708, 2294 col_2709, 2295 col_2710, 2296 col_2712, 2297 col_2713, 2298 col_2714, 2299 col_2715, 2300 col_2716, 2301 col_2717, 2302 col_2718, 2303 col_2719, 2304 col_2720, 2305 col_2721, 2306 col_2722, 2307 col_2723, 2308 col_2724, 2309 col_2725, 2310 col_2726, 2311 col_2727, 2312 col_2728, 2313 col_2729, 2314 col_2730, 2315 col_2731, 2316 col_2732, 2317 col_2735, 2318 col_2737, 2319 col_2738, 2320 col_2739, 2321 col_2740, 2322 col_2741, 2323 col_2742, 2324 col_2743, 2325 col_2744, 2326 col_2745, 2327 col_2746, 2328 col_2747, 2329 col_2748, 2330 col_2749, 2331 col_2750, 2332 col_2751, 2333 col_2752, 2334 col_2758, 2335 col_2759, 2336 col_2760, 2337 col_2761, 2338 col_2762, 2339 col_2763, 2340 col_2764, 2341 col_2765, 2342 col_2766, 2343 col_2767, 2344 col_2768, 2345 col_2769, 2346 col_2770, 2347 col_2771, 2348 col_2772, 2349 col_2773, 2350 col_2774, 2351 col_2775, 2352 col_2776, 2353 col_2777, 2354 col_2779, 2355 col_2780, 2356 col_2782, 2357 col_2783, 2358 col_2784, 2359 col_2785, 2360 col_2787, 2361 col_2788, 2362 col_2789, 2363 col_2790, 2364 col_2791, 2365 col_2792, 2366 col_2793, 2367 col_2794, 2368 col_2795, 2369 col_2796, 2370 col_2797, 2371 col_2798, 2372 col_2799, 2373 col_2800, 2374 col_2803, 2375 col_2804, 2376 col_2805, 2377 col_2806, 2378 col_2807, 2379 col_2808, 2380 col_2809, 2381 col_2810, 2382 col_2811, 2383 col_2812, 2384 col_2813, 2385 col_2814, 2386 col_2815, 2387 col_2816, 2388 col_2817, 2389 col_2818, 2390 col_2819, 2391 col_2820, 2392 col_2821, 2393 col_2822, 2394 col_2823, 2395 col_2824, 2396 col_2825, 2397 col_2826, 2398 col_2827, 2399 col_2828, 2400 col_2829, 2401 col_2830, 2402 col_2831, 2403 col_2832, 2404 col_2833, 2405 col_2834, 2406 col_2835, 2407 col_2836, 2408 col_2837, 2409 col_2838, 2410 col_2839, 2411 col_2840, 2412 col_2841, 2413 col_2842, 2414 col_2843, 2415 col_2844, 2416 col_2845, 2417 col_2846, 2418 col_2848, 2419 col_2849, 2420 col_2855, 2421 col_2856, 2422 col_2857, 2423 col_2859, 2424 col_2860, 2425 col_2861, 2426 col_2862, 2427 col_2863, 2428 col_2864, 2429 col_2865, 2430 col_2866, 2431 col_2867, 2432 col_2868, 2433 col_2869, 2434 col_2870, 2435 col_2871, 2436 col_2872, 2437 col_2873, 2438 col_2874, 2439 col_2875, 2440 col_2876, 2441 col_2877, 2442 col_2878, 2443 col_2879, 2444 col_2880, 2445 col_2881, 2446 col_2882, 2447 col_2883, 2448 col_2884, 2449 col_2885, 2450 col_2886, 2451 col_2887, 2452 col_2888, 2453 col_2889, 2454 col_2890, 2455 col_2891, 2456 col_2892, 2457 col_2893, 2458 col_2894, 2459 col_2895, 2460 col_2896, 2461 col_2897, 2462 col_2898, 2463 col_2899, 2464 col_2900, 2465 col_2901, 2466 col_2902, 2467 col_2903, 2468 col_2904, 2469 col_2905, 2470 col_2906, 2471 col_2908, 2472 col_2909, 2473 col_2913, 2474 col_2914, 2475 col_2915, 2476 col_2916, 2477 col_2920, 2478 col_2921, 2479 col_2922, 2480 col_2923, 2481 col_2924, 2482 col_2925, 2483 col_2926, 2484 col_2927, 2485 col_2928, 2486 col_2929, 2487 col_2930, 2488 col_2931, 2489 col_2932, 2490 col_2933, 2491 col_2934, 2492 col_2935, 2493 col_2936, 2494 col_2937, 2495 col_2938, 2496 col_2939, 2497 col_2940, 2498 col_2941, 2499 col_2942, 2500 col_2943, 2501 col_2944, 2502 col_2945, 2503 col_2946, 2504 col_2947, 2505 col_2948, 2506 col_2949, 2507 col_2950, 2508 col_2951, 2509 col_2952, 2510 col_2953, 2511 col_2954, 2512 col_2955, 2513 col_2956, 2514 col_2957, 2515 col_2958, 2516 col_2959, 2517 col_2960, 2518 col_2961, 2519 col_2962, 2520 col_2963, 2521 col_2964, 2522 col_2965, 2523 col_2966, 2524 col_2967, 2525 col_2968, 2526 col_2969, 2527 col_2970, 2528 col_2971, 2529 col_2972, 2530 col_2973, 2531 col_2974, 2532 col_2976, 2533 col_2979, 2534 col_2980, 2535 col_2982, 2536 col_2983, 2537 col_2986, 2538 col_2987, 2539 col_2988, 2540 col_2989, 2541 col_2990, 2542 col_2991, 2543 col_2992, 2544 col_2993, 2545 col_2994, 2546 col_2995, 2547 col_2996, 2548 col_2997, 2549 col_2998, 2550 col_2999, 2551 col_3000, 2552 col_3001, 2553 col_3002, 2554 col_3003, 2555 col_3004, 2556 col_3005, 2557 col_3006, 2558 col_3007, 2559 col_3008, 2560 col_3009, 2561 col_3010, 2562 col_3011, 2563 col_3013, 2564 col_3017, 2565 col_3018, 2566 col_3019, 2567 col_3020, 2568 col_3021, 2569 col_3022, 2570 col_3023, 2571 col_3024, 2572 col_3025, 2573 col_3026, 2574 col_3027, 2575 col_3028, 2576 col_3029, 2577 col_3030, 2578 col_3031, 2579 col_3032, 2580 col_3033, 2581 col_3034, 2582 col_3035, 2583 col_3036, 2584 col_3037, 2585 col_3038, 2586 col_3040, 2587 col_3041, 2588 col_3042, 2589 col_3043, 2590 col_3044, 2591 col_3045, 2592 col_3046, 2593 col_3047, 2594 col_3048, 2595 col_3049, 2596 col_3050, 2597 col_3051, 2598 col_3052, 2599 col_3053, 2600 col_3054, 2601 col_3055, 2602 col_3056, 2603 col_3057, 2604 col_3058, 2605 col_3059, 2606 col_3060, 2607 col_3061, 2608 col_3062, 2609 col_3063, 2610 col_3064, 2611 col_3065, 2612 col_3066, 2613 col_3069, 2614 col_3070, 2615 col_3071, 2616 col_3072, 2617 col_3073, 2618 col_3074, 2619 col_3075, 2620 col_3077, 2621 col_3078, 2622 col_3079, 2623 col_3080, 2624 col_3083, 2625 col_3084, 2626 col_3085, 2627 col_3086, 2628 col_3087, 2629 col_3088, 2630 col_3089, 2631 col_3090, 2632 col_3091, 2633 col_3092, 2634 col_3093, 2635 col_3094, 2636 col_3095, 2637 col_3096, 2638 col_3097, 2639 col_3098, 2640 col_3099, 2641 col_3100, 2642 col_3101, 2643 col_3103, 2644 col_3104, 2645 col_3105, 2646 col_3106, 2647 col_3108, 2648 col_3109, 2649 col_3113, 2650 col_3114, 2651 col_3115, 2652 col_3116, 2653 col_3117, 2654 col_3118, 2655 col_3119, 2656 col_3120, 2657 col_3121, 2658 col_3122, 2659 col_3123, 2660 col_3124, 2661 col_3125, 2662 col_3126, 2663 col_3127, 2664 col_3129, 2665 col_3130, 2666 col_3131, 2667 col_3134, 2668 col_3135, 2669 col_3136, 2670 col_3137, 2671 col_3138, 2672 col_3139, 2673 col_3140, 2674 col_3141, 2675 col_3142, 2676 col_3143, 2677 col_3144, 2678 col_3145, 2679 col_3146, 2680 col_3147, 2681 col_3148, 2682 col_3149, 2683 col_3150, 2684 col_3151, 2685 col_3152, 2686 col_3153, 2687 col_3154, 2688 col_3155, 2689 col_3156, 2690 col_3159, 2691 col_3160, 2692 col_3161, 2693 col_3162, 2694 col_3163, 2695 col_3164, 2696 col_3165, 2697 col_3166, 2698 col_3167, 2699 col_3168, 2700 col_3169, 2701 col_3170, 2702 col_3171, 2703 col_3172, 2704 col_3173, 2705 col_3174, 2706 col_3176, 2707 col_3177, 2708 col_3178, 2709 col_3179, 2710 col_3180, 2711 col_3184, 2712 col_3188, 2713 col_3191, 2714 col_3196, 2715 col_3197, 2716 col_3198, 2717 col_3199, 2718 col_3200, 2719 col_3201, 2720 col_3202, 2721 col_3205, 2722 col_3206, 2723 col_3208 ; Format Datatype = Standard Symbols="01" missing = '?' ; Matrix temporariaDMH84R1 111011111010011110110000000001111111001000101101111110000000000000011011111111001110111001101111011001100010000000000000000101000000000111101111001111111100000011111111000011011101100111000110000000000001111111110111111111100011111111101110000000000101111011011111110001100000001111111110011000000001000001010001110011111110000000111111111100100000000110010000000000111111100111101111111111010000000000000000000010111111101111110000011110111111110001001110111111111111111111110111100010000110000011100000110001001011100000000000110111111100100001111111111000000101110111000111100111000000011110111111111110110000000001100001101100011101100010000001000001111110000110000000111100000111110010111111111110011111011100000111111011101110011?1111111111101110000001101111111111111111100010000001111?1111000000110111111101000001111111111100001101101111111001110011000111111100011111111101001111110000100000000110011000001110001001000000110000001110011110111110111111111110111111111100011110000000000011110000001001100000000011100111111110011111100111011111111010110011110010111011111110000000011001000000000000000000000001011011000000111100000011011101000000110011110011111000111111100111010000100000001111111101000001110111000000011100111000011111111?100000001111000000111111111001011011111001111111110000111010100011100111111000000010111101001110111111100010011100000000000111111111110000001111100111111011111111111010001111111111100011111101111111111111011100000000011100000111111111111111111111110110000000000001111111110000111111110111000000011011100000000000011110000000000001111000011111111000001111111000000101111100111100001111111111110000000??10000111111101011111100110111101111111000001111110111111101111111111100011101010111111111100101111100100110110000000001000001101100001111110001110100000010000110000000110000000000001110011111000000000011100101001110001111110111110111000110000011100000000110001000001111000000110111010111000011011011111011100110000000000110101111101111000011100000???0011111111111111101111011111111111111110001110001100000011011000000101100000000011000001000001000001111110111110000000000010000010001111111111000000001110110111011110000110111100000000001110011000000?001111110011111010001111110111100001111110000000011111110001100111011001100000001000011111111110111111111111111111111111000000000001000111111111111101111011011111011000000011111101011111111101011001110111111100111101111101011110001100001011011111111110010111000111111011011110011110000111111110001111100110111111000011111111111000011111000001111111111111101111100000010000000111110000000111111100001111110000010111111000000000010111011100111111111000000001010000000001101111111001110100000000111011011111101010000001110100011011 boyliiMVZ148929 111011111010011110110000000001111111000000000001111110000000000000001011111111001110111001101111011001111010000000000000000101000000000111101111001111111100000011111111000011011101100111000111000000000101111001110111110000000011111111101110000000000101111110011111100001100000001111111110011000000001000001010001110011111110000000111111111100100000000110010000000000011111100111101111111111010000000000000000000010111101101111110000011110111111110001001110111111111111111111110101100011000110000011100000110001001011100000000000110111111100111111000111111000000111110111000110000111000000011110111111101110110000000001100001101100011101100010000001000001111110000110000000111100000001010010100000000110011111011100000111111011101110011?1111111111101010000001101111111111111111100010000001111?1111000000100111110101000001000011111100001101101011111001110011000111111100011111111101001001110000100000001110011000001110001111000001110000001110011110111110111101111000101111111100011110100000000011110000001001100000000011100111111110011111111111010111111010110011110010111011111110000000011001000000000000000000000001011011000000111100000011011101111111110011110011111000111111100111010000110000001111111101000001110111000000011100111000011111111?100000001111000000111111111001011011111001011111110000111010100011100111111000000010111101111110111111100010011100000000000111111111110000001000000111111011111111111011001111111111100011111101111011111111011100000000011100000111111111111111111111110110000000000001111111110000111111111111111000011011100011110110011110000000000001111000011111111000001111111000000110111100111110001111111101110000000??10000111111101011111100110111101111111100101111110111111101111100111100011101010111111111100101111100100110111101001001000001101100001111110001110100000000000011111111110000000000001110011111000000000011100101000000000000010111110111000111111111100000000010001000001111000000110111010111000011011011111011100110000000000110101111101111000011100000???0011110111111111101111011111111111111110001110011100000011011000000101100000000011000001000011000001111110111110000000000000000010001101111111101011000010110111011110000110111100000000000000011000000?001111110011111010001111110111100001111110000000011111110001100111011001100000001000011111111110111111111111111111111111000000000001000111000001111101111011011111011000000011111101011111100000000000010111100100111101111101011110001100001011011111111110010111000111111011011110011110000111111110001111100110111111000011111111111000011111000001111111111111101111100000010000000111110000000111111100001111110000010111111000000000011111010100111100011000000001010000000001101111111001111100000000111011011111111010111111110100011011 luteiventris_MT_MVZ191016 111011111010011110110000000001111111000000000001111110000000000000011011111111001110111001101111011001111010000000000000000101000000000111101111001111111100000011111110000011011101100111000111000000000101111111110111110000000011111111101010000000000000001011011111110001100000001111111110011000000001000001010001110011111110000000111111111100100000000110010000000000111111100111101111111111010000000000000000000010111111101111110000011110111111110001001110111111111111111111110101100011000110000011000000110001001011100000000000110011111100111111000111111000000111110111000110000111000000011110111111111110110000000001100001101100011101100010000001000001111110000110000000111100000001110010100000000110011111011100000111111011101110011?1111111111111110000001101111111111111111100010000001111?1111000000100111111101000001111111111100001101101011111001110011000111111100011111111101001000000000000000001110011000001110001001000001110000001110011110111110111101111000101111111100011110100000000011110000001001100000000011100111111110011111111111011111111010110011110011111011111110000000011001000000000000000000000001011011000000111100000011011101000000110011110011111000111111100111010000110000001111111101000001110111000000011100111000011111111?100000001111000000111111111001011011111001011111110000111010100011100111100000000000001101111110111111100010011100000000000111111111110000001111000111111011111111111010001111111111100011111101111011111111011100000000011100000111111111111111111111110110000000000001111111110000111111111111111000011011100000000110011110000000000001111000011111111000001111111000000110111100111110001111111111110000000??10000111111101010001100110111100000001100101111110111111101111100111100011101010111111111100101111100100110111101001001000001101100001111110001110100000000000011111111110000000000001110010000000000000011100101001110001111110111110111000110000011100000000111001000001111000000110111010111000011011011111011100110000000000110101111101111000011100000???0011110111111111101111011111111111111110001110001100000011011000000101100000000011000001000011000001111110000010000000000000000010001101111111101011001110110111011110000110111100000000001110011000000?001111110011111010001111110111100001110110000000011111110001100111011001100000001000011111111110111111111111111111111111000000000001000111000001000101111011011101011000000011111101011111111111011001110111111100111101111101011110001100001011011111111110010111000111111011011110011110000111111110001111100110111111000011111111111000011111000001111111111111101111100000010000000111110000000111111100001111110000010111111000000000010111010100111100011000000001010000000001101111111001111100000000111011011111111010111111110100011011 luteiventris_WA_MVZ225749 111011111010011110110000000001111111000000000001111110000000000000011011111111001110111001101111011001111010000000000000000101000000000111101111001111111100000011111110000011011101100111000111000000000101111111110111110000000011111111101010000000000000001011011111110001100000001111111110011000000001000001010001110011111110000000111111111100100000000110010000000000111111100111101111111111010000000000000000000010111111101111110000011110111111110001001110111111111111111111110101100011000110000011000000110001001011100000000000110011111100111111000111111000000111110111000110000111000000011110111111111110110000000001100001101100011101100010000001000001111110000110000000111100000001110010100000000110011111011100000111111011101110011?1111111111111110000001101111111111111111100010000001111?1111000000100111111101000001111111111100001101101011111001110011000111111100011111111101001000000000000000001110011000001110001001000101110000001110011110111110111101111000101111111100011110100000000011110000001001100000000011100111111110011111111111011111111010110011110011111011111110000000011001000000000000000000000001011011000000111100000011011101000000110011110011111000111111100111010000110000001111111101000001110111000000011100111000011111111?100000001111000000111111111001011011111001011111110000111010100011100111100000000000001101111110111111100010011100000000000111111111110000001111000111111011111111111010001111111111100011111101111011111111011100000000011100000111111111111111111111110110000000000001111111110000111111111111111000011011100000000110011110000000000001111000011111111000001111111000000110111100111110001111111111110000000??10000111111101010001100110111100000001100101111110111111101111100111100011101010111111111100101111100100110111101001001000001101100001111110001110100000000000011111111110000000000001110010000000000000011100101001110001111110111110111000110000011100000000111001000001111000000110111010111000011011011111011100110000000000110101111101111000011100000???0011110111111111101111011111111111111110001110001100000011011000000101100000000011000001000011000001111110000010000000000000000010001101111111101011001110110111011110000110111100000000001110011000000?001111110011111010001111110111100001111110000000011111110001100111011001100000001000011111111110111111111111111111111111000000000001000111000001000101111011011101011000000011111101011111111111011001110111111100111101111101011110001100001011011111111110010111000111111011011110011110000111111110001111100110111111000011111111111000011111000001111111111111101111100000010000000111110000000111111100001111110000010111111000000000010111010100111100011000000001010000000001101111111001111100000000111011011111111010111111110100011011 muscosaMVZ149006 111011111010011110110000000001111111001000101101111110000000000000011011111111001110111001101111011001111010000000000000000000000000000111101111001111111100000011111111000011011101100111000111000000000101111111110111110000000011111111101110000000000101111011011111110001100000001111111110011000000001000001010001110011111110000000111111111100100000000110010000000000111111100001101111111111010000000000000000000010111111101111110000011110111111110001001110000111111111111111110111100011000110000011100000110001001011100000000000110111111100111111111111111000000111110111000110000111000000011110111111111110110000000001100001101100011101100010000001000001111110000110000000111100000001110010100000000110011111011100000111111011101110011?1111111111101110000001101111111111111111100010000001111?1111000000100111111101000001111111111100001101101011111001110011000111111100011111111101001000000000100000001110011000001110001001000001110000001110011110111110111101111000101111111100011110100000000011100000001001100000000011100111111110011111111111001111111010110011110010111011111110000000011001000000000000000000000001011011000000111100000011011101000000110011110011111000111111100111010000110000001111111101000001110111000000011100111000011111111?100000001111000000111111111001011011111001111111110000111010100011100111111000000010111101111110111111100010011111111110000111111111110000001111000111111011111111111010001111111111100011111101111011111111011000000000000100000111111111111100111111110110000000000001111111110000111111111111111000011011100000000110011110000000000001111000011111111000001111111000000110111100111110001111111111110000000??10000111111101011111100110111101111111100101111110111111101111100111100011101010110100000000000111100100110111111001001000001101100001111110001110100000000000011111111110000000000001110011111000000000011100101001110001111110111110111000110000011100000000110001000001111000000110111010111000011011011111011100110000000000110101111101111000011100000???0011110111111111101111011110000000111110001110001100000011011000000101100000000011000001000011000001111110111110000000000000000010001101110000101011001110110111011110000110111100000000001110011000000?001111110011111010001111110111100001111110000000011111110001100111011001100000001000011111111110111111111111111111111111000000000001000111000001111101111011011111011000000011111101010000000001011001110111111100111101111101011110001100001011011111111110010111000111111011011110011110000111111110001111100110111111000011111111101000001111000001111111111111101111100000010000000111110000000111111100001111110000010111111000000000010111010100111100011000000001010000000001101111111000000000000000001011011111111010111111110100011011 auroraMVZ13957 011011111010011110110000000001111111001000101101111110000000000000011011111111001110111001101111011001111010000000000000000101000000000111101111001111111100000011111111000011011101100111000111000000000101111111110111110000000011111111101110000000000101111011011111110001100000001111111110011000000001000001010001110011111110000000111111111100100000000110010000000000111111100001101111111011010000000000000000000010111111101111110000011110111111110001001110000111111111111111110111100011000110000011100000110001001011100000000000110111111100111111111111111000000111110111000110011111000000011110111111111110110000000001100001101100011101100010000001000001111110000110000000111100000001110010100000000110011111011100000111111011101110011?1111111111101110000001111111111111111110100010000001111?1111000000100111111101000001111111111100001101101011111001110011100111111100011111111101001001110000100000001110011000001110001001000001110000001110011111111110111101111000101111111100011110100000000011110000001001100000000011100111111110011110111111011111100010110011110010111011111110000000011001000000000000000000000001011011000000111000000011011101000000110010110011111000111111100111010000110000001111111101000001110111000000011100111000011111111?100000001111000000101111111001011011111001111111110000111010100011100111111111111110111101111110111111100010011111111110000111111111110000001111000111111011111111111010001111100011100011111101111011111111011000000000000100000111111111111100111111110110000000000001111111110000111111111111111000011011100000000110011110000000000001111000011111111000001111111000000110111100111110001111111111110000000??10000111111101011011100110111101111111100101111110111111101111100111100011101010110111111100101111100100110111111001001000001101100001111110001110100000000000011111111110000000000001110011111000000000011100101001110001111110111110111000110000011100000000110001000001111000000110111010111000011011011111011000110000000000110101111101111000011100000???0011110111111111101011011111111111111110001110001100000011011000000101100000000011000001000011000001111110111110000000000000100010001100010000101011001110110111011110000110111100000000001110011000000?001111110011111010001111110111100001111110000000011111110001100111011001100000001000011111111110111111111111111111111111000000000001000111000001111101111011011111011000000011111101011111111111011001110111111100111101111101111110001100001011011111111110010111000111111011011110011110000111111110001111100110111111000011111111111111111111000001111011111111101110100000010000000111110000000111001100001111110000010111111000000000010111010100111100011000000001010000000001101111111001111100000000111011011111111010111111110100011011 cascadaeMVZ148946 111011111010011110110000000001111111001000101101111110000000000000011011111111001110111001101111011001111010000000000000000101000000000111101111001111111100000011111111000011011101100111000111000000000101111111110111110000000011111111101110000000000101111011011111110001100000001000011110011000000001000001010001110011111110000000111111111100100000000110010000000000111111100001101111111011010000000000000000000010111111101111110000011110111111110001001110000111111111111111110111100011000110000011100000110001001011100000000000110111111100111111111111111000000111110111000110000111000000011110111111111110110000000001100001101100011101100010000001000001111110000110000000111100000001110010100000000110011111011100000111111011101110011?1111111111101110000001111111111111111110100010000001111?1111000000100111111101000001111111111100001101101011111001110011100111111100011111111101001001110000100000001110011000001110001001000001110000001110011110111110111101111000101111111100011110100000000011110000001001100000000011100111111110011111111110001111111010110011110010111011111110000000011001000000000000000000000001011011000000111100000011011101000000110011110011111000111111100111010000110000001111111101000001110111000000011100111000011111111?100000001111000000111111111001011011111001111111110000111010100011100111111111111110111101111110111111100010011111111110000111111111110000001111000111111011111111111010001111111111100011111101111011111111011000000000000100000111111111111100111111110110000000000001111111110000111111111111111000011011100000000110011110000000000001111000011111111000001111111000000110111100111110001111111111110000000??10000111111101011011100110111101111111100101111110111111101111100111100011101010110111111100101111100100110111111001001000001101100001111110001110100000000000011111111110000000000001110011111000000000011100001001110001111110111110111000110000011100000000110001000001111000000110111010111000011010011111011100110000000000110101111101111000011100000???0011110111111111101011011111111111111110001110001100000011011000000101100000000011000001000011000001111110111110000000000000100010001100010000101011001110110111011110000110111100000000001110011000000?001111110011111010001111110111100001111110000000011111110001100111011001100000001000011111111110111111111111111111111111000000000001000111000001111101111011011111011000000011111101011111111111011001110111111100111101111101111110001100001011001111111110010001000111111011011110011110000111111110001111100110111111000011111111111111111111000001111011111111101111100000010000000111110000000111111100001111110000010111111000000000010111010100111100011000000001010000000001101111111001111100000000111011011111111010111111110100011011 sylvaticaMVZ137426 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sylvaticaDMH84R43 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septentrionalesDCC3588 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pipiensJSF1119 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dunniJSF1017 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montezumaeJAC8836 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sp_2_mex_JSF1106 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chiricahuensisJSF1063 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subaquavocalis 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chiricahuensisJSF1092 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palustrisJSF1110 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magnaocularisJSF1073 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yavapaiensisJSF1085 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oncaLVT3542 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sp_8_PueblaJAC9467 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macroglossaJAC10472 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macroglossaJSF7933 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sp_4_Panama 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sp_5_CostaRichDMH86_210 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sp_6_CostaRicaDMH86_225 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; END; garli-2.1-release/example/partition/exampleRuns/dna+Mkv/garli.conf000066400000000000000000000026561241236125200253060ustar00rootroot00000000000000[general] datafname = dnaPlusGapCoding.nex constraintfile = none streefname = random attachmentspertaxon = 100 ofprefix = mixedDnaMkv randseed = -1 availablememory = 512 logevery = 10 saveevery = 100 refinestart = 1 outputeachbettertopology = 0 outputcurrentbesttopology = 0 enforcetermconditions = 1 genthreshfortopoterm = 10000 scorethreshforterm = 0.001 significanttopochange = 0.01 outputphyliptree = 0 outputmostlyuselessfiles = 0 writecheckpoints = 0 restart = 0 outgroup = 1 resampleproportion = 1.0 inferinternalstateprobs = 0 outputsitelikelihoods = 0 optimizeinputonly = 0 collapsebranches = 1 searchreps = 2 bootstrapreps = 0 linkmodels = 0 subsetspecificrates = 1 [model1] datatype = nucleotide ratematrix = 6rate statefrequencies = estimate ratehetmodel = gamma numratecats = 4 invariantsites = estimate [model2] datatype = standardvariable ratematrix = 1rate statefrequencies = equal ratehetmodel = none numratecats = 1 invariantsites = none [master] nindivs = 4 holdover = 1 selectionintensity = 0.5 holdoverpenalty = 0 stopgen = 5000000 stoptime = 5000000 startoptprec = 0.5 minoptprec = 0.01 numberofprecreductions = 10 treerejectionthreshold = 50.0 topoweight = 0.01 modweight = 0.002 brlenweight = 0.002 randnniweight = 0.1 randsprweight = 0.3 limsprweight = 0.6 intervallength = 100 intervalstostore = 5 limsprrange = 6 meanbrlenmuts = 5 gammashapebrlen = 1000 gammashapemodel = 1000 uniqueswapbias = 0.1 distanceswapbias = 1.0 garli-2.1-release/example/partition/exampleRuns/dna+Mkv/mixedDnaMkv.best.all.tre000066400000000000000000000130611241236125200277570ustar00rootroot00000000000000#NEXUS begin trees; translate 1 temporariaDMH84R1, 2 boyliiMVZ148929, 3 luteiventris_MT_MVZ191016, 4 luteiventris_WA_MVZ225749, 5 muscosaMVZ149006, 6 auroraMVZ13957, 7 cascadaeMVZ148946, 8 sylvaticaMVZ137426, 9 sylvaticaDMH84R43, 10 septentrionalesDCC3588, 11 grylioMVZ175945, 12 okaloosae, 13 clamitansJSF1118, 14 heckscheriMVZ164908, 15 catesbianaX12841, 16 catesbianaDMH84R2, 17 virgatipesMVZ175944, 18 maculataKU195258, 19 vibicariaMVZ11035, 20 warszewitshiiJSF1127, 21 palmipesVenAMNHA118801, 22 palmipesEcuKU204425, 23 Sp_1_ecuadorQCAZ13219, 24 bwanaQCAZ13964, 25 vaillantiKU195299, 26 julianiTNHC60324, 27 sierramadrensisKU195181, 28 psilonotaKU195119, 29 zweifeliJAC7514, 30 tarahumaraeKU194596, 31 pustulosaJAC10555, 32 pipiensJSF1119, 33 pipiensY10945, 34 dunniJSF1017, 35 montezumaeJAC8836, 36 sp_2_mex_JSF1106, 37 chiricahuensisJSF1063, 38 subaquavocalis, 39 chiricahuensisJSF1092, 40 palustrisJSF1110, 41 areolataJSF1111, 42 sevosaUSC8236, 43 capitoSLU003, 44 spectabilisJAC8622, 45 omiltemanaJAC7413, 46 sp_3_MichoacanJSF955, 47 tlalociJSF1083, 48 neovolcanicaJSF960, 49 berlandieriJSF1136, 50 blairiJSF830, 51 sphenocephalaUSC7448, 52 utriculariaJSF845, 53 forreriJSF1065, 54 magnaocularisJSF1073, 55 sp_7_JaliscoJSF1000, 56 yavapaiensisJSF1085, 57 oncaLVT3542, 58 sp_8_PueblaJAC9467, 59 macroglossaJAC10472, 60 macroglossaJSF7933, 61 taylori286, 62 sp_4_Panama, 63 sp_5_CostaRichDMH86_210, 64 sp_6_CostaRicaDMH86_225; tree rep1 = [&U][!GarliScore -61937.27][!GarliModel S 1.748119 0.117688 M1 r 2.02871 7.73017 2.08171 0.81780 13.87204 1.00000 e 0.33609 0.22000 0.14234 0.30157 a 0.77482 p 0.43349 M2 e 0.50000 0.50000 ](1:0.17941819,(((17:0.06825516,(10:0.06404833,(11:0.12781767,((13:0.01322966,12:0.01059399):0.01795642,((16:0.00288901,15:0.00000001):0.04973784,14:0.06608698):0.00515382):0.02250318):0.00259060):0.01306057):0.09016985,(9:0.00303898,8:0.01059628):0.12975904):0.04107838,(((((33:0.01083574,32:0.00980447):0.06568282,((35:0.03005125,34:0.01711179):0.02591346,(36:0.04132485,((38:0.01794724,37:0.05565104):0.00746844,39:0.02099720):0.01978111):0.01085310):0.04896704):0.07005818,((((44:0.08985910,(45:0.10773335,46:0.06086692):0.00230276):0.00186814,((50:0.02187182,(49:0.01488051,(48:0.00280650,47:0.00799698):0.00835376):0.01491409):0.05591723,(52:0.02326401,51:0.03043222):0.03142776):0.00959553):0.00698122,(((((63:0.05709074,62:0.03795675):0.00893365,64:0.07385451):0.00885168,(61:0.10427631,(60:0.01002483,59:0.01774937):0.02794954):0.02298569):0.01330538,((58:0.13602956,(56:0.01535670,57:0.00574102):0.04484603):0.01988304,(54:0.16533287,55:0.09977092):0.03730440):0.01279570):0.00771718,53:0.10901502):0.00866640):0.05440755,((41:0.07072630,(42:0.00555842,43:0.01977683):0.03502007):0.02045156,40:0.02932419):0.03976203):0.03506653):0.18208566,(((29:0.09791568,28:0.16416832):0.03315658,(31:0.18015935,30:0.05514847):0.02975015):0.08448688,27:0.20030436):0.06478838):0.01363196,(((19:0.11827193,20:0.22348354):0.17416492,18:0.11924062):0.04168588,((24:0.06669992,((22:0.00429721,21:0.17963317):0.04867471,23:0.03337586):0.01870529):0.03359377,(25:0.12644528,26:0.13213941):0.04378362):0.08666865):0.03257370):0.06186000):0.11992959,(((4:0.00071035,3:0.00797849):0.06036142,2:0.12355860):0.01304412,(5:0.05131282,(6:0.02882514,7:0.03874389):0.03464354):0.03484968):0.00626508); tree rep2BEST = [&U][!GarliScore -61937.27015625][!GarliModel S 1.748173 0.117625 M1 r 2.02754 7.72587 2.08045 0.81707 13.86626 1.00000 e 0.33610 0.22001 0.14233 0.30156 a 0.77477 p 0.43346 M2 e 0.50000 0.50000 ](1:0.17947672,((((18:0.11927940,(19:0.11831549,20:0.22356705):0.17422723):0.04169982,((24:0.06672385,(23:0.03338531,(22:0.00429782,21:0.17969580):0.04868685):0.01871139):0.03360464,(26:0.13218472,25:0.12648334):0.04379631):0.08669472):0.03258189,((((28:0.16422475,29:0.09794713):0.03316677,(30:0.05516490,31:0.18022082):0.02975735):0.08451121,27:0.20037305):0.06481076,(((((39:0.02100270,(38:0.01795203,37:0.05566678):0.00747012):0.01978719,36:0.04133912):0.01085512,(34:0.01711666,35:0.03005836):0.02592314):0.04898104,(32:0.00980526,33:0.01083913):0.06570138):0.07008223,(((53:0.10905227,((((62:0.03796830,63:0.05710692):0.00893463,64:0.07387502):0.00885535,(61:0.10430643,(60:0.01002699,59:0.01775339):0.02795678):0.02299225):0.01331127,((58:0.13607075,(57:0.00574277,56:0.01535922):0.04485556):0.01988942,(54:0.16539077,55:0.09980517):0.03731602):0.01279929):0.00771907):0.00866886,(((51:0.03044061,52:0.02326996):0.03143913,((49:0.01488440,(47:0.00799776,48:0.00280743):0.00835570):0.01491740,50:0.02187916):0.05593376):0.00959852,(44:0.08988473,(46:0.06088936,45:0.10777023):0.00230236):0.00186844):0.00698250):0.05442347,(40:0.02933233,((43:0.01978146,42:0.00556070):0.03502584,41:0.07074693):0.02045751):0.03977517):0.03507764):0.18214887):0.01363732):0.06188598,((17:0.06827047,(10:0.06406817,((((15:0.00000001,16:0.00288964):0.04975386,14:0.06610646):0.00515252,(13:0.01323407,12:0.01059606):0.01796138):0.02250923,11:0.12785695):0.00259199):0.01306556):0.09020261,(9:0.00303671,8:0.01060155):0.12980121):0.04109502):0.11996864,((5:0.05132921,(6:0.02883419,7:0.03875302):0.03465309):0.03485991,((4:0.00071070,3:0.00797950):0.06038468,2:0.12359976):0.01304585):0.00626743); end; [M1 begin paup; clear; gett file=mixedDnaMkv.best.all.tre storebr; lset userbr nst=6 rmat=(2.02754326 7.72587188 2.08045394 0.81707243 13.86625709) base=(0.33609716 0.22000583 0.14233462) rates=gamma shape= 0.77476879 ncat=4 pinv= 0.43346220; end; ] garli-2.1-release/example/partition/exampleRuns/dna+Mkv/mixedDnaMkv.best.tre000066400000000000000000000067611241236125200272210ustar00rootroot00000000000000#NEXUS begin trees; translate 1 temporariaDMH84R1, 2 boyliiMVZ148929, 3 luteiventris_MT_MVZ191016, 4 luteiventris_WA_MVZ225749, 5 muscosaMVZ149006, 6 auroraMVZ13957, 7 cascadaeMVZ148946, 8 sylvaticaMVZ137426, 9 sylvaticaDMH84R43, 10 septentrionalesDCC3588, 11 grylioMVZ175945, 12 okaloosae, 13 clamitansJSF1118, 14 heckscheriMVZ164908, 15 catesbianaX12841, 16 catesbianaDMH84R2, 17 virgatipesMVZ175944, 18 maculataKU195258, 19 vibicariaMVZ11035, 20 warszewitshiiJSF1127, 21 palmipesVenAMNHA118801, 22 palmipesEcuKU204425, 23 Sp_1_ecuadorQCAZ13219, 24 bwanaQCAZ13964, 25 vaillantiKU195299, 26 julianiTNHC60324, 27 sierramadrensisKU195181, 28 psilonotaKU195119, 29 zweifeliJAC7514, 30 tarahumaraeKU194596, 31 pustulosaJAC10555, 32 pipiensJSF1119, 33 pipiensY10945, 34 dunniJSF1017, 35 montezumaeJAC8836, 36 sp_2_mex_JSF1106, 37 chiricahuensisJSF1063, 38 subaquavocalis, 39 chiricahuensisJSF1092, 40 palustrisJSF1110, 41 areolataJSF1111, 42 sevosaUSC8236, 43 capitoSLU003, 44 spectabilisJAC8622, 45 omiltemanaJAC7413, 46 sp_3_MichoacanJSF955, 47 tlalociJSF1083, 48 neovolcanicaJSF960, 49 berlandieriJSF1136, 50 blairiJSF830, 51 sphenocephalaUSC7448, 52 utriculariaJSF845, 53 forreriJSF1065, 54 magnaocularisJSF1073, 55 sp_7_JaliscoJSF1000, 56 yavapaiensisJSF1085, 57 oncaLVT3542, 58 sp_8_PueblaJAC9467, 59 macroglossaJAC10472, 60 macroglossaJSF7933, 61 taylori286, 62 sp_4_Panama, 63 sp_5_CostaRichDMH86_210, 64 sp_6_CostaRicaDMH86_225; tree bestREP2 = [&U][!GarliScore -61937.270156][!GarliModel S 1.748173 0.117625 M1 r 2.02754 7.72587 2.08045 0.81707 13.86626 1.00000 e 0.33610 0.22001 0.14233 0.30156 a 0.77477 p 0.43346 M2 e 0.50000 0.50000 ](1:0.17947672,((((18:0.11927940,(19:0.11831549,20:0.22356705):0.17422723):0.04169982,((24:0.06672385,(23:0.03338531,(22:0.00429782,21:0.17969580):0.04868685):0.01871139):0.03360464,(26:0.13218472,25:0.12648334):0.04379631):0.08669472):0.03258189,((((28:0.16422475,29:0.09794713):0.03316677,(30:0.05516490,31:0.18022082):0.02975735):0.08451121,27:0.20037305):0.06481076,(((((39:0.02100270,(38:0.01795203,37:0.05566678):0.00747012):0.01978719,36:0.04133912):0.01085512,(34:0.01711666,35:0.03005836):0.02592314):0.04898104,(32:0.00980526,33:0.01083913):0.06570138):0.07008223,(((53:0.10905227,((((62:0.03796830,63:0.05710692):0.00893463,64:0.07387502):0.00885535,(61:0.10430643,(60:0.01002699,59:0.01775339):0.02795678):0.02299225):0.01331127,((58:0.13607075,(57:0.00574277,56:0.01535922):0.04485556):0.01988942,(54:0.16539077,55:0.09980517):0.03731602):0.01279929):0.00771907):0.00866886,(((51:0.03044061,52:0.02326996):0.03143913,((49:0.01488440,(47:0.00799776,48:0.00280743):0.00835570):0.01491740,50:0.02187916):0.05593376):0.00959852,(44:0.08988473,(46:0.06088936,45:0.10777023):0.00230236):0.00186844):0.00698250):0.05442347,(40:0.02933233,((43:0.01978146,42:0.00556070):0.03502584,41:0.07074693):0.02045751):0.03977517):0.03507764):0.18214887):0.01363732):0.06188598,((17:0.06827047,(10:0.06406817,((((15:0.00000001,16:0.00288964):0.04975386,14:0.06610646):0.00515252,(13:0.01323407,12:0.01059606):0.01796138):0.02250923,11:0.12785695):0.00259199):0.01306556):0.09020261,(9:0.00303671,8:0.01060155):0.12980121):0.04109502):0.11996864,((5:0.05132921,(6:0.02883419,7:0.03875302):0.03465309):0.03485991,((4:0.00071070,3:0.00797950):0.06038468,2:0.12359976):0.01304585):0.00626743); end; [ S 1.748173 0.117625 M1 r 2.02754 7.72587 2.08045 0.81707 13.86626 1.00000 e 0.33610 0.22001 0.14233 0.30156 a 0.77477 p 0.43346 M2 e 0.50000 0.50000 ] garli-2.1-release/example/partition/exampleRuns/dna+Mkv/mixedDnaMkv.log00.log000066400000000000000000002622041241236125200271700ustar00rootroot00000000000000Search rep 1 (of 2) random seed = 853654 gen best_like time optPrecision 0 -101113.0297 28 0.5 10 -100820.8477 29 0.5 20 -99463.88968 29 0.5 30 -98493.98737 30 0.5 40 -98312.10553 30 0.5 50 -96647.35981 31 0.5 60 -94451.32369 32 0.5 70 -94064.80284 32 0.5 80 -92886.97454 33 0.5 90 -91512.29965 33 0.5 100 -90517.35366 34 0.5 110 -90300.81805 35 0.5 120 -88132.16511 35 0.5 130 -87799.9098 36 0.5 140 -86355.32816 36 0.5 150 -86125.06835 37 0.5 160 -86024.54664 37 0.5 170 -84979.84955 38 0.5 180 -84043.90482 39 0.5 190 -83362.52924 39 0.5 200 -83291.80983 40 0.5 210 -83244.34687 40 0.5 220 -82563.02346 41 0.5 230 -82522.44927 41 0.5 240 -82244.84075 42 0.5 250 -82198.90106 43 0.5 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garli-2.1-release/example/partition/exampleRuns/dna+Mkv/mixedDnaMkv.screen.log000066400000000000000000001157271241236125200275350ustar00rootroot00000000000000Running GARLI-PART Version 2.0.1008 (17 Mar 2011) ->Single processor version<- ############################################################## This is GARLI 2.0, the first "official" release including partitioned models. It is a merging of official release 1.0 and beta version GARLI-PART 0.97 Briefly, it includes models for nucleotides, amino acids, codons, and morphology-like characters, any of which can be mixed together and applied to different subsets of data. General program usage is extensively documented here: http://www.nescent.org/wg_garli/ see this page for details on partitioned usage: http://www.nescent.org/wg_garli/Partition_testing_version and this page for details on Mkv mophology model usage: http://www.nescent.org/wg_garli/Mkv_morphology_model PLEASE LET ME KNOW OF ANY PROBLEMS AT: garli.support@gmail.com ############################################################## This version has undergone much testing, but is still a BETA VERSION. - Please check results carefully! - Compiled Mar 21 2011 13:13:18 using Intel icc compiler version 9.10 Using NCL version 2.1.10 ####################################################### Reading config file garli.conf ################################################### READING OF DATA Attempting to read data file in Nexus format (using NCL): dnaPlusGapCoding.nex ... Reading TAXA block... successful Reading CHARACTERS block... found dna data... successful Reading CHARACTERS block... found standard data... successful ################################################### PARTITIONING OF DATA AND MODELS CHECK: DIFFERENT MODEL TYPES AND MODEL PARAMETERS APPLY TO EACH DATA SUBSET (no linkage) GARLI data subset 1 CHARACTERS block #1 ("Untitled DATA Block 1GapsAsMissing") Data read as Nucleotide data, modeled as Nucleotide data Summary of data: 64 sequences. 2040 constant characters. 1025 parsimony-informative characters. 137 uninformative variable characters. 9 characters were completely missing or ambiguous (removed). 3202 total characters (3211 before removing empty columns). 1989 unique patterns in compressed data matrix. Pattern processing required < 1 second GARLI data subset 2 CHARACTERS block #2 ("Untitled DATA Block 1GapsAsBinary") Data read as Standard k-state data, variable only, modeled as Standard k-state data, variable only NOTE: entirely missing characters removed from matrix: 736 792 1244 1644 1645 1993-1995 2195 Subset of data with 2 states: chars 1-735 737-791 793-1243 1245-1643 1646-1992 1996-2194 2196-2723 Summary of data: 64 sequences. 0 constant characters. 1530 parsimony-informative characters. 1184 uninformative variable characters. 2714 total characters. 768 unique patterns in compressed data matrix. Pattern processing required 1 second(s) ################################################### NOTE: Unlike many programs, the amount of system memory that Garli will use can be controlled by the user. (This comes from the availablememory setting in the configuration file. Availablememory should NOT be set to more than the actual amount of physical memory that your computer has installed) For this dataset: Mem level availablememory setting great >= 165 MB good approx 164 MB to 106 MB low approx 105 MB to 44 MB very low approx 43 MB to 34 MB the minimum required availablememory is 34 MB You specified that Garli should use at most 512.0 MB of memory. Garli will actually use approx. 246.5 MB of memory **Your memory level is: great (you don't need to change anything)** ####################################################### Found outgroup specification: 1 ####################################################### STARTING RUN >>>Search rep 1 (of 2)<<< MODEL REPORT - Parameters are at their INITIAL values (not yet optimized) Model 1 Number of states = 4 (nucleotide data) Nucleotide Relative Rate Matrix: 6 rates AC = 1.000, AG = 4.000, AT = 1.000, CG = 1.000, CT = 4.000, GT = 1.000 Equilibrium State Frequencies: estimated (ACGT) 0.3406 0.2086 0.1521 0.2987 Rate Heterogeneity Model: 4 discrete gamma distributed rate categories, alpha param estimated 0.5000 with an invariant (invariable) site category, proportion estimated 0.1593 Substitution rate categories under this model: rate proportion 0.0000 0.1593 0.0334 0.2102 0.2519 0.2102 0.8203 0.2102 2.8944 0.2102 Model 2 Number of states = 2 (standard data) Character change matrix: One rate (symmetric one rate Mkv model) Equilibrium State Frequencies: equal (0.50, fixed) Rate Heterogeneity Model: no rate heterogeneity Subset rate multipliers: 1.00 1.00 Starting with seed=853654 creating random starting tree... Initial ln Likelihood: -128174.5818 optimizing: starting branch lengths, alpha shape, prop. invar, rel rates, eq freqs, subset rates... pass 1:+22928.633 (branch=20244.73 scale= 59.25 alpha=919.94 freqs=140.51 rel rates= 92.29 pinv=508.82 subset rates=963.09) pass 2:+ 2693.685 (branch=2114.86 scale= 2.80 alpha=211.33 freqs= 20.51 rel rates= 27.46 pinv=212.97 subset rates=103.76) pass 3:+ 785.400 (branch= 283.03 scale= 17.67 alpha=289.84 freqs= 6.02 rel rates= 21.63 pinv= 81.62 subset rates= 85.59) pass 4:+ 238.687 (branch= 129.63 scale= 13.69 alpha= 18.44 freqs= 2.42 rel rates= 6.25 pinv= 0.00 subset rates= 68.26) pass 5:+ 96.971 (branch= 49.27 scale= 7.20 alpha= 0.01 freqs= 0.07 rel rates= 7.49 pinv= 0.00 subset rates= 32.94) pass 6:+ 51.721 (branch= 15.02 scale= 16.85 alpha= 0.01 freqs= 0.16 rel rates= 1.87 pinv= 1.06 subset rates= 16.76) pass 7:+ 43.183 (branch= 16.32 scale= 7.03 alpha= 0.02 freqs= 0.24 rel rates= 4.16 pinv= 1.70 subset rates= 13.72) pass 8:+ 34.358 (branch= 6.09 scale= 11.96 alpha= 0.67 freqs= 0.19 rel rates= 2.77 pinv= 2.29 subset rates= 10.39) pass 9:+ 26.268 (branch= 0.57 scale= 13.26 alpha= 0.01 freqs= 0.22 rel rates= 2.23 pinv= 1.50 subset rates= 8.49) pass10:+ 23.507 (branch= 1.12 scale= 11.18 alpha= 0.01 freqs= 0.26 rel rates= 2.44 pinv= 1.23 subset rates= 7.28) pass11:+ 18.732 (branch= 0.01 scale= 9.66 alpha= 0.01 freqs= 0.20 rel rates= 1.70 pinv= 0.88 subset rates= 6.26) pass12:+ 16.213 (branch= 0.01 scale= 8.09 alpha= 0.01 freqs= 0.26 rel rates= 1.78 pinv= 0.69 subset rates= 5.37) pass13:+ 12.915 (branch= 0.01 scale= 6.85 alpha= 0.01 freqs= 0.29 rel rates= 1.11 pinv= 0.06 subset rates= 4.57) pass14:+ 10.495 (branch= 0.01 scale= 5.37 alpha= 0.01 freqs= 0.38 rel rates= 0.02 pinv= 0.99 subset rates= 3.71) pass15:+ 9.366 (branch= 0.10 scale= 4.40 alpha= 0.02 freqs= 0.22 rel rates= 0.75 pinv= 0.63 subset rates= 3.25) pass16:+ 8.907 (branch= 0.01 scale= 4.62 alpha= 0.02 freqs= 0.22 rel rates= 0.73 pinv= 0.03 subset rates= 3.28) pass17:+ 7.469 (branch= 0.01 scale= 3.78 alpha= 0.03 freqs= 0.24 rel rates= 0.65 pinv= 0.04 subset rates= 2.71) pass18:+ 8.518 (branch= 0.01 scale= 3.11 alpha= 0.56 freqs= 0.27 rel rates= 1.61 pinv= 0.62 subset rates= 2.34) pass19:+ 6.269 (branch= 0.01 scale= 3.10 alpha= 0.00 freqs= 0.31 rel rates= 0.54 pinv= 0.03 subset rates= 2.28) pass20:+ 4.812 (branch= 0.01 scale= 2.60 alpha= 0.01 freqs= 0.30 rel rates= 0.02 pinv= 0.04 subset rates= 1.83) pass21:+ 4.453 (branch= 0.01 scale= 1.57 alpha= 0.01 freqs= 0.20 rel rates= 0.54 pinv= 0.84 subset rates= 1.29) pass22:+ 4.078 (branch= 0.00 scale= 2.20 alpha= 0.02 freqs= 0.24 rel rates= 0.02 pinv= 0.01 subset rates= 1.58) pass23:+ 4.356 (branch= 0.49 scale= 1.49 alpha= 0.02 freqs= 0.11 rel rates= 1.13 pinv= 0.04 subset rates= 1.08) pass24:+ 5.268 (branch= 0.58 scale= 1.69 alpha= 0.03 freqs= 0.22 rel rates= 1.58 pinv= 0.03 subset rates= 1.14) pass25:+ 4.941 (branch= 1.04 scale= 1.19 alpha= 0.03 freqs= 0.28 rel rates= 1.41 pinv= 0.04 subset rates= 0.95) pass26:+ 3.345 (branch= 0.46 scale= 1.10 alpha= 0.64 freqs= 0.31 rel rates= 0.01 pinv= 0.05 subset rates= 0.77) pass27:+ 2.213 (branch= 0.00 scale= 0.69 alpha= 0.00 freqs= 0.21 rel rates= 0.01 pinv= 0.74 subset rates= 0.55) pass28:+ 2.547 (branch= 0.00 scale= 0.81 alpha= 0.00 freqs= 0.07 rel rates= 0.98 pinv= 0.03 subset rates= 0.66) pass29:+ 1.942 (branch= 0.00 scale= 0.99 alpha= 0.01 freqs= 0.18 rel rates= 0.02 pinv= 0.02 subset rates= 0.72) pass30:+ 1.351 (branch= 0.00 scale= 0.58 alpha= 0.01 freqs= 0.04 rel rates= 0.67 pinv= 0.04 subset rates= 0.00) pass31:+ 0.855 (branch= 0.00 scale= 0.00 alpha= 0.02 freqs= 0.14 rel rates= 0.69 pinv= 0.00 subset rates= 0.00) pass32:+ 0.092 (branch= 0.00 scale= 0.00 alpha= 0.01 freqs= 0.05 rel rates= 0.01 pinv= 0.02 subset rates= 0.00) lnL after optimization: -101113.0297 gen current_lnL precision last_tree_imp 0 -101113.0297 0.500 0 100 -90517.3537 0.500 100 200 -83291.8098 0.500 199 300 -78503.3448 0.500 298 400 -73032.9079 0.500 400 500 -69757.1888 0.500 499 600 -67518.7818 0.500 593 700 -66093.8913 0.500 697 800 -65360.8432 0.500 799 900 -64392.4094 0.500 898 1000 -63053.1325 0.500 992 1100 -62393.8347 0.500 1092 1200 -62063.3513 0.500 1157 1300 -62005.3529 0.500 1278 1400 -61997.1106 0.500 1278 1500 -61993.8298 0.500 1278 1600 -61988.1565 0.500 1278 1700 -61984.6113 0.500 1278 1800 -61976.3742 0.500 1278 Optimization precision reduced Optimizing parameters... improved 10.998 lnL Optimizing branchlengths... improved 3.264 lnL 1900 -61951.6576 0.451 1278 2000 -61949.5978 0.451 1278 2100 -61947.1059 0.451 1278 2200 -61946.0770 0.451 1278 2300 -61944.0182 0.451 1278 Optimization precision reduced Optimizing parameters... improved 0.032 lnL Optimizing branchlengths... improved 0.571 lnL 2400 -61942.7941 0.402 1278 2500 -61942.3538 0.402 1278 2600 -61942.0551 0.402 1278 2700 -61941.7881 0.402 1278 2800 -61941.5493 0.402 1278 Optimization precision reduced Optimizing parameters... improved 0.017 lnL Optimizing branchlengths... improved 0.000 lnL 2900 -61941.3428 0.353 1278 3000 -61940.8061 0.353 1278 3100 -61940.6832 0.353 1278 3200 -61940.4818 0.353 1278 3300 -61940.4333 0.353 1278 Optimization precision reduced Optimizing parameters... improved 0.007 lnL Optimizing branchlengths... improved 0.452 lnL 3400 -61939.7576 0.304 1278 3500 -61939.6917 0.304 1278 3600 -61939.5526 0.304 1278 3700 -61939.3828 0.304 1278 3800 -61939.3664 0.304 1278 Optimization precision reduced Optimizing parameters... improved 0.012 lnL Optimizing branchlengths... improved 0.000 lnL 3900 -61939.2876 0.255 1278 4000 -61939.2292 0.255 1278 4100 -61939.1071 0.255 1278 4200 -61938.9824 0.255 1278 4300 -61938.9732 0.255 1278 Optimization precision reduced Optimizing parameters... improved 0.004 lnL Optimizing branchlengths... improved 0.328 lnL 4400 -61938.6275 0.206 1278 4500 -61938.5428 0.206 1278 4600 -61938.5114 0.206 1278 4700 -61938.5110 0.206 1278 4800 -61938.4303 0.206 1278 Optimization precision reduced Optimizing parameters... improved 0.004 lnL Optimizing branchlengths... improved 0.000 lnL 4900 -61938.4244 0.157 1278 5000 -61938.3613 0.157 1278 5100 -61938.3570 0.157 1278 5200 -61938.3257 0.157 1278 5300 -61938.3232 0.157 1278 Optimization precision reduced Optimizing parameters... improved 0.003 lnL Optimizing branchlengths... improved 0.139 lnL 5400 -61938.1805 0.108 1278 5500 -61938.1691 0.108 1278 5600 -61938.1620 0.108 1278 5700 -61938.1601 0.108 1278 5800 -61938.1528 0.108 1278 Optimization precision reduced Optimizing parameters... improved 0.001 lnL Optimizing branchlengths... improved 0.000 lnL 5900 -61938.1409 0.059 1278 6000 -61938.1409 0.059 1278 6100 -61938.1409 0.059 1278 6200 -61938.1399 0.059 1278 6300 -61938.1322 0.059 1278 Optimization precision reduced Optimizing parameters... improved 0.001 lnL Optimizing branchlengths... improved 0.540 lnL 6400 -61937.5856 0.010 1278 6500 -61937.5849 0.010 1278 6600 -61937.5756 0.010 1278 6700 -61937.5746 0.010 1278 6800 -61937.5711 0.010 1278 6900 -61937.5698 0.010 1278 7000 -61937.5698 0.010 1278 7100 -61937.5698 0.010 1278 7200 -61937.5698 0.010 1278 7300 -61937.5675 0.010 1278 7400 -61937.5669 0.010 1278 7500 -61937.5637 0.010 1278 7600 -61937.5637 0.010 1278 7700 -61937.5603 0.010 1278 7800 -61937.5603 0.010 1278 7900 -61937.5599 0.010 1278 8000 -61937.5599 0.010 1278 8100 -61937.5595 0.010 1278 8200 -61937.5594 0.010 1278 8300 -61937.5567 0.010 1278 8400 -61937.5495 0.010 1278 8500 -61937.5477 0.010 1278 8600 -61937.5457 0.010 1278 8700 -61937.5457 0.010 1278 8800 -61937.5356 0.010 1278 8900 -61937.5356 0.010 1278 9000 -61937.5311 0.010 1278 9100 -61937.5311 0.010 1278 9200 -61937.5310 0.010 1278 9300 -61937.5260 0.010 1278 9400 -61937.5255 0.010 1278 9500 -61937.5236 0.010 1278 9600 -61937.5228 0.010 1278 9700 -61937.5167 0.010 1278 9800 -61937.5157 0.010 1278 9900 -61937.5157 0.010 1278 10000 -61937.5156 0.010 1278 10100 -61937.5156 0.010 1278 10200 -61937.5152 0.010 1278 10300 -61937.5147 0.010 1278 10400 -61937.5120 0.010 1278 10500 -61937.5120 0.010 1278 10600 -61937.5071 0.010 1278 10700 -61937.5071 0.010 1278 10800 -61937.5062 0.010 1278 10900 -61937.5042 0.010 1278 11000 -61937.5042 0.010 1278 11100 -61937.5042 0.010 1278 11200 -61937.5025 0.010 1278 11300 -61937.5019 0.010 1278 11400 -61937.5004 0.010 1278 11500 -61937.5004 0.010 1278 11600 -61937.5004 0.010 1278 11700 -61937.4882 0.010 1278 11800 -61937.4882 0.010 1278 11900 -61937.4863 0.010 1278 12000 -61937.4863 0.010 1278 12100 -61937.4861 0.010 1278 12200 -61937.4861 0.010 1278 12300 -61937.4861 0.010 1278 12400 -61937.4861 0.010 1278 12500 -61937.4861 0.010 1278 12600 -61937.4861 0.010 1278 12700 -61937.4741 0.010 1278 12800 -61937.4710 0.010 1278 12900 -61937.4710 0.010 1278 13000 -61937.4706 0.010 1278 13100 -61937.4693 0.010 1278 13200 -61937.4693 0.010 1278 13300 -61937.4673 0.010 1278 13400 -61937.4606 0.010 1278 13500 -61937.4604 0.010 1278 13600 -61937.4589 0.010 1278 13700 -61937.4589 0.010 1278 13800 -61937.4589 0.010 1278 13900 -61937.4589 0.010 1278 14000 -61937.4565 0.010 1278 14100 -61937.4565 0.010 1278 14200 -61937.4565 0.010 1278 14300 -61937.4564 0.010 1278 14400 -61937.4525 0.010 1278 14500 -61937.4506 0.010 1278 14600 -61937.4495 0.010 1278 14700 -61937.4421 0.010 1278 14800 -61937.4401 0.010 1278 14900 -61937.4401 0.010 1278 15000 -61937.4395 0.010 1278 Optimizing parameters... improved 0.000 lnL Optimizing branchlengths... improved 0.000 lnL 15100 -61937.4392 0.010 1278 15200 -61937.4392 0.010 1278 15300 -61937.4383 0.010 1278 15400 -61937.4383 0.010 1278 15500 -61937.4372 0.010 1278 15600 -61937.4372 0.010 1278 15700 -61937.4349 0.010 1278 15800 -61937.4340 0.010 1278 15900 -61937.4340 0.010 1278 16000 -61937.4339 0.010 1278 16100 -61937.4333 0.010 1278 16200 -61937.4332 0.010 1278 16300 -61937.4332 0.010 1278 16400 -61937.4332 0.010 1278 Reached termination condition! last topological improvement at gen 1278 Improvement over last 500 gen = 0.00072 Current score = -61937.4332 Performing final optimizations... pass 1 : -61937.3675 (branch= 0.0652 alpha= 0.0000 pinv= 0.0002 eq freqs= 0.0001 rel rates= 0.0002 subset rates= 0.0000) pass 2 : -61937.3404 (branch= 0.0271 alpha= 0.0000 pinv= 0.0000 eq freqs= 0.0000 rel rates= 0.0001 subset rates= 0.0000) pass 3 : -61937.2994 (branch= 0.0408 alpha= 0.0000 pinv= 0.0000 eq freqs= 0.0000 rel rates= 0.0000 subset rates= 0.0000) pass 4 : -61937.2857 (branch= 0.0137 alpha= 0.0000 pinv= 0.0000 eq freqs= 0.0000 rel rates= 0.0000 subset rates= 0.0000) pass 5 : -61937.2775 (branch= 0.0082 alpha= 0.0000 pinv= 0.0000 eq freqs= 0.0000 rel rates= 0.0000 subset rates= 0.0000) pass 6 : -61937.2740 (branch= 0.0035 alpha= 0.0000 pinv= 0.0000 eq freqs= 0.0000 rel rates= 0.0000 subset rates= 0.0000) pass 7 : -61937.2719 (branch= 0.0021 alpha= 0.0000 pinv= 0.0000 eq freqs= 0.0000 rel rates= 0.0000 subset rates= 0.0000) pass 8 : -61937.2714 (branch= 0.0006 alpha= 0.0000 pinv= 0.0000 eq freqs= 0.0000 rel rates= 0.0000 subset rates= 0.0000) pass 9 : -61937.2709 (branch= 0.0005 alpha= 0.0000 pinv= 0.0000 eq freqs= 0.0000 rel rates= 0.0000 subset rates= 0.0000) pass 10: -61937.2707 (branch= 0.0002 alpha= 0.0000 pinv= 0.0000 eq freqs= 0.0000 rel rates= 0.0000 subset rates= 0.0000) pass 11: -61937.2706 (branch= 0.0001 alpha= 0.0000 pinv= 0.0000 eq freqs= 0.0000 rel rates= 0.0000 subset rates= 0.0000) pass 12: -61937.2705 (branch= 0.0001 alpha= 0.0000 pinv= 0.0000 eq freqs= 0.0000 rel rates= 0.0000 subset rates= 0.0000) pass 13: -61937.2705 (branch= 0.0000 alpha= 0.0000 pinv= 0.0000 eq freqs= 0.0000 rel rates= 0.0000 subset rates= 0.0000) pass 14: -61937.2705 (branch= 0.0000 alpha= 0.0000 pinv= 0.0000 eq freqs= 0.0000 rel rates= 0.0000 subset rates= 0.0000) pass 15: -61937.2705 (branch= 0.0000 alpha= 0.0000 pinv= 0.0000 eq freqs= 0.0000 rel rates= 0.0000 subset rates= 0.0000) Looking for minimum length branches... Final score = -61937.2705 Time used so far = 0 hours, 13 minutes and 15 seconds MODEL REPORT - Parameter values are FINAL Model 1 Number of states = 4 (nucleotide data) Nucleotide Relative Rate Matrix: 6 rates AC = 2.029, AG = 7.730, AT = 2.082, CG = 0.818, CT = 13.872, GT = 1.000 Equilibrium State Frequencies: estimated (ACGT) 0.3361 0.2200 0.1423 0.3016 Rate Heterogeneity Model: 4 discrete gamma distributed rate categories, alpha param estimated 0.7748 with an invariant (invariable) site category, proportion estimated 0.4335 Substitution rate categories under this model: rate proportion 0.0000 0.4335 0.0902 0.1416 0.3968 0.1416 0.9497 0.1416 2.5633 0.1416 Model 2 Number of states = 2 (standard data) Character change matrix: One rate (symmetric one rate Mkv model) Equilibrium State Frequencies: equal (0.50, fixed) Rate Heterogeneity Model: no rate heterogeneity Subset rate multipliers: 1.75 0.12 NOTE: Collapsing of minimum length branches was requested (collapsebranches = 1) No branches were short enough to be collapsed. >>>Completed Search rep 1 (of 2)<<< >>>Search rep 2 (of 2)<<< MODEL REPORT - Parameters are at their INITIAL values (not yet optimized) Model 1 Number of states = 4 (nucleotide data) Nucleotide Relative Rate Matrix: 6 rates AC = 1.000, AG = 4.000, AT = 1.000, CG = 1.000, CT = 4.000, GT = 1.000 Equilibrium State Frequencies: estimated (ACGT) 0.3406 0.2086 0.1521 0.2987 Rate Heterogeneity Model: 4 discrete gamma distributed rate categories, alpha param estimated 0.5000 with an invariant (invariable) site category, proportion estimated 0.1593 Substitution rate categories under this model: rate proportion 0.0000 0.1593 0.0334 0.2102 0.2519 0.2102 0.8203 0.2102 2.8944 0.2102 Model 2 Number of states = 2 (standard data) Character change matrix: One rate (symmetric one rate Mkv model) Equilibrium State Frequencies: equal (0.50, fixed) Rate Heterogeneity Model: no rate heterogeneity Subset rate multipliers: 1.00 1.00 Starting with seed=1646090181 creating random starting tree... Initial ln Likelihood: -128061.7427 optimizing: starting branch lengths, alpha shape, prop. invar, rel rates, eq freqs, subset rates... pass 1:+23690.219 (branch=20808.93 scale= 99.25 alpha=972.18 freqs=122.54 rel rates=106.53 pinv=565.82 subset rates=1014.96) pass 2:+ 2933.758 (branch=2250.01 scale= 0.00 alpha=212.57 freqs= 5.19 rel rates= 20.80 pinv=316.40 subset rates=128.79) pass 3:+ 724.336 (branch= 270.25 scale= 15.49 alpha=316.53 freqs= 8.90 rel rates= 17.79 pinv= 0.00 subset rates= 95.37) pass 4:+ 207.720 (branch= 117.56 scale= 10.72 alpha= 7.55 freqs= 3.55 rel rates= 6.88 pinv= 0.00 subset rates= 61.47) pass 5:+ 102.899 (branch= 57.65 scale= 8.44 alpha= 0.01 freqs= 0.16 rel rates= 6.68 pinv= 0.00 subset rates= 29.96) pass 6:+ 73.722 (branch= 38.02 scale= 16.81 alpha= 0.00 freqs= 0.23 rel rates= 2.12 pinv= 2.74 subset rates= 13.80) pass 7:+ 58.202 (branch= 26.89 scale= 15.27 alpha= 0.01 freqs= 0.80 rel rates= 1.32 pinv= 2.70 subset rates= 11.20) pass 8:+ 30.447 (branch= 1.55 scale= 14.46 alpha= 0.02 freqs= 0.32 rel rates= 2.60 pinv= 2.39 subset rates= 9.13) pass 9:+ 23.616 (branch= 0.00 scale= 12.60 alpha= 0.02 freqs= 0.73 rel rates= 0.60 pinv= 1.91 subset rates= 7.76) pass10:+ 19.063 (branch= 0.00 scale= 10.99 alpha= 0.02 freqs= 0.19 rel rates= 0.04 pinv= 1.20 subset rates= 6.63) pass11:+ 16.098 (branch= 0.00 scale= 7.87 alpha= 0.02 freqs= 0.06 rel rates= 1.31 pinv= 1.48 subset rates= 5.37) pass12:+ 15.004 (branch= 0.00 scale= 8.25 alpha= 0.02 freqs= 0.09 rel rates= 1.28 pinv= 0.05 subset rates= 5.31) pass13:+ 13.824 (branch= 0.00 scale= 6.36 alpha= 0.02 freqs= 0.20 rel rates= 2.01 pinv= 0.85 subset rates= 4.39) pass14:+ 11.018 (branch= 0.00 scale= 5.78 alpha= 0.02 freqs= 0.25 rel rates= 0.94 pinv= 0.05 subset rates= 3.98) pass15:+ 11.213 (branch= 0.00 scale= 4.74 alpha= 0.02 freqs= 0.33 rel rates= 2.05 pinv= 0.66 subset rates= 3.41) pass16:+ 9.111 (branch= 0.00 scale= 4.33 alpha= 0.02 freqs= 0.35 rel rates= 1.29 pinv= 0.04 subset rates= 3.08) pass17:+ 8.520 (branch= 0.00 scale= 3.60 alpha= 0.02 freqs= 0.19 rel rates= 2.05 pinv= 0.06 subset rates= 2.60) pass18:+ 8.284 (branch= 0.00 scale= 2.92 alpha= 0.02 freqs= 0.83 rel rates= 1.56 pinv= 0.74 subset rates= 2.21) pass19:+ 5.273 (branch= 0.00 scale= 2.92 alpha= 0.03 freqs= 0.23 rel rates= 0.02 pinv= 0.03 subset rates= 2.04) pass20:+ 5.987 (branch= 0.97 scale= 2.15 alpha= 0.02 freqs= 0.13 rel rates= 0.57 pinv= 0.64 subset rates= 1.49) pass21:+ 5.609 (branch= 0.00 scale= 2.34 alpha= 0.66 freqs= 0.13 rel rates= 0.69 pinv= 0.02 subset rates= 1.77) pass22:+ 5.315 (branch= 0.00 scale= 2.08 alpha= 0.01 freqs= 0.14 rel rates= 1.51 pinv= 0.02 subset rates= 1.56) pass23:+ 3.197 (branch= 0.00 scale= 1.70 alpha= 0.01 freqs= 0.20 rel rates= 0.02 pinv= 0.03 subset rates= 1.24) pass24:+ 2.603 (branch= 0.00 scale= 1.09 alpha= 0.01 freqs= 0.10 rel rates= 0.02 pinv= 0.53 subset rates= 0.86) pass25:+ 2.881 (branch= 0.00 scale= 1.06 alpha= 0.01 freqs= 0.01 rel rates= 0.91 pinv= 0.02 subset rates= 0.87) pass26:+ 2.260 (branch= 0.00 scale= 1.19 alpha= 0.02 freqs= 0.12 rel rates= 0.02 pinv= 0.02 subset rates= 0.89) pass27:+ 2.010 (branch= 0.00 scale= 0.77 alpha= 0.02 freqs= 0.01 rel rates= 0.54 pinv= 0.04 subset rates= 0.63) pass28:+ 1.596 (branch= 0.00 scale= 0.82 alpha= 0.03 freqs= 0.07 rel rates= 0.02 pinv= 0.03 subset rates= 0.62) pass29:+ 0.640 (branch= 0.00 scale= 0.54 alpha= 0.03 freqs= 0.01 rel rates= 0.02 pinv= 0.05 subset rates= 0.00) pass30:+ 0.090 (branch= 0.00 scale= 0.00 alpha= 0.03 freqs= 0.00 rel rates= 0.02 pinv= 0.04 subset rates= 0.00) lnL after optimization: -100067.2273 gen current_lnL precision last_tree_imp 0 -100067.2273 0.500 0 100 -89028.2502 0.500 99 200 -78816.4419 0.500 200 300 -74605.0115 0.500 298 400 -69536.0283 0.500 399 500 -66577.3643 0.500 496 600 -65606.7330 0.500 599 700 -64212.7525 0.500 696 800 -63896.2512 0.500 785 900 -63005.8058 0.500 871 1000 -62503.7469 0.500 984 1100 -62075.5268 0.500 1094 1200 -62069.1589 0.500 1094 1300 -62032.3206 0.500 1094 1400 -62020.3538 0.500 1094 1500 -62017.2148 0.500 1094 1600 -62012.6899 0.500 1094 Optimization precision reduced Optimizing parameters... improved 12.137 lnL Optimizing branchlengths... improved 4.032 lnL 1700 -61983.3416 0.451 1094 1800 -61970.0107 0.451 1094 1900 -61956.5501 0.451 1094 2000 -61951.8397 0.451 1094 2100 -61948.0607 0.451 1094 Optimization precision reduced Optimizing parameters... improved 0.096 lnL Optimizing branchlengths... improved 0.000 lnL 2200 -61946.2281 0.402 1094 2300 -61945.4277 0.402 1094 2400 -61944.9809 0.402 1094 2500 -61944.4784 0.402 1094 2600 -61943.4366 0.402 1094 Optimization precision reduced Optimizing parameters... improved 0.015 lnL Optimizing branchlengths... improved 0.000 lnL 2700 -61942.9618 0.353 1094 2800 -61942.8073 0.353 1094 2900 -61942.4636 0.353 1094 3000 -61941.8507 0.353 1094 3100 -61941.7820 0.353 1094 Optimization precision reduced Optimizing parameters... improved 0.003 lnL Optimizing branchlengths... improved 0.000 lnL 3200 -61941.5451 0.304 1094 3300 -61941.3977 0.304 1094 3400 -61941.2943 0.304 1094 3500 -61941.2515 0.304 1094 3600 -61941.1456 0.304 1094 Optimization precision reduced Optimizing parameters... improved 0.004 lnL Optimizing branchlengths... improved 0.000 lnL 3700 -61941.0659 0.255 1094 3800 -61940.8330 0.255 1094 3900 -61940.5704 0.255 1094 4000 -61940.4515 0.255 1094 4100 -61940.1327 0.255 1094 Optimization precision reduced Optimizing parameters... improved 0.006 lnL Optimizing branchlengths... improved 0.264 lnL 4200 -61939.7760 0.206 1094 4300 -61939.7143 0.206 1094 4400 -61939.6269 0.206 1094 4500 -61939.5873 0.206 1094 4600 -61939.4914 0.206 1094 Optimization precision reduced Optimizing parameters... improved 0.002 lnL Optimizing branchlengths... improved 0.174 lnL 4700 -61939.2405 0.157 1094 4800 -61939.1400 0.157 1094 4900 -61939.1103 0.157 1094 5000 -61939.0333 0.157 1094 5100 -61939.0119 0.157 1094 Optimization precision reduced Optimizing parameters... improved 0.003 lnL Optimizing branchlengths... improved 0.278 lnL 5200 -61938.7196 0.108 1094 5300 -61938.7190 0.108 1094 5400 -61938.6364 0.108 1094 5500 -61938.6269 0.108 1094 5600 -61938.5618 0.108 1094 Optimization precision reduced Optimizing parameters... improved 0.002 lnL Optimizing branchlengths... improved 0.170 lnL 5700 -61938.3371 0.059 1094 5800 -61938.2973 0.059 1094 5900 -61938.2567 0.059 1094 6000 -61938.1962 0.059 1094 6100 -61938.1731 0.059 1094 Optimization precision reduced Optimizing parameters... improved 0.001 lnL Optimizing branchlengths... improved 0.638 lnL 6200 -61937.5340 0.010 1094 6300 -61937.5291 0.010 1094 6400 -61937.5169 0.010 1094 6500 -61937.5161 0.010 1094 6600 -61937.5138 0.010 1094 6700 -61937.5132 0.010 1094 6800 -61937.5128 0.010 1094 6900 -61937.5112 0.010 1094 7000 -61937.5098 0.010 1094 7100 -61937.5098 0.010 1094 7200 -61937.5098 0.010 1094 7300 -61937.5098 0.010 1094 7400 -61937.5092 0.010 1094 7500 -61937.5092 0.010 1094 7600 -61937.5084 0.010 1094 7700 -61937.5084 0.010 1094 7800 -61937.5079 0.010 1094 7900 -61937.5079 0.010 1094 8000 -61937.5079 0.010 1094 8100 -61937.5079 0.010 1094 8200 -61937.5017 0.010 1094 8300 -61937.5002 0.010 1094 8400 -61937.4964 0.010 1094 8500 -61937.4902 0.010 1094 8600 -61937.4899 0.010 1094 8700 -61937.4872 0.010 1094 8800 -61937.4872 0.010 1094 8900 -61937.4872 0.010 1094 9000 -61937.4867 0.010 1094 9100 -61937.4867 0.010 1094 9200 -61937.4851 0.010 1094 9300 -61937.4822 0.010 1094 9400 -61937.4821 0.010 1094 9500 -61937.4821 0.010 1094 9600 -61937.4756 0.010 1094 9700 -61937.4722 0.010 1094 9800 -61937.4722 0.010 1094 9900 -61937.4711 0.010 1094 10000 -61937.4711 0.010 1094 10100 -61937.4707 0.010 1094 10200 -61937.4676 0.010 1094 10300 -61937.4660 0.010 1094 10400 -61937.4625 0.010 1094 10500 -61937.4582 0.010 1094 10600 -61937.4582 0.010 1094 10700 -61937.4555 0.010 1094 10800 -61937.4555 0.010 1094 10900 -61937.4555 0.010 1094 11000 -61937.4498 0.010 1094 11100 -61937.4428 0.010 1094 11200 -61937.4428 0.010 1094 11300 -61937.4412 0.010 1094 11400 -61937.4412 0.010 1094 11500 -61937.4412 0.010 1094 11600 -61937.4411 0.010 1094 11700 -61937.4411 0.010 1094 11800 -61937.4399 0.010 1094 11900 -61937.4352 0.010 1094 12000 -61937.4346 0.010 1094 12100 -61937.4332 0.010 1094 12200 -61937.4332 0.010 1094 12300 -61937.4332 0.010 1094 12400 -61937.4332 0.010 1094 12500 -61937.4332 0.010 1094 12600 -61937.4302 0.010 1094 12700 -61937.4294 0.010 1094 12800 -61937.4251 0.010 1094 12900 -61937.4251 0.010 1094 13000 -61937.4250 0.010 1094 13100 -61937.4230 0.010 1094 13200 -61937.4229 0.010 1094 13300 -61937.4229 0.010 1094 13400 -61937.4229 0.010 1094 13500 -61937.4226 0.010 1094 13600 -61937.4222 0.010 1094 13700 -61937.4205 0.010 1094 13800 -61937.4205 0.010 1094 13900 -61937.4167 0.010 1094 14000 -61937.4167 0.010 1094 14100 -61937.4167 0.010 1094 14200 -61937.4167 0.010 1094 14300 -61937.4165 0.010 1094 14400 -61937.4164 0.010 1094 14500 -61937.4161 0.010 1094 14600 -61937.4128 0.010 1094 14700 -61937.4128 0.010 1094 14800 -61937.4128 0.010 1094 14900 -61937.4128 0.010 1094 15000 -61937.4128 0.010 1094 Optimizing parameters... improved 0.001 lnL Optimizing branchlengths... improved 0.024 lnL 15100 -61937.3883 0.010 1094 15200 -61937.3883 0.010 1094 15300 -61937.3883 0.010 1094 15400 -61937.3883 0.010 1094 15500 -61937.3867 0.010 1094 15600 -61937.3854 0.010 1094 15700 -61937.3854 0.010 1094 15800 -61937.3854 0.010 1094 15900 -61937.3784 0.010 1094 16000 -61937.3782 0.010 1094 16100 -61937.3782 0.010 1094 16200 -61937.3782 0.010 1094 16300 -61937.3782 0.010 1094 16400 -61937.3782 0.010 1094 Reached termination condition! last topological improvement at gen 1094 Improvement over last 500 gen = 0.00018 Current score = -61937.3782 Performing final optimizations... pass 1 : -61937.3696 (branch= 0.0082 alpha= 0.0000 pinv= 0.0001 eq freqs= 0.0001 rel rates= 0.0002 subset rates= 0.0000) pass 2 : -61937.3177 (branch= 0.0518 alpha= 0.0000 pinv= 0.0000 eq freqs= 0.0000 rel rates= 0.0000 subset rates= 0.0000) pass 3 : -61937.2981 (branch= 0.0195 alpha= 0.0000 pinv= 0.0000 eq freqs= 0.0001 rel rates= 0.0000 subset rates= 0.0000) pass 4 : -61937.2874 (branch= 0.0106 alpha= 0.0000 pinv= 0.0000 eq freqs= 0.0000 rel rates= 0.0000 subset rates= 0.0000) pass 5 : -61937.2795 (branch= 0.0079 alpha= 0.0000 pinv= 0.0000 eq freqs= 0.0000 rel rates= 0.0000 subset rates= 0.0000) pass 6 : -61937.2754 (branch= 0.0041 alpha= 0.0000 pinv= 0.0000 eq freqs= 0.0000 rel rates= 0.0000 subset rates= 0.0000) pass 7 : -61937.2729 (branch= 0.0025 alpha= 0.0000 pinv= 0.0000 eq freqs= 0.0000 rel rates= 0.0000 subset rates= 0.0000) pass 8 : -61937.2715 (branch= 0.0007 alpha= 0.0000 pinv= 0.0000 eq freqs= 0.0000 rel rates= 0.0000 subset rates= 0.0007) pass 9 : -61937.2712 (branch= 0.0003 alpha= 0.0000 pinv= 0.0000 eq freqs= 0.0000 rel rates= 0.0000 subset rates= 0.0000) pass 10: -61937.2709 (branch= 0.0003 alpha= 0.0000 pinv= 0.0000 eq freqs= 0.0000 rel rates= 0.0000 subset rates= 0.0000) pass 11: -61937.2706 (branch= 0.0003 alpha= 0.0000 pinv= 0.0000 eq freqs= 0.0000 rel rates= 0.0000 subset rates= 0.0000) pass 12: -61937.2705 (branch= 0.0001 alpha= 0.0000 pinv= 0.0000 eq freqs= 0.0000 rel rates= 0.0000 subset rates= 0.0000) pass 13: -61937.2703 (branch= 0.0001 alpha= 0.0000 pinv= 0.0000 eq freqs= 0.0000 rel rates= 0.0000 subset rates= 0.0000) pass 14: -61937.2703 (branch= 0.0000 alpha= 0.0000 pinv= 0.0000 eq freqs= 0.0000 rel rates= 0.0000 subset rates= 0.0000) pass 15: -61937.2702 (branch= 0.0001 alpha= 0.0000 pinv= 0.0000 eq freqs= 0.0000 rel rates= 0.0000 subset rates= 0.0000) pass 16: -61937.2702 (branch= 0.0000 alpha= 0.0000 pinv= 0.0000 eq freqs= 0.0000 rel rates= 0.0000 subset rates= 0.0000) pass 17: -61937.2702 (branch= 0.0000 alpha= 0.0000 pinv= 0.0000 eq freqs= 0.0000 rel rates= 0.0000 subset rates= 0.0000) pass 18: -61937.2702 (branch= 0.0000 alpha= 0.0000 pinv= 0.0000 eq freqs= 0.0000 rel rates= 0.0000 subset rates= 0.0000) Looking for minimum length branches... Final score = -61937.2702 Time used = 0 hours, 26 minutes and 44 seconds MODEL REPORT - Parameter values are FINAL Model 1 Number of states = 4 (nucleotide data) Nucleotide Relative Rate Matrix: 6 rates AC = 2.028, AG = 7.726, AT = 2.080, CG = 0.817, CT = 13.866, GT = 1.000 Equilibrium State Frequencies: estimated (ACGT) 0.3361 0.2200 0.1423 0.3016 Rate Heterogeneity Model: 4 discrete gamma distributed rate categories, alpha param estimated 0.7748 with an invariant (invariable) site category, proportion estimated 0.4335 Substitution rate categories under this model: rate proportion 0.0000 0.4335 0.0902 0.1416 0.3968 0.1416 0.9497 0.1416 2.5633 0.1416 Model 2 Number of states = 2 (standard data) Character change matrix: One rate (symmetric one rate Mkv model) Equilibrium State Frequencies: equal (0.50, fixed) Rate Heterogeneity Model: no rate heterogeneity Subset rate multipliers: 1.75 0.12 NOTE: Collapsing of minimum length branches was requested (collapsebranches = 1) No branches were short enough to be collapsed. >>>Completed Search rep 2 (of 2)<<< ####################################################### Completed 2 replicate search(es) (of 2). NOTE: Unless the following output indicates that search replicates found the same topology, you should assume that they found different topologies. Results: Replicate 1 : -61937.2705 Replicate 2 : -61937.2702 (best) (same topology as 1) Parameter estimates across search replicates: Partition model subset 1: r(AC) r(AG) r(AT) r(CG) r(CT) r(GT) pi(A) pi(C) pi(G) pi(T) alpha pinv rep 1: 2.029 7.73 2.082 0.8178 13.87 1 0.336 0.220 0.142 0.302 0.775 0.433 rep 2: 2.028 7.726 2.08 0.8171 13.87 1 0.336 0.220 0.142 0.302 0.775 0.433 Partition model subset 2: Model contains no estimated parameters Treelengths and subset rate multipliers: TL R(1) R(2) rep 1: 6.388 1.748 0.118 rep 2: 6.390 1.748 0.118 Saving final trees from all search reps to mixedDnaMkv.best.all.tre Saving final tree from best search rep (#2) to mixedDnaMkv.best.tre ####################################################### garli-2.1-release/example/partition/exampleRuns/mkv/000077500000000000000000000000001241236125200226325ustar00rootroot00000000000000garli-2.1-release/example/partition/exampleRuns/mkv/Lewis2001.nex000066400000000000000000000311351241236125200247370ustar00rootroot00000000000000 #NEXUS [ Title: INCONGRUENCE BETWEEN MORPHOLOGICAL DATA SETS: AN EXAMPLE FROM THE EVOLUTION OF ENDOPARASITISM AMONG PARASITIC WASPS (HYMENOPTERA: BRACONIDAE) Authors: DONALD L. J. QUICKE AND ROBERT BELSHAW Journal: SYSTEMATIC BIOLOGY 48(3): 436-454 Contents: THREE NEXUS FILES, IDENTICAL EXCEPT FOR DIFFERENT ALIGNMENTS OF ONE GENE This represents: File 1: data file with 28S D2 2:1 gap to substitution cost alignment except all of the sequence data has been removed ] BEGIN CHARACTERS; DIMENSIONS NEWTAXA NTAX=30 NCHAR=118; FORMAT labels MISSING=? SYMBOLS= "0 1 2 3 4 5"; OPTIONS MSTAXA=POLYMORPH [gap=newstate]; [DJZ - removing eliminate command, adding assumptions block eliminate 1-45 74-82 85 86 105 108-112 114 116-.; [leaves female+larval character set] ] CHARLABELS [1] antennsensilla [2] antBarlin [3] 'm/f_ant=' [4] Maxillary_palp [5] labial_palp [6] cyclostome [7] laciniaround [8] laciniashort [9] hypoclypset [10] occipital_carina [11] Prontal_dorsum [12] prepectal_carina [13] Notauli [14] scut_sulc [15] scutellum [16] propodeum [17] Central_areola [18] antenna_cleaner [19] tib [20] FWR [21] '2m-cu' [22] '1-SR+M' [23] R_to_margin [24] 'FW1-SR' [25] 'r-m' [26] 'FW2-SR' [27] MCU [28] 'FW_m-cu' [29] 'FW2-M' [30] FWa [31] 3CU1 [32] 'HW2-CU' [33] 'HW_cu-a' [34] HW_submarginal [35] 'HWm-cu' [36] secondary_hamuli [37] petiole [38] numspir [39] T2_spir [40] 'T2-T3articulation' [41] 'MT4-7_apodemes' [42] MT8sculpt [43] MS8 [44] rectal_pads [45] chromosomes [46] prongs [47] ovip_sheath [48] GAoverlap [49] ovip_dor_valve [50] ovip_shape [51] ovip_ridge [52] Ovip [53] sperone [54] DVsculp [55] rachies [56] egg_canal [57] ctenidia [58] ctenidia [59] valvillus [60] valvillus [61] valpos [62] lower_valve [63] lv_seal [64] no_serrations [65] spermatheca [66] venom_app [67] res_sculp [68] VGA [69] Res_Div [70] VGins [71] ven_gland [72] no_ins [73] ovarioles [74] cercifused [75] cerci_setae [76] articulated_cusp [77] basal_ring [78] aedeagus [79] vas_def [80] Testes [81] sperm [82] sperm_morphol [83] egg [84] synovigeny [85] yolky [86] teratocytes [87] teratocyte_origin [88] lar_processes [89] L1_mandib_shape [90] L1_A_spines [91] L1_T3_spines.2 [92] l1spinsfused [93] l1spinesgroups [94] L1_sensilla [95] L1_labral_sensilla [96] lar_antenna [97] Larval_mandible [98] larmandib [99] Larv_spir [100] 'l-spirac' [101] hypostspur [102] stipital [103] larEpist [104] ringsclerite [105] anal_vesicle [106] postvencomm [107] instars [108] Ovip_into_gang [109] idiobiont [110] ectopar [111] final_external_feeding [112] mummif [113] mum [114] pupate [115] emergence_hole [116] paralysis [117] host_feeding [118] hostaphid ; STATELABELS 1 scattered one_rank 2_ranks, 2 small_hole medium_hole entire, 3 unequal equal, 4 six five four three, 5 four three two one, 6 no yes, 7 one three, 8 one three, 9 no yes, 10 complete abs_dors completely_abs, 11 simple_or_two_lateral_pits dorsope_and_2_lt_pits, 12 present absent, 13 present absent, 14 crenulate_or_with_1_carina smooth, 15 with_posterior_cren_groove without_posterior_crenulated_gr, 16 areola_complete areola_incomplete, 17 large small, 19 pegs_absent present, 20 contig_with_parastigma lost, 21 pres absent, 22 present_complete present_but_incomplete totally_absent, 23 yes no, 24 present lost, 25 present absent, 26 present absent, 27 fully_sclerotized largely_absent, 28 present absent, 29 part_present absent, 30 present absent, 31 present absent, 32 present absent, 33 present absent, 34 present absent, 35 present absent, 36 on_spur_beyond_r on_spur_overlapping on_C+SC+R, 37 unfused fused, 38 seven six five, 39 in_notum in_laterotergite, 40 fixed flexible, 41 _ _ _ _ _ _ small 'long,_thin_processes', 42 smooth microsculpture, 43 pointed_anteriorly square_anteriorly, 44 six four two, 45 four five six seven eight more_than_8, 46 absent present, 47 pointed truncate, 48 not trans, 49 lumen_divided lumen_entire, 50 straightish strongly_curved, 51 dorsal_ridge_absent present, 52 simple nodus notch double_nodus, 53 absent present, 54 present_ctenid pres_setae abs, 55 not to_end, 56 closed_by_LVs closed_by_DV, 57 otherwise 'with_scale-like_combsctenidiact', 58 without_sock_seta with_sock_seta, 59 many two one none, 60 no_fringe fringe, 61 apical medial basal, 62 flaps_absent flaps_normal flaps_large, 63 fades_out scaly_and_detached, 64 <3 '=3' >3, 65 white black, 66 muscular not_so, 67 spiral not_spiral, 68 otherwise with_long_prim_duct_and_anterio, 69 undivided divided, 70 in_spiral_part not_in_spiral_part, 71 anterior medial posterior, 72 one two many, 73 one two 'three-seven' eight_or_more, 74 cerci_separate cerci_fused_fo_TIX, 75 _many five four three, 76 present fused, 77 wide_laterally unifomly_narrow produced_medially, 78 normal reduced, 79 posterior anterior, 80 fused_above_gut separate_or_fised_below, 81 long medium short, 82 normal abnormal_morphology, 83 ovoid 'lemon-shaped' with_long_process, 84 synovigenic proovigenic, 85 anhydropic hydropic, 87 from_polar_bodies from_delamination_of_serosa, 88 absent pair_below_tail, 89 sickle_and_narrow broad_base_with_hook_blade, 90 absent single_row multiple_rows, 91 present absent, 92 not_fused fused_and_branching, 93 not_grouped grouped, 94 without_group_of_3 with_group_of_3, 95 absent present, 96 papilliform disc absent, 97 toothed smooth, 98 cross_or_meet separate, 99 prothorax mesothorax, 100 divided undivided, 101 present absent, 102 simple paddle baloon, 103 present absent_or_v_reduced, 104 absent present, 105 absent present, 106 present absent, 107 five four three, 108 not yes, 109 idiobiont koinobiont, 110 ecto endo, 111 present absent, 112 no_mummy mummy, 113 pale always_black, 114 internal external, 115 A B, 116 permanent temporary none, 117 present absent, 118 not_aphid aphid, ; [ 11111111112 2 2 22222 2 23 3 3333333344444444445555555555666666666677777777778888888888 9 9999999999000000000 0111 1 1111 ] [ 123 4 5678901234567890 1 2 34567 8 90 1 2345678901234567890123456789012345678901234567890123456789 0 1234567890123456789 0123 4 5678 ] MATRIX [ ] Aphidius_rhopalosiphi 120 2 1000001011101001 1 2 10011 0 01 1 1111210101012301?10121001103??0011000002101311011101111000 0 10101211?1111000101 1110 1 1211 Aphidius_ervi 120 2 1000001011101001 1 2 10011 0 01 1 1111210101012101?10121001103??0011000002101311011101111000 0 101012111111100?101 1110 1 1211 Diaeretiella_rapae 120 (23) 2000001011101001 1 2 10111 1 01 1 1111210101012201?10121?01?03??00?1000002101311011??11?1000 0 101012111111100?1?1 1110 1 1211 Lysiplebus_confusus 120 (23) 300000101111?001 1 2 10111 1 01 1 1111210101012200?10121001?03??101100000??01311011??1111000 0 101011111111100?1?1 1110 1 1211 Pauesia_unilachni 120 2 1000001011100001 1 2 10011 0 01 1 1111210100012200010?21?01?03???0110000?2?01311011??01?1?00 0 10100211?111100?1?1 1110 1 1211 Pauesia_juniperorum 120 2 1000000001100001 1 2 10011 0 01 (01) 1111210100012201010??1?01?03???0110000???01311011??11?1?00 0 10100211?111100?1?1 1110 1 1211 Binodoxys_acalephae 120 2 2000000011100001 1 2 10111 1 11 ? 1111210100112410?1100??01?0????0?10000?2?0111?0????11?1?01 0 00000211?102100?1?1 1110 1 0211 Trioxys_pallidus 120 2 2000000011100001 1 2 10111 1 11 ? 1111210100112410111001001?03???00100000?101111011??11?1001 0 000002111102100?101 1110 1 0211 Monoctonus_pseudoplatani 120 2 1000001011100001 1 2 10011 0 11 ? 1111210100112300110021?01?020200110100?2?001110????11?1?11 0 00000211?102100?111 1110 1 0111 Praon_volucre 120 2 100000000111?001 1 1 10110 (01) 01 (01) 1101200100012000010021001003??20101100???10200111100111111 0 0101021?1110110?1?1 1110 0 ?211 Praon_abjectum 120 2 100000000111?001 1 (12) 10110 (01) 01 (01) 1101200100012000010?21001003??20101100???10200111100111111 0 0101021?1110110?1?1 1110 0 ?211 [ Praon_dorsale 120 2 100000000111?001 1 1 10110 (01) 01 0 1101200100012000010021001003??20101100???10200111100111111 0 0101021?1110110?1?1 1110 0 ?211 ] Pseudopraon_mindariphagum 120 2 2000000001100001 1 2 10110 0 11 0 1101200100?1??00010?2?????0????0??1100????02001?????????1? ? ???????1????1?0???1 1110 (01) 0211 Dyscritulus_planiceps 120 2 1000000001100001 1 2 10110 0 01 0 110120010001200001002???1?0???20?01000?00102001????01???11 0 010102111110110?1?1 1110 0 ?211 Ephedrus_plagiator 121 2 2000001001100001 1 0 00000 0 01 0 1101200100012301010021001003??2020010002010111011?00111111 0 000102111110100?101 1111 1 0211 Ephedrus_californicus 121 2 2000001001100001 1 0 00000 0 01 0 1101200100012301010021001003??20200100020101110111?0111111 0 000102101110100?101 1111 1 0211 [ Ephedrus_validus 121 2 2000001001100001 1 0 00000 0 01 0 1101200100012301010021001003??2020010002010111011??0111111 0 000102111110100?101 1111 1 0211 Ephedrus_persicae 121 2 2000001001100001 1 0 00000 0 01 (01) 1101200100012301010021001003??2020010002010111011??0111111 0 000102101110100?101 1111 1 0211 ] Sathon_falcatus 211 1 101102011011?001 1 0 10100 0 00 0 1001001110001500011001201101121121110012010011011210111?00 2 1????20010001010101 100? 0 ?210 Chelonus_sp. 010 1 001100000011?100 1 1 01000 0 00 0 1001102010001200010021201101111?11110012010011011210111100 (01) 0000?10010101010101 100? 0 ?210 Cenocoelius_analis 010 0 0011000000101000 1 0 00000 0 00 0 1001100110011?000100212010001?11211100110300110112121???0? ? ?????100?01010????1 100? 0 ?2?0 Eubazus_semirugosus 010 0 0011000000000000 1 0 01100 0 00 0 1001000110011?000100212010001?1101110012030011011210111?00 0 000??1001010101?101 100? 0 ?210 Acampsis_alternipes 010 0 0011000000001100 1 0 00000 0 00 0 0001000110011?000100011010021011?111001?030011011??0110?00 2 100001001010101??11 100? 0 ?210 Alysia_lucicola 100 0 0000020000100100 1 0 00000 0 01 0 10002001100115000000200?0012001010000102010012001000110?01 (12) 000??11100001000101 110? 1 ?110 Aleiodes_coxalis 100 0 010000000011?001 1 0 00000 0 01 0 10010000100115000000000?0012101021000002010012000000000?01 2 1????1100000000?001 1111 1 ?110 Heterospilus_prosopidis 100 0 0100100000100011 1 1 00000 0 01 0 1000200010001?00000030010012001020000112110012000000000?01 1 000??00000000001000 000? 0 ?000 Hecabolus_sp. 100 0 010010000011?011 1 0 00000 0 01 0 1000200010001?00000030010012001020000112110012000000000?01 1 000??00000001001000 000? 0 ?0?0 Bracon_sp. 100 1 010012010011?101 1 0 00000 0 01 0 1001200010011500000010010012001020000012210012000000000?01 2 1????00000000001000 000? 0 ?000 Colastes_incertus 100 0 010000010011?001 1 0 00000 0 01 0 1000200010001?000000100100120010?000010?010012000000000?01 1 000??00000000001000 000? 0 ?000 Rhyssalus_sp. 100 0 0100100000101000 1 0 00000 0 00 0 100020011000??000000100100110010?01110020100?100?000000?0? ? ?????0000000000??00 000? 0 ?000 Histeromerus_mystacinus 110 0 010011011011?000 (01) 0 00000 0 00 0 1000100110002?00000000?1001101100011100201000000???0000?0? ? ?????0000000000??00 000? 0 ?010 Xorides_praecatorius 010 1 0001000000000000 0 2 0???0 0 01 0 0001010000000?00000011000001001020000000020000010000000?0? ? ?????000?000000??00 000? 0 ?0?0 Alomyia_debellator 010 1 000100001011?000 0 2 0???0 0 01 0 0001010000000?000100100000011010201100000?0000000000??0?0? ? ?????111001000????1 111? 1 ?2?0 ; END; begin assumptions; exset * larfem = 1-45 74-82 85 86 105 108-112 114 116-.; end; garli-2.1-release/example/partition/exampleRuns/mkv/garli.conf000066400000000000000000000024211241236125200245760ustar00rootroot00000000000000[general] datafname = lewis2001.nex constraintfile = none streefname = random attachmentspertaxon = 100 ofprefix = mkv randseed = -1 availablememory = 512 logevery = 10 saveevery = 100 refinestart = 1 outputeachbettertopology = 0 outputcurrentbesttopology = 0 enforcetermconditions = 1 genthreshfortopoterm = 10000 scorethreshforterm = 0.001 significanttopochange = 0.01 outputphyliptree = 0 outputmostlyuselessfiles = 0 writecheckpoints = 0 restart = 0 outgroup = 1 resampleproportion = 1.0 inferinternalstateprobs = 0 outputsitelikelihoods = 0 optimizeinputonly = 0 collapsebranches = 1 searchreps = 5 bootstrapreps = 0 linkmodels = 0 subsetspecificrates = 0 [model1] datatype = standardvariable ratematrix = 1rate statefrequencies = equal ratehetmodel = none numratecats = 1 invariantsites = none [master] nindivs = 4 holdover = 1 selectionintensity = 0.5 holdoverpenalty = 0 stopgen = 5000000 stoptime = 5000000 startoptprec = 0.5 minoptprec = 0.01 numberofprecreductions = 10 treerejectionthreshold = 50.0 topoweight = 0.01 modweight = 0.002 brlenweight = 0.002 randnniweight = 0.1 randsprweight = 0.3 limsprweight = 0.6 intervallength = 100 intervalstostore = 5 limsprrange = 6 meanbrlenmuts = 5 gammashapebrlen = 1000 gammashapemodel = 1000 uniqueswapbias = 0.1 distanceswapbias = 1.0 garli-2.1-release/example/partition/exampleRuns/mkv/mkv.best.all.tre000066400000000000000000000115251241236125200256520ustar00rootroot00000000000000#NEXUS begin trees; translate 1 Aphidius_rhopalosiphi, 2 Aphidius_ervi, 3 Diaeretiella_rapae, 4 Lysiplebus_confusus, 5 Pauesia_unilachni, 6 Pauesia_juniperorum, 7 Binodoxys_acalephae, 8 Trioxys_pallidus, 9 Monoctonus_pseudoplatani, 10 Praon_volucre, 11 Praon_abjectum, 12 Pseudopraon_mindariphagum, 13 Dyscritulus_planiceps, 14 Ephedrus_plagiator, 15 Ephedrus_californicus, 16 Sathon_falcatus, 17 Chelonus_sp., 18 Cenocoelius_analis, 19 Eubazus_semirugosus, 20 Acampsis_alternipes, 21 Alysia_lucicola, 22 Aleiodes_coxalis, 23 Heterospilus_prosopidis, 24 Hecabolus_sp., 25 Bracon_sp., 26 Colastes_incertus, 27 Rhyssalus_sp., 28 Histeromerus_mystacinus, 29 Xorides_praecatorius, 30 Alomyia_debellator; tree rep1 = [&U][!GarliScore -472.39083][!GarliModel M1 e 0.50000 0.50000 M2 e 0.33333 0.33333 0.33333 M3 e 0.25000 0.25000 0.25000 0.25000 ](24:0.02076503,23:0.00000001,(((((20:0.13910242,19:0.00000001,18:0.08202657):0.08612723,(16:0.20549705,17:0.00000001):0.02277555):0.35723526,((((((1:0.00000001,4:0.06853868,2:0.00000001,3:0.00000001):0.02387432,6:0.00000001):0.02003674,5:0.02834557):0.22046080,(7:0.00000001,8:0.00000001):0.11235003):0.03972373,9:0.03258248):0.29754135,(10:0.00000001,13:0.03089219,11:0.00000001,12:0.00000001):0.08992231,(14:0.00000001,15:0.01994227):0.06222771):0.12443750):0.37074536,21:0.00000001):0.25289579,26:0.00000001,(22:0.12959685,((28:0.06286434,27:0.00000001):0.09631312,(30:0.27621754,29:0.00938861):0.13478015):0.02632217,25:0.04773151):0.06564413):0.06578588); tree rep2 = [&U][!GarliScore -472.39058759][!GarliModel M1 e 0.50000 0.50000 M2 e 0.33333 0.33333 0.33333 M3 e 0.25000 0.25000 0.25000 0.25000 ]((((9:0.03255822,((12:0.00000001,13:0.03086586,11:0.00000001,10:0.00000001):0.08991187,(((16:0.20511647,17:0.00000001):0.02288206,(20:0.13832030,(18:0.08210741,19:0.00000001):0.00109863):0.08546662):0.35740524,(((22:0.12959239,((28:0.06287536,27:0.00000001):0.09633755,(30:0.27628400,29:0.00944828):0.13464285):0.02629110,25:0.04777096):0.06563332,26:0.00000001,(23:0.00000001,24:0.02076454):0.06578299):0.25282094,21:0.00000001):0.37038197):0.12488484,(15:0.01994297,14:0.00000001):0.06223081):0.29746932):0.03967022,(7:0.00000001,8:0.00000001):0.11245268):0.22038020,5:0.02831839):0.02007387,(3:0.00000001,4:0.06853775,1:0.00000001,2:0.00000001):0.02386163,6:0.00000001); tree rep3BEST = [&U][!GarliScore -472.39057697][!GarliModel M1 e 0.50000 0.50000 M2 e 0.33333 0.33333 0.33333 M3 e 0.25000 0.25000 0.25000 0.25000 ]((((7:0.00000001,8:0.00000001):0.11236048,(((12:0.00000001,10:0.00000001,11:0.00000001,13:0.03088157):0.08988849,(((20:0.13825439,(18:0.08204068,19:0.00000001):0.00123999):0.08527720,(16:0.20500271,17:0.00000001):0.02298487):0.35721979,(((((30:0.27621511,29:0.00946667):0.13454604,(28:0.06286195,27:0.00000001):0.09631244):0.02630013,25:0.04771927,22:0.12958639):0.06564011,26:0.00000001,(24:0.02076360,23:0.00000001):0.06578090):0.25273699,21:0.00000001):0.37026723):0.12499145,(15:0.01994165,14:0.00000001):0.06218053):0.29756654,9:0.03257892):0.03967214):0.22041684,5:0.02831043):0.02007330,6:0.00000001,(4:0.06854944,3:0.00000001,1:0.00000001,2:0.00000001):0.02384508); tree rep4 = [&U][!GarliScore -473.59637940][!GarliModel M1 e 0.50000 0.50000 M2 e 0.33333 0.33333 0.33333 M3 e 0.25000 0.25000 0.25000 0.25000 ](7:0.00000001,((5:0.02859651,(6:0.00000001,(2:0.00000001,4:0.06878983,3:0.00000001,1:0.00000001):0.02397891):0.01997552):0.22267001,(9:0.03307470,(((((17:0.02941573,16:0.16447154):0.02704881,(19:0.02950486,18:0.05584294,(20:0.00000001,(22:0.00000001,(25:0.04803852,(21:0.23899086,26:0.00000001,(24:0.02083388,23:0.00000001):0.06602439):0.06454996,((30:0.27792394,29:0.00925617):0.13545962,(27:0.00000001,28:0.06312749):0.09675542):0.02638458):0.13076371):0.58849982):0.12916443):0.05524582):0.46784652,15:0.00000001):0.02024653,14:0.00000001):0.06391240,(12:0.00000001,13:0.03046742,10:0.00000001,11:0.00000001):0.09126425):0.32776636):0.03879655):0.11236473,8:0.00000001); tree rep5 = [&U][!GarliScore -474.58596822][!GarliModel M1 e 0.50000 0.50000 M2 e 0.33333 0.33333 0.33333 M3 e 0.25000 0.25000 0.25000 0.25000 ](((((6:0.00000001,(2:0.00000001,4:0.06842016,3:0.00000001,1:0.00000001):0.02382922):0.01998616,5:0.02840229):0.22120056,(7:0.00000001,8:0.00000001):0.11160898):0.03835873,9:0.03333404):0.33000315,(13:0.03028206,12:0.00000001,11:0.00000001,10:0.00000001):0.09023672,((15:0.00000001,(((19:0.03455914,20:0.11703538,18:0.05560975):0.05824676,(16:0.15890988,17:0.03352756):0.02222396):0.14540449,(30:0.00000001,(29:0.04447756,((27:0.00000001,28:0.06299443):0.09294946,(22:0.12906664,(21:0.23729208,26:0.00000001,(24:0.02071497,23:0.00000001):0.06568807):0.06384650,25:0.04837874):0.02978368):0.10242674):0.27754259):0.36830668):0.32276881):0.02023717,14:0.00000001):0.06449326); end; garli-2.1-release/example/partition/exampleRuns/mkv/mkv.best.tre000066400000000000000000000030431241236125200250770ustar00rootroot00000000000000#NEXUS begin trees; translate 1 Aphidius_rhopalosiphi, 2 Aphidius_ervi, 3 Diaeretiella_rapae, 4 Lysiplebus_confusus, 5 Pauesia_unilachni, 6 Pauesia_juniperorum, 7 Binodoxys_acalephae, 8 Trioxys_pallidus, 9 Monoctonus_pseudoplatani, 10 Praon_volucre, 11 Praon_abjectum, 12 Pseudopraon_mindariphagum, 13 Dyscritulus_planiceps, 14 Ephedrus_plagiator, 15 Ephedrus_californicus, 16 Sathon_falcatus, 17 Chelonus_sp., 18 Cenocoelius_analis, 19 Eubazus_semirugosus, 20 Acampsis_alternipes, 21 Alysia_lucicola, 22 Aleiodes_coxalis, 23 Heterospilus_prosopidis, 24 Hecabolus_sp., 25 Bracon_sp., 26 Colastes_incertus, 27 Rhyssalus_sp., 28 Histeromerus_mystacinus, 29 Xorides_praecatorius, 30 Alomyia_debellator; tree bestREP3 = [&U][!GarliScore -472.390577][!GarliModel M1 e 0.50000 0.50000 M2 e 0.33333 0.33333 0.33333 M3 e 0.25000 0.25000 0.25000 0.25000 ]((((7:0.00000001,8:0.00000001):0.11236048,(((12:0.00000001,10:0.00000001,11:0.00000001,13:0.03088157):0.08988849,(((20:0.13825439,(18:0.08204068,19:0.00000001):0.00123999):0.08527720,(16:0.20500271,17:0.00000001):0.02298487):0.35721979,(((((30:0.27621511,29:0.00946667):0.13454604,(28:0.06286195,27:0.00000001):0.09631244):0.02630013,25:0.04771927,22:0.12958639):0.06564011,26:0.00000001,(24:0.02076360,23:0.00000001):0.06578090):0.25273699,21:0.00000001):0.37026723):0.12499145,(15:0.01994165,14:0.00000001):0.06218053):0.29756654,9:0.03257892):0.03967214):0.22041684,5:0.02831043):0.02007330,6:0.00000001,(4:0.06854944,3:0.00000001,1:0.00000001,2:0.00000001):0.02384508); end; garli-2.1-release/example/partition/exampleRuns/mkv/mkv.log00.log000066400000000000000000007270031241236125200250630ustar00rootroot00000000000000Search rep 1 (of 5) random seed = 113156 gen best_like time optPrecision 0 -837.7144974 0 0.5 10 -791.6582983 0 0.5 20 -788.5939546 0 0.5 30 -728.0455255 0 0.5 40 -700.8589295 0 0.5 50 -684.1875509 0 0.5 60 -656.053475 0 0.5 70 -646.6480566 0 0.5 80 -617.5543937 0 0.5 90 -593.6563126 0 0.5 100 -592.1303299 0 0.5 110 -580.9060046 0 0.5 120 -580.7826629 0 0.5 130 -580.7768646 0 0.5 140 -580.1642304 0 0.5 150 -580.0719047 0 0.5 160 -568.3336942 0 0.5 170 -568.2356884 0 0.5 180 -565.584049 0 0.5 190 -553.489975 0 0.5 200 -550.9261357 0 0.5 210 -546.6750075 0 0.5 220 -543.805642 0 0.5 230 -543.6681609 0 0.5 240 -541.6612477 0 0.5 250 -541.0826976 0 0.5 260 -540.2029033 0 0.5 270 -538.1727277 0 0.5 280 -537.9648868 0 0.5 290 -537.8103573 0 0.5 300 -502.7223063 0 0.5 310 -499.4549509 1 0.5 320 -498.7955468 1 0.5 330 -498.5456577 1 0.5 340 -498.4386578 1 0.5 350 -498.3220932 1 0.5 360 -498.211985 1 0.5 370 -497.4340217 1 0.5 380 -496.2663349 1 0.5 390 -496.2220897 1 0.5 400 -496.2153387 1 0.5 410 -485.8509913 1 0.5 420 -483.867697 1 0.5 430 -483.5487594 1 0.5 440 -483.3767652 1 0.5 450 -483.3212687 1 0.5 460 -483.2930257 1 0.5 470 -483.1821162 1 0.5 480 -482.6826421 1 0.5 490 -482.6225988 1 0.5 500 -482.5724142 1 0.5 510 -482.5038997 1 0.5 520 -482.4021884 1 0.5 530 -481.5523521 1 0.5 540 -481.1860514 1 0.5 550 -481.1164963 1 0.5 560 -481.0453212 1 0.5 570 -481.03956 1 0.5 580 -480.543966 1 0.5 590 -480.4624003 1 0.5 600 -480.2204569 1 0.5 610 -480.1418839 1 0.5 620 -480.0352612 1 0.5 630 -479.8552166 1 0.5 640 -479.8136461 1 0.5 650 -479.7600011 1 0.5 660 -479.7432575 1 0.5 670 -479.71597 1 0.5 680 -479.6143496 1 0.5 690 -479.5298891 1 0.5 700 -479.5175821 1 0.5 710 -479.3908236 1 0.5 720 -479.3538857 1 0.5 730 -479.3518035 1 0.5 740 -479.1500315 1 0.5 750 -479.1500315 1 0.5 760 -479.1420152 1 0.5 770 -479.0863859 1 0.5 780 -479.0647858 1 0.5 790 -479.0471871 1 0.5 800 -479.0438303 1 0.5 810 -479.0194435 2 0.5 820 -478.9604586 2 0.5 830 -478.9114993 2 0.5 840 -478.8956001 2 0.5 850 -478.7588453 2 0.5 860 -478.7413206 2 0.5 870 -478.7352527 2 0.5 880 -478.7175979 2 0.5 890 -478.6999553 2 0.5 900 -478.6735155 2 0.5 910 -478.6549961 2 0.5 920 -478.6421259 2 0.5 930 -478.6396956 2 0.5 940 -478.6126241 2 0.5 950 -478.5596244 2 0.5 960 -478.5482065 2 0.5 970 -478.5001446 2 0.5 980 -478.3427258 2 0.5 990 -478.3427246 2 0.5 1000 -478.3271506 2 0.5 1010 -478.3012042 2 0.5 1020 -478.2427383 2 0.5 1030 -478.2343125 2 0.5 1040 -478.1727291 2 0.5 1050 -478.1135566 2 0.5 1060 -478.0925006 2 0.5 1070 -478.051884 2 0.5 1080 -477.9921349 2 0.5 1090 -477.9221866 2 0.5 1100 -477.8446607 2 0.5 1110 -477.8016656 2 0.5 1120 -477.7982012 2 0.5 1130 -477.7564708 2 0.5 1140 -477.7309658 2 0.5 1150 -476.9066408 2 0.5 1160 -476.6747283 2 0.5 1170 -476.599305 2 0.5 1180 -476.5966333 2 0.5 1190 -476.5877518 2 0.5 1200 -476.5509562 2 0.5 1210 -476.442408 2 0.5 1220 -476.4054952 2 0.5 1230 -476.3757308 2 0.5 1240 -476.3138875 2 0.5 1250 -476.3015896 2 0.5 1260 -476.2711478 2 0.5 1270 -476.172468 2 0.5 1280 -476.1423962 2 0.5 1290 -476.0989647 2 0.5 1300 -476.0617085 2 0.5 1310 -476.0483133 2 0.5 1320 -476.0452088 2 0.5 1330 -476.0379163 3 0.5 1340 -476.0313601 3 0.5 1350 -475.9883792 3 0.5 1360 -475.9417086 3 0.5 1370 -475.9027716 3 0.5 1380 -475.8630764 3 0.5 1390 -475.8603859 3 0.5 1400 -475.8285691 3 0.5 1410 -475.8092286 3 0.5 1420 -475.7716081 3 0.5 1430 -475.7447593 3 0.5 1440 -475.6647265 3 0.5 1450 -475.6319333 3 0.5 1460 -475.6284735 3 0.5 1470 -475.6263159 3 0.5 1480 -475.5927871 3 0.5 1490 -475.5927816 3 0.5 1500 -475.5767425 3 0.5 1510 -475.5690082 3 0.5 1520 -475.5571747 3 0.5 1530 -475.5442818 3 0.5 1540 -475.4884646 3 0.5 1550 -475.4811278 3 0.5 1560 -475.4777695 3 0.5 1570 -475.4294735 3 0.5 1580 -475.4289111 3 0.5 1590 -475.4277059 3 0.5 1600 -475.4252515 3 0.5 1610 -475.40522 3 0.5 1620 -475.3968628 3 0.5 1630 -475.364993 3 0.5 1640 -475.3555946 3 0.5 1650 -475.3524741 3 0.5 1660 -475.2922714 3 0.5 1670 -475.286876 3 0.5 1680 -475.2774021 3 0.5 1690 -475.2773429 3 0.5 1700 -475.2730209 3 0.5 1710 -475.2395116 3 0.451 1720 -475.2366846 3 0.451 1730 -475.2349692 3 0.451 1740 -475.233922 3 0.451 1750 -475.233922 3 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-474.6024067 233 0.01 15880 -474.6023965 233 0.01 15890 -474.6023965 233 0.01 15900 -474.6023965 233 0.01 15910 -474.6023393 233 0.01 15920 -474.6023393 233 0.01 15930 -474.6023381 233 0.01 15940 -474.6022832 233 0.01 15950 -474.6022832 233 0.01 15960 -474.6022832 233 0.01 15970 -474.6022832 233 0.01 15980 -474.6022832 233 0.01 15990 -474.6022832 233 0.01 16000 -474.6022832 234 0.01 16010 -474.6022832 234 0.01 16020 -474.6022832 234 0.01 16030 -474.6022832 234 0.01 16040 -474.6022832 234 0.01 16050 -474.6022832 234 0.01 16060 -474.6022832 234 0.01 16070 -474.6022832 234 0.01 16080 -474.6022832 234 0.01 16090 -474.6022832 234 0.01 16100 -474.6022055 234 0.01 Score after final optimization: -474.5859682 Final -474.5859682 234 0.01 garli-2.1-release/example/partition/exampleRuns/mkv/mkv.screen.log000066400000000000000000002032551241236125200254170ustar00rootroot00000000000000Running GARLI-PART Version 2.0.1008 (17 Mar 2011) ->Single processor version<- ############################################################## This is GARLI 2.0, the first "official" release including partitioned models. It is a merging of official release 1.0 and beta version GARLI-PART 0.97 Briefly, it includes models for nucleotides, amino acids, codons, and morphology-like characters, any of which can be mixed together and applied to different subsets of data. General program usage is extensively documented here: http://www.nescent.org/wg_garli/ see this page for details on partitioned usage: http://www.nescent.org/wg_garli/Partition_testing_version and this page for details on Mkv mophology model usage: http://www.nescent.org/wg_garli/Mkv_morphology_model PLEASE LET ME KNOW OF ANY PROBLEMS AT: garli.support@gmail.com ############################################################## This version has undergone much testing, but is still a BETA VERSION. - Please check results carefully! - Compiled Mar 21 2011 13:13:18 using Intel icc compiler version 9.10 Using NCL version 2.1.10 ####################################################### Reading config file garli.conf ################################################### READING OF DATA Attempting to read data file in Nexus format (using NCL): lewis2001.nex ... Reading CHARACTERS block... found standard data... successful Reading ASSUMPTIONS block... successful ################################################### PARTITIONING OF DATA AND MODELS GARLI data subset 1 CHARACTERS block #1 ("Untitled CHARACTERS Block 1") Data read as Standard k-state data, variable only, modeled as Standard k-state data, variable only Subset of data with 2 states: chars 46-51 53 55-58 60 63 65-70 84 87-89 91-95 97-101 103 104 106 107 113 115 Summary of data: 30 sequences. 0 constant characters. 39 parsimony-informative characters. 0 uninformative variable characters. 39 total characters. 39 unique patterns in compressed data matrix. Pattern processing required < 1 second Part ambig. char's of taxon Chelonus_sp. converted to full ambiguity: char 90 Part ambig. char's of taxon Alysia_lucicola converted to full ambiguity: char 90 Subset of data with 3 states: chars 54 61 62 64 71 72 83 90-102 \ 6 Summary of data: 30 sequences. 0 constant characters. 10 parsimony-informative characters. 0 uninformative variable characters. 10 total characters. 10 unique patterns in compressed data matrix. Pattern processing required < 1 second Subset of data with 4 states: chars 52 59 73 Summary of data: 30 sequences. 0 constant characters. 0 parsimony-informative characters. 3 uninformative variable characters. 3 total characters. 3 unique patterns in compressed data matrix. Pattern processing required < 1 second NOTE: No characters found with 5 observed states. NOTE: No characters found with 6 observed states. ################################################### NOTE: Unlike many programs, the amount of system memory that Garli will use can be controlled by the user. (This comes from the availablememory setting in the configuration file. Availablememory should NOT be set to more than the actual amount of physical memory that your computer has installed) For this dataset: Mem level availablememory setting great >= 1 MB good approx 0 MB to 1 MB low approx 0 MB to 1 MB very low approx 0 MB to 1 MB the minimum required availablememory is 1 MB You specified that Garli should use at most 512.0 MB of memory. Garli will actually use approx. 0.6 MB of memory **Your memory level is: great (you don't need to change anything)** ####################################################### STARTING RUN >>>Search rep 1 (of 5)<<< MODEL REPORT - Parameters are at their INITIAL values (not yet optimized) Model 1 Number of states = 2 (standard data) Character change matrix: One rate (symmetric one rate Mkv model) Equilibrium State Frequencies: equal (0.50, fixed) Rate Heterogeneity Model: no rate heterogeneity Model 2 Number of states = 3 (standard data) Character change matrix: One rate (symmetric one rate Mkv model) Equilibrium State Frequencies: equal (0.33, fixed) Rate Heterogeneity Model: no rate heterogeneity Model 3 Number of states = 4 (standard data) Character change matrix: One rate (symmetric one rate Mkv model) Equilibrium State Frequencies: equal (0.25, fixed) Rate Heterogeneity Model: no rate heterogeneity Starting with seed=113156 creating random starting tree... NOTE: Model contains no mutable parameters! Setting model mutation weight to zero. Initial ln Likelihood: -1296.0557 optimizing: starting branch lengths... pass 1:+ 364.588 (branch= 364.59 scale= 0.00) pass 2:+ 66.578 (branch= 63.37 scale= 3.21) pass 3:+ 24.948 (branch= 23.25 scale= 1.69) pass 4:+ 1.187 (branch= 0.53 scale= 0.66) pass 5:+ 1.040 (branch= 1.04 scale= 0.00) pass 6:+ 0.000 (branch= 0.00 scale= 0.00) lnL after optimization: -837.7145 gen current_lnL precision last_tree_imp 0 -837.7145 0.500 0 100 -592.1303 0.500 95 200 -550.9261 0.500 198 300 -502.7223 0.500 293 400 -496.2153 0.500 379 500 -482.5724 0.500 480 600 -480.2205 0.500 599 700 -479.5176 0.500 599 800 -479.0438 0.500 706 900 -478.6735 0.500 706 1000 -478.3272 0.500 706 1100 -477.8447 0.500 1006 1200 -476.5510 0.500 1153 1300 -476.0617 0.500 1153 1400 -475.8286 0.500 1153 1500 -475.5767 0.500 1153 1600 -475.4253 0.500 1153 1700 -475.2730 0.500 1153 Optimization precision reduced Optimizing parameters... improved 0.000 lnL Optimizing branchlengths... improved 0.002 lnL 1800 -475.1729 0.451 1153 1900 -475.1232 0.451 1153 2000 -474.7475 0.451 1914 2100 -474.6530 0.451 1914 2200 -474.3547 0.451 2182 2300 -474.2897 0.451 2182 2400 -474.1635 0.451 2182 2500 -474.1124 0.451 2182 2600 -474.0612 0.451 2182 2700 -473.9885 0.451 2182 Optimization precision reduced Optimizing parameters... improved 0.000 lnL Optimizing branchlengths... improved 0.000 lnL 2800 -473.9058 0.402 2744 2900 -473.7521 0.402 2846 3000 -473.7250 0.402 2846 3100 -473.6863 0.402 2846 3200 -473.6766 0.402 2846 3300 -473.6522 0.402 2846 3400 -473.6455 0.402 2846 Optimization precision reduced Optimizing parameters... improved 0.000 lnL Optimizing branchlengths... improved 0.000 lnL 3500 -473.6274 0.353 2846 3600 -473.5061 0.353 3576 3700 -473.4862 0.353 3576 3800 -473.4791 0.353 3576 3900 -473.4632 0.353 3576 4000 -473.4431 0.353 3576 4100 -473.4328 0.353 3576 Optimization precision reduced Optimizing parameters... improved 0.000 lnL Optimizing branchlengths... improved 0.000 lnL 4200 -473.4217 0.304 3576 4300 -473.4091 0.304 3576 4400 -473.3964 0.304 3576 4500 -473.3896 0.304 3576 4600 -473.3704 0.304 4588 4700 -473.3686 0.304 4588 4800 -473.3595 0.304 4588 4900 -473.3544 0.304 4588 5000 -473.3466 0.304 4588 5100 -473.3397 0.304 4588 Optimization precision reduced Optimizing parameters... improved 0.000 lnL Optimizing branchlengths... improved 0.000 lnL 5200 -473.3341 0.255 4588 5300 -473.3251 0.255 4588 5400 -473.3183 0.255 4588 5500 -473.2540 0.255 5457 5600 -473.1386 0.255 5457 5700 -473.0787 0.255 5457 5800 -473.0601 0.255 5457 5900 -473.0481 0.255 5457 6000 -472.9455 0.255 5996 6100 -472.9401 0.255 5996 6200 -472.9357 0.255 5996 6300 -472.9343 0.255 5996 6400 -472.9339 0.255 5996 6500 -472.9274 0.255 5996 Optimization precision reduced Optimizing parameters... improved 0.000 lnL Optimizing branchlengths... improved 0.000 lnL 6600 -472.9270 0.206 5996 6700 -472.9227 0.206 5996 6800 -472.9208 0.206 5996 6900 -472.9158 0.206 5996 7000 -472.9152 0.206 5996 Optimization precision reduced Optimizing parameters... improved 0.000 lnL Optimizing branchlengths... improved 0.000 lnL 7100 -472.9117 0.157 5996 7200 -472.8881 0.157 7137 7300 -472.8873 0.157 7137 7400 -472.8860 0.157 7137 7500 -472.8830 0.157 7137 7600 -472.8824 0.157 7137 7700 -472.8805 0.157 7137 Optimization precision reduced Optimizing parameters... improved 0.000 lnL Optimizing branchlengths... improved 0.168 lnL 7800 -472.6943 0.108 7137 7900 -472.6573 0.108 7137 8000 -472.6449 0.108 7137 8100 -472.6326 0.108 7137 8200 -472.6290 0.108 7137 Optimization precision reduced Optimizing parameters... improved 0.000 lnL Optimizing branchlengths... improved 0.000 lnL 8300 -472.6197 0.059 7137 8400 -472.5536 0.059 8357 8500 -472.5409 0.059 8357 8600 -472.5364 0.059 8357 8700 -472.5310 0.059 8357 8800 -472.5256 0.059 8357 8900 -472.5249 0.059 8357 Optimization precision reduced Optimizing parameters... improved 0.000 lnL Optimizing branchlengths... improved 0.066 lnL 9000 -472.4570 0.010 8357 9100 -472.4491 0.010 8357 9200 -472.4424 0.010 8357 9300 -472.4399 0.010 8357 9400 -472.4338 0.010 8357 9500 -472.4322 0.010 8357 9600 -472.4291 0.010 8357 9700 -472.4275 0.010 8357 9800 -472.4256 0.010 8357 9900 -472.4229 0.010 8357 10000 -472.4203 0.010 8357 10100 -472.4203 0.010 8357 10200 -472.4200 0.010 8357 10300 -472.4200 0.010 8357 10400 -472.4197 0.010 8357 10500 -472.4191 0.010 8357 10600 -472.4179 0.010 8357 10700 -472.4176 0.010 8357 10800 -472.4172 0.010 8357 10900 -472.4136 0.010 8357 11000 -472.4115 0.010 8357 11100 -472.4102 0.010 8357 11200 -472.4099 0.010 8357 11300 -472.4096 0.010 8357 11400 -472.4092 0.010 8357 11500 -472.4087 0.010 8357 11600 -472.4086 0.010 8357 11700 -472.4082 0.010 8357 11800 -472.4080 0.010 8357 11900 -472.4078 0.010 8357 12000 -472.4075 0.010 8357 12100 -472.4074 0.010 8357 12200 -472.4073 0.010 8357 12300 -472.4068 0.010 8357 12400 -472.4066 0.010 8357 12500 -472.4065 0.010 8357 12600 -472.4064 0.010 8357 12700 -472.4060 0.010 8357 12800 -472.4059 0.010 8357 12900 -472.4058 0.010 8357 13000 -472.4057 0.010 8357 13100 -472.4056 0.010 8357 13200 -472.4056 0.010 8357 13300 -472.4056 0.010 8357 13400 -472.4055 0.010 8357 13500 -472.4054 0.010 8357 13600 -472.4054 0.010 8357 13700 -472.4054 0.010 8357 13800 -472.4053 0.010 8357 13900 -472.4052 0.010 8357 14000 -472.4052 0.010 8357 14100 -472.4048 0.010 8357 14200 -472.4048 0.010 8357 14300 -472.4047 0.010 8357 14400 -472.4045 0.010 8357 14500 -472.4045 0.010 8357 14600 -472.4045 0.010 8357 14700 -472.4045 0.010 8357 14800 -472.4045 0.010 8357 14900 -472.4045 0.010 8357 15000 -472.4044 0.010 8357 Optimizing parameters... improved 0.000 lnL Optimizing branchlengths... improved 0.000 lnL 15100 -472.4042 0.010 8357 15200 -472.4042 0.010 8357 15300 -472.4041 0.010 8357 15400 -472.4041 0.010 8357 15500 -472.4039 0.010 8357 15600 -472.4039 0.010 8357 15700 -472.4036 0.010 8357 15800 -472.4036 0.010 8357 15900 -472.4034 0.010 8357 16000 -472.4034 0.010 8357 16100 -472.4032 0.010 8357 16200 -472.4031 0.010 8357 16300 -472.4031 0.010 8357 16400 -472.4031 0.010 8357 16500 -472.4030 0.010 8357 16600 -472.4028 0.010 8357 16700 -472.4028 0.010 8357 16800 -472.4027 0.010 8357 16900 -472.4027 0.010 8357 17000 -472.4026 0.010 8357 17100 -472.4026 0.010 8357 17200 -472.4025 0.010 8357 17300 -472.4025 0.010 8357 17400 -472.4024 0.010 8357 17500 -472.4022 0.010 8357 17600 -472.4022 0.010 8357 17700 -472.4022 0.010 8357 17800 -472.4021 0.010 8357 17900 -472.4020 0.010 8357 18000 -472.4020 0.010 8357 18100 -472.4020 0.010 8357 18200 -472.4020 0.010 8357 18300 -472.4019 0.010 8357 18400 -472.4018 0.010 8357 18500 -472.4018 0.010 8357 18600 -472.4017 0.010 8357 18700 -472.4015 0.010 8357 18800 -472.4014 0.010 8357 18900 -472.4014 0.010 8357 19000 -472.4014 0.010 8357 Reached termination condition! last topological improvement at gen 8357 Improvement over last 500 gen = 0.00042 Current score = -472.4014 Performing final optimizations... pass 1 : -472.4014 (branch= 0.0000) pass 2 : -472.4014 (branch= 0.0000) pass 3 : -472.3948 (branch= 0.0066) pass 4 : -472.3919 (branch= 0.0029) pass 5 : -472.3915 (branch= 0.0004) pass 6 : -472.3915 (branch= 0.0000) pass 7 : -472.3915 (branch= 0.0000) pass 8 : -472.3912 (branch= 0.0003) pass 9 : -472.3910 (branch= 0.0002) pass 10: -472.3909 (branch= 0.0001) pass 11: -472.3908 (branch= 0.0000) pass 12: -472.3908 (branch= 0.0000) pass 13: -472.3908 (branch= 0.0000) Looking for minimum length branches... Final score = -472.3908 Time used so far = 0 hours, 0 minutes and 50 seconds MODEL REPORT - Parameter values are FINAL Model 1 Number of states = 2 (standard data) Character change matrix: One rate (symmetric one rate Mkv model) Equilibrium State Frequencies: equal (0.50, fixed) Rate Heterogeneity Model: no rate heterogeneity Model 2 Number of states = 3 (standard data) Character change matrix: One rate (symmetric one rate Mkv model) Equilibrium State Frequencies: equal (0.33, fixed) Rate Heterogeneity Model: no rate heterogeneity Model 3 Number of states = 4 (standard data) Character change matrix: One rate (symmetric one rate Mkv model) Equilibrium State Frequencies: equal (0.25, fixed) Rate Heterogeneity Model: no rate heterogeneity NOTE: Collapsing of minimum length branches was requested (collapsebranches = 1) 8 branches were collapsed. >>>Completed Search rep 1 (of 5)<<< >>>Search rep 2 (of 5)<<< MODEL REPORT - Parameters are at their INITIAL values (not yet optimized) Model 1 Number of states = 2 (standard data) Character change matrix: One rate (symmetric one rate Mkv model) Equilibrium State Frequencies: equal (0.50, fixed) Rate Heterogeneity Model: no rate heterogeneity Model 2 Number of states = 3 (standard data) Character change matrix: One rate (symmetric one rate Mkv model) Equilibrium State Frequencies: equal (0.33, fixed) Rate Heterogeneity Model: no rate heterogeneity Model 3 Number of states = 4 (standard data) Character change matrix: One rate (symmetric one rate Mkv model) Equilibrium State Frequencies: equal (0.25, fixed) Rate Heterogeneity Model: no rate heterogeneity Starting with seed=44926809 creating random starting tree... Initial ln Likelihood: -1318.0450 optimizing: starting branch lengths... pass 1:+ 430.059 (branch= 430.06 scale= 0.00) pass 2:+ 43.968 (branch= 37.57 scale= 6.40) pass 3:+ 15.540 (branch= 12.62 scale= 2.92) pass 4:+ 13.620 (branch= 10.43 scale= 3.19) pass 5:+ 2.101 (branch= 1.37 scale= 0.73) pass 6:+ 0.440 (branch= 0.44 scale= 0.00) lnL after optimization: -812.3165 gen current_lnL precision last_tree_imp 0 -812.3165 0.500 0 100 -631.8927 0.500 95 200 -515.5765 0.500 183 300 -513.6662 0.500 281 400 -495.7026 0.500 389 500 -489.8560 0.500 486 600 -487.9821 0.500 540 700 -487.2379 0.500 695 800 -477.6723 0.500 794 900 -477.1914 0.500 832 1000 -476.5205 0.500 982 1100 -475.5301 0.500 1044 1200 -475.2139 0.500 1044 1300 -474.8837 0.500 1044 1400 -474.6604 0.500 1044 1500 -474.3814 0.500 1044 1600 -474.1658 0.500 1044 Optimization precision reduced Optimizing parameters... improved 0.000 lnL Optimizing branchlengths... improved 0.079 lnL 1700 -473.9456 0.451 1044 1800 -473.8144 0.451 1044 1900 -473.6242 0.451 1883 2000 -473.2478 0.451 1883 2100 -473.1190 0.451 1883 2200 -473.0786 0.451 1883 2300 -473.0143 0.451 1883 2400 -472.9732 0.451 1883 Optimization precision reduced Optimizing parameters... improved 0.000 lnL Optimizing branchlengths... improved 0.000 lnL 2500 -472.9234 0.402 1883 2600 -472.8918 0.402 1883 2700 -472.8750 0.402 1883 2800 -472.8232 0.402 1883 2900 -472.8046 0.402 1883 Optimization precision reduced Optimizing parameters... improved 0.000 lnL Optimizing branchlengths... improved 0.000 lnL 3000 -472.7937 0.353 1883 3100 -472.7611 0.353 1883 3200 -472.7356 0.353 1883 3300 -472.7169 0.353 1883 3400 -472.7028 0.353 1883 Optimization precision reduced Optimizing parameters... improved 0.000 lnL Optimizing branchlengths... improved 0.000 lnL 3500 -472.6782 0.304 1883 3600 -472.6583 0.304 1883 3700 -472.6535 0.304 1883 3800 -472.6366 0.304 1883 3900 -472.6268 0.304 1883 Optimization precision reduced Optimizing parameters... improved 0.000 lnL Optimizing branchlengths... improved 0.000 lnL 4000 -472.6163 0.255 1883 4100 -472.5964 0.255 1883 4200 -472.5787 0.255 1883 4300 -472.5698 0.255 1883 4400 -472.5472 0.255 1883 Optimization precision reduced Optimizing parameters... improved 0.000 lnL Optimizing branchlengths... improved 0.000 lnL 4500 -472.5449 0.206 1883 4600 -472.5388 0.206 1883 4700 -472.5311 0.206 1883 4800 -472.5289 0.206 1883 4900 -472.5262 0.206 1883 Optimization precision reduced Optimizing parameters... improved 0.000 lnL Optimizing branchlengths... improved 0.000 lnL 5000 -472.5023 0.157 4939 5100 -472.4963 0.157 4939 5200 -472.4951 0.157 4939 5300 -472.4922 0.157 4939 5400 -472.4812 0.157 4939 5500 -472.4718 0.157 4939 Optimization precision reduced Optimizing parameters... improved 0.000 lnL Optimizing branchlengths... improved 0.000 lnL 5600 -472.4685 0.108 4939 5700 -472.4673 0.108 4939 5800 -472.4662 0.108 4939 5900 -472.4640 0.108 4939 6000 -472.4630 0.108 4939 Optimization precision reduced Optimizing parameters... improved 0.000 lnL Optimizing branchlengths... improved 0.000 lnL 6100 -472.4625 0.059 4939 6200 -472.4612 0.059 4939 6300 -472.4563 0.059 4939 6400 -472.4541 0.059 4939 6500 -472.4538 0.059 4939 Optimization precision reduced Optimizing parameters... improved 0.000 lnL Optimizing branchlengths... improved 0.038 lnL 6600 -472.4158 0.010 4939 6700 -472.4158 0.010 4939 6800 -472.4154 0.010 4939 6900 -472.4151 0.010 4939 7000 -472.4147 0.010 4939 7100 -472.4139 0.010 4939 7200 -472.4137 0.010 4939 7300 -472.4130 0.010 4939 7400 -472.4130 0.010 4939 7500 -472.4106 0.010 4939 7600 -472.4105 0.010 4939 7700 -472.4103 0.010 4939 7800 -472.4101 0.010 4939 7900 -472.4098 0.010 4939 8000 -472.4094 0.010 4939 8100 -472.4093 0.010 4939 8200 -472.4093 0.010 4939 8300 -472.4090 0.010 4939 8400 -472.4089 0.010 4939 8500 -472.4087 0.010 4939 8600 -472.4086 0.010 4939 8700 -472.4085 0.010 4939 8800 -472.4084 0.010 4939 8900 -472.4083 0.010 4939 9000 -472.4081 0.010 4939 9100 -472.4080 0.010 4939 9200 -472.4080 0.010 4939 9300 -472.4078 0.010 4939 9400 -472.4076 0.010 4939 9500 -472.4072 0.010 4939 9600 -472.4071 0.010 4939 9700 -472.4067 0.010 4939 9800 -472.4067 0.010 4939 9900 -472.4067 0.010 4939 10000 -472.4066 0.010 4939 10100 -472.4063 0.010 4939 10200 -472.4061 0.010 4939 10300 -472.4060 0.010 4939 10400 -472.4060 0.010 4939 10500 -472.4060 0.010 4939 10600 -472.4058 0.010 4939 10700 -472.4058 0.010 4939 10800 -472.4056 0.010 4939 10900 -472.4055 0.010 4939 11000 -472.4054 0.010 4939 11100 -472.4052 0.010 4939 11200 -472.4052 0.010 4939 11300 -472.4052 0.010 4939 11400 -472.4051 0.010 4939 11500 -472.4050 0.010 4939 11600 -472.4050 0.010 4939 11700 -472.4050 0.010 4939 11800 -472.4050 0.010 4939 11900 -472.4046 0.010 4939 12000 -472.4045 0.010 4939 12100 -472.4045 0.010 4939 12200 -472.4044 0.010 4939 12300 -472.4044 0.010 4939 12400 -472.4043 0.010 4939 12500 -472.4040 0.010 4939 12600 -472.4040 0.010 4939 12700 -472.4040 0.010 4939 12800 -472.4038 0.010 4939 12900 -472.4038 0.010 4939 13000 -472.4037 0.010 4939 13100 -472.4035 0.010 4939 13200 -472.4033 0.010 4939 13300 -472.4033 0.010 4939 13400 -472.4032 0.010 4939 13500 -472.4031 0.010 4939 13600 -472.4031 0.010 4939 13700 -472.4029 0.010 4939 13800 -472.4029 0.010 4939 13900 -472.4029 0.010 4939 14000 -472.4028 0.010 4939 14100 -472.4026 0.010 4939 14200 -472.4024 0.010 4939 14300 -472.4024 0.010 4939 14400 -472.4024 0.010 4939 14500 -472.4023 0.010 4939 14600 -472.4023 0.010 4939 14700 -472.4023 0.010 4939 14800 -472.4023 0.010 4939 14900 -472.4021 0.010 4939 15000 -472.4020 0.010 4939 Optimizing parameters... improved 0.000 lnL Optimizing branchlengths... improved 0.000 lnL 15100 -472.4020 0.010 4939 15200 -472.4020 0.010 4939 15300 -472.4020 0.010 4939 15400 -472.4020 0.010 4939 15500 -472.4019 0.010 4939 15600 -472.4019 0.010 4939 15700 -472.4019 0.010 4939 15800 -472.4019 0.010 4939 15900 -472.4017 0.010 4939 16000 -472.4017 0.010 4939 16100 -472.4016 0.010 4939 16200 -472.4015 0.010 4939 16300 -472.4013 0.010 4939 16400 -472.4013 0.010 4939 16500 -472.4012 0.010 4939 16600 -472.4012 0.010 4939 Reached termination condition! last topological improvement at gen 4939 Improvement over last 500 gen = 0.00046 Current score = -472.4012 Performing final optimizations... pass 1 : -472.4012 (branch= 0.0000) pass 2 : -472.4012 (branch= 0.0000) pass 3 : -472.3949 (branch= 0.0063) pass 4 : -472.3918 (branch= 0.0031) pass 5 : -472.3913 (branch= 0.0005) pass 6 : -472.3912 (branch= 0.0002) pass 7 : -472.3911 (branch= 0.0001) pass 8 : -472.3910 (branch= 0.0001) pass 9 : -472.3908 (branch= 0.0002) pass 10: -472.3907 (branch= 0.0001) pass 11: -472.3906 (branch= 0.0000) pass 12: -472.3906 (branch= 0.0000) pass 13: -472.3906 (branch= 0.0000) Looking for minimum length branches... Final score = -472.3906 Time used so far = 0 hours, 1 minutes and 34 seconds MODEL REPORT - Parameter values are FINAL Model 1 Number of states = 2 (standard data) Character change matrix: One rate (symmetric one rate Mkv model) Equilibrium State Frequencies: equal (0.50, fixed) Rate Heterogeneity Model: no rate heterogeneity Model 2 Number of states = 3 (standard data) Character change matrix: One rate (symmetric one rate Mkv model) Equilibrium State Frequencies: equal (0.33, fixed) Rate Heterogeneity Model: no rate heterogeneity Model 3 Number of states = 4 (standard data) Character change matrix: One rate (symmetric one rate Mkv model) Equilibrium State Frequencies: equal (0.25, fixed) Rate Heterogeneity Model: no rate heterogeneity NOTE: Collapsing of minimum length branches was requested (collapsebranches = 1) 7 branches were collapsed. >>>Completed Search rep 2 (of 5)<<< >>>Search rep 3 (of 5)<<< MODEL REPORT - Parameters are at their INITIAL values (not yet optimized) Model 1 Number of states = 2 (standard data) Character change matrix: One rate (symmetric one rate Mkv model) Equilibrium State Frequencies: equal (0.50, fixed) Rate Heterogeneity Model: no rate heterogeneity Model 2 Number of states = 3 (standard data) Character change matrix: One rate (symmetric one rate Mkv model) Equilibrium State Frequencies: equal (0.33, fixed) Rate Heterogeneity Model: no rate heterogeneity Model 3 Number of states = 4 (standard data) Character change matrix: One rate (symmetric one rate Mkv model) Equilibrium State Frequencies: equal (0.25, fixed) Rate Heterogeneity Model: no rate heterogeneity Starting with seed=1129809455 creating random starting tree... Initial ln Likelihood: -1345.1873 optimizing: starting branch lengths... pass 1:+ 462.206 (branch= 460.91 scale= 1.29) pass 2:+ 46.315 (branch= 41.48 scale= 4.84) pass 3:+ 8.901 (branch= 7.46 scale= 1.45) pass 4:+ 0.399 (branch= 0.40 scale= 0.00) lnL after optimization: -827.3663 gen current_lnL precision last_tree_imp 0 -827.3663 0.500 0 100 -554.0794 0.500 97 200 -519.0429 0.500 200 300 -495.8588 0.500 299 400 -491.6494 0.500 383 500 -489.6575 0.500 486 600 -485.5977 0.500 553 700 -484.4369 0.500 665 800 -483.6662 0.500 791 900 -483.1040 0.500 791 1000 -482.5508 0.500 947 1100 -481.4178 0.500 1088 1200 -479.8625 0.500 1193 1300 -478.7363 0.500 1277 1400 -477.3675 0.500 1379 1500 -476.5981 0.500 1469 1600 -476.2200 0.500 1469 1700 -475.8149 0.500 1469 1800 -475.6773 0.500 1469 1900 -475.5862 0.500 1892 2000 -475.0435 0.500 1953 2100 -474.6464 0.500 2047 2200 -474.5245 0.500 2047 2300 -474.2834 0.500 2047 2400 -474.1793 0.500 2047 2500 -474.0827 0.500 2047 2600 -474.0282 0.500 2047 Optimization precision reduced Optimizing parameters... improved 0.000 lnL Optimizing branchlengths... improved 0.003 lnL 2700 -473.8768 0.451 2047 2800 -473.8100 0.451 2047 2900 -473.7925 0.451 2047 3000 -473.7515 0.451 2963 3100 -473.6539 0.451 2963 3200 -473.6069 0.451 2963 3300 -473.5546 0.451 2963 3400 -473.4800 0.451 2963 3500 -473.4100 0.451 2963 Optimization precision reduced Optimizing parameters... improved 0.000 lnL Optimizing branchlengths... improved 0.017 lnL 3600 -473.3227 0.402 2963 3700 -473.2814 0.402 2963 3800 -473.2520 0.402 2963 3900 -473.2171 0.402 2963 4000 -473.1817 0.402 2963 Optimization precision reduced Optimizing parameters... improved 0.000 lnL Optimizing branchlengths... improved 0.000 lnL 4100 -473.1732 0.353 2963 4200 -473.1482 0.353 2963 4300 -473.1308 0.353 2963 4400 -473.1219 0.353 2963 4500 -473.1118 0.353 2963 Optimization precision reduced Optimizing parameters... improved 0.000 lnL Optimizing branchlengths... improved 0.000 lnL 4600 -473.1050 0.304 2963 4700 -473.1015 0.304 2963 4800 -473.0905 0.304 2963 4900 -473.0630 0.304 4831 5000 -473.0574 0.304 4831 5100 -473.0441 0.304 4831 5200 -473.0302 0.304 4831 5300 -473.0260 0.304 4831 5400 -473.0225 0.304 4831 Optimization precision reduced Optimizing parameters... improved 0.000 lnL Optimizing branchlengths... improved 0.000 lnL 5500 -473.0122 0.255 4831 5600 -473.0074 0.255 4831 5700 -473.0044 0.255 4831 5800 -472.9855 0.255 5743 5900 -472.9802 0.255 5743 6000 -472.9770 0.255 5743 6100 -472.9697 0.255 5743 6200 -472.9691 0.255 5743 6300 -472.9684 0.255 5743 Optimization precision reduced Optimizing parameters... improved 0.000 lnL Optimizing branchlengths... improved 0.000 lnL 6400 -472.9103 0.206 6363 6500 -472.7831 0.206 6363 6600 -472.7122 0.206 6363 6700 -472.6699 0.206 6363 6800 -472.6517 0.206 6363 6900 -472.6270 0.206 6363 Optimization precision reduced Optimizing parameters... improved 0.000 lnL Optimizing branchlengths... improved 0.000 lnL 7000 -472.6085 0.157 6363 7100 -472.6020 0.157 6363 7200 -472.5927 0.157 6363 7300 -472.5865 0.157 6363 7400 -472.5660 0.157 6363 Optimization precision reduced Optimizing parameters... improved 0.000 lnL Optimizing branchlengths... improved 0.017 lnL 7500 -472.5448 0.108 6363 7600 -472.5350 0.108 6363 7700 -472.5283 0.108 6363 7800 -472.5265 0.108 6363 7900 -472.5211 0.108 6363 Optimization precision reduced Optimizing parameters... improved 0.000 lnL Optimizing branchlengths... improved 0.000 lnL 8000 -472.4754 0.059 7923 8100 -472.4617 0.059 7923 8200 -472.4586 0.059 7923 8300 -472.4552 0.059 7923 8400 -472.4521 0.059 7923 8500 -472.4492 0.059 7923 Optimization precision reduced Optimizing parameters... improved 0.000 lnL Optimizing branchlengths... improved 0.017 lnL 8600 -472.4288 0.010 7923 8700 -472.4266 0.010 7923 8800 -472.4230 0.010 7923 8900 -472.4226 0.010 7923 9000 -472.4214 0.010 7923 9100 -472.4198 0.010 7923 9200 -472.4185 0.010 7923 9300 -472.4181 0.010 7923 9400 -472.4177 0.010 7923 9500 -472.4175 0.010 7923 9600 -472.4173 0.010 7923 9700 -472.4160 0.010 7923 9800 -472.4160 0.010 7923 9900 -472.4160 0.010 7923 10000 -472.4155 0.010 7923 10100 -472.4152 0.010 7923 10200 -472.4148 0.010 7923 10300 -472.4145 0.010 7923 10400 -472.4140 0.010 7923 10500 -472.4138 0.010 7923 10600 -472.4138 0.010 7923 10700 -472.4137 0.010 7923 10800 -472.4132 0.010 7923 10900 -472.4130 0.010 7923 11000 -472.4125 0.010 7923 11100 -472.4124 0.010 7923 11200 -472.4123 0.010 7923 11300 -472.4121 0.010 7923 11400 -472.4121 0.010 7923 11500 -472.4118 0.010 7923 11600 -472.4115 0.010 7923 11700 -472.4114 0.010 7923 11800 -472.4114 0.010 7923 11900 -472.4114 0.010 7923 12000 -472.4112 0.010 7923 12100 -472.4108 0.010 7923 12200 -472.4106 0.010 7923 12300 -472.4106 0.010 7923 12400 -472.4102 0.010 7923 12500 -472.4102 0.010 7923 12600 -472.4101 0.010 7923 12700 -472.4100 0.010 7923 12800 -472.4100 0.010 7923 12900 -472.4100 0.010 7923 13000 -472.4099 0.010 7923 13100 -472.4096 0.010 7923 13200 -472.4094 0.010 7923 13300 -472.4094 0.010 7923 13400 -472.4092 0.010 7923 13500 -472.4091 0.010 7923 13600 -472.4088 0.010 7923 13700 -472.4086 0.010 7923 13800 -472.4086 0.010 7923 13900 -472.4084 0.010 7923 14000 -472.4084 0.010 7923 14100 -472.4083 0.010 7923 14200 -472.4081 0.010 7923 14300 -472.4081 0.010 7923 14400 -472.4076 0.010 7923 14500 -472.4075 0.010 7923 14600 -472.4074 0.010 7923 14700 -472.4073 0.010 7923 14800 -472.4073 0.010 7923 14900 -472.4073 0.010 7923 15000 -472.4072 0.010 7923 Optimizing parameters... improved 0.000 lnL Optimizing branchlengths... improved 0.000 lnL 15100 -472.4072 0.010 7923 15200 -472.4070 0.010 7923 15300 -472.4070 0.010 7923 15400 -472.4069 0.010 7923 15500 -472.4067 0.010 7923 15600 -472.4066 0.010 7923 15700 -472.4066 0.010 7923 15800 -472.4065 0.010 7923 15900 -472.4064 0.010 7923 16000 -472.4064 0.010 7923 16100 -472.4063 0.010 7923 16200 -472.4063 0.010 7923 16300 -472.4063 0.010 7923 16400 -472.4062 0.010 7923 16500 -472.4061 0.010 7923 16600 -472.4061 0.010 7923 16700 -472.4061 0.010 7923 16800 -472.4061 0.010 7923 16900 -472.4061 0.010 7923 17000 -472.4060 0.010 7923 17100 -472.4058 0.010 7923 17200 -472.4058 0.010 7923 17300 -472.4058 0.010 7923 17400 -472.4058 0.010 7923 17500 -472.4057 0.010 7923 17600 -472.4057 0.010 7923 17700 -472.4055 0.010 7923 17800 -472.4055 0.010 7923 17900 -472.4052 0.010 7923 18000 -472.4048 0.010 7923 18100 -472.4048 0.010 7923 18200 -472.4048 0.010 7923 18300 -472.4047 0.010 7923 18400 -472.4047 0.010 7923 18500 -472.4046 0.010 7923 18600 -472.4046 0.010 7923 Reached termination condition! last topological improvement at gen 7923 Improvement over last 500 gen = 0.00024 Current score = -472.4046 Performing final optimizations... pass 1 : -472.4046 (branch= 0.0000) pass 2 : -472.4046 (branch= 0.0000) pass 3 : -472.3930 (branch= 0.0116) pass 4 : -472.3918 (branch= 0.0012) pass 5 : -472.3912 (branch= 0.0006) pass 6 : -472.3912 (branch= 0.0000) pass 7 : -472.3910 (branch= 0.0002) pass 8 : -472.3909 (branch= 0.0001) pass 9 : -472.3908 (branch= 0.0001) pass 10: -472.3907 (branch= 0.0001) pass 11: -472.3906 (branch= 0.0001) pass 12: -472.3906 (branch= 0.0000) pass 13: -472.3906 (branch= 0.0000) pass 14: -472.3906 (branch= 0.0000) Looking for minimum length branches... Final score = -472.3906 Time used so far = 0 hours, 2 minutes and 22 seconds MODEL REPORT - Parameter values are FINAL Model 1 Number of states = 2 (standard data) Character change matrix: One rate (symmetric one rate Mkv model) Equilibrium State Frequencies: equal (0.50, fixed) Rate Heterogeneity Model: no rate heterogeneity Model 2 Number of states = 3 (standard data) Character change matrix: One rate (symmetric one rate Mkv model) Equilibrium State Frequencies: equal (0.33, fixed) Rate Heterogeneity Model: no rate heterogeneity Model 3 Number of states = 4 (standard data) Character change matrix: One rate (symmetric one rate Mkv model) Equilibrium State Frequencies: equal (0.25, fixed) Rate Heterogeneity Model: no rate heterogeneity NOTE: Collapsing of minimum length branches was requested (collapsebranches = 1) 7 branches were collapsed. >>>Completed Search rep 3 (of 5)<<< >>>Search rep 4 (of 5)<<< MODEL REPORT - Parameters are at their INITIAL values (not yet optimized) Model 1 Number of states = 2 (standard data) Character change matrix: One rate (symmetric one rate Mkv model) Equilibrium State Frequencies: equal (0.50, fixed) Rate Heterogeneity Model: no rate heterogeneity Model 2 Number of states = 3 (standard data) Character change matrix: One rate (symmetric one rate Mkv model) Equilibrium State Frequencies: equal (0.33, fixed) Rate Heterogeneity Model: no rate heterogeneity Model 3 Number of states = 4 (standard data) Character change matrix: One rate (symmetric one rate Mkv model) Equilibrium State Frequencies: equal (0.25, fixed) Rate Heterogeneity Model: no rate heterogeneity Starting with seed=1786917786 creating random starting tree... Initial ln Likelihood: -1259.6816 optimizing: starting branch lengths... pass 1:+ 411.545 (branch= 410.14 scale= 1.40) pass 2:+ 34.424 (branch= 31.96 scale= 2.47) pass 3:+ 4.884 (branch= 4.25 scale= 0.63) pass 4:+ 1.721 (branch= 1.72 scale= 0.00) pass 5:+ 0.047 (branch= 0.05 scale= 0.00) lnL after optimization: -807.0589 gen current_lnL precision last_tree_imp 0 -807.0589 0.500 0 100 -601.5939 0.500 100 200 -526.3617 0.500 196 300 -512.4135 0.500 278 400 -483.5238 0.500 390 500 -481.4931 0.500 471 600 -481.0061 0.500 471 700 -480.0821 0.500 683 800 -477.5502 0.500 729 900 -477.1062 0.500 729 1000 -476.7058 0.500 729 1100 -476.2214 0.500 729 1200 -475.8903 0.500 729 1300 -475.6326 0.500 1292 1400 -475.4871 0.500 1292 1500 -475.3881 0.500 1292 1600 -475.1881 0.500 1598 1700 -474.9285 0.500 1598 1800 -474.7453 0.500 1598 1900 -474.6094 0.500 1887 2000 -474.5233 0.500 1887 2100 -474.4728 0.500 2088 2200 -474.4253 0.500 2088 2300 -474.3405 0.500 2088 2400 -474.2962 0.500 2088 2500 -474.2187 0.500 2088 2600 -474.1912 0.500 2088 Optimization precision reduced Optimizing parameters... improved 0.000 lnL Optimizing branchlengths... improved 0.001 lnL 2700 -474.1788 0.451 2088 2800 -474.1712 0.451 2088 2900 -474.1538 0.451 2088 3000 -474.1458 0.451 2088 3100 -474.1336 0.451 2088 Optimization precision reduced Optimizing parameters... improved 0.000 lnL Optimizing branchlengths... improved 0.001 lnL 3200 -474.1318 0.402 2088 3300 -474.1252 0.402 2088 3400 -474.1154 0.402 2088 3500 -474.1050 0.402 2088 3600 -474.0923 0.402 2088 Optimization precision reduced Optimizing parameters... improved 0.000 lnL Optimizing branchlengths... improved 0.000 lnL 3700 -474.0853 0.353 2088 3800 -474.0781 0.353 2088 3900 -474.0710 0.353 2088 4000 -474.0676 0.353 2088 4100 -474.0557 0.353 2088 Optimization precision reduced Optimizing parameters... improved 0.000 lnL Optimizing branchlengths... improved 0.000 lnL 4200 -474.0534 0.304 2088 4300 -474.0477 0.304 2088 4400 -474.0391 0.304 2088 4500 -474.0353 0.304 2088 4600 -474.0334 0.304 2088 Optimization precision reduced Optimizing parameters... improved 0.000 lnL Optimizing branchlengths... improved 0.000 lnL 4700 -474.0283 0.255 2088 4800 -474.0282 0.255 2088 4900 -474.0262 0.255 2088 5000 -474.0228 0.255 2088 5100 -474.0183 0.255 2088 Optimization precision reduced Optimizing parameters... improved 0.000 lnL Optimizing branchlengths... improved 0.000 lnL 5200 -474.0155 0.206 2088 5300 -474.0118 0.206 2088 5400 -474.0089 0.206 2088 5500 -474.0077 0.206 2088 5600 -474.0053 0.206 2088 Optimization precision reduced Optimizing parameters... improved 0.000 lnL Optimizing branchlengths... improved 0.000 lnL 5700 -474.0043 0.157 2088 5800 -474.0024 0.157 2088 5900 -474.0009 0.157 2088 6000 -474.0001 0.157 2088 6100 -473.9987 0.157 2088 Optimization precision reduced Optimizing parameters... improved 0.000 lnL Optimizing branchlengths... improved 0.000 lnL 6200 -473.9969 0.108 2088 6300 -473.9937 0.108 2088 6400 -473.9927 0.108 2088 6500 -473.9916 0.108 2088 6600 -473.9905 0.108 2088 Optimization precision reduced Optimizing parameters... improved 0.000 lnL Optimizing branchlengths... improved 0.000 lnL 6700 -473.9882 0.059 2088 6800 -473.9871 0.059 2088 6900 -473.9863 0.059 2088 7000 -473.9859 0.059 2088 7100 -473.9843 0.059 2088 Optimization precision reduced Optimizing parameters... improved 0.000 lnL Optimizing branchlengths... improved 0.042 lnL 7200 -473.9424 0.010 2088 7300 -473.9419 0.010 2088 7400 -473.9416 0.010 2088 7500 -473.9414 0.010 2088 7600 -473.9411 0.010 2088 7700 -473.9398 0.010 2088 7800 -473.9393 0.010 2088 7900 -473.9384 0.010 2088 8000 -473.9377 0.010 2088 8100 -473.9376 0.010 2088 8200 -473.9352 0.010 2088 8300 -473.9337 0.010 2088 8400 -473.9331 0.010 2088 8500 -473.9321 0.010 2088 8600 -473.9303 0.010 2088 8700 -473.8109 0.010 8647 8800 -473.7338 0.010 8717 8900 -473.6620 0.010 8717 9000 -473.6447 0.010 8717 9100 -473.6360 0.010 8717 9200 -473.6289 0.010 8717 9300 -473.6274 0.010 8717 9400 -473.6244 0.010 8717 9500 -473.6190 0.010 8717 9600 -473.6187 0.010 8717 9700 -473.6164 0.010 8717 9800 -473.6158 0.010 8717 9900 -473.6154 0.010 8717 10000 -473.6148 0.010 8717 10100 -473.6148 0.010 8717 10200 -473.6146 0.010 8717 10300 -473.6141 0.010 8717 10400 -473.6141 0.010 8717 10500 -473.6139 0.010 8717 10600 -473.6138 0.010 8717 10700 -473.6136 0.010 8717 10800 -473.6134 0.010 8717 10900 -473.6134 0.010 8717 11000 -473.6132 0.010 8717 11100 -473.6132 0.010 8717 11200 -473.6130 0.010 8717 11300 -473.6129 0.010 8717 11400 -473.6129 0.010 8717 11500 -473.6128 0.010 8717 11600 -473.6128 0.010 8717 11700 -473.6126 0.010 8717 11800 -473.6124 0.010 8717 11900 -473.6124 0.010 8717 12000 -473.6122 0.010 8717 12100 -473.6122 0.010 8717 12200 -473.6122 0.010 8717 12300 -473.6122 0.010 8717 12400 -473.6121 0.010 8717 12500 -473.6120 0.010 8717 12600 -473.6119 0.010 8717 12700 -473.6119 0.010 8717 12800 -473.6119 0.010 8717 12900 -473.6118 0.010 8717 13000 -473.6118 0.010 8717 13100 -473.6118 0.010 8717 13200 -473.6117 0.010 8717 13300 -473.6116 0.010 8717 13400 -473.6116 0.010 8717 13500 -473.6115 0.010 8717 13600 -473.6113 0.010 8717 13700 -473.6113 0.010 8717 13800 -473.6113 0.010 8717 13900 -473.6112 0.010 8717 14000 -473.6112 0.010 8717 14100 -473.6112 0.010 8717 14200 -473.6112 0.010 8717 14300 -473.6109 0.010 8717 14400 -473.6108 0.010 8717 14500 -473.6107 0.010 8717 14600 -473.6106 0.010 8717 14700 -473.6106 0.010 8717 14800 -473.6106 0.010 8717 14900 -473.6105 0.010 8717 15000 -473.6105 0.010 8717 Optimizing parameters... improved 0.000 lnL Optimizing branchlengths... improved 0.000 lnL 15100 -473.6104 0.010 8717 15200 -473.6104 0.010 8717 15300 -473.6101 0.010 8717 15400 -473.6101 0.010 8717 15500 -473.6100 0.010 8717 15600 -473.6100 0.010 8717 15700 -473.6099 0.010 8717 15800 -473.6098 0.010 8717 15900 -473.6098 0.010 8717 16000 -473.6098 0.010 8717 16100 -473.6098 0.010 8717 16200 -473.6098 0.010 8717 16300 -473.6097 0.010 8717 16400 -473.6097 0.010 8717 16500 -473.6097 0.010 8717 16600 -473.6096 0.010 8717 16700 -473.6096 0.010 8717 16800 -473.6095 0.010 8717 16900 -473.6094 0.010 8717 17000 -473.6093 0.010 8717 17100 -473.6093 0.010 8717 17200 -473.6092 0.010 8717 17300 -473.6091 0.010 8717 17400 -473.6091 0.010 8717 17500 -473.6090 0.010 8717 17600 -473.6089 0.010 8717 17700 -473.6089 0.010 8717 17800 -473.6089 0.010 8717 17900 -473.6088 0.010 8717 18000 -473.6088 0.010 8717 18100 -473.6088 0.010 8717 18200 -473.6087 0.010 8717 18300 -473.6087 0.010 8717 18400 -473.6086 0.010 8717 18500 -473.6086 0.010 8717 18600 -473.6084 0.010 8717 18700 -473.6084 0.010 8717 18800 -473.6084 0.010 8717 Reached termination condition! last topological improvement at gen 8717 Improvement over last 500 gen = 0.00033 Current score = -473.6084 Performing final optimizations... pass 1 : -473.6084 (branch= 0.0000) pass 2 : -473.6084 (branch= 0.0000) pass 3 : -473.5997 (branch= 0.0087) pass 4 : -473.5967 (branch= 0.0030) pass 5 : -473.5967 (branch= 0.0000) pass 6 : -473.5967 (branch= 0.0000) pass 7 : -473.5967 (branch= 0.0000) pass 8 : -473.5966 (branch= 0.0001) pass 9 : -473.5966 (branch= 0.0001) pass 10: -473.5964 (branch= 0.0001) pass 11: -473.5964 (branch= 0.0001) pass 12: -473.5964 (branch= 0.0000) Looking for minimum length branches... Final score = -473.5964 Time used so far = 0 hours, 3 minutes and 11 seconds MODEL REPORT - Parameter values are FINAL Model 1 Number of states = 2 (standard data) Character change matrix: One rate (symmetric one rate Mkv model) Equilibrium State Frequencies: equal (0.50, fixed) Rate Heterogeneity Model: no rate heterogeneity Model 2 Number of states = 3 (standard data) Character change matrix: One rate (symmetric one rate Mkv model) Equilibrium State Frequencies: equal (0.33, fixed) Rate Heterogeneity Model: no rate heterogeneity Model 3 Number of states = 4 (standard data) Character change matrix: One rate (symmetric one rate Mkv model) Equilibrium State Frequencies: equal (0.25, fixed) Rate Heterogeneity Model: no rate heterogeneity NOTE: Collapsing of minimum length branches was requested (collapsebranches = 1) 7 branches were collapsed. >>>Completed Search rep 4 (of 5)<<< >>>Search rep 5 (of 5)<<< MODEL REPORT - Parameters are at their INITIAL values (not yet optimized) Model 1 Number of states = 2 (standard data) Character change matrix: One rate (symmetric one rate Mkv model) Equilibrium State Frequencies: equal (0.50, fixed) Rate Heterogeneity Model: no rate heterogeneity Model 2 Number of states = 3 (standard data) Character change matrix: One rate (symmetric one rate Mkv model) Equilibrium State Frequencies: equal (0.33, fixed) Rate Heterogeneity Model: no rate heterogeneity Model 3 Number of states = 4 (standard data) Character change matrix: One rate (symmetric one rate Mkv model) Equilibrium State Frequencies: equal (0.25, fixed) Rate Heterogeneity Model: no rate heterogeneity Starting with seed=1429655032 creating random starting tree... Initial ln Likelihood: -1219.3334 optimizing: starting branch lengths... pass 1:+ 350.904 (branch= 350.90 scale= 0.00) pass 2:+ 56.702 (branch= 53.13 scale= 3.58) pass 3:+ 20.452 (branch= 17.31 scale= 3.14) pass 4:+ 3.274 (branch= 2.54 scale= 0.73) pass 5:+ 2.093 (branch= 1.31 scale= 0.78) pass 6:+ 1.698 (branch= 1.70 scale= 0.00) pass 7:+ 0.095 (branch= 0.09 scale= 0.00) lnL after optimization: -784.1160 gen current_lnL precision last_tree_imp 0 -784.1160 0.500 0 100 -593.8019 0.500 100 200 -533.4957 0.500 192 300 -516.5071 0.500 287 400 -508.1399 0.500 397 500 -489.8206 0.500 499 600 -483.5815 0.500 564 700 -480.9720 0.500 670 800 -480.2684 0.500 773 900 -479.5095 0.500 773 1000 -479.1002 0.500 773 1100 -478.7379 0.500 773 1200 -478.3594 0.500 773 1300 -477.9320 0.500 773 Optimization precision reduced Optimizing parameters... improved 0.000 lnL Optimizing branchlengths... improved 0.244 lnL 1400 -477.3303 0.451 773 1500 -476.9782 0.451 1469 1600 -476.6121 0.451 1469 1700 -476.4869 0.451 1469 1800 -476.4165 0.451 1469 1900 -476.2181 0.451 1469 2000 -476.1044 0.451 1469 Optimization precision reduced Optimizing parameters... improved 0.000 lnL Optimizing branchlengths... improved 0.001 lnL 2100 -475.8182 0.402 1469 2200 -475.6823 0.402 1469 2300 -475.5295 0.402 1469 2400 -475.4160 0.402 1469 2500 -475.3457 0.402 1469 Optimization precision reduced Optimizing parameters... improved 0.000 lnL Optimizing branchlengths... improved 0.002 lnL 2600 -475.3139 0.353 1469 2700 -475.2752 0.353 1469 2800 -475.2199 0.353 1469 2900 -475.1825 0.353 1469 3000 -475.1627 0.353 1469 Optimization precision reduced Optimizing parameters... improved 0.000 lnL Optimizing branchlengths... improved 0.000 lnL 3100 -475.1412 0.304 1469 3200 -475.1168 0.304 1469 3300 -475.0961 0.304 1469 3400 -475.0782 0.304 1469 3500 -475.0663 0.304 1469 Optimization precision reduced Optimizing parameters... improved 0.000 lnL Optimizing branchlengths... improved 0.000 lnL 3600 -475.0616 0.255 1469 3700 -474.8098 0.255 1469 3800 -474.7550 0.255 1469 3900 -474.7510 0.255 1469 4000 -474.7427 0.255 1469 Optimization precision reduced Optimizing parameters... improved 0.000 lnL Optimizing branchlengths... improved 0.000 lnL 4100 -474.7305 0.206 1469 4200 -474.7217 0.206 1469 4300 -474.7104 0.206 1469 4400 -474.7037 0.206 1469 4500 -474.7028 0.206 1469 Optimization precision reduced Optimizing parameters... improved 0.000 lnL Optimizing branchlengths... improved 0.000 lnL 4600 -474.7019 0.157 1469 4700 -474.6963 0.157 1469 4800 -474.6919 0.157 1469 4900 -474.6899 0.157 1469 5000 -474.6870 0.157 1469 Optimization precision reduced Optimizing parameters... improved 0.000 lnL Optimizing branchlengths... improved 0.000 lnL 5100 -474.6848 0.108 1469 5200 -474.6823 0.108 1469 5300 -474.6807 0.108 1469 5400 -474.6786 0.108 1469 5500 -474.6774 0.108 1469 Optimization precision reduced Optimizing parameters... improved 0.000 lnL Optimizing branchlengths... improved 0.000 lnL 5600 -474.6750 0.059 1469 5700 -474.6731 0.059 1469 5800 -474.6718 0.059 1469 5900 -474.6693 0.059 1469 6000 -474.6675 0.059 1469 Optimization precision reduced Optimizing parameters... improved 0.000 lnL Optimizing branchlengths... improved 0.043 lnL 6100 -474.6238 0.010 1469 6200 -474.6194 0.010 1469 6300 -474.6183 0.010 1469 6400 -474.6182 0.010 1469 6500 -474.6179 0.010 1469 6600 -474.6176 0.010 1469 6700 -474.6174 0.010 1469 6800 -474.6173 0.010 1469 6900 -474.6170 0.010 1469 7000 -474.6139 0.010 1469 7100 -474.6139 0.010 1469 7200 -474.6136 0.010 1469 7300 -474.6131 0.010 1469 7400 -474.6126 0.010 1469 7500 -474.6123 0.010 1469 7600 -474.6120 0.010 1469 7700 -474.6117 0.010 1469 7800 -474.6116 0.010 1469 7900 -474.6115 0.010 1469 8000 -474.6113 0.010 1469 8100 -474.6108 0.010 1469 8200 -474.6105 0.010 1469 8300 -474.6100 0.010 1469 8400 -474.6099 0.010 1469 8500 -474.6092 0.010 1469 8600 -474.6088 0.010 1469 8700 -474.6088 0.010 1469 8800 -474.6086 0.010 1469 8900 -474.6084 0.010 1469 9000 -474.6081 0.010 1469 9100 -474.6079 0.010 1469 9200 -474.6076 0.010 1469 9300 -474.6076 0.010 1469 9400 -474.6076 0.010 1469 9500 -474.6072 0.010 1469 9600 -474.6070 0.010 1469 9700 -474.6067 0.010 1469 9800 -474.6067 0.010 1469 9900 -474.6065 0.010 1469 10000 -474.6064 0.010 1469 10100 -474.6064 0.010 1469 10200 -474.6062 0.010 1469 10300 -474.6062 0.010 1469 10400 -474.6062 0.010 1469 10500 -474.6062 0.010 1469 10600 -474.6061 0.010 1469 10700 -474.6061 0.010 1469 10800 -474.6061 0.010 1469 10900 -474.6060 0.010 1469 11000 -474.6060 0.010 1469 11100 -474.6060 0.010 1469 11200 -474.6060 0.010 1469 11300 -474.6060 0.010 1469 11400 -474.6059 0.010 1469 11500 -474.6058 0.010 1469 11600 -474.6057 0.010 1469 11700 -474.6057 0.010 1469 11800 -474.6057 0.010 1469 11900 -474.6057 0.010 1469 12000 -474.6056 0.010 1469 12100 -474.6056 0.010 1469 12200 -474.6056 0.010 1469 12300 -474.6055 0.010 1469 12400 -474.6055 0.010 1469 12500 -474.6055 0.010 1469 12600 -474.6055 0.010 1469 12700 -474.6055 0.010 1469 12800 -474.6054 0.010 1469 12900 -474.6054 0.010 1469 13000 -474.6052 0.010 1469 13100 -474.6050 0.010 1469 13200 -474.6050 0.010 1469 13300 -474.6049 0.010 1469 13400 -474.6048 0.010 1469 13500 -474.6048 0.010 1469 13600 -474.6046 0.010 1469 13700 -474.6042 0.010 1469 13800 -474.6040 0.010 1469 13900 -474.6040 0.010 1469 14000 -474.6039 0.010 1469 14100 -474.6039 0.010 1469 14200 -474.6039 0.010 1469 14300 -474.6038 0.010 1469 14400 -474.6035 0.010 1469 14500 -474.6034 0.010 1469 14600 -474.6033 0.010 1469 14700 -474.6032 0.010 1469 14800 -474.6030 0.010 1469 14900 -474.6030 0.010 1469 15000 -474.6028 0.010 1469 Optimizing parameters... improved 0.000 lnL Optimizing branchlengths... improved 0.000 lnL 15100 -474.6028 0.010 1469 15200 -474.6028 0.010 1469 15300 -474.6027 0.010 1469 15400 -474.6027 0.010 1469 15500 -474.6027 0.010 1469 15600 -474.6026 0.010 1469 15700 -474.6025 0.010 1469 15800 -474.6024 0.010 1469 15900 -474.6024 0.010 1469 16000 -474.6023 0.010 1469 16100 -474.6022 0.010 1469 Reached termination condition! last topological improvement at gen 1469 Improvement over last 500 gen = 0.00041 Current score = -474.6022 Performing final optimizations... pass 1 : -474.6022 (branch= 0.0000) pass 2 : -474.5995 (branch= 0.0027) pass 3 : -474.5871 (branch= 0.0124) pass 4 : -474.5871 (branch= 0.0000) pass 5 : -474.5868 (branch= 0.0003) pass 6 : -474.5866 (branch= 0.0001) pass 7 : -474.5864 (branch= 0.0002) pass 8 : -474.5862 (branch= 0.0002) pass 9 : -474.5861 (branch= 0.0002) pass 10: -474.5860 (branch= 0.0000) pass 11: -474.5860 (branch= 0.0000) pass 12: -474.5860 (branch= 0.0000) pass 13: -474.5860 (branch= 0.0000) Looking for minimum length branches... Final score = -474.5860 Time used = 0 hours, 3 minutes and 54 seconds MODEL REPORT - Parameter values are FINAL Model 1 Number of states = 2 (standard data) Character change matrix: One rate (symmetric one rate Mkv model) Equilibrium State Frequencies: equal (0.50, fixed) Rate Heterogeneity Model: no rate heterogeneity Model 2 Number of states = 3 (standard data) Character change matrix: One rate (symmetric one rate Mkv model) Equilibrium State Frequencies: equal (0.33, fixed) Rate Heterogeneity Model: no rate heterogeneity Model 3 Number of states = 4 (standard data) Character change matrix: One rate (symmetric one rate Mkv model) Equilibrium State Frequencies: equal (0.25, fixed) Rate Heterogeneity Model: no rate heterogeneity NOTE: Collapsing of minimum length branches was requested (collapsebranches = 1) 7 branches were collapsed. >>>Completed Search rep 5 (of 5)<<< ####################################################### Completed 5 replicate search(es) (of 5). NOTE: Unless the following output indicates that search replicates found the same topology, you should assume that they found different topologies. Results: Replicate 1 : -472.3908 Replicate 2 : -472.3906 Replicate 3 : -472.3906 (best) (same topology as 2) Replicate 4 : -473.5964 Replicate 5 : -474.5860 Parameter estimates across search replicates: Partition model subset 1: Model contains no estimated parameters Partition model subset 2: Model contains no estimated parameters Partition model subset 3: Model contains no estimated parameters Treelengths: TL rep 1: 3.623 rep 2: 3.622 rep 3: 3.621 rep 4: 3.807 rep 5: 3.572 Saving final trees from all search reps to mkv.best.all.tre Saving final tree from best search rep (#3) to mkv.best.tre ####################################################### garli-2.1-release/example/partition/exampleRuns/partitionedDna+Mkv/000077500000000000000000000000001241236125200255335ustar00rootroot00000000000000garli-2.1-release/example/partition/exampleRuns/partitionedDna+Mkv/dnaPlusGapCoding.nex000066400000000000000000015222241241236125200314410ustar00rootroot00000000000000#NEXUS BEGIN TAXA; TITLE Untitled_TAXA_Block_1; DIMENSIONS NTax = 64; TAXLABELS temporariaDMH84R1 boyliiMVZ148929 luteiventris_MT_MVZ191016 luteiventris_WA_MVZ225749 muscosaMVZ149006 auroraMVZ13957 cascadaeMVZ148946 sylvaticaMVZ137426 sylvaticaDMH84R43 septentrionalesDCC3588 grylioMVZ175945 okaloosae clamitansJSF1118 heckscheriMVZ164908 catesbianaX12841 catesbianaDMH84R2 virgatipesMVZ175944 maculataKU195258 vibicariaMVZ11035 warszewitshiiJSF1127 palmipesVenAMNHA118801 palmipesEcuKU204425 Sp_1_ecuadorQCAZ13219 bwanaQCAZ13964 vaillantiKU195299 julianiTNHC60324 sierramadrensisKU195181 psilonotaKU195119 zweifeliJAC7514 tarahumaraeKU194596 pustulosaJAC10555 pipiensJSF1119 pipiensY10945 dunniJSF1017 montezumaeJAC8836 sp_2_mex_JSF1106 chiricahuensisJSF1063 subaquavocalis chiricahuensisJSF1092 palustrisJSF1110 areolataJSF1111 sevosaUSC8236 capitoSLU003 spectabilisJAC8622 omiltemanaJAC7413 sp_3_MichoacanJSF955 tlalociJSF1083 neovolcanicaJSF960 berlandieriJSF1136 blairiJSF830 sphenocephalaUSC7448 utriculariaJSF845 forreriJSF1065 magnaocularisJSF1073 sp_7_JaliscoJSF1000 yavapaiensisJSF1085 oncaLVT3542 sp_8_PueblaJAC9467 macroglossaJAC10472 macroglossaJSF7933 taylori286 sp_4_Panama sp_5_CostaRichDMH86_210 sp_6_CostaRicaDMH86_225; END; BEGIN CHARACTERS; TITLE Untitled_DATA_Block_1GapsAsMissing; LINK TAXA = Untitled_TAXA_Block_1; DIMENSIONS NChar=3211; FORMAT Datatype=DNA; Matrix temporariaDMH84R1 ACA?CTTGT?A??AGTG?GC?T????????GACCTGTAG??TT???T?TT?AACAAACTA????GCG??????????GGTG?ACAAACAT??GGT?TTTAATCT??TG?TGTT?GA??TT???TT?A???????T?AC???????C?AA?????????CCAACT?ACAA??CCAGTAACGAC????G??CCCGAATATG??C?TA?AT?TAT?AT??CGG???AT?ACT?????????T??ACACTGTCGTTG?CTATCGTTAT???CTTGGTTGTA?TCT??????????A?CGATATA?ATGGAAT?CTGAGAT???AC????CTCC???TCAACTCGC??CT????????CTCTAAAT?????TG?T??C?AATG??TAGATACTA???????ATAAAACTTTC??C????????GCCATTAC??T??????????AAAAATTGACAGTA??TACA?ACATCACAGG?AT??T??????????????????TC?CTGCAATGTC?ACATTCCTG????C?TCCC?CGCAGCAA???C??CGC?TTATGAAGTCTCTCTCATCAT?ATAT???C????TA?????AGGAC?????CA???T?TG?TGAA?TTATTT???T????CAT?C???CC?TGCCCCA??A????TTAAACGATCTTAA??????T?CTC?TAG???TAGTT??ACAA???????ATAGT?ACGAATCTAGTTCT?CT?????????TGGA????TT?CA???AAA?AC???T??????T?????AGCTCG????AA???????CCCTT?TCCA????CCAGTGATC??G?CTTACCTTAACA??CCTAG?GTAA?????AAAATC?TAG?GCA??TA?TATACCTAGAA?TGA??????CTG?CGATCTATTGCAAGTATTTAA???TTCA??????ACCT?TAGT??????AT?GCATTAA?T?????GTCTACATAAAAAT????AA?CC?ACATAAT??ATATT??TCTAGAGAATT???GTAGCACC???GGGATCACT?C??AACCCA????CA?CAAA?ATT?????A?AACCCC??GT?????TAA???C??CAT???GGG???TA??????ACTG??TTCAA?CATCGA?ACGGCTTCGAGT?AATTATAAAAAGAAA?AT??GCATTAGA??TG?????????TAAAGA??????A??GA?????????ACTATT??TCCAATTAT??GCCCTGTAT??TTA?CGGTAAATGTAAT?C?TT?ATA?TATCATGG??A?ATC?AATTCCTATA????????CA??A???????????????????????C?GA?AA??????GATC??????AA?TAT?TGG??????CCAAAC??CTAGCT??TCAAA???GAAAAGC??TTCC?T????A?GC??????AATGCAAA?TAACCA?????TTAAG?CCAA???????TGT??ATACGTTCACAA????TGGCAAACTC?C???????TGAG??????TTTAAATCT??G?CC?TCGTG??GAAGATTCTTG????GTC?T?A???AAT??CGACTAC???????A?CACAG?TTA?A?TCTCTCC?TTTTATA???T??AAA???????????AGCATATTGCAT??????TAATTA?T?ATAAATTGAAGTAA?GCTCTTACTGAATGACTAGG?GA?CTC??TGAACTGATTA???GGAACA?TACGTTTGGTATA?ACCGTAT?????????GTT?T??T??TCTAGCTGAAGCGCTGACACTACCT?AAT????????????TATCATACGCT????GGACCCAAAACAA?CAAAA???AT????CG?ACT???A????A?????AACTATC?????T??T?????AATC????GCAGTATTAC?????ACAAGAATT??????GAAA?AGGAC??CAAA????GAAAACGAGCAAA?????????ACGA????CTACTGTCAG?GCT?GATAGTGATTAACT??GCT?ATAA?AACACTG?????CTAAGG?CCGAAATTA?AAAGTTGTGAAG???TTA?A?C?CTATGGACAT??A?AGAGT??A?GT?TCAA?AAC?????????G?????TTC?GTACTAAT????TTCAGT???AAA?T??????A????GT???????CTA????????????TCACGC??TCATAGTC??????????ACCATA?GT?TGT?A??TAATC???ATATTC?ACTGCTCT?ATA???GT?????AATACACG????????TTTGA?A??A?????TTTCC??????TAT?GTT?T?TTT????TC?TA?CGATA?TCA??AG??????????TT?T?TAGGA?TCTA????AAAAA??????????TGTGGATAGCTTATCGCT?TGAGATGA?ATAATAACCAAGTACGTACCA???TTTG?TC?TA?CTC??????AAA?ACT??????ACTCA?GC?????????AC?????G?????CT?????ATCCGG?TTTTTT????????A???TC?G?C??GT?A???ACTCCTGTATAA????????ACC?TT?CGA?ATGA????TACT?GATCC??????????TCA??CC????????A?TCTTGG??AGAATG?A???AATAGCG?GCGC????CCATTT????????TTAACTC???TA??GAT?AC??AT???????C????CACCCGATAG?GGTAAGGAAGTGTGTAGAGATAATA?GT?A?????????A???TACGGCATCCAGTCTACG?AATC?AC?TGCTAGT?GAA???????AACAATTC?A?TCGCAAACC?G?AT??CTT?CTATGTC??TTTA?CGTAC?CA?CTACAATTGA???AG????C?GA?CATTCTGAAA??A?GTA???TCATAT?GT?CTGGA??GCCTTAATCC????TTCTAGTA???TTTAA??CT?AACTTA????CAATGTTGTTA????AGATTC?CG??C??CATAGATCATATCATC?AATAT??????ATATA???????TTAGA???????TATTAATT????CGCTTA?????A?CTACTTCA???????T???TTG?ATA?TGA??AGCAGCCAAA????A??AGA??A?T?????????ATA?CCTGTAAAT??TGA?C????????CTAGC?CT?CTTAGG?A?A?C?????TTGATACGAGTATTA?G???GGTC?TTAACA boyliiMVZ148929 ACT?CCCGC?A??AGTG?GC?T????????GACCTGTAG??T?????????AACCAACTA????GTG???????????GTG?ACAAACCC??GGT?TTTAATCT??CG?TAAT?GA??TTGA?TT?A???????C?AC???????G?AA?????????CCCATT?ACAG??CCAGTAACGAC????G??CTCGAATATA??C?TA?GT?TAT?AT??CGG???ATAACC?????????AG?ACACCG??GCTG?ATAGC????????TTTAGTTGTA?TCT??????????A?CGGTATACATGGAA??CTGAGG????AC????CTCC???TTTACTCGC??CT????????CTTTAAAT?????TG?T??C?AATG??TTGATACTA???????CTGATATTTTC??C????????GCCATTAC??A???????????AAAGTTGACAAAA??CACA?ACATTACAGG?AT??T??????????????????TT?CTTAAAT?TC?ACATTCCTG????T?TTCC?TGCCTTAA???A??CGC?TTAAGAAGACTCACTAATCCT?A?AT???CC???TT?????AGCAC?????CA???T?TA?AGCG?TCATTT???T????CTT?C???TT?AATCCTA??AGAACTTAAA???TCTTCA??????TTCCC?TAG???TTG????TTAT???????ATAGT?ACGAATATAG?CCA?GT?????????TGTA????TC?CA???AAA?AC???T??????C?????AGCTTG????AA???????TTTAT?TTCA????CC??GGT?C??A?C????????ACA??CCTGG?GTAG?????AAAATC?CAA?GCA??TA?TATAATTAGAA?T?T??????CTG?CGATATACAGTCCGTACTCAT???TTTG??????ACCT?TAGT??????A??ACATT?A?T?????GTC????AAAAAAT????AA?CC?A?ACATT??ATAAT??TCTCGAGGACT???GAAGGAAT???GGGACGAAT?C??A??CTA????CA?TAAT?ATT?????AATACCCC??AT?????TAA???CTACAG???AGG??ATA??????ACTG??TTCAA?CATCAA?TCTG?TTTGA???AA?TATAATAAGCAC?AT??AGGTTAGA?CAG?????????CAAAGA??????A??GA?????????ACTATT??TCCAATTTT??GCCTTCTATTATTA?C?GTAAATGGAAT?C?TT?AAA?TACCATGG??C?ATC?AATTCCTATA????????CG??A???????????????????????C?GA?AA??????GATC??????AA?CAT?CGGTTCCCACTAGAC??GTAGAT??TTAAA???GAAAATT??TTCA?T????ATGC??????AATGGGCA?TCAACA?????TTAGG?CCAC???????CGT??ATACGTCCACAT????AGACAAACTT?C???????TGTG??????TATACATCC??G?CC?TAGTA??G?TAATTCTTA????GTT?A?A???AAT??CAACTGC???????A?CACAG?GTAGATTCTCTCC?TTTTATA???C??GAA???????????AACATATTGCAT??????T????A?C?ATAAATTGAAACAA?GCTCTTACTGAACGATTAAG?GATCTC??TGAACTGATTA???GGGACG?TACG?CTGGAATA?ACCGTAT?????????GTT?A??T??TCCAGACGAAGCACAGACAATTCTT?ACT????????????TATCATCTGCC????AGACTCAACAAAAGTAGAAGACAT????CG?GAT???ATTTAA?GC??AACTATC?????T??T?????AACA????GCAGTACTGC?????ACGAGAACT??????GAAAA?TGAC??CAAAC???AAAAACAAG?AAA?????????TTGC????ATACTGCCAA?GCT?GATAGAAATTAACT??GCT?ATAG?AATACTGG??C?CTAAGG?TCGAAGCTA?TAAATT??GGAG???TCA?A?C?TTAAAAACAT??A?AGGGC??A?GT?CAAT?AGGAA?C??A??C?????GTT?GTACTAAG????ATCAGT???AAA?A????????????TGAAATCCCCC????????????TCACGC??TCATTGTA??????????ACTATA?GC?TGT?A??TA???????????C?GCTACTGT?ATA???GTCAAGGAGTATACG????????ATT?A?A??T?????TTTCC??????TAT?TTT?T?TTT????TC?CA?CGATA?CCA??AG??????????TT?T?TAGGA?TCTA????AAAAA??????????TGTA?ATAGGTTATCGCT?TAAGATGA?ATAATAGCTAAGTACGTACCA???TCTG?TA?TATCCC??????AAA?TTT??????ACTTA?GC?????????AT?????G????TCT?????ACCCGG?TCTTTT????????A????T?G?T??GT?A???AT?CCTGTACGAT?A?AT????C?TA?CGC?ACGC????TACA?GAATC???????????????CT????????A?TTTTGG??AAAATA?A???AACAGTG?GAGC????CCAATT????????CTAATAT???TA??GTC?AC??AT???????C????CACTTGATAG?GGTGAGGAAATGCGTGGAGTTTATG?GT?A?????????A???CATAGTAG?????CCGTC?GAAC?AT?TTCCAGG?CAA???????AACAATTC?A?TTGTGA????????????T?GTAC??C??TTTA?CGTAC?CA?CTACTATTGA???AG????C?AA?CATTCTTAAA??A?GTA???TCCAAT?GT?CTGGA??GCTGTATCCC????CTCTTGTA???TTTTC??CT?AACTTA????GAATGTTGATA????AGCCCC?CA??C??CATCGATTATATCGTC?AATAA??????ATGTA???????TTAGG???????TGGTAATT????CACTAA?????A?TTACTTCA???????T???CCGTATA?T?A??AGCA???AAA????A??TGA??T?A?????????ATA?CCAGTAAAT??CGAAC????????CCTGT?CT?CTTAGAAA?A?TCAGAATTGATACAAGTATTA?G???GGTC?TTAACA luteiventris_MT_MVZ191016 ACC?CTCGT?A??AGTG?GC?T????????GACCTGTAG??T?????????AACAAACTA????GTG??????????GGTG?ACAAACCT??GGT?TTTGACCT??CG?TATT?GA??TTGA?TT?A???????C?AC???????T?AA?????????CCAATT?ACAA??CCCGTAACGAC????G??CTTGAATA?A??A?TA?AT?TAT?AT??CGG???ACAACC?????????AG?ACATCGTCGCTG?TCATC????????TTTAGTTGCA?T?T???????????????TATA?ATGGAAT?CTGAGGT???AC????CTCC???TTCACTCGC??CT????????CTCTAAAT?????TG?T??T?AATG??TTGATACTA???????ATGAGGCTTTC??C????????GCCATTAC??A??????????AGAAGTTGACAAAA??TACA?ACATCACAGG?AC??T??????????????????TT?CCAAAATGTC?ACAGTCCTG????T?TACC?CGCTGTAA???C??TGC?TTAGGAAGCCTCACTCGTCAT?T?AT???CC???TA?????AGT?C?????CC???T?TG?GATA?TCACTT???T????CTT?C???AC??GCCCTA??AGAACTTAGA???TCTTAA??????TTCCC?TAG???CAG????TCAA???????AAAGT?ACGAATGTAGTTCA?AT?????????TGTA????TT?CA???AAA?AC???T??????C?????AGCTCG????AA???????TCAAT?TCCA????CC??AATTC??A?C????????GCA??CCTGG?GTAA?????AAAATC?TAA?GCA??TA?GATGAATAGAATTGT??????CTG?CGATATATAGCATGTATTCAT???TTTA??????ACCT?TAGT??????A??AGATTAA?T?????GTCAACACAAAAAT????AA?CC?A?ACATT??ATAAT??TCTAGAGAACT???GAAGCAAT???GGGACAATT?C??A??????????A?CAAA?ATT?????ATTACCCC??AT?????CAA???C??CAC???GGG??ATA??????ACTG??TTCAA?CATCAA?TCAG?TTCGA???AA?TATAATAAGTAT?AT??GCATTAGA?CCG?????????CAAAGA??????A??GA?????????ACTATT??TCCAATTTT??GCCTTATCTTATTA?CAATAAATGAAAC?C?TT?ACA?TACCATGG??CTATC?AATTCCTATA????????CA??A???????????????????????C?GA?AA??????GATC??????AA?TAT?CGG??????CTAGAC??CTAGAT??TTAAA???GAAAAGT??TCTA?T????ACGC??????AATGGGTA?TCAACA?????TTAAG?TCAA???????CGT??ATACGTCCACAA????AGACAGACTA?C???????TGAG??????TGTATATCT??G?CC?TTGTA??G?TAATTGCTG????GTC?C?A???AAT??CAACT?????????????CAG?TTAGATTCACTCC?TTTTATA???C??GAT???????????ATCATATTGCAT??????TAAA?A?C?ATAAATTGAAATAA?GCTCTTACTGAATGACTAGG?GA?CTC??TGAACTGATCA???GGAACA?TACG?CTGGTATA?ACCGTAT?????????GTT?A??C??TCTAGACGAAGCGCCGACAATGCTT?ACT????????????TATCATTTGCT????AGACTCAATAAAAGTACAAGACAA????CG?GAT???A????A?TC??AACCATC?????T??T?????AATA????GCAGTACTGC?????ACAAGATCT??????AAAAT?TGAC??CAAAT???AAAAACTAGCAAA?????????CCGA????ATACTGCCAT?GCT?GATAG???TTAACT??GCT?ATAA???????GG??C?CTAAGA?CCGAAGCTA?TAATTT??GGAG???TTA?A?C?TTATAAACAT??A?AGGGT??A?GT?TAAT?AGAAA?T??A??G?????ATT?GTACTAAA????ACCAAT???AAA?A????????????TGAAGTCCTTC????????????TTACGC??TC????TA??????????ACTATA?GT?TGT?A??TAATC???ACATTC?GCTCCTGT?ATA???GT?????AGTACACG????????TTTAATA??T?????CTTAC??????TAT?TTT?T?TTT????CC?CA?CGATA?CCA??AG??????????CT?T?TAGGA?ACTA????AAAAA??????????CGTA?ATAGGTTATCGAT?TAAGATAA?ATAATAACCAAGTACGTACCA???TTTG?TA?TA?CCC??????AAA?ATT??????ACTGA?GC?????????AT?????G????TCT?????ACCTGG?G????T????????A????A?G?T??GC?A???AC?CCTGTACAAT?G?AA??ACC?CA?CGT?ACGT????TACA?GATCC??????????TCA??CT????????A?TTTTGG??AAAATA?A???AACAGTG?GCGC????CCA?TT????????CTAACAT???TA??GAT?AC??AT???????T????CACTTGATAG?GGTGAGGAATTGTGTTGAGCTAATG?GT?A?????????A???CACGACAT?????C???T?TATC?AC?TCTCA?G?GAA???????AACAATTT?A?TTGTAAATCAG?AT??CTT?CTATGCC??TTTA?CGTAC?CA?CTACAATTGG???AG????A?AA?CATTTCAAAA??A?GTA???TTTTAT?GT?CTAGA??GCCCTATGCT????CTCTAGTA???TATCC??CT?AACTTA????CAATGTCGATA????AGGTCC?CA??C??CATCGATAATATCGTC?GTTAA??????ATATA???????TCAGG???????TAATAATT????CACTTA?????A?CTACTTCG???????T???ACG?ATA?T?A??AGCA???AAA????T??CGA??C?C?????????ATA?CCTGTAAAT??TGAAC????????CCAAC?CA?CTTAGAAA?A?TCAAAATTGATATGAGTATTA?C???GGTC?ATAACA luteiventris_WA_MVZ225749 ACC?CTCGT?A??AGTG?GC?T????????GACCTGTAG??T?????????AACAAACTA????GTG??????????GGTG?ACAAACCT??GGT?TTTGACCT??CG?TATT?GA??TTGA?TT?A???????C?AC???????T?AA?????????CCAATT?ACAA??CCCGTAACGAC????G??CTTGAATA?A??T?TA?AT?TAT?AT??CGG???ACAACC?????????AG?ACATCGTCGCTG?TTATC????????TTTAGTTGCA?T?T???????????????TATA?ATGGAAT?CTGAGGT???AC????CTCC???TTCACTCGC??CT????????CTCTAAAT?????TG?T??T?AATG??TTGATACTA???????ATGAGGCTTTC??C????????GCCATTAC??A??????????AGAAGTTGACAAAA??TACA?ACATCACAGG?AC??T??????????????????TT?CCAAAATGTC?ACAGTCCTG????T?TACC?CGCTGTAA???C??TGC?TTAGGAAGTCTCACTCGTCAT?T?AT???CC???TA?????AGT?C?????CC???T?TG?GATA?TCACTT???T????CTT?C???AC??GCCCTA??AGAACTTAGA???TCTTAA??????TTCCC?TAG???CAG????TCAA???????ATAGT?ACGAATGTAGTTCA?AT?????????TGTA????TT?CA???AAA?AC???T??????C?????AGCTCG????AA???????TCAAT?TCCA????CC??AATTC??A?C????????GCA??CCTGG?GTAA?????AAAATC?TAA?GCA??TA?AATAAATAGAATTGT??????CTG?CGATATATAGCATGTATTCAT???TTTA??????ACCT?TAGT??????A??AGATTAA?T?????GTCAACACAAAAAT????AA?CC?A?ACATT??ATAAT??TCTAGAGAACT???GAAGCAAT???GGGACAATT?C??A??????????A?CAAA?ATT?????ATTACCCC??AT?????CAA???C??CAC???GGGA?ATA??????ACTG??TTCAA?CATCAA?TCAG?TTCGA???AA?TATAATAAGTAT?AT??GCATTAGA?CTG?????????CAAAGA??????A??GA?????????ACTATT??TCCAATTTT??GCCTTATCTTATTA?CAATAAATGAAAC?C?TT?ACA?TACCATGG??CTATC?AATTCCTATA????????CA??A???????????????????????C?GA?AA??????GATC??????AA?TAT?CGG??????CTAGAC??CTAGAT??TTAAA???GAAAAGT??TCTA?T????ACGC??????AATGGGTA?TCAACA?????TTAAG?TCAA???????CGT??ATACGTCCACAA????AGACAGACTA?C???????TGAG??????TGTATATCT??G?CC?TTGTA??G?TAATTGCTG????GTC?C?A???AAT??CAACT?????????????CAG?TTAGATTCACTCC?TTTTATA???C??GAT???????????ATCATATTGCAT??????TAAT?A?C?ATAAATTGAAACAA?GCTCTTACTGAATGACTAGG?GA?CTC??TGAACTGATCA???GGAACA?TACG?CTGGTATA?ACCGTAT?????????GTT?A??C??TCTAGACGAAGCGCCGACAATTCTT?ACT????????????TATCATTTGCT????AGACTCAATAAAAGTACAAGACAT????CG?GAT???A????A?TC??AACCATC?????T??T?????AATA????GCAGTACTGC?????ACAAGATCT??????AAAAC?TGAC??CAAAT???AAAAACTAGCAAA?????????CCGA????ATACTGCCAT?GCT?GATAG???TTAACT??GCT?ATAA???????GG??C?CTAAGA?CCGAAGCTA?TAATTT??GGAG???TTA?A?C?TTATAAACAT??A?AGGGT??A?GT?TAAT?AGAAA?T??A??G?????ATT?GTACTAAA????ACCAAT???AAA?A????????????TGAAGTCCTTC????????????TTACGC??TC????TA??????????ACTATA?GT?TGT?A??TAATC???ACATTC?GCTCCTAT?ATA???GT?????AGTACACG????????TTTAATA??T?????CTTAC??????TAT?TTT?T?TTT????CC?CA?CGATA?TCA??AG??????????CT?T?TAGGA?ACTA????AAAAA??????????CGTA?ATAGGTTATCGAT?TAAGATAA?ATAATAACCAAGTACGTACCA???TTTG?TA?TA?CCC??????AAA?ATT??????ACTGA?GC?????????AT?????G????TCT?????ACCTGG?G????T????????A????A?G?T??GC?A???AC?CCTGTACAAT?G?AA??ACC?CA?CGC?ACGC????TACA?GATCC??????????TCC??CT????????A?TTTTGG??AAAATA?A???AACAGTG?GCGC????CCACTT????????CTAACAT???TA??GAC?AC??AT???????C????CACTTGATAG?GGTGAGGAATTGTGTAGAGCTAATG?GT?A?????????A???CACGACAT?????C???T?TATC?AC?TCTCA?A?GAA???????AACAATTT?A?TTGTAAATCAG?AT??CTT?CTATGCC??TTTA?CGTAC?CA?CTACAATTGG???AG????T?AA?CATTCCAAAA??A?GTA???TTTTAT?GT?CTAGA??GCCATATGCT????CTCTAGTA???TATCC??CT?AACTTA????CAATGTCGATA????AGGTTC?CA??C??CATCGATAATATCGTC?GTTAA??????ATATA???????TCAGG???????TAATAATT????CACTTA?????A?CTACTTCG???????T???TCG?ATA?T?A??AGCA???AAA????A??CGA??C?C?????????ATA?CCTGTAAAT??TGAAC????????CCAAC?CA?CTTAGAAA?A?TCAAAATTGATATGAGTATTA?T???GGTC?ATAACA muscosaMVZ149006 ACT?CCCGT?A??AGTG?GC?T????????GACCTGTAG??TT???T?TT?AACAAACTA????GTG??????????GGTG?ACAAACCT??GGC?TTTGACCT??CG?TCTT?GA??TTGA?TT?A???????C?AC??????????A?????????CCAATT?ACAA??TCCGTAACGAC????A??CTCGAATATA??C?TA?AT?TAT?AT??CGG???ATAACC?????????AG?ACACCGTCGCTG?TTATC????????TTTAGTTGTA?TCT??????????A?CGGTATA?ATGGAAT?CTGAGGT???AC????CTCC???TATACTCGC??CT????????CTTTAAAT?????TG?T??T?TATG??TAGATACTA???????CTAAAACTTTC??C????????GCCATTAC??A??????????AGAAGTTGACACAA????CG?ACATCACGGG?AT??A??????????????????TC?CCAAAATGTC?ACATTCCTG????T?TACT?CGCTGTAA???G??AGC????TGAAGCTTCCCTTCTTGT?TTAT???CC???TA?????AGTAC?????CG???T?TA?AGAA?TCACTT???T????CTT?C???CA?AATCCTA??AGAACTTAAACGATCTTAA??????TTCCC?TAG???TAG????CCAA???????ATAGT?ACGAATTTAGTTCT?CT?????????TGAA????TC?CA???AAA?AC???T??????T?????AGCTCG????AA???????TGAAT?TTCA????CC??AGTTC??A?C????????ACA??CCTGG?GTAA?????AAAATC?CAA?GCA??TA?TATAGATAGAA?TGT??????CTG?CGATTTATAGCCTGTATTCAT???TTTA??????ACCT?TAGT??????A??ACATTAA?T?????GTCACCAAAAAAAT????AA?TC?A?ACAAT??ATAAT??TCTAGAGAATT???GAAGCAAT???GGGATCATT?C??A?????????CA?TAAG?ATT?????ATTACCCA??AT?????TAA???C??CAA???GGG??ATA??????ACTG??TTCAA?CAACAG?TCAG?TTGGA???TA?AGTAATAAGAAA?AT??GTATTAAA?CTG?????????CAAAG???????A??GA?????????ATTATT??TCCAATTCT??GCCTTATATTATCA??TATAAATGTAAT?C?TT?ATA?TGCCATGG??C?ATC?AATTCCTATA????????CA??A???????????????????????C?GA?AA??????GATC??????AA?TAT?CGG??????CAACAC??TTAGAT??TTAAA???GAAAAGC??TTCA?T????AAGC??????AATGAGAA?TCAATA?????TTAGG?CCTT???????TGT??ATACGTTCACAA????AGGCAAACTC?C???????TGAG??????TACATATGT??G?CC?TTGTA??GATAATTCTTG????GTC?C?A???AAT??CAACTAC???????A?CTCAG?TTAGATTCGCCCC?TTTTATA???C??GATGTGTTGT????AACATATTGCGT??????TAAT?A?C?ATAAATTGAAATAA?GCTCTTACTGAATGACTAGG?GA?CTC??TGAACTGATCA???GGACCA?TACG?CTGGTATA?ACCGTA????????????T?A??T??TCTAGAAGAAGCG??GACAATCCTT?ACT????????????TATCTTTTGCT????AGACTCAAAAAAAGTAAAAAACAT????CG?AAT???A????A?CC??AACCATC?????T??T?????AACA????GCAGTACTAC?????ACGAGAACT??????GAATC?TGAC??CAAAC???AAAAACTAGCAAA?????????TCGA????TTACTGCCAA?GCT?GATAGCAATTAACT??GCT?ATAG?AACACTGG??C?CTAAGT?CCGAAGCTA?TAAATT??GGAG???TTA?A?C?CT?T???????????GGGC??A?GT?AAAT?AGAAACT??G??G?????ATT?GTACTAAA????ACTAAT???ATA?A????????????TGAAGTTCTCC????????????TCACGC??TCATAGTA??????????ACAATA?GT?TGT?A??TAACC???ACATTC?TCTACTTT?ATA???GC?????AGTATACA????????CTTGA?A??C?????CTTAC??????TAT?TTT?T?TCT????TC?CA?CGATA?GCA??AG??????????TT?T?TAGGA?TCTA????AAAGA??????????CGTA?ATAGTACACCGCT?TAAGATAA?ATTATAA???????CGTACCA???TTCG?TA?CA?CCC??????AAA?ATT??????ACTCA?GC?????????AT?????G????TCT?????ACACGG?TCTTTT????????A????T?G?T??GT?A???AC?CCTGT????T?G?AA??ACC?CC?CGC?ATGT????TACA?GATTC??????????TCT??CT????????A?TCTCGG??AAAACA?A???AACAGTG?GCGC????CCATTT????????GTAATAT???TA??GTC?AC??AT???????C????CACTTGATAG?GGTAAGGAATTGTGTAGAGCTAATG?GT?A?????????A???CATGACAT?????CTCTC?AATC?AC?TATCAGA?GAA???????AGCAATTC?A?T?????????G?AT??CTT?CTATGCC??TTTA?TGTAC?CA?CTACAATTGG???AG????C?AA?CATTTCAAAA??A?GTA???TCTAAT?GT?CTGGA??GCCATATGCC????TTCTAGTA???TTTCC??CT?AACTTA????AAATGTCGA?A?????GGCTC?CA??C??CATCGATAATATCATC?GATAA??????ATTTA???????TCAGG???????CGATAATT????CACTTA?????A?CTACTTCA???????C???TCG?ATA?T?A??AGCT???AAA????A??CGA??T?A?????????ATA?CCTGTAAAT?????????????????AGT?CT?CTTAGAAA?A?TCAAAATTGATATGAGTATTA?A???GGTC?CTGACA auroraMVZ13957 ?TC?CCCAT?A??AGTG?GC?T????????GACCTGTAG??TT???T?TT?AACAAACTA????GTG??????????GGTG?ACAAACTC??GGC?TTTTACCT??TG?TCAT?GA??TCGA?TT?A???????C?AT???????A?AA?????????CCAATT?ACAA??CCCGTAACGAC????A??CTCGTGTATA??T?TA?AT?TAT?AT??CGG???ATAACC?????????AG?ACACTGTCGCTG?TTATC????????TTTAGTTGTA?TCT??????????A?CGGTATA?ATGGAAT?CTGAGAT???AC????CACC???TTTACTCGC??CT????????TTCTAAAT?????TG?T??T?AATA??TTGATACTA???????ATAAAACTTTC??C????????GCCATTAC??A??????????AGAAGTTGACAAAA????CA?ACATCAC?GG?AC??A??????????????????TC?CCTAAATGTT?ACATTCCTG????T?TGCC?TGCTGTAA???T??AGC????AGAAGTTTCTCTTTTCAT?TTAT???CC???TG?????AGTGC?????CG???T?TA?GAAA?TCACTT???T????CTT?C???CA?AATCCTA??AGAACTTAAACGATCTTGA??????TTCCC?TAG???CTG??TCCCAA???????ACAGT?ACGAATATAGTCTA?TT?????????TGCA????TC?CA???AAA?AC???T??????T?????AGCTTG????AA???????TTTAT?TTCA????CC??ACCTC??A?C????????ACA??CCTGG?GTAA?????AAAATC?CAA?GCA??TA?CATCGATAGAA?TGT??????CTGGCGATAAATAGTATGTA?TCTT???TTTA??????ACCT?TAGT??????A??ATACTAA?T?????GTCATCAAAAAGAT????AA?TC?A?ATAAT??ATAAT??TCTAGAGAACTA??GAAGCAAT???GGGATTATT?C??A??CAT????CA?CAAG?ATT?????ATTACCCC??AT?????TAA???C??CAA???GGG??ATA??????ACTG??TTCAATCAACAA?TCAG?TTCGA???AA?AATAACAAGGAC?AT??GTATTAGA?CTG?????????CAAAGA??????A??GA?????????ATTACT??TCCAATTTT??GCCTTAT?TTATCA?CTGTAAATGAA???C?TT?AAA?TGCCATGG??T?ATC?AATTCCTATA????????CA??A???????????????????????C?GA?AA??????GAT???????AG?TAT?CGG??????CCATAC??T?AGAT??TTAAA???GAAAAGT??TTCC?T????ATGC??????AATGAAAA?TCATCA?????TTAGG?TCAT???????TGT??ATACGTTCACAA????AGGCAAACTC?C???????TGAG??????T?CATATTT??G?CC?TTGTA??GATAATTCTTG????ATC?C?A???AAT??CAACTGCGCGAAAAA?CACAG?TTAGATTCGCCCC?TTTTATA???T??GATGTATTGT????AACATATTGCAT??????TAAT?A?T?ATAAATTGAAGTAA?GCTCTTACCGAATGACTACG?GA?CTC??TGAAC???TCA???GGATCA?TACG?CTGGTATA?ACCGTA????????????T?A??T??TCCAGACGAAGCG??GACAATCCTT?ATT????????????TATCATTTACT????AGACTCAATAAAAGTAAAAGACAT????CG?AAT???A????A?TC??AACCATC?????T??T?????AATA????GCAGTAATAC?????ACAAGAACT??????GAATC?TGAC??CAAAC???AAAAACTAGCAAA?????????TCAA????TTACTGCCAG?GCT?GATAGC?ATTAACT??GCT?ATAA?AACACTGG??C?CTAAGT?CCGAAGTTT?AAAATT??GGAG???TTA?A?C?TT?TAAACAT??G?AGGGT??A?GT?CTAT?AGAATCT??G??A?????ATC?GTACTAAG????ACAATT???ATA?A????????????TGAAGTTCTTC????????????TCTCGC??TCATAGTA??????????ACAATA?GT?CGT?A??TAATC???ATATTC?GCTGCTGT?ATA???GC?????AATATACG????????CTTAA?A??C?????CTTAC??????TGT?TTT?T?TTT????TC?CA?CGATA?TC???AG??????????TT?T?TAGGA?TCTA????AAAGA??????????CGTA?ATAGCCTATCGCT?TG?GATAA?ATTATAGCCAAGTACGTATCA???TTCG?TG?CA?CCC??????AAA?ATT??????ACTCA?GC?????????AT?????G????CCT?????ACCCGG?CCTTTT????????A????T?GTT??GT?A???AT?C??GT????T?G?AG??ACC?CT?CGG?ATGT????TACA?GACTC??????????TCA??CT????????A?TCTTGG??AAAACA?A???AACAGTG?GCGC????CCATTT????????TTAATAT???TA??GTT?AC??AT???????C????CACTTGATAG?GGTCAGGAATTGTGTGGAGTTAATG?GT?A?????????A???CAGGACAT?????CTTTT?GATC?AC?TTTCACG?GAA???????AGCAATTT?A?TAGCAAATCAG?AT??CTT?TTATGCC??TTTA?CGTGC?CAACTACATTTGG???AG????T?GA?CATTTCTAAA??A?GTA???TCTTAT?GT?CTGGA??GCCATATGCC????TTCTAGTA???TTTCC??CT?AACTTA????AAATGTCGATATATTAGGTTC?CA??C??CATCGA?AATATCGTC?AAT?A??????ATTTA???????TCAGG???????TAAT??TT????CCCTAA?????A?CTACTTCA???????C???TCG?ATA?T?A??AGCA???AAA????A??TGA??T?A?????????ATA?CCTGTGAAC??TGAAC????????CTAGC?CT?CTTAGAAA?A?TCAAAATTGATATGAGTATTA?G???GGTC?ATGACA cascadaeMVZ148946 ATC?CCCAC?A??AGTG?GC?T????????GACCTGTAG??TT???T?TT?AACGAACTA????GTG??????????GGTG?ACAAACTT??GGC?TTTAATCT??TG?TCAT?CA??TCGA?TT?A???????T?AT???????A?AA?????????CCCATT?ACAA??CCCGTAACGAC????A??CTCGAGTATA??T?TA?AT?TAT?AT??CGG???ATAACC?????????GG?ACACCGACGCTG?TCACC????????TTTTGTTGTA?TCT??????????A?CGATATA?ATGGAAT?CTGAGAT???AC????CTCC???T????TCGC??CT????????CTCTAAAT?????TG?T??C?TATA??TTGATACTA???????TTAAAACTTTC??C????????GCCATTAC??A??????????AGAAATTGACAAAA????CG?ACACCAC?GG?AC??A??????????????????CC?CATTAATGTT?ACATTCCTG????T?TGCC?CGCTGTAA???T??AGC????AGAGGACTCTCTTCTTAT?TTAT???CC???TA?????AGTGC?????CG???T?TG?GGAG?TCACTT???T????CTT?C???TC?AATCCTA??AGAGCTTAAACGATCTTAA??????TTCCC?TAG???CAG????CTAA???????ACAAT?ACGAATCTAGTCTA?TT?????????CGCA????TT?CA???AAA?AC???T??????T?????AGCTCG????AA???????CTTAT?TCCA????CC??AACTC??A?C????????ACA??CCTGG?GTAA?????AAAATC?CCA?GCA??TA?TATAGATAGAA?TGC??????CTGGCGATATATAGTATGTA?TCCT???TTTA??????ACCT?TAGT??????A??ACATTAA?T?????GTCACCAGAAAGAT????AA?TC?A?ACAGT??ATAAT??TCTAGAGAACTA??GAAGCAAC???GGGATCATT?C??A??CAT????CA?CAAA?ATT?????ATTACCCC??AT?????TAA???C??CAA???GGG??ATA??????ACTG??TTCAA?CAACAA?TCAG?TTTGA???AA?AATAACAAGAAC?AT??GTATTAGA?CCG?????????TAAAGA??????A??GA?????????ATTACT??TCCAATTTT??GCCTTATTTTATC???CGTAAATGGAAT?C?TT?ATA?TACCATGG??C?ATC?AATTCCTATA????????CA??A???????????????????????C?GA?AA??????GATC??????AA?TAT?CGG??????CCACAC??TTAGAT??TTAAA???GAAAAGT??TTCC?T????ATGC??????AATGAGAT?TCAATA?????TTAGG?CCAT???????AGT??ATACGTACACAA????AAGCAAACTC?C???????TGAG??????TACATATTT??G?CC?TTGTA??GATAATTCTTA????ATC?C?A???AAT??CAACTACACGAAGAA?CGCAG?TTAGATTCGCCCC?TTTTATA???C??GATGTATTGT????AACATATTGCAT??????TAAT?A?C?ATAAATTGAAGTAA?GCTCTTACCGAATGACTATG?GA?CTC??TGCACTGATCA???GGATCA?TACG?CTGGCATA?ACCGTA????????????T?A??T??TCCAGACGAAGCG??GACAATCCCT?AAT????????????TATCATCTGCT????AGACTCAACAAAAGTACAGGACAT????CG?AAT???A????A?TC??AACCATC?????T??T?????AATA????GCAGTATTAC?????ACAAGTGCT??????GAATC?TGAC??CAAAC???AAAAACTAGTAAA?????????TCGA????TTACTGCCAG?GCT?GTTAGC?ATTAATT??GCT?ATAA?AACACTGG??C?CTAAGT?TCGAAGCTA?TAAATT??GGAG???TCA?A?C?TT?TAAACAT??A?AGGGT??A?GT?CAAC?AGAATCT??G??G?????ATC?GTACTAAG????ACTACT???ATA?A????????????TGAAGTTCCTC????????????TCTCGC??TCATAGTA??????????ACAATA?GT?C?T?A??TAATC???ATATTC?GCTGCTAT?ATA???GC?????AATATACA????????CTTGA?A??C?????TTTAC??????TAT?TTT?C?TTT????TC?C??CGATA?TCA??AG??????????TT?T?TAGGA?TCTA????AAAGA??????????CGTA?ATAGTCTCTCGTT?TG?GATAA?ATAATAGCCAAGTACGTATCA???TTCG?TG?CA?CCC??????AAA?ACT??????ACTAA?GC?????????AA?????G????CCT?????ACTCGG?CCTTTT????????A????T?GTC??GT?A???AC?C??GT????T?A?AA??ACC?CT?CGG?AAGT????TACA?GACTC??????????TCA??CT????????A?TCTTGG??AAAACA?A???AACAGTG?GCGC????CCATTT????????TTAATAT???TA??GTT?AC??AT???????C????CACATGATAG?GGTGAGGAATTATGTGGAGTTAATG?GT?A?????????A???CAGGACAT?????CTCTT?GATC?AC?TACCACA?GAA???????AGCAATTT?A?TCGCAAATCAG?AT??CTT?TTACGCC??TTTA?CGTTC?CAACTACTTTTGA???AG????T?AA??TTTTCTAAA??A???A???TTCTAT?GT?CTGGA??GCCATATGCC????TTCTAGTA???TTTCC??CT?AACTTA????AAATGTCGATATACTAGGTCC?CA??C??CATCGA?AATATCGTC?GGTAA??????ATTTA???????TCAGG???????TTATGATT????CCCTCA?????A?CCACTTCA???????C???TTG?ATA?T?A??AGCA???AAA????A??TGA??T?A?????????ATA?CCTGTGAAC??CGAAC????????CTAGC?CT?CTTAGAAA?A?TCAAAATTGATATGAGTATTA?A???GGTC?CTTACA sylvaticaMVZ137426 ACC?CCTGT?A??AGTG?GC?T????????GTCCTGTTG??TT???T?TT?AACTAA??A????GTG??????????GGTG?ACA????????GT?TTTGACCT???????????????TGA?TT?A???????C?AC???????A?AA?????????CCGATT?ACAA??CCCGTAATGTC????A????????TATA??C?TA??T?TAT?AT??CAG???ACAACCTTCAT??A?AG?ACAAAGTCGCTG?TTAAC????????CATAGTTGTTCTCT??????????A?CGCTATA?ATGGAAT?CTGAGGT???AC????CTCC???TCTACTCGC??CT????????CCCTAAAT?????TG?T??T?AATG??TAGATACTA???????ATGAAACTTTC??C????????GCCTTTAC??A??????????ACAAGTTGACACAA??TACG??CGCTACTGG?AT??CGT??????????A?TAC?TT?CACAAA????????AT?CTG????T?TATT?CGCCGTAAA?CTGTTGC?T??CGAA?ATTCTCTCACCAT?TTAT???CA???TA?????AGTAC?????CT???T?TA?AGAA?TCACTT???TCTGGCGTGC???TC?AGACCTA??AGAACTTAAACGA?CTTAA??????TTCTC?TGG???CAG?????CAC????????TAGT?ACAAATTTAGTCCC?AT?????????AGAA????TTTCA???AAA?AC???T??????C?????AGCTAA????AA???????TAGTT?TTCA????CC??ATATC??A?C????????GCA??GTCGG?ATAT?????GAAATC?G???????????GATAGATAG????GC??????CTG?AGA???ATAGTCTATATTCGT???TTTT??????ACCT?TAGT??????A??AAAATAA?T?????GTCAA????????T????AA?CA?A?AT??T??ATAAT??TCTAGGGAATT???GAAGCAAA???GGGACTATT?C??A??CTA????CA?CTCT?ATT?????ACTACCCT??AT?????CAAT??C??CAA???GGG??ATAT?????ACCG??TTCAA?CATCAA?TTAG?TTCGA???AA?C?TA?AAAGGAA?AT??ATATTAAA?CCG?????????TAGAGA??????A??GATC??ACCCAATTATT??CACA?TTTT??GCT???TATTATTA?CAGTAAACGAAAG?C?TT?GTA?CACCATGG??C?ATC?????CCTACA????????CA??A???????????????????????C?GA?AA??????GCTC??????A??????TGG??????CCAGAC??CTAGCT??????????GAAAAGC??TCTA?T????ATAC??????AATGACAA?TCATCA?????TTAGG?ACAA???????AGT??ATACGTTCACAA????TGGCAGACCC?C???????TGCG???????????ATTC??G?AC?TCGTG??GATAAATATTA????GTT?C?AATTAAT???GACTGC???????A?CCCAG?ATAGATTCCCTCC?TTTTATA???C??AAG???????????A?CATATTGC?TTACGT?CAAC?A?C????AGTTGGAGAAA?GCTCCTGCTGAACGATTAAG?GA?CTC??TCATTTGATTG???GGTTCA?TACG?TTGGTATA?TCCGCAA?????????GTT?A??C??T????AGGAAGCGCCAACTATCCCT?ACT????????????CATCT???????????GACTTATTACAAGAA?AAAACAT????CG?GAT???A????A?TT??AACCTCCCGCATT??T?????AATA??CCGCAGTAATAC?????ACAAGAACT??????GAAATATGAC??CAAAT???AAAAACTCGCAAA?????????TCCA????TTA?TGACAAAGCT?GGTAGCAACTGACT??GCT?ATAA?A?TATTGG??C?CTACGA?CCTAAGTAT?AAAATT?TGG??????TA?A?C?TTACAAACAT??G?AGAGA??A?GT?GAAC?GGAAA?T??G?CA?????CTG?GTACTAAG????ATAACT???AAA?A??????A????GTGAAGTTCTAT????????????TCACGC??TCATAGTA??????????ACAATA?GT?TGT?G??TAATC???ATATTC?G?????TT?ATA???GT?????GTTATACG????????TTTGA?A??A?????TTTAC??????TAT?TTC?T?TTT????TC?CA?CAATA??CG??AG??????????TT?C?TAGGA?TATA????AAAGA??????????AGGA?ATAGCACACCGCT?TGAGATAA?ATCA??ACCAAGT????ATCA???TTTG?TC?AA?CCC??????AAA?ATT??????ACTTA?GT?????????????????????TTT?????ATCCGG?CTTTTT????????A???TT?G?C??GT?A???AT?CCTGTATAAT?C?AA??ACC?AT?CGC?AAGC????TACA?GATTT??????????T?A??CT????????A?TCTTGG??ACAACG?A???AACAGTG?GTGC????CCATTTATATATGGATAATAT???TA??GCT?AC??AT???????C????CACTTGATAG?GGTCAGGAAATATGTGGAGCTAATG?GT?A?????????A???TATGCTAA?????CTGTC?GACC?AT?TCCTAGC?AAA???????AGCAATCG?C?TTGCAAATCAG?GT??CTT?TTGCGTC??TTTA?TGTGC?CA?CTACAGTTGA???AG????C?AA?CGTTCTCAAA??AAGTA???CCACAT?GT?CTCGC??GCCGTATGCC???????T?GTA???TGTTC??CT?AACTTA????TAATATTGGTA????AGAGTC?CG??C??CATCGATCATAT?GTC?CATAT??????ATGTA???????TCAGA???????GGGTA?????????CTTA?????A?CCACTTCA???????C???TTA?ATA?T?A??A?CA???AAA????T??AGG??C?A?????????AAG?CCAGTAAAT??CGAACA???????CTCGC?CA?CTT?GC?????TCATAATTGATATGGGTATTA?T???GGTC?ATAACA sylvaticaDMH84R43 ACC?CCTGT?A??AGTG?GC?T????????GTCCTGTTG??TT???T?TT?AACTAA??A????GTG??????????GGTG?ACA????????GT?TTTGACCT???????????????TGA?TT?A???????C?AC???????A?AA?????????CCGATT?ACAA??CCCGTAATGTC????A????????TATA??C?TA??T?TAT?AT??CAG???ACGACCTTCAT??A?AG?ACAATGTCGCTG?TTAAC????????CATAGTTGTTCTCT??????????A?CGCTATA?ATGGAAT?CTGAGAT???AC????CTCC???TCTGCTCGC??CT????????CCCTAAAT?????TG?T??T?AATG??TAGATACTA???????ATGAAACTTTC??C????????GCCTTTAC??A??????????ACAAGTTGACACAA??TACG??CGCTACTGG?AT??TGT??????????A?TAC?TT?CAAAAA????????AT?CTG????T?TCTT?CGCCGTAAA?CTGCTGC?T??CGAA?ATTCTCTCACCAT?TTAT???CA???TA?????AGTAC?????CT???T?TA?AGAA?TCACTT???TCTGGCGTGC???TC?AGTCCTA??AGAACTTAAACGA?CTTAA??????TTCTC?TGG???CAG?????CAC????????AAGT?ACAAATTTAGTCCT?AT?????????AGAA????TCTCA???AAA?AC???T??????A?????AGCTAA????AA???????TAGTT?TTCA????CC??ATATC??A?C????????GCA??GTCGG?GTAT?????GAAATC?G???????????GATAGATAG????GC??????CTG?AGA???ATAGTCTATATTCGT???TTTC??????ACCT?TAGT??????A??AAAATAA?T?????GTCAA????????T????AA?CA?A?ATAAT??ATAAT??TCTGGGGAATT???GAAGCAAT???GGGACCATT?C??A??CTA????CA?CTCC?ATT?????ACTACCCT??AT?????CAAT??C??CAA???GGG??ATAT?????ACCG??TTCAA?CATCAA?TTAG?TTCGA???AA?C?TA?AAAGGAA?AT??ATATTAAA?CCG?????????TAGAGA??????A??GATC??ACCCAATTATT??CACAATTTT??GCT???TATTATTA?CAGTAAACGAAAG?C?TT?GTA?CACCATGG??C?ATC?????CCTACA????????CA??A???????????????????????C?GA?AA??????GCTC??????A??????TGG??????CTAGAC??CTAGCT??????????GAAAAGC??TCTA?T????ATAC??????AATGACAA?TCATCA?????TTAGG?ACAA???????CGT??ATACGTTCACAA????TGGCAGACCC?C???????TGCG???????????ATTC??G?AC?TCGTA??GATAAATATTA????GTT?C?AATTAAT???GACTGC???????A?CCCAG?ATAGATTCCCTCC?TTTTATA???C??AAG???????????A?CATATTGC?TTACGT?CAAC?A?C????AGTTGGAGAAA?GCTCCTGCTGAACGATTAAG?GA?CTC??TCATTTGATTG???GGTTCA?TACG?TTGGTATA?TCCGCAA?????????GTT?A??C??T????AGGAAGCGCCAACTATTCGT?ACT????????????CATCT???????????GACTTATTACAAGAA?AAAACAT????CG?GAT???A????A?TT??AACCTCCCGCATT??T?????AATA??CCGCAGTAATAC?????ACAAGAACT??????GAAATATGAC??CAAAT???AAAAACTTGCAAA?????????TCGA????TTA?TGACAAAGCT?GGTAGCAAATGACT??GCT?ATAA?A?TATTGG??C?CTACGA?CCTAAGTAT?AAAATT?TGG??????TA?A?C?TTACAAACAT??G?AGAGA??A?GT?GAAC?GGAAA?T??G?CA?????CTG?GTACTAAG????ATAAGT???AAA?A??????A????GTGAAGTTCTAT????????????TCACGC??TCATAGTA??????????ACAATA?GT?TGT?G??TAATC???ATATTC?G?????TT?ATA???GT?????GTTATACG????????TTTGA?A??A?????TTTAC??????TAT?TTC?T?TTT????TC?CA?CAATA??CG??AG??????????TT?C?TAGGA?TATA????AAAGA??????????AGGA?ATAGCACACCGCT?TGAGATAA?ATCA??ACCAAGT????ATCA???TCTG?TC?AA?CCC??????AAA?ATT??????ACTTA?GT?????????????????????TTT?????ATCCGG?CTTTTT????????A???TT?G?T??GT?A???AT?CCTGTATAAT?C?AA??ACC?AT?CGC?ATGC????TACA?GATTT??????????T?A??CT????????A?TCTTGG??ACAATG?A???AACAGTG?GTGC????CCATTTATATATGGCTAATAC???TA??GCT?AC??AT???????C????CACTTGATAG?GGTGAGGAAATATGTGGAGCTAATG?GT?A?????????A???TATGCCAA?????CTGTC?GACC?AT?TCCTAGC?AAA???????AGCAATCG?C?TTGCAAATCAG?GT??CTT?TTGCGTC??TTTA?TGTGC?CA?CTACAGTTGA???AG????C?AA?CGTTCTCAAA??AAGTA???TCACAT?GT?CTCGC??GCCGTATGCC???????T?GTA???TGTTC??CT?AACTTA????CAATATTGATA????AGAGTC?CG??C??CATCGATCATAT?GTC?CATAT??????ATGTA???????TCAGA???????GAGTA?????????CTTA?????A?CCACTTCA???????C???TTA?ATA?T?A??A?CA???AAA????T??AGG??C?A?????????AAG?CCAGTAAAT??CGAACA???????CTTGC?CA?CTT?GC?????TCAAAATTGATATGGGTATTA?T???GGTC?ATAACA septentrionalesDCC3588 ACTTCTC???AT?AGTG?GC?T????????GTCCTGTCG??TT???T?TT?AAGTAACTG????GTG??????????GGTG?ACA????????GT?TTTAATTT??TAGTATCAAAAGTTGA?TT?A???????C?AC???????C?AA?????????CCTATT?ATAG??CCCGTAATGAC????A????????TATA??T?TA?GT?TAT?????CAG???ATGACCTTCAC??A?CC?ACCGTGTCGCTG?CCATT????????TCTAGTTGTA?TCT??????????A?CGTTATA?ATGGAAT?CTGAGAT???AC????CTCC???TTTACTCGC??CT????????TTTTAAAT?????TG?T??T?GATG??TGGATACCA???????ATGAAACTTTC??C????????GCCATTAC??A??????????AAAAATTGACAGAA??CACA??CGCCACCGG?GT??GGT??????????A?TAC?CA?CAAAAA?GTA?ACACT?CTG????TCTGCC?CACCGCAAA?CT??TGC?CTAAGAA?CTTCTCTCATCAT?TTAT???CC???TA?????AGTAC?????CT???TTTG?TGGA?TCACTT???T????CATGC????A?GGTCCTA??AGATCTTAAACGA?CTTAA??????TTCTC?TGG???CCG????CCAC???????CAAGT?ACAA?TATAGTTCT?AT?????????AGT?????TC?CA???AAA?AC???T??????A?????AGCTAG????AA???????ATGTT?TTCA????CC??ACATC??C?C????????CCA??ATTGG?ATAT?????AAAATC?TAA?GCA??TA?GATATA?AG???TGA??????CTG?CGATCCATAGCTCGTATTCGT???TTTT??????A?????ATT??????A??CTATTAA?T?????GTCCC????????T????AA?TA?A?ATACT??ATAAT??TCTCGAGAACT???GAAGCAAC???GGGATTATT?C??A??CTG????CA?CATA?ATT?????ACTACCCT??AT?????CAAT??C??CAA???AGG??ATAT?????ACCG??TT???????CAA?TCTG?TTTGA???AA?C?TA?TAAGCAG?AT??ACCTTAGA?CCG?????????CAAAGA??????A??GATC??ACTCTATTAAT??TCCAATTCT??GCTTTGTATTATTA?CAGTAAACGCAAG?C?TT?ACA?CGCCATGG??T?ATC?GATCCCTATA????????CA??G???????????????????????C?GA?AA??????GATC??????TA?AGT?CGG??????CCAGAC??CTAGTT??TTAAA???GAAAAAT??TCCA?T????A?AC??????AATGAAAA?TCAACAC????TTAGG?ACAG???????AGT??ATACGTGCACAA????CGACAGACCC?C???????TGTG???????????ATTA??G?CC?TCGTA??GACAAATCTTA????GTT?C?A???AAT??CAACTGC???????A?CACAG?TTAGATTCTCTTC?TTTTACA???C??AAG???????????A?CATATTGCAT??????CAAT?A?C????AGTTGAAGAAC?GCTCTTGCCGAATGATTA?G?GA?CTC??TTATTTGATTG???GGAACA?TAC?????GTATA?TACGCAA?????????GTT?T??C??T????GAGAAGCTC??ACACTGCTT?ACT????????????CATCA???????????GACCCAACACAAGCA?AAAACAT????CG?GAT???A????A?AT??AACCATCCACATT??T?????AAGA??CCGCAGTAATCC?????ACGAGAACTAACAAAGAAAAATGAC??CAAAC???AAA?ACTTGCAAA?????????CCGA?????TACTGACAGAGCT?GGTAGTA?TTCACT??GCT?ATAA?A?TTC??G??C?CTACGA?CCCAAGCAG?AAAATT?TGATG???TTA?A?C?CTATAAACAT??A??AAGC??A?GT?TAATGGGAAA?T????????????TA?GCACTAAAATTGATAATT???A??????????A????GTGAAATGCTGC????????????TCACGC??TCATGGTG??????????ACAA???GC?TGT?A??TAATC???ATATTC?G?????AT???A???GT?????AGTATACG????????CTTGA?A??A?????TTTAC??????TAT?TTC?C?TCT????TC?CA?CAATA??CA??AG??????????CT?C?TAGGA?CATA????AAAAA??????????AGG??????AACATCGTT?CGAGATTG?ATTA??ACCAAGTCCGTACCA???TATG?TT?AA?CCC??????AAAGCTT??????TCTTA?GT?????????CC?????G????CCT?????ATCCGG?TGTTTT????????A???TC?G?T??GTGA???AT?TCTGTATAAT?G?AA??ACC?AC?CGT?ATGT????TACA?GATTT??????????TCC??CT????????A?TATTGG??AAAATT?A???AACAGTG?TCGC?????????????????????ACAT???TA??GCA?AC??TT???????C????CACTCGATAG?GGTAAGGAACTATGTGGAG??AACA?GT?ACCATTAAATT???TACGTTAA?????CTACC?GGCC?AT?TGTCAGT?AGA???????AGCAATCT?C?TTGCCAATCAG?GT??CTT?TTGCGTC??TTTT?CGTAC?CA?CTACTATTGA???AG????C?AA?CGTTATAAAA??AAGTA???CTGTAT?GT?CTAGC??GCTGTATACC????CC?T?GTA???TATTC??CT?AACTTA????CAATGTTGAT?????AGACAC?CG??C??CATTG???????CGTC?AATAT??????ATGTACAAGACCTCTGG???????GAATAATT????CCCTCA?????A?TTAATTCA???????G???TCA?ATA?T?A??A?CA???AAA????A??AGG??C?C?????????ATA?CCCGTAAAT??CGA???????????AAGC?CA?CTT?GTAA?A?TCACAATTGATATAAGTATTA?T???GGTC?ATCACA grylioMVZ175945 ACTCCTC???AT?AGTG?AC?T????????GTCCTTTTG??TT???T?CT?AAGTAACTG????GTG??????????GGTG?ACA????????GT?TTTAACTT??TAGTATCACA??TTGA?CT?A???????C?AC???????A?AA?????????CCAATT?GCAA??CCCGTAATGACT???A????????TATA??C?TA?TT?TAT?????CAG???ATGACCTTCAT??A?CA?ACTGTGTCGTTG?CTAAC????????CTTAGTTGTC?TCT??????????A?CGCTATA?ATGGAAT?CTGAGAT???AC????CTCC???TTTACTCGC??CT????????ACTTAAAT?????TG?T??T?GATG??TCGATACCA???????ATGATACTTTC??T????????GCCATTAC??A??????????AAAAATTGTCAGGA??CACA??CGTCACTGG?AT??GGT??????????A?TAC?TC?CAAGAA?GTC?ACATT?CTG????T?TGTC?CACCGCAAA?CT??TGC?CTAAGAA?ACTCCCTCCTCAT?CTAT???CC???TA?????GGCAC?????CC???T?TT?TGGC?TCATTT???T????CATGC????A?GGTCCTA??AGAACTTAGGCGA?CTTGA??????TTCCC?TAG???CAG????TCAC???????CTAAT?ACAA?TATAGTTCA?TT?????????AGCA????TC?CA???AAA?AC???T??????C?????AGCTAG????AA???????AAACT?TTCA????CC??ATATC??G?C????????CCA??ATCGG?ATAC?????ACAACC?CGA?GCA??TA?TATAAGTCG???TAA??????CTG?TGATCTACCGCTTGTAATCGT???TTTC??????A?????ATC???????????ACTAA?T?????GTCAT????????T????AA?TA?A?ACAAT??ATAAT??TCTTGAGAACT???AAAGCAAC???GGGACTATT?CTCA??CCG????CA?CACG?ATT?????ATTACCCT??AT?????CAAT??C??CAA???GGG??ATAT?????ACCG??TTCGA?CACCAA?TCTG?TTAGA???AA?C?TA?TAAGAAG?AT??AACTTAAA?CTG?????????CAAAGA??????A??GATC??ACTCTATTACT??TCCACTTTT??GTTTTGTACTATTA?CAGTAAACGAAAG?C?TT?ATA?CCCCA?????????????TCCCTATA????????CA??A???????????????????????C?GA?AA??????GGTC??????CT?AGT?CGG??????TTAAA??????GTT??TTAAA???GAAAAGT??TTCA?T????A?CC??????AATGGGAA?TCAACA?????TTAGG?ACAG???????CGT??ATACGTGCACAA????CGACAGACCA?C???????TGAG???????????ATCT??G?AC?TCGTA???ACAAATCTTC????GTT?C?A???AAT??CAACTGC???????A?CTCAG?CTAAATTCTCTAC?TTTTATA???A??AAG???????????A?CATATTGCCT???????AAT?A?CC???AGCTGAAGGAA?GCTCTTGCTGAAT?ATTATG?GA?CTC??TTAATTGAT????????????TAC?????GTATA?AATGCAA?????????GTT?A??T??T????ACGAAGCCC??ACAATGCCC?A??????????????CATCT???????????GACCCAACACAAGAT?AAAACAT????CG?GAT???A????AATT??AACTATCCGCATT??T?????AAAA??CCGCAGTATTCC?????ACGAGAAA?CACAAAGAAATATGAC??CAAAT???CAA?ACCTGCAAA?????????CCGA????TTACTGACACAGCTAGGTAGTA?TTTACT??GCT?ATAA?A?CTC??G??C?CTAAG??ACTAAGCAG?AAAA????????????TA?A?C?CTATAAACAT??A??GAGT??A?GT?CGACGGAGAA?T??GTCA?????ATC?GTACTAAGATTGACTACT????AA?A??????A????GTGAAATTCCAT????????????TCACGC??TCATGGTG??????????ACAATA?GC?TGT?A??TAACC???AGATTC?A?????TT?ATA???GT?????ACTATACG????????TTTGA?A??A?????ATTAC??????TAT?ATC?T?TTT????CC?CA?CAATA??CA??AG??????????TT?T?TAGGA?CATA????A??TA??????????AGG??????AACGTCGTT?TGAGATCG?ATAA??ACCAAGTACGTACTA???TATG?TCTAA?CCC??????AAAGATT??????ACTCA?GC?????????TA?????G????TTT?????ATTAGG?CCTTTTAAGATCCTA???TC?G?T??GTGA???AT?TCTGTATAAT?G?AA??ACC?AT?CGC?ATGC????TATC?G???T??????????TCA??CTAGACAC?CA?TATTGG??AAAAAT?A???AACAGTG?TTGC?????????????????????ATAT???TA??GCT?AC??AT???????T????CACTT?ATAG?GGTTAGGAACTATGTGGAG??GATA?GT?ACTAT??????????ATATTAA?????CTAGA?GATC?CT?TGTCAGA?AGA???????AACAATCT?T?TTACCAATCAG?AT??CTT?TTGTGT???TTTG?TGTCC??A?CTATTATTGA???AG????T?AA?CATTTTCAAA??AAGTA???CTGCAT?GT?CTTGC??GCCGTATACC????TC?T?GTA???T?TCC??CT?AACTTA????TAATGTTGCT?????AGACAC?CG??C??CATTG???????CGTC?AATAT??????ATATA???????TTTGG???????AAATAACT????CACTTA?????A?TTAAT?CA???????A???TTA?ATA?T?A??A?CA???AAA????T??AGA??T?C?????????ATA?CCTGTAAAT??CGA???????????CAGC?CA?CTT?GCAA?A?TCAGAATTGATACATGTATAC?T???GGTC?TTTACA okaloosae ACTTCTT???AT?AGTG?GC?T????????GTCCTGTTG??CT???T?TT?AAGTAGCTA????ATG??????????GGTG?ACA????????GT?TTTAATTT??CAGTATCAAA??TTGA?TT?A???????C?AC???????A?AA?????????CCAATT?ACAA??CCCGTAATGAC????A????????TATA??C?TA?AT?TAT?????CAG???ATGACCTTCTT??A?TA?ACCGAGTCGCTG?CTAAC????????TATAGTTGCA?TCT??????????A?CGCTATA?ATGGAAT?CTGAGAT???AC????CTCC???TTTTCTCGC??CT????????AATTAAAT?????T?????C?GATG??TAGATACCA???????ATGAAACTTTC??C????????GCCGTTAC??A??????????ACAAATTGTCAGTA??CACA??CGTCACTGG?AT??GGT??????????A?TAC?TT?CACGAA?GTA?ACACT?CTG????T?TATC?CACCGCAAA?CC??AGC?CTAAGAA?CCTCTCTCTTCAT?TTAT???CC???TA?????AGCAC?????CT???T?TG?TGGA?TCACTT???T????CATGC????G?AGCCCTA??AGAACTTAAACGA?CTTAA??????TTCTC?TAG???CTG????TCAC???????CAAGT?ACAA?TATAGTTCT?GT?????????AGTA????TC?CA???AAA?AC???T??????A?????AGCTAG????AA???????AAGGT?TTCA????CC??ACGTC??A?C????????CCA??ATCGG?ATAT?????AAAATC?TAACGCA??TA?TATAAATAG???TAG??????CTG?CGATCCATAGCCTGTATTCAT???TTTC??????A?????ATT??????A??CTATTAA?T?????GTCAC????????T????AA?TA?A?ACAGT??ATAAT??TCTAGCGAATT???AAAGCAAT???GGGACTATT?C??A??CTG????CA?CATA?ATT?????ACTACC???????????CAAT??C??CAA???GGG??ATAT?????ACCG??TTCGA?CATCAA?TCTG?TTCGA???AA?C?TA?TAAGAAT?AT??ATATTAGA?CCG?????????CAAAGA??????A??GATCTCACCCTATTACT??ACCAATTCT??GCTTTGTACTATTA?CAGTAAATGCAAA?C?TT?GTA?CACCATGG??T?ATC?GATTCCTATA????????TG??A???????????????????????C?GA?AA??????GATC??????TA?AGT?CGG??????TTAGAC??CTAGTT??TTAAA???GAAAAGC??TCCA?T????A?AC??????AATGAGAA?TCAACA?A???TTAGG?GCAG???????TGT??ATACGTCCACAA????TGACAGACCT?C???????TGTG???????????ATTG??G?CC?TTGTA??GATAAATCTTA????GTT?T?A???AAT??CGACTGC???????A?CACAG?CTAGATTCACTCC?TTTTATA???C??GAA???????????A?CATATTGCAT??????CAAT?A?C????AGCTGAAGAAG?GCTCTTGCCGAGTGATTATG?GA?CTC??TTAATTGATTG???GGAACA?TAC?????GTATA?TATGCAA?????????GTT?A??C??T????GGGAAGCTC??AC?????TT?A??????????????CATCC???????????GACCCAATACAAGAA?AAAACAT????CG?AAT???A????A?CT??AACCATCCGCATT??T?????AAA???CCGCAGTAATTC?????ACAAGAACTGACAAAGAAATATGAC??CAAAC???GAA?ACTCGCAAA?????????TCGA????TTACTGACAAAGCT?GGTAGAA?TTAACT??GCT?ATAC?A?TCT??G??C?CTAC?T?CCCAAGCAG?AAAATT?TGGAG???TTA?A?C?CTATAAACAT??A??GTGA??A?GT?CGACGGAAAA?T??G?CA?????ATA?GTACTAAGATTGATAATT???ATA?A??????A????GTGAAATTCTAC????????????TCACGC??TCATGGTA??????????ACAATA?GC?CGT?A??TAATC???AGATTC?G?????CT?ACA???GT?????ATTATACA????????TTTGA?A??A?????TTTAC??????TAT?TTC?T?TCT????TC?CA?C???????A??AG??????????CT?C?TAGGA?CATA????AAAAA??????????AGG??????AATGTCGCT?TGAGATTG?ATCA??GCCAAGTACGTACCA???TATG?TA?AA?CCC??????AAAGCTT??????ACTTA?GT?????????AT?????G????TCT?????ATCCGG?TCTTTT????????A???TT?G?T??GTGA???AT?TCTGTATAAT?G?AA??ACC?AT?CGT?ATGT????TACA?GATTT??????????TCA??CT????????A?TCTTGG??AAAATT?A???AACAGTG?TCGC?????????????????????ATAT???TA??GTT?AC??AT???????C????CACCCGATAG?GGTCAGGAACTATGTGGAG??AATA?GT?ACTATTAAATT???TACGCTAA?????CTACT?GATC?AT?TACCAGA?AGA???????AACAATTTCT?TTGCCAATCAG?AT??CTT?TTGTGTC??TTTA?TGTAC?CA?CTACTATTGG???AG????T?AA?CATTTTAAAAAAAAGTA???CCGTAT?GT?CTAAC??GCCGTATTCC????TC?T?GTA???TGTTC??CC?AACTTA????CAATGTTGGT?????AGCTGC?CG??C??CATCG???????CGTC?AATAT??????ATGTA???????TCTGG???????GAATAATT????CACTCA?????A?TTAATTCG???????A???TCA?ATA?T?A??A?CA???AAG????A??AGG??C?G?????????ATA?CCTTTAAAT??CGA???????????TAGC?CA?CTT?GTAA?A?CCGCAATTGATATAAGTATTAAT???GGTC?CTAACA clamitansJSF1118 ACTTCTC???AT?AGTG?GC?T????????GTCCTGTTG??TT???T?TT?AAGTAGCTG????ATG??????????GGTG?ACA????????GT?TTTAATTT??TAGTATCAAA??TTGA?TT?A???????C?AC???????A?AA?????????CCAATT?ACAA??CCTGTAATGAC????A????????TATA??C?TA?AT?TAT?????CAG???ATGACCTTCAT??A?TA?ACCGAGTCGCTG?CTAAC????????TATAGTTGCA?TCT??????????A?CGCTATA?ATGGAAT?CTGAGAT???AC????CTCC???TTTTCTCGC??CT????????AATTAAAT?????T?????T?GATG??TTGATACCA???????TTGAAACTTTC??C????????GCCGTTAC??A??????????ATAAATTGTCAGTA??CACA??CGTCACTGG?AT??GGT??????????A?TAC?TT?CACGAA?GTA?ACACT?CTG????T?TATC?CACCGCAAA?CC??TGC?CTAAGAA?CCTCTCTCTTCAT?TTAT???CC???TA?????TGAAC?????CT???T?TG?CGGA?TCACTT???T????CATGC????G?AGCCCTA??AGACCTTAAACGA?CTTAA??????TTCTC?TAG???CAG????TCAC???????CAAGT?ACAA?TATAGTTCT?GT?????????AGTA????TA?CA???AAA?AC???T??????A?????AGCTAG????AA???????AAGGT?TTCA????CC??ATGTC??A?C????????CCA??ATCGG?ATAT?????AAAATC?TAACGCA??TA?GATAAATAG???TGG??????CTG?CGATCCATAGCCTGTATTCAT???TTTC??????A?????ATT??????A??CTATTAA?T?????GTCAC????????T????AA?TA?A?ACAGT??ATAAT??TCTAGAGAAAT???AAAGCAAC???GGGACTATT?C??A??CCG????CA?CATA?ATT?????ACTACC???????????CAAT??C??CAA???GGG??ATAT?????ACCG??TTCGA?CATCAA?TCTG?TTCGA???AA?C?TA?TAAGAAT?AT??ATCTTAGA?CCG?????????CAAAGA??????A??GATCTCACCCTATTACT??ACCAATTCT??GCTTTGTACTATTA?CAGTAAATGCAAG?C?TT?GCA?CACCATGG??T?ATC?TATCTCTATA????????TG??A???????????????????????C?GA?AA??????GATC??????CA?AGT?CGG??????TTAGAC??CTAGTT??TTAAA???GAAAAGC??TCCA?T????A?AC??????AATGAGAA?TCAACA?G???TTAGG?GCAG???????TGT??ATACGTTCACAA????TGACATACCT?C???????TGTG???????????ATTG??G?CC?TTGTA??GACAAATCTTA????GTT?C?A???AAT??CGACTGC???????A?CACAG?CTAGATTCACTCC?TTTTATA???C??GAA???????????A?CATATTGCAT??????CAAT?A?C????AGCTGAAGAAG?GCTCTTGCCGAATGATTATG?GA?CTC??TTAATTGATTG???GGAACA?TAC?????GTATA?TACGCAA?????????GTT?A??C??T????GGGAAGCTC??AC?????TT?A??????????????CATCC???????????GACCCAACACAAGAA?AAAACAT????CG?AAT???A????A?CT??AACCATCCGCATT??T?????AAA???CCGCAGTAATTC?????ACAAGAACTAACAAAGAAATATGAC??CAAAC???AAA?ACTCGCAAA?????????TCGA????TTACTGACAAAGCT?GGTAGAA?TTAACT??GCT?ATAC?A?TCT??G??C?CTAC?G?CTCAAGC??????ATT?TGGCG???TTA?A?C?CTATAAACTT??A??GTGA??A?GT?CGACGGAAAA?T??G?CA?????ATA?GTACTAAGATTGATAACT???ATA?A??????A????GTGAAATTCTAC????????????TCACGC??TCATGGTA??????????ACAATA?GC?TGT?A??TAATC???AGATTC?A?????TT?ACA???GT?????ATTATACA????????TTTGA?A??A?????TTTAC??????TAT?TTC?T?TCT????TC?CA?C???????A??AG??????????CT?C?TAGGA?CATA????AAAAA??????????AGG??????AATGTCGTT?TGAGATTG?ATCA??GCCAAGTACGTACCA???TATG?TA?AA?CCC??????AAAGCTT??????ACTTA?GT?????????GT?????G????TCT?????ATCCGG?TCTTTT????????A???TT?G?T??GTGA???AT?TCTGTATAAT?A?AA??ACC?AT?CGT?ATGT????TACA?GATTT???????????CA??CT????????A?TCTTGG??AAAATT?A???AACAGTG?TCGC?????????????????????ATAT???TA??GTT?AC??CT???????C????CACCCGATAG?GGTCAGGAATTATGTGGAG??AATA?GT?ACTATTAAATT???TACGCTAA?????CTACT?GATC?AT?TACCAGA?AGA???????AACAATTCCT?TTGCCAATCAG?AT??CTT?TTGCGTC??TTTA?TGTAC?CA?CTACTATTGG???AG????T?AA?CATTTTAAAAAAAAGTA???CCGTAT?GT?CTAAC??GCCGTATTCC????TC?T?GTA???TATTC??CT?AACTTACTCACAATGTTGAT?????AGCTAC?CG??C??CATCG???????CGTC?AATAT??????ATGTA???????TTTGG???????GAATAATT????CACTCA?????A?TTAATTCA???????A???TCA?ATA?T?A??A?CA???AAG????A??AGG??C?C?????????ATA?CCTTTAAAA??CGA???????????CAGC?CA?CTT?GTAA?A?CCGCAATTGATATAAGTATCAAT???GGTC?ATAACA heckscheriMVZ164908 ACTTCTC???AT?AGTG?GC?T????????GTCCTGTTG??TT???T?CT?AAGCAGCTG????GTT??????????GGTG?ACA????????GC?TTTAATTT??TAGTGTCAAA??TTGA?TT?A???????C?AC???????A?AA?????????CCAATT?ACAA??CCCGTAATGAC????A????????TATA??C?TA?AT?TAT?????CAG???ATGACCTTCAT??A?TA?ACCGGGTCGCTG?CCAAC????????TAGAGTTGCA?TCT??????????A?CGCTATA?ATGGAAT?C??AGAT???AC????CTCC???TCTGCTCGC??CT????????AATTAAAT?????T?????A?GATG??TAGATACCA???????GTAATACTTTC??T????????GCCGTTAC??A??????????ACAAATTGCCAATA??CACA??CGTCACTGG?AC??TGT??????????A?TAC?CT?CAAGAA?GTA?ACATT?CTG????T?TATC?CACCGCAAA?CC??TGC?CTAGGAA?TCTCTCTCT???????AT???CC???TA?????AGCAC?????CT???T?TC?TGAA?TCACTT???T????CATGC????G?AGCCCTA??AGAACTTAAACGA?CTTAA??????T?CTC?TGG???AAG????TCAC???????CAAGT?ACAA?TATAGTTCT?GTCCACTATTTAGTA????TC?CA????????C???T??????T?????AGCTAG????AA???????AAGAT?TTGA????CC??AAATC??A?C????????CCA??ATCGG?ATAC?????AAAATC?TAA?GCA??TA?GATAAATAG???TGG??????CTG?CGATAAATAGCCTGTATTCAC???TTTC??????A?????ATT??????A??ATATTAA?T?????GTCAC????????T????AA?TA?A?ACAGT??ATAAT??TCTAGAGAATT???AAAGCAAA???GGGACTATT?C??A??CCGATAGCA?CACA?ATT?????ACTACCCT??AT?????TAAT??C??CAA???GGG??ATAT?????ACTG??TTCGA?CATCAA?TCTG?TTCGA???AA?C?TA?TAAGAAA?AT??ATATTAGA?CCG?????????CAAAGA??????A??GACCTCACCCTATTACT??CCCAATTTT??GCTTTATTCTATTA?CAGTAAACGGAAG?C?TT?GCA?CACCA??G??T?ATC?GATACCTATA????????CA??A???????????????????????C?GA?AATAAACAGATCTATAACTT?AGT?CGG??????CTAGAC??TTAGTT??TTAAA???GAAAAGT??TTCA?T????A?AC??????AATGAAAA?TCAATA?T???TTAGG?GCAG???????TGT??ATACGTGCACAAACAGCGACAGACCT?C???????TGTG???????????ATAG??G?TC?TCGTA??GACAAATTTTA????GTT?C?A???AAT??CTACTGC???????A?CACAG?CTAGATTCCCTCC?TTTTACA???C??AAA???????????A?CATATTGCAT??????TAAT?A?C????AGCTGAAGAAA?GCTCTTACAGAATGATTAAG?GA?CTC??TTAATTGA??G???GGAACA?TAC?????GTATA?TACGCAT?????????GTT?A??A??T????GAGAAGCAC??AC?????TT?A??????????????CATCT???????????GACCCAACACAAGAA?AAAACAT????CG?AAT???A????A?TT??AACCATCCGCATT??T?????AAAA??CCGCAGTAATCC?????ACGAGAACTAACAAAGAAAAATGAC??CAAAC???GAA?ACTCGCAAA?????????TCTA????TTACTGACAAAGCT?GGTAGTA?TTAACT??GCT?ATAA?A?TAC??G??C?CTACGA?TCGAAGCAG?AAAATT?TGGAG???TTA?A?C?CTATAAACAT??A??CAGT??A?GT?TGACGAAAAA?T??G?CA?????ATG?GCACTACGATTGATAACT???AAA?A??????A????GTGAAATTCCAT????????????TCACGC??TCATGGTA??????????ACAATA?GT?TGT?A??TAATC???AGATTC?G?????CT?ACA???GT?????AATATACA????????TTTGA?A??A?????TTTAC??????TAT?TTC?C?TCT????TC?CA?C???????A??AG??????????CT?C?TAGGA?TATA????AAAAA??????????AGG??????GATATCGCT?TGAGATTG?ATTA??ACCAAGTA?GTACCA???TATG?TA?AA?CCC??????AAAGCTT??????ACTTA?GT?????????CT?????G????TCT?????ATCCGG?CCTTTT????????A???TT?G?T??GTGA???AT?TCTGTATAAT?G?AA??ACC?AC?CGC?ATGT????TACA?GATTT??????????TCG??CT????????A????????????ATT?A???AACAGTG?TCGC?????????????????????ATAC???TA??GTT?AC??AT???????C????CACCCGATAG?GGTTAGGAACTATGTCGAA??A????GT?ACTATTAAATT???TATGCTAA?????CTACT?CATC?AT?TATCAGC?AGA???????AACAATTCCC?TTGCCAATCAG?AT??CTT?TTGCGTC??TTCA?TGTAC?CA?CTACTATTGA???AG????A?AA?CATTTTGAAA??AAGTA???CCATAT?GT?CTAGC??GCCTTATTCC????TC?T?GTA???TATTC??CT?AACTTA????CAATGTTGAT?????AGTCAC?CG??C??CATTG???????CGTC?AATAA??????ATGTA???????TTTGA???????GAATAATT????CACTCA?????A?TTAATTCT???????A???CCA?ATA?T?A??A?CA???AAA????A??AGG??C?C?????????ATA?CCTTTAAAC??TGA???????????TAAC?CA?CTT?GTAA?ACCCATAATTGATATAAGTATATAA???GGTC?AT?ACA catesbianaX12841 ACTTCTC???AT?AGTG?GC?T????????GTCCTGTTG??TT???T?TT?AAGG?ACTG????GTG??????????GGTG?ACA????????GT?TTTAATTT??CAGTATCAAA??TTGA?TT?A???????C?AC???????A?AA?????????CCAATT?ACAA??CCCGTAACGAC????T????????TATA??A?TA?GT?TAT?????CAG???ATGACCTTCAT??A?TA?ACCAGGTCGCTG?CCAAC????????TACAGTTGTG?TCT??????????A?CGTTATA?ATGGAAT?CTGAGAT???AC????CTCC???TTTGCTCGC??CT????????AATTAAAT?????T?????C?GATG??TTGATACCA???????ATGATACTTTC??C????????GCCGTTAC??A??????????ATAAGTTGTCAATA??CACA??CGTCACTGG?AT??TGC??????????A?TAC?TT?CACGAA?GTA?ACATT?CTG????C?TATT?CACCGCAAA?CC??CGC?CTAAGAA?CTTCTCTCTTTAT?TTAT???TC???TA?????AGTAC?????CC???T?TG?TGGA?TCACTT???T????CATGC????G?GGTCCTA??GGAACTTACACGA?CTTAA????????????????????G????TCAC???????CAAGT?ACAA?TATAGTTCT?GT?????????GGTA????TT?CA???AAA?AC???T??????A?????AGCTAG????AA???????AAGTT?TTCA????CC??AAATC??A?C????????TCA??TTAGG?ATAT?????AAAATC?TAA?GCA??TA?GATAGATAG???TGG??????CTG?TGATACATTGCTTGTATTCAT???TTTC??????A?????ATT??????A?ACAACTAA?T?????GTCAT????????T????AA?CAAA?ACAGT??ATAAT??TCTAGAGAATT???AAAGCAAC???GGGATTATT?C??A??CTG????CA?CATG?ATT?????ACTACCCT??AT?????CAAT??C??CGA???GGG??ATAT?????ACCG??TTCGA?CATCAA?CCCG?TTTGA???AA?T?TA?AAAGAAC?AA??ATTTT?GA?CCG?????????CAAAGA??????A??GACCTCACCCTATTACT??TCCAATTCT??GCTTTGTACTATTA?CAGTAAACGCAAG?C?TT?GCA?CACCATGG??A?ATC?GATCCCTATA????????CA??A???????????????????????C?GA?AA??????GATC??????CA?AGT?CGG??????CAAGAC??CTAGTT??TTAAA???GAAAAGC??TTCA?T????A?AC??????AATGAAAA?TTAACA?A???TTAGGGACAG???????CGT??ATACGTCCACAA????AGACAGACCG?C???????TGTG???????????ATCG??G?CC?TTGTA??GACAAATCTTA????GTT?C?A???AAT??CAACTGC???????A?CACAG?ATAGATTCTCTCC?TTTTACA???C??CAA???????????A?CATATTGCAT??????CAAT?A?C????AGCTGAAGA?A?GCTCTTGCCGAATGATTACG?GA?CTC??TTAATTGA??G???GGAACA?TAC?????GTATA?TACGCAA?????????GTT?A??C??T????G?GAAGCCC??AC?????TT?A??????????????CATCA???????????GACCCAACACAAGAA?AAAATAT????CG?AAT???A????A?CT??AACCATCCGCATT??T?????AAAA??TCGCAGTATTCC?????ACGAGAACTAACAA?GAAATATGAC??CAAAC???GAA?ACTCGCAAA?????????TCGA????TTACTGACAAAGCT?GGTAGTA?TTAACT??GCT?ATAA?A?TCC??G??C?CTACGA?CCCAATCAG?AAAATT?TGGAG???TTA?A?C?TTATAAACAT??A??CTGC??A?GT?CGACGGAAAA?A??G?CA?????TTA?GTACTAAGATTGATAACC???AAA?A??????A????GTAAAATACTAT????????????TCACGC??TCATGGTA??????????ACAATA?GC?CGT?A??TAATC???AGATTC?G?????TT?ACA???GT?????GTTATACA????????TTTAA?A??A?????TTTAC??????TTT?TTA?C?TTT????TC?TA?C???????A??AG??????????TT?C?TAGGA?CGTA????AAAAA??????????AGG??????AACATCGTT??GAGATTG?ATCA??ACCAAGTACGTACCA???TAAG?TA?AA?CCC??????AAAGATT??????ACTGA?GC?????????CT?????G????TCT?????ATCCGG?TCTTTT????????A???TT?G?T??GTGA???AT?TCTGTATAAT?A?AA??ACC?AC?CGT?ATGT????TACA?GACTT??????????TCA??CT????????A?TGTTGG??AAAATT?A???AACAGTG?TCGC?????????????????????ACAT???TA??GTG?AC??AT???????C????CACCCGATAG?GGTTAGGAACTATGTTGAG??AATA?GT?ACTATTAAATT???TATGCTAA?????CTACC?AATC?AT?TGTCAAA?AGA???????AACAATTCCT?TTGTCAATCAG?AA??CTT?TTGCGTC??TTTAGTGTAC?CA?CTACTATTGG???AG????A?AA?CGTTTTGAAA??AAGTA???CCGAAT?GT?CTAGC??GCCGTATTCC????TC?T?GTA???TGTTC??CT?AACTTA????TAATGTTGAT?????AGCTTC?CG??C??CATTG???????CGTC?AATAT??????ATGTA???????TTTGA???????GAATAATT????CTCTAA?????A?TTAATTCA???????A???GCA?ATA?T?A??A?CA???AAA????A??AGG??T?T?????????ATA?TCCCTAAAT??CGA???????????TAGC?CA?CTT?GTAA?A?CCATAATTGATATAGGTATTAAT???GGTC?ATAACA catesbianaDMH84R2 ACTTCTC???AT?AGTG?GC?T????????GTCCTGTTG??TT???T?TT?AAGG?ACTG????GTG??????????GGTG?ACA????????GT?TTTAATTT??CAGTATCAAA??TTGA?TT?A???????C?AC???????A?AA?????????CCAATT?ACAA??CCCGTAACGAC????T????????TATA??A?TA?GT?TAT?????CAG???ATGACCTTCAT??A?TA?ACCAGGTCGCTG?CCAAC????????TACAGTTGTG?TCT??????????A?CGTTATA?ATGGAAT?CTGAGAT???AC????CTCC???TTTGCTCGC??CT????????AACTAAAT?????T?????C?GATG??TTGATACCA???????ATGATACTTTC??C????????GCCGTTAC??A??????????ATAAGTTGTCAATA??CACA??CGTCACTGG?AT??TGC??????????A?TAC?TT?CACGAA?GTA?ACATT?CTG????C?TATT?CACCGCAAA?CC??CGC?CTAAGAA?CTTCTCTCTTTAT?TTAT???TC???TA?????AGTAC?????CC???T?TG?TGGA?TCACTT???T????CATGC????G?GGTCCTA??GGAACTTACACGA?CTTAA????????????????????G????TCAC???????CAAGT?ACAA?TATAGTTCT?GT?????????GGTA????TT?CA???AAA?AC???T??????A?????AGCTAG????AA???????AAGTT?TTCA????CC??GAATC??A?C????????TCA??TTAGG?ATAT?????AAAATC?TAA?GCA??TA?GATAGATAG???TGG??????CTG?TGATACATTGCTTGTATTCAT???TTTC??????A?????ATT??????A?ACAACTAA?T?????GTCAT????????T????AA?CAAA?ACAGT??ATAAT??TCTAGAGAATT???AAAGCAAC???GGGATTATT?C??A??CTG????CA?CATG?ATT?????ACTACCCT??AT?????CAAT??C??CGA???GGG??ATAT?????ACCG??TTCGA?CATCAA?CCCG?TTTGA???AA?T?TA?AAAGAAC?AA??ATTTT?GA?CCG?????????CAAAGA??????A??GACCTCACCCTATTACT??TCCAATTCT??GCTTTGTACTATTA?CAGTAAACGCAAG?C?TT?GCA?CACCATGG??A?ATC?GATCCCTATA????????CC??A???????????????????????C?GA?AA??????GATC??????CA?AGT?CGG??????CAAAAC??CTAGTT??TTAAA???GAAAAGT??TTCA?T????A?AC??????AATGAAAA?TTAACA?A???TTAGGGACAG???????CGT??ATACGTCCACAA????AGACAGACCG?C???????TGTG???????????ATCG??G?TC?TTGTA??GACAAATCTTA????GTT?C?A???AAT??CAACTGC???????A?CACAG?ATAGATTCTCTCC?TTTTACA???C??CAA???????????A?CATATTGCAT??????CAAT?A?C????AGCTGAAGA?A?GCTCTTGCCGAATGATTACG?GA?CTC??TTAATTGA??G???GGAACA?TAC?????GTATA?TACGCAA?????????GTT?A??C??T????G?GAAGCCC??AC?????TT?A??????????????CATCA???????????GACCCAACACAAGAA?AAAATAT????CG?AAT???A????A?CT??AACCATCCGCATT??T?????AAAA??TCGCAGTATTCC?????ACGAGAACTAACAA?GAAATATGAC??CAAAC???GAA?ACTCGCAAA?????????TCGA????TTACTGACAAAGCT?GGTAGTA?TTAACT??GCT?ATAA?A?TCC??G??C?CTACGA?CCCAATCAG?AAAATT?TGGAG???TTA?A?C?TTATAAACAT??A??CAGC??A?GT?CGACGGAAAA?G??G?CA?????TTA?GTACTAAGATTGATAACC???AAA?A??????A????GTAAAATACTAT????????????TCACGC??TCATGGTA??????????ACAATA?GC?CGT?A??TAATC???AGATTC?G?????TT?ACA???GT?????GTTATACA????????TTTAA?A??A?????TTTAC??????TTT?TTA?C?TTT????TC?TA?C???????A??AG??????????TT?C?TAGGA?CGTA????AAAAA??????????AGG??????AACATCGTT??GAGATTG?ATCA??ACCAAGTACGTACCA???TAAG?TA?AA?CCC??????AAAGATT??????ACTGA?GC?????????CT?????G????TCT?????ATCCGG?TCTTTT????????A???TT?G?T??GTGA???AT?TCTGTATAAT?A?AA??ACC?AC?CGT?ATGT????TACA?GACTT??????????TCA??CT????????A?TGTTGG??AAAATT?A???AACAGTG?TCGC?????????????????????ACAT???TA??GTG?AC??AT???????C????CACCCGATAG?GGTTAGGAACTATGTTGAG??AATA?GT?ACTATTAAATT???TATGCTAA?????CTACC?AATC?AT?TGTCAAA?AGA???????AACAATTCCT?TTGTCAATCAG?AA??CTT?TTGCGTC??TTTAGTGTAC?CA?CTACTATTGG???AA????A?AA?CGTTTTGAAA??AAGTA???CCGAAT?GT?CTAGC??GCCGTATTCC????TC?T?GTA???TGTTC??CT?AACTTA????TAATGTTGAT?????AGCGTC?CG??C??CATTG???????CGTC?AATAT??????ATGTA???????TTTGA???????GAATAATT????CTCTAA?????A?TTAATTCA???????A???GCA?ATA?T?A??A?CA???AAA????A??AGG??T?T?????????ATA?TCCCTAAAT??CGA???????????TAGC?CA?CTT?GTAA?A?CCATAATTGATATAGGTATTAAT???GGTC?ATAACA virgatipesMVZ175944 ACCTCTTAT?AT?AGTG?GC?T????????GTCCTG?CG??TT???T?TT?GAGTAACTG????GTG??????????AGTG?ACA????????GC?TTTAATTT??CAGTAACAAA??TTGA?TT?A???????C?AC???????A?AA?????????CCAATT?ACAA??CCCGTAAAGAC????A????????TATA??C?TAAAT?TAT?????CAA???ATGACCTTCAT??A?AA?ACCAAGTCGCTG?CT??????????????AGTTGTG?TCT??????????A?CGCTATA?ATGGAAT?CTGAGGT???AC????CTCC???TTCACTCGC??CT????????ACTTAAAT?????TG?T??G?GATG??TAGATACCA???????ATTAAATTTTC??C????????GCCGTTAC??C??????????ATAAGTTGGCAGAA??CACA??CGCCACCGG?AT??ACT??????????A?TAC?TT?CAGGAA?GTA?ACATT?CTG????T?TATC?CACCGCAAA?CC??TGC?CTAAGAA?TCTCTCTCCTTAC?TTAT???TC???TA?????AGCAC?????CT???T?TA?TGGA?TCACTT???T????CATGC????A?AAACCTA??AGAACT?AAACGA?CTTAA??????TTCCC?TGG???CAG????ACAC???????CAAGT?ACAA?TCTAGTTCT?AT?????????AGTA????TT?CA???AAA?AC???T??????A?????AGCTAG????AA???????AGGTT?TTCA????CC??ACATC??A?C????????CCA??ACCGG?ATAT?????AAAACC?TCA?GCA??TA?AATAGGTAG???TGA??????CTG?CGATTCATAGTTTGTATTCGT???TTTG??????A?????ATT??????A??ATATTAA?T?????GTCAT????????T????AA?TA?A?ACAGT??ATAAT??TCTAGAGAACT???AAAGCAAT???GGGTCTAAT?C??A??CTG????CA?CATTTATT?????ACTACCCT??AT?????CAAT??C??CAA???GGG??ATAT?????ACCG??TTCGA?CATCAA?TCTG?TTCGA???AA?A?TA?AAA?AAG?AT??TTTTTAGA?CTG?????????CAAAGA??????A??TATC??ACCCTATTATT??CCCAATTCT??GCTTTGTGCTATTA?CTGTAAATGCAAG?C?TT?GCA?CATCATGG??A?ATC?GATCCCTATA????????CA??A???????????????????????C?GA?AA??????GACC??????TA?AGT?CGG??????CTAGAC??CTAGTT??TTAAA???GAAAAGC??TTCA?T????A?GC??????AATGAGAA?TTAACA?????TTAGG?ACAC???????CGT??ATACGTTCACAC????C???AGACCT?C???????TGTG???????????ATAG??G?TC?TCGTA??GAAAAATCTTC????GTT?C?A???AAA??CGACTAC???????A?CTCAG?CTAGATTCCCTCC?TTTTAT????C??CAT???????????A?CATATTG?GT??????CAAT?A?C????AATTGAAGAAA?GCTCTTGCCGAATGATTACG?GA?CTC??TCAATTGATTG???GGAACT?TACG?TTG?TATA?TA?GCAA?????????GTT?T??T??T????GAGAAGCCC??ACAATACTT?ACT????????????CATCC???????????GACCTAATACAAGAA?AAGACAT????CG?GAT???A????A?CT??AACTATCCGCATT??T?????AAAA??CCGCAGTATTCC?????ACCAGAGATAACAAAGAAATATGAC??CAAAC???AAAAACTTGCAAA?????????TTGA????TTACTGACAGAGCT?GGTAGTA?CTGACT??GCT?ATAA?A?TCT??G??C?CTATGT?CCTAAGCAG?AAAATT?TGATG???TGA?A?C?TTATAAACAT??A??GGGA??A?GT?CTAC?TGAAC?T??G?CA?????ATT?GTACTAAAATTGATTACT???AAA?A??????A????GTAAAATCCCAT????????????TCACGC??TCATAGTA??????????ACAATA?GC?TGT?C??TAATC???ATATTC?G?????CT?ATA???GT?????AGTATACG????????TTTAA?A??A?????TTTAC??????TAT?TTC?T?TTT????TC?CA?AAATA??CA??AG??????????CT?C?TAGGA?CATA????AAAAA??????????AGG??????AGCGTCGCT?TGAGATTG?ATTA??GCCAAGTACGTACCA????ATG?TC?AA?CCC??????AAAGTTT??????ACTCA?GT?????????GC?????G????CCT?????ATCTGG?TCTTTT????????A???TC?G?T??GTGA???AT?TCTGTATAAT?G?AA??ACC?TT?CGT?ATGT????TACA?GATTT??????????TCA??CT????????A?TATTGG??ATAATT?A???AACAGTG?CCGC?????????????????????ATAT???TA??GTT?AC??AT???????C????C?CCCGATAG?GGTTAGGAACTATGTGGAGATAATA?GT?ACAATTAAATT???TATGCCAA?????CTTTC?AATC?AT?TTTCAGC?AAA???????AACAATCT?C?TTGCCAATCAG?AT??CTT?TTGCGTC??TTAA?CGTAC?TA?CTACTT??GG???AG????T?AA?CGTTTTCAAA??AAGTA???CCATAT?GT?CTTGC??GCCTTATACC????AC?T?GTA???TATCC??CC?AACTTA????AAATGTTGCT?????AGATAC?CG??C??CATTG???????CGTC?AATAT??????ATGTA???????TCTGG???????GAGTAATT????CTCTCA?????A?TCA?TTCA???????A???TTG?ATA?T?A??A?CA???AAA????A??AGGTCC?A?????????ATA?CCTATAAAC??CGA???????????CAGC?CA?CTT?GTAA?A?TCATAATTGATATATGTATTA?T???GGTC?ATAACA maculataKU195258 ACC?CCCGT?A??AGTG?GC?T????????GTCCTGTCG??TT???T?CT?AACTAACTA????GTA???TA?AC??GGTG?ACAATCTT??GGT?TTT??CCT??TA?TATT?TA??TTAA?TT?A???????C?AC???????G?AA?????????CCAATT?ACAA??TCCGTAATGAC????AA???????TGTATAC?TA?AT???T?AT??CAT???ATGACTTTCAT??A?AG?ACATAGACGTTG?CCAAC????????TTTAGTTGTC?TCC??????????A?CGATTTA?ATGGAAT?CTGAGGTAT?AC????CTCC???TTTACTCGC??CT????????CCGTAAA??????TG?T??T?CATT??TCGATACTA???????ATGAAATTTTC??C????????GCCCTTAC??A??????????A??AGTTGGCACAAA?CACA??CAATA?TGG?AC??TGA??????????A?CAC?GC?CTGAAATGTA?ACATT?CTG????T?TATA?AGCCATAAA?CA??AGC?TTTAGAA?TTTCTCTCATTTT?TTAT???CC???TA?????AGTAC?????CG???T?TG?GGAG?TCACTT???T????CGTGC???GT?CACCCCA??AGAACTTAAGCGATCTTAA??????TTCTC?TAG???TAG????ACAT???????CTAAT?ACAAATA?AGTTCC?GT?????????AGGACTT?TC?CA???AAA?AC???T??????A?????AGCTCG????AA?TATGCTTTGTT?TCTA????CC??ACGTC??A????????????A??AACGGTATAC?????AAAATC?AAA?A?A??TA?AA????????????C??????CTT?TGATAAAT??????????TAT???TTTA??????ACCT??AGT??????A??CT??TAA?T?????GCCGC????????T????TA?CC?A?ACACT??ATAA???TCTCGTGAACT???AAAGCAAA???GG?????TT?C??A??CGA????CA?TACA?ATT?????ACTACCCC??AT?????TAAT?????CAG???GGG??ATAT?????ACTG??TTCAA?CATCAA?CCAG?TTAGA???AA?C?TAATAAGGAT?AT??CACT??GA?CAG?????????CAGAGA??????A??GA?????????GTTAAT??TCCAATTCT??GCTTTTTGTTATTA?CGGTAAATGCAAT?C?TT?AAA?TACCATGG??T?????AATCCCT?T?????????CA??A???????????????????????C?GA?AA????????AC??????CA?AATACGG??????TTACAC??CTAGT???TTAAA???GAAAAGT??TTCA?T????AGGT???????ATGGAGG?TCAATA?????TTAGG?ACGT???????TGT??ATACGTCCACCG????AGAC??ACTT?C???????TGAGG??????????ATT?????AC?TCGTA??GAAAATTCCTT????GTG?C?A???AAT??TAACTAC???????AACACAG?CTAGATTCTCTCC?TTTTATA???C??AAA???????????A?CATATTGCAT??????TAGT?A?C?ATAAGTTGAACTAG?GCTCT???AGAATGATTATG?GA?CTC??TTCATTGATCG???GGAACA?TACG?TAGGTATA?ACCGTACCTTCACCAGGTT?C??A??T????AAGAAGCACCGACCT??CCT?ATTTC??????????CATCTTCAGCC????AGACTCAACACAAGAATAACACAT????CG?GAT???A????A?CA??ACCCAACCCC?TT??T?????AAAC??GTGCAGTTTTAC?????ACGAGAACT??????AGAGAATGAC??CAAAC???TAAAACTTGCAAA?????????TCAA????TTACTGACAGAGCT?GGTAGTAATTTACT??GCT?ATAA?A?TGATGG??C?C??AGCTTCAAAACAG?AAAATT?TGGAG???TAA?A?C?TTACACGCAT??A?GGCGA??G?GT?CGAT?ATAAT?T??G?CA?????GTA?GTACTA??????ACCAAT???AAA?G??????A????GTGAACTTCAAC????????????TCGCGC??TCA??GTC??????????ACAATA?GT?AGT?A??AAATC???A??TTC?G?????TT?ATT???G????????TCTACG????????CTTAA?A??T?????TTTAC??????TGT?ATC?A?TAT????CC?CA?CAATA??CG??AG??????????CT?T?TA?GA?TATA????AAAAA??????????AGTA?ACTGTCCCTCGAT?TGAG?TAG?A??A??ACCAAGTACGTACCA???TCTG?TG?AA?CTC??????AAA?AAT??????GCTAA?GA?????????AT?????G????ACT?????ATTTGG?TATTT?????????A???CC?G?C??GT?A???CC?TCTGTATAAC?TGAA??ACC?AT?CG??A????????ACA?GATCT??????????TCC??CA????????A?TCTTGG??ACAATA?A???AA??????????????CCATTT????????GTAA??????TA??GAC?AC??AT???????C????CACTCGATAG?GGATAGGAAATCTGTGAAA??AATG?GT?A?????????A???CATAGCAT?????CTACC?AATC?GT?TTCCAGG?AAA???????ATCAA?CT?C?TCGCAAGCC?G?AC??CTT?TTGAGCC??TTTT?AGTTC?CA?CTACAGTTGG???AG????C?AA??ATTTTT?AA??A?GTA???CT?CAT?GT?CTCGC??GCTTTAAGCT????TT?T?GTA???TATTC??CA?AACTTA????TAATGTTGGTA????AGTTAT?CG??C??CATCGAT???ATCATC?TATAC??????ATATA???????TCAGG???????AGATGATT????CCCTTA?????A?CGACTTCA???????C???CCG?ATA?T?A??A?CG???AAA????T??AGG??T?A?????????ATA?CCCGTAAAT??TTAAC????????CAAGC?CA?CTT?GTAA?T?TAGGAATTG?TACGAGTATCA?T???GGTC?GTGACA vibicariaMVZ11035 ACT?C?????????????GC?T????????GACCTGTGG??TT???T?TT?CATT????A????ATG???TA?GC??GGTG?ACAACCAA??GGT?TTT??TCTGACCGTAGC?CA??TTGA?TT?A???????CGAT???????G?GA?????????CCAA?????????????TAACGAC????G?T??????TATA??T?TA?TT???T?AT??CAG???ATAACCCTCAT??ACGT?ACAAAGGCGCTG?CTATC????????TTC?GTTGGT?TCT??????????A?CGTTCTA?ATGGAAT?CT?????????C????CTCC???TTCC?TCGC??CT????????CTCTAAA??????TG?T??T?CATG??TAGATACTA???????CTGAAACTTTC??C????????GCCATTAC??ATTTTTTAC??A??AGTTGACAAAA??CACCT?CATTGCCGG?AC??TTA??????????A?TAC?TC?CTAAAATGTA?ACACT?CTG????T?TATA?A?????????C???TGC?TTTAGGA?TTTCTCCCGACCT?ACAA???CC???TA?????AGTAC?????CA???T?TTTAGAG?TCATTT???T????CATGC???TC?CGCCCCA??GGAACTTAGACGATCTTAA??????TT?GCCTAG???CAG????TCAC???????GAAT??ACAAATGTAGTTCC?TT?????????AGAACGTATT?CA???AAA?AC???T??????G?????AGCTCG????AA?TACGCTTAAAT?TCTA????CC??TATTC??A????????????A??AACGGTTTAC?????AA??????????TA??TA?CA????????????T??????CTT?CG????????????????TTTGCATTTG??????ACCT?TAAC??????A??GG??TAA?T?????GCCGT????????T????AA?CT?A?ACAAT??ATAA???T??AGAGAATT???AAAGCAGA???GG?????TT?C??A??CTA????CACTAGT?ATT?????ACTACCCC??AT?????TAAT??C??CAC???GGG??ATAT?????ACCG??TTCAA?CAACAT?TCTG?TTAGA???TA?T??AACAAGCGC?ATCA?ACT??GA?CTA?????????TAAAGT??????A?????????????GCTAC???TCCAATT?T??GCTTTATCCTATAA?CAGTAAATGAAAA?C?TC?GAA?TACCATGG??T?ATC?GATCCCT?TA????????CA??A???????????????????????C?GATAA????????AC??????TA?AATATGG??????TTAGAC??CTAGTT??TTAAA???AAAAAGC??TTCA?T????AGGT??????AATGATAACTCAATA?????TCA?G?AAGA???????CGT??ATACGTACACAA????AG?C??ACCG?C???????TGAG??????????????TCCG??C?TTGTC??GAGAAT?CTTG????GTG?C?A???AAT??TAACTAC???????A?CTCAG?TTAGATTCACTGC?TTTTATATTAT??GAA???????????A?CATATTGCAT??????TAAC?A?C?ATAAGCTGAATTAG?GC?CA???AGAATGACTATG?GA?CTC??TCCGTTGATTG???GGGACA?TACG?CTGGTATA?ACTGTAC?????????ATT?A??C??T????GCGAAGCGCTGACCATACTT?ATTTC??????????CA?CCTATGCT????AAACCCAACAAAAGAAAAAAAAAT????CG?GATGTAA????A?TA??ATCCA??CGC?TT??T?????AATC??CTG???????AC?????ACAAGA?????????AAAAAATGAC??CAAAAAATCAAAACTCGAA?A?????????TCTAAAC?TTACTGACAAAGCT?GGTAGTTATTCA?T??GCT?ATAG?A?CGATGG??C?CTAGGT?TCAAAACAG??AAATT?TGAGG???TAA?AT?????????CAT??A?AGCGA??A?GT?TAAC?AATAA?A??G?CA?????TTA?GTACTA???????AGAAT???AAA?G??????A????GTAAA???CA?C????????????TCACGC??ACA??ATA??????????ACAATA?GT?CGT????TAACC???AAATTC?G?????AT?ATA???GT?????ACTGTACG????????TTTGA?A??CACGAACTTAC??????TAT?CTC?G?TTT????CC?CA?CAGTA??CG??AG??????????CT?T?TAGG????TG????AAAGA??????????GGAA?ACAG?TAATCGAT?TAAGATAG?A??A??GCCAAGTACGTACCAAATTTTG?TA?AA?CCC??????AAA?GCT??????ACTAA?GA?????????CA?????G????TTT?????ATCTGG?CATTT?????????A???CT?G?C??AT?A???AT?CCTGTATAAT?CGAT??ACC?TA??GG?A????????TTA?GATCT??????????TCC??CA????????A?TATTGG??ACAACG?A???AATA?TG?TCGC????CCATTT????????ATAATGT???TA??GCC?AC??AT????????????CACTTGATAG?GGATAGGAAATTTGTCAAG??AATG?GT?A?????????A???TACGGTAT?????CAGTC??????????TGCAGC?AAA???????ATCAATCA?T?TTGCAAATCAA?TC??CTT?TTGAGCC??TTTT?CGTAC?CA?CTACAGTTGG??????????????????TTA?AA??A?GTA???CTTCAT?GT?CTTGT??GCTATAAACT????CC?T?GTA???TATAT??CA?AACTTA????TAATGTTGGTA????AGGTCC?CA??C??CATTGTTTATATCGTC?GATAC??????ATATA???????TCAGGTCCCATAAGATGATT????CTCTAA?????ATAA?CTTCA???????ACGATCG???????????????????G????AAGAGA??A?T?????????ATA?CCCGTAAAA??TGAAC????????CTAGC??????T?GTAA?C?TCAAA?TTG?TATGAGTATCC?A???GGTC?TTGACA warszewitshiiJSF1127 ACA?CCTGT????AGTG?GC?T????????GT?CTGTGG??TT???T?CT?CAAA???TA????ATG???TA?G????GTG?AC?ATCGC??GGC?TTT??CCT??CAGCAAC?TA??TTAA?GT?A???????C?AC???????T?AA?????????TC???????????????????GAC????G?A??????CATA??T?TA?CT???T?AG??CAA???ATAAACCTCAC??ACGC?ACAAAGACGCTG?ACAAC?????GTCTTC?????TC?TCT??????????A?C?GTCTA?ATGGAAT?CTGA????????ACAGCTCC???TTT??????????????????CTGTAAA??????TG?T??T?CATG??TGGATACTACATTTCCCTAAACT?TTC??C????????GCCATTA???A??????????A??AGTTGACAAAA??CACA??CATTACTGG?AC??TTC??????????A?TAC?TA?CCCGAATGTGCACACT?CTG????C?TATA?A?????????CT??TGC?CTTAGAA?CCTCT?????AAT?ACAT???CC???TA?????GGTAC?????CA???T?TCTA?AG?TCACTT???T????CATGC???GA?GATCCCA??GGAACTTAGACGATCTT?A??????TT?GCTTAGATATAG????ATAC???????CAAT??ACAAATCTAGTACA?TT?????????AGAACATCTT?CA???AAA?A??????????????????????G????AA?TACGCCATGTT?TTCA????CC??AATTC??A????????????A??AAAAGTAT?G?????CA??????????TA??TA?AA????????????T??????TTT?CGATCTAC??????????TTC???TTTG??????ACCT?TAAT??????A??AG??TAA?T?????GCCGT????????T????AA?CT?A?ATAAT??ATAA???T??AGAGAATT???AGAGCACA???GG?????TT?C??A??CAG????CACCAAT?AAT?????ATTACCCC??ATTAGAACAAT??C??CAA???GGG??ATAT?????ACCG??TTCAA?CATCGG?ACAG?TTCGA???TA?C??AATAAGCGT?ATTA?CTT??GA?CCA?????????CAAAGT??????A?????????????GTTAT???CCCAATT?T??GCTTTC?TCTATAA?CTCTAAACGACAA?C?TC?GAA?GTCCATGG??C?ATC?GATCCCT?CA????????CC??A???????????????????????C?GATAA????????AC??????AA?AATATGG??????CTAGAC??CTAGTT??TGAAA???AAAAAGC??CTTA?T????ACAC??????AATGACAA?TAAATA?????TCAGG?TAGG???????GGT??ATACGTGCACAG????AG?C??ACCA?C???????TGAG???????????ATTA??A??CGTTGTT??GAAAAT?ATTG????GTG?T?A???AAT??TAACTAC???????A?CTCAA?TTAAATTCACTCCCTT?T?TACTGT??AAA???????????A?CATATTGCAT??????CAGC?A?C?ATAAGCTGAACAAA?GC?CA???AGATCGATTATG?GA?CTC??TCCGTTGATTG???GGAACC?TACG?CTGG??TA?CTCGTAT?????????ATT?G??C??T????GCGAAGCGCCGACCTTACATCATTTC??????????CA?CATTCGCC????GAACTCAATAAAAGCAGAATATAC????CG?GTT???A????A?AA??ATCAA??CGC?TC??T?????AACG???TG???????AC?????ATAAGAATT??????AGAGAATGAC??CAAAGAATCAAAACTTGAAAA?????????TCCA????TTACTGACATAGCT?GGTAGTCATTTATT??GCC?ATAA?A?CCATGA??C?CTAGGA?CCAAAACAA?AAAATT?TGATG???TTA?ATC?TTATAAACATA?AAAGTGC??A?GT?ATAC?GAAAA?C??G?CA?????TTA?GTACTA??????GCAAAT???AAA?G??????A????GTCATCTTCT?T????????????TCCCGC??ACA??ATA??????????ACAATA?GT?TGT?C??TAATC???AAATTC?G?????AT?ATC???GT?????AATCTACATATGAAATCTT???A??A?????TATTCTTCCTCTAT?CTC?C?TCT????TC?CA?CTGTA??CT??AG??????????CT?T?TAGGA?CATG????AAAAA??????????GGAT?ATGG?CTATCGTT?TAA?ATAGAA??A??ACC?AGTACGTACCAAACTTTG?TA?TA?CCC??????AAA?AAT??????ACTA???G?????????TT?????G????TAT?????AAATGG?TATTT?????????A???CT?G?T??AT?A???A??CCTGTATAAT?AGAC??ACC?AA?CGTGA????????ACA?GATCT??????????TCT??CA????????A?TGTTGG??ACAACG?A???AATA?TG?TCGC????C??????????????????GT???TA??GCT?AC??AT????????????CACATGATAG?GGATAGGAAGTTTGTCAAG??TATG?GT?A?????????A???TACGGCAT?????CAACT??????????CATAGC?AAA???????ACC?ATCT?TGT?ACAAATCAA?CC??CTT?TTGCGAC??TTCC?TGTAC?CA?CTACAATTGT???AG????T?GA??ATTTCA?AA??A?GTA???TACAA??GT?ATAGTA?GCTGTAACCT????AC?T?GTA???TGTCT??CA?AACCTA????T??TGTTGATA????AGGCTCGCG??C??CATCGATAGTAT??????ATAG??????ATATA???????TTAGGTTCCATAGAATGATT????CTCTAA?????A?AG?CTTCA???????A???TTG???????????????????G????TAGGGG??A?AATTCACCACATA?CCCGTAAAT??CGA?C????????CTGGT?CA?CTT?GAAA?C?TCGAA?TTG?TATGCGTATCC?GAG?GGTC?TTGACA palmipesVenAMNHA118801 TCT?CCCGG?A??AGTG?GC?T????????GCCCTATCG??TT???T?AT?AATAACCTA????GTGAGATAAAA??GGTG?ACAATCTT??GGC?TTTGATCT??TAGTGTT?TA??TTAG?TT?A???????C?AC???????T?AA?????????CCTATT?GCAA??CCTGTAACGAC????A????????CATA??G?TA?AT???T?AT??CAA???AATACCTTCAT??A?CT?ACAGGGACGTTG?TAATC????????TGGAGTTGTT?TCT??????????A?CG?TTTA?ATGGAAG?CTGAAAT???AC????CTCC????CTACTCGC???T????????CAATACAT?????TG?T??A?C??????CGATACTA???????TTAAAAATTTC??T????????GCCTTTAC???????????????????TGCCAAAA??CACA??CCGTACTGG?AG??ATG??????????A?TAC?CT?CAAT?ATGCG?ACAGT?CTG????T?TGTA?TGCTACAAA?CC??GGC?CTTTGAA?CTTCTCCCGTCTTCCCAT???TC???TA?????TGTAC?????CC???T?TC?TGAG?TCAATT???T????CGTGC???CC?AATCCC???????CTTAGACGATCTTAA??????TTC?C?TAG???AAG????TTAT???????CCACTCACAAATTTAGTCTC?AT?????????TG??????TC????????A?AC???T??????A?????AGCTCG????AA?TACGCCAC????CGTACAT?CC??GATTC??A????????????A??AAAGGCATAC?????ACCATC?TGG?GCG??TA?GA????????????T??????CTT?AGATCTATCACTCG?ATTCAA???TTTT????????????????????????AT??TAA?T?????GACAA????????T????AA?CG????????????AA???TCTTGAGAAAT???GAAGCAAC???GA?????TT?C??A??TAA????CA?TACG?ATT?????AGCACCCA??AT????????T??C??CAG???GGG??ATAA?????ACCG??TTCAA?CACCTA?CAAG?TTGGA???TA?C?TAATAAGTAT?AT??TTTTCAAA?CTG?????????CA?????????????AA?????????ACTACC??ACCAATTCT??GCCTTATTTTATTA?CGGTAAATG??????????ACA?TTCCATGG??T?ATC?AATCTCT?TACCCATACTCA??A???????????????????????C?GA?AA????????TC??????TC?AAT?CGA??????TTAAAC??CTAGTT??TTAAA???GAAAAAC??TTCA?T????AACT??????AATGGTAG?TTAACA?????TTAGG?GCGG???????TGT??ATACGTCCACAT????CGAC??ACCT?C???????TGAG???????????ATTG??G?AC?TTGTC??GATAAATCTTG????GTG?C?A???AAC??TAACTAC???????A?CTCAA?ATAGATTCCCACC?TTTTATA???T??CAT???????????A?CATATTGCAT????????AT?A?C?ATAAGTTGAACAAA?GCTCT???AGGGTGAT???GAGA?CTC???????CGATTG???A?GACA?TACG?CCGGCATA?ATCGTAC?????????GTT?T??C??T????AAGAAGCCC??ACCGTCCCT?ATT????????????CATCATCTACTCT??AGACCCAAAAC?AGAAAAATACAC????CG?AAT???A????A?ACCCAACCAACCAC?TT??T?????AATA??TTGCAGTACTTC?????ATTAGGAAT??????AGAC??TGAC??CAAAT???TAAAACTTGCAA??????????TCAA???GTTACTGACACAGCT?GATAGTAATTTACT??GCT?ATAC?A?CTCT??????????AA?TCTAAGTATACAAATT??GTTG???TAA?A???CTATAGACAT??T??GAGT??A?GT?AGAC?CAGAA?C??A?CT?????CT??GTACTA??????ATCAGT???AAA?G??????A????GTA?ATTTCGAA????????????TT???C??TCA??GTA??????????ACTATA?GG?TGT?A??TAACC???ACATTC?T?????TT??TT???GT?????AATGCACG????????TTTAA?ATAC?????TTTCC??????TAT?TTC???TAT????CC?CA?TAGTA??CG??CG??????????TT?T?TAGGA?CATA????AAACA??????????AGAA?ATAGCACTTCAGTCTAAGACAG?ATTA??ACCAAGTACGTACC????TTTGATT?TA?CCC??????AAA?GT???????TCTCA?GA?????????AT?????G????TTT?????ACCTGG?TATTT?????????A???TC?G?TG?GT?A???AC?CCT?CATAAT?G?A???ACCAGT?CGT?TAGTCATATATC?GATTT??????????TCT??CT????????A?TTTTGG??AAAACA?ACATAAG?????ACGC????CCAATT????????ATAATAC???TC??GTT?AC??CT???????C????CACCTGATAG?GG?AAGG?????TG???????AATA?GT?A?????????G???AACGGCAT?????CATCA?GAAC?TT?TGTCAAC?AA??????????????AT?C?TTGTAAATCAG?AA??CT???????????T????TGT???CA?CTACACTTGAGT?AG????T?AA??ATTCT????????GTA???CTACAT?GT?CTAGC??GCATTAAACC????A????GTA???TGTTT??CA?AACTTA????TAATGTTGGTA????AGGGAC?CA??C??CATAGATCATATCACC?AATAT??????ATATA???????TTAGG???????AAGTAACTTTATCCCTTA?????A?CCACTTCA???????A???ATG?ATA?T?A??A?C????AAA????A??TGA??G?T?????????ATA?CCAG???A????GAAC????????CGAGC?CA?CTT?GTAA?G?TCATAATTGATACGGGTATAA?A??TGGTC?CTGATA palmipesEcuKU204425 ACT?CCCGG?A??AGCG?GC?T????????GCCCTGTTG??TT???T?TT?AATTACCTA????GTGAGTTACAT??GGTG?ACAATCCA??GGC?TTTGATCT??CAGTACT?TA??TTAG?TT?A???????C?AT???????A?AA?????????CCAATT?ACAA??CCCGTAACGAC????A????????TATA??G?TA?GT???T?AT??CAG???ATTAACTTCAT??A?AT?ACAGAGACGTTG?TTAAC????????TGGAGTTGTC?TCC??????????A?CG?TGTA?ATGGAAA?CTGAGCT???AC????CTCC????CTACTCGC??CT????????CCATACAT?????TG?T??C?C??????TGATACTA???????CTAAAAATTTC??T????????GCCATTAC??A??????????A??AGTTGACAAAA??CACA??CAGTACCGG?AC??TTA??????????A?TAC?CT?CAAA?ATGCG?ACAGT?CTG????C?TATA?TGCTACAAA?CC??GGC?TTTCGAA?TTTCTCCCATCCTCTTAT???CC???TA?????GGTAC?????CT???T?TC?AGAG?TCAATT???T????CGTGC???CT?AATCCC???????CTTAAACGATCTTAA??????TTCCC?TAG???AAG????TCAT???????CGACT?ACAAATATAGTTCC?CT?????????CGAA????TA????????A?AC???T??????A?????AGCTTG????AA?TATGCCCTGAT?CTTA????CC??GAATC??C????????????A??ACAGGTATAC?????ACCATC?TGA?GCG??TA?AA????????????T??????CTT?CGATCTATAGTTTG?ATTCAA???TTTA??????ACCT?TAGT??????A??AT??TAA?T?????GCCGA????????T????AA?CG????????????AA???TCTCGAGAAAT???GAAGCAAT???GG?????TT?C??A??TAA????CA?TATT?ATT?????ACTACCCT??AT????????T??C??CAG???GGG??ATAA?????ACTG??TTCAA?CACCAA?CAAG?TTGGA???TA?C?TAATAAGAAC?AT??CTCTCAAA?CTG?????????CAAAGA??????A??AA?????????GCTATT??ACCAATTTT??GCTTTATATTATTA?CAGTAAATG??????????ACA?TACCATGG??T?ATC?AATCTCT?TA????????CA??A???????????????????????C?GA?AA????????TC??????CA?AAT?CGA??????CCAGAC??CTAGTT??TTAAA???GAAAAGA??TACA?T????AGTT??????AATGACAG?TTATCA?????TTAGG?ACGA???????TGT??ATACGTCCACAT????AGGC??ACCC?C???????TGAG???????????ATCA??G?AC?TTGTC??GAGAAATCTTA????GTG?T?A???AAC??TAACTGC???????A?CCCAG?TTAGATTCCCACC?TTTTATA???C??CAA???????????A?CATATTGCAT??????TAGT?A?C?ATAAGTTGAATCAA?GCTCT???AGAGTGAT???GAGA?CTT???????AGATTG???GGAACT?TACG?CTGGTATA?ATCGTAC?????????GTT?T??T??T????GGGAAGCGCCAACCTTACCT?AAT????????????CATCGTTCGCT????AGACCCAACAC?AGAAAAATACAT????CG?AAT???A????A?ATGCATCTAACCAC?TT??T?????AATG??TCGCAGTACTGC?????ATCAGGACT??????AGACCATGAC??CAAAC???TAAAACTCGCAA??????????TCAA???GTTACTGACAAAGCT?GATAGTAATTAACT??GCT?ATAC?A?CTCT??????????GA?TCAAAGTAGAAAAATT?TGGAG???TTA?A???TTACAGACAT??T??AAGG??A?GT?CGAT?GAGAA?T??A?CA?????TT??GTACTA??????AGGAGT???AAA?G??????A????GTG?ACTTCGAC????????????TCACGC??TCA??GTG??????????ACCATA?GG?TGT?A??TAATC???ACATTC?T?????CT??TT???GT?????AATGCACG????????TTTAA?A??C?????TTTCC??????TAT?TTC?T?TAT????TC?CA?CAATA??CG??TG??????????TT?T?TAGGA?TATA????AAATA??????????AGAA?ATAGTACTTCACTCTGAGACAG?ATAA??ACCAAGTACGTACC????TTTGATT?TA?CCC??????AAA?GTT??????ACTGA?GA?????????AT?????G????TTT?????ACTTGG?CATTT?????????A???TC?G?T??GT?A???AC?CCT?CATAAT?A?AG??ACC?CC?CGT?CCGC????TATA?GACCT??????????TCT??CT????????A?TATTGG??AAAACG?A???AAGGGTG?ACGC????CCATTT????????GTAATAC???TC??GTT?AC??AT???????C????CACATGATAG?GGGCAGG?????TGTGAAG??AATG?GT?A?????????G???CATGGCAT?????CTCTT?AAAC?AT?TACCAGC?AAA???????ATCAATAT?C?TCGCAAGTCAG?AA??CT???????????T????TGT???CA?CTACAGTTGG???AG????T?AA??ATTTTC?AA??A?GTA???CTATAT?GT?CTTGC??GCTTTAAGCC????CC?T?GTA???TGTTT??CA?AACTTA????AAATGTTGATA????AGACAC?CA??C??CATAGATAATATCGCC?AATAC??????ATATA???????TTAGG???????AAGTAATT????CTCTCA?????A?CAACTTCA???????T???TTG?ATA?T?A??A?CA???AAG????G??CGA??A?A?????????ATA?CCAGTAAA????GAAC????????CAAGC?CA?CTT?GTAA?A?TCGAAATTGATATGAGTATTA?T??TGGTC?ATGATA Sp_1_ecuadorQCAZ13219 ACT?CTTGA?A??AGCG?GC?T????????GCCCTGTCG??TT???T?TT?AATAACCTA????GTG???TACAC??GGTG?ACAATCTA??GGT?TTTAATCT??TAGTACT?TA??TTAG?TT?A???????C?AT???????G?AA?????????CCAATT?ACAA??CCTGTAACGAC????A????????TATA??G?TA?CT???T?ATTTCAA???ATTACCTTCGT??A?AC?ACAAAGACGTTG?TAATC????????TAGAGTTGTT?TCC??????????A?CG?TGTA?ATGGAAT?CTGAGAT???AC????CTCC????CTACTCGC??CT????????TCATACAT?????TG?T??C?C??????CGATACTA???????CTAAAACTTTC??T????????GCCATTAC??A??????????A??AGTTGACAAAA??CACT??CAGTACCGG?AA??CGA??????????A?TAC?AT?CAGAAATGCG?ACAGT?CTG????T?TATA?CGCTACAA??CT??AGC?TTTTGAA?TTTCTCCCATTTTCTTAT???CC???TA?????GGTAC?????CC???T?TG?AGAG?TCAATT???T????CGTGC???CT?AACCCCA??AGAACTTAAGCGATCTTAA??????TTCTC?TAG???AAG????TCAT???????CGATT?ACAAATCTAGTTCC?CT?????????CGAA????TA????????A?AC???T??????A?????AGCTCG????AA?TATGCTCTGAT?CCTA????CC??GATTC??G????????????A??ACAGGTATAT?????ACCATC?AGA?GCG??TA?AA????????????T??????CTT?TGATCTATAGCTTG?ATTCGT???TTTA??????ACCT?TAGT??????A??AT??TAA?T?????GCCAT????????T????AA?TG????????????AA???TCTAGCGAACT???GAAGCAAC???GG?????TT?C??A??TAA????CA?TATA?AAT?????ACTACCCT??AT?????TAAT??C??CAG???GGG??ATAA?????ACTG??TTCAA?CACCAA?CAAG?TTGGA???AA?C?TAATAAGAAC?AT??CTTT??AA?CCG?????????CAAAGA??????A??AA?????????GTTATT??CCCAATTAT??GCTTTTTATTATTA?CAGTAAATGTAAA?C?TT?GCA?TGCCATGG??T?ATC?AATCTCT?TA????????CA??A???????????????????????C?GA?AA????????TC??????CA?AAT?CGA??????CTAGAC??CTAGCT??TCAAA???GAAAAGT??TACA?TACAAAGTT??????AATGATAG?TCATCA?????TTAGG?ACGA???????CGT??ATACGTCCACAT????AGGC??ACCC?C???????TGAG???????????ATTT??G?AC?TCATA??GAGAAATTTTG????GTG?T?A???AAC??TCACTAC???????A?CCCAG?TTAGATTCTCACC?TTTTATA???CTCTAA???????????A?CATATTGCAT??????TAGT?A?C?ATAAGTTGAATCAA?GCTCT???AGAATGATCAGGAGA?CTT???????CGATTG???GGAACA?TACG?CTGGTATA?ATCGTAC?????????GTT?A??T??T????GTGAAGCTCTAACCCTACCT?AAT????????????CATCGTTTGCT????AGACTCAACAA?AGAATAATACAT????CG?AAT???A????A?ATGCATCTAACCAC?TT??T?????AATG??TCGCAGTATTTC?????ATTAGTACT??????AGACCATGAC??CAAAT???AAAAACTCGCAA??????????TCAA???GTTACTGACAAAGCT?GATAGGAATTGACT??GCT?ATAT?A?CTTT??????????GA?TCAAAGCATATAAATT?TGGAG???TTA?A?C?CTATAAACAT??T??GAGG??A?GT?TTAT?GTGAA?T??A?CG?????CT??GTACTA??????AGAAGT???ATA?G??????A????GTG?ACTTCGAC????????????TCACGC??TCA??GTG??????????ACAATA?GG?TGT?A??TAACC???ACATTC?T?????AT??TT???GT?????AATGTACG????????TTTAA?A??C?????TTTTC??????TAT?T??????AT????TC?CA?CAATA??CG??TG??????????TT?T?TAGGA?TATA????AAATA??????????AGAA?ACAGTACCTCGGTCTGAGACAG?ATTA??ACCAAGTACGTACC????TTTGATT?CA?CCC??????AAA?TTT??????ACTGA?GA?????????TT?????G????TCT?????ATTCGG?CATTT?????????A???TC?G?T??GT?A???AC?CCT?CATAAA?A?AG??ACC?CC?CGT?CCGC????TATA?GACCT??????????TCT??CT????????A?TATTGG??AAAACG?A???AAGAGTG?ATGC????CCATTT????????GTAATAC???TC??GTC?AC??AT???????C????CACATGATAG?GGGTAGG?????TGTGAAG??AATG?GT?A?????????G???CATGATAT?????CTTCT?GACC?AT?TATCAGC?AAA???????ACCAATAT?C?TCGCAAGTCAG?AT??CT???????????T????TGT???CA?CTACCGTTGG???AG????A?AA??ATTGTT?AA??A?GTA???CCACAT?GT?CTTGC??GCTTTAAGCT????TC?T?GTA???TGTTT??CA?AACTTA????TAATGTTGATA????AGGTAC?CA??C??CATAGATAATATCGCC?AATAC??????ATATA???????TCAGG???????AAGTAATT????CCCTTA?????A?CGACTTCA???????T???CTG?ATA?T?A??A?CA???AAG????A??GGA??A?C?????????ATA?CCAGTAAA????GAAC????????CCAGT?CA?CTT?GTAA?A?TCGAAATTGATACGAGTATTA?T???GGTC?GTGATA bwanaQCAZ13964 ACT?CCCGC?A??AGTG?GC?T????????GCCCTGTTG??TT???T?TT?AACTATCTA????GTG???TACAT??GGTG?ACA???????GGT?TTTGATCT??TAGTATC?TA??TTAG?TT?A???????C?AT???????A?AA?????????ACAATT?ACAA??CC??TAATGAC????T????????TATA??GCTA?AT???T?AT??CAG???ATAAACTACAT??A?AC?ACAAGGACGTTG?TGAAC????????TTGAGTTGTC?TCC??????????A?CG?TGTA?ATGGAAT?CTGAGAT???AC????CTCC????CCACTCGC??CT????????CTATACAT?????TG?T??C?C??????TGA???TA???????ATAA?ATTTTC??C????????GCCATTAC??A??????????A??AGTTGACAAAA??CACA??CAGCACCGG?AG??AAA??????????A?TAC?CC?CCGAAATGCG?ACAGT?CTG????C?TATA?CGCTACAAA?CT??AGC?TTTCGAA?CTTCTCCTATTCTTTTAT???CC???TA?????GGCAC?????CC???T?TT?AGAG?TCAATT???T????CGTGC???CTCTGTCCTA??GGAACTTAAACGATCTTAA??????TTCTC?TAG???TGG????TCACACC????CGAAT?ACAAATCTAGTTCC?CT?????????CGAA????TG????????A?AC???T??????ACGTCTAGCTTG????AA?TATGCCCTGAT?CCTA????CC??GAGTC??G????????????A??ACAGGTATAA?????ACCATC?AGA?GCG??TA?AA????????????A??????CTT?CGATCTATAGCGTG?ATTCTT???TTAA??????ACCT?TAGT??????A??GT??TAA?T?????GCCGA????????T????AA?TG????????????AA???TCTAGAGAACT???GTAGCACT???GG?????TT?C??A??TAA????CA?TACC?ACT?????ACTACCCT??AT?????TAAT??C??CAG???GGG??ATAA?????ACTG??TTCAA?CACCAA?CAAG?TTAGA???AA?C?TAATAAGGAT?AT??ACTTTAAA?CTG?????????CAAAGA??????A??GA?????????GTTATT??TCCAATTTT??GCTTTGTATTATTA?CAGTAAATGTGAC?C?TT?GCA?TACCATGG??T?ATC?AATCTCT?TA????????CA??A???????????????????????C?GA?AA????????TC??????CA?AAT?TGA??????CTAGAC??CTAGTT??TTAAA???GAAAAGC??TACA?T????AGTT??????AATGATAG?TCACCA?????TTAGG?CCGA???????AGT??ATACGTCCACAT????AAAC??ACCC?C???????TGCG???????????ATTT??G?AC?TCGTA??GAGAAATTTTG????GTG?T?A???AAC??TAACTAC???????A?CCCAG?ATAGATTCACACC?TTTTATA???C??TAA???????????A?CATATTGCAT??????TAGT?A?C?ATAAGTTGAACTAA?GCTCT???AGATTGATCACGAGA?CTT???CCATCGATTG???AGAACA?TACG?CTGGTATA?AACGTAC?????????GTT?T??A??T????GTGAAGCGCCGACCTTACCT?AA?????????????CATCATTTGCT????AGACCCAATAC?AGAATAATACAT????CG?AAT???A????A?ACGCATCCAACCAC?TTCTT?????AATG??TCGCAGTACTTC?????ATTAGTACT??????AGATCATGA?????AAT???AAAAACTCGCAA??????????GCAT???GTTACTGACAAAGCT?GATAGTAATTAACT??GCT?ATAT?A?TTTTGG??CACTAAGA?TCAAAGCATATAAATT?TGGTG???TTA?A?C?TTAAAAACAT??T??GAGA??A?GT?TTAC?GTGAA?T??A?CG?????TT??GTACTA??????AAAAGT???AAA?G??????A????GTG?ACTTCGAT????????????TCACGC??TCA??GTG??????????ACAATA?GG?TGT?A??TAACC???ACATTC?T?????AT??TA???GT?????AATGCACG????????TTTAA?A??C?????TCTTC??????TAT?TTT?A?TAT????TC?CA?CAATA??CA???????????????T?T?TAGGA?TATA????AAATA??????????AGTA?A????ATATCGATCTGAGATAG?ATCA??ACCAAGTACGTACC????TTCGATC?TA?CCC??????AAA?CTT??????ACTTA?GA?????????AT?????G????ACT?????ATTTGG?CATTT?????????A???TC?G?T??GT????????CCT?CATAAT?A?AG??ACC?CT?CGC?CTGC????TATA?GACCT??????????TCT??CT????????A?TATTGG??AAAAAC?A???AAGAGTG?ACGC????CCATTT????????GTAATAC???TC??GCC?AC??AT???????C????CACCTGATAG?GGATAGG?????TGTGAAG??AATG?GT?A?????????A???CATGATAT?????CTACC?CACC?AC?TCATAGC?AAA???????ACCAATAC?C?TTGCAAGTCAG?AT??CT???????????T????TGT???CA?CTACAATTGG???AG????C?AA??ATTTTA?AA??A?GTA???TCATAT?GT?CTTGC??GCTCTAAACT????CA?T?GTA???TGTTT??CA?AAC?????????ATGTTGATA????AGACAC?CA??C??CATAGATAATATCGCC?AATAC??????ATATA???????TTAGG???????AAATGATT????CTCTCA?????A?TGACTTCA???????A???CTG?ATA?T?A??A?CA???AAG????A??AGG??A?T?????????ATA?CCAATAAA????GAAC????????CTAGT?CA?CTT?GTAA?A?TCGGAATTGATACGAGTATTA?A???GGTC?TTGATA vaillantiKU195299 ????????C?A??AGTG?GC?T????????GCCCT?T?G??TT???T?TT?CACCACCTA????GTG???TA?AC??GGTG?GCAATCCA??GGC?TTTGATCT??TAGTTGC?T????TAG?CT?A???????C?AT???????ATAA?????????CCGATT?ACAA??CCCGTAACGAC????A????????AATA??G?TA?TT???T?AT??CAG???ATAACTTTCAT??A?AT?ACATAGACGTTG?TAACC????????CGTAGTTGTC?TC???????????A?CGCTGTA?ATGGAAT?CTGAGAT???AC????CTCC????TTACTCGC??CT????????CTATACAT?????TG?T??C?C??????CGATACTA???????TTAAAATTTTC???????????GCCATTAC??A??????????A??AGTTGACATAA??TACA??CAACACCGG?AG??TGG??????????A?TAC?TC?CCTAAATGCG?ACAAT?CTG????T????A?CGCCATAAA?CC??AGC?TTTAGAA?CTTCCCCCATTCTTATAT???CC???????????GTACACTACCA???T?TT?AGAA?TCAATT???T????CGTGC???CA?GATCCCA??GGAACTTAAACGATCT??????????TCCC?TAG????GG????TCAT???????CAAAT?ACAAATGTAGTTCG?CT?????????GGGA????TT????????A?ACTTGT????????????AGCTAG????AA?TATGCCCTGGT?CCTA????CC??GCTTC??A????????????A??ACTGGTATAA???????CATC?TGA?GCA??TA?TA????????????C??????CTC?TGATGTATGGTACG?ACTCGA???T?TA??????ACCT?TAGT??????A??AT??TAA?TT????GCCAA????????T????AA???????CAAT??ATAA???TCTAGAGAACT???GAAGCAAA???GC?????TT?C??A??TAA????CA?TATA?ATT?????ACTACC?T??AT?????TAAT??C??CAA???GGG??ATAA?????ACTG??TTCAA?CACCAA?TATG?TTAGA???AA?C?TAGTAAGAAC?AT??CCCT??TA?CGGCTAG?????CAAAGA??????A??GA?????????GC??TT??CCCAATTTT??GCTTTCTATTATTA?CAATAAAAGAGAT?C?TT?GCA?CACCATGG??A?ATCGAATTTCT?TA????????CT??T??????????????????????????A?AA????????TC??????CA?AAT?TGG??????TTATAC??ATAGTT??TTAAA???GAAAAGC??TACA?T????AGCT??????AATAAAAG?TTATCA?????TTAGG?ACGA???????GGT??ATACGTTCACAT????CTTC??A???????????????G???????????ATTT??G?GC?TAGTG??GAGAAATATTA????GTT?T?A???AAT??TAACTTC???????A?CTCAG?CTAGATTCACAC???TTTACA???T??AAA???????????A?CATATTGCAT??????TAGT?A?C?ATAAGTTGAATTAA?GCTCT???AGAATGATCACGAGA?CTC??TCTATCGATT????GGATCT?TACG?TTGGTATA?ACCGTAT?????????GTT?A??A??T????GAGAAGCTCCAACCATACCT?ATT????????????CATCGTTTACT????AAACACATTAT?AGAAGAAAACAT????CG?AAT???A????A?AC?CATCCAACCGC?TT??T?????AACG??TCGCA??ATTGC?????A?CAGTACT??????AAAACATGAC??CAAAC???AAAAACCCGCAA??????????TCAA???GCTACTGGCAACGCT?GATAGTAATTGACT??GCT?ATATGA?CTCTGA??C?CTAAGA?TCA?AGCAG?CAAATT?TGATG???T?A?A?C?ATATAAACAT??A?CGTGA??A?GT?TCAC?ATAAA?T???????????CT??GTACTA??????AGCAGT???AAA?G??????A????GTTAACTACTGC????????????TCACGC??TCA??GTG??????????ACTATA?GG?TGT?G??TAACC???AAATGC?G?????GT?ATA???GT?????CATGG?CG????????ATTGA?A????????????C??????TAT?TTT?G?TTT????TC?CA?CAATA??CG??TG??????????CT?C?TAGGA?CATA????AAATA??????????AGAA?ACAGCTCCTCGATCTGAGACAG?ATTA??ACCAATTA?GTATC????TTTGCTT?TA?CCT??????AAA?A????????GCTCA?GA?????????AT?????G????TC????????T??G?CATTT?????????A???CC?G?G??GT?A???AT?CCTGTATAAT?A?AT??ACC?TT?CGA?ATGT????TATA?GATTC??????????TCTAACT????????ATTCTTGG??AAAATA?A???AATAGTG?ATGC????CCATTT????????GTAATAT???TA??GTC?AC??AT???????C????CACATGATA??GGAAAGG?????TGTGTAG??AATA?GT?A?????????A???CATGGTAT?????CATAGCAACCAAC?TGCCA????AA???????ATCAATTC?T?TTGCAACTCAG?AC??CT???????????T????CGT???CA?CTACGGTTGA???AG????T?AA??ATTTTT?AA??A?GTA???CCACAT?GT?CTTGC??GCTCCAAGCC????TC?T?G?A???TGTTC??CA?AACTTA????GAATGTTGATA????AGAGAT?CG??C??CATCGATTATATCCC??AATAC??????ATATA???????TTAGA???????AAGTAATT????CTC????????????????CA???????A???TTC?ATA?T?A??A?CA???AAT????G??TGG??C?T?????????ATA?CCGGTAAA????GAAC????????CCGGT?CA?CTT?GTAA?G?TCTTAATTGATACGAGTATCA?C???GGTC?GTGATA julianiTNHC60324 ????????T?A??AGTG?GC?T????????GCCCT?TCG??TT???T?AT?AATAACCTA????GTG???TA?AT??GGTG?ACAATCCA??GGC?TTTGATCT??TAGTCCC?A????TAG?TT?C???????C?AT???????G?AA?????????CCAATT???????CCCGTAACGGC????A????????TATA??G?TA?ATG??T?AT??C??????TAACCTTCGT??A?AC?ACAAA?GCGTTG????CC????????CTTAGTTGAT?TC???????????A?CGTTGTA?ATGGAAT?CTGAAAT???AC????CTCC????CTACTCGCAACT????????TAATACAT?????TG?T??C?C??????AGATACTA???????TTAATACTTTC??C????????GCCATTAC??A??????????A??AATTGACAAAA??CACC??CAATACCGG?AA??TGT??????????A?TAC?TT?CATAAATGCG?ACACT?CTG????G????G?AGCCATAAA?CG??AGC?TTTTGAA?CCTCTCTCATACTTTCAT???CC???????????GTAC?????CC???T?TC?AGAA?TCGATT???T????CGTGC???TT?ATACCCA??AGACCTTAAACGATCTTAA??????TTCTC?TAG???AAG????TCAT???????CAAAT?ACAAATATAGTTTA?CT?????????GGGA????TT????????A?AC???T??????A?????AGCTCG????AA?TATGCTC?AAT?CCTA????CC??AAAT???A????????????A??ACTGGTATAG?????ACCATC?CGA?ACA??TA?AA????????????C??????CTT?TGATTTATGGTGCG?ACTCGT???TTTA??????ACCT?TAGC??????A??AC??TAA?T?????GCCAA????????T????AA?TG?A?ATAGT??ATAA???TCTTGAGAACT???GAAGCAAA???GT?????TT?C??A??TAT????CA?TACA?ATT?????ACTACC?T??AT?????TAAT??C??CGA???GGG??ATAA?????ACTG??TTCAA?CATCAA?AAAG?TCAGA???AA?C?TAGTAAGAAT?AT??TTATTAAA?CCG?TTG?????CAGAGA??????A??GA?????????GT?ATT??TCCAATTCT??GCTTTGTATTATTA?CAATAAAAGTGAT?C?TT?GTA?AACCATGG??T?ATCGAATTTCT?TA????????CA??A???????????????????????C?GA?AA????????TC??????AA?AAT?CG???????TTAGAC??TTAGTT??TTAAA???GAAAATG??TTTG?T????AGCT??????AATG?AGG?CCATCA?????TTAGG?ACGA???????TGT??ATACGTTCACAT????ATGC??ACCA?C???????TGCG???????????ATTC??G?CC?TCGTATCGAAAAATTTTG????GTT?T?A???AAT??TCACTAC???????A?CTC???ATAGATTCACGCC?TTTTATA???T??CAC???????????A?CATATTGCAT??????TAGT?A?T?ATAAGATGAATTAA?GCTCT???AGAATGATCAAGAGA?CTC??TCCTTCGATCG???GGAACT?TACG?TTGGTATA?GTCGTAC?????????GTT?A??A??T????GTGAAGCACCGACCTTTCAT?ATT????????????CATCTTTTGCT????AGACCCAGTAT????AAAAAATAT????CG?AAT???A????A?ACACATCCAACCAC?TT??T?????AATG??TCGCAGTAATAC?????ATTAGTCCT??????GGAGCATGAC??CAAAT???GAAAACTTGCAA??????????TCTA???GTTACTGACAGCGCT?GATAGTAATTGTCT??GCT?ATAT?A?ATCTG??????????T?TCA?AGCAC?TAAATT?TGAAG???T?A?A?C?ATACAAACAT??A?ACCGT??A?GT?CCAC?GTAAG?T???????????AT??GTACTA??????ATGAAT???AAA?G??????A????GTTAGCTCCTAC????????????TCACGC??TCA??GTA??????????ACCATA?GG?TGT?C??TAACCATAATATTC?G?????GT?ATA???GT?????TATATACG????????TTTAA?A??C?????TTTAC??????TAT?TTC?T?TTT????TC?CA?CAATA??CG??TG??????????AT?T?T?GGACCATA????AAATA??????????AGAA?ACAGCTCGTCGGTCTGAGACAG?ATTA??ACCAAATACGTACC????TTCGATA?CA?CCC??????AAA?CCT??????GCTGA?GA?????????ACAGGA?G????TTT?????ATACGG?TGTTT?????????A???CT?G?C??GT?A???TT?CCTGTACATT?T?AG??ACC?CT?CGC?AGGC????TATC?GACCT??????????TCT??CT????????A?TCTTGG??AAAA????????AGAGTG?GTGCCCCGCCATTT????????GTA???????????????AC??TT???????C????CACGTGATAG?GGACAGG?????TGTGAAG??AATA?GT?A?????????A???C??GAAAT?????CTCACCTATC?AC?TACCAAA?AGA???????ATCAATTC?C?TCGCAAGCCAG?CC??CT???????????T????TGT???CA?CTACGATTGA???AG????T?AA??ATTA???AA??A?GTA???CCAT?T?GT?CTTGC?TGCCT??AACCTCA?TC?T?GTA???TTTTC??CA?AACTTA????TAA??TCGATA????AGAAAC?CG??C??CATTGATTATATCGCC?CGTAT??????ATATA???????TCAGA???????AAATAATT????CACTTA?????A?AAGCTTCA???????T???CTG?ATA?T?A??A?CA???AAA????A??AGG??C?T?????????ATA?TCCGTAAA????GAAC????????CTAGT?CG?CTT?GCAA?A?TCGAAATTGATATGAGTATCG?A???GGTC?GTGATA sierramadrensisKU195181 ACA?CCAGT?A??AGTG?GC?T????????GTCCTGTCG??CT???T?CT?AACCAACTA????GCG??????????GGTG?TCAAACGT??GG??TTTGAACT??CAGCATC?AA??TTGATTT?A???????C?AT???????A?AA?????????TCGATT?ACAA??ACTGTAATGAC????C????????TATA??T?TA?TT???T?AT??CAG???ATAACCTTCAT??A?AG????ATGGCGATG?TGGGC????????TCTAGTTGTT?TCA??????????A?CGTTCTA?ATGGAAT?CTGAGTT???AC????CTCC???TCTGCTCGC??CT????????CCATAAAT?????AG?T??A?AATG??TAGATACTA???????ATAAAACTTTC??C????????GCCATTAC??A??????????A??AATTGACAAAA??CACG??CACCACCGGTAC??GGT??????????A?TAC?ATACAAAAATGTA?ACATT?CTG????G?T?????GCTATAAA?C????????TAGG????ATCTCTCTTCCT?CTAT???CC???TA?????AGTAC?????CA???T?TG?GACT?TCAC?T???T????CGTGC???TG?ATACCAG??GGAACTTAAACGA????????????TTCTC?TAG???GTG????CTAT???????TAAAT?ACGAAT???????T?GT?????????CGTA????TC?CA???AAA?AC???T??????C?????AGCTTG????AAATATGCGTAAGT?TTCA????CC??GTGTCC?A????????????A??AAAGGTATAC?????AACATC?TGA?ACA??TA?TA????????????AT?????CTA?GGATATATAGCGAG?ACTTAT???TTTT??????ACCT?TAGT??????A??A??????????????TCAG????????TTTTTAA?TT?A?ATACT??ATAAT??TCTTGAGAACT???AAAGCAG????GG?????TT?C??A??CTA????CA?TGCT?ATT?????ATTACCCT??AT?????CAAT??C??CAA???AGG??ATAC?????ACTG??TTCAA?CACCAA?CCAG?TTAGA???AA???TAAGAAGAA??AT??ATATTAGA?CCG?????????CAAAGA??????A??GA?????????AATATT??TCCATT?AT??GCTCTCTCTTATTA?CAGTAAATGCAAT?C?TT?GCATTACCATG?TTT?ATC?AATACCTATA????????CT??A???????????????????????C????AA????????AC??????AA?AAT?GGA??????TCAAAC??CTAGT??????????????AAGC??TTCA?T????AAAT??????A??GGTAA?TTAGCA?????TTAGG?GCAC???????CGT??ATACGTCCACAA????GGCC??ACCT?CCGGAATATGGG???????????AT????G?T?????????GAGGATTATTACC??GTT?C?A???AAA??TAACTCC???????A?CTCAG??TAGATTCCCCC????TTACA???T??CAT???????????A?CATATTGCAT??????TAAT?ACC?ATAAGTTGAAGCAG?GCTCT???GGAATGATTAAG??A?CAT??TCCATTGATTGA??GGGGCG?TACG?TTGGTA?A?CTCGTAT?????????GTTCT??T??T????GAGAAGCGCAGACTCTACTT?AGA????????????CATCTTCTGCT????TGACCTAACACAA??AAAAAAAGT????CG?CGT???A????A?TC??ATCCATCCACATC??T?????AACA??AAGCAGTAATTC?????ACAAGGCTT??????ATAATATGACAACAAAT???CAA?ACATGCAAA?????????TCGC????TTA?TGACAAAGTT?GGTAGAAAGTTACT??GCT?ATAG?A?TACTGT??C?CTAAGC?CC????????GAAATT?T?ACGGT?TCA?A?C?TTACAAGCAT??A?GGTGG??ATGT?TAAT?GAGAA?T??G?CA?????GTT?ATACTA??????AATAGC???AAA?A??????A????GTAAATTCCATC????????????TCACGC??ACATTGTG??????????ACAATAAGT?TGT?A??TAAT????ACATTC?G?????AT?ATG???GT?????CCTATACG????????CTTAA?A??C?????TTTAC??????TCT?TTT???TCT????TT?CA?CGATA??CG??AG??????????TT?T?TAGGA??????????AAAA??????????AG?A?ACGGACTATCGTT?TGAGACAG?ATCA??CCCAAGTACGTATCA???TTCG?TA?AA?CCC??????AAA?ATT??????ACTAA????????????TC?????G????CCTA????ATTCGG?CCTTT?????????A???CT?G?T??GT?A???AC?CCTGTATAAT?A?AC??ACC?TC?CGC?ATGT????TAC???AACT??????????TCC??CT????????A?TCTTGG??ATAATT?A???AATAGTG?ACGC????CCATTT????????CTTACAT???TA??GCC?AC??AT???????C????CACCCG???????TTAGGAAGTTTGTGGAAG????G?GTTA??????????????ATGGTAT?????CTGGA???CC?AA?TC??AGA??TA???????AAC?ATCA?C?TTGAAACTCAG?CC??C????????????TTTG?GGTCC?CA?CTACGATTGA???AG????T?GA??ATTTTCAAA??A?GTA???CTGTAT?GT?CTCGC??GCCAT?TACA????CC?T?GTA???TTTTC??CAGAACTTA????AAATGTAG?????????CTAC?CG??C??CATCGCTTAT?TCGCC?TATAC??????ATATA???????TGAGG???????AGATAATT????CTCTTA?????A?CGATTTCA???????T????TG?ATA?T?A??A?CA???AAA????A??AGG??A?T?????????ATA?CCGGTAA?A??TGAAC????????C?GAC?CC?CTT?GTAG?A?TCTAAATTGATACGGGTATCA?G???GGTC?TTAACA psilonotaKU195119 ACC?CA??C??????CG?GC?T???????????CTGTAG??TT???TTTT?AACTACCTAA?TCGCG??????????GGTG?GCAATCCC???GT?TTTCA?CT??CAGCATC?CA??T?????TTAAC?????T?ATCCTCTT?T?AA?????????TCAATT?AT????CCTGTAACGAC?CCCG????????TATA??C?TA?AT???T?AT??CCG???ATGACTTTCAT??A?AG????TA??CGTTG?TAGAC????????TATAGTTGTA?TCC?????????????GCTTTA?ATGGAAT?CTGAACT???AC????CTCC???TCTACTCGC??CT????????CCCTAAAT?????TG?T??C?AATG??TCGATACCA???????TTAAAAATTT???T????????GCCCTTAC??A??????????A??AGTTGCCATAA??CACG??CCTCGCCGGTAA??ATG??????????A?TAC?CC?CGA??ATGTA?ACATT?CTG????C?TATT?AGCTATAAA?C????????TACGAA?ACTC?ATCTCATT?CTGC???CCC??TA?????AGTAC?????CG???T?TT?AGTAATCATTT???T????CATGC???TA?AATCCTA??GGACCTTACACGATCTTAAAATCG?TTCTC?TGG???TTG????TCAT???????TAAAT?ACGAATATAGTCCA?CT?????????AGAA????TG?CA???AAA?AC???T??????T?????A?CTCG????AA?TA?GCTTAGTT?TTCA????CC??ACATCC?A????????????A??AATGGTATAT?????GTAATC?CCA?GCA??TA?AA????????????T??????CTA?CGATCCATTTCGTG?ACTCAC???TTTA??????ACCT?TAGA??????A??AA??TAA?T?????GCCAG????????T???????TA?A?ATA????ATAAT??TCTAGTGAATT???GAAGCAAT???G????????????????CA????CA?CACT?ATTTCACTATTACCCC??AT?????TAATTCC??CAA???AGG??ATAT?????ACCG??TTCAA?CATC?A?CCAG?T?AGA???AA???TAAAAAGAAA?AT??CTGTTAGA?CAA?????????CAAAGA??????A??GA?????????TATATT??TCCATTTCT??GTTCTATTCTATTATCGGTAAAAGGAAG?CGTT?ACATCTCCATGGTCT?ATC?AATTACTATA????????CA??G???????????????????????C????AA????????AC??????TA?TAT?CGG??????CTAGAC??TTAGC??????????????AAGT??TATG???????AGC??????AATGCAAA?TCACCA?????TTA?????AAC????????T??ATTCGTCCACAA????TGGC??ACTA?C???????TGCG???????????ATCG??GTTC?TTGTT??G??AAA???????????????????AAA??TTACTGC???????A?CTCAG?ATACATTCTCCCC?TTTTATA???C??G?A???????G???A?CATATTGCGT??????TAAT?A?C?ATAAGCTGAAGGAG?GCTCT???AGAAAGA???CG?GA?CTT??TCTATTGATTG???GGATCA?TACG?TTGGCA?G?ATTGTAC?????????GT??A??G??T?????AGAAGCGCTAACCATACCT?ATT????????????CATCCTCTGCT????CGACCCAGCACAAGAAAAAAAAGT????CG?AAT???A????A?TA??ACCTATCCACATC??T?????AACG??CTGCAGTACTTC?????ACAAGAA?????????TAATACGACAACAAAC???AAA?ACCCGC??A?????????CCTC????TTA?TGACAAAGCT?GATAGCAACTAACT??G?T????C?A?C??TGG??C?CTAAGA?CC????????TAAATT?TGATGGT?TGACA?CCATACA????T??A?GGCGC??A?GT?TAGT?TGGAT?T??G?CA?????GTA?GTACTA??????AAAAGT???AAA?A??????A????GTTAACTACACA????????????TCACGC???CATGGTG???????????CGATA?GCCCGTAA??TAAC????ATATTC?G?????TT?ATA???GT?????CATATACA????????TTTGA?A??C?????CTTAC??????TCT?TCC???TTTTACCGC?CA?CAATA???G??AG??????????TT?T?TAGGA?CGTC???CAAAAA??????????AGCA?ACAGACCATCGAT?TAAGACAG?ATTA??CCCAAGTACAT?CCA???TCAG?TG?AA?CCC??????AAA?ATT??????ACTCA?GT?????????GT?????G????TATGATAGATTCGG?CTTTT?????????A???CT?G?T??GT?A???AT?CCTGTATAAT?A?ACT?ACC?TT?CGT?ATGT????TTCA?GACAT??????????TCG????????????A?TTTTGG??ATAACT?A???AACAGTG?ATGC????CCATTT????????TTAAT?????????????????GT???????C????C??????????????AGA????TTGTCGAATCGATG?GT?A?????????A????ATGGTAT?????CTAC????????A?TTCCAAT??TA???????AAC?ATAA?T?TTGTAAATCAG?AT??CTT?TTAGGAC??T????TGTCC?CA?CTACACTTGC???AG????GAAA??ATTACAAAA??A?GTAT??CC???T?GT?CTAGC??GCAATACACA????TT?T?GTA???TGTTT??CT?AACTTA????GAATGTAGTTA????AGGTAC?CG??C??CATAGATC??ATCG??????AT??????ATATA???????TCAGG???????AGGTAATT????CACTTA?????A?CGTTTTCA???????C???ATG?ATA?T?A??A?CT???AAA????A??GGG??A?G??????????TA?CCTGTACAT??CGAAC????????CGGGC?CA?CTT?GCAATA?TCTTAATTGATAT?AGTATTA???????TC?GT?ACA zweifeliJAC7514 ????CACGT?A??AGTG?GC?T????????GTCCTGTCG??TT???T?TT?AACCAACTAATGCGCG??????????GGTG?TCAATCCC??GGT?TTTGATCT??AAGCATC?CA??T?????T?AAA?????C?AT???????C?AA?????????TCAATT?AT????CCTGTAATGAC?CCCG????????TATA??C?TA?AT???T?AT??CAA???ATAACGTTCAT??A?AA????CGGACGTTG?TTGAC????????TTTAGTTGTT?TCC??????????A?CGCTTTA?A?GGAAT?CTGAAGT???AC????CTCC???TATACTCGC???T????????CCGTAAAT?????TG?T??C?AATA??TAGATACTA???????GTAAAATTTT???T????????GCCATTAC??A????????TAA??ACTTGTCATAA??CACA??CATCACCGGTAA??CTA??????????A?CAC?CC?CTA??ATGTA?ACATT?CTG????T?TTTT?TGCTATAAA?C????????TATGGA?ATTCTATCTTTTT?TGAC???CCC??TA?????CGTAC?????CA???T?TG?AGTAATCATTT???T????CGTGC???CA?TTCCCTG??TGAACTTAAACGATCTTAA??????TTCGC?TAG???ATG????ACAT???????TAAGT?ACGAATGTAGTCCT?AT?????????GGTA????TT?CA???AAA?AC???T??????C?????AGCTCG????AA?TATGCTTGGCT?TCCA????CC??GTTTCC?A????????????A??AACGGTATAT?????AAAATC?AAA?ACA??TA?TA????????????C???????TG?TGATACATGATGTG?ATTCTC???TTTA??????ACCT?TAGG??????A??AA??TAA?T?????GCCGA????????T????AA?TA?A?ATACT??ATAAT??TCTAGTGAATT???G?AGCAACTATG????????????????CA????CA?CAAT?ATT?????ATTACCCC??AT?????TAAT??C??CAA???AGG??ATAT?????ACTG??TTCAA?CACCAA?TCAG?T?TGA???AA???TAACAAGAAA?AT??CCGTTAGA?CGA?????????CAAAGA??????A??GA?????????AGTATT??TCCACTTCT??GCTTTGTTCTATTATCTGTAAATGTAAA?C?TT?GTATGCCCATGGTCT?ATC?GATCACTATA????????CT??A???????????????????????C????AA????????AC??????AG?AAT?CGG??????CTAGAC??CTAGC??????????????AAGG??TACG???????AGC??????AATGTCAA?TCAACA?????TTAGG?ACA????????????????CGTTCACGA????AGGC??ACTC?C???????TGTG???????????ATTT??GCTC?TTGTT??GAGAAATATTC????GTC?C?A???AAT??TTACTAC???????A?CACAA?TTAGATTCACCCC?TTTTACA???C??GAA???????G???A?CATATTGCAT??????TAAT?A?C?ATAAGCTGAAGTAA?GCTCT???AGAATGA???CG?GA?CTC??TCTATTGATTG?CGGGGCCA?TA?G?TTGGTA?A?ACTGTAT?????????GT??A??T??T????ATGAAGCGCTAACCGTTCAT?ACT????????????CATCTTTTGCC????AGACTTAATAAAAGAACAGAAAGC????CG?TAT???A????A?TA??ACCTAACCACATT??T?????AACA??TTGCAGTATTCC?????ACAAGAATT??????GCACTACGACAACAAAT???AAA?ACTCGCAAA?????????CCAT????TTA?TGACAGAGCT?GGTAGCAACTAACT??GCC?ATAA?A?T??TGG??C?CTAAGC?CC????????CAAATT?TGATGGT?TCACA?CGTTACA????T?TA?GGCGA??A?GTCTAAC?AAGAT?T??A?CG?????GTA?GTACTA??????ATAAAT???AAA?A????????????????TTTCACG????????????TCACGC??TCATAATA??????????ACTATA?GTATGT?A??TAAT????ATATCC?G?????TC?ATG???GT?????CATTTACA????????TTTAA?A??C?????CTTAC??????TCT?ACC???TTT????TC?CA?CAGTA??CG??AG??????????CT?T?TAGGA?TGTCTATCAAAAA??????????AGCA?ACTGTCTGTCTTT?TGAGACTG?ATTA??ACCAAGTACGTACCA???TTAG?TA?AA?CCC??????AAA?GTT??????ACTTA?GT?????????CT?????G????TTTG????ATTCGG?CTTTT?????????A???CT?G?C??GT?A???AC?CCTGTATAAT?A?AC??ACC?CT?CGT?ATGT????TACA?GATAT??????????TCC????????????A?TTTCGG??ATAACA?A???AATAGTG?AAGC????CCATTT????????CTAATAC???TA??GTT?AT??GTCACACTTC????C??????????????AGG?????TGTAGAATTGATA?GT?A?????????A????ACGGAAT?????CCAA????????A?TCCCAAA??TA???????AAC?ATCA?T?TTAAAAGTCAG?AC??CTT?ATAAGAC??T?TA?CGTAC?CA?CTACACTTGT???AG????CACA??ATTATAAAA??A?GTA???CT???T????CTAGC??GCACTACACA????TC?T?GTA???TTTTC??CT?AACTTA????AAATGTTGGTA????AG?AGC?CG??C??CATAGATA??ATCG??????AC??????ATCTA???????TCAGG???????AAATAATT????CACTTA?????A?TGATTTCA???????T???ATG?ATA?T?A??A?CA???AAAGAACC??AGA??T?T?????????ATA?CCCGTAAAT??TGAAC????????CCGAC?CG?CTT?GAAG?A?T???AATTGATAT?AGTATTA???????TC?GTGACA tarahumaraeKU194596 A?C?CCC???A??AGTG?GC?T????????GTCCTGTTG??TTTTGT?CT?GACCAACTAATGTGCG??????????GGTG?ACAATCTT??GGC?TTTGATCT??CAGTGTC?AA??TTGA?CT?AAC?????C?AT???????C?AA?????????TCTATT?AT????CCTGTAAAGAC?CCCG????????TATA??C?TA?AT???T?AT??CAG???ACAACCTTCAT??A?AAG???TGGACGTTG?TTGAC????????TCCAGTTGTT?TCC??????????A?CGC?CTA?ATGGAAC?CTGACAT??TAC????CTCC???TTTACTCGC??CT????????CCTTAAAT?????TG?T??T?AATA??TAGATACCA???????GTAAAATTTTC??T????????GCCATTAC??G??????????A??AGTTGACAAAA??CACA??CATCACCGGTAT??CAA??????????A?TAC?TT?CAG??ATGTA?ACACT?CTG????C?TATT?AGCTATAAA?C????????TACGAA?TTTCTACCTTTTT?TCAC???CCC??TAGCATGAG?AC?????CC???T?TA?AGTAATCACTTACTT????CGTGC???TA?TGGCCCA??GGAACTTAAACGATCTTGA??????TTCTC?TAG???TGG????TCAT???????TGAGT?ACGAATATAGTCCC?TT?????????GG???????????????AA?AC???T??????C?????AGCTTG????AA?TATGCCTAGTT?TCCA????CC??AAATCC?A????????????A??AACAGTATAA?????GAAATC?TAA?GCA??TA?GA????????????T??????CTG?CGATACATA?TACG?ACTCAC???TTTA??????ACCT?TAGA??????A??AT??TAA?T?????GCCGC????????T????AA??G?A?ATACT??ATAAT??TCTAGAAAAAT???GAAGCAAT???G????????????????TA????CA?TACC?ATT?????ATTACCCT??AT?????TAAT??C??CAA???GGG??ATAT?????ACTG??TTCAA?CACCAA?TCAG?T?TGA???AA???TAACAAGAAC?AT??CCGTTAGACCAG?????????CAAAGA??????A??GA?????????AGTACT??CCCACTTCT??GCTTTATTCTATTATCGGTAAACGCAAG?C?TT?GCATTCCCATGGTTT?ATC?AATGACTATA????????CA??A???????????????????????C????AA????????AC??????AT?ATT?CGG??????CTAAAC??TTAGT??????????????AAGA??TATA???????AGC??????AATGAGGA?TCAACA?????TTAGG?TCAAC??????TGT??ATACGTCCACAT????AGGC??ACTC?C???????TGAG???????????ATCG??GTGC?TTGTC??GAGAAATTTTA????GTA?C?A???AAC??TAACTAC???????A?CGCAG?ATAGATTCTCTCC?TTTTATA???C??AAA???????G???A?CATATTGCAT??????GAAT?G?C?ATAAGCTGAAGTAG?GCTCT???GGAATGATTACG?GA?CTC??TCTATTGATTG???AGGTCA?TACG?TTGGTA?A?ATCGTAT?????????GT??A??C??T????ATGAAGCGCTAACCATACTT?ACC????????????CATCTTCCGCT????AGACTTAATATAAGAAGAGAAAGT????CG?AAT???A????A?CC??ATCTACCCACATT??T????????A??CTGCAGTATTTC?????ACAAGGAAT??????AAAATACGACAACAAAT???CAA?ACTTGCAAA?????????CCTT????TTA?TGACAGAGCT?GGTAGCCATTAACT??GCT?ATAA?A?T??TGG??C?CTAAGA?CC????????CAAATT?TGCTGAT?TTA?A?CATTACA????T??A?GGCGC??A?GT?TGAC?AAGAA?T??G?CC?????GTT?GTACTA??????ACAATT???AAA?A??????A????GTAAATTTCATA????????????TCACGC??TCATAGTG??????????ACAATA?GTATGT?A??TAAT????ATATCC?T?????CT?ATG???GT?????CATATACA????????CTTAA?A??T?????CTTCC??????TCT?TCC???T???????CCCA?C?ATA??CG??AG??????????TT?T?TAGGA?TATC???TAATAA??????????AGCA?ACAGACTTCCGCT?TGAGACAG?ATCA??TCCA?GTACGTATC????TTAG?TT?AA?CCC??????AAA?ATC??????ACTTA?GT?????????GT?????GTTTATCTA????AT?CGG?TTTTT?????????A???TC?G?T??GT?A???AC?CCTGTACAAT?G?AT??ACC?TC?CGT?ATGA????TACA?GATAT??????????TCG????????????A?TTTTGG??ATAAAT?A???AATAGTG?ACGC????CCATTT????????TTAATCC???TA??GCT?AT??GT???????C????C??????????????AGG????CTGTAGAATTGATG?GT?A?????????A????ATGGCAT?????CTATT?AATC?AAATCCCAGA??CA???????AAC?ATCT?C?TCGTAATTCAA?AC??ATT?ATAGGTC??T?TA?TGTAC?CA?CTACAATTGT???AG????CAAA??ATTCTAAAA??A?GTA???CC???T?GT?CTAAC??GCATTATACA????CC?T?GT????TTTTC??CA?AACCTA????AAATGTAGATA????AGGTTC?CG??C??CATCGATTGTATC???????AC??????ATATA???????TCAGG???????AGATAATT????CACTTA?????A?CGATTTCA???????A???TTG?ATA?T?A??A?CA???AAA????A??TGG??C?C?????????ATA?CCTATAAAT??TGAAC????????CTAGC?CG?CTT?GCAA?A?TCTAAATTGATAT?AGTATCA?A???GGTC?GTGACA pustulosaJAC10555 AAT?CCCGA?A??AGTGTGC?T????????GTCCTGTAG??TTTTGT?CT?GACTACCTATTGTGCG??????????GGTG?ACAATCCC??GGC?TTTAACCT??GCGCAAC?AA??T?????T?CAT?????C?AT???????C?AA?????????TCAATT?AT????TCTGTAATGAC?CCCG????????TGTA??A?TA?AT???T?AT??CAA???ACAACGTTCGT??A?AA????TTGACGTTG?TAGTC????????TTCAGTTGAA?TCC??????????A?CGT?CTA?ATGGAAT?CTGAAAT???AC????CTCC???TTTCCTCGC??CT????????ACTCAAAT?????TG?T??C?AATA??TAGATACTA???????A??AAAATTTC??T????????GCCATTAC??A??????????A??AATTGACAAA????????????CACCGGTAT??AGA??????????G?TAC?CC?CAG??ATGTA?ACAAT?CTG????C?TGTT?AGCTGTAAATC????????TAAGAA?TTTCTATTCTTTT?CTAC???CCC??TA?????AGCGC?????CT???T?TA?TGTAATCAC?????T????CATGC???TT?TACCCTA??TGAGCTTACACGATCTTGA??????TTCGC?TAG???CAG????ACAC???C???TAAAT?ACGAATATAGTCTC?CT?????????GG???????????????AA?AC???T??????A?????AGCTAG????AA?T?????TTATT?TCCA????CC??ACATCC?C????????????A??AAAGGTATATAACGTGAAATC?TAA?GCA??TA?AA????????????T??????CTG?CGATTTATG?CTCG?ATTCAC???TTTA??????ACCT?TAGA??????A??AA??TAA?T?????GCCAT????????T????AA?TA?A?ATATT??ATAAT??TCCAGAGAATT???GAAGCAGT???G????????????????CA????CA?CACT?ATT?????ACCACCCT??AT?????TAAT??C??CAA???AGG??ATAT?????ACTG??TTCAA?CATCAA????????CGA???AA???TAAC?AGAAA?AT??TCGTTAAGCCAG?????????CAAA????????A??GA?????????AGTAA??????ACTTTT??GCTTTATCATATTATCTGTAAATGAAAT?C?TT?GTATTTCCATGGTCT?ATC?GATTACTATA????????CC??A???????????????????????C????AA????????AC??????CC?GAT?CGG??????CTAAAC??TTAGT??????????????AAGC??TGCA???????GGTCTATTAAATGAAGT?TAAACA?????TTAGG?CCCAC??????TGTATATACGTTCACGT????AGAC??ACAC?C???????TGGG???????????ATTA??GATC?TTATC??GATAAATATTA????GTT?A?A???AAC??TGACTAC???????A?CTCAG?TTAGATTCCCACC?TTTTACA???T??GAA???????G???A?CA???T?CAT??????TAAT?A?C?ATAAGCTGAAACAG?GCTCT???GGAATGATTACG?GA?CCC??TCTATTGATTG????AAACA?TACG?TTGATA?A?ATAGTAT?????????GT??A??G??T????AGGAAGCGCCAACCATGC???ATC????????????CATCATTCGCC??ATAGACCTAATATAAGAAAAGAAAGT????CG?GAT???A????A?TC??ATCTACCCACATC??T????????A??CCGCAGTAC?CC?????ACTAGGAAT??????AAAATACGACAACAAAT???TAA?ACTTGCAAA?????????CCAC????TTA?TGACAAAGCT?GGTAGAAATTATCT??GCTCATAG?A?G??TGA??C?CTAAAA?CC????????CAAATT?AGATGATCTCA?A?CATTATA????T??A?GGCGG??A?GT?TGAT?GAAAA?C??G?CC?????ATT?GTACTA??????AAAAAT???ATA?A??????A????GTAAATTCCCCA????????????TCACGC??TCATGGTA??????????ACGATA?GTTCGT?GCTTAAC????ACATCC?G?????AT?ATG???GT?????CATATACA????????TTTAA?A??T?????CTTAC??????TCT?TCCA??TTT????TT?CA?C?ATAG?CT??AG??????????TT?C?TA?GA?TATC???CAAAAA??????????GGAA?ACAGTACATCGAT?TGAGACAG?ATCA??TCCA?GTTCGTATCA???TTAG?TG?AA?CCC??????AAA?AGC??????ACTTA?GT?????????AA?????G????ACTG????ACTCGG?TTTTT?????????A???TT?G?T??GT?A???AC?CCTGTATAAT?A?AC??ACC?TA?CAG?CTGT????TGTA?GATAC??????????TCT????????????A?TCTCGG??ACAATG?A???AATAGTG?ATGC????CCATTT????????CTGACCC???TA??GCC?ATGT?T???????T????C??????????????AGG????CTGTAGAATTAATA?GT?A?????????A????AGGGAAT?????CGGTT?TATC?AA?TCTCAAC??TA???????AAC?ATCT?T?TCGTAAACCAA?AC??CTT?TTAAGTC??T?TA?CGTGC?CA?CTACAATTGT???AG????TAGA??TTTTTCAAA??A?GTA???CC???T?GT?CTTAC??GCATTA?TCA????TC?T?GTA???????????????CTTA????GAATGTAGATA????AGGTAC?CG??T??CATC?ATTCTATC???????AC??????ATGTA???????TTTGG???????AGATAATT????CA????????????????TCA???????C???TTG?ATA?T?A??A?CA???AAT????C??TGG??????????????ATATCCTGTAAAACCTGAAC????????CCTAC?CG?CTT?ATA??A?T?CGAATTGATAT?AG?ATTA?A???GGTC?TTGACA pipiensJSF1119 ACC?CCCGC?A??AGCG?GC?T????????GTCCTGTTG??CT???T?TT?CATAAACTA????GTG??????????GGTG?ACAAACTC??GGTTTTTAATCT??GGGTACC?TA??TTAG?AT?A???????C?AA???????G?TA?????????CCAATT?ACAAAACCTGTAATGCC????T????????GATA??C?TA?TT???T?AT??CAG???ATAACCTT??T??A?TT????TAGACGTTG?TTATC????????TACAGTTGCA???CCCTGTCCTACA?CGATCTA?ATGGAAC?CTGAGCT????C????CTCCCTATTCGCTCGC???C????????CTTTAAAT?????TG?T??A?AATG??TCGATACTA???????ATGAAAATTTC??T????????GCCATTAC??A??????????A??ATTTGACATAA?ACACT???A??AC?GG?AT??TAAACAAACTCTTCATAC?A??CCCGAATGTG?ACACT?CTG????C?TTTA?CGCTATAAA?CA??GGCCTTAAGCA?AA??????ATTT??GTAT???CC???TA?????GGTAC????????????TG?AGAT?TCGTTT???T????CATGC???GC?TATCCAA??GGATCTTAAACGATCTTAA??????TTCTC?TAG???ATG????ACAC???????CGAAT?ACAAATATAGTTCT?AT?????????TGAG????TC?CA???AAA?AC???T??????G?????AGCTAG????AA?TATGCTTAATT?TACA????CC??TAATC??A????????????A??GTTGGCATAT?????GAAATC?TAT?ACAAGTA?GA????????????A??????TTG?TGATATATCGGATG?ACTCAT???TTTACGTCACACCT?TCGT??????A??AT???AA?T?????GACGC????????T????AT?TA????TACT??ATAAT??TCTGGAGAACT?CCGAAGCAGC???GG?????TT?C??A??CTA????CA?CAAT?AAT?????ATTACCCT??AT?????TAAT??C??CAG???AGG??ATATCTCCGACTG??TTCAA?CATCAA?TCGG?TTTG????AA?C?TAACAAGAAA?AT??GTATTAGA?CTG?????????CAAAGG??????A??GA?????????GATATT??TCCAATTTT??GCTTTATACTATTA?CTGTAAATGAAGG?C?TT?ATA?GACCATGG??C?ATC?AAT??CTCTA????????CA??GCAAATTT????????????AAT?C????A?????????GC??????CG?TAT?CGG??????CAATAC??CTAGTT??TTAAATTTGA?AAGA??TACAAT????AAGC??????AATAACAA?TCAATA??TTCTTAGG?GCAA???????TGT??ATACGTACACAG????AGAC??ACCC?C???????TGTG???????????CTAA??G?CC?TCCTA??GATAACTATTG??TTGTA?C?A???AAT??TATCTCC???????A?CCCAG?CTATATTCTCCCC?TTTTATA???T??CAG???????????A????ATTGCAT??????TAAT?A?C?ATAAGCTGAAGCAG?GCTCT???A??ATGATTAAG?GA?ACC??TCTATTGATTG???AGGACA?TAC???????ATAAGTTGCAC??????????TT?G??T??T????????????CTGACTATACCT?ACT????????????CATCTTTCGCT????TGACTCAAAATAAGAATATAAAAC????CA?AGT???A????A?AC??ATCCATCCACATT??T?????AATA??CTGCAGTATCCC??????CCAG?ACT??????AAATCATGAC??CAAAT?????AAACTCGCAAATGTTGTG??TTAA????CTA?TGACATAGCT?GGTAGTAATTATCT??GCT?ATAA?A?AACTGG??C?CTAAGA?CTCAATCAC?TAAATT?TGAAG???TAA?A?C?CTATAC????????AGAGG??A?GT?TGTC?GGAAG?T??G?CA?????GTA?GTACTA??????A?AAA??????A?A??????A????GTGAACTACCCT?AGGATAGAC?TTCACGC??TCATAATA??????????ACCATA?GT?TGT?A??TAATC?????????CG?????TT??TACTTAT?????ACTACACT????????TTTGA?A??T?????CCTAC??????TTT?CTT?G?TAT????CC?GA?CAATA??????????????????????????GA?TTTA????AAAGA??????????AGGA?ACAGCTCATCATT?TGAGACGG?ATCA??AC??AGTACGTACCA???TCTG?TT?GA?CCC???????AA?ATT??????ACTTA?GT?????????GA?????G????TAT?????ATCC?G?ATTTT?????????A???CC?G?C??GT?ATGCAT?CCTGTACAATCA?A???ACC?GG?CGT?A??T????TTCA?GACTT??????????TCC??CT????????A?TATTGG??ATAATT?A???AATAGTG?TC???????CATTT????????TTAATATTTCTAACGCT?AC??CT???????T??????????AT?G?GGTCAGGAAGTTTGTGGAGTTAATG?GT?A?????????AACATATGACAT?????CTGTC?AAAC?AA?TTCCAGC?GTA???????AGCAATCA????TGTAATTCAC?AT??CTTGTTATGCC??TTCA?GGTCC?TA?CTACCGTTGG???AG????G?GA??A??????????A?GTA???CCGTAT?GT?CTTGT??GCTATATACA????CT?T?GTA???TCTTT??CA?AACTTA????TAATGTTGTTA????????AC?CG??T??CAT?GGTA???????C?AATAT??????ATATA???????TTAGG???????AGATAATT????CTCTTA?????????????CATTACTACT???TTA?ATA?T?A??A?CA???AAA????A??AGA??T?G?????????ATA?CCA?TAAAT??CGAAC????????TCAGA?CT?CTT?GAAA?T?TCGAAATTGATACGAGTATTA?G???GGTC?TTAACA pipiensY10945 ACC?CCCGC?A??AGCG?GC?T????????GTCCTGTCG??CT???T?TT?CATAAACTA????GTG??????????GGTG?ACAAACTC??GGTTTTTAATCT??GGGTACC?TA??TTAG?AT?A???????C?AA???????G?TA?????????CCAATT?ACAAAACCTGTAATGCC????T????????GATA??C?TA?AT???T?AT??CAG???ATAACCTT??TACA?TT????TAGACGTTG?TTATC????????TACAGTTGCA???CCCTGTCCTACA?CGATCTA?ATGGAAC?CTGAGCT????C????CTCCCTATTCACTCGC???C????????CTTTAAAT?????TG?T??A?AATG??TCGATACT????????????GAATTTC??T????????GCCATTAC??A??????????A??ATTTGACATAA?ACACC???ACCAC?GG?AT??TAAACAAACTCTTCATAC?A??CCCGAATGTG?ACATT?CTG????C?TATA?AGCTATAAA?CA??GGCCTTAAGAA?AA??????ATTT??GTAT???CC???TA?????GGTAC????????????TG?AGAT?TCGTTT???T????CATGC???GC?TATCCAA??GGATCTTAAACGATCTTAA??????TTCTC?TAG???ATG????ACAC???????CGAAT?ACAAATATAGTTCT?AT?????????TGAG????TC?CA???AAA?AC???T??????G?????AGCTAG????AA?TATGCTTAATT?TACA????CC??TGATC??A????????????A??GTTGGCATAT?????GAAATC?TAT?ACAAGTA?GA????????????A??????TTG?TGATATATCGGATG?ACTCAT???TTTACGTCACACCT?TCGT??????A??AT???AA?T?????GCCGC????????T????TT?TA????TACT??ATAAT??TCTGGAGAACT?CCGAAGCAGC???GG?????TT?C??A??CTA????CA?CAAT?AAT?????ACTACCCT??AT?????TAAT??C??CAG???AGG??ATATCTCCGACTG??TTCAA?CATCAA?TCGG?TTTG????AA?C?TAACAAGAAA?AT??GTATTAGA?CTG?????????CAAAGG??????A??GA?????????GATATT??TCCAATTTT??GCTTTATACTATTA?CTGTAAATGAAGG?C?TT?ATA?GACCATGG??C?ATC?AAT??CTCTA????????CA??GCAAATTT????????????AAT?C????A?????????GC??????CG?CAT?CGG??????CAATAC??CTAGTT??TTAAATTTGA?AAGA??TACAGT????AAGC??????AATAACAA?TCAATA??TTCTTAGG?GCAG???????TGT??ATACGTACACAA????AGAC??ACCC?C???????TGTG???????????CTAA??G?CC?TCCTA??GATAACTATTG????G??????????????TATCTCC???????A?CCCAG?CTATATTCTCCCC?TTTTATA???T??CAA???????????A????ATTGCAT??????TAAT?A?C?ATAAGCTGAAGCAG?GCTCT???A??ATGATTAAG?GA?ACC??TCTATTGATTG???AGGACA?TAC???????ATAAGTTGCAC??????????TT?A??C??T????????????CTGACTATACCT?ACT????????????CATCTTTCGCT????TGACTCAAGATAAGAATATAAAAC????CA?AGT???A????A?AC??ATCCATCCACATT??T?????AATA??CTGCAGTATCCC??????CCAG?ACT??????AAATCATGAC??CAAAT?????AAACTCGCAAATGTTGTG??TTAA????CTA?TGACATAGCT?GGTAGTAATTTTCT??GCT?ATAA?A?AACTGG??C?CTAAGG?CTCAACCAC?TAAATT?TGAAG???TAA?A?C?CTATAC????????AGAGG??A?GT?TGTC?GGAAG?T??G?CA?????GTA?GTACTA??????A??????????A?G??????A????GTGAATTACCCT?AGGATAGAC?TTCACGC??TCATAATA??????????ACCATA?GT?TGT?A??TAATC?????????CG?????TT??TACTTAT?????AATACACT????????TTTGA?A??C?????CCTAC??????TTT?CTT?G?TAT????CC?GA?CAATA??????????????????????????GA?TTTA????AAAGA??????????AGAA?ACAGCTCATCATT?TGAGACGG?ATCA??AC??AGTACGTACCA???TCTG?TA?GA?CCC???????AA?ATT??????ACTCA?GT?????????GA?????G????TAT?????ATCC?G?ATTTT?????????A???CC?G?C??GT?ATGCAC?CCTGTACAATCA?A???ACC?GG?CGT?A??T????TACA?GACTT??????????TCT??CT????????A?TATTGG??ATAACT?A???AATAGTG?AC???????CATTT????????TTAATATCTCTAACGCT?AC??CT???????T??????????AT?G?GGTTAGGAAGTTTGTGGAGTTAATG?GT?A?????????AACATATGGCAT?????CTGTT?AAAC?AA?TTTCAGT?GTA???????AGCAATCT????TGTAATCCAC?AT??CTTGTTACGCC??TTCA?GGTCC?TA?CTACAGTTGG???AG????G?GA??A??????????A?GTA???CCGTAT?GT?CTTGT??GCTATATACA????CT?T?GTA???TCTTT??CA?AACTTA????TAATGTTGTTA????????AC?CG??T??CAT?GGTA???????C?AATAT??????ATATA???????TTAGG???????AGATAATT????CTCTTA?????????????CATTACTACT???TTA?ATA?T?A??A?CA???AAA????A??AGA??T?G?????????ATA?CCAGTAAAT??CGAAC????????TCAGA?CT?CTT?GAAA?T?TCGAAATTGATACGAGTATTA?G???GGTC?TTAACA dunniJSF1017 ACC?CCCGT?A??AGCG????T????????GTCCTGTTG??TT???T?TT?CACAAACTA????GTG??????????GGTG?GCAAACTT??GGTCTTTAATCT??TGGTACC?TA??TTAG?AT?A???????T?AA???????G?TA?????????TCAATT?ACAAAACCTGTAATGTC????C????????TATA??T?TA?AT?????AT??CAG???ATAACCTT??T??A?AT????TCGACGTTG?TT??????????????AGTTGCA???C??????????A?CGATCTA?ATGGAAC?CTGAGCT????C????CTCCCTATTTACTCGC???T????????CTTTAAAT?????TG?T??C?AATG??TCGATACTA???????ATCAAACTTTC??G????????GCCATTAC?????????????A??ACTTGACATAA??CACC???ACCAC?GG?AA??CAG??????????CATAC?A??CGTGAATGTG?ACACT?CTG????C?TATG?AGCTATAAA?CC??AGCCTTAAGAA?GA??????ATTT??GTAT???CC???TA?????CGTAC????????????TT?AAAT?TCGTTT???T????CATGC???GG?CAACCTA??GGATCTTAAACGATCTTAA??????TTCTC?TAG???TTG????ACAT???????CGAAT?ACAAATTTAGTTCA?CT?????????TGTG????TT?CA???AAA?AC???T??????G?????AGCTAG????AA?TATGCTTTATT?TACA????CC??TACTC??A????????????A??GACGGCATAT?????GAAATC?CAT?ACAAATA?TA????????????A??????CTG?TGATATATTTACTG?ACTCAT???TTTT??????ACCT?TCGT??????A??AC??TAA?T?????GCCGT????????T????AA?CA????TACT??ATAAT??TCTGGAGAACT?TCGAAGCA????????????TT?C??A??CTT?????A?GATT?AAT?????ATTACCCT??AT?????TAAT??C??CAG???AGG??ATATATCCGACTG??TTCAA?CACCAA?TCGG?TTCGA???AA?C?TAATAAGAAA?AT??ACATTAGA?CCG?????????CAAAGG??????A??GA?????????GA?ATT??TCCAATTTT??GCTTTGTGCTATTA?CTGTAAA??AAAA?C?TT?GTA?GTCCATGG??C?ATC?AATT?CTATG????????CA??GCAAATCT????????????TAT?C????A?????????GC??????AG?CAT?CGG??????TTAAAC??CTAGTT??TTAAATTTAA?AAGG??TATAGT????AAGC??????AATAATAA?TCAACA??TTCTTAGG?TCAG???????GGT??ATACGTTCACAG????CGTC??ACCT?C???????TGAG???????????CTTA??G?CC?TTCTA??GATAAATATTA????GTAGC?A???AAT??TGTCTAC???????A?CCCAG?TTACATTCCCCCC?TTTTACA???C??TAA???????????A?CATATTGCAT??????CACT?A?C?ATAAGATGAAGTAG?GCTCT???A??ATGATTACG?GA?ACT??TCTATTGATCG???AGAACA??????????????AGTTGTAC??????????TT?A??A??T????????????CTGACCATGCCT?ACC????????????CA????CTGCT????AGACTCAAAATATGAAAACAAAAT????CA?AGT???A????A?CC??ATCCAACCACATT??T?????AATA??CTGCAGTATTCC??????CTAG?ACT??????AAAAAATGAC??CAAAT???TAAAACCTGCAAACGTTGTG??TTAA????TTA?TGACATAGCT?GGTAGTAACTTTCT??GCT?ATAA?A?AACTGG??C?CTAAGA?CCTAACCAA?AAAATT?TGACG???TAA?A?C?TTATAA????????AGTGT??A?GT?TTTA?AAAAA?C??G?CA?????ATA?GTACTA??????A?CAGT???AAA?G??????A????GTGAATTACTCG?AGGATAGAC?TTCACGC??TCATGATG??????????ACCATA?GC?TGT?G??TAAAC?????????CA?????TT??TATTTGT?????ACTACACT????????TTTGA?A??T?????TCTTC???????CT?CTT?G?TAT????TC?GA?CAATA??????????????????????????GA?TTTA????AAAGA??????????AGTA?ACAGTCTATCGCT?TGAGACAG?ATCA??AC??AGTACGTACCA???TCTG?TA?GA?CCC???????AA?GCTGTAAAGACTTA?GT?????????AC????CG????TAT?????ATCTGG?ATTTT?????????A???CC?G?T??GT?ATGTAT?CCTGTACAAT?A?A???ACC?GG?CGT?A??C????TCTA?GACTT??????????TCT??CT????????A?TGTCGG??ATAACT?A???AATAGTG?AC???????CATTT????????TTAATATCTCTA??GCT?AC??CT???????C??????????AT?G?GGTTAGGAAGTTTG?GGAGCTAATA?GT?A?????????C???TAAGATAT?????CTGCT?AAAC?AA?TTACAGT?GTA???????AGCAATCC????AGTAACCCAC?GT??CTTGTTACG??????CA?AGTCC?TA?CTATAATTGA???AG????G?GA??A??????????A?GTA???CCATAT?GT?CTTGT??GCTTTATACA????CT?T?GTA???TCTTT??CA?TACTTA????TAATGTTGCTA????????AT?CG??T??CAT?GATA???????C?AATAA??????ATATA???????TAAGG???????GGTTAATT????CCCTCA?????????????CA???????T???TTA?ATA?T?A??A?CA???AAT????C??AGG??T?G?????????ATA?CCAGTAAAT??TGAAC????????TTAGA?CT?CTT?GAAA?A?TCGAAATTGATACGAGTATCA?G???GGTC?TTGACA montezumaeJAC8836 ACC?CCAGT?A??AGCG????T????????GTCCTGTCG??TT???T?CT?CACCAACTA????GTG??????????GGTG?GCAAACTT??GGTCTTTAATCT??TGGTACC?TA??TTAG?AT?A???????T?AA???????G?TA?????????CCGATT?ATAAGACCTGTAATGCC????T????????TATA??T?TA?GT?????AT??AAG???ATAACCTT??T??A?AT????TCGACGTTG?TT??????????????AGT???A???C??????????A?CGATTTA?ATGGAAC?CTGAGCT????C????CTCCCTATATACTCGC???T????????CGTTAAAT?????TG?T??T?AATG??TCGATACTA???????TTCAAATTTTC??G????????GCCATTAC?????????????A??ATTTGACATAA??CACC???ACCAC?GG?AA??CAG??????????CATAC?A??CGAGAATGTG?ACACT?CTG????T?TATG?AGCTATAAA?CC??AGCCTTAAGAA?GA??????ATTT??ATAT???CC???TA?????AGTAC????????????TA?AAAT?TCGTTT???T????CATGC???AG?CATCCTA??GGATCTTAAACGATCTTAA??????TTCTC?TAG???TAG????ACAT???????CGAAT??CAAATTTAGTTCA?TT?????????TGCG????TT?CA???AAA?AC???T??????G?????AGCTAG????AA?TATGCTTAATT?TACA????CC??TACTC??A????????????A??GACAGCATAT?????GAAATC?CAC?ACAAGTA?TA????????????A??????CTG?TGATGTATTTACTG?ATTTAT???TTTT??????ACCT?TCGT??????A??AT??TAA?T?????GCCGT????????T????AA?CA????TATC??ATAAT??TCTGGAGAACT?TCGAAGCA????????????TT?C??A??CTT?????A?CATA?ACT?????ATTACCCC??AT?????TAAT??C??CAG???AGG??ATATATCCAACTG??TTCAA?CAACAA?TCGG?TTCGA???AA?C?TAATAAGAAA?AT??ACATTAGA?CCG?????????CAAAGG??????A??GA?????????GA?ATT??TCCAATTTT??GCTTTGTACTATTA?CCGTAAA??GAAG?C?TT?TTA?GTCCATGG??T?ATC?AATT?CTATG????????CA??GCAAATCT????????????TAT?C????A?????????GC??????AA?CAT?CGG??????TTAGAC??CTAGTT??TTAAATTTAA?AAGG??TATAGT????AAAC??????AATAATAA?TTAACA??TTCTTAGG?TCAG???????AGT??ATACGTTCACAG????CGTC??ACCT?C???????TGAG???????????CTTG??G?AC?TTCTA??GATAAATTTTA????GTAGC?A???AAC??TATCTCC???????A?CTCAG?TTACATTCCCCCC?TTTTACA???C??TAA???????????A?CATATTGCAT??????CACT?A?C?ATAAGATGAAGAAG?GCTCT???A??ACGATTATG?GA?ACC??TCTATTGATCG???AGAACC??????????????AGTTGTAC??????????TT?A??A??T????????????CTAACCATGCGT?ACC????????????CA????CTGCT????AGACCCAAGATATGAAAATAAAAC????CA?GGT???A????A?TC??ATCCAACCACATT??T?????AACA??TTGCAGTACTACGCCAG?CTAG?ACT??????AAAAAATGAC??CAAAT???TAAAACTTGCAAACGTTGTG??TTAT????TTA?TGACATAGCT?GGTAGTAATTTTCT??GCT?ATAA?A?AACTGG??C?CTAAGG?CCTAACCAA?AAAATT?TGAAG???TAA?A?C?TTATAA????????AGAGT??A?GT?TTTA?AAAAG?C??G?CA?????ATA?GTACTA??????A?CAGA???AAA?G??????A????GTGAATTACCCG?AGGATAGAC?TTCACGC??TCATGATA??????????ACCATA?GT?TGT?G??TAACC?????????CA?????TT??TAATTGT?????ACTACACC????????TTTGA?A??T?????TCTCC???????TT?TTT?G?TAT????TC?GA?CAATA??????????????????????????GA?CTTA????AAAGA??????????CGTA?ACAGTCTATCGCT?TGAGACGG?ATCA??AC??AGTACGTACCA???TCTG?TA?GA?CCC???????AA?GCTGTGAAGACTTA?GT?????????AC????CG????TAT?????ATCCGG?ATTTT?????????A???CC?G?T??GT?ATGTAT?CCTGTACAAT?A?A???ACC?GG?CGT?A??C????TCTA?GACAT??????????TCG??CT????????A?TGTCGG??ATAACT?A???AATAGTG?AC???????CATTT????????CTAATATTTCTA??GCC?AC??CT???????C??????????AT?G?GGTTAGGAAGTTTG?GGAGTTAATA?GT?A?????????A???TATGATAT?????CTGCT?AAAC?AA?TTACAGT?GTA???????AGCAATCC????TGTAACACAC?GTAACTTGTTAAG??????TA?AGTCC?TA?CTACAATTGA???AA????G?GA??A??????????A?GTA???CCATAT?GT?CTCGT??GCTCTACACA????CT?T?GTA???TCTTT??C??TACTTA????TAATGTTGCTA????????AC?CG??T??CAT?GATA???????C?AATAA??????ATATA???????TAAGG???????GGTTAATT????CTCTCA?????????????CA???????T???TTA?ATA?T?A??A?CA???AAC????C??AGG??T?G?????????ATA?CCAGTAAAT??TGAAC????????TTAGA?CT?CTT?GAAA?A?TCGAAATTGATACAAGTATGA?G???GGTC?TTAACA sp_2_mex_JSF1106 ACC?CTCGT?A??AGCG????T????????GTCCTGTTG??TT???T?TT?CATAAACTA????GTG??????????GGTG?GCAAACTT??GGTCTTTAACCT??CGGTACC?TA??TTAG?AT?A???????T?AA???????G?TA?????????CCAATT?ATAAGACCTGTAATGCC????T????????TATA??C?TA?GT???T?AT??CAG???ATAACCTT??T??A?AT????CCGACGCTG?TT??????????????AGTTGCA???C??????????A?CGATTTA?ATGGAAC?CTGAGTT????C????CTCCCTATTTACTCGC???C????????ACTTAAATGCATCTG?T??G?AATG??TCGATACTA???????ATCAAGTTTTC??C????????GCCATTACCT???????????A??ACTTGACATAA??CACT???ATCAC?GG?AA??TAA??????????CATAC?A??CAAGAATGTG?ACACT?CTG????C?TGTG?AGCTATAAA?CC??AGCCTTAAGAA?G??????????????TAC???CC???TA?????GGTAC????????????TA?AAAT?TCGTTT???T????CATGC???G??????CCA??GGATCTTAAACGATCTTAA??????TTCTC?TAG???TCG????AC?????????CGAAT?ACAAATATAGTTCA?TT?????????TGCG????TT?CA???AAA?AC???T??????G?????AGCTGA????AA?TATGCTTAATT?TACA????CC??TATTC??A????????????A??GTTGGCATAC?????GAAATC?TAT?ACAAGTA?CA????????????A??????C?G?T?ATGTATTTGTAG?ACTCAT???TTTC??????ACCT?TCGT??????A??AC??TAA?T?????GCCGA????????T????AA?CA????TACT??ATAAT??TCAGGAGAACT?CCGAAGCA????????????TT?C??A??CCT?????A?TATT?AAT?????ACTACCCT??AT?????TAAT??C??CAG???AGG??ATATTTCCAACTG??TTCAA?CACCAA?TTGG?TTCGA???AA?C?TAATAAGAAA?AT??GCATTAGA?CTG?????????CAAAGG??????A??GA?????????GA?ATT??TCCAATTAT??GCTTTGTACTATTA?CTGTAAA??AAGG?C?TT?GTA?GACCATGG??T?ATC?TATT?CTTTA????????CA??GCAAATCT????????????CAT?C????A?????????GC??????AT?CAT?CGG??????TTAGAC??CTAGTT??TTAAATTTAA?AAGG??TATAGT????AAGC??????AATAATAA?TAAACA??TTCTTAGG?CCAA???????AGT??ATACGTCCACAG????AGAC??ACCT?C???????TGAG???????????C?AA??G?TC?T???????ATAAATATTA????GTAGC?A???AAT??TATCTAC???????A?CTCAG?ATATATTCCCTAC?TTTTACA???C??TAA???????????A?CATATTGCAT??????CAAT?A?C?ATAAGCTGAAGTAG?GCTCT???A??ATGACTATG?GA?ACT??TCTATTGATCG???AGGACT??????????????AGCTGTAC??????????TT?A??A??T????????????CTGACCTTACAT?ACC????????????CA????TTGCT????AGACCCAAAATAAGAATATAAAAT????CA?AGT???A????A?CC??ATCTAACCACATT??T?????AATA??CTGCAGTACTTC??????CTAG?ACT??????AAAATATGAC??CAAAT???CAAAACTTGCAAATGTTATA??TTAA????TTA?TGACATAGCT?GGTAGCAATTTTCT??GCT?ATAA?A?AACTGG??C?CTAAGG?CCCAACCAA?TAAATT?TGAAG???TGA?A???TTATAC????????AGGGC??A?GT?TACA?AAAAG?A??G?CA?????ATG?GTACTA??????A?AAGT???AAA?G??????A????GTAAACTACTCG?AGGATAGAC?TTCACGC??TCATGATA??????????ACCATA?GC?TGT?G??TAATC?????????CA?????TT??TATCTGT?????AATACACT????????TTTAA?A??T?????TCTCC???????TT?CTA?G?TAT????TC?GA?AAATA??????????????????????????GA?TTTA????AAAGA??????????AGAA?ACAGCCCATCGCT?TGAGATGG?ATCA??AC??AGTACGTACCA???TTTG?TA?GA?CCC???????AA?ACT??????ACTTA?GT?????????AC????CG????TAT?????ATCCGG?ACTTT?????????A???CT?G?C??GT?ATGTAT?CCTGTACAAT?A?A???ACC?GG?CGT?A??C????TTTC??ACCT??????????TCT??CT????????A?TGTCGG??ATAATT?A???AATAGTG?AC???????CATTT????????CTAATATTTCTA??GCT?AC??CT???????C??????????AT?G?GGTAAGGAAGTCTG?GGAGTTAATA?GT?A?????????A???TATGATAT?????CCGCT?AAAC?AA?TTACAAA?GTA???????AGCAATCC????TGTAACCCAC?GT??CTTGTTATGCC??TTTA?CG?????A?CTACAATTGA???AA????G?GA??T??????????A?GTA???TGATAT?GT?CTCGT??GCTCTATTCA????TT?T?GTA???TCTCT??CA?AACTTA????CAATGTCGTTA????????AC?CG??T??CAT?GATA???????C?AATAA??????ATATA???????TAAGG???????GGTTAATT????CCCTCA?????????????CA???????C???TTA?ATA?T?A??A?CA???AAC????A??AGA??C?G?????????ATA?CCAGTAAAT??TGAAC????????TAAGA?CT?CTT?GGAA?T?TCGGAATTGATACGGGTATCA?G???GGTC?TTAACA chiricahuensisJSF1063 ACT?CTTGT?A??AGCG????T????????GTCCTGTTG??TT???T?CT?CAAAAACTA????GTG??????????GGTG?CCAAACCT??GGTCTTTAATCT??TGGTACC?TA??TTAG?AT?A???????T?AA???????G?TA?????????CCAATT?ATAAGACCTGTAATGCC????A????????TATA??C?TA?GT???T?AT??CAG???ATTACCTT??T??A?AT????CAGACGTTG?TT??????????????AGTTGTT???C??????????A?CGATTTA?ATGGAAC?CTGAGTT????C????CTCCCTATTTACTCGC???T????????CTTTAAAT?????TG?T??T?A?????TCGATACTA???????GTCAGATTTTC??C????????GCCATTAC?????????????A??ACTTGACATAA??CACT???ACCAC?GG?AA??TAA??????????CATACTA??CGTGAATGTG?ACACT?CTG????T?TGTG?AGCCATAAA?CC??AGCCTTAGGAA?AA??????ATAT??GTCT???CC???TA?????GGTAC????????????TA?AGAA?TCGTTT???T????CATGC???GG?CATCCTA??GGATCTTAAACGATCTTAA??????TTCAC?TAG???ACG????ACAT???????CAAAT?ACAAATATAGTCCA?CT?????????CGCG????TC?CA???AAA?AC???T??????G?????AGCTGG????AA?TATGCTTAATT?TACA????CC??TATTC??A????????????A??ATAGGCATAT?????GAAATC?TAT?ACAAGTA?CA????????????A??????CTG?TGATGTATTTGTAG?ATTCAT???TTTT??????ACCT?TTGT??????A??AT??TAA?T?????GCCTT????????T????AG?CA????TACT??ATAAT??TCTGGAGAACT?CCGAAGCA????????????TT?C??A??CTT?????A?TATT?AAT?????ACTACCCT??AT?????TAAT??C??CAG???GGG??ATATTTCCCACTG??TTCAA?CATC???TCGG?TTTGA???AA?C?TAACAAGAAA?AT??GTATTAGA?CTG?????????CAAAGG??????A??GA?????????TA?ATT??TCCAATTAT??GCTTTGTACTATTA?CCGTAAA??AGGG?C?TT?GTA?GACCATGG??G?ATC?AATT?CTTTA????????????????ATTT????????????AAT?C??????????????GC??????TA?CAT?AGG??????TTAGAC??CTAGTT??TAAAATTTAA?AAGA??TATAGT????AAGC??????AATATTAA?TTAACA??TTCTTAGG?TCAA???????AGT??ATACGTCCACAG????CGAC??ACCT?C???????TGCG???????????CTCG??G?AC?TCCTA??GATAAATATTC????GTA???A???AAT??TATCTGC???????A?CTCAG?ACATATTCTCTCC?TTTTACA???C??TAA???????????A?CATATTGCAT??????CAAT?A?C?ATAAGCTGAAGTAG?GCTCT???A??ATGAC?ATG?GA?ACT??TCTATTGATAG???AGGACT??????????????AGTTGTAA??????????TT?A??A??T????????????CTTACCGTACAT?ACT????????????CA????TCGCT????AGACTAAAAATAAGAAAATAAAAC????CA?AGT???A????A?CC??ATCTACCCACATT??T?????AATA??CTGCAGTAATTC??????CCAG?CCT??????AAAATATGAC??CAAAT???GAAAACTCGCAAATGTTATG??TTGT????TTA?TGACATAGCT?GGTAGTAATTCTCT??GCT?ATAA?A?AACTGG??C?CTAGGG?ACTAACCAA?GAAATT?TGAAG???TGA?A???TTAAAT????????AGAGT??G?GT?TGCA?TAAAG?A??G?CA?????CTG?GTACTA??????A?TATC???AAA?G??????A????GTGAACTACTCACAGGATAGAC?TTCACGC??TCATGATCTAATACGTCTACCATA?GC?TGT?G??TAACC?????????CA?????CT??TATTTGT?????AATCCACT????????TTTAA?A??T?????CCTCC???????TT?TTT?A?TAT????TC?GA?CAATA??????????????????????????GA?CTTA????AAAGA??????????A?????????CCATCACT?TGAGACGG?ATCA??AC????TACGTACCA???TCTG?TC?GA?CCC???????AA?ACT??????GCTCA?GT?????????AC????CG????TAT?????ATTCGG?ATTTT?????????A???CT?G?T??GT?ATGTAC?CCTGTACAAT?C?A???ACC?GA?CGT?A??C????TTTA??ATCT??????????TCC??CT????????A?TATCGG??ATAATT?A???AATAGTG?AC???????CATTT????????CTAATATCTCTA??GCT?AC??CT???????C??????????AT?G?GGTAAGGAAGTTTG?GGAGCTAATAAGT?A?????????G???CATGACAT?????CTGTT?AAGC?AA?TTACAGG?GCA???????AGCAATCT????TGTAAATCAC?GT??TTTGTTATGAC??TTTA?CGTCC?TA?CTACAATTGA??CAA????A?GA??T??????????A?GTA???CAATAT?GT?CTCAT??GCCCTATACA????GT?T?GTA???T?????????????TA????TAATGTTGCTA????????AC?CG??T??CA??GATT???????C?AATAA??????ATATA???????TCAGG???????GACTAATT????CCCTTA?????????????CA???????T???TTA?ATAAT?A??A?CA???AAT????G??AGA??C?C?????????ATA?CCAGTAAAT??TGAAC????????TAAGA?CT?CTT?GAAA?T?TCGAAATTGATATGGGTATCA?A???GGTC?ATCACA subaquavocalis ACC?CCAGT?A?AAGCG????T????????GCCCTGTTG??TT???T?CT?AATAAACTA????GTG??????????GGTG?GCAAACCT??GGTCTTTAACCT??CGGTACC?CA??TTAG?AT?A???????T?AA???????G?TA?????????CCGATT?ATAAGACCTGTAATGCC????A????????TATA??C?TA?GT???T?AT??CAG???ATAACCTT??T??A?AT????TAGACGTTG?TT??????????????AGTTGCG???C??????????A?CGATTTA?ATGGAAC?CTGAGTT????C????CTCCCTATTTACTCGC???T????????CCTTAAAT?????TG?T??G?AATG??TTGATACTA???????ATCAAATTTTC??C????????GCCATTAC?????????????A??ACTTGACATAA??CACC???ACCAC?GG?AA??TAA??????????CATAC?G??CATGAATGTG?ACACT?CTG????A?TATG?TGCTATAAA?CC??AGCCTTAAGAC?AA??????ATTT??GTCT???CC???TA?????GGTAC????????????TA?AAAT?TCGTTT???T????CATGC???GG?CATCCTA??GGATCTTAAACGATCTTAA??????TTCTC?TAG???TCG????ACAC???????CAAAT?ACAAATATAGTCCA?CT?????????TGCG????TT?CA???AAA?AC???T??????G?????AGCTAG????AA?TATGCTTAATT?TGCA????CC??TGTTC??A????????????A??GTAGGCATAT?????GAAATC?TAT?ACAAGTA?TA????????????T??????CTG?TGATATATATGTAG?ACTCAT???TTTT??????ACCT?TCGT??????A??AT??TAA?T?????GCCTT????????T????AG?CA????TACT??ATAAT??TCTGGAAAACT?ACGAAGCA????????????TT?C??A??CCT?????A?TAGC?AAT?????ACTACCCT??AT?????TAAT??C??CAG???AGG??ATATTTCCCACTG??TTCAA?CATCAA?TCGG?TTCGA???AA?C?TAAAAAGAAA?AT??GCATTAGA?CTG?????????CAAAGG??????AC?GA?????????GA?ATT??TCCAATTGT??GCTTTGTACTATTA?CTGTAAA??AAGG?C?TT?ATA?AACCATGG??T?ATC?AATT?CTATA????????????????ATCT????????????TAT?C????A?????????GC??????AA?CAT?AGG??????TTAGAC??CTAGTT??TTAAATTTAA?AAGG??TATAGT????AAGC??????AATAAGAA?TAAACA??TTCTTAGG?TCAA???????AGT??ATACGTCCACAG????AGAC??ACCT?C???????TGCG???????????CTCG??G?AC?TCCTA??GATAAATATTA????GTAGT?A???AAT??TATCTTC???????A?CACAG?ATACATTCCCTCC?TTTTACA???C??TAA???????????A?CATATTGCAT??????CAAT?A?C?ATAAGTTGAAGTAG?GCTCT???A??ATGACTATG?GA?ACT??TCTATTGATCG???AGGACT??????????????AGTTGTAC??????????TT?A??A??T????????????CTTACCATACAT?ACC????????????CA????TCGCT????AGACTCAAAATAAGAAAATAAAAC????CA?AGT???A????A?CC??ATCCACCCGCATT??T?????AATA??CTGCAGTATTCC??????CTAG?ACT??????AAAATATGAC??CAAAT???GAAAACTCGCAAATGTTATG??TTGA????TTA?TGACATAGCT?GGTAGTAATTTTCT??GCT?ATAA?A?AATTGG??C?CTAGGA?CCCAACCAA?AAAATT?TGAAG???TCA?A???TTAAAT????????GGAGC??G?GT?TACA?AAAAG?A??G?CA?????TTG?GCACTA??????A?CAAC???AAA?G??????A????GTAAACTACCCGTAGGATAAAC?TTCACGC??TCATGATT??????????ACCATA?GC?TGT?G??TAACC?????????CA?????TT??TATTTGT?????AGTACACT????????CTTAA?A??T?????CCTCC???????TT?CTT?G?TAT????TC?GA?CAATA??????????????????????????GA?CTTA????AAAGA??????????AGAA?ACAGCCCATCGTT?TGAGACGG?ATCA??AC??AGTACGTACCA???TCTG?TA?GA?CCC???????AA?GCT??????ACTTA?GT?????????TC????CG????CAT?????ATCCGG?ATTTT?????????A???CT?G?T??GT?ATGTAC?CCTGTACAAT?A?A???ACC?GG?CGT??????????CTA??ACTT??????????TCC??CT????????A?TATCGG??ATAATT?A???AATAGTG?AC???????CATTT????????TTAATATCTCTA??GCT?AC??CT???????C??????????AT?G?GGTAAGGAAGTTTG?GGAGCTAATAAGT?A?????????A???CATGATAT?????CTGCT?AAAC?AA?TCACAGA?GTA???????AGCAATCC????TGTGATCCAC?GT??CTTGTTATGAC??TTTA?TGTTC?TA?CTACAATTGA???AA????G?GA??T??????????A?GTA?GTCAATAT?GT?CTCGT??GCTCTATGCA????TT?T?GTA???T?????????????TA????TAATGTTGTTA????????AC?CG??T??CAT?GATT???????C?AATAA??????ATATA???????TCAGG???????GGTTAATT????CCCTTA?????????????CC???????T???TTA?ATA?T?A??A?CA???AAT????G??AGA??C?C?????????ATA?CCAGTAAAT??TGAAC????????TAAGA?CT?CTT?GAAA?T?TCGAAATTGATACGGGTATCA?A???GGTC?ATAATA chiricahuensisJSF1092 ACC?CCCGT?A??AGCG????T????????GTCCTGTTG??TT???T?T??AATAAACTA????GTG??????????GGTG?GCAAACTT??GGTCTTTAACCT??CGGTACC?AA??TTAG?AT?A???????T?AA???????G?TA?????????CCCATT?ATAAGACCTGTAATGCC????T????????TATA??C?TA?GT???T?AT??CAG???ATAACCTT??T??A?AT????TAGACGTTG?TT??????????????AGTTGCG???C??????????A?CGATTTA?ATGGAAC?CTGAGTT????C????CTCCCTATTTTCTCGC???T????????CCTTAAAT?????TG?T??G?AATG??TTGATACTA???????ACCAAATTTTC??C????????GCCATTAC?????????????A??ACTTGACATAA??CACC???ACCAC?GG?AA??TAA??????????CATAC?G??CGTGAATGTG?ACACT?CTG????T?TATG?AGCTATAAA?CC??AGCCTTACGAA?AA??????TTTC??G????????????A?????GGTAC????????????TA?AAAT?TCGTTT???T????CATGC???GG?TACCCTA??GGATCTTAAACGATCTTAA??????TTCTC?TAG???TCG????ACAC???????CAAAT?ACAAATATAGTCCA?CT?????????TG?????????CA???AAA?AC???T??????G?????AGCTAG????AA?TATGCTTAATT?TACA????CC??CAGTC??G????????????A??GTAGGCATAT?????AAAATC?TAT?ACAAGTA?TA????????????A??????CTG?TGATATATTTGTAG?ACTCAT???TTTT??????ACCT?TCGT??????A??AC??TAA?T?????GCCTC????????T????AG?CA????AATT??ATAAT??TCTAGAGAATT?CCGAAGCA????????????CT?C??A??CTT?????A?TATC?AAT?????ATTACCCT??AT?????CAAT??C??CAG???AGG??ATATTTCCTACTG??TTCAA?CATCCA?TCGG?TTCGA???AA?C?TAATAAGAAA?AT??ACATTAGA?CTG?????????CAAAGG??????A??GA?????????GA?ATT??TCCAATTGT??GCTTTGTACTATTA?CTGTAAA??AAGG?C?TT?GTA?GACCATGG??T?ATC?AATT?CTATA????????????????ATCT????????????TAT?C????A?????????GC??????AA?CAT?AGG??????TTAGAC??CTAGTT??TTAAATTTAA?AAGG??TACAGT????AAGC??????AATAATAA?TAAACA??TTCTTAGG?CCAA???????AGT??ATACGTTCACAG????AGAC??ACCT?C???????TGCG???????????CTAG??G?AC?TCCTA??GATAAATATTA????GTAGC?A???AAT??TATCTGC???????A?CCCAG?ATACATTCCCTCC?TTTTACA???C??CAA???????????A?CATATTGCAT??????CAAT?A?C?ATAAGTTGAAATAG?GCTCT???A??ATGACTATG?GA?ACT??TCTATTGATCG???AGGACT??????????????AGTTGTAC??????????TT?A??A??T????????????CTTACCATGCAT?ACC????????????CA????TCGCT????AGA???AAAATAAGAATATAAAAC????CA?AGT???A????A?CC??ATCCACCCACATT??T?????AAAA??CTGCAGTACTCC??????CTAG?ACT??????ACAATATGAC??CAAAT???GAAAACTCGTAAATGTTATG??TTGA????TTA?TGACATAGCT?GGTAGTAATTTTCT??GCT?ATAA?A?AACTGG??C?CTAGGG?CCCAACCAA?GAAATT?TGAAG???TTA?A???TTATAT????????AGAGA??G?GT?TACA?AAAAG?A??G?CA?????ATG?GTACTA??????A?CAAC???AAA?G??????A????GTGAACTACTCGTAGGATAGAC?TTCACGC??TCATGATA??????????ACCATA?GC?TGT?G??TAACC?????????CA?????CT??TATTTGT?????AATACACT????????TTTAA?A??T?????CCTCC???????TT?CTC?G?TAT????TC?GA?CAATA??????????????????????????GA?CTTA????AAAGA??????????AGAA?ACAGCCCATCGTT?TGAGACGG?ATCA??AC??AGTACGTACCA???TCTG?TG?GA?CCC???????AA?GCT??????ACTTA?GT?????????GC????CG????TAT?????ATCCGG?ATTTT?????????A???CC?G?T??GT?ATGTAC?CCTGTACAAT?T?A???ACC?GA?CGT?A??C????TCTA??ACTT??????????TCC??CT????????A?TATCGG??ATAATT?A???AATAGTG?AC???????CATTT????????CTAAGATCTCTA??GCT?AC??CT???????C??????????AT?G?GGTCAGGAAATTTG?GGAGCTAATAAGT?A?????????A???CATGATAT?????CTGCT?AAAC?AA?TTACAGG?GTA???????AGCAATCC????CGTAACCCAC?GT??CTTGTTATGAC??TTTA?TGTCC?TA?CTACAATTGA???AA????G?GA??T??????????A?GTA???CAATAT?GT?CTCGT??GCTTTATGCA????TT?T?GTA???TCTC???CA?AACTTA????TAATGTTGTTA????????AC?CG??T??CAT?GATT???????C?AATAA??????ATATA???????TTAGG???????GGTTAATT????CCCTTA?????????????CA???????T???TTA?ATA?T?A??A?CA???AAT????G??AGA??T?C?????????ATA?CCAGTAAAT??TGAAC????????TAAGA?CT?CTT?GAAA?T?TCGAAATTGATATGGGTATCA?A???GGTC?TTAACA palustrisJSF1110 ACT?CTCGC?A??AGCG?GC?T????????GTCCTGTAG??TT???T?TT?AATTAACTA????GTG??????????GGTG?GCAAACTT??GGCCTTTAAACT??AGGTATT?CA??TTAA?AT?A???????C?AC???????A?TA?????????CCGATT?ACAAAATCTGTAATGGC????T????????AATA??C?TA?AT???A?AT??CAG???ATGACCTT??T??A?CC????GGGACGATG?TCACC????????TTTAGTTGCA?TCC??????????A?CGGTTTA?ATGGAAC?CTGAGCT????C????CTCC???TTCACTCGC???TA???ATTACCTTAACT?????TG?T??A?AATGTGTAGATACTA???????ATGAAGCTTTC??C????????GCCATTAC??A??????????A??ATTTGTCACAA??CACT???GTCAC?GG?AC??TAT??????????AATAC?A??CCTAAATGTA?ACACT?CTG????T?TTT??A????TAAA?CT??GGC?TTACGAA?A?TCACTCATTTT?GGAC???CC???TA?????AGTAC?????CA???T?TA?AGAT?TCGTTT???T????CGTGC???GC?GACCCGT??GGAACTTAGACGATCTTAA??????TTCCC?TAG???TCG????ACAA???????CTAAT?ACAAATATAGTCCA?T??????????TGAG????TT?CA???AAA?AC???T??????G?????AGCTTG????AA?TACGCATAGCT?TACA????CC??TAGTC??A????????????A??GTCGGCGTAC?????AAAATC?TAA?GCAATTA?AA????????????A??????CTG?CGATGTATCGTTTG?ACTCAT???TTTA??????ACCT?TCGTATCC??A??AT??TAA?T?????GCCGA????????T????AA?TA?A?ATACT??ATAAT??TCTGGAGAATT???GAAGCAGC???GG?????TTGC??A??CTA????CA?CATC?AAT?????ATTACCCC??AT?????CAAT??C??CAA???AGG??ATATCTCCAACTG??TTCAA?CATCAA?TCCG?TTTGA???AA?T?TAACAAGAAA?AT??CTATTAGA?CCG?????????CAAAGG?????????GA?????????AATATT??TCCAATTCT??GCTTTATACTATTA?CTGTAAATGGAAA?C?TT?GTA?GACCATGG??C?ATC?AATTTCTATA????????CA??GCAAATCTGTT??ATCTTTACAT?C????A?????????GC??????AA?AAT?AGG??????CTAGAC??TTAGTT??TTAAA???GA?AAGG??TACA?T????AAGC??????AATA???A?TCAGTA?????T?AGG?CCAA???????TGT??ATACGTACACAA????TGGC??ACCT?C???????TGAG???????????ATTC??G?TC?TTTTA??GATAAATATTA????GTA?C?A???AAT??TAACTAC???????A?CCCAG?TTACATTCACCCC?TTTTATA???C??TAG???????????A?CATATTGCTT??????CAAT?A?C?ATAAGTTGAAGCAG?GCTCT???A??TTGATTATG?GA?ACC??TCTATTGATTG???AG?ACA?TAC???????ATA?ACTGTAC??????????TT?A??C??T????????????CCGACC???????ACC????????????CATCATTTGCT????AGACTCAATACAAGAAAAACAAAT????CA?AAT???A????A?CC??ATCCATCCGCATT??T?????AACA??C???AGTATTTC?????ACTAG?ACT??????AAAATATGAC??CAAAA???GAAAACTTGTAAAAGTTATG??TTAA????TTA?TGACATAGCT?GGTAGTAACTCACT??GCT?ATAA?A?AACTGG??C?CTAAAA?CCCAAATAA?TAAATT?TGGCG???TTA?A?C?CTATAA????????AGAGG??A?GT?AATC?AGAAA?T??G?CT?????CTG?GTACTA??????A?CAAT???AGA?A??????A????GTTAATTACTAT?AGGGTAGAC?TTCACGC??TCATGGTT??????????ACAATA?GT?TGT?A??TAACC?????????CG?????TT??TAGCTGT?????ATTCCACT????????TTTGA?A??T?????CCTAC??????TAT?TTT?C?TA?????CC?GA?CAATA??CG??AG??????????CT?T?TAGGA?TTTA????AAACA??????????AGCA?ATAGCACCTCATT?CGAGACCG?ATCA??AC??AGTACGTACCA???TCTG?TA?GA?CCC??????AAA?GTT??????ACTTA?GC?????????AC?????G????AAT?????ATTCGGAATTTT?????????A???CT?G?T??GT?A???AC?CCTGTACCAT?A?AT??A?????????????T????TACA?GACTT??????????TCA??CT????????A?TCTTGG??ACGATT?A???AAAAGTG?ACGC????CCATTT????????TTAACATCTCTA??GCT?AC??AT???????C????CACCCGAT?G?GGTAAGGAAGTTTGTAGAGTTAATG?GT?A?????????C???TATGGCAT?????CTCTC?GAAC?AG?TCTCAGT?ATA???????AGCAATCA????TACAAGCCAT?AT??CTTGATA?GCC??TTTA?AGTC??TA?CTACAGTTGA???AG????G?CA??A??????????A?GTA???CCACAT?GT?CTTGC??GCTATATCCA????TT?T?GTA???TCTTT??CA?AACTTA????TAATGTTGCTA????????AC?CG??T??CA??GATA???????C?A?TAA??????ATATA??????????????????????TAATT????CTCTCA?????????????CA???????T???TTA?ATA?T?A??A?CA???AAA????G??AGG??T?C?????????ATA?CCGGTAAAT??TGAAC????????CCAGA?CC?CTT?GCAA?T?CCGCAATTGATACGAGTATTA?T???GGTC?CTAACA areolataJSF1111 ACT?CTCGC?A??AGCG?GC?T????????GTCCTGTAG??TT???T?TT?AATTACCTA????GTG??????????G?TG?GCAAACTCGCGGCCTTTCAACT??AGGCATG?CA??TTAA?AT?A???????C?AC???????G?TA?????????CCAATT?ATAAAGCCTGTAATGTC????T????????AATA??C?TA?TT???T?AT??CAG???ATAACTTT??C??A?AC????GGGACGCTG?TCATC????????TTTAGTTGCA?TCC??????????A?CGATTTA?ATGGAAC?CTGAGCT????C????CTCC???TTCACTCGC???TA???ATTACCCTAAAT?????TG?T??A?AATGTATAGATACTA???????ATGAAG?????????????????CCATTAC??A??????????A??ATTTGCCACAA??CACT???ATCAC?GG?AC??CAC??????????AAAAC?A??CATAAATGTA?ACATT?C??????C?TCT??A????TAAA?CT??GGC?TTATGAA?A?TCACCCATTTT?GGAC???CC???TA?????AGTAC?????CA???T?TA?AAAT?TCGTTT???T????CATGC???GT?GTTCCAT??GGAACTTAGACGATCTTAA??????TTCCC?TAG???CTG????ACAA???????TTATT?ACAAATATAGTCCA?TT?????????TGAG????TT?CA???AAA?AC???TCTAATAG?????AGCTCG????AA?TACGCTTAATT?TACA????CC??AACTC??A????????????A??GTTGGCATAC?????AAAATC?TAG?GCAATTA?AA????????????T??????CTG?TGATGTATTGTCTG?ATTCTC???TTTA??????ACCT?TCGT??????A??AT??TAA?T?????GCCGG????????T????AA?TA?A?ATACT??ATAATAATCTTGAGAATT???GAAGCAGG???GG?????TT?C??A??CTA????CA?CATT?AGT?????ACTACCCC??AT?????CAAT??C??CAA???AGG??ATATTCCCGACTG??T?CAA?CATCAACTCGG?TTTGA???AA?T?TAAGAAGAAA?AT??TTATTAAA?CGG?????????CAAAGG??????A??GA?????????AATATT??CCCAATTTT??GCTTTTTACTATTA?CTGTAAACGGAAA?C?TT?GCA?GACCATGG??C?ATC?AATTTCTGTA????????CA??GCAAATCTATT??ATCTTT?CATTC????A?????????GC??????GA?AAT?TGG??????TTAGAC??CTAGAT??TTAAA???GA?AAGA??TACA?T????AAGC??????AATA???A?TTAATA?????T?AGG?CCCA???????TGT??ATACGTGCACAA????CGGC??ACCT?C???????TGCG?GC????????ATTA??G?TC?TCTTA??GATAAATCTTA????GTA?T?A???AAT??TAACTAC???????A?CTCAG?ATACATTCACCCC?TTTTATA???C??TAG???????????A?CATATTGCTT??????TAAT?T?C?ATAAGCTGAAGCAA?GCTCT???A??GTGATTATG?GA?ACC??TCTATTGATCG???AG?ACA?TAC???????ATA?ACTGTAC??????????TT?A??C??T????????????CCGACC???????ACC??CAACTCGCGACATCATTTGCC????AGACTCAATATAAGAATAACAAAC????CA?AAT???A????A?TC??ACCCATCCGCATT??T?????AATA??C???AGTACTTC?????ACTAG?GCT??????AAAACATGAC??CAAAA???AAAAAC???TAA?????ATG??TTAA????TTA?TGACATAGCT?GGTAGCAACTGACT??GCT?ATAA?A?AACTGG??C?CTAAAG?TCCAAATAA?TAAATT?TGGTG???TGA?A?C?CTATAA????????AGAGA??A?GT?AATT?AGCAA?T??G?CA?????CTG?GTACTA??????A?AAGT???AGA?ACCTTGTA????GTTAATTACTAA?AGGATACAC?TTCACGC??TCATGGTT??????????ACTATG?GC?CGT?G??TAACC?????????CG?????AT??TAGCTGT?????AATACACT????????TTTGA?A??T?????TTTAC??????TAT?TTT?G?TA?????CC?GA?CAATA??CG??AG??????????CT?T?TAGGA?TTTA????AAATA??????????AGTA?ATAGCTCATCAAT?CGAGACGG?ATTA??AC??AGTACATACCA???TCTG?TA?GA?CCC??????AAA?ATT??????ACTTA?GTCCTATTTCTTC?????G????TAT?????ATCCGG?TTTTT?????????A???CA?G?T??GT?A???AT?CCTGTACCAC?A?AT??A?????????????T????TACA?GAAGT??????????TCT??CT????????A?TTTTGGGAATGATT?A???AAAAGTG?ACGC????CCATTT????????TTAACATCTCTA??GCT?AC??AT???????CGTTACACACGAT?G?GGTCAGGAGGTTTGTAGAGTTAATG?GT?A?????????C???TAAGGCAT?????CTCCC?GAAC?AG?TTTCAGG?AAA???????AGCAATCA????TGAAAACCAC?AT??TTTGATA?GCC??TTCA?AGTCC?TA?CTACAGTTGG???AG????G?TA??A??????????A?GTA???TAACAT?GT?CTTGC??GCTCTATTCA????CT?T?GTA???TTTTT??CA?AACTTA????CAATG?TGATA????????AC?CG??TATCAT?GATA???????C?AAT????????ATATA??????????????????????TAATT????CCCTCA?????????????CA???????T???TCA?ATA?T?A??A?CA???AAA????C??AGG??T?T?????????ATA?CCAGTAAAA??TGAAC????????CCAGA?CC?CTT?GTAG?C?TC?TAATTGATACGAGTATTA?A???GGTC?TTAACA sevosaUSC8236 ACT?CTTGT?A??AGCG?GC?T????????GTCCTGTAG??TT???T?TT?AACCAACTA????GTG??????????GGTG?TCAAACTT??GGTCTTTTAACT??GGGTATC?CA??TTAA?AT?A???????C?AT???????A?TA?????????CCGATT?ACAAAGTCTGTAATGCC????T????????AATA??C?TA?CT???T?AT??CAG???ATAACTTT??T??A?AC????GGGACGTTG?TAACC????????TCTAGTTGCA?TCC??????????A?CGATTTA?ATGGAAC?CTGAGTT????C????CTCC???TTCACTCGC???TA???ATTACCTTAAAT?????TG?T??A?AATGTATAGATACTA???????ATGAAG?????????????????CCATTAC??A??????????A??ATTTGTCACAA??CACT???ATCAC?GG?AC??TAC??????????AATAC?G??CATAAATGTA?ACACT?CTGTATAT?TTT??A????TAAA?CC??GGC?TTACGAA?AATCACTCATTTT?GGAC???CC???TA?????AGTAC?????CA???T?TA?AGAT?TCGTTT???T????CATGC???GA?GTCCCAT??GGAACTTAGACGATCTTAA??????TTCCC?TGG???TTG????ACAA???????CTAAT?ACAAATATAGTCCA?CT?????????TGAG????TT?CA???AAA?AC???T??????G?????AGCTTG????AA?TACGCCTAACT?TACA????CC??TA??????????????????A??GATGGCATAC?????AAAATC?TAA?GCAAC?A?AA????????????A??????CTG?TGATATATTGTCTG?ACTCTT???TTTG??????ACCT?TCGT??????A??AT??TAA?T?????GCCGA????????T????AA?TA?A?ATACT??ATAAT??TCTCGCGAATT???GAAGCAAC???GG?????TT?C??A??CTA????CA?CATT?AAT?????ATTACCCC??AT?????CAAT??C??CAA???AGG??ATATTTCCGACTG??TTCAA?CATCAACTCGG?TTTGA???GA?T?TAAAAAGAAA?AT??TAGTTAGA?CGG?????????CAAAGG??????A??GA?????????AATATT??TCCAATTTT??GCTCTATACTATTA?CCGTAAACGAAAA?C?TT?GTA?GACCATGG??C?ATC?AATTTCTATA????????CA??GCAAATTTGTC??ATCTTTATAT?C????A????????????????????????TGG??????TTAGAC??CTAGTT??TTAAA???GA?AAG???TACA?T????AAGC??????AATA???A?TCAATA?????T?AGG?CCCC???????CGT??ATACGTACACAA????TGGC??ACCT?C???????TGTG???????????ATCA??G?TC?TTTTG??GATAAATATTA????GTA?T?A???AAT??TAACTAC???????A?CCCAG?CTACATTCACACC?TTTTATA???C??TAA???????????A?CATATTGCTT??????CAAT?A?C?ATAAGTTGAAGCAG?GCTCT???A??TTGATTACG?GA?ACC??TCTATTGATTG???A??ACA?TAC???????ATA?ACTGTAC??????????TT?A??T??T????????????CCGACC???????ATC????????????CA?CATTTGCC????AGACTCAATATAAGAATAATAAAT????CA?AAT???A????A?TC??ACCCATCCGCATT??T?????AATA??C???AGTATTTC?????ACCAG?ACT??????AAAACATGAC??CAAAA???GAAAACTTGTAAAAGTTATA??TTAA????TTA?TGACATAGCT?GGTAGTAATAGACT??GCT?ATAA?A?AACTGG??C?CTA?AG?CCTAAATAA?TAAATT?TGGCG???TGA?A?C?CTATAA????????AGAGT??A?GT?AATC?AGTCT?T??G?CA?????CTG?GTACTA??????A?AAAT???AGA?A??????A????GTCAATTACTTG?AGGATAGAC?TTCACGC??TCATGGTT??????????ACAATA?GT?TGT?G??TAATC?????????CG?????TT??TAGCTGT?????AATCCACT????????TTTAA?A??G?????CCTAC??????TAT?TTC?A?TA?????CC?GA?CAATA??CA??AG??????????CT?T?TAGGA?CTTA????AAATA??????????AGCA?ATAGCTCGTCAAT?CGAGACGG?ATCA??AC??AGTACATACCA???TCTG?TC?AA?CCC??????AAA?ATT??????ACTTA?GTCCTATTTCTTC?????G????TAT?????ATCTGG?ATTTT?????????A???GT?G?T??GT?A???AC?CCTGTACCAT?A?AC??A?????????????T????TACA?GACAT??????????TCC??CT????????A?TATTGG??ACGAAT?A???AAAAGTG?ACGC????CCATTT????????TTAACATTTCTA??GCT?AC??AT???????C????CACCCGAT?G?GGTTAGGAAGTTTGTGGAGTTTATG?GT?A?????????T???TATGGCAT?????CTACC?GTGC?AG?TTCCAGT?ATA???????ACCAATCA????TGTAAACCAC?GT??CTTGATA?GCC??TTTA?GGTTC?TA?CTACAGTTGA???AG????G?TA??A??????????A?GTA???CTATATAGT?CTTGC??GCTTTATTCA????TT?T?GTA???TGTTT??CA?AACTTA????TAATGTTGTTA????????AC?CG??C??CAT?GATA???????C?AAT????????ATATA??????????????????????TAATT????CCCTCA?????????????CA???????C???TTA?ATA?T?A??A?CA???AAG????A??AGG??T?T?????????ATA?CCAGTAAAT??TGAAC????????TCGGA?CT?CTT?GCAA?T?TCGTAATTGATACGAGTATTA?T???GGTC?CTAACA capitoSLU003 ACC?CTTGC?A??AGCG?GC?T????????GTCCTGTA???TT???T?TT?AACCAACTA????GTG??????????GGTG?TCAAACTT??GGCCTTTTAACT??AGGTATC?CA??TTAA?AT?A???????C?AT???????A?TA?????????CCGATT?ACAAAGTCTGTAATGCC????T????????AATA??C?TA?CT???T?AT??CAG???ATAACTTT??T??A?AC????AGGACGTTG?TTACC????????TTTAGTTGCA?TCC??????????A?CGATTTA?ATGGAAC?CTGAGTT????C????CTCC????TCACTCGC???TA???ATTACCCTAAAT?????TG?T??A?AATGTATAGATACTA???????ATGAAG?????????????????CCATTAC??A????????????????TGTCACAA??CACT???ATCAC?GG?AG??CAC??????????AATAC?G??CATAAATGTA?ACACT?CTGTATAT?TTT??A????TAAA?CT??GGC?TTACGAA?AATCACTCATTTC?GGAC???CC???TA?????AGTAC?????CA???T?TA?CGAT?TCGTTT???T????CATGC???GC?GTCCCAT??GGACCTTAGACGATCTTAA??????TTCCC?TGG???TAG????ACAA???????CTAAT?ACAAATATAGTCCA?CT?????????TGAG????TT?CA???AAA?AC???T??????G?????AGCTTG????AA?TACGCCTAGCT?TACA????CC??TA??????????????????A??GATGGCATAT?????AAAATC?TAA?GCAAC?A?AA????????????A??????CTG?TGATATATTGTCTG?ATTCTT???TTTG??????ACCT?TCGT??????A??AT??TAA?T?????GCCGA????????T????AA?TA?A?ATACT??ATAAT??TCTCGCGAATT???GAAGCAAT???GG?????TT?C??A??CTA????CA?CACT?AAT?????ATTACCCC??AT?????CAAT??C??CAA???AGG??ATATTTCCGACTG??TTCAA?CATCAACTCGG?TTTGA???GA?T?TAAAAAGAAA?AT??TATTTAGA?CAG?????????CAAAGG??????A??GA?????????AATATT??CCCAATTTT??GCTTTATACTATTA?CCGTAAACGAAAA?C?TT?GTA?GACCATGG??T?ATC?GATTTCTATA????????CA??GCAAATCTGTC??ATCTTTATAT?C????A????????????????????????TGG??????TTAGAC??CTAGTT??TTAAA???GA?AAG???TACA?T????AAGC??????AATA???A?TCAATA?????T?AGG?CCTC???????TGT??ATACGTACACAA????CGAC??ACCT?C???????TGCG???????????ATTA??G?TC?TTTTG??GATAAATATTA????ATA?T?A???AAT??TAACTAC???????A?CCCAG?CTACATTCACACC?TTTTATA???C??TAA???????????A?CATATTGCTT??????CAAT?A?C?ATAAGTTGAAGCAG?GCTCT???A??TTGATTACG?GA?ACC??TCTATTGATTG???AG?ACA?TAC???????ATA?ACTGTAC??????????TT?A??C??T????????????CCGACC???????ATC????????????CA?CATTTGCC????AGACTCAACAGAAGAATAAAAAAT????CA?AAT???A????A?TC??ACCTATCCGCATT??T?????AATA??C???AGTATTTC?????ACTAG?ACT??????AAAATATGAC??CAAAG???GAAAACTTGTAAAAGTTATA??TTAA????TTA?TGACATAGCT?GGTAGTAACAGACT??GCT?ATAA?A?AACTGG??C?CTA?AG?CCTAAATAA?TAAATT?TGGTG???TGA?A?C?CTATAA????????AGAGT??A?GT?AATC?AGTCT?T??G?CA?????CTG?GTACTA??????A?AATT???AGA?A??????A????GTCAATTACTCG?AGGATAGAC?TTCACGC??TCATGGTT??????????ACAATA?GT?TGT?G??TAATC?????????CG?????AT??TAGCTGT?????AATTCACT????????TTTAA?A??T?????CCTAC??????TAT?TTC?A?TA?????TC?GA?CAATA??CA??AG??????????CT?T?TAGGA?CTTA????AAATA??????????AGCA?ATAGCTTGTCATT?CGAGACGG?ATCA??AC??AGTACATACCA???TATG?TC?GA?CCC??????AAA?ATT??????ACTTA?GTCCTATTTCTTT?????G????TAT?????ATCTGG?ATTTT?????????A???GT?G?T??GT?A???AC?CCTGTACCAT?A?AC??A?????????????T????TACA?GACAT??????????TCC??CT????????A?TATTGG??ACGAAT?A???AAAAGTG?ATGC????CCATTT????????TTAATATTTCTA??GCT?AC??AT???????C????CACCCGAT?G?GGTTAGGAAGTCTGTGGAGTTTATG?GT?A?????????T???TATGGCAT?????CTACC?GCGC?AG?TCCCAGC?ATA???????ACCAATCA????TGTAAACCAC?GT??CTTGATA?GCC??TTTA?GGTCC?TA?CTACAGTTGA???AG????G?TA??G??????????A?GTA???CTATATAGT?CTTGC??GCTTTATTCA????TT?T?GTA???TATTT??CA?AACTTA????TAATGTTGTTA????????AC?CG??C??CAT?GATA???????C?CAT????????A?ATA??????????????????????TAATT????CCCTCA?????????????CA???????C???TTA?ATA?T?A??A?CA???AAG????G??AGG??T?T?????????ATA?CCAGTGAAT??TGAAC????????TTGGA?CT?CTT?GTAA?C?TCGCAATTGATACGAGTATTA?T???GGTC?CTAACA spectabilisJAC8622 ATT?CCCGC?A??AGCG?GC?T????????G?CCTGTCG??TT???T?GT?AACAAACTA????ATG??????????GGTG?GCAAACTT??GGCCTTTGATCT??AGGTACC?TA??TTAA?TT?A???????C?AC???????C?TA?????????CCCATT?ACAAAGCCTGTAATGCC????C????????AATA??T?TA?AT???T?AT??CAG???ACAACCTT??T??A?AT????TAGACGTTG?ATACC????????TATAGTTGTA?TCC??????????A?CGATTTA?ATGGAAC?CTGAATT????C????CTCC???TTCACTCGC???TA???ACTGCTTTAAAT?????TG?T??T?AATGTATAGATACTA???????CTGAAACTTTCAAC????????GCCATTAC??A??????????A??ATTTGCCATAA??CACT???ACCTC?GG?AT??CAG??????????AATAC?T??CATGAATGT??ACATT?CTG????T?TCCGGAGCCATA?A?CT??CGC?TTAGGAA?GGTCACTCATTCT?GAAT???CC???TA?????AGTAC?????CA???T?TG?AGGT?T?GTTT???T????CTTGC???GT?CATCCAT??AGAACTTAGACGATCTTAA?????CTTCCC?TAG???CCG????TTAG???????CGAAT?ACAAATTTAGTTCG?AT?????????TGCG????TC?CATTGAAA?AC???T??????T?????AG?TTG????AA?TATGCCTGGTTCTAAA????CC??CAGTC??A????????????A??GTCAGTATAA?????GAAATC?CCA?GCAATTA?TA????????????A?CACCACTG?TGA????????CAG?ACTCTT???TTTA??????ACCT???GT??????A??AT??TAA?T??????CCAA????????T????AA?TA?A?ACACT??ATAAT??TCTGGTGAATT???CAAGCAGG???GG?????CT?C??A??CTA????CA?TATT?AAT?????ATTACCCT??AT?????C?AT??C??CGGTGGAGG??ATATTTCCAACTG??TTCAA?CACCAA?TCGG?TTTGA???AA?T?TAAAAAGAAA?AC??TCTTTATA?CAG?????????AAAAGG??????A??GA?????????AATATTGATTCAAT?TT??GCTTTGTACTATTA?CGGTAAACGGAAG?C?TT?GTA?GCCCATGG??A?ATC?AATCTCTACA????????CA??ACAAATCTATC??ATCTTTATAC?C????A?????????GC??????GATTAT?TGG??????CTA?AC??TTAGTT??TCAAA???GA?AAAA??TAAA?T????AAGC??????AATA???A?TCAATA?????TTAGG?GCAA???????CGT??ATTCGTACACAA????TGGC??ACCA?C???????TGTG???????????ATTA??G?TC?TCCTA??GAGAAATCCTT????GTA?T?A???AAT??TAACTAC???????A?CTCAG?ATACATTCTCTCC?TTT????????????C???????????A?CATATTGCTT??????CAAT?T?C?ATAAGTTGAAGCAG?GCTCT???A??CTGATTATG?GA?CCT??TCCATTGA??????AGAACA?TAC???????AT??GTTGTAC??????????TT?A??A??T????????????CCGACCATTCCT?ACC????????????CATCTTCTACT????AGACCCAACACAAGAA??ATATAT?????????????A????A?CC??ATCCATCCGCATT??T?????AAAA??C???AGTATTCC?????ATCAG?ACT??????AAATTATGAC??CAAAT???CAAAACTTGTAAAAGATATG???TAA????T????GACATAGCT?GGTAGTAACTAGCT??GCT?ATAA?A?AACTGAAAC?CTAAGA?CCTAAATAA?TAAATT?TGGCG???TAA?A?C?TTATAC????????AGAGA??A?GT?AATC?AGAAA?T??G?CT?????TTA?GCACTA??????A?AACC???AAA?G??????A????GTCAATTACTTT?AGGATAGAC?TTCACGC??TCATGGTG??????????ACCATA?GC?TGT?C??TAACC?????????CG?????AT??TATCTGT?????AATCCACC????????ATTGA?A??T?????ACTAC??????TTT?CTT?A?TCT????CC?GA?CAATA??CG??AG??????????GT?C?TAGGA?CTTA????AATGA??????????AGAA?ATGGCTCATCA???TTAG?CGT?ATTA??AC??AGTACGAATCA???TCTG?TG?AA?CCC??????AAA?AT???????ACTCA?GT?????????CA?????G????TAT?????ATCTGG?TCTTT?????????AAAACT?G?T??GT?A???AC?CCTGT?CAAT?A?AC?TACC?AA?CGT?G??T????TGCA?GACTT??????????TCA??CT????????A?TTTTGG??ACGATATA???AATAGTG?ACGC????CCATTT????????TTGATATCTCTA??GTA?AC??AT???????C????CACCCGAT?G?GGTAAGGAAGTTTGTGGAATTAATG?GT?A?????????G???TATGGAAT?????CTATC?GAGC?AA?TGC?AGC?ATA???????ATCAATCA????AGCAAACCAT?A???CTTGATA?GCC??TTTC?AGTT??TA?CTACCGTTGA???AGATCAG?GA??A??????????A?GTA???CTGTAT?GT?CTTGC??GCTTTACGCA????TT?T?GTA???TATATTCCA?AACTTA????TAATGTTGA??????????AC?CG??T??CAT?GATG???????C?AATAA??????ATATA??????????????????????TAATT????CCCTCA?????????????CA???????A???TTA?ATA?T?A??A?CA???AAA????C??AGG??C?C?????????ATA??CAGTAAAT??CGAAC?CCTATCCCAAGA?CT?CTT?GCAA?T?TCGGAATTGATACGGGTATTA?A???GGTC?TTAACA omiltemanaJAC7413 ATC?CTCGC?A??AGCG?GC?T????????GTCCTGACG??TT???T?AT?CACGATCTA????GCG??????????GGTG?GCA???????GGACTTTGATCT??AGGTACT?CA??TTAA?AT?A???????C?AC???????A?TA?????????CCCATT?ACAAAGCCTGTAATGTC????T????????GATA??T?TA?AT???T?AT??CAG???ATAACTTT??T??A?AT????TTGACGTTG?ATATC????????TCAAGTTGTT?TCC??????????A?CGATCTA?ATGGAAC?CTGAATT????C????CTCC???TTCACTCGC???TAAAGGCTATTCTAAAT?????TG?T??A?AATGTATAGATACTA???????GTGAAACTTTCTAT????????GCCATTAC??A??????????A??AATTGTCACAA??CACT???CCCAC?GG?AT??TAA??????????AATAC?T??CATGAATGTA??CATT?CTG????T?TCTG?AGCTATA?A?CC??AGC?TTAGGAA?GGTCACCCACATT?AGAT???C????TA?????AG?AC?????CT???T?TC?GGGT?TCGCTT???T????CCTGC???GC?AGTCCGT??AGAACTTAGGCGATCTTG??????CTTCTC?TAG???TCG????TTAC????ACCCAAAT?ACAAATATAGTTCG?CT?????????CGCG????TT?CATTGAAA?AC???T??????G?????AGCTCG????AA?TATGCTTGGTT?TAAA????CC??AAGTC??A????????????A??????GCATAA?????AAAATC?CAA?ACAATTA?AA????????????G??????CTG?TGA????????CAG?ATTCAT???TTTA??????ACCT???GT??????A??AT??TAA?T??????CCAA????????T????AA?TA?A?ACACT??ATAAT??TCTAGTGAATT???GAAGCAGG???GG?????CT?C??A??CCA????CA?CACT?AAT?????ATTACCCC??AT??????AAT??C??CAG???AGG??ATATATTTTACTG??TTCAA?CACCAA?TCGG?TTATA???AA?T?TAATAAGGAA?AT??ACTTTACG?CCG?????????CATAG???????A??GA?????????AATATC??TTCAATTTT??GCTTTATTCTATTA?CTGTAAATGGAAG?C?TT?ATA?GACCATGG??A?ATC?AATATCTATA????????CA??GCAAATTTATT??ATCTTTACAC?C????A??????????C??????AA?CAT?TGG??????CAA?AC??CTAGTT??TTAAA???G????????TATA?T????ATGC??????AATA???A?TCAACA?????TTAAG?ACAA?AATTGCTGT??ATTCGTACACAA????CAGC??ACCA?C???????TGCG???????????ATCA??G?TC?TCCTA??GACAAATTTTG????GTG?T?A???AAT??TAACTGC???????A?CTCAG?CTACATTCCCTCT?TTT????????????T???????????A?CATA?TGCTT???????AAT?A?C?ATAAGATGAAATAA?GCTCT???A??TTGATTACG?GA?ACC??TCCATTGATTG???AGGACG?TAC???????ATA?GCTGTAC??????????TT?GCCT??T????????????CCGACCGTTCTT?AGC????????????CATCTTATACA????AGACTCAGCACAAG?A??ATAAAC????CA?AAT???A????A?CC??ACCCATCCGCATT??T?????AATA??C???AGTAATCC?????A?AAG?ACT??????AAAATATGAC??CAAAT???AAAAACTTGCAAAAGATATG??TTAA????TTA?TGAC????CT?GGTAGTAACTAACT??GCT?ATAA?A?AACTGT??C?CTACGG?CCTAAACAA?CAAATT?TGGGG???TAA?A?C?TTAC???????????????????GT?GATC?ACTAA?C??G?CT?????CTA?GCACTA??????A?AAGC???AAA?G??????A????GTTAATTTCATT?AGGATAGACTTTCACGC??TCATAGTA??????????ACTATA?GT?TGT?G??TAATC?????????CG?????GT??TACCTGT?????AATAAACC????????ATTAA?A??T?????TCTAC??????TTT??TT?A?TCT????TC?GATCAATA??CG??AG??????????TT?CCTAGG?????A????AAAGATATTT???AAAGAA?ACAGTTTATCA???TGAGACGC?ATCA??AC??AGTACGTATCA???TCTG?TT?GA?CTC??????AAA?ATC??????GCTCA?GC?????????GA?????G????TAT?????ATCCGG?CCTTT?????????A???CT?G?C??GT?A???AC?CCTGT?CAAT?A?AT?TACC?AC?CGT?A??T????TACA?GATCT??????????TCT??CT????????A?TATTGG??ACGAATTA???AATAGTG?ACGC????CCATTT????????TTAATGTCTCTG??GCAGAC??A????????C????CACTCGAT?G?GGTAAGGAAGTTTGTGGAAATAATG?GT?A?????????A???AATGGGAT?????CTACC?GAAC?AA?TAA?AGA?ATA???????AACAATCA????TGTAAACCAT?A?????TGATA?GCC??TTTA?AGTCC?TA?CTACAGTTGA???AGAACGA?GA??G??????????A?GTA???CCATAT?GT?CTTGC??GCTTTACGCA????AT?T?GTA???TCTTT??CA?AACTTA????TAATGTTGGTA????????AC?CA??T??CAT?GATG???????C?AATAT??????ATTTA??????????????????????TAATT????CTCTCA?????????????CA???????T???TTA?ATA?T?A??A?CA???AAA????A??AGG??T?T?????????ATA??CGATAAAT??TAA???CCTATCCCAAG?????CTT?GCAA?T?TCGGAATTGATACGTGTATCA?G???GGTC?CTAACA sp_3_MichoacanJSF955 ACA?CTCGC?A??AGCG?GC?T????????GTCCTGTCG??TT???T?GT?GACAAACTA????GTG???????????GTG?GCAAACTT??GGCCTTTGATCT??AGGTACC?TA??TTAA?AT?A???????C?AC???????G?TA?????????CCCATT?ACAAAGCCTGTAACGAC????T????????AATA??T?TA?AT???T?AT??CAG???ATAACTTT??T??A?AC????TAGACGTTG?ATACC????????TTTAGTTGTA?TCC??????????A?CGA?TTA?ATGGAAC?CTGAATT????C????CTCC???TTCGCTCGC???TA???GTTACTTTAAAT?????TG?T??T?AATGTATAGATACTA???????ATGAAATTCTCTAC????????GCCATTAC??A??????????A??AGTTGTCATAA??CACT???ACCAC?GG?AT??CAA??????????AATAC?A??CATGAATGTA?ACAGT?CTG????T?TTTG?AGCTATA?A?CT??GGC?TTAGGAA?AGTCACTAATTCT?GGAT???CC???TA?????AGCAC?????CT???T?TC?GGGC?TCGTTT???T????CCTGC???GC?AGCC??T??AG??CTTAAACGATCTTA??????CTTCCC?TAG???GAG????TTA???????????AT?ACAAATATAGTTCA?TT?????????CGCG????TT?CATTGAAA?AC???T??????A?????AGCTTG????AA?TATGCCTAGCT?TAAA????CC??TAGTC??A????????????A??GTTGGCATAA?????AAAATC?CAA?GCAACTA?TA????????????A??????CTG?TGA????????TAG?ACTCAT???TTTA??????ACCT???GT??????A??AT??TAA?T??????CCGA????????T????AA?TA?A?ACATT??ATAAT??TCTGGTGAAAT???GAAGCAGA???GG?????CT?C??A??CCA????CA?CATC?AAT?????ATTACCCC??AT?????CAAT??C??CAG???AGG??ATATTTCCGACTG??TTCAA?CACCAA?TTGG?TTCGA???AA?T?TAATAAGCAA?AT??CCATTACA?CTG?????????CAAAGG??????A??GA?????????AATATT??TTCAATTTT??GCTTTGTTCTATTA?CGGTAAATGGAAA?C?TT?GTA?GACCATGG??A?ATC?AATCTCTATA????????CA??ACAAATCTATC??ATCTTTATAC?C????A?????????GC??????GT?TAT?TGG??????CTA?AC??TTAGTT??TTAAA???GA?AAAA??TACA?T????AAGT??????AATA???A?TCAATA?????TTAGG?CCAA???????TGT??ATTCGTACACAA????TGGC??ACCT?C???????TGTG???????????ATCA??G?TC?TGCTG??GACAAATACTA????GTG?T?A???AAT??TAACTAC???????A?CTCAG?TTACATTCGCCCC?TTT????????????A???????????A?CATATTGCTT??????CAAT?A?C?ATAAGCTGAAGTAG?GCTCT???A??CTGATTATG?GA?ACC??TTCATTGATCG???AGAACATTAC???????ATA?ATTGTAT??????????TT?A??C??T????????????CCAACAGTTCTT?ACC????????????CATCATCTACC????AGACACAATATAAGAA??AAAAAT????CATAAT???A????A?TT??ATCCATCCGCATT??T?????AGCA??C???AGTAGTTC???????AAG?ACT??????CAAATATGAC??CAAAT???AAAAACTTGCAAA??????G??TTAA????TTA?TGACATAGCT?GGTAGTAATTACCT??GCT?ATAA?A?AGCTGG??C?CTAAGG?CCTAAATA??TAAATT?TGGGG???TAA?A???CTATGA????????AAAGGCCA?GT?AATT?AGAAA?T??G?CA?????CTT?GCACTA??????A?AAAT???AAA?G??????A????GTTAATTACTTC?GGGATAGAC?CTCGCGCCTTCATGGTA??????????ACCATA?GT?TGT?G??TAAAC?????????CA?????AT??TACATGT?????AGTACACT????????TTTAA?A??T?????TCTAC??????TCT?CTA?A?TTT????CC?GA?CGATA??CG??AG??????????TTAT?TAGGA?TTTA????AAAAA??????????AGAA?ACAGCTTATCA???TGAGACGT?ATCA??AC??AGTACGTATCA???TTTG?TA?GA?CCC??????AA??GTC??????ACTTA?GT?????????TA?????G????CAT????????????CCTTT?????????A???CC?G?T??GT?A???AT?CCTGT?CAAT?A?AT?TACC?AG?CGT?A??T????TACA?GAACT??????????TCC??CT????????A?TATTGG??ACGACTTA???AACAGTG?ACGC????CCATTT????????TTAACATTTCTA??GCA?AC??GT???????C????CACCCGAT?G?GGTGAGGAAGTTTGTGGAATTAATG?GT?A?????????T???TATGGAAT?????CTACC?GAAC?AA?TAC?AAT?ATA???????AGCAATCA????AGCAAACCAT?A?????TGATA?GTC??TTTT?AGTTC?TA?CTACAGTTGG???AGACCAG?GA??A??????????A?GTA???CCATAT?GT?CTTGC??GCTTAACGCA????TT?T?GTA???TTTTT??CA?AACTTA????TAATGTTGATA????????AC?CG??C??CAT?GATA???????C?AATAA??????ATATA??????????????????????TAATT????CCCTCA?????????????CA???????T???TCA?ATA?T?A????CG???AAA????A??AGG??T?T?????????ATA?CCAGTAAAT??CGAAC?CCTATCCCCAAA?CC?CTT?GTAA?T?TCGTAATTGATAAGAGTATTA?A???GGTC?TTAACA tlalociJSF1083 ACT?CTCGC?A??AGCG?GC?T????????GTCCTGTCG??TT???T?TT?AACG?ACTA????GTG??????????GGTG?GCAAACAC??GGCTTTTAAGCT??AGGTACC?CA??TTAA?CT?A???????C?AT???????A?TA?????????CCCATT?ACAAAGCCTGTAATGCC????T????????AATA??T?TA?AT???T????????????TAACTTT??C??A?AT????TTGACGTTG?ACACC????????CCTAGTTGTT?TCC??????????A?CGATTTA?ATGGAAC?CTG?ATT????C????CTCC???TCCC???GC???TA???GCTTTTTTAAAT?????TG?T??T?AATGTATTGATACTA???????CTGAAACTTTCTAT????????GCCTTTAC??A??????????A??AATTGTCACAA??TACC????????????AC??TAA??????????AATAC?A??CATGAATGTA?ACATT?CTG????T?TCTA?AGCTATA?A?CT??GGC?TTAGGAA?GTTCACTCATATT?GGTT???CC???TA?????AGTAC?????CT???T?TG?TGAT?TCGTTT???T????CCTGC???GC?AAACCAC??AGAACTTAGACGATCTTAA?????TTTCTC?TTG???TTG????TTAC???????CAAAT?ACAAATGTAGTTTA?TT?????????TGTG????TT?CATTGAAA?AC???T??????G?????AGCTTG????AA?TATGCCTAATT?TAAA????CC??CAGTC?TA????????????A??GTCTGTCTAA?????AAAACC?CAA?GCAACTA?TA????????????G??????CTG?TGA????????TAG?ATTCAT???TTTA??????ACCT???GT??????A??AC??TAA?T??????CCAC????????T????AATTA?A?ATATT??ATAAT??TCTCGCGAATT???GAAGCAGT???GG?????CT?C??A??CCC????CA?TATC?AAT?????ATTACCCT??AT?????CAAT??C??CAG???AGG??ATATTTCCAACTG??TTCAA?CACCAA?TCGG?TTCGA???AA?T?TAATAAGAAA?AT??CCTTTAAA?CTG?????????CAAAGGCTAATCA??GA?????????CATATT??TTCAATTTT??GCCTTGTACTATTA?CAGTAAATGGAAA?C?TT?TTA?GACCATGG??A?ATC?AATTTCTATA????????CA??ACAAATTTTTT??ATCTCTATAC?C????A?????????GC??????AA?CAT?TGG??????CTA?ACTACTAGTT????AAA???GA?AAAA??TACA?T????AAGC??????AATA???A?TCAATA?????TCAGG?CCAA???????TGT??ATTCGTACACAA????TGTC??ACTT?C???????T?????????????????T??G?CC?TCTTA??GACAAATCCTA????GTG?C?A???AAT??TAACTAC?????????CTCAG?TTACATTCGCCCC?TTT????????????G???????????A?CATATTGCTT??????CAAT?A?T?ATAAGTTGAAGAAG?GCTCT???A??TTGATTAGG?GA?ACC??TCCACTGATTG???AGAACG?TAC???????ATA?GTTGTAC??????????TT?A??C??T????????????CCGACCGTACTT?ACC????????????TATCCTCCACT????GGACCCAATACAAGAA??ATAAAT????CA?AAT???A????A?AC??ACCCAACCACATT??T?????AACA??C???AGTATTTC?????ACAAG?ACT??????AAACTATGAC??CAAAT???GAA??CTTGCAAAAGTTTTG??TTAA????TTA?TGACACAGCT?GGTAGTAATTAACT??GCT?ATAA?A?AGCTGG??C?CTAAGA?TTAAAGTAT?TAAATT?TGACG???TGA?A?C?CTATAA????????AGAGT??A?GT?TATT?AGCAA?T??G?CGAACGATTA?GCACTA??????A?CAGT???AAA?G??????A????GTTAAGTCCTTC?AGGATAGAC?TTCACGC??TCATGGTA??????????ACCATA?GT?TGT?G??TAATC?????????CG?????GTC?TATATGT?????AATACACT????????TTTAA?A??T?????CCTAC??????TTT?CTC?A?TTT????AC?GA?CAATA??CG??AG??????????CT?C?T?????TTTA????AAAGA??????????AGAA?ACAGCCCGTCC???TGAGACGT?ATCA??AC??AGTACGTATCA???TTTG?TG?AA?CCC??????AAA?GTC??????ACTTA?GC?????????GA?????G????TCT?????ATTCGG?TCTTT?????????A???CA?G?T??GT?A???AC?CCTGT?CAAT?A?AC?TACC?ATACGC?A??T????TACA?GATAT??????????TCG??CT????????A?TATTGG??ATGATCTA???AATAGTG???????????????????????TTAACACTTCTA??GTA?AC??AT???????C????CACCCGAT?G?GGTAAGGAAGTTTGTGGAATTGATG?GT?A?????????T???TATGGAAT?????CTGTT?AAAC?AA?TAT?AGT?ATAATTTCATAGCAATCA????CGCAAACCAT?A???CTTGATA?GCC??TTTA?AGTCC?TA?CTACAGTTGA???AGTTCAA?GA??A??????????A?GCA???CCATAT?GT?CTTGC??GCTGTACACT???TTT?T?GTA???TCTCT??CA?AACTTA????CAATGTTGATA????????AC?CA??T??CAT?GATG???????C?AATAGACTTT?ATATA??????????????????????TAATT????CTCTCA?????????????CA???????C???TTA?ATA?T?A??A?CA???AAA????A??TGG??T?T?????????ATA?CCAGTAAAT??CGAAC?CCTACCCCCAGC?CC?CTT?GCAA?T?TCGGAATTGATACGAGTATTA?A???GGTC?CTAACA neovolcanicaJSF960 ACT?CTTGC?A??AGCG?GC?T????????GTCCTGTCG??TT???T?TT?GACG?ACTA????GTG??????????GGTG?GCAAACAC??GGCTTTTAAGCT??AGGTACC?CA??TTAA?CT?A???????C?AT???????A?TA?????????CCCATT?ACAAAGCCTGTAATGCC????T????????AATA??T?TA?AT???T?AT??CAG???ATAACTTT??C??A?AT????TTGACGTTG?ACACC????????CCTAGTTGTT?TCC??????????A?CGATTTA?ATGGAAC?CTG?ACT????C????CTCC???TCCC???GC???TA???GCTTTTTTAAAT?????TG?T??T?AATGTATAGATACTA???????TTGAAACTTTCTAT????????GCCTTTAC??A??????????A??AATTGTCACAA??TACC????????????AC??TAA??????????AATAC?A??CATGAATGTA?ACATT?CTG????T?TCTG?AGCTATA?A?CT??GGC?TTAGGAA?GTTCACTCATTTT?GGTT???CC???TA?????AGTAC?????CT???T?TG?TGAT?TCGTTT???T????CCTGC???GC?AAACCAC??AGAACTTAGACGATCTTAA?????TTTCTC?TTG???TTG????TTAC???????CAAAT?ACAAATGTAGTTTA?TT?????????TGCG????TT?CATTGAAA?AC???T??????G?????AGCTTG????AA?TATGCCTAATT?TAAA????CC??CAGTC?TA????????????A??GTCTGTCTAA?????AAAACC?CAA?GCAACTA?TA????????????G??????CTG?TGA????????TAG?ATTCAT???TTTA??????ACCT???GT??????A??AC??TAA?T??????CCAC????????T????AATTA?A?ATATT??ATAAT??TCTCGCGAATT???GAAGCAGT???GG?????CT?C??A??CCC????CA?TATC?AAT?????ATTACCCT??AT?????CAAT??C??CAG???AGG??ATATTTCCAACTG??TTCAA?CACCAA?TCGG?TTTGA???AA?T?TAATAAGAAA?AT??TCTTTAAA?CTG?????????CAAAGGCTAATCA??GA?????????CATACT??TTCAATTTT??GCCTTGTACTATTA?CAGTAAATGGAAA?C?TT?TTA?GACCATGG??A?ATC?AATTTCTATA????????CA??ACAAATTTTTT??ATCTCTATAC?C????A?????????GC??????AA?CAT?TGG??????CTA?AC??CTAGTT????AAA???GA?AAAA??TACA?T????AAGC??????AATA???A?TCAATA?????TCAGG?TCAA???????TGT??ATTCGTACACAA????TGGC??ACTT?C???????T?????????????????T??G?CC?TCTTA??GACAAATCCTA????GTG?C?A???AAT??TGACTAC?????????CTCAG?TTACATTCGCCCC?TTT????????????G???????????A?CATATTGCTT??????CAAT?A?T?ATAAGTTGAAGAAG?GCTCT???A??TTGATTAGG?GA?ACC??TCCACTGATTG???AGAACG?TAC???????ATA?GTTGTAC??????????TT?T??C??T????????????CCGACCGTACTT?ACC????????????TATCCTCCACT????GGACCCAATACAAGAA??ATAAAT????CA?AAT???A????A?AC??ACCCAACCACATT??T?????AACA??C???AGTATTTC?????ACAAG?ACT??????AAACTATGAC??CAAAT???GAA??CTTGCAAAAGTTTTG??TTAA????TTA?TGACATAGCT?GGTAGTAATTAACT??GCT?ATAA?A?AGCTGG??C?CTAAGA?TTAAAGTAT?TAAATT?TGACG???TGA?A?C?CTATAA????????AGAGT??A?GT?AATT?AGCAA?T??G?CAAACGATTA?GCACTA??????A?CAGT???AAA?G??????A????GTTAAGTACTTC?AGGATAGAC?TTCACGC??TCATGGTA??????????ACCATA?GT?TGT?G??TAATC?????????CG?????GTC?TATATGT?????AATACACT????????TTTAA?A??T?????CCTAC??????TTT?CTC?A?TTT????AC?GA?CAATA??CG??AG??????????CT?C?T?????TTTA????AAAGA??????????AGAA?ACAGCCCGTCC???TGAGACGT?ATCA??A???AGTACGTATCA???TTTG?TG?AA?CCC??????AAA?GTC??????ACTTA?GC?????????GA?????G????CCT?????ATTCGG?TCTTT?????????A???CA?G?T??GT?A???GC?CCTGT?CAAT?A?AC?TACC?AT?CGC?A??T????TACA?GATAT??????????TCG??CT????????A?TATTGG??ATGATCTA???AATAGTG???????????????????????TTAATACTTCTA??GTA?AC??AT???????C????CACTCGAT?G?GGTAAGGAAGTTTGTGGAACTGATG?GT?A?????????T???TATGGAAT?????CTGTT?GAAC?AA?TAT?AGT?ATAATTTCATAGCAATCA????CGCAAACCAT?A???CTTGATA?GCC??TTTA?AGTCC?TA?CTACAGTTGA???AGTCCAA?GA??A??????????A?GCA???CCATAT?GT?CTTGC??GCTGTACACT???TTT?T?GTA???TCTCT??CA?AACTTA????CAATGTTGATA????????AC?CA??T??CAT?GATG???????C?AATAAACTTT?ATATA??????????????????????TAATT????CTCTCA?????????????CA???????C???TTA?ATA?T?A??A?CA???AAA????A??TGG??T?T?????????ATA?CCAGTAAAT??CGAAC?CCTACCCCCAGC?CC?CTT?GCAA?T?TCGGAATTGATACGAGTATTA?A???GGTC?CTAACA berlandieriJSF1136 ACT?CTTGC?A??AGCG?GC?T????????GTCCTGTCG??TT???T?GT?AACA?ACTA????GTG??????????GGTG?GCAAACAC??GGCCTTTAAGCT??AGGTACC?CA??TTAA?CT?A???????C?AT???????A?TA?????????CCCATT?ACAAAACCTGTAATGCC????T????????AATA??T?TA?AT???T?AT??CAG???ATAACTTT??C??A?AT????TTGACGTTG?ACACC????????CCTAGTTGTT?TCC??????????A?CGATTTA?ATGGAAC?CTG?ATT????C????CTCC???TCGC???GC???TA???GCTTTTTTAAAT?????TG?T??T?AATGTATAGATACTA???????ATGAAACTTTCTAT????????GCCTTTAC??A??????????A??AATTGTCACAA??TACC????????????AC??TAA??????????AATAC?A??CACGAATGTA?ACATT?CTG????T?TCTG?AGCTATA?A?CT??GGC?TTAGGAA?GGTCACTCATTTT?AGTT???CC???TA?????AGCAC?????CT???T?TA?TGAT?TCGTTT???T????CCTGC???GC?AACCCAC??AGAGCTTAGACGATCTTAA?????TTTCTC?TAG???TTG????TTAC???????CAAAT?ACAAATGTAGTTCA?TT?????????TGCG????TT?CATTGAAA?AC???T??????G?????AGCTTG????AA?TATGCCTAATT?TAAA????CC??CCGTC?TA????????????A??GTCTGTCTAA?????GAAACC?CAA?GCAATTA?TA????????????G??????CTG?TGA????????TAG?ATTCCT???TTTA??????ACCT???GT??????A??AC??TAA?T??????CCAT????????T????AATTA?A?AAATT??ATAAT??TCTCGCGAATT???GAAGCAGC???GG?????CT?C??A??CCC????CA?TTCC?AAT?????ATTACCCT??AT?????CAAT??C??CAG???AGG??ATATTTCCAACTG??TTCAA?CACCAA?TCGG?TTTGA???AA?T?TAAAAAGAAA?AT??CCCTTAAA?CTG?????????CAAAGGCTAATCA??GA?????????TATATT??TTCAATTTT??GCCTTGTACTATTA?CAGTAAATGGAAA?C?TT?TTA?GACCATGG??A?ATC?AATTTCTCTA????????CA??ACAAATTTATT??ATTTCTATAC?C????A?????????GC??????AA?CAT?TGG??????CTA?AC??CTAGTT????AAA???GA?AAAA??TACA?T????AAGC??????AA????????CAATA?????TCAGG?TCAA???????CGT??ATTCGTACACAA????TGAC??ACTT?C???????TGTG???????????ATTA??G?CC?TCTTA??GACAAATCCTA????GTG?C?A???AAT??TAACTAC?????????CTCAG?CTACATTCGCCCC?TTT????????????G???????????A?CATATTGCTT??????CAAT?A?T?ATAAGTTGAAGAAG?GCTCT???A??TTGATTAGG?GA?ACC??TCCACTGATTG???AGAACG?TAC???????ATA?GTTGTAC??????????TT?A??C??T????????????CCGACCGTACAT?ACC????????????TATCATCCACT????GGACCCAATACAAGAA??ATAAAT????CA?AAT???A????A?AC??ACCCAACCACATT??T?????AACA??C???AGTATTTC?????ACAAG?ACT??????AGACTATGAC??CAAAT???GAA??CCTGCAAAAGTTATG??TTAA????ATA?TGACATAGCT?GGTAGTAATTAACT??GCT?ATAA?A?AGCTGG??C?CTAAGG?TTAAAGTAT?TAAATT?TGATG???TGA?A?C?CTATAA????????AGAGG??A?GT?AATT?AGCAA?T??G?CGAACGATTA?GCACTA??????A?TAGT???AAA?G??????A????GTTAAATACTTC?AGGATAGAC?TTCACGC??TCATGGTA??????????ACCATA?GT?TGT?G??TAATC?????????CG?????GTC?TATGTGT?????AATACACT????????TTTAA?A??T?????CCTAC??????TTT?CTC?A?TT???????????????A??CG??AG??????????CT?C?T?????TTTA????AAAGA??????????AGAA?ACAGCCCGTCC???TGAGACGT?ATCA??AC??AGTACGTATCA???TTTG?TG?AA?CCC??????AAA?GTC??????ACTTA?GC?????????GA?????G????CCT?????ATTCGG?TCTTT?????????A???CA?G?T??GT?A???AC?CCTGT?CAAT?A?AC?TACC?AT?CGC?A??T????TACA?GATAT??????????TCG??CT????????A?TTTTGG??ATGATCTA???AATAGTG???????????????????????TTAATACTTCTA??GTA?AC??AT???????C????CACTCGAT?G?GGTAAGGAAGTTTGTGGAATTGATG?GT?A?????????T???TATGGAAT?????CTGTT?AAAC?AA?TAT?AGT?ATAATTTCATAGCAATCA????CGCAAACCAT?A???CTTGATA?GTC??TTTA?AGTCC?TA?CTACAGTTGA???AGTTCCA?GA??A??????????A?GCA???CCATAT?GT?CTTGC??GCTGTACACT???TAT?T?GTA???TCTCT??CA?AACTTA????CAATGTTGATA????????AC?CA??T??CAT?GATG???????C?GATAAACTTT?ATATA??????????????????????TAATT????CTCTCA?????????????CA???????C???TTA?ATA?T?A??A?CA???AAA????A??TGG??C?T?????????ATA?CCAGTAAAT??CGAAC?CCTATCCCCAGC?CC?CTT?GCAA?T?TCGGAATTGATACGAGTATTA?A???GGTC?CTAACA blairiJSF830 ACT?CTTGC?A??AGCG?GC?T????????GTCCTGTCG??TT???T?GT?AACA?ACTA????GTG??????????GGTG?GCAAACAC??GGCCTTTAATCT??AGGTACC?CA??TTAA?CT?A??CGATAC?AT???????A?TA?????????CCCATT?ACAAAGCCTGTAATGCC????T????????AATA??T?TA?AT???T?AT??CAG???ATAACTTT??C??A?AT????TTGACGTTG?ACACC????????CCTAGTTGTT?TCC??????????A?CGATTTA?ATGGAAC?CTG?ATT????C????CTCC???TTCC???GC???TA???GTTTCTTTAAAT?????TG?T??T?A?TGTATAGATACTA???????ATGAAACTTTCTAC????????GCCTTTAC??T??????????A??AATTGACACAA??TACC????????????AC??CAA??????????AATAC?T??CATGAATGTA?ACATT?CTG????T?TCTG?AGCTATA?A?CT??CGC?TTAGGAA?GGTCACTCATTTT?AGTT???CC???TA?????AGTAC?????CT???T?TC?TGAT?TCGTTT???T????CCTGC???GT?AAACCAT??AGAACTTAGACGATCTTAA?????TTTCTC?TAG???TTG????TTAC???????CAAAT?ACAAATGTAGTTCA?TT?????????TGCG????TT?CATTGAAA?AC???T??????G?????AGCTAG????AA?TATGCCTAATT?TAAA????CC??CCGTC?TT????????????A??GTTTGTATAA?????AAAACC?CAA?GCAACTA?CA????????????G??????CTG?TGA????????TAG?ATTCAC???TTTA??????ACCT???GT??????A??AT??TAA?T??????CCAC????????T????AATTA?A?ACATT??ATAAT??TCTGGCGAATT???G?AGCAGT???GG?????CT?C??A??CCA????CA?TATT?AAT?????ACTACCCT??AT?????CAAT??C??CAG???AGG??ATATATCCTACTG??TTCAA?CACCAA?TCGG?TTTGA???AA?T?TAACAAGAAA?AT??TCTTTAAA?CTG?????????CAAAGG??????A??GA?????????AATATT??TTCAATTTT??GCCTTGTACTATTA?CAGTAAATGGAAA?C?TT?TTA?GACCATGG??A?ATC?AATTTCTATA????????CA??ACAAATTTGTT??ATCTTTATAC?C????A?????????GC??????GA?CAT?TGG??????CTA?AC??CTAGTT????AAA???GA?AAAA??TACA?T????AAGC??????AATA???A?TCAATA?????TCAGG?TCAA???????CGT??ATTCGTACACAA????TGGC??ACTT?C???????TGTG???????????ATTA??G?CC?TATTA??GACAAATGCTA????GTG?C?A???AAT??TAACTAC?????????CCCAG?CTACATTCGCCCC?TTT????????????G???????????A?CATATTGCCT??????CAAT?A?C?ATAAGTTGAAGAAG?GCTCT???A??TTGATTAGG?GA?ACC??TCCACTGATTG???AGAACG?TAC???????ATA?GTTGTAT??????????TT?A??C??T????????????CCGACCGTACTT?ACC????????????CATCCTCTACT????AGACCCAATATAAGAA??ATAAAT????CA?AAT???A????A?AC??ATCCATCCACATT??T?????AACA??C???AGTATTTC?????ACAAG?ACT??????ATAATATGAC??CAAAT???AAA??CTTGCAAAAGTTATG??TTAA????TTA?TGACATAGCT?GGTAGTAATTAACT??GCT?ATAA?A?AGCTGG??C?CTACGG?TTAAAATAC?TAAATT?TG???????????????TATAA????????AGAGG??A?GT?GATT?AGCAA?T??G?CGAACGATTA?GCACTA??????A?CAGT???AAA?G??????A????GTTAAATACTAC?AGGATAGAC?TTCACGC??TCATGGTA??????????ACAATA?GT?TGT?G??TAATC?????????CG?????GTC?TATATGT?????AGTACACT????????TTTAA?A??T?????CCTAC??????TAT?CTT?A?TTT????AC?GA?CAATA??CG??AG??????????CT?C?T?????CTTA????AAAGA??????????AGAA?ACAGCCCATCC???TGAGACGT?ATCA??AC??AGTACGTATCA???TTTG?TG?AA?CCC??????AAA?GTC??????ACTTA?GC?????????GA?????G????CTT?????ATCCGG?TCTTT?????????A???CA?G?T?TGT?A???AC?CCTGT?CAAT?A?AC?TACC?AT?CGC?A??T????TACA?GATAT??????????TCA??CT????????A?TTTTGG??ATGATCCA???AATAGTG???????????????????????TTAATATCTCTA??GCC?AC??AT???????C????CACACGAT?G?GGTAAGGAAGTTTGTGGAACTGATG?GT?A?????????T???TATGGAAT?????CTGCC?GAAC?AA?TAT?AGT?ATAATTTCATAGCAATCA????TGCAAACCAT?A???CTTGATA?GCC??TTTA?AGTCC?TA?CTACGGTTGA???AGTTCAG?GA??A??????????A?GT?????CATAT?GT?CTTGC??GCTGTACTCT???TTT?T?GTAATATCTTT??CA?AACTTA????CAATGTTGTTA????????AC?CA??T??CAT?GATG???????C?AATAAACTTT?ATATA??????????????????????TAATT????CCCTCA?????????????CA???????C???TTA?ATA?T?A??A?CA???AAA????A??CGG??C?T?????????ATA?CCAGTAAAT??CGAAC?CCTATCCCCAGT?CC?CTT?GCAA?T?TCGGAATTGATACGAGTATTA?G???GGTC?CTAACA sphenocephalaUSC7448 ACT?CTCGT?A??AGCG?GC?T????????GTCCTGTTG??TT???T?GT?AACC?ACTA????GTG??????????GGTG?GCAAACTT??GGCTTTTGACCT??TAGTGCC?AA??TTAA?AT?A???????C?AC???????G?TA?????????CCTATT?ACAAAGCCTGTAATGCC????T????????AATA??T?TA?AT???T?AT??CAG???ATAACTTT??C??A?AC????ACGACGTTG?ACAAC????????TCTAGTTGCA?TCT??????????A?CGGTTTA?ATGGAAC?CTGAATT????C????CTCC???TCCGCTCGC???TA???ATTACTTTAAAT?????TG?T??T?AATGTATAGATACTA???????TTGAAATTTTCTAC????????GCCTTTAC??A??????????A??AATTGTCACAA??TACT???AACAC??G?AT??TAA??????????AATAC?T??CAAGAATGTA?ACATT?CTG????T?TTTG?AGCTATA?A?CC??GGC?TTAGGAA?GGTCACTCATTTT?AGAT???CC???TA?????GGTAC?????CT???T?TA?GGAC?TCGTTT???T????CCTGC???AA?AGGCCAG??AGAACTTAGACGATCTTAA?????TTTCCC?TAG???GTG????TCAC???????CAAAT?ACAAATATAGTTCT?TT?????????CGTG????TT?CATTGAAA?AC???T??????G?????AGCTCGAT?TAA?TATGCATAGCT?TAAA????CC??TCGTC??A????????????A??GTCGGCATAA?????AAAATC?CAA?GCAACTA?AA????????????C??????CTA?TGA????????TAG?ACTCTC???TTTA??????ACCT???GT??????A??AA??TAA?T??????CCAT????????T????AA?TA?A?ATACT??ATAAT??TCTAGTGAATT???GAAGCAGC???GG?????CT?C??A??CCA????CA?TATT?AAT?????ATTACCCC??AT?????CAAT??C??CAG???AGG??ATATTTTCAACTG??TTCAA?CACCAA?CCGG?TTCGA???AA?T?TAATAAGAAT?AT??TTTTTAAA?CCG?????????CAAAGG??????A??GA?????????AATATT??ATCAATTTT??GCTTTGTTCTATTA?CGGTAAAAGGAAA?C?TT?GTA?GACCATGG??A?ATC?AATTTCTACA????????CA??ACAAATCTTTA??ATCTTTATAC?C????A?????????GC??????AA?CAT?TGG??????CTA?AC??CTAGTT??TTAAA???GA?AAAC??TGCA?T????AAGC??????AATA???A?TCAATA?????TTAGG?TCAA???????TGT??ATTCGTACACAA????TGGC??ACCT?C???????TGAG???????????ATTC??G?CC?TCCTA??GACAAATCCTT????GTG?C?A???AAT??TAACTAC?????????CCCAG?CTACATTCGCTCC?TTT??????????????????????????????TTGCTT??????CAAT?A?C?ATAAGTTGGAGAAG?GCTCT???A??CTGATTAGG?GA?ACC??TCCATTGATTG???AGAACG?TAC???????ATA?CTTGTAT??????????TT?A??C?????????????????GACCGTACCT?ATC????????????CA????????????????CCCAATACAAGAA??ATAAAT????CA?GAT???A????A?TT??ATCCATCCGCATT??T?????AACT??C???AGTATTAC?????ACAAG?ACT??????AAAATATGAC??CAAAT???AAAAACTCGCAAAAGTTATG??TTCA????TTA?TGACATAGCT?GGTAGTAATT?ACT??GCT?ATAA?A?AATTGG??C?CTAAGG?CCTAAATAT?CAAATT?TGGAG???TGA?A?C?ATACGA????????AGAGA??A?GT?GATC?AGAAA?T??G?CG?????CTT?GTACTA??????A?CATT???AAA?G??????A????GTTAATTACTTT?AGGATAAAC?TTCACGC??TCATGGTT??????????ACCATA?GT?TGT?G??TAATC?????????CG?????ATC?TCCCTGT?????AGTACACT????????TTTAA?A??T?????CCTAC??????TCT?CTT?A?TTT????TC?GA?CAATA??CG??AG??????????CT?C?TAGGA?TTTA????AAAGA??????????AGAA?ACAGCTTATCA???TGAGACGT?ATTA??AC??AGTACGTATCA???TTTG?TT?GA?CCC??????AAA?GTC??????ACTTA?GC?????????AA?????G????AAT?????ATCCGG?TTTTT?????????A???CT?G?T??GT?A???AT?CCTGT?CAAT?A?AC?TACC?AT?CGC?A??T????TACA?GATTTGCTCGGTACATCT??CT????????A?TCTTGG??ACGACCTA???AATAGCG?ACGC????CCATTT????????TTAATATTTCTA??GCT?AC??AT???????C????CACCCGAT?G?GGTAAGGAAGTTTGTGGAACTAATG?GT?A?????????T???TACGTAAT?????CTGCC?GAAC?AA?TAT?AGA?ATA???????AGCAATCA????TGAAAACCAT?A???CTTGATA?GCC??TTTA?AGTCC?TA?CTACAGTTGA???AGTACAG?GA??A??????????A?GTA???C??TAT?GT?CTTGC??GCTTTACGCA????TT?T?GTA???TCTTT??CA?AACTTA????CAATGTTGATA????????AC?CG??T??CAT?GATC???????CCAATAC??????ATATA??????????????????????TAATT????CCCTCA?????????????CA???????C???TCA?ATA?T?A??A?CA???AAA????A??TGG??T?T?????????ATA?CCAGTAAAT??CGAAC?CCTATCTCCAGA?CC?CTT?GCAA?T?TCGGAATTGATACGAGTATTA?A???GGTC?TTAACA utriculariaJSF845 ACT?CTTGC?A??AGCG?GC?T????????GTCCTGTTG??TT???T?GT?AACC?ACTA????GTG??????????GGTG?GCAAACTC??GGCTTTTGAACT??AAGTATC?CA??TTAA?AT?A???????C?AC???????G?TA?????????CCCATT?ACAAAGCCTGTAATGCC????T????????AATA??T?TA?AT???T?AT??CAG???ATAACTTT??C??A?AT????TCGACGTTG?ATAAC????????TCTAGTTGTA?TCC??????????A?CGGTTTA?ATGGAAC?CTGAATT????C????CTCC???TCCACTCGC???TA???GTTACTTTAAAT?????TG?T??T?AATGTATAGATACTA???????ATGAAATTTTCTAC????????GCCTTTAC??A??????????A??AATTGTCACAA??TACT???AACAC??G?AC??TAA??????????AATAC?T??CATGAATGTA?ACATT?CTG????T?TATG?AGCTATA?A?CC??GGC?TT?GGAA?TGTCACTCATCGT?AGAT???CC???TA?????AGTAC?????CT???T?TA?GGAT?TCGTTT???T????CCTGC???GT?AGGCCAG??AGAACTTAGACGATCTTAA?????CTTCCC?TAG???TTG????TCAC???????CAGAT?ACAAATATAGTTCT?TT?????????CGTG????TT?CATTGAAA?AC???T??????G?????AGCTTGATATAA?TATGCTTAGTT?TAAA????CC??TCGTC??A????????????A??GTTGGCATAA?????AAAATC?CAA?GCAATTA?AA????????????A??????CTA?TGA????????TAG?ACTCAC???TTTA??????ACCT???GT??????A??AA??TAA?T??????CCAC????????T????AA?TA?A?ATACT??ATAAT??TCTAGTGAATT???GAAGCAGA???GG?????CT?C??A??CCA????CA?CATA?AAT?????ATTACCCT??AT?????TAAT??C??CAG???AGG??ATATTTTCAACTG??TTCAA?CACCAA?CCGG?TTTGA???AA?T?TAACAAGAAA?AT??TCTTTAAA?CTG?????????CAAAGG??????A??GA?????????AATATT??ATCAATTTT??GCTTTGTTCTATTA?CGGTAAATGGAAA?C?TT?GTA?GACCATGG??A?ATC?AATTTCTACA????????CA??ACAAATTTATC??ATCTTTATAC?C????A?????????AC??????AA?CAT?TGG??????CTA?AC??CTAGTT??TTAAA???GA?AAAA??TGCG?T????AAGC??????AATA???A?TCAATA?????TTAGG?ACAG???????TGT??ATTCGTACACAA????TGGC??ACCT?C???????TGAG???????????ATCC??G?TC?TCCTA??GACAAATCCTT????GTG?TAA???AAT??TAACTAC?????????CCCAG?CTACATTCGCTCC?TTT??????????????????????????????TTGCTT??????CAAT?A?C?ATAAGTTGGAGAAG?GCTCT???A??CTGATTAGG?GA?ACT??TCCATTGATTG???AGAACG?TAC???????ATA?GCTGTAT??????????TT?A??T??T????????????CCGACCGTACCT?ACC????????????CATCATCTACT????AGACCCACTACAAGAA??ATAAAT????CA?GAT???A????A?CC??AGCCATCCGCATT??T?????AACT??C???AGTATTAC?????ACTAG?ATT??????AAAATATGAC??CAAAC???AAAAACTCGCAAAGGTTGTG??TTAA????TTA?TGACACAGCT?GGTAGTGATT?ACT??GCT?ATAA?A?AACTGG??C?CTAAGG?CCTAAATAC?CAAATT?TGGAG???TAA?A?C?ATATTA????????AGAGA??A?GT?GATC?ATAAA?T??G?CG?????CTT?GTACTA??????A?CATT???AAA?G??????A????GTTAACTACTTT?AGGATAGAC?TTCACGC??TCATGGTG??????????ACCATA?GT?TGT?G??TAATC?????????CG?????TTC?TACCTGT?????AATGCACT????????TTTGA?A??T?????CCTAC??????TCT?CTT?A?TTT????TC?GA?CAATA??CG??AG??????????CT?C?TAGGA?TTTA????AAAGA??????????AGAA?ACAGCTTA??A???TGAGACGT?ATCA??AC??AGTACGTATCA???TTTG?TA?GA?CCC??????AAA?GTC??????GCTTA?GC?????????AA?????G????AAT?????ATCCGG?TCTTT?????????A???CT?G?T??GT?A???AC?CCTGT?CAAT?A?AT?TACC?AT?CGC?A??T????TATA?GACCTGCTCGTCACATCT??CT????????A?TGTTGG??ACGACCTA???AATAGTG?ACGC????CCATTT????????TTAATATTTCTA??GCA?AC??AT???????C????CACTCGAT?G?GGTAAGGATGTTTGTGGAACTAATG?GT?A?????????T???TATGTAAT?????CCGTC?CAAC?AA?TAC?AGT?ATA???????AGCAATCA????TGAAAACCAT?A???CTTGATA?GCC??TTTA?AGTCC?TA?CTACAGTTGA???AGTCCAG?GA??A??????????A?GTA???CCATAT?GT?CTTGC??GCTTTACGCA????TT?T?GTA???TCTTT??CA?AACTTA????CAAT????????????????C?CG??T??CAT?GATA???????CCAATAC??????ATATA??????????????????????TTATT????CCCTCA?????????????CA???????C???TTA?ATA?T?A??A?CA???AAA????A??AGG??T?T?????????ATA?CCAATAAAT??CGAAC?CCTATCTCAAGA?CC?CTT?GCAA?T?TCGAAATTGATACGAGTATTA?G???GGTC?CTAACA forreriJSF1065 AC???ACAT?A??AGCG?GC?T????????GTCCTGTAG??TT???T?CT?AATGAACTA????GCG??????????GGTG?GCAAACCT??GGCCTTTGACCT??AGGTACC?TA??TTAA?AT?A???????C?AC???????A?TA?????????CCCACT?ACAAAACCTGTAACGTC????T????????AATA??C?TA?TT???T?AT??CAG???ATAACCTT??T??A?AC????TAGACGCTG?ATATT????????TTAAGTTGTA?TCT??????????A?CGATTTA?ATGGAAACCTGAACT????C????CTCC???TTCACTCGC????????G?TGCTCTAAAT?????TG?TGAT?AATGTATAGATAC????????????AAACTTTCTAC????????GCCATTAC??A??????????A??ACTTGTCACAA??CACT???ATCAC?GG?AT??TTA??????????AATAC?A??CATGAATGTA?ACATT?CTG????T?TTTG?CGCCATA?A?CC??GGC?TTA?GAA?GGTCACTCATTTT?AGAT???CC???TA?????TGCCC?????CT???T?TA?AGGT?TCGTTT???T????CCTTC???TC?GACCCATAGCGAACTTAGACGATCTTAA?????CTTCCC?TAG???CTG????TCAT???????CGAAT?ACAAATATAGTTCA?TT?????????CGCG????TT?CA???AAA?AC???T??????G?????AGCTTG????????ATGCTCGGTT?TGAA???ACC??CAATC??A????????????A??GTCGGCATAA?????AAAATC?AAA?ACAAATA?AA????????????G??????CTG?TGA????????TAC?AATCAC???TTTA??????ACCT???GA??????A??GA??TAA?T??????CCAA????????T????AA?TA?A?ATACC??ATAAT??TCGAGCAAACT???GAAGCAGT???GG?????CT?C??A??CAG????CA?AACT?AAT?????ATTACCCC??AT?????CAAT??C??CAG???AGG??ATATCCCTTACTG??TTCAA?CAACAA?TCGG?TTCGA???AA?T?TAACAAGAACAAT??ACCTTAAA?CCG?????????CAAAGG??????A??GA?????????AATAT???????????T??GCTTTGC??TATTA?CGGTAAATGCAAA?C?TT?GTA?GACC?TGG??A?ATC?AATTTCTATA????????CA??ACAAATCTTTC??ATCTTTACAC?C????A?????????GC??????GA?CAT?TGG??????CTA?AC??CTAGTT??TTAAA???GA?AAGA??TACG?T????AAAC??????AATA???A?TCAGTA?????TTAGG?GCGA???????TGT??ATTCGTGCACAA????TC?C??ACTT?C???????TGTG???????????ATCA??G?TC?TCTTA??GATATATTCTT????GTA?T?A???AATATTGACTAC???????A?CCCAG?CTACATTCGCCCC?TTT????????????T????????CGCA?CATATTGCTT?????CTAAT?A?C?ATAAGTTGAAGGAA?GCTCT???A??ATGATTACG?GA?ACTTTTCCGTCGATTG???AGAACA?TAC????????????ATGTAC??????????TT?A??C??T????????????CTGACCATACTT?ATC????????????CATCATTCACT????AGATTCAAAACAAGAACAACAAAT????CA?AAT???A????A?TC??ATCTATCCGCATT??T?????AATG??C???AGTATTTC?????ACGAG?ACT??????GAATTACG?C??CAAAT???AAAAACTTGCAAAAGCTGTG??CTAA????TTA?TGACATAGCT?GGTAGTAATTCAAT??GCT?ATAT?A?AACTGG??C?CTAAGG?CCTAAATAG?GAAATT?TGGGG???TAA?A?C?TTACAT????????AAAGA??A?GT?TATC?AAAAA?T??G?CA?????GTT?GCACTA??????A?AAAT???AAA?G??????A????GTTAACTACTTT?AGGATAGAC?TTCACGC??TCATGGTA??????????ACTATA?GT?TGT?G??TAATC?????????CG?????AT??TACCTGT?????ACTACACC????????CTTGA?A??A?????CTTAC??????TCT?CCT?G?TCT????TC?GA?CAATA??CGTCAG??????????TT?T?TAGGA?TTTA????AAAGA??????????AGCA?ACAGTTCGTCA???TGAGACGT?ATCA??AC??AGTTCGTATCA???TCTG?TA?GA?CCC??????AAA?ATC??????ACTTATGC?????????CA?????G????TTT?????ACCCGG?TCTTT?????????A???CT?G?T??GT?A???AC?CCTGT?CAAT?T?AT?TACC?AA?CGC?A??T????TATA?GATCC??????????TCA??CT????????A?TA??GG??ACGATCTA???AACGGTG?ACGC????CCATTT????????CTAATAT?TCTA??GCA?AC??AT???????C????CACCCGAT?G?GGTAAGGAAGTATGTGGAATTA?TG?GT?A?????????G???TATGGAAT?????????T?AATC?AA?TAT?AAC?ATA???????AACAATCT????TGCAAACCAT?A???CTTGATA?G????????????CC?TA?CTACAGTTGA???AGCCCGA?GA??A??????????A?GTA???CTATAT?GT?CTCGG??GCTTTATACA????CT?T?GTA???TCTTC??CA?AACTTA????TAATG?TGATA????????GC?CG??T??CAT?GATG???????C?AATAA??????ATCTA??????????????????????TAATT????CACTCA?????????????CA???????C???TTA?ATA?T?A??A?CA???AAA????A??GGA??T?T?????????AT??CCAGTAAAT??CGAAC?CCAATCCCAAGA?CTTCTT?GTAT?T?TTGGAATTGATACATGTATTA?G???GGTCTTTAACA magnaocularisJSF1073 GCG?CTCGC????AGCG?GCAT????????GTCCTGTTG??TT???T?TT?CATGAGCTA????ACG????????GCGGTG?????ACCT??GGCTTTTAACCT??A?GTAAC?TA??TTAA?AT?T???????C?AC???????A?TA?????????CCTATT?ACAAAGCCTGTAACGTC????A????????AATA??T?TA?A?????CAT??CA???????ACTTT??C??A?CT????TAG????TG?ACATC????????CTATGTTGTG?TCC??????????A?CGGTCTA?ATGGAAC????AATT????C????CTCC???TCCACTCGC????????GTTACCCTAGAT?????TG????ATAATGTATCGATACTA???????GTAAAACTTTCTAC????????GCCGTTAC??A??????????A??ATTTGGCATAA??CACT???AACAC?GG??CACTTA??????????ATTAC?A??CTAAAATGTA?ACACT?CTG????T?TACG?AGCTATA?A?CA??AGC?TTAAGAA?GATCACTCGCCTT?TAATTCACC???TA?????CGTAC?????CT???T?TA?AGGT?TCATTT???T????CTTGC???GG?GAGCCAT??AGAGTTTAAACGATCTTTA?????CTTCCC?TAG???TTG????ACAC???????CAAAT?ACAAATTTAGTTCG?TT?????????TGAG????TC?CA???AAA?AC???T??????T?????AGCTC?????AA?TATGCCTAGTT?TAAA????CC??TTTTC??A????????????A??GTTAGTATAA?????AAAATC?TAA?GCAACTA?AA????????????A??????CTG?CGA????????GAG?ATTCAC???TTCA??????ACCT???GT????CTA??GA??TAA?T??????CCAG????????T????AA?TA?A?ATACT??ATAAT??TCGTGCGAACT???G?????AT????????????????A??CTA????CA?AATG?AAT?????ATTACCCTGAAT?????TAAT??C??CAA???GGG??ATATTAC?????GCGTTCAA?CAACAA?CCGG?TTCGA??TAA?A?TAA?AAGAAA?AT??CT?TTAAT?CCG????TTATTCAAAGG??????A?TGA?????????AATATT??TTCAATTTT??GAATTTTTTTATTA?CAGTAAATGGA?A?C?TTAGTA?GACCATGG??A?ATC?AATTTCTATA????????CAACGCAAATATTTC??ATCTTTACAT?C????A?????????AC??????AA?TAT?CGG??????TTA?AC??GTAGCT??TTAAA???GA?AAGAA?TACA?T????AAGC??????AATA???A?TTATTA?????TTAGG?GCG????????AGT??ATCCGTGCACTC????AGCC??ACCC?C???????TGCG???CAT?????ATTA??G?TC?TCCTA??GACAAATCCTC????GTA?C?A???AAA??TA???GC???????A?CCCAG?CTACATTCACA?C?TTT????????????A????????CGCA?CATATTGCTT??????CAAT?A?T?ATAAGTTGAAAAAG?GCTCT???A??GTGATTATG?GA?ACC??TCGGTAGATTG???AGAGCG?TAC???????ATA?ACTGTAC??????????TT?T??C??T????????????CCGACAATTC???ATC????????????CATCCTTTGCG????AGACCTAAAAAAAGAATAACAAAT????CA?AGT???A????A?TC??ATCTACCCACTTT??T?????A?CG??C???AGTACTCC?????ACAA??ACT??????AAAGCA?G?C??CAAAT???TAAAACTTGCAAACGTTATG??TTTC????TTA?TGACA???CT?GATAGTAAATAATTTAGCC?AT???A?AACTGA??C?CTAAGC?TCTAAATAG?TAAATT?TGTAG???TCA?A?C?CTATAA????????AGTGA??A?GT?GATC?CCAAG?A??G?CC?????GTG?GTACTA??????A?GAGC???ACA?G??????A????GTCAATTGCCCT?AGGTTAGAC?TTCACGC??TTATGGTA??????????ACCATA?GT?TGT?G??TAACC?????????CG?????AT??TACCTGT?????TCTTCACT????????ATTAA?A??T?????CCTAC????????TATTC?A?TCT????TC?GA?CGATA??CG??GG???????????T?T?TAGGA??TTA????AAATA??????????AGGA?ACAGTCAGTC?????GAGATGT?ATCA??AC??AGTACGTGTCA?????TG?TT?GA?CCCTGATAAAAA?GCT??????GCTTA?GC?????????AA?????G????TAT?????ATGCGG?TATTT?????????G???CC?G?T??GT?A???AA?CCTGT?C???????????CC?AT?CAC?G??C????TTCT?GAATT??????????TCA??CT????????A?TCTTGG??ACGATCCA???AATAGTG?ACGC????CCATTT????????TTAAAATTTCTA??GCA?AC??AT???????C????CACATGAT?G?GGTCA??AAATCTGTAGAATTAATG?GT?A?????????A???TAGGGAAT?????CTGCT?AACC?AA?TGA?ACT?ATA???????ATCAATCA????TGCAAGCCAC?A???ATTGATA?GCC??TTCA?AGTCC?TA?CTACAATTGA???AGCACGA?GAA?A??????????A?GTA???ACAAAT?GT?CTC?C??GCTTTAACCA????CC?T?GTA???TCTTT??CA?GACTTA????TAATTTTGGTA????????AC?CG??T??CAT?GATG???????C?AGTAA??????ATGTA??????????????????????TAATT????CTCTCA?????????????CA???????C???TTA?ATA?T????A?CA???AAA????A??AGG??T?C?????????ATA?CCCGTAAAT??TGAAC?CCTATCCTTAGA?CT????????A?T?TCGAAATTGATACATGTATAA?A???GGTC?TTAATA sp_7_JaliscoJSF1000 ACA?CTTGT?A??AGCG?GCATTAAAA???GTCCTGTTG??TT???T?CT?AATAAACTA????GCG??????????GGTG?TCAAAC???????CTTTGATCT??A?GTGCC?AA??TTAA?AT?A???????C?AT???????A?TA?????????CCAATT?ATAAAGCCTGTAATGCC????C????????AATA??T?TA?AT???CAAT??CAG???ATAACTTT??C??A?AT????TCG?????G?GCATC????????TTTAGTTGTA?TCC??????????A?CGATCTA?ATGGAAC?CTGAATT????C????CTCC???TCTCCTCGC????????GTTGCGTTAAAT?????TGCT??T?AATGTATTGATACTA???????ATAAAACTTTCTAC????????GCCGTTAC??A??????????A??ATTTGTCACAA??CACC???ACCAC?GG?ATACACA??????????AATAC?A??C?AGAATGTA?ACACT?CTG????G?TACG?AGCCATA?A?CC??AGC?TTAGGAA?AGTCACTCATCTT?AAATTCACC???TA?????TGTAC?????CTAGCT?TC?GGGC?TCGTTT???T????CCTGC???GC?AACCCAT??AGAGCTTAGACGATCTTAA?????CTTCCC?TAG????TG????TCAC???????TGAAT?ACAAATCTAGTTCA?TT?????????CGTG????TA?CA???AAA?AC???T??????T?????AGCTC?????AA?TATGCCCAGTT?TAAA????CC??TTATC??AC???????????A??GTTGGTATAA?????AAAATCAGAA?GCAACTA?TA????????????G??????CTG?TGA????????AAA?ACTCTT???TTCA??????ACCT???GT??????A??GA??TAA?T?CCAT?CCGA????????T????AA?TA?A?ACATT??ATAAT??TCGGGCGAATT???GAAGCAGC???AG?????CT?C??A??CTA????CA?CAAC?AAT?????ATTACCCCGAAT?????CAAT??C??CA????CGG??ATATCTCCAACTG??TTCAA?CACCAA?TCGG?TTTGA??TAA?C?TAAAAAGAAG?AT??TC?TTAAT?CTG?????????CAAAGG??????A?CGA?????????AATACT??TTC?ATTCT??GAATTCTATTATTA?CAGTAAATGCAAA?C?TT?ATA?GACCATGG??A?ATC?AATATCTATA????????CA??ACAAATTTATT??ATCTA????T?C????A?????????CC??????AA???T?TGG??????CTA?AC??TTAGCT??TAAAA???TA?AAGA??TACA?T????AAAC??????AATA???A?TCAATA?????TAAGG?ACGA???????AGT??ATTCGTGCACAA????CGGC??ACCT?C???????TGTG???????????TTCC??G?TC?TTCTA??GATAAATGCTC????GTA?T?A???AAC??TAACTGC???????A?CCCAG?CTACATTCGCA?C?TTT????????????A????????CGCA?CATATTGCTT??????CAAT?G?T?ATAAGTTGAAGTAT?GCTCT???A??ATGATTACG?GA?ATC??TCCGTAGATTG???AGGACA?TAC???????ATA?ATTGTAC??????????TT?C??C??T????????????CCGACCGTACTT?ATC????????????CATCTTTTACG????AGACCCAAAAAAAGAACAATAAAT????CA?ACT???A????A?CC??ACCTAACCACATA??T?????A?GA??C???AGTGTTCC?????ACAA??TTT??????TAAGCA?G?C??CAAAT???TAAAACTCGCAAACGTTATG??TTAA????TTA?TGACAGAGCT?GGTAGTCACTAATTTAGCC?ATAA?A?AGCTGA??C?CTAAGC?ACTAAATAA?CAAATT?TGGGG???TCA?A?C?CTATAC????????AGAGA??A?GT?CATC?TTAAA?C??G?CG?????TTG?GAACTA??????A?AATT???AAA?G??????A????GTTAACTACTTT?AGGATAGAC?TTCACGC??TTATAGTA??????????ACCATA?GT?CGT?A??TAATC?????????CG?????GT??TAACTGT?????AATTTACT????????ATTCA?A??T?????CCTAC??????TATAGTT?A?TCT????AC?GA?CAATA??CG??AG??????????CT?T?TAGGA??TTA????AAACA???????????GAA?ACCGCTCGCCA???TGAGACAT?ATTA??AC??AGTACGTATCA???TT?G?TT?AA?CCC??????AAA?TCC??????ACTTA?GC?????????CA?????G????TTT?????ATCTGG?CTTTT?????????A???CT?G?T??GT?A???GC?CCTGT?CACT?A?AT?TACC?AG?CGT?A??A????TCCA?GATCC??????????TCT??CT????????A?TATTGG??ACGATGTA???AACAGTG?ACGC????CCATTT????????TT????????????GCA?AC??AT???????C????CACCC?AT?G?GGCAA??AACTTT??GGAAATAATG?GT?A?????????T???TATGACAT?????CTTTT?AAAC?AA?TAC?AGA?ACA???????AGCAATCG????TGTAACCCAC?A???CTTGATA?GCC??TTTA?AGTTC?TA?CTAAAGTTGA???AGTTCAG?AA??A??????????A?GTA???CTATAT?GT?CTTGC??GCTTTACACA????TT?T?GTA???TCTTT??CG?AACATA????AAATTTTGATA????????AC?CG??T??CAT?G?TA???????C?AATAA??????ATATA??????????????????????TAATT????CTCTTA?????????????CA???????T???TTT?ATA?T?A??A?CA???AAA????G??GGG??C?C?????????ATA?CCGGTAAAC??CGAAC?CTTATCCCACGAACC????????A?T?TCGAAATTGATACAGGTATTG?G????GTC??TGACA yavapaiensisJSF1085 ACA?CTCGC?A??AGCG?GC?T????????GTCCTGTCGTGTT???T?TT?AACGAACTA????GCG??????????GGTGTGCAAACTT??GGCCTTTCACCT??AGGTACC?TA??TTAA?AT?A???????C?AC???????A?TA?????????CCTATT?ACAAAACCTGTAATGCC????T????????AATA??T?TA?AT???TAAT??C??????TAACTTT??T??A?AT????CCGACGTTG?GCAAC????????AATA??TGTA?TCC??????????A?CGATTTA?A?GGAAT?CTGAATT????C????CTCC???TTCACTCGC????????GTTCCTCTAAAT?????TG?T??A?AATATATAGATACTA???????CTGA?ATTTTCTATCACCTTATGCCATTAC??A??????????A??ATTTGCCACAA??CACT???ACCAC?GG?ATACAAA??????????AGCAC?A??CACGAATGTA?ACATT?CTG????T?TTCA?AGCCATA?A?CC??GGC?TTAGGAA?AGTCACTTATCTT?GAATTCACC?GCTA?????AGTAC?????CA???T?TG?AGGC?TCGTTT???T????CCTGC???AA?AAACCAT??AGAACTTAGACGATCTTAA?????CTTCAC?TAG???CCG????TCAT???????TAAAT?ACAAATCTAGTTCA?AT?????????CGTG????TT?CA???AAA?AC???T??????G?????AGCTT?????AA?TATGCCTAGTT?TAAA????CC??TTATC??A????????????A??GTTGGCATAA?????GAAACC?CAA?GCAA?TA?GA????????????G??????CTG?TGA????????AAG?ATTCAT???TTCA??????ACCT???GT??????A??GT??TAA?T??????CCAA????????T????AA?TC?A?ACATT??ATAAT??TCGAGTGAATT???GAAGCAGC???GG?????TT?C??A??CAA????CA?CATG?AAT?????ATTACCCC??AT?????CAAT??C??CAG???AGG??ATATTCCCCACAG??TTCAA?CAACAA?TCAG?TTTGA???AA?C?TAACAAGGAA?AT??TCTTTATG?CCG?????????CAAAGG??????A?CGA?????????AATACT??TTCAATTTT??GCATTATATTATTA?CGGTAAATGGAGA?C?TT?GCA?GACCATGG??G?ATC?AATTTCTTTA????????CA??ACAAATCTATCTCATCTTTATAC?C????A?????????GC??????GA?TAT?CGG??????CCA?AC??TTAGCT??TTAAA???GA?AAGA??TACA?T????ATGC??????AATA???A?TCAATA?????TTAGG?ACAT???????TGT??ATCCGTACACAA????TGGC??ACCT?C???????TGCG???????????ATTA??G?TC?TTTTA??GATAAATATTT????GTA?T?A???AAT??TAACTAC???????A?CTCAG?CTATATTCGCC?C?TTT????????????A????????CGCA?CATATTGCTT??????CAAT?A?C?ATAAGATGAAGTTG?GCTCT???A??ATGATTAAG?GA?ACC??TCCGTTGATTG???AGAACA?TAC???????ATA?ACTGTAC??????????TT?A??C??T????????????CCGACCATACTT?ACC????????????CATCTTCAACC????AGACCTAACAAAAGAATAATAAAT????CA?AAT???A????A?AC??ACCTACCCGCATT??TTTAATA?TA??C???AGTACTCC?????ATAA??ACT??????AAAGTA?G?C??CAAAT???AAAAACTTGCAAAAGTTATG??TTAA????TTA?TGCCAAAGCT?GGTAGTGATCAACT??GCT?ATAG?A?T?CTGA??C?CTAAGG?CCTAAATAC?TAAATT?TGGAG???TGA?A?C?TTATAT????????AGATG??A?GT?AATC?AAAAA?T??G?CC?????CTA?GCACTA??????A?AAGT???AAA?G??????A????GTTAACTACATC?ATGATAGAC?TTCACGC??TCATGGTA??????????ACTATA?GT?TGT?G??TAATC?????????CG?????AT??????????????AATTTACT????????CTTGA?A??T?????CCTCC??????TCT?CTT?A?TAT????TC?GA?CAATA??CG??AG??????????TT?T?TAGGA?TCTA????AAAGA??????????AGGG?ACAGATTGTCA???TGAGACGT?ATCA??AC??AGTACGTAACA???TTTG?TG?GA?CCC??????AAA?GTC??????ACTCA?GC?????????TA?????G????CAT?????ATCCGG?TCTTT?????????A???CT?G?T??GT?A???AC?CCTGT?CAAT?G?AT?TACC?AT?CGC?G??C????TACA?GATCT??????????TCA??CT????????A?TATTGG??ATGATCTA???AAGAGTG?ACGC????CCATTT????????TTAATATTTCTA??GCA?AC??AT???????C????CACTCGAT?G?GGTAAGAAAGTTTGTGGAATTAATG?GT?A?????????A???TACGATAT?????CTATC?TAAC?AA?TAG?AGA?TTA???????AGCAATCA????CGCAAACCATTA???CTTGGTA?GCC??T??A?AGTAC?TA?CTACAATTGA???AGTTCAG?AA??A??????????A?GTA???CCATAT?GT?CTTGC??GCCCTACGCA????TT?T?GTA???TCTTT??CA?AACTTA????TAATGTTGGTA????????AC?CG??T??CAT?GATA???????C?AATAA??????ATATA??????????????????????TTATT????CTCTAA?????????????CA???????C???TTA?ATA?T?A??A?CA???AAA????G??AGG??T?C?????????ATA?CCGGTAAAT??TGAAC?CCTATCCCCGGA?CG?CTT?GTAA?T?TCGGAATTGATATGAGTATTA?G???GGTC?GTAACA oncaLVT3542 ACA?CTCGC?A??AGCG?GC?T????????GTCCTGTCGTGTT???T?TT?AACGAACTA????GCG??????????GGTG?GCAAACAT??GGCCTTTTACCT??AGGTATC?TA??TTAA?AT?A???????C?AC???????A?TA?????????CCCATT?ACAAACCCTGTAATGCC????T????????AATA??T?TA?AT???TAAT??C??????TAACTTT??T??A?AT????TCGACGTTG?GCAAC????????TATA??TGTG?TCC??????????A?CGATTTA?A?GGAAT?CTGAATT????C????CTCC???TTCACTCGC????????GTTCCTTTAAAT?????TG?T??A?AATGTATAGATACTA???????CTGA?ACTTTCTATCACCTTATGCCATTAC??A??????????A??ACTTGTCACAA??CACT???ACCAC?GG?ATACGAA??????????AACAC?A??CGCGAATGTA?ACATT?CTG????T?TTCA?AGCCATA?A?CC??GGC?TTAGGAA?AATCACTCATCTT?GAATTCACC?GCTA?????AGTAC?????CA???T?TG?AGGT?TCGTTT???T????CCTGC???AA?AAACCAT??AGAACTTAGACGATCTTAA?????CTTCTC?TAG???CCG????TCAT???????TAAGT?ACAAATCTAGTTCA?AT?????????CGCG????TT?CA???AAA?AC???T??????G?????AGCTT?????AA?TATGCCTAGTT?TAAA????CC??TTATC??A????????????A??GTTGGCATAA?????GAAACC?CAA?GCAA?TA?GA????????????G??????CTG?TGA????????AAG?ATTCAT???TTCA??????ACCT???GT??????A??GT??TAA?T??????CCAA????????T????AA?TC?A?ACATT??ATAAT??TCGAGTGAATT???GAAGCAGC???GG?????TT?C??A??CAA????CA?CATG?AAT?????ATTACCCC??AT?????CAAT??C??CAG???AGG??ATATTCTCCACAG??TTCAA?CAACAA?TCGG?TTTGA???AA?C?TAAAAAGGAA?AT??TCTTTAAG?CCG?????????CAAAGG??????A?CGA?????????AATACT??TTCAATTTT??GCATTATACTATTA?CGGTAAATGGAGA?C?TT?GGA?GACCATGG??G?ATC?AATTTCTTTA????????CA??ACAAATCTATCTCATCTTTATAC?C????A?????????GC??????GA?TAT?CGG??????CTA?AC??TTAGCT??TTAAA???GA?AAGA??TACA?T????ATGC??????AATA???A?TCAATA?????TTAGG?ACAT???????TGT??ATCCGTGCACAA????TGGC??ACCT?C???????TGCG???????????ATTA??G?CC?TTTTA??GATAAATATTT????GTA?T?A???AAT??TAACTAC???????A?CTCAG?CTATATTCGCC?C?TTT????????????A????????CGCA?CATATTGCTT??????CAAT?A?C?ATAAGATGAAGTAG?GCTCT???A??ATGATTAAG?GA?ACC??TCCGTTGATTG???AGAACA?TAC???????ATA?ACTGTAC??????????TT?G??A??T????????????CCGACCATACTT?ATC????????????CATCTTCAACC????AGACCTAAAAAAAGAACAATAAAT????CA?AAT???A????A?AC??ACCTACCCGCATT??T?????A?TA??C???AGTATTTC?????ACAA??ACT??????AAAGTA?G?C??CAAAT???AAAAACTTGCAAAAGTTATG??TTAA????TTA?TGCCAAAGCT?GGTAGTGATCGACT??GCT?ATAG?A?TGCTGA??C?CTAAGG?CCTAAATAC?AAAATT?TGGGG???TGA?A?C?TTATAT????????AGATG??A?GT?AATC?AAAAA?T??G?CC?????CTA?GCACTA??????A?AAGT???AAA?G??????A????GTTAACTACATC?ATGATAGAC?TTCACGC??TCATGGTG??????????ACTATA?GT?TGT?G??TAATC?????????CG?????AT??????????????AATTTACT????????CTTGA?A??T?????CCTCC??????TCT?CTT?A?TAT????TC?GA?CAATA??CG??AG??????????TT?T?TAGGA?TTTA????AAAGA??????????AGGG?ACAGCTTGTCA???TGAGACGT?ATCA??AC??AGTACGTAACA???TTTG?TG?GA?CCC??????AAA?GTC??????ACTCA?GC?????????TA?????G????CAT?????ATCCGG?TCTTT?????????A???CT?G?T??GT?A???AC?CCTGT?CAAT?G?AT?TACC?AA?CGC?G??C????TACA?GATCT??????????TCA??CT????????A?TATTGG??ATGATCTA???AATAGTG?ACGC????CCATTT????????TTAATATTTCTA??GCA?AC??AT???????C????CACTTGAT?G?GGTAAGAAAGTTTGTGGAATTAATG?GT?A?????????A???TACGATAT?????CTATC?TAAC?AA?TAG?AGA?TTA???????AGCAATCA????AGCAAACCATTA???CTTGGTA?GCC??TTTA?AGTAC?TA?CTACAATTGA???AGTTCAG?AA??A??????????A?GTA???CCATAT?GT?CTTGC??GCTCTACGCA????TT?T?GTA???TCTTT??CA?AACTTA????TAATGTTGGTA????????AC?CG??T??CAT?GATA???????C?AATAA??????ATATA??????????????????????TTATT????CACTCA?????????????CA???????C???TTA?ATA?T?A??A?CA???AAA????G??AGG??T?C?????????ATA?CCAGTAAAT??TGAAC?CCTATCCCTGGA?CC?CTT?GTAA?T?TCGGAATTGATACGAGTATTA?A???GGTC?GTAACA sp_8_PueblaJAC9467 ACA?CTTGC?A??AGTG?GC?T????????GTCCTGTAGTGTT???T?TTAAATCAACTA????GCG??????????GGTG?GCAAACTT??GGCCTTTAATCT??AGGAACT?CA??TTAA?TT?A???????C?AC???????G?TA?????????CCCATT?ACAAAACCTGTAATGCC????T????????AATA??C?TA?AT???TAAA??C??????TAACTTT??T??A?AC????CTG???TTG?GTACT????????TCTAGTTGTG?TCC??????????A?CGATTTA?ATGGAA??CTGAAAT????C????CTCC???TCCACTCGC????????ATTTCGTTAAAT?????TG?T??T?AATGTATTGATACCA???????GTAAAATTTTCTACCACCTCACGCCATTAC??A??????????A??ATTTGCCACAA??CACT???AT????????TACGGA??????????AATAC?G??CGTAAAT?TA?ACAGT?CTG????C?TCTG?AGCTATA?A?CT??AGC?TTATGAA?AGTCACTCATTTT?GAACCCACC???TA?????AGAAC?????CT???T?TA?TGGC?TCGTTT???T????CCTGC???GA?CAACCAT??AGAACTTAAACGATCTTAA?????CTTCAC?TAG???ATG????TCAC???????TGAAT?ACAA???TAGTTCA?AT?????????CGCG????TC?CA???AAA?AC???T??????G?????AGCTT?????AA?TATGCCTAATT?CCAA????CC??ACATC??A????????????ACTTTCGGGCTAC?????GAAAT???????????????????????????????????CTG?CGA????????TAT?AATCAT???TTCA??????ACCT???GT??????A??AC??TAA?T??????CC?A????????T????AA?CA?A?ATACT??ATAAT??TCGCGTGAATT???GAAGCAGC???GA?????CT?C??A??CCA????CA?CATT?AAT?????ATTACCCC??AT?????CAAT??C??CAG???AGG??ATATCTCCAACTG??TTCAA?CAGCAA?TCGG?TTA?A???AA?T?TAACAAGAAA?AT??ATTTTAAG?CGG?????????CACAGG??????A?TGA?????????AATAAT??TTCAATTTTATGCATTATCT??????CTGTAAATGTAAG?C?TT?ATA?GACCATGG??A?ATC?AATTTCTATA????????CG??GCAAATCTATC??ATTTTTACAT?C????A?????????AC??????AA?TAT?GGG??????CAA?AC??CTAGCT??TT?AA???GA?AAGA??TCTA?T????AAAC??????AATA???A?TAAATA?????TTAGG?GCAT???????GGT??ATTCGTACACAA????TGGC??ACCT?C???????TGCG???????????ATCG??G?GC?TCCTG??GAAA?ATTCTT????GTA?C?A???AAT??TAACTA????????A?CTCAG?TTACATTCGC??C?TTT????????????C????????CGCA?CATATTGCTT??????CAGT?A?T?ATAAGTTGAAGCAG?GCTCC???A??ATGATTATG?GA?ACC??TCCAT?GATTC???AGAACG?TAC???????ATA?ACTGTAT??????????TT?A??C??T????????????CCGACCATACCT?ACC????????????CATCCTT????????????CCAAAAAAAGAATAACAGAT????CA?AAT???A????A?TC??ATCTATCCG?ATG??T?????A?TA??C???AGTACTTC?????ATAA??A????????AAAGTA?G?C??CAAAT???AAAAACTCGCAAAAGTTATG??TTAA????TTA?TGACAAAGCT?GGTAGTAACTAATT??GC??ATAG?A?TCCTGG??C?CTAGGG?CCTAAATAC?TAAATT?TGGCG???TCA?A?C?TTATAT????????AGAGG??A?GT?TCCC?ACAAA?C??G?CA?????GTTAGCACTA??????A?AAGT???AAA?G??????A????GTAAACTACATT?ATGATAAAC?TTCACGC??TCATGGTA??????????ACAATA?GT?TGT?G??TAATC?????????CG?????GT??TACTTGT?????ACTTCACT????????CTTGA?A??T?????CCTCC??????TTT?TTT?A?TTT????CC?AA?CAATA??CG??AG??????????CT?T?TAGGA?TTTA????AAAGA??????????TGAA?ACAGCCCG?CA???TGAGACGA?ATTA??AC??AGTACGTAACA???CTTG?TA?GA?CCC??????AAA?ATC??????ACTTA?GC?????????TG?????G????CAT?????ATTCGG?ACTTT?????????A???CCAG?C??GT?A???AG?CCTGT?CAAT?A?AT?TACC?AA?CGC?G??T????TGTA?GACCT??????????TCC??CT????????A?TTTAGG??AAGACTTA???AATAGTGTACGC????CCATTT????????TTAACATCTCTA????A?AC??AT???????C????CACACGAT?G?GGTAAGAAAGTTTGTGGACTTAATG?GT?A?????????G???CACGGCAT?????CTTTC?AAAC?AA?TGT?AGT?CTA???????AGCAATCG????TGCAAGA??G?A???CTTGATA?GCC??TTTA?TGTTCCTA?CTACAATTGA???ATTCCAA?AA??A??????????A?GTA???CCTCAT?GT?????C??GCTCTACTCA????TT?T??TA???TTTTT??CA?AACTTA????CAATGTCG?TA????????AC?CGTAT??CAT?GATA???????C?AATAA?????AATATA??????????????????????TAATT????CTCTCACATGT????????CA???????C???TTA?ATA?T?AAAA?CA???AAA????A??GGG??T?T?????????ATA?CCAGTAAAC??TGAAC?TCTATCCCAGGA?CC?CTT?GCAA?T?TCGAA?TTGATACGAGTATGT?A???GGTC?TTTACA macroglossaJAC10472 ACC?CTCGC?A??AGCG?GC?T????????GTCCTGTTG??TT???T?TT?AA????CTA????GCG??????????GGTG?GCAAGCCT??GGCCTTTGACCT??AGGTACC?AA??TTAA?AT?A???????C?AC???????A?TA?????????CCTATTTACAAAACCTGTAATGTC????T????????A??A??T?TA?AT???TAAT??CAG???ATAACTTT??T??A?TC????TTGACGTTG?ACATC????????CATAGTTGTA?TCC??????????A?CGATTTA?ATGGAAC?CTGAACT????C????CTCC???TTCACTCGC????????GCTGCTCTAAAT?????TG?T??T?AATGTATGGATACTA???????CTGAAATTTTCTAC????????GCCATTAC??A??????????A??ATTTGCCACAA??CACA???TCCAC?GG?AT??AAA??????????AATAC?A??CTAGAATGTA?ACATT?CTG????T?TACA?CGCTATA?A?CC??GGC?TTAGGAA?AGTCACTCATTAT?GAAT???CC???TA?????AGTAC?????CT???T?TT?GGGT?TCGTTT???T????CCTGC???GC?AGTCCAC??AGAACTTAAACGATCTTAA?????CTTCTC?TAG???TCG????TCAC???????TGAAT?ACAAATTTAGTTCAATT?????????CGTG????TC?CA???AAA?AC???T??????A?????AGCTC?????AA?TATGCCTAATT?TAAA????CC??TAGTC??A????????????A??GTCGGCATAA?????AAGACC?CAA?ACAACTA?CA????????????A??????CTG?TGA????????AAG?ACTCCT???TTCA??????ACCT???GT??????A??AT??TAA?T??????CCAG????????T????AA?TA?A?ATACT??ATAAT??TCGGGCGGAAT???AAAGCAGG???GG?????CT?C??A??CCA????CA?CACT?AAT?????ATTACCCC??AT?????CAAT??C??CAC???AGG?GATATTCTCCACTG??TTCAA?CAACAA?GCGG?TTTAA???AA?T?TAATAAGAAA?AT??ACTTTAAG?CTG?????????CAAAGG??????A??GA?????????ACTATT??CTCACTTTT??GCATTGTCCTATTA?CAGTAAATGGAAA?C?TT?TTA?GACCATGG??A?ATC?AATTTCTATA????????CA??GCAAATTTGTT??ATCTTTACAC?CT???A?????????GC??????GA?TAT?TGG??????TCA?AC??CTAGCTCTTTAAA???GA?AAGA??TGTA?T????AGGC??????AATA???A?TTACTA?????TTAGG?ATAT???????TGT??ATTCGTGCACAA????CGGC??ACTT?C???????TGTG???????????ATCA??G?AC?TCCTA??GACAAATTCTA????ATG?T?A???AAT??TTACTGC???????A?CCCAGTCTACATTCTCA?C?TTT????????????A????????CGCA?CATAT???TT???????AAT?A?C?ATAAGTTGGAATAG?GCTCT???G??ATGATTAAG?GA?ACC??TATGTTGATAG???AGATCA?TAC???????ATA?ACTGTAC??????????TT?A??CCGT????????????CTGACCGTACCT?ACC????????????CATCTTCTACC????AGACTCAAAATAAGAATAATAGAT????CA?AAT???A????A?TC??ATCCATCCGCATT??T?????A?CA??C???AGTAC?TC?????ACAAG?ACT??????TAAATATG?C??CAAAA???AAAAACTTGTAAAGGTTATG??TTAA????TTA?TGACAAAGCT?GGTAGTAATTGATT??GCT?ATAA?A?AACTGG??C?CTAAGG?CCTAAATAC?AAAATT?TGGGG???TGA?A?C?CTATAT????????AGAGG??A?GT?GATC?AATAA?T??G?CG?????TTG?GCACTA??????A?TAAT???AAA?G??????A????GTTAACTACTCT?AGGATAAAC?TTCACGC??TCATGATA??????????ACAATA?GT?TGT?G??TAATC?????????CG?????TT??TACTTGT?????ATTACACT????????TTTAA?A??C?????CCTAC??????TCT?CTC?T?TTT????TC?GA?CGATA??CG??A????????????T?C?TAGGA?TCTA????AAAGA??????????AGAA?ACAGTTCATCA???TGAGACGT?ATCA??AC??AGTACGTATCA???TTTG?TT?GA?CCC??????AAA?ATC??????ACTAA?GT?????????AA?????G????CAT?????ATTCGG?CATTT?????????A???CT?G?T??GT?A???AC?CCTGT?CAAT?A?AG?TACC?AG?CGT?A??A????TACA?GACTT??????????TCT??CT????????A?TGT?GG??ACGATCTA???AATAGTG?ACGC????CCATTT????????CTAATATCTCTA??GCA?AC??AT???????A????CACACGAT?G??GTAAGGAAGTTTGTGGAATTAATG?GT?A?????????A???TATGGAAT?????CCTCT?GACC?AA?TGG?AGA?ACA???????AGCAATCA????TGCAAGCCAA?A???CTTGCTA?GCC??TTTA?AGTTC?TA?CTACAGTTGA???AGTT?????A??A??????????A?GTA???CCCAAT?GT?CTCGC??GCT????GCA????TT?T?GTA???TCTCT??CA?AACTTA????TAATGTTGATA????????GC?CA??T??CAT?GGTG???????C?AATAA??????ATATA??????????????????????TAATT????CTCTTA?????????????CA???????C???TTA?ATA?T?A??A?CA???AAT????A??AGG??T?G?????????ATA?CCAGTAAAT??CGAAC?CCTATCCCCAGA?CT?CT??GCAA?T?TCGAAATTGATAAGAGTATTA?G???GGTC?TTGACA macroglossaJSF7933 ACC?CTCGC?A??AGCG?GC?T????????GTCCTGTCG??TT???T?TT?AA????CTA????GCG??????????GGTG?GCAAACTT??GGCCTTTGATCT??AGGTACC?TA??TTAA?AT?A???????C?AC???????A?TA?????????CCTATTTACAAAGCCTGTAATGTC????T????????A??A??T?TA?AT???TAAT??CAA???ATAACTTT??T??A?TC????TAGACGTTG?ACATC????????CATAGTTGTA?TCC??????????A?CGATTTA?ATGGAAC?CTGAACT????C????CTCC???TTCGCTCGC????????GCTGCACTAAAT?????TG?T??T?AATGTATAGATACTA???????TTGAAATTTTCTAC????????GCCATTAC??A??????????A??ATTTGCCACAA??CACA???TCCAC?GG?AT??AAA??????????AATAC?G??CTAAAATGTA?ACATT?CTG????T?TACA?CGCAATA?A?CT??GGC?TTAGGAA?AGTCACTCATTAT?AAAT???CC???TA?????AGTAC?????CT???T?TG?GGGT?TCGTTT???T????CCTGC???GC?GGTCCAC??AGAACTTAGACGATCTTAA?????CTTCTC?TAG???TCG????TCAC???????TGAAT?ACAAATTTAGTTCA?TT?????????CGCG????TC?CA???AAA?AC???T??????A?????AGCTA?????AA?TATGCCTAACT?TAAA????CC??TAGTC??A????????????A??GTCGGCATAA?????AAGATC?CAA?ACAACTA?CA????????????A??????CTG?TGA????????AAG?ACTCCT???TTCA??????ACCT???GT??????A??AT??TAA?T??????CCAG????????T????AA?TA?A?ATACT??ATAAT??TCGGGCGAAAT???AAAGCAGG???GG?????CT?C??A??CCA????CA?CACT?AAT?????ATTACCCC??AT?????CAAT??C??CAG???AGG?GATATTTTCTACTG??TTCAA?CAACAA?TCGG?TTTGA???AA?T?TAAAAAGAAA?AT??ACTTTAAG?CTG?????????CAAAGG??????A??GA?????????ACTACT??CTCACTTAT??GCATTGTCCTATTA?CAGTAAATGGAAG?C?TT?TTA?ACCCATGG??A?ATC?AATTTCTATA????????CA??ACAAATTTGTT??ATCTTTATAC?CT???A?????????GC??????GA?TAT?TGG??????TCA?AC??CTAGCTCTTTAAA???GA?AAGA??TATA?T????AAGC??????AATA???A?TTACTA?????TTAGG?ATAT???????TGT??ATTCGTGCACAA????CGGC??ACTT?C???????TGTG???????????ATCA??G?AC?TCCTA??GACAAATCCTA????GTG?T?A???AAT??TTACTGC???????A?CCCAGTCTACATTCTCC?C?TTT????????????A????????CGCA?CATAT???TT??????CAAT?A?C?ATAAGTTGGAATAG?GCTCT???G??ATGATTAAG?GA?ACC??TCCGTTGATTG???AGAACA?TAC???????ATA?ACCGTAC??????????TT?A??CCGT????????????CCGACCGTACCT?ACC????????????CATCCTCTACC????AGACTCAAAACAAGAACAATAGAT????CA?AGT???A????A?TC??ATCCATCCGCATT??T?????A?TA??C???AGTAC?CC?????ACAAG?ACT??????AAAATATG?C??CAAAA???AAAAACTTGTAAAGGTTATG??TTAA????TTA?TGACAAAGCT?GGTAGTAATTGATT??GCT?ATAA?A?AACTGC??C?CTAAGG?CCTAAATAC?AAAATT?TGAGG???TGA?A?C?CTATAT????????AGAGG??A?GT?GATC?AATAA?T??G?CG?????TTG?GCACTA??????A?CAAT???AAA?G??????A????GTTAACTACTTT?AGGATAAAC?TTCACGC??TCATGATA??????????ACAATA?GT?TGT?G??TAATC?????????CG?????CT??TACTTGT?????AGTACACT????????TTTAA?A??C?????CCTGC??????TCT?CTT?T?TGT????TC?GA?CGATA??CG??AG??????????TT?C?TAGGA?TCTA????AAAGA??????????AGAA?ACAGCTCATCA???TGAGACGT?ATCA??AC??AGTACGTATCA???TTTG?TT?GA?CCC??????AAA?ATC??????ACTCA?GT?????????AA?????G????CAT?????ATTCGG?TATTT?????????A???CT?G?C??GT?A???AC?CCTGT?CAAT?A?AG?TACC?AG?CGT?A??A????TACA?GACGT??????????TCC??CT????????A?TGTTGG??ACGATCTA???AATAGTG?ACGC????CCATTT????????CTAATATCTCTA??GCA?AC??CT???????A????CACACGAT?G??GTAAGGAAGTTTGTGGAATTAATG?GT?A?????????A???TATGGAAT?????CCTCT?GACC?AA?TAG?AGT?ACA???????AGCAATCA????TGCAAGCCAA?A???CTTGCTA?GCC??TTTA?AGTTC?TA?CTACAGTTGA???AGTC?????A??A??????????A?GTA???CCCAAT?GT?CTCGC??GCT????GCA????CT?T?GTA???TCTTT??CA?AACTTA????TAATGTTGATA????????AC?CA??T??CAT?GGTG???????C?AATAA??????ATATA??????????????????????TAATT????CTCTTA?????????????CA???????C???TTA?ATA?T?A??A?CA???AAT????C??AGG??T?G?????????ATA?CCAGTAAAT??CGAAC?CCTATCCCTAGA?CT?CTT?GCAA?T?TCGAAATTGATAAGAGTATTA?G???GGTC?TTAACA taylori286 ATA?CTCGC?A??AGCG?GC?T?????TCAGTCCTGTCG??TT???T?TT?AACGAACTA????GCG??????????GGTG?GCAAACCT??GGCCTTTGACCT??AGGAAAC?TA??TCAA?AT?A???????C?AC???????A?TA?????????ACC?TTTACAAAGCCTGTAATGCC????T????????GATA??T?TA?GT???TAAT??CAG???ATAACCTT??T??A?AC????CCGACGTTG?ATATC????????CACAGTTGTA?TCC??????????A?CGTTTTA?ATGGAAC?CTGAACT????C????CTCC???TCCACTCGC????????GTTGCCCTAAAT?????TG?T??G?AATGTATAGATACTA???????ATAAGACTTTCTAC????????GCCATTAC??A??????????A??AGTTGTCACAA??CACA???TCCAC?GG?AA??AGA??????????AATAC?G??CAGGAATGTA?ACATT?CTG????T?TTAA?C??TATA?A?CA??GGC?TTAGGAA?AATCACTCATTTT?AAAT???CC???TA?????TGTAC?????CT???T?TC?GGGT?TCGTTT???T????CCTGCAAAGA?G?CCCAT??AGAACTTAGACGATCTTAA?????CTTCCC?CAG???TTG????ACA????????CGAAT?ACAAATCTAGTCT?????????????CGTG????TA?CA???AAA?AC???T??????A?????AGCTC?????AA?TATGCCTAATT?CAAA????CC??ATATC??A????????????A??GTCAGCATAG?????AGGATC?CAA?ACAATTA?GA????????????G??????CTA?TGA????????TTG?ACTCAT???TTTA??????ACCT???GT??????A??AT??TAA?T??????TCAC????????T????AA?TC?A?ATACTGCATAAT??TCGGGTGAATT???AAAGCAGT???GG?????CT?C??A??CTA????CA?CATT?AAT?????ATTACCCC??AT?????CAAT??C??CAG???AGG?GATATTTACTACTG??TTCAA?CAACAA?CCGG?TCTGA????A?T?TA??AAGAAG?AT??GTTTTAAG?CTG?????????CAAAGG??????A??GA?????????ACTATT??TTCACTTTT??ATA??GTGTTATTA?CAGTAAATGCACATC?TT?GTA?ATCCATGG??A?ATC?AATTTCTATA????????CA??ACAAATCTATG??ATTTTTATAC?C????A?????????GC??????AA?AAT?TGA??????TCA?AC??CTAGCT??TTAAA???GA?AAGA?CTATA?T????ATAC??????AATA???A?TCATTA?????TTAGG?ATAA???????TGT??ATTCGTGCACAA????AAGC??ACCT?C????????GTG???????????ATTT??G?AC?TTCTA??GAAAAATTCTA????GTG?A?A???AAC??TCACTGC???????A?CTCAA?TTACATTCGCT?C?TTT????????????T????????CGCA?CATATTGCTT??????TAAT?A?C?ATAAGCTGAA??AG?GCTCT???A??ATGATTATG?GA?ACT??TCCATTGATTG???GGAACA?TAC???????ATA?TCCGTAC??????????TT?A??CCGT????????????CCGACAGTATCT?ACC????????????CATCAT??ACC????AGACCCAAAAAAAGAACAAGAGAT????CA?TGT???A????A?TC??ATC??TCCACATT??T?????A?CG??C???AGTAA?CC?????ACAAG?ACT??????AAAACATG?C??CAAAA???GAAAACTTGTAAAAGTTATG??TTAA????TTA?TGACATAGCT?GGTAGTAATTTATT??GCT?ATAA?A?ACTTGG??C?CTAAGG?CCCAAATAC?AAATTT?TGTAG???TGA?A?C?TTATAT????????AGA????A?GT?CATC?AACAA?T??G?CG?????TTG?GCACTA??????A?AAAT???AAA?G??????ATGTCGTTAACTACTGC?GTGATAAAC?TTCACGC??TCATGATT??????????ACAATA?GT?TGT?G??TAATC?????????CG?????TT??TATTTGT?????ATTGCACT????????TTTAA?A??C?????CCTAC??????TCT?TTT?CTTTT????TC?GA?CGAT???CG??AG??????????TT?T?TAGGA?CTTA????AAAGA??????????AGGA?ACAGCTACTCA???TGAGACGT?ATCA??AC??AGTACGTACGA???TGTG?TA?GA?C????????AAA?ATC??????GCTGA?GC?????????AA?????G????CAT?????ATTCGG?CTTTT?????????A???CT?G?T??GT?A???AC?CCTGT?CAAT?A?A??TACC?AA?CGC?A??A????TCTC?GATTT??????????TCT??CC????????A?TATTGG??ATGATTTA???AACAGTG?ACGC????CCATTT????????TTAATATTTCTT??GCA?AC??AT???????A????CACACGAT?GA?GCAAGGAAGTTTGTGGAT??AATG?GT?A?????????A???TATGAAAT?????CCTCG?AATC?AA?TTT?AGATACA???????AGCAATCA????TGCAAAACAT?G???CTTGATA?GTC??TTTA?AGTAC?TA?CTACAGTT?A???AGTT?????A??A??????????A?GTA???TTATAT?GT?TTCGT??GCT????GCA????CT?T?GTA???TCTTT??CA?AACTTA????AAATGTTGTTA????????AC?CA??T??CAT?GGTA???????C?AATAA??????ATATA??????????????????????TAACT????CTCTTA?????????????CA???????C???CTA?ATA?T?A??A?CA???AAT????A??AGA??T?G?????????ATA?CCAGTAAAT??TGAAC?CCTATCCCTAGA?CC???????AA?T?TCGGAATTGATATGAGTATCG?G???GGTC?CTAACA sp_4_Panama ACC?CTCGC?A??AGCG?GC?T????????GTCCTATCG??TT???T?TT?AATGAACTA????GCG??????????GGTG?GCAAACTT??GGCTTTTGACCT??AGGTACC?TA??TTAA??T?A???????C?AC???????A?TA?????????CCCATT?ACAAAGCCTGTAATGTC????T????????AATA??T?TA?AT???CAAT??CCG???ATAACTTT??T??A?AC????TAGACGTTG?ACAAC????????TATAGTTGTA?TCC??????????A?CGATTTA?ATGGAAC?CTGAATT????C????CTCC???TCCACTCGC????????CTTACCCTAAAT?????TG?T??T?AATGTATAGATACTA???????ATGAAACTTTCTAC????????GCCATTAC??A??????????A??ATTTGCCACAA??CACT???ACCAC?GG?AT??GAG??????????AATAC?G??CGTAAATGTA?ACATT?CTG????T?TTCG?AGCTATA?A?CC??GGC?TTAGGAA?GGTCACTCTTTCT?GGAT???CC???TA?????AGTAC?????CT???T?TA?GGGT?TCGTTT???T????CCTGC???AC?AGCCCAT??GGAACTTAGACGATCTTAA?????TTTCTC?TAG???TAG????TCAC???????TAAAT?ACAAATTTAGTTCA?TT?????????CGCG????TC?CA???AAACAC???T??????G?????AGCTT?????AA?TATGCTTTGTT?TGAA????CC??TAGTC??A????????????A??GTCGGCATAA?????AAGATC?CGA?TCAATTA?AA????????????A??????CTG?TGA????????CAG?ATTCAT???TTCA??????ACCT???GT??????A??AT??TAA?T??????CCAG????????T????AA?TA?A?ACAGT??ATAAT??TCGGGCGAATT???GAAGTAAG???GG?????CT?C??A??CTA????CA?CGTA?AAT?????ATTACCCC??AT?????CAAT??C??CAG???AGG?GATATTCCCAACTG??TTCAA?CAACAA?TCAG?TTTGA???AA?T?TAACAAGAAC?AT??ACTTTAAG?CTG?????????CAAAGG??????A??GA?????????AATATT??TTCAATTTT??GCATTGTACTATTA?CAGTAAATGGAAA?C?TT?GTA?GACCATGG??A?ATC?AATTACTATA????????CA??ACAAATCTATT??ATCTTTACAC?C????A?????????GC??????GC?TAT?TAG??????CCA?AC??TTAGCT??TTAAA???GA?AAGC??TACA?T????AAGC??????AATA???A?TTAATA?????TTAGG?GCAA???????TGT??ATTCGTGCACAA????TAGC??ACCT?C???????TGTG???????????ATTA??G?CC?TTCTA??GACAAATCCTA????GTA?T?A???AAA??TCACTGC???????A?CCCAG?TTACATTCGCC?C?TTT????????????A????????CGCA?CATATTGCTT??????CAAT?A?C?ATAAGTTGAAGCAG?GCTCT???A??ATGATTATG?GA?ACC??TCCGTTGATTG???AGAACA?TAC???????ATA?ACTGTAC??????????TT?A??CCGT????????????CTGACCGTTCTT?ACC????????????CATCTTCTACC????AGACCCAAGAAAAGAAGAATAAATTATACA?AAT???A????A?TC??ATCCAACCGCATT??T?????A?AA??C???AGTGA?TC?????ACAAG?ACT??????AAAAT????????????????AAAACTTGCAAAAGTTATG??TTCA????TTA?TGACATAGCT?GGTAGTCATTAATT??GCT?ATAA?A?AACTGG??C?CTAAGG?CCTAAATAT?CAAATT?TGGAG???TGA?A?C?CT?TAT????????AGAGG??A?GT?TGTC?AAAAA?T??G?CG?????CTA?GTACTA??????A?A?ATATGAAA?G??????A????GTTAACTACT???AGGATAAAC?TTTACGC??TCATGGTA??????????ACAATA?GT?AGT?A??TAATC?????????CG?????TT??TACTTGT?????AGTACACT????????TTTAA?A??T?????CCTAC??????TTT?CTT?A?TCT????CC?GA?CGATA??CG??AGTGACACAGTTTT?A?TAGGA?CTTA????AAAGA??????????AGAA?ACAGTTTATCA???TGAGACAT?ATCA??AC??AGTACGTATCA???TCTG?TA?GA?CCC??????AAA?GTC??????GCTTA?GC?????????GA?????G????CAT?????ATCCGG?CCTCT?????????A???CT?G?T??GT?A???AC?CCTGT?CAAC?A?AT?TACC?AG?CGA?A??A????TACA?GATTT??????????TCA??CT????????A?TGTTGG??ATGATCTA???AAC???G?ACGC????CCATTT????????TTAATATCTCTA??GCA?AC??AT???????C????CACCCGAT?G??GTAAGGAAATTTGTGGAACTAATG?GT?A?????????A???TATGGAAT?????CCTTT?GAGC?AA?TAC?AGG?ATA???????AGCAATCA????CGCAAGCCAT?A???CTTGATA?GCC??TTTA?AGTTC?TA?CTACAGTTGA???AGTCCAA?GA??A??????????A?GTA???CCATAT?GTCCTCGC??GCCTTATGCA????CT?T?GTA???ACTTT??CA?AACTTA????CAATGTTGATA????????AC?CG??T??CAT?GGTA???????C?CATAA??????ATATA??????????????????????TAATT????CTCTTA?????????????CA???????C???TTA?ATA?T?A??A?CA???AAA????A??AGG??T?A?????????ATA?CCAGTAAAT??CGAAC?CCTATCCCAAGG?CT?CTT?GCAA?T?TCGCAATTGATACAAGTATTA?C???GGTC?ATAACA sp_5_CostaRichDMH86_210 ACA?CTCGTTA??AGCG?GC?T????????GTCCTGTCG??TT???T?TT?AATAAACTA????GCG??????????GGTG?GCAAACCT??GGCCTTTG?TCT??AGGCACC?CA??TTAA?GT?A???????C?AC???????A?CA?????????CCTATT?AAAAAGCCTGTAACGCC????C????????AATA??T?TA?GT???CAAT??CAGTTTATAACTTT??T??A?AC????AAGACGTTG?ATAAC????????TATCGTTGTT?TCC??????????ATCGATTTA?ATGGAAC?CTGAACT????C????CTCC???TCCACTCGC????????GTTGTTTTAAAT?????TG?T??C?AATGTATAGATACTA???????ATGAGATTTTCTAC????????GCCATTAC??A??????????A??ATTTGCCACAA??CACT???ACCAC?GG?AA??TAA??????????AATAC?A??CAAGAATGTA?ACACT?CTG????T?TTCG?AGCTATA?A?CC??AGC?TTAGGAA?AGTCACTCATTTT?GATT???CC???TA?????AGTAC?????CC???T?TT?GGGT?TCGTTT???T????CCTGC???AC?AGG?CAT??AGAGCTTAAGCGATCTTAA?????TTTCTC?TAG???TAG????TCAT???????TGAAT?ACAAATTTAGTTCA?TT?????????CGCG????TC?CA???AAA?AC???T??????G?????AGCTT?????AA?TATGCCTAGTT?TAAA????CC??TAGTC??A????????????A??GTTAGAATAA?????AAGATC?CGA?ACAATTA?AA????????????G??????CTG?TGA????????CAG?ACTCAT???TTCA??????ACCT???GT??????A??AC??TAA?T??????CCGG????????T????AA?TA?A?ATAAT??ATAAT??TCGTGCGAATT???GAAGTAAA???GG?????CT?C??A??CTA????CA?CATA?AAT?????ATTACCCT??AT?????CAAT??C??CAG???AGG?GA???TCCCAACTG??TTCAA?CAACAA?CCAG?TTTGA???AA?T?TAAGAAGAAA?AT??ACCTTAAG?CTG?????????CAAAGG??????A??GA?????????AATATT??CTCAATTCT??GCATTGTACTA??A?CAGTAAATGAAAA?C?TT?GTA?GACCATGG??A?ATC?AATTTCTATA????????CA??ACAAATCTATC??ATCTTTAAAC?C????A?????????GC??????GA?CAT?TGG??????CCA?AC??CTAGCT??TTAAA???GA?AAGG???ACA?T????AAGC??????AATA???A?TCAATA?????TTAGG?GCAA???????CGT??ATTCGTGCACAA????TGGC??ACCT?C???????TGCG???????????ATTA??G?TC?TCCTA??GACAAAT??TA????GTA?T?A???AAG??TCACTGC???????A?CCCAG?TTACATTCGCA?C?TTT????????????A????????CGCA?CATATTGCTT??????CAAT?A?T?ATAAGCTGAAGCAATGCTCT???T??TTGATTACG?GA?ATC??TTCGTTGATTG???AGAACA?TAC???????ATA?GCTGTAC??????????TT?A??TCGT????????????CTGACCATCCTT?AAC????????????CATCCTCTACC????AGACCCAAAACAAGAAGAATAAAT????CA?AAT???A????A?TA??ATCCAACCACATT??T?????A?AGTCC???AGTAA?TC?????ACAAG?ACT??????AAAGT????????????????AAAACTTGCAAAGGTTATG??TTAA????TTA?TGACACAGCT?GGTA??TATTGATT??GCT?ATAA?A?AACTGG??C?CTAAGA?CCTAAACAT?CAAATT?TGGGG???TGA?A?C?CT?TAT????????AGAGG??A?GT?CG?????AAA?CGTA?CG?????ATT?GCACTA??????A?AAGT???AAAAG??????A????GTCAACTGCCTC?AGGATAAAC?TTTACGC??TCATGGTA??????????ACAATA?GT?TGT?G??TAATC?????????CG?????CT??TACTTGC?????AATACACT????????TTTAA?A??C?????CCTAC??????TTT?TTT?C?TTT????TT?GA?CGATA??CG??CG??????????TT?T?TAGGA?TTTA????AAAGA??????????AGAA?ATAGCTTATCA???TGAGACAT?ATCA??AC??AGTACGTATCA???TCTG?TA?GA?CCC??????AAA?ATC??????CCTTA?GC?????????GA?????G????CAT?????ATCCGG?TCTTT?????????A???CT?G?T??GT?A???AC?CCTGT?CAAT?A?AT?TACC?AG?CGT?A??A????TACACGACCT??????????TCC??CT????????A?TGTTGG??ATGATCTA???AATAGTG?ACGC????CCATTT????????TTAATATCTCTA??GCA?AC??AT???????C????CACCCGAT?G??GTTAGGAAG??????GAACTGATG?GT?A?????????A???TATGGAAT???????TTT?GAAC?AA?TTT?AGA?ATA???????AACAATCA????TGCAAACCAT?A???CTTGATA?GTCTATTTA?AGTAC?TA??TACAGTTGA???AGTCCAC?GA??A??????????A?GTA???CCATAT?GTCCTCGC??GCTATATGCA????CT?T?GTA???TCTTT??CA?AACTTA????CAATGTTGATA????????AC?CG??T??CAT?GGTA???????C?AATAA??????ATATA??????????????????????TAATT????CTCTAA?????????????CA???????C???TTA?ATA?T?A??A?CA???AAA????A??AGG??T?A?????????ATA?CCAGTAAAT??CGAAC?CCTATCCCCAGA?CT?CTT?GCAA?T?TCGAAATTGATACGAGTATTA?G???GGTC?TTAACA sp_6_CostaRicaDMH86_225 AAC?CTCGC?A??AGCG?GC?T????????GTCCTGTAG??TT???T?TT?AATGAACTA????GCG??????????GGTG?GCAAACCC??GGCCTTTGATCT??AGGTATC?TA??TTAA?AT?T???????C?AC??????CA?TATAGTCGGAACCCATT?ACAAATCCTGTAATGCC????C????????AATA??T?TA?AT???TAAT??CAG???ATAACTTT??T??A?GC????TAGACGTTGAATAAC????????TGTAGTTGTA?TCC??????????A?CGATTTA?ATGGAAC?CTGAATT????C????CTCC???TTCACTCGC????????GCTGCTCTAAAT?????TG?T??T?GATATAT?GATACCA???????ATGAAACTTTCTAC????????GCCATTAC??A??????????A??ACTTGTCACAA??CACA???AGTAC?GG?AC??GAA??????????AATAC?A??CATGAATGTA?ACACT?CTG????A?TTCG?AACTATA?A??A??CGC?TTAGGAA?AGTCACTCATTTT?GAAT???CC???TA?????AGTAC?????CC???T?TT?GGGT?TCGCTT???T????CCTGC???AT?GGTCCAT??AGATCTTAGACGATCTTAA?????CTTCTC?TAG???CAG????CCAC???????AGAAT?ACAAATTTAGTTCA?AT?????????AGTG????TC?CA???AAA?AC???T??????G?????AGCTT?????AA?TATGCTTTGTT?TAGA????CC??TCATC??A????????????A??GTT??????A?????AAGATC?CA??ACAACTA?CA????????????G??????CTC?CGA????????CAG?ACTCCA???TTCA??????ACCT???AT??????A??AC??TAAGT??????CCAG????????T????AA?TA?A?ACAAT??ATAAT??TCGGGAGAATT???GGAGTAAG???GG?????CT?C??A??CCA????CA?GATA?AAT?????ATTACCCC??AT?????CAAT??C??CAG???AGG?GATATTCCCTACTG??TTCAA?CACCAA?TCAG?TTTGA???AA?T?TAAAAAGAAA?AT??ACTTTAAG?CTG?????????CAAAGG??????A??GA?????????AATATT??TTCAATTTT??GCATTGTACTATTA?CAGTAAATGAAAA?C?TT?GTA?GACCATGG??A?ATC?AATTTCTACA????????CA??ACAAATTTATC??ATCTTTCTAC?C????A?????????GC??????AA?TAT?TGG??????CCA?AC??CTAGCT??TTAAA???GA?AAGA??TC?????????GGC??????AATA???A?CTAATA?????TTAGG?ACAA???????TGT??ATTCGTGCACAA????TGGC??ACCT?C???????TGTG???????????ATTA??G?TC?TTCTT??GACAAATGATG????GTA?T?A???AAA??TCACTGC???????A?CCCAG?CTACATTCACC?C?TTT????????????T????????CGCA?CATATTGCTT??????CAAT?A?C?ATAAGTTGAAGAAG?GCTCT???A??TTGATTACG?GA?ACC??TTCGTTGATTG???AGAACA?TAC???????ATA?ACTGTAT??????????TT?A??CCGT????????????CTGACCGTCCTT?ACC????????????CATCCTCCACC????AGACCCAAAACAAGAAGAACAAAT????CA?AAT???A????A?CC??ATCTAACCGCATT??T?????A?CG??C???AGTAA?TC?????ACAAG?CCT??????AAATTATG?C??CAAAT???AA???CTTGCAAAAGTTATG??ATAA????TTA?TGACACAGCT?GGTAGTCATTGATT??GCT?ATGA?A?AACTGG??C?CTAAGA?TCTAAATAC?TAAATT?TGTAG???TGA?A?C?TT?TAT????????AGGGA??A?GT?TGTC?AAAAA?T??G?CG?????CTC?GGACTA??????A?CAAT???AAA?G??????A????GTTAACTACTTT?AGGATAAAC?TTCACGC??TCATAGTA??????????ACAATG?GT?TGT?G??TAATC?????????CG?????CT??TACATGT?????AATACACT????????TTTGA?A??A?????CCTTC???????TT?CTT?A?TCT????CC?GA?TGATA??CG??AG??????????TT?T?TAGGA?TGTA????AAAGA??????????AGAA?ACAGCTTGTCG???TGAGACGT?ATTA??AC??AGTACGTATCA???TCTG?TT?GA?CCC??????AAA?GTC??????GCTCA?GT?????????TA?????G????CAT?????ATTAGG?CTTTT?????????A???CT?G?C??GT?A???AC?CCTGT?CAAT?A?AT?TACC?AT?CGT?A??A????TACA?GATTT??????????TCC??CT????????A?TGTTGG??ATGATTTA???AATAGCG?ACGC????CCATTT????????CTAATATTTCTA??GGA?AC??AT???????C????CACCTGAT?G??GTAAGGAAGTCTGTGG???TAATG?GT?A?????????A???TAAGGTAT?????CCTCA?GA?C?AG?TAT?AGT?ATA???????AACAATCA????TGC?ATCCAT?A???CTTGATA?GCC??TTTA?AGTCC?TA?CTACAGTTGC???AGTTCAG?GA??A??????????A?GTA???CCATAT?GTCCTCGT??GCCTTATGCA????CT?T?GTA???TCTCT??CT?AACTTA????TAATGTTGGTA????????AC?CG??T??CAT?GGTT???????C?AATAA??????ATATA??????????????????????TAATT????CCCTTA?????????????CA???????A???TCA?ATA?T?A??A?CA???AAA????C??GGG??GAA?????????ATA?CCAGTAAAT??TGAAC?CCTATCCTCAGA?CC?CTT?GCAA?T?TCGGAATTGATACGAGTATTA?A???GGTC?CTAACA ; END; BEGIN CHARACTERS; TITLE Untitled_DATA_Block_1GapsAsBinary; LINK TAXA = Untitled_TAXA_Block_1; DIMENSIONS NChar=2723; CharStateLabels 1 col_1, 2 col_2, 3 col_3, 4 col_4, 5 col_5, 6 col_6, 7 col_7, 8 col_8, 9 col_9, 10 col_10, 11 col_11, 12 col_12, 13 col_13, 14 col_14, 15 col_15, 16 col_16, 17 col_17, 18 col_18, 19 col_19, 20 col_20, 21 col_21, 22 col_23, 23 col_24, 24 col_25, 25 col_26, 26 col_27, 27 col_28, 28 col_29, 29 col_30, 30 col_31, 31 col_32, 32 col_33, 33 col_36, 34 col_37, 35 col_38, 36 col_39, 37 col_40, 38 col_41, 39 col_43, 40 col_44, 41 col_45, 42 col_46, 43 col_47, 44 col_48, 45 col_49, 46 col_50, 47 col_51, 48 col_54, 49 col_55, 50 col_56, 51 col_57, 52 col_58, 53 col_59, 54 col_61, 55 col_62, 56 col_63, 57 col_64, 58 col_68, 59 col_69, 60 col_70, 61 col_71, 62 col_72, 63 col_73, 64 col_74, 65 col_75, 66 col_76, 67 col_77, 68 col_78, 69 col_79, 70 col_82, 71 col_83, 72 col_84, 73 col_85, 74 col_86, 75 col_87, 76 col_88, 77 col_89, 78 col_90, 79 col_91, 80 col_92, 81 col_93, 82 col_94, 83 col_95, 84 col_96, 85 col_100, 86 col_101, 87 col_102, 88 col_105, 89 col_106, 90 col_107, 91 col_108, 92 col_109, 93 col_110, 94 col_111, 95 col_112, 96 col_113, 97 col_114, 98 col_115, 99 col_116, 100 col_117, 101 col_118, 102 col_119, 103 col_120, 104 col_121, 105 col_122, 106 col_123, 107 col_124, 108 col_126, 109 col_128, 110 col_129, 111 col_130, 112 col_131, 113 col_132, 114 col_133, 115 col_134, 116 col_136, 117 col_139, 118 col_140, 119 col_141, 120 col_142, 121 col_143, 122 col_144, 123 col_145, 124 col_146, 125 col_147, 126 col_148, 127 col_150, 128 col_151, 129 col_152, 130 col_153, 131 col_154, 132 col_155, 133 col_156, 134 col_157, 135 col_158, 136 col_161, 137 col_162, 138 col_163, 139 col_164, 140 col_165, 141 col_166, 142 col_167, 143 col_168, 144 col_169, 145 col_170, 146 col_171, 147 col_172, 148 col_173, 149 col_174, 150 col_175, 151 col_176, 152 col_177, 153 col_178, 154 col_179, 155 col_183, 156 col_184, 157 col_185, 158 col_186, 159 col_188, 160 col_189, 161 col_190, 162 col_191, 163 col_192, 164 col_193, 165 col_194, 166 col_195, 167 col_197, 168 col_198, 169 col_200, 170 col_201, 171 col_203, 172 col_206, 173 col_207, 174 col_208, 175 col_209, 176 col_210, 177 col_211, 178 col_212, 179 col_213, 180 col_214, 181 col_215, 182 col_216, 183 col_217, 184 col_218, 185 col_219, 186 col_220, 187 col_221, 188 col_222, 189 col_223, 190 col_224, 191 col_225, 192 col_226, 193 col_230, 194 col_231, 195 col_232, 196 col_233, 197 col_234, 198 col_235, 199 col_236, 200 col_237, 201 col_238, 202 col_240, 203 col_241, 204 col_242, 205 col_243, 206 col_244, 207 col_247, 208 col_248, 209 col_249, 210 col_250, 211 col_251, 212 col_252, 213 col_254, 214 col_255, 215 col_256, 216 col_257, 217 col_258, 218 col_259, 219 col_260, 220 col_261, 221 col_262, 222 col_263, 223 col_264, 224 col_265, 225 col_266, 226 col_267, 227 col_268, 228 col_269, 229 col_270, 230 col_271, 231 col_272, 232 col_273, 233 col_274, 234 col_275, 235 col_276, 236 col_278, 237 col_279, 238 col_280, 239 col_281, 240 col_282, 241 col_283, 242 col_284, 243 col_285, 244 col_286, 245 col_287, 246 col_288, 247 col_289, 248 col_290, 249 col_291, 250 col_292, 251 col_293, 252 col_294, 253 col_295, 254 col_296, 255 col_297, 256 col_301, 257 col_303, 258 col_308, 259 col_309, 260 col_310, 261 col_311, 262 col_312, 263 col_313, 264 col_314, 265 col_315, 266 col_316, 267 col_317, 268 col_318, 269 col_319, 270 col_320, 271 col_321, 272 col_322, 273 col_323, 274 col_324, 275 col_325, 276 col_330, 277 col_331, 278 col_332, 279 col_333, 280 col_334, 281 col_335, 282 col_336, 283 col_337, 284 col_338, 285 col_339, 286 col_340, 287 col_341, 288 col_342, 289 col_343, 290 col_344, 291 col_345, 292 col_346, 293 col_347, 294 col_348, 295 col_349, 296 col_350, 297 col_351, 298 col_352, 299 col_353, 300 col_361, 301 col_362, 302 col_363, 303 col_364, 304 col_365, 305 col_366, 306 col_368, 307 col_369, 308 col_370, 309 col_371, 310 col_372, 311 col_374, 312 col_376, 313 col_377, 314 col_378, 315 col_379, 316 col_380, 317 col_381, 318 col_382, 319 col_385, 320 col_386, 321 col_387, 322 col_388, 323 col_389, 324 col_390, 325 col_391, 326 col_392, 327 col_393, 328 col_394, 329 col_395, 330 col_396, 331 col_397, 332 col_398, 333 col_399, 334 col_400, 335 col_401, 336 col_403, 337 col_404, 338 col_405, 339 col_406, 340 col_407, 341 col_408, 342 col_409, 343 col_410, 344 col_411, 345 col_412, 346 col_413, 347 col_414, 348 col_415, 349 col_416, 350 col_417, 351 col_418, 352 col_419, 353 col_426, 354 col_427, 355 col_428, 356 col_429, 357 col_430, 358 col_431, 359 col_432, 360 col_433, 361 col_434, 362 col_435, 363 col_436, 364 col_437, 365 col_438, 366 col_439, 367 col_440, 368 col_441, 369 col_442, 370 col_443, 371 col_444, 372 col_445, 373 col_453, 374 col_454, 375 col_455, 376 col_456, 377 col_457, 378 col_458, 379 col_459, 380 col_460, 381 col_461, 382 col_462, 383 col_463, 384 col_464, 385 col_465, 386 col_466, 387 col_467, 388 col_468, 389 col_469, 390 col_470, 391 col_471, 392 col_472, 393 col_474, 394 col_475, 395 col_477, 396 col_478, 397 col_479, 398 col_480, 399 col_481, 400 col_482, 401 col_483, 402 col_484, 403 col_485, 404 col_486, 405 col_487, 406 col_488, 407 col_489, 408 col_490, 409 col_491, 410 col_492, 411 col_493, 412 col_494, 413 col_496, 414 col_497, 415 col_499, 416 col_501, 417 col_502, 418 col_504, 419 col_505, 420 col_506, 421 col_507, 422 col_508, 423 col_509, 424 col_510, 425 col_511, 426 col_514, 427 col_516, 428 col_517, 429 col_518, 430 col_519, 431 col_520, 432 col_521, 433 col_523, 434 col_524, 435 col_525, 436 col_526, 437 col_527, 438 col_528, 439 col_529, 440 col_530, 441 col_531, 442 col_532, 443 col_533, 444 col_534, 445 col_535, 446 col_536, 447 col_537, 448 col_538, 449 col_539, 450 col_540, 451 col_541, 452 col_542, 453 col_543, 454 col_544, 455 col_545, 456 col_546, 457 col_547, 458 col_548, 459 col_549, 460 col_550, 461 col_552, 462 col_553, 463 col_554, 464 col_555, 465 col_556, 466 col_557, 467 col_558, 468 col_559, 469 col_560, 470 col_561, 471 col_562, 472 col_563, 473 col_564, 474 col_565, 475 col_566, 476 col_567, 477 col_568, 478 col_569, 479 col_570, 480 col_571, 481 col_572, 482 col_573, 483 col_574, 484 col_575, 485 col_576, 486 col_577, 487 col_578, 488 col_579, 489 col_580, 490 col_581, 491 col_582, 492 col_583, 493 col_584, 494 col_585, 495 col_586, 496 col_587, 497 col_588, 498 col_590, 499 col_591, 500 col_593, 501 col_594, 502 col_595, 503 col_596, 504 col_597, 505 col_598, 506 col_599, 507 col_600, 508 col_601, 509 col_602, 510 col_603, 511 col_604, 512 col_607, 513 col_609, 514 col_612, 515 col_614, 516 col_617, 517 col_618, 518 col_619, 519 col_620, 520 col_621, 521 col_623, 522 col_624, 523 col_625, 524 col_626, 525 col_630, 526 col_632, 527 col_633, 528 col_634, 529 col_635, 530 col_636, 531 col_637, 532 col_638, 533 col_639, 534 col_640, 535 col_641, 536 col_642, 537 col_643, 538 col_644, 539 col_645, 540 col_646, 541 col_647, 542 col_648, 543 col_649, 544 col_650, 545 col_651, 546 col_653, 547 col_657, 548 col_658, 549 col_659, 550 col_660, 551 col_661, 552 col_662, 553 col_663, 554 col_664, 555 col_665, 556 col_666, 557 col_667, 558 col_668, 559 col_669, 560 col_670, 561 col_671, 562 col_672, 563 col_673, 564 col_674, 565 col_675, 566 col_676, 567 col_677, 568 col_678, 569 col_679, 570 col_680, 571 col_681, 572 col_682, 573 col_683, 574 col_684, 575 col_685, 576 col_687, 577 col_688, 578 col_689, 579 col_690, 580 col_691, 581 col_693, 582 col_694, 583 col_695, 584 col_696, 585 col_697, 586 col_698, 587 col_699, 588 col_700, 589 col_701, 590 col_702, 591 col_703, 592 col_704, 593 col_706, 594 col_707, 595 col_708, 596 col_712, 597 col_713, 598 col_714, 599 col_715, 600 col_716, 601 col_717, 602 col_718, 603 col_719, 604 col_720, 605 col_721, 606 col_722, 607 col_723, 608 col_724, 609 col_725, 610 col_726, 611 col_727, 612 col_728, 613 col_729, 614 col_730, 615 col_731, 616 col_732, 617 col_733, 618 col_736, 619 col_737, 620 col_738, 621 col_739, 622 col_740, 623 col_741, 624 col_742, 625 col_743, 626 col_744, 627 col_745, 628 col_746, 629 col_747, 630 col_748, 631 col_749, 632 col_750, 633 col_751, 634 col_752, 635 col_753, 636 col_754, 637 col_755, 638 col_756, 639 col_757, 640 col_758, 641 col_759, 642 col_760, 643 col_761, 644 col_762, 645 col_763, 646 col_764, 647 col_765, 648 col_766, 649 col_767, 650 col_768, 651 col_769, 652 col_770, 653 col_771, 654 col_772, 655 col_773, 656 col_774, 657 col_775, 658 col_776, 659 col_777, 660 col_778, 661 col_779, 662 col_780, 663 col_781, 664 col_782, 665 col_783, 666 col_784, 667 col_785, 668 col_786, 669 col_787, 670 col_788, 671 col_789, 672 col_790, 673 col_792, 674 col_793, 675 col_794, 676 col_795, 677 col_796, 678 col_801, 679 col_802, 680 col_803, 681 col_804, 682 col_807, 683 col_808, 684 col_811, 685 col_812, 686 col_813, 687 col_814, 688 col_815, 689 col_816, 690 col_817, 691 col_818, 692 col_819, 693 col_820, 694 col_821, 695 col_822, 696 col_823, 697 col_824, 698 col_825, 699 col_826, 700 col_827, 701 col_828, 702 col_830, 703 col_831, 704 col_832, 705 col_833, 706 col_834, 707 col_835, 708 col_836, 709 col_837, 710 col_838, 711 col_839, 712 col_840, 713 col_842, 714 col_843, 715 col_844, 716 col_845, 717 col_846, 718 col_847, 719 col_848, 720 col_849, 721 col_850, 722 col_851, 723 col_852, 724 col_853, 725 col_854, 726 col_855, 727 col_856, 728 col_857, 729 col_858, 730 col_859, 731 col_860, 732 col_861, 733 col_862, 734 col_863, 735 col_864, 736 col_865, 737 col_866, 738 col_867, 739 col_868, 740 col_869, 741 col_870, 742 col_871, 743 col_872, 744 col_873, 745 col_874, 746 col_875, 747 col_876, 748 col_877, 749 col_878, 750 col_879, 751 col_880, 752 col_881, 753 col_882, 754 col_883, 755 col_884, 756 col_885, 757 col_886, 758 col_887, 759 col_888, 760 col_890, 761 col_892, 762 col_893, 763 col_894, 764 col_895, 765 col_896, 766 col_897, 767 col_898, 768 col_899, 769 col_900, 770 col_901, 771 col_902, 772 col_903, 773 col_904, 774 col_905, 775 col_906, 776 col_907, 777 col_908, 778 col_912, 779 col_913, 780 col_914, 781 col_916, 782 col_919, 783 col_920, 784 col_921, 785 col_922, 786 col_923, 787 col_924, 788 col_925, 789 col_926, 790 col_927, 791 col_928, 792 col_929, 793 col_930, 794 col_931, 795 col_932, 796 col_933, 797 col_934, 798 col_935, 799 col_936, 800 col_937, 801 col_938, 802 col_939, 803 col_940, 804 col_941, 805 col_942, 806 col_943, 807 col_944, 808 col_945, 809 col_946, 810 col_947, 811 col_948, 812 col_949, 813 col_950, 814 col_951, 815 col_952, 816 col_953, 817 col_954, 818 col_955, 819 col_956, 820 col_957, 821 col_960, 822 col_961, 823 col_962, 824 col_963, 825 col_964, 826 col_965, 827 col_966, 828 col_967, 829 col_968, 830 col_969, 831 col_971, 832 col_972, 833 col_973, 834 col_974, 835 col_975, 836 col_976, 837 col_977, 838 col_978, 839 col_979, 840 col_980, 841 col_981, 842 col_982, 843 col_983, 844 col_984, 845 col_985, 846 col_986, 847 col_987, 848 col_988, 849 col_989, 850 col_990, 851 col_991, 852 col_994, 853 col_995, 854 col_996, 855 col_998, 856 col_999, 857 col_1008, 858 col_1009, 859 col_1010, 860 col_1012, 861 col_1013, 862 col_1014, 863 col_1015, 864 col_1016, 865 col_1017, 866 col_1018, 867 col_1019, 868 col_1020, 869 col_1021, 870 col_1022, 871 col_1023, 872 col_1024, 873 col_1025, 874 col_1026, 875 col_1027, 876 col_1028, 877 col_1029, 878 col_1030, 879 col_1031, 880 col_1032, 881 col_1033, 882 col_1034, 883 col_1035, 884 col_1036, 885 col_1037, 886 col_1038, 887 col_1039, 888 col_1040, 889 col_1041, 890 col_1042, 891 col_1043, 892 col_1044, 893 col_1045, 894 col_1047, 895 col_1052, 896 col_1056, 897 col_1057, 898 col_1058, 899 col_1059, 900 col_1060, 901 col_1062, 902 col_1067, 903 col_1068, 904 col_1069, 905 col_1070, 906 col_1071, 907 col_1072, 908 col_1073, 909 col_1074, 910 col_1075, 911 col_1076, 912 col_1077, 913 col_1078, 914 col_1079, 915 col_1080, 916 col_1081, 917 col_1082, 918 col_1083, 919 col_1084, 920 col_1085, 921 col_1086, 922 col_1089, 923 col_1090, 924 col_1091, 925 col_1092, 926 col_1096, 927 col_1097, 928 col_1098, 929 col_1099, 930 col_1100, 931 col_1101, 932 col_1102, 933 col_1103, 934 col_1104, 935 col_1105, 936 col_1106, 937 col_1107, 938 col_1108, 939 col_1109, 940 col_1111, 941 col_1112, 942 col_1114, 943 col_1115, 944 col_1116, 945 col_1117, 946 col_1118, 947 col_1119, 948 col_1120, 949 col_1121, 950 col_1123, 951 col_1124, 952 col_1125, 953 col_1126, 954 col_1127, 955 col_1128, 956 col_1129, 957 col_1130, 958 col_1131, 959 col_1132, 960 col_1134, 961 col_1135, 962 col_1136, 963 col_1137, 964 col_1138, 965 col_1139, 966 col_1141, 967 col_1142, 968 col_1143, 969 col_1144, 970 col_1146, 971 col_1147, 972 col_1148, 973 col_1150, 974 col_1153, 975 col_1154, 976 col_1157, 977 col_1158, 978 col_1159, 979 col_1161, 980 col_1163, 981 col_1164, 982 col_1167, 983 col_1168, 984 col_1171, 985 col_1172, 986 col_1173, 987 col_1174, 988 col_1175, 989 col_1176, 990 col_1177, 991 col_1178, 992 col_1179, 993 col_1182, 994 col_1183, 995 col_1184, 996 col_1185, 997 col_1186, 998 col_1187, 999 col_1188, 1000 col_1189, 1001 col_1190, 1002 col_1191, 1003 col_1192, 1004 col_1193, 1005 col_1194, 1006 col_1195, 1007 col_1196, 1008 col_1197, 1009 col_1198, 1010 col_1199, 1011 col_1200, 1012 col_1201, 1013 col_1202, 1014 col_1203, 1015 col_1204, 1016 col_1205, 1017 col_1208, 1018 col_1209, 1019 col_1211, 1020 col_1212, 1021 col_1213, 1022 col_1214, 1023 col_1215, 1024 col_1216, 1025 col_1217, 1026 col_1218, 1027 col_1219, 1028 col_1220, 1029 col_1221, 1030 col_1223, 1031 col_1224, 1032 col_1228, 1033 col_1229, 1034 col_1230, 1035 col_1231, 1036 col_1232, 1037 col_1233, 1038 col_1234, 1039 col_1235, 1040 col_1236, 1041 col_1237, 1042 col_1238, 1043 col_1239, 1044 col_1240, 1045 col_1241, 1046 col_1247, 1047 col_1248, 1048 col_1249, 1049 col_1250, 1050 col_1251, 1051 col_1252, 1052 col_1253, 1053 col_1254, 1054 col_1255, 1055 col_1256, 1056 col_1257, 1057 col_1258, 1058 col_1262, 1059 col_1267, 1060 col_1268, 1061 col_1269, 1062 col_1270, 1063 col_1271, 1064 col_1272, 1065 col_1273, 1066 col_1274, 1067 col_1275, 1068 col_1276, 1069 col_1277, 1070 col_1278, 1071 col_1279, 1072 col_1280, 1073 col_1281, 1074 col_1282, 1075 col_1283, 1076 col_1286, 1077 col_1288, 1078 col_1289, 1079 col_1290, 1080 col_1291, 1081 col_1292, 1082 col_1293, 1083 col_1294, 1084 col_1295, 1085 col_1296, 1086 col_1297, 1087 col_1298, 1088 col_1299, 1089 col_1300, 1090 col_1301, 1091 col_1302, 1092 col_1303, 1093 col_1304, 1094 col_1305, 1095 col_1306, 1096 col_1307, 1097 col_1308, 1098 col_1309, 1099 col_1310, 1100 col_1311, 1101 col_1312, 1102 col_1313, 1103 col_1314, 1104 col_1315, 1105 col_1316, 1106 col_1317, 1107 col_1318, 1108 col_1319, 1109 col_1320, 1110 col_1321, 1111 col_1322, 1112 col_1323, 1113 col_1324, 1114 col_1325, 1115 col_1326, 1116 col_1327, 1117 col_1328, 1118 col_1329, 1119 col_1330, 1120 col_1331, 1121 col_1332, 1122 col_1333, 1123 col_1334, 1124 col_1335, 1125 col_1336, 1126 col_1337, 1127 col_1338, 1128 col_1339, 1129 col_1340, 1130 col_1341, 1131 col_1342, 1132 col_1343, 1133 col_1344, 1134 col_1345, 1135 col_1346, 1136 col_1347, 1137 col_1348, 1138 col_1349, 1139 col_1350, 1140 col_1351, 1141 col_1352, 1142 col_1353, 1143 col_1354, 1144 col_1357, 1145 col_1358, 1146 col_1359, 1147 col_1360, 1148 col_1361, 1149 col_1362, 1150 col_1363, 1151 col_1367, 1152 col_1369, 1153 col_1370, 1154 col_1371, 1155 col_1372, 1156 col_1373, 1157 col_1374, 1158 col_1377, 1159 col_1378, 1160 col_1379, 1161 col_1380, 1162 col_1381, 1163 col_1382, 1164 col_1383, 1165 col_1384, 1166 col_1385, 1167 col_1386, 1168 col_1387, 1169 col_1388, 1170 col_1389, 1171 col_1390, 1172 col_1391, 1173 col_1392, 1174 col_1393, 1175 col_1394, 1176 col_1395, 1177 col_1396, 1178 col_1397, 1179 col_1399, 1180 col_1400, 1181 col_1401, 1182 col_1402, 1183 col_1403, 1184 col_1404, 1185 col_1405, 1186 col_1406, 1187 col_1407, 1188 col_1408, 1189 col_1411, 1190 col_1412, 1191 col_1413, 1192 col_1414, 1193 col_1415, 1194 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1261 col_1502, 1262 col_1503, 1263 col_1504, 1264 col_1505, 1265 col_1506, 1266 col_1507, 1267 col_1508, 1268 col_1509, 1269 col_1510, 1270 col_1511, 1271 col_1512, 1272 col_1513, 1273 col_1514, 1274 col_1515, 1275 col_1516, 1276 col_1517, 1277 col_1518, 1278 col_1519, 1279 col_1520, 1280 col_1521, 1281 col_1522, 1282 col_1523, 1283 col_1524, 1284 col_1525, 1285 col_1526, 1286 col_1527, 1287 col_1528, 1288 col_1529, 1289 col_1531, 1290 col_1533, 1291 col_1534, 1292 col_1535, 1293 col_1536, 1294 col_1537, 1295 col_1538, 1296 col_1539, 1297 col_1540, 1298 col_1541, 1299 col_1542, 1300 col_1543, 1301 col_1544, 1302 col_1545, 1303 col_1546, 1304 col_1547, 1305 col_1548, 1306 col_1549, 1307 col_1550, 1308 col_1551, 1309 col_1552, 1310 col_1553, 1311 col_1554, 1312 col_1555, 1313 col_1556, 1314 col_1557, 1315 col_1559, 1316 col_1560, 1317 col_1561, 1318 col_1562, 1319 col_1563, 1320 col_1564, 1321 col_1565, 1322 col_1566, 1323 col_1567, 1324 col_1568, 1325 col_1569, 1326 col_1570, 1327 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1394 col_1649, 1395 col_1657, 1396 col_1658, 1397 col_1659, 1398 col_1661, 1399 col_1664, 1400 col_1667, 1401 col_1668, 1402 col_1669, 1403 col_1671, 1404 col_1672, 1405 col_1675, 1406 col_1677, 1407 col_1678, 1408 col_1679, 1409 col_1680, 1410 col_1682, 1411 col_1683, 1412 col_1685, 1413 col_1689, 1414 col_1690, 1415 col_1691, 1416 col_1692, 1417 col_1693, 1418 col_1694, 1419 col_1695, 1420 col_1696, 1421 col_1697, 1422 col_1698, 1423 col_1699, 1424 col_1700, 1425 col_1701, 1426 col_1702, 1427 col_1703, 1428 col_1704, 1429 col_1705, 1430 col_1706, 1431 col_1707, 1432 col_1708, 1433 col_1709, 1434 col_1710, 1435 col_1711, 1436 col_1712, 1437 col_1713, 1438 col_1714, 1439 col_1715, 1440 col_1716, 1441 col_1717, 1442 col_1718, 1443 col_1719, 1444 col_1720, 1445 col_1721, 1446 col_1722, 1447 col_1723, 1448 col_1724, 1449 col_1725, 1450 col_1726, 1451 col_1728, 1452 col_1732, 1453 col_1733, 1454 col_1734, 1455 col_1735, 1456 col_1736, 1457 col_1737, 1458 col_1738, 1459 col_1739, 1460 col_1740, 1461 col_1741, 1462 col_1742, 1463 col_1743, 1464 col_1744, 1465 col_1745, 1466 col_1747, 1467 col_1748, 1468 col_1750, 1469 col_1751, 1470 col_1752, 1471 col_1753, 1472 col_1754, 1473 col_1755, 1474 col_1756, 1475 col_1757, 1476 col_1758, 1477 col_1759, 1478 col_1760, 1479 col_1761, 1480 col_1762, 1481 col_1763, 1482 col_1764, 1483 col_1765, 1484 col_1766, 1485 col_1767, 1486 col_1770, 1487 col_1771, 1488 col_1772, 1489 col_1773, 1490 col_1774, 1491 col_1775, 1492 col_1776, 1493 col_1777, 1494 col_1779, 1495 col_1780, 1496 col_1781, 1497 col_1782, 1498 col_1783, 1499 col_1784, 1500 col_1785, 1501 col_1786, 1502 col_1787, 1503 col_1788, 1504 col_1789, 1505 col_1790, 1506 col_1791, 1507 col_1792, 1508 col_1795, 1509 col_1796, 1510 col_1797, 1511 col_1798, 1512 col_1799, 1513 col_1800, 1514 col_1801, 1515 col_1802, 1516 col_1803, 1517 col_1804, 1518 col_1805, 1519 col_1806, 1520 col_1807, 1521 col_1808, 1522 col_1809, 1523 col_1810, 1524 col_1811, 1525 col_1812, 1526 col_1813, 1527 col_1819, 1528 col_1820, 1529 col_1821, 1530 col_1822, 1531 col_1824, 1532 col_1825, 1533 col_1827, 1534 col_1828, 1535 col_1829, 1536 col_1832, 1537 col_1833, 1538 col_1834, 1539 col_1835, 1540 col_1836, 1541 col_1837, 1542 col_1838, 1543 col_1839, 1544 col_1840, 1545 col_1841, 1546 col_1842, 1547 col_1843, 1548 col_1844, 1549 col_1846, 1550 col_1847, 1551 col_1848, 1552 col_1849, 1553 col_1851, 1554 col_1852, 1555 col_1853, 1556 col_1854, 1557 col_1855, 1558 col_1859, 1559 col_1860, 1560 col_1861, 1561 col_1862, 1562 col_1863, 1563 col_1864, 1564 col_1865, 1565 col_1866, 1566 col_1867, 1567 col_1869, 1568 col_1870, 1569 col_1872, 1570 col_1873, 1571 col_1874, 1572 col_1875, 1573 col_1876, 1574 col_1877, 1575 col_1878, 1576 col_1879, 1577 col_1880, 1578 col_1881, 1579 col_1882, 1580 col_1883, 1581 col_1884, 1582 col_1885, 1583 col_1886, 1584 col_1887, 1585 col_1888, 1586 col_1889, 1587 col_1890, 1588 col_1891, 1589 col_1892, 1590 col_1895, 1591 col_1896, 1592 col_1897, 1593 col_1898, 1594 col_1899, 1595 col_1900, 1596 col_1901, 1597 col_1904, 1598 col_1905, 1599 col_1906, 1600 col_1907, 1601 col_1908, 1602 col_1909, 1603 col_1910, 1604 col_1911, 1605 col_1912, 1606 col_1913, 1607 col_1914, 1608 col_1915, 1609 col_1919, 1610 col_1920, 1611 col_1921, 1612 col_1922, 1613 col_1923, 1614 col_1924, 1615 col_1925, 1616 col_1926, 1617 col_1927, 1618 col_1928, 1619 col_1929, 1620 col_1930, 1621 col_1931, 1622 col_1932, 1623 col_1933, 1624 col_1934, 1625 col_1935, 1626 col_1936, 1627 col_1937, 1628 col_1938, 1629 col_1939, 1630 col_1941, 1631 col_1942, 1632 col_1943, 1633 col_1944, 1634 col_1945, 1635 col_1946, 1636 col_1947, 1637 col_1948, 1638 col_1949, 1639 col_1950, 1640 col_1951, 1641 col_1952, 1642 col_1953, 1643 col_1954, 1644 col_1955, 1645 col_1956, 1646 col_1957, 1647 col_1961, 1648 col_1962, 1649 col_1963, 1650 col_1964, 1651 col_1965, 1652 col_1966, 1653 col_1967, 1654 col_1968, 1655 col_1969, 1656 col_1973, 1657 col_1974, 1658 col_1975, 1659 col_1976, 1660 col_1979, 1661 col_1984, 1662 col_1985, 1663 col_1986, 1664 col_1987, 1665 col_1990, 1666 col_1992, 1667 col_1994, 1668 col_1995, 1669 col_1997, 1670 col_1998, 1671 col_1999, 1672 col_2000, 1673 col_2001, 1674 col_2002, 1675 col_2003, 1676 col_2004, 1677 col_2005, 1678 col_2006, 1679 col_2007, 1680 col_2008, 1681 col_2009, 1682 col_2010, 1683 col_2011, 1684 col_2012, 1685 col_2013, 1686 col_2014, 1687 col_2015, 1688 col_2016, 1689 col_2017, 1690 col_2018, 1691 col_2019, 1692 col_2020, 1693 col_2021, 1694 col_2022, 1695 col_2023, 1696 col_2026, 1697 col_2027, 1698 col_2028, 1699 col_2029, 1700 col_2030, 1701 col_2031, 1702 col_2032, 1703 col_2033, 1704 col_2034, 1705 col_2035, 1706 col_2036, 1707 col_2038, 1708 col_2039, 1709 col_2040, 1710 col_2041, 1711 col_2042, 1712 col_2043, 1713 col_2044, 1714 col_2045, 1715 col_2046, 1716 col_2047, 1717 col_2048, 1718 col_2049, 1719 col_2050, 1720 col_2051, 1721 col_2052, 1722 col_2053, 1723 col_2054, 1724 col_2055, 1725 col_2056, 1726 col_2057, 1727 col_2058, 1728 col_2059, 1729 col_2060, 1730 col_2061, 1731 col_2062, 1732 col_2063, 1733 col_2064, 1734 col_2065, 1735 col_2066, 1736 col_2067, 1737 col_2068, 1738 col_2069, 1739 col_2070, 1740 col_2071, 1741 col_2072, 1742 col_2073, 1743 col_2074, 1744 col_2075, 1745 col_2076, 1746 col_2077, 1747 col_2078, 1748 col_2079, 1749 col_2082, 1750 col_2085, 1751 col_2086, 1752 col_2087, 1753 col_2088, 1754 col_2089, 1755 col_2091, 1756 col_2092, 1757 col_2093, 1758 col_2094, 1759 col_2095, 1760 col_2096, 1761 col_2097, 1762 col_2098, 1763 col_2099, 1764 col_2100, 1765 col_2101, 1766 col_2102, 1767 col_2103, 1768 col_2104, 1769 col_2105, 1770 col_2106, 1771 col_2108, 1772 col_2109, 1773 col_2116, 1774 col_2117, 1775 col_2118, 1776 col_2119, 1777 col_2120, 1778 col_2121, 1779 col_2122, 1780 col_2123, 1781 col_2124, 1782 col_2125, 1783 col_2126, 1784 col_2127, 1785 col_2128, 1786 col_2129, 1787 col_2130, 1788 col_2131, 1789 col_2132, 1790 col_2133, 1791 col_2134, 1792 col_2135, 1793 col_2136, 1794 col_2137, 1795 col_2138, 1796 col_2139, 1797 col_2140, 1798 col_2141, 1799 col_2142, 1800 col_2143, 1801 col_2144, 1802 col_2145, 1803 col_2146, 1804 col_2147, 1805 col_2148, 1806 col_2149, 1807 col_2150, 1808 col_2151, 1809 col_2152, 1810 col_2153, 1811 col_2154, 1812 col_2155, 1813 col_2157, 1814 col_2158, 1815 col_2159, 1816 col_2160, 1817 col_2161, 1818 col_2162, 1819 col_2163, 1820 col_2164, 1821 col_2165, 1822 col_2166, 1823 col_2167, 1824 col_2168, 1825 col_2169, 1826 col_2170, 1827 col_2173, 1828 col_2174, 1829 col_2175, 1830 col_2177, 1831 col_2178, 1832 col_2179, 1833 col_2181, 1834 col_2182, 1835 col_2183, 1836 col_2184, 1837 col_2187, 1838 col_2188, 1839 col_2189, 1840 col_2190, 1841 col_2191, 1842 col_2192, 1843 col_2193, 1844 col_2194, 1845 col_2195, 1846 col_2196, 1847 col_2197, 1848 col_2201, 1849 col_2202, 1850 col_2203, 1851 col_2206, 1852 col_2208, 1853 col_2210, 1854 col_2211, 1855 col_2212, 1856 col_2213, 1857 col_2216, 1858 col_2217, 1859 col_2218, 1860 col_2219, 1861 col_2220, 1862 col_2221, 1863 col_2222, 1864 col_2223, 1865 col_2224, 1866 col_2225, 1867 col_2226, 1868 col_2227, 1869 col_2228, 1870 col_2230, 1871 col_2231, 1872 col_2232, 1873 col_2233, 1874 col_2234, 1875 col_2237, 1876 col_2238, 1877 col_2239, 1878 col_2240, 1879 col_2241, 1880 col_2242, 1881 col_2243, 1882 col_2244, 1883 col_2245, 1884 col_2246, 1885 col_2247, 1886 col_2248, 1887 col_2249, 1888 col_2250, 1889 col_2251, 1890 col_2252, 1891 col_2256, 1892 col_2259, 1893 col_2260, 1894 col_2261, 1895 col_2262, 1896 col_2263, 1897 col_2264, 1898 col_2265, 1899 col_2266, 1900 col_2270, 1901 col_2271, 1902 col_2272, 1903 col_2274, 1904 col_2275, 1905 col_2276, 1906 col_2277, 1907 col_2278, 1908 col_2279, 1909 col_2280, 1910 col_2281, 1911 col_2282, 1912 col_2283, 1913 col_2284, 1914 col_2285, 1915 col_2287, 1916 col_2288, 1917 col_2289, 1918 col_2290, 1919 col_2291, 1920 col_2292, 1921 col_2293, 1922 col_2294, 1923 col_2296, 1924 col_2297, 1925 col_2298, 1926 col_2299, 1927 col_2300, 1928 col_2301, 1929 col_2302, 1930 col_2303, 1931 col_2304, 1932 col_2305, 1933 col_2306, 1934 col_2307, 1935 col_2308, 1936 col_2309, 1937 col_2310, 1938 col_2311, 1939 col_2312, 1940 col_2313, 1941 col_2314, 1942 col_2315, 1943 col_2316, 1944 col_2317, 1945 col_2318, 1946 col_2319, 1947 col_2320, 1948 col_2321, 1949 col_2322, 1950 col_2323, 1951 col_2324, 1952 col_2325, 1953 col_2326, 1954 col_2327, 1955 col_2328, 1956 col_2329, 1957 col_2330, 1958 col_2331, 1959 col_2332, 1960 col_2333, 1961 col_2334, 1962 col_2335, 1963 col_2336, 1964 col_2337, 1965 col_2338, 1966 col_2339, 1967 col_2340, 1968 col_2341, 1969 col_2342, 1970 col_2343, 1971 col_2344, 1972 col_2345, 1973 col_2346, 1974 col_2347, 1975 col_2348, 1976 col_2349, 1977 col_2350, 1978 col_2351, 1979 col_2352, 1980 col_2353, 1981 col_2354, 1982 col_2355, 1983 col_2356, 1984 col_2357, 1985 col_2358, 1986 col_2359, 1987 col_2360, 1988 col_2363, 1989 col_2364, 1990 col_2365, 1991 col_2366, 1992 col_2367, 1993 col_2368, 1994 col_2369, 1995 col_2370, 1996 col_2371, 1997 col_2372, 1998 col_2373, 1999 col_2374, 2000 col_2375, 2001 col_2376, 2002 col_2377, 2003 col_2378, 2004 col_2379, 2005 col_2380, 2006 col_2381, 2007 col_2382, 2008 col_2386, 2009 col_2387, 2010 col_2388, 2011 col_2389, 2012 col_2390, 2013 col_2391, 2014 col_2392, 2015 col_2394, 2016 col_2395, 2017 col_2396, 2018 col_2400, 2019 col_2402, 2020 col_2403, 2021 col_2405, 2022 col_2406, 2023 col_2408, 2024 col_2409, 2025 col_2410, 2026 col_2411, 2027 col_2412, 2028 col_2413, 2029 col_2414, 2030 col_2415, 2031 col_2416, 2032 col_2417, 2033 col_2418, 2034 col_2421, 2035 col_2422, 2036 col_2423, 2037 col_2424, 2038 col_2425, 2039 col_2426, 2040 col_2427, 2041 col_2429, 2042 col_2432, 2043 col_2435, 2044 col_2437, 2045 col_2438, 2046 col_2439, 2047 col_2440, 2048 col_2441, 2049 col_2442, 2050 col_2443, 2051 col_2444, 2052 col_2445, 2053 col_2447, 2054 col_2448, 2055 col_2450, 2056 col_2451, 2057 col_2452, 2058 col_2453, 2059 col_2454, 2060 col_2455, 2061 col_2456, 2062 col_2457, 2063 col_2462, 2064 col_2463, 2065 col_2464, 2066 col_2465, 2067 col_2466, 2068 col_2467, 2069 col_2468, 2070 col_2469, 2071 col_2470, 2072 col_2471, 2073 col_2472, 2074 col_2473, 2075 col_2474, 2076 col_2475, 2077 col_2476, 2078 col_2477, 2079 col_2478, 2080 col_2479, 2081 col_2480, 2082 col_2481, 2083 col_2482, 2084 col_2483, 2085 col_2484, 2086 col_2485, 2087 col_2486, 2088 col_2487, 2089 col_2489, 2090 col_2490, 2091 col_2491, 2092 col_2492, 2093 col_2493, 2094 col_2494, 2095 col_2495, 2096 col_2496, 2097 col_2497, 2098 col_2498, 2099 col_2499, 2100 col_2500, 2101 col_2501, 2102 col_2503, 2103 col_2504, 2104 col_2505, 2105 col_2506, 2106 col_2507, 2107 col_2508, 2108 col_2509, 2109 col_2510, 2110 col_2511, 2111 col_2512, 2112 col_2513, 2113 col_2514, 2114 col_2515, 2115 col_2517, 2116 col_2518, 2117 col_2519, 2118 col_2520, 2119 col_2522, 2120 col_2524, 2121 col_2526, 2122 col_2527, 2123 col_2530, 2124 col_2531, 2125 col_2532, 2126 col_2533, 2127 col_2534, 2128 col_2535, 2129 col_2536, 2130 col_2537, 2131 col_2539, 2132 col_2540, 2133 col_2541, 2134 col_2543, 2135 col_2544, 2136 col_2545, 2137 col_2546, 2138 col_2547, 2139 col_2548, 2140 col_2549, 2141 col_2550, 2142 col_2551, 2143 col_2552, 2144 col_2553, 2145 col_2554, 2146 col_2555, 2147 col_2556, 2148 col_2557, 2149 col_2558, 2150 col_2559, 2151 col_2560, 2152 col_2561, 2153 col_2562, 2154 col_2563, 2155 col_2564, 2156 col_2565, 2157 col_2566, 2158 col_2567, 2159 col_2568, 2160 col_2569, 2161 col_2570, 2162 col_2571, 2163 col_2572, 2164 col_2573, 2165 col_2574, 2166 col_2577, 2167 col_2578, 2168 col_2579, 2169 col_2580, 2170 col_2581, 2171 col_2582, 2172 col_2584, 2173 col_2585, 2174 col_2586, 2175 col_2587, 2176 col_2588, 2177 col_2589, 2178 col_2590, 2179 col_2591, 2180 col_2592, 2181 col_2593, 2182 col_2594, 2183 col_2595, 2184 col_2596, 2185 col_2597, 2186 col_2598, 2187 col_2599, 2188 col_2600, 2189 col_2601, 2190 col_2602, 2191 col_2603, 2192 col_2604, 2193 col_2605, 2194 col_2606, 2195 col_2607, 2196 col_2608, 2197 col_2610, 2198 col_2611, 2199 col_2612, 2200 col_2613, 2201 col_2614, 2202 col_2615, 2203 col_2616, 2204 col_2617, 2205 col_2618, 2206 col_2619, 2207 col_2620, 2208 col_2621, 2209 col_2623, 2210 col_2624, 2211 col_2625, 2212 col_2626, 2213 col_2627, 2214 col_2628, 2215 col_2629, 2216 col_2630, 2217 col_2632, 2218 col_2633, 2219 col_2634, 2220 col_2635, 2221 col_2636, 2222 col_2637, 2223 col_2638, 2224 col_2639, 2225 col_2640, 2226 col_2641, 2227 col_2642, 2228 col_2643, 2229 col_2644, 2230 col_2645, 2231 col_2646, 2232 col_2647, 2233 col_2648, 2234 col_2649, 2235 col_2650, 2236 col_2651, 2237 col_2652, 2238 col_2653, 2239 col_2654, 2240 col_2655, 2241 col_2656, 2242 col_2657, 2243 col_2658, 2244 col_2659, 2245 col_2660, 2246 col_2661, 2247 col_2662, 2248 col_2663, 2249 col_2664, 2250 col_2665, 2251 col_2666, 2252 col_2667, 2253 col_2668, 2254 col_2669, 2255 col_2670, 2256 col_2671, 2257 col_2672, 2258 col_2673, 2259 col_2674, 2260 col_2675, 2261 col_2676, 2262 col_2677, 2263 col_2678, 2264 col_2679, 2265 col_2680, 2266 col_2681, 2267 col_2682, 2268 col_2683, 2269 col_2684, 2270 col_2685, 2271 col_2686, 2272 col_2687, 2273 col_2688, 2274 col_2689, 2275 col_2690, 2276 col_2691, 2277 col_2692, 2278 col_2693, 2279 col_2694, 2280 col_2695, 2281 col_2696, 2282 col_2697, 2283 col_2698, 2284 col_2699, 2285 col_2700, 2286 col_2701, 2287 col_2702, 2288 col_2703, 2289 col_2704, 2290 col_2705, 2291 col_2706, 2292 col_2707, 2293 col_2708, 2294 col_2709, 2295 col_2710, 2296 col_2712, 2297 col_2713, 2298 col_2714, 2299 col_2715, 2300 col_2716, 2301 col_2717, 2302 col_2718, 2303 col_2719, 2304 col_2720, 2305 col_2721, 2306 col_2722, 2307 col_2723, 2308 col_2724, 2309 col_2725, 2310 col_2726, 2311 col_2727, 2312 col_2728, 2313 col_2729, 2314 col_2730, 2315 col_2731, 2316 col_2732, 2317 col_2735, 2318 col_2737, 2319 col_2738, 2320 col_2739, 2321 col_2740, 2322 col_2741, 2323 col_2742, 2324 col_2743, 2325 col_2744, 2326 col_2745, 2327 col_2746, 2328 col_2747, 2329 col_2748, 2330 col_2749, 2331 col_2750, 2332 col_2751, 2333 col_2752, 2334 col_2758, 2335 col_2759, 2336 col_2760, 2337 col_2761, 2338 col_2762, 2339 col_2763, 2340 col_2764, 2341 col_2765, 2342 col_2766, 2343 col_2767, 2344 col_2768, 2345 col_2769, 2346 col_2770, 2347 col_2771, 2348 col_2772, 2349 col_2773, 2350 col_2774, 2351 col_2775, 2352 col_2776, 2353 col_2777, 2354 col_2779, 2355 col_2780, 2356 col_2782, 2357 col_2783, 2358 col_2784, 2359 col_2785, 2360 col_2787, 2361 col_2788, 2362 col_2789, 2363 col_2790, 2364 col_2791, 2365 col_2792, 2366 col_2793, 2367 col_2794, 2368 col_2795, 2369 col_2796, 2370 col_2797, 2371 col_2798, 2372 col_2799, 2373 col_2800, 2374 col_2803, 2375 col_2804, 2376 col_2805, 2377 col_2806, 2378 col_2807, 2379 col_2808, 2380 col_2809, 2381 col_2810, 2382 col_2811, 2383 col_2812, 2384 col_2813, 2385 col_2814, 2386 col_2815, 2387 col_2816, 2388 col_2817, 2389 col_2818, 2390 col_2819, 2391 col_2820, 2392 col_2821, 2393 col_2822, 2394 col_2823, 2395 col_2824, 2396 col_2825, 2397 col_2826, 2398 col_2827, 2399 col_2828, 2400 col_2829, 2401 col_2830, 2402 col_2831, 2403 col_2832, 2404 col_2833, 2405 col_2834, 2406 col_2835, 2407 col_2836, 2408 col_2837, 2409 col_2838, 2410 col_2839, 2411 col_2840, 2412 col_2841, 2413 col_2842, 2414 col_2843, 2415 col_2844, 2416 col_2845, 2417 col_2846, 2418 col_2848, 2419 col_2849, 2420 col_2855, 2421 col_2856, 2422 col_2857, 2423 col_2859, 2424 col_2860, 2425 col_2861, 2426 col_2862, 2427 col_2863, 2428 col_2864, 2429 col_2865, 2430 col_2866, 2431 col_2867, 2432 col_2868, 2433 col_2869, 2434 col_2870, 2435 col_2871, 2436 col_2872, 2437 col_2873, 2438 col_2874, 2439 col_2875, 2440 col_2876, 2441 col_2877, 2442 col_2878, 2443 col_2879, 2444 col_2880, 2445 col_2881, 2446 col_2882, 2447 col_2883, 2448 col_2884, 2449 col_2885, 2450 col_2886, 2451 col_2887, 2452 col_2888, 2453 col_2889, 2454 col_2890, 2455 col_2891, 2456 col_2892, 2457 col_2893, 2458 col_2894, 2459 col_2895, 2460 col_2896, 2461 col_2897, 2462 col_2898, 2463 col_2899, 2464 col_2900, 2465 col_2901, 2466 col_2902, 2467 col_2903, 2468 col_2904, 2469 col_2905, 2470 col_2906, 2471 col_2908, 2472 col_2909, 2473 col_2913, 2474 col_2914, 2475 col_2915, 2476 col_2916, 2477 col_2920, 2478 col_2921, 2479 col_2922, 2480 col_2923, 2481 col_2924, 2482 col_2925, 2483 col_2926, 2484 col_2927, 2485 col_2928, 2486 col_2929, 2487 col_2930, 2488 col_2931, 2489 col_2932, 2490 col_2933, 2491 col_2934, 2492 col_2935, 2493 col_2936, 2494 col_2937, 2495 col_2938, 2496 col_2939, 2497 col_2940, 2498 col_2941, 2499 col_2942, 2500 col_2943, 2501 col_2944, 2502 col_2945, 2503 col_2946, 2504 col_2947, 2505 col_2948, 2506 col_2949, 2507 col_2950, 2508 col_2951, 2509 col_2952, 2510 col_2953, 2511 col_2954, 2512 col_2955, 2513 col_2956, 2514 col_2957, 2515 col_2958, 2516 col_2959, 2517 col_2960, 2518 col_2961, 2519 col_2962, 2520 col_2963, 2521 col_2964, 2522 col_2965, 2523 col_2966, 2524 col_2967, 2525 col_2968, 2526 col_2969, 2527 col_2970, 2528 col_2971, 2529 col_2972, 2530 col_2973, 2531 col_2974, 2532 col_2976, 2533 col_2979, 2534 col_2980, 2535 col_2982, 2536 col_2983, 2537 col_2986, 2538 col_2987, 2539 col_2988, 2540 col_2989, 2541 col_2990, 2542 col_2991, 2543 col_2992, 2544 col_2993, 2545 col_2994, 2546 col_2995, 2547 col_2996, 2548 col_2997, 2549 col_2998, 2550 col_2999, 2551 col_3000, 2552 col_3001, 2553 col_3002, 2554 col_3003, 2555 col_3004, 2556 col_3005, 2557 col_3006, 2558 col_3007, 2559 col_3008, 2560 col_3009, 2561 col_3010, 2562 col_3011, 2563 col_3013, 2564 col_3017, 2565 col_3018, 2566 col_3019, 2567 col_3020, 2568 col_3021, 2569 col_3022, 2570 col_3023, 2571 col_3024, 2572 col_3025, 2573 col_3026, 2574 col_3027, 2575 col_3028, 2576 col_3029, 2577 col_3030, 2578 col_3031, 2579 col_3032, 2580 col_3033, 2581 col_3034, 2582 col_3035, 2583 col_3036, 2584 col_3037, 2585 col_3038, 2586 col_3040, 2587 col_3041, 2588 col_3042, 2589 col_3043, 2590 col_3044, 2591 col_3045, 2592 col_3046, 2593 col_3047, 2594 col_3048, 2595 col_3049, 2596 col_3050, 2597 col_3051, 2598 col_3052, 2599 col_3053, 2600 col_3054, 2601 col_3055, 2602 col_3056, 2603 col_3057, 2604 col_3058, 2605 col_3059, 2606 col_3060, 2607 col_3061, 2608 col_3062, 2609 col_3063, 2610 col_3064, 2611 col_3065, 2612 col_3066, 2613 col_3069, 2614 col_3070, 2615 col_3071, 2616 col_3072, 2617 col_3073, 2618 col_3074, 2619 col_3075, 2620 col_3077, 2621 col_3078, 2622 col_3079, 2623 col_3080, 2624 col_3083, 2625 col_3084, 2626 col_3085, 2627 col_3086, 2628 col_3087, 2629 col_3088, 2630 col_3089, 2631 col_3090, 2632 col_3091, 2633 col_3092, 2634 col_3093, 2635 col_3094, 2636 col_3095, 2637 col_3096, 2638 col_3097, 2639 col_3098, 2640 col_3099, 2641 col_3100, 2642 col_3101, 2643 col_3103, 2644 col_3104, 2645 col_3105, 2646 col_3106, 2647 col_3108, 2648 col_3109, 2649 col_3113, 2650 col_3114, 2651 col_3115, 2652 col_3116, 2653 col_3117, 2654 col_3118, 2655 col_3119, 2656 col_3120, 2657 col_3121, 2658 col_3122, 2659 col_3123, 2660 col_3124, 2661 col_3125, 2662 col_3126, 2663 col_3127, 2664 col_3129, 2665 col_3130, 2666 col_3131, 2667 col_3134, 2668 col_3135, 2669 col_3136, 2670 col_3137, 2671 col_3138, 2672 col_3139, 2673 col_3140, 2674 col_3141, 2675 col_3142, 2676 col_3143, 2677 col_3144, 2678 col_3145, 2679 col_3146, 2680 col_3147, 2681 col_3148, 2682 col_3149, 2683 col_3150, 2684 col_3151, 2685 col_3152, 2686 col_3153, 2687 col_3154, 2688 col_3155, 2689 col_3156, 2690 col_3159, 2691 col_3160, 2692 col_3161, 2693 col_3162, 2694 col_3163, 2695 col_3164, 2696 col_3165, 2697 col_3166, 2698 col_3167, 2699 col_3168, 2700 col_3169, 2701 col_3170, 2702 col_3171, 2703 col_3172, 2704 col_3173, 2705 col_3174, 2706 col_3176, 2707 col_3177, 2708 col_3178, 2709 col_3179, 2710 col_3180, 2711 col_3184, 2712 col_3188, 2713 col_3191, 2714 col_3196, 2715 col_3197, 2716 col_3198, 2717 col_3199, 2718 col_3200, 2719 col_3201, 2720 col_3202, 2721 col_3205, 2722 col_3206, 2723 col_3208 ; Format Datatype = Standard Symbols="01" missing = '?' ; Matrix temporariaDMH84R1 111011111010011110110000000001111111001000101101111110000000000000011011111111001110111001101111011001100010000000000000000101000000000111101111001111111100000011111111000011011101100111000110000000000001111111110111111111100011111111101110000000000101111011011111110001100000001111111110011000000001000001010001110011111110000000111111111100100000000110010000000000111111100111101111111111010000000000000000000010111111101111110000011110111111110001001110111111111111111111110111100010000110000011100000110001001011100000000000110111111100100001111111111000000101110111000111100111000000011110111111111110110000000001100001101100011101100010000001000001111110000110000000111100000111110010111111111110011111011100000111111011101110011?1111111111101110000001101111111111111111100010000001111?1111000000110111111101000001111111111100001101101111111001110011000111111100011111111101001111110000100000000110011000001110001001000000110000001110011110111110111111111110111111111100011110000000000011110000001001100000000011100111111110011111100111011111111010110011110010111011111110000000011001000000000000000000000001011011000000111100000011011101000000110011110011111000111111100111010000100000001111111101000001110111000000011100111000011111111?100000001111000000111111111001011011111001111111110000111010100011100111111000000010111101001110111111100010011100000000000111111111110000001111100111111011111111111010001111111111100011111101111111111111011100000000011100000111111111111111111111110110000000000001111111110000111111110111000000011011100000000000011110000000000001111000011111111000001111111000000101111100111100001111111111110000000??10000111111101011111100110111101111111000001111110111111101111111111100011101010111111111100101111100100110110000000001000001101100001111110001110100000010000110000000110000000000001110011111000000000011100101001110001111110111110111000110000011100000000110001000001111000000110111010111000011011011111011100110000000000110101111101111000011100000???0011111111111111101111011111111111111110001110001100000011011000000101100000000011000001000001000001111110111110000000000010000010001111111111000000001110110111011110000110111100000000001110011000000?001111110011111010001111110111100001111110000000011111110001100111011001100000001000011111111110111111111111111111111111000000000001000111111111111101111011011111011000000011111101011111111101011001110111111100111101111101011110001100001011011111111110010111000111111011011110011110000111111110001111100110111111000011111111111000011111000001111111111111101111100000010000000111110000000111111100001111110000010111111000000000010111011100111111111000000001010000000001101111111001110100000000111011011111101010000001110100011011 boyliiMVZ148929 111011111010011110110000000001111111000000000001111110000000000000001011111111001110111001101111011001111010000000000000000101000000000111101111001111111100000011111111000011011101100111000111000000000101111001110111110000000011111111101110000000000101111110011111100001100000001111111110011000000001000001010001110011111110000000111111111100100000000110010000000000011111100111101111111111010000000000000000000010111101101111110000011110111111110001001110111111111111111111110101100011000110000011100000110001001011100000000000110111111100111111000111111000000111110111000110000111000000011110111111101110110000000001100001101100011101100010000001000001111110000110000000111100000001010010100000000110011111011100000111111011101110011?1111111111101010000001101111111111111111100010000001111?1111000000100111110101000001000011111100001101101011111001110011000111111100011111111101001001110000100000001110011000001110001111000001110000001110011110111110111101111000101111111100011110100000000011110000001001100000000011100111111110011111111111010111111010110011110010111011111110000000011001000000000000000000000001011011000000111100000011011101111111110011110011111000111111100111010000110000001111111101000001110111000000011100111000011111111?100000001111000000111111111001011011111001011111110000111010100011100111111000000010111101111110111111100010011100000000000111111111110000001000000111111011111111111011001111111111100011111101111011111111011100000000011100000111111111111111111111110110000000000001111111110000111111111111111000011011100011110110011110000000000001111000011111111000001111111000000110111100111110001111111101110000000??10000111111101011111100110111101111111100101111110111111101111100111100011101010111111111100101111100100110111101001001000001101100001111110001110100000000000011111111110000000000001110011111000000000011100101000000000000010111110111000111111111100000000010001000001111000000110111010111000011011011111011100110000000000110101111101111000011100000???0011110111111111101111011111111111111110001110011100000011011000000101100000000011000001000011000001111110111110000000000000000010001101111111101011000010110111011110000110111100000000000000011000000?001111110011111010001111110111100001111110000000011111110001100111011001100000001000011111111110111111111111111111111111000000000001000111000001111101111011011111011000000011111101011111100000000000010111100100111101111101011110001100001011011111111110010111000111111011011110011110000111111110001111100110111111000011111111111000011111000001111111111111101111100000010000000111110000000111111100001111110000010111111000000000011111010100111100011000000001010000000001101111111001111100000000111011011111111010111111110100011011 luteiventris_MT_MVZ191016 111011111010011110110000000001111111000000000001111110000000000000011011111111001110111001101111011001111010000000000000000101000000000111101111001111111100000011111110000011011101100111000111000000000101111111110111110000000011111111101010000000000000001011011111110001100000001111111110011000000001000001010001110011111110000000111111111100100000000110010000000000111111100111101111111111010000000000000000000010111111101111110000011110111111110001001110111111111111111111110101100011000110000011000000110001001011100000000000110011111100111111000111111000000111110111000110000111000000011110111111111110110000000001100001101100011101100010000001000001111110000110000000111100000001110010100000000110011111011100000111111011101110011?1111111111111110000001101111111111111111100010000001111?1111000000100111111101000001111111111100001101101011111001110011000111111100011111111101001000000000000000001110011000001110001001000001110000001110011110111110111101111000101111111100011110100000000011110000001001100000000011100111111110011111111111011111111010110011110011111011111110000000011001000000000000000000000001011011000000111100000011011101000000110011110011111000111111100111010000110000001111111101000001110111000000011100111000011111111?100000001111000000111111111001011011111001011111110000111010100011100111100000000000001101111110111111100010011100000000000111111111110000001111000111111011111111111010001111111111100011111101111011111111011100000000011100000111111111111111111111110110000000000001111111110000111111111111111000011011100000000110011110000000000001111000011111111000001111111000000110111100111110001111111111110000000??10000111111101010001100110111100000001100101111110111111101111100111100011101010111111111100101111100100110111101001001000001101100001111110001110100000000000011111111110000000000001110010000000000000011100101001110001111110111110111000110000011100000000111001000001111000000110111010111000011011011111011100110000000000110101111101111000011100000???0011110111111111101111011111111111111110001110001100000011011000000101100000000011000001000011000001111110000010000000000000000010001101111111101011001110110111011110000110111100000000001110011000000?001111110011111010001111110111100001110110000000011111110001100111011001100000001000011111111110111111111111111111111111000000000001000111000001000101111011011101011000000011111101011111111111011001110111111100111101111101011110001100001011011111111110010111000111111011011110011110000111111110001111100110111111000011111111111000011111000001111111111111101111100000010000000111110000000111111100001111110000010111111000000000010111010100111100011000000001010000000001101111111001111100000000111011011111111010111111110100011011 luteiventris_WA_MVZ225749 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chiricahuensisJSF1092 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omiltemanaJAC7413 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sp_3_MichoacanJSF955 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tlalociJSF1083 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neovolcanicaJSF960 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berlandieriJSF1136 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blairiJSF830 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sphenocephalaUSC7448 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utriculariaJSF845 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forreriJSF1065 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magnaocularisJSF1073 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sp_7_JaliscoJSF1000 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yavapaiensisJSF1085 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oncaLVT3542 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sp_8_PueblaJAC9467 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macroglossaJAC10472 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macroglossaJSF7933 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taylori286 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sp_4_Panama 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sp_5_CostaRichDMH86_210 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sp_6_CostaRicaDMH86_225 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; END; begin sets; charset one (CHARACTERS = Untitled_DATA_Block_1GapsAsMissing) = 1-.\3; charset two (CHARACTERS = Untitled_DATA_Block_1GapsAsMissing) = 2-.\3; charset three (CHARACTERS = Untitled_DATA_Block_1GapsAsMissing) = 3-.\3; charpartition byPos = 1:one, 2:two, 3:three; end; garli-2.1-release/example/partition/exampleRuns/partitionedDna+Mkv/garli.conf000066400000000000000000000033121241236125200274770ustar00rootroot00000000000000[general] datafname = dnaPlusGapCoding.nex constraintfile = none streefname = random attachmentspertaxon = 100 ofprefix = mixedDnaMkv randseed = -1 availablememory = 512 logevery = 10 saveevery = 100 refinestart = 1 outputeachbettertopology = 0 outputcurrentbesttopology = 0 enforcetermconditions = 1 genthreshfortopoterm = 10000 scorethreshforterm = 0.001 significanttopochange = 0.01 outputphyliptree = 0 outputmostlyuselessfiles = 0 writecheckpoints = 0 restart = 0 outgroup = 1 resampleproportion = 1.0 inferinternalstateprobs = 0 outputsitelikelihoods = 0 optimizeinputonly = 0 collapsebranches = 1 searchreps = 2 bootstrapreps = 0 linkmodels = 0 subsetspecificrates = 1 [model1] datatype = nucleotide ratematrix = 6rate statefrequencies = estimate ratehetmodel = gamma numratecats = 4 invariantsites = estimate [model2] datatype = nucleotide ratematrix = 6rate statefrequencies = estimate ratehetmodel = gamma numratecats = 4 invariantsites = estimate [model3] datatype = nucleotide ratematrix = 6rate statefrequencies = estimate ratehetmodel = gamma numratecats = 4 invariantsites = estimate [model4] datatype = standardvariable ratematrix = 1rate statefrequencies = equal ratehetmodel = none numratecats = 1 invariantsites = none [master] nindivs = 4 holdover = 1 selectionintensity = 0.5 holdoverpenalty = 0 stopgen = 5000000 stoptime = 5000000 startoptprec = 0.5 minoptprec = 0.01 numberofprecreductions = 10 treerejectionthreshold = 50.0 topoweight = 0.01 modweight = 0.002 brlenweight = 0.002 randnniweight = 0.1 randsprweight = 0.3 limsprweight = 0.6 intervallength = 100 intervalstostore = 5 limsprrange = 6 meanbrlenmuts = 5 gammashapebrlen = 1000 gammashapemodel = 1000 uniqueswapbias = 0.1 distanceswapbias = 1.0 garli-2.1-release/example/partition/exampleRuns/partitionedDna+Mkv/mixedDnaMkv.best.all.tre000066400000000000000000000130611241236125200321620ustar00rootroot00000000000000#NEXUS begin trees; translate 1 temporariaDMH84R1, 2 boyliiMVZ148929, 3 luteiventris_MT_MVZ191016, 4 luteiventris_WA_MVZ225749, 5 muscosaMVZ149006, 6 auroraMVZ13957, 7 cascadaeMVZ148946, 8 sylvaticaMVZ137426, 9 sylvaticaDMH84R43, 10 septentrionalesDCC3588, 11 grylioMVZ175945, 12 okaloosae, 13 clamitansJSF1118, 14 heckscheriMVZ164908, 15 catesbianaX12841, 16 catesbianaDMH84R2, 17 virgatipesMVZ175944, 18 maculataKU195258, 19 vibicariaMVZ11035, 20 warszewitshiiJSF1127, 21 palmipesVenAMNHA118801, 22 palmipesEcuKU204425, 23 Sp_1_ecuadorQCAZ13219, 24 bwanaQCAZ13964, 25 vaillantiKU195299, 26 julianiTNHC60324, 27 sierramadrensisKU195181, 28 psilonotaKU195119, 29 zweifeliJAC7514, 30 tarahumaraeKU194596, 31 pustulosaJAC10555, 32 pipiensJSF1119, 33 pipiensY10945, 34 dunniJSF1017, 35 montezumaeJAC8836, 36 sp_2_mex_JSF1106, 37 chiricahuensisJSF1063, 38 subaquavocalis, 39 chiricahuensisJSF1092, 40 palustrisJSF1110, 41 areolataJSF1111, 42 sevosaUSC8236, 43 capitoSLU003, 44 spectabilisJAC8622, 45 omiltemanaJAC7413, 46 sp_3_MichoacanJSF955, 47 tlalociJSF1083, 48 neovolcanicaJSF960, 49 berlandieriJSF1136, 50 blairiJSF830, 51 sphenocephalaUSC7448, 52 utriculariaJSF845, 53 forreriJSF1065, 54 magnaocularisJSF1073, 55 sp_7_JaliscoJSF1000, 56 yavapaiensisJSF1085, 57 oncaLVT3542, 58 sp_8_PueblaJAC9467, 59 macroglossaJAC10472, 60 macroglossaJSF7933, 61 taylori286, 62 sp_4_Panama, 63 sp_5_CostaRichDMH86_210, 64 sp_6_CostaRicaDMH86_225; tree rep1 = [&U][!GarliScore -61937.27][!GarliModel S 1.748119 0.117688 M1 r 2.02871 7.73017 2.08171 0.81780 13.87204 1.00000 e 0.33609 0.22000 0.14234 0.30157 a 0.77482 p 0.43349 M2 e 0.50000 0.50000 ](1:0.17941819,(((17:0.06825516,(10:0.06404833,(11:0.12781767,((13:0.01322966,12:0.01059399):0.01795642,((16:0.00288901,15:0.00000001):0.04973784,14:0.06608698):0.00515382):0.02250318):0.00259060):0.01306057):0.09016985,(9:0.00303898,8:0.01059628):0.12975904):0.04107838,(((((33:0.01083574,32:0.00980447):0.06568282,((35:0.03005125,34:0.01711179):0.02591346,(36:0.04132485,((38:0.01794724,37:0.05565104):0.00746844,39:0.02099720):0.01978111):0.01085310):0.04896704):0.07005818,((((44:0.08985910,(45:0.10773335,46:0.06086692):0.00230276):0.00186814,((50:0.02187182,(49:0.01488051,(48:0.00280650,47:0.00799698):0.00835376):0.01491409):0.05591723,(52:0.02326401,51:0.03043222):0.03142776):0.00959553):0.00698122,(((((63:0.05709074,62:0.03795675):0.00893365,64:0.07385451):0.00885168,(61:0.10427631,(60:0.01002483,59:0.01774937):0.02794954):0.02298569):0.01330538,((58:0.13602956,(56:0.01535670,57:0.00574102):0.04484603):0.01988304,(54:0.16533287,55:0.09977092):0.03730440):0.01279570):0.00771718,53:0.10901502):0.00866640):0.05440755,((41:0.07072630,(42:0.00555842,43:0.01977683):0.03502007):0.02045156,40:0.02932419):0.03976203):0.03506653):0.18208566,(((29:0.09791568,28:0.16416832):0.03315658,(31:0.18015935,30:0.05514847):0.02975015):0.08448688,27:0.20030436):0.06478838):0.01363196,(((19:0.11827193,20:0.22348354):0.17416492,18:0.11924062):0.04168588,((24:0.06669992,((22:0.00429721,21:0.17963317):0.04867471,23:0.03337586):0.01870529):0.03359377,(25:0.12644528,26:0.13213941):0.04378362):0.08666865):0.03257370):0.06186000):0.11992959,(((4:0.00071035,3:0.00797849):0.06036142,2:0.12355860):0.01304412,(5:0.05131282,(6:0.02882514,7:0.03874389):0.03464354):0.03484968):0.00626508); tree rep2BEST = [&U][!GarliScore -61937.27015625][!GarliModel S 1.748173 0.117625 M1 r 2.02754 7.72587 2.08045 0.81707 13.86626 1.00000 e 0.33610 0.22001 0.14233 0.30156 a 0.77477 p 0.43346 M2 e 0.50000 0.50000 ](1:0.17947672,((((18:0.11927940,(19:0.11831549,20:0.22356705):0.17422723):0.04169982,((24:0.06672385,(23:0.03338531,(22:0.00429782,21:0.17969580):0.04868685):0.01871139):0.03360464,(26:0.13218472,25:0.12648334):0.04379631):0.08669472):0.03258189,((((28:0.16422475,29:0.09794713):0.03316677,(30:0.05516490,31:0.18022082):0.02975735):0.08451121,27:0.20037305):0.06481076,(((((39:0.02100270,(38:0.01795203,37:0.05566678):0.00747012):0.01978719,36:0.04133912):0.01085512,(34:0.01711666,35:0.03005836):0.02592314):0.04898104,(32:0.00980526,33:0.01083913):0.06570138):0.07008223,(((53:0.10905227,((((62:0.03796830,63:0.05710692):0.00893463,64:0.07387502):0.00885535,(61:0.10430643,(60:0.01002699,59:0.01775339):0.02795678):0.02299225):0.01331127,((58:0.13607075,(57:0.00574277,56:0.01535922):0.04485556):0.01988942,(54:0.16539077,55:0.09980517):0.03731602):0.01279929):0.00771907):0.00866886,(((51:0.03044061,52:0.02326996):0.03143913,((49:0.01488440,(47:0.00799776,48:0.00280743):0.00835570):0.01491740,50:0.02187916):0.05593376):0.00959852,(44:0.08988473,(46:0.06088936,45:0.10777023):0.00230236):0.00186844):0.00698250):0.05442347,(40:0.02933233,((43:0.01978146,42:0.00556070):0.03502584,41:0.07074693):0.02045751):0.03977517):0.03507764):0.18214887):0.01363732):0.06188598,((17:0.06827047,(10:0.06406817,((((15:0.00000001,16:0.00288964):0.04975386,14:0.06610646):0.00515252,(13:0.01323407,12:0.01059606):0.01796138):0.02250923,11:0.12785695):0.00259199):0.01306556):0.09020261,(9:0.00303671,8:0.01060155):0.12980121):0.04109502):0.11996864,((5:0.05132921,(6:0.02883419,7:0.03875302):0.03465309):0.03485991,((4:0.00071070,3:0.00797950):0.06038468,2:0.12359976):0.01304585):0.00626743); end; [M1 begin paup; clear; gett file=mixedDnaMkv.best.all.tre storebr; lset userbr nst=6 rmat=(2.02754326 7.72587188 2.08045394 0.81707243 13.86625709) base=(0.33609716 0.22000583 0.14233462) rates=gamma shape= 0.77476879 ncat=4 pinv= 0.43346220; end; ] garli-2.1-release/example/partition/exampleRuns/partitionedDna+Mkv/mixedDnaMkv.best.tre000066400000000000000000000067611241236125200314240ustar00rootroot00000000000000#NEXUS begin trees; translate 1 temporariaDMH84R1, 2 boyliiMVZ148929, 3 luteiventris_MT_MVZ191016, 4 luteiventris_WA_MVZ225749, 5 muscosaMVZ149006, 6 auroraMVZ13957, 7 cascadaeMVZ148946, 8 sylvaticaMVZ137426, 9 sylvaticaDMH84R43, 10 septentrionalesDCC3588, 11 grylioMVZ175945, 12 okaloosae, 13 clamitansJSF1118, 14 heckscheriMVZ164908, 15 catesbianaX12841, 16 catesbianaDMH84R2, 17 virgatipesMVZ175944, 18 maculataKU195258, 19 vibicariaMVZ11035, 20 warszewitshiiJSF1127, 21 palmipesVenAMNHA118801, 22 palmipesEcuKU204425, 23 Sp_1_ecuadorQCAZ13219, 24 bwanaQCAZ13964, 25 vaillantiKU195299, 26 julianiTNHC60324, 27 sierramadrensisKU195181, 28 psilonotaKU195119, 29 zweifeliJAC7514, 30 tarahumaraeKU194596, 31 pustulosaJAC10555, 32 pipiensJSF1119, 33 pipiensY10945, 34 dunniJSF1017, 35 montezumaeJAC8836, 36 sp_2_mex_JSF1106, 37 chiricahuensisJSF1063, 38 subaquavocalis, 39 chiricahuensisJSF1092, 40 palustrisJSF1110, 41 areolataJSF1111, 42 sevosaUSC8236, 43 capitoSLU003, 44 spectabilisJAC8622, 45 omiltemanaJAC7413, 46 sp_3_MichoacanJSF955, 47 tlalociJSF1083, 48 neovolcanicaJSF960, 49 berlandieriJSF1136, 50 blairiJSF830, 51 sphenocephalaUSC7448, 52 utriculariaJSF845, 53 forreriJSF1065, 54 magnaocularisJSF1073, 55 sp_7_JaliscoJSF1000, 56 yavapaiensisJSF1085, 57 oncaLVT3542, 58 sp_8_PueblaJAC9467, 59 macroglossaJAC10472, 60 macroglossaJSF7933, 61 taylori286, 62 sp_4_Panama, 63 sp_5_CostaRichDMH86_210, 64 sp_6_CostaRicaDMH86_225; tree bestREP2 = [&U][!GarliScore -61937.270156][!GarliModel S 1.748173 0.117625 M1 r 2.02754 7.72587 2.08045 0.81707 13.86626 1.00000 e 0.33610 0.22001 0.14233 0.30156 a 0.77477 p 0.43346 M2 e 0.50000 0.50000 ](1:0.17947672,((((18:0.11927940,(19:0.11831549,20:0.22356705):0.17422723):0.04169982,((24:0.06672385,(23:0.03338531,(22:0.00429782,21:0.17969580):0.04868685):0.01871139):0.03360464,(26:0.13218472,25:0.12648334):0.04379631):0.08669472):0.03258189,((((28:0.16422475,29:0.09794713):0.03316677,(30:0.05516490,31:0.18022082):0.02975735):0.08451121,27:0.20037305):0.06481076,(((((39:0.02100270,(38:0.01795203,37:0.05566678):0.00747012):0.01978719,36:0.04133912):0.01085512,(34:0.01711666,35:0.03005836):0.02592314):0.04898104,(32:0.00980526,33:0.01083913):0.06570138):0.07008223,(((53:0.10905227,((((62:0.03796830,63:0.05710692):0.00893463,64:0.07387502):0.00885535,(61:0.10430643,(60:0.01002699,59:0.01775339):0.02795678):0.02299225):0.01331127,((58:0.13607075,(57:0.00574277,56:0.01535922):0.04485556):0.01988942,(54:0.16539077,55:0.09980517):0.03731602):0.01279929):0.00771907):0.00866886,(((51:0.03044061,52:0.02326996):0.03143913,((49:0.01488440,(47:0.00799776,48:0.00280743):0.00835570):0.01491740,50:0.02187916):0.05593376):0.00959852,(44:0.08988473,(46:0.06088936,45:0.10777023):0.00230236):0.00186844):0.00698250):0.05442347,(40:0.02933233,((43:0.01978146,42:0.00556070):0.03502584,41:0.07074693):0.02045751):0.03977517):0.03507764):0.18214887):0.01363732):0.06188598,((17:0.06827047,(10:0.06406817,((((15:0.00000001,16:0.00288964):0.04975386,14:0.06610646):0.00515252,(13:0.01323407,12:0.01059606):0.01796138):0.02250923,11:0.12785695):0.00259199):0.01306556):0.09020261,(9:0.00303671,8:0.01060155):0.12980121):0.04109502):0.11996864,((5:0.05132921,(6:0.02883419,7:0.03875302):0.03465309):0.03485991,((4:0.00071070,3:0.00797950):0.06038468,2:0.12359976):0.01304585):0.00626743); end; [ S 1.748173 0.117625 M1 r 2.02754 7.72587 2.08045 0.81707 13.86626 1.00000 e 0.33610 0.22001 0.14233 0.30156 a 0.77477 p 0.43346 M2 e 0.50000 0.50000 ] garli-2.1-release/example/partition/exampleRuns/partitionedDna+Mkv/mixedDnaMkv.log00.log000066400000000000000000002622041241236125200313730ustar00rootroot00000000000000Search rep 1 (of 2) random seed = 853654 gen best_like time optPrecision 0 -101113.0297 28 0.5 10 -100820.8477 29 0.5 20 -99463.88968 29 0.5 30 -98493.98737 30 0.5 40 -98312.10553 30 0.5 50 -96647.35981 31 0.5 60 -94451.32369 32 0.5 70 -94064.80284 32 0.5 80 -92886.97454 33 0.5 90 -91512.29965 33 0.5 100 -90517.35366 34 0.5 110 -90300.81805 35 0.5 120 -88132.16511 35 0.5 130 -87799.9098 36 0.5 140 -86355.32816 36 0.5 150 -86125.06835 37 0.5 160 -86024.54664 37 0.5 170 -84979.84955 38 0.5 180 -84043.90482 39 0.5 190 -83362.52924 39 0.5 200 -83291.80983 40 0.5 210 -83244.34687 40 0.5 220 -82563.02346 41 0.5 230 -82522.44927 41 0.5 240 -82244.84075 42 0.5 250 -82198.90106 43 0.5 260 -80830.67788 43 0.5 270 -80297.86066 44 0.5 280 -79288.43519 44 0.5 290 -78557.51741 45 0.5 300 -78503.34478 45 0.5 310 -77614.54839 46 0.5 320 -76538.03957 46 0.5 330 -76464.59157 47 0.5 340 -76374.06924 47 0.5 350 -75778.65476 48 0.5 360 -75766.14777 49 0.5 370 -74619.89639 49 0.5 380 -74463.70233 50 0.5 390 -73808.87366 50 0.5 400 -73032.90792 51 0.5 410 -73006.33487 51 0.5 420 -72906.56377 52 0.5 430 -72490.89132 52 0.5 440 -72402.7476 53 0.5 450 -72367.59175 53 0.5 460 -71459.52105 54 0.5 470 -70690.6552 55 0.5 480 -70688.32998 55 0.5 490 -69774.10168 55 0.5 500 -69757.18877 56 0.5 510 -69755.54355 56 0.5 520 -69108.13277 57 0.5 530 -68939.8519 58 0.5 540 -68894.61966 58 0.5 550 -68894.61966 58 0.5 560 -68796.96332 59 0.5 570 -68790.00753 59 0.5 580 -68444.31206 60 0.5 590 -67838.85468 60 0.5 600 -67518.7818 61 0.5 610 -67517.0523 61 0.5 620 -67487.23389 62 0.5 630 -67414.65302 62 0.5 640 -67035.16876 63 0.5 650 -66784.98145 63 0.5 660 -66750.10446 64 0.5 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garli-2.1-release/example/partition/exampleRuns/partitionedDna+Mkv/mixedDnaMkv.screen.log000066400000000000000000000171501241236125200317270ustar00rootroot00000000000000Running GARLI Version 2.01.1067 (18 May 2012) ->Single processor version for 64-bit OS<- ############################################################## This is GARLI 2.0, the first "official" release including partitioned models. It is a merging of official release 1.0 and beta version GARLI-PART 0.97 Briefly, it includes models for nucleotides, amino acids, codons, and morphology-like characters, any of which can be mixed together and applied to different subsets of data. General program usage is extensively documented here: http://www.nescent.org/wg/garli/ see this page for details on partitioned usage: http://www.nescent.org/wg/garli/Partition_testing_version and this page for details on Mkv mophology model usage: http://www.nescent.org/wg/garli/Mkv_morphology_model PLEASE LET ME KNOW OF ANY PROBLEMS AT: garli.support@gmail.com ############################################################## Compiled Jul 2 2012 15:52:18 using GNU gcc compiler version 4.2.1 Using NCL version 2.1.17 ####################################################### Reading config file garli.conf ################################################### READING OF DATA Attempting to read data file in Nexus format (using NCL): dnaPlusGapCoding.nex ... Reading TAXA block... successful Reading CHARACTERS block... found dna data... successful Reading CHARACTERS block... found standard data... successful Reading SETS block... successful ################################################### PARTITIONING OF DATA AND MODELS CHECK: DIFFERENT MODEL TYPES AND MODEL PARAMETERS APPLY TO EACH DATA SUBSET (no linkage) GARLI data subset 1 CHARACTERS block #1 ("Untitled DATA Block 1GapsAsMissing") CHARPARTITION subset #1 ("1") Data read as Nucleotide data, modeled as Nucleotide data Summary of data: 64 sequences. 687 constant characters. 338 parsimony-informative characters. 44 uninformative variable characters. 2 characters were completely missing or ambiguous (removed). 1069 total characters (1071 before removing empty columns). 787 unique patterns in compressed data matrix. Pattern processing required < 1 second GARLI data subset 2 CHARACTERS block #1 ("Untitled DATA Block 1GapsAsMissing") CHARPARTITION subset #2 ("2") Data read as Nucleotide data, modeled as Nucleotide data Summary of data: 64 sequences. 687 constant characters. 341 parsimony-informative characters. 39 uninformative variable characters. 3 characters were completely missing or ambiguous (removed). 1067 total characters (1070 before removing empty columns). 800 unique patterns in compressed data matrix. Pattern processing required < 1 second GARLI data subset 3 CHARACTERS block #1 ("Untitled DATA Block 1GapsAsMissing") CHARPARTITION subset #3 ("3") Data read as Nucleotide data, modeled as Nucleotide data Summary of data: 64 sequences. 666 constant characters. 346 parsimony-informative characters. 54 uninformative variable characters. 4 characters were completely missing or ambiguous (removed). 1066 total characters (1070 before removing empty columns). 799 unique patterns in compressed data matrix. Pattern processing required < 1 second GARLI data subset 4 CHARACTERS block #2 ("Untitled DATA Block 1GapsAsBinary") Data read as Standard k-state data, variable only, modeled as Standard k-state data, variable only NOTE: entirely missing characters removed from matrix: 736 792 1244 1644 1645 1993-1995 2195 Subset of data with 2 states: chars 1-735 737-791 793-1243 1245-1643 1646-1992 1996-2194 2196-2723 Summary of data: 64 sequences. 0 constant characters. 1530 parsimony-informative characters. 1184 uninformative variable characters. 2714 total characters. 768 unique patterns in compressed data matrix. Pattern processing required < 1 second ################################################### NOTE: Unlike many programs, the amount of system memory that Garli will use can be controlled by the user. (This comes from the availablememory setting in the configuration file. Availablememory should NOT be set to more than the actual amount of physical memory that your computer has installed) For this dataset: Mem level availablememory setting great >= 196 MB good approx 195 MB to 126 MB low approx 125 MB to 52 MB very low approx 52 MB to 40 MB the minimum required availablememory is 40 MB You specified that Garli should use at most 512.0 MB of memory. Garli will actually use approx. 293.0 MB of memory **Your memory level is: great (you don't need to change anything)** ####################################################### Found outgroup specification: 1 ####################################################### STARTING RUN >>>Search rep 1 (of 2)<<< MODEL REPORT - Parameters are at their INITIAL values (not yet optimized) Model 1 Number of states = 4 (nucleotide data) Nucleotide Relative Rate Matrix: 6 rates AC = 1.000, AG = 4.000, AT = 1.000, CG = 1.000, CT = 4.000, GT = 1.000 Equilibrium State Frequencies: estimated (ACGT) 0.3211 0.2053 0.1693 0.3043 Rate Heterogeneity Model: 4 discrete gamma distributed rate categories, alpha param estimated 0.5000 with an invariant (invariable) site category, proportion estimated 0.1607 Substitution rate categories under this model: rate proportion 0.0000 0.1607 0.0334 0.2098 0.2519 0.2098 0.8203 0.2098 2.8944 0.2098 Model 2 Number of states = 4 (nucleotide data) Nucleotide Relative Rate Matrix: 6 rates AC = 1.000, AG = 4.000, AT = 1.000, CG = 1.000, CT = 4.000, GT = 1.000 Equilibrium State Frequencies: estimated (ACGT) 0.3490 0.2203 0.1345 0.2962 Rate Heterogeneity Model: 4 discrete gamma distributed rate categories, alpha param estimated 0.5000 with an invariant (invariable) site category, proportion estimated 0.1610 Substitution rate categories under this model: rate proportion 0.0000 0.1610 0.0334 0.2098 0.2519 0.2098 0.8203 0.2098 2.8944 0.2098 Model 3 Number of states = 4 (nucleotide data) Nucleotide Relative Rate Matrix: 6 rates AC = 1.000, AG = 4.000, AT = 1.000, CG = 1.000, CT = 4.000, GT = 1.000 Equilibrium State Frequencies: estimated (ACGT) 0.3516 0.2003 0.1525 0.2956 Rate Heterogeneity Model: 4 discrete gamma distributed rate categories, alpha param estimated 0.5000 with an invariant (invariable) site category, proportion estimated 0.1562 Substitution rate categories under this model: rate proportion 0.0000 0.1562 0.0334 0.2110 0.2519 0.2110 0.8203 0.2110 2.8944 0.2110 Model 4 Number of states = 2 (standard data) Character change matrix: One rate (symmetric one rate Mkv model) Equilibrium State Frequencies: equal (0.50, fixed) Rate Heterogeneity Model: no rate heterogeneity Subset rate multipliers: 1.00 1.00 1.00 1.00 Starting with seed=558821 creating random starting tree... Initial ln Likelihood: -130238.3436 optimizing: starting branch lengths, alpha shape, prop. invar, rel rates, eq freqs, subset rates... pass 1:+23019.697 (branch=20315.82 scale=171.66 alpha=920.61 freqs=166.50 rel rates=112.55 pinv=545.55 subset rates=787.00) pass 2:+ 3142.907 (branch=2540.85 scale= 1.40 alpha=173.03 freqs= 16.15 rel rates= 15.76 pinv=257.52 subset rates=138.20) pass 3:+ 835.620 (branch= 380.39 scale= 8.60 alpha=272.65 freqs= 5.15 rel rates= 18.80 pinv= 82.85 subset rates= 67.19) pass 4:+ 340.762 (branch= 189.10 scale= 12.96 alpha= 33.80 freqs= 6.94 rel rates= 10.24 pinv= 0.00 subset rates= 87.73) garli-2.1-release/example/partition/templateConfigs/000077500000000000000000000000001241236125200226565ustar00rootroot00000000000000garli-2.1-release/example/partition/templateConfigs/garli.3diffModels.bigData.conf000066400000000000000000000031221241236125200303110ustar00rootroot00000000000000[general] datafname = YOURDATAFILE.nex constraintfile = none streefname = stepwise attachmentspertaxon = 100 ofprefix = 3diffModels randseed = -1 availablememory = 512 logevery = 10 saveevery = 100 refinestart = 1 outputeachbettertopology = 0 outputcurrentbesttopology = 0 enforcetermconditions = 1 genthreshfortopoterm = 10000 scorethreshforterm = 0.001 significanttopochange = 0.01 outputphyliptree = 0 outputmostlyuselessfiles = 0 writecheckpoints = 0 restart = 0 outgroup = 1 resampleproportion = 1.0 inferinternalstateprobs = 0 outputsitelikelihoods = 0 optimizeinputonly = 0 collapsebranches = 1 searchreps = 5 bootstrapreps = 0 linkmodels = 0 subsetspecificrates = 1 [model1] datatype = nucleotide ratematrix = ( 0 1 2 2 3 4 ) statefrequencies = estimate ratehetmodel = gamma numratecats = 4 invariantsites = none [model2] datatype = nucleotide ratematrix = ( 0 1 2 1 0 3 ) statefrequencies = estimate ratehetmodel = gamma numratecats = 4 invariantsites = none [model3] datatype = nucleotide ratematrix = ( 0 1 2 3 1 0 ) statefrequencies = estimate ratehetmodel = gamma numratecats = 4 invariantsites = estimate [master] nindivs = 4 holdover = 1 selectionintensity = 0.5 holdoverpenalty = 0 stopgen = 5000000 stoptime = 5000000 startoptprec = 0.5 minoptprec = 0.01 numberofprecreductions = 10 treerejectionthreshold = 50.0 topoweight = 0.01 modweight = 0.002 brlenweight = 0.002 randnniweight = 0.1 randsprweight = 0.3 limsprweight = 0.6 intervallength = 100 intervalstostore = 5 limsprrange = 6 meanbrlenmuts = 5 gammashapebrlen = 1000 gammashapemodel = 1000 uniqueswapbias = 0.1 distanceswapbias = 1.0 garli-2.1-release/example/partition/templateConfigs/garli.3diffModels.smallData.conf000066400000000000000000000031171241236125200306640ustar00rootroot00000000000000[general] datafname = YOURDATAFILE.nex constraintfile = none streefname = stepwise attachmentspertaxon = 50 ofprefix = 3diffModels randseed = -1 availablememory = 512 logevery = 10 saveevery = 100 refinestart = 1 outputeachbettertopology = 0 outputcurrentbesttopology = 0 enforcetermconditions = 1 genthreshfortopoterm = 5000 scorethreshforterm = 0.001 significanttopochange = 0.01 outputphyliptree = 0 outputmostlyuselessfiles = 0 writecheckpoints = 0 restart = 0 outgroup = 1 resampleproportion = 1.0 inferinternalstateprobs = 0 outputsitelikelihoods = 0 optimizeinputonly = 0 collapsebranches = 1 searchreps = 5 bootstrapreps = 0 linkmodels = 0 subsetspecificrates = 1 [model1] datatype = nucleotide ratematrix = ( 0 1 2 2 3 4 ) statefrequencies = estimate ratehetmodel = gamma numratecats = 4 invariantsites = none [model2] datatype = nucleotide ratematrix = ( 0 1 2 1 0 3 ) statefrequencies = estimate ratehetmodel = gamma numratecats = 4 invariantsites = none [model3] datatype = nucleotide ratematrix = ( 0 1 2 3 1 0 ) statefrequencies = estimate ratehetmodel = gamma numratecats = 4 invariantsites = estimate [master] nindivs = 4 holdover = 1 selectionintensity = 0.5 holdoverpenalty = 0 stopgen = 5000000 stoptime = 5000000 startoptprec = 0.5 minoptprec = 0.01 numberofprecreductions = 5 treerejectionthreshold = 50.0 topoweight = 0.01 modweight = 0.002 brlenweight = 0.002 randnniweight = 0.1 randsprweight = 0.3 limsprweight = 0.6 intervallength = 100 intervalstostore = 5 limsprrange = 6 meanbrlenmuts = 5 gammashapebrlen = 1000 gammashapemodel = 1000 uniqueswapbias = 0.1 distanceswapbias = 1.0 garli-2.1-release/example/partition/templateConfigs/garli.mixedDnaMkv.conf000066400000000000000000000026521241236125200270360ustar00rootroot00000000000000[general] datafname = YOURDATAFILE.nex constraintfile = none streefname = random attachmentspertaxon = 100 ofprefix = mixedDnaMkv randseed = -1 availablememory = 512 logevery = 10 saveevery = 100 refinestart = 1 outputeachbettertopology = 0 outputcurrentbesttopology = 0 enforcetermconditions = 1 genthreshfortopoterm = 10000 scorethreshforterm = 0.001 significanttopochange = 0.01 outputphyliptree = 0 outputmostlyuselessfiles = 0 writecheckpoints = 0 restart = 0 outgroup = 1 resampleproportion = 1.0 inferinternalstateprobs = 0 outputsitelikelihoods = 0 optimizeinputonly = 0 collapsebranches = 1 searchreps = 5 bootstrapreps = 0 linkmodels = 0 subsetspecificrates = 1 [model1] datatype = nucleotide ratematrix = 6rate statefrequencies = estimate ratehetmodel = gamma numratecats = 4 invariantsites = estimate [model2] datatype = standardvariable ratematrix = 1rate statefrequencies = equal ratehetmodel = none numratecats = 1 invariantsites = none [master] nindivs = 4 holdover = 1 selectionintensity = 0.5 holdoverpenalty = 0 stopgen = 5000000 stoptime = 5000000 startoptprec = 0.5 minoptprec = 0.01 numberofprecreductions = 10 treerejectionthreshold = 50.0 topoweight = 0.01 modweight = 0.002 brlenweight = 0.002 randnniweight = 0.1 randsprweight = 0.3 limsprweight = 0.6 intervallength = 100 intervalstostore = 5 limsprrange = 6 meanbrlenmuts = 5 gammashapebrlen = 1000 gammashapemodel = 1000 uniqueswapbias = 0.1 distanceswapbias = 1.0 garli-2.1-release/example/partition/templateConfigs/garli.mkv.conf000066400000000000000000000024241241236125200254210ustar00rootroot00000000000000[general] datafname = YOURDATAFILE.nex constraintfile = none streefname = random attachmentspertaxon = 100 ofprefix = mkv randseed = -1 availablememory = 512 logevery = 10 saveevery = 100 refinestart = 1 outputeachbettertopology = 0 outputcurrentbesttopology = 0 enforcetermconditions = 1 genthreshfortopoterm = 10000 scorethreshforterm = 0.001 significanttopochange = 0.01 outputphyliptree = 0 outputmostlyuselessfiles = 0 writecheckpoints = 0 restart = 0 outgroup = 1 resampleproportion = 1.0 inferinternalstateprobs = 0 outputsitelikelihoods = 0 optimizeinputonly = 0 collapsebranches = 1 searchreps = 5 bootstrapreps = 0 linkmodels = 0 subsetspecificrates = 0 [model1] datatype = standardvariable ratematrix = 1rate statefrequencies = equal ratehetmodel = none numratecats = 1 invariantsites = none [master] nindivs = 4 holdover = 1 selectionintensity = 0.5 holdoverpenalty = 0 stopgen = 5000000 stoptime = 5000000 startoptprec = 0.5 minoptprec = 0.01 numberofprecreductions = 10 treerejectionthreshold = 50.0 topoweight = 0.01 modweight = 0.002 brlenweight = 0.002 randnniweight = 0.1 randsprweight = 0.3 limsprweight = 0.6 intervallength = 100 intervalstostore = 5 limsprrange = 6 meanbrlenmuts = 5 gammashapebrlen = 1000 gammashapemodel = 1000 uniqueswapbias = 0.1 distanceswapbias = 1.0 garli-2.1-release/example/partition/templateConfigs/garli.oneModelType.bigData.conf000066400000000000000000000024351241236125200305640ustar00rootroot00000000000000[general] datafname = YOURDATAFILE.nex constraintfile = none streefname = stepwise attachmentspertaxon = 100 ofprefix = oneModelType randseed = -1 availablememory = 512 logevery = 10 saveevery = 100 refinestart = 1 outputeachbettertopology = 0 outputcurrentbesttopology = 0 enforcetermconditions = 1 genthreshfortopoterm = 10000 scorethreshforterm = 0.001 significanttopochange = 0.01 outputphyliptree = 0 outputmostlyuselessfiles = 0 writecheckpoints = 0 restart = 0 outgroup = 1 resampleproportion = 1.0 inferinternalstateprobs = 0 outputsitelikelihoods = 0 optimizeinputonly = 0 collapsebranches = 1 searchreps = 5 bootstrapreps = 0 linkmodels = 0 subsetspecificrates = 1 [model1] datatype = nucleotide ratematrix = 6rate statefrequencies = estimate ratehetmodel = gamma numratecats = 4 invariantsites = none [master] nindivs = 4 holdover = 1 selectionintensity = 0.5 holdoverpenalty = 0 stopgen = 5000000 stoptime = 5000000 startoptprec = 0.5 minoptprec = 0.01 numberofprecreductions = 10 treerejectionthreshold = 50.0 topoweight = 0.01 modweight = 0.002 brlenweight = 0.002 randnniweight = 0.1 randsprweight = 0.3 limsprweight = 0.6 intervallength = 100 intervalstostore = 5 limsprrange = 6 meanbrlenmuts = 5 gammashapebrlen = 1000 gammashapemodel = 1000 uniqueswapbias = 0.1 distanceswapbias = 1.0 garli-2.1-release/example/partition/templateConfigs/garli.oneModelType.smallData.conf000066400000000000000000000024321241236125200311300ustar00rootroot00000000000000[general] datafname = YOURDATAFILE.nex constraintfile = none streefname = stepwise attachmentspertaxon = 50 ofprefix = oneModelType randseed = -1 availablememory = 512 logevery = 10 saveevery = 100 refinestart = 1 outputeachbettertopology = 0 outputcurrentbesttopology = 0 enforcetermconditions = 1 genthreshfortopoterm = 5000 scorethreshforterm = 0.001 significanttopochange = 0.01 outputphyliptree = 0 outputmostlyuselessfiles = 0 writecheckpoints = 0 restart = 0 outgroup = 1 resampleproportion = 1.0 inferinternalstateprobs = 0 outputsitelikelihoods = 0 optimizeinputonly = 0 collapsebranches = 1 searchreps = 5 bootstrapreps = 0 linkmodels = 0 subsetspecificrates = 1 [model1] datatype = nucleotide ratematrix = 6rate statefrequencies = estimate ratehetmodel = gamma numratecats = 4 invariantsites = none [master] nindivs = 4 holdover = 1 selectionintensity = 0.5 holdoverpenalty = 0 stopgen = 5000000 stoptime = 5000000 startoptprec = 0.5 minoptprec = 0.01 numberofprecreductions = 5 treerejectionthreshold = 50.0 topoweight = 0.01 modweight = 0.002 brlenweight = 0.002 randnniweight = 0.1 randsprweight = 0.3 limsprweight = 0.6 intervallength = 100 intervalstostore = 5 limsprrange = 6 meanbrlenmuts = 5 gammashapebrlen = 1000 gammashapemodel = 1000 uniqueswapbias = 0.1 distanceswapbias = 1.0 garli-2.1-release/project/000077500000000000000000000000001241236125200155345ustar00rootroot00000000000000garli-2.1-release/project/standardGarliVC/000077500000000000000000000000001241236125200205445ustar00rootroot00000000000000garli-2.1-release/project/standardGarliVC/BOINCGarli.sln000066400000000000000000000143001241236125200230710ustar00rootroot00000000000000 Microsoft Visual Studio Solution File, Format Version 9.00 # Visual Studio 2005 Project("{8BC9CEB8-8B4A-11D0-8D11-00A0C91BC942}") = "libboinc", "..\..\..\boinc_samples\win_build\libboinc.vcproj", "{E8F6BD7E-461A-4733-B7D8-37B09A099ED8}" EndProject Project("{8BC9CEB8-8B4A-11D0-8D11-00A0C91BC942}") = "libboincapi", "..\..\..\boinc_samples\win_build\libboincapi.vcproj", "{0BC1DB36-030A-4321-B387-1CEE2611E329}" EndProject Project("{8BC9CEB8-8B4A-11D0-8D11-00A0C91BC942}") = "BOINCGarli", "BOINCGarli.vcproj", "{E40AF4AF-9D7C-4CA5-A6F3-758C7364EB7F}" ProjectSection(ProjectDependencies) = postProject {B88D3B05-C6E0-4094-9B11-7AA14A9A824B} = {B88D3B05-C6E0-4094-9B11-7AA14A9A824B} {0BC1DB36-030A-4321-B387-1CEE2611E329} = {0BC1DB36-030A-4321-B387-1CEE2611E329} {E8F6BD7E-461A-4733-B7D8-37B09A099ED8} = {E8F6BD7E-461A-4733-B7D8-37B09A099ED8} EndProjectSection EndProject Project("{2150E333-8FDC-42A3-9474-1A3956D46DE8}") = "Solution Items", "Solution Items", "{4E038CCC-3A90-41E6-B2E6-A553BBDB1BCE}" EndProject Project("{8BC9CEB8-8B4A-11D0-8D11-00A0C91BC942}") = "libncl", "..\..\..\ncl-2.0\VC6\libncl\libncl.vcproj", "{B88D3B05-C6E0-4094-9B11-7AA14A9A824B}" EndProject Global GlobalSection(SolutionConfigurationPlatforms) = preSolution BOINC_Debug|Win32 = BOINC_Debug|Win32 BOINC_Debug|x64 = BOINC_Debug|x64 BOINC_Release|Win32 = BOINC_Release|Win32 BOINC_Release|x64 = BOINC_Release|x64 Debug|Win32 = Debug|Win32 Debug|x64 = Debug|x64 Release|Win32 = Release|Win32 Release|x64 = Release|x64 EndGlobalSection GlobalSection(ProjectConfigurationPlatforms) = postSolution {E8F6BD7E-461A-4733-B7D8-37B09A099ED8}.BOINC_Debug|Win32.ActiveCfg = Debug|Win32 {E8F6BD7E-461A-4733-B7D8-37B09A099ED8}.BOINC_Debug|Win32.Build.0 = Debug|Win32 {E8F6BD7E-461A-4733-B7D8-37B09A099ED8}.BOINC_Debug|x64.ActiveCfg = Debug|x64 {E8F6BD7E-461A-4733-B7D8-37B09A099ED8}.BOINC_Debug|x64.Build.0 = Debug|x64 {E8F6BD7E-461A-4733-B7D8-37B09A099ED8}.BOINC_Release|Win32.ActiveCfg = Release|Win32 {E8F6BD7E-461A-4733-B7D8-37B09A099ED8}.BOINC_Release|Win32.Build.0 = Release|Win32 {E8F6BD7E-461A-4733-B7D8-37B09A099ED8}.BOINC_Release|x64.ActiveCfg = BOINC_Release|x64 {E8F6BD7E-461A-4733-B7D8-37B09A099ED8}.BOINC_Release|x64.Build.0 = BOINC_Release|x64 {E8F6BD7E-461A-4733-B7D8-37B09A099ED8}.Debug|Win32.ActiveCfg = Debug|Win32 {E8F6BD7E-461A-4733-B7D8-37B09A099ED8}.Debug|Win32.Build.0 = Debug|Win32 {E8F6BD7E-461A-4733-B7D8-37B09A099ED8}.Debug|x64.ActiveCfg = Debug|x64 {E8F6BD7E-461A-4733-B7D8-37B09A099ED8}.Debug|x64.Build.0 = Debug|x64 {E8F6BD7E-461A-4733-B7D8-37B09A099ED8}.Release|Win32.ActiveCfg = Release|Win32 {E8F6BD7E-461A-4733-B7D8-37B09A099ED8}.Release|Win32.Build.0 = Release|Win32 {E8F6BD7E-461A-4733-B7D8-37B09A099ED8}.Release|x64.ActiveCfg = Release|x64 {E8F6BD7E-461A-4733-B7D8-37B09A099ED8}.Release|x64.Build.0 = Release|x64 {0BC1DB36-030A-4321-B387-1CEE2611E329}.BOINC_Debug|Win32.ActiveCfg = Debug|Win32 {0BC1DB36-030A-4321-B387-1CEE2611E329}.BOINC_Debug|Win32.Build.0 = Debug|Win32 {0BC1DB36-030A-4321-B387-1CEE2611E329}.BOINC_Debug|x64.ActiveCfg = Debug|x64 {0BC1DB36-030A-4321-B387-1CEE2611E329}.BOINC_Debug|x64.Build.0 = Debug|x64 {0BC1DB36-030A-4321-B387-1CEE2611E329}.BOINC_Release|Win32.ActiveCfg = Release|Win32 {0BC1DB36-030A-4321-B387-1CEE2611E329}.BOINC_Release|Win32.Build.0 = Release|Win32 {0BC1DB36-030A-4321-B387-1CEE2611E329}.BOINC_Release|x64.ActiveCfg = BOINC_Release|x64 {0BC1DB36-030A-4321-B387-1CEE2611E329}.BOINC_Release|x64.Build.0 = BOINC_Release|x64 {0BC1DB36-030A-4321-B387-1CEE2611E329}.Debug|Win32.ActiveCfg = Debug|Win32 {0BC1DB36-030A-4321-B387-1CEE2611E329}.Debug|Win32.Build.0 = Debug|Win32 {0BC1DB36-030A-4321-B387-1CEE2611E329}.Debug|x64.ActiveCfg = Debug|x64 {0BC1DB36-030A-4321-B387-1CEE2611E329}.Debug|x64.Build.0 = Debug|x64 {0BC1DB36-030A-4321-B387-1CEE2611E329}.Release|Win32.ActiveCfg = Release|Win32 {0BC1DB36-030A-4321-B387-1CEE2611E329}.Release|Win32.Build.0 = Release|Win32 {0BC1DB36-030A-4321-B387-1CEE2611E329}.Release|x64.ActiveCfg = Release|x64 {0BC1DB36-030A-4321-B387-1CEE2611E329}.Release|x64.Build.0 = Release|x64 {E40AF4AF-9D7C-4CA5-A6F3-758C7364EB7F}.BOINC_Debug|Win32.ActiveCfg = BOINC_Debug|Win32 {E40AF4AF-9D7C-4CA5-A6F3-758C7364EB7F}.BOINC_Debug|Win32.Build.0 = BOINC_Debug|Win32 {E40AF4AF-9D7C-4CA5-A6F3-758C7364EB7F}.BOINC_Debug|x64.ActiveCfg = BOINC_Debug|Win32 {E40AF4AF-9D7C-4CA5-A6F3-758C7364EB7F}.BOINC_Release|Win32.ActiveCfg = BOINC_Release|Win32 {E40AF4AF-9D7C-4CA5-A6F3-758C7364EB7F}.BOINC_Release|Win32.Build.0 = BOINC_Release|Win32 {E40AF4AF-9D7C-4CA5-A6F3-758C7364EB7F}.BOINC_Release|x64.ActiveCfg = BOINC_Release|Win32 {E40AF4AF-9D7C-4CA5-A6F3-758C7364EB7F}.Debug|Win32.ActiveCfg = Debug|Win32 {E40AF4AF-9D7C-4CA5-A6F3-758C7364EB7F}.Debug|Win32.Build.0 = Debug|Win32 {E40AF4AF-9D7C-4CA5-A6F3-758C7364EB7F}.Debug|x64.ActiveCfg = Debug|Win32 {E40AF4AF-9D7C-4CA5-A6F3-758C7364EB7F}.Release|Win32.ActiveCfg = Release|Win32 {E40AF4AF-9D7C-4CA5-A6F3-758C7364EB7F}.Release|Win32.Build.0 = Release|Win32 {E40AF4AF-9D7C-4CA5-A6F3-758C7364EB7F}.Release|x64.ActiveCfg = Release|Win32 {B88D3B05-C6E0-4094-9B11-7AA14A9A824B}.BOINC_Debug|Win32.ActiveCfg = Debug|Win32 {B88D3B05-C6E0-4094-9B11-7AA14A9A824B}.BOINC_Debug|Win32.Build.0 = Debug|Win32 {B88D3B05-C6E0-4094-9B11-7AA14A9A824B}.BOINC_Debug|x64.ActiveCfg = Debug|Win32 {B88D3B05-C6E0-4094-9B11-7AA14A9A824B}.BOINC_Release|Win32.ActiveCfg = Release|Win32 {B88D3B05-C6E0-4094-9B11-7AA14A9A824B}.BOINC_Release|Win32.Build.0 = Release|Win32 {B88D3B05-C6E0-4094-9B11-7AA14A9A824B}.BOINC_Release|x64.ActiveCfg = Release|Win32 {B88D3B05-C6E0-4094-9B11-7AA14A9A824B}.Debug|Win32.ActiveCfg = Debug|Win32 {B88D3B05-C6E0-4094-9B11-7AA14A9A824B}.Debug|Win32.Build.0 = Debug|Win32 {B88D3B05-C6E0-4094-9B11-7AA14A9A824B}.Debug|x64.ActiveCfg = Debug|Win32 {B88D3B05-C6E0-4094-9B11-7AA14A9A824B}.Release|Win32.ActiveCfg = Release|Win32 {B88D3B05-C6E0-4094-9B11-7AA14A9A824B}.Release|Win32.Build.0 = Release|Win32 {B88D3B05-C6E0-4094-9B11-7AA14A9A824B}.Release|x64.ActiveCfg = Release|Win32 EndGlobalSection GlobalSection(SolutionProperties) = preSolution HideSolutionNode = FALSE EndGlobalSection EndGlobal garli-2.1-release/project/standardGarliVC/BOINCGarli.vcproj000066400000000000000000000536721241236125200236170ustar00rootroot00000000000000 garli-2.1-release/project/standardGarliVC/standardGarli.sln000066400000000000000000000043501241236125200240430ustar00rootroot00000000000000 Microsoft Visual Studio Solution File, Format Version 9.00 # Visual Studio 2005 Project("{8BC9CEB8-8B4A-11D0-8D11-00A0C91BC942}") = "standardGarli", "standardGarli.vcproj", "{E40AF4AF-9D7C-4CA5-A6F3-758C7364EB7F}" ProjectSection(ProjectDependencies) = postProject {B88D3B05-C6E0-4094-9B11-7AA14A9A824B} = {B88D3B05-C6E0-4094-9B11-7AA14A9A824B} EndProjectSection EndProject Project("{8BC9CEB8-8B4A-11D0-8D11-00A0C91BC942}") = "libncl", "..\..\..\ncl-2.0\VC6\libncl\libncl.vcproj", "{B88D3B05-C6E0-4094-9B11-7AA14A9A824B}" EndProject Global GlobalSection(SolutionConfigurationPlatforms) = preSolution Debug|Win32 = Debug|Win32 OMP_Debug|Win32 = OMP_Debug|Win32 OMP_Release|Win32 = OMP_Release|Win32 Release|Win32 = Release|Win32 EndGlobalSection GlobalSection(ProjectConfigurationPlatforms) = postSolution {E40AF4AF-9D7C-4CA5-A6F3-758C7364EB7F}.Debug|Win32.ActiveCfg = Debug|Win32 {E40AF4AF-9D7C-4CA5-A6F3-758C7364EB7F}.Debug|Win32.Build.0 = Debug|Win32 {E40AF4AF-9D7C-4CA5-A6F3-758C7364EB7F}.OMP_Debug|Win32.ActiveCfg = OMP_Debug|Win32 {E40AF4AF-9D7C-4CA5-A6F3-758C7364EB7F}.OMP_Debug|Win32.Build.0 = OMP_Debug|Win32 {E40AF4AF-9D7C-4CA5-A6F3-758C7364EB7F}.OMP_Release|Win32.ActiveCfg = OMP_Release|Win32 {E40AF4AF-9D7C-4CA5-A6F3-758C7364EB7F}.OMP_Release|Win32.Build.0 = OMP_Release|Win32 {E40AF4AF-9D7C-4CA5-A6F3-758C7364EB7F}.Release|Win32.ActiveCfg = Release|Win32 {E40AF4AF-9D7C-4CA5-A6F3-758C7364EB7F}.Release|Win32.Build.0 = Release|Win32 {B88D3B05-C6E0-4094-9B11-7AA14A9A824B}.Debug|Win32.ActiveCfg = Debug|Win32 {B88D3B05-C6E0-4094-9B11-7AA14A9A824B}.Debug|Win32.Build.0 = Debug|Win32 {B88D3B05-C6E0-4094-9B11-7AA14A9A824B}.OMP_Debug|Win32.ActiveCfg = Debug|Win32 {B88D3B05-C6E0-4094-9B11-7AA14A9A824B}.OMP_Debug|Win32.Build.0 = Debug|Win32 {B88D3B05-C6E0-4094-9B11-7AA14A9A824B}.OMP_Release|Win32.ActiveCfg = Release|Win32 {B88D3B05-C6E0-4094-9B11-7AA14A9A824B}.OMP_Release|Win32.Build.0 = Release|Win32 {B88D3B05-C6E0-4094-9B11-7AA14A9A824B}.Release|Win32.ActiveCfg = Release|Win32 {B88D3B05-C6E0-4094-9B11-7AA14A9A824B}.Release|Win32.Build.0 = Release|Win32 EndGlobalSection GlobalSection(SolutionProperties) = preSolution HideSolutionNode = FALSE EndGlobalSection EndGlobal garli-2.1-release/project/standardGarliVC/standardGarli.vcproj000066400000000000000000000315631241236125200245600ustar00rootroot00000000000000 garli-2.1-release/src/000077500000000000000000000000001241236125200146555ustar00rootroot00000000000000garli-2.1-release/src/Makefile.am000066400000000000000000000021511241236125200167100ustar00rootroot00000000000000AM_CPPFLAGS = @CPPFLAGS@ AM_LDFLAGS = @LDFLAGS@ bin_PROGRAMS = Garli EXTRA_DIST = threadfunc.cpp \ mpifuncs.cpp noinst_HEADERS = \ adaptation.h \ bipartition.h \ clamanager.h \ condlike.h \ configoptions.h \ configreader.h \ datamatr.h \ defs.h \ errorexception.h \ funcs.h \ garlireader.h \ individual.h \ linalg.h \ memchk.h \ model.h \ mpifuncs.h \ optimizationinfo.h \ outputman.h \ population.h \ reconnode.h \ rng.h \ sequencedata.h \ set.h \ stopwatch.h \ threaddcls.h \ translatetable.h \ tree.h \ treenode.h \ utility.h Garli_SOURCES = \ adaptation.cpp \ bipartition.cpp \ condlike.cpp \ configoptions.cpp \ configreader.cpp \ datamatr.cpp \ funcs.cpp \ garlimain.cpp \ garlireader.cpp \ individual.cpp \ linalg.cpp \ model.cpp \ optimization.cpp \ population.cpp \ rng.cpp \ sequencedata.cpp \ set.cpp \ translatetable.cpp \ tree.cpp \ treenode.cpp \ mpitrick.cpp Garli_LDADD = $(LDADD) @GARLI_LIBS@ install-exec-hook: cd $(DESTDIR)$(bindir) && \ mv -f Garli$(EXEEXT) Garli-$(VERSION)$(EXEEXT) && \ $(LN_S) Garli-$(VERSION)$(EXEEXT) Garli$(EXEEXT) garli-2.1-release/src/Makefile.ser000066400000000000000000000073721241236125200171160ustar00rootroot00000000000000#NOTE THIS IS AN OLD AND CRAPPY MAKEFILE #SEE THE INSTALL FILE FOR COMPILATION INSTRUCTIONS AND DO NOT USE #THIS UNLESS YOU HAVE TO #GARLI version 0.97 makefile #NOTE - this is from the poor make system of version 0.951 and earlier. The configure # script mechanism should now be used instead if possible #to use, follow steps 1, 2 and optionally 3 #(1) choose one of these compile types #options are: #gcc_any gnu compiler gcc, any architecture #gcc_osx_universal gcc on OSX to make a universal binary #xlc_ppc970 ibm compiler for Power PC 970 architecture #icc_any Intel compiler icc (commercial). \ icc code is much faster on intel hardware COMPILE_TYPE = gcc_any #(2) these need to be adjusted to the correct path to a compiled #copy of Paul Lewis's Nexus Class Library #(NCL is available here: http://hydrodictyon.eeb.uconn.edu/ncl) #See the NCL documentation for details on compiling it. #Note that you may be able to safely ignore some errors during #NCL compilation (Garli only needs the static library libncl.a) NCL_INCLUDES = ../../ncl-2.0/src LIB_NCL = ../../ncl-2.0/src/libncl.a #(3)set this to yes to compile a version that uses MPI to fork #multiple serial runs across processors (using the same config) #This is an advanced option and should generally be = no unless #you know what you are doing MPI_RUN_SPLITTER = no #gcc: gnu compiler #linux or OSX binary that will work on the machine type that it is compiled on ifeq ($(COMPILE_TYPE),gcc_any) CC = g++ CC_FLAGS = -O3 -fstrict-aliasing -fomit-frame-pointer -funroll-loops \ -fsigned-char -DNDEBUG -DUNIX -I$(NCL_INCLUDES) endif #gcc: gnu compiler #OSX universal binary (may only compile on intel machines) ifeq ($(COMPILE_TYPE),gcc_osx_universal) CC = g++ CC_FLAGS = -O3 -fstrict-aliasing -arch i386 -arch ppc -fomit-frame-pointer \ -funroll-loops -DUNIX -DNDEBUG -include defs.h -I$(NCL_INCLUDES) endif #icc: intel compiler, any machine type ifeq ($(COMPILE_TYPE),icc_any) CC = icpc CC_FLAGS = -O2 -ip -fno-alias -DUNIX -DNDEBUG -I$(NCL_INCLUDES) endif #xlc: IBM compiler, PowerPC 970 processor ifeq ($(COMPILE_TYPE),xlc_ppc970) CC = xlC CC_FLAGS = -qsourcetype=c++ -qarch=ppc970 -qtune=ppc970 -qenablevmx \ -qaltivec -q64 -O3 -qalias=ansi -qunroll=yes -qchars=signed \ -qinclude=defs.h -I$(NCL_INCLUDES) -DUNIX -DNDEBUG endif ifeq ($(MPI_RUN_SPLITTER), yes) CC = mpicxx CC_FLAGS += -DSUBROUTINE_GARLI endif EXEC = Garli-Part-0.97 OBJECT_LIST = condlike.o datamatr.o individual.o\ population.o rng.o set.o\ garlireader.o translatetable.o tree.o treenode.o\ funcs.o configreader.o configoptions.o\ bipartition.o model.o linalg.o adaptation.o sequencedata.o\ optimization.o ifeq ($(MPI_RUN_SPLITTER), yes) OBJECT_LIST += mpitrick.o endif Garli-Part-0.97 : $(OBJECT_LIST) garlimain.o $(CC) $(CC_FLAGS) -v -o $(EXEC) $(OBJECT_LIST) $(LIB_NCL) garlimain.o #this forces garlimain.cpp to always be recompiled, which ensures that the #"compiled on XXX" message will be current garlimain.o:: $(CC) $(CC_FLAGS) -c -I. garlimain.cpp .cpp.o: $(CC) -c $(CC_FLAGS) -I. $*.cpp condlike.o: condlike.h defs.h configreader.o: configreader.h defs.h configoptions.o: configoptions.h defs.h tree.o: tree.h funcs.h defs.h clamanager.h optimization.o: tree.h funcs.h defs.h population.o: population.h clamanager.h defs.h individual.o: individual.h clamanager.h defs.h datamatr.o: datamatr.h defs.h model.o: model.h defs.h funcs.o: funcs.h defs.h linalg.o: linalg.h defs.h treenode.o: treenode.h defs.h bipartition.o: bipartition.h defs.h translatetable.o:translatetable.h defs.h set.o: set.h defs.h rng.o: rng.h defs.h adaptation.o: adaptation.h defs.h sequencedata.o: sequencedata.h defs.h garlireader.o: garlireader.h defs.h garli-2.1-release/src/adaptation.cpp000066400000000000000000000452201241236125200175100ustar00rootroot00000000000000// GARLI version 2.0 source code // Copyright 2005-2011 Derrick J. Zwickl // email: garli.support@gmail.com // // This program is free software: you can redistribute it and/or modify // it under the terms of the GNU General Public License as published by // the Free Software Foundation, either version 3 of the License, or // (at your option) any later version. // // This program is distributed in the hope that it will be useful, // but WITHOUT ANY WARRANTY; without even the implied warranty of // MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the // GNU General Public License for more details. // // You should have received a copy of the GNU General Public License // along with this program. If not, see . #include #include #include using namespace std; #include "defs.h" #include "funcs.h" #include "adaptation.h" #include "math.h" #include "configoptions.h" #include "individual.h" /* The next 3 lovely lines are used to cause the build to fail. This is useful for making sure that our automated build system is finding build errors */ #if defined(CAUSE_A_BUILD_ERROR) && CAUSE_A_BUILD_ERROR # error "CAUSE_A_BUILD_ERROR is defined" #endif Adaptation::Adaptation(const GeneralGamlConfig *gc){ intervalsToStore=gc->intervalsToStore; intervalLength=gc->intervalLength; startOptPrecision = branchOptPrecision = gc->startOptPrec; minOptPrecision = gc->minOptPrec; numPrecReductions=gc->numPrecReductions; if(gc->numPrecReductions > 0)//changing prec reduction to linear rather than geometric //precReductionFactor = pow((minOptPrecision/startOptPrecision), 1.0/numPrecReductions); precReductionFactor = (startOptPrecision - minOptPrecision)/FLOAT_TYPE(numPrecReductions); else precReductionFactor = gc->precReductionFactor; reset=false; topoWeight = gc->topoWeight; modWeight = gc->modWeight; brlenWeight = gc->brlenWeight; origRandNNIweight = randNNIweight = gc->randNNIweight; randSPRweight = gc->randSPRweight; limSPRweight = gc->limSPRweight; limSPRrange = gc->limSPRrange; #ifdef GANESH randPECRweight = gc->randPECRweight; #endif FLOAT_TYPE tot = topoWeight+modWeight+brlenWeight; topoMutateProb = topoWeight / tot; modelMutateProb = modWeight / tot; #ifdef GANESH tot = randNNIweight + randSPRweight + limSPRweight + randPECRweight; randNNIprob = randNNIweight / tot; randSPRprob = randSPRweight / tot; limSPRprob = limSPRweight / tot; randPECRprob = randPECRweight / tot; #else tot = randNNIweight + randSPRweight + limSPRweight; randNNIprob = randNNIweight / tot; randSPRprob = randSPRweight / tot; limSPRprob = limSPRweight / tot; #endif exNNIprob = 0.0; exlimSPRprob = 0.0; lastgenscore = 0.0; laststepscore=0.0; improveOverStoredIntervals=0.0; recTopImproveSize = 1.0; randNNI = new FLOAT_TYPE[intervalsToStore]; randNNInum = new int[intervalsToStore]; exNNI = new FLOAT_TYPE[intervalsToStore]; exNNInum = new int[intervalsToStore]; randSPR = new FLOAT_TYPE[intervalsToStore]; randSPRnum = new int[intervalsToStore]; #ifdef GANESH randPECR = new FLOAT_TYPE[intervalsToStore]; randPECRnum = new int[intervalsToStore]; #endif limSPR = new FLOAT_TYPE[intervalsToStore]; limSPRnum = new int[intervalsToStore]; exlimSPR = new FLOAT_TYPE[intervalsToStore]; exlimSPRnum = new int[intervalsToStore]; randRecom = new FLOAT_TYPE[intervalsToStore]; randRecomnum = new int[intervalsToStore]; bipartRecom = new FLOAT_TYPE[intervalsToStore]; bipartRecomnum = new int[intervalsToStore]; onlyBrlen = new FLOAT_TYPE[intervalsToStore]; onlyBrlennum = new int[intervalsToStore]; improvetotal = new FLOAT_TYPE[intervalsToStore]; anyModel = new FLOAT_TYPE[intervalsToStore]; anyModelnum = new int[intervalsToStore]; #ifdef MPI_VERSION bestFromRemote=new FLOAT_TYPE[intervalsToStore];bestFromRemoteNum=new int[intervalsToStore]; #endif for(unsigned i=0;iminOptPrec; numPrecReductions=gc->numPrecReductions; if(gc->numPrecReductions > 0) precReductionFactor = (gc->startOptPrec- minOptPrecision)/FLOAT_TYPE(numPrecReductions); else precReductionFactor = gc->precReductionFactor; topoWeight = gc->topoWeight; modWeight = gc->modWeight; brlenWeight = gc->brlenWeight; origRandNNIweight = randNNIweight = gc->randNNIweight; randSPRweight = gc->randSPRweight; limSPRweight = gc->limSPRweight; limSPRrange = gc->limSPRrange; } void Adaptation::WriteToCheckpoint(OUTPUT_CLASS &out) const{ //this function assumes that it has been passed an MFILE that is already open for //binary writing //7/13/07 changing this to calculate the actual size of the chunk of scalars //(the number of bytes between the start of the object and the first nonscalar //data member) rather than counting the number of each type and adding it up //manually. This should make it work irrespective of things like memory padding //for data member alignment, which could vary between platforms and compilers intptr_t scalarSize = (intptr_t) &improvetotal - (intptr_t) this; out.WRITE_TO_FILE(this, scalarSize, 1); //now the arrays, which should be of length intervalsToStore out.WRITE_TO_FILE(improvetotal, sizeof(FLOAT_TYPE), intervalsToStore); out.WRITE_TO_FILE(randNNI, sizeof(FLOAT_TYPE), intervalsToStore); out.WRITE_TO_FILE(randNNInum, sizeof(int), intervalsToStore); out.WRITE_TO_FILE(exNNI, sizeof(FLOAT_TYPE), intervalsToStore); out.WRITE_TO_FILE(exNNInum, sizeof(int), intervalsToStore); out.WRITE_TO_FILE(randSPR, sizeof(FLOAT_TYPE), intervalsToStore); out.WRITE_TO_FILE(randSPRnum, sizeof(int), intervalsToStore); out.WRITE_TO_FILE(limSPR, sizeof(FLOAT_TYPE), intervalsToStore); out.WRITE_TO_FILE(limSPRnum, sizeof(int), intervalsToStore); out.WRITE_TO_FILE(exlimSPR, sizeof(FLOAT_TYPE), intervalsToStore); out.WRITE_TO_FILE(exlimSPRnum, sizeof(int), intervalsToStore); out.WRITE_TO_FILE(randRecom, sizeof(FLOAT_TYPE), intervalsToStore); out.WRITE_TO_FILE(randRecomnum, sizeof(int), intervalsToStore); out.WRITE_TO_FILE(bipartRecom, sizeof(FLOAT_TYPE), intervalsToStore); out.WRITE_TO_FILE(bipartRecomnum, sizeof(int), intervalsToStore); out.WRITE_TO_FILE(onlyBrlen, sizeof(FLOAT_TYPE), intervalsToStore); out.WRITE_TO_FILE(onlyBrlennum, sizeof(int), intervalsToStore); out.WRITE_TO_FILE(anyModel, sizeof(FLOAT_TYPE), intervalsToStore); out.WRITE_TO_FILE(anyModelnum, sizeof(int), intervalsToStore); } /* void Adaptation::WriteToCheckpoint(ofstream &out) const{ //this function assumes that it has been passed a stream that is already open for //binary writing assert(out.good()); //7/13/07 changing this to calculate the actual size of the chunk of scalars //(the number of bytes between the start of the object and the first nonscalar //data member) rather than counting the number of each type and adding it up //manually. This should make it work irrespective of things like memory padding //for data member alignment, which could vary between platforms and compilers intptr_t scalarSize = (intptr_t) &improvetotal - (intptr_t) this; out.write((char *) this, (streamsize) scalarSize); //now the arrays, which should be of length intervalsToStore out.write((char *) improvetotal, sizeof(FLOAT_TYPE)*intervalsToStore); out.write((char *) randNNI, sizeof(FLOAT_TYPE)*intervalsToStore); out.write((char *) randNNInum, sizeof(int)*intervalsToStore); out.write((char *) exNNI, sizeof(FLOAT_TYPE)*intervalsToStore); out.write((char *) exNNInum, sizeof(int)*intervalsToStore); out.write((char *) randSPR, sizeof(FLOAT_TYPE)*intervalsToStore); out.write((char *) randSPRnum, sizeof(int)*intervalsToStore); out.write((char *) limSPR, sizeof(FLOAT_TYPE)*intervalsToStore); out.write((char *) limSPRnum, sizeof(int)*intervalsToStore); out.write((char *) exlimSPR, sizeof(FLOAT_TYPE)*intervalsToStore); out.write((char *) exlimSPRnum, sizeof(int)*intervalsToStore); out.write((char *) randRecom, sizeof(FLOAT_TYPE)*intervalsToStore); out.write((char *) randRecomnum, sizeof(int)*intervalsToStore); out.write((char *) bipartRecom, sizeof(FLOAT_TYPE)*intervalsToStore); out.write((char *) bipartRecomnum, sizeof(int)*intervalsToStore); out.write((char *) onlyBrlen, sizeof(FLOAT_TYPE)*intervalsToStore); out.write((char *) onlyBrlennum, sizeof(int)*intervalsToStore); out.write((char *) anyModel, sizeof(FLOAT_TYPE)*intervalsToStore); out.write((char *) anyModelnum, sizeof(int)*intervalsToStore); } */ void Adaptation::ReadFromCheckpoint(FILE *in){ //this function assumes that it has been passed a FILE* that is already open for //binary reading //7/13/07 changing this to calculate the actual size of the chunk of scalars //(the number of bytes between the start of the object and the first nonscalar //data member) rather than counting the number of each type and adding it up //manually. This should make it work irrespective of things like memory padding //for data member alignment, which could vary between platforms and compilers intptr_t scalarSize = (intptr_t) &improvetotal - (intptr_t) this; fread((char *) this, 1, scalarSize, in); if(ferror(in) || feof(in)){//this mainly checks for a zero-byte file throw ErrorException("Error reading checkpoint file .adap.check.\n\tA problem may have occured writing the file to disk, or the file may have been overwritten or truncated.\n\tUnfortunately you'll need to start the run again from scratch."); } //now the arrays, which should be of length intervalsToStore fread((char *) improvetotal, sizeof(FLOAT_TYPE), intervalsToStore, in); fread((char *) randNNI, sizeof(FLOAT_TYPE), intervalsToStore, in); fread((char *) randNNInum, sizeof(int), intervalsToStore, in); fread((char *) exNNI, sizeof(FLOAT_TYPE), intervalsToStore, in); fread((char *) exNNInum, sizeof(int), intervalsToStore, in); fread((char *) randSPR, sizeof(FLOAT_TYPE), intervalsToStore, in); fread((char *) randSPRnum, sizeof(int), intervalsToStore, in); fread((char *) limSPR, sizeof(FLOAT_TYPE), intervalsToStore, in); fread((char *) limSPRnum, sizeof(int), intervalsToStore, in); fread((char *) exlimSPR, sizeof(FLOAT_TYPE), intervalsToStore, in); fread((char *) exlimSPRnum, sizeof(int), intervalsToStore, in); fread((char *) randRecom, sizeof(FLOAT_TYPE), intervalsToStore, in); fread((char *) randRecomnum, sizeof(int), intervalsToStore, in); fread((char *) bipartRecom, sizeof(FLOAT_TYPE), intervalsToStore, in); fread((char *) bipartRecomnum, sizeof(int), intervalsToStore, in); fread((char *) onlyBrlen, sizeof(FLOAT_TYPE), intervalsToStore, in); fread((char *) onlyBrlennum, sizeof(int), intervalsToStore, in); fread((char *) anyModel, sizeof(FLOAT_TYPE), intervalsToStore, in); fread((char *) anyModelnum, sizeof(int), intervalsToStore, in); } void Adaptation::PrepareForNextInterval(){ //if we're on the first generation of a new recording period, shift everything over for(int i=intervalsToStore-1; i>0; i--){ improvetotal[i] = improvetotal[i-1]; randNNI[i] = randNNI[i-1];randNNInum[i] = randNNInum[i-1]; exNNI[i] = exNNI[i-1]; exNNInum[i] = exNNInum[i-1]; randSPR[i] = randSPR[i-1];randSPRnum[i] = randSPRnum[i-1]; limSPR[i] = limSPR[i-1]; limSPRnum[i] = limSPRnum[i-1]; exlimSPR[i] = exlimSPR[i-1]; exlimSPRnum[i] = exlimSPRnum[i-1]; #ifdef GANESH randPECR[i] = randPECR[i-1]; randPECRnum[i] = randPECRnum[i-1]; #endif // taxonSwap[i] = taxonSwap[i-1];taxonSwapnum[i] = taxonSwapnum[i-1]; randRecom[i] = randRecom[i-1];randRecomnum[i] = randRecomnum[i-1]; bipartRecom[i]=bipartRecom[i-1];bipartRecomnum[i] = bipartRecomnum[i-1]; onlyBrlen[i] = onlyBrlen[i-1];onlyBrlennum[i] = onlyBrlennum[i-1]; anyModel[i] = anyModel[i-1]; anyModelnum[i] = anyModelnum[i-1]; // slopes[i] = slopes[i-1]; #ifdef MPI_VERSION bestFromRemote[i] = bestFromRemote[i-1]; bestFromRemoteNum[i] = bestFromRemoteNum[i-1]; #endif // fromRemoteSubtree[i] = fromRemoteSubtree[i-1]; // fromRemoteNonSubtree[i] = fromRemoteNonSubtree[i-1]; }// end of for loop // clean up for next entry improvetotal[0] = 0.0; randNNI[0] = 0.0;randNNInum[0] = 0; exNNI[0] = 0.0;exNNInum[0] = 0; randSPR[0] = 0.0;randSPRnum[0] = 0; limSPR[0] = 0.0;limSPRnum[0] = 0; exlimSPR[0] = 0.0;exlimSPRnum[0] = 0; #ifdef GANESH randPECR[0] = 0.0;randPECRnum[0] = 0; #endif // taxonSwap[0] = 0.0;taxonSwapnum[0] = 0; randRecom[0] = 0.0;randRecomnum[0] = 0; bipartRecom[0]=0.0;bipartRecomnum[0]=0; onlyBrlen[0] = 0.0;onlyBrlennum[0] = 0; anyModel[0] = 0.0;anyModelnum[0] = 0; #ifdef MPI_VERSION bestFromRemote[0]=0.0;bestFromRemoteNum[0]=0; #endif // fromRemoteSubtree[0] = 0.0; // fromRemoteNonSubtree[0] = 0.0; } void Adaptation::BeginProbLog(ofstream &plog, int gen){ plog << "gen\tmod\ttopo\tbrlen\tNNI\trandSPR\tlimSPR\n"; //gen could be non-zero if we restarted OutputProbs(plog, gen); } void Adaptation::OutputProbs(ofstream &plog, int gen){ plog << gen << "\t" << modelMutateProb << "\t" << topoMutateProb << "\t" << (1.0-modelMutateProb-topoMutateProb) << "\t"; plog << randNNIprob << "\t" << randSPRprob << "\t" << limSPRprob << endl; } void Adaptation::UpdateProbs(){ FLOAT_TYPE topoTot=0.0, modTot=0.0, onlyBrlenTot=0.0; int numTopos=0, numMod=0, numOnlyBrlen=0; FLOAT_TYPE totRandNNI=0.0, totLimSPR=0.0, totRandSPR=0.0; int totNumRandNNI=0, totNumLimSPR=0, totNumRandSPR=0; FLOAT_TYPE totBipartRecom=0.0; int totNumBipartRecom=0; #ifdef MPI_VERSION FLOAT_TYPE totalFromRemote; #endif #ifdef GANESH FLOAT_TYPE totRandPECR=0.0; int totNumRandPECR=0; #endif for(unsigned i=0;i0) perBrlen=(onlyBrlenTot/numOnlyBrlen); else perBrlen= 0.0; FLOAT_TYPE perBipartRecom; if(totNumBipartRecom > 0) perBipartRecom=(totBipartRecom/totNumBipartRecom); else perBipartRecom=0.0; //version 0.95b3 - The reduction of precision that used to appear here has been //moved to Adaptation::ReducePrecision, which is called from Run, MasterMaster and //RemoteSubtreeWorker when lastTopoImprove is > that #int * intLength generations ago perTopo += topoWeight; perModel += modWeight; perBrlen += brlenWeight; FLOAT_TYPE tot=perTopo+perModel+perBrlen; FLOAT_TYPE brlenOnlyMut; //only update these probs if model mutations are turned off completely //or if some model mutations have been done (ie not in subtree mode) if(anyModelnum[0]!=0 || FloatingPointEquals(modWeight, 0.0, 1e-10)){ brlenOnlyMut=perBrlen/tot; modelMutateProb = perModel/tot; topoMutateProb = perTopo/tot; } //enforce a minimum probability if(modWeight != 0.0 && topoWeight != 0.0){ FLOAT_TYPE minProb= (FLOAT_TYPE) 0.02; if(topoMutateProb < minProb){ modelMutateProb -= minProb - topoMutateProb; topoMutateProb=minProb; } if(modelMutateProb < minProb){ topoMutateProb -= minProb - modelMutateProb; modelMutateProb=minProb; } if(1.0 - (modelMutateProb + topoMutateProb) < minProb){ FLOAT_TYPE diff=minProb - (FLOAT_TYPE)(1.0 - (modelMutateProb + topoMutateProb)); if(modelMutateProb - diff/2.0 > .02 && topoMutateProb - diff/2.0 > .02){ modelMutateProb -= diff/(FLOAT_TYPE)2.0; topoMutateProb -= diff/(FLOAT_TYPE)2.0; } else{ if(modelMutateProb - diff/2.0 < .02){ topoMutateProb -= diff; } else modelMutateProb -= diff; } brlenOnlyMut=minProb; } } // brlenOnlyMut = 1.0 - topoMutateProb - modelMutateProb; // } /* else{ scaler=(1-brlenOnlyMut) / (topoMutateProb + modelMutateProb); modelMutateProb *= scaler; topoMutateProb *= scaler; } */ if(totNumRandNNI==0) totNumRandNNI=1; if(totNumLimSPR==0) totNumLimSPR=1; if(totNumRandSPR==0) totNumRandSPR=1; #ifdef GANESH if(totNumRandPECR==0) totNumRandPECR=1; #endif //Because NNI's chosen by an SPR mutator are marked as NNI's, this needs to be done to keep from //giving NNI's some prob even when the weight was 0.0 FLOAT_TYPE perRandNNI= (randNNIweight == ZERO_POINT_ZERO ? ZERO_POINT_ZERO : totRandNNI/totNumRandNNI + randNNIweight); FLOAT_TYPE perLimSPR= (limSPRweight == ZERO_POINT_ZERO ? ZERO_POINT_ZERO : totLimSPR/totNumLimSPR + limSPRweight); FLOAT_TYPE perRandSPR= (limSPRweight == ZERO_POINT_ZERO ? ZERO_POINT_ZERO : totRandSPR/totNumRandSPR + randSPRweight); #ifdef GANESH FLOAT_TYPE perRandPECR=totRandPECR/totNumRandPECR + randPECRweight; tot=perRandNNI+perLimSPR+perRandSPR+perRandPECR; randNNIprob=perRandNNI/tot; randSPRprob=perRandSPR/tot; limSPRprob=perLimSPR/tot; randPECRprob=perRandPECR/tot; #else tot=perRandNNI+perLimSPR+perRandSPR; if(tot > ZERO_POINT_ZERO){ randNNIprob=perRandNNI/tot; randSPRprob=perRandSPR/tot; limSPRprob=perLimSPR/tot; } #endif } garli-2.1-release/src/adaptation.h000066400000000000000000000063601241236125200171570ustar00rootroot00000000000000// GARLI version 2.0 source code // Copyright 2005-2011 Derrick J. Zwickl // email: garli.support@gmail.com // // This program is free software: you can redistribute it and/or modify // it under the terms of the GNU General Public License as published by // the Free Software Foundation, either version 3 of the License, or // (at your option) any later version. // // This program is distributed in the hope that it will be useful, // but WITHOUT ANY WARRANTY; without even the implied warranty of // MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the // GNU General Public License for more details. // // You should have received a copy of the GNU General Public License // along with this program. If not, see . #ifndef ADAPTION_H #define ADAPTION_H #include using namespace std; #include "configoptions.h" class MFILE; class Adaptation{ public: //here are all of the scalars: //4 ints, 1 bool, 23 FLOAT_TYPEs unsigned intervalLength; unsigned intervalsToStore; FLOAT_TYPE lastgenscore; FLOAT_TYPE laststepscore; FLOAT_TYPE improveOverStoredIntervals; bool reset; FLOAT_TYPE startOptPrecision; FLOAT_TYPE branchOptPrecision; FLOAT_TYPE minOptPrecision; FLOAT_TYPE precReductionFactor; int numPrecReductions; FLOAT_TYPE topoWeight; FLOAT_TYPE modWeight; FLOAT_TYPE brlenWeight; FLOAT_TYPE randNNIweight; FLOAT_TYPE origRandNNIweight; FLOAT_TYPE randSPRweight; FLOAT_TYPE limSPRweight; FLOAT_TYPE recTopImproveSize; FLOAT_TYPE exNNIprob; FLOAT_TYPE exlimSPRprob; FLOAT_TYPE topoMutateProb; FLOAT_TYPE randNNIprob; FLOAT_TYPE randSPRprob; FLOAT_TYPE limSPRprob; FLOAT_TYPE modelMutateProb; unsigned limSPRrange; //the arrays. All will be of length intervalsToStore FLOAT_TYPE *improvetotal; FLOAT_TYPE *randNNI; int *randNNInum; FLOAT_TYPE *exNNI; int *exNNInum; FLOAT_TYPE *randSPR; int *randSPRnum; FLOAT_TYPE *limSPR; int *limSPRnum; FLOAT_TYPE *exlimSPR; int *exlimSPRnum; FLOAT_TYPE *randRecom; int *randRecomnum; FLOAT_TYPE *bipartRecom; int *bipartRecomnum; FLOAT_TYPE *onlyBrlen; int *onlyBrlennum; FLOAT_TYPE *anyModel; int *anyModelnum; #ifdef MPI_VERSION FLOAT_TYPE *fromRemoteSubtree; FLOAT_TYPE *fromRemoteNonSubtree; int *bestFromRemoteNum; FLOAT_TYPE *bestFromRemote; #endif Adaptation(const GeneralGamlConfig *gc); ~Adaptation(); public: void SetChangeableVariablesFromConfAfterReadingCheckpoint(const GeneralGamlConfig *gc); void PrepareForNextInterval(); void UpdateProbs(); void OutputProbs(ofstream &plog, int gen); void BeginProbLog(ofstream &plot, int gen); bool ReducePrecision(){ if(FloatingPointEquals(branchOptPrecision, minOptPrecision, 1e-10) || numPrecReductions == 0) return false; if(topoMutateProb > .01 || FloatingPointEquals(topoWeight, 0.0, 1e-10)){ //changing this to a linear reduction in prec. Geometric was too fast //branchOptPrecision*=precReductionFactor; branchOptPrecision -= precReductionFactor; if((branchOptPrecision < minOptPrecision) || (branchOptPrecision - minOptPrecision) < (precReductionFactor / 2.0)) branchOptPrecision=minOptPrecision; return true; } else return false; } void WriteToCheckpoint(OUTPUT_CLASS &) const; void ReadFromCheckpoint(FILE *); }; #endif garli-2.1-release/src/bipartition.cpp000066400000000000000000000256751241236125200177240ustar00rootroot00000000000000// GARLI version 2.0 source code // Copyright 2005-2011 Derrick J. Zwickl // email: garli.support@gmail.com // // This program is free software: you can redistribute it and/or modify // it under the terms of the GNU General Public License as published by // the Free Software Foundation, either version 3 of the License, or // (at your option) any later version. // // This program is distributed in the hope that it will be useful, // but WITHOUT ANY WARRANTY; without even the implied warranty of // MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the // GNU General Public License for more details. // // You should have received a copy of the GNU General Public License // along with this program. If not, see . #include "defs.h" #include "bipartition.h" #include "reconnode.h" int Bipartition::nBlocks; int Bipartition:: blockBits; int Bipartition::ntax; unsigned int Bipartition::largestBlockDigit; unsigned int Bipartition::allBitsOn; char * Bipartition::str; unsigned int Bipartition::partialBlockMask; bool Constraint::allBackbone; bool Constraint::anyBackbone; bool Constraint::sharedMask; //note that this is "less than" for sorting purposes, not in a subset sense bool BipartitionLessThan(const Bipartition &lhs, const Bipartition &rhs){ int i; for(i=0;i rhs.rep[i]) return false; else if(lhs.rep[i] < rhs.rep[i]) return true; } if(((lhs.rep[i]) & lhs.partialBlockMask) > ((rhs.rep[i]) & lhs.partialBlockMask)) return false; else return true; } void Constraint::SetConstraintStatics(bool allBack, bool anyBack, bool oneMask){ Constraint::allBackbone = allBack; Constraint::anyBackbone = anyBack; Constraint::sharedMask = oneMask; } void Bipartition::SetBipartitionStatics(int nt){ Bipartition::blockBits=sizeof(int)*8; Bipartition::ntax=nt; Bipartition::nBlocks=(int)ceil((FLOAT_TYPE)nt/(FLOAT_TYPE)Bipartition::blockBits); Bipartition::largestBlockDigit=1<<(Bipartition::blockBits-1); Bipartition::allBitsOn=(unsigned int)(pow(2.0, Bipartition::blockBits)-1); Bipartition::str=new char[nt+1]; Bipartition::str[nt] = '\0'; Bipartition::SetPartialBlockMask(); } void Bipartition::SetPartialBlockMask(){ partialBlockMask=0; unsigned int bit=largestBlockDigit; for(int b=0;b> 1; } if(ntax%blockBits == 0) partialBlockMask=allBitsOn; } //this function does 2 things //1. Fills this bipartition with the bitwise intersection of a backbone mask and a mask //representing a subset of taxa in a growing tree. Note that it is safe to call this when the //constraint is not a backbone and/or when the partialMask is NULL. In that case it will fill //the bipartition with one or the other, or with all bits on if their if neither //2. Checks if there is a meaningful intersection between the created joint mask and //the constraint. That means at least 2 bits are "on" on each site of the constrained bipartition bool Bipartition::MakeJointMask(const Constraint &constr, const Bipartition *partialMask){ if(constr.IsBackbone()){ //this just uses Bipartition::Operator=() *this = *(constr.GetBackboneMask()); if(partialMask != NULL)//in this case we'll need to test for meaningful intersection below this->AndEquals(*partialMask); else//here we don't need to check, since a backbone constraint and its own mask must be meaningful return true; } else if(partialMask != NULL){ //in this case we'll need to test for meaningful intersection below *this = *(partialMask); } else{ FillAllBits(); return true; } Bipartition temp; temp = constr.GetBipartition(); temp.AndEquals(*this); if(temp.MoreThanOneBitSet() == false) return false; temp = constr.GetBipartition(); temp.Complement(); temp.AndEquals(*this); if(temp.MoreThanOneBitSet() == false) return false; return true; } bool Bipartition::IsCompatibleWithBipartition(const Bipartition &constr) const{ //using buneman's 4 point condition. At least one of the four intersections must be empty //A & B if(HasIntersection(constr, NULL) == false) return true; //A & ~B if(HasIntersectionWithComplement(constr, NULL) == false) return true; //~A & B if(ComplementHasIntersection(constr, NULL) == false) return true; //~A & ~B if(ComplementHasIntersectionWithComplement(constr, NULL) == false) return true; return false; } bool Bipartition::OldIsCompatibleWithBipartition(const Bipartition &constr) const{ //using buneman's 4 point condition. At least one of the four intersections must be empty bool compat=true; int i; //A & B for(i=0;irep[i]; unsigned int bit = largestBlockDigit; for(int j=0;j= ntax) break; if(bit & m){ if(bit & t){ if(strcmp(left.c_str(), "(")) left += ','; sprintf(temp, "%d", (i*blockBits + j + 1)); left += temp; } else{ if(strcmp(right.c_str(), "(")) right += ','; sprintf(temp, "%d", (i*blockBits + j + 1)); right += temp; } } bit = bit >> 1; } } } else{ for(int i=0;i= ntax) break; if(bit & t){ if(strcmp(left.c_str(), "(")) left += ','; sprintf(temp, "%d", (i*blockBits + j + 1)); left += temp; } else{ if(strcmp(right.c_str(), "(")) right += ','; sprintf(temp, "%d", (i*blockBits + j + 1)); right += temp; } bit = bit >> 1; } } } left += ')'; right += ')'; out = left + " | " + right; } garli-2.1-release/src/bipartition.h000066400000000000000000000442011241236125200173530ustar00rootroot00000000000000// GARLI version 2.0 source code // Copyright 2005-2011 Derrick J. Zwickl // email: garli.support@gmail.com // // This program is free software: you can redistribute it and/or modify // it under the terms of the GNU General Public License as published by // the Free Software Foundation, either version 3 of the License, or // (at your option) any later version. // // This program is distributed in the hope that it will be useful, // but WITHOUT ANY WARRANTY; without even the implied warranty of // MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the // GNU General Public License for more details. // // You should have received a copy of the GNU General Public License // along with this program. If not, see . #ifndef BIPARTITION #define BIPARTITION #include #include #include #include #include #include using namespace std; #include "errorexception.h" class Constraint; class Bipartition{ public: unsigned int *rep; static int nBlocks; static int blockBits; static int ntax; static unsigned int largestBlockDigit; static unsigned int allBitsOn; static char* str; static unsigned int partialBlockMask;//this can be used to mask out the bits that // aren't used in the last block. This becomes //important if we start doing complements. Bits //that represent actual taxa are ON Bipartition(){ rep=new unsigned int[nBlocks]; ClearBipartition(); } Bipartition(const Bipartition &b){//copy constructor rep=new unsigned int[nBlocks]; memcpy(rep, b.rep, nBlocks*sizeof(int)); //for(int i=0;irep[i]; } } void operator-=(const Bipartition *rhs){ //note that this assumes that rhs is a subset of this or vice versa!!! if(rhs->IsASubsetOf(*this)){ for(int i=0;irep[i]; } } else{ for(int i=0;irep[i]; } } } void operator=(const Bipartition *rhs){ memcpy(rep, rhs->rep, nBlocks*sizeof(int)); } void operator=(const Bipartition &rhs){ memcpy(rep, rhs.rep, nBlocks*sizeof(int)); } int CountOnBits() const{ int num=0; for(int i=1;i<=ntax;i++) if(ContainsTaxon(i)) num++; return num; } bool MoreThanOneBitSet() const{ int num = 0; int i = 0; for(i=0;i 1) return true; } if((rep[i] & partialBlockMask) & ((rep[i] & partialBlockMask) - 1)) return true; if(rep[i] & partialBlockMask) num++; if(num > 1) return true; return false; } void FlipBits(const Bipartition &flip){ //this just flips the bits in the passed in bipartition EqualsXORComplement(flip); } void FillAllBits(){ //the argument here is what to fill each _byte_ of the ints with memset(rep, 0xFF, sizeof(int) * nBlocks); } bool EqualsEquals(const Bipartition &rhs) const{ //assert(this->ContainsTaxon(1)); //assert(rhs.ContainsTaxon(1)); int i; for(i=0;iContainsTaxon(1)); //assert(rhs.ContainsTaxon(1)); int i; for(i=0;iContainsTaxon(1)); //assert(rhs.ContainsTaxon(1)); int i; for(i=0;i>((t-1)%blockBits)) return true; return false; } //if you know the taxon is in the first block, it is unnecessary and very slow to do //the divisions in ContainsTaxon inline bool ContainsFirstTaxon() const{ if(rep[0] & largestBlockDigit) return true; return false; } void FillWithXORComplement(const Bipartition &lhs, const Bipartition &rhs){ for(int i=0;irep[i]); } if(sum) return true; if((rep[i] & rhs.rep[i]) & (mask->rep[i] & partialBlockMask)) return true; } #else else{ for(i=0;irep[i]){ return true; } } if(((rep[i] & rhs.rep[i]) & (mask->rep[i] & partialBlockMask))) return true; } #endif return false; } bool HasIntersectionWithComplement(const Bipartition &rhs, const Bipartition *mask) const{ int i; if(!mask){ for(i=0;irep[i]); } if(sum) return true; if((rep[i] & ~rhs.rep[i]) & (mask->rep[i] & partialBlockMask)) return true; } #else else{ for(i=0;irep[i]){ return true; } } if(((rep[i] & ~rhs.rep[i]) & (mask->rep[i] & partialBlockMask))) return true; } #endif return false; } bool ComplementHasIntersection(const Bipartition &rhs, const Bipartition *mask) const{ int i; if(!mask){ for(i=0;irep[i]); } if(sum) return true; if((~rep[i] & rhs.rep[i]) & (mask->rep[i] & partialBlockMask)) return true; } #else else{ for(i=0;irep[i]){ return true; } } if(((~rep[i] & rhs.rep[i]) & (mask->rep[i] & partialBlockMask))) return true; } #endif return false; } bool ComplementHasIntersectionWithComplement(const Bipartition &rhs, const Bipartition *mask) const{ int i; if(!mask){ for(i=0;irep[i]); } if(sum) return true; if((~rep[i] & ~rhs.rep[i]) & (mask->rep[i] & partialBlockMask)) return true; } #else else{ for(i=0;irep[i]){ return true; } } if(((~rep[i] & ~rhs.rep[i]) & (mask->rep[i] & partialBlockMask))) return true; } #endif return false; } bool IsCompatibleWithNegativeBipartition(const Bipartition &bip) const{ //To be consistent with a negative bipartition (constraint) neither the bipartition //or its complement can BE the constraint bipartition if(this->EqualsEquals(bip)==false && this->ComplementEqualsEquals(bip)==false) return true; else return false; } bool IsCompatibleWithNegativeBipartitionWithMask(const Bipartition &bip, const Bipartition &mask) const{ //To be consistent with a negative bipartition (constraint) neither the masked bipartition //or its masked complement can BE the constraint bipartition if(this->MaskedEqualsEquals(bip, mask)==false && this->MaskedComplementEqualsEquals(bip, mask)==false) return true; else return false; } bool IsASubsetOf(const Bipartition &target) const{ //returns true if this is a subset of target int i; for(i=0;i 0 && taxNum <= ntax); ClearBipartition(); //8-19-05 fixed this bit of stupidity //rep[(taxNum)/blockBits]|=(largestBlockDigit>>((taxNum-1)%blockBits)); rep[(taxNum-1)/blockBits]|=(largestBlockDigit>>((taxNum-1)%blockBits)); return this; } char * Output(){ for(int i=0;i= ntax) break; if(t&largestBlockDigit) str[i*blockBits+j]='*'; else str[i*blockBits+j]='-'; t=t<<1; } } return str; } /* void BinaryOutput(ofstream &out){ int size = nBlocks * sizeof(unsigned int); out.write((char*) rep, size); } */ void BinaryOutput(OUTPUT_CLASS &out){ out.WRITE_TO_FILE(rep, sizeof(unsigned int), nBlocks); } void BinaryInput(FILE* &in){ fread((char*) rep, sizeof(unsigned int), nBlocks, in); } vector NodenumsFromBipart(){ vector nodes; for(int i=1;i<=ntax;i++) if(ContainsTaxon(i)) nodes.push_back(i); return nodes; } void BipartFromNodenums(const vector & nodes){ ClearBipartition(); Bipartition temp; for(vector::const_iterator it = nodes.begin();it != nodes.end();it++) *this += temp.TerminalBipart(*it); } void BipartFromNodenums(const std::set & nodes){ ClearBipartition(); Bipartition temp; for(set::const_iterator it = nodes.begin();it != nodes.end();it++) *this += temp.TerminalBipart(*it); } }; bool BipartitionLessThan(const Bipartition &lhs, const Bipartition &rhs); class Constraint{ //a very simple class that just contains a bipartition and whether //it is a negative or positive constraint Bipartition con; bool positive; //now adding the potential for a backbone constraint //BACKBONE bool backbone; Bipartition backboneMask; //an outgroup is a special type of positive constraint bool outgroup; public: //if these are true (and they almost always will be), it makes checking constraint validity much easier static bool allBackbone; static bool anyBackbone; static bool sharedMask; Constraint() { backbone=false; }; Constraint(const Bipartition *b, bool pos){ positive=pos; con=b; backbone=false; } Constraint(const Bipartition *b, const Bipartition *m, bool pos){ positive=pos; con=b; backbone=true; backboneMask = m; } static void SetConstraintStatics(bool allBack, bool anyBack, bool oneMask); bool IsPositive() const {return positive;} bool IsBackbone() const {return backbone;} bool IsOutgroup() const {return outgroup;} const Bipartition *GetBipartition() const {return &con;} const Bipartition *GetBackboneMask() const {return &backboneMask;} void SetAsOutgroup() {outgroup = true;} void Standardize() {con.Standardize();} void ReadDotStarConstraint(const char *c){ Bipartition b; size_t len=strlen(c); con.ClearBipartition(); if(c[0] == '+') positive=true; else if(c[0] == '-') positive=false; else throw ErrorException("constraint string must start with \'+\' (positive constraint) or \'-\' (negative constraint)"); for(unsigned i=1;i. #ifndef CLA_MANAGER #define CLA_MANAGER #include #include #include #include "condlike.h" #include "model.h" using namespace std; extern int memLevel; #ifdef UNIX #include #endif #undef CLA_DEBUG class ClaSpecifier{ public: int claIndex; //this is just the number of the corresponding cla - there is a 1 to 1 correspondence int modelIndex; int dataIndex; ClaSpecifier(int c, int m, int d):claIndex(c), modelIndex(m), dataIndex(d){}; }; extern vector claSpecs; class ClaManager{ int numNodes;//the number of nodes in each tree int numRates; int numClas; int numHolders; int maxUsed; CondLikeArraySet **allClas; //these are the actual sets of arrays to be used in calculations, but will assigned to //nodes via a CondLikeArrayHolder. There may be a limited number CondLikeArrayHolder *holders; //there will be enough of these such that every node and direction could //have a unique one, although many will generally be shared vector claStack; vector holderStack; public: //PARTITION //ClaManager(int nnod, int nClas, int nHolders, int nchar, int nrates) : numNodes(nnod), numClas(nClas), numHolders(nHolders), numRates(nrates){ /* ClaManager(int nnod, int numClas, int nHolders, const ModelPartition *mods, const DataPartition *data) : numNodes(nnod), numHolders(nHolders){ maxUsed=0; allClas=new CondLikeArraySet*[numClas]; claStack.reserve(numClas); for(int i=numClas-1;i>=0;i--){ allClas[i]=new CondLikeArraySet; //for(vector::iterator modit = mods->models.begin();modit != mods->models.end();modit++){ for(int m = 0;m < mods->NumModels();m++){ const Model *thisMod = mods->GetModel(m); CondLikeArray *thisCLA = new CondLikeArray(data->GetSubset(thisMod->dataIndex)->NChar(), thisMod->NStates(), thisMod->NRateCats(), thisMod->dataIndex); allClas[i]->AddCLA(thisCLA); allClas[i]->Allocate(); } claStack.push_back(allClas[i]); } holders = new CondLikeArrayHolder[numHolders]; holderStack.reserve(numHolders); for(int i=numHolders-1;i>=0;i--) holderStack.push_back(i); } */ ClaManager(int nnod, int nClas, int nHolders, const ModelPartition *mods, const DataPartition *data) : numNodes(nnod), numClas(nClas), numHolders(nHolders){ maxUsed=0; allClas=new CondLikeArraySet*[numClas]; claStack.reserve(numClas); for(int i=numClas-1;i>=0;i--){ allClas[i]=new CondLikeArraySet; //for(vector::iterator modit = mods->models.begin();modit != mods->models.end();modit++){ //for(int m = 0;m < mods->NumModels();m++){ for(vector::iterator specs = claSpecs.begin();specs != claSpecs.end();specs++){ const Model *thisMod = mods->GetModel((*specs).modelIndex); CondLikeArray *thisCLA = new CondLikeArray(data->GetSubset((*specs).dataIndex)->NChar(), (thisMod->IsOrientedGap() ? 3: thisMod->NStates()), thisMod->NRateCats()); allClas[i]->AddCLA(thisCLA); } allClas[i]->Allocate(); claStack.push_back(allClas[i]); } holders = new CondLikeArrayHolder[numHolders]; holderStack.reserve(numHolders); for(int i=numHolders-1;i>=0;i--) holderStack.push_back(i); } ~ClaManager(){ if(allClas!=NULL){ for(int i=0;i 0); int index=holderStack[holderStack.size()-1]; IncrementCla(index); holderStack.pop_back(); return index; } inline void ClaManager::FillHolder(int index, int dir){ holders[index].theSet = AssignFreeCla(); holders[index].reclaimLevel=dir; } inline int ClaManager::GetReclaimLevel(int index){ if(holders[index].theSet == NULL) return -1; return holders[index].GetReclaimLevel(); } inline void ClaManager::SetReclaimLevel(int index, int lvl){ assert(index > -1); if(holders[index].theSet == NULL) assert(0); //return; holders[index].SetReclaimLevel(lvl); } inline void ClaManager::ReserveCla(int index, bool temp/*=true*/){ if(temp==true) holders[index].tempReserved=true; else holders[index].reserved=true; } inline void ClaManager::UnreserveCla(int index){ // holders[index].tempReserved=false; holders[index].reserved=false; if(memLevel>1) holders[index].SetReclaimLevel(1); } inline void ClaManager::ReclaimSingleCla(int index){ //this simply removes the cla from a holder. It is equivalent to just //dirtying it if only a single tree shares the holder if(holders[index].theSet==NULL) return; claStack.push_back(holders[index].theSet); holders[index].SetReclaimLevel(0); holders[index].theSet=NULL; } inline void ClaManager::CountClaTotals(int &clean, int &tempres, int &res, int &assigned){ for(int i=0;i -1); return (holders[index].theSet == NULL); } inline int ClaManager::SetDirty(int index){ //there are only two options here: //1. Cla is being made dirty, and only node node points to it // ->null the holder's cla pointer and return the same index //2. Cla is being made dirty, and multiple nodes point to it // ->remove this node from the holder (decrement) and assign a new one assert(index != -1); if(holders[index].numAssigned==1){ if(holders[index].theSet != NULL){ holders[index].SetReclaimLevel(0); claStack.push_back(holders[index].theSet); holders[index].theSet=NULL; } } else{ DecrementCla(index); index=holderStack[holderStack.size()-1]; holderStack.pop_back(); IncrementCla(index); } return index; } inline void ClaManager::IncrementCla(int index){ holders[index].numAssigned++; } inline void ClaManager::DecrementCla(int index){ assert(index != -1); if(holders[index].numAssigned==1){ holderStack.push_back(index); if(holders[index].theSet != NULL){ assert(find(claStack.begin(), claStack.end(), holders[index].theSet) == claStack.end()); //assert(holders[index].theSet->NStates()==4); claStack.push_back(holders[index].theSet); } holders[index].Reset(); } else{ holders[index].numAssigned--; //this is important! holders[index].tempReserved=false; } } inline void ClaManager::CheckClaHolders(){ int used=0; int reclaim2=0; for(int i=0;iSetDirty(true); assignedClaArray[i]=0; } } */ /* void OutputClaReport(){ ofstream cla("clareport.log"); for(int i=1;inodeNum << "\t" << allClas[i]->IsDirty() << "\n"; } } */ /* int NumAssigned(int index){ return assignedClaArray[index]; } */ /* void OutputClaInfo(ostream &str, int index, int nd){ str << nd << "\t" << index << "\t" << allClas[index]->nodeNum << "\t" << allClas[index]->IsDirty(nd) << "\t" << assignedClaArray[index] << "\t" << allClas[index]->GetReclaimLevel() << "\n"; } */ #endif garli-2.1-release/src/condlike.cpp000066400000000000000000000112741241236125200171560ustar00rootroot00000000000000// GARLI version 2.0 source code // Copyright 2005-2011 Derrick J. Zwickl // email: garli.support@gmail.com // // This program is free software: you can redistribute it and/or modify // it under the terms of the GNU General Public License as published by // the Free Software Foundation, either version 3 of the License, or // (at your option) any later version. // // This program is distributed in the hope that it will be useful, // but WITHOUT ANY WARRANTY; without even the implied warranty of // MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the // GNU General Public License for more details. // // You should have received a copy of the GNU General Public License // along with this program. If not, see . // // NOTE: Portions of this source adapted from GAML source, written by Paul O. Lewis #include "defs.h" #include "condlike.h" #include "clamanager.h" #include "utility.h" #undef ALIGN_CLAS #define CLA_ALIGNMENT 32 CondLikeArray::~CondLikeArray(){ //with partitioning, the entire allocation is managed and deleted by the //condlikearrayset, so don't delete anything here arr = NULL; underflow_mult = NULL; /* if( arr ){ #ifndef ALIGN_CLAS delete []arr; #else DeleteAlignedArray(arr); #endif arr=NULL; } if(underflow_mult!=NULL) delete []underflow_mult; */ } void CondLikeArray::Allocate( int nk, int ns, int nr /* = 1 */ ){ if( arr ){ #ifndef ALIGN_CLAS delete []arr; #else DeleteAlignedArray(arr); #endif arr=NULL; } nrates = nr; nsites = ns; nstates = nk; #ifndef ALIGN_CLAS arr=new FLOAT_TYPE[nk*nr*ns]; #else arr = NewAlignedArray(nk*nr*ns, CLA_ALIGNMENT); #endif if(arr==NULL){ throw ErrorException("GARLI had a problem allocating memory! Try reducing the availablememory setting."); } //DJZ 1-5-05 - the underflow mult used to be ns*nr in length, //but only needs to be ns underflow_mult=new int[ns]; if(underflow_mult==NULL){ throw ErrorException("GARLI had a problem allocating memory! Try reducing the availablememory setting."); } } CondLikeArraySet* ClaManager::AssignFreeCla(){ #ifdef CLA_DEBUG ofstream deb("cladebug.log", ios::app); #endif if(claStack.empty() == true) RecycleClas(); CondLikeArraySet *arr=claStack[claStack.size()-1]; assert(arr != NULL); claStack.pop_back(); if(numClas - (int)claStack.size() > maxUsed) maxUsed=numClas - (int)claStack.size(); return arr; } void ClaManager::RecycleClas(){ int numReclaimed=0; for(int i=0;i 50) return; } if(numReclaimed > 10) return; for(int i=0;i 0); } void CondLikeArraySet::Allocate() { unsigned size = 0, usize = 0; for(vector::iterator cit = theSets.begin();cit != theSets.end();cit++){ size += (*cit)->RequiredSize(); usize += (*cit)->NChar(); } try{ rawAllocation = new FLOAT_TYPE[size]; } catch(std::bad_alloc){ throw ErrorException("Problem allocating cond. likelihood array (len = %d). Out of mem?\n\tNote: to use > 4GB of memory, you will need a 64-bit version of GARLI.", size); } try{ rawUnder = new int[usize]; } catch(std::bad_alloc){ throw ErrorException("Problem allocating underflow multiplier array (len = %d). Out of mem?", usize); } unsigned offset = 0, uoffset = 0; for(vector::iterator cit = theSets.begin();cit != theSets.end();cit++){ (*cit)->Assign(&rawAllocation[offset], &rawUnder[uoffset]); offset += (*cit)->RequiredSize(); uoffset += (*cit)->NChar(); } } garli-2.1-release/src/condlike.h000066400000000000000000000073271241236125200166270ustar00rootroot00000000000000// GARLI version 2.0 source code // Copyright 2005-2011 Derrick J. Zwickl // email: garli.support@gmail.com // // This program is free software: you can redistribute it and/or modify // it under the terms of the GNU General Public License as published by // the Free Software Foundation, either version 3 of the License, or // (at your option) any later version. // // This program is distributed in the hope that it will be useful, // but WITHOUT ANY WARRANTY; without even the implied warranty of // MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the // GNU General Public License for more details. // // You should have received a copy of the GNU General Public License // along with this program. If not, see . // // NOTE: Portions of this source adapted from GAML source, written by Paul O. Lewis // -------- // Example showing how the array is laid out: // ------------------------------------------ // 4 bases, 3 sites, 5 rate categories // // 11111111112222222222333333333344444444445555555555 // 012345678901234567890123456789012345678901234567890123456789 // ------------------------------------------------------------ // 012301230123012301230123012301230123012301230123012301230123 <== base b // 000011112222333344440000111122223333444400001111222233334444 <== rate r // 000000000000000000001111111111111111111122222222222222222222 <== site s // indexing: ^ this is s*4*5 + r*4 + b = 22 // // If there is only one rate category... // 4 bases, 3 sites // // 11 // 012345678901 // ------------ // 012301230123 <== base b // 000000000000 <== rate r // 000011112222 <== site s // indexing: ^ this is s*4*1 + b = 10 // #ifndef __CONDLIKE_H #define __CONDLIKE_H #include #include using namespace std; #include "defs.h" //****************************************************************************** // CondLikeArray // class CondLikeArray{ //this is a CLA for a single model friend class CondLikeArrayIterator; unsigned nsites, nrates, nstates; public: FLOAT_TYPE* arr; int* underflow_mult; unsigned rescaleRank; CondLikeArray(int nsit, int nsta, int nrat) : nsites(nsit), nrates(nrat), nstates(nsta), arr(NULL), underflow_mult(NULL), rescaleRank(1){} CondLikeArray() : nsites(0), nrates(0), nstates(0), arr(0), underflow_mult(0), rescaleRank(1){} ~CondLikeArray(); int NStates() const { return nstates; } int NChar() const {return nsites;} int NRateCats() const {return nrates;} int RequiredSize() const {return nsites * nstates * nrates;} void Assign(FLOAT_TYPE *alloc, int * under) {arr = alloc; underflow_mult = under;} void Allocate( int nk, int ns, int nr = 1 ); }; class CondLikeArraySet{ //this is a set of CLAs, one for each model public: vector theSets; FLOAT_TYPE *rawAllocation; int *rawUnder; CondLikeArraySet() : rawAllocation(NULL), rawUnder(NULL){}; ~CondLikeArraySet() { for(int i = 0;i < theSets.size();i++) delete theSets[i]; theSets.clear(); delete []rawAllocation; delete []rawUnder; } void Allocate(); void AddCLA(CondLikeArray *cla ){ theSets.push_back(cla); } CondLikeArray *GetCLA(int index){ return theSets[index]; } }; class CondLikeArrayHolder{ public: short numAssigned; short reclaimLevel; bool tempReserved; bool reserved; //CondLikeArray *theArray; CondLikeArraySet *theSet; CondLikeArrayHolder() : theSet(NULL), numAssigned(0), reclaimLevel(0), reserved(false) , tempReserved(false){} ~CondLikeArrayHolder() {theSet = NULL;} int GetReclaimLevel() {return reclaimLevel;} void SetReclaimLevel(int lvl) {reclaimLevel = lvl;} void Reset(){reclaimLevel=0;numAssigned=0,tempReserved=false;reserved=false;theSet=NULL;} }; #endif garli-2.1-release/src/configoptions.cpp000066400000000000000000000612421241236125200202470ustar00rootroot00000000000000// GARLI version 2.1 source code // Copyright 2005-2014 Derrick J. Zwickl // email: garli.support@gmail.com // // This program is free software: you can redistribute it and/or modify // it under the terms of the GNU General Public License as published by // the Free Software Foundation, either version 3 of the License, or // (at your option) any later version. // // This program is distributed in the hope that it will be useful, // but WITHOUT ANY WARRANTY; without even the implied warranty of // MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the // GNU General Public License for more details. // // You should have received a copy of the GNU General Public License // along with this program. If not, see . #include #include #include #include #include using namespace std; #include "defs.h" #include "configoptions.h" #include "configreader.h" #include "errorexception.h" ///////////////////////////////////////////////////////////////////////// // GamlConfig::General methods ////////////////////////////////////////// ///////////////////////////////////////////////////////////////////////// GeneralGamlConfig::GeneralGamlConfig(){ //Default values for everything //output related ofprefix = "ofprefix"; logevery = 10; saveevery = 100; outputTreelog = false; outputMostlyUselessFiles = false; outputPhylipTree = false; outputCurrentBestTopology = false; collapseBranches = false; outputSitelikelihoods = 0; reportRunProgress = 0; //starting the run randseed = -1; bootstrapSeed = -1; streefname = "random"; refineStart = true; refineEnd = true; //general run details datafname = "datafname"; constraintfile = "\0"; availableMemory = -1; megsClaMemory = 512; restart = false; checkpoint = false; significantTopoChange = (FLOAT_TYPE)0.01; searchReps = 1; //this isn't for general consumption, but lets me easily enable hacked in features runmode = 0; scoreOnly = false; ignoreStopCodons = false; workPhaseDivision = false; attachmentsPerTaxon = 50; siteWindowLength = 0; siteWindowStride = 0; combineAdjacentIdenticalGapPatterns = false; usePatternManager = true; rootAtBranchMidpoint = false; useOptBoundedForBlen = false; optimizeInputOnly = false; //this should really not be necessary, but for some reason not explicitly initializing it was causing problems with icc parameterValueString = ""; //finishing the run enforceTermConditions = true; lastTopoImproveThresh = 10000; improveOverStoredIntervalsThresh = (FLOAT_TYPE)0.05; stopgen = UINT_MAX; stoptime = UINT_MAX; swapTermThreshold = 0; linkModels = false; subsetSpecificRates = true; //general population stuff nindivs = 4; holdover = 1; selectionIntensity = 0.5; holdoverPenalty = 0.0; startOptPrec = 0.5; minOptPrec = (FLOAT_TYPE)0.01; numPrecReductions = 20; precReductionFactor = (FLOAT_TYPE)0.9; treeRejectionThreshold = 50.0; //parameters affecting proportion of mutations topoWeight = 1.0; randNNIweight = (FLOAT_TYPE)0.1; randSPRweight = (FLOAT_TYPE)0.3; limSPRweight = (FLOAT_TYPE)0.6; modWeight = (FLOAT_TYPE)0.05; brlenWeight = (FLOAT_TYPE)0.2; intervalLength = 100; intervalsToStore = 5; //parameters affecting other details of mutations meanBrlenMuts = 5.0; gammaShapeBrlen = 1000; gammaShapeModel = 1000; limSPRrange = 6; uniqueSwapBias = (FLOAT_TYPE)0.1; distanceSwapBias = 1.0; //optional analyses inferInternalStateProbs = false; bootstrapReps = 0; resampleProportion = 1.0; sendInterval = 60.0; //these macros are all defined in the defs.h file //but could be overriden in the config minBrlen = DEF_MIN_BRLEN; maxBrlen = DEF_MAX_BRLEN; startingBrlen = DEF_STARTING_BRLEN; } int GeneralGamlConfig::Read(const char* fname, bool isMaster /*=false*/) { ConfigReader cr; if (cr.Load(fname) != 0) { printf("ERROR: GamlConfig::General::Read(%s) failed.\n", fname); return -1; } cr.MakeAllSection(); int errors = 0; errors += cr.SetSection("general"); if(errors < 0) throw ErrorException("Didn't find [general] section in config file\n (this section heading is required)"); errors += cr.GetUnsignedOption("logevery", logevery); errors += cr.GetUnsignedOption("saveevery", saveevery); int found=cr.GetPositiveNonZeroDoubleOption("megsclamemory", megsClaMemory, true); found += cr.GetPositiveNonZeroDoubleOption("availablememory", availableMemory, true); if(found == -2) throw ErrorException("Either \"megsclamemory\" or \"availablememory\" must be specified in conf!"); errors += cr.GetStringOption("datafname", datafname); errors += cr.GetStringOption("ofprefix", ofprefix); errors += cr.GetStringOption("streefname", streefname); cr.GetStringOption("constraintfile", constraintfile, true); errors += cr.GetIntNonZeroOption("randseed", randseed); cr.GetIntNonZeroOption("bootstrapseed", bootstrapSeed, true); errors += cr.GetBoolOption("refinestart", refineStart); cr.GetBoolOption("refineend", refineEnd, true); errors += cr.GetBoolOption("outputeachbettertopology", outputTreelog); errors += cr.GetBoolOption("enforcetermconditions", enforceTermConditions); errors += cr.GetUnsignedNonZeroOption("genthreshfortopoterm", lastTopoImproveThresh); errors += cr.GetPositiveNonZeroDoubleOption("scorethreshforterm", improveOverStoredIntervalsThresh); cr.GetPositiveNonZeroDoubleOption("significanttopochange", significantTopoChange, true); cr.GetUnsignedNonZeroOption("attachmentspertaxon", attachmentsPerTaxon, true); cr.GetUnsignedOption("outputsitelikelihoods", outputSitelikelihoods, true); cr.GetBoolOption("reportrunprogress", reportRunProgress, true); cr.GetBoolOption("optimizeinputonly", optimizeInputOnly, true); cr.GetBoolOption("ignorestopcodons", ignoreStopCodons, true); cr.GetBoolOption("outputmostlyuselessfiles", outputMostlyUselessFiles, true); cr.GetBoolOption("outputphyliptree", outputPhylipTree, true); cr.GetBoolOption("collapsebranches", collapseBranches, true); cr.GetIntOption("genthreshforswapterm", swapTermThreshold, true); cr.GetStringOption("arbitrarystring", arbitraryString, true); cr.GetUnsignedOption("windowlength", siteWindowLength, true); cr.GetUnsignedOption("windowstride", siteWindowStride, true); cr.GetBoolOption("usepatternmanager", usePatternManager, true); cr.GetStringOption("parametervaluestring", parameterValueString, true); cr.GetBoolOption("combineadjacentidenticalgappatterns", combineAdjacentIdenticalGapPatterns, true); cr.GetBoolOption("rootatbranchmidpoint", rootAtBranchMidpoint, true); cr.GetBoolOption("useoptboundedforblen", useOptBoundedForBlen, true); //changed the wording of this from besttree to besttopology, to match outputeachbettertopology //still allow besttree, since that is what I told Maddison, and I think has already been incorporated //into Mesquite int ret = cr.GetBoolOption("outputcurrentbesttopology", outputCurrentBestTopology, true); if(ret < 0) cr.GetBoolOption("outputcurrentbesttree", outputCurrentBestTopology, true); cr.GetBoolOption("restart", restart, true); cr.GetBoolOption("writecheckpoints", checkpoint, true); cr.GetUnsignedNonZeroOption("searchreps", searchReps, true); cr.GetUnsignedOption("runmode", runmode, true); cr.GetBoolOption("scoreonly", scoreOnly, true); //These three used to be in the [master] section, for no apparent reason. Now allowed in [general] //as well. If in both, will be overridden by master cr.GetUnsignedOption("bootstrapreps", bootstrapReps, true); cr.GetPositiveNonZeroDoubleOption("resampleproportion", resampleProportion, true); cr.GetBoolOption("inferinternalstateprobs", inferInternalStateProbs, true); cr.GetBoolOption("workphasedivision", workPhaseDivision, true); bool multipleModelsFound = ReadPossibleModelPartition(cr); if(!multipleModelsFound){//if we didn't find multiple models in separate model sections, look for them in ConfigModelSettings configModSet; int settingsFound = 0; settingsFound += cr.GetStringOption("ratehetmodel", configModSet.rateHetModel, true); settingsFound += cr.GetUnsignedOption("numratecats", configModSet.numRateCats, true); settingsFound += cr.GetStringOption("statefrequencies", configModSet.stateFrequencies, true); settingsFound += cr.GetStringOption("ratematrix", configModSet.rateMatrix, true); settingsFound += cr.GetStringOption("invariantsites", configModSet.proportionInvariant, true); settingsFound += cr.GetStringOption("datatype", configModSet.datatype, true); settingsFound += cr.GetStringOption("geneticcode", configModSet.geneticCode, true); if(settingsFound == -7) throw ErrorException("No model descriptions found in config file. Proper setup is either:\n\t1. Model settings found somewhere under [general] heading,\n\t applying to all data subsets\n\t2. Separate model settings for each partition subset\n\t found under different headings [model1], [model2]. etc"); configModelSets.push_back(configModSet); } cr.GetBoolOption("linkmodels", linkModels, true); cr.GetBoolOption("subsetspecificrates", subsetSpecificRates, true); cr.GetStringOption("outgroup", outgroupString, true); if(isMaster){ errors += cr.SetSection("master"); if(errors < 0) throw ErrorException("Didn't find [master] section in config file\n (this section heading is required)"); } else{ errors += cr.SetSection("remote"); if(errors < 0) throw ErrorException("Didn't find [remote] section in config file\n (this section heading is required)"); } errors += cr.GetUnsignedNonZeroOption("nindivs", nindivs); errors += cr.GetUnsignedOption("holdover", holdover); errors += cr.GetPositiveNonZeroDoubleOption("selectionintensity", selectionIntensity); errors += cr.GetDoubleOption("holdoverpenalty", holdoverPenalty); errors += cr.GetUnsignedNonZeroOption("stopgen", stopgen); errors += cr.GetUnsignedNonZeroOption("stoptime", stoptime); errors += cr.GetPositiveNonZeroDoubleOption("startoptprec", startOptPrec); errors += cr.GetPositiveNonZeroDoubleOption("minoptprec", minOptPrec); //changing this to specify either the number of reductions in the precision or the //multiplier as before. Prefer the number, since it should be easier to specify. // found=0; found=cr.GetIntOption("numberofprecreductions", numPrecReductions, true); found += cr.GetPositiveNonZeroDoubleOption("precreductionfactor", precReductionFactor, true); if(found == -2) throw ErrorException("Error: either \"numberofprecreductions\" (preferably) or \"precreductionfactor\" must be specified in conf!"); errors += cr.GetPositiveDoubleOption("topoweight", topoWeight); errors += cr.GetPositiveDoubleOption("modweight", modWeight); errors += cr.GetPositiveDoubleOption("brlenweight", brlenWeight); errors += cr.GetPositiveDoubleOption("randnniweight", randNNIweight); errors += cr.GetPositiveDoubleOption("randsprweight", randSPRweight); errors += cr.GetPositiveDoubleOption("limsprweight", limSPRweight); cr.GetPositiveNonZeroDoubleOption("uniqueswapbias", uniqueSwapBias, true); cr.GetPositiveNonZeroDoubleOption("distanceswapbias", distanceSwapBias, true); cr.GetDoubleOption("treerejectionthreshold", treeRejectionThreshold, true); cr.GetUnsignedOption("bootstrapreps", bootstrapReps, true); cr.GetPositiveNonZeroDoubleOption("resampleproportion", resampleProportion, true); cr.GetBoolOption("inferinternalstateprobs", inferInternalStateProbs, true); #ifdef MPI_VERSION if(bootstrapReps != 0) throw ErrorException("Sorry, Bootstrap not yet implemented in parallel GARLI!"); #endif #ifdef MPI_VERSION if(isMaster==false) errors += cr.GetDoubleOption("sendinterval", sendInterval); #endif #ifdef GANESH errors += cr.GetDoubleOption("randpecrweight", randPECRweight); #endif // errors += cr.GetUnsignedOption("gammashapebrlen", gammaShapeBrlen); // errors += cr.GetUnsignedOption("gammashapemodel", gammaShapeModel); errors += cr.GetPositiveNonZeroDoubleOption("gammashapebrlen", gammaShapeBrlen); errors += cr.GetPositiveNonZeroDoubleOption("gammashapemodel", gammaShapeModel); errors += cr.GetUnsignedNonZeroOption("limsprrange", limSPRrange); errors += cr.GetUnsignedNonZeroOption("intervallength", intervalLength); errors += cr.GetUnsignedNonZeroOption("intervalstostore", intervalsToStore); errors += cr.GetPositiveNonZeroDoubleOption("meanbrlenmuts", meanBrlenMuts); cr.GetPositiveNonZeroDoubleOption("minbrlen", minBrlen, true); cr.GetPositiveNonZeroDoubleOption("maxbrlen", maxBrlen, true); cr.GetPositiveNonZeroDoubleOption("startingbrlen", startingBrlen, true); #ifdef INCLUDE_PERTURBATION errors += cr.SetSection("perturbation"); errors += cr.GetIntOption("perttype", pertType); errors += cr.GetDoubleOption("pertthresh", pertThresh); errors += cr.GetIntOption("minpertinterval", minPertInterval); errors += cr.GetIntOption("maxpertsnoimprove", maxPertsNoImprove); errors += cr.GetBoolOption("restartafterabandon", restartAfterAbandon); errors += cr.GetIntOption("gensbeforerestart", gensBeforeRestart); errors += cr.GetDoubleOption("ratchetproportion", ratchetProportion); errors += cr.GetDoubleOption("ratchetoffthresh", ratchetOffThresh); errors += cr.GetIntOption("ratchetmaxgen", ratchetMaxGen); errors += cr.GetIntOption("nnitargetaccepts", nniTargetAccepts); errors += cr.GetIntOption("nnimaxattempts", nniMaxAttempts); errors += cr.GetIntOption("numsprcycles", numSprCycles); errors += cr.GetIntOption("sprpertrange", sprPertRange); #endif return errors; } bool GeneralGamlConfig::ReadPossibleModelPartition(ConfigReader &cr){ string origSection = cr.GetCurrentSection(); bool foundAnyModels = false; for(int modelNum = 0; ;modelNum++){ char modName[10]; sprintf(modName, "model%d", modelNum); int found = cr.SetSection(modName); //models need to appear consecuatively, but can start at 0 (old) or 1 (new) if(modelNum == 0 && found < 0){ continue; } else if(found < 0){ cr.SetSection(origSection.c_str()); return foundAnyModels; } else{ foundAnyModels = true; ConfigModelSettings configModSet; cr.GetStringOption("ratehetmodel", configModSet.rateHetModel, true); cr.GetUnsignedOption("numratecats", configModSet.numRateCats, true); cr.GetStringOption("statefrequencies", configModSet.stateFrequencies, true); cr.GetStringOption("ratematrix", configModSet.rateMatrix, true); cr.GetStringOption("invariantsites", configModSet.proportionInvariant, true); cr.GetStringOption("datatype", configModSet.datatype, true); cr.GetStringOption("geneticcode", configModSet.geneticCode, true); configModelSets.push_back(configModSet); } } //we shouldn't be getting to here return false; } int GeneralGamlConfig::Serialize(char** buf_, int* size_) const { int& size = *size_; char*& buf = *buf_; // calculate the size first size = 0; size += sizeof(logevery); size += sizeof(saveevery); size += sizeof(megsClaMemory); size += (int)datafname.length() + 1; size += (int)ofprefix.length() + 1; size += (int)streefname.length() + 1; // allocate the buffer buf = new char[size]; // populate the buffer char* p = buf; for(int i=0;i 0) { printf("ERROR: GamlConfig::Read(): reading [general] produced %d errors.\n", gerrors); } merrors = -mc.Read(fname); if (merrors > 0) { printf("ERROR: GamlConfig::Read(): reading [master] produced %d errors.\n", merrors); } rerrors = -rc.Read(fname); if (rerrors > 0) { printf("ERROR: GamlConfig::Read(): reading [remote] produced %d errors.\n", rerrors); } if (gerrors || merrors || rerrors) return -1; return 0; } int GamlConfig::Serialize(char** buf_, int* size_) const{ //there's no need to serialize and send the master conf info int& size = *size_; char*& buf = *buf_; int gsize, msize, rsize; char *gbuf, *mbuf, *rbuf; gc.Serialize(&gbuf, &gsize); // mc.Serialize(&mbuf, &msize); rc.Serialize(&rbuf, &rsize); size = gsize + rsize + sizeof(int)*2; // size = gsize + msize + rsize + sizeof(int)*3; char* p = buf = new char[size]; // put in the sizes memcpy(p, &gsize, sizeof(gsize)); p += sizeof(gsize); // memcpy(p, &msize, sizeof(msize)); // p += sizeof(msize); memcpy(p, &rsize, sizeof(rsize)); p += sizeof(rsize); // put in the data memcpy(p, gbuf, gsize); p += gsize; // memcpy(p, mbuf, msize); // p += msize; memcpy(p, rbuf, rsize); p += rsize; delete [] gbuf; // delete [] mbuf; delete [] rbuf; return size; } int GamlConfig::Deserialize(char* buf, int size) { int gsize, msize, rsize; char* p = buf; memcpy(&gsize, p, sizeof(gsize)); p += sizeof(gsize); */ /* memcpy(&msize, p, sizeof(msize)); p += sizeof(msize); */ /* memcpy(&rsize, p, sizeof(rsize)); p += sizeof(rsize); gc.Deserialize(p, gsize); p += gsize; rc.Deserialize(p, rsize); p += rsize; // sanity checks assert(p-buf == size); return 0; } bool GamlConfig::operator==(const GamlConfig& rhs) const { if ( gc == rhs.gc && mc == rhs.mc && rc == rhs.rc ) return true; return false; } */ garli-2.1-release/src/configoptions.h000066400000000000000000000137171241236125200177200ustar00rootroot00000000000000// GARLI version 2.1 source code // Copyright 2005-2014 Derrick J. Zwickl // email: garli.support@gmail.com // // This program is free software: you can redistribute it and/or modify // it under the terms of the GNU General Public License as published by // the Free Software Foundation, either version 3 of the License, or // (at your option) any later version. // // This program is distributed in the hope that it will be useful, // but WITHOUT ANY WARRANTY; without even the implied warranty of // MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the // GNU General Public License for more details. // // You should have received a copy of the GNU General Public License // along with this program. If not, see . #ifndef CONFIGOPTIONS_H #define CONFIGOPTIONS_H #include using std::string; using std::vector; class ConfigReader; class ConfigModelSettings{ public: //model settings string datatype; string geneticCode; string stateFrequencies; //equal, estimate, emprical, fixed string rateMatrix; //6rate, 2rate, 1rate, fixed, custom( string proportionInvariant; //none, fixed, estimate string rateHetModel; //gamma, gammafixed, flex, none unsigned numRateCats; ConfigModelSettings(){ stateFrequencies = "estimate"; rateMatrix = "6rate"; proportionInvariant = "estimate"; rateHetModel = "gamma"; numRateCats = 4; datatype = "dna"; geneticCode = "standard"; } }; class GeneralGamlConfig{ public: //these options will be the same regardless of whether a population is master or remote //output related string ofprefix; unsigned logevery; unsigned saveevery; bool outputTreelog; bool outputMostlyUselessFiles; bool outputPhylipTree; bool outputCurrentBestTopology; bool collapseBranches; //this is just a string that I can use for whatever I want in special runmodes string arbitraryString; unsigned int siteWindowLength; unsigned int siteWindowStride; bool combineAdjacentIdenticalGapPatterns; bool usePatternManager; bool rootAtBranchMidpoint; bool useOptBoundedForBlen; string parameterValueString; bool optimizeInputOnly; //starting the run int randseed; int bootstrapSeed; string streefname; bool refineStart; bool refineEnd; //general run details string datafname; string constraintfile; FLOAT_TYPE megsClaMemory; FLOAT_TYPE availableMemory; bool restart; bool checkpoint; FLOAT_TYPE significantTopoChange; string outgroupString; unsigned searchReps; unsigned runmode; unsigned outputSitelikelihoods; bool reportRunProgress; bool scoreOnly; bool ignoreStopCodons; //finishing the run bool enforceTermConditions; unsigned lastTopoImproveThresh; FLOAT_TYPE improveOverStoredIntervalsThresh; unsigned stopgen; unsigned stoptime; int swapTermThreshold; unsigned attachmentsPerTaxon; bool workPhaseDivision; //this holds descriptions of models, possible > 1 in the case of partitioning /* //model settings string datatype; string geneticCode; string stateFrequencies; //equal, estimate, emprical, fixed string rateMatrix; //6rate, 2rate, 1rate, fixed, custom( string proportionInvariant; //none, fixed, estimate string rateHetModel; //gamma, gammafixed, flex, none unsigned numRateCats; */ vector configModelSets; bool linkModels;//full linkage for partitioned models / no linkage bool subsetSpecificRates;//whether models are linked or not, separate rate multiplier for each subset //all of the following options can vary between master and remote //general population stuff unsigned nindivs; unsigned holdover; FLOAT_TYPE selectionIntensity; FLOAT_TYPE holdoverPenalty; FLOAT_TYPE startOptPrec; FLOAT_TYPE minOptPrec; int numPrecReductions; FLOAT_TYPE precReductionFactor; //deprecated FLOAT_TYPE treeRejectionThreshold; //parameters affecting proportion of mutations FLOAT_TYPE topoWeight; FLOAT_TYPE randNNIweight; FLOAT_TYPE randSPRweight; FLOAT_TYPE limSPRweight; // FLOAT_TYPE randPECRweight; FLOAT_TYPE modWeight; FLOAT_TYPE brlenWeight; unsigned intervalLength; unsigned intervalsToStore; //parameters affecting other details of mutations FLOAT_TYPE meanBrlenMuts; FLOAT_TYPE gammaShapeBrlen; FLOAT_TYPE gammaShapeModel; unsigned limSPRrange; FLOAT_TYPE uniqueSwapBias; FLOAT_TYPE distanceSwapBias; //optional analyses unsigned bootstrapReps; FLOAT_TYPE resampleProportion; bool inferInternalStateProbs; #ifdef INCLUDE_PERTURBATION //perturbation parameters int pertType; FLOAT_TYPE pertThresh; int minPertInterval; int maxPertsNoImprove; bool restartAfterAbandon; int gensBeforeRestart; FLOAT_TYPE ratchetProportion; FLOAT_TYPE ratchetOffThresh; int ratchetMaxGen; int nniTargetAccepts; int nniMaxAttempts; int numSprCycles; int sprPertRange; #endif //the number of seconds between remote tree sends (parallel only) FLOAT_TYPE sendInterval; //by default these come from the defs.h file, but could be overriden FLOAT_TYPE minBrlen; FLOAT_TYPE maxBrlen; FLOAT_TYPE startingBrlen; // methods GeneralGamlConfig(); int Read(const char*, bool isMaster=false); bool ReadPossibleModelPartition(ConfigReader &cr); int Serialize(char**, int*) const; int Deserialize(char*, int); bool operator==(const GeneralGamlConfig&) const; }; class MasterGamlConfig: public GeneralGamlConfig{ public: //parallel behavior parameters-stored in pop->paraMan on master only FLOAT_TYPE startUpdateThresh; FLOAT_TYPE minUpdateThresh; FLOAT_TYPE updateReductionFactor; int subtreeInterval; FLOAT_TYPE subtreeStartThresh; int minSubtreeSize; int targetSubtreeSize; FLOAT_TYPE orphanFactor; int maxRecomIndivs; /* int pertType; FLOAT_TYPE pertThresh; FLOAT_TYPE pertAmount; int maxPertsNoImprove; FLOAT_TYPE ratchetProportion; FLOAT_TYPE ratchetOffThresh; int ratchetMaxGen; FLOAT_TYPE nniAcceptThresh; int numSprCycles; int sprPertRange; */ int bootstrapReps; FLOAT_TYPE bootTermThresh; // methods MasterGamlConfig(); int Read(const char*, bool isMaster=false); }; #endif garli-2.1-release/src/configreader.cpp000066400000000000000000000460221241236125200200150ustar00rootroot00000000000000// GARLI version 2.0 source code // Copyright 2005-2011 Derrick J. Zwickl // email: garli.support@gmail.com // // This program is free software: you can redistribute it and/or modify // it under the terms of the GNU General Public License as published by // the Free Software Foundation, either version 3 of the License, or // (at your option) any later version. // // This program is distributed in the hope that it will be useful, // but WITHOUT ANY WARRANTY; without even the implied warranty of // MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the // GNU General Public License for more details. // // You should have received a copy of the GNU General Public License // along with this program. If not, see . #include #include #include #include #include #include using namespace std; #include "defs.h" #include "configreader.h" #include "errorexception.h" #include "funcs.h" int ConfigReader::UNKNOWN=0; int ConfigReader::SECTION=1; int ConfigReader::OPTION=2; ConfigReader::ConfigReader() { } ConfigReader::ConfigReader(const char* filename) { Load(filename); } ConfigReader::~ConfigReader() { } int ConfigReader::Load(const char* filename) { FILE* file; // clear all currently loaded sections sections.clear(); #ifndef BOINC file = fopen(filename, "r"); #else char input_path[512]; boinc_resolve_filename(filename, input_path, sizeof(input_path)); file = boinc_fopen(input_path, "r"); #endif if (file == NULL) throw ErrorException("could not open file \"%s\".", filename); int type; string sectionName, name, val; Options options; bool first = true; while (!feof(file)) { type = ReadSectionOrOption(file, name, val); if (type == SECTION) { if (!first) { sections.insert(pair(sectionName, options)); options.clear(); } else first = false; sectionName = name; } else if (type == OPTION) { options.insert(pair(name, val)); } } // insert the last section sections.insert(pair(sectionName, options)); fclose(file); return 0; } /***************************************************************************************** ** Save() ** *****************************************************************************************/ int ConfigReader::Save(const char* filename) { FILE* file = fopen(filename, "w"); if (file == NULL) { printf("Error opening file \"%s\" for writing.\n", filename); return -1; } map::iterator sit = sections.begin(); map::iterator oit; while (sit != sections.end()) { // write the section fprintf(file, "[%s]\n", sit->first.c_str()); // write the options oit = sit->second.begin(); while (oit != sit->second.end()) { fprintf(file, "%s = %s\n", oit->first.c_str(), oit->second.c_str()); ++oit; } fprintf(file, "\n"); ++sit; } fclose(file); return 0; } /****************************************************************************************/ /*** AddSection() ***/ /****************************************************************************************/ int ConfigReader::AddSection(const char* _name) { int rv; string name; Sections::iterator it; name = _name; TrimWhiteSpace(name); it = sections.find(name); if (it == sections.end()) rv = 0; else rv = 1; sections.insert(pair(name, Options())); return rv; } /****************************************************************************************/ /*** RemoveSection() ***/ /****************************************************************************************/ int ConfigReader::RemoveSection(const char* _name) { int rv; string name; Sections::iterator it; name = _name; TrimWhiteSpace(name); it = sections.find(name); if (it == sections.end()) // section doesn't exist, bomb out rv = -1; else { sections.erase(it); rv = 0; } return rv; } /****************************************************************************************/ /*** SetSection() ***/ /****************************************************************************************/ int ConfigReader::SetSection(const char* name) { cur_section = name; TrimWhiteSpace(cur_section); Sections::iterator sit = sections.find(cur_section); if (sit == sections.end()) return -1; return 0; } /****************************************************************************************/ /*** GetSection() ***/ /****************************************************************************************/ const string ConfigReader::GetCurrentSection() { return cur_section; } /****************************************************************************************/ /*** SetOption() ***/ /****************************************************************************************/ int ConfigReader::SetOption(const char* _option, const char* _val) { int rv; string option, val; Sections::iterator sit; Options::iterator oit; sit = sections.find(cur_section); if (sit == sections.end()) // section doesn't exist...bomb out rv = -1; else { option = _option; val = _val; TrimWhiteSpace(option); TrimWhiteSpace(val); oit = sit->second.find(option); if (oit == sit->second.end()) { // option doesn't exist, create it rv = 0; } else { // option exists, overwrite sit->second.erase(oit); rv = 1; } sit->second.insert(pair(option, val)); } return rv; } /****************************************************************************************/ /*** RemoveOption() ***/ /****************************************************************************************/ int ConfigReader::RemoveOption(const char* _option) { int rv; string option; Sections::iterator sit; Options::iterator oit; sit = sections.find(cur_section); if (sit == sections.end()) // section doesn't exist, bomb out rv = -2; else { option = _option; TrimWhiteSpace(option); oit = sit->second.find(option); if (oit == sit->second.end()) { // option doesn't exist, bomb out rv = -1; } else { // option exists, remove it sit->second.erase(oit); rv = 0; } } return rv; } /****************************************************************************************/ /*** GetStringOption() ***/ /****************************************************************************************/ int ConfigReader::GetStringOption(const char* _option, string& val, bool optional /*=false*/) { int rv; string option; Sections::iterator sit; Options::iterator oit; sit = sections.find(cur_section); if (sit == sections.end()) // section doesn't exist, bomb out rv = -2; else { option = _option; TrimWhiteSpace(option); oit = sit->second.find(option); if (oit == sit->second.end()) { // option doesn't exist, bomb out rv = -1; if(!optional) throw ErrorException("could not find string configuration entry \"%s\"", option.c_str()); } else { // option exists, get the value val = oit->second; rv = 0; } } return rv; } /****************************************************************************************/ /*** GetBoolOption() ***/ /****************************************************************************************/ int ConfigReader::GetBoolOption(const char* option, bool& val, bool optional /*=false*/) { int rv; string str; if (GetStringOption(option, str, optional) == 0) { // option exists // lower case it for (int i = 0; i < (int)str.length(); ++i) str[i] = tolower(str[i]); if (str == "true") val = true; else if (str == "false") val = false; else if(isdigit(str[0]) != 0){ if (atoi(str.c_str()) != 0) val = true; else val = false; } else throw ErrorException("expecting boolean (0 or 1) for entry \"%s\", found %s", option, str.c_str()); rv = 0; } else{ rv = -1; if(!optional) throw ErrorException("could not find boolean configuration entry \"%s\"", option); } return rv; } /****************************************************************************************/ /*** GetIntOption() ***/ /****************************************************************************************/ int ConfigReader::GetIntOption(const char* option, int& val, bool optional /*=false*/) { int rv; string str; FLOAT_TYPE dummy;//read into a FLOAT_TYPE first to check bounds if (GetStringOption(option, str, optional) == 0) { // option exists dummy = (FLOAT_TYPE) atof(str.c_str()); if(dummy > (INT_MAX-1)) throw ErrorException("entry for option \"%s\" (%s) is greater than its max (%u)" , option, str.c_str(), (INT_MAX-1)); if(fabs(dummy - (int)dummy) > 0.0) throw ErrorException("entry for option \"%s\" (%s) is not an integer" , option, str.c_str()); val = (int) dummy; rv = 0; } else{ rv = -1; if(!optional) throw ErrorException("could not find integer configuration entry \"%s\"", option); } return rv; } /****************************************************************************************/ /*** GetIntNonZeroOption() ***/ /****************************************************************************************/ int ConfigReader::GetIntNonZeroOption(const char* option, int& val, bool optional /*=false*/) { int rv; string str; FLOAT_TYPE dummy;//read into a FLOAT_TYPE first to check bounds if (GetStringOption(option, str, optional) == 0) { // option exists dummy = (FLOAT_TYPE) atof(str.c_str()); if(dummy > (INT_MAX-1)) throw ErrorException("entry for option \"%s\" (%s) is greater than its max (%u)" , option, str.c_str(), (INT_MAX-1)); if(FloatingPointEquals(dummy, ZERO_POINT_ZERO, 1e-8)) throw ErrorException("entry for option \"%s\" cannot be zero", option); if(!(FloatingPointEquals(dummy, (int)dummy, 1e-8))) throw ErrorException("entry for option \"%s\" (%s) is not an integer" , option, str.c_str()); val = (int) dummy; rv = 0; } else{ rv = -1; if(!optional) throw ErrorException("could not find integer configuration entry \"%s\"", option); } return rv; } /****************************************************************************************/ /*** GetUnsignedOption() ***/ /****************************************************************************************/ int ConfigReader::GetUnsignedOption(const char* option, unsigned& val, bool optional /*=false*/) { int rv; string str; FLOAT_TYPE dummy;//read into a FLOAT_TYPE first to check sign and bounds if (GetStringOption(option, str, optional) == 0) { // option exists dummy = (FLOAT_TYPE) atof(str.c_str()); if(dummy < 0.0) throw ErrorException("entry for option \"%s\" must be >=0", option); if(dummy > (UINT_MAX-1)) throw ErrorException("entry for option \"%s\" (%s) is greater than its max (%u)" , option, str.c_str(), (UINT_MAX-1)); if(fabs(dummy - (unsigned)dummy) > 0.0) throw ErrorException("entry for option \"%s\" (%s) is not an integer" , option, str.c_str()); val = (unsigned) dummy; rv = 0; } else{ rv = -1; if(!optional) throw ErrorException("could not find unsigned integer configuration entry \"%s\"", option); } return rv; } /****************************************************************************************/ /*** GetUnsignedOption() ***/ /****************************************************************************************/ int ConfigReader::GetUnsignedNonZeroOption(const char* option, unsigned& val, bool optional /*=false*/) { int rv; string str; FLOAT_TYPE dummy;//read into a FLOAT_TYPE first to check sign and bounds if (GetStringOption(option, str, optional) == 0) { // option exists dummy = (FLOAT_TYPE) atof(str.c_str()); if(!(dummy > 0.0)) throw ErrorException("entry for option \"%s\" must be >0", option); if(dummy > (UINT_MAX-1)) throw ErrorException("entry for option \"%s\" (%s) is greater than its max (%u)" , option, str.c_str(), (UINT_MAX-1)); if(!(FloatingPointEquals(dummy, (unsigned)dummy, 1e-8))) throw ErrorException("entry for option \"%s\" (%s) is not an integer" , option, str.c_str()); val = (unsigned) dummy; rv = 0; } else{ rv = -1; if(!optional) throw ErrorException("could not find unsigned integer configuration entry \"%s\"", option); } return rv; } /****************************************************************************************/ /*** GetIntRangeOption() ***/ /****************************************************************************************/ int ConfigReader::GetIntRangeOption(const char* option, int& val1, int& val2) { int rv; string str; if (GetStringOption(option, str) == 0) { // option exists // split up the string int len = (int)str.length(); int i = (int)str.find(' ', 0); if (i < 0) rv = -1; else { val1 = atoi(str.substr(0, i).c_str()); val2 = atoi(str.substr(i+1, len-i).c_str()); rv = 0; } } else{ rv = -1; throw ErrorException("could not find integer range configuration entry \"%s\"", option); } return rv; } /****************************************************************************************/ /*** GetFloatOption() ***/ /****************************************************************************************/ int ConfigReader::GetFloatOption(const char* option, float& val) { int rv; string str; if (GetStringOption(option, str) == 0) { // option exists val = (float)atof(str.c_str()); rv = 0; } else rv = -1; return rv; } /****************************************************************************************/ /*** GetFloatRangeOption() ***/ /****************************************************************************************/ int ConfigReader::GetFloatRangeOption(const char* option, float& val1, float& val2) { int rv; string str; if (GetStringOption(option, str) == 0) { // option exists // split up the string int len = (int)str.length(); int i = (int)str.find(' ', 0); if (i < 0) rv = -1; else { val1 = (float)atof(str.substr(0, i).c_str()); val2 = (float)atof(str.substr(i+1, len-i).c_str()); rv = 0; } } else rv = -1; return rv; } /****************************************************************************************/ /*** GetDoubleOption() ***/ /****************************************************************************************/ int ConfigReader::GetDoubleOption(const char* option, FLOAT_TYPE& val, bool optional /*=false*/) { int rv; string str; if (GetStringOption(option, str, optional) == 0) { // option exists val = (FLOAT_TYPE) atof(str.c_str()); rv = 0; } else{ rv = -1; if(!optional) throw ErrorException("error: could not find float configuration entry \"%s\"", option); } return rv; } /****************************************************************************************/ /*** GetPositiveDoubleOption() ***/ /****************************************************************************************/ //this is just a version of GetDoubleOption that checks that the value is non-negative int ConfigReader::GetPositiveDoubleOption(const char* option, FLOAT_TYPE& val, bool optional /*=false*/) { int rv; string str; if (GetStringOption(option, str, optional) == 0) { // option exists val = (FLOAT_TYPE) atof(str.c_str()); if(val < 0.0) throw ErrorException("configuration entry \"%s\" cannot be negative", option); rv = 0; } else{ rv = -1; if(!optional) throw ErrorException("could not find float configuration entry \"%s\"", option); } return rv; } /****************************************************************************************/ /*** GetPositiveNonZeroDoubleOption() ***/ /****************************************************************************************/ //this is just a version of GetDoubleOption that checks that the value is non-negative, and that it is not //zero. atof returns zero when it encounters an error, which is very annoying behavior. When the entry must //be nonzero, at least we can check for that int ConfigReader::GetPositiveNonZeroDoubleOption(const char* option, FLOAT_TYPE& val, bool optional /*=false*/) { int rv; string str; if (GetStringOption(option, str, optional) == 0) { // option exists val = (FLOAT_TYPE) atof(str.c_str()); if(val == ZERO_POINT_ZERO) throw ErrorException("configuration entry \"%s\" cannot be zero (possible problems reading this entry)", option); if(val < 0.0) throw ErrorException("configuration entry \"%s\" cannot be negative", option); rv = 0; } else{ rv = -1; if(!optional) throw ErrorException("could not find float configuration entry \"%s\"", option); } return rv; } /****************************************************************************************/ /*** GetDoubleRangeOption() ***/ /****************************************************************************************/ int ConfigReader::GetDoubleRangeOption(const char* option, FLOAT_TYPE& val1, FLOAT_TYPE& val2) { int rv; string str; if (GetStringOption(option, str) == 0) { // option exists // split up the string int len = (int)str.length(); int i = (int)str.find(' ', 0); if (i < 0) rv = -1; else { val1 = (FLOAT_TYPE) atof(str.substr(0, i).c_str()); val2 = (FLOAT_TYPE) atof(str.substr(i+1, len-i).c_str()); rv = 0; } } else{ rv = -1; throw ErrorException("could not find float range configuration entry \"%s\"", option); } return rv; } /***************************************************************************************** ** PRIVATE METHODS *********************************************************************** *****************************************************************************************/ int ConfigReader::ReadSectionOrOption(FILE* file, string& name, string& val) { string line; size_t index; size_t len; int type = UNKNOWN; do { len = ReadLine(file, line); if (line.find('=') < len) type = OPTION; else if (line.find('[') < len && line.find(']') < len) type = SECTION; } while (type == UNKNOWN && !feof(file)); if (type == SECTION) { line.erase(line.find('['), 1); line.erase(line.find(']'), 1); name = line; } else if (type == OPTION) { index = line.find('='); val = line.substr(index+1); name = line.substr(0, index); } TrimWhiteSpace(name); if (type == OPTION) TrimWhiteSpace(val); return type; } int ConfigReader::ReadLine(FILE* file, string& line) { char ch; line = ""; fread(&ch, sizeof(char), 1, file); while (ch != '\n' && ch != '\r' && !feof(file)) { line += ch; fread(&ch, sizeof(char), 1, file); } return (int)line.length(); } void ConfigReader::TrimWhiteSpace(string& str) { int index; if (str.length() == 0) return; index = (int)str.find(' ', 0); while (index != -1 && index < (int)str.length()) { while (index < (int)str.length()-1 && str[index+1] == ' ') str.erase(index+1, 1); index = (int)str.find(' ', index+1); } if (str.find(' ', 0) == 0) str.erase(0, 1); if ( (str.length() > 0) && (str.find(' ', str.length()-1) == str.length()-1) ) str.erase(str.length()-1, 1); } //this just takes master and general and combines them into a single "all" section //which will allow ignoring of section headings in general, but still deal with old //configs void ConfigReader::MakeAllSection(){ map ops = sections["general"]; ops.insert(sections["master"].begin(), sections["master"].end()); string name="all"; sections.insert(pair(name, ops)); } garli-2.1-release/src/configreader.h000066400000000000000000000051441241236125200174620ustar00rootroot00000000000000// GARLI version 2.0 source code // Copyright 2005-2011 Derrick J. Zwickl // email: garli.support@gmail.com // // This program is free software: you can redistribute it and/or modify // it under the terms of the GNU General Public License as published by // the Free Software Foundation, either version 3 of the License, or // (at your option) any later version. // // This program is distributed in the hope that it will be useful, // but WITHOUT ANY WARRANTY; without even the implied warranty of // MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the // GNU General Public License for more details. // // You should have received a copy of the GNU General Public License // along with this program. If not, see . #ifndef CONFIGREADER_H #define CONFIGREADER_H #include #include #include //using std::string; //using std::map; typedef map Options; typedef map Sections; class ConfigReader { public: ConfigReader(); ConfigReader(const char*); ~ConfigReader(); int Load(const char*); int Save(const char*); int AddSection(const char*); int RemoveSection(const char*); int SetSection(const char*); const string GetCurrentSection(); void MakeAllSection(); int SetOption(const char*, const char*); int RemoveOption(const char*); int GetStringOption(const char*, string&, bool optional=false); int GetBoolOption(const char*, bool&, bool optional=false); int GetIntOption(const char*, int&, bool optional=false); int GetIntNonZeroOption(const char*, int&, bool optional=false); int GetIntRangeOption(const char*, int&, int&); int GetUnsignedOption(const char* option, unsigned& val, bool optional=false); int GetUnsignedNonZeroOption(const char* option, unsigned& val, bool optional=false); int GetFloatOption(const char*, float&); int GetFloatRangeOption(const char*, float&, float&); int GetDoubleOption(const char*, FLOAT_TYPE&, bool optional=false); int GetDoubleRangeOption(const char*, FLOAT_TYPE&, FLOAT_TYPE&); int GetPositiveDoubleOption(const char*, FLOAT_TYPE&, bool optional=false); int GetPositiveNonZeroDoubleOption(const char* option, FLOAT_TYPE& val, bool optional=false); Sections::const_iterator BeginSection() const { return sections.begin(); } Sections::const_iterator EndSection() const { return sections.end(); } private: static int UNKNOWN; static int SECTION; static int OPTION; private: int ReadSectionOrOption(FILE* file, string& name, string& val); int ReadLine(FILE* file, string& line); void TrimWhiteSpace(string& str); Sections sections; string cur_section; }; #endif garli-2.1-release/src/datamatr.cpp000066400000000000000000001601401241236125200171600ustar00rootroot00000000000000// GARLI version 2.1 source code // Copyright 2005-2014 Derrick J. Zwickl // email: garli.support@gmail.com // // This program is free software: you can redistribute it and/or modify // it under the terms of the GNU General Public License as published by // the Free Software Foundation, either version 3 of the License, or // (at your option) any later version. // // This program is distributed in the hope that it will be useful, // but WITHOUT ANY WARRANTY; without even the implied warranty of // MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the // GNU General Public License for more details. // // You should have received a copy of the GNU General Public License // along with this program. If not, see . // // NOTE: Portions of this source adapted from GAML source, written by Paul O. Lewis #include #include #include #include #include using namespace std; #include "defs.h" #include "datamatr.h" #include "rng.h" #include "nxsstring.h" #include "errorexception.h" #include "outputman.h" #include "model.h" #include "garlireader.h" #include "stopwatch.h" //extern ModelSpecification modSpec; #define MAX_TAXON_LABEL 100 extern rng rnd; extern OutputManager outman; bool my_pair_compare(pair fir, pair sec) {return fir.second < sec.second;} int SitePattern::numTax; int SitePattern::maxNumStates; int numCompares; bool SitePattern::operator<(const SitePattern &rhs) const{ //zero state sites (all missing) will now be shuffled to the start (previously the end) and removed later //potentially constant sites always need to come just after that //sorting will first be by number of states (fast), then by the state vectors (slow) assert(numStates > -1 && rhs.numStates > -1); numCompares++; if(numStates < rhs.numStates) return true; if(numStates > rhs.numStates) return false; assert(stateVec.empty() == false); assert(stateVec.size() == rhs.stateVec.size()); //this lexigraphically compares the vector contents if(stateVec < rhs.stateVec) return true; return false; } bool SitePattern::operator==(const SitePattern &rhs) const{ return (stateVec == rhs.stateVec); } //CalcPatternTypeAndNumStates determines whether pattern a is constant, informative, or missing //The passed in vector is used as scratch, and is assumed to already be of size maxNumStates //This ALSO has the side effect of filling in the SitePattern::numStates field, which is necessary for sorting. int SitePattern::CalcPatternTypeAndNumStates( vector &stateCounts ){ bool ambig = false; //any total or partial ambiguity int nStates = 0; bool informative = false; bool constant = false; bool missing = false; //fill the scratch array with zeros std::fill(stateCounts.begin(), stateCounts.end(), 0); //count the number of times each state occurs, and whether there are any partially //ambiguous characters (currently only allowed for nuc data) unsigned char full_ambig = (maxNumStates == 4 ? 15 : maxNumStates); if(maxNumStates == 4){ for(vector::iterator sit = stateVec.begin();sit != stateVec.end();sit++){ unsigned char c = *sit; if(c != full_ambig && (c & (c - 1))){ ambig = true; break; } else if(c != full_ambig){ stateCounts[(c > 1) + (c > 2) + (c > 4)]++; } } } else { for(vector::iterator sit = stateVec.begin();sit != stateVec.end();sit++){ unsigned char c = *sit; if(c != full_ambig){ stateCounts[c]++; } } } if(!ambig){ //no partial ambiguity (all AA and codon will come this way) //without ambiguity, having 2+ states with 2+ counts means informativeness int numDoubles = 0; for(int s = 0; s < maxNumStates; s++ ){ if(stateCounts[s] > 0){ nStates++; if(stateCounts[s] > 1){ numDoubles++; } } } if(nStates == 0){ missing = true; assert(numDoubles == 0); } else if(nStates == 1){ constant = true; assert(numDoubles < 2); } else{ if(numDoubles > 1){ informative = true; } } } else{ assert(maxNumStates == 4); //this very convoluted scheme (worked out by Mark) must be used to determine informativeness //if partial ambiguity is allowed (only for nuc data currently) multiset pat; unsigned char conStates = 15; for(vector::iterator sit = stateVec.begin();sit != stateVec.end();sit++){ unsigned char c = *sit; pat.insert(c); conStates &= c; } //constant sites are possible with partial ambiguity if some resolution gives a single state if(conStates){ if(conStates == 15) missing = true; else constant = true; } else{ vector< pair > stateScores; for(unsigned state=0;state < 4;state++){ int sc = 0; for(multiset::iterator it=pat.begin();it != pat.end();it++){ if(!((*it) & (1 << state))){ sc++; } } stateScores.push_back(pair(state, sc)); } sort(stateScores.begin(), stateScores.end(), my_pair_compare); int minStar = stateScores[0].second; if(minStar > 1){ set uPat; for(multiset::iterator it=pat.begin();it != pat.end();it++) uPat.insert(*it); int minScore = MinScore(uPat, minStar); if(minScore < minStar){ informative = true; } } } } if(missing){ type = MISSING; nStates = 0; } else if(constant){ type = CONSTANT; nStates = 1; } else if(informative){ type = INFORMATIVE; nStates = max(2, nStates); } else{ type = UNINFORM_VARIABLE; nStates = max(2, nStates); } //Note that numStates here may not be the true number of states in the //case of ambiguity, but it really only matters that it is accurate in //discriminating 0/1/1+ states because code elsewhere depends on it. numStates = nStates; return type; } //this is used for determining informative sites when there is partial ambiguity int SitePattern::MinScore(set patt, int bound, unsigned char bits/*=15*/, int prevSc/*=0*/) const{ if(patt.size() == 0) return 0; int min_sc_this_lvl = 9999; int curr_sc_this_lvl = 9999; for(unsigned s2 = 0;s2 < 4;s2++){ unsigned char thisBit = (1 << s2); if(bits & thisBit){ set remaining; for(set::iterator it=patt.begin();it != patt.end();it++){ if(!(*it & thisBit)) remaining.insert(*it); } if(remaining.size() > 0){ if(prevSc + 1 < bound) curr_sc_this_lvl = 1 + MinScore(remaining, bound, bits & ~thisBit, prevSc+1); else curr_sc_this_lvl = bound - prevSc; } else return 0; if(curr_sc_this_lvl < min_sc_this_lvl) min_sc_this_lvl = curr_sc_this_lvl; if(min_sc_this_lvl == 0 || min_sc_this_lvl + prevSc < bound) return min_sc_this_lvl; } } return min_sc_this_lvl; } //Collapse merges like patterns, transfering over the counts and site numbers represented by each sucessive identical column. //Patterns that are assigned zero counts here will be removed in Pack(), but will still contribute to the numNonMissingRealSitesInOrigMatrix, except //for those with zero states (= missing) void PatternManager::NewCollapse(){ list::iterator first; list::iterator second = patterns.begin(); while(second != patterns.end()){ first = second++; while(second != patterns.end() && (*first == *second)){ (*first).count += (*second).count; (*first).siteNumbers.insert((*first).siteNumbers.end(), (*second).siteNumbers.begin(), (*second).siteNumbers.end()); //if a wtset was used, this definitely doesn't need to be the case //assert((*first).count == (*first).siteNumbers.size()); (*second).count = 0; second++; } } //this will zero the count of all missing pats, which will make them not get put into the uniquePatterns //list in NewPack for(list::iterator pit = patterns.begin();pit != patterns.end();pit++){ if((*pit).numStates == 0){ (*pit).count = 0; } } } void PatternManager::NewSort(){ //this is the stl list sort function, using SitePattern::operator< patterns.sort(); } // This version of pack copies unique patterns from the patterns list into the uniquePatterns list void PatternManager::NewPack(){ for(list::iterator pit = patterns.begin();pit != patterns.end();pit++){ if(pit->numStates > 0){ if(pit->count > 0){ uniquePatterns.push_back(*pit); } } } pman_numPatterns = uniquePatterns.size(); compressed = true; } //This does all necessary processing in the patman (assuming that it has already been filled with data) //up to the point when the compressed matrix can be copied back into //this will only be used for nuc/AA/codon data void PatternManager::ProcessPatterns(){ CalcPatternTypesAndNumStates(); NewSort(); NewCollapse(); NewPack(); NewDetermineConstantSites(); } //it would really make more sense to do this after packing, but the number of states //is needed in pattern comparison in sorting. This also does what Summarize used to, //filling the counts of various types of patterns //THIS DOES NOT CURRENTLY SUPPORT CONDITIONING PATTERNS! void PatternManager::CalcPatternTypesAndNumStates(){ //this is just a scratch array to be used repeatedly in PatternType vector s(maxNumStates); pman_numMissingChars = pman_numConstantChars = pman_numInformativeChars = pman_numUninformVariableChars = pman_numNonMissingRealCountsInOrigMatrix = 0; pman_numRealSitesInOrigMatrix = patterns.size(); for(list::iterator pit = patterns.begin();pit != patterns.end();pit++){ int t = pit->CalcPatternTypeAndNumStates(s); //Fixed 2 bugs - It is important to calculate numNonMissingRealCountsInOrigMatrix here from counts of the //the generally unpacked data, because it could effectively be partially packed due to the use of a wtset, //but also need to keep separate track of the number of columns in the orig matrix with numRealSitesInOrigMatrix if( t != SitePattern::MISSING ) pman_numNonMissingRealCountsInOrigMatrix += pit->count; if( t == SitePattern::MISSING ) pman_numMissingChars += pit->count; else if( t == SitePattern::CONSTANT ) pman_numConstantChars += pit->count; else if( t == SitePattern::INFORMATIVE ) pman_numInformativeChars += pit->count; else{ assert(t == SitePattern::UNINFORM_VARIABLE); pman_numUninformVariableChars += pit->count; } } pman_numNonMissingChars = pman_numRealSitesInOrigMatrix - pman_numMissingChars; if( pman_numNonMissingChars == 0 ){ throw ErrorException("Matrix is made up entirely of missing characters (?, -, or N)!"); } } //note where all of the constant sites are, and what state they are. //this is kind of ugly, but will never be rate limiting void PatternManager::NewDetermineConstantSites(){ assert(compressed); lastConstant=-1; list::iterator pat = uniquePatterns.begin(); assert(pat->numStates > 0); while(pat != uniquePatterns.end() && pat->numStates == 1){ lastConstant++; pat++; } int t = 0; int thisCon = 0; if(maxNumStates == 4){ for(pat = uniquePatterns.begin();thisCon++ <= lastConstant;pat++){ t = 0; char c=15; while(t < numTax){ char ch = pat->stateVec[t]; c = c & ch; t++; } assert(c != 0); pat->constStates = c; } } else{//not allowing ambiguity for codon/AA's, so this is a bit easier for(pat = uniquePatterns.begin();thisCon++ <= lastConstant;pat++){ t = 0; char c = maxNumStates; do{ c = pat->stateVec[t]; t++; }while(c == maxNumStates && t < numTax); assert(t <= numTax); pat->constStates = c; } } } //The following are for copying the results of the pattern processing back into the old fields of DataMatrix //This takes the unique pattern types and uses their siteNumbers vector to map back to the original //ordering of sites, as used to tbe stored in the number array. void PatternManager::FillNumberVector(vector &nums) const{ if(nums.size() != patterns.size()){ nums.clear(); nums.resize(patterns.size()); } //this is necessary so that all missing patterns, which should already have been removed from //uniquePatterns, will properly show up as -1 in the number array, since they will not be overwritten //with other values below for(vector::iterator nit = nums.begin();nit != nums.end();nit++) (*nit) = -1; int p = 0; for(list::const_iterator pit = uniquePatterns.begin();pit != uniquePatterns.end();pit++){ for(vector::const_iterator nit = (*pit).siteNumbers.begin(); nit != (*pit).siteNumbers.end();nit++) nums[*nit] = p; p++; } } void PatternManager::FillCountVector(vector &counts) const{ counts.clear(); for(list::const_iterator pit = uniquePatterns.begin();pit != uniquePatterns.end();pit++){ counts.push_back((*pit).count); } } void PatternManager::FillNumStatesVector(vector &ns) const{ ns.clear(); for(list::const_iterator pit = uniquePatterns.begin();pit != uniquePatterns.end();pit++){ ns.push_back((*pit).numStates); } } void PatternManager::FillConstStatesVector(vector &cs) const{ int c = 0; for(list::const_iterator pit = uniquePatterns.begin();pit != uniquePatterns.end();pit++){ cs.push_back((*pit).constStates); c++; } } //Takes the data out of the SitePattern list and copies into the DataMatrix 2d matrix void PatternManager::FillTaxaXCharMatrix(unsigned char **mat) const{ for(int t = 0;t < numTax;t++){ int c = 0; for(list::const_iterator cit = uniquePatterns.begin();cit != uniquePatterns.end();cit++){ mat[t][c++] = (*cit).stateVec[t]; } } } void PatternManager::FillIntegerValues(int &_nMissing, int &_nConstant, int &_nVarUninform, int &_nInformative, int &_lastConst, int &_numRealSitesInOrigMatrix, int &_numNonMissingRealCountsInOrigMatrix, int &_numNonMissingRealSitesInOrigMatrix, int &_numPatterns) const { _numRealSitesInOrigMatrix = pman_numRealSitesInOrigMatrix; _numNonMissingRealCountsInOrigMatrix = pman_numNonMissingRealCountsInOrigMatrix; _numNonMissingRealSitesInOrigMatrix = pman_numNonMissingChars; _numPatterns = pman_numPatterns; _nMissing = pman_numMissingChars; _nConstant = pman_numConstantChars; _nVarUninform = pman_numUninformVariableChars; _nInformative = pman_numInformativeChars; _lastConst = lastConstant; } void DataMatrix::OutputDataSummary() const{ //outman.UserMessage("\n#######################################################"); outman.UserMessage("\tSummary of data:"); outman.UserMessage("\t %d sequences.", NTax()); outman.UserMessage("\t %d constant characters.", NConstant() - numConditioningPatterns); outman.UserMessage("\t %d parsimony-informative characters.", NInformative()); outman.UserMessage("\t %d uninformative variable characters.", NVarUninform()); int total = NConstant() + NInformative() + NVarUninform() - numConditioningPatterns; if(NMissing() > 0){ outman.UserMessage("\t %d characters were completely missing or ambiguous (removed).", NMissing()); //outman.UserMessage("\t %d total characters (%d before removing empty columns).", total, GapsIncludedNChar() - numConditioningPatterns); outman.UserMessage("\t %d total characters (%d before removing empty columns).", total, numNonMissingRealCountsInOrigMatrix + NMissing()); } else outman.UserMessage("\t %d total characters.", total); assert(total == numNonMissingRealCountsInOrigMatrix); outman.UserMessage("\t %d unique patterns in compressed data matrix.", NChar() - numConditioningPatterns); outman.flush(); } void DataMatrix::ProcessPatterns() { Stopwatch stoppy; stoppy.Start(); if(usePatternManager){ patman.ProcessPatterns(); GetDataFromPatternManager(); patman.Reset(); } else{ Summarize(); Collapse(); DetermineConstantSites(); } CalcEmpiricalFreqs(); ReserveOriginalCounts(); OutputDataSummary(); int t = stoppy.SplitTime(); /*There isn't much point in outputting all of this clutter if(t == 0) outman.UserMessage("\tPattern processing required < 1 second"); else outman.UserMessage("\tPattern processing required %d second(s)", stoppy.SplitTime()); if(numCompares > 0) outman.UserMessage("\t%d pattern comparisons were needed", numCompares); outman.UserMessage(""); */ } //this pulls all of the processed data back out of the patman into the old fields of DataMatrix void DataMatrix::GetDataFromPatternManager(){ ResizeCharacterNumberDependentVariables(patman.NChar()) ; patman.FillNumberVector(newNumber); patman.FillCountVector(newCount); patman.FillNumStatesVector(newNumStates); patman.FillConstStatesVector(newConstStates); patman.FillIntegerValues(numMissingChars, numConstantChars, numVariableUninformChars, numInformativeChars, lastConstant, numRealSitesInOrigMatrix, numNonMissingRealCountsInOrigMatrix, numNonMissingRealSitesInOrigMatrix, numPatterns); patman.FillTaxaXCharMatrix(matrix); if(patman.compressed) dense = 1; } DataMatrix::~DataMatrix(){ if( count ) MEM_DELETE_ARRAY(count); // count is of length numPatterns if( numStates ) MEM_DELETE_ARRAY(numStates); // numStates is of length numPatterns if( number ) MEM_DELETE_ARRAY(number); // number is of length numPatterns if( origDataNumber ) MEM_DELETE_ARRAY(origDataNumber); // origDataNumber is of length numPatterns if( taxonLabel ) { for( int j = 0; j < nTaxAllocated; j++ ) MEM_DELETE_ARRAY( taxonLabel[j] ); // taxonLabel[j] is of length strlen(taxonLabel[j])+1 MEM_DELETE_ARRAY(taxonLabel); // taxonLabel is of length nTax } if( matrix ) { for( int j = 0; j < nTaxAllocated; j++ ) MEM_DELETE_ARRAY(matrix[j]); // matrix[j] is of length numPatterns MEM_DELETE_ARRAY(matrix); // matrix is of length nTax } if(constStates!=NULL) delete []constStates; if(origCounts!=NULL) delete []origCounts; } void DataMatrix::SetTaxonLabel(int i, const char* s) { if( taxonLabel && (i < nTax) ) ReplaceTaxonLabel(i, s); } void DataMatrix::ReplaceTaxonLabel( int i, const char* s ) { assert( taxonLabel ); if( taxonLabel[i] ) { MEM_DELETE_ARRAY(taxonLabel[i]); // taxonLabel[i] is of length strlen(taxonLabel[i])+1 } int newLength = (strlen(s)+1); if(newLength > MAX_TAXON_LABEL) throw ErrorException("Sorry, taxon name %s for taxon #%d is too long (max length=%d)", s, i+1, MAX_TAXON_LABEL); MEM_NEW_ARRAY(taxonLabel[i],char,newLength); strcpy(taxonLabel[i], s); } // // PositionOf returns position (starting from 0) of taxon whose name // matches the string s in the taxonLabel list // int DataMatrix::PositionOf( char* s ) const { int i; for( i = 0; i < nTax; i++ ) { if( strcmp( taxonLabel[i], s ) == 0 ) break; } assert( i < nTax ); return i; } int DataMatrix::MinScore(set patt, int bound, unsigned char bits/*=15*/, int prevSc/*=0*/) const{ if(patt.size() == 0) return 0; int min_sc_this_lvl = 9999; int curr_sc_this_lvl = 9999; for(unsigned s2 = 0;s2 < 4;s2++){ unsigned char thisBit = (1 << s2); if(bits & thisBit){ set remaining; for(set::iterator it=patt.begin();it != patt.end();it++){ if(!(*it & thisBit)) remaining.insert(*it); } if(remaining.size() > 0){ if(prevSc + 1 < bound) curr_sc_this_lvl = 1 + MinScore(remaining, bound, bits & ~thisBit, prevSc+1); else curr_sc_this_lvl = bound - prevSc; } else return 0; if(curr_sc_this_lvl < min_sc_this_lvl) min_sc_this_lvl = curr_sc_this_lvl; if(min_sc_this_lvl == 0 || min_sc_this_lvl + prevSc < bound) return min_sc_this_lvl; } } return min_sc_this_lvl; } // // PatternType determines whether pattern k is constant, informative, or missing //it used to try to determine autapomorphies, although not correctly // int DataMatrix::PatternType( int k , unsigned int *stateCounts) const{ assert(k < numPatterns); if( k >= numPatterns ) return 0; int retval; bool ambig = false; //any total or partial ambiguity int nStates = 0; bool informative = false; bool constant = false; bool missing = false; //fill the scratch array with zeros memset(stateCounts, 0x00, maxNumStates * sizeof(*stateCounts)); //count the number of times each state occurs, and whether there are any partially //ambiguous characters (currently only allowed for nuc data) unsigned char full_ambig = (maxNumStates == 4 ? 15 : maxNumStates); if(maxNumStates == 4){ for(int t = 0; t < nTax; t++ ){ unsigned char c = Matrix( t, k ); if(c != full_ambig && (c & (c-1))){ ambig = true; break; } else if(c != full_ambig) stateCounts[(c > 1) + (c > 2) + (c > 4)]++; } } else { for(int t = 0; t < nTax; t++ ){ unsigned char c = Matrix( t, k ); if(c != full_ambig) stateCounts[c]++; } } if(!ambig){ //no partial ambiguity (all AA and codon will come this way) //without ambiguity, having 2+ states with 2+ counts means informativeness int numDoubles = 0; for(int s = 0; s < maxNumStates; s++ ){ if(stateCounts[s] > 0){ nStates++; if(stateCounts[s] > 1) numDoubles++; } } if(nStates == 0){ missing = true; assert(numDoubles == 0); } else if(nStates == 1){ constant = true; assert(numDoubles < 2); } else{ if(numDoubles > 1) informative = true; } } else{ //this very convoluted scheme must be used to determine informativeness //if ambiguity is allowed (only for nuc data currently) multiset patt; unsigned char conStates = 15; for(int t = 0;t < nTax;t++){ unsigned char c = Matrix( t, k ); patt.insert(c); conStates &= c; } //constant sites are possible with ambiguity of some resolution gives a single state if(conStates){ if(conStates == 15) missing = true; else constant = true; } else{ vector< pair > stateScores; for(unsigned state=0;state < 4;state++){ int sc = 0; for(multiset::iterator it=patt.begin();it != patt.end();it++){ if(!((*it) & (1 << state))){ sc++; } } stateScores.push_back(pair(state, sc)); } sort(stateScores.begin(), stateScores.end(), my_pair_compare); int minStar = stateScores[0].second; if(minStar > 1){ set uPatt; for(multiset::iterator it=patt.begin();it != patt.end();it++) uPatt.insert(*it); int minScore = MinScore(uPatt, minStar); if(minScore < minStar){ informative = true; } } } } if(missing){ retval = PT_MISSING; nStates = 0; } else if(constant){ retval = PT_CONSTANT; nStates = 1; } else if(informative){ retval = PT_INFORMATIVE | PT_VARIABLE; nStates = max(2, nStates); } else{ retval = PT_VARIABLE; nStates = max(2, nStates); } /* ofstream deb; if(k==0) deb.open("pat.log"); else deb.open("pat.log", ios::app); deb << k << "\t" << constant << "\t" << informative << "\t" << nStates << "\n"; deb.close(); */ //Note that numStates here may not be the true number of states in the //case of ambiguity, but it really only matters that it is accurate in //discriminating 0/1/1+ states because code elsewhere depends on it. numStates[k] = nStates; return retval; } // // Summarize tallies number of constant, informative, and autapomorphic characters // void DataMatrix::Summarize(){ assert( numPatterns > 0 ); numMissingChars = numConstantChars = numInformativeChars = numVariableUninformChars = numNonMissingRealCountsInOrigMatrix = 0; //this is just a scratch array to be used repeatedly in PatternType vector s(maxNumStates); numRealSitesInOrigMatrix = numPatterns - numConditioningPatterns; for(int k = 0; k < numPatterns; k++ ) { int ptFlags = PatternType(k, &s[0]); //Fixed 2 bugs - It is important to calculate numNonMissingRealCountsInOrigMatrix here from counts of the //the generally unpacked data, because it could effectively be partially packed due to the use of a wtset, //but also need to keep separate track of the number of columns in the orig matrix with numRealSitesInOrigMatrix if( ptFlags != PT_MISSING && k >= numConditioningPatterns) numNonMissingRealCountsInOrigMatrix += count[k]; if( ptFlags == PT_MISSING ) numMissingChars += count[k]; else if( ptFlags & PT_CONSTANT ) numConstantChars += count[k]; else if( ptFlags & PT_INFORMATIVE ) numInformativeChars += count[k]; else{ assert(ptFlags & PT_VARIABLE); numVariableUninformChars += count[k]; } } numNonMissingRealSitesInOrigMatrix = numRealSitesInOrigMatrix - numMissingChars; if( numConstantChars + numInformativeChars + numVariableUninformChars == 0 ){ throw ErrorException("Matrix is made up entirely of missing characters (?, -, or N)!"); } } // // NewMatrix deletes old matrix, taxonLabel, count, and number // arrays and creates new ones // void DataMatrix::NewMatrix( int taxa, int sites ) { //allocate an extra taxon unless there previously wasn't one int extraTax = 1; if(nTaxAllocated > 0){ extraTax = nTaxAllocated - nTax; } // delete taxon labels if( taxonLabel ) { int i; for( i = 0; i < nTaxAllocated; i++ ) MEM_DELETE_ARRAY(taxonLabel[i]); // taxonLabel[i] is of length strlen(taxonLabel[i])+1 MEM_DELETE_ARRAY(taxonLabel); // taxonLabel is of length nTax } // create new array of taxon label pointers if( taxa > 0 ) { MEM_NEW_ARRAY(taxonLabel,char*,taxa + extraTax); for( int i = 0; i < taxa + extraTax; i++ ) taxonLabel[i] = NULL; } // delete data matrix and count and number arrays if( matrix ) { int j; for( j = 0; j < taxa + extraTax; j++ ) MEM_DELETE_ARRAY(matrix[j]); // matrix[j] has length numPatterns MEM_DELETE_ARRAY(matrix); // matrix has length nTax } if(usePatternManager == false){ if( count ) { MEM_DELETE_ARRAY(count); //count has length numPatterns } if( numStates ) { MEM_DELETE_ARRAY(numStates); // numStates has length numPatterns } } //yarg - this is used even when usePatMan == true, when converting from nuc to AA matrix if( number ) { MEM_DELETE_ARRAY(number); // number has length numPatterns } if( origDataNumber ) { MEM_DELETE_ARRAY(origDataNumber); // origDataNumber has length numPatterns } // create new data matrix, and new count and number arrays // all counts are initially 1, and characters are numbered // sequentially from 0 to numPatterns-1 if( taxa > 0 && sites > 0 ) { MEM_NEW_ARRAY(matrix,unsigned char*,taxa + extraTax); MEM_NEW_ARRAY(number,int,sites); MEM_NEW_ARRAY(origDataNumber,int,sites); for( int j = 0; j < sites; j++ ) { number[j] = j; //number[j] = ( j < numConditioningPatterns ? -1 : j - numConditioningPatterns); //in the case of conditioning patterns or partitioning this will be updated later anyway origDataNumber[j] = j; } if(usePatternManager == false){ MEM_NEW_ARRAY(count,int,sites); MEM_NEW_ARRAY(numStates,int,sites); for( int j = 0; j < sites; j++ ) { count[j] = 1; numStates[j] = 1; } } for( int i = 0; i < taxa + extraTax; i++ ) { matrix[i]=new unsigned char[sites]; //MEM_NEW_ARRAY(matrix[i],unsigned char,sites); //memset( matrix[i], 0xff, taxa*sizeof(unsigned char) ); memset( matrix[i], 0xff, sites*sizeof(unsigned char) ); } } // set dimension variables to new values nTax = taxa; nTaxAllocated = nTax + extraTax; //these will likely be updated later numRealSitesInOrigMatrix = numNonMissingRealSitesInOrigMatrix = sites - numConditioningPatterns; nonZeroCharCount = numPatterns = sites; } void DataMatrix::ResizeCharacterNumberDependentVariables(int nCh) { //ONLY CALL THIS BEFORE GETTING DATA FROM PATMAN, OTHERWISE SOME VARIABLES //WILL BE WRONG!!! numPatterns = nCh; // delete data matrix and count and number arrays if( matrix ) { int j; for( j = 0; j < nTaxAllocated; j++ ) MEM_DELETE_ARRAY(matrix[j]); // matrix[j] has length numPatterns MEM_DELETE_ARRAY(matrix); // matrix has length nTax } if( count ) { MEM_DELETE_ARRAY(count); //count has length numPatterns } if( numStates ) { MEM_DELETE_ARRAY(numStates); // numStates has length numPatterns } // create new data matrix, and new count and number arrays // all counts are initially 1, and characters are numbered // sequentially from 0 to numPatterns-1 if( numPatterns > 0 ) { MEM_NEW_ARRAY(matrix,unsigned char*,nTaxAllocated); MEM_NEW_ARRAY(count,int,numPatterns); MEM_NEW_ARRAY(numStates,int,numPatterns); for( int j = 0; j < numPatterns; j++ ) { count[j] = 1; numStates[j] = 1; } for( int i = 0; i < nTaxAllocated; i++ ) { matrix[i]=new unsigned char[numPatterns]; memset( matrix[i], 0xff, numPatterns*sizeof(unsigned char) ); } } //set dimension variables to new values, which actually MUST be updated elsewhere to be correct //see note at top of func nonZeroCharCount = numRealSitesInOrigMatrix = numNonMissingRealSitesInOrigMatrix = numPatterns; } //deprecated DataMatrix& DataMatrix::operator =(const DataMatrix& d){ assert(0); NewMatrix( d.NTax(), d.NChar() ); int i, j; for( i = 0; i < nTax; i++ ) { SetTaxonLabel(i, d.TaxonLabel(i) ); } for( j = 0; j < numPatterns; j++ ) { SetCount(j, d.Count(j) ); origCounts[j] = d.origCounts[j]; number[j] = d.Number(j); numStates[j] = d.NumStates(j); } for( i = 0; i < nTax; i++ ) { for( j = 0; j < numPatterns; j++ ) SetMatrix(i, j, d.Matrix(i, j)); } return *this; } // // Pack simply deletes sites having a count of zero // void DataMatrix::Pack(){ int i, j, newNChar = 0; // determine dimensions of new matrix for( j = 0; j < numPatterns; j++ ) { if( count[j] ) newNChar++; } //DEBUG - something was going wrong and causing crashes in some cases (only AA's?) when a new matrix //was created with the same dimensions as the original. Haven't figured out why yet, //but this avoids the crash at least. if(newNChar == numPatterns){ dense = true; return; } // create new matrix and count arrays and fill unsigned char** newMatrix; MEM_NEW_ARRAY(newMatrix,unsigned char*,nTaxAllocated); int* newCount; MEM_NEW_ARRAY(newCount,int,newNChar); int* newNumStates; MEM_NEW_ARRAY(newNumStates,int,newNChar); for( i = 0; i < nTaxAllocated; i++ ) MEM_NEW_ARRAY(newMatrix[i],unsigned char,newNChar); i = 0; for( j = 0; j < numPatterns; j++ ) { if( count[j] ) { for( int k = 0; k < nTax; k++ ) newMatrix[k][i] = matrix[k][j]; newCount[i] = count[j]; newNumStates[i] = numStates[j]; i++; } else{//as we remove columns, shift all the greater numbers over for(int c=0;c < numPatterns;c++){ if(number[c] >= i) number[c]--; } } } // delete old matrix and count arrays if( count ) MEM_DELETE_ARRAY(count); // count has length numPatterns if( numStates ) MEM_DELETE_ARRAY(numStates); // numStates has length numPatterns if( matrix ) { for( i = 0; i < nTaxAllocated; i++ ) MEM_DELETE_ARRAY(matrix[i]); // matrix[i] has length numPatterns MEM_DELETE_ARRAY(matrix); // matrix has length nTax } // set count, matrix and numStates to their new counterparts count = newCount; numStates = newNumStates; matrix = newMatrix; numPatterns = newNChar; nonZeroCharCount = numPatterns; } void DataMatrix::DetermineConstantSites(){ //note where all of the constant sites are, and what they are //this is kind of ugly, but will never be rate limiting lastConstant=-1; assert(numStates[0] > 0); while(numStates[lastConstant+1]==1) lastConstant++; constStates=new int[lastConstant+1]; int t; if(maxNumStates == 4){ for(int i=0;intax-1, taxon numbers 1->ntax } return -1; } // // ComparePatterns returns: // 0 complete identity // -1 if i less than j // 1 if i greater than j // int DataMatrix::ComparePatterns( const int i, const int j ) const{ //DJZ 10/28/03 altering this to always put constant patterns at the start, which will //make implementing invariant sites much easier. int cmp = 0; if(numStates[i]==1){ if(numStates[j]==1){ if(Matrix(0,i) < Matrix(0,j)) return -1; //else return 1; else{ for( int k = 0; k < nTax; k++ ) { int same = ( Matrix( k, i ) == Matrix( k, j ) ); if( !same ) { FLOAT_TYPE diff = ( (FLOAT_TYPE)Matrix( k, i ) - (FLOAT_TYPE)Matrix( k, j ) ); cmp = ( diff < 0.0 ? -1 : 1 ); break; } } return cmp; } } else return -1; } else if(numStates[j]==1){ return 1; } for( int k = 0; k < nTax; k++ ) { int same = ( Matrix( k, i ) == Matrix( k, j ) ); if( !same ) { FLOAT_TYPE diff = ( (FLOAT_TYPE)Matrix( k, i ) - (FLOAT_TYPE)Matrix( k, j ) ); cmp = ( diff < 0.0 ? -1 : 1 ); break; } } return cmp; } // // Collapse merges like patterns // void DataMatrix::Collapse(){ int i = 0, j = 1; assert(nonZeroCharCount == numPatterns); Sort(); while( i < numPatterns ) { while( j < numPatterns && ComparePatterns( i, j ) == 0 ) { // pattern j same as pattern i count[i] += count[j]; count[j] = 0; j++; } i = j++; } //DJZ 10/28/03 get rid of all missing patterns int q=numPatterns-1; while(numStates[q] == 0){ //This sets the number to -1 for an all missing site, indicating that none of the packed //matrix columns corresponds to it for(i = 0;i < numPatterns;i++){ if(number[i]==q) number[i]=-1; } count[q--]=0; //NO, this is now done in Summarize()!! //when all missing columns are deleted, remove them from the total number of characters //numNonMissingRealSitesInOrigMatrix--; } Pack(); assert(nonZeroCharCount == numPatterns); } // // EliminateAdjacentIdenticalColumns sets the count of successive identical patterns // in the original alignment to zero (usually applied to gaps) // i.e., adjacent identical patterns count as only one observation // 5/17/12 Ooops, changed to only do this for non-constant columns void DataMatrix::EliminateAdjacentIdenticalColumns(){ //this needs to happen here to know the number of state counts, but will be redone later Summarize(); int i = 0, j = 1; assert(nonZeroCharCount == numPatterns); int numCombined = 0; while( i < NChar() ) { //need to avoid subtracting zero state chars here (blank cols) since the will be removed already //oops, and one state characters, since we don't want to collapse all present columns while( numStates[i] > 1 && j < NChar() && ComparePatterns( i, j ) == 0 ) { // pattern j same as pattern i count[j] = 0; //when columns are eliminated, remove them from the total number of characters numNonMissingRealSitesInOrigMatrix--; numCombined++; j++; } i = j++; } outman.UserMessage(" ***%d IDENTICAL ADJACENT CHARACTERS ELIMINATED***", numCombined); } // // BSort implements a simple bubblesort // void DataMatrix::BSort( int byCounts /* = 0 */ ){ int swap, k; for( int i = 0; i < numPatterns-1; i++ ) { for( int j = i+1; j < numPatterns; j++ ) { if( byCounts ) swap = ( count[i] < count[j] ? 1 : 0 ); else swap = ( ComparePatterns( i, j ) > 0 ? 1 : 0 ); if( swap ) { SwapCharacters( i, j ); k = count[i]; count[i] = count[j]; count[j] = k; k = numStates[i]; numStates[i] = numStates[j]; numStates[j] = k; k = number[i]; number[i] = number[j]; number[j] = k; } } } } void DataMatrix::DebugSaveQSortState( int top, int bottom, int ii, int jj, int xx, const char* title ) { ofstream qsf( "qsstate.txt", ios::out | ios::app ); qsf << endl << title << endl; int i, j; for( j = 0; j < numPatterns; j++ ) { qsf << setw(6) << j << " "; for( i = 0; i < nTax; i++ ) qsf << DatumToChar( Matrix( i, j ) ); if( j == top ) qsf << " <-- top "; if( j == ii ) qsf << " <-- i "; if( j == bottom ) qsf << " <-- bottom"; if( j == jj ) qsf << " <-- j "; if( j == xx ) qsf << " <-- x "; qsf << endl; } qsf.close(); } // // QSort implements the quicksort algorithm // void DataMatrix::QSort( int top, int bottom ) { int i = top; int j = bottom; int x = ( top + bottom ) / 2; //DebugSaveQSortState( top, bottom, i, j, x, "Entering QSort" ); do { while( ComparePatterns( i, x ) < 0 && i < bottom ) i++ ; while( ComparePatterns( x, j ) < 0 && j > top ) j-- ; if( i <= j ) { //DebugSaveQSortState( top, bottom, i, j, x, "Just about to swap i and j" ); SwapCharacters( i, j ); if( x == i ) // keep track of the reference pattern! x = j; else if( x == j ) x = i; i++; if(j) j--; //DebugSaveQSortState( top, bottom, i, j, x, "Just after swapping" ); } } while( i <= j ); if( top < j ) QSort( top, j ); if( i < bottom ) QSort( i, bottom ); } int DataMatrix::GetToken( istream& in, char* tokenbuf, int maxlen, bool acceptComments /*=true*/ ) { int ok = 1; int i; char ch = ' '; // skip leading whitespace while( in && ( isspace(ch) || ch == '[' ) ){ in.get(ch); if(ch == '[' && acceptComments==false) return -1; } if( !in ) return 0; tokenbuf[0] = ch; tokenbuf[1] = '\0'; tokenbuf[maxlen-1] = '\0'; for( i = 1; i < maxlen-1; i++ ) { in.get(ch); if( isspace(ch) || ch == ']' ) break; tokenbuf[i] = ch; tokenbuf[i+1] = '\0'; } if( i >= maxlen-1 ) ok = 0; return ok; } int DataMatrix::GetToken( FILE *in, char* tokenbuf, int maxlen){ int ok = 1; int i; char ch = ' '; // skip leading whitespace while( !ferror(in) && ( isspace(ch) || ch == '[' ) ){ ch = getc(in); } if( ferror(in) ) return 0; tokenbuf[0] = ch; tokenbuf[1] = '\0'; tokenbuf[maxlen-1] = '\0'; for( i = 1; i < maxlen-1; i++ ) { ch = getc(in); if( isspace(ch) || ch == ']' ) break; tokenbuf[i] = ch; tokenbuf[i+1] = '\0'; } if( i >= maxlen-1 ) ok = 0; return ok; } // // Read reads in data from a file int DataMatrix::ReadPhylip( const char* infname){ //PARTITION ModelSpecification *modSpec = modSpecSet.GetModSpec(0); char ch; bool isNexus=false; FILE *inf; #ifdef BOINC char input_path[512]; boinc_resolve_filename(infname, input_path, sizeof(input_path)); inf = boinc_fopen(input_path, "r"); #else inf = fopen(infname, "r"); #endif if(ferror(inf)) throw ErrorException("problem opening datafile %s for reading", infname); // get comments (note: comments only allowed at the beginning of the file) int end_of_comments = 0; while( !end_of_comments ){ ch = getc(inf); if( ch != '/' ) { ungetc(ch, inf); end_of_comments = 1; } else { // ch is a slash, ignore rest of this line while( ch != '\n' && ch != '\r' && ch != EOF) { ch = getc(inf); } } } // get the dimensions of the data file int num_taxa=0, num_chars=0; fscanf(inf, "%d %d", &num_taxa, &num_chars); NewMatrix( num_taxa, num_chars ); // read in the data, including taxon names int blockStartNum = 0, charNum, i; bool firstPass = true; bool allDataRead = false; bool interleaved = false; while(allDataRead == false){ //loop over the taxa, doing so multiple times for interleaved data for( i = 0; i < num_taxa; i++ ) { if(firstPass){ // get name for taxon i char taxon_name[ MAX_TAXON_LABEL ]; int ok = GetToken(inf, taxon_name, MAX_TAXON_LABEL); if( !ok ) { throw ErrorException("problem reading data: name for taxon #%d too long", i+1); } SetTaxonLabel( i, taxon_name ); } // get data for taxon i unsigned char datum; for( charNum = blockStartNum; charNum < num_chars; charNum++ ) { if(firstPass == false && charNum == blockStartNum){ do{ ch = getc(inf); }while(isspace(ch) && ch != EOF); } else{ do{ ch = getc(inf); }while(ch == ' ' || ch == '\t'); } if(ch == '['){//if there is a comment here, which is how the "color" used to be represented while (ch != ']' && ch != EOF) ch = getc(inf); ch = getc(inf); } if( ch == '.' ){ datum = Matrix( 0, charNum ); } else if(ch == '\n' || ch == '\r'){ //file must be interleaved (or broken) if(!interleaved && i != 0) throw ErrorException("Unexpected line break found while reading data for taxon %s", TaxonLabel(i)); else{ interleaved = true; break; } } else{ if(modSpec->IsAminoAcid() && modSpec->IsCodonAminoAcid() == false) datum = CharToDatum(ch); else datum = CharToBitwiseRepresentation(ch); } SetMatrix( i, charNum, datum ); } } if(charNum == num_chars && i == num_taxa) allDataRead = true; else{ firstPass = false; blockStartNum = charNum; } } // read in the line containing the counts do{ ch = getc(inf); }while(ch != EOF && isspace(ch)); if( !feof(inf) ) { if(isdigit(ch) == false) throw ErrorException("Found extraneous information at end of phylip formatted datafile"); ungetc(ch, inf); int i; char buf[10]; for( i = 0; i < num_chars; i++ ) { int ok = GetToken( inf, buf, 10); if(feof(inf)) break; int cnt = atoi(buf); SetCount( i, cnt ); } if(i != num_chars) throw ErrorException("problem reading pattern counts"); else dense = 1; //DJZ 9-13-06 //It is very important to properly set the numNonMissingRealSitesInOrigMatrix variable now //to be the sum of the counts, otherwise bootstrapping after reading //a .cond file will give wrong resampling!!!!! numNonMissingRealSitesInOrigMatrix=0; for(int i=0;iIsAminoAcid() && modSpec->IsCodonAminoAcid() == false) datum = CharToDatum(ch); else datum = CharToBitwiseRepresentation(ch); SetMatrix( i, charNum, datum ); } } fclose(inf); return 1; } void DataMatrix::DumpCounts( const char* s ) { ofstream tmpf( "tmpfile.txt", ios::out | ios::app ); tmpf << endl << endl; if(s) { tmpf << s << endl; } for( int j = 0; j < numPatterns; j++ ) { tmpf << j << " " << Count(j) << endl; } tmpf << endl; } // // saves data under the name s but with extension changed to '.mlt' // if third argument supplied, a NEXUS file ending in '.nex' is saved also // int DataMatrix::Save( const char* path, char* newfname /* = 0 */, char* #if defined( AUTOSAVE_NEXUS ) nxsfname /* = 0 */ #endif ) { int i, j;//, nchar_total; char newpath[ MAXPATH ]; strcpy( newpath, path ); #if defined( AUTOSAVE_NEXUS ) // ________________________________________ // | | // | save uncompressed data to file nxspath | // |________________________________________| // int k; char nxspath[ MAXPATH ]; strcat( nxspath, ".nex" ); cerr << endl << "Opening file '" << nxspath << "' for saving..." << endl; ofstream nxsf( nxspath ); if( !nxsf ) { cerr << endl << "Error: could not open file '" << nxspath << "' for saving" << endl; return 0; } nchar_total = 0; for( j = 0; j < numPatterns; j++ ) nchar_total += Count(j); nxsf << "#nexus" << endl << endl; nxsf << "begin data;" << endl; nxsf << " dimensions ntax=" << nTax << " nchar=" << nchar_total << ";" << endl; nxsf << " format missing=? datatype=standard;" << endl; nxsf << " matrix" << endl; for( i = 0; i < nTax; i++ ) { nxsf << TaxonLabel(i) << " "; nxsf << " [" << TaxonColor(i) << "] "; for( j = 0; j < numPatterns; j++ ) { for( k = 0; k < Count(j); k++ ) { nxsf << DatumToChar( Matrix( i, j ) ); } } nxsf << endl; } nxsf << ";" << endl; nxsf << "end;" << endl << endl; if( !nxsf ) { cerr << endl << "Error saving data to file '" << nxspath << "': disk full?" << endl; return 0; } nxsf.close(); if( nxsfname ) { strcpy( nxsfname, nxspath ); } #endif // _______________________________________ // | | // | save compressed data to file newpath | // |_______________________________________| // //strcat( newpath, ".comp" ); outman.UserMessage("Opening file \"%s\" for saving...", newpath); ofstream outf( newpath ); if( !outf ) throw ErrorException("Error: could not open file \"%s\"", newpath); /* nchar_total = 0; for( j = 0; j < numPatterns; j++ ) { int k = PatternType(j); if( (k & PT_CONSTANT) && !InvarCharsExpected() ) continue; nchar_total++; } */ outf << "#NEXUS\nbegin data;\ndimensions ntax=" <MakeWeightSetString(str, "packed"); outf << str.c_str() << "\n;end;\n"; outf.close(); return 1; // save a line containing the counts for each character for( j = 0; j < numPatterns; j++ ) { // int k = PatternType(j); // if( (k & PT_CONSTANT) && !InvarCharsExpected() ) continue; outf << Count(j) << ' '; } outf << endl; // save a line containing the number of states for each character for( j = 0; j < numPatterns; j++ ) { // int k = PatternType(j); // if( (k & PT_CONSTANT) && !InvarCharsExpected() ) continue; outf << NumStates(j) << ' '; } outf << endl; if( !outf ) { cerr << endl << "Error saving data to file '" << newpath << "': disk full?" << endl; return 0; } outf.close(); /* cjb if( newfname ) { strcpy( newfname, newpath ); } */ return 1; } void DataMatrix::WriteCollapsedData(){ //write the data matrix for(int i=0;i= 5.0) outman.UserMessage("WARNING: The resampleproportion setting is the proportion to resample,\nNOT the percentage (1.0 = 100%%).\nThe value you specified (%.2f) is a very large proportion.", resampleProportion); int originalSeed = rnd.seed(); rnd.set_seed(seedToUse); //This is a little dumb, but since there are parallel counts and origCounts variables depending on whether the new PatternManager //is being used, need to alias them so that the remainder of this function works unchanged const int *origCountsAlias; if(newOrigCounts.size() > 0){ origCountsAlias = &newOrigCounts[0]; } else origCountsAlias = origCounts; int *countsAlias; if(newCount.size() > 0){ countsAlias = &newCount[0]; } else countsAlias = count; FLOAT_TYPE *cumProbs = new FLOAT_TYPE[numPatterns]; FLOAT_TYPE p=0.0; cumProbs[0]=(FLOAT_TYPE) origCountsAlias[0] / ((FLOAT_TYPE) numNonMissingRealCountsInOrigMatrix); countsAlias[0] = 0; for(int i = 1;i < numPatterns;i++){ cumProbs[i] = cumProbs[i-1] + (FLOAT_TYPE) origCountsAlias[i] / ((FLOAT_TYPE) numNonMissingRealCountsInOrigMatrix); countsAlias[i] = 0; } cumProbs[numPatterns - 1] = 1.0; //ofstream deb("counts.log", ios::app); //ofstream deb("counts.log"); //round to nearest int int numToSample = (int) (((FLOAT_TYPE)numNonMissingRealCountsInOrigMatrix * resampleProportion) + 0.5); if(numToSample != numNonMissingRealCountsInOrigMatrix) outman.UserMessage("Resampling %d characters (%.2f%%).\n", numToSample, resampleProportion*100); for(int c=0;c cumProbs[pat]) pat++; countsAlias[pat]++; } /* for(int i = 0;i < numPatterns;i++) deb << i << "\t" << origCountsAlias[i] << "\t" << countsAlias[i] << endl; */ //take a count of the number of chars that were actually resampled nonZeroCharCount = 0; int numZero = 0; int totCounts = 0; for(int d=0;d 0) { nonZeroCharCount++; totCounts += countsAlias[d]; } else numZero++; } delete []cumProbs; assert(totCounts == numNonMissingRealCountsInOrigMatrix); assert(nonZeroCharCount + numZero == numPatterns); int nextSeed = rnd.seed(); rnd.set_seed(originalSeed); return nextSeed; } void DataMatrix::CheckForIdenticalTaxonNames(){ const char *name1, *name2; vector< pair > identicals; for(int t1=0;t1 0){ outman.UserMessage("Error! Multiple sequences with same name encountered!:"); for(vector< pair >::iterator it=identicals.begin() ; it != identicals.end() ; it++){ outman.UserMessage("\t%s : numbers %d and %d", TaxonLabel((*it).first), (*it).first+1, (*it).second+1); } throw(ErrorException("Terminating. Please make all sequence names unique!")); } } void DataMatrix::GetStringOfOrigDataColumns(string &str) const{ //note that GetSetAsNexusString takes zero offset indeces and converts them to //char nums, ie adds 1 to each NxsUnsignedSet chars; for(int c = numConditioningPatterns;c < numRealSitesInOrigMatrix + numConditioningPatterns;c++) chars.insert(origDataNumber[c]); str = NxsSetReader::GetSetAsNexusString(chars); } void DataMatrix::CountMissingCharsByColumn(vector &vec){ for(int c = 0;c < numPatterns;c++){ int missing = 0; for(int t = 0;t < nTax;t++){ if(Matrix(t, c) == fullyAmbigChar) missing++; } vec.push_back(missing); } } void DataMatrix::MakeWeightSetString(NxsCharactersBlock &charblock, std::string &wtstring, string name){ NxsTransformationManager &transformer = charblock.GetNxsTransformationManagerRef(); //this is a list of IntWeightToIndexSet objects NxsTransformationManager::ListOfIntWeights intWeights; NxsUnsignedSet dummy; //the charset was empty, implying that all characters in this block will go into a single matrix for(int i = 0;i < charblock.GetNumChar();i++) dummy.insert(i); for(int countNum = 0;dummy.size() > 0;countNum++){ //this is a pair > NxsTransformationManager::IntWeightToIndexSet weightToIndex; weightToIndex.first = countNum; for(NxsUnsignedSet::iterator it = dummy.begin();it != dummy.end();){ int thisCount = Count(*it); if(thisCount == countNum){ weightToIndex.second.insert(*it); int err = *it++; dummy.erase(err); } else it++; } if(weightToIndex.second.size() > 0) intWeights.push_back(weightToIndex); } transformer.AddIntWeightSet("bootstrapped", intWeights, true); ostringstream out; transformer.WriteWtSet(out); wtstring = out.str(); } void DataMatrix::MakeWeightSetString(std::string &wtstring, string name){ NxsTransformationManager transformer;// = charblock.GetNxsTransformationManagerRef(); //this is a list of IntWeightToIndexSet objects NxsTransformationManager::ListOfIntWeights intWeights; NxsUnsignedSet dummy; //the charset was empty, implying that all characters in this block will go into a single matrix for(int i = 0;i < numPatterns;i++) dummy.insert(i); for(int countNum = 0;dummy.size() > 0;countNum++){ //this is a pair > NxsTransformationManager::IntWeightToIndexSet weightToIndex; weightToIndex.first = countNum; for(NxsUnsignedSet::iterator it = dummy.begin();it != dummy.end();){ int thisCount = Count(*it); if(thisCount == countNum){ weightToIndex.second.insert(*it); int err = *it++; dummy.erase(err); } else it++; } if(weightToIndex.second.size() > 0) intWeights.push_back(weightToIndex); } transformer.AddIntWeightSet(name.c_str(), intWeights, true); ostringstream out; transformer.WriteWtSet(out); wtstring = out.str(); } garli-2.1-release/src/datamatr.h000066400000000000000000000457271241236125200166420ustar00rootroot00000000000000// GARLI version 2.1 source code // Copyright 2005-2014 Derrick J. Zwickl // email: garli.support@gmail.com // // This program is free software: you can redistribute it and/or modify // it under the terms of the GNU General Public License as published by // the Free Software Foundation, either version 3 of the License, or // (at your option) any later version. // // This program is distributed in the hope that it will be useful, // but WITHOUT ANY WARRANTY; without even the implied warranty of // MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the // GNU General Public License for more details. // // You should have received a copy of the GNU General Public License // along with this program. If not, see . // // NOTE: Portions of this source adapted from GAML source, written by Paul O. Lewis #ifndef __DATAMATR_H #define __DATAMATR_H #include #include #include #include #include #include using namespace std; #include "ncl.h" #include "errorexception.h" class GarliReader; typedef FLOAT_TYPE** DblPtrPtr; #define MAX_STATES (8*sizeof(unsigned char)) #define FIRST_STATE (0x01) #define LAST_STATE (0x80) #define MISSING_DATA (0xf) // all bits set to 1 #if defined( CPLUSPLUS_EXCEPTIONS ) # define THROW_BADSTATE(a) throw XBadState(a) #else # define THROW_BADSTATE(a) BadState(a) #endif class SitePattern{ public: int count; int origCount; int numStates; int constStates; static int numTax; static int maxNumStates; vector stateVec; vector siteNumbers; enum patternType{ MISSING = 1, CONSTANT = 2, UNINFORM_VARIABLE = 3, INFORMATIVE = 4 }type; SitePattern(){Reset();} SitePattern(const SitePattern &rhs){ Reset(); siteNumbers = rhs.siteNumbers; stateVec = rhs.stateVec; count = rhs.count; origCount = rhs.origCount; numStates = rhs.numStates; constStates = rhs.constStates; } ~SitePattern(){ stateVec.clear(); siteNumbers.clear(); } void Reset(){ count = origCount = numStates = constStates = -1; stateVec.clear(); siteNumbers.clear(); // if(numTax > 0) // stateVec.reserve(numTax); } static void SetStatics(int nt, int ns){ numTax = nt; maxNumStates = ns; } //bool PatternLessThan(const SitePattern &lhs, const SitePattern &rhs) const; bool operator==(const SitePattern &rhs) const; bool operator<(const SitePattern &rhs) const; void AddChar(const unsigned char c){ stateVec.push_back(c); } void SetCount(int c) { count = origCount = c; } int CalcPatternTypeAndNumStates(vector &stateCounts); int MinScore(set patt, int bound, unsigned char bits=15, int prevSc=0) const; }; //An alternate and several order of magnitude faster means of packing data. The functionality is really the //same as that in DataMatrix functions, it just uses better classes and STL sorting. To keep from changing lots of //code, even if this is used the results are copied back into their usual locations in DataMatrix. //Also need to keep around DataMatrix packing for certain types of data. //THIS DOES NOT CURRENTLY SUPPORT CONDITIONING PATTERNS, NOR IS IS CURRENTLY USED FOR NON-SEQUENCE DATA class PatternManager{ friend class DataMatrix; int numTax; int maxNumStates; int pman_numPatterns; //this is the CURRENT number of patterns, so will change during packing int pman_numRealSitesInOrigMatrix; int pman_numNonMissingRealCountsInOrigMatrix; int pman_numNonMissingChars; int pman_numMissingChars; int pman_numConstantChars; int pman_numInformativeChars; int pman_numUninformVariableChars; int lastConstant; bool compressed; //dense list patterns; list uniquePatterns; vector constStates; ~PatternManager(){ patterns.clear(); uniquePatterns.clear(); constStates.clear(); } virtual void NewCollapse(); virtual void NewPack(); virtual void NewSort(); virtual void NewDetermineConstantSites(); public: void Initialize(int nt, int max){ Reset(); numTax = nt; maxNumStates = max; SitePattern::maxNumStates = max; SitePattern::numTax = nt; } void Reset(){ numTax = maxNumStates = pman_numRealSitesInOrigMatrix = pman_numNonMissingChars = pman_numPatterns = pman_numMissingChars = pman_numConstantChars = pman_numInformativeChars = lastConstant = pman_numUninformVariableChars = 0; compressed = false; patterns.clear(); uniquePatterns.clear(); constStates.clear(); } void AddPattern(const SitePattern &add){ patterns.push_back(add); } //these are named along the lines of the old DataMatrix members int NChar() const { if(uniquePatterns.empty()) return -1; else return uniquePatterns.size(); } void ProcessPatterns(); void CalcPatternTypesAndNumStates(); //funcs for getting info back out of the patman into the datamatrix object void FillNumberVector(vector &nums) const; void FillTaxaXCharMatrix(unsigned char **mat) const; void FillNumStatesVector(vector &ns) const; void FillCountVector(vector &counts) const; void FillConstStatesVector(vector &cs) const; void FillIntegerValues(int &numMissingChars, int &numConstantChars, int &numVariableUninformChars, int &numInformativeChars, int &lastConstant, int &numRealSitesInOrigMatrix, int &numNonMissingRealCountsInOrigMatrix, int &totNChar, int &NChar) const; }; // Note: the class below has pure virtual member functions class DataMatrix{ protected: //This currently all becomes a bit of a nightmare when there are conditioning patterns included in the matrix, a la mkv. //Some of the below include those counts (numConstantChars, numPatterns), but many don't (anything with OrigMatrix in the name) int nTax; int nTaxAllocated; //allocate more than nTax to allow for the addition of dummy taxa //this will only be used during allocating and deallocation //if a dummy taxon is created then nTax will be incremented int numPatterns; //This is the size of the *CURRENT* *INTERNAL* representation of a datamatrix. //Thus, depending on when during the data pattern processing procedure, it may be //the same size as the true matrix, include zero count sites, or only consist of unique //patterns. After processing it will be the number of unique patterns that are //looped over in likelihood calculations, so is the most frequently used size value. //It DOES always include conditioning patterns. unsigned numConditioningPatterns; //Extra dummy characters added to the start of the matrix (currently all constant) //In terms of packing and processing, they aren't treated differently, and it is //REQUIRED that they will pack and appear as the first N characters in the matrix. //They are also the first N characters in the matrix BEFORE packing as well. int numRealSitesInOrigMatrix; //The actual number of columns in the data matrix read in, WITHOUT excluded chars //or conditioning patterns, but with all missing. This is mainly for outputting //things with reference to the orig matrix. int numNonMissingRealSitesInOrigMatrix; //as numRealSitesInOrigMatrix, with all missing columns removed int numNonMissingRealCountsInOrigMatrix; //The actual number of effective characters in the data matrix read in. //Will differ from numRealSitesInOrigMatrix in that all missing columns aren't //included, and because of any wtsets. Does not contain missing or conditioning patterns //This is critically used in bootstrap resampling. int nonZeroCharCount; //this is the number of character patterns that have non-zero //counts after bootstrap resampling. Zero count characters can //be avoided in the conditional likelihood calcs, but how this //is done varies depending on the context //only used when outputting something relative to input alignment int numMissingChars; int numConstantChars; int numInformativeChars; int numVariableUninformChars; int dense; //whether the data has been sorted and identical patterns combined unsigned char** matrix; PatternManager patman; int* count; int* origCounts; //maping of chars to columns. indeces are original char numbers, values are the packed column representing that char //both start at 0, so offset upon output int* number; /*in the partitioned context number maps the columns of the original partition subset to the columns of the compressed matrix. So, number[j] is the column of the packed matrix that represents column j of the partition subset. So, this may have no relationship to the original data matrix before the subsets were even made. origDataNumber then maps the columns of the uncompressed subset to the original full datamatrix. Thus, number[j] is the compressed column that represents uncompressed subset column j (many-to-one mapping) origDataNumber[j] is the column of the orignal matrix that corresponds to uncompressed subset column j (one-to-one mapping) example (zero offset): partition by codon position, so sub1 = {0, 3, 6, ...}, sub2 = {1, 4, 7, ...} and sub3 = {2, 5, 8, ...} each subset is its own datamatrix object, with its own number and origDataNumber arrays. so, sub1->number[0] is the column of the compressed sub1 matrix that represents the first column of sub1 (same for sub2 and sub3) sub1->number[1] is the column of the compressed sub2 matrix that represents the second column of sub2 (same for sub2 and sub3) sub1->origDataNumber[0] = 0 sub2->origDataNumber[0] = 1 sub1->origDataNumber[1] = 3 sub2->origDataNumber[1] = 4 etc. the values in number must the shuffled around as the matrix is compressed the values in origDataNumber are set when SetMatrix is called, and don't change thereafter */ int* origDataNumber; //These are new correlates to the old dynamicaly allocated arrays. They will be filled from //the pattern manager. vector newNumber; vector newNumStates; vector newCount; vector newOrigCounts; vector newConstStates; vector newTaxonLabel; char** taxonLabel; int lastConstant; int *constStates;//the state (or states) that a constant site contains unsigned char fullyAmbigChar; protected: int* numStates; int maxNumStates; bool useDefaultWeightsets; string wtsetName; bool usePatternManager; protected: char info[80]; virtual void SwapCharacters( int i, int j ); virtual int ComparePatterns( const int i, const int j ) const; void BSort( int byCounts = 0 ); void DebugSaveQSortState( int top, int bottom, int ii, int jj, int xx, const char* title ); void QSort( int top, int bottom ); void ReplaceTaxonLabel( int i, const char* s ); public: enum { PT_MISSING = 0x0000, PT_CONSTANT = 0x0001, PT_INFORMATIVE = 0x0002, PT_VARIABLE = 0x0004 }; public: DataMatrix() : dense(0), nTax(0), numPatterns(0), matrix(0), count(0), number(0), taxonLabel(0), numStates(0), numMissingChars(0), numConstantChars(0), numInformativeChars(0), numVariableUninformChars(0), lastConstant(-1), constStates(0), origCounts(0), fullyAmbigChar(15), useDefaultWeightsets(true), usePatternManager(false), nTaxAllocated(0), origDataNumber(0), numConditioningPatterns(0) { memset( info, 0x00, 80 ); } DataMatrix( int ntax, int nchar ) : nTax(ntax), numPatterns(nchar), dense(0), matrix(0), count(0), number(0), taxonLabel(0), numStates(0), numMissingChars(0), numConstantChars(0), numInformativeChars(0), numVariableUninformChars(0), lastConstant(-1), constStates(0), origCounts(0), fullyAmbigChar(15), useDefaultWeightsets(true), usePatternManager(false), nTaxAllocated(0), origDataNumber(0), numConditioningPatterns(0) { memset( info, 0x00, 80 ); NewMatrix(ntax, nchar); } virtual ~DataMatrix(); // pure virtual functions - must override in derived class virtual unsigned char CharToDatum( char ch ) const = 0; virtual unsigned char CharToBitwiseRepresentation( char ch ) const= 0; virtual char DatumToChar( unsigned char d ) const = 0; virtual unsigned char FirstState() const = 0; virtual unsigned char LastState() const = 0; virtual void CalcEmpiricalFreqs() = 0; // virtual FLOAT_TYPE Freq( unsigned char, int = 0) = 0; // virtual functions - can override in derived class virtual FLOAT_TYPE TransitionProb( int /*i*/, int /*j*/ , int /*site*/, FLOAT_TYPE /*brlen*/) { return 0.0; } virtual int NumStates(int j) const { return ( numStates && (j < numPatterns) ? numStates[j] : 0 ); } void SetUsePatternManager(bool tf) {usePatternManager = tf;} bool GetUsePatternManager() const {return usePatternManager;} void ProcessPatterns(); void OutputDataSummary() const; void GetDataFromPatternManager(); // functions for getting the data in and out int GetToken( istream& in, char* tokenbuf, int maxlen, bool acceptComments=true ); int GetToken( FILE *in, char* tokenbuf, int maxlen); int ReadPhylip( const char* filename); int ReadFasta( const char* filename); int Save( const char* filename, char* newfname = 0, char* nxsfname = 0 ); char* DataType() { return info; } int unsigned charToInt( unsigned char d ) const { return (int)d; } int NTax() const { return nTax; } void SetNTax(int ntax) { nTax = ntax; } virtual int NChar() const { return numPatterns; } int TotalNChar() const { return numNonMissingRealSitesInOrigMatrix; } int GapsIncludedNChar() const { return numRealSitesInOrigMatrix; } void SetNChar(int nchar) { numPatterns = nchar; } unsigned NumConditioningPatterns() const{return numConditioningPatterns;} int BootstrappedNChar() {return nonZeroCharCount;} void Flush() { NewMatrix( 0, 0 ); } int Dense() const { return dense; } //argument here is column number from uncompressed subset //return val is compressed pattern representing that column int Number(int j) const{ if(newNumber.size() > 0) return newNumber[j]; assert(j < numRealSitesInOrigMatrix + numConditioningPatterns); return number[j]; } //argument here is column number from uncompressed subset //return val is column from original full matrix before partitioning int OrigDataNumber(int j) const{ assert(j < numRealSitesInOrigMatrix + numConditioningPatterns); return origDataNumber[j]; } virtual int Count(int j) const{ if(newCount.size() > 0) return newCount[j]; assert(j < numPatterns); return count[j]; } virtual int CountByOrigIndex(int j) const{ if(newCount.size() > 0) if(newNumber.size() > 0){ assert(newCount.size() > j); return newCount[newNumber[j]]; } assert(j < numRealSitesInOrigMatrix + numConditioningPatterns); return count[number[j]]; } virtual const int *GetCounts() const { if(newCount.size() > 0) return &(newCount[0]); return count; } const int *GetConstStates() const { if(newConstStates.size() > 0) return &(newConstStates[0]); return constStates; } void SetCount(int j, int c){ if(newCount.size() > 0){ assert(newCount.size() > j); newCount[j] = c; } else if( count && (j < numPatterns) ) count[j] = c; } void SetNumStates(int j, int c){ if( numStates && (j < numPatterns) ) numStates[j] = c; } const char* TaxonLabel(int i) const{ return ( taxonLabel && (i < nTax) ? taxonLabel[i] : 0 ); } void SetTaxonLabel(int i, const char* s); int TaxonNameToNumber(const NxsString &name) const; void CopyNamesFromOtherMatrix(const DataMatrix *dat){ assert(taxonLabel); for(int t=0;tTaxonLabel(t)); } void BeginNexusTreesBlock(ofstream &treeout) const; void BeginNexusTreesBlock(string &trans) const; virtual void CreateMatrixFromNCL(const NxsCharactersBlock *, NxsUnsignedSet &charset) = 0; virtual unsigned char Matrix( int i, int j ) const { assert( matrix ); assert( i >= 0 ); assert( i < nTax ); assert( j >= 0 ); assert( j < numPatterns ); return (unsigned char)matrix[i][j]; } unsigned char *GetRow( int i) const { assert( matrix ); assert( i >= 0 ); assert( i < nTax ); return matrix[i]; } virtual void SetMatrix( int i, int j, unsigned char c){ if(matrix && (i < nTax) && (j < numPatterns)) matrix[i][j] = c; } void SetOriginalDataNumber(const int subsetMatColumn, const int origMatColumn){ origDataNumber[subsetMatColumn] = origMatColumn; } int MatrixExists() const { return ( matrix && nTax>0 && numPatterns>0 ? 1 : 0 ); } int NMissing() const { return numMissingChars; } int NConstant() const { return numConstantChars; } int LastConstant() const {return lastConstant;} int NInformative() const { return numInformativeChars; } int NVarUninform() const { return numVariableUninformChars; } DataMatrix& operator =(const DataMatrix&); void Sort( int byCounts = 0 ){ byCounts; QSort( 0, NChar()-1 ); } virtual int PatternType( int , unsigned int *) const; // returns PT_XXXX constant indicating type of pattern void Summarize(); // fills in numConstantChars, numInformativeChars, and numVariableUninformChars data members virtual void Collapse(); void EliminateAdjacentIdenticalColumns(); virtual void Pack(); void NewMatrix(int nt, int nc); // flushes old matrix, creates new one void ResizeCharacterNumberDependentVariables(int nCh); int PositionOf( char* s ) const; // returns pos (0..nTax-1) of taxon named s void DumpCounts( const char* s ); void WriteCollapsedData(); //DZ void SaveNexus(const char* filename, int iosFlags /* = 0 */); //DZ virtual void DetermineConstantSites(); void ExplicitDestructor(); // cjb - totally clear the DataMatrix and revert it to its original state as if it was just constructed void CheckForIdenticalTaxonNames(); bool DidUseDefaultWeightsets() const {return (wtsetName.length() > 0);} string WeightsetName() const { return wtsetName;} //for determining parsimony informative chars int MinScore(set patt, int bound, unsigned char bits=15, int sc=0) const; void GetStringOfOrigDataColumns(string &str) const; public: void ReserveOriginalCounts(){ if(usePatternManager == false){ if(origCounts == NULL) origCounts = new int[numPatterns]; } else assert(newOrigCounts.size() == 0); for(int i=0;i 0){ assert(newCount.size() > i); newOrigCounts.push_back(newCount[i]); } else origCounts[i] = count[i]; } } void RestoreOriginalCounts(){ if(origCounts == NULL) return; for(int i=0;i 0){ assert(newCount.size() > i); newCount[i] = newOrigCounts[i]; } else count[i] = origCounts[i]; } } void Reweight(FLOAT_TYPE prob); virtual int BootstrapReweight(int seedToUse, FLOAT_TYPE resampleProportion); void CountMissingCharsByColumn(vector &vec); void MakeWeightSetString(NxsCharactersBlock &charblock, string &wtstring, string name); void MakeWeightSetString(std::string &wtstring, string name); }; #endif garli-2.1-release/src/defs.h000066400000000000000000000077511241236125200157610ustar00rootroot00000000000000// GARLI version 2.1 source code // Copyright 2005-2014 Derrick J. Zwickl // email: garli.support@gmail.com // // This program is free software: you can redistribute it and/or modify // it under the terms of the GNU General Public License as published by // the Free Software Foundation, either version 3 of the License, or // (at your option) any later version. // // This program is distributed in the hope that it will be useful, // but WITHOUT ANY WARRANTY; without even the implied warranty of // MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the // GNU General Public License for more details. // // You should have received a copy of the GNU General Public License // along with this program. If not, see . // #ifndef DEFS #define DEFS #if defined(HAVE_CONFIG_H) #include "config.h" #endif //these will be defined by either the Microsoft compiler //or the intel compiler when openmp support is turned on //by compiling with /openmp (ms) or -openmp (icc) //Nothing else should need to be defined anywhere to get //openMP working #if defined (__OPENMP) || defined (_OPENMP) #include "omp.h" #define OPEN_MP #define OMP_INTINTCLA #define OMP_INTTERMCLA #define OMP_TERMDERIV #define OMP_INTDERIV #define OMP_INTINTCLA_NSTATE #define OMP_INTTERMCLA_NSTATE #define OMP_TERMDERIV_NSTATE #define OMP_INTDERIV_NSTATE #define OMP_INTSCORE_NSTATE #define OMP_TERMSCORE_NSTATE #endif /* #ifndef NDEBUG #undef NDEBUG #endif */ #define USE_COUNTS_IN_BOOT //#undef OPT_DEBUG #define ONE_BRANCH_INS_DEL //The ONLY thing that should need to be done to turn on memcheck leak detection //should be defining MONITORING_ALLOCATION here #undef MONITORING_ALLOCATION #include "memchk.h" #define ADAPTIVE_BOUNDED_OPT #define ALT_NR_BAIL #define PUSH_TO_MIN_BLEN #define SUM_AA_REL_RATES #define NEW_BUMPING #define STOCHASTIC_STARTING_BLENS #undef IGNORE_SMALL_TOPO_IMP #undef INCLUDE_PERTURBATION #undef SUBTREE_VERSION //#undef ENABLE_CUSTOM_PROFILER //#undef SINGLE_PRECISION_FLOATS //#undef SWAP_BASED_TERMINATION //#undef OUTPUT_UNIQUE_TREES #undef VARIABLE_OPTIMIZATION #undef INPUT_RECOMBINATION #define NUM_INPUT 12 //#undef ALLOW_SINGLE_SITE #undef EQUIV_CALCS typedef double MODEL_FLOAT; #ifdef SINGLE_PRECISION_FLOATS typedef float FLOAT_TYPE; #define ONE_POINT_ZERO 1.0f #define ZERO_POINT_FIVE 0.5f #define ZERO_POINT_ZERO 0.0f #define DEF_MIN_BRLEN 1e-8f #define DEF_MAX_BRLEN 100.0f #define DEF_STARTING_BRLEN 0.05f #define GARLI_FP_EPS FLT_EPSILON #define LUMP_LIKES #if !defined(LUMP_FREQ) #define LUMP_FREQ 400 #endif #else typedef double FLOAT_TYPE; #define ONE_POINT_ZERO 1.0 #define ZERO_POINT_FIVE 0.5 #define ZERO_POINT_ZERO 0.0 #define DEF_MIN_BRLEN 1e-8 #define DEF_MAX_BRLEN 100.0 #define DEF_STARTING_BRLEN 0.05 #define GARLI_FP_EPS DBL_EPSILON #if !defined(LUMP_FREQ) #define LUMP_FREQ 400 #endif #endif #define MAXPATH 256 #define DEF_PRECISION 8 #define MEM_DELETE_ARRAY(v) { delete [] v; v=NULL; } #define MEM_NEW_ARRAY(a,t,n) { a = new t[n]; } #ifdef BOINC #define WRITE_TO_FILE(ptr, size, count) write((void *) ptr, (size_t) size, (size_t) count) #define OUTPUT_CLASS MFILE #include "boinc_api.h" #include "filesys.h" #ifdef _WIN32 #include "boinc_win.h" #else #include "config.h" #endif #else #define WRITE_TO_FILE(ptr, size, count) write((const char *) ptr, (streamsize) size*count) #define OUTPUT_CLASS ofstream #endif //mpi message tags #ifdef MPI_VERSION #define TAG_PARAMS_SIZE 1 #define TAG_PARAMS 2 #define TAG_DATA_SIZE 3 #define TAG_DATA 4 #define TAG_TREE_STRINGS_COUNT 5 #define TAG_TREE_STRINGS_SIZE 6 #define TAG_TREE_STRINGS 7 #define TAG_CONFIG 8 #define TAG_QUIT 9 #define TAG_KAPPAS 10 #define TAG_NINDIVS 11 #define TAG_ACCEPT_COUNT 12 #define TAG_TREE_STRINGS_REQUEST 13 #define TAG_SCORE 14 #define TAG_PIS 15 #define TAG_MODEL 16 #define TAG_REMOTE_TYPE_SWITCH 17 #define TAG_SUBTREE_DEFINE 18 #define TAG_SUBTREE_ITERATION 19 #define TAG_PERTURB 20 #endif #endif garli-2.1-release/src/errorexception.h000066400000000000000000000060641241236125200201040ustar00rootroot00000000000000// GARLI version 1.00 source code // Copyright 2005-2010 Derrick J. Zwickl // email: garli.support@gmail.com // // This program is free software: you can redistribute it and/or modify // it under the terms of the GNU General Public License as published by // the Free Software Foundation, either version 3 of the License, or // (at your option) any later version. // // This program is distributed in the hope that it will be useful, // but WITHOUT ANY WARRANTY; without even the implied warranty of // MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the // GNU General Public License for more details. // // You should have received a copy of the GNU General Public License // along with this program. If not, see . #ifndef ERROREXCEPTION #define ERROREXCEPTION #include #include #include #include #include "outputman.h" using namespace std; extern OutputManager outman; #define BUFFER_LENGTH 500 class ErrorException{ public: char *message; size_t messlen; //char message[5000]; //char message[400]; ErrorException(){ message = NULL; } /* ErrorException(const char *fmt, ...){ message = new char[500]; va_list vl; va_start(vl, fmt); vsprintf(message, fmt, vl); assert(strlen(message) < 5000); va_end(vl); } */ ErrorException(const ErrorException &other){ messlen = strlen(other.message); message = new char[messlen + 1]; strcpy(message, other.message); } ErrorException(const char *fmt, ...){ messlen = BUFFER_LENGTH; message = new char[messlen]; va_list vl; va_start(vl, fmt); int len = vsnprintf(message, messlen, fmt, vl); va_end(vl); if((len > -1 && len < messlen) == false){//default buffer is not long enough or there was an error delete []message; message = NULL; //char *longmessage = NULL; if(len > -1){//on unix systems vsnprintf returns the required length. There is some //some ambiguity about whether it includes the null termination or not, but //the number passed to vsnprintf should definitely include it. message = new char[len+2]; va_start(vl, fmt); vsnprintf(message, len+1, fmt, vl); va_end(vl); } else{ #if defined(_MSC_VER) //on windows a negative value means that the length wasn't engough messlen = BUFFER_LENGTH * 2; message = new char[messlen+1]; va_start(vl, fmt); while(vsnprintf(message, messlen, fmt, vl) < 0){ delete []message; messlen *= 2; message = new char[messlen+1]; va_end(vl); va_start(vl, fmt); } va_end(vl); #else //otherwise negative means a formatting error sprintf(message, "(problem formatting some program output...)"); return; #endif } //message = longmessage; } } ~ErrorException(){ delete []message; } void Print(ostream &out){ outman.UserMessage("ERROR!: %s\n\n", message); //out << "ERROR!: " << message << endl << endl; } void Print(FILE *out){ fprintf(out, "ERROR!: %s\n\n", message); } }; class UnscoreableException{ public: UnscoreableException(){}; }; #endif garli-2.1-release/src/funcs.cpp000066400000000000000000000772201241236125200165070ustar00rootroot00000000000000// GARLI version 0.96b8 source code // Copyright 2005-2008 Derrick J. Zwickl // email: garli.support@gmail.com // // This program is free software: you can redistribute it and/or modify // it under the terms of the GNU General Public License as published by // the Free Software Foundation, either version 3 of the License, or // (at your option) any later version. // // This program is distributed in the hope that it will be useful, // but WITHOUT ANY WARRANTY; without even the implied warranty of // MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the // GNU General Public License for more details. // // You should have received a copy of the GNU General Public License // along with this program. If not, see . // // NOTE: Portions of this source adapted from: // Press, W. H., B. P. Flannery, S. A. Teukolsky, and W. T. Vetterling. 1992. // Numerical Recipes in C : The Art of Scientific Computing. Cambridge University Press, Cambridge. #if defined(_MSC_VER) //POL 23-Feb-2006 VC doesn't have this header, and it was not needed to compile //# include #else # include #endif #include "defs.h" #include "funcs.h" #include "population.h" #include "tree.h" #include "outputman.h" #include "garlireader.h" extern OutputManager outman; #undef ROOT_OPT #define FOURTH_ROOT #define LOG_MIN_BRLEN log(min_brlen) bool FloatingPointEquals(const FLOAT_TYPE first, const FLOAT_TYPE sec, const FLOAT_TYPE epsilon){ FLOAT_TYPE diff = fabs(first - sec); return (diff < epsilon); } //this is for sticking info about what is defined into log files, for later checking void OutputImportantDefines(){ outman.DebugMessage("#####\nThe following are/are not defined:"); #ifdef RESCALE_ARRAY_LENGTH outman.DebugMessage("RESCALE_ARRAY_LENGTH = %d", RESCALE_ARRAY_LENGTH); #endif outman.DebugMessageNoCR("LUMP_LIKES : "); #ifdef LUMP_LIKES outman.DebugMessage("%d", LUMP_FREQ); #else outman.DebugMessage("no"); #endif #ifdef DEBUG_SCORES outman.DebugMessage("DEBUG_SCORES"); #endif #ifdef OPT_DEBUG outman.DebugMessage("OPT_DEBUG"); #endif #ifdef VARIABLE_OPTIMIZATION outman.DebugMessage("VARIABLE_OPTIMIZATION"); #endif #ifdef NO_EVOLUTION outman.DebugMessage("NO_EVOLUTION"); #endif #ifdef SWAP_BASED_TERMINATION outman.DebugMessage("SWAP_BASED_TERMINATION"); #endif #ifdef ADAPTIVE_BOUNDED_OPT outman.DebugMessage("ADAPTIVE_BOUNDED_OPT = yes"); #else outman.DebugMessage("ADAPTIVE_BOUNDED_OPT = no"); #endif #ifdef PUSH_TO_MIN_BLEN outman.DebugMessage("PUSH_TO_MIN_BLEN = yes"); #else outman.DebugMessage("PUSH_TO_MIN_BLEN = no"); #endif #ifdef DEBUG_MESSAGES outman.DebugMessage("DEBUG_MESSAGES = yes"); #else outman.DebugMessage("DEBUG_MESSAGES = no"); #endif outman.DebugMessage("#####\n"); } #ifdef BROOK_GPU #include //#include //using namespace brook::internal; void BranchLike2 (::brook::stream des, ::brook::stream res, ::brook::stream pmat); void SecondBranchLike (::brook::stream des, ::brook::stream part, ::brook::stream res, ::brook::stream pmat); void Product4 (::brook::stream des1, ::brook::stream des2, ::brook::stream res); brook::stream LCLstream(brook::getStreamType(( float4 *)0), 890, -1); brook::stream RCLstream(brook::getStreamType(( float4 *)0), 890, -1); brook::stream deststream(brook::getStreamType(( float4 *)0), 890, -1); // brook::stream tempstream(brook::getStreamType(( float4 *)0), 890, -1); // brook::stream tempstream2(brook::getStreamType(( float4 *)0), 890, -1); brook::stream Lprstream(brook::getStreamType(( float *)0), 16, -1); brook::stream Rprstream(brook::getStreamType(( float *)0), 16, -1); #endif //a variety of functions that don't belong to any class #if defined(SINGLE_PRECISION_FLOATS) && (!defined(_MSC_VER)) || (defined(BOINC) && defined (_WIN32)) //Overloaded versions of min and max that take different types for the two arguments //This should not be used in hot code when possible, and conditional comp should //be used to make two different versions of the code float min(const double first, const float second) {return min((float) first, second);} float min(const float first, const double second) {return min(first, (float) second);} float max(const double first, const float second) {return max((float) first, second);} float max(const float first, const double second) {return max(first, (float) second);} #endif int FileExists( const char* s ) { #ifdef POWERMAC_VERSION // cjb if( s && access( s, 0 ) == 0 ) return 1; #else if (s) { ifstream test(s); if(test.good()) { test.close(); return 1; } } #endif return 0; } /* // GetRestartParams extracts the following pieces of information from // the first line of the state file: // 1. the last generation done on previous run (prev_generations) // - we want to start this run with generation prev_generations // 2. the time recorded after the last generation was completed (prev_time) // - we'll start counting seconds with prev_time rather than zero // 3. the random number seed with which to begin the run (randomSeed) // - we thus start exactly where we left off before // void GetRestartParams( Parameters& params ) { if( !FileExists( params.statefname ) ) throw ErrorException("Error opening state file: %s", params.statefname); ifstream sf( params.statefname ); sf >> params.prev_generations >> params.prev_time >> params.randomSeed; sf.close(); rnd.set_seed( params.randomSeed ); } */ int GetToken( FILE *in, char* tokenbuf, int maxlen){ int ok = 1; int i; char ch = ' '; // skip leading whitespace while( in && ( isspace(ch) || ch == '[' ) ){ ch = getc(in); } if( !in ) return 0; tokenbuf[0] = ch; tokenbuf[1] = '\0'; tokenbuf[maxlen-1] = '\0'; for( i = 1; i < maxlen-1; i++ ) { ch = getc(in); if( isspace(ch) || ch == ']' ) break; tokenbuf[i] = ch; tokenbuf[i+1] = '\0'; } if( i >= maxlen-1 ) ok = 0; return ok; } bool FileIsNexus(const char *name){ if (!FileExists(name)) { throw ErrorException("could not open file: %s!", name); } bool nexus = false; FILE *inf; #ifdef BOINC inf = boinc_fopen(name, "r"); #else inf = fopen(name, "r"); #endif char buf[1024]; GetToken(inf, buf, 1024); if(!(_stricmp(buf, "#NEXUS"))) nexus = true; fclose(inf); return nexus; } bool FileIsFasta(const char *name){ if (!FileExists(name)) { throw ErrorException("could not open file: %s!", name); } bool fasta = false; FILE *inf; #ifdef BOINC inf = boinc_fopen(name, "r"); #else inf = fopen(name, "r"); #endif char buf[1024]; GetToken(inf, buf, 1024); if(buf[0] == '>') fasta = true; fclose(inf); return fasta; } /* //the ReadData within the GarliReader should now be used to read all data files bool ReadData(const char* filename) { bool usedNCL = false; if (!FileExists(filename)) { throw ErrorException("data file not found: %s!", filename); } if(FileIsNexus(filename)){ outman.UserMessage("Attempting to read data file in Nexus format (using NCL): %s ...", filename); GarliReader &reader = GarliReader::GetInstance(); #ifdef FACTORY reader.ReadFilepath(filename, MultiFormatReader::NEXUS_FORMAT); #else int err = reader.HandleExecute(filename, true); if(err) throw ErrorException("Problem reading nexus datafile"); #endif //moving error checking and finding of correct char block into individual CreateMatrix functions // NxsCharactersBlock *chars = reader.GetCharactersBlock(); // if(modSpec.IsAminoAcid() && modSpec.IsCodonAminoAcid()==false && chars->GetDataType() != NxsCharactersBlock::protein) // throw ErrorException("protein data specified, but nexus file does not contain protein data!"); //FACTORY // data->CreateMatrixFromNCL(reader); usedNCL = true; } else if(1) assert(0); /* else if(FileIsFasta(filename)){ outman.UserMessage("Attempting to read data file in Fasta format: %s ...", filename); data->ReadFasta(filename); } else{ outman.UserMessage("Attempting to read data file in Phylip format: %s ...", filename); data->ReadPhylip(filename); } */ /* if(modSpec.IsCodon()){ assert(0); if(modSpec.IsVertMitoCode()){ static_cast(data)->SetVertMitoCode(); } static_cast(data)->FillCodonMatrix(false); } else if(modSpec.IsCodonAminoAcid()){ assert(0); if(modSpec.IsVertMitoCode()){ static_cast(data)->SetVertMitoCode(); } static_cast(data)->SetAminoAcid(); static_cast(data)->FillCodonMatrix(true); } */ /* // report summary statistics about the data data->Summarize(); outman.UserMessage("\nData summary:"); outman.UserMessage(" %d taxa", data->NTax()); outman.UserMessage(" %d total characters.", data->NChar()); outman.UserMessage(" %d constant characters.", data->NConstant()); outman.UserMessage(" %d parsimony-informative characters.", data->NInformative()); outman.UserMessage(" %d autapomorphic characters.", data->NAutapomorphic()); //int total = data->NConstant() + data->NInformative() + data->NAutapomorphic(); outman.flush(); //if(modSpec.IsNucleotide()){ if(1){ // try to compress if (!data->Dense()) { outman.UserMessage("Compressing data matrix..."); data->Collapse(); outman.UserMessage("%d columns in data matrix after compression.", data->NChar()); } else { outman.UserMessage("Datafile already compressed."); outman.UserMessage("%d columns in compressed data matrix.\n", data->NChar()); } if(modSpec.IsNucleotide()){ data->DetermineConstantSites(); // if(!data->Dense()) data->Save(filename, "new"); } else if(modSpec.IsAminoAcid()){ // static_cast(data)->DetermineConstantAASites(); } } */ /* return usedNCL; } */ int ReadData(GeneralGamlConfig *conf, NucleotideData* data) { assert(0); // regurgitate params specified /* if( params.restart ) { outman.UserMessage("Restarting using state file \"%s\"", params.statefname); GetRestartParams( const_cast(params) ); outman.UserMessage("random number seed set to %d", params.randomSeed); outman.UserMessage("last generation from previous run was %d", params.prev_generations); outman.UserMessage("starting with previous elapsed time, which was %d seconds", params.prev_time); } */ // const_cast(params).BriefReport( cout ); // outman.UserMessage(""); // Check to be sure data file exists // /* if( !FileExists( conf->datafname.c_str() ) ) throw ErrorException("data file does not exist: %s", conf->datafname.c_str()); // Read in the data matrix outman.flush(); outman.UserMessage("Reading data file %s...", conf->datafname.c_str()); data->Read( conf->datafname.c_str() ); // report summary statistics about data data->Summarize(); outman.UserMessage(" %d constant characters.", data->NConstant()); outman.UserMessage(" %d parsimony-informative characters.", data->NInformative()); outman.UserMessage(" %d autapomorphic characters.", data->NAutapomorphic()); int total = data->NConstant() + data->NInformative() + data->NAutapomorphic(); outman.UserMessage(" %d total characters.", total); outman.flush(); //DZ Only compress and write data to file if dense=0 (data is not already compressed) if(!(data->Dense())){ outman.UserMessage("Compressing data file..."); data->Collapse(); data->Save("compdata.nex", "new"); outman.UserMessage(" %d columns in data matrix after compression", data->NChar()); } else outman.UserMessage("Datafile already compressed.\n %d columns in compressed data matrix", data->NChar()); data->DetermineConstantSites(); outman.UserMessage(""); outman.flush(); */ return 0; } int RandomInt(int lb, int ub) { return lb + rand() % (ub-lb+1); } FLOAT_TYPE RandomFrac() { return (FLOAT_TYPE) (rand() / RAND_MAX); } FLOAT_TYPE RandomDouble(FLOAT_TYPE lb, FLOAT_TYPE ub) { return lb + RandomFrac() * (ub - lb); } //Bracketing func from Numerical Recipies //attempts to use parabolic fit to bracket, otherwise //uses golden section #define GOLD 1.618034 #define CGOLD 0.3819660 #define ZEPS 1.0e-10 #define GLIMIT 100.0 //#define GLIMIT 1.6 #define ITMAX 50 #define TINY 1.0e-20 #define SHFT(a,b,c,d) a=b;b=c;c=d; #define SIGN(a,b) ((b)>ZERO_POINT_ZERO ? fabs(a) : -fabs(a)) #define FMAX(a,b) ((a)>(b) ? (a):(b)) //This version takes a node pointer and optimizes blens int mnbrak(FLOAT_TYPE *ax, FLOAT_TYPE *bx, FLOAT_TYPE *cx, FLOAT_TYPE *fa, FLOAT_TYPE *fb, FLOAT_TYPE *fc, FLOAT_TYPE (*func)(TreeNode*, Tree*, FLOAT_TYPE), TreeNode *thisnode, Tree *thistree){ FLOAT_TYPE ulim, u, r, q, fu; // ofstream brak("brakdebug.log", ios::app); // brak.precision(10); // brak << "node " << thisnode->nodeNum << "\n"; *fa=(*func)(thisnode, thistree, *ax); *fb=(*func)(thisnode, thistree, *bx); *fc=(*func)(thisnode, thistree, *cx); //hopefully we passsed in a good bracket. If so, get out. if(*fb < *fa && *fb < *fc) return 0; /* if(*fb > *fa){ SHFT(dum, *ax, *bx, dum) SHFT(dum, *fb, *fa, dum) } *cx=(*bx)+GOLD*(*bx-*ax); *cx = (*cx > min_brlen ? (*cx < DEF_MAX_BRLEN ? *cx : DEF_MAX_BRLEN) : min_brlen); *fc=(*func)(thisnode, thistree, *cx); */ while(*fb>*fc){ r=(*bx-*ax)*(*fb-*fc); q=(*bx-*cx)*(*fb-*fa); u=(*bx)-((*bx-*cx)*q-(*bx-*ax)*r)/(FLOAT_TYPE)(2.0*SIGN(FMAX(fabs(q-r),TINY), q-r)); u = (FLOAT_TYPE)(u > DEF_MIN_BRLEN ? (u < DEF_MAX_BRLEN ? u : DEF_MAX_BRLEN) : DEF_MIN_BRLEN); ulim=(FLOAT_TYPE)((*bx)+GLIMIT*(*cx-*bx)); if((*bx-u)*(u-*cx)>ZERO_POINT_ZERO){ fu=(*func)(thisnode, thistree, u); if(fu < *fc){ *ax=*bx; *bx=u; *fa=*fb; *fb=fu; return 0; } else if(fu > *fb){ *cx=u; *fc=fu; return 0; } u=(FLOAT_TYPE)((*cx)+GOLD*(*cx-*bx)); //DZ 10/27/03 don't let this evaluate totally insane blens /* if(u>=.69){ //=ln(2) if(max(*ax, max(*bx, *cx)) < .69) u=.69; else{ u=max(*ax, max(*bx, *cx)) + .69; } limited=true; } */ fu=(*func)(thisnode, thistree, u); } else if((*cx-u)*(u-ulim)>ZERO_POINT_ZERO){ //DZ 10/27/03 don't let this evaluate totally insane blens /* if(u>=.69){ //=ln(2) if(max(*ax, max(*bx, *cx)) < .69) u=.69; else{ u=max(*ax, max(*bx, *cx)) + .69; } limited=true; } */ fu=(*func)(thisnode, thistree, u); if(fu <*fc){ SHFT(*bx, *cx, u, *cx+(FLOAT_TYPE)GOLD*(*cx-*bx)); SHFT(*fb, *fc, fu, (*func)(thisnode, thistree, u)); } } else if((u-ulim)*(ulim-*cx) >ZERO_POINT_ZERO){ u=ulim; //DZ 10/27/03 don't let this evaluate totally insane blens /* if(u>=.69){ //=ln(2) if(max(*ax, max(*bx, *cx)) < .69) u=.69; else{ u=max(*ax, max(*bx, *cx)) + .69; } limited=true; } */ fu=(*func)(thisnode, thistree, u); } else{ u=(*cx)+(FLOAT_TYPE)GOLD*(*cx-*bx); fu=(*func)(thisnode, thistree, u); } SHFT(*ax, *bx, *cx, u) SHFT(*fa, *fb, *fc, fu) /* if(((*ax < -18.42) && (*bx < -18.42)) || ((*ax<-10) && (*bx<-10) && (*cx>1))){ //DZ 12-18-03 if our three best points are all < ln(1e-8), just give up and take that as a blen //the MLE is probably essentially 0. Note that sometimes when ax and bx are very small this //func tries very large values for cx, which I think is a bug. This hack also avoids that return 1; } */ } return 0; } //This version takes a node pointer and optimizes blens FLOAT_TYPE brent(FLOAT_TYPE ax, FLOAT_TYPE bx, FLOAT_TYPE cx, FLOAT_TYPE (*f)(TreeNode *, Tree*, FLOAT_TYPE), FLOAT_TYPE tol, FLOAT_TYPE *xmin, TreeNode *thisnode, Tree *thistree){ int iter; FLOAT_TYPE a, b, d, etemp, fu, fv, fw, fx, p, q, r, tol1, tol2, u, v, w, x, xm; FLOAT_TYPE e=ZERO_POINT_ZERO; a=(ax < cx ? ax : cx); //make a the smallest of the three bracket points b=(ax > cx ? ax : cx); //and b the largest x=w=v=bx; //make x the current minimum, as well as w and v fw=fv=fx=(*f)(thisnode, thistree, x); for(iter=1;iter<=ITMAX;iter++){ xm=ZERO_POINT_FIVE*(a+b); //xm is the midpoint of the bracket (of a and b) tol2=(FLOAT_TYPE)(2.0*(tol1=(FLOAT_TYPE)(tol*fabs(x)+ZEPS))); if (fabs(x-xm) <= (tol2-ZERO_POINT_FIVE*(b-a))){ //termination condition *xmin=x; //if the distance between x and bracket mean is < return fx; } if (fabs(e) > tol1){ //construct a trial parabolic fit r=(x-w)*(fx-fv); q=(x-v)*(fx-fw); p=(x-v)*q-(x-w)*r; q=(FLOAT_TYPE)(2.0*(q-r)); if(q>ZERO_POINT_ZERO) p=-p; q=fabs(q); etemp=e; e=d; if(fabs(p) >= fabs(ZERO_POINT_FIVE*q*etemp)||p<=q*(a-x) || p>=q*(b-x)) //determine if the parabolic fit is good d=(FLOAT_TYPE)(CGOLD*(e=(x>=xm?a-x:b-x))); //if not else{ //if so, take the parabolic step d=p/q; u=x+d; if(u-a < tol2||b-u=xm?a-x:b-x))); //e is the distance moved in the step before last } //d is golden section of that (.38.... times) u=(fabs(d) >= tol1 ? x+d : x+SIGN(tol1,d));//u is the next point to be evaluated //it is x+d or ? fu=(*f)(thisnode, thistree, u); if(fu<=fx){ //if our new try at u is better than the previous min at x if(u>=x) a=x; else b=x; //if u is > x, x becomes a, otherwise it becomes b SHFT(v,w,x,u); //w becomes v, x becomes w and u becomes x SHFT(fv,fw,fx,fu); } else{ //if our new try at u is worse than the old min at x if(u*worstOuterL){//the min must be to the left (or maybe between) of our evals, so don't bother //evaluating the bestOuter we passed in, which we know is to the right. Either evaluate the min, //or if worstOuter already is the the min evaluate a point between the current evals if(*worstOuter==effectiveMin){ SHFT(dum, *bestOuter, *worstOuter, *mid) SHFT(dum, *bestOuterL, *worstOuterL, *midL) *mid=(FLOAT_TYPE)((*worstOuter+*bestOuter)*ZERO_POINT_FIVE); *midL=(*func)(thisnode, thistree, *mid, true); if(*bestOuterL < *midL && !(*mid > sweetspot)) return 1; } else if(!(*worstOuter > sweetspot)){ SHFT(dum, *mid, *worstOuter, dum) SHFT(dum, *midL, *worstOuterL, dum) *bestOuter=(FLOAT_TYPE)effectiveMin; *bestOuterL=(*func)(thisnode, thistree, *bestOuter, true); if(*bestOuterL < *midL && !(*mid > sweetspot)) return 1; } else{ *bestOuter=(FLOAT_TYPE)(sweetspot-.02); possibleZeroMLE=true; SHFT(dum, *worstOuter, *mid, dum) SHFT(dum, *worstOuterL, *midL, dum) *bestOuterL=(*func)(thisnode, thistree, *bestOuter, true); } } else *bestOuterL=(*func)(thisnode, thistree, *bestOuter, true); /*There are a pretty limited number of cases for each loop here case 1: We have three points that define a bracket -> exit with 0 case 2: We have three points with sucessively better scores to the right 2a: The curvature implied by the three points is convex or only slightly concave which makes the parabolic estimate very poor. -> Do a GOLD step. 2b: The parabolic estimate is between mid and bestOuter -> take parabolic step 2c: The parabolic estimate is to the right of bestOuter -> ? case 3: We have three points with sucessively better scores to the left 3a: The best score is at the minimum allowed value, suggesting a possible zero MLE 3a1: Parabolic estimate is between mid and minimum value. ?? 3a2: Parabolic estimate is < minimum -> return zeroMLE=true 3b: The parabolic estimate is between mid and bestOuter -> take parabolic step 3c: The parabolic estimate is to the left of bestOuter -> take parabolic step */ if(*worstOuterL < *bestOuterL){ SHFT(dum, *worstOuter, *bestOuter, dum) SHFT(dum, *worstOuterL, *bestOuterL, dum) } do{ if(*midL < *worstOuterL && *midL < *bestOuterL){//case 1, got a bracket if(*bestOuter==effectiveMin && (*worstOuter - *mid)> .2){ //nextTry=(*mid+*worstOuter)*.5; nextTry=(FLOAT_TYPE)0.16; nextTryL=(*func)(thisnode, thistree, nextTry, true); assert(nextTryL < *worstOuterL); if(nextTryL < *midL){ SHFT(*bestOuter, *mid, nextTry, dum) SHFT(*bestOuterL, *midL, nextTryL, dum) } else if(nextTryL < *bestOuterL){ SHFT(*worstOuter, *bestOuter, nextTry, dum) SHFT(*worstOuterL, *bestOuterL, nextTryL, dum) } else{ *worstOuter=nextTry; *worstOuterL=nextTryL; } } return 0; } else{ FLOAT_TYPE diffMidBestL=(*midL-*bestOuterL); FLOAT_TYPE diffMidBest=(*mid-*bestOuter); FLOAT_TYPE diffMidWorstL=(*worstOuterL-*midL); FLOAT_TYPE diffMidWorst=(*worstOuter-*mid); if(*worstOuter < *bestOuter){ //case 2 //check the curvature FLOAT_TYPE slopeRatio=(diffMidBestL/diffMidBest) / (diffMidWorstL/diffMidWorst); if(slopeRatio > 0.9){ nextTry=(FLOAT_TYPE)((*bestOuter)+2.0*(*bestOuter-*mid));//case 2a } else{ //case 2b and 2c r=diffMidWorst*diffMidBestL; q=diffMidBest*diffMidWorstL; nextTry=(FLOAT_TYPE)((*mid)-(diffMidBest*q-(*mid-*worstOuter)*r)/(2.0*SIGN(FMAX(fabs(q-r),TINY), q-r))); if(/*nextTry > *bestOuter && */fabs(nextTry-*bestOuter) < smallShift){ //if the parabolic estimate is very near our current best it tends to take //a while to get the bracket, so just push it a little further to the right nextTry += (FLOAT_TYPE)smallShift; } } } else if(*worstOuter > *bestOuter){ //case 3 r=diffMidWorst*diffMidBestL; q=diffMidBest*diffMidWorstL; nextTry=(FLOAT_TYPE)((*mid)-(diffMidBest*q-diffMidWorst*r)/(2.0*SIGN(FMAX(fabs(q-r),TINY), q-r))); if(*bestOuter==effectiveMin){ if(nextTry < effectiveMin || *mid < sweetspot || possibleZeroMLE) return 1; //case 3a2 else {//case 3a1 //just go with the parabolic } } else { if(possibleZeroMLE==true) nextTry=(FLOAT_TYPE)effectiveMin; } } } assert(nextTry >= effectiveMin); nextTryL=(*func)(thisnode, thistree, nextTry, true); if(nextTryL < *bestOuterL){ if((*mid-nextTry) * (nextTry-*bestOuter)>ZERO_POINT_ZERO){//if the proposed point is between mid and bestOuter SHFT(dum, *worstOuter, *mid, nextTry); SHFT(dum, *worstOuterL, *midL, nextTryL); } else{ SHFT(*worstOuter, *mid, *bestOuter, nextTry); SHFT(*worstOuterL, *midL, *bestOuterL, nextTryL); } } else{ if((*mid-nextTry)*(nextTry-*bestOuter)>ZERO_POINT_ZERO){//if the proposed point is between mid and bestOuter assert(nextTryL < *midL);//if this isn't the case there are multiple optima SHFT(dum, *worstOuter, *mid, nextTry); SHFT(dum, *worstOuterL, *midL, nextTryL); } else{ if(nextTryL < *midL){ SHFT(*worstOuter, *mid, *bestOuter, nextTry); SHFT(*worstOuterL, *midL, *bestOuterL, nextTryL); } else{ SHFT(dum, *mid, *bestOuter, dum); SHFT(dum, *midL, *bestOuterL, dum); *worstOuter=nextTry; *worstOuterL=nextTryL; } } } }while(1); assert(0); return 0; } //I'm reworking this a bit to better use the information that has already been generated in the bracketing function //since we already have those function evaluations, we might as well pass them in and use them FLOAT_TYPE DZbrent(FLOAT_TYPE ax, FLOAT_TYPE bx, FLOAT_TYPE cx, FLOAT_TYPE fa, FLOAT_TYPE fx, FLOAT_TYPE fc, FLOAT_TYPE (*f)(TreeNode *, Tree*, FLOAT_TYPE, bool), FLOAT_TYPE tol, FLOAT_TYPE *xmin, TreeNode *thisnode, Tree *thistree){ int iter; FLOAT_TYPE a, b, d, etemp, fu, fv, fw/*, fx*/, p, q, r, tol1, tol2, u, v, w, x, xm; FLOAT_TYPE e=ZERO_POINT_ZERO; if((fxnodeNum << "\n"; a=(ax < cx ? ax : cx); //make a the smallest of the three bracket points b=(ax > cx ? ax : cx); //and b the largest if(ax>cx){ FLOAT_TYPE dummy=fa; fa=fc; fc=dummy; } x=bx; //make x the current minimum if(fa=xm?a-x:b-x); //set e to the larger of the two bracket intervals d=(FLOAT_TYPE)CGOLD*e; // assert(a<=x && x<=b); // fw=fv=fx=(*f)(thisnode, thistree, x); for(iter=1;iter<=ITMAX;iter++){ xm=(FLOAT_TYPE)ZERO_POINT_FIVE*(a+b); //xm is the midpoint of the bracket (of a and b) tol2=(FLOAT_TYPE)(2.0*(tol1=(FLOAT_TYPE)(tol*fabs(x)+ZEPS))); /* if (fabs(x-xm) <= (tol2-ZERO_POINT_FIVE*(b-a))){ //termination condition *xmin=x; return fx; } */// if (fabs(e) > tol1){ //construct a trial parabolic fit r=(x-w)*(fx-fv); q=(x-v)*(fx-fw); p=(x-v)*q-(x-w)*r; q=(FLOAT_TYPE)2.0*(q-r); if(q>ZERO_POINT_ZERO) p=-p; q=fabs(q); etemp=e; e=d; if(fabs(p) >= fabs(ZERO_POINT_FIVE*q*etemp)||p<=q*(a-x) || p>=q*(b-x)){ //determine if the parabolic fit is good d=(FLOAT_TYPE)(CGOLD*(e=(x>=xm?a-x:b-x))); //if not u=(fabs(d) >= tol1 ? x+d : x+SIGN(tol1,d)); } else{ //if so, take the parabolic step d=p/q; u=x+d; FLOAT_TYPE alph=w-u; FLOAT_TYPE beta=x-u; paraMinlnL=(((fx) * alph*alph) - ((fw) * beta*beta)) / (alph*alph - beta*beta); if(paraOK==true){ //the estimation error in the parabolic step always seems to at least half each iteration, //hence the division by 2.0 FLOAT_TYPE estlnL=(FLOAT_TYPE)(paraMinlnL - paraErr*ZERO_POINT_FIVE); if((fx - estlnL) < tol){ *xmin=x; return fx; } } para=true; if(u-a < tol2||b-u=xm?a-x:b-x)); //e is the distance moved in the step before last //d is golden section of that (.38.... times) u=(fabs(d) >= tol1 ? x+d : x+SIGN(tol1,d)); } */ //assert(a<=u && u<=b); //for some reason this occasionally proposes a new value //that is not within the bracket. If that happens force //the new point to be within a and b if(!(a<=u && u<=b)){ ofstream S("brakmiss.log", ios::app); S.precision(12); S << fa << "\t" << fw << "\t" << fv << "\t" << fu << endl; S << a << "\t" << b << "\t" << u << endl; S.close(); u=a/b; } fu=(*f)(thisnode, thistree, u, false); if(para==true){ paraErr=fu - paraMinlnL; if(fabs(paraErr) < paraErrCrit) paraOK=true; para=false; } if(!(fu>fx)){ //if our new try at u is better than the previous min at x if(u>=x) a=x; else b=x; //if u is > x, x becomes a, otherwise it becomes b SHFT(v,w,x,u); //w becomes v, x becomes w and u becomes x SHFT(fv,fw,fx,fu); } //DJZ 1/21/04 Rewrote this loop. I think that it was buggy, and made no sense //to me previously. Was updating variables such that w and v were not either a or b //(The bracket) but were older evaluations. This resulted in terrible cases where //the interval {w, x, v} didn't even contain a minimum at all, but {a, x, b} did. else{ //if our new try at u is worse than the old min at x if(u &stateVec, const FLOAT_TYPE *cla, int nchar, int nstates){ //what is passed in here is really the unscaled posterior values for each state, marginalized across rates (including any invariant class). //thus, the state frqeuencies have already been figured in and nothing needs to be done in CalcProbs besides divide each by the sum //note that this clas then only uses the first nstates x nchar portion, instead of the usual nstates x nchar x nrates for(int c=0;c. #ifndef FUNCS_H #define FUNCS_H //a variety of functions that don't belong to any class #include #include "population.h" #include "sequencedata.h" #ifdef UNIX #include #endif extern rng rnd; class StateSet{ protected: vector states; int numStates; public: StateSet(int ns){ numStates = ns; assert(numStates == 4 || numStates == 20 || numStates == 21); if(numStates == 4){ states.push_back("A"); states.push_back("C"); states.push_back("G"); states.push_back("T"); } else if(numStates > 19){ states.push_back("A"); states.push_back("C"); states.push_back("D"); states.push_back("E"); states.push_back("F"); states.push_back("G"); states.push_back("H"); states.push_back("I"); states.push_back("K"); states.push_back("L"); states.push_back("M"); states.push_back("N"); states.push_back("P"); states.push_back("Q"); states.push_back("R"); states.push_back("S"); states.push_back("T"); states.push_back("V"); states.push_back("W"); states.push_back("Y"); } if(numStates == 21) states.push_back("Z"); } StateSet(const GeneticCode *code){ numStates = code->NumStates(); for(int s = 0;s < numStates;s++) states.push_back(code->LookupCodonDisplayFromIndex(s)); } void OutputInternalStateHeader(ofstream &out) const{ out << "site\tbestState(prob)\t"; for(int s = 0;s < numStates;s++) out << "prob(" << states[s] << ")\t"; out << endl; } const string GetState(int s) const{ return states[s]; } }; class InternalState{ protected: int best; int numStates; vector probs; public: InternalState(int ns){ numStates = ns; probs.resize(numStates); } void CalcProbs(const FLOAT_TYPE *tots){ FLOAT_TYPE tot=0.0; best = 0; FLOAT_TYPE bestVal = ZERO_POINT_ZERO; for(int s = 0;s < numStates;s++) tot += tots[s]; for(int i=0;i bestVal){ bestVal = probs[i]; best = i; } } } void Output(ofstream &out, const StateSet &states) const{ out << states.GetState(best) << "(" << probs[best] << ")\t"; for(int s = 0;s < numStates;s++) out << probs[s] << "\t"; out << endl; } }; bool FloatingPointEquals(const FLOAT_TYPE first, const FLOAT_TYPE sec, const FLOAT_TYPE epsilon); #if defined(SINGLE_PRECISION_FLOATS) && (!defined(_MSC_VER)) || (defined(BOINC) && defined (_WIN32)) //Overloaded versions of min and max that take different types for the two arguments //This should not be used in hot code when possible, and conditional comp should //be used to make two different versions of the code float min(const double first, const float second); float min(const float first, const double second); float max(const double first, const float second); float max(const float first, const double second); #endif void OutputImportantDefines(); int FileExists(const char* s); bool FileIsFasta(const char *name); bool FileIsNexus(const char *name); int ReadData(GeneralGamlConfig *, SequenceData* data); bool ReadData(const char* filename); //void GetRestartParams(Parameters& params); int RandomInt(int lb, int ub); FLOAT_TYPE RandomFrac(); FLOAT_TYPE RandomDouble(FLOAT_TYPE lb, FLOAT_TYPE ub); int mnbrak(FLOAT_TYPE *ax, FLOAT_TYPE *bx, FLOAT_TYPE *cx, FLOAT_TYPE *fa, FLOAT_TYPE *fb, FLOAT_TYPE *fc, FLOAT_TYPE (*func)(TreeNode*, Tree*, FLOAT_TYPE), TreeNode *thisnode, Tree *thistree); int DZbrak(FLOAT_TYPE *ax, FLOAT_TYPE *bx, FLOAT_TYPE *cx, FLOAT_TYPE *fa, FLOAT_TYPE *fb, FLOAT_TYPE *fc, FLOAT_TYPE (*func)(TreeNode*, Tree*, FLOAT_TYPE), TreeNode *thisnode, Tree *thistree); FLOAT_TYPE brent(FLOAT_TYPE ax, FLOAT_TYPE bx, FLOAT_TYPE cx, FLOAT_TYPE (*f)(TreeNode *, Tree*, FLOAT_TYPE), FLOAT_TYPE tol, FLOAT_TYPE *xmin, TreeNode *thisnode, Tree *thistree); FLOAT_TYPE DZbrent(FLOAT_TYPE ax, FLOAT_TYPE bx, FLOAT_TYPE cx, FLOAT_TYPE fa, FLOAT_TYPE fb, FLOAT_TYPE fc, FLOAT_TYPE (*f)(TreeNode *, Tree*, FLOAT_TYPE), FLOAT_TYPE tol, FLOAT_TYPE *xmin, TreeNode *thisnode, Tree *thistree); void DirichletRandomVariable (FLOAT_TYPE *alp, FLOAT_TYPE *z, int n); void InferStatesFromCla(vector &stateVec, const FLOAT_TYPE *cla, int nchar, int nstates); FLOAT_TYPE CalculateHammingDistance(const char *str1, const char *str2, const int *counts, int nchar, int nstates); void SampleBranchLengthCurve(FLOAT_TYPE (*func)(TreeNode*, Tree*, FLOAT_TYPE, bool), TreeNode *thisnode, Tree *thistree); int DZbrak(FLOAT_TYPE *worstOuter, FLOAT_TYPE *mid, FLOAT_TYPE *bestOuter, FLOAT_TYPE *worstOuterL, FLOAT_TYPE *midL, FLOAT_TYPE *bestOuterL, FLOAT_TYPE (*func)(TreeNode*, Tree*, FLOAT_TYPE, bool), TreeNode *thisnode, Tree *thistree); FLOAT_TYPE DZbrent(FLOAT_TYPE ax, FLOAT_TYPE bx, FLOAT_TYPE cx, FLOAT_TYPE fa, FLOAT_TYPE fx, FLOAT_TYPE fc, FLOAT_TYPE (*f)(TreeNode *, Tree*, FLOAT_TYPE, bool), FLOAT_TYPE tol, FLOAT_TYPE *xmin, TreeNode *thisnode, Tree *thistree); /* void CalcFullCLAInternalInternalRateHet(FLOAT_TYPE *dest, const FLOAT_TYPE *LCL, const FLOAT_TYPE *RCL, const FLOAT_TYPE *Lpr, const FLOAT_TYPE *Rpr, int nchar); void CalcFullCLATerminalTerminalRateHet(FLOAT_TYPE *dest, const FLOAT_TYPE *LCL, const FLOAT_TYPE *RCL, const FLOAT_TYPE *Lpr, const FLOAT_TYPE *Rpr, unsigned char *Ldata, unsigned char *Rdata, int nchar); void CalcFullCLAInternalTerminalRateHet(FLOAT_TYPE *dest, const FLOAT_TYPE *CL1, const FLOAT_TYPE *pr1, const FLOAT_TYPE *pr2, unsigned char *data2, int nchar); void CalcFullCLAInternalInternal(FLOAT_TYPE *dest, const FLOAT_TYPE *LCL, const FLOAT_TYPE *RCL, const FLOAT_TYPE *Lpr, const FLOAT_TYPE *Rpr, int nchar); void CalcFullCLATerminalTerminal(FLOAT_TYPE *dest, const FLOAT_TYPE *LCL, const FLOAT_TYPE *RCL, const FLOAT_TYPE *Lpr, const FLOAT_TYPE *Rpr, unsigned char *Ldata, unsigned char *Rdata, int nchar); void CalcFullCLAInternalTerminal(FLOAT_TYPE *dest, const FLOAT_TYPE *CL1, const FLOAT_TYPE *pr1, const FLOAT_TYPE *pr2, unsigned char *data2, int nchar); */ int gsl_min_find_bracket(FLOAT_TYPE (*f)(TreeNode *, Tree*, FLOAT_TYPE),FLOAT_TYPE *x_minimum,FLOAT_TYPE * f_minimum,FLOAT_TYPE * x_lower, FLOAT_TYPE * f_lower, FLOAT_TYPE * x_upper, FLOAT_TYPE * f_upper, size_t eval_max, TreeNode *thisnode, Tree *thistree); inline void ArrayMultiply(FLOAT_TYPE *dest, const FLOAT_TYPE *source, int num){ //simply multiplies each element in dest by the corresponding element in source, up to num for(register int i=0;i void ScrambleArray(int n, T ar[]) { int times = n*2; int x, y; T temp; for (int i = 0; i < times; ++i) { // x = rand() % n; // y = rand() % n; x = rnd.random_int(RAND_MAX) % n; y = rnd.random_int(RAND_MAX) % n; if (x != y) { temp = ar[x]; ar[x] = ar[y]; ar[y] = temp; } } }; #endif garli-2.1-release/src/garli.br000066400000000000000000000100171241236125200162770ustar00rootroot00000000000000 kernel void BranchLike2(float4 des<>, out float4 res<>, float pmat[16]){ res.x = des.x * pmat[0] + des.y * pmat[1] + des.z * pmat[2] + des.w * pmat[3]; res.y = des.x * pmat[4] + des.y * pmat[5] + des.z * pmat[6] + des.w * pmat[7]; res.z = des.x * pmat[8] + des.y * pmat[9] + des.z * pmat[10] + des.w * pmat[11]; res.w = des.x * pmat[12] + des.y * pmat[13] + des.z * pmat[14] + des.w * pmat[15]; } kernel void SecondBranchLike(float4 des<>, float4 part<>, out float4 res<>, float pmat[16]){ res.x = part.x * (des.x * pmat[0] + des.y * pmat[1] + des.z * pmat[2] + des.w * pmat[3]); res.y = part.y * (des.x * pmat[4] + des.y * pmat[5] + des.z * pmat[6] + des.w * pmat[7]); res.z = part.z * (des.x * pmat[8] + des.y * pmat[9] + des.z * pmat[10] + des.w * pmat[11]); res.w = part.w * (des.x * pmat[12] + des.y * pmat[13] + des.z * pmat[14] + des.w * pmat[15]); } /* kernel void SingleLikeKernel(float4 des<>, float4 des2<>, out float4 res<>, float pmat[32]){ res.x = des.x * pmat[0] + des.y * pmat[1] + des.z * pmat[2] + des.w * pmat[3]; res.y = des.x * pmat[4] + des.y * pmat[5] + des.z * pmat[6] + des.w * pmat[7]; res.z = des.x * pmat[8] + des.y * pmat[9] + des.z * pmat[10] + des.w * pmat[11]; res.w = des.x * pmat[12] + des.y * pmat[13] + des.z * pmat[14] + des.w * pmat[15]; res.x *= (des2.x * pmat[16] + des2.y * pmat[17] + des2.z * pmat[18] + des2.w * pmat[19]); res.y *= (des2.x * pmat[20] + des2.y * pmat[21] + des2.z * pmat[22] + des2.w * pmat[23]); res.z *= (des2.x * pmat[24] + des2.y * pmat[25] + des2.z * pmat[26] + des2.w * pmat[27]); res.w *= (des2.x * pmat[28] + des2.y * pmat[29] + des2.z * pmat[30] + des2.w * pmat[31]); } */ /* kernel void Product(float des1<>, float des2<>, out float res<>){ res = des1 * des2; } kernel void Product4(float4 des1<>, float4 des2<>, out float4 res<>){ res.x = des1.x * des2.x; res.y = des1.y * des2.y; res.z = des1.z * des2.z; res.w = des1.w * des2.w; } */ /* void DoOneBranchLike(float *des, float *res, float *pmat, int len){ float4 desstream<1>, resstream<1>; //float pmatstream<4, 4>; float pmatstream<16>; streamRead(desstream, des); streamRead(resstream, res); streamRead(pmatstream, pmat); BranchLike2(desstream, resstream, pmatstream); streamWrite(resstream, res); } */ /* kernel void BranchLikeSecond(float4 des<>, float4 first<>, out float4 res<>, float pmat[4][4]){ res.x = first.x * (des.x * pmat[0][0] + des.y * pmat[0][1] + des.z * pmat[0][2] + des.w * pmat[0][3]); res.y = first.y * (des.x * pmat[1][0] + des.y * pmat[1][1] + des.z * pmat[1][2] + des.w * pmat[1][3]); res.z = first.z * (des.x * pmat[2][0] + des.y * pmat[2][1] + des.z * pmat[2][2] + des.w * pmat[2][3]); res.w = first.w * (des.x * pmat[3][0] + des.y * pmat[3][1] + des.z * pmat[3][2] + des.w * pmat[3][3]); } kernel void BranchLike2(float4 des<>, float4 des2<>, out float4 res<>, float pmat[4][4], float pmat2[4][4]){ res.x = (des.x * pmat[0][0] + des.y * pmat[0][1] + des.z * pmat[0][2] + des.w * pmat[0][3]) * (des2.x * pmat2[0][0] + des2.y * pmat2[0][1] + des2.z * pmat2[0][2] + des2.w * pmat2[0][3]); res.y = (des.x * pmat[1][0] + des.y * pmat[1][1] + des.z * pmat[1][2] + des.w * pmat[1][3]) * (des2.x * pmat2[1][0] + des2.y * pmat2[1][1] + des2.z * pmat2[1][2] + des2.w * pmat2[1][3]); res.z = (des.x * pmat[2][0] + des.y * pmat[2][1] + des.z * pmat[2][2] + des.w * pmat[2][3]) * (des2.x * pmat2[2][0] + des2.y * pmat2[2][1] + des2.z * pmat2[2][2] + des2.w * pmat2[2][3]); res.w = (des.x * pmat[3][0] + des.y * pmat[3][1] + des.z * pmat[3][2] + des.w * pmat[3][3]) * (des2.x * pmat2[3][0] + des2.y * pmat2[3][1] + des2.z * pmat2[3][2] + des2.w * pmat2[3][3]); } */ /* kernel void BranchLike(float4 des<>, out float4 res<>, float pmat[4][4]){ res.x = des.x * pmat[0][0] + des.y * pmat[0][1] + des.z * pmat[0][2] + des.w * pmat[0][3]; res.y = des.x * pmat[1][0] + des.y * pmat[1][1] + des.z * pmat[1][2] + des.w * pmat[1][3]; res.z = des.x * pmat[2][0] + des.y * pmat[2][1] + des.z * pmat[2][2] + des.w * pmat[2][3]; res.w = des.x * pmat[3][0] + des.y * pmat[3][1] + des.z * pmat[3][2] + des.w * pmat[3][3]; } */garli-2.1-release/src/garlimain.cpp000066400000000000000000001122741241236125200173330ustar00rootroot00000000000000// GARLI version 2.1 source code // Copyright 2005-2014 Derrick J. Zwickl // email: garli.support@gmail.com // // This program is free software: you can redistribute it and/or modify // it under the terms of the GNU General Public License as published by // the Free Software Foundation, either version 3 of the License, or // (at your option) any later version. // // This program is distributed in the hope that it will be useful, // but WITHOUT ANY WARRANTY; without even the implied warranty of // MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the // GNU General Public License for more details. // // You should have received a copy of the GNU General Public License // along with this program. If not, see . // // NOTE: Portions of this source adapted from GAML source, written by Paul O. Lewis #define PROGRAM_NAME "GARLI" #define MAJOR_VERSION "2" #define MINOR_VERSION "1" //DON'T mess with the following 2 lines!. They are auto substituted by svn. #define SVN_REV "$Rev$" #define SVN_DATE "$Date$" //allocation monitoring stuff from Paul, Mark and Dave #define WRITE_MEM_REPORT_TO_FILE #define INSTANTIATE_MEMCHK #ifdef WIN32 #include #endif #ifdef MPI_VERSION #include "mpi.h" #endif #include "defs.h" #include "population.h" #include "individual.h" #include "adaptation.h" #include "sequencedata.h" #include "garlireader.h" #include "funcs.h" #include "tree.h" #include "errorexception.h" #include "outputman.h" #ifdef WIN32 #include #define PID_FUNC() _getpid() typedef int pid_type; #else #include #include #define PID_FUNC() getpid() typedef pid_t pid_type; #endif #ifdef MAC_FRONTEND #import #import "MFEInterfaceClient.h" #endif #ifdef CUDA_GPU #include "cudaman.h" CudaManager *cudaman; int cuda_device_number=0; #endif OutputManager outman; bool interactive; bool is64bit = false; vector claSpecs; vector dataSubInfo; //This is annoying, but the substituted rev and date from svn are in crappy format. //Get what we need from them //revision string looks like this: $Rev$ std::string GetSvnRev(){ string temp = SVN_REV; string ret; for(int i=0;i 1) { int curarg=1; while(curargMPI Parallel Version<-\nNote: this version divides a number of independent runs across processors."); outman.UserMessage("It is not the multipopulation parallel Garli algorithm.\n(but is generally a better use of resources)"); #endif #if defined(OPEN_MP) outman.UserMessageNoCR("->OpenMP multithreaded version for multiple processors/cores"); #elif !defined(SUBROUTINE_GARLI) outman.UserMessageNoCR("->Single processor version"); #endif if(is64bit) outman.UserMessage(" for 64-bit OS<-"); else outman.UserMessage(" for 32-bit OS<-"); #ifdef SINGLE_PRECISION_FLOATS outman.UserMessage("->Single precision floating point version<-\n"); #endif #ifdef CUDA_GPU outman.UserMessage("->CUDA GPU version<-\n"); #endif outman.UserMessage("##############################################################"); outman.UserMessage(" This is GARLI 2.1: maximum likelihood phylogenetic inference"); outman.UserMessage(" using nucleotide, amino acid, codon and morphology-like data,"); outman.UserMessage(" as well as partitioned models."); outman.UserMessage(" General program usage is extensively documented here:"); outman.UserMessage(" http://www.nescent.org/wg/garli/"); outman.UserMessage(" See this page for details on partitioned model usage:"); outman.UserMessage(" http://www.nescent.org/wg_garli/Using_partitioned_models"); outman.UserMessage(" and this page for details on Mkv mophology model usage:"); outman.UserMessage(" http://www.nescent.org/wg_garli/Mkv_morphology_model"); outman.UserMessage(" PLEASE LET ME KNOW OF ANY PROBLEMS AT:"); outman.UserMessage(" garli.support@gmail.com"); outman.UserMessage("##############################################################"); outman.UserMessageNoCR("Compiled %s %s", __DATE__, __TIME__); #if defined (_MSC_VER) outman.UserMessage(" using Microsoft C++ compiler version %.2f", _MSC_VER/100.0); #elif defined(__INTEL_COMPILER) outman.UserMessage(" using Intel icc compiler version %.2f", __INTEL_COMPILER/100.0); #elif defined(__GNUC__) outman.UserMessage(" using GNU gcc compiler version %d.%d.%d", __GNUC__, __GNUC_MINOR__, __GNUC_PATCHLEVEL__); #else outman.UserMessage(""); #endif #ifdef NCL_NAME_AND_VERSION outman.UserMessage("Using %s", NCL_NAME_AND_VERSION); #endif OutputImportantDefines(); outman.UserMessage("\n#######################################################"); outman.UserMessage("Reading config file %s", conf_name.c_str()); if(confOK == false) throw ErrorException("Error in config file...aborting"); #ifdef SUBROUTINE_GARLI if(conf.randseed != -1) throw ErrorException("You cannot specify a random number seed with the MPI version. This would cause all of the\n\tindependent MPI processes to give exactly identical results. Set randomseed to -1"); #endif //This is pretty hacky. Create one modSpec now because it is needed //to read the data (to identify the expected type of sequence for phylip and //fasta files), then add more later if there are multiple char blocks or CharPartitions //in the case of a Nexus datafile, we actually don't need to know the datatype //in advance, and can handle data subsets with different datatypes modSpecSet.AddModSpec(conf.configModelSets[0]); //read the datafile with the NCL-based GarliReader - should allow nexus, phylip and fasta outman.UserMessage("###################################################\nREADING OF DATA"); GarliReader &reader = GarliReader::GetInstance(); bool usedNCL = reader.ReadData(datafile.c_str(), *modSpecSet.GetModSpec(0)); if(! usedNCL) throw ErrorException("There was a problem reading the data file."); //assuming a single taxa block if(reader.GetNumTaxaBlocks() > 1) throw ErrorException("Expecting only one taxa block in datafile"); NxsTaxaBlock *taxblock = reader.GetTaxaBlock(0); //currently data subsets will be created for each separate characters block, and/or for each //part of a char partition within a characters block int numCharBlocks = reader.GetNumCharactersBlocks(taxblock); if(numCharBlocks == 0) throw ErrorException("No character data (in characters/data blocks) found in datafile"); vector > effectiveMatrices; outman.UserMessage("\n###################################################\nPARTITIONING OF DATA AND MODELS"); //loop over characters blocks for(int c = 0;c < numCharBlocks;c++){ NxsCharactersBlock *charblock = reader.GetCharactersBlock(taxblock, c); string cbName = charblock->GetTitle(); NxsAssumptionsBlock *assblock = NULL; NxsUnsignedSet charSet; bool foundCharPart = false; int numAssBlocks = reader.GetNumAssumptionsBlocks(charblock); if(numAssBlocks > 0){ //loop over assumptions blocks for this charblock for(int a = 0;a < numAssBlocks;a++){ assblock = reader.GetAssumptionsBlock(charblock, a); int numParts = assblock->GetNumCharPartitions(); if(numParts > 1) throw ErrorException("Found more than one CHARPARTITION referring to CHARACTERS block %s in a single ASSUMPTIONS or SETS blocks", charblock->GetTitle().c_str()); else if(numParts == 1){ if(foundCharPart == true) throw ErrorException("Found more than one CHARPARTITION referring to CHARACTERS block %s in multiple ASSUMPTIONS or SETS blocks", charblock->GetTitle().c_str());\ else foundCharPart = true; //get the name of the charpartition vector charPartNames; assblock->GetCharPartitionNames(charPartNames); const NxsPartition *part = assblock->GetCharPartition(charPartNames[0]); int numSubsets = part->size(); int subsetNum = 0; //loop over the partition subsets, each of which creates a data subset in GARLI for(NxsPartition::const_iterator subit = part->begin();subit != part->end();subit++){ charSet = (*subit).second; dataSubInfo.push_back(DataSubsetInfo(effectiveMatrices.size(), c, cbName, subsetNum, (*subit).first, DataSubsetInfo::NUCLEOTIDE, DataSubsetInfo::NUCLEOTIDE)); effectiveMatrices.push_back(make_pair(charblock, charSet)); subsetNum++; } } } if(foundCharPart == false){//no charpart found dataSubInfo.push_back(DataSubsetInfo(effectiveMatrices.size(), c, cbName, -1, "", DataSubsetInfo::NUCLEOTIDE, DataSubsetInfo::NUCLEOTIDE)); effectiveMatrices.push_back(make_pair(charblock, charSet)); } } else{ //no assumptions block found, dataSubInfo.push_back(DataSubsetInfo(effectiveMatrices.size(), c, cbName, -1, "", DataSubsetInfo::NUCLEOTIDE, DataSubsetInfo::NUCLEOTIDE)); effectiveMatrices.push_back(make_pair(charblock, charSet)); } } //report on how data and models line up, and deal with a few unsupported possibilites if(conf.linkModels && conf.configModelSets.size() > 1) throw ErrorException("Multiple model subsets specified, but linkmodels = 1"); if(effectiveMatrices.size() > 1){ if(conf.configModelSets.size() == 1){//only one model description found if(conf.linkModels) outman.UserMessage("\nCHECK: ONE MODEL APPLIES TO ALL DATA SUBSETS\n\t(full linkage, all parameters shared)\n"); else outman.UserMessage("\nCHECK: ONE MODEL TYPE APPLIES TO ALL DATA SUBSETS,\n\tBUT WITH INDEPENDENT MODEL PARAMETERS (no linkage)\n"); } else{//mulitple model descriptions found if(conf.configModelSets.size() != effectiveMatrices.size()) throw ErrorException("Multiple data subsets and model subsets specified, but numbers don't match"); else outman.UserMessage("\nCHECK: DIFFERENT MODEL TYPES AND MODEL PARAMETERS APPLY\n\tTO EACH DATA SUBSET (no linkage)\n"); } } else if(conf.configModelSets.size() != 1) throw ErrorException("Multiple models specified, but only one data subset found"); //set this modSpecSet.SetInferSubsetRates(conf.subsetSpecificRates && effectiveMatrices.size() > 1); //now create a datamatrix object for each effective matrix //because of exsets some subsets of a charpart could contain no characters, //but I'm not going to deal with that right now, and will crap out //EFFECTIVE matrices ( = datasubsets) are the actual chunks of data specified by separate charblocks and/or charpartitions //There is a one to one matching between effective matrices and CLAs. EXCEPT in the case of Nstate data, //in which case multiple matrices will be spawned for(int dataChunk = 0;dataChunk < effectiveMatrices.size();dataChunk++){ ModelSpecification *modSpec = NULL; //for Mk type data the number of actual matrices created can be > the number of actual data chunks //e.g., a first characters block might spawn 3 matrices for 2 state, 3 state and 4 state characters //sucessive char blocks then need to take that into account //a dataSubset is equivalent to a matrix in this respect int nextMatrixNum = dataPart.NumSubsets(); if(dataChunk > 0){ if(conf.linkModels){//linkage means that all clas/matrices point to the same model/modSpec //EXCEPT in the case of Mk type data with different numbers of states. That is taken //care of below. claSpecs.push_back(ClaSpecifier(dataChunk,0,dataChunk)); modSpec = modSpecSet.GetModSpec(0); } else{//models are not linked ... if(conf.configModelSets.size() == 1)//but are all described by the same settings in the config file modSpecSet.AddModSpec(conf.configModelSets[0]); else{ //each has its own description in the config modSpecSet.AddModSpec(conf.configModelSets[dataChunk]); } claSpecs.push_back(ClaSpecifier(nextMatrixNum, nextMatrixNum, nextMatrixNum)); modSpec = modSpecSet.GetModSpec(nextMatrixNum); } } else{ //if this is the first model, it must correspond to the first modSpec modSpec = modSpecSet.GetModSpec(0); claSpecs.push_back(ClaSpecifier(0,0,0)); } if(conf.linkModels && (modSpec->IsMkTypeModel() || modSpec->IsOrientedGap())) throw ErrorException("Model linkage cannot be used with Mk/Mkv models (nor does it\n\tneed to be, since there are no estimated parameters).\n\tSet linkmodels = 0"); //defaults here are NUCLEOTIDE, so make changes as necessary if(modSpec->IsCodon()) dataSubInfo[dataChunk].usedAs = DataSubsetInfo::CODON; else if(modSpec->IsCodonAminoAcid()) dataSubInfo[dataChunk].usedAs = DataSubsetInfo::AMINOACID; else if(modSpec->IsAminoAcid()) dataSubInfo[dataChunk].readAs = dataSubInfo[dataChunk].usedAs = DataSubsetInfo::AMINOACID; else if(modSpec->IsNState()) dataSubInfo[dataChunk].readAs = dataSubInfo[dataChunk].usedAs = DataSubsetInfo::NSTATE; else if(modSpec->IsNStateV()) dataSubInfo[dataChunk].readAs = dataSubInfo[dataChunk].usedAs = DataSubsetInfo::NSTATEV; else if(modSpec->IsOrderedNState()) dataSubInfo[dataChunk].readAs = dataSubInfo[dataChunk].usedAs = DataSubsetInfo::ORDNSTATE; else if(modSpec->IsOrderedNStateV()) dataSubInfo[dataChunk].readAs = dataSubInfo[dataChunk].usedAs = DataSubsetInfo::ORDNSTATEV; else if(modSpec->IsOrientedGap()) dataSubInfo[dataChunk].readAs = dataSubInfo[dataChunk].usedAs = DataSubsetInfo::ORIENTEDGAP; else if(modSpec->IsBinaryNotAllZeros()) dataSubInfo[dataChunk].readAs = dataSubInfo[dataChunk].usedAs = DataSubsetInfo::BINARY_NOT_ALL_ZEROS; else if(modSpec->IsBinary()) dataSubInfo[dataChunk].readAs = dataSubInfo[dataChunk].usedAs = DataSubsetInfo::BINARY; dataSubInfo[dataChunk].Report(); //outman.UserMessage(""); // Create the data object //for nstate data the effective matrices will be further broken up into implied matrices that each have the same number of observed states //the implied matrix number will be that number of states int actuallyUsedImpliedMatrixIndex = 0; int maxObservedStates = effectiveMatrices[dataChunk].first->GetMaxObsNumStates(false); //for Mk the impliedMatrix number is the number of states for(int impliedMatrix = 2;impliedMatrix < (modSpec->IsMkTypeModel() ? maxObservedStates + 1 : 3);impliedMatrix++){ if(modSpec->IsMkTypeModel() && !modSpec->IsOrientedGap()){ bool isOrdered = (modSpec->IsOrderedNState() || modSpec->IsOrderedNStateV()); bool isBinary = modSpec->IsBinary() || modSpec->IsBinaryNotAllZeros(); bool isConditioned = (modSpec->IsNStateV() || modSpec->IsOrderedNStateV() || modSpec->IsBinaryNotAllZeros()); //data = new NStateData(impliedMatrix, (modSpec->IsNStateV() || modSpec->IsOrderedNStateV()), (modSpec->IsOrderedNState() || modSpec->IsOrderedNStateV())); data = new NStateData(impliedMatrix, isOrdered, isBinary, isConditioned); } else if(modSpec->IsOrientedGap()) data = new OrientedGapData(); else if(modSpec->IsAminoAcid() && modSpec->IsCodonAminoAcid() == false) data = new AminoacidData(); else //all other data will be read into a DNA matrix and //then converted if necessary data = new NucleotideData(); //it really shouldn't be necessary to use the PatternManager on non-sequence data if(modSpec->IsNucleotide() || modSpec->IsAminoAcid() || modSpec->IsCodon()) data->SetUsePatternManager(conf.usePatternManager); else data->SetUsePatternManager(0); //if no charpart was specified, the second argument here will be empty data->CreateMatrixFromNCL(effectiveMatrices[dataChunk].first, effectiveMatrices[dataChunk].second); #ifdef SINGLE_PRECISION_FLOATS if(modSpec->IsMkTypeModel() || modSpec->IsOrientedGap()) throw ErrorException("Sorry, Mk/Mkv type models have not yet been tested with single precision."); #endif if(data->NChar() == 0){ //if there weren't any characters with a certain number of states, //just get rid of the matrix. This could in theory also work for //totally excluded subsets, but that gets complicated because it //isn't clear how the indexing of models specified in the config //file should work assert(modSpec->IsMkTypeModel()); outman.UserMessage("NOTE: No characters found with %d observed states.", impliedMatrix); delete data; } else{//now we have a data matrix object created, already filtered for the correct sites or number of states if(modSpec->IsMkTypeModel()){ if(modSpec->IsGammaRateHet() || modSpec->IsFlexRateHet()) throw ErrorException("Sorry, rate heterogeneity cannot be used with Mk/Mkv models yet.\n\tSet ratehetmodel = none."); if(actuallyUsedImpliedMatrixIndex > 0){ //the specs are being added as we read and create subsets, so we can add them for the implied matrices //as we go claSpecs.push_back(ClaSpecifier(nextMatrixNum + actuallyUsedImpliedMatrixIndex, nextMatrixNum + actuallyUsedImpliedMatrixIndex, nextMatrixNum + actuallyUsedImpliedMatrixIndex)); //clone the current datasubset info, which applies to all of the implied matrices within this effective matrix dataSubInfo.push_back(dataSubInfo[dataChunk]); //also clone the modspec. This isn't really necessary (or good) except that the number of states is stored by the modspecs if(conf.linkModels) modSpecSet.AddModSpec(conf.configModelSets[0]); else//there may be only a single model set specified, but no linkage modSpecSet.AddModSpec(conf.configModelSets[conf.configModelSets.size() > 1 ? dataChunk : 0]); modSpec = modSpecSet.GetModSpec(modSpecSet.NumSpecs() - 1); } modSpec->SetNStates(impliedMatrix); } else if(modSpec->IsOrientedGap()){ if(modSpec->IsGammaRateHet() || modSpec->IsFlexRateHet()) throw ErrorException("Sorry, rate heterogeneity cannot be used with gap models yet.\n\tSet ratehetmodel = none."); if(modSpecSet.InferSubsetRates()) outman.UserMessage("WARNING - YOU SHOULD TURN OFF SUBSET SPECIFIC RATE ESTIMATION WHEN USING GAP MODELS"); if(actuallyUsedImpliedMatrixIndex > 0){ //the specs are being added as we read and create subsets, so we can add them for the implied matrices //as we go claSpecs.push_back(ClaSpecifier(nextMatrixNum + actuallyUsedImpliedMatrixIndex, nextMatrixNum + actuallyUsedImpliedMatrixIndex, nextMatrixNum + actuallyUsedImpliedMatrixIndex)); //clone the current datasubset info, which applies to all of the implied matrices within this effective matrix dataSubInfo.push_back(dataSubInfo[dataChunk]); //also clone the modspec. This isn't really necessary (or good) except that the number of states is stored by the modspecs if(conf.linkModels) modSpecSet.AddModSpec(conf.configModelSets[0]); else//there may be only a single model set specified, but no linkage modSpecSet.AddModSpec(conf.configModelSets[conf.configModelSets.size() > 1 ? dataChunk : 0]); modSpec = modSpecSet.GetModSpec(modSpecSet.NumSpecs() - 1); } } else if(modSpec->IsCodon()){ rawPart.AddSubset(data); const NucleotideData *nuc = dynamic_cast(data); CodonData *dat; if(nuc != NULL) dat = new CodonData(nuc, modSpec->geneticCode, conf.ignoreStopCodons); else throw ErrorException("Attempted to create codon matrix from non-nucleotide data"); //this probably shouldn't go here, but... if(modSpec->IsF1x4StateFrequencies()) dat->SetF1X4Freqs(); else if(modSpec->IsF3x4StateFrequencies()) dat->SetF3X4Freqs(); else if(modSpec->IsEmpiricalStateFrequencies()) dat->SetCodonTableFreqs(); data = dat; } else if(modSpec->IsCodonAminoAcid()){ rawPart.AddSubset(data); const NucleotideData *nuc = dynamic_cast(data); AminoacidData *dat; if(nuc != NULL) dat = new AminoacidData(nuc, modSpec->geneticCode, conf.ignoreStopCodons); else throw ErrorException("Attempted to translate to amino acids from non-nucleotide data"); data = dat; } dataPart.AddSubset(data); if(modSpec->IsMkTypeModel()){ outman.UserMessage("\tSubset of data with %d states:", impliedMatrix); string chars; data->GetStringOfOrigDataColumns(chars); outman.UserMessage("\t chars%s", chars.c_str()); } if(conf.combineAdjacentIdenticalGapPatterns && (modSpec->IsOrientedGap() || modSpec->IsBinaryNotAllZeros())){ if(conf.usePatternManager) throw ErrorException("Sorry, the pattern manager can't be used with gap collapsing currently"); data->EliminateAdjacentIdenticalColumns(); } data->ProcessPatterns(); dataSubInfo[dataChunk + actuallyUsedImpliedMatrixIndex].totalCharacters = data->TotalNChar(); dataSubInfo[dataChunk + actuallyUsedImpliedMatrixIndex].uniqueCharacters = data->NChar(); actuallyUsedImpliedMatrixIndex++; } } //subset specific rates will be set if: //1. subsetspecificrates = 1 in the conf // and //2a. a partition is actually specified via multiple char blocks or a charpart // and/or //2b. nstate (Mk) model is specified, characters have different numbers of observed states //(2b. is what needs to be taken care of here because we don't know whether there will //be implied blocks in advance) if(conf.subsetSpecificRates && modSpecSet.InferSubsetRates() == false) if(actuallyUsedImpliedMatrixIndex > 1) modSpecSet.SetInferSubsetRates(true); } //this depends on the fact that an extra taxon slot was allocated but not yet used if(modSpecSet.AnyOrientedGap()){ NxsTaxaBlock *tax = reader.GetTaxaBlock(0); if(!tax->IsAlreadyDefined("ROOT")) dataPart.AddDummyRoots(); } outman.UserMessage("\n###################################################"); //could deallocate the storage in the NCL reader here, which saves a bit of memory but isn't critical //reader.DeleteCharacterBlocksFromFactories(); //allocate the population pop = new Population(); pop->usedNCL = usedNCL; pop->Setup(&conf, &dataPart, &rawPart, 1, (validateMode == true ? -1 : 0)); pop->SetOutputDetails(); outman.UserMessage("STARTING RUN"); if(runTests){ outman.UserMessage("starting internal tests..."); pop->RunTests(); outman.UserMessage("******Successfully completed tests.******"); return 0; } if(conf.optimizeInputOnly) conf.runmode = 11; if(validateMode){ //validate mode skips some allocation in pop::Setup, and then executes pop::ValidateInput, //which is essentially a stripped down version of pop::SeedPopWithStartingTree pop->ValidateInput(1); outman.UserMessage("VALIDATION COMPLETE. Check output above for information and possible errors."); } //the runmodes are essentially a hidden way of causing different (often very different) program //behavior at runtime. not really for user consumption else if(conf.runmode != 0){ if(conf.runmode == 1) pop->ApplyNSwaps(10); else if(conf.runmode == 7) pop->VariableStartingTreeOptimization(false); else if(conf.runmode == 9) pop->VariableStartingTreeOptimization(true); else if(conf.runmode == 8){ throw ErrorException("Sorry, site rate estimation is not yet implemented in this version."); #ifdef OPEN_MP throw ErrorException("can't estimate site rates in openmp version!"); #endif #ifndef ALLOW_SINGLE_SITE throw ErrorException("the program must be compiled with ALLOW_SINGLE_SITE defined in defs.h to use site rate estimation (runmode = 8)!"); #endif pop->OptimizeSiteRates(); } else if(conf.runmode == 11){ pop->OptimizeInputAndWriteSitelikelihoods(); } else if(conf.runmode == 12){ pop->OptimizeInputAndWriteSitelikelihoodsAndTryRootings(); } else if(conf.runmode > 20){ pop->GenerateTreesOnly(conf.runmode); } else if(conf.runmode > 1) //this is runmodes 2-6 pop->SwapToCompletion(conf.startOptPrec); } else{ //if no checkpoint files are actually found conf->restart will be set to zero if(pop->conf->restart) pop->conf->restart = pop->ReadStateFiles(); pop->SetOutputDetails(); if(pop->conf->bootstrapReps == 0){//NOT bootstrapping pop->PerformSearch(); } else //Bootstrap() in turn calls PerformSearch for each boot rep pop->Bootstrap(); pop->FinalizeOutputStreams(2); } dataPart.Delete(); if(pop != NULL){ delete pop; pop = NULL; } }catch(ErrorException &err){ if(outman.IsLogSet() == false){ outman.SetLogFile("ERROR.log"); if(interactive == false) UsageMessage(argv[0]); } outman.UserMessage("\nERROR: %s\n\n", err.message); if(pop != NULL){ pop->FinalizeOutputStreams(0); pop->FinalizeOutputStreams(1); pop->FinalizeOutputStreams(2); } #ifdef MAC_FRONTEND NSAutoreleasePool *pool = [[NSAutoreleasePool alloc] init]; NSString *messageForInterface = [NSString stringWithUTF8String:err.message]; [[MFEInterfaceClient sharedClient] didEncounterError:messageForInterface]; [pool release]; #endif if(interactive==true){ outman.UserMessage("\n-Press enter to close program.-"); char d=getchar(); } return 1; } catch(int error){ if(error==Population::nomem) cout << "not able to allocate enough memory!!!" << endl; } dataPart.Delete(); modSpecSet.Delete(); if(interactive==true){ outman.UserMessage("\n-Press enter to close program.-"); char d=getchar(); } outman.CloseLogFile(); } #if defined(MONITORING_ALLOCATION) && !defined(NDEBUG) #if defined(WRITE_MEM_REPORT_TO_FILE) char filename[50]; #ifndef WIN32 int rank=0; // MPI_Comm_rank(MPI_COMM_WORLD, &rank); sprintf(filename, "memcheck%d.txt", rank); #else strcpy(filename, "memcheck.txt"); #endif ofstream memf(filename); MEMCHK_REPORT(memf) memf.close(); #else MEMCHK_REPORT(cout) #endif #endif return 0; }; garli-2.1-release/src/garlireader.cpp000066400000000000000000001330421241236125200176450ustar00rootroot00000000000000// GARLI version 2.1 source code // Copyright 2005-2014 Derrick J. Zwickl // email: zwickl@nescent.org // // This program is free software: you can redistribute it and/or modify // it under the terms of the GNU General Public License as published by // the Free Software Foundation, either version 3 of the License, or // (at your option) any later version. // // This program is distributed in the hope that it will be useful, // but WITHOUT ANY WARRANTY; without even the implied warranty of // MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the // GNU General Public License for more details. // // You should have received a copy of the GNU General Public License // along with this program. If not, see . // This file was adapted from from the BasicCmdLine example provided as // part of the NCL // Copyright (C) 1999-2002 Paul O. Lewis // // This file is part of NCL (Nexus Class Library). // // NCL is free software; you can redistribute it and/or modify // it under the terms of the GNU General Public License as published by // the Free Software Foundation; either version 2 of the License, or // (at your option) any later version. // // NCL is distributed in the hope that it will be useful, // but WITHOUT ANY WARRANTY; without even the implied warranty of // MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the // GNU General Public License for more details. // // You should have received a copy of the GNU General Public License // along with NCL; if not, write to the Free Software Foundation, Inc., // 59 Temple Place, Suite 330, Boston, MA 02111-1307 USA // #include "defs.h" #include "ncl.h" #include "garlireader.h" #include "outputman.h" #include "errorexception.h" #include "model.h" #include #include int GARLI_main( int argc, char* argv[] ); extern OutputManager outman; /*---------------------------------------------------------------------------------------------------------------------- | The constructor simply passes along `i' to the base class constructor. Nothing else needs to be done. */ MyNexusToken::MyNexusToken( istream &i) /* the input file stream attached to the NEXUS file to be read */ : NxsToken(i) { } /*---------------------------------------------------------------------------------------------------------------------- | Overrides the NxsToken::OutputComment virtual function (which does nothing) to display output comments [!comments | like this one beginning with an exclamation point]. The output comment is passed through the OutputManager */ void MyNexusToken::OutputComment( const NxsString &msg) /* the output comment to be displayed */ { size_t pos; string s; //changing this again - just eating the Garli output comments s = "GarliScore"; pos = msg.find(s); if(pos != string::npos){ //outman.UserMessage("This is apparently a tree inferred by Garli in a previous run. Its score was %s", msg.substr(s.length()).c_str()); return; } s = "GarliModel"; pos = msg.find(s); if(pos != string::npos){ //outman.UserMessage("Garli's model parameter values used in inferring this tree:\n\t%s", msg.substr(s.length()).c_str()); return; } s = "****NOTE";//this is a note about the parameter values either being from a run that was terimated early or that //they are only optimal for a certain tree. This is mainly for output when reading the trees in PAUP //and we will just ignore them here pos = msg.find(s); if(pos != string::npos) return; outman.UserMessage("\nCOMMENT FOUND IN NEXUS FILE (output verbatim):"); outman.UserMessage(msg); outman.UserMessage("(END OF NEXUS COMMENT)"); } /*---------------------------------------------------------------------------------------------------------------------- | Initializes the `id' data member to "GarliReader" and calls the FactoryDefaults member function to perform the | remaining initializations. The data member `'' is set to NULL so that memory will be allocated for it in | FactoryDefaults. */ GarliReader::GarliReader() { //none of these besides garliBlock are being used anymore taxa = NULL; trees = NULL; assumptions = NULL; distances = NULL; characters = NULL; data = NULL; next_command = NULL; garliBlock = NULL; ClearContent(); } /*---------------------------------------------------------------------------------------------------------------------- | Closes `logf' if it is open and deletes memory allocated to `next_command'. */ GarliReader::~GarliReader() { //this is a little odd, since ClearContent will reallocate a few things, but //it also ensures that a bunch of other things are deleted ClearContent(); assert(next_command != NULL); delete [] next_command; if (logf_open) logf.close(); if(garliBlock != NULL){ Detach(garliBlock); delete garliBlock; garliBlock = NULL; } } //DJZ THIS WAS ADDED DIRECTLY FROM NxsReader, since Public Reader apparently deprecates this old API and //Add just asserts zero there. /*---------------------------------------------------------------------------------------------------------------------- | Adds `newBlock' to the end of the list of NxsBlock objects growing from `blockList'. If `blockList' points to NULL, | this function sets `blockList' to point to `newBlock'. Calls SetNexus method of `newBlock' to inform `newBlock' of | the NxsReader object that now owns it. This is useful when the `newBlock' object needs to communicate with the | outside world through the NxsReader object, such as when it issues progress reports as it is reading the contents | of its block. */ /* void GarliReader::Add( NxsBlock *newBlock) // a pointer to an existing block object { assert(newBlock != NULL); newBlock->SetNexus(this); if (!blockList) blockList = newBlock; else { // Add new block to end of list // NxsBlock *curr; for (curr = blockList; curr && curr->next;) curr = curr->next; assert(curr && !curr->next); curr->next = newBlock; } } /*---------------------------------------------------------------------------------------------------------------------- | Called by the NxsReader object when a block named `blockName' is entered. Allows program to notify user of | progress in parsing the NEXUS file. Also gives program the opportunity to ask user if it is ok to purge data | currently contained in this block. If user is asked whether existing data should be deleted, and the answer comes | back no, then then return false, otherwise return true. Overrides pure virtual function in class NxsReader. */ bool GarliReader::EnteringBlock( NxsString blockName) /* the name of the block just entered */ { message = "Reading "; message += blockName; message += " block..."; PrintMessage(false); //3/25/08 if we already found a Garli block with a model (e.g. in the dataset file) //we should crap out, since we don't know which one the user meant to use //this is a change from previous behavior, in which I wanted the second to just override the first. if(blockName.Equals("GARLI") && FoundModelString()) throw ErrorException("Multiple GARLI blocks found (possibly in multiple files).\n\tRemove or comment out all but one."); return true; } /*---------------------------------------------------------------------------------------------------------------------- | Called by the NxsReader object when exiting a block named `blockName'. Allows program to notify user of progress | in parsing the NEXUS file. Virtual function that overrides the pure virtual function in the base class NxsReader. */ void GarliReader::ExitingBlock( NxsString blockName) /* the name of the block just exited */ { //message to indicate that we sucessfully read whatever block this was string mess; if(blockName.Equals("CHARACTERS")){ switch (static_cast(currBlock)->GetDataType()){ case NxsCharactersBlock::dna: mess = " found dna data..."; break; case NxsCharactersBlock::rna: mess = " found rna data..."; break; case NxsCharactersBlock::protein: mess = " found protein data..."; break; case NxsCharactersBlock::standard: mess = " found standard data..."; break; case NxsCharactersBlock::nucleotide: mess = " found nucleotide data..."; break; case NxsCharactersBlock::continuous: mess = " found continuous data..."; break; } } mess += " successful"; outman.UserMessage(mess); } //Delete only the characters blocks, which can take a substantial amount of memory, and aren't needed after //Garli's matrices are created void GarliReader::DeleteCharacterBlocksFromFactories() { for(vector::iterator cit = charactersBlockVec.begin();cit != charactersBlockVec.end();cit++){ RemoveBlockFromUsedBlockList(*cit); delete *cit; } } //This used to be the FactoryDefaults function, but was changes to be consistent with //the higher level functions in PublicReader. It clears out/resets everything that //was in the reader AND gets the reader ready to do further reading - thus it is //a function that returns the reader to the state it was just after allocation. It //is NOT a deallocater, although it will properly deallocate things if necessary //to get back to the initial state void GarliReader::ClearContent() { DeleteBlocksFromFactories(); MultiFormatReader::ClearContent(); inf_open = false; logf_open = false; quit_now = false; message.clear(); //tell the reader to nuke identical taxa blocks, which could be created by reading a data/characters block //and then another file with a trees block and taxa block cullIdenticalTaxaBlocks(); treesBlockTemplate->SetAllowImplicitNames(true); if(garliBlock != NULL){ Detach(garliBlock); delete garliBlock; garliBlock = NULL; } garliBlock = new GarliBlock(); Add(garliBlock); if (next_command == NULL) next_command = new char[COMMAND_MAXLEN + 1]; next_command[0] = '\0'; //none of the following should be getting used, but delete just in case if (trees != NULL) { assert(0); Detach(trees); delete trees; trees = NULL; } if (taxa != NULL) { assert(0); Detach(taxa); delete taxa; taxa = NULL; } if (assumptions != NULL) { Detach(assumptions); delete assumptions; assumptions = NULL; } if (distances != NULL) { Detach(distances); delete distances; distances = NULL; } if (characters != NULL) { assert(0); Detach(characters); delete characters; characters = NULL; } if (data != NULL) { assert(0); Detach(data); delete data; data = NULL; } } //DJZ this is my function, replacing an old one that appeared in funcs.cpp //simpler now, since it uses NxsMultiFormatReader bool GarliReader::ReadData(const char* filename, const ModelSpecification &mSpec){ //first use a few of my crappy functions to try to diagnose the type of file and data //then call the NxsMultiFormatReader functions to process it if (!FileExists(filename)) { throw ErrorException("data file not found: %s!", filename); } //if it is Nexus, don't need to specify anything else in advance if(FileIsNexus(filename)){ outman.UserMessage("Attempting to read data file in Nexus format (using NCL):\n\t%s ...", filename); ReadFilepath(filename, NEXUS_FORMAT); } else{//if this isn't nexus we'll try a bunch of formats to see if we can get something to work //the idea here is that we create an ordered list of formats to try, then we try them typedef pair FormatPair; list formatsToTry; NxsString name; if(FileIsFasta(filename)){ //IsAminoAcid() returns true with codon-aminoacid datatype if(mSpec.IsAminoAcid() && mSpec.IsCodonAminoAcid() == false){ formatsToTry.push_back(FormatPair(FASTA_AA_FORMAT, "Fasta amino acid")); } else{ if(mSpec.IsRna() == false) formatsToTry.push_back(FormatPair(FASTA_DNA_FORMAT, "Fasta DNA")); formatsToTry.push_back(FormatPair(FASTA_RNA_FORMAT, "Fasta RNA")); } } else{//otherwise assume phylip format //IsAminoAcid() returns true with codon-aminoacid datatype if(mSpec.IsAminoAcid() && mSpec.IsCodonAminoAcid() == false){ formatsToTry.push_back(FormatPair(RELAXED_PHYLIP_AA_FORMAT, "relaxed Phylip amino acid")); formatsToTry.push_back(FormatPair(INTERLEAVED_RELAXED_PHYLIP_AA_FORMAT, "interleaved relaxed Phylip amino acid")); formatsToTry.push_back(FormatPair(PHYLIP_AA_FORMAT, "strict Phylip amino acid")); formatsToTry.push_back(FormatPair(INTERLEAVED_PHYLIP_AA_FORMAT, "interleaved strict Phylip amino acid")); } else{ if(mSpec.IsRna() == false){ formatsToTry.push_back(FormatPair(RELAXED_PHYLIP_DNA_FORMAT, "relaxed Phylip DNA")); formatsToTry.push_back(FormatPair(INTERLEAVED_RELAXED_PHYLIP_DNA_FORMAT, "interleaved relaxed Phylip DNA")); formatsToTry.push_back(FormatPair(PHYLIP_DNA_FORMAT, "strict Phylip DNA")); formatsToTry.push_back(FormatPair(INTERLEAVED_PHYLIP_DNA_FORMAT, "interleaved strict Phylip DNA")); } formatsToTry.push_back(FormatPair(RELAXED_PHYLIP_RNA_FORMAT, "relaxed Phylip RNA")); formatsToTry.push_back(FormatPair(INTERLEAVED_RELAXED_PHYLIP_RNA_FORMAT, "interleaved relaxed Phylip RNA")); formatsToTry.push_back(FormatPair(PHYLIP_RNA_FORMAT, "strict Phylip RNA")); formatsToTry.push_back(FormatPair(INTERLEAVED_PHYLIP_RNA_FORMAT, "interleaved strict Phylip RNA")); } } //now start trying formats bool success; for(list::iterator formIt = formatsToTry.begin();formIt != formatsToTry.end();formIt++){ success = true; try{ outman.UserMessage("Attempting to read data file %s as\n\t%s format (using NCL) ...", filename, (*formIt).second.c_str()); ReadFilepath(filename, (*formIt).first); } catch(NxsException &err){ NexusError(err.msg, err.pos, err.line, err.col, false); outman.UserMessage("Problem reading data file as %s format...\n", (*formIt).second.c_str()); success = false; } catch(ErrorException &err){ //Sometimes NCL raises a NxsException, but then catches it and passes it onto my NexusError, //which throws an ErrorException. So, need to catch both types of exceptions here outman.UserMessage("Problem reading data file as %s format...\n", (*formIt).second.c_str()); success = false; } if(success) break; } if(success == false) throw ErrorException("\nUnable to read data file %s in any format.\n", filename); else outman.UserMessage("\nData read successfully."); } return true; } //verifies that we got the right number/type of blocks and returns the Characters block to be used const NxsCharactersBlock *GarliReader::CheckBlocksAndGetCorrectCharblock(const ModelSpecification &mSpec) const{ const int numTaxaBlocks = GetNumTaxaBlocks(); if(numTaxaBlocks > 1) throw ErrorException("Either more than one taxa block was found in the data file\n\tor multiple blocks had different taxon sets."); else if(numTaxaBlocks == 0) throw ErrorException("No taxa information was provided by NCL.\n\tThere may have been a problem reading the data file.\n\tCheck output above."); const NxsTaxaBlock *taxablock = GetTaxaBlock(0); const int numCharBlocks = GetNumCharactersBlocks(taxablock); outman.UserMessageNoCR(""); if(numCharBlocks == 0) throw ErrorException("No character data was provided by NCL. There may have been a problem reading\n\tthe data file, the data might be of the wrong type for the specified model,\n\tor the data might be in an invalid format or not aligned properly. Check output above."); //now check that we only have one of the charblock types that we want int correctIndex = -1; for(int c = 0;c < GetNumCharactersBlocks(taxablock);c++){ const NxsCharactersBlock *charblock = GetCharactersBlock(taxablock, c); if((charblock->GetDataType() == NxsCharactersBlock::dna || charblock->GetDataType() == NxsCharactersBlock::nucleotide) && (mSpec.IsNucleotide() || mSpec.IsCodon() || mSpec.IsCodonAminoAcid())){ if(correctIndex > -1) throw ErrorException("More than one block containing nucleotide data was found."); else correctIndex = c; } //rna data is not allowed as input for codon or codon-aminoacid analyses else if(charblock->GetDataType() == NxsCharactersBlock::rna && (mSpec.IsNucleotide() || mSpec.IsRna())){ if(correctIndex > -1) throw ErrorException("More than one block containing nucleotide data was found."); else correctIndex = c; } else if(charblock->GetDataType() == NxsCharactersBlock::protein && (mSpec.IsAminoAcid() && ! mSpec.IsCodonAminoAcid())){ if(correctIndex > -1) throw ErrorException("More than one block containing amino acid (protein) data was found."); else correctIndex = c; } } if(correctIndex == -1){ if(mSpec.IsNucleotide()) throw ErrorException("A data file was read, but no nucleotide data was found."); else if(mSpec.IsRna()) throw ErrorException("A data file was read, but no RNA data was found."); else if(mSpec.IsAminoAcid()) throw ErrorException("A data file was read, but no amino acid (protein) data was found."); else if(mSpec.IsCodon()) throw ErrorException("DNA data is required as input for codon models.\n\tA data file was read, but none was found."); else if(mSpec.IsCodonAminoAcid()) throw ErrorException("DNA data is required as input for codon translated amino acid models.\n\tA data file was read, but none was found."); } return GetCharactersBlock(taxablock, correctIndex); } /*---------------------------------------------------------------------------------------------------------------------- | Returns true if file named `fn' already exists, false otherwise. */ bool GarliReader::FileExists( const char *fn) const /* the name of the file to check */ { bool exists = false; FILE *fp = fopen(fn, "r"); if (fp != NULL) { fclose(fp); exists = true; } return exists; } //DJZ there are my crappy functions to try to diagnose file type bool GarliReader::FileIsNexus(const char *name) const{ if (!FileExists(name)) { throw ErrorException("could not open file: %s!", name); } bool nexus = false; FILE *inf; #ifdef BOINC inf = boinc_fopen(name, "r"); #else inf = fopen(name, "r"); #endif char buf[1024]; GetToken(inf, buf, 1024); if(!(_stricmp(buf, "#NEXUS"))) nexus = true; fclose(inf); return nexus; } bool GarliReader::FileIsFasta(const char *name) const{ if (!FileExists(name)) { throw ErrorException("could not open file: %s!", name); } bool fasta = false; FILE *inf; #ifdef BOINC inf = boinc_fopen(name, "r"); #else inf = fopen(name, "r"); #endif char buf[1024]; GetToken(inf, buf, 1024); if(buf[0] == '>') fasta = true; fclose(inf); return fasta; } int GarliReader::GetToken( FILE *in, char* tokenbuf, int maxlen) const{ int ok = 1; int i; char ch = ' '; // skip leading whitespace while( in && ( isspace(ch) || ch == '[' ) ){ ch = getc(in); } if( !in ) return 0; tokenbuf[0] = ch; tokenbuf[1] = '\0'; tokenbuf[maxlen-1] = '\0'; for( i = 1; i < maxlen-1; i++ ) { ch = getc(in); if( isspace(ch) || ch == ']' ) break; tokenbuf[i] = ch; tokenbuf[i+1] = '\0'; } if( i >= maxlen-1 ) ok = 0; return ok; } /*---------------------------------------------------------------------------------------------------------------------- | Called whenever a file name needs to be read from either the command line or a file. Expects next token to be "=" | followed by the token representing the file name. Call this function after, say, the keyword "file" has been read | in the following LOG command: |> | log file=doofus.txt start replace; |> | Note that this function will read only the "=doofus.txt " leaving "start replace;" in the stream for reading at | a later time. */ NxsString GarliReader::GetFileName( NxsToken &token) /* the token used to read from `in' */ { // Eat the equals sign // token.GetNextToken(); if (!token.Equals("=")) { errormsg = "Expecting an equals sign, but found "; errormsg += token.GetToken(); errormsg += " instead"; throw NxsException(errormsg, token.GetFilePosition(), token.GetFileLine(), token.GetFileColumn()); } // Now get the filename itself // token.GetNextToken(); return token.GetToken(); } /*---------------------------------------------------------------------------------------------------------------------- | Called when the END or ENDBLOCK command needs to be parsed from within the GarliReader block. Basically just | checks to make sure the next token in the data file is a semicolon. */ void GarliBlock::HandleEndblock( NxsToken &token) /* the token used to read from `in' */ { // Get the semicolon following END or ENDBLOCK token // token.GetNextToken(); if (!token.Equals(";")) { errormsg = "Expecting ';' to terminate the END or ENDBLOCK command, but found "; errormsg += token.GetToken(); errormsg += " instead"; throw NxsException(errormsg, token.GetFilePosition(), token.GetFileLine(), token.GetFileColumn()); } } /*---------------------------------------------------------------------------------------------------------------------- | Handles everything after the EXECUTE keyword and the terminating semicolon. Purges all blocks before executing | file specified, and no warning is given of this. | DJZ THIS IS NOT THE VERSION OF HandleExecute USED. See the other overloaded version below. */ void GarliReader::HandleExecute( NxsToken &token) /* the token used to read from `in' */ { // Issuing the EXECUTE command from within a file is a no-no (at least in this program) // if (inf_open) { errormsg = "Cannot issue execute command from within a GarliReader block"; throw NxsException(errormsg, token.GetFilePosition(), token.GetFileLine(), token.GetFileColumn()); } // Get the file name to execute (note: if filename contains underscores, these will be // automatically converted to spaces; user should surround such filenames with single quotes) // token.GetNextToken(); NxsString fn = token.GetToken(); // Get the semicolon terminating the EXECUTE command // token.GetNextToken(); if (!token.Equals(";")) { errormsg = "Expecting ';' to terminate the EXECUTE command, but found "; errormsg += token.GetToken(); errormsg += " instead"; throw NxsException(errormsg, token.GetFilePosition(), token.GetFileLine(), token.GetFileColumn()); } if (FileExists(fn.c_str())) { cerr << endl; cerr << "Opening " << fn << "..." << endl; ifstream inf(fn.c_str(), ios::binary | ios::in); inf_open = true; MyNexusToken ftoken(inf); try { Execute(ftoken); } catch(NxsException &x) { NexusError(errormsg, x.pos, x.line, x.col); } if (inf_open) inf.close(); inf_open = false; // Users are allowed to put DATA blocks in their NEXUS files, but internally the data is always // stored in a NxsCharacterBlock object. // if (characters->IsEmpty() && !data->IsEmpty()) { data->TransferTo(*characters); } } // if (FileExists(fn.c_str())) else { cerr << endl; cerr << "Oops! Could not find specified file: " << fn << endl; } } int GarliReader::HandleExecute(const char *filename, bool purge) { // The filename to execute is passed in // NxsString fn = filename; int ret = 0; if (FileExists(fn.c_str())) { ifstream inf(fn.c_str(), ios::binary | ios::in); inf_open = true; MyNexusToken ftoken(inf); try{ Execute(ftoken); } catch(NxsException &x){ //DJZ 3/24/08 this was a bug that I inherited from the NCL example BasicCmdLine //the actual error message in x.msg was never getting printed because the empty //errormsg member of NexusBlock was being passed instead of the error stored in the //NxsException //NexusError(errormsg, x.pos, x.line, x.col); NexusError(x.msg, x.pos, x.line, x.col); ret = 1;//error } if (inf_open) inf.close(); inf_open = false; // Users are allowed to put DATA blocks in their NEXUS files, but internally the data is always // stored in a NxsCharacterBlock object. // if (characters->IsEmpty() && !data->IsEmpty()) { data->TransferTo(*characters); } } // if (FileExists(fn.c_str())) else { outman.UserMessage("Sorry, could not find specified file: %s", fn.c_str()); ret = 1; } return ret; } /*---------------------------------------------------------------------------------------------------------------------- | Called when the HELP command needs to be parsed from within the GarliReader block. */ void GarliReader::HandleHelp( NxsToken &token) /* the token used to read from `in' */ { // Retrieve all tokens for this command, stopping only in the event // of a semicolon or an unrecognized keyword // for (;;) { token.GetNextToken(); if (token.Equals(";")) { break; } else { errormsg = "Unexpected keyword ("; errormsg += token.GetToken(); errormsg += ") encountered reading HELP command"; throw NxsException(errormsg, token.GetFilePosition(), token.GetFileLine(), token.GetFileColumn()); } } message = "\nExamples of use of available commands:"; message += "\n help -> shows this message"; message += "\n log file=mylog.txt start -> opens log file named mylog.txt"; message += "\n log stop -> closes current log file"; message += "\n exe mydata.nex -> executes nexus file mydata.nex"; message += "\n show -> reports on blocks currently stored"; message += "\n quit -> terminates application"; message += "\n"; PrintMessage(); } void GarliReader::HandleGarliReader( NxsToken &token){ /* the token used to read from `in' */ } /*This would need to be rewritten for the new Factory/Multiformat system void GarliReader::HandleShow( NxsToken &token) { // Retrieve all tokens for this command, stopping only in the event // of a semicolon or an unrecognized keyword // for (;;) { token.GetNextToken(); if (token.Equals(";")) break; else { errormsg = "Unexpected keyword ("; errormsg += token.GetToken(); errormsg += ") encountered reading HELP command"; throw NxsException(errormsg, token.GetFilePosition(), token.GetFileLine(), token.GetFileColumn()); } } message = "\nNexus blocks currently stored:"; PrintMessage(); if (!taxa->IsEmpty()) { cerr << "\n TAXA block found" << endl; taxa->Report(cerr); if (logf_open) taxa->Report(logf); } if (!trees->IsEmpty()) { cerr << "\n TREES block found" << endl; trees->Report(cerr); if (logf_open) trees->Report(logf); } if (!assumptions->IsEmpty()) { cerr << "\n ASSUMPTIONS block found" << endl; assumptions->Report(cerr); if (logf_open) assumptions->Report(logf); } if (!distances->IsEmpty()) { cerr << "\n DISTANCES block found" << endl; distances->Report(cerr); if (logf_open) distances->Report(logf); } if (!characters->IsEmpty()) { cerr << "\n CHARACTERS block found" << endl; characters->Report(cerr); if (logf_open) characters->Report(logf); if (!charBlocks.empty()) { cerr << "\n " << charBlocks.size() << " CHARACTERS block found" << endl; for(vector::iterator it=charBlocks.begin();it != charBlocks.end();it++){ (*it)->Report(cerr); if (logf_open) (*it)->Report(logf); } } } if (!data->IsEmpty()) { cerr << "\n DATA block found" << endl; data->Report(cerr); if (logf_open) data->Report(logf); } } */ /*---------------------------------------------------------------------------------------------------------------------- | Called when the LOG command needs to be parsed from within the GarliReader block. */ void GarliReader::HandleLog( NxsToken &token) /* the token used to read from `in' */ { bool starting = false; bool stopping = false; bool appending = false; bool replacing = false; bool name_provided = false; NxsString logfname; // Retrieve all tokens for this command, stopping only in the event // of a semicolon or an unrecognized keyword // for (;;) { token.GetNextToken(); if (token.Equals(";")) { break; } else if (token.Abbreviation("STOp")) { stopping = true; } else if (token.Abbreviation("STArt")) { starting = true; } else if (token.Abbreviation("Replace")) { replacing = true; } else if (token.Abbreviation("Append")) { appending = true; } else if (token.Abbreviation("File")) { logfname = GetFileName(token); name_provided = true; } else { errormsg = "Unexpected keyword ("; errormsg += token.GetToken(); errormsg += ") encountered reading LOG command"; throw NxsException(errormsg, token.GetFilePosition(), token.GetFileLine(), token.GetFileColumn()); } } // Check for incompatible combinations of keywords // if (stopping && (starting || appending || replacing || name_provided)) { errormsg = "Cannot specify STOP with any of the following START, APPEND, REPLACE, FILE"; throw NxsException(errormsg, token.GetFilePosition(), token.GetFileLine(), token.GetFileColumn()); } if (appending && replacing) { errormsg = "Cannot specify APPEND and REPLACE at the same time"; throw NxsException(errormsg, token.GetFilePosition(), token.GetFileLine(), token.GetFileColumn()); } if (logf_open && (starting || name_provided || appending || replacing)) { errormsg = "Cannot start log file since log file is already open"; throw NxsException(errormsg, token.GetFilePosition(), token.GetFileLine(), token.GetFileColumn()); } // Is user closing an open log file? // if (stopping) { logf.close(); logf_open = false; message = "\nLog file closed"; PrintMessage(); return; } // If this far, must be attempting to open a log file // if (!name_provided) { errormsg = "Must provide a file name when opening a log file\n"; errormsg += "e.g., log file=doofus.txt start replace;"; throw NxsException(errormsg, token.GetFilePosition(), token.GetFileLine(), token.GetFileColumn()); } if (appending) { logf_open = true; logf.open(logfname.c_str(), ios::out | ios::app); message = "\nAppending to log file "; message += logfname; PrintMessage(); } else if (replacing) { logf_open = true; logf.open(logfname.c_str()); message = "\nReplacing log file "; message += logfname; PrintMessage(); } else { bool exists = FileExists(logfname.c_str()); bool userok = true; if (exists && !UserQuery("Ok to replace?", "Log file specified already exists", GarliReader::UserQueryEnum(GarliReader::uq_yes | GarliReader::uq_no))) userok = false; if (userok) { logf_open = true; logf.open(logfname.c_str()); } if (exists && userok) { message = "\nReplacing log file "; message += logfname; } else if (userok) { message = "\nLog file "; message += logfname; message += " opened"; } else { message = "\nLog command aborted"; } PrintMessage(); } } /*---------------------------------------------------------------------------------------------------------------------- | Accepts a string in the form of a GarliReader block containing one command and processes it just like a real | GarliReader block in a NEXUS data file. */ void GarliReader::HandleNextCommand() { std::istringstream cmdin(next_command); MyNexusToken token(cmdin); try { assert(garliBlock); garliBlock->Read(token); } catch(NxsException &x) { NexusError(errormsg, x.pos, x.line, x.col); } } /*---------------------------------------------------------------------------------------------------------------------- | Called when an error is encountered in a NEXUS file. Allows program to give user details of the error as well as | the precise location of the error. */ void GarliReader::NexusError( NxsString msg, /* the error message */ file_pos , /* the point in the NEXUS file where the error occurred */ long line, /* the line in the NEXUS file where the error occurred */ long col, /* the column in the NEXUS file where the error occurred */ bool throwExcept /*=true*/) /* whether to throw an actual exception or just output the error message */ { message = "\n"; message += msg; PrintMessage(); if (1) { message = "Line: "; message += line; PrintMessage(); message = "Column: "; message += col; PrintMessage(); } if(throwExcept) throw ErrorException("NCL encountered a problem reading the dataset."); } /*---------------------------------------------------------------------------------------------------------------------- | Begins with the command just entered by the user, which is stored in the data member `next_command', adds a | semicolon (if the user failed to supply one), and then adds the string "end;" so the whole bundle looks like a | very short GarliReader block. This is then passed to HandleNextCommand, which processes it just like a real | GarliReader block in a NEXUS data file. */ void GarliReader::PreprocessNextCommand() { // If user failed to add the terminating semicolon, we'll do it now. We will also remove the line feed // at the end and add the command "end;" to the end of the line (see explanation below). // unsigned len = strlen(next_command); assert(len > 0); // Remove any whitespace characters from end of string entered by user // unsigned i = len; while (i > 0 && next_command[i-1] == ' ' || next_command[i-1] == '\t' || next_command[i-1] == '\n') i--; // If character at position i - 1 is a semicolon, put '\0' terminator at position i; // otherwise, put a semicolon at position i and terminator at i + 1 // if (next_command[i-1] != ';') { next_command[i] = ';'; i++; } assert(i <= COMMAND_MAXLEN); next_command[i] = '\0'; // Now add a semicolon at the beginning and terminate with an "END;" command // so that we can pretend this is simply a very short private NEXUS block // containing only one command. This allows us to simply use the Read // function we inherited from the base class BstBase to process the command. // len = strlen(next_command); assert(len < COMMAND_MAXLEN-2); NxsString tmp = ";"; tmp += next_command; tmp += "end;"; strcpy(next_command, tmp.c_str()); } /*---------------------------------------------------------------------------------------------------------------------- | All output is funneled through here. Writes string currently stored in `message' (a NxsString data member) to the | output file stream, if open, and also to the console via cerr. Places a newline after the string if `linefeed' is | true. | DJZ - funneling all messages through my OutputManager, which already outputs to the screen and a log file */ void GarliReader::PrintMessage( bool linefeed) /* if true, places newline character after message */ { if(linefeed) outman.UserMessage(message); else outman.UserMessageNoCR(message); /* cerr << message; if (linefeed) cerr << endl; if (logf_open) { logf << message; if (linefeed) logf << endl; } */ } /*---------------------------------------------------------------------------------------------------------------------- | This function provides the ability to read everything following the block name (which is read by the NxsReader | object) to the END or ENDBLOCK statement. Characters are read from the input stream `in'. Overrides the virtual | function in the base class. */ void GarliBlock::Read( NxsToken &token) /* the token used to read from `in' */ { isEmpty = false; //if we already read a garli block with a model string, clear it modelString.clear(); // This should be the semicolon after the block name // token.GetNextToken(); if (!token.Equals(";")) { errormsg = "Expecting ';' after "; errormsg += id; errormsg += " block name, but found "; errormsg += token.GetToken(); errormsg += " instead"; throw NxsException(errormsg, token.GetFilePosition(), token.GetFileLine(), token.GetFileColumn()); } for (;;) {//only allowing three things to happen here //1. endblock is reached, sucessfully exiting the garli block //2. something besides an endblock is read. This is interpreted as part of the model string, with minimal error checking //3. eof is hit before an endblock //we want to allow hyphens in parenthetical notation, since otherwise they are individual nexus tokens. //this gets reset after every read token.SetLabileFlagBit(NxsToken::hyphenNotPunctuation); token.GetNextToken(); if (token.Abbreviation("ENdblock")) { HandleEndblock(token); break; } else if(token.AtEOF() == false){ NxsString s = token.GetToken(); if(s.size() > 1 && (s.IsADouble() == false && s.IsALong() == false && s.find("M") > s.length())){ errormsg = "Unexpected character(s) in Garli block.\n See manual for model parameter format."; throw NxsException(errormsg, token.GetFilePosition(), token.GetFileLine(), token.GetFileColumn()); } if(token.IsPunctuationToken() == false){//toss semicolons and such modelString += token.GetToken(); modelString += ' '; } } else { errormsg = "Unexpected end of file encountered before \"end;\" or\n \"endblock;\" command in Garli block"; throw NxsException(errormsg, token.GetFilePosition(), token.GetFileLine(), token.GetFileColumn()); } } /* else if (token.Abbreviation("GarliReader")) { HandleGarliReader(token); } else if (token.Abbreviation("Help")) { HandleHelp(token); } else if (token.Abbreviation("Log")) { HandleLog(token); } else if (token.Abbreviation("EXecute")) { HandleExecute(token); } else if (token.Abbreviation("Show")) { HandleShow(token); } else if (token.Abbreviation("Quit")) { quit_now = true; message = "\nGarliReader says goodbye\n"; PrintMessage(); break; } else { SkippingCommand(token.GetToken()); do { token.GetNextToken(); } while (!token.AtEOF() && !token.Equals(";")); if (token.AtEOF()) { errormsg = "Unexpected end of file encountered"; throw NxsException(errormsg, token.GetFilePosition(), token.GetFileLine(), token.GetFileColumn()); } } */ } /*---------------------------------------------------------------------------------------------------------------------- | This function outputs a brief report of the contents of this GarliReader block. Overrides the virtual function | in the NxsBlock base class. */ void GarliBlock::Report( ostream &out) const /* the output stream to which to write the report */ { /* message.clear(); PrintMessage(); out << message << '\n'; message = id; message += " block contains..."; PrintMessage(); out << message << '\n'; */ } /*---------------------------------------------------------------------------------------------------------------------- | Runs the command line interpreter, allowing GarliReader to interact with user. Typically, this is the only | function called in main after a GarliReader object is created. If `infile_name' is non-NULL, the first command | executed by the command interpreter will be "EXECUTE `infile_name'". | DJZ - not currently used, since I'm just using NCL to parse the datafile */ void GarliReader::Run( char *infile_name) /* the name of the NEXUS data file to execute (can be NULL) */ { taxa = new NxsTaxaBlock(); trees = new NxsTreesBlock(taxa); #if defined(NCL_MAJOR_VERSION) && (NCL_MAJOR_VERSION >= 2) && (NCL_MINOR_VERSION >= 1) trees->SetAllowImplicitNames(true); #endif assumptions = new NxsAssumptionsBlock(taxa); characters = new NxsCharactersBlock(taxa, assumptions); distances = new NxsDistancesBlock(taxa); data = new NxsDataBlock(taxa, assumptions); Add(taxa); Add(trees); Add(assumptions); Add(characters); Add(distances); Add(data); Add(garliBlock); if (infile_name != NULL) { strcpy(next_command, "exe "); strncat(next_command, infile_name, 252); PreprocessNextCommand(); //DEBUG // HandleNextCommand(); } quit_now = false; while (!quit_now) { cerr << endl; cerr << "GarliReader> "; //cin.getline(next_command, COMMAND_MAXLEN); unsigned i = 0; for(;;) { int ch = cin.get(); if (i > COMMAND_MAXLEN) { cerr << endl; cerr << "Error: the length of any one command cannot exceed "; cerr << COMMAND_MAXLEN << " characters" << endl; break; } else if (ch == 10 || ch == 13) break; next_command[i++] = (char)ch; next_command[i] = '\0'; } PreprocessNextCommand(); //DEBUG // HandleNextCommand(); } } /*---------------------------------------------------------------------------------------------------------------------- | Called when program does not recognize a block name encountered in a NEXUS file. Virtual function that overrides | the virtual function in the base class NxsReader. */ void GarliReader::SkippingBlock( NxsString blockName) /* the unrecognized block name */ { message = "Skipping unknown block ("; message += blockName; message += ")"; PrintMessage(); } /*---------------------------------------------------------------------------------------------------------------------- | This function is called when an unknown command named `commandName' is about to be skipped. This version of the | function (which is identical to the base class version) does nothing (i.e., no warning is issued that a command | was unrecognized). Modify this virtual function to provide such warnings to the user (or eliminate it altogether | since the base class version already does what this does). */ void GarliReader::SkippingCommand( NxsString commandName) /* the name of the command being skipped */ { message = "Skipping unknown command ("; message += commandName; message += ")"; PrintMessage(); } /*---------------------------------------------------------------------------------------------------------------------- | Called by the NxsReader object when skipping a block named blockName that has been disabled. Allows program to | notify user of progress in parsing the NEXUS file. Virtual function that overrides the virtual function in the | base class NxsReader. */ void GarliReader::SkippingDisabledBlock( NxsString ) /* the name of the block just exited */ { } /*---------------------------------------------------------------------------------------------------------------------- | Returns true if response is either "ok" or "yes", and returns false if response is either "no" or "cancel". | This is a general query function that can handle many situations. The possible responses are enumerated in | GarliReader::UserQueryEnum: uq_cancel, uq_ok, uq_yes, and uq_no. Not yet fully implemented: only handles uq_ok | alone or the (uq_yes | uq_no) combination. */ bool GarliReader::UserQuery( NxsString mb_message, /* the question posed to the user */ NxsString mb_title, /* the title of the message box */ GarliReader::UserQueryEnum mb_choices) /* bit combination of uq_xx values indicating which buttons to show */ { const bool yes_no = (mb_choices == (GarliReader::uq_yes | GarliReader::uq_no)); const bool ok_only = (mb_choices == GarliReader::uq_ok); assert(ok_only || yes_no); // Still working on other choices if (ok_only) { cerr << endl; cerr << mb_title << endl; cerr << " " << mb_message; cerr << " (press return to acknowledge) "; cin.getline(next_command, COMMAND_MAXLEN); return true; } cerr << endl; cerr << mb_title << endl; cerr << " " << mb_message; cerr << " (y/n) "; cin.getline(next_command, COMMAND_MAXLEN); // This could be made much simpler by just checking first letter: if 'y' then // assume yes, if 'n' assume no. // bool yep = (next_command[0] == 'y' && next_command[1] == '\0'); bool nope = (next_command[0] == 'n' && next_command[1] == '\0'); while (!yep && !nope) { cerr << endl; cerr << "Must answer by typing either y or n and then pressing the Enter key" << endl; cerr << endl; cerr << mb_title << endl; cerr << " " << mb_message; cerr << " (y/n) "; cin.getline(next_command, COMMAND_MAXLEN); yep = (next_command[0] == 'y' && next_command[1] == '\0'); nope = (next_command[0] == 'n' && next_command[1] == '\0'); } return yep; } /*---------------------------------------------------------------------------------------------------------------------- | Called if an "output comment" is encountered in a NEXUS data file. An output comment is a comment [text enclosed in | square brackets] that begins with an exclamation point. [!This is an example of a NEXUS output comment]. Output | comments are supposed to be displayed when encountered. Modify this function's body to display output comments, | which are made available as they are encountered via the `msg' argument. */ inline void GarliReader::OutputComment(const NxsString &msg) { size_t pos; string s; //changing this again - just eating the Garli output comments s = "GarliScore"; pos = msg.find(s); if(pos != string::npos){ //outman.UserMessage("This is apparently a tree inferred by Garli in a previous run. Its score was %s", msg.substr(s.length()).c_str()); return; } s = "GarliModel"; pos = msg.find(s); if(pos != string::npos){ //outman.UserMessage("Garli's model parameter values used in inferring this tree:\n\t%s", msg.substr(s.length()).c_str()); return; } s = "****NOTE";//this is a note about the parameter values either being from a run that was terimated early or that //they are only optimal for a certain tree. This is mainly for output when reading the trees in PAUP //and we will just ignore them here pos = msg.find(s); if(pos != string::npos) return; outman.UserMessage("\nCOMMENT FOUND IN NEXUS FILE (output verbatim):"); outman.UserMessage(msg); outman.UserMessage("(END OF NEXUS COMMENT)"); } GarliReader & GarliReader::GetInstance() { static GarliReader gr; return gr; } //This doesn't really have anything to do with the GarliReader class, it just acts on the passed in charblock string GarliReader::GetDefaultIntWeightSet(const NxsCharactersBlock *charblock, vector &charWeights) { const NxsTransformationManager transformer = charblock->GetNxsTransformationManagerRef(); string wset = transformer.GetDefaultWeightSetName(); if(wset.length() > 0){ charWeights = transformer.GetDefaultIntWeights(); if(charWeights.size() == 0) throw ErrorException("Default weightSet \"%s\" contains non-integer weights", wset.c_str()); } return wset; } garli-2.1-release/src/garlireader.h000066400000000000000000000242301241236125200173100ustar00rootroot00000000000000// GARLI version 2.1 source code // Copyright 2005-2014 Derrick J. Zwickl // email: garli.support@gmail.com // // This program is free software: you can redistribute it and/or modify // it under the terms of the GNU General Public License as published by // the Free Software Foundation, either version 3 of the License, or // (at your option) any later version. // // This program is distributed in the hope that it will be useful, // but WITHOUT ANY WARRANTY; without even the implied warranty of // MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the // GNU General Public License for more details. // // You should have received a copy of the GNU General Public License // along with this program. If not, see . // This file was adapted from from the BasicCmdLine example provided as // part of the NCL // Copyright (C) 1999-2002 Paul O. Lewis // // This file is part of NCL (Nexus Class Library). // // NCL is free software; you can redistribute it and/or modify // it under the terms of the GNU General Public License as published by // the Free Software Foundation; either version 2 of the License, or // (at your option) any later version. // // NCL is distributed in the hope that it will be useful, // but WITHOUT ANY WARRANTY; without even the implied warranty of // MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the // GNU General Public License for more details. // // You should have received a copy of the GNU General Public License // along with NCL; if not, write to the Free Software Foundation, Inc., // 59 Temple Place, Suite 330, Boston, MA 02111-1307 USA // #ifndef NCL_GarliReader_H #define NCL_GarliReader_H #define COMMAND_MAXLEN 255 #include "ncl.h" #include "nxsmultiformat.h" class ModelSpecification; //the reader is no longer derived from NexusBlock itself which was done such that it was it's own //custom block (a bit weird). Garli block is separate entity now. class GarliBlock: public NxsBlock{ public: GarliBlock():NxsBlock(){ id ="GARLI"; } NxsString modelString; char *next_command; void Read(NxsToken &token); void HandleEndblock(NxsToken &token); const NxsString GetModelString(){return modelString;} bool ModelStringWasRead(){return modelString.empty() == false;} void Clear() { modelString.clear();} void Report(ostream &out) const; // void HandleNextCommand(); // void NexusError(NxsString msg, file_pos pos, long line, long col); }; /*---------------------------------------------------------------------------------------------------------------------- | GarliReader provides a template for creating a program that reads NEXUS data files and provides a basic command | line. After compiling GarliReader, you will already have a program that understands the following commands, either | typed in at the console or provided in a GarliReader block in a NEXUS data file (exception is the execute command, | which can only be entered at the console). Keywords in the descriptions below are given in uppercase, however the | commands themselves are case-insensitive. Lower-case indicates a parameter supplied by the user (e.g., "filename" | would be replaced by the actual name of the file). Square brackets indicate optional keywords or subcommands. |> | EXECUTE filename; | | LOG [options]; | | Option Action | ------------------------------------------------------ | FILE=filename specifies name of log file to start | START indicates logging is to be started | STOP indicates logging is to be stopped | APPEND append to log file if it already exists | REPLACE replace log file without asking | | QUIT; |> | See the Read function for details and to add other commands. | | To change the name of the program (which is also the prompt name and the name of the program's private NEXUS | block), replace all occurrences of GarliReader with the name of your program (also search for the string | "GarliReader" and replace with an appropriate string at each occurrence). | | This class handles reading and storage for the NxsReader block GarliReader. It also serves as the main class for | the program GarliReader, acting as both a NxsReader object (in order to be capable of parsing data files) as well | as a NxsBlock object (in order to be able to process commands in a GarliReader block). | | Adding a new data member? Don't forget to: |~ | o Describe it in the class header comment at the top of "GarliReader.h" | o Initialize it (unless it is self-initializing) in the constructor and reinitialize it in the Reset function | o Describe the initial state in the constructor documentation | o Delete memory allocated to it in both the destructor and Reset function | o Report it in some way in the Report function |~ */ class GarliReader : public MultiFormatReader { friend class NxsBlock; public: static GarliReader & GetInstance(); enum UserQueryEnum /* enumeration used with UserQuery member function to specify which choices to provide the user */ { uq_cancel = 0x01, /* provide opportunity to cancel */ uq_ok = 0x02, /* provide opportunity to answer ok */ uq_yes = 0x04, /* provide opportunity to answer yes */ uq_no = 0x08 /* provide opportunity to answer no */ }; GarliReader(); virtual ~GarliReader(); bool EnteringBlock(NxsString blockName); void ExitingBlock(NxsString blockName); void ExecuteStarting(); void ExecuteStopping(); void OutputComment(const NxsString &msg); void HandleNextCommand(); void NexusError(NxsString msg, file_pos pos, long line, long col){ NexusError(msg, pos, line, col, true); } void NexusError(NxsString msg, file_pos pos, long line, long col, bool throwExcept); void PreprocessNextCommand(); void PrintMessage(bool linefeed = true); // virtual void Report(ostream &out); void Run(char *infile_name); void SkippingBlock(NxsString blockName); void SkippingCommand(NxsString commandName); void SkippingDisabledBlock(NxsString blockName); virtual bool UserQuery(NxsString mb_message, NxsString mb_title, GarliReader::UserQueryEnum mb_choices = GarliReader::uq_ok); //a bunch of hacky stuff here got removed when going to the new NLC Factory API and deriving the reader //from Multiformat Reader- I don't need to worry about multiple charblocks and such myself. Many functions //that were here are now further up in the inheritance chain and not part of my code //char blocks protected: bool inf_open; /* true if `inf' is currently open */ bool logf_open; /* true if `logf' is currently open */ bool quit_now; /* set to false at beginning of Run and turns true only when QUIT command processed */ ofstream logf; /* the log file output stream */ NxsString message; /* workspace for composing output strings */ //none of these should be getting used with the new Factory/MultiformatReader system NxsTreesBlock *trees; /* pointer to NxsTreesBlock object */ NxsTaxaBlock *taxa; /* pointer to NxsTaxaBlock object */ NxsAssumptionsBlock *assumptions; /* pointer to NxsAssumptionsBlock object */ NxsDistancesBlock *distances; /* pointer to NxsDistancesBlock object */ NxsCharactersBlock *characters; /* pointer to NxsCharactersBlock object */ NxsDataBlock *data; /* pointer to NxsDataBlock object */ //this still is being used GarliBlock *garliBlock; NxsString errormsg; char *next_command; /* workspace for processing next command entered interactively by user */ unsigned CharLabelToNumber(NxsString s) const; bool FileExists(const char* fn) const; bool FileIsNexus(const char *name) const; bool FileIsFasta(const char *name) const; int GetToken( FILE *in, char* tokenbuf, int maxlen) const; NxsString GetFileName(NxsToken& token); void HandleEndblock(NxsToken& token); void HandleShow(NxsToken& token); void HandleHelp(NxsToken& token); void HandleLog(NxsToken& token); void HandleExecute(NxsToken& token); void HandleGarliReader(NxsToken &token); public: int HandleExecute(const char *filename, bool purge); string GetModelString(){ return garliBlock->GetModelString(); } bool FoundModelString() {return garliBlock->ModelStringWasRead();} void ClearModelString() {garliBlock->Clear();} //this removes and deallocates everything in the reader and gets it ready //for further reading void ClearContent(); //delete only the characters blocks, which can take a substantial amount of memory, and aren't needed after //Garli's matrices are created void DeleteCharacterBlocksFromFactories(); bool ReadData(const char* filename, const ModelSpecification &modspec); const NxsCharactersBlock *CheckBlocksAndGetCorrectCharblock(const ModelSpecification &modspec) const; static string GetDefaultIntWeightSet(const NxsCharactersBlock *charblock, vector &wset); }; /*---------------------------------------------------------------------------------------------------------------------- | The MyNexusToken class provides a NxsToken-derived object that can display output comments as it encounters them. | The virtual function NxsToken::OutputComment is overridden in this class for this purpose. */ class MyNexusToken : public NxsToken { public: MyNexusToken(istream &i); void OutputComment(const NxsString &msg); }; /*---------------------------------------------------------------------------------------------------------------------- | Will be called by NxsReader::Execute after the initial "#NEXUS" keyword is found in a NEXUS file but before other | tokens are read. Add code here if you need to do any initializations prior to encountering any NEXUS blocks in a | NEXUS data file. */ inline void GarliReader::ExecuteStarting() { } /*---------------------------------------------------------------------------------------------------------------------- | Will be called by NxsReader::Execute just before it exits after reading to the end of a NEXUS data file (or until | encountering a LEAVE command between NEXUS blocks. Add code here if you need to clean up any memory allocated in | ExecuteStarting. */ inline void GarliReader::ExecuteStopping() { } #endif garli-2.1-release/src/individual.cpp000066400000000000000000001150131241236125200175120ustar00rootroot00000000000000// GARLI version 0.96b8 source code // Copyright 2005-2008 Derrick J. Zwickl // email: garli.support@gmail.com // // This program is free software: you can redistribute it and/or modify // it under the terms of the GNU General Public License as published by // the Free Software Foundation, either version 3 of the License, or // (at your option) any later version. // // This program is distributed in the hope that it will be useful, // but WITHOUT ANY WARRANTY; without even the implied warranty of // MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the // GNU General Public License for more details. // // You should have received a copy of the GNU General Public License // along with this program. If not, see . // // NOTE: Portions of this source adapted from GAML source, written by Paul O. Lewis #include #include #include using namespace std; #include "defs.h" #include "set.h" #include "funcs.h" #include "adaptation.h" #include "model.h" #include "tree.h" #include "population.h" #include "condlike.h" #include "sequencedata.h" #include "treenode.h" #include "individual.h" #include "errorexception.h" #include "outputman.h" #include "reconnode.h" #include "utility.h" extern int memLevel; extern int calcCount; extern OutputManager outman; extern FLOAT_TYPE globalBest; #define MUTUALLY_EXCLUSIVE_MUTS #undef VARIABLE_OPTIMIZATION // // // Methods for class Individual // // Individual::Individual() : dirty(1), fitness(0.0), reproduced(false), willreproduce(false), parent(-1), willrecombine(false), recombinewith(-1), topo(-1), mutated_brlen(0), mutation_type(0), accurateSubtrees(0){ treeStruct=NULL; // mod=new Model(); } Individual::Individual(const Individual *other) : dirty(1), fitness(0.0), reproduced(false), willreproduce(false), parent(-1), willrecombine(false), recombinewith(-1), topo(-1), mutated_brlen(0), mutation_type(0), accurateSubtrees(0){ //mod=new Model(); treeStruct=new Tree(); CopyNonTreeFields(other); treeStruct->MimicTopo(other->treeStruct); dirty=false; treeStruct->lnL=other->fitness; treeStruct->modPart = &modPart; } Individual::~Individual(){ if(treeStruct!=NULL) delete treeStruct; //if(mod!=NULL) delete mod; } void Individual::CopySecByStealingFirstTree(Individual * sourceOfTreePtr, const Individual *sourceOfInformation){ CopyNonTreeFields(sourceOfInformation); treeStruct=sourceOfTreePtr->treeStruct; treeStruct->CopyBranchLens(sourceOfInformation->treeStruct); treeStruct->CopyClaIndeces(sourceOfInformation->treeStruct,1); dirty=false; } void Individual::CopySecByRearrangingNodesOfFirst(Tree * sourceOfTreePtr, const Individual *sourceOfInformation, bool CLAassigned /*=false*/){ CopyNonTreeFields(sourceOfInformation); treeStruct=sourceOfTreePtr; for(int i=treeStruct->getNumTipsTotal()+1;i<(2*treeStruct->getNumTipsTotal()-2);i++) treeStruct->allNodes[i]->attached=false; //DZ 10-28 changing this treeStruct->MimicTopo(sourceOfInformation->treeStruct); treeStruct->CopyClaIndeces(sourceOfInformation->treeStruct,CLAassigned); dirty=false; treeStruct->lnL=sourceOfInformation->fitness; modPart.CopyModelPartition(&sourceOfInformation->modPart); treeStruct->modPart = &modPart; } void Individual::DuplicateIndivWithoutCLAs(const Individual *sourceOfInformation){ CopyNonTreeFields(sourceOfInformation); if(treeStruct == NULL) treeStruct = new Tree; for(int i=treeStruct->getNumTipsTotal()+1;i<(2*treeStruct->getNumTipsTotal()-2);i++) treeStruct->allNodes[i]->attached=false; treeStruct->MimicTopo(sourceOfInformation->treeStruct); dirty=true; treeStruct->lnL=sourceOfInformation->fitness; modPart.CopyModelPartition(&sourceOfInformation->modPart); treeStruct->modPart = &modPart; } void Individual::Mutate(FLOAT_TYPE optPrecision, Adaptation *adap){ //this is the original version of mutate, and will be called by both //master and remote when they are mutating a tree that does not have //its subtrees properly defined. FLOAT_TYPE r = rnd.uniform(); //DJZ 1-5-05 Moving branch length mutation to be before topo, so that if both are performed //the upward sweep needed for blen optimization in the topo mutation will automatically recalc //nodes that were dirtied by the blen mutation, and the score of the tree can be finalized at //an internal node after the last branch is optimized, rather than waiting until CalcAverageFitness //when it will require a sweep down to the root #ifndef MUTUALLY_EXCLUSIVE_MUTS if(adap->branchOptPrecision != adap->minOptPrecision || r > adap->modelMutateProb + adap->topoMutateProb){ #else if(r >= adap->modelMutateProb + adap->topoMutateProb){ #endif mutated_brlen=treeStruct->BrlenMutate(); if(mutated_brlen > 0){ mutation_type |= brlen; dirty=true; } } try{ if(r <= adap->topoMutateProb){ r = rnd.uniform(); if(rlimSPRprob){ int reconDist = treeStruct->TopologyMutator(optPrecision, adap->limSPRrange, 0); if(reconDist == 1 || reconDist == -1) mutation_type |= randNNI; else if(reconDist < 0) mutation_type |= limSPRCon; else mutation_type |= limSPR; if(!FloatingPointEquals(treeStruct->lnL, -ONE_POINT_ZERO, 1.0e-8)){ fitness=treeStruct->lnL; dirty=false; } else dirty=true; } else if (r< adap->randSPRprob + adap->limSPRprob){ int reconDist = treeStruct->TopologyMutator(optPrecision, -1, 0); if(reconDist < 0){ if(reconDist == -1) mutation_type |= randNNI; else if(reconDist < -1 * (int)adap->limSPRrange) mutation_type |= randSPRCon; else mutation_type |= limSPRCon; } else { if(reconDist == 1) mutation_type |= randNNI; else if(reconDist > (int) adap->limSPRrange) mutation_type |= randSPR; else mutation_type |= limSPR; } if(!FloatingPointEquals(treeStruct->lnL, -ONE_POINT_ZERO, 1.0e-8)){ fitness=treeStruct->lnL; dirty=false; } else dirty=true; } else { treeStruct->TopologyMutator(optPrecision, 1, 0); mutation_type |= randNNI; if(!FloatingPointEquals(treeStruct->lnL, -ONE_POINT_ZERO, 1.0e-8)){ fitness=treeStruct->lnL; dirty=false; } else dirty=true; } } // end if of topomutation //model mutations else if( r < adap->modelMutateProb + adap->topoMutateProb){ mutation_type |= modPart.PerformModelMutation(); treeStruct->MakeAllNodesDirty(); dirty = true; } //be sure that we have an accurate score before any CLAs get invalidated CalcFitness(0); } catch(UnscoreableException &ex){ //in some situations the tree just underflows no matter what - I've only seen this and only //throw this from orientedGap models with very poor trees. outman.DebugMessage("WARNING - created individual deemed unscorable!"); treeStruct->lnL = -FLT_MAX; SetFitness(-FLT_MAX); } /* FLOAT_TYPE lnL = fitness; dirty = true; treeStruct->MakeAllNodesDirty(); CalcFitness(0); if(!FloatingPointEquals(lnL, fitness, 1e-3)){ outman.UserMessage("DEBUG - scoring problem:%f vs %f", lnL, fitness); //throw ErrorException("DEBUG - scoring problem:%f vs %f", lnL, fitness); } */ // treeStruct->calcs=calcCount; // calcCount=0; } void Individual::CalcFitness(int subtreeNode){ if(dirty || FloatingPointEquals(treeStruct->lnL, ZERO_POINT_ZERO, max(1.0e-8, GARLI_FP_EPS * 2.0)) || FloatingPointEquals(treeStruct->lnL, -ONE_POINT_ZERO, max(1.0e-8, GARLI_FP_EPS * 2))){ if(subtreeNode>0 && accurateSubtrees==true){ treeStruct->Score( subtreeNode ); } else treeStruct->Score( ); fitness = treeStruct->lnL; dirty = 0; } else{ assert(!FloatingPointEquals(treeStruct->lnL, -ONE_POINT_ZERO, 1e-8)); fitness = treeStruct->lnL; } if(memLevel > 0) treeStruct->RemoveTempClaReservations(); } void Individual::MakeRandomTree(int nTax){ treeStruct=new Tree(); int n = nTax; Set taxset(n); for( int i = 1; i <= n; i++ ) taxset += i; int placeInAllNodes=n+1; if(treeStruct->constraints.empty() == true){ // add nodes randomly for( int i = 0; i < n; i++ ) { int pos = rnd.random_int( taxset.Size() ); int k = taxset[pos]; treeStruct->RandomlyAttachTip(k, placeInAllNodes ); taxset -= k; } } else{ // add nodes randomly, ensuring that the resulting partial tree is compatible with constraints Bipartition mask; for( int i = 0; i < n; i++ ) { int pos = rnd.random_int( taxset.Size() ); int k = taxset[pos]; treeStruct->RandomlyAttachTipWithConstraints(k, placeInAllNodes, &mask ); taxset -= k; } #ifndef NDEBUG for(vector::iterator conit=treeStruct->constraints.begin();conit!=treeStruct->constraints.end();conit++){ TreeNode *check = NULL; if((*conit).IsBackbone()) check = treeStruct->ContainsMaskedBipartitionOrComplement(*(*conit).GetBipartition(), *(*conit).GetBackboneMask()); else check = treeStruct->ContainsBipartitionOrComplement(*(*conit).GetBipartition()); if((*conit).IsPositive()) assert(check != NULL); else assert(check == NULL); } #endif } if(treeStruct->dummyRootBranchMidpoint) treeStruct->MoveDummyRootToBranchMidpoint(); treeStruct->AssignCLAsFromMaster(); } void Individual::MakeStepwiseTree(int nTax, int attachesPerTaxon, FLOAT_TYPE optPrecision ){ treeStruct=new Tree(); treeStruct->modPart = &modPart; treeStruct->AssignCLAsFromMaster(); Individual scratchI; scratchI.treeStruct=new Tree(); Tree *scratchT = scratchI.treeStruct; scratchT->modPart = &scratchI.modPart; scratchT->AssignCLAsFromMaster(); scratchI.CopySecByRearrangingNodesOfFirst(scratchT, this, true); int n = nTax; Set taxset(n); for( int i = 1; i <= n; i++ ) taxset += i; int placeInAllNodes=n+1; // ofstream stepout("stepwise.log"); outman.UserMessage("number of taxa added:"); Bipartition mask;//mask is used for constrained trees for(int i = 0;i<3;i++){//add the first 3 int pos = rnd.random_int( taxset.Size() ); int k = taxset[pos]; if(treeStruct->constraints.empty()) scratchT->RandomlyAttachTip(k, placeInAllNodes ); else scratchT->RandomlyAttachTipWithConstraints(k, placeInAllNodes, &mask ); taxset -= k; } //use information on the similarity between sequences to choose first stepwise additions /* const SequenceData *dat = treeStruct->data; int nstates = mod->NStates(); FLOAT_TYPE **pdist = New2DArray(dat->NTax(), dat->NTax()); for(int i=0;iGetRow(i), (char*) dat->GetRow(j), dat->GetCounts(), dat->NChar(), nstates); pdist[j][i] = pdist[i][j]; } } //add the first 3 //be careful because the taxa are indexed from 1->ntax int pos = rnd.random_int( taxset.Size() ); int first = (taxset[pos]); scratchT->RandomlyAttachTip(first, placeInAllNodes ); taxset -= first; //add the furthest taxon to that int sec = 1; FLOAT_TYPE maxDist = pdist[first-1][sec-1]; for(int i=sec+1;i<=dat->NTax();i++){ if(pdist[first-1][i-1] > maxDist){ sec = i; maxDist = pdist[first-1][sec-1]; } } scratchT->RandomlyAttachTip(sec, placeInAllNodes ); taxset -= sec; //add the furthest taxon to that (which may in fact be close to first, but should not have a pdist = 0 to it) int third = (first == 1 ? 2 : 1); maxDist = pdist[sec-1][third-1]; for(int i=third+1;i<=dat->NTax();i++){ if(pdist[sec-1][i] > maxDist && i != first && pdist[first-1][third-1] > ZERO_POINT_ZERO){ third = i; maxDist = pdist[sec-1][third-1]; } } scratchT->RandomlyAttachTip(third, placeInAllNodes ); taxset -= third; */ CopySecByRearrangingNodesOfFirst(treeStruct, &scratchI, true); for( int i = 3; i < n; i++ ) { //select a random node int pos = rnd.random_int( taxset.Size() ); int k = taxset[pos]; taxset -= k; //add the node randomly - this is a little odd, but for the existing swap collecting machinery //to work right, the taxon to be added needs to already be in the tree if(treeStruct->constraints.empty()) scratchT->RandomlyAttachTip(k, placeInAllNodes ); else scratchT->RandomlyAttachTipWithConstraints(k, placeInAllNodes, &mask ); TreeNode *added = scratchT->allNodes[k]; scratchT->SweepDirtynessOverTree(added); scratchT->OptimizeBranchesWithinRadius(added->anc, optPrecision, 0, NULL); //backup what we have now CopySecByRearrangingNodesOfFirst(treeStruct, &scratchI, true); FLOAT_TYPE bestScore = scratchT->lnL; //collect reconnection points - this will automatically filter for constraints scratchT->GatherValidReconnectionNodes(scratchT->NTax()*2, added, NULL, &mask); // stepout << i << "\t" << k << "\t" << bestScore << "\t"; //start swappin int num=0; //for(list::iterator b = scratchT->sprRang.begin();b != scratchT->sprRang.end();b++){ ReconList attempted; while(num < attachesPerTaxon && scratchT->sprRang.size() > 0){ int connectNum = rnd.random_int(scratchT->sprRang.size()); listIt broken = scratchT->sprRang.NthElement(connectNum); //try a reattachment point scratchT->SPRMutate(added->nodeNum, &(*broken), optPrecision, 0); //record the score broken->chooseProb = scratchT->lnL; attempted.AddNode(*broken); scratchT->sprRang.RemoveNthElement(connectNum); // stepout << scratchT->lnL << "\t"; //restore the tree scratchI.CopySecByRearrangingNodesOfFirst(scratchT, this, true); num++; } //now find the best score ReconNode *best = NULL; //For debugging, add to random place, to check correct filtering of attachment points for constraints /* if(attempted.size() != 0) best = attempted.RandomReconNode(); */ for(list::iterator b = attempted.begin();b != attempted.end();b++){ if((*b).chooseProb > bestScore){ best = &(*b); bestScore = (*b).chooseProb; } } //if we didn't find anything better than the initial random attachment we don't need to do anything if(best != NULL){ scratchT->SPRMutate(added->nodeNum, best, optPrecision, 0); } else scratchT->Score(); scratchI.CalcFitness(0); // stepout << scratchT->lnL << endl; CopySecByRearrangingNodesOfFirst(treeStruct, &scratchI, true); //outman.UserMessage(" %d %f", i+1, scratchT->lnL); outman.UserMessageNoCR(" %d ", i+1); outman.flush(); //when we've added half the taxa optimize alpha, flex or omega if(i == (n/2)){ FLOAT_TYPE improve = 0.0; for(int modnum = 0;modnum < modPart.NumModels();modnum++){ Model *mod = scratchI.modPart.GetModel(modnum); const ModelSpecification *modSpec = mod->GetCorrespondingSpec(); if(modSpec->IsCodon())//optimize omega even if there is only 1 improve += scratchT->OptimizeOmegaParameters(optPrecision, modnum); else if(mod->NRateCats() > 1){ if(modSpec->IsFlexRateHet()){//Flex rates //no longer doing alpha first, it was too hard to know if the flex rates had been partially optimized //already during making of a stepwise tree improve += scratchT->OptimizeFlexRates(optPrecision, modnum); } else if(modSpec->fixAlpha == false){//normal gamma //do NOT let alpha go too low here - on bad or random starting trees the branch lengths get crazy long improve += scratchT->OptimizeBoundedParameter(modnum, optPrecision, mod->Alpha(), 0, 0.05, 999.9, &Model::SetAlpha); } } if(modSpec->includeInvariantSites && !modSpec->fixInvariantSites) improve += scratchT->OptimizeBoundedParameter(modnum, optPrecision, mod->PropInvar(), 0, 1.0e-8, mod->maxPropInvar, &Model::SetPinv); } if(modSpecSet.InferSubsetRates()){ improve += scratchT->OptimizeSubsetRates(optPrecision); } outman.UserMessageNoCR("\nOptimizing parameters... improved %.3f lnL", improve); scratchT->Score(); FLOAT_TYPE start=scratchT->lnL; scratchT->OptimizeAllBranches(optPrecision); FLOAT_TYPE bimprove = max(scratchT->lnL - start, 0.0); outman.UserMessage("\nOptimizing branchlengths... improved %.3f lnL", bimprove); } } // stepout.close(); outman.UserMessage(""); scratchI.treeStruct->RemoveTreeFromAllClas(); delete scratchI.treeStruct; scratchI.treeStruct=NULL; } void Individual::GetStartingConditionsFromFile(const char* fname, int rank, int nTax, bool restart /*=false*/){ //using a startfile for the initial conditions //12-28-05 This part used to check whether a tree had previously been read in before going into //this loop. Now it goes in regardlesss, since it needs to for bootstrapping from a starting tree if (!FileExists(fname)) throw ErrorException("starting model/tree file \"%s\" does not exist!", fname); ifstream stf( fname, ios::in ); if (!stf) throw ErrorException("starting model/tree file \"%s\" could not be opened!", fname); bool foundModel, foundTree, numericalTaxa; int strlen; char c; if(restart == false){ //first we need to determine whether there is a model and/or a treestring and //check if the taxon numbers or names are present in the tree string c=' '; c=stf.get(); if(c=='#'){//nexus tree files should now be going through NCL elsewhere, so we shouldn't be here assert(0); throw ErrorException("Sorry, GARLI does not yet read Nexus tree files. See manual for starting tree/model format."); } strlen=1; foundModel=false; foundTree=false; numericalTaxa=true; while(c!='\n' && c!='\r' && c!=';' && stf.eof()==false){ if(foundModel==false && foundTree==false){ if(isalpha(c)){ //changing from b for base freqs to e, for equilibrium freqs if(c=='r'||c=='R'||c=='b'||c=='B'||c=='e'||c=='E'||c=='a'||c=='A'||c=='p'||c=='P'||c=='i'||c=='I'||c=='f'||c=='o'||c=='O'||c=='M'||c=='m'||c=='S'||c=='s') foundModel=true; else throw ErrorException("Unknown model parameter specification! \"%c\"", c); } } if(foundTree==false && c=='('){ foundTree=true; } if(foundTree==true){ if(isalpha(c) && c!='e' && c!='E'){//for scientific notation numericalTaxa=false; } } strlen++; c=stf.get(); } } else{//if we are restarting, we can a few things for granted //also note the the rank will be incremented by 1, since //we want to skip the first line, which had non-tree info on it assert(0); foundModel=foundTree=numericalTaxa=true; rank++; strlen = (int)((nTax*2)*(10+DEF_PRECISION)+ (FLOAT_TYPE) log10((FLOAT_TYPE) ((FLOAT_TYPE)nTax)*nTax*2)); } //we know what we need to, now reopen the file stf.close(); stf.clear(); stf.open( fname, ios::in ); char *temp=new char[strlen + 100]; //if this is a remote population in a parallel run or a multirep run, find the proper tree (ie line number) int effectiveRank=rank; for(int r=0;r 1) // throw ErrorException("Specification of model parameter values is not yet supported with partitioned models"); string modString; do{ c=stf.get(); modString += c; //}while(c != '(' && c != '\r' && c != '\n' && !stf.eof()); }while(stf.peek() != '(' && stf.peek() != '\r' && stf.peek() != '\n' && !stf.eof()); while((stf.peek() == '\n' || stf.peek() == '\r') && stf.eof() == false) stf.get(c); modPart.ReadGarliFormattedModelStrings(modString); } if(foundTree==true){ string treeString; char c; stf.get(c); do{ treeString += c; stf.get(c); }while(c != '\n' && c!= '\r' && stf.eof() == false); while((stf.peek() == '\n' || stf.peek() == '\r') && stf.eof() == false) stf.get(c); //the call to the tree constructor can change the seed because random branch lengths are generated when the tree doesn't //have them. So, store and restore the seed, mainly for output purposes (the seed output to the screen happens after this //call int seed = rnd.seed(); //now allowing polytomies, since they will be taken care of in Population::SeedPopulationWithStartingTree treeStruct=new Tree(treeString.c_str(), numericalTaxa, true); //treeStruct=new Tree(treeString.c_str(), numericalTaxa); //check that any defined constraints are present in the starting tree int conNum=1; for(vector::iterator conit=treeStruct->constraints.begin();conit!=treeStruct->constraints.end();conit++){ TreeNode *check = NULL; if((*conit).IsBackbone()) check = treeStruct->ContainsMaskedBipartitionOrComplement(*(*conit).GetBipartition(), *(*conit).GetBackboneMask()); else check = treeStruct->ContainsBipartitionOrComplement(*(*conit).GetBipartition()); if(((*conit).IsPositive() && check == NULL) || ((*conit).IsPositive() == false && check != NULL)) throw ErrorException("Starting tree not compatible with constraint number %d!!!", conNum); } treeStruct->AssignCLAsFromMaster(); } //if no tree is found the making of the random tree will now be taken care of back in Population::SeedPopulationWithStartingTree //else MakeRandomTree(nTax); if(restart == false){ if(!foundTree && !foundModel) throw ErrorException("No starting tree or model was found in the specified starting conditions\n\tfile %s.\n\tIf it is a Nexus file it must start with #NEXUS\n\tOtherwise see manual for information on starting condition format.", fname); if(foundTree==true) outman.UserMessage("Obtained starting tree %d from file %s", effectiveRank+1, fname); else{ outman.UserMessage("No starting tree found in file %s", fname); } if(foundModel==true){ outman.UserMessage("Obtained starting or fixed model parameter values from file %s", fname); string m; modPart.FillGarliFormattedModelStrings(m); outman.UserMessage("This is the current full model string:"); outman.UserMessage("%s", m.c_str()); } else{ //this checks whether we have already gotten some parameter values from file, which might have come from a garli block in the datafile if(!(modSpecSet.GotAnyParametersFromFile())){ outman.UserMessage("No starting model parameter values found in %s\nUsing default parameter values", fname); } } outman.UserMessage(""); } for(int m=0;m < modPart.NumModels();m++){ modPart.GetModel(m)->UpdateQMat(); } stf.close(); delete []temp; } void Individual::GetStartingTreeFromNCL(const NxsTreesBlock *treesblock, int rank, int nTax, bool restart /*=false*/){ assert(treeStruct == NULL); int totalTrees = treesblock->GetNumTrees(); int effectiveRank = rank % totalTrees; //the call to the tree constructor can change the seed because random branch lengths are generated when the tree doesn't //have them. So, store and restore the seed, mainly for output purposes (the seed output to the screen happens after this //call int seed = rnd.seed(); //we will get the tree string from NCL with taxon numbers (starting at 1), regardless of how it was initially read in const NxsFullTreeDescription &t = treesblock->GetFullTreeDescription(effectiveRank); if(t.AllTaxaAreIncluded() == false && !treeStruct->someOrientedGap) throw ErrorException("Starting tree description must contain all taxa."); string ts = t.GetNewick(); ts += ";"; treeStruct=new Tree(ts.c_str(), true, true); rnd.set_seed(seed); //check that any defined constraints are present in the starting tree int conNum=1; for(vector::iterator conit=treeStruct->constraints.begin();conit!=treeStruct->constraints.end();conit++){ TreeNode *check = NULL; if((*conit).IsBackbone()) check = treeStruct->ContainsMaskedBipartitionOrComplement(*(*conit).GetBipartition(), *(*conit).GetBackboneMask()); else check = treeStruct->ContainsBipartitionOrComplement(*(*conit).GetBipartition()); if(((*conit).IsPositive() && check == NULL) || ((*conit).IsPositive() == false && check != NULL)) throw ErrorException("Starting tree not compatible with constraint number %d!!!", conNum); } treeStruct->AssignCLAsFromMaster(); for(int m=0;m < modPart.NumModels();m++){ modPart.GetModel(m)->UpdateQMat(); } } void Individual::RefineStartingConditions(bool optModel, FLOAT_TYPE branchPrec){ //This has been deprecated in favor of Population::InitialOptimization, which is essentially the same code assert(0); bool optOmega, optAlpha, optFlex, optPinv, optFreqs, optRelRates, optSubsetRates; optOmega = optAlpha = optFlex = optPinv = optFreqs = optRelRates = optSubsetRates = false; bool optInsDel = false; if(optModel){ for(int modnum = 0;modnum < modPart.NumModels();modnum++){ Model *mod = modPart.GetModel(modnum); const ModelSpecification *modSpec = mod->GetCorrespondingSpec(); if(modSpec->numRateCats > 1 && modSpec->IsNonsynonymousRateHet() == false && modSpec->IsFlexRateHet() == false && modSpec->fixAlpha == false) optAlpha = true; if(modSpec->IsFlexRateHet()) optFlex = true; if(modSpec->includeInvariantSites && modSpec->fixInvariantSites == false) optPinv = true; if(modSpec->IsCodon() && !modSpec->fixOmega) optOmega = true; if(modSpec->IsOrientedGap()) optInsDel = true; if(modSpec->IsCodon() == false && modSpec->fixStateFreqs == false && modSpec->IsEqualStateFrequencies() == false && modSpec->IsEmpiricalStateFrequencies() == false) optFreqs = true; //this is the case of forced freq optimization with codon models. For everything to work they must be set as both not fixed but empirical if(modSpec->IsCodon() && modSpec->fixStateFreqs == false && modSpec->IsEqualStateFrequencies() == false && modSpec->IsEmpiricalStateFrequencies() == true) optFreqs = true; if(modSpec->fixRelativeRates == false && (modSpec->Nst() > 1 || modSpec->IsEstimateAAMatrix() || modSpec->IsTwoSerineRateMatrix())) optRelRates = true; } //oops, bug fixed 10/2/12 - subset rates weren't getting opt in linked models //modSpecSet.inferSubsetRates is already getting set only if conf.inferSubsetRates //is true and there are multiple matrices, but not necessarily multiple models //if(modSpecSet.InferSubsetRates() && modSpecSet.NumSpecs() > 1) if(modSpecSet.InferSubsetRates()) optSubsetRates = true; } outman.UserMessageNoCR("optimizing: starting branch lengths"); if(optAlpha) outman.UserMessageNoCR(", alpha shape"); if(optPinv) outman.UserMessageNoCR(", prop. invar"); if(optRelRates) outman.UserMessageNoCR(", rel rates"); if(optFreqs) outman.UserMessageNoCR(", eq freqs"); if(optOmega) outman.UserMessageNoCR(", dN/dS (aka omega) parameters"); if(optInsDel){ outman.UserMessageNoCR(", ins rate"); outman.UserMessageNoCR(", del rate"); } if(optSubsetRates) outman.UserMessageNoCR(", subset rates"); outman.UserMessage("..."); FLOAT_TYPE improve=(FLOAT_TYPE)999.9; CalcFitness(0); for(int i=1;improve > branchPrec;i++){ FLOAT_TYPE alphaOptImprove=0.0, pinvOptImprove = 0.0, omegaOptImprove = 0.0, flexOptImprove = 0.0, optImprove=0.0, scaleOptImprove=0.0, subsetRateImprove=0.0, rateOptImprove=0.0; FLOAT_TYPE freqOptImprove=0.0, insDelImprove = 0.0; CalcFitness(0); FLOAT_TYPE passStart=Fitness(); optImprove=treeStruct->OptimizeAllBranches(branchPrec); CalcFitness(0); FLOAT_TYPE trueImprove= Fitness() - passStart; assert(trueImprove >= -1.0); if(trueImprove < ZERO_POINT_ZERO) trueImprove = ZERO_POINT_ZERO; vector blens; treeStruct->StoreBranchlengths(blens); scaleOptImprove=treeStruct->OptimizeTreeScale(branchPrec); CalcFitness(0); //if some of the branch lengths were at the minimum or maximum boundaries the scale optimization //can actually worsen the score. If so, return them to their original lengths. if(scaleOptImprove < ZERO_POINT_ZERO){ treeStruct->RestoreBranchlengths(blens); CalcFitness(0); scaleOptImprove = ZERO_POINT_ZERO; } CalcFitness(0); if(optModel){ for(int modnum = 0;modnum < modPart.NumModels();modnum++){ Model *mod = modPart.GetModel(modnum); const ModelSpecification *modSpec = mod->GetCorrespondingSpec(); if(modSpec->IsCodon()){ if(!modSpec->fixOmega) omegaOptImprove += treeStruct->OptimizeOmegaParameters(branchPrec, modnum); } else if(mod->NRateCats() > 1){ if(modSpec->IsFlexRateHet()){//Flex rates //no longer doing alpha first, it was too hard to know if the flex rates had been partially optimized //already during making of a stepwise tree //if(i == 1) rateOptImprove = treeStruct->OptimizeAlpha(branchPrec); //if(i == 1 && modSpec.gotFlexFromFile==false) rateOptImprove = treeStruct->OptimizeBoundedParameter(branchPrec, mod->Alpha(), 0, 1.0e-8, 999.9, &Model::SetAlpha); flexOptImprove += treeStruct->OptimizeFlexRates(branchPrec, modnum); } else if(modSpec->fixAlpha == false){//normal gamma //rateOptImprove = treeStruct->OptimizeAlpha(branchPrec); //do NOT let alpha go too low here - on bad or random starting trees the branch lengths get crazy long //rateOptImprove = treeStruct->OptimizeBoundedParameter(branchPrec, mod->Alpha(), 0, 1.0e-8, 999.9, &Model::SetAlpha); //alphaOptImprove += treeStruct->OptimizeBoundedParameter(branchPrec, mod->Alpha(), 0, 0.05, 999.9, modnum, &Model::SetAlpha); alphaOptImprove += treeStruct->OptimizeBoundedParameter(modnum, branchPrec, mod->Alpha(), 0, 0.05, 999.9, &Model::SetAlpha); } } if(modSpec->includeInvariantSites && !modSpec->fixInvariantSites) pinvOptImprove += treeStruct->OptimizeBoundedParameter(modnum, branchPrec, mod->PropInvar(), 0, 1.0e-8, mod->maxPropInvar, &Model::SetPinv); if(modSpec->IsOrientedGap()){ insDelImprove += treeStruct->OptimizeInsertDeleteRates(branchPrec, modnum); } if(modSpec->IsCodon() == false && modSpec->fixStateFreqs == false && modSpec->IsEqualStateFrequencies() == false && modSpec->IsEmpiricalStateFrequencies() == false) freqOptImprove += treeStruct->OptimizeEquilibriumFreqs(branchPrec, modnum); if(modSpec->fixRelativeRates == false && (modSpec->Nst() > 1 || modSpec->IsEstimateAAMatrix() || modSpec->IsTwoSerineRateMatrix())) rateOptImprove += treeStruct->OptimizeRelativeNucRates(branchPrec, modnum); } if(optSubsetRates){ subsetRateImprove += treeStruct->OptimizeSubsetRates(branchPrec); } } improve=scaleOptImprove + trueImprove + alphaOptImprove + pinvOptImprove + flexOptImprove + omegaOptImprove + rateOptImprove + freqOptImprove + subsetRateImprove + insDelImprove; outman.precision(8); outman.UserMessageNoCR("pass%2d:+%9.3f (branch=%7.2f scale=%6.2f", i, improve, trueImprove, scaleOptImprove); if(optOmega) outman.UserMessageNoCR(" omega=%6.2f", omegaOptImprove); if(optAlpha) outman.UserMessageNoCR(" alpha=%6.2f", alphaOptImprove); if(optFreqs) outman.UserMessageNoCR(" freqs=%6.2f", freqOptImprove); if(optRelRates) outman.UserMessageNoCR(" rel rates=%6.2f", rateOptImprove); if(optFlex) outman.UserMessageNoCR(" flex=%6.2f", flexOptImprove); if(optPinv) outman.UserMessageNoCR(" pinv=%6.2f", pinvOptImprove); if(optInsDel){ outman.UserMessageNoCR(" ins/del=%6.2f", insDelImprove); } if(optSubsetRates) outman.UserMessageNoCR(" subset rates=%6.2f", subsetRateImprove); outman.UserMessage(")"); } treeStruct->nodeOptVector.clear(); } void Individual::ReadTreeFromFile(istream & inf) { char tmp[256]; char ch = ' '; NxsString s; while( inf ) { inf.get( tmp, 255, '\n' ); inf.get(ch); tmp[255] = '\0'; s += tmp; if( ch == '\n' ) break; else s += ch; } treeStruct=new Tree(s.c_str(), true); } void Individual::CopyNonTreeFields(const Individual* ind ){ fitness = ind->fitness; accurateSubtrees=ind->accurateSubtrees; modPart.CopyModelPartition(&ind->modPart); dirty = ind->dirty; topo=ind->topo; } /* 7/21/06 needs to be fixed to correspond to changes in tree for constraints void Individual::SubtreeMutate(int subdomain, FLOAT_TYPE optPrecision, vector const &subtreeMemberNodes, Adaptation *adap){ //this version is used only by remotes when they have had a subtree defined for them //it will mutate only within that subtree, and because we know that the next mutation //will also be within that subtree we can get away without recalculating some likelihood //arrays //because we don't do model mutations during subtree mode, factor the modelMutateProb out FLOAT_TYPE effectiveTopoProb=adap->topoMutateProb / (1.0/(1.0-adap->modelMutateProb)); FLOAT_TYPE r = rnd.uniform(); #ifndef MUTUALLY_EXCLUSIVE_MUTS if(adap->branchOptPrecision != adap->minOptPrecision || r > effectiveTopoProb){ #else if(r >= effectiveTopoProb){ #endif mutated_brlen=treeStruct->BrlenMutateSubset( subtreeMemberNodes ); if(mutated_brlen > 0){ mutation_type |= brlen; dirty=true; } } if(r < effectiveTopoProb){ r = rnd.uniform(); int cut; if(rrandNNIprob){ //the node passed to the nni function can only be an internal node, so //pick from the first part of the list which contains the internals cut = subtreeMemberNodes[(int)(rnd.uniform()*(subtreeMemberNodes.size()/2-1))]; int branch = rnd.uniform() < .5; treeStruct->NNIMutate(cut, branch, optPrecision, subdomain); mutation_type |= randNNI; if(treeStruct->lnL !=-1.0){ fitness=treeStruct->lnL; dirty=false; } else dirty=true; } else if(r < adap->randNNIprob + adap->randSPRprob){ int broken; //the nodes passed to the spr function can be internals or terminals, so //choose anywhere in the list do{ cut=subtreeMemberNodes[(int)(rnd.uniform()*subtreeMemberNodes.size())]; vector SPRList; SPRList.reserve(subtreeMemberNodes.size()); treeStruct->allNodes[subdomain]->right->getSPRList(cut,SPRList); treeStruct->allNodes[subdomain]->left->getSPRList(cut,SPRList); broken=SPRList[(int)(rnd.uniform()*SPRList.size())]; }while(treeStruct->allNodes[broken]->next==treeStruct->allNodes[cut] || treeStruct->allNodes[broken]->prev==treeStruct->allNodes[cut]); //reattaching to cut's sib recreates the same tree, so avoid treeStruct->SPRMutate(cut, broken, optPrecision, subdomain, 0); mutation_type |= randSPR; if(treeStruct->lnL !=-1.0){ fitness=treeStruct->lnL; dirty=false; } else dirty=true; } else{//limited spr //the nodes passed to the spr function can be internals or terminals, so //choose anywhere in the list TreeNode *sib; do{ cut=subtreeMemberNodes[(int)(rnd.uniform()*subtreeMemberNodes.size())]; if(treeStruct->allNodes[cut]->next != NULL) sib=treeStruct->allNodes[cut]->next; else sib=treeStruct->allNodes[cut]->prev; }while(treeStruct->allNodes[cut]->anc->nodeNum == subdomain && sib->left==NULL); treeStruct->SPRMutate(cut, -1, optPrecision, subdomain, adap->limSPRrange); mutation_type |= limSPR; if(treeStruct->lnL !=-1.0){ fitness=treeStruct->lnL; dirty=false; } else dirty=true; } } /* else{ assert(TaxonSwapList.size>0); FLOAT_TYPE s2, s1 = params->rnd.uniform(); int randint2, randint1 = TaxonSwapList.size * s1 + 1; if(randint1>TaxonSwapList.size) randint1 = TaxonSwapList.size; do{ s2 = params->rnd.uniform(); randint2 = TaxonSwapList.size * s2 + 1; }while(randint2==randint1); if(randint2>TaxonSwapList.size) randint2 = TaxonSwapList.size; treeStruct->TaxonSwap(randint1, randint2, optPrecision); mutation_type |= taxonSwap; } *//* CalcFitness(subdomain); treeStruct->calcs=calcCount; calcCount=0; } */ /*7/21/06 needs to be fixed to correspond to changes in tree for constraints void Individual::NonSubtreeMutate(const ParallelManager *pMan, FLOAT_TYPE optPrecision, Adaptation *adap) {//this version is used only by the master when subtree mode is active //it will make a mutation on one of the nodes that are not contained within //a subtree, which are in a vector that is passed in //because we don't do model mutations during subtree mode, factor the modelMutateProb out FLOAT_TYPE effectiveTopoProb=adap->topoMutateProb / (1.0/(1.0-adap->modelMutateProb)); FLOAT_TYPE r = rnd.uniform(); #ifndef MUTUALLY_EXCLUSIVE_MUTS if(adap->branchOptPrecision != adap->minOptPrecision || r >= effectiveTopoProb){ #else if(r >= effectiveTopoProb){ #endif mutated_brlen=treeStruct->BrlenMutateSubset(pMan->nonSubtreeNodesforSPR); if(mutated_brlen > 0){ mutation_type |= brlen; dirty=true; } } if(r < effectiveTopoProb){ FLOAT_TYPE r = rnd.uniform(); if(r<(adap->randNNIprob/(1.0-adap->randSPRprob)) && (pMan->nonSubtreeNodesforNNI.size() > 0)){ int randint1; do{ randint1 = pMan->nonSubtreeNodesforNNI[(int)(pMan->nonSubtreeNodesforNNI.size() * rnd.uniform())]; }while(randint1<=params->data->NTax()); int branch = rnd.uniform() < .5; treeStruct->NNIMutate(randint1,branch,optPrecision, 0); mutation_type |= randNNI; if(treeStruct->lnL !=-1.0){ fitness=treeStruct->lnL; dirty=false; } else dirty=true; } else if(pMan->nonSubtreeNodesforSPR.size() > 3){ int randint1, randint2; bool done; do{ done=false; randint1 = pMan->nonSubtreeNodesforSPR[(int)(pMan->nonSubtreeNodesforSPR.size() * rnd.uniform())]; randint2 = pMan->nonSubtreeNodesforSPR[(int)(pMan->nonSubtreeNodesforSPR.size() * rnd.uniform())]; //check that the cut node (randint1) is not an ancestor of the attachment node (randint2) TreeNode *tmp=treeStruct->allNodes[randint2]; while((tmp->nodeNum != 0) && (tmp->nodeNum != randint1)){ tmp=tmp->anc; } if(tmp->nodeNum==0) done=true; //check if the nodes are siblings tmp=treeStruct->allNodes[randint1]->anc; if(tmp->left->nodeNum==randint2) done=false; if(tmp->left->next->nodeNum==randint2) done=false; if(tmp->left->next->next != NULL) if(tmp->left->next->next->nodeNum == randint2) done=false; }while(done == false || treeStruct->allNodes[randint1]->anc->nodeNum==randint2); treeStruct->SPRMutate(randint1, randint2, optPrecision, pMan->nonSubtreeNodesforNNI); mutation_type |= limSPR; if(treeStruct->lnL !=-1.0){ fitness=treeStruct->lnL; dirty=false; } else dirty=true; } } */ /* else{ assert(TaxonSwapList.size>0); FLOAT_TYPE s2, s1 = params->rnd.uniform(); int randint2, randint1 = TaxonSwapList.size * s1 + 1; if(randint1>TaxonSwapList.size) randint1 = TaxonSwapList.size; do{ s2 = params->rnd.uniform(); randint2 = TaxonSwapList.size * s2 + 1; }while(randint2==randint1); if(randint2>TaxonSwapList.size) randint2 = TaxonSwapList.size; treeStruct->TaxonSwap(randint1, randint2, optPrecision); mutation_type |= taxonSwap; } */ /* CalcFitness(0); treeStruct->calcs=calcCount; calcCount=0; } */ garli-2.1-release/src/individual.h000066400000000000000000000127141241236125200171630ustar00rootroot00000000000000// GARLI version 2.0 source code // Copyright 2005-2011 Derrick J. Zwickl // email: garli.support@gmail.com // // This program is free software: you can redistribute it and/or modify // it under the terms of the GNU General Public License as published by // the Free Software Foundation, either version 3 of the License, or // (at your option) any later version. // // This program is distributed in the hope that it will be useful, // but WITHOUT ANY WARRANTY; without even the implied warranty of // MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the // GNU General Public License for more details. // // You should have received a copy of the GNU General Public License // along with this program. If not, see . #ifndef INDIVIDUAL_H #define INDIVIDUAL_H #include "tree.h" #include "model.h" class CondLikeArray; class Tree; class ParallelManager; class Adaptation; class Individual { FLOAT_TYPE fitness; bool dirty; // individual becomes dirty if mutated in any way public: int mutation_type; //here we define the binary equivalents of the mutation types, so that they can all be rolled //into a single int with bit flags enum { //normal mutation types randNNI = 0x0001, //1 randSPRCon = 0x0002, //2 randSPR = 0x0004, //4 limSPR = 0x0008, //8 limSPRCon = 0x0010, //16 randRecom = 0x0020, //32 bipartRecom = 0x0040, //64 taxonSwap = 0x1000, //4096 subtreeRecom= 0x2000, //8192 brlen = 0x0080, //128 rates = 0x0100, //256 pi = 0x0200, //512 alpha = 0x0400, //1024 pinv = 0x0800, //2048 subsetRate = 0x4000, //16384 muScale = 0x10000, //65536 indel = 0x20000, //131072 #ifdef GANESH randPECR = 0x4000, //16384 #endif rerooted = 0x8000, //32768 - this is needed because in many senses a tree that has been rerooted //is a new topology (for example the left, right and anc pointers from a particular nodenum //won't be the same before and after rerooting) although the likelihood is the same //compostite types #ifdef GANESH anyTopo = (randNNI | exNNI | randSPR | limSPR | exlimSPR | randRecom | bipartRecom | taxonSwap | subtreeRecom | randPECR ) , #else anyTopo = (randNNI | randSPRCon | randSPR | limSPR | limSPRCon | randRecom | bipartRecom | taxonSwap | subtreeRecom ) , #endif anyModel = rates | pi | alpha | pinv | muScale | subsetRate | indel }; int mutated_brlen;//the number of brlen muts bool accurateSubtrees; //Model *mod; ModelPartition modPart; Tree *treeStruct; bool reproduced; bool willreproduce; bool willrecombine; int recombinewith; int parent,topo; Individual(); Individual(const Individual *other); ~Individual(); FLOAT_TYPE Fitness() const { return fitness; } void SetDirty() { dirty = true; } bool IsDirty() const { return dirty; } void SetFitness( FLOAT_TYPE f ) { fitness = f; dirty=false; } void GetStartingConditionsFromFile(const char *fname, int rank, int nTax, bool restart=false); void GetStartingTreeFromNCL(const NxsTreesBlock *treesblock, int rank, int nTax, bool restart=false); void RefineStartingConditions(bool optModel, FLOAT_TYPE branchPrec); void CalcFitness(int subtreeNode); void ReadTreeFromFile(istream & inf); // void Mutate(int, FLOAT_TYPE); void Mutate(FLOAT_TYPE optPrecision, Adaptation *adap); // void SubtreeMutate(int subdomain, FLOAT_TYPE optPrecision, vector const &subtreeList, Adaptation *adap); // void NonSubtreeMutate(const ParallelManager *, FLOAT_TYPE optPrecision, Adaptation *adap); void CrossOverWith( Individual& so, FLOAT_TYPE optPrecision); void CopyNonTreeFields(const Individual* ind ); void CopyByStealingTree(Individual* ind ); void CopySecByStealingFirstTree(Individual * sourceOfTreePtr, const Individual *sourceOfInformation); void CopySecByRearrangingNodesOfFirst(Tree * sourceOfTreePtr, const Individual *sourceOfInformation, bool CLAassigned=false); void DuplicateIndivWithoutCLAs(const Individual *sourceOfInformation); void ResetIndiv(); void MakeRandomTree(int nTax); void MakeStepwiseTree(int nTax, int attemptsPerTaxon, FLOAT_TYPE optPrecision ); }; inline void Individual::CopyByStealingTree(Individual* ind ){ CopyNonTreeFields(ind); treeStruct=ind->treeStruct; } inline void Individual::ResetIndiv(){ reproduced=willreproduce=willrecombine=false; recombinewith=-1; mutation_type=mutated_brlen=0; } #define BIPART_BASED_RECOM inline void Individual::CrossOverWith( Individual& so , FLOAT_TYPE optPrecision){ //check if the models are the same, which will allow the replicated parts of the trees //to use the same clas #ifdef BIPART_BASED_RECOM //this will return -1 if no recombination actually occured int x=-1; x=treeStruct->BipartitionBasedRecombination(so.treeStruct, modPart.IsModelPartitionEqual(&so.modPart), optPrecision); //if we don't find a bipart based recom that does much good, do a normal one if(x==-1){ /* treeStruct->RecombineWith( so.treeStruct, mod->IsModelEqual(so.mod), optPrecision); mutation_type |= randRecom; */ mutation_type=0; recombinewith=-1; } else{ // recombinewith+=100; mutation_type |= bipartRecom; fitness=treeStruct->lnL; dirty=false; CalcFitness(0); } #else treeStruct->RecombineWith( so.treeStruct, params->rnd , kappa, mod->IsModelEqual(so.mod)); dirty=1; #endif } #endif garli-2.1-release/src/linalg.cpp000066400000000000000000000775661241236125200166540ustar00rootroot00000000000000// GARLI version 2.0 source code // Copyright 2005-2011 Derrick J. Zwickl // email: garli.support@gmail.com // // This program is free software: you can redistribute it and/or modify // it under the terms of the GNU General Public License as published by // the Free Software Foundation, either version 3 of the License, or // (at your option) any later version. // // This program is distributed in the hope that it will be useful, // but WITHOUT ANY WARRANTY; without even the implied warranty of // MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the // GNU General Public License for more details. // // You should have received a copy of the GNU General Public License // along with this program. If not, see . // // NOTE: Portions of this source adapted from MrBayes source code (Huelsenbeck and Ronquist) // I believe that they originaly appeared in PAUP* source code #if defined(_MSC_VER) //POL 23-Feb-2006 VC requires these headers instead if using the std namespace # include # include # include # include #else # include # include # include # include #endif using namespace std; #include "defs.h" #include "linalg.h" #undef NO_ERROR #undef ERROR #define NO_ERROR 0 #define ERROR 1 #undef FALSE #undef TRUE #define FALSE 0 #define TRUE 1 static void LUBackSubst (MODEL_FLOAT **a, int n, int *indx, MODEL_FLOAT *b); static int EigenRG (int n, MODEL_FLOAT **a, MODEL_FLOAT *wr, MODEL_FLOAT *wi, MODEL_FLOAT **z, int *iv1, MODEL_FLOAT *fv1); static void Balanc (int n, MODEL_FLOAT **a, int *pLow, int *pHigh, MODEL_FLOAT *scale); static void Exchange (int j, int k, int l, int m, int n, MODEL_FLOAT **a, MODEL_FLOAT *scale); static void ElmHes (int n, int low, int high, MODEL_FLOAT **a, int *intchg); static void ElTran (int n, int low, int high, MODEL_FLOAT **a, int *intchg, MODEL_FLOAT **z); static int Hqr2 (int n, int low, int high, MODEL_FLOAT **h, MODEL_FLOAT *wr, MODEL_FLOAT *wi, MODEL_FLOAT **z); static void BalBak (int n, int low, int high, MODEL_FLOAT *scale, int m, MODEL_FLOAT **z); static void CDiv (MODEL_FLOAT ar, MODEL_FLOAT ai, MODEL_FLOAT br, MODEL_FLOAT bi, MODEL_FLOAT *cr, MODEL_FLOAT *ci); //inline static MODEL_FLOAT D_sign (MODEL_FLOAT a, MODEL_FLOAT b); #ifdef SINGLE_PRECISION_FLOATS #define TINY 1.0e-20f #else #define TINY 1.0e-20 #endif #if !defined(MAX) # define MAX(a,b) (((a) > (b)) ? (a) : (b)) #endif #if !defined(MIN) # define MIN(a,b) (((a) < (b)) ? (a) : (b)) #endif /*-------------------------------------------------------------------------------------------------- | | D_sign | | "Sign" function. */ inline static MODEL_FLOAT D_sign (MODEL_FLOAT a, MODEL_FLOAT b){ MODEL_FLOAT x = (a >= 0 ? a : -a); return (b >= 0 ? x : -x); } /*-------------------------------------------------------------------------------------------------- | | InvertMatrix | | Invert matrix 'a' using LU-decomposition technique, storing inverse in 'a_inv'. Matrix 'a' | is destroyed. Returns ERROR if matrix is singular, NO_ERROR otherwise. */ int InvertMatrix (MODEL_FLOAT **a, int n, MODEL_FLOAT *col, int *indx, MODEL_FLOAT **a_inv) /* **a = matrix represented as vector of row pointers */ /* n = order of matrix */ /* *col = work vector of size n */ /* *indx = work vector of size n */ /* **a_inv = inverse of input matrix a (matrix a is destroyed) */ { int rc, i, j; rc = LUDecompose(a, n, col, indx, (MODEL_FLOAT *)NULL); if (rc == FALSE) { for (j = 0; j < n; j++) { for (i = 0; i < n; i++) col[i] = 0.0; col[j] = 1.0; LUBackSubst(a, n, indx, col); for (i = 0; i < n; i++) a_inv[i][j] = col[i]; } } return rc; } /*-------------------------------------------------------------------------------------------------- | | LUDecompose | | Replace matrix 'a' with its LU-decomposition. Returns ERROR if matrix is singular, NO_ERROR | otherwise. */ int LUDecompose (MODEL_FLOAT **a, int n, MODEL_FLOAT *vv, int *indx, MODEL_FLOAT *pd) /* **a = the matrix whose LU-decomposition is wanted */ /* n = order of a */ /* *vv = work vector of size n (stores implicit scaling of each row) */ /* *indx => row permutation according to partial pivoting sequence */ /* *pd => 1 if number of row interchanges was even, -1 if odd (NULL OK) */ { int i, imax, j, k; MODEL_FLOAT big, dum, sum, temp, d; d = 1.0; for (i = 0; i < n; i++) { big = 0.0; for (j = 0; j < n; j++) { if ((temp = fabs(a[i][j])) > big) big = temp; } if (big == 0.0) { printf("singular matrix in routine LUDecompose"); return ERROR; } vv[i] = ONE_POINT_ZERO / big; } for (j = 0; j < n; j++) { for (i = 0; i < j; i++) { sum = a[i][j]; for (k = 0; k < i; k++) sum -= a[i][k] * a[k][j]; a[i][j] = sum; } big = 0.0; for (i = j; i < n; i++) { sum = a[i][j]; for (k = 0; k < j; k++) sum -= a[i][k] * a[k][j]; a[i][j] = sum; dum = vv[i] * fabs(sum); if (dum >= big) { big = dum; imax = i; } } if (j != imax) { for (k = 0; k < n; k++) { dum = a[imax][k]; a[imax][k] = a[j][k]; a[j][k] = dum; } d = -d; vv[imax] = vv[j]; } indx[j] = imax; if (a[j][j] == 0.0) a[j][j] = TINY; if (j != n - 1) { dum = ONE_POINT_ZERO / (a[j][j]); for (i = j + 1; i < n; i++) a[i][j] *= dum; } } if (pd != NULL) *pd = d; return NO_ERROR; } /*-------------------------------------------------------------------------------------------------- | | LUBackSubst | | Perform back-substition into LU-decomposed matrix in order to obtain inverse. */ void LUBackSubst (MODEL_FLOAT **a, int n, int *indx, MODEL_FLOAT *b) { int i, ip, j, ii = -1; MODEL_FLOAT sum; for (i = 0; i < n; i++) { ip = indx[i]; sum = b[ip]; b[ip] = b[i]; if (ii >= 0) { for (j = ii; j <= i - 1; j++) sum -= a[i][j] * b[j]; } else if (sum != 0.0) ii = i; b[i] = sum; } for (i = n - 1; i >= 0; i--) { sum = b[i]; for (j = i + 1; j < n; j++) sum -= a[i][j] * b[j]; b[i] = sum / a[i][i]; } } /*-------------------------------------------------------------------------------------------------- | | EigenRealGeneral | | Calculate eigenvalues and eigenvectors of a general real matrix assuming that all eigenvalues | are real, using routines from the public domain EISPACK package. */ int EigenRealGeneral (int n, MODEL_FLOAT **a, MODEL_FLOAT *v, MODEL_FLOAT *vi, MODEL_FLOAT **u, int *iwork, MODEL_FLOAT *work) /* n = order of a */ /* **a = input matrix in row-ptr representation; will be destroyed */ /* *v = array of size 'n' to receive eigenvalues */ /* *vi = work vector of size 'n' for imaginary components of eigenvalues */ /* **u = matrix in row-ptr representation to receive eigenvectors */ /* *iwork = work vector of size 'n' */ /* *work = work vector of size 'n' */ { int i, rc; rc = EigenRG (n, a, v, vi, u, iwork, work); if (rc != NO_ERROR) { puts("\nInternal error in 'EigenRealGeneral'."); printf ("rc = %d\n", rc); return ERROR; } for (i = 0; i < n; i++) { if (vi[i] != 0.0) return RC_COMPLEX_EVAL; } return NO_ERROR; } /*-------------------------------------------------------------------------------------------------- | | EigenRG | | This subroutine calls the recommended sequence of subroutines from the eigensystem subroutine | package (EISPACK) to find the eigenvalues of a real general matrix. It was converted from | Fortran to C by David Swofford. | | ON INPUT: | | n is the order of the matrix 'a' | | a contains the real general matrix | | ON OUTPUT: | | wr and wi contain the real and imaginary parts, respectively, of the eigenvalues. | Complex conjugate pairs of eigenvalues appear consecutively with the eigenvalue having the | positive imaginary part first. | | z contains the real and imaginary parts of the eigenvectors. If the j-th eigenvalue is | real, the j-th column of z contains its eigenvector. If the j-th eigenvalue is complex | with positive imaginary part, the j-th and (j+1)-th columns of z contain the real and | imaginary parts of its eigenvector. The conjugate of this vector is the eigenvector for | the conjugate eigenvalue. | | ierr is an integer output variable set equal to an error completion code described in the | documentation for Hqr and Hqr2. The normal completion code is zero. | | iv1 and fv1 are temporary storage vectors of size n */ int EigenRG (int n, MODEL_FLOAT **a, MODEL_FLOAT *wr, MODEL_FLOAT *wi, MODEL_FLOAT **z, int *iv1, MODEL_FLOAT *fv1) { static int is1, is2; int ierr; Balanc (n, a, &is1, &is2, fv1); ElmHes (n, is1, is2, a, iv1); ElTran (n, is1, is2, a, iv1, z); ierr = Hqr2 (n, is1, is2, a, wr, wi, z); if (ierr == 0) BalBak (n, is1, is2, fv1, n, z); return ierr; } /*-------------------------------------------------------------------------------------------------- | | Balanc | | EISPACK routine translated from Fortran to C by David Swofford. Modified EISPACK comments | follow. | | This subroutine is a translation of the algol procedure BALANCE, Num. Math. 13, 293-304(1969) | by Parlett and Reinsch. Handbook for Auto. Comp., Vol. II-Linear Algebra, 315-326( 1971). | | This subroutine balances a real matrix and isolates eigenvalues whenever possible. | | ON INPUT: | | n is the order of the matrix. | | a contains the input matrix to be balanced. | | ON OUTPUT: | | a contains the balanced matrix. | | low and high are two integers such that a(i,j) is equal to zero if | (1) i is greater than j and | (2) j=1,...,low-1 or i=high+1,...,n. | | scale contains information determining the permutations and scaling factors used. | | Suppose that the principal submatrix in rows low through high has been balanced, that p(j) | denotes the index interchanged with j during the permutation step, and that the elements of the | diagonal matrix used are denoted by d(i,j). Then | scale(j) = p(j), for j = 1,...,low-1 | = d(j,j), j = low,...,high | = p(j) j = high+1,...,n. | The order in which the interchanges are made is n to high+1, then 1 to low-1. | | Note that 1 is returned for high if high is zero formally. */ void Balanc (int n, MODEL_FLOAT **a, int *pLow, int *pHigh, MODEL_FLOAT *scale) { MODEL_FLOAT c, f, g, r, s, b2; int i, j, k, l, m, noconv; b2 = FLT_RADIX * FLT_RADIX; k = 0; l = n - 1; /* search for rows isolating an eigenvalue and push them down */ for (j = l; j >= 0; j--) { for (i = 0; i <= l; i++) { if (i != j) { if (a[j][i] != 0.0) goto next_j1; } } # if 0 /* bug that dave caught */ m = l; Exchange(j, k, l, m, n, a, scale); if (l < 0) goto leave; else j = --l; # else m = l; Exchange(j, k, l, m, n, a, scale); if (--l < 0) goto leave; # endif next_j1: ; } /* search for columns isolating an eigenvalue and push them left */ for (j = k; j <= l; j++) { for (i = k; i <= l; i++) { if (i != j) { if (a[i][j] != 0.0) goto next_j; } } m = k; Exchange(j, k, l, m, n, a, scale); k++; next_j: ; } /* now balance the submatrix in rows k to l */ for (i = k; i <= l; i++) scale[i] = 1.0; /* iterative loop for norm reduction */ do { noconv = FALSE; for (i = k; i <= l; i++) { c = 0.0; r = 0.0; for (j = k; j <= l; j++) { if (j != i) { c += fabs(a[j][i]); r += fabs(a[i][j]); } } /* guard against zero c or r due to underflow */ if ((c != 0.0) && (r != 0.0)) { g = r / FLT_RADIX; f = 1.0; s = c + r; while (c < g) { f *= FLT_RADIX; c *= b2; } g = r * FLT_RADIX; while (c >= g) { f /= FLT_RADIX; c /= b2; } /* now balance */ if ((c + r) / f < s * .95) { g = ONE_POINT_ZERO / f; scale[i] *= f; noconv = TRUE; for (j = k; j < n; j++) a[i][j] *= g; for (j = 0; j <= l; j++) a[j][i] *= f; } } } } while (noconv); leave: *pLow = k; *pHigh = l; } /*-------------------------------------------------------------------------------------------------- | | Exchange | | Support function for EISPACK routine Balanc. */ void Exchange (int j, int k, int l, int m, int n, MODEL_FLOAT **a, MODEL_FLOAT *scale) { int i; MODEL_FLOAT f; scale[m] = (MODEL_FLOAT)j; if (j != m) { for (i = 0; i <= l; i++) { f = a[i][j]; a[i][j] = a[i][m]; a[i][m] = f; } for (i = k; i < n; i++) { f = a[j][i]; a[j][i] = a[m][i]; a[m][i] = f; } } } /*-------------------------------------------------------------------------------------------------- | | ElmHes | | EISPACK routine translated from Fortran to C by David Swofford. Modified EISPACK comments | follow. | | This subroutine is a translation of the algol procedure ELMHES, Num. Math. 12, 349-368(1968) by | Martin and Wilkinson. Handbook for Auto. Comp., Vol. II-Linear Algebra, 339-358 (1971). | | Given a real general matrix, this subroutine reduces a submatrix situated in rows and columns | low through high to upper Hessenberg form by stabilized elementary similarity transformations. | | ON INPUT: | | n is the order of the matrix. | | low and high are integers determined by the balancing subroutine BALANC. If BALANC has not | been used, set low=1, high=n. | | a contains the input matrix. | | ON OUTPUT: | | a contains the Hessenberg matrix. The multipliers which were used in the reduction are | stored in the remaining triangle under the Hessenberg matrix. | | int contains information on the rows and columns interchanged in the reduction. Only | elements low through high are used. */ void ElmHes (int n, int low, int high, MODEL_FLOAT **a, int *intchg) { int i, j, m; MODEL_FLOAT x, y; int la, mm1, kp1, mp1; la = high - 1; kp1 = low + 1; if (la < kp1) return; for (m = kp1; m <= la; m++) { mm1 = m - 1; x = 0.0; i = m; for (j = m; j <= high; j++) { if (fabs(a[j][mm1]) > fabs(x)) { x = a[j][mm1]; i = j; } } intchg[m] = i; if (i != m) { /* interchange rows and columns of a */ for (j = mm1; j < n; j++) { y = a[i][j]; a[i][j] = a[m][j]; a[m][j] = y; } for (j = 0; j <= high; j++) { y = a[j][i]; a[j][i] = a[j][m]; a[j][m] = y; } } if (x != 0.0) { mp1 = m + 1; for (i = mp1; i <= high; i++) { y = a[i][mm1]; if (y != 0.0) { y /= x; a[i][mm1] = y; for (j = m; j < n; j++) a[i][j] -= y * a[m][j]; for (j = 0; j <= high; j++) a[j][m] += y * a[j][i]; } } } } } /*-------------------------------------------------------------------------------------------------- | | ElTran | | EISPACK routine translated from Fortran to C by David Swofford. Modified EISPACK comments | follow. | | This subroutine is a translation of the algol procedure ELMTRANS, Num. Math. 16, 181-204 (1970) | by Peters and Wilkinson. Handbook for Auto. Comp., Vol. II-Linear Algebra, 372-395 (1971). | | This subroutine accumulates the stabilized elementary similarity transformations used in the | reduction of a real general matrix to upper Hessenberg form by ElmHes. | | ON INPUT: | | n is the order of the matrix. | | low and high are integers determined by the balancing subroutine Balanc. if Balanc has | not been used, set low=1, high=n. | | a contains the multipliers which were used in the reduction by ElmHes in its lower triangle | below the subdiagonal. | | intchg contains information on the rows and columns interchanged in the reduction by ElmHes. | Only elements low through high are used. | | ON OUTPUT: | | z contains the transformation matrix produced in the reduction by ElmHes. */ void ElTran (int n, int low, int high, MODEL_FLOAT **a, int *intchg, MODEL_FLOAT **z) { int i, j, mp; /* initialize z to identity matrix */ for (j = 0; j < n; j++) { for (i = 0; i < n; i++) z[i][j] = 0.0; z[j][j] = 1.0; } for (mp = high - 1; mp >= low + 1; mp--) { for (i = mp + 1; i <= high; i++) z[i][mp] = a[i][mp-1]; i = intchg[mp]; if (i != mp) { for (j = mp; j <= high; j++) { z[mp][j] = z[i][j]; z[i][j] = 0.0; } z[i][mp] = 1.0; } } } /*-------------------------------------------------------------------------------------------------- | | Hqr2 | | EISPACK routine translated from Fortran to C by David Swofford. Modified EISPACK comments | follow. | | This subroutine is a translation of the algol procedure HQR2, Num. Math. 16, 181-204 (1970) by | Peters and Wilkinson. Handbook for Auto. Comp., Vol. II-Linear Algebra, 372-395 (1971). | | This subroutine finds the eigenvalues and eigenvectors of a real upper Hessenberg matrix by | the QR method. The eigenvectors of a real general matrix can also be found if ElmHes and | ElTran or OrtHes and OrTran have been used to reduce this general matrix to Hessenberg form | and to accumulate the similarity transformations. | | ON INPUT: | | n is the order of the matrix | | low and high are integers determined by the balancing subroutine Balanc. If Balanc has not | been used, set low=0, high=n-1. | | h contains the upper Hessenberg matrix | | z contains the transformation matrix produced by ElTran after the reduction by ElmHes, or | by OrTran after the reduction by OrtHes, if performed. If the eigenvectors of the | Hessenberg matrix are desired, z must contain the identity matrix. | | ON OUTPUT: | | h has been destroyed | | wr and wi contain the real and imaginary parts, respectively, of the eigenvalues. The | eigenvalues are unordered except that complex conjugate pairs of values appear consecutively | with the eigenvalue having the positive imaginary part first. If an error exit is made, the | eigenvalues should be correct for indices ierr,...,n-1. | | z contains the real and imaginary parts of the eigenvectors. If the i-th eigenvalue is | real, the i-th column of z contains its eigenvector. If the i-th eigenvalue is complex with | positive imaginary part, the i-th and (i+1)-th columns of z contain the real and imaginary | parts of its eigenvector. The eigenvectors are unnormalized. If an error exit is made, | none of the eigenvectors has been found. | | Return value is set to: | zero for normal return, | j if the limit of 30*n iterations is exhausted while the j-th eigenvalue is | being sought. | | Calls CDiv for complex division. */ //DJZ - Intel compiler 10.0 -O2 optimization breaks this function //so this pragma reduces the optimization level #if (defined(__INTEL_COMPILER) && __INTEL_COMPILER >= 1000) #pragma intel optimization_level 1 #endif int Hqr2 (int n, int low, int high, MODEL_FLOAT **h, MODEL_FLOAT *wr, MODEL_FLOAT *wi, MODEL_FLOAT **z) { int i, j, k, l, m, na, en, notlas, mp2, itn, its, enm2, twoRoots; MODEL_FLOAT norm, p, q, r, s, t, w, x, y, ra, sa, vi, vr, zz, tst1, tst2; /* store roots isolated by Balanc and compute matrix norm */ norm = 0.0; k = 0; for (i = 0; i < n; i++) { for (j = k; j < n; j++) norm += fabs(h[i][j]); k = i; if ((i < low) || (i > high)) { wr[i] = h[i][i]; wi[i] = 0.0; } } en = high; t = 0.0; itn = n * 30; /* search for next eigenvalues */ while (en >= low) { its = 0; na = en - 1; enm2 = na - 1; twoRoots = FALSE; /* look for single small sub-diagonal element */ for (;;) { for (l = en; l > low; l--) { s = fabs(h[l-1][l-1]) + fabs(h[l][l]); if (s == 0.0) s = norm; tst1 = s; tst2 = tst1 + fabs(h[l][l-1]); if (tst2 == tst1) break; } /* form shift */ x = h[en][en]; if (l == en) break; y = h[na][na]; w = h[en][na] * h[na][en]; if (l == na) { twoRoots = TRUE; break; } if (itn == 0) { /* set error -- all eigenvalues have not converged after 30*n iterations */ return en; } if ((its == 10) || (its == 20)) { /* form exceptional shift */ t += x; for (i = low; i <= en; i++) h[i][i] -= x; s = fabs(h[en][na]) + fabs(h[na][enm2]); x = s * (MODEL_FLOAT) 0.75; y = x; w = s * (MODEL_FLOAT)-0.4375 * s; } its++; --itn; /* look for two consecutive small sub-diagonal elements */ for (m = enm2; m >= l; m--) { zz = h[m][m]; r = x - zz; s = y - zz; p = (r * s - w) / h[m+1][m] + h[m][m+1]; q = h[m+1][m+1] - zz - r - s; r = h[m+2][m+1]; s = fabs(p) + fabs(q) + fabs(r); p /= s; q /= s; r /= s; if (m == l) break; tst1 = fabs(p) * (fabs(h[m-1][m-1]) + fabs(zz) + fabs(h[m+1][m+1])); tst2 = tst1 + fabs(h[m][m-1]) * (fabs(q) + fabs(r)); if (tst2 == tst1) break; } mp2 = m + 2; for (i = mp2; i <= en; i++) { h[i][i-2] = 0.0; if (i != mp2) h[i][i-3] = 0.0; } /* MODEL_FLOAT qr step involving rows l to en and columns m to en */ for (k = m; k <= na; k++) { notlas = (k != na); if (k != m) { p = h[k][k-1]; q = h[k+1][k-1]; r = 0.0; if (notlas) r = h[k+2][k-1]; x = fabs(p) + fabs(q) + fabs(r); if (x == 0.0) continue; p /= x; q /= x; r /= x; } s = D_sign(sqrt(p*p + q*q + r*r), p); if (k != m) h[k][k-1] = -s * x; else if (l != m) h[k][k-1] = -h[k][k-1]; p += s; x = p / s; y = q / s; zz = r / s; q /= p; r /= p; if (!notlas) { /* row modification */ for (j = k; j < n; j++) { p = h[k][j] + q * h[k+1][j]; h[k][j] -= p * x; h[k+1][j] -= p * y; } j = MIN(en, k + 3); /* column modification */ for (i = 0; i <= j; i++) { p = x * h[i][k] + y * h[i][k+1]; h[i][k] -= p; h[i][k+1] -= p * q; } /* accumulate transformations */ for (i = low; i <= high; i++) { p = x * z[i][k] + y * z[i][k+1]; z[i][k] -= p; z[i][k+1] -= p * q; } } else { /* row modification */ for (j = k; j < n; j++) { p = h[k][j] + q * h[k+1][j] + r * h[k+2][j]; h[k][j] -= p * x; h[k+1][j] -= p * y; h[k+2][j] -= p * zz; } j = MIN(en, k + 3); /* column modification */ for (i = 0; i <= j; i++) { p = x * h[i][k] + y * h[i][k+1] + zz * h[i][k+2]; h[i][k] -= p; h[i][k+1] -= p * q; h[i][k+2] -= p * r; } /* accumulate transformations */ for (i = low; i <= high; i++) { p = x * z[i][k] + y * z[i][k+1] + zz * z[i][k+2]; z[i][k] -= p; z[i][k+1] -= p * q; z[i][k+2] -= p * r; } } } } if (twoRoots) { /* two roots found */ p = (y - x) / (MODEL_FLOAT) 2.0; q = p * p + w; zz = sqrt(fabs(q)); h[en][en] = x + t; x = h[en][en]; h[na][na] = y + t; /* DLS 28aug96: Changed "0.0" to "-1e-12" below. Roundoff errors can cause this value to dip ever-so-slightly below zero even when eigenvalue is not complex. */ if (q >= -1e-12) { /* real pair */ zz = p + D_sign(zz, p); wr[na] = x + zz; wr[en] = wr[na]; if (zz != 0.0) wr[en] = x - w/zz; wi[na] = 0.0; wi[en] = 0.0; x = h[en][na]; s = fabs(x) + fabs(zz); p = x / s; q = zz / s; r = sqrt(p*p + q*q); p /= r; q /= r; /* row modification */ for (j = na; j < n; j++) { zz = h[na][j]; h[na][j] = q * zz + p * h[en][j]; h[en][j] = q * h[en][j] - p * zz; } /* column modification */ for (i = 0; i <= en; i++) { zz = h[i][na]; h[i][na] = q * zz + p * h[i][en]; h[i][en] = q * h[i][en] - p * zz; } /* accumulate transformations */ for (i = low; i <= high; i++) { zz = z[i][na]; z[i][na] = q * zz + p * z[i][en]; z[i][en] = q * z[i][en] - p * zz; } } else { /* complex pair */ wr[na] = x + p; wr[en] = x + p; wi[na] = zz; wi[en] = -zz; } en = enm2; } else { /* one root found */ h[en][en] = x + t; wr[en] = h[en][en]; wi[en] = 0.0; en = na; } } /* All roots found. Backsubstitute to find vectors of upper triangular form */ if (norm == 0.0) return 0; for (en = n - 1; en >= 0; en--) { p = wr[en]; q = wi[en]; na = en - 1; /* DLS 28aug96: Changed "0.0" to -1e-12 below (see comment above) */ if (q < -1e-12) { /* complex vector */ m = na; /* last vector component chosen imaginary so that eigenvector matrix is triangular */ if (fabs(h[en][na]) > fabs(h[na][en])) { h[na][na] = q / h[en][na]; h[na][en] = -(h[en][en] - p) / h[en][na]; } else CDiv(0.0, -h[na][en], h[na][na] - p, q, &h[na][na], &h[na][en]); h[en][na] = 0.0; h[en][en] = 1.0; enm2 = na - 1; if (enm2 >= 0) { for (i = enm2; i >= 0; i--) { w = h[i][i] - p; ra = 0.0; sa = 0.0; for (j = m; j <= en; j++) { ra += h[i][j] * h[j][na]; sa += h[i][j] * h[j][en]; } if (wi[i] < 0.0) { zz = w; r = ra; s = sa; } else { m = i; if (wi[i] == 0.0) CDiv(-ra, -sa, w, q, &h[i][na], &h[i][en]); else { /* solve complex equations */ x = h[i][i+1]; y = h[i+1][i]; vr = (wr[i] - p) * (wr[i] - p) + wi[i] * wi[i] - q * q; vi = (wr[i] - p) * (MODEL_FLOAT)2.0 * q; if ((vr == 0.0) && (vi == 0.0)) { tst1 = norm * (fabs(w) + fabs(q) + fabs(x) + fabs(y) + fabs(zz)); vr = tst1; do { vr *= (MODEL_FLOAT) 0.01; tst2 = tst1 + vr; } while (tst2 > tst1); } CDiv(x * r - zz * ra + q * sa, x * s - zz * sa - q * ra, vr, vi, &h[i][na], &h[i][en]); if (fabs(x) > fabs(zz) + fabs(q)) { h[i+1][na] = (-ra - w * h[i][na] + q * h[i][en]) / x; h[i+1][en] = (-sa - w * h[i][en] - q * h[i][na]) / x; } else CDiv(-r - y * h[i][na], -s - y * h[i][en], zz, q, &h[i+1][na], &h[i+1][en]); } /* overflow control */ tst1 = fabs(h[i][na]); tst2 = fabs(h[i][en]); t = MAX(tst1, tst2); if (t != 0.0) { tst1 = t; tst2 = tst1 + ONE_POINT_ZERO / tst1; if (tst2 <= tst1) { for (j = i; j <= en; j++) { h[j][na] /= t; h[j][en] /= t; } } } } } } /* end complex vector */ } else if (q == 0.0) { /* real vector */ m = en; h[en][en] = 1.0; if (na >= 0) { for (i = na; i >= 0; i--) { w = h[i][i] - p; r = 0.0; for (j = m; j <= en; j++) r += h[i][j] * h[j][en]; if (wi[i] < 0.0) { zz = w; s = r; continue; } else { m = i; if (wi[i] == 0.0) { t = w; if (t == 0.0) { tst1 = norm; t = tst1; do { t *= (MODEL_FLOAT) 0.01; tst2 = norm + t; } while (tst2 > tst1); } h[i][en] = -r / t; } else { /* solve real equations */ x = h[i][i+1]; y = h[i+1][i]; q = (wr[i] - p) * (wr[i] - p) + wi[i] * wi[i]; t = (x * s - zz * r) / q; h[i][en] = t; if (fabs(x) > fabs(zz)) h[i+1][en] = (-r - w * t) / x; else h[i+1][en] = (-s - y * t) / zz; } /* overflow control */ t = fabs(h[i][en]); if (t != 0.0) { tst1 = t; tst2 = tst1 + ONE_POINT_ZERO / tst1; if (tst2 <= tst1) { for (j = i; j <= en; j++) h[j][en] /= t; } } } } } /* end real vector */ } } /* end back substitution */ /* vectors of isolated roots */ for (i = 0; i < n; i++) { if ((i < low) || (i > high)) { for (j = i; j < n; j++) z[i][j] = h[i][j]; } } /* multiply by transformation matrix to give vectors of original full matrix */ for (j = n - 1; j >= low; j--) { m = MIN(j, high); for (i = low; i <= high; i++) { zz = 0.0; for (k = low; k <= m; k++) zz += z[i][k] * h[k][j]; z[i][j] = zz; } } return 0; } /*-------------------------------------------------------------------------------------------------- | | BalBak | | EISPACK routine translated from Fortran to C by David Swofford. Modified EISPACK comments | follow. | | This subroutine is a translation of the algol procedure BALBAK, Num. Math. 13, 293-304 (1969) | by Parlett and Reinsch. Handbook for Auto. Comp., vol. II-Linear Algebra, 315-326 (1971). | | This subroutine forms the eigenvectors of a real general matrix by back transforming those of | the corresponding balanced matrix determined by Balanc. | | ON INPUT: | | n is the order of the matrix. | | low and high are integers determined by Balanc. | | scale contains information determining the permutations and scaling factors used by Balanc. | | m is the number of columns of z to be back transformed. | | z contains the real and imaginary parts of the eigenvectors to be back transformed in its | first m columns. | | ON OUTPUT: | | z contains the real and imaginary parts of the transformed eigenvectors in its first m | columns. */ void BalBak (int n, int low, int high, MODEL_FLOAT *scale, int m, MODEL_FLOAT **z) { int i, j, k, ii; MODEL_FLOAT s; if (m != 0) { if (high != low) { for (i = low; i <= high; i++) { s = scale[i]; /* left hand eigenvectors are back transformed if this statement is replaced by s = 1.0/scale[i] */ for (j = 0; j < m; j++) z[i][j] *= s; } } for (ii = 0; ii < n; ii++) { i = ii; if ((i < low) || (i > high)) { if (i < low) i = low - ii; k = (int)scale[i]; if (k != i) { for (j = 0; j < m; j++) { s = z[i][j]; z[i][j] = z[k][j]; z[k][j] = s; } } } } } } /*-------------------------------------------------------------------------------------------------- | | CDiv | | Complex division, (cr,ci) = (ar,ai)/(br,bi) */ void CDiv (MODEL_FLOAT ar, MODEL_FLOAT ai, MODEL_FLOAT br, MODEL_FLOAT bi, MODEL_FLOAT *cr, MODEL_FLOAT *ci) { MODEL_FLOAT s, ais, bis, ars, brs; s = fabs(br) + fabs(bi); ars = ar / s; ais = ai / s; brs = br / s; bis = bi / s; s = brs*brs + bis*bis; *cr = (ars*brs + ais*bis) / s; *ci = (ais*brs - ars*bis) / s; } //these are from John's MCMC.c file void CalcCijk (MODEL_FLOAT *c_ijk, int n, const MODEL_FLOAT **u, const MODEL_FLOAT **v) { /* precalculate values for faster matrix mult in GTRChangeMatrix and GTRDerivatives */ MODEL_FLOAT *pc = c_ijk; for (int i=0; i= 0.0); } } void CalcPij (const MODEL_FLOAT *c_ijk, int n, const MODEL_FLOAT *eigenValues, MODEL_FLOAT v, MODEL_FLOAT r, MODEL_FLOAT **p, MODEL_FLOAT *EigValexp) { register int nsq = n * n; MODEL_FLOAT sum; const MODEL_FLOAT *ptr; MODEL_FLOAT *pMat = p[0]; MODEL_FLOAT vr = v * r; MODEL_FLOAT *g = EigValexp; for (int k=0; k 0.0f); #ifdef _SINGLE_PRECISION_FLOATS *pMat++ = (sum < ZERO_POINT_ZERO) ? FLT_MIN : sum; #else *pMat++ = (sum < ZERO_POINT_ZERO) ? ZERO_POINT_ZERO : sum; #endif } #else for(i=0; i #include #include #include #include #define CREATE_MEMCHK \ MemoryAccountant memAccountant; \ memTracker = &memAccountant; \ memTracker->StartRecording(); #define MEMCHK_REPORT(o) \ memTracker->StopRecording(); \ memAccountant.Summarize(o); using namespace std; class MemoryAccountant; #if defined (INSTANTIATE_MEMCHK) //struct dmalloc_t dmalloc; MemoryAccountant* memTracker = NULL; #else //extern struct dmalloc_t dmalloc; extern MemoryAccountant* memTracker; #endif /*---------------------------------------------------------------------------------------------------------------------- | Structure used to store accounting information for one individual memory allocation. */ class MemoryInfo { public: void *ptr; unsigned filename_index; unsigned line_number; unsigned num_bytes; enum { Mem_Array = 0x01, /* was allocated using the new [] operator */ Mem_Op_Err_Free_Array = 0x02, /* was allocated using the new, but freed with delete [] */ Mem_Op_Err_New_Array = 0x04, /* was allocated using the new[], but freed with delete */ Mem_Mid_Array_Delete = 0x08 /* delete called with an address in the middle of array */ }; unsigned flag; bool array_allocation; MemoryInfo(bool is_arr = false); }; inline MemoryInfo::MemoryInfo( bool is_arr) /*true if the memory was allocated using new [] */ { flag = is_arr ? (unsigned) Mem_Array : 0U; ptr = (void *)NULL; filename_index = 0L; line_number = 0L; num_bytes = 0L; } /*---------------------------------------------------------------------------------------------------------------------- | Keeps track of memory allocations and deletions if AddMemoryInfo is called after every call to the new operator and | MarkDeleted is called for every call to the delete or delete [] operator, which can be done using macros so that | memory accounting is not done in the release version. */ class MemoryAccountant { typedef vector MemInfoVector; typedef vector FileNameVector; FileNameVector filenames; /* vector of source file names used to provide filename_index element in the MemoryInfo struct */ MemoryInfo tmp; /* workspace used when adding a new mem_info element */ MemInfoVector mem_info; /* vector of allocation records */ unsigned nBad; /* number of remaining allocs that have not yet been deleted */ unsigned nUnknown; /* number of deletes on non-NULL pointers that are not in our database */ unsigned nAllocs; /* total number of allocs that we have caught with our overload of new */ unsigned long numBytesAllocated; /* total number of bytes from allocs that we have caught with our overload of new */ unsigned long currentlyAllocated; /* the number of bytes that are allocated but haven't been freed (only catchs allocations through our new operator) */ unsigned long peakAllocation; /* the most bytes that are ever allocated at one time (only catchs allocations through our new operator) */ public: static bool recording; /* if true, records allocations and deletions; otherwise, ignores them */ MemoryAccountant(); ~MemoryAccountant(); void StartRecording(); void StopRecording(); void AddMemoryInfo(void *p, string fn, unsigned ln, unsigned long b = 0L, bool is_arr = false); void MarkDeleted(void *p, bool is_arr = false); void Summarize(ostream &out); }; inline void MemoryAccountant::StartRecording() { recording = true; } inline void MemoryAccountant::StopRecording() { recording = false; } inline void *operator new (size_t size, const char *file, int line) { void *p = malloc(size); if (p == NULL) throw std::bad_alloc(); if (memTracker != NULL && MemoryAccountant::recording) memTracker->AddMemoryInfo(p, file, (unsigned)line, size, false); return p; } inline void operator delete (void *p) { if (p != NULL) { if (memTracker != NULL && MemoryAccountant::recording) memTracker->MarkDeleted(p, false); free(p); } } #if !defined (_MSC_VER) inline void * operator new [] (size_t size, const char *file, int line) { void *p = malloc (size); if (p == NULL) throw std::bad_alloc(); if (memTracker != NULL && MemoryAccountant::recording) memTracker->AddMemoryInfo(p, file, (unsigned) line, size, true); return p; } inline void operator delete [](void *p) { if (p != NULL) { if (memTracker != NULL && MemoryAccountant::recording) memTracker->MarkDeleted(p, true); free(p); } } #endif //!defined (_MSC_VER) #define NEW new (__FILE__, __LINE__) #define new NEW #if defined (INSTANTIATE_MEMCHK) bool MemoryAccountant::recording = false; /*---------------------------------------------------------------------------------------------------------------------- | Default constructor does nothing currently. */ MemoryAccountant::MemoryAccountant() : nBad(0L), nUnknown(0L), nAllocs(0L), numBytesAllocated(0L), peakAllocation(0L), currentlyAllocated(0L) { } /*---------------------------------------------------------------------------------------------------------------------- | Destructor does nothing currently. */ MemoryAccountant::~MemoryAccountant() { filenames.erase(filenames.begin(), filenames.end()); } /*---------------------------------------------------------------------------------------------------------------------- | Fills in fields of temporary MemoryInfo structure tmp and pushes it onto the mem_info vector. */ void MemoryAccountant::AddMemoryInfo( void *p, /* pointer to the allocated memory */ string fn, /* name of file where allocation occurred */ unsigned ln, /* line number where allocation occurred */ unsigned long b, /* number of bytes allocated (defaults to 0L, which means number of bytes is not being tracked) */ bool is_arr) /* true if the allocated using the new [] operator */ { if (!MemoryAccountant::recording) return; nAllocs++; numBytesAllocated += b; currentlyAllocated += b; if (currentlyAllocated > peakAllocation) peakAllocation = currentlyAllocated; // Attempt to find fn in the stored vector of file names // FileNameVector::iterator i = find(filenames.begin(), filenames.end(), fn); if (i == filenames.end()) { // fn has not previously been encountered // tmp.filename_index = filenames.size(); filenames.push_back(fn); } else { // fn has not previously been encountered // tmp.filename_index = (unsigned) (i - filenames.begin()); } tmp.ptr = p; tmp.flag = is_arr ? (unsigned) MemoryInfo::Mem_Array : 0U; tmp.num_bytes = b; tmp.line_number = ln; mem_info.push_back(tmp); nBad++; } /*---------------------------------------------------------------------------------------------------------------------- | Finds pointer p in mem_info vector and sets it to NULL, marking it as having been deleted. */ void MemoryAccountant::MarkDeleted( void *p, /* the pointer to be marked */ bool is_arr) /*true if the memory is being freed with the delete [] operator */ { if (!recording || p == NULL) return; bool found = false; bool middleArrayError = false; MemInfoVector::iterator i; for (i = mem_info.begin(); i != mem_info.end(); i++) { if (i->ptr == p) { if (is_arr) { if (!(i->flag & MemoryInfo::Mem_Array)) i->flag |= MemoryInfo::Mem_Op_Err_Free_Array; } else { if (i->flag & MemoryInfo::Mem_Array) i->flag |= MemoryInfo::Mem_Op_Err_New_Array; } i->ptr = NULL; nBad--; found = true; currentlyAllocated -= i->num_bytes; break; } else if (i->ptr < p && ((unsigned long) i->ptr + (unsigned long) i->num_bytes > (unsigned long) p)) { middleArrayError = true; i->flag |= MemoryInfo::Mem_Mid_Array_Delete; assert(middleArrayError == false); } } assert(! (found && middleArrayError)); // shouldn't be possible to trip the middle of an array error and later find the mem object if (!found) nUnknown++; } /*---------------------------------------------------------------------------------------------------------------------- | Summarizes memory allocations recorded, displaying total number of allocations, number of allocations currently | not deleted, and file name and line number for remaining undeleted elements. */ void MemoryAccountant::Summarize( ostream &out) /* output stream for showing summary */ { out << "\n\nMemory Report" << endl; out << "\nTotal allocations: " << nAllocs << " ("<< numBytesAllocated <<" bytes)"< 1) { out << " "; if (mi.num_bytes > 0L) out << mi.num_bytes << " bytes "; else out << "unknown number of bytes "; if (mi.ptr != NULL) out << "remaining from allocation at "; if (mi.flag & MemoryInfo::Mem_Op_Err_Free_Array) out << "allocated with new but freed with delete []. Allocation at "; else if (mi.flag & MemoryInfo::Mem_Op_Err_New_Array) out << "allocated with new [] but freed with delete. Allocation at "; else if (mi.flag & MemoryInfo::Mem_Mid_Array_Delete) out << "allocated but deletion occurred from the middle. Allocation at "; string s = filenames[mi.filename_index]; out << s; out << " (" << mi.line_number << ")" << endl; } } } # endif //defined (INSTANTIATE_MEMCHK) # else // #if defined(MONITORING_ALLOCATION) && !defined(NDEBUG # define CREATE_MEMCHK # define MEMCHK_REPORT(o) # endif // #if defined(MONITORING_ALLOCATION) && !defined(NDEBUG) #endif // #ifndef __MEMORYACCOUNTANT garli-2.1-release/src/model.cpp000066400000000000000000006241671241236125200165010ustar00rootroot00000000000000// GARLI version 2.1 source code // Copyright 2005-2014 Derrick J. Zwickl // email: garli.support@gmail.com // // This program is free software: you can redistribute it and/or modify // it under the terms of the GNU General Public License as published by // the Free Software Foundation, either version 3 of the License, or // (at your option) any later version. // // This program is distributed in the hope that it will be useful, // but WITHOUT ANY WARRANTY; without even the implied warranty of // MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the // GNU General Public License for more details. // // You should have received a copy of the GNU General Public License // along with this program. If not, see . #include #include using namespace std; #include "defs.h" #include "utility.h" #include "linalg.h" #include "model.h" #include "individual.h" #include "sequencedata.h" #include "rng.h" #undef ALIGN_MODEL Profiler ProfCalcPmat("CalcPmat "); Profiler ProfCalcEigen("CalcEigen "); extern rng rnd; extern vector dataSubInfo; FLOAT_TYPE Model::mutationShape; FLOAT_TYPE PointNormal (FLOAT_TYPE prob); FLOAT_TYPE IncompleteGamma (FLOAT_TYPE x, FLOAT_TYPE alpha, FLOAT_TYPE LnGamma_alpha); FLOAT_TYPE PointChi2 (FLOAT_TYPE prob, FLOAT_TYPE v); Model::~Model(){ if(stateFreqs.empty() == false){ for(int i=0;i<(int)stateFreqs.size();i++) delete stateFreqs[i]; } if(relNucRates.empty() == false){ if(nst==6 || nst == -1){ //3/25/08 this needed to change a bit for arbitrary matrices //since some of the elements might be aliased for(int i=0;i<(int)relNucRates.size();i++){ if(relNucRates[i] != NULL){ for(int j=i+1;j<(int)relNucRates.size();j++){ if(relNucRates[j] == relNucRates[i]) relNucRates[j] = NULL; } delete relNucRates[i]; relNucRates[i] = NULL; } } } else if(nst==2){ delete relNucRates[0]; delete relNucRates[1]; } else if(nst==1) delete relNucRates[0]; } if(modSpec->IsCodon()){ for(int r=0;r::iterator delit=paramsToMutate.begin();delit!=paramsToMutate.end();delit++) delete *(delit); Delete2DArray(eigvals); delete []eigvalsimag; delete []iwork; delete []work; delete []col; delete []indx; if(c_ijk != NULL) Delete2DArray(c_ijk); delete []EigValexp; delete []EigValderiv; delete []EigValderiv2; delete []blen_multiplier; #ifndef ALIGN_MODEL Delete3DArray(eigvecs); Delete2DArray(teigvecs); Delete3DArray(inveigvecs); //Delete3DArray(pmat); Delete3DArray(pmat1); Delete3DArray(pmat2); Delete3DArray(qmat); Delete3DArray(tempqmat); Delete3DArray(deriv1); Delete3DArray(deriv2); #ifdef SINGLE_PRECISION_FLOATS Delete3DArray(fpmat1); Delete3DArray(fpmat2); Delete3DArray(fderiv1); Delete3DArray(fderiv2); #endif #else Delete2DAlignedArray(eigvecs); Delete2DAlignedArray(teigvecs); Delete2DAlignedArray(inveigvecs); Delete3DAlignedArray(pmat); Delete2DAlignedArray(qmat); Delete2DAlignedArray(tempqmat); Delete3DAlignedArray(deriv1); Delete3DAlignedArray(deriv2); #endif } void Model::AllocateEigenVariables(){ #ifndef ALIGN_MODEL //a bunch of allocation here for all of the qmatrix->eigenvector->pmatrix related variables eigvalsimag=new MODEL_FLOAT[nstates]; iwork=new int[nstates]; work=new MODEL_FLOAT[nstates]; col=new MODEL_FLOAT[nstates]; indx=new int[nstates]; EigValexp=new MODEL_FLOAT[nstates*NRateCats()]; EigValderiv=new MODEL_FLOAT[nstates*NRateCats()]; EigValderiv2=new MODEL_FLOAT[nstates*NRateCats()]; //create the matrix for the eigenvectors eigvecs=New3DArray(NRateCats(), nstates, nstates); //create a temporary matrix to hold the eigenvectors that will be destroyed during the invertization teigvecs=New2DArray(nstates,nstates); //create the matrix for the inverse eigenvectors inveigvecs=New3DArray(NRateCats(), nstates, nstates); //allocate the pmats pmat1=New3DArray(NRateCats(), nstates, nstates); pmat2=New3DArray(NRateCats(), nstates, nstates); #ifdef SINGLE_PRECISION_FLOATS //allocate single precision versions of the matrices fpmat1=New3DArray(NRateCats(), nstates, nstates); fpmat2=New3DArray(NRateCats(), nstates, nstates); fderiv1=New3DArray(NRateCats(), nstates, nstates); fderiv2=New3DArray(NRateCats(), nstates, nstates); #endif //it is actually less efficient to precalc the c_ijk for codon models due to the immense //size of the matrix. So don't allocate it at all. if(modSpec->IsCodon() == false){ c_ijk=New2DArray(1,nstates*nstates*nstates); } else c_ijk = NULL; //allocate qmat and tempqmat //if this is a model with multiple qmats (like multi-omega models or mixtures) //it needs to be bigger if(modSpec->IsNonsynonymousRateHet() == false){ qmat=New3DArray(1, nstates,nstates); tempqmat=New3DArray(1, nstates,nstates); blen_multiplier = new FLOAT_TYPE[1]; eigvals=New2DArray(1, nstates);//eigenvalues } else{ qmat=New3DArray(NRateCats(), nstates, nstates); tempqmat=New3DArray(NRateCats(), nstates, nstates); blen_multiplier = new FLOAT_TYPE[NRateCats()]; eigvals=New2DArray(NRateCats(), nstates);//eigenvalues } deriv1=New3DArray(NRateCats(), nstates, nstates); deriv2=New3DArray(NRateCats(), nstates, nstates); #else //a bunch of allocation here for all of the qmatrix->eigenvector->pmatrix related variables eigvals=new MODEL_FLOAT[nstates];//eigenvalues eigvalsimag=new MODEL_FLOAT[nstates]; iwork=new int[nstates]; work=new MODEL_FLOAT[nstates]; col=new MODEL_FLOAT[nstates]; indx=new int[nstates]; c_ijk=new MODEL_FLOAT[nstates*nstates*nstates]; EigValexp=new MODEL_FLOAT[nstates*NRateCats()]; //create the matrix for the eigenvectors eigvecs=New2DAlignedArray(nstates,nstates); //create a temporary matrix to hold the eigenvectors that will be destroyed during the invertization teigvecs=New2DAlignedArray(nstates,nstates); //create the matrix for the inverse eigenvectors inveigvecs=New2DAlignedArray(nstates,nstates); //allocate the pmat pmat=New3DAlignedArray(NRateCats(), nstates, nstates); //allocate qmat and tempqmat qmat=New2DAlignedArray(nstates,nstates); tempqmat=New2DAlignedArray(nstates,nstates); deriv1=New3DAlignedArray(NRateCats(), nstates, nstates); deriv2=New3DAlignedArray(NRateCats(), nstates, nstates); #endif } void Model::UpdateQMat(){ //recalculate the qmat from the statefreqs and rates if(modSpec->IsCodon()){ UpdateQMatCodon(); return; } else if(modSpec->IsOrientedGap()){ return; } else if(modSpec->IsAminoAcid()){ UpdateQMatAminoAcid(); return; } else if(modSpec->IsNState() || modSpec->IsNStateV() || modSpec->IsBinary() || modSpec->IsBinaryNotAllZeros()){ UpdateQMatNState(); return; } else if(modSpec->IsOrderedNState() || modSpec->IsOrderedNStateV()){ UpdateQMatOrderedNState(); return; } if(nstates==4){ qmat[0][0][1]=*relNucRates[0] * *stateFreqs[1]; //a * piC qmat[0][0][2]=*relNucRates[1] * *stateFreqs[2]; //b * piG qmat[0][0][3]=*relNucRates[2] * *stateFreqs[3]; //c * piT qmat[0][1][2]=*relNucRates[3] * *stateFreqs[2]; //d * piG qmat[0][1][3]=*relNucRates[4] * *stateFreqs[3]; //e * piT qmat[0][2][3]=*stateFreqs[3]; //f(=1) * piT qmat[0][1][0]=*relNucRates[0] * *stateFreqs[0]; //a * piA qmat[0][2][0]=*relNucRates[1] * *stateFreqs[0]; //b * piA qmat[0][2][1]=*relNucRates[3] * *stateFreqs[1]; //d * piC qmat[0][3][0]=*relNucRates[2] * *stateFreqs[0]; //c * piA qmat[0][3][1]=*relNucRates[4] * *stateFreqs[1]; //e * piC qmat[0][3][2]=*stateFreqs[2]; //f(=1) * piG } else {//this isn't being used - see UpdateQmatCodon and UpdateQmatAminoAcid //general nstate x nstate method int rnum=0; for(int i=0;iC or C->A nuc change //bit 16 = A->G or G->A nuc change //bit 32 = A->T or T->A nuc change //bit 64 = C->G or G->C nuc change //bit 128 = C->T or T->C nuc change //bit 256 = G->T or T->G nuc change //although it seems a little wacky, I'm going to fill the 64x64 matrix, and then eliminate the //rows and columns that are stop codons, since they differ for different codes and the following //stuff would be hell without regularity. The static qmatLookup is only calculated once anyway. int tempqmatLookup[64*64]; for(int q=0;q<64*64;q++) tempqmatLookup[q]=0; //its easier to do this in 4 x 4 blocks for(int i=0;i<16;i++){ for(int j=0;j<16;j++){ for(int ii=0;ii<4;ii++){ for(int jj=0;jj<4;jj++){ if(i==j){//on diagonal 4x4 if(ii!=jj){ //all the cells in this subsection are 1 nuc change away tempqmatLookup[64*(i*4+ii) + (j*4+jj)] = 1; if((ii+jj)%2 == 0) tempqmatLookup[64*(i*4+ii) + (j*4+jj)] |= 2; if((ii==0 && jj==1) || (ii==1 && jj==0)) tempqmatLookup[64*(i*4+ii) + (j*4+jj)] |= 8; else if((ii==0 && jj==2) || (ii==2 && jj==0)) tempqmatLookup[64*(i*4+ii) + (j*4+jj)] |= 16; else if((ii==0 && jj==3) || (ii==3 && jj==0)) tempqmatLookup[64*(i*4+ii) + (j*4+jj)] |= 32; else if((ii==1 && jj==2) || (ii==2 && jj==1)) tempqmatLookup[64*(i*4+ii) + (j*4+jj)] |= 64; else if((ii==1 && jj==3) || (ii==3 && jj==1)) tempqmatLookup[64*(i*4+ii) + (j*4+jj)] |= 128; else if((ii==2 && jj==3) || (ii==3 && jj==2)) tempqmatLookup[64*(i*4+ii) + (j*4+jj)] |= 256; } } else if(floor(i/4.0)==floor(j/4.0)){//near diagonal 4x4, some cells differ at the 2nd pos if(ii==jj){ //the diagonal cells in this subsection are 1 nuc change away tempqmatLookup[64*(i*4+ii) + (j*4+jj)] = 1; if(abs(i-j) == 2) tempqmatLookup[64*(i*4+ii) + (j*4+jj)] |= 2; if((i%4==0 && j%4==1) || (i%4==1 && j%4==0)) tempqmatLookup[64*(i*4+ii) + (j*4+jj)] |= 8; else if((i%4==0 && j%4==2) || (i%4==2 && j%4==0)) tempqmatLookup[64*(i*4+ii) + (j*4+jj)] |= 16; else if((i%4==0 && j%4==3) || (i%4==3 && j%4==0)) tempqmatLookup[64*(i*4+ii) + (j*4+jj)] |= 32; else if((i%4==1 && j%4==2) || (i%4==2 && j%4==1)) tempqmatLookup[64*(i*4+ii) + (j*4+jj)] |= 64; else if((i%4==1 && j%4==3) || (i%4==3 && j%4==1)) tempqmatLookup[64*(i*4+ii) + (j*4+jj)] |= 128; else if((i%4==2 && j%4==3) || (i%4==3 && j%4==2)) tempqmatLookup[64*(i*4+ii) + (j*4+jj)] |= 256; } } else{//far from diagonal 4x4, some cells differ at the 1nd pos if(i%4 == j%4){ if(ii==jj){ //the diagonal cells in this subsection are 1 nuc change away tempqmatLookup[64*(i*4+ii) + (j*4+jj)] = 1; if(abs(i-j) ==8) tempqmatLookup[64*(i*4+ii) + (j*4+jj)] |= 2; if((i/4==0 && j/4==1) || (i/4==1 && j/4==0)) tempqmatLookup[64*(i*4+ii) + (j*4+jj)] |= 8; else if((i/4==0 && j/4==2) || (i/4==2 && j/4==0)) tempqmatLookup[64*(i*4+ii) + (j*4+jj)] |= 16; else if((i/4==0 && j/4==3) || (i/4==3 && j/4==0)) tempqmatLookup[64*(i*4+ii) + (j*4+jj)] |= 32; else if((i/4==1 && j/4==2) || (i/4==2 && j/4==1)) tempqmatLookup[64*(i*4+ii) + (j*4+jj)] |= 64; else if((i/4==1 && j/4==3) || (i/4==3 && j/4==1)) tempqmatLookup[64*(i*4+ii) + (j*4+jj)] |= 128; else if((i/4==2 && j/4==3) || (i/4==3 && j/4==2)) tempqmatLookup[64*(i*4+ii) + (j*4+jj)] |= 256; } } } } } } } //mark the nonsynonymous changes with |4 and the stops with -1 for(int from=0;from<64;from++){ for(int to=0;to<64;to++){ int fromAA = code->CodonLookup(from); int toAA = code->CodonLookup(to); //if one of the codons is a stop if(fromAA == 20 || toAA == 20) tempqmatLookup[64*from + to] = -1; //if this is a viable 1 nucleotide change else if(tempqmatLookup[64*from + to] & 1){ //if this is a nonsynonymous change if(fromAA != toAA) tempqmatLookup[64*from + to] |= 4; } } } #ifdef CODON_QMAT_HACK //WHEN PLAYING HERE, REMEMBER THAT THESE INDECES ARE WITH RESPECT TO THE //WHOLE 64X64 MATRIX //Put in whatever ad hoc alterations to the codon matrix //Hack in single changes for serine -> serine double hits //giving them a rate of omega X the first pos change //AGC->TCC /* tempqmatLookup[629] = 1 | 4 | 32; //AGT->TCT tempqmatLookup[759] = 1 | 4 | 32; //TCT->AGT tempqmatLookup[3531] = 1 | 4 | 32; //TCC->AGC tempqmatLookup[3401] = 1 | 4 | 32; */ //all transitions between //the two sets of serine codons //AGC->TCN tempqmatLookup[628] = 1 | 4 | 32; tempqmatLookup[629] = 1 | 4 | 32; tempqmatLookup[630] = 1 | 4 | 32; tempqmatLookup[631] = 1 | 4 | 32; //AGT->TCN tempqmatLookup[756] = 1 | 4 | 32; tempqmatLookup[757] = 1 | 4 | 32; tempqmatLookup[758] = 1 | 4 | 32; tempqmatLookup[759] = 1 | 4 | 32; //TCN->AGC tempqmatLookup[3337] = 1 | 4 | 32; tempqmatLookup[3401] = 1 | 4 | 32; tempqmatLookup[3465] = 1 | 4 | 32; tempqmatLookup[3529] = 1 | 4 | 32; //TCN->AGT tempqmatLookup[3339] = 1 | 4 | 32; tempqmatLookup[3403] = 1 | 4 | 32; tempqmatLookup[3467] = 1 | 4 | 32; tempqmatLookup[3531] = 1 | 4 | 32; #endif //remove the columns and rows representing stops int reducedCell = 0; for(int fullCell=0;fullCell<64*64;fullCell++){ if(tempqmatLookup[fullCell] != -1) qmatLookup[reducedCell++] = tempqmatLookup[fullCell]; } //assert(reducedCell == nstates*nstates); /* ofstream deb("debugQmat.log"); for(int from=0;from1 appart qmat[w][i][j]=0.0; } else{ qmat[w][i][j] = *stateFreqs[j]; if(nst == 2){ if(qmatLookup[i*nstates+j] & 2){//if the difference is a transition qmat[w][i][j] *= *relNucRates[1]; } } else if(nst == 6){ if(qmatLookup[i*nstates+j] & 8) qmat[w][i][j] *= *relNucRates[0]; else if(qmatLookup[i*nstates+j] & 16) qmat[w][i][j] *= *relNucRates[1]; else if(qmatLookup[i*nstates+j] & 32) qmat[w][i][j] *= *relNucRates[2]; else if(qmatLookup[i*nstates+j] & 64) qmat[w][i][j] *= *relNucRates[3]; else if(qmatLookup[i*nstates+j] & 128) qmat[w][i][j] *= *relNucRates[4]; else if(qmatLookup[i*nstates+j] & 256) qmat[w][i][j] *= *relNucRates[5]; } if(qmatLookup[i*nstates+j]&4){ //this is where omega or AA property stuff will go //double phi=pow(abs((composition[i]-scalerC->val)/(composition[j]-scalerC->val)),Vc->val); //double phi=pow(abs((polarity[i]-scalerC->val)/(polarity[j]-scalerC->val)),Vc->val); //double phi=pow(exp(abs(polarity[i]-polarity[j])),Vc->val); //double phi=pow(exp(abs(composition[i]-composition[j])),Vc->val); //double phi=pow(1-abs(polarity[i]-polarity[j]),Vc->val); /* double compdist=abs(composition[i]-composition[j]); double poldist=abs(polarity[i]-polarity[j]); double voldist=abs(molvol[i]-molvol[j]); double comp, pol, vol; */ /* //this is essentially Yang's geometric relationship for each distance separately //there is a separate 'b' variable for each property, and a single 'a' double comp=exp(-Vc->val*abs(composition[i]-composition[j])); double pol=exp(-Vc->val*abs(composition[i]-composition[j])); double vol=exp(-Vc->val*abs(composition[i]-composition[j])); qmat[i][j] *= comp; qmat[i][j] *= scalerC->val; //overall omega type thing qmat[i][j] *= pol; qmat[i][j] *= vol; */ int nParams = 0; if(nParams==5){ //this is essentially Yang's linear relationship for each distance separately //there is a separate 'b' variable for each property, and a single 'a' /* comp=(1-scalerC->val*compdist); pol=(1-scalerP->val*poldist); vol=(1-scalerM->val*voldist); qmat[i][j] *= comp; qmat[i][j] *= omega->val; //overall omega type thing qmat[i][j] *= pol; qmat[i][j] *= vol; */ } if(nParams==0){ qmat[w][i][j] *= *omegas[w]; //overall omega } if(nParams==1){ //raw distances with overall omega /* comp=1.0-compdist; pol=1.0-poldist; vol=1.0-voldist; qmat[i][j] *= comp; qmat[i][j] *= omega->val; //overall omega qmat[i][j] *= pol; qmat[i][j] *= vol; */ } else if(nParams==4){ //powered distances with overall omega /* comp=pow((1.001-compdist),powerC->val-1.0); pol=pow((1.001-poldist),powerP->val-1.0); vol=pow((1.001-voldist),powerM->val-1.0); qmat[i][j] *= comp; qmat[i][j] *= omega->val; //overall omega type thing qmat[i][j] *= pol; qmat[i][j] *= vol; */ } else if(nParams==7){ //powered distances with scalers and overall omega /* comp=pow(1-scalerC->val * abs(composition[i]-composition[j]),powerC->val); pol=pow(1-scalerP->val * abs(polarity[i]-polarity[j]),powerP->val); vol=pow(1-scalerM->val * abs(molvol[i]-molvol[j]),powerM->val); qmat[i][j] *= comp; qmat[i][j] *= omega->val; //overall omega type thing qmat[i][j] *= pol; qmat[i][j] *= vol; */ } /* //raw distances with overall omega double comp=1-abs(composition[i]-composition[j]); double pol=1-abs(polarity[i]-polarity[j]); double vol=1-abs(molvol[i]-molvol[j]); qmat[i][j] *= comp; qmat[i][j] *= scalerC->val; //overall omega type thing qmat[i][j] *= pol; qmat[i][j] *= vol; */ /* //raw distances converted to single euclidian and not rescaled to new max, with overall omega double eucdist=1 - sqrt(comp*comp + pol*pol + vol*vol); if(eucdist<0){ eucdist=0; } qmat[i][j] *= eucdist; qmat[i][j] *= scalerC->val; //overall omega type thing */ /* //powered distances converted to a euclidian with overall omega, not rescaled double comp=pow(1-abs(composition[i]-composition[j]),scalerC->val); double pol=pow(1-abs(polarity[i]-polarity[j]),powerP->val); double vol=pow(1-abs(molvol[i]-molvol[j]),powerM->val); double eucdist=1 - sqrt((1-comp)*(1-comp) + (1-pol)*(1-pol) + (1-vol)*(1-vol)); qmat[i][j] *= eucdist; qmat[i][j] *= Vc->val; //overall omega type thing */ /* //powered distances converted to a euclidian with overall omega, rescaled to max double comp=pow(1-abs(composition[i]-composition[j]),scalerC->val); double pol=pow(1-abs(polarity[i]-polarity[j]),powerP->val); double vol=pow(1-abs(molvol[i]-molvol[j]),powerM->val); double eucdist=1 - (sqrt((1-comp)*(1-comp) + (1-pol)*(1-pol) + (1-vol)*(1-vol)))/sqrt(3.0); qmat[i][j] *= eucdist; qmat[i][j] *= Vc->val; //overall omega type thing */ /* //raw distances converted to single euclidian rescaled to euc max, with overall omega double eucdist=1.0 - sqrt(comp*comp + pol*pol + vol*vol) / sqrt(3)); //make sure to rescale the max here qmat[i][j] *= eucdist; qmat[i][j] *= scalerC->val; //overall omega type thing */ //raw distances with estimated weights, converted to single euclidian, with overall omega //the weights are really only relative, with the composition weight fixed to 1 /* //distance rescaled to new max value double eucdist=sqrt(comp*comp + powerP->val*pol*pol + powerM->val*vol*vol); double eucmax=sqrt(1 + powerP->val*powerP->val + powerM->val*powerM->val); qmat[i][j] *= 1.0 - eucdist/eucmax; qmat[i][j] *= scalerC->val; //overall omega type thing */ /* //raw distances converted to single euclidian rescaled to euc max, with overall omega //also with a parameter like the 'b' in Yang's linear model double eucdist=1.0 - Vc->val*sqrt(comp*comp + pol*pol + vol*vol) / sqrt(3); //make sure to rescale the max here if(eucdist<0) eucdist=0.0; qmat[i][j] *= eucdist; qmat[i][j] *= scalerC->val; //overall omega type thing */ //raw distances converted to single euclidian rescaled to euc max, with overall omega /* //raised to an estimated power double eucdist=pow(1.0 - sqrt(comp*comp + pol*pol + vol*vol) / sqrt(3), powerP->val); //make sure to rescale the max here if(eucdist<0) eucdist=0.0; qmat[i][j] *= eucdist; qmat[i][j] *= scalerC->val; //overall omega type thing */ /* //alternative forumulation allowing the curve to go above 1. //infers "intercept" and power //double eucdist=sqrt(comp*comp + pol*pol + vol*vol) / sqrt(3.0); //make sure to rescale the max here double eucdist=comp; //make sure to rescale the max here eucdist=pow((1.0-(eucdist)*(eucdist-powerP->val)), scalerC->val); qmat[i][j] *= eucdist; qmat[i][j] *= Vc->val; //overall omega type thing */ //raw distances converted to single euclidian rescaled to euc max, with overall omega /* //raised to an estimated power, with a scaler (ie 'b' on the euc) <0 set to 0 double eucdist=1.0 - powerP->val * sqrt(comp*comp + pol*pol + vol*vol) / sqrt(3); //make sure to rescale the max here if(eucdist<0){ eucdist=0.0; } eucdist=pow(eucdist, Vc->val); qmat[i][j] *= eucdist; qmat[i][j] *= scalerC->val; //overall omega type thing */ //raw distances with estimated weights, converted to single euclidian, with overall omega //the weights are really only relative, with the composition weight fixed to 1 //distance rescaled to new max value, also with the b of Yang /* double eucdist=scalerC->val * sqrt(comp*comp + powerP->val*pol*pol + powerM->val*vol*vol); double eucmax=sqrt(1 + powerP->val*powerP->val + powerM->val*powerM->val); eucdist = 1.0 - eucdist/eucmax; if(eucdist<0) eucdist=0.0; qmat[i][j] *= eucdist; qmat[i][j] *= Vc->val; //overall omega type thing */ //raw distances with estimated weights, converted to single euclidian, with overall omega //the weights are really only relative, with the composition weight fixed to 1 /* //distance rescaled to new max value, also with the b of Yang double eucdist=scalerC->val * sqrt(comp*comp + powerP->val*pol*pol + powerM->val*vol*vol); double eucmax=sqrt(1 + powerP->val*powerP->val + powerM->val*powerM->val); eucdist=1.0 - eucdist/eucmax; if(eucdist<0) eucdist=0.0; eucdist = pow(eucdist, scalerP->val); qmat[i][j] *= eucdist; qmat[i][j] *= Vc->val; //overall omega type thing */ /* //this is called "3propPowerDenom" double comp=pow(1-(abs(composition[i]-composition[j])/(1+scalerC->val)),Vc->val); double pol=pow(1-(abs(polarity[i]-polarity[j])/(1+scalerP->val)),powerP->val); double vol=pow(1-(abs(molvol[i]-molvol[j])/(1+scalerM->val)),powerM->val); qmat[i][j] *= comp; qmat[i][j] *= pol; qmat[i][j] *= vol; */ } } } } } //set diags to sum rows to 0 and calculate the branch length rescaling factor //note the there is really only a single rescaler, but it is stored separately //for each omega model double sum, weightedDiagSum; blen_multiplier[0] = 0.0; for(int w=0;w &results){ results.clear(); UpdateQMatCodon(); //calc the S and NS blens separately vector sumS, sumNS; sumS.resize(NRateCats()); sumNS.resize(NRateCats()); double weightedSumS, weightedSumNS; double tempSumS, tempSumNS; for(int w=0;wIsJonesAAMatrix()) MultiplyByJonesAAMatrix(); else if(modSpec->IsDayhoffAAMatrix()) MultiplyByDayhoffAAMatrix(); else if(modSpec->IsWAGAAMatrix()) MultiplyByWAGAAMatrix(); else if(modSpec->IsMtMamAAMatrix()) MultiplyByMtMamAAMatrix(); else if(modSpec->IsMtRevAAMatrix()) MultiplyByMtRevAAMatrix(); else if(modSpec->IsEstimateAAMatrix() || modSpec->IsTwoSerineRateMatrix() || modSpec->IsUserSpecifiedRateMatrix()){ vector::iterator r = relNucRates.begin(); for(int from=0;fromIsNonsynonymousRateHet() ? NRateCats() : 1; memcpy(**tempqmat, **qmat, effectiveModels*nstates*nstates*sizeof(MODEL_FLOAT)); for(int m=0;mIsCodon() == false) CalcCijk(&c_ijk[m][0], nstates, (const MODEL_FLOAT**) eigvecs[m], (const MODEL_FLOAT**) inveigvecs[m]); } eigenDirty=false; ProfCalcEigen.Stop(); } //this just copies elements from a double precision matrix into a single precision one void ChangeMatrixPrecision(int elements, double ***pmat, float ***fpmat){ for(int e=0;emodSpec->IsOrientedGap()){ if(!(blen1 < ZERO_POINT_ZERO)){ CalcOrientedGapPmat(blen1, pmat1); #ifdef SINGLE_PRECISION_FLOATS ChangeMatrixPrecision(nstates * nstates * modSpec->numRateCats, pmat1, fpmat1); mat1 = **fpmat1; #else mat1 = **pmat1; #endif } if(!(blen2 < ZERO_POINT_ZERO)){ CalcOrientedGapPmat(blen2, pmat2); #ifdef SINGLE_PRECISION_FLOATS ChangeMatrixPrecision(nstates * nstates * modSpec->numRateCats, pmat2, fpmat2); mat2 = **fpmat2; #else mat2 = **pmat2; #endif } } else{ if(!(blen1 < ZERO_POINT_ZERO)){ AltCalcPmat(blen1, pmat1); #ifdef SINGLE_PRECISION_FLOATS ChangeMatrixPrecision(nstates * nstates * modSpec->numRateCats, pmat1, fpmat1); mat1 = **fpmat1; #else mat1 = **pmat1; #endif } if(!(blen2 < ZERO_POINT_ZERO)){ AltCalcPmat(blen2, pmat2); #ifdef SINGLE_PRECISION_FLOATS ChangeMatrixPrecision(nstates * nstates * modSpec->numRateCats, pmat2, fpmat2); mat2 = **fpmat2; #else mat2 = **pmat2; #endif } } /* for(int i=0;i 1.0, this opens the //possibility of overflow, so reduce the blen here. If the artificial reduction is too much //the normal rescaling will kick in #ifdef ONE_BRANCH_INS_DEL //10/11/10 - more changes after some thought. Looks like we DO need to account for ins -> del //on a single branch, making mat[0][2] non-zero. Actual probs are determined via a convolution //that integrate over all possible placements of ins then del on the branch. The denominator for //both still has the treelength in it, so will be taken care of at the root. mat[0][2] will //only appear for fully gap sites. The 0.1 will be figured in again to avoid overflow. //this full term is: //mat[0][0][1] = (1.0 - expMu) / (mu * TL); //but the (mu * TL) will be factored in at the root mat[0][0][1] = (1.0 - expMu); //(actually mat[0][2] is now figured in at the root, so not even used from the pmat //mat[0][0][2] = (blen / TL) - (1.0 - expMu) / (mu * TL); #else mat[0][0][1] = blen * 0.1; //prob of insert, uniform along branches mat[0][0][2] = 0.0; //insert and del on same branch (should be 0?) #endif mat[0][1][2] = 1.0 - expMu; //deletion //mat[0][1][1] = 1.0 - mat[0][1][2]; //no deletion mat[0][1][1] = expMu; //no deletion mat[0][2][2] = 1.0; //stay deleted (?) mat[0][1][0] = mat[0][2][0] = mat[0][2][1] = ZERO_POINT_ZERO; /* //earlier abandoned stuff worked out with Mark mat[0][0][0] = expLam; mat[0][0][1] = ((expLam - expMu) * lambda) / (mu - lambda); mat[0][0][2] = (mu - (mu * expLam) + (expMu - 1.0) * lambda) / (mu - lambda); mat[0][1][1] = expMu; mat[0][1][2] = 1.0 - expMu; mat[0][2][2] = 1.0; mat[0][1][0] = mat[0][2][0] = mat[0][2][1] = ZERO_POINT_ZERO; */ //from Rivas and Eddy /* double psi = IndelPsi(blen); double gamma = IndelGamma(blen); double delProb = (1.0 - psi) * gamma; //here R&E multiply by pi(j) for the base being inserted. Not sure if I should have any //value here or not. double insProb = psi; //psi * 0.25; mat[0][0][0] = ONE_POINT_ZERO - insProb; mat[0][0][1] = insProb; //we used to have a value here, but not sure if it is necessary when conditioning on a base making it to the present day mat[0][0][2] = ZERO_POINT_ZERO; mat[0][1][1] = ONE_POINT_ZERO - delProb; mat[0][1][2] = delProb; */ /* ofstream mats("mats.log", ios::app); mats << lambda << "\t" << mu << "\t" << blen << "\t"; //endl; for(int f = 0;f < 3;f++){ for(int t = 0;t < 3;t++){ mats << mat[0][f][t] << "\t"; } //mats << endl; } mats << endl; */ /* //my initial attempt at this int ns = 3; //insertions 0 -> 1 mat[0][0][1] = 1.0 - exp(-rateI * blen); //deletions 1 -> 2 mat[0][1][2] = 1.0 - exp(-rateD * blen); //stay null 0 -> 0 mat[0][0][0] = 1.0 - mat[0][0][1]; //stay inserted 1 -> 1 mat[0][1][1] = 1.0 - mat[0][1][2]; //stay deleted 2 -> 2 mat[0][2][2] = 1.0; mat[0][0][2] = mat[0][1][0] = mat[0][2][0] = mat[0][2][1] = ZERO_POINT_ZERO; */ /* //insertions 0 -> 1 **mat[0 * ns + 1] = 1.0 - exp(-rateI * blen); //deletions 1 -> 2 **mat[1 * ns + 2] = 1.0 - exp(-rateD * blen); //stay null 0 -> 0 **mat[0 * ns + 0] = 1.0 - **mat[0 * ns + 1]; //stay inserted 1 -> 1 **mat[1 * ns + 1] = 1.0 - **mat[1 * ns + 2]; //stay deleted 2 -> 2 **mat[2 * ns + 2] = 1.0; */ } void Model::CalcPmat(MODEL_FLOAT blen, MODEL_FLOAT *metaPmat, bool flip /*=false*/){ assert(0); /* ProfCalcPmat.Start(); assert(flip == false); //this is a bit of a hack to avoid requiring the fuction calling this one to know if //this is a nucleotide, AA or codon model if(NStates() > 4){ if(NStates() == 20){ CalcPmatNState(blen, metaPmat); } else{ FLOAT_TYPE ***ptr; AltCalcPmat(blen, ptr); memcpy(metaPmat, **ptr, nstates*nstates*NRateCats()*sizeof(FLOAT_TYPE)); } ProfCalcPmat.Stop(); return; } //this will be a wacky pmat calculation that combines the pmats for all of the rates FLOAT_TYPE tmpFreqs[4]; for(int i=0;iIsFlexRateHet())//if we're using flex rates, pinv should already be included //in the rate normalization, and doesn't need to be figured in here tempblen=(blen * blen_multiplier[0] * rateMults[r]); else tempblen=(blen * blen_multiplier[0] * rateMults[r]) / (ONE_POINT_ZERO-*propInvar); CalcPij(c_ijk[0], nstates, eigvals[0], 1, tempblen, pmat[0], EigValexp); } else if(nst==2 || modSpec.IsEqualStateFrequencies() == false){ //remember that relNucRates[1] is kappa for nst=2 models FLOAT_TYPE PI, A, K=*relNucRates[1]; FLOAT_TYPE R=tmpFreqs[0]+tmpFreqs[2]; FLOAT_TYPE Y=ONE_POINT_ZERO - R; blen_multiplier[0]=(ZERO_POINT_FIVE/((R*Y)+K*((tmpFreqs[0])*((tmpFreqs[2]))+(tmpFreqs[1])*((tmpFreqs[3]))))); FLOAT_TYPE tempblen ; if(NoPinvInModel()==true || modSpec.IsFlexRateHet())//if we're using flex rates, pinv should already be included //in the rate normalization, and doesn't need to be figured in here tempblen=(blen * blen_multiplier[0] * rateMults[r]); else tempblen=(blen * blen_multiplier[0] * rateMults[r]) / (ONE_POINT_ZERO-*propInvar); FLOAT_TYPE expblen=exp(-tempblen); for(register int f=0;f<4;f++){ for(register int t=0;t<4;t++){ if(f==t){ if(t==0||t==2) PI = R; else PI = Y; A=ONE_POINT_ZERO + PI * (K - ONE_POINT_ZERO); (**pmat)[f*4+t]=(tmpFreqs[t])+(tmpFreqs[t])*((ONE_POINT_ZERO/PI)-ONE_POINT_ZERO)*expblen+((PI-(tmpFreqs[t]))/PI)*exp(-A*tempblen); assert((**pmat)[f*4+t] > ZERO_POINT_ZERO); assert((**pmat)[f*4+t] < ONE_POINT_ZERO); } else if((f+t)%2){ (**pmat)[f*4+t]=((tmpFreqs[t]))*(ONE_POINT_ZERO-expblen);//tranversion assert((**pmat)[f*4+t] > ZERO_POINT_ZERO); assert((**pmat)[f*4+t] < ONE_POINT_ZERO); } else{ if(t==0||t==2) PI=R; else PI = Y; A=ONE_POINT_ZERO + PI * (K - ONE_POINT_ZERO); (**pmat)[f*4+t]=(tmpFreqs[t])+(tmpFreqs[t])*((ONE_POINT_ZERO/PI)-ONE_POINT_ZERO)*expblen-((tmpFreqs[t])/PI)*exp(-A*tempblen);//transition assert((**pmat)[f*4+t] > ZERO_POINT_ZERO); assert((**pmat)[f*4+t] < ONE_POINT_ZERO); } } } } else if(nst==1){ blen_multiplier[0]=(FLOAT_TYPE)(4.0/3.0); // } FLOAT_TYPE tempblen ; if(NoPinvInModel()==true || modSpec.IsFlexRateHet())//if we're using flex rates, pinv should already be included //in the rate normalization, and doesn't need to be figured in here tempblen=(blen * blen_multiplier[0] * rateMults[r]); else tempblen=(blen * blen_multiplier[0] * rateMults[r]) / (ONE_POINT_ZERO-*propInvar); FLOAT_TYPE expblen=exp(-tempblen); for(register int f=0;f<4;f++){ for(register int t=0;t<4;t++){ if(f==t) (**pmat)[f*4+t]=expblen+(FLOAT_TYPE) 0.25*(ONE_POINT_ZERO-expblen); else (**pmat)[f*4+t]=(FLOAT_TYPE)0.25*((ONE_POINT_ZERO)-expblen); } } } if(flip==true){ //copy and flip the calculated pmat into the metaPmat for(int i=0;i<4;i++) for(int j=0;j<4;j++){ metaPmat[i*16+j+r*4]=pmat[0][0][i+j*4]; } } else{ //Copy the pmats into the metaPmat in order for(int i=0;i<4;i++) for(int j=0;j<4;j++){ metaPmat[i*4+j+r*16]=pmat[0][0][i*4+j]; } } } ProfCalcPmat.Stop(); */ } void Model::CalcPmatNState(FLOAT_TYPE blen, MODEL_FLOAT *metaPmat){ assert(0); /* if(eigenDirty==true) CalcEigenStuff(); if(modSpec.IsNonsynonymousRateHet()){ for(int w=0;wIsNonsynonymousRateHet() == false){ if(NoPinvInModel()==true || modSpec->IsFlexRateHet())//if we're using flex rates, pinv should already be included //in the rate normalization, and doesn't need to be figured in here scaledEigVal = eigvals[0][k]*rateMults[rate]*blen_multiplier[0]; else scaledEigVal = eigvals[0][k]*rateMults[rate]*blen_multiplier[0]/(ONE_POINT_ZERO-*propInvar); } else{ scaledEigVal = eigvals[rate][k]*blen_multiplier[rate]; } EigValexp[k+rateOffset] = exp(scaledEigVal * dlen); EigValderiv[k+rateOffset] = scaledEigVal*EigValexp[k+rateOffset]; EigValderiv2[k+rateOffset] = scaledEigVal*EigValderiv[k+rateOffset]; } } if(NStates() > 59){//using precalced eigvecs X inveigvecs (c_ijk) is less efficient for codon models, and I //don't want a conditional in the inner loop for(int rate=0;rateIsNonsynonymousRateHet()) model = rate; const unsigned rateOffset = nstates*rate; for (int i = 0; i < nstates; i++){ for (int j = 0; j < nstates; j++){ MODEL_FLOAT sum_p=ZERO_POINT_ZERO; MODEL_FLOAT sum_d1p=ZERO_POINT_ZERO; MODEL_FLOAT sum_d2p = ZERO_POINT_ZERO; for (int k = 0; k < nstates; k++){ const MODEL_FLOAT x = eigvecs[model][i][k]*inveigvecs[model][k][j]; sum_p += x*EigValexp[k+rateOffset]; sum_d1p += x*EigValderiv[k+rateOffset]; sum_d2p += x*EigValderiv2[k+rateOffset]; } pmat1[rate][i][j] = (sum_p > ZERO_POINT_ZERO ? sum_p : ZERO_POINT_ZERO); deriv1[rate][i][j] = sum_d1p; deriv2[rate][i][j] = sum_d2p; } } } } else{ // aminoacids or nucleotides for(int rate=0;rate ZERO_POINT_ZERO ? sum_p : ZERO_POINT_ZERO); deriv1[rate][i][j] = sum_d1p; deriv2[rate][i][j] = sum_d2p; } } } } #ifdef SINGLE_PRECISION_FLOATS ChangeMatrixPrecision(nstates * nstates * NRateCats(), deriv1, fderiv1); ChangeMatrixPrecision(nstates * nstates * NRateCats(), deriv2, fderiv2); ChangeMatrixPrecision(nstates * nstates * NRateCats(), pmat1, fpmat1); one=fderiv1; two=fderiv2; pr=fpmat1; #else one=deriv1; two=deriv2; pr=pmat1; #endif } bool DoubleAbsLessThan(double &first, double &sec){return fabs(first) <= fabs(sec);} void Model::AltCalcPmat(FLOAT_TYPE dlen, MODEL_FLOAT ***&pmat){ if(eigenDirty==true) CalcEigenStuff(); for(int rate=0;rateIsNonsynonymousRateHet() == false){ if(NoPinvInModel()==true || modSpec->IsFlexRateHet())//if we're using flex rates, pinv should already be included //in the rate normalization, and doesn't need to be figured in here scaledEigVal = eigvals[0][k]*rateMults[rate]*blen_multiplier[0]; else scaledEigVal = eigvals[0][k]*rateMults[rate]*blen_multiplier[0]/(ONE_POINT_ZERO-*propInvar); } else{ scaledEigVal = eigvals[rate][k]*blen_multiplier[rate]; } EigValexp[k+rateOffset] = exp(scaledEigVal * dlen); } } if(NStates() == 20 || NStates() == 21){ for(int rate=0;rate ZERO_POINT_ZERO ? sum_p : ZERO_POINT_ZERO); } } } } else if(NStates()>59){ for(int rate=0;rateIsNonsynonymousRateHet()) model = rate; const unsigned rateOffset = nstates*rate; #ifdef OPEN_MP #pragma omp parallel for #endif for (int i = 0; i < nstates; i++){ for (int j = 0; j < nstates; j++){ MODEL_FLOAT sum_p=ZERO_POINT_ZERO; for (int k = 0; k < nstates; k++){ const MODEL_FLOAT x = eigvecs[model][i][k]*inveigvecs[model][k][j]; sum_p += x*EigValexp[k+rateOffset]; } pmat[rate][i][j] = (sum_p > ZERO_POINT_ZERO ? sum_p : ZERO_POINT_ZERO); /* //This was an attempt to improve floating point accuracy by avoiding the summing //of numbers with very different magnitudes. It was somewhat helpful, but only //necessary in odd cases and came with a horrible overhead FLOAT_TYPE sum_pBig=ZERO_POINT_ZERO; FLOAT_TYPE sum_pSmall=ZERO_POINT_ZERO; FLOAT_TYPE sum_pBig2=ZERO_POINT_ZERO; FLOAT_TYPE sum_pSmall2=ZERO_POINT_ZERO; for (int k = 0; k < nstates; k++){ const FLOAT_TYPE x = eigvecs[model][i][k]*inveigvecs[model][k][j]; if(x < ZERO_POINT_ZERO){ if(x < -1e-4) sum_pSmall += x*EigValexp[k+rateOffset]; else sum_pSmall2 += x*EigValexp[k+rateOffset]; } else{ if(x > 1e-4) sum_pBig += x*EigValexp[k+rateOffset]; else sum_pBig2 += x*EigValexp[k+rateOffset]; } } // FLOAT_TYPE tot = sum_pBig2 + sum_pSmall2; // tot += sum_pBig + sum_pSmall; FLOAT_TYPE tot = sum_pBig2 + sum_pBig; tot += (sum_pSmall2 + sum_pSmall); sum_p = tot; */ } } } } else if(modSpec->IsMkTypeModel()){ for(int rate=0;rate ZERO_POINT_ZERO ? sum_p : ZERO_POINT_ZERO); } } } } else{ for(int rate=0;rate ZERO_POINT_ZERO ? sum_p : ZERO_POINT_ZERO); } } } } } void Model::SetDefaultModelParameters(SequenceData *data){ //some of these depend on having read the data already //also note that this resets the values in the case of //bootstrapping. Any of this could be overridden by //values specified in a start file for(vector::iterator pit=paramsToMutate.begin();pit != paramsToMutate.end();pit++){ (*pit)->SetToDefaultValues(); } if(modSpec->numRateCats > 1 && modSpec->IsNonsynonymousRateHet() == false){ if(modSpec->IsFlexRateHet()){ //if alpha is only being used to manipulate the flex rates, it wouldn't be reset above SetAlpha(0, 0.5); } DiscreteGamma(rateMults, rateProbs, *alpha); } //this is a somewhat odd place to do this, but ends up making the most sense if(modSpec->IsCodon()){ if(code == NULL) SetCode(static_cast(data)->GetCode()); } if((modSpec->IsEqualStateFrequencies() == false && (modSpec->IsCodon() && modSpec->IsUserSpecifiedStateFrequencies()) == false && modSpec->IsPrecaledAAFreqs() == false) || (modSpec->IsF3x4StateFrequencies() || modSpec->IsF1x4StateFrequencies())){ //if the state freqs aren't equal, they will either start at the empirical values //or be fixed at them //if using the F3x4 or F1x4 flavors, they should have already be calculated and stored in the data empirical frequency field FLOAT_TYPE *f = new FLOAT_TYPE[nstates]; data->GetEmpiricalFreqs(f); SetPis(f, false, true); delete []f; } if(modSpec->includeInvariantSites==false){ SetPinv(ZERO_POINT_ZERO, false); SetMaxPinv(ZERO_POINT_ZERO); } else{ //if there are no constant sites, warn user that Pinv should not be used //if(data->NConstant() == 0) throw(ErrorException("This dataset contains no constant characters!\nInference of the proportion of invariant sites is therefore meaningless.\nPlease set invariantsites to \"none\"")); if(data->NConstant() == 0){ outman.UserMessage("This dataset contains no constant characters!\nInference of the proportion of invariant sites is therefore meaningless.\nSetting invariantsites to \"none\"."); outman.UserMessage("(If this is a partitioned model, you may ignore the previous message)"); SetPinv(ZERO_POINT_ZERO, false); SetMaxPinv(ZERO_POINT_ZERO); modSpec->includeInvariantSites = false; } else{ SetPinv((FLOAT_TYPE)0.25 * ((FLOAT_TYPE)data->NConstant()/(data->NConstant()+data->NInformative()+data->NVarUninform())), false); SetMaxPinv((FLOAT_TYPE)data->NConstant()/(data->NConstant()+data->NInformative()+data->NVarUninform())); if(modSpec->IsFlexRateHet()) NormalizeRates(); else AdjustRateProportions(); } } eigenDirty = true; } void Model::MutateRates(){ //paramsToMutate[1]->Mutator(Model::mutationShape); //assert(Rates(0) == Rates(2)); /* int rateToChange=int(rnd.uniform()*(nst)); if(rateToChangernd.uniform()-.5)); if(rates[rateToChange]>99.9) rates[rateToChange]=99.9; } else{//if we alter the reference rate GT (fixed to 1.0) //scale all of the other rates //FLOAT_TYPE scaler=exp(MODEL_CHANGE_SCALER * (params->rnd.uniform()-.5)); FLOAT_TYPE scaler= rnd.gamma( Model::mutationShape ); for(int i=0;iMutator(Model::mutationShape); // paramsToMutate[0]->Mutator(Model::mutationShape); // dirty=true; //alternative:change one pi with a multiplier and rescale the rest /* int piToChange=int(rnd.uniform()*4.0); FLOAT_TYPE newPi=pi[piToChange] * rnd.gamma( Model::mutationShape ); for(int b=0;b<4;b++) if(b!=piToChange) pi[b] *= (1.0-newPi)/(1.0-pi[piToChange]); pi[piToChange]=newPi; dirty=true; */ } /* void Model::MutateRateProbs(){ int ProbToChange=int(rnd.uniform()*(FLOAT_TYPE) NRateCats()); FLOAT_TYPE newProb=rateProbs[ProbToChange] * rnd.gamma( Model::mutationShape / 10.0 ); for(int b=0;brnd.uniform()-.5)); *alpha *=rnd.gamma( Model::mutationShape ); DiscreteGamma(rateMults, rateProbs, *alpha); //change the proportion of rates in each gamma cat } void Model::MutatePropInvar(){ // propInvar *= exp(MODEL_CHANGE_SCALER * (params->rnd.uniform()-.5)); FLOAT_TYPE mult=rnd.gamma( Model::mutationShape ); if(*propInvar == maxPropInvar && (mult > 1.0)) mult=1.0/mult; *propInvar *= mult; *propInvar = (*propInvar > maxPropInvar ? maxPropInvar : *propInvar); //change the proportion of rates in each gamma cat for(int i=0;iIsCodon()){ //code = from->code; if(code == NULL) SetCode(from->code); for(int i=0;iomegas[i]); for(int i=0;iomegaProbs[i]); } //CANNOT memcpy stateFreqs and relNucRates because they are vectors of pointers, not of doubles //Arg, had twoserine bug here. If AA, rel rates need to be copied: //1) If IsEstimateAAMatrix - matrix needs to be propagated as it changes //2) If is estimated two-serine model (i.e., modSpec.IsTwoSerineRateMatrix() && !modSpec.fixRelativeRates) - as above //THE FOLLOWING TWO ONE-TIME CASES COULD BE REMOVED FROM HERE AND ONLY CALLED DURING INITIAL CLONING, AND I LOOKED INTO //THIS, BUT IT GETS NASTY AND POTENTIALLY BUGGY TO TRY IT. THESE ARE RARE USE CASES, SO I WON'T WORRY ABOUT THE OVERHEAD, //WHICH MAY BE LARGE //3) If IsUserSpecifiedRateMatrix (=fixed) - Doesn't need to be copied during run since it won't be changing, but DOES need to be copied //when the initial tree is cloned into the pop, to overwrite the default parameter values //This is what was missing in two serine bug: //4) If is a fixed two-serine matrix (i.e., modSpec.IsTwoSerineRateMatrix() && modSpec.fixRelativeRates) - for same reason as previous, //to overwrite default values //if(modSpec.IsAminoAcid() == false || modSpec.IsEstimateAAMatrix() || (modSpec.IsTwoSerineRateMatrix() && !modSpec.fixRelativeRates) || (modSpec.IsAminoAcid() && modSpec.IsUserSpecifiedRateMatrix())) if((modSpec->IsAminoAcid() == false && modSpec->IsMkTypeModel() == false && modSpec->IsOrientedGap() == false) || modSpec->IsEstimateAAMatrix() || modSpec->IsTwoSerineRateMatrix() || (modSpec->IsAminoAcid() && modSpec->IsUserSpecifiedRateMatrix())) for(int i=0;irelNucRates[i]); for(int i=0;istateFreqs[i]); //memcpy(pi, from->pi, sizeof(FLOAT_TYPE)*4); memcpy(rateMults, from->rateMults, sizeof(FLOAT_TYPE)*NRateCats()); memcpy(rateProbs, from->rateProbs, sizeof(FLOAT_TYPE)*NRateCats()); if(modSpec->IsGammaRateHet()) *alpha=*(from->alpha); *propInvar=*(from->propInvar); maxPropInvar = from->maxPropInvar; if(from->eigenDirty == false){ //copy the already calculated eigen variables, which are nontrivial to //calculate for non-nucleotide models CopyEigenVariables(from); eigenDirty = false; } else eigenDirty=true; if(modSpec->IsOrientedGap()){ *insertRate = *from->insertRate; *deleteRate = *from->deleteRate; } } void Model::CopyEigenVariables(const Model *from){ int effectiveModels = modSpec->IsNonsynonymousRateHet() ? NRateCats() : 1; memcpy(**qmat, **from->qmat, effectiveModels*nstates*nstates*sizeof(MODEL_FLOAT)); memcpy(**eigvecs, **from->eigvecs, NRateCats()*nstates*nstates*sizeof(MODEL_FLOAT)); memcpy(**inveigvecs, **from->inveigvecs, NRateCats()*nstates*nstates*sizeof(MODEL_FLOAT)); memcpy(*eigvals, *from->eigvals, effectiveModels * nstates * sizeof(MODEL_FLOAT)); memcpy(blen_multiplier, from->blen_multiplier, effectiveModels * sizeof(FLOAT_TYPE)); //c_ijk isn't allocated or used for codon models if(c_ijk != NULL) memcpy(*c_ijk, *from->c_ijk, effectiveModels*nstates*nstates*nstates*sizeof(MODEL_FLOAT)); } void Model::SetModel(FLOAT_TYPE *model_string){ int slot=0; for(int i=0;i1) *alpha=model_string[slot++]; DiscreteGamma(rateMults, rateProbs, *alpha); //using whether or not this individual had a PI of >0 in the first //place to decide whether we should expect one in the string. //Seems safe. if(*propInvar!=ZERO_POINT_ZERO) *propInvar=model_string[slot++]; eigenDirty=true; } FLOAT_TYPE Model::TRatio() const{ FLOAT_TYPE numerator = *relNucRates[1] * ( *stateFreqs[0]**stateFreqs[2] + *stateFreqs[1]**stateFreqs[3] ); FLOAT_TYPE denominator = ( *stateFreqs[0] + *stateFreqs[2] ) * ( *stateFreqs[1] + *stateFreqs[3] ); return ( numerator / denominator ); } bool Model::IsModelEqual(const Model *other) const { assert(0); //this will need to be generalized if other models are introduced for(int i=0;i<6;i++) if(!FloatingPointEquals(*relNucRates[i], *(other->relNucRates[i]), 1e-15)) return false; for(int i=0;istateFreqs[i]), 1e-15)) return false; if(!modSpec->IsCodon() && NRateCats() > 1){ for(int i=0;iNRateCats();i++){ if(!FloatingPointEquals(rateMults[i], other->rateMults[i], 1e-15)) return false; if(!FloatingPointEquals(rateProbs[i], other->rateProbs[i], 1e-15)) return false; } } else if(modSpec->IsCodon()){ for(int i=0;iNRateCats();i++){ if(!FloatingPointEquals(Omega(i), other->Omega(i), 1e-15)) return false; if(!FloatingPointEquals(OmegaProb(i), other->OmegaProb(i), 1e-15)) return false; } } /* if(rateMults[0] != other->rateMults[0]) return false; if(rateMults[1] != other->rateMults[1]) return false; if(rateMults[2] != other->rateMults[2]) return false; if(rateMults[3] != other->rateMults[3]) return false; if(rateProbs[0] != other->rateProbs[0]) return false; if(rateProbs[1] != other->rateProbs[1]) return false; if(rateProbs[2] != other->rateProbs[2]) return false; if(rateProbs[3] != other->rateProbs[3]) return false; */ if(alpha!=other->alpha) return false; if(propInvar!=other->propInvar) return false; return true; } //a bunch of the gamma rate het machinery //from MrBayes /*------------------------------------------------------------------------------- | | | Discretization of gamma distribution with equal proportions in each | | category. | | | -------------------------------------------------------------------------------*/ #ifdef SINGLE_PRECISION_FLOATS #define POINTGAMMA(prob,alpha,beta) PointChi2(prob,2.0f*(alpha))/(2.0f*(beta)) #else #define POINTGAMMA(prob,alpha,beta) PointChi2(prob,2.0*(alpha))/(2.0*(beta)) #endif /* ------------------------------------------------------------------------------ | | | Returns z so That Prob{xx || x<=1) | | (2) continued fraction otherwise | | | | RATNEST FORTRAN by | | Bhattacharjee, G. P. 1970. The incomplete gamma integral. Applied | | Statistics, 19:285-287 (AS32) | | | -------------------------------------------------------------------------------*/ FLOAT_TYPE IncompleteGamma (FLOAT_TYPE x, FLOAT_TYPE alpha, FLOAT_TYPE LnGamma_alpha){ int i; #ifdef SINGLE_PRECISION_FLOATS FLOAT_TYPE p = alpha, g = LnGamma_alpha, accurate = GARLI_FP_EPS, overflow = 1e30f, factor, gin = 0.0f, rn = 0.0f, a = 0.0f, b = 0.0f, an = 0.0f, dif = 0.0f, term = 0.0f, pn[6]; #else FLOAT_TYPE p = alpha, g = LnGamma_alpha, accurate = 1e-8, overflow = 1e30, factor, gin = 0.0, rn = 0.0, a = 0.0, b = 0.0, an = 0.0, dif = 0.0, term = 0.0, pn[6]; #endif if (x == ZERO_POINT_ZERO) return (ZERO_POINT_ZERO); if (x < 0 || p <= 0) return (-ONE_POINT_ZERO); factor = exp(p*log(x)-x-g); if (x>1 && x>=p) goto l30; gin = ONE_POINT_ZERO; term = ONE_POINT_ZERO; rn = p; l20: rn++; term *= x/rn; gin += term; if (term > accurate) goto l20; gin *= factor/p; goto l50; l30: a = ONE_POINT_ZERO-p; b = a+x+ONE_POINT_ZERO; term = ZERO_POINT_ZERO; pn[0] = ONE_POINT_ZERO; pn[1] = x; pn[2] = x+1; pn[3] = x*b; gin = pn[2]/pn[3]; l32: a++; b += 2.0; term++; an = a*term; for (i=0; i<2; i++) pn[i+4] = b*pn[i+2]-an*pn[i]; if (pn[5] == 0) goto l35; rn = pn[4]/pn[5]; dif = fabs(gin-rn); if (dif>accurate) goto l34; if (dif<=accurate*rn) goto l42; l34: gin = rn; l35: for (i=0; i<4; i++) pn[i] = pn[i+2]; if (fabs(pn[4]) < overflow) goto l32; for (i=0; i<4; i++) pn[i] /= overflow; goto l32; l42: gin = ONE_POINT_ZERO-factor*gin; l50: return (gin); } inline FLOAT_TYPE LnGamma (FLOAT_TYPE alp){ /* FLOAT_TYPE cof[6]; cof[0]=76.18009172947146; cof[1]=-86.50532032941677; cof[2]=24.01409824083091; cof[3]=-1.231739572450155; cof[4]=0.1208650973866179e-2; cof[5]=-0.5395239384953e-5; FLOAT_TYPE xx=alp; FLOAT_TYPE yy=alp; FLOAT_TYPE tmp=xx + 5.5 - (xx + 0.5) * log(xx + 5.5); FLOAT_TYPE ser = 1.000000000190015; for(int j=0;j<5;j++){ ser += (cof[j] / ++yy); } return log(2.5066282746310005*ser/xx)-tmp; } */ FLOAT_TYPE x = alp, f=ZERO_POINT_ZERO, z; if (x < 7) { f = ONE_POINT_ZERO; z = x-ONE_POINT_ZERO; while (++z < 7.0) f *= z; x = z; f = -log(f); } z = ONE_POINT_ZERO/(x*x); #ifdef SINGLE_PRECISION_FLOATS return (f + (x-0.5f)*log(x) - x + 0.918938533204673f + (((-0.000595238095238f*z+0.000793650793651f)*z-0.002777777777778f)*z + 0.083333333333333f)/x); #else return (f + (x-0.5)*log(x) - x + 0.918938533204673 + (((-0.000595238095238*z+0.000793650793651)*z-0.002777777777778)*z + 0.083333333333333)/x); #endif } FLOAT_TYPE PointChi2 (FLOAT_TYPE prob, FLOAT_TYPE v){ #ifdef SINGLE_PRECISION_FLOATS //potential error e needs to be increased here //because of lesser decimal precision of floats FLOAT_TYPE e = 0.5e-4f, aa = 0.6931471805f, p = prob, g, xx, c, ch, a = 0.0f, q = 0.0f, p1 = 0.0f, p2 = 0.0f, t = 0.0f, x = 0.0f, b = 0.0f, s1, s2, s3, s4, s5, s6; if (p < 0.000002f || p > 0.999998f || v <= 0.0f) return (-1.0f); g = LnGamma (v*ZERO_POINT_FIVE); xx = v/2.0f; c = xx - ONE_POINT_ZERO; if (v >= -1.24f*log(p)) goto l1; #else FLOAT_TYPE e = 0.5e-6, aa = 0.6931471805, p = prob, g, xx, c, ch, a = 0.0, q = 0.0, p1 = 0.0, p2 = 0.0, t = 0.0, x = 0.0, b = 0.0, s1, s2, s3, s4, s5, s6; if (p < 0.000002 || p > 0.999998 || v <= 0.0) return (-ONE_POINT_ZERO); g = LnGamma (v*ZERO_POINT_FIVE); xx = v/2.0; c = xx - ONE_POINT_ZERO; if (v >= -1.24*log(p)) goto l1; #endif ch = pow((p*xx*exp(g+xx*aa)), ONE_POINT_ZERO/xx); if (ch-e < ZERO_POINT_ZERO) return (ch); goto l4; #ifdef SINGLE_PRECISION_FLOATS l1: if (v > 0.32f) goto l3; ch = 0.4f; a = log(ONE_POINT_ZERO-p); l2: q = ch; p1 = ONE_POINT_ZERO+ch*(4.67f+ch); p2 = ch*(6.73f+ch*(6.66f+ch)); t = -0.5f+(4.67f+2.0f*ch)/p1 - (6.73f+ch*(13.32f+3.0f*ch))/p2; ch -= (ONE_POINT_ZERO-exp(a+g+0.5f*ch+c*aa)*p2/p1)/t; if (fabs(q/ch-ONE_POINT_ZERO)-0.01f <= ZERO_POINT_ZERO) goto l4; else goto l2; l3: x = PointNormal (p); p1 = 0.222222f/v; ch = v*pow((x*sqrt(p1)+ONE_POINT_ZERO-p1), 3.0f); if (ch > 2.2f*v+6.0f) ch = -2.0f*(log(ONE_POINT_ZERO-p)-c*log(0.5f*ch)+g); #else l1: if (v > 0.32) goto l3; ch = 0.4; a = log(ONE_POINT_ZERO-p); l2: q = ch; p1 = ONE_POINT_ZERO+ch*(4.67+ch); p2 = ch*(6.73+ch*(6.66+ch)); t = -0.5+(4.67+2.0*ch)/p1 - (6.73+ch*(13.32+3.0*ch))/p2; ch -= (ONE_POINT_ZERO-exp(a+g+0.5*ch+c*aa)*p2/p1)/t; if (fabs(q/ch-ONE_POINT_ZERO)-0.01 <= ZERO_POINT_ZERO) goto l4; else goto l2; l3: x = PointNormal (p); p1 = 0.222222/v; ch = v*pow((x*sqrt(p1)+ONE_POINT_ZERO-p1), 3.0); if (ch > 2.2*v+6.0) ch = -2.0*(log(ONE_POINT_ZERO-p)-c*log(0.5*ch)+g); #endif l4: q = ch; p1 = ZERO_POINT_FIVE*ch; if ((t = IncompleteGamma (p1, xx, g)) < ZERO_POINT_ZERO) { printf ("\nerr IncompleteGamma"); return (-ONE_POINT_ZERO); } p2 = p-t; t = p2*exp(xx*aa+g+p1-c*log(ch)); b = t/ch; #ifdef SINGLE_PRECISION_FLOATS a = 0.5f*t-b*c; s1 = (210.0f+a*(140.0f+a*(105.0f+a*(84.0f+a*(70.0f+60.0f*a))))) / 420.0f; s2 = (420.0f+a*(735.0f+a*(966.0f+a*(1141.0f+1278.0f*a))))/2520.0f; s3 = (210.0f+a*(462.0f+a*(707.0f+932.0f*a)))/2520.0f; s4 = (252.0f+a*(672.0f+1182.0f*a)+c*(294.0f+a*(889.0f+1740.0f*a)))/5040.0f; s5 = (84.0f+264.0f*a+c*(175.0f+606.0f*a))/2520.0f; s6 = (120.0f+c*(346.0f+127.0f*c))/5040.0f; ch += t*(1+0.5f*t*s1-b*c*(s1-b*(s2-b*(s3-b*(s4-b*(s5-b*s6)))))); #else a = 0.5*t-b*c; s1 = (210.0+a*(140.0+a*(105.0+a*(84.0+a*(70.0+60.0*a))))) / 420.0; s2 = (420.0+a*(735.0+a*(966.0+a*(1141.0+1278.0*a))))/2520.0; s3 = (210.0+a*(462.0+a*(707.0+932.0*a)))/2520.0; s4 = (252.0+a*(672.0+1182.0*a)+c*(294.0+a*(889.0+1740.0*a)))/5040.0; s5 = (84.0+264.0*a+c*(175.0+606.0*a))/2520.0; s6 = (120.0+c*(346.0+127.0*c))/5040.0; ch += t*(1+0.5*t*s1-b*c*(s1-b*(s2-b*(s3-b*(s4-b*(s5-b*s6)))))); #endif if (fabs(q/ch-ONE_POINT_ZERO) > e) goto l4; return (ch); } //function taken from MB and hard wired for use here void Model::DiscreteGamma(FLOAT_TYPE *rates, FLOAT_TYPE *props, FLOAT_TYPE shape){ bool median=false; int i; FLOAT_TYPE gap05 = ZERO_POINT_FIVE/NRateCats(), t, factor = shape/shape*NRateCats(), lnga1; if (median){ for (i=0; iIsNucleotide()); outf << "begin paup;\nclear;\ngett file=" << treefname << " storebr;\nlset userbr "; if(nst == 2) outf << "nst=2 trat= " << TRatio(); else if(nst == 1) outf << "nst=1 "; else{ if(modSpec->IsArbitraryRateMatrix()) outf << "nst=6 rclass=" << modSpec->arbitraryRateMatrixString.c_str() << " rmat=(" << Rates(0) << " " << Rates(1) << " " << Rates(2) << " " << Rates(3) << " " << Rates(4) << ")"; else outf << "nst=6 rmat=(" << Rates(0) << " " << Rates(1) << " " << Rates(2) << " " << Rates(3) << " " << Rates(4) << ")"; } if(modSpec->IsEqualStateFrequencies() == true) outf << " base=eq "; else if(modSpec->IsEmpiricalStateFrequencies() == true) outf << " base=emp "; else outf << " base=(" << StateFreq(0) << " " << StateFreq(1) << " " << StateFreq(2) << ")"; if(modSpec->IsFlexRateHet() == false){ if(NRateCats()>1) outf << " rates=gamma shape= " << Alpha() << " ncat=" << NRateCats(); else outf << " rates=equal"; outf << " pinv= " << PropInvar(); outf << ";\nend;\n"; } else{ outf << " pinv= " << PropInvar(); outf << " [FLEX RATES:\t"; for(int i=0;iIsArbitraryRateMatrix()) sprintf(temp,"nst=6 rclass=%s rmat=(%f %f %f %f %f)", modSpec->arbitraryRateMatrixString.c_str(), Rates(0), Rates(1), Rates(2), Rates(3), Rates(4)); else sprintf(temp,"nst=6 rmat=(%f %f %f %f %f)", Rates(0), Rates(1), Rates(2), Rates(3), Rates(4)); str += temp; } if(modSpec->IsEqualStateFrequencies()) str +=" base=eq "; else if(modSpec->IsEmpiricalStateFrequencies()) str += " base=emp "; else{ sprintf(temp," base=( %f %f %f)", StateFreq(0), StateFreq(1), StateFreq(2)); str += temp; } if(modSpec->IsFlexRateHet()==false){ if(NRateCats()>1){ sprintf(temp, " rates=gamma shape=%f ncat=%d", Alpha(), NRateCats()); str += temp; } else str += " rates=equal"; sprintf(temp, " pinv=%f;\nend;\n", PropInvar());; str += temp; } else{ sprintf(temp, " pinv=%f [FLEX RATES:\t", PropInvar()); str += temp; for(int i=0;iIsNucleotide() || modSpec->IsCodon()) outf << " r " << Rates(0) << " " << Rates(1) << " " << Rates(2) << " " << Rates(3) << " " << Rates(4); outf << " e " ; for(int i=0;iIsFlexRateHet()){ outf << " f "; for(int i=0;i1 && modSpec->IsNonsynonymousRateHet() == false) outf << " a " << Alpha(); } if(PropInvar()!=ZERO_POINT_ZERO) outf << " p " << PropInvar(); outf << " "; */ } void Model::FillModelOrHeaderStringForTable(string &s, bool model) const{ s.clear(); char cStr[500]; if(modSpec->IsCodon()){ for(int i=0;iIsNucleotide() || modSpec->IsCodon() || modSpec->IsEstimateAAMatrix() || modSpec->IsTwoSerineRateMatrix()){ if(model){ //sprintf(cStr, " %5.2f %5.2f %5.2f %5.2f %5.2f %5.2f", Rates(0), Rates(1), Rates(2), Rates(3), Rates(4), 1.0); for(int st = 0;st < relNucRates.size();st++){ sprintf(cStr," %6.4g", Rates(st)); s += cStr; } } else{ string states; //Z is second serine type if(modSpec->IsAminoAcid()) states="ACDEFGHIKLMNPQRSTVWYZ"; else states="ACGT"; char rStr[50]; for(int from=0;from<(modSpec->IsCodon() ? 4 - 1 : NStates() - 1);from++){ for(int to=from+1;to<(modSpec->IsCodon() ? 4 : NStates());to++){ sprintf(rStr, "r(%c%c)", states[from], states[to]); sprintf(cStr," %6s", rStr); s += cStr; } } /* char rStr[50]; sprintf(rStr, "r(AC)"); sprintf(cStr," %5s", rStr); s += cStr; sprintf(rStr, "r(AG)"); sprintf(cStr," %5s", rStr); s += cStr; sprintf(rStr, "r(AT)"); sprintf(cStr," %5s", rStr); s += cStr; sprintf(rStr, "r(CG)"); sprintf(cStr," %5s", rStr); s += cStr; sprintf(rStr, "r(CT)"); sprintf(cStr," %5s", rStr); s += cStr; sprintf(rStr, "r(GT)"); sprintf(cStr," %5s", rStr); s += cStr; */ } } /* if(modSpec.IsNucleotide()){ if(model){ sprintf(cStr," %5.3f %5.3f %5.3f %5.3f ", StateFreq(0), StateFreq(1), StateFreq(2), StateFreq(3)); s += cStr; } else{ char pStr[50]; sprintf(pStr, "pi(A)"); sprintf(cStr,"%5s ", pStr); s += cStr; sprintf(pStr, "pi(C)"); sprintf(cStr,"%5s ", pStr); s += cStr; sprintf(pStr, "pi(G)"); sprintf(cStr,"%5s ", pStr); s += cStr; sprintf(pStr, "pi(T)"); sprintf(cStr,"%5s ", pStr); s += cStr; } } */ //else if(modSpec.IsAminoAcid()){ if(modSpec->IsNucleotide() || (modSpec->IsAminoAcid() && (modSpec->fixStateFreqs == false && modSpec->IsEqualStateFrequencies() == false && modSpec->IsEmpiricalStateFrequencies() == false))){ if(model){ for(int st = 0;st < stateFreqs.size();st++){ sprintf(cStr," %5.3f", StateFreq(st)); s += cStr; } } else{ char pStr[50]; string states; //Z is extra serine, and won't be shown in normal models because there are only 20 AA's if(modSpec->IsAminoAcid()) states="ACDEFGHIKLMNPQRSTVWYZ"; else states="ACGT"; for(int st = 0;st < stateFreqs.size();st++){ sprintf(pStr,"pi(%c)", states[st]); sprintf(cStr," %5s", pStr); s += cStr; } } } if(modSpec->IsFlexRateHet()){ for(int i=0;iIsGammaRateHet()){ if(model) sprintf(cStr, " %5.3f", Alpha()); else{ sprintf(cStr, " %5s", "alpha"); } s += cStr; } } if(PropInvar()!=ZERO_POINT_ZERO){ if(model) sprintf(cStr, " %5.3f", PropInvar()); else{ sprintf(cStr, " %5s", "pinv"); } s += cStr; } if(modSpec->IsOrientedGap()){ if(model) sprintf(cStr, " %5.3f %5.3f", *insertRate, *deleteRate); else{ sprintf(cStr, " %5s %5s", "ins", "del"); } s += cStr; } } void Model::OutputAminoAcidRMatrixMessage(ostream &out){ out << "Estimated AA rate matrices:" << endl;; out << "NOTE THAT THIS FUNCTION IS FAIRLY EXPERIMENTAL, SO CHECK YOUR OUTPUT AND LET ME KNOW OF ANY PROBLEMS\n" << endl;; out << "GARLI's order of AA's is alphabetically BY SINGLE LETTER CODE, i.e.:\n ACDEFGHIKLMNPQRSTVWY" << endl; out << "The correspondence with the 3-letter codes and full names is this:" << endl; out << "A\tAla\tAlanine\nC\tCys\tCysteine\nD\tAsp\tAspartic Acid\nE\tGlu\tGlutamic Acid\nF\tPhe\tPhenylalanine\nG\tGly\tGlycine\nH\tHis\tHistidine\n"; out << "I\tIle\tIsoleucine\nK\tLys\tLysine\nL\tLeu\tLeucine\nM\tMet\tMethionine\nN\tAsn\tAsparagine\nP\tPro\tProline\nQ\tGln\tGlutamine\nR\tArg\tArginine\n"; out << "S\tSer\tSerine\nT\tThr\tThreonine\nV\tVal\tValine\nW\tTrp\tTryptophan\nY\tTyr\tTyrosine\n" << endl; out << "Unfortunately, I beleive that GARLI, PAML, and MrBayes all have different orderings of the amino acids. PAML" << endl; out << "is alphabetical by three-letter code, MrBayes is alphabetical by full name (same as PAML, but swap Gln and Glu), GARLI" << endl; out << "is alphabetical by single letter code. Additionally, I believe that PAML takes the below diagonal matrix as input,"<< endl; out << "while GARLI and MrBayes take the upper." << endl; out << "I COULD BE WRONG ABOUT THIS, AND YOU SHOULD VERIFY THAT THE ABOVE FACTS ARE TRUE BEFORE USING THE BELOW MATRICES" << endl; out << "IN ANOTHER PROGRAM" << endl; out << "Following are the matrix inferred by GARLI in GARLI's order, then the same matrices ordered by the other systems" << endl; out << "described above. Both the above and below diagonal versions appear for each." << endl; out << "The entries are scaled such that the mean rate is 100. It can be rescaled by any constant factor without" << endl; out << "changing its meaning. Entries on the diagonal are all zero." << endl; out << "\nThe ABOVE diagonal SINGLE LETTER order is what would be fed back into GARLI as a starting condition to use this matrix" << endl; out << "in future analyses. Here is a GARLI block that could be used to do this. The values could be fixed for further analyses" << endl; out << "by setting \"ratematrix = fixed\" in the configuration file, or it could be used as starting values for another run estimating" << endl; out << "the full matrix by leaving \"ratematrix = estimate\". The block itself could be put in the same file as a NEXUS" << endl; out << "data matrix, or put in a file (which must start with #NEXUS) specified on the streefname line of the configuarion file.\n" << endl; } void Model::OutputAminoAcidRMatrixArray(ostream &out, int modNum, int treeNum){ //assert(el.size() == 400); //first make a full 20x20 matrix assert(modSpec->IsAminoAcid()); vector el(nstates * nstates, ZERO_POINT_ZERO); vector::iterator r = relNucRates.begin(); FLOAT_TYPE tot = ZERO_POINT_ZERO; for(int from=0;fromfixStateFreqs == false && modSpec->IsEqualStateFrequencies() == false && modSpec->IsEmpiricalStateFrequencies() == false) out << "[below the rate matrix is a line begining with \"e\" that specifies the estimated AA frequencies in GARLI format]" << endl; out << "M" << modNum+1 << " r "; FLOAT_TYPE scaleTo = 100.0 * ((nstates * nstates) - nstates)/2.0; for(int from=0;fromfixStateFreqs == false && modSpec->IsEqualStateFrequencies() == false && modSpec->IsEmpiricalStateFrequencies() == false){ out << "\ne "; for(int st = 0;st < nstates;st++) out << StateFreq(st) << " "; } } out << ";\nend;\n" << endl; //21st state is extra serine int corThree[21] = {0, 14, 11, 2, 1, 13, 3, 5, 6, 7, 9, 8, 10, 4, 12, 15, 16, 18, 19, 17, 20}; int corFull[21] = {0, 14, 11, 2, 1, 3, 13, 5, 6, 7, 9, 8, 10, 4, 12, 15, 16, 18, 19, 17, 20}; out << "This is the SINGLE LETTER order (GARLI), above diagonal matrix\n" << endl; out << "(this is what appears in the above GARLI block)\n"; for(int from=0;fromIsCodon()){ if(modSpec->IsVertMitoCode()) outman.UserMessage(" Number of states = 60 (codon data, vertebrate mitochondrial code)"); else if(modSpec->IsInvertMitoCode()) outman.UserMessage(" Number of states = 62 (codon data, invertebrate mitochondrial code)"); else outman.UserMessage(" Number of states = 61 (codon data, standard code)"); } else if(modSpec->IsAminoAcid()){ if(modSpec->nstates == 20) outman.UserMessage(" Number of states = 20 (amino acid data)"); else if(modSpec->nstates == 21) outman.UserMessage(" Number of states = 21 (amino acid data, experimental matrix with two serine types)"); } else if(modSpec->IsNState() || modSpec->IsNStateV()) outman.UserMessage(" Number of states = %d (standard data)", nstates); else if(modSpec->IsOrderedNState() || modSpec->IsOrderedNStateV()) outman.UserMessage(" Number of states = %d (ordered standard data)", nstates); else if(modSpec->IsBinary() || modSpec->IsBinaryNotAllZeros()) outman.UserMessage(" Number of states = 2 (binary data)"); else if(modSpec->IsOrientedGap()) outman.UserMessage(" Number of states = 2 (0/1 coding of gaps)"); else outman.UserMessage(" Number of states = 4 (nucleotide data)"); if(modSpec->IsNucleotide() || modSpec->IsCodon()){ if(modSpec->IsCodon() && modSpec->numRateCats == 1){ if(!modSpec->fixOmega) outman.UserMessageNoCR(" One estimated dN/dS ratio (aka omega) = %f\n", Omega(0)); else outman.UserMessageNoCR(" One dN/dS ratio (aka omega).\n Value provided by user (fixed) = %f\n", Omega(0)); } if(modSpec->IsCodon()) outman.UserMessageNoCR(" Nucleotide Relative Rate Matrix Assumed by Codon Model: "); else outman.UserMessageNoCR(" Nucleotide Relative Rate Matrix: "); if(nst == 6){ if(modSpec->IsArbitraryRateMatrix()) outman.UserMessage("\n User specified matrix type: %s ", modSpec->arbitraryRateMatrixString.c_str()); else outman.UserMessage(" 6 rates "); if(modSpec->fixRelativeRates == true) outman.UserMessage(" Values specified by user (fixed)"); //else outman.UserMessage(""); outman.UserMessage(" AC = %.3f, AG = %.3f, AT = %.3f, CG = %.3f, CT = %.3f, GT = %.3f", Rates(0), Rates(1), Rates(2), Rates(3), Rates(4), 1.0); } else if(nst == 2){ outman.UserMessageNoCR(" 2 rates (transition and transversion) K param = %.4f", Rates(1)); if(modSpec->IsCodon() == false) outman.UserMessage(" (ti/tv = %.4f)", TRatio()); else outman.UserMessage(""); } else outman.UserMessage(" 1 rate"); } else if(modSpec->IsAminoAcid()){ outman.UserMessageNoCR(" Amino Acid Rate Matrix: "); if(modSpec->IsJonesAAMatrix()) outman.UserMessage("Jones"); else if(modSpec->IsDayhoffAAMatrix()) outman.UserMessage("Dayhoff"); else if(modSpec->IsPoissonAAMatrix()) outman.UserMessage("Poisson"); else if(modSpec->IsWAGAAMatrix()) outman.UserMessage("WAG"); else if(modSpec->IsMtMamAAMatrix()) outman.UserMessage("MtMam"); else if(modSpec->IsMtRevAAMatrix()) outman.UserMessage("MtRev"); else if(modSpec->IsEstimateAAMatrix()) outman.UserMessage("Estimated (189 free parameters)"); else if(modSpec->IsUserSpecifiedRateMatrix()) outman.UserMessage(" values specified by user (fixed)"); else if(modSpec->IsTwoSerineRateMatrix() && !modSpec->fixRelativeRates) outman.UserMessage("Experimental model with two serine types\n Matrix estimated (209 free parameters)"); else if(modSpec->IsTwoSerineRateMatrix()) outman.UserMessage("Experimental model with two serine types\n Matrix specified by user."); } else if(modSpec->IsNState()){ outman.UserMessage(" Character change matrix:\n One rate (symmetric one rate Mk model)"); } else if(modSpec->IsNStateV()){ outman.UserMessage(" Character change matrix:\n One rate (symmetric one rate Mkv model)"); } else if(modSpec->IsOrderedNState()){ outman.UserMessage(" Character change matrix:\n One rate (ordered symmetric one rate Mk model)"); } else if(modSpec->IsOrderedNStateV()){ outman.UserMessage(" Character change matrix:\n One rate (ordered symmetric one rate Mkv model)"); } else if(modSpec->IsOrientedGap()){ outman.UserMessage(" Character change matrix: irreversible matrix\n deletion rate parameter only estimated if using a partitioned\n model without subset rates"); outman.UserMessage(" deletion rate = %.3f", *deleteRate); } else if(modSpec->IsBinary()){ outman.UserMessage(" Character change matrix:\n Binary (2-state symmetric one rate model)"); } else if(modSpec->IsBinaryNotAllZeros()){ outman.UserMessage(" Character change matrix:\n Binary, no all-zero columns (2-state symmetric one rate model)"); } outman.UserMessageNoCR(" Equilibrium State Frequencies: "); if(modSpec->IsEqualStateFrequencies()){ if(modSpec->IsCodon()){ if(modSpec->IsVertMitoCode()) outman.UserMessage("equal (1/60 = 0.01667, fixed)"); else if(modSpec->IsInvertMitoCode()) outman.UserMessage("equal (1/62 = 0.01613, fixed)"); else outman.UserMessage("equal (1/61 = 0.01639, fixed)"); } else if(modSpec->IsAminoAcid()) outman.UserMessage("equal (0.05, fixed)"); else if(modSpec->IsMkTypeModel()) outman.UserMessage("equal (%.2f, fixed)", 1.0/nstates); else if(modSpec->IsOrientedGap()){ outman.UserMessage("proportion of inserted sites parameter"); outman.UserMessage(" insert proportion = %.3f", *insertRate); } else outman.UserMessage("equal (0.25, fixed)"); } else if(modSpec->IsF3x4StateFrequencies()) outman.UserMessage("\n empirical values calculated by F3x4 method (fixed)"); else if(modSpec->IsF1x4StateFrequencies()) outman.UserMessage("\n empirical values calculated by F1x4 method (fixed)"); else if(modSpec->IsEmpiricalStateFrequencies()){ if(modSpec->IsAminoAcid()) outman.UserMessage("empirical (observed) values (+F)"); else outman.UserMessage("empirical (observed) values, fixed:"); } else if(modSpec->IsJonesAAFreqs()) outman.UserMessage("Jones"); else if(modSpec->IsWAGAAFreqs()) outman.UserMessage("WAG"); else if(modSpec->IsMtMamAAFreqs()) outman.UserMessage("MtMam"); else if(modSpec->IsMtRevAAFreqs()) outman.UserMessage("MtRev"); else if(modSpec->IsDayhoffAAFreqs()) outman.UserMessage("Dayhoff"); else if(modSpec->IsUserSpecifiedStateFrequencies()) outman.UserMessage("specified by user (fixed)"); else outman.UserMessage("estimated"); if(!modSpec->IsEqualStateFrequencies()){ if(modSpec->IsCodon()) outman.UserMessageNoCR(" (AAA, AAC, AAG, AAT, ACA, ... etc)\n "); else if(modSpec->IsAminoAcid() && !modSpec->IsTwoSerineRateMatrix()) outman.UserMessageNoCR(" (ACDEFGHIKLMNPQRSTVWY)\n "); else if(modSpec->IsAminoAcid() && modSpec->IsTwoSerineRateMatrix()) outman.UserMessageNoCR(" (ACDEFGHIKLMNPQRSTVWYZ) (Z=ACG and AGT Serines)\n "); else outman.UserMessageNoCR(" (ACGT) "); for(int i=0;i0 && (i+1)!= nstates && !((i+1)%5)) outman.UserMessageNoCR("\n "); } outman.UserMessage(""); } outman.UserMessage(" Rate Heterogeneity Model:"); if(modSpec->numRateCats == 1){ if(modSpec->includeInvariantSites == false) outman.UserMessage(" no rate heterogeneity"); else{ if(modSpec->fixInvariantSites == true) outman.UserMessage(" only an invariant (invariable) site category,\n proportion specified by user (fixed)\n %.4f", PropInvar()); else outman.UserMessage(" only an invariant (invariable) site category, proportion estimated\n %.4f", PropInvar()); } } else{ outman.UserMessageNoCR(" %d ", modSpec->numRateCats); if(modSpec->IsNonsynonymousRateHet()){ if(!modSpec->fixOmega){ outman.UserMessage("nonsynonymous rate categories, rate and proportion of each estimated\n (this is effectively the M3 model of PAML)"); } else{ outman.UserMessage("nonsynonymous rate categories, rate and proportion of each provided by user (fixed)\n (this is effectively the M3 model of PAML)"); } outman.UserMessage(" dN/dS\tProportion"); for(int i=0;inumRateCats;i++) outman.UserMessage(" %5.4f\t%5.4f", Omega(i), OmegaProb(i)); } else if(modSpec->IsFlexRateHet() == false){ if(modSpec->fixAlpha == true) outman.UserMessage("discrete gamma distributed rate categories,\n alpha param specified by user (fixed)\n %.4f", Alpha()); else outman.UserMessage("discrete gamma distributed rate categories, alpha param estimated\n %.4f", Alpha()); if(modSpec->includeInvariantSites == true){ if(modSpec->fixInvariantSites == true) outman.UserMessage(" with an invariant (invariable) site category,\n proportion specified by user (fixed)\n %.4f", PropInvar()); else outman.UserMessage(" with an invariant (invariable) site category, proportion estimated\n %.4f", PropInvar()); } outman.UserMessage(" Substitution rate categories under this model:\n rate\tproportion"); if(modSpec->includeInvariantSites == true) outman.UserMessage(" %5.4f\t%5.4f", 0.0, PropInvar()); for(int r=0;rnumRateCats;r++) outman.UserMessage(" %5.4f\t%5.4f", rateMults[r], rateProbs[r]); } else{ outman.UserMessage("FLEX rate categories, rate and proportion of each estimated"); if(modSpec->includeInvariantSites == true){ if(modSpec->fixInvariantSites == true) outman.UserMessage(" with an invariant (invariable) site category,\n proportion specified by user (fixed)"); else outman.UserMessage(" with an invariant (invariable) site category, proportion estimated"); } outman.UserMessage(" Estimated substitution rate categories:\n rate\tproportion"); for(int r=0;rnumRateCats;r++) outman.UserMessage(" %5.4f\t%5.4f", rateMults[r], rateProbs[r]); } } outman.UserMessage(""); } #define MODEL_OUTPUT_PREC 5 void Model::FillGarliFormattedModelString(string &s) const{ char temp[1000]; int prec = MODEL_OUTPUT_PREC; if(modSpec->IsCodon()){ s += " o"; for(int i=0;iIsNucleotide() || modSpec->IsCodon() || modSpec->IsEstimateAAMatrix() || modSpec->IsTwoSerineRateMatrix() || (modSpec->IsAminoAcid() && modSpec->IsUserSpecifiedRateMatrix())){ //sprintf(temp," r %.*f %.*f %.*f %.*f %.*f", prec, Rates(0), prec, Rates(1), prec, Rates(2), prec, Rates(3), prec, Rates(4)); //s += temp; s += " r "; for(int st = 0;st < relNucRates.size();st++){ sprintf(temp," %.*f", prec, Rates(st)); s += temp; } } if(modSpec->IsNucleotide()){ sprintf(temp," e %.*f %.*f %.*f %.*f", prec, StateFreq(0), prec, StateFreq(1), prec, StateFreq(2), prec, StateFreq(3)); s += temp; } else if(!IsOrientedGap()){ sprintf(temp," e "); s += temp; for(int i=0;iIsFlexRateHet()){ s += " f "; for(int i=0;iIsGammaRateHet()){ sprintf(temp, " a %.*f", prec, Alpha()); s += temp; } } if(PropInvar()!=ZERO_POINT_ZERO){ sprintf(temp, " p %.*f", prec, PropInvar()); s += temp; } if(modSpec->IsOrientedGap()){ sprintf(temp, " i %f d %f", *insertRate, *deleteRate); s += temp; } s += " "; } /* void Model::ReadModelFromFile(NexusToken &token){ token.GetNextToken(); do{ if(token.Equals("r")){//rate parameters token.GetNextToken(); FLOAT_TYPE r[5]; for(int i=0;i<5;i++){ r[i]=atof(token.GetToken().c_str()); token.GetNextToken(); } SetRmat(r); if(token.IsNumericalToken()) token.GetNextToken();//this is necessary incase GT is included } else if(token.Equals("b")){ token.GetNextToken(); FLOAT_TYPE b[3]; for(int i=0;i<3;i++){ b[i]=atof(token.GetToken().c_str()); token.GetNextToken(); } SetPis(b); if(token.IsNumericalToken()) token.GetNextToken();//this is necessary incase T is included } else if(token.Equals("a")){ token.GetNextToken(); SetAlpha(atof(token.GetToken().c_str())); token.GetNextToken(); } else if(token.Equals("p")){ token.GetNextToken(); SetPinv(atof(token.GetToken().c_str())); token.GetNextToken(); } else if(token.Begins("(") == false){ token.GetNextToken(); } }while(token.Begins("(") == false); UpdateQMat(); } */ void Model::ReadGarliFormattedModelString(string &modString){ istringstream stf(modString, stringstream::in); char c; NxsString temp; c=stf.get(); do{//read parameter values identified by single letter identifier. Each section should //take care of advancing to the following letter if(c == 'R' || c == 'r'){//rate parameters if(modSpec->IsAminoAcid() && modSpec->IsEstimateAAMatrix() == false && modSpec->IsUserSpecifiedRateMatrix() == false && modSpec->IsTwoSerineRateMatrix() == false) throw ErrorException("Amino acid rate matrix parameters cannot be specified unless \"ratematrix = fixed\" or \"ratematrix = estimate\" are used."); //FLOAT_TYPE r[6]; vector r; //for(int i=0;i<5;i++){ for(int i=0;i> temp; if(temp.size() == 0) throw(ErrorException("Unexpected end of model string while reading rate matrix parameters.\nExamine file and check manual for format.\n")); if(temp[0] != '.' && (!isdigit(temp[0]))) throw(ErrorException("Problem reading rate matrix parameters from file (maybe too few rates?).\n\tFor amino acid models 190 rates should be specified, (or 189 rates if the last rate is assumed to be 1.0).\n\tFor nucleotide models 6 should be specified (or 5 if the last rate is assumed to be 1.0).\n\tExamine file and check manual or website for format.\n")); //r[i]=(FLOAT_TYPE)atof(temp.c_str()); r.push_back((FLOAT_TYPE)atof(temp.c_str())); } do{ c=stf.get(); }while(!stf.eof() && (c == ' ' || c == '\t')); if(isdigit(c) || c=='.'){//read the GT rate, if specified string v; v = c; while((!isalpha(c) || c=='e' || c == 'E') && !stf.eof()){ c=stf.get(); if(isdigit(c) || c=='.' || c=='e' || c=='E' || c == '-') v += c; else if(c == ' ' || c == '\t'){ c=stf.get(); if(isdigit(c) || c=='.') throw ErrorException("It appears that too many relative rates was specified in the model string.\n\tFor amino acid models 190 rates should be specified, (or 189 rates if the last rate is assumed to be 1.0).\n\tFor nucleotide models 6 should be specified (or 5 if the last rate is assumed to be 1.0)."); break; } } //r[5] = atof(v.c_str()); r.push_back((FLOAT_TYPE)atof(v.c_str())); } //else r[5] = ONE_POINT_ZERO; else r.push_back(ONE_POINT_ZERO); if(r.size() != relNucRates.size()){ if(modSpec->IsAminoAcid()) throw ErrorException("It appears that too few relative rates were specified in the model string (found %d).\n\tFor amino acid models 190 rates should be specified, (or 189 rates if the last rate is assumed to be 1.0).", r.size()); else throw ErrorException("Incorrect number of relative rates specified in the model string.\t6 rates should be specified, (or 5 rates if the G-T rate is assumed to be 1.0)."); } SetRmat(&r[0], true, true); modSpec->gotRmatFromFile=true; } else if(c == 'E' || c == 'e' || c == 'b' || c == 'B'){//base freqs //7/12/07 changing this to pay attention to the 4th state, if specified //although it should be calcuable from the other three, having exact restartability //sometimes requires that it is taken as is //FLOAT_TYPE b[4]; int nstates = modSpec->nstates; vector b(nstates); for(int i=0;i> temp; if(temp.size() == 0) throw(ErrorException("Unexpected end of model string while reading equilibrium frequency parameters.\nExamine file and check manual for format.\n")); if(temp[0] != '.' && (!isdigit(temp[0]))) throw(ErrorException("Problem reading equilibrium state frequency parameters from file.\nExamine file and check manual for format.\n")); b[i]=(FLOAT_TYPE)atof(temp.c_str()); } do{ c=stf.get(); }while(!stf.eof() && (c == ' ' || c == '\t')); if(isdigit(c) || c=='.'){ string v; v = c; while(!isalpha(c) && !stf.eof()){ c=stf.get(); if(isdigit(c) || c=='.') v += c; else if(c == ' ' || c == '\t'){ c=stf.get(); if(isdigit(c) || c=='.') throw ErrorException("It appears that too many equilibrium frequencies were specified in the model string.\n\tFor amino acid models 20 should be specified, (or 19 if the last is assumed to make the sum 1.0).\n\tFor nucleotide models 4 (or 3 if the last is assumed to make the sum 1.0)."); break; } } b[nstates-1]=(FLOAT_TYPE)atof(v.c_str()); } else{ FLOAT_TYPE tot = ZERO_POINT_ZERO; for(int i=0;iIsCodon() && modSpec->fixStateFreqs == false && modSpec->IsEmpiricalStateFrequencies()) SetPis(&b[0], false, true); else SetPis(&b[0], true, true); modSpec->gotStateFreqsFromFile=true; } else if(c == 'A' || c == 'a'){//alpha shape if(modSpec->IsFlexRateHet()) throw(ErrorException("Config file specifies ratehetmodel = flex, but starting model contains alpha!\n")); if(modSpec->IsNonsynonymousRateHet()) throw(ErrorException("Config file specifies ratehetmodel = nonsynonymous, but starting model contains alpha!\n")); temp.clear(); stf >> temp; if(temp.size() == 0) throw(ErrorException("Unexpected end of model string while reading alpha parameter.\nExamine file and check manual for format.\n")); if(temp[0] != '.' && (!isdigit(temp[0]))) throw(ErrorException("Problem reading alpha parameter from file.\nExamine file and check manual for format.\n")); SetAlpha((FLOAT_TYPE)atof(temp.c_str()), true); c=stf.get(); modSpec->gotAlphaFromFile=true; } //apropriating "i" for insert rate //else if(c == 'P' || c == 'p' || c == 'i' || c == 'I'){//proportion invariant else if(c == 'P' || c == 'p'){//proportion invariant temp.clear(); stf >> temp; if(temp.size() == 0) throw(ErrorException("Unexpected end of model string while reading proportion of invariant sites parameter.\nExamine file and check manual for format.\n")); if(temp[0] != '.' && (!isdigit(temp[0]))) throw(ErrorException("Problem reading proportion of invariant sites parameter from file.\nExamine file and check manual for format.\n")); FLOAT_TYPE p=(FLOAT_TYPE)atof(temp.c_str()); SetPinv(p, true); c=stf.get(); modSpec->gotPinvFromFile=true; } else if(c == 'F' || c == 'f'){//flex rates if(modSpec->IsFlexRateHet()==false) throw(ErrorException("Flex rate parameters specified, but ratehetmodel is not flex!\n")); FLOAT_TYPE rates[20]; FLOAT_TYPE probs[20]; for(int i=0;i> temp; if(temp.size() == 0) throw(ErrorException("Unexpected end of model string while reading flex rate parameters.\nExamine file and check manual for format.\n")); if(isalpha(temp[0])) throw ErrorException("Problem with flex rates specification in starting condition file"); rates[i]=(FLOAT_TYPE)atof(temp.c_str()); temp.clear(); stf >> temp; if(temp.size() == 0) throw(ErrorException("Unexpected end of model string while reading flex rate parameters.\nExamine file and check manual for format.\n")); if(isalpha(temp[0])) throw ErrorException("Problem with flex rates specification in starting condition file"); probs[i]=(FLOAT_TYPE)atof(temp.c_str()); } SetFlexRates(rates, probs, true); NormalizeRates(); c=stf.get(); modSpec->gotFlexFromFile=true; } else if(c == 'O' || c == 'o'){//omega parameters if(modSpec->IsCodon() == false) throw ErrorException("Omega parameters specified for non-codon model?"); FLOAT_TYPE rates[20]; FLOAT_TYPE probs[20]; if(NRateCats() == 1){//just a single omega value to get, maybe with a proportion of 1.0 following it temp.clear(); stf >> temp; if(temp.size() == 0) throw(ErrorException("Unexpected end of model string while reading omega parameters.\nExamine file and check manual for format.\n")); if(isalpha(temp[0])) throw ErrorException("Problem with omega parameter specification in starting condition file"); rates[0]=(FLOAT_TYPE)atof(temp.c_str()); do{ c=stf.get(); }while(!stf.eof() && (c == ' ' || c == '\t')); if(isdigit(c) || c=='.'){ string v; v = c; temp.clear(); stf >> temp; if(temp.size() == 0) throw(ErrorException("Unexpected end of model string while reading omega parameters.\nExamine file and check manual for format.\n")); v += temp; if(FloatingPointEquals(atof(v.c_str()), ONE_POINT_ZERO, 1.0e-5) == false) throw ErrorException("Problem with omega parameter specification in starting condition file\n(wrong number of rate cats specified in config?)"); do{ c=stf.get(); }while(!stf.eof() && (c == ' ' || c == '\t')); if(isdigit(c) || c == '.') throw ErrorException("Problem with omega parameter specification in starting condition file"); } probs[0] = ONE_POINT_ZERO; SetOmegas(rates, probs); } else{ for(int i=0;i> temp; if(temp.size() == 0) throw(ErrorException("Unexpected end of model string while reading omega parameters.\nExamine file and check manual for format.\n")); if(isalpha(temp[0])) throw ErrorException("Problem with omega parameter specification in starting condition file"); rates[i]=(FLOAT_TYPE)atof(temp.c_str()); temp.clear(); stf >> temp; if(temp.size() == 0) throw(ErrorException("Unexpected end of model string while reading omega parameters.\nExamine file and check manual for format.\n")); if(isalpha(temp[0])) throw ErrorException("Problem with omega parameter specification in starting condition file"); probs[i]=(FLOAT_TYPE)atof(temp.c_str()); } do{ c=stf.get(); }while(!stf.eof() && (c == ' ' || c == '\t')); if(isdigit(c) || c == '.') throw ErrorException("Problem with omega parameter specification in starting condition file"); SetOmegas(rates, probs); } modSpec->gotOmegasFromFile=true; } else if(c == 'n'){ //the number of cats should now be set in the config file c=stf.get(); assert(0); } else if(c == 'I' || c == 'i'){ stf >> temp; if(temp[0] != '.' && (!isdigit(temp[0]))) throw(ErrorException("Problem reading insertion rate parameter from file.\nExamine file and check manual for format.\nNote that the proportion of invariable sites parameter is specified with \"p\", not \"i\"")); if(! NxsString(temp).IsADouble()) throw(ErrorException("Problem reading insertion rate parameter from file.\nExamine file and check manual for format.\nNote that the proportion of invariable sites parameter is specified with \"p\", not \"i\"")); FLOAT_TYPE i = (FLOAT_TYPE)atof(temp.c_str()); if(insertRate == NULL) throw ErrorException("insert rate (%f) specified for non-gap model! Check configuration.", i); SetInsertRate(0, i); do{c=stf.get();}while(c==' '); modSpec->gotInsertFromFile=true; } else if(c == 'D' || c == 'd'){ stf >> temp; if(temp[0] != '.' && (!isdigit(temp[0]))) throw(ErrorException("Problem reading deletion rate parameter from file.\nExamine file and check manual for format.\n")); if(! NxsString(temp).IsADouble()) throw(ErrorException("Problem reading deletion rate parameter from file.\nExamine file and check manual for format.\n")); FLOAT_TYPE d = (FLOAT_TYPE)atof(temp.c_str()); if(deleteRate == NULL) throw ErrorException("delete rate (%f) specified for non-gap model! Check configuration.", d); SetDeleteRate(0, d); do{c=stf.get();}while(c==' '); modSpec->gotDeleteFromFile=true; } else if(isalpha(c)) throw(ErrorException("Unknown model parameter specification in file.\nExamine file and check manual for format.\n")); else if(c != '(') c=stf.get(); }while(c != '(' && c != '\r' && c != '\n' && !stf.eof()); /* this isn't necessary with the new definition of the insert "rate" as a proportion //adjust the indel rates, if necessary if(IsOrientedGap() && *insertRate > *deleteRate){ if(modSpec->gotInsertFromFile && modSpec->gotDeleteFromFile && *insertRate > *deleteRate - 1.0e-2) throw ErrorException("Insertion and deletion rates specified are not compatible. Insertion rate must be < deletion rate"); else if(modSpec->gotInsertFromFile) *deleteRate = *insertRate + 0.001; else if(modSpec->gotDeleteFromFile) *insertRate = *deleteRate - 0.001; } */ } void Model::CreateModelFromSpecification(int modnum){ modSpec = modSpecSet.GetModSpec(modnum); nstates = modSpec->nstates; if(modSpec->IsNucleotide() || modSpec->IsCodon()) nst = modSpec->Nst(); else nst = -1; if(IsOrientedGap()){ insertRate = new FLOAT_TYPE; *insertRate = 0.5; //AbsoluteRate *ins = new AbsoluteRate((FLOAT_TYPE **) &insertRate, modnum); InsertProportion *ins = new InsertProportion((FLOAT_TYPE **) &insertRate, modnum); ins->SetWeight(1); paramsToMutate.push_back(ins); //del rate may be optimized elsewhere, but not randomly during GA //(optimized if part. model and no SSR) deleteRate = new FLOAT_TYPE; *deleteRate = 1.0; /* DeleteRate *del = new DeleteRate((FLOAT_TYPE **) &deleteRate, modnum); del->SetWeight(1); paramsToMutate.push_back(del); */ } else{ insertRate = deleteRate = NULL; } nRateCats = modSpec->numRateCats; //deal with rate het models propInvar = new FLOAT_TYPE; includeInvariantSites = modSpec->includeInvariantSites; if(includeInvariantSites){ assert(modSpec->IsCodon() == false); *propInvar=(FLOAT_TYPE)0.2; if(modSpec->fixInvariantSites == false){ ProportionInvariant *pi = new ProportionInvariant("proportion invariant", (FLOAT_TYPE **) &propInvar, modnum); pi->SetWeight(1); paramsToMutate.push_back(pi); } } else *propInvar=ZERO_POINT_ZERO; if(NRateCats() > 1 && modSpec->IsNonsynonymousRateHet() == false){ //assert(modSpec.IsNucleotide() || modSpec.IsAminoAcid()); alpha = new FLOAT_TYPE; *alpha = ZERO_POINT_FIVE; if(modSpec->IsFlexRateHet() == false){ DiscreteGamma(rateMults, rateProbs, *alpha); if(modSpec->fixAlpha == false){ AlphaShape *a= new AlphaShape("alpha", &alpha, modnum); a->SetWeight(1); paramsToMutate.push_back(a); } } else{ //start the flex rates out being equivalent to //a gamma with alpha=.5 DiscreteGamma(rateMults, rateProbs, ZERO_POINT_FIVE); if(modSpec->includeInvariantSites == true) NormalizeRates(); vector dummy; dummy.reserve(NRateCats()); for(int i=0;iSetWeight((FLOAT_TYPE)NRateCats()); paramsToMutate.push_back(rateP); dummy.clear(); for(int i=0;iSetWeight((FLOAT_TYPE)NRateCats()); paramsToMutate.push_back(rateM); } } else{ rateMults[0]=ONE_POINT_ZERO; rateProbs[0]=ONE_POINT_ZERO; alpha=NULL; } //deal with the state frequencies for(int i=0;iIsEqualStateFrequencies() == false && modSpec->fixStateFreqs == false){ StateFrequencies *s=new StateFrequencies(&stateFreqs[0], nstates, modnum); s->SetWeight(nstates); paramsToMutate.push_back(s); } if(modSpec->IsAminoAcid()){ if(modSpec->IsJonesAAFreqs()) SetJonesAAFreqs(); if(modSpec->IsDayhoffAAFreqs()) SetDayhoffAAFreqs(); if(modSpec->IsWAGAAFreqs()) SetWAGAAFreqs(); if(modSpec->IsMtMamAAFreqs()) SetMtMamAAFreqs(); if(modSpec->IsMtRevAAFreqs()) SetMtRevAAFreqs(); } //deal with the relative rates if(modSpec->IsAminoAcid() == false){ if(nst==6){ if(modSpec->IsArbitraryRateMatrix()){ //user specified rate matrix type, like rclass = (a b c d e f) in paup //trying to do this as generically as possible string matrixSpec = modSpec->GetArbitraryRateMatrixString(); int pos = 0; char characters[10]; // int usedCharacters = 0; FLOAT_TYPE **params = new FLOAT_TYPE*[6]; for(int r=0;r<6;r++){ while (pos < matrixSpec.size() && !isalnum(matrixSpec[pos])) pos++; bool newChar = true; char thisChar = matrixSpec[pos]; for(int c=0;cSetWeight(6); paramsToMutate.push_back(r); } else{//normal GTR //make the transitions higher to begin with for(int i=0;i<6;i++){ FLOAT_TYPE *d=new FLOAT_TYPE; relNucRates.push_back(d); } *relNucRates[0]=*relNucRates[2]=*relNucRates[3]=*relNucRates[5] = ONE_POINT_ZERO; *relNucRates[1]=*relNucRates[4] = 4.0; } if(modSpec->fixRelativeRates == false){ RelativeRates *r=new RelativeRates("Rate matrix", &relNucRates[0], 6, 1e-3, 999.9, modnum); r->SetWeight(6); paramsToMutate.push_back(r); } } else if(nst==2){ FLOAT_TYPE *a=new FLOAT_TYPE; FLOAT_TYPE *b=new FLOAT_TYPE; *a=ONE_POINT_ZERO; *b=4.0; relNucRates.push_back(a); relNucRates.push_back(b); relNucRates.push_back(a); relNucRates.push_back(a); relNucRates.push_back(b); relNucRates.push_back(a); if(modSpec->fixRelativeRates == false){ RelativeRates *r=new RelativeRates("Rate matrix", &b, 1, 1e-3, 999.9, modnum); r->SetWeight(2); paramsToMutate.push_back(r); } } else if(nst==1){ FLOAT_TYPE *a=new FLOAT_TYPE; *a=ONE_POINT_ZERO; for(int i=0;i<6;i++) relNucRates.push_back(a); } } else{//estimating or fixing the aminoacid rate matrix - a two serine matrix is either estimated or fixed and user specified, so goes through here regardless if(modSpec->fixRelativeRates == false || modSpec->IsUserSpecifiedRateMatrix() || modSpec->IsTwoSerineRateMatrix()){ int seed = rnd.seed(); int matrixRates = nstates * (nstates - 1) / 2; //for(int i=0;i<190;i++){ for(int i=0;i < matrixRates;i++){ FLOAT_TYPE *d=new FLOAT_TYPE; //*d = ONE_POINT_ZERO; if(i == (matrixRates - 1)) *d = 1.0; else *d = max(rnd.gamma(1), MIN_REL_RATE); relNucRates.push_back(d); } rnd.set_seed(seed); #ifdef SUM_AA_REL_RATES this->NormalizeSumConstrainedRelativeRates(true, -1); #endif if((modSpec->IsUserSpecifiedRateMatrix() == false) && ((modSpec->IsTwoSerineRateMatrix() && modSpec->fixRelativeRates) == false)){ #ifdef SUM_AA_REL_RATES SumConstrainedRelativeRates *r = new SumConstrainedRelativeRates("Rate matrix", &relNucRates[0], matrixRates, SUM_TO * 1.0e-6/(double)matrixRates, SUM_TO * 1.0e6/(double)matrixRates, SUM_TO, modnum); #else RelativeRates *r=new RelativeRates("Rate matrix", &relNucRates[0], 190, 1e-3, 9999.9, modnum); #endif r->SetWeight(matrixRates); paramsToMutate.push_back(r); } } } AllocateEigenVariables();//these need to be allocated regardless of //nst because I don't feel like simplifying the deriv calcs for simpler //models. Pmat calcs for simpler models are simplified, and don't //require the Eigen stuff if(modSpec->IsMkTypeModel() || modSpec->IsOrientedGap()){ //NSTATE - nothing needs to be done here right now } else if(modSpec->IsCodon() == false) UpdateQMat(); else if(modSpec->IsCodon()){ FLOAT_TYPE *d; for(int i=0;ifixOmega){ if(NRateCats() > 1){ RateProportions *omegaP=new RateProportions(&omegaProbs[0], NRateCats(), modnum); omegaP->SetWeight((FLOAT_TYPE)NRateCats()); paramsToMutate.push_back(omegaP); } RateMultipliers *omegaM=new RateMultipliers(&omegas[0], NRateCats(), modnum); omegaM->SetWeight((FLOAT_TYPE)NRateCats()); paramsToMutate.push_back(omegaM); } /* FLOAT_TYPE *NS=new FLOAT_TYPE; *NS = 0.5; FLOAT_TYPE *S=new FLOAT_TYPE; *S = 1.0; omegas.push_back(NS); omegas.push_back(S); RelativeRates *o=new RelativeRates("Omega", &omega[0], 2); o->SetWeight(2); paramsToMutate.push_back(o); */ //this is hopefully not needed here (and can't be here now that //the genetic code is not static), since the code has yet to be set //it will be updated later //UpdateQMatCodon(); } eigenDirty=true; } void Model::SetMtMamAAFreqs(){ *stateFreqs[0] = 0.0692 ; *stateFreqs[14] = 0.0184 ; *stateFreqs[11] = 0.0400 ; *stateFreqs[2] = 0.0186 ; *stateFreqs[1] = 0.0065 ; *stateFreqs[13] = 0.0238 ; *stateFreqs[3] = 0.0236 ; *stateFreqs[5] = 0.0557 ; *stateFreqs[6] = 0.0277 ; *stateFreqs[7] = 0.0905 ; *stateFreqs[9] = 0.1675 ; *stateFreqs[8] = 0.0221 ; *stateFreqs[10] = 0.0561 ; *stateFreqs[4] = 0.0611 ; *stateFreqs[12] = 0.0536 ; *stateFreqs[15] = 0.0725 ; *stateFreqs[16] = 0.0870 ; *stateFreqs[18] = 0.0293 ; *stateFreqs[19] = 0.0340 ; *stateFreqs[17] = 0.0428 ; } void Model::SetMtRevAAFreqs(){ *stateFreqs[0] = 0.0720 ; *stateFreqs[14] = 0.0190 ; *stateFreqs[11] = 0.0390 ; *stateFreqs[2] = 0.0190 ; *stateFreqs[1] = 0.0060 ; *stateFreqs[13] = 0.0250 ; *stateFreqs[3] = 0.0240 ; *stateFreqs[5] = 0.0560 ; *stateFreqs[6] = 0.0280 ; *stateFreqs[7] = 0.0880 ; *stateFreqs[9] = 0.1690 ; *stateFreqs[8] = 0.0230 ; *stateFreqs[10] = 0.0540 ; *stateFreqs[4] = 0.0610 ; *stateFreqs[12] = 0.0540 ; *stateFreqs[15] = 0.0720 ; *stateFreqs[16] = 0.0860 ; *stateFreqs[18] = 0.0290 ; *stateFreqs[19] = 0.0330 ; *stateFreqs[17] = 0.0430 ; } void Model::SetJonesAAFreqs(){ *stateFreqs[0] =0.076748; *stateFreqs[14]=0.051691; *stateFreqs[11]=0.042645; *stateFreqs[2]=0.051544; *stateFreqs[1]=0.019803; *stateFreqs[13]=0.040752; *stateFreqs[3]=0.06183; *stateFreqs[5]=0.073152; *stateFreqs[6]=0.022944; *stateFreqs[7]=0.053761; *stateFreqs[9]=0.091904; *stateFreqs[8]=0.058676; *stateFreqs[10]=0.023826; *stateFreqs[4]=0.040126; *stateFreqs[12]=0.050901; *stateFreqs[15]=0.068765; *stateFreqs[16]=0.058565; *stateFreqs[18]=0.014261; *stateFreqs[19]=0.032101; *stateFreqs[17]=0.066005; } void Model::SetDayhoffAAFreqs(){ *stateFreqs[0] =0.087127; *stateFreqs[14] =0.040904; *stateFreqs[11] =0.040432; *stateFreqs[2] =0.046872; *stateFreqs[1] =0.033474; *stateFreqs[13] =0.038255; *stateFreqs[3] =0.04953; *stateFreqs[5] =0.088612; *stateFreqs[6] =0.033618; *stateFreqs[7] =0.036886; *stateFreqs[9] =0.085357; *stateFreqs[8] =0.080482; *stateFreqs[10] =0.014753; *stateFreqs[4] =0.039772; *stateFreqs[12] =0.05068; *stateFreqs[15] =0.069577; *stateFreqs[16] =0.058542; *stateFreqs[18] =0.010494; *stateFreqs[19] =0.029916; *stateFreqs[17] =0.064718; } void Model::SetWAGAAFreqs(){ *stateFreqs[0]=0.0866279; *stateFreqs[14]=0.043972; *stateFreqs[11]=0.0390894; *stateFreqs[2]=0.0570451; *stateFreqs[1]=0.0193078; *stateFreqs[13]=0.0367281; *stateFreqs[3]=0.0580589; *stateFreqs[5]=0.0832518; *stateFreqs[6]=0.0244313; *stateFreqs[7]=0.048466; *stateFreqs[9]=0.086209; *stateFreqs[8]=0.0620286; *stateFreqs[10]=0.0195027; *stateFreqs[4]=0.0384319; *stateFreqs[12]=0.0457631; *stateFreqs[15]=0.0695179; *stateFreqs[16]=0.0610127; *stateFreqs[18]=0.0143859; *stateFreqs[19]=0.0352742; *stateFreqs[17]=0.0708956; } int Model::PerformModelMutation(){ //the ModelPartition version of this is now being called assert(0); if(paramsToMutate.empty()) return 0; BaseParameter *mut = SelectModelMutation(); assert(mut != NULL); mut->Mutator(mutationShape); int retType; if(mut->Type() == RELATIVERATES){ UpdateQMat(); retType=Individual::rates; eigenDirty=true; } else if(mut->Type() == STATEFREQS){ UpdateQMat(); retType=Individual::pi; eigenDirty=true; } else if(mut->Type() == PROPORTIONINVARIANT){ //this max checking should really be rolled into the parameter class *propInvar = (*propInvar > maxPropInvar ? maxPropInvar : *propInvar); //the non invariant rates need to be rescaled even if there is only 1 if(modSpec->IsFlexRateHet() == false) AdjustRateProportions(); else NormalizeRates(); retType=Individual::pinv; } else if(mut->Type() == ALPHASHAPE){ DiscreteGamma(rateMults, rateProbs, *alpha); retType=Individual::alpha; } else if(mut->Type() == RATEPROPS || mut->Type() == RATEMULTS){ //flex rates and omega muts come through here //enforce an ordering of the rate multipliers, so that they can't "cross" one another if(NRateCats() > 1) CheckAndCorrectRateOrdering(); if(modSpec->IsFlexRateHet() == true) NormalizeRates(); else if(modSpec->IsCodon()){ //this normalization could really be taken care of in the mutator, but this general purpose //function does a better job of enforcing minimum values NormalizeSumConstrainedValues(&omegaProbs[0], NRateCats(), ONE_POINT_ZERO, 1.0e-5, -1); //eigen stuff needs to be recalced for changes to nonsynonymous rates eigenDirty = true; } retType=Individual::alpha; } else if(mut->Type() == INSERTPROPORTION || mut->Type() == DELETERATE){ retType=Individual::indel; } return retType; } BaseParameter *Model::SelectModelMutation(){ CalcMutationProbsFromWeights(); if(paramsToMutate.empty() == true) return NULL; FLOAT_TYPE r=rnd.uniform(); vector::iterator it; for(it=paramsToMutate.begin();it!=paramsToMutate.end();it++){ if((*it)->GetProb() > r) return *it; } it--; return *it; } void Model::CalcMutationProbsFromWeights(){ FLOAT_TYPE tot=ZERO_POINT_ZERO, running=ZERO_POINT_ZERO; for(vector::iterator it=paramsToMutate.begin();it!=paramsToMutate.end();it++){ tot += (*it)->GetWeight(); } for(vector::iterator it=paramsToMutate.begin();it!=paramsToMutate.end();it++){ running += (*it)->GetWeight() / tot; (*it)->SetProb(running); } } FLOAT_TYPE Model::GetTotalModelMutationWeight(){ FLOAT_TYPE tot=ZERO_POINT_ZERO; for(vector::iterator it=paramsToMutate.begin();it!=paramsToMutate.end();it++){ tot += (*it)->GetWeight(); } return tot; } /* void Model::OutputBinaryFormattedModel(OUTPUT_CLASS &out) const{ FLOAT_TYPE *r = new FLOAT_TYPE; for(int i=0;i<5;i++){ *r = Rates(i); out.write((char *) r, sizeof(FLOAT_TYPE)); } for(int i=0;iflexRates==true){ for(int i=0;i1){ *r = Alpha(); out.write((char *) r, sizeof(FLOAT_TYPE)); } } if(PropInvar()!=ZERO_POINT_ZERO){ *r = PropInvar(); out.write((char *) r, sizeof(FLOAT_TYPE)); } delete r; } */ void Model::OutputBinaryFormattedModel(OUTPUT_CLASS &out) const{ FLOAT_TYPE *r = new FLOAT_TYPE; // 1/17/14 The assert here was diallowing non-sequence data to be checkpointed. Don't recall that being intentional, and tests run fine. // Added check of number of rates to avoid section entirely in non-sequence case if(NumRelRates() > 0 && (modSpec->IsAminoAcid() == false || modSpec->IsUserSpecifiedRateMatrix() || modSpec->IsEstimateAAMatrix() || modSpec->IsTwoSerineRateMatrix())){ if(modSpec->IsAminoAcid()) assert(NumRelRates() == 190 || NumRelRates() == 210); // else // assert(NumRelRates() == 6); for(int i=0;iIsCodon()){ for(int i=0;iIsFlexRateHet()){ for(int i=0;iIsGammaRateHet()){ *r = Alpha(); out.WRITE_TO_FILE(r, sizeof(FLOAT_TYPE), 1); } if(PropInvar()!=ZERO_POINT_ZERO){ *r = PropInvar(); out.WRITE_TO_FILE(r, sizeof(FLOAT_TYPE), 1); } if(IsOrientedGap()){ *r = *insertRate; out.WRITE_TO_FILE(r, sizeof(FLOAT_TYPE), 1); *r = *deleteRate; out.WRITE_TO_FILE(r, sizeof(FLOAT_TYPE), 1); } delete r; } void Model::ReadBinaryFormattedModel(FILE *in){ // 1/17/14 The assert here was diallowing non-sequence data to be checkpointed. Don't recall that being intentional, and tests run fine. // Added check of number of rates to avoid section entirely in non-sequence case if(NumRelRates() > 0 && (modSpec->IsAminoAcid() == false || modSpec->IsUserSpecifiedRateMatrix() || modSpec->IsEstimateAAMatrix() || modSpec->IsTwoSerineRateMatrix())){ if(modSpec->IsAminoAcid()) assert(NumRelRates() == 190 || NumRelRates() == 210); // else // assert(NumRelRates() == 6); FLOAT_TYPE *r = new FLOAT_TYPE[NumRelRates()]; for(int i=0;iIsCodon()){ FLOAT_TYPE o; for(int i=0;iIsFlexRateHet()){ for(int i=0;iIsGammaRateHet()){ FLOAT_TYPE a; assert(ferror(in) == false); fread((char*) &a, sizeof(FLOAT_TYPE), 1, in); SetAlpha(a, false); } } if(PropInvar()!=ZERO_POINT_ZERO){ FLOAT_TYPE p; fread((char*) &p, sizeof(FLOAT_TYPE), 1, in); SetPinv(p, false); } if(IsOrientedGap()){ FLOAT_TYPE x; fread((char*) &x, sizeof(FLOAT_TYPE), 1, in); *insertRate = x; fread((char*) &x, sizeof(FLOAT_TYPE), 1, in); *deleteRate = x; } } void Model::MultiplyByJonesAAMatrix(){ int modNum=0; MODEL_FLOAT **qmatOffset = qmat[modNum]; qmatOffset[0][1] *= 0.056; qmatOffset[1][0] *= 0.056; qmatOffset[0][2] *= 0.081; qmatOffset[2][0] *= 0.081; qmatOffset[0][3] *= 0.105; qmatOffset[3][0] *= 0.105; qmatOffset[0][4] *= 0.015; qmatOffset[4][0] *= 0.015; qmatOffset[0][5] *= 0.179; qmatOffset[5][0] *= 0.179; qmatOffset[0][6] *= 0.027; qmatOffset[6][0] *= 0.027; qmatOffset[0][7] *= 0.036; qmatOffset[7][0] *= 0.036; qmatOffset[0][8] *= 0.035; qmatOffset[8][0] *= 0.035; qmatOffset[0][9] *= 0.03; qmatOffset[9][0] *= 0.03; qmatOffset[0][10] *= 0.054; qmatOffset[10][0] *= 0.054; qmatOffset[0][11] *= 0.054; qmatOffset[11][0] *= 0.054; qmatOffset[0][12] *= 0.194; qmatOffset[12][0] *= 0.194; qmatOffset[0][13] *= 0.057; qmatOffset[13][0] *= 0.057; qmatOffset[0][14] *= 0.058; qmatOffset[14][0] *= 0.058; qmatOffset[0][15] *= 0.378; qmatOffset[15][0] *= 0.378; qmatOffset[0][16] *= 0.475; qmatOffset[16][0] *= 0.475; qmatOffset[0][17] *= 0.298; qmatOffset[17][0] *= 0.298; qmatOffset[0][18] *= 0.009; qmatOffset[18][0] *= 0.009; qmatOffset[0][19] *= 0.011; qmatOffset[19][0] *= 0.011; qmatOffset[1][2] *= 0.01; qmatOffset[2][1] *= 0.01; qmatOffset[1][3] *= 0.005; qmatOffset[3][1] *= 0.005; qmatOffset[1][4] *= 0.078; qmatOffset[4][1] *= 0.078; qmatOffset[1][5] *= 0.059; qmatOffset[5][1] *= 0.059; qmatOffset[1][6] *= 0.069; qmatOffset[6][1] *= 0.069; qmatOffset[1][7] *= 0.017; qmatOffset[7][1] *= 0.017; qmatOffset[1][8] *= 0.007; qmatOffset[8][1] *= 0.007; qmatOffset[1][9] *= 0.023; qmatOffset[9][1] *= 0.023; qmatOffset[1][10] *= 0.031; qmatOffset[10][1] *= 0.031; qmatOffset[1][11] *= 0.034; qmatOffset[11][1] *= 0.034; qmatOffset[1][12] *= 0.014; qmatOffset[12][1] *= 0.014; qmatOffset[1][13] *= 0.009; qmatOffset[13][1] *= 0.009; qmatOffset[1][14] *= 0.113; qmatOffset[14][1] *= 0.113; qmatOffset[1][15] *= 0.223; qmatOffset[15][1] *= 0.223; qmatOffset[1][16] *= 0.042; qmatOffset[16][1] *= 0.042; qmatOffset[1][17] *= 0.062; qmatOffset[17][1] *= 0.062; qmatOffset[1][18] *= 0.115; qmatOffset[18][1] *= 0.115; qmatOffset[1][19] *= 0.209; qmatOffset[19][1] *= 0.209; qmatOffset[2][3] *= 0.767; qmatOffset[3][2] *= 0.767; qmatOffset[2][4] *= 0.004; qmatOffset[4][2] *= 0.004; qmatOffset[2][5] *= 0.13; qmatOffset[5][2] *= 0.13; qmatOffset[2][6] *= 0.112; qmatOffset[6][2] *= 0.112; qmatOffset[2][7] *= 0.011; qmatOffset[7][2] *= 0.011; qmatOffset[2][8] *= 0.026; qmatOffset[8][2] *= 0.026; qmatOffset[2][9] *= 0.007; qmatOffset[9][2] *= 0.007; qmatOffset[2][10] *= 0.015; qmatOffset[10][2] *= 0.015; qmatOffset[2][11] *= 0.528; qmatOffset[11][2] *= 0.528; qmatOffset[2][12] *= 0.015; qmatOffset[12][2] *= 0.015; qmatOffset[2][13] *= 0.049; qmatOffset[13][2] *= 0.049; qmatOffset[2][14] *= 0.016; qmatOffset[14][2] *= 0.016; qmatOffset[2][15] *= 0.059; qmatOffset[15][2] *= 0.059; qmatOffset[2][16] *= 0.038; qmatOffset[16][2] *= 0.038; qmatOffset[2][17] *= 0.031; qmatOffset[17][2] *= 0.031; qmatOffset[2][18] *= 0.004; qmatOffset[18][2] *= 0.004; qmatOffset[2][19] *= 0.046; qmatOffset[19][2] *= 0.046; qmatOffset[3][4] *= 0.005; qmatOffset[4][3] *= 0.005; qmatOffset[3][5] *= 0.119; qmatOffset[5][3] *= 0.119; qmatOffset[3][6] *= 0.026; qmatOffset[6][3] *= 0.026; qmatOffset[3][7] *= 0.012; qmatOffset[7][3] *= 0.012; qmatOffset[3][8] *= 0.181; qmatOffset[8][3] *= 0.181; qmatOffset[3][9] *= 0.009; qmatOffset[9][3] *= 0.009; qmatOffset[3][10] *= 0.018; qmatOffset[10][3] *= 0.018; qmatOffset[3][11] *= 0.058; qmatOffset[11][3] *= 0.058; qmatOffset[3][12] *= 0.018; qmatOffset[12][3] *= 0.018; qmatOffset[3][13] *= 0.323; qmatOffset[13][3] *= 0.323; qmatOffset[3][14] *= 0.029; qmatOffset[14][3] *= 0.029; qmatOffset[3][15] *= 0.03; qmatOffset[15][3] *= 0.03; qmatOffset[3][16] *= 0.032; qmatOffset[16][3] *= 0.032; qmatOffset[3][17] *= 0.045; qmatOffset[17][3] *= 0.045; qmatOffset[3][18] *= 0.01; qmatOffset[18][3] *= 0.01; qmatOffset[3][19] *= 0.007; qmatOffset[19][3] *= 0.007; qmatOffset[4][5] *= 0.005; qmatOffset[5][4] *= 0.005; qmatOffset[4][6] *= 0.04; qmatOffset[6][4] *= 0.04; qmatOffset[4][7] *= 0.089; qmatOffset[7][4] *= 0.089; qmatOffset[4][8] *= 0.004; qmatOffset[8][4] *= 0.004; qmatOffset[4][9] *= 0.248; qmatOffset[9][4] *= 0.248; qmatOffset[4][10] *= 0.043; qmatOffset[10][4] *= 0.043; qmatOffset[4][11] *= 0.01; qmatOffset[11][4] *= 0.01; qmatOffset[4][12] *= 0.017; qmatOffset[12][4] *= 0.017; qmatOffset[4][13] *= 0.004; qmatOffset[13][4] *= 0.004; qmatOffset[4][14] *= 0.005; qmatOffset[14][4] *= 0.005; qmatOffset[4][15] *= 0.092; qmatOffset[15][4] *= 0.092; qmatOffset[4][16] *= 0.012; qmatOffset[16][4] *= 0.012; qmatOffset[4][17] *= 0.062; qmatOffset[17][4] *= 0.062; qmatOffset[4][18] *= 0.053; qmatOffset[18][4] *= 0.053; qmatOffset[4][19] *= 0.536; qmatOffset[19][4] *= 0.536; qmatOffset[5][6] *= 0.023; qmatOffset[6][5] *= 0.023; qmatOffset[5][7] *= 0.006; qmatOffset[7][5] *= 0.006; qmatOffset[5][8] *= 0.027; qmatOffset[8][5] *= 0.027; qmatOffset[5][9] *= 0.006; qmatOffset[9][5] *= 0.006; qmatOffset[5][10] *= 0.014; qmatOffset[10][5] *= 0.014; qmatOffset[5][11] *= 0.081; qmatOffset[11][5] *= 0.081; qmatOffset[5][12] *= 0.024; qmatOffset[12][5] *= 0.024; qmatOffset[5][13] *= 0.026; qmatOffset[13][5] *= 0.026; qmatOffset[5][14] *= 0.137; qmatOffset[14][5] *= 0.137; qmatOffset[5][15] *= 0.201; qmatOffset[15][5] *= 0.201; qmatOffset[5][16] *= 0.033; qmatOffset[16][5] *= 0.033; qmatOffset[5][17] *= 0.047; qmatOffset[17][5] *= 0.047; qmatOffset[5][18] *= 0.055; qmatOffset[18][5] *= 0.055; qmatOffset[5][19] *= 0.008; qmatOffset[19][5] *= 0.008; qmatOffset[6][7] *= 0.016; qmatOffset[7][6] *= 0.016; qmatOffset[6][8] *= 0.045; qmatOffset[8][6] *= 0.045; qmatOffset[6][9] *= 0.056; qmatOffset[9][6] *= 0.056; qmatOffset[6][10] *= 0.033; qmatOffset[10][6] *= 0.033; qmatOffset[6][11] *= 0.391; qmatOffset[11][6] *= 0.391; qmatOffset[6][12] *= 0.115; qmatOffset[12][6] *= 0.115; qmatOffset[6][13] *= 0.597; qmatOffset[13][6] *= 0.597; qmatOffset[6][14] *= 0.328; qmatOffset[14][6] *= 0.328; qmatOffset[6][15] *= 0.073; qmatOffset[15][6] *= 0.073; qmatOffset[6][16] *= 0.046; qmatOffset[16][6] *= 0.046; qmatOffset[6][17] *= 0.011; qmatOffset[17][6] *= 0.011; qmatOffset[6][18] *= 0.008; qmatOffset[18][6] *= 0.008; qmatOffset[6][19] *= 0.573; qmatOffset[19][6] *= 0.573; qmatOffset[7][8] *= 0.021; qmatOffset[8][7] *= 0.021; qmatOffset[7][9] *= 0.229; qmatOffset[9][7] *= 0.229; qmatOffset[7][10] *= 0.479; qmatOffset[10][7] *= 0.479; qmatOffset[7][11] *= 0.047; qmatOffset[11][7] *= 0.047; qmatOffset[7][12] *= 0.01; qmatOffset[12][7] *= 0.01; qmatOffset[7][13] *= 0.009; qmatOffset[13][7] *= 0.009; qmatOffset[7][14] *= 0.022; qmatOffset[14][7] *= 0.022; qmatOffset[7][15] *= 0.04; qmatOffset[15][7] *= 0.04; qmatOffset[7][16] *= 0.245; qmatOffset[16][7] *= 0.245; qmatOffset[7][17] *= 0.961; qmatOffset[17][7] *= 0.961; qmatOffset[7][18] *= 0.009; qmatOffset[18][7] *= 0.009; qmatOffset[7][19] *= 0.032; qmatOffset[19][7] *= 0.032; qmatOffset[8][9] *= 0.014; qmatOffset[9][8] *= 0.014; qmatOffset[8][10] *= 0.065; qmatOffset[10][8] *= 0.065; qmatOffset[8][11] *= 0.263; qmatOffset[11][8] *= 0.263; qmatOffset[8][12] *= 0.021; qmatOffset[12][8] *= 0.021; qmatOffset[8][13] *= 0.292; qmatOffset[13][8] *= 0.292; qmatOffset[8][14] *= 0.646; qmatOffset[14][8] *= 0.646; qmatOffset[8][15] *= 0.047; qmatOffset[15][8] *= 0.047; qmatOffset[8][16] *= 0.103; qmatOffset[16][8] *= 0.103; qmatOffset[8][17] *= 0.014; qmatOffset[17][8] *= 0.014; qmatOffset[8][18] *= 0.01; qmatOffset[18][8] *= 0.01; qmatOffset[8][19] *= 0.008; qmatOffset[19][8] *= 0.008; qmatOffset[9][10] *= 0.388; qmatOffset[10][9] *= 0.388; qmatOffset[9][11] *= 0.012; qmatOffset[11][9] *= 0.012; qmatOffset[9][12] *= 0.102; qmatOffset[12][9] *= 0.102; qmatOffset[9][13] *= 0.072; qmatOffset[13][9] *= 0.072; qmatOffset[9][14] *= 0.038; qmatOffset[14][9] *= 0.038; qmatOffset[9][15] *= 0.059; qmatOffset[15][9] *= 0.059; qmatOffset[9][16] *= 0.025; qmatOffset[16][9] *= 0.025; qmatOffset[9][17] *= 0.18; qmatOffset[17][9] *= 0.18; qmatOffset[9][18] *= 0.052; qmatOffset[18][9] *= 0.052; qmatOffset[9][19] *= 0.024; qmatOffset[19][9] *= 0.024; qmatOffset[10][11] *= 0.03; qmatOffset[11][10] *= 0.03; qmatOffset[10][12] *= 0.016; qmatOffset[12][10] *= 0.016; qmatOffset[10][13] *= 0.043; qmatOffset[13][10] *= 0.043; qmatOffset[10][14] *= 0.044; qmatOffset[14][10] *= 0.044; qmatOffset[10][15] *= 0.029; qmatOffset[15][10] *= 0.029; qmatOffset[10][16] *= 0.226; qmatOffset[16][10] *= 0.226; qmatOffset[10][17] *= 0.323; qmatOffset[17][10] *= 0.323; qmatOffset[10][18] *= 0.024; qmatOffset[18][10] *= 0.024; qmatOffset[10][19] *= 0.018; qmatOffset[19][10] *= 0.018; qmatOffset[11][12] *= 0.015; qmatOffset[12][11] *= 0.015; qmatOffset[11][13] *= 0.086; qmatOffset[13][11] *= 0.086; qmatOffset[11][14] *= 0.045; qmatOffset[14][11] *= 0.045; qmatOffset[11][15] *= 0.503; qmatOffset[15][11] *= 0.503; qmatOffset[11][16] *= 0.232; qmatOffset[16][11] *= 0.232; qmatOffset[11][17] *= 0.016; qmatOffset[17][11] *= 0.016; qmatOffset[11][18] *= 0.008; qmatOffset[18][11] *= 0.008; qmatOffset[11][19] *= 0.07; qmatOffset[19][11] *= 0.07; qmatOffset[12][13] *= 0.164; qmatOffset[13][12] *= 0.164; qmatOffset[12][14] *= 0.074; qmatOffset[14][12] *= 0.074; qmatOffset[12][15] *= 0.285; qmatOffset[15][12] *= 0.285; qmatOffset[12][16] *= 0.118; qmatOffset[16][12] *= 0.118; qmatOffset[12][17] *= 0.023; qmatOffset[17][12] *= 0.023; qmatOffset[12][18] *= 0.006; qmatOffset[18][12] *= 0.006; qmatOffset[12][19] *= 0.01; qmatOffset[19][12] *= 0.01; qmatOffset[13][14] *= 0.31; qmatOffset[14][13] *= 0.31; qmatOffset[13][15] *= 0.053; qmatOffset[15][13] *= 0.053; qmatOffset[13][16] *= 0.051; qmatOffset[16][13] *= 0.051; qmatOffset[13][17] *= 0.02; qmatOffset[17][13] *= 0.02; qmatOffset[13][18] *= 0.018; qmatOffset[18][13] *= 0.018; qmatOffset[13][19] *= 0.024; qmatOffset[19][13] *= 0.024; qmatOffset[14][15] *= 0.101; qmatOffset[15][14] *= 0.101; qmatOffset[14][16] *= 0.064; qmatOffset[16][14] *= 0.064; qmatOffset[14][17] *= 0.017; qmatOffset[17][14] *= 0.017; qmatOffset[14][18] *= 0.126; qmatOffset[18][14] *= 0.126; qmatOffset[14][19] *= 0.02; qmatOffset[19][14] *= 0.02; qmatOffset[15][16] *= 0.477; qmatOffset[16][15] *= 0.477; qmatOffset[15][17] *= 0.038; qmatOffset[17][15] *= 0.038; qmatOffset[15][18] *= 0.035; qmatOffset[18][15] *= 0.035; qmatOffset[15][19] *= 0.063; qmatOffset[19][15] *= 0.063; qmatOffset[16][17] *= 0.112; qmatOffset[17][16] *= 0.112; qmatOffset[16][18] *= 0.012; qmatOffset[18][16] *= 0.012; qmatOffset[16][19] *= 0.021; qmatOffset[19][16] *= 0.021; qmatOffset[17][18] *= 0.025; qmatOffset[18][17] *= 0.025; qmatOffset[17][19] *= 0.016; qmatOffset[19][17] *= 0.016; qmatOffset[18][19] *= 0.071; qmatOffset[19][18] *= 0.071; } void Model::MultiplyByMtMamAAMatrix(){ int modNum=0; MODEL_FLOAT **qmatOffset = qmat[modNum]; qmatOffset [ 0 ][ 14 ] *= 0.0337 ; qmatOffset [ 14 ][ 0 ] *= 0.0337 ; qmatOffset [ 0 ][ 11 ] *= 0.0021 ; qmatOffset [ 11 ][ 0 ] *= 0.0021 ; qmatOffset [ 0 ][ 2 ] *= 0.0116 ; qmatOffset [ 2 ][ 0 ] *= 0.0116 ; qmatOffset [ 0 ][ 1 ] *= 0.0000 ; qmatOffset [ 1 ][ 0 ] *= 0.0000 ; qmatOffset [ 0 ][ 13 ] *= 0.0000 ; qmatOffset [ 13 ][ 0 ] *= 0.0000 ; qmatOffset [ 0 ][ 3 ] *= 0.0000 ; qmatOffset [ 3 ][ 0 ] *= 0.0000 ; qmatOffset [ 0 ][ 5 ] *= 0.0821 ; qmatOffset [ 5 ][ 0 ] *= 0.0821 ; qmatOffset [ 0 ][ 6 ] *= 0.0084 ; qmatOffset [ 6 ][ 0 ] *= 0.0084 ; qmatOffset [ 0 ][ 7 ] *= 0.0790 ; qmatOffset [ 7 ][ 0 ] *= 0.0790 ; qmatOffset [ 0 ][ 9 ] *= 0.0221 ; qmatOffset [ 9 ][ 0 ] *= 0.0221 ; qmatOffset [ 0 ][ 8 ] *= 0.0000 ; qmatOffset [ 8 ][ 0 ] *= 0.0000 ; qmatOffset [ 0 ][ 10 ] *= 0.0800 ; qmatOffset [ 10 ][ 0 ] *= 0.0800 ; qmatOffset [ 0 ][ 4 ] *= 0.0000 ; qmatOffset [ 4 ][ 0 ] *= 0.0000 ; qmatOffset [ 0 ][ 12 ] *= 0.0558 ; qmatOffset [ 12 ][ 0 ] *= 0.0558 ; qmatOffset [ 0 ][ 15 ] *= 0.3601 ; qmatOffset [ 15 ][ 0 ] *= 0.3601 ; qmatOffset [ 0 ][ 16 ] *= 0.7170 ; qmatOffset [ 16 ][ 0 ] *= 0.7170 ; qmatOffset [ 0 ][ 18 ] *= 0.0053 ; qmatOffset [ 18 ][ 0 ] *= 0.0053 ; qmatOffset [ 0 ][ 19 ] *= 0.0000 ; qmatOffset [ 19 ][ 0 ] *= 0.0000 ; qmatOffset [ 0 ][ 17 ] *= 0.4191 ; qmatOffset [ 17 ][ 0 ] *= 0.4191 ; qmatOffset [ 14 ][ 11 ] *= 0.0042 ; qmatOffset [ 11 ][ 14 ] *= 0.0042 ; qmatOffset [ 14 ][ 2 ] *= 0.0000 ; qmatOffset [ 2 ][ 14 ] *= 0.0000 ; qmatOffset [ 14 ][ 1 ] *= 0.1958 ; qmatOffset [ 1 ][ 14 ] *= 0.1958 ; qmatOffset [ 14 ][ 13 ] *= 0.2590 ; qmatOffset [ 13 ][ 14 ] *= 0.2590 ; qmatOffset [ 14 ][ 3 ] *= 0.0000 ; qmatOffset [ 3 ][ 14 ] *= 0.0000 ; qmatOffset [ 14 ][ 5 ] *= 0.0190 ; qmatOffset [ 5 ][ 14 ] *= 0.0190 ; qmatOffset [ 14 ][ 6 ] *= 0.2443 ; qmatOffset [ 6 ][ 14 ] *= 0.2443 ; qmatOffset [ 14 ][ 7 ] *= 0.0000 ; qmatOffset [ 7 ][ 14 ] *= 0.0000 ; qmatOffset [ 14 ][ 9 ] *= 0.0063 ; qmatOffset [ 9 ][ 14 ] *= 0.0063 ; qmatOffset [ 14 ][ 8 ] *= 0.0526 ; qmatOffset [ 8 ][ 14 ] *= 0.0526 ; qmatOffset [ 14 ][ 10 ] *= 0.0000 ; qmatOffset [ 10 ][ 14 ] *= 0.0000 ; qmatOffset [ 14 ][ 4 ] *= 0.0000 ; qmatOffset [ 4 ][ 14 ] *= 0.0000 ; qmatOffset [ 14 ][ 12 ] *= 0.0095 ; qmatOffset [ 12 ][ 14 ] *= 0.0095 ; qmatOffset [ 14 ][ 15 ] *= 0.0032 ; qmatOffset [ 15 ][ 14 ] *= 0.0032 ; qmatOffset [ 14 ][ 16 ] *= 0.0000 ; qmatOffset [ 16 ][ 14 ] *= 0.0000 ; qmatOffset [ 14 ][ 18 ] *= 0.0168 ; qmatOffset [ 18 ][ 14 ] *= 0.0168 ; qmatOffset [ 14 ][ 19 ] *= 0.0000 ; qmatOffset [ 19 ][ 14 ] *= 0.0000 ; qmatOffset [ 14 ][ 17 ] *= 0.0000 ; qmatOffset [ 17 ][ 14 ] *= 0.0000 ; qmatOffset [ 11 ][ 2 ] *= 0.9097 ; qmatOffset [ 2 ][ 11 ] *= 0.9097 ; qmatOffset [ 11 ][ 1 ] *= 0.0000 ; qmatOffset [ 1 ][ 11 ] *= 0.0000 ; qmatOffset [ 11 ][ 13 ] *= 0.0084 ; qmatOffset [ 13 ][ 11 ] *= 0.0084 ; qmatOffset [ 11 ][ 3 ] *= 0.0000 ; qmatOffset [ 3 ][ 11 ] *= 0.0000 ; qmatOffset [ 11 ][ 5 ] *= 0.0495 ; qmatOffset [ 5 ][ 11 ] *= 0.0495 ; qmatOffset [ 11 ][ 6 ] *= 0.4822 ; qmatOffset [ 6 ][ 11 ] *= 0.4822 ; qmatOffset [ 11 ][ 7 ] *= 0.0200 ; qmatOffset [ 7 ][ 11 ] *= 0.0200 ; qmatOffset [ 11 ][ 9 ] *= 0.0000 ; qmatOffset [ 9 ][ 11 ] *= 0.0000 ; qmatOffset [ 11 ][ 8 ] *= 0.4296 ; qmatOffset [ 8 ][ 11 ] *= 0.4296 ; qmatOffset [ 11 ][ 10 ] *= 0.0221 ; qmatOffset [ 10 ][ 11 ] *= 0.0221 ; qmatOffset [ 11 ][ 4 ] *= 0.0063 ; qmatOffset [ 4 ][ 11 ] *= 0.0063 ; qmatOffset [ 11 ][ 12 ] *= 0.0347 ; qmatOffset [ 12 ][ 11 ] *= 0.0347 ; qmatOffset [ 11 ][ 15 ] *= 0.4696 ; qmatOffset [ 15 ][ 11 ] *= 0.4696 ; qmatOffset [ 11 ][ 16 ] *= 0.1158 ; qmatOffset [ 16 ][ 11 ] *= 0.1158 ; qmatOffset [ 11 ][ 18 ] *= 0.0063 ; qmatOffset [ 18 ][ 11 ] *= 0.0063 ; qmatOffset [ 11 ][ 19 ] *= 0.1643 ; qmatOffset [ 19 ][ 11 ] *= 0.1643 ; qmatOffset [ 11 ][ 17 ] *= 0.0000 ; qmatOffset [ 17 ][ 11 ] *= 0.0000 ; qmatOffset [ 2 ][ 1 ] *= 0.0000 ; qmatOffset [ 1 ][ 2 ] *= 0.0000 ; qmatOffset [ 2 ][ 13 ] *= 0.0516 ; qmatOffset [ 13 ][ 2 ] *= 0.0516 ; qmatOffset [ 2 ][ 3 ] *= 0.5991 ; qmatOffset [ 3 ][ 2 ] *= 0.5991 ; qmatOffset [ 2 ][ 5 ] *= 0.0832 ; qmatOffset [ 5 ][ 2 ] *= 0.0832 ; qmatOffset [ 2 ][ 6 ] *= 0.0116 ; qmatOffset [ 6 ][ 2 ] *= 0.0116 ; qmatOffset [ 2 ][ 7 ] *= 0.0000 ; qmatOffset [ 7 ][ 2 ] *= 0.0000 ; qmatOffset [ 2 ][ 9 ] *= 0.0000 ; qmatOffset [ 9 ][ 2 ] *= 0.0000 ; qmatOffset [ 2 ][ 8 ] *= 0.0000 ; qmatOffset [ 8 ][ 2 ] *= 0.0000 ; qmatOffset [ 2 ][ 10 ] *= 0.0000 ; qmatOffset [ 10 ][ 2 ] *= 0.0000 ; qmatOffset [ 2 ][ 4 ] *= 0.0053 ; qmatOffset [ 4 ][ 2 ] *= 0.0053 ; qmatOffset [ 2 ][ 12 ] *= 0.0021 ; qmatOffset [ 12 ][ 2 ] *= 0.0021 ; qmatOffset [ 2 ][ 15 ] *= 0.0168 ; qmatOffset [ 15 ][ 2 ] *= 0.0168 ; qmatOffset [ 2 ][ 16 ] *= 0.0000 ; qmatOffset [ 16 ][ 2 ] *= 0.0000 ; qmatOffset [ 2 ][ 18 ] *= 0.0000 ; qmatOffset [ 18 ][ 2 ] *= 0.0000 ; qmatOffset [ 2 ][ 19 ] *= 0.0000 ; qmatOffset [ 19 ][ 2 ] *= 0.0000 ; qmatOffset [ 2 ][ 17 ] *= 0.0105 ; qmatOffset [ 17 ][ 2 ] *= 0.0105 ; qmatOffset [ 1 ][ 13 ] *= 0.0000 ; qmatOffset [ 13 ][ 1 ] *= 0.0000 ; qmatOffset [ 1 ][ 3 ] *= 0.0000 ; qmatOffset [ 3 ][ 1 ] *= 0.0000 ; qmatOffset [ 1 ][ 5 ] *= 0.0000 ; qmatOffset [ 5 ][ 1 ] *= 0.0000 ; qmatOffset [ 1 ][ 6 ] *= 0.3211 ; qmatOffset [ 6 ][ 1 ] *= 0.3211 ; qmatOffset [ 1 ][ 7 ] *= 0.0432 ; qmatOffset [ 7 ][ 1 ] *= 0.0432 ; qmatOffset [ 1 ][ 9 ] *= 0.0284 ; qmatOffset [ 9 ][ 1 ] *= 0.0284 ; qmatOffset [ 1 ][ 8 ] *= 0.0000 ; qmatOffset [ 8 ][ 1 ] *= 0.0000 ; qmatOffset [ 1 ][ 10 ] *= 0.0000 ; qmatOffset [ 10 ][ 1 ] *= 0.0000 ; qmatOffset [ 1 ][ 4 ] *= 0.0074 ; qmatOffset [ 4 ][ 1 ] *= 0.0074 ; qmatOffset [ 1 ][ 12 ] *= 0.0000 ; qmatOffset [ 12 ][ 1 ] *= 0.0000 ; qmatOffset [ 1 ][ 15 ] *= 0.3654 ; qmatOffset [ 15 ][ 1 ] *= 0.3654 ; qmatOffset [ 1 ][ 16 ] *= 0.1200 ; qmatOffset [ 16 ][ 1 ] *= 0.1200 ; qmatOffset [ 1 ][ 18 ] *= 0.0684 ; qmatOffset [ 18 ][ 1 ] *= 0.0684 ; qmatOffset [ 1 ][ 19 ] *= 0.5580 ; qmatOffset [ 19 ][ 1 ] *= 0.5580 ; qmatOffset [ 1 ][ 17 ] *= 0.0000 ; qmatOffset [ 17 ][ 1 ] *= 0.0000 ; qmatOffset [ 13 ][ 3 ] *= 0.2885 ; qmatOffset [ 3 ][ 13 ] *= 0.2885 ; qmatOffset [ 13 ][ 5 ] *= 0.0000 ; qmatOffset [ 5 ][ 13 ] *= 0.0000 ; qmatOffset [ 13 ][ 6 ] *= 0.5791 ; qmatOffset [ 6 ][ 13 ] *= 0.5791 ; qmatOffset [ 13 ][ 7 ] *= 0.0000 ; qmatOffset [ 7 ][ 13 ] *= 0.0000 ; qmatOffset [ 13 ][ 9 ] *= 0.0211 ; qmatOffset [ 9 ][ 13 ] *= 0.0211 ; qmatOffset [ 13 ][ 8 ] *= 0.2548 ; qmatOffset [ 8 ][ 13 ] *= 0.2548 ; qmatOffset [ 13 ][ 10 ] *= 0.0232 ; qmatOffset [ 10 ][ 13 ] *= 0.0232 ; qmatOffset [ 13 ][ 4 ] *= 0.0000 ; qmatOffset [ 4 ][ 13 ] *= 0.0000 ; qmatOffset [ 13 ][ 12 ] *= 0.0537 ; qmatOffset [ 12 ][ 13 ] *= 0.0537 ; qmatOffset [ 13 ][ 15 ] *= 0.0316 ; qmatOffset [ 15 ][ 13 ] *= 0.0316 ; qmatOffset [ 13 ][ 16 ] *= 0.0000 ; qmatOffset [ 16 ][ 13 ] *= 0.0000 ; qmatOffset [ 13 ][ 18 ] *= 0.0000 ; qmatOffset [ 18 ][ 13 ] *= 0.0000 ; qmatOffset [ 13 ][ 19 ] *= 0.0569 ; qmatOffset [ 19 ][ 13 ] *= 0.0569 ; qmatOffset [ 13 ][ 17 ] *= 0.0347 ; qmatOffset [ 17 ][ 13 ] *= 0.0347 ; qmatOffset [ 3 ][ 5 ] *= 0.0232 ; qmatOffset [ 5 ][ 3 ] *= 0.0232 ; qmatOffset [ 3 ][ 6 ] *= 0.0232 ; qmatOffset [ 6 ][ 3 ] *= 0.0232 ; qmatOffset [ 3 ][ 7 ] *= 0.0000 ; qmatOffset [ 7 ][ 3 ] *= 0.0000 ; qmatOffset [ 3 ][ 9 ] *= 0.0000 ; qmatOffset [ 9 ][ 3 ] *= 0.0000 ; qmatOffset [ 3 ][ 8 ] *= 0.2264 ; qmatOffset [ 8 ][ 3 ] *= 0.2264 ; qmatOffset [ 3 ][ 10 ] *= 0.0000 ; qmatOffset [ 10 ][ 3 ] *= 0.0000 ; qmatOffset [ 3 ][ 4 ] *= 0.0000 ; qmatOffset [ 4 ][ 3 ] *= 0.0000 ; qmatOffset [ 3 ][ 12 ] *= 0.0000 ; qmatOffset [ 12 ][ 3 ] *= 0.0000 ; qmatOffset [ 3 ][ 15 ] *= 0.0221 ; qmatOffset [ 15 ][ 3 ] *= 0.0221 ; qmatOffset [ 3 ][ 16 ] *= 0.0042 ; qmatOffset [ 16 ][ 3 ] *= 0.0042 ; qmatOffset [ 3 ][ 18 ] *= 0.0000 ; qmatOffset [ 18 ][ 3 ] *= 0.0000 ; qmatOffset [ 3 ][ 19 ] *= 0.0000 ; qmatOffset [ 19 ][ 3 ] *= 0.0000 ; qmatOffset [ 3 ][ 17 ] *= 0.0211 ; qmatOffset [ 17 ][ 3 ] *= 0.0211 ; qmatOffset [ 5 ][ 6 ] *= 0.0000 ; qmatOffset [ 6 ][ 5 ] *= 0.0000 ; qmatOffset [ 5 ][ 7 ] *= 0.0000 ; qmatOffset [ 7 ][ 5 ] *= 0.0000 ; qmatOffset [ 5 ][ 9 ] *= 0.0000 ; qmatOffset [ 9 ][ 5 ] *= 0.0000 ; qmatOffset [ 5 ][ 8 ] *= 0.0000 ; qmatOffset [ 8 ][ 5 ] *= 0.0000 ; qmatOffset [ 5 ][ 10 ] *= 0.0000 ; qmatOffset [ 10 ][ 5 ] *= 0.0000 ; qmatOffset [ 5 ][ 4 ] *= 0.0000 ; qmatOffset [ 4 ][ 5 ] *= 0.0000 ; qmatOffset [ 5 ][ 12 ] *= 0.0000 ; qmatOffset [ 12 ][ 5 ] *= 0.0000 ; qmatOffset [ 5 ][ 15 ] *= 0.1179 ; qmatOffset [ 15 ][ 5 ] *= 0.1179 ; qmatOffset [ 5 ][ 16 ] *= 0.0000 ; qmatOffset [ 16 ][ 5 ] *= 0.0000 ; qmatOffset [ 5 ][ 18 ] *= 0.0000 ; qmatOffset [ 18 ][ 5 ] *= 0.0000 ; qmatOffset [ 5 ][ 19 ] *= 0.0011 ; qmatOffset [ 19 ][ 5 ] *= 0.0011 ; qmatOffset [ 5 ][ 17 ] *= 0.0053 ; qmatOffset [ 17 ][ 5 ] *= 0.0053 ; qmatOffset [ 6 ][ 7 ] *= 0.0000 ; qmatOffset [ 7 ][ 6 ] *= 0.0000 ; qmatOffset [ 6 ][ 9 ] *= 0.0274 ; qmatOffset [ 9 ][ 6 ] *= 0.0274 ; qmatOffset [ 6 ][ 8 ] *= 0.0000 ; qmatOffset [ 8 ][ 6 ] *= 0.0000 ; qmatOffset [ 6 ][ 10 ] *= 0.0000 ; qmatOffset [ 10 ][ 6 ] *= 0.0000 ; qmatOffset [ 6 ][ 4 ] *= 0.0000 ; qmatOffset [ 4 ][ 6 ] *= 0.0000 ; qmatOffset [ 6 ][ 12 ] *= 0.0558 ; qmatOffset [ 12 ][ 6 ] *= 0.0558 ; qmatOffset [ 6 ][ 15 ] *= 0.0211 ; qmatOffset [ 15 ][ 6 ] *= 0.0211 ; qmatOffset [ 6 ][ 16 ] *= 0.0011 ; qmatOffset [ 16 ][ 6 ] *= 0.0011 ; qmatOffset [ 6 ][ 18 ] *= 0.0000 ; qmatOffset [ 18 ][ 6 ] *= 0.0000 ; qmatOffset [ 6 ][ 19 ] *= 1.6057 ; qmatOffset [ 19 ][ 6 ] *= 1.6057 ; qmatOffset [ 6 ][ 17 ] *= 0.0000 ; qmatOffset [ 17 ][ 6 ] *= 0.0000 ; qmatOffset [ 7 ][ 9 ] *= 0.2443 ; qmatOffset [ 9 ][ 7 ] *= 0.2443 ; qmatOffset [ 7 ][ 8 ] *= 0.0063 ; qmatOffset [ 8 ][ 7 ] *= 0.0063 ; qmatOffset [ 7 ][ 10 ] *= 0.3980 ; qmatOffset [ 10 ][ 7 ] *= 0.3980 ; qmatOffset [ 7 ][ 4 ] *= 0.0600 ; qmatOffset [ 4 ][ 7 ] *= 0.0600 ; qmatOffset [ 7 ][ 12 ] *= 0.0053 ; qmatOffset [ 12 ][ 7 ] *= 0.0053 ; qmatOffset [ 7 ][ 15 ] *= 0.0000 ; qmatOffset [ 15 ][ 7 ] *= 0.0000 ; qmatOffset [ 7 ][ 16 ] *= 0.3790 ; qmatOffset [ 16 ][ 7 ] *= 0.3790 ; qmatOffset [ 7 ][ 18 ] *= 0.0000 ; qmatOffset [ 18 ][ 7 ] *= 0.0000 ; qmatOffset [ 7 ][ 19 ] *= 0.0168 ; qmatOffset [ 19 ][ 7 ] *= 0.0168 ; qmatOffset [ 7 ][ 17 ] *= 2.3375 ; qmatOffset [ 17 ][ 7 ] *= 2.3375 ; qmatOffset [ 9 ][ 8 ] *= 0.0042 ; qmatOffset [ 8 ][ 9 ] *= 0.0042 ; qmatOffset [ 9 ][ 10 ] *= 0.6412 ; qmatOffset [ 10 ][ 9 ] *= 0.6412 ; qmatOffset [ 9 ][ 4 ] *= 0.2590 ; qmatOffset [ 4 ][ 9 ] *= 0.2590 ; qmatOffset [ 9 ][ 12 ] *= 0.0453 ; qmatOffset [ 12 ][ 9 ] *= 0.0453 ; qmatOffset [ 9 ][ 15 ] *= 0.0779 ; qmatOffset [ 15 ][ 9 ] *= 0.0779 ; qmatOffset [ 9 ][ 16 ] *= 0.0358 ; qmatOffset [ 16 ][ 9 ] *= 0.0358 ; qmatOffset [ 9 ][ 18 ] *= 0.0126 ; qmatOffset [ 18 ][ 9 ] *= 0.0126 ; qmatOffset [ 9 ][ 19 ] *= 0.0263 ; qmatOffset [ 19 ][ 9 ] *= 0.0263 ; qmatOffset [ 9 ][ 17 ] *= 0.1053 ; qmatOffset [ 17 ][ 9 ] *= 0.1053 ; qmatOffset [ 8 ][ 10 ] *= 0.0621 ; qmatOffset [ 10 ][ 8 ] *= 0.0621 ; qmatOffset [ 8 ][ 4 ] *= 0.0000 ; qmatOffset [ 4 ][ 8 ] *= 0.0000 ; qmatOffset [ 8 ][ 12 ] *= 0.0190 ; qmatOffset [ 12 ][ 8 ] *= 0.0190 ; qmatOffset [ 8 ][ 15 ] *= 0.0684 ; qmatOffset [ 15 ][ 8 ] *= 0.0684 ; qmatOffset [ 8 ][ 16 ] *= 0.0526 ; qmatOffset [ 16 ][ 8 ] *= 0.0526 ; qmatOffset [ 8 ][ 18 ] *= 0.0000 ; qmatOffset [ 18 ][ 8 ] *= 0.0000 ; qmatOffset [ 8 ][ 19 ] *= 0.0705 ; qmatOffset [ 19 ][ 8 ] *= 0.0705 ; qmatOffset [ 8 ][ 17 ] *= 0.0000 ; qmatOffset [ 17 ][ 8 ] *= 0.0000 ; qmatOffset [ 10 ][ 4 ] *= 0.0116 ; qmatOffset [ 4 ][ 10 ] *= 0.0116 ; qmatOffset [ 10 ][ 12 ] *= 0.0000 ; qmatOffset [ 12 ][ 10 ] *= 0.0000 ; qmatOffset [ 10 ][ 15 ] *= 0.0495 ; qmatOffset [ 15 ][ 10 ] *= 0.0495 ; qmatOffset [ 10 ][ 16 ] *= 0.7276 ; qmatOffset [ 16 ][ 10 ] *= 0.7276 ; qmatOffset [ 10 ][ 18 ] *= 0.0137 ; qmatOffset [ 18 ][ 10 ] *= 0.0137 ; qmatOffset [ 10 ][ 19 ] *= 0.0000 ; qmatOffset [ 19 ][ 10 ] *= 0.0000 ; qmatOffset [ 10 ][ 17 ] *= 0.8760 ; qmatOffset [ 17 ][ 10 ] *= 0.8760 ; qmatOffset [ 4 ][ 12 ] *= 0.0179 ; qmatOffset [ 12 ][ 4 ] *= 0.0179 ; qmatOffset [ 4 ][ 15 ] *= 0.0948 ; qmatOffset [ 15 ][ 4 ] *= 0.0948 ; qmatOffset [ 4 ][ 16 ] *= 0.0084 ; qmatOffset [ 16 ][ 4 ] *= 0.0084 ; qmatOffset [ 4 ][ 18 ] *= 0.0000 ; qmatOffset [ 18 ][ 4 ] *= 0.0000 ; qmatOffset [ 4 ][ 19 ] *= 0.7181 ; qmatOffset [ 19 ][ 4 ] *= 0.7181 ; qmatOffset [ 4 ][ 17 ] *= 0.0063 ; qmatOffset [ 17 ][ 4 ] *= 0.0063 ; qmatOffset [ 12 ][ 15 ] *= 0.2127 ; qmatOffset [ 15 ][ 12 ] *= 0.2127 ; qmatOffset [ 12 ][ 16 ] *= 0.0821 ; qmatOffset [ 16 ][ 12 ] *= 0.0821 ; qmatOffset [ 12 ][ 18 ] *= 0.0074 ; qmatOffset [ 18 ][ 12 ] *= 0.0074 ; qmatOffset [ 12 ][ 19 ] *= 0.0084 ; qmatOffset [ 19 ][ 12 ] *= 0.0084 ; qmatOffset [ 12 ][ 17 ] *= 0.0000 ; qmatOffset [ 17 ][ 12 ] *= 0.0000 ; qmatOffset [ 15 ][ 16 ] *= 0.6465 ; qmatOffset [ 16 ][ 15 ] *= 0.6465 ; qmatOffset [ 15 ][ 18 ] *= 0.0179 ; qmatOffset [ 18 ][ 15 ] *= 0.0179 ; qmatOffset [ 15 ][ 19 ] *= 0.1127 ; qmatOffset [ 19 ][ 15 ] *= 0.1127 ; qmatOffset [ 15 ][ 17 ] *= 0.0000 ; qmatOffset [ 17 ][ 15 ] *= 0.0000 ; qmatOffset [ 16 ][ 18 ] *= 0.0000 ; qmatOffset [ 18 ][ 16 ] *= 0.0000 ; qmatOffset [ 16 ][ 19 ] *= 0.0000 ; qmatOffset [ 19 ][ 16 ] *= 0.0000 ; qmatOffset [ 16 ][ 17 ] *= 0.2495 ; qmatOffset [ 17 ][ 16 ] *= 0.2495 ; qmatOffset [ 18 ][ 19 ] *= 0.0147 ; qmatOffset [ 19 ][ 18 ] *= 0.0147 ; qmatOffset [ 18 ][ 17 ] *= 0.0000 ; qmatOffset [ 17 ][ 18 ] *= 0.0000 ; qmatOffset [ 19 ][ 17 ] *= 0.0000 ; qmatOffset [ 17 ][ 19 ] *= 0.0000 ; } void Model::MultiplyByMtRevAAMatrix(){ int modNum=0; MODEL_FLOAT **qmatOffset = qmat[modNum]; qmatOffset [ 0 ][ 14 ] *= 23.18 ; qmatOffset [ 14 ][ 0 ] *= 23.18 ; qmatOffset [ 0 ][ 11 ] *= 26.95 ; qmatOffset [ 11 ][ 0 ] *= 26.95 ; qmatOffset [ 0 ][ 2 ] *= 17.67 ; qmatOffset [ 2 ][ 0 ] *= 17.67 ; qmatOffset [ 0 ][ 1 ] *= 59.93 ; qmatOffset [ 1 ][ 0 ] *= 59.93 ; qmatOffset [ 0 ][ 13 ] *= 1.9 ; qmatOffset [ 13 ][ 0 ] *= 1.9 ; qmatOffset [ 0 ][ 3 ] *= 9.77 ; qmatOffset [ 3 ][ 0 ] *= 9.77 ; qmatOffset [ 0 ][ 5 ] *= 120.71 ; qmatOffset [ 5 ][ 0 ] *= 120.71 ; qmatOffset [ 0 ][ 6 ] *= 13.9 ; qmatOffset [ 6 ][ 0 ] *= 13.9 ; qmatOffset [ 0 ][ 7 ] *= 96.49 ; qmatOffset [ 7 ][ 0 ] *= 96.49 ; qmatOffset [ 0 ][ 9 ] *= 25.46 ; qmatOffset [ 9 ][ 0 ] *= 25.46 ; qmatOffset [ 0 ][ 8 ] *= 8.36 ; qmatOffset [ 8 ][ 0 ] *= 8.36 ; qmatOffset [ 0 ][ 10 ] *= 141.88 ; qmatOffset [ 10 ][ 0 ] *= 141.88 ; qmatOffset [ 0 ][ 4 ] *= 6.37 ; qmatOffset [ 4 ][ 0 ] *= 6.37 ; qmatOffset [ 0 ][ 12 ] *= 54.31 ; qmatOffset [ 12 ][ 0 ] *= 54.31 ; qmatOffset [ 0 ][ 15 ] *= 387.86 ; qmatOffset [ 15 ][ 0 ] *= 387.86 ; qmatOffset [ 0 ][ 16 ] *= 480.72 ; qmatOffset [ 16 ][ 0 ] *= 480.72 ; qmatOffset [ 0 ][ 18 ] *= 1.9 ; qmatOffset [ 18 ][ 0 ] *= 1.9 ; qmatOffset [ 0 ][ 19 ] *= 6.48 ; qmatOffset [ 19 ][ 0 ] *= 6.48 ; qmatOffset [ 0 ][ 17 ] *= 195.06 ; qmatOffset [ 17 ][ 0 ] *= 195.06 ; qmatOffset [ 14 ][ 11 ] *= 13.24 ; qmatOffset [ 11 ][ 14 ] *= 13.24 ; qmatOffset [ 14 ][ 2 ] *= 1.9 ; qmatOffset [ 2 ][ 14 ] *= 1.9 ; qmatOffset [ 14 ][ 1 ] *= 103.33 ; qmatOffset [ 1 ][ 14 ] *= 103.33 ; qmatOffset [ 14 ][ 13 ] *= 220.99 ; qmatOffset [ 13 ][ 14 ] *= 220.99 ; qmatOffset [ 14 ][ 3 ] *= 1.9 ; qmatOffset [ 3 ][ 14 ] *= 1.9 ; qmatOffset [ 14 ][ 5 ] *= 23.03 ; qmatOffset [ 5 ][ 14 ] *= 23.03 ; qmatOffset [ 14 ][ 6 ] *= 165.23 ; qmatOffset [ 6 ][ 14 ] *= 165.23 ; qmatOffset [ 14 ][ 7 ] *= 1.9 ; qmatOffset [ 7 ][ 14 ] *= 1.9 ; qmatOffset [ 14 ][ 9 ] *= 15.58 ; qmatOffset [ 9 ][ 14 ] *= 15.58 ; qmatOffset [ 14 ][ 8 ] *= 141.4 ; qmatOffset [ 8 ][ 14 ] *= 141.4 ; qmatOffset [ 14 ][ 10 ] *= 1.9 ; qmatOffset [ 10 ][ 14 ] *= 1.9 ; qmatOffset [ 14 ][ 4 ] *= 4.69 ; qmatOffset [ 4 ][ 14 ] *= 4.69 ; qmatOffset [ 14 ][ 12 ] *= 23.64 ; qmatOffset [ 12 ][ 14 ] *= 23.64 ; qmatOffset [ 14 ][ 15 ] *= 6.04 ; qmatOffset [ 15 ][ 14 ] *= 6.04 ; qmatOffset [ 14 ][ 16 ] *= 2.08 ; qmatOffset [ 16 ][ 14 ] *= 2.08 ; qmatOffset [ 14 ][ 18 ] *= 21.95 ; qmatOffset [ 18 ][ 14 ] *= 21.95 ; qmatOffset [ 14 ][ 19 ] *= 1.9 ; qmatOffset [ 19 ][ 14 ] *= 1.9 ; qmatOffset [ 14 ][ 17 ] *= 7.64 ; qmatOffset [ 17 ][ 14 ] *= 7.64 ; qmatOffset [ 11 ][ 2 ] *= 794.38 ; qmatOffset [ 2 ][ 11 ] *= 794.38 ; qmatOffset [ 11 ][ 1 ] *= 58.94 ; qmatOffset [ 1 ][ 11 ] *= 58.94 ; qmatOffset [ 11 ][ 13 ] *= 173.56 ; qmatOffset [ 13 ][ 11 ] *= 173.56 ; qmatOffset [ 11 ][ 3 ] *= 63.05 ; qmatOffset [ 3 ][ 11 ] *= 63.05 ; qmatOffset [ 11 ][ 5 ] *= 53.3 ; qmatOffset [ 5 ][ 11 ] *= 53.3 ; qmatOffset [ 11 ][ 6 ] *= 496.13 ; qmatOffset [ 6 ][ 11 ] *= 496.13 ; qmatOffset [ 11 ][ 7 ] *= 27.1 ; qmatOffset [ 7 ][ 11 ] *= 27.1 ; qmatOffset [ 11 ][ 9 ] *= 15.16 ; qmatOffset [ 9 ][ 11 ] *= 15.16 ; qmatOffset [ 11 ][ 8 ] *= 608.7 ; qmatOffset [ 8 ][ 11 ] *= 608.7 ; qmatOffset [ 11 ][ 10 ] *= 65.41 ; qmatOffset [ 10 ][ 11 ] *= 65.41 ; qmatOffset [ 11 ][ 4 ] *= 15.2 ; qmatOffset [ 4 ][ 11 ] *= 15.2 ; qmatOffset [ 11 ][ 12 ] *= 73.31 ; qmatOffset [ 12 ][ 11 ] *= 73.31 ; qmatOffset [ 11 ][ 15 ] *= 494.39 ; qmatOffset [ 15 ][ 11 ] *= 494.39 ; qmatOffset [ 11 ][ 16 ] *= 238.46 ; qmatOffset [ 16 ][ 11 ] *= 238.46 ; qmatOffset [ 11 ][ 18 ] *= 10.68 ; qmatOffset [ 18 ][ 11 ] *= 10.68 ; qmatOffset [ 11 ][ 19 ] *= 191.36 ; qmatOffset [ 19 ][ 11 ] *= 191.36 ; qmatOffset [ 11 ][ 17 ] *= 1.9 ; qmatOffset [ 17 ][ 11 ] *= 1.9 ; qmatOffset [ 2 ][ 1 ] *= 1.9 ; qmatOffset [ 1 ][ 2 ] *= 1.9 ; qmatOffset [ 2 ][ 13 ] *= 55.28 ; qmatOffset [ 13 ][ 2 ] *= 55.28 ; qmatOffset [ 2 ][ 3 ] *= 583.55 ; qmatOffset [ 3 ][ 2 ] *= 583.55 ; qmatOffset [ 2 ][ 5 ] *= 56.77 ; qmatOffset [ 5 ][ 2 ] *= 56.77 ; qmatOffset [ 2 ][ 6 ] *= 113.99 ; qmatOffset [ 6 ][ 2 ] *= 113.99 ; qmatOffset [ 2 ][ 7 ] *= 4.34 ; qmatOffset [ 7 ][ 2 ] *= 4.34 ; qmatOffset [ 2 ][ 9 ] *= 1.9 ; qmatOffset [ 9 ][ 2 ] *= 1.9 ; qmatOffset [ 2 ][ 8 ] *= 2.31 ; qmatOffset [ 8 ][ 2 ] *= 2.31 ; qmatOffset [ 2 ][ 10 ] *= 1.9 ; qmatOffset [ 10 ][ 2 ] *= 1.9 ; qmatOffset [ 2 ][ 4 ] *= 4.98 ; qmatOffset [ 4 ][ 2 ] *= 4.98 ; qmatOffset [ 2 ][ 12 ] *= 13.43 ; qmatOffset [ 12 ][ 2 ] *= 13.43 ; qmatOffset [ 2 ][ 15 ] *= 69.02 ; qmatOffset [ 15 ][ 2 ] *= 69.02 ; qmatOffset [ 2 ][ 16 ] *= 28.01 ; qmatOffset [ 16 ][ 2 ] *= 28.01 ; qmatOffset [ 2 ][ 18 ] *= 19.86 ; qmatOffset [ 18 ][ 2 ] *= 19.86 ; qmatOffset [ 2 ][ 19 ] *= 21.21 ; qmatOffset [ 19 ][ 2 ] *= 21.21 ; qmatOffset [ 2 ][ 17 ] *= 1.9 ; qmatOffset [ 17 ][ 2 ] *= 1.9 ; qmatOffset [ 1 ][ 13 ] *= 75.24 ; qmatOffset [ 13 ][ 1 ] *= 75.24 ; qmatOffset [ 1 ][ 3 ] *= 1.9 ; qmatOffset [ 3 ][ 1 ] *= 1.9 ; qmatOffset [ 1 ][ 5 ] *= 30.71 ; qmatOffset [ 5 ][ 1 ] *= 30.71 ; qmatOffset [ 1 ][ 6 ] *= 141.49 ; qmatOffset [ 6 ][ 1 ] *= 141.49 ; qmatOffset [ 1 ][ 7 ] *= 62.73 ; qmatOffset [ 7 ][ 1 ] *= 62.73 ; qmatOffset [ 1 ][ 9 ] *= 25.65 ; qmatOffset [ 9 ][ 1 ] *= 25.65 ; qmatOffset [ 1 ][ 8 ] *= 1.9 ; qmatOffset [ 8 ][ 1 ] *= 1.9 ; qmatOffset [ 1 ][ 10 ] *= 6.18 ; qmatOffset [ 10 ][ 1 ] *= 6.18 ; qmatOffset [ 1 ][ 4 ] *= 70.8 ; qmatOffset [ 4 ][ 1 ] *= 70.8 ; qmatOffset [ 1 ][ 12 ] *= 31.26 ; qmatOffset [ 12 ][ 1 ] *= 31.26 ; qmatOffset [ 1 ][ 15 ] *= 277.05 ; qmatOffset [ 15 ][ 1 ] *= 277.05 ; qmatOffset [ 1 ][ 16 ] *= 179.97 ; qmatOffset [ 16 ][ 1 ] *= 179.97 ; qmatOffset [ 1 ][ 18 ] *= 33.6 ; qmatOffset [ 18 ][ 1 ] *= 33.6 ; qmatOffset [ 1 ][ 19 ] *= 254.77 ; qmatOffset [ 19 ][ 1 ] *= 254.77 ; qmatOffset [ 1 ][ 17 ] *= 1.9 ; qmatOffset [ 17 ][ 1 ] *= 1.9 ; qmatOffset [ 13 ][ 3 ] *= 313.56 ; qmatOffset [ 3 ][ 13 ] *= 313.56 ; qmatOffset [ 13 ][ 5 ] *= 6.75 ; qmatOffset [ 5 ][ 13 ] *= 6.75 ; qmatOffset [ 13 ][ 6 ] *= 582.4 ; qmatOffset [ 6 ][ 13 ] *= 582.4 ; qmatOffset [ 13 ][ 7 ] *= 8.34 ; qmatOffset [ 7 ][ 13 ] *= 8.34 ; qmatOffset [ 13 ][ 9 ] *= 39.7 ; qmatOffset [ 9 ][ 13 ] *= 39.7 ; qmatOffset [ 13 ][ 8 ] *= 465.58 ; qmatOffset [ 8 ][ 13 ] *= 465.58 ; qmatOffset [ 13 ][ 10 ] *= 47.37 ; qmatOffset [ 10 ][ 13 ] *= 47.37 ; qmatOffset [ 13 ][ 4 ] *= 19.11 ; qmatOffset [ 4 ][ 13 ] *= 19.11 ; qmatOffset [ 13 ][ 12 ] *= 137.29 ; qmatOffset [ 12 ][ 13 ] *= 137.29 ; qmatOffset [ 13 ][ 15 ] *= 54.11 ; qmatOffset [ 15 ][ 13 ] *= 54.11 ; qmatOffset [ 13 ][ 16 ] *= 94.93 ; qmatOffset [ 16 ][ 13 ] *= 94.93 ; qmatOffset [ 13 ][ 18 ] *= 1.9 ; qmatOffset [ 18 ][ 13 ] *= 1.9 ; qmatOffset [ 13 ][ 19 ] *= 38.82 ; qmatOffset [ 19 ][ 13 ] *= 38.82 ; qmatOffset [ 13 ][ 17 ] *= 19 ; qmatOffset [ 17 ][ 13 ] *= 19 ; qmatOffset [ 3 ][ 5 ] *= 28.28 ; qmatOffset [ 5 ][ 3 ] *= 28.28 ; qmatOffset [ 3 ][ 6 ] *= 49.12 ; qmatOffset [ 6 ][ 3 ] *= 49.12 ; qmatOffset [ 3 ][ 7 ] *= 3.31 ; qmatOffset [ 7 ][ 3 ] *= 3.31 ; qmatOffset [ 3 ][ 9 ] *= 1.9 ; qmatOffset [ 9 ][ 3 ] *= 1.9 ; qmatOffset [ 3 ][ 8 ] *= 313.86 ; qmatOffset [ 8 ][ 3 ] *= 313.86 ; qmatOffset [ 3 ][ 10 ] *= 1.9 ; qmatOffset [ 10 ][ 3 ] *= 1.9 ; qmatOffset [ 3 ][ 4 ] *= 2.67 ; qmatOffset [ 4 ][ 3 ] *= 2.67 ; qmatOffset [ 3 ][ 12 ] *= 12.83 ; qmatOffset [ 12 ][ 3 ] *= 12.83 ; qmatOffset [ 3 ][ 15 ] *= 54.71 ; qmatOffset [ 15 ][ 3 ] *= 54.71 ; qmatOffset [ 3 ][ 16 ] *= 14.82 ; qmatOffset [ 16 ][ 3 ] *= 14.82 ; qmatOffset [ 3 ][ 18 ] *= 1.9 ; qmatOffset [ 18 ][ 3 ] *= 1.9 ; qmatOffset [ 3 ][ 19 ] *= 13.12 ; qmatOffset [ 19 ][ 3 ] *= 13.12 ; qmatOffset [ 3 ][ 17 ] *= 21.14 ; qmatOffset [ 17 ][ 3 ] *= 21.14 ; qmatOffset [ 5 ][ 6 ] *= 1.9 ; qmatOffset [ 6 ][ 5 ] *= 1.9 ; qmatOffset [ 5 ][ 7 ] *= 5.98 ; qmatOffset [ 7 ][ 5 ] *= 5.98 ; qmatOffset [ 5 ][ 9 ] *= 2.41 ; qmatOffset [ 9 ][ 5 ] *= 2.41 ; qmatOffset [ 5 ][ 8 ] *= 22.73 ; qmatOffset [ 8 ][ 5 ] *= 22.73 ; qmatOffset [ 5 ][ 10 ] *= 1.9 ; qmatOffset [ 10 ][ 5 ] *= 1.9 ; qmatOffset [ 5 ][ 4 ] *= 1.9 ; qmatOffset [ 4 ][ 5 ] *= 1.9 ; qmatOffset [ 5 ][ 12 ] *= 1.9 ; qmatOffset [ 12 ][ 5 ] *= 1.9 ; qmatOffset [ 5 ][ 15 ] *= 125.93 ; qmatOffset [ 15 ][ 5 ] *= 125.93 ; qmatOffset [ 5 ][ 16 ] *= 11.17 ; qmatOffset [ 16 ][ 5 ] *= 11.17 ; qmatOffset [ 5 ][ 18 ] *= 10.92 ; qmatOffset [ 18 ][ 5 ] *= 10.92 ; qmatOffset [ 5 ][ 19 ] *= 3.21 ; qmatOffset [ 19 ][ 5 ] *= 3.21 ; qmatOffset [ 5 ][ 17 ] *= 2.53 ; qmatOffset [ 17 ][ 5 ] *= 2.53 ; qmatOffset [ 6 ][ 7 ] *= 12.26 ; qmatOffset [ 7 ][ 6 ] *= 12.26 ; qmatOffset [ 6 ][ 9 ] *= 11.49 ; qmatOffset [ 9 ][ 6 ] *= 11.49 ; qmatOffset [ 6 ][ 8 ] *= 127.67 ; qmatOffset [ 8 ][ 6 ] *= 127.67 ; qmatOffset [ 6 ][ 10 ] *= 11.97 ; qmatOffset [ 10 ][ 6 ] *= 11.97 ; qmatOffset [ 6 ][ 4 ] *= 48.16 ; qmatOffset [ 4 ][ 6 ] *= 48.16 ; qmatOffset [ 6 ][ 12 ] *= 60.97 ; qmatOffset [ 12 ][ 6 ] *= 60.97 ; qmatOffset [ 6 ][ 15 ] *= 77.46 ; qmatOffset [ 15 ][ 6 ] *= 77.46 ; qmatOffset [ 6 ][ 16 ] *= 44.78 ; qmatOffset [ 16 ][ 6 ] *= 44.78 ; qmatOffset [ 6 ][ 18 ] *= 7.08 ; qmatOffset [ 18 ][ 6 ] *= 7.08 ; qmatOffset [ 6 ][ 19 ] *= 670.14 ; qmatOffset [ 19 ][ 6 ] *= 670.14 ; qmatOffset [ 6 ][ 17 ] *= 1.9 ; qmatOffset [ 17 ][ 6 ] *= 1.9 ; qmatOffset [ 7 ][ 9 ] *= 329.09 ; qmatOffset [ 9 ][ 7 ] *= 329.09 ; qmatOffset [ 7 ][ 8 ] *= 19.57 ; qmatOffset [ 8 ][ 7 ] *= 19.57 ; qmatOffset [ 7 ][ 10 ] *= 517.98 ; qmatOffset [ 10 ][ 7 ] *= 517.98 ; qmatOffset [ 7 ][ 4 ] *= 84.67 ; qmatOffset [ 4 ][ 7 ] *= 84.67 ; qmatOffset [ 7 ][ 12 ] *= 20.63 ; qmatOffset [ 12 ][ 7 ] *= 20.63 ; qmatOffset [ 7 ][ 15 ] *= 47.7 ; qmatOffset [ 15 ][ 7 ] *= 47.7 ; qmatOffset [ 7 ][ 16 ] *= 368.43 ; qmatOffset [ 16 ][ 7 ] *= 368.43 ; qmatOffset [ 7 ][ 18 ] *= 1.9 ; qmatOffset [ 18 ][ 7 ] *= 1.9 ; qmatOffset [ 7 ][ 19 ] *= 25.01 ; qmatOffset [ 19 ][ 7 ] *= 25.01 ; qmatOffset [ 7 ][ 17 ] *= 1222.94 ; qmatOffset [ 17 ][ 7 ] *= 1222.94 ; qmatOffset [ 9 ][ 8 ] *= 14.88 ; qmatOffset [ 8 ][ 9 ] *= 14.88 ; qmatOffset [ 9 ][ 10 ] *= 537.53 ; qmatOffset [ 10 ][ 9 ] *= 537.53 ; qmatOffset [ 9 ][ 4 ] *= 216.06 ; qmatOffset [ 4 ][ 9 ] *= 216.06 ; qmatOffset [ 9 ][ 12 ] *= 40.1 ; qmatOffset [ 12 ][ 9 ] *= 40.1 ; qmatOffset [ 9 ][ 15 ] *= 73.61 ; qmatOffset [ 15 ][ 9 ] *= 73.61 ; qmatOffset [ 9 ][ 16 ] *= 126.4 ; qmatOffset [ 16 ][ 9 ] *= 126.4 ; qmatOffset [ 9 ][ 18 ] *= 32.44 ; qmatOffset [ 18 ][ 9 ] *= 32.44 ; qmatOffset [ 9 ][ 19 ] *= 44.15 ; qmatOffset [ 19 ][ 9 ] *= 44.15 ; qmatOffset [ 9 ][ 17 ] *= 91.67 ; qmatOffset [ 17 ][ 9 ] *= 91.67 ; qmatOffset [ 8 ][ 10 ] *= 91.37 ; qmatOffset [ 10 ][ 8 ] *= 91.37 ; qmatOffset [ 8 ][ 4 ] *= 6.44 ; qmatOffset [ 4 ][ 8 ] *= 6.44 ; qmatOffset [ 8 ][ 12 ] *= 50.1 ; qmatOffset [ 12 ][ 8 ] *= 50.1 ; qmatOffset [ 8 ][ 15 ] *= 105.79 ; qmatOffset [ 15 ][ 8 ] *= 105.79 ; qmatOffset [ 8 ][ 16 ] *= 136.33 ; qmatOffset [ 16 ][ 8 ] *= 136.33 ; qmatOffset [ 8 ][ 18 ] *= 24 ; qmatOffset [ 18 ][ 8 ] *= 24 ; qmatOffset [ 8 ][ 19 ] *= 51.17 ; qmatOffset [ 19 ][ 8 ] *= 51.17 ; qmatOffset [ 8 ][ 17 ] *= 1.9 ; qmatOffset [ 17 ][ 8 ] *= 1.9 ; qmatOffset [ 10 ][ 4 ] *= 90.82 ; qmatOffset [ 4 ][ 10 ] *= 90.82 ; qmatOffset [ 10 ][ 12 ] *= 18.84 ; qmatOffset [ 12 ][ 10 ] *= 18.84 ; qmatOffset [ 10 ][ 15 ] *= 111.16 ; qmatOffset [ 15 ][ 10 ] *= 111.16 ; qmatOffset [ 10 ][ 16 ] *= 528.17 ; qmatOffset [ 16 ][ 10 ] *= 528.17 ; qmatOffset [ 10 ][ 18 ] *= 21.71 ; qmatOffset [ 18 ][ 10 ] *= 21.71 ; qmatOffset [ 10 ][ 19 ] *= 39.96 ; qmatOffset [ 19 ][ 10 ] *= 39.96 ; qmatOffset [ 10 ][ 17 ] *= 387.54 ; qmatOffset [ 17 ][ 10 ] *= 387.54 ; qmatOffset [ 4 ][ 12 ] *= 17.31 ; qmatOffset [ 12 ][ 4 ] *= 17.31 ; qmatOffset [ 4 ][ 15 ] *= 64.29 ; qmatOffset [ 15 ][ 4 ] *= 64.29 ; qmatOffset [ 4 ][ 16 ] *= 33.85 ; qmatOffset [ 16 ][ 4 ] *= 33.85 ; qmatOffset [ 4 ][ 18 ] *= 7.84 ; qmatOffset [ 18 ][ 4 ] *= 7.84 ; qmatOffset [ 4 ][ 19 ] *= 465.58 ; qmatOffset [ 19 ][ 4 ] *= 465.58 ; qmatOffset [ 4 ][ 17 ] *= 6.35 ; qmatOffset [ 17 ][ 4 ] *= 6.35 ; qmatOffset [ 12 ][ 15 ] *= 169.9 ; qmatOffset [ 15 ][ 12 ] *= 169.9 ; qmatOffset [ 12 ][ 16 ] *= 128.22 ; qmatOffset [ 16 ][ 12 ] *= 128.22 ; qmatOffset [ 12 ][ 18 ] *= 4.21 ; qmatOffset [ 18 ][ 12 ] *= 4.21 ; qmatOffset [ 12 ][ 19 ] *= 16.21 ; qmatOffset [ 19 ][ 12 ] *= 16.21 ; qmatOffset [ 12 ][ 17 ] *= 8.23 ; qmatOffset [ 17 ][ 12 ] *= 8.23 ; qmatOffset [ 15 ][ 16 ] *= 597.21 ; qmatOffset [ 16 ][ 15 ] *= 597.21 ; qmatOffset [ 15 ][ 18 ] *= 38.58 ; qmatOffset [ 18 ][ 15 ] *= 38.58 ; qmatOffset [ 15 ][ 19 ] *= 64.92 ; qmatOffset [ 19 ][ 15 ] *= 64.92 ; qmatOffset [ 15 ][ 17 ] *= 1.9 ; qmatOffset [ 17 ][ 15 ] *= 1.9 ; qmatOffset [ 16 ][ 18 ] *= 9.99 ; qmatOffset [ 18 ][ 16 ] *= 9.99 ; qmatOffset [ 16 ][ 19 ] *= 38.73 ; qmatOffset [ 19 ][ 16 ] *= 38.73 ; qmatOffset [ 16 ][ 17 ] *= 204.54 ; qmatOffset [ 17 ][ 16 ] *= 204.54 ; qmatOffset [ 18 ][ 19 ] *= 26.25 ; qmatOffset [ 19 ][ 18 ] *= 26.25 ; qmatOffset [ 18 ][ 17 ] *= 5.37 ; qmatOffset [ 17 ][ 18 ] *= 5.37 ; qmatOffset [ 19 ][ 17 ] *= 1.9 ; qmatOffset [ 17 ][ 19 ] *= 1.9 ; } void Model::MultiplyByDayhoffAAMatrix(){ int modNum=0; MODEL_FLOAT **qmatOffset = qmat[modNum]; qmatOffset[0][1] *= 0.036; qmatOffset[0][2] *= 0.12; qmatOffset[0][3] *= 0.198; qmatOffset[0][4] *= 0.018; qmatOffset[0][5] *= 0.24; qmatOffset[0][6] *= 0.023; qmatOffset[0][7] *= 0.065; qmatOffset[0][8] *= 0.026; qmatOffset[0][9] *= 0.041; qmatOffset[0][10] *= 0.072; qmatOffset[0][11] *= 0.098; qmatOffset[0][12] *= 0.25; qmatOffset[0][13] *= 0.089; qmatOffset[0][14] *= 0.027; qmatOffset[0][15] *= 0.409; qmatOffset[0][16] *= 0.371; qmatOffset[0][17] *= 0.208; qmatOffset[0][19] *= 0.024; qmatOffset[1][0] *= 0.036; qmatOffset[1][5] *= 0.011; qmatOffset[1][6] *= 0.028; qmatOffset[1][7] *= 0.044; qmatOffset[1][12] *= 0.019; qmatOffset[1][14] *= 0.023; qmatOffset[1][15] *= 0.161; qmatOffset[1][16] *= 0.016; qmatOffset[1][17] *= 0.049; qmatOffset[1][19] *= 0.096; qmatOffset[2][0] *= 0.12; qmatOffset[2][3] *= 1.153; qmatOffset[2][5] *= 0.125; qmatOffset[2][6] *= 0.086; qmatOffset[2][7] *= 0.024; qmatOffset[2][8] *= 0.071; qmatOffset[2][11] *= 0.905; qmatOffset[2][12] *= 0.013; qmatOffset[2][13] *= 0.134; qmatOffset[2][15] *= 0.095; qmatOffset[2][16] *= 0.066; qmatOffset[2][17] *= 0.018; qmatOffset[3][0] *= 0.198; qmatOffset[3][2] *= 1.153; qmatOffset[3][5] *= 0.081; qmatOffset[3][6] *= 0.043; qmatOffset[3][7] *= 0.061; qmatOffset[3][8] *= 0.083; qmatOffset[3][9] *= 0.011; qmatOffset[3][10] *= 0.03; qmatOffset[3][11] *= 0.148; qmatOffset[3][12] *= 0.051; qmatOffset[3][13] *= 0.716; qmatOffset[3][14] *= 0.001; qmatOffset[3][15] *= 0.079; qmatOffset[3][16] *= 0.034; qmatOffset[3][17] *= 0.037; qmatOffset[3][19] *= 0.022; qmatOffset[4][0] *= 0.018; qmatOffset[4][5] *= 0.015; qmatOffset[4][6] *= 0.048; qmatOffset[4][7] *= 0.196; qmatOffset[4][9] *= 0.157; qmatOffset[4][10] *= 0.092; qmatOffset[4][11] *= 0.014; qmatOffset[4][12] *= 0.011; qmatOffset[4][14] *= 0.014; qmatOffset[4][15] *= 0.046; qmatOffset[4][16] *= 0.013; qmatOffset[4][17] *= 0.012; qmatOffset[4][18] *= 0.076; qmatOffset[4][19] *= 0.698; qmatOffset[5][0] *= 0.24; qmatOffset[5][1] *= 0.011; qmatOffset[5][2] *= 0.125; qmatOffset[5][3] *= 0.081; qmatOffset[5][4] *= 0.015; qmatOffset[5][6] *= 0.01; qmatOffset[5][8] *= 0.027; qmatOffset[5][9] *= 0.007; qmatOffset[5][10] *= 0.017; qmatOffset[5][11] *= 0.139; qmatOffset[5][12] *= 0.034; qmatOffset[5][13] *= 0.028; qmatOffset[5][14] *= 0.009; qmatOffset[5][15] *= 0.234; qmatOffset[5][16] *= 0.03; qmatOffset[5][17] *= 0.054; qmatOffset[6][0] *= 0.023; qmatOffset[6][1] *= 0.028; qmatOffset[6][2] *= 0.086; qmatOffset[6][3] *= 0.043; qmatOffset[6][4] *= 0.048; qmatOffset[6][5] *= 0.01; qmatOffset[6][7] *= 0.007; qmatOffset[6][8] *= 0.026; qmatOffset[6][9] *= 0.044; qmatOffset[6][11] *= 0.535; qmatOffset[6][12] *= 0.094; qmatOffset[6][13] *= 0.606; qmatOffset[6][14] *= 0.24; qmatOffset[6][15] *= 0.035; qmatOffset[6][16] *= 0.022; qmatOffset[6][17] *= 0.044; qmatOffset[6][18] *= 0.027; qmatOffset[6][19] *= 0.127; qmatOffset[7][0] *= 0.065; qmatOffset[7][1] *= 0.044; qmatOffset[7][2] *= 0.024; qmatOffset[7][3] *= 0.061; qmatOffset[7][4] *= 0.196; qmatOffset[7][6] *= 0.007; qmatOffset[7][8] *= 0.046; qmatOffset[7][9] *= 0.257; qmatOffset[7][10] *= 0.336; qmatOffset[7][11] *= 0.077; qmatOffset[7][12] *= 0.012; qmatOffset[7][13] *= 0.018; qmatOffset[7][14] *= 0.064; qmatOffset[7][15] *= 0.024; qmatOffset[7][16] *= 0.192; qmatOffset[7][17] *= 0.889; qmatOffset[7][19] *= 0.037; qmatOffset[8][0] *= 0.026; qmatOffset[8][2] *= 0.071; qmatOffset[8][3] *= 0.083; qmatOffset[8][5] *= 0.027; qmatOffset[8][6] *= 0.026; qmatOffset[8][7] *= 0.046; qmatOffset[8][9] *= 0.018; qmatOffset[8][10] *= 0.243; qmatOffset[8][11] *= 0.318; qmatOffset[8][12] *= 0.033; qmatOffset[8][13] *= 0.153; qmatOffset[8][14] *= 0.464; qmatOffset[8][15] *= 0.096; qmatOffset[8][16] *= 0.136; qmatOffset[8][17] *= 0.01; qmatOffset[8][19] *= 0.013; qmatOffset[9][0] *= 0.041; qmatOffset[9][3] *= 0.011; qmatOffset[9][4] *= 0.157; qmatOffset[9][5] *= 0.007; qmatOffset[9][6] *= 0.044; qmatOffset[9][7] *= 0.257; qmatOffset[9][8] *= 0.018; qmatOffset[9][10] *= 0.527; qmatOffset[9][11] *= 0.034; qmatOffset[9][12] *= 0.032; qmatOffset[9][13] *= 0.073; qmatOffset[9][14] *= 0.015; qmatOffset[9][15] *= 0.017; qmatOffset[9][16] *= 0.033; qmatOffset[9][17] *= 0.175; qmatOffset[9][18] *= 0.046; qmatOffset[9][19] *= 0.028; qmatOffset[10][0] *= 0.072; qmatOffset[10][3] *= 0.03; qmatOffset[10][4] *= 0.092; qmatOffset[10][5] *= 0.017; qmatOffset[10][7] *= 0.336; qmatOffset[10][8] *= 0.243; qmatOffset[10][9] *= 0.527; qmatOffset[10][11] *= 0.001; qmatOffset[10][12] *= 0.017; qmatOffset[10][13] *= 0.114; qmatOffset[10][14] *= 0.09; qmatOffset[10][15] *= 0.062; qmatOffset[10][16] *= 0.104; qmatOffset[10][17] *= 0.258; qmatOffset[11][0] *= 0.098; qmatOffset[11][2] *= 0.905; qmatOffset[11][3] *= 0.148; qmatOffset[11][4] *= 0.014; qmatOffset[11][5] *= 0.139; qmatOffset[11][6] *= 0.535; qmatOffset[11][7] *= 0.077; qmatOffset[11][8] *= 0.318; qmatOffset[11][9] *= 0.034; qmatOffset[11][10] *= 0.001; qmatOffset[11][12] *= 0.042; qmatOffset[11][13] *= 0.103; qmatOffset[11][14] *= 0.032; qmatOffset[11][15] *= 0.495; qmatOffset[11][16] *= 0.229; qmatOffset[11][17] *= 0.015; qmatOffset[11][18] *= 0.023; qmatOffset[11][19] *= 0.095; qmatOffset[12][0] *= 0.25; qmatOffset[12][1] *= 0.019; qmatOffset[12][2] *= 0.013; qmatOffset[12][3] *= 0.051; qmatOffset[12][4] *= 0.011; qmatOffset[12][5] *= 0.034; qmatOffset[12][6] *= 0.094; qmatOffset[12][7] *= 0.012; qmatOffset[12][8] *= 0.033; qmatOffset[12][9] *= 0.032; qmatOffset[12][10] *= 0.017; qmatOffset[12][11] *= 0.042; qmatOffset[12][13] *= 0.153; qmatOffset[12][14] *= 0.103; qmatOffset[12][15] *= 0.245; qmatOffset[12][16] *= 0.078; qmatOffset[12][17] *= 0.048; qmatOffset[13][0] *= 0.089; qmatOffset[13][2] *= 0.134; qmatOffset[13][3] *= 0.716; qmatOffset[13][5] *= 0.028; qmatOffset[13][6] *= 0.606; qmatOffset[13][7] *= 0.018; qmatOffset[13][8] *= 0.153; qmatOffset[13][9] *= 0.073; qmatOffset[13][10] *= 0.114; qmatOffset[13][11] *= 0.103; qmatOffset[13][12] *= 0.153; qmatOffset[13][14] *= 0.246; qmatOffset[13][15] *= 0.056; qmatOffset[13][16] *= 0.053; qmatOffset[13][17] *= 0.035; qmatOffset[14][0] *= 0.027; qmatOffset[14][1] *= 0.023; qmatOffset[14][3] *= 0.001; qmatOffset[14][4] *= 0.014; qmatOffset[14][5] *= 0.009; qmatOffset[14][6] *= 0.24; qmatOffset[14][7] *= 0.064; qmatOffset[14][8] *= 0.464; qmatOffset[14][9] *= 0.015; qmatOffset[14][10] *= 0.09; qmatOffset[14][11] *= 0.032; qmatOffset[14][12] *= 0.103; qmatOffset[14][13] *= 0.246; qmatOffset[14][15] *= 0.154; qmatOffset[14][16] *= 0.026; qmatOffset[14][17] *= 0.024; qmatOffset[14][18] *= 0.201; qmatOffset[14][19] *= 0.008; qmatOffset[15][0] *= 0.409; qmatOffset[15][1] *= 0.161; qmatOffset[15][2] *= 0.095; qmatOffset[15][3] *= 0.079; qmatOffset[15][4] *= 0.046; qmatOffset[15][5] *= 0.234; qmatOffset[15][6] *= 0.035; qmatOffset[15][7] *= 0.024; qmatOffset[15][8] *= 0.096; qmatOffset[15][9] *= 0.017; qmatOffset[15][10] *= 0.062; qmatOffset[15][11] *= 0.495; qmatOffset[15][12] *= 0.245; qmatOffset[15][13] *= 0.056; qmatOffset[15][14] *= 0.154; qmatOffset[15][16] *= 0.55; qmatOffset[15][17] *= 0.03; qmatOffset[15][18] *= 0.075; qmatOffset[15][19] *= 0.034; qmatOffset[16][0] *= 0.371; qmatOffset[16][1] *= 0.016; qmatOffset[16][2] *= 0.066; qmatOffset[16][3] *= 0.034; qmatOffset[16][4] *= 0.013; qmatOffset[16][5] *= 0.03; qmatOffset[16][6] *= 0.022; qmatOffset[16][7] *= 0.192; qmatOffset[16][8] *= 0.136; qmatOffset[16][9] *= 0.033; qmatOffset[16][10] *= 0.104; qmatOffset[16][11] *= 0.229; qmatOffset[16][12] *= 0.078; qmatOffset[16][13] *= 0.053; qmatOffset[16][14] *= 0.026; qmatOffset[16][15] *= 0.55; qmatOffset[16][17] *= 0.157; qmatOffset[16][19] *= 0.042; qmatOffset[17][0] *= 0.208; qmatOffset[17][1] *= 0.049; qmatOffset[17][2] *= 0.018; qmatOffset[17][3] *= 0.037; qmatOffset[17][4] *= 0.012; qmatOffset[17][5] *= 0.054; qmatOffset[17][6] *= 0.044; qmatOffset[17][7] *= 0.889; qmatOffset[17][8] *= 0.01; qmatOffset[17][9] *= 0.175; qmatOffset[17][10] *= 0.258; qmatOffset[17][11] *= 0.015; qmatOffset[17][12] *= 0.048; qmatOffset[17][13] *= 0.035; qmatOffset[17][14] *= 0.024; qmatOffset[17][15] *= 0.03; qmatOffset[17][16] *= 0.157; qmatOffset[17][19] *= 0.028; qmatOffset[18][4] *= 0.076; qmatOffset[18][6] *= 0.027; qmatOffset[18][9] *= 0.046; qmatOffset[18][11] *= 0.023; qmatOffset[18][14] *= 0.201; qmatOffset[18][15] *= 0.075; qmatOffset[18][19] *= 0.061; qmatOffset[19][0] *= 0.024; qmatOffset[19][1] *= 0.096; qmatOffset[19][3] *= 0.022; qmatOffset[19][4] *= 0.698; qmatOffset[19][6] *= 0.127; qmatOffset[19][7] *= 0.037; qmatOffset[19][8] *= 0.013; qmatOffset[19][9] *= 0.028; qmatOffset[19][11] *= 0.095; qmatOffset[19][14] *= 0.008; qmatOffset[19][15] *= 0.034; qmatOffset[19][16] *= 0.042; qmatOffset[19][17] *= 0.028; qmatOffset[19][18] *= 0.061; //here are the zero entries qmatOffset[0][18]=qmatOffset[18][0]=0.0; qmatOffset[2][14]=qmatOffset[14][2]=0.0; qmatOffset[1][11]=qmatOffset[11][1]=0.0; qmatOffset[1][2]=qmatOffset[2][1]=0.0; qmatOffset[9][2]=qmatOffset[2][9]=0.0; qmatOffset[10][2]=qmatOffset[2][10]=0.0; qmatOffset[4][2]=qmatOffset[2][4]=0.0; qmatOffset[18][2]=qmatOffset[2][18]=0.0; qmatOffset[19][2]=qmatOffset[2][19]=0.0; qmatOffset[13][1]=qmatOffset[1][13]=0.0; qmatOffset[3][1]=qmatOffset[1][3]=0.0; qmatOffset[9][1]=qmatOffset[1][9]=0.0; qmatOffset[8][1]=qmatOffset[1][8]=0.0; qmatOffset[10][1]=qmatOffset[1][10]=0.0; qmatOffset[4][1]=qmatOffset[1][4]=0.0; qmatOffset[18][1]=qmatOffset[1][18]=0.0; qmatOffset[4][13]=qmatOffset[13][4]=0.0; qmatOffset[18][13]=qmatOffset[13][18]=0.0; qmatOffset[19][13]=qmatOffset[13][19]=0.0; qmatOffset[4][3]=qmatOffset[3][4]=0.0; qmatOffset[18][3]=qmatOffset[3][18]=0.0; qmatOffset[7][5]=qmatOffset[5][7]=0.0; qmatOffset[18][5]=qmatOffset[5][18]=0.0; qmatOffset[19][5]=qmatOffset[5][19]=0.0; qmatOffset[10][6]=qmatOffset[6][10]=0.0; qmatOffset[18][7]=qmatOffset[7][18]=0.0; qmatOffset[4][8]=qmatOffset[8][4]=0.0; qmatOffset[18][8]=qmatOffset[8][18]=0.0; qmatOffset[18][10]=qmatOffset[10][18]=0.0; qmatOffset[19][10]=qmatOffset[10][19]=0.0; qmatOffset[18][12]=qmatOffset[12][18]=0.0; qmatOffset[19][12]=qmatOffset[12][19]=0.0; qmatOffset[18][16]=qmatOffset[16][18]=0.0; qmatOffset[17][18]=qmatOffset[18][17]=0.0; } void Model::MultiplyByWAGAAMatrix(){ int modNum=0; MODEL_FLOAT **qmatOffset = qmat[modNum]; qmatOffset[14][0] *= 1.75252; qmatOffset[0][14] *= 1.75252; qmatOffset[11][0] *= 1.61995; qmatOffset[0][11] *= 1.61995; qmatOffset[11][14] *= 2.0187; qmatOffset[14][11] *= 2.0187; qmatOffset[2][0] *= 2.34804; qmatOffset[0][2] *= 2.34804; qmatOffset[2][14] *= 0.468033; qmatOffset[14][2] *= 0.468033; qmatOffset[2][11] *= 17.251; qmatOffset[11][2] *= 17.251; qmatOffset[1][0] *= 3.26324; qmatOffset[0][1] *= 3.26324; qmatOffset[1][14] *= 1.67824; qmatOffset[14][1] *= 1.67824; qmatOffset[1][11] *= 0.842805; qmatOffset[11][1] *= 0.842805; qmatOffset[1][2] *= 0.0962568; qmatOffset[2][1] *= 0.0962568; qmatOffset[13][0] *= 2.88691; qmatOffset[0][13] *= 2.88691; qmatOffset[13][14] *= 9.64477; qmatOffset[14][13] *= 9.64477; qmatOffset[13][11] *= 4.90465; qmatOffset[11][13] *= 4.90465; qmatOffset[13][2] *= 1.95972; qmatOffset[2][13] *= 1.95972; qmatOffset[13][1] *= 0.313977; qmatOffset[1][13] *= 0.313977; qmatOffset[3][0] *= 5.02923; qmatOffset[0][3] *= 5.02923; qmatOffset[3][14] *= 1.39535; qmatOffset[14][3] *= 1.39535; qmatOffset[3][11] *= 3.00956; qmatOffset[11][3] *= 3.00956; qmatOffset[3][2] *= 19.6173; qmatOffset[2][3] *= 19.6173; qmatOffset[3][1] *= 0.0678423; qmatOffset[1][3] *= 0.0678423; qmatOffset[3][13] *= 17.3783; qmatOffset[13][3] *= 17.3783; qmatOffset[5][0] *= 4.50138; qmatOffset[0][5] *= 4.50138; qmatOffset[5][14] *= 1.85767; qmatOffset[14][5] *= 1.85767; qmatOffset[5][11] *= 3.57627; qmatOffset[11][5] *= 3.57627; qmatOffset[5][2] *= 2.75024; qmatOffset[2][5] *= 2.75024; qmatOffset[5][1] *= 0.974403; qmatOffset[1][5] *= 0.974403; qmatOffset[5][13] *= 1.04868; qmatOffset[13][5] *= 1.04868; qmatOffset[5][3] *= 1.80382; qmatOffset[3][5] *= 1.80382; qmatOffset[6][0] *= 1.00707; qmatOffset[0][6] *= 1.00707; qmatOffset[6][14] *= 6.79042; qmatOffset[14][6] *= 6.79042; qmatOffset[6][11] *= 12.5704; qmatOffset[11][6] *= 12.5704; qmatOffset[6][2] *= 2.95706; qmatOffset[2][6] *= 2.95706; qmatOffset[6][1] *= 0.791065; qmatOffset[1][6] *= 0.791065; qmatOffset[6][13] *= 13.6438; qmatOffset[13][6] *= 13.6438; qmatOffset[6][3] *= 1.81116; qmatOffset[3][6] *= 1.81116; qmatOffset[6][5] *= 0.792457; qmatOffset[5][6] *= 0.792457; qmatOffset[7][0] *= 0.614288; qmatOffset[0][7] *= 0.614288; qmatOffset[7][14] *= 0.594093; qmatOffset[14][7] *= 0.594093; qmatOffset[7][11] *= 1.76099; qmatOffset[11][7] *= 1.76099; qmatOffset[7][2] *= 0.125304; qmatOffset[2][7] *= 0.125304; qmatOffset[7][1] *= 0.540574; qmatOffset[1][7] *= 0.540574; qmatOffset[7][13] *= 0.361952; qmatOffset[13][7] *= 0.361952; qmatOffset[7][3] *= 0.404776; qmatOffset[3][7] *= 0.404776; qmatOffset[7][5] *= 0.0967499; qmatOffset[5][7] *= 0.0967499; qmatOffset[7][6] *= 0.439075; qmatOffset[6][7] *= 0.439075; qmatOffset[9][0] *= 1.26431; qmatOffset[0][9] *= 1.26431; qmatOffset[9][14] *= 1.58126; qmatOffset[14][9] *= 1.58126; qmatOffset[9][11] *= 0.417907; qmatOffset[11][9] *= 0.417907; qmatOffset[9][2] *= 0.269452; qmatOffset[2][9] *= 0.269452; qmatOffset[9][1] *= 1.22101; qmatOffset[1][9] *= 1.22101; qmatOffset[9][13] *= 2.76265; qmatOffset[13][9] *= 2.76265; qmatOffset[9][3] *= 0.490144; qmatOffset[3][9] *= 0.490144; qmatOffset[9][5] *= 0.194782; qmatOffset[5][9] *= 0.194782; qmatOffset[9][6] *= 1.58695; qmatOffset[6][9] *= 1.58695; qmatOffset[9][7] *= 10.0752; qmatOffset[7][9] *= 10.0752; qmatOffset[8][0] *= 2.8795; qmatOffset[0][8] *= 2.8795; qmatOffset[8][14] *= 17.0032; qmatOffset[14][8] *= 17.0032; qmatOffset[8][11] *= 9.57014; qmatOffset[11][8] *= 9.57014; qmatOffset[8][2] *= 1.52466; qmatOffset[2][8] *= 1.52466; qmatOffset[8][1] *= 0.23523; qmatOffset[1][8] *= 0.23523; qmatOffset[8][13] *= 12.3754; qmatOffset[13][8] *= 12.3754; qmatOffset[8][3] *= 8.21158; qmatOffset[3][8] *= 8.21158; qmatOffset[8][5] *= 1.18692; qmatOffset[5][8] *= 1.18692; qmatOffset[8][6] *= 2.82919; qmatOffset[6][8] *= 2.82919; qmatOffset[8][7] *= 1.02892; qmatOffset[7][8] *= 1.02892; qmatOffset[8][9] *= 0.818336; qmatOffset[9][8] *= 0.818336; qmatOffset[10][0] *= 2.83893; qmatOffset[0][10] *= 2.83893; qmatOffset[10][14] *= 2.17063; qmatOffset[14][10] *= 2.17063; qmatOffset[10][11] *= 0.629813; qmatOffset[11][10] *= 0.629813; qmatOffset[10][2] *= 0.32966; qmatOffset[2][10] *= 0.32966; qmatOffset[10][1] *= 1.24069; qmatOffset[1][10] *= 1.24069; qmatOffset[10][13] *= 4.9098; qmatOffset[13][10] *= 4.9098; qmatOffset[10][3] *= 1.00125; qmatOffset[3][10] *= 1.00125; qmatOffset[10][5] *= 0.553173; qmatOffset[5][10] *= 0.553173; qmatOffset[10][6] *= 1.28409; qmatOffset[6][10] *= 1.28409; qmatOffset[10][7] *= 13.5273; qmatOffset[7][10] *= 13.5273; qmatOffset[10][9] *= 15.4228; qmatOffset[9][10] *= 15.4228; qmatOffset[10][8] *= 2.9685; qmatOffset[8][10] *= 2.9685; qmatOffset[4][0] *= 0.668808; qmatOffset[0][4] *= 0.668808; qmatOffset[4][14] *= 0.326346; qmatOffset[14][4] *= 0.326346; qmatOffset[4][11] *= 0.305538; qmatOffset[11][4] *= 0.305538; qmatOffset[4][2] *= 0.148478; qmatOffset[2][4] *= 0.148478; qmatOffset[4][1] *= 1.26464; qmatOffset[1][4] *= 1.26464; qmatOffset[4][13] *= 0.317481; qmatOffset[13][4] *= 0.317481; qmatOffset[4][3] *= 0.257789; qmatOffset[3][4] *= 0.257789; qmatOffset[4][5] *= 0.158647; qmatOffset[5][4] *= 0.158647; qmatOffset[4][6] *= 2.15858; qmatOffset[6][4] *= 2.15858; qmatOffset[4][7] *= 3.36628; qmatOffset[7][4] *= 3.36628; qmatOffset[4][9] *= 6.72059; qmatOffset[9][4] *= 6.72059; qmatOffset[4][8] *= 0.282261; qmatOffset[8][4] *= 0.282261; qmatOffset[4][10] *= 3.78302; qmatOffset[10][4] *= 3.78302; qmatOffset[12][0] *= 4.57074; qmatOffset[0][12] *= 4.57074; qmatOffset[12][14] *= 2.15896; qmatOffset[14][12] *= 2.15896; qmatOffset[12][11] *= 0.619836; qmatOffset[11][12] *= 0.619836; qmatOffset[12][2] *= 1.34714; qmatOffset[2][12] *= 1.34714; qmatOffset[12][1] *= 0.347612; qmatOffset[1][12] *= 0.347612; qmatOffset[12][13] *= 2.96563; qmatOffset[13][12] *= 2.96563; qmatOffset[12][3] *= 2.16806; qmatOffset[3][12] *= 2.16806; qmatOffset[12][5] *= 0.773901; qmatOffset[5][12] *= 0.773901; qmatOffset[12][6] *= 2.21205; qmatOffset[6][12] *= 2.21205; qmatOffset[12][7] *= 0.317506; qmatOffset[7][12] *= 0.317506; qmatOffset[12][9] *= 1.32127; qmatOffset[9][12] *= 1.32127; qmatOffset[12][8] *= 1.76944; qmatOffset[8][12] *= 1.76944; qmatOffset[12][10] *= 0.544368; qmatOffset[10][12] *= 0.544368; qmatOffset[12][4] *= 0.51296; qmatOffset[4][12] *= 0.51296; qmatOffset[15][0] *= 10.7101; qmatOffset[0][15] *= 10.7101; qmatOffset[15][14] *= 3.88965; qmatOffset[14][15] *= 3.88965; qmatOffset[15][11] *= 12.6274; qmatOffset[11][15] *= 12.6274; qmatOffset[15][2] *= 3.40533; qmatOffset[2][15] *= 3.40533; qmatOffset[15][1] *= 4.4726; qmatOffset[1][15] *= 4.4726; qmatOffset[15][13] *= 3.26906; qmatOffset[13][15] *= 3.26906; qmatOffset[15][3] *= 2.23982; qmatOffset[3][15] *= 2.23982; qmatOffset[15][5] *= 4.2634; qmatOffset[5][15] *= 4.2634; qmatOffset[15][6] *= 2.35176; qmatOffset[6][15] *= 2.35176; qmatOffset[15][7] *= 1.01497; qmatOffset[7][15] *= 1.01497; qmatOffset[15][9] *= 1.09535; qmatOffset[9][15] *= 1.09535; qmatOffset[15][8] *= 3.07289; qmatOffset[8][15] *= 3.07289; qmatOffset[15][10] *= 1.5693; qmatOffset[10][15] *= 1.5693; qmatOffset[15][4] *= 1.7346; qmatOffset[4][15] *= 1.7346; qmatOffset[15][12] *= 5.12592; qmatOffset[12][15] *= 5.12592; qmatOffset[16][0] *= 6.73946; qmatOffset[0][16] *= 6.73946; qmatOffset[16][14] *= 1.76155; qmatOffset[14][16] *= 1.76155; qmatOffset[16][11] *= 6.45016; qmatOffset[11][16] *= 6.45016; qmatOffset[16][2] *= 1.19107; qmatOffset[2][16] *= 1.19107; qmatOffset[16][1] *= 1.62992; qmatOffset[1][16] *= 1.62992; qmatOffset[16][13] *= 2.72592; qmatOffset[13][16] *= 2.72592; qmatOffset[16][3] *= 2.61419; qmatOffset[3][16] *= 2.61419; qmatOffset[16][5] *= 0.717545; qmatOffset[5][16] *= 0.717545; qmatOffset[16][6] *= 1.50385; qmatOffset[6][16] *= 1.50385; qmatOffset[16][7] *= 4.63305; qmatOffset[7][16] *= 4.63305; qmatOffset[16][9] *= 1.03778; qmatOffset[9][16] *= 1.03778; qmatOffset[16][8] *= 4.40689; qmatOffset[8][16] *= 4.40689; qmatOffset[16][10] *= 4.81721; qmatOffset[10][16] *= 4.81721; qmatOffset[16][4] *= 0.546192; qmatOffset[4][16] *= 0.546192; qmatOffset[16][12] *= 2.52719; qmatOffset[12][16] *= 2.52719; qmatOffset[16][15] *= 13.9104; qmatOffset[15][16] *= 13.9104; qmatOffset[18][0] *= 0.35946; qmatOffset[0][18] *= 0.35946; qmatOffset[18][14] *= 3.69815; qmatOffset[14][18] *= 3.69815; qmatOffset[18][11] *= 0.228503; qmatOffset[11][18] *= 0.228503; qmatOffset[18][2] *= 0.412312; qmatOffset[2][18] *= 0.412312; qmatOffset[18][1] *= 2.27837; qmatOffset[1][18] *= 2.27837; qmatOffset[18][13] *= 0.685467; qmatOffset[13][18] *= 0.685467; qmatOffset[18][3] *= 0.497433; qmatOffset[3][18] *= 0.497433; qmatOffset[18][5] *= 1.07071; qmatOffset[5][18] *= 1.07071; qmatOffset[18][6] *= 0.834267; qmatOffset[6][18] *= 0.834267; qmatOffset[18][7] *= 0.675128; qmatOffset[7][18] *= 0.675128; qmatOffset[18][9] *= 2.1139; qmatOffset[9][18] *= 2.1139; qmatOffset[18][8] *= 0.436898; qmatOffset[8][18] *= 0.436898; qmatOffset[18][10] *= 1.63857; qmatOffset[10][18] *= 1.63857; qmatOffset[18][4] *= 4.86017; qmatOffset[4][18] *= 4.86017; qmatOffset[18][12] *= 0.442935; qmatOffset[12][18] *= 0.442935; qmatOffset[18][15] *= 1.6641; qmatOffset[15][18] *= 1.6641; qmatOffset[18][16] *= 0.352251; qmatOffset[16][18] *= 0.352251; qmatOffset[19][0] *= 0.764894; qmatOffset[0][19] *= 0.764894; qmatOffset[19][14] *= 1.21225; qmatOffset[14][19] *= 1.21225; qmatOffset[19][11] *= 3.45058; qmatOffset[11][19] *= 3.45058; qmatOffset[19][2] *= 1.03489; qmatOffset[2][19] *= 1.03489; qmatOffset[19][1] *= 1.72794; qmatOffset[1][19] *= 1.72794; qmatOffset[19][13] *= 0.723509; qmatOffset[13][19] *= 0.723509; qmatOffset[19][3] *= 0.623719; qmatOffset[3][19] *= 0.623719; qmatOffset[19][5] *= 0.329184; qmatOffset[5][19] *= 0.329184; qmatOffset[19][6] *= 12.3072; qmatOffset[6][19] *= 12.3072; qmatOffset[19][7] *= 1.33502; qmatOffset[7][19] *= 1.33502; qmatOffset[19][9] *= 1.26654; qmatOffset[9][19] *= 1.26654; qmatOffset[19][8] *= 0.423423; qmatOffset[8][19] *= 0.423423; qmatOffset[19][10] *= 1.36128; qmatOffset[10][19] *= 1.36128; qmatOffset[19][4] *= 20.5074; qmatOffset[4][19] *= 20.5074; qmatOffset[19][12] *= 0.686449; qmatOffset[12][19] *= 0.686449; qmatOffset[19][15] *= 2.50053; qmatOffset[15][19] *= 2.50053; qmatOffset[19][16] *= 0.925072; qmatOffset[16][19] *= 0.925072; qmatOffset[19][18] *= 7.8969; qmatOffset[18][19] *= 7.8969; qmatOffset[17][0] *= 6.37375; qmatOffset[0][17] *= 6.37375; qmatOffset[17][14] *= 0.800207; qmatOffset[14][17] *= 0.800207; qmatOffset[17][11] *= 0.623538; qmatOffset[11][17] *= 0.623538; qmatOffset[17][2] *= 0.484018; qmatOffset[2][17] *= 0.484018; qmatOffset[17][1] *= 3.18413; qmatOffset[1][17] *= 3.18413; qmatOffset[17][13] *= 0.957268; qmatOffset[13][17] *= 0.957268; qmatOffset[17][3] *= 1.87059; qmatOffset[3][17] *= 1.87059; qmatOffset[17][5] *= 0.594945; qmatOffset[5][17] *= 0.594945; qmatOffset[17][6] *= 0.376062; qmatOffset[6][17] *= 0.376062; qmatOffset[17][7] *= 24.8508; qmatOffset[7][17] *= 24.8508; qmatOffset[17][9] *= 5.72027; qmatOffset[9][17] *= 5.72027; qmatOffset[17][8] *= 0.970464; qmatOffset[8][17] *= 0.970464; qmatOffset[17][10] *= 6.54037; qmatOffset[10][17] *= 6.54037; qmatOffset[17][4] *= 2.06492; qmatOffset[4][17] *= 2.06492; qmatOffset[17][12] *= 1.0005; qmatOffset[12][17] *= 1.0005; qmatOffset[17][15] *= 0.739488; qmatOffset[15][17] *= 0.739488; qmatOffset[17][16] *= 4.41086; qmatOffset[16][17] *= 4.41086; qmatOffset[17][18] *= 1.1609; qmatOffset[18][17] *= 1.1609; qmatOffset[17][19] *= 1; qmatOffset[19][17] *= 1; } ModelPartition::ModelPartition(){ for(int i=0;iNumModels();m++) models.push_back(modSets[i]->GetModel(m)); } //numSubsetRates will be = # specs in the case of no linkage //but in the case of linkage with different subset rates #specs will be 1 and numSubsetRates > 1 //separate subset rates will always be stored for each data subset, but they won't be changed if //they aren't actually being estiamted if(dataSubInfo.size() > 1){ int totalCharacters = 0; /* for(int d = 0;d < dataSubInfo.size();d++){ totalCharacters += dataSubInfo[d].totalCharacters; } for(int d = 0;d < dataSubInfo.size();d++){ subsetRates.push_back(1.0); subsetProportions.push_back(dataSubInfo[d].totalCharacters / (FLOAT_TYPE) totalCharacters); } */ //if we're in MKV mode and this is a subset that MKV will be applied to, need to compensate for dummy char for(int d = 0;d < dataSubInfo.size();d++){ totalCharacters += dataSubInfo[d].totalCharacters - (dataSubInfo[d].usedAs == DataSubsetInfo::NSTATEV ? 1 : 0); } for(int d = 0;d < dataSubInfo.size();d++){ subsetRates.push_back(1.0); subsetProportions.push_back((dataSubInfo[d].totalCharacters - (dataSubInfo[d].usedAs == DataSubsetInfo::NSTATEV ? 1 : 0))/ (FLOAT_TYPE) totalCharacters); } #ifndef NDEBUG double propTot = 0.0; for(int d = 0;d < dataSubInfo.size();d++) propTot += subsetProportions[d]; assert(FloatingPointEquals(propTot, 1.0, 1e-6)); #endif if(modSpecSet.InferSubsetRates()){ vector dummy; for(int d = 0;d < dataSubInfo.size();d++) dummy.push_back(&subsetRates[d]); SubsetRates *rm = new SubsetRates(&dummy[0], dataSubInfo.size(), -1); rm->SetWeight(dataSubInfo.size() * 2); allParamsToMutate.push_back(rm); } } else{ subsetRates.push_back(1.0); subsetProportions.push_back(1.0); } CollectMutableParameters(); } //This is the size in KB not elements. KB is used because the number of bytes can be larger than UNSIGNED_MAX on very large datasets double ModelPartition::CalcRequiredCLAsizeKB(const DataPartition *dat){ unsigned size = 0; double size2 = 0; double KB = 1024; for(vector::iterator specs = claSpecs.begin();specs != claSpecs.end();specs++){ const Model *thisMod = GetModel((*specs).modelIndex); size2 += (dat->GetSubset((*specs).dataIndex)->NChar() / KB) * (thisMod->NStates() * thisMod->NRateCats() * sizeof(FLOAT_TYPE) + sizeof(int)); size += (thisMod->NStates() * thisMod->NRateCats() * dat->GetSubset((*specs).dataIndex)->NChar()) * sizeof(FLOAT_TYPE); size += dat->GetSubset((*specs).dataIndex)->NChar() * sizeof(int); } assert(size2 * 1024 == size); return size2; } //this is the size in BYTES not elements unsigned ModelPartition::CalcRequiredCLAsize(const DataPartition *dat){ unsigned size = 0; for(vector::iterator specs = claSpecs.begin();specs != claSpecs.end();specs++){ const Model *thisMod = GetModel((*specs).modelIndex); size += (thisMod->NStates() * thisMod->NRateCats() * dat->GetSubset((*specs).dataIndex)->NChar()) * sizeof(FLOAT_TYPE); size += dat->GetSubset((*specs).dataIndex)->NChar() * sizeof(int); } return size; } //these are just stolen directly from the corresponding Model:: functions for now BaseParameter *ModelPartition::SelectModelMutation(){ CalcMutationProbsFromWeights(); if(allParamsToMutate.empty() == true) return NULL; FLOAT_TYPE r=rnd.uniform(); vector::iterator it; for(it=allParamsToMutate.begin();it!=allParamsToMutate.end();it++){ if((*it)->GetProb() > r) return *it; } it--; return *it; } void ModelPartition::CalcMutationProbsFromWeights(){ FLOAT_TYPE tot=ZERO_POINT_ZERO, running=ZERO_POINT_ZERO; for(vector::iterator it=allParamsToMutate.begin();it!=allParamsToMutate.end();it++){ tot += (*it)->GetWeight(); } for(vector::iterator it=allParamsToMutate.begin();it!=allParamsToMutate.end();it++){ running += (*it)->GetWeight() / tot; (*it)->SetProb(running); } } int ModelPartition::PerformModelMutation(){ if(allParamsToMutate.empty()) return 0; BaseParameter *mut = SelectModelMutation(); assert(mut != NULL); mut->Mutator(Model::mutationShape); int retType; if(mut->Type() == RELATIVERATES){ for(vector::iterator mit = mut->modelsThatInclude.begin();mit != mut->modelsThatInclude.end();mit++){ models[*mit]->UpdateQMat(); models[*mit]->eigenDirty=true; } retType=Individual::rates; } else if(mut->Type() == STATEFREQS){ for(vector::iterator mit = mut->modelsThatInclude.begin();mit != mut->modelsThatInclude.end();mit++){ models[*mit]->UpdateQMat(); models[*mit]->eigenDirty=true; } retType=Individual::pi; } else if(mut->Type() == PROPORTIONINVARIANT){ //this max checking should really be rolled into the parameter class //DEBUG PARTITION - need to put this check somewhere - since the pinv value can be shared //across subsets with different obs numbers of invariants, not sure how it should be //limited // *propInvar = (*propInvar > maxPropInvar ? maxPropInvar : *propInvar); //the non invariant rates need to be rescaled even if there is only 1 for(vector::iterator mit = mut->modelsThatInclude.begin();mit != mut->modelsThatInclude.end();mit++){ *(models[*mit]->propInvar) = (*(models[*mit]->propInvar) > (models[*mit]->maxPropInvar) ? (models[*mit]->maxPropInvar) : *(models[*mit]->propInvar)); if(modSpecSet.GetModSpec(*mit)->IsFlexRateHet() == false) models[*mit]->AdjustRateProportions(); else models[*mit]->NormalizeRates(); } retType=Individual::pinv; } else if(mut->Type() == ALPHASHAPE){ for(vector::iterator mit = mut->modelsThatInclude.begin();mit != mut->modelsThatInclude.end();mit++) models[*mit]->DiscreteGamma(models[*mit]->rateMults, models[*mit]->rateProbs, *models[*mit]->alpha); retType=Individual::alpha; } else if(mut->Type() == RATEPROPS || mut->Type() == RATEMULTS){ for(vector::iterator mit = mut->modelsThatInclude.begin();mit != mut->modelsThatInclude.end();mit++){ //flex rates and omega muts come through here //enforce an ordering of the rate multipliers, so that they can't "cross" one another if(models[*mit]->NRateCats() > 1) models[*mit]->CheckAndCorrectRateOrdering(); if(modSpecSet.GetModSpec(*mit)->IsFlexRateHet() == true) models[*mit]->NormalizeRates(); else if(modSpecSet.GetModSpec(*mit)->IsCodon()){ //this normalization could really be taken care of in the mutator, but this general purpose //function does a better job of enforcing minimum values models[*mit]->NormalizeSumConstrainedValues(&models[*mit]->omegaProbs[0], models[*mit]->NRateCats(), ONE_POINT_ZERO, 1.0e-5, -1); //eigen stuff needs to be recalced for changes to nonsynonymous rates models[*mit]->eigenDirty = true; } } retType=Individual::alpha; } else if(mut->Type() == SUBSETRATE){ NormalizeSubsetRates(); retType=Individual::subsetRate; } else if(mut->Type() == INSERTPROPORTION || mut->Type() == DELETERATE){ retType=Individual::indel; } return retType; } void ModelPartition::ReadGarliFormattedModelStrings(string &modstr){ NxsString mod(modstr.c_str()); NxsString::to_lower(mod); try{ while(mod.length() > 0){ //now, read through the string, figuring out where each of the model strings start and end, and what numbers they are size_t start = mod.find("m"); size_t start2 = mod.find("s"); if(start < start2){ if(start == string::npos) throw ErrorException("Problem reading model parameter string."); mod.erase(0, 1); int space = mod.find(" "); if(space == string::npos) throw ErrorException("Problem reading model parameter string."); //space here is the number of elements, not a range //string num = mod.substr(0, space); NxsString num = mod.substr(0, space).c_str(); if(!num.IsALong()) throw ErrorException("Expecting a model number, found %s!", num.c_str()); int modNum = atoi(num.c_str()); int modIndex = modNum - 1; if(modNum == 0) throw ErrorException("Model numbers in param strings should begin with M1, not M0!", modNum); if(modIndex >= models.size()) throw ErrorException("Model number appearing in param string (%d) is too large!", modNum); mod.erase(0, space + 1); //now we've eaten off everything up to the actual model string. figure out where it ends for this model. //find_first_of looks for the first occurence of the letters m or s. size_t end = mod.find_first_of("ms"); if(end == string::npos){ if(mod.length() == 0) throw ErrorException("Problem reading model parameter string."); end = mod.length(); } string thismod = mod.substr(0, end); mod.erase(0, end); GetModelSet(modIndex)->GetModel(0)->ReadGarliFormattedModelString(thismod); } else if(start2 != string::npos){ size_t space = mod.find(" "); if(space == string::npos) throw ErrorException("Problem reading subset rate parameters from file."); mod.erase(0, space + 1); vector ssr; NxsString val; for(int m = 0;m < NumSubsetRates();m++){ space = mod.find(" "); if(space == string::npos){ if(mod.length() == 0){ throw(ErrorException("Problem reading subset rate parameters from file. Wrong number of rates?", val.c_str())); } } val = mod.substr(0, space).c_str(); mod.erase(0, space + 1); if(! val.IsADouble()) throw ErrorException("Problem reading subset rate parameters from file. Expected a number, found %s.", val.c_str()); ssr.push_back(atof(val.c_str())); } SetSubsetRates(ssr, true); } else{ //if there is only one model and the M0 wasn't specified, then try to read it anyway if(models.size() == 1){ GetModelSet(0)->GetModel(0)->ReadGarliFormattedModelString(mod); break; } else throw ErrorException("Problem reading model specification string"); } } } catch(ErrorException &mess){ outman.UserMessage("\nERROR. There was a problem with the model specification string near this point:\n\"%s\"", mod.c_str()); outman.UserMessage("\nProper format for specification of model parameters in the partitioned\nversion is as follows. Neither subset rates nor all models are required to\nappear. Line breaks are ignored, but the string must be terminated with a \";\".\nThe first model is M1. Omit the <>'s in the following."); outman.UserMessage("\n\nS \nM \nM \n ;"); outman.UserMessage("\nExample for 3 models:\nS 0.551458 0.302705 2.145837\nM1 r 1.959444 2.571568 1.406484 1.406484 3.725263 e 0.310294 0.176855 0.297080 0.215771 a 0.410964\nM2 r 4.366321 7.061605 1.603498 7.061605 4.366321 e 0.269302 0.163670 0.160508 0.406520 a 0.361294\nM3 r 1.000000 4.908101 3.372480 0.457829 4.908101 e 0.156505 0.353697 0.287843 0.201954 a 4.098323 p 0.034152;"); outman.UserMessage("\nWhen there is only one model (i.e., unpartitioned analyses), the \"M1\" part\nthat indicates the model number need not appear."); throw mess; } } void ModelPartition::FillGarliFormattedModelStrings(string &s) const{ char temp[50]; if(modSpecSet.InferSubsetRates()){ s += " S "; for(int r = 0;r < NumSubsetRates();r++){ sprintf(temp, " %f ", SubsetRate(r)); s += temp; } } for(int m = 0;m < modSets.size(); m++){ sprintf(temp, " M%d" , m + 1); s += temp; GetModelSet(m)->GetModel(0)->FillGarliFormattedModelString(s); } } void ModelPartition::WriteModelPartitionCheckpoint(OUTPUT_CLASS &out) const { //subsetProportions are data dependent, not free variables, so don't need to write if(NumModelSets() > 1){ FLOAT_TYPE *dummy = new FLOAT_TYPE; for(int s = 0;s < NumSubsetRates();s++){ *dummy = subsetRates[s]; out.WRITE_TO_FILE(dummy, sizeof(FLOAT_TYPE), 1); } delete dummy; } for(int m = 0;m < modSets.size(); m++){ GetModelSet(m)->WriteModelSetCheckpoint(out); } } void ModelPartition::ReadModelPartitionCheckpoint(FILE *in) { if(NumModelSets() > 1){ FLOAT_TYPE *dummy = new FLOAT_TYPE; vector rates; for(int s = 0;s < NumSubsetRates();s++){ assert(ferror(in) == false); fread(dummy, sizeof(FLOAT_TYPE), 1, in); rates.push_back(*dummy); } SetSubsetRates(rates, false); delete dummy; } for(int m = 0;m < modSets.size(); m++){ GetModelSet(m)->ReadModelSetCheckpoint(in); } } garli-2.1-release/src/model.h000066400000000000000000002232431241236125200161340ustar00rootroot00000000000000// GARLI version 2.1 source code // Copyright 2005-2014 Derrick J. Zwickl // email: garli.support@gmail.com // // This program is free software: you can redistribute it and/or modify // it under the terms of the GNU General Public License as published by // the Free Software Foundation, either version 3 of the License, or // (at your option) any later version. // // This program is distributed in the hope that it will be useful, // but WITHOUT ANY WARRANTY; without even the implied warranty of // MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the // GNU General Public License for more details. // // You should have received a copy of the GNU General Public License // along with this program. If not, see . #ifndef _MODEL_ #define _MODEL_ #if !defined(_MSC_VER) #define _stricmp strcasecmp #endif #include #include #include #include using namespace std; #include "rng.h" #include "sequencedata.h" #include "configoptions.h" #include "errorexception.h" class ModelSpecification; class ModelSpecificationSet; class MFILE; class Individual; class ClaSpecifier; extern rng rnd; //extern ModelSpecification modSpec; extern ModelSpecificationSet modSpecSet; extern bool FloatingPointEquals(const FLOAT_TYPE first, const FLOAT_TYPE sec, const FLOAT_TYPE epsilon); #ifdef SINGLE_PRECISION_FLOATS #ifndef SUM_TO #define SUM_TO 1900.0f #endif //min rate is 1.0e6 times less than the mean //max is just less than SUM_TO #define MIN_REL_RATE (SUM_TO * (1.0e-6f/190.0f)) #define MAX_REL_RATE (SUM_TO - (189.0f * MIN_REL_RATE)) #else #ifndef SUM_TO #define SUM_TO 1900.0 #endif //min rate is 1.0e6 times less than the mean //max is just less than SUM_TO #define MIN_REL_RATE (SUM_TO * (1.0e-6/190.0)) #define MAX_REL_RATE (SUM_TO - (189.0 * MIN_REL_RATE)) #endif enum{//the types STATEFREQS = 1, RELATIVERATES = 2, ALPHASHAPE = 3, RATEMULTS = 4, RATEPROPS = 5, PROPORTIONINVARIANT = 6, SUBSETRATE = 7, DELETERATE = 8, INSERTPROPORTION = 9 }; class BaseParameter { protected: NxsString name; int type; int numElements; bool fixed; FLOAT_TYPE maxv,minv; FLOAT_TYPE mutationWeight; FLOAT_TYPE mutationProb; vector vals; //this will be aliased to the actual //parameter value within the model class (sneaky!) vector default_vals; public: vector modelsThatInclude;//this isn't currently being used, but will eventually be used to only dirty models that changed BaseParameter() { numElements=1; maxv=minv=0.0; } BaseParameter(const char *n, FLOAT_TYPE **dv, int t, int numE, FLOAT_TYPE mn, FLOAT_TYPE mx, int modNum) { vals.reserve(6); default_vals.reserve(6); name=n; type=t; numElements=numE; for(int i=0;i 1) out << "Current value:\t"; else out << "Current values:\t"; for(int i=0;i maxv) ok = false; else if(newFreq < minv) ok = false; //all of this checking should almost never be necessary, but if the rescaling after //changing one rate would push one of the others over a boundary, just draw another //freqToChange and multiplier else{ for(int b=0;b= 1e-4); if(b!=freqToChange){ if(*vals[b] * rescaleBy > maxv || *vals[b] * rescaleBy < minv){ ok = false; break; } } } } }while(! ok); for(int b=0;b= minv && *vals[i] <= maxv); } }; class RelativeRates:public BaseParameter{ public: // 5/9/06 now enforcing non-zero minimum relative rate to avoid problems in the linear algebra functions RelativeRates(const char *c, FLOAT_TYPE **dv, int numE, FLOAT_TYPE min, FLOAT_TYPE max, int modnum):BaseParameter(c, dv, RELATIVERATES, numE, min, max, modnum){}; void Mutator(FLOAT_TYPE mutationShape){ if(numElements > 1){ int rateToChange=int(rnd.uniform()*(numElements)); //3/25/08 had to change this to allow arbitrary rate matrices mutation //of a rate other than GT might actually alter GT, so we need to actually check //whether it is 1.0 or not //if(rateToChangemaxv) *vals[rateToChange]=maxv; if(*vals[rateToChange]maxv) *vals[i]=maxv; if(*vals[i]maxv) *vals[rateToChange]=maxv; if(*vals[rateToChange]maxv) *vals[i]=maxv; if(*vals[i]maxv) *vals[0]=maxv; if(*vals[0] 1); int rateToChange=int(rnd.uniform()*(numElements)); FLOAT_TYPE newVal = *vals[rateToChange] * rnd.gamma( mutationShape ); if(newVal>maxv) newVal=maxv; if(newVal maxv) *vals[rateToChange] = maxv; if(*vals[rateToChange] < minv) *vals[rateToChange] = minv; //This rescaling is pretty poor, and can result in rates ending up //below or above the limits. It should be followed up by a call of //NormalizeSumConstrainedValues FLOAT_TYPE newTot = 1.0 - *vals[rateToChange]; FLOAT_TYPE oldTot = 0.0; for(int i=0;imaxv) *vals[rateToChange]=maxv; } }; class AlphaShape:public BaseParameter{ public: AlphaShape(const char *c, FLOAT_TYPE **dv, int modnum):BaseParameter(c, dv, ALPHASHAPE, 1, (FLOAT_TYPE)0.05, (FLOAT_TYPE)999.9, modnum){}; void Mutator(FLOAT_TYPE mutationShape){ *vals[0] *=rnd.gamma( mutationShape ); if(*vals[0]maxv) *vals[0]=maxv; } }; class ProportionInvariant:public BaseParameter{ public: ProportionInvariant(const char *c, FLOAT_TYPE **dv, int modnum):BaseParameter(c, dv, PROPORTIONINVARIANT, 1, (FLOAT_TYPE)0.0001, (FLOAT_TYPE)0.9999, modnum){}; void Mutator(FLOAT_TYPE mutationShape){ *vals[0] *=rnd.gamma( mutationShape ); if(*vals[0] < minv) *vals[0] = minv; if(*vals[0] > maxv) *vals[0] = maxv; } }; class InsertProportion:public BaseParameter{ public: InsertProportion(FLOAT_TYPE **dv, int modnum):BaseParameter("Insert prop.", dv, INSERTPROPORTION, 1, (FLOAT_TYPE)0.0001, (FLOAT_TYPE)0.9999, modnum){}; void Mutator(FLOAT_TYPE mutationShape){ *vals[0] *=rnd.gamma( mutationShape ); if(*vals[0] < minv) *vals[0] = minv; if(*vals[0] > maxv) *vals[0] = maxv; } }; class SubsetRates:public BaseParameter{ public: SubsetRates(FLOAT_TYPE **dv, int numE, int modnum):BaseParameter("Subset rate", dv, SUBSETRATE, numE, (FLOAT_TYPE)0.0, (FLOAT_TYPE)1.0, modnum){}; void Mutator(FLOAT_TYPE mutationShape){ int rateToChange=int(rnd.uniform()*(numElements)); *vals[rateToChange] *= rnd.gamma( mutationShape ); if(*vals[rateToChange]>maxv) *vals[rateToChange]=maxv; } }; class DeleteRate:public BaseParameter{ public: DeleteRate(FLOAT_TYPE **dv, int modnum):BaseParameter("Delete rate", dv, DELETERATE, 1, (FLOAT_TYPE)0.0001, (FLOAT_TYPE)999.0, modnum){}; void Mutator(FLOAT_TYPE mutationShape){ *vals[0] *= rnd.gamma( mutationShape ); if(*vals[0] > maxv) *vals[0] = maxv; if(*vals[0] < minv) *vals[0] = minv; } }; class ModelSpecification{ //this will hold the model specification as a global variable //so that any models allocated will immediately know what they are public: bool isSetup; int nstates; int numRateCats; bool fixStateFreqs; bool fixRelativeRates; string arbitraryRateMatrixString; // bool fixSubstitutionRates; //bool flexRates; bool fixInvariantSites; bool fixAlpha; bool fixOmega; bool includeInvariantSites; bool gotRmatFromFile; bool gotStateFreqsFromFile; bool gotAlphaFromFile; bool gotFlexFromFile; bool gotPinvFromFile; bool gotOmegasFromFile; bool gotInsertFromFile; bool gotDeleteFromFile; enum{ DNA = 0, RNA = 1, CODON = 2, AMINOACID = 3, CODONAMINOACID = 4, NSTATE = 5, NSTATEV = 6, ORDNSTATE = 7, ORDNSTATEV = 8, ORIENTEDGAP = 9, BINARY = 10, BINARY_NOTALLZEROS = 11 }datatype; enum{ EQUAL = 0, EMPIRICAL = 1, ESTIMATE = 2, F1X4 = 3, F3X4 = 4, JONES = 5, DAYHOFF = 6, WAG = 8, MTMAM = 9, MTREV = 10, USERSPECIFIED = 20 }stateFrequencies; enum{ NST1 = 0, NST2 = 1, NST6 = 2, ARBITRARY = 3, JONESMAT = 5, DAYHOFFMAT = 6, POISSON = 7, WAGMAT = 8, MTMAMMAT = 9, MTREVMAT = 10, ESTIMATEDAAMAT = 11, TWOSERINEMAT = 12, USERSPECIFIEDMAT = 20 }rateMatrix; enum{ NONE = 0, GAMMA = 1, FLEX = 2, NONSYN = 3 }rateHetType; enum{ STANDARD = 0, VERTMITO = 1, INVERTMITO = 2, STANDARDTWOSERINE = 3, VERTMITOTWOSERINE = 4, INVERTMITOTWOSERINE = 5 }geneticCode; ModelSpecification(){ nstates=4; //this is the default model SetGTR(); SetGammaRates(); SetNumRateCats(4, false); SetInvariantSites(); datatype=DNA; gotRmatFromFile = gotStateFreqsFromFile = gotAlphaFromFile = gotFlexFromFile = gotPinvFromFile = gotOmegasFromFile = gotInsertFromFile = gotDeleteFromFile = false; geneticCode=STANDARD; isSetup = false; } bool IsCodon() const {return datatype == CODON;} bool IsNucleotide() const {return (datatype == DNA || datatype == RNA);} bool IsRna() const {return (datatype == RNA);} //rna will be treated identically to dna almost everywhere, but it might be good to know when reading bool IsAminoAcid() const {return (datatype == AMINOACID || datatype == CODONAMINOACID);}//for most purposes codon-aminoacid should be considered AA bool IsCodonAminoAcid() const {return datatype == CODONAMINOACID;} bool IsNState() const {return datatype == NSTATE;} bool IsOrderedNState() const {return datatype == ORDNSTATE;} bool IsNStateV() const {return datatype == NSTATEV;} bool IsOrderedNStateV() const {return datatype == ORDNSTATEV;} bool IsOrientedGap() const {return datatype == ORIENTEDGAP;} bool IsBinary() const {return datatype == BINARY;} bool IsBinaryNotAllZeros() const {return datatype == BINARY_NOTALLZEROS;} //DO NOT INCLUDE OrientedGap here! bool IsMkTypeModel() const {return (IsNState() || IsNStateV() || IsOrderedNState() || IsOrderedNStateV() || IsBinary() || IsBinaryNotAllZeros());} bool GotAnyParametersFromFile() const { return gotRmatFromFile || gotStateFreqsFromFile || gotAlphaFromFile || gotFlexFromFile || gotPinvFromFile || gotOmegasFromFile; } //A number of canned model setups void SetJC(){ nstates=4; rateMatrix = NST1; SetEqualStateFreqs(); fixRelativeRates=true; } void K2P(){ nstates=4; rateMatrix = NST2; SetEqualStateFreqs(); fixRelativeRates=false; } void SetF81(){ nstates=4; rateMatrix = NST1; SetEstimateStateFreqs(); fixRelativeRates=true; } void SetHKY(){ nstates=4; rateMatrix = NST2; SetEstimateStateFreqs(); fixRelativeRates=false; } void SetGTR(){ nstates=4; rateMatrix = NST6; SetEstimateStateFreqs(); fixRelativeRates=false; } //this is the default, and shouldn't really need to be explicitly set //this and SetRna depend on the default model settings from the constructor void SetDna(){ datatype = DNA; } void SetRna(){ datatype = DNA; } void SetCodon(){ datatype = CODON; rateMatrix = NST2; stateFrequencies = EQUAL; nstates = 61;//this might be overridden if a nonstandard code is set numRateCats = 1; fixRelativeRates=false; fixOmega = false; RemoveInvariantSites(); } void SetAminoAcid(){ datatype = AMINOACID; rateMatrix = WAGMAT; stateFrequencies = WAG; nstates = 20; fixRelativeRates=true; } void SetCodonAminoAcid(){ datatype = CODONAMINOACID; rateMatrix = WAGMAT; stateFrequencies = WAG; nstates = 20; fixRelativeRates=true; } void SetNState(){ datatype = NSTATE; rateMatrix = NST1; stateFrequencies = EQUAL; nstates = -1; //this will need to be reset later fixRelativeRates=true; fixStateFreqs=true; } void SetOrderedNState(){ datatype = ORDNSTATE; rateMatrix = NST1; stateFrequencies = EQUAL; nstates = -1; //this will need to be reset later fixRelativeRates=true; fixStateFreqs=true; } void SetNStateV(){ datatype = NSTATEV; rateMatrix = NST1; stateFrequencies = EQUAL; nstates = -1; //this will need to be reset later fixRelativeRates=true; fixStateFreqs=true; } void SetOrderedNStateV(){ datatype = ORDNSTATEV; rateMatrix = NST1; stateFrequencies = EQUAL; nstates = -1; //this will need to be reset later fixRelativeRates=true; fixStateFreqs=true; } void SetOrientedGap(){ datatype = ORIENTEDGAP; rateMatrix = NST1; stateFrequencies = EQUAL; nstates = 3; fixRelativeRates=true; fixStateFreqs=true; } void SetBinary(){ datatype = BINARY; rateMatrix = NST1; stateFrequencies = EQUAL; nstates = 2; fixRelativeRates=true; fixStateFreqs=true; } void SetBinaryNotAllZeros(){ datatype = BINARY_NOTALLZEROS; rateMatrix = NST1; stateFrequencies = EQUAL; nstates = 2; fixRelativeRates=true; fixStateFreqs=true; } void SetNStates(int ns){ nstates = ns; } void SetGammaRates(){ rateHetType = GAMMA; fixAlpha=false; } void SetFlexRates(){ if(includeInvariantSites==true) throw(ErrorException("Invariant sites models should not be (and don't need to be) used\n with the \"flex\" model of rate heterogeneity, since flex is able to\n incorporate a class of sites with a rate of effectively zero")); rateHetType = FLEX; } void SetNumRateCats(int nrates, bool test){//correct behavior here depends on the fact that the default //model includes gamma with 4 rate cats if(test ==true){ if(rateHetType == NONE && nrates > 1) throw(ErrorException("ratehetmodel set to \"none\", but numratecats is equal to %d!", nrates)); if(rateHetType != NONE && nrates == 1){ if(rateHetType == GAMMA && fixAlpha == false) throw(ErrorException("ratehetmodel set to \"gamma\", but numratecats is equal to 1!")); else if(rateHetType == GAMMA && fixAlpha == true) throw(ErrorException("ratehetmodel set to \"gammafixed\", but numratecats is equal to 1!")); else if(rateHetType == FLEX) throw(ErrorException("ratehetmodel set to \"flex\", but numratecats is equal to 1!")); //now allowing this to signify a single omega param // else if(rateHetType == NONSYN) // throw(ErrorException("ratehetmodel set to \"nonsynonymous\", but numratecats is equal to 1!")); } } if(nrates < 1) throw(ErrorException("1 is the minimum value for numratecats.")); if(nrates > 20) throw(ErrorException("20 is the maximum value for numratecats.")); numRateCats=nrates; } void SetInvariantSites(){ if(rateHetType == NONSYN) throw(ErrorException("invariant sites cannot be used with nonsynonymous rate variation")); includeInvariantSites=true; fixInvariantSites=false; } void RemoveInvariantSites(){ includeInvariantSites=false; fixInvariantSites=false; } void SetEmpiricalStateFreqs(){ stateFrequencies = EMPIRICAL; fixStateFreqs=true; } //this is a hack to allow estimation of codon frequencies //other parts of the code break if stateFreqs aren't f1x4/f3x4/emp in a codon model //but leaving fixStateFreqs as false allows them to be estimated void SetFakeEmpiricalStateFreqs(){ stateFrequencies = EMPIRICAL; fixStateFreqs=false; } void SetEqualStateFreqs(){ stateFrequencies = EQUAL; fixStateFreqs=true; } void SetUserSpecifiedStateFreqs(){ stateFrequencies = USERSPECIFIED; fixStateFreqs=true; } void SetEstimateStateFreqs(){ stateFrequencies = ESTIMATE; fixStateFreqs=false; } void SetF1X4StateFreqs(){ stateFrequencies = F1X4; fixStateFreqs = true; } void SetF3X4StateFreqs(){ stateFrequencies = F3X4; fixStateFreqs = true; } void SetFixedAlpha(){ fixAlpha=true; } void SetFixedInvariantSites(){ fixInvariantSites=true; includeInvariantSites=true; } void SetUserSpecifiedRateMatrix(){ rateMatrix = USERSPECIFIEDMAT; fixRelativeRates=true; } void SetJonesAAMatrix(){ rateMatrix = JONESMAT; fixRelativeRates=true; } void SetPoissonAAMatrix(){ rateMatrix = POISSON; fixRelativeRates=true; } void SetDayhoffAAMatrix(){ rateMatrix = DAYHOFFMAT; fixRelativeRates=true; } void SetWAGAAMatrix(){ rateMatrix = WAGMAT; fixRelativeRates=true; } void SetMtMamAAMatrix(){ rateMatrix = MTMAMMAT; fixRelativeRates=true; } void SetMtRevAAMatrix(){ rateMatrix = MTREVMAT; fixRelativeRates=true; } void SetEstimatedAAMatrix(){ rateMatrix = ESTIMATEDAAMAT; fixRelativeRates=false; } void SetTwoSerineRateMatrix(){ rateMatrix = TWOSERINEMAT; nstates = 21; fixRelativeRates=false; } void SetJonesAAFreqs(){ stateFrequencies = JONES; fixStateFreqs=true; } void SetDayhoffAAFreqs(){ stateFrequencies = DAYHOFF; fixStateFreqs=true; } void SetWAGAAFreqs(){ stateFrequencies = WAG; fixStateFreqs=true; } void SetMtMamAAFreqs(){ stateFrequencies = MTMAM; fixStateFreqs=true; } void SetMtRevAAFreqs(){ stateFrequencies = MTREV; fixStateFreqs=true; } void SetOmegaModel(){ rateHetType = NONSYN; numRateCats = 3; fixOmega = false; } void SetFixedOmegaModel(){ rateHetType = NONSYN; numRateCats = 3; fixOmega = true; } int Nst() const { if(rateMatrix == NST1) return 1; else if(rateMatrix == NST2) return 2; else if(rateMatrix == NST6 || rateMatrix == ARBITRARY) return 6; else if(rateMatrix == USERSPECIFIEDMAT && datatype != AMINOACID && datatype != CODONAMINOACID) return 6; //estimation of AA matrices is now legal //else assert(0); return -1; } bool IsJonesAAFreqs() const {return (stateFrequencies == JONES);} bool IsJonesAAMatrix() const {return (rateMatrix == JONESMAT);} bool IsDayhoffAAFreqs() const {return (stateFrequencies == DAYHOFF);} bool IsDayhoffAAMatrix() const {return (rateMatrix == DAYHOFFMAT);} bool IsWAGAAFreqs() const {return (stateFrequencies == WAG);} bool IsWAGAAMatrix() const {return (rateMatrix == WAGMAT);} bool IsMtMamAAFreqs() const {return (stateFrequencies == MTMAM);} bool IsMtMamAAMatrix() const {return (rateMatrix == MTMAMMAT);} bool IsMtRevAAFreqs() const {return (stateFrequencies == MTREV);} bool IsMtRevAAMatrix() const {return (rateMatrix == MTREVMAT);} bool IsEstimateAAMatrix() const {return (rateMatrix == ESTIMATEDAAMAT);} bool IsVertMitoCode() const {return (geneticCode == VERTMITO);} bool IsInvertMitoCode() const {return (geneticCode == INVERTMITO);} bool IsTwoSerineCode() const {return (geneticCode == STANDARDTWOSERINE);} bool IsTwoSerineVertMitoCode() const {return (geneticCode == VERTMITOTWOSERINE);} bool IsTwoSerineInvertMitoCode() const {return (geneticCode == INVERTMITOTWOSERINE);} bool IsPoissonAAMatrix() const {return (rateMatrix == POISSON);} bool IsUserSpecifiedRateMatrix() const {return rateMatrix == USERSPECIFIEDMAT;} bool IsTwoSerineRateMatrix() const {return rateMatrix == TWOSERINEMAT;} bool IsArbitraryRateMatrix() const {return rateMatrix == ARBITRARY;} const string GetArbitraryRateMatrixString() const {return arbitraryRateMatrixString;} bool IsEqualStateFrequencies() const {return stateFrequencies == EQUAL;} bool IsEmpiricalStateFrequencies() const {return stateFrequencies == EMPIRICAL;} bool IsUserSpecifiedStateFrequencies() const {return stateFrequencies == USERSPECIFIED;} bool IsF3x4StateFrequencies() const {return stateFrequencies == F3X4;} bool IsF1x4StateFrequencies() const {return stateFrequencies == F1X4;} bool IsPrecaledAAFreqs() const {return (IsAminoAcid() && (stateFrequencies == DAYHOFF || stateFrequencies == JONES || stateFrequencies == WAG || stateFrequencies == MTMAM || stateFrequencies == MTREV));} bool IsFlexRateHet() const {return rateHetType == FLEX;} bool IsGammaRateHet() const {return rateHetType == GAMMA;} bool IsNonsynonymousRateHet() const {return rateHetType == NONSYN;} void SetStateFrequencies(const char *str){ if((datatype == NSTATE || datatype == NSTATEV) && _stricmp(str, "equal") != 0) throw(ErrorException("Invalid setting for statefrequencies: %s\n\tOnly equal state frequencies are currently available for the standard data type", str)); if(_stricmp(str, "equal") == 0) SetEqualStateFreqs(); else if(_stricmp(str, "estimate") == 0){ if(datatype == CODON) throw ErrorException("Sorry, ML estimation of equilibrium frequencies is not available under\ncodon models. Try statefrequencies = empirical"); //else if(datatype == AMINOACID || datatype == CODONAMINOACID) outman.UserMessage("\nWARNING: to obtain good ML estimates of equilibrium aminoacid frequencies you\n\tmay need to run for a very long time or increase the modweight.\n\tConsider statefrequencies = empirical instead.\n"); SetEstimateStateFreqs(); } else if(_stricmp(str, "estimateF") == 0){ //HACK - unfix freqs for codons, to cause estimation if(datatype != CODON) throw ErrorException("Sorry, forced estimation of frequencies (estimateF) is only for codon models"); SetFakeEmpiricalStateFreqs(); outman.UserMessage("\n\n\nCUSTOM USAGE - ESTIMATING CODON FREQS BY ML\n\n\n"); } else if(_stricmp(str, "empirical") == 0) SetEmpiricalStateFreqs(); else if(_stricmp(str, "fixed") == 0) SetUserSpecifiedStateFreqs(); else if(datatype == CODON && _stricmp(str, "f1x4") == 0) SetF1X4StateFreqs(); else if(datatype == CODON && _stricmp(str, "f3x4") == 0) SetF3X4StateFreqs(); else if((datatype == AMINOACID || datatype == CODONAMINOACID) && _stricmp(str, "jones") == 0) SetJonesAAFreqs(); else if((datatype == AMINOACID || datatype == CODONAMINOACID) && _stricmp(str, "dayhoff") == 0) SetDayhoffAAFreqs(); else if((datatype == AMINOACID || datatype == CODONAMINOACID) && _stricmp(str, "wag") == 0) SetWAGAAFreqs(); else if((datatype == AMINOACID || datatype == CODONAMINOACID) && _stricmp(str, "mtmam") == 0) SetMtMamAAFreqs(); else if((datatype == AMINOACID || datatype == CODONAMINOACID) && _stricmp(str, "mtrev") == 0) SetMtRevAAFreqs(); else throw(ErrorException("Invalid setting for statefrequencies: %s\n\tOptions for all datatypes: equal, empirical, fixed\n\tFor all datatypes besides codon: estimate\n\tFor aminoacid datatype only: poisson, dayhoff, jones, wag, mtmam, mtrev\n\tFor codon datatype only: F1X4, F3X4", str)); } void SetRateMatrix(const char *str){ if(datatype == AMINOACID || datatype == CODONAMINOACID){ if(_stricmp(str, "jones") == 0) SetJonesAAMatrix(); else if(_stricmp(str, "dayhoff") == 0) SetDayhoffAAMatrix(); else if(_stricmp(str, "poisson") == 0) SetPoissonAAMatrix(); else if(_stricmp(str, "wag") == 0) SetWAGAAMatrix(); else if(_stricmp(str, "mtmam") == 0) SetMtMamAAMatrix(); else if(_stricmp(str, "mtrev") == 0) SetMtRevAAMatrix(); else if(_stricmp(str, "estimate") == 0){ outman.UserMessage("\nWARNING: to obtain good ML estimates of the aminoacid rate matrix (189 free parameters)\n\tyou may need to run for a very long time or increase the modweight.\n\tDo not attempt this unless you have a very large amount of data.\n"); SetEstimatedAAMatrix(); } else if(_stricmp(str, "fixed") == 0) SetUserSpecifiedRateMatrix(); else if(_stricmp(str, "twoserine") == 0 || _stricmp(str, "twoserinefixed") == 0){ if(datatype != CODONAMINOACID) throw(ErrorException("Sorry, codon input data (with the codon-aminoacid datatype) are currently required for the Two-Serine model")); if(stateFrequencies != EMPIRICAL && stateFrequencies != ESTIMATE && stateFrequencies != USERSPECIFIED) throw(ErrorException("Sorry, empirical, estimated or fixed must be used as the statefrequencies setting for the Two-Serine model")); if( ! (IsTwoSerineCode() || IsTwoSerineVertMitoCode() || IsTwoSerineInvertMitoCode())) throw(ErrorException("To use the twoserine rate matrix the genetic code must be \"standardtwoserine\" \"vertmitotwoserine\" or \"invertmitotwoserine\".")); SetTwoSerineRateMatrix(); if(_stricmp(str, "twoserinefixed") == 0) fixRelativeRates = true; } else throw(ErrorException("Sorry, %s is not a valid aminoacid rate matrix. \n\t(Options are: dayhoff, jones, poisson, wag, mtmam, mtrev, estimate, fixed)", str)); } else if(datatype == NSTATE || datatype == NSTATEV){ if(_stricmp(str, "1rate") != 0) throw(ErrorException("Sorry, %s is not a valid ratematrix setting for the standard data type.\n\tOnly 1rate matrices are currently allowed.", str)); else rateMatrix = NST1; } else{ if(_stricmp(str, "6rate") == 0) rateMatrix = NST6; else if(_stricmp(str, "2rate") == 0) rateMatrix = NST2; else if(_stricmp(str, "1rate") == 0) rateMatrix = NST1; else if(_stricmp(str, "fixed") == 0) SetUserSpecifiedRateMatrix(); else if(str[0] == '('){ rateMatrix = ARBITRARY; arbitraryRateMatrixString = str; } else{ if(datatype == CODON) throw(ErrorException("Unknown setting for codon ratematrix: %s\n\t(options are: 6rate, 2rate, 1rate, fixed)", str)); else throw(ErrorException("Unknown setting for dna/rna ratematrix: %s\n\t(options are: 6rate, 2rate, 1rate, fixed)", str)); } } } void SetProportionInvariant(const char *str){ //if the entry didn't appear, depend on the correct default being set by the datatype specification if(_stricmp(str, "unspecified") == 0) return; if(_stricmp(str, "none") == 0) RemoveInvariantSites(); //else if(datatype == CODON || datatype == AMINOACID) throw(ErrorException("Sorry, invariant sites not yet supported with Codon/Aminoacid data")); else if(datatype == CODON){ if(_stricmp(str, "fixed") == 0 || _stricmp(str, "estimate") == 0) throw ErrorException("Invariant sites cannot be used with codon models.\n Try ratehetmodel = nonsynonymous to allow dN/dS variation across sites"); else throw(ErrorException("Unknown setting for proportioninvariant: %s\n\t(only valid option for codon models is none)", str)); } else if(_stricmp(str, "fixed") == 0) SetFixedInvariantSites(); else if(_stricmp(str, "estimate") == 0) SetInvariantSites(); else throw(ErrorException("Unknown setting for proportioninvariant: %s\n\t(options are: none, fixed, estimate)", str)); } void SetRateHetModel(const char *str){ // if((datatype != DNA) && (datatype != AMINOACID) && _stricmp(str, "none")) throw(ErrorException("Sorry, rate heterogeneity not yet supported with Codon/Aminoacid data")); if(datatype == CODON){ if(_stricmp(str, "nonsynonymous") == 0) SetOmegaModel(); else if(_stricmp(str, "nonsynonymousfixed") == 0) SetFixedOmegaModel(); else if(_stricmp(str, "none") == 0){ SetNumRateCats(1, false); rateHetType = NONE; } else if(_stricmp(str, "gamma") == 0) throw ErrorException("Gamma rate heterogeneity cannot be used with codon models.\n Try ratehetmodel = nonsynonymous to allow dN/dS variation across sites"); else throw(ErrorException("Unknown setting for ratehetmodel: %s\n\t(options for codon datatype are: nonsynonymous, nonsynonymousfixed, none)", str)); } else{ if(_stricmp(str, "gamma") == 0) SetGammaRates(); else if(_stricmp(str, "gammafixed") == 0){ SetGammaRates(); SetFixedAlpha(); } else if(_stricmp(str, "flex") == 0) SetFlexRates(); else if(_stricmp(str, "none") == 0){ SetNumRateCats(1, false); rateHetType = NONE; } else throw(ErrorException("Unknown setting for ratehetmodel: %s\n\t(options are for nucleotide or aminoacid data are: gamma, gammafixed, flex, none)", str)); } } void SetDataType(const char *str){ if(_stricmp(str, "codon") == 0) SetCodon(); else if(_stricmp(str, "codon-aminoacid") == 0) SetCodonAminoAcid(); else if(_stricmp(str, "aminoacid") == 0) SetAminoAcid(); else if(_stricmp(str, "protein") == 0) SetAminoAcid(); else if(_stricmp(str, "dna") == 0) str; else if(_stricmp(str, "rna") == 0) SetRna(); else if(_stricmp(str, "nucleotide") == 0) str; else if(_stricmp(str, "nstate") == 0) SetNState(); else if(_stricmp(str, "standard") == 0) SetNState(); else if(_stricmp(str, "standardordered") == 0) SetOrderedNState(); else if(_stricmp(str, "mk") == 0) SetNState(); else if(_stricmp(str, "standardvariable") == 0) SetNStateV(); else if(_stricmp(str, "standardvariableordered") == 0) SetOrderedNStateV(); else if(_stricmp(str, "standardorderedvariable") == 0) SetOrderedNStateV(); else if(_stricmp(str, "mkv") == 0) SetNStateV(); else if(_stricmp(str, "indelmixturemodel") == 0) SetOrientedGap(); else if(_stricmp(str, "gapmixturemodel") == 0) SetOrientedGap(); else if(_stricmp(str, "orientedgap") == 0) SetOrientedGap(); else if(_stricmp(str, "binary") == 0) SetBinary(); else if(_stricmp(str, "binarynotallzeros") == 0) SetBinaryNotAllZeros(); else throw(ErrorException("Unknown setting for datatype: %s\n\t(options are: codon, codon-aminoacid, aminoacid, nucleotide, standard[ordered], standardvariable[ordered], indelmixturemodel, binary, binarynotallzeros)", str)); } void SetGeneticCode(const char *str){ if(datatype != DNA && datatype != RNA){ if(_stricmp(str, "standard") == 0) geneticCode = STANDARD; else if(_stricmp(str, "vertmito") == 0){ geneticCode = VERTMITO; if(datatype == CODON) nstates = 60; } else if(_stricmp(str, "invertmito") == 0){ geneticCode = INVERTMITO; if(datatype == CODON) nstates = 62; } else if(_stricmp(str, "standardtwoserine") == 0) geneticCode = STANDARDTWOSERINE; else if(_stricmp(str, "vertmitotwoserine") == 0) geneticCode = INVERTMITOTWOSERINE; else if(_stricmp(str, "invertmitotwoserine") == 0) geneticCode = VERTMITOTWOSERINE; else throw(ErrorException("Unknown genetic code: %s\n\t(options are: standard, vertmito, invertmito)", str)); } } //PARTITION void SetupModSpec(const ConfigModelSettings &conf){ SetDataType(conf.datatype.c_str()); SetGeneticCode(conf.geneticCode.c_str()); SetStateFrequencies(conf.stateFrequencies.c_str()); SetRateMatrix(conf.rateMatrix.c_str()); SetProportionInvariant(conf.proportionInvariant.c_str()); SetRateHetModel(conf.rateHetModel.c_str()); SetNumRateCats(conf.numRateCats, true); isSetup = true; } /* void SetupModSpec(const GeneralGamlConfig &conf){ SetDataType(conf.datatype.c_str()); SetGeneticCode(conf.geneticCode.c_str()); SetStateFrequencies(conf.stateFrequencies.c_str()); SetRateMatrix(conf.rateMatrix.c_str()); SetProportionInvariant(conf.proportionInvariant.c_str()); SetRateHetModel(conf.rateHetModel.c_str()); SetNumRateCats(conf.numRateCats, true); isSetup = true; } */ }; class ModelSpecificationSet{ //a set of ModelSpecifications, each corresponding to a data subset/modelset vector modSpecs; bool inferSubsetRates; public: ModelSpecificationSet(){ inferSubsetRates = true; } ~ModelSpecificationSet(){ for(int i = 0;i < modSpecs.size();i++) delete modSpecs[i]; modSpecs.clear(); } void Delete(){ for(int i = 0;i < modSpecs.size();i++) delete modSpecs[i]; modSpecs.clear(); inferSubsetRates = true; } void AddModSpec(const ConfigModelSettings &conf){ ModelSpecification * mod = new ModelSpecification; mod->SetupModSpec(conf); modSpecs.push_back(mod); } ModelSpecification *GetModSpec(int num) const{ if(num > -1 == false || num >= modSpecs.size()) throw ErrorException("tried to access invalid ModSpec number"); return modSpecs[num]; } bool IsSetup(int num){return GetModSpec(num)->isSetup;} int NumSpecs() const {return modSpecs.size();} void SetInferSubsetRates(bool i){inferSubsetRates = i;} bool InferSubsetRates(){return inferSubsetRates;} bool GotAnyParametersFromFile() { for(vector::iterator msit = modSpecs.begin();msit != modSpecs.end();msit++) if((*msit)->GotAnyParametersFromFile()) return true; return false; } bool AnyOrientedGap(){ for(vector::iterator msit = modSpecs.begin();msit != modSpecs.end();msit++){ if((*msit)->IsOrientedGap()){ return true; } } return false; } }; class Model{ friend class ModelPartition; friend class ModelSet; int nst; int nstates; int nRateCats; ModelSpecification *modSpec;//pointer to the corresponding ModelSpecification int effectiveModels;//this is the number of models with different Q matrices //it does not include things like gamma or flex rates, //in which it is only the overall rate that varies bool includeInvariantSites; vector stateFreqs; vector relNucRates; //this is essentially a temporary scratch variable used during optimization. See SetReferenceRelativeNucRate FLOAT_TYPE currentRefRateScale; int arbitraryMatrixIndeces[6];//this just keeps track of which rate parameters are aliased to single parameters vector omegas; vector omegaProbs; bool eigenDirty; FLOAT_TYPE *blen_multiplier; //this is the rescaling factor to make the mean rate in the qmat = 1 FLOAT_TYPE rateMults[20]; FLOAT_TYPE rateProbs[20]; FLOAT_TYPE *alpha; FLOAT_TYPE *propInvar; FLOAT_TYPE *insertRate; FLOAT_TYPE *deleteRate; //variables used for the eigen process if nst=6 int *iwork, *indx; MODEL_FLOAT **eigvals, *eigvalsimag, ***eigvecs, ***inveigvecs, **teigvecs, *work, *temp, *col, **c_ijk, *EigValexp, *EigValderiv, *EigValderiv2; MODEL_FLOAT ***qmat, ***pmat1, ***pmat2; MODEL_FLOAT ***tempqmat; #ifdef SINGLE_PRECISION_FLOATS //these are used so that the transition matrices can be computed in double precision and //then copied to sinlge precision for use in the CLA/Deriv functions FLOAT_TYPE ***fpmat1, ***fpmat2; FLOAT_TYPE ***fderiv1, ***fderiv2; #endif //Newton Raphson crap MODEL_FLOAT ***deriv1, ***deriv2; //this will be a little bigger than necessary with some codes, but dynamically allocating a static is a bit of a pain //Making these no longer static, to allow different codes for different //partition subsets //static int qmatLookup[62*62]; //static GeneticCode *code; int qmatLookup[62*62]; GeneticCode *code; public: // static bool noPinvInModel; // static bool useFlexRates; // static int nRateCats; static FLOAT_TYPE mutationShape; FLOAT_TYPE maxPropInvar; vector paramsToMutate; ~Model(); Model(int num){ code = NULL; stateFreqs.reserve(4); relNucRates.reserve(6); paramsToMutate.reserve(5); //DEBUG - we should probably move this out of here. It assumes that the //global modspec has been setup assert(modSpecSet.IsSetup(num)); CreateModelFromSpecification(num); } void CalcMutationProbsFromWeights(); FLOAT_TYPE GetTotalModelMutationWeight(); BaseParameter *SelectModelMutation(); const vector *GetMutableParameters(){return ¶msToMutate;} int PerformModelMutation(); void CreateModelFromSpecification(int); void SetCode(GeneticCode *c){ code = c; FillQMatLookup(); } const ModelSpecification *GetCorrespondingSpec() const {return modSpec;} private: void AllocateEigenVariables(); void CalcEigenStuff(); public: void CalcPmat(MODEL_FLOAT blen, MODEL_FLOAT *metaPmat, bool flip =false); void CalcPmats(FLOAT_TYPE blen1, FLOAT_TYPE blen2, FLOAT_TYPE *&mat1, FLOAT_TYPE *&mat2); void CalcPmatNState(FLOAT_TYPE blen, MODEL_FLOAT *metaPmat); void CalcDerivatives(FLOAT_TYPE, FLOAT_TYPE ***&, FLOAT_TYPE ***&, FLOAT_TYPE ***&); void CalcDerivativesOrientedGap(FLOAT_TYPE, FLOAT_TYPE ***&, FLOAT_TYPE ***&, FLOAT_TYPE ***&); void OutputPmats(ofstream &deb); void AltCalcPmat(FLOAT_TYPE dlen, MODEL_FLOAT ***&pr); void CalcOrientedGapPmat(FLOAT_TYPE blen, MODEL_FLOAT ***&mat); void UpdateQMat(); void UpdateQMatCodon(); void CalcSynonymousBranchlengthProportions(vector &results); void UpdateQMatAminoAcid(); void UpdateQMatNState(); void UpdateQMatOrderedNState(); void DiscreteGamma(FLOAT_TYPE *, FLOAT_TYPE *, FLOAT_TYPE); bool IsModelEqual(const Model *other) const ; void CopyModel(const Model *from); void CopyEigenVariables(const Model *from); void SetModel(FLOAT_TYPE *model_string); void OutputPaupBlockForModel(ofstream &, const char *) const; void FillPaupBlockStringForModel(string &str, const char *treefname) const; void OutputGarliFormattedModel(ostream &) const; void FillGarliFormattedModelString(string &s) const; void OutputBinaryFormattedModel(OUTPUT_CLASS &) const; void ReadGarliFormattedModelString(string &); void OutputHumanReadableModelReportWithParams() const; void FillModelOrHeaderStringForTable(string &s, bool m) const; void OutputAminoAcidRMatrixArray(ostream &out, int modNum, int treeNum); void OutputAminoAcidRMatrixMessage(ostream &out); void ReadBinaryFormattedModel(FILE *); void FillQMatLookup(); void SetJonesAAFreqs(); void SetMtMamAAFreqs(); void SetMtRevAAFreqs(); void SetDayhoffAAFreqs(); void SetWAGAAFreqs(); void MultiplyByJonesAAMatrix(); void MultiplyByMtMamAAMatrix(); void MultiplyByMtRevAAMatrix(); void MultiplyByDayhoffAAMatrix(); void MultiplyByWAGAAMatrix(); //model mutations void MutateRates(); void MutatePis(); void MutateAlpha(); void MutatePropInvar(); void MutateRateProbs(); void MutateRateMults(); //Accessor functions FLOAT_TYPE StateFreq(int p) const{ return *stateFreqs[p];} FLOAT_TYPE TRatio() const; FLOAT_TYPE Rates(int r) const { return *relNucRates[r];} int NumRelRates() const {return relNucRates.size();} int NRateCats() const {return nRateCats;} FLOAT_TYPE *GetRateMults() {return rateMults;} FLOAT_TYPE Alpha() const {return *alpha;} FLOAT_TYPE PropInvar() const { return *propInvar;} bool NoPinvInModel() const { return ! (modSpec->includeInvariantSites);} FLOAT_TYPE MaxPinv() const{return maxPropInvar;} int NStates() const {return nstates;} int NumMutatableParams() const {return (int) paramsToMutate.size();} int Nst() const {return nst;} const int *GetArbitraryRateMatrixIndeces() const {return arbitraryMatrixIndeces;} const GeneticCode *GetGeneticCode(){return code;} bool IsNucleotide() const {return modSpec->IsNucleotide();} bool IsOrientedGap() const {return modSpec->IsOrientedGap();} bool IsNState() const {return modSpec->IsNState();} bool IsNStateV() const {return modSpec->IsNStateV();} bool IsOrderedNState() const {return modSpec->IsOrderedNState();} bool IsOrderedNStateV() const {return modSpec->IsOrderedNStateV();} bool IsBinary() const {return modSpec->IsBinary();} bool IsBinaryNotAllZeros() const {return modSpec->IsBinaryNotAllZeros();} const ModelSpecification *GetModSpec() const {return modSpec;} FLOAT_TYPE InsertRate() const {return *insertRate;} FLOAT_TYPE DeleteRate() const {return *deleteRate;} //these are the old freqs, no longer used. Came from TKF I think FLOAT_TYPE AbsenceFrequency() const {return (1.0 / ((*insertRate / *deleteRate) + 1.0));} FLOAT_TYPE PresenceFrequency() const {return (*insertRate / *deleteRate) / ((*insertRate / *deleteRate) + 1.0);} //these were from Rivas and Eddy, also not used currently FLOAT_TYPE IndelPsi(FLOAT_TYPE blen) const {return (InsertRate() / (InsertRate() + DeleteRate()) * (1.0 - exp(-(InsertRate() + DeleteRate()) * blen)));} FLOAT_TYPE IndelGamma(FLOAT_TYPE blen) const {return (DeleteRate() / (InsertRate() + DeleteRate()) * (1.0 - exp(-(InsertRate() + DeleteRate()) * blen)));} //Setting things void SetDefaultModelParameters(SequenceData *data); void SetRmat(FLOAT_TYPE *r, bool checkValidity, bool renormalize){ if(checkValidity == true && modSpec->IsAminoAcid() == false){ if(nst==1){ if((FloatingPointEquals(r[0], r[1], 1.0e-5) && FloatingPointEquals(r[1], r[2], 1.0e-5) && FloatingPointEquals(r[2], r[3], 1.0e-5) && FloatingPointEquals(r[3], r[4], 1.0e-5) && FloatingPointEquals(r[4], r[5], 1.0e-5)) == false) throw(ErrorException("Config file specifies ratematrix = 1rate, but starting model has nonequal rates!\n")); } else if(nst==2){ if((FloatingPointEquals(r[0], r[2], 1.0e-5) && FloatingPointEquals(r[2], r[3], 1.0e-5) && FloatingPointEquals(r[1], r[4], 1.0e-5) && FloatingPointEquals(r[3], r[5], 1.0e-5)) == false) throw(ErrorException("Config file specifies ratematrix = 2rate, but starting model parameters do not match!\n")); } else if(nst==6 && modSpec->IsArbitraryRateMatrix()){ for(int rate1=0;rate1<6-1;rate1++){ for(int rate2=rate1+1;rate2<6;rate2++){ if(arbitraryMatrixIndeces[rate1] == arbitraryMatrixIndeces[rate2]){ if(!FloatingPointEquals(r[rate1], r[rate2], max(1.0e-8, GARLI_FP_EPS * 2.0))) throw(ErrorException("Provided relative rate parameters don't obey the ratematix specification!\n\tGiven this spec: %s, rates %d and %d should be equal.\n", modSpec->arbitraryRateMatrixString.c_str(), rate1+1, rate2+1)); } } } } } /* if(FloatingPointEquals(r[5], ONE_POINT_ZERO, 1.0e-5) == false){ //if an alternate GTR paramterization is used in which GT != 1.0, rescale the rates for(int i=0;i<5;i++) r[i] /= r[5]; } for(int i=0;i<5;i++) *relNucRates[i]=r[i]; *relNucRates[5]=1.0; eigenDirty=true; */ //if we're reading in from a binary checkpoint we may not want to renormalize if were close because //the scores need to match exactly //if we're constraining the matrix by summing the rates AND this is an AA model, do that //otherwise do the normal fix at 1.0 constraint if(modSpec->IsAminoAcid()) assert(modSpec->IsEstimateAAMatrix() || modSpec->IsUserSpecifiedRateMatrix() || modSpec->IsTwoSerineRateMatrix()); #ifdef SUM_AA_REL_RATES if(modSpec->IsAminoAcid()){ for(int i=0;iNormalizeSumConstrainedRelativeRates(true, -1); } else{ #else if(1){ #endif if(renormalize){ int refRate = relNucRates.size()-1; if(FloatingPointEquals(r[refRate], ONE_POINT_ZERO, 1.0e-5) == false){ for(int i=0;iIsEqualStateFrequencies() && (FloatingPointEquals(b[0], b[1], 1.0e-5) && FloatingPointEquals(b[1], b[2], 1.0e-5)) == false) throw(ErrorException("Config file specifies equal statefrequencies,\nbut starting model has nonequal frequencies!\n")); if(modSpec->IsEmpiricalStateFrequencies()) throw(ErrorException("Config file specifies empirical statefrequencies,\nbut starting model contains frequencies!\nTry statefrequencies = fixed or statefrequencies = estimate.")); if(modSpec->IsPrecaledAAFreqs()) throw(ErrorException("Config file specifies \"named\" amino acid statefrequencies,\nbut starting model contains frequencies!\nTry statefrequencies = fixed or statefrequencies = estimate.")); // } } FLOAT_TYPE freqTot = 0.0; for(int i=0;iIsFlexRateHet() == false) throw ErrorException("Flex rate values specified in start file,\nbut ratehetmodel is not flex in conf file."); for(int r=0;r= 1e-4); } } void SetEquilibriumFreq(int which, FLOAT_TYPE val){ assert(which < this->nstates); #ifdef OLD_EQ_RESCALE FLOAT_TYPE rescale = (FLOAT_TYPE)((1.0 - val)/(1.0 - *stateFreqs[which])); for(int b=0;b= MIN_REL_RATE); *relNucRates[which] = val; NormalizeSumConstrainedRelativeRates(true, which); /* FLOAT_TYPE initial = *relNucRates[which]; FLOAT_TYPE rescale = ((SUM_TO - val) / (SUM_TO - initial)); FLOAT_TYPE minSum = ZERO_POINT_ZERO; FLOAT_TYPE nonMin = ZERO_POINT_ZERO; bool someMin = false; for(int i=0;i= ZERO_POINT_ZERO){ *relNucRates[i] *= rescale; if(*relNucRates[i] < MIN_REL_RATE){ *relNucRates[i] = -1.0; minSum += MIN_REL_RATE; someMin = true; } else sum += *relNucRates[i]; } } } }while(someMin); } for(int i=0;i MAX_REL_RATE && enforceBounds){ *relNucRates[i] = MAX_REL_RATE; sum += *relNucRates[i]; } else{ sum += *relNucRates[i]; } } FLOAT_TYPE nonMinTarget = SUM_TO - minSum; for(int i=0;i ZERO_POINT_ZERO){ *vals[i] *= rescale; if(*vals[i] < minVal){ *vals[i] = -1.0; minSum += minVal; someMin = true; } else sum += *vals[i]; } } } }while(someMin); } for(int i=0;inumRateCats > 1); *alpha=val; DiscreteGamma(rateMults, rateProbs, *alpha); //This is odd, but we need to call normalize rates here if we are just using a gamma distrib to get starting rates for //flex. Flex expects that the rates will be normalized including pinv elsewhere if(modSpec->IsFlexRateHet()) NormalizeRates(); } void SetDeleteRate(int which, FLOAT_TYPE val){ *deleteRate = val; } void SetInsertRate(int which, FLOAT_TYPE val){ *insertRate = val; } //these are the bounds on a particular rate that keep it from crossing a neighboring rate when rescaling happens FLOAT_TYPE EffectiveLowerFlexBound(int which){ assert(which != 0); assert(which < NRateCats()); FLOAT_TYPE whichProd = rateMults[which] * rateProbs[which]; FLOAT_TYPE factor = rateMults[which - 1] / ((rateMults[which] * (1.0 - whichProd)) + (whichProd * rateMults[which - 1])); FLOAT_TYPE thisVal = rateMults[which] * factor; FLOAT_TYPE lowerVal = rateMults[which - 1] * (1.0 - factor * rateMults[which] * rateProbs[which]) / (1.0 - rateMults[which] * rateProbs[which]); assert(FloatingPointEquals(thisVal, lowerVal, 1e-4)); return max(thisVal, lowerVal) + GARLI_FP_EPS; } FLOAT_TYPE EffectiveUpperFlexBound(int which){ assert(which < NRateCats() - 1); FLOAT_TYPE whichProd = rateMults[which] * rateProbs[which]; FLOAT_TYPE factor = rateMults[which + 1] / ((rateMults[which] * (1.0 - whichProd)) + (whichProd * rateMults[which + 1])); FLOAT_TYPE thisVal = rateMults[which] * factor; FLOAT_TYPE upperVal = rateMults[which + 1] * (1.0 - factor * rateMults[which] * rateProbs[which]) / (1.0 - rateMults[which] * rateProbs[which]); assert(FloatingPointEquals(thisVal, upperVal, 1e-4)); return min(thisVal, upperVal) - GARLI_FP_EPS; } void SetFlexRate(int which, FLOAT_TYPE val){ assert(which < NRateCats()); rateMults[which] = val; NormalizeRates(which, which); eigenDirty = true; } void SetFlexProb(int which, FLOAT_TYPE val){ assert(which < NRateCats()); rateProbs[which] = val; //here the proportion that changed should remain constant, but there isn't anything wrong with //the corresponding rate changing when rescaling NormalizeRates(which, -1); eigenDirty = true; } void SetOmega(int which, FLOAT_TYPE val){ assert(which < NRateCats()); *omegas[which] = val; eigenDirty = true; } void SetOmegaProb(int which, FLOAT_TYPE val){ assert(which < NRateCats()); assert(val >= 0.0); assert(val == val); *omegaProbs[which] = val; NormalizeSumConstrainedValues(&omegaProbs[0], NRateCats(), ONE_POINT_ZERO, 1.0e-5, which); /* FLOAT_TYPE newTot = 1.0 - *omegaProbs[which]; FLOAT_TYPE oldTot = 0.0; for(int i=0;i 0.0); *omegaProbs[i] *= newTot / oldTot; } #ifndef NDEBUG newTot = 0.0; for(int i=0;i= 0.0); assert(*omegas[which] == *omegas[which]); return *omegas[which]; } FLOAT_TYPE OmegaProb(int which) const{ assert(which < NRateCats()); assert(*omegaProbs[which] >= 0.0); assert(*omegaProbs[which] == *omegaProbs[which]); return *omegaProbs[which]; } void SetAlpha(FLOAT_TYPE a, bool checkValidity){ if(checkValidity == true) if(modSpec->numRateCats==1) throw(ErrorException("Config file specifies ratehetmodel = none, but starting model contains alpha!\n")); *alpha=a; DiscreteGamma(rateMults, rateProbs, *alpha); //This is odd, but we need to call normalize rates here if we are just using a gamma distrib to get starting rates for //flex. Flex expects that the rates will be normalized including pinv elsewhere if(modSpec->IsFlexRateHet()) NormalizeRates(); } void SetPinv(FLOAT_TYPE p, bool checkValidity){ if(checkValidity == true){ if(modSpec->includeInvariantSites == false && p!=0.0) throw(ErrorException("Config file specifies invariantsites = none, but starting model contains it!\n")); else if(modSpec->includeInvariantSites == true && p == 0.0){ outman.UserMessage("WARNING: Config file specifies estimation of invariantsites, but starting model sets it to zero!\n"); p = 1.0e-8; //throw(ErrorException("Config file specifies invariantsites, but starting model sets it to zero!\n")); } } *propInvar=p; //change the proportion of rates in each gamma cat for(int i=0;i 1); if(modSpec->IsNonsynonymousRateHet()){ bool done; do{ done = true; for(int f=0;f *omegas[f+1]){ //outman.UserMessage("prevented: %f %f", *omegas[f], *omegas[f+1]); FLOAT_TYPE dum = *omegas[f+1]; *omegas[f+1] = *omegas[f]; *omegas[f] = dum; dum = *omegaProbs[f+1]; *omegaProbs[f+1] = *omegaProbs[f]; *omegaProbs[f] = dum; done = false; } } }while(!done); } else if(modSpec->IsFlexRateHet()){ bool done; do{ done = true; for(int f=0;f rateMults[f+1]){ FLOAT_TYPE dum = rateMults[f+1]; rateMults[f+1] = rateMults[f]; rateMults[f] = dum; dum = rateProbs[f+1]; rateProbs[f+1] = rateProbs[f]; rateProbs[f] = dum; done = false; } } }while(!done); } else assert(0); } void AdjustRateProportions(){ //this will change the gamma class probs when pinv changes for(int i=0;i aliasedRates; for(int i=0;i -1) sum /= (ONE_POINT_ZERO - rateProbs[toRemainConstant]); for(int i=0;i -1){ rateToRemainConstantContrib = rateMults[rateToRemainConstant]*rateProbs[rateToRemainConstant]; //this means that it isn't possible to rescale and keep one of the rate/probs constant if(rateToRemainConstantContrib > ONE_POINT_ZERO) rateToRemainConstant = -1; } for(int i=0;i -1) sum /= (ONE_POINT_ZERO - (rateMults[rateToRemainConstant] * rateProbs[rateToRemainConstant])); for(int i=0;i rateMults[r]){ OK = false; break; } if(r == NRateCats()) OK = true; if(rateToRemainConstant == -1) assert(OK); if(!OK){//restore the rates and try again for(int r=0;r ZERO_POINT_ZERO); assert(rateProbs[i] < ONE_POINT_ZERO); assert(rateMults[i] > ZERO_POINT_ZERO); } sum += *propInvar; assert(fabs(sum - 1.0) < 0.0001); sum=0.0; for(int i=0;iIsNonsynonymousRateHet()) for(int i=0;i mods; vector modelProbs; vector modelMutationProbs; public: ModelSet(int m){ //currently one model per set int numModels = 1; for(int i=0;istateFreqs[0] != NULL); unsigned num = 0; for(vector::const_iterator modit = m->mods.begin();modit != m->mods.end();modit++){ Model *mod; if(num >= mods.size()){ mod = new Model(num); mods.push_back(mod); } else mod = mods[num]; mod->CopyModel(*modit); num++; } } bool IsModelSetEqual(const ModelSet *other) const{ bool equal = true; for(unsigned i=0;iIsModelEqual(other->GetModel(i)); if(!equal) return equal; } return equal; } void CollectMutableParameters(vector ¶mVec){ for(vector::const_iterator modit = mods.begin();modit != mods.end();modit++){ const vector *tempVec = (*modit)->GetMutableParameters(); for(unsigned i=0;isize();i++) paramVec.push_back((*tempVec)[i]); } } //currently nothing in ModelSet to save, since no mixing void WriteModelSetCheckpoint(OUTPUT_CLASS &out) const{ for(vector::const_iterator modit = mods.begin();modit != mods.end();modit++){ (*modit)->OutputBinaryFormattedModel(out); } } void ReadModelSetCheckpoint(FILE *in){ for(vector::iterator modit = mods.begin();modit != mods.end();modit++){ (*modit)->ReadBinaryFormattedModel(in); } } void SetDefaultModelSetParameters(SequenceData *data){ for(vector::iterator modit = mods.begin();modit != mods.end();modit++){ (*modit)->SetDefaultModelParameters(data); } } }; class ModelPartition{ //a collection of model sets, each of which corresponds to a set of characters vector modSets; //the models here are the same as included in the modSets - it will sometimes //be handy to access them directly, and it will often be not matter what the //hierarchy is to calculate things as the model level (ie, pmats, CLAs etc) vector models; vector allParamsToMutate; // FLOAT_TYPE globalRateScaler; vector subsetRates; vector subsetProportions; public: ModelPartition(); ~ModelPartition(){ for(int i = 0;i < modSets.size();i++) delete modSets[i]; modSets.clear(); //these are just pulled from the modsets, so don't need to be deleted models.clear(); allParamsToMutate.clear(); } void CopyModelPartition(const ModelPartition *mp){ unsigned num = 0; for(vector::const_iterator setit = mp->modSets.begin();setit != mp->modSets.end();setit++){ ModelSet *modSet; if(num >= modSets.size()){ modSet = new ModelSet(num); modSets.push_back(modSet); } else modSet = modSets[num]; modSet->CopyModelSet(*setit); num++; } //subsetProportions are just proportional to the number of total chars in each data subset, so they won't vary assert(NumSubsetRates() == mp->NumSubsetRates()); for(int d = 0;d < subsetRates.size();d++){ subsetRates[d] = mp->subsetRates[d]; } } unsigned NumModelSets() const {return modSets.size();} unsigned NumModels() const {return models.size();} unsigned NumMutableParams() const {return allParamsToMutate.size();} unsigned NumSubsetRates() const {return subsetRates.size();} FLOAT_TYPE SubsetRate(int i) const {return subsetRates[i];} //can't think of anything else that really needs to get reset here void Reset(){ for(int d = 0;d < subsetRates.size();d++){ subsetRates[d] = 1.0; } } void SetSubsetRate(int which, FLOAT_TYPE val){ assert(which < subsetRates.size()); subsetRates[which] = val; NormalizeSubsetRates(which); } void SetSubsetRates(const vector vals, bool renormalize){ assert(NumSubsetRates() == vals.size()); subsetRates.clear(); for(int i = 0;i < vals.size();i++) subsetRates.push_back(vals[i]); if(renormalize) NormalizeSubsetRates(); } int PerformModelMutation(); BaseParameter *SelectModelMutation(); void CalcMutationProbsFromWeights(); unsigned CalcRequiredCLAsize(const DataPartition *dat); //this is the size in BYTES not elements double CalcRequiredCLAsizeKB(const DataPartition *dat); //this is the size in KB not elements ModelSet *GetModelSet(int ms) const{ if(ms < 0 || ms < modSets.size() == false) throw ErrorException("Attemped to access invalid ModelSet number"); return modSets[ms]; } Model *GetModel(int m) const{ if(m < 0 || (m < models.size()) == false) throw ErrorException("Attemped to access invalid Model number"); return models[m]; } bool IsModelPartitionEqual(const ModelPartition *other) const{ bool equal = true; for(unsigned i=0;iIsModelSetEqual(other->GetModelSet(i)); if(!equal) return equal; } return equal; } void CollectMutableParameters(){ for(vector::const_iterator setit = modSets.begin();setit != modSets.end();setit++){ vector setParams; (*setit)->CollectMutableParameters(setParams); for(vector::iterator pit = setParams.begin();pit != setParams.end();pit++) allParamsToMutate.push_back(*pit); setParams.clear(); } } void NormalizeSubsetRates(int toRemainConstant = -1){ //optionally, pass the number of one of the rates to hold constant //the proportions don't change, so this is simpler than flex rates assert(subsetRates.size() > 1); double toRemainConstantContrib; if(toRemainConstant > -1){ toRemainConstantContrib = subsetRates[toRemainConstant]*subsetProportions[toRemainConstant]; //this means that it isn't possible to rescale and keep one of the rate/probs constant if(toRemainConstantContrib > ONE_POINT_ZERO) toRemainConstant = -1; } FLOAT_TYPE sum = 0.0; for(int i=0;i -1) sum /= (ONE_POINT_ZERO - (subsetRates[toRemainConstant] * subsetProportions[toRemainConstant])); for(int i=0;iOutputHumanReadableModelReportWithParams(); } if(modSpecSet.InferSubsetRates()){ outman.UserMessageNoCR("Subset rate multipliers:\n "); for(int d = 0;d < subsetRates.size();d++) outman.UserMessageNoCR("%6.2f", SubsetRate(d)); outman.UserMessage(""); } } void ReadGarliFormattedModelStrings(string &modstr); void FillGarliFormattedModelStrings(string &s) const; void WriteModelPartitionCheckpoint(OUTPUT_CLASS &out) const; void ReadModelPartitionCheckpoint(FILE *in); }; typedef void (Model::*SetParamFunc) (int, FLOAT_TYPE); #define CALL_SET_PARAM_FUNCTION(object, ptrToMember) ((object).*(ptrToMember)) #endif garli-2.1-release/src/mpifuncs.cpp000066400000000000000000001102771241236125200172150ustar00rootroot00000000000000// GARLI version 0.94 source code // Copyright 2005 by Derrick J. Zwickl // All rights reserved. // // This code may be used and modified for non-commercial purposes // but redistribution in any form requires written permission. // Please contact: // // Derrick Zwickl // Integrative Biology, UT // 1 University Station, C0930 // Austin, TX 78712 // email: garli.support@gmail.com // // Note: In 2006 moving to NESCENT (The National // Evolutionary Synthesis Center) for a postdoc // NOTE: Portions of this source adapted from GAML source, written by Paul O. Lewis // all of the mpi related code appears here or in threadfuncs.cpp #ifdef MPI_VERSION #include #include #include #include "mpifuncs.h" #include "defs.h" #include "population.h" #include "individual.h" #include "configoptions.h" #include "configreader.h" #include "funcs.h" #include "stopwatch.h" #include "threaddcls.h" #include "adaptation.h" #include "errorexception.h" // globals Stopwatch *g_sw=NULL; long int* g_gen = NULL; extern rng rnd; FILE *fhandle; int MPIMain(int argc, char** argv) { MPI_Init(&argc, &argv); int rank, nprocs; MPI_Comm_rank(MPI_COMM_WORLD, &rank); MPI_Comm_size(MPI_COMM_WORLD, &nprocs); bool poo=true; //if(rank==0) while (poo) ; try{ if (rank == 0) { MasterGamlConfig conf; int err=conf.Read("garli.conf", true); if(err != 0){ send_quit_messages(nprocs); throw ErrorException("Error in config file (Master)...aborting."); } LogConfig(conf); // Create the data object NucleotideData data; ReadData(conf.datafname.c_str(), &data); // start the remote nodes going... StartProcs(conf, data); // start yourself! MasterMaster(conf, data); } else { // rank != 0 int from, tag, size; char* buf; NucleotideData data; GeneralGamlConfig conf; if (conf.Read("garli.conf") < 0) { throw ErrorException("Error in config file (Remote)...aborting."); } //no longer sending the conf, just letting the remote read it from file // for (int i = 0; i < 2; ++i) { RecvMPIMessage(&buf, &size, &from, &tag, true); assert(from == 0); // sanity check if (tag == TAG_DATA) { data.Deserialize(buf, size); debug_mpi("receieved data from %d", from); } /* else if (tag == TAG_CONFIG) { conf.Deserialize(buf, size); debug_mpi("received conf from %d", from); } */ else { debug_mpi("ERROR: received unexpected message from %d with tag %d", from, tag); debug_mpi("aborting from MPIMain()"); } delete [] buf; // } // LogConfig(conf); RemoteMaster(conf, data); } }catch(ErrorException &err){ err.Print(cout); } // time to kill some global vars delete [] node_results; MPI_Finalize(); return 0; } int StartProcs(const GeneralGamlConfig& conf, NucleotideData& data) { // debug_mpi("entering StartProcs()"); char* conf_buf, *data_buf; int conf_size, data_size; GeneralGamlConfig ctest; // NucleotideData dtest; data.Serialize(&data_buf, &data_size); // conf.Serialize(&conf_buf, &conf_size); // sanity check: make sure the serialization code works /* dtest.Deserialize(data_buf, data_size); assert(data == dtest); ctest.Deserialize(conf_buf, conf_size); assert(conf == ctest); */ int nprocs; MPI_Comm_size(MPI_COMM_WORLD, &nprocs); for (int i = 1; i < nprocs; ++i) { SendMPIMessage(data_buf, data_size, i, TAG_DATA); debug_mpi("sent data to node %d", i); // SendMPIMessage(conf_buf, conf_size, i, TAG_CONFIG); // debug_mpi("sent conf to node %d", i); } delete [] data_buf; // delete [] conf_buf; debug_mpi("leaving StartProcs()"); return 0; } /* threaded MasterMaster */ /* i would prefer that the thread initialization code happen in MPIMain(), but * it needs Population pop, which is declared here */ int MasterMaster(MasterGamlConfig& conf, NucleotideData& data) { Parameters params; params.SetParams(conf, data); LogParams(params); int nprocs; MPI_Comm_size(MPI_COMM_WORLD, &nprocs); bool poo=true; // while(poo); Tree::alpha = params.gammaShapeBrlen; Tree::meanBrlenMuts = params.meanBrlenMuts; Population pop; // debug_mpi("about to setup"); pop.Setup(params, &conf, nprocs, 0); g_sw=&pop.stopwatch; // debug_mpi("setup"); g_gen = &pop.gen; pop.CalcAverageFitness(); // start the thread pthread_t thread; thread_arg_t targ; pthread_mutex_init(&lock_pm, NULL); pthread_mutex_init(&lock_pop, NULL); pthread_cond_init(&cond_pm, NULL); g_quit_time = false; g_processing_message = false; targ.conf = &const_cast(conf); targ.pop = &pop; targ.nprocs = nprocs; pthread_create(&thread, NULL, thread_func2, (void*)&targ); cout << "Master running..." << endl; pop.gen=0; while (!g_quit_time){ pthread_mutex_lock(&lock_pop); pop.keepTrack(); pop.OutputFate(); if (pop.gen % conf.logevery == 0) pop.OutputLog(); ++pop.gen; pop.NextGeneration(); if(pop.gen % pop.params->saveEvery == 0) pop.CreateTreeFile( pop.params->treefname ); if(pop.gen % pop.adap->intervalLength == 0){ bool reduced=false; if(pop.gen-pop.lastTopoImprove >= pop.adap->intervalsToStore*pop.adap->intervalLength){ reduced=pop.adap->ReducePrecision(); } if(reduced){ pop.lastTopoImprove=pop.gen; pop.indiv[pop.bestIndiv].treeStruct->OptimizeAllBranches(pop.adap->branchOptPrecision); pop.indiv[pop.bestIndiv].SetDirty(); pop.CalcAverageFitness(); //DJZ 2/20/06 //reducing parallel remote update thresh based on same criteria as opt precision double prev=pop.paraMan->updateThresh; pop.paraMan->ReduceUpdateThresh(); debug_mpi("Remote update threshold reduced from %f to %f", prev, pop.paraMan->updateThresh); } /* else if(!(pop.gen%(pop.adap->intervalLength*pop.adap->intervalsToStore))){ pop.indiv[pop.bestIndiv].treeStruct->OptimizeAllBranches(pop.adap->branchOptPrecision); pop.indiv[pop.bestIndiv].SetDirty(); pop.CalcAverageFitness(); } */ if(pop.enforceTermConditions == true && pop.gen-pop.lastTopoImprove > pop.lastTopoImproveThresh && pop.adap->improveOverStoredIntervals < pop.improveOverStoredIntervalsThresh && pop.adap->branchOptPrecision == pop.adap->minOptPrecision){ // && pop.paraMan->updateThresh == pop.paraMan->minUpdateThresh){ cout << "Reached termination condition!\nlast topological improvement at gen " << pop.lastTopoImprove << endl; cout << "Improvement over last " << pop.adap->intervalsToStore*pop.adap->intervalLength << " gen = " << pop.adap->improveOverStoredIntervals << endl; g_quit_time=true; break; } pop.CheckSubtrees(); #ifdef INCLUDE_PERTURBATION pop.CheckPerturbParallel(); #endif } pthread_mutex_unlock(&lock_pop); pthread_mutex_lock(&lock_pm); while (g_processing_message) pthread_cond_wait(&cond_pm, &lock_pm); pthread_mutex_unlock(&lock_pm); } //DJZ 3-1-06 Need to give control back to the thread one more time so that it can deal with any final messages pthread_mutex_unlock(&lock_pop); pthread_mutex_lock(&lock_pm); while (g_processing_message) pthread_cond_wait(&cond_pm, &lock_pm); pthread_mutex_unlock(&lock_pm); pop.FinalOptimization(); pop.FinalizeOutputStreams(); pthread_join(thread, NULL); return 0; } /* old MasterMaster int MasterMaster(const GamlConfig& conf, NucleotideData& data) { Parameters params; params.SetParams(conf, data, true); LogParams(params); Tree::brlen_mu = params.brlenMutProb; Tree::mu = params.topoMutProb; Tree::lambda = params.crossoverProb; Tree::alpha = params.gammaShape; int nprocs; MPI_Comm_size(MPI_COMM_WORLD, &nprocs); Population pop; pop.Setup(params, conf, nprocs, 0); g_gen = &pop.gen; for (int i = 1; i < nprocs; ++i) { if (i < conf.hybridp.nt*nprocs) debug_mpi("node %d is shielded migrants", i); else debug_mpi("node %d is alpha male replication", i); } if (conf.method == "fde") MasterFullDuplexExchange(pop, conf); else if (conf.method == "sm") MasterShieldedMigrants(pop, conf); else if (conf.method == "amr") MasterAlphaMaleReplication(pop, conf); else if (conf.method == "hybrid") MasterHybrid(pop, conf); else { debug_mpi("ERROR: unknown method (GamlConfig::General::method): %s", conf.method.c_str()); MPI_Abort(MPI_COMM_WORLD, -1); } return 0; } */ int RemoteMaster(GeneralGamlConfig& conf, NucleotideData& data) { debug_mpi("starting RemoteMaster()..."); int rank; MPI_Comm_rank(MPI_COMM_WORLD, &rank); Parameters params; params.SetParams(conf, data); //the seed has already been set in SetParams above, but we need to //modify it so that all remotes are not the same rnd.set_seed((rank+1) * rnd.init_seed()); LogParams(params); Tree::alpha = params.gammaShapeBrlen; Tree::meanBrlenMuts = params.meanBrlenMuts; int nprocs; MPI_Comm_size(MPI_COMM_WORLD, &nprocs); bool poo=true; // while(poo); Population pop; pop.Setup(params, &conf, nprocs, rank); g_sw=&pop.stopwatch; g_gen = &pop.gen; //for now all nodes will be SW debug_mpi("doing remote subtree worker"); RemoteSubtreeWorker(pop, conf); /* if (conf.method == "fde") assert(0); // RemoteFullDuplexExchange(pop, conf); //DJZ changed this 9/10/03 to avoid occasional disparity in the acutal # of sm nodes and the number of shields allocated else if (conf.method == "sm" || (conf.method == "hybrid" && rank <= (int) (conf.hybridp.nt*(nprocs-1)))){ // else if (conf.method == "sm" || (conf.method == "hybrid" && rank < conf.hybridp.nt*nprocs)){ debug_mpi("doing remote shielded migrants"); RemoteShieldedMigrants(pop, conf); } else if (conf.method == "amr" || conf.method == "hybrid") { debug_mpi("doing remote alpha male replication"); RemoteAlphaMaleReplication(pop, conf); } else { debug_mpi("ERROR: unknown method (GamlConfig::General::method): %s", conf.method.c_str()); MPI_Abort(MPI_COMM_WORLD, -1); } */ return 0; } /* int RemoteMaster(const GamlConfig& conf, NucleotideData& data) { debug_mpi("starting RemoteMaster()..."); Parameters params; params.SetParams(conf, data, false); LogParams(params); Tree::brlen_mu = params.brlenMutProb; Tree::mu = params.topoMutProb; Tree::lambda = params.crossoverProb; Tree::alpha = params.gammaShape; Population pop; pop.Setup(params, conf.max_nindivs); char* tree_strings_in; char* tree_strings_out; int trans_count = 0; // tree's must be scored before calling next generation so call it here so it doesn't crash on gen == 1 pop.CalcAverageFitness(); pop.gen = 1; while (true) { if (pop.gen % conf.interval == 0) { debug_mpi("send interval reached."); // sanity check: make sure param's nindivs == pop's current/original size assert( (params.nindivs == pop.current_size) && (pop.current_size == pop.original_size) ); // see if there is a quit message int flag; MPI_Status status; MPI_Iprobe(0, MPI_ANY_TAG, MPI_COMM_WORLD, &flag, &status); if (flag) { if (status.MPI_TAG == TAG_QUIT) { debug_mpi("quit message received."); break; } else // sanity check: should not get here assert(status.MPI_TAG == TAG_QUIT); } // save old scores for TransLog() pop.CalcAverageFitness(); double* old_scores = new double[params.nindivs]; for (int j = 0; j < params.nindivs; ++j) old_scores[j] = pop.indiv[j].Fitness(); // send tree strings to node pop.ShrinkPopulation(params.nindivs, &tree_strings_out); // allocates buf on heap SendResultsToNode(0, pop.original_size, tree_strings_out); debug_mpi("sent %d tree strings to master...", pop.original_size); char* p = tree_strings_out; for (int i = 0; i < pop.original_size; ++i) { debug_mpi("%s", p); p += strlen(p) +1; } // wait for tree's to be sent back. // must probe this incase a quit message is sent int nindivs; MPI_Probe(MPI_ANY_SOURCE, MPI_ANY_TAG, MPI_COMM_WORLD, &status); if (status.MPI_SOURCE != 0) assert(status.MPI_SOURCE == 0); // should not receive messages from any node except the master if (status.MPI_TAG == TAG_QUIT) { debug_mpi("quit message received while waiting for tree strings."); break; } else if (status.MPI_TAG == TAG_TREE_STRINGS_COUNT) { GetResultsFromNode(0, &nindivs, &tree_strings_in); debug_mpi("received %d tree strings from master...", nindivs); char* p = tree_strings_in; for (int i = 0; i < pop.original_size; ++i) { debug_mpi("%s", p); p += strlen(p) +1; } // sanity check: make sure we're getting back as many as we sent out assert(nindivs == pop.original_size); pop.ExtendPopulation(nindivs, tree_strings_in); pop.CalcAverageFitness(); // save new scores for TransLog() double* new_scores = new double[params.nindivs]; for (int j = 0; j < params.nindivs; ++j) new_scores[j] = pop.indiv[j].Fitness(); // remote nodes send all indivs and replace all indivs int* temp = new int[params.nindivs+1]; for (int j = 0; j < params.nindivs; ++j) temp[j] = j; TransLog(trans_count++, params.nindivs, nindivs, tree_strings_out, nindivs, tree_strings_in, temp, temp, old_scores, new_scores); delete [] tree_strings_in; delete [] tree_strings_out; delete [] new_scores; delete [] old_scores; delete [] temp; } else // sanity check: should not get here! assert(status.MPI_TAG != TAG_QUIT || status.MPI_TAG != TAG_TREE_STRINGS_COUNT); } else { pop.NextGeneration(); ++(pop.gen); } } // end while (true) return 0; } */ /* returns: how many nodes have results. pre condition: nodes[] must be of size nprocs-1 post condition: nodes[] will hold >which< nodes have results. example: if remote nodes 1, 2 and 5 have results, then return == 3 nodes[] == {1, 2, 5, (garbage)...} */ int PollForResults(int nodes[]) { int nprocs, flag, count = 0; MPI_Status status; MPI_Comm_size(MPI_COMM_WORLD, &nprocs); for (int i = 2 ; i < nprocs; ++i) { MPI_Iprobe(i, TAG_TREE_STRINGS_COUNT, MPI_COMM_WORLD, &flag, &status); if (flag) nodes[count++] = status.MPI_SOURCE; } return count; } bool PollForResults(int n) { int flag; MPI_Status status; MPI_Iprobe(n, TAG_TREE_STRINGS_COUNT, MPI_COMM_WORLD, &flag, &status); if (flag) return 1; else return 0; } /* results format: 4 bytes (int) - nindivs 4 bytes (int) - size of tree strings including null terminators n bytes (char) - tree strings seperated by NULLs, terminated by a double NULL returns: which node it received from side affects: if returns 0, then tree_strings is allocated on the heap */ int GetResultsFromNode(int node, int* n_, char** tree_strings_) { int& n = *n_; char*& tree_strings = *tree_strings_; int buf_size = 0; MPI_Status status; // sanity check: make sure node_num is a valid node int nprocs; MPI_Comm_size(MPI_COMM_WORLD, &nprocs); assert( ((node >= 0) && (node < nprocs)) || (node == MPI_ANY_SOURCE) ); MPI_Recv(&n, 1, MPI_INT, node, TAG_TREE_STRINGS_COUNT, MPI_COMM_WORLD, &status); assert(n > 0); if (node == MPI_ANY_SOURCE) node = status.MPI_SOURCE; MPI_Recv(&buf_size, 1, MPI_INT, node, TAG_TREE_STRINGS_SIZE, MPI_COMM_WORLD, &status); assert(buf_size > 0); tree_strings = new char[buf_size]; MPI_Recv(tree_strings, buf_size, MPI_CHAR, node, TAG_TREE_STRINGS, MPI_COMM_WORLD, &status); // sanity check: the number of tree strings in tree_strings should == nindivs int count = 0; char* p = tree_strings; while (*p) { p += strlen(p) + 1; ++count; } assert(count == n); // sanity check: make sure p ended up at the end of the string ++p; assert(p-tree_strings == buf_size); return status.MPI_SOURCE; } /* results format: 4 bytes (int) - nindivs 4 bytes (int) - size of tree strings including null terminators n bytes (char) - tree strings seperated by NULLs, terminated by a double NULL pre conditions: 0 < node < nprocs n > 0 tree_strings must have n tree_strings seperated by NULLs */ int SendResultsToNode(int node, int n, char* tree_strings) { // sanity check: make sure node is a valid number int nprocs; MPI_Comm_size(MPI_COMM_WORLD, &nprocs); assert( (node >= 0) && (node < nprocs) ); // sanity check: make sure the actual number of tree strings in tree_strings is equal to n int count = 0; char* p = tree_strings; while (*p) { p += strlen(p) + 1; ++count; } assert(count == n); int buf_size = (p+1)-tree_strings; // sanity check: make sure i calculated buf_size correctly p = tree_strings; count = 0; while (*p || *(p+1)) { // this is basically strlen() for string terminated by two NULLs instead of one ++count; ++p; } assert(count+2 == buf_size); MPI_Status status; MPI_Send(&n, 1, MPI_INT, node, TAG_TREE_STRINGS_COUNT, MPI_COMM_WORLD); MPI_Send(&buf_size, 1, MPI_INT, node, TAG_TREE_STRINGS_SIZE, MPI_COMM_WORLD); MPI_Send(tree_strings, buf_size, MPI_CHAR, node, TAG_TREE_STRINGS, MPI_COMM_WORLD); return 0; } int ReceiveParams(Parameters* params_, int node) { Parameters& params = *params_; int size; MPI_Status status; MPI_Recv(&size, 1, MPI_INT, node, TAG_PARAMS_SIZE, MPI_COMM_WORLD, &status); char* buf = new char[size]; MPI_Recv(buf, size, MPI_CHAR, node, TAG_PARAMS, MPI_COMM_WORLD, &status); params.Deserialize(buf); delete [] buf; return 0; } int ReceiveData(NucleotideData* data_, int node) { NucleotideData& data = *data_; int size; MPI_Status status; MPI_Recv(&size, 1, MPI_INT, node, TAG_DATA_SIZE, MPI_COMM_WORLD, &status); char* buf = new char[size]; MPI_Recv(buf, size, MPI_CHAR, node, TAG_DATA, MPI_COMM_WORLD, &status); data.Deserialize(buf, size); delete [] buf; return 0; } int debug_mpi(const char* fmt, ...) { static bool first_call = true; static int rank; static char fname[13]; if (first_call) { MPI_Comm_rank(MPI_COMM_WORLD, &rank); if (rank < 10) sprintf(fname, "node0%d.log", rank); else sprintf(fname, "node%d.log", rank); fhandle = fopen(fname, "w"); first_call = false; } if(g_sw != NULL) fprintf(fhandle, "g %d, t %d: ", (g_gen && *g_gen != 0 ? *g_gen : -1), g_sw->SplitTime()); else fprintf(fhandle, "g %d, t %d: ", (g_gen && *g_gen != 0 ? *g_gen : -1), 0); va_list vl; va_start(vl, fmt); vfprintf(fhandle, fmt, vl); va_end(vl); fprintf(fhandle, "\n"); fflush(fhandle); return 0; } int TransLog(int to_who_or_count, int nindivs, int n, char* str_out, int m, char* str_in, int* to_send, int* to_replace, double* old_scores, double* new_scores) { char fname[64]; char temp_buf[64]; int rank; static int* counts; static bool first_call = true; MPI_Comm_rank(MPI_COMM_WORLD, &rank); if (first_call && rank == 0) { int nprocs; MPI_Comm_size(MPI_COMM_WORLD, &nprocs); counts = new int[nprocs]; // yeah yeah, so i'm never deallocating this. it'll be free'd when the process ends....=) memset(counts, 0, sizeof(int)*nprocs); } if (rank < 10) sprintf(fname, "trans0%d.log", rank); else sprintf(fname, "trans%d.log", rank); FILE* translog; if (first_call) { translog = fopen(fname, "w"); first_call = false; } else translog = fopen(fname, "a"); assert(translog); if (rank == 0) sprintf(temp_buf, "%d-%d", to_who_or_count, counts[to_who_or_count]++); else sprintf(temp_buf, "%d-%d", rank, to_who_or_count); fprintf(translog, "transmission %s\n", temp_buf); fprintf(translog, "send count = %d\n", n); for (int j = 0; j < n; ++j) { fprintf(translog, "%s\n", str_out); str_out += strlen(str_out) + 1; } fprintf(translog, "recv count = %d\n", m); for (int j = 0; j < m; ++j) { fprintf(translog, "%s\n", str_in); str_in += strlen(str_in) + 1; } fprintf(translog, "indivs sent ="); for (int j = 0; j < n; ++j) { fprintf(translog, " %d", to_send[j]); } fprintf(translog, "\n"); fprintf(translog, "indivs replaced ="); for (int j = 0; j < m; ++j) { fprintf(translog, " %d", to_replace[j]); } fprintf(translog, "\n"); fprintf(translog, "old scores\n"); for (int j = 0; j < nindivs; ++j) fprintf(translog, "%f\n", old_scores[j]); fprintf(translog, "new scores\n"); for (int j = 0; j < nindivs; ++j) fprintf(translog, "%f\n", new_scores[j]); fprintf(translog, "\n"); fflush(translog); fclose(translog); return 0; } int RecvMPIMessage(char** buf_, int* size_, int* who_, int* tag_, bool block) { int& who = *who_; int& tag = *tag_; int& size = *size_; char*& buf = *buf_; int flag; MPI_Status status; MPI_Iprobe(MPI_ANY_SOURCE, MPI_ANY_TAG, MPI_COMM_WORLD, &flag, &status); if (flag == 0) { if (block == false) return 0; else MPI_Probe(MPI_ANY_SOURCE, MPI_ANY_TAG, MPI_COMM_WORLD, &status); } MPI_Get_count(&status, MPI_CHAR, &size); buf = new char[size]; MPI_Recv(buf, size, MPI_CHAR, status.MPI_SOURCE, status.MPI_TAG, MPI_COMM_WORLD, &status); who = status.MPI_SOURCE; tag = status.MPI_TAG; return 1; } int RecvMPIMessage(char** buf_, int* size_, int who, int* tag_, bool block) { int& tag = *tag_; int& size = *size_; char*& buf = *buf_; int flag; MPI_Status status; MPI_Iprobe(who, MPI_ANY_TAG, MPI_COMM_WORLD, &flag, &status); if (flag == 0) { if (block == false) return 0; else MPI_Probe(who, MPI_ANY_TAG, MPI_COMM_WORLD, &status); } MPI_Get_count(&status, MPI_CHAR, &size); buf = new char[size]; MPI_Recv(buf, size, MPI_CHAR, who, status.MPI_TAG, MPI_COMM_WORLD, &status); tag = status.MPI_TAG; return 1; } int RecvMPIMessage(char** buf_, int* size_, int* who_, int tag, bool block) { int& who = *who_; int& size = *size_; char*& buf = *buf_; int flag; MPI_Status status; MPI_Iprobe(MPI_ANY_SOURCE, tag, MPI_COMM_WORLD, &flag, &status); if (flag == 0) { if (block == false) return 0; else MPI_Probe(MPI_ANY_SOURCE, tag, MPI_COMM_WORLD, &status); } MPI_Get_count(&status, MPI_CHAR, &size); buf = new char[size]; MPI_Recv(buf, size, MPI_CHAR, status.MPI_SOURCE, tag, MPI_COMM_WORLD, &status); who = status.MPI_SOURCE; return 1; } int RecvMPIMessage(char** buf_, int* size_, int who, int tag, bool block) { int& size = *size_; char*& buf = *buf_; int flag; MPI_Status status; MPI_Iprobe(who, tag, MPI_COMM_WORLD, &flag, &status); if (flag == 0) { if (block == false) return 0; else MPI_Probe(who, tag, MPI_COMM_WORLD, &status); } MPI_Get_count(&status, MPI_CHAR, &size); buf = new char[size]; MPI_Recv(buf, size, MPI_CHAR, who, tag, MPI_COMM_WORLD, &status); return 1; } int SendMPIMessage(char* buf, int size, int who, int tag) { return MPI_Send(buf, size, MPI_CHAR, who, tag, MPI_COMM_WORLD); } int LogConfig(const GeneralGamlConfig& conf) { debug_mpi("logging GamlConfig structure..."); debug_mpi("[general]"); debug_mpi("logevery = %d", conf.logevery); debug_mpi("saveevery = %d", conf.saveevery); debug_mpi("datafname = %s", conf.datafname.c_str()); debug_mpi("streefname = %s", conf.streefname.c_str()); debug_mpi("ofprefix = %s", conf.ofprefix.c_str()); debug_mpi("[master]"); debug_mpi("holdover = %d", conf.holdover); debug_mpi("nindivs = %d %d", conf.min_nindivs, conf.max_nindivs); debug_mpi("stopgen = %d", conf.stopgen); debug_mpi("[remote]"); debug_mpi("holdover = %d", conf.holdover); debug_mpi("nindivs = %d %d", conf.min_nindivs, conf.max_nindivs); debug_mpi("stopgen = %d", conf.stopgen); debug_mpi("done logging GamlConfig structure"); return 0; } int LogParams(const Parameters& params) { debug_mpi("logging Parameters (partial) structure..."); debug_mpi("holdover = %d", params.holdover); debug_mpi("nindivs = %d", params.nindivs); debug_mpi("randomSeed = %d", params.randomSeed); return 0; } int LogTreeStrings(const char* tree_strings) { int count = 0; const char* p = tree_strings; while (*p) { p += strlen(p) + 1; ++count; } debug_mpi("%d tree strings:", count); p = tree_strings; while (*p) { debug_mpi("%s", p); p += strlen(p) + 1; } return 0; } int LogKappas(const double* kappa_probs, const int count) { debug_mpi("%d kappa probs:", count); for (int i = 0; i < count; ++i) debug_mpi("%f", kappa_probs[i]); return 0; } int LogPis(const double* pis, const int count) { debug_mpi("%d pis:", count); for (int i = 0; i < count; ++i) for(int b=0;b<4;b++) debug_mpi("%f, ", pis[i*4+b]); return 0; } int CountTreeStrings(char* p) { int count = 0; while (*p) { p += strlen(p) + 1; ++count; } return count; } // string length for a string that is terminated by a double null int strlen2(char* p) { int count = 0; while (*p || *(p+1)) { ++count; ++p; } return count; } int CalcMaxIndivs(const NucleotideData& data, int mem) { const int KB = 1024; const int MB = KB*KB; int sizeof_treenode = 4*data.NChar()*sizeof(double); debug_mpi("sizeof_treenode = %d", sizeof_treenode); int num_internal_treenode_per_indiv = data.NTax()-2; int size_of_terminal_indivs=sizeof(int) * data.NChar(); debug_mpi("num_internal_treenode_per_indiv = %d", num_internal_treenode_per_indiv); int sizeof_indiv = sizeof_treenode*num_internal_treenode_per_indiv*2; sizeof_indiv+=size_of_terminal_indivs*data.NTax(); debug_mpi("sizeof_indiv = %d", sizeof_indiv); debug_mpi("retval = %d", mem*MB / sizeof_indiv); return mem*MB / sizeof_indiv; } int RemoteShieldedMigrants(Population& pop, const GeneralGamlConfig& conf) { /* int size, rank, nprocs, count = conf.numshields, restart_count = 0; int *which = new int[count]; char *tree_strings, fname[64]; double *models, old_score; MPI_Comm_rank(MPI_COMM_WORLD, &rank); MPI_Comm_size(MPI_COMM_WORLD, &nprocs); if (rank < 10) sprintf(fname, "log0%d.log", rank); else sprintf(fname, "log%d.log", rank); pop.CalcAverageFitness(); old_score = pop.bestFitness; //start the tree log file pop.CreateTreeLog(rank); for (pop.gen = 1; pop.gen < conf.stopgen; ++pop.gen) { if (pop.gen % conf.repeatthresh == 0) { if (pop.bestFitness - old_score <= conf.scorethresh) { debug_mpi("repeat thresh=%d", conf.repeatthresh); debug_mpi("score threshold exceeded, restarting population (%f, %f)", old_score, pop.bestFitness); pop.Restart(1, rank, nprocs, restart_count++); pop.CalcAverageFitness(); } old_score = pop.bestFitness; } if (pop.gen % conf.logevery == 0) pop.Log(fname, 0.0); if (pop.gen % conf.interval == 0) { //Check for a message to change remote type int tag; RecvMPIMessage(&tree_strings, &size, 0, &tag, false); if(tag == TAG_REMOTE_TYPE_SWITCH){ //call RemoteAlphaMaleReplication debug_mpi("\tchanging remote type to AMR...\n"); RemoteAlphaMaleReplication(pop, conf); return 0;//if we return from RemoteAMR, everything must be done, so return } debug_mpi("STARTING SYNCHRONOUS COMMUNICATION (node %d)", 0); pop.GetNBestIndivIndices(&which, count); //pop.GetNRandomIndivIndices(&which, conf.numshieldedpernode); // alternatively pop.GetSpecifiedTreeStrings(&tree_strings, count, which); SendMPIMessage(tree_strings, strlen2(tree_strings)+2, 0, TAG_TREE_STRINGS); int model_size=pop.GetSpecifiedModels(&models, count, which); SendMPIMessage((char*) models, count*sizeof(double)*model_size, 0, TAG_MODEL); debug_mpi("\tsent: %d tree strings", count); debug_mpi("\tsent: %d models, size: %d", count, model_size); delete [] tree_strings; delete [] models; } //adding this to make log files of trees for each population // if(pop.gen % pop.params->saveEvery == 0) pop.AppendTreeToTreeLog( rank ); pop.NextGeneration(); pop.OutputFate(); } SendMPIMessage(NULL, 0, 0, TAG_QUIT); debug_mpi("STARTING SYNCHRONOUS COMMUNICATION (node %d)", 0); debug_mpi("\tsent: quit message"); delete [] which; // TODO what to do on quit? debug_mpi("quitting"); */ return 0; } /* int RemoteAlphaMaleReplication(Population& pop, const GeneralGamlConfig& conf) { int which, *all, size, rank, tag; char *tree_strings, *buf; double score, *models; char fname[32]; all = new int[pop.params->nindivs]; for (int i = 0; i < pop.params->nindivs; ++i) all[i] = i; MPI_Comm_rank(MPI_COMM_WORLD, &rank); if (rank < 10) sprintf(fname, "log0%d.log", rank); else sprintf(fname, "log%d.log", rank); pop.CalcAverageFitness(); for (pop.gen = 1; pop.gen < conf.stopgen; ++pop.gen) { if (pop.gen % conf.logevery == 0) pop.OutputLog(); if (pop.gen % conf.interval == 0) { debug_mpi("STARTING SYNCHRONOUS COMMUNICATION (node 0)"); score = pop.bestFitness; SendMPIMessage((char*)&score, sizeof(double), 0, TAG_SCORE); debug_mpi("\tsend: score = %f", score); RecvMPIMessage(&tree_strings, &size, 0, &tag); if (tag == TAG_TREE_STRINGS_REQUEST) { debug_mpi("\trecv: tree strings request"); which = (int)pop.cumfit[pop.total_size-1][0]; pop.GetSpecifiedTreeStrings(&tree_strings, 1, &which); SendMPIMessage(tree_strings, strlen2(tree_strings)+2, 0, TAG_TREE_STRINGS); int model_size=pop.GetSpecifiedModels(&models, 1, &which); SendMPIMessage((char*)models, sizeof(double)*model_size, 0, TAG_MODEL); debug_mpi("\tsend: %d tree string", 1); debug_mpi("\tsend: %d models, size:%d", 1, model_size*sizeof(double)); delete [] tree_strings; delete [] models; } else if (tag == TAG_TREE_STRINGS) { debug_mpi("\trecv: %d tree strings", CountTreeStrings(tree_strings)); RecvMPIMessage(&buf, &size, 0, TAG_MODEL); models=(double*) buf; pop.ReplicateSpecifiedIndividuals(pop.total_size, all, tree_strings, models); pop.CalcAverageFitness(); delete [] tree_strings; delete [] models; } else { debug_mpi("alpha male replication recved bad message tag"); assert(false); } } //adding this to make log files of trees for each population // if(pop.gen % pop.params->saveEvery == 0) pop.AppendTreeToTreeLog( rank ); // if(pop.gen % 100) pop.NNIoptimization(); pop.NextGeneration(); pop.OutputFate(); } debug_mpi("sending quit message"); SendMPIMessage(NULL, 0, 0, TAG_QUIT); delete [] all; return 0; } */ void RemoteSendBestTree(Population& pop){ int *which=new int; char *tree_strings; double *models; pop.GetNBestIndivIndices(&which, 1); pop.GetSpecifiedTreeStrings(&tree_strings, 1, which); int size=strlen2(tree_strings)+2; // debug_mpi("about to send treestrings..."); SendMPIMessage(tree_strings, size, 0, TAG_TREE_STRINGS); debug_mpi("\tsent ind %d, lnL %f", *which, pop.indiv[*which].Fitness()); // debug_mpi("about to send modelstrings..."); int model_size=pop.GetSpecifiedModels(&models, 1, which); SendMPIMessage((char*) models, sizeof(double)*model_size, 0, TAG_MODEL); // debug_mpi("about to send subdef..."); char std[5]; sprintf(std, "%d", pop.subtreeDefNumber); SendMPIMessage(std, strlen(std)+2, 0, TAG_SUBTREE_ITERATION); if(pop.subtreeDefNumber!=0){ char stn[10]; sprintf(stn, "%d", pop.subtreeNode); SendMPIMessage(stn, strlen(stn)+2, 0, TAG_SUBTREE_DEFINE); debug_mpi("\tvalid for subtree def %d, node %d", pop.subtreeDefNumber, pop.subtreeNode); } else debug_mpi("\tno defined subtree"); //finally, send the score double score = pop.indiv[*which].Fitness(); SendMPIMessage((char*)&score, sizeof(double), 0, TAG_SCORE); delete which; delete [] tree_strings; delete [] models; } int RemoteSubtreeWorker(Population& pop, const GeneralGamlConfig& conf){ int *which, size, rank, tag; char *tree_strings, *buf; double score, *models; bool perturb; which=new int[5]; MPI_Comm_rank(MPI_COMM_WORLD, &rank); pop.CalcAverageFitness(); int lastSend=g_sw->SplitTime(); cout << "Remote number " << rank << " running..." << endl; for (pop.gen = 1; pop.gen < conf.stopgen;){ pop.keepTrack(); pop.OutputFate(); if (pop.gen % conf.logevery == 0) pop.OutputLog(); ++pop.gen; pop.NextGeneration(); if(pop.gen % pop.adap->intervalLength == 0){ bool reduced=false; if(pop.gen-pop.lastTopoImprove >= pop.adap->intervalsToStore*pop.adap->intervalLength){ reduced=pop.adap->ReducePrecision(); } if(reduced){ pop.lastTopoImprove=pop.gen; pop.indiv[pop.bestIndiv].treeStruct->OptimizeAllBranches(pop.adap->branchOptPrecision); pop.indiv[pop.bestIndiv].SetDirty(); pop.CalcAverageFitness(); } /* else if(!(pop.gen%(pop.adap->intervalLength*pop.adap->intervalsToStore))){ pop.indiv[pop.bestIndiv].treeStruct->OptimizeAllBranches(pop.adap->branchOptPrecision); pop.indiv[pop.bestIndiv].SetDirty(); pop.CalcAverageFitness(); } */ } if(g_sw->SplitTime() - lastSend > conf.sendInterval){ debug_mpi("SYNCH COMM (node 0)"); //send our best individual to the master RemoteSendBestTree(pop); lastSend=g_sw->SplitTime(); if(pop.params->stoptime - g_sw->SplitTime() < 0){ debug_mpi("time limit of %d seconds reached...", pop.params->stoptime); break; } } //Check for a new tree from the master bool firstmessage=true; bool gotmessage=false; int subtreeNode; while(RecvMPIMessage(&tree_strings, &size, 0, &tag, false)==true){ //check for a quit message if(tag == TAG_QUIT) { debug_mpi("\trecv: quit message"); delete [] which; debug_mpi("quitting"); return 0; } // bool gotNewIndiv=false; int recievedDefNumber; debug_mpi("SYNCH COMM (node 0)"); gotmessage=true; assert(tag == TAG_TREE_STRINGS || tag==TAG_PERTURB); if(firstmessage==false) debug_mpi("\tfound a newer message..."); if(tag != TAG_PERTURB){ gotNewIndiv=true; RecvMPIMessage(&buf, &size, 0, &tag, true); assert(tag == TAG_MODEL); models=(double*) buf; debug_mpi("\tgot new ind" ); RecvMPIMessage(&buf, &size, 0, &tag, true); // if(tag != TAG_PERTURB){ perturb=false; assert(tag == TAG_SUBTREE_DEFINE); subtreeNode=atoi(buf); if(subtreeNode!=0){ delete []buf; RecvMPIMessage(&buf, &size, 0, &tag, true); assert(tag == TAG_SUBTREE_ITERATION); recievedDefNumber=atoi(buf); debug_mpi("\tworking on subtree def %d, node %d", recievedDefNumber, subtreeNode); } else recievedDefNumber=0; } else{ pop.pertMan->pertType=atoi(tree_strings); perturb=true; } //if the current best and the new tree are either both accurate for the same subtree def or both //inaccurate for subtrees, just replace the worst individual, rather than the // whole pop, that way if the tree is old and worse that what the remote // already has it won't matter if(gotNewIndiv){ *which=(int)pop.cumfit[0][0]; debug_mpi("\treplacing indiv %d", *which); pop.ReplaceSpecifiedIndividuals(1, which, tree_strings, models); if(recievedDefNumber!=pop.subtreeDefNumber || (pop.subtreeNode!=0 && subtreeNode!=0)){ pop.AssignSubtree(subtreeNode, *which); pop.CalcAverageFitness(); debug_mpi("\tfilling pop with clones of %d", *which); pop.SetNewBestIndiv(*which); pop.FillPopWithClonesOfBest(); pop.subtreeDefNumber=recievedDefNumber; } delete [] models; delete [] buf; } #ifdef INCLUDE_PERTURBATION if(perturb==true){ pop.CalcAverageFitness(); if(pop.pertMan->pertType==1){ debug_mpi("peforming NNI perturbation..."); int toReplace=(pop.bestIndiv == 0 ? 1 : 0); pop.AppendTreeToTreeLog(-1, pop.bestIndiv); pop.NNIPerturbation(pop.bestIndiv, toReplace); pop.SetNewBestIndiv(toReplace); pop.FillPopWithClonesOfBest(); pop.AppendTreeToTreeLog(-1, pop.bestIndiv); } else if(pop.pertMan->pertType==2){ debug_mpi("peforming SPR perturbation..."); int toReplace=(pop.bestIndiv == 0 ? 1 : 0); pop.AppendTreeToTreeLog(-1, pop.bestIndiv); pop.SPRPerturbation(pop.bestIndiv, toReplace); pop.SetNewBestIndiv(toReplace); pop.FillPopWithClonesOfBest(); pop.AppendTreeToTreeLog(-1, pop.bestIndiv); } else assert(0); } #endif delete [] tree_strings; tag=0; firstmessage=false; } if(gotmessage==true){ // if(pop.subtreeNode != subtreeNode) pop.AssignSubtree(subtreeNode); pop.CalcAverageFitness(); debug_mpi("\tbest score= %f", pop.indiv[*which].Fitness()); pop.AppendTreeToTreeLog(-1, *which); } } SendMPIMessage(NULL, 0, 0, TAG_QUIT); debug_mpi("\tsent: quit message"); delete [] which; pop.FinalizeOutputStreams(); debug_mpi("quitting"); return 0; } #endif // #ifdef MPI_VERSION garli-2.1-release/src/mpifuncs.h000066400000000000000000000055701241236125200166610ustar00rootroot00000000000000// GARLI version 0.93 source code // Copyright 2005 by Derrick J. Zwickl // All rights reserved. // // This code may be used and modified for non-commercial purposes // but redistribution in any form requires written permission. // Please contact: // // Derrick Zwickl // Integrative Biology, UT // 1 University Station, C0930 // Austin, TX 78712 // email: garli.support@gmail.com // // Note: In 2006 moving to NESCENT (The National // Evolutionary Synthesis Center) for a postdoc #ifdef MPI_VERSION #ifndef MPIFUNCS_H #define MPIFUNCS_H #include "configoptions.h" #include "sequencedata.h" #include "parameters.h" #include "population.h" #include "threaddcls.h" int MPIMain(int arc, char** argv); int StartProcs(const GeneralGamlConfig&, NucleotideData&); int MasterMaster(MasterGamlConfig&, NucleotideData&); int RemoteMaster(GeneralGamlConfig&, NucleotideData&); int MasterFullDuplexExchange(Population& pop, const MasterGamlConfig& conf); int RemoteFullDuplexExchange(Population& pop, const GeneralGamlConfig& conf); int MasterShieldedMigrants(Population& pop, const MasterGamlConfig& conf); int RemoteShieldedMigrants(Population& pop, const GeneralGamlConfig& conf); int RemoteAlphaMaleReplication(Population& pop, const GeneralGamlConfig& conf); int RemoteSubtreeWorker(Population& pop, const GeneralGamlConfig& conf); int MasterAlphaMaleReplication(Population& pop, const MasterGamlConfig& conf); int MasterHybrid(Population& pop, const MasterGamlConfig& conf); int MasterLastCall(Population& pop, int master_mem); //int DoMasterSM(Population& pop, const GamlConfig& conf, int who, transferred_data_t results); //int DoMasterAMR(Population& pop, const GamlConfig& conf, int who, transferred_data_t results); int CalcMaxIndivs(const NucleotideData&, int); // buf, size in bytes, from, tag, blocking int RecvMPIMessage(char**, int*, int*, int*, bool block = true); int RecvMPIMessage(char**, int*, int, int*, bool block = true); int RecvMPIMessage(char**, int*, int*, int, bool block = true); int RecvMPIMessage(char**, int*, int, int, bool block = true); // buf, size, to, tag int SendMPIMessage(char*, int, int, int); int PollForResults(int nodes[]); bool PollForResults(int n); int GetResultsFromNode(int node_num, int* nindivs_, char** tree_strings_); int SendResultsToNode(int node, int n, char* tree_strings); int ReceiveParams(Parameters* params_, int node); int ReceiveData(NucleotideData* data_, int node); int debug_mpi(const char* fmt, ...); int LogConfig(const GeneralGamlConfig&); int LogParams(const Parameters& params); int LogTreeStrings(const char* tree_strings); int LogKappas(const double* kappa_probs, const int count); int LogPis(const double* pis, const int count); int TransLog(int count, int nindivs, int n, char* str_out, int m, char* str_in, int* to_send, int* to_replace, double* old_scores, double* new_scores); int strlen2(char* p); int CountTreeStrings(char* p); #endif #endif garli-2.1-release/src/mpitrick.cpp000066400000000000000000000230541241236125200172070ustar00rootroot00000000000000// GARLI version 1.00 source code // Copyright 2005-2010 Derrick J. Zwickl // email garli.support@gmail.com // // This program is free software: you can redistribute it and/or modify // it under the terms of the GNU General Public License as published by // the Free Software Foundation, either version 3 of the License, or // (at your option) any later version. // // This program is distributed in the hope that it will be useful, // but WITHOUT ANY WARRANTY; without even the implied warranty of // MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the // GNU General Public License for more details. // // You should have received a copy of the GNU General Public License // along with this program. If not, see . #if defined(SUBROUTINE_GARLI) || defined(OLD_SUBROUTINE_GARLI) #include "mpi.h" #include #include #include #include "defs.h" #include "string.h" #include #include "funcs.h" #include "outputman.h" using namespace std; int SubGarliMain(int); void UsageMessage(char *execName); extern OutputManager outman; //old (parallel batch) and new (parallel replicates) mpi behavior now rolled into a single function #if(1) int jobloop(int, int, MPI_Comm, int numJobs); string MyFormattedTime(){ time_t rawtime; struct tm * timeinfo; time(&rawtime); timeinfo = localtime(&rawtime); string s = asctime(timeinfo); s.erase(s.end()-1, s.end()); return s; } int main(int argc,char **argv){ if(argc == 2){ if(!strcmp(argv[1], "--help") || !strcmp(argv[1], "--h") || !strcmp(argv[1], "-help") || !strcmp(argv[1], "-h")){ UsageMessage(argv[0]); return 0; } } int rc = MPI_Init(&argc,&argv); if(rc != MPI_SUCCESS){ outman.SetLogFile("mpi_messages.log"); outman.UserMessage("Error starting MPI. Terminating."); MPI_Abort(MPI_COMM_WORLD, rc); } MPI_Comm comm,mycomm; int nproc, rank; comm = MPI_COMM_WORLD; MPI_Comm_size(comm,&nproc); MPI_Comm_rank(comm,&rank); timespec wait; int numJobsTotal = 0; if(rank == 0){ outman.SetLogFile("mpi_messages.log"); outman.UserMessageNoCR("MPI Garli started with command line: "); for(int i=0;i 1){ if(! isdigit(argv[1][0])){ outman.UserMessage("***ERROR***:GARLI is expecting \n\tor\n\t \n\tGot %s", argv[1]); UsageMessage(argv[0]); MPI_Finalize(); return 1; } else numJobsTotal = atoi(argv[1]); } else numJobsTotal = nproc; #else if(argc == 1 || (argv[1][0] != '-' && !isdigit(argv[1][0]))){ outman.UserMessage("***ERROR***:Garli is expecting the number of jobs to be run to follow\n\tthe executable name on the command line\n"); UsageMessage(argv[0]); MPI_Finalize(); return 1; } else{ if(argv[1][0] == '-') numJobsTotal = atoi(&argv[1][1]); else numJobsTotal = atoi(&argv[1][0]); } #endif outman.UserMessage("#####%d total executions of the config file were requested######", numJobsTotal); } else{//wait a moment for proc 0 to output to the messages file, then attach to the stream wait.tv_sec = 1; wait.tv_nsec=0; nanosleep(&wait, NULL); outman.SetLogFileForAppend("mpi_messages.log"); } //These barriers really shouldn't be necessary, but adding them seemed to resolve a weird issue that Jelesko //was having where processes with rank >= 8 were hanging in the Bcast until some of the first 8 were completely //finished and returned from the job loop //outman.UserMessage("#####Process %d approaching barrier 1 at %s######", rank, MyFormattedTime().c_str()); MPI_Barrier(comm); //outman.UserMessage("#####Process %d passed barrier 1 at %s######", rank, MyFormattedTime().c_str()); //send all of the processors the number of jobs total MPI_Bcast(&numJobsTotal, 1, MPI_INT, 0, comm); // outman.UserMessage("#####Process %d passed broadcast, approaching barrier 2 at %s######", rank, MyFormattedTime().c_str()); MPI_Barrier(comm); // outman.UserMessage("#####Process %d passed barrier 2 at %s######", rank, MyFormattedTime().c_str()); //DEBUG //if startjob lockfiles exist at this point that must mean that a previous run bailed. Remove them. //Then processes will start those same runs and possibly restart from checkpoint (if any were being //written and restart=1 was specified). Otherwise they will just start them over. if(rank == 0){ for(int j = 0;j < numJobsTotal;j++){ char startfile[100], donefile[100]; sprintf(startfile, ".s-lock%d", j); sprintf(donefile, ".d-lock%d", j); if(FileExists(donefile)){ outman.UserMessage("It appears that run %d was completed in a previous MPI invocation.\n\tRun %d will not be re-run unless the hidden file \"%s\" and any checkpoint files for this run (if present) are deleted from this directory.", j, j, donefile); } else if(FileExists(startfile)){ outman.UserMessage("It appears that run %d was started but not completed in a previous MPI invocation.\n\tRun %d will either be re-run or restarted from a checkpoint (if restart = 1 was specified in the GARLI config file).", j, j); remove(startfile); } } } int jobsCompleted = jobloop(rank,nproc,mycomm,numJobsTotal); outman.SetLogFileForAppend("mpi_messages.log"); if(jobsCompleted > -1){ #ifdef OLD_SUBROUTINE_GARLI outman.UserMessage("process %d finished, did %d run(s), no more configs to execute at %s. Waiting for other procs...", rank, jobsCompleted, MyFormattedTime().c_str()); #else outman.UserMessage("process %d finished, did %d run(s), no further runs to do at %s. Waiting for other procs...", rank, jobsCompleted, MyFormattedTime().c_str()); #endif } MPI_Barrier(comm); if(rank == 0) outman.UserMessage("all processes completed at %s", MyFormattedTime().c_str()); else nanosleep(&wait, NULL);//this is just to keep proper ordering in the output file outman.UserMessage("process %d terminating", rank); //Not sure if deleting lock files should or should not be done. /* if(rank == 0){ char temp[100]; for(int i=0;i.screen.log files for details on what went wrong"); return -1; } jobsCompleted++; lock.open(donefile); lock.close(); remove(startfile); jobNum++; } } return jobsCompleted; } #elif defined(__OLD_SUBROUTINE_GARLI) void jobloop(int, int, MPI_Comm, int numJobs=-1); int main(int argc,char **argv) { MPI_Comm comm,mycomm; int ntids,mytid; MPI_Init(&argc,&argv); comm = MPI_COMM_WORLD; MPI_Comm_size(comm,&ntids); MPI_Comm_rank(comm,&mytid); MPI_Comm_split(comm,mytid,mytid,&mycomm); if(mytid == 0){ outman.SetLogFile("mpi_messages.log"); outman.UserMessageNoCR("MPI Garli started with command line: "); for(int i=0;i 1){ if(! isdigit(argv[1][0])){ cout << "I'm confused.\nExpecting \n or\n \nGot " << argv[1] << endl; MPI_Finalize(); return 1; } jobloop(mytid,ntids,mycomm, atoi(argv[1])); } else jobloop(mytid, ntids, mycomm); MPI_Finalize(); return 0; } void jobloop(int mytid,int ntids,MPI_Comm comm, int numJobs /*=-1*/){ if(numJobs < 0) numJobs=ntids; int jobNum=mytid; cout << "total proc: " << ntids << " total jobs: " << numJobs << endl; while(jobNum < numJobs){ timespec wait; wait.tv_sec = mytid * 2; wait.tv_nsec=0; nanosleep(&wait, NULL); SubGarliMain(jobNum); jobNum += ntids; } return; } #endif #endif garli-2.1-release/src/optimization.cpp000066400000000000000000004151071241236125200201170ustar00rootroot00000000000000// GARLI version 2.1 source code // Copyright 2005-2014 Derrick J. Zwickl // email: garli.support@gmail.com // // This program is free software: you can redistribute it and/or modify // it under the terms of the GNU General Public License as published by // the Free Software Foundation, either version 3 of the License, or // (at your option) any later version. // // This program is distributed in the hope that it will be useful, // but WITHOUT ANY WARRANTY; without even the implied warranty of // MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the // GNU General Public License for more details. // // You should have received a copy of the GNU General Public License // along with this program. If not, see . #include "defs.h" #include "tree.h" #include "model.h" #include "funcs.h" #include "outputman.h" //a bunch of functions from the Tree class, relating to optimization #include "utility.h" Profiler ProfIntDeriv ("IntDeriv "); Profiler ProfTermDeriv("TermDeriv "); Profiler ProfModDeriv ("ModDeriv "); Profiler ProfNewton ("Newton-Raphson"); extern Profiler ProfEQVectors; #if !defined(STEP_TOL) #ifdef SINGLE_PRECISION_FLOATS #define STEP_TOL 1.0e-6 #else #define STEP_TOL 1.0e-8 #endif #endif extern FLOAT_TYPE globalBest; extern int optCalcs; #define FOURTH_ROOT #ifdef OPT_DEBUG #include "optimizationinfo.h" OptimizationInfo optInfo; ofstream opt("optimization.log"); //ofstream der("derivs.log"); ofstream optsum("optsummary.log"); ofstream curves("curves.log"); #endif #ifdef FOURTH_ROOT #define effectiveMin 0.01 #define effectiveMax 1.77827941 #elif ROOT_OPT #define effectiveMin 0.0001 #define effectiveMax 3.16227766 #else #define effectiveMin=min_brlen #define effectiveMax=max_brlen #endif inline FLOAT_TYPE CallBranchLike(TreeNode *thisnode, Tree *thistree, FLOAT_TYPE blen, bool brak /*=false*/){ brak; #ifdef FOURTH_ROOT thisnode->dlen=blen*blen*blen*blen; #elif ROOT_OPT thisnode->dlen=blen*blen; #else thisnode->dlen=blen; #endif FLOAT_TYPE like=thistree->BranchLike(thisnode)*-1; optCalcs++; #ifdef OPT_DEBUG if(brak) optInfo.BrakAdd(blen, like); else optInfo.BrentAdd(blen, like); #endif return like; } void Tree::OptimizeBranchesInArray(int *nodes, int numNodes, FLOAT_TYPE optPrecision){ //this takes an array of nodeNums (branches) to be optimized and does so for(int i=0;ileft, optPrecision, 0, numNodesTotal, true, improve, true); improve = RecursivelyOptimizeBranches(root->left->next, optPrecision, 0, numNodesTotal, true, improve, true); improve = RecursivelyOptimizeBranches(root->right, optPrecision, 0, numNodesTotal, true, improve, true); return improve; } int Tree::PushBranchlengthsToMin(){ int num = 0; pair derivs; for(int i=1;i < numNodesTotal;i++){ if(allNodes[i]->dlen < 1.0e-4 && !(FloatingPointEquals(allNodes[i]->dlen, min_brlen, 1e-9))){ if(useOptBoundedForBlen){ //in this case (mainly oriented gap) there is no deriv function, so just set the blens and //count on them being re-optimized if wrong SetBranchLength(allNodes[i], min_brlen); num++; } else{ derivs = CalcDerivativesRateHet(allNodes[i]->anc, allNodes[i]); if(derivs.first < ZERO_POINT_ZERO){ //outman.DebugMessage("(branch %d: %.9f -> %.9f", i, allNodes[i]->dlen, 1e-8); SetBranchLength(allNodes[i], min_brlen); num++; } else outman.DebugMessage("pos d1\t%.9f\t%.9f", allNodes[i]->dlen, derivs.first); } } } return num; } FLOAT_TYPE Tree::OptimizeTreeScale(FLOAT_TYPE optPrecision){ if(FloatingPointEquals(lnL, -ONE_POINT_ZERO, max(1.0e-8, GARLI_FP_EPS * 2.0))) Score(); Score(); FLOAT_TYPE start=lnL; FLOAT_TYPE prev=lnL; FLOAT_TYPE cur; FLOAT_TYPE scale; FLOAT_TYPE t; FLOAT_TYPE lastChange=(FLOAT_TYPE)9999.9; FLOAT_TYPE effectiveScale = ONE_POINT_ZERO; //this measures the change in scale relative to what it began at. FLOAT_TYPE upperBracket = FLT_MAX; //the smallest value we know of with a negative d1 (relative to inital scale of 1.0!) FLOAT_TYPE lowerBracket = FLT_MIN; //the largest value we know of with a positive d1 (relative to inital scale of 1.0!) FLOAT_TYPE incr; #undef DEBUG_SCALE_OPT #ifdef DEBUG_SCALE_OPT ofstream deb("scaleTrace.log"); deb.precision(20); for(int s=0;s<50;s++){ FLOAT_TYPE scale=0.5 + s*.025; ScaleWholeTree(scale); Score(); deb << scale << "\t" << lnL << endl; ScaleWholeTree(ONE_POINT_ZERO/scale); } deb.close(); #endif while(1){ //reversed this now so the reduction in scale is done first when getting the //derivs. This works better if some blens are at DEF_MAX_BLEN because the //scaling up causes them to hit the max and the relative blens to change #ifdef SINGLE_PRECISION_FLOATS incr=0.005f; #else incr=0.0001; #endif scale=ONE_POINT_ZERO-incr; ScaleWholeTree(scale); Score(); cur=lnL; ScaleWholeTree(ONE_POINT_ZERO/scale);//return the tree to its original scale FLOAT_TYPE d12=(cur-prev)/-incr; scale=ONE_POINT_ZERO + incr; ScaleWholeTree(scale); Score(); cur=lnL; ScaleWholeTree(ONE_POINT_ZERO/scale);//return the tree to its original scale FLOAT_TYPE d11=(cur-prev)/incr; FLOAT_TYPE d1=(d11+d12)*ZERO_POINT_FIVE; FLOAT_TYPE d2=(d11-d12)/incr; FLOAT_TYPE est = -d1/d2; FLOAT_TYPE estImprove = d1*est + d2*(est*est*ZERO_POINT_FIVE); //return conditions. Leave if the estimated improvement is < precision of if the points straddle the optimum if((d11 - d12) == ZERO_POINT_ZERO || (d11 > ZERO_POINT_ZERO && d12 < ZERO_POINT_ZERO) || (d11 < ZERO_POINT_ZERO && d12 > ZERO_POINT_ZERO) || (estImprove < optPrecision && d2 < ZERO_POINT_ZERO)){ lnL = prev; return prev-start; } if(d2 < ZERO_POINT_ZERO){ est = max(min((FLOAT_TYPE)0.1, est), (FLOAT_TYPE)-0.1); t=ONE_POINT_ZERO + est; } else{//if we have lots of data, move //very slowly here //if(data->NInformative() > 500){ if(0){ if(d1 > ZERO_POINT_ZERO) t=(FLOAT_TYPE)1.01; else t=(FLOAT_TYPE)0.99; } else{ if(d1 > ZERO_POINT_ZERO) t=(FLOAT_TYPE)1.05; else t=(FLOAT_TYPE)0.95; } } //update the brackets if(d1 <= ZERO_POINT_ZERO && effectiveScale < upperBracket) upperBracket = effectiveScale; else if(d1 > ZERO_POINT_ZERO && effectiveScale > lowerBracket) lowerBracket = effectiveScale; //if the surface is wacky and we are going to shoot past one of our brackets //take evasive action by going halfway to the bracket if((effectiveScale * t) <= lowerBracket){ t = (lowerBracket + effectiveScale) * ZERO_POINT_FIVE / effectiveScale; } else if((effectiveScale * t) >= upperBracket){ t = (upperBracket + effectiveScale) * ZERO_POINT_FIVE / effectiveScale; } scale=t; effectiveScale *= scale; ScaleWholeTree(scale); Score(); cur=lnL; lastChange = cur - prev; prev=cur; } return -1; } //The newer, more convoluted OptBounded from the trunk FLOAT_TYPE Tree::SetAndEvaluateParameter(int modnum, int which, FLOAT_TYPE val, FLOAT_TYPE &bestKnownScore, FLOAT_TYPE &bestKnownVal, void (Model::*SetParam)(int, FLOAT_TYPE)){ if(which > -1){ Model *mod = modPart->GetModel(modnum); CALL_SET_PARAM_FUNCTION(*mod, SetParam)(which, val); MakeAllNodesDirty(); } else{//A negative which means that this is a branchlength being set. In that case the SetParam function is just a dummy function of Model //that allow this function to be called SetBranchLength(allNodes[-which], val); } Score(); if(lnL > bestKnownScore){ bestKnownVal = val; bestKnownScore = lnL; } return lnL; } //This checks whether bestVal is significantly better than the otherScore, and if so takes it. Otherwise otherVal is taken, //which could represent the current value that the optimizer is at, or the initial value. //This is ONLY called when we are about to return from OptBounded and want to know whether we should take: //-A step to a bracket (best) that evals slightly higher than the initial (other and current) point (first optimization pass) // In this case we want the best to be significantly better than the initial=current=other. tolerance should be > 0 //-Revert to the best known value if we took a step and ended up worsening the score. // In this case we want to take best if it is at all better than the current. tolerance == 0 bool Tree::CheckScoreAndRestore(int modnum, int which, void (Model::*SetParam)(int, FLOAT_TYPE), FLOAT_TYPE otherScore, FLOAT_TYPE otherVal, FLOAT_TYPE bestScore, FLOAT_TYPE bestVal, FLOAT_TYPE tolerance){ Model *mod = modPart->GetModel(modnum); bool restored = false; if(otherScore + tolerance < bestScore){ // outman.DebugMessage("Rest %.12f", otherScore - bestScore); if(which > -1) CALL_SET_PARAM_FUNCTION(*mod, SetParam)(which, bestVal); else{//A negative which means that this is a branchlength being set. In that case the SetParam function is just a dummy function of Model //that allow this function to be called SetBranchLength(allNodes[-which], bestVal); } otherScore = bestScore; restored = true; } else{ if(otherScore < bestScore) outman.DebugMessage("Stay %.12f (would have gone)", otherScore - bestScore); /* else outman.DebugMessage("Stay %.12f", otherScore - bestScore); */ if(which > -1) CALL_SET_PARAM_FUNCTION(*mod, SetParam)(which, otherVal); else{//A negative which means that this is a branchlength being set. In that case the SetParam function is just a dummy function of Model //that allow this function to be called SetBranchLength(allNodes[-which], otherVal); } } MakeAllNodesDirty(); lnL = otherScore; return restored; } void Tree::TraceLikelihoodForParameter(int modnum, int which, FLOAT_TYPE init, FLOAT_TYPE min, FLOAT_TYPE max, FLOAT_TYPE interval, void (Model::*SetParam)(int, FLOAT_TYPE), bool append){ Model *mod = modPart->GetModel(modnum); ofstream curves; if(append) curves.open("lcurve.log", ios::app); else curves.open("lcurve.log"); curves.precision(12); curves << "\n"; FLOAT_TYPE dummy = -1; FLOAT_TYPE dummy2 = -1; for(double c = min; c <= max ; c += interval){ FLOAT_TYPE v = SetAndEvaluateParameter(modnum, which, c, dummy, dummy2, SetParam); curves << c << "\t" << v << "\n"; } curves.close(); if(which > -1){ CALL_SET_PARAM_FUNCTION(*mod, SetParam)(which, init); MakeAllNodesDirty(); } else{//A negative which means that this is a branchlength being set. In that case the SetParam function is just a dummy function of Model //that allow this function to be called SetBranchLength(allNodes[-which], init); } Score(); } FLOAT_TYPE Tree::OptimizeBoundedParameter(int modnum, FLOAT_TYPE optPrecision, FLOAT_TYPE initialVal, int which, FLOAT_TYPE lowBound, FLOAT_TYPE highBound, void (Model::*SetParam)(int, FLOAT_TYPE), FLOAT_TYPE targetScoreDigits /* DP = 9, SP = 5 */){ if(FloatingPointEquals(lnL, -ONE_POINT_ZERO, max(1.0e-8, GARLI_FP_EPS * 2.0))) Score(); #ifdef SINGLE_PRECISION_FLOATS FLOAT_TYPE baseIncr = min(max(0.001*optPrecision, 1.0e-5f), initialVal * 0.01); #else FLOAT_TYPE baseIncr = min(max(0.001*optPrecision, 1.0e-6), initialVal * 0.01); #endif //DEBUG //for blen opt (which < 0) super small incrs don't make sense. If initialVal is at 1e-8 //it will get bumped below if(which < 0){ baseIncr = max(baseIncr, min_brlen); } //this first bit of checking and bumping used to use epsilon rather than the default baseIncr assert(initialVal > lowBound - baseIncr && initialVal < highBound + baseIncr); FLOAT_TYPE curVal = initialVal; FLOAT_TYPE initialScore, curScore; initialScore = curScore = lnL; FLOAT_TYPE bestKnownScore = initialScore; FLOAT_TYPE bestKnownVal = initialVal; #ifdef NEW_BUMPING FLOAT_TYPE requiredWindow = (baseIncr * 1.0001) * 2.0; FLOAT_TYPE actualWindow = highBound - lowBound; if(actualWindow < requiredWindow){ //if the bounds are so tight that we can't be > baseIncr * 1.0001 from both, exit outman.DebugMessage("NEWER: OptimizeBoundedParameter: bounds fully constrain parameter %.6f <- %.6f -> %.6f, desired amount = %.6f", lowBound, curVal, highBound, requiredWindow); //SetAndEvaluateParameter(which, initialVal, bestKnownScore, bestKnownVal, SetParam); return 0.0; } //the new version FLOAT_TYPE boundBumped = -1.0; //if possible, bump enough that we could have two legal increases in incr below to allow sufficient lnL diffs FLOAT_TYPE bumpAmt = baseIncr * 25.0001; //if(initialVal - lowBound < bumpAmt){ if(lowBound + bumpAmt > initialVal){ //were closer than we'd like to be to the low bound //but, if we bump what we would like we might go past the other bound, in that case decrease bump if(lowBound + bumpAmt > highBound - bumpAmt){ bumpAmt = actualWindow / 2.0; outman.DebugMessage("halved: base = %.6f, ideal = %.6f, actual = %.6f", baseIncr, baseIncr * 25.0001, bumpAmt); } boundBumped = fabs(curVal - (lowBound + bumpAmt)); curVal = lowBound + bumpAmt; curScore = SetAndEvaluateParameter(modnum, which, curVal, bestKnownScore, bestKnownVal, SetParam); } else if(highBound - bumpAmt < initialVal){ if(highBound - bumpAmt < lowBound + bumpAmt){ bumpAmt = actualWindow / 2.0; outman.DebugMessage("halved: base = %.6f, ideal = %.6f, actual = %.6f", baseIncr, baseIncr * 25.0001, bumpAmt); } curVal = highBound - bumpAmt; boundBumped = initialVal - curVal; curScore = SetAndEvaluateParameter(modnum, which, curVal, bestKnownScore, bestKnownVal, SetParam); } #else //the older version FLOAT_TYPE boundBumped = -1.0; //if possible, bump enough that we could have one legal increase in incr below to allow sufficient lnL diffs FLOAT_TYPE bumpAmt = baseIncr * 5.0001; if(initialVal - lowBound < bumpAmt){ if(lowBound + bumpAmt > highBound) bumpAmt = baseIncr * 1.0001; // outman.DebugMessage("NEW: OptimizeBoundedParameter: value bumped off low bound %.6f -> %.6f", initialVal, lowBound + bumpAmt); boundBumped = fabs(curVal - (lowBound + bumpAmt)); curVal = lowBound + bumpAmt; if(curVal > highBound){ outman.DebugMessage("Bumped past other (high) bound!"); curVal = initialVal; } curScore = SetAndEvaluateParameter(modnum, which, curVal, bestKnownScore, bestKnownVal, SetParam); } else if(highBound - initialVal < bumpAmt){ // outman.DebugMessage("NEW: OptimizeBoundedParameter: value bumped off high bound %.6f -> %.6f", initialVal, highBound - bumpAmt);s if(highBound - bumpAmt < lowBound) bumpAmt = baseIncr * 1.0001; boundBumped = fabs(curVal - (highBound - bumpAmt)); curVal = highBound - bumpAmt; if(curVal < lowBound){ outman.DebugMessage("Bumped past other (low) bound!"); curVal = initialVal; } curScore = SetAndEvaluateParameter(modnum, which, curVal, bestKnownScore, bestKnownVal, SetParam); } //if the bounds are so tight that we can't be > baseIncr from both, exit //If we were close to one bound we should have already been bumped off of it. If we're still close to a bound then the bump must have pushed //us too near the opposite bound. give up in that case if(curVal - lowBound < baseIncr || highBound - curVal < baseIncr){ outman.DebugMessage("NEW: OptimizeBoundedParameter: bounds fully constrain parameter %.6f <- %.6f -> %.6f, desired amount = %.6f", lowBound, curVal, highBound, bumpAmt * 2); SetAndEvaluateParameter(modnum, which, initialVal, bestKnownScore, bestKnownVal, SetParam); return 0.0; } #endif FLOAT_TYPE lowerEval, higherEval; FLOAT_TYPE lowerEvalScore, higherEvalScore; FLOAT_TYPE lastChange=(FLOAT_TYPE)9999.9; FLOAT_TYPE upperBracket = highBound; //the smallest value we know of with a negative d1, or the minimum allowed value FLOAT_TYPE lowerBracket = lowBound; //the largest value we know of with a positive d1 , or the maximum allowed value FLOAT_TYPE incr, diffDigits = 100.0; int lowBoundOvershoot = 0; int upperBoundOvershoot = 0; int positiveD2Num = 0; int pass = 0, incrIncreases = 0; #ifdef OPT_BOUNDED_LOG // ofstream log("optbounded.log", ios::app); // log.precision(10); char name[50]; sprintf(name, "%s.optbounded.log", ofprefix.c_str()); ofstream log(name, ios::app); log.precision(10); #endif #ifdef OPT_BOUNDED_TRACE // if(which > -1){ ofstream curves("lcurve.log", ios::app); curves.precision(12); curves << "\n"; ofprefix = "SLs"; string oname = ofprefix; oname += ".sitelikes.log"; Model *mod = modPart->GetModel(modnum); sitelikeLevel = 1; double inc = curVal / 20.0; for(double c = curVal / 20.0; c < curVal * 20.0 ; c += inc){ FLOAT_TYPE v = SetAndEvaluateParameter(modnum, which, c, bestKnownScore, bestKnownVal, SetParam); curves << c << "\t" << v << "\n"; ofstream ordered(oname.c_str(), ios::app); ordered.precision(10); ordered << "1" << "\t" << -lnL << "\n"; ordered.close(); sitelikeLevel = -1; } curves.close(); sitelikeLevel = 0; SetAndEvaluateParameter(modnum, which, curVal, bestKnownScore, bestKnownVal, SetParam); /* ofstream ordered(oname.c_str(), ios::app); ordered.precision(10); ordered << "1" << "\t" << -lnL << "\n"; ordered.close(); */ //} #endif FLOAT_TYPE incrLimit; bool limited = false; //we'll always know what the current score is at the top of this loop while(1){ //baseIncr will be a sort of ideal increment, but it may be limited because of closeness //to a min or max bracket incrLimit = min(curVal - lowerBracket, upperBracket - curVal); incr = baseIncr; if(incr > incrLimit){ incr = incrLimit / 1.0001; limited = true; //outman.DebugMessage("OptimizeBoundedParameter: incr limited by bound.\n\tpass=%d initlnL=%.6f curlnL=%.6f initVal=%.6f curVal=%.6f lbound=%.6f hbound=%.6f incr=%.10f baseIncr=%.6f", pass, initialScore, lnL, initialVal, curVal, lowerBracket, upperBracket, incr, baseIncr); if(baseIncr/incrLimit > 100.0) outman.DebugMessage("OptimizeBoundedParameter: incr very limited by bound. Ratio is %.6f (curVal = %f)", baseIncr/incrLimit, curVal); } //evaluate a point just above the current value higherEval = curVal+incr; higherEvalScore = SetAndEvaluateParameter(modnum, which, higherEval, bestKnownScore, bestKnownVal, SetParam); #ifdef ADAPTIVE_BOUNDED_OPT bool cont = false; //There are a few things that could happen here //1. The incr has already be limited by closeness to a bound above - move on //2. Test lnL diffs. // 2a. The difference in lnLs values is sufficiently large for accurate derivatives - move on // 2b. The difference in lnLs is not sufficient - increase incr // 2b1. The increased incr is still within any bounds - go bck to 2 // 2b2. The increased incr is greater than allowed by one bound. Limit it and break. while(pass == 0 && !cont && !limited){ //we want differences in likelihood of greater than targetScoreDigits orders of magnitude //less than the total likelihoods. The determination of this amount will be taken care //of by the caller, and will vary by DP or SP (currently mostly 9 and 5) or the parameter //begin optimized (lower for codon) FLOAT_TYPE diff = fabs(curScore - higherEvalScore); if(diff != ZERO_POINT_ZERO)//otherwise diffDigits has been initialized to 100 above to force incr increase diffDigits = log10(-curScore / diff); if(diffDigits > targetScoreDigits){ incrIncreases++; baseIncr *= 5.0; //if the increased increment would be greater than what we are allowed by our bound //we'll have to use the limited incr. We'll try the increased baseIncr on the next pass. if(baseIncr > incrLimit){ incr = incrLimit / 1.0001; cont = true; outman.DebugMessage("OptimizeBoundedParameter: adaptive increase in incr limited by bound (%s).\n\tpass=%d initlnL=%.6f curlnL=%.6f initVal=%.6f curVal=%.6f incr=%.10f baseIncr=%.6f", (boundBumped > ZERO_POINT_ZERO ? "boundBumped" : "no boundBump"), pass, initialScore, curScore, initialVal, curVal, incr, baseIncr); } else incr = baseIncr; //apply the new increment and check score difference again higherEval = curVal+incr; higherEvalScore = SetAndEvaluateParameter(modnum, which, higherEval, bestKnownScore, bestKnownVal, SetParam); } else cont = true; } #endif //we'll never move to a point closer than this (except maybe on exit) //this ensures that we'll be able to have the low evaluation point just inside the bound //without limiting incr FLOAT_TYPE veryCloseToBound = baseIncr * 1.0001; //we'll exit if closer than this, and will take the point with the best known score, which //could be much closer yet FLOAT_TYPE closeToBound = baseIncr * 1.0002; //evaluate a point just below the current value lowerEval = curVal-incr; lowerEvalScore = SetAndEvaluateParameter(modnum, which, lowerEval, bestKnownScore, bestKnownVal, SetParam); FLOAT_TYPE d11=(higherEvalScore-curScore)/incr; FLOAT_TYPE d12=(lowerEvalScore-curScore)/-incr; FLOAT_TYPE d1=(d11+d12)*ZERO_POINT_FIVE; FLOAT_TYPE d2=(d11-d12)/incr; FLOAT_TYPE est=-d1/d2; FLOAT_TYPE proposed = curVal + est; #ifdef OPT_BOUNDED_LOG log << pass << "\t" << incr << "\t" << incrIncreases << "\t" << diffDigits << "\t"; log << lowBound << "\t" << lowerBracket << "\t" << lowerEval << "\t" << curVal << "\t" << higherEval << "\t" << upperBracket << "\t" << highBound << "\t"; log << lowerEvalScore << "\t" << curScore << "\t" << higherEvalScore << "\t"; log << d1 << "\t" << d2 << "\t" << est << "\t" << proposed << "\t"; #endif //if the two derivative estimates are equal d2 is zero or undefined and bad things happen. This is a bit of a hack, but works since it kicks in the pos d2 machinery if(d11 - d12 == 0){ d2 = 42.0; // outman.DebugMessage("***equal d1's: %.4f", d11); } if(d1 == ZERO_POINT_ZERO){ outman.DebugMessage("****d1 is zero! d11=%.4f d12=%.4f", d11, d12); } //if the evaluation points straddle the optimum (or minimum), leave now //in cases where the likelihood is unstable we can apparently straddle but end up with a worse likelihood //than what we had initially. In that case there isn't a lot we can do. Restore the initial value and exit. //occasionally d1 can also end up 0, so behave the same then. if((d11 * d12 < ZERO_POINT_ZERO) || (d1 == ZERO_POINT_ZERO)){ if(d11 > 0.0){ outman.DebugMessage("MINIMUM! %.6f %.6f %.6f", lowerEvalScore, curScore, higherEvalScore); //TraceLikelihoodForParameter(which, curVal, curVal-(baseIncr * 5), curVal+(baseIncr * 5), 1e-5, SetParam, false); } //on the first pass here the curVal will be the best val, so this won't do anything //on later passes it should also be the best, unless there are weird stability issues bool restored = CheckScoreAndRestore(modnum, which, SetParam, curScore, curVal, bestKnownScore, bestKnownVal, ZERO_POINT_ZERO); // if(restored) outman.DebugMessage("took best: init=%.6f cur=%.6f best=%.6f initV=%.6f curV=%.6f bestV=%.6f", initialScore, curScore, bestKnownScore, initialVal, curVal, bestKnownVal); // else outman.DebugMessage("took current: init=%.6f cur=%.6f best=%.6f initV=%.6f curV=%.6f bestV=%.6f", initialScore, curScore, bestKnownScore, initialVal, curVal, bestKnownVal); #ifdef OPT_BOUNDED_LOG log << "\t" << bestKnownVal << "\treturn1" << (restored ? "_best" : "") << endl; log.close(); #endif return lnL-initialScore; } #ifdef OPT_BOUNDED_LOG if(boundBumped > ZERO_POINT_ZERO) log << "BB-"; if(d2 < ZERO_POINT_ZERO) log << "NR-"; #endif //second derivative is positive, so can't use NR. Bump the value arbitrarily. //if this overshoots a bound it will be dealt with below if(d2 > ZERO_POINT_ZERO){ positiveD2Num++; FLOAT_TYPE amtToBump; if(d1 > ZERO_POINT_ZERO){ if((positiveD2Num + 1) % 3 == 0){ amtToBump = ((upperBracket + curVal) * ZERO_POINT_FIVE) - curVal; #ifdef OPT_BOUNDED_LOG log << "B1/2"; #endif } else{ //proposed=curVal*(FLOAT_TYPE)(ONE_POINT_ZERO+0.02*positiveD2Num); amtToBump = max(closeToBound, (curVal * (FLOAT_TYPE)(0.02*positiveD2Num))); #ifdef OPT_BOUNDED_LOG log << "B2P"; #endif } proposed = curVal + amtToBump; } else {//cycle through a number of arbitrary value changes here if(positiveD2Num % 3 == 0 || (pass == 0 && boundBumped > ZERO_POINT_ZERO)){ amtToBump = (curVal - (lowerBracket + veryCloseToBound)); #ifdef OPT_BOUNDED_LOG log << "BtoB"; #endif } else if((positiveD2Num + 2) % 3 == 0){ amtToBump = (curVal * (FLOAT_TYPE)(0.02*positiveD2Num)); #ifdef OPT_BOUNDED_LOG log << "B2P"; #endif } else if((positiveD2Num + 1) % 3 == 0){ amtToBump = curVal - ((curVal + lowerBracket) * ZERO_POINT_FIVE); #ifdef OPT_BOUNDED_LOG log << "B1/2"; #endif } //SHOULD THIS BE ctb or vctb? amtToBump = max(veryCloseToBound, amtToBump); proposed = curVal - amtToBump; } } //we're proposing below the bound if(d1 < ZERO_POINT_ZERO && proposed < lowerBracket + veryCloseToBound){ //if we're already very close to that bound, exit //if(prevVal - lowerBracket - epsilon < epsilon * ZERO_POINT_FIVE){ if(curVal - (lowerBracket + closeToBound) <= ZERO_POINT_ZERO){ bool restored = CheckScoreAndRestore(modnum, which, SetParam, curScore, curVal, bestKnownScore, bestKnownVal, (pass > 0 ? ZERO_POINT_ZERO : STEP_TOL)); /* if(restored) outman.DebugMessage("LOW:took bestKnown: init=%.6f cur=%.6f best=%.6f initV=%.6f curV=%.6f bestV=%.6f", initialScore, curScore, bestKnownScore, initialVal, curVal, bestKnownVal); else outman.DebugMessage("LOW:took current: init=%.6f cur=%.6f best=%.6f initV=%.6f curV=%.6f bestV=%.6f", initialScore, curScore, bestKnownScore, initialVal, curVal, bestKnownVal); */ #ifdef OPT_BOUNDED_LOG log << "\t" << bestKnownVal << "\treturn2" << (restored ? "_best" : "") << endl; log.close(); #endif return lnL-initialScore; } lowBoundOvershoot++; //The previous behavior for low/high bracket overshooting caused rare problems because it automatically //tried a value just inside the bracket if it was more than the first overshoot. If the derivs at both //the low and high brackets propose a value past the other, this can ping-pong back and forth making only //very tiny moves inward, and crap out once 1000 reps have been completed. Now just try near the bound once if(lowBoundOvershoot == 2 || (lowBoundOvershoot == 1 && boundBumped > ZERO_POINT_ZERO)){ //this used to jump to 1/2 * baseIncr from bound proposed = lowerBracket + veryCloseToBound; #ifdef OPT_BOUNDED_LOG log << "LtoB"; #endif } else{//jump halfway to bound, unless that is too close FLOAT_TYPE delta = curVal - (curVal + lowerBracket) * ZERO_POINT_FIVE; delta = max(veryCloseToBound, delta); proposed = curVal - delta; #ifdef OPT_BOUNDED_LOG log << "L1/2"; #endif } } //we're proposing above the bound else if(d1 > ZERO_POINT_ZERO && proposed > upperBracket - veryCloseToBound){ //if we're already very close to that bound, exit if(upperBracket - closeToBound - curVal <= ZERO_POINT_ZERO){ bool restored = CheckScoreAndRestore(modnum, which, SetParam, curScore, curVal, bestKnownScore, bestKnownVal, (pass > 0 ? ZERO_POINT_ZERO : STEP_TOL)); // if(restored) outman.DebugMessage("HIGH:took bestKnown: init=%.6f cur=%.6f best=%.6f initV=%.6f curV=%.6f bestV=%.6f", initialScore, curScore, bestKnownScore, initialVal, curVal, bestKnownVal); // else outman.DebugMessage("HIGH:took current: init=%.6f cur=%.6f best=%.6f initV=%.6f curV=%.6f bestV=%.6f", initialScore, curScore, bestKnownScore, initialVal, curVal, bestKnownVal); #ifdef OPT_BOUNDED_LOG log << "\t" << bestKnownVal << "\treturn3" << (restored ? "_best" : "") << endl; log.close(); #endif return lnL-initialScore; } upperBoundOvershoot++; if(upperBoundOvershoot == 2 || (upperBoundOvershoot == 1 && boundBumped > ZERO_POINT_ZERO)){ proposed = upperBracket - veryCloseToBound; #ifdef OPT_BOUNDED_LOG log << "LtoB"; #endif } else{ FLOAT_TYPE delta = (curVal + upperBracket) * ZERO_POINT_FIVE - curVal; delta = max(veryCloseToBound, delta); proposed = curVal + delta; #ifdef OPT_BOUNDED_LOG log << "L1/2"; #endif } } FLOAT_TYPE estImprove; if(d2 < ZERO_POINT_ZERO) estImprove = d1*(proposed - curVal) + (d2 * (proposed - curVal) * (proposed - curVal)) * ZERO_POINT_FIVE; else estImprove = 9999.9; //The expected amount of improvement from an NR move is low //require that we didn't significantly worsen the likelihood overall or on the last pass if(estImprove < optPrecision && curScore >= initialScore - 1.0e-6 && lastChange > -1.0e-6){ bool restored = CheckScoreAndRestore(modnum, which, SetParam, curScore, curVal, bestKnownScore, bestKnownVal, (pass > 0 ? ZERO_POINT_ZERO : STEP_TOL)); /* if(bestKnownScore > curScore) outman.DebugMessage("IMPROVE:took best: init=%.6f cur=%.6f best=%.6f initV=%.6f curV=%.6f bestV=%.6f", initialScore, curScore, bestKnownScore, initialVal, curVal, bestKnownVal); else outman.DebugMessage("IMPROVE:took current: init=%.6f cur=%.6f best=%.6f initV=%.6f curV=%.6f bestV=%.6f", initialScore, curScore, bestKnownScore, initialVal, curVal, bestKnownVal); */ #ifdef OPT_BOUNDED_LOG log << "\t" << bestKnownVal << "\treturn4" << (restored ? "_best" : "") << endl; log.close(); #endif return lnL-initialScore; } //don't allow infinite looping if something goes wrong if(pass > 1000){ //bool restored = CheckScoreAndRestore(which, SetParam, curScore, curVal, initialScore, initialVal); bool worsened = !CheckScoreAndRestore(modnum, which, SetParam, initialScore, initialVal, bestKnownScore, bestKnownVal, ZERO_POINT_ZERO); if(worsened){ outman.UserMessage("OptimizeBoundedParameter: 1000 passes, but score worsened.\n\tpass=%d initlnL=%.6f curlnL=%.6f initVal=%.6f curVal=%.6f d11=%.6f d12=%.6f incr=%.10f baseIncr=%.10f", pass, initialScore, curScore, initialVal, curVal, d11, d12, incr, baseIncr); outman.UserMessage("****Please report this message to garli.support@gmail.com****"); } else{ outman.UserMessage("OptimizeBoundedParameter: 1000 passes without termination.\n\tpass=%d initlnL=%.6f curlnL=%.6f initVal=%.6f curVal=%.6f d11=%.6f d12=%.6f incr=%.10f baseIncr=%.10f", pass, initialScore, curScore, initialVal, curVal, d11, d12, incr, baseIncr); outman.UserMessage("****Please report this message to garli.support@gmail.com****"); } return lnL-initialScore; } assert(proposed >= lowerBracket && proposed <= upperBracket); if((lowerBracket + closeToBound > proposed) && (upperBracket - closeToBound < proposed)){ //this means the point we moved to isn't > closeToBound from both bounds bool restored = CheckScoreAndRestore(modnum, which, SetParam, curScore, curVal, bestKnownScore, bestKnownVal, (pass > 0 ? ZERO_POINT_ZERO : STEP_TOL)); #ifdef OPT_BOUNDED_LOG log << "\t" << bestKnownVal << "\treturn5" << (restored ? "_best" : "") << endl; log.close(); #endif return lnL-initialScore; } //update the brackets and take the move if(d1 <= ZERO_POINT_ZERO && curVal < upperBracket) upperBracket = curVal; else if(d1 > ZERO_POINT_ZERO && curVal > lowerBracket) lowerBracket = curVal; #ifdef OPT_BOUNDED_LOG log << "\t" << estImprove << "\t" << proposed << endl; #endif FLOAT_TYPE proposedScore = SetAndEvaluateParameter(modnum, which, proposed, bestKnownScore, bestKnownVal, SetParam); lastChange = proposedScore - curScore; curScore = proposedScore; curVal = proposed; pass++; } return -1; } FLOAT_TYPE Tree::OptimizeBranchLength(FLOAT_TYPE optPrecision, TreeNode *nd, bool goodGuess){ nd->alreadyOptimized=true; FLOAT_TYPE improve; #ifdef OPT_DEBUG optsum << nd->nodeNum << "\t" << nd->dlen << "\t"; #endif #ifdef BRENT improve = BrentOptimizeBranchLength(optPrecision, nd, goodGuess); #else //don't optimize the length of the entirely missing dummy branch if(rootWithDummy && nd->nodeNum == numTipsTotal) return 0.0; //improve = NewtonRaphsonOptimizeBranchLength(optPrecision, nd, goodGuess); //abandoning use of goodGuess. Doesn't seem to be reducing opt passes, which //was the point. ProfNewton.Start(); if(useOptBoundedForBlen){ double before = nd->dlen; #ifdef OPT_BOUNDED_LOG char name[50]; sprintf(name, "%s.optbounded.log", ofprefix.c_str()); ofstream log(name, ios::app); log << -nd->nodeNum << "\n"; log.close(); #endif //the first argument here is the modnum, which doesn't matter for this hack way of optimizing blens //improve = OptimizeBoundedParameter(0, optPrecision, nd->dlen, -nd->nodeNum, min_brlen, max_brlen, &Model::SetBranchlengthDummy); //bounds will be 1/10th of current blen or min_brlen on the low side, and 10x the current or 1e-4 if it is less than that improve = OptimizeBoundedParameter(0, optPrecision, nd->dlen, -nd->nodeNum, max(nd->dlen * 0.1, min_brlen), min(max(nd->dlen * 10.0, 1.0e-4), max_brlen), &Model::SetBranchlengthDummy); //outman.UserMessage("%d\t%f -> %f", nd->nodeNum, before, nd->dlen); } else{ improve = NewtonRaphsonOptimizeBranchLength(optPrecision, nd, true); } ProfNewton.Stop(); #endif #ifdef OPT_DEBUG optsum << nd->dlen << "\t" << improve << "\t" << lnL << endl; /* ofstream opttrees; if(num == 1) opttrees.open("everyTree.tre"); else opttrees.open("everyTree.tre", ios::app); char treeString[20000]; root->MakeNewick(treeString, false, true); opttrees << "utree tree" << num++ << "_" << nd->nodeNum << "=" << treeString << ";" << endl; opttrees.close(); */ #endif return improve; } void Tree::SetNodesUnoptimized(){ root->left->SetUnoptimized(); root->left->next->SetUnoptimized(); root->right->SetUnoptimized(); } void Tree::OptimizeBranchesWithinRadius(TreeNode *nd, FLOAT_TYPE optPrecision, int subtreeNode, TreeNode *prune){ nodeOptVector.clear(); SetNodesUnoptimized(); #ifdef EQUIV_CALCS if(dirtyEQ){ ProfEQVectors.Start(); root->SetEquivalentConditionalVectors(data); ProfEQVectors.Stop(); dirtyEQ=false; } #endif FLOAT_TYPE totalIncrease=ZERO_POINT_ZERO, prunePointIncrease=ZERO_POINT_ZERO, thisIncr, pruneRadIncrease=ZERO_POINT_ZERO; //for codon models, numerical instability can cause problems if a //branch length is super short and its MLE is large. This is very //rare, but hard to detect when it is happening. So, raise the blens //before all of the optimization if they are very small //PARTITION - I guess this needs to happen if any of the subsets are codon //and the subset mult should be figured in for(int m = 0;m < modSpecSet.NumSpecs();m++){ if(modSpecSet.GetModSpec(m)->IsCodon()){ FLOAT_TYPE subsRate = modPart->SubsetRate(m); if(nd->left->dlen * subsRate < 1e-4) SetBranchLength(nd->left, 1e-4 / subsRate); if(nd->right->dlen * subsRate < 1e-4) SetBranchLength(nd->right, 1e-4 / subsRate); if(nd->dlen * subsRate < 1e-4) SetBranchLength(nd, 1e-4 / subsRate); } } #ifdef CHECK_LNL_BEFORE_RAD FLOAT_TYPE leftIncrease=ZERO_POINT_ZERO, rightIncrease=ZERO_POINT_ZERO, ancIncrease=ZERO_POINT_ZERO; leftIncrease = OptimizeBranchLength(optPrecision, nd->left, false); ancIncrease = OptimizeBranchLength(optPrecision, nd, false); rightIncrease = OptimizeBranchLength(optPrecision, nd->right, false); if(leftIncrease > ZERO_POINT_ZERO) nodeOptVector.push_back(nd->left); if(ancIncrease > ZERO_POINT_ZERO) nodeOptVector.push_back(nd); if(rightIncrease > ZERO_POINT_ZERO) nodeOptVector.push_back(nd->right); totalIncrease = leftIncrease + rightIncrease + ancIncrease; #else totalIncrease += OptimizeBranchLength(optPrecision, nd->left, false); totalIncrease+= OptimizeBranchLength(optPrecision, nd, false); totalIncrease += OptimizeBranchLength(optPrecision, nd->right, false); nodeOptVector.push_back(nd->left); nodeOptVector.push_back(nd); nodeOptVector.push_back(nd->right); #endif if(prune!=NULL){ prunePointIncrease = OptimizeBranchLength(optPrecision, prune, true); if(prunePointIncrease > ZERO_POINT_ZERO) nodeOptVector.push_back(prune); totalIncrease+=prunePointIncrease; #ifdef OPT_DEBUG optsum << "prune total\t" << prunePointIncrease << endl; #endif } #ifdef OPT_DEBUG optsum << "3/4 branch total\t" << totalIncrease << endl; if(lnL < globalBest - treeRejectionThreshold) optsum << "bailing early\t"; optsum << "Scores:" << globalBest << "\t" << lnL << "\t" << globalBest-lnL << endl; #endif #ifdef CHECK_LNL_BEFORE_RAD bool fullOpt = false; if(lnL > globalBest){ fullOpt = true; } #endif assert(!FloatingPointEquals(lnL, -1.0, 1e-8)); if(lnL < globalBest - treeRejectionThreshold){ return; } //now spread out int rad=10; if(rad>0){ #ifdef CHECK_LNL_BEFORE_RAD if(((rightIncrease > ZERO_POINT_ZERO) || fullOpt) && nd->right->left!=NULL && nd->right->left->alreadyOptimized==false) totalIncrease += RecursivelyOptimizeBranches(nd->right->left, optPrecision, subtreeNode, rad, false, ZERO_POINT_ZERO); if(((leftIncrease > ZERO_POINT_ZERO) || fullOpt) && nd->left->left!=NULL && nd->left->left->alreadyOptimized==false) totalIncrease += RecursivelyOptimizeBranches(nd->left->left, optPrecision, subtreeNode, rad, false, ZERO_POINT_ZERO); if(((ancIncrease > ZERO_POINT_ZERO)) || fullOpt){ #else if(nd->right->left!=NULL && nd->right->left->alreadyOptimized==false) totalIncrease += RecursivelyOptimizeBranches(nd->right->left, optPrecision, subtreeNode, rad, false, ZERO_POINT_ZERO); if(nd->left->left!=NULL && nd->left->left->alreadyOptimized==false) totalIncrease += RecursivelyOptimizeBranches(nd->left->left, optPrecision, subtreeNode, rad, false, ZERO_POINT_ZERO); if(1){ #endif if(nd->anc!=root && nd->anc->alreadyOptimized==false) totalIncrease += RecursivelyOptimizeBranchesDown(nd->anc, nd, optPrecision, subtreeNode, rad, ZERO_POINT_ZERO); else{ if(root->left != nd && root->left->alreadyOptimized==false) totalIncrease += RecursivelyOptimizeBranches(root->left, optPrecision, subtreeNode, rad, true, ZERO_POINT_ZERO); if(root->left->next != nd && root->left->next->alreadyOptimized==false) totalIncrease += RecursivelyOptimizeBranches(root->left->next, optPrecision, subtreeNode, rad, true, ZERO_POINT_ZERO); if(root->right != nd && root->right->alreadyOptimized==false) totalIncrease += RecursivelyOptimizeBranches(root->right, optPrecision, subtreeNode, rad, true, ZERO_POINT_ZERO); } } if(prunePointIncrease > ZERO_POINT_ZERO){//now doing a radius opt at the prune point starting from the 4 branches attached to that branch //in other words, this is no longer centered on a node, but on a branch if(prune->left != NULL){ if(prune->right->alreadyOptimized==false) pruneRadIncrease += RecursivelyOptimizeBranches(prune->right, optPrecision, subtreeNode, rad, true, ZERO_POINT_ZERO); if(prune->left->alreadyOptimized==false) pruneRadIncrease += RecursivelyOptimizeBranches(prune->left, optPrecision, subtreeNode, rad, true, ZERO_POINT_ZERO); } if(prune == root){ assert(0); //pruneRadIncrease += RecursivelyOptimizeBranches(prune->left->next, optPrecision, subtreeNode, rad, false, ZERO_POINT_ZERO); } else{//this RecursivelyOptimizeBranchesDown will implicitly also optimize prune's next or prev if(prune->anc!=root){ pruneRadIncrease += RecursivelyOptimizeBranchesDown(prune->anc, prune, optPrecision, subtreeNode, rad, ZERO_POINT_ZERO); } else{ if(root->left != prune && root->left->alreadyOptimized==false) totalIncrease += RecursivelyOptimizeBranches(root->left, optPrecision, subtreeNode, rad, true, ZERO_POINT_ZERO); if(root->left->next != prune && root->left->next->alreadyOptimized==false) totalIncrease += RecursivelyOptimizeBranches(root->left->next, optPrecision, subtreeNode, rad, true, ZERO_POINT_ZERO); if(root->right != prune && root->right->alreadyOptimized==false) totalIncrease += RecursivelyOptimizeBranches(root->right, optPrecision, subtreeNode, rad, true, ZERO_POINT_ZERO); } } // if(prune->next != NULL) pruneRadIncrease += RecursivelyOptimizeBranches(prune->next, optPrecision, subtreeNode, rad, true, ZERO_POINT_ZERO); // if(prune->prev != NULL) pruneRadIncrease += RecursivelyOptimizeBranches(prune->prev, optPrecision, subtreeNode, rad, true, ZERO_POINT_ZERO); // } /* if(prune->right->left!=NULL) totalIncrease += RecursivelyOptimizeBranches(prune->right->left, optPrecision, subtreeNode, rad, false, ZERO_POINT_ZERO); if(prune->left->left!=NULL) totalIncrease += RecursivelyOptimizeBranches(prune->left->left, optPrecision, subtreeNode, rad, false, ZERO_POINT_ZERO); if(prune==root){ if(prune->left->next->left!=NULL) totalIncrease += RecursivelyOptimizeBranches(prune->left->next->left, optPrecision, subtreeNode, rad, false, ZERO_POINT_ZERO); } else{ if(prune->anc!=root) totalIncrease += RecursivelyOptimizeBranchesDown(prune->anc, prune, optPrecision, subtreeNode, rad, ZERO_POINT_ZERO); else{ if(prune->nodeNum!=subtreeNode){ if(root->left != prune) totalIncrease += RecursivelyOptimizeBranches(root->left, optPrecision, subtreeNode, rad, true, ZERO_POINT_ZERO); if(root->left->next != prune) totalIncrease += RecursivelyOptimizeBranches(root->left->next, optPrecision, subtreeNode, rad, true, ZERO_POINT_ZERO); if(root->right != prune) totalIncrease += RecursivelyOptimizeBranches(root->right, optPrecision, subtreeNode, rad, true, ZERO_POINT_ZERO); } } } */ totalIncrease += pruneRadIncrease; #ifdef OPT_DEBUG optsum << "pruneRadOpt total\t" << pruneRadIncrease << endl; #endif } } #ifdef OPT_DEBUG optsum << "radiusopt intial pass total\t" << totalIncrease << endl; #endif FLOAT_TYPE postIncr=ZERO_POINT_ZERO; TreeNode *finalNode; //no longer need to do this because checking if nodes have already been optimized before doing it again /* if(nodeOptVector.empty()==false && prune != NULL){//remove any duplicate entries caused by an overlaping radius around connector and prune for(list::iterator fir=nodeOptVector.begin();fir!=nodeOptVector.end();fir++){ list::iterator sec=fir; sec++; if(sec==nodeOptVector.end()) break; for(;sec!=nodeOptVector.end();){ if(*fir == *sec){ list::iterator del=sec; if(sec != nodeOptVector.end()) sec++; nodeOptVector.erase(del); } else if(sec != nodeOptVector.end()) sec++; } } } */ if(nodeOptVector.empty()) finalNode = nd->right; else{ while(nodeOptVector.empty() == false){ list::iterator it=nodeOptVector.begin(); while(it!=nodeOptVector.end()){ if(nodeOptVector.size() == 1) finalNode=*it; thisIncr=OptimizeBranchLength(optPrecision, *(it), true); postIncr+= thisIncr; if(!(thisIncr > optPrecision)){ list::iterator del=it; it++; nodeOptVector.erase(del); } else it++; } } } assert(!FloatingPointEquals(lnL, -1.0, 1e-8)); totalIncrease += postIncr; // if(fourBranchTot > treeRejectionThreshold ) cout << "r\t" << (totalIncrease - fourBranchTot) << endl; // else if(treeRejectionThreshold < (totalIncrease - fourBranchTot)) cout << (totalIncrease - fourBranchTot) << endl; #ifdef OPT_DEBUG optsum << "postopt total\t" << postIncr << endl; optsum << "total\t" << totalIncrease << endl; #endif } FLOAT_TYPE Tree::RecursivelyOptimizeBranches(TreeNode *nd, FLOAT_TYPE optPrecision, int subtreeNode, int radius, bool dontGoNext, FLOAT_TYPE scoreIncrease, bool ignoreDelta/*=false*/){ FLOAT_TYPE delta = ZERO_POINT_ZERO; if(nd->alreadyOptimized == false) delta = OptimizeBranchLength(optPrecision, nd, true); scoreIncrease += delta; if(!(delta < optPrecision)) nodeOptVector.push_back(nd); // if(radius==0) cout << "hit max radius!" <left!=NULL && radius>1 && (!(delta < optPrecision) || ignoreDelta == true)){ /*if(nd->left->alreadyOptimized == false)*/ scoreIncrease += RecursivelyOptimizeBranches(nd->left, optPrecision, subtreeNode, radius-1, false, 0, ignoreDelta); } if(nd->next!=NULL && dontGoNext==false){ if(nd->next->alreadyOptimized == false) scoreIncrease += RecursivelyOptimizeBranches(nd->next, optPrecision, subtreeNode, radius, false, 0, ignoreDelta); } if(memLevel > 1) RemoveTempClaReservations(); return scoreIncrease; } FLOAT_TYPE Tree::RecursivelyOptimizeBranchesDown(TreeNode *nd, TreeNode *calledFrom, FLOAT_TYPE optPrecision, int subtreeNode, int radius, FLOAT_TYPE scoreIncrease){ FLOAT_TYPE delta = ZERO_POINT_ZERO; if(nd->alreadyOptimized==false)//because the next or prev of calledFrom could be unoptimized //even if nd has been, this check needs to be done here, rather than before calling this func delta = OptimizeBranchLength(optPrecision, nd, true); scoreIncrease += delta; if(nd->nodeNum == subtreeNode){ return scoreIncrease; } if(!(delta < optPrecision)) nodeOptVector.push_back(nd); // if(radius==0) cout << "hit max radius!" <left!=NULL && nd->left!=calledFrom && radius>1){ if(nd->left->alreadyOptimized == false) scoreIncrease += RecursivelyOptimizeBranches(nd->left, optPrecision, subtreeNode, radius, true, 0); } else if(radius>1 && nd->left->next->alreadyOptimized == false) scoreIncrease += RecursivelyOptimizeBranches(nd->left->next, optPrecision, subtreeNode, radius, false, 0); if(nd->anc!=root && radius>1 && !(delta < optPrecision)){ if(nd->anc->alreadyOptimized == false) scoreIncrease += RecursivelyOptimizeBranchesDown(nd->anc, nd, optPrecision, subtreeNode, radius-1, 0); } if(nd->anc==root){ if(radius>1 && !(delta < optPrecision)){ if(nd->next!=NULL){ if(nd->next->alreadyOptimized == false) scoreIncrease += RecursivelyOptimizeBranches(nd->next, optPrecision, subtreeNode, radius-1, true, 0); } else if(nd->prev->prev->alreadyOptimized == false) scoreIncrease += RecursivelyOptimizeBranches(nd->prev->prev, optPrecision, subtreeNode, radius-1, true, 0); if(nd->prev!=NULL){ if(nd->prev->alreadyOptimized == false) scoreIncrease += RecursivelyOptimizeBranches(nd->prev, optPrecision, subtreeNode, radius-1, true, 0); } else if(nd->next->next->alreadyOptimized == false) scoreIncrease += RecursivelyOptimizeBranches(nd->next->next, optPrecision, subtreeNode, radius-1, true, 0); } } if(memLevel > 1) RemoveTempClaReservations(); return scoreIncrease; } pair Tree::CalcDerivativesRateHet(TreeNode *nd1, TreeNode *nd2){ //nd1 and nd2 are the nodes on either side of the branch of interest //nd1 will always be the "lower" one, and will always be internal, while //nd2 can be internal or terminal CondLikeArray *claOne=NULL, *claTwo=NULL; CondLikeArraySet *setOne=NULL, *setTwo=NULL; if(nd1->left == nd2) setOne=GetClaUpLeft(nd1, true); else if(nd1->right == nd2) setOne=GetClaUpRight(nd1, true); else //nd1 must be the root, and nd2 it's middle des setOne=GetClaDown(nd1, true); //this must happen BEFORE the derivs are calced, or the prmat won't be current for this branch! if(nd2->left != NULL) setTwo=GetClaDown(nd2, true); FLOAT_TYPE ***deriv1, ***deriv2, ***prmat; FLOAT_TYPE d1=ZERO_POINT_ZERO, d2=ZERO_POINT_ZERO, d1tot=ZERO_POINT_ZERO, d2tot=ZERO_POINT_ZERO; //zero out lnL here, since the looping over the various models below will just add to it lnL = ZERO_POINT_ZERO; for(vector::iterator specs = claSpecs.begin();specs != claSpecs.end();specs++){ Model *mod = modPart->GetModel((*specs).modelIndex); ProfModDeriv.Start(); mod->CalcDerivatives(nd2->dlen * modPart->SubsetRate((*specs).dataIndex), prmat, deriv1, deriv2); ProfModDeriv.Stop(); claOne = setOne->GetCLA((*specs).claIndex); if(setTwo != NULL) claTwo = setTwo->GetCLA((*specs).claIndex); bool isNucleotide = mod->IsNucleotide(); if(nd2->left == NULL){ char *childData=nd2->tipData[(*specs).dataIndex]; ProfTermDeriv.Start(); if(isNucleotide == false){ if(mod->NRateCats() > 1) GetDerivsPartialTerminalNStateRateHet(claOne, **prmat, **deriv1, **deriv2, childData, d1, d2, (*specs).modelIndex, (*specs).dataIndex); else GetDerivsPartialTerminalNState(claOne, **prmat, **deriv1, **deriv2, childData, d1, d2, (*specs).modelIndex, (*specs).dataIndex); } else { #ifdef OPEN_MP assert(nd2->ambigMap.size() > (*specs).dataIndex); assert(nd2->ambigMap[(*specs).dataIndex] != NULL); GetDerivsPartialTerminal(claOne, **prmat, **deriv1, **deriv2, childData, d1, d2, (*specs).modelIndex, (*specs).dataIndex, nd2->ambigMap[(*specs).dataIndex]); // GetDerivsPartialTerminal(claOne, **prmat, **deriv1, **deriv2, childData, d1, d2, modIndex, nd2->ambigMap, (*specs).modelIndex, (*specs).dataIndex); #else GetDerivsPartialTerminal(claOne, **prmat, **deriv1, **deriv2, childData, d1, d2, (*specs).modelIndex, (*specs).dataIndex); #endif } assert(d1 == d1); ProfTermDeriv.Stop(); } else { ProfIntDeriv.Start(); #ifdef EQUIV_CALCS GetDerivsPartialInternalEQUIV(claOne, claTwo, **prmat, **deriv1, **deriv2, d1, d2, nd2->tipData, (*specs).modelIndex, (*specs).dataIndex); #else if(isNucleotide == false){ if(mod->NRateCats() > 1) GetDerivsPartialInternalNStateRateHet(claOne, claTwo, **prmat, **deriv1, **deriv2, d1, d2, (*specs).modelIndex, (*specs).dataIndex); else GetDerivsPartialInternalNState(claOne, claTwo, **prmat, **deriv1, **deriv2, d1, d2, (*specs).modelIndex, (*specs).dataIndex); } else GetDerivsPartialInternal(claOne, claTwo, **prmat, **deriv1, **deriv2, d1, d2, (*specs).modelIndex, (*specs).dataIndex); #endif ProfIntDeriv.Stop(); } assert(d1 == d1); //account for the different rate scaling factors here // d1tot += d1 ; // d2tot += d2; d1tot += d1 * modPart->SubsetRate((*specs).dataIndex); d2tot += d2 * modPart->SubsetRate((*specs).dataIndex) * modPart->SubsetRate((*specs).dataIndex); } assert(d1 == d1); assert(d2 == d2); return pair(d1tot, d2tot); } FLOAT_TYPE Tree::BranchLike(TreeNode *optNode){ bool scoreOK=true; do{ try{ if(optNode->anc->left==optNode){ optNode->anc->claIndexDown = claMan->SetDirty(optNode->anc->claIndexDown); optNode->anc->claIndexUR = claMan->SetDirty(optNode->anc->claIndexUR); GetClaUpLeft(optNode->anc); } else if(optNode->anc->right==optNode){ optNode->anc->claIndexDown = claMan->SetDirty(optNode->anc->claIndexDown); optNode->anc->claIndexUL = claMan->SetDirty(optNode->anc->claIndexUL); GetClaUpRight(optNode->anc); } else { optNode->anc->claIndexUL = claMan->SetDirty(optNode->anc->claIndexUL); optNode->anc->claIndexUR = claMan->SetDirty(optNode->anc->claIndexUR); GetClaDown(optNode->anc); } //now sum as if this were the root ConditionalLikelihoodRateHet(ROOT, optNode->anc); return lnL; } catch(int){ scoreOK=false; MakeAllNodesDirty(); rescaleEvery -= 2; ofstream resc("rescale.log", ios::app); resc << "rescale reduced to " << rescaleEvery << endl; resc.close(); if(rescaleEvery<2) rescaleEvery=2; } }while(scoreOK==false); return 0; } void Tree::SampleBlenCurve(TreeNode *nd, ofstream &out){ FLOAT_TYPE initialLen=nd->dlen; Score(); out << nd->dlen << "\t" << lnL << "\n"; SetBranchLength(nd, (FLOAT_TYPE)1e-4); for(int i=0;i<15;i++){ Score(); out << nd->dlen << "\t" << lnL << "\n"; SetBranchLength(nd, nd->dlen * (FLOAT_TYPE)2.0); } SetBranchLength(nd, initialLen); } void Tree::CalcEmpiricalDerivatives(TreeNode *nd, FLOAT_TYPE &D1, FLOAT_TYPE &D2){ FLOAT_TYPE start_blen = nd->dlen; FLOAT_TYPE incr; FLOAT_TYPE blen_used = start_blen; //derivs will be too small to avoid floating point error //REMEMBER THAT THIS IS NOT ALWAYS ENOUGH THOUGH, AND ESPECIALLY THE //SEC DERIV CAN STILL BE FAIRLY WRONG double precCompensationFactor = 1.0; double digits = ceil(log10(-lnL)); if(digits > 4) precCompensationFactor = pow(10.0, digits - 4); if(blen_used > 1e-6){ incr = blen_used * 0.001 * precCompensationFactor; blen_used = max(min(start_blen, max_brlen - incr), min_brlen + incr); SetBranchLength(nd, blen_used); } else if(blen_used > min_brlen + 1.0e-8) incr = blen_used * 0.01 * precCompensationFactor; else{ /* ofstream deb("bcurve.log"); deb.precision(14); SampleBlenCurve(nd, deb); deb.close(); */ incr =1.0e-8 * precCompensationFactor * 10; blen_used = max(start_blen, min_brlen * 2.0 + incr); SetBranchLength(nd, blen_used); } MakeAllNodesDirty(); Score(); FLOAT_TYPE start=lnL; /* ofstream deb; deb.open("clas.log"); OutputNthClaAcrossTree(deb, root, 274); deb.close(); */ SetBranchLength(nd, blen_used + incr); // MakeAllNodesDirty(); Score(); /* deb.open("clas.log"); OutputNthClaAcrossTree(deb, root, 274); deb.close(); */ FLOAT_TYPE empD11= (lnL - start)/incr; // SetBranchLength(nd, prevDLen); // Score(); SetBranchLength(nd, blen_used - incr); // MakeAllNodesDirty(); Score(); /* deb.open("clas.log"); OutputNthClaAcrossTree(deb, root, 274); deb.close(); */ FLOAT_TYPE empD12 = (lnL - start)/-incr; D1=(empD11+empD12)*.5; D2=(empD11-empD12)/incr; SetBranchLength(nd, start_blen); // MakeAllNodesDirty(); //Note that setting this isn't important except for proper score output when OPT_DEBUG is on lnL = start; } #ifdef SPOOF_NEWTON_RAPHSON //this allows the ability to play with optimization without actually disrupting program flow FLOAT_TYPE Tree::NewtonRaphsonOptimizeBranchLength(FLOAT_TYPE precision1, TreeNode *nd, bool goodGuess){ FLOAT_TYPE origLen = nd->dlen; Score(); FLOAT_TYPE origScore = lnL; FLOAT_TYPE estNRImprove = NewtonRaphsonSpoof(precision1, nd, goodGuess); FLOAT_TYPE nrLen = nd->dlen; Score(); FLOAT_TYPE nrScore = lnL; SetBranchLength(nd, origLen); FLOAT_TYPE estBestImprove = NewtonRaphsonSpoof(0.0001, nd, goodGuess); FLOAT_TYPE bestLen = nd->dlen; Score(); FLOAT_TYPE bestScore = lnL; FLOAT_TYPE trueNRImprove = nrScore - origScore; FLOAT_TYPE trueBestImprove = bestScore - origScore; SetBranchLength(nd, nrLen); ofstream spoof("optspoof.log", ios::app); spoof << nd->nodeNum << "\t" << origLen << "\t" << origScore << "\t" << goodGuess << "\n"; spoof << "\t" << nrLen << "\t" << nrScore << "\t" << estNRImprove << "\t" << trueNRImprove << "\n"; spoof << "\t" << bestLen << "\t" << bestScore << "\t" << estBestImprove << "\t" << trueBestImprove << "\n"; spoof.close(); return estNRImprove; } FLOAT_TYPE Tree::NewtonRaphsonSpoof(FLOAT_TYPE precision1, TreeNode *nd, bool goodGuess){ #else FLOAT_TYPE Tree::NewtonRaphsonOptimizeBranchLength(FLOAT_TYPE precision1, TreeNode *nd, bool goodGuess){ #endif /* if(goodGuess==false && (nd->dlen < 0.0001 || nd->dlen > .1)){ SetBranchLength(nd, (FLOAT_TYPE).001); } */ //if(nd->dlen==min_brlen){ #ifdef OPT_DEBUG /* if(nd->nodeNum == 8){ ofstream scr("NRcurve.log"); scr.precision(20); assert(scr.good()); scr.precision(15); FLOAT_TYPE initDlen = nd->dlen; for(FLOAT_TYPE d=1e-8;d<.5;d*=1.33){ nd->dlen = d; SweepDirtynessOverTree(nd); Score(); scr << d << "\t" << lnL << endl; } nd->dlen=initDlen; SweepDirtynessOverTree(nd); scr.close(); } */ #endif // nd->dlen=.3254; // SweepDirtynessOverTree(nd); /* if(nd->nodeNum==67){ ofstream deb("curves.log"); SampleBlenCurve(nd, deb); deb.close(); } */ // MakeAllNodesDirty(); /* FLOAT_TYPE start, empD11, empD12, empD1, empD2; if(nd->nodeNum == 4 && nd->dlen < 1e-7 && nd->anc->nodeNum==12 && nd->next != NULL && nd->next->nodeNum==10){ //if(0){ SetBranchLength(nd, 1.0e-5); MakeAllNodesDirty(); Score(); } */ #ifdef OPT_DEBUG // ofstream log("optimization.log", ios::app); // log.precision(10); opt.precision(8); opt << nd->nodeNum << "\t" << nd->dlen << "\t" << lnL < -2){ Score(nd->anc->nodeNum); } FLOAT_TYPE poo = lnL; MakeAllNodesDirty(); Score(nd->anc->nodeNum); assert(FloatingPointEquals(poo, lnL, 1e-4)); */ FLOAT_TYPE delta; #endif FLOAT_TYPE totalEstImprove=ZERO_POINT_ZERO; int iter=0; FLOAT_TYPE abs_d1_prev=FLT_MAX; const FLOAT_TYPE v_onEntry=nd->dlen; FLOAT_TYPE v=nd->dlen; FLOAT_TYPE v_prev = nd->dlen; /* in case we don't like the new value (see below) */ bool moveOn = false; FLOAT_TYPE prevScore=lnL; FLOAT_TYPE curScore=lnL; int negProposalNum=0; FLOAT_TYPE knownMin=min_brlen, knownMax=max_brlen; FLOAT_TYPE d1, d2, estScoreDelta, estDeltaNR; FLOAT_TYPE initialL; do{ bool scoreOK; int sweeps=0; #undef EMPERICAL_DERIVS #ifndef EMPERICAL_DERIVS pair derivs; do{ //this part just catches the exception that could be thrown by the rescaling //function if it decides that the current rescaleEvery is too large try{ scoreOK=true; derivs = CalcDerivativesRateHet(nd->anc, nd); if(iter == 0) initialL = lnL; optCalcs++; }catch(int err){ scoreOK=false; if(err==1){ MakeAllNodesDirty(); rescaleEvery -= 2; ofstream resc("rescale.log", ios::app); resc << "rescale reduced to " << rescaleEvery << endl; resc.close(); if(rescaleEvery<2) throw(ErrorException("Problem with rescaling in branchlength optimization.\nPlease report this error (and the details of your analysis) to garli.support@gmail.com.")); } else if(err==2){ //this is necessary because rarely it is possible that attempted optimization at nodes //across the tree causes more than a single set of clas to be in use, which can cause //clas to run out if we are in certain memory situations assert(sweeps==0); SweepDirtynessOverTree(nd); sweeps++; } } }while(scoreOK==false); d1=derivs.first; d2=derivs.second; #else if(iter == 0){ if(lnL > -2) Score(nd->anc->nodeNum); initialL = lnL; } CalcEmpiricalDerivatives(nd, d1, d2); #endif #ifdef OPT_DEBUG FLOAT_TYPE empD1, empD2; // if(nd->nodeNum == 67 && nd->anc->nodeNum==96){// && nd->anc->nodeNum==12 && nd->next != NULL && nd->next->nodeNum==10){ // SetBranchLength(nd, 0.01); // CalcEmpiricalDerivatives(nd, empD1, empD2); // opt << empD1 << "\t" << empD2 << "\t" << nd->dlen + (-empD1/empD2) << "\t"; // d1 = empD1; // d2 = empD2; // } #endif estDeltaNR=-d1/d2; //estimated change in score by a Taylor series estScoreDelta = d1*estDeltaNR + (d2 * estDeltaNR * estDeltaNR * ZERO_POINT_FIVE); if(d1 <= ZERO_POINT_ZERO && nd->dlen < knownMax) knownMax = nd->dlen; else if(d1 > ZERO_POINT_ZERO && nd->dlen > knownMin) knownMin = nd->dlen; #ifdef OPT_DEBUG opt << nd->dlen << "\t" << lnL << "\t" << d1 << "\t" << d2 << "\t" << estScoreDelta << "\t"; #endif FLOAT_TYPE abs_d1 = fabs(d1); if (d2 >= ZERO_POINT_ZERO){//curvature is wrong for NR use //this does NOT only happen when the peak is at the min, as I used to think #ifdef OPT_DEBUG opt << "d2 > 0\t"; #endif //Not allowing this escape anymore if(fabs(d1) < ONE_POINT_ZERO){//don't bother doing anything if the surface is this flat #ifdef OPT_DEBUG opt << "very small d1.\t"; #endif // return totalEstImprove; } if(d1 <= ZERO_POINT_ZERO){//if d1 is negative, try shortening arbitrarily, or go halfway to the knownMin FLOAT_TYPE proposed; #ifdef SINGLE_PRECISION_FLOATS if(FloatingPointEquals(nd->dlen, min_brlen, 1.0e-8f)){ #ifdef OPT_DEBUG opt << "already at min, return\n"; #endif return totalEstImprove; } if(knownMin == min_brlen){ if(nd->dlen <= 1.0e-4f) proposed = min_brlen; else if(nd->dlen <= 0.005f) proposed = 1.0e-4f; else if(nd->dlen <= 0.05f) proposed = nd->dlen * 0.1f; else proposed = nd->dlen * 0.25f; } #else if(FloatingPointEquals(nd->dlen, min_brlen, 1.0e-8)){ //DEBUG if(lnL < initialL - pow(10.0, -6.0+ceil(log10(-lnL)))){ outman.DebugMessage("Score worsened by %.6f, restoring blen, exiting", initialL - lnL); SetBranchLength(nd, v_onEntry); //9/25/13 There was a dumb bug here where even if the blen was reset a positive //improvement could be returned. This code only gets executed very infrequently, //and the bug only caused issues under very specific and even more rare conditions. //e.g., when only this branch was in the optimize queue totalEstImprove = ZERO_POINT_ZERO; Score(); } assert(lnL >= initialL - pow(10.0, -6.0+ceil(log10(-lnL)))); #ifdef OPT_DEBUG opt << "already at min, return\n"; #endif return totalEstImprove; } if(knownMin == min_brlen){ if(nd->dlen <= 1.0e-4) proposed = min_brlen; else if(nd->dlen <= 0.005) proposed = 1.0e-4; else if(nd->dlen <= 0.05) proposed = nd->dlen * 0.1; else proposed = nd->dlen * 0.25; } #endif else proposed = (knownMin + nd->dlen) * ZERO_POINT_FIVE; if(iter > 0 || proposed == min_brlen){//don't let this bail out on the first iteration based on the estimated //change if we are jumping to an arbitrary point, because we are just trying to get to a point //where we can actually trust the derivs FLOAT_TYPE estImp = d1*(proposed - nd->dlen) + (d2 * (proposed - nd->dlen) * (proposed - nd->dlen) * ZERO_POINT_FIVE); if(estImp < precision1){ //DEBUG - this shouldn't really be bailing because of low potential improvement //unless the likelihood is at least as good as it was coming in if(lnL >= initialL - 1.0e-4){ #ifdef OPT_DEBUG opt << "imp to proposed " << proposed << " < prec, return\n"; #endif return totalEstImprove; } #ifdef OPT_DEBUG else opt << "don't return!"; #endif } } v=proposed; totalEstImprove += precision1; } else{//d1 > 0.0, try increasing the blen by 10 or 2 if knownMax==max_brlen, otherwise try a step half-way to the knownMax FLOAT_TYPE proposed; if(knownMax == max_brlen){ #ifdef SINGLE_PRECISION_FLOATS if(nd->dlen < 0.1f) proposed = nd->dlen * 10.0f; else proposed = min(nd->dlen * 2.0f, max_brlen); #else if(nd->dlen < 0.1) proposed = nd->dlen * 10.0; else proposed = min(nd->dlen * 2.0, max_brlen); #endif } else proposed = (knownMax + nd->dlen) * ZERO_POINT_FIVE; if(iter > 0){//don't let this bail out on the first iteration based on the estimated //change if we are jumping to an arbitrary point, because we are just trying to get to a point //where we can actually trust the derivs FLOAT_TYPE estImp = d1*(proposed - nd->dlen) + (d2 * (proposed - nd->dlen) * (proposed - nd->dlen) * ZERO_POINT_FIVE); if(estImp < precision1){ //DEBUG - this shouldn't really be bailing because of low potential improvement //unless the likelihood is at least as good as it was coming in if(lnL >= initialL - 1.0e-4){ #ifdef OPT_DEBUG opt << "imp to prop < prec, return\n"; #endif return totalEstImprove; } #ifdef OPT_DEBUG else opt << "don't return!"; #endif } } v=proposed; totalEstImprove += precision1; } } else{//trying NR is feasible if(d1 < ZERO_POINT_ZERO && FloatingPointEquals(nd->dlen, min_brlen, 1.0e-8)){ #ifdef OPT_DEBUG opt << "already at min, return\n"; #endif return totalEstImprove; } if(d1 > ZERO_POINT_ZERO && FloatingPointEquals(nd->dlen, max_brlen, 1.0e-8)){ #ifdef OPT_DEBUG opt << "already at max, return\n"; #endif return totalEstImprove; } //12/9/07 now requiring the actual likelihood to improve. Single optimization passes with AA and Codon //models were fairly often moving to worse likelihoods but indicating that the function should return //since the deriv calculations are now calculating the true likelihood, this has no real overhead #ifdef NR_EXIT_96 if(estScoreDelta < precision1 && (iter == 0 || lnL >= initialL)){ #elif defined(NR_EXIT_R340) if(estScoreDelta < precision1 && (iter == 0 || lnL + 1.0e-8 >= initialL)){ #elif defined(NR_EXIT_R343) if(estScoreDelta < precision1 && (iter == 0 || lnL + max(1.0e-7, GARLI_FP_EPS * 10.0) >= initialL)){ #else //this will gradually increase the tolerated amount of score worsening (due to floating point imprecision) //as the iterations go on. If possible we'd still like to see very close scores, but if we're having //trouble getting close after many iterations we don't want to terminate the program. If something is //horribly wrong with the scores this will still cause termination. if(estScoreDelta < precision1 && (iter == 0 || lnL + ((iter < 10 ? 1 : iter) * max(1.0e-7, GARLI_FP_EPS * 10.0)) >= initialL)){ #endif #ifdef OPT_DEBUG opt << "delta < prec, return\n"; if(curScore==-ONE_POINT_ZERO){ Score(nd->anc->nodeNum); } #endif return totalEstImprove; } else{/* Take the Newton-Raphson step */ bool noNR = false; if(iter > 10) { //If we've taken a lot of NR steps without bracketing the peak (diagnosed by //the knownMax or knownMin being equal to the max or min brlen), make //some agresssive moves. This is often useful when the tree/branch lengths are //terrible and numerical instability creeps into the derivative and likelihood //calculations, which can cause the NR moves based on the derivs to be extremely //conservative. That only really seems to be an issue in codon models if(d1 > ZERO_POINT_ZERO){ if(FloatingPointEquals(knownMax, max_brlen, 1e-8)){ if((iter > 20) && (nd->dlen > (max_brlen/2.0))) v = max_brlen; else{ if(v < 0.2) v = min((v + max_brlen)*0.5, v*5.0); else v = min((v + max_brlen)*0.5, v*2.0); } noNR = true; #ifdef OPT_DEBUG opt << "IgnoreNRUp\t"; #endif } else if(((iter - 20) > 0) && ((iter - 20) % 10 == 0)){ //another annoying special case (only for codon models I think) //it is possible for the derivs to apparently be //correct but for the NR estimate to still be so conservative that it takes forever //to converge. The above code can take care of that if we've never been to the right //of the peak (knownMax == max_brlen), but this can also happen if we were to right //of the peak at one point and jumped all the way to the min length. In that case, //try a jump to the midpoint of the bracket or 100x the current length, //whichever is less v = min((v + knownMax)*0.5, v*100.0); #ifdef OPT_DEBUG opt << "IgnoreNRUp2\t"; #endif } } else{ if(FloatingPointEquals(knownMin, min_brlen, 1e-8)){ if(iter > 20) v = min_brlen; else v = (v + min_brlen)*0.5; noNR = true; #ifdef OPT_DEBUG opt << "IgnoreNRDown\t"; #endif } } } if(noNR == false) v += estDeltaNR; #ifdef OPT_DEBUG opt << v << "\t"; #endif } if ((iter != 0) && (abs_d1 > abs_d1_prev)){ //not doing anything special here. This generally means that we overshot the peak, but //should get it from the other side #ifdef OPT_DEBUG opt << "d1 increased!\t"; #endif } if (v <= knownMin){ negProposalNum++; if(knownMin == min_brlen){ FLOAT_TYPE deltaToMin=min_brlen-nd->dlen; FLOAT_TYPE scoreDeltaToMin = (deltaToMin * d1 + (deltaToMin*deltaToMin*d2*ZERO_POINT_FIVE)); if(scoreDeltaToMin < precision1){ #ifdef OPT_DEBUG opt << "imp to MIN < prec, return\n"; #endif return totalEstImprove; } #ifdef SINGLE_PRECISION_FLOATS else if(negProposalNum==1 && nd->dlen > 1e-4f && v_prev != 1e-4f){ //try a somewhat smaller length before going all the way to the min if(nd->dlen < .005f ) v = 1e-4f; else if(nd->dlen < 0.05f) v = nd->dlen * 0.1f; else v = nd->dlen * .25f; #else else if(negProposalNum==1 && nd->dlen > 1e-4 && v_prev != 1e-4){ //try a somewhat smaller length before going all the way to the min if(nd->dlen < .005 ) v = 1e-4; else if(nd->dlen < 0.05) v = nd->dlen * 0.1; else v = nd->dlen * 0.25; #endif FLOAT_TYPE delta=v - nd->dlen; totalEstImprove += (delta * d1 + (delta*delta*d2*ZERO_POINT_FIVE)); } else{ v = min_brlen; totalEstImprove += scoreDeltaToMin; } } else{//knownMin is > absolute min, so we must already have a better guess //go half way to that guess FLOAT_TYPE proposed = (knownMin + nd->dlen) * ZERO_POINT_FIVE; FLOAT_TYPE deltaToMin=proposed-nd->dlen; FLOAT_TYPE scoreDeltaToMin = (deltaToMin * d1 + (deltaToMin*deltaToMin*d2*ZERO_POINT_FIVE)); #ifdef ALT_NR_BAIL //For exit, this used to not require that the lnL had improved from the starting value, only that the expected improvement //for the next jump was small. Now require improvement, but with a bit of scoring error tolerance. This will probbably //only come up with SP, in which case the max number of passes will be taken and then the initial blen will be restored below if(scoreDeltaToMin < precision1){ // outman.DebugMessage("would have bailed\t%.6f\t%.6f\t%.6f\t%.6f\t%.6f", lnL, knownMin, nd->dlen, scoreDeltaToMin, (lnL - initialL)); #ifdef OPT_DEBUG opt << "would have bailed\t" << scoreDeltaToMin << "\t" << (lnL - initialL); #endif } if(scoreDeltaToMin < precision1 && lnL + ((iter < 10 ? 1 : iter) * max(1.0e-7, GARLI_FP_EPS * 10.0)) >= initialL){ #else if(scoreDeltaToMin < precision1){ #endif #ifdef OPT_DEBUG opt << "imp to knownMIN < prec, return\n"; #endif return totalEstImprove; } v=proposed; totalEstImprove += scoreDeltaToMin; } } else if (v >= knownMax){ if(knownMax == max_brlen){ FLOAT_TYPE deltaToMax=max_brlen - nd->dlen; FLOAT_TYPE scoreDeltaToMax = (deltaToMax * d1 + (deltaToMax*deltaToMax*d2*ZERO_POINT_FIVE)); if(scoreDeltaToMax < precision1){ #ifdef OPT_DEBUG opt << "imp to MAX < prec, return\n"; #endif return totalEstImprove; } else{ v = max_brlen; totalEstImprove += scoreDeltaToMax; } } else{//knownMax is < absolute max, so we must already have a better guess //go half way to that guess FLOAT_TYPE proposed = (knownMax + nd->dlen) * ZERO_POINT_FIVE; FLOAT_TYPE deltaToMax=proposed-nd->dlen; FLOAT_TYPE scoreDeltaToMax = (deltaToMax * d1 + (deltaToMax*deltaToMax*d2*ZERO_POINT_FIVE)); #ifdef ALT_NR_BAIL //For exit, this used to not require that the lnL had improved from the starting value, only that the expected improvement //for the next jump was small. Now require improvement, but with a bit of scoring error tolerance. This will probbably //only come up with SP, in which case the max number of passes will be taken and then the initial blen will be restored below if(scoreDeltaToMax < precision1){ // outman.DebugMessage("would have bailed\t%.6f\t%.6f\t%.6f\t%.6f\t%.6f", lnL, knownMin, nd->dlen, scoreDeltaToMax, (lnL - initialL)); #ifdef OPT_DEBUG opt << "would have bailed\t" << scoreDeltaToMax << "\t" << (lnL - initialL); #endif } if(scoreDeltaToMax < precision1 && lnL + ((iter < 10 ? 1 : iter) * max(1.0e-7, GARLI_FP_EPS * 10.0)) >= initialL){ #else if(scoreDeltaToMax < precision1){ #endif #ifdef OPT_DEBUG opt << "imp to knownMAX < prec, return\n"; #endif return totalEstImprove; } v=proposed; totalEstImprove += scoreDeltaToMax; } } else totalEstImprove += estScoreDelta; abs_d1_prev = abs_d1; } assert(v >= min_brlen); assert(v >= knownMin); assert(v <= knownMax); SetBranchLength(nd, v); #ifdef OPT_DEBUG //Score(nd->anc->nodeNum); if(curScore != -ONE_POINT_ZERO){ if(lnL < curScore){ cout << lnL << "\t" << curScore << endl; if(curScore - lnL < .005){ //don't want to have different logic when OPT_DEBUG is on // SetBranchLength(nd, v_prev); // Score(nd->anc->nodeNum); // return lnL; } else {//assert(0); FLOAT_TYPE poo=lnL; SetBranchLength(nd, v_prev); MakeAllNodesDirty(); Score(nd->anc->nodeNum); assert(fabs(prevScore - lnL) < .01); SetBranchLength(nd, v); MakeAllNodesDirty(); Score(nd->anc->nodeNum); assert(fabs(poo - lnL) < .01); } } } curScore=lnL; delta=prevScore - lnL; opt << v << "\t" << "\n"; opt.flush(); #endif prevScore=lnL; v_prev=v; iter++; if(iter>50){ /* ofstream deb("optdeb.log"); deb << "initial length " << v_onEntry << endl; deb << "current length " << nd->dlen << endl; deb << "prev length " << v_prev << endl; deb << "d1 " << d1 << " d2 " << d2 << endl; deb << "neg proposal num " << negProposalNum << endl; deb.close(); */ if(iter > 100){ outman.DebugMessage("100 passes in NR!"); Score(nd->anc->nodeNum); //now going to allow escape after 100 passes in all SP runs, and in DP codon runs. This should only happen due to numerical problems, and these //are situations where numerical problems are known to occur. //Update - when blen MLE is very large I've found cases where there are true multiple blen optima for a single blen trace. So, allowing an //exemption in that case if the lnL loss was minor. #ifndef SINGLE_PRECISION_FLOATS bool someCodon = false; for(int m = 0;m < modSpecSet.NumSpecs();m++){ if(modSpecSet.GetModSpec(m)->IsCodon()){ someCodon = true; } } //DEBUG PART - not sure how to transfer this from trunk if(someCodon == false && (nd->dlen < 0.5 && v_onEntry < 0.5) && (initialL > lnL + 0.1)) throw(ErrorException("Problem with branchlength optimization. Please report this error (and the details of your analysis) to garli.support@gmail.com.\n")); else if(nd->dlen < 0.5 && v_onEntry < 0.5) outman.UserMessage("Notice: possible problem with branchlength optimization.\nIf you see this message frequently, please report it (and the details of your analysis) to garli.support@gmail.com.\nIf you only see it once, you may ignore it.\n"); else outman.DebugMessage("NOTE 100 passes in NR, long blens involved. \nDetails: nd=%d init=%f cur=%f prev=%d d1=%f d2=%f neg=%d", nd->nodeNum, v_onEntry, v_prev, nd->dlen, d1, d2, negProposalNum); #endif outman.DebugMessage(">>>>%.6f %.6f <<<<", initialL, lnL); if(lnL > initialL){ outman.DebugMessage("Score improved by %.6f, exiting", initialL - lnL); #ifdef OPT_DEBUG opt << "100 passes, score improved, keeping blen " << v << endl; #endif return totalEstImprove; } else{ outman.DebugMessage("Score worsened by %.6f, restoring blen, exiting", initialL - lnL); #ifdef OPT_DEBUG opt << "100 passes, score worsened, restoring initial blen " << v_onEntry << endl; #endif SetBranchLength(nd, v_onEntry); Score(); return ZERO_POINT_ZERO; } } /* ofstream scr("NRcurve.log"); scr.precision(20); assert(scr.good()); scr.precision(15); FLOAT_TYPE initDlen = nd->dlen; for(FLOAT_TYPE d=1e-8;d<.5;d*=1.33){ nd->dlen = d; SweepDirtynessOverTree(nd); Score(); scr << d << "\t" << lnL << endl; } nd->dlen=initDlen; SweepDirtynessOverTree(nd); scr.close(); bool poo=true; outman.UserMessage("long opt: %d", iter); ofstream deb("longopt.log", ios::app); deb << iter << "\t" << precision1 << "\t" << nd->nodeNum << "\t" << v_onEntry << "\t" << nd->dlen << "\t" << d1 << "\t" << d2 << "\t" << estDeltaNR << "\t" << estScoreDelta << "\t" << negProposalNum << "\n"; deb.close(); */ // while(poo){ // opt.close(); // } //assert(iter<=50); } }while(moveOn==false); #ifdef OPT_DEBUG opt << "final\t" << nd->dlen << "\t" << lnL << endl; #endif assert(0);//shouldn't be exiting this way return totalEstImprove; } /* void Tree::RecursivelyOptimizeBranches(TreeNode *nd, FLOAT_TYPE optPrecision, int subtreeNode, int radius, int centerNode, bool dontGoNext){ FLOAT_TYPE prevScore=lnL; #ifdef BRENT BrentOptimizeBranchLength(optPrecision, nd, false); FLOAT_TYPE delta=lnL - prevScore; bool continueOpt=(delta*2.0 > optPrecision ? true : false); #else bool continueOpt = NewtonRaphsonOptimizeBranchLength(optPrecision, nd); // continueOpt=true; #endif if(nd->left!=NULL && radius>1 && continueOpt) RecursivelyOptimizeBranches(nd->left, optPrecision, subtreeNode, radius-1, centerNode, false); if(nd->next!=NULL && dontGoNext==false){ RecursivelyOptimizeBranches(nd->next, optPrecision, subtreeNode, radius, centerNode, false); } } void Tree::RecursivelyOptimizeBranchesDown(TreeNode *nd, TreeNode *calledFrom, FLOAT_TYPE optPrecision, int subtreeNode, int radius, int ){ FLOAT_TYPE prevScore=lnL; #ifdef BRENT BrentOptimizeBranchLength(optPrecision, nd, false); FLOAT_TYPE delta=lnL - prevScore; bool continueOpt=(delta*2.0 > optPrecision ? true : false); #else bool continueOpt = NewtonRaphsonOptimizeBranchLength(optPrecision, nd); // continueOpt=true; #endif if(nd->left!=NULL && nd->left!=calledFrom && radius>1) RecursivelyOptimizeBranches(nd->left, optPrecision, subtreeNode, radius, 0, true); else if(radius>1) RecursivelyOptimizeBranches(nd->left->next, optPrecision, subtreeNode, radius, 0, false); if(nd->anc!=root && radius>1 && continueOpt){ RecursivelyOptimizeBranchesDown(nd->anc, nd, optPrecision, subtreeNode, radius-1, 0); } if(nd->anc==root){ if(radius>1 && continueOpt){ if(nd->next!=NULL) RecursivelyOptimizeBranches(nd->next, optPrecision, subtreeNode, radius-1, 0, true); else RecursivelyOptimizeBranches(nd->prev->prev, optPrecision, subtreeNode, radius-1, 0, true); if(nd->prev!=NULL) RecursivelyOptimizeBranches(nd->prev, optPrecision, subtreeNode, radius-1, 0, true); else RecursivelyOptimizeBranches(nd->next->next, optPrecision, subtreeNode, radius-1, 0, true); } } } */ /* void Tree::OptimizeBranchesAroundNode(TreeNode *nd, FLOAT_TYPE optPrecision, int subtreeNode){ //this function will optimize the three branches (2 descendents and one anc) connecting //to it. It assumes that everything that is dirty has been marked so. //by default there is only a single optimization pass over the three nodes FLOAT_TYPE precision1, precision2; if(subtreeNode==0) SetAllTempClasDirty(); precision1=optPrecision;// * 0.5; if(optPrecision > .2) precision2=0.0; else precision2=precision1 * 0.5; if(nd != root){ BrentOptimizeBranchLength(precision1, nd, false); BrentOptimizeBranchLength(precision1, nd->left, false); BrentOptimizeBranchLength(precision1, nd->right, false); } else{ BrentOptimizeBranchLength(precision1, nd->left, false); BrentOptimizeBranchLength(precision1, nd->left->next, false); BrentOptimizeBranchLength(precision1, nd->right, false); } if(precision2 > 0){ //if were're doing multiple optimization passes, only this stuff needs to be set dirty claMan->SetDirty(nd->nodeNum, nd->claIndex, true); claMan->SetTempDirty(nd->nodeNum, true); if(nd != root) claMan->SetTempDirty(nd->anc->nodeNum, true); if(nd != root){ BrentOptimizeBranchLength(precision2, nd, false); BrentOptimizeBranchLength(precision2, nd->left, false); BrentOptimizeBranchLength(precision2, nd->right, false); } else { BrentOptimizeBranchLength(precision2, nd->left, false); BrentOptimizeBranchLength(precision2, nd->left->next, false); BrentOptimizeBranchLength(precision2, nd->right, false); } } //these must be called after all optimization passes are done around this node TraceDirtynessToRoot(nd); if(subtreeNode==0) SetAllTempClasDirty(); else SetTempClasDirtyWithinSubtree(subtreeNode); } */ /* inline FLOAT_TYPE CallBranchLike(TreeNode *thisnode, Tree *thistree, FLOAT_TYPE blen){ thisnode->dlen=exp(blen); return thistree->BranchLike(thisnode)*-1; } inline FLOAT_TYPE CallBranchLikeRateHet(TreeNode *thisnode, Tree *thistree, FLOAT_TYPE blen){ thisnode->dlen=blen; FLOAT_TYPE like=thistree->BranchLikeRateHet(thisnode)*-1; #ifdef OPT_DEBUG ofstream opt("optimization.log" ,ios::app); opt.precision(11); opt << thisnode->dlen << "\t" << like << "\n\t"; opt.close(); ofstream opttrees("opttrees.tre", ios::app); char treeString[20000]; thistree->root->MakeNewick(treeString, false); opttrees << "utree tree1=" << treeString << ";" << endl; opttrees.close(); //if(thisnode->left!=NULL) thistree->TraceDirtynessToRoot(thisnode); ofstream scr("optscores.log", ios::app); scr.precision(10); scr << like << "\t" << blen << endl; scr.close(); #endif thistree->RerootHere(thisnode->nodeNum); thistree->MakeAllNodesDirty(); thistree->Score(thistree->data); return like; } FLOAT_TYPE Tree::BrentOptimizeBranchLength(FLOAT_TYPE accuracy_cutoff, TreeNode *here, bool firstPass){ //we pass the node whose branch length whose blen we want to optimize, but note that the //calculations occur at the node below that //if firstPass is true, we have no idea what a reasonable value for the blen is, so //use a wide bracket. If it is false, try a fairly tight bracket around the current val FLOAT_TYPE a, b, c, fa, fb, fc, minimum, minScore=0.0; FLOAT_TYPE blen=here->dlen; assert(blen>=min_brlen); if(here->anc){ if(firstPass){ if(blen<1e-6){ a=min_brlen; if(blen!=min_brlen){ b=blen; } else{ b=min_brlen*100; lnL=-1; } c=min_brlen*10000.0; } else{ if(blen<.0001){ a=.000001; b=blen; c=.01; } else if(blen<.1){ a=.0001; b=blen; c=.1; } else { a=.1; b=blen; c=.75; } } } else{ //tighter if(blen > 1e-6){ a=blen*.66; b=blen; c=blen*1.5; } else{ a=min_brlen; if(blen!=min_brlen){ b=blen; } else{ b=min_brlen*100; lnL=-1; } c=min_brlen*10000.0; } } #ifdef OPT_DEBUG ofstream opt("optimization.log" ,ios::app); opt << "node " << here->nodeNum << "\t" << here->dlen << "\n"; // opt << "\t" << a << "\t" << b << "\t" << c << "\n"; #endif if(mod->NRateCats()==1){ mnbrak(&a, &b, &c, &fa, &fb, &fc, CallBranchLike, here, this); // opt << a << "\t" << b << "\t" << c << "\t"; brent(a, b, c, CallBranchLike, accuracy_cutoff, &minimum, here, this); } else{ #ifdef OPT_DEBUG opt << "brak\t"; opt.close(); #endif fb=lnL; int zeroMLE = DZbrak(&a, &b, &c, &fa, &fb, &fc, CallBranchLikeRateHet, here, this); bool flatSurface=false; if(fa-fb + fc-fb < .000001) flatSurface=true; //braka=fa; //brakb=fb; #ifdef OPT_DEBUG ofstream opt("optimization.log" ,ios::app); // opt << "bracket\t" << a << "\t" << fa << "\n\t" << b << "\t" << fb << "\n\t" << c << "\t" << fc << endl; opt << "brent\t"; opt.close(); #endif if(zeroMLE==0 && flatSurface==false) //if the bracket suggests that the MLE is very near 0, don't bother calling brent minScore=DZbrent(a, b, c, fa, fb, fc, CallBranchLikeRateHet, accuracy_cutoff, &minimum, here, this); else if(zeroMLE==1){ minimum=(min_brlen); if(a==min_brlen) minScore=fa; else if(c==min_brlen) minScore=fc; else minScore=-1; } else{ minimum=b; minScore=fb; } } FLOAT_TYPE min_len=minimum; // FLOAT_TYPE min_len=exp(minimum); here->dlen = (min_len > min_brlen ? (min_len < max_brlen ? min_len : max_brlen) : min_brlen); #ifdef OPT_DEBUG opt.open("optimization.log" ,ios::app); opt.precision(9); opt << "final " << "\t" << minScore << "\t" << here->dlen << "\n"; opt.close(); #endif // claMan->SetTempDirty(-1, true); /* MakeAllNodesDirty(); SetAllTempClasDirty(); if(minScore!=0.0){ // TraceDirtynessToRoot(here); Score(Tree::data); assert(abs(lnL+minScore) <.001); } } SweepDirtynessOverTree(here); lnL=minScore; return minScore; } */ FLOAT_TYPE Tree::BrentOptimizeBranchLength(FLOAT_TYPE accuracy_cutoff, TreeNode *here, bool goodGuess){ //we pass the node whose branch length whose blen we want to optimize, but note that the //calculations occur at the node below that //if firstPass is true, we have no idea what a reasonable value for the blen is, so //use a wide bracket. If it is false, try a fairly tight bracket around the current val FLOAT_TYPE a, b, c, fa, fb, fc, minimum, minScore=ZERO_POINT_ZERO; FLOAT_TYPE blen=here->dlen; FLOAT_TYPE min_len; assert(blen>=min_brlen); FLOAT_TYPE initialScore; fb=initialScore=CallBranchLike(here, this, sqrt(sqrt(here->dlen)), true); if(here->anc){ #ifndef FOURTH_ROOT // if(here->anc){ if(firstPass){ if(!(blen>1e-6)){ a=min_brlen; if(blen!=min_brlen){ b=blen; } else{ b=min_brlen*100; lnL=-1; } c=min_brlen*10000.0; } else{ if((blen>0.0001)){ a=.000001; b=blen; c=.01; } else if(!(blen>0.1)){ a=.0001; b=blen; // c=blen*2.0; c=blen*16.0; } else { a=.01; b=blen; c=blen*2.0; //c=.75; } } } else{ //tighter if(blen >= 1e-6){ a=blen*.66; b=blen; c=blen*1.5; } else{ a=min_brlen; if(blen!=min_brlen){ b=blen; } else{ b=min_brlen*100; lnL=-1; } c=min_brlen*10000.0; } } #endif #ifdef FOURTH_ROOT /* if(blen < min_brlen*10){ a=.01; b=a+.05; fb=-1; c=b+.05; } else{ b=sqrt(sqrt(blen)); if(goodGuess==false){ a=(b <= 0.06 ? .01 : b-0.05); c=b+0.05; } else{ a=(b <= 0.026 ? .01 : b-0.025); c=b+0.025; } } */ #elif ROOT_OPT a=sqrt(a); b=sqrt(b); c=sqrt(c); #endif #ifdef OPT_DEBUG optInfo.Setup(here->nodeNum, blen, accuracy_cutoff, goodGuess, a, b, c); // SampleBranchLengthCurve(CallBranchLike, here, this); optInfo.Report(curves); bool trueMin=optInfo.IsMinAtMinAllowableLength(); curves.flush(); optInfo.Setup(here->nodeNum, blen, accuracy_cutoff, goodGuess, a, b, c); #endif int zeroMLE = DZbrak(&a, &b, &c, &fa, &fb, &fc, CallBranchLike, here, this); #ifdef OPT_DEBUG /* if(trueMin != zeroMLE){ assert(0); } */ #endif bool flatSurface=false; if(fa-fb + fc-fb < .000001){ flatSurface=true; } if(zeroMLE==0 && flatSurface==false) //if the bracket suggests that the MLE is very near 0, don't bother calling brent minScore=DZbrent(a, b, c, fa, fb, fc, CallBranchLike, accuracy_cutoff, &minimum, here, this); else if(zeroMLE==1){ #ifdef FOURTH_ROOT assert(c==effectiveMin); minimum=c; minScore=fc; #elif ROOT_OPT FLOAT_TYPE sqrtmin=sqrt(min_brlen); minimum=sqrtmin; if(a==sqrtmin) minScore=fa; else if(c==sqrtmin) minScore=fc; else minScore=-1; #else minimum=(min_brlen); if(a==min_brlen) minScore=fa; else if(c==min_brlen) minScore=fc; else minScore=-1; #endif } else{ minimum=b; minScore=fb; } #ifdef FOURTH_ROOT min_len=minimum*minimum*minimum*minimum; #elif ROOT_OPT if(zeroMLE) min_len=minimum; else min_len=minimum*minimum; #else min_len=minimum; #endif } // if(here->dlen != min_len){ here->dlen = (min_len > min_brlen ? (min_len < max_brlen ? min_len : max_brlen) : min_brlen); SweepDirtynessOverTree(here); // } assert(minScore!=-1); /* if(minScore == -1){ minScore=CallBranchLike(here, this, here->dlen, false); } */ lnL=-minScore; #ifdef OPT_DEBUG optInfo.Report(opt); opt << "final\t" << minimum << "\t" << minScore << endl; // optsum << here->nodeNum << "\t" << blen << "\t" << min_len << "\t" << initialScore - minScore << endl; #endif return initialScore - minScore; } void Tree::GetDerivsPartialTerminal(const CondLikeArray *partialCLA, const FLOAT_TYPE *prmat, const FLOAT_TYPE *d1mat, const FLOAT_TYPE *d2mat, const char *Ldat, FLOAT_TYPE &d1Tot, FLOAT_TYPE &d2Tot, int modIndex, int dataIndex, const unsigned *ambigMap /*=NULL*/){ //this function assumes that the pmat is arranged with the 16 entries for the //first rate, followed by 16 for the second, etc. const FLOAT_TYPE *partial=partialCLA->arr; const SequenceData *data = dataPart->GetSubset(dataIndex); Model *mod = modPart->GetModel(modIndex); const int nchar=data->NChar(); const int nRateCats=mod->NRateCats(); const char *Ldata=Ldat; const int *countit=data->GetCounts(); const FLOAT_TYPE *rateProb=mod->GetRateProbs(); const int lastConst=data->LastConstant(); const int *conBases=data->GetConstStates(); const FLOAT_TYPE prI=mod->PropInvar(); FLOAT_TYPE freqs[4]; for(int i=0;i<4;i++) freqs[i]=mod->StateFreq(i); #ifdef UNIX posix_madvise((void*)partial, nchar*4*nRateCats*sizeof(FLOAT_TYPE), POSIX_MADV_SEQUENTIAL); #endif FLOAT_TYPE tot1=ZERO_POINT_ZERO, tot2=ZERO_POINT_ZERO, totL=ZERO_POINT_ZERO, grandSumL=ZERO_POINT_ZERO;//can't use d1Tot and d2Tot in OMP reduction because they are references FLOAT_TYPE siteL, siteD1, siteD2; FLOAT_TYPE La, Lc, Lg, Lt; FLOAT_TYPE D1a, D1c, D1g, D1t; FLOAT_TYPE D2a, D2c, D2g, D2t; FLOAT_TYPE unscaledlnL; vector siteLikes(nchar); #ifdef OUTPUT_SITEDERIVS vector siteD1s(nchar); vector siteD2s(nchar); #endif #ifdef OMP_TERMDERIV #ifdef LUMP_LIKES #pragma omp parallel for private(partial, Ldata, siteL, siteD1, siteD2, unscaledlnL, La, Lc, Lg, Lt, D1a, D1c, D1g, D1t, D2a, D2c, D2g, D2t) reduction(+ : tot1, tot2, totL, grandSumL) #else #pragma omp parallel for private(partial, Ldata, siteL, siteD1, siteD2, unscaledlnL, La, Lc, Lg, Lt, D1a, D1c, D1g, D1t, D2a, D2c, D2g, D2t) reduction(+ : tot1, tot2, totL) #endif for(int i=0;iarr[i*4*nRateCats]; #else for(int i=0;i 0){ #else if(1){ #endif La=Lc=Lg=Lt=D1a=D1c=D1g=D1t=D2a=D2c=D2g=D2t=ZERO_POINT_ZERO; if(*Ldata > -1){ //no ambiguity for(int r=0;rNoPinvInModel() == false) && (i<=lastConst)){ FLOAT_TYPE btot=ZERO_POINT_ZERO; if(conBases[i]&1) btot+=freqs[0]; if(conBases[i]&2) btot+=freqs[1]; if(conBases[i]&4) btot+=freqs[2]; if(conBases[i]&8) btot+=freqs[3]; //6-27-05 fixed this to calc derivs correctly if constant site has been rescaled siteL = ((La*freqs[0]+Lc*freqs[1]+Lg*freqs[2]+Lt*freqs[3]) + (prI*btot)*exp((FLOAT_TYPE)partialCLA->underflow_mult[i])); } else siteL = ((La*freqs[0]+Lc*freqs[1]+Lg*freqs[2]+Lt*freqs[3])); assert(La >= 0.0f && Lc >= 0.0f && Lg >= 0.0f && Lt >= 0.0f); assert(La < 1.0e30 && Lc < 1.0e30 && Lg < 1.0e30 && Lt < 1.0e30); unscaledlnL = log(siteL) - partialCLA->underflow_mult[i]; totL += unscaledlnL * countit[i]; siteD1 = (((D1a*freqs[0]+D1c*freqs[1]+D1g*freqs[2]+D1t*freqs[3])) / siteL); tot1+= siteD1 * countit[i]; siteD2=((D2a*freqs[0]+D2c*freqs[1]+D2g*freqs[2]+D2t*freqs[3]) / siteL) - (siteD1 * siteD1); tot2 += siteD2 * countit[i]; } #ifndef OMP_TERMDERIV else{ //partial+=4*nRateCats; if(!(*Ldata < 0)) Ldata++; else if(*Ldata == -4) Ldata++; else{ int nstates=-1 * *(Ldata++); for(int s=0;sunderflow_mult, NULL); } #ifdef OUTPUT_SITEDERIVS ofstream ord("orderedSiteDerivs.term.log"); ofstream packed("packedSiteDerivs.term.log"); OutputSiteDerivatives(dataIndex, siteLikes, siteD1s, siteD2s, partialCLA->underflow_mult, NULL, ord, packed); ord.close(); packed.close(); #endif d1Tot = tot1; d2Tot = tot2; lnL += totL; /* double poo = lnL; MakeAllNodesDirty(); Score(); assert(FloatingPointEquals(lnL, poo, 1e-8)); */ } void Tree::GetDerivsPartialTerminalNState(const CondLikeArray *partialCLA, const FLOAT_TYPE *prmat, const FLOAT_TYPE *d1mat, const FLOAT_TYPE *d2mat, const char *Ldat, FLOAT_TYPE &d1Tot, FLOAT_TYPE &d2Tot, int modIndex, int dataIndex){ //this function assumes that the pmat is arranged with nstates^2 entries for the //first rate, followed by nstates^2 for the second, etc. const FLOAT_TYPE *partial=partialCLA->arr; const SequenceData *data = dataPart->GetSubset(dataIndex); Model *mod = modPart->GetModel(modIndex); const int nRateCats=mod->NRateCats(); const int nchar = data->NChar(); const int *countit = data->GetCounts(); const int nstates = mod->NStates(); const char *Ldata = Ldat; const FLOAT_TYPE *rateProb=mod->GetRateProbs(); const int lastConst=data->LastConstant(); const int *conStates=data->GetConstStates(); const FLOAT_TYPE prI=mod->PropInvar(); const int numCondPats = data->NumConditioningPatterns(); vector freqs(nstates); for(int i=0;iStateFreq(i); #ifdef UNIX posix_madvise((void*)partial, nchar*nstates*nRateCats*sizeof(FLOAT_TYPE), POSIX_MADV_SEQUENTIAL); #endif FLOAT_TYPE tot1=ZERO_POINT_ZERO, tot2=ZERO_POINT_ZERO, totL=ZERO_POINT_ZERO, grandSumL=ZERO_POINT_ZERO;//can't use d1Tot and d2Tot in OMP reduction because they are references FLOAT_TYPE siteL, siteD1, siteD2; FLOAT_TYPE unscaledlnL; FLOAT_TYPE logLikeConditioningFactor = ZERO_POINT_ZERO; FLOAT_TYPE conditioningLikeSum = ZERO_POINT_ZERO; FLOAT_TYPE conditioningD1Sum = ZERO_POINT_ZERO; FLOAT_TYPE conditioningD2Sum = ZERO_POINT_ZERO; FLOAT_TYPE probVariable = ZERO_POINT_ZERO; vector siteLikes(nchar); #ifdef OUTPUT_SITEDERIVS vector siteD1s(nchar); vector siteD2s(nchar); #endif if(nRateCats == 1){ #ifdef OMP_TERMDERIV_NSTATE #ifdef LUMP_LIKES #pragma omp parallel for if(numCondPats == 0) private(partial, Ldata, siteL, siteD1, siteD2, unscaledlnL) reduction(+ : tot1, tot2, totL, grandSumL) #else #pragma omp parallel for if(numCondPats == 0) private(partial, Ldata, siteL, siteD1, siteD2, unscaledlnL) reduction(+ : tot1, tot2, totL) #endif for(int i=0;iarr[i*nstates*nRateCats]; #else for(int i=0;i 0){//this check speeds us up in the case of bootstrapping #else if(1){ #endif siteL = siteD1 = siteD2 = ZERO_POINT_ZERO; if(*Ldata != nstates){ //no ambiguity for(int from=0;fromNoPinvInModel() == false) && (i<=lastConst)){ siteL += (prI*freqs[conStates[i]] * exp((FLOAT_TYPE)partialCLA->underflow_mult[i])); } unscaledlnL=log(siteL) - partialCLA->underflow_mult[i]; if(numCondPats > 0){ assert(unscaledlnL < ZERO_POINT_ZERO); if(i < numCondPats){ if(partialCLA->underflow_mult[i] == 0){ conditioningLikeSum += siteL; conditioningD1Sum += siteD1; conditioningD2Sum += siteD2; } else{ outman.DebugMessage("SCALED MKV SCALER = %d (%f)", partialCLA->underflow_mult[i], exp((double)partialCLA->underflow_mult[i])); double unscaler = exp((double)(partialCLA->underflow_mult[i])); //Guard against this over or underflowing, which I think are very unlikely. If it does, just ignore this site if(unscaler == unscaler){ double unscaled = siteL / unscaler; double unscaledD1 = siteD1 / unscaler; double unscaledD2 = siteD2 / unscaler; if(unscaled == unscaled){ conditioningLikeSum += unscaled; assert(unscaledD1 == unscaledD1); conditioningD1Sum += unscaledD1; assert(unscaledD2 == unscaledD2); conditioningD2Sum += unscaledD2; } } } if(i == numCondPats - 1){ probVariable = (ONE_POINT_ZERO - conditioningLikeSum); logLikeConditioningFactor = -log(probVariable); } //these are just for site deriv output unscaledlnL = siteL; siteD1 = siteD1; siteD2 = siteD2; } else{ //condition the likelihood on variability FLOAT_TYPE condlnL = unscaledlnL + logLikeConditioningFactor; assert(condlnL < ZERO_POINT_ZERO); totL += condlnL * countit[i]; //condition the first deriv FLOAT_TYPE condD1 = (siteD1 + ((siteL * conditioningD1Sum) / probVariable)) / siteL; //condition the second FLOAT_TYPE t1 = conditioningLikeSum - ONE_POINT_ZERO; FLOAT_TYPE condD2 = ((-siteD1 * siteD1 * t1 * t1) + siteL * ((t1 * t1 * siteD2) + siteL * (conditioningD1Sum * conditioningD1Sum - t1 * conditioningD2Sum))) / (siteL * siteL * t1 * t1); tot1 += countit[i] * condD1; tot2 += countit[i] * condD2; assert(tot1 == tot1); assert(tot2 == tot2); //these are just for site deriv output unscaledlnL = condlnL; siteD1 = condD1; siteD2 = condD2; } } else if(unscaledlnL < ZERO_POINT_ZERO){ totL += unscaledlnL * countit[i]; siteD1 /= siteL; tot1 += countit[i] * siteD1; tot2 += countit[i] * ((siteD2 / siteL) - siteD1*siteD1); assert(tot1 == tot1); assert(tot2 == tot2); } Ldata++; partial+=nstates*nRateCats; } else{ #ifdef OPEN_MP //this is a little strange, but the arrays needs to be advanced in the case of OMP (if this function is not OMP enabled) //because sections of the CLAs corresponding to sites with count=0 are skipped //over in OMP instead of being eliminated partial+=nstates*nRateCats; #endif Ldata++; } if(sitelikeLevel != 0){ siteLikes[i] = unscaledlnL; } #ifdef OUTPUT_SITEDERIVS siteD1s[i] = siteD1; siteD2s[i] = siteD2; #endif #ifdef LUMP_LIKES if((i + 1) % LUMP_FREQ == 0){ grandSumL += totL; totL = ZERO_POINT_ZERO; } } totL += grandSumL; #else } #endif if(sitelikeLevel != 0){ OutputSiteLikelihoods(dataIndex, siteLikes, partialCLA->underflow_mult, NULL); } #ifdef OUTPUT_SITEDERIVS ofstream ord("orderedSiteDerivs.term.log"); ofstream packed("packedSiteDerivs.term.log"); OutputSiteDerivatives(dataIndex, siteLikes, siteD1s, siteD2s, partialCLA->underflow_mult, NULL, ord, packed); ord.close(); packed.close(); #endif } else{ //I don't think that this is being used, as there is a separate function for PartialTermNStateRateHet assert(0); /* #ifdef OMP_TERMDERIV_NSTATE #pragma omp parallel for private(partial, Ldata, siteL, siteD1, siteD2) reduction(+ : tot1, tot2, totL) for(int i=0;iarr[i*nstates*nRateCats]; #else for(int i=0;iNoPinvInModel() == false) && (i<=lastConst)){ siteL += (prI*freqs[conStates[i]] * exp((FLOAT_TYPE)partialCLA->underflow_mult[i])); } FLOAT_TYPE unscaledlnL=log(siteL) - partialCLA->underflow_mult[i]; if(unscaledlnL < ZERO_POINT_ZERO){ totL += unscaledlnL * countit[i]; siteD1 /= siteL; tot1 += countit[i] * siteD1; tot2 += countit[i] * ((siteD2 / siteL) - siteD1*siteD1); assert(tot1 == tot1); assert(tot2 == tot2); } } else{ #ifdef OPEN_MP //this needs to be advanced in the case of openmp, regardless of whether //this function actually has OMP enabled or not. partial += nstates * nRateCats; #endif Ldata++; } } */ } d1Tot = tot1; d2Tot = tot2; lnL += totL; } void Tree::GetDerivsPartialTerminalNStateRateHet(const CondLikeArray *partialCLA, const FLOAT_TYPE *prmat, const FLOAT_TYPE *d1mat, const FLOAT_TYPE *d2mat, const char *Ldat, FLOAT_TYPE &d1Tot, FLOAT_TYPE &d2Tot, int modIndex, int dataIndex){ //this function assumes that the pmat is arranged with nstates^2 entries for the //first rate, followed by nstates^2 for the second, etc. const FLOAT_TYPE *partial=partialCLA->arr; const SequenceData *data = dataPart->GetSubset(dataIndex); Model *mod = modPart->GetModel(modIndex); const int nRateCats=mod->NRateCats(); const int nchar = data->NChar(); const int *countit = data->GetCounts(); const int nstates = mod->NStates(); const char *Ldata = Ldat; const FLOAT_TYPE *rateProb=mod->GetRateProbs(); const int lastConst=data->LastConstant(); const int *conStates=data->GetConstStates(); const FLOAT_TYPE prI=mod->PropInvar(); const int numCondPats = data->NumConditioningPatterns(); vector freqs(nstates); for(int i=0;iStateFreq(i); #ifdef UNIX posix_madvise((void*)partial, nchar*nstates*nRateCats*sizeof(FLOAT_TYPE), POSIX_MADV_SEQUENTIAL); #endif FLOAT_TYPE tot1=ZERO_POINT_ZERO, tot2=ZERO_POINT_ZERO, totL=ZERO_POINT_ZERO, grandSumL=ZERO_POINT_ZERO;//can't use d1Tot and d2Tot in OMP reduction because they are references FLOAT_TYPE siteL, siteD1, siteD2; FLOAT_TYPE rateL, rateD1, rateD2; FLOAT_TYPE unscaledlnL; FLOAT_TYPE logLikeConditioningFactor = ZERO_POINT_ZERO; FLOAT_TYPE conditioningLikeSum = ZERO_POINT_ZERO; FLOAT_TYPE conditioningD1Sum = ZERO_POINT_ZERO; FLOAT_TYPE conditioningD2Sum = ZERO_POINT_ZERO; FLOAT_TYPE probVariable = ZERO_POINT_ZERO; vector siteLikes(nchar); #ifdef OUTPUT_SITEDERIVS vector siteD1s(nchar); vector siteD2s(nchar); #endif #ifdef OMP_TERMDERIV_NSTATE #ifdef LUMP_LIKES #pragma omp parallel for if(numCondPats == 0) private(partial, Ldata, siteL, siteD1, siteD2, rateL, rateD1, rateD2, unscaledlnL) reduction(+ : tot1, tot2, totL, grandSumL) #else #pragma omp parallel for if(numCondPats == 0) private(partial, Ldata, siteL, siteD1, siteD2, rateL, rateD1, rateD2, unscaledlnL) reduction(+ : tot1, tot2, totL) #endif for(int i=0;iarr[i*nstates*nRateCats]; #else for(int i=0;iNoPinvInModel() == false) && (i<=lastConst)){ siteL += (prI*freqs[conStates[i]] * (exp((FLOAT_TYPE)partialCLA->underflow_mult[i]))); } unscaledlnL=log(siteL) - partialCLA->underflow_mult[i]; if(numCondPats > 0){ //CONDITIONING HERE HAS NEVER BEEN TESTED, SINCE NO STANDARD DATA AND RATE HET assert(unscaledlnL < ZERO_POINT_ZERO); if(i < numCondPats){ if(partialCLA->underflow_mult[i] == 0){ conditioningLikeSum += siteL; conditioningD1Sum += siteD1; conditioningD2Sum += siteD2; } else{ outman.DebugMessage("SCALED MKV SCALER = %d (%f)", partialCLA->underflow_mult[i], exp((double)(partialCLA->underflow_mult[i]))); double unscaler = exp((double)(partialCLA->underflow_mult[i])); //Guard against this over or underflowing, which I think are very unlikely. If it does, just ignore this site if(unscaler == unscaler){ double unscaled = siteL / unscaler; double unscaledD1 = siteD1 / unscaler; double unscaledD2 = siteD2 / unscaler; if(unscaled == unscaled){ conditioningLikeSum += unscaled; assert(unscaledD1 == unscaledD1); conditioningD1Sum += unscaledD1; assert(unscaledD2 == unscaledD2); conditioningD2Sum += unscaledD2; } } } if(i == numCondPats - 1){ probVariable = (ONE_POINT_ZERO - conditioningLikeSum); logLikeConditioningFactor = -log(probVariable); } } else{ //condition the likelihood on variability FLOAT_TYPE condlnL = unscaledlnL + logLikeConditioningFactor; assert(condlnL < ZERO_POINT_ZERO); totL += condlnL * countit[i]; //condition the first deriv FLOAT_TYPE condD1 = (siteD1 + ((siteL * conditioningD1Sum) / probVariable)) / siteL; //condition the second FLOAT_TYPE t1 = conditioningLikeSum - ONE_POINT_ZERO; FLOAT_TYPE condD2 = ((-siteD1 * siteD1 * t1 * t1) + siteL * ((t1 * t1 * siteD2) + siteL * (conditioningD1Sum * conditioningD1Sum - t1 * conditioningD2Sum))) / (siteL * siteL * t1 * t1); tot1 += countit[i] * condD1; tot2 += countit[i] * condD2; assert(tot1 == tot1); assert(tot2 == tot2); //these are just for site deriv output unscaledlnL = condlnL; siteD1 = condD1; siteD2 = condD2; } } else if(unscaledlnL < ZERO_POINT_ZERO){ totL += unscaledlnL * countit[i]; siteD1 /= siteL; tot1 += countit[i] * siteD1; siteD2 = ((siteD2 / siteL) - siteD1*siteD1); tot2 += countit[i] * siteD2; assert(siteL == siteL); assert(totL == totL); assert(tot1 == tot1); assert(tot2 == tot2); } } else{ #ifdef OPEN_MP //this needs to be advanced in the case of openmp, regardless of whether //this function actually has OMP enabled or not. partial += nstates * nRateCats; #endif Ldata++; } if(sitelikeLevel != 0){ siteLikes[i] = unscaledlnL; } #ifdef OUTPUT_SITEDERIVS siteD1s[i] = siteD1; siteD2s[i] = siteD2; #endif #ifdef LUMP_LIKES if((i + 1) % LUMP_FREQ == 0){ grandSumL += totL; totL = ZERO_POINT_ZERO; } } totL += grandSumL; #else } #endif if(sitelikeLevel != 0){ OutputSiteLikelihoods(dataIndex, siteLikes, partialCLA->underflow_mult, NULL); } #ifdef OUTPUT_SITEDERIVS ofstream ord("orderedSiteDerivs.term.log"); ofstream packed("packedSiteDerivs.term.log"); OutputSiteDerivatives(dataIndex, siteLikes, siteD1s, siteD2s, partialCLA->underflow_mult, NULL, ord, packed); ord.close(); packed.close(); #endif d1Tot = tot1; d2Tot = tot2; lnL += totL; } void Tree::GetDerivsPartialInternal(const CondLikeArray *partialCLA, const CondLikeArray *childCLA, const FLOAT_TYPE *prmat, const FLOAT_TYPE *d1mat, const FLOAT_TYPE *d2mat, FLOAT_TYPE &d1Tot, FLOAT_TYPE &d2Tot, int modIndex, int dataIndex){ //this function assumes that the pmat is arranged with the 16 entries for the //first rate, followed by 16 for the second, etc. const FLOAT_TYPE *CL1=childCLA->arr; const FLOAT_TYPE *partial=partialCLA->arr; const SequenceData *data = dataPart->GetSubset(dataIndex); Model *mod = modPart->GetModel(modIndex); const int nchar=data->NChar(); const int nRateCats=mod->NRateCats(); const int *countit=data->GetCounts(); const FLOAT_TYPE *rateProb=mod->GetRateProbs(); const int lastConst=data->LastConstant(); const int *conBases=data->GetConstStates(); const FLOAT_TYPE prI=mod->PropInvar(); FLOAT_TYPE freqs[4]; for(int i=0;i<4;i++) freqs[i]=mod->StateFreq(i); #ifdef UNIX posix_madvise((void*)partial, nchar*4*nRateCats*sizeof(FLOAT_TYPE), POSIX_MADV_SEQUENTIAL); posix_madvise((void*)CL1, nchar*4*nRateCats*sizeof(FLOAT_TYPE), POSIX_MADV_SEQUENTIAL); #endif FLOAT_TYPE tot1=ZERO_POINT_ZERO, tot2=ZERO_POINT_ZERO, totL=ZERO_POINT_ZERO, grandSumL=ZERO_POINT_ZERO;//can't use d1Tot and d2Tot in OMP reduction because they are references FLOAT_TYPE siteL, siteD1, siteD2; FLOAT_TYPE La, Lc, Lg, Lt; FLOAT_TYPE D1a, D1c, D1g, D1t; FLOAT_TYPE D2a, D2c, D2g, D2t; FLOAT_TYPE Ra, Rc, Rg, Rt; FLOAT_TYPE unscaledlnL=ZERO_POINT_ZERO; vector siteLikes(nchar); #ifdef OUTPUT_SITEDERIVS vector siteD1s(nchar); vector siteD2s(nchar); #endif #ifdef OMP_INTDERIV #ifdef LUMP_LIKES #pragma omp parallel for private(partial, CL1, siteL, siteD1, siteD2, unscaledlnL, La, Lc, Lg, Lt, D1a, D1c, D1g, D1t, D2a, D2c, D2g, D2t, Ra, Rc, Rg, Rt) reduction(+ : tot1, tot2, totL, grandSumL) #else #pragma omp parallel for private(partial, CL1, siteL, siteD1, siteD2, unscaledlnL, La, Lc, Lg, Lt, D1a, D1c, D1g, D1t, D2a, D2c, D2g, D2t, Ra, Rc, Rg, Rt) reduction(+ : tot1, tot2, totL) #endif for(int i=0;iarr[4*i*nRateCats]); CL1 = &(childCLA->arr[4*i*nRateCats]); #else for(int i=0;i 0){ #else if(1){ #endif La=Lc=Lg=Lt=D1a=D1c=D1g=D1t=D2a=D2c=D2g=D2t=ZERO_POINT_ZERO; for(int r=0;rNoPinvInModel() == false) && (i<=lastConst)){ FLOAT_TYPE btot=ZERO_POINT_ZERO; if(conBases[i]&1) btot+=freqs[0]; if(conBases[i]&2) btot+=freqs[1]; if(conBases[i]&4) btot+=freqs[2]; if(conBases[i]&8) btot+=freqs[3]; //6-27-05 fixed this to calc derivs correctly if constant site has been rescaled siteL = ((La*freqs[0]+Lc*freqs[1]+Lg*freqs[2]+Lt*freqs[3]) + (prI*btot)*exp((FLOAT_TYPE)childCLA->underflow_mult[i]+partialCLA->underflow_mult[i])); } else siteL = ((La*freqs[0]+Lc*freqs[1]+Lg*freqs[2]+Lt*freqs[3])); assert(La >= 0.0f && Lc >= 0.0f && Lg >= 0.0f && Lt >= 0.0f); assert(La < 1.0e30 && Lc < 1.0e30 && Lg < 1.0e30 && Lt < 1.0e30); siteD1 = (((D1a*freqs[0]+D1c*freqs[1]+D1g*freqs[2]+D1t*freqs[3])) / siteL); unscaledlnL = log(siteL) - childCLA->underflow_mult[i] - partialCLA->underflow_mult[i]; totL += unscaledlnL * countit[i]; tot1+= countit[i] * siteD1; siteD2=((D2a*freqs[0]+D2c*freqs[1]+D2g*freqs[2]+D2t*freqs[3]) / siteL) - (siteD1 * siteD1); tot2 += countit[i] * siteD2; assert(d2Tot == d2Tot); // assert(tot1 < 1.0e10 && tot2 < 1.0e10); } #ifndef OMP_INTDERIV else{ // partial+=4*nRateCats; // CL1+=4*nRateCats; } #endif if(sitelikeLevel != 0){ siteLikes[i] = unscaledlnL; } #ifdef OUTPUT_SITEDERIVS siteD1s[i] = siteD1; siteD2s[i] = siteD2; #endif #ifdef LUMP_LIKES if((i + 1) % LUMP_FREQ == 0){ grandSumL += totL; totL = ZERO_POINT_ZERO; } } totL += grandSumL; #else } #endif if(sitelikeLevel != 0){ OutputSiteLikelihoods(dataIndex, siteLikes, childCLA->underflow_mult, partialCLA->underflow_mult); } #ifdef OUTPUT_SITEDERIVS ofstream ord("orderedSiteDerivs.term.log"); ofstream packed("packedSiteDerivs.term.log"); OutputSiteDerivatives(dataIndex, siteLikes, siteD1s, siteD2s, partialCLA->underflow_mult, NULL, ord, packed); ord.close(); packed.close(); #endif d1Tot = tot1; d2Tot = tot2; lnL += totL; } void Tree::GetDerivsPartialInternalNStateRateHet(const CondLikeArray *partialCLA, const CondLikeArray *childCLA, const FLOAT_TYPE *prmat, const FLOAT_TYPE *d1mat, const FLOAT_TYPE *d2mat, FLOAT_TYPE &d1Tot, FLOAT_TYPE &d2Tot, int modIndex, int dataIndex){ //this function assumes that the pmat is arranged with the nstates^2 entries for the //first rate, followed by nstates^2 for the second, etc. const FLOAT_TYPE *CL1=childCLA->arr; const FLOAT_TYPE *partial=partialCLA->arr; const SequenceData *data = dataPart->GetSubset(dataIndex); Model *mod = modPart->GetModel(modIndex); const int nchar = data->NChar(); const int *countit = data->GetCounts(); const int nstates = mod->NStates(); const int nRateCats = mod->NRateCats(); const FLOAT_TYPE *rateProb=mod->GetRateProbs(); const int lastConst=data->LastConstant(); const int *conStates=data->GetConstStates(); const FLOAT_TYPE prI=mod->PropInvar(); const int numCondPats = data->NumConditioningPatterns(); vector freqs(nstates); for(int i=0;iStateFreq(i); #ifdef UNIX posix_madvise((void*)partial, nchar*nstates*nRateCats*sizeof(FLOAT_TYPE), POSIX_MADV_SEQUENTIAL); posix_madvise((void*)CL1, nchar*nstates*nRateCats*sizeof(FLOAT_TYPE), POSIX_MADV_SEQUENTIAL); #endif FLOAT_TYPE tot1=ZERO_POINT_ZERO, tot2=ZERO_POINT_ZERO, totL = ZERO_POINT_ZERO, grandSumL = ZERO_POINT_ZERO;//can't use d1Tot and d2Tot in OMP reduction because they are references FLOAT_TYPE siteL, siteD1, siteD2; FLOAT_TYPE tempL, tempD1, tempD2; FLOAT_TYPE rateL, rateD1, rateD2; FLOAT_TYPE unscaledlnL; FLOAT_TYPE logLikeConditioningFactor = ZERO_POINT_ZERO; FLOAT_TYPE conditioningLikeSum = ZERO_POINT_ZERO; FLOAT_TYPE conditioningD1Sum = ZERO_POINT_ZERO; FLOAT_TYPE conditioningD2Sum = ZERO_POINT_ZERO; FLOAT_TYPE probVariable = ZERO_POINT_ZERO; vector siteLikes(nchar); #ifdef OUTPUT_SITEDERIVS vector siteD1s(nchar); vector siteD2s(nchar); #endif #ifdef OMP_INTDERIV_NSTATE #ifdef LUMP_LIKES #pragma omp parallel for if(numCondPats == 0) private(partial, CL1, siteL, siteD1, siteD2, unscaledlnL, tempL, tempD1, tempD2, rateL, rateD1, rateD2) reduction(+ : tot1, tot2, totL, grandSumL) #else #pragma omp parallel for if(numCondPats == 0) private(partial, CL1, siteL, siteD1, siteD2, unscaledlnL, tempL, tempD1, tempD2, rateL, rateD1, rateD2) reduction(+ : tot1, tot2, totL) #endif for(int i=0;iarr[nRateCats * nstates * i]); CL1 = &(childCLA->arr[nRateCats * nstates * i]); #else for(int i=0;i 0){//this check speeds us up in the case of bootstrapping #else if(1){ #endif siteL = siteD1 = siteD2 = ZERO_POINT_ZERO; for(int rate=0;rateNoPinvInModel() == false) && (i<=lastConst)){ siteL += (prI*freqs[conStates[i]] * exp((FLOAT_TYPE)partialCLA->underflow_mult[i]) * exp((FLOAT_TYPE)childCLA->underflow_mult[i])); } unscaledlnL=log(siteL) - partialCLA->underflow_mult[i] - childCLA->underflow_mult[i]; if(numCondPats > 0){ //CONDITIONING HERE HAS NEVER BEEN TESTED, SINCE NO STANDARD DATA AND RATE HET assert(unscaledlnL < ZERO_POINT_ZERO); if(i < numCondPats){ if(partialCLA->underflow_mult[i] + childCLA->underflow_mult[i] == 0){ conditioningLikeSum += siteL; conditioningD1Sum += siteD1; conditioningD2Sum += siteD2; } else{ outman.DebugMessage("SCALED MKV SCALER = %d (%f)", partialCLA->underflow_mult[i] + childCLA->underflow_mult[i], exp((double)(partialCLA->underflow_mult[i] + childCLA->underflow_mult[i]))); double unscaler = exp((double)(partialCLA->underflow_mult[i] + childCLA->underflow_mult[i])); //Guard against this over or underflowing, which I think are very unlikely. If it does, just ignore this site if(unscaler == unscaler){ double unscaled = siteL / unscaler; double unscaledD1 = siteD1 / unscaler; double unscaledD2 = siteD2 / unscaler; if(unscaled == unscaled){ conditioningLikeSum += unscaled; assert(unscaledD1 == unscaledD1); conditioningD1Sum += unscaledD1; assert(unscaledD2 == unscaledD2); conditioningD2Sum += unscaledD2; } } } if(i == numCondPats - 1){ probVariable = (ONE_POINT_ZERO - conditioningLikeSum); logLikeConditioningFactor = -log(probVariable); } //these are just for site deriv output unscaledlnL = siteL; siteD1 = siteD1; siteD2 = siteD2; } else{ //condition the likelihood on variability FLOAT_TYPE condlnL = unscaledlnL + logLikeConditioningFactor; assert(condlnL < ZERO_POINT_ZERO); totL += condlnL * countit[i]; //condition the first deriv FLOAT_TYPE condD1 = (siteD1 + ((siteL * conditioningD1Sum) / probVariable)) / siteL; //condition the second FLOAT_TYPE t1 = conditioningLikeSum - ONE_POINT_ZERO; FLOAT_TYPE condD2 = ((-siteD1 * siteD1 * t1 * t1) + siteL * ((t1 * t1 * siteD2) + siteL * (conditioningD1Sum * conditioningD1Sum - t1 * conditioningD2Sum))) / (siteL * siteL * t1 * t1); tot1 += countit[i] * condD1; tot2 += countit[i] * condD2; assert(tot1 == tot1); assert(tot2 == tot2); //these are just for site deriv output unscaledlnL = condlnL; siteD1 = condD1; siteD2 = condD2; } } else if(unscaledlnL < ZERO_POINT_ZERO){ totL += unscaledlnL * countit[i]; siteD1 /= siteL; tot1 += countit[i] * siteD1; siteD2 = ((siteD2 / siteL) - siteD1*siteD1); tot2 += countit[i] * siteD2; assert(tot1 == tot1); assert(tot2 == tot2); } } if(sitelikeLevel != 0){ siteLikes[i] = unscaledlnL; } #ifdef OUTPUT_SITEDERIVS siteD1s[i] = siteD1; siteD2s[i] = siteD2; #endif #ifdef LUMP_LIKES if((i + 1) % LUMP_FREQ == 0){ grandSumL += totL; totL = ZERO_POINT_ZERO; } } totL += grandSumL; #else } #endif if(sitelikeLevel != 0){ OutputSiteLikelihoods(dataIndex, siteLikes, childCLA->underflow_mult, partialCLA->underflow_mult); } #ifdef OUTPUT_SITEDERIVS ofstream ord("orderedSiteDerivs.log"); ofstream packed("packedSiteDerivs.log"); OutputSiteDerivatives(dataIndex, siteLikes, siteD1s, siteD2s, childCLA->underflow_mult, NULL, ord, packed); ord.close(); packed.close(); #endif d1Tot = tot1; d2Tot = tot2; lnL += totL; } void Tree::GetDerivsPartialInternalNState(const CondLikeArray *partialCLA, const CondLikeArray *childCLA, const FLOAT_TYPE *prmat, const FLOAT_TYPE *d1mat, const FLOAT_TYPE *d2mat, FLOAT_TYPE &d1Tot, FLOAT_TYPE &d2Tot, int modIndex, int dataIndex){ //this function assumes that the pmat is arranged with the nstates^2 entries for the //first rate, followed by nstates^2 for the second, etc. const FLOAT_TYPE *CL1=childCLA->arr; const FLOAT_TYPE *partial=partialCLA->arr; const SequenceData *data = dataPart->GetSubset(dataIndex); Model *mod = modPart->GetModel(modIndex); const int nchar = data->NChar(); const int *countit = data->GetCounts(); const int nstates = mod->NStates(); const int nRateCats = mod->NRateCats(); const FLOAT_TYPE *rateProb=mod->GetRateProbs(); const int lastConst=data->LastConstant(); const int *conStates=data->GetConstStates(); const FLOAT_TYPE prI=mod->PropInvar(); const int numCondPats = data->NumConditioningPatterns(); vector freqs(nstates); for(int i=0;iStateFreq(i); #ifdef UNIX posix_madvise((void*)partial, nchar*nstates*nRateCats*sizeof(FLOAT_TYPE), POSIX_MADV_SEQUENTIAL); posix_madvise((void*)CL1, nchar*nstates*nRateCats*sizeof(FLOAT_TYPE), POSIX_MADV_SEQUENTIAL); #endif FLOAT_TYPE tot1=ZERO_POINT_ZERO, tot2=ZERO_POINT_ZERO, totL = ZERO_POINT_ZERO, grandSumL = ZERO_POINT_ZERO;//can't use d1Tot and d2Tot in OMP reduction because they are references FLOAT_TYPE siteL, siteD1, siteD2; FLOAT_TYPE tempL, tempD1, tempD2; FLOAT_TYPE unscaledlnL; FLOAT_TYPE logLikeConditioningFactor = ZERO_POINT_ZERO; FLOAT_TYPE conditioningLikeSum = ZERO_POINT_ZERO; FLOAT_TYPE conditioningD1Sum = ZERO_POINT_ZERO; FLOAT_TYPE conditioningD2Sum = ZERO_POINT_ZERO; FLOAT_TYPE probVariable = ZERO_POINT_ZERO; vector siteLikes(nchar); #ifdef OUTPUT_SITEDERIVS vector siteD1s(nchar); vector siteD2s(nchar); #endif #ifdef OMP_INTDERIV_NSTATE #ifdef LUMP_LIKES #pragma omp parallel for if(numCondPats == 0) private(partial, CL1, siteL, siteD1, siteD2, unscaledlnL, tempL, tempD1, tempD2) reduction(+ : tot1, tot2, totL, grandSumL) #else #pragma omp parallel for if(numCondPats == 0) private(partial, CL1, siteL, siteD1, siteD2, unscaledlnL, tempL, tempD1, tempD2) reduction(+ : tot1, tot2, totL) #endif for(int i=0;iarr[nstates*i]); CL1 = &(childCLA->arr[nstates*i]); #else for(int i=0;i 0){//this check speeds us up in the case of bootstrapping #else if(1){ #endif siteL = siteD1 = siteD2 = ZERO_POINT_ZERO; for(int from=0;fromNoPinvInModel() == false) && (i<=lastConst)){ siteL += (prI*freqs[conStates[i]] * exp((FLOAT_TYPE)partialCLA->underflow_mult[i]) * exp((FLOAT_TYPE)childCLA->underflow_mult[i])); } unscaledlnL = log(siteL) - partialCLA->underflow_mult[i] - childCLA->underflow_mult[i]; if(numCondPats > 0){ assert(unscaledlnL < ZERO_POINT_ZERO); if(i < numCondPats){ if(partialCLA->underflow_mult[i] + childCLA->underflow_mult[i] == 0){ conditioningLikeSum += siteL; conditioningD1Sum += siteD1; conditioningD2Sum += siteD2; } else{ outman.DebugMessage("SCALED MKV SCALER = %d (%f)", partialCLA->underflow_mult[i] + childCLA->underflow_mult[i], exp((double)(partialCLA->underflow_mult[i] + childCLA->underflow_mult[i]))); double unscaler = exp((double)(partialCLA->underflow_mult[i] + childCLA->underflow_mult[i])); //Guard against this over or underflowing, which I think are very unlikely. If it does, just ignore this site if(unscaler == unscaler){ double unscaled = siteL / unscaler; double unscaledD1 = siteD1 / unscaler; double unscaledD2 = siteD2 / unscaler; if(unscaled == unscaled){ conditioningLikeSum += unscaled; assert(unscaledD1 == unscaledD1); conditioningD1Sum += unscaledD1; assert(unscaledD2 == unscaledD2); conditioningD2Sum += unscaledD2; } } } if(i == numCondPats - 1){ probVariable = (ONE_POINT_ZERO - conditioningLikeSum); logLikeConditioningFactor = -log(probVariable); } //these are just for site deriv output unscaledlnL = siteL; siteD1 = siteD1; siteD2 = siteD2; } else{ //condition the likelihood on variability FLOAT_TYPE condlnL = unscaledlnL + logLikeConditioningFactor; assert(condlnL < ZERO_POINT_ZERO); totL += condlnL * countit[i]; //condition the first deriv FLOAT_TYPE condD1 = (siteD1 + ((siteL * conditioningD1Sum) / probVariable)) / siteL; //condition the second FLOAT_TYPE t1 = conditioningLikeSum - ONE_POINT_ZERO; FLOAT_TYPE condD2 = ((-siteD1 * siteD1 * t1 * t1) + siteL * ((t1 * t1 * siteD2) + siteL * (conditioningD1Sum * conditioningD1Sum - t1 * conditioningD2Sum))) / (siteL * siteL * t1 * t1); tot1 += countit[i] * condD1; tot2 += countit[i] * condD2; assert(tot1 == tot1); assert(tot2 == tot2); //these are just for site deriv output unscaledlnL = condlnL; siteD1 = condD1; siteD2 = condD2; } } else if(unscaledlnL < ZERO_POINT_ZERO){ totL += unscaledlnL * countit[i]; siteD1 /= siteL; tot1 += countit[i] * siteD1; siteD2 = ((siteD2 / siteL) - siteD1*siteD1); tot2 += countit[i] * siteD2; assert(tot1 == tot1); assert(tot2 == tot2); } partial += nstates; CL1 += nstates; } if(sitelikeLevel != 0){ siteLikes[i] = unscaledlnL; } #ifdef OUTPUT_SITEDERIVS siteD1s[i] = siteD1; siteD2s[i] = siteD2; #endif #ifdef LUMP_LIKES if((i + 1) % LUMP_FREQ == 0){ grandSumL += totL; totL = ZERO_POINT_ZERO; } } totL += grandSumL; #else } #endif if(sitelikeLevel != 0){ OutputSiteLikelihoods(dataIndex, siteLikes, childCLA->underflow_mult, partialCLA->underflow_mult); } #ifdef OUTPUT_SITEDERIVS ofstream ord("orderedSiteDerivs.int.log"); ofstream packed("packedSiteDerivs.int.log"); OutputSiteDerivatives(dataIndex, siteLikes, siteD1s, siteD2s, partialCLA->underflow_mult, childCLA->underflow_mult, ord, packed); ord.close(); packed.close(); #endif d1Tot = tot1; d2Tot = tot2; lnL += totL; } void Tree::GetDerivsPartialInternalEQUIV(const CondLikeArray *partialCLA, const CondLikeArray *childCLA, const FLOAT_TYPE *prmat, const FLOAT_TYPE *d1mat, const FLOAT_TYPE *d2mat, FLOAT_TYPE &d1Tot, FLOAT_TYPE &d2Tot, char *equiv, int modIndex, int dataIndex){ //this function assumes that the pmat is arranged with the 16 entries for the //first rate, followed by 16 for the second, etc. //this needs to be updated before the Equiv calcs will work assert(0); FLOAT_TYPE *CL1=childCLA->arr; FLOAT_TYPE *partial=partialCLA->arr; const SequenceData *data = dataPart->GetSubset(dataIndex); Model *mod = modPart->GetModel(modIndex); const int nchar=data->NChar(); const int nRateCats=mod->NRateCats(); #ifdef UNIX posix_madvise((void*)partial, nchar*4*nRateCats*sizeof(FLOAT_TYPE), POSIX_MADV_SEQUENTIAL); posix_madvise((void*)CL1, nchar*4*nRateCats*sizeof(FLOAT_TYPE), POSIX_MADV_SEQUENTIAL); #endif FLOAT_TYPE siteL; FLOAT_TYPE La, Lc, Lg, Lt; FLOAT_TYPE D1a, D1c, D1g, D1t; FLOAT_TYPE D2a, D2c, D2g, D2t; FLOAT_TYPE eLa, eLc, eLg, eLt; FLOAT_TYPE eD1a, eD1c, eD1g, eD1t; FLOAT_TYPE eD2a, eD2c, eD2g, eD2t; FLOAT_TYPE tot1=ZERO_POINT_ZERO, tot2=ZERO_POINT_ZERO;//can't use d1Tot and d2Tot in OMP reduction because they are references const int *countit=data->GetCounts(); const FLOAT_TYPE *rateProb=mod->GetRateProbs(); assert(nRateCats == 1); const int lastConst=data->LastConstant(); const int *conBases=data->GetConstStates(); const FLOAT_TYPE prI=mod->PropInvar(); FLOAT_TYPE freqs[4]; for(int i=0;i<4;i++) freqs[i]=mod->StateFreq(i); int rOff =0; for(int i=0;iNoPinvInModel() == false) && (i<=lastConst)){ FLOAT_TYPE btot=ZERO_POINT_ZERO; if(conBases[i]&1) btot+=freqs[0]; if(conBases[i]&2) btot+=freqs[1]; if(conBases[i]&4) btot+=freqs[2]; if(conBases[i]&8) btot+=freqs[3]; //6-27-05 fixed this to calc derivs correctly if constant site has been rescaled siteL = ((La*freqs[0]+Lc*freqs[1]+Lg*freqs[2]+Lt*freqs[3]) + (prI*btot)*exp((FLOAT_TYPE)childCLA->underflow_mult[i]+partialCLA->underflow_mult[i])); } else siteL = ((La*freqs[0]+Lc*freqs[1]+Lg*freqs[2]+Lt*freqs[3])); FLOAT_TYPE tempD1 = (((D1a*freqs[0]+D1c*freqs[1]+D1g*freqs[2]+D1t*freqs[3])) / siteL); #ifdef SINGLE_PRECISION_FLOATS if(fabs(tempD1) < 1.0e8f){ assert(d1Tot == d1Tot); FLOAT_TYPE siteD2=((D2a*freqs[0]+D2c*freqs[1]+D2g*freqs[2]+D2t*freqs[3])); tot1 += countit[i] * tempD1; tot2 += countit[i] * ((siteD2 / siteL) - tempD1*tempD1); } #else assert(d1Tot == d1Tot); FLOAT_TYPE siteD2=((D2a*freqs[0]+D2c*freqs[1]+D2g*freqs[2]+D2t*freqs[3])); tot1 += countit[i] * tempD1; tot2 += countit[i] * ((siteD2 / siteL) - tempD1*tempD1); #endif assert(d2Tot == d2Tot); // assert(tot1 < 1.0e10 && tot2 < 1.0e10); } else{ partial+=4*nRateCats; CL1+=4*nRateCats; } } d1Tot = tot1; d2Tot = tot2; } garli-2.1-release/src/optimizationinfo.h000066400000000000000000000047571241236125200204450ustar00rootroot00000000000000// GARLI version 2.0 source code // Copyright 2005-2011 Derrick J. Zwickl // email: garli.support@gmail.com // // This program is free software: you can redistribute it and/or modify // it under the terms of the GNU General Public License as published by // the Free Software Foundation, either version 3 of the License, or // (at your option) any later version. // // This program is distributed in the hope that it will be useful, // but WITHOUT ANY WARRANTY; without even the implied warranty of // MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the // GNU General Public License for more details. // // You should have received a copy of the GNU General Public License // along with this program. If not, see . #ifndef OPTIMIZATION_INFO #define OPTIMIZATION_INFO typedef pair pd; bool IsEvalLess(pd lhs, pd rhs){ return lhs.second < rhs.second; } class OptimizationInfo{ int node; FLOAT_TYPE initLen; FLOAT_TYPE precision; bool goodGuess; FLOAT_TYPE initBracket[3]; vector brakEvals; vector brentEvals; public: OptimizationInfo(){}; void Setup(int n, FLOAT_TYPE len, FLOAT_TYPE prec, bool gg, FLOAT_TYPE low, FLOAT_TYPE mid, FLOAT_TYPE high){ brakEvals.clear(); brentEvals.clear(); node=n; initLen=len; precision=prec; goodGuess=gg; initBracket[0]=low; initBracket[1]=mid; initBracket[2]=high; } void Report(ofstream &out){ out.precision(12); out << "node\t" << node << "\tlen\t" << initLen << "\tprecision\t" << precision; if(goodGuess==true) out << "\t(good guess)\n"; else out << "\t(weak guess)\n"; out << "init Bracket\t" << initBracket[0] << "\t" << initBracket[1] << "\t" << initBracket[2] << "\n"; out << "brak"; for(vector::iterator it=brakEvals.begin();it!=brakEvals.end();it++){ out << "\t"<< (*it).first << "\t" << (*it).second << "\n"; } if(brentEvals.empty() == false) { out << "brent"; for(vector::iterator it=brentEvals.begin();it!=brentEvals.end();it++){ out << "\t"<< (*it).first << "\t" << (*it).second << "\n"; } } } void BrakAdd(FLOAT_TYPE val, FLOAT_TYPE score){ brakEvals.push_back(make_pair(val, score)); } void BrentAdd(FLOAT_TYPE val, FLOAT_TYPE score){ brentEvals.push_back(make_pair(val, score)); } bool IsMinAtMinAllowableLength(){ vector::iterator minEval = min_element(brakEvals.begin(),brakEvals.end(), IsEvalLess); return FloatingPointEquals((*minEval).first, 0.01, 1e-10); } }; #endif garli-2.1-release/src/outputman.h000066400000000000000000000213311241236125200170620ustar00rootroot00000000000000// GARLI version 2.1 source code // Copyright 2005-2014 Derrick J. Zwickl // email: garli.support@gmail.com // // This program is free software: you can redistribute it and/or modify // it under the terms of the GNU General Public License as published by // the Free Software Foundation, either version 3 of the License, or // (at your option) any later version. // // This program is distributed in the hope that it will be useful, // but WITHOUT ANY WARRANTY; without even the implied warranty of // MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the // GNU General Public License for more details. // // You should have received a copy of the GNU General Public License // along with this program. If not, see . #ifndef OUTPUTMANAGER #define OUTPUTMANAGER #include #include #include #include //#include using namespace std; class fmtflags; #define BUFFER_LENGTH 500 class OutputManager{ char message[BUFFER_LENGTH+1]; ostream *defaultOut; ofstream logOut; bool noOutput; bool log; public: OutputManager(){ noOutput=false; log=false; defaultOut=&cout; } ~OutputManager(){ if(log==true) logOut.close(); } bool IsLogSet(){ return (log == true); } void SetOutputStream(ostream &out){ defaultOut=&out; } void SetLogFile(const char *logname){ log=true; if(logOut.is_open()){ logOut.close(); logOut.clear(); } logOut.open(logname); } ofstream *GetLogStream(){ if(log == true) return &logOut; else return NULL; } ostream *GetOutputStream(){ if(noOutput) return NULL; else return defaultOut; } void SetLogFileForAppend(const char *logname){ log=true; if(logOut.is_open()){ logOut.close(); logOut.clear(); } logOut.open(logname, ios::app); } void CloseLogFile(){ logOut.close(); } void SetNoOutput(bool o){ noOutput=o; } void precision(const int p){ defaultOut->precision(p); if(log==true) logOut.precision(p); } void setf(const std::ios_base::fmtflags &flags){ defaultOut->setf(flags); if(log==true) logOut.setf(flags); } void unsetf(const std::ios_base::fmtflags &flags){ defaultOut->unsetf(flags); if(log==true) logOut.unsetf(flags); } void UserMessage(const char *fmt, ...){ va_list vl; va_start(vl, fmt); int len = vsnprintf(message, BUFFER_LENGTH, fmt, vl); va_end(vl); if(len > -1 && len < BUFFER_LENGTH){ Print(*defaultOut); } else{//default buffer is not long enough or there was an error char *longmessage = NULL; if(len > -1){//on unix systems vsnprintf returns the required length. There is some //some ambiguity about whether it includes the null termination or not, but //the number passed to vsnprintf should definitely include it. longmessage = new char[len+2]; va_start(vl, fmt); vsnprintf(longmessage, len+1, fmt, vl); va_end(vl); } else{ #if defined(_MSC_VER) //on windows a negative value means that the length wasn't engough int len2 = BUFFER_LENGTH * 2; longmessage = new char[len2+1]; va_start(vl, fmt); while(vsnprintf(longmessage, len2, fmt, vl) < 0){ delete []longmessage; len2 *= 2; longmessage = new char[len2+1]; va_end(vl); va_start(vl, fmt); } va_end(vl); #else //otherwise negative means a formatting error Print(*defaultOut, "(problem formatting some program output...)"); if(longmessage) delete []longmessage; return; #endif } Print(*defaultOut, longmessage); if(longmessage) delete []longmessage; } } void UserMessageNoCR(const char *fmt, ...){ va_list vl; va_start(vl, fmt); int len = vsnprintf(message, BUFFER_LENGTH, fmt, vl); va_end(vl); if(len > -1 && len < BUFFER_LENGTH){ PrintNoCR(*defaultOut); } else{//default buffer is not long enough or there was an error char *longmessage = NULL; if(len > -1){//on unix systems vsnprintf returns the required length. There is some //some ambiguity about whether it includes the null termination or not, but //the number passed to vsnprintf should definitely include it. longmessage = new char[len+2]; va_start(vl, fmt); vsnprintf(longmessage, len+1, fmt, vl); va_end(vl); } else{ #if defined(_MSC_VER) //on windows a negative value means that the length wasn't engough int len2 = BUFFER_LENGTH * 2; longmessage = new char[len2+1]; va_start(vl, fmt); while(vsnprintf(longmessage, len2, fmt, vl) < 0){ delete []longmessage; len2 *= 2; longmessage = new char[len2+1]; va_end(vl); va_start(vl, fmt); } va_end(vl); #else //otherwise negative means a formatting error Print(*defaultOut, "(problem formatting some program output...)"); if(longmessage) delete []longmessage; return; #endif } PrintNoCR(*defaultOut, longmessage); if(longmessage) delete []longmessage; } } void DebugMessage(const char *fmt, ...){ #ifdef DEBUG_MESSAGES va_list vl; va_start(vl, fmt); int len = vsnprintf(message, BUFFER_LENGTH, fmt, vl); va_end(vl); if(len > -1 && len < BUFFER_LENGTH){ Print(*defaultOut); } else{//default buffer is not long enough or there was an error char *longmessage = NULL; if(len > -1){//on unix systems vsnprintf returns the required length. There is some //some ambiguity about whether it includes the null termination or not, but //the number passed to vsnprintf should definitely include it. longmessage = new char[len+2]; va_start(vl, fmt); vsnprintf(longmessage, len+1, fmt, vl); va_end(vl); } else{ #if defined(_MSC_VER) //on windows a negative value means that the length wasn't engough int len2 = BUFFER_LENGTH * 2; longmessage = new char[len2+1]; va_start(vl, fmt); while(vsnprintf(longmessage, len2, fmt, vl) < 0){ delete []longmessage; len2 *= 2; longmessage = new char[len2+1]; va_end(vl); va_start(vl, fmt); } va_end(vl); #else //otherwise negative means a formatting error Print(*defaultOut, "(problem formatting some program output...)"); if(longmessage) delete []longmessage; return; #endif } Print(*defaultOut, longmessage); if(longmessage) delete []longmessage; } #endif } void DebugMessageNoCR(const char *fmt, ...){ #ifdef DEBUG_MESSAGES va_list vl; va_start(vl, fmt); int len = vsnprintf(message, BUFFER_LENGTH, fmt, vl); va_end(vl); if(len > -1 && len < BUFFER_LENGTH){ PrintNoCR(*defaultOut); } else{//default buffer is not long enough or there was an error char *longmessage = NULL; if(len > -1){//on unix systems vsnprintf returns the required length. There is some //some ambiguity about whether it includes the null termination or not, but //the number passed to vsnprintf should definitely include it. longmessage = new char[len+2]; va_start(vl, fmt); vsnprintf(longmessage, len+1, fmt, vl); va_end(vl); } else{ #if defined(_MSC_VER) //on windows a negative value means that the length wasn't engough int len2 = BUFFER_LENGTH * 2; longmessage = new char[len2+1]; va_start(vl, fmt); while(vsnprintf(longmessage, len2, fmt, vl) < 0){ delete []longmessage; len2 *= 2; longmessage = new char[len2+1]; va_end(vl); va_start(vl, fmt); } va_end(vl); #else //otherwise negative means a formatting error Print(*defaultOut, "(problem formatting some program output...)"); if(longmessage) delete []longmessage; return; #endif } PrintNoCR(*defaultOut, longmessage); if(longmessage) delete []longmessage; } #endif } /* void UserMessageNoCR(const char *fmt, ...){ va_list vl; va_start(vl, fmt); vsprintf(message, fmt, vl); va_end(vl); PrintNoCR(*defaultOut); } */ void UserMessage(const string &mess){ Print(*defaultOut, mess); } void UserMessageNoCR(const string &mess){ PrintNoCR(*defaultOut, mess); } void flush(){ if(noOutput == false) defaultOut->flush(); if(log==true) logOut.flush(); } void Print(ostream &out){ if(noOutput == false) out << message << endl; if(log==true) logOut << message << endl; } void PrintNoCR(ostream &out){ if(noOutput == false) out << message; if(log==true) logOut << message; } void Print(ostream &out, const string &mess){ if(noOutput == false) out << mess << endl; if(log==true) logOut << mess << endl; } void PrintNoCR(ostream &out, const string &mess){ if(noOutput == false) out << mess; if(log==true) logOut << mess; } }; #endif garli-2.1-release/src/population.cpp000066400000000000000000011202641241236125200175610ustar00rootroot00000000000000// GARLI version 2.1 source code // Copyright 2005-2014 Derrick J. Zwickl // email garli.support@gmail.com // // This program is free software: you can redistribute it and/or modify // it under the terms of the GNU General Public License as published by // the Free Software Foundation, either version 3 of the License, or // (at your option) any later version. // // This program is distributed in the hope that it will be useful, // but WITHOUT ANY WARRANTY; without even the implied warranty of // MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the // GNU General Public License for more details. // // You should have received a copy of the GNU General Public License // along with this program. If not, see . // // NOTE: Portions of this source adapted from GAML source, written by Paul O. Lewis #ifdef MPI_VERSION #include #endif #include #include #include #include #include #include #include #include using namespace std; #ifdef WIN32 #include #include #endif #ifdef MAC_FRONTEND #import #import "MFEInterfaceClient.h" #endif #include "defs.h" #include "population.h" #include "individual.h" #include "sequencedata.h" #include "tree.h" #include "funcs.h" #include "clamanager.h" #include "stopwatch.h" #include "bipartition.h" #include "adaptation.h" #include "errorexception.h" #include "outputman.h" #include "model.h" #include "garlireader.h" #ifdef ENABLE_CUSTOM_PROFILER #include "utility.h" extern Profiler ProfIntInt; extern Profiler ProfIntTerm; extern Profiler ProfTermTerm; extern Profiler ProfRescale; extern Profiler ProfScoreInt; extern Profiler ProfScoreTerm; extern Profiler ProfIntDeriv; extern Profiler ProfTermDeriv; extern Profiler ProfCalcPmat; extern Profiler ProfCalcEigen; extern Profiler ProfModDeriv; extern Profiler ProfNewton; extern Profiler ProfEQVectors; #endif extern OutputManager outman; extern bool interactive; bool swapBasedTerm = false; int memLevel; int calcCount=0; int optCalcs; ModelSpecificationSet modSpecSet; ofstream outf, paupf; int tempGlobal=1; bool uniqueSwapTried; FLOAT_TYPE globalBest; #undef PERIODIC_SCORE_DEBUG #undef NNI_SPECTRUM #undef MASTER_DOES_SUBTREE bool output_tree=false; int CheckRestartNumber(const string str); int debug_mpi(const char* fmt, ...); int QuitNow(); void InterruptMessage( int ); void ClearDebugLogs(); int askQuitNow = 0; int QuitNow() { char ch = '?'; cerr << endl << "Quit? (y/n) -->"; do { cin.get(ch); } while( ch != 'y' && ch != 'n' ); if( ch == 'n' ) askQuitNow = 0; return ( ch == 'y' ? 1 : 0 ); } #ifdef WIN32 // A function to get a single character from the windows console. Provided by POL. // Prompts user with string s, then returns the first character typed. If any problems arise // (e.g. cannot obtain handle to console input buffer), bails out by returning the null // character (i.e. '\0'). char AskUser(std::string s) { HANDLE h; DWORD num_chars_read, new_console_mode, prev_console_mode; char char_buffer[2]; // may be able to get away with [1] // Output the prompt string std::cerr << s << std::endl; // Get handle to console input buffer h = GetStdHandle(STD_INPUT_HANDLE); if (h == INVALID_HANDLE_VALUE) return '\0'; // Save the current input mode (will restore it before we leave this function) if (!GetConsoleMode(h, &prev_console_mode) ) return '\0'; // Set new console mode. There are five mode flags defined in wincon.h (ENABLE_LINE_INPUT, ENABLE_ECHO_INPUT, // ENABLE_PROCESSED_INPUT, ENABLE_WINDOW_INPUT and ENABLE_MOUSE_INPUT), only ENABLE_PROCESSED_INPUT is useful // to us, and we specifically want to avoid ENABLE_LINE_INPUT because it requires the user to press the enter // key before ReadConsole returns (much better to have this function return the instant the user presses any // key). new_console_mode = ENABLE_PROCESSED_INPUT; if (!SetConsoleMode(h, new_console_mode)) return '\0'; // Read 1 character and place it in char_buffer. num_chars_read should be 1 afterwards. Note that // the last argument is reserved and must be NULL. if (!ReadConsole(h, char_buffer, 1, &num_chars_read, NULL)) return '\0'; // Be nice and return console mode to its previous value if (!SetConsoleMode(h, prev_console_mode)) return '\0'; return char_buffer[0]; } #endif void InterruptMessage( int ) { askQuitNow = 1; } void TurnOnSignalCatching() {//if SIGINT (generally Ctrl-C) isn't already set to be ignored, set it to the custom handler if( signal( SIGINT, SIG_IGN ) != SIG_IGN ){ signal( SIGINT, InterruptMessage ); } } void TurnOffSignalCatching() {//if SIGINT (generally Ctrl-C) isn't already set to be ignored, set it back to the default if( signal( SIGINT, SIG_IGN ) != SIG_IGN ){ signal( SIGINT, SIG_DFL ); } } bool CheckForUserSignal(){ //this will be set if the user raises a signal with ctrl-C if(askQuitNow == 1){ char c; if(interactive == false){ //The run will begin terminating gracefully after this returns, but turn off further catching //in case the user wants to fully kill the run fully TurnOffSignalCatching(); return true; } else{ #if defined (WIN32) c = AskUser("Perform final branch-length optimization and terminate now? (y/n)"); #else outman.UserMessage("Perform final branch-length optimization and terminate now? (y/n)"); #ifdef MAC_FRONTEND NSAutoreleasePool *pool = [[NSAutoreleasePool alloc] init]; BOOL shouldQuit = [[MFEInterfaceClient sharedClient] programShouldTerminate]; c = shouldQuit ? 'y' : 'n'; [pool release]; #else c = getchar(); #endif #endif if(c=='y'){ //as above, give up further catching TurnOffSignalCatching(); #ifdef MAC cin.get(); #endif return true; } else{ //the user changed their mind askQuitNow = 0; TurnOnSignalCatching(); outman.UserMessage("continuing ..."); #ifndef MAC_FRONTEND #ifndef WIN32 cin.get(); #endif #endif return false; } } } return false; } // // // Methods for class Population // // void ClearDebugLogs(){ //most of the debug logs just append to the current log, so clear them out here #ifndef NDEBUG // ofstream pert("pertreport.log"); // pert.close(); #ifdef MPI_VERSION ofstream subrec("subrec.log"); subrec.close(); ofstream optp("partscores.log"); optp.close(); #endif /* ofstream brak("brakdebug.log"); brak.close(); ofstream brak2("brakmiss.log"); brak2.close(); ofstream opt("optimization.log"); opt.close(); ofstream optt("opttrees.tre"); optt.close(); ofstream opts("optscores.log"); opts.close(); ofstream optb("blendeb.log"); optb.close(); */ #endif } Population::~Population() { if(indiv != NULL){ for (unsigned i = 0; i < total_size; ++i) { for (unsigned j = 0; j < total_size; ++j) { if (newindiv[i].treeStruct == indiv[j].treeStruct) { newindiv[i].treeStruct = NULL; break; } } } } if( indiv!=NULL ) MEM_DELETE_ARRAY(indiv); // indiv has length params.nindivs if( newindiv!=NULL ) MEM_DELETE_ARRAY(newindiv); // newindiv has length params.nindivs ClearStoredTrees(); if( cumfit!=NULL ) { for( unsigned i = 0; i < total_size; i++ ) MEM_DELETE_ARRAY(cumfit[i]); // cumfit[i] has length 2 MEM_DELETE_ARRAY(cumfit); // cumfit has length params.nindivs } if( treeString!=NULL) MEM_DELETE_ARRAY(treeString); for(vector::iterator vit=unusedTrees.begin();vit!=unusedTrees.end();vit++){ delete *vit; } unusedTrees.clear(); if(claMan!=NULL){ delete claMan; } if(paraMan!=NULL){ delete paraMan; } #ifdef INCLUDE_PERTURBATION if(pertMan!=NULL){ delete pertMan; } #endif if(Bipartition::str!=NULL) delete []Bipartition::str; for(vector::iterator delit=unusedTrees.begin();delit!=unusedTrees.end();delit++) delete *delit; if(adap!=NULL) delete adap; Tree::attemptedSwaps.ClearAttemptedSwaps(); //This shouldn't have been getting deleted. It was created as a local in main and then just //aliased in Population // if(rawPart != NULL) // rawPart->Delete(); } void Population::ErrorMsg( char* msgstr, int len ) { switch( error ) { case nomem: strncpy( msgstr, "not enough memory", len ); break; case nofile: strncpy( msgstr, "parameter file not found", len ); break; case baddimen: strncpy( msgstr, "bad dimensions specified", len ); break; default: strncpy( msgstr, "undocumented error", len ); } } void Population::CheckForIncompatibleConfigEntries(){ //DEBUG - fill this in better //PARTITION - disallow a number of things that aren't implemented/tested with partitioned models if(dataPart->NumSubsets() > 1){ if(conf->linkModels && modSpecSet.GetModSpec(0)->IsEmpiricalStateFrequencies()) throw ErrorException("Sorry, empirical state frequencies can't be used with partitioned models when models are linked"); } for(int ms = 0;ms < modSpecSet.NumSpecs();ms++){ ModelSpecification *modSpec = modSpecSet.GetModSpec(ms); //if no model mutations will be performed, parameters cannot be estimated. if(conf->modWeight == ZERO_POINT_ZERO){ if(modSpec->fixStateFreqs == false) throw(ErrorException("if model mutation weight is set to zero,\nstatefrequencies cannot be set to estimate!")); if(modSpec->includeInvariantSites == true && modSpec->fixInvariantSites == false) throw(ErrorException("if model mutation weight is set to zero,\ninvariantsites cannot be set to estimate!")); if(modSpec->IsAminoAcid() == false && modSpec->Nst() > 1 && modSpec->fixRelativeRates == false) throw(ErrorException("if model mutation weight is set to zero, ratematrix\nmust be fixed or 1rate!")); if((modSpec->numRateCats > 1 && modSpec->IsFlexRateHet() == false && modSpec->fixAlpha == false && modSpec->IsCodon() == false) || (modSpec->IsCodon() && !modSpec->fixOmega)) throw(ErrorException("if model mutation weight is set to zero,\nratehetmodel must be set to gammafixed, nonsynonymousfixed or none!")); } if((modSpec->IsNStateV() || modSpec->IsOrderedNStateV() || modSpec->IsBinaryNotAllZeros() || modSpec->IsOrientedGap()) && (_stricmp(conf->streefname.c_str(), "stepwise") == 0)) throw ErrorException("Sorry, stepwise addition starting trees currently cannot be used when\n\ta conditioned model (datatype = standardvariable,\n\tstandardvariableordered, binarynotallzeros or indelmixturemodel)\n\tis used for any data.\n\tTry streefname = random, or provide your own starting tree."); if(conf->inferInternalStateProbs && ! (modSpec->IsNucleotide() || modSpec->IsAminoAcid() || modSpec->IsCodon())) throw ErrorException("Sorry, internal states can currently only be inferred for nucleotide, amino acid and codon models"); } if(conf->inferInternalStateProbs && conf->bootstrapReps > 0) throw(ErrorException("You cannont infer internal states during a bootstrap run!")); if(conf->outputSitelikelihoods > 0 && conf->bootstrapReps > 0) throw(ErrorException("You cannont output site likelihoods during a bootstrap run!")); if(conf->startOptPrec < conf->minOptPrec) throw ErrorException("startoptprec must be equal to or greater than minoptprec"); if(!conf->checkpoint && conf->workPhaseDivision) throw ErrorException("workphasedivision mode only makes sense if checkpoints are written (writecheckpoints = 1)"); } void Population::Setup(GeneralGamlConfig *c, DataPartition *d, DataPartition *rawD, int nprocs, int r){ bool validateMode = false; if(r < 0){ validateMode = true; r = 0; } stopwatch.Start(); //most of the allocation occurs here or in children //set various things rank=r; conf=c; dataPart = d; rawPart = rawD; subtreeNode=0; CheckForIncompatibleConfigEntries(); //put info that was read from the config file in its place if(rank == 0) total_size = conf->nindivs + nprocs-1; else total_size = conf->nindivs; swapTermThreshold = conf->swapTermThreshold; if(swapTermThreshold != 0) //this is a global that Tree needs access to swapBasedTerm = true; else swapBasedTerm = false; //set two model statics Model::mutationShape = conf->gammaShapeModel; //check and warn if different codes have been selected for different subsets - this is experimental for(vector::iterator c = claSpecs.begin();c != claSpecs.end();c++){ if(modSpecSet.GetModSpec((*c).modelIndex)->IsCodon()){ for(vector::iterator c2 = c+1;c2 != claSpecs.end();c2++){ if(modSpecSet.GetModSpec((*c2).modelIndex)->IsCodon()){ if(modSpecSet.GetModSpec((*c).modelIndex)->geneticCode != modSpecSet.GetModSpec((*c2).modelIndex)->geneticCode){ outman.UserMessage("\n################\nWARNING: Different genetic codes have been specified among partition subsets."); outman.UserMessage("This is experimental - check your results carefully!!!\n################\n"); } } } } } #ifdef INPUT_RECOMBINATION total_size = conf->nindivs + NUM_INPUT; #endif adap=new Adaptation(conf); #ifdef INCLUDE_PERTURBATION pertMan = new PerturbManager(conf); #endif //instantiate the ParallelManager if(rank==0){ MasterGamlConfig *mastConf = (MasterGamlConfig*) (conf); paraMan = new ParallelManager(dataPart->NTax(), nprocs, mastConf); } //use RTTI to check if the data subsets are nuclotide, and if so make ambig strings for(int ds = 0;ds < dataPart->NumSubsets();ds++){ NucleotideData *nuc = dynamic_cast(dataPart->GetSubset(ds)); if(nuc != NULL) nuc->MakeAmbigStrings(); } //allocate the treeString //remember that we also encode internal node numbers sometimes FLOAT_TYPE taxsize=log10((FLOAT_TYPE) ((FLOAT_TYPE)dataPart->NTax())*dataPart->NTax()*2); stringSize=(int)((dataPart->NTax()*2)*(10+DEF_PRECISION)+taxsize); treeString=new char[stringSize]; treeString[stringSize - 1]='\0'; //allocate the indiv array indiv = new Individual[total_size]; newindiv = new Individual[total_size]; for (unsigned i = conf->nindivs; i < total_size; i++) { indiv[i].reproduced = indiv[i].willreproduce = 1; newindiv[i].reproduced = newindiv[i].willreproduce = 1; indiv[i].parent=i; newindiv[i].parent=i; } cumfit = new FLOAT_TYPE*[total_size]; for(unsigned i = 0; !error && i < total_size; i++ ) cumfit[i] = new FLOAT_TYPE[2]; //instantiate the clamanager and figure out how much memory to snatch FLOAT_TYPE memToUse; //this gives a bit of leeway in normal runs, when total mem usage may get significantly higher than the actual CLA usage //but not much is used when just scoring/optimizing one tree FLOAT_TYPE memUsageMult = ((conf->scoreOnly || conf->optimizeInputOnly) ? 1.05 : 1.25); outman.UserMessage("NOTE: Unlike many programs, the amount of system memory that Garli will\nuse can be controlled by the user."); if(conf->availableMemory > 0){ outman.UserMessage("(This comes from the availablememory setting in the configuration file."); outman.UserMessage("Availablememory should NOT be set to more than the actual amount of "); outman.UserMessage("physical memory that your computer has installed)"); memToUse=(FLOAT_TYPE)(1.0/memUsageMult)*conf->availableMemory; } else{ outman.UserMessage("\nMemory to be used for conditional likelihood arrays specified as %.1f MB", conf->megsClaMemory); memToUse=conf->megsClaMemory; } const int KB = 1024; double claSizePerNodeKB = indiv[0].modPart.CalcRequiredCLAsizeKB(dataPart); int numNodesPerIndiv = dataPart->NTax()-2; int idealClas = 3 * total_size * numNodesPerIndiv; int maxClas = (int)((memToUse*KB)/ claSizePerNodeKB); int numClas; int L0=(int) (numNodesPerIndiv * total_size * 2);//a downward and one upward set for each tree int L1=(int) (numNodesPerIndiv * total_size + 2*total_size + numNodesPerIndiv); //at least a downward set and a full root set for every tree, plus one other set int L2=(int) (numNodesPerIndiv * 2.0 + 2*total_size);//a downward set for the best, one other full set and enough for each root direction int L3; if(conf->scoreOnly || conf->optimizeInputOnly){ L3=(int) (numNodesPerIndiv * 1.0 + 2);//one full set plus a few extra } else{ L3=(int) (numNodesPerIndiv * 1.5 - 2 + 2*total_size);//one full set, enough to reserve at least all of the full internals of the //best indiv and enough for each root } if(maxClas >= L0){ numClas = min(maxClas, idealClas); memLevel = 0; } else{ numClas=maxClas; if(maxClas >= L1) memLevel = 1; else if(maxClas >= L2) memLevel = 2; else if(maxClas >= L3) memLevel = 3; else memLevel=-1; } outman.precision(4); outman.UserMessage("\nFor this dataset:"); outman.UserMessage(" Mem level availablememory setting"); outman.UserMessage(" great >= %.0f MB", ceil(L0 * (claSizePerNodeKB/(FLOAT_TYPE)KB)) * memUsageMult); outman.UserMessage(" good approx %.0f MB to %.0f MB", ceil(L0 * ((FLOAT_TYPE)claSizePerNodeKB/KB)) * memUsageMult - 1, ceil(L1 * ((FLOAT_TYPE)claSizePerNodeKB/KB)) * memUsageMult); outman.UserMessage(" low approx %.0f MB to %.0f MB", ceil(L1 * ((FLOAT_TYPE)claSizePerNodeKB/KB)) * memUsageMult - 1, ceil(L2 * ((FLOAT_TYPE)claSizePerNodeKB/KB)) * memUsageMult); outman.UserMessage(" very low approx %.0f MB to %.0f MB", ceil(L2 * ((FLOAT_TYPE)claSizePerNodeKB/KB)) * memUsageMult - 1, ceil(L3 * ((FLOAT_TYPE)claSizePerNodeKB/KB)) * memUsageMult); outman.UserMessage("the minimum required availablememory is %.0f MB", ceil(L3 * ((FLOAT_TYPE)claSizePerNodeKB/KB)) * memUsageMult ); if(conf->scoreOnly || conf->optimizeInputOnly){ outman.UserMessage("\nNOTE: Less memory is required when scoring or optimizing fixed trees.\n\tminimum of %.0f availablememory would be required to search\n", ceil(((int) (numNodesPerIndiv * 1.5 - 2 + 2*total_size)) * ((FLOAT_TYPE)claSizePerNodeKB/KB)) * 1.25); } outman.UserMessage("\nYou specified that Garli should use at most %.1f MB of memory.", conf->availableMemory); outman.UserMessage("\nGarli will actually use approx. %.1f MB of memory", memUsageMult*(FLOAT_TYPE)numClas*(FLOAT_TYPE)claSizePerNodeKB/(FLOAT_TYPE)KB); if( ! (conf->scoreOnly || conf->optimizeInputOnly)){ if(memLevel == 0) outman.UserMessage("**Your memory level is: great (you don't need to change anything)**"); else if(memLevel == 1) outman.UserMessage("**Your memory level is: good (you don't need to change anything)**"); else if(memLevel == 2) outman.UserMessage("**Your memory level is: low\n\t(you may want to increase the availablememory setting)**"); else if(memLevel == 3) outman.UserMessage("**Your memory level is: very low\n\t(if possible, you should increase the availablememory setting)**"); else if(memLevel == -1) outman.UserMessage("**NOT ENOUGH MEMORY\n\t(you must increase the availablememory setting)**"); } outman.UserMessage("\n#######################################################"); /* outman.precision(4); outman.UserMessage("allocating memory...\nusing %.1f MB for conditional likelihood arrays. Memlevel= %d", (FLOAT_TYPE)numClas*(FLOAT_TYPE)claSizePerNode/(FLOAT_TYPE)MB, memLevel); outman.UserMessage("For this dataset:"); outman.UserMessage("level 0: >= %.0f megs", ceil(L0 * (claSizePerNode/(FLOAT_TYPE)MB))); outman.UserMessage("level 1: %.0f megs to %.0f megs", ceil(L0 * ((FLOAT_TYPE)claSizePerNode/MB))-1, ceil(L1 * ((FLOAT_TYPE)claSizePerNode/MB))); outman.UserMessage("level 2: %.0f megs to %.0f megs", ceil(L1 * ((FLOAT_TYPE)claSizePerNode/MB))-1, ceil(L2 * ((FLOAT_TYPE)claSizePerNode/MB))); outman.UserMessage("level 3: %.0f megs to %.0f megs", ceil(L2 * ((FLOAT_TYPE)claSizePerNode/MB))-1, ceil(L3 * ((FLOAT_TYPE)claSizePerNode/MB))); outman.UserMessage("not enough mem: <= %.0f megs\n", ceil(L3 * ((FLOAT_TYPE)claSizePerNode/MB))-1); */ if(memLevel==-1 && !validateMode) throw ErrorException("Not enough memory specified in config file (availablememory)!"); //increasing this more to allow for the possiblility of needing a set for all nodes for both the indiv and newindiv arrays //if we do tons of recombination idealClas *= 2; if(!validateMode) claMan=new ClaManager(dataPart->NTax()-2, numClas, idealClas, &indiv[0].modPart, dataPart); //setup the bipartition statics Bipartition::SetBipartitionStatics(dataPart->NTax()); //set the tree statics Tree::SetTreeStatics(claMan, dataPart, conf); //load any constraints GetConstraints(); //try to get nexus starting tree/trees from file, which we don't want to do within the PerformSearch loop if((_stricmp(conf->streefname.c_str(), "random") != 0) && (_stricmp(conf->streefname.c_str(), "stepwise") != 0)) if(FileIsNexus(conf->streefname.c_str())){ LoadNexusStartingConditions(); } } void Population::LoadNexusStartingConditions(){ GarliReader & reader = GarliReader::GetInstance(); NxsTaxaBlock *tax = NULL; NxsTreesBlock *treesblock = NULL; if(reader.GetNumTaxaBlocks() == 1) tax = reader.GetTaxaBlock(0); else //I think this check happens in NCL as well, but best to be safe throw ErrorException("multiple non-identical taxa blocks have been read"); //this actually is also checked in SetTreeStatics if(Tree::rootWithDummy && !tax->IsAlreadyDefined("ROOT")){ string n = "ROOT"; tax->AppendNewLabel(n); } if(usedNCL && strcmp(conf->streefname.c_str(), conf->datafname.c_str()) == 0){ //in this case we should have already read in the tree when getting the data, so check that we have either one //trees block for this taxa block or a garli block if(reader.GetNumTreesBlocks(tax) == 0 && reader.FoundModelString() == false) throw ErrorException("No nexus trees block or Garli block was found in file %s,\n which was specified as source of starting tree and/or model", conf->streefname.c_str()); else if(reader.GetNumTreesBlocks(tax) > 1) throw ErrorException("Expecting only one trees block in file %s (not sure which to use)", conf->streefname.c_str()); else if(reader.GetNumTreesBlocks(tax) == 1) startingTreeInNCL = true; else startingTreeInNCL = false; } else{ //use NCL to get trees from the specified file outman.UserMessage("Loading starting model and/or tree from file %s", conf->streefname.c_str()); //it isn't easy to remove a previous trees block in factory mode, so we need to do this int initNumTreesBlocks = reader.GetNumTreesBlocks(tax); try{ reader.ReadFilepath(conf->streefname.c_str(), MultiFormatReader::NEXUS_FORMAT); } catch (const NxsException & x){ throw ErrorException("%s", x.msg.c_str()); } int afterNumTreesBlocks = reader.GetNumTreesBlocks(tax);; if(afterNumTreesBlocks - initNumTreesBlocks > 1){//we added more than one trees block throw ErrorException("Expecting only one trees block in file %s (not sure which to use)", conf->streefname.c_str()); } //otherwise we want the last one because others may have been read with the data else if(afterNumTreesBlocks == initNumTreesBlocks)//we didnt' add any tree blocks startingTreeInNCL = false; else //we found exactly one trees block. WE NEED TO BE SURE THAT WE USE THE LATEST ONE LATER in SeeedPop startingTreeInNCL = true; //we read the file, but didn't find either if(startingTreeInNCL == false && reader.FoundModelString() == false) throw ErrorException("No nexus trees block or Garli block was found in file %s,\n which was specified as the source of starting model and/or tree", conf->streefname.c_str()); } if(reader.FoundModelString()) startingModelInNCL = true; } //return population more or less to what it looked like just after Setup() void Population::Reset(){ if(adap != NULL) delete adap; adap=new Adaptation(conf); lastTopoImprove = lastPrecisionReduction = gen = 0; //conf->restart indicates whether the current rep was restarted, so if we complete one rep and then //move on to another it should be false conf->restart = false; finishedRep = false; finishedGenerations = false; genTermination = false; bestFitness = prevBestFitness = -(FLT_MAX); initialRefinePass = finalRefinePass = 0; for(unsigned i=0;iRemoveTreeFromAllClas(); for(unsigned j=0;jRemoveTreeFromAllClas(); delete newindiv[i].treeStruct; newindiv[i].treeStruct=NULL; } } Tree::attemptedSwaps.ClearAttemptedSwaps(); } void Population::ApplyNSwaps(int numSwaps){ Individual *ind0 = &newindiv[0]; ind0->GetStartingConditionsFromFile(conf->streefname.c_str(), 0, dataPart->NTax()); ind0->treeStruct->modPart = &ind0->modPart; //ind0->GetStartingConditionsFromNCL( File(conf->streefname.c_str(), 0, data->NTax()); Individual *repResult = new Individual(ind0); storedTrees.push_back(repResult); for(int s=0;streeStruct->TopologyMutator(0.01, 10, 0); ind0->SetDirty(); ind0->CalcFitness(0); Individual *repResult = new Individual(ind0); storedTrees.push_back(repResult); } WriteStoredTrees("swapped.tre"); } void Population::SwapToCompletion(FLOAT_TYPE optPrecision){ SeedPopulationWithStartingTree(currentSearchRep); InitializeOutputStreams(); if(conf->runmode == 2) indiv[0].treeStruct->DeterministicSwapperByDist(&indiv[0], optPrecision, conf->limSPRrange, false); else if(conf->runmode == 3) indiv[0].treeStruct->DeterministicSwapperByCut(&indiv[0], optPrecision, conf->limSPRrange, false); else if(conf->runmode == 4) indiv[0].treeStruct->DeterministicSwapperRandom(&indiv[0], optPrecision, conf->limSPRrange); else if(conf->runmode == 5) indiv[0].treeStruct->DeterministicSwapperByDist(&indiv[0], optPrecision, conf->limSPRrange, true); else if(conf->runmode == 6) indiv[0].treeStruct->DeterministicSwapperByCut(&indiv[0], optPrecision, conf->limSPRrange, true); else if(conf->runmode == 13) indiv[0].treeStruct->GenerateTopologiesAtSprDistance(&indiv[0], optPrecision, conf->limSPRrange); bestIndiv = 0; FinalOptimization(); WriteTreeFile(besttreefile.c_str(), -1); /* double imp = 999.9; do{ imp = indiv[0].treeStruct->OptimizeAllBranches(optPrecision); optPrecision /= 1.5; }while(imp > 0.0); */ outman.UserMessage("final score: %f, %d sec", indiv[0].treeStruct->lnL, stopwatch.SplitTime()); } //this is mainly for debugging purposes, to ensure that we are able to make all trees or all trees //compatible with any constraints void Population::GenerateTreesOnly(int nTrees){ SeedPopulationWithStartingTree(1); InitializeOutputStreams(); if((_stricmp(conf->streefname.c_str(), "random") == 0)){ outman.UserMessageNoCR("Making random trees compatible with constraints... "); for(int i=0;iNTax()); AppendTreeToTreeLog(-1, 0); indiv[0].treeStruct->RemoveTreeFromAllClas(); delete indiv[0].treeStruct; indiv[0].treeStruct=NULL; if(!(i % 100)) outman.UserMessageNoCR("%d ", i); } } else if((_stricmp(conf->streefname.c_str(), "stepwise") == 0)){ outman.UserMessageNoCR("Making stepwise trees compatible with constraints... "); for(int i=0;iNTax(), conf->attachmentsPerTaxon, adap->branchOptPrecision); AppendTreeToTreeLog(-1, 0); indiv[0].treeStruct->RemoveTreeFromAllClas(); delete indiv[0].treeStruct; indiv[0].treeStruct=NULL; if(!(i % 100)) outman.UserMessageNoCR("%d ", i); } } FinalizeOutputStreams(0); } void Population::RunTests(){ //test a number of functions to ensure that any code changes haven't broken anything //it assumes that Setup has been called SeedPopulationWithStartingTree(1); // InitializeOutputStreams(); #ifdef NDEBUG outman.UserMessage("WARNING: You are running internal tests with NDEBUG defined!\nIt should not be defined for full error checking."); #endif if(conf->bootstrapReps > 0){ outman.UserMessage("\nBootstrap reweighting..."); //if this is the first rep if(nextBootstrapSeed == 0) nextBootstrapSeed = rnd.seed(); lastBootstrapSeed = nextBootstrapSeed; nextBootstrapSeed = dataPart->BootstrapReweight(lastBootstrapSeed, conf->resampleProportion); } //DEBUG // Individual *ind0 = &newindiv[0]; // Individual *ind1 = &newindiv[1]; Individual *ind0 = &indiv[0]; Individual *ind1 = &indiv[1]; Tree *tree0 = ind0->treeStruct; Tree *tree1 = ind1->treeStruct; //ind0->MakeRandomTree(data->NTax()); //ind0->MakeStepwiseTree(dataPart->NTax(), conf->attachmentsPerTaxon, adap->branchOptPrecision); //ind0->treeStruct->modPart=&ind0->modPart; #ifdef SINGLE_PRECISION_FLOATS int sigFigs = ceil(log10(-tree0->lnL)); double tol = pow(10.0f, sigFigs-7) * 2.0; #else double tol = 0.001; #endif //check that the score was correct coming out of MakeStepwiseTree FLOAT_TYPE scr = tree0->lnL; tree0->MakeAllNodesDirty(); ind0->SetDirty(); ind0->CalcFitness(0); //this only really tests for major scoring problems in the optimization functions scr = tree0->lnL; tree0->OptimizeAllBranches(adap->branchOptPrecision); assert(tree0->lnL + tol > scr); assert(tree0->lnL * 2 < scr); //test rescaling scr = tree0->lnL; int r = Tree::rescaleEvery; Tree::rescaleEvery = 2; tree0->MakeAllNodesDirty(); ind0->SetDirty(); ind0->CalcFitness(0); if(FloatingPointEquals(ind0->Fitness(), scr, tol) == false){ throw ErrorException("Failed rescaling test: freq %d=%f, freq 2=%f", r, scr, ind0->Fitness()); } Tree::rescaleEvery = r; tree1=new Tree(); ind1->CopySecByRearrangingNodesOfFirst(tree1, ind0); tree1->modPart=&ind1->modPart; ind0->SetDirty(); ind0->CalcFitness(0); ind1->SetDirty(); ind1->CalcFitness(0); assert(ind0->Fitness() == ind1->Fitness()); tree0->MakeAllNodesDirty(); tree1->MakeAllNodesDirty(); for(int i=0;i<100;i++){ tree0->RerootHere(tree0->GetRandomInternalNode()); tree1->RerootHere(tree1->GetRandomInternalNode()); tree0->CalcBipartitions(true); tree1->CalcBipartitions(true); //check rerooting and bipartition comparisons assert(tree0->IdenticalTopologyAllowingRerooting(tree1->root)); ind0->SetDirty(); ind1->SetDirty(); //check minimal recalculation scoring (proper readjustment of CLAs during rerooting) tree0->Score(tree0->GetRandomInternalNode()); tree1->Score(tree1->GetRandomInternalNode()); if(FloatingPointEquals(tree0->lnL, tree1->lnL, tol) == false){ throw ErrorException("failed min recalc test: %f diff vs %f allowed", tree0->lnL - tree1->lnL, tol); } //check full rescoring from arbitrary nodes in the trees tree0->MakeAllNodesDirty(); tree1->MakeAllNodesDirty(); tree0->Score(tree0->GetRandomInternalNode()); tree1->Score(tree1->GetRandomInternalNode()); if(FloatingPointEquals(tree0->lnL, tree1->lnL, tol) == false){ throw ErrorException("failed score at arbitrary node test: %f diff vs %f allowed", tree0->lnL - tree1->lnL, tol); } //check that the derivative funcs are outputing the correct score TreeNode *nd = tree0->allNodes[tree0->GetRandomNonRootNode()]; tree0->CalcDerivativesRateHet(nd->anc, nd); nd = tree1->allNodes[tree1->GetRandomNonRootNode()]; tree1->CalcDerivativesRateHet(nd->anc, nd); if(FloatingPointEquals(tree0->lnL, tree1->lnL, tol) == false){ throw ErrorException("failed derivative scoring test: %f diff vs %f allowed", tree0->lnL - tree1->lnL, tol); } } } void Population::ResetMemLevel(int numNodesPerIndiv, int numClas){ assert(0); //Deprecated /* const int KB = 1024; const int MB = KB*KB; int claSizePerNode = (4 * modSpec.numRateCats * data->NChar() * sizeof(FLOAT_TYPE)) + (data->NChar() * sizeof(int)); int sizeOfIndiv = claSizePerNode * numNodesPerIndiv; int idealClas = 3 * total_size * numNodesPerIndiv; int L0=(int) (numNodesPerIndiv * total_size * 2);//a downward and one upward set for each tree int L1=(int) (numNodesPerIndiv * total_size + 2*total_size + numNodesPerIndiv); //at least a downward set and a full root set for every tree, plus one other set int L2=(int) (numNodesPerIndiv * 2.0 + 2*total_size);//a downward set for the best, one other full set and enough for each root direction int L3=(int) (numNodesPerIndiv * 1.5 - 2 + 2*total_size);//one full set, enough to reserve at least all of the full internals of the //best indiv and enough for each root if(numClas >= L0) memLevel = 0; else if(numClas >= L1) memLevel = 1; else if(numClas >= L2) memLevel = 2; else if(numClas >= L3) memLevel = 3; else memLevel=-1; assert(memLevel >= 0); */ } void Population::GetConstraints(){ //first see if there are any constraints if((strlen(conf->constraintfile.c_str()) != 0) && (_stricmp(conf->constraintfile.c_str(), "none") != 0)){ if(FileIsNexus(conf->constraintfile.c_str())) throw ErrorException("Sorry, Garli doesn't allow constraint trees in Nexus format.\n See the manual for proper constraint format."); ifstream con(conf->constraintfile.c_str()); if(con.good() == false) throw ErrorException("Could not open constraint file %s!", conf->constraintfile.c_str()); if(con.good()){ outman.UserMessage("Loading constraints from file %s", conf->constraintfile.c_str()); Tree::LoadConstraints(con, dataPart->NTax()); } } } //This is a stripped down version of SeedPopWithStartingTree that loads and validates //starting conditions but doesn't score or require CLAs to have been allocated void Population::ValidateInput(int rep){ //create the first indiv, and then copy the tree and clas //this is really annoying and hacky - the maxPinv value is held by each model, and is data dependent (maxPinv can't be > obs pinv) //But, since a single model may apply to multiple data, need to be sure that the maxPinv is > the highest obs pinv of any of them //now always setting the model default for each data subset (which due to linkage might reset the model several times), but this //shouldn't be problematic. Note that the other data dependent model thing is empirical base freqs, but that will be disallowed //elsewhere when there is linkage. FLOAT_TYPE maxPinv = ZERO_POINT_ZERO; for(vector::iterator c = claSpecs.begin();c != claSpecs.end();c++){ for(int m = 0;m < indiv[0].modPart.NumModels();m++){ if((*c).modelIndex == m){ indiv[0].modPart.GetModel(m)->SetDefaultModelParameters(dataPart->GetSubset((*c).dataIndex)); if(indiv[0].modPart.GetModel(m)->MaxPinv() > maxPinv) maxPinv = indiv[0].modPart.GetModel(m)->MaxPinv(); } } } //we should only need to do this crap if the models are linked, but not currently allowing linking of some models but not others if(conf->linkModels && modSpecSet.GetModSpec(0)->includeInvariantSites == true){ assert(indiv[0].modPart.NumModels() == 1); if(maxPinv > ZERO_POINT_ZERO == false) throw ErrorException("invariantsites = estimate was specified, but no data subsets contained constant characters!"); indiv[0].modPart.GetModel(0)->SetMaxPinv(maxPinv); indiv[0].modPart.GetModel(0)->SetPinv(maxPinv * 0.25, false); } //DEBUG - need to stick this in somewhere more natural so that it gets reset after a rep completes indiv[0].modPart.Reset(); //This is getting very complicated. Here are the allowable combinations. //streefname not specified (random or stepwise) //Case 1 - no gblock in datafile //Case 2 - found gblock in datafile //streefname specified //specified file is same as datafile //Case 3 - Found trees block only //Case 4 - Found gblock only (create random tree) //Case 5 - Found both //specified file not same as datafile //NOTE that all of these are also possible with a gblock found in the datafile //3/25/08 Change - a second gblock is not allowed (it will throw an exception //upon reading the second in GarliReader::EnteringBlock), nor are both a garli block //with the data and model params in the old format in the streefname //specified streefname is Nexus //Case 6 - Found trees block only //Case 7 - Found gblock only (create random tree) (if a gblock was already read it will crap out) //Case 8 - Found both (if a gblock was already read it will crap out) //specified streefname is not Nexus //Case 9 - found a tree //Case 10 - found a model (create random tree) (if a gblock was already read it will crap out) //Case 11 - found both (if a gblock was already read it will crap out) GarliReader & reader = GarliReader::GetInstance(); #ifdef INPUT_RECOMBINATION if(0) #else if((_stricmp(conf->streefname.c_str(), "random") != 0) && (_stricmp(conf->streefname.c_str(), "stepwise") != 0)) //some starting file has been specified - Cases 3-11 #endif { //we already checked in Setup whether NCL has trees for us. A starting model in Garli block will //be handled below, although both a garli block (in the data) and an old style model specification //are not allowed if(startingTreeInNCL){//cases 3, 5, 6 and 8 //CAREFUL here - we may have more than one trees block because a tree could appear with the //dataset and in a different starting tree file. The factory api allows this fine, so we //need to be sure to grab the last trees block. Checking for whether the starting tree //file contained multiple trees blocks was already done in LoadNexusStartingConditions const NxsTreesBlock *treesblock = reader.GetTreesBlock(reader.GetTaxaBlock(0), reader.GetNumTreesBlocks(reader.GetTaxaBlock(0)) - 1); assert(treesblock != NULL); //this should verify some aspects of the tree description and change everything to taxon numbers treesblock->ProcessAllTrees(); int numTrees = treesblock->GetNumTrees(); if(numTrees > 0){ int treeNum = (rank+rep-1) % numTrees; indiv[0].GetStartingTreeFromNCL(treesblock, treeNum, dataPart->NTax()); outman.UserMessage("Obtained starting tree %d from Nexus", treeNum+1); } else throw ErrorException("Problem getting tree(s) from NCL!"); } else if(strcmp(conf->streefname.c_str(), conf->datafname.c_str()) != 0 && !FileIsNexus(conf->streefname.c_str())){ //cases 9-11 if the streef file is not the same as the datafile, and it isn't Nexus //use the old garli starting model/tree format outman.UserMessage("Obtaining starting conditions from file %s", conf->streefname.c_str()); indiv[0].GetStartingConditionsFromFile(conf->streefname.c_str(), rank + rep - 1, dataPart->NTax()); } indiv[0].SetDirty(); } if(reader.FoundModelString()) startingModelInNCL = true; if(startingModelInNCL || conf->parameterValueString.length() > 0){ //crap out if we already got some parameters above in an old style starting conditions file #ifndef SUBROUTINE_GARLI if(modSpecSet.GotAnyParametersFromFile() && (currentSearchRep == 1 && (conf->bootstrapReps == 0 || currentBootstrapRep == 1))) throw ErrorException("Found model parameters specified in a Nexus GARLI block with the dataset,\n\tand in the starting condition file (streefname).\n\tPlease use one or the other."); #endif if(startingModelInNCL && conf->parameterValueString.length() > 0) throw ErrorException("Found model parameters specified in the configuration file and in the dataset or starting condition file (streefname).\n\tPlease use one or the other."); //model string from garli block, which could have come either in starting condition file //or in file with Nexus dataset. Cases 2, 4, 5, 7 and 8 come through here. string modString; if(startingModelInNCL) modString = reader.GetModelString(); else modString = conf->parameterValueString; if(modString.length() > 0) indiv[0].modPart.ReadGarliFormattedModelStrings(modString); if(startingModelInNCL) outman.UserMessage("Obtained starting or fixed model parameter values from Nexus:"); else outman.UserMessage("Obtained starting or fixed model parameter values from configuration file:"); } //The model params should be set to their initial values by now, so report them if(conf->bootstrapReps == 0 || (currentBootstrapRep == 1 && currentSearchRep == 1)){ outman.UserMessage("MODEL REPORT - Parameters are at their INITIAL values (not yet optimized)"); indiv[0].modPart.OutputHumanReadableModelReportWithParams(); } outman.UserMessage("Starting with seed=%d\n", rnd.seed()); //Here we'll error out if something was fixed but didn't appear for(int ms = 0;ms < modSpecSet.NumSpecs();ms++){ const ModelSpecification *modSpec = modSpecSet.GetModSpec(ms); if((_stricmp(conf->streefname.c_str(), "random") == 0) || (_stricmp(conf->streefname.c_str(), "stepwise") == 0)){ //if no streefname file was specified, the param values should be in a garli block with the dataset if(modSpec->IsNucleotide() && modSpec->IsUserSpecifiedStateFrequencies() && !modSpec->gotStateFreqsFromFile) throw(ErrorException("state frequencies specified as fixed, but no\n\tGarli block found in %s!!" , conf->datafname.c_str())); else if(modSpec->fixAlpha && !modSpec->gotAlphaFromFile) throw(ErrorException("alpha parameter specified as fixed, but no\n\tGarli block found in %s!!" , conf->datafname.c_str())); else if(modSpec->fixInvariantSites && !modSpec->gotPinvFromFile) throw(ErrorException("proportion of invariant sites specified as fixed, but no\n\tGarli block found in %s!!" , conf->datafname.c_str())); else if(modSpec->IsUserSpecifiedRateMatrix() && !modSpec->gotRmatFromFile) throw(ErrorException("relative rate matrix specified as fixed, but no\n\tGarli block found in %s!!" , conf->datafname.c_str())); else if(modSpec->IsCodon() && modSpec->fixOmega && !modSpec->gotOmegasFromFile) throw(ErrorException("rate het model set to nonsynonymousfixed, but no\n\tGarli block found in %s!!" , conf->datafname.c_str())); } else{ if((modSpec->IsNucleotide() || modSpec->IsAminoAcid()) && modSpec->IsUserSpecifiedStateFrequencies() && !modSpec->gotStateFreqsFromFile) throw ErrorException("state frequencies specified as fixed, but no\n\tparameter values found in %s or %s!", conf->streefname.c_str(), conf->datafname.c_str()); else if(modSpec->fixAlpha && !modSpec->gotAlphaFromFile) throw ErrorException("alpha parameter specified as fixed, but no\n\tparameter values found in %s or %s!", conf->streefname.c_str(), conf->datafname.c_str()); else if(modSpec->fixInvariantSites && !modSpec->gotPinvFromFile) throw ErrorException("proportion of invariant sites specified as fixed, but no\n\tparameter values found in %s or %s!", conf->streefname.c_str(), conf->datafname.c_str()); else if(modSpec->IsUserSpecifiedRateMatrix() && !modSpec->gotRmatFromFile) throw ErrorException("relative rate matrix specified as fixed, but no\n\tparameter values found in %s or %s!", conf->streefname.c_str(), conf->datafname.c_str()); else if(modSpec->IsCodon() && modSpec->fixOmega && !modSpec->gotOmegasFromFile) throw ErrorException("rate het model set to nonsynonymousfixed, but no\n\tparameter values found in %s or %s!", conf->streefname.c_str(), conf->datafname.c_str()); } } //the treestruct could be null if there was a start file that contained no tree if((_stricmp(conf->streefname.c_str(), "random") != 0) && (_stricmp(conf->streefname.c_str(), "stepwise") != 0) && (indiv[0].treeStruct != NULL)){ bool foundPolytomies = indiv[0].treeStruct->ArbitrarilyBifurcate(); if(foundPolytomies) outman.UserMessage("WARNING: Polytomies found in start tree. These were arbitrarily resolved."); indiv[0].treeStruct->root->CheckTreeFormation(); indiv[0].treeStruct->root->CheckforPolytomies(); } //if there are not mutable params in the model, remove any weight assigned to the model if(indiv[0].modPart.NumMutableParams() == 0) { if((conf->bootstrapReps == 0 && currentSearchRep == 1) || (currentBootstrapRep == 1 && currentSearchRep == 1)) outman.UserMessage("NOTE: Model contains no mutable parameters!\nSetting model mutation weight to zero.\n"); adap->modelMutateProb=ZERO_POINT_ZERO; adap->UpdateProbs(); } } void Population::SeedPopulationWithStartingTree(int rep){ for(unsigned i=0;iRemoveTreeFromAllClas(); if(newindiv[i].treeStruct != NULL) newindiv[i].treeStruct->RemoveTreeFromAllClas(); } //create the first indiv, and then copy the tree and clas //this is really annoying and hacky - the maxPinv value is held by each model, and is data dependent (maxPinv can't be > obs pinv) //But, since a single model may apply to multiple data, need to be sure that the maxPinv is > the highest obs pinv of any of them //now always setting the model default for each data subset (which due to linkage might reset the model several times), but this //shouldn't be problematic. Note that the other data dependent model thing is empirical base freqs, but that will be disallowed //elsewhere when there is linkage. FLOAT_TYPE maxPinv = ZERO_POINT_ZERO; for(vector::iterator c = claSpecs.begin();c != claSpecs.end();c++){ for(int m = 0;m < indiv[0].modPart.NumModels();m++){ if((*c).modelIndex == m){ indiv[0].modPart.GetModel(m)->SetDefaultModelParameters(dataPart->GetSubset((*c).dataIndex)); if(indiv[0].modPart.GetModel(m)->MaxPinv() > maxPinv) maxPinv = indiv[0].modPart.GetModel(m)->MaxPinv(); } } } //we should only need to do this crap if the models are linked, but not currently allowing linking of some models but not others if(conf->linkModels && modSpecSet.GetModSpec(0)->includeInvariantSites == true){ assert(indiv[0].modPart.NumModels() == 1); if(maxPinv > ZERO_POINT_ZERO == false) throw ErrorException("invariantsites = estimate was specified, but no data subsets contained constant characters!"); indiv[0].modPart.GetModel(0)->SetMaxPinv(maxPinv); indiv[0].modPart.GetModel(0)->SetPinv(maxPinv * 0.25, false); } //DEBUG - need to stick this in somewhere more natural so that it gets reset after a rep completes indiv[0].modPart.Reset(); //This is getting very complicated. Here are the allowable combinations. //streefname not specified (random or stepwise) //Case 1 - no gblock in datafile //Case 2 - found gblock in datafile //streefname specified //specified file is same as datafile //Case 3 - Found trees block only //Case 4 - Found gblock only (create random tree) //Case 5 - Found both //specified file not same as datafile //NOTE that all of these are also possible with a gblock found in the datafile //3/25/08 Change - a second gblock is not allowed (it will throw an exception //upon reading the second in GarliReader::EnteringBlock), nor are both a garli block //with the data and model params in the old format in the streefname //specified streefname is Nexus //Case 6 - Found trees block only //Case 7 - Found gblock only (create random tree) (if a gblock was already read it will crap out) //Case 8 - Found both (if a gblock was already read it will crap out) //specified streefname is not Nexus //Case 9 - found a tree //Case 10 - found a model (create random tree) (if a gblock was already read it will crap out) //Case 11 - found both (if a gblock was already read it will crap out) GarliReader & reader = GarliReader::GetInstance(); #ifdef INPUT_RECOMBINATION if(0) #else if((_stricmp(conf->streefname.c_str(), "random") != 0) && (_stricmp(conf->streefname.c_str(), "stepwise") != 0)) //some starting file has been specified - Cases 3-11 #endif { //we already checked in Setup whether NCL has trees for us. A starting model in Garli block will //be handled below, although both a garli block (in the data) and an old style model specification //are not allowed if(startingTreeInNCL){//cases 3, 5, 6 and 8 //CAREFUL here - we may have more than one trees block because a tree could appear with the //dataset and in a different starting tree file. The factory api allows this fine, so we //need to be sure to grab the last trees block. Checking for whether the starting tree //file contained multiple trees blocks was already done in LoadNexusStartingConditions const NxsTreesBlock *treesblock = reader.GetTreesBlock(reader.GetTaxaBlock(0), reader.GetNumTreesBlocks(reader.GetTaxaBlock(0)) - 1); assert(treesblock != NULL); //this should verify some aspects of the tree description and change everything to taxon numbers treesblock->ProcessAllTrees(); int numTrees = treesblock->GetNumTrees(); if(numTrees > 0){ int treeNum = (rank+rep-1) % numTrees; indiv[0].GetStartingTreeFromNCL(treesblock, treeNum, dataPart->NTax()); outman.UserMessage("Obtained starting tree %d from Nexus", treeNum+1); } else throw ErrorException("Problem getting tree(s) from NCL!"); } else if(strcmp(conf->streefname.c_str(), conf->datafname.c_str()) != 0 && !FileIsNexus(conf->streefname.c_str())){ //cases 9-11 if the streef file is not the same as the datafile, and it isn't Nexus //use the old garli starting model/tree format outman.UserMessage("Obtaining starting conditions from file %s", conf->streefname.c_str()); indiv[0].GetStartingConditionsFromFile(conf->streefname.c_str(), rank + rep - 1, dataPart->NTax()); } indiv[0].SetDirty(); } if(reader.FoundModelString()) startingModelInNCL = true; if(startingModelInNCL || conf->parameterValueString.length() > 0){ //crap out if we already got some parameters above in an old style starting conditions file #ifndef SUBROUTINE_GARLI if(modSpecSet.GotAnyParametersFromFile() && (currentSearchRep == 1 && (conf->bootstrapReps == 0 || currentBootstrapRep == 1))) throw ErrorException("Found model parameters specified in a Nexus GARLI block with the dataset,\n\tand in the starting condition file (streefname).\n\tPlease use one or the other."); #endif if(startingModelInNCL && conf->parameterValueString.length() > 0) throw ErrorException("Found model parameters specified in the configuration file and in the dataset or starting condition file (streefname).\n\tPlease use one or the other."); //model string from garli block, which could have come either in starting condition file //or in file with Nexus dataset. Cases 2, 4, 5, 7 and 8 come through here. string modString; if(startingModelInNCL) modString = reader.GetModelString(); else modString = conf->parameterValueString; if(modString.length() > 0) indiv[0].modPart.ReadGarliFormattedModelStrings(modString); if(startingModelInNCL) outman.UserMessage("Obtained starting or fixed model parameter values from Nexus:"); else outman.UserMessage("Obtained starting or fixed model parameter values from configuration file:"); } //Here we'll error out if something was fixed but didn't appear for(int ms = 0;ms < modSpecSet.NumSpecs();ms++){ const ModelSpecification *modSpec = modSpecSet.GetModSpec(ms); if((_stricmp(conf->streefname.c_str(), "random") == 0) || (_stricmp(conf->streefname.c_str(), "stepwise") == 0)){ //if no streefname file was specified, the param values should be in a garli block with the dataset if(modSpec->IsNucleotide() && modSpec->IsUserSpecifiedStateFrequencies() && !modSpec->gotStateFreqsFromFile) throw(ErrorException("state frequencies specified as fixed, but no\n\tGarli block found in %s!!" , conf->datafname.c_str())); else if(modSpec->fixAlpha && !modSpec->gotAlphaFromFile) throw(ErrorException("alpha parameter specified as fixed, but no\n\tGarli block found in %s!!" , conf->datafname.c_str())); else if(modSpec->fixInvariantSites && !modSpec->gotPinvFromFile) throw(ErrorException("proportion of invariant sites specified as fixed, but no\n\tGarli block found in %s!!" , conf->datafname.c_str())); else if(modSpec->IsUserSpecifiedRateMatrix() && !modSpec->gotRmatFromFile) throw(ErrorException("relative rate matrix specified as fixed, but no\n\tGarli block found in %s!!" , conf->datafname.c_str())); else if(modSpec->IsCodon() && modSpec->fixOmega && !modSpec->gotOmegasFromFile) throw(ErrorException("rate het model set to nonsynonymousfixed, but no\n\tGarli block found in %s!!" , conf->datafname.c_str())); } else{ if((modSpec->IsNucleotide() || modSpec->IsAminoAcid()) && modSpec->IsUserSpecifiedStateFrequencies() && !modSpec->gotStateFreqsFromFile) throw ErrorException("state frequencies specified as fixed, but no\n\tparameter values found in %s or %s!", conf->streefname.c_str(), conf->datafname.c_str()); else if(modSpec->fixAlpha && !modSpec->gotAlphaFromFile) throw ErrorException("alpha parameter specified as fixed, but no\n\tparameter values found in %s or %s!", conf->streefname.c_str(), conf->datafname.c_str()); else if(modSpec->fixInvariantSites && !modSpec->gotPinvFromFile) throw ErrorException("proportion of invariant sites specified as fixed, but no\n\tparameter values found in %s or %s!", conf->streefname.c_str(), conf->datafname.c_str()); else if(modSpec->IsUserSpecifiedRateMatrix() && !modSpec->gotRmatFromFile) throw ErrorException("relative rate matrix specified as fixed, but no\n\tparameter values found in %s or %s!", conf->streefname.c_str(), conf->datafname.c_str()); else if(modSpec->IsCodon() && modSpec->fixOmega && !modSpec->gotOmegasFromFile) throw ErrorException("rate het model set to nonsynonymousfixed, but no\n\tparameter values found in %s or %s!", conf->streefname.c_str(), conf->datafname.c_str()); } if(conf->modWeight == ZERO_POINT_ZERO) if(modSpec->IsCodon() && modSpec->gotOmegasFromFile == false) throw(ErrorException("sorry, to turn off model mutations you must provide omega values in a codon model.\nSet modweight to > 0.0 or provide omega values.")); } //The model params should be set to their initial values by now, so report them if(conf->bootstrapReps == 0 || (currentBootstrapRep == 1 && currentSearchRep == 1)){ outman.UserMessage("MODEL REPORT - Parameters are at their INITIAL values (not yet optimized)"); indiv[0].modPart.OutputHumanReadableModelReportWithParams(); } outman.UserMessage("Starting with seed=%d\n", rnd.seed()); //A random tree specified, or a starting file was specified but contained no tree if(_stricmp(conf->streefname.c_str(), "stepwise") == 0){ if(Tree::constraints.empty()) outman.UserMessage("creating likelihood stepwise addition starting tree..."); else outman.UserMessage("creating likelihood stepwise addition starting tree (compatible with constraints)..."); //5/20/08 If we're making a stepwise tree, we depend on the extern globalBest being zero to keep the optimization //during the stepwise creation to be localized to just the three branches (the radius optimization only happens if //the lnL of created tree is within a threshold of the global best). Having global best = zero effectively turns //off all radius opt. There was a bug here because it wasn't getting reset before starting search reps after the //first. This caused the stepwise to be slow, and to not be reproducible when the seed from a rep > 1 was specified //as the initial seed for a new run globalBest = ZERO_POINT_ZERO; //DEBUG - haven't worked this out with the rooted tree yet, since the fake root needs to be in the tree before it is scored assert(!indiv[0].treeStruct->rootWithDummy); indiv[0].MakeStepwiseTree(dataPart->NTax(), conf->attachmentsPerTaxon, adap->branchOptPrecision); } else if(_stricmp(conf->streefname.c_str(), "random") == 0 || indiv[0].treeStruct == NULL){ if(Tree::constraints.empty()) outman.UserMessage("creating random starting tree..."); else outman.UserMessage("creating random starting tree (compatible with constraints)..."); indiv[0].MakeRandomTree(dataPart->NTax()); indiv[0].SetDirty(); } assert(indiv[0].treeStruct != NULL); bool foundPolytomies = indiv[0].treeStruct->ArbitrarilyBifurcate(); if(foundPolytomies) outman.UserMessage("WARNING: Polytomies found in start tree. These were arbitrarily resolved."); indiv[0].treeStruct->root->CheckTreeFormation(); indiv[0].treeStruct->root->CheckforPolytomies(); if(!indiv[0].treeStruct->rootWithDummy) indiv[0].treeStruct->CheckBalance(); indiv[0].treeStruct->modPart=&indiv[0].modPart; try{ indiv[0].CalcFitness(0); } catch(UnscoreableException &ex){ throw ErrorException("Initial individual unscorable, perhaps due to poorness of starting tree.\n\tTry providing a tree if you previously tried random."); } //check the current likelihood now to know how accurate we can expect them to be later #ifdef SINGLE_PRECISION_FLOATS Tree::expectedPrecision = pow(10.0, - (double) ((int) FLT_DIG - ceil(log10(-indiv[0].Fitness())))); #else Tree::expectedPrecision = pow(10.0, - (double) ((int) DBL_DIG - ceil(log10(-indiv[0].Fitness())))); #endif // outman.UserMessage("expected likelihood precision = %.4e", Tree::expectedPrecision); //if there are not mutable params in the model, remove any weight assigned to the model if(indiv[0].modPart.NumMutableParams() == 0) { if((conf->bootstrapReps == 0 && currentSearchRep == 1) || (currentBootstrapRep == 1 && currentSearchRep == 1)) outman.UserMessage("NOTE: Model contains no mutable parameters!\nSetting model mutation weight to zero.\n"); adap->modelMutateProb=ZERO_POINT_ZERO; adap->UpdateProbs(); } outman.precision(10); outman.UserMessage("Initial ln Likelihood: %.4f", indiv[0].Fitness()); #ifdef MAC_FRONTEND NSAutoreleasePool *pool = [[NSAutoreleasePool alloc] init]; [[MFEInterfaceClient sharedClient] didBeginInitializingSearch]; [pool release]; #endif if(conf->refineStart==true && !conf->scoreOnly){ //12/26/07 now only passing the first argument here ("optModel") as false if no model muts are used //if single parameters are fixed that will be checked in the Refine function itself //5/15/14 Moved the initial refinement phase to the Population level, which makes more sense and //mirrors the final optimization behavior //indiv[0].RefineStartingConditions(adap->modWeight != ZERO_POINT_ZERO, adap->branchOptPrecision); InitialOptimization(&indiv[0], adap->modWeight != ZERO_POINT_ZERO, adap->branchOptPrecision); indiv[0].CalcFitness(0); outman.UserMessage("lnL after optimization: %.4f", indiv[0].Fitness()); } globalBest=bestFitness=prevBestFitness=indiv[0].Fitness(); //don't bother allocating any further indivs if we will only use one if(conf->optimizeInputOnly || conf->scoreOnly) return; #ifndef INPUT_RECOMBINATION for(unsigned i=1;imodPart=&indiv[i].modPart; } #else for(unsigned i=1;inindivs;i++){ if(indiv[i].treeStruct == NULL) indiv[i].treeStruct = new Tree(); indiv[i].CopySecByRearrangingNodesOfFirst(indiv[i].treeStruct, &indiv[0]); indiv[i].treeStruct->modPart=&indiv[i].modPart; } //string inputs="sphinx.input6000.goodmod.tre"; for(unsigned i=conf->nindivs;istreefname.c_str(), i-conf->nindivs, dataPart->NTax()); indiv[i].treeStruct->modPart=&indiv[i].modPart; indiv[i].SetDirty(); //indiv[i].RefineStartingConditions((adap->modWeight == ZERO_POINT_ZERO || modSpec->fixAlpha == true) == false, adap->branchOptPrecision); indiv[i].CalcFitness(0); } #endif CalcAverageFitness(); } //Copied almost exactly from Individual::RefineStartingConditions. For various reasons it is easier //to have it at the population level. void Population::InitialOptimization(Individual *ind, bool optModel, FLOAT_TYPE branchPrec){ bool optOmega, optAlpha, optFlex, optPinv, optFreqs, optRelRates, optSubsetRates; optOmega = optAlpha = optFlex = optPinv = optFreqs = optRelRates = optSubsetRates = false; bool optInsDel = false; ModelPartition &modPart = ind->modPart; Tree *treeStruct = ind->treeStruct; if(optModel){ for(int modnum = 0;modnum < modPart.NumModels();modnum++){ Model *mod = modPart.GetModel(modnum); const ModelSpecification *modSpec = mod->GetCorrespondingSpec(); if(modSpec->numRateCats > 1 && modSpec->IsNonsynonymousRateHet() == false && modSpec->IsFlexRateHet() == false && modSpec->fixAlpha == false) optAlpha = true; if(modSpec->IsFlexRateHet()) optFlex = true; if(modSpec->includeInvariantSites && modSpec->fixInvariantSites == false) optPinv = true; if(modSpec->IsCodon() && !modSpec->fixOmega) optOmega = true; if(modSpec->IsOrientedGap()) optInsDel = true; if(modSpec->IsCodon() == false && modSpec->fixStateFreqs == false && modSpec->IsEqualStateFrequencies() == false && modSpec->IsEmpiricalStateFrequencies() == false) optFreqs = true; //this is the case of forced freq optimization with codon models. For everything to work they must be set as both not fixed but empirical if(modSpec->IsCodon() && modSpec->fixStateFreqs == false && modSpec->IsEqualStateFrequencies() == false && modSpec->IsEmpiricalStateFrequencies() == true) optFreqs = true; if(modSpec->fixRelativeRates == false && (modSpec->Nst() > 1 || modSpec->IsEstimateAAMatrix() || modSpec->IsTwoSerineRateMatrix())) optRelRates = true; } //oops, bug fixed 10/2/12 - subset rates weren't getting opt in linked models //modSpecSet.inferSubsetRates is already getting set only if conf.inferSubsetRates //is true and there are multiple matrices, but not necessarily multiple models //if(modSpecSet.InferSubsetRates() && modSpecSet.NumSpecs() > 1) if(modSpecSet.InferSubsetRates()) optSubsetRates = true; } outman.UserMessageNoCR("optimizing: starting branch lengths"); if(optAlpha) outman.UserMessageNoCR(", alpha shape"); if(optPinv) outman.UserMessageNoCR(", prop. invar"); if(optRelRates) outman.UserMessageNoCR(", rel rates"); if(optFreqs) outman.UserMessageNoCR(", eq freqs"); if(optOmega) outman.UserMessageNoCR(", dN/dS (aka omega) parameters"); if(optInsDel){ outman.UserMessageNoCR(", ins rate"); outman.UserMessageNoCR(", del rate"); } if(optSubsetRates) outman.UserMessageNoCR(", subset rates"); outman.UserMessage("..."); FLOAT_TYPE improve=(FLOAT_TYPE)999.9; ind->CalcFitness(0); assert(initialRefinePass < 1); for(initialRefinePass = 1;improve > branchPrec;initialRefinePass++){ FLOAT_TYPE alphaOptImprove=0.0, pinvOptImprove = 0.0, omegaOptImprove = 0.0, flexOptImprove = 0.0, optImprove=0.0, scaleOptImprove=0.0, subsetRateImprove=0.0, rateOptImprove=0.0; FLOAT_TYPE freqOptImprove=0.0, insDelImprove = 0.0; ind->CalcFitness(0); FLOAT_TYPE passStart = ind->Fitness(); optImprove=treeStruct->OptimizeAllBranches(branchPrec); ind->CalcFitness(0); FLOAT_TYPE trueImprove = ind->Fitness() - passStart; assert(trueImprove >= -1.0); if(trueImprove < ZERO_POINT_ZERO) trueImprove = ZERO_POINT_ZERO; vector blens; treeStruct->StoreBranchlengths(blens); scaleOptImprove=treeStruct->OptimizeTreeScale(branchPrec); ind->CalcFitness(0); //if some of the branch lengths were at the minimum or maximum boundaries the scale optimization //can actually worsen the score. If so, return them to their original lengths. if(scaleOptImprove < ZERO_POINT_ZERO){ treeStruct->RestoreBranchlengths(blens); ind->CalcFitness(0); scaleOptImprove = ZERO_POINT_ZERO; } ind->CalcFitness(0); if(optModel){ for(int modnum = 0;modnum < modPart.NumModels();modnum++){ Model *mod = modPart.GetModel(modnum); const ModelSpecification *modSpec = mod->GetCorrespondingSpec(); if(modSpec->IsCodon()){ if(!modSpec->fixOmega) omegaOptImprove += treeStruct->OptimizeOmegaParameters(branchPrec, modnum); } else if(mod->NRateCats() > 1){ if(modSpec->IsFlexRateHet()){//Flex rates //no longer doing alpha first, it was too hard to know if the flex rates had been partially optimized //already during making of a stepwise tree //if(i == 1) rateOptImprove = treeStruct->OptimizeAlpha(branchPrec); //if(i == 1 && modSpec.gotFlexFromFile==false) rateOptImprove = treeStruct->OptimizeBoundedParameter(branchPrec, mod->Alpha(), 0, 1.0e-8, 999.9, &Model::SetAlpha); flexOptImprove += treeStruct->OptimizeFlexRates(branchPrec, modnum); } else if(modSpec->fixAlpha == false){//normal gamma //rateOptImprove = treeStruct->OptimizeAlpha(branchPrec); //do NOT let alpha go too low here - on bad or random starting trees the branch lengths get crazy long //rateOptImprove = treeStruct->OptimizeBoundedParameter(branchPrec, mod->Alpha(), 0, 1.0e-8, 999.9, &Model::SetAlpha); //alphaOptImprove += treeStruct->OptimizeBoundedParameter(branchPrec, mod->Alpha(), 0, 0.05, 999.9, modnum, &Model::SetAlpha); alphaOptImprove += treeStruct->OptimizeBoundedParameter(modnum, branchPrec, mod->Alpha(), 0, 0.05, 999.9, &Model::SetAlpha); } } if(modSpec->includeInvariantSites && !modSpec->fixInvariantSites) pinvOptImprove += treeStruct->OptimizeBoundedParameter(modnum, branchPrec, mod->PropInvar(), 0, 1.0e-8, mod->maxPropInvar, &Model::SetPinv); if(modSpec->IsOrientedGap()){ insDelImprove += treeStruct->OptimizeInsertDeleteRates(branchPrec, modnum); } if(modSpec->IsCodon() == false && modSpec->fixStateFreqs == false && modSpec->IsEqualStateFrequencies() == false && modSpec->IsEmpiricalStateFrequencies() == false) freqOptImprove += treeStruct->OptimizeEquilibriumFreqs(branchPrec, modnum); if(modSpec->fixRelativeRates == false && (modSpec->Nst() > 1 || modSpec->IsEstimateAAMatrix() || modSpec->IsTwoSerineRateMatrix())) rateOptImprove += treeStruct->OptimizeRelativeNucRates(branchPrec, modnum); } if(optSubsetRates){ subsetRateImprove += treeStruct->OptimizeSubsetRates(branchPrec); } } improve=scaleOptImprove + trueImprove + alphaOptImprove + pinvOptImprove + flexOptImprove + omegaOptImprove + rateOptImprove + freqOptImprove + subsetRateImprove + insDelImprove; outman.precision(8); outman.UserMessageNoCR("pass%2d:+%9.3f (branch=%7.2f scale=%6.2f", initialRefinePass, improve, trueImprove, scaleOptImprove); if(optOmega) outman.UserMessageNoCR(" omega=%6.2f", omegaOptImprove); if(optAlpha) outman.UserMessageNoCR(" alpha=%6.2f", alphaOptImprove); if(optFreqs) outman.UserMessageNoCR(" freqs=%6.2f", freqOptImprove); if(optRelRates) outman.UserMessageNoCR(" rel rates=%6.2f", rateOptImprove); if(optFlex) outman.UserMessageNoCR(" flex=%6.2f", flexOptImprove); if(optPinv) outman.UserMessageNoCR(" pinv=%6.2f", pinvOptImprove); if(optInsDel){ outman.UserMessageNoCR(" ins/del=%6.2f", insDelImprove); } if(optSubsetRates) outman.UserMessageNoCR(" subset rates=%6.2f", subsetRateImprove); outman.UserMessageNoCR(")"); UpdateFractionDone(1); if(conf->reportRunProgress) outman.UserMessageNoCR(" %14.2f %14.2f", 0.01 * (int) ceil(rep_fraction_done * 100), 0.01 * (int) ceil(tot_fraction_done * 100)); outman.UserMessage(""); } initialRefinePass = -1; treeStruct->nodeOptVector.clear(); } /* This is deprecated in favor of model based function void Population::OutputModelReport(){ //Report on the model setup outman.UserMessage("MODEL REPORT:"); if(modSpec->IsCodon()){ if(modSpec->IsVertMitoCode()) outman.UserMessage(" Number of states = 60 (codon data, vertebrate mitochondrial code)"); else if(modSpec->IsInvertMitoCode()) outman.UserMessage(" Number of states = 62 (codon data, invertebrate mitochondrial code)"); else outman.UserMessage(" Number of states = 61 (codon data, standard code)"); } else if(modSpec->IsAminoAcid()) outman.UserMessage(" Number of states = 20 (amino acid data)"); else outman.UserMessage(" Number of states = 4 (nucleotide data)"); if(modSpec->IsAminoAcid() == false){ if(modSpec->IsCodon() && modSpec->numRateCats == 1) outman.UserMessageNoCR(" One estimated dN/dS ratio (aka omega)\n"); if(modSpec->IsCodon()) outman.UserMessageNoCR(" Nucleotide Relative Rate Matrix Assumed by Codon Model:\n "); else outman.UserMessageNoCR(" Nucleotide Relative Rate Matrix: "); if(modSpec->Nst() == 6){ if(modSpec->IsArbitraryRateMatrix()) outman.UserMessage("User specified matrix type: %s", modSpec->arbitraryRateMatrixString.c_str()); else outman.UserMessage("6 rates"); if(modSpec->fixRelativeRates == true) outman.UserMessage("values specified by user (fixed)"); } else if(modSpec->Nst() == 2) outman.UserMessage("2 rates (transition and transversion)"); else outman.UserMessage("1 rate"); } else{ outman.UserMessageNoCR(" Amino Acid Rate Matrix: "); if(modSpec->IsJonesAAMatrix()) outman.UserMessage("Jones"); else if(modSpec->IsDayhoffAAMatrix()) outman.UserMessage("Dayhoff"); else if(modSpec->IsPoissonAAMatrix()) outman.UserMessage("Poisson"); else if(modSpec->IsWAGAAMatrix()) outman.UserMessage("WAG"); else if(modSpec->IsMtMamAAMatrix()) outman.UserMessage("MtMam"); else if(modSpec->IsMtRevAAMatrix()) outman.UserMessage("MtRev"); } outman.UserMessageNoCR(" Equilibrium State Frequencies: "); if(modSpec->IsEqualStateFrequencies()){ if(modSpec->IsCodon()){ if(modSpec->IsVertMitoCode()) outman.UserMessage("equal (1/60 = 0.01667, fixed)"); else if(modSpec->IsInvertMitoCode()) outman.UserMessage("equal (1/62 = 0.01613, fixed)"); else outman.UserMessage("equal (1/61 = 0.01639, fixed)"); } else if(modSpec->IsAminoAcid()) outman.UserMessage("equal (0.05, fixed)"); else outman.UserMessage("equal (0.25, fixed)"); } else if(modSpec->IsF3x4StateFrequencies()) outman.UserMessage("empirical values calculated by F3x4 method (fixed)"); else if(modSpec->IsF1x4StateFrequencies()) outman.UserMessage("empirical values calculated by F1x4 method (fixed)"); else if(modSpec->IsEmpiricalStateFrequencies()) outman.UserMessage("empirical values (fixed)"); else if(modSpec->IsJonesAAFreqs()) outman.UserMessage("Jones"); else if(modSpec->IsWAGAAFreqs()) outman.UserMessage("WAG"); else if(modSpec->IsMtMamAAFreqs()) outman.UserMessage("MtMam"); else if(modSpec->IsMtRevAAFreqs()) outman.UserMessage("MtRev"); else if(modSpec->IsDayhoffAAFreqs()) outman.UserMessage("Dayhoff"); else if(modSpec->IsUserSpecifiedStateFrequencies()) outman.UserMessage("specified by user (fixed)"); else outman.UserMessage("estimated"); outman.UserMessage(" Rate Heterogeneity Model:"); if(modSpec->numRateCats == 1){ if(modSpec->includeInvariantSites == false) outman.UserMessage(" no rate heterogeneity"); else{ if(modSpec->fixInvariantSites == true) outman.UserMessage(" only an invariant (invariable) site category,\n proportion specified by user (fixed)"); else outman.UserMessage(" only an invariant (invariable) site category,\n proportion estimated"); } } else{ outman.UserMessageNoCR(" %d ", modSpec->numRateCats); if(modSpec->IsNonsynonymousRateHet()){ outman.UserMessage("nonsynonymous rate categories, rate and proportion of each estimated\n (this is effectively the M3 model of PAML)"); } else if(modSpec->IsFlexRateHet() == false){ if(modSpec->fixAlpha == true) outman.UserMessage("discrete gamma distributed rate cats,\n alpha param specified by user (fixed)"); else outman.UserMessage("discrete gamma distributed rate cats, alpha param estimated"); if(modSpec->includeInvariantSites == true){ if(modSpec->fixInvariantSites == true) outman.UserMessage(" with an invariant (invariable) site category,\n proportion specified by user (fixed)"); else outman.UserMessage(" with an invariant (invariable) site category, proportion estimated"); } } else{ outman.UserMessage("FLEX rate categories, rate and proportion of each estimated"); if(modSpec->includeInvariantSites == true){ if(modSpec->fixInvariantSites == true) outman.UserMessage(" with an invariant (invariable) site category,\n proportion specified by user (fixed)"); else outman.UserMessage(" with an invariant (invariable) site category, proportion estimated"); } } } outman.UserMessage(""); } */ /* void Population::WriteStateFiles(){ char str[100]; //write the adaptation info checkpoint in binary format sprintf(str, "%s.adap.check", conf->ofprefix.c_str()); ofstream out(str, ios::binary | ios::out); adap->WriteToCheckpoint(out); out.close(); //write the state of the population, including the seed, generation, elapsed time, //lastTopoImprove and specifications of the current individuals sprintf(str, "%s.pop.check", conf->ofprefix.c_str()); ofstream pout(str); pout.precision(10); WritePopulationCheckpoint(pout); pout.close(); //if we are keeping track of swaps, write a checkpoint for that if(conf->uniqueSwapBias != ONE_POINT_ZERO){ sprintf(str, "%s.swaps.check", conf->ofprefix.c_str()); ofstream sout(str); Tree::attemptedSwaps.WriteSwapCheckpoint(sout); sout.close(); } } */ //#define OLD_CHECK #ifdef OLD_CHECK void Population::WriteStateFiles(){ char name[100]; //write the adaptation info checkpoint in binary format sprintf(name, "%s.adap.check", conf->ofprefix.c_str()); #ifdef BOINC char physical_name[256]; boinc_resolve_filename(name, physical_name, sizeof(physical_name)); MFILE out; out.open(physical_name, "wb"); #else ofstream out(name, ios::out | ios::binary); #endif adap->WriteToCheckpoint(out); out.close(); //write the state of the population, including the seed, generation, elapsed time, //lastTopoImprove and specifications of the current individuals sprintf(name, "%s.pop.check", conf->ofprefix.c_str()); #ifdef BOINC MFILE pout; boinc_resolve_filename(name, physical_name, sizeof(physical_name)); pout.open(physical_name, "wb"); #else ofstream pout(name, ios::out | ios::binary); #endif WritePopulationCheckpoint(pout); pout.close(); //if we are keeping track of swaps, write a checkpoint for that if(conf->uniqueSwapBias != ONE_POINT_ZERO){ sprintf(name, "%s.swaps.check", conf->ofprefix.c_str()); #ifdef BOINC MFILE sout; boinc_resolve_filename(name, physical_name, sizeof(physical_name)); sout.open(physical_name, "wb"); #else ofstream sout(name, ios::out | ios::binary); #endif Tree::attemptedSwaps.WriteSwapCheckpoint(sout); sout.close(); } } #else void Population::WriteStateFiles(){ char aname[128]; char pname[128]; char sname[128]; sprintf(aname, "%s.adap.check", conf->ofprefix.c_str()); sprintf(pname, "%s.pop.check", conf->ofprefix.c_str()); sprintf(sname, "%s.swaps.check", conf->ofprefix.c_str()); //1. write the adaptation info checkpoint in binary format //2. write the state of the population, including the seed, generation, elapsed time, // lastTopoImprove and specifications of the current individuals //3. if we are keeping track of swaps, write a checkpoint for that #ifdef BOINC //The BOINC provided MFILE class handily allows writing to it before it is actually //open and attached to any file. It buffers the information, and flushes it upon closing //of the file. This is good, because all of the checkpoint information can be gathered //and then written all at once, making it less likely that the checkpoint will be corrupted //by the program being terminated mid-checkpoint MFILE aout; MFILE pout; MFILE sout; adap->WriteToCheckpoint(aout); WritePopulationCheckpoint(pout); if(conf->uniqueSwapBias != ONE_POINT_ZERO) Tree::attemptedSwaps.WriteSwapCheckpoint(sout); char aphysical_name[512]; char pphysical_name[512]; char sphysical_name[512]; boinc_resolve_filename(aname, aphysical_name, sizeof(aphysical_name)); boinc_resolve_filename(pname, pphysical_name, sizeof(pphysical_name)); boinc_resolve_filename(sname, sphysical_name, sizeof(sphysical_name)); aout.open(aphysical_name, "wb"); aout.close(); pout.open(pphysical_name, "wb"); pout.close(); if(conf->uniqueSwapBias != ONE_POINT_ZERO){ sout.open(sphysical_name, "wb"); sout.close(); } boinc_checkpoint_completed(); #else //it would be nice to be able to do something like what is done for BOINC above (buffering output and //then writing all at once), maybe with a stringstream, but I couldn't get that to work ofstream aout(aname, ios::out | ios::binary); adap->WriteToCheckpoint(aout); aout.close(); ofstream pout(pname, ios::out | ios::binary); WritePopulationCheckpoint(pout); pout.close(); if(conf->uniqueSwapBias != ONE_POINT_ZERO){ ofstream sout(sname, ios::out | ios::binary); Tree::attemptedSwaps.WriteSwapCheckpoint(sout); sout.close(); } #endif } #endif //Returns whether or not checkpoints were actually found and read bool Population::ReadStateFiles(){ char name[100]; //read the adaptation binary checkpoint sprintf(name, "%s.adap.check", conf->ofprefix.c_str()); FILE *in; #ifdef BOINC char physical_name[100]; boinc_resolve_filename(name, physical_name, sizeof(physical_name)); in = boinc_fopen(physical_name, "rb"); #else if(FileExists(name) == false){ #if defined(SUBROUTINE_GARLI) || defined(OLD_SUBROUTINE_GARLI) //for the MPI version we don't care if checkpoint files weren't found return false; #else throw(ErrorException("Could not find checkpoint file %s!\nEither the previous run was not writing checkpoints (checkpoint = 0),\nthe checkpoint files were moved/deleted or the ofprefix setting\nin the config file was changed.", name)); #endif } in = fopen(name, "rb"); #endif adap->ReadFromCheckpoint(in); fclose(in); //Read the population checkpoint ReadPopulationCheckpoint(); //need to reset these here, although really only because asserts check that the values never decrease rep_fraction_done = tot_fraction_done = 0.0; #ifdef BOINC boinc_fraction_done(tot_fraction_done); #endif //Read the swap checkpoint, if necessary if(conf->uniqueSwapBias != ONE_POINT_ZERO){ sprintf(name, "%s.swaps.check", conf->ofprefix.c_str()); FILE *sin; #ifdef BOINC boinc_resolve_filename(name, physical_name, sizeof(physical_name)); sin = boinc_fopen(physical_name, "rb"); #else if(FileExists(name) == false) throw(ErrorException("Could not find checkpoint file %s!\nEither the previous run was not writing checkpoints (checkpoint = 0),\nthe file was moved/deleted or the ofprefix setting\nin the config file was changed.", name)); sin = fopen(name, "rb"); #endif Tree::attemptedSwaps.ReadBinarySwapCheckpoint(sin); fclose(sin); } return true; } /* void Population::WritePopulationCheckpoint(ofstream &out) { long currentSeed = rnd.seed(); out.write((char*) ¤tSeed, sizeof(currentSeed)); long currentTime = stopwatch.SplitTime(); out.write((char*) ¤tTime, sizeof(currentTime)); //7/13/07 changing this to calculate the actual size of the chunk of scalars //(the number of bytes between the start of the object and the first nonscalar //data member) rather than counting the number of each type and adding it up //manually. This should make it work irrespective of things like memory padding //for data member alignment, which could vary between platforms and compilers intptr_t scalarSize = (intptr_t) &fraction_done - (intptr_t) this + sizeof(fraction_done); out.write((char*) this, (streamsize) scalarSize); for(unsigned i=0;iOutputBinaryFormattedModel(out); indiv[i].treeStruct->OutputBinaryFormattedTree(out); } } */ void Population::WritePopulationCheckpoint(OUTPUT_CLASS &out) { assert(!timeTermination && !userTermination); long currentSeed = rnd.seed(); out.WRITE_TO_FILE(¤tSeed, sizeof(currentSeed), 1); int currentTime = stopwatch.SplitTime(); out.WRITE_TO_FILE(¤tTime, sizeof(currentTime), 1); //7/13/07 changing this to calculate the actual size of the chunk of scalars //(the number of bytes between the start of the object and the first nonscalar //data member) rather than counting the number of each type and adding it up //manually. This should make it work irrespective of things like memory padding //for data member alignment, which could vary between platforms and compilers intptr_t scalarSize = (intptr_t) &rep_fraction_done - (intptr_t) this + sizeof(rep_fraction_done); out.WRITE_TO_FILE(this, (streamsize) scalarSize, 1); //save the current members of the population for(unsigned i=0;iOutputBinaryFormattedTree(out); } //write any individuals that we may have stored from previous search reps for(vector::iterator it = storedTrees.begin(); it != storedTrees.end() ; it++){ (*it)->modPart.WriteModelPartitionCheckpoint(out); (*it)->treeStruct->OutputBinaryFormattedTree(out); } } void Population::ReadPopulationCheckpoint(){ char str[100]; sprintf(str, "%s.pop.check", conf->ofprefix.c_str()); if(FileExists(str) == false) throw(ErrorException("Could not find checkpoint file %s!\nEither the previous run was not writing checkpoints (checkpoint = 0),\nthe file was moved/deleted or the ofprefix setting\nin the config file was changed.", str)); #ifdef BOINC char physical_name[100]; boinc_resolve_filename(str, physical_name, sizeof(physical_name)); FILE *pin = boinc_fopen(physical_name, "rb"); #else FILE *pin = fopen(str, "rb"); #endif long seed; fread((char *) &seed, sizeof(seed), 1, pin); if(ferror(pin) || feof(pin)){//this mainly checks for a zero-byte file throw ErrorException("Error reading checkpoint file %s.\n\tA problem may have occured writing the file to disk, or the file may have been overwritten or truncated.\n\tUnfortunately you'll need to start the run again from scratch.", str); } rnd.set_seed(seed); int t; fread((char *) &t, sizeof(t), 1, pin); stopwatch.AddPreviousTime(t); //7/13/07 changing this to calculate the actual size of the chunk of scalars //(the number of bytes between the start of the object and the first nonscalar //data member) rather than counting the number of each type and adding it up //manually. This should make it work irrespective of things like memory padding //for data member alignment, which could vary between platforms and compilers intptr_t scalarSize = (intptr_t) &rep_fraction_done - (intptr_t) this + sizeof(rep_fraction_done); fread(this, scalarSize, 1, pin); //if were restarting a bootstrap run we need to change to the bootstrapped data //now, so that scoring below is correct if(conf->bootstrapReps > 0){ int s = dataPart->BootstrapReweight(lastBootstrapSeed, conf->resampleProportion); //this should be the case because what was written to the checkpoint for nextBootstrapSeed //should have come out of BootstrapReweight when it was originally called with lastBootstrapSeed assert(s == nextBootstrapSeed); } for(unsigned i=0;iSetDefaultModelSetParameters(dataPart->GetSubset(m)); } indiv[i].modPart.ReadModelPartitionCheckpoint(pin); indiv[i].treeStruct = new Tree(); indiv[i].treeStruct->ReadBinaryFormattedTree(pin); indiv[i].treeStruct->AssignCLAsFromMaster(); indiv[i].treeStruct->modPart=&indiv[i].modPart; indiv[i].SetDirty(); indiv[i].treeStruct->root->CheckTreeFormation(); indiv[i].CalcFitness(0); } //if we are doing multiple reps, there should have been one tree per completed rep written to file //remember that currentSearchRep starts at 1 for(int i=1;i<(finishedRep == false ? currentSearchRep : currentSearchRep+1);i++){ Individual *ind = new Individual; for(int m = 0;m < modSpecSet.NumSpecs();m++){ //it would make more sense to have this happen at a lower level, but the data are needed ind->modPart.GetModelSet(m)->SetDefaultModelSetParameters(dataPart->GetSubset(m)); } ind->modPart.ReadModelPartitionCheckpoint(pin); ind->treeStruct = new Tree(); ind->treeStruct->ReadBinaryFormattedTree(pin); ind->treeStruct->AssignCLAsFromMaster(); ind->treeStruct->modPart=&ind->modPart; ind->SetDirty(); ind->treeStruct->root->CheckTreeFormation(); ind->CalcFitness(0); ind->treeStruct->RemoveTreeFromAllClas(); storedTrees.push_back(ind); } //as far as the TopologyList is concerned, each individual will be considered different ntopos = total_size; if(fabs(bestFitness - indiv[bestIndiv].Fitness()) > 0.01) throw ErrorException("Problem reading checkpoint files. Scores of stored trees don't match calculated scores."); CalcAverageFitness(); globalBest = bestFitness; } //Depending on the generation, output to various files during the GA search void Population::WriteGenerationOutput(){ if(conf->outputMostlyUselessFiles) OutputFate(); if(conf->logevery > 0 && !(gen % conf->logevery)) OutputLog(); if(conf->saveevery > 0 && !(gen % conf->saveevery)){ if(best_output & WRITE_CONTINUOUS){ string outname = besttreefile; outname += ".current"; WriteTreeFile( outname.c_str(), -1); } outman.UserMessageNoCR("%-8d %-14.4f %-9.3f %6d ", gen, BestFitness(), adap->branchOptPrecision, lastTopoImprove); if(swapBasedTerm) outman.UserMessageNoCR("%14d ", indiv[bestIndiv].treeStruct->attemptedSwaps.GetUnique()); if(conf->reportRunProgress) outman.UserMessageNoCR("%14.2f %14.2f", 0.01 * (int) ceil(rep_fraction_done * 100), 0.01 * (int) ceil(tot_fraction_done * 100)); outman.UserMessage(""); if(conf->outputMostlyUselessFiles){ swapLog << gen << "\t"; indiv[bestIndiv].treeStruct->attemptedSwaps.SwapReport(swapLog); } } } void Population::Run(){ // calcCount=0; optCalcs=0; #ifdef VARIABLE_OPTIMIZATION // var << "type\tdist\tinitlnL\tnoBail.01\tnoBail.5\t3B.01\t3B.5\tdef.01\tdefdef\n"; #endif /* if(conf->restart == false){ if(conf->bootstrapReps == 0) outman.UserMessage("Running Genetic Algorithm with initial seed=%d", rnd.init_seed()); } else{ outman.UserMessage("Restarting Genetic Algorithm from checkpoint"); outman.UserMessage("generation %d, seed %d, best lnL %.3f", gen, rnd.init_seed(), BestFitness()); } */ #ifdef MAC_FRONTEND NSAutoreleasePool *pool = [[NSAutoreleasePool alloc] init]; [[MFEInterfaceClient sharedClient] didBeginRun]; [pool release]; #endif CalcAverageFitness(); outman.precision(6); outman.UserMessageNoCR("%-8s %-14s %-8s %-14s ", "gen", "current_lnL", "precision", "last_tree_imp"); if(swapBasedTerm) outman.UserMessageNoCR("%-14s ", "swaps_on_cur"); if(conf->reportRunProgress) outman.UserMessageNoCR("%-14s %-14s", "rep_prop_done", "tot_prop_done"); outman.UserMessage(""); outman.UserMessageNoCR("%-8d %-14.4f %-9.3f %6d ", gen, BestFitness(), adap->branchOptPrecision, lastTopoImprove); if(swapBasedTerm) outman.UserMessageNoCR("%14d ", indiv[bestIndiv].treeStruct->attemptedSwaps.GetUnique()); if(finishedGenerations == false) UpdateFractionDone(2); if(conf->reportRunProgress) outman.UserMessageNoCR("%14.2f %14.2f", 0.01 * (int) ceil(rep_fraction_done * 100), 0.01 * (int) ceil(tot_fraction_done * 100)); outman.UserMessage(""); OutputLog(); if(conf->outputMostlyUselessFiles) OutputFate(); gen++; for (; gen < conf->stopgen+1; ++gen){ //this is set true if the generation loop was exited normally but final optimization was not done if(finishedGenerations) break; NextGeneration(); UpdateFractionDone(2); if(swapBasedTerm){ if(uniqueSwapTried){ lastUniqueSwap = gen; uniqueSwapTried = false; } } keepTrack(); WriteGenerationOutput(); #ifndef BOINC userTermination = CheckForUserSignal(); if(userTermination){ outman.UserMessage("NOTE: ****Run terminated by user interuption ..."); break; } #endif #ifdef PERIODIC_SCORE_DEBUG if(gen % 500 == 0 ||gen==1) OutputFilesForScoreDebugging(&indiv[bestIndiv], tempGlobal++); #endif #ifdef NNI_SPECTRUM if(gen % 1000 == 0 || gen==1) NNISpectrum(bestIndiv); #endif if(!(gen%adap->intervalLength)){ outman.precision(10); bool reduced=false; if(gen-(max(lastTopoImprove, lastPrecisionReduction)) >= adap->intervalsToStore*adap->intervalLength){ //this allows the program to bail if numPrecReductions < 0, which can be handy to get to this point //with checkpointing in and then restart from the same checkpoint with various values of numPrecReductions if(adap->numPrecReductions < 0) return; reduced=adap->ReducePrecision(); } //optimize params if we just reduced prec or if we are at the min prec and we've run for a while since the last reduction if(reduced || ((gen - lastPrecisionReduction >= (adap->intervalLength * 50)) && (gen % (adap->intervalLength * 50) == 0) && (FloatingPointEquals(adap->branchOptPrecision, conf->minOptPrec, 1.0e-8)))){ if(reduced){ lastPrecisionReduction=gen; outman.UserMessage("Optimization precision reduced "); } //Added in this optimization of rate het params at prec reduction, //mainly to help with optimization in partitioned models FLOAT_TYPE improve = 0.0; Tree *bestTree = indiv[bestIndiv].treeStruct; for(int modnum = 0;modnum < indiv[bestIndiv].modPart.NumModels();modnum++){ Model *mod = indiv[bestIndiv].modPart.GetModel(modnum); const ModelSpecification *modSpec = mod->GetCorrespondingSpec(); if(modSpec->IsCodon())//optimize omega even if there is only 1 improve += bestTree->OptimizeOmegaParameters(adap->branchOptPrecision, modnum); else if(mod->NRateCats() > 1){ if(modSpec->IsFlexRateHet()){//Flex rates improve += bestTree->OptimizeFlexRates(adap->branchOptPrecision, modnum); } else if(modSpec->fixAlpha == false){//normal gamma improve += bestTree->OptimizeBoundedParameter(modnum, adap->branchOptPrecision, mod->Alpha(), 0, min(mod->Alpha(), 0.05), 999.9, &Model::SetAlpha); } } if(modSpec->includeInvariantSites && !modSpec->fixInvariantSites) improve += bestTree->OptimizeBoundedParameter(modnum, adap->branchOptPrecision, mod->PropInvar(), 0, min(mod->PropInvar(), 1.0e-8), mod->maxPropInvar, &Model::SetPinv); if(modSpec->IsCodon() == false && modSpec->fixStateFreqs == false && modSpec->IsEqualStateFrequencies() == false && modSpec->IsEmpiricalStateFrequencies() == false){ FLOAT_TYPE paramOpt = bestTree->OptimizeEquilibriumFreqs(adap->branchOptPrecision, modnum); if(paramOpt < ZERO_POINT_ZERO && paramOpt > -1e-8)//avoid printing very slightly negative values paramOpt = ZERO_POINT_ZERO; improve += paramOpt; outman.DebugMessage("eq freq opt = %.4f", paramOpt); } if(modSpec->fixRelativeRates == false && modSpec->Nst() > 1 && modSpec->IsAminoAcid() == false){ FLOAT_TYPE paramOpt = bestTree->OptimizeRelativeNucRates(adap->branchOptPrecision, modnum); if(paramOpt < ZERO_POINT_ZERO && paramOpt > -1e-8)//avoid printing very slightly negative values paramOpt = ZERO_POINT_ZERO; improve += paramOpt; outman.DebugMessage("rel rates opt = %.4f", paramOpt); } if(modSpec->IsEstimateAAMatrix() || (modSpec->IsTwoSerineRateMatrix() && !modSpec->fixRelativeRates)){ FLOAT_TYPE paramOpt = bestTree->OptimizeRelativeNucRates(adap->branchOptPrecision, modnum); if(paramOpt < ZERO_POINT_ZERO && paramOpt > -1e-8)//avoid printing very slightly negative values paramOpt = ZERO_POINT_ZERO; improve += paramOpt; outman.DebugMessage("rel rates opt = %.4f", paramOpt); } if(modSpec->IsOrientedGap()){ FLOAT_TYPE paramOpt = bestTree->OptimizeInsertDeleteRates(adap->branchOptPrecision, modnum); if(paramOpt < ZERO_POINT_ZERO && paramOpt > -1e-8)//avoid printing very slightly negative values paramOpt = ZERO_POINT_ZERO; improve += paramOpt; outman.DebugMessage("ins/del opt = %.4f", paramOpt); } } if(modSpecSet.InferSubsetRates()){ improve += bestTree->OptimizeSubsetRates(adap->branchOptPrecision); } if(!(FloatingPointEquals(adap->modWeight, 0.0, 1e-8))) outman.UserMessage(" Optimizing parameters... improved %8.3f lnL", improve); ///// FLOAT_TYPE before=bestFitness; //under some conditions (very steep lopsided likelihood curve for branch lengths) //the blen opt can actually make the score worse bestTree->OptimizeAllBranches(adap->branchOptPrecision); indiv[bestIndiv].SetDirty(); CalcAverageFitness(); FLOAT_TYPE bImp = bestFitness - before - improve; if(bImp < ZERO_POINT_ZERO && bImp > -1e-4)//avoid printing very slightly negative values bImp = ZERO_POINT_ZERO; outman.UserMessage(" Optimizing branchlengths... improved %8.3f lnL", bImp); //This is important so that new better topos can be properly identified in the next gen! adap->lastgenscore = BestFitness(); } //automatic termination conditions if(conf->enforceTermConditions == true){ if(swapBasedTerm && !FloatingPointEquals(adap->topoMutateProb, ZERO_POINT_ZERO, max(1.0e-8, GARLI_FP_EPS * 2.0))){ assert(swapTermThreshold != 0); if(swapTermThreshold < 0 && (gen - lastUniqueSwap > abs(swapTermThreshold))){ break; } else { if(swapTermThreshold > 0 && (gen - lastUniqueSwap > swapTermThreshold) && (gen-max(lastTopoImprove, lastPrecisionReduction) > conf->lastTopoImproveThresh || FloatingPointEquals(adap->topoMutateProb, ZERO_POINT_ZERO, max(1.0e-8, GARLI_FP_EPS * 2.0))) && (gen > adap->intervalsToStore * adap->intervalLength) && adap->improveOverStoredIntervals < conf->improveOverStoredIntervalsThresh && (FloatingPointEquals(adap->branchOptPrecision, adap->minOptPrecision, max(1.0e-8, GARLI_FP_EPS * 2.0)) || adap->numPrecReductions==0)){ if(adap->topoMutateProb > ZERO_POINT_ZERO) outman.UserMessage("Reached termination condition!\nlast topological improvement at gen %d", lastTopoImprove); else outman.UserMessage("Reached termination condition!\n"); outman.UserMessage("Improvement over last %d gen = %.5f", adap->intervalsToStore*adap->intervalLength, adap->improveOverStoredIntervals); outman.UserMessage("Last new topology swap at gen %d", lastUniqueSwap); break; } } } else{ if((gen-max(lastTopoImprove, lastPrecisionReduction) > conf->lastTopoImproveThresh || FloatingPointEquals(adap->topoMutateProb, ZERO_POINT_ZERO, max(1.0e-8, GARLI_FP_EPS * 2.0))) && (gen > adap->intervalsToStore * adap->intervalLength) && (adap->improveOverStoredIntervals < conf->improveOverStoredIntervalsThresh) && (FloatingPointEquals(adap->branchOptPrecision, adap->minOptPrecision, max(1.0e-8, GARLI_FP_EPS * 2.0)) || adap->numPrecReductions==0)){ if(adap->topoMutateProb > ZERO_POINT_ZERO) outman.UserMessage("Reached termination condition!\nlast topological improvement at gen %d", lastTopoImprove); else outman.UserMessage("Reached termination condition!\n"); outman.UserMessage("Improvement over last %d gen = %.5f", adap->intervalsToStore*adap->intervalLength, adap->improveOverStoredIntervals); break; } } } #ifdef INCLUDE_PERTURBATION CheckPerturbSerial(); #endif } if(ShouldCheckpoint(true) == true) WriteStateFiles(); if(stopwatch.ThisExecutionSplitTime() > conf->stoptime){ outman.UserMessage("NOTE: ****Specified time limit (%d seconds) reached...", conf->stoptime); //Time termination can be used a sort of "pause" along with checkpointing. Checkpoints may be //written very infrequently though (large saveevery), so spit one out now. //Always do this in BOINC mode. #ifndef BOINC if(conf->checkpoint) #endif WriteStateFiles(); timeTermination = true; break; } if(gen == conf->stopgen){ //stopgen is essentially a stopping condition, treated just like the flexible automated criterion. outman.UserMessage("NOTE: ****Specified generation limit (%d) reached...", conf->stopgen); genTermination = true; } #ifdef INCLUDE_PERTURBATION if(pertMan->pertAbandoned==true && pertMan->restartAfterAbandon==true && (gen - pertMan->lastPertGeneration > pertMan->gensBeforeRestart)){ params->starting_tree=""; pertMan->lastPertGeneration=gen; pertMan->pertAbandoned=false; pertMan->numPertsNoImprove=0; bestSinceRestart.SetFitness(-1e100); if(BestFitness() > allTimeBest.Fitness()) StoreAllTimeBest(); SeedPopulationWithStartingTree(); outman.UserMessage("restarting ...."); } #endif } //Allow killing during FinalOpt TurnOffSignalCatching(); //checkpoint immediately before final opt, if finishedGenerations isn't set, which will //indicate that we've already written and restarted from a checkpoint written here if(!(finishedGenerations || timeTermination || userTermination)){ //this will indicate that we've finished the loop over generations through automatic means //and that we are just before finalOpt (i.e., finishedGenerations is true but finishedRep is //false). This will be critical for restarting from a checkpoint written just before finalOpt if(!(timeTermination || userTermination)) finishedGenerations = true; #ifndef BOINC //non-BOINC checkpointing if(ShouldCheckpoint(false)) #endif WriteStateFiles(); if(conf->workPhaseDivision){ //second workphasedivision exit WriteStateFiles(); outman.UserMessage("\nNOTE: Terminating run before final optimization and writing checkpoint"); outman.UserMessage("because workphasedivision configuration entry was set."); workPhaseTermination = true; return; } } //don't optimize if checkpointing is happening and the run was prematurely killed if(conf->refineEnd && !(conf->checkpoint && (timeTermination || userTermination))){ UpdateFractionDone(3); BetterFinalOptimization(); finishedRep = true; //finishedRep must be true for the following two functions to output correctly OutputLog(); if(conf->outputTreelog && treeLog.is_open()) AppendTreeToTreeLog(-1); } //outman.UserMessage("Maximum # clas used = %d out of %d", claMan->MaxUsedClas(), claMan->NumClas()); //outman.UserMessage("%d conditional likelihood calculations\n%d branch optimization passes", calcCount, optCalcs); UpdateFractionDone(4); } FLOAT_TYPE Population::GenerationFractionDone(){ //This just pulls out some complicated (and ad hoc) code out of UpdateFractionDone that assigns a proportion done //to a point during the search (generation) phase of a run. bool willReduce = (FloatingPointEquals(adap->startOptPrecision, adap->minOptPrecision, 1e-6) == false) && (adap->numPrecReductions > 0); //the 0.45 here is for rounding purposes. Don't want to round down if precReductionFactor ends up being //slightly more than the diff due to floating point rep int reduction_number = willReduce ? (int) (0.45 + ((adap->startOptPrecision - adap->branchOptPrecision) / adap->precReductionFactor)) : 0; int remaining_reductions = willReduce ? adap->numPrecReductions - reduction_number : 0; double evalInterval = (adap->intervalLength * adap->intervalsToStore); FLOAT_TYPE genProportionDone = 0.0; //these should add up to one const FLOAT_TYPE preReduceSplit = 0.2; const FLOAT_TYPE duringReduceSplit = 0.5; const FLOAT_TYPE postReduceSplit = 0.3; if(willReduce && remaining_reductions == adap->numPrecReductions){ //We've done a decent number of gen, but haven't yet reduced the prec. This will be linear until the minimum //possible number of generations before a prec reduction could happen have passed, thereafter it will be asymptotic FLOAT_TYPE linearProportion = 0.5; if(gen <= evalInterval) genProportionDone = preReduceSplit * linearProportion * (gen / evalInterval); else genProportionDone = preReduceSplit * (linearProportion + (1.0 - linearProportion) * (1.0 - (evalInterval / (FLOAT_TYPE) gen))); } else if(willReduce && remaining_reductions > 0){ //during reduction phase - divide evenly among the precision reductions FLOAT_TYPE perReduction = duringReduceSplit / ((FLOAT_TYPE) adap->numPrecReductions - 1.0); //between one reduction and the next, linear, then asymptotic FLOAT_TYPE sinceLastReduction = gen - lastPrecisionReduction; FLOAT_TYPE linearProportion = 0.5; genProportionDone = preReduceSplit + (perReduction * (reduction_number - 1)); if(sinceLastReduction <= evalInterval) //genProportionDone +=((reduction_number - 1) * perReduction) + (duringReduceSplit * perReduction * ((FLOAT_TYPE) sinceLastReduction / evalInterval)); genProportionDone += perReduction * linearProportion * ((FLOAT_TYPE) sinceLastReduction / evalInterval); else //genProportionDone += ((reduction_number - 1) * perReduction) + (duringReduceSplit * perReduction) + (1.0 - duringReduceSplit) * perReduction * (1.0 - (evalInterval / (FLOAT_TYPE) sinceLastReduction)); genProportionDone += perReduction * (linearProportion + (1.0 - linearProportion) * (1.0 - (evalInterval / (FLOAT_TYPE) sinceLastReduction))); } else if(remaining_reductions == 0){ //this is linear until we get past the absolute minimum point that the run could have finished FLOAT_TYPE sinceLastReduction = gen - lastPrecisionReduction; //the chance of going over the minimum # gen in the last phase is small with lower # of taxa, which makes for a big jump //in proportion because the asymptotic phase isn't entered at all. Scale the proportion where the asymp phase starts //with the # of taxa double linearProportion = max(0.5, 0.9 - 0.10 * (dataPart->NTax() / 50.0)); genProportionDone = preReduceSplit + duringReduceSplit; if(sinceLastReduction <= conf->lastTopoImproveThresh){ genProportionDone += postReduceSplit * linearProportion * (sinceLastReduction / (FLOAT_TYPE) conf->lastTopoImproveThresh); } //thereafter it is conservatively asymptotic else{ assert( (1.0 - (conf->lastTopoImproveThresh / (FLOAT_TYPE) sinceLastReduction)) >= 0.0); genProportionDone += postReduceSplit * (linearProportion + (1.0 - linearProportion) * min(1.0, (1.0 - (conf->lastTopoImproveThresh / (FLOAT_TYPE) sinceLastReduction)))); } } assert(genProportionDone >= 0.0 && genProportionDone <= 1.0); return genProportionDone; } void Population::UpdateFractionDone(int phase){ #ifndef BOINC if(!conf->reportRunProgress) return; #endif //Update the proportion done. This is mainly for BOINC, but might be used elsewhere. // //-This uses the "search phase" and other info to automatically determine the progress using a fairly arbitrary algorithm. //-The fractions done are members of Population (rep_fraction_done and tot_fraction_done), so values will be maintained // until updated, and stored in checkpoints. //-The fractions should never reduce during the course of a run, or after restarting from checkpoint. //-It is generally not clear when various phases will end, so the intent is to have a portion of each phase increase the // fraction linearly, and then switch to an assymptotic approach to some value. //-The searchphasedivision setting complicates things a bit. In this case the run terms immediately after initial opt, // and immediately before final opt. During the generation cycle it will generally be time limited, but doesn't need // to be. During each of those phases it will be assumed that the whole fraction done refers to just that phase, and // that upon restarting from checkpoint the fraction done will reset, as opposed to continuing from the previous value. //phases: //0 - data has been read and everything allocated OR a search has just started a new replicate //1 - during initial optimization, based on optimization pass //2 - during generation cycle, this can be difficult, because no real a priori way to know if run will (or is intended to) // stop due to stoptime, stopgen or auto termination. So, will use the max of those. // The calculations for fraction done assuming autotermination also define these phases: // pre-reduction - before first precision reduction // reduction - while reductions are happening // terminal - remaining gens after min prec reached //3 - final opt, based on optimization pass //4 - a replicate is entirely done //Called from: //0 - if not restart at start of loop over reps in PerformSearch (reset rep_fraction_done) //1 FLOAT_TYPE afterSetup = 0.05; FLOAT_TYPE beforeTerm = 0.95; FLOAT_TYPE workDuration = beforeTerm - afterSetup; //under normal conditions, these should add up to 1.0, and divide workDuration into sub-intervals FLOAT_TYPE initialOptProportionOfWork = -1.0; FLOAT_TYPE finalOptProportionOfWork = -1.0; FLOAT_TYPE genProportionOfWork = -1.0; if(conf->workPhaseDivision){ //because in this mode it stops right after initial opt, and starts right before final initialOptProportionOfWork = finalOptProportionOfWork = genProportionOfWork = 1.0; } else{ initialOptProportionOfWork = 0.15; finalOptProportionOfWork = 0.15; genProportionOfWork = 1.0 - initialOptProportionOfWork - finalOptProportionOfWork; } FLOAT_TYPE newFract = -1.0; FLOAT_TYPE newRepFract = -1.0; if(phase == 0){ //This will reset for the start of a new rep. The fraction of completion contibuted by previous finished reps //will be included below. //newFract = afterSetup; rep_fraction_done = newRepFract = 0.0; } else if(phase == 1){ //during initial optimization FLOAT_TYPE linearUntilPass = 10; FLOAT_TYPE linearProportion = 0.5; if(initialRefinePass < 0) //this means that initial opt has completed newRepFract = initialOptProportionOfWork; else if(initialRefinePass <= linearUntilPass) newRepFract = initialOptProportionOfWork * linearProportion * min(1.0, (initialRefinePass / linearUntilPass)); else newRepFract = initialOptProportionOfWork * (linearProportion + (1.0 - linearProportion) * max(0.0, 1.0 - linearUntilPass / (FLOAT_TYPE) initialRefinePass)); } else if(phase == 2){ //during generations newRepFract = conf->workPhaseDivision == true ? 0.0 : initialOptProportionOfWork; //No real way of knowing what will cause termination of this run. Choose the max. //In the case of time term it will "reset" upon a restart from checkpoint, which might //cause odd changes in the fraction done in some cases, but whatever. FLOAT_TYPE timeFract = min(1.0, (FLOAT_TYPE) stopwatch.ThisExecutionSplitTime() / conf->stoptime); FLOAT_TYPE genLimitFract = min(1.0, (FLOAT_TYPE) gen / conf->stopgen); FLOAT_TYPE genFract = conf->enforceTermConditions ? GenerationFractionDone() : 0.0; //An odd case can happen here, where the generation phase is nearly done (giving high fraction done) //but a run is likely to be time limited, and after a restart the generation fraction calculation //will still be very high. That would make the fraction done immediately upon restart to be very high //and to just hang there for a long time. So, to be conservative downweight the generation fraction if the //stoptime is < 5 hr. if(conf->stoptime < 5 * 60 * 60) newRepFract += genProportionOfWork * max(max(timeFract, genLimitFract), genFract * 0.5); else newRepFract += genProportionOfWork * max(max(timeFract, genLimitFract), genFract); } else if(phase == 3){ //during final optimization FLOAT_TYPE linearUntilPass = 20; FLOAT_TYPE linearProportion = 0.5; newRepFract = conf->workPhaseDivision == true ? 0.0 : initialOptProportionOfWork + genProportionOfWork; if(finalRefinePass <= linearUntilPass) newRepFract += finalOptProportionOfWork * linearProportion * min(1.0, finalRefinePass / linearUntilPass); else newRepFract += finalOptProportionOfWork * (linearProportion + (1.0 - linearProportion) * max(0.0, 1.0 - linearUntilPass / finalRefinePass)); } else if(phase == 4){ newRepFract = 1.0; } //outman.DebugMessage("newrep, oldrep: %f, %f, %d", newRepFract, rep_fraction_done, phase); assert(newRepFract >= 0.0 && newRepFract <= 1.0); assert(newRepFract + 0.00001 >= rep_fraction_done); //Now, prorate the newRepFract, which corresponds to the work portion of a rep and ranges from 0.0 to 1.0 if(newFract < 0.0){ //In searchphasereduction, don't prorate, since each segment makes up its own 0-1 fraction if(conf->workPhaseDivision) newFract = afterSetup + workDuration * newRepFract; else{ int totSearches = conf->searchReps * (conf->bootstrapReps > 0 ? conf->bootstrapReps : 1); int curSearch = currentSearchRep + (currentBootstrapRep > 0 ? currentBootstrapRep - 1 : 0) * conf->searchReps; FLOAT_TYPE perRepProportion = 1.0 / totSearches; FLOAT_TYPE completedRepProportion = (curSearch - 1.0) * perRepProportion; newFract = afterSetup + workDuration * (completedRepProportion + (newRepFract * perRepProportion)); } } //outman.DebugMessage("newtot, oldtot: %f, %f, %d", newFract, tot_fraction_done, phase); assert(newFract >= 0.0 && newFract <= 1.0); assert(newFract + 0.00001 >= tot_fraction_done); rep_fraction_done = newRepFract; tot_fraction_done = newFract; #ifdef BOINC boinc_fraction_done(tot_fraction_done); #endif //outman.DebugMessage("newrep, newtot: %f, %f, %d", rep_fraction_done, tot_fraction_done, phase); } //this is a final opt adapted from final opt of trunk version 1.0 void Population::BetterFinalOptimization(){ outman.setf(ios::fixed); outman.precision(5); outman.UserMessage("Current score = %.4f", BestFitness()); #ifdef INCLUDE_PERTURBATION if(pertMan->ratcheted) TurnOffRatchet(); if(allTimeBest != NULL){ if(BestFitness() < allTimeBest->Fitness()){ RestoreAllTimeBest(); } } #endif //This was a little dangerous since any subsequent scoring of any of the trees would cause problems //probably not that important anyway. /* for(unsigned i=0;iRemoveTreeFromAllClas(); } */ outman.UserMessage("Performing final optimizations..."); #ifdef MAC_FRONTEND NSAutoreleasePool *pool = [[NSAutoreleasePool alloc] init]; [[MFEInterfaceClient sharedClient] didBeginBranchOptimization]; [pool release]; #endif FLOAT_TYPE incr; assert(finalRefinePass < 1); finalRefinePass = 1; double freqOptImprove, nucRateOptImprove, pinvOptImprove, alphaOptImprove, omegaOptImprove, flexOptImprove, subRateOpt, insDelOptImprove; double paramOpt, blenOptImprove; paramOpt = blenOptImprove = freqOptImprove = nucRateOptImprove = pinvOptImprove = alphaOptImprove = omegaOptImprove = flexOptImprove = subRateOpt = insDelOptImprove = ZERO_POINT_ZERO; FLOAT_TYPE precThisPass = max(adap->branchOptPrecision * pow(ZERO_POINT_FIVE, finalRefinePass), (FLOAT_TYPE)1e-10); FLOAT_TYPE paramPrecThisPass = max(adap->branchOptPrecision*0.1, 0.01); bool optAnyModel = FloatingPointEquals(conf->modWeight, ZERO_POINT_ZERO, 1e-8) == false; bool goingToExit; Individual *optInd = &indiv[bestIndiv]; Tree *optTree = optInd->treeStruct; adap->branchOptPrecision = min(adap->branchOptPrecision, 0.01); string blenS; do{ //during each pass we'll keep track of a few things // incr = total improvement this pass. this controls termination of opt // summed improvement for each param/blens since last output. If not outputting // every pass it won't be zeroed and so will accumulate. The output string // will be constructed each pass, but only output sometimes precThisPass = max(adap->branchOptPrecision * pow(ZERO_POINT_FIVE, finalRefinePass), (FLOAT_TYPE)1e-10); paramPrecThisPass = max(precThisPass, 1e-5); string outString; char temp[50]; FLOAT_TYPE passStart=optInd->Fitness(); //back up the current branch lengths in case something goes wrong in the blen optimization vector blens; optTree->StoreBranchlengths(blens); //remember that what is returned from OptAllBranches isn't the true increase in score, just an estimate incr=optTree->OptimizeAllBranches(precThisPass); optInd->CalcFitness(0); FLOAT_TYPE trueImprove= optInd->Fitness() - passStart; //In very rare cases the score can come out very slightly worse (or apparently worse due to numerical instability issues) after //optimizing all of the branches. In general this is taken care of at a lower level, but if it percolates up to here we'll ignore //the last set of changes and pretend they never happened. if(trueImprove < ZERO_POINT_ZERO){ outman.DebugMessage("OptimizeAllBranches worsened score by %f. Restoring previous branch lengths...", trueImprove); optTree->RestoreBranchlengths(blens); trueImprove = ZERO_POINT_ZERO; optInd->SetDirty(); optInd->CalcFitness(0); } blenOptImprove += trueImprove; incr = trueImprove; sprintf(temp, "(branch= %4.4f", blenOptImprove); blenS = temp; optInd->CalcFitness(0); //these strings will be overwritten each time one of the parameter types are optimized //always with the sum total of improvement due to that param, be it over models, passes, etc. //this means that each will only appear once, even in partitioned models string omegaS, alphaS, flexS, pinvS, freqsS, relRatesS, insDelS, subsetS; omegaS = alphaS = flexS = pinvS = freqsS = relRatesS = insDelS = subsetS = ""; for(int m = 0;m < indiv[bestIndiv].modPart.NumModels();m++){ Model *mod = indiv[bestIndiv].modPart.GetModel(m); const ModelSpecification *modSpec = mod->GetCorrespondingSpec(); bool optOmega, optAlpha, optFlex, optPinv, optFreqs, optRelRates, optInsDel; optOmega = optAlpha = optFlex = optPinv = optFreqs = optRelRates = optInsDel = false; if(modSpec->IsCodon() && ! modSpec->fixOmega) optOmega = true; else if(modSpec->numRateCats > 1 && !modSpec->IsCodon()){ if(modSpec->IsFlexRateHet()) optFlex = true; else if(modSpec->fixAlpha == false) optAlpha = true; } if(modSpec->includeInvariantSites && !modSpec->fixInvariantSites) optPinv = true; if(modSpec->IsCodon() == false && modSpec->fixStateFreqs == false && modSpec->IsEqualStateFrequencies() == false && modSpec->IsEmpiricalStateFrequencies() == false) optFreqs = true; //this is the case of forced freq optimization with codon models. For everything to work they must be set as both not fixed but empirical if(modSpec->IsCodon() && modSpec->fixStateFreqs == false && modSpec->IsEqualStateFrequencies() == false && modSpec->IsEmpiricalStateFrequencies() == true) optFreqs = true; if((modSpec->fixRelativeRates == false) && ((modSpec->Nst() > 1 && modSpec->IsAminoAcid() == false) || modSpec->IsEstimateAAMatrix() || modSpec->IsTwoSerineRateMatrix())) optRelRates = true; if(modSpec->IsOrientedGap()) optInsDel = true; //this is taken from the improved version in the trunk, and is a bit redundant in this context. //the output strings will be generated every time that any of the params are optimized, and will //then be updated the next time the same parameter type is optimized in a different model. The //last model to optimize a given param will make the correct string that will eventually get output if(optOmega) { paramOpt = optTree->OptimizeOmegaParameters(paramPrecThisPass, m); if(paramOpt < ZERO_POINT_ZERO && paramOpt > -1e-8)//avoid printing very slightly negative values paramOpt = ZERO_POINT_ZERO; omegaOptImprove += paramOpt; sprintf(temp, " omega= %4.4f", omegaOptImprove); omegaS = temp; incr += paramOpt; } if(optAlpha){ paramOpt = optTree->OptimizeBoundedParameter(m, paramPrecThisPass, mod->Alpha(), 0, min(0.05, mod->Alpha()), max(999.9, mod->Alpha()), &Model::SetAlpha); if(paramOpt < ZERO_POINT_ZERO && paramOpt > -1e-8)//avoid printing very slightly negative values paramOpt = ZERO_POINT_ZERO; alphaOptImprove += paramOpt; sprintf(temp, " alpha= %4.4f", alphaOptImprove); alphaS = temp; incr += paramOpt; } if(optFlex){ //Flex opt is tough, give it more passes if they are helping FLOAT_TYPE p = 0.0; paramOpt = 0.0; int innerPass = 0; do{ p = optTree->OptimizeFlexRates(paramPrecThisPass, m); paramOpt += p; }while(p > trueImprove && innerPass++ < 5); if(paramOpt < ZERO_POINT_ZERO && paramOpt > -1e-8)//avoid printing very slightly negative values paramOpt = ZERO_POINT_ZERO; flexOptImprove += paramOpt; sprintf(temp, " flex rates= %4.4f", flexOptImprove); flexS = temp; incr += paramOpt; } if(optPinv){ paramOpt = optTree->OptimizeBoundedParameter(m, paramPrecThisPass, mod->PropInvar(), 0, min(1.0e-8,mod->PropInvar()), mod->maxPropInvar, &Model::SetPinv); if(paramOpt < ZERO_POINT_ZERO && paramOpt > -1e-8)//avoid printing very slightly negative values paramOpt = ZERO_POINT_ZERO; pinvOptImprove += paramOpt; sprintf(temp, " pinv= %4.4f", pinvOptImprove); pinvS = temp; incr += paramOpt; } if(optFreqs){ paramOpt = optTree->OptimizeEquilibriumFreqs(paramPrecThisPass, m); if(paramOpt < ZERO_POINT_ZERO && paramOpt > -1e-8)//avoid printing very slightly negative values paramOpt = ZERO_POINT_ZERO; freqOptImprove += paramOpt; sprintf(temp, " eq freqs= %4.4f", freqOptImprove); freqsS = temp; incr += paramOpt; } if(optRelRates){ paramOpt = optTree->OptimizeRelativeNucRates(paramPrecThisPass, m); if(paramOpt < ZERO_POINT_ZERO && paramOpt > -1e-8)//avoid printing very slightly negative values paramOpt = ZERO_POINT_ZERO; nucRateOptImprove += paramOpt; sprintf(temp, " rel rates= %4.4f", nucRateOptImprove); relRatesS = temp; incr += paramOpt; } if(optInsDel){ paramOpt = optTree->OptimizeInsertDeleteRates(paramPrecThisPass, m); if(paramOpt < ZERO_POINT_ZERO && paramOpt > -1e-8)//avoid printing very slightly negative values paramOpt = ZERO_POINT_ZERO; insDelOptImprove += paramOpt; sprintf(temp, " ins/del rates= %4.4f", insDelOptImprove); insDelS = temp; incr += paramOpt; } optInd->CalcFitness(0); } if(modSpecSet.InferSubsetRates()){ paramOpt = indiv[bestIndiv].treeStruct->OptimizeSubsetRates(max(adap->branchOptPrecision*0.1, 0.001)); if(paramOpt < ZERO_POINT_ZERO && paramOpt > -1e-8)//avoid printing very slightly negative values paramOpt = ZERO_POINT_ZERO; subRateOpt += paramOpt; sprintf(temp, " subset rates= %4.4f", subRateOpt); subsetS = temp; paramOpt += subRateOpt; } optInd->CalcFitness(0); outString = blenS + omegaS + alphaS + flexS + pinvS + freqsS + relRatesS + insDelS + subsetS; goingToExit = !(incr > 1.0e-5 || precThisPass > 1.0e-4 || finalRefinePass < 10); UpdateFractionDone(3); if(finalRefinePass < 20 || (finalRefinePass % 10 == 0) || goingToExit){ if(finalRefinePass > 20 && (goingToExit || (finalRefinePass % 10 == 0))) outman.UserMessage(" optimization up to ..."); outman.UserMessageNoCR("pass %-2d: %.4f %s)", finalRefinePass, optInd->Fitness(), outString.c_str()); if(conf->reportRunProgress) outman.UserMessageNoCR(" %14.2f %14.2f", 0.01 * (int) ceil(rep_fraction_done * 100), 0.01 * (int) ceil(tot_fraction_done * 100)); outman.UserMessage(""); paramOpt = blenOptImprove = freqOptImprove = nucRateOptImprove = pinvOptImprove = alphaOptImprove = omegaOptImprove = flexOptImprove = subRateOpt = insDelOptImprove = ZERO_POINT_ZERO; } finalRefinePass++; }while(!goingToExit); finalRefinePass = -1; #ifdef PUSH_TO_MIN_BLEN double init = indiv[bestIndiv].treeStruct->lnL; int num = indiv[bestIndiv].treeStruct->PushBranchlengthsToMin(); indiv[bestIndiv].treeStruct->Score(); double aft = indiv[bestIndiv].treeStruct->lnL; double imp=indiv[bestIndiv].treeStruct->OptimizeAllBranches(precThisPass); indiv[bestIndiv].treeStruct->Score(); double fin = indiv[bestIndiv].treeStruct->lnL; outman.UserMessage("Looking for minimum length branches..."); indiv[bestIndiv].CalcFitness(0); outman.DebugMessage("%d branches pushed to min.\nScore after opt: %.9f\nScore after push: %.9f\nScore after reopt: %.9f", num, init, aft, fin); #endif outman.UserMessage("Final score = %.4f", indiv[bestIndiv].Fitness()); unsigned totalSecs = stopwatch.SplitTime(); unsigned secs = totalSecs % 60; totalSecs -= secs; unsigned min = (totalSecs % 3600)/60; totalSecs -= min * 60; unsigned hours = totalSecs / 3600; if(conf->searchReps == currentSearchRep && (conf->bootstrapReps == 0 || conf->bootstrapReps == currentBootstrapRep )) outman.UserMessage("Time used = %d hours, %d minutes and %d seconds", hours, min, secs); else outman.UserMessage("Time used so far = %d hours, %d minutes and %d seconds", hours, min, secs); log << "Score after final optimization: " << indiv[bestIndiv].Fitness() << endl; //not sure how this would be done partitioned /* if(modSpec.IsCodon()){ vector sProps; indiv[bestIndiv].treeStruct->mod->CalcSynonymousBranchlengthProportions(sProps); outman.UserMessage("Proportion of branchlengths that are Synonymous: %.5f", sProps[sProps.size()-1]); } */ #ifdef MAC_FRONTEND pool = [[NSAutoreleasePool alloc] init]; [[MFEInterfaceClient sharedClient] reportFinalScore:BestFitness()]; [pool release]; #endif outman.unsetf(ios::fixed); if(conf->outputTreelog && treeLog.is_open()) AppendTreeToTreeLog(-1); #ifdef ENABLE_CUSTOM_PROFILER char fname[100]; sprintf(fname, "%s.profileresults.log", conf->ofprefix.c_str()); #ifdef BOINC char physical_name[100]; boinc_resolve_filename(fname, physical_name, sizeof(physical_name)); ofstream prof(physical_name); //MFILE prof; //prof.open(physical_name, "w"); #else ofstream prof(fname); #endif /* //FROM WRITETREEFILE #ifdef BOINC char physical_name[100]; boinc_resolve_filename(fname, physical_name, sizeof(physical_name)); MFILE outf; outf.open(physical_name, "w"); #else ofstream outf; outf.open( filename.c_str() ); outf.precision(8); #endif //... #ifdef BOINC const char *s = trans.c_str(); outf.write(s, sizeof(char), trans.length()); s = str.c_str(); outf.write(s, sizeof(char), str.length()); theInd->treeStruct->root->MakeNewick(treeString, false, true); size_t len = strlen(treeString); outf.write(treeString, sizeof(char), len); str = ";\nend;\n"; s = str.c_str(); outf.write(s, sizeof(char), str.length()); #else outf << trans; outf << str; outf.setf( ios::floatfield, ios::fixed ); outf.setf( ios::showpoint ); theInd->treeStruct->root->MakeNewick(treeString, false, true); outf << treeString << ";\n"; outf << "end;\n"; #endif */ char str[256]; sprintf(str, "dataset: %s\tstart:%s\n", conf->datafname.c_str(), conf->streefname.c_str()); prof << "dataset: " << conf->datafname << "\t" << "start: " << conf->streefname << endl; prof << "seed: " << conf->randseed << "\t" << "refine: " << (conf->refineStart == true) << endl; prof << "start prec: " << conf->startOptPrec << "\t" << "final prec: " << adap->branchOptPrecision << endl; #ifdef SINGLE_PRECISION_FLOATS prof << "Single precision\n"; #else prof << "Double precision\n"; #endif unsigned s = stopwatch.SplitTime(); prof << "Total Runtime: " << s << "\tnumgen: " << gen << "\tFinalScore: " << indiv[bestIndiv].Fitness() << "\n"; outman.SetOutputStream(prof); indiv[bestIndiv].modPart.OutputHumanReadableModelReportWithParams(); prof << "Function\t\tcalls\ttime\tTperC\t%runtime" << endl; ProfIntInt.Report(prof, s); ProfIntTerm.Report(prof, s); ProfTermTerm.Report(prof, s); ProfRescale.Report(prof, s); ProfScoreInt.Report(prof, s); ProfScoreTerm.Report(prof, s); ProfIntDeriv.Report(prof, s); ProfTermDeriv.Report(prof, s); ProfCalcPmat.Report(prof, s); ProfCalcEigen.Report(prof, s); ProfModDeriv.Report(prof, s); ProfNewton.Report(prof, s); ProfEQVectors.Report(prof, s); prof.close(); outman.SetOutputStream(cout); #endif /* cout << "intterm calls " << inttermcalls << " time " << inttermtime/(double)(ticspersec.QuadPart) << endl; cout << "termterm calls " << termtermcalls << " time " << termtermtime/(double)(ticspersec.QuadPart) << endl; cout << "rescale calls " << rescalecalls << " time " << rescaletime/(double)(ticspersec.QuadPart) << " numrescales " << numactualrescales << endl; cout << "totalopt calls " << totaloptcalls << " time " << totalopttime/(double)(ticspersec.QuadPart) << endl; cout << "calcderiv calls " << calcderivcalls << " time " << calcderivtime/(double)(ticspersec.QuadPart) << endl; cout << "derivgetclas calls " << derivgetclascalls << " time " << derivgetclastime/(double)(ticspersec.QuadPart) << endl; cout << "derivint calls " << derivintcalls << " time " << derivinttime/(double)(ticspersec.QuadPart) << endl; cout << "derivterm calls " << derivtermcalls << " time " << derivtermtime/(double)(ticspersec.QuadPart) << endl; cout << "modderiv calls " << modderivcalls << " time " << modderivtime/(double)(ticspersec.QuadPart) << endl; cout << "pmat calls " << pmatcalls << " time " << pmattime/(double)(ticspersec.QuadPart) << endl; */ } //this is the original partitioned final opt void Population::FinalOptimization(){ //DEPRECATED in favor of BetterFinalOptimization assert(0); outman.setf(ios::fixed); outman.precision(5); outman.UserMessage("Current score = %.4f", BestFitness()); #ifdef INCLUDE_PERTURBATION if(pertMan->ratcheted) TurnOffRatchet(); if(allTimeBest != NULL){ if(BestFitness() < allTimeBest->Fitness()){ RestoreAllTimeBest(); } } #endif for(unsigned i=0;iRemoveTreeFromAllClas(); } outman.UserMessage("Performing final optimization..."); #ifdef MAC_FRONTEND NSAutoreleasePool *pool = [[NSAutoreleasePool alloc] init]; [[MFEInterfaceClient sharedClient] didBeginBranchOptimization]; [pool release]; #endif int pass=1; FLOAT_TYPE incr; double paramOpt, subRateOpt, paramTot; paramTot = ZERO_POINT_ZERO; do{ paramOpt = ZERO_POINT_ZERO; for(int m = 0;m < indiv[bestIndiv].modPart.NumModels();m++){ const ModelSpecification *modSpec = indiv[bestIndiv].modPart.GetModel(m)->GetCorrespondingSpec(); if(modSpec->IsFlexRateHet()) paramOpt = indiv[bestIndiv].treeStruct->OptimizeFlexRates(max(adap->branchOptPrecision*0.1, 0.001), m); else if(modSpec->IsCodon()) paramOpt = indiv[bestIndiv].treeStruct->OptimizeOmegaParameters(max(adap->branchOptPrecision*0.1, 0.001), m); paramTot += paramOpt; if(modSpec->IsFlexRateHet()){ outman.UserMessage("Flex optimization: %f", paramTot); } else if(modSpec->IsCodon()){ outman.UserMessage("Omega optimization: %f", paramTot); } } if(modSpecSet.InferSubsetRates()){ subRateOpt = indiv[bestIndiv].treeStruct->OptimizeSubsetRates(max(adap->branchOptPrecision*0.1, 0.001)); outman.UserMessage("Subset rate optimization: %f", subRateOpt); paramOpt += subRateOpt; } }while(paramOpt > ZERO_POINT_ZERO); do{ incr=indiv[bestIndiv].treeStruct->OptimizeAllBranches(max(adap->branchOptPrecision * pow(ZERO_POINT_FIVE, pass), (FLOAT_TYPE)1e-10)); indiv[bestIndiv].CalcFitness(0); outman.UserMessage("\tpass %d %.4f", pass++, indiv[bestIndiv].Fitness()); }while(incr > .00001 || pass < 10); outman.UserMessage("Final score = %.4f", indiv[bestIndiv].Fitness()); unsigned totalSecs = stopwatch.SplitTime(); unsigned secs = totalSecs % 60; totalSecs -= secs; unsigned min = (totalSecs % 3600)/60; totalSecs -= min * 60; unsigned hours = totalSecs / 3600; if(conf->searchReps == currentSearchRep && (conf->bootstrapReps == 0 || conf->bootstrapReps == currentBootstrapRep )) outman.UserMessage("Time used = %d hours, %d minutes and %d seconds", hours, min, secs); else outman.UserMessage("Time used so far = %d hours, %d minutes and %d seconds", hours, min, secs); log << "Score after final optimization: " << indiv[bestIndiv].Fitness() << endl; #ifdef MAC_FRONTEND pool = [[NSAutoreleasePool alloc] init]; [[MFEInterfaceClient sharedClient] reportFinalScore:BestFitness()]; [pool release]; #endif outman.unsetf(ios::fixed); #ifdef ENABLE_CUSTOM_PROFILER char fname[100]; sprintf(fname, "%s.profileresults.log", conf->ofprefix.c_str()); ofstream prof(fname); prof << "dataset: " << conf->datafname << "\t" << "start: " << conf->streefname << endl; prof << "seed: " << conf->randseed << "\t" << "refine: " << (conf->refineStart == true) << endl; prof << "start prec: " << conf->startOptPrec << "\t" << "final prec: " << adap->branchOptPrecision << endl; #ifdef SINGLE_PRECISION_FLOATS prof << "Single precision\n"; #else prof << "Double precision\n"; #endif unsigned s = stopwatch.SplitTime(); prof << "Total Runtime: " << s << "\tnumgen: " << gen << "\tFinalScore: " << indiv[bestIndiv].Fitness() << "\n"; outman.SetOutputStream(prof); indiv[bestIndiv].modPart.OutputHumanReadableModelReportWithParams(); prof << "Function\t\tcalls\ttime\tTperC\t%runtime" << endl; ProfIntInt.Report(prof, s); ProfIntTerm.Report(prof, s); ProfTermTerm.Report(prof, s); ProfRescale.Report(prof, s); ProfScoreInt.Report(prof, s); ProfScoreTerm.Report(prof, s); ProfIntDeriv.Report(prof, s); ProfTermDeriv.Report(prof, s); ProfCalcPmat.Report(prof, s); ProfCalcEigen.Report(prof, s); ProfModDeriv.Report(prof, s); ProfNewton.Report(prof, s); ProfEQVectors.Report(prof, s); prof.close(); outman.SetOutputStream(cout); #endif /* cout << "intterm calls " << inttermcalls << " time " << inttermtime/(double)(ticspersec.QuadPart) << endl; cout << "termterm calls " << termtermcalls << " time " << termtermtime/(double)(ticspersec.QuadPart) << endl; cout << "rescale calls " << rescalecalls << " time " << rescaletime/(double)(ticspersec.QuadPart) << " numrescales " << numactualrescales << endl; cout << "totalopt calls " << totaloptcalls << " time " << totalopttime/(double)(ticspersec.QuadPart) << endl; cout << "calcderiv calls " << calcderivcalls << " time " << calcderivtime/(double)(ticspersec.QuadPart) << endl; cout << "derivgetclas calls " << derivgetclascalls << " time " << derivgetclastime/(double)(ticspersec.QuadPart) << endl; cout << "derivint calls " << derivintcalls << " time " << derivinttime/(double)(ticspersec.QuadPart) << endl; cout << "derivterm calls " << derivtermcalls << " time " << derivtermtime/(double)(ticspersec.QuadPart) << endl; cout << "modderiv calls " << modderivcalls << " time " << modderivtime/(double)(ticspersec.QuadPart) << endl; cout << "pmat calls " << pmatcalls << " time " << pmattime/(double)(ticspersec.QuadPart) << endl; */ } //figures out the best individual that has been stored and returns index, optionally summarizes the final trees/models that have been stored int Population::EvaluateStoredTrees(bool report){ double bestL=-FLT_MAX; int bestRep; if(report){ outman.UserMessage("\n#######################################################\n\nCompleted %d replicate search(es) (of %d).", storedTrees.size(), conf->searchReps); if(conf->searchReps > 1 && (storedTrees.size() > 1)) outman.UserMessage("\nNOTE: Unless the following output indicates that search replicates found the\n\tsame topology, you should assume that they found different topologies."); outman.UserMessage("Results:"); } for(unsigned r=0;rtreeStruct->CalcBipartitions(true); if(storedTrees[r]->Fitness() > bestL){ bestL = storedTrees[r]->Fitness(); bestRep = r; } } if(report){ for(unsigned r=0;rcollapseBranches){ /* if(storedTrees[r]->treeStruct->IdenticalTopologyAllowingRerooting(storedTrees[r2]->treeStruct->root) && storedTrees[r2]->treeStruct->IdenticalTopologyAllowingRerooting(storedTrees[r]->treeStruct->root)) break; */ //This is where only collapsing branches upon output gets annoying. We really want to check //whether the collapsed trees are the same, but we're no longer storing them. So, generate the collapsed //trees and check. A set of collapsed trees could be generated in adavance, so doing this every time is //a bit of extra work Individual tempInd, tempInd2; tempInd.DuplicateIndivWithoutCLAs(storedTrees[r]); tempInd2.DuplicateIndivWithoutCLAs(storedTrees[r2]); int num = 0; tempInd.treeStruct->root->CollapseMinLengthBranches(num); tempInd2.treeStruct->root->CollapseMinLengthBranches(num); tempInd.treeStruct->CalcBipartitions(true); tempInd2.treeStruct->CalcBipartitions(true); if(tempInd.treeStruct->IdenticalTopologyAllowingRerooting(tempInd2.treeStruct->root) && tempInd2.treeStruct->IdenticalTopologyAllowingRerooting(tempInd.treeStruct->root)) break; } else if(storedTrees[r]->treeStruct->IdenticalTopologyAllowingRerooting(storedTrees[r2]->treeStruct->root)) break; } if(r == bestRep && conf->searchReps > 1) outman.UserMessageNoCR("Replicate %d : %.4f (best)", r+1, storedTrees[r]->Fitness()); else outman.UserMessageNoCR("Replicate %d : %.4f ", r+1, storedTrees[r]->Fitness()); if(r2 < r) outman.UserMessageNoCR(" (same topology as %d)", r2+1); if((userTermination || timeTermination) && r == storedTrees.size() - 1) outman.UserMessageNoCR(" (TERMINATED PREMATURELY) ", r2+1); outman.UserMessage(""); } if(conf->searchReps > 1) outman.UserMessage("\nParameter estimates across search replicates:"); else outman.UserMessage("\nParameter estimates:"); for(int part = 0;part < storedTrees[0]->modPart.NumModels();part++){ if(storedTrees[0]->modPart.NumModels() > 1) outman.UserMessage("\nPartition model subset %d:", part + 1); Model *tree0mod = storedTrees[0]->modPart.GetModel(part); if(tree0mod->GetMutableParameters()->size() > 0){ string s; tree0mod->FillModelOrHeaderStringForTable(s, false); outman.UserMessage(" %s", s.c_str()); for(unsigned i=0;imodPart.GetModel(part)->FillModelOrHeaderStringForTable(s, true); outman.UserMessage("rep%2d: %s", i+1, s.c_str()); } if(storedTrees[0]->modPart.GetModel(part)->GetModSpec()->IsOrientedGap()){ outman.UserMessage("\t **ins = proportion of columns that experienced an insertion"); outman.UserMessage("\t **del = deletion rate relative to nucleotide substitution rate"); } } else{ outman.UserMessage("\t Model contains no estimated parameters"); } } outman.UserMessageNoCR("\nTreelengths"); if(modSpecSet.InferSubsetRates()) outman.UserMessageNoCR(" and subset rate multipliers"); outman.UserMessage(":"); string line; char cStr[100]; sprintf(cStr, " %4s ", "TL"); line = cStr; if(modSpecSet.InferSubsetRates()){ for(int d = 0;d < dataPart->NumSubsets();d++){ char oStr[10]; sprintf(oStr, "R(%d)", d + 1); sprintf(cStr, " %5s", oStr); line += cStr; } } outman.UserMessage(" %s", line.c_str()); for(unsigned i=0;itreeStruct->Treelength()); line = cStr; if(modSpecSet.InferSubsetRates()){ for(int d = 0;d < dataPart->NumSubsets();d++){ sprintf(cStr, " %5.3f", storedTrees[i]->modPart.SubsetRate(d)); line += cStr; } } outman.UserMessage("rep%2d: %s", i+1, line.c_str()); } } bool firstEstAArmat = true; for(int part = 0;part < storedTrees[0]->modPart.NumModels();part++){ const Model *tree0mod = storedTrees[0]->modPart.GetModel(part); const ModelSpecification *modSpec = tree0mod->GetCorrespondingSpec(); for(unsigned i=0;imodPart.GetModel(part); if((modSpec->IsEstimateAAMatrix() || modSpec->IsTwoSerineRateMatrix()) && conf->bootstrapReps == 0){ string n = conf->ofprefix.c_str(); n += ".AArmatrix.dat"; ofstream mat; if(firstEstAArmat){ mat.open(n.c_str()); treeImod->OutputAminoAcidRMatrixMessage(mat); firstEstAArmat = false; outman.UserMessage("Estimated amino acid rate matrix/matrices saved to %s.AArmatrix.dat", conf->ofprefix.c_str()); } else mat.open(n.c_str(), ios::app); treeImod->OutputAminoAcidRMatrixArray(mat, part, i); mat << endl; mat.close(); } } } return bestRep; } void Population::ClearStoredTrees(){ for(vector::iterator it=storedTrees.begin();it!=storedTrees.end();it++){ delete (*it)->treeStruct; (*it)->treeStruct=NULL; delete (*it); } storedTrees.clear(); } void Population::Bootstrap(){ //if we're not restarting if(conf->restart == false) currentBootstrapRep = 1; for( ;currentBootstrapRep<=conf->bootstrapReps;currentBootstrapRep++){ #ifdef MAC_FRONTEND NSAutoreleasePool *pool = [[NSAutoreleasePool alloc] init]; [[MFEInterfaceClient sharedClient] didBeginBootstrapReplicate:rep]; [pool release]; #endif if(conf->restart == false){ outman.UserMessage("\nBootstrap reweighting..."); //if this is the first rep use the bootstrapseed if one was specified, //or the current seed (which could have come from a specified randseed or could have been generated randomly) if(nextBootstrapSeed == 0){ assert(currentBootstrapRep == 1); if(conf->bootstrapSeed > 0) nextBootstrapSeed = conf->bootstrapSeed; else nextBootstrapSeed = rnd.seed(); } lastBootstrapSeed = nextBootstrapSeed; nextBootstrapSeed = dataPart->BootstrapReweight(lastBootstrapSeed, conf->resampleProportion); } PerformSearch(); //In workphasedivision mode we could have gotten here because PerformSearch returned early after initial //optimization, before final optimzation or after final optimization. //if(workPhaseTermination && !(currentSearchRep == conf->searchReps && (conf->bootstrapReps == 0 || currentBootstrapRep == conf->bootstrapReps))) if(workPhaseTermination) break; Reset(); if(!userTermination && !timeTermination){ #ifdef MAC_FRONTEND pool = [[NSAutoreleasePool alloc] init]; [[MFEInterfaceClient sharedClient] didCompleteBoostrapReplicate:rep]; [pool release]; #endif } else { if(userTermination) outman.UserMessage("abandoning bootstrap rep %d.... terminating\n", currentBootstrapRep); break; } } } /* OLD VERSION void Population::Bootstrap(){ data->ReserveOriginalCounts(); stopwatch.Start(); CatchInterrupt(); for(int rep=1;rep <= (int) conf->bootstrapReps;rep++){ lastTopoImprove = lastPrecisionReduction = gen = 0; outman.UserMessage("bootstrap replicate %d (seed %d)", rep, rnd.seed()); #ifdef MAC_FRONTEND NSAutoreleasePool *pool = [[NSAutoreleasePool alloc] init]; [[MFEInterfaceClient sharedClient] didBeginBootstrapReplicate:rep]; [pool release]; #endif data->BootstrapReweight(); SeedPopulationWithStartingTree(); Run(); if(userTermination == false){ adap->branchOptPrecision = adap->startOptPrecision; FinishBootstrapRep(rep); outman.UserMessage("finished with bootstrap rep %d\n", rep); #ifdef MAC_FRONTEND pool = [[NSAutoreleasePool alloc] init]; [[MFEInterfaceClient sharedClient] didCompleteBoostrapReplicate:rep]; [pool release]; #endif } else { outman.UserMessage("abandoning bootstrap rep %d ....terminating", rep); break; } } FinalizeOutputStreams(); } */ //this function manages multiple search replicates, setting up the population //and then calling Run(). It can be called either directly from main(), or //from Bootstrap() void Population::PerformSearch(){ if(conf->restart == false) currentSearchRep = 1; else{ outman.UserMessage("\nRestarting from checkpoint..."); if(finishedRep == true){ //if we've restarted but the last checkpoint written apparently represents //the state of the population immediately after the completion of a replicate currentSearchRep++; if(currentSearchRep > conf->searchReps && (conf->bootstrapReps == 0 || currentBootstrapRep == conf->bootstrapReps)){ outman.UserMessage("The checkpoint loaded indicates that this run already completed.\nTo start a new run set restart to 0 and change the output\nfile prefix (ofprefix)."); restartedAfterTermination = true; return; } else{//we need to initialize the output here, while the population still knows that this was a restart (before calling Reset) InitializeOutputStreams(); Reset(); } } else InitializeOutputStreams(); } for(;currentSearchRep<=conf->searchReps;currentSearchRep++){ string s; if(conf->restart == false){ //this will reset the rep_fraction_done at the start of each rep UpdateFractionDone(0); if(currentSearchRep > 1){ Reset(); //this just changes what the rng has stored as the init seed ix0, for output purposes rnd.set_seed(rnd.seed()); } } //ensure that the user can ctrl-c kill the program during creation of each stepwise addition tree TurnOffSignalCatching(); GetRepNums(s); if(conf->restart == false){ //the fraction done is set to 1% here, indicating the this rep is ready to go //if we restarted, the fraction should already have been set when reading the state files if(s.length() > 0) outman.UserMessage("\n>>>%s<<<", s.c_str()); SeedPopulationWithStartingTree(currentSearchRep); //can't initialize output until after the pop is seeded, unless restarting (which happens above) InitializeOutputStreams(); //write a checkpoint, since the refinement (and maybe making a stepwise tree) could have taken a good while #ifndef BOINC //non-BOINC checkpointing if(ShouldCheckpoint(false) == true) #endif WriteStateFiles(); if(conf->workPhaseDivision){ WriteStateFiles(); //first workphasedivision exit point outman.UserMessage("\nNOTE: Terminating run after initial optimization and writing"); outman.UserMessage("checkpoint because workphasedivision configuration entry was set."); workPhaseTermination = true; UpdateFractionDone(4); break; } UpdateFractionDone(1); } else{ //in this case the progress should have been read from checkpoint and will maintain that value going forward adap->SetChangeableVariablesFromConfAfterReadingCheckpoint(conf); if(currentSearchRep > conf->searchReps) throw ErrorException("rep number in checkpoint (%d) is larger than total rep specified in config (%d)", currentSearchRep, conf->searchReps); outman.UserMessage("%s generation %d, seed %d, best lnL %.3f", s.c_str(), gen, rnd.init_seed(), indiv[bestIndiv].Fitness()); } #ifndef BOINC //Start catching Ctrl-C's TurnOnSignalCatching(); #endif if(!conf->scoreOnly) Run(); //for most purposes, these two types of termination are premature and treated identically //gen termination is treated as normal termination besides some warnings //bool prematureTermination = (userTermination || timeTermination); bool prematureTermination = conf->checkpoint ? genTermination : (genTermination | timeTermination || userTermination); //if we're checkpointing and terminated prematurely just bail without doing anything else if(conf->checkpoint && (userTermination || timeTermination || workPhaseTermination)){ #ifndef BOINC if(!workPhaseTermination){ outman.UserMessage("\nNOTE: A CHECKPOINTED RUN (writecheckpoints = 1) WAS PREMATURELY"); outman.UserMessage("TERMINATED. OUTPUT FILES (tree files, etc.) WILL NOT BE" ); outman.UserMessage("FINALIZED SO THAT THE RUN CAN BE RESTARTED WHERE IT LEFT OFF"); outman.UserMessage("(set restart = 1 in the config file). IF YOU WANT TO USE THE"); outman.UserMessage("PARTIAL OUTPUT FILES WITHOUT RESTARTING YOU WILL NEED TO MANUALLY"); outman.UserMessage("ADD \"end;\" TO THE TREE FILES.\n"); } #endif UpdateFractionDone(4); break; } outman.UserMessage(""); if(userTermination) outman.UserMessage("MODEL REPORT - SEARCH TERMINATED BY USER"); else if(timeTermination) outman.UserMessage("MODEL REPORT - SEARCH TERMINATED AFTER REACHING TIME LIMIT"); else if(genTermination) outman.UserMessage("MODEL REPORT - SEARCH TERMINATED AFTER REACHING GENERATION LIMIT"); else outman.UserMessage("MODEL REPORT - Parameter values are FINAL"); indiv[bestIndiv].modPart.OutputHumanReadableModelReportWithParams(); //this rep is over //11/28/09 We will now always store the final individual in the stored trees array, //even if prematureTerm if(Tree::outgroup != NULL) OutgroupRoot(&indiv[bestIndiv], bestIndiv); //this individual will be stored in the storedTrees array until population deletes it much later, Individual *repResult = new Individual(&indiv[bestIndiv]); //Note that the collapsed individual is intentionally not stored here. It will be re-collapsed on //output to file, and the collapsing here is just for this message if(conf->collapseBranches){ Individual repResultColl(&indiv[bestIndiv]); int numCollapsed = 0; repResultColl.treeStruct->root->CollapseMinLengthBranches(numCollapsed); outman.UserMessage("NOTE: Collapsing of minimum length branches was requested (collapsebranches = 1)");\ if(numCollapsed == 0) outman.UserMessage(" No branches were short enough to be collapsed.\n"); else outman.UserMessage(" %d branches were collapsed.\n", numCollapsed); if(repResult->treeStruct->constraints.empty() == false){ for(vector::iterator con=repResult->treeStruct->constraints.begin();con!=repResult->treeStruct->constraints.end();con++){ if(con->IsPositive()){ outman.UserMessage("\nNOTE: If collapsing of minimum length branches is requested (collapsebranches = 1) in a run with\n\ta positive constraint, it is possible for a constrained branch itself to be collapsed.\n\tIf you care, be careful to check whether this has happened or turn off branch collapsing.\n"); break; } } } } storedTrees.push_back(repResult); //output site likelihoods if requested if(conf->outputSitelikelihoods > 0){ outman.UserMessage("Saving site likelihoods to file %s.sitelikes.log ...", conf->ofprefix.c_str()); for( int set = 0;set < dataPart->NumSubsets();set++){ if( dataPart->GetSubset(set)->DidUseDefaultWeightsets() == true) outman.UserMessage("WARNING: Site likelihoods are being output when wtset %s is in effect.\n Sites with weight > 1 will only be output once!", dataPart->GetSubset(set)->WeightsetName().c_str()); } if( (userTermination || timeTermination || genTermination) ){ outman.UserMessage("WARNING: Site likelihoods being output on prematurely terminated search ..."); } //This has to work somewhat differently for partitioning. As far as the tree functions know we will always //be in append mode for the sitelike output (negative sitelike value). The pop will have to nuke any //existing file here the first time through and put in the header indiv[bestIndiv].treeStruct->sitelikeLevel = -(int) conf->outputSitelikelihoods; ofstream ordered; indiv[bestIndiv].treeStruct->ofprefix = conf->ofprefix; string oname = indiv[bestIndiv].treeStruct->ofprefix + ".sitelikes.log"; if(currentSearchRep == 1){ ordered.open(oname.c_str()); ordered << "Tree\t-lnL\tSite\t-lnL"; if(conf->outputSitelikelihoods > 1) ordered << "\tunder1\tunder2"; ordered << "\n"; ordered.close(); } indiv[bestIndiv].treeStruct->Score(); ordered.open(oname.c_str(), ios::app); ordered.precision(12); ordered << currentSearchRep << "\t" << -indiv[bestIndiv].treeStruct->lnL << "\n"; ordered.close(); } //warn if the normal auto-term conditions weren't used if(userTermination || timeTermination || genTermination){ if(s.length() > 0 && (userTermination || timeTermination)) outman.UserMessage(">>>Terminated %s<<<", s.c_str()); outman.UserMessage("%s", TerminationWarningMessage().c_str()); } else{ //I think that this should only be reported here if there is > 1 search rep per boot rep, since it should really be noting that //a given rep has finished, and the overall boot rep doesn't really finish until after the summary across search reps if(s.length() > 0 && (conf->bootstrapReps == 0 || (conf->bootstrapReps > 0 && conf->searchReps > 1))) outman.UserMessage(">>>Completed %s<<<", s.c_str()); } int best=0; //If this is the last search of a run, bootstrap rep, or it was killed prematurely //Note that EvaluateStoredTrees does some summary output for the model estimates from those trees if((currentSearchRep == conf->searchReps) || prematureTermination){ if(storedTrees.size() > 0){ best=EvaluateStoredTrees(true); //recombine final trees /* if(total_size > 2){ for(int i=0;itreeStruct->root->claIndexDown == -1) storedTrees[i]->treeStruct->AssignCLAsFromMaster(); for(int i=0;itotal_size;i++){ //only the best indiv has clas assigned at this point if(i != bestIndiv) indiv[i].CopySecByRearrangingNodesOfFirst(indiv[i].treeStruct, storedTrees[best], false); else indiv[i].CopySecByRearrangingNodesOfFirst(indiv[i].treeStruct, storedTrees[best], true); } int holdover = 2; for(int rounds=0;rounds<10;rounds++){ for(int i=holdover;iCalcAverageFitness(); for(int i=holdover;i indiv[0].Fitness()){ indiv[1].CopySecByRearrangingNodesOfFirst(indiv[1].treeStruct, &indiv[0], true); indiv[0].CopySecByRearrangingNodesOfFirst(indiv[0].treeStruct, &indiv[i], true); } else if(indiv[i].Fitness() > indiv[1].Fitness()){ indiv[1].CopySecByRearrangingNodesOfFirst(indiv[1].treeStruct, &indiv[i], true); } } for(int i=holdover;iCalcAverageFitness(); } } string s = "recom."; s += besttreefile; this->WriteTreeFile(s.c_str()); Individual *repResult = new Individual(&indiv[0]); storedTrees.push_back(repResult); outman.UserMessage("Best topology created by recombination: %f", indiv[0].Fitness()); */ } } //write the best trees from all completed reps: //at the end of each rep //at the end of all reps //if termination was premature and we're told to write in that case (the premature tree will be included) if( ( (! prematureTermination) && (all_best_output & WRITE_REP_TERM)) || ( (! prematureTermination) && (currentSearchRep == conf->searchReps) && (all_best_output & WRITE_REPSET_TERM)) || ( (prematureTermination) && (storedTrees.size() > 1) && (all_best_output & WRITE_PREMATURE))){ if(storedTrees.size() > 0){ if(prematureTermination || currentSearchRep == conf->searchReps)//message only if last outman.UserMessage("\nSaving final trees from all search reps to %s.all.tre", besttreefile.c_str()); WriteStoredTrees(besttreefile.c_str()); } } //write the best overall tree: //at the end of each rep //at the end of all reps ////if termination was premature and we're told to write in that case (the premature tree will be written if it is best) if( ( (! prematureTermination) && (best_output & WRITE_REP_TERM)) || ( (! prematureTermination) && (currentSearchRep == conf->searchReps) && (best_output & WRITE_REPSET_TERM)) || ( (prematureTermination) && (best_output & WRITE_PREMATURE))){ //the first two options here write trees from the storedTrees array, the last writes the best from the current population outman.UserMessage("\nSaving final tree from best search rep (#%d) to %s.tre", best + 1, besttreefile.c_str()); if(conf->searchReps > 1 && storedTrees.size() > 0){ WriteTreeFile(besttreefile.c_str(), best, conf->collapseBranches); } else if(storedTrees.size() == 1) WriteTreeFile(besttreefile.c_str(), 0, conf->collapseBranches); else WriteTreeFile(besttreefile.c_str(), -1, conf->collapseBranches); } if(conf->bootstrapReps > 0){ //write best boot tree if: //end of single search rep of many, and we're supposed to (not normal) //end of search rep set or single search //premature termination and we're told to //premature termination and we've already stored a tree (due to change, even a single termed run will now be in here) if( ( (! prematureTermination) && (bootlog_output & WRITE_REP_TERM)) || ( (! prematureTermination) && (currentSearchRep == conf->searchReps) && (bootlog_output & WRITE_REPSET_TERM)) || ( (prematureTermination) && (bootlog_output & WRITE_PREMATURE)) || ( (prematureTermination) && storedTrees.size() > 0)){ if(conf->searchReps > 1 && storedTrees.size() > 0){ //we're doing multiple searches per boot rep, and have successfully completed at least one replicate //(although the present replicate could have been prematurely terminated) char temp_buf[100]; char suffix[100]; sprintf(suffix, "boot.tre"); DetermineFilename(bootlog_output, temp_buf, suffix); outman.UserMessage("\nSaving tree from best search rep (#%d) to bootstrap file %s\n", best+1, temp_buf); if(prematureTermination && best == storedTrees.size() - 1) outman.UserMessage("WARNING: Tree from prematurely terminated search saved to bootstrap file"); FinishBootstrapRep(storedTrees[best], currentBootstrapRep); } else if(storedTrees.size() == 1){ //We just successfully completed a one-search-rep bootstrap replicate FinishBootstrapRep(storedTrees[0], currentBootstrapRep); char temp_buf[100]; char suffix[100]; sprintf(suffix, "boot.tre"); DetermineFilename(bootlog_output, temp_buf, suffix); outman.UserMessage("\nSaving best tree to bootstrap file %s\n", temp_buf); if(prematureTermination) outman.UserMessage("WARNING: Tree from prematurely terminated search saved to bootstrap file"); } else //This rep was prematurely killed, but we're supposed to write it FinishBootstrapRep(&indiv[bestIndiv], currentBootstrapRep); if(!prematureTermination){ outman.UserMessage(">>>Completed Bootstrap rep %d<<<", currentBootstrapRep); } } else{ if(prematureTermination && !(bootlog_output & WRITE_PREMATURE)) outman.UserMessage("Not saving search rep to bootstrap file due to early termination"); } } if(conf->inferInternalStateProbs == true){ //don't infer internals states unless at least one rep successfully completed if((prematureTermination == false && currentSearchRep == conf->searchReps) || (prematureTermination && storedTrees.size() > 0)){ //this is important to ensure that there are enough free clas for a temp set to be allocated, //since recycling won't happen in this usage (it could if implemented, but I don't see the benefit) for(int i = 0;i < total_size;i++){ if(indiv[i].treeStruct != NULL) indiv[i].treeStruct->MakeAllNodesDirty(); if(newindiv[i].treeStruct != NULL) newindiv[i].treeStruct->MakeAllNodesDirty(); } if(storedTrees.size() > 0){//careful here, the trees in the storedTrees array don't have clas assigned outman.UserMessage("Inferring internal state probabilities on best tree... saving to file %s.internalstates.log\n", conf->ofprefix.c_str()); Individual *theInd; Individual tempInd; if(Tree::outgroup != NULL){ tempInd.DuplicateIndivWithoutCLAs(storedTrees[best]); OutgroupRoot(&tempInd, -1); theInd = &tempInd; } else theInd = storedTrees[best]; //InferAllInternalStateProbs will deal with assigning clas, since neither the tree in storedTrees nor the potentially temp tree have them theInd->treeStruct->InferAllInternalStateProbs(conf->ofprefix.c_str()); if(prematureTermination && best == storedTrees.size() - 1) outman.UserMessage("WARNING: Internal states inferred on tree from prematurely terminated search\n"); } } else if(prematureTermination){ outman.UserMessage(">>>Internal state probabilities not inferred due to premature termination<<<\n"); } } //finalize anything that needs it at rep end FinalizeOutputStreams(0); //finalize anything that needs it at the end of the repset if(currentSearchRep == conf->searchReps || prematureTermination) { FinalizeOutputStreams(1); outman.UserMessage("#######################################################"); } if(userTermination == true || timeTermination == true) break; #ifndef BOINC if(ShouldCheckpoint(false) == true || conf->workPhaseDivision ) #endif //write a checkpoint that will indicate that the rep is done and results have been written to file //the gen will be UINT_MAX, as it is after a rep has terminated, which will tell the function that reads //the checkpoint to set finishedrep = true. This automatically happens in the BOINC case WriteStateFiles(); if(conf->workPhaseDivision && !(currentSearchRep == conf->searchReps && (conf->bootstrapReps == 0 || currentBootstrapRep == conf->bootstrapReps))){ //third workphasedivision exit point - if this is the end of the whole run, don't do this. outman.UserMessage("\nNOTE: Terminating run after final optimization and writing checkpoint"); outman.UserMessage("because workphasedivision configuration entry was set."); workPhaseTermination = true; UpdateFractionDone(4); break; } //this needs to be set here so that the population is reset at the top of this loop before the next rep conf->restart = false; } ClearStoredTrees(); } void Population::OptimizeInputAndWriteSitelikelihoods(){ log_output = fate_output = swaplog_output = treelog_output = problog_output = Population::DONT_OUTPUT; InitializeOutputStreams(); //find out how many trees we have GarliReader & reader = GarliReader::GetInstance(); const NxsTreesBlock *treesblock = reader.GetTreesBlock(reader.GetTaxaBlock(0), reader.GetNumTreesBlocks(reader.GetTaxaBlock(0)) - 1); if(treesblock == NULL || !strcmp(conf->streefname.c_str(), "random") || !strcmp(conf->streefname.c_str(), "stepwise")) throw ErrorException("You must specify a nexus treefile to use this runmode."); int numTrees = treesblock->GetNumTrees(); string oname = conf->ofprefix + ".sitelikes.log"; ofstream ordered; ordered.open(oname.c_str()); ordered << "Tree\t-lnL\tSite\t-lnL\n"; ordered.close(); bestIndiv = 0; conf->searchReps = numTrees; //loop over the trees for(int t = 1;t <= numTrees;t++){ currentSearchRep = t; if(!conf->scoreOnly){ outman.UserMessage("Optimizing tree %d ...", t); SeedPopulationWithStartingTree(t); bestIndiv = 0; BetterFinalOptimization(); } else outman.UserMessage("Scoring tree %d ...", t); outman.UserMessage("Writing site likelihoods for tree %d ...", t); indiv[0].treeStruct->sitelikeLevel = - (max((int)conf->outputSitelikelihoods, 1)); indiv[0].treeStruct->ofprefix = conf->ofprefix; indiv[0].treeStruct->Score(); ordered.open(oname.c_str(), ios::app); ordered.precision(10); ordered << t << "\t" << -indiv[0].treeStruct->lnL << "\n"; ordered.close(); Individual *repResult = new Individual(&indiv[0]); storedTrees.push_back(repResult); Reset(); } bool coll = conf->collapseBranches; conf->collapseBranches = false; EvaluateStoredTrees(true); if(coll) outman.UserMessage("\nNOTE: collapsebranches setting ignored when writing and comparing optimized trees..."); outman.UserMessage("\nWriting optimized trees and models to %s.all.tre", besttreefile.c_str()); WriteStoredTrees(besttreefile.c_str()); FinalizeOutputStreams(0); FinalizeOutputStreams(1); FinalizeOutputStreams(2); } void Population::OptimizeInputAndWriteSitelikelihoodsAndTryRootings(){ log_output = fate_output = swaplog_output = treelog_output = problog_output = Population::DONT_OUTPUT; //log_output = fate_output = swaplog_output = problog_output = Population::DONT_OUTPUT; InitializeOutputStreams(); //assert(Tree::someOrientedGap); //find out how many trees we have GarliReader & reader = GarliReader::GetInstance(); const NxsTreesBlock *treesblock = reader.GetTreesBlock(reader.GetTaxaBlock(0), reader.GetNumTreesBlocks(reader.GetTaxaBlock(0)) - 1); if(treesblock == NULL || treesblock->GetNumTrees() > 1) throw ErrorException("You must specify a treefile with exactly one tree to use this runmode."); // int numTrees = treesblock->GetNumTrees(); // assert(numTrees == 1); bestIndiv = 0; //start the sitelike file string oname = conf->ofprefix + ".sitelikes.log"; ofstream ordered; ordered.open(oname.c_str()); ordered << "Tree\t-lnL\tSite\t-lnL\n"; ordered.close(); Tree::useOptBoundedForBlen = true; currentSearchRep = 1; outman.UserMessage("Optimizing tree %d ...", 1); conf->refineStart = false; SeedPopulationWithStartingTree(currentSearchRep); bestIndiv = 0; assert(indiv[0].treeStruct->dummyRoot); //the number of branches on which the root could be attached conf->searchReps = (dataPart->NTax() - 1) * 2 - 3; double initIns = 0.05, initDel = 0.1; for(int m = 0;m < indiv[0].modPart.NumModels();m++){ if(indiv[0].modPart.GetModel(m)->IsOrientedGap()){ initIns = indiv[0].modPart.GetModel(m)->InsertRate(); initDel = indiv[0].modPart.GetModel(m)->DeleteRate(); } } #ifdef OPT_BOUNDED_LOG char name[50]; sprintf(name, "%s.optbounded.log", conf->ofprefix.c_str()); ofstream log(name); log.close(); indiv[0].treeStruct->ofprefix = conf->ofprefix; indiv[1].treeStruct->ofprefix = conf->ofprefix; #endif BetterFinalOptimization(); //this will stick the current tree into the treelog conf->outputTreelog = true; InitializeOutputStreams(); outman.UserMessage("Writing site likelihoods for tree %d ...", 1); indiv[0].treeStruct->sitelikeLevel = -1; indiv[0].treeStruct->ofprefix = conf->ofprefix; indiv[0].treeStruct->Score(); //put the score of the initial indiv in the file ordered.open(oname.c_str(), ios::app); ordered.precision(10); ordered << "0\t" << -indiv[0].treeStruct->lnL << "\t"; indiv[0].treeStruct->root->MakeNewick(treeString, false, true, false); ordered << treeString << "\n"; ordered.close(); //store the indiv Individual *repResult = new Individual(&indiv[0]); storedTrees.push_back(repResult); //return the rates to initial vals to have them reopt each time bool resetInsDel = false; if(resetInsDel){ for(int m = 0;m < indiv[0].modPart.NumModels();m++){ if(indiv[0].modPart.GetModel(m)->IsOrientedGap()){ indiv[0].modPart.GetModel(m)->SetInsertRate(0, initIns); indiv[0].modPart.GetModel(m)->SetDeleteRate(0, initDel); indiv[0].treeStruct->MakeAllNodesDirty(); } } } else indiv[0].treeStruct->MakeAllNodesDirty(); indiv[0].CalcFitness(0); //copy the tree and model into indiv[1] outman.UserMessage("Rooting at nodes across tree..."); Tree *indiv1Tree = indiv[1].treeStruct; indiv[1].CopySecByRearrangingNodesOfFirst(indiv1Tree, &indiv[0]); //get ready to swap on indiv[1] indiv1Tree->GatherValidReconnectionNodes(99999, indiv1Tree->dummyRoot, NULL); indiv1Tree->sprRang.SortByDist(); int tnum = 2; for(listIt broken = indiv1Tree->sprRang.begin();broken != indiv1Tree->sprRang.end();broken++){ //try a reattachment point for the dummy root taxon indiv1Tree->SPRMutate(indiv1Tree->dataPart->NTax(), &(*broken), 0.01, 0); //optimize the result bestIndiv = 1; indiv[bestIndiv].CalcFitness(0); bestFitness = indiv[bestIndiv].Fitness(); //this will make the various trees have different names when they are appended to the treelog //in BetterFinalOpt gen = tnum; //AppendTreeToTreeLog(0, 1); BetterFinalOptimization(); outman.UserMessage("%d\tnode\t%d\tlnL\t%f", tnum, (*broken).nodeNum, indiv1Tree->lnL); //output the sitelikes indiv1Tree->ofprefix = conf->ofprefix; indiv1Tree->sitelikeLevel = -1; indiv1Tree->Score(); //add the total score and the tree ordered.open(oname.c_str(), ios::app); ordered.precision(10); ordered << tnum << "\t" << -indiv1Tree->lnL << "\t"; indiv[1].treeStruct->root->MakeNewick(treeString, false, true, false); ordered << treeString << "\n"; ordered.close(); //store the indiv and write the tree to file repResult = new Individual(&indiv[1]); storedTrees.push_back(repResult); indiv[1].CopySecByRearrangingNodesOfFirst(indiv1Tree, &indiv[0], true); tnum++; } bool coll = conf->collapseBranches; conf->collapseBranches = false; EvaluateStoredTrees(true); if(coll) outman.UserMessage("\nNOTE: collapsebranches setting ignored when writing and comparing optimized trees..."); outman.UserMessage("\nWriting optimized trees and models to %s.all.tre", besttreefile.c_str()); WriteStoredTrees(besttreefile.c_str()); FinalizeOutputStreams(0); FinalizeOutputStreams(1); FinalizeOutputStreams(2); } void Population::VariableStartingTreeOptimization(bool reducing){ currentSearchRep = 1; SeedPopulationWithStartingTree(currentSearchRep); InitializeOutputStreams(); string filename = conf->ofprefix + ".var.log"; ofstream out(filename.c_str()); out.precision(10); filename = conf->ofprefix + ".randblens.tre"; ofstream randTrees(filename.c_str()); dataPart->BeginNexusTreesBlock(randTrees); filename = conf->ofprefix + ".optblens.tre"; ofstream optTrees(filename.c_str()); dataPart->BeginNexusTreesBlock(optTrees); typedef vector doubvec; //this is a vector of vectors, with each entry in the higher level vector being a vector //with all of the final rep scores for a given precision vector finalScores; typedef vector intvec; //vector of vectors, number of passes per rep per prec vector numPasses; vector numDerivCalcs; //a triple vector, with the branch lengths for each branch, rep and prec typedef vector doubdoubvec; vector allBlens; vector prec; //get the precision values to use from the arbitrarystring entry in the config file stringstream s; s.str(conf->arbitraryString); string p; while(!s.eof()){ s >> p; double x = atof(p.c_str()); prec.push_back(x); } int numReps = conf->searchReps; double prec1; int numNodes = indiv[0].treeStruct->getNumNodesTotal(); for(int rep = 0;rep < numReps;rep++){ //for each rep, rerandomize the branch lengths and output the tree indiv[0].treeStruct->RandomizeBranchLengthsExponential(conf->gammaShapeBrlen); indiv[0].treeStruct->root->MakeNewick(treeString, false, true, false); randTrees << "tree r" << rep << " = [&U] " << treeString << ";\n"; //store this randomization indiv[1].CopySecByRearrangingNodesOfFirst(indiv[1].treeStruct, &indiv[0], true); indiv[0].SetDirty(); indiv[0].CalcFitness(0); double imp = 999.9; double prevScore = indiv[0].Fitness(); // outman.UserMessage("%f\t%f\t", prec[p], indiv[0].Fitness()); for(int precNum=0;precNum < prec.size() && (!reducing || (reducing && precNum < 1)) ;precNum++){ prec1 = prec[precNum]; int pass=0; prevScore = indiv[0].Fitness(); outman.UserMessage("%f\t%d", indiv[0].Fitness(), pass); do{ if(reducing) prec1 = prec[min(pass, (int)prec.size()-1)]; indiv[0].treeStruct->OptimizeAllBranches(prec1); indiv[0].SetDirty(); indiv[0].CalcFitness(0); // indiv[0].treeStruct->OptimizeTreeScale(prec1); imp = indiv[0].Fitness() - prevScore; prevScore = indiv[0].Fitness(); outman.UserMessage("%f\t%f\t%d", indiv[0].Fitness(), prec1, optCalcs); pass++; }while(imp > prec1 || pass < prec.size()); outman.UserMessage("%f\t%d\n", indiv[0].Fitness(), pass); if(rep == 0){ doubvec scoreTemp; scoreTemp.push_back(indiv[0].Fitness()); finalScores.push_back(scoreTemp); intvec passTemp; passTemp.push_back(pass); numPasses.push_back(passTemp); intvec calcsTemp; calcsTemp.push_back(optCalcs); numDerivCalcs.push_back(calcsTemp); doubvec tempBlens; doubdoubvec tempBlens2; for(int b=1;ballNodes[b]->dlen); tempBlens2.push_back(tempBlens); allBlens.push_back(tempBlens2); } else{ finalScores[precNum].push_back(indiv[0].Fitness()); numPasses[precNum].push_back(pass); numDerivCalcs[precNum].push_back(optCalcs); doubvec tempBlens; for(int b=1;ballNodes[b]->dlen); allBlens[precNum].push_back(tempBlens); } indiv[0].treeStruct->root->MakeNewick(treeString, false, true, false); optTrees << "tree p" << prec1 << ".r" << rep << " = [&U] " << treeString << ";\n"; //restore the randomization indiv[0].CopySecByRearrangingNodesOfFirst(indiv[0].treeStruct, &indiv[1], true); indiv[0].SetDirty(); indiv[0].CalcFitness(0); // scoresThisPrec.push_back(indiv[0].Fitness()); // passesThisPrec.push_back(pass); // derivCalcsThisPrec.push_back(optCalcs); // for(int b=1;ballNodes[b]->dlen); // blensThisPrec.push_back(blensThisRep); // blensThisRep.clear(); optCalcs = 0; //indiv[0].CopySecByRearrangingNodesOfFirst(indiv[0].treeStruct, &tempIndiv, true); } // finalScores.push_back(scoresThisPrec); // numPasses.push_back(passesThisPrec); // numDerivCalcs.push_back(derivCalcsThisPrec); // allBlens.push_back(blensThisPrec); // AppendTreeToTreeLog(0, 0); // scoresThisPrec.clear(); // passesThisPrec.clear(); // derivCalcsThisPrec.clear(); // blensThisPrec.clear(); // out << prec[p] << "\t"; } // out << "\n"; for(int precNum = 0;precNum < finalScores.size();precNum++){ for(int rep = 0;rep < finalScores[precNum].size();rep++){ // for(vector::iterator it = finalScores.begin();it != scores.end();it++){ out << prec[precNum] << "\t" << rep << "\t" << finalScores[precNum][rep] << "\t" << numPasses[precNum][rep] << "\t" << numDerivCalcs[precNum][rep] << endl; /* for(vector::iterator it = scores.begin();it != scores.end();it++){ out << (*it)[rep] << "\t"; } for(vector::iterator it = passes.begin();it != passes.end();it++){ out << (*it)[rep] << "\t"; } out << "\n"; */ } } ofstream blens; for(int precNum = 0;precNum < finalScores.size();precNum++){ char filename[100]; if(reducing) sprintf(filename, "blens.%s.final.log", conf->ofprefix.c_str()); else sprintf(filename, "blens.%s.%f.log", conf->ofprefix.c_str(), prec[precNum]); blens.open(filename); blens << "branch#\tfullyOpt\treps...\n"; //careful here - the number of nodes includes the root, which has no blen and wasn't put into the //blen vector. So, the indexing is [actualNodeNum - 1] for(int bnum=0;bnum top ) j-- ; if( i <= j ) { for( int k = 0; k < 2; k++ ) { FLOAT_TYPE y = scoreArray[i][k]; scoreArray[i][k] = scoreArray[j][k]; scoreArray[j][k] = y; } i++; if(j) j--; } } while( i <= j ); if( top < j ) QuickSort( scoreArray, top, j ); if( i < bottom ) QuickSort( scoreArray, i, bottom ); } FLOAT_TYPE Population::CalcAverageFitness(){ FLOAT_TYPE total = ZERO_POINT_ZERO; for(unsigned i = 0; i < total_size; i++ ){ // evaluate fitness if(indiv[i].IsDirty()){ indiv[i].CalcFitness(subtreeNode); } assert(indiv[i].Fitness() != 1); total += indiv[i].Fitness(); cumfit[i][0] = (FLOAT_TYPE)i; cumfit[i][1] = indiv[i].Fitness(); } FLOAT_TYPE avg = total / (FLOAT_TYPE)total_size; // Sort fitnesses from low to high (bad to good) QuickSort( cumfit, 0, total_size-1 ); // keep track of which individual is most fit each generation we've stored the //fitnesses as ln-likelihoods in cumfit, so cumfit[0] will be the _least_ fit individual int mostFit = total_size-1; #ifndef NO_EVOLUTION bestAccurateIndiv=bestIndiv = (int)cumfit[mostFit][0]; #else bestAccurateIndiv=bestIndiv = 0; #endif //if subtree mode is active, we also want to find the best accurate indiv if(rank==0){ while(paraMan->subtreeModeActive==true && indiv[bestAccurateIndiv].accurateSubtrees==false){ mostFit--; bestAccurateIndiv=(int)cumfit[mostFit][0]; } assert(mostFit>=0); } // keep track of all-time best if( indiv[bestIndiv].Fitness() > prevBestFitness ){ prevBestFitness = bestFitness; globalBest = bestFitness = indiv[bestIndiv].Fitness(); } if(memLevel>0){ //if we are at some level of memory restriction, mark the clas of the old best //for reclamation, and protect those of the new best SetNewBestIndiv(bestIndiv); } CalculateReproductionProbabilies(cumfit, conf->selectionIntensity, total_size); return avg; /* Here's Paul's original selection criterion, based solely on rank // // relative fitnesses are assigned based solely on position // of individual in sorted array - we forget the likelihoods (or treelengths) // at this point. This allows the likelihoods to be close together // and still get a healthy distribution of relative fitnesses so that // there is real differential reproduction FLOAT_TYPE n = (FLOAT_TYPE)total_size; FLOAT_TYPE nn = n * ( n + 1.0 ); FLOAT_TYPE incr = 2.0 / nn; FLOAT_TYPE cum = incr; cumfit[0][1] = cum; for( i = 1; i < total_size; i++ ) { cum += incr; cumfit[i][1] = cumfit[i-1][1] + cum; } */ } void Population::CalculateReproductionProbabilies(FLOAT_TYPE **scoreArray, FLOAT_TYPE selectionIntensity, int indivsInArray){ //DJZ 2-28-06 Generalizing this so that it can be used in multiple places with different //subsets of individuals and selection intensities. The 2-d array passed in (indivsInArray x 2) //has the scores in the [x][1] slots, and the indiv numbers in the [x][0] slots, and should already //be sorted from low to high (bad to good). The reproduction probs will be placed in the [x][1] before returning. //Probability of reproduction based on more or less on AIC weights, although //the strength of selection can be varied by changing the selectionIntensity //A selectionIntensity of 0.5 makes this equivalent to AIC weights, while //smaller number makes the selection less severe FLOAT_TYPE *deltaAIC=new FLOAT_TYPE[indivsInArray]; FLOAT_TYPE tot=ZERO_POINT_ZERO; for(int i=0;iholdoverPenalty; } else deltaAIC[indivsInArray-1]=ZERO_POINT_ZERO; deltaAIC[indivsInArray-1]=exp(-selectionIntensity * deltaAIC[indivsInArray-1]); tot+=deltaAIC[indivsInArray-1]; for(int i=0;ignufname ); assert( gnuf ); // set labels gnuf << "set xlabel \"Generation\"" << endl; gnuf << "set ylabel \"Fitness\"" << endl; gnuf << "set title \"" << params->plottitle << "\"" << endl; // alternate title containing config settings gnuf << "#set title \""; gnuf << "N=" << conf->nindivs; gnuf << " h=" << params->holdover; gnuf << " b=" << params->meanBrlenMuts; gnuf << " s=" << params->gammaShapeBrlen; gnuf << " seed=" << rnd.init_seed(); gnuf << '\"' << endl; // make alternate output to mif file available gnuf << "#set terminal mif" << endl; gnuf << "#set output \"" << params->ofprefix << ".mif\"" << endl; // place legend on graph strcpy( tmpstr, "set key 5000,-45000.0" ); gnuf << tmpstr << endl; // finally, the plot command gnuf << "plot \"" << params->logfname; if( params->fatlog ) { gnuf << "\" using 1:3 title \"best\" with lines"; gnuf << ", \"" << params->logfname; gnuf << "\" using 1:2 title \"average\" with lines" << endl; } else { gnuf << "\" using 1:2 with lines" << endl; } gnuf << "pause -1" << endl; gnuf.close(); } */ void Population::DetermineParentage(){ //determine each individual's parentage unsigned parent; FLOAT_TYPE r; for(unsigned i = 0; i < conf->nindivs; i++ ){ #ifndef NO_EVOLUTION if( i < conf->holdover ){// copy best individual's genotype to next generation if(rank==0){ if(paraMan->subtreeModeActive==true) parent=bestAccurateIndiv; else parent=bestIndiv; } else parent=bestIndiv; } else if(rank==0) if(i==1 && rank==0 && indiv[bestIndiv].accurateSubtrees==true && paraMan->ReadyForSubtreeRecom(gen)){ //if subtree mode is on and we haven't tried a subtreeRecom in a while, set up an individual for that parent=bestIndiv; newindiv[i].mutation_type=Individual::subtreeRecom; } else {// find a parent r = rnd.uniform(); for( parent = 0; parent < total_size; parent++ ){ if( r < cumfit[parent][1] ) break; } parent = (int)cumfit[parent][0]; #ifdef INPUT_RECOMBINATION paraMan->maxRecomIndivs = 3; paraMan->nremotes = NUM_INPUT; if(rank==0 && paraMan->subtreeModeActive==false && i>= (conf->nindivs - paraMan->maxRecomIndivs)){ /* int *mates=new int[paraMan->nremotes]; for(int j=0;jnremotes;j++) mates[j]=conf->nindivs+j; ScrambleArray(paraMan->nremotes, mates); */ int foo=2; FLOAT_TYPE **recomSelect=new FLOAT_TYPE *[paraMan->nremotes]; for(int q=0;qnremotes;q++) recomSelect[q]=new FLOAT_TYPE[2]; int potentialPartners=0; for(int r=0;rnremotes;r++){ int ind=conf->nindivs+r; recomSelect[r][0]=(FLOAT_TYPE)(ind); if(ind==parent //don't recombine with your parent || (indiv[parent].topo == indiv[ind].topo) //don't recombine with another of the same topo || (indiv[ind].willrecombine == true))//don't recombine with someone who is already doing so recomSelect[r][1]=-1e100; else{ recomSelect[r][1]=indiv[ind].Fitness(); potentialPartners++; } } if(potentialPartners > 0){ QuickSort(recomSelect, 0, paraMan->nremotes-1); CalculateReproductionProbabilies(recomSelect, 0.001, paraMan->nremotes); int mateIndex; int curMate; // find someone else to recombine with FLOAT_TYPE r=rnd.uniform(); for( mateIndex=0;mateIndex < paraMan->nremotes;mateIndex++) if( r < recomSelect[mateIndex][1]) break; curMate=recomSelect[mateIndex][0]; newindiv[i].recombinewith=curMate; indiv[curMate].willrecombine=true; //this will be a new topology, so mark it as topo -1. This will be dealt with when we update the topolist newindiv[i].topo=-1; } for(int q=0;qnremotes;q++) delete []recomSelect[q]; delete []recomSelect; } #endif #ifdef MPI_VERSION //new bipart recom conditions, 9-25-05 //DJZ 2-28-06 making recombination partner weakly tied to fitness (selctionIntensity of 0.01) rather than random if(rank==0 && paraMan->subtreeModeActive==false && i>= (conf->nindivs - paraMan->maxRecomIndivs)){ /* int *mates=new int[paraMan->nremotes]; for(int j=0;jnremotes;j++) mates[j]=conf->nindivs+j; ScrambleArray(paraMan->nremotes, mates); */ int foo=2; FLOAT_TYPE **recomSelect=new FLOAT_TYPE *[paraMan->nremotes]; for(int q=0;qnremotes;q++) recomSelect[q]=new FLOAT_TYPE[2]; int potentialPartners=0; for(int r=0;rnremotes;r++){ int ind=conf->nindivs+r; recomSelect[r][0]=(FLOAT_TYPE)(ind); if(ind==parent //don't recombine with your parent || (indiv[parent].topo == indiv[ind].topo) //don't recombine with another of the same topo || (indiv[ind].willrecombine == true))//don't recombine with someone who is already doing so recomSelect[r][1]=-1e100; else{ recomSelect[r][1]=indiv[ind].Fitness(); potentialPartners++; } } if(potentialPartners > 0){ QuickSort(recomSelect, 0, paraMan->nremotes-1); CalculateReproductionProbabilies(recomSelect, 0.01, paraMan->nremotes); int mateIndex; int curMate; // find someone else to recombine with FLOAT_TYPE r=rnd.uniform(); for( mateIndex=0;mateIndex < paraMan->nremotes;mateIndex++) if( r < recomSelect[mateIndex][1]) break; curMate=recomSelect[mateIndex][0]; newindiv[i].recombinewith=curMate; indiv[curMate].willrecombine=true; //this will be a new topology, so mark it as topo -1. This will be dealt with when we update the topolist newindiv[i].topo=-1; } for(int q=0;qnremotes;q++) delete []recomSelect[q]; delete []recomSelect; } #endif } #else //ifdef NO_EVOLUTION parent = 0; #endif newindiv[i].parent=parent; if(newindiv[i].mutation_type==Individual::subtreeRecom) newindiv[i].topo=-1; //VERIFY else newindiv[i].topo=indiv[parent].topo; indiv[ parent ].willreproduce=true; } } void Population::FindTreeStructsForNextGeneration(){ //find treestructs for all of the newindivs, either by getting an unused one from the previous //generation or by getting one from the unusedTree stack for(unsigned i = 0; i < total_size; i++ ){ //see if the parent indiv has already been used in the new generation, or if it will recombine if( i < conf->nindivs && (indiv[newindiv[i].parent].reproduced||indiv[newindiv[i].parent].willrecombine )){ //use a tree from the unused Indiv stack. If it is empty, create an extra indiv that will //eventually make it's way /back to that stack. At most we should only ever have nindiv //trees in the unused stack Tree *destPtr; if(unusedTrees.empty()){//create a new tree Tree *ttree=new Tree(); destPtr=ttree; } else{ destPtr=*(unusedTrees.end()-1); unusedTrees.pop_back(); } newindiv[i].CopySecByRearrangingNodesOfFirst(destPtr,&indiv[newindiv[i].parent]); } else{ //if the tree will not be used in recombination and has not already been used newindiv[i].CopyByStealingTree(&indiv[newindiv[i].parent]); indiv[ newindiv[i].parent].reproduced=true; if(i>conf->nindivs) newindiv[i].mutation_type=indiv[i].mutation_type; } } } void Population::PerformMutation(int indNum){ Individual *ind=&newindiv[indNum]; Individual *par=&indiv[newindiv[indNum].parent]; //FLOAT_TYPE beforeScore; bool recomPerformed; switch(ind->mutation_type){ /* case Individual::exNNI: //exNNI and exlimSPR trump all other mutation types beforeScore=par->Fitness(); NNIoptimization(indNum, 1); if(beforeScore==ind->Fitness()){ topologies[ind->topo]->exNNItried=true; } //ind->accurateSubtrees=false; break; case Individual::exlimSPR: assert(0); SPRoptimization(indNum); ind->accurateSubtrees=false; break; */ case Individual::subtreeRecom: //perform subtree recom, which melds together the different subtrees worked on by the //remote nodes recomPerformed=SubtreeRecombination(indNum); if(recomPerformed==false) ind->mutation_type=0; // ind->treeStruct->calcs=calcCount; // calcCount=0; ind->CalcFitness(0); break; default: if(ind->recombinewith>-1){// perform recombination Individual *recompar=&indiv[ind->recombinewith]; //don't want to standardize biparts anymore ind->treeStruct->CalcBipartitions(false); recompar->treeStruct->CalcBipartitions(false); ind->CrossOverWith( *recompar, adap->branchOptPrecision); ind->accurateSubtrees=false; // ind->treeStruct->calcs=calcCount; // calcCount=0; } if(ind->recombinewith==-1){//all types of "normal" mutation that occur at the inidividual level if(rank==0){//if we are the master if(ind->accurateSubtrees==false || paraMan->subtreeModeActive==false){ ind->Mutate(adap->branchOptPrecision, adap); if(output_tree){ treeLog << " tree gen" << gen << "." << indNum << "= [&U] [" << ind->Fitness() << "][ "; string modstr; ind->modPart.FillGarliFormattedModelStrings(modstr); ind->treeStruct->root->MakeNewick(treeString, false, true); treeLog << modstr.c_str() << "]" << treeString << ";" << endl; output_tree=false; } //reclaim clas if the created tree has essentially no chance of reproducing if(((ind->Fitness() - BestFitness()) < (-11.5/conf->selectionIntensity))){ ind->treeStruct->ReclaimUniqueClas(); } } else{ assert(0);//7/21/06 subtree mode would need to be updated to work again //if subtree mode is on and we are the master, mutate one of the nodes //that isn't in a subtree, or alternatively pick a subtree and mutate it /* #ifndef MASTER_DOES_SUBTREE if(paraMan->fewNonSubtreeNodes != true) ind->NonSubtreeMutate(paraMan, adap->branchOptPrecision, adap); else ind->SubtreeMutate(subtreeNode, adap->branchOptPrecision, subtreeMemberNodes, adap); #else ind->SubtreeMutate(subtreeNode, adap->branchOptPrecision, subtreeMemberNodes, adap); #endif */ } } else{//if we are a remote node if(subtreeNode==0) ind->Mutate(adap->branchOptPrecision, adap); else{ assert(0); //ind->SubtreeMutate(subtreeNode, adap->branchOptPrecision, subtreeMemberNodes, adap); } } } } //check the accuracy of the subtrees #ifndef NDEBUG if(rank==0 && ind->accurateSubtrees==true) paraMan->CheckSubtreeAccuracy(ind->treeStruct); #endif } void Population::NextGeneration(){ DetermineParentage(); FindTreeStructsForNextGeneration(); //return any treestructs from the indivs that won't be used in recombination //and weren't used to make the newindivs. This is necessary to keep from having //too many CLAs in use at any one time for(unsigned j=0;jnindivs;j++){ if(indiv[j].reproduced==false && indiv[j].willrecombine==false){ //this reclaims all indiv's treestructs who have no offspring and no recombination partner indiv[j].treeStruct->RemoveTreeFromAllClas(); unusedTrees.push_back(indiv[j].treeStruct); indiv[j].treeStruct=NULL; } } //to simplify all of the scoring that will be coming up (without passing //a bunch of crap), set the models of the trees to correspond to that of the individuals UpdateTreeModels(); //this loop is only for mutation and recom, so start from holdover for(unsigned indnum = conf->holdover; indnum < conf->nindivs; indnum++ ){ PerformMutation(indnum); } UpdateTreeModels(); //the only trees that we need to return at this point are ones that //did not reproduce AND were used in recom. Those that weren't used //in recom were already reclaimed above, and the treestructs set to NULL for(unsigned j=0;jnindivs;j++){ if(indiv[j].reproduced==false && indiv[j].treeStruct!=NULL){ indiv[j].treeStruct->RemoveTreeFromAllClas(); unusedTrees.push_back(indiv[j].treeStruct); } //reset all of the individuals indiv[j].ResetIndiv(); } // swap newindiv and indiv for(unsigned i=0;inindivs;i++) indiv[i].ResetIndiv(); for(unsigned i=conf->nindivs;ibranchOptPrecision; //some extra debugging info /* fate << "\t" << indiv[i].topo << "\t"; fate << indiv[i].treeStruct->calcs << "\t"; indiv[i].treeStruct->calcs=0; int c, tr, r; indiv[i].treeStruct->CountNumReservedClas(c, tr, r); fate << c << "\t" << tr << "\t" << r << "\t"; // */ fate << "\n"; } // fate << claMan->NumFreeClas() << "\n"; if(gen%20 ==0) fate.flush(); } void Population::OutputFilesForScoreDebugging(Individual *ind /*=NULL*/, int num){ //create three files, one with all of the trees in each gen in nexus format //one with a paup block specifiying the scoring of the trees, and one containing //a list of the scores from GAML if(rank > 0) return; //ofstream outf; //ofstream paupf; #ifdef NNI_SPECTRUM char fname1[30]; char fname2[30]; sprintf(fname1, "toscore%d.tre", gen); sprintf(fname2, "toscore%d.nex", gen); if(num==1){ outf.open(fname1); paupf.open(fname2); } else{ outf.open(fname1, ios::app); paupf.open(fname2, ios::app); } #endif if(gen==1 && ind==NULL || num==1){ dataPart->BeginNexusTreesBlock(outf); paupf << "#nexus\n\n"; paupf << "begin paup;\n"; paupf << "set warnreset=no incr=auto;\n"; paupf << "execute " << conf->ofprefix.c_str() << ".nex;\n"; #ifndef NNI_SPECTRUM paupf << "gett file=toscore.tre storebr;" << endl; #else paupf << "gett file=" << outf << " storebr;" << endl; #endif } if(ind==NULL){ for(unsigned i=0;iStateFreq(0) << " " << indiv[i].mod->StateFreq(1) << " " << indiv[i].mod->StateFreq(2) << ");\n" << "lsc " << (gen-1)*conf->nindivs+i+1; else paupf << "nst=6 rmat=(" << indiv[i].mod->Rates(0) << " " << indiv[i].mod->Rates(1) << " " << indiv[i].mod->Rates(2) << " " << indiv[i].mod->Rates(3) << " " << indiv[i].mod->Rates(4) << ") " << " base=(" << indiv[i].mod->StateFreq(0) << " " << indiv[i].mod->StateFreq(1) << " " << indiv[i].mod->StateFreq(2) << ") "; #ifdef FLEX_RATES paupf << "[FLEX RATES] "; #else if(indiv[i].mod->NRateCats()>1) paupf << "rates=gamma shape=" << indiv[i].mod->Alpha() << " "; paupf << "pinv=" << indiv[i].mod->PropInvar() << " "; #endif */ if(modSpecSet.GetModSpec(0)->Nst()==2) paupf << "nst=2 trat=" << indiv[i].modPart.GetModel(0)->Rates(0) << " base=(" << indiv[i].modPart.GetModel(0)->StateFreq(0) << " " << indiv[i].modPart.GetModel(0)->StateFreq(1) << " " << indiv[i].modPart.GetModel(0)->StateFreq(2) << ");\n" << "lsc " << (gen-1)*conf->nindivs+i+1; else paupf << "nst=6 rmat=(" << indiv[i].modPart.GetModel(0)->Rates(0) << " " << indiv[i].modPart.GetModel(0)->Rates(1) << " " << indiv[i].modPart.GetModel(0)->Rates(2) << " " << indiv[i].modPart.GetModel(0)->Rates(3) << " " << indiv[i].modPart.GetModel(0)->Rates(4) << ") " << " base=(" << indiv[i].modPart.GetModel(0)->StateFreq(0) << " " << indiv[i].modPart.GetModel(0)->StateFreq(1) << " " << indiv[i].modPart.GetModel(0)->StateFreq(2) << ") "; #ifdef FLEX_RATES paupf << "[FLEX RATES] "; #else if(indiv[i].modPart.GetModel(0)->NRateCats()>1) paupf << "rates=gamma shape=" << indiv[i].modPart.GetModel(0)->Alpha() << " "; paupf << "pinv=" << indiv[i].modPart.GetModel(0)->PropInvar() << " "; #endif if(gen==1 && i==0) paupf << ";\n" << "lsc " << (gen-1)*total_size+i+1 << "/scorefile=paupscores.txt replace;\n"; else paupf << ";\n" << "lsc " << (gen-1)*total_size+i+1 << "/scorefile=paupscores.txt append;\n"; } } else{ outf << " utree " << num << "= "; ind->treeStruct->root->MakeNewick(treeString, false, true); outf << treeString << ";\n"; //DEBUG PARTITION /* paupf << "lset userbr "; if(modSpec->Nst()==2) paupf << "nst=2 trat=" << ind->mod->Rates(0) << " base=(" << ind->mod->StateFreq(0) << " " << ind->mod->StateFreq(1) << " " << ind->mod->StateFreq(2) << ");\nlsc "; else paupf << "nst=6 rmat=(" << ind->mod->Rates(0) << " " << ind->mod->Rates(1) << " " << ind->mod->Rates(2) << " " << ind->mod->Rates(3) << " " << ind->mod->Rates(4) << ") " << " base=(" << ind->mod->StateFreq(0) << " " << ind->mod->StateFreq(1) << " " << ind->mod->StateFreq(2) << ") "; #ifdef FLEX_RATES paupf << "[FLEX RATES] "; #else if(ind->mod->NRateCats()>1) paupf << "rates=gamma shape=" << ind->mod->Alpha() << " "; paupf << "pinv=" << ind->mod->PropInvar() << " "; #endif */ paupf << "lset userbr "; if(modSpecSet.GetModSpec(0)->Nst()==2) paupf << "nst=2 trat=" << ind->modPart.GetModel(0)->Rates(0) << " base=(" << ind->modPart.GetModel(0)->StateFreq(0) << " " << ind->modPart.GetModel(0)->StateFreq(1) << " " << ind->modPart.GetModel(0)->StateFreq(2) << ");\nlsc "; else paupf << "nst=6 rmat=(" << ind->modPart.GetModel(0)->Rates(0) << " " << ind->modPart.GetModel(0)->Rates(1) << " " << ind->modPart.GetModel(0)->Rates(2) << " " << ind->modPart.GetModel(0)->Rates(3) << " " << ind->modPart.GetModel(0)->Rates(4) << ") " << " base=(" << ind->modPart.GetModel(0)->StateFreq(0) << " " << ind->modPart.GetModel(0)->StateFreq(1) << " " << ind->modPart.GetModel(0)->StateFreq(2) << ") "; #ifdef FLEX_RATES paupf << "[FLEX RATES] "; #else if(ind->modPart.GetModel(0)->NRateCats()>1) paupf << "rates=gamma shape=" << ind->modPart.GetModel(0)->Alpha() << " "; paupf << "pinv=" << ind->modPart.GetModel(0)->PropInvar() << " "; #endif #ifndef NNI_SPECTRUM if(num==1) paupf << ";\n" << "lsc " << num << "/scorefile=paupscores.txt replace;\n"; else paupf << ";\n" << "lsc " << num << "/scorefile=paupscores.txt append;\n"; #else if(num==1) paupf << ";\n" << "lsc " << num << "/scorefile=paupscores" << gen << ".txt replace;\n"; else paupf << ";\n" << "lsc " << num << "/scorefile=paupscores" << gen << ".txt append;\n"; #endif } #ifdef NNI_SPECTRUM outf.close(); paupf.close(); #endif } //this assumes that the tree to be appended is a member of the population //if indNum is -1, then the bestIndiv from the pop is used void Population::AppendTreeToTreeLog(int mutType, int indNum /*=-1*/){ if(treeLog.is_open() == false || conf->outputTreelog==false) return; const Individual *ind; int i = (indNum >= 0 ? indNum : bestIndiv); ind=&indiv[i]; // if(Tree::outgroup != NULL) // OutgroupRoot(ind, i); int num = 0; Individual tempInd; const Individual *theInd; if(Tree::outgroup != NULL || conf->collapseBranches){ tempInd.DuplicateIndivWithoutCLAs(ind); if(Tree::outgroup != NULL) OutgroupRoot(&tempInd, -1); /* //Can't decide if these should be collapsed or not here. Thinking no. if(conf->collapseBranches){ tempInd.treeStruct->root->CollapseMinLengthBranches(num); outman.UserMessage("%d COLLAPSED", num); } */ theInd = &tempInd; } else theInd = ind; if(finishedRep) treeLog << " tree final= [&U] [" << theInd->Fitness() << "][ "; else treeLog << " tree gen" << gen << "= [&U] [" << theInd->Fitness() << "\tmut=" << mutType << "][ "; string modstr; ind->modPart.FillGarliFormattedModelStrings(modstr); theInd->treeStruct->root->MakeNewick(treeString, false, true); treeLog << modstr.c_str() << "]" << treeString << ";" << endl; } void Population::FinishBootstrapRep(const Individual *ind, int rep){ if(bootLog.is_open() == false) return; int num = 0; Individual tempInd; const Individual *theInd; if(Tree::outgroup != NULL || conf->collapseBranches){ tempInd.DuplicateIndivWithoutCLAs(ind); if(Tree::outgroup != NULL) OutgroupRoot(&tempInd, -1); if(conf->collapseBranches){ tempInd.treeStruct->root->CollapseMinLengthBranches(num); // outman.UserMessage("%d COLLAPSED", num); } theInd = &tempInd; } else theInd = ind; bootLog << " tree bootrep" << rep << "= [&U] [" << theInd->Fitness() << " "; string modstr; theInd->modPart.FillGarliFormattedModelStrings(modstr); theInd->treeStruct->root->MakeNewick(treeString, false, true); bootLog << modstr.c_str() << "] " << treeString << ";" << endl; if(conf->outputPhylipTree) WritePhylipTree(bootLogPhylip); } bool Population::OutgroupRoot(Individual *ind, int indnum){ //if indnum != -1 the individual is in the indiv array, and a few extra things need to be done ind->treeStruct->root->CheckforPolytomies(); ind->treeStruct->CalcBipartitions(true); Bipartition b = *(Tree::outgroup); b.Standardize(); TreeNode *r = ind->treeStruct->ContainsBipartitionOrComplement(b); if(r == NULL){ //this means that there isn't a bipartition separating the outgroup and ingroup //so outgroup rooting is not possible return false; } TreeNode *temp = r; while(temp->IsTerminal() == false) temp=temp->left; if(Tree::outgroup->ContainsTaxon(temp->nodeNum) == false || r->IsTerminal()) r = r->anc; if(r->IsNotRoot()){ // outman.UserMessage("REROOTED"); ind->treeStruct->RerootHere(r->nodeNum); if(indnum != -1){ ind->SetDirty(); ind->CalcFitness(0); } return true; } else return false; } void Population::WriteTreeFile( const char* treefname, int indnum, bool collapse /*=false*/ ){ assert( treefname ); string filename = treefname; filename += ".tre"; //output an individual from the storedTrees if an indnum is passed in //otherwise the best in the population const Individual *ind; if(indnum == -1){ ind = &indiv[bestIndiv]; } else{ assert(indnum < storedTrees.size()); ind = storedTrees[indnum]; } int num = 0; Individual tempInd; const Individual *theInd; if(Tree::outgroup != NULL || (conf->collapseBranches && collapse)){ tempInd.DuplicateIndivWithoutCLAs(ind); if(Tree::outgroup != NULL) OutgroupRoot(&tempInd, -1); if(conf->collapseBranches && collapse){ tempInd.treeStruct->root->CollapseMinLengthBranches(num); // outman.UserMessage("%d COLLAPSED", num); } theInd = &tempInd; } else theInd = ind; #ifdef INCLUDE_PERTURBATION if(allTimeBest != NULL){ if(best->Fitness() < allTimeBest->Fitness() || pertMan->ratcheted==true) return; } #endif #ifdef BOINC char physical_name[100]; boinc_resolve_filename(filename.c_str(), physical_name, sizeof(physical_name)); MFILE outf; outf.open(physical_name, "w"); #else ofstream outf; outf.open( filename.c_str() ); outf.precision(8); #endif string trans; string str; dataPart->BeginNexusTreesBlock(trans); //data->BeginNexusTreesBlock(outf); char temp[101]; if(indnum == -1) sprintf(temp, "tree best = [&U][!GarliScore %f][!GarliModel ", theInd->Fitness()); else sprintf(temp, "tree bestREP%d = [&U][!GarliScore %f][!GarliModel ", indnum+1, theInd->Fitness()); str += temp; string modstr; ind->modPart.FillGarliFormattedModelStrings(modstr); str += modstr; str += "]"; #ifdef BOINC const char *s = trans.c_str(); outf.write(s, sizeof(char), trans.length()); s = str.c_str(); outf.write(s, sizeof(char), str.length()); theInd->treeStruct->root->MakeNewick(treeString, false, true); size_t len = strlen(treeString); outf.write(treeString, sizeof(char), len); str = ";\nend;\n"; s = str.c_str(); outf.write(s, sizeof(char), str.length()); #else outf << trans; outf << str; outf.setf( ios::floatfield, ios::fixed ); outf.setf( ios::showpoint ); theInd->treeStruct->root->MakeNewick(treeString, false, true); outf << treeString << ";\n"; outf << "end;\n"; #endif //add a paup block setting the model params str = ""; if(modSpecSet.GetModSpec(0)->IsNucleotide()){ if(ind->modPart.NumModels() == 1){ ind->modPart.GetModel(0)->FillPaupBlockStringForModel(str, filename.c_str()); } else{ str += "[\n"; //modstr was already filled above //ind->modPart.FillGarliFormattedModelStrings(modstr); str += modstr; str += "\n]\n"; modstr.clear(); } } #ifdef BOINC s = str.c_str(); outf.write(s, sizeof(char), str.length()); if((userTermination || timeTermination) && (indnum == storedTrees.size() - 1)){ //str = "[!****NOTE: GARLI Run was terminated before termination condition was reached!\nLikelihood scores, topologies and model estimates obtained may not be fully optimal!****\n]"; str = "["; str += TerminationWarningMessage(); str += "]\n"; s = str.c_str(); outf.write(s, sizeof(char), str.length()); } #else outf << str; //if(indnum < 0 && (userTermination || timeTermination)) if((userTermination || timeTermination) && (indnum == storedTrees.size() - 1)) outf << "[" << TerminationWarningMessage().c_str() << "]" << endl; #endif outf.close(); if(conf->outputPhylipTree){//output a phylip formatted tree if desired char phyname[85]; sprintf(phyname, "%s.phy", treefname); ofstream phytree(phyname); phytree.precision(8); WritePhylipTree(phytree); phytree.close(); } } void Population::WriteStoredTrees( const char* treefname ){ assert( treefname ); string name; name = treefname; name += ".all.tre"; ofstream outf( name.c_str() ); outf.precision(8); dataPart->BeginNexusTreesBlock(outf); ofstream phytree; if(conf->outputPhylipTree){ char phyname[85]; sprintf(phyname, "%s.all.phy", treefname); phytree.open(phyname); phytree.precision(8); } int bestRep = EvaluateStoredTrees(false); Individual tempInd; for(unsigned r=0;rcollapseBranches){ tempInd.DuplicateIndivWithoutCLAs(storedTrees[r]); if(Tree::outgroup != NULL) OutgroupRoot(&tempInd, -1); if(conf->collapseBranches){ int num = 0; tempInd.treeStruct->root->CollapseMinLengthBranches(num); // outman.UserMessage("%d COLLAPSED", num); } curInd = &tempInd; } else curInd = storedTrees[r]; if(r == bestRep) outf << "tree rep" << r+1 << "BEST = [&U][!GarliScore " << curInd->Fitness() << "][!GarliModel "; else outf << "tree rep" << r+1 << " = [&U][!GarliScore " << curInd->Fitness() << "][!GarliModel "; string mods; curInd->modPart.FillGarliFormattedModelStrings(mods); outf << mods; outf << "]"; outf.setf( ios::floatfield, ios::fixed ); outf.setf( ios::showpoint ); curInd->treeStruct->root->MakeNewick(treeString, false, true); outf << treeString << ";\n"; if(conf->outputPhylipTree){//output a phylip formatted tree if requested WritePhylipTree(phytree); } } /* for(unsigned r=0;rFitness() << "][!GarliModel "; else outf << "tree rep" << r+1 << " = [&U][!GarliScore " << storedTrees[r]->Fitness() << "][!GarliModel "; storedTrees[r]->mod->OutputGarliFormattedModel(outf); outf << "]"; outf.setf( ios::floatfield, ios::fixed ); outf.setf( ios::showpoint ); if(Tree::outgroup != NULL) OutgroupRoot(storedTrees[r], -1); storedTrees[r]->treeStruct->root->MakeNewick(treeString, false, true); outf << treeString << ";\n"; if(conf->outputPhylipTree){//output a phylip formatted tree if requested WritePhylipTree(phytree); } } */ outf << "end;\n"; // if(modSpecSet.GetModSpec(0)->IsNucleotide()){ //add a paup block setting the model params //PARTITION //storedTrees[bestRep]->mod->OutputPaupBlockForModel(outf, name.c_str()); if(storedTrees[bestRep]->modPart.NumModels() == 1 && storedTrees[bestRep]->modPart.GetModel(0)->IsNucleotide()){ storedTrees[bestRep]->modPart.GetModel(0)->OutputPaupBlockForModel(outf, name.c_str()); outf << "[!****NOTE: The model parameters loaded are the final model estimates****\n****from GARLI for the best scoring search replicate (#" << bestRep + 1 << ").****\n****The best model parameters for other trees may vary.****]" << endl; } else{ for(int m = 0;m < storedTrees[bestRep]->modPart.NumModels();m++){ //DEBUG if(storedTrees[bestRep]->modPart.GetModel(m)->IsNucleotide()){ char mStr[20]; sprintf(mStr, "[M%d\n", m + 1); outf << mStr; storedTrees[bestRep]->modPart.GetModel(m)->OutputPaupBlockForModel(outf, name.c_str()); outf << "\n]\n"; } } } // } if(userTermination || timeTermination){ outf << "[" << TerminationWarningMessage().c_str() << "]"; } outf.close(); if(conf->outputPhylipTree) phytree.close(); } //CAREFUL HERE! This function assumes the the treestring was just //filled with MakeNewick, making a tree with taxon NUMBERS in the specification. //This function then just reads that treestring and translates to taxon NAMES //on the fly and outputs everything to the string passed in, which needs to //be already open void Population::WritePhylipTree(ofstream &phytree){ char *loc=treeString; NxsString temp; while(*loc){ if(*loc == ':'){ temp += *loc++; while(*loc != ',' && *loc != ')') temp += *loc++; phytree << temp.c_str(); temp=""; } if(isdigit(*loc) == false) phytree << *loc++; else{ while(isdigit(*loc)) temp += *loc++; //The stored taxon names will have been gotten with GetEscaped, and thus might //have quotes around them if they have Nexus punctuation. The quotes probably //shouldn't appear in the phylip output. However, if the names have three single //quotes this corresponds to a single literal quote, in which case it will be output NxsString pname = dataPart->TaxonLabel(atoi(temp.c_str())-1); if(pname[0] == '\'' && pname[pname.size()-1] == '\''){ pname.erase(pname.end()-1); pname.erase(pname.begin()); } if(pname[0] == '\'' && pname[1] == '\'' ){ pname.erase(pname.end()-1); pname.erase(pname.begin()); } phytree << pname.c_str(); //phytree << data->TaxonLabel(atoi(temp.c_str())-1); temp=""; } } phytree << ";" << endl; } char * Population::MakeNewick(int i, bool internalNodes) { indiv[i].treeStruct->root->MakeNewick(treeString, internalNodes, true); assert(!treeString[stringSize-1]); return treeString; } //DZ 7-7 This function will get rid of multiple references to the same treeStruct //from different individuals. This keeps FLOAT_TYPE deletion from occuring in the destructor. //Not the most elegant, but it works. void Population::EliminateDuplicateTreeReferences(){ bool dupe; vector tstructs; //go through the indiv array for(unsigned i=0;inindivs;i++){ //check if we have already encountered this treeStruct dupe=false; for(vector::iterator tit=tstructs.begin();tit!=tstructs.end();tit++){ if(indiv[i].treeStruct==(*tit)){ dupe=true; indiv[i].treeStruct=NULL; break; } } if(dupe==false){ tstructs.push_back(indiv[i].treeStruct); } } //go through the newindiv array for(unsigned i=0;inindivs;i++){ //check if we have already encountered this treeStruct dupe=false; for(vector::iterator tit=tstructs.begin();tit!=tstructs.end();tit++){ if(newindiv[i].treeStruct==(*tit)){ dupe=true; newindiv[i].treeStruct=NULL; break; } } if(dupe==false){ tstructs.push_back(newindiv[i].treeStruct); } } //go through the unusedTree vector for(vector::iterator vit=unusedTrees.begin();vit!=unusedTrees.end();vit++){ dupe=false; for(vector::iterator tit=tstructs.begin();tit!=tstructs.end();tit++){ if((*vit)==(*tit)){ dupe=true; unusedTrees.erase(vit); vit--; break; } } } } void Population::CheckAllTrees(){//debugging function for(unsigned i=0;inindivs;i++){ //check that trees are properly formed indiv[i].treeStruct->root->CheckforLeftandRight(); indiv[i].treeStruct->root->CheckforPolytomies(); indiv[i].treeStruct->root->CheckTreeFormation(); //check that no individuals point to the same treeStruct for(unsigned j=i+1;jnindivs;j++) assert(!(indiv[i].treeStruct==indiv[j].treeStruct)); } } void Population::CheckTreesVsClaManager(){ //go through each node for each tree and make sure that the numbers in the assignedClaArray are correct /* int numCopies=claMan->NumCopies(); int numNodes=claMan->NumNodes(); int count; for(int n=0;nallNodes[claMan->ReverseConvertNodeIndex(n)]->claIndex==c) count++; } claMan->CheckAssignedNumber(count, n, c); } } */ } /* int Population::SwapIndividuals(int n, const char* tree_strings_in, FLOAT_TYPE* kappa_probs_in, char** tree_strings_out_, FLOAT_TYPE** kappa_probs_out_) { char*& tree_strings_out = *tree_strings_out_; FLOAT_TYPE*& kappa_probs_out = *kappa_probs_out_; int* indivs_to_send; GetNRandomIndivIndices(&indivs_to_send, n); GetSpecifiedTreeStrings(&tree_strings_out, n, indivs_to_send); GetSpecifiedKappas(&kappa_probs_out, n, indivs_to_send); // determine what to replace out (don't send out our best indiv!) int* indivs_to_replace = new int[n]; for (int i = 0; i < n; ++i) { if (indivs_to_send[i] == (int)cumfit[current_size-1][0]) { indivs_to_replace[i] = ((rand() % (current_size-1))+1 + indivs_to_send[i]) % current_size; assert(indivs_to_replace[i] != (int)cumfit[current_size-1][0]); } else indivs_to_replace[i] = indivs_to_send[i]; } EliminateDuplicateTreeReferences(); int x; const char *p = tree_strings_in; for (int i = 0; i < n; ++i) { x = indivs_to_replace[i]; assert(x != -1); // make sure we're not at the end of the array assert(x != (int)cumfit[current_size-1][0]); // make sure we're not replacing best // put in the new tree indiv[x].treeStruct->RemoveTreeFromAllClas(); delete indiv[x].treeStruct; indiv[x].treeStruct = new Tree(p, params->data, sharedcl); indiv[x].treeStruct->AssignCLAsFromMaster(); // put in the kappa prob indiv[x].kappa = kappa_probs_in[i]; // set some other stuff indiv[x].SetDirty(); indiv[x].parent = -1; p += strlen(p) + 1; } delete [] indivs_to_replace; delete [] indivs_to_send; return 0; } */ /* This is all old parallel stuff not currently being used int Population::ReplaceSpecifiedIndividuals(int count, int* which_array, const char* tree_strings, FLOAT_TYPE* model_string) { //assert(count < CountTreeStrings(tree_strings)); // sanity check int which; for (int i = 0; i < count; ++i) { which = which_array[i]; Individual *ind=&indiv[which]; ind->treeStruct->RemoveTreeFromAllClas(); topologies[ind->topo]->RemoveInd(which); ind->topo=-1; ind->mutation_type=-1; delete ind->treeStruct; ind->treeStruct = new Tree(tree_strings, true); ind->treeStruct->AssignCLAsFromMaster(); ind->mod->SetModel(model_string); ind->treeStruct->modPart=&ind->modPart; ind->SetDirty(); tree_strings += strlen(tree_strings)+1; ind->treeStruct->modPart=&ind->modPart; } CompactTopologiesList(); UpdateTopologyList(indiv); return 0; } int Population::GetNRandomIndivIndices(int** indiv_list, int n) { int* ar = new int[total_size]; for (unsigned i = 0; i < total_size; ++i) ar[i] = i; ScrambleArray(total_size, ar); *indiv_list = new int[n]; for (int i = 0; i < n; ++i) (*indiv_list)[i] = ar[i]; delete [] ar; return 0; } int Population::GetNBestIndivIndices(int** indiv_list, int n) { *indiv_list = new int[n]; for (int i = 0; i < n; ++i) (*indiv_list)[i] = (int)cumfit[total_size-i-1][0]; return 0; } int Population::GetSpecifiedTreeStrings(char** tree_strings_, int n, int* indiv_list) { char*& tree_strings = *tree_strings_; int buf_size = 0; for (int i = 0; i < n; ++i) // calc the buff size buf_size += (int) strlen(MakeNewick(indiv_list[i], true)) + 1; char* p = tree_strings = new char[buf_size+1]; for (int i = 0; i < n; ++i) p += strlen(strcpy(p, MakeNewick(indiv_list[i], true))) + 1; *p = 0; assert(p-tree_strings == buf_size); return 0; } int Population::GetSpecifiedModels(FLOAT_TYPE** model_string, int n, int* indiv_list){ FLOAT_TYPE *&model = *model_string; int string_size=0; //first calculate the appropriate size of the string and allocate it int nrates=modSpec->Nst()-1; string_size+=n*nrates; string_size+=n*4;//the pi's if(indiv[indiv_list[0]].mod->NRateCats()>1) string_size+=1*n; #ifdef FLEX_RATES assert(0); #else if(indiv[indiv_list[0]].mod->PropInvar()!=ZERO_POINT_ZERO) string_size+=1*n; #endif model=new FLOAT_TYPE[string_size]; int slot=0; for (int i = 0; i < n; ++i){ //get the rates for(int r=0;rRates(r); //get the pi's for(int b=0;b<4;b++) model[slot++] = indiv[indiv_list[i]].mod->StateFreq(b); #ifdef FLEX_RATES assert(0); #else //get alpha if we are using rate het if(indiv[indiv_list[0]].mod->NRateCats()>1) model[slot++] = indiv[indiv_list[i]].mod->Alpha(); //get pinv if we are using invariant sites if(indiv[indiv_list[0]].mod->PropInvar()!=ZERO_POINT_ZERO) model[slot++] = indiv[indiv_list[i]].mod->PropInvar(); #endif } return slot; } */ void Population::OutputLog() { //log << gen << "\t" << bestFitness << "\t" << stopwatch.SplitTime() << "\t" << adap->branchOptPrecision << endl; if(!finishedRep) { log << gen << "\t" << BestFitness() << "\t" << stopwatch.SplitTime() << "\t" << adap->branchOptPrecision; if(conf->reportRunProgress) log << "\t" << 0.01 * (int) ceil(rep_fraction_done * 100) << "\t" << 0.01 * (int) ceil(tot_fraction_done * 100); log << endl; #ifdef MAC_FRONTEND NSAutoreleasePool *pool = [[NSAutoreleasePool alloc] init]; NSDictionary *progressDict = [NSDictionary dictionaryWithObjectsAndKeys:[NSNumber numberWithInt:gen], @"generation", [NSNumber numberWithDouble:BestFitness()], @"likelihood", [NSNumber numberWithInt:stopwatch.SplitTime()], @"time", [NSNumber numberWithDouble:adap->branchOptPrecision], @"precision", [NSNumber numberWithInt:lastTopoImprove], @"lastImprovement", nil]; [[MFEInterfaceClient sharedClient] reportProgress:progressDict]; [pool release]; #endif } else{ CalcAverageFitness(); log << "Final\t" << BestFitness() << "\t" << stopwatch.SplitTime() << "\t" << adap->branchOptPrecision << endl; } } /* int Population::ReplicateSpecifiedIndividuals(int count, int* which, const char* tree_string, FLOAT_TYPE *model_string){ assert(count > 0 && count <= (int)total_size); for (int i = 0; i < count; ++i) { indiv[which[i]].treeStruct->RemoveTreeFromAllClas(); delete indiv[which[i]].treeStruct; indiv[which[i]].treeStruct = new Tree(tree_string, true); indiv[which[i]].treeStruct->AssignCLAsFromMaster(); indiv[which[i]].mod->SetModel(model_string); indiv[which[i]].treeStruct->modPart=&indiv[which[i]].modPart; indiv[which[i]].SetDirty(); indiv[which[i]].treeStruct->modPart=&indiv[which[i]].modPart; } return 0; } */ void Population::UpdateTreeModels(){ for(unsigned ind=0;indmodPart=&newindiv[ind].modPart; // indiv[ind].treeStruct->mod=indiv[ind].mod; } } FLOAT_TYPE Population::IndivFitness(int i) { return indiv[i].Fitness(); } void Population::OutputModelAddresses(){ ofstream mods("modeldeb.log", ios::app); for(unsigned i=0;imodPart << "\n"; mods << "newindiv " << i << "\t" << &newindiv[i].modPart << "\t" << newindiv[i].treeStruct->modPart << "\n"; } mods << endl; } bool Population::NNIoptimization(unsigned indivIndex, int steps){ Individual currentBest; Individual tempIndiv1, tempIndiv2, *best; int beginNode, endNode, optiNode; FLOAT_TYPE bestNNIFitness; FLOAT_TYPE startingFitness; bool betterScore=false; // ofstream outf("nnidebug.tre"); // ofstream scr("nniscores.tre"); ofstream out; beginNode = newindiv[indivIndex].treeStruct->getNumTipsTotal() + 1; endNode = beginNode * 2 - 5; startingFitness = indiv[newindiv[indivIndex].parent].Fitness(); bestNNIFitness = -FLT_MAX; steps = min(max(0,steps),newindiv[indivIndex].treeStruct->getNumTipsTotal()-3); indivIndex = min(max(0,(int)indivIndex),(int)conf->nindivs-1); //DJZ while(unusedTrees.size()<3){ Tree *temp=new Tree(); unusedTrees.push_back(temp); } tempIndiv1.treeStruct=*(unusedTrees.end()-1); unusedTrees.pop_back(); tempIndiv2.treeStruct=*(unusedTrees.end()-1); unusedTrees.pop_back(); currentBest.treeStruct=*(unusedTrees.end()-1); unusedTrees.pop_back(); // tempIndiv1.CopySecByRearrangingNodesOfFirst(tempIndiv1.treeStruct, &newindiv[indivIndex]); tempIndiv2.CopySecByRearrangingNodesOfFirst(tempIndiv2.treeStruct, &newindiv[indivIndex]); currentBest.CopySecByRearrangingNodesOfFirst(currentBest.treeStruct, &newindiv[indivIndex]); for(int i =0;i" << endNode<NNIMutate(optiNode,0, adap->branchOptPrecision, 0); tempIndiv2.treeStruct->NNIMutate(optiNode,1, adap->branchOptPrecision, 0); //newindiv[0].treeStruct->SetAllTempClasDirty(); tempIndiv1.SetDirty(); tempIndiv2.SetDirty(); tempIndiv1.CalcFitness(0); tempIndiv2.CalcFitness(0); FLOAT_TYPE improvement = (FLOAT_TYPE)0.01; improvement = adap->recTopImproveSize; if(tempIndiv1.Fitness() > (bestNNIFitness) || tempIndiv2.Fitness() > (bestNNIFitness)){ if(tempIndiv1.Fitness() > tempIndiv2.Fitness()) best=&tempIndiv1; else best=&tempIndiv2; bestNNIFitness = best->Fitness(); currentBest.CopySecByRearrangingNodesOfFirst(currentBest.treeStruct, best, true); if(bestNNIFitness > startingFitness) betterScore=true; } //if the best tree we've found by NNI is better than what we started with, use it //for successive NNI attempts in this function if(bestNNIFitness > startingFitness){ tempIndiv1.CopySecByRearrangingNodesOfFirst(tempIndiv1.treeStruct, ¤tBest, true); tempIndiv2.CopySecByRearrangingNodesOfFirst(tempIndiv2.treeStruct, ¤tBest, true); } else{//otherwise, revert to the starting tree tempIndiv1.CopySecByRearrangingNodesOfFirst(tempIndiv1.treeStruct, &newindiv[indivIndex], true); tempIndiv2.CopySecByRearrangingNodesOfFirst(tempIndiv2.treeStruct, &newindiv[indivIndex], true); } } //end of loop through all possible NNIs //copy the best tree that we found back into the population, whether or not it was better than what we //started with newindiv[indivIndex].CopySecByRearrangingNodesOfFirst(newindiv[indivIndex].treeStruct, ¤tBest, true); } //end of loop through steps //Return the treestructs that we used temporarily back to the unused tree vector tempIndiv1.treeStruct->RemoveTreeFromAllClas(); unusedTrees.push_back(tempIndiv1.treeStruct); tempIndiv1.treeStruct=NULL; tempIndiv2.treeStruct->RemoveTreeFromAllClas(); unusedTrees.push_back(tempIndiv2.treeStruct); tempIndiv2.treeStruct=NULL; currentBest.treeStruct->RemoveTreeFromAllClas(); unusedTrees.push_back(currentBest.treeStruct); currentBest.treeStruct=NULL; // newindiv[indivIndex].treeStruct->SetAllTempClasDirty(); // newindiv[indivIndex].mutation_type |= Individual::exNNI; return betterScore; } /* End of methods added by Yufeng Zhang*/ void Population::NNISpectrum(int sourceInd){ Individual tempIndiv1, tempIndiv2; int optiNode; FLOAT_TYPE previousFitness; FLOAT_TYPE scorediff=ZERO_POINT_ZERO; //FLOAT_TYPE thresh=pertMan->nniAcceptThresh; int numNodes=indiv[sourceInd].treeStruct->getNumTipsTotal()-3; int *nodeArray=new int[numNodes]; for(int i=0;iMakeAllNodesDirty(); tempGlobal=1; OutputFilesForScoreDebugging(&tempIndiv1, tempGlobal++); FLOAT_TYPE localprec; FLOAT_TYPE prec[7]={(FLOAT_TYPE).5, (FLOAT_TYPE).25, (FLOAT_TYPE).1, (FLOAT_TYPE).05, (FLOAT_TYPE).01, (FLOAT_TYPE).005, (FLOAT_TYPE).001}; for(int q=0;q<7;q++){ localprec=prec[q]; char filename[50]; // sprintf(filename, "%d.%.4fscores.log", gen, localprec); ofstream temp(filename); temp.precision(12); temp << "start\t" << BestFitness() << "\n"; for(int i=0;iNNIMutate(optiNode,0, localprec, 0); tempIndiv2.treeStruct->NNIMutate(optiNode,1, localprec, 0); // tempIndiv1.SetDirty(); // tempIndiv2.SetDirty(); tempIndiv1.SetFitness(tempIndiv1.treeStruct->lnL); tempIndiv2.SetFitness(tempIndiv2.treeStruct->lnL); temp << tempIndiv1.Fitness() << "\n" << tempIndiv2.Fitness() << "\n"; if(q==0){ OutputFilesForScoreDebugging(&tempIndiv1, tempGlobal++); OutputFilesForScoreDebugging(&tempIndiv2, tempGlobal++); } FLOAT_TYPE diff1=tempIndiv1.Fitness() - previousFitness; FLOAT_TYPE diff2=tempIndiv2.Fitness() - previousFitness; tempIndiv1.CopySecByRearrangingNodesOfFirst(tempIndiv1.treeStruct, &indiv[sourceInd], true); tempIndiv2.CopySecByRearrangingNodesOfFirst(tempIndiv2.treeStruct, &indiv[sourceInd], true); } temp.close(); } //Return the treestructs that we used temporarily back to the unused tree vector tempIndiv1.treeStruct->RemoveTreeFromAllClas(); unusedTrees.push_back(tempIndiv1.treeStruct); tempIndiv1.treeStruct=NULL; tempIndiv2.treeStruct->RemoveTreeFromAllClas(); unusedTrees.push_back(tempIndiv2.treeStruct); tempIndiv2.treeStruct=NULL; delete []nodeArray; } #ifdef INCLUDE_PERTURBATION void Population::NNIPerturbation(int sourceInd, int indivIndex){ Individual currentBest; Individual tempIndiv1, tempIndiv2, *best; int optiNode; FLOAT_TYPE previousFitness; // bool betterScore=false; FLOAT_TYPE scorediff=ZERO_POINT_ZERO; FLOAT_TYPE thresh=pertMan->nniAcceptThresh; int nummoves=0; ofstream out; // int numNodes=indiv[indivIndex].treeStruct->getNumTipsTotal()-3; // int *nodeArray=new int[numNodes]; /* for(int i=0;iGetRandomInternalNode(); //get all of the nodes, in order // nodeArray[i]=numNodes+i+4; } */ // ScrambleArray(numNodes, nodeArray); previousFitness = indiv[sourceInd].Fitness(); //DJZ while(unusedTrees.size()<3){ Tree *temp=new Tree(); unusedTrees.push_back(temp); } tempIndiv1.treeStruct=*(unusedTrees.end()-1); unusedTrees.pop_back(); tempIndiv2.treeStruct=*(unusedTrees.end()-1); unusedTrees.pop_back(); currentBest.treeStruct=*(unusedTrees.end()-1); unusedTrees.pop_back(); // tempIndiv1.CopySecByRearrangingNodesOfFirst(tempIndiv1.treeStruct, &indiv[sourceInd]); tempIndiv2.CopySecByRearrangingNodesOfFirst(tempIndiv2.treeStruct, &indiv[sourceInd]); currentBest.CopySecByRearrangingNodesOfFirst(currentBest.treeStruct, &indiv[sourceInd]); int n=2; //make all the nodes dirty of all of the trees in the actual population, since they //will be replaced by the perturbed individual and will take up valuable clas for(unsigned i=0;iMakeAllNodesDirty(); char filename[50]; if(rank < 10) sprintf(filename, "pertreport0%d.log", rank); else sprintf(filename, "pertreport%d.log", rank); ofstream pert(filename, ios::app); pert.precision(10); pert << "gen\t" << gen << "\tstart\t" << BestFitness() << "\n"; outman.UserMessage("Performing NNI Perturbation. Starting score= %.4f", BestFitness()); /* char filename[50]; FLOAT_TYPE localprec=.5; sprintf(filename, "%d.%.4fscores.log", gen, localprec); ofstream temp(filename); temp.precision(12); temp << "start\t" << BestFitness() << "\n"; */ // for(int i=0;inniTargetAccepts) && (attempts <= pertMan->nniMaxAttempts);){ if(! (attempts++ % (pertMan->nniMaxAttempts/20))) outman.UserMessage("."); outman.flush(); optiNode=indiv[indivIndex].treeStruct->GetRandomInternalNode(); // optiNode=nodeArray[i]; // tempIndiv1.treeStruct->NNIMutate(optiNode,0, localprec, 0); // tempIndiv2.treeStruct->NNIMutate(optiNode,1, localprec, 0); tempIndiv1.treeStruct->NNIMutate(optiNode,0, adap->branchOptPrecision, 0); tempIndiv2.treeStruct->NNIMutate(optiNode,1, adap->branchOptPrecision, 0); // tempIndiv1.SetDirty(); // tempIndiv2.SetDirty(); tempIndiv1.SetFitness(tempIndiv1.treeStruct->lnL); tempIndiv2.SetFitness(tempIndiv2.treeStruct->lnL); // temp << tempIndiv1.Fitness() << "\n" << tempIndiv2.Fitness() << "\n"; FLOAT_TYPE diff1=tempIndiv1.Fitness() - previousFitness; FLOAT_TYPE diff2=tempIndiv2.Fitness() - previousFitness; //ignore NNI's that improve the fitness, because they are probably just undoing a previous NNI // if(((diff1 < ZERO_POINT_ZERO) && (diff1 + thresh > ZERO_POINT_ZERO)) || ((diff2 < ZERO_POINT_ZERO) && (diff2 + thresh > ZERO_POINT_ZERO))){ // if((diff1 < ZERO_POINT_ZERO) && ((diff1 > diff2) || (diff2 >= ZERO_POINT_ZERO))) best=&tempIndiv1; if(diff1 < ZERO_POINT_ZERO || diff2 < ZERO_POINT_ZERO){ if((diff1 < ZERO_POINT_ZERO) && ((diff1 > diff2) || (diff2 >= ZERO_POINT_ZERO))) best=&tempIndiv1; else best=&tempIndiv2; FLOAT_TYPE acceptanceProb=exp(-conf->selectionIntensity * (previousFitness - best->Fitness())); if(rnd.uniform() < acceptanceProb){ FLOAT_TYPE thisdiff=best->Fitness() - previousFitness; assert(thisdiff < 0); scorediff += thisdiff; accepts++; previousFitness = best->Fitness(); pert << accepts << "\t" << optiNode << "\t" << thisdiff << "\n"; currentBest.CopySecByRearrangingNodesOfFirst(currentBest.treeStruct, best, true); } } tempIndiv1.CopySecByRearrangingNodesOfFirst(tempIndiv1.treeStruct, ¤tBest, true); tempIndiv2.CopySecByRearrangingNodesOfFirst(tempIndiv2.treeStruct, ¤tBest, true); } indiv[indivIndex].CopySecByRearrangingNodesOfFirst(indiv[indivIndex].treeStruct, ¤tBest, true); //Return the treestructs that we used temporarily back to the unused tree vector tempIndiv1.treeStruct->RemoveTreeFromAllClas(); unusedTrees.push_back(tempIndiv1.treeStruct); tempIndiv1.treeStruct=NULL; tempIndiv2.treeStruct->RemoveTreeFromAllClas(); unusedTrees.push_back(tempIndiv2.treeStruct); tempIndiv2.treeStruct=NULL; currentBest.treeStruct->RemoveTreeFromAllClas(); unusedTrees.push_back(currentBest.treeStruct); currentBest.treeStruct=NULL; UpdateTopologyList(indiv); indiv[indivIndex].SetDirty(); indiv[indivIndex].CalcFitness(0); AssignNewTopology(indiv, indivIndex); UpdateTopologyList(indiv); SetNewBestIndiv(indivIndex); indiv[indivIndex].treeStruct->calcs=calcCount; calcCount=0; indiv[indivIndex].mutation_type=-1; pert << "end\t" << indiv[indivIndex].Fitness() << "\n"; outman.UserMessage("Completed Perturbation.\n %d NNI's accepted in %d attempts. Current score= %.4f", accepts, attempts, BestFitness()); // delete []nodeArray; } void Population::TurnOffRatchet(){ data->RestoreOriginalCounts(); pertMan->ratcheted=false; claMan->MakeAllHoldersDirty(); for(unsigned i=0;ilastPertGeneration=gen; adap->reset=true; outman.UserMessage("Returning to normal character weighting..."); char filename[50]; if(rank < 10) sprintf(filename, "pertreport0%d.log", rank); else sprintf(filename, "pertreport%d.log", rank); ofstream pert(filename, ios::app); pert << "Returning to normal character weighting..." << endl; pert.close(); } void Population::RestoreAllTimeBest(){ UpdateTopologyList(indiv); topologies[indiv[0].topo]->RemoveInd(0); CompactTopologiesList(); indiv[0].CopySecByRearrangingNodesOfFirst(indiv[0].treeStruct, allTimeBest, true); indiv[0].treeStruct->AssignCLAsFromMaster(); AssignNewTopology(indiv, 0); indiv[0].CalcFitness(0); SetNewBestIndiv(0); FillPopWithClonesOfBest(); CalcAverageFitness(); } void Population::RestoreBestForPert(){ UpdateTopologyList(indiv); topologies[indiv[0].topo]->RemoveInd(0); CompactTopologiesList(); indiv[0].CopySecByRearrangingNodesOfFirst(indiv[0].treeStruct, bestSinceRestart, true); indiv[0].treeStruct->AssignCLAsFromMaster(); AssignNewTopology(indiv, 0); indiv[0].CalcFitness(0); SetNewBestIndiv(0); FillPopWithClonesOfBest(); CalcAverageFitness(); char filename[50]; if(rank < 10) sprintf(filename, "pertreport0%d.log", rank); else sprintf(filename, "pertreport%d.log", rank); ofstream pert(filename, ios::app); pert.precision(10); pert << "restoring best individual with score of " << indiv[0].Fitness() << "\n"; pert.close(); outman.UserMessage("restoring best individual with score of %.4f\n %d perturbation(s) performed without improvement.", bestSinceRestart->Fitness(), pertMan->numPertsNoImprove); } void Population::StoreBestForPert(){ if(BestFitness() > allTimeBest->Fitness()) StoreAllTimeBest(); if(bestSinceRestart->treeStruct==NULL){ if(unusedTrees.empty()){ Tree *temp=new Tree(); unusedTrees.push_back(temp); } bestSinceRestart->treeStruct=*(unusedTrees.end()-1); unusedTrees.pop_back(); } bestSinceRestart->CopySecByRearrangingNodesOfFirst(bestSinceRestart->treeStruct, &indiv[bestIndiv]); bestSinceRestart->topo=-1; //need to do this to be sure that the bestSinceRestart isn't tying up clas bestSinceRestart->treeStruct->RemoveTreeFromAllClas(); char filename[50]; if(rank < 10) sprintf(filename, "pertreport0%d.log", rank); else sprintf(filename, "pertreport%d.log", rank); ofstream pert(filename, ios::app); pert.precision(10); pert << "storing best individual with score of " << bestSinceRestart->Fitness() << "\n"; pert.close(); outman.UserMessage("storing best individual with score of %.4f", bestSinceRestart->Fitness()); } void Population::StoreAllTimeBest(){ WriteTreeFile(besttreefile); if(allTimeBest->treeStruct==NULL){ if(unusedTrees.empty()){ Tree *temp=new Tree(); unusedTrees.push_back(temp); } allTimeBest->treeStruct=*(unusedTrees.end()-1); unusedTrees.pop_back(); } allTimeBest->CopySecByRearrangingNodesOfFirst(allTimeBest->treeStruct, &indiv[bestIndiv]); allTimeBest->topo=-1; //need to do this to be sure that the alltimebest isn't tying up clas allTimeBest->treeStruct->RemoveTreeFromAllClas(); } #endif void Population::keepTrack(){ if(((gen-1)%adap->intervalLength)==0){ if(gen>1) adap->PrepareForNextInterval(); } //remember that the indiv and newindiv arrays have already been switched, so the newindivs are the parents of the indivs //adap->reset will be true if we've Ratcheted, in which case the scores will be noncomparable //so reset these values if(adap->reset==true){ adap->laststepscore=BestFitness(); adap->lastgenscore=BestFitness(); adap->reset=false; } if(gen==1) adap->lastgenscore = adap->laststepscore = newindiv[0].Fitness(); adap->improvetotal[0] = BestFitness() - adap->laststepscore; for(unsigned i=0;inindivs;i++){ // FLOAT_TYPE scoreDif=indiv[i].Fitness() - newindiv[indiv[i].parent].Fitness(); FLOAT_TYPE scoreDif=indiv[i].Fitness() - adap->lastgenscore; int typ = indiv[i].mutation_type; if(typ > 0){ if(scoreDif>0){ // if(i==bestIndiv){ //keep track of when the last significant beneficial topo mutation occured //this will be used for the stopping criterion, precision reduction and update reduction in the parallel version #ifndef NO_EVOLUTION #ifdef IGNORE_SMALL_TOPO_IMP if(typ&Individual::anyTopo){ if(i == bestIndiv && scoreDif > significantTopoChange){ indiv[0].treeStruct->attemptedSwaps.ClearAttemptedSwaps(); indiv[0].treeStruct->CalcBipartitions(true); indiv[i].treeStruct->CalcBipartitions(true); if(indiv[0].treeStruct->IdenticalTopology(indiv[i].treeStruct->root)==false){ lastTopoImprove=gen; if(i == bestIndiv){ AppendTreeToTreeLog(indiv[bestIndiv].mutation_type); } } } else{//just ignore this small improvement. Kill the individual's chance //of reproducing FLOAT_TYPE scr=indiv[i].Fitness(); indiv[i].SetFitness(-FLT_MAX); indiv[i].treeStruct->lnL = -FLT_MAX; CalcAverageFitness(); bestIndiv=(int) cumfit[3][0]; indiv[i].SetFitness(scr); } } #else if(typ&Individual::anyTopo || adap->topoWeight==ZERO_POINT_ZERO){ //clearing of the swaps records needs to be done for _any_ new best topo, not //just ones that are significantly better indiv[0].treeStruct->CalcBipartitions(true); indiv[i].treeStruct->CalcBipartitions(true); bool sameTopo = indiv[0].treeStruct->IdenticalTopologyAllowingRerooting(indiv[i].treeStruct->root); //if this is a new best and isn't the same topology, clear the swaplist if(i == bestIndiv && sameTopo == false) indiv[0].treeStruct->attemptedSwaps.ClearAttemptedSwaps(); if(scoreDif > conf->significantTopoChange){ //if this is a new best, it is a different topology and it is significantly better //update the lastTopoImprove if(sameTopo == false){ lastTopoImprove=gen; if(i == bestIndiv){ AppendTreeToTreeLog(indiv[bestIndiv].mutation_type); } } else if(adap->topoWeight==ZERO_POINT_ZERO){ if(i == bestIndiv){ AppendTreeToTreeLog(indiv[bestIndiv].mutation_type); } } } } #endif #endif if(typ&(Individual::randNNI)){ adap->randNNI[0] += scoreDif; } // if(typ&(Individual::exNNI)) adap->exNNI[0] += scoreDif; if(typ&(Individual::randSPR)) adap->randSPR[0] += scoreDif; if(typ&(Individual::limSPR)) adap->limSPR[0] += scoreDif; // if(typ&(Individual::exlimSPR)) adap->exlimSPR[0] += scoreDif; #ifdef GANESH if(typ&(Individual::randPECR)) adap->randPECR[0] += scoreDif; #endif // if(typ&(Individual::taxonSwap)) adap->taxonSwap[0] += scoreDif; if(typ == (Individual::brlen)) adap->onlyBrlen[0] += scoreDif; if(typ&(Individual::bipartRecom)) adap->bipartRecom[0] += scoreDif; if(typ&(Individual::randRecom)) adap->randRecom[0] += scoreDif; if(typ&(Individual::anyModel)) adap->anyModel[0] += scoreDif; #ifdef MPI_VERSION if(scoreDif > adap->branchOptPrecision){ if(typ&(Individual::bipartRecom)) adap->bestFromRemote[0] += scoreDif; if(typ&(Individual::bipartRecom)) adap->bestFromRemoteNum[0] ++; if(typ&(Individual::subtreeRecom)) adap->bestFromRemote[0] += scoreDif; if(typ&(Individual::subtreeRecom)) adap->bestFromRemoteNum[0] ++; } #endif // } } if(typ&(Individual::randNNI)) adap->randNNInum[0]++; // if(typ&(Individual::exNNI)) adap->exNNInum[0]++; if(typ&(Individual::randSPR)) adap->randSPRnum[0]++; if(typ&(Individual::limSPR)) adap->limSPRnum[0]++; // if(typ&(Individual::exlimSPR)) adap->exlimSPRnum[0]++; #ifdef GANESH if(typ&(Individual::randPECR)) adap->randPECRnum[0]++; #endif // if(typ&(Individual::taxonSwap)) adap->taxonSwapnum[0]++; if(typ == (Individual::brlen)) adap->onlyBrlennum[0]++; if(typ&(Individual::bipartRecom)) adap->bipartRecomnum[0]++; if(typ&(Individual::randRecom)) adap->randRecomnum[0]++; if(typ&(Individual::anyModel)) adap->anyModelnum[0]++; } } adap->lastgenscore=BestFitness(); //things to do on the final generation of an interval if(gen%adap->intervalLength==0){ //improveOverStoredIntervals is only used on generations that are multiples of intervalLength //so it won't contain the improvement in the latest interval until it's end adap->improveOverStoredIntervals=ZERO_POINT_ZERO; for(unsigned i=0;iintervalsToStore;i++) adap->improveOverStoredIntervals += adap->improvetotal[i]; if(adap->improveOverStoredIntervals < ZERO_POINT_ZERO) adap->improveOverStoredIntervals = ZERO_POINT_ZERO; //update the mutation probailities if(gen>=(adap->intervalLength*adap->intervalsToStore*ZERO_POINT_FIVE)){ adap->UpdateProbs(); if(conf->outputMostlyUselessFiles) adap->OutputProbs(probLog, gen); } adap->laststepscore=BestFitness(); } } int ParallelManager::DetermineSubtrees(Tree *tr, ofstream &scr){ //Determine what the best node we could choose to be the root would be in terms of //the partitioning efficiency TreeNode *nd=tr->root; int bestRoot=0, orphans=ntax; bool done=false; FLOAT_TYPE bestScore=ZERO_POINT_ZERO; FLOAT_TYPE thisScore; ClearSubtrees(); //trying new partitioning function int one=0, two=0, three=0; vector sub1, sub2, sub3; if(nd->left->left) NewPartition(nd->left, one, sub1); if(nd->left->next->left) NewPartition(nd->left->next, two, sub2); if(nd->right->left) NewPartition(nd->right, three, sub3); for(vector::iterator it = sub1.begin();it!=sub1.end();it++){ bestScore += (*it)->score; (*it)->Log(scr); orphans -= (*it)->taxa; delete *it; } for(vector::iterator it = sub2.begin();it!=sub2.end();it++){ bestScore += (*it)->score; (*it)->Log(scr); orphans -= (*it)->taxa; delete *it; } for(vector::iterator it = sub3.begin();it!=sub3.end();it++){ bestScore += (*it)->score; (*it)->Log(scr); orphans -= (*it)->taxa; delete *it; } bestScore += pow((FLOAT_TYPE)(orphans), orphanFactor); scr << "root " << nd->nodeNum << " score " << bestScore << " orphans " << orphans << endl; sub1.clear(); sub2.clear(); sub3.clear(); nd=nd->left; while(!done){ if(nd->CountTerminals(0) > minSubtreeSize){ orphans=ntax; //ClearSubtrees(); if(nd->left->left) NewPartition(nd->left, one, sub1); if(nd->right->left) NewPartition(nd->right, two, sub2); NewPartitionDown(nd->anc, nd, three, sub3); thisScore=ZERO_POINT_ZERO; for(vector::iterator it = sub1.begin();it!=sub1.end();it++){ thisScore += (*it)->score; (*it)->Log(scr); orphans -= (*it)->taxa; delete *it; } for(vector::iterator it = sub2.begin();it!=sub2.end();it++){ thisScore += (*it)->score; (*it)->Log(scr); orphans -= (*it)->taxa; delete *it; } for(vector::iterator it = sub3.begin();it!=sub3.end();it++){ thisScore += (*it)->score; (*it)->Log(scr); orphans -= (*it)->taxa; delete *it; } thisScore+=pow((FLOAT_TYPE)(orphans), orphanFactor); scr << "root " << nd->nodeNum << " score " << thisScore << " orphans " << orphans << endl; sub1.clear(); sub2.clear(); sub3.clear(); if(thisScore < bestScore){ bestScore=thisScore; bestRoot=nd->nodeNum; } } if(nd->left != NULL){ nd=nd->left; } else if(nd->next != NULL){ nd=nd->next; } else{ while(nd->anc!=NULL){ nd=nd->anc; if(nd->next != NULL){ nd=nd->next; break; } } if(nd->anc==NULL){ done=true; } } } return bestRoot; /* if(nd->left->CountBranches(0)>1) Partition(nd->left); if(nd->left->next->CountBranches(0)>1) Partition(nd->left->next); if(nd->right->CountBranches(0)>1) Partition(nd->right); FLOAT_TYPE bestScore=ScorePartitioning(nd->nodeNum, pscores); FLOAT_TYPE thisScore; nd=nd->left; while(!done){ if(nd->CountTerminals(0) > minSubtreeSize){ ClearSubtrees(); Partition(nd->left); Partition(nd->right); PartitionDown(nd->anc, nd); thisScore=ScorePartitioning(nd->nodeNum, pscores); if(thisScore < bestScore){ bestScore=thisScore; bestRoot=nd->nodeNum; } } if(nd->left != NULL){ nd=nd->left; } else if(nd->next != NULL){ nd=nd->next; } else{ while(nd->anc!=NULL){ nd=nd->anc; if(nd->next != NULL){ nd=nd->next; break; } } if(nd->anc==NULL){ done=true; } } } return bestRoot; */ } void Population::StartSubtreeMode(){ OutputFate(); gen++; bool subtreesOK=false; int attempt=1; int origMinSubtreeSize=paraMan->minSubtreeSize; int origTargetSubtreeSize=paraMan->targetSubtreeSize; FLOAT_TYPE origOrphanFactor=paraMan->orphanFactor; ofstream pscores("partscores.log", ios::app); do{ pscores << "gen " << gen << " attempt " << attempt << endl; int bestRoot=paraMan->DetermineSubtrees(indiv[bestIndiv].treeStruct, pscores); //now we need to reroot to the best root found pscores << "best root=" << bestRoot << "\n"; if(bestRoot!=0){ indiv[bestIndiv].treeStruct->RerootHere(bestRoot); } //mark all of the trees inaccurate for(unsigned i=0;iroot; int orphans=paraMan->ntax; bool done=false; FLOAT_TYPE bestScore=ZERO_POINT_ZERO; //trying new partitioning function int one=0, two=0, three=0; vector sub1, sub2, sub3; if(nd->left->left) paraMan->NewPartition(nd->left, one, sub1); if(nd->left->next->left) paraMan->NewPartition(nd->left->next, two, sub2); if(nd->right->left) paraMan->NewPartition(nd->right, three, sub3); for(vector::iterator it = sub1.begin();it!=sub1.end();it++){ bestScore += (*it)->score; orphans -= (*it)->taxa; paraMan->subtrees.push_back(*it); } for(vector::iterator it = sub2.begin();it!=sub2.end();it++){ bestScore += (*it)->score; orphans -= (*it)->taxa; paraMan->subtrees.push_back(*it); } for(vector::iterator it = sub3.begin();it!=sub3.end();it++){ bestScore += (*it)->score; orphans -= (*it)->taxa; paraMan->subtrees.push_back(*it); } sub1.clear(); sub2.clear(); sub3.clear(); if((int)paraMan->subtrees.size() > paraMan->nremotes){ paraMan->targetSubtreeSize = (int) (paraMan->targetSubtreeSize * 1.05); attempt++; pscores << "too many subtrees, increasing target size to paraMan->targetSubtreeSize..." << endl; } else subtreesOK=true; }while(subtreesOK==false); //set these back to their original values paraMan->minSubtreeSize=origMinSubtreeSize; paraMan->targetSubtreeSize=origTargetSubtreeSize; paraMan->orphanFactor=origOrphanFactor; paraMan->PrepareForSubtreeMode(&indiv[bestIndiv], gen); if(paraMan->fewNonSubtreeNodes==true) AssignSubtree(paraMan->ChooseSubtree(), bestIndiv); #ifdef MASTER_DOES_SUBTREE AssignSubtree(paraMan->subtrees[(int)(rnd.uniform()*paraMan->subtrees.size())]->nodeNum, bestIndiv); #endif } void Population::StopSubtreeMode(){ paraMan->subtreeModeActive=false; paraMan->subtreeDefGeneration = gen; for(unsigned i=0;itreeStruct->root); sort(nonSubtreeNodesforNNI.begin(), nonSubtreeNodesforNNI.end()); sort(nonSubtreeNodesforSPR.begin(), nonSubtreeNodesforSPR.end()); if(nonSubtreeNodesforSPR.size() < 20) fewNonSubtreeNodes=true; subtreeDefScore=ind->Fitness(); } //Partition with loose lower bound and upper bound void ParallelManager::Partition(TreeNode *pointer){ if(pointer->left==NULL) return; int largestOrphan=5; // int min=20; // int min = ntax < 100 ? 10 : 20; //int target=params->data->NTax()/subMan->nremotes; int n1 = pointer->left->CountTerminals(0); int n2 = pointer->right->CountTerminals(0); int n = n1 + n2; if(nlargestOrphan && n1largestOrphan && n2=minSubtreeSize)){ Subtree *st = new Subtree(pointer->nodeNum, n, pointer->dlen, ZERO_POINT_ZERO); subtrees.push_back(st); } else{ if(n1>=minSubtreeSize) Partition(pointer->left); if(n2>=minSubtreeSize) Partition(pointer->right); } } void ParallelManager::NewPartition(TreeNode *pointer, int &orphans, vector &subtreesAbove){ vector subtreesUpLeft, subtreesUpRight; FLOAT_TYPE scoreAbove=ZERO_POINT_ZERO; int orphansHere=0, orphansLeft=0, orphansRight=0; int n1 = pointer->left->CountTerminals(0); int n2 = pointer->right->CountTerminals(0); int n = n1 + n2; if(n=minSubtreeSize){ NewPartition(pointer->left, orphansLeft, subtreesUpLeft); for(vector::iterator it = subtreesUpLeft.begin();it!=subtreesUpLeft.end();it++){ subtreesAbove.push_back(*it); scoreAbove += (*it)->score; } subtreesUpLeft.clear(); } else orphansHere += n1; if(n2>=minSubtreeSize){ NewPartition(pointer->right, orphansRight, subtreesUpRight); for(vector::iterator it = subtreesUpRight.begin();it!=subtreesUpRight.end();it++){ subtreesAbove.push_back(*it); scoreAbove += (*it)->score; } subtreesUpRight.clear(); } else orphansHere += n2; orphans=orphansLeft+orphansRight+orphansHere; scoreAbove += pow((FLOAT_TYPE)orphans, orphanFactor); FLOAT_TYPE scoreHere = pow((FLOAT_TYPE)(targetSubtreeSize-n), 2); if(scoreAbove > scoreHere){ for(vector::iterator it = subtreesAbove.begin();it!=subtreesAbove.end();it++){ Subtree *del=*it; delete del; } subtreesAbove.clear(); Subtree *poo=new Subtree(pointer->nodeNum, n, pointer->dlen, pow((FLOAT_TYPE)(targetSubtreeSize-n), 2)); subtreesAbove.push_back(poo); orphans=0; } } void ParallelManager::NewPartitionDown(TreeNode *pointer, TreeNode *calledFrom, int &orphans, vector &subtreesAbove){ int n, n1, n2; TreeNode *sib, *anc; vector subtreesUpLeft, subtreesUpRight; FLOAT_TYPE scoreAbove=ZERO_POINT_ZERO; int orphansHere=0, orphansLeft=0, orphansRight=0; if(pointer->nodeNum != 0){ if(pointer->left==calledFrom) sib=pointer->right; else sib=pointer->left; anc=pointer->anc; n1 = sib->CountTerminals(0); n2 = anc->CountTerminalsDown(0, pointer); n = n1 + n2; if(n=minSubtreeSize){ NewPartition(sib, orphansLeft, subtreesUpLeft); for(vector::iterator it = subtreesUpLeft.begin();it!=subtreesUpLeft.end();it++){ subtreesAbove.push_back(*it); scoreAbove += (*it)->score; } subtreesUpLeft.clear(); } else orphansHere += n1; if(n2>=minSubtreeSize){ NewPartitionDown(anc, pointer, orphansRight, subtreesUpRight); for(vector::iterator it = subtreesUpRight.begin();it!=subtreesUpRight.end();it++){ subtreesAbove.push_back(*it); scoreAbove += (*it)->score; } subtreesUpRight.clear(); } else orphansHere += n2; orphans=orphansLeft+orphansRight+orphansHere; scoreAbove += pow((FLOAT_TYPE)orphans, orphanFactor); } else{ TreeNode *nd1, *nd2; if(pointer->left==calledFrom){ nd1=pointer->left->next; nd2=pointer->right; } else if(pointer->left->next==calledFrom){ nd1=pointer->left; nd2=pointer->right; } else { nd1=pointer->left; nd2=pointer->left->next; } n1 = nd1->CountTerminals(0); n2 = nd2->CountTerminals(0); n = n1 + n2; if(n=minSubtreeSize){ NewPartition(nd1, orphansLeft, subtreesUpLeft); for(vector::iterator it = subtreesUpLeft.begin();it!=subtreesUpLeft.end();it++){ subtreesAbove.push_back(*it); scoreAbove += (*it)->score; } subtreesUpLeft.clear(); } else orphansHere += n1; if(n2>=minSubtreeSize){ NewPartition(nd2, orphansRight, subtreesUpRight); for(vector::iterator it = subtreesUpRight.begin();it!=subtreesUpRight.end();it++){ subtreesAbove.push_back(*it); scoreAbove += (*it)->score; } subtreesUpRight.clear(); } else orphansHere += n2; orphans=orphansLeft+orphansRight+orphansHere; scoreAbove += pow((FLOAT_TYPE)orphans, orphanFactor); } FLOAT_TYPE scoreHere = (FLOAT_TYPE)(targetSubtreeSize-n)*(targetSubtreeSize-n); if(scoreAbove > scoreHere){ for(vector::iterator it = subtreesAbove.begin();it!=subtreesAbove.end();it++){ delete *it; } subtreesAbove.clear(); Subtree *poo=new Subtree(pointer->nodeNum, n, calledFrom->dlen, pow((FLOAT_TYPE)(targetSubtreeSize-n), 2)); subtreesAbove.push_back(poo); orphans=0; } } void ParallelManager::PartitionDown(TreeNode *pointer, TreeNode *calledFrom){ // int min=20; int min = ntax < 100 ? 10 : 20; int largestOrphan=5; // int target=params->data->NTax()/subMan->nremotes; #ifdef MPI_VERSION int target=2*ntax/nremotes; #else int target=2*ntax/9; #endif TreeNode *sib, *anc; if(pointer->nodeNum != 0){ if(pointer->left==calledFrom) sib=pointer->right; else sib=pointer->left; anc=pointer->anc; int n1 = sib->CountTerminals(0); int n2 = anc->CountTerminalsDown(0, pointer); int n = n1 + n2; if(nlargestOrphan && n1largestOrphan && n2=min)){ Subtree *st = new Subtree(pointer->nodeNum, n, calledFrom->dlen, ZERO_POINT_ZERO); subtrees.push_back(st); } else{ if(n1>=min) Partition(sib); if(n2>=min) PartitionDown(anc, pointer); } } else{ TreeNode *nd1, *nd2; if(pointer->left==calledFrom){ nd1=pointer->left->next; nd2=pointer->right; } else if(pointer->left->next==calledFrom){ nd1=pointer->left; nd2=pointer->right; } else { nd1=pointer->left; nd2=pointer->left->next; } int n1 = nd1->CountTerminals(0); int n2 = nd2->CountTerminals(0); int n = n1 + n2; if(nlargestOrphan && n1largestOrphan && n2=min)){ Subtree *st = new Subtree(pointer->nodeNum, n, calledFrom->dlen, ZERO_POINT_ZERO); subtrees.push_back(st); } else{ if(n1>=min) Partition(nd1); if(n2>=min) Partition(nd2); } } } /* End of methods added by Yufeng Zhang*/ void Population::CheckSubtrees(){ //this function will determine whether the subtree mode should be turned on or off //and whether the subtrees should be recalculated //if subtrees are currently active, see how many trees we have that have accurate subtrees //also include any remotes that we have assigned a subtree to, since the next time we //communicate with them we will get a tree that has accurate subtrees if(paraMan->subtreeModeActive==true){ int count=0; for(unsigned i=0;inindivs) count++; // if(indiv[i].Fitness() > subMan->currentBest) subMan->currentBest=indiv[i].Fitness(); } } /* for(int j=1;j<=paraMan->nremotes;j++) if(paraMan->remoteSubtreeAssign[j] > 0) count++; if(count==0){ //if nothing is accurate, we obviously need to recalc the subtrees paraMan->needUpdate=true; } if(count>=total_size) subMan->perturb=false; */ //other conditions for recalcing the subtrees can be put here. /* if((bestFitness - paraMan->subtreeDefScore) > paraMan->recalcThresh){ paraMan->needUpdate=true; } */ } if(paraMan->subtreeModeActive==false){ //determine some conditions for starting/restarting subtree mode here //this should probably depend at least partially on the master's score //having settled down and model mutations not helping much if((int)gen - paraMan->subtreeDefGeneration > paraMan->subtreeInterval && adap->improveOverStoredIntervals < paraMan->subtreeStartThresh){ paraMan->subtreeModeActive=true; paraMan->needUpdate=true; } } if(paraMan->subtreeModeActive==true && paraMan->needUpdate==true){ StartSubtreeMode(); } if(paraMan->subtreeModeActive==true){ //determine some conditions for stopping subtree mode here if((int)gen - paraMan->subtreeDefGeneration > paraMan->subtreeInterval){ StopSubtreeMode(); } } } void Population::FillPopWithClonesOfBest(){ Individual *best=&indiv[bestIndiv]; best->treeStruct->modPart=&best->modPart; for(unsigned i=0;inindivs;i++){ if(&indiv[i]!=best){ indiv[i].treeStruct->RemoveTreeFromAllClas(); indiv[i].CopySecByRearrangingNodesOfFirst(indiv[i].treeStruct,best); indiv[i].mutation_type=-1; } indiv[i].treeStruct->modPart=&indiv[i].modPart; } CalcAverageFitness(); } void Population::AssignSubtree(int st, int indNum){ subtreeNode=st; //we'll do all of this stuff if we are assigning a new subtree or if //we are assigning 0 (turning off subtree mode) for(unsigned i=0;inindivs;i++){ indiv[i].accurateSubtrees=false; newindiv[i].accurateSubtrees=false; indiv[i].treeStruct->UnprotectClas(); } ResetMemLevel(dataPart->NTax()-2,claMan->NumClas()); indiv[bestIndiv].treeStruct->ProtectClas(); subtreeMemberNodes.clear(); //add all of the nodenums in the subtree into the subtreeMemberNodes //note that the subtree node itself is not added if(subtreeNode!=0){ if(rank==0) assert(indiv[indNum].accurateSubtrees==true); indiv[indNum].treeStruct->allNodes[subtreeNode]->left->AddNodesToList(subtreeMemberNodes); sort(subtreeMemberNodes.begin(),subtreeMemberNodes.end()); reverse(subtreeMemberNodes.begin(),subtreeMemberNodes.end()); for(unsigned i=0;inindivs;i++){ indiv[i].accurateSubtrees=true; newindiv[i].accurateSubtrees=true; } indiv[indNum].SetDirty(); indiv[indNum].CalcFitness(subtreeNode); if(rank!=0){//if we are the master and are going to choose a subtree, don't do this indiv[indNum].treeStruct->SetupClasForSubtreeMode(subtreeNode); int nodesNeedingClas=((int)subtreeMemberNodes.size())/2+2;//the nodes in the subtree, plus the subnode itself and it's anc ResetMemLevel(nodesNeedingClas,claMan->NumClas()); } } /* else{ // subMan->active=false; for(int i=0;inindivs;i++){ indiv[i].accurateSubtrees=false; newindiv[i].accurateSubtrees=false; indiv[i].treeStruct->UnprotectClas(); } ResetMemLevel(params->data->NTax()-2,claMan->NumClas()); indiv[bestIndiv].treeStruct->ProtectClas(); } */ } bool Population::SubtreeRecombination(int indivIndex){ //try recombining the trees from the remotes working on different subtrees //in various combinations to give the optimal recombinant Individual currentBest; Individual tempIndiv1; bool betterScore=false; //DJZ while(unusedTrees.size()<2){ Tree *temp=new Tree(); unusedTrees.push_back(temp); } tempIndiv1.treeStruct=*(unusedTrees.end()-1); unusedTrees.pop_back(); currentBest.treeStruct=*(unusedTrees.end()-1); unusedTrees.pop_back(); bool *recomEligable=new bool[total_size]; #undef FAKE_PARALLEL int poo=1; #ifndef FAKE_PARALLEL int count=0; for(unsigned i=conf->nindivs;ilocalSubtreeAssign[i-conf->nindivs+1] > 0)){ paraMan->CheckSubtreeAccuracy(indiv[i].treeStruct); recomEligable[i]=true; if(i>=conf->nindivs)count++; } else recomEligable[i]=false; } if(count < (paraMan->nremotes)/2){ delete []recomEligable; return false; } #else int count=0; for(int i=0;iCheckSubtreeAccuracy(newindiv[i].treeStruct); #endif recomEligable[i]=true; } else recomEligable[i]=false; } #endif ofstream subrec("subrec.log", ios::app); subrec.precision(10); subrec << "Subdef " << paraMan->subtreeDefNumber << ", " << (int)paraMan->subtrees.size() << " subtrees, defined gen " << paraMan->subtreeDefGeneration << "\n"; subrec << "Last full recom gen " << paraMan->lastFullRecom <<"\n"; subrec << "nodenum\tsize\tpriority\tassigned\tbrlen\n"; int totnode=0; for(vector::iterator it=paraMan->subtrees.begin();it!=paraMan->subtrees.end();it++){ subrec << (*it)->nodeNum << "\t" << (*it)->taxa << "\t" << (*it)->priority << "\t" << (*it)->numAssigned << "\t" << (*it)->blen << "\n"; totnode+=(*it)->taxa; } subrec << totnode << " taxa are in a subtree\nremote\tlocalnode\tassignednode\tscore\n"; for(int p=1;pnremotes+1;p++){ subrec << p << "\t" << paraMan->localSubtreeAssign[p] << "\t" << paraMan->remoteSubtreeAssign[p] << "\t" << indiv[p+conf->nindivs-1].Fitness() << "\n"; } subrec << "gen\t" << gen << "\nstart\t" << indiv[newindiv[indivIndex].parent].Fitness() << "\n"; //this is important newindiv[indivIndex].CalcFitness(0); //we don't want to do this anymore // newindiv[indivIndex].treeStruct->ProtectClas(); tempIndiv1.CopySecByRearrangingNodesOfFirst(tempIndiv1.treeStruct, &newindiv[indivIndex]); currentBest.CopySecByRearrangingNodesOfFirst(currentBest.treeStruct, &newindiv[indivIndex]); recomEligable[indivIndex]=false; #ifndef FAKE_PARALLEL for(unsigned who=conf->nindivs;whoSubtreeBasedRecombination(indiv[who].treeStruct, paraMan->localSubtreeAssign[who - conf->nindivs + 1], false, adap->branchOptPrecision); #else tempIndiv1.treeStruct->SubtreeBasedRecombination(newindiv[who].treeStruct, subtreeNode , tempIndiv1.mod->IsModelEqual(newindiv[who].mod), adap->branchOptPrecision); #endif // OutputFilesForScoreDebugging(&tempIndiv1, poo++); // paupf.flush(); // outf.flush(); tempIndiv1.SetDirty(); tempIndiv1.CalcFitness(subtreeNode); /* ofstream poo("debug.log"); poo.precision(10); tempIndiv1.treeStruct->OutputFirstClaAcrossTree(poo, tempIndiv1.treeStruct->root); poo.close(); */ subrec << "with " << who << "\t(node " << paraMan->localSubtreeAssign[who - conf->nindivs + 1] << ")\t" << tempIndiv1.Fitness() << "\n"; if(tempIndiv1.Fitness() > currentBest.Fitness()){ //if the recombinant we create is better, make it the current best, mark it as //ineligable so we don't try to add it again, and start back at the first eligable //recominant currentBest.CopySecByRearrangingNodesOfFirst(currentBest.treeStruct, &tempIndiv1, true); recomEligable[who]=false; //who=conf->nindivs-1; } else{ tempIndiv1.CopySecByRearrangingNodesOfFirst(tempIndiv1.treeStruct, ¤tBest, true); if(tempIndiv1.Fitness()==currentBest.Fitness()) recomEligable[who]=false; } } } newindiv[indivIndex].CopySecByRearrangingNodesOfFirst(newindiv[indivIndex].treeStruct, ¤tBest, true); subrec << "end\t" << currentBest.Fitness() << endl; subrec.close(); //Return the treestructs that we used temporarily back to the unused tree vector tempIndiv1.treeStruct->RemoveTreeFromAllClas(); unusedTrees.push_back(tempIndiv1.treeStruct); tempIndiv1.treeStruct=NULL; currentBest.treeStruct->RemoveTreeFromAllClas(); unusedTrees.push_back(currentBest.treeStruct); currentBest.treeStruct=NULL; delete []recomEligable; paraMan->lastFullRecom=gen; return true; } FLOAT_TYPE ParallelManager::ScorePartitioning(int nodeNum, ofstream &pscores){ int size=(int)subtrees.size(); if(size<2 /*|| size>(nremotes-1)*/) return FLT_MAX; FLOAT_TYPE blenScore=ZERO_POINT_ZERO, subScore=ZERO_POINT_ZERO, fosterScore=ZERO_POINT_ZERO; int fosterTerms=ntax; int allots[1024]; #ifndef MPI_VERSION nremotes=9; #endif int a=0; for(vector::iterator it=subtrees.begin();it!=subtrees.end();it++){ blenScore -= log((FLOAT_TYPE)(*it)->blen); subScore += (*it)->taxa * (*it)->taxa; fosterTerms-=(*it)->taxa; allots[a++]=(*it)->taxa; (*it)->numAssigned=1; } int left=nremotes-size; while(left>0){ int maxnum=0, max=0; for(int q=0;qmaxnum){ maxnum=allots[q]; max=q; } } subtrees[max]->numAssigned++; allots[max]=subtrees[max]->taxa/subtrees[max]->numAssigned; left--; } int maxallot=0; for(int q=0;qmaxallot){ maxallot=allots[q]; } } subScore = sqrt(subScore); if(fosterTerms> (ntax/20)) fosterScore = (FLOAT_TYPE)(fosterTerms-(ntax/20))*5; else fosterScore=0; blenScore*=2.0; FLOAT_TYPE tot= subScore + blenScore + fosterScore + maxallot; pscores << nodeNum << "\ts=" << size << "\tscr=" << tot << "\tfost=" << fosterTerms << "\tblenscr=" << blenScore << "\tsubscr=" << subScore << "\tallotscr" << maxallot << "\n"; for(vector::iterator it=subtrees.begin();it!=subtrees.end();it++){ pscores << (*it)->nodeNum << "\t"; pscores << (*it)->taxa << "\t"; pscores << (*it)->blen << "\n"; } pscores << "\n"; return tot; } int ParallelManager::ChooseSubtree(){ /* subroutine to decide which sub tree to work on then. currently we select subtree randomly, and the chance to select a specific subtree is depends on the size of the subtree and how many nodes it is assignged to */ int totalsize = 0; int temp = 0; int size=(int)subtrees.size(); if(size<=0) return (0); //DJZ see if any subtrees have not been assigned bool allassigned=false; int unassignedCount=0; for(int q=0;qnumAssigned=0; for(int i=1;i<=nremotes;i++){ if(remoteSubtreeAssign[i]>0){ int j=0; while(subtrees[j]->nodeNum!=remoteSubtreeAssign[i]) j++; subtrees[j]->numAssigned++; } } for(int i=0;inumAssigned==false) unassignedCount++; } if(unassignedCount==0) allassigned=true; int nd, max=0; for(int i = 0;ipriority = subtrees[i]->taxa / subtrees[i]->numAssigned; else if(subtrees[i]->numAssigned==0) subtrees[i]->priority = subtrees[i]->taxa; else subtrees[i]->priority = 0; totalsize += subtrees[i]->priority; if(subtrees[i]->priority>max){ nd=i; max=subtrees[i]->priority; } } //assign the node with the highest priority overall //or the unassigned one with the highest priority subtrees[nd]->numAssigned++; return subtrees[nd]->nodeNum; /*//assign a node randomly in proportion to it's size and assignedness for(int i=1;i<=size;i++) { if((randnum>=(temp*1.0/totalsize))&&(randnum<((temp+p[i])*1.0/totalsize))){ assigned[i]+=1; return(node[i]); } else temp += p[i]; } */ //debug_mpi("problem in selectnode..."); return (subtrees[0]->nodeNum); } void ParallelManager::FindNonSubtreeNodes(TreeNode *nd){ bool subNode=false; for(int i=0;i<(int)subtrees.size();i++) if(nd->nodeNum==subtrees[i]->nodeNum) subNode=true; if(nd->nodeNum!=0){ nonSubtreeNodesforSPR.push_back(nd->nodeNum); if(subNode==false && nd->nodeNum>ntax) nonSubtreeNodesforNNI.push_back(nd->nodeNum); } if(subNode==false){ if(nd->left) FindNonSubtreeNodes(nd->left); if(nd->right) FindNonSubtreeNodes(nd->right); if(nd->anc==NULL) FindNonSubtreeNodes(nd->left->next); } } void Population::GetRepNums(string &s){ char buf[100]; if(conf->bootstrapReps > 0){ sprintf(buf, "Bootstrap rep %d (of %d) ", currentBootstrapRep, conf->bootstrapReps); s += buf; } if(conf->searchReps > 1){ sprintf(buf, "Search rep %d (of %d)", currentSearchRep, conf->searchReps); s += buf; } } void Population::OutputRepNums(ofstream &out){ if(conf->bootstrapReps > 0 || conf->searchReps > 1){ if(conf->bootstrapReps > 0) out << "Bootstrap rep " << currentBootstrapRep << " (of " << conf->bootstrapReps << ") "; if(conf->searchReps > 1) out << "Search rep " << currentSearchRep << " (of " << conf->searchReps << ")"; out << "\n"; } } void Population::SetOutputDetails(){ /* DONT_OUTPUT = 0, REPLACE = 1, APPEND = 2, NEWNAME = 4, WRITE_CONTINUOUS = 8, WRITE_REP_TERM = 16, WRITE_REPSET_TERM = 32, WRITE_PREMATURE = 64, FINALIZE_REP_TERM = 128, FINALIZE_REPSET_TERM = 256, FINALIZE_FULL_TERM = 512, FINALIZE_PREMATURE = 1024, WARN_PREMATURE = 2048, NEWNAME_PER_REP = 4096 */ //not restarted from checkpoint //-replace all files that will be output if(conf->restart == false){ screen_output = (output_details) (REPLACE | WRITE_CONTINUOUS | WARN_PREMATURE); log_output = (output_details) (REPLACE | WRITE_CONTINUOUS | WARN_PREMATURE); if(conf->outputMostlyUselessFiles) fate_output = problog_output = swaplog_output = (output_details) (REPLACE | WRITE_CONTINUOUS); else fate_output = problog_output = swaplog_output = (output_details) (DONT_OUTPUT); //not bootstrap if(conf->bootstrapReps == 0){ bootlog_output = (output_details) (DONT_OUTPUT); best_output = (output_details) (REPLACE | WRITE_REPSET_TERM | WRITE_PREMATURE | WARN_PREMATURE); if(conf->outputCurrentBestTopology) best_output = (output_details) (best_output | WRITE_CONTINUOUS); //normal 1 rep if(conf->searchReps == 1){ all_best_output = (output_details) DONT_OUTPUT; treelog_output = (output_details) (conf->outputTreelog ? (REPLACE | WRITE_CONTINUOUS | FINALIZE_REP_TERM | FINALIZE_PREMATURE | WARN_PREMATURE) : DONT_OUTPUT); } //normal multirep run else if(conf->searchReps > 1){ all_best_output = (output_details) (REPLACE | WRITE_REP_TERM | WRITE_PREMATURE | WARN_PREMATURE); //treelog_output = (output_details) (conf->outputTreelog ? (REPLACE | WRITE_CONTINUOUS | FINALIZE_REP_TERM | FINALIZE_PREMATURE | WARN_PREMATURE | NEWNAME_PER_REP) : DONT_OUTPUT); treelog_output = (output_details) (conf->outputTreelog ? (REPLACE | WRITE_CONTINUOUS | FINALIZE_REP_TERM | FINALIZE_PREMATURE | WARN_PREMATURE | NEWNAME_PER_REP) : DONT_OUTPUT); } if(conf->scoreOnly) log_output = treelog_output = fate_output = problog_output = swaplog_output = (output_details) DONT_OUTPUT; } //bootstrapping else { best_output = all_best_output = treelog_output = (output_details) DONT_OUTPUT; //bootstrap, 1 OR multiple search reps per bootstrap rep //bootlog_output = (output_details) (REPLACE | WRITE_REPSET_TERM | FINALIZE_FULL_TERM | FINALIZE_PREMATURE); //WORK - should this include WRITE)PREMATURE? bootlog_output = (output_details) (REPLACE | WRITE_REPSET_TERM | FINALIZE_FULL_TERM | FINALIZE_PREMATURE); } } else{//restarted screen_output = (output_details) (APPEND | WRITE_CONTINUOUS | WARN_PREMATURE); log_output = (output_details) (APPEND | WRITE_CONTINUOUS | WARN_PREMATURE); if(conf->outputMostlyUselessFiles) fate_output = problog_output = swaplog_output = (output_details) (APPEND | WRITE_CONTINUOUS); else fate_output = problog_output = swaplog_output = (output_details) (DONT_OUTPUT); //restarted, not bootstrap if(conf->bootstrapReps == 0){ bootlog_output = (output_details) (DONT_OUTPUT); best_output = (output_details) (REPLACE | WRITE_REPSET_TERM | WRITE_PREMATURE | WARN_PREMATURE); if(conf->outputCurrentBestTopology) best_output = (output_details) (best_output | WRITE_CONTINUOUS); //restarted 1 rep search if(conf->searchReps == 1){ all_best_output = (output_details) DONT_OUTPUT; treelog_output = (output_details) (conf->outputTreelog ? (APPEND | WRITE_CONTINUOUS | FINALIZE_REP_TERM | FINALIZE_PREMATURE | WARN_PREMATURE) : DONT_OUTPUT); } //restarted multirep run else if(conf->bootstrapReps == 0 && conf->searchReps > 1){ all_best_output = (output_details) (REPLACE | WRITE_REP_TERM | WARN_PREMATURE); treelog_output = (output_details) (conf->outputTreelog ? (APPEND | WRITE_CONTINUOUS | FINALIZE_REP_TERM | FINALIZE_PREMATURE | WARN_PREMATURE | NEWNAME_PER_REP) : DONT_OUTPUT); } } else {//restarted bootstrap, 1 OR multiple search reps per bootstrap rep bootlog_output = (output_details) (APPEND | WRITE_REPSET_TERM | FINALIZE_FULL_TERM | FINALIZE_PREMATURE); best_output = all_best_output = treelog_output = (output_details) DONT_OUTPUT; } } } void Population::DetermineFilename(output_details details, char *outname, string suffix){ char restartString[20]; char runString[20]; if(conf->restart && (details & NEWNAME)) sprintf(restartString, ".restart%d", CheckRestartNumber(conf->ofprefix)); else restartString[0]='\0'; if(conf->searchReps > 1 && (details & NEWNAME_PER_REP)) sprintf(runString, ".rep%d", currentSearchRep); else runString[0]='\0'; sprintf(outname, "%s%s%s.%s", conf->ofprefix.c_str(), runString, restartString, suffix.c_str()); } void Population::InitializeOutputStreams(){ char temp_buf[100]; char suffix[100]; sprintf(suffix, "best"); DetermineFilename(best_output, temp_buf, suffix); //sprintf(temp_buf, "%s%s.best", conf->ofprefix.c_str(), restartString); besttreefile = temp_buf; if(fate_output != DONT_OUTPUT){ //initialize the fate file if(fate.is_open() == false){ char suffix[100]; sprintf(suffix, "fate0%d.log", rank); DetermineFilename(fate_output, temp_buf, suffix); if(fate_output & APPEND) fate.open(temp_buf, ios::app); else fate.open(temp_buf); fate.precision(10); } #ifdef MPI_VERSION fate << "gen\tind\tparent\trecomWith\tscore\tMutType\t#brlen\taccurateSubtrees\tTime\tprecision\n"; #else if(conf->restart) fate << "Restarting from checkpoint...\n"; OutputRepNums(fate); fate << "gen\tind\tparent\tscore\tMutType\t#brlen\tTime\tprecision\n"; #endif } if(problog_output != DONT_OUTPUT){ //initialize the problog if(probLog.is_open() == false){ char suffix[100]; sprintf(suffix, "problog0%d.log", rank); DetermineFilename(problog_output, temp_buf, suffix); if(problog_output & APPEND) probLog.open(temp_buf, ios::app); else probLog.open(temp_buf); if(!probLog.good()) throw ErrorException("problem opening problog"); } if(conf->restart) probLog << "Restarting from checkpoint...\n"; OutputRepNums(probLog); adap->BeginProbLog(probLog, gen); } //initialize the swaplog if(swaplog_output != DONT_OUTPUT){ if(conf->uniqueSwapBias != 1.0){ if(swapLog.is_open() == false){ char suffix[100]; sprintf(suffix, "swaplog0%d.log", rank); DetermineFilename(swaplog_output, temp_buf, suffix); if(swaplog_output & APPEND) swapLog.open(temp_buf, ios::app); else swapLog.open(temp_buf); } if(conf->restart) swapLog << "Restarting from checkpoint...\n"; OutputRepNums(swapLog); swapLog << "gen\tuniqueSwaps\ttotalSwaps\n"; } } //initialize the log file if(log_output != DONT_OUTPUT){ if(log.is_open() == false){ char suffix[100]; sprintf(suffix, "log0%d.log", rank); DetermineFilename(log_output, temp_buf, suffix); if(log_output & APPEND) log.open(temp_buf, ios::app); else log.open(temp_buf); log.precision(10); } OutputRepNums(log); if(conf->restart == false) log << "random seed = " << rnd.init_seed() << "\n"; else{ if(finishedRep == false) if(finishedGenerations == true) log << "Restarting run before final optimization " << ", seed " << rnd.init_seed() << ", best lnL " << indiv[bestIndiv].Fitness() << endl; else log << "Restarting run at generation " << gen << ", seed " << rnd.init_seed() << ", best lnL " << indiv[bestIndiv].Fitness() << endl; else log << "Restarting from checkpoint...\n"; } log << "gen\tbest_like\ttime\toptPrecision\n"; } //initialize the treelog if(treelog_output != DONT_OUTPUT){ if(treeLog.is_open() == false){ char suffix[100]; sprintf(suffix, "treelog0%d.tre", rank); DetermineFilename(treelog_output, temp_buf, suffix); if(treelog_output & APPEND) treeLog.open(temp_buf, ios::app); else treeLog.open(temp_buf); treeLog.precision(10); } treeLog.precision(10); if((conf->restart == false && conf->searchReps == 1) || (conf->restart == false && (treelog_output & NEWNAME_PER_REP)) || (conf->restart && (treelog_output & NEWNAME))) dataPart->BeginNexusTreesBlock(treeLog); AppendTreeToTreeLog(-1, -1); } //initialize the bootstrap tree file if(bootlog_output != DONT_OUTPUT){ if(rank==0 && bootLog.is_open() == false){ char suffix[100]; sprintf(suffix, "boot.tre"); DetermineFilename(bootlog_output, temp_buf, suffix); if(bootlog_output & APPEND){ if(FileExists(temp_buf) && FileIsNexus(temp_buf)){ //this will verify whether we previously started a trees block in the bootstrap file //if so, we shouldn't do so again. If not, we need to start it now bootLog.open(temp_buf, ios::app); } else{ bootLog.open(temp_buf, ios::app); dataPart->BeginNexusTreesBlock(bootLog); } } else{ bootLog.open(temp_buf); dataPart->BeginNexusTreesBlock(bootLog); } bootLog.precision(10); } if(conf->outputPhylipTree){ if(!(bootLogPhylip.is_open())){ char suffix[100]; sprintf(suffix, "boot.phy"); DetermineFilename(bootlog_output, temp_buf, suffix); if(bootlog_output & APPEND) bootLogPhylip.open(temp_buf, ios::app); else bootLogPhylip.open(temp_buf); bootLogPhylip.precision(10); } } } ClearDebugLogs(); #ifdef DEBUG_SCORES outf.open("toscore.tre"); paupf.open("toscore.nex"); #endif #ifdef VARIABLE_OPTIMIZATION outf.open("toscore.tre"); paupf.open("toscore.nex"); #endif #ifdef PERIODIC_SCORE_DEBUG outf.open("toscore.tre"); paupf.open("toscore.nex"); #endif } /* OLD WAY void Population::InitializeOutputStreams(){ char temp_buf[100]; char restart[12]; if(conf->restart == true){ //check if this run has been restarted before. If so, increment the restart number sprintf(restart, ".restart%d", CheckRestartNumber(conf->ofprefix)); } else restart[0]='\0'; sprintf(temp_buf, "%s%s.best.tre", conf->ofprefix.c_str(), restart); besttreefile = temp_buf; if(conf->outputMostlyUselessFiles){ //initialize the fate file if (rank > 9) sprintf(temp_buf, "%s%s.fate%d.log", conf->ofprefix.c_str(), restart, rank); else sprintf(temp_buf, "%s%s.fate0%d.log", conf->ofprefix.c_str(), restart, rank); fate.open(temp_buf); fate.precision(10); #ifdef MPI_VERSION fate << "gen\tind\tparent\trecomWith\tscore\tMutType\t#brlen\taccurateSubtrees\tTime\tprecision\n"; #else fate << "gen\tind\tparent\tscore\tMutType\t#brlen\tTime\tprecision\n"; #endif //initialize the problog if (rank > 9) sprintf(temp_buf, "%s%s.problog%d.log", conf->ofprefix.c_str(), restart, rank); else sprintf(temp_buf, "%s%s.problog0%d.log", conf->ofprefix.c_str(), restart, rank); probLog.open(temp_buf); if(!probLog.good()) throw ErrorException("problem opening problog"); adap->BeginProbLog(probLog, gen); //initialize the swaplog if(conf->uniqueSwapBias != ONE_POINT_ZERO){ sprintf(temp_buf, "%s%s.swap.log", conf->ofprefix.c_str(), restart); swapLog.open(temp_buf); swapLog << "gen\tuniqueSwaps\ttotalSwaps\n"; } } //initialize the log file if (rank > 9) sprintf(temp_buf, "%s%s.log%d.log", conf->ofprefix.c_str(), restart, rank); else sprintf(temp_buf, "%s%s.log0%d.log", conf->ofprefix.c_str(), restart, rank); log.open(temp_buf); log.precision(10); if(conf->restart == false) log << "random seed = " << rnd.init_seed() << "\n"; else log << "Restarting run at generation " << gen << ", seed " << rnd.init_seed() << ", best lnL " << BestFitness() << endl; log << "gen\tbest_like\ttime\toptPrecision\n"; //initialize the treelog if(conf->outputTreelog){ if (rank > 9) sprintf(temp_buf, "%s%s.treelog%d.tre", conf->ofprefix.c_str(), restart, rank); else sprintf(temp_buf, "%s%s.treelog0%d.tre", conf->ofprefix.c_str(), restart, rank); treeLog.open(temp_buf); treeLog.precision(10); data->BeginNexusTreesBlock(treeLog); } //initialize the bootstrap tree file if(conf->bootstrapReps > 0 && rank==0){ sprintf(temp_buf, "%s%s.boot.tre", conf->ofprefix.c_str(), restart); bootLog.open(temp_buf); bootLog.precision(10); data->BeginNexusTreesBlock(bootLog); if(conf->outputPhylipTree == true){ sprintf(temp_buf, "%s%s.boot.phy", conf->ofprefix.c_str(), restart); bootLogPhylip.open(temp_buf); bootLogPhylip.precision(10); } } ClearDebugLogs(); #ifdef DEBUG_SCORES outf.open("toscore.tre"); paupf.open("toscore.nex"); #endif #ifdef VARIABLE_OPTIMIZATION outf.open("toscore.tre"); paupf.open("toscore.nex"); #endif #ifdef PERIODIC_SCORE_DEBUG outf.open("toscore.tre"); paupf.open("toscore.nex"); #endif } */ void Population::SetNewBestIndiv(int indivIndex){ //this should be called when a new best individual is set outside of //CalcAverageFitness. Particularly important for parallel. bestIndiv=indivIndex; globalBest=bestFitness=prevBestFitness=BestFitness(); for(unsigned i=0;iUnprotectClas(); } } indiv[bestIndiv].treeStruct->ProtectClas(); } void Population::FinalizeOutputStreams(int type){ /*the types are: repTerm = 0 repsetTerm = 1 fullTerm = 2 */ //things should already have been finalized at the end of the previous execution if(restartedAfterTermination) return; bool prematureTermination = conf->checkpoint ? genTermination : (genTermination | timeTermination | userTermination); if(prematureTermination && type == 0){ if(log_output & WARN_PREMATURE) log << TerminationWarningMessage().c_str() << endl; if(treelog_output & WARN_PREMATURE) if(treeLog.is_open()) treeLog << TerminationWarningMessage().c_str() << endl; } //in these cases the termination is essentially a pause, so don't finalize or write anything if(workPhaseTermination || (conf->checkpoint && (timeTermination || userTermination))) return; bool repTerm, repsetTerm, fullTerm; if(prematureTermination){ fullTerm = false; repsetTerm = false; repTerm = false; } else if(type == 2){ fullTerm = true; repsetTerm = false; repTerm = false; } else if(type == 1) { fullTerm = false; repsetTerm = true; repTerm = false; } else { fullTerm = false; repsetTerm = false; repTerm = true; } //if(((conf->bootstrapReps == 0 || currentBootstrapRep == conf->bootstrapReps) && (currentSearchRep == conf->searchReps)) || prematureTermination == true){ if(log.is_open()){ if((prematureTermination && (log_output & FINALIZE_PREMATURE)) || (!prematureTermination && ((repTerm && (log_output & FINALIZE_REP_TERM)) || (repsetTerm && (log_output & FINALIZE_REPSET_TERM)) || (fullTerm && (log_output & FINALIZE_FULL_TERM)))) ) log.close(); } if(fate.is_open()){ if((prematureTermination && (fate_output & FINALIZE_PREMATURE)) || (!prematureTermination && ((repTerm && (fate_output & FINALIZE_REP_TERM)) || (repsetTerm && (fate_output & FINALIZE_REPSET_TERM)) || (fullTerm && (fate_output & FINALIZE_FULL_TERM)))) ) fate.close(); } if(probLog.is_open()){ if((prematureTermination && (problog_output & FINALIZE_PREMATURE)) || (!prematureTermination && ((repTerm && (problog_output & FINALIZE_REP_TERM)) || (repsetTerm && (problog_output & FINALIZE_REPSET_TERM)) || (fullTerm && (problog_output & FINALIZE_FULL_TERM)))) ) probLog.close(); } if(swapLog.is_open()){ if((prematureTermination && (swaplog_output & FINALIZE_PREMATURE)) || (!prematureTermination && ((repTerm && (swaplog_output & FINALIZE_REP_TERM)) || (repsetTerm && (swaplog_output & FINALIZE_REPSET_TERM)) || (fullTerm && (swaplog_output & FINALIZE_FULL_TERM)))) ) swapLog.close(); } if(treeLog.is_open()){ if((prematureTermination && (treelog_output & FINALIZE_PREMATURE)) || (!prematureTermination && ((repTerm && (treelog_output & FINALIZE_REP_TERM)) || (repsetTerm && (treelog_output & FINALIZE_REPSET_TERM)) || (fullTerm && (treelog_output & FINALIZE_FULL_TERM)))) ){ treeLog << "end;\n"; treeLog.close(); } } if(bootLog.is_open()){ if((prematureTermination && (bootlog_output & FINALIZE_PREMATURE)) || (!prematureTermination && ((repTerm && (bootlog_output & FINALIZE_REP_TERM)) || (repsetTerm && (bootlog_output & FINALIZE_REPSET_TERM)) || (fullTerm && (bootlog_output & FINALIZE_FULL_TERM)))) ){ bootLog << "end;\n"; bootLog.close(); } } if(bootLogPhylip.is_open()){ if((prematureTermination && (bootlog_output & FINALIZE_PREMATURE)) || (!prematureTermination && ((repTerm && (bootlog_output & FINALIZE_REP_TERM)) || (repsetTerm && (bootlog_output & FINALIZE_REPSET_TERM)) || (fullTerm && (bootlog_output & FINALIZE_FULL_TERM)))) ) bootLogPhylip.close(); } #ifdef DEBUG_SCORES outf << "end;\n"; outf.close(); paupf << "end;\n"; paupf.close(); #endif } /* OLD WAY void Population::FinalizeOutputStreams(){ if(prematureTermination == true){ log << "***NOTE: Run was terminated before termination condition was reached!\nLikelihood scores, topologies and model estimates obtained may not be fully optimal!***" << endl; if(treeLog.is_open()) treeLog << "[! ***NOTE: GARLI run was terminated before termination condition was reached!\nLikelihood scores, topologies and model estimates obtained may not be fully optimal!***]" << endl; } fate.close(); log.close(); if(treeLog.is_open()){ AppendTreeToTreeLog(-1); treeLog << "end;\n"; treeLog.close(); } if(bootLog.is_open()){ bootLog << "end;\n"; bootLog.close(); } if(bootLogPhylip.is_open()) bootLogPhylip.close(); probLog.close(); if(swapLog.is_open()) swapLog.close(); #ifdef DEBUG_SCORES outf << "end;\n"; outf.close(); paupf << "end;\n"; paupf.close(); #endif } */ void Population::FindLostClas(){ vector arr; for(unsigned i=0;iIsDirty(t->allNodes[0]->claIndexDown))) arr.push_back(claMan->GetCla(t->allNodes[0]->claIndexDown)); if(! (claMan->IsDirty(t->allNodes[0]->claIndexUL))) arr.push_back(claMan->GetCla(t->allNodes[0]->claIndexUL)); if(! (claMan->IsDirty(t->allNodes[0]->claIndexUR))) arr.push_back(claMan->GetCla(t->allNodes[0]->claIndexUR)); for(int n=t->getNumTipsTotal()+1;ngetNumNodesTotal();n++){ if(! (claMan->IsDirty(t->allNodes[n]->claIndexDown))) arr.push_back(claMan->GetCla(t->allNodes[n]->claIndexDown)); if(! (claMan->IsDirty(t->allNodes[n]->claIndexUL))) arr.push_back(claMan->GetCla(t->allNodes[n]->claIndexUL)); if(! (claMan->IsDirty(t->allNodes[n]->claIndexUR))) arr.push_back(claMan->GetCla(t->allNodes[n]->claIndexUR)); } } sort(arr.begin(), arr.end()); for(vector::iterator vit=arr.begin();vit!=arr.end();){ if(*(vit)==*(vit+1)) arr.erase(vit+1); else vit++; if((vit+1)==arr.end()){ break; } } assert(arr.size() + claMan->NumFreeClas() == claMan->NumClas()); } void Population::LogNewBestFromRemote(FLOAT_TYPE scorediff, int ind){ #ifdef MPI_VERSION adap->bestFromRemoteNum[0]++; adap->bestFromRemote[0]+=scorediff; indiv[0].treeStruct->CalcBipartitions(true); indiv[ind].treeStruct->CalcBipartitions(true); //check if this is a new topology if(indiv[0].treeStruct->IdenticalTopology(indiv[ind].treeStruct->root) == false){ debug_mpi("\ttopo different from prev best"); AppendTreeToTreeLog(-1, ind); if(scorediff > significantTopoChange) lastTopoImprove=gen; } else{ debug_mpi("\ttopo same as prev best"); // AppendTreeToTreeLog(0, ind); } #endif } void Population::CheckRemoteReplaceThresh(){ #ifdef MPI_VERSION if(gen < adap->intervalLength * adap->intervalsToStore) return; FLOAT_TYPE totBestFromRemote=ZERO_POINT_ZERO; int totBestFromRemoteNum=0; for(int i=0;iintervalsToStore;i++){ totBestFromRemoteNum += adap->bestFromRemoteNum[i]; totBestFromRemote += adap->bestFromRemote[i]; } if(totBestFromRemoteNum==0) paraMan->ReduceUpdateThresh(); #endif } /* 7/21/06 needs to be updated void Population::SPRoptimization(int indivIndex){ // for(int i=0;inindivs;i++) // indiv[i].ResetIndiv(); // bool topoChange=false; for(int reps=0;reps<1;reps++){ int cutnum = newindiv[indivIndex].treeStruct->GetRandomNonRootNode(); SPRoptimization(indivIndex, adap->limSPRrange, cutnum); } /* if(topoChange==true){ if(topologies[indiv[bestIndiv].topo]->nInds>1){ topologies[indiv[bestIndiv].topo]->RemoveInd(bestIndiv); indiv[bestIndiv].topo=ntopos++; topologies[indiv[bestIndiv].topo]->AddInd(bestIndiv); assert(topologies[indiv[bestIndiv].topo]->nInds==1); } topologies[indiv[bestIndiv].topo]->gensAlive=0; TopologyList::ntoposexamined++; UpdateTopologyList(indiv); } */ // CalcAverageFitness(); // OutputFilesForScoreDebugging(); //} /* 7/21/06 needs to be updated bool Population::SPRoptimization(int indivIndex, int range, int cutnum ){ //DJZ 1/23/04 this is based on the NNI optimization function by Alan (ie, exhaustive nnis) //the main difference is that only one node will be used as the node to be cut off and //reattached, but then all reattachment points within a radius will be tried. //the marking of nodes as dirty is also necessarily different subset sprRange; Individual currentBest; Individual tempIndiv1; FLOAT_TYPE bestSPRFitness; bool topoChange=false; ofstream outf("sprdebug.tre"); ofstream scr("sprscores.tre"); scr.precision(10); //DJZ while(unusedTrees.size()<2){ Tree *temp=new Tree(); unusedTrees.push_back(temp); } //bestSPRFitness = indiv[newindiv[indivIndex].parent].Fitness(); bestSPRFitness = -1e100; tempIndiv1.treeStruct=*(unusedTrees.end()-1); unusedTrees.pop_back(); currentBest.treeStruct=*(unusedTrees.end()-1); unusedTrees.pop_back(); // tempIndiv1.CopySecByRearrangingNodesOfFirst(tempIndiv1.treeStruct, &newindiv[indivIndex]); currentBest.CopySecByRearrangingNodesOfFirst(currentBest.treeStruct, &newindiv[indivIndex]); TreeNode **thenodes=tempIndiv1.treeStruct->allNodes; //choose the nodenum to be cut // int cutnum = params->rnd.random_int(tempIndiv1.treeStruct->numNodesTotal -1 ) +1; TreeNode *cutnode= thenodes[cutnum]; //now determine the nodes that fall within the reattachment radius //this is Alan's code for putting together the subset, with a bit of my alteration //the subset will be centered on cutnode's anc, AKA connector sprRange.setseed(cutnode->anc->nodeNum); int connector=cutnode->anc->nodeNum; for(int i = 0;ileft!=NULL) sprRange.addelement(cur->left->nodeNum, i+1, sprRange.pathlength[k]+cur->left->dlen); if(cur->right!=NULL) sprRange.addelement(cur->right->nodeNum, i+1, sprRange.pathlength[k]+cur->right->dlen); if(cur->anc!=NULL) sprRange.addelement(cur->anc->nodeNum, i+1, sprRange.pathlength[k]+cur->dlen); }// end of loop through element of current subset }// end of loop to findrange } if(cutnode->next != NULL) sprRange.elementremove(cutnode->next->nodeNum); if(cutnode->prev != NULL) sprRange.elementremove(cutnode->prev->nodeNum); sprRange.elementremove(connector); //connecting to the sib recreates the original tree //remove the nodes that are actually part of the subtree being cut, starting with connector thenodes[cutnum]->RemoveSubTreeFromSubset(sprRange, true); sprRange.compact(); int broken=0; while(sprRange.element[broken]!=0){ tempIndiv1.treeStruct->SPRMutate(cutnum, sprRange.element[broken++], adap->branchOptPrecision, 0, 0); if(sprRange.element[broken]==0 && sprRange.element[broken+1]!=0) broken++; //indiv[indivIndex].treeStruct->SetAllTempClasDirty(); //Because a large section of the tree will be shared between the different attachment //points, we should see a decent savings by only making the temp clas dirty that we //know might change, which should only be those that are considered as reattachments. //newindiv[indivIndex].treeStruct->SetSpecifiedTempClasDirty(sprRange.element); tempIndiv1.SetDirty(); tempIndiv1.CalcFitness(0); //debug the scoring of the spr trees /* outf << " utree " << gen << sprRange.element[broken] << "= "; tempIndiv1.treeStruct->root->MakeNewick(treeString); outf << treeString << ";" << endl; scr << tempIndiv1.Fitness() << endl; // */ // if(tempIndiv1.Fitness() > (bestSPRFitness + 0.01)) /* if(tempIndiv1.Fitness() > bestSPRFitness) { bestSPRFitness = tempIndiv1.Fitness(); currentBest.CopySecByRearrangingNodesOfFirst(currentBest.treeStruct, &tempIndiv1, true); topoChange=true; } //make the tempIndiv equal to the starting tree tempIndiv1.CopySecByRearrangingNodesOfFirst(tempIndiv1.treeStruct, &newindiv[indivIndex], true); } //end of loop through all possible NNIs if(topoChange==true){ newindiv[indivIndex].CopySecByRearrangingNodesOfFirst(newindiv[indivIndex].treeStruct, ¤tBest, true); } //Return the treestructs that we used temporarily back to the unused tree vector tempIndiv1.treeStruct->RemoveTreeFromAllClas(); unusedTrees.push_back(tempIndiv1.treeStruct); tempIndiv1.treeStruct=NULL; currentBest.treeStruct->RemoveTreeFromAllClas(); unusedTrees.push_back(currentBest.treeStruct); currentBest.treeStruct=NULL; // newindiv[indivIndex].treeStruct->SetAllTempClasDirty(); newindiv[indivIndex].mutation_type |= Individual::exlimSPR; return topoChange; } */ /* 7/21/06 needs to be update void Population::SPRPerturbation(int sourceInd, int indivIndex){ assert(0); //7/21/06 needs to be fixed to deal with changes made in //constraint implementation /* Individual currentBest; Individual tempIndiv1; Individual *source=&indiv[sourceInd]; int range=pertMan->sprPertRange; FLOAT_TYPE thresh=10000.0; // ofstream outf("sprdebug.tre"); // ofstream scr("sprscores.tre"); // scr.precision(10); //DJZ while(unusedTrees.size()<2){ Tree *temp=new Tree(); unusedTrees.push_back(temp); } tempIndiv1.treeStruct=*(unusedTrees.end()-1); unusedTrees.pop_back(); currentBest.treeStruct=*(unusedTrees.end()-1); unusedTrees.pop_back(); for(int cycle=0;cycle < pertMan->numSprCycles;cycle++){ FLOAT_TYPE previousFitness=source->Fitness(); FLOAT_TYPE bestDiff=-thresh; if(cycle==0){ tempIndiv1.CopySecByRearrangingNodesOfFirst(tempIndiv1.treeStruct, source); currentBest.CopySecByRearrangingNodesOfFirst(currentBest.treeStruct, source); } else{ tempIndiv1.CopySecByRearrangingNodesOfFirst(tempIndiv1.treeStruct, source, true); currentBest.CopySecByRearrangingNodesOfFirst(currentBest.treeStruct, source, true); } TreeNode **thenodes=tempIndiv1.treeStruct->allNodes; //this is a little odd, but just call a normal SPRMutate with the proper range //so that the possible reattachment points within that range are gathered properly int cutnum=tempIndiv1.treeStruct->SPRMutate(range, adap->branchOptPrecision); tempIndiv1.CopySecByRearrangingNodesOfFirst(tempIndiv1.treeStruct, source, true); char filename[50]; sprintf(filename, "pertreport%d.log", rank); ofstream pert(filename, ios::app); pert.precision(10); subset *sprRange=&(tempIndiv1.treeStruct->sprRange); pert.precision(10); // pert2.precision(10); pert << "gen " << gen << " start " << source->Fitness() << "\t" << sprRange->total << " possible attachments\n"; int bestDist=0; int broken=sprRange->total-1; while(broken>=0){ if(! (broken % (int)ceil((FLOAT_TYPE)sprRange->total/5))) outman.UserMessageNoCR("."); outman.flush(); tempIndiv1.treeStruct->SPRMutate(cutnum, sprRange->element[broken--], adap->branchOptPrecision, 0, 0); if(sprRange->element[broken]==0 && broken>0) broken--; tempIndiv1.SetFitness(tempIndiv1.treeStruct->lnL);; //divide the score difference by the square root of the node distance, to favor longer moves FLOAT_TYPE diff=(tempIndiv1.Fitness()-previousFitness)/sqrt(sprRange->front[broken]+1.0); if(diff>0) diff=(tempIndiv1.Fitness()-previousFitness)*(sprRange->front[broken]+1); // pert2 << "node=\t" << sprRange->element[broken] << "\tdist=\t" << sprRange->front[broken] << "\tscore=\t" << tempIndiv1.Fitness() << "\t" << diff << "\t" << tempIndiv1.Fitness()-previousFitness << "\n"; if(diff > bestDiff){ bestDiff=diff; currentBest.CopySecByRearrangingNodesOfFirst(currentBest.treeStruct, &tempIndiv1, true); bestDist=sprRange->front[broken]; pert << diff << "\t" << bestDist << "\n"; } tempIndiv1.CopySecByRearrangingNodesOfFirst(tempIndiv1.treeStruct, source, true); } if(bestDiff>-thresh){ indiv[indivIndex].CopySecByRearrangingNodesOfFirst(indiv[indivIndex].treeStruct, ¤tBest, true); } //set this tree up as the source for the next cycle source=&indiv[indivIndex]; indiv[indivIndex].mutation_type |= Individual::exlimSPR; pert << "end score=" << currentBest.Fitness() << endl; outman.UserMessage("Accepted SPR with range of %d. Current score= %.4f", bestDist, indiv[indivIndex].Fitness()); } //Return the treestructs that we used temporarily back to the unused tree vector tempIndiv1.treeStruct->RemoveTreeFromAllClas(); unusedTrees.push_back(tempIndiv1.treeStruct); tempIndiv1.treeStruct=NULL; currentBest.treeStruct->RemoveTreeFromAllClas(); unusedTrees.push_back(currentBest.treeStruct); currentBest.treeStruct=NULL; } */ /* void Population::CheckPerturbParallel(){ if(paraMan->perturbModeActive==true){ if(paraMan->allSent == false){ for(int i=1;i<=paraMan->nremotes;i++){ if(paraMan->needToSend[i]==true) break; if(i==paraMan->nremotes) paraMan->allSent=true; } if(paraMan->allSent==true){ //the pert generation is recorded as when we sent our last message pertMan->lastPertGeneration=gen; } } //keep the perturbModeActive flag true for a while so the master doesn't replace the remotes perturbed trees if(gen - pertMan->lastPertGeneration > pertMan->minPertInterval){ paraMan->perturbModeActive=false; // pertMan->lastPertGeneration=gen; } } else if(pertMan->pertAbandoned==false && (gen - pertMan->lastPertGeneration) >= pertMan->minPertInterval && (adap->improveOverStoredIntervals < pertMan->pertThresh)/* && (adap->branchOptPrecision == adap->minOptPrecision)*/ /*){ /* for(int i=1;i<=paraMan->nremotes;i++){ paraMan->needToSend[i]=true; } paraMan->perturbModeActive=true; paraMan->allSent=false; pertMan->lastPertGeneration=gen; } } */ /* 7/21/06 needs to be updated void Population::CheckPerturbSerial(){ if(pertMan->pertType < 3 ){ if(pertMan->pertType==1 && (gen - pertMan->lastPertGeneration) >= pertMan->minPertInterval/2 && adap->randNNIweight != adap->origRandNNIweight){ adap->randNNIweight=adap->origRandNNIweight; // pertMan->lastPertGeneration=gen; } if(pertMan->pertAbandoned==false && (gen - pertMan->lastPertGeneration) >= pertMan->minPertInterval && (adap->improveOverStoredIntervals < pertMan->pertThresh) /*&& (adap->branchOptPrecision == adap->minOptPrecision)*/ /*){ if(pertMan->numPertsNoImprove <= pertMan->maxPertsNoImprove){ if(BestFitness() > bestSinceRestart.Fitness()){ StoreBestForPert(); pertMan->numPertsNoImprove=0; } else{ //if we haven't done better than the best we had before the previous perturbation, restore to //that point and perturb again pertMan->numPertsNoImprove++; RestoreBestForPert(); //abandoning perturbations if(pertMan->numPertsNoImprove > pertMan->maxPertsNoImprove){ pertMan->pertAbandoned=true; pertMan->lastPertGeneration=gen; return; } } if(pertMan->pertType==1){ int indToReplace = (bestIndiv==0 ? 1 : 0); NNIPerturbation(bestIndiv, indToReplace); SetNewBestIndiv(indToReplace); FillPopWithClonesOfBest(); AppendTreeToTreeLog(-1, bestIndiv); //try disallowing NNIs immediately after the perturbation adap->randNNIweight=0.0; adap->randNNIprob=0.0; } else if(pertMan->pertType==0){ //branch length perturbation int indToReplace = (bestIndiv==0 ? 1 : 0); indiv[indToReplace].treeStruct->PerturbAllBranches(); indiv[indToReplace].SetDirty(); indiv[indToReplace].CalcFitness(0); SetNewBestIndiv(indToReplace); FillPopWithClonesOfBest(); } else{ FLOAT_TYPE startscore=BestFitness(); FLOAT_TYPE curscore; // do{ int indToReplace = (bestIndiv==0 ? 1 : 0); int source=bestIndiv; outman.UserMessage("Performing SPR Perturbation. Starting score=%.4f", BestFitness()); SPRPerturbation(source, indToReplace); indiv[indToReplace].CalcFitness(0); SetNewBestIndiv(indToReplace); FillPopWithClonesOfBest(); curscore=indiv[indToReplace].Fitness(); AppendTreeToTreeLog(-1, bestIndiv); } pertMan->lastPertGeneration=gen; adap->reset=true; gen++; OutputFate(); } } } else if(pertMan->pertType==3){ if(pertMan->ratcheted==false){ if(pertMan->pertAbandoned==false && (gen - pertMan->lastPertGeneration) >= pertMan->minPertInterval && adap->improveOverStoredIntervals < pertMan->pertThresh){ if(BestFitness() > bestSinceRestart.Fitness()){ StoreBestForPert(); pertMan->numPertsNoImprove=0; } else{ //if we haven't done better than the best we had before the previous perturbation, restore to //that point and reweight again RestoreBestForPert(); pertMan->numPertsNoImprove++; //abandoning perturbations if(pertMan->numPertsNoImprove > pertMan->maxPertsNoImprove){ pertMan->pertAbandoned=true; return; } } pertMan->ratcheted=true; params->data->ReserveOriginalCounts(); params->data->Reweight(pertMan->ratchetProportion); claMan->MakeAllHoldersDirty(); for(int i=0;ilastPertGeneration=gen; pertMan->scoreAfterRatchet=BestFitness(); adap->reset=true; gen++; OutputFate(); outman.UserMessage("Performing ratcheting: reweighting %.1f percent of characters.", pertMan->ratchetProportion*100); char filename[50]; if(rank < 10) sprintf(filename, "pertreport0%d.log", rank); else sprintf(filename, "pertreport%d.log", rank); ofstream pert(filename, ios::app); pert << "Performing ratcheting: reweighting " << pertMan->ratchetProportion*100 << " percent of characters." << endl; pert.close(); } } //turn ratchet off else{ if((gen - pertMan->lastPertGeneration) >= pertMan->ratchetMaxGen || BestFitness() - pertMan->scoreAfterRatchet > pertMan->ratchetOffThresh){ TurnOffRatchet(); gen++; OutputFate(); } } } } */ //SINGLE SITE FUNCTIONS void Population::OptimizeSiteRates(){ SeedPopulationWithStartingTree(1); const SequenceData *data = dataPart->GetSubset(0); //store a backup of the exisiting tree and blens Individual tempIndiv; tempIndiv.treeStruct=new Tree(); tempIndiv.CopySecByRearrangingNodesOfFirst(tempIndiv.treeStruct, &indiv[0]); char filename[100]; sprintf(filename, "%s.siterates.log", conf->ofprefix.c_str()); ofstream out(filename); Tree::min_brlen = 1.0e-20; Tree::max_brlen = 1.0e10; const int lastConst=data->LastConstant(); typedef pair float_pair; float_pair rateAndScore; vector allRates; for(int i=0;iNChar();i++){ if(i <= lastConst) rateAndScore = pair(ZERO_POINT_ZERO, indiv[0].modPart.GetModel(0)->StateFreq((data->GetConstStates())[i])); else{ indiv[0].treeStruct->MakeAllNodesDirty(); Tree::siteToScore = i; rateAndScore = indiv[0].treeStruct->OptimizeSingleSiteTreeScale(adap->branchOptPrecision); //restore the original blens indiv[0].CopySecByRearrangingNodesOfFirst(indiv[0].treeStruct, &tempIndiv, true); } allRates.push_back(rateAndScore); } out << "site#\tsiteRate\tsitelnL" << endl; for(int i=0;iGapsIncludedNChar();i++){ int packedColumn = data->Number(i); assert(packedColumn < (int)allRates.size()); out << i+1 << "\t"; if(packedColumn == -1) out << "NA\tNA" << endl; else out << allRates[packedColumn].first << "\t" << allRates[packedColumn].second << endl; } out.close(); outman.UserMessage("Site-rate estimation complete."); } garli-2.1-release/src/population.h000066400000000000000000000434211241236125200172240ustar00rootroot00000000000000// GARLI version 2.1 source code // Copyright 2005-2014 Derrick J. Zwickl // email: garli.support@gmail.com // // This program is free software: you can redistribute it and/or modify // it under the terms of the GNU General Public License as published by // the Free Software Foundation, either version 3 of the License, or // (at your option) any later version. // // This program is distributed in the hope that it will be useful, // but WITHOUT ANY WARRANTY; without even the implied warranty of // MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the // GNU General Public License for more details. // // You should have received a copy of the GNU General Public License // along with this program. If not, see . // // NOTE: Portions of this source adapted from GAML source, written by Paul O. Lewis #ifndef POPULATION_H #define POPULATION_H #include #include #include #include using namespace std; #include "configoptions.h" #include "individual.h" #include "stopwatch.h" #include "errorexception.h" class CondLikeArray; class Tree; class ClaManager; class Adaptation; class Subtree{ public: int nodeNum; int taxa; FLOAT_TYPE blen; int numAssigned; int priority; FLOAT_TYPE score; Subtree(int nn, int t, FLOAT_TYPE b, FLOAT_TYPE s){ nodeNum=nn; taxa=t; priority=1; numAssigned=0; blen=b; score=s; } void Log(ofstream &out){ out << nodeNum << "\t" << taxa << "\t" << score << "\n"; } }; class ParallelManager{ public: bool subtreeModeActive; bool perturbModeActive; int nremotes; int ntax; //variables related to subtree mode int subtreeDefNumber; int subtreeDefGeneration; FLOAT_TYPE subtreeDefScore; //the score of the tree that the subtrees were initially calculated on int lastFullRecom; //the last time we tried a subtree based recombination vector subtrees; bool fewNonSubtreeNodes; int *remoteSubtreeAssign; // which node the remote was most recently assigned int *localSubtreeAssign; // which node the remotes _were_ working on for the most recent tree received bool needUpdate; //this means that the subtrees determined are no longer guaranteed to be valid for any trees in the population bool beforeFirstSubtree; FLOAT_TYPE updateThresh; FLOAT_TYPE startUpdateThresh; FLOAT_TYPE minUpdateThresh; FLOAT_TYPE updateReductionFactor; int subtreeInterval; FLOAT_TYPE subtreeStartThresh; int minSubtreeSize; int targetSubtreeSize; FLOAT_TYPE orphanFactor; int maxRecomIndivs; // FLOAT_TYPE recalcThresh; //the score threshold for forcing a recalc of the subtrees // FLOAT_TYPE subtreeThresh; //the score threshold for sending a remote a new tree if it //is currently working on a subtree // FLOAT_TYPE nonSubtreeThresh; //the score threshold for sending a remote a new tree if subtree //mode is off bool perturb; bool *needToSend; bool allSent; public: vector nonSubtreeNodesforNNI; vector nonSubtreeNodesforSPR; void FindNonSubtreeNodes(TreeNode *nd); public: ParallelManager(int _ntax, int nproc, const MasterGamlConfig *mc){ ntax=_ntax; needUpdate=true; subtreeModeActive=false; perturbModeActive=false; perturb=false; subtreeDefGeneration =-1; subtreeDefScore=-1; subtreeDefNumber=0; lastFullRecom=-1; beforeFirstSubtree=true; nremotes=nproc-1; remoteSubtreeAssign=new int[nproc]; localSubtreeAssign=new int[nproc]; needToSend=new bool[nproc]; for(int i=0;isubtreeRecalcThresh; updateThresh = startUpdateThresh = mc->startUpdateThresh; minUpdateThresh = mc->minUpdateThresh; //DJZ 2/20/06 //making the reduction factor depend on the min and max updateThreshes //and the number of reductions requested for the optprecision to //go from its start to min //updateReductionFactor = mc->updateReductionFactor; updateReductionFactor=pow((FLOAT_TYPE) minUpdateThresh/startUpdateThresh, (FLOAT_TYPE) 1.0/ (mc->numPrecReductions)); // subtreeThresh = mc->subtreeUpdateThresh; // nonSubtreeThresh = mc->nonsubtreeUpdateThresh; subtreeInterval = mc->subtreeInterval; subtreeStartThresh = mc->subtreeStartThresh; minSubtreeSize=mc->minSubtreeSize; targetSubtreeSize=mc->targetSubtreeSize; orphanFactor=mc->orphanFactor; maxRecomIndivs=mc->maxRecomIndivs; } ~ParallelManager(){ if(remoteSubtreeAssign != NULL) delete []remoteSubtreeAssign; if(localSubtreeAssign != NULL) delete []localSubtreeAssign; if(needToSend != NULL) delete []needToSend; } bool ReadyForSubtreeRecom(int gen){ return (subtreeModeActive==true && (gen - lastFullRecom >= subtreeInterval/2)); } void ReduceUpdateThresh(){ updateThresh *= updateReductionFactor; if(updateThresh < minUpdateThresh) updateThresh=minUpdateThresh; } void CheckSubtreeAccuracy(const Tree *tr){ for(int i=0;i<(int)subtrees.size();i++){ int countnum= tr->allNodes[subtrees[i]->nodeNum]->CountTerminals(0); assert(countnum== subtrees[i]->taxa); } } void ClearSubtrees(){ for(int i=0;i<(int)subtrees.size();i++){ delete subtrees[i]; } subtrees.clear(); } int DetermineSubtrees(Tree *tr, ofstream &); void Partition(TreeNode *pointer); void NewPartition(TreeNode *pointer, int &orphans, vector &subtreesAbove); void NewPartitionDown(TreeNode *pointer, TreeNode *calledFrom, int &orphans, vector &subtreesAbove); void PartitionDown(TreeNode *pointer, TreeNode *calledFrom); FLOAT_TYPE ScorePartitioning(int nodeNum, ofstream &pscores); void PrepareForSubtreeMode(Individual *ind, int gen); int ChooseSubtree(); }; #ifdef INCLUDE_PERTURBATION class PerturbManager{ public: int lastPertGeneration; bool pertAbandoned; int numPertsNoImprove; FLOAT_TYPE prevPertScore; FLOAT_TYPE scoreAfterRatchet; int pertType; FLOAT_TYPE pertThresh; int minPertInterval; int maxPertsNoImprove; bool restartAfterAbandon; int gensBeforeRestart; FLOAT_TYPE ratchetProportion; FLOAT_TYPE ratchetOffThresh; int ratchetMaxGen; bool ratcheted; FLOAT_TYPE nniAcceptThresh; int nniTargetAccepts; int nniMaxAttempts; int numSprCycles; int sprPertRange; public: PerturbManager(){ pertAbandoned=true; ratcheted=false; pertThresh=0.0; } PerturbManager(const GeneralGamlConfig *conf){ lastPertGeneration=-1; pertAbandoned=false; numPertsNoImprove=0; prevPertScore=-1; scoreAfterRatchet=-1; pertType = conf->pertType; if(pertType!=1 && pertType!=3){ throw ErrorException("Sorry, only pertTypes 1 and 3 and currently supported!"); } pertThresh = conf->pertThresh; minPertInterval = conf->minPertInterval; maxPertsNoImprove = conf->maxPertsNoImprove; restartAfterAbandon = conf->restartAfterAbandon; gensBeforeRestart = conf->gensBeforeRestart; nniTargetAccepts = conf->nniTargetAccepts; nniMaxAttempts = conf->nniMaxAttempts; ratchetProportion = conf->ratchetProportion; ratchetOffThresh = conf->ratchetOffThresh; ratchetMaxGen = conf->ratchetMaxGen; ratcheted=false; numSprCycles = conf->numSprCycles; sprPertRange = conf->sprPertRange; } }; #endif class Population{ private: int rank;//denotes which processor this is. 0 if serial int bestIndiv; int bestAccurateIndiv; int subtreeNode; int subtreeDefNumber; unsigned gen; unsigned currentBootstrapRep; int lastBootstrapSeed; int nextBootstrapSeed; unsigned currentSearchRep; //termination related variables unsigned lastTopoImprove; unsigned lastPrecisionReduction; unsigned lastUniqueSwap; unsigned total_size; //this will be equal to conf->nindiv, except in //the case of the parallel master unsigned ntopos; //this indicates that we've exited the generation loop in Run(), but if //finishedRep is false that means that we still have to do final opt. bool finishedGenerations; //when a single search replicate is finished (not a bootstrap rep) bool finishedRep; //These start at 0, are set to 1 when optimization starts, and when each pass ends they increment. When a refine //phase finishes it is set to -1. Mainly important to report % done in boincWordDivision mode, but could in theory //be adapted to checkpointing during initial/final optimization. int initialRefinePass; int finalRefinePass; FLOAT_TYPE bestFitness; FLOAT_TYPE prevBestFitness; FLOAT_TYPE tot_fraction_done; FLOAT_TYPE rep_fraction_done;//make sure this remains the last scalar in the class for checkpointing to work public: GeneralGamlConfig *conf; ClaManager *claMan; Adaptation *adap; Individual* indiv; private: Individual* newindiv; vector subtreeMemberNodes; #ifdef INCLUDE_PERTURBATION PerturbManager *pertMan; #endif ParallelManager *paraMan; //DJZ adding these streams directly to the class so that they can be opened once and left open ofstream fate; ofstream log; ofstream treeLog; ofstream probLog; ofstream bootLog; ofstream bootLogPhylip; ofstream swapLog; string besttreefile; char *treeString; int stringSize; //if the user killed the run bool userTermination; //specified stoptime FOR THIS EXECUTION was reached. bool timeTermination; //specified stopgen was reached. Note that this is PER REP, so it can be reset. bool genTermination; //termination due to workphasedivision setting, i.e. after initial opt, before final opt //and immediately after final opt bool workPhaseTermination; //This just means that the run was restarted but the checkpoint indicated that the run had //actually finished. The flag mainly ensures that output files are not finalized multiple //times (e.g., end; written to tree files) bool restartedAfterTermination; vector unusedTrees; //trees that are being stored for some reason, for example the //best from a number of reps vector storedTrees; public: enum { nomem=1, nofile, baddimen }; int error; bool usedNCL; bool startingTreeInNCL; bool startingModelInNCL; //this is the number of generations that the run must continue without finding //a new better swap to terminate, on top of other stopping conditions, default=0 //if it is NEGATIVE, then abs(this) superseeds all other term cond and only this //must be met. set in conf with swaptermthreshold = # int swapTermThreshold; enum output_details { DONT_OUTPUT = 0, REPLACE = 1, APPEND = 2, NEWNAME = 4, WRITE_CONTINUOUS = 8, WRITE_REP_TERM = 16, WRITE_REPSET_TERM = 32, WRITE_PREMATURE = 64, FINALIZE_REP_TERM = 128, FINALIZE_REPSET_TERM = 256, FINALIZE_FULL_TERM = 512, FINALIZE_PREMATURE = 1024, WARN_PREMATURE = 2048, NEWNAME_PER_REP = 4096 }; output_details screen_output; output_details log_output; output_details best_output; output_details all_best_output; output_details treelog_output; output_details fate_output; output_details problog_output; output_details swaplog_output; output_details bootlog_output; FLOAT_TYPE** cumfit;//allocated in setup, deleted in dest DataPartition *dataPart; DataPartition *rawPart;//this will hold the original data as read in, before it might be converted //to codons or aminoacid Stopwatch stopwatch; #ifdef INCLUDE_PERTURBATION Individual *allTimeBest; //this is only used for perturbation or ratcheting Individual *bestSinceRestart; #endif public: Population() : error(0), conf(NULL), usedNCL(false), startingTreeInNCL(false), startingModelInNCL(false), bestFitness(-(FLT_MAX)), bestIndiv(0), currentSearchRep(1), prevBestFitness(-(FLT_MAX)),indiv(NULL), newindiv(NULL), cumfit(NULL), gen(0), paraMan(NULL), subtreeDefNumber(0), claMan(NULL), treeString(NULL), adap(NULL), rep_fraction_done(ZERO_POINT_ZERO), tot_fraction_done(ZERO_POINT_ZERO), userTermination(false), timeTermination(false), genTermination(false), workPhaseTermination(false), restartedAfterTermination(false), currentBootstrapRep(0), finishedRep(false), lastBootstrapSeed(0), nextBootstrapSeed(0), dataPart(NULL), rawPart(NULL), swapTermThreshold(0), finishedGenerations(false), initialRefinePass(0), finalRefinePass(0) #ifdef INCLUDE_PERTURBATION pertMan(NULL), allTimeBest(NULL), bestSinceRestart(NULL), #endif { lastTopoImprove = 0; lastPrecisionReduction = 0; } ~Population(); void QuickSort( FLOAT_TYPE **scoreArray, int top, int bottom ); FLOAT_TYPE BestFitness() { // assert(bestFitness == indiv[bestIndiv].Fitness()); return bestFitness; } #if !defined( PARALLEL_MPI_VERSION ) void Run(); #endif void SetOutputDetails(); void DetermineFilename(output_details details, char *outname, string suffix); //functions added for multiple replicate searches void WriteStoredTrees( const char* treefname ); void OutputRepNums(ofstream &out); void GetRepNums(string &s); void PerformSearch(); int EvaluateStoredTrees(bool report); void ClearStoredTrees(); char *TreeStructToNewick(int i); char *MakeNewick(int, bool); void CreateGnuPlotFile(); void WritePopulationCheckpoint(OUTPUT_CLASS &out) ; void ReadPopulationCheckpoint(); void WriteStateFiles(); bool ReadStateFiles(); void GetConstraints(); void WriteTreeFile( const char* treefname, int indnum, bool collapse = false); void WritePhylipTree(ofstream &phytree); void Setup(GeneralGamlConfig *conf, DataPartition *, DataPartition *, int nprocs = 1, int rank = 0); void Reset(); int Restart(int type, int rank, int nprocs, int restart_count); void SeedPopulationWithStartingTree(int rep);//mult rep change FLOAT_TYPE CalcAverageFitness(); void CalculateReproductionProbabilies(FLOAT_TYPE **scoreArray, FLOAT_TYPE selectionIntensity, int indivsInArray); void NextGeneration(); void DetermineParentage(); void FindTreeStructsForNextGeneration(); void PerformMutation(int indNum); void UpdateFractionDone(int phase); FLOAT_TYPE GenerationFractionDone(); bool OutgroupRoot(Individual *ind, int indnum); void LoadNexusStartingConditions(); void VariableStartingTreeOptimization(bool reducing); void OptimizeSiteRates(); void OptimizeInputAndWriteSitelikelihoods(); void OptimizeInputAndWriteSitelikelihoodsAndTryRootings(); int IsError() const { return error; } void ErrorMsg( char* msgstr, int len ); void CompactTopologiesList(); void EliminateDuplicateTreeReferences(); friend istream& operator >>( istream& inf, Population& p ); friend ostream& operator <<( ostream& outf, Population& p ); int ExtendPopulation(int, char*, FLOAT_TYPE*); int ShrinkPopulation(int, char**, FLOAT_TYPE**); int SwapIndividuals(int, const char*, FLOAT_TYPE*, char**, FLOAT_TYPE**); int ReplaceSpecifiedIndividuals(int, int*, const char*, FLOAT_TYPE*); int ReplicateSpecifiedIndividuals(int, int*, const char*, FLOAT_TYPE *); void FillPopWithClonesOfBest(); int GetNRandomIndivIndices(int**, int); int GetNBestIndivIndices(int**, int); int GetSpecifiedTreeStrings(char**, int, int*); int GetSpecifiedRates(FLOAT_TYPE**, int, int*); int GetSpecifiedPis(FLOAT_TYPE**, int , int*); int GetSpecifiedModels(FLOAT_TYPE** model_string, int n, int* indiv_list); void CheckAllTrees(); void CheckIndividuals(); void CheckTreesVsClaManager(); FLOAT_TYPE IndivFitness(int i); void NNIPerturbation(int sourceInd, int indivIndex); void NNISpectrum(int sourceInd); void NNIoptimization(); // void SPRoptimization(int indivIndex); bool NNIoptimization(unsigned IndivIndex, int steps); // bool SPRoptimization(int indivIndex, int range, int cutnum ); void SPRPerturbation(int sourceInd, int indivIndex); void keepTrack(); void DetermineSubsets(int); void Partition(TreeNode *pointer); void PartitionDown(TreeNode *pointer, TreeNode *calledFrom); void CheckPerturbSerial(); void CheckPerturbParallel(); void StoreBestForPert(); void StoreAllTimeBest(); void RestoreBestForPert(); void RestoreAllTimeBest(); void CheckSubtrees(); void AssignSubtree(int st, int indNum); bool SubtreeRecombination(int); void StartSubtreeMode(); void StopSubtreeMode(); private: int prResizeIndividualArray(int, char* = NULL, FLOAT_TYPE* = NULL); int prResizeNewIndividualArray(int); int prResizeTopologyListArray(int); int prResizeCumFitArray(int); public: void InitializeOutputStreams(); void FinalizeOutputStreams(int type); void AppendTreeToTreeLog(int mutType, int indNum=-1); void FinishBootstrapRep(const Individual *ind, int rep); void UpdateTreeModels(); void WriteGenerationOutput(); void OutputFate(); void OutputLog(); void OutputModelReport(); void OutputModelAddresses(); void OutputClaReport(Individual *arr); void OutputFilesForScoreDebugging(Individual *ind=NULL, int num=0); void RunTests(); void GenerateTreesOnly(int nTrees); void ApplyNSwaps(int numSwaps); void SwapToCompletion(FLOAT_TYPE optPrecision); void CheckForIncompatibleConfigEntries(); void Bootstrap(); void FindLostClas(); void FinalOptimization(); void BetterFinalOptimization(); void InitialOptimization(Individual *ind, bool optModel, FLOAT_TYPE branchPrec); void ResetMemLevel(int numNodesPerIndiv, int numClas); void SetNewBestIndiv(int indivIndex); void LogNewBestFromRemote(FLOAT_TYPE, int); void CheckRemoteReplaceThresh(); void TurnOffRatchet(); unsigned Gen()const {return gen;} void ValidateInput(int rep); string TerminationWarningMessage(){ return string("\nNOTE: ***Search was terminated before full auto-termination condition was reached!\nLikelihood scores, topologies and model estimates obtained may not be fully optimal!***\n"); } bool ShouldCheckpoint(bool checkGeneration) const{ #ifndef BOINC //non-BOINC checkpointing if(conf->checkpoint == true && conf->scoreOnly == false && conf->optimizeInputOnly == false && (checkGeneration == false || (gen % conf->saveevery) == 0)) return true; #else //BOINC checkpointing can occur whenever the BOINC client wants it to if(boinc_time_to_checkpoint()) return true; #endif return false; } }; #endif garli-2.1-release/src/reconnode.h000066400000000000000000000326321241236125200170100ustar00rootroot00000000000000// GARLI version 2.0 source code // Copyright 2005-2011 Derrick J. Zwickl // email: garli.support@gmail.com // // This program is free software: you can redistribute it and/or modify // it under the terms of the GNU General Public License as published by // the Free Software Foundation, either version 3 of the License, or // (at your option) any later version. // // This program is distributed in the hope that it will be useful, // but WITHOUT ANY WARRANTY; without even the implied warranty of // MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the // GNU General Public License for more details. // // You should have received a copy of the GNU General Public License // along with this program. If not, see . #ifndef _RECONNODE_ #define _RECONNODE_ #include #include #include #include "rng.h" #ifdef UNIX #include "unistd.h" #endif extern rng rnd; using namespace std; class ReconNode; typedef list::iterator listIt; class ReconNode{ public: unsigned short nodeNum; unsigned short reconDist; FLOAT_TYPE pathlength; FLOAT_TYPE weight; FLOAT_TYPE chooseProb; bool withinCutSubtree; ReconNode(unsigned short nn, unsigned short rd, float pl, bool wcs=false) : nodeNum(nn), reconDist(rd), pathlength(pl), withinCutSubtree(wcs) {} void Report(ofstream &deb){ deb << nodeNum << "\t" << reconDist << "\t" << pathlength << "\t" << weight << "\t" << chooseProb << "\t" << withinCutSubtree << "\n"; } bool operator<(const ReconNode &rhs){ return reconDist < rhs.reconDist; } }; class DistEquals:public binary_function{ public: result_type operator()(first_argument_type i, second_argument_type j) const{ return (result_type) (i.reconDist==j); } }; class DistEqualsWithinCutSubtree:public binary_function{ public: result_type operator()(first_argument_type i, second_argument_type j) const{ return (result_type) (i.reconDist==j && i.withinCutSubtree==true); } }; class NodeEquals:public binary_function{ public: result_type operator()(first_argument_type i, second_argument_type j) const{ return (result_type) (i.nodeNum==j); } }; class ReconList{ unsigned num; list l; public: ReconList(){ num = 0; } listIt begin(){ return l.begin(); } listIt end(){ return l.end(); } listIt GetFirstNodeAtDist(int Dist){ return find_if(l.begin(),l.end(),bind2nd(DistEquals(), Dist)); } listIt GetFirstNodeAtDistWithinCutSubtree(int Dist){ return find_if(l.begin(),l.end(),bind2nd(DistEqualsWithinCutSubtree(), Dist)); } void clear() { l.clear(); num=0; } unsigned size() { assert(num == l.size()); return num; } void print(const char *fn){ ofstream out(fn); for(listIt it=l.begin();it!=l.end();it++){ out << it->nodeNum << "\t" << it->reconDist << "\t" << it->pathlength << endl; } out.close(); } void CalcProbsFromWeights(){ //this just fills the chooseProb field by dividing the prob between the nodes in proportion to their weight FLOAT_TYPE weightSum = 0.0, running = 0.0; for(listIt it=l.begin();it!=l.end();it++){ weightSum += (*it).weight; } for(listIt it=l.begin();it!=l.end();it++){ running += (*it).weight/weightSum; (*it).chooseProb = (float) running; } } void RemoveNodesOfDist(int dist){ //remove_if(l.begin(), l.end(), bind2nd(DistEquals(), dist)); for(listIt it=l.begin();it!=l.end();){ if((*it).reconDist==dist){ it=l.erase(it); num--; } else it++; } } listIt NthElement(int index){ listIt ret=l.begin(); int i=0; while(i++nodeNum; } void Reverse(){ reverse(l.begin(), l.end()); } ReconNode *RandomReconNode(){ return &(*(NthElement(rnd.random_int((int)l.size())))); } ReconNode *ChooseNodeByWeight(){ FLOAT_TYPE prob = rnd.uniform(); listIt it=l.begin(); for(;it!=l.end();it++){ if(prob < (*it).chooseProb) return &(*it); } //we should only get here due to a little rounding error it--; return &(*it); } void AddNode(ReconNode &nd){ //this just duplicates the added ReconNode via the default copy constructor, assumably added from another list l.push_back(nd); num++; } void AddNode(int nn, int rd, float pl, bool withinCutSubtree=false){ //first verify that we don't already have this node in the list if(find_if(l.begin(),l.end(),bind2nd(NodeEquals(), nn)) != l.end()) return; l.push_back(ReconNode(nn, rd, pl, withinCutSubtree)); num++; } void SortByDist(){ l.sort(); } void DebugReport(){ ofstream deb("recons.log"); for(listIt it=l.begin();it!=l.end();it++){ (*it).Report(deb); } deb.close(); } }; class Swap; bool SwapLessThan(const Swap &lhs, const Swap &rhs); bool SwapLessThanDist(const Swap &lhs, const Swap &rhs); class Swap{ Bipartition b; unsigned short count; unsigned short cutnum; unsigned short brokenum; unsigned short reconDist; public: //default constructor does not initialize the bipart, since there would be some overhead Swap() : count(0), cutnum(0), brokenum(0), reconDist(0){} Swap(Bipartition &swap, int cut, int broke, int dist){ b=&swap; count=1; cutnum=cut; brokenum=broke; reconDist=dist; } //copy constructor Swap(const Swap &s){ b=s.b; count=s.count; cutnum=s.cutnum; brokenum=s.brokenum; reconDist=s.reconDist; } //this is just like the constructor, but doesn't require the bipartition //to be allocated every time void Setup(Bipartition &swap, int cut, int broke, int dist){ b=&swap; count=1; cutnum=cut; brokenum=broke; reconDist=dist; } Swap(FILE* &in){ b.BinaryInput(in); intptr_t scalarSize = (intptr_t) &(reconDist) - (intptr_t) &(count) + sizeof(reconDist); fread(&count, scalarSize, 1, in); } void Increment(){ count++; } int Count()const { return count; } int ReconDist() const{ return reconDist; } void SetCount(int c){ count = c; } void Output(ofstream &out){ out << b.Output() << "\t" << count << "\t" << cutnum << "\t" << brokenum << "\t" << reconDist << endl; } /* void BinaryOutput(ofstream &out){ b.BinaryOutput(out); intptr_t scalarSize = (intptr_t) &reconDist - (intptr_t) &count + sizeof(reconDist); out.write((char*)&count, (streamsize) scalarSize); } */ void BinaryOutput(OUTPUT_CLASS &out){ b.BinaryOutput(out); intptr_t scalarSize = (intptr_t) &reconDist - (intptr_t) &count + sizeof(reconDist); out.WRITE_TO_FILE(&count, (streamsize) scalarSize, 1); } unsigned BipartitionBlock(int block) const{ return b.rep[block]; } bool operator<(const Swap &rhs){ //note that this is "less than" for sorting purposes, not in a subset sense //it is a strict weak ordering, so it returns false in the case of possible equality //ordering is based first on bip, then on reconDist int i; for(i=0;i rhs.BipartitionBlock(i)) return false; else if(BipartitionBlock(i) < rhs.BipartitionBlock(i)) return true; } if(((BipartitionBlock(i)) & Bipartition::partialBlockMask) < ((rhs.BipartitionBlock(i)) & Bipartition::partialBlockMask)) return true; else if(((BipartitionBlock(i)) & Bipartition::partialBlockMask) == ((rhs.BipartitionBlock(i)) & Bipartition::partialBlockMask)){ //bipartitions are equal if(reconDist < rhs.reconDist) return true; //dists are not else if(reconDist == rhs.reconDist) if(cutnum < rhs.cutnum) return true;//cutnum is not } return false; } bool operator==(const Swap &rhs){ assert(rhs.b.ContainsTaxon(1)); bool bipEqual = b.EqualsEquals(rhs.b); if(bipEqual == false) return false; //if the bips are equal but the distances are different, the pre-swap topos must be different //so we want to consider this a different swap if(reconDist != rhs.reconDist) return false; if(reconDist == 1){//NNI's with different cuts and brokens can give the same topo return true; } else if((cutnum == rhs.cutnum) && (brokenum == rhs.brokenum)){ return true; } return false; } }; class AttemptedSwapList{ list swaps; list::iterator> indeces; unsigned unique; unsigned total; public: AttemptedSwapList(){ unique=total=0; } int GetUnique() {return unique;} int GetTotal() {return total;} void ClearAttemptedSwaps(){ swaps.clear(); indeces.clear(); unique=total=0; } list::iterator end(){ return swaps.end(); } /* void WriteSwapCheckpoint(ofstream &out){ intptr_t scalarSize = (intptr_t) &total - (intptr_t) &unique + sizeof(total); out.write((char*) &unique, (streamsize) scalarSize); for(list::iterator it=swaps.begin();it != swaps.end(); it++){ (*it).BinaryOutput(out); } } */ void WriteSwapCheckpoint(OUTPUT_CLASS &out){ intptr_t scalarSize = (intptr_t) &total - (intptr_t) &unique + sizeof(total); out.WRITE_TO_FILE(&unique, scalarSize, 1); for(list::iterator it=swaps.begin();it != swaps.end(); it++){ (*it).BinaryOutput(out); } } void ReadBinarySwapCheckpoint(FILE* &in){ assert(ferror(in) == false); intptr_t scalarSize = (intptr_t) &total - (intptr_t) &unique + sizeof(total); fread(&unique, scalarSize, 1, in); if(ferror(in) || feof(in)){//this mainly checks for a zero-byte file throw ErrorException("Error reading checkpoint file .swaps.check.\n\tA problem may have occured writing the file to disk, or the file may have been overwritten or truncated.\n\tUnfortunately you'll need to start the run again from scratch."); } for(unsigned i=0;i::iterator it=swaps.begin();it != swaps.end(); it++) tot += (*it).Count(); if(tot != total) throw ErrorException("problem reading swap checkpoint!"); } void ReadSwapCheckpoint(ifstream &in, int ntax){ assert(in.good()); Bipartition *b; char *str=new char[ntax+2]; int count, cut, broke, dist; in >> str; while(in.good() && !in.eof()){ b=new Bipartition(str); in >> count; in >> cut; in >> broke; in >> dist; Swap swap(*b, cut, broke, dist); swap.SetCount(count); unique++; total+=count; swaps.push_back(swap); in >> str; } IndexSwaps(); delete []str; } void IndexSwaps(){ indeces.clear(); int increment=(int) sqrt((FLOAT_TYPE)unique); int count=0; for(list::iterator it=swaps.begin();it != swaps.end(); it++){ if(count % increment == 0) indeces.push_back(it); count++; } } bool AddSwap(Bipartition &bip, int cut, int broke, int dist){ //see if the bipartition already exists in the list //if so, increment the count, otherwise add it assert(bip.ContainsTaxon(1)); Swap swap; swap.Setup(bip, cut, broke, dist); bool found; list::iterator it = FindSwap(swap, found); if(found == false){ bool reindex=false; //if we're adding this before the first index, be sure to reindex if(it == swaps.begin() && indeces.empty()==false) reindex=true; swaps.insert(it, swap); unique++; total++; if(unique==100 || (unique % 1000)==0 || reindex==true) IndexSwaps(); } else{ (*it).Increment(); total++; } assert(swaps.size() == unique); return (found == false);//return value is true if the swap is _unique_ } list::iterator FindSwap(Swap &swap, bool &found){ //this function returns the matching swap if found in the list //or the swap that would come immediately after it if not list::iterator start; if(indeces.size() == 0) start=swaps.begin(); else{ for(list::iterator>::iterator indexit=indeces.begin();;indexit++){ if(indexit == indeces.end()){ start = *(--indexit); break; } else if(swap < (*(*indexit))){ if(indexit != indeces.begin()) start = *(--indexit); else start = *(indeces.begin()); break; } } } for(list::iterator it = start;it != swaps.end();it++){ if(swap == (*it)){ found=true; return it; } if(swap < (*it)){ found=false; return it; } } /* //a complete search from the start for(list::iterator it = swaps.begin();it != swaps.end();it++){ if(swap == (*it)){ found=true; return it; } if(swap < (*it)){ found=false; return it; //return swaps.end(); } } */ found=false; return swaps.end(); } void SwapReport(ofstream &swapLog){ unsigned int distTotCounts[200]; unsigned int distUniqueCounts[200]; for(int i=0;i<200;i++){ distTotCounts[i]=distUniqueCounts[i]=0; } for(list::iterator it = swaps.begin();it != swaps.end();it++){ distUniqueCounts[(*it).ReconDist() - 1]++; distTotCounts[(*it).ReconDist() - 1] += (*it).Count(); } swapLog << "\t" << GetUnique() << "\t" << GetTotal() << "\t" ; for(int i=0;i<200;i++){ if(i > 5 && distUniqueCounts[i] == 0) break; swapLog << distUniqueCounts[i] << "\t" << distTotCounts[i] << "\t"; } swapLog << endl; } void AttemptedSwapDump(ofstream &deb){ deb << "\t" << GetUnique() << "\t" << GetTotal() << "\n" ; for(list::iterator it = swaps.begin();it != swaps.end();it++){ (*it).Output(deb); } } }; #endif garli-2.1-release/src/rng.cpp000066400000000000000000000506701241236125200161570ustar00rootroot00000000000000// GARLI version 2.0 source code // Copyright 2005-2011 Derrick J. Zwickl // email: garli.support@gmail.com // // This program is free software: you can redistribute it and/or modify // it under the terms of the GNU General Public License as published by // the Free Software Foundation, either version 3 of the License, or // (at your option) any later version. // // This program is distributed in the hope that it will be useful, // but WITHOUT ANY WARRANTY; without even the implied warranty of // MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the // GNU General Public License for more details. // // You should have received a copy of the GNU General Public License // along with this program. If not, see . // // NOTE: Portions of this source adapted from GAML source, written by Paul O. Lewis #include #include using namespace std; #include "defs.h" #include "rng.h" rng rnd; rng::rng() : ix0(1L), ix(1L), ifault(0) { randomize(); } void rng::dememorize( int spin /* = 100 */ ) { for( int k = 0; k < spin; k++ ) uniform(); } void rng::randomize( int spin /* = 100 */ ) { time_t timer; ix=ix0=(long) time(&timer); dememorize( spin ); } long rng::random_long(long max) { if (max == 0) return 0; long return_val = max; while( return_val == max ) return_val = (long)( (FLOAT_TYPE)max * uniform() ); return return_val; } int rng::random_int(int max) { if (max == 0) return 0; int return_val = max; while( return_val == max ) return_val = (int)( (FLOAT_TYPE)max * uniform() ); return return_val; } float rng::random_float(float max) { if (max == 0.0) return 0.0; float return_val = max; while (return_val == max) return_val = (float)( (FLOAT_TYPE)max * uniform() ); return return_val; } FLOAT_TYPE rng::random_FLOAT_TYPE(FLOAT_TYPE max) { if (max == 0.0) return 0.0; FLOAT_TYPE return_val = max; while (return_val == max) return_val = max * uniform(); return return_val; } FLOAT_TYPE rng::exponential(FLOAT_TYPE lambda) { FLOAT_TYPE x = 0.0; while( x <= 0.0 || x > 1.0 ) x = ONE_POINT_ZERO - uniform(); x = -log(x) / lambda; return x; } FLOAT_TYPE rng::gamln( FLOAT_TYPE x ) { // ====================================================================== // NIST Guide to Available Math Software. // Source for module GAMLN from package CMLIB. // Retrieved from TIBER on Wed Apr 29 17:30:20 1998. // ====================================================================== // WRITTEN BY D. E. AMOS, SEPTEMBER, 1977. // // REFERENCES // SAND-77-1518 // // COMPUTER APPROXIMATIONS BY J.F.HART, ET.AL., SIAM SERIES IN // APPLIED MATHEMATICS, WILEY, 1968, P.135-136. // // NBS HANDBOOK OF MATHEMATICAL FUNCTIONS, AMS 55, BY // M. ABRAMOWITZ AND I.A. STEGUN, DECEMBER. 1955, P.257. // // ABSTRACT // GAMLN COMPUTES THE NATURAL LOG OF THE GAMMA FUNCTION FOR // X.GT.0. A RATIONAL CHEBYSHEV APPROXIMATION IS USED ON // 8.LT.X.LT.1000., THE ASYMPTOTIC EXPANSION FOR X.GE.1000. AND // A RATIONAL CHEBYSHEV APPROXIMATION ON 2.LT.X.LT.3. FOR // 0.LT.X.LT.8. AND X NON-INTEGRAL, FORWARD OR BACKWARD // RECURSION FILLS IN THE INTERVALS 0.LT.X.LT.2 AND // 3.LT.X.LT.8. FOR X=1.,2.,...,100., GAMLN IS SET TO // NATURAL LOGS OF FACTORIALS. // // DESCRIPTION OF ARGUMENTS // // INPUT // X - X.GT.0 // // OUTPUT // GAMLN - NATURAL LOG OF THE GAMMA FUNCTION AT X // // ERROR CONDITIONS // IMPROPER INPUT ARGUMENT - A FATAL ERROR static FLOAT_TYPE xlim1 = (FLOAT_TYPE)8.; static FLOAT_TYPE xlim2 = (FLOAT_TYPE)1e3; static FLOAT_TYPE rtwpil = (FLOAT_TYPE).918938533204673; static FLOAT_TYPE p[5] = { (FLOAT_TYPE)7.66345188e-4,(FLOAT_TYPE)-5.9409561052e-4,(FLOAT_TYPE) 7.936431104845e-4,(FLOAT_TYPE)-.00277777775657725,(FLOAT_TYPE) .0833333333333169 }; static FLOAT_TYPE q[2] = { (FLOAT_TYPE)-.00277777777777778,(FLOAT_TYPE).0833333333333333 } ; static FLOAT_TYPE pcoe[9] = { (FLOAT_TYPE).00297378664481017,(FLOAT_TYPE) .0092381945590276,(FLOAT_TYPE).109311595671044,(FLOAT_TYPE).398067131020357, (FLOAT_TYPE)2.15994312846059,(FLOAT_TYPE)6.33806799938727,(FLOAT_TYPE) 20.7824725317921,(FLOAT_TYPE)36.0367725300248,(FLOAT_TYPE)62.0038380071273 } ; static FLOAT_TYPE qcoe[4] = { (FLOAT_TYPE)1.,(FLOAT_TYPE)-8.90601665949746,(FLOAT_TYPE) 9.82252110471399,(FLOAT_TYPE)62.003838007127 }; static FLOAT_TYPE gln[100] = { (FLOAT_TYPE)0.,(FLOAT_TYPE)0.,(FLOAT_TYPE).693147180559945,( FLOAT_TYPE)1.79175946922806,(FLOAT_TYPE)3.17805383034795,(FLOAT_TYPE) 4.78749174278205,(FLOAT_TYPE)6.5792512120101,(FLOAT_TYPE)8.52516136106541,( FLOAT_TYPE)10.6046029027453,(FLOAT_TYPE)12.8018274800815,(FLOAT_TYPE) 15.1044125730755,(FLOAT_TYPE)17.5023078458739,(FLOAT_TYPE)19.9872144956619,( FLOAT_TYPE)22.5521638531234,(FLOAT_TYPE)25.1912211827387,(FLOAT_TYPE) 27.8992713838409,(FLOAT_TYPE)30.6718601060807,(FLOAT_TYPE)33.5050734501369,( FLOAT_TYPE)36.3954452080331,(FLOAT_TYPE)39.3398841871995,(FLOAT_TYPE) 42.3356164607535,(FLOAT_TYPE)45.3801388984769,(FLOAT_TYPE)48.4711813518352,( FLOAT_TYPE)51.6066755677644,(FLOAT_TYPE)54.7847293981123,(FLOAT_TYPE) 58.0036052229805,(FLOAT_TYPE)61.261701761002,(FLOAT_TYPE)64.5575386270063,( FLOAT_TYPE)67.8897431371815,(FLOAT_TYPE)71.257038967168,(FLOAT_TYPE) 74.6582363488302,(FLOAT_TYPE)78.0922235533153,(FLOAT_TYPE)81.557959456115,( FLOAT_TYPE)85.0544670175815,(FLOAT_TYPE)88.5808275421977,(FLOAT_TYPE) 92.1361756036871,(FLOAT_TYPE)95.7196945421432,(FLOAT_TYPE)99.3306124547874,( FLOAT_TYPE)102.968198614514,(FLOAT_TYPE)106.631760260643,(FLOAT_TYPE) 110.320639714757,(FLOAT_TYPE)114.034211781462,(FLOAT_TYPE)117.771881399745,( FLOAT_TYPE)121.533081515439,(FLOAT_TYPE)125.317271149357,(FLOAT_TYPE) 129.123933639127,(FLOAT_TYPE)132.952575035616,(FLOAT_TYPE)136.802722637326,( FLOAT_TYPE)140.673923648234,(FLOAT_TYPE)144.565743946345,(FLOAT_TYPE) 148.477766951773,(FLOAT_TYPE)152.409592584497,(FLOAT_TYPE)156.360836303079,( FLOAT_TYPE)160.331128216631,(FLOAT_TYPE)164.320112263195,(FLOAT_TYPE) 168.327445448428,(FLOAT_TYPE)172.352797139163,(FLOAT_TYPE)176.395848406997,( FLOAT_TYPE)180.456291417544,(FLOAT_TYPE)184.533828861449,(FLOAT_TYPE) 188.628173423672,(FLOAT_TYPE)192.739047287845,(FLOAT_TYPE)196.86618167289,( FLOAT_TYPE)201.009316399282,(FLOAT_TYPE)205.168199482641,(FLOAT_TYPE) 209.342586752537,(FLOAT_TYPE)213.532241494563,(FLOAT_TYPE)217.736934113954,( FLOAT_TYPE)221.95644181913,(FLOAT_TYPE)226.190548323728,(FLOAT_TYPE) 230.439043565777,(FLOAT_TYPE)234.701723442818,(FLOAT_TYPE)238.978389561834,( FLOAT_TYPE)243.268849002983,(FLOAT_TYPE)247.572914096187,(FLOAT_TYPE) 251.890402209723,(FLOAT_TYPE)256.22113555001,(FLOAT_TYPE)260.564940971863,( FLOAT_TYPE)264.921649798553,(FLOAT_TYPE)269.29109765102,(FLOAT_TYPE) 273.673124285694,(FLOAT_TYPE)278.067573440366,(FLOAT_TYPE)282.47429268763,( FLOAT_TYPE)286.893133295427,(FLOAT_TYPE)291.32395009427,(FLOAT_TYPE) 295.766601350761,(FLOAT_TYPE)300.220948647014,(FLOAT_TYPE)304.686856765669,( FLOAT_TYPE)309.164193580147,(FLOAT_TYPE)313.652829949879,(FLOAT_TYPE) 318.152639620209,(FLOAT_TYPE)322.663499126726,(FLOAT_TYPE)327.185287703775,( FLOAT_TYPE)331.717887196928,(FLOAT_TYPE)336.261181979198,(FLOAT_TYPE) 340.815058870799,(FLOAT_TYPE)345.379407062267,(FLOAT_TYPE)349.95411804077,( FLOAT_TYPE)354.539085519441,(FLOAT_TYPE)359.134205369575 }; /* System generated locals */ long int i__1; FLOAT_TYPE ret_val=0.0; /* Local variables */ static FLOAT_TYPE dgam; static long int i__; static FLOAT_TYPE t, dx, px, qx, rx, xx; static long int ndx, nxm; static FLOAT_TYPE sum, rxx; if ( x <= (FLOAT_TYPE)0.) { goto L90; } else { goto L5; } L5: ndx = (long int)x; t = x - (FLOAT_TYPE) ndx; if (t == (FLOAT_TYPE)0.) { goto L51; } dx = xlim1 - x; if (dx < (FLOAT_TYPE)0.) { goto L40; } /* RATIONAL CHEBYSHEV APPROXIMATION ON 2.LT.X.LT.3 FOR GAMMA(X) */ nxm = ndx - 2; px = pcoe[0]; for (i__ = 2; i__ <= 9; ++i__) { /* L10: */ px = t * px + pcoe[i__ - 1]; } qx = qcoe[0]; for (i__ = 2; i__ <= 4; ++i__) { /* L15: */ qx = t * qx + qcoe[i__ - 1]; } dgam = px / qx; if (nxm > 0) { goto L22; } if (nxm == 0) { goto L25; } /* BACKWARD RECURSION FOR 0.LT.X.LT.2 */ dgam /= t + (FLOAT_TYPE)1.; if (nxm == -1) { goto L25; } dgam /= t; ret_val = log(dgam); return ret_val; /* FORWARD RECURSION FOR 3.LT.X.LT.8 */ L22: xx = t + (FLOAT_TYPE)2.; i__1 = nxm; for (i__ = 1; i__ <= i__1; ++i__) { dgam *= xx; /* L24: */ xx += (FLOAT_TYPE)1.; } L25: ret_val = log(dgam); return ret_val; /* X.GT.XLIM1 */ L40: rx = (FLOAT_TYPE)1. / x; rxx = rx * rx; if (x - xlim2 < (FLOAT_TYPE)0.) { goto L41; } px = q[0] * rxx + q[1]; ret_val = px * rx + (x - (FLOAT_TYPE).5) * log(x) - x + rtwpil; return ret_val; /* X.LT.XLIM2 */ L41: px = p[0]; sum = (x - (FLOAT_TYPE).5) * log(x) - x; for (i__ = 2; i__ <= 5; ++i__) { px = px * rxx + p[i__ - 1]; /* L42: */ } ret_val = px * rx + sum + rtwpil; return ret_val; /* TABLE LOOK UP FOR INTEGER ARGUMENTS LESS THAN OR EQUAL 100. */ L51: if (ndx > 100) { goto L40; } ret_val = gln[ndx - 1]; return ret_val; L90: cerr << "GAMLN ARGUMENT IS LESS THAN OR EQUAL TO ZERO " << endl; return ret_val; } #if 0 // // Algorithm 291. Pike, M. C., and I. D. Hill. 1966. Logarithm of the // gamma function. Commun. Ass. Comput. Mach. 9: 684. // (translated to C++ by Paul O. Lewis, November, 1996) // // This procedure evaluates the natural logarithm of gamma(x) for all x > 0, // accurate to 10 decimal places. Stirling's formula is used for the central // polynomial part of the procedure // FLOAT_TYPE rng::loggamma( FLOAT_TYPE x ) { FLOAT_TYPE f = 0.0; FLOAT_TYPE z; if( x < 7.0 ) { f = 1.0; for( z = x - 1.0; z < 7.0; z += 1.0 ) { x = z; f *= z; } x += 1.0; f -= log(f); } z = 1.0 / (x*x); FLOAT_TYPE v1 = f + (x - 0.5)*log(x) - x + 0.918938533204673; FLOAT_TYPE v2 = -0.000595238095238*z; FLOAT_TYPE v3 = ( v2 - 0.000793650793651 )*z; FLOAT_TYPE v4 = ( v3 - 0.0002777777777778 )*z; FLOAT_TYPE v5 = ( v4 + 0.083333333333333 ); FLOAT_TYPE v6 = v5 / x; return (v1 + v6); } #endif // // Algorithm AS 32. Bhattacharjee, G. P. 1970. The incomplete gamma integral. // Appl. Statist. 19: 285-187. // (translated to C++ by Paul O. Lewis, November, 1996) // // Computes incomplete gamma ratio for positive values of // arguments x and p. g must be supplied and should be equal to // ln( gamma(p) ). // ifault = 1 if p <= 0. ifault = 2 if x < 0 else ifault = 0 // Uses series expansion if p > x or x <= 1, otherwise a // continued fraction approximation. // FLOAT_TYPE rng::gamain( FLOAT_TYPE x, FLOAT_TYPE p, FLOAT_TYPE g ) { FLOAT_TYPE pn[6]; // define accuracy and initialize FLOAT_TYPE acu = (FLOAT_TYPE)1.0e-8; const FLOAT_TYPE oflo = (FLOAT_TYPE)1.0e30; FLOAT_TYPE gin = 0.0; FLOAT_TYPE term; FLOAT_TYPE rn; ifault = 0; // test for admissibility of arguments if( p <= 0.0 ) ifault = 1; if( x < 0.0 ) ifault = 2; if( ifault > 0 || x == 0.0 ) return gin; FLOAT_TYPE factor = exp( p * log(x) - x - g ); if( x > 1.0 && x >= p ) goto label30; // calculation by series expansion gin = 1.0; term = 1.0; rn = p; for(;;) { rn += 1.0; term *= (x / rn); gin += term; if( term <= acu ) break; } gin *= ( factor / p ); return gin; // calculation by continued fraction FLOAT_TYPE a, b, an, dif; int i; label30: // <<<<<<<<<<<<<<< label 30 a = ONE_POINT_ZERO - p; b = a + x + ONE_POINT_ZERO; term = 0.0; pn[0] = 1.0; pn[1] = x; pn[2] = x + ONE_POINT_ZERO; pn[3] = x*b; gin = pn[2] / pn[3]; label32: // <<<<<<<<<<<<<<< label 32 a += 1.0; b += 2.0; term += 1.0; an = a * term; pn[4] = b*pn[2] - an*pn[0]; pn[5] = b*pn[3] - an*pn[1]; if( pn[5] == 0.0 ) goto label35; rn = pn[4] / pn[5]; dif = fabs( gin - rn ); if( dif > acu ) goto label34; if( dif <= acu*rn ) { gin = ONE_POINT_ZERO - factor*gin; return gin; } label34: // <<<<<<<<<<<<<<< label 34 gin = rn; label35: // <<<<<<<<<<<<<<< label 35 for( i = 0; i < 4; i++ ) pn[i] = pn[i+2]; if( fabs(pn[4]) < oflo ) goto label32; for( i = 0; i < 4; i++ ) pn[i] /= oflo; goto label32; } // // Algorithm AS 70. Odeh, R. E., and Evans, J. O. 1974. Percentage points // of the normal distribution. Appl. Statist. 23: 96-97. // (translated to C++ by Paul O. Lewis, November, 1996) // // gauinv finds percentage points of the normal distribution. // FLOAT_TYPE rng::gauinv( FLOAT_TYPE p ) { #ifdef SINGLE_PRECISION_FLOATS const FLOAT_TYPE zero = 0.0f; const FLOAT_TYPE one = 1.0f; const FLOAT_TYPE half = 0.5f; const FLOAT_TYPE alimit = 1.0e-20f; const FLOAT_TYPE p0 = -0.322232431088f; const FLOAT_TYPE p1 = -1.0f; const FLOAT_TYPE p2 = -0.342242088547f; const FLOAT_TYPE p3 = -0.0204231210245f; const FLOAT_TYPE p4 = -0.0000453642210148f; const FLOAT_TYPE q0 = 0.099348462606f; const FLOAT_TYPE q1 = 0.58858157495f; const FLOAT_TYPE q2 = 0.531103462366f; const FLOAT_TYPE q3 = 0.10353775285f; const FLOAT_TYPE q4 = 0.0038560700634f; #else const FLOAT_TYPE zero = 0.0; const FLOAT_TYPE one = 1.0; const FLOAT_TYPE half = 0.5; const FLOAT_TYPE alimit = 1.0e-20; const FLOAT_TYPE p0 = -0.322232431088; const FLOAT_TYPE p1 = -1.0; const FLOAT_TYPE p2 = -0.342242088547; const FLOAT_TYPE p3 = -0.0204231210245; const FLOAT_TYPE p4 = -0.0000453642210148; const FLOAT_TYPE q0 = 0.099348462606; const FLOAT_TYPE q1 = 0.58858157495; const FLOAT_TYPE q2 = 0.531103462366; const FLOAT_TYPE q3 = 0.10353775285; const FLOAT_TYPE q4 = 0.0038560700634; #endif ifault = 1; FLOAT_TYPE ps = p; if( ps > half ) ps = one - ps; if( ps < alimit ) return zero; ifault = 0; if( ps == half ) return zero; FLOAT_TYPE yi = sqrt( log( one / (ps*ps) ) ); FLOAT_TYPE retval = yi + ((((yi*p4 + p3)*yi + p2)*yi + p1)*yi + p0) / ((((yi*q4 + q3)*yi + q2)*yi + q1)*yi + q0); if( p < half ) retval = -retval; return retval; } #define A_A 16807L #define B_B15 32768L #define B_B16 65536L #define P_P 2147483647L FLOAT_TYPE rng::uniform() { //long a, p, b15, b16, xhi, xalo, leftlo, fhi, k; long xhi, xalo, leftlo, fhi, k; // // Uniform pseudorandom number generator // Provided by J. Monahan, Statistics Dept., N.C. State University // From Schrage, ACM Trans. Math. Software 5:132-138 (1979) // Translated to C by Paul O. Lewis, Dec. 10, 1992 // xhi = ix / B_B16; xalo = (ix - xhi * B_B16) * A_A; leftlo = xalo / B_B16; fhi = xhi * A_A + leftlo; k = fhi / B_B15; ix = (((xalo - leftlo * B_B16) - P_P) + (fhi - k * B_B15) * B_B16) + k; if (ix < 0) ix += P_P; return ix * (FLOAT_TYPE)4.6566128575e-10; } // // Algorithm AS 91. Best, D. J., and D. E. Roberts. 1975. The percentage // points of the chi-square distribution. Appl. Statist. 24(3): 385-388. // (translated to C++ by Paul O. Lewis, November, 1996) // // To evaluate the percentage points of the chi-squared // probability distribuiton function. // p must lie in the range 0.000002 to 0.999998 // v must be positive // g must be supplied and should be equal to ln(gamma(v/2.0)) // // ifault values: // 0: everything went fine // 1: p was not in range 0.000002 to 0.999998 // 2: v was not positive // 3: // FLOAT_TYPE rng::ppchi2( FLOAT_TYPE p, FLOAT_TYPE v ) { #ifdef SINGLE_PRECISION_FLOATS const FLOAT_TYPE e = 0.5e-4f; const FLOAT_TYPE aa = 0.69314718f; #else const FLOAT_TYPE e = 0.5e-6; const FLOAT_TYPE aa = 0.6931471805; #endif FLOAT_TYPE ch, a, b, q, p1, p2, t, x; FLOAT_TYPE s1, s2, s3, s4, s5, s6; #if 0 FLOAT_TYPE g = gammln( v / 2.0 ); #else FLOAT_TYPE g = gamln( v / (FLOAT_TYPE)2.0 ); #endif // after defining accuracy and ln(2), test arguments and initialize ifault = 1; if( p < 0.000002 || p > 0.999998 ) return -1.0; ifault = 2; if( v <= 0.0 ) return -ONE_POINT_ZERO; ifault = 0; FLOAT_TYPE xx = (FLOAT_TYPE)0.5 * v; FLOAT_TYPE c = xx - ONE_POINT_ZERO; // starting approximation for small chi-squared if( v >= -1.24 * log(p) ) goto label1; ch = pow( (p * xx * exp( g + xx * aa)), (ONE_POINT_ZERO / xx) ); if( ch - e < 0.0 ) return ch; else goto label4; // starting approximation for v less than or equal to 0.32 label1: if( v > 0.32 ) goto label3; ch = (FLOAT_TYPE)0.4; a = log( ONE_POINT_ZERO - p ); label2: q = ch; p1 = ONE_POINT_ZERO + ch * ( (FLOAT_TYPE)4.67 + ch ); p2 = ch * ( (FLOAT_TYPE)6.73 + ch * ( (FLOAT_TYPE)6.66 + ch )); t = (FLOAT_TYPE)-0.5 + ( (FLOAT_TYPE)4.67 + (FLOAT_TYPE)2.0*ch ) / p1 - ( (FLOAT_TYPE)6.73 + ch*( (FLOAT_TYPE)13.32 + (FLOAT_TYPE)3.0*ch )) / p2; ch -= ( ONE_POINT_ZERO - exp( a + g + (FLOAT_TYPE)0.5*ch + c*aa ) * p2 / p1 ) / t; if( fabs( q/ch - 1.0 ) - 0.01 <= 0.0 ) goto label4; else goto label2; // call to algorithm AS 70 - note that p has been tested above label3: x = gauinv( p ); // starting approximation using Wilson and Hilferty estimate p1 = (FLOAT_TYPE) 0.222222 / v; ch = v * pow( x*sqrt(p1) + ONE_POINT_ZERO - p1, 3 ); // starting approximation for p tending to 1 if( ch > 2.2 * v + 6.0 ) ch = (FLOAT_TYPE)-2.0 * ( log( ONE_POINT_ZERO - p ) - c * log((FLOAT_TYPE) 0.5 * ch ) + g ); // call to algorithm AS 32 and calculation of seven term Taylor series label4: q = ch; p1 = (FLOAT_TYPE)0.5 * ch; p2 = p - gamain( p1, xx, g ); if( ifault != 0 ) { ifault = 3; return -ONE_POINT_ZERO; } t = p2 * exp( xx*aa + g + p1 - c * log(ch) ); b = t / ch; #ifdef SINGLE_PRECISION_FLOATS a = 0.5f*t - b*c; s1 = ( 210.0f + a*( 140.0f + a*( 105.0f + a*(84.0f + a*( 70.0f + a*60.0f ))))) / 420.0f; s2 = ( 420.0f + a*( 735.0f + a*( 966.0f + a*( 1141.0f + a*1278.0f)))) / 2520.0f; s3 = ( 210.0f + a*( 462.0f + a*( 707.0f + a*932.0f))) / 2520.0f; s4 = ( 252.0f + a*( 672.0f + a*1182.0f) + c*( 204.0f + a*( 889.0f + a*1740.0f))) / 5040.0f; s5 = ( 84.0f + a*264.0f + c*(175.0f + a*606.0f)) / 2520.0f; s6 = ( 120.0f + c*( 346.0f + c*127.0f)) / 5040.0f; ch += t*( 1.0f + 0.5f*t*s1 - b*c*( s1 - b*( s2 - b*( s3 - b*( s4 - b*( s5 - b*s6)))))); #else a = 0.5*t - b*c; s1 = ( 210.0 + a*( 140.0 + a*( 105.0 + a*(84.0 + a*( 70.0 + a*60.0 ))))) / 420.0; s2 = ( 420.0 + a*( 735.0 + a*( 966.0 + a*( 1141.0 + a*1278.0)))) / 2520.0; s3 = ( 210.0 + a*( 462.0 + a*( 707.0 + a*932.0))) / 2520.0; s4 = ( 252.0 + a*( 672.0 + a*1182.0) + c*( 204.0 + a*( 889.0 + a*1740.0))) / 5040.0; s5 = ( 84.0 + a*264.0 + c*(175.0 + a*606.0)) / 2520.0; s6 = ( 120.0 + c*( 346.0 + c*127.0)) / 5040.0; ch += t*( 1.0 + 0.5*t*s1 - b*c*( s1 - b*( s2 - b*( s3 - b*( s4 - b*( s5 - b*s6)))))); #endif // cout << q << "\t" << ch << "\t" << fabs( q/ch - ONE_POINT_ZERO ) << endl; if( fabs( q/ch - ONE_POINT_ZERO ) > e ) goto label4; return ch; } //this is from MB void rng::DirichletRandomVariable (FLOAT_TYPE *alp, FLOAT_TYPE *z, int n){ int i; FLOAT_TYPE sum; sum = 0.0; for(i=0; i. // NOTE: Portions of this source adapted from GAML source, written by Paul O. Lewis // // Some portions of the source code file for which this is // the header were written by others and thus no copyright // is claimed for those portions. Methods derived from // other works include gamln, uniform, gamain, gauinv, // and ppchi2. // #ifndef __RNG_H #define __RNG_H #ifndef __TIME_H # include #endif #include #include #include using namespace std; class rng { protected: long ix0, ix; int ifault; protected: //FLOAT_TYPE loggamma( FLOAT_TYPE x ); FLOAT_TYPE gamln( FLOAT_TYPE x ); FLOAT_TYPE gauinv( FLOAT_TYPE p ); FLOAT_TYPE gamain( FLOAT_TYPE x, FLOAT_TYPE p, FLOAT_TYPE g ); FLOAT_TYPE ppchi2( FLOAT_TYPE p, FLOAT_TYPE v ); public: rng(); long seed() { return ix; } long init_seed() { return ix0; } void randomize( int spin = 100 ); void set_seed(long s) { ix = ix0 = s; } void dememorize( int spin = 100 ); int random_int(int); long random_long(long); float random_float(float); FLOAT_TYPE random_FLOAT_TYPE(FLOAT_TYPE); FLOAT_TYPE uniform(); FLOAT_TYPE exponential(FLOAT_TYPE); FLOAT_TYPE gamma( FLOAT_TYPE shape ){ FLOAT_TYPE g=-1; do{ g = (FLOAT_TYPE)( ppchi2( uniform(), (FLOAT_TYPE)2.0*shape ) / (FLOAT_TYPE)(2.0*shape) ); }while( g < 0.0); assert(g > 0.0); return g; } FLOAT_TYPE gamma_two_param(FLOAT_TYPE alpha, FLOAT_TYPE beta){ FLOAT_TYPE g=-1; do{ g = (FLOAT_TYPE)( ppchi2( uniform(), (FLOAT_TYPE)2.0*alpha ) / (FLOAT_TYPE)(2.0*beta) ); }while( g < 0.0); assert(g > 0.0); return g; } //DZ 11-3-02 addition int random_binomial(int n, FLOAT_TYPE p); void DirichletRandomVariable (FLOAT_TYPE *alp, FLOAT_TYPE *z, int n); }; //DJZ 11-3-02 Added by me. Stolen from ProbabLib 1.0, by Antonio Larrosa //DJZ 3-29-04 Altering this to have the distribution mean specified, which //should make picking a value for different datasets a bit easier inline int rng::random_binomial(int n, FLOAT_TYPE mean){ FLOAT_TYPE p=mean/n; FLOAT_TYPE t=(FLOAT_TYPE) p/((FLOAT_TYPE)1.0-p); FLOAT_TYPE u=uniform(); FLOAT_TYPE p0=pow((FLOAT_TYPE)((FLOAT_TYPE)1.0-p),n); FLOAT_TYPE g=p0; unsigned int k=0; while (u>g){ p0*=t*(n-k)/(FLOAT_TYPE)(k+1.0); g+=p0; k++; } return k; } /*inline int rng::random_binomial(int n, FLOAT_TYPE p){ FLOAT_TYPE t=p/(1.0-p); FLOAT_TYPE u=uniform(); FLOAT_TYPE p0=pow((1.0-p),n); FLOAT_TYPE g=p0; unsigned int k=0; while (u>g){ p0*=t*(n-k)/(k+1.0); g+=p0; k++; } return k; } */ #endif garli-2.1-release/src/sequencedata.cpp000066400000000000000000001711511241236125200200310ustar00rootroot00000000000000// GARLI version 2.0 source code // Copyright 2005-2011 Derrick J. Zwickl // email: garli.support@gmail.com // // This program is free software: you can redistribute it and/or modify // it under the terms of the GNU General Public License as published by // the Free Software Foundation, either version 3 of the License, or // (at your option) any later version. // // This program is distributed in the hope that it will be useful, // but WITHOUT ANY WARRANTY; without even the implied warranty of // MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the // GNU General Public License for more details. // // You should have received a copy of the GNU General Public License // along with this program. If not, see . // // NOTE: Portions of this source adapted from GAML source, written by Paul O. Lewis #include "defs.h" #include "sequencedata.h" #include "garlireader.h" #include "rng.h" #include extern rng rnd; extern OutputManager outman; extern bool FloatingPointEquals(const FLOAT_TYPE first, const FLOAT_TYPE sec, const FLOAT_TYPE epsilon); #undef DEBUG_CALCFREQ #undef DEBUG_CALCPRMATRIX #undef DEBUGGING_PRMATRICES #undef DEBUGGING_PATTERN_PROBS #if defined( DEBUGGING_PATTERN_PROBS ) # include #endif //this depends on the fact that a spare taxon was allocated void SequenceData::AddDummyRootToExistingMatrix(){ assert(nTaxAllocated > nTax); nTax++; SetTaxonLabel( nTax - 1, "ROOT"); for(int c = 0;c < numPatterns;c++){ SetMatrix( nTax - 1, c, maxNumStates); } } //this depends on the fact that a spare taxon was allocated void NucleotideData::AddDummyRootToExistingMatrix(){ assert(nTaxAllocated > nTax); nTax++; SetTaxonLabel( nTax - 1, "ROOT"); for(int c = 0;c < numPatterns;c++){ SetMatrix( nTax - 1, c, 15); } } void NucleotideData::CalcEmpiricalFreqs(){ empStateFreqs=new FLOAT_TYPE[4];//this is a member of the class, and where the final freqs will be stored empStateFreqs[0]=empStateFreqs[1]=empStateFreqs[2]=empStateFreqs[3]=0.0; //these are all temporary and local FLOAT_TYPE freqSumNoAmbig[4] = {0.0, 0.0, 0.0, 0.0}; FLOAT_TYPE freqSumAmbig[4] = {0.0, 0.0, 0.0, 0.0}; FLOAT_TYPE nonAmbigTotal = 0.0; FLOAT_TYPE ambigTotal = 0.0; vector ambigStates; vector ambigCounts; for( int i = 0; i < NTax(); i++ ) { for( int j = 0; j < NChar(); j++ ) { char thischar=(char) Matrix( i, j ); int nstates=0; //first figure out how many states we've got if(thischar & 1) nstates++; if(thischar & 2) nstates++; if(thischar & 4) nstates++; if(thischar & 8) nstates++; if(nstates==1){ if(thischar & 1) freqSumNoAmbig[0] += (FLOAT_TYPE) Count(j); if(thischar & 2) freqSumNoAmbig[1] += (FLOAT_TYPE) Count(j); if(thischar & 4) freqSumNoAmbig[2] += (FLOAT_TYPE) Count(j); if(thischar & 8) freqSumNoAmbig[3] += (FLOAT_TYPE) Count(j); nonAmbigTotal += Count(j); } else if(nstates < 4){ //now divide the states up to the bases //division will be equal for this pass, and refined below if(thischar & 1) freqSumAmbig[0] += (FLOAT_TYPE) Count(j)/nstates; if(thischar & 2) freqSumAmbig[1] += (FLOAT_TYPE) Count(j)/nstates; if(thischar & 4) freqSumAmbig[2] += (FLOAT_TYPE) Count(j)/nstates; if(thischar & 8) freqSumAmbig[3] += (FLOAT_TYPE) Count(j)/nstates; ambigTotal += Count(j); //these will store a list of the ambiguous characters so that iterations //below don't require going through the whole dataset again ambigStates.push_back(thischar); ambigCounts.push_back(Count(j)); } } } bool allPresent = true; for(int j=0;j<4;j++){ if((freqSumNoAmbig[j] + freqSumAmbig[j]) == 0.0){ outman.UserMessage("WARNING: Not all nucleotides are observed in this subset of data!\nYou may have partitioned too finely or are analyzing very strange data.\nBeware!!!"); allPresent = false; break; } } if(!allPresent){ for(int j=0;j<4;j++) freqSumNoAmbig[j] += ONE_POINT_ZERO; nonAmbigTotal += 4.0; } for(int j=0;j<4;j++){ empStateFreqs[j] = (freqSumNoAmbig[j] + freqSumAmbig[j]) / (nonAmbigTotal + ambigTotal); } //now iterate to refine the emp freqs to account for partial ambiguity if(ambigStates.size() > 0){ bool continueIterations; do{ continueIterations = false; freqSumAmbig[0]=freqSumAmbig[1]=freqSumAmbig[2]=freqSumAmbig[3]=0.0; for(unsigned i=0;i max(1.0e-8, GARLI_FP_EPS * 2.0)) continueIterations = true; empStateFreqs[j] = tempFreqs[j]; } }while(continueIterations); } #if defined( DEBUG_CALCFREQ ) cerr << endl << "Frequency of A: " << p[0] << endl; cerr << "Frequency of C: " << p[1] << endl; cerr << "Frequency of G: " << p[2] << endl; cerr << "Frequency of T: " << p[3] << endl; cerr << "Total : " << ( p[0] + p[1] + p[2] + p[3] ) << endl; cerr << endl << "Program stopped after calculating base frequencies because" << endl; cerr << "DEBUG_CALCFREQS was #define'd in source code file \"mlhky.cpp\" " << endl; cerr << endl << "Press Enter key to continue..." << endl; char ch = '\0'; cin.get(ch); exit(0); #endif } void AminoacidData::CalcEmpiricalFreqs(){ empStateFreqs=new FLOAT_TYPE[maxNumStates];//this is a member of the class, and where the final freqs will be stored for(int i=0;i -1); empStateFreqs[thischar] += (FLOAT_TYPE) Count(j); total += (FLOAT_TYPE) Count(j); } } } //check whether this might be nucleotide data in disguise if((empStateFreqs[0]+empStateFreqs[1]+empStateFreqs[5]+empStateFreqs[16])/total > 0.90) throw ErrorException("Model specified as aminoacid, but nucleotide data found!"); FLOAT_TYPE freqTot = 0.0; bool allPresent = true; for(int j=0;j -1); empStateFreqs[thischar] += (FLOAT_TYPE) Count(j); total += (FLOAT_TYPE) Count(j); } } } FLOAT_TYPE freqTot = 0.0; bool allPresent = true; for(int j=0;j1 || numstates==0 || numstates==4) totalStates++; } char *thisString=new char[totalStates]; #ifdef OPEN_MP unsigned *thisMap=new unsigned[NChar()]; #endif //now do it for real int index=0; for(int j=0;jWeightsetName(); if(defWtsName.length() > 0 && useDefaultWeightsets) outman.UserMessage("NOTE: Cannot use default wtsets with DNA to Codon translation! Wtset \"%s\" ignored.", defWtsName.c_str()); nonZeroCharCount = numPatterns = dnaData->NChar()/3; nTax = dnaData->NTax(); if(dnaData->NChar() % 3 != 0) throw ErrorException("Codon datatype specified, but number of nucleotides not divisible by 3!"); NewMatrix(nTax, numPatterns); patman.Initialize(nTax, maxNumStates); //this will just map from the bitwise format to the index format (A, C, G, T = 0, 1, 2, 3) //partial ambiguity is mapped to total ambiguity currently short bitwiseToIndexFormat[16] = {15,0,1,15,2,15,15,15,3,15,15,15,15,15,15,15}; //keep track of the empirical base freqs at the codon positions, for possible use //in the F1x4 or F3x4 methods of calculating the equilibrium codon freqs empBaseFreqsPos1[0]=empBaseFreqsPos1[1]=empBaseFreqsPos1[2]=empBaseFreqsPos1[3]=ZERO_POINT_ZERO; empBaseFreqsPos2[0]=empBaseFreqsPos2[1]=empBaseFreqsPos2[2]=empBaseFreqsPos2[3]=ZERO_POINT_ZERO; empBaseFreqsPos3[0]=empBaseFreqsPos3[1]=empBaseFreqsPos3[2]=empBaseFreqsPos3[3]=ZERO_POINT_ZERO; FLOAT_TYPE total = ZERO_POINT_ZERO; int tax=0, thisCodonNum; for(int tax=0;taxMatrix(tax, cod*3); short p2 = dnaData->Matrix(tax, cod*3+1); short p3 = dnaData->Matrix(tax, cod*3+2); pos1 = bitwiseToIndexFormat[p1]; pos2 = bitwiseToIndexFormat[p2]; pos3 = bitwiseToIndexFormat[p3]; thisCodonNum=(pos1)*16 + (pos2)*4 + pos3; if(pos1==15||pos2==15||pos3==15){//check for gaps or ambiguity if(pos1+pos2+pos3 != 45){ //warn about gaps or ambiguity in codons if(firstAmbig){ outman.UserMessageNoCR("Gaps or ambiguity codes found within codon for taxon %s.\n\tCodons coded as missing for that taxon: ", dnaData->TaxonLabel(tax)); firstAmbig = false; } outman.UserMessageNoCR("%d ", cod+1); } thisCodonNum=64; } else{ empBaseFreqsPos1[pos1] += ONE_POINT_ZERO; empBaseFreqsPos2[pos2] += ONE_POINT_ZERO; empBaseFreqsPos3[pos3] += ONE_POINT_ZERO; total += ONE_POINT_ZERO; } char prot; //note that a return code of 20 from the codon lookup indicates a stop codon, but a protein code of 20 generally means total ambiguity if(thisCodonNum != 64){ prot = code.CodonLookup(thisCodonNum); if(prot == 20){ string c; char b[4]={'A','C','G','T'}; c += b[pos1]; c += b[pos2]; c += b[pos3]; if(ignoreStops == true){ outman.UserMessage("Warning: stop codon %s found at codon site %d (nuc site %d) in taxon %s.\n\tTreating as missing data because ignorestopcodons = 1 is set in configuration file.", c.c_str(), cod+1, cod*3+1, dnaData->TaxonLabel(tax)); thisCodonNum=64; } else throw ErrorException("Stop codon %s found at codon site %d (nuc site %d) in taxon %s. Bailing out.\nBe sure that your alignment is properly in frame, or set ignorestopcodons = 1 in the\n[general] section of your configuration file to treat as missing.", c.c_str(), cod+1, cod*3+1, dnaData->TaxonLabel(tax)); } } if(thisCodonNum == 64)//missing or ambiguous matrix[tax][cod] = maxNumStates; else matrix[tax][cod] = code.Map64stateToNonStops(thisCodonNum); } if(firstAmbig == false) outman.UserMessage(""); } for(int b=0;b<4;b++){ empBaseFreqsAllPos[b] = (empBaseFreqsPos1[b] + empBaseFreqsPos2[b] + empBaseFreqsPos3[b]) / (3.0 * total); empBaseFreqsPos1[b] /= total; empBaseFreqsPos2[b] /= total; empBaseFreqsPos3[b] /= total; } //copy matrix into alternative PatternManager for pattern sorting if(usePatternManager){ SitePattern thisPat; for(int cod=0;codnchar order now anyway thisPat.siteNumbers.push_back(number[cod]); thisPat.SetCount(1); patman.AddPattern(thisPat); thisPat.Reset(); } } } void AminoacidData::FillAminoacidMatrixFromDNA(const NucleotideData *dnaData, GeneticCode *code, bool ignoreStops){ //first we need to convert the nucleotide data to codons, and then translate the codons to AA's //codons are ordered AAA, AAC, AAG, AAT, ACA, ... TTT short pos1, pos2, pos3; string defWtsName = dnaData->WeightsetName(); if(defWtsName.length() > 0 && useDefaultWeightsets) outman.UserMessage("NOTE: Cannot use default wtsets with DNA to Aminoacid translation! Wtset \"%s\" ignored.", defWtsName.c_str()); nonZeroCharCount = numPatterns = dnaData->NChar()/3; nTax = dnaData->NTax(); if(dnaData->NChar() % 3 != 0) throw ErrorException("Codon to Aminoacid translation specified, but number of nucleotides not divisible by 3!"); NewMatrix(nTax, numPatterns); patman.Initialize(nTax, maxNumStates); int tax=0, thisCodonNum; for(int tax=0;tax treatedAsMissing; for(int cod = 0;cod < numPatterns;cod++){ int posArr[3] = {dnaData->Matrix(tax, cod*3), dnaData->Matrix(tax, cod*3+1), dnaData->Matrix(tax, cod*3+2)}; int prot = -1; bool breaker; string stopString; if(posArr[0] + posArr[1] + posArr[0] == 45) //all positions missing prot = maxNumStates; else{ //All this determines what possible codons any ambiguity could resolve to, and whether those codons encode //the same protein. If so, use that protein. If not, treat as N. vector< vector > allPos; allPos.push_back(vector()); allPos.push_back(vector()); allPos.push_back(vector()); for(int pos = 0;pos < 3;pos++){ if(posArr[pos] & 1) allPos[pos].push_back(0); if(posArr[pos] & 2) allPos[pos].push_back(1); if(posArr[pos] & 4) allPos[pos].push_back(2); if(posArr[pos] & 8) allPos[pos].push_back(3); } breaker = false; stopString.clear(); for(vector::iterator fit = allPos[0].begin();fit != allPos[0].end();fit++){ for(vector::iterator sit = allPos[1].begin();sit != allPos[1].end();sit++){ for(vector::iterator tit = allPos[2].begin();tit != allPos[2].end();tit++){ int thisCodonResolution = (*fit)*16 + (*sit)*4 + *tit; //note that a return code of 20 (or 21 for the two serine model) from the codon lookup indicates a stop codon, but a protein code of 20 generally means total ambiguity int thisResolutionProt = code->CodonLookup(thisCodonResolution); if(thisResolutionProt == maxNumStates){ char b[4]={'A','C','G','T'}; stopString += b[*fit]; stopString += b[*sit]; stopString += b[*tit]; if(ignoreStops == true){ outman.UserMessage("Warning: stop codon %s found at codon site %d (nuc site %d) in taxon %s.\n\tTreating as missing data because ignorestopcodons = 1 is set in configuration file.", stopString.c_str(), cod+1, cod*3+1, dnaData->TaxonLabel(tax)); treatedAsMissing.push_back(cod+1); prot = maxNumStates; breaker = true; break; } } else if(prot < 0) //translating the first (or only) codon resolution to amino acid prot = thisResolutionProt; else if(thisResolutionProt != prot){ //another resolution codes for a different AA prot = maxNumStates; treatedAsMissing.push_back(cod+1); breaker = true; break; } else{ outman.DebugMessage("Gaps or ambiguity codes found within codon at codon site %d (nuc site %d)\n\tfor taxon %s.\n\tResolutions of ambiguity encode a single aminoacid.", cod+1, cod*3+1, dnaData->TaxonLabel(tax)); } } if(breaker) break; } if(breaker) break; } } if(stopString.length() > 0 && breaker == false){ //Some resolution of ambiguity resulted in a stop. If stops are being ignored, breaker would have been set above and this would already have been treated as missing. if(prot < 0){ //Unambiguous stop found throw ErrorException("Stop codon %s found at codon site %d (nuc site %d) in taxon %s. Bailing out.\nBe sure that your alignment is properly in frame, or set ignorestopcodons = 1 in the\n[general] section of your configuration file to treat as missing.", stopString.c_str(), cod+1, cod*3+1, dnaData->TaxonLabel(tax)); } else{ //Could be a stop, but some ambiguity resolution results in a valid AA. Treat as missing. outman.UserMessage("Warning: a resolution of ambiguity results in a stop codon at codon site %d (nuc site %d) in taxon %s.\n\tTreating site as missing data.", cod+1, cod*3+1, dnaData->TaxonLabel(tax)); prot = maxNumStates; treatedAsMissing.push_back(cod+1); } } matrix[tax][cod] = prot; } if(treatedAsMissing.size() > 0){ outman.UserMessageNoCR("Some sites treated as missing data for taxon %s due to ambiguity in translation.\n\tAminoacids coded as missing for that taxon: ", dnaData->TaxonLabel(tax)); for(vector::iterator vit = treatedAsMissing.begin();vit != treatedAsMissing.end();vit++) outman.UserMessageNoCR("%d ", *vit); outman.UserMessage("\n"); } } //copy matrix into alternative PatternManager for pattern sorting if(usePatternManager){ SitePattern thisPat; for(int cod=0;codnchar order now anyway thisPat.siteNumbers.push_back(number[cod]); thisPat.SetCount(1); patman.AddPattern(thisPat); thisPat.Reset(); } } } void CodonData::CalcF1x4Freqs(){ //this assumes that the empirical base freqs have already been calculated in FillCodonMatrixFromDNA assert(fabs(empBaseFreqsAllPos[0] + empBaseFreqsAllPos[1] + empBaseFreqsAllPos[2] + empBaseFreqsAllPos[3] - 1.0) < 1.0e-4); FLOAT_TYPE total = ZERO_POINT_ZERO; int stops=0; for(int base1=0;base1<4;base1++){ for(int base2=0;base2<4;base2++){ for(int base3=0;base3<4;base3++){ if(code.CodonLookup(base1*16+base2*4+base3) != 20){ empStateFreqs[base1*16+base2*4+base3 - stops] = empBaseFreqsAllPos[base1] * empBaseFreqsAllPos[base2] * empBaseFreqsAllPos[base3]; total += empStateFreqs[base1*16+base2*4+base3 - stops]; } else stops++; } } } //now normalize, because the stop codons will make the total of the 60 or 61 allowed codons < 1.0 for(int s=0;sGetDataType() != NxsCharactersBlock::dna && charblock->GetDataType() != NxsCharactersBlock::rna && charblock->GetDataType() != NxsCharactersBlock::nucleotide ) throw ErrorException("Tried to create nucleotide matrix from non-nucleotide data.\n\tCheck the datatype settings in your datafile in the characters\n\tor data block and the datatype setting in your Garli config file."); int numOrigTaxa = charblock->GetNTax(); int numActiveTaxa = charblock->GetNumActiveTaxa(); if(charset.empty()){ //the charset was empty, implying that all characters in this block will go into a single matrix (actually, for nstate //might be split anyway). Create an effective charset that contains all of the characters, which will be filtered //for exclusions and for the right number of max states for(int i = 0;i < charblock->GetNumChar();i++) charset.insert(i); } //deal with any exclusions NxsUnsignedSet excluded = charblock->GetExcludedIndexSet(); const NxsUnsignedSet *realCharSet = & charset; NxsUnsignedSet charsetMinusExcluded; if (!excluded.empty()) { int origSize = charset.size(); set_difference(charset.begin(), charset.end(), excluded.begin(), excluded.end(), inserter(charsetMinusExcluded, charsetMinusExcluded.begin())); //only output a message about excluded characters if there is actually an intersection of the exset with the //characters in this subset. Otherwise multiple subsets in a partition will report the same exclusions. //Also, if there are a ton of excluded chars, truncate the message. if(charsetMinusExcluded.size() != origSize){ NxsUnsignedSet actuallyExcluded; set_intersection(charset.begin(), charset.end(), excluded.begin(), excluded.end(), inserter(actuallyExcluded, actuallyExcluded.begin())); NxsString exstr = NxsString(NxsSetReader::GetSetAsNexusString(actuallyExcluded).c_str()).ShortenTo(500); outman.UserMessage("\tExcluded character numbers:%s\n\t", exstr.c_str()); } realCharSet = &charsetMinusExcluded; } int numActiveChar = realCharSet->size(); if(numActiveChar == 0){ throw ErrorException("Sorry, fully excluded characters blocks or partition subsets are not currently supported."); } NewMatrix( numActiveTaxa, numActiveChar ); patman.Initialize(numActiveTaxa, maxNumStates); //get weightset if one was specified vector charWeights; if(useDefaultWeightsets){ wtsetName = GarliReader::GetDefaultIntWeightSet(charblock, charWeights); if(charWeights.size() > 0){ assert(charWeights.size() == charblock->GetNumChar()); outman.UserMessage("\tFound wtset \"%s\" with data, applying...", wtsetName.c_str()); for(int i = 0;i < charWeights.size();i++){ if(charWeights[i] == 0){ throw ErrorException("Sorry, wtsets including sites with zero weight are not allowed in GARLI.\nTry using an exset to exclude the site."); } } } } // read in the data, including taxon names int i=0; for( int origTaxIndex = 0; origTaxIndex < numOrigTaxa; origTaxIndex++ ) { if(charblock->IsActiveTaxon(origTaxIndex)){ //Now storing names as escaped Nexus values - this means: //if they have underscores - store with underscores //if they have spaces within single quotes - store with underscores //if they have punctuation within single parens (including spaces) - store with single quotes maintained NxsString tlabel = charblock->GetTaxonLabel(origTaxIndex); SetTaxonLabel(i, NxsString::GetEscaped(tlabel).c_str()); int j = 0; for(NxsUnsignedSet::const_iterator cit = realCharSet->begin(); cit != realCharSet->end();cit++){ if(i == 0) SetOriginalDataNumber(j, *cit); unsigned char datum = '\0'; if(charblock->IsGapState(origTaxIndex, *cit) == true) datum = 15; else if(charblock->IsMissingState(origTaxIndex, *cit) == true) datum = 15; else{ int nstates = charblock->GetNumStates(origTaxIndex, *cit); for(int s=0;sGetState(origTaxIndex, *cit, s)); } } if(i == 0 && charWeights.size() > 0) SetCount(j, charWeights[*cit]); SetMatrix( i, j, datum ); j++; } i++; } } if(usePatternManager){ //optionally also read into the alternative pattern manager, this is taxa loop inside char loop bool haveWeights = !charWeights.empty(); SitePattern thisPat; int charNum = 0; for(NxsUnsignedSet::const_iterator cit = realCharSet->begin(); cit != realCharSet->end();cit++){ int tax = 0; for( int origTaxIndex = 0; origTaxIndex < numOrigTaxa; origTaxIndex++ ) { if(charblock->IsActiveTaxon(origTaxIndex)){ unsigned char datum = '\0'; if(charblock->IsGapState(origTaxIndex, *cit) == true) datum = 15; else if(charblock->IsMissingState(origTaxIndex, *cit) == true) datum = 15; else{ int nstates = charblock->GetNumStates(origTaxIndex, *cit); for(int s=0;sGetState(origTaxIndex, *cit, s)); } } thisPat.AddChar(datum); } } thisPat.siteNumbers.push_back(charNum++); thisPat.SetCount((haveWeights ? charWeights[*cit] : 1)); patman.AddPattern(thisPat); thisPat.Reset(); } } } void AminoacidData::CreateMatrixFromNCL(const NxsCharactersBlock *charblock, NxsUnsignedSet &charset){ if(charblock->GetDataType() != NxsCharactersBlock::protein) throw ErrorException("Tried to create amino acid matrix from non-amino acid data.\n\t(Did you mean to use datatype = codon-aminoacid?)"); int numOrigTaxa = charblock->GetNTax(); int numActiveTaxa = charblock->GetNumActiveTaxa(); if(charset.empty()){ //the charset was empty, implying that all characters in this block will go into a single matrix (actually, for nstate //might be split anyway). Create an effective charset that contains all of the characters, which will be filtered //for exclusions and for the right number of max states for(int i = 0;i < charblock->GetNumChar();i++) charset.insert(i); } //deal with any exclusions NxsUnsignedSet excluded = charblock->GetExcludedIndexSet(); const NxsUnsignedSet *realCharSet = & charset; NxsUnsignedSet charsetMinusExcluded; if (!excluded.empty()) { string exsetName = NxsSetReader::GetSetAsNexusString(excluded); outman.UserMessage("Excluded characters: %s\n\t", exsetName.c_str()); set_difference(charset.begin(), charset.end(), excluded.begin(), excluded.end(), inserter(charsetMinusExcluded, charsetMinusExcluded.begin())); realCharSet = &charsetMinusExcluded; } int numActiveChar = realCharSet->size(); if(numActiveChar == 0){ throw ErrorException("Sorry, fully excluded characters blocks or partition subsets are not currently supported."); } NewMatrix( numActiveTaxa, numActiveChar ); patman.Initialize(numActiveTaxa, maxNumStates); //get weightset if one was specified vector charWeights; if(useDefaultWeightsets){ wtsetName = GarliReader::GetDefaultIntWeightSet(charblock, charWeights); if(charWeights.size() > 0){ assert(charWeights.size() == charblock->GetNumChar()); outman.UserMessage("\tFound wtset \"%s\" with data, applying...", wtsetName.c_str()); for(int i = 0;i < charWeights.size();i++){ if(charWeights[i] == 0){ throw ErrorException("Sorry, wtsets including sites with zero weight are not allowed in GARLI.\nTry using an exset to exclude the site."); } } } } // read in the data, including taxon names int i=0; for( int origTaxIndex = 0; origTaxIndex < numOrigTaxa; origTaxIndex++ ) { if(charblock->IsActiveTaxon(origTaxIndex)){ //Now storing names as escaped Nexus values - this means: //if they have underscores - store with underscores //if they have spaces within single quotes - store with underscores //if they have punctuation within single parens (including spaces) - store with single quotes maintained NxsString tlabel = charblock->GetTaxonLabel(origTaxIndex); SetTaxonLabel(i, NxsString::GetEscaped(tlabel).c_str()); int j = 0; bool firstAmbig = true; for(NxsUnsignedSet::const_iterator cit = realCharSet->begin(); cit != realCharSet->end();cit++){ if(i == 0) SetOriginalDataNumber(j, *cit); unsigned char datum = '\0'; if(charblock->IsGapState(origTaxIndex, *cit) == true) datum = maxNumStates; else if(charblock->IsMissingState(origTaxIndex, *cit) == true) datum = maxNumStates; else{ int nstates = charblock->GetNumStates(origTaxIndex, *cit); //need to deal with the possibility of multiple states represented in matrix //just convert to full ambiguity if(nstates == 1) datum = CharToDatum(charblock->GetState(origTaxIndex, *cit, 0)); else{ if(firstAmbig){ outman.UserMessageNoCR("\tPart ambig. char's of taxon %s converted to full ambiguity:\n\t char ", TaxonLabel(origTaxIndex)); firstAmbig = false; } outman.UserMessageNoCR(" %d ", *cit+1); datum = CharToDatum('?'); } } if(i == 0 && charWeights.size() > 0) SetCount(j, charWeights[*cit]); SetMatrix( i, j, datum ); j++; } if(firstAmbig == false) outman.UserMessage(""); i++; } } //read the same data into the alternate pattern sorting machinery, which only makes sense looping over tax within char if(usePatternManager){ SitePattern thisPat; bool haveWeights = !charWeights.empty(); int charNum = 0; for(NxsUnsignedSet::const_iterator cit = realCharSet->begin(); cit != realCharSet->end();cit++){ int tax = 0; for( int origTaxIndex = 0; origTaxIndex < numOrigTaxa; origTaxIndex++ ) { if(charblock->IsActiveTaxon(origTaxIndex)){ unsigned char datum = '\0'; if(charblock->IsGapState(origTaxIndex, *cit) == true) datum = maxNumStates; else if(charblock->IsMissingState(origTaxIndex, *cit) == true) datum = maxNumStates; else{ int nstates = charblock->GetNumStates(origTaxIndex, *cit); //need to deal with the possibility of multiple states represented in matrix //just convert to full ambiguity if(nstates == 1) datum = CharToDatum(charblock->GetState(origTaxIndex, *cit, 0)); else{ datum = CharToDatum('?'); } } thisPat.AddChar(datum); } } thisPat.siteNumbers.push_back(charNum++); thisPat.SetCount((haveWeights ? charWeights[*cit] : 1)); patman.AddPattern(thisPat); thisPat.Reset(); } } } /* void BinaryData::CreateMatrixFromNCL(const NxsCharactersBlock *charblock, NxsUnsignedSet &origCharset){ if(charblock->GetDataType() != NxsCharactersBlock::standard) throw ErrorException("Tried to create binary matrix from non-standard data.\n\t(Did you mean to use datatype = binary?)"); //this creates a copy of the charset that we can screw with here without hosing the one that was passed in, //which might be needed elsewhere NxsUnsignedSet charset = origCharset; int numOrigTaxa = charblock->GetNTax(); int numActiveTaxa = charblock->GetNumActiveTaxa(); if(charset.empty()){ //the charset was empty, implying that all characters in this block will go into a single matrix (actually, for nstate //might be split anyway). Create an effective charset that contains all of the characters, which will be filtered //for exclusions and for the right number of max states for(int i = 0;i < charblock->GetNumIncludedChars();i++) charset.insert(i); } NxsUnsignedSet excluded = charblock->GetExcludedIndexSet(); const NxsUnsignedSet *realCharSet = & charset; NxsUnsignedSet charsetMinusExcluded; if (!excluded.empty()) { set_difference(charset.begin(), charset.end(), excluded.begin(), excluded.end(), inserter(charsetMinusExcluded, charsetMinusExcluded.begin())); realCharSet = &charsetMinusExcluded; } int numOrigChar = charset.size(); int numActiveChar = realCharSet->size(); if(numActiveChar == 0){ throw ErrorException("Sorry, fully excluded characters blocks or partition subsets are not currently supported."); } NewMatrix( numActiveTaxa, numActiveChar ); // read in the data, including taxon names int i=0; for( int origTaxIndex = 0; origTaxIndex < numOrigTaxa; origTaxIndex++ ) { if(charblock->IsActiveTaxon(origTaxIndex)){ //store the taxon names based on NCL's "escaped" version, which will properly deal //with whether quotes are necessary, etc. No conversion needed at output. NxsString tlabel = charblock->GetTaxonLabel(origTaxIndex); SetTaxonLabel(i, NxsString::GetEscaped(tlabel).c_str()); int j = 0; bool firstAmbig = true; for(NxsUnsignedSet::const_iterator cit = realCharSet->begin(); cit != realCharSet->end();cit++){ if(i == 0) SetOriginalDataNumber(j, *cit); unsigned char datum = '\0'; if(charblock->IsGapState(origTaxIndex, *cit) == true) datum = 2; else if(charblock->IsMissingState(origTaxIndex, *cit) == true) datum = 2; else{ int nstates = charblock->GetNumStates(origTaxIndex, *cit); //need to deal with the possibility of multiple states represented in matrix //just convert to full ambiguity if(nstates == 1) datum = CharToDatum(charblock->GetState(origTaxIndex, *cit, 0)); else{ if(firstAmbig){ outman.UserMessageNoCR("Partially ambiguous characters of taxon %s converted to full ambiguity:\n\t", TaxonLabel(origTaxIndex)); firstAmbig = false; } outman.UserMessageNoCR("%d ", *cit+1); datum = CharToDatum('?'); } } SetMatrix( i, j++, datum ); } if(firstAmbig == false) outman.UserMessage(""); i++; } } } */ void NStateData::CreateMatrixFromNCL(const NxsCharactersBlock *charblock, NxsUnsignedSet &origCharset){ if(charblock->GetDataType() != NxsCharactersBlock::standard) throw ErrorException("Tried to create n-state matrix from non-standard data.\n\t(Did you mean to use datatype = standard?)"); //this creates a copy of the charset that we can screw with here without hosing the one that was passed in, //which might be needed elsewhere NxsUnsignedSet charset = origCharset; int numOrigTaxa = charblock->GetNTax(); int numActiveTaxa = charblock->GetNumActiveTaxa(); //Not allowing wtsets here, mainly due to lazyness if(useDefaultWeightsets){ vector charWeights; string defWtsName = GarliReader::GetDefaultIntWeightSet(charblock, charWeights); if(charWeights.size() > 0){ outman.UserMessage("NOTE: Default wtsets cannot currently be used with non-sequence data! Wtset \"%s\" ignored.", defWtsName.c_str()); } } if(charset.empty()){ //the charset was empty, implying that all characters in this block will go into a single matrix (actually, for nstate //might be split anyway). Create an effective charset that contains all of the characters, which will be filtered //for exclusions and for the right number of max states for(int i = 0;i < charblock->GetNumChar();i++) charset.insert(i); } //deal with any exclusions NxsUnsignedSet excluded = charblock->GetExcludedIndexSet(); NxsUnsignedSet *realCharSet = & charset; NxsUnsignedSet charsetMinusExcluded; if (!excluded.empty()) { string exsetName = NxsSetReader::GetSetAsNexusString(excluded); outman.UserMessage("Excluded characters: %s\n\t", exsetName.c_str()); set_difference(charset.begin(), charset.end(), excluded.begin(), excluded.end(), inserter(charsetMinusExcluded, charsetMinusExcluded.begin())); realCharSet = &charsetMinusExcluded; } int numActiveChar = realCharSet->size(); if(numActiveChar == 0){ throw ErrorException("Sorry, fully excluded characters blocks or partition subsets are not currently supported."); } //first count the number of characters with the number of observed states that was specified for //this matrix, create a matrix with those dimensions and grab them from the charblock and make a matrix. //If not, just return and the function that called this should be able to check if any characters were actually read, and act accordingly //remove_if(realCharSet->begin(), realCharSet->end(), charblock->GetObsNumStates); NxsUnsignedSet consts; NxsUnsignedSet missing; for(NxsUnsignedSet::const_iterator cit = realCharSet->begin(); cit != realCharSet->end();){ unsigned num = *cit; cit++; //this gets the actual number of observed states, not including gaps or ?'s -, //but, there are no gaps in standard data (or 0/1), so only options are #'s and ?'s //ns = 1 are chars that are potentially constant (i.e., 0000, 1111, 000?, 111? etc) int ns = charblock->GetObsNumStates(num, false); if(ns == 1) consts.insert(num); //the maxNumStates == 2 part here is so that the message is only output when reading the first standard data matrix else if(ns == 0 && maxNumStates == 2) missing.insert(num); if(datatype == BINARY || datatype == BINARY_NOT_ALL_ZEROS){ if(ns > 2){ throw ErrorException("More than two character states found in binary data (character %d)!", num + 1); } } //If not binary data, toss any char with #states not equal to the prespecified maxNumStates for this matrix. //For binary data accept all chars with any number of states, because any with > 2 should already have caused //an error above, and any with zero will be ignored for calculations but will matter for formatting of SL output if(!(datatype == BINARY || datatype == BINARY_NOT_ALL_ZEROS)){ if(ns == 0 || ns != maxNumStates){ realCharSet->erase(num); } } } if(missing.size() > 0){ string str = NxsSetReader::GetSetAsNexusString(missing); outman.UserMessage("\tNOTE: entirely missing characters removed from matrix: %s", str.c_str()); } //verify that we're not breaking the assumptions of these datatypes. Anything entering here //is potentially constant, ignoring the effect of ambiguity. This is never ok with Mkv, because //we don't know how many states the ? might resolve to. With binary no zeros this is ok if //there is ambiguity, in which case minStates == 1 and maxStates == 2 if(consts.size() > 0 && (datatype == ONLY_VARIABLE || datatype == BINARY_NOT_ALL_ZEROS)){ string c = NxsSetReader::GetSetAsNexusString(consts); if(datatype == BINARY_NOT_ALL_ZEROS){ for(NxsUnsignedSet::iterator cit = consts.begin(); cit != consts.end();){ int num = *cit; cit++; std::set minStates = charblock->GetNamedStateSetOfColumn(num); std::set maxStates = charblock->GetMaximalStateSetOfColumn(num); assert(minStates.size() == 1); assert(maxStates.size() <= 2); if(maxStates.find(1) != maxStates.end()) consts.erase(num); } if(consts.size() > 0){ string c = NxsSetReader::GetSetAsNexusString(consts); throw ErrorException("Constant characters of state 0 are not allowed when using the binarynotallzeros datatype (as opposed to plain binary).\nChange to datatype = binary\n\tor exclude them by adding this to your nexus datafile:\nbegin assumptions;\nexset * const = %s;\nend;", c.c_str()); } } else{ string c = NxsSetReader::GetSetAsNexusString(consts); string title = charblock->GetTitle(); throw ErrorException("Constant characters are not allowed when using the Mkv model\n\t(as opposed to Mk), because it assumes that all characters\n\tare variable. Ambiguity does not count as a state.\n\tChange to datatype = standard or exclude them by adding this\n\tto your nexus datafile:\nbegin assumptions;\nlink characters='%s';\nexset * const = %s;\nend;", title.c_str(), c.c_str()); } } //maxNumStates = 2 here is only so that the message is output when creating the first standard matrix else if(consts.size() > 0 && !(datatype == BINARY) && maxNumStates == 2){ string c = NxsSetReader::GetSetAsNexusString(consts); outman.UserMessage("\t****\n\tWARNING - Constant characters found in standard data matrix (sites %s)", c.c_str()); outman.UserMessage("\tCurrently these will be ignored because including them in the"); outman.UserMessage("\tlikelihood calculations would require knowledge of how many states"); outman.UserMessage("\twere possible for those columns (i.e., 1 state was observed, but "); outman.UserMessage("\twas that out of 2 possible, or 3 or 4, etc)\n\t****"); } if(realCharSet->size() == 0) return; //Make room for dummy conditioning (generally constant) character(s) here. //For anything besides BINARY_NOT_ALL_ZEROS the # will be equal to maxNumStates //although for symetrical Mkv that many are not needed because they are all equal //it defaults to zero in the constructor if(datatype == ONLY_VARIABLE || datatype == BINARY_NOT_ALL_ZEROS){ if(datatype == BINARY_NOT_ALL_ZEROS) numConditioningPatterns = 1; else numConditioningPatterns = maxNumStates; } NewMatrix( numActiveTaxa, realCharSet->size() + numConditioningPatterns); map nclStateIndexToGarliState; vector< map > stateMaps; bool recodeSkipped = false; if(modeltype == UNORDERED && !(datatype == BINARY || datatype == BINARY_NOT_ALL_ZEROS)) recodeSkipped = true; if(recodeSkipped){ //Recode characters that skip states (assuming numerical order of states) to not skip any. i.e., recode a //char with states 0 1 5 7 to 0 1 2 3 and assume that it has 4 states //With assumptions block "options gapmode=newstate" things get even more confusing. GetNamedStateSetOfColumn //returns the gap as a code of -2, in which case the mapping would be -2 0 1 5 7 -> 0 1 2 3 4 5 if(datatype == ONLY_VARIABLE) //add in the conditioning patterns such that the character numbers match up later for(int i = 0;i < numConditioningPatterns;i++) stateMaps.push_back(nclStateIndexToGarliState); for(NxsUnsignedSet::const_iterator cit = realCharSet->begin(); cit != realCharSet->end();cit++){ set stateSet = charblock->GetNamedStateSetOfColumn(*cit); int myIndex = 0; for(set::iterator sit = stateSet.begin();sit != stateSet.end();sit++){ nclStateIndexToGarliState.insert(pair(*sit, myIndex++)); } stateMaps.push_back(nclStateIndexToGarliState); nclStateIndexToGarliState.clear(); } } else{//for ordered data we don't want to remove unobserved states if(charblock->GetGapModeSetting() == CharactersBlock::GAP_MODE_NEWSTATE){ throw ErrorException("Cannot use ordered Mk/Mkv data with gapmode=newstate. Recode the state or choose unordered."); } } // read in the data, including taxon names int effectiveTax=0; for( int origTaxIndex = 0; origTaxIndex < numOrigTaxa; origTaxIndex++ ) { if(charblock->IsActiveTaxon(origTaxIndex)){ //store the taxon names based on NCL's "escaped" version, which will properly deal //with whether quotes are necessary, etc. No conversion needed at output. NxsString tlabel = charblock->GetTaxonLabel(origTaxIndex); SetTaxonLabel(effectiveTax, NxsString::GetEscaped(tlabel).c_str()); //this accounts for conditioning patterns int effectiveChar = 0; //For each taxon, add the dummy constant character(s). This will be one of each possible constant //state, except for BINARY_NOT_ALL_ZEROS, where it will be only state 0 //Since there is no correspondence btwn these characters and the original alignments, the origDataNumber //is -1 if(numConditioningPatterns > 0){ for(int s = 0; s < (datatype == BINARY_NOT_ALL_ZEROS ? 1 : maxNumStates); s ++){ if(effectiveTax == 0) SetOriginalDataNumber(s, -1); SetMatrix( effectiveTax, effectiveChar++, s); } } bool firstAmbig = true; for(NxsUnsignedSet::const_iterator cit = realCharSet->begin(); cit != realCharSet->end();cit++){ if(effectiveTax == 0) SetOriginalDataNumber(effectiveChar, *cit); unsigned char datum = '\0'; if(charblock->IsGapState(origTaxIndex, *cit) == true){ if(datatype == BINARY || datatype == BINARY_NOT_ALL_ZEROS) throw ErrorException("Cannot use gap characters with binary datatype. Recode to 0 and 1"); //if gapmode=newstate is on (default is gapmode=missing) then need handle the gap properly //changes in NCL should now have it correctly reporting the number of states with gaps {in, ex}cluded if(charblock->GetGapModeSetting() == CharactersBlock::GAP_MODE_NEWSTATE){ if(recodeSkipped){ datum = stateMaps[effectiveChar][NXS_GAP_STATE_CODE]; } else{ assert(0); datum = maxNumStates - 1; } } else{ datum = maxNumStates; } } else if(charblock->IsMissingState(origTaxIndex, *cit) == true){ datum = maxNumStates; } else{ int nstates = charblock->GetNumStates(origTaxIndex, *cit); //need to deal with the possibility of multiple states represented in matrix //just convert to full ambiguity if(nstates == 1){ NxsDiscreteStateCell nclIndex = charblock->GetStateIndex(origTaxIndex, *cit, 0); if(recodeSkipped) datum = stateMaps[effectiveChar][nclIndex]; else datum = nclIndex; } else{ if(firstAmbig){ outman.UserMessageNoCR("\tPart ambig. char's of taxon %s converted to full ambiguity:\n\t char ", TaxonLabel(origTaxIndex)); firstAmbig = false; } outman.UserMessageNoCR(" %d ", *cit+1); datum = maxNumStates; } } SetMatrix( effectiveTax, effectiveChar++, datum); } if(firstAmbig == false) outman.UserMessage(""); effectiveTax++; } } //verify that every allowed state was observed for each character #ifndef NDEBUG bool found; if(recodeSkipped){ for(int c = numConditioningPatterns;c < numPatterns;c++){ for(int s = 0;s < maxNumStates;s++){ found = false; for(int t = 0;t < nTax;t++){ if(Matrix(t, c) == s){ found = true; break; } } if(!found){ outman.UserMessage("\nWARNING - some state in a %d-state character appeared only as part\n\tof an ambiguity code, e.g., a column with states 0, 1 and (12).", maxNumStates); outman.UserMessage("\tThe ambiguity code will be treated as missing data,\n\tbut the character will still be considered to have %d states.\n", maxNumStates); } } } } #endif } //this is a virtual overload for NState because it might have to deal with the dummy char, which shouldn't be included in the resampling int NStateData::BootstrapReweight(int seedToUse, FLOAT_TYPE resampleProportion){ //a seed is passed in and used for the reweighting - Either for restarting or not //Either way we'll return the seed at the end of the reweighting, to be stored as the Population::nextBootstrapSeed //which allows exactly the same bootstraped datasets to be used in multiple runs, but with different //settings for the actual search if(resampleProportion >= 5.0) outman.UserMessage("WARNING: The resampleproportion setting is the proportion to resample,\nNOT the percentage (1.0 = 100%%).\nThe value you specified (%.2f) is a very large proportion.", resampleProportion); int originalSeed = rnd.seed(); rnd.set_seed(seedToUse); //This is a little dumb, but since there are parallel counts and origCounts variables depending on whether the new PatternManager //is being used, need to alias them so that the remainder of this function works unchanged const int *origCountsAlias; if(newOrigCounts.size() > 0){ origCountsAlias = &newOrigCounts[0]; } else origCountsAlias = origCounts; int *countsAlias; if(newCount.size() > 0){ countsAlias = &newCount[0]; } else countsAlias = count; //for nstate data this will include the conditioning chars, but they will //have a resample prob of zero FLOAT_TYPE *cumProbs = new FLOAT_TYPE[numPatterns]; assert(origCountsAlias[0] > 0 && origCountsAlias[1] > 0); for(int i = 0;i < numConditioningPatterns;i++) cumProbs[i] = ZERO_POINT_ZERO; cumProbs[numConditioningPatterns]=(FLOAT_TYPE) origCountsAlias[numConditioningPatterns] / ((FLOAT_TYPE) numNonMissingRealCountsInOrigMatrix); for(int i=numConditioningPatterns + 1;i 0); } for(int q=numConditioningPatterns;q cumProbs[pat]) pat++; countsAlias[pat]++; } /* for(int i = 0;i < numPatterns;i++) deb << i << "\t" << cumProbs[i] << "\t" << origCountsAlias[i] << "\t" << countsAlias[i] << endl; */ //take a count of the number of chars that were actually resampled nonZeroCharCount = 0; int numZero = 0; int totCounts = 0; for(int d = numConditioningPatterns;d < numPatterns;d++){ if(countsAlias[d] > 0) { nonZeroCharCount++; totCounts += countsAlias[d]; } else numZero++; } if(datatype == ONLY_VARIABLE) assert(countsAlias[0] == 1); assert(totCounts == numNonMissingRealCountsInOrigMatrix); assert(nonZeroCharCount + numZero == numPatterns - numConditioningPatterns); delete []cumProbs; int nextSeed = rnd.seed(); rnd.set_seed(originalSeed); return nextSeed; } void OrientedGapData::CreateMatrixFromNCL(const NxsCharactersBlock *charblock, NxsUnsignedSet &origCharset){ if(charblock->GetDataType() != NxsCharactersBlock::standard) throw ErrorException("Tried to create n-state matrix from non-standard data.\n\t(Did you mean to use datatype = nstate?)"); //this creates a copy of the charset that we can screw with here without hosing the one that was passed in, //which might be needed elsewhere NxsUnsignedSet charset = origCharset; int numOrigTaxa = charblock->GetNTax(); int numActiveTaxa = charblock->GetNumActiveTaxa(); //Not allowing wtsets here, mainly due to lazyness if(useDefaultWeightsets){ vector charWeights; string defWtsName = GarliReader::GetDefaultIntWeightSet(charblock, charWeights); if(charWeights.size() > 0){ outman.UserMessage("NOTE: Default wtsets cannot currently be used with non-sequence data! Wtset \"%s\" ignored.", defWtsName.c_str()); } } if(charset.empty()){ //the charset was empty, implying that all characters in this block will go into a single matrix (actually, for nstate //might be split anyway). Create an effective charset that contains all of the characters, which will be filtered //for exclusions and for the right number of max states for(int i = 0;i < charblock->GetNumChar();i++) charset.insert(i); } NxsUnsignedSet excluded = charblock->GetExcludedIndexSet(); NxsUnsignedSet *realCharSet = & charset; NxsUnsignedSet charsetMinusExcluded; if (!excluded.empty()) { set_difference(charset.begin(), charset.end(), excluded.begin(), excluded.end(), inserter(charsetMinusExcluded, charsetMinusExcluded.begin())); realCharSet = &charsetMinusExcluded; } int numActiveChar = realCharSet->size(); if(numActiveChar == 0){ throw ErrorException("Sorry, fully excluded characters blocks or partition subsets are not currently supported."); } if(realCharSet->size() == 0) return; // the dummy root is now taken care of outside of here in a non-datatype specific way // int myEffectiveTaxa = numActiveTaxa + 1; bool allGapChar = true; //Make room for dummy conditioning (all zero) character here. //it defaults to zero in the constructor if(datatype == ONLY_VARIABLE || allGapChar){ numConditioningPatterns = 1; } //make room for a dummy constant character here NewMatrix( numActiveTaxa, realCharSet->size() + numConditioningPatterns); // read in the data, including taxon names int effectiveTax=0; for( int origTaxIndex = 0; origTaxIndex < numOrigTaxa; origTaxIndex++ ) { if(charblock->IsActiveTaxon(origTaxIndex)){ //store the taxon names based on NCL's "escaped" version, which will properly deal //with whether quotes are necessary, etc. No conversion needed at output. NxsString tlabel = charblock->GetTaxonLabel(origTaxIndex); SetTaxonLabel(effectiveTax, NxsString::GetEscaped(tlabel).c_str()); int effectiveChar = 0; //add the dummy character if(numConditioningPatterns > 0){ if(effectiveTax == 0) SetOriginalDataNumber(0, -1); if(tlabel != "ROOT") SetMatrix( effectiveTax, effectiveChar++, 0); else SetMatrix( effectiveTax, effectiveChar++, maxNumStates); } bool firstAmbig = true; for(NxsUnsignedSet::const_iterator cit = realCharSet->begin(); cit != realCharSet->end();cit++){ if(effectiveTax == 0) SetOriginalDataNumber(effectiveChar, *cit); unsigned char datum = '\0'; if(charblock->IsGapState(origTaxIndex, *cit) == true) datum = 0; else if(charblock->IsMissingState(origTaxIndex, *cit) == true){ datum = maxNumStates; } else{ int nstates = charblock->GetNumStates(origTaxIndex, *cit); if(nstates == 1){ int nclIndex = charblock->GetStateIndex(origTaxIndex, *cit, 0); datum = nclIndex; } else{ if(firstAmbig){ outman.UserMessageNoCR("\tPart ambig. char's of taxon %s converted to full ambiguity:\n\t char ", TaxonLabel(origTaxIndex)); firstAmbig = false; } outman.UserMessageNoCR(" %d ", *cit+1); datum = maxNumStates; } } SetMatrix( effectiveTax, effectiveChar++, datum); } if(firstAmbig == false) outman.UserMessage(""); effectiveTax++; } } } garli-2.1-release/src/sequencedata.h000066400000000000000000000640241241236125200174760ustar00rootroot00000000000000// GARLI version 2.0 source code // Copyright 2005-2011 Derrick J. Zwickl // email: garli.support@gmail.com // // This program is free software: you can redistribute it and/or modify // it under the terms of the GNU General Public License as published by // the Free Software Foundation, either version 3 of the License, or // (at your option) any later version. // // This program is distributed in the hope that it will be useful, // but WITHOUT ANY WARRANTY; without even the implied warranty of // MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the // GNU General Public License for more details. // // You should have received a copy of the GNU General Public License // along with this program. If not, see . // // NOTE: Portions of this source adapted from GAML source, written by Paul O. Lewis #ifndef _SEQUENCE_DATA_ #define _SEQUENCE_DATA_ #include using namespace std; #include "defs.h" #include "datamatr.h" //#include "model.h" class SequenceData : public DataMatrix{ public: SequenceData() : DataMatrix() { maxNumStates=4; strcpy( info, "DNA" ); empStateFreqs=NULL; numConditioningPatterns = 0;} SequenceData( int ntax, int nchar ) : DataMatrix( ntax, nchar ) { maxNumStates=4; strcpy( info, "DNA" ); empStateFreqs=NULL; numConditioningPatterns = 0;} virtual ~SequenceData() { if(empStateFreqs != NULL) delete []empStateFreqs; } protected: FLOAT_TYPE *empStateFreqs; // overrides of base class's virtual fuctions virtual unsigned char CharToDatum( char ch ) const = 0; virtual unsigned char CharToBitwiseRepresentation( char ch ) const; virtual char DatumToChar( unsigned char d ) const; virtual unsigned char FirstState() const { return 0; } virtual unsigned char LastState() const { return 3; } virtual int NumStates(int) const { return 4; } public: virtual void CreateMatrixFromNCL(const NxsCharactersBlock *, NxsUnsignedSet &charset) = 0; virtual void CalcEmpiricalFreqs() = 0; virtual void GetEmpiricalFreqs(FLOAT_TYPE *f) const{ assert(empStateFreqs); for(int i=0;i ambigStrings; #ifdef OPEN_MP vector ambigToCharMap; #endif public: NucleotideData() : SequenceData() {fullyAmbigChar = 15;} NucleotideData( int ntax, int nchar ) : SequenceData( ntax, nchar ) {fullyAmbigChar = 15;} ~NucleotideData() { for(vector::iterator delit=ambigStrings.begin();delit!=ambigStrings.end();delit++) delete [](*delit); #ifdef OPEN_MP for(vector::iterator delit=ambigToCharMap.begin();delit!=ambigToCharMap.end();delit++) delete [](*delit); #endif } unsigned char CharToDatum(char d) const; void CalcEmpiricalFreqs(); void CreateMatrixFromNCL(const NxsCharactersBlock *charblock, NxsUnsignedSet &charset); void MakeAmbigStrings(); void AddDummyRootToExistingMatrix(); char *GetAmbigString(int i) const{ return ambigStrings[i]; } #ifdef OPEN_MP unsigned *GetAmbigToCharMap(int i) const{ return ambigToCharMap[i]; } #endif }; class GeneticCode{ //mapping from codon number (ordered AAA, AAC, AAG, AAT, ACA, etc) to //amino acid number (0-19). Stop codons are 20. //except for two-serine models, when they are 21 and the serine with two codons is state 20 int codonTable[64]; int map64toNonStops[64]; vector stops; //this holds the correspondence between the state indeces and actual codons //for display purposes. Stops are removed and thus any mapIndexToCodonDisplay[index] //gives the codon for that index vector mapIndexToCodonDisplay; public: enum{ STANDARD= 0, VERTMITO = 1, INVERTMITO = 2, STANDARDTWOSERINE = 3, VERTMITOTWOSERINE = 4, INVERTMITOTWOSERINE = 5 }codeName; GeneticCode(){ SetStandardCode(); } void SetStandardCode(){ codonTable[ 0 ]= 8; codonTable[ 1 ]= 11; codonTable[ 2 ]= 8; codonTable[ 3 ]= 11; codonTable[ 4 ]= 16; codonTable[ 5 ]= 16; codonTable[ 6 ]= 16; codonTable[ 7 ]= 16; codonTable[ 8 ]= 14; codonTable[ 9 ]= 15; codonTable[ 10 ]= 14; codonTable[ 11 ]= 15; codonTable[ 12 ]= 7; codonTable[ 13 ]= 7; codonTable[ 14 ]= 10; codonTable[ 15 ]= 7; codonTable[ 16 ]= 13; codonTable[ 17 ]= 6; codonTable[ 18 ]= 13; codonTable[ 19 ]= 6; codonTable[ 20 ]= 12; codonTable[ 21 ]= 12; codonTable[ 22 ]= 12; codonTable[ 23 ]= 12; codonTable[ 24 ]= 14; codonTable[ 25 ]= 14; codonTable[ 26 ]= 14; codonTable[ 27 ]= 14; codonTable[ 28 ]= 9; codonTable[ 29 ]= 9; codonTable[ 30 ]= 9; codonTable[ 31 ]= 9; codonTable[ 32 ]= 3; codonTable[ 33 ]= 2; codonTable[ 34 ]= 3; codonTable[ 35 ]= 2; codonTable[ 36 ]= 0; codonTable[ 37 ]= 0; codonTable[ 38 ]= 0; codonTable[ 39 ]= 0; codonTable[ 40 ]= 5; codonTable[ 41 ]= 5; codonTable[ 42 ]= 5; codonTable[ 43 ]= 5; codonTable[ 44 ]= 17; codonTable[ 45 ]= 17; codonTable[ 46 ]= 17; codonTable[ 47 ]= 17; codonTable[ 48 ]= 20; codonTable[ 49 ]= 19; codonTable[ 50 ]= 20; codonTable[ 51 ]= 19; codonTable[ 52 ]= 15; codonTable[ 53 ]= 15; codonTable[ 54 ]= 15; codonTable[ 55 ]= 15; codonTable[ 56 ]= 20; codonTable[ 57 ]= 1; codonTable[ 58 ]= 18; codonTable[ 59 ]= 1; codonTable[ 60 ]= 9; codonTable[ 61 ]= 4; codonTable[ 62 ]= 9; codonTable[ 63 ]= 4; map64toNonStops[0]=0; map64toNonStops[1]=1; map64toNonStops[2]=2; map64toNonStops[3]=3; map64toNonStops[4]=4; map64toNonStops[5]=5; map64toNonStops[6]=6; map64toNonStops[7]=7; map64toNonStops[8]=8; map64toNonStops[9]=9; map64toNonStops[10]=10; map64toNonStops[11]=11; map64toNonStops[12]=12; map64toNonStops[13]=13; map64toNonStops[14]=14; map64toNonStops[15]=15; map64toNonStops[16]=16; map64toNonStops[17]=17; map64toNonStops[18]=18; map64toNonStops[19]=19; map64toNonStops[20]=20; map64toNonStops[21]=21; map64toNonStops[22]=22; map64toNonStops[23]=23; map64toNonStops[24]=24; map64toNonStops[25]=25; map64toNonStops[26]=26; map64toNonStops[27]=27; map64toNonStops[28]=28; map64toNonStops[29]=29; map64toNonStops[30]=30; map64toNonStops[31]=31; map64toNonStops[32]=32; map64toNonStops[33]=33; map64toNonStops[34]=34; map64toNonStops[35]=35; map64toNonStops[36]=36; map64toNonStops[37]=37; map64toNonStops[38]=38; map64toNonStops[39]=39; map64toNonStops[40]=40; map64toNonStops[41]=41; map64toNonStops[42]=42; map64toNonStops[43]=43; map64toNonStops[44]=44; map64toNonStops[45]=45; map64toNonStops[46]=46; map64toNonStops[47]=47; map64toNonStops[48]=-1; map64toNonStops[49]=48; map64toNonStops[50]=-1; map64toNonStops[51]=49; map64toNonStops[52]=50; map64toNonStops[53]=51; map64toNonStops[54]=52; map64toNonStops[55]=53; map64toNonStops[56]=-1; map64toNonStops[57]=54; map64toNonStops[58]=55; map64toNonStops[59]=56; map64toNonStops[60]=57; map64toNonStops[61]=58; map64toNonStops[62]=59; map64toNonStops[63]=60; stops.clear(); stops.push_back(48); stops.push_back(50); stops.push_back(56); FillIndexToCodonDisplayMap(); } void SetStandardTwoSerineCode(){ //because the stops don't change location, I don't think that anything else needs to be changed here //the two lone serines become the 20th state codonTable[ 9 ]= 20; //AGC codonTable[ 11 ]= 20; //AGT //the three stop codons become the 21st state codonTable[ 48 ]= 21; codonTable[ 50 ]= 21; codonTable[ 56 ]= 21; } void SetVertMitoCode(){ SetStandardCode(); codonTable[56] = 18; //TGA codonTable[8] = 20; //AGA codonTable[10] = 20; //AGG codonTable[12] = 10; //ATA map64toNonStops[0]=0; map64toNonStops[1]=1; map64toNonStops[2]=2; map64toNonStops[3]=3; map64toNonStops[4]=4; map64toNonStops[5]=5; map64toNonStops[6]=6; map64toNonStops[7]=7; map64toNonStops[8]=-1; map64toNonStops[9]=8; map64toNonStops[10]=-1; map64toNonStops[11]=9; map64toNonStops[12]=10; map64toNonStops[13]=11; map64toNonStops[14]=12; map64toNonStops[15]=13; map64toNonStops[16]=14; map64toNonStops[17]=15; map64toNonStops[18]=16; map64toNonStops[19]=17; map64toNonStops[20]=18; map64toNonStops[21]=19; map64toNonStops[22]=20; map64toNonStops[23]=21; map64toNonStops[24]=22; map64toNonStops[25]=23; map64toNonStops[26]=24; map64toNonStops[27]=25; map64toNonStops[28]=26; map64toNonStops[29]=27; map64toNonStops[30]=28; map64toNonStops[31]=29; map64toNonStops[32]=30; map64toNonStops[33]=31; map64toNonStops[34]=32; map64toNonStops[35]=33; map64toNonStops[36]=34; map64toNonStops[37]=35; map64toNonStops[38]=36; map64toNonStops[39]=37; map64toNonStops[40]=38; map64toNonStops[41]=39; map64toNonStops[42]=40; map64toNonStops[43]=41; map64toNonStops[44]=42; map64toNonStops[45]=43; map64toNonStops[46]=44; map64toNonStops[47]=45; map64toNonStops[48]=-1; map64toNonStops[49]=46; map64toNonStops[50]=-1; map64toNonStops[51]=47; map64toNonStops[52]=48; map64toNonStops[53]=49; map64toNonStops[54]=50; map64toNonStops[55]=51; map64toNonStops[56]=52; map64toNonStops[57]=53; map64toNonStops[58]=54; map64toNonStops[59]=55; map64toNonStops[60]=56; map64toNonStops[61]=57; map64toNonStops[62]=58; map64toNonStops[63]=59; stops.clear(); stops.push_back(8); stops.push_back(10); stops.push_back(48); stops.push_back(50); FillIndexToCodonDisplayMap(); } //this should be called AFTER SetVertMitoCode() void SetVertMitoTwoSerineCode(){ //because the stops don't change location, I don't think that anything else needs to be changed here //the two lone serines become the 20th state codonTable[ 9 ]= 20; //AGC codonTable[ 11 ]= 20; //AGT //the four stop codons become the 21st state codonTable[8] = 21; //AGA codonTable[10] = 21; //AGG codonTable[ 48 ]= 21; codonTable[ 50 ]= 21; } void SetInvertMitoCode(){ SetStandardCode(); codonTable[56] = 18; //TGA codonTable[8] = 15; //AGA codonTable[10] = 15; //AGG codonTable[12] = 10; //ATA map64toNonStops[0]=0; map64toNonStops[1]=1; map64toNonStops[2]=2; map64toNonStops[3]=3; map64toNonStops[4]=4; map64toNonStops[5]=5; map64toNonStops[6]=6; map64toNonStops[7]=7; map64toNonStops[8]=8; map64toNonStops[9]=9; map64toNonStops[10]=10; map64toNonStops[11]=11; map64toNonStops[12]=12; map64toNonStops[13]=13; map64toNonStops[14]=14; map64toNonStops[15]=15; map64toNonStops[16]=16; map64toNonStops[17]=17; map64toNonStops[18]=18; map64toNonStops[19]=19; map64toNonStops[20]=20; map64toNonStops[21]=21; map64toNonStops[22]=22; map64toNonStops[23]=23; map64toNonStops[24]=24; map64toNonStops[25]=25; map64toNonStops[26]=26; map64toNonStops[27]=27; map64toNonStops[28]=28; map64toNonStops[29]=29; map64toNonStops[30]=30; map64toNonStops[31]=31; map64toNonStops[32]=32; map64toNonStops[33]=33; map64toNonStops[34]=34; map64toNonStops[35]=35; map64toNonStops[36]=36; map64toNonStops[37]=37; map64toNonStops[38]=38; map64toNonStops[39]=39; map64toNonStops[40]=40; map64toNonStops[41]=41; map64toNonStops[42]=42; map64toNonStops[43]=43; map64toNonStops[44]=44; map64toNonStops[45]=45; map64toNonStops[46]=46; map64toNonStops[47]=47; map64toNonStops[48]=-1; map64toNonStops[49]=48; map64toNonStops[50]=-1; map64toNonStops[51]=49; map64toNonStops[52]=50; map64toNonStops[53]=51; map64toNonStops[54]=52; map64toNonStops[55]=53; map64toNonStops[56]=54; map64toNonStops[57]=55; map64toNonStops[58]=56; map64toNonStops[59]=57; map64toNonStops[60]=58; map64toNonStops[61]=59; map64toNonStops[62]=60; map64toNonStops[63]=61; stops.clear(); stops.push_back(48); stops.push_back(50); FillIndexToCodonDisplayMap(); } //this should be called AFTER SetInvertMitoCode() void SetInvertMitoTwoSerineCode(){ //because the stops don't change location, I don't think that anything else needs to be changed here //the two lone serines become the 20th state codonTable[ 9 ]= 20; //AGC codonTable[ 11 ]= 20; //AGT //the two stop codons become the 21st state codonTable[ 48 ]= 21; codonTable[ 50 ]= 21; } int CodonLookup(int i){ assert(i >= 0 && i < 64); return codonTable[i]; } int Map64stateToNonStops(int i){ assert(i >= 0 && i < 64); assert(map64toNonStops[i] != -1); return map64toNonStops[i]; } void FillIndexToCodonDisplayMap(){ //this assumes that the correct genetic code has already been set mapIndexToCodonDisplay.clear(); char nucs[4] = {'A', 'C', 'G', 'T'}; //char cod[3]; char *cod = new char[4]; for(int f = 0;f < 4;f++){ for(int s = 0;s < 4;s++){ for(int t = 0;t < 4;t++){ if(CodonLookup(f * 16 + s * 4 + t) != 20){ sprintf(cod, "%c%c%c", nucs[f], nucs[s], nucs[t]); mapIndexToCodonDisplay.push_back(cod); } } } } delete []cod; } const string LookupCodonDisplayFromIndex(int index) const{ return mapIndexToCodonDisplay[index]; } int NumStates() const {return mapIndexToCodonDisplay.size();} }; class CodonData : public SequenceData { GeneticCode code; //these are for use in the F1x4 or F3x4 methods of calculating the state freqs FLOAT_TYPE empBaseFreqsPos1[4]; FLOAT_TYPE empBaseFreqsPos2[4]; FLOAT_TYPE empBaseFreqsPos3[4]; FLOAT_TYPE empBaseFreqsAllPos[4]; enum{ NOT_EMPIRICAL = 0, CODON_TABLE = 1, F1X4 = 2, F3X4 = 3 }empType; // int empType; //codon table = 0 //F1x4 = 1 //F3x4 = 2 public: CodonData() : SequenceData(){ maxNumStates = 61; code.SetStandardCode(); empType = NOT_EMPIRICAL; fullyAmbigChar = maxNumStates; } CodonData(const NucleotideData *dat, int genCode, bool ignoreStops=false) : SequenceData(){ assert(dat->Dense() == false); if(genCode == GeneticCode::STANDARD){ code.SetStandardCode(); maxNumStates = 61; } else if(genCode == GeneticCode::VERTMITO){ code.SetVertMitoCode(); maxNumStates = 60; } else if(genCode == GeneticCode::INVERTMITO){ code.SetInvertMitoCode(); maxNumStates = 62; } else{ throw ErrorException("Sorry, only the standard, vert mito and invert mito codes can be used with codon models"); } usePatternManager = dat->GetUsePatternManager(); FillCodonMatrixFromDNA(dat, ignoreStops); CopyNamesFromOtherMatrix(dat); empType = NOT_EMPIRICAL; fullyAmbigChar = maxNumStates; } ~CodonData(){} void FillCodonMatrixFromDNA(const NucleotideData *, bool ignoreStops); unsigned char CharToDatum(char c) const{ //this shouldn't be getting called, as it makes no sense for codon data assert(0); return 0; } void CreateMatrixFromNCL(const NxsCharactersBlock *, NxsUnsignedSet &charset){ //this also should not be getting called. The codon matrix //is created from a DNA matrix that has been read in, possibly //by the NCL assert(0); } GeneticCode* GetCode() {return &code;} //void SetEmpType(int t) {empType = t;} void SetF1X4Freqs(){empType = F1X4;} void SetF3X4Freqs(){empType = F3X4;} void SetCodonTableFreqs(){empType = CODON_TABLE;} void CalcEmpiricalFreqs(); void CalcF1x4Freqs(); void CalcF3x4Freqs(); void BaseFreqXPositionReport(); //int ComparePatterns( const int i, const int j ) const; void SetVertMitoCode() {code.SetVertMitoCode();} void SetInvertMitoCode() {code.SetInvertMitoCode();} }; class AminoacidData : public SequenceData{ public: AminoacidData() : SequenceData(){ maxNumStates = 20; fullyAmbigChar = maxNumStates; } AminoacidData(const NucleotideData *dat, int genCode, bool ignoreStops=false) : SequenceData(){ maxNumStates = 20; GeneticCode c; if(genCode == GeneticCode::STANDARD) c.SetStandardCode(); else if(genCode == GeneticCode::VERTMITO) c.SetVertMitoCode(); else if(genCode == GeneticCode::INVERTMITO) c.SetInvertMitoCode(); else{ if(genCode == GeneticCode::STANDARDTWOSERINE){ c.SetStandardTwoSerineCode(); } else if(genCode == GeneticCode::VERTMITOTWOSERINE){ c.SetVertMitoCode(); c.SetVertMitoTwoSerineCode(); } else if(genCode == GeneticCode::INVERTMITOTWOSERINE){ c.SetInvertMitoCode(); c.SetInvertMitoTwoSerineCode(); } else assert(0); maxNumStates = 21; } usePatternManager = dat->GetUsePatternManager(); FillAminoacidMatrixFromDNA(dat, &c, ignoreStops); CopyNamesFromOtherMatrix(dat); fullyAmbigChar = maxNumStates; } void FillAminoacidMatrixFromDNA(const NucleotideData *dat, GeneticCode *code, bool ignoreStops); void CalcEmpiricalFreqs(); unsigned char CharToDatum(char d) const; void CreateMatrixFromNCL(const NxsCharactersBlock *, NxsUnsignedSet &charset); }; class DataPartition { private: vector dataSubsets; int nTax; public: void AddSubset(SequenceData* sub){ dataSubsets.push_back(sub); nTax = sub->NTax(); } SequenceData *GetSubset(int num) const{ if(num < 0 || (num < dataSubsets.size()) == false) throw ErrorException("Tried to access invalid subset number"); return dataSubsets[num]; } void Delete(){ for(vector::iterator it = dataSubsets.begin();it != dataSubsets.end(); it++) delete *it; dataSubsets.clear(); } int NTax() const {return nTax;} int NumSubsets() const {return dataSubsets.size();} void BeginNexusTreesBlock(string &trans) const {dataSubsets[0]->BeginNexusTreesBlock(trans);} void BeginNexusTreesBlock(ofstream &out) const {dataSubsets[0]->BeginNexusTreesBlock(out);} NxsString TaxonLabel(int t) const {return dataSubsets[0]->TaxonLabel(t);} int TaxonNameToNumber(NxsString name) const{return dataSubsets[0]->TaxonNameToNumber(name);} void AddDummyRoots(){ nTax++; for(int p = 0;p < NumSubsets();p++){ dataSubsets[p]->AddDummyRootToExistingMatrix(); assert(nTax == dataSubsets[p]->NTax()); } } int BootstrapReweight(int seedToUse, FLOAT_TYPE resampleProportion){ int nextSeed = seedToUse; for(int p = 0;p < NumSubsets();p++){ outman.UserMessage("\tSubset %d: Random seed for bootstrap reweighting: %d", p + 1, nextSeed); SequenceData *curData = GetSubset(p); nextSeed = curData->BootstrapReweight(nextSeed, resampleProportion); } return nextSeed; } }; class DataSubsetInfo{ public: int garliSubsetNum; int charblockNum; string charblockName; int partitionSubsetNum; string partitionSubsetName; enum type{ NUCLEOTIDE = 0, AMINOACID = 1, CODON = 2, NSTATE= 3, NSTATEV = 4, ORDNSTATE = 5, ORDNSTATEV = 6, ORIENTEDGAP = 7, BINARY = 8, BINARY_NOT_ALL_ZEROS = 9 }readAs, usedAs; int totalCharacters; int uniqueCharacters; string outputNames[10];//{"Nucleotide data", "Amino acid data", "Codon data"}; DataSubsetInfo(int gssNum, int cbNum, string cbName, int psNum, string psName, type rAs, type uAs) : garliSubsetNum(gssNum), charblockNum(cbNum), charblockName(cbName), partitionSubsetNum(psNum), partitionSubsetName(psName), readAs(rAs), usedAs(uAs){ outputNames[NUCLEOTIDE]="Nucleotide data"; outputNames[AMINOACID]="Amino acid data"; outputNames[CODON]="Codon data"; outputNames[NSTATE]="Standard k-state data"; outputNames[NSTATEV]="Standard k-state data, variable only"; outputNames[ORDNSTATE]="Standard ordered k-state data"; outputNames[ORDNSTATEV]="Standard ordered k-state data, variable only"; outputNames[ORIENTEDGAP]="Gap-coded data, oriented with respect to time"; outputNames[BINARY]="Binary data"; outputNames[BINARY_NOT_ALL_ZEROS]="Binary data, no constant state 0 chars"; } void Report(){ outman.UserMessage("GARLI data subset %d", garliSubsetNum+1); outman.UserMessage("\tCHARACTERS block #%d (\"%s\")", charblockNum+1, charblockName.c_str()); if(partitionSubsetNum >= 0) outman.UserMessage("\tCHARPARTITION subset #%d (\"%s\")", partitionSubsetNum+1, partitionSubsetName.c_str()); outman.UserMessage("\tData read as %s,\n\tmodeled as %s", outputNames[readAs].c_str(), outputNames[usedAs].c_str()); } }; /* // // Mk type model, with binary data class BinaryData : public SequenceData{ public: BinaryData() : SequenceData(){ maxNumStates = 2; } unsigned char CharToDatum(char d); char DatumToChar( unsigned char d ); void CreateMatrixFromNCL(const NxsCharactersBlock *, NxsUnsignedSet &charset); void CalcEmpiricalFreqs(){ //BINARY - this might actually make sense for gap encoding } //this is just a virtual overload that avoids doing anything if determine const is called with inappropriate data void DetermineConstantSites(){}; }; inline unsigned char BinaryData::CharToDatum( char ch ){ unsigned char datum; if( ch == '0' || ch == '-' ) datum = 0; else if( ch == '1' || ch == '+' ) datum = 1; else if( ch == '?' ) datum = 2; else THROW_BADSTATE(ch); return datum; } inline char BinaryData::DatumToChar( unsigned char d ){ char ch = 'X'; // ambiguous if( d == 2 ) ch = '?'; else if( d == 0 ) ch = '0'; else if( d == 1 ) ch = '1'; return ch; } */ // // Mk or Mkv type model, with n-state data class NStateData : public SequenceData{ public: enum{ ALL = 0, ONLY_VARIABLE = 1, ONLY_INFORM = 2, BINARY = 3, BINARY_NOT_ALL_ZEROS = 4 }datatype; enum{ UNORDERED = 0, ORDERED = 1 }modeltype; NStateData() : SequenceData(){ maxNumStates = 99; } NStateData(int ns) : SequenceData(){ maxNumStates = ns; } //NStateData(int ns, bool isMkv, bool isOrdered) : SequenceData(){' NStateData(int ns, bool isOrdered, bool isBinary, bool isConditioned) : SequenceData(){ if(isBinary){ if(isConditioned) datatype = BINARY_NOT_ALL_ZEROS; else datatype = BINARY; } else if(isConditioned) datatype = ONLY_VARIABLE; else datatype = ALL; if(isOrdered) modeltype = ORDERED; else modeltype = UNORDERED; maxNumStates = ns; } void SetNumStates(int ns){maxNumStates = ns;} virtual unsigned char CharToDatum(char d) const; virtual char DatumToChar( unsigned char d ) const; virtual void CreateMatrixFromNCL(const NxsCharactersBlock *, NxsUnsignedSet &charset); void CalcEmpiricalFreqs(){ //BINARY - this might actually make sense for gap encoding } //this is a virtual overload for NState because it might have to deal with the conditioning chars, which shouldn't be included in the resampling int BootstrapReweight(int restartSeed, FLOAT_TYPE resampleProportion); //this is just a virtual overload that avoids doing anything if determine const is called with inappropriate data void DetermineConstantSites(){}; }; inline unsigned char NStateData::CharToDatum( char ch ) const{ unsigned char datum; if( ch == '0') datum = 0; else if( ch == '1') datum = 1; else if( ch == '2') datum = 2; else if( ch == '3') datum = 3; else if( ch == '4') datum = 4; else if( ch == '5') datum = 5; else if( ch == '6') datum = 6; else if( ch == '7') datum = 7; else if( ch == '8') datum = 8; else if( ch == '9') datum = 9; else if( ch == '?') datum = 99; else throw ErrorException("Unknown character \"%c\" in NStateData::CharToDatum", ch); return datum; } inline char NStateData::DatumToChar( unsigned char d ) const{ //NSTATE - not sure how this should work, but it isn't that important anyway char ch = 'X'; // ambiguous /* if( d == 2 ) ch = '?'; else if( d == 0 ) ch = '0'; else if( d == 1 ) ch = '1'; */ return ch; } class OrientedGapData : public NStateData{ public: OrientedGapData() : NStateData(){ maxNumStates = 2; } OrientedGapData(int ns) : NStateData(){ assert(0); } OrientedGapData(int ns, bool isMkv) : NStateData(){ assert(0); } void SetNumStates(int ns){maxNumStates = ns;} virtual void CreateMatrixFromNCL(const NxsCharactersBlock *, NxsUnsignedSet &charset); void CalcEmpiricalFreqs(){ //BINARY - this might actually make sense for gap encoding } //this is just a virtual overload that avoids doing anything if determine const is called with inappropriate data void DetermineConstantSites(){}; }; #endif garli-2.1-release/src/set.cpp000066400000000000000000000033321241236125200161550ustar00rootroot00000000000000// set.h // Copyright © 1998 by Paul O. Lewis // All rights reserved. // // This code may be used and modified for non-commercial purposes // but redistribution in any form requires written permission. // Please contact: // // Paul O. Lewis, Assistant Professor // 167 Castetter Hall // Department of Biology // The University of New Mexico // Albuquerque, NM 87131-1091 // Phone: (505) 277-6681 // Fax: (505) 277-0304 // email: lewisp@unm.edu // http://biology.unm.edu/~lewisp/pol.html // // Note: moving January 1, 1999, to the Department of Ecology and // Evolutionary Biology, University of Connecticut // // Associated source code file: "set.cpp" // #include using namespace std; #include "defs.h" #include "set.h" DNASet& DNASet::operator|=( DNASet& b ) { set |= b.set; return *this; } DNASet operator|( DNASet& a, DNASet& b ) { return DNASet( a.set | b.set ); } DNASet& DNASet::operator&=( DNASet& b ) { set &= b.set; return *this; } DNASet operator&( DNASet& a, DNASet& b ) { return DNASet( a.set & b.set ); } Set::Set( int startsz ) : sz(startsz), next(0) { arr=new int[startsz]; } Set::~Set() { delete []arr; } Set& Set::operator-=( const int i ) { for( int j = 0; j < next; j++ ) { if( arr[j] != i ) continue; // arr[j] equals i arr[j] = arr[--next]; arr[next] = 0; break; } return *this; } void Set::Realloc( int newsz ) { if( newsz <= sz ) return; int* newarr; newarr=new int[newsz]; memset( newarr, 0x00, newsz*sizeof(int) ); for( int i = 0; i < sz; i++ ) newarr[i] = arr[i]; delete []arr; sz = newsz; arr = newarr; } garli-2.1-release/src/set.h000066400000000000000000000065471241236125200156350ustar00rootroot00000000000000// set.h // Copyright © 1998 by Paul O. Lewis // All rights reserved. // // This code may be used and modified for non-commercial purposes // but redistribution in any form requires written permission. // Please contact: // // Paul O. Lewis, Assistant Professor // 167 Castetter Hall // Department of Biology // The University of New Mexico // Albuquerque, NM 87131-1091 // Phone: (505) 277-6681 // Fax: (505) 277-0304 // email: lewisp@unm.edu // http://biology.unm.edu/~lewisp/pol.html // // Note: moving January 1, 1999, to the Department of Ecology and // Evolutionary Biology, University of Connecticut // // Associated source code file: "set.cpp" // #ifndef __SET_H #define __SET_H #include // Note: the data type unsigned char and the macro MISSING_DATA // should be defined here the same way they are defined // in the file "datamatr.h" #define MISSING_DATA (0xf) class Set { int sz; int next; int* arr; void Realloc( int newsz ); public: Set() : sz(0), arr(0), next(0) {} Set( int startsz ); ~Set(); int Size() const { return next; } int Empty() const { return (sz==0); } Set& operator+=( const int i ); Set& operator-=( const int i ); int operator[]( const int i ) const; }; class DNASet { int set; public: enum { BASE_A = 0x01, BASE_C = 0x02, BASE_G = 0x04, BASE_T = 0x08, BASE_MISSING = 0x0f }; DNASet() : set(0) {} DNASet( int s ) : set(s) {} int Empty() const { return (set==0); } void Flush() { set = 0; } DNASet& operator=( const DNASet& d ) { set = d.set; return *this; } DNASet& operator+=( const unsigned char i ); DNASet& operator-=( const unsigned char i ); // set intersection is mapped to the bitwise AND operator DNASet& operator|=( DNASet& b ); friend DNASet operator|( DNASet& a, DNASet& b ); // set union is mapped to the bitwise OR operator DNASet& operator&=( DNASet& b ); friend DNASet operator&( DNASet& a, DNASet& b ); }; inline Set& Set::operator+=( const int i ) { if( next == sz ) Realloc( sz + 5 ); arr[next++] = i; return *this; } inline int Set::operator[]( const int i ) const { assert( i >= 0 ); assert( i < next ); return arr[i]; } inline DNASet& DNASet::operator+=( const unsigned char i ) { // Note: this function is designed to take values of type unsigned char // see file datamatr.h before changing the relationship between // bases and unsigned char values, specifically the function SequenceData::DatumToChar // // BUGBUG: unsigned char should be defined in its own header file along with // the necessary conversion functions such as DatumToChar and CharToDatum if( i == 0 ) set |= BASE_A; else if( i == 1 ) set |= BASE_C; else if( i == 2 ) set |= BASE_G; else if( i == 3 ) set |= BASE_T; else if( i == MISSING_DATA ) set |= BASE_MISSING; return *this; } inline DNASet& DNASet::operator-=( const unsigned char i ) { // Note: this function is designed to take values of type unsigned char // see file datamatr.h before changing the relationship between // bases and unsigned char values, specifically the function SequenceData::DatumToChar if( i == 0 ) set &= ~BASE_A; else if( i == 1 ) set &= ~BASE_C; else if( i == 2 ) set &= ~BASE_G; else if( i == 3 ) set &= ~BASE_T; else if( i == MISSING_DATA ) set &= ~BASE_MISSING; // this does nothing, but is aesthetically pleasing! return *this; } #endif garli-2.1-release/src/stopwatch.h000066400000000000000000000046331241236125200170500ustar00rootroot00000000000000// GARLI version 2.0 source code // Copyright 2005-2011 Derrick J. Zwickl // email: garli.support@gmail.com // // This program is free software: you can redistribute it and/or modify // it under the terms of the GNU General Public License as published by // the Free Software Foundation, either version 3 of the License, or // (at your option) any later version. // // This program is distributed in the hope that it will be useful, // but WITHOUT ANY WARRANTY; without even the implied warranty of // MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the // GNU General Public License for more details. // // You should have received a copy of the GNU General Public License // along with this program. If not, see . #ifndef STOPWATCH_H #define STOPWATCH_H #ifndef UNIX #include #else #include #endif class Stopwatch { public: Stopwatch() { Restart(); } #ifndef UNIX void Restart() { time(&start_time); } void Start() { time(&start_time); this_execution_start_time = start_time; } int SplitTime() { time(&end_time); return (int)(end_time - start_time); } int ThisExecutionSplitTime() { time(&end_time); return (int)(end_time - this_execution_start_time); } //this is for restarting void AddPreviousTime(time_t t){ start_time -= t; } #else void Restart() { gettimeofday(&start_time, NULL); } void Start() { gettimeofday(&start_time, NULL); this_execution_start_time = start_time; } int SplitTime() { gettimeofday(&end_time, NULL); return end_time.tv_sec - start_time.tv_sec; } int ThisExecutionSplitTime() { gettimeofday(&end_time, NULL); return end_time.tv_sec - this_execution_start_time.tv_sec; } //this is for restarting void AddPreviousTime(int t){ start_time.tv_sec -= t; } #endif private: #ifndef UNIX //these are with respect to the entire run, summing across any possible restarts from checkpoint time_t start_time, end_time; //this is the time at which the binary was most recently started, possibly as a restart from checkpoint //this can be used to interpret stoptime as a time since invocation, rather than the total amount of time //used by the search, summed across restarts time_t this_execution_start_time; #else timeval start_time, restart_time, end_time; timeval this_execution_start_time; #endif }; #endif garli-2.1-release/src/threaddcls.h000066400000000000000000000021041241236125200171400ustar00rootroot00000000000000#ifndef THREADDCLS_H #define THREADDCLS_H #include class MasterGamlConfig; class Population; struct transferred_data_t { char *tree_strings; unsigned int ts_size; double *kappas; unsigned int k_size; double score; int tag; }; struct thread_arg_t { int nprocs; MasterGamlConfig *conf; Population *pop; }; extern transferred_data_t *node_results; extern pthread_mutex_t lock_pm; extern pthread_mutex_t lock_pop; extern pthread_cond_t cond_pm; extern bool g_quit_time; extern bool g_processing_message; void *master_poller(void *varg); void *thread_func2(void *varg); void purge_results(transferred_data_t *r); void copy_results(transferred_data_t *lhs, transferred_data_t rhs); bool valid_results(transferred_data_t r); void send_quit_messages(int); int process_message(char *buf, int size, int who, int tag, thread_arg_t *targ); void DoMasterSM(char *buf, int size, int who, int tag, thread_arg_t *targ); void DoMasterAMR(char *buf, int size, int who, int tag, thread_arg_t *targ); int DoMasterSW(char *buf, int size, int who, int tag, thread_arg_t *targ); #endif garli-2.1-release/src/threadfunc.cpp000066400000000000000000000375231241236125200175160ustar00rootroot00000000000000 // GARLI version 0.94 source code // Copyright 2005 by Derrick J. Zwickl // All rights reserved. // // This code may be used and modified for non-commercial purposes // but redistribution in any form requires written permission. // Please contact: // // Derrick Zwickl // Integrative Biology, UT // 1 University Station, C0930 // Austin, TX 78712 // email: garli.support@gmail.com // // Note: In 2006 moving to NESCENT (The National // Evolutionary Synthesis Center) for a postdoc // all of the mpi related code appears here or in mpifuncs.cpp #ifdef MPI_VERSION #include "defs.h" #include "threaddcls.h" #include "mpifuncs.h" #include "individual.h" // local vars transferred_data_t *node_results; pthread_mutex_t lock_pm; pthread_mutex_t lock_pop; pthread_cond_t cond_pm; bool g_quit_time; bool g_processing_message; int remote_types[32]; #define AMR 1 #define SM 2 #define SW 3 extern int calcCount; void *thread_func2(void *varg) { int who, size, tag, quits = 0; thread_arg_t *targs = (thread_arg_t*)varg; MasterGamlConfig *conf = targs->conf; char *buf; bool poo=true; // while(poo); //initialize the array that shows what type of remote each node is int method = 0, nprocs = targs->nprocs; for(who=0;whogc.method == "sm" || (conf->gc.method == "hybrid" && who <= (int) (conf->gc.hybridpercent*(nprocs -1)))) remote_types[who]=SM; else if (conf->gc.method == "amr" || (conf->gc.method == "hybrid" && who > conf->gc.hybridpercent*(nprocs -1))) remote_types[who]=AMR; else debug_mpi("ERROR: can't determine method proper type of remote, remote #%d", who); */ } timespec sleepTime; sleepTime.tv_nsec=50000000; sleepTime.tv_sec=0; int nextStart=1, nextRemote; int maxNumReceives=2, numReceives; while (quits < targs->nprocs-1) { nextRemote=nextStart; bool checkedAll=false, received=false; do{ who=nextRemote; received=RecvMPIMessage(&buf, &size, who, &tag, false); if(received==true) assert(tag==TAG_TREE_STRINGS || tag==TAG_QUIT); nextRemote=(nextRemote<(targs->nprocs-1) ? nextRemote+1 : 1); if(nextRemote==nextStart) checkedAll=true; }while(received==false && checkedAll==false); nextStart=(nextStart<(targs->nprocs-1) ? nextStart+1 : 1); if(checkedAll==true) nanosleep(&sleepTime, NULL); if(received==true){ if (tag == TAG_QUIT){ ++quits; debug_mpi("received quit message from %d. %d quits received.", who, quits); } else { pthread_mutex_lock(&lock_pm); g_processing_message = true; pthread_mutex_unlock(&lock_pm); pthread_mutex_lock(&lock_pop); quits += process_message(buf, size, who, tag, targs); pthread_mutex_unlock(&lock_pop); // if(numReceives == maxNumReceives){ pthread_mutex_lock(&lock_pm); g_processing_message = false; pthread_cond_signal(&cond_pm); pthread_mutex_unlock(&lock_pm); // numReceives=0; // } // else numReceives++; } delete []buf; numReceives++; } if(g_quit_time == true){ send_quit_messages(nprocs); break; } /* if((checkedAll==true && g_processing_message==true) || (received==true && numReceives >= maxNumReceives)){ pthread_mutex_lock(&lock_pm); g_processing_message = false; pthread_cond_signal(&cond_pm); pthread_mutex_unlock(&lock_pm); numReceives=0; }*/ // else numReceives++; } debug_mpi("thread terminating"); g_quit_time = true; } void send_quit_messages(int np){ for(int i=1;iconf; Population *pop = targ->pop; int method = 0, nprocs = targ->nprocs; int foundQuit=0; if(remote_types[who]==SM) DoMasterSM(buf, size, who, tag, targ); else if(remote_types[who]==AMR) DoMasterAMR(buf, size, who, tag, targ); else if(remote_types[who]==SW) foundQuit=DoMasterSW(buf, size, who, tag, targ); else debug_mpi("ERROR: can't determine method to use in process_message(), remote #%d", who); return foundQuit; } void DoMasterSM(char *buf, int size, int who, int tag, thread_arg_t *targ) { MasterGamlConfig *conf = targ->conf; Population *pop = targ->pop; int count, start_shield, *which = new int;//[conf->gc.numshields]; char *tree_strings; double *models; assert(tag == TAG_TREE_STRINGS); tree_strings = buf; count = CountTreeStrings(tree_strings); RecvMPIMessage(&buf, &size, who, &tag, true); assert(tag == TAG_MODEL); models=(double*)buf; // print some messages debug_mpi("SYNCHRONOUS COMMUNICATION (%d, SM)", who); debug_mpi("\trecv: %d tree strings", count); debug_mpi("\trecv: %d models", count); bool poo=true; //while(poo); *which = start_shield = pop->params->nindivs + (who-1); // for (int i = 0; i < conf->gc.numshields; ++i) // which[i] = start_shield++; pop->ReplaceSpecifiedIndividuals(count, which, tree_strings, models); pop->CalcAverageFitness(); delete [] which; //this will be deleted back where the initial call to RecvMPIMessage was made //delete [] tree_strings; delete [] (char*)models; //under certain conditions, tell the remote to become an AMR node if(((double)rand()/RAND_MAX)<.002){ debug_mpi("sent message to change to AMR to remote %d", who); SendMPIMessage(NULL, 0, who, TAG_REMOTE_TYPE_SWITCH); //get rid of any SM messages that might be waiting while(RecvMPIMessage(&buf, &size, who, &tag, false)==true); //update the remote_types array remote_types[who]=AMR; } } void DoMasterAMR(char *buf, int size, int who, int tag, thread_arg_t *targ) { MasterGamlConfig *conf = targ->conf; Population *pop = targ->pop; int count, start_shield, which; char *tree_strings, *model_buf; double *models, score; assert(tag == TAG_SCORE); memcpy(&score, buf, sizeof(double)); //delete [] buf; // print some messages debug_mpi("SYNCHRONOUS COMM (%d, AMR, %f)", who, pop->bestFitness); debug_mpi("\trecv: score %f", score); if (score > pop->bestFitness) { SendMPIMessage(NULL, 0, who, TAG_TREE_STRINGS_REQUEST); debug_mpi("\tsent: TAG_TREE_STRINGS_REQUEST"); RecvMPIMessage(&buf, &size, who, &tag, true); assert(tag == TAG_TREE_STRINGS); tree_strings = buf; debug_mpi("\trecv: %d tree strings", CountTreeStrings(tree_strings)); RecvMPIMessage(&buf, &size, who, &tag, true); assert(tag == TAG_MODEL); models=(double*)buf; which = pop->total_size-1; pop->ReplaceSpecifiedIndividuals(1, &which, tree_strings, models); pop->CalcAverageFitness(); debug_mpi("score sent: %f, score calced: %f", score, pop->IndivFitness(which)); //assert(abs(score - pop->IndivFitness(which))<.00001); } else { which = (int)pop->cumfit[pop->total_size-1][0]; pop->GetSpecifiedTreeStrings(&tree_strings, 1, &which); int model_size=pop->GetSpecifiedModels(&models, 1, &which); SendMPIMessage(tree_strings, strlen2(tree_strings)+2, who, TAG_TREE_STRINGS); debug_mpi("\tsent: %d tree strings", CountTreeStrings(tree_strings)); model_buf = (char*)models; SendMPIMessage(model_buf, sizeof(double)*model_size, who, TAG_MODEL); debug_mpi("\tsent: %d models", 1); } delete [] tree_strings; delete [] models; } int DoMasterSW(char *buf, int size, int who, int tag, thread_arg_t *targ) { MasterGamlConfig *conf = targ->conf; Population *pop = targ->pop; int count, start_shield, *which = new int;//[conf->gc.numshields]; char *tree_strings, *model_buf; char *out_tree_strings; char *defbuf, *subbuf; double *out_models; double *models; int remoteSubtreeDef, remoteSubtreeNode; ParallelManager *paraMan=(pop->paraMan); //first get the tree and model from the remote and include it in //the master population. If there are multiple messages, chuck //earlier ones and just get the most recent bool firstmessage=true; do{ debug_mpi("Remote %d", who); assert(tag == TAG_TREE_STRINGS || tag == TAG_QUIT); if(tag==TAG_QUIT) return 1; tree_strings = buf; count = CountTreeStrings(tree_strings); // debug_mpi("about to get model strings..."); RecvMPIMessage(&buf, &size, who, &tag, true); assert(tag == TAG_MODEL); models=(double*)buf; // debug_mpi("about to get subdef strings..."); //determine what the remote was doing when it sent this tree RecvMPIMessage(&defbuf, &size, who, &tag, true); assert(tag==TAG_SUBTREE_ITERATION); remoteSubtreeDef=atoi(defbuf); if(remoteSubtreeDef>0){ RecvMPIMessage(&subbuf, &size, who, &tag, true); assert(tag==TAG_SUBTREE_DEFINE); remoteSubtreeNode=atoi(subbuf); if(remoteSubtreeDef==paraMan->subtreeDefNumber) paraMan->localSubtreeAssign[who]=remoteSubtreeNode; else paraMan->localSubtreeAssign[who]=0; delete []subbuf; } //DJZ 5-18-05 else { paraMan->localSubtreeAssign[who]=0; remoteSubtreeNode=0; } double score; char *scoreBuf; RecvMPIMessage(&scoreBuf, &size, who, &tag, true); assert(tag==TAG_SCORE); memcpy(&score, scoreBuf, sizeof(double)); // debug_mpi("recieved score of %f", score); delete []scoreBuf; if(firstmessage==false) debug_mpi("\tfound another tree from remote %d", who); *which = start_shield = pop->params->nindivs + (who-1); pop->ReplaceSpecifiedIndividuals(count, which, tree_strings, models); pop->indiv[*which].SetFitness(score); if(firstmessage==false) delete []tree_strings; delete [](char*)models; delete []defbuf; firstmessage=false; }while(RecvMPIMessage(&buf, &size, who, &tag, false)==true); bool subtreesCurrent = ((remoteSubtreeDef == paraMan->subtreeDefNumber) && remoteSubtreeDef > 0); if(paraMan->subtreeModeActive==false || subtreesCurrent==false){ pop->indiv[*which].accurateSubtrees=false; pop->newindiv[*which].accurateSubtrees=false; } else { pop->indiv[*which].accurateSubtrees=true; pop->newindiv[*which].accurateSubtrees=true; } // debug_mpi("about to CalcFitness..."); double prevBestScore=pop->BestFitness(); // pop->indiv[*which].CalcFitness(0); pop->indiv[*which].treeStruct->calcs=calcCount; pop->CalcAverageFitness(); //reclaim clas if the new tree has essentially no chance of reproducing if(((pop->indiv[*which].Fitness() - pop->indiv[pop->bestIndiv].Fitness()) < (-11.5/pop->params->selectionIntensity))){ // debug_mpi("about to reclaim..."); pop->indiv[*which].treeStruct->ReclaimUniqueClas(); } //Now, take a look at what we got from the remote and decide what to do double inscore=pop->indiv[*which].Fitness(); double scorediff=prevBestScore - inscore; debug_mpi("\tnew ind - def %d - node %d - lnL: %f", remoteSubtreeDef, remoteSubtreeNode, inscore); if(scorediff < 0) debug_mpi("\tPrev Best=%f, diff=%f (new best)", prevBestScore, scorediff); else debug_mpi("\tPrev Best=%f, diff=%f", prevBestScore, scorediff); // debug_mpi("\tbest=%d, bestAc=%d, bestlnL=%f, bestAcclnL=%f", pop->bestIndiv, pop->bestAccurateIndiv, pop->BestFitness(), pop->indiv[pop->bestAccurateIndiv].Fitness()); bool recalcSubtrees=false; if(scorediff < -0.01){ pop->LogNewBestFromRemote(-scorediff, *which); } int subtreeNum; bool send=false; //there are really 8 possible cases here //1. Subtree mode active, got accurate tree, score good -> do nothing //2. score bad -> send best accurate tree //3. inaccurate tree, score good -> recalc subtrees, send? //4. score bad -> send best accurate tree //5. Subtree mode inactive, got accurate tree, score good -> send best tree //6. score bad -> send best tree //7. inaccurate tree, score good -> do nothing //8. score bad -> send best tree //so, 2 "do nothings" 3 "send best", 2 "send best accurate" and 1 "subtree recalc" //if subtree mode isn't active, send the remote our best tree if the //tree we got from it is worse by some amount, or if it is still working //on a subtree double updateThresh=paraMan->updateThresh; if(paraMan->subtreeModeActive==false){ if((paraMan->perturbModeActive==false && (scorediff > updateThresh || remoteSubtreeDef>0))/* || (paraMan->needToSend[who]==true)*/){ debug_mpi("\tupdate thresh = %f, send indiv", updateThresh); //cases 5, 6 and 8 *which = (int)pop->cumfit[pop->total_size-1][0]; subtreeNum=0; send=true; } else debug_mpi("\tupdate thresh = %f", updateThresh); } else if(paraMan->subtreeModeActive==true){ //cases 1-4 if((scorediff > updateThresh) || (subtreesCurrent==false)/* || paraMan->perturb==true*/){ //cases 2 and 4. send the best accurate tree *which=pop->bestAccurateIndiv; if(paraMan->remoteSubtreeAssign[who] != 0) subtreeNum=paraMan->remoteSubtreeAssign[who]; else subtreeNum=paraMan->ChooseSubtree(); debug_mpi("\tsend best accurate ind, %f (best=%f)", pop->indiv[*which].Fitness(), pop->bestFitness); // debug_mpi("\tperturb=%d, bestFit=%f, indFit=%f", paraMan->perturb, pop->bestFitness, pop->indiv[*which].Fitness()); send=true; } else if(recalcSubtrees==true && subtreesCurrent==false){ //case 3 //if the new inaccurate tree that came in is better than what we have, //recalcuate the subtrees, and send the same tree back, but with a //subtree asignment pop->StartSubtreeMode(); debug_mpi("Recalculating subtrees"); subtreeNum=paraMan->ChooseSubtree(); send=true; } } if(paraMan->needToSend[who]){ char pertbuf[5]; int perttype = (pop->pertMan->pertType > 0 ? pop->pertMan->pertType : (int)(rnd.uniform() * 2 + 1)); sprintf(pertbuf, "%d", perttype); SendMPIMessage(pertbuf, strlen(pertbuf)+2, who, TAG_PERTURB); debug_mpi("sending pertub message to %d, type %d", who, perttype); paraMan->needToSend[who]=false; } if(send==true){ pop->GetSpecifiedTreeStrings(&out_tree_strings, 1, which); assert(*out_tree_strings == '('); int model_size=pop->GetSpecifiedModels(&out_models, 1, which); SendMPIMessage(out_tree_strings, strlen2(out_tree_strings)+2, who, TAG_TREE_STRINGS); SendMPIMessage((char*)out_models, sizeof(double)*model_size, who, TAG_MODEL); /* if(paraMan->needToSend[who]){ char pertbuf[5]; int perttype = (pop->pertMan->pertType > 0 ? pop->pertMan->pertType : (rnd.uniform() * 2 + 1)); sprintf(pertbuf, "%d", subtreeNum); SendMPIMessage(NULL, 0, who, TAG_PERTURB); debug_mpi("sending pertub message to %d, type %d", who, perttype); paraMan->needToSend[who]=false; } */ // else{ char stn[5]; sprintf(stn, "%d", subtreeNum); SendMPIMessage(stn, strlen(stn)+2, who, TAG_SUBTREE_DEFINE); debug_mpi("\tsent ind %d, lnL %f", *which, pop->indiv[*which].Fitness()); if(subtreeNum > 0){ //if this node was already assigned a subtree, be sure to subtract the old one from the assigned array sprintf(stn, "%d", paraMan->subtreeDefNumber); debug_mpi("\tsubdef %d, node %d", paraMan->subtreeDefNumber, subtreeNum); SendMPIMessage(stn, strlen(stn)+2, who, TAG_SUBTREE_ITERATION); } // } paraMan->remoteSubtreeAssign[who]=subtreeNum; delete []out_models; delete []out_tree_strings; } #ifndef NDEBUG if(paraMan->subtreeModeActive && paraMan->subtreeDefNumber==remoteSubtreeDef){ //if we think that this remote gave us a tree with accurate subtrees, check paraMan->CheckSubtreeAccuracy(pop->indiv[which[0]].treeStruct); } #endif //the tree_strings that were passed in will be deleted back //where the initial call to RecvMPIMessage was made delete [] which; pop->CalcAverageFitness(); return 0; } void purge_results(transferred_data_t *r) { if (r->tree_strings) delete [] r->tree_strings; if (r->kappas) delete [] r->kappas; memset(r, 0, sizeof(transferred_data_t)); } void copy_results(transferred_data_t *lhs, transferred_data_t rhs) { memcpy(lhs, &rhs, sizeof(transferred_data_t)); if (rhs.tree_strings) { lhs->tree_strings = new char[rhs.ts_size]; memcpy(lhs->tree_strings, rhs.tree_strings, rhs.ts_size); } if (rhs.kappas) { lhs->kappas = new double[rhs.k_size/sizeof(double)]; memcpy(lhs->kappas, rhs.kappas, rhs.k_size); } } bool valid_results(transferred_data_t r) { if (r.tree_strings && r.kappas) return true; if (r.tag == TAG_SCORE) return true; return false; } #endifgarli-2.1-release/src/translatetable.cpp000066400000000000000000000046331241236125200203740ustar00rootroot00000000000000// GARLI version 2.0 source code // Copyright 2005-2011 Derrick J. Zwickl // email: garli.support@gmail.com // // This program is free software: you can redistribute it and/or modify // it under the terms of the GNU General Public License as published by // the Free Software Foundation, either version 3 of the License, or // (at your option) any later version. // // This program is distributed in the hope that it will be useful, // but WITHOUT ANY WARRANTY; without even the implied warranty of // MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the // GNU General Public License for more details. // // You should have received a copy of the GNU General Public License // along with this program. If not, see . #include "defs.h" #include "translatetable.h" #include "datamatr.h" #include "sequencedata.h" void TranslateTable::SetTaxonName( int i, const char* s ){ assert( Check(i-1) ); assert( nTax ); SetName(i-1, s); } TranslateTable::TranslateTable( SequenceData* d ) : nTax(0){ assert( d ); nTax = d->NTax(); Alloc(); for( int i = 0; i < nTax; i++ ) SetTaxonName( i+1, d->TaxonLabel(i) ); } void TranslateTable::Alloc(){ assert( nTax ); MEM_NEW_ARRAY(taxonName,char*,nTax); for( int i = 0; i < nTax; i++ ) taxonName[i] = 0; } void TranslateTable::Destroy(){ for( int i = 0; i < nTax; i++ ) { int nmlen = (int)strlen( taxonName[i] ); assert(nmlen > 0); MEM_DELETE_ARRAY(taxonName[i]); // taxonName[i] has length nmlen+1 } MEM_DELETE_ARRAY(taxonName); // taxonName has length nTax taxonName = 0; nTax = 0; } void TranslateTable::SetName( int i, const char* s ){ assert(s); int nmlen; if( taxonName[i] ) { nmlen = (int)strlen( taxonName[i] ); MEM_DELETE_ARRAY(taxonName[i]); // taxonName[i] has length nmlen+1 } nmlen = (int)strlen(s); MEM_NEW_ARRAY(taxonName[i],char,nmlen+1); assert( taxonName[i] ); strcpy( taxonName[i], s ); } int TranslateTable::Find( const char* s ){ assert(s); int taxonNumber = 0; for( int i = 0; i < nTax; i++ ) { if( strcmp( taxonName[i], s ) == 0 ) { taxonNumber = i+1; break; } } return taxonNumber; } ostream& operator<<( ostream& out, TranslateTable& tt ){ out << "translate" << endl; for( int i = 0; i < tt.nTax-1; i++ ) { out << " " << (i+1) << ' ' << tt.taxonName[i] << ',' << endl; } out << " " << tt.nTax << ' ' << tt.taxonName[tt.nTax-1] << endl; out << " ;" << endl; return out; } garli-2.1-release/src/translatetable.h000066400000000000000000000032021241236125200200300ustar00rootroot00000000000000// GARLI version 2.0 source code // Copyright 2005-2011 Derrick J. Zwickl // email: garli.support@gmail.com // // This program is free software: you can redistribute it and/or modify // it under the terms of the GNU General Public License as published by // the Free Software Foundation, either version 3 of the License, or // (at your option) any later version. // // This program is distributed in the hope that it will be useful, // but WITHOUT ANY WARRANTY; without even the implied warranty of // MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the // GNU General Public License for more details. // // You should have received a copy of the GNU General Public License // along with this program. If not, see . // // NOTE: Portions of this source adapted from GAML source, written by Paul O. Lewis #ifndef _TRANSTABLE #define _TRANSTABLE #include #include #include #include using namespace std; class SequenceData; class TranslateTable { int nTax; char** taxonName; int Check( int i ) { return (i >= 0 && i. // // NOTE: Portions of this source adapted from GAML source, written by Paul O. Lewis #include #include #include #include #ifdef UNIX #include #endif using namespace std; #include "defs.h" #include "sequencedata.h" #include "clamanager.h" #include "funcs.h" #include "stopwatch.h" #include "model.h" #include "tree.h" #include "reconnode.h" #include "garlireader.h" #include "utility.h" Profiler ProfIntInt ("ClaIntInt "); Profiler ProfIntTerm ("ClaIntTerm "); Profiler ProfTermTerm ("ClaTermTerm "); Profiler ProfRescale ("Rescale "); Profiler ProfScoreInt ("ScoreInt "); Profiler ProfScoreTerm("ScoreTerm "); Profiler ProfEQVectors("EQVectors "); extern bool swapBasedTerm; /* FLOAT_TYPE precalcThresh[30]; FLOAT_TYPE precalcMult[30]; int precalcIncr[30] = {1, 3, 5, 7, 10, 12, 14, 17, 19, 21, 24, 26, 28, 30, 33, 35, 37, 40, 42, 44, 47, 49, 51, 53, 56, 58, 60, 63, 65, 67}; */ extern rng rnd; extern bool output_tree; extern bool uniqueSwapTried; #ifdef VARIABLE_OPTIMIZATION ofstream var("variable.log"); ofstream uni("unique.log"); #endif #ifdef OUTPUT_UNIQUE_TREES ofstream uni("unique.log"); #endif //external global variables extern int calcCount; extern int optCalcs; extern ofstream opt; extern ofstream optsum; extern int memLevel; extern vector dataSubInfo; //extern ModelSpecification modSpec; //Tree static definitions FLOAT_TYPE Tree::meanBrlenMuts; FLOAT_TYPE Tree::alpha; FLOAT_TYPE Tree::min_brlen; // branch lengths never below this value FLOAT_TYPE Tree::max_brlen; FLOAT_TYPE Tree::exp_starting_brlen; // expected starting branch length ClaManager *Tree::claMan; list Tree::nodeOptVector; const DataPartition *Tree::dataPart; unsigned Tree::rescaleEvery; FLOAT_TYPE Tree::rescaleBelow; FLOAT_TYPE Tree::reduceRescaleBelow; FLOAT_TYPE Tree::bailOutBelow; FLOAT_TYPE Tree::treeRejectionThreshold; vector Tree::constraints; AttemptedSwapList Tree::attemptedSwaps; FLOAT_TYPE Tree::uniqueSwapBias; FLOAT_TYPE Tree::distanceSwapBias; FLOAT_TYPE Tree::expectedPrecision; bool Tree::rootWithDummy; bool Tree::dummyRootBranchMidpoint; bool Tree::someOrientedGap; bool Tree::useOptBoundedForBlen; FLOAT_TYPE Tree::uniqueSwapPrecalc[500]; FLOAT_TYPE Tree::distanceSwapPrecalc[1000]; FLOAT_TYPE Tree::rescalePrecalcThresh[RESCALE_ARRAY_LENGTH]; FLOAT_TYPE Tree::rescalePrecalcMult[RESCALE_ARRAY_LENGTH]; int Tree::rescalePrecalcIncr[RESCALE_ARRAY_LENGTH]; Bipartition *Tree::outgroup = NULL; int Tree::siteToScore = -1; void InferStatesFromCla(char *states, FLOAT_TYPE *cla, int nchar); FLOAT_TYPE CalculateHammingDistance(const char *str1, const char *str2, int nchar); void SampleBranchLengthCurve(FLOAT_TYPE (*func)(TreeNode*, Tree*, FLOAT_TYPE, bool), TreeNode *thisnode, Tree *thistree); FLOAT_TYPE CalculatePDistance(const char *str1, const char *str2, int nchar); inline FLOAT_TYPE CallBranchLike(TreeNode *thisnode, Tree *thistree, FLOAT_TYPE blen, bool brak); //basic function to deal with the odd data string format that I use for nuc data const char *AdvanceDataPointer(const char *arr, int num){ for(int a=0;a -1 || *arr == -4) arr++; else{ int states = -1 * *arr; do{ arr++; }while (states-- > 0); } } return arr; } void Tree::SetTreeStatics(ClaManager *claMan, const DataPartition *data, const GeneralGamlConfig *conf){ Tree::claMan=claMan; Tree::dataPart=data; #ifdef SINGLE_PRECISION_FLOATS Tree::rescaleEvery = 6; Tree::rescaleBelow = exp(-1.0f); //this is 0.368 Tree::reduceRescaleBelow = 1.0e-30; Tree::bailOutBelow = 1.0e-30; FLOAT_TYPE maxMult = 1.0 / bailOutBelow; for(int i=0;i<30;i++){ Tree::rescalePrecalcIncr[i] = i*3 - (int) log(rescaleBelow); Tree::rescalePrecalcThresh[i] = exp((FLOAT_TYPE)(-rescalePrecalcIncr[i])); Tree::rescalePrecalcMult[i] = min(exp((FLOAT_TYPE)(rescalePrecalcIncr[i])), maxMult); } FLOAT_TYPE minVal = 1.0e-10f; FLOAT_TYPE maxVal = 1.0e10f; #else Tree::rescaleEvery=16; Tree::rescaleBelow = exp(-24.0); //this is 1.026e-10 Tree::reduceRescaleBelow = 1.0e-190; Tree::bailOutBelow = 1.0e-250; FLOAT_TYPE maxMult = 1.0 / bailOutBelow; for(int i=0;iuniqueSwapBias; Tree::distanceSwapBias = conf->distanceSwapBias; for(int i=0;i<500;i++){ Tree::uniqueSwapPrecalc[i] = (FLOAT_TYPE) pow(Tree::uniqueSwapBias, i); //if(Tree::uniqueSwapPrecalc[i] != Tree::uniqueSwapPrecalc[i]) Tree::uniqueSwapPrecalc[i]=0.0f; if(Tree::uniqueSwapPrecalc[i] < minVal) Tree::uniqueSwapPrecalc[i] = minVal; if(Tree::uniqueSwapPrecalc[i] > maxVal) Tree::uniqueSwapPrecalc[i] = maxVal; } for(int i=0;i<1000;i++){ Tree::distanceSwapPrecalc[i] = (FLOAT_TYPE) pow(Tree::distanceSwapBias, i); //if(Tree::distanceSwapPrecalc[i] != Tree::distanceSwapPrecalc[i]) Tree::distanceSwapPrecalc[i]=0.0f; if(Tree::distanceSwapPrecalc[i] < minVal) Tree::distanceSwapPrecalc[i] = minVal; if(Tree::distanceSwapPrecalc[i] > maxVal) Tree::distanceSwapPrecalc[i] = maxVal; } Tree::meanBrlenMuts = conf->meanBrlenMuts; Tree::alpha = conf->gammaShapeBrlen; Tree::treeRejectionThreshold = conf->treeRejectionThreshold; Tree::min_brlen = conf->minBrlen; Tree::max_brlen = conf->maxBrlen; Tree::exp_starting_brlen = conf->startingBrlen; Tree::someOrientedGap = false; for(vector::iterator it = dataSubInfo.begin();it != dataSubInfo.end();it++){ if((*it).readAs == DataSubsetInfo::ORIENTEDGAP) Tree::someOrientedGap = true; } string outString = conf->outgroupString; if(someOrientedGap){ //Tree::rescaleEvery = 2; Tree::rootWithDummy = true; Tree::useOptBoundedForBlen = true; Tree::dummyRootBranchMidpoint = conf->rootAtBranchMidpoint; //set the dummy taxon as the effective outgroup if(conf->outgroupString.length() > 0) outman.UserMessage("WARNING - specified outgroup (%s) being ignored due to inference of a rooted true", conf->outgroupString.c_str()); char num[10]; sprintf(num, "%d", data->NTax()); outString = num; GarliReader &reader = GarliReader::GetInstance(); NxsTaxaBlock *tax = reader.GetTaxaBlock(0); if(!tax->IsAlreadyDefined("ROOT")){ string n = "ROOT"; tax->AppendNewLabel(n); } } else{ Tree::rootWithDummy = false; Tree::useOptBoundedForBlen = conf->useOptBoundedForBlen; } //deal with the outgroup specification, if there is one if(outString.length() > 0){ if(outgroup) outgroup->ClearBipartition(); else outgroup = new Bipartition(); GarliReader &reader = GarliReader::GetInstance(); if(reader.GetTaxaBlock(0)->GetNTax() > 0){ //now using NCL to much more rigorously and flexibly read the outgroup specification NxsString tax(outString.c_str()); tax += ";"; std::istringstream s(tax); NxsToken tok(s); tok.GetNextToken(); NxsUnsignedSet iset; try{ NxsSetReader::ReadSetDefinition(tok, *reader.GetTaxaBlock(0), "outgroup", "GARLI configuration", &iset); if(!rootWithDummy) outman.UserMessage("Found outgroup specification: %s", NxsSetReader::GetSetAsNexusString(iset).c_str()); } catch (const NxsException & x){ throw ErrorException("%s", x.msg.c_str()); } //the set has been read as indeces, so change to taxon numbers before passing to the bipart func NxsUnsignedSet nset; for(NxsUnsignedSet::const_iterator it = iset.begin();it != iset.end(); it++) nset.insert(*it + 1); outgroup->BipartFromNodenums(nset); } else{//the old half-assed outgroup reader vector nums; unsigned pos1=0, pos2; while(pos1 < outString.size()){ pos2 = outString.find(" ", pos1+1); string tax = outString.substr(pos1, pos2 - pos1); tax = NxsString::strip_whitespace(tax); for(string::iterator it = tax.begin();it != tax.end();it++) if(isdigit(*it) == false) throw ErrorException("problem in outgroup specification.\nExpecting taxon numbers separated by spaces, found %s.", tax.c_str()); nums.push_back(atoi(tax.c_str())); pos1 = pos2; } outman.UserMessageNoCR("Found outgroup specification: "); for(vector::iterator it = nums.begin();it != nums.end();it++) outman.UserMessageNoCR("%d ", *it); outman.UserMessage("\n"); outgroup->BipartFromNodenums(nums); } outman.UserMessage("\n#######################################################"); } } //this assumes that a tree string has been passed in with *s pointing to the first char of a blen //description, and reads and advances the string up to the next non-blen character. The string that //was interpreted as the branch length is placed into the NxsString passed in double ReadBranchlength(const char *&s, NxsString &blen){ blen = ""; while(*(s+1) && *(s+1)!=')'&& *(s+1)!=',' && *(s+1)!=';'){ blen += *(s+1); s++; } s++; double len; if(NxsString::to_double(blen.c_str(), &len) == false) throw ErrorException("Problem reading tree description. Illegal branch-length specification: \"%s\"", blen.c_str()); return len; } //DJZ 4-28-04 //adding the ability to read in treestrings in which the internal node numbers are specified. I'd like to make the //internal numbers be specified the way that internal node labels are according to the newick format, ie directly after //the closing paren that represents the internal node. But, that makes going from string -> tree annoying //because by the time the internal node number would be read the treeNode structure would have already been created. //So, the internal node numbers will go just BEFORE the opening paren that represents that node //Example: 50(1:.05, 2:.02):.1 signifies a node numbered 50 that is ancestral to 1 and 2. Tree::Tree(const char* s, bool numericalTaxa, bool allowPolytomies /*=false*/, bool allowMissingTaxa /*=false*/){ //if we are using this constructor, we can't guarantee that the tree will be specified unrooted (with //a trifurcating root), so use an allocation function that is guaranteed to have enough room and then //trifurcate and delete if necessary //this should strip out any crap in the tree string, although much of it would be disregarded below anyway string editedString = NxsString::strip_whitespace(s); s = editedString.c_str(); AllocateTree(true); TreeNode *temp=root; root->attached=true; int current=numTipsTotal+1; bool cont=false; numBranchesAdded = 0; while(*s){ cont = false; if(*s == ';') break; // ignore semicolons else if(*s == ' ' || *s == '\t') s++; //DEBUG //break; // ignore spaces else if(*s == ')'){ //we're closing a paren, moving a node toward the root assert(temp->anc); if(!temp->anc) throw ErrorException("Problem reading tree description. Mismatched parentheses?"); temp=temp->anc; s++; //while(*s && !isgraph(*s)) //an internal node label might appear here, so ignore anything up to one of these valid next characters while(*s && (*s != ',') && (*s != ':') && (*s != ',') && (*s != ')') && (*s != ';')) s++; if(*s==':'){//adding a branch length NxsString len; temp->dlen = ReadBranchlength(s, len); if(temp->dlen < min_brlen){ outman.UserMessage("->Branch of length %s is less than min of %.1e. Setting to min.", len.c_str(), min_brlen); temp->dlen = min_brlen; } else if (temp->dlen > max_brlen){ outman.UserMessage("->Branch of length %s is greater than max of %.0f. Setting to max.", len.c_str(), max_brlen); temp->dlen = max_brlen; } } else if(*s==','||*s==')'){ temp->dlen=Tree::exp_starting_brlen; #ifdef STOCHASTIC_STARTING_BLENS temp->dlen *= rnd.gamma(1.0); #endif } else { if(*s==';'){ s++; while(*s){ if(*s != ' ' && *s != '\t') outman.UserMessage("Warning: extraneous character (%c) found after ; in tree description", *s); s++; } break; } else if(*s == ' ' || *s == '\t') s++; else if(*s == '\0' || *s == '\n' || *s == '\r') break; else throw ErrorException("Unexpected character found in tree description at this point: %s", s); // assert(!*s || *s==';'); } } else if(*s == ','){ assert(temp->anc); if(!temp->anc) throw ErrorException("Problem reading tree description. Mismatched parentheses?"); temp=temp->anc; if(*(s+1)!='(') { s++; } cont = true; } if(*s == '(' || isdigit(*s) || cont==true){ //here we're about to add a node of some sort if(*(s+1)=='('){//add an internal node if(current >= numNodesTotal) throw ErrorException("Problem reading tree description. Extra taxa?"); temp=temp->AddDes(allNodes[current++]); numBranchesAdded++; numNodesAdded++; s++; } else{ //this gets ugly. At this point we could be adding an internal node with the internal node //num specifed, or a terminal node. Either way the next characters in the string will be //digits. We'll have to look ahead to see what the next non-digit character is. If it's //a '(', we know we are adding a prenumbered internal if(*s=='(') { s++; } int i=0; bool term=true; while(isdigit(*(s+i))) i++; if(*(s+i) == '(') term=false; //add an internal node with the nodenum specified in the string - this is my non-standard hack if(term == false){ NxsString num; num = *s; while(isdigit(*(s+1))){ assert(*s); num += *++s; } int internalnodeNum = atoi( num.c_str() ); temp=temp->AddDes(allNodes[internalnodeNum]); numBranchesAdded++; numNodesAdded++; s++; } else{//add a terminal node // read taxon name NxsString name; name = *s; int taxonnodeNum; if(numericalTaxa==true){ while(isdigit(*(s+1))){ assert(*s); name += *++s; } taxonnodeNum = atoi( name.c_str() ); if(taxonnodeNum == 0) throw ErrorException("Unexpected character(s) found in tree description \"%s!\"", name.c_str()); if(taxonnodeNum > numTipsTotal) throw ErrorException("Taxon number in tree description (%d) is greater than\n\tnumber of taxa in dataset!", taxonnodeNum); } else{ while(*(s+1) != ':' && *(s+1) != ',' && *(s+1) != ')'){ assert(*s); name += *++s; } //This is a bit annoying. If the tree string came directly from NCL then GetEscaped should get any //names to match the names present in the datamatrix (whether Nexus or not). But, if the tree string //came from a start file with just a newick string there are various possibilities. First try interpreting //the name as-is. If that doesn't work, try GetEscaped. If that doesn't work, try removing quotes (if any) //before calling GetEscaped taxonnodeNum = dataPart->TaxonNameToNumber(name); if(taxonnodeNum < 0){ NxsString esc = NxsString::GetEscaped(name).c_str(); taxonnodeNum = dataPart->TaxonNameToNumber(esc); } if(taxonnodeNum < 0){ if(name.c_str()[0] == '\'' && name.c_str()[name.size()-1] == '\''){ NxsString esc2; for(int c=1;cTaxonNameToNumber(esc); } } if(taxonnodeNum < 0){ throw ErrorException("Unknown taxon \"%s\" encountered in tree description!\nIf you have spaces in your taxon names, try replacing them with underscores.", name.c_str()); } } if(allNodes[taxonnodeNum]->attached == true) throw ErrorException("Taxon \"%s\" seems to appear in the tree description twice!\nCheck the tree string.", name.c_str()); else{ temp=temp->AddDes(allNodes[taxonnodeNum]); numBranchesAdded++; numNodesAdded++; numTipsAdded++; } s++; while(*s == ' ' || *s == '\t') s++;;//eat any spaces here if(*s!=':' && *s!=',' && *s!=')'){ throw ErrorException("Problem parsing tree string! Expecting \":\" or \",\" or \")\", found %c", *s); s--; ofstream str("treestring.log", ios::app); str << s << endl; str.close(); assert(0); } if(*s==':'){ NxsString len; temp->dlen = ReadBranchlength(s, len); if(temp->dlen < min_brlen){ outman.UserMessage("->Branch of length %s is less than min of %.1e. Setting to min.", len.c_str(), min_brlen); temp->dlen = min_brlen; } else if (temp->dlen > max_brlen){ outman.UserMessage("->Branch of length %s is greater than max of %.0f. Setting to max.", len.c_str(), max_brlen); temp->dlen = max_brlen; } } else{ temp->dlen = Tree::exp_starting_brlen; #ifdef STOCHASTIC_STARTING_BLENS temp->dlen *= rnd.gamma(1.0); #endif } } } } } //See if the fake ROOT taxon is in the tree, and place it if necessary. Note that the extra tip is //allNodes[numTipsTotal] (and numNodesTotal includes that extra tip) because allNodes[0] is the root, but //the extra connector is allNodes[numNodesTotal - 1], i.e., the last node allocated. Note that during the run the dummy root //will always be the same node, but its anc (the dummy connector) won't be if(rootWithDummy){ assert(dummyRoot); SetBranchLength(dummyRoot, 0.01); if(root->left->next == root->right){ //if the root only has two descendents (i.e., it is a rooted tree) then add the dummy root there //there will be no connector, and all connectors should already have been used. assert(numNodesAdded == numNodesTotal - 2); root->AddDes(dummyRoot); numBranchesAdded++; numNodesAdded++; numTipsAdded++; } //else if(numNodesAdded == numNodesTotal - 3){ else if( dummyRoot->attached == false ){ //tree didn't have dummy in it, nor was it rooted. Toss in anywhere //(numNodesTotal - 1) is the "extra" node allocated for possibly unrooted //trees, which will be elminated below int connector = numNodesTotal - 2; assert(allNodes[connector]->attached == false); if(constraints.size() == 0) RandomlyAttachTip(numTipsTotal, connector); else{ Bipartition mask; vector n; for(int tax = 1;tax < numTipsTotal;tax++) n.push_back(tax); mask.BipartFromNodenums(n); RandomlyAttachTipWithConstraints(numTipsTotal, connector, &mask ); } } else//the input tree must have had the dummy in it already assert(dummyRoot->attached == true); if(dummyRootBranchMidpoint) MoveDummyRootToBranchMidpoint(); } if(root->left->next==root->right){ MakeTrifurcatingRoot(true, false); } else { EliminateNode(2*dataPart->NTax()-2); } assert(root->left->next!=root->right); if((allowMissingTaxa == false) && (numTipsAdded != numTipsTotal) && !rootWithDummy) throw ErrorException("Number of taxa in tree description (%d) not equal to number of taxa in dataset (%d)!", numTipsAdded, numTipsTotal); root->CheckforLeftandRight(); if(allowPolytomies == false) root->CheckforPolytomies(); root->CheckTreeFormation(); bipartCond = DIRTY; assert(numBranchesAdded == numNodesAdded - 1); if(!allowMissingTaxa) assert(numTipsAdded == numTipsTotal); if(!allowPolytomies) assert(numNodesAdded == numNodesTotal); } Tree::Tree(){ AllocateTree(false); } //we might want the extra node here if we are reading in a user tree that could be rooted (with a basal bifurcation rather than trifurcation) //the standard Tree() constructor used to be hard coded to take care of the withExtraNode = no case, while AllocateTree did yes. Otherwise //they were almost identical, so have been combined void Tree::AllocateTree(bool withExtraNode){ if(withExtraNode) numNodesTotal = 2*dataPart->NTax()-1; else numNodesTotal = 2*dataPart->NTax()-2; allNodes=new TreeNode*[numNodesTotal]; for(int i=0;ibipart=new Bipartition(); } root=allNodes[0]; root->attached=true; //PARTITION modPart = NULL; AssignDataToTips(); numTipsAdded=0; numNodesAdded=1;//root numTipsTotal=dataPart->NTax(); lnL=0.0; if(rootWithDummy) dummyRoot = allNodes[numTipsTotal]; else dummyRoot = NULL; calcs=0; sitelikeLevel = 0; numBranchesAdded=0; taxtags=new int[numTipsTotal+1]; bipartCond = DIRTY; #ifdef EQUIV_CALCS //need to do the root too, since that node is sometimes stolen allNodes[0]->tipData = new char[dataPart->NChar()]; for(int i=dataPart->NTax()+1;itipData = new char[data->NChar()]; } dirtyEQ=true; #endif } void Tree::AssignDataToTips(){ //TODO FOR MIXING - this assumes that 1 data subset = one cla for(int c = 0;c < claSpecs.size();c++){ SequenceData *curData = dataPart->GetSubset(c); for(int t=1;t<=dataPart->NTax();t++){ //if(isNucleotide){ if(modSpecSet.GetModSpec(claSpecs[c].modelIndex)->IsNucleotide()){ //allNodes[t]->tipData=static_cast(curData)->GetAmbigString(t-1); allNodes[t]->tipData.push_back(static_cast(curData)->GetAmbigString(t-1)); #ifdef OPEN_MP //allNodes[t]->ambigMap=static_cast(curData)->GetAmbigToCharMap(t-1); allNodes[t]->ambigMap.push_back(static_cast(curData)->GetAmbigToCharMap(t-1)); #endif } else{ //allNodes[t]->tipData=(char *)(curData)->GetRow(t-1); allNodes[t]->tipData.push_back((char *)(curData)->GetRow(t-1)); #ifdef OPEN_MP //even though there is no ambig map for non-nuc data, we need to put a dummy into the vector //so that the data index matches up with the correct element in the vector allNodes[t]->ambigMap.push_back(NULL); #endif } } } #ifdef OPEN_MP assert(allNodes[1]->ambigMap.size() == claSpecs.size()); #endif } Tree::~Tree(){ if(taxtags!=NULL) delete []taxtags; if(allNodes!=NULL){ for(int x=0; xdlen*=rnd.gamma( Tree::alpha ); allNodes[branch]->dlen = (allNodes[branch]->dlen > min_brlen ? (allNodes[branch]->dlen < max_brlen ? allNodes[branch]->dlen : max_brlen) : min_brlen); SweepDirtynessOverTree(allNodes[branch]); } } return numBrlenMuts; } void Tree::PerturbAllBranches(){ for(int i=numTipsTotal+1;idlen*=rnd.gamma(100); } MakeAllNodesDirty(); } void Tree::RandomizeBranchLengths(FLOAT_TYPE lowLimit, FLOAT_TYPE highLimit){ FLOAT_TYPE range = (highLimit - lowLimit); for(int i=1;idlen = lowLimit + (rnd.uniform() * range); } MakeAllNodesDirty(); } void Tree::RandomizeBranchLengthsExponential(FLOAT_TYPE lambda){ for(int i=1;idlen = rnd.exponential(lambda); } /* FLOAT_TYPE low = log(lowLimit); FLOAT_TYPE high = log(highLimit); FLOAT_TYPE range = high - low; for(int i=1;idlen = exp(low + rnd.uniform() * range); } */ MakeAllNodesDirty(); } void Tree::ScaleWholeTree(FLOAT_TYPE factor/*=-1.0*/){ if(factor==-1.0) factor = rnd.gamma( Tree::alpha ); //9-12-06 Stupid! Why the hell was this only scaling the internals? //for(int i=numTipsTotal;idlen*=factor; allNodes[i]->dlen = (allNodes[i]->dlen > min_brlen ? (allNodes[i]->dlen < max_brlen ? allNodes[i]->dlen : max_brlen) : min_brlen); assert(!(allNodes[i]->dlen < min_brlen)); } MakeAllNodesDirty(); lnL=-ONE_POINT_ZERO; } //this returns the average tree length for the whole dataset, and might need to be scaled for a given subset if SSR is being used FLOAT_TYPE Tree::Treelength(){ FLOAT_TYPE tot = 0.0; for(int i=1;idlen; } return tot; } int Tree::BrlenMutateSubset(vector const &subtreeMemberNodes){ int numBrlenMuts; do{ numBrlenMuts=rnd.random_binomial((int)subtreeMemberNodes.size(), meanBrlenMuts); }while(numBrlenMuts==0); for(int i=0;idlen*=rnd.gamma( Tree::alpha ); SweepDirtynessOverTree(allNodes[branch]); allNodes[branch]->dlen = (allNodes[branch]->dlen > min_brlen ? (allNodes[branch]->dlen < max_brlen ? allNodes[branch]->dlen : max_brlen) : min_brlen); } return numBrlenMuts; } void Tree::MakeTrifurcatingRoot(bool reducenodes, bool clasAssigned ){ //reducenodes should only =1 if this function is called after generating a random tree //or after reading in a tree with a bifurcating root. DO NOT call with reducenodes=1 if //this is being used after one of the initial root branches was pruned off //clasAssigned should be true if the clas have been assigned to the nodes by the claManager. //(ie, not right after tree creation) TreeNode *t1, *removedNode; vector rootDesc; assert(root->left->next==root->right); if(root->left->IsInternal()){ removedNode = root->left; root->right->dlen += removedNode->dlen; rootDesc.push_back(root->right); } else{ removedNode = root->right; root->left->dlen += removedNode->dlen; rootDesc.push_back(root->left); } if(clasAssigned){ removedNode->claIndexDown=claMan->SetDirty(removedNode->claIndexDown); removedNode->claIndexUL=claMan->SetDirty(removedNode->claIndexUL); removedNode->claIndexUR=claMan->SetDirty(removedNode->claIndexUR); } t1 = removedNode->left; while(t1){ rootDesc.push_back(t1); t1 = t1->next; } //now we have all of the new desc of the root //disconnect the old ones root->left = root->right = NULL; for(unsigned t=0;tAddDes(rootDesc[t]); /* if(root->left->IsInternal()){ removedNode=root->left; t1=root->left->left; t2=root->left->right; l=root->left->dlen; root->right->dlen+=l; root->left->attached=false; if(clasAssigned){ root->left->claIndexDown=claMan->SetDirty(root->left->claIndexDown); root->left->claIndexUL=claMan->SetDirty(root->left->claIndexUL); root->left->claIndexUR=claMan->SetDirty(root->left->claIndexUR); } root->left=t1; t1->next=t2; t2->prev=t1; t2->next=root->right; root->right->prev=t2; t1->anc=root; t2->anc=root; } else { removedNode=root->right; t1=root->right->left; t2=root->right->right; l=root->right->dlen; root->left->dlen+=l; root->right->attached=false; if(clasAssigned){ root->right->claIndexDown=claMan->SetDirty(root->right->claIndexDown); root->right->claIndexUL=claMan->SetDirty(root->right->claIndexUL); root->right->claIndexUR=claMan->SetDirty(root->right->claIndexUR); } root->left->next=t1; t1->prev=root->left; t1->next=t2; t2->prev=t1; t2->next=NULL; t1->anc=root; t2->anc=root; root->right=t2; } */ if(reducenodes==1){ //we need to permanently get rid of the node that was removed and decrement the nodeNums of those greater //than it. SortAllNodesArray(); EliminateNode(removedNode->nodeNum); numBranchesAdded--; numNodesAdded--; } } bool Tree::ArbitrarilyBifurcate(){ //note that this assumes that the root has been already been made into at least a trichotomy if(numNodesAdded == numNodesTotal) return false; //first figure out which internal nodenums haven't been used yet int placeInAllNodes=1; while(allNodes[placeInAllNodes]->attached == true) placeInAllNodes++; vector nodes; TreeNode *curNode = root; TreeNode *desNode; bool goingDown = false; bool polytomiesFound = false; while(numNodesAdded < numNodesTotal){ if(curNode->IsInternal() && !goingDown){ desNode = curNode->left; nodes.push_back(desNode); while(desNode->next){ desNode = desNode->next; nodes.push_back(desNode); } if((curNode != root && nodes.size() > 2) || (curNode == root && nodes.size() > 3)){ polytomiesFound = true; bipartCond = DIRTY; int first = rnd.random_int(nodes.size()); int second; do{ second = rnd.random_int(nodes.size()); }while(first == second); TreeNode *move1 = nodes[first]; TreeNode *move2 = nodes[second]; TreeNode *nextInternal = allNodes[placeInAllNodes]; curNode->RemoveDes(move1); curNode->RemoveDes(move2); nextInternal->AddDes(move1); nextInternal->AddDes(move2); curNode->AddDes(nextInternal); nextInternal->dlen=Tree::exp_starting_brlen; #ifdef STOCHASTIC_STARTING_BLENS nextInternal->dlen *= rnd.gamma(1.0); #endif placeInAllNodes++; numNodesAdded++; } else{ if(curNode->left && !goingDown){ curNode = curNode->left; } else if(curNode->next){ curNode = curNode->next; goingDown = false; } else{ curNode = curNode->anc; goingDown = true; } } } else{ if(curNode->next){ curNode = curNode->next; goingDown = false; } else{ curNode = curNode->anc; goingDown = true; } } nodes.clear(); } assert(numNodesAdded == numNodesTotal); return polytomiesFound; } void Tree::RandomlyAttachTip(int nodenum , int &placeInAllNodes){ assert(nodenum>0 && nodenum<=numTipsTotal); //should be adding a terminal TreeNode* nd=allNodes[nodenum]; nd->dlen = Tree::exp_starting_brlen; #ifdef STOCHASTIC_STARTING_BLENS nd->dlen *= rnd.gamma(1.0); #endif if(nd->dlen < min_brlen) nd->dlen = min_brlen; else if(nd->dlen > max_brlen) nd->dlen = max_brlen; nd->next=nd->prev=NULL;//in case this node was connected in some other tree //Make sure that the root has 3 decendents if(numBranchesAdded<3) {root->AddDes(nd); } else {// If we're not adding directly to the root node, then we will need // a connector node and make the new terminal its left des TreeNode* connector=allNodes[placeInAllNodes++]; numNodesAdded++; connector->dlen = Tree::exp_starting_brlen; #ifdef STOCHASTIC_STARTING_BLENS connector->dlen *= rnd.gamma(1.0); #endif nd->dlen = (nd->dlen > min_brlen ? nd->dlen : min_brlen); connector->left=connector->right=NULL; connector->AddDes(nd); //select a branch to break with the connector int k = rnd.random_int( numBranchesAdded ) + 1; TreeNode* otherDes = root->FindNode( k ); assert(otherDes); // replace puts connection in the tree where otherDes had been otherDes->SubstituteNodeWithRespectToAnc(connector); //add otherDes back to the tree as the sister to the new tip connector->AddDes(otherDes); numBranchesAdded++;//numBranchesAdded needs to be incremented twice because a total of two branches have been added } numBranchesAdded++; numNodesAdded++; numTipsAdded++; bipartCond = DIRTY; } void Tree::RandomlyAttachTipWithConstraints(int nodenum, int &placeInAllNodes, Bipartition *mask){ //the trick here with the constraints is that only a subset of the taxa will be in the //growing tree. To properly determine bipartition comptability a mask consisting of only //the present taxa will need to be used assert(nodenum>0 && nodenum<=numTipsTotal); //should be adding a terminal TreeNode* nd=allNodes[nodenum]; Bipartition temp; *mask += temp.TerminalBipart(nodenum); nd->dlen = Tree::exp_starting_brlen; #ifdef STOCHASTIC_STARTING_BLENS nd->dlen *= rnd.gamma(1.0); #endif if(nd->dlen < min_brlen) nd->dlen = min_brlen; else if(nd->dlen > max_brlen) nd->dlen = max_brlen; nd->next=nd->prev=NULL;//in case this node was connected in some other tree //Make sure that the root has 3 decendents if(numBranchesAdded<3) {root->AddDes(nd); } else {// If we're not adding directly to the root node, then we will need // a connector node and make the new terminal its left des TreeNode* connector=allNodes[placeInAllNodes++]; numNodesAdded++; connector->dlen = Tree::exp_starting_brlen; #ifdef STOCHASTIC_STARTING_BLENS connector->dlen *= rnd.gamma(1.0); #endif connector->dlen = (connector->dlen > min_brlen ? connector->dlen : min_brlen); connector->left=connector->right=NULL; connector->AddDes(nd); //select a branch to break with the connector int k; TreeNode *otherDes; bool compat; Bipartition proposed; nd->CalcBipartition(false); do{ k = rnd.random_int( numBranchesAdded ) + 1; otherDes = root->FindNode( k ); compat=true; CalcBipartitions(true); proposed.FillWithXORComplement(*(nd->bipart), *(otherDes->bipart)); //6/23/09 This call was moved here from within SwapAllowedByConstraint. This saves a lot //of work when looping over many constraints for a single swap that really only requires a single adjustment. //Doing the adjustment isn't necessary for positive non-backbone constraints with no mask (and isn't always //necessary when there is a mask either), but there will always be a mask here since we're building a partial tree. AdjustBipartsForSwap(nd->nodeNum, otherDes->nodeNum); for(vector::iterator conit=constraints.begin();conit!=constraints.end();conit++){ //if the taxon being added isn't in the backbone, it can go anywhere if(((*conit).IsBackbone() == false) || (*conit).GetBackboneMask()->ContainsTaxon(nd->nodeNum)){ ReconNode broken(otherDes->nodeNum, 0, 0.0, false); compat = SwapAllowedByConstraint((*conit), nd, &broken, proposed, mask); if(compat == false) break; } } }while(compat == false); // replace puts connection in the tree where otherDes had been otherDes->SubstituteNodeWithRespectToAnc(connector); //add otherDes back to the tree as the sister to the new tip connector->AddDes(otherDes); numBranchesAdded++;//numBranchesAdded needs to be incremented twice because a total of two branches have been added bipartCond = DIRTY; } numBranchesAdded++; numNodesAdded++; numTipsAdded++; } void Tree::MimicTopologyButNotInternNodeNums(TreeNode *copySource,TreeNode *replicate,int &placeInAllNodes){ //used in recombine so internal node nodeNums don't have to match TreeNode *tempno=copySource->left; assert(copySource->left); while(tempno) {if(tempno->left) {//tempno isn't a terminal placeInAllNodes=FindUnusedNode(placeInAllNodes); allNodes[placeInAllNodes]->dlen=tempno->dlen; // allNodes[placeInAllNodes]->CopyOneClaIndex(copySource, claMan); MimicTopologyButNotInternNodeNums(tempno,replicate->AddDes(allNodes[placeInAllNodes]),placeInAllNodes); } else {allNodes[tempno->nodeNum]->dlen=tempno->dlen; replicate->AddDes(allNodes[tempno->nodeNum]); } tempno=tempno->next; } } void Tree::RecombineWith( Tree *t, bool sameModel, FLOAT_TYPE optPrecision ){ //note that this function will loop infinately right now if the tree is too small //(ie, there are no suitable nodes to choose to recombine with) //mark all of the tags as present in this; for(int i=1;i<=numTipsTotal;i++) taxtags[i]=0; // Pick a random internal node that is the source of the subtree that will be copied into both trees int k; TreeNode* cop; bool sfound=false; while(!sfound){//find a non trivial clade to add to this //k = rnd.random_int( t->numBranchesAdded-1); //cop = t->root->FindNode( ++k); //don't bother picking terminal nodes k=t->GetRandomInternalNode(); cop=t->allNodes[k]; if(cop->left->left || cop->right->left){ // cop isn't a two node sub tree if(cop->anc) //check to make sure there are at least 2 nodes "below"cop on the source tree {if(cop->anc->anc) sfound=true; else {if(t->root->left!=cop) {if(t->root->left->left) sfound=true; } if(!sfound && t->root->left->next!=cop) {if(t->root->left->next->left) sfound=true; } if(!sfound && t->root->right!=cop) {if(t->root->right->left) sfound=true; } } } } } //Prune terminals off of this to prepare for attachement of a copy of cop cop->left->MarkTerminals(taxtags); for(int i=1;i<=numTipsTotal;i++){ if(taxtags[i]){ //before removing the tip, trace dirtyness from its anc to the root //make sure to set any TraceDirtynessToRoot(allNodes[i]->anc); // TraceDirtynessToRoot(allNodes[i]); allNodes[i]->Prune(); if(root->left->next==root->right) MakeTrifurcatingRoot(false, true); } } int numAttachedToRoot=root->CountBranches(0); //what we'd like to do now is make the nodeNums of the subtree that will be attached to this //the same as they were in the source tree. This will require swapping some nodes in the allNodes array, //but will simplify other things, and allow us not to recalc some clas. This is a bit dangerous though, as //the nodeNums in this that correspond to those in the cop subtree are now technically free, but are still //marked as attached. There should still be one node in this marked as unattached that will be used for //the connector SwapAndFreeNodes(cop); // Pick a random node whose branch we will bisected by the new subtree. int n = rnd.random_int( numAttachedToRoot ); TreeNode* broken = root->FindNode( ++n ); assert(broken->anc);//broken can't be the root; //DZ 7-6 rewritting this so that broken keeps it's original dlen and connector has a new one //generated. Exactly how this would be best done is not clear. For now picking uniform[0.05,0.2] TreeNode *connector; int nextUnconnectedNode=FindUnusedNode(numTipsTotal+1); connector=allNodes[nextUnconnectedNode]; connector->left=connector->right=NULL; broken->SubstituteNodeWithRespectToAnc(connector); connector->AddDes(broken); connector->AddDes(allNodes[cop->nodeNum]); MimicTopo(cop, 1, sameModel); //place connector midway along the broken branch connector->dlen=broken->dlen*ZERO_POINT_FIVE; broken->dlen-=connector->dlen; TraceDirtynessToRoot(connector); OptimizeBranchesAroundNode(connector, optPrecision, 0); } TreeNode *Tree::ContainsBipartition(const Bipartition &bip){ //note that this doesn't work for terminals (but there's no reason to call for them anyway) //find a taxon that appears "on" in the bipartition //turning this back on int tax=bip.FirstPresentTaxon(); //now start moving down the tree from taxon 1 until a bipart that //conflicts or a match is found //TreeNode *nd=allNodes[1]->anc; TreeNode *nd=allNodes[tax]->anc; while(nd->anc){ if(nd->bipart->IsASubsetOf(bip) == false) return NULL; else if(nd->bipart->EqualsEquals(bip)) return nd; else nd=nd->anc; } return NULL; } TreeNode *Tree::ContainsBipartitionOrComplement(const Bipartition &bip){ //this version will detect if the same bipartition exists in the trees, even //if it is in different orientation, which could happen due to rooting //differences //NOTE: This requires that the bipartitions are "standardized" meaning that //the one bit is always "on". In general in other places we do not need that //to be the case if(bipartCond != CLEAN_STANDARDIZED){ if(bipartCond == CLEAN_UNSTANDARDIZED) root->StandardizeBipartition(); else CalcBipartitions(true); } //find a taxon that appears "on" in the bipartition int tax=bip.FirstPresentTaxon(); //now start moving down the tree from that taxon until a bipart that //conflicts or a match is found //7/17/07 changing this to start from the trivial terminal branch, rather //then its anc TreeNode *nd=allNodes[tax]; while(nd->anc){ if(nd->bipart->IsASubsetOf(bip) == false) break; else if(nd->bipart->EqualsEquals(bip)) return nd; else nd=nd->anc; } //find a taxon that is NOT "on" in the bipartition tax=bip.FirstNonPresentTaxon(); //now start moving down the tree from that taxon until a bipart that //conflicts or a match is found //7/17/07 changing this to start from the trivial terminal branch, rather //then its anc nd=allNodes[tax]; while(nd->anc){ //if(nd->bipart->ComplementIsASubsetOf(bip) == false){ if(bip.IsASubsetOf(*nd->bipart) == false){ return NULL; } else if(nd->bipart->EqualsEquals(bip)) return nd; else nd=nd->anc; } return NULL; } TreeNode *Tree::ContainsMaskedBipartitionOrComplement(const Bipartition &bip, const Bipartition &mask){ //as in ContainsMaskedBipartitionOrComplement, but bits not on in the //mask are ignored //NOTE: This requires that the bipartitions are "standardized" meaning that //the one bit is always "on". In general in other places we do not want that //to be the case if(bipartCond != CLEAN_STANDARDIZED){ if(bipartCond == CLEAN_UNSTANDARDIZED) root->StandardizeBipartition(); else CalcBipartitions(true); } //find a taxon that appears "on" in the bipartition and is on in the mask Bipartition temp = bip; temp.AndEquals(mask); int tax=temp.FirstPresentTaxon(); //now start moving down the tree from that taxon until we find a //match or reach the root TreeNode *nd=allNodes[tax]->anc; temp = bip; temp.Complement(); while(nd->anc){ if(nd->bipart->MaskedEqualsEquals(bip, mask)) return nd; if(nd->bipart->MaskedEqualsEquals(temp, mask)) return nd; else nd=nd->anc; } //find a taxon that is NOT "on" in the bipartition temp = bip; temp.Complement(); temp.AndEquals(mask); tax=temp.FirstPresentTaxon(); //now start moving down the tree from that taxon until we find a //match or reach the root nd=allNodes[tax]->anc; temp = bip; temp.Complement(); while(nd->anc){ if(nd->bipart->MaskedEqualsEquals(bip, mask)) return nd; if(nd->bipart->MaskedEqualsEquals(temp, mask)) return nd; else nd=nd->anc; } return NULL; } int Tree::SubtreeBasedRecombination( Tree *t, int recomNodeNum, bool sameModel, FLOAT_TYPE optPrecision){ //this will work more or less like the normal bipartition based recombination, except //that the node at which the recombination will occur will be passed in from the population //which knows what subtree each remote is working on //we are assuming that the recomNodeNum represents the same bipartition (subtree) in each tree TreeNode *tonode=allNodes[recomNodeNum]; TreeNode *fromnode=t->allNodes[recomNodeNum]; tonode->MarkUnattached(true); SwapAndFreeNodes(fromnode); //manually set up the base of the subtree in the totree and point tonode to it TreeNode *tempanc=tonode->anc; TreeNode *tempnext=tonode->next; TreeNode *tempprev=tonode->prev; if(tempanc->left==tonode){ tempanc->left=allNodes[fromnode->nodeNum]; tonode=tempanc->left; } else if(tempanc->right==tonode){ tempanc->right=allNodes[fromnode->nodeNum]; tonode=tempanc->right; } else{ tempanc->left->next=allNodes[fromnode->nodeNum]; tonode=tempanc->left->next; } tonode->anc=tempanc; tonode->next=tempnext; tonode->prev=tempprev; if(tempnext) tempnext->prev=tonode; if(tempprev) tempprev->next=tonode; MimicTopo(fromnode, 1, sameModel); if(sameModel==true) CopyClaIndecesInSubtree(fromnode, true); else DirtyNodesInSubtree(tonode); SweepDirtynessOverTree(tonode); //try branch length optimization of tonode's branch, to make sure it fits in it's new tree background OptimizeBranchLength(optPrecision, tonode, true); return 1; } bool Tree::IdenticalSubtreeTopology(const TreeNode *other){ //This should not be called with the root, and only detects identical subtrees //in the same orientation (ie rooting can fool it) assert(other->IsNotRoot()); bool identical; if(other->IsRoot() == false){ if(other->IsTerminal()) return true; identical=(ContainsBipartition(*other->bipart) != NULL); if(identical==true){ identical=IdenticalSubtreeTopology(other->left); if(identical==true) identical=IdenticalSubtreeTopology(other->right); } } return identical; } bool Tree::IdenticalTopology(const TreeNode *other){ //this is intitially called with the root, it will detect any difference in the //overall topology, but assumes the same rooting bool identical; //NOTE: This requires that the bipartitions are "standardized" meaning that //the one bit is always "on". In general in other places we do not need that //to be the case if(bipartCond != CLEAN_STANDARDIZED){ if(bipartCond == CLEAN_UNSTANDARDIZED) root->StandardizeBipartition(); else CalcBipartitions(true); } if(other->IsRoot() == false){ if(other->IsTerminal()) return true; identical= (ContainsBipartition(*other->bipart) != NULL); if(identical==true){ identical=IdenticalTopology(other->left); if(identical==true) identical=IdenticalTopology(other->right); } } else{ TreeNode *nd=other->left; while(nd != NULL){ identical=IdenticalTopology(nd); if(identical == false){ return identical; } nd=nd->next; } } return identical; } //this is the corrected version from the trunk that accurately detects identical trees //with collapsed branches bool Tree::IdenticalTopologyAllowingRerooting(const TreeNode *other){ //this is intitially called with the root, it will detect any difference in the //overall topology bool identical = true; //NOTE: This requires that the bipartitions are "standardized" meaning that //the one bit is always "on". In general in other places we do not need that //to be the case if(bipartCond != CLEAN_STANDARDIZED){ if(bipartCond == CLEAN_UNSTANDARDIZED) root->StandardizeBipartition(); else CalcBipartitions(true); } if(other->IsTerminal()) return true; if(other->IsRoot() == false) identical = (ContainsBipartitionOrComplement(*other->bipart) != NULL); TreeNode *nd=other->left; while(identical && nd != NULL){ identical = IdenticalTopologyAllowingRerooting(nd); if(identical == false) break; nd=nd->next; } return identical; /* if(other->IsRoot() == false){ if(other->IsTerminal()) return true; identical= (ContainsBipartitionOrComplement(*other->bipart) != NULL); if(identical==true){ identical=IdenticalTopologyAllowingRerooting(other->left); if(identical==true) identical=IdenticalTopologyAllowingRerooting(other->right); } } else{ TreeNode *nd=other->left; while(nd != NULL){ identical=IdenticalTopologyAllowingRerooting(nd); if(identical == false){ return identical; } nd=nd->next; } } return identical; */ } int Tree::BipartitionBasedRecombination( Tree *t, bool sameModel, FLOAT_TYPE optPrecision){ //find a bipartition that is shared between the trees TreeNode *tonode, *fromnode; bool found=false; int tries=0; CalcBipartitions(true); t->CalcBipartitions(true); while(!found && (++tries<50)){ int i; do{ i=GetRandomInternalNode(); //WTF!!! How did this work? }while((allNodes[i]->left->IsTerminal() && allNodes[i]->right->IsTerminal())); //}while((t->allNodes[i]->left->IsTerminal() && t->allNodes[i]->right->IsTerminal())); //fromnode=t->ContainsBipartition(allNodes[i]->bipart); //fromnode=t->ContainsBipartition(*allNodes[i]->bipart); fromnode=t->ContainsBipartitionOrComplement(*allNodes[i]->bipart); if(fromnode != NULL){ //OK the biparts match, but see if they share the same clas!!!! //Not much point in scoring them then. tonode=allNodes[i]; if(!((tonode->nodeNum == fromnode->nodeNum) && (tonode->claIndexDown == fromnode->claIndexDown))){ if(IdenticalSubtreeTopology(fromnode->left)==false) found=true; if(found==false) if(IdenticalSubtreeTopology(fromnode->right)==false) found=true; } } } //sum the two subtrees as if they were the root to see which is better in score /* if(found==true){ FLOAT_TYPE toscore, fromscore; toscore=SubTreeScore(tonode); fromscore=t->SubTreeScore(fromnode); if(fromscore > (toscore + .1)){ found=true; break; } else found=false; } */ if(found==true){ tonode->MarkUnattached(true); SwapAndFreeNodes(fromnode); //manually set up the base of the subtree in the totree and point tonode to it TreeNode *tempanc=tonode->anc; TreeNode *tempnext=tonode->next; TreeNode *tempprev=tonode->prev; if(tempanc->left==tonode){ tempanc->left=allNodes[fromnode->nodeNum]; tonode=tempanc->left; } else if(tempanc->right==tonode){ tempanc->right=allNodes[fromnode->nodeNum]; tonode=tempanc->right; } else{ tempanc->left->next=allNodes[fromnode->nodeNum]; tonode=tempanc->left->next; } tonode->anc=tempanc; tonode->next=tempnext; tonode->prev=tempprev; if(tempnext) tempnext->prev=tonode; if(tempprev) tempprev->next=tonode; MimicTopo(fromnode, 1, sameModel); if(sameModel==true) CopyClaIndecesInSubtree(fromnode, true); else DirtyNodesInSubtree(tonode); //try branch length optimization of tonode's branch, to make sure it fits in it's new tree background SweepDirtynessOverTree(tonode); //OptimizeBranchLength(optPrecision, tonode, true); OptimizeBranchesWithinRadius(tonode, optPrecision, 0, NULL); Score(tonode->nodeNum); bipartCond = DIRTY; } else return -1; return 1; } //this is essentially a version of TopologyMutator that goes through cut nodes in order //and for each cut node goes through the broken nodes in order. The swaps are performed //on a temporary tree void Tree::DeterministicSwapperByCut(Individual *source, double optPrecision, int range, bool furthestFirst){ TreeNode *cut; int swapNum=0; Individual tempIndiv; tempIndiv.treeStruct=new Tree(); tempIndiv.CopySecByRearrangingNodesOfFirst(tempIndiv.treeStruct, source); //ensure that the starting tree is optimal up to the required precision FLOAT_TYPE imp = 999.9; do{ imp = tempIndiv.treeStruct->OptimizeAllBranches(optPrecision); }while(imp > 0.0); outman.UserMessage("starting score:%f", tempIndiv.treeStruct->lnL); char str[50]; if(furthestFirst) sprintf(str, "determImpsCutR.%d.%f.tre", range, optPrecision); else sprintf(str, "determImpsCut.%d.%f.tre", range, optPrecision); ofstream better(str); better.precision(9); dataPart->BeginNexusTreesBlock(better); #ifdef OUTPUT_ALL if(furthestFirst) sprintf(str, "determAllCutR.%d.%f.tre", range, optPrecision); else sprintf(str, "determAllCut.%d.%f.tre", range, optPrecision); ofstream all(str); dataPart->BeginNexusTreesBlock(all); #endif if(furthestFirst) sprintf(str, "determCutR%d.%f.log", range, optPrecision); else sprintf(str, "determCut%d.%f.log", range, optPrecision); FILE *log = fopen(str, "w"); //allocate a treeString double taxsize=log10((double) ((double)dataPart->NTax())*dataPart->NTax()*2); int stringSize=(int)((dataPart->NTax()*2)*(10+DEF_PRECISION)); char *treeString=new char[stringSize]; stringSize--; treeString[stringSize]='\0'; bool newBest=false; attemptedSwaps.ClearAttemptedSwaps(); int startC, c=1; int acceptedSwaps = 0; startC = c; while(1){ cut=tempIndiv.treeStruct->allNodes[c]; tempIndiv.treeStruct->GatherValidReconnectionNodes(range, cut, NULL); tempIndiv.treeStruct->sprRang.SortByDist(); if(furthestFirst) tempIndiv.treeStruct->sprRang.Reverse(); for(list::iterator b = tempIndiv.treeStruct->sprRang.begin();b != tempIndiv.treeStruct->sprRang.end();b++){ ReconNode *broken = &(*b); //log the swap about to be performed. Although this func goes through the swaps in order, //there will be duplication because of the way that NNIs are performed. Two different cut //nodes can be reconnected with an NNI such that the same topology results bool unique=false; Bipartition proposed; CalcBipartitions(true); proposed.FillWithXORComplement(*cut->bipart, *tempIndiv.treeStruct->allNodes[broken->nodeNum]->bipart); unique = attemptedSwaps.AddSwap(proposed, cut->nodeNum, broken->nodeNum, broken->reconDist); if(unique){ swapNum++; if(swapNum %100 == 0) fprintf(log, "%d\t%d\t%f\n", swapNum, acceptedSwaps, lnL); if(broken->withinCutSubtree == true){ tempIndiv.treeStruct->ReorientSubtreeSPRMutate(cut->nodeNum, broken, optPrecision); } else{ tempIndiv.treeStruct->SPRMutate(cut->nodeNum, broken, optPrecision, 0); } #ifdef OUTPUT_ALL tempIndiv.treeStruct->root->MakeNewick(treeString, false, true); all << "tree " << c << "." << b->reconDist << "= [&U][" << lnL << "]" << treeString << ";" << endl; #endif if(tempIndiv.treeStruct->lnL > (lnL+optPrecision)){ outman.UserMessage("%f\t%f\t%d\t%d", tempIndiv.treeStruct->lnL, lnL - tempIndiv.treeStruct->lnL, c, b->reconDist); source->CopySecByRearrangingNodesOfFirst(source->treeStruct, &tempIndiv, true); lnL = tempIndiv.treeStruct->lnL; tempIndiv.treeStruct->root->MakeNewick(treeString, false, true); better << "tree " << c << "." << b->reconDist << "= [&U][" << lnL << "]" << treeString << ";" << endl; newBest = true; acceptedSwaps++; attemptedSwaps.ClearAttemptedSwaps(); break; } else{ tempIndiv.CopySecByRearrangingNodesOfFirst(tempIndiv.treeStruct, source, true); } } } c++; if(c == numNodesTotal) c = 1; if(newBest == true){ startC = c; newBest = false; } else if(c == startC){ outman.UserMessage("done. %d swaps, %d accepted", swapNum, acceptedSwaps); break; } } better << "end;"; better.close(); delete []treeString; fclose(log); tempIndiv.treeStruct->RemoveTreeFromAllClas(); delete tempIndiv.treeStruct; tempIndiv.treeStruct=NULL; } //this is essentially a version of TopologyMutator that goes through cut nodes in order //and for each cut node goes through the broken nodes in order. It the swaps are performed //on a temporary tree void Tree::DeterministicSwapperByDist(Individual *source, double optPrecision, int range, bool furthestFirst){ TreeNode *cut; int swapNum=0; Individual tempIndiv; tempIndiv.treeStruct=new Tree(); tempIndiv.CopySecByRearrangingNodesOfFirst(tempIndiv.treeStruct, source); //ensure that the starting tree is optimal up to the required precision FLOAT_TYPE imp = 999.9; do{ imp = tempIndiv.treeStruct->OptimizeAllBranches(optPrecision); }while(imp > 0.0); outman.UserMessage("starting score:%f", tempIndiv.treeStruct->lnL); char str[50]; if(furthestFirst) sprintf(str, "determImpsDistR.%d.%f.tre", range, optPrecision); else sprintf(str, "determImpsDist.%d.%f.tre", range, optPrecision); ofstream better(str); better.precision(9); dataPart->BeginNexusTreesBlock(better); #ifdef OUTPUT_ALL if(furthestFirst) sprintf(str, "determAllDistR.%d.%f.tre", range, optPrecision); else sprintf(str, "determAllDist.%d.%f.tre", range, optPrecision); ofstream all(str); dataPart->BeginNexusTreesBlock(all); #endif if(furthestFirst) sprintf(str, "determDistR%d.%f.log", range, optPrecision); else sprintf(str, "determDist%d.%f.log", range, optPrecision); FILE *log = fopen(str, "w"); //allocate a treeString double taxsize=log10((double) ((double)dataPart->NTax())*dataPart->NTax()*2); int stringSize=(int)((dataPart->NTax()*2)*(10+DEF_PRECISION)); char *treeString=new char[stringSize]; stringSize--; treeString[stringSize]='\0'; bool newBest=false; attemptedSwaps.ClearAttemptedSwaps(); int startC, c=1; int currentDist; if(furthestFirst) currentDist = range; else currentDist = 1; int acceptedSwaps = 0; startC = c; do{ cut=allNodes[c]; //outman.UserMessageNoCR("cut=%d ", c); GatherValidReconnectionNodes(range, cut, NULL); sprRang.SortByDist(); for(list::iterator b = sprRang.GetFirstNodeAtDist(currentDist);b != sprRang.end() && b->reconDist == currentDist;b++){ ReconNode *broken = &(*b); //log the swap about to be performed. Although this func goes through the swaps in order, //there will be duplication because of the way that NNIs are performed. Two different cut //nodes can be reconnected with an NNI such that the same topology results bool unique=false; Bipartition proposed; CalcBipartitions(true); proposed.FillWithXORComplement(*cut->bipart, *allNodes[broken->nodeNum]->bipart); unique = attemptedSwaps.AddSwap(proposed, cut->nodeNum, broken->nodeNum, broken->reconDist); if(unique){ swapNum++; if(broken->withinCutSubtree == true){ tempIndiv.treeStruct->ReorientSubtreeSPRMutate(cut->nodeNum, broken, optPrecision); } else{ tempIndiv.treeStruct->SPRMutate(cut->nodeNum, broken, optPrecision, 0); } #ifdef OUTPUT_ALL tempIndiv.treeStruct->root->MakeNewick(treeString, false, true); all << "tree " << c << "." << b->reconDist << "= [&U][" << lnL << "]" << treeString << ";" << endl; #endif if(tempIndiv.treeStruct->lnL > (lnL+optPrecision)){ outman.UserMessage("%f\t%f\t%d\t%d", tempIndiv.treeStruct->lnL, lnL - tempIndiv.treeStruct->lnL, c, b->reconDist); source->CopySecByRearrangingNodesOfFirst(source->treeStruct, &tempIndiv, true); lnL = tempIndiv.treeStruct->lnL; tempIndiv.treeStruct->root->MakeNewick(treeString, false, true); better << "tree " << c << "." << b->reconDist << "= [&U][" << lnL << "]" << treeString << ";" << endl; newBest = true; acceptedSwaps++; attemptedSwaps.ClearAttemptedSwaps(); break; } else{ tempIndiv.CopySecByRearrangingNodesOfFirst(tempIndiv.treeStruct, source, true); } if(swapNum %100 == 0) fprintf(log, "%d\t%d\t%f\n", swapNum, acceptedSwaps, lnL); } } c++; if(c == numNodesTotal) c = 1; if(newBest == true){ startC = c; if(furthestFirst) currentDist = range; else currentDist = 1; newBest = false; } else if(c == startC){ if(furthestFirst) currentDist--; else currentDist++; outman.UserMessage("dist = %d", currentDist); } }while(currentDist <= range && currentDist > 0); outman.UserMessage("done. %d swaps, %d accepted", swapNum, acceptedSwaps); better << "end;"; better.close(); delete []treeString; fclose(log); tempIndiv.treeStruct->RemoveTreeFromAllClas(); delete tempIndiv.treeStruct; tempIndiv.treeStruct=NULL; } void Tree::FillAllSwapsList(ReconList *cuts, int reconLim){ CalcBipartitions(true); for(int i=1;iOptimizeAllBranches(optPrecision); }while(imp > 0.0); outman.UserMessage("starting score:%f", tempIndiv.treeStruct->lnL); char str[50]; sprintf(str, "determImpsRand.%d.%f.tre", range, optPrecision); ofstream better(str); better.precision(9); dataPart->BeginNexusTreesBlock(better); #ifdef OUTPUT_ALL sprintf(str, "determAllRand.%d.%f.tre", range, optPrecision); ofstream all(str); dataPart->BeginNexusTreesBlock(all); #endif sprintf(str, "determRand%d.%f.log", range, optPrecision); FILE *log = fopen(str, "w"); //allocate a treeString double taxsize=log10((double) ((double)dataPart->NTax())*dataPart->NTax()*2); int stringSize=(int)((dataPart->NTax()*2)*(10+DEF_PRECISION)); char *treeString=new char[stringSize]; stringSize--; treeString[stringSize]='\0'; bool newBest=false; attemptedSwaps.ClearAttemptedSwaps(); int c=1; //zeroth element won't be used, for clarity of indexing vector cuts(numNodesTotal+1); vector cutWeights(numNodesTotal+1); tempIndiv.treeStruct->FillAllSwapsList(&cuts[0], range); unsigned swapsLeft = tempIndiv.treeStruct->FillWeightsForAllSwaps(&cuts[0], &cutWeights[0]); int acceptedSwaps = 0; int swapsOnCurrent=0; do{ double r = rnd.uniform(); c = 1; while(cutWeights[c] < r) c++; cut = tempIndiv.treeStruct->allNodes[c]; listIt b = cuts[c].NthElement(rnd.random_int(cuts[c].size())); ReconNode *broken = &(*b); //log the swap about to be performed. Although this func goes through the swaps in order, //there will be duplication because of the way that NNIs are performed. Two different cut //nodes can be reconnected with an NNI such that the same topology results bool unique=false; newBest = false; Bipartition proposed; CalcBipartitions(true); proposed.FillWithXORComplement(*(cut->bipart), *(tempIndiv.treeStruct->allNodes[broken->nodeNum]->bipart)); unique = attemptedSwaps.AddSwap(proposed, cut->nodeNum, broken->nodeNum, broken->reconDist); if(unique){ swapNum++; swapsOnCurrent++; if(broken->withinCutSubtree == true){ tempIndiv.treeStruct->ReorientSubtreeSPRMutate(cut->nodeNum, broken, optPrecision); } else{ tempIndiv.treeStruct->SPRMutate(cut->nodeNum, broken, optPrecision, 0); } #ifdef OUTPUT_ALL tempIndiv.treeStruct->root->MakeNewick(treeString, false, true); all << "tree " << c << "." << b->nodeNum << "." << b->reconDist << "." << swapsOnCurrent << " = [&U][" << lnL << "]" << treeString << ";" << endl; #endif if(tempIndiv.treeStruct->lnL > (lnL+optPrecision)){ outman.UserMessage("%f\t%f\t%d\t%d", tempIndiv.treeStruct->lnL, lnL - tempIndiv.treeStruct->lnL, c, b->reconDist); source->CopySecByRearrangingNodesOfFirst(source->treeStruct, &tempIndiv, true); lnL = tempIndiv.treeStruct->lnL; tempIndiv.treeStruct->root->MakeNewick(treeString, false, true); better << "tree " << c << "." << b->nodeNum << "." << b->reconDist << "= [&U][" << lnL << "]" << treeString << ";" << endl; newBest = true; acceptedSwaps++; outman.UserMessage("%d swaps before reset", swapsOnCurrent); swapsOnCurrent = 0; attemptedSwaps.ClearAttemptedSwaps(); tempIndiv.treeStruct->FillAllSwapsList(&cuts[0], range); } else{ tempIndiv.CopySecByRearrangingNodesOfFirst(tempIndiv.treeStruct, source, true); } } else{ if(broken->reconDist != 1) throw ErrorException("nonunique swap > NNI found! %d %d %d", c, b->nodeNum, b->reconDist); } if(newBest == false)//if the swap either wasn't better or wasn't unique cuts[c].RemoveElement(b); if(swapNum %100 == 0) fprintf(log, "%d\t%d\t%f\n", swapNum, acceptedSwaps, lnL); swapsLeft = tempIndiv.treeStruct->FillWeightsForAllSwaps(&cuts[0], &cutWeights[0]); }while(swapsLeft); outman.UserMessage("%d swaps before completion", swapsOnCurrent); /* while(1){ int attempts = 0; do{ c = GetRandomNonRootNode(); if(attempts++ > numNodesTotal){ int n=1; while(completed[n] && n < numNodesTotal) n++; if(n == numNodesTotal){ outman.UserMessage("done. %d swaps, %d accepted", swapNum, acceptedSwaps); better << "end;"; better.close(); delete []treeString; fclose(log); tempIndiv.treeStruct->RemoveTreeFromAllClas(); delete tempIndiv.treeStruct; tempIndiv.treeStruct=NULL; return; } } }while(completed[c]); cut=allNodes[c]; //outman.UserMessageNoCR("cut=%d ", c); GatherValidReconnectionNodes(range, cut, NULL); //for(list::iterator b = sprRang.GetFirstNodeAtDist(currentDist);b != sprRang.end() && b->reconDist == currentDist;b++){ bool noSwapFound = true; listIt b; while(sprRang.size() > 0){ b = sprRang.NthElement(rnd.random_int(sprRang.size())); ReconNode *broken = &(*b); //log the swap about to be performed. Although this func goes through the swaps in order, //there will be duplication because of the way that NNIs are performed. Two different cut //nodes can be reconnected with an NNI such that the same topology results bool unique=false; Bipartition proposed; CalcBipartitions(true); proposed.FillWithXORComplement(cut->bipart, allNodes[broken->nodeNum]->bipart); unique = attemptedSwaps.AddSwap(proposed, cut->nodeNum, broken->nodeNum, broken->reconDist); if(unique){ swapNum++; if(broken->withinCutSubtree == true){ tempIndiv.treeStruct->ReorientSubtreeSPRMutate(cut->nodeNum, broken, optPrecision); } else{ tempIndiv.treeStruct->SPRMutate(cut->nodeNum, broken, optPrecision, 0); } #ifdef OUTPUT_ALL tempIndiv.treeStruct->root->MakeNewick(treeString, false, true); all << "tree " << c << "." << b->reconDist << "= [&U][" << lnL << "]" << treeString << ";" << endl; #endif if(tempIndiv.treeStruct->lnL > (lnL+optPrecision)){ outman.UserMessage("%f\t%f\t%d\t%d", tempIndiv.treeStruct->lnL, lnL - tempIndiv.treeStruct->lnL, c, b->reconDist); source->CopySecByRearrangingNodesOfFirst(source->treeStruct, &tempIndiv, true); lnL = tempIndiv.treeStruct->lnL; tempIndiv.treeStruct->root->MakeNewick(treeString, false, true); better << "tree " << c << "." << b->reconDist << "= [&U][" << lnL << "]" << treeString << ";" << endl; newBest = true; acceptedSwaps++; attemptedSwaps.ClearAttemptedSwaps(); for(int i=0;iOptimizeAllBranches(optPrecision); }while(imp > 0.0); outman.UserMessage("starting score:%f", tempIndiv.treeStruct->lnL); char str[50]; sprintf(str, "allswaps.SPR%d.tre", range); ofstream all(str); dataPart->BeginNexusTreesBlock(all); sprintf(str, "allswaps.SPR%d.log", range); FILE *log = fopen(str, "w"); //allocate a treeString double taxsize=log10((double) ((double)dataPart->NTax())*dataPart->NTax()*2); int stringSize=(int)((dataPart->NTax()*2)*(10+DEF_PRECISION)); char *treeString=new char[stringSize]; stringSize--; treeString[stringSize]='\0'; //bool newBest=false; int acceptedSwaps = 0; tempIndiv.treeStruct->root->MakeNewick(treeString, false, true); all << "tree start = [&U][" << lnL << "]" << treeString << ";" << endl; for(int cutnum=1;cutnumallNodes[cutnum]; tempIndiv.treeStruct->CalcBipartitions(true); tempIndiv.treeStruct->GatherValidReconnectionNodes(range, cut, NULL); //tempIndiv.treeStruct->FillAllSwapsList(range); ReconList *cutSwapList = &tempIndiv.treeStruct->sprRang; for(listIt b=cutSwapList->begin();b!=cutSwapList->end();b++){ //listIt b = cut.NthElement(rnd.random_int(cuts[c].size())); ReconNode *broken = &(*b); //log the swap about to be performed. Although this func goes through the swaps in order, //there will be duplication because of the way that NNIs are performed. Two different cut //nodes can be reconnected with an NNI such that the same topology results bool unique=false; Bipartition proposed; CalcBipartitions(true); proposed.FillWithXORComplement(*(cut->bipart), *(tempIndiv.treeStruct->allNodes[broken->nodeNum]->bipart)); unique = attemptedSwaps.AddSwap(proposed, cut->nodeNum, broken->nodeNum, broken->reconDist); if(unique){ swapNum++; swapsOnCurrent++; if(broken->withinCutSubtree == true){ tempIndiv.treeStruct->ReorientSubtreeSPRMutate(cut->nodeNum, broken, optPrecision); } else{ tempIndiv.treeStruct->SPRMutate(cut->nodeNum, broken, optPrecision, 0); } tempIndiv.treeStruct->root->MakeNewick(treeString, false, true); all << "tree " << cutnum << "." << b->nodeNum << "." << b->reconDist << "." << swapsOnCurrent << " = [&U][" << lnL << "]" << treeString << ";" << endl; tempIndiv.CopySecByRearrangingNodesOfFirst(tempIndiv.treeStruct, source, true); } else{ if(broken->reconDist != 1) throw ErrorException("nonunique swap > NNI found! %d %d %d", cutnum, b->nodeNum, b->reconDist); } //if(swapNum %100 == 0) fprintf(log, "%d\t%d\t%f\n", swapNum, acceptedSwaps, lnL); } } //outman.UserMessage("%d swaps before completion", swapsOnCurrent); all << "end;" << endl; } //this function now returns the reconnection distance, with it being negative if its a //subtree reorientation swap int Tree::TopologyMutator(FLOAT_TYPE optPrecision, int range, int subtreeNode){ //All topology mutations go through here now. Range will be 1 in the case of NNI's //Range will be some small number in the case of limSPR's and will be 999999 in the case //of random SPR's TreeNode *cut; ReconNode *broken; bool unique; #ifdef EQUIV_CALCS dirtyEQ = true; #endif int err=0; int ret=0; int tryNum = 0; do{ do{ cut=allNodes[GetRandomNonRootNode()]; GatherValidReconnectionNodes(range, cut, NULL); }while(sprRang.size()==0); if((FloatingPointEquals(uniqueSwapBias, 1.0, max(1.0e-8, GARLI_FP_EPS * 2.0)) && FloatingPointEquals(distanceSwapBias, 1.0, max(1.0e-8, GARLI_FP_EPS * 2))) || range < 0) broken = sprRang.RandomReconNode(); else{//only doing this on limSPR and NNI err = AssignWeightsToSwaps(cut); err = err && (tryNum++ < 5); if((!swapBasedTerm) || (swapBasedTerm && !err)){ //this was a stupid bug. Err was being paid attention by looping over the //outer do loop because it was not being reset below when returning from ReorientSubtreeSPR //as it is with normal SPR if(!swapBasedTerm) err = 0; sprRang.CalcProbsFromWeights(); broken = sprRang.ChooseNodeByWeight(); } } if((!swapBasedTerm) || (swapBasedTerm && !err)){ //log the swap about to be performed if( ! ((uniqueSwapBias == 1.0 && distanceSwapBias == 1.0) || range < 0)){ Bipartition proposed; CalcBipartitions(true); proposed.FillWithXORComplement(*(cut->bipart), *(allNodes[broken->nodeNum]->bipart)); unique = attemptedSwaps.AddSwap(proposed, cut->nodeNum, broken->nodeNum, broken->reconDist); uniqueSwapTried = uniqueSwapTried || unique; //uniqueSwapTried = uniqueSwapTried || attemptedSwaps.AddSwap(proposed, cut->nodeNum, broken->nodeNum, broken->reconDist); } //else if(! ((uniqueSwapBias == 1.0 && distanceSwapBias == 1.0) && range < 0)){ else{ //this means that we are doing an unlimited SPR, which we don't keep track of unique = false; } if(broken->withinCutSubtree == true){ #ifdef OPT_DEBUG optsum << "reorientSPR\t" << broken->reconDist << "\t" << range << "\n"; #endif #ifdef VARIABLE_OPTIMIZATION if(unique == true) ReorientSubtreeSPRMutateDummy(cut->nodeNum, broken, optPrecision); else return broken->reconDist * -1; #endif ReorientSubtreeSPRMutate(cut->nodeNum, broken, optPrecision); ret=broken->reconDist * -1; } else{ #ifdef OPT_DEBUG optsum << "SPR\t" << broken->reconDist << "\t" << range << "\n"; #endif #ifdef VARIABLE_OPTIMIZATION if(unique == true) err=SPRMutateDummy(cut->nodeNum, broken, optPrecision, subtreeNode); else return broken->reconDist; #endif err=SPRMutate(cut->nodeNum, broken, optPrecision, subtreeNode); ret=broken->reconDist; } #ifdef OUTPUT_UNIQUE_TREES if(unique == true){ output_tree = true; //uni.precision(9); if(broken->withinCutSubtree == false) uni << "SPR" << "\t" << broken->reconDist << "\t" << lnL << "\t" << cut->nodeNum << "\t" << broken->nodeNum << "\n"; else uni << "reSPR" << "\t" << broken->reconDist << "\t" << lnL << "\t" << cut->nodeNum << "\t" << broken->nodeNum << "\n"; } #endif } }while(err); #ifndef NDEBUG for(vector::iterator conit=constraints.begin();conit!=constraints.end();conit++){ TreeNode *check = NULL; if((*conit).IsBackbone()) check = ContainsMaskedBipartitionOrComplement(*(*conit).GetBipartition(), *(*conit).GetBackboneMask()); else check = ContainsBipartitionOrComplement(*(*conit).GetBipartition()); if((*conit).IsPositive()) assert(check != NULL); else assert(check == NULL); } #endif return ret; } void Tree::GatherValidReconnectionNodes(int maxDist, TreeNode *cut, const TreeNode *subtreeNode, Bipartition *partialMask /*=NULL*/){ /* 7/11/06 making this function more multipurpose It now assumes that the cut branch has NOT YET BEEN DETACHED. This is important so that when branches are chosen without a viable reconnection due to a constraint another cut can be chosen without having the put the tree back together again 1. Gather all nodes within maxRange. This can include nodes that are des of the cut node. In this case the portion of the tree containing the root is considered the subtree to be reattached, and the swap would be done by ReorientSubtreeSPRMutate 2. Keep information on the potential reconnection nodes, including reconnection distance and branchlength distance. This allows for various schemes of differentially weighting the swaps. 3. filter out reconnection nodes incompatible with constraints */ sprRang.clear(); const TreeNode *center=cut->anc; //add the descendent branches if(center->left != cut) sprRang.AddNode(center->left->nodeNum, 0, (float) center->left->dlen); if(center->left->next != cut) sprRang.AddNode(center->left->next-> nodeNum, 0, (float) center->left->next->dlen); //add either the center node itself or the third descendent in the case of the root if(center->IsNotRoot()){ if(center->anc != subtreeNode) sprRang.AddNode(center->nodeNum, 0, (float) center->dlen); } else{ if(center->left->next->next != cut) sprRang.AddNode(center->left->next->next->nodeNum, 0, (float) center->left->next->next->dlen); } assert(sprRang.size() == 2); for(int curDist = 0; curDist < maxDist || maxDist < 0; curDist++){ list::iterator it=sprRang.GetFirstNodeAtDist(curDist); if(it == sprRang.end()){ break; //need this to break out of loop when curDist exceeds any branches in the tree } for(; it != sprRang.end() && it->reconDist == curDist; it++){ TreeNode *cur=allNodes[it->nodeNum]; assert(cur->IsNotRoot()); if(cur->left!=NULL && cur->left!=cut) sprRang.AddNode(cur->left->nodeNum, curDist+1, (float) (it->pathlength + cur->left->dlen)); if(cur->right!=NULL && cur->right!=cut) sprRang.AddNode(cur->right->nodeNum, curDist+1, (float) (it->pathlength + cur->right->dlen)); if(cur->next!=NULL && cur->next!=cut){ sprRang.AddNode(cur->next->nodeNum, curDist+1, (float) (it->pathlength + cur->next->dlen)); if(cur->next->next!=NULL && cur->next->next!=cut){//if cur is the left descendent of the root sprRang.AddNode(cur->next->next->nodeNum, curDist+1, (float) (it->pathlength + cur->next->next->dlen)); } } if(cur->prev!=NULL && cur->prev!=cut){ sprRang.AddNode(cur->prev->nodeNum, curDist+1, (float) (it->pathlength + cur->prev->dlen)); if(cur->prev->prev!=NULL && cur->prev->prev!=cut){//if cur is the right descendent of the root sprRang.AddNode(cur->prev->prev->nodeNum, curDist+1, (float) (it->pathlength + cur->prev->prev->dlen)); } } if(cur->anc->nodeNum != 0){//if the anc is not the root, add it. if(cur->anc!=subtreeNode){ sprRang.AddNode(cur->anc->nodeNum, curDist+1, (float) (it->pathlength + cur->anc->dlen)); } } } } if(maxDist != 1 && cut->IsInternal()){ //Gather nodes within the cut subtree to allow SPRs in which the portion of the tree containing //the root is considered the subtree to be reattached //start by adding cut's left and right sprRang.AddNode(cut->left->nodeNum, 0, (float) cut->left->dlen, true); sprRang.AddNode(cut->right->nodeNum, 0, (float) cut->right->dlen, true); for(int curDist = 0; curDist < maxDist || maxDist < 0; curDist++){ list::iterator it=sprRang.GetFirstNodeAtDistWithinCutSubtree(curDist); if(it == sprRang.end()){ break; //need this to break out of loop when curDist exceeds any branches in the tree } for(; it != sprRang.end() && it->reconDist == curDist; it++){ TreeNode *cur=allNodes[it->nodeNum]; if(cur->left!=NULL) sprRang.AddNode(cur->left->nodeNum, curDist+1, (float) (it->pathlength + cur->left->dlen), true); if(cur->right!=NULL) sprRang.AddNode(cur->right->nodeNum, curDist+1, (float) (it->pathlength + cur->right->dlen), true); if(cur->next!=NULL){ sprRang.AddNode(cur->next->nodeNum, curDist+1, (float) (it->pathlength + cur->next->dlen), true); } } } } //remove general unwanted nodes from the subset sprRang.RemoveNodesOfDist(0); //remove branches adjacent to cut // if(maxDist != 1) // sprRang.RemoveNodesOfDist(1); //remove branches equivalent to NNIs //now deal with constraints, if any if(constraints.size() > 0){ /* int ok =0; int bad = 0; int calls = 0; int attach = sprRang.size(); */ bool bypass = false; //6/30/09 If all constraints are backbone on the same set of taxa, check that both sides of the split where the tree was broken //actually appear in the backbone mask. Otherwise the swap is always valid and we can skip the whole following loop. //This is very helpful when, for example, a terminal taxon not in the backbone is cut. CalcBipartitions(true); if(Constraint::allBackbone && Constraint::sharedMask){ if(!(constraints[0].GetBackboneMask()->HasIntersection(*cut->bipart, NULL)) || !(constraints[0].GetBackboneMask()->HasIntersectionWithComplement(*cut->bipart, NULL))){ bypass = true; } } if(!bypass && sprRang.size() != 0){ Bipartition proposed; listIt it=sprRang.begin(); do{ TreeNode* broken=allNodes[it->nodeNum]; CalcBipartitions(true); proposed.FillWithXORComplement(*(cut->bipart), *(allNodes[broken->nodeNum]->bipart)); bool allowed = true; //6/23/09 This call was moved here from within SwapAllowedByConstraint. This saves a lot //of work when looping over many constraints for a single swap that really only requires a single adjustment. //Doing the adjustment isn't necessary for positive non-backbone constraints with no mask (and isn't always //necessary when there is a mask either) so skip this if we can if(it->withinCutSubtree == false && (partialMask || Constraint::anyBackbone || constraints[0].IsPositive() == false)) AdjustBipartsForSwap(cut->nodeNum, broken->nodeNum); for(vector::iterator conit=constraints.begin();conit!=constraints.end();conit++){ // calls++; allowed = SwapAllowedByConstraint((*conit), cut, &*it, proposed, partialMask); if(!allowed) break; } // if(allowed) ok++; // else bad++; if(!allowed) it=sprRang.RemoveElement(it); else it++; }while(it != sprRang.end()); } /* if(bypass) outman.UserMessage("%d max range, %d attach, %d calls, %d ok, %d bad, BYPASSED", maxDist, attach, calls, ok, bad); else outman.UserMessage("%d max range, %d attach, %d calls, %d ok, %d bad", maxDist, attach, calls, ok, bad); */ } } //same as the normal GatherValidReconnectionNodes, but fills ReconList passed in, not the normal tree one //6/23/09 I don't think that this has been updated for the most recent constraint implementation, so shouldn't be being used void Tree::GatherValidReconnectionNodes(ReconList &thisList, int maxDist, TreeNode *cut, const TreeNode *subtreeNode, Bipartition *partialMask /*=NULL*/){ assert(0); const TreeNode *center=cut->anc; //add the descendent branches if(center->left != cut) thisList.AddNode(center->left->nodeNum, 0, (float) center->left->dlen); if(center->left->next != cut) thisList.AddNode(center->left->next-> nodeNum, 0, (float) center->left->next->dlen); //add either the center node itself or the third descendent in the case of the root if(center->IsNotRoot()){ if(center->anc != subtreeNode) thisList.AddNode(center->nodeNum, 0, (float) center->dlen); } else{ if(center->left->next->next != cut) thisList.AddNode(center->left->next->next->nodeNum, 0, (float) center->left->next->next->dlen); } assert(thisList.size() == 2); for(int curDist = 0; curDist < maxDist || maxDist < 0; curDist++){ //list::iterator it=thisList.GetFirstNodeAtDist(curDist); listIt it=thisList.GetFirstNodeAtDist(curDist); if(it == thisList.end()){ break; //need this to break out of loop when curDist exceeds any branches in the tree } for(; it != thisList.end() && it->reconDist == curDist; it++){ TreeNode *cur=allNodes[it->nodeNum]; assert(cur->IsNotRoot()); if(cur->left!=NULL && cur->left!=cut) thisList.AddNode(cur->left->nodeNum, curDist+1, (float) (it->pathlength + cur->left->dlen)); if(cur->right!=NULL && cur->right!=cut) thisList.AddNode(cur->right->nodeNum, curDist+1, (float) (it->pathlength + cur->right->dlen)); if(cur->next!=NULL && cur->next!=cut){ thisList.AddNode(cur->next->nodeNum, curDist+1, (float) (it->pathlength + cur->next->dlen)); if(cur->next->next!=NULL && cur->next->next!=cut){//if cur is the left descendent of the root thisList.AddNode(cur->next->next->nodeNum, curDist+1, (float) (it->pathlength + cur->next->next->dlen)); } } if(cur->prev!=NULL && cur->prev!=cut){ thisList.AddNode(cur->prev->nodeNum, curDist+1, (float) (it->pathlength + cur->prev->dlen)); if(cur->prev->prev!=NULL && cur->prev->prev!=cut){//if cur is the right descendent of the root thisList.AddNode(cur->prev->prev->nodeNum, curDist+1, (float) (it->pathlength + cur->prev->prev->dlen)); } } if(cur->anc->nodeNum != 0){//if the anc is not the root, add it. if(cur->anc!=subtreeNode){ thisList.AddNode(cur->anc->nodeNum, curDist+1, (float) (it->pathlength + cur->anc->dlen)); } } } } if(maxDist != 1 && cut->IsInternal()){ //Gather nodes within the cut subtree to allow SPRs in which the portion of the tree containing //the root is considered the subtree to be reattached //start by adding cut's left and right thisList.AddNode(cut->left->nodeNum, 0, (float) cut->left->dlen, true); thisList.AddNode(cut->right->nodeNum, 0, (float) cut->right->dlen, true); for(int curDist = 0; curDist < maxDist || maxDist < 0; curDist++){ //list::iterator it=thisList.GetFirstNodeAtDistWithinCutSubtree(curDist); listIt it=thisList.GetFirstNodeAtDistWithinCutSubtree(curDist); if(it == thisList.end()){ break; //need this to break out of loop when curDist exceeds any branches in the tree } for(; it != thisList.end() && it->reconDist == curDist; it++){ TreeNode *cur=allNodes[it->nodeNum]; if(cur->left!=NULL) thisList.AddNode(cur->left->nodeNum, curDist+1, (float) (it->pathlength + cur->left->dlen), true); if(cur->right!=NULL) thisList.AddNode(cur->right->nodeNum, curDist+1, (float) (it->pathlength + cur->right->dlen), true); if(cur->next!=NULL){ thisList.AddNode(cur->next->nodeNum, curDist+1, (float) (it->pathlength + cur->next->dlen), true); } } } } //remove general unwanted nodes from the subset thisList.RemoveNodesOfDist(0); //remove branches adjacent to cut //try removing nni's that would be dupes for(listIt it = thisList.begin(); it != thisList.end();){ if(cut->nodeNum > (*it).nodeNum) it = thisList.RemoveElement(it); else it++; } //now deal with constraints, if any if(constraints.size() > 0){ Bipartition scratch; for(vector::iterator conit=constraints.begin();conit!=constraints.end();conit++){ if(thisList.size() != 0){ listIt it=thisList.begin(); do{ //if(AllowedByConstraint(&(*conit), cut, broken, scratch) == false) it=thisList.RemoveElement(it); if(SwapAllowedByConstraint((*conit), cut, &*it, scratch, partialMask) == false) it=thisList.RemoveElement(it); else it++; }while(it != thisList.end()); } else return; } } } bool Tree::AssignWeightsToSwaps(TreeNode *cut){ //Assign weights to each swap (reconnection node) based on //some criterion CalcBipartitions(true); Bipartition proposed; list::iterator thisSwap; bool someUnique = false; Swap tmp; for(listIt it = sprRang.begin();it != sprRang.end();it++){ bool found; CalcBipartitions(true); proposed.FillWithXORComplement(*(cut->bipart), *(allNodes[(*it).nodeNum]->bipart)); tmp.Setup(proposed, cut->nodeNum, (*it).nodeNum, (*it).reconDist); thisSwap = attemptedSwaps.FindSwap(tmp, found); if(found == false){ someUnique = true; if((*it).reconDist - 1 < 1000) (*it).weight = distanceSwapPrecalc[(*it).reconDist - 1]; else (*it).weight = distanceSwapPrecalc[999]; } else{ if((*thisSwap).Count() < 500) (*it).weight = uniqueSwapPrecalc[(*thisSwap).Count()]; else (*it).weight = uniqueSwapPrecalc[499]; if((*it).reconDist - 1 < 1000) (*it).weight *= distanceSwapPrecalc[(*it).reconDist - 1]; else (*it).weight *= distanceSwapPrecalc[999]; /* if((*it).reconDist - 1 < 1000 && (*thisSwap).Count() < 500) (*it).weight = uniqueSwapPrecalc[(*thisSwap).Count()] * distanceSwapPrecalc[(*it).reconDist - 1]; else (*it).weight = 0.0; */ } } return someUnique==false; } int Tree::SPRMutateDummy(int cutnum, ReconNode *broke, FLOAT_TYPE optPrecision, int subtreeNode){ //this is just a spoof version of SPRMutate that will perform the same mutation //several times with different optimiation settings, but will otherwise //maintain exactly the same program flow because it resets the seed #ifndef VARIABLE_OPTIMIZATION assert(0); #else Individual tempIndiv; tempIndiv.treeStruct=new Tree(); Individual sourceIndiv; sourceIndiv.treeStruct=this; sourceIndiv.mod->CopyModel(this->mod); int savedSeed; var.precision(10); var << "SPR" << "\t" << broke->reconDist << "\t" << lnL << "\t"; tempIndiv.CopySecByRearrangingNodesOfFirst(tempIndiv.treeStruct, &sourceIndiv); // FLOAT_TYPE prec[5]={(FLOAT_TYPE).01, (FLOAT_TYPE).5, (FLOAT_TYPE).01, (FLOAT_TYPE).01, (FLOAT_TYPE).01}; FLOAT_TYPE origThresh = treeRejectionThreshold; /* treeRejectionThreshold = 10000; for(int i=0;i<1;i++){ savedSeed = rnd.seed(); optCalcs = 0; tempIndiv.treeStruct->SPRMutate(cutnum, broke, optPrecision, 0); var << tempIndiv.treeStruct->lnL << "\t" << optCalcs << "\t"; optCalcs = 0; rnd.set_seed(savedSeed); tempIndiv.CopySecByRearrangingNodesOfFirst(tempIndiv.treeStruct, &sourceIndiv, true); } treeRejectionThreshold = -10000; for(int i=0;i<1;i++){ savedSeed = rnd.seed(); optCalcs = 0; tempIndiv.treeStruct->SPRMutate(cutnum, broke, optPrecision, 0); var << tempIndiv.treeStruct->lnL << "\t" << optCalcs << "\t"; optCalcs = 0; rnd.set_seed(savedSeed); tempIndiv.CopySecByRearrangingNodesOfFirst(tempIndiv.treeStruct, &sourceIndiv, true); } */ /* for(int i=0;i<1;i++){ treeRejectionThreshold = origThresh; savedSeed = rnd.seed(); optCalcs = 0; tempIndiv.treeStruct->SPRMutate(cutnum, broke, optPrecision, 0); var << tempIndiv.treeStruct->lnL << "\t" << optCalcs << "\t"; optCalcs = 0; rnd.set_seed(savedSeed); tempIndiv.CopySecByRearrangingNodesOfFirst(tempIndiv.treeStruct, &sourceIndiv, true); } */ treeRejectionThreshold = origThresh; tempIndiv.treeStruct->RemoveTreeFromAllClas(); delete tempIndiv.treeStruct; tempIndiv.treeStruct=NULL; sourceIndiv.treeStruct=NULL; optCalcs = 0; SPRMutate(cutnum, broke, optPrecision, 0); var << lnL << "\t" << optCalcs << "\n"; optCalcs = 0; #endif return 1; } // 7/21/06 This function is now called by TopologyMutator to actually do the rearrangement //It has the cut and broken nodenums passed in. It also does NNI's int Tree::SPRMutate(int cutnum, ReconNode *broke, FLOAT_TYPE optPrecision, int subtreeNode){ //if the optPrecision passed in is < 0 it means that we're just trying to //make the tree structure for some reason, but don't have CLAs allocated //and don't intend to do blen opt bool createTopologyOnly=false; if(optPrecision < 0.0) createTopologyOnly=true; TreeNode* cut = allNodes[cutnum]; TreeNode *broken = allNodes[broke->nodeNum]; TreeNode *connector=NULL; TreeNode *sib; //note that this assignment of the sib can be overridden below if cut is attached to the root or the subtreeNode if(cut->next!=NULL) sib=cut->next; else sib=cut->prev; //determine who the connector node will be. It will be cut->anc unless that is the root //if cut->anc is the root, connector will be one of cut's siblings, which is freed when //the basal trichotomy is reestablished after removing cut. if(cut->anc->IsNotRoot()){ if(cut->anc->nodeNum != subtreeNode){ connector=cut->anc; } else{ //cut is attached to the subtreeNode, so we will have to use it's sib as the connector connector=sib; sib=connector->left; } } else{ if(root->left!=cut && root->left->IsInternal()) connector = root->left; else if(root->left->next!=cut && root->left->next->IsInternal()) connector = root->left->next; else if(root->right!=cut && root->right->IsInternal()) connector = root->right; else{//this should be quite rare, and means that the three descendents of the root //are cut and two terminals, so no viable swap exists, just try again return -1; } } //all clas below cut will need to be recalced if(createTopologyOnly == false) SweepDirtynessOverTree(cut); TreeNode *replaceForConn; if(cut->anc->anc){ if(cut->anc->nodeNum != subtreeNode){ //cut is not connected to the root, so we can steal it's ancestor as the new connector if(cut==connector->left){ assert(cut->next==connector->right); replaceForConn=connector->right; } else{ assert(cut==connector->right); replaceForConn=connector->left; } SetBranchLength(replaceForConn, min(max_brlen, replaceForConn->dlen+connector->dlen)); connector->SubstituteNodeWithRespectToAnc(replaceForConn); } else{//cut is attached to the subtreeNode, so we will have to use it's sib as the connector //connector's two children become the subtreeNodes new children, and connector's dlen gets added to subtreeNodes TreeNode *subnode=allNodes[subtreeNode]; SetBranchLength(subnode, min(max_brlen, subnode->dlen+connector->dlen)); SweepDirtynessOverTree(connector); subnode->left=connector->left; subnode->right=connector->right; connector->left->anc=subnode; connector->right->anc=subnode; } } else{//cut is connected to the root so we need to steal a non terminal sib node as the connector if(createTopologyOnly == false) MakeNodeDirty(root); //Disconnect cut from the root if(cut==root->left){ root->left=cut->next; cut->next->prev=NULL; } else if(cut==root->right){ root->right=cut->prev; cut->prev->next=NULL; } else{ assert(cut->prev==root->left && cut->next==root->right);//can only have a basal trifucation, or we're in trouble cut->prev->next=cut->next; cut->next->prev=cut->prev; } //root is now bifurcation //preserve branch length info if(root->right==connector){ SetBranchLength(root->left, min(max_brlen, root->left->dlen+connector->dlen)); sib=root->left; } else{ SetBranchLength(root->right, min(max_brlen, root->right->dlen+connector->dlen)); sib=root->right; } //add the connectors two desccendants as descendants of the root assert(connector->right==connector->left->next); connector->SubstituteNodeWithRespectToAnc(connector->left); root->AddDes(connector->right); } //establish correct topology for connector and cut nodes if(createTopologyOnly == false) MakeNodeDirty(connector); cut->anc=connector; connector->left=connector->right=cut; connector->next=connector->prev=connector->anc=cut->next=cut->prev=NULL; broken->SubstituteNodeWithRespectToAnc(connector); connector->AddDes(broken); assert(connector->right == broken); SetBranchLength(connector, max(min_brlen, broken->dlen*ZERO_POINT_FIVE)); SetBranchLength(broken, connector->dlen); if(createTopologyOnly == false){ SweepDirtynessOverTree(connector, cut); if(broke->reconDist > 1) OptimizeBranchesWithinRadius(connector, optPrecision, subtreeNode, sib); else OptimizeBranchesWithinRadius(connector, optPrecision, subtreeNode, NULL); } bipartCond = DIRTY; //#ifdef EXTRA_ROOT_OPT if(createTopologyOnly == false && cut == dummyRoot){ //do some extra optimization when the root branch is moved, since it is a tough move to accept outman.DebugMessageNoCR("root move: %.4f ", lnL); for(int modnum = 0;modnum < modPart->NumModels();modnum++){ const ModelSpecification *modSpec = modPart->GetModel(modnum)->GetCorrespondingSpec(); if(modSpec->IsOrientedGap()){ OptimizeInsertDeleteRates(optPrecision, modnum); outman.DebugMessageNoCR("-> %.4f ", lnL); OptimizeAllBranches(optPrecision); } } outman.DebugMessage("-> %.4f", lnL); } //#endif return 0; } void Tree::ReorientSubtreeSPRMutateDummy(int oroot, ReconNode *nroot, FLOAT_TYPE optPrecision){ //this is just a spoof version of SPRMutate that will perform the same mutation //several times with different optimiation settings, but will otherwise //maintain exactly the same program flow because it resets the seed #ifndef VARIABLE_OPTIMIZATION assert(0); #else Individual tempIndiv; tempIndiv.treeStruct=new Tree(); Individual sourceIndiv; sourceIndiv.treeStruct=this; sourceIndiv.mod->CopyModel(this->mod); int savedSeed; var.precision(10); var << "reSPR" << "\t" << nroot->reconDist << "\t" << lnL << "\t"; //FLOAT_TYPE prec[5]={(FLOAT_TYPE).01, (FLOAT_TYPE).5, (FLOAT_TYPE).01, (FLOAT_TYPE).01, (FLOAT_TYPE).01}; tempIndiv.CopySecByRearrangingNodesOfFirst(tempIndiv.treeStruct, &sourceIndiv); FLOAT_TYPE origThresh = treeRejectionThreshold; /* treeRejectionThreshold = 10000; for(int i=0;i<1;i++){ savedSeed = rnd.seed(); optCalcs = 0; tempIndiv.treeStruct->ReorientSubtreeSPRMutate(oroot, nroot, optPrecision); var << tempIndiv.treeStruct->lnL << "\t" << optCalcs << "\t"; optCalcs = 0; rnd.set_seed(savedSeed); tempIndiv.CopySecByRearrangingNodesOfFirst(tempIndiv.treeStruct, &sourceIndiv, true); } treeRejectionThreshold = -10000; for(int i=0;i<1;i++){ savedSeed = rnd.seed(); optCalcs = 0; tempIndiv.treeStruct->ReorientSubtreeSPRMutate(oroot, nroot, optPrecision); var << tempIndiv.treeStruct->lnL << "\t" << optCalcs << "\t"; optCalcs = 0; rnd.set_seed(savedSeed); tempIndiv.CopySecByRearrangingNodesOfFirst(tempIndiv.treeStruct, &sourceIndiv, true); } */ /* for(int i=0;i<1;i++){ treeRejectionThreshold = origThresh; savedSeed = rnd.seed(); optCalcs = 0; tempIndiv.treeStruct->ReorientSubtreeSPRMutate(oroot, nroot, optPrecision); var << tempIndiv.treeStruct->lnL << "\t" << optCalcs << "\t"; optCalcs = 0; rnd.set_seed(savedSeed); tempIndiv.CopySecByRearrangingNodesOfFirst(tempIndiv.treeStruct, &sourceIndiv, true); } */ treeRejectionThreshold = origThresh; tempIndiv.treeStruct->RemoveTreeFromAllClas(); delete tempIndiv.treeStruct; tempIndiv.treeStruct=NULL; sourceIndiv.treeStruct=NULL; optCalcs = 0; ReorientSubtreeSPRMutate(oroot, nroot, optPrecision); var << lnL << "\t" << optCalcs << "\n"; optCalcs = 0; #endif } void Tree::ReorientSubtreeSPRMutate(int oroot, ReconNode *nroot, FLOAT_TYPE optPrecision){ //this is used to allow the other half of SPR rearrangements in which //the part of the tree containing the root is considered the subtree //to be attached. Terminology is VERY confusing here. newRoot is the //branch to be bisected (rooted at). oldRoot is the node that is at the //base of the subtree currently. After the rearrangement it will still //be at the base of the subtree, but in the middle of a different branch //if the optPrecision passed in is < 0 it means that we're just trying to //make the tree structure for some reason, but don't have CLAs allocated //and don't intend to do blen opt bool createTopologyOnly=false; if(optPrecision < 0.0) createTopologyOnly=true; TreeNode *newroot=allNodes[nroot->nodeNum]; TreeNode *oldroot=allNodes[oroot]; //these are the only blens that need to be dealt with specially FLOAT_TYPE fusedBlen = min(max_brlen, oldroot->left->dlen + oldroot->right->dlen); FLOAT_TYPE dividedBlen = max(ZERO_POINT_FIVE * newroot->dlen, min_brlen); //first detatch the subtree and make it free floating. This will //leave oroot in its place and fuse two branches in the subtree //into a branch connecting one of oroots des to its other des //This makes that des a tricotomy with a NULL anc. Then the rotating //begins. if(createTopologyOnly == false){ SweepDirtynessOverTree(oldroot->left); SweepDirtynessOverTree(oldroot->right); } TreeNode *prunePoint; TreeNode *tempRoot; if(oldroot->left->IsInternal()){ tempRoot=oldroot->left; prunePoint=oldroot->right; } else{ tempRoot=oldroot->right; prunePoint=oldroot->left; } tempRoot->AddDes(prunePoint); //prunePoint->dlen=fusedBlen; SetBranchLength(prunePoint, fusedBlen); tempRoot->anc=NULL; if(createTopologyOnly == false) MakeNodeDirty(tempRoot); //collect each of the nodes that will need to be flipped vector path; path.reserve(10); TreeNode *tmp=newroot->anc; while(tmp){ path.push_back(tmp); tmp=tmp->anc; } reverse(path.begin(),path.end()); for(vector::iterator it=path.begin();(it+1)!=path.end();it++){ (*it)->MoveDesToAnc(*(it+1)); } //now disconnect the oldroot oldroot->left = NULL; oldroot->right = NULL; //and add the new des TreeNode *oldanc=newroot->anc; oldanc->RemoveDes(newroot); oldroot->AddDes(oldanc); oldroot->AddDes(newroot); SetBranchLength(oldroot->left, dividedBlen); SetBranchLength(oldroot->right, dividedBlen); if(createTopologyOnly == false){ SweepDirtynessOverTree(newroot); SweepDirtynessOverTree(oldroot); SweepDirtynessOverTree(tempRoot); SweepDirtynessOverTree(prunePoint); if(nroot->reconDist > 1) OptimizeBranchesWithinRadius(oldroot, optPrecision, 0, prunePoint); else OptimizeBranchesWithinRadius(oldroot, optPrecision, 0, NULL); } bipartCond = DIRTY; } void Tree::LoadConstraints(ifstream &con, int nTaxa){ string temp;//=new char[numTipsTotal + 100]; Constraint constr; int conNum=0; do{ temp.clear(); char c; con.get(c); do{ temp += c; con.get(c); }while(c != '\n' && c!= '\r' && con.eof() == false); while((con.peek() == '\n' || con.peek() == '\r') && con.eof() == false){ con.get(c); } //getline works strangely on some compilers. temp should end with ; or \0 , but //might end with \r or \n size_t len=temp.length(); char last=temp.c_str()[len-1]; while(last == '\r' || last == '\n' || last == ' '){ temp.erase(len-1, 1); len--; last=temp.c_str()[len-1]; } if(temp[0] != '\0'){ if(temp[0] != '+' && temp[0] != '-') throw ErrorException("constraint string must start with \'+\' (positive constraint) or \'-\' (negative constraint)"); if(temp[1] == '.' || temp[1] == '*'){//if individual biparts are specified in *. format //while(temp[temp.length()-1] == ' ') temp.erase(temp.length()-1);//eat any spaces at the end if(len != nTaxa+1) throw ErrorException("constraint # %d does not have the correct number of characters!\n(has %d) constraint strings must start with \n\'+\' (positive constraint) or \'-\' (negative constraint)\nfollowed by either a ...*** type specification\nor a constraint in newick format. \nNote that backbone constraints cannot be specified in ...*** format.", conNum, len); constr.ReadDotStarConstraint(temp.c_str()); constraints.push_back(constr); conNum++; } else if(temp[1] == '('){//if a constraint tree in parenthetical notation is used bool numericalTaxa=true; for(unsigned i=0;i bip; contree.root->GatherConstrainedBiparitions(bip); if(bip.size() == 0) throw ErrorException("Specified constraint does not constrain any relationships.\n\tSee manual for constraint format"); if(pos==false && (bip.size() > 1)) throw ErrorException("Sorry, GARLI can currently only handle a single negatively (conversely) constrainted branch (bipartition):-("); //BACKBONE - see if all taxa appear in this constraint or if its a backbone if(contree.numTipsAdded < contree.numTipsTotal){ Bipartition mask = *(contree.root->bipart); //complement the mask if necessary TreeNode *n=contree.root; while(n->IsInternal()) n = n->left; if(mask.ContainsTaxon(n->nodeNum) == false) mask.Complement(); for(vector::iterator bit=bip.begin();bit!=bip.end();bit++){ constraints.push_back(Constraint(&(*bit), &mask, pos)); conNum++; } } else{ for(vector::iterator bit=bip.begin();bit!=bip.end();bit++){ constraints.push_back(Constraint(&(*bit), pos)); conNum++; } } } else{ throw ErrorException("problem with constraint # %d\nconstraint strings must start with \n\'+\' (positive constraint) or \'-\' (negative constraint)\nfollowed by either a ...*** type specification\nor a constraint in newick format", conNum, len); } } }while(con.eof() == false); //make sure the constraints are compatible with each other! bool allBackbone = true; bool anyBackbone = false; bool sameMask = true; for(vector::iterator first=constraints.begin();first!=constraints.end();first++){ if(first->IsBackbone() == false){ allBackbone = false; sameMask = false; } else anyBackbone = true; for(vector::iterator sec=first+1;sec!=constraints.end();sec++){ if((*first).IsPositive() != (*sec).IsPositive()) throw ErrorException("cannot mix positive and negative constraints!"); if(((*first).IsPositive()==false) && ((*sec).IsPositive()==false)) throw ErrorException("Sorry, GARLI can currently only handle a single negatively (conversely) constrainted branch :-("); if((*first).ConstraintIsCompatibleWithConstraint((*sec)) == false) throw ErrorException("constraints are not compatible with one another!"); if(allBackbone && sameMask && first->IsBackbone() && sec->IsBackbone() && first == constraints.begin()){ if(first->GetBackboneMask()->EqualsEquals(*sec->GetBackboneMask()) == false) sameMask = false; } } } Constraint::SetConstraintStatics(allBackbone, anyBackbone, sameMask); //summarize the constraint info to the screen string str; int num=1; if(allBackbone){ outman.UserMessage("All constraints are backbone"); if(sameMask) outman.UserMessage("All constraints involve the same backbone set of taxa"); else outman.UserMessage("Constraints involve differing sets of taxa"); } else if(anyBackbone) outman.UserMessage("Some constraints are backbone"); if(constraints[0].IsPositive()){ outman.UserMessage("Found %d positively constrained bipartition(s)", constraints.size()); for(vector::iterator first=constraints.begin();first!=constraints.end();first++){ (*first).NumericalOutput(str); if((*first).IsBackbone()) outman.UserMessage(" Bipartition %d (backbone): %s", num, str.c_str()); else outman.UserMessage(" Bipartition %d: %s", num, str.c_str()); num++; } } else{ outman.UserMessage("Found 1 negatively (conversely) constrained bipartition"); constraints[0].NumericalOutput(str); if(constraints[0].IsBackbone()) outman.UserMessage(" Bipartition %d (backbone): %s", num, str.c_str()); else outman.UserMessage(" Bipartition %d: %s", num, str.c_str()); } } //this just "fakes" the swapping of the subtree rooted at cut to a postition as the sister of broken by adjusting the //biparts across the tree. This should only be used for NORMAL SPR's not subtree reorient SPR's void Tree::AdjustBipartsForSwap(int cut, int broken){ //first be sure the biparts are current CalcBipartitions(true); if(allNodes[cut]->anc->IsNotRoot()) allNodes[cut]->anc->RecursivelyAddOrRemoveSubtreeFromBipartitions(*(allNodes[cut]->bipart)); if(allNodes[broken]->anc->IsNotRoot()) allNodes[broken]->anc->RecursivelyAddOrRemoveSubtreeFromBipartitions(*(allNodes[cut]->bipart)); bipartCond = TEMP_ADJUSTED; } //test whether the attachment of branch "cut" (subtree or tip) to branch "broken" (subtree or tip) is allowed by //any constraints. The general purpose Constraint::BipartitionIsCompatibleWithConstraint function (which takes care of //positive and negative constraints, backbone or not) is called to check if the bipartition created by the union //of cut and broken is itself allowable. Depending on the type of constraint, other checks may also need to be done. bool Tree::SwapAllowedByConstraint(const Constraint &constr, TreeNode *cut, ReconNode *broken, const Bipartition &proposed, const Bipartition *partialMask) { //for a normal positive constraint with no mask we only need to check the bipartition about to be created if(constr.IsPositive() && !constr.IsBackbone() && partialMask==NULL) return constr.BipartitionIsCompatibleWithConstraint(proposed, NULL); else{ //otherwise we need to check bipartitions across the tree bool compat; /*(check for meaningful intersection of constraint and partial/backbone mask here)*/ Bipartition jointMask; bool meaningfulIntersection = jointMask.MakeJointMask(constr, partialMask); if(!meaningfulIntersection) return true; if(!broken->withinCutSubtree){ //if this is a normal SPR swap in which the cut subtree has the same orientation after the swap then we can //check the bipartition about to be created, and if that passes then adjust the bipartitions across the tree and //recursively check the rest of the tree compat = constr.BipartitionIsCompatibleWithConstraint(proposed, &jointMask); if(compat == false) return compat; //6/23/09 This call was moved up one level, so that it MUST called in RandomlyAttachTipWithConstraints or GatherValidReconnectionNodes //before calling SwapAllowedByConstraint. This saves a lot of work when looping over many constraints for a single swap //that really only requires a single adjustment //AdjustBipartsForSwap(cut->nodeNum, broken->nodeNum); compat = RecursiveAllowedByConstraintWithMask(constr, &jointMask, root); } else{ Tree propTree; propTree.MimicTopo(this); propTree.ReorientSubtreeSPRMutate(cut->nodeNum, broken, -1.0); compat = (constr.IsPositive()) == (propTree.ContainsMaskedBipartitionOrComplement(*constr.GetBipartition(), jointMask) != NULL); } return compat; } } /* bool Tree::TaxonAdditionAllowedByPositiveConstraintWithMask(Constraint *constr, Bipartition *mask, TreeNode *toAdd, TreeNode *broken){ Bipartition proposed; proposed.FillWithXORComplement(toAdd->bipart, broken->bipart); bool compat = constr->BipartitionIsCompatibleWithConstraint(&proposed, mask); if(compat==false) return compat; //This is a little sneaky here. Cut has not been added to the tree, but since we are going up from broken //and it is present in the mask it will effectively appear in biparts in that direction else if(broken->IsInternal()){ compat=RecursiveAllowedByConstraintWithMask(constr, mask, broken); } return compat; } bool Tree::TaxonAdditionAllowedByNegativeConstraintWithMask(Constraint *constr, Bipartition *mask, TreeNode *toAdd, TreeNode *broken){ Bipartition proposed; proposed.FillWithXORComplement(toAdd->bipart, broken->bipart); bool compat = constr->BipartitionIsCompatibleWithConstraint(&proposed, mask); if(compat==true) return compat; else if(broken->IsInternal()) compat=RecursiveAllowedByConstraintWithMask(constr, mask, broken); return compat; } bool Tree::TaxonAdditionAllowedByPositiveBackboneConstraintWithMask(Constraint *constr, Bipartition *mask, TreeNode *toAdd, TreeNode *broken){ Bipartition proposed; proposed.FillWithXORComplement(toAdd->bipart, broken->bipart); bool compat = constr->BipartitionIsCompatibleWithConstraint(&proposed, mask); if(compat==false) return compat; else{ if(broken->anc->IsNotRoot()) broken->anc->RecursivelyAddOrRemoveSubtreeFromBipartitions(toAdd->bipart); bipartCond = TEMP_ADJUSTED; compat = RecursiveAllowedByConstraintWithMask(constr, mask, root); CalcBipartitions(false); } return compat; } bool Tree::TaxonAdditionAllowedByNegativeBackboneConstraintWithMask(Constraint *constr, Bipartition *mask, TreeNode *toAdd, TreeNode *broken){ // Bipartition jointMask=*(constr->GetBackboneMask()); // jointMask.AndEquals(mask); Bipartition proposed; proposed.FillWithXORComplement(toAdd->bipart, broken->bipart); //bool compat = constr->IsCompatibleWithConstraintWithMask(&proposed, &jointMask); bool compat = constr->BipartitionIsCompatibleWithConstraint(&proposed, mask); if(compat==false) return compat; else{ if(broken->anc->IsNotRoot()) broken->anc->RecursivelyAddOrRemoveSubtreeFromBipartitions(toAdd->bipart); bipartCond = TEMP_ADJUSTED; compat = RecursiveAllowedByConstraintWithMask(constr, mask, root); CalcBipartitions(false); } return compat; } */ //This can be called with the root, and it then recurces through the tree until it finds a bipartition that conflicts //with the constraint. Unlike the ContainsBipartition functions, it doesn't actually require that the actual tree //to be checked has been made (i.e. that the swap has been done) - just that the bipartitions have been altered //as if it had. It therefore has lower overhead when checking swaps and should be preferred. The mask passed in should only be //should include any backbone constraint and/or a mask containing those taxa present in a growing tree bool Tree::RecursiveAllowedByConstraintWithMask(const Constraint &constr, const Bipartition *jointMask, const TreeNode *nd){ bool compat = true; if(nd->IsNotRoot()) compat = constr.BipartitionIsCompatibleWithConstraint(*nd->bipart, jointMask); if(compat==false) return compat; if(nd->left->IsInternal()) compat=RecursiveAllowedByConstraintWithMask(constr, jointMask, nd->left); if(compat==false) return compat; if(nd->left->next->IsInternal()) compat=RecursiveAllowedByConstraintWithMask(constr, jointMask, nd->left->next); if(compat==false) return compat; if(nd->left->next->next != NULL)//this would be the right dec of the root if(nd->left->next->next->IsInternal()) compat=RecursiveAllowedByConstraintWithMask(constr, jointMask, nd->left->next->next); return compat; } //DJZ 8-11-04 This version is only for the master doing SPRs on nodes that aren't in a subtree when subtree //mode is on. Basically the only difference is that if the ancestor of the cut node is the root, we need to //choose one of the other nonSubtree nodes to make a connector to avoid screwing up the subtree partitioning void Tree::SPRMutate(int cutnum, int broknum, FLOAT_TYPE optPrecision, const vector &nonSubNodes) { assert( numBranchesAdded > 3 ); assert(0);//needst to be verified TreeNode* cut = allNodes[cutnum]; assert(cut!=NULL); SweepDirtynessOverTree(cut->anc); TreeNode *connector; if(cut->anc->IsNotRoot()){ connector=cut->anc; } else{ bool foundAConn=false; connector=cut->prev; while(connector && !foundAConn)//try previous sibs {if(connector->left && find(nonSubNodes.begin(),nonSubNodes.end(),connector->nodeNum)!=nonSubNodes.end())//not a terminal foundAConn=true; else connector=connector->prev; } if(!foundAConn) {connector=cut->next;//that didn't work try the next sibs while(connector && !foundAConn)//try previous sibs {if(connector->left && find(nonSubNodes.begin(),nonSubNodes.end(),connector->nodeNum)!=nonSubNodes.end())//not a terminal foundAConn=true; else connector=connector->next; } } if(!foundAConn) return;//oops by chance we picked a trivial branch to cut, so it goes (if you want to call SPRMutate again that would make sure the tree always changes topo } SweepDirtynessOverTree(cut); TreeNode *replaceForConn; if(cut->anc->anc){ //cut is not connected to the root, so we can steal it's ancestor as the new connector if(cut==connector->left){ replaceForConn=connector->right; } else{ replaceForConn=connector->left; } replaceForConn->dlen+=connector->dlen; connector->SubstituteNodeWithRespectToAnc(replaceForConn); } else{//cut is connected to the root so we need to steal a non terminal sib node as the connector //this makes the root totally dirty MakeNodeDirty(root); //Disconnect cut from the root if(cut==root->left){ root->left=cut->next; cut->next->prev=NULL; } else if(cut==root->right){ root->right=cut->prev; cut->prev->next=NULL; } else{ assert(cut->prev==root->left && cut->next==root->right);//can only have a basal trifucation, or we're in trouble cut->prev->next=cut->next; cut->next->prev=cut->prev; } //root is now bifurcation //preserve branch length info if(root->right==connector) root->left->dlen+= connector->dlen; else root->right->dlen+= connector->dlen; //add the connectors two desccendants as descendants of the root assert(connector->right==connector->left->next); connector->SubstituteNodeWithRespectToAnc(connector->left); root->AddDes(connector->right); MakeNodeDirty(connector); } //establish correct topology for connector and cut nodes cut->anc=connector; connector->left=connector->right=cut; connector->next=connector->prev=connector->anc=cut->next=cut->prev=NULL; TreeNode *broken=allNodes[broknum]; broken->SubstituteNodeWithRespectToAnc(connector); connector->AddDes(broken); double len = max(broken->dlen*ZERO_POINT_FIVE, min_brlen); SetBranchLength(connector, len); SetBranchLength(broken, len); SweepDirtynessOverTree(connector, cut); MakeNodeDirty(connector); #ifdef OPT_DEBUG opt << "SPR\n"; #endif OptimizeBranchesWithinRadius(connector, optPrecision, 0, NULL); bipartCond = DIRTY; } void Tree::MimicTopo(const Tree *source){ //DZ 10-25-02 This should be much easier and faster using the allnodes array rather //than being recursive. Notice that even if the allNodes array of source is not //ordered according to nodeNum, the new tree will be. TreeNode **allNs=source->allNodes; for(int i=0;inumNodesTotal;i++){ if(allNs[i]->anc!=NULL) allNodes[i]->anc=allNodes[allNs[i]->anc->nodeNum]; else allNodes[i]->anc=NULL; if(allNs[i]->left!=NULL){ allNodes[i]->left=allNodes[allNs[i]->left->nodeNum]; allNodes[i]->right=allNodes[allNs[i]->right->nodeNum]; } else{ allNodes[i]->left=NULL; allNodes[i]->right=NULL; } if(allNs[i]->next!=NULL) allNodes[i]->next=allNodes[allNs[i]->next->nodeNum]; else allNodes[i]->next=NULL; if(allNs[i]->prev!=NULL) allNodes[i]->prev=allNodes[allNs[i]->prev->nodeNum]; else allNodes[i]->prev=NULL; allNodes[i]->dlen=allNs[i]->dlen; allNodes[i]->attached=true; } numNodesTotal=source->numNodesTotal; numNodesAdded=source->numNodesAdded; numTipsAdded=source->numTipsAdded; numBranchesAdded=source->numBranchesAdded; bipartCond = DIRTY; } //this version is used for just copying a subtree, //but assumes that the nodenums will match. Automatically //copys the cla indeces too void Tree::MimicTopo(TreeNode *nd, bool firstNode, bool sameModel){ //firstNode will be true if this is the base of the subtree to be copied. //if it is true, the anc, next and prev should not be copied for that node //Above the firstNode, nodes will be assumed to be the same nodenum in both trees. This //allows replicating nodeNums from a certain subtree up, but not in the rest of the tree //The cla info will only be copied if the models are identical for the individuals (sameModel==true) //otherwise the replicated nodes will be marked as dirty TreeNode *mnd; mnd=allNodes[nd->nodeNum]; mnd->attached=true; if(!firstNode){ //stuff that should not be done for the root of the subtree if(nd->anc){ mnd->anc=allNodes[nd->anc->nodeNum]; } else{ mnd->anc=NULL; } if(nd->next){ mnd->next=allNodes[nd->next->nodeNum]; MimicTopo(nd->next, false, sameModel); } else mnd->next=NULL; if(nd->prev){ mnd->prev=allNodes[nd->prev->nodeNum]; } else mnd->prev=NULL; } //this should apply to all nodes if(nd->left){ //if this is not a terminal mnd->left=allNodes[nd->left->nodeNum]; mnd->right=allNodes[nd->right->nodeNum]; MimicTopo(nd->left, false, sameModel); } else mnd->right=mnd->left=NULL;; //the clas are now taken care of back where this was called /* if(nd->left){ if(sameModel==true) mnd->CopyOneClaIndex(nd, claMan, DOWN); else mnd->claIndexDown=claMan->SetDirty(mnd->claIndexDown); } */ mnd->dlen=nd->dlen; bipartCond = DIRTY; } void Tree::CopyClaIndecesInSubtree(const TreeNode *from, bool remove){ //the bool argument "remove" designates whether the tree currently has cla arrays //assigned to it or not (if not, it must have come from the unused tree vector) //note that we assume that the node numbers and topologies match within the subtree assert(from->anc); //do the clas down if(remove) claMan->DecrementCla(allNodes[from->nodeNum]->claIndexDown); allNodes[from->nodeNum]->claIndexDown=from->claIndexDown; if(allNodes[from->nodeNum]->claIndexDown != -1) claMan->IncrementCla(allNodes[from->nodeNum]->claIndexDown); //do the clas up left if(remove) claMan->DecrementCla(allNodes[from->nodeNum]->claIndexUL); allNodes[from->nodeNum]->claIndexUL=from->claIndexUL; if(allNodes[from->nodeNum]->claIndexUL != -1) claMan->IncrementCla(allNodes[from->nodeNum]->claIndexUL); //do the clas up right if(remove) claMan->DecrementCla(allNodes[from->nodeNum]->claIndexUR); allNodes[from->nodeNum]->claIndexUR=from->claIndexUR; if(allNodes[from->nodeNum]->claIndexUR != -1) claMan->IncrementCla(allNodes[from->nodeNum]->claIndexUR); if(from->left->IsInternal()) CopyClaIndecesInSubtree(from->left, remove); if(from->right->IsInternal()) CopyClaIndecesInSubtree(from->right, remove); } void Tree::DirtyNodesInSubtree(TreeNode *nd){ MakeNodeDirty(nd); if(nd->left->IsInternal()) DirtyNodesInSubtree(nd->left); if(nd->right->IsInternal()) DirtyNodesInSubtree(nd->right); } void Tree::RescaleRateHet(CondLikeArray *destCLA, int dataIndex){ SequenceData *curData = dataPart->GetSubset(dataIndex); FLOAT_TYPE *destination=destCLA->arr; int *underflow_mult=destCLA->underflow_mult; const int *c= curData->GetCounts(); const int nsites = destCLA->NChar(); const int nRateCats = destCLA->NRateCats(); //check if any clas are getting close to underflow #ifdef UNIX posix_madvise(destination, sizeof(FLOAT_TYPE)*4*nRateCats*nsites, POSIX_MADV_SEQUENTIAL); posix_madvise(underflow_mult, sizeof(int)*nsites, POSIX_MADV_SEQUENTIAL); #endif FLOAT_TYPE large1 = 0.0, large2 = 0.0; for(int i=0;i 0){ #else if(1){ #endif //for some reason optimzation in gcc 2.95 breaks the more optimal version of this code //this version is safer #if defined(__GNUC__) && __GNUC__ < 3 small1 = FLT_MAX; large1 = FLT_MIN; for(int r=0;r largest_abs) { largest_abs = absvalue; large1 = destination[j]; } } #else large1= (destination[0] > destination[2] ? destination[0] : destination[2]); large2= (destination[1] > destination[3] ? destination[1] : destination[3]); large1= (large1 > large2 ? large1 : large2); for(int r=1;r destination[2 + r*4] ? destination[0 + r*4] : destination[2 + r*4]); large1= (large1 > large2 ? large1 : large2); large2= (destination[1 + r*4] > destination[3 + r*4] ? destination[1 + r*4] : destination[3 + r*4]); large1= (large1 > large2 ? large1 : large2); } #endif #endif if(large1 < rescaleBelow){ //we aren't rescaling enough if(large1 < reduceRescaleBelow){ //but the frequency can be increased. throw out of here, reduce the rescaleEvery and try scoring again if(rescaleEvery > 2){ outman.UserMessage("WARNING: Increasing rescaling frequency (site = %d L = %g data = %d)", i, large1, dataIndex); throw(1); } //uh oh, we must have already reduced rescale as far as possible, and it still isn't enough. Bail out. else if(large1 < bailOutBelow){ outman.UserMessage("Can't rescale sufficiently, exiting (site = %d L = %g data = %d)", i, large1, dataIndex); outman.UserMessage("You might try providing a better starting tree, or checking the accuracy of your alignment"); throw(1); } //we can't rescale any more frequently, but we're not yet at critical levels else{ outman.UserMessage("WARNING: Can't increase rescaling further (site = %d L = %g data = %d)", i, large1, dataIndex); } } int index = 0; while(((index + 1) < RESCALE_ARRAY_LENGTH) && (Tree::rescalePrecalcThresh[index + 1] > large1)){ index++; } int incr = Tree::rescalePrecalcIncr[index]; underflow_mult[i]+=incr; FLOAT_TYPE mult=Tree::rescalePrecalcMult[index]; assert(large1 * mult < 1.0); for(int r=0;r -1) break; #endif } else{ #ifdef OPEN_MP //this is a little strange, but dest only needs to be advanced in the case of OMP //because sections of the CLAs corresponding to sites with count=0 are skipped //over in OMP instead of being eliminated destination += 4 * nRateCats; #endif } } destCLA->rescaleRank=0; } void Tree::RescaleRateHetNState(CondLikeArray *destCLA, int dataIndex){ SequenceData *curData = dataPart->GetSubset(dataIndex); FLOAT_TYPE *destination=destCLA->arr; int *underflow_mult=destCLA->underflow_mult; const int nsites = destCLA->NChar(); const int nstates = destCLA->NStates(); const int nRateCats = destCLA->NRateCats(); const int *c = curData->GetCounts(); //check if any clas are getting close to underflow #ifdef UNIX posix_madvise(destination, sizeof(FLOAT_TYPE)*nstates*nRateCats*nsites, POSIX_MADV_SEQUENTIAL); posix_madvise(underflow_mult, sizeof(int)*nsites, POSIX_MADV_SEQUENTIAL); #endif FLOAT_TYPE large1 = 0.0; for(int i=0;i 0){ #else if(1){ #endif #if (defined(_MSC_VER) || defined(__INTEL_COMPILER)) && !defined(SINGLE_PRECISION_FLOATS) //This is a neat trick for quickly finding the approximately largest //value of an array of doubles, but it only works on littleendian //systems. There's no easy way of detecting endianness at compile //time that I've been able to find, but since x86 machines are always //littleendian, this should be safe int size = nstates * nRateCats; unsigned int absvalue, largest_abs = 0; for (int j = 0; j < size; j++) { // Get upper 32 bits of a[i] and shift out sign bit: absvalue = *((unsigned int*)&destination[j] + 1) * 2; // Find numerically largest element (approximately): if (absvalue > largest_abs) { largest_abs = absvalue; large1 = destination[j]; } } assert(largest_abs > 0); #else large1 = (destination[0] > destination[1]) ? destination[0] : destination[1]; for(int s=2;s large1) ? destination[s] : large1; } #endif if(large1 < rescaleBelow){ //we aren't rescaling enough if(large1 < reduceRescaleBelow){ //but the frequency can be increased. throw out of here, reduce the rescaleEvery and try scoring again if(rescaleEvery > 2){ outman.UserMessage("WARNING: Increasing rescaling frequency (site = %d L = %g data = %d)", i, large1, dataIndex); throw(1); } //uh oh, we must have already reduced rescale as far as possible, and it still isn't enough. Bail out. else if(large1 < bailOutBelow){ //poor blens can very rarely kill a gap model if(someOrientedGap) throw(UnscoreableException()); else{ outman.UserMessage("Can't rescale sufficiently, exiting (site = %d L = %g data = %d)", i, large1, dataIndex); outman.UserMessage("You might try providing a better starting tree, or checking the accuracy of your alignment"); throw(1); } } //we can't rescale any more frequently, but we're not yet at critical levels else{ outman.UserMessage("WARNING: Can't increase rescaling further (site = %d L = %g data = %d)", i, large1, dataIndex); } } int index = 0; while(((index + 1) < RESCALE_ARRAY_LENGTH) && (Tree::rescalePrecalcThresh[index + 1] > large1)){ index++; } int incr = Tree::rescalePrecalcIncr[index]; underflow_mult[i]+=incr; FLOAT_TYPE mult=Tree::rescalePrecalcMult[index]; assert(large1 * mult < 1.0); for(int q=0;q -1) break; #endif } else{ #ifdef OPEN_MP //this is a little strange, but dest only needs to be advanced in the case of OMP //because sections of the CLAs corresponding to sites with count=0 are skipped //over in OMP instead of being eliminated destination += nstates * nRateCats; #endif } } destCLA->rescaleRank=0; } int Tree::ConditionalLikelihoodRateHet(int direction, TreeNode* nd, bool returnUnscaledSitePosteriors /*=false*/){ //note that fillFinalCLA just refers to whether we actually want to calc a CLA //representing the contribution of the entire tree vs just calcing the score //The only reason I can think of for doing that is to calc internal state probs //the fuction will then return a pointer to the CLA /*NOTE - if a dummy gap rooting is being used, it is assumed that this will be called with nd = dummy->anc*/ assert(this != NULL); calcCount++; CondLikeArraySet *destCLA=NULL; TreeNode* Lchild, *Rchild; CondLikeArraySet *LCLA=NULL, *RCLA=NULL, *partialCLA=NULL; //FLOAT_TYPE *Rprmat = NULL, *Lprmat = NULL; FLOAT_TYPE blen1, blen2; if(direction != ROOT){ //the only complicated thing here will be to set up the two children depending on the direction //get all of the clas, underflow mults and pmat set up here, then the actual calc loops below //won't depend on direction if(direction==DOWN){ Lchild=nd->left; Rchild=nd->right; if(Lchild->IsInternal()) LCLA=GetClaDown(Lchild); if(Rchild->IsInternal()) RCLA=GetClaDown(Rchild); blen1 = Lchild->dlen; blen2 = Rchild->dlen; } else if(direction==UPRIGHT || direction==UPLEFT){ if(nd->anc){ Lchild=nd->anc; if(nd->anc->left==nd) LCLA=GetClaUpLeft(Lchild); else if(nd->anc->right==nd) LCLA=GetClaUpRight(Lchild); else//watch out here. This is the case in which we want the cla at the root including the left //and right, but not the middle. We will confusingly store this in the root's DOWN cla LCLA=GetClaDown(Lchild); blen1 = nd->dlen; if(direction==UPRIGHT) Rchild=nd->left; else Rchild=nd->right; } else{ if(direction==UPRIGHT){ Lchild=nd->left; Rchild=nd->left->next; } else{ Lchild=nd->left->next; Rchild=nd->right; } if(Lchild->IsInternal()) LCLA=GetClaDown(Lchild); blen1 = Lchild->dlen; } if(Rchild->IsInternal()) RCLA=GetClaDown(Rchild); blen2 = Rchild->dlen; } if(direction==DOWN) destCLA=GetClaDown(nd, false); else if(direction==UPRIGHT) destCLA=GetClaUpRight(nd, false); else if(direction==UPLEFT) destCLA=GetClaUpLeft(nd, false); UpdateCLAs(destCLA, LCLA, RCLA, Lchild, Rchild, blen1, blen2); } if(direction==ROOT){ //at the root we need to include the contributions of 3 branches. Check if we have a //valid CLA that already represents two of these three. If so we can save a bit of //computation. This will mainly be the case during blen optimization, when when we //only change one of the branches again and again. TreeNode *child; CondLikeArraySet *childCLA=NULL; /*Here the dummy root (if used) needs to be the last taxon combined. This rooting isn't currently necessary except in the case of oriented gap models. Otherwise it becomes difficult to enforce the single-insert-on-tree rule. Note that this assumes that this function was called with dummy->anc, so that it must be one of the descendent taxa.*/ if(rootWithDummy){ if(nd->left == dummyRoot){ child=nd->left; assert(!child->IsInternal()); partialCLA = GetClaUpLeft(nd, claMan->IsDirty(nd->claIndexUL)); } else if(nd->right == dummyRoot){ child=nd->right; assert(!child->IsInternal()); partialCLA = GetClaUpRight(nd, claMan->IsDirty(nd->claIndexUR)); } else if(nd->left->next == dummyRoot){ child = nd->left->next; assert(!child->IsInternal()); partialCLA = GetClaDown(nd, claMan->IsDirty(nd->claIndexDown)); } else assert(0); blen1 = child->dlen; } else{//not dummy rooting if(claMan->IsDirty(nd->claIndexUL) == false){ partialCLA=GetClaUpLeft(nd, false); child=nd->left; if(child->IsInternal()){ childCLA=GetClaDown(child, true); } blen1 = child->dlen; } else if(claMan->IsDirty(nd->claIndexUR) == false){ partialCLA=GetClaUpRight(nd, false); child=nd->right; if(child->IsInternal()){ childCLA=GetClaDown(child, true); } blen1 = child->dlen; } else{//both of the UP clas must be dirty. We'll use the down one as the //partial, and calc it now if necessary if(claMan->IsDirty(nd->claIndexDown) == true) partialCLA=GetClaDown(nd, true); else partialCLA=GetClaDown(nd, false); if(nd->anc!=NULL){ child=nd->anc; if(child->left==nd){ childCLA=GetClaUpLeft(child, true); } else if(child->right==nd){ childCLA=GetClaUpRight(child, true); } else{ //the node down that we want to get must be the root, and this //node must be it's middle des. Remember that the cla for that //direction is stored as the root DOWN direction childCLA=GetClaDown(child); } blen1 = nd->dlen; } else{ child=nd->left->next; if(child->IsInternal()){ childCLA=GetClaDown(child, true); } blen1 = child->dlen; } } } if(returnUnscaledSitePosteriors == false) GetTotalScore(partialCLA, childCLA, child, blen1); else return FillStatewiseUnscaledPosteriors(partialCLA, childCLA, child, blen1); /* mod->CalcPmats(blen1, -1.0, Lprmat, Rprmat); if(fillFinalCLA==false){ if(childCLA!=NULL){//if child is internal ProfScoreInt.Start(); if(modSpec.IsNucleotide()) lnL = GetScorePartialInternalRateHet(partialCLA, childCLA, &Lprmat[0]); else lnL = GetScorePartialInternalNState(partialCLA, childCLA, &Lprmat[0]); ProfScoreInt.Stop(); } else{ ProfScoreTerm.Start(); if(modSpec.IsNucleotide()) lnL = GetScorePartialTerminalRateHet(partialCLA, &Lprmat[0], child->tipData); else lnL = GetScorePartialTerminalNState(partialCLA, &Lprmat[0], child->tipData); ProfScoreTerm.Stop(); } } else{ //this is only for inferring internal states //careful! This will have to be returned manually!! int wholeTreeIndex=claMan->AssignClaHolder(); claMan->FillHolder(wholeTreeIndex, ROOT); claMan->ReserveCla(wholeTreeIndex); if(childCLA!=NULL)//if child is internal CalcFullCLAPartialInternalRateHet(claMan->GetCla(wholeTreeIndex), childCLA, &Lprmat[0], partialCLA); else CalcFullCLAPartialTerminalRateHet(claMan->GetCla(wholeTreeIndex), partialCLA, &Lprmat[0], child->tipData); return wholeTreeIndex; } */ } return -1; } void Tree::GetTotalScore(CondLikeArraySet *partialCLAset, CondLikeArraySet *childCLAset, TreeNode *child, FLOAT_TYPE blen1){ FLOAT_TYPE *Rprmat = NULL, *Lprmat = NULL; CondLikeArray *partialCLA=NULL, *childCLA=NULL; FLOAT_TYPE modlnL; lnL = ZERO_POINT_ZERO; //NOTE: for sitelike output the caller should already have set the sitelike mode on the tree and prepared //the sitelike output file (ofprefix + ".sitelikes.log"), adding a header or clearing it out first. The sitelike //level should generally be negative when partitioned so that each subset appends on to the file. See how //this is done in PerformSearch. This function IS responsible for resetting the sitelike level and turning off //sitelike output for future scorings. for(vector::iterator specs = claSpecs.begin();specs != claSpecs.end();specs++){ Model *mod = modPart->GetModel((*specs).modelIndex); if(! mod->IsOrientedGap())//we don't actually use a pmat with final scoring in gap model, so no need to calc it here mod->CalcPmats(blen1 * modPart->SubsetRate((*specs).dataIndex), -1.0, Lprmat, Rprmat); partialCLA = partialCLAset->GetCLA((*specs).claIndex); bool isNucleotide = mod->IsNucleotide(); if(childCLAset != NULL) childCLA = childCLAset->GetCLA((*specs).claIndex); if(childCLA!=NULL){//if child is internal //when doing oriented gap we assume that the tree must be rooted, thus the child must be the dummy tip assert(! mod->IsOrientedGap()); ProfScoreInt.Start(); if(isNucleotide) modlnL = GetScorePartialInternalRateHet(partialCLA, childCLA, &Lprmat[0], (*specs).modelIndex, (*specs).dataIndex); else modlnL = GetScorePartialInternalNState(partialCLA, childCLA, &Lprmat[0], (*specs).modelIndex, (*specs).dataIndex); ProfScoreInt.Stop(); } else{ ProfScoreTerm.Start(); if(isNucleotide) modlnL = GetScorePartialTerminalRateHet(partialCLA, &Lprmat[0], child->tipData[(*specs).dataIndex], (*specs).modelIndex, (*specs).dataIndex); else if(mod->IsOrientedGap()){ modlnL = GetScorePartialTerminalOrientedGap(partialCLA, &Lprmat[0], child->tipData[(*specs).dataIndex], (*specs).modelIndex, (*specs).dataIndex); } else modlnL = GetScorePartialTerminalNState(partialCLA, &Lprmat[0], child->tipData[(*specs).dataIndex], (*specs).modelIndex, (*specs).dataIndex); ProfScoreTerm.Stop(); } lnL += modlnL; } //sitelike output is non-persistent, so clear it out here sitelikeLevel = 0; } //this is more or less a clone of GetTotalScore that fills a cla set with the necessary values to calculate internal state reconstructions //and returns the corresponding cla index int Tree::FillStatewiseUnscaledPosteriors(CondLikeArraySet *partialCLAset, CondLikeArraySet *childCLAset, TreeNode *child, FLOAT_TYPE blen1){ FLOAT_TYPE *Rprmat = NULL, *Lprmat = NULL; CondLikeArray *partialCLA=NULL, *childCLA=NULL, *destCLA=NULL; //careful! The cla will have to be returned manually by the caller int posteriorClaIndex=claMan->AssignClaHolder(); claMan->FillHolder(posteriorClaIndex, ROOT); claMan->ReserveCla(posteriorClaIndex); CondLikeArraySet *destCLAset = claMan->GetCla(posteriorClaIndex); //note that the NState functions are used here for both nuc and other datatypes for(vector::iterator specs = claSpecs.begin();specs != claSpecs.end();specs++){ Model *mod = modPart->GetModel((*specs).modelIndex); ModelSpecification *modSpec = modSpecSet.GetModSpec((*specs).modelIndex); assert( modSpec->IsNucleotide() || modSpec->IsAminoAcid() || modSpec->IsCodon() ); mod->CalcPmats(blen1 * modPart->SubsetRate((*specs).dataIndex), -1.0, Lprmat, Rprmat); partialCLA = partialCLAset->GetCLA((*specs).claIndex); destCLA = destCLAset->GetCLA((*specs).claIndex); if(childCLAset != NULL) childCLA = childCLAset->GetCLA((*specs).claIndex); if(childCLA!=NULL){//if child is internal GetStatewiseUnscaledPosteriorsPartialInternalNState(destCLA, partialCLA, childCLA, &Lprmat[0], (*specs).modelIndex, (*specs).dataIndex); } else{ GetStatewiseUnscaledPosteriorsPartialTerminalNState(destCLA, partialCLA, &Lprmat[0], child->tipData[(*specs).dataIndex], (*specs).modelIndex, (*specs).dataIndex); } } return posteriorClaIndex; } void Tree::UpdateCLAs(CondLikeArraySet *destCLAset, CondLikeArraySet *firstCLAset, CondLikeArraySet *secCLAset, TreeNode *firstChild, TreeNode *secChild, FLOAT_TYPE blen1, FLOAT_TYPE blen2){ FLOAT_TYPE *Rprmat = NULL, *Lprmat = NULL; CondLikeArray *destCLA=NULL, *firstCLA=NULL, *secCLA=NULL; for(vector::iterator specs = claSpecs.begin();specs != claSpecs.end();specs++){ Model *mod = modPart->GetModel((*specs).modelIndex); mod->CalcPmats(blen1 * modPart->SubsetRate((*specs).dataIndex), blen2 * modPart->SubsetRate((*specs).dataIndex), Lprmat, Rprmat); destCLA = destCLAset->GetCLA((*specs).claIndex); bool isNucleotide = mod->IsNucleotide(); if(firstCLAset != NULL) firstCLA = firstCLAset->GetCLA((*specs).claIndex); if(secCLAset != NULL) secCLA = secCLAset->GetCLA((*specs).claIndex); if(firstCLAset!=NULL && secCLAset!=NULL){ //two internal children ProfIntInt.Start(); if(isNucleotide) CalcFullCLAInternalInternal(destCLA, firstCLA, secCLA, &Lprmat[0], &Rprmat[0], (*specs).modelIndex, (*specs).dataIndex); else if(mod->IsOrientedGap()) CalcFullCLAOrientedGap(destCLA, &Lprmat[0], &Rprmat[0], firstCLA, secCLA, NULL, NULL, (*specs).modelIndex, (*specs).dataIndex); else CalcFullCLAInternalInternalNState(destCLA, firstCLA, secCLA, &Lprmat[0], &Rprmat[0], (*specs).modelIndex, (*specs).dataIndex); ProfIntInt.Stop(); } else if(firstCLAset==NULL && secCLAset==NULL){ //two terminal children ProfTermTerm.Start(); if(isNucleotide) CalcFullCLATerminalTerminal(destCLA, &Lprmat[0], &Rprmat[0], firstChild->tipData[(*specs).dataIndex], secChild->tipData[(*specs).dataIndex], (*specs).modelIndex, (*specs).dataIndex); else if(mod->IsOrientedGap()) CalcFullCLAOrientedGap(destCLA, &Lprmat[0], &Rprmat[0], NULL, NULL, firstChild->tipData[(*specs).dataIndex], secChild->tipData[(*specs).dataIndex], (*specs).modelIndex, (*specs).dataIndex); else CalcFullCLATerminalTerminalNState(destCLA, &Lprmat[0], &Rprmat[0], firstChild->tipData[(*specs).dataIndex], secChild->tipData[(*specs).dataIndex], (*specs).modelIndex, (*specs).dataIndex); ProfTermTerm.Stop(); } else{ //one terminal, one internal ProfIntTerm.Start(); if(isNucleotide == false){ if(mod->IsOrientedGap()){ if(firstCLAset==NULL) CalcFullCLAOrientedGap(destCLA, &Lprmat[0], &Rprmat[0], NULL, secCLA, firstChild->tipData[(*specs).dataIndex], NULL, (*specs).modelIndex, (*specs).dataIndex); else CalcFullCLAOrientedGap(destCLA, &Lprmat[0], &Rprmat[0], firstCLA, NULL, NULL, secChild->tipData[(*specs).dataIndex], (*specs).modelIndex, (*specs).dataIndex); } else{ if(firstCLAset==NULL) CalcFullCLAInternalTerminalNState(destCLA, secCLA, &Rprmat[0], &Lprmat[0], firstChild->tipData[(*specs).dataIndex], (*specs).modelIndex, (*specs).dataIndex); else CalcFullCLAInternalTerminalNState(destCLA, firstCLA, &Lprmat[0], &Rprmat[0], secChild->tipData[(*specs).dataIndex], (*specs).modelIndex, (*specs).dataIndex); } } else{ #ifdef OPEN_MP if(firstCLA==NULL){ assert(firstChild->ambigMap.size() > (*specs).dataIndex); assert(firstChild->ambigMap[(*specs).dataIndex] != NULL); } else{ assert(secChild->ambigMap.size() > (*specs).dataIndex); assert(secChild->ambigMap[(*specs).dataIndex] != NULL); } if(firstCLA==NULL) CalcFullCLAInternalTerminal(destCLA, secCLA, &Rprmat[0], &Lprmat[0], firstChild->tipData[(*specs).dataIndex], firstChild->ambigMap[(*specs).dataIndex], (*specs).modelIndex, (*specs).dataIndex); else CalcFullCLAInternalTerminal(destCLA, firstCLA, &Lprmat[0], &Rprmat[0], secChild->tipData[(*specs).dataIndex], secChild->ambigMap[(*specs).dataIndex], (*specs).modelIndex, (*specs).dataIndex); } #else if(firstCLA==NULL) CalcFullCLAInternalTerminal(destCLA, secCLA, &Rprmat[0], &Lprmat[0], firstChild->tipData[(*specs).dataIndex], NULL, (*specs).modelIndex, (*specs).dataIndex); else CalcFullCLAInternalTerminal(destCLA, firstCLA, &Lprmat[0], &Rprmat[0], secChild->tipData[(*specs).dataIndex], NULL, (*specs).modelIndex, (*specs).dataIndex); } #endif ProfIntTerm.Stop(); } if(destCLA->rescaleRank >= rescaleEvery){ ProfRescale.Start(); if(isNucleotide) RescaleRateHet(destCLA, (*specs).dataIndex); else RescaleRateHetNState(destCLA, (*specs).dataIndex); ProfRescale.Stop(); } } } int Tree::Score(int rootNodeNum /*=0*/){ TreeNode *rootNode=allNodes[rootNodeNum]; #ifdef EQUIV_CALCS if(dirtyEQ){ ProfEQVectors.Start(); root->SetEquivalentConditionalVectors(data); ProfEQVectors.Stop(); dirtyEQ=false; } #endif bool scoreOK=true; do{ try{ scoreOK=true; if(rootWithDummy){ assert(rootNodeNum == 0); ConditionalLikelihoodRateHet( ROOT, dummyRoot->anc); } else ConditionalLikelihoodRateHet( ROOT, rootNode); } #if defined(NDEBUG) catch(int){ #else catch(int err){ #endif assert(err==1); scoreOK=false; MakeAllNodesDirty(); rescaleEvery -= 2; ofstream resc("rescale.log", ios::app); resc << "rescale reduced to " << rescaleEvery << endl; resc.close(); if(rescaleEvery<2) throw(ErrorException("Problem with rescaling during tree scoring.\nPlease report this error (and the details of your analysis) to garli.support@gmail.com.")); } }while(scoreOK==false); return 1; } /* FLOAT_TYPE Tree::SubTreeScore( TreeNode *nd){ //calculates the likelihood of the tree above the node passed in FLOAT_TYPE lnL = 0.0; int nSites = data->NChar(); int ck; if(claMan->IsDirty(nd->claIndexDown)){ if(mod->NRateCats()==1) ConditionalLikelihood( DOWN, nd); else ConditionalLikelihoodRateHet(DOWN, nd); } FLOAT_TYPE *cla=claMan->GetCla(nd->claIndexDown)->arr; int *underflow_mult=claMan->GetCla(nd->claIndexDown)->underflow_mult; // loop over all patterns long FLOAT_TYPE Lk; FLOAT_TYPE siteL; int ufcount=0; const int *countit=data->GetCounts(); if(mod->PropInvar()==0.0){ if(mod->NRateCats()==1){//no invariants or gamma for( int k = 0; k < nSites; k++ ){ Lk = mod->Pi(0) * cla[0] + mod->Pi(1) * cla[1] + mod->Pi(2) * cla[2] + mod->Pi(3) * cla[3]; if(Lk<1e-300){ printf("Underflow! site %d, multiplier %d\n", k, underflow_mult[k]); ufcount++; } cla+=4; siteL = (log( Lk ) - underflow_mult[k]); lnL += ( *countit++ * siteL); } } else{//gamma, no invariants for( int k = 0; k < nSites; k++ ){ Lk = mod->Pi(0) * cla[0] + mod->Pi(1) * cla[1] + mod->Pi(2) * cla[2] + mod->Pi(3) * cla[3]; Lk += mod->Pi(0) * cla[4] + mod->Pi(1) * cla[5] + mod->Pi(2) * cla[6] + mod->Pi(3) * cla[7]; Lk += mod->Pi(0) * cla[8] + mod->Pi(1) * cla[9] + mod->Pi(2) * cla[10] + mod->Pi(3) * cla[11]; Lk += mod->Pi(0) * cla[12] + mod->Pi(1) * cla[13] + mod->Pi(2) * cla[14] + mod->Pi(3) * cla[15]; if(Lk<1e-300){ printf("Underflow! site %d, multiplier %d\n", k, underflow_mult[k]); ufcount++; } cla+=16; //this is hard coded for 4 equal sized rate cats siteL = (log( Lk*.25 ) - underflow_mult[k]); lnL += ( *countit * siteL); countit++; } } } else { FLOAT_TYPE prI=mod->PropInvar(); int lastConst=data->LastConstant(); const int *conBases=data->GetConstBases(); if(mod->NRateCats()==1){//invariants without gamma for( int k = 0; k < nSites; k++ ){ assert(0); //this isn't valid :mod->Pi(conBases[k]), because the con bases are coded as 1 2 4 8 for amiguity Lk = mod->Pi(0) * cla[0] + mod->Pi(1) * cla[1] + mod->Pi(2) * cla[2] + mod->Pi(3) * cla[3]; if(Lk<1e-300){ printf("Underflow! site %d, multiplier %d\n", k, underflow_mult[k]); ufcount++; } cla+=4; if(k > lastConst){ siteL = log( Lk * (1.0-prI)) - underflow_mult[k]; lnL += ( *countit++ * siteL); } else{ siteL = log( Lk * (1.0-prI) + (prI * mod->Pi(conBases[k])) * exp((FLOAT_TYPE)underflow_mult[k])); lnL += ( *countit++ * (siteL + underflow_mult[k])); } } } else{//gamma and invariants FLOAT_TYPE scaledGammaProp=0.25 * (1.0-prI); assert(0); //this isn't valid :mod->Pi(conBases[k]), because the con bases are coded as 1 2 4 8 for amiguity for( int k = 0; k < nSites; k++ ){ Lk = mod->Pi(0) * cla[0] + mod->Pi(1) * cla[1] + mod->Pi(2) * cla[2] + mod->Pi(3) * cla[3]; Lk += mod->Pi(0) * cla[4] + mod->Pi(1) * cla[5] + mod->Pi(2) * cla[6] + mod->Pi(3) * cla[7]; Lk += mod->Pi(0) * cla[8] + mod->Pi(1) * cla[9] + mod->Pi(2) * cla[10] + mod->Pi(3) * cla[11]; Lk += mod->Pi(0) * cla[12] + mod->Pi(1) * cla[13] + mod->Pi(2) * cla[14] + mod->Pi(3) * cla[15]; if(Lk<1e-300){ printf("Underflow! site %d, multiplier %d\n", k, underflow_mult[k]); ufcount++; } cla+=16; if(k > lastConst){ siteL = log( Lk * scaledGammaProp) - underflow_mult[k]; lnL += ( *countit++ * siteL); } else{ siteL = log( Lk * scaledGammaProp + (prI * mod->Pi(conBases[k])) * exp((FLOAT_TYPE)underflow_mult[k])); lnL += ( *countit++ * (siteL + underflow_mult[k])); } } } } return lnL; } */ /* FLOAT_TYPE Tree::SubTreeScoreRateHet( TreeNode *nd){ //calculates the likelihood of the tree above the node passed in FLOAT_TYPE sublnL = 0.0; int nSites = data->NChar(); int ck; if(claMan->IsDirty(nd->claIndexDown)) ConditionalLikelihoodRateHet(DOWN, nd); FLOAT_TYPE *cla=claMan->GetCla(nd->claIndexDown)->arr; int *underflow_mult=claMan->GetCla(nd->claIndexDown)->underflow_mult; // loop over all patterns long FLOAT_TYPE Lk; int ufcount=0; const int *countit=data->GetCounts(); for( int k = 0; k < nSites; k++ ){ Lk = mod->Pi(0) * cla[0] + mod->Pi(1) * cla[1] + mod->Pi(2) * cla[2] + mod->Pi(3) * cla[3]; Lk += mod->Pi(0) * cla[4] + mod->Pi(1) * cla[5] + mod->Pi(2) * cla[6] + mod->Pi(3) * cla[7]; Lk += mod->Pi(0) * cla[8] + mod->Pi(1) * cla[9] + mod->Pi(2) * cla[10] + mod->Pi(3) * cla[11]; Lk += mod->Pi(0) * cla[12] + mod->Pi(1) * cla[13] + mod->Pi(2) * cla[14] + mod->Pi(3) * cla[15]; if(Lk<1e-300){ printf("Underflow! site %d, multiplier %d\n", k, underflow_mult[k]); ufcount++; } cla+=16; sublnL += ( *countit * (log( Lk*.25 ) - underflow_mult[k]) ); countit++; } return sublnL; } */ void Tree::TraceDirtynessToRoot(TreeNode *nd){ SweepDirtynessOverTree(nd); /* while(nd){ if(nd->nodeNum==0 || nd->nodeNum>numTipsTotal) nd->claIndexDown=claMan->SetDirty(nd->claIndexDown, true); nd=nd->anc; } */ } void Tree::SweepDirtynessOverTree(TreeNode *nd, TreeNode *from/*=NULL*/){ lnL=-1; //this will be the case if we are simply making the tree structure but //never intend to score it if(nd->IsInternal() && nd->claIndexDown == -1){ return; } if(from==NULL){ //if this is the branch where the dirtyness starts if(nd->IsInternal()){ nd->claIndexUL=claMan->SetDirty(nd->claIndexUL); nd->claIndexUR=claMan->SetDirty(nd->claIndexUR); if(nd->left->IsInternal()) SweepDirtynessOverTree(nd->left, nd); if(nd->right->IsInternal()) SweepDirtynessOverTree(nd->right, nd); } if(nd->anc!=NULL) SweepDirtynessOverTree(nd->anc, nd); } else{ //if the change was below, invalidating clas above, also if the change //was on the path connecting to the central des of the root if(from==nd->anc || (nd->IsRoot() && from==nd->left->next)){ nd->claIndexUL=claMan->SetDirty(nd->claIndexUL); nd->claIndexUR=claMan->SetDirty(nd->claIndexUR); if(nd->left->IsInternal()) SweepDirtynessOverTree(nd->left, nd); if(nd->right->IsInternal()) SweepDirtynessOverTree(nd->right, nd); } else if(from==nd->left){ nd->claIndexUR=claMan->SetDirty(nd->claIndexUR); nd->claIndexDown=claMan->SetDirty(nd->claIndexDown); if(nd->right->IsInternal()) SweepDirtynessOverTree(nd->right, nd); if(nd->anc!=NULL) SweepDirtynessOverTree(nd->anc, nd); else if(nd->left->next->IsInternal()) SweepDirtynessOverTree(nd->left->next, nd); } else if(from==nd->right){ nd->claIndexUL=claMan->SetDirty(nd->claIndexUL); nd->claIndexDown=claMan->SetDirty(nd->claIndexDown); if(nd->left->IsInternal()) SweepDirtynessOverTree(nd->left, nd); if(nd->anc!=NULL) SweepDirtynessOverTree(nd->anc, nd); else if(nd->left->next->IsInternal()) SweepDirtynessOverTree(nd->left->next, nd); } } } void Tree::TraceDirtynessToNode(TreeNode *nd, int tonode){ if(nd->nodeNum==0 || nd->nodeNum>numTipsTotal) nd->claIndexDown=claMan->SetDirty(nd->claIndexDown); while(nd->nodeNum!=tonode){ nd=nd->anc; if(nd->nodeNum==0 || nd->nodeNum>numTipsTotal) nd->claIndexDown=claMan->SetDirty(nd->claIndexDown); } } void Tree::SortAllNodesArray(){ //this function will simply sort the nodes in the allNodes **TreeNode array by their nodeNum //having the nodes always in order will make some other operations much simpler //the root(nodenum=0) and terminals(nodenums=1->Ntax) should already be in order, so just sort //starting at Ntax+1. I'm making up a kind of wacky algorithm for this. DZ 10-30-02 for(int i=numTipsTotal+1;inodeNum!=i){ while(allNodes[i]->nodeNum!=i){ TreeNode *toPlace=allNodes[i]; int rightPlace=toPlace->nodeNum; TreeNode *temp=allNodes[rightPlace];//copy the node that is in toPlace's rightful place allNodes[rightPlace]=allNodes[i]; //put toPlace where it belongs allNodes[i]=temp; //put the node that was moved in allNodes[i]; } } } } void Tree::EliminateNode(int nn){ //DZ 10-30-02 this function will permenantly get rid of a node and correct all of the other nodeNums so that //there isn't a hole in the middle. I think this just needs to be called when an inital tree is made trifrcating //at the root. This isn't the prettiest thing, but I can't think of an obvious way to make a tree that has 3 des //from the root in the first place delete allNodes[nn]; for(int i=nn;inodeNum=i; } allNodes[numNodesTotal-1]=NULL; numNodesTotal--; //now make a new allNodes array of the proper length TreeNode **newNodes=new TreeNode*[numNodesTotal]; memcpy(newNodes, allNodes, sizeof(TreeNode*)*numNodesTotal); delete []allNodes; allNodes=newNodes; } //CAREFUL! This is called from CheckBalance and assumes that this tree //does not share CLAs with any other. void Tree::RotateNodesAtRoot(TreeNode *newroot){ //DZ 11-3-02 This can be used to rebalance the tree //I'm assuming that this will be called with one of the des of the root; assert(newroot->anc==root); assert(newroot->IsInternal()); //detach the newroot from root, making it bifurcating if(newroot==root->left){ root->left=newroot->next; root->left->prev=NULL; //DEBUG int temp = root->claIndexDown; root->claIndexDown = root->claIndexUL; root->claIndexUL = temp; } else if(newroot==root->left->next){ root->left->next=root->right; root->right->prev=root->left; } else{ root->right=root->left->next; root->right->next=NULL; //DEBUG int temp = root->claIndexDown; root->claIndexDown = root->claIndexUR; root->claIndexUR = temp; } //now make the root the middle des of newroot and correct the dlens root->anc=newroot; newroot->left->next=root; root->prev=newroot->left; root->next=newroot->right; newroot->right->prev=root; root->dlen=newroot->dlen; newroot->dlen=-1; newroot->anc=NULL; newroot->next=newroot->prev=NULL; //now make the new root nodeNum 0 in the allNodes array TreeNode *tempnode=root; //DEBUG /* int tempindexdown=root->claIndexDown; root->claIndexDown=newroot->claIndexDown; newroot->claIndexDown=tempindexdown; int tempindexUL=root->claIndexUL; root->claIndexUL=newroot->claIndexUL; newroot->claIndexUL=tempindexUL; int tempindexUR=root->claIndexUR; root->claIndexUR=newroot->claIndexUR; newroot->claIndexUR=tempindexUR; */ root=newroot; allNodes[0]=newroot; tempnode->nodeNum=root->nodeNum; root->nodeNum=0; allNodes[tempnode->nodeNum]=tempnode; bipartCond = DIRTY; //this form of setdirty won't shift every copy to a new topo, but will set them to dirty // claMan->SetDirtyButDoNotMove(0, root->claIndex); // claMan->SetDirtyButDoNotMove(tempnode->nodeNum, tempnode->claIndex); // root->claIndexDown=claMan->SetDirty(root->claIndexDown); // tempnode->claIndexDown=claMan->SetDirty(tempnode->claIndexDown); } //CAREFUL here! This function assumes that this tree and ONLY this tree //points to a set of CLAs. The indeces should all be valid on exit //but strange things may happen if other trees also point to them. void Tree::CheckBalance(){ //evaluate the average depth of all branches in the tree int lb=0, mb=0, rb=0; int ls=0, ms=0, rs=0; int llb=0, lrb=0, mlb=0, mrb=0, rlb=0, rrb=0; int lls=0, lrs=0, mls=0, mrs=0, rls=0, rrs=0; int lastRot=0; if(root->left->IsInternal()){ root->left->left->CountSubtreeBranchesAndDepth(llb, lls, 3, true); root->left->right->CountSubtreeBranchesAndDepth(lrb, lrs, 3, true); lb=llb+lrb+2; ls=lls+lrs+4; } if(root->left->next->IsInternal()){ root->left->next->left->CountSubtreeBranchesAndDepth(mlb, mls, 3, true); root->left->next->right->CountSubtreeBranchesAndDepth(mrb, mrs, 3, true); mb=mlb+mrb+2; ms=mls+mrs+4; } if(root->right->IsInternal()){ root->right->left->CountSubtreeBranchesAndDepth(rlb, rls, 3, true); root->right->right->CountSubtreeBranchesAndDepth(rrb, rrs, 3, true); rb=rlb+rrb+2; rs=rls+rrs+4; } /* int dl=0, dm=0, dr=0; root->left->CalcDepth(dl); root->left->next->CalcDepth(dm); root->right->CalcDepth(dr); */ do{ int cur=ls+ms+rs+3; int rotLeft=(lls-llb+lrs-lrb+2+ms+mb+rs+rb+5); int rotMid=(mls-mlb+mrs-mrb+2+ls+lb+rs+rb+5); int rotRight=(rls-rlb+rrs-rrb+2+ms+mb+ls+lb+5); if(cur<=rotLeft&&cur<=rotMid&&cur<=rotRight) return; else if(rotLeftleft); lastRot=1; } else if(rotMidleft->next); lastRot=2; } else if(rotRightright); lastRot=3; } lb=mb=rb=ls=ms=rs=llb=lrb=mlb=mrb=rlb=rrb=lls=lrs=mls=mrs=rls=rrs=0; if(root->left->IsInternal()){ root->left->left->CountSubtreeBranchesAndDepth(llb, lls, 3, true); root->left->right->CountSubtreeBranchesAndDepth(lrb, lrs, 3, true); lb=llb+lrb+2; ls=lls+lrs+4; } if(root->left->next->IsInternal()){ root->left->next->left->CountSubtreeBranchesAndDepth(mlb, mls, 3, true); root->left->next->right->CountSubtreeBranchesAndDepth(mrb, mrs, 3, true); mb=mlb+mrb+2; ms=mls+mrs+4; } if(root->right->IsInternal()){ root->right->left->CountSubtreeBranchesAndDepth(rlb, rls, 3, true); root->right->right->CountSubtreeBranchesAndDepth(rrb, rrs, 3, true); rb=rlb+rrb+2; rs=rls+rrs+4; } /* root->left->CalcDepth(dl); root->left->next->CalcDepth(dm); root->right->CalcDepth(dr); */ }while(1); } void Tree::SwapAndFreeNodes(TreeNode *cop){ assert(cop->left);//only swap internal nodes int tofree=cop->nodeNum; //we need to actually swap the memory addresses of the nodes in the allnodes array so that all other node pointers in the //tree stay correct if(allNodes[tofree]->attached){ //find a node to swap with int unused=FindUnusedNode(numTipsTotal+1); TreeNode *tempnode=allNodes[unused]; //swap the adresses of the nodes allNodes[unused]=allNodes[tofree]; allNodes[tofree]=tempnode; //now adjust the nodeNums and claIndeces int temp=allNodes[unused]->nodeNum; allNodes[unused]->nodeNum=allNodes[tofree]->nodeNum; allNodes[tofree]->nodeNum=temp; MakeNodeDirty(allNodes[unused]); MakeNodeDirty(allNodes[tofree]); /* temp=allNodes[unused]->claIndexDown; allNodes[unused]->claIndexDown=allNodes[tofree]->claIndexDown; allNodes[tofree]->claIndexDown=temp; temp=allNodes[unused]->claIndexUL; allNodes[unused]->claIndexUL=allNodes[tofree]->claIndexUL; allNodes[tofree]->claIndexUL=temp; temp=allNodes[unused]->claIndexUR; allNodes[unused]->claIndexUR=allNodes[tofree]->claIndexUR; allNodes[tofree]->claIndexUR=temp; */ //set the nodes to dirty // assert(0); // allNodes[tofree]->claIndex=claMan->SetDirty(allNodes[tofree]->nodeNum, allNodes[tofree]->claIndex, true); // allNodes[unused]->claIndex=claMan->SetDirty(allNodes[unused]->nodeNum, allNodes[unused]->claIndex, true); allNodes[unused]->attached=true; allNodes[tofree]->attached=true;//actual its not attached, but we need to mark it as such so it isn't used as a connector } else//this is odd, but if a node will need to be used to //mimic nodenums in the subtree, but was already unattached, //we need to mark it as attached so that it isn't used for //some other purpose. allNodes[tofree]->attached=true; if(cop->left->left) SwapAndFreeNodes(cop->left); if(cop->right->left) SwapAndFreeNodes(cop->right); } void Tree::CalcBipartitions(bool standardize){ if(!(bipartCond == CLEAN_STANDARDIZED && standardize == true) && !(bipartCond == CLEAN_UNSTANDARDIZED && standardize == false)){ if(bipartCond == CLEAN_UNSTANDARDIZED && standardize == true) root->StandardizeBipartition(); else root->CalcBipartition(standardize); if(standardize) bipartCond = CLEAN_STANDARDIZED; else bipartCond = CLEAN_UNSTANDARDIZED; } // root->VerifyBipartition(standardize); } void Tree::OutputBipartitions(){ ofstream out("biparts.log", ios::app); root->OutputBipartition(out); } /* void Tree::SetDistanceBasedBranchLengthsAroundNode(TreeNode *nd){ FLOAT_TYPE D1, D2, D3, k1, k2, k3, k4, a, b, c; TreeNode *T1, *T2, *T3, *T4; FindNearestTerminalUp(nd->left, T1, k1); FindNearestTerminalUp(nd->right, T2, k2); FindNearestTerminalsDown(nd->anc, nd, T3, T4, k3, k4); // FindNearestTerminalUp(nd->, T2, k2); if(k4tipData, T2->tipData, data->NChar())/(1.0-mod->PropInvar()) - k1 -k2; D2=CalculatePDistance(T1->tipData, T3->tipData, data->NChar())/(1.0-mod->PropInvar()) - k1 -k3; D3=CalculatePDistance(T2->tipData, T3->tipData, data->NChar())/(1.0-mod->PropInvar()) - k2 -k3; #endif b=(D3-D2+D1)*0.5; if(b < min_brlen) b=min_brlen; a=D1-b; if(a < min_brlen) a=min_brlen; c=D2-a; if(c < min_brlen) c=min_brlen; nd->left->dlen=a; nd->right->dlen=b; nd->dlen=c; SweepDirtynessOverTree(nd->left); SweepDirtynessOverTree(nd); SweepDirtynessOverTree(nd->right); } void Tree::FindNearestTerminalUp(TreeNode *start, TreeNode *&term, FLOAT_TYPE &dist){ dist=999999.9; int nodeDist=9999; sprRange.clear(); sprRange.setseed(start->nodeNum); int range=10; for(int i = 0;ileft!=NULL){ sprRange.addelement(cur->left->nodeNum, i+1, sprRange.pathlength[k]+cur->left->dlen); sprRange.addelement(cur->right->nodeNum, i+1, sprRange.pathlength[k]+cur->right->dlen); } else{ //if(sprRange.pathlength[k]left) sprRange.setseed(start->right->nodeNum, start->right->dlen); else sprRange.setseed(start->left->nodeNum, start->left->dlen); int range=10; for(int i = 0;ileft!=NULL){ sprRange.addelement(cur->left->nodeNum, i+1, sprRange.pathlength[k]+cur->left->dlen); sprRange.addelement(cur->right->nodeNum, i+1, sprRange.pathlength[k]+cur->right->dlen); } else{ //if(sprRange.pathlength[k]anc != NULL){ sprRange.setseed(start->anc->nodeNum, start->dlen); for(int i = 0;ileft!=NULL){ if(cur->left!=from->anc) sprRange.addelement(cur->left->nodeNum, i+1, sprRange.pathlength[k]+cur->left->dlen); if(cur->right!=from->anc) sprRange.addelement(cur->right->nodeNum, i+1, sprRange.pathlength[k]+cur->right->dlen); } else{ //if(sprRange.pathlength[k]anc) sprRange.addelement(cur->anc->nodeNum, i+1, sprRange.pathlength[k]+cur->dlen); else sprRange.addelement(cur->left->next->nodeNum, i+1, sprRange.pathlength[k]+cur->left->next->dlen); } } } } else{ if(from!=start->left->next) sprRange.setseed(start->left->next->nodeNum, start->left->next->dlen); else sprRange.setseed(start->right->nodeNum, start->right->dlen); int range=10; for(int i = 0;ileft!=NULL){ sprRange.addelement(cur->left->nodeNum, i+1, sprRange.pathlength[k]+cur->left->dlen); sprRange.addelement(cur->right->nodeNum, i+1, sprRange.pathlength[k]+cur->right->dlen); } else{ //if(sprRange.pathlength[k] .2) precision2=0.0; else precision2=precision1 * 0.5; if(nd != root){ BrentOptimizeBranchLength(precision1, nd, false); BrentOptimizeBranchLength(precision1, nd->left, false); BrentOptimizeBranchLength(precision1, nd->right, false); } else{ BrentOptimizeBranchLength(precision1, nd->left, false); BrentOptimizeBranchLength(precision1, nd->left->next, false); BrentOptimizeBranchLength(precision1, nd->right, false); } */ /* if(precision2 > 0){ //if were're doing multiple optimization passes, only this stuff needs to be set dirty claMan->SetDirty(nd->nodeNum, nd->claIndex, true); claMan->SetTempDirty(nd->nodeNum, true); if(nd != root) claMan->SetTempDirty(nd->anc->nodeNum, true); if(nd != root){ BrentOptimizeBranchLength(precision2, nd, false); BrentOptimizeBranchLength(precision2, nd->left, false); BrentOptimizeBranchLength(precision2, nd->right, false); } else { BrentOptimizeBranchLength(precision2, nd->left, false); BrentOptimizeBranchLength(precision2, nd->left->next, false); BrentOptimizeBranchLength(precision2, nd->right, false); } } */ /* //these must be called after all optimization passes are done around this node TraceDirtynessToRoot(nd); if(subtreeNode==0) SetAllTempClasDirty(); else SetTempClasDirtyWithinSubtree(subtreeNode); */ } void Tree::RerootHere(int newroot){ //DJZ 1-5-05 adding functionality to adjust the direction of existing clas //so that they are still valid in the new context, rather than just dirtying everything //DJZ 11/19/07 removing CLA adjustment code because it was buggy and didn't check the //number of individuals that pointed to the same CLA, and so sometimes screwed things up. //REMEMBER that the mutation_type of the individual this is called for needs to be // "|= rerooted" so that the topo numbers are updated properly TreeNode *nroot=allNodes[newroot]; TreeNode *prevnode=nroot; TreeNode *curnode=nroot->anc; TreeNode *nextnode=nroot->anc->anc; //this is necessary to properly dirty clas TreeNode *lastOnPath=nroot; while(lastOnPath->anc != root) lastOnPath = lastOnPath->anc; SweepDirtynessOverTree(lastOnPath); //first trace down to the old root and fix all the blens //Each branch with take the length of its descendent on that path //this will be easiest recursively nroot->FlipBlensToRoot(0); SweepDirtynessOverTree(nroot); //now take the new root's current ancestor and make it the middle des //note that the existing cla directions at this node are still valid nroot->left->next=curnode; curnode->next=nroot->right; nroot->right->prev=curnode; curnode->prev=nroot->left; //this needs to work slightly differently if the old root is the anc of the new one if(curnode!=root){ if(prevnode==curnode->left){ curnode->left=curnode->anc; //curnode->AdjustClasForReroot(UPLEFT); } else{ curnode->right=curnode->anc; //curnode->AdjustClasForReroot(UPRIGHT); } // SweepDirtynessOverTree(curnode); curnode->left->next=curnode->right; curnode->left->prev=NULL; curnode->right->prev=curnode->left; curnode->right->next=NULL; prevnode=curnode; curnode=nextnode; nextnode=nextnode->anc; } curnode->anc=prevnode; nroot->anc=NULL; while(curnode!=root){ if(prevnode==curnode->left){ curnode->left=nextnode; //curnode->AdjustClasForReroot(UPLEFT); } else{ curnode->right=nextnode; //curnode->AdjustClasForReroot(UPRIGHT); } // SweepDirtynessOverTree(curnode); curnode->left->next=curnode->right; curnode->left->prev=NULL; curnode->right->prev=curnode->left; curnode->right->next=NULL; curnode->anc=prevnode; prevnode=curnode; curnode=nextnode; nextnode=nextnode->anc; } //now deal with the old root, which is now curnode if(prevnode==curnode->left){ curnode->left=curnode->right->prev; curnode->left->prev=NULL; //curnode->AdjustClasForReroot(UPLEFT); } else if(prevnode==curnode->left->next){ curnode->left->next=curnode->right; curnode->right->prev=curnode->left; //clas don't need to be adjusted in this case } else{ curnode->right=curnode->left->next; curnode->right->next=NULL; //curnode->AdjustClasForReroot(UPRIGHT); } MakeNodeDirty(curnode); curnode->anc=prevnode; //now we just need to make the newroot node0 and swap it with the old root, which means moving the //_data_ to node 0, not just swapping the memory addresses SwapNodeDataForReroot(nroot); root->CheckTreeFormation(); bipartCond = DIRTY; // MakeAllNodesDirty(); // Score(); } void Tree::SwapNodeDataForReroot(TreeNode *nroot){ TreeNode tempold; tempold.left=root->left; tempold.right=root->right; tempold.next=root->next; tempold.prev=root->prev; //note that we need to watch out here if the new root is currently the anc of the old root if(root->anc==nroot) tempold.anc=root; else tempold.anc=root->anc; tempold.dlen=root->dlen; tempold.claIndexDown=root->claIndexDown; tempold.claIndexUL=root->claIndexUL; tempold.claIndexUR=root->claIndexUR; TreeNode tempnew; tempnew.left=nroot->left; tempnew.right=nroot->right; tempnew.next=nroot->next; tempnew.prev=nroot->prev; tempnew.anc=nroot->anc; tempnew.dlen=nroot->dlen; tempnew.claIndexDown=nroot->claIndexDown; tempnew.claIndexUL=nroot->claIndexUL; tempnew.claIndexUR=nroot->claIndexUR; root->left=tempnew.left; root->left->anc=root; root->right=tempnew.right; root->right->anc=root; root->left->next->anc=root; root->prev=root->next=NULL; root->anc=NULL; root->dlen=-1; root->claIndexDown=tempnew.claIndexDown; root->claIndexUL=tempnew.claIndexUL; root->claIndexUR=tempnew.claIndexUR; MakeNodeDirty(root); nroot->left=tempold.left; nroot->left->anc=nroot; nroot->right=tempold.right; nroot->next=tempold.next; if(nroot->next) nroot->next->prev=nroot; nroot->prev=tempold.prev; if(nroot->prev) nroot->prev->next=nroot; nroot->right->anc=nroot; nroot->anc=tempold.anc; nroot->claIndexDown=tempold.claIndexDown; nroot->claIndexUL=tempold.claIndexUL; nroot->claIndexUR=tempold.claIndexUR; MakeNodeDirty(nroot); if(nroot->anc->left==root){ nroot->anc->left=nroot; nroot->prev=NULL; nroot->next=nroot->anc->right; nroot->next->prev=nroot; } else if(nroot->anc->right==root){ nroot->anc->right=nroot; nroot->next=NULL; nroot->prev=nroot->anc->left; nroot->prev->next=nroot; } else{ nroot->anc->left->next=nroot; // nroot->next=NULL; nroot->prev=nroot->anc->left; // nroot->prev->next=nroot; } nroot->dlen=tempold.dlen; } void Tree::MakeNodeDirty(TreeNode *nd){ if(nd->claIndexDown != -1) nd->claIndexDown=claMan->SetDirty(nd->claIndexDown); if(nd->claIndexUL != -1) nd->claIndexUL=claMan->SetDirty(nd->claIndexUL); if(nd->claIndexUR != -1) nd->claIndexUR=claMan->SetDirty(nd->claIndexUR); } void Tree::RemoveTempClaReservations(){ if(memLevel > 1){ for(int i=numTipsTotal+1;iClearTempReservation(allNodes[i]->claIndexDown); } } for(int i=numTipsTotal+1;iClearTempReservation(allNodes[i]->claIndexUR); } for(int i=numTipsTotal+1;iClearTempReservation(allNodes[i]->claIndexUL); } } void Tree::ReclaimUniqueClas(){ for(int i=numTipsTotal+1;iGetNumAssigned(allNodes[i]->claIndexDown) == 1){ claMan->ReclaimSingleCla(allNodes[i]->claIndexDown); } if(claMan->GetNumAssigned(allNodes[i]->claIndexUL) == 1){ claMan->ReclaimSingleCla(allNodes[i]->claIndexUL); } if(claMan->GetNumAssigned(allNodes[i]->claIndexUR) == 1){ claMan->ReclaimSingleCla(allNodes[i]->claIndexUR); } } } void Tree::MarkUpwardClasToReclaim(int subtreeNode){ //if we are somewhat low on clas, mark some reclaimable that were //used tracing the likelihood upward for blen optimization assert(0); if(subtreeNode==0){ /* if(memLevel==2){ if(allNodes[0]->claIndexUL > 0) claMan->MarkReclaimable(allNodes[0]->claIndexUL, 2); if(allNodes[0]->claIndexUR > 0) claMan->MarkReclaimable(allNodes[0]->claIndexUR, 2); } */ for(int i=numTipsTotal+1;iMarkReclaimable(allNodes[i]->claIndexUL, 2, false); // claMan->MarkReclaimable(allNodes[i]->claIndexUR, 2, false); } } else{ for(int i=numTipsTotal+1;inodeNum != subtreeNode) && (allNodes[i]->nodeNum != allNodes[subtreeNode]->anc->nodeNum)){ if(allNodes[i]->claIndexUL > 0){ // claMan->MarkReclaimable(allNodes[i]->claIndexUL, 2, false); } if(allNodes[i]->claIndexUR > 0){ // claMan->MarkReclaimable(allNodes[i]->claIndexUR, 2, false); } } } } } void Tree::MarkDownwardClasToReclaim(int subtreeNode){ //if we're calling this, we must really be desperate for clas //this should only be called after the tree has been scored assert(0); if(subtreeNode==0){ if(memLevel<3){ for(int i=numTipsTotal+1;iMarkReclaimable(allNodes[i]->claIndexDown, 1); } } else{ for(int i=numTipsTotal+1;iMarkReclaimable(allNodes[i]->claIndexDown, 1, false); } } } else{ return; //I think that this is safe, since in general many fewer node will be necessary in subtree mode for(int i=numTipsTotal+1;inodeNum != subtreeNode) && (allNodes[i]->nodeNum != allNodes[subtreeNode]->anc->nodeNum)){ if(allNodes[i]->claIndexUL > 0){ // claMan->MarkReclaimable(allNodes[i]->claIndexUL, 1); } } } } } void Tree::MarkClasNearTipsToReclaim(int subtreeNode){ assert(0); if(subtreeNode==0){ for(int i=1;iMarkReclaimable(allNodes[i]->anc->claIndexDown, 1, false); } } else{ return; //I think that this is safe, since in general many fewer node will be necessary in subtree mode for(int i=numTipsTotal+1;inodeNum != subtreeNode) && (allNodes[i]->nodeNum != allNodes[subtreeNode]->anc->nodeNum)){ if(allNodes[i]->claIndexUL > 0){ // claMan->MarkReclaimable(allNodes[i]->claIndexUL, 1); } } } } } //PARTITION void Tree::OutputNthClaAcrossTree(ofstream &deb, TreeNode *nd, int site, int modIndex){ //int site=0; int nstates = modPart->GetModel(modIndex)->NStates(); int rateCats = modPart->GetModel(modIndex)->NRateCats(); int index=nstates * rateCats * site; bool outputDirtyClas = false; if(nd->IsInternal()){ if(claMan->IsDirty(nd->claIndexDown) == false){ deb << nd->nodeNum << "\t0\t" << nd->claIndexDown << "\t"; const CondLikeArray *cla = claMan->GetCla(nd->claIndexDown)->theSets[modIndex]; for(int i=0;iarr[index+i] << "\t"; deb << cla->underflow_mult[site]; deb <<"\n"; } else if(outputDirtyClas){ deb << nd->nodeNum << "\t0\t" << nd->claIndexDown << "\n"; } } if(nd->IsInternal()){ if(claMan->IsDirty(nd->claIndexUL) == false){ deb << nd->nodeNum << "\t1\t" << nd->claIndexUL << "\t"; const CondLikeArray *cla = claMan->GetCla(nd->claIndexUL)->theSets[modIndex]; for(int i=0;iarr[index+i] << "\t"; deb << cla->underflow_mult[site]; deb <<"\n"; } else if(outputDirtyClas){ deb << nd->nodeNum << "\t1\t" << nd->claIndexUL << "\n"; } } if(nd->IsInternal()){ if(claMan->IsDirty(nd->claIndexUR) == false){ deb << nd->nodeNum << "\t2\t" << nd->claIndexUR << "\t"; const CondLikeArray *cla = claMan->GetCla(nd->claIndexUR)->theSets[modIndex]; for(int i=0;iarr[index+i] << "\t"; deb << cla->underflow_mult[site]; deb <<"\n"; } else if(outputDirtyClas){ deb << nd->nodeNum << "\t2\t" << nd->claIndexUR << "\n"; } } if(nd->IsInternal()) OutputNthClaAcrossTree(deb, nd->left, site, modIndex); if(nd->next!=NULL) OutputNthClaAcrossTree(deb, nd->next, site, modIndex); } void Tree::CountNumReservedClas(int &clean, int &tempRes, int&res){ clean=0; tempRes=0; res=0; if(claMan->IsDirty(allNodes[0]->claIndexDown)==false){ clean++; res += (claMan->IsClaReserved(allNodes[0]->claIndexDown)); tempRes += (claMan->IsClaTempReserved(allNodes[0]->claIndexDown)); } if(claMan->IsDirty(allNodes[0]->claIndexUL)==false){ clean++; res += (claMan->IsClaReserved(allNodes[0]->claIndexUL)); tempRes += (claMan->IsClaTempReserved(allNodes[0]->claIndexUL)); } if(claMan->IsDirty(allNodes[0]->claIndexUR)==false){ clean++; res += (claMan->IsClaReserved(allNodes[0]->claIndexUR)); tempRes += (claMan->IsClaTempReserved(allNodes[0]->claIndexUR)); } for(int i=numTipsTotal+1;iIsDirty(allNodes[i]->claIndexDown)==false){ clean++; res += (claMan->IsClaReserved(allNodes[i]->claIndexDown)); tempRes += (claMan->IsClaTempReserved(allNodes[i]->claIndexDown)); } if(claMan->IsDirty(allNodes[i]->claIndexUL)==false){ clean++; res += (claMan->IsClaReserved(allNodes[i]->claIndexUL)); tempRes += (claMan->IsClaTempReserved(allNodes[i]->claIndexUL)); } if(claMan->IsDirty(allNodes[i]->claIndexUR)==false){ clean++; res += (claMan->IsClaReserved(allNodes[i]->claIndexUR)); tempRes += (claMan->IsClaTempReserved(allNodes[i]->claIndexUR)); } } } void Tree::SetupClasForSubtreeMode(int subtreeNode){ TreeNode *subnode=allNodes[subtreeNode]; claMan->ReserveCla(subnode->claIndexDown, false); claMan->ReserveCla(subnode->claIndexUL, false); claMan->ReserveCla(subnode->claIndexUR, false); if(subnode->anc != root){ if(subnode->anc->left==subnode) claMan->ReserveCla(subnode->anc->claIndexUL, false); else if(subnode->anc->right==subnode) claMan->ReserveCla(subnode->anc->claIndexUR, false); } DirtyNodesOutsideOfSubtree(root, subtreeNode); } void Tree::DirtyNodesOutsideOfSubtree(TreeNode *nd, int subtreeNode){ if(nd != root){ claMan->ReclaimSingleCla(nd->claIndexDown); claMan->ReclaimSingleCla(nd->claIndexUL); claMan->ReclaimSingleCla(nd->claIndexUR); } if(nd->left->IsInternal() && nd->left->nodeNum != subtreeNode && nd->left->nodeNum != allNodes[subtreeNode]->anc->nodeNum){ DirtyNodesOutsideOfSubtree(nd->left, subtreeNode); } if(nd->right->IsInternal() && nd->right->nodeNum != subtreeNode && nd->right->nodeNum != allNodes[subtreeNode]->anc->nodeNum){ DirtyNodesOutsideOfSubtree(nd->right, subtreeNode); } if(nd->IsRoot() && nd->left->next->IsInternal() && nd->left->next->nodeNum != subtreeNode && nd->left->next->nodeNum != allNodes[subtreeNode]->anc->nodeNum){ DirtyNodesOutsideOfSubtree(nd->left->next, subtreeNode); } } void Tree::OutputValidClaIndeces(){ ofstream cla("claind.log"); if(claMan->IsDirty(allNodes[0]->claIndexDown)==false){ cla << "0\t" << allNodes[0]->claIndexDown << "\t" << claMan->GetNumAssigned(allNodes[0]->claIndexDown) << "\t" << claMan->GetReclaimLevel(allNodes[0]->claIndexDown) << "\t" << claMan->IsClaReserved(allNodes[0]->claIndexDown) <<"\n"; } for(int i=numTipsTotal+1;iclaIndexDown << "\t" << claMan->GetNumAssigned(allNodes[i]->claIndexDown) << "\t" << claMan->GetReclaimLevel(allNodes[i]->claIndexDown) << "\t" << claMan->IsClaReserved(allNodes[i]->claIndexDown) << "\n"; } cla.close(); } void Tree::GetInternalStateString(char *string, int nodeNum){ assert(0); // Score(nodeNum); // InferStatesFromCla(string, claMan->GetTempCla()->arr, data->NChar()); } void Tree::InferAllInternalStateProbs(const char *ofprefix){ char filename[80]; sprintf(filename, "%s.internalstates.log", ofprefix); ofstream out(filename); out.precision(5); AssignCLAsFromMaster(); RecursivelyCalculateInternalStateProbs(root, out); out.close(); } void Tree::RecursivelyCalculateInternalStateProbs(TreeNode *nd, ofstream &out){ if(nd->IsInternal()) RecursivelyCalculateInternalStateProbs(nd->left, out); if(nd->next) RecursivelyCalculateInternalStateProbs(nd->next, out); if(nd->IsInternal()){ //what this now returns is really the unscaled posterior values for each state, marginalized across rates (including any invariant class). //thus, the state frqeuencies have already been figured in and nothing needs to be done in InferStatesFromCla besides divide each by the sum //note that this clas then only uses the first nstates x nchar portion, instead of the usual nstates x nchar x nrates int wholeTreeIndex = ConditionalLikelihoodRateHet(ROOT, nd, true); CondLikeArraySet *CLAset = claMan->GetCla(wholeTreeIndex); //output newick strings with both names and numbers indicating which node this corresponds to string subtreeString; nd->MakeNewickForSubtree(subtreeString, dataPart, false, false, false); out << "node " << nd->nodeNum << "\t" << subtreeString.c_str() << "\t"; subtreeString.clear(); nd->MakeNewickForSubtree(subtreeString, dataPart, false, false, true); out << subtreeString.c_str() << endl; for(vector::iterator c = claSpecs.begin() ; c != claSpecs.end() ; c++){ const CondLikeArray *thisCLA = CLAset->GetCLA((*c).claIndex); const ModelSpecification *modSpec = modSpecSet.GetModSpec((*c).modelIndex); vector stateProbs; InferStatesFromCla(stateProbs, thisCLA->arr, thisCLA->NChar(), thisCLA->NStates()); //this just maps the indecies used in the clas to actual states StateSet *states; if(modSpec->IsNucleotide()) states = new StateSet(4); else if(modSpec->IsAminoAcid()){ if(modSpec->IsTwoSerineRateMatrix()) states = new StateSet(21); else states = new StateSet(20); } else states = new StateSet(modPart->GetModel((*c).modelIndex)->GetGeneticCode()); states->OutputInternalStateHeader(out); //now map the posteriors of each packed state to the original char order const SequenceData *data = dataPart->GetSubset((*c).dataIndex); for(int s=data->NumConditioningPatterns();sGapsIncludedNChar() + data->NumConditioningPatterns();s++){ //out << s+1 << "\t"; out << data->OrigDataNumber(s) + 1 << "\t"; if(data->Number(s) > -1) stateProbs[data->Number(s)].Output(out, *states); else out << "Entirely uninformative character (gaps,N's or ?'s)\n"; } //return the cla that we used temporarily claMan->ClearTempReservation(wholeTreeIndex); claMan->DecrementCla(wholeTreeIndex); delete states; } } } void Tree::ClaReport(ofstream &cla){ int totDown=0; int totUL=0; int totUR=0; cla << "root\t" << claMan->GetReclaimLevel(root->claIndexDown) << "\t" << claMan->GetNumAssigned(root->claIndexDown)<< "\t" << claMan->GetClaNumber(root->claIndexDown); cla << "\n\t" << claMan->GetReclaimLevel(root->claIndexUL) << "\t" << claMan->GetNumAssigned(root->claIndexUL) << "\t" << claMan->GetClaNumber(root->claIndexUL); cla << "\n\t" << claMan->GetReclaimLevel(root->claIndexUR) << "\t" << claMan->GetNumAssigned(root->claIndexUR) << "\t" << claMan->GetClaNumber(root->claIndexUR) << "\n"; // cla << "\t" << claMan->GetNumAssigned(root->claIndexDown) << "\t" << claMan->GetNumAssigned(root->claIndexUL) << "\t" << claMan->GetNumAssigned(root->claIndexUR) << "\n"; for(int i=numTipsTotal+1;iGetReclaimLevel(n->claIndexDown) << "\t" << claMan->GetNumAssigned(n->claIndexDown) << "\t" << claMan->GetClaNumber(n->claIndexDown); cla << "\n\t" << claMan->GetReclaimLevel(n->claIndexUL) << "\t" << claMan->GetNumAssigned(n->claIndexUL) << "\t" << claMan->GetClaNumber(n->claIndexUL); cla << "\n\t" << claMan->GetReclaimLevel(n->claIndexUR) << "\t" << claMan->GetNumAssigned(n->claIndexUR) << "\t" << claMan->GetClaNumber(n->claIndexUR) << "\n"; totDown += claMan->GetReclaimLevel(n->claIndexDown); totUL += claMan->GetReclaimLevel(n->claIndexUL); totUR += claMan->GetReclaimLevel(n->claIndexUR); } cla << "tots\t" << totDown << "\t" << totUL << "\t" << totUR << endl; // cla.close(); } FLOAT_TYPE Tree::CountClasInUse(){ FLOAT_TYPE inUse=0.0; if(claMan->IsDirty(root->claIndexDown) == false) inUse += ONE_POINT_ZERO/claMan->GetNumAssigned(root->claIndexDown); if(claMan->IsDirty(root->claIndexUL) == false) inUse += ONE_POINT_ZERO/claMan->GetNumAssigned(root->claIndexUL); if(claMan->IsDirty(root->claIndexUR) == false) inUse += ONE_POINT_ZERO/claMan->GetNumAssigned(root->claIndexUR); for(int i=numTipsTotal+1;iIsDirty(n->claIndexDown) == false) inUse += ONE_POINT_ZERO/claMan->GetNumAssigned(n->claIndexDown); if(claMan->IsDirty(n->claIndexUL) == false) inUse += ONE_POINT_ZERO/claMan->GetNumAssigned(n->claIndexUL); if(claMan->IsDirty(n->claIndexUR) == false) inUse += ONE_POINT_ZERO/claMan->GetNumAssigned(n->claIndexUR); } return inUse; } void Tree::OutputSiteLikelihoods(int partNum, vector &likes, const int *under1, const int *under2){ //output level 1 is user-level output, just site nums and site likes //output level 2 is for debugging, includes underflow multipliers and output of site likes in packed order const SequenceData *data = dataPart->GetSubset(partNum); assert(sitelikeLevel != 0); //a negative sitelike level means append, but the absolute value meanings are the same bool append = sitelikeLevel < 0; int effectiveSitelikeLevel = abs(sitelikeLevel); ofstream ordered, packed; string oname = ofprefix + ".sitelikes.log"; ordered.open(oname.c_str(), (append == true ? ios::app : ios::out)); if(effectiveSitelikeLevel > 1){ string pname = ofprefix + ".packedSiteLikes.log"; packed.open(pname.c_str(), (append == true ? ios::app : ios::out)); } assert(effectiveSitelikeLevel > 0); assert(likes.size() == data->NChar());; if(!append){ ordered << "Tree\t-lnL\tSite\t-lnL"; if(effectiveSitelikeLevel > 1) ordered << "\tunder1\tunder2"; ordered << "\n"; } ordered.setf(ios::fixed, ios::floatfield); ordered.precision(8); packed.precision(8); int startPat = (effectiveSitelikeLevel > 1 ? 0 : data->NumConditioningPatterns()); for(int site = startPat;site < data->GapsIncludedNChar() + data->NumConditioningPatterns();site++){ int col = data->Number(site); int origCol = data->OrigDataNumber(site); if(col == -1){ ordered << "\t\t" << origCol + 1 << "\t-"; if(effectiveSitelikeLevel > 1) ordered << "\t-\t-"; ordered << "\n"; } else{ ordered << "\t\t" << origCol + 1 << "\t" << -likes[col]; if(effectiveSitelikeLevel > 1){ ordered << "\t" << under1[col]; if(under2 != NULL) ordered << "\t" << under2[col]; else ordered << "\t-"; } ordered << "\n"; } } if(effectiveSitelikeLevel > 1){ packed << "Partition subset " << partNum + 1 << "\npackedIndex\ttruelnL\tunder1\tunder2" << endl; for(int c = 0;c < data->NChar();c++){ packed << c << "\t" << likes[c] << "\t" << under1[c]; if(under2 != NULL) packed << "\t" << under2[c] << endl; else packed << "\t-" << endl; } } ordered.close(); if(packed.is_open()) packed.close(); } void Tree::OutputSiteDerivatives(int partNum, vector &likes, vector &d1s, vector &d2s, const int *under1, const int *under2, ofstream &ordered, ofstream &packed){ const SequenceData *data = dataPart->GetSubset(partNum); assert(d1s.size() == data->NChar());; ordered << "Partition subset " << partNum + 1 << "\nsite#\ttruelnL\td1\td2\tunder1\tunder2" << endl; packed << "Partition subset " << partNum + 1 << "\npackedIndex\ttruelnL\td1\td2\tunder1\tunder2" << endl; ordered.precision(10); packed.precision(10); for(int site = 0;site < data->GapsIncludedNChar() + data->NumConditioningPatterns();site++){ int col = data->Number(site); if(col == -1) ordered << site+1 << "\tgap\t-\t-\t-\t-"; else{ ordered << site+1 << "\t" << (likes.size() > 0 ? likes[col] : 0.0) << "\t" << d1s[col] << "\t" << d2s[col] << "\t" << under1[col]; if(under2 != NULL) ordered << "\t" << under2[col] << endl; else ordered << "\t-" << endl; } } for(int c = 0;c < data->NChar();c++){ packed << c << "\t" << (likes.size() > 0 ? likes[c] : 0.0) << "\t" << d1s[c] << "\t" << d2s[c] << "\t" << under1[c]; if(under2 != NULL) packed << "\t" << under2[c] << endl; else packed << "\t-" << endl; } } FLOAT_TYPE Tree::GetScorePartialTerminalNState(const CondLikeArray *partialCLA, const FLOAT_TYPE *prmat, const char *Ldat, int modIndex, int dataIndex){ //this function assumes that the pmat is arranged with the nstates^2 entries for the //first rate, followed by nstates^2 for the second, etc. const FLOAT_TYPE *partial=partialCLA->arr; const int *underflow_mult=partialCLA->underflow_mult; const SequenceData *data = dataPart->GetSubset(dataIndex); Model *mod = modPart->GetModel(modIndex); const int nstates = mod->NStates(); const int nRateCats = mod->NRateCats(); const int nchar = data->NChar(); const int *countit=data->GetCounts(); const char *Ldata = Ldat; const FLOAT_TYPE *rateProb=mod->GetRateProbs(); const int lastConst=data->LastConstant(); const int *conStates=data->GetConstStates(); const FLOAT_TYPE prI=mod->PropInvar(); const int numCondPats = data->NumConditioningPatterns(); #ifdef UNIX posix_madvise((void*)partial, nchar*nstates*nRateCats*sizeof(FLOAT_TYPE), POSIX_MADV_SEQUENTIAL); #endif vector freqs(nstates); for(int i=0;iStateFreq(i); FLOAT_TYPE siteL, unscaledlnL, totallnL = ZERO_POINT_ZERO, grandSumlnL=ZERO_POINT_ZERO; FLOAT_TYPE logConditioningFactor = ZERO_POINT_ZERO; FLOAT_TYPE conditioningLikeSum = ZERO_POINT_ZERO; vector siteLikes(nchar); if(siteToScore > 0) Ldat += siteToScore; if(nRateCats == 1){ #ifdef OMP_TERMSCORE_NSTATE #ifdef LUMP_LIKES #pragma omp parallel for if(numCondPats == 0) private(partial, Ldata, siteL, unscaledlnL) reduction(+ : totallnL, grandSumlnL) #else #pragma omp parallel for if(numCondPats == 0) private(partial, Ldata, siteL, unscaledlnL) reduction(+ : totallnL) #endif for(int i=0;iarr[i*nstates]; #else for(int i=0;i 0){ #else if(1){ #endif siteL = 0.0; if(*Ldata < nstates){ //no ambiguity for(int from=0;fromNoPinvInModel() == false) && (i<=lastConst)){ if(underflow_mult[i] == 0) siteL += prI*freqs[conStates[i]]; else siteL += prI*freqs[conStates[i]]*exp((FLOAT_TYPE)underflow_mult[i]); } unscaledlnL = (log(siteL) - underflow_mult[i]); assert(siteL > ZERO_POINT_ZERO);//this should be positive assert(unscaledlnL < 1.0e-4);//this should be negative or zero //rounding error in multiplying a site that is fully ambiguous across the tree //(which might not have been removed from the data because we are only scoring a //partial tree during stepwise addition) can cause the unscaledlnL to be slightly //> zero. If that is the case, just ignore it if(numCondPats > 0){ assert(unscaledlnL < ZERO_POINT_ZERO); if(i < numCondPats){ if(underflow_mult[i] == 0) conditioningLikeSum += siteL; else{ outman.DebugMessage("SCALED MKV SCALER = %d (%f)", (underflow_mult[i]), exp((double)underflow_mult[i])); double unscaler = exp((FLOAT_TYPE)underflow_mult[i]); //Guard against this over or underflowing, which I think are very unlikely. If it does, just ignore this siteL if(unscaler == unscaler){ double unscaled = siteL / unscaler; if(unscaled == unscaled) conditioningLikeSum += unscaled; } } if(i == numCondPats - 1) logConditioningFactor = -log(ONE_POINT_ZERO - conditioningLikeSum); } else{ unscaledlnL += logConditioningFactor; totallnL += (countit[i] * unscaledlnL); } assert(unscaledlnL < ZERO_POINT_ZERO); } else if(unscaledlnL < ZERO_POINT_ZERO) totallnL += (countit[i] * unscaledlnL); #ifdef ALLOW_SINGLE_SITE if(siteToScore > -1) break; #endif } else{//nothing needs to be done if the count for this site is 0 } Ldata++; #ifdef LUMP_LIKES if((i + 1) % LUMP_FREQ == 0){ grandSumlnL += totallnL; totallnL = ZERO_POINT_ZERO; } #endif if(sitelikeLevel != 0) siteLikes[i] = unscaledlnL; } } else{//multiple rates FLOAT_TYPE rateL; #ifdef OMP_TERMSCORE_NSTATE #ifdef LUMP_LIKES #pragma omp parallel for if(numCondPats == 0) private(partial, Ldata, siteL, rateL, unscaledlnL) reduction(+ : totallnL, grandSumlnL) #else #pragma omp parallel for if(numCondPats == 0) private(partial, Ldata, siteL, rateL, unscaledlnL) reduction(+ : totallnL) #endif for(int i=0;iarr[i*nstates*nRateCats]; #else for(int i=0;i 0){ #else if(1){ #endif siteL = ZERO_POINT_ZERO; if(*Ldata < nstates){ //no ambiguity for(int rate=0;rateNoPinvInModel() == false) && (i<=lastConst)){ if(underflow_mult[i] == 0) siteL += prI*freqs[conStates[i]]; else siteL += prI*freqs[conStates[i]]*exp((FLOAT_TYPE)underflow_mult[i]); } unscaledlnL = (log(siteL) - underflow_mult[i]); assert(siteL > ZERO_POINT_ZERO);//this should be positive assert(unscaledlnL < 1.0e-4);//this should be negative or zero //rounding error in multiplying a site that is fully ambiguous across the tree //(which might not have been removed from the data because we are only scoring a //partial tree during stepwise addition) can cause the unscaledlnL to be slightly //> zero. If that is the case, just ignore it if(numCondPats > 0){ assert(unscaledlnL < ZERO_POINT_ZERO); if(i < numCondPats){ if(underflow_mult[i] == 0) conditioningLikeSum += siteL; else{ outman.DebugMessage("SCALED MKV SCALER = %d (%f)", (underflow_mult[i], exp((double)underflow_mult[i]))); double unscaler = exp((FLOAT_TYPE)underflow_mult[i]); //Guard against this over or underflowing, which I think are very unlikely. If it does, just ignore this siteL if(unscaler == unscaler){ double unscaled = siteL / unscaler; if(unscaled == unscaled) conditioningLikeSum += unscaled; } } if(i == numCondPats - 1) logConditioningFactor = -log(ONE_POINT_ZERO - conditioningLikeSum); } else{ unscaledlnL += logConditioningFactor; totallnL += (countit[i] * unscaledlnL); } assert(unscaledlnL < ZERO_POINT_ZERO); } else if(unscaledlnL < ZERO_POINT_ZERO) totallnL += (countit[i] * unscaledlnL); #ifdef ALLOW_SINGLE_SITE if(siteToScore > -1) break; #endif } Ldata++; #ifdef LUMP_LIKES if((i + 1) % LUMP_FREQ == 0){ grandSumlnL += totallnL; totallnL = ZERO_POINT_ZERO; } #endif if(sitelikeLevel != 0) siteLikes[i] = unscaledlnL; } } if(sitelikeLevel != 0){ OutputSiteLikelihoods(dataIndex, siteLikes, underflow_mult, NULL); } #ifdef LUMP_LIKES totallnL += grandSumlnL; #endif return totallnL; } FLOAT_TYPE Tree::GetScorePartialTerminalOrientedGap(const CondLikeArray *partialCLA, const FLOAT_TYPE *prmat, const char *Ldat, int modIndex, int dataIndex){ //this assumes that Ldat is the all-missing data from the dummy taxon used in rooting. So, neither it nor the pmat are actually used below //Ldat should be from fully ambiguous dummy taxon that is added for rooting purposes assert(Ldat[0] == 2); const FLOAT_TYPE *partial=partialCLA->arr; const int *underflow_mult=partialCLA->underflow_mult; const SequenceData *data = dataPart->GetSubset(dataIndex); Model *mod = modPart->GetModel(modIndex); const int claStates = 3; const int nchar = data->NChar(); const int *countit=data->GetCounts(); FLOAT_TYPE siteL, totallnL = ZERO_POINT_ZERO, unscaledlnL = ZERO_POINT_ZERO; vector freqs(4); vector siteLikes(nchar); bool allGapChar = true; //rescaler for conditioning on not seeing all gap columns double condScaler = ZERO_POINT_ZERO; FLOAT_TYPE oneInsertProportion = mod->InsertRate(); assert(oneInsertProportion >= 0.0001); double mu = mod->DeleteRate(); double TL = Treelength(); double TLrescaler = 1.0 / (TL * mu); bool outputCategorySitelikes = false; FILE *breakdown = NULL; if(outputCategorySitelikes && sitelikeLevel != 0) breakdown = fopen("likeMixBreakdown.log", "a"); for(int i=0;i 0){ //include the treelength and mu once here, not in the insert prob in the pmat double oneInsert = oneInsertProportion * partial[1] * TLrescaler; double noInsert = (1.0 - oneInsertProportion) * partial[2]; siteL = oneInsert + noInsert; if(outputCategorySitelikes && sitelikeLevel != 0) fprintf(breakdown, "%d\t%.3f\t%.3f\n", i, (oneInsert == 0.0 ? 0.0 : log(oneInsert) - (double) underflow_mult[i]), (noInsert == 0.0 ? 0.0 : log(noInsert) - (double) underflow_mult[i])); partial += claStates; if(i == 0 && allGapChar){ double sum = 0.0; double allGapLike = siteL / exp((double) underflow_mult[i]); //condScaler = -log(ONE_POINT_ZERO - siteL / exp((double) underflow_mult[i])); #ifdef ONE_BRANCH_INS_DEL //also need to figure in prob of single branch insert then delete for each branch //the full term here for each branch would be //(blen / TL) - (1.0 - expMu) / (mu * TL); //or (blen - (1.0 - expMu) / mu)) / TL //for(int i = 1;i < numTipsTotal - 1;i++){ for(int i = 1;i < numNodesTotal;i++){ //skip the dummy root branch if(allNodes[i] != dummyRoot){ double expMu = exp(-mu * allNodes[i]->dlen); //the TL would appear in the denominator of both of the following terms sum += (allNodes[i]->dlen - (1.0 - expMu) / mu); } } //the oneInsertProportion needs to appear here because this single branch ins->del scenario //is only relavent for the class with one insert sum *= oneInsertProportion / TL; #endif condScaler = -log(1.0 - (allGapLike + sum)); assert(condScaler > 0.0); //this is just for sitelike purposes unscaledlnL = condScaler; //outman.UserMessage("mu\t%f\tTL\t%f\toneInsert\t%f\tnoInsert\t%f\tallGapLike\t%f\tsum\t%f\tcondScaler\t%f", mu, TL, oneInsert, noInsert, allGapLike, sum, condScaler); } else{ unscaledlnL = log(siteL) - underflow_mult[i] + condScaler; assert(siteL > ZERO_POINT_ZERO);//this should be positive assert(unscaledlnL < 1.0e-4);//this should be negative or zero //rounding error in multiplying a site that is fully ambiguous across the tree //(which might not have been removed from the data because we are only scoring a //partial tree during stepwise addition) can cause the unscaledlnL to be slightly //> zero. If that is the case, just ignore it if(unscaledlnL < ZERO_POINT_ZERO) totallnL += (countit[i] * unscaledlnL); assert(unscaledlnL == unscaledlnL); assert(unscaledlnL < 0.0); assert(unscaledlnL > -10000.0); } } else{//nothing needs to be done if the count for this site is 0 } if(sitelikeLevel != 0) siteLikes[i] = unscaledlnL; } if(sitelikeLevel != 0){ OutputSiteLikelihoods(dataIndex, siteLikes, underflow_mult, NULL); } if(outputCategorySitelikes && sitelikeLevel != 0) fclose(breakdown); //Previous Rivas and Eddy style method /* //This is the p parameter from the geometric length distribution. Setting it dependent //on the actual seq length L, it is p = ( L / (L + 1)). So, 0.999 is expected len of 1000 double p = 0.999; //freqs of gaps and bases doesn't come in here as it would with a nuc model. However, //a factor of p multiplies the conditional of a non-gap base as part of the seq. length prior //so, just include it here in the freq freqs[0] = freqs[3] = 0.0; freqs[1] = 1.0; freqs[2] = p; //this calculates the product of the (1 - psi) factors that come from each branch double runningTot = 1.0; for(int i = 1;i < numNodesTotal;){ runningTot *= (1.0 - mod->IndelPsi(allNodes[i]->dlen * modPart->SubsetRate(modIndex))); assert(runningTot > 0.0); i++; if(i == numTipsTotal) i++; } double extraColTerm = (1.0 - p) * runningTot; for(int i=0;i 0){ siteL = ZERO_POINT_ZERO; for(int from = 0;from < claStates;from++){ siteL += partial[from] * freqs[from]; } partial += claStates; if(i == 0 && allGapChar){ if(underflow_mult[i] == 0) condScaler = -log(ONE_POINT_ZERO - siteL); else condScaler = ZERO_POINT_ZERO; } else{ unscaledlnL = log(siteL) - underflow_mult[i] + condScaler; assert(siteL > ZERO_POINT_ZERO);//this should be positive assert(unscaledlnL < 1.0e-4);//this should be negative or zero //rounding error in multiplying a site that is fully ambiguous across the tree //(which might not have been removed from the data because we are only scoring a //partial tree during stepwise addition) can cause the unscaledlnL to be slightly //> zero. If that is the case, just ignore it if(unscaledlnL < ZERO_POINT_ZERO) totallnL += (countit[i] * unscaledlnL); assert(unscaledlnL == unscaledlnL); assert(unscaledlnL < 0.0); assert(unscaledlnL > -100.0); } } else{//nothing needs to be done if the count for this site is 0 } if(sitelikeLevel != 0) siteLikes.push_back(unscaledlnL); } if(sitelikeLevel != 0){ OutputSiteLikelihoods(dataIndex, siteLikes, underflow_mult, NULL); } assert(extraColTerm == extraColTerm); assert(extraColTerm > 0.0); assert(extraColTerm < 1.0); totallnL += log(extraColTerm); */ //DEBUG //this takes into account the sequence length /* double ins = mod->InsertRate(); double del = mod->DeleteRate(); int numNoIndels = 1497; double term = 0.0; // double term = log(1.0 - (ins / del)) + numNoIndels * log(ins / del); //add in a factor for the constant columns double expectedDels = Treelength() * del * modPart->SubsetRate(modIndex); //this would be ln(pi * exp(-expectedDels)), so simplifies to: double term2 = numNoIndels * (log(freqs[2]) - expectedDels) ; outman.DebugMessage("%f\t%f\t%f\t%f\t%f\t%f", totallnL + term + term2, totallnL, term, term2, mod->InsertRate(), mod->DeleteRate()); totallnL += (term + term2); */ return totallnL; } FLOAT_TYPE Tree::GetScorePartialTerminalRateHet(const CondLikeArray *partialCLA, const FLOAT_TYPE *prmat, const char *Ldata, int modIndex, int dataIndex){ //this function assumes that the pmat is arranged with the 16 entries for the //first rate, followed by 16 for the second, etc. const FLOAT_TYPE *partial=partialCLA->arr; const int *underflow_mult=partialCLA->underflow_mult; const SequenceData *data = dataPart->GetSubset(dataIndex); Model *mod = modPart->GetModel(modIndex); const int nRateCats=mod->NRateCats(); const int nchar=data->NChar(); const int *countit=data->GetCounts(); const FLOAT_TYPE *rateProb=mod->GetRateProbs(); const int lastConst=data->LastConstant(); const int *conBases=data->GetConstStates(); const FLOAT_TYPE prI=mod->PropInvar(); FLOAT_TYPE freqs[4]; for(int i=0;i<4;i++) freqs[i]=mod->StateFreq(i); #ifdef UNIX posix_madvise((void*)partial, nchar*4*nRateCats*sizeof(FLOAT_TYPE), POSIX_MADV_SEQUENTIAL); #endif #ifdef ALLOW_SINGLE_SITE if(siteToScore > 0) Ldata = AdvanceDataPointer(Ldata, siteToScore); #endif FLOAT_TYPE siteL, unscaledlnL, totallnL = ZERO_POINT_ZERO, grandSumlnL=ZERO_POINT_ZERO; FLOAT_TYPE La, Lc, Lg, Lt; vector siteLikes(nchar); for(int i=0;i 0){ #else if(1){ #endif La=Lc=Lg=Lt=ZERO_POINT_ZERO; if(*Ldata > -1){ //no ambiguity for(int rate=0;rateNoPinvInModel() == false) && (i<=lastConst)){ FLOAT_TYPE btot=0.0; if(conBases[i]&1) btot+=freqs[0]; if(conBases[i]&2) btot+=freqs[1]; if(conBases[i]&4) btot+=freqs[2]; if(conBases[i]&8) btot+=freqs[3]; if(underflow_mult[i]==0) siteL = ((La*freqs[0]+Lc*freqs[1]+Lg*freqs[2]+Lt*freqs[3]) + prI*btot); else siteL = ((La*freqs[0]+Lc*freqs[1]+Lg*freqs[2]+Lt*freqs[3]) + (prI*btot*exp((FLOAT_TYPE)underflow_mult[i]))); } else siteL = ((La*freqs[0]+Lc*freqs[1]+Lg*freqs[2]+Lt*freqs[3])); unscaledlnL = (log(siteL) - underflow_mult[i]); totallnL += (countit[i] * unscaledlnL); #ifdef ALLOW_SINGLE_SITE if(siteToScore > -1) break; #endif } else{ #ifdef OPEN_MP //this is a little strange, but partial only needs to be advanced in the case of OMP //because sections of the CLAs corresponding to sites with count=0 are skipped //over in OMP instead of being eliminated partial += 4*nRateCats; #endif if(*Ldata > -1 || *Ldata == -4) Ldata++; else{ int states = -1 * *Ldata; do{ Ldata++; }while (states-- > 0); } } #ifdef LUMP_LIKES if((i + 1) % LUMP_FREQ == 0){ grandSumlnL += totallnL; totallnL = ZERO_POINT_ZERO; } #endif if(sitelikeLevel != 0) siteLikes[i] = unscaledlnL; } #ifdef LUMP_LIKES totallnL += grandSumlnL; #endif if(sitelikeLevel != 0){ OutputSiteLikelihoods(dataIndex, siteLikes, underflow_mult, NULL); } return totallnL; } FLOAT_TYPE Tree::GetScorePartialInternalRateHet(const CondLikeArray *partialCLA, const CondLikeArray *childCLA, const FLOAT_TYPE *prmat, int modIndex, int dataIndex){ //this function assumes that the pmat is arranged with the 16 entries for the //first rate, followed by 16 for the second, etc. const FLOAT_TYPE *CL1=childCLA->arr; const FLOAT_TYPE *partial=partialCLA->arr; const int *underflow_mult1=partialCLA->underflow_mult; const int *underflow_mult2=childCLA->underflow_mult; const SequenceData *data = dataPart->GetSubset(dataIndex); Model *mod = modPart->GetModel(modIndex); const int nchar=data->NChar(); const int nRateCats=mod->NRateCats(); const int *countit=data->GetCounts(); const FLOAT_TYPE *rateProb=mod->GetRateProbs(); const FLOAT_TYPE prI=mod->PropInvar(); const int lastConst=data->LastConstant(); const int *conBases=data->GetConstStates(); FLOAT_TYPE freqs[4]; for(int i=0;i<4;i++) freqs[i]=mod->StateFreq(i); #ifdef UNIX posix_madvise((void*)partial, nchar*4*nRateCats*sizeof(FLOAT_TYPE), POSIX_MADV_SEQUENTIAL); posix_madvise((void*)CL1, nchar*4*nRateCats*sizeof(FLOAT_TYPE), POSIX_MADV_SEQUENTIAL); #endif FLOAT_TYPE siteL, unscaledlnL, totallnL = ZERO_POINT_ZERO, grandSumlnL=ZERO_POINT_ZERO; FLOAT_TYPE La, Lc, Lg, Lt; vector siteLikes(nchar); for(int i=0;i 0){ #else if(1){ #endif La=Lc=Lg=Lt=ZERO_POINT_ZERO; for(int rate=0;rateNoPinvInModel() == false) && (i<=lastConst)){ FLOAT_TYPE btot=ZERO_POINT_ZERO; if(conBases[i]&1) btot+=freqs[0]; if(conBases[i]&2) btot+=freqs[1]; if(conBases[i]&4) btot+=freqs[2]; if(conBases[i]&8) btot+=freqs[3]; if(underflow_mult1[i] + underflow_mult2[i] == 0) siteL = ((La*freqs[0]+Lc*freqs[1]+Lg*freqs[2]+Lt*freqs[3]) + prI*btot); else siteL = ((La*freqs[0]+Lc*freqs[1]+Lg*freqs[2]+Lt*freqs[3]) + (prI*btot*exp((FLOAT_TYPE)underflow_mult1[i]+underflow_mult2[i]))); } else siteL = ((La*freqs[0]+Lc*freqs[1]+Lg*freqs[2]+Lt*freqs[3])); unscaledlnL = (log(siteL) - underflow_mult1[i] - underflow_mult2[i]); totallnL += (countit[i] * unscaledlnL); #ifdef ALLOW_SINGLE_SITE if(siteToScore > -1) break; #endif } else{ #ifdef OPEN_MP //this is a little strange, but the arrays only needs to be advanced in the case of OMP //because sections of the CLAs corresponding to sites with count=0 are skipped //over in OMP instead of being eliminated partial+=4*nRateCats; CL1+=4*nRateCats; #endif } #ifdef LUMP_LIKES if((i + 1) % LUMP_FREQ == 0){ grandSumlnL += totallnL; totallnL = ZERO_POINT_ZERO; } #endif if(sitelikeLevel != 0) siteLikes[i] = unscaledlnL; } #ifdef LUMP_LIKES totallnL += grandSumlnL; #endif if(sitelikeLevel != 0){ OutputSiteLikelihoods(dataIndex, siteLikes, underflow_mult1, underflow_mult2); } return totallnL; } FLOAT_TYPE Tree::GetScorePartialInternalNState(const CondLikeArray *partialCLA, const CondLikeArray *childCLA, const FLOAT_TYPE *prmat, int modIndex, int dataIndex){ //this function assumes that the pmat is arranged with nstates^2 entries for the //first rate, followed by nstate^2 for the second, etc. const FLOAT_TYPE *CL1=childCLA->arr; const FLOAT_TYPE *partial=partialCLA->arr; const int *underflow_mult1=partialCLA->underflow_mult; const int *underflow_mult2=childCLA->underflow_mult; const SequenceData *data = dataPart->GetSubset(dataIndex); Model *mod = modPart->GetModel(modIndex); const int nchar=data->NChar(); const int *countit=data->GetCounts(); const int nRateCats = mod->NRateCats(); const int nstates = mod->NStates(); const FLOAT_TYPE *rateProb=mod->GetRateProbs(); const FLOAT_TYPE prI=mod->PropInvar(); const int lastConst=data->LastConstant(); const int *conStates=data->GetConstStates(); const int numCondPats = data->NumConditioningPatterns(); #ifdef UNIX posix_madvise((void*)partial, nchar*nstates*nRateCats*sizeof(FLOAT_TYPE), POSIX_MADV_SEQUENTIAL); posix_madvise((void*)CL1, nchar*nstates*nRateCats*sizeof(FLOAT_TYPE), POSIX_MADV_SEQUENTIAL); #endif vector freqs(nstates); for(int i=0;iStateFreq(i); FLOAT_TYPE siteL, unscaledlnL, totallnL = ZERO_POINT_ZERO, grandSumlnL=ZERO_POINT_ZERO; FLOAT_TYPE logConditioningFactor = ZERO_POINT_ZERO; FLOAT_TYPE conditioningLikeSum = ZERO_POINT_ZERO; vector siteLikes(nchar); if(nRateCats == 1){ #ifdef OMP_INTSCORE_NSTATE #ifdef LUMP_LIKES #pragma omp parallel for if(numCondPats == 0) private(partial, CL1, siteL, unscaledlnL) reduction(+ : totallnL, grandSumlnL) #else #pragma omp parallel for if(numCondPats == 0) private(partial, CL1, siteL, unscaledlnL) reduction(+ : totallnL) #endif for(int i=0;iarr[nstates*i]); CL1 = &(childCLA->arr[nstates*i]); #else for(int i=0;i 0){ #else if(1){ #endif siteL = 0.0; for(int from=0;fromNoPinvInModel() == false) && (i<=lastConst)){ if(underflow_mult1[i] + underflow_mult2[i] == 0) siteL += prI*freqs[conStates[i]]; else siteL += prI*freqs[conStates[i]]*exp((FLOAT_TYPE)underflow_mult1[i]+(FLOAT_TYPE)underflow_mult2[i]); } CL1 += nstates; partial += nstates; unscaledlnL = (log(siteL) - underflow_mult1[i] - underflow_mult2[i]); assert(siteL > ZERO_POINT_ZERO);//this should be positive assert(unscaledlnL < 1.0e-4);//this should be negative or zero //rounding error in multiplying a site that is fully ambiguous across the tree //(which might not have been removed from the data because we are only scoring a //partial tree during stepwise addition) can cause the unscaledlnL to be slightly //> zero. If that is the case, just ignore it if(numCondPats > 0){ assert(unscaledlnL < ZERO_POINT_ZERO); if(i < numCondPats){ if(underflow_mult1[i] + underflow_mult2[i] == 0) conditioningLikeSum += siteL; else{ outman.DebugMessage("SCALED MKV SCALER = %d (%f)", (underflow_mult1[i] + underflow_mult2[i]), exp((double)underflow_mult1[i] + underflow_mult2[i])); double unscaler = exp((FLOAT_TYPE)underflow_mult1[i] + underflow_mult2[i]); //Guard against this over or underflowing, which I think are very unlikely. If it does, just ignore this siteL if(unscaler == unscaler){ double unscaled = siteL / unscaler; if(unscaled == unscaled) conditioningLikeSum += unscaled; } } if(i == numCondPats - 1) logConditioningFactor = -log(ONE_POINT_ZERO - conditioningLikeSum); } else{ unscaledlnL += logConditioningFactor; totallnL += (countit[i] * unscaledlnL); } assert(unscaledlnL < ZERO_POINT_ZERO); } else if(unscaledlnL < ZERO_POINT_ZERO) totallnL += (countit[i] * unscaledlnL); #ifdef ALLOW_SINGLE_SITE if(siteToScore > -1) break; #endif } else{//nothing needs to be done if the count for this site is 0 } #ifdef LUMP_LIKES if((i + 1) % LUMP_FREQ == 0){ grandSumlnL += totallnL; totallnL = ZERO_POINT_ZERO; } #endif if(sitelikeLevel != 0) siteLikes[i] = unscaledlnL; } } else{ FLOAT_TYPE siteL, tempL, rateL; #ifdef OMP_INTSCORE_NSTATE #ifdef LUMP_LIKES #pragma omp parallel for if(numCondPats == 0) private(partial, CL1, siteL, tempL, rateL, unscaledlnL) reduction(+ : totallnL, grandSumlnL) #else #pragma omp parallel for if(numCondPats == 0) private(partial, CL1, siteL, tempL, rateL, unscaledlnL) reduction(+ : totallnL) #endif for(int i=0;iarr[nRateCats*nstates*i]); CL1 = &(childCLA->arr[nRateCats*nstates*i]); #else for(int i=0;i 0){ #else if(1){ #endif siteL = ZERO_POINT_ZERO; for(int rate=0;rateNoPinvInModel() == false) && (i<=lastConst)){ if(underflow_mult1[i] + underflow_mult2[i] == 0) siteL += prI*freqs[conStates[i]]; else siteL += prI*freqs[conStates[i]]*exp((FLOAT_TYPE)underflow_mult1[i]+(FLOAT_TYPE)underflow_mult2[i]); } unscaledlnL = (log(siteL) - underflow_mult1[i] - underflow_mult2[i]); assert(siteL > ZERO_POINT_ZERO);//this should be positive assert(unscaledlnL < 1.0e-4);//this should be negative or zero //rounding error in multiplying a site that is fully ambiguous across the tree //(which might not have been removed from the data because we are only scoring a //partial tree during stepwise addition) can cause the unscaledlnL to be slightly //> zero. If that is the case, just ignore it if(numCondPats > 0){ assert(unscaledlnL < ZERO_POINT_ZERO); if(i < numCondPats){ if(underflow_mult1[i] + underflow_mult2[i] == 0) conditioningLikeSum += siteL; else outman.DebugMessage("SCALED MKV SCALER = %d (%f)", (underflow_mult1[i] + underflow_mult2[i]), exp((double)underflow_mult1[i] + underflow_mult2[i])); double unscaler = exp((FLOAT_TYPE)underflow_mult1[i] + underflow_mult2[i]); //Guard against this over or underflowing, which I think are very unlikely. If it does, just ignore this siteL if(unscaler == unscaler){ double unscaled = siteL / unscaler; if(unscaled == unscaled) conditioningLikeSum += unscaled; } } if(i == numCondPats - 1) logConditioningFactor = -log(ONE_POINT_ZERO - conditioningLikeSum); else{ unscaledlnL += logConditioningFactor; totallnL += (countit[i] * unscaledlnL); } assert(unscaledlnL < ZERO_POINT_ZERO); } else if(unscaledlnL < ZERO_POINT_ZERO) totallnL += (countit[i] * unscaledlnL); #ifdef ALLOW_SINGLE_SITE if(siteToScore > -1) break; #endif } else{ //nothing needs to be done if the count of this site is 0 } #ifdef LUMP_LIKES if((i + 1) % LUMP_FREQ == 0){ grandSumlnL += totallnL; totallnL = ZERO_POINT_ZERO; } #endif if(sitelikeLevel != 0) siteLikes[i] = unscaledlnL; } } #ifdef LUMP_LIKES totallnL += grandSumlnL; #endif if(sitelikeLevel != 0){ OutputSiteLikelihoods(dataIndex, siteLikes, underflow_mult1, underflow_mult2); } return totallnL; } void Tree::GetStatewiseUnscaledPosteriorsPartialInternalNState(CondLikeArray *destCLA, const CondLikeArray *partialCLA, const CondLikeArray *childCLA, const FLOAT_TYPE *prmat, int modIndex, int dataIndex){ FLOAT_TYPE *dest=destCLA->arr; const FLOAT_TYPE *CL1=childCLA->arr; const FLOAT_TYPE *partial=partialCLA->arr; const int *underflow_mult1=partialCLA->underflow_mult; const int *underflow_mult2=childCLA->underflow_mult; const SequenceData *data = dataPart->GetSubset(dataIndex); Model *mod = modPart->GetModel(modIndex); const int nchar=data->NChar(); const int *countit=data->GetCounts(); const int nRateCats = mod->NRateCats(); const int nstates = mod->NStates(); const FLOAT_TYPE *rateProb=mod->GetRateProbs(); const FLOAT_TYPE prI=mod->PropInvar(); const int lastConst=data->LastConstant(); const int *conStates=data->GetConstStates(); #ifdef UNIX posix_madvise((void*)partial, nchar*nstates*nRateCats*sizeof(FLOAT_TYPE), POSIX_MADV_SEQUENTIAL); posix_madvise((void*)CL1, nchar*nstates*nRateCats*sizeof(FLOAT_TYPE), POSIX_MADV_SEQUENTIAL); #endif vector freqs(nstates); for(int i=0;iStateFreq(i); //note that we don't need to zero the whole thing //this was not guaranteed to be safe //memset(dest, 0, nchar * nstates * sizeof(FLOAT_TYPE)); for(int d = 0;d < nchar * nstates;d++) dest[d] = ZERO_POINT_ZERO; for(int i=0;iNoPinvInModel() == false) && (i<=lastConst)){ //conStates has different meaning with nuc and other models. //With nuc it is the base in 1, 2, 4, 8 notation (possibly mulitple bits set if ambiguity) //With other models it is the state index, starting at 0 FLOAT_TYPE pinvRescaler = ONE_POINT_ZERO; //if the site is constant but was rescaled, this must be done if((underflow_mult1[i] + underflow_mult2[i]) != 0) pinvRescaler = exp((FLOAT_TYPE)underflow_mult1[i]+(FLOAT_TYPE)underflow_mult2[i]); if(nstates > 4){ dest[conStates[i]] += prI * freqs[conStates[i]] * pinvRescaler; } else{ if(conStates[i]&1) dest[0] += prI * freqs[0] * pinvRescaler; if(conStates[i]&2) dest[1] += prI * freqs[1] * pinvRescaler; if(conStates[i]&4) dest[2] += prI * freqs[2] * pinvRescaler; if(conStates[i]&8) dest[3] += prI * freqs[3] * pinvRescaler; } } dest += nstates; } } void Tree::GetStatewiseUnscaledPosteriorsPartialTerminalNState(CondLikeArray *destCLA, const CondLikeArray *partialCLA, const FLOAT_TYPE *prmat, const char *Ldata, int modIndex, int dataIndex){ //this function assumes that the pmat is arranged with the nstates^2 entries for the //first rate, followed by nstates^2 for the second, etc. FLOAT_TYPE *dest=destCLA->arr; const FLOAT_TYPE *partial=partialCLA->arr; const int *underflow_mult=partialCLA->underflow_mult; const SequenceData *data = dataPart->GetSubset(dataIndex); Model *mod = modPart->GetModel(modIndex); const int nstates = mod->NStates(); const int nRateCats = mod->NRateCats(); const int nchar = data->NChar(); const FLOAT_TYPE *rateProb=mod->GetRateProbs(); const int lastConst=data->LastConstant(); const int *conStates=data->GetConstStates(); const FLOAT_TYPE prI=mod->PropInvar(); #ifdef UNIX posix_madvise((void*)partial, nchar*nstates*nRateCats*sizeof(FLOAT_TYPE), POSIX_MADV_SEQUENTIAL); #endif FLOAT_TYPE totallnL=ZERO_POINT_ZERO, grandSumlnL=ZERO_POINT_ZERO; vector freqs(nstates); for(int i=0;iStateFreq(i); //note that we don't need to zero the whole thing //this was not guaranteed to be safe //memset(dest, 0, nchar * nstates * sizeof(FLOAT_TYPE)); for(int d = 0;d < nchar * nstates;d++) dest[d] = ZERO_POINT_ZERO; for(int i=0;iNoPinvInModel() == false) && (i<=lastConst)){ //conStates has different meaning with nuc and other models. //With nuc it is the base in 1, 2, 4, 8 notation (possibly mulitple bits set if ambiguity) //With other models it is the state index, starting at 0 FLOAT_TYPE pinvRescaler = ONE_POINT_ZERO; //if the site is constant but was rescaled, this must be done if(underflow_mult[i] != 0) pinvRescaler = exp((FLOAT_TYPE)underflow_mult[i]); if(nstates > 4){ dest[conStates[i]] += prI * freqs[conStates[i]] * pinvRescaler; } else{ if(conStates[i]&1) dest[0] += prI * freqs[0] * pinvRescaler; if(conStates[i]&2) dest[1] += prI * freqs[1] * pinvRescaler; if(conStates[i]&4) dest[2] += prI * freqs[2] * pinvRescaler; if(conStates[i]&8) dest[3] += prI * freqs[3] * pinvRescaler; } } Ldata++; dest += nstates; } } void Tree::LocalMove(){ assert(0); //This is not working //this will all assume that there are no polytomies besides the root TreeNode *a, *b, *c, *d; int cPosition; //pick a random TreeNode and set up the rest of the nodes in relation to it// int tmp=numTipsTotal+rnd.random_int(numTipsTotal-3)+1; TreeNode *u=allNodes[tmp]; //set up the vicinity of u TreeNode *v=u->anc; //STANDARDIZE by making v->left=u if(u!=v->left){ if(u==v->left->next){ if(v->IsRoot()){ TreeNode *tempnode=v->left; TreeNode *tempnode2; if(v->left->next==u) tempnode2=u->next; else tempnode2=v->left->next; v->left=u; u->next=tempnode; tempnode->next=tempnode2; tempnode2->next=NULL; } else{ v->RotateDescendents(); /* TreeNode *tempnode=v->left; v->left=u; u->next=tempnode; tempnode->next=NULL; */ } } else{ //v must be the root, and u must be v->left->next->next TreeNode *tempnode=v->left; TreeNode *tempnode2=v->left->next; v->left=u; u->next=tempnode; u->next->next=tempnode2; tempnode2->next=NULL; } } //determine a and b if(rnd.uniform()<.5){ a=u->left; b=a->next; } else{ b=u->left; a=b->next; } //STANDARDIZE by making u->left=a if(u->left!=a){ u->RotateDescendents(); /* u->left=a; u->left->next=b; b->next=NULL; */ } //set up the vicinity of v if(v->IsRoot()){ //if v is the root if(rnd.uniform()<.05){ c=u->next; d=c->next; //STANDARDIZE by making c=v->left->next->next u->next=d; d->next=c; c->next=NULL; cPosition=2; } else{ d=u->next; c=d->next;//left->next->next if(c==NULL){ c=c; } cPosition=2; } } else{ //if v is not the root... if(rnd.uniform()<.5){ c=u->next; cPosition=1;//left->next d=v->anc; //STANDARDIZE by making d->left=v if cPosition==1 if(d->left!=v){ if(d->anc!=NULL){ d->RotateDescendents(); /* TreeNode *tempnode=d->left; d->left=v; v->next=tempnode; tempnode->next=NULL; */ } else{ TreeNode *tempnode=d->left; TreeNode *tempnode2; if(d->left->next==v) tempnode2=v->next; else tempnode2=d->left->next; d->left=v; v->next=tempnode; tempnode->next=tempnode2; tempnode2->next=NULL; } } } else{ d=u->next; c=v->anc; cPosition=3;//anc //STANDARDIZE by making c->left=v if cPosition==3 if(c->left!=v){ if(c->IsRoot()){ TreeNode *tempnode=c->left; TreeNode *tempnode2; if(tempnode->next==v) tempnode2=v->next; else tempnode2=tempnode->next; c->left=v; v->next=tempnode; tempnode->next=tempnode2; tempnode2->next=NULL; } else{ c->RotateDescendents(); /* TreeNode *tempnode=c->left; c->left=v; v->next=tempnode; tempnode->next=NULL; */ } } } } /*Now that things are set up, we can count on the following being true: u->left=a; u->left->next=b; v->left=a; if(v->anc!=NULL){ v->left->next=c && d->left=v (case 1) else c->left=v && v->left->next=d (case 2) } else{ v->left->next->next=c && v->left->next=d (case 3) } */ //Ok, the nodes are defined. //Calculate the backbone length and the new length FLOAT_TYPE m; FLOAT_TYPE changing_blens[3]; // FLOAT_TYPE new_blens[3]; changing_blens[0]=a->dlen; changing_blens[1]=u->dlen; if(cPosition==3){ changing_blens[2]=v->dlen; } else { changing_blens[2]=c->dlen; } m=changing_blens[0]+changing_blens[1]+changing_blens[2]; FLOAT_TYPE r=rnd.uniform(); // FLOAT_TYPE tuning=.25; // FLOAT_TYPE tuning=.1; FLOAT_TYPE mprime=m*exp((FLOAT_TYPE).5*(rnd.uniform()-(FLOAT_TYPE).5)); FLOAT_TYPE x, y; //choose whether to "detach" u or v. Don't actually detach anything though if(rnd.uniform()<.5){ //detach u //calculate x and y x=rnd.uniform()*mprime; y=(a->dlen+u->dlen) * (mprime/m); if(xdlen=x; u->dlen=y-x; if(cPosition==3) v->dlen=mprime-y; else c->dlen = mprime-y; TraceDirtynessToRoot(a->anc); // tree->AdjustCLArrayFlagsBelow(a->anc, curMove); } else{ //case 1 if(cPosition==1){ u->left=b; u->left->next=c; c->next=NULL; c->anc=u; v->left->next=a; a->anc=v; a->next=NULL; a->dlen=y; u->dlen=x-y; c->dlen=mprime-x; TraceDirtynessToRoot(c->anc); //tree->AdjustCLArrayFlagsBelow(c->anc, curMove); } //case 3 else if(cPosition==2){ u->left=b; u->left->next=c; c->next=NULL; c->anc=u; v->left->next->next=a; a->anc=v; a->next=NULL; a->dlen=y; u->dlen=x-y; c->dlen=mprime-x; TraceDirtynessToRoot(c->anc); //tree->AdjustCLArrayFlagsBelow(c->anc, curMove); } //case 2 else{//u and v physically swap positions in this case v->left=a; a->anc=v; a->next=d; d->next=NULL; u->left=v; u->next=v->next; v->next=b; b->next=NULL; u->anc=c; v->anc=u; c->left=u; a->dlen=y; v->dlen=x-y; u->dlen=mprime-x; TraceDirtynessToRoot(a->anc); //tree->AdjustCLArrayFlagsBelow(a->anc, curMove); } } } else{ //"detach" v x=a->dlen*(mprime/m); y=rnd.uniform() * mprime; if(xdlen=x; u->dlen=y-x; if(cPosition==3) v->dlen=mprime-y; else c->dlen=mprime-y; TraceDirtynessToRoot(a->anc); // tree->AdjustCLArrayFlagsBelow(a->anc, curMove); } else{ //case 1 if(cPosition==1){ u->left=b; u->left->next=c; c->next=NULL; c->anc=u; v->left->next=a; a->anc=v; a->next=NULL; a->dlen=y; u->dlen=x-y; c->dlen=mprime-x; TraceDirtynessToRoot(c->anc); // tree->AdjustCLArrayFlagsBelow(c->anc, curMove); } //case 3 else if(cPosition==2){ u->left=b; u->left->next=c; c->next=NULL; c->anc=u; v->left->next->next=a; a->anc=v; a->next=NULL; a->dlen=y; u->dlen=x-y; c->dlen=mprime-x; TraceDirtynessToRoot(c->anc); // tree->AdjustCLArrayFlagsBelow(c->anc, curMove); } //case 2 else{//u and v physically swap positions in this case v->left=a; a->anc=v; a->next=d; d->next=NULL; u->left=v; u->next=v->next; v->next=b; b->next=NULL; u->anc=c; v->anc=u; c->left=u; a->dlen=y; v->dlen=x-y; u->dlen=mprime-x; TraceDirtynessToRoot(a->anc); // tree->AdjustCLArrayFlagsBelow(a->anc, curMove); } } } } void Tree::NNIMutate(int node, int branch, FLOAT_TYPE optPrecision, int subtreeNode){ assert(0); TreeNode* connector=NULL; TreeNode* cut=NULL; TreeNode* broken=NULL; TreeNode* sib=NULL; assert(nodeIsInternal()); if(branch==0){ cut=connector->left; sib=connector->right; } else{ cut=connector->right; sib=connector->left; } SweepDirtynessOverTree(cut); //cut will be attached to connector's next or prev if(connector->next!=NULL) broken=connector->next; else{ if(connector->anc==root){ //special case if connector's anc is root and connector is the rightmost decendent broken=connector->prev->prev; } else broken=connector->prev; } //take out connector and substitute cut's sib for it connector->SubstituteNodeWithRespectToAnc(sib); //establish correct topology for connector and cut nodes connector->left=connector->right=cut; connector->next=connector->prev=connector->anc=cut->next=cut->prev=NULL; //assign branchlengths such that the previous blen of broken is divided between //broken and connector //cut will keep its original blen. Connector's old blen will be added to sib sib->dlen+=connector->dlen; if(broken->dlen*.5 > min_brlen){ connector->dlen=broken->dlen*(FLOAT_TYPE).5; broken->dlen-=connector->dlen; } else connector->dlen=broken->dlen=min_brlen; //put everything in its place broken->SubstituteNodeWithRespectToAnc(connector); connector->AddDes(broken); //try some branch length optimization SweepDirtynessOverTree(connector, cut); MakeNodeDirty(connector); #ifdef OPT_DEBUG opt << "NNI\n"; optsum << "NNI\n"; #endif OptimizeBranchesWithinRadius(connector, optPrecision, subtreeNode, NULL); } /* void Tree::OutputBinaryFormattedTree(ofstream &out) const{ for(int i=0;iOutputBinaryNodeInfo(out); } out.write((char*) &lnL, sizeof(FLOAT_TYPE)); out.write((char*) &numTipsTotal, sizeof(numTipsTotal)); out.write((char*) &numTipsAdded, sizeof(numTipsAdded)); out.write((char*) &numNodesAdded, sizeof(numNodesAdded)); out.write((char*) &numBranchesAdded, sizeof(numBranchesAdded)); out.write((char*) &numNodesTotal, sizeof(numNodesTotal)); } */ void Tree::OutputBinaryFormattedTree(OUTPUT_CLASS &out) const{ out.WRITE_TO_FILE(&numTipsTotal, sizeof(numTipsTotal), 1); out.WRITE_TO_FILE(&lnL, sizeof(FLOAT_TYPE), 1); out.WRITE_TO_FILE(&numTipsAdded, sizeof(numTipsAdded), 1); out.WRITE_TO_FILE(&numNodesAdded, sizeof(numNodesAdded), 1); out.WRITE_TO_FILE(&numBranchesAdded, sizeof(numBranchesAdded), 1); out.WRITE_TO_FILE(&numNodesTotal, sizeof(numNodesTotal), 1); for(int i=0;iOutputBinaryNodeInfo(out); } } void Tree::ReadBinaryFormattedTree(FILE *in){ //this allows a check that the checkpoint was written for the same //dataset that was specified in the conf int expectedNumTipsTotal = numTipsTotal; fread((char*) &numTipsTotal, sizeof(numTipsTotal), 1, in); if(numTipsTotal != expectedNumTipsTotal){ int wrong = numTipsTotal; numTipsTotal = expectedNumTipsTotal; throw ErrorException("Number of taxa from checkpoint (%d) is not the same as in the current\n\tdatafile (%d)! The checkpoint seems to be from a different run!", wrong, expectedNumTipsTotal); } fread((char*) &lnL, sizeof(FLOAT_TYPE), 1, in); fread((char*) &numTipsAdded, sizeof(numTipsAdded), 1, in); fread((char*) &numNodesAdded, sizeof(numNodesAdded), 1, in); fread((char*) &numBranchesAdded, sizeof(numBranchesAdded), 1, in); fread((char*) &numNodesTotal, sizeof(numNodesTotal), 1, in); int dum; fread((char*) &dum, sizeof(dum), 1, in); allNodes[0]->left = allNodes[dum]; fread((char*) &dum, sizeof(dum), 1, in); allNodes[0]->right = allNodes[dum]; fread((char*) &dum, sizeof(dum), 1, in); if(dum == 0) allNodes[0]->prev = NULL; else allNodes[0]->prev = allNodes[dum]; fread((char*) &dum, sizeof(dum), 1, in); if(dum == 0) allNodes[0]->next = NULL; else allNodes[0]->next = allNodes[dum]; fread((char*) &dum, sizeof(dum), 1, in); if(dum == 0) allNodes[0]->anc = NULL; else allNodes[0]->anc = allNodes[dum]; fread((char*) &allNodes[0]->dlen, sizeof(FLOAT_TYPE), 1, in); // double d; for(int i=1;i<=numTipsTotal;i++){ fread(&dum, sizeof(dum), 1, in); if(dum == 0) allNodes[i]->prev = NULL; else allNodes[i]->prev = allNodes[dum]; fread(&dum, sizeof(dum), 1, in); if(dum == 0) allNodes[i]->next = NULL; else allNodes[i]->next = allNodes[dum]; //all non-root nodes will have an anc, which might be nodenum 0 (the root) //so, don't test for zero here fread(&dum, sizeof(dum), 1, in); allNodes[i]->anc = allNodes[dum]; fread(&(allNodes[i]->dlen), sizeof(FLOAT_TYPE), 1, in); } for(int i=numTipsTotal+1;ileft = allNodes[dum]; fread((char*) &dum, sizeof(dum), 1, in); allNodes[i]->right = allNodes[dum]; fread((char*) &dum, sizeof(dum), 1, in); if(dum == 0) allNodes[i]->prev = NULL; else allNodes[i]->prev = allNodes[dum]; fread((char*) &dum, sizeof(dum), 1, in); if(dum == 0) allNodes[i]->next = NULL; else allNodes[i]->next = allNodes[dum]; //all non-root nodes will have an anc, which might be nodenum 0 (the root) //so, don't test for zero here fread((char*) &dum, sizeof(dum), 1, in); allNodes[i]->anc = allNodes[dum]; fread((char*) &allNodes[i]->dlen, sizeof(FLOAT_TYPE), 1, in); } } FLOAT_TYPE Tree::OptimizeInsertDeleteRates(FLOAT_TYPE prec, int modnum){ FLOAT_TYPE improve = 0.0; FLOAT_TYPE insProp, del; insProp = modPart->GetModel(modnum)->InsertRate(); del = modPart->GetModel(modnum)->DeleteRate(); //insert rate here is really the proportion of sites in the one insert category improve += OptimizeBoundedParameter(modnum, prec, insProp, 0, max(1e-4, insProp / 1.5), min(insProp * 1.5, 0.9999), &Model::SetInsertRate); //don't optimize del rate in these cases because it becomes non-identifiable if(modSpecSet.NumSpecs() != 1 && modSpecSet.InferSubsetRates() == false) improve += OptimizeBoundedParameter(modnum, prec, del, 0, max(0.001, del / 1.5), min(del * 1.5, 999.9), &Model::SetDeleteRate); return improve; } FLOAT_TYPE Tree::OptimizeOmegaParameters(FLOAT_TYPE prec, int modnum){ FLOAT_TYPE omegaImprove=ZERO_POINT_ZERO; FLOAT_TYPE minVal = 1.0e-5; int i=0; Model *mod = modPart->GetModel(modnum); #undef DEBUG_OMEGA_OPT //codon models can be a little unstable, so make the difference in scores that we're looking for in OptBounded a bit larger. 9 is the default value. //it really shouldn't matter in almost all cases. FLOAT_TYPE scoreDiffTarget; #ifdef SINGLE_PRECISION_FLOATS scoreDiffTarget = 4.0; #else scoreDiffTarget = 7.0; #endif //limiting change in any one pass double maxRateChangeProportion = 2.0; //this is the allowed proportion of change, i.e., x is bounded by x/maxRateChangeProportion and x * maxRateChangeProportion double maxProbChange = 0.10; //this is the actual allowed magnitude of change, i.e, x - maxProbChange aned x + maxProbChange double curVal; //give the first rate more leeway in the down direction, since it may want to approach zero if(mod->NRateCats() == 1){ curVal = mod->Omega(i); omegaImprove += OptimizeBoundedParameter(modnum, prec, curVal, 0, max(minVal, curVal / 5.0), max(min(9999.9, curVal * maxRateChangeProportion), 0.01), &Model::SetOmega, scoreDiffTarget); } else{ curVal = mod->Omega(i); omegaImprove += OptimizeBoundedParameter(modnum, prec, curVal, i, max(minVal, curVal / 5.0), min(mod->Omega(i+1), max(curVal * maxRateChangeProportion, 0.01)), &Model::SetOmega, scoreDiffTarget); #ifdef DEBUG_OMEGA_OPT for(int j=0;jNRateCats();j++) outman.UserMessage("%f\t%f", mod->Omega(j), mod->OmegaProb(j)); #endif curVal = mod->OmegaProb(i); omegaImprove += OptimizeBoundedParameter(modnum, prec, curVal, i, max(minVal, curVal-maxProbChange), min((ONE_POINT_ZERO - (minVal * (FLOAT_TYPE)(mod->NRateCats() - 1))), curVal+maxProbChange), &Model::SetOmegaProb, scoreDiffTarget); #ifdef DEBUG_OMEGA_OPT for(int j=0;jNRateCats();j++) outman.UserMessage("%f\t%f", mod->Omega(j), mod->OmegaProb(j)); #endif for(i=1;i < mod->NRateCats()-1;i++){ curVal = mod->Omega(i); omegaImprove += OptimizeBoundedParameter(modnum, prec, curVal, i, max(mod->Omega(i-1), curVal / maxRateChangeProportion), min(mod->Omega(i+1), max(curVal * maxRateChangeProportion, 0.01)), &Model::SetOmega, scoreDiffTarget); #ifdef DEBUG_OMEGA_OPT for(int j=0;jNRateCats();j++) outman.UserMessage("%f\t%f", mod->Omega(j), mod->OmegaProb(j)); #endif curVal = mod->OmegaProb(i); omegaImprove += OptimizeBoundedParameter(modnum, prec, curVal, i, max(minVal, curVal-maxProbChange), min((ONE_POINT_ZERO - (minVal * (FLOAT_TYPE)(mod->NRateCats() - 1))), curVal+maxProbChange), &Model::SetOmegaProb, scoreDiffTarget); #ifdef DEBUG_OMEGA_OPT for(int j=0;jNRateCats();j++) outman.UserMessage("%f\t%f", mod->Omega(j), mod->OmegaProb(j)); #endif } curVal = mod->Omega(i); omegaImprove += OptimizeBoundedParameter(modnum, prec, curVal, i, max(mod->Omega(i-1), curVal / maxRateChangeProportion), min(9999.9, curVal * maxRateChangeProportion), &Model::SetOmega, scoreDiffTarget); #ifdef DEBUG_OMEGA_OPT for(int j=0;jNRateCats();j++) outman.UserMessage("%f\t%f", mod->Omega(j), mod->OmegaProb(j)); #endif curVal = mod->OmegaProb(i); omegaImprove += OptimizeBoundedParameter(modnum, prec, curVal, i, max(minVal, curVal-maxProbChange), min((ONE_POINT_ZERO - (minVal * (FLOAT_TYPE)(mod->NRateCats() - 1))), curVal+maxProbChange), &Model::SetOmegaProb, scoreDiffTarget); #ifdef DEBUG_OMEGA_OPT for(int j=0;jNRateCats();j++) outman.UserMessage("%f\t%f", mod->Omega(j), mod->OmegaProb(j)); #endif } /* if(mod->NRateCats() == 1) omegaImprove += OptimizeBoundedParameter(prec, mod->Omega(i), 0, minVal, 9999.9, &Model::SetOmega); else{ omegaImprove += OptimizeBoundedParameter(prec, mod->Omega(i), i, minVal, mod->Omega(1), &Model::SetOmega); // for(int j=0;jNRateCats();j++) // cout << mod->Omega(j) << "\t" << mod->OmegaProb(j) << endl; omegaImprove += OptimizeBoundedParameter(prec, mod->OmegaProb(i), i, minVal, ONE_POINT_ZERO, &Model::SetOmegaProb); // for(int j=0;jNRateCats();j++) // cout << mod->Omega(j) << "\t" << mod->OmegaProb(j) << endl; for(i=1;i < mod->NRateCats()-1;i++){ omegaImprove += OptimizeBoundedParameter(prec, mod->Omega(i), i, mod->Omega(i-1), mod->Omega(i+1), &Model::SetOmega); // for(int j=0;jNRateCats();j++) // cout << mod->Omega(j) << "\t" << mod->OmegaProb(j) << endl; omegaImprove += OptimizeBoundedParameter(prec, mod->OmegaProb(i), i, minVal, ONE_POINT_ZERO, &Model::SetOmegaProb); // for(int j=0;jNRateCats();j++) // cout << mod->Omega(j) << "\t" << mod->OmegaProb(j) << endl; } omegaImprove += OptimizeBoundedParameter(prec, mod->Omega(i), i, mod->Omega(i-1), 9999.9, &Model::SetOmega); // for(int j=0;jNRateCats();j++) // cout << mod->Omega(j) << "\t" << mod->OmegaProb(j) << endl; omegaImprove += OptimizeBoundedParameter(prec, mod->OmegaProb(i), i, minVal, ONE_POINT_ZERO, &Model::SetOmegaProb); // for(int j=0;jNRateCats();j++) // cout << mod->Omega(j) << "\t" << mod->OmegaProb(j) << endl; } */ return omegaImprove; } FLOAT_TYPE Tree::OptimizeEquilibriumFreqs(FLOAT_TYPE prec, int modnum){ FLOAT_TYPE freqImprove=ZERO_POINT_ZERO; int i=0; Model *mod = modPart->GetModel(modnum); //limiting change in any one pass much more double maxChangeProportion = 1.2; for(i=0;i < mod->NStates();i++){ double curVal = mod->StateFreq(i); freqImprove += OptimizeBoundedParameter(modnum, prec, curVal, i, min(max((mod->NStates() > 4 ? 0.0001 : 0.01), curVal / maxChangeProportion), curVal), max(min(0.96, curVal * maxChangeProportion), curVal), &Model::SetEquilibriumFreq); } return freqImprove; } FLOAT_TYPE Tree::OptimizeRelativeNucRates(FLOAT_TYPE prec, int modnum){ FLOAT_TYPE rateImprove=ZERO_POINT_ZERO; FLOAT_TYPE minVal = 1.0e-5; int i=0; FLOAT_TYPE scoreOnEntry = lnL; const ModelSpecification *modSpec = modSpecSet.GetModSpec(modnum); Model *mod = modPart->GetModel(modnum); //codon models can be a little unstable, so make the difference in scores that we're looking for in OptBounded a bit larger. 9 is the default value. //it really shouldn't matter in almost all cases. FLOAT_TYPE scoreDiffTarget; #ifdef SINGLE_PRECISION_FLOATS if(modSpec->IsCodon()) scoreDiffTarget = 4.0; else scoreDiffTarget = 5.0; #else if(modSpec->IsCodon()) scoreDiffTarget = 7.0; else scoreDiffTarget = 9.0; #endif //limiting change in any one pass double maxChangeProportion = 5.0; //assert(mod->Nst() > 1); if(mod->Nst() == 2){ //this was wrong - it should be Rates(1) i.e., K that is being optimized here double curVal = mod->Rates(1); rateImprove += OptimizeBoundedParameter(modnum, prec, curVal, 1, max(min(1.0e-3, curVal), curVal / maxChangeProportion), min(max(999.0, curVal), curVal * maxChangeProportion), &Model::SetRelativeNucRate, scoreDiffTarget); /* rateImprove += OptimizeBoundedParameter(prec, mod->Rates(0), 0, max(0.05, mod->Rates(0) / maxChangeProportion), min(999.0, mod->Rates(0) * maxChangeProportion), &Model::SetRelativeNucRate); */ } else if(modSpec->IsNucleotide() || modSpec->IsCodon()){ /* char temp[100]; int oprec = 4; sprintf(temp," r %.*f %.*f %.*f %.*f %.*f", oprec, mod->Rates(0), oprec, mod->Rates(1), oprec, mod->Rates(2), oprec, mod->Rates(3), oprec, mod->Rates(4)); outman.UserMessage("%s", temp); */ for(i=0;i < 5;i++){ bool skip = false; if(modSpec->IsArbitraryRateMatrix()){ if(mod->GetArbitraryRateMatrixIndeces()[i] == mod->GetArbitraryRateMatrixIndeces()[5]) skip = true; } if(!skip){ double curVal = mod->Rates(i); rateImprove += OptimizeBoundedParameter(modnum, prec, curVal, i, max(min(1.0e-3, curVal), curVal / maxChangeProportion), min(max(999.0, curVal), curVal * maxChangeProportion), &Model::SetRelativeNucRate, scoreDiffTarget); } } } else if(modSpec->IsAminoAcid()){ #ifdef DEBUG_MESSAGES /* string s; mod->FillModelOrHeaderStringForTable(s, true); ofstream tab("valTable.log", ios::app); tab << lnL << "\t" << s.c_str() << "\t" << endl; tab.close(); */ #endif list reopt; #ifdef SUM_AA_REL_RATES mod->NormalizeSumConstrainedRelativeRates(true, -1); for(i=0;i < mod->NumRelRates();i++) reopt.push_back(i); #else for(i=0;i < mod->NumRelRates()-1;i++) reopt.push_back(i); #endif int pass = 0; while(reopt.size() != 0 && pass < 5){ double beflnL = lnL; list::iterator it = reopt.begin(); int num = reopt.size(); while(it != reopt.end()){ double beflnL = lnL; double befval = mod->Rates(*it); #ifdef SUM_AA_REL_RATES FLOAT_TYPE minV = max(MIN_REL_RATE, befval / maxChangeProportion); if(minV < SUM_TO * 1.0e-3/190.0){ minV = min(befval, MIN_REL_RATE); } FLOAT_TYPE maxV = min(MAX_REL_RATE, befval * maxChangeProportion); if(maxV < SUM_TO * 1.0e-3/190.0) maxV = SUM_TO * 1.0e-3/190.0; rateImprove += OptimizeBoundedParameter(modnum, prec, befval, *it, minV, maxV, &Model::SetSumConstrainedRelativeRate, scoreDiffTarget); #else FLOAT_TYPE minV = max(min(1.0e-3, befval), befval / maxChangeProportion); if(minV < 0.01) minV = min(befval, 1.0e-3); FLOAT_TYPE maxV = min(max(9999.0, befval), befval * maxChangeProportion); if(maxV < 0.01) maxV = 0.01; rateImprove += OptimizeBoundedParameter(modnum, prec, befval, *it, minV, maxV, &Model::SetRelativeNucRate, scoreDiffTarget); #endif if(FloatingPointEquals(lnL, beflnL, 1e-8)){ list::iterator del=it; it++; reopt.erase(del); } else it++; } pass++; outman.DebugMessage("reoptimized %d. improvement %.6f", num, lnL - beflnL); #ifdef DEBUG_MESSAGES /* string s; mod->FillModelOrHeaderStringForTable(s, true); ofstream tab("valTable.log", ios::app); tab << lnL << "\t" << s.c_str() << endl; tab.close(); */ #endif } FLOAT_TYPE currDiff = lnL - scoreOnEntry; outman.UserMessage("summed improve %.8f actual diff %.8f, %.8f, %.8f", rateImprove, currDiff, scoreOnEntry, lnL); if(currDiff < rateImprove && currDiff >= 0.0) rateImprove = currDiff; string modstr; modPart->FillGarliFormattedModelStrings(modstr); ofstream modlog("models.log", ios::app); modlog << lnL << "\t" << modstr.c_str() << "\t"; char treeString[5000]; modlog.setf( ios::floatfield, ios::fixed ); modlog.setf( ios::showpoint ); root->MakeNewick(treeString, false, true); modlog << treeString << ";\n"; modlog.close(); } return rateImprove; } FLOAT_TYPE Tree::OptimizeFlexRates(FLOAT_TYPE prec, int modnum){ FLOAT_TYPE flexImprove=ZERO_POINT_ZERO; FLOAT_TYPE minVal = 1.0e-5; int i=0; //PARTITION Model *mod = modPart->GetModel(modnum); //limiting change in any one pass double maxRateChangeProp = 1.5; double maxProbChange = 0.10; double curVal; //very tight increments really seems to help flex optimization FLOAT_TYPE scoreDiffTarget; #ifdef SINGLE_PRECISION_FLOATS scoreDiffTarget = 5.0; #else scoreDiffTarget = 10.0; #endif #ifdef DEBUG_FLEX_OPT outman.UserMessage("%.4f", lnL); for(int j=0;jNRateCats();j++) outman.UserMessage("%f\t%f", mod->FlexRate(j), mod->FlexProb(j)); #endif //the EffectiveXXXFlexBound functions here just give the values at //which the rate currently being optimized would cross the one above //or below it due to rescaling of the rates to keep the mean rate 1.0 curVal = mod->FlexRate(i); flexImprove += OptimizeBoundedParameter(modnum, prec, curVal, i, max(min(minVal, curVal), curVal / maxRateChangeProp), min(mod->EffectiveUpperFlexBound(i), curVal * maxRateChangeProp), &Model::SetFlexRate, scoreDiffTarget); #ifdef DEBUG_FLEX_OPT outman.UserMessage("%.4f", lnL); for(int j=0;jNRateCats();j++) outman.UserMessage("%f\t%f", mod->FlexRate(j), mod->FlexProb(j)); #endif curVal = mod->FlexProb(i); flexImprove += OptimizeBoundedParameter(modnum, prec, curVal, i, max(min(minVal, curVal), curVal-maxProbChange), min((ONE_POINT_ZERO - (minVal * (FLOAT_TYPE)(mod->NRateCats() - 1))), curVal + maxProbChange), &Model::SetFlexProb, scoreDiffTarget); #ifdef DEBUG_FLEX_OPT outman.UserMessage("%.4f", lnL); for(int j=0;jNRateCats();j++) outman.UserMessage("%f\t%f", mod->FlexRate(j), mod->FlexProb(j)); #endif for(i=1;i < mod->NRateCats()-1;i++){ curVal = mod->FlexRate(i); flexImprove += OptimizeBoundedParameter(modnum, prec, curVal, i, max(mod->EffectiveLowerFlexBound(i), curVal / maxRateChangeProp), min(mod->EffectiveUpperFlexBound(i), curVal * maxRateChangeProp), &Model::SetFlexRate, scoreDiffTarget); #ifdef DEBUG_FLEX_OPT outman.UserMessage("%.4f", lnL); for(int j=0;jNRateCats();j++) outman.UserMessage("%f\t%f", mod->FlexRate(j), mod->FlexProb(j)); #endif curVal = mod->FlexProb(i); flexImprove += OptimizeBoundedParameter(modnum, prec, curVal, i, max(min(minVal, curVal), curVal-maxProbChange), min((ONE_POINT_ZERO - (minVal * (FLOAT_TYPE)(mod->NRateCats() - 1))), curVal + maxProbChange), &Model::SetFlexProb, scoreDiffTarget); #ifdef DEBUG_FLEX_OPT outman.UserMessage("%.4f", lnL); for(int j=0;jNRateCats();j++) outman.UserMessage("%f\t%f", mod->FlexRate(j), mod->FlexProb(j)); #endif } curVal = mod->FlexRate(i); flexImprove += OptimizeBoundedParameter(modnum, prec, curVal, i, max(mod->EffectiveLowerFlexBound(i), curVal / maxRateChangeProp), min(max(curVal, 999.9), curVal * maxRateChangeProp), &Model::SetFlexRate, scoreDiffTarget); #ifdef DEBUG_FLEX_OPT outman.UserMessage("%.4f", lnL); for(int j=0;jNRateCats();j++) outman.UserMessage("%f\t%f", mod->FlexRate(j), mod->FlexProb(j)); #endif curVal = mod->FlexProb(i); flexImprove += OptimizeBoundedParameter(modnum, prec, curVal, i, max(min(minVal, curVal), curVal-maxProbChange), min((ONE_POINT_ZERO - (minVal * (FLOAT_TYPE)(mod->NRateCats() - 1))), curVal + maxProbChange), &Model::SetFlexProb, scoreDiffTarget); #ifdef DEBUG_FLEX_OPT outman.UserMessage("%.4f", lnL); for(int j=0;jNRateCats();j++) outman.UserMessage("%f\t%f", mod->FlexRate(j), mod->FlexProb(j)); #endif return flexImprove; } FLOAT_TYPE Tree::OptimizeSubsetRates(FLOAT_TYPE prec){ FLOAT_TYPE subrateImprove=ZERO_POINT_ZERO; FLOAT_TYPE minVal = 1.0e-5; int i=0; //DEBUG //store the current values in case we end up making the lnL worse Score(); FLOAT_TYPE start = lnL; vector initVals; for(int i = 0;i < modPart->NumSubsetRates();i++) initVals.push_back(modPart->SubsetRate(i)); //limiting change in any one pass double maxRateChangeProp = 0.9; for(i=0;i < modPart->NumSubsetRates();i++){ subrateImprove += OptimizeBoundedParameter(prec, modPart->SubsetRate(i), i, max(minVal, modPart->SubsetRate(i)*maxRateChangeProp), modPart->SubsetRate(i) / maxRateChangeProp, modPart, &ModelPartition::SetSubsetRate); } Score(); FLOAT_TYPE after = lnL; //if the optimization made things at all worse we'll revert to the old values, //but not complain about it unless it is a meaningful difference if(after < start){ if(!FloatingPointEquals(after, start, 1.0e-3)) outman.DebugMessage("##NOTE: SUBSET RATE OPT WORSENED SCORE##"); modPart->SetSubsetRates(initVals, false); MakeAllNodesDirty(); Score(); after = lnL; assert(FloatingPointEquals(after, start, 1e-6)); subrateImprove = ZERO_POINT_ZERO; } assert(after - start + 1e-6> 0.0); return subrateImprove; } void Tree::CalcFullCLAInternalInternalEQUIV(CondLikeArray *destCLA, const CondLikeArray *LCLA, const CondLikeArray *RCLA, const FLOAT_TYPE *Lpr, const FLOAT_TYPE *Rpr, const char *leftEQ, const char *rightEQ, int modIndex, int dataIndex){ //this function assumes that the pmat is arranged with the 16 entries for the //first rate, followed by 16 for the second, etc. FLOAT_TYPE *dest=destCLA->arr; const FLOAT_TYPE *LCL=LCLA->arr; const FLOAT_TYPE *RCL=RCLA->arr; FLOAT_TYPE L1, L2, L3, L4, R1, R2, R3, R4; const SequenceData *data = dataPart->GetSubset(dataIndex); Model *mod = modPart->GetModel(modIndex); const int nRateCats = mod->NRateCats(); const int nchar = data->NChar(); assert(nRateCats == 1); #ifdef UNIX posix_madvise(dest, nchar*4*nRateCats*sizeof(FLOAT_TYPE), POSIX_MADV_SEQUENTIAL); posix_madvise((void *)LCL, nchar*4*nRateCats*sizeof(FLOAT_TYPE), POSIX_MADV_SEQUENTIAL); posix_madvise((void *)RCL, nchar*4*nRateCats*sizeof(FLOAT_TYPE), POSIX_MADV_SEQUENTIAL); #endif for(int i=0;i= 0); dest += 4; LCL += 4; RCL += 4; } const int *left_mult=LCLA->underflow_mult; const int *right_mult=RCLA->underflow_mult; int *undermult=destCLA->underflow_mult; for(int i=0;irescaleRank = 2 + LCLA->rescaleRank + RCLA->rescaleRank; } void Tree::CalcFullCLAInternalInternal(CondLikeArray *destCLA, const CondLikeArray *LCLA, const CondLikeArray *RCLA, const FLOAT_TYPE *Lpr, const FLOAT_TYPE *Rpr, int modIndex, int dataIndex){ //this function assumes that the pmat is arranged with the 16 entries for the //first rate, followed by 16 for the second, etc. FLOAT_TYPE *dest=destCLA->arr; const FLOAT_TYPE *LCL=LCLA->arr; const FLOAT_TYPE *RCL=RCLA->arr; FLOAT_TYPE L1, L2, L3, L4, R1, R2, R3, R4; const SequenceData *data = dataPart->GetSubset(dataIndex); Model *mod = modPart->GetModel(modIndex); const int nRateCats = mod->NRateCats(); const int nchar = data->NChar(); const int *counts = data->GetCounts(); #ifdef UNIX posix_madvise(dest, nchar*4*nRateCats*sizeof(FLOAT_TYPE), POSIX_MADV_SEQUENTIAL); posix_madvise((void *)LCL, nchar*4*nRateCats*sizeof(FLOAT_TYPE), POSIX_MADV_SEQUENTIAL); posix_madvise((void *)RCL, nchar*4*nRateCats*sizeof(FLOAT_TYPE), POSIX_MADV_SEQUENTIAL); #endif if(nRateCats == 4){//the unrolled 4 rate version #ifdef OMP_INTINTCLA #pragma omp parallel for private(dest, LCL, RCL, L1, L2, L3, L4, R1, R2, R3, R4) for(int i=0;iarr[index]); LCL = &(LCLA->arr[index]); RCL= &(RCLA->arr[index]); #else for(int i=0;i 0){ #else if(1){ #endif L1=((Lpr[0]*LCL[0])+(Lpr[1]*LCL[1]))+((Lpr[2]*LCL[2])+(Lpr[3]*LCL[3])); L2=((Lpr[4]*LCL[0])+(Lpr[5]*LCL[1]))+((Lpr[6]*LCL[2])+(Lpr[7]*LCL[3])); L3=((Lpr[8]*LCL[0])+(Lpr[9]*LCL[1]))+((Lpr[10]*LCL[2])+(Lpr[11]*LCL[3])); L4=((Lpr[12]*LCL[0])+(Lpr[13]*LCL[1]))+((Lpr[14]*LCL[2])+(Lpr[15]*LCL[3])); R1=((Rpr[0]*RCL[0])+(Rpr[1]*RCL[1]))+((Rpr[2]*RCL[2])+(Rpr[3]*RCL[3])); R2=((Rpr[4]*RCL[0])+(Rpr[5]*RCL[1]))+((Rpr[6]*RCL[2])+(Rpr[7]*RCL[3])); R3=((Rpr[8]*RCL[0])+(Rpr[9]*RCL[1]))+((Rpr[10]*RCL[2])+(Rpr[11]*RCL[3])); R4=((Rpr[12]*RCL[0])+(Rpr[13]*RCL[1]))+((Rpr[14]*RCL[2])+(Rpr[15]*RCL[3])); dest[0] = L1 * R1; dest[1] = L2 * R2; dest[2] = L3 * R3; dest[3] = L4 * R4; dest+=4; LCL+=4; RCL+=4; L1=(Lpr[16+0]*LCL[0]+Lpr[16+1]*LCL[1])+(Lpr[16+2]*LCL[2]+Lpr[16+3]*LCL[3]); L2=(Lpr[16+4]*LCL[0]+Lpr[16+5]*LCL[1])+(Lpr[16+6]*LCL[2]+Lpr[16+7]*LCL[3]); L3=(Lpr[16+8]*LCL[0]+Lpr[16+9]*LCL[1])+(Lpr[16+10]*LCL[2]+Lpr[16+11]*LCL[3]); L4=(Lpr[16+12]*LCL[0]+Lpr[16+13]*LCL[1])+(Lpr[16+14]*LCL[2]+Lpr[16+15]*LCL[3]); R1=(Rpr[16+0]*RCL[0]+Rpr[16+1]*RCL[1])+(Rpr[16+2]*RCL[2]+Rpr[16+3]*RCL[3]); R2=(Rpr[16+4]*RCL[0]+Rpr[16+5]*RCL[1])+(Rpr[16+6]*RCL[2]+Rpr[16+7]*RCL[3]); R3=(Rpr[16+8]*RCL[0]+Rpr[16+9]*RCL[1])+(Rpr[16+10]*RCL[2]+Rpr[16+11]*RCL[3]); R4=(Rpr[16+12]*RCL[0]+Rpr[16+13]*RCL[1])+(Rpr[16+14]*RCL[2]+Rpr[16+15]*RCL[3]); dest[0] = L1 * R1; dest[1] = L2 * R2; dest[2] = L3 * R3; dest[3] = L4 * R4; dest+=4; LCL+=4; RCL+=4; L1=(Lpr[32+0]*LCL[0]+Lpr[32+1]*LCL[1])+(Lpr[32+2]*LCL[2]+Lpr[32+3]*LCL[3]); L2=(Lpr[32+4]*LCL[0]+Lpr[32+5]*LCL[1])+(Lpr[32+6]*LCL[2]+Lpr[32+7]*LCL[3]); L3=(Lpr[32+8]*LCL[0]+Lpr[32+9]*LCL[1])+(Lpr[32+10]*LCL[2]+Lpr[32+11]*LCL[3]); L4=(Lpr[32+12]*LCL[0]+Lpr[32+13]*LCL[1])+(Lpr[32+14]*LCL[2]+Lpr[32+15]*LCL[3]); R1=(Rpr[32+0]*RCL[0]+Rpr[32+1]*RCL[1])+(Rpr[32+2]*RCL[2]+Rpr[32+3]*RCL[3]); R2=(Rpr[32+4]*RCL[0]+Rpr[32+5]*RCL[1])+(Rpr[32+6]*RCL[2]+Rpr[32+7]*RCL[3]); R3=(Rpr[32+8]*RCL[0]+Rpr[32+9]*RCL[1])+(Rpr[32+10]*RCL[2]+Rpr[32+11]*RCL[3]); R4=(Rpr[32+12]*RCL[0]+Rpr[32+13]*RCL[1])+(Rpr[32+14]*RCL[2]+Rpr[32+15]*RCL[3]); dest[0] = L1 * R1; dest[1] = L2 * R2; dest[2] = L3 * R3; dest[3] = L4 * R4; dest+=4; LCL+=4; RCL+=4; L1=(Lpr[48+0]*LCL[0]+Lpr[48+1]*LCL[1])+(Lpr[48+2]*LCL[2]+Lpr[48+3]*LCL[3]); L2=(Lpr[48+4]*LCL[0]+Lpr[48+5]*LCL[1])+(Lpr[48+6]*LCL[2]+Lpr[48+7]*LCL[3]); L3=(Lpr[48+8]*LCL[0]+Lpr[48+9]*LCL[1])+(Lpr[48+10]*LCL[2]+Lpr[48+11]*LCL[3]); L4=(Lpr[48+12]*LCL[0]+Lpr[48+13]*LCL[1])+(Lpr[48+14]*LCL[2]+Lpr[48+15]*LCL[3]); R1=(Rpr[48+0]*RCL[0]+Rpr[48+1]*RCL[1])+(Rpr[48+2]*RCL[2]+Rpr[48+3]*RCL[3]); R2=(Rpr[48+4]*RCL[0]+Rpr[48+5]*RCL[1])+(Rpr[48+6]*RCL[2]+Rpr[48+7]*RCL[3]); R3=(Rpr[48+8]*RCL[0]+Rpr[48+9]*RCL[1])+(Rpr[48+10]*RCL[2]+Rpr[48+11]*RCL[3]); R4=(Rpr[48+12]*RCL[0]+Rpr[48+13]*RCL[1])+(Rpr[48+14]*RCL[2]+Rpr[48+15]*RCL[3]); dest[0] = L1 * R1; dest[1] = L2 * R2; dest[2] = L3 * R3; dest[3] = L4 * R4; dest+=4; LCL+=4; RCL+=4; #ifdef ALLOW_SINGLE_SITE if(siteToScore > -1) break; #endif } } } else{//the general N rate version int r; #ifdef OMP_INTINTCLA int index; #pragma omp parallel for private(r, index, dest, LCL, RCL, L1, L2, L3, L4, R1, R2, R3, R4) for(int i=0;iarr[index]); LCL = &(LCLA->arr[index]); RCL= &(RCLA->arr[index]); #else for(int i=0;i 0){ #else if(1){ #endif for(r=0;r -1) break; #endif } } } const int *left_mult=LCLA->underflow_mult; const int *right_mult=RCLA->underflow_mult; int *undermult=destCLA->underflow_mult; for(int i=0;irescaleRank = 2 + LCLA->rescaleRank + RCLA->rescaleRank; } void Tree::CalcFullCLAInternalInternalNState(CondLikeArray *destCLA, const CondLikeArray *LCLA, const CondLikeArray *RCLA, const FLOAT_TYPE *Lpr, const FLOAT_TYPE *Rpr, int modIndex, int dataIndex){ //this function assumes that the pmat is arranged with the 16 entries for the //first rate, followed by 16 for the second, etc. FLOAT_TYPE *dest=destCLA->arr; const FLOAT_TYPE *LCL=LCLA->arr; const FLOAT_TYPE *RCL=RCLA->arr; FLOAT_TYPE L1, R1; const SequenceData *data = dataPart->GetSubset(dataIndex); Model *mod = modPart->GetModel(modIndex); const int nRateCats = mod->NRateCats(); const int nstates = mod->NStates(); const int nchar = data->NChar(); const int *counts = data->GetCounts(); #ifdef UNIX posix_madvise(dest, nchar*nstates*nRateCats*sizeof(FLOAT_TYPE), POSIX_MADV_SEQUENTIAL); posix_madvise((void *)LCL, nchar*nstates*nRateCats*sizeof(FLOAT_TYPE), POSIX_MADV_SEQUENTIAL); posix_madvise((void *)RCL, nchar*nstates*nRateCats*sizeof(FLOAT_TYPE), POSIX_MADV_SEQUENTIAL); #endif #ifdef OMP_INTINTCLA_NSTATE #pragma omp parallel for private(dest, LCL, RCL, L1, R1) for(int i=0;iarr[nRateCats * nstates * i]); LCL = &(LCLA->arr[nRateCats * nstates * i]); RCL= &(RCLA->arr[nRateCats * nstates * i]); #else for(int i=0;i 0){ #else if(1) #endif for(int rate=0;rate= 0.0); assert(dest[-nstates*nRateCats] == dest[-nstates*nRateCats]); #ifdef ALLOW_SINGLE_SITE if(siteToScore > -1) break; #endif } } const int *left_mult=LCLA->underflow_mult; const int *right_mult=RCLA->underflow_mult; int *undermult=destCLA->underflow_mult; for(int i=0;irescaleRank = 2 + LCLA->rescaleRank + RCLA->rescaleRank; } void Tree::CalcFullCLATerminalTerminal(CondLikeArray *destCLA, const FLOAT_TYPE *Lpr, const FLOAT_TYPE *Rpr, const char *Ldata, const char *Rdata, int modIndex, int dataIndex){ //this function assumes that the pmat is arranged with the 16 entries for the //first rate, followed by 16 for the second, etc. FLOAT_TYPE *dest=destCLA->arr; const SequenceData *data = dataPart->GetSubset(dataIndex); Model *mod = modPart->GetModel(modIndex); const int nRateCats = mod->NRateCats(); const int nchar = data->NChar(); const int *counts = data->GetCounts(); #ifdef UNIX posix_madvise(dest, nchar*4*nRateCats*sizeof(FLOAT_TYPE), POSIX_MADV_SEQUENTIAL); #endif #ifdef ALLOW_SINGLE_SITE if(siteToScore > 0){ Ldata = AdvanceDataPointer(Ldata, siteToScore); Rdata = AdvanceDataPointer(Rdata, siteToScore); } #endif for(int i=0;i 0){ #else if(1){ #endif if(*Ldata > -1 && *Rdata > -1){ for(int r=0;r -1) || (*Rdata == -4 && *Ldata > -1)){//total ambiguity of left, right or both if(*Ldata == -4 && *Rdata == -4) //total ambiguity of both for(int i=0;i< (4*nRateCats);i++) *(dest++) = ONE_POINT_ZERO; else if(*Ldata == -4){//total ambiguity of left for(int i=0;i=ZERO_POINT_ZERO); } } else{//total ambiguity of right for(int i=0;i=ZERO_POINT_ZERO); } } Ldata++; Rdata++; } else {//partial ambiguity of left, right or both if(*Ldata>-1){//unambiguous left for(int i=0;i=ZERO_POINT_ZERO); } Ldata++; } else{ if(*Ldata==-4){//fully ambiguous left for(int i=0;i< (4*nRateCats);i++){ *(dest+i)=ONE_POINT_ZERO; } Ldata++; } else{//partially ambiguous left int nstates=-*(Ldata++); for(int q=0;q< (4*nRateCats);q++) dest[q]=0; for(int i=0;i-1){//unambiguous right for(int i=0;i tempcla(4*nRateCats); for(int i=0;i -1) break; #endif } else{//if the count for this site is 0 #ifdef OPEN_MP //this is a little strange, but dest only needs to be advanced in the case of OMP //because sections of the CLAs corresponding to sites with count=0 are skipped //over in OMP instead of being eliminated dest += 4 * nRateCats; #endif if(*Ldata > -1 || *Ldata == -4) Ldata++; else{ int states = -1 * *Ldata; do{ Ldata++; }while (states-- > 0); } if(*Rdata > -1 || *Rdata == -4) Rdata++; else{ int states = -1 * *Rdata; do{ Rdata++; }while (states-- > 0); } } } for(int site=0;siteunderflow_mult[site]=0; } destCLA->rescaleRank=2; } void Tree::CalcFullCLATerminalTerminalNState(CondLikeArray *destCLA, const FLOAT_TYPE *Lpr, const FLOAT_TYPE *Rpr, const char *Ldata, const char *Rdata, int modIndex, int dataIndex){ //this function assumes that the pmat is arranged with the 16 entries for the //first rate, followed by 16 for the second, etc. FLOAT_TYPE *dest=destCLA->arr; const SequenceData *data = dataPart->GetSubset(dataIndex); Model *mod = modPart->GetModel(modIndex); const int nRateCats = mod->NRateCats(); const int nstates = mod->NStates(); const int nchar = data->NChar(); const int *counts = data->GetCounts(); #ifdef UNIX posix_madvise(dest, nchar*nstates*nRateCats*sizeof(FLOAT_TYPE), POSIX_MADV_SEQUENTIAL); #endif if(siteToScore > 0){ Ldata += siteToScore; Rdata += siteToScore; } for(int i=0;i 0){ #else if(1){ #endif if(*Ldata < nstates && *Rdata < nstates){ for(int rate=0;rate -1) break; #endif } else{ #ifdef OPEN_MP //this is a little strange, but dest only needs to be advanced in the case of OMP //because sections of the CLAs corresponding to sites with count=0 are skipped //over in OMP instead of being eliminated dest += nRateCats*nstates; #endif Ldata++; Rdata++; } } for(int site=0;siteunderflow_mult[site]=0; } destCLA->rescaleRank=2; } //this will not be very fast, but is generalized to account for all types of nodes void Tree::CalcFullCLAOrientedGap(CondLikeArray *destCLA, const FLOAT_TYPE *Lpr, const FLOAT_TYPE *Rpr, const CondLikeArray *LCLA, const CondLikeArray *RCLA, const char *Ldata, const char *Rdata, int modIndex, int dataIndex){ assert((LCLA == NULL && Ldata) || (Ldata == NULL && LCLA)); assert((RCLA == NULL && Rdata) || (Rdata == NULL && RCLA)); const FLOAT_TYPE *LCL = NULL; const FLOAT_TYPE *RCL = NULL; if(LCLA) LCL = LCLA->arr; if(RCLA) RCL = RCLA->arr; const SequenceData *data = dataPart->GetSubset(dataIndex); Model *mod = modPart->GetModel(modIndex); const int nchar = data->NChar(); const int *counts = data->GetCounts(); const int pmatStates = mod->NStates(); const int claStates = mod->NStates(); FLOAT_TYPE *dest=destCLA->arr; FLOAT_TYPE **tipStates = New2DArray(3, 4); bool gapIsState0 = true; int gapState = (gapIsState0 ? 0 : 1); int baseState = (gapIsState0 ? 1 : 0); //gap with base in subtree should always be zero at tip //gap at tip tipStates[gapState][0] = 1.0; tipStates[gapState][2] = tipStates[gapState][1] = 0.0; //base at tip tipStates[baseState][0] = tipStates[baseState][1] = 0.0; tipStates[baseState][2] = 1.0; //missing data tipStates[2][0] = 1.0; tipStates[2][1] = 0.0; //?? tipStates[2][2] = 1.0; const FLOAT_TYPE *left, *right; //conditioning on zero or 1 insert. cla[0] is now essentially an indicator func of "no bases in subtree" //the categories also amount to state freqs at the root, being gap (one insert site, cla[1]) or base (no inserts, cla[2]) for(int i=0;i 0){ if(Ldata) left = tipStates[*Ldata]; else{ left = &LCL[0]; LCL += claStates; } if(Rdata) right = tipStates[*Rdata]; else{ right = &RCL[0]; RCL += claStates; } //element 0 is just an indicator of when NO bases observed in subtree (i.e., 1 = no bases, 0 = bases) dest[0] = left[0] * right[0]; //the pr[0][0] (never inserted to never inserted transition) that would appear here can be set to 1.0 dest[1] = left[0] * (Rpr[0 * claStates + 1] * right[2] + right[1]) + right[0] * (Lpr[0 * claStates + 1] * left[2] + left[1]); //dest[1] = left[0] * (Rpr[0 * claStates + 1] * right[2] + Rpr[0 * claStates + 0] * right[1]) + right[0] * (Lpr[0 * claStates + 1] * left[2] + Lpr[0 * claStates + 0] * left[1]); dest[2] = (left[2] * Lpr[1 * claStates + 1] + left[0] * Lpr[1 * claStates + 2]) * (right[2] * Rpr[1 * claStates + 1] + right[0] * Rpr[1 * claStates + 2]); //DEBUG if(!(dest[1] > 0.0 || dest[2] > 0.0)) outman.UserMessage("%f\t%f\t%f\t%f\t%f\t%f\t%f\t%f\t%f\t", left[0], left[1], left[2], right[0], right[1], right[2], dest[0], dest[1], dest[2]); assert(dest[1] > 0.0 || dest[2] > 0.0); dest += claStates; } if(Ldata) Ldata++; if(Rdata) Rdata++; } /* //initial attempt with dynamic programming trick for only 1 insert. 4 states in CLA //gap at tip const int pmatStates = mod->NStates(); const int claStates = mod->NStates() + 1; tipStates[gapState][0] = tipStates[gapState][3] = 1.0; tipStates[gapState][2] = tipStates[gapState][1] = 0.0; //base at tip tipStates[baseState][0] = tipStates[baseState][1] = tipStates[baseState][3] = 0.0; tipStates[baseState][2] = 1.0; //missing data tipStates[2][0] = 0.0; tipStates[2][1] = tipStates[2][2] = tipStates[2][3] = 1.0; for(int i=0;i 0){ if(Ldata) left = tipStates[*Ldata++]; else left = &LCL[i * claStates]; if(Rdata) right = tipStates[*Rdata++]; else right = &RCL[i * claStates]; dest[0] = left[0] * Lpr[0] * right[0] * Rpr[0]; assert(dest[0] <= 1.0); // p(no base L) * p(no ins L) * (p(ins R) * p(base R) + p(no ins R) * p(no base R)) + p(no base R) * p(no ins R) * (p(ins L) * p(base L) + p(no ins L) * p(no base L) dest[1] = left[0] * Lpr[0] * (Rpr[1] * right[2] + Rpr[0] * right[1]) + right[0] * Rpr[0] * (Lpr[1] * left[2] + Lpr[0] * left[1]); assert(dest[1] <= 1.0); dest[2] = (left[2] * Lpr[1 * pmatStates + 1] + left[3] * Lpr[1 * pmatStates + 2]) * (right[2] * Rpr[1 * pmatStates + 1] + right[3] * Rpr[1 * pmatStates + 2]); assert(dest[2] <= 1.0); if(! dest[2] > 0.0) outman.DebugMessage("(left[2] (%f) * Lpr[1 * pmatStates + 1] (%f) + left[3] (%f) * Lpr[1 * pmatStates + 2]) (%f) * (right[2] (%f) * Rpr[1 * pmatStates + 1] (%f) + right[3] (%f) * Rpr[1 * pmatStates + 2] (%f))", left[2], Lpr[1 * pmatStates + 1], left[3], Lpr[1 * pmatStates + 2], right[2], Rpr[1 * pmatStates + 1], right[3], Rpr[1 * pmatStates + 2]); //assert(dest[2] > 0.0); dest[3] = (left[3] * right[3]); assert(dest[3] <= 1.0); dest += 4; } } */ for(int i=0;iunderflow_mult[i] = (Ldata ? 0 : LCLA->underflow_mult[i]) + (Rdata ? 0 : RCLA->underflow_mult[i]); destCLA->rescaleRank = (Ldata ? 0 : LCLA->rescaleRank) + (Rdata ? 0 : RCLA->rescaleRank) + 2; Delete2DArray(tipStates); } void Tree::CalcFullCLAInternalTerminal(CondLikeArray *destCLA, const CondLikeArray *LCLA, const FLOAT_TYPE *pr1, const FLOAT_TYPE *pr2, char *dat2, const unsigned *ambigMap, int modIndex, int dataIndex){ //this function assumes that the pmat is arranged with the 16 entries for the //first rate, followed by 16 for the second, etc. FLOAT_TYPE *des=destCLA->arr; FLOAT_TYPE *dest=des; const FLOAT_TYPE *CL=LCLA->arr; const FLOAT_TYPE *CL1=CL; const char *data2=dat2; FLOAT_TYPE L1, L2, L3, L4; const SequenceData *data = dataPart->GetSubset(dataIndex); Model *mod = modPart->GetModel(modIndex); const int nchar = data->NChar(); const int nRateCats = mod->NRateCats(); const int *counts = data->GetCounts(); #ifdef UNIX posix_madvise(dest, nchar*4*nRateCats*sizeof(FLOAT_TYPE), POSIX_MADV_SEQUENTIAL); posix_madvise((void*)CL1, nchar*4*nRateCats*sizeof(FLOAT_TYPE), POSIX_MADV_SEQUENTIAL); #endif #ifdef ALLOW_SINGLE_SITE if(siteToScore > 0) data2 = AdvanceDataPointer(data2, siteToScore); #endif if(nRateCats==4){//unrolled 4 rate version #ifdef OMP_INTTERMCLA #pragma omp parallel for private(dest, CL1, data2, L1, L2, L3, L4) for(int i=0;i 0){ #else if(1){ #endif if(*data2 > -1){ //no ambiguity L1 = ((pr1[0]*CL1[0]+pr1[1]*CL1[1])+(pr1[2]*CL1[2]+pr1[3]*CL1[3])); L2 = ((pr1[4]*CL1[0]+pr1[5]*CL1[1])+(pr1[6]*CL1[2]+pr1[7]*CL1[3])); L3 = ((pr1[8]*CL1[0]+pr1[9]*CL1[1])+(pr1[10]*CL1[2]+pr1[11]*CL1[3])); L4 = ((pr1[12]*CL1[0]+pr1[13]*CL1[1])+(pr1[14]*CL1[2]+pr1[15]*CL1[3])); dest[0] = L1 * pr2[*data2]; dest[1] = L2 * pr2[*data2+4]; dest[2] = L3 * pr2[*data2+8]; dest[3] = L4 * pr2[*data2+12]; dest+=4; CL1+=4; L1 = ((pr1[16]*CL1[0]+pr1[17]*CL1[1])+(pr1[18]*CL1[2]+pr1[19]*CL1[3])); L2 = ((pr1[20]*CL1[0]+pr1[21]*CL1[1])+(pr1[22]*CL1[2]+pr1[23]*CL1[3])); L3 = ((pr1[24]*CL1[0]+pr1[25]*CL1[1])+(pr1[26]*CL1[2]+pr1[27]*CL1[3])); L4 = ((pr1[28]*CL1[0]+pr1[29]*CL1[1])+(pr1[30]*CL1[2]+pr1[31]*CL1[3])); dest[0] = L1 * pr2[*data2+16]; dest[1] = L2 * pr2[*data2+4+16]; dest[2] = L3 * pr2[*data2+8+16]; dest[3] = L4 * pr2[*data2+12+16]; dest+=4; CL1+=4; L1 = ((pr1[32]*CL1[0]+pr1[33]*CL1[1])+(pr1[34]*CL1[2]+pr1[35]*CL1[3])); L2 = ((pr1[36]*CL1[0]+pr1[37]*CL1[1])+(pr1[38]*CL1[2]+pr1[39]*CL1[3])); L3 = ((pr1[40]*CL1[0]+pr1[41]*CL1[1])+(pr1[42]*CL1[2]+pr1[43]*CL1[3])); L4 = ((pr1[44]*CL1[0]+pr1[45]*CL1[1])+(pr1[46]*CL1[2]+pr1[47]*CL1[3])); dest[0] = L1 * pr2[*data2+32]; dest[1] = L2 * pr2[*data2+4+32]; dest[2] = L3 * pr2[*data2+8+32]; dest[3] = L4 * pr2[*data2+12+32]; dest+=4; CL1+=4; L1 = ((pr1[48]*CL1[0]+pr1[49]*CL1[1])+(pr1[50]*CL1[2]+pr1[51]*CL1[3])); L2 = ((pr1[52]*CL1[0]+pr1[53]*CL1[1])+(pr1[54]*CL1[2]+pr1[55]*CL1[3])); L3 = ((pr1[56]*CL1[0]+pr1[57]*CL1[1])+(pr1[58]*CL1[2]+pr1[59]*CL1[3])); L4 = ((pr1[60]*CL1[0]+pr1[61]*CL1[1])+(pr1[62]*CL1[2]+pr1[63]*CL1[3])); dest[0] = L1 * pr2[*data2+48]; dest[1] = L2 * pr2[*data2+4+48]; dest[2] = L3 * pr2[*data2+8+48]; dest[3] = L4 * pr2[*data2+12+48]; dest+=4; CL1+=4; data2++; } else if(*data2 == -4){//total ambiguity dest[0] = ( pr1[0]*CL1[0]+pr1[1]*CL1[1]+pr1[2]*CL1[2]+pr1[3]*CL1[3]); dest[1] = ( pr1[4]*CL1[0]+pr1[5]*CL1[1]+pr1[6]*CL1[2]+pr1[7]*CL1[3]); dest[2] = ( pr1[8]*CL1[0]+pr1[9]*CL1[1]+pr1[10]*CL1[2]+pr1[11]*CL1[3]); dest[3] = ( pr1[12]*CL1[0]+pr1[13]*CL1[1]+pr1[14]*CL1[2]+pr1[15]*CL1[3]); dest[4] = ( pr1[16]*CL1[4]+pr1[17]*CL1[5]+pr1[18]*CL1[6]+pr1[19]*CL1[7]); dest[5] = ( pr1[20]*CL1[4]+pr1[21]*CL1[5]+pr1[22]*CL1[6]+pr1[23]*CL1[7]); dest[6] = ( pr1[24]*CL1[4]+pr1[25]*CL1[5]+pr1[26]*CL1[6]+pr1[27]*CL1[7]); dest[7] = ( pr1[28]*CL1[4]+pr1[29]*CL1[5]+pr1[30]*CL1[6]+pr1[31]*CL1[7]); dest[8] = ( pr1[32]*CL1[8]+pr1[33]*CL1[9]+pr1[34]*CL1[10]+pr1[35]*CL1[11]); dest[9] = ( pr1[36]*CL1[8]+pr1[37]*CL1[9]+pr1[38]*CL1[10]+pr1[39]*CL1[11]); dest[10] = ( pr1[40]*CL1[8]+pr1[41]*CL1[9]+pr1[42]*CL1[10]+pr1[43]*CL1[11]); dest[11] = ( pr1[44]*CL1[8]+pr1[45]*CL1[9]+pr1[46]*CL1[10]+pr1[47]*CL1[11]); dest[12] = ( pr1[48]*CL1[12]+pr1[49]*CL1[13]+pr1[50]*CL1[14]+pr1[51]*CL1[15]); dest[13] = ( pr1[52]*CL1[12]+pr1[53]*CL1[13]+pr1[54]*CL1[14]+pr1[55]*CL1[15]); dest[14] = ( pr1[56]*CL1[12]+pr1[57]*CL1[13]+pr1[58]*CL1[14]+pr1[59]*CL1[15]); dest[15] = ( pr1[60]*CL1[12]+pr1[61]*CL1[13]+pr1[62]*CL1[14]+pr1[63]*CL1[15]); dest+=16; data2++; CL1+=16; } else {//partial ambiguity //first figure in the ambiguous terminal int nstates=-1 * *(data2++); for(int j=0;j<16;j++) dest[j]=ZERO_POINT_ZERO; for(int s=0;s -1) break; #endif } else{ data2 = AdvanceDataPointer(data2, 1); } } } else{//general N rate version #ifdef OMP_INTTERMCLA #pragma omp parallel for private(dest, CL1, data2, L1, L2, L3, L4) for(int i=0;i 0){ #else if(1){ #endif if(*data2 > -1){ //no ambiguity for(int r=0;r -1) break; #endif } else{ data2 = AdvanceDataPointer(data2, 1); } } } for(int i=0;iunderflow_mult[i]=LCLA->underflow_mult[i]; destCLA->rescaleRank=LCLA->rescaleRank+2; } void Tree::CalcFullCLAInternalTerminalNState(CondLikeArray *destCLA, const CondLikeArray *LCLA, const FLOAT_TYPE *pr1, const FLOAT_TYPE *pr2, char *dat2, int modIndex, int dataIndex){ //this function assumes that the pmat is arranged with the 16 entries for the //first rate, followed by 16 for the second, etc. FLOAT_TYPE *des=destCLA->arr; FLOAT_TYPE *dest=des; const FLOAT_TYPE *CL=LCLA->arr; const FLOAT_TYPE *CL1=CL; const char *data2=dat2; const SequenceData *data = dataPart->GetSubset(dataIndex); Model *mod = modPart->GetModel(modIndex); const int nchar = data->NChar(); const int nRateCats = mod->NRateCats(); const int nstates = mod->NStates(); const int *counts = data->GetCounts(); #ifdef UNIX posix_madvise(dest, nchar*nstates*nRateCats*sizeof(FLOAT_TYPE), POSIX_MADV_SEQUENTIAL); posix_madvise((void*)CL1, nchar*nstates*nRateCats*sizeof(FLOAT_TYPE), POSIX_MADV_SEQUENTIAL); #endif if(siteToScore > 0) data2 += siteToScore; #ifdef OMP_INTTERMCLA_NSTATE #pragma omp parallel for private(dest, CL1, data2) for(int i=0;i 0){ #else if(1){ #endif for(int rate=0;rate -1) break; #endif } else data2++; } for(int i=0;iunderflow_mult[i]=LCLA->underflow_mult[i]; destCLA->rescaleRank=LCLA->rescaleRank+2; } void Tree::CalcFullCLAPartialInternalRateHet(CondLikeArray *destCLA, const CondLikeArray *LCLA, const FLOAT_TYPE *pr1, CondLikeArray *partialCLA, int modIndex, int dataIndex){ //this function assumes that the pmat is arranged with the 16 entries for the //first rate, followed by 16 for the second, etc. FLOAT_TYPE *dest=destCLA->arr; FLOAT_TYPE *CL1=LCLA->arr; FLOAT_TYPE *partial=partialCLA->arr; const SequenceData *data = dataPart->GetSubset(dataIndex); Model *mod = modPart->GetModel(modIndex); const int nchar = data->NChar(); const int nRateCats = mod->NRateCats(); #ifdef UNIX posix_madvise(dest, nchar*4*nRateCats*sizeof(FLOAT_TYPE), POSIX_MADV_SEQUENTIAL); posix_madvise((void*)CL1, nchar*4*nRateCats*sizeof(FLOAT_TYPE), POSIX_MADV_SEQUENTIAL); posix_madvise(partial, nchar*4*nRateCats*sizeof(FLOAT_TYPE), POSIX_MADV_SEQUENTIAL); #endif if(nRateCats==4){ for(int i=0;i=ZERO_POINT_ZERO); } } else{ for(int i=0;i=ZERO_POINT_ZERO); } } } for(int site=0;siteunderflow_mult[site]=partialCLA->underflow_mult[site] + LCLA->underflow_mult[site]; } } void Tree::CalcFullCLAPartialTerminalRateHet(CondLikeArray *destCLA, const CondLikeArray *partialCLA, const FLOAT_TYPE *Lpr, char *Ldata, int modIndex, int dataIndex){ //this function assumes that the pmat is arranged with the 16 entries for the //first rate, followed by 16 for the second, etc. FLOAT_TYPE *dest=destCLA->arr; FLOAT_TYPE *partial=partialCLA->arr; const SequenceData *data = dataPart->GetSubset(dataIndex); Model *mod = modPart->GetModel(modIndex); const int nchar = data->NChar(); const int nRateCats = mod->NRateCats(); #ifdef UNIX posix_madvise(dest, nchar*4*nRateCats*sizeof(FLOAT_TYPE), POSIX_MADV_SEQUENTIAL); posix_madvise((void*)partial, nchar*4*nRateCats*sizeof(FLOAT_TYPE), POSIX_MADV_SEQUENTIAL); #endif for(int i=0;i -1){ //no ambiguity for(int i=0;i=ZERO_POINT_ZERO); } Ldata++; } else if(*Ldata == -4){ //total ambiguity for(int i=0;i<4*nRateCats;i++) *(dest++) = *(partial++); Ldata++; } else{ //partial ambiguity //first figure in the ambiguous terminal char nstates=-1 * *(Ldata++); for(int q=0;q<4*nRateCats;q++) dest[q]=0; for(int i=0;i=ZERO_POINT_ZERO); } Ldata++; } //now add the partial for(int r=0;runderflow_mult[i]=partialCLA->underflow_mult[i]; } //SINGLE SITE FUNCTIONS pair Tree::OptimizeSingleSiteTreeScale(FLOAT_TYPE optPrecision){ //this is silly, but the site likelihood calculating function will do it for the //correct single site, but using the pattern count of the first character. So, we'll //need to divide by this count to get the proper site like //PARTITION FLOAT_TYPE siteCount = (FLOAT_TYPE) dataPart->GetSubset(0)->Count(0); //FLOAT_TYPE siteCount = (FLOAT_TYPE) data->Count(0); Score(); FLOAT_TYPE prev=lnL/siteCount; FLOAT_TYPE cur; FLOAT_TYPE scale; FLOAT_TYPE t; FLOAT_TYPE lastChange=(FLOAT_TYPE)9999.9; FLOAT_TYPE effectiveScale = ONE_POINT_ZERO; //this measures the change in scale relative to what it began at. FLOAT_TYPE upperBracket = FLT_MAX; //the smallest value we know of with a negative d1 (relative to inital scale of 1.0!) FLOAT_TYPE lowerBracket = FLT_MIN; //the largest value we know of with a positive d1 (relative to inital scale of 1.0!) FLOAT_TYPE incr; #ifdef DEBUG_SCALE_OPT ofstream deb("scaleTrace.log"); deb.precision(20); for(int s=0;s<50;s++){ FLOAT_TYPE scale=0.5 + s*.025; ScaleWholeTree(scale); Score(); deb << scale << "\t" << lnL << endl; ScaleWholeTree(ONE_POINT_ZERO/scale); } deb.close(); #endif if(FloatingPointEquals(lnL, ZERO_POINT_ZERO, max(1.0e-8, GARLI_FP_EPS * 2.0))){ return pair(-ONE_POINT_ZERO, ZERO_POINT_ZERO); } int pass=1; while(1){ //reversed this now so the reduction in scale is done first when getting the //derivs. This works better if some blens are at DEF_MAX_BLEN because the //scaling up causes them to hit the max and the relative blens to change #ifdef SINGLE_PRECISION_FLOATS incr=0.005f; #else incr=0.005; #endif scale=ONE_POINT_ZERO-incr; ScaleWholeTree(scale); Score(); cur=lnL/siteCount; ScaleWholeTree(ONE_POINT_ZERO/scale);//return the tree to its original scale FLOAT_TYPE d12=(cur-prev)/-incr; if(pass == 1 && fabs(d12) < max(1.0e-8, GARLI_FP_EPS * 2.0)){ //The surface looks suspiciously flat. Test if the likelihood //is really invariant for different scales (which means that //the site is all missing or only has an observed state for one taxon) ScaleWholeTree(1.1); Score(); FLOAT_TYPE s = lnL/siteCount; ScaleWholeTree(1.0/1.1); if(fabs(prev - s) < max(1.0e-8, GARLI_FP_EPS * 2.0)) return pair(-ONE_POINT_ZERO, prev); } scale=ONE_POINT_ZERO + incr; ScaleWholeTree(scale); Score(); cur=lnL/siteCount; ScaleWholeTree(ONE_POINT_ZERO/scale);//return the tree to its original scale FLOAT_TYPE d11=(cur-prev)/incr; FLOAT_TYPE d1=(d11+d12)*ZERO_POINT_FIVE; FLOAT_TYPE d2=(d11-d12)/incr; FLOAT_TYPE est = -d1/d2; FLOAT_TYPE estImprove = d1*est + d2*(est*est*ZERO_POINT_FIVE); //return conditions if(estImprove < optPrecision && d2 < ZERO_POINT_ZERO){ ScaleWholeTree(ONE_POINT_ZERO/effectiveScale); //cout << pass << endl; return pair(effectiveScale, prev); } if(d2 < ZERO_POINT_ZERO){ est = max(min((FLOAT_TYPE)0.5, est), (FLOAT_TYPE)-0.5); t=ONE_POINT_ZERO + est; } else{ if(d1 > ZERO_POINT_ZERO) t=(FLOAT_TYPE)2.0; else t=(FLOAT_TYPE)0.5; } //update the brackets if(d1 <= ZERO_POINT_ZERO && effectiveScale < upperBracket) upperBracket = effectiveScale; else if(d1 > ZERO_POINT_ZERO && effectiveScale > lowerBracket) lowerBracket = effectiveScale; //if the surface is wacky and we are going to shoot past one of our brackets //take evasive action by going halfway to the bracket if((effectiveScale * t) <= lowerBracket){ t = (lowerBracket + effectiveScale) * ZERO_POINT_FIVE / effectiveScale; } else if((effectiveScale * t) >= upperBracket){ t = (upperBracket + effectiveScale) * ZERO_POINT_FIVE / effectiveScale; } scale=t; effectiveScale *= scale; if(effectiveScale > 100.0) return pair(100.0, prev); ScaleWholeTree(scale); if(effectiveScale < 1e-4){ //The rate is essentially zero. Invariant sites should be getting caught //before even calling this func, so this probably won't be visited ScaleWholeTree(1.0/effectiveScale); return pair(effectiveScale, prev); } Score(); cur=lnL/siteCount; lastChange = cur - prev; prev=cur; pass++; } assert(0); } void Tree::C4(const FLOAT_TYPE *a){ printf("%f %f %f %f\n", a[0], a[1], a[2], a[3]); } garli-2.1-release/src/tree.h000066400000000000000000001104301241236125200157640ustar00rootroot00000000000000// GARLI version 2.1 source code // Copyright 2005-2014 Derrick J. Zwickl // email: garli.support@gmail.com // // This program is free software: you can redistribute it and/or modify // it under the terms of the GNU General Public License as published by // the Free Software Foundation, either version 3 of the License, or // (at your option) any later version. // // This program is distributed in the hope that it will be useful, // but WITHOUT ANY WARRANTY; without even the implied warranty of // MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the // GNU General Public License for more details. // // You should have received a copy of the GNU General Public License // along with this program. If not, see . // // NOTE: Portions of this source adapted from GAML source, written by Paul O. Lewis #ifndef __TREE_H #define __TREE_H #include #include using namespace std; #include "defs.h" #include "rng.h" #include "treenode.h" #include "clamanager.h" #include "model.h" #include "sequencedata.h" #include "reconnode.h" #undef BRENT class SequenceData; class NucleotideData; class ClaManager; class GeneralGamlConfig; class ModelPartition; class Individual; extern rng rnd; #define RESCALE_ARRAY_LENGTH 90 class Tree{ protected: int numTipsTotal; int numTipsAdded; int numNodesAdded; int numBranchesAdded; int numNodesTotal; int *taxtags;//int[ntax+1] used in tagging terminals in recombine function //allocated in SharedTreeConstruction, deleted in dest public: FLOAT_TYPE lnL; // holds likelihood score ModelPartition *modPart; TreeNode *root; TreeNode *dummyRoot;//when we are dummy rootinging this will just alias allNodes[numTipsTotal] TreeNode **allNodes; ReconList sprRang; #ifdef EQUIV_CALCS bool dirtyEQ; #endif //a bunch of statics static FLOAT_TYPE meanBrlenMuts; static FLOAT_TYPE alpha; //alpha shape of blen mutation, not gamma rate het static FLOAT_TYPE min_brlen; static FLOAT_TYPE max_brlen; static FLOAT_TYPE exp_starting_brlen; static ClaManager *claMan; static const DataPartition *dataPart; static FLOAT_TYPE treeRejectionThreshold; static vector constraints; static AttemptedSwapList attemptedSwaps; static FLOAT_TYPE uniqueSwapBias; static FLOAT_TYPE distanceSwapBias; static unsigned rescaleEvery; static FLOAT_TYPE rescaleBelow; static FLOAT_TYPE reduceRescaleBelow; static FLOAT_TYPE bailOutBelow; static list nodeOptVector; static bool useOptBoundedForBlen; static bool rootWithDummy; static bool dummyRootBranchMidpoint; static bool someOrientedGap; static FLOAT_TYPE uniqueSwapPrecalc[500]; static FLOAT_TYPE distanceSwapPrecalc[1000]; static FLOAT_TYPE expectedPrecision; static FLOAT_TYPE rescalePrecalcThresh[RESCALE_ARRAY_LENGTH]; static FLOAT_TYPE rescalePrecalcMult[RESCALE_ARRAY_LENGTH]; static int rescalePrecalcIncr[RESCALE_ARRAY_LENGTH]; static Bipartition *outgroup; static int siteToScore; int calcs; //this controls the amount of site likelihood output. It is easier to just set it for the whole //tree instead of passing it around a lot. 0 = no sitelikes, 1 = user level sitelikes, 2 = debugging //it is NOT PERSISTENT, so after OutputSitelikes is called it is reset to 0 int sitelikeLevel; string ofprefix; enum{//the directions for sweeping of CLAs DOWN = 1, UPLEFT = 2, UPRIGHT = 3, ROOT = 4 }; enum{ DIRTY = 0, CLEAN_STANDARDIZED = 1, CLEAN_UNSTANDARDIZED = 2, TEMP_ADJUSTED = 3 }bipartCond; public: //construction and allocation functions Tree(); Tree(NucleotideData*,CondLikeArray **sharedcl); Tree(const char*, bool numericalTaxa, bool allowPolytomies=false, bool allowMissingTaxa=false); void AllocateTree(bool withExtraNode); void AssignDataToTips(); void AssignCLAsFromMaster(); //destructor ~Tree(); //functions for manipulating and making trees void RandomlyAttachTip(int nodenum, int & ); void RandomlyAttachTipWithConstraints(int nodenum, int &placeInAllNodes, Bipartition *mask); void MakeTrifurcatingRoot(bool reducenodes, bool clasAssigned); bool ArbitrarilyBifurcate(); void SortAllNodesArray(); void EliminateNode(int nn); int FindUnusedNode(int start); inline void SetBranchLength(TreeNode *nd, FLOAT_TYPE len, bool dummyRootDontRecurse=false); bool IdenticalSubtreeTopology(const TreeNode *other); bool IdenticalTopology(const TreeNode *other); bool IdenticalTopologyAllowingRerooting(const TreeNode *other); void RotateNodesAtRoot(TreeNode *newroot); void RerootHere(int newroot); void SwapNodeDataForReroot(TreeNode *nroot); void CheckBalance(); void SwapAndFreeNodes(TreeNode *cop); void OutputBinaryFormattedTree(OUTPUT_CLASS &) const; void ReadBinaryFormattedTree(FILE *); //functions for copying trees void MimicTopologyButNotInternNodeNums(TreeNode *copySource,TreeNode *replicate,int &placeInAllNodes); void MimicTopo(TreeNode *, bool firstNode, bool sameModel); void MimicTopo(const Tree *source); void CopyBranchLens(const Tree *s); void CopyClaIndeces(const Tree *source, bool remove); // mutation functions int TopologyMutator(FLOAT_TYPE optPrecision, int range, int subtreeNode); void DeterministicSwapperByDist(Individual *source, double optPrecision, int range, bool furthestFirst); void DeterministicSwapperByCut(Individual *source, double optPrecision, int range, bool furthestFirst); void DeterministicSwapperRandom(Individual *source, double optPrecision, int range); void GenerateTopologiesAtSprDistance(Individual *source, double optPrecision, int range); void GatherValidReconnectionNodes(ReconList &list, int maxDist, TreeNode *cut, const TreeNode *subtreeNode, Bipartition *partialMask=NULL); void GatherValidReconnectionNodes(int maxRange, TreeNode *cut, const TreeNode *subtreeNode, Bipartition *partialMask=NULL); void FillAllSwapsList(ReconList *cuts, int reconLim); unsigned FillWeightsForAllSwaps(ReconList *cuts, double *); bool AssignWeightsToSwaps(TreeNode *cut); int SPRMutate(int cutnum, ReconNode *broke, FLOAT_TYPE optPrecision, int subtreeNode); int SPRMutateDummy(int cutnum, ReconNode *broke, FLOAT_TYPE optPrecision, int subtreeNode); void ReorientSubtreeSPRMutate(int oldRoot, ReconNode *newRoot, FLOAT_TYPE optPrecision); void ReorientSubtreeSPRMutateDummy(int oldRoot, ReconNode *newRoot, FLOAT_TYPE optPrecision); int BrlenMutate(); int BrlenMutateSubset(const vector &subtreeList); void ScaleWholeTree(FLOAT_TYPE factor=-1.0); FLOAT_TYPE Treelength(); //deprecated mutation functions int VariableSPRMutate(int range, FLOAT_TYPE optPrecision); void SPRMutate(int cutnum, int broknum, FLOAT_TYPE optPrecision, const vector &nonSubNodes); void NNIMutate(int node, int branch, FLOAT_TYPE optPrecision, int subtreeNode); void VariableNNIMutate(int node, int branch, FLOAT_TYPE optPrecision, int subtreeNode); void LocalMove(); //recombination int BipartitionBasedRecombination( Tree *t, bool sameModel, FLOAT_TYPE optPrecision); int SubtreeBasedRecombination( Tree *t, int recomNodeNum, bool sameModel, FLOAT_TYPE optPrecision); void RecombineWith( Tree *t, bool sameModel , FLOAT_TYPE optPrecision ); //functions for dealing with constraints and bipartitions static void LoadConstraints(ifstream &con, int nTaxa); bool SwapAllowedByConstraint(const Constraint &constr, TreeNode *cut, ReconNode *broken, const Bipartition &proposed, const Bipartition *partialMask); //functions for determining if adding a particular taxon to a particular place in a growing tree is allowed by any constraints //this is used if the taxon to be added is NOT already in the tree (so not for testing the allowability of swaps) //these depend on the bipartitions across the tree NOT being standardized (not all having the taxon 1 bit on) bool TaxonAdditionAllowedByPositiveConstraintWithMask(Constraint *constr, Bipartition *mask, TreeNode *toAdd, TreeNode *broken); bool TaxonAdditionAllowedByNegativeConstraintWithMask(Constraint *constr, Bipartition *mask, TreeNode *toAdd, TreeNode *broken); bool TaxonAdditionAllowedByPositiveBackboneConstraintWithMask(Constraint *constr, Bipartition *mask, TreeNode *toAdd, TreeNode *broken); bool TaxonAdditionAllowedByNegativeBackboneConstraintWithMask(Constraint *constr, Bipartition *mask, TreeNode *toAdd, TreeNode *broken); bool RecursiveAllowedByConstraintWithMask(const Constraint &constr, const Bipartition *partialMask, const TreeNode *nd); //bool RecursiveAllowedByNegativeConstraintWithMask(Constraint *constr, Bipartition *mask, TreeNode *nd); void CalcBipartitions(bool standardize); void OutputBipartitions(); TreeNode *ContainsBipartition(const Bipartition &bip); TreeNode *ContainsBipartitionOrComplement(const Bipartition &bip); TreeNode *ContainsMaskedBipartitionOrComplement(const Bipartition &bip, const Bipartition &mask); void AdjustBipartsForSwap(int cut, int broken); // functions for computing likelihood bool ConditionalLikelihood(int direction, TreeNode* nd); int ConditionalLikelihoodRateHet(int direction, TreeNode* nd, bool fillFinalCLA=false); FLOAT_TYPE GetScorePartialTerminalRateHet(const CondLikeArray *partialCLA, const FLOAT_TYPE *prmat, const char *Ldata, int modIndex, int dataIndex); FLOAT_TYPE GetScorePartialTerminalNState(const CondLikeArray *partialCLA, const FLOAT_TYPE *prmat, const char *Ldata, int modIndex, int dataIndex); FLOAT_TYPE GetScorePartialInternalRateHet(const CondLikeArray *partialCLA, const CondLikeArray *childCLA, const FLOAT_TYPE *prmat, int modIndex, int dataIndex); FLOAT_TYPE GetScorePartialInternalNState(const CondLikeArray *partialCLA, const CondLikeArray *childCLA, const FLOAT_TYPE *prmat, int modIndex, int dataIndex); int Score(int rootNodeNum =0); FLOAT_TYPE GetScorePartialTerminalOrientedGap(const CondLikeArray *partialCLA, const FLOAT_TYPE *prmat, const char *Ldata, int modIndex, int dataIndex); //functions to optimize blens and params pair CalcDerivativesRateHet(TreeNode *nd1, TreeNode *nd2); FLOAT_TYPE NewtonRaphsonOptimizeBranchLength(FLOAT_TYPE precision1, TreeNode *nd, bool goodGuess); #ifdef OPT_DEBUG FLOAT_TYPE NewtonRaphsonSpoof(FLOAT_TYPE precision1, TreeNode *nd, bool goodGuess); #endif void GetDerivsPartialTerminal(const CondLikeArray *partialCLA, const FLOAT_TYPE *prmat, const FLOAT_TYPE *d1mat, const FLOAT_TYPE *d2mat, const char *Ldata, FLOAT_TYPE &d1Tot, FLOAT_TYPE &d2Tot, int modIndex, int dataIndex, const unsigned *ambigMap =NULL); void GetDerivsPartialTerminalNState(const CondLikeArray *partialCLA, const FLOAT_TYPE *prmat, const FLOAT_TYPE *d1mat, const FLOAT_TYPE *d2mat, const char *Ldata, FLOAT_TYPE &d1Tot, FLOAT_TYPE &d2Tot, int modIndex, int dataIndex); void GetDerivsPartialTerminalNStateRateHet(const CondLikeArray *partialCLA, const FLOAT_TYPE *prmat, const FLOAT_TYPE *d1mat, const FLOAT_TYPE *d2mat, const char *Ldata, FLOAT_TYPE &d1Tot, FLOAT_TYPE &d2Tot, int modIndex, int dataIndex); void GetDerivsPartialInternal(const CondLikeArray *partialCLA, const CondLikeArray *childCLA, const FLOAT_TYPE *prmat, const FLOAT_TYPE *d1mat, const FLOAT_TYPE *d2mat, FLOAT_TYPE &d1, FLOAT_TYPE &d2, int modIndex, int dataIndex); void GetDerivsPartialInternalNState(const CondLikeArray *partialCLA, const CondLikeArray *childCLA, const FLOAT_TYPE *prmat, const FLOAT_TYPE *d1mat, const FLOAT_TYPE *d2mat, FLOAT_TYPE &d1, FLOAT_TYPE &d2, int modIndex, int dataIndex); void GetDerivsPartialInternalNStateRateHet(const CondLikeArray *partialCLA, const CondLikeArray *childCLA, const FLOAT_TYPE *prmat, const FLOAT_TYPE *d1mat, const FLOAT_TYPE *d2mat, FLOAT_TYPE &d1Tot, FLOAT_TYPE &d2Tot, int modIndex, int dataIndex); void GetDerivsPartialInternalEQUIV(const CondLikeArray *partialCLA, const CondLikeArray *childCLA, const FLOAT_TYPE *prmat, const FLOAT_TYPE *d1mat, const FLOAT_TYPE *d2mat, FLOAT_TYPE &d1, FLOAT_TYPE &d2, char *equiv, int modIndex, int dataIndex); void CalcFullCLAInternalInternal(CondLikeArray *destCLA, const CondLikeArray *LCLA, const CondLikeArray *RCLA, const FLOAT_TYPE *Lpr, const FLOAT_TYPE *Rpr, int modIndex, int dataIndex); void CalcFullCLATerminalTerminal(CondLikeArray *destCLA, const FLOAT_TYPE *Lpr, const FLOAT_TYPE *Rpr, const char *Ldata, const char *Rdata, int modIndex, int dataIndex); void CalcFullCLAInternalTerminal(CondLikeArray *destCLA, const CondLikeArray *LCLA, const FLOAT_TYPE *pr1, const FLOAT_TYPE *pr2, char *data2, const unsigned *ambigMap, int modIndex, int dataIndex); void CalcFullCLAInternalTerminalOpenMP(CondLikeArray *destCLA, const CondLikeArray *LCLA, const FLOAT_TYPE *pr1, const FLOAT_TYPE *pr2, char *data2, const unsigned *ambigMap, int modIndex, int dataIndex); void CalcFullCLAInternalInternalEQUIV(CondLikeArray *destCLA, const CondLikeArray *LCLA, const CondLikeArray *RCLA, const FLOAT_TYPE *Lpr, const FLOAT_TYPE *Rpr,const char *leftEQ, const char *rightEQ, int modIndex, int dataIndex); void CalcFullCLAPartialInternalRateHet(CondLikeArray *destCLA, const CondLikeArray *LCLA, const FLOAT_TYPE *pr1, CondLikeArray *partialCLA, int modIndex, int dataIndex); void CalcFullCLAPartialTerminalRateHet(CondLikeArray *destCLA, const CondLikeArray *partialCLA, const FLOAT_TYPE *Lpr, char *Ldata, int modIndex, int dataIndex); void CalcFullCLAInternalInternalNState(CondLikeArray *destCLA, const CondLikeArray *LCLA, const CondLikeArray *RCLA, const FLOAT_TYPE *Lpr, const FLOAT_TYPE *Rpr, int modIndex, int dataIndex); void CalcFullCLAInternalTerminalNState(CondLikeArray *destCLA, const CondLikeArray *LCLA, const FLOAT_TYPE *pr1, const FLOAT_TYPE *pr2, char *data2, int modIndex, int dataIndex); void CalcFullCLATerminalTerminalNState(CondLikeArray *destCLA, const FLOAT_TYPE *Lpr, const FLOAT_TYPE *Rpr, const char *Ldata, const char *Rdata, int modIndex, int dataIndex); //for all internal state recon void GetStatewiseUnscaledPosteriorsPartialInternalNState(CondLikeArray *destCLA, const CondLikeArray *partialCLA, const CondLikeArray *childCLA, const FLOAT_TYPE *prmat, int modIndex, int dataIndex); void GetStatewiseUnscaledPosteriorsPartialTerminalNState(CondLikeArray *destCLA, const CondLikeArray *partialCLA, const FLOAT_TYPE *prmat, const char *Ldata, int modIndex, int dataIndex); int FillStatewiseUnscaledPosteriors(CondLikeArraySet *partialCLAset, CondLikeArraySet *childCLAset, TreeNode *child, FLOAT_TYPE blen1); /* void CalcFullCLATerminalTerminalOrientedGap(CondLikeArray *destCLA, const FLOAT_TYPE *Lpr, const FLOAT_TYPE *Rpr, const char *Ldata, const char *Rdata, int modIndex, int dataIndex); void CalcFullCLAInternalTerminalOrientedGap(CondLikeArray *destCLA, const CondLikeArray *LCLA, const FLOAT_TYPE *pr1, const FLOAT_TYPE *pr2, char *data2, int modIndex, int dataIndex); void CalcFullCLAInternalInternalOrientedGap(CondLikeArray *destCLA, const CondLikeArray *LCLA, const CondLikeArray *RCLA, const FLOAT_TYPE *Lpr, const FLOAT_TYPE *Rpr, int modIndex, int dataIndex); */ //new version that should work for any node - for each child data or a CLA will be passed in void CalcFullCLAOrientedGap(CondLikeArray *destCLA, const FLOAT_TYPE *Lpr, const FLOAT_TYPE *Rpr, const CondLikeArray *LCLA, const CondLikeArray *RCLA, const char *Ldata, const char *Rdata, int modIndex, int dataIndex); void UpdateCLAs(CondLikeArraySet *destCLA, CondLikeArraySet *firstCLA, CondLikeArraySet *secCLA, TreeNode *firstChild, TreeNode *secChild, FLOAT_TYPE blen1, FLOAT_TYPE blen2); void GetTotalScore(CondLikeArraySet *partialCLA, CondLikeArraySet *childCLA, TreeNode *child, FLOAT_TYPE blen1); FLOAT_TYPE OptimizeBranchLength(FLOAT_TYPE optPrecision, TreeNode *nd, bool goodGuess); FLOAT_TYPE OptimizeAllBranches(FLOAT_TYPE optPrecision); int PushBranchlengthsToMin(); void OptimizeBranchesAroundNode(TreeNode *nd, FLOAT_TYPE optPrecision, int subtreeNode); void OptimizeBranchesWithinRadius(TreeNode *nd, FLOAT_TYPE optPrecision, int subtreeNode, TreeNode *prune); void OptimizeBranchesInArray(int *nodes, int numNodes, FLOAT_TYPE optPrecision); FLOAT_TYPE RecursivelyOptimizeBranches(TreeNode *nd, FLOAT_TYPE optPrecision, int subtreeNode, int radius, bool dontGoNext, FLOAT_TYPE scoreIncrease, bool ignoreDelta=false); FLOAT_TYPE RecursivelyOptimizeBranchesDown(TreeNode *nd, TreeNode *calledFrom, FLOAT_TYPE optPrecision, int subtreeNode, int radius, FLOAT_TYPE scoreIncrease); FLOAT_TYPE BrentOptimizeBranchLength(FLOAT_TYPE accuracy_cutoff, TreeNode *here, bool firstPass); FLOAT_TYPE BranchLike(TreeNode *optNode); FLOAT_TYPE OptimizeAlpha(FLOAT_TYPE, int modnum); FLOAT_TYPE OptimizeOmegaParameters(FLOAT_TYPE prec, int modnum); FLOAT_TYPE OptimizeFlexRates(FLOAT_TYPE prec, int modnum); FLOAT_TYPE OptimizeEquilibriumFreqs(FLOAT_TYPE prec, int modnum); FLOAT_TYPE OptimizeRelativeNucRates(FLOAT_TYPE prec, int modnum); FLOAT_TYPE OptimizeInsertDeleteRates(FLOAT_TYPE prec, int modnum); FLOAT_TYPE OptimizeSubsetRates(FLOAT_TYPE prec); //the new versions from the trunk #ifdef SINGLE_PRECISION_FLOATS FLOAT_TYPE OptimizeBoundedParameter(int modnum, FLOAT_TYPE optPrecision, FLOAT_TYPE initialVal, int which, FLOAT_TYPE lowBound, FLOAT_TYPE highBound, void (Model::*SetParam)(int, FLOAT_TYPE), FLOAT_TYPE targetScoreDigits = 5.0f); #else FLOAT_TYPE OptimizeBoundedParameter(int modnum, FLOAT_TYPE optPrecision, FLOAT_TYPE initialVal, int which, FLOAT_TYPE lowBound, FLOAT_TYPE highBound, void (Model::*SetParam)(int, FLOAT_TYPE), FLOAT_TYPE targetScoreDigits = 9.0); #endif FLOAT_TYPE OptimizeBoundedParameter(FLOAT_TYPE optPrecision, FLOAT_TYPE prevVal, int which, FLOAT_TYPE lowBound, FLOAT_TYPE highBound, int modnum, void (Model::*SetParam)(int, FLOAT_TYPE)); template FLOAT_TYPE OptimizeBoundedParameter(FLOAT_TYPE optPrecision, FLOAT_TYPE prevVal, int which, FLOAT_TYPE lowBound, FLOAT_TYPE highBound, T *obj, void (T::*SetParam)(int, FLOAT_TYPE)); template void TraceParameterLikelihood(ofstream &out, int which, FLOAT_TYPE prevVal, FLOAT_TYPE startVal, FLOAT_TYPE endVal, FLOAT_TYPE incr, T *obj, void (T::*SetParam)(int, FLOAT_TYPE)); void TraceLikelihoodForParameter(int modnum, int which, FLOAT_TYPE init, FLOAT_TYPE min, FLOAT_TYPE max, FLOAT_TYPE interval, void (Model::*SetParam)(int, FLOAT_TYPE), bool append); //FLOAT_TYPE OptimizeBoundedParameter(FLOAT_TYPE optPrecision, FLOAT_TYPE prevVal, int which, FLOAT_TYPE lowBound, FLOAT_TYPE highBound, void (Model::*SetParam)(int, FLOAT_TYPE)); FLOAT_TYPE SetAndEvaluateParameter(int modnum, int which, FLOAT_TYPE val, FLOAT_TYPE &bestKnownScore, FLOAT_TYPE &bestKnownVal, void (Model::*SetParam)(int, FLOAT_TYPE)); bool CheckScoreAndRestore(int modnum, int which, void (Model::*SetParam)(int, FLOAT_TYPE), FLOAT_TYPE otherScore, FLOAT_TYPE otherVal, FLOAT_TYPE bestScore, FLOAT_TYPE bestVal, FLOAT_TYPE tolerance); FLOAT_TYPE OptimizeTreeScale(FLOAT_TYPE); FLOAT_TYPE OptimizePinv(); void SetNodesUnoptimized(); void RescaleRateHet(CondLikeArray *destCLA, int dataIndex); void RescaleRateHetNState(CondLikeArray *destCLA, int dataIndex); void StoreBranchlengths(vector &blens){ for(int n=1;ndlen); assert(blens.size() == numNodesTotal - 1); } void RestoreBranchlengths(vector &blens){ for(int n=1;n %.6f = %.6f", bef, lnL, bef - lnL); assert(FloatingPointEquals(bef, lnL, tol)); } return; } pair OptimizeSingleSiteTreeScale(FLOAT_TYPE optPrecision); //functions for dealing with conditional likelihood arrays void MarkUpwardClasToReclaim(int subtreeNode); void MarkDownwardClasToReclaim(int subtreeNode); void MarkClasNearTipsToReclaim(int subtreeNode); void ProtectClas(); void UnprotectClas(); inline CondLikeArraySet *GetClaDown(TreeNode *nd, bool calc=true); inline CondLikeArraySet *GetClaUpLeft(TreeNode *nd, bool calc=true); inline CondLikeArraySet *GetClaUpRight(TreeNode *nd, bool calc=true); void OutputValidClaIndeces(); void OutputNthClaAcrossTree(ofstream &deb, TreeNode *nd, int site, int modIndex); void ClaReport(ofstream &cla); FLOAT_TYPE CountClasInUse(); void OutputSiteLikelihoods(int partnum, vector &likes, const int *under1, const int *under2); void OutputSiteDerivatives(int partNum, vector &likes, vector &d1s, vector &d2s, const int *under1, const int *under2, ofstream &ordered, ofstream &packed); void CountNumReservedClas(int &, int &, int&); void CheckClaAssignments(TreeNode *nd); void RemoveTempClaReservations(); void SetupClasForSubtreeMode(int subtreeNode); void DirtyNodesOutsideOfSubtree(TreeNode *nd, int subtreeNodeAnc); void CopyClaIndecesInSubtree(const TreeNode *from, bool remove); void DirtyNodesInSubtree(TreeNode *nd); void ReclaimUniqueClas(); void RemoveTreeFromAllClas(); void TraceDirtynessToRoot(TreeNode *nd); void TraceDirtynessToNode(TreeNode *nd, int tonode); void SweepDirtynessOverTree(TreeNode *nd, TreeNode *from=NULL); void MakeNodeDirty(TreeNode *nd); void MakeAllNodesDirty(); //accessor funcs bool IsGood() const {return root->IsGood();} int NTax() const {return numTipsTotal;} int getNumTipsTotal() const {return numTipsTotal;} int getNumNodesTotal() const {return numNodesTotal;} int GetRandomInternalNode() const {return numTipsTotal+rnd.random_int(numTipsTotal-3)+1;} int GetRandomTerminalNode() const {return rnd.random_int(numTipsTotal)+1;} int GetRandomNonRootNode() const {return rnd.random_int(numNodesTotal-1)+1;} //odds and ends void PerturbAllBranches(); void RandomizeBranchLengths(FLOAT_TYPE lowLimit, FLOAT_TYPE highLimit); void RandomizeBranchLengthsExponential(FLOAT_TYPE lambda); int NodeToNodeDistance(int num1, int num2); int NodesToRoot(TreeNode *nd); void SampleBlenCurve(TreeNode *nd, ofstream &out); void CalcEmpiricalDerivatives(TreeNode *nd, FLOAT_TYPE &D1, FLOAT_TYPE &D2); void SetDistanceBasedBranchLengthsAroundNode(TreeNode *nd); void FindNearestTerminalUp(TreeNode *start, TreeNode *&, FLOAT_TYPE &dist); void FindNearestTerminalsDown(TreeNode *start, TreeNode *from, TreeNode *&term1, TreeNode *&term2, FLOAT_TYPE &dist1, FLOAT_TYPE &dist2); void OutputTreeStructure(TreeNode *); void GetInternalStateString(char *string, int nodeNum); void RecursivelyCalculateInternalStateProbs(TreeNode *nd, ofstream &out); void InferAllInternalStateProbs(const char *ofprefix); void MoveDummyRootToBranchMidpoint(); static void SetTreeStatics(ClaManager *, const DataPartition *, const GeneralGamlConfig *); void C4(const FLOAT_TYPE *a); }; inline void Tree::CopyBranchLens(const Tree *s){ for(int i=1;idlen=s->allNodes[i]->dlen; } inline void Tree::MakeAllNodesDirty(){ root->claIndexDown=claMan->SetDirty(root->claIndexDown); root->claIndexUL=claMan->SetDirty(root->claIndexUL); root->claIndexUR=claMan->SetDirty(root->claIndexUR); for(int i=numTipsTotal+1;iclaIndexDown=claMan->SetDirty(allNodes[i]->claIndexDown); allNodes[i]->claIndexUL=claMan->SetDirty(allNodes[i]->claIndexUL); allNodes[i]->claIndexUR=claMan->SetDirty(allNodes[i]->claIndexUR); } lnL=-ONE_POINT_ZERO; } inline int Tree::FindUnusedNode(int start){ for(int i=start;iattached)) {allNodes[i]->left=allNodes[i]->right=NULL; return i; } assert(0); return -1; } inline void Tree::AssignCLAsFromMaster(){ //remember that the root's down cla is actually the one that goes up //the middle des if(claMan == NULL) return; assert(allNodes[0]->claIndexDown==-1); allNodes[0]->claIndexDown=claMan->AssignClaHolder(); allNodes[0]->claIndexUL=claMan->AssignClaHolder(); allNodes[0]->claIndexUR=claMan->AssignClaHolder(); for(int i=numTipsTotal+1;iclaIndexDown==-1); allNodes[i]->claIndexDown=claMan->AssignClaHolder(); allNodes[i]->claIndexUL=claMan->AssignClaHolder(); allNodes[i]->claIndexUR=claMan->AssignClaHolder(); } } inline void Tree::CopyClaIndeces(const Tree *from, bool remove){ //the bool argument "remove" designates whether the tree currently has cla arrays //assigned to it or not (if not, it must have come from the unused tree vector) //do the clas down if(remove) claMan->DecrementCla(allNodes[0]->claIndexDown); allNodes[0]->claIndexDown=from->allNodes[0]->claIndexDown; if(allNodes[0]->claIndexDown != -1) claMan->IncrementCla(allNodes[0]->claIndexDown); #ifdef EQUIV_CALCS if(from->dirtyEQ == false){ memcpy(allNodes[0]->tipData, from->allNodes[0]->tipData, data->NChar()*sizeof(char)); for(int i=numTipsTotal+1;itipData, from->allNodes[i]->tipData, data->NChar()*sizeof(char)); dirtyEQ = false; } else dirtyEQ = true; #endif for(int i=numTipsTotal+1;iDecrementCla(allNodes[i]->claIndexDown); allNodes[i]->claIndexDown=from->allNodes[i]->claIndexDown; if(allNodes[i]->claIndexDown != -1) claMan->IncrementCla(allNodes[i]->claIndexDown); } //do the clas up left if(remove) claMan->DecrementCla(allNodes[0]->claIndexUL); allNodes[0]->claIndexUL=from->allNodes[0]->claIndexUL; if(allNodes[0]->claIndexUL != -1) claMan->IncrementCla(allNodes[0]->claIndexUL); for(int i=numTipsTotal+1;iDecrementCla(allNodes[i]->claIndexUL); allNodes[i]->claIndexUL=from->allNodes[i]->claIndexUL; if(allNodes[i]->claIndexUL != -1) claMan->IncrementCla(allNodes[i]->claIndexUL); } //do the clas up right if(remove) claMan->DecrementCla(allNodes[0]->claIndexUR); allNodes[0]->claIndexUR=from->allNodes[0]->claIndexUR; if(allNodes[0]->claIndexUR != -1) claMan->IncrementCla(allNodes[0]->claIndexUR); for(int i=numTipsTotal+1;iDecrementCla(allNodes[i]->claIndexUR); allNodes[i]->claIndexUR=from->allNodes[i]->claIndexUR; if(allNodes[i]->claIndexUR != -1) claMan->IncrementCla(allNodes[i]->claIndexUR); } } inline void Tree::RemoveTreeFromAllClas(){ if(root->claIndexDown != -1){ claMan->DecrementCla(root->claIndexDown); root->claIndexDown=-1; } if(root->claIndexUL != -1){ claMan->DecrementCla(root->claIndexUL); root->claIndexUL=-1; } if(root->claIndexUR != -1){ claMan->DecrementCla(root->claIndexUR); root->claIndexUR=-1; } for(int i=numTipsTotal+1;iclaIndexDown != -1){ claMan->DecrementCla(allNodes[i]->claIndexDown); allNodes[i]->claIndexDown=-1; } if(allNodes[i]->claIndexUL != -1){ claMan->DecrementCla(allNodes[i]->claIndexUL); allNodes[i]->claIndexUL=-1; } if(allNodes[i]->claIndexUR != -1){ claMan->DecrementCla(allNodes[i]->claIndexUR); allNodes[i]->claIndexUR=-1; } } } inline void Tree::SetBranchLength(TreeNode *nd, FLOAT_TYPE len, bool dummyRootDontRecurse /*=false*/){ assert(!(len < min_brlen) && !(len > max_brlen)); nd->dlen=len; //the dontRecurse bit here just keeps it from bouncing back and forth setting the //lengths of the two "root" branches, since changing one triggers a change to the other //There are are few posibilities: //1. nd->anc is the root and dummyRoot->anc is the root // The other branch to adjust is the descendent of the root that is not the dummyRoot nor nd //2. nd->anc is not the root // 2a. nd is the branch "above" where dummyRoot attaches // In this case dummyRoot is the next or prev of nd, and the other branch to adjust is nd->anc // 2b. nd is the branch "below" where dummyRoot attaches // In this case dummyRoot is left or right of nd. The branch to adjust is the other descendent of nd // 3. nd is not related to the dummyRooted branch if(rootWithDummy && dummyRootBranchMidpoint && dummyRootDontRecurse == false){ TreeNode *otherNode = NULL; if(nd->anc == root && dummyRoot->anc == root){ otherNode = root->left; do{ if(otherNode != dummyRoot && otherNode != nd){ break; } else otherNode = otherNode->next; }while(otherNode); } else{ if(nd->prev == dummyRoot || nd->next == dummyRoot) otherNode = nd->anc; else{ if(nd->left == dummyRoot) otherNode = nd->right; else if(nd->right == dummyRoot) otherNode = nd->left; } } if(otherNode) SetBranchLength(otherNode, len, true); } SweepDirtynessOverTree(nd); } inline void Tree::MoveDummyRootToBranchMidpoint(){ TreeNode *branch1, *branch2; if(dummyRoot->anc == root){ if(root->left != dummyRoot){ branch1 = root->left; if(root->left->next != dummyRoot){ branch2 = root->left->next; assert(root->right == dummyRoot); } else{ branch2 = root->right; } } else{ branch1 = root->left->next; branch2 = root->right; } } else{ branch1 = dummyRoot->anc; branch2 = (dummyRoot->next ? dummyRoot->next : dummyRoot->prev); } double sum = branch1->dlen + branch2->dlen; //this should automatically adjust the length of branch2 because of code in SetBranchLength SetBranchLength(branch1, sum / 2.0); } inline CondLikeArraySet *Tree::GetClaDown(TreeNode *nd, bool calc/*=true*/){ if(claMan->IsDirty(nd->claIndexDown)){ if(calc==true){ ConditionalLikelihoodRateHet(DOWN, nd); } else claMan->FillHolder(nd->claIndexDown, 1); } if(memLevel > 1) claMan->ReserveCla(nd->claIndexDown); return claMan->GetCla(nd->claIndexDown); } inline CondLikeArraySet *Tree::GetClaUpLeft(TreeNode *nd, bool calc/*=true*/){ if(claMan->IsDirty(nd->claIndexUL)){ if(calc==true){ ConditionalLikelihoodRateHet(UPLEFT, nd); } else claMan->FillHolder(nd->claIndexUL, 2); } if(memLevel > 0) claMan->ReserveCla(nd->claIndexUL); return claMan->GetCla(nd->claIndexUL); } inline CondLikeArraySet *Tree::GetClaUpRight(TreeNode *nd, bool calc/*=true*/){ if(claMan->IsDirty(nd->claIndexUR)){ if(calc==true){ ConditionalLikelihoodRateHet(UPRIGHT, nd); } else claMan->FillHolder(nd->claIndexUR, 2); } if(memLevel > 0) claMan->ReserveCla(nd->claIndexUR); return claMan->GetCla(nd->claIndexUR); } inline void Tree::ProtectClas(){ if(memLevel != 3){ for(int i=numTipsTotal+1;iReserveCla(allNodes[i]->claIndexDown, false); } } else{ for(int i=numTipsTotal+1;ileft->IsInternal() && allNodes[i]->right->IsInternal()) claMan->ReserveCla(allNodes[i]->claIndexDown, false); } } } inline void Tree::UnprotectClas(){ for(int i=numTipsTotal+1;iclaIndexDown > -1) claMan->UnreserveCla(allNodes[i]->claIndexDown); } } inline int Tree::NodeToNodeDistance(int num1, int num2){ TreeNode *nd1=allNodes[num1]; TreeNode *nd2=allNodes[num2]; int dist=0; int height1=NodesToRoot(nd1); int height2=NodesToRoot(nd2); while(height1 > height2){ nd1=nd1->anc; dist++; height1--; } while(height2 > height1){ nd2=nd2->anc; dist++; height2--; } while(nd1 != nd2){ nd1=nd1->anc; nd2=nd2->anc; dist += 2; } return dist; } inline int Tree::NodesToRoot(TreeNode *nd){ int i=0; while(nd->anc){ nd=nd->anc; i++; } return i; } template void Tree::TraceParameterLikelihood(ofstream &out, int which, FLOAT_TYPE prevVal, FLOAT_TYPE startVal, FLOAT_TYPE endVal, FLOAT_TYPE incr, T *obj, void (T::*SetParam)(int, FLOAT_TYPE)){ for(FLOAT_TYPE val = startVal;val <=endVal;val += incr){ CALL_SET_PARAM_FUNCTION(*obj, SetParam)(which, val); MakeAllNodesDirty(); Score(); out << val << "\t" << lnL << endl; } CALL_SET_PARAM_FUNCTION(*obj, SetParam)(which, prevVal); MakeAllNodesDirty(); Score(); out << prevVal << "\t" << lnL << endl; } //a templated version template FLOAT_TYPE Tree::OptimizeBoundedParameter(FLOAT_TYPE optPrecision, FLOAT_TYPE prevVal, int which, FLOAT_TYPE lowBound, FLOAT_TYPE highBound, T *obj, void (T::*SetParam)(int, FLOAT_TYPE)){ FLOAT_TYPE epsilon = min(optPrecision, (FLOAT_TYPE) 1.0e-5); assert(prevVal > lowBound - epsilon && prevVal < highBound + epsilon); //if the initial value is already very near or equal to a bound, bump it off a tad so the emirical derivs below work right. if(prevVal - lowBound < epsilon){ prevVal = lowBound + epsilon; CALL_SET_PARAM_FUNCTION(*obj, SetParam)(which, prevVal); MakeAllNodesDirty(); } else if(highBound - prevVal < epsilon){ prevVal = highBound - epsilon; CALL_SET_PARAM_FUNCTION(*obj, SetParam)(which, prevVal); MakeAllNodesDirty(); } if(FloatingPointEquals(lnL, -ONE_POINT_ZERO, 1e-8)) Score(); FLOAT_TYPE start, prev, cur; prev = start = cur = lnL; FLOAT_TYPE lastChange=(FLOAT_TYPE)9999.9; FLOAT_TYPE upperBracket = highBound; //the smallest value we know of with a negative d1, or the minimum allowed value FLOAT_TYPE lowerBracket = lowBound; //the largest value we know of with a positive d1 , or the maximum allowed value FLOAT_TYPE incr; int lowBoundOvershoot = 0; int upperBoundOvershoot = 0; int positiveD2Num = 0; int pass = 0; /* ofstream curves("lcurve.log"); curves.precision(8); curves << endl; for(double c = max(prevVal - 0.01, lowBound); c < min(prevVal + 0.01, highBound) ; c += 0.001){ CALL_SET_PARAM_FUNCTION(*obj, SetParam)(which, c); MakeAllNodesDirty(); Score(); curves << c << "\t" << lnL << endl;; } curves.close(); CALL_SET_PARAM_FUNCTION(*obj, SetParam)(which, prevVal); MakeAllNodesDirty(); Score(); */ while(1){ #ifdef SINGLE_PRECISION_FLOATS incr=0.005f; #else incr=min(0.0001*optPrecision, min(prevVal - lowerBracket, upperBracket - prevVal)); //incr=min(0.0001, min(prevVal - lowerBracket, upperBracket - prevVal)); #endif CALL_SET_PARAM_FUNCTION(*obj, SetParam)(which, prevVal+incr); MakeAllNodesDirty(); Score(); cur=lnL; FLOAT_TYPE d11=(cur-prev)/incr; CALL_SET_PARAM_FUNCTION(*obj, SetParam)(which, prevVal-incr); MakeAllNodesDirty(); Score(); cur=lnL; FLOAT_TYPE d12=(cur-prev)/-incr; FLOAT_TYPE d1=(d11+d12)*ZERO_POINT_FIVE; //if the evaluation points straddle the optimum, leave now if((d11 - d12) == ZERO_POINT_ZERO || (d11 > ZERO_POINT_ZERO && d12 < ZERO_POINT_ZERO) || (d11 < ZERO_POINT_ZERO && d12 > ZERO_POINT_ZERO)){ CALL_SET_PARAM_FUNCTION(*obj, SetParam)(which, prevVal);; MakeAllNodesDirty(); lnL = prev; return prev-start; } FLOAT_TYPE d2=(d11-d12)/incr; FLOAT_TYPE est=-d1/d2; FLOAT_TYPE proposed = prevVal + est; // outman.UserMessage("%f\t%f\t%f\t%f\t%f", d1, d2, prevVal, est, proposed); if(d2 > ZERO_POINT_ZERO){ positiveD2Num++; if(d1 > ZERO_POINT_ZERO) proposed=prevVal*(FLOAT_TYPE)(ONE_POINT_ZERO+0.01*positiveD2Num); else proposed=prevVal*(FLOAT_TYPE)(ONE_POINT_ZERO-0.01*positiveD2Num); } if(d1 < ZERO_POINT_ZERO && proposed < (lowerBracket + epsilon)){ if(prevVal - lowerBracket - epsilon < epsilon * ZERO_POINT_FIVE){ CALL_SET_PARAM_FUNCTION(*obj, SetParam)(which, prevVal);; MakeAllNodesDirty(); lnL = prev; return prev-start; } lowBoundOvershoot++; if(lowBoundOvershoot > 1) proposed = lowerBracket + epsilon; else proposed = (prevVal + lowerBracket) * ZERO_POINT_FIVE; } else if(d1 > ZERO_POINT_ZERO && proposed > upperBracket - epsilon){ if(upperBracket - epsilon - prevVal < epsilon * ZERO_POINT_FIVE){ CALL_SET_PARAM_FUNCTION(*obj, SetParam)(which, prevVal);; MakeAllNodesDirty(); lnL = prev; return prev-start; } upperBoundOvershoot++; if(upperBoundOvershoot > 1) proposed = upperBracket - epsilon; else proposed = (prevVal + upperBracket) * ZERO_POINT_FIVE; } FLOAT_TYPE estImprove; if(d2 < ZERO_POINT_ZERO) estImprove = d1*(proposed - prevVal) + (d2 * (proposed - prevVal) * (proposed - prevVal)) * ZERO_POINT_FIVE; else estImprove = 9999.9; //require that we didn't significantly worsen the likelihood if(estImprove < optPrecision && prev >= start - 1.0e-6){ CALL_SET_PARAM_FUNCTION(*obj, SetParam)(which, prevVal);; MakeAllNodesDirty(); lnL = prev; return prev-start; } //don't allow infinite looping if something goes wrong if(pass > 1000){ throw ErrorException("too many passes in OptimizeBoundedParameter"); } //update the brackets if(d1 <= ZERO_POINT_ZERO && prevVal < upperBracket) upperBracket = prevVal; else if(d1 > ZERO_POINT_ZERO && prevVal > lowerBracket) lowerBracket = prevVal; CALL_SET_PARAM_FUNCTION(*obj, SetParam)(which, proposed);; MakeAllNodesDirty(); Score(); lastChange = lnL - prev; prev=lnL; prevVal=proposed; pass++; } return -1; } #endif garli-2.1-release/src/treenode.cpp000066400000000000000000000507521241236125200171770ustar00rootroot00000000000000// GARLI version 2.1 source code // Copyright 2005-2014 Derrick J. Zwickl // email: garli.support@gmail.com // // This program is free software: you can redistribute it and/or modify // it under the terms of the GNU General Public License as published by // the Free Software Foundation, either version 3 of the License, or // (at your option) any later version. // // This program is distributed in the hope that it will be useful, // but WITHOUT ANY WARRANTY; without even the implied warranty of // MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the // GNU General Public License for more details. // // You should have received a copy of the GNU General Public License // along with this program. If not, see . // // NOTE: Portions of this source adapted from GAML source, written by Paul O. Lewis #include #include #include using namespace std; #include "defs.h" #include "treenode.h" #include "clamanager.h" #include "bipartition.h" #include "errorexception.h" #include "outputman.h" #include "sequencedata.h" extern OutputManager outman; #undef DEBUG_RECOMBINEWITH TreeNode::TreeNode( const int no ) : left(0), right(0), next(0), prev(0), anc(0), tipData(0L), bipart(0L) { attached =false; claIndexDown=-1; claIndexUL=-1; claIndexUR=-1; nodeNum = no; dlen = 0.0; /* GANESH added this */ #ifdef PECR_SET_PARSIMONY_BRLEN /* every node is a leaf (no descendants) when created */ leaf_mask = true; #endif } TreeNode::~TreeNode(){ if(bipart!=NULL) delete bipart; } TreeNode* TreeNode::AddDes(TreeNode *d){ //leaves blens as-is, ignores the current values //of anc, prev and next for d d->anc=this; d->next=NULL; if(left){ if(right){ right->next=d; d->prev=right; right=d; } else{ right=d; left->next=d; d->prev=left; } } else{ left=d; d->prev=NULL; } d->attached=true; /* GANESH added this */ #ifdef PECR_SET_PARSIMONY_BRLEN /* not a leaf any more once we add descendants */ leaf_mask = false; #endif return d; } void TreeNode::RemoveDes(TreeNode *d){ //leaves blens as-is assert(d->anc == this); //remove d from this if(d->prev != NULL) d->prev->next=d->next; if(d->next != NULL) d->next->prev=d->prev; if(d == left) left=d->next; else if(d == right) right=d->prev; d->next = d->prev = NULL; d->anc=NULL; } void TreeNode::MoveDesToAnc(TreeNode *d){ //this assumes that the anc is currently NULL, //and makes the specified des the new anc. //*this is added as a des of that anc //the blen of the des is trasfered to this assert(anc == NULL); dlen = d->dlen; d->dlen=-1; RemoveDes(d); //now add this to d d->AddDes(this); } char *TreeNode::MakeNewick(char *s, bool internalNodes, bool branchLengths, bool highPrec /*=false*/) const{ if(left){ if(internalNodes==true && nodeNum!=0){ sprintf(s, "%d", nodeNum); while(*s)s++; } *s++='('; s=left->MakeNewick(s, internalNodes, branchLengths, highPrec); if(anc){ if(branchLengths==true){ *s++=':'; if(highPrec == false) sprintf(s, "%.8lf", dlen); else sprintf(s, "%.10lf", dlen); while(*s)s++; } } else {*s='\0'; return s; } } else { sprintf(s, "%d", nodeNum); while(*s)s++; if(branchLengths==true){ *s++=':'; if(highPrec == false) sprintf(s, "%.8lf", dlen); else sprintf(s, "%.10lf", dlen); while(*s)s++; } } if(next){ *s++=','; s=next->MakeNewick(s, internalNodes, branchLengths, highPrec); } else { if(anc){ *s++=')'; } } return s; } void TreeNode::MakeNewick(string &outStr, const DataPartition *data, bool internalNodes, bool branchLengths, bool taxonNames /*=false*/, bool highPrec /*=false*/) const{ char s[500]; if(left){ if(internalNodes==true && nodeNum!=0){ sprintf(s, "%d", nodeNum); outStr += s; } outStr += '('; left->MakeNewick(outStr, data, internalNodes, branchLengths, taxonNames, highPrec); if(anc){ if(branchLengths==true){ outStr += ':'; if(highPrec == false){ sprintf(s, "%.8lf", dlen); outStr += s; } else{ sprintf(s, "%.10lf", dlen); outStr += s; } } } else return; } else { //sprintf(s, "%d", nodeNum); //outStr += s; if(taxonNames && data) outStr += data->TaxonLabel(nodeNum - 1); else{ sprintf(s, "%d", nodeNum); outStr += s; } if(branchLengths==true){ outStr += ':'; if(highPrec == false) sprintf(s, "%.8lf", dlen); else sprintf(s, "%.10lf", dlen); outStr += s; } } if(next){ outStr += ','; next->MakeNewick(outStr, data, internalNodes, branchLengths, taxonNames, highPrec); } else { if(anc){ outStr += ')'; } } } void TreeNode::MakeNewickForSubtree(char *s) const{ assert(left); *s++='('; s=left->MakeNewick(s, false, false); *s++=';'; *s++='\0'; } void TreeNode::MakeNewickForSubtree(string &s, const DataPartition *data, bool internalNodes, bool branchLengths, bool taxonNames, bool highPrec) const{ assert(left); s += '('; left->MakeNewick(s, data, internalNodes, branchLengths, taxonNames, highPrec); //s += ';'; } //MTH void TreeNode::Prune() { //DZ 7-6 removing adjustments to branch lengths when pruning, which just result in adding a whole bunch of length //to the whole tree, making the dlens get longer and longer and longer as the run progresses assert(anc);//never call with this=root attached=false; if(anc->anc) {//not connected to the root if(anc->left->next==anc->right) {TreeNode *sis; if(anc->left==this) sis=anc->right; else sis=anc->left; // sis->dlen+=anc->dlen; anc->SubstituteNodeWithRespectToAnc(sis); anc->attached=false; } else { anc=anc; assert(0);//internal polytomy } } else { //these assume a trifurcating root if(anc->left==this){ anc->left=next; anc->left->prev=NULL; anc->left->next=anc->right; } else if(anc->right==this){ anc->right=prev; anc->right->next=NULL; anc->left->next=anc->right; } else {assert(anc->left==prev && anc->right==next); next->prev=prev; prev->next=next; assert(anc->left && anc->right); TreeNode *temp; if(anc->left->left){ //anc->right->dlen+=anc->left->dlen; temp=anc->left; temp->SubstituteNodeWithRespectToAnc(temp->left); anc->AddDes(temp->right); temp->attached=false; } else if(anc->right->left){ //anc->left->dlen+=anc->right->dlen; temp=anc->right; temp->SubstituteNodeWithRespectToAnc(temp->left); anc->AddDes(temp->right); temp->attached=false; } } } } //MTH void TreeNode::SubstituteNodeWithRespectToAnc(TreeNode *subs)//note THIS DOESN't do anything with numBranchesAdded or any other tree fields that describe the tree { //this function moves subs into the place in the tree that had been occupied by this // it is called in swapping and addRandomNode and can't be called with the root as this // Nothing is done with branch lengths OR LEFT OR RIGHT (this and subs still keep their descendants) subs->anc=anc; subs->prev=prev; subs->next=next; assert(anc); if(anc->left==this) anc->left=subs; if(anc->right==this) anc->right=subs; if(next) next->prev=subs; if(prev) prev->next=subs; subs->attached=true; attached=false; next=prev=anc=NULL; } int TreeNode::CountBranches(int s){ if(left) left->CountBranches(++s); if(nodeNum==0) left->next->CountBranches(++s); if(right) right->CountBranches(++s); return s; } int TreeNode::CountTerminals(int s){ if(left) s=left->CountTerminals(s); else s++; if(nodeNum==0) s=left->next->CountTerminals(s); if(right) s=right->CountTerminals(s); return s; } int TreeNode::CountTerminalsDown(int s, TreeNode *calledFrom){ TreeNode *sib; if(nodeNum!=0){ if(left==calledFrom) sib=right; else sib=left; if(sib) s=sib->CountTerminals(s); else s++; s=anc->CountTerminalsDown(s, this); } else { if(left!=calledFrom) s=left->CountTerminals(s); if(left->next!=calledFrom) s=left->next->CountTerminals(s); if(right!=calledFrom) s=right->CountTerminals(s); } return s; } void TreeNode::CountSubtreeBranchesAndDepth(int &branches, int &sum, int depth, bool first) const{ //this is the version to use if you want to be //sure not to jump to another subtree (ie, don't //go ->next from the calling node) if(left){ sum+=depth; left->CountSubtreeBranchesAndDepth(++branches, sum, depth+1, false); } if(next&&!first){ sum+=depth-1; next->CountSubtreeBranchesAndDepth(++branches, sum, depth, false); } } void TreeNode::CalcDepth(int &dep){ dep++; int l=0, r=0; if(left){ left->CalcDepth(l); } if(right){ right->CalcDepth(r); } dep += (r > l ? r : l); } void TreeNode::MarkTerminals(int *taxtags){ if(left) left->MarkTerminals(taxtags); else taxtags[nodeNum]=1; if(next) next->MarkTerminals(taxtags); } void TreeNode::MarkUnattached(bool includenode){ attached=false; if(left) left->MarkUnattached(false); if(next&&includenode==false) next->MarkUnattached(false); } TreeNode* TreeNode::FindNode( int &n, TreeNode *tempno){ //my version DZ. It returns nodeNum n if(left&&tempno!=NULL){ tempno=left->FindNode(n, tempno); } if(next&&tempno!=NULL){ tempno=left->FindNode(n, tempno); } if(nodeNum==n){ tempno=this; } return tempno; } //MTH TreeNode* TreeNode::FindNode( int &n){ //note that this function does NOT look for the node with nodeNum n, but rather //counts nodes and returns the nth one that it finds n--; if(n<0) return this; if(left) {TreeNode* tempno; tempno=left->FindNode(n); if(tempno) return tempno; } if(next) return next->FindNode(n); return NULL; } bool TreeNode::IsGood() { if(attached || !anc) {if(!left && right) return false; if(!anc) {if(nodeNum!=0 || next || prev) return false; } else {TreeNode *tempno; tempno=anc->left; bool found=false; int nsibs=0; while(tempno) {if(tempno->anc!=anc) return false; if(tempno==this) found=true; tempno=tempno->next; nsibs++; if(nsibs>3) return false; } if(!found) return false; } if(left){ if(!left->IsGood()) return false; } if(next) return next->IsGood(); return true; } else return false; } void TreeNode::CountNumberofNodes(int &nnodes){ if(left!=NULL){ left->CountNumberofNodes(nnodes); } if(next!=NULL){ next->CountNumberofNodes(nnodes); } nnodes++; } void TreeNode::CheckforLeftandRight(){ if(left!=NULL){ left->CheckforLeftandRight(); } if(next!=NULL){ next->CheckforLeftandRight(); } if((left&&!right)||(right&&!left)){ throw ErrorException("There appears to be a problem with a tree specification string.\n\tCheck for extra parentheses."); } } void TreeNode::FindCrazyLongBranches(){ if(left!=NULL){ left->FindCrazyLongBranches(); } if(next!=NULL){ next->FindCrazyLongBranches(); } if(dlen>1.0){ outman.UserMessage("WTF?"); } } void TreeNode::FindCrazyShortBranches(){ if(left!=NULL){ left->FindCrazyShortBranches(); } if(next!=NULL){ next->FindCrazyShortBranches(); } if(anc&&dlen<.0001){ outman.UserMessage("WTF?"); } } void TreeNode::CheckTreeFormation() { #ifndef NDEBUG //make sure that nodes that this node points to also point back (ie this->ldes->anc=this) if(left){ assert(left->anc==this); left->CheckTreeFormation(); } if(right){ assert(right->anc==this); } if(next){ assert(next->prev==this); next->CheckTreeFormation(); } if(prev){ assert(prev->next==this); } assert(!anc||dlen>0.0); #endif } void TreeNode::CheckforPolytomies(){ if(IsInternal()){ left->CheckforPolytomies(); } if(next!=NULL){ next->CheckforPolytomies(); } if(anc!=NULL){ if(left!=NULL){ if(left->next!=right){ //polytomous tree should have been dealt with earlier. throw ErrorException("Input tree has polytomies!!"); } } } } void TreeNode::OutputNodeConnections(){ TreeNode *nd; if(IsInternal()){ cout << nodeNum << "\t"; nd = left; while(nd){ cout << nd->nodeNum << "\t"; nd = nd->next; } cout << dlen << "\t" << endl; nd = left; while(nd){ nd->OutputNodeConnections(); nd = nd->next; } } else{ cout << nodeNum << "\t" << dlen << endl; } } Bipartition* TreeNode::VerifyBipartition(bool standardize){ Bipartition before = *bipart; if(IsInternal()){//not terminal TreeNode *nd=left; *bipart = nd->CalcBipartition(standardize); //the standardization needs to happen AFTER the child unstandardized bipart is used here if(standardize) nd->bipart->Standardize(); nd=nd->next; do{ *bipart += nd->CalcBipartition(standardize); //the standardization needs to happen AFTER the child unstandardized bipart is used here if(standardize) nd->bipart->Standardize(); nd=nd->next; }while(nd != NULL); assert(bipart->EqualsEquals(before)); return bipart; } else if(IsNotRoot()){//terminal bipart=bipart->TerminalBipart(nodeNum); return bipart; } return NULL; } Bipartition* TreeNode::CalcBipartition(bool standardize){ if(IsInternal()){//not terminal TreeNode *nd=left; *bipart = nd->CalcBipartition(standardize); //the standardization needs to happen AFTER the child unstandardized bipart is used here if(standardize) nd->bipart->Standardize(); nd=nd->next; do{ *bipart += nd->CalcBipartition(standardize); //the standardization needs to happen AFTER the child unstandardized bipart is used here if(standardize) nd->bipart->Standardize(); nd=nd->next; }while(nd != NULL); return bipart; } else if(IsNotRoot()){//terminal bipart=bipart->TerminalBipart(nodeNum); return bipart; } return NULL; } void TreeNode::StandardizeBipartition(){ if(IsInternal()){//not terminal TreeNode *nd=left; do{ nd->StandardizeBipartition(); nd=nd->next; }while(nd != NULL); } bipart->Standardize(); } void TreeNode::GatherConstrainedBiparitions(vector &biparts) { if(IsInternal()){ TreeNode *nd=left; do{ nd->GatherConstrainedBiparitions(biparts); nd=nd->next; }while(nd != NULL); if(IsNotRoot()){ Bipartition b(*bipart); biparts.push_back(b); } } } void TreeNode::OutputBipartition(ostream &out){ if(left&&anc){ left->OutputBipartition(out); left->next->OutputBipartition(out); out << bipart->Output() << endl; } else if(!anc){ left->OutputBipartition(out); left->next->OutputBipartition(out); left->next->next->OutputBipartition(out); } } void TreeNode::RotateDescendents(){ //don't call this with the root! assert(anc); TreeNode* tmp=right; right=left; left=tmp; left->prev=NULL; left->next=right; right->next=NULL; } void TreeNode::AddNodesToList(vector &list){ list.push_back(nodeNum); if(IsInternal()) left->AddNodesToList(list); if(next!=NULL) next->AddNodesToList(list); } void TreeNode::FlipBlensToRoot(TreeNode *from){ if(anc!=NULL) anc->FlipBlensToRoot(this); if(from==NULL) dlen=-1; else dlen=from->dlen; } void TreeNode::FlipBlensToNode(TreeNode *from, TreeNode *stopNode){ //for rerooting a subtree //each node gets the get blen of the previous node (one of //its des) assert(IsNotRoot()); assert(from != NULL); assert(stopNode != NULL); if(anc != stopNode) anc->FlipBlensToNode(this, stopNode); else dlen=from->dlen; } void TreeNode::PrintSubtreeMembers(ofstream &out){ if(IsTerminal()) out << nodeNum << "\t"; else left->PrintSubtreeMembers(out); if(next!=NULL) next->PrintSubtreeMembers(out); } void TreeNode::AdjustClasForReroot(int dir){ //11/19/07 this was really, really dumb! CLA's were //being reoriented even when more than one tree //was pointing to them. There isn't an easy way //of checking the number of users of a cla from here //so just deprecating this function for now. assert(0); int tmp=claIndexDown; if(dir==2){//the ancestor and left des have been swapped claIndexDown=claIndexUL; claIndexUL=tmp; } else if(dir==3){//the ancestor and right des have been swapped claIndexDown=claIndexUR; claIndexUR=tmp; } else assert(0); } void TreeNode::RecursivelyAddOrRemoveSubtreeFromBipartitions(const Bipartition &subtree){ //this function just tricks nodes down to the root into thinking //that a taxon is in their subtree by flipping its bit in the bipartition //this obviously needs to be undone by calcing the biparts if the true //tree bipartitions are needed bipart->FlipBits(subtree); if(anc->IsNotRoot()) anc->RecursivelyAddOrRemoveSubtreeFromBipartitions(subtree); } //unsigned MATCH_II=0, MATCH_TT=0, MATCH_IT=0, TOT_II=0, TOT_IT=0, TOT_TT=0; /* void TreeNode::SetEquivalentConditionalVectors(const SequenceData *data){ if(nodeNum == 0){ if(left->IsInternal()) left->SetEquivalentConditionalVectors(data); if(left->next->IsInternal()) left->next->SetEquivalentConditionalVectors(data); if(right->IsInternal()) right->SetEquivalentConditionalVectors(data); return; } if(left->IsTerminal() && right->IsTerminal()){ unsigned char *leftSeq = data->GetRow(left->nodeNum-1); unsigned char *rightSeq = data->GetRow(right->nodeNum-1); char lastLeft, lastRight; lastLeft = leftSeq[0]; lastRight = rightSeq[0]; tipData[0] = 0; for(int i=1;iNChar();i++){ bool match=true; if(leftSeq[i] != lastLeft){ lastLeft = leftSeq[i]; match=false; } if(rightSeq[i] != lastRight){ lastRight = rightSeq[i]; match=false; } // MATCH_TT += match; // TOT_TT++; tipData[i] = match; } } else if(left->IsInternal() && right->IsInternal()){ left->SetEquivalentConditionalVectors(data); right->SetEquivalentConditionalVectors(data); for(int i=0;iNChar();i++){ tipData[i] = left->tipData[i] && right->tipData[i]; // MATCH_II += tipData[i]; // TOT_II++; } } else if(left->IsTerminal()){ right->SetEquivalentConditionalVectors(data); unsigned char *leftSeq = data->GetRow(left->nodeNum-1); char lastLeft; lastLeft = leftSeq[0]; tipData[0] = 0; for(int i=1;iNChar();i++){ bool match=true; if(leftSeq[i] != lastLeft){ lastLeft = leftSeq[i]; match=false; } // MATCH_IT += right->tipData[i] && match; // TOT_IT++; tipData[i] = right->tipData[i] && match; } } else { left->SetEquivalentConditionalVectors(data); unsigned char *rightSeq = data->GetRow(right->nodeNum-1); char lastRight; lastRight = rightSeq[0]; tipData[0] = 0; for(int i=1;iNChar();i++){ bool match=true; if(rightSeq[i] != lastRight){ lastRight = rightSeq[i]; match=false; } // MATCH_IT += right->tipData[i] && match; // TOT_IT++; tipData[i] = left->tipData[i] && match; } } } */ /* void TreeNode::OutputBinaryNodeInfo(ofstream &out) const{ int zero = 0; if(this->IsInternal()){ out.write((char*) &(left->nodeNum), sizeof(int)); out.write((char*) &(right->nodeNum), sizeof(int)); } if(prev == NULL) out.write((char*) &zero, sizeof(int)); else out.write((char*) &(prev->nodeNum), sizeof(int)); if(next == NULL) out.write((char*) &zero, sizeof(int)); else out.write((char*) &(next->nodeNum), sizeof(int)); if(anc == NULL) out.write((char*) &zero, sizeof(int)); else out.write((char*) &(anc->nodeNum), sizeof(int)); out.write((char*) &dlen, sizeof(FLOAT_TYPE)); } */ void TreeNode::OutputBinaryNodeInfo(OUTPUT_CLASS &out) const{ int zero = 0; if(this->IsInternal()){ out.WRITE_TO_FILE(&(left->nodeNum), sizeof(int), 1); out.WRITE_TO_FILE(&(right->nodeNum), sizeof(int), 1); } if(prev == NULL) out.WRITE_TO_FILE(&zero, sizeof(int), 1); else out.WRITE_TO_FILE(&(prev->nodeNum), sizeof(int), 1); if(next == NULL) out.WRITE_TO_FILE(&zero, sizeof(int), 1); else out.WRITE_TO_FILE(&(next->nodeNum), sizeof(int), 1); if(anc == NULL) out.WRITE_TO_FILE(&zero, sizeof(int), 1); else out.WRITE_TO_FILE(&(anc->nodeNum), sizeof(int), 1); out.WRITE_TO_FILE(&dlen, sizeof(FLOAT_TYPE), 1); } void TreeNode::CollapseMinLengthBranches(int &num){ if(this->IsInternal()){ TreeNode *nd = left; do{ if(FloatingPointEquals(nd->dlen, DEF_MIN_BRLEN, 2e-8) && nd->IsInternal()){ TreeNode *childNode = nd->left; //Note that Prune requires that the subtree pruned is not part of a polytomy //this means that the collapsing must start at the root and work upward. //Also note that Prune automatically detatches the anc of the pruned subtree //since it then only has one child. childNode->Prune(); AddDes(childNode); //this resets the node checking to the left des of the current node //when a branch is removed. This duplicates some effort but is safe. nd = left; num++; } else nd = nd->next; }while(nd); left->CollapseMinLengthBranches(num); } if(next) next->CollapseMinLengthBranches(num); } garli-2.1-release/src/treenode.h000066400000000000000000000106341241236125200166370ustar00rootroot00000000000000// GARLI version 2.1 source code // Copyright 2005-2014 Derrick J. Zwickl // email: garli.support@gmail.com // // This program is free software: you can redistribute it and/or modify // it under the terms of the GNU General Public License as published by // the Free Software Foundation, either version 3 of the License, or // (at your option) any later version. // // This program is distributed in the hope that it will be useful, // but WITHOUT ANY WARRANTY; without even the implied warranty of // MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the // GNU General Public License for more details. // // You should have received a copy of the GNU General Public License // along with this program. If not, see . // // NOTE: Portions of this source adapted from GAML source, written by Paul O. Lewis #ifndef __TREE_NODE_H #define __TREE_NODE_H #include #include #include using namespace std; #include "condlike.h" #include "clamanager.h" #include "bipartition.h" class NucleotideData; class MFILE; class TreeNode{ public: TreeNode* left,* right,* next,* prev,* anc; int nodeNum; int claIndexDown; int claIndexUL; int claIndexUR; FLOAT_TYPE dlen; bool attached; bool alreadyOptimized; Bipartition *bipart; vector tipData; #ifdef OPEN_MP //unsigned *ambigMap; vector ambigMap; #endif TreeNode( const int i = -1 ); ~TreeNode(); //functions for manipulating nodes within a tree TreeNode * AddDes(TreeNode *);//returns argument void RemoveDes(TreeNode *d); void MoveDesToAnc(TreeNode *d); void SubstituteNodeWithRespectToAnc(TreeNode *subs); int CountBranches(int s); int CountTerminals(int s); int CountTerminalsDown(int s, TreeNode *calledFrom); void CountSubtreeBranchesAndDepth(int &branches, int &sum, int depth, bool first) const; void MarkTerminals(int *taxtags); void Prune(); TreeNode* FindNode( int &n); TreeNode* FindNode( int &n,TreeNode *tempno); void CountNumberofNodes(int &nnodes); void MarkUnattached(bool includenode); void RotateDescendents(); void AdjustClasForReroot(int dir); void AddNodesToList(vector &list); void FlipBlensToRoot(TreeNode *from); void FlipBlensToNode(TreeNode *from, TreeNode *stopNode); void RecursivelyAddOrRemoveSubtreeFromBipartitions(const Bipartition &subtree); void CollapseMinLengthBranches(int &); //misc functions char *MakeNewick(char *s, bool internalNodes, bool branchLengths, bool highPrec=false) const; void MakeNewick(string &outStr, const DataPartition *data, bool internalNodes, bool branchLengths, bool taxonNames = false, bool highPrec = false) const; void MakeNewickForSubtree(char *s) const; void MakeNewickForSubtree(string &s, const DataPartition *data, bool internalNodes, bool branchLengths, bool taxonNames = false, bool highPrec = false) const; bool IsGood(); bool IsTerminal() const{ return left == NULL; } bool IsInternal() const{ return left != NULL; } bool IsRoot() const{ return anc==NULL; } bool IsNotRoot() const{ return anc!=NULL; } void CalcDepth(int &dep); void CopyOneClaIndex(const TreeNode *from, ClaManager *claMan, int dir); Bipartition* CalcBipartition(bool standardize); Bipartition* VerifyBipartition(bool standardize); void StandardizeBipartition(); void GatherConstrainedBiparitions(vector &biparts); void OutputBipartition(ostream &out); void PrintSubtreeMembers(ofstream &out); void SetUnoptimized(){ alreadyOptimized=false; if(left) left->SetUnoptimized(); if(right) right->SetUnoptimized(); } void SetEquivalentConditionalVectors(const SequenceData *data); void OutputBinaryNodeInfo(OUTPUT_CLASS &out) const; //debugging functions for checking tree formation void CheckforPolytomies(); void CheckforLeftandRight(); void FindCrazyLongBranches(); void FindCrazyShortBranches(); void CheckTreeFormation(); void OutputNodeConnections(); }; inline void TreeNode::CopyOneClaIndex(const TreeNode *from, ClaManager *claMan, int dir){ const int *indexF; int *indexT; if(dir==1){ indexF=&from->claIndexDown; indexT=&claIndexDown; } else if(dir==2){ indexF=&from->claIndexUL; indexT=&claIndexUL; } else if(dir==3){ indexF=&from->claIndexUR; indexT=&claIndexUR; } else assert(0); claMan->DecrementCla(*indexT); claMan->IncrementCla(*indexF); *indexT=*indexF; } #endif garli-2.1-release/src/utility.h000066400000000000000000000211211241236125200165260ustar00rootroot00000000000000// GARLI version 2.0 source code // Copyright 2005-2011 Derrick J. Zwickl // email: garli.support@gmail.com // // This program is free software: you can redistribute it and/or modify // it under the terms of the GNU General Public License as published by // the Free Software Foundation, either version 3 of the License, or // (at your option) any later version. // // This program is distributed in the hope that it will be useful, // but WITHOUT ANY WARRANTY; without even the implied warranty of // MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the // GNU General Public License for more details. // // You should have received a copy of the GNU General Public License // along with this program. If not, see . #ifndef GAML_UTIL_HPP #define GAML_UTIL_HPP //some code from Mark Holder for allocating flattened matrices, and other misc stuff #include #include #include #include #include #ifdef _MSC_VER #include #else #include #endif using namespace std; #include "errorexception.h" #define DBL_ALIGN 32 template T ***New3DArray(unsigned f , unsigned s , unsigned t); template T **New2DArray(unsigned f , unsigned s); template void Delete3DArray (T ***temp); template void Delete2DArray (T **temp); //aligned versions template T ***New3DAlignedArray(unsigned f , unsigned s , unsigned t, unsigned a); template T **New2DAlignedArray(unsigned f , unsigned s, unsigned a); template void Delete3DAlignedArray (T ***temp); template void Delete2DAlignedArray (T **temp); template T *NewAlignedArray(unsigned len, unsigned align ){ #ifdef _MSC_VER return (T*) _aligned_malloc(sizeof(T)*len, align); #endif } template void DeleteAlignedArray(T *a){ #ifdef _MSC_VER _aligned_free(a); #endif } /*-------------------------------------------------------------------------------------------------------------------------- | Allocates a three dimensional array of FLOAT_TYPEs as one contiguous block of memory | the dimensions are f two dimensional arrays that are s by t. | the array is set up so that | for(i = 0 ; i < f ; i++) | for (j = 0 ; j < s ; j++) | for (k = 0 ; k < t; k++) | array[i][j][k]; | would be the same order of access as: | | T *temp = **array; | for (i = 0 ; i < f*s*t ; i++) | { | *temp++; | } */ template T ***New3DArray(unsigned f , unsigned s , unsigned t) { assert(f > 0 && s > 0 && t> 0); T ***temp; try{ temp = new T **[f]; *temp = new T *[f * s]; **temp = new T[f * s * t]; for (unsigned sIt = 1 ; sIt < s ; sIt++) temp[0][sIt] = temp[0][sIt-1] + t ; for (unsigned fIt = 1 ; fIt < f ; fIt ++) { temp[fIt] = temp[fIt -1] + s ; temp[fIt][0] = temp[fIt -1][0] + (s*t); for (unsigned sIt = 1 ; sIt < s ; sIt++) temp[fIt][sIt] = temp[fIt][sIt-1] + t ; } } catch(std::bad_alloc){ throw ErrorException("Problem allocating 3D array (%d X %d X %d = %.2f MB). Out of mem?", f, s, t, (f * s * t * sizeof(T)) / (1024.0 * 1024.0)); } return temp; } /*-------------------------------------------------------------------------------------------------------------------------- | Delete a Three Dimensional Array that has been allocated using New3DArray */ template void Delete3DArray (T ***temp) { assert(temp); //these asserts aren't necessary, but right now I can't think of a case in which they'd fail other than following an allocation error assert(*temp); assert(**temp); if (temp) { if (*temp) { if (**temp) delete [] **temp; delete [] * temp; } delete [] temp; } } /*-------------------------------------------------------------------------------------------------------------------------- | Allocates a two dimensional array of FLOAT_TYPEs as one contiguous block of memory | the dimensions are f by s. | the array is set up so that | | for(i = 0 ; i < f ; i++) | for (j = 0 ; j < s ; j++) | array[i][j]; | | would be the same order of access as: | | T *temp = **array; | for (i = 0 ; i < f*s*t ; i++) | *temp++; */ template T **New2DArray(unsigned f , unsigned s) { assert(f > 0 && s > 0); T **temp; try{ temp = new T *[f]; *temp = new T [f * s]; for (unsigned fIt = 1 ; fIt < f ; fIt ++) temp[fIt] = temp[fIt -1] + s ; } catch(std::bad_alloc){ throw ErrorException("Problem allocating 2D array (%d X %d = %.2f MB). Out of mem?", f, s, (f * s * sizeof(T)) / (1024.0 * 1024.0)); } return temp; } /*-------------------------------------------------------------------------------------------------------------------------- | Delete a 2 Dimensional Array New2DArray */ template inline void Delete2DArray (T **temp) { assert(temp); //these asserts aren't necessary, but right now I can't think of a case in which they'd fail other than following an allocation error assert(*temp); if (temp) { if (*temp) delete [] * temp; delete [] temp; } } //aligned version template T ***New3DAlignedArray(unsigned f , unsigned s , unsigned t) { assert(f > 0 && s > 0 && t> 0); T ***temp; temp = new T **[f]; *temp = new T *[f * s]; **temp = new T[f * s * t]; **temp = NewAlignedArray(f * s * t, DBL_ALIGN); for (unsigned sIt = 1 ; sIt < s ; sIt++) temp[0][sIt] = temp[0][sIt-1] + t ; for (unsigned fIt = 1 ; fIt < f ; fIt ++) { temp[fIt] = temp[fIt -1] + s ; temp[fIt][0] = temp[fIt -1][0] + (s*t); for (unsigned sIt = 1 ; sIt < s ; sIt++) temp[fIt][sIt] = temp[fIt][sIt-1] + t ; } return temp; } /*-------------------------------------------------------------------------------------------------------------------------- | Delete a Three Dimensional Array that has been allocated using New3DArray */ template void Delete3DAlignedArray (T ***temp) { assert(temp); //these asserts aren't necessary, but right now I can't think of a case in which they'd fail other than following an allocation error assert(*temp); assert(**temp); if (temp) { if (*temp) { if (**temp) DeleteAlignedArray(**temp); delete [] * temp; } delete [] temp; } } template T **New2DAlignedArray(unsigned f , unsigned s) { assert(f > 0 && s > 0); T **temp; temp = new T *[f]; *temp = NewAlignedArray(f * s, DBL_ALIGN); for (unsigned fIt = 1 ; fIt < f ; fIt ++) temp[fIt] = temp[fIt -1] + s ; return temp; } /*-------------------------------------------------------------------------------------------------------------------------- | Delete a 2 Dimensional Array New2DArray */ template inline void Delete2DAlignedArray (T **temp) { assert(temp); //these asserts aren't necessary, but right now I can't think of a case in which they'd fail other than following an allocation error assert(*temp); if (temp) { if (*temp) DeleteAlignedArray(*temp); delete [] temp; } } class Profiler{ #ifdef _MSC_VER LONGLONG totalTics; int numCalls; string name; bool inuse; LARGE_INTEGER start; LARGE_INTEGER end; LARGE_INTEGER ticsPerSec; #else unsigned totalTics; int numCalls; string name; bool inuse; timeval start; timeval end; struct timezone tz; int ticsPerSec; #endif public: Profiler(string n){ name = n; totalTics = 0; numCalls = 0; inuse=false; #ifdef _MSC_VER QueryPerformanceFrequency(&ticsPerSec); #else ticsPerSec = 1000000; #endif } void Start(){ #ifdef ENABLE_CUSTOM_PROFILER if(inuse){ cout << "Error! Don't use this on recursive functions!" << endl; exit(1); } inuse=true; numCalls++; #ifdef _MSC_VER QueryPerformanceCounter(&start); #else gettimeofday(&start, &tz); #endif #endif } void Stop(){ #ifdef ENABLE_CUSTOM_PROFILER if(!inuse){ cout << "Error! Profiler was not started!" << endl; exit(1); } #ifdef _MSC_VER QueryPerformanceCounter(&end); totalTics += end.QuadPart - start.QuadPart; #else gettimeofday(&end, &tz); totalTics += end.tv_usec - start.tv_usec + (end.tv_sec - start.tv_sec)*1000000; // cout << end.tv_usec - start.tv_usec + (end.tv_usec - start.tv_usec)*100000 << endl; #endif inuse=false; #endif } void Report(ostream &out, int progTime){ #ifdef ENABLE_CUSTOM_PROFILER #ifdef _MSC_VER FLOAT_TYPE seconds = totalTics/(FLOAT_TYPE)ticsPerSec.QuadPart; #else FLOAT_TYPE seconds = totalTics/(FLOAT_TYPE)ticsPerSec; #endif out << setw( 10 ) << name.c_str() << "\t" << setw( 10 )<< numCalls << "\t"; out.precision(4); out << setw( 10 ) << seconds << "\t" << setw( 10 ) << seconds/(FLOAT_TYPE)numCalls << "\t" << setw( 10 ) << seconds*100/(FLOAT_TYPE)progTime << endl; #endif } }; #endif // garli-2.1-release/tests/000077500000000000000000000000001241236125200152305ustar00rootroot00000000000000garli-2.1-release/tests/Makefile.am000066400000000000000000000001571241236125200172670ustar00rootroot00000000000000 check-local: $(srcdir)/runtests.sh $(srcdir) $(top_builddir)/src/Garli$(EXEEXT) @NCL_BIN_DIR@/NEXUSvalidator garli-2.1-release/tests/check/000077500000000000000000000000001241236125200163055ustar00rootroot00000000000000garli-2.1-release/tests/check/a.G3.conf000066400000000000000000000024151241236125200176460ustar00rootroot00000000000000[general] datafname = data/z.11x30.stop.nex constraintfile = none streefname = stepwise attachmentspertaxon = 50 ofprefix = ch.a.G3 randseed = -1 availablememory = 512 logevery = 10 saveevery = 100 refineend = 0 refinestart = 1 outputeachbettertopology = 0 outputcurrentbesttopology = 0 enforcetermconditions = 1 genthreshfortopoterm = 2000 scorethreshforterm = 0.05 significanttopochange = 0.01 outputphyliptree = 0 outputmostlyuselessfiles = 1 writecheckpoints = 1 restart = 0 outgroup = 1-2 outputsitelikelihoods = 0 collapsebranches = 1 usepatternmanager = 1 searchreps = 1 ignorestopcodons = 1 datatype = codon-aminoacid ratematrix = jones statefrequencies = empirical ratehetmodel = gamma numratecats = 3 invariantsites = estimate [master] nindivs = 4 holdover = 1 selectionintensity = 0.5 holdoverpenalty = 0 stopgen = 5000 stoptime = 5 startoptprec = 0.5 minoptprec = 0.01 numberofprecreductions = 1 treerejectionthreshold = 50.0 topoweight = 1.0 modweight = 0.05 brlenweight = 0.2 randnniweight = 0.1 randsprweight = 0.3 limsprweight = 0.6 intervallength = 100 intervalstostore = 5 uniqueswapbias = 0.1 distanceswapbias = 1.0 limsprrange = 6 meanbrlenmuts = 5 gammashapebrlen = 1000 gammashapemodel = 1000 bootstrapreps = 1 resampleproportion = 1.0 inferinternalstateprobs = 0 garli-2.1-release/tests/check/a.G4.conf000077500000000000000000000023561241236125200176560ustar00rootroot00000000000000[general] datafname = data/z.11x30.AA.fas constraintfile = none streefname = stepwise attachmentspertaxon = 50 ofprefix = ch.a.G4 randseed = -1 availablememory = 512 logevery = 10 saveevery = 100 refineend = 0 refinestart = 1 outputeachbettertopology = 0 outputcurrentbesttopology = 0 enforcetermconditions = 1 genthreshfortopoterm = 2000 scorethreshforterm = 0.05 significanttopochange = 0.01 outputphyliptree = 0 outputmostlyuselessfiles = 1 writecheckpoints = 1 restart = 0 outgroup = 2 outputsitelikelihoods = 1 collapsebranches = 1 usepatternmanager = 1 searchreps = 1 datatype = aminoacid ratematrix = jones statefrequencies = empirical ratehetmodel = gamma numratecats = 4 invariantsites = estimate [master] nindivs = 4 holdover = 1 selectionintensity = 0.5 holdoverpenalty = 0 stopgen = 5000 stoptime = 5 startoptprec = 0.5 minoptprec = 0.01 numberofprecreductions = 1 treerejectionthreshold = 50.0 topoweight = 1.0 modweight = 0.05 brlenweight = 0.2 randnniweight = 0.1 randsprweight = 0.3 limsprweight = 0.6 intervallength = 100 intervalstostore = 5 uniqueswapbias = 0.1 distanceswapbias = 1.0 limsprrange = 6 meanbrlenmuts = 5 gammashapebrlen = 1000 gammashapemodel = 1000 bootstrapreps = 0 resampleproportion = 1.0 inferinternalstateprobs = 1 garli-2.1-release/tests/check/a.conf000077500000000000000000000023231241236125200173770ustar00rootroot00000000000000[general] datafname = data/z.11x30.phy constraintfile = none streefname = stepwise attachmentspertaxon = 50 ofprefix = ch.a randseed = -1 availablememory = 512 logevery = 10 saveevery = 100 refineend = 0 refinestart = 1 outputeachbettertopology = 0 outputcurrentbesttopology = 0 enforcetermconditions = 1 genthreshfortopoterm = 2000 scorethreshforterm = 0.05 significanttopochange = 0.01 outputphyliptree = 0 outputmostlyuselessfiles = 1 writecheckpoints = 1 restart = 0 outgroup = 2 outputsitelikelihoods = 1 collapsebranches = 1 searchreps = 1 datatype = codon-aminoacid ratematrix = jones statefrequencies = empirical ratehetmodel = none numratecats = 1 invariantsites = none [master] nindivs = 4 holdover = 1 selectionintensity = 0.5 holdoverpenalty = 0 stopgen = 5000 stoptime = 5 startoptprec = 0.5 minoptprec = 0.01 numberofprecreductions = 1 treerejectionthreshold = 50.0 topoweight = 1.0 modweight = 0.05 brlenweight = 0.2 randnniweight = 0.1 randsprweight = 0.3 limsprweight = 0.6 intervallength = 100 intervalstostore = 5 uniqueswapbias = 0.1 distanceswapbias = 1.0 limsprrange = 6 meanbrlenmuts = 5 gammashapebrlen = 1000 gammashapemodel = 1000 bootstrapreps = 0 resampleproportion = 1.0 inferinternalstateprobs = 1 garli-2.1-release/tests/check/c.M3x2.conf000077500000000000000000000023511241236125200201320ustar00rootroot00000000000000[general] datafname = data/z.11x30.phy constraintfile = none streefname = stepwise attachmentspertaxon = 50 ofprefix = ch.c.M3x2 randseed = -1 availablememory = 512 logevery = 10 saveevery = 10 refineend = 0 refinestart = 1 outputeachbettertopology = 0 outputcurrentbesttopology = 0 enforcetermconditions = 1 genthreshfortopoterm = 2000 scorethreshforterm = 0.05 significanttopochange = 0.01 outputphyliptree = 0 outputmostlyuselessfiles = 1 writecheckpoints = 1 restart = 0 outgroup = 1 outputsitelikelihoods = 1 collapsebranches = 1 searchreps = 1 datatype = codon geneticcode = standard ratematrix = 6rate statefrequencies = f3x4 ratehetmodel = nonsynonymous numratecats = 2 invariantsites = none [master] nindivs = 4 holdover = 1 selectionintensity = 0.5 holdoverpenalty = 0 stopgen = 5000 stoptime = 10 startoptprec = 0.5 minoptprec = 0.01 numberofprecreductions = 1 treerejectionthreshold = 50.0 topoweight = 1.0 modweight = 0.05 brlenweight = 0.2 randnniweight = 0.1 randsprweight = 0.3 limsprweight = 0.6 intervallength = 100 intervalstostore = 5 uniqueswapbias = 0.1 distanceswapbias = 1.0 limsprrange = 6 meanbrlenmuts = 5 gammashapebrlen = 1000 gammashapemodel = 1000 bootstrapreps = 0 resampleproportion = 1.0 inferinternalstateprobs = 1 garli-2.1-release/tests/check/c.conf000066400000000000000000000023621241236125200174010ustar00rootroot00000000000000[general] datafname = data/z.11x30.phy constraintfile = none streefname = stepwise attachmentspertaxon = 50 ofprefix = ch.c randseed = -1 availablememory = 512 logevery = 10 saveevery = 10 refineend = 0 refinestart = 1 outputeachbettertopology = 0 outputcurrentbesttopology = 0 enforcetermconditions = 1 genthreshfortopoterm = 2000 scorethreshforterm = 0.05 significanttopochange = 0.01 outputphyliptree = 0 outputmostlyuselessfiles = 1 writecheckpoints = 1 restart = 0 outgroup = 1-2 outputsitelikelihoods = 0 collapsebranches = 1 usepatternmanager = 1 searchreps = 1 datatype = codon geneticcode = standard ratematrix = 6rate statefrequencies = f3x4 ratehetmodel = none numratecats = 1 invariantsites = none [master] nindivs = 4 holdover = 1 selectionintensity = 0.5 holdoverpenalty = 0 stopgen = 5000 stoptime = 5 startoptprec = 0.5 minoptprec = 0.01 numberofprecreductions = 1 treerejectionthreshold = 50.0 topoweight = 1.0 modweight = 0.05 brlenweight = 0.2 randnniweight = 0.1 randsprweight = 0.3 limsprweight = 0.6 intervallength = 100 intervalstostore = 5 uniqueswapbias = 0.1 distanceswapbias = 1.0 limsprrange = 6 meanbrlenmuts = 5 gammashapebrlen = 1000 gammashapemodel = 1000 bootstrapreps = 1 resampleproportion = 1.0 inferinternalstateprobs = 0 garli-2.1-release/tests/check/g.dnaBnoZ.conf000077500000000000000000000026441241236125200207450ustar00rootroot00000000000000[general] datafname = data/dnaGap.8x1K.nex constraintfile = none streefname = data/dnaGap.8x1K.nex attachmentspertaxon = 50 ofprefix = ch.g.dnaBnoZ randseed = -1 availablememory = 512 logevery = 10 saveevery = 100 refinestart = 1 outputeachbettertopology = 0 outputcurrentbesttopology = 0 enforcetermconditions = 1 genthreshfortopoterm = 10000 scorethreshforterm = 0.05 significanttopochange = 0.01 outputphyliptree = 0 outputmostlyuselessfiles = 0 writecheckpoints = 1 restart = 0 outgroup = 1 subsetspecificrates = 1 collapsebranches = 1 usepatternmanager = 1 searchreps = 2 outputsitelikelihoods = 0 [model1] datatype = nucleotide ratematrix = 6rate statefrequencies = estimate ratehetmodel = gamma numratecats = 4 invariantsites = estimate [model2] datatype = binaryNotAllZeros ratematrix = 1rate statefrequencies = equal ratehetmodel = none numratecats = 1 invariantsites = none [master] nindivs = 4 holdover = 1 selectionintensity = 0.5 holdoverpenalty = 0 stopgen = 5000000 stoptime = 5 startoptprec = 0.5 minoptprec = 0.01 numberofprecreductions = 2 treerejectionthreshold = 50.0 topoweight = 1.0 modweight = 0.05 brlenweight = 0.2 randnniweight = 0.1 randsprweight = 0.3 limsprweight = 0.6 intervallength = 100 intervalstostore = 5 limsprrange = 6 meanbrlenmuts = 5 gammashapebrlen = 1000 gammashapemodel = 1000 uniqueswapbias = 0.1 distanceswapbias = 1.0 bootstrapreps = 0 resampleproportion = 1.0 inferinternalstateprobs = 0 garli-2.1-release/tests/check/g.dnaMix.conf000077500000000000000000000026411241236125200206270ustar00rootroot00000000000000[general] datafname = data/dnaGap.8x1K.nex constraintfile = none streefname = data/dnaGap.8x1K.nex attachmentspertaxon = 50 ofprefix = ch.g.dnaMix randseed = -1 availablememory = 512 logevery = 10 saveevery = 100 refinestart = 1 outputeachbettertopology = 0 outputcurrentbesttopology = 0 enforcetermconditions = 1 genthreshfortopoterm = 10000 scorethreshforterm = 0.05 significanttopochange = 0.01 outputphyliptree = 0 outputmostlyuselessfiles = 0 writecheckpoints = 1 restart = 0 outgroup = 1 subsetspecificrates = 0 collapsebranches = 1 usepatternmanager = 1 searchreps = 2 outputsitelikelihoods = 0 [model1] datatype = nucleotide ratematrix = 6rate statefrequencies = estimate ratehetmodel = gamma numratecats = 4 invariantsites = estimate [model2] datatype = gapmixturemodel ratematrix = 1rate statefrequencies = equal ratehetmodel = none numratecats = 1 invariantsites = none [master] nindivs = 4 holdover = 1 selectionintensity = 0.5 holdoverpenalty = 0 stopgen = 5000000 stoptime = 5 startoptprec = 0.5 minoptprec = 0.01 numberofprecreductions = 2 treerejectionthreshold = 50.0 topoweight = 1.0 modweight = 0.05 brlenweight = 0.2 randnniweight = 0.1 randsprweight = 0.3 limsprweight = 0.6 intervallength = 100 intervalstostore = 5 limsprrange = 6 meanbrlenmuts = 5 gammashapebrlen = 1000 gammashapemodel = 1000 uniqueswapbias = 0.1 distanceswapbias = 1.0 bootstrapreps = 0 resampleproportion = 1.0 inferinternalstateprobs = 0 garli-2.1-release/tests/check/n.G3.conf000077500000000000000000000023541241236125200176700ustar00rootroot00000000000000[general] datafname = data/z.11x30.phy constraintfile = none streefname = stepwise attachmentspertaxon = 50 ofprefix = ch.n.G3 randseed = -1 availablememory = 512 logevery = 10 saveevery = 200 refineend = 0 refinestart = 1 outputeachbettertopology = 0 outputcurrentbesttopology = 0 enforcetermconditions = 1 genthreshfortopoterm = 10000 scorethreshforterm = 0.05 significanttopochange = 0.01 outputphyliptree = 0 outputmostlyuselessfiles = 1 writecheckpoints = 1 restart = 0 outgroup = 1 outputsitelikelihoods = 1 collapsebranches = 1 usepatternmanager = 1 searchreps = 1 datatype = nucleotide ratematrix = 6rate statefrequencies = estimate ratehetmodel = gamma numratecats = 3 invariantsites = estimate [master] nindivs = 4 holdover = 1 selectionintensity = 0.5 holdoverpenalty = 0 stopgen = 10000 stoptime = 5 startoptprec = 0.5 minoptprec = 0.01 numberofprecreductions = 1 treerejectionthreshold = 50.0 topoweight = 1.0 modweight = 0.05 brlenweight = 0.2 randnniweight = 0.1 randsprweight = 0.3 limsprweight = 0.6 intervallength = 100 intervalstostore = 5 limsprrange = 6 meanbrlenmuts = 5 gammashapebrlen = 1000 gammashapemodel = 1000 uniqueswapbias = 0.1 distanceswapbias = 1.0 bootstrapreps = 0 resampleproportion = 1.0 inferinternalstateprobs = 1 garli-2.1-release/tests/check/n.G4.conf000066400000000000000000000023541241236125200176660ustar00rootroot00000000000000[general] datafname = data/z.11x30.phy constraintfile = none streefname = stepwise attachmentspertaxon = 50 ofprefix = ch.n.G4 randseed = -1 availablememory = 512 logevery = 10 saveevery = 200 refineend = 0 refinestart = 1 outputeachbettertopology = 0 outputcurrentbesttopology = 0 enforcetermconditions = 1 genthreshfortopoterm = 10000 scorethreshforterm = 0.05 significanttopochange = 0.01 outputphyliptree = 0 outputmostlyuselessfiles = 1 writecheckpoints = 1 restart = 0 outgroup = 1 outputsitelikelihoods = 0 collapsebranches = 1 usepatternmanager = 1 searchreps = 1 datatype = nucleotide ratematrix = 6rate statefrequencies = estimate ratehetmodel = gamma numratecats = 4 invariantsites = estimate [master] nindivs = 4 holdover = 1 selectionintensity = 0.5 holdoverpenalty = 0 stopgen = 10000 stoptime = 5 startoptprec = 0.5 minoptprec = 0.01 numberofprecreductions = 1 treerejectionthreshold = 50.0 topoweight = 1.0 modweight = 0.05 brlenweight = 0.2 randnniweight = 0.1 randsprweight = 0.3 limsprweight = 0.6 intervallength = 100 intervalstostore = 5 limsprrange = 6 meanbrlenmuts = 5 gammashapebrlen = 1000 gammashapemodel = 1000 uniqueswapbias = 0.1 distanceswapbias = 1.0 bootstrapreps = 1 resampleproportion = 1.0 inferinternalstateprobs = 0 garli-2.1-release/tests/check/n.conf000077500000000000000000000023441241236125200174170ustar00rootroot00000000000000[general] datafname = data/z.11x30.phy constraintfile = none streefname = stepwise attachmentspertaxon = 50 ofprefix = ch.n randseed = -1 availablememory = 512 logevery = 10 saveevery = 200 refineend = 0 refinestart = 1 outputeachbettertopology = 0 outputcurrentbesttopology = 0 enforcetermconditions = 1 genthreshfortopoterm = 10000 scorethreshforterm = 0.05 significanttopochange = 0.01 outputphyliptree = 0 outputmostlyuselessfiles = 1 writecheckpoints = 1 restart = 0 outgroup = 1 outputsitelikelihoods = 1 collapsebranches = 1 usepatternmanager = 1 searchreps = 1 datatype = nucleotide ratematrix = 6rate statefrequencies = estimate ratehetmodel = none numratecats = 1 invariantsites = none [master] nindivs = 4 holdover = 1 selectionintensity = 0.5 holdoverpenalty = 0 stopgen = 10000 stoptime = 5 startoptprec = 0.5 minoptprec = 0.01 numberofprecreductions = 1 treerejectionthreshold = 50.0 topoweight = 1.0 modweight = 0.05 brlenweight = 0.2 randnniweight = 0.1 randsprweight = 0.3 limsprweight = 0.6 intervallength = 100 intervalstostore = 5 limsprrange = 6 meanbrlenmuts = 5 gammashapebrlen = 1000 gammashapemodel = 1000 uniqueswapbias = 0.1 distanceswapbias = 1.0 bootstrapreps = 0 resampleproportion = 1.0 inferinternalstateprobs = 1 garli-2.1-release/tests/check/n.const.conf000077500000000000000000000023771241236125200205520ustar00rootroot00000000000000[general] datafname = data/z.11x30.phy constraintfile = data/z.pos.const.tre streefname = stepwise attachmentspertaxon = 50 ofprefix = ch.n.const randseed = -1 availablememory = 512 logevery = 10 saveevery = 200 refineend = 0 refinestart = 1 outputeachbettertopology = 0 outputcurrentbesttopology = 0 enforcetermconditions = 1 genthreshfortopoterm = 10000 scorethreshforterm = 0.05 significanttopochange = 0.01 outputphyliptree = 0 outputmostlyuselessfiles = 1 writecheckpoints = 1 restart = 0 outgroup = 1 outputsitelikelihoods = 1 collapsebranches = 1 usepatternmanager = 1 searchreps = 1 datatype = nucleotide ratematrix = 6rate statefrequencies = estimate ratehetmodel = gamma numratecats = 4 invariantsites = estimate [master] nindivs = 4 holdover = 1 selectionintensity = 0.5 holdoverpenalty = 0 stopgen = 10000 stoptime = 5 startoptprec = 0.5 minoptprec = 0.01 numberofprecreductions = 1 treerejectionthreshold = 50.0 topoweight = 1.0 modweight = 0.05 brlenweight = 0.2 randnniweight = 0.1 randsprweight = 0.3 limsprweight = 0.6 intervallength = 100 intervalstostore = 5 limsprrange = 6 meanbrlenmuts = 5 gammashapebrlen = 1000 gammashapemodel = 1000 uniqueswapbias = 0.1 distanceswapbias = 1.0 bootstrapreps = 0 resampleproportion = 1.0 inferinternalstateprobs = 1 garli-2.1-release/tests/check/p.3diff.conf000066400000000000000000000030621241236125200204060ustar00rootroot00000000000000[general] datafname = data/z.byPos.11x2178.nex constraintfile = none streefname = data/p.3diff.start attachmentspertaxon = 50 ofprefix = ch.p.3diff randseed = -1 availablememory = 512 logevery = 10 saveevery = 100 refinestart = 1 outputeachbettertopology = 0 outputcurrentbesttopology = 0 enforcetermconditions = 1 genthreshfortopoterm = 5000 scorethreshforterm = 0.001 significanttopochange = 0.01 outputphyliptree = 0 outputmostlyuselessfiles = 0 writecheckpoints = 1 restart = 0 usepatternmanager = 1 searchreps = 1 collapsebranches = 1 linkmodels = 0 subsetspecificrates = 1 [model1] datatype = nucleotide ratematrix = ( 0 1 2 2 3 4 ) statefrequencies = estimate ratehetmodel = gamma numratecats = 4 invariantsites = none [model2] datatype = nucleotide ratematrix = ( 0 1 2 1 0 3 ) statefrequencies = estimate ratehetmodel = gamma numratecats = 4 invariantsites = none [model3] datatype = nucleotide ratematrix = ( 0 1 2 3 1 0 ) statefrequencies = estimate ratehetmodel = gamma numratecats = 4 invariantsites = estimate [master] nindivs = 4 holdover = 1 selectionintensity = 0.5 holdoverpenalty = 0 stopgen = 5000 stoptime = 5 startoptprec = 0.5 minoptprec = 0.01 numberofprecreductions = 5 treerejectionthreshold = 50.0 topoweight = 0.01 modweight = 0.002 brlenweight = 0.002 randnniweight = 0.1 randsprweight = 0.3 limsprweight = 0.6 intervallength = 100 intervalstostore = 5 limsprrange = 6 meanbrlenmuts = 5 gammashapebrlen = 1000 gammashapemodel = 1000 uniqueswapbias = 0.1 distanceswapbias = 1.0 bootstrapreps = 1 resampleproportion = 1.0 inferinternalstateprobs = 0 garli-2.1-release/tests/check/p.mk.ssr.conf000077500000000000000000000023631241236125200206360ustar00rootroot00000000000000[general] datafname = data/L2001.30x52.nex constraintfile = none streefname = data/L.start attachmentspertaxon = 100 ofprefix = ch.p.mk.ssr randseed = -1 availablememory = 512 logevery = 10 saveevery = 100 refinestart = 1 outputeachbettertopology = 0 outputcurrentbesttopology = 0 enforcetermconditions = 1 genthreshfortopoterm = 100 scorethreshforterm = 0.001 significanttopochange = 0.01 outputphyliptree = 0 outputmostlyuselessfiles = 0 writecheckpoints = 1 restart = 0 usepatternmanager = 1 searchreps = 5 collapsebranches = 1 linkmodels = 0 subsetspecificrates = 1 [model1] datatype = standard ratematrix = 1rate statefrequencies = equal ratehetmodel = none numratecats = 1 invariantsites = none [master] nindivs = 4 holdover = 1 selectionintensity = 0.5 holdoverpenalty = 0 stopgen = 5000000 stoptime = 5 startoptprec = 0.01 minoptprec = 0.01 numberofprecreductions = 10 treerejectionthreshold = 50.0 topoweight = 0.01 modweight = 0.002 brlenweight = 0.002 randnniweight = 0.1 randsprweight = 0.3 limsprweight = 0.6 intervallength = 100 intervalstostore = 5 limsprrange = 6 meanbrlenmuts = 5 gammashapebrlen = 1000 gammashapemodel = 1000 uniqueswapbias = 0.1 distanceswapbias = 1.0 bootstrapreps = 0 resampleproportion = 1.0 inferinternalstateprobs = 0 garli-2.1-release/tests/check/p.mkO.ssr.conf000077500000000000000000000023731241236125200207560ustar00rootroot00000000000000[general] datafname = data/L2001.30x52.nex constraintfile = none streefname = data/L.start attachmentspertaxon = 100 ofprefix = ch.p.mkO.ssr randseed = -1 availablememory = 512 logevery = 10 saveevery = 100 refinestart = 1 outputeachbettertopology = 0 outputcurrentbesttopology = 0 enforcetermconditions = 1 genthreshfortopoterm = 100 scorethreshforterm = 0.001 significanttopochange = 0.01 outputphyliptree = 0 outputmostlyuselessfiles = 0 writecheckpoints = 1 restart = 0 usepatternmanager = 1 searchreps = 5 collapsebranches = 1 linkmodels = 0 subsetspecificrates = 1 [model1] datatype = standardordered ratematrix = 1rate statefrequencies = equal ratehetmodel = none numratecats = 1 invariantsites = none [master] nindivs = 4 holdover = 1 selectionintensity = 0.5 holdoverpenalty = 0 stopgen = 5000000 stoptime = 5 startoptprec = 0.01 minoptprec = 0.01 numberofprecreductions = 10 treerejectionthreshold = 50.0 topoweight = 0.01 modweight = 0.002 brlenweight = 0.002 randnniweight = 0.1 randsprweight = 0.3 limsprweight = 0.6 intervallength = 100 intervalstostore = 5 limsprrange = 6 meanbrlenmuts = 5 gammashapebrlen = 1000 gammashapemodel = 1000 uniqueswapbias = 0.1 distanceswapbias = 1.0 bootstrapreps = 0 resampleproportion = 1.0 inferinternalstateprobs = 0 garli-2.1-release/tests/check/p.mkv.ssr.conf000077500000000000000000000023741241236125200210260ustar00rootroot00000000000000[general] datafname = data/L2001.30x52.nex constraintfile = none streefname = data/L.start attachmentspertaxon = 100 ofprefix = ch.p.mkv.ssr randseed = -1 availablememory = 512 logevery = 10 saveevery = 100 refinestart = 1 outputeachbettertopology = 0 outputcurrentbesttopology = 0 enforcetermconditions = 1 genthreshfortopoterm = 100 scorethreshforterm = 0.001 significanttopochange = 0.01 outputphyliptree = 0 outputmostlyuselessfiles = 0 writecheckpoints = 1 restart = 0 usepatternmanager = 1 searchreps = 5 collapsebranches = 1 linkmodels = 0 subsetspecificrates = 1 [model1] datatype = standardvariable ratematrix = 1rate statefrequencies = equal ratehetmodel = none numratecats = 1 invariantsites = none [master] nindivs = 4 holdover = 1 selectionintensity = 0.5 holdoverpenalty = 0 stopgen = 5000000 stoptime = 5 startoptprec = 0.01 minoptprec = 0.01 numberofprecreductions = 10 treerejectionthreshold = 50.0 topoweight = 0.01 modweight = 0.002 brlenweight = 0.002 randnniweight = 0.1 randsprweight = 0.3 limsprweight = 0.6 intervallength = 100 intervalstostore = 5 limsprrange = 6 meanbrlenmuts = 5 gammashapebrlen = 1000 gammashapemodel = 1000 uniqueswapbias = 0.1 distanceswapbias = 1.0 bootstrapreps = 1 resampleproportion = 1.0 inferinternalstateprobs = 0 garli-2.1-release/tests/check/p.mkvO.ssr.conf000077500000000000000000000024041241236125200211370ustar00rootroot00000000000000[general] datafname = data/L2001.30x52.nex constraintfile = none streefname = data/L.start attachmentspertaxon = 100 ofprefix = ch.p.mkvO.ssr randseed = -1 availablememory = 512 logevery = 10 saveevery = 100 refinestart = 1 outputeachbettertopology = 0 outputcurrentbesttopology = 0 enforcetermconditions = 1 genthreshfortopoterm = 100 scorethreshforterm = 0.001 significanttopochange = 0.01 outputphyliptree = 0 outputmostlyuselessfiles = 0 writecheckpoints = 1 restart = 0 usepatternmanager = 1 searchreps = 5 collapsebranches = 1 linkmodels = 0 subsetspecificrates = 1 [model1] datatype = standardorderedvariable ratematrix = 1rate statefrequencies = equal ratehetmodel = none numratecats = 1 invariantsites = none [master] nindivs = 4 holdover = 1 selectionintensity = 0.5 holdoverpenalty = 0 stopgen = 5000000 stoptime = 5 startoptprec = 0.01 minoptprec = 0.01 numberofprecreductions = 10 treerejectionthreshold = 50.0 topoweight = 0.01 modweight = 0.002 brlenweight = 0.002 randnniweight = 0.1 randsprweight = 0.3 limsprweight = 0.6 intervallength = 100 intervalstostore = 5 limsprrange = 6 meanbrlenmuts = 5 gammashapebrlen = 1000 gammashapemodel = 1000 uniqueswapbias = 0.1 distanceswapbias = 1.0 bootstrapreps = 0 resampleproportion = 1.0 inferinternalstateprobs = 0 garli-2.1-release/tests/const/000077500000000000000000000000001241236125200163565ustar00rootroot00000000000000garli-2.1-release/tests/const/n.neg.const.conf000066400000000000000000000023471241236125200213650ustar00rootroot00000000000000[general] datafname = data/z.11x30.phy constraintfile = data/z.neg.const.tre streefname = stepwise attachmentspertaxon = 50 ofprefix = con.n.neg.const randseed = -1 availablememory = 512 logevery = 10 saveevery = 500 refinestart = 1 outputeachbettertopology = 0 outputcurrentbesttopology = 0 enforcetermconditions = 1 genthreshfortopoterm = 2000 scorethreshforterm = 0.05 significanttopochange = 0.01 outputphyliptree = 0 outputmostlyuselessfiles = 0 writecheckpoints = 0 restart = 0 outgroup = 1 outputsitelikelihoods = 0 collapsebranches = 1 searchreps = 1 datatype = nucleotide ratematrix = 6rate statefrequencies = estimate ratehetmodel = gamma numratecats = 4 invariantsites = estimate [master] nindivs = 4 holdover = 1 selectionintensity = 0.5 holdoverpenalty = 0 stopgen = 5000000 stoptime = 5000000 startoptprec = 0.5 minoptprec = 0.01 numberofprecreductions = 1 treerejectionthreshold = 50.0 topoweight = 1.0 modweight = 0.05 brlenweight = 0.2 randnniweight = 0.1 randsprweight = 0.3 limsprweight = 0.6 intervallength = 100 intervalstostore = 5 limsprrange = 6 meanbrlenmuts = 5 gammashapebrlen = 1000 gammashapemodel = 1000 uniqueswapbias = 0.1 distanceswapbias = 1.0 bootstrapreps = 0 resampleproportion = 1.0 inferinternalstateprobs = 0 garli-2.1-release/tests/const/n.negBack.const.conf000066400000000000000000000023611241236125200221420ustar00rootroot00000000000000[general] datafname = data/z.11x30.phy constraintfile = data/z.negBack.const.tre streefname = stepwise attachmentspertaxon = 50 ofprefix = con.n.negBack.const randseed = -1 availablememory = 512 logevery = 10 saveevery = 500 refinestart = 1 outputeachbettertopology = 0 outputcurrentbesttopology = 0 enforcetermconditions = 1 genthreshfortopoterm = 2000 scorethreshforterm = 0.05 significanttopochange = 0.01 outputphyliptree = 0 outputmostlyuselessfiles = 0 writecheckpoints = 0 restart = 0 outgroup = 1-3 outputsitelikelihoods = 0 collapsebranches = 1 searchreps = 1 datatype = nucleotide ratematrix = 6rate statefrequencies = estimate ratehetmodel = gamma numratecats = 4 invariantsites = estimate [master] nindivs = 4 holdover = 1 selectionintensity = 0.5 holdoverpenalty = 0 stopgen = 5000000 stoptime = 5000000 startoptprec = 0.5 minoptprec = 0.01 numberofprecreductions = 1 treerejectionthreshold = 50.0 topoweight = 1.0 modweight = 0.05 brlenweight = 0.2 randnniweight = 0.1 randsprweight = 0.3 limsprweight = 0.6 intervallength = 100 intervalstostore = 5 limsprrange = 6 meanbrlenmuts = 5 gammashapebrlen = 1000 gammashapemodel = 1000 uniqueswapbias = 0.1 distanceswapbias = 1.0 bootstrapreps = 0 resampleproportion = 1.0 inferinternalstateprobs = 0 garli-2.1-release/tests/const/n.pos.const.conf000066400000000000000000000023771241236125200214200ustar00rootroot00000000000000[general] datafname = data/z.11x30.phy constraintfile = data/z.pos.const.tre streefname = stepwise attachmentspertaxon = 50 ofprefix = con.n.pos.const randseed = -1 availablememory = 512 logevery = 10 saveevery = 500 refinestart = 1 outputeachbettertopology = 0 outputcurrentbesttopology = 0 enforcetermconditions = 1 genthreshfortopoterm = 2000 scorethreshforterm = 0.05 significanttopochange = 0.01 outputphyliptree = 0 outputmostlyuselessfiles = 0 writecheckpoints = 0 restart = 0 outgroup = 1-5 outputsitelikelihoods = 0 collapsebranches = 1 usepatternmanager = 1 searchreps = 1 datatype = nucleotide ratematrix = 6rate statefrequencies = estimate ratehetmodel = gamma numratecats = 4 invariantsites = estimate [master] nindivs = 4 holdover = 1 selectionintensity = 0.5 holdoverpenalty = 0 stopgen = 5000000 stoptime = 5000000 startoptprec = 0.5 minoptprec = 0.01 numberofprecreductions = 1 treerejectionthreshold = 50.0 topoweight = 1.0 modweight = 0.05 brlenweight = 0.2 randnniweight = 0.1 randsprweight = 0.3 limsprweight = 0.6 intervallength = 100 intervalstostore = 5 limsprrange = 6 meanbrlenmuts = 5 gammashapebrlen = 1000 gammashapemodel = 1000 uniqueswapbias = 0.1 distanceswapbias = 1.0 bootstrapreps = 0 resampleproportion = 1.0 inferinternalstateprobs = 0 garli-2.1-release/tests/const/n.posBack.const.conf000066400000000000000000000024071241236125200221730ustar00rootroot00000000000000[general] datafname = data/z.11x30.phy constraintfile = data/z.posBack.const.tre streefname = stepwise attachmentspertaxon = 50 ofprefix = con.n.posBack.const randseed = -1 availablememory = 512 logevery = 10 saveevery = 500 refinestart = 1 outputeachbettertopology = 0 outputcurrentbesttopology = 0 enforcetermconditions = 1 genthreshfortopoterm = 2000 scorethreshforterm = 0.05 significanttopochange = 0.01 outputphyliptree = 0 outputmostlyuselessfiles = 0 writecheckpoints = 0 restart = 0 outgroup = 1 2 outputsitelikelihoods = 0 collapsebranches = 1 usepatternmanager = 1 searchreps = 1 datatype = nucleotide ratematrix = 6rate statefrequencies = estimate ratehetmodel = gamma numratecats = 4 invariantsites = estimate [master] nindivs = 4 holdover = 1 selectionintensity = 0.5 holdoverpenalty = 0 stopgen = 5000000 stoptime = 5000000 startoptprec = 0.5 minoptprec = 0.01 numberofprecreductions = 1 treerejectionthreshold = 50.0 topoweight = 1.0 modweight = 0.05 brlenweight = 0.2 randnniweight = 0.1 randsprweight = 0.3 limsprweight = 0.6 intervallength = 100 intervalstostore = 5 limsprrange = 6 meanbrlenmuts = 5 gammashapebrlen = 1000 gammashapemodel = 1000 uniqueswapbias = 0.1 distanceswapbias = 1.0 bootstrapreps = 0 resampleproportion = 1.0 inferinternalstateprobs = 0 garli-2.1-release/tests/data/000077500000000000000000000000001241236125200161415ustar00rootroot00000000000000garli-2.1-release/tests/data/L.start000066400000000000000000000023101241236125200174070ustar00rootroot00000000000000#NEXUS Begin trees; [Treefile saved Fri Aug 13 17:03:40 2010] [! >Data file = /home/zwickl/googleCodeRepo/branches/partitioning/tests/gettingScores/data/L2001.30x52.nex > >Processing TREES block from file "mkv.search.best.tre": > Keeping: trees from file (replacing trees in memory) > 1 tree read from file ] Translate 1 Aphidius_rhopalosiphi, 2 Aphidius_ervi, 3 Diaeretiella_rapae, 4 Lysiplebus_confusus, 5 Pauesia_unilachni, 6 Pauesia_juniperorum, 7 Binodoxys_acalephae, 8 Trioxys_pallidus, 9 Monoctonus_pseudoplatani, 10 Praon_volucre, 11 Praon_abjectum, 12 Pseudopraon_mindariphagum, 13 Dyscritulus_planiceps, 14 Ephedrus_plagiator, 15 Ephedrus_californicus, 16 Sathon_falcatus, 17 Chelonus_sp., 18 Cenocoelius_analis, 19 Eubazus_semirugosus, 20 Acampsis_alternipes, 21 Alysia_lucicola, 22 Aleiodes_coxalis, 23 Heterospilus_prosopidis, 24 Hecabolus_sp., 25 Bracon_sp., 26 Colastes_incertus, 27 Rhyssalus_sp., 28 Histeromerus_mystacinus, 29 Xorides_praecatorius, 30 Alomyia_debellator ; tree PAUP_1 = [&U] (1,(2,(3,(4,((5,((7,8),(9,(((((10,13),11),12),(((16,17),((18,19),20)),(21,((((22,25),((27,28),(29,30))),(23,24)),26)))),(14,15))))),6))))); End; garli-2.1-release/tests/data/L2001.30x52.nex000066400000000000000000000311671241236125200201430ustar00rootroot00000000000000 #NEXUS [ Title: INCONGRUENCE BETWEEN MORPHOLOGICAL DATA SETS: AN EXAMPLE FROM THE EVOLUTION OF ENDOPARASITISM AMONG PARASITIC WASPS (HYMENOPTERA: BRACONIDAE) Authors: DONALD L. J. QUICKE AND ROBERT BELSHAW Journal: SYSTEMATIC BIOLOGY 48(3): 436-454 Contents: THREE NEXUS FILES, IDENTICAL EXCEPT FOR DIFFERENT ALIGNMENTS OF ONE GENE This represents: File 1: data file with 28S D2 2:1 gap to substitution cost alignment except all of the sequence data has been removed ] BEGIN CHARACTERS; DIMENSIONS NEWTAXA NTAX=30 NCHAR=118; FORMAT labels MISSING=? SYMBOLS= "0 1 2 3 4 5"; OPTIONS MSTAXA=POLYMORPH [gap=newstate]; [DJZ - removing eliminate command, adding assumptions block eliminate 1-45 74-82 85 86 105 108-112 114 116-.; [leaves female+larval character set] ] CHARLABELS [1] antennsensilla [2] antBarlin [3] 'm/f_ant=' [4] Maxillary_palp [5] labial_palp [6] cyclostome [7] laciniaround [8] laciniashort [9] hypoclypset [10] occipital_carina [11] Prontal_dorsum [12] prepectal_carina [13] Notauli [14] scut_sulc [15] scutellum [16] propodeum [17] Central_areola [18] antenna_cleaner [19] tib [20] FWR [21] '2m-cu' [22] '1-SR+M' [23] R_to_margin [24] 'FW1-SR' [25] 'r-m' [26] 'FW2-SR' [27] MCU [28] 'FW_m-cu' [29] 'FW2-M' [30] FWa [31] 3CU1 [32] 'HW2-CU' [33] 'HW_cu-a' [34] HW_submarginal [35] 'HWm-cu' [36] secondary_hamuli [37] petiole [38] numspir [39] T2_spir [40] 'T2-T3articulation' [41] 'MT4-7_apodemes' [42] MT8sculpt [43] MS8 [44] rectal_pads [45] chromosomes [46] prongs [47] ovip_sheath [48] GAoverlap [49] ovip_dor_valve [50] ovip_shape [51] ovip_ridge [52] Ovip [53] sperone [54] DVsculp [55] rachies [56] egg_canal [57] ctenidia [58] ctenidia [59] valvillus [60] valvillus [61] valpos [62] lower_valve [63] lv_seal [64] no_serrations [65] spermatheca [66] venom_app [67] res_sculp [68] VGA [69] Res_Div [70] VGins [71] ven_gland [72] no_ins [73] ovarioles [74] cercifused [75] cerci_setae [76] articulated_cusp [77] basal_ring [78] aedeagus [79] vas_def [80] Testes [81] sperm [82] sperm_morphol [83] egg [84] synovigeny [85] yolky [86] teratocytes [87] teratocyte_origin [88] lar_processes [89] L1_mandib_shape [90] L1_A_spines [91] L1_T3_spines.2 [92] l1spinsfused [93] l1spinesgroups [94] L1_sensilla [95] L1_labral_sensilla [96] lar_antenna [97] Larval_mandible [98] larmandib [99] Larv_spir [100] 'l-spirac' [101] hypostspur [102] stipital [103] larEpist [104] ringsclerite [105] anal_vesicle [106] postvencomm [107] instars [108] Ovip_into_gang [109] idiobiont [110] ectopar [111] final_external_feeding [112] mummif [113] mum [114] pupate [115] emergence_hole [116] paralysis [117] host_feeding [118] hostaphid ; STATELABELS 1 scattered one_rank 2_ranks, 2 small_hole medium_hole entire, 3 unequal equal, 4 six five four three, 5 four three two one, 6 no yes, 7 one three, 8 one three, 9 no yes, 10 complete abs_dors completely_abs, 11 simple_or_two_lateral_pits dorsope_and_2_lt_pits, 12 present absent, 13 present absent, 14 crenulate_or_with_1_carina smooth, 15 with_posterior_cren_groove without_posterior_crenulated_gr, 16 areola_complete areola_incomplete, 17 large small, 19 pegs_absent present, 20 contig_with_parastigma lost, 21 pres absent, 22 present_complete present_but_incomplete totally_absent, 23 yes no, 24 present lost, 25 present absent, 26 present absent, 27 fully_sclerotized largely_absent, 28 present absent, 29 part_present absent, 30 present absent, 31 present absent, 32 present absent, 33 present absent, 34 present absent, 35 present absent, 36 on_spur_beyond_r on_spur_overlapping on_C+SC+R, 37 unfused fused, 38 seven six five, 39 in_notum in_laterotergite, 40 fixed flexible, 41 _ _ _ _ _ _ small 'long,_thin_processes', 42 smooth microsculpture, 43 pointed_anteriorly square_anteriorly, 44 six four two, 45 four five six seven eight more_than_8, 46 absent present, 47 pointed truncate, 48 not trans, 49 lumen_divided lumen_entire, 50 straightish strongly_curved, 51 dorsal_ridge_absent present, 52 simple nodus notch double_nodus, 53 absent present, 54 present_ctenid pres_setae abs, 55 not to_end, 56 closed_by_LVs closed_by_DV, 57 otherwise 'with_scale-like_combsctenidiact', 58 without_sock_seta with_sock_seta, 59 many two one none, 60 no_fringe fringe, 61 apical medial basal, 62 flaps_absent flaps_normal flaps_large, 63 fades_out scaly_and_detached, 64 <3 '=3' >3, 65 white black, 66 muscular not_so, 67 spiral not_spiral, 68 otherwise with_long_prim_duct_and_anterio, 69 undivided divided, 70 in_spiral_part not_in_spiral_part, 71 anterior medial posterior, 72 one two many, 73 one two 'three-seven' eight_or_more, 74 cerci_separate cerci_fused_fo_TIX, 75 _many five four three, 76 present fused, 77 wide_laterally unifomly_narrow produced_medially, 78 normal reduced, 79 posterior anterior, 80 fused_above_gut separate_or_fised_below, 81 long medium short, 82 normal abnormal_morphology, 83 ovoid 'lemon-shaped' with_long_process, 84 synovigenic proovigenic, 85 anhydropic hydropic, 87 from_polar_bodies from_delamination_of_serosa, 88 absent pair_below_tail, 89 sickle_and_narrow broad_base_with_hook_blade, 90 absent single_row multiple_rows, 91 present absent, 92 not_fused fused_and_branching, 93 not_grouped grouped, 94 without_group_of_3 with_group_of_3, 95 absent present, 96 papilliform disc absent, 97 toothed smooth, 98 cross_or_meet separate, 99 prothorax mesothorax, 100 divided undivided, 101 present absent, 102 simple paddle baloon, 103 present absent_or_v_reduced, 104 absent present, 105 absent present, 106 present absent, 107 five four three, 108 not yes, 109 idiobiont koinobiont, 110 ecto endo, 111 present absent, 112 no_mummy mummy, 113 pale always_black, 114 internal external, 115 A B, 116 permanent temporary none, 117 present absent, 118 not_aphid aphid, ; [ 11111111112 2 2 22222 2 23 3 3333333344444444445555555555666666666677777777778888888888 9 9999999999000000000 0111 1 1111 ] [ 123 4 5678901234567890 1 2 34567 8 90 1 2345678901234567890123456789012345678901234567890123456789 0 1234567890123456789 0123 4 5678 ] MATRIX [ ] Aphidius_rhopalosiphi 120 2 1000001011101001 1 2 10011 0 01 1 1111210101012301?10121001103??0011000002101311011101111000 0 10101211?1111000101 1110 1 1211 Aphidius_ervi 120 2 1000001011101001 1 2 10011 0 01 1 1111210101012101?10121001103??0011000002101311011101111000 0 101012111111100?101 1110 1 1211 Diaeretiella_rapae 120 (23) 2000001011101001 1 2 10111 1 01 1 1111210101012201?10121?01?03??00?1000002101311011??11?1000 0 101012111111100?1?1 1110 1 1211 Lysiplebus_confusus 120 (23) 300000101111?001 1 2 10111 1 01 1 1111210101012200?10121001?03??101100000??01311011??1111000 0 101011111111100?1?1 1110 1 1211 Pauesia_unilachni 120 2 1000001011100001 1 2 10011 0 01 1 1111210100012200010?21?01?03???0110000?2?01311011??01?1?00 0 10100211?111100?1?1 1110 1 1211 Pauesia_juniperorum 120 2 1000000001100001 1 2 10011 0 01 (01) 1111210100012201010??1?01?03???0110000???01311011??11?1?00 0 10100211?111100?1?1 1110 1 1211 Binodoxys_acalephae 120 2 2000000011100001 1 2 10111 1 11 ? 1111210100112410?1100??01?0????0?10000?2?0111?0????11?1?01 0 00000211?102100?1?1 1110 1 0211 Trioxys_pallidus 120 2 2000000011100001 1 2 10111 1 11 ? 1111210100112410111001001?03???00100000?101111011??11?1001 0 000002111102100?101 1110 1 0211 Monoctonus_pseudoplatani 120 2 1000001011100001 1 2 10011 0 11 ? 1111210100112300110021?01?020200110100?2?001110????11?1?11 0 00000211?102100?111 1110 1 0111 Praon_volucre 120 2 100000000111?001 1 1 10110 (01) 01 (01) 1101200100012000010021001003??20101100???10200111100111111 0 0101021?1110110?1?1 1110 0 ?211 Praon_abjectum 120 2 100000000111?001 1 (12) 10110 (01) 01 (01) 1101200100012000010?21001003??20101100???10200111100111111 0 0101021?1110110?1?1 1110 0 ?211 [ Praon_dorsale 120 2 100000000111?001 1 1 10110 (01) 01 0 1101200100012000010021001003??20101100???10200111100111111 0 0101021?1110110?1?1 1110 0 ?211 ] Pseudopraon_mindariphagum 120 2 2000000001100001 1 2 10110 0 11 0 1101200100?1??00010?2?????0????0??1100????02001?????????1? ? ???????1????1?0???1 1110 (01) 0211 Dyscritulus_planiceps 120 2 1000000001100001 1 2 10110 0 01 0 110120010001200001002???1?0???20?01000?00102001????01???11 0 010102111110110?1?1 1110 0 ?211 Ephedrus_plagiator 121 2 2000001001100001 1 0 00000 0 01 0 1101200100012301010021001003??2020010002010111011?00111111 0 000102111110100?101 1111 1 0211 Ephedrus_californicus 121 2 2000001001100001 1 0 00000 0 01 0 1101200100012301010021001003??20200100020101110111?0111111 0 000102101110100?101 1111 1 0211 [ Ephedrus_validus 121 2 2000001001100001 1 0 00000 0 01 0 1101200100012301010021001003??2020010002010111011??0111111 0 000102111110100?101 1111 1 0211 Ephedrus_persicae 121 2 2000001001100001 1 0 00000 0 01 (01) 1101200100012301010021001003??2020010002010111011??0111111 0 000102101110100?101 1111 1 0211 ] Sathon_falcatus 211 1 101102011011?001 1 0 10100 0 00 0 1001001110001500011001201101121121110012010011011210111?00 2 1????20010001010101 100? 0 ?210 Chelonus_sp. 010 1 001100000011?100 1 1 01000 0 00 0 1001102010001200010021201101111?11110012010011011210111100 (01) 0000?10010101010101 100? 0 ?210 Cenocoelius_analis 010 0 0011000000101000 1 0 00000 0 00 0 1001100110011?000100212010001?11211100110300110112121???0? ? ?????100?01010????1 100? 0 ?2?0 Eubazus_semirugosus 010 0 0011000000000000 1 0 01100 0 00 0 1001000110011?000100212010001?1101110012030011011210111?00 0 000??1001010101?101 100? 0 ?210 Acampsis_alternipes 010 0 0011000000001100 1 0 00000 0 00 0 0001000110011?000100011010021011?111001?030011011??0110?00 2 100001001010101??11 100? 0 ?210 Alysia_lucicola 100 0 0000020000100100 1 0 00000 0 01 0 10002001100115000000200?0012001010000102010012001000110?01 (12) 000??11100001000101 110? 1 ?110 Aleiodes_coxalis 100 0 010000000011?001 1 0 00000 0 01 0 10010000100115000000000?0012101021000002010012000000000?01 2 1????1100000000?001 1111 1 ?110 Heterospilus_prosopidis 100 0 0100100000100011 1 1 00000 0 01 0 1000200010001?00000030010012001020000112110012000000000?01 1 000??00000000001000 000? 0 ?000 Hecabolus_sp. 100 0 010010000011?011 1 0 00000 0 01 0 1000200010001?00000030010012001020000112110012000000000?01 1 000??00000001001000 000? 0 ?0?0 Bracon_sp. 100 1 010012010011?101 1 0 00000 0 01 0 1001200010011500000010010012001020000012210012000000000?01 2 1????00000000001000 000? 0 ?000 Colastes_incertus 100 0 010000010011?001 1 0 00000 0 01 0 1000200010001?000000100100120010?000010?010012000000000?01 1 000??00000000001000 000? 0 ?000 Rhyssalus_sp. 100 0 0100100000101000 1 0 00000 0 00 0 100020011000??000000100100110010?01110020100?100?000000?0? ? ?????0000000000??00 000? 0 ?000 Histeromerus_mystacinus 110 0 010011011011?000 (01) 0 00000 0 00 0 1000100110002?00000000?1001101100011100201000000???0000?0? ? ?????0000000000??00 000? 0 ?010 Xorides_praecatorius 010 1 0001000000000000 0 2 0???0 0 01 0 0001010000000?00000011000001001020000000020000010000000?0? ? ?????000?000000??00 000? 0 ?0?0 Alomyia_debellator 010 1 000100001011?000 0 2 0???0 0 01 0 0001010000000?000100100000011010201100000?0000000000??0?0? ? ?????111001000????1 111? 1 ?2?0 ; END; begin assumptions; exset * larfem = 1-45 74-82 85 86 105 108-112 114 116-.; wtset * equal = 1 : 1-.; end; garli-2.1-release/tests/data/a.G3.start000066400000000000000000000013051241236125200177070ustar00rootroot00000000000000#NEXUS begin trees; translate 1 MorNa6, 2 ClownNa6, 3 AraNa6, 4 puffNa6, 5 NewZebra, 6 SterNa6, 7 eelNa6, 8 catNa6, 9 AptNa6, 10 PinniNa6, 11 tetra; tree bestREP1 = [&U][!GarliScore -6240.261727][!GarliModel e 0.05006 0.02522 0.04579 0.05796 0.07502 0.05608 0.00778 0.08819 0.04880 0.11266 0.04378 0.05006 0.02961 0.02522 0.03726 0.05520 0.04441 0.09058 0.02170 0.03463 a 0.43677 ](8:0.05935897,((4:0.03051927,11:0.05580100):0.10931448,((1:0.22446286,(2:0.04508832,3:0.04743270):0.01175049):0.03129708,5:0.06562443):0.01061253):0.02441771,(((10:0.09191045,7:0.14789299):0.09056644,6:0.16711511):0.03643131,9:0.05177230):0.04070844); end; [ begin garli; M1 a 0.43677 ; end; ] garli-2.1-release/tests/data/a.G4.start000066400000000000000000000040341241236125200177120ustar00rootroot00000000000000#NEXUS begin trees; translate 1 MorNa6, 2 ClownNa6, 3 AraNa6, 4 puffNa6, 5 NewZebra, 6 SterNa6, 7 eelNa6, 8 catNa6, 9 AptNa6, 10 PinniNa6, 11 tetra; tree bestREP2 = [&U][!GarliScore -6237.258402][!GarliModel e 0.05006 0.02522 0.04579 0.05796 0.07502 0.05608 0.00778 0.08819 0.04880 0.11266 0.04378 0.05006 0.02961 0.02522 0.03726 0.05520 0.04441 0.09058 0.02170 0.03463 a 0.46160 ](((7:0.14599226,10:0.09055204):0.09024955,6:0.16570677):0.03527064,9:0.05078852,(8:0.05838226,((4:0.02994310,11:0.05488274):0.10831868,(5:0.06486452,((2:0.04405548,3:0.04678517):0.01139601,1:0.22237979):0.03096996):0.01020390):0.02406689):0.04011937); end; [the rate matrix is LG] begin garli; a 0.46160 r 243.500 38.656 101.598 24.819 202.114 35.106 14.657 52.486 38.675 109.961 27.080 115.206 94.882 41.586 462.446 209.301 249.250 17.679 21.420 6.120 0.342 108.123 55.689 62.662 31.366 1.298 58.110 87.426 51.728 7.374 8.297 52.294 272.397 111.863 191.672 65.557 114.020 512.992 1.704 82.657 90.697 1.046 27.681 1.475 2.499 496.584 38.588 51.201 12.126 121.332 41.661 3.714 2.924 13.217 1.840 34.127 41.467 4.330 176.791 6.816 16.996 52.994 41.030 403.888 35.606 59.867 59.141 23.971 7.616 11.743 8.764 66.732 108.855 2.340 253.635 175.976 8.758 9.241 3.508 5.158 35.396 16.142 64.046 240.373 763.432 30.472 0.852 29.019 4.330 13.651 140.640 19.268 26.214 38.171 170.218 12.701 7.503 26.266 5.349 10.652 68.211 35.836 43.286 441.125 49.779 470.891 237.387 96.850 57.157 11.643 58.408 519.152 15.561 405.499 418.074 18.734 7.658 7.127 12.423 6.271 101.128 1041.770 10.923 22.747 13.451 64.234 209.847 38.184 316.401 618.860 73.241 111.216 18.118 4.882 12.907 617.519 6.694 24.365 56.980 29.529 17.833 29.635 166.574 60.617 29.314 36.294 9.768 163.622 47.361 33.942 197.646 185.746 68.105 47.085 15.827 165.890 73.554 392.126 195.720 8.187 4.439 59.873 61.073 32.531 130.905 55.905 29.006 9.306 8.767 274.689 119.723 105.666 20.576 23.107 25.174 83.950 56.641 16.717 58.071 30.761 633.164 9.623 24.345 39.184 214.061 13.776 24.050 18.539 24.390 308.333 ; end; garli-2.1-release/tests/data/a.start000066400000000000000000000012261241236125200174410ustar00rootroot00000000000000#NEXUS begin trees; translate 1 MorNa6, 2 ClownNa6, 3 AraNa6, 4 puffNa6, 5 NewZebra, 6 SterNa6, 7 eelNa6, 8 catNa6, 9 AptNa6, 10 PinniNa6, 11 tetra; tree bestREP2 = [&U][!GarliScore -6430.816437][!GarliModel e 0.05006 0.02522 0.04579 0.05796 0.07502 0.05608 0.00778 0.08819 0.04880 0.11266 0.04378 0.05006 0.02961 0.02522 0.03726 0.05520 0.04441 0.09058 0.02170 0.03463 ](8:0.05249357,((6:0.13492838,(10:0.07773538,7:0.12477691):0.07362245):0.03624041,9:0.04716527):0.03542008,((((3:0.04217772,2:0.04130586):0.01296054,1:0.17849475):0.02682993,5:0.05697633):0.01047972,(11:0.04999547,4:0.02773113):0.09034148):0.02247935); end; garli-2.1-release/tests/data/c.M3x2.start000066400000000000000000000023111241236125200201670ustar00rootroot00000000000000#NEXUS begin trees; translate 1 MorNa6, 2 ClownNa6, 3 AraNa6, 4 puffNa6, 5 NewZebra, 6 SterNa6, 7 eelNa6, 8 catNa6, 9 AptNa6, 10 PinniNa6, 11 tetra; tree bestREP1 = [&U][!GarliScore -12956.123517][!GarliModel o 0.02001 0.69648 0.29356 0.30352 r 1.26027 2.44850 1.25881 0.68658 1.39601 1.00000 e 0.01794 0.03488 0.03086 0.01518 0.01142 0.01807 0.00652 0.00841 0.00953 0.01217 0.00828 0.01041 0.00941 0.05068 0.04378 0.02810 0.00778 0.00489 0.01744 0.00289 0.00866 0.01016 0.00226 0.00853 0.00339 0.00803 0.00289 0.00514 0.00715 0.02860 0.04165 0.01518 0.02045 0.03074 0.03751 0.01505 0.01192 0.02208 0.00427 0.01179 0.01656 0.01681 0.01342 0.00928 0.00803 0.02597 0.04353 0.01305 0.02195 0.01267 0.00765 0.01305 0.00339 0.00853 0.01593 0.02170 0.00928 0.00615 0.04692 0.01393 0.02810 ](9:0.17302410,((10:0.30682011,7:0.28202315):0.15919286,6:0.34705233):0.07921800,(8:0.28133143,(5:0.47952034,(((2:0.34814105,1:0.71924216):0.06101346,3:0.38140945):0.21550738,(4:0.15923502,11:0.18232271):0.63467009):0.12460134):0.17191830):0.26462974); end; begin garli; M1 o 0.02001 0.69648 0.29356 0.30352 r 1.26027 2.44850 1.25881 0.68658 1.39601 1.00000; end; garli-2.1-release/tests/data/c.start000066400000000000000000000022521241236125200174430ustar00rootroot00000000000000#NEXUS begin trees; translate 1 MorNa6, 2 ClownNa6, 3 AraNa6, 4 puffNa6, 5 NewZebra, 6 SterNa6, 7 eelNa6, 8 catNa6, 9 AptNa6, 10 PinniNa6, 11 tetra; tree bestREP2 = [&U][!GarliScore -13269.229793][!GarliModel o 0.08740 1.00000 r 1.26512 2.36171 1.15119 0.70580 1.40199 1.00000 e 0.01794 0.03488 0.03086 0.01518 0.01142 0.01807 0.00652 0.00841 0.00953 0.01217 0.00828 0.01041 0.00941 0.05068 0.04378 0.02810 0.00778 0.00489 0.01744 0.00289 0.00866 0.01016 0.00226 0.00853 0.00339 0.00803 0.00289 0.00514 0.00715 0.02860 0.04165 0.01518 0.02045 0.03074 0.03751 0.01505 0.01192 0.02208 0.00427 0.01179 0.01656 0.01681 0.01342 0.00928 0.00803 0.02597 0.04353 0.01305 0.02195 0.01267 0.00765 0.01305 0.00339 0.00853 0.01593 0.02170 0.00928 0.00615 0.04692 0.01393 0.02810 ](((10:0.29317580,7:0.27300094):0.15273335,6:0.32741524):0.08784407,9:0.16518210,((5:0.45695619,((4:0.16177501,11:0.17290062):0.58276496,(3:0.36040616,(1:0.68292488,2:0.33639981):0.07210723):0.21341644):0.13001699):0.17097973,8:0.27115449):0.25827687); end; begin garli; M1 o 0.08740 1.00000 r 1.26512 2.36171 1.15119 0.70580 1.40199 1.00000 ; end; garli-2.1-release/tests/data/dnaGap.8x1K.nex000066400000000000000000001032511241236125200206030ustar00rootroot00000000000000#NEXUS BEGIN TAXA; TITLE Untitled_TAXA_Block_1; DIMENSIONS NTax = 8; TAXLABELS temporariaDMH84R1 boyliiMVZ148929 luteiventris_MT_MVZ191016 luteiventris_WA_MVZ225749 muscosaMVZ149006 auroraMVZ13957 cascadaeMVZ148946 sylvaticaMVZ137426; END; BEGIN CHARACTERS; TITLE Untitled_DATA_Block_1GapsAsMissing; LINK TAXA = Untitled_TAXA_Block_1; DIMENSIONS NChar=1000; FORMAT Datatype=DNA; Matrix temporariaDMH84R1 ?AATAAAGCGCAAAGGTAC?TATA?ACT??????A??ATGATAACCTACTC???C??TTT?CCCCA???TACAGTAACTGTAC??T?TAATCTA??CT?ACG??????????????CCAAATTT?AACGACCCAAGC????????TACATGCTCCCCATGAA???A?C?????????TTTAAC?GGG?AAGGATA????AA?????????GA?GA???TTAAGTGCG????T????A???GAATTC?G?CACCAT??TA?CAGAT?A?T????????T???????TACG?????TCC???CGATGTGCAA??ACTTGGAAG?CT?GACGT??CTG???TCAAA?TCCAGTCGAAGA??????????AG?GGA??????TAACTATTTTGAT?ACTT?AGTATC???AATACA?TT??AG???????AC??CACGTCTA????ATGCAAA?T?T???????C?GACCTACA?TA??????C???TGC????A?A??T?TCTG?????GCA?AAA?TCTGCA??????CCTGT???ATC????CT?GA?T?A???TGAAAACA?AAA??AC???TACT?C?TGCTT???GTCTGACTTACCTACCGTCAG??TAA??CAACC???????TATTCCGTTCC??CAATAG????TT?T??????CGACTT??AGCT????GG??GCGT?TATTAAGAA??AGCG?????TA??????TA??TCTGAACGACTAC?TACTC??CCT????????G?????CGATCT??GACTAA??GTCCAGCTCGA?TTAT?CA???AAGTTTTTCC?C???GAAA?CCAGGCA?????A?AG???TTAATGA?G???????T??GTGA??????????ATCAGTTATACTAGTAA??????????????????AG???AGTCTTC??AT????TGA?GC?GATATGCTAATG?T??CATC?????????TCGTCT?????????GCCC????????TTAATTG????A?AAGT boyliiMVZ148929 ?AATGTAGCGAAACAGCAC?TATA?GTT??????A??ATGACAACCTACTC???CTTTT???????????CTCATAACTGCAC??T?TAATT?????T?ACGTAG?ATG???????CTTAATTT?AATG???????C????????TACACGCTCTCATTGAA???A?C?????????TAAAAC?GGC?GGGGATA????AA?????????GA?GA???CTAAGTGCG????T????T???GAATAC???CACAAT??TA?CAGAC?A?C????????TAC?????TGTG?????TCT???CAATGTGCAA??CCTTGAAGG?TC?GACCT??CTC???TTAGC?ACCAGTCGAAGACT?TATTTCCAG?GGA??????TAACTAATTTGGT?CCTT?ATTATT???GACATA?TT??AG???????ATTACACGTCTA????CCGCAAA?T?T???????C?GACCTACA?CA??????C???TGC????A?A??C?TTCG?????GTA?GTA?CCTGCA??????CCTGT??AATT????CT?AA?TCA???TGTAAAC??AAA???C???TACT?C?TAATTCACCTCTGCCGTCCCTACCGTCAG??TAA??CAACC???????TATTCCGTTTC??CAG??????????T??????GGATTT??AGTT????GG??GTA???TCTAAAAA??AGCG?????AA??????TG??TAAG??CTCCTAC???TTC??CCT????????G?????CGATCT??AGTTAA??GTCAAGTTCGA?TTAT?CA???AAGTATT?CC?C???GAAA?CCAG??A?????A?AG???TTAATGA?G??C?CATT??GTAA??????????ATCAGCCATACTAACAAACT?????????A??CATAG???AGTCTTC??AG????TAA?GT?GCTATGCTATTG?T??CATC?????????TAGATT?????????GCCA??????CATTAGTTG???GA?AATT luteiventris_MT_MVZ191016 ?AATATAGCGCAAAAGCAC?TA?A?GCT??????A??ATGACAACCTATTC???T?ATT???????????CAGATAACTGTAC??T?TAATCTC??CT?ACATGG?A?G???????CCTAATTC?AATG???????C????????TCCGTGCTCCCATCGAA???A?C?????????TAAAAC?GGC?GGGGATA????AA?????????GA?GA???TTAAGTGCG????T????A???GAGTAC?G?CATCAT??TA?CAAAT?A?A????????TAC?????TGTG?????TCT???CGATACGCAA??CCTTGGAGG?TC?GACAT??CTA???TCGAC?ACCAGTTGAAGATC?TGTTTCCAG?GGA??????TAACTAAATTGAT?CCTT?ATTATC???AATATA??T??CG???????ATCACACGTCTA????ACGTAAA?T?T???????C?GACCTCTA?TA??????C???TGC????A?A??C?TTTG?????GTA?GTA?CTTGCA??????CCTGA??AATC????CT?AA?T?A???TGTAAA???AAA??GC???TACT?C?TGGTTCAACTCTGTCATC??TACCGTCAG??TAA??CAACC???????TATTCCGTTTC??CAATTG????TT?T??????AGATTT??AGTT????GG??GCAT?AACTAAAAA??AGCG?????AA??????TA??TGAG??CTACT?T?TATTC??CCT????????G?????CGATCT??TGTTAA???TCAAGCTCGA?TTAT?CA???A??TCTT?CC?T???GAAG?CTAGGTA?????A?AG???TTAATGA?G??G?CGTT??GTAA??????????ATCAGTTATACTAACAAATT?????????A??AATAG???CTTCTTC??AT????TAA?GT?GATATGCTACTG?T??CATC?????????TCGACT?????????GCCA??????CATCAGTTG???CG?AATT luteiventris_WA_MVZ225749 ?AATATAGCGCAAAAGCAC?TA?A?GCT??????A??ATGACAACCTATTC???T?ATT???????????CTGATAACTGTAC??T?TAATCTC??CT?ACATGG?A?G???????CCTAATTC?AATG???????C????????TCCGTGCTCCCATCGAA???A?C?????????TAAGAC?GGC?GGGGATA????AA?????????GA?GA???TTAAGTGCG????T????A???GAGTAC?G?CACCAT??TA?CAGAT?A?A????????TAC?????TGTG?????TCT???CGATACGCAA??CCTTGGAGG?TC?GACAT??CTA???TCAAC?ACCAGTTGAAGACC?TATTTCCAG?GGA??????TAACTAAATTGAT?CCTT?ATTATC???AATATA??T??CG???????ATCACACGTCTA????ACGTAAA?T?T???????C?GACCTTTA?TA??????C???TGC????A?A??C?TTTG?????GTA?GTA?CTTGCA??????CCTGA??AATC????CT?AA?T?A???TGCAAACA?AAA??GC???TACT?C?TGGTTCAACTCTGTCATC??TACCGTCAG??TAA??CAACC???????TATTCCGTTTC??CAATTG????TT?T??????AGATTT??AGTT????GG??GCAT?AACTAAAAA??AGCG?????AA??????TA??TGAG??CTACT?C?TATTC??CCT????????G?????CGATCT??TGTTAA???TCAAGCTCGA?TTAT?CA???A??TCTT?CC?T???GAAG?CTAGGCA?????A?AG???TTAATGA?G??G?CGTT??GTAA??????????ATCAGTTATACTAACAAATT?????????A??AATAG???CTTCTTC??AT????TAA?GT?GATATGCTACTG?T??CATC?????????TCGACT?????????GCCA??????CATCAGTTG???CG?AATT muscosaMVZ149006 ?AATACAGCGCAAAAGCAC?TATA?GCT??????A??ATGACAACCTACTC???T?TTTT?ATCCA???TTCAATAACTGCAC??T?TAATTTT??CT?ACTTAG?ATG???????CTTAATCC?AATG????????????????????TGCTCCCCTCAAA???A?A?????????TTAAACTGGT?GG??????????A?????????GA?GA???TTACGTGCG????T????A???GAGTCC?G?CACCAT??TA?CAGAC?A?T????????TAC?????TGCG?????TCT???CGATCCGCAA??CCT???AAG?TT?GACGT??CTA???TCAAA?TCCAGTAGAAGAAC?TATTTCCAG?GGA??????CAACTAATTTGGT?ACTT?ACTATC???AATATA?TT??TG???????ATAACACGTTTA????TCGCAAA?T?T???????C?GACCTACA?TA??????CATTTGC????A?A??C?TTTG?????GGA?ATA?TCTGCA??????CCTGA??AATT????CT?AA?T?A???TGCAAACA?AAA??CC???TACT?C?TGGTT???TTCTATCGTCCCTACCGTCAG??TGG??CAACC???????TATTCCATTCC??CAGTTG????TT?T??????GGAATT??AGCT????GG??GCAT?AACTAAAAA??AGCG?????AA??????TC??TGTG??CGACTAC?CATTC??CCT????????G?????CGATCT??AGTTAA??GTCAAGTTCGA?TTAT?CA???AAGTATT?CC?T???GAAA?CCAGGCA?????A?AG???TTAATGA?G??T?CGTT??GTAA??????????ATCAGTCACACTAACAAATT?????????A??AATAG???TATCTTC??AC????TTA?GT?GATATGTTACTG?T??CATC?????????TCGACT?????????GCCA??????TATTAGTTG???TA?AAGT auroraMVZ13957 ?AATACAGCGCAAAAGCACTTATA?GTT??????A??ATGACAACCTACTC???A?TTTT?GTCCA???TCTTATAACCGCAC??T?TAATCCG??CT?ACATAG?ATG???????CCTAATTT?AATG????????????????????TGCTCCCCTCGAA???A?A?????????TAAAACTGGT?GGGGATAA???AA?????????AA??A???TTAAGTGCG????T????A???GAGTCC?G?CATTAT??TA?CAGAT?A?T????????TAC?????TGAG?????TCT???CGATCCG????????????????C?GACGT??CTA???TAAAT?TCCAGTAGAA?AAC?TCTTTCCA??GGA??????TAACTAATTTGGT?ACTT?ACTATC???AATACA?TT??TG???????ATCACACGTTTA????TTGTAAA?T?T???????C?GACCTACA?TA??????C???CGC????A?G??T?TTTG?????GTA?ATA?TCTGCA??????CCTGT??AATA????CT?AA?T?A???TGTAAACA?AAA??AC???TACT?C?TGATT???TTCTAACATACCTACCGTCA???????????CC???????TATTCCATTTC??CAGTTG????TT?T??????GGAATT??AGTT????GG??GCAT?AACTAAAAA??AGCG?????AA??????TT??TGAG??CTACTAC?TGCTC??CCT????????G?????CGATCT??AATTAA??GTCTAGCTCGA?TTAT?CA???AAGTTTT?CC?A???GAAA?CCAGGTA?????A?AG???TTAATGA?G??C?CGTT??GTAA??????????ATCAGTCATACTAACAAATT?????????A??AATAG???CGTCTCC??TC????TTA?GT?GTTATGCTAATG?T??CATC?????????TCGACT?????????GTCA??????CATTAGTTG???TG?AAGT cascadaeMVZ148946 ?AATATAGCGCAAAAGCAC?TATA?GTT??????A??ATGACAACCTACTC???A?TTTT?GTCCA???TTTCATAACTGCAC??T?TAATTTT??CT?ACATAG?ATG???????CCTAATTT?AATG????????????????????TGCTCCCCTCAAA???A?A?????????TAAAACTGGC?GGGGATA????AA?????????AA?GA???TTAAGTGCG????T????G???GAGTCC?G?CATCAT??TA?AAGAT?A?C????????TAC?????TAAG?????TCC???CGATCCGCAA??ACT???AGG?CT?GACGT??CTA???TTAAT?TCCAGTAGAAGAAC?TATTTCCAG?GGA??????TAACTAATTTGGT?ACTT?ACTATC???AATACA?TT??AG???????ATCACACGTTTA????CCGTAAA?T?T???????C?GACCTACA?TA??????C???TGC????A?A??C?TTCG?????GCA?TTA?TCTGTA??????CCTGC??AATT????CT?AA?T?A???TGCAAACA?AAA??AC???TACT?C?TAATT???TTCTACTGTACCCACCGTCA???????????CC???????TATTCCATTTC??CAGT?G????TT?T???????GAACT??AGTT????GG??GCAT?AACTAAAAA??AGCG?????AA??????TA??TAAG??CTACTAC?TGTTC??CCT????????G?????CGATCT??AATTAA??GTCTAGCTCGA?TTAT?CA???AAGTTTT?CC?G???GAAA?CCAGGTA?????A?AG???TTAATGA?G??T?CGTT??GTAA??????????ATCAGTCATACTAATAAATT?????????A??AATAG???CGTCTTC??AC????TCA?GT?GATATGCTATTG?T??CATC?????????TCGACT?????????GCCA??????CATTAATTG???CG?AAGT sylvaticaMVZ137426 ?AATACATCGTGACATTAC?TA???ATT??????A??ATGACGACTTACTC???T?ATTC?TTCCA???TTCGATAACTGCAC??T?TAATCCA??CT?ACGTAG?ATG???????CCTGATTT?AAAG???????C????????TTCATGCTCTCCACAAA???A?T?????????TATAAC?GGA?AAGAATA????AA?????????AA?GA???TCAGGTGCG????T????A???AAATAC?G?CTTAAT??TA?AAGAT?A?T????????TAC?????TGTG?????TCA???CGATATGCGA??ACTTGAAAG?TC?GACGT??CTG???TCAAT?ACCAGTTGAGGACT?TGTTTCCAG?GGA??????CAACTAATTTGGTGACAT?ATTATC???TA???A?TT??AG???????ACCACACGTTTA????AAGTAAA?T?T???????C?GACCTACA?TA??????T???TGC????A?A??C?T??G?????GAA?GCAACCTGCA??????CCTGA??AATA????CT?AA?T?C???TGTAAACA?AAAATAC???TACT?C?TGCTT???TCCTGACGTGCCCATCGTCAG??TAA??CGACCT?A???TTATTCCGTTCC??CGACAG????TT?T??????GGACTT??AGTT????GG??GCAT?AACTGATAA??AGCG?????AA??????TT??TTAGAACAACTAC?CATTCATCCT????????G?????CGATCT??AATTAA??GTCAAGCTCGG?TTGT?CA???AAGTATT?CT?T???GAAA?CTAGGAA?????A?AG???TTAATAC?G??T?CGTT??GTAA??????????ATCAGTTACACTAACAGCAC?????????A??GATAA???CATTTTG??ATGAAATA???C?GTT?CGCTACTGTCTTCTTC?????????TCG??????????????TG??????AATCAT?TG???AA?AAGT ; END; BEGIN CHARACTERS; TITLE Untitled_DATA_Block_1GapsAsBinary; LINK TAXA = Untitled_TAXA_Block_1; DIMENSIONS NChar=1000; CharStateLabels 1 col_1, 2 col_2, 3 col_3, 4 col_4, 5 col_5, 6 col_6, 7 col_7, 8 col_8, 9 col_9, 10 col_10, 11 col_11, 12 col_12, 13 col_13, 14 col_14, 15 col_15, 16 col_16, 17 col_17, 18 col_18, 19 col_19, 20 col_20, 21 col_21, 22 col_22, 23 col_23, 24 col_24, 25 col_25, 26 col_26, 27 col_27, 28 col_28, 29 col_29, 30 col_30, 31 col_31, 32 col_32, 33 col_33, 34 col_34, 35 col_35, 36 col_36, 37 col_37, 38 col_38, 39 col_39, 40 col_40, 41 col_41, 42 col_42, 43 col_43, 44 col_44, 45 col_45, 46 col_46, 47 col_47, 48 col_48, 49 col_49, 50 col_50, 51 col_51, 52 col_52, 53 col_53, 54 col_54, 55 col_55, 56 col_56, 57 col_57, 58 col_58, 59 col_59, 60 col_60, 61 col_61, 62 col_62, 63 col_63, 64 col_64, 65 col_65, 66 col_66, 67 col_67, 68 col_68, 69 col_69, 70 col_70, 71 col_71, 72 col_72, 73 col_73, 74 col_74, 75 col_75, 76 col_76, 77 col_77, 78 col_78, 79 col_79, 80 col_80, 81 col_81, 82 col_82, 83 col_83, 84 col_84, 85 col_85, 86 col_86, 87 col_87, 88 col_88, 89 col_89, 90 col_90, 91 col_91, 92 col_92, 93 col_93, 94 col_94, 95 col_95, 96 col_96, 97 col_97, 98 col_98, 99 col_99, 100 col_100, 101 col_101, 102 col_102, 103 col_103, 104 col_104, 105 col_105, 106 col_106, 107 col_107, 108 col_108, 109 col_109, 110 col_110, 111 col_111, 112 col_112, 113 col_113, 114 col_114, 115 col_115, 116 col_116, 117 col_117, 118 col_118, 119 col_119, 120 col_120, 121 col_121, 122 col_122, 123 col_123, 124 col_124, 125 col_125, 126 col_126, 127 col_127, 128 col_128, 129 col_129, 130 col_130, 131 col_131, 132 col_132, 133 col_133, 134 col_134, 135 col_135, 136 col_136, 137 col_137, 138 col_138, 139 col_139, 140 col_140, 141 col_141, 142 col_142, 143 col_143, 144 col_144, 145 col_145, 146 col_146, 147 col_147, 148 col_148, 149 col_149, 150 col_150, 151 col_151, 152 col_152, 153 col_153, 154 col_154, 155 col_155, 156 col_156, 157 col_157, 158 col_158, 159 col_159, 160 col_160, 161 col_161, 162 col_162, 163 col_163, 164 col_164, 165 col_165, 166 col_166, 167 col_167, 168 col_168, 169 col_169, 170 col_170, 171 col_171, 172 col_172, 173 col_173, 174 col_174, 175 col_175, 176 col_176, 177 col_177, 178 col_178, 179 col_179, 180 col_180, 181 col_181, 182 col_182, 183 col_183, 184 col_184, 185 col_185, 186 col_186, 187 col_187, 188 col_188, 189 col_189, 190 col_190, 191 col_191, 192 col_192, 193 col_193, 194 col_194, 195 col_195, 196 col_196, 197 col_197, 198 col_198, 199 col_199, 200 col_200, 201 col_201, 202 col_202, 203 col_203, 204 col_204, 205 col_205, 206 col_206, 207 col_207, 208 col_208, 209 col_209, 210 col_210, 211 col_211, 212 col_212, 213 col_213, 214 col_214, 215 col_215, 216 col_216, 217 col_217, 218 col_218, 219 col_219, 220 col_220, 221 col_221, 222 col_222, 223 col_223, 224 col_224, 225 col_225, 226 col_226, 227 col_227, 228 col_228, 229 col_229, 230 col_230, 231 col_231, 232 col_232, 233 col_233, 234 col_234, 235 col_235, 236 col_236, 237 col_237, 238 col_238, 239 col_239, 240 col_240, 241 col_241, 242 col_242, 243 col_243, 244 col_244, 245 col_245, 246 col_246, 247 col_247, 248 col_248, 249 col_249, 250 col_250, 251 col_251, 252 col_252, 253 col_253, 254 col_254, 255 col_255, 256 col_256, 257 col_257, 258 col_258, 259 col_259, 260 col_260, 261 col_261, 262 col_262, 263 col_263, 264 col_264, 265 col_265, 266 col_266, 267 col_267, 268 col_268, 269 col_269, 270 col_270, 271 col_271, 272 col_272, 273 col_273, 274 col_274, 275 col_275, 276 col_276, 277 col_277, 278 col_278, 279 col_279, 280 col_280, 281 col_281, 282 col_282, 283 col_283, 284 col_284, 285 col_285, 286 col_286, 287 col_287, 288 col_288, 289 col_289, 290 col_290, 291 col_291, 292 col_292, 293 col_293, 294 col_294, 295 col_295, 296 col_296, 297 col_297, 298 col_298, 299 col_299, 300 col_300, 301 col_301, 302 col_302, 303 col_303, 304 col_304, 305 col_305, 306 col_306, 307 col_307, 308 col_308, 309 col_309, 310 col_310, 311 col_311, 312 col_312, 313 col_313, 314 col_314, 315 col_315, 316 col_316, 317 col_317, 318 col_318, 319 col_319, 320 col_320, 321 col_321, 322 col_322, 323 col_323, 324 col_324, 325 col_325, 326 col_326, 327 col_327, 328 col_328, 329 col_329, 330 col_330, 331 col_331, 332 col_332, 333 col_333, 334 col_334, 335 col_335, 336 col_336, 337 col_337, 338 col_338, 339 col_339, 340 col_340, 341 col_341, 342 col_342, 343 col_343, 344 col_344, 345 col_345, 346 col_346, 347 col_347, 348 col_348, 349 col_349, 350 col_350, 351 col_351, 352 col_352, 353 col_353, 354 col_354, 355 col_355, 356 col_356, 357 col_357, 358 col_358, 359 col_359, 360 col_360, 361 col_361, 362 col_362, 363 col_363, 364 col_364, 365 col_365, 366 col_366, 367 col_367, 368 col_368, 369 col_369, 370 col_370, 371 col_371, 372 col_372, 373 col_373, 374 col_374, 375 col_375, 376 col_376, 377 col_377, 378 col_378, 379 col_379, 380 col_380, 381 col_381, 382 col_382, 383 col_383, 384 col_384, 385 col_385, 386 col_386, 387 col_387, 388 col_388, 389 col_389, 390 col_390, 391 col_391, 392 col_392, 393 col_393, 394 col_394, 395 col_395, 396 col_396, 397 col_397, 398 col_398, 399 col_399, 400 col_400, 401 col_401, 402 col_402, 403 col_403, 404 col_404, 405 col_405, 406 col_406, 407 col_407, 408 col_408, 409 col_409, 410 col_410, 411 col_411, 412 col_412, 413 col_413, 414 col_414, 415 col_415, 416 col_416, 417 col_417, 418 col_418, 419 col_419, 420 col_420, 421 col_421, 422 col_422, 423 col_423, 424 col_424, 425 col_425, 426 col_426, 427 col_427, 428 col_428, 429 col_429, 430 col_430, 431 col_431, 432 col_432, 433 col_433, 434 col_434, 435 col_435, 436 col_436, 437 col_437, 438 col_438, 439 col_439, 440 col_440, 441 col_441, 442 col_442, 443 col_443, 444 col_444, 445 col_445, 446 col_446, 447 col_447, 448 col_448, 449 col_449, 450 col_450, 451 col_451, 452 col_452, 453 col_453, 454 col_454, 455 col_455, 456 col_456, 457 col_457, 458 col_458, 459 col_459, 460 col_460, 461 col_461, 462 col_462, 463 col_463, 464 col_464, 465 col_465, 466 col_466, 467 col_467, 468 col_468, 469 col_469, 470 col_470, 471 col_471, 472 col_472, 473 col_473, 474 col_474, 475 col_475, 476 col_476, 477 col_477, 478 col_478, 479 col_479, 480 col_480, 481 col_481, 482 col_482, 483 col_483, 484 col_484, 485 col_485, 486 col_486, 487 col_487, 488 col_488, 489 col_489, 490 col_490, 491 col_491, 492 col_492, 493 col_493, 494 col_494, 495 col_495, 496 col_496, 497 col_497, 498 col_498, 499 col_499, 500 col_500, 501 col_501, 502 col_502, 503 col_503, 504 col_504, 505 col_505, 506 col_506, 507 col_507, 508 col_508, 509 col_509, 510 col_510, 511 col_511, 512 col_512, 513 col_513, 514 col_514, 515 col_515, 516 col_516, 517 col_517, 518 col_518, 519 col_519, 520 col_520, 521 col_521, 522 col_522, 523 col_523, 524 col_524, 525 col_525, 526 col_526, 527 col_527, 528 col_528, 529 col_529, 530 col_530, 531 col_531, 532 col_532, 533 col_533, 534 col_534, 535 col_535, 536 col_536, 537 col_537, 538 col_538, 539 col_539, 540 col_540, 541 col_541, 542 col_542, 543 col_543, 544 col_544, 545 col_545, 546 col_546, 547 col_547, 548 col_548, 549 col_549, 550 col_550, 551 col_551, 552 col_552, 553 col_553, 554 col_554, 555 col_555, 556 col_556, 557 col_557, 558 col_558, 559 col_559, 560 col_560, 561 col_561, 562 col_562, 563 col_563, 564 col_564, 565 col_565, 566 col_566, 567 col_567, 568 col_568, 569 col_569, 570 col_570, 571 col_571, 572 col_572, 573 col_573, 574 col_574, 575 col_575, 576 col_576, 577 col_577, 578 col_578, 579 col_579, 580 col_580, 581 col_581, 582 col_582, 583 col_583, 584 col_584, 585 col_585, 586 col_586, 587 col_587, 588 col_588, 589 col_589, 590 col_590, 591 col_591, 592 col_592, 593 col_593, 594 col_594, 595 col_595, 596 col_596, 597 col_597, 598 col_598, 599 col_599, 600 col_600, 601 col_601, 602 col_602, 603 col_603, 604 col_604, 605 col_605, 606 col_606, 607 col_607, 608 col_608, 609 col_609, 610 col_610, 611 col_611, 612 col_612, 613 col_613, 614 col_614, 615 col_615, 616 col_616, 617 col_617, 618 col_618, 619 col_619, 620 col_620, 621 col_621, 622 col_622, 623 col_623, 624 col_624, 625 col_625, 626 col_626, 627 col_627, 628 col_628, 629 col_629, 630 col_630, 631 col_631, 632 col_632, 633 col_633, 634 col_634, 635 col_635, 636 col_636, 637 col_637, 638 col_638, 639 col_639, 640 col_640, 641 col_641, 642 col_642, 643 col_643, 644 col_644, 645 col_645, 646 col_646, 647 col_647, 648 col_648, 649 col_649, 650 col_650, 651 col_651, 652 col_652, 653 col_653, 654 col_654, 655 col_655, 656 col_656, 657 col_657, 658 col_658, 659 col_659, 660 col_660, 661 col_661, 662 col_662, 663 col_663, 664 col_664, 665 col_665, 666 col_666, 667 col_667, 668 col_668, 669 col_669, 670 col_670, 671 col_671, 672 col_672, 673 col_673, 674 col_674, 675 col_675, 676 col_676, 677 col_677, 678 col_678, 679 col_679, 680 col_680, 681 col_681, 682 col_682, 683 col_683, 684 col_684, 685 col_685, 686 col_686, 687 col_687, 688 col_688, 689 col_689, 690 col_690, 691 col_691, 692 col_692, 693 col_693, 694 col_694, 695 col_695, 696 col_696, 697 col_697, 698 col_698, 699 col_699, 700 col_700, 701 col_701, 702 col_702, 703 col_703, 704 col_704, 705 col_705, 706 col_706, 707 col_707, 708 col_708, 709 col_709, 710 col_710, 711 col_711, 712 col_712, 713 col_713, 714 col_714, 715 col_715, 716 col_716, 717 col_717, 718 col_718, 719 col_719, 720 col_720, 721 col_721, 722 col_722, 723 col_723, 724 col_724, 725 col_725, 726 col_726, 727 col_727, 728 col_728, 729 col_729, 730 col_730, 731 col_731, 732 col_732, 733 col_733, 734 col_734, 735 col_735, 736 col_736, 737 col_737, 738 col_738, 739 col_739, 740 col_740, 741 col_741, 742 col_742, 743 col_743, 744 col_744, 745 col_745, 746 col_746, 747 col_747, 748 col_748, 749 col_749, 750 col_750, 751 col_751, 752 col_752, 753 col_753, 754 col_754, 755 col_755, 756 col_756, 757 col_757, 758 col_758, 759 col_759, 760 col_760, 761 col_761, 762 col_762, 763 col_763, 764 col_764, 765 col_765, 766 col_766, 767 col_767, 768 col_768, 769 col_769, 770 col_770, 771 col_771, 772 col_772, 773 col_773, 774 col_774, 775 col_775, 776 col_776, 777 col_777, 778 col_778, 779 col_779, 780 col_780, 781 col_781, 782 col_782, 783 col_783, 784 col_784, 785 col_785, 786 col_786, 787 col_787, 788 col_788, 789 col_789, 790 col_790, 791 col_791, 792 col_792, 793 col_793, 794 col_794, 795 col_795, 796 col_796, 797 col_797, 798 col_798, 799 col_799, 800 col_800, 801 col_801, 802 col_802, 803 col_803, 804 col_804, 805 col_805, 806 col_806, 807 col_807, 808 col_808, 809 col_809, 810 col_810, 811 col_811, 812 col_812, 813 col_813, 814 col_814, 815 col_815, 816 col_816, 817 col_817, 818 col_818, 819 col_819, 820 col_820, 821 col_821, 822 col_822, 823 col_823, 824 col_824, 825 col_825, 826 col_826, 827 col_827, 828 col_828, 829 col_829, 830 col_830, 831 col_831, 832 col_832, 833 col_833, 834 col_834, 835 col_835, 836 col_836, 837 col_837, 838 col_838, 839 col_839, 840 col_840, 841 col_841, 842 col_842, 843 col_843, 844 col_844, 845 col_845, 846 col_846, 847 col_847, 848 col_848, 849 col_849, 850 col_850, 851 col_851, 852 col_852, 853 col_853, 854 col_854, 855 col_855, 856 col_856, 857 col_857, 858 col_858, 859 col_859, 860 col_860, 861 col_861, 862 col_862, 863 col_863, 864 col_864, 865 col_865, 866 col_866, 867 col_867, 868 col_868, 869 col_869, 870 col_870, 871 col_871, 872 col_872, 873 col_873, 874 col_874, 875 col_875, 876 col_876, 877 col_877, 878 col_878, 879 col_879, 880 col_880, 881 col_881, 882 col_882, 883 col_883, 884 col_884, 885 col_885, 886 col_886, 887 col_887, 888 col_888, 889 col_889, 890 col_890, 891 col_891, 892 col_892, 893 col_893, 894 col_894, 895 col_895, 896 col_896, 897 col_897, 898 col_898, 899 col_899, 900 col_900, 901 col_901, 902 col_902, 903 col_903, 904 col_904, 905 col_905, 906 col_906, 907 col_907, 908 col_908, 909 col_909, 910 col_910, 911 col_911, 912 col_912, 913 col_913, 914 col_914, 915 col_915, 916 col_916, 917 col_917, 918 col_918, 919 col_919, 920 col_920, 921 col_921, 922 col_922, 923 col_923, 924 col_924, 925 col_925, 926 col_926, 927 col_927, 928 col_928, 929 col_929, 930 col_930, 931 col_931, 932 col_932, 933 col_933, 934 col_934, 935 col_935, 936 col_936, 937 col_937, 938 col_938, 939 col_939, 940 col_940, 941 col_941, 942 col_942, 943 col_943, 944 col_944, 945 col_945, 946 col_946, 947 col_947, 948 col_948, 949 col_949, 950 col_950, 951 col_951, 952 col_952, 953 col_953, 954 col_954, 955 col_955, 956 col_956, 957 col_957, 958 col_958, 959 col_959, 960 col_960, 961 col_961, 962 col_962, 963 col_963, 964 col_964, 965 col_965, 966 col_966, 967 col_967, 968 col_968, 969 col_969, 970 col_970, 971 col_971, 972 col_972, 973 col_973, 974 col_974, 975 col_975, 976 col_976, 977 col_977, 978 col_978, 979 col_979, 980 col_980, 981 col_981, 982 col_982, 983 col_983, 984 col_984, 985 col_985, 986 col_986, 987 col_987, 988 col_988, 989 col_989, 990 col_990, 991 col_991, 992 col_992, 993 col_993, 994 col_994, 995 col_995, 996 col_996, 997 col_997, 998 col_998, 999 col_999, 1000 col_1000 ; Format Datatype = Standard Symbols="01" missing = '?' ; Matrix temporariaDMH84R1 ?11111111111111111101111?111??????1??11111111111111???100111?11111???11111111111111??1?1111111??11?111000?000???????11111111?111111111111????????11111111111111111???1?1?????????1111110111?11111110???11?????????11?11???111111111????1????1???111111?1?111111??11?11111?1?1????????100?????1111?????111???1111111111??111111111?11?11111??111???11111?11111111111100?000000011?111??????111111111111101111?111111???111111?11??11???????110011111111????1111111?1?1???????1?11111111?11??????1000111????1?1??1?1111?????111?1110111111??????11111??0111????11?11?101???11111111?1110011???1111?1?11111000111111111111111111111??111??111110?0???011111111111??111111????11?1??????111111??1111????11??1111?111111111??1111?????11??????11??1111111111111?1111100111????????1?????111111??111111??11111111111?1111?11???1111111111?1???1111?1111111?????1?11???1111111?1??0?0001??1111??????????11111111111111111000?????????0??00011???1111111??110000111?11?11111111111101001111?????????111111?????????1111??????001111111???01?1111 boyliiMVZ148929 ?11111111111111111101111?111??????1??11111111111111???111110?00000???01111111111111??1?1111100??01?111111?111???????11111111?111100000001????????11111111111111111???1?1?????????1111110111?11111110???11?????????11?11???111111111????1????1???111111?0?111111??11?11111?1?1????????111?????1111?????111???1111111111??111111111?11?11111??111???11111?11111111111111?111111111?111??????111111111111101111?111111???111111?11??11???????111111111111????1111111?1?1???????1?11111111?11??????1000111????1?1??1?1111?????111?1110111111??????11111??1111????11?11?111???11111110?1110001???1111?1?11111111111111111111111111111??111??111110?0???011111111111??111000????00?1??????111111??1111????11??1110?011111111??1111?????11??????11??1111001111111?0011100111????????1?????111111??111111??11111111111?1111?11???1111111011?1???1111?1111001?????1?11???1111111?1??1?1111??1111??????????11111111111111111111?????????1??11111???1111111??110000111?11?11111111111101001111?????????111111?????????1111??????111111111???11?1111 luteiventris_MT_MVZ191016 ?11111111111111111101101?111??????1??11111111111111???101110?00000???01111111111111??1?1111111??11?111111?101???????11111111?111100000001????????11111111111111111???1?1?????????1111110111?11111110???11?????????11?11???111111111????1????1???111111?1?111111??11?11111?1?1????????111?????1111?????111???1111111111??111111111?11?11111??111???11111?11111111111111?111111111?111??????111111111111101111?111111???111111?01??11???????111111111111????1111111?1?1???????1?11111111?11??????1000111????1?1??1?1111?????111?1110111111??????11111??1111????11?11?101???11111100?1110011???1111?1?11111111111111111100111111111??111??111110?0???011111111111??111111????11?1??????111111??1111????11??1111?111111111??1111?????11??????11??1111001111101?1111100111????????1?????111111??111111??01111111111?1111?11???1001111011?1???1111?1111111?????1?11???1111111?1??1?1111??1111??????????11111111111111111111?????????1??11111???1111111??110000111?11?11111111111101001111?????????111111?????????1111??????111111111???11?1111 luteiventris_WA_MVZ225749 ?11111111111111111101101?111??????1??11111111111111???101110?00000???01111111111111??1?1111111??11?111111?101???????11111111?111100000001????????11111111111111111???1?1?????????1111110111?11111110???11?????????11?11???111111111????1????1???111111?1?111111??11?11111?1?1????????111?????1111?????111???1111111111??111111111?11?11111??111???11111?11111111111111?111111111?111??????111111111111101111?111111???111111?01??11???????111111111111????1111111?1?1???????1?11111111?11??????1000111????1?1??1?1111?????111?1110111111??????11111??1111????11?11?101???11111111?1110011???1111?1?11111111111111111100111111111??111??111110?0???011111111111??111111????11?1??????111111??1111????11??1111?111111111??1111?????11??????11??1111001111101?1111100111????????1?????111111??111111??01111111111?1111?11???1001111011?1???1111?1111111?????1?11???1111111?1??1?1111??1111??????????11111111111111111111?????????1??11111???1111111??110000111?11?11111111111101001111?????????111111?????????1111??????111111111???11?1111 muscosaMVZ149006 ?11111111111111111101111?111??????1??11111111111111???101111?11111???11111111111111??1?1111111??11?111111?111???????11111111?111100000000????????00001111111111111???1?1?????????1111111111?11000000???01?????????11?11???111111111????1????1???111111?1?111111??11?11111?1?1????????111?????1111?????111???1111111111??111000111?11?11111??111???11111?11111111111111?111111111?111??????111111111111101111?111111???111111?11??11???????111111111111????1111111?1?1???????1?11111111?11??????1111111????1?1??1?1111?????111?1110111111??????11111??1111????11?11?101???11111111?1110011???1111?1?11111000111111111111111111111??111??111110?0???011111111111??111111????11?1??????111111??1111????11??1111?111111111??1111?????11??????11??1111001111111?1111100111????????1?????111111??111111??11111111111?1111?11???1111111011?1???1111?1111111?????1?11???1111111?1??1?1111??1111??????????11111111111111111111?????????1??11111???1111111??110000111?11?11111111111101001111?????????111111?????????1111??????111111111???11?1111 auroraMVZ13957 ?11111111111111111111111?111??????1??11111111111111???101111?11111???11111111111111??1?1111111??11?111111?111???????11111111?111100000000????????00001111111111111???1?1?????????1111111111?11111111???11?????????11?01???111111111????1????1???111111?1?111111??11?11111?1?1????????111?????1111?????111???1111111000??000000000?01?11111??111???11111?11111111110111?111111110?111??????111111111111101111?111111???111111?11??11???????111111111111????1111111?1?1???????1?11111111?11??????1000111????1?1??1?1111?????111?1110111111??????11111??1111????11?11?101???11111111?1110011???1111?1?11111000111111111111111111110??000??000110?0???011111111111??111111????11?1??????111111??1111????11??1111?111111111??1111?????11??????11??1111001111111?1111100111????????1?????111111??111111??11111111111?1111?11???1111111011?1???1111?1111111?????1?11???1111111?1??1?1111??1111??????????11111111111111111111?????????1??11111???1111111??110000111?11?11111111111101001111?????????111111?????????1111??????111111111???11?1111 cascadaeMVZ148946 ?11111111111111111101111?111??????1??11111111111111???101111?11111???11111111111111??1?1111111??11?111111?111???????11111111?111100000000????????00001111111111111???1?1?????????1111111111?11111110???11?????????11?11???111111111????1????1???111111?1?111111??11?11111?1?1????????111?????1111?????111???1111111111??111000111?11?11111??111???11111?11111111111111?111111111?111??????111111111111101111?111111???111111?11??11???????111111111111????1111111?1?1???????1?11111111?11??????1000111????1?1??1?1111?????111?1110111111??????11111??1111????11?11?101???11111111?1110011???1111?1?11111000111111111111111111110??000??000110?0???011111111111??111101????11?1??????011111??1111????11??1111?111111111??1111?????11??????11??1111001111111?1111100111????????1?????111111??111111??11111111111?1111?11???1111111011?1???1111?1111111?????1?11???1111111?1??1?1111??1111??????????11111111111111111111?????????1??11111???1111111??110000111?11?11111111111101001111?????????111111?????????1111??????111111111???11?1111 sylvaticaMVZ137426 ?11111111111111111101100?111??????1??11111111111111???101111?11111???11111111111111??1?1111111??11?111111?111???????11111111?111100000001????????11111111111111111???1?1?????????1111110111?11111110???11?????????11?11???111111111????1????1???111111?1?111111??11?11111?1?1????????111?????1111?????111???1111111111??111111111?11?11111??111???11111?11111111111111?111111111?111??????111111111111111111?111111???110001?11??11???????111111111111????1111111?1?1???????1?11111111?11??????1000111????1?1??1?1001?????111?1111111111??????11111??1111????11?11?101???11111111?1111111???1111?1?11111000111111111111111111111??111??111111?1???111111111111??111111????11?1??????111111??1111????11??1111?111111111??1111?????11??????11??1111111111111?1111111111????????1?????111111??111111??11111111111?1111?11???1111111011?1???1111?1111111?????1?11???1111111?1??1?1111??1111??????????11111111111111111111?????????1??11111???1111111??111111110?01?11101111111111111111?????????111000?????????0011??????111111011???11?1111 ; END; Begin trees; [Treefile saved Tue Jan 11 10:45:47 2011] [! >Data file = /Users/zwickl/Desktop/GarliDEV/bugsAndExperiments/orientedGapTests/mixedModel.Oct2010/indelibleSims//multiSite/zipfian1.5/del2Xins/ranaDepth1.0/totRate0.08/19/onlyDNA.nex >Tree(s) input to PAUP* as user-defined tree(s) ] Translate 1 temporariaDMH84R1, 2 boyliiMVZ148929, 3 luteiventris_MT_MVZ191016, 4 luteiventris_WA_MVZ225749, 5 muscosaMVZ149006, 6 auroraMVZ13957, 7 cascadaeMVZ148946, 8 sylvaticaMVZ137426 ; tree PAUP_1 = [&U] (1,(((2,(3,4)),(5,(6,7))),8)); End; garli-2.1-release/tests/data/expected.scr000066400000000000000000000006471241236125200204620ustar00rootroot00000000000000a.conf 6434.50916 a.G3.conf 6242.85802 a.G4.conf 6247.02222 c.conf 13269.229793 c.M3x2.conf 12956.1235 n.conf 14486.03829 n.G4.conf 13917.56622 n.G5.conf 13918.08407 p.mk.conf 474.1864 p.mkO.conf 494.9700 p.mkO.ssr.conf 477.6848 p.mk.ssr.conf 467.6783 p.mkv.conf 472.3906 p.mkvO.conf 493.667 p.mkvO.ssr.conf 474.6455633 p.mkv.ssr.conf 464.5473 p.3diff.conf 13306.20608 g.dnaBnoZ.conf 3350.2345 g.dnaMix.conf 3325.98222 garli-2.1-release/tests/data/moore.matK90-120.nex000066400000000000000000000107271241236125200213510ustar00rootroot00000000000000#NEXUS Begin data; Dimensions ntax=86 nchar=30; Format datatype=dna gap=-; Matrix Acorus ---ATGGAA------------GAATTCAAA Amborella ---ATGGAG------------GAATTACGA Anethum ---ATGGAG------------GAATTCCAA Antirrhinum ---ATGGAG------------GAAATCCAA Arabidopsis CAAATGGAT------------AAATTTCAA Atropa ---ATGGAA------------GAAATCCAA Aucuba ---ATGGAG------------GAATTCCAA Berberidopsis ---ATGGAG------------GAATTCCAA Brassica CAAATGGAG------------AAATTTCAA Bulnesia ---ATGAAG------------AAATTTCGA Buxus ---ATGAAG------------GAATTACAA Calycanthus ---ATGGAG------------GAATTACAA Ceratophyllum ---ATGGAA------------GAATCGCAT Chloranthus ---ATGGAA------------GAATTACAA Citrus ---ATGGAG------------GAATTTCAA Coffea ---ATGGAG------------GAAATTCAA Cornus ---ATGGAG------------GAATTCCAA Cucumis ---ATGGAG------------GAATTTCAA Cuscuta ---ACGGAG------------GACTTCAAA Cycas ---ATGGAT------------AAGTTTCGA Daucus ---ATGGAG------------GAATTCCAA Dillenia ---ATGGAG------------GAATTCCAA Dioscorea AAAATAGAA------------GAATTACAA Drimys ---ATGGAG------------GAATTACAA Ehretia ---ATGGAG------------GAAATTCAA Elaeis ---ATGGAA------------GAATTACAA Epifagus ------------------------------ Eucalyptus ---ATGGAG------------GAATTTCAA Euonymus ---ATGGAA------AAAGAAAAATTCCAA Ficus ---ATGGCG------------GAATTTCAA Ginkgo ---ATGGAT------------AAGTTCAAA Glycine ---ATGGAG------------GAATCTCGA Gossypium ---ATGGAG------------GAATTTCAA Gunnera ---ATGGAG------------GAATTCAAA Helianthus ---ATGGAG------------AAATTCCAA Heuchera ---ATGGGG------------GAATTTCAA Ilex ---ATGGAG------------GAATTCCAA Illicium ---ATGGAG------------GAATTACAA Ipomoea ---ATGGAG------------GAAATTCAA Jasminum ---ATGCAG------------GAAATCAAA Lactuca ---ATGGAG------------AAATTCCAA Lemna ---ATGGAA------------GAATTCAAA Liquidambar ---ATGGAG------------GAATCTCAA Liriodendron ---ATGGAG------------GAATTACAA Lonicera ---ATGGAG------------GAATTCAAA Lotus ---ATGGAG------------GAATATCAG Manihot ---ATGGAG------------GAA------ Medicago ---ATGAAG------------GAATATCAA Meliosma ---ATGGAG------------GAATTACAA Morus ---ATGGCG------------GAATTTCAA Musa ---ATGGAA------------GAATTACAA Nandina ---ATGGAA------------GAATTAAAA Nelumbo ---ATGGAG------------GAATTACAA Nerium ---ATGGAA------------GAAATCCAA Nicotiana ---ATGGAA------------GAAATCCAA Nuphar ---ATGGAGAAATTGCAATACGAATTGCAA Nymphaea CAAATGGAAAAATTGCAATACGAATTGCAA Oenothera ---ATGGAG------------GAATTCCCG Oryza CAAATGGAA------------AAATTCGAG Oxalis ---ATGTAT------------AAATATCAA Panax ---ATGGAG------------GAATTCCAA Passiflora ---ATAGAG------------AAATATCAA Pelargonium ---ATGGAA------------GAATTTCAA Phalaenopsis ------------------------------ Phaseolus ---ATGGAG------------AAATATCAA Phoradendron ---ATGGAA------------CAATTCCAA Pinus ---ATGGAT------------GAGTTCCAT Piper ---ATGGAA------------AAATTCAAA Platanus ---ATGGAA------------GAATTACAA Plumbago ---ATGGAA------------GAATTCCAA Populus AAAATAGAG------------AAATCTCAA Quercus ---ATGGAG------------GAATTTCAA Ranunculus ---ATGGAG------------GAATTACAA Rhododendron ---ATGGAG------------GAATTCAAA Scaevola ATGATGGAG------------GAATTCCAA Solanum_lycopersicum ---ATGGAA------------GAAATCCAC Spinacia ---ATGGAA------------GAATTCCAA Staphylea ---ATGGAG------------GAATTTCAA Trachelium ---ATGGCG------------GAATTTCAA Triticum CAAATGGAA------------AAATTCGAA Trochodendron ---ATGGGG------------GAATTAGAA Typha ---ATGAAA------------CAATTACAT Vitis ---ATGGAA------------GGAGTTCAA Ximenia ---ATGGAG------------AAATTCCAA Yucca ---ATGGAA------------GAATTACAA Zea CAAATGGAA------------AAATTCGAA ; End; garli-2.1-release/tests/data/moore.start000066400000000000000000000056041241236125200203460ustar00rootroot00000000000000#NEXUS Begin trees; [Treefile saved Wed Feb 16 13:33:03 2011] [! >Data file = /home/zwickl/googleCodeRepo/branches/clean-partitioning/tests/moore86x30.nex >Heuristic search settings: > Optimality criterion = parsimony > Character-status summary: > Of 30 total characters: > All characters are of type 'unord' > All characters have equal weight > 6 characters are constant > 5 variable characters are parsimony-uninformative > Number of parsimony-informative characters = 19 > Gaps are treated as "missing" > Starting tree(s) obtained via stepwise addition > Addition sequence: simple (reference taxon = Acorus) > Number of trees held at each step during stepwise addition = 1 > Branch-swapping algorithm: tree-bisection-reconnection (TBR) > Steepest descent option not in effect > Initial 'MaxTrees' setting = 100 > Zero-length branches not collapsed > 'MulTrees' option not in effect; only 1 tree will be saved > Topological constraints not enforced > Trees are unrooted > >Heuristic search completed > Total number of rearrangements tried = 433936 > Score of best tree(s) found = 93 > Number of trees retained = 1 > Time used = <1 sec (CPU time = 0.06 sec) ] Translate 1 Acorus, 2 Amborella, 3 Anethum, 4 Antirrhinum, 5 Arabidopsis, 6 Atropa, 7 Aucuba, 8 Berberidopsis, 9 Brassica, 10 Bulnesia, 11 Buxus, 12 Calycanthus, 13 Ceratophyllum, 14 Chloranthus, 15 Citrus, 16 Coffea, 17 Cornus, 18 Cucumis, 19 Cuscuta, 20 Cycas, 21 Daucus, 22 Dillenia, 23 Dioscorea, 24 Drimys, 25 Ehretia, 26 Elaeis, 27 Epifagus, 28 Eucalyptus, 29 Euonymus, 30 Ficus, 31 Ginkgo, 32 Glycine, 33 Gossypium, 34 Gunnera, 35 Helianthus, 36 Heuchera, 37 Ilex, 38 Illicium, 39 Ipomoea, 40 Jasminum, 41 Lactuca, 42 Lemna, 43 Liquidambar, 44 Liriodendron, 45 Lonicera, 46 Lotus, 47 Manihot, 48 Medicago, 49 Meliosma, 50 Morus, 51 Musa, 52 Nandina, 53 Nelumbo, 54 Nerium, 55 Nicotiana, 56 Nuphar, 57 Nymphaea, 58 Oenothera, 59 Oryza, 60 Oxalis, 61 Panax, 62 Passiflora, 63 Pelargonium, 64 Phalaenopsis, 65 Phaseolus, 66 Phoradendron, 67 Pinus, 68 Piper, 69 Platanus, 70 Plumbago, 71 Populus, 72 Quercus, 73 Ranunculus, 74 Rhododendron, 75 Scaevola, 76 Solanum_lycopersicum, 77 Spinacia, 78 Staphylea, 79 Trachelium, 80 Triticum, 81 Trochodendron, 82 Typha, 83 Vitis, 84 Ximenia, 85 Yucca, 86 Zea ; tree PAUP_1 = [&U] (1,(((((((((((((((((((((((((((((((((((((((((2,3,34),(((((((((4,19,27,6),7),16),25),55),76),32),70),78)),(((8,22),36),43)),10),11),14),((((15,(35,41)),71),(29,47)),60)),(((17,(39,54)),48),52)),(((18,42),46),(56,57))),24),26),28),((30,50),(((37,68),(65,84)),73))),38),44),(12,51)),((5,9),(13,33))),20),(((53,(64,66)),77),85)),69),72),81),83),23),31),40),45),49),21),58),59),61),62),63),67),74),75),79),80),82),86)); End; garli-2.1-release/tests/data/n.G4.start000066400000000000000000000016401241236125200177270ustar00rootroot00000000000000#NEXUS begin trees; translate 1 MorNa6, 2 ClownNa6, 3 AraNa6, 4 puffNa6, 5 NewZebra, 6 SterNa6, 7 eelNa6, 8 catNa6, 9 AptNa6, 10 PinniNa6, 11 tetra; tree bestREP2 = [&U][!GarliScore -13930.518123][!GarliModel r 1.53159 3.88003 1.55869 1.28339 4.73849 1.00000 e 0.24425 0.24251 0.24974 0.26350 a 1.25046 p 0.30177 ]((8:0.09946229,(9:0.05193751,(6:0.10492481,(7:0.08952527,10:0.10060054):0.04846139):0.02384170):0.06722100):0.04417065,((4:0.05079817,11:0.05129394):0.15580771,((2:0.10536255,1:0.20236259):0.02015659,3:0.10962909):0.05348449):0.04497161,5:0.15298556); end; begin paup; clear; gett file=21.GTRIG.best.tre storebr; lset userbr nst=6 rmat=(1.531593 3.880025 1.558690 1.283386 4.738494) base=( 0.244246 0.242510 0.249740) rates=gamma shape=1.250460 ncat=4 pinv=0.301772; end; begin garli; M1 r 1.53159 3.88003 1.55869 1.28339 4.73849 1.00000 e 0.24425 0.24251 0.24974 0.26350 a 1.25046 p 0.30177 ; end; garli-2.1-release/tests/data/n.G5.start000066400000000000000000000016421241236125200177320ustar00rootroot00000000000000#NEXUS begin trees; translate 1 MorNa6, 2 ClownNa6, 3 AraNa6, 4 puffNa6, 5 NewZebra, 6 SterNa6, 7 eelNa6, 8 catNa6, 9 AptNa6, 10 PinniNa6, 11 tetra; tree bestREP2 = [&U][!GarliScore -13931.023312][!GarliModel r 1.53027 3.88100 1.55292 1.28472 4.74166 1.00000 e 0.24445 0.24238 0.24960 0.26357 a 1.34382 p 0.30984 ](((11:0.05135093,4:0.05087766):0.15612255,(3:0.10987345,(2:0.10558309,1:0.20301026):0.02021699):0.05373359):0.04514408,(8:0.09967691,(9:0.05197581,(6:0.10515979,(7:0.08974519,10:0.10077975):0.04865254):0.02389496):0.06736931):0.04428274,5:0.15340923); end; begin garli; M1 r 1.53027 3.88100 1.55292 1.28472 4.74166 1.00000 e 0.24445 0.24238 0.24960 0.26357 a 1.34382 p 0.30984 ; end; begin paup; clear; gett file=35.GTRIG5.best.tre storebr; lset userbr nst=6 rmat=(1.530273 3.880998 1.552918 1.284724 4.741660) base=( 0.244447 0.242378 0.249602) rates=gamma shape=1.343822 ncat=5 pinv=0.309837; end; garli-2.1-release/tests/data/n.start000066400000000000000000000001651241236125200174570ustar00rootroot00000000000000#NEXUS begin garli; M1 r 1.86905 3.67120 1.22140 1.85314 4.41432 1.00000 e 0.25543 0.20855 0.23822 0.29780 ; end; garli-2.1-release/tests/data/p.3diff.start000066400000000000000000000013661241236125200204570ustar00rootroot00000000000000#NEXUS Begin trees; [Treefile saved Mon Aug 16 15:53:08 2010] [! >Data file = /home/zwickl/googleCodeRepo/branches/partitioning/tests/partTestWork/seqAndMixed/3parts.diffModelTypes/zakonEtAl2006.11tax.nex > >Processing TREES block from file "3diffModels.byCodonPos.best.tre": > Keeping: trees from file (replacing trees in memory) > 1 tree read from file ] Translate 1 MorNa6, 2 ClownNa6, 3 AraNa6, 4 puffNa6, 5 NewZebra, 6 SterNa6, 7 eelNa6, 8 catNa6, 9 AptNa6, 10 PinniNa6, 11 tetra ; tree PAUP_1 = [&U] (1,(2,(3,((4,11),(5,(((6,(7,10)),9),8)))))); End; begin garli; S 0.540677 0.300693 2.158629 M2 e 0.1 0.2 0.3 0.4 a 0.4 M1 r 1 10 2 2 1e-2 1.00000 a 0.6 M3 r 1.0 2.0 3.0 5.0 2.0 e 0.4 0.2 0.3 0.1 p 0.01 end; garli-2.1-release/tests/data/z.11x2178.AA.nex000077500000000000000000000221461241236125200203500ustar00rootroot00000000000000#NEXUS [written Wed Sep 29 15:31:28 CDT 2010 by Mesquite version 2.73 (build 544) at zwickl-WORK/129.237.138.169] [NOTE HERE THAT CODONs 513 and 646 OF PINNI HAD A Y IN IT, AND SO WAS BEING TOSSED BY GARLI IN CODON-AA THE AMBIGUITY ACTUALLY STILL ONLY ALLOWS IT TO CODE FOR ONE AA (L and K respectively, SO MESQUITE JUST TRANSLATES IT NORMALLY THAT AA IS TAKEN OUT HERE. EDIT: Since GARLI now translates and allows codon ambiguity if it doesn't cause protein ambiguity, those AAs have been added back in. ] BEGIN TAXA; TITLE Taxa; DIMENSIONS NTAX=11; TAXLABELS MorNa6 ClownNa6 AraNa6 puffNa6 NewZebra SterNa6 eelNa6 catNa6 AptNa6 PinniNa6 tetra ; END; BEGIN CHARACTERS; TITLE Protein_translation_of_Character_Matrix; DIMENSIONS NCHAR=727; FORMAT DATATYPE = Protein GAP = - MISSING = ?; MATRIX MorNa6 PVTPHFEHVLSVGNLVFSGIFAGEMVLKIIAMDPYYYFQVGWNVFDSIIVTMSMVEMVLADVEGLSVLRSFRLLRVFKLAKSWPTLNMLLTIIGNSVGALGNLTVVLAIIVFIFAVVGMQLFAKNYKDCVCKIAEDCELPRWHMHDFFHSFLIVFRILCGEWIETMWDCMEVANRNMCLVLFLMVMIIGNLVVLNLFLALLLSSFSGDNLQMADDDGELNNLQLSALRITRAIDWVKAYVRGLIWKILGKQPRVLDGLSHWATFTVPIAQEESDLEDGVSECSTVDYVPPPPDEVEEPEPVEPEACYTDNCLRRCPCLVLDTSEGRGKTWWNLRRTCYTIVEHDYFESSIIFMILLSSGALAFEDIYLERRRTIKILLEYADKVFSYVFVIEMLLKWVAYGYKVYFTNAWCWLDFLIVDVSLVSLAASIMGYSELGPIKSLRTLRALRPLRALSRFEGMRVVVNALVGAVPAIFNVMLVCLIFWLIFSIMGVNLFAGTFYHCLNTTTGEMFTIDVVNNYSECLALMHTNEVRWANVRVNYDNVGMGYLSLLQVSTFKGWMEIMYAAVDSRKVGQQPSYEANLYMYVYFVIFIIFGSFFTLNLFIGVIIDNFNQQKNKMGG-DCFMTEEQKKYYDAMKKLGNKKPAKPIPRPTGKIPGLVYDFISQQAFDIFIMVLICLNMVTMMVEEDDQSEQKTDMLGKINAVFIVVFSSECLLKMIALRQYFFT- ClownNa6 PMSPEFDHMLSVGNLVFTGIFTAEMVLKLIAMDPYYYFQVGWNIFDSIIVTLSLVELGLANVQGLSVLRSFRLLRVFKLAKSWPTLNMLIKIIGNSVGALGNLTLVLAIIVFIFAVVGMQLFGKTYKDCVCKIASDCELPRWHMNDFFHSFLIVFRILCGEWIETMWDCMEVAGAGMCLVVFMMVMVIGNLVVLNLFLALLLSSFSGDNLAGGDEDGEMNNLQIAIGRITRGIDWVKAFVMGLVWRVMGKKPKMLDGLSHWVTLSVPMAQEESDLEDDSSECSTVDYRPPEPVEEEEPEQVEPVECFTDDCVRRCPCLTVDITQGKGRTWWNLRKTCYTIVEHDYFETFIIFMILLSSGALAFEDIYIERRRTIKIILEYADKVFTYVFVVEMLLKWVAYGFKTYFTNAWCWLDFLIVDVSLISLTANLMGYSELGPIKSLRTLRALRPLRALSRFEGMRVVVNALVGAILSIFNVLLVCLIFWLIFSIMGVNLFAGKFYRCINTTTEELLPVEIVNNKSDCLNLMHTNEVRWVNVKVNYDNVGLGYLSLLQVATFKGWMDIMYAAVDSREVEEQPLYEENLYMYLYFVIFIIFGSFFTLNLFIGAIIDNFNQQKKKLGGKDIFMTEEQKKYYNAMKKLGSKKPVKPIPRPTNKIQGVVFDFISQQFFDIFIMVLICLNMVTMMVETDDQSQEKENILNQINLVFIVIFTSECVLKMFALRHYFFT- AraNa6 PMSPAFDHMLTVGNLVFTGIFTAEMVFKLIAMDPYHYFQVGWNIFDSIIVTLSLVELGLANVQGLSVLRSFRLLRVFKLAKSWPTLNMLIKIIGNSVGALGNLTLVLAIIVFIFAVVGMQLFGKSYKDCVCKIAEDCELPRWHMNDFFHSFLIVFRILCGEWIETMWDCMEVAGAGMCLVVFMMVMVIGNLVVLNLFLALLLSSFSGDNLAGGDDDGEMNNLQIAIGRITRGIDWIKAFAMGFIWKLLGKKAKMLDGLSHWVTLSVPIAQGESDLEDDSSECSTVDYRPPEPEEEEEPEQQEPEACFTEDCFRRMPCLMVDITQGKGKTWWKLRKTCFTIVEHGYFETFIIFMILLSSGALAFEDIYIEKRRVIKIILEYADKVFTYVFVIEMVLKWVAYGFKVYFTNAWCWLDFLIVDVSLISLTANLMGYSELGPIKSLRTLRALRPLRALSRFEGMRVVVNALVGAILSIFNVLLVCLIFWLIFSIMGVNLFAGKFYYCINTTSEERLPIDVVNNKSDCMALMHTNEVRWVNVKVNYDNVGLGYLSLLQVATFKGWMDIMYAAVDSREVGEQPSYEVNIYMYLYFVIFIIFGSFFTLNLFIGVIIDNFNQQKKKLGGKDIFMTEEQKKYYNAMKKLGSKKPVKPIPRPSNKIQGMVFDFITQQFFDIFIMVLICLNMVTMMVETDDQSEDKENVLYQINLVFIVIFTCECVLKMFALRQYFFT- puffNa6 PMTEEFDYMLSVGNLVFTGIFAAEMFFKLIAMDPYYYFQVGWNIFDSIIVTLSLVELGLANVQGLSVLRSFRLLRVFKLAKSWPTLNMLIKIIGNSVGALGNLTLVLAIIVFIFAVVGMQLFGKSYKDCVCKISSDCELPRWHMNDFFHSFLIVFRILCGEWIETMWDCMEVAGAGMCLVVFMMVMVIGNLVVLNLFLALLLSSFSGDNLSVGDDDGELNNLQIAIGRITRGGNWLKAFFIGTLQRVLGREPKLADGIANCLSITVPIALGESDSEGDSSVCSTVDYQPPEPEEEEEPDLVEPEACFTDNCVKRWPCLNVDISQGKGKKWWNLRKTCFTIVEHDWFETFIIFMILLSSGALAFEDIYIERRRTVKIVLEFADKVFTFIFVIEMLLKWVAYGFKTYFTNAWCWLDFFIVDISLISLSANLMGFSDLGPIKSLRTLRALRPLRALSRFEGMRVVVNALIGAIPSIFNVLLVCLIFWLIFSIMGVNLFAGKFYRCINTTTAELFPISVVNNKSDCVALQATQEARWVNVKVNYDNVAKGYLSLLQIATFKGWMDIMYPAVDSREVEEQPSYEINLYMYIYFVIFIIFGSFFTLNLFIGVIIDNFNQQKKKLGDKDIFMTEEQKKYYEAMKKLGSKKPQKPIPRPANLIQGLVFDFISQQFFDIFIMVLICLNMVTMMVETDDQSPAKEDFLFKVNVAFIVVFTGECTLKLIALRHYFFT- NewZebra PMSPHFEHVLSVGNLVFTGIFTAEMVFKLIAMDPYYYFQVGWNIFDSIIVTLSLVELGLANVQGLSVLRSFRLLRVFKLAKSWPTLNMLIKIIGNSVGALGNLTLVLAIIVFIFAVVGMQLFGKSYKDCVCKISEDCELPRWHMNDFFHSFLIVFRILCGEWIETMWDCMEVAGASMCLIVFMMVMVIGNLVVLNLFLALLLSSFSGDNLSGGDDDGEMNNLQIAIGRITRGIDWVKALVASMVQRILGKKPKMADGLTNCLTLTVPIARCESDVEGDSSVCSTVDYQPPEPVEEEEPEPEEPEACFTEGCIRRCACLSVDITEGWGKKWWNLRRTCFTIVEHDYFETFIIFMILLSSGALAFEDINIERRRVIKIILEYADKVFTYIFIVEMLLKWVAYGFKTYFTNAWCWLDFLIVDVSLVSLTANLMGYSELGAIKSLRTLRALRPLRALSRFEGMRVVVNALVGAIPSIFNVLLVCLIFWLIFSIMGVNLFAGKFYHCINTTTEERIPMDVVNNKSDCMALMYTNEVRWVNVKVNYDNVGLGYLSLLQIATFKGWMDIMYAAVDSREVDEQPSYEINLYMYLYFVIFIIFGSFFTLNLFIGVIIDNFNQQKSKFGGKDIFMTEEQKKYYNAMKKLGAKKRPKPIPRPSNIIQGLVFDFISKQFFDIFIMVLICLNMVTMMIETDDQSAEKEYVLYQINLVFIVVFTSECVLKLFALRQYFFT- SterNa6 PMSETFQHVLTIGNLVFTTIFTAEMVSKIIALDPYYYFQVGWNIFDCIIVTLSLVELSLSNMPGLSVLRSFRLMRIFKLAKSWPTLNMLIKIIGNSMGALGNLTFVLAIVIFIFAVVGFQLFGKSYKDNVCKVSADCTLPRWHMNDFFHSFLIVFRILCGEWIETMWDCMEVDGVPMCLTVFMMVMVIGNLVMLNLFLALLLSSFSCDNLAAPDDDSEVTNIQISIVRISRGISWVKKFIVGTAWWIMGRKPKIVDGITNYVVLNVPIAKGESEVEDDSSICSSVDYELLQPEEEKE-EPVDPEACFTENCVRYFPCLDVDITQGKGKIWWNLRCTCYNIVEHHYFENFLIFMILLSSGVLAFEDVNIERRRVIKTMLEYADIVFTYIFVVEMFLKWTAYGFKAYFTSAWCWLDFFIVDVSVISLVANVLGYAELGPVRSLRTFRALRPLRALSRFEGMRVVVNALLGAIPSIMNVLLVCLIFWLIFSIMGVNLFAGKFYRCINTTTDEVLSTEQVNNRSECMALMHTNEVRWVNLKVNYDNVGQGYLSLLQVATFKGWMGIMYGAVDSREVEDQPSYEINLYMYLYFVIFITFGSFFILNLFIGVIIDNFNRQKQKLGGDDLFMTDEQKKYYAAMKKLGSKKPLKPIPRPSNMVQGVVFDFISQKFFDISIMVLICLNMVIMMVEADDQSEEKENVLYQINIIFIV?FTGESLLKLFGLRHYFFT- eelNa6 PMNESFQSLLSAGNLVFTTIFAAEMVLKIIALDPYYYFQQTWNIFDSIIVSLSLLELGLSNMQGMSVLRSLRLLRIFKLAKSWPTLNILIKIICNSVGALGNLTIVLAIIVFIFALVGFQLFGKNYKEYVCKISDDCELPRWHMNDFFHSFLIVFRALCGEWIETMWDCMEVGGVPMCLAVYMMVIIIGNLVMLNLFLALLLSSFSSDNLSSIEEDDEVNSLQVASERISRAKNWVKIFITGTVLWIQGKKPKIVDGITNCVTLNLPIVKGESEIEEDSSVCSTVDYSPSEQEEPEELESKDPEACFTEKCIWRFPFLDVDITQGKGKIWWNLRRTCYTIVEHDYFETFIIFMILLSSGVLAFEDIYIWRRRVIKVILEYADKVFTYVFIVEMLLKWVAYGFKRYFTDAWCWLDFVIVGASIMGITSSLLGYEELGAIKNLRTIRALRPLRALSRFEGMKVVVRALLGAIPSIMNVLLVCLMFWLIFSIMGVNLFAGKFYRCINTTTDEILPVEEVNNRSDCMALMYTNEVRWVNLKVNYDNAGMGYLSLLQVSTFKGWMDIMYAAVDSREVEDQPIYEINVYMYLYFVIFIVFGAFFTLNLFIGVIIDNFNRQKQKLGGEDLFMTEEQKKYYNAMKKLGSKKAAKCIPRPSNVVQGVVYDIVTQPFTDIFIMALICINMVAMMVESEDQSQVKKDILSQINVIFVIIFTVECLLKLLALRQYFFT- catNa6 PMSSNFEHVLSVGNLVFTGIFTAEMVFKLIALDPFYYFQVGWNIFDSIIVTLSLVELGLANVQGLSVLRSFRLLRVFKLAKSWPTLNMLIKIIGNSVGALGNLTLVLAIVVFIFAVVGMQLFGKSYKDCVCKIAEDCELPRWHMNDFFHSFLIVFRILCGEWIETMWDCMEVAGAGMCLVVFLMVMVIGNLVVLNLFLALLLSSFSGDNLSAGDEDGEMNNLQIAIGRITRGIDWVKSFIIGLVQQILCRKPKMADRLTNCLTLNVPIAKAESDVEEDSSMCSTVDYRPPESEEEEEPEPVEPEACFTENCVRRCPCLNLDITQGRGKSWWNLRRTCYTIVEHDYFETFIIFMILLSSGALAFDDIYIERRRVIKIILEYADQVFTYIFVIEMLLKWVAYGFKTYFTNAWCWLDFFIVDVSLIGLTANLLGYSELGPIKSLRTLRALRPLRALSRFEGMRVVVNALLGAIPSIMNVLLVCLIFWLIFSIMGVNLFAGKYYRCINTTTEELLPIEQVNNMSDCIALMHTKEARWVNVKVNFDNVGLGYLSLLQEATFKGWMDIMYAAVDSREVEEQPSYEINIYMYLYFVIFIIFGSSFTLNLFIGVIIDNFNQQKQKFGGEDLFMTEEQKKYYNAMKKLGSKKPVKPIPRPANMIQGIVFDFISQQFFDIFIMVLICLNKVTMMIETDDQSAEKEYVLYQINLIFIVVFTGECILKMFALRQYFFT- AptNa6 ---------LTVGNLVFTGIFTAEMVFKLIAMDPYYYFQVGWNIFDSIIVTLSLVELGLANVQGLSVLRSFRLLRVFKLAKSWPTLNMLIKIIGNSVGALGNLTLVLAIIVFIFAVVGMQLFGKSYKDCVCKIALDCELPRWHMTDFFHSFLIVFRILCGEWIETMWDCMEVAGPSMCLIVFMLVMVIGNLVVLNLFLALLLSSFSGDNLSASDDDSEINNLQIATGRISRAIGWVKNFIISTVQWVLGRKPKMVDGMTNCVVLNVPIAKGESEIEGDYSVCSTADYRPPEPEEEKVPETNDPEACFTENCVRRFPCLNVDITQGKGKSWWNLRRTCYIIVEHDYFETFIIFMILLSSGALAFEDIYIERRKMIKIILEYADKIFTYVFIMEMLLKWVAYGFKTYFTNAWCWLDFLIVDVSIISLTANLLGYSELGPIKSLRTLRALRPLRALSRFEGMRVVVNALVGAIPSIMNVLLVCLIFWLIFSIMGVNLFAGKFYRCINTTTEELLPMEEVNNRSDCMALMHTNEVRWVNVKVNYDNVALGYLSLLQVATFKGWMDIMYAAVDSREVEEQPSYEINLYMYLYFVIFIILGSFFTLNLFIGVIIDNFNRQKQKFGGEDLFMTEEQKKYYNAMKKLGSKKPVKPIPRPTNVIQGVVFDLISQQFFDIFIMVLICLNMVTMMVETDDQSKEKEHILYQINVIFIVVFTGECLLKMFALRQYFFT- PinniNa6 PMSETFDYVLSTGNLVFTIIFAAEMVLKLIAMDPYYYFQQTWNIFDFFIVSLSLVEMGLANMQGLSVLRSFRLLRIFKLAKSWPTLNILIKIICNSVGALGNLTIVLAIIVFIFALVGMQLFGKNYKEFVCKISADCTLPRWHMNDFFHSFLIVFRCLCGEWIETMWDCMEVGGVPMCLSVYMMVIIIGNLVVLNLFLALLLSSFSGDNLTANDDDQEDNNILIAAERISRAKLWVKGFIIRTVLGMLGKEPKIVNGLANGVVLNVPIAKGESETEDDSSVCSTVDYSPPNPEEPEEPEPDNPEDCLTEECVSRFPWLNVDITQPKGKSWWNLRRTCYVIVEHDYFETFIIFMILLSSGALAFEDIYIERRRVIKIILEYADKVFTYIFIAEMLLKWVAYGFKKYFSDAWCWLDFLIVDVSIISLTANLLGYSELGPIKSLRTLRALRPLRALSRFEGMRVVVKALVGAIPSIVNVLLVCLMFWLIFSIMGVNLFAGKFYRCINTTTEETMPLEEVNNRSDCNALMYTNEVRWVNLKVNYDNAGMGYLSLLQVATFKGWMDIMYAAVDSRGVEDQPIYEINVYMYLYFVIFIVFGSFFTLNLFIGVIIDNFNRQKQKLGGDDLFMTEEQKKYYDAMKKLGSKKPVKVIPRPSNKILGVLYDIVNQRVTDIFIMSLIWLNMVTMMVETDDQSEEKKNVLYQINLIFIIIFTGECLLKLLALRHYFFT- tetra PMTQEFDYMLSVGNLVFTGIFAAEMFFKLIAMDPYYYFQVGWNIFDSIIVTLSLVELGLANVQGLSVLRSFRLLRVFKLAKSWPTLNMLIKIIGSSVGALGNLTLVLAIIVFIFAVVGMQLFGKSYKDCVCKISTECELPRWHMNDFFHSFLIVFRILCGEWIENMWACMEVAGAGMCLVVFMMVMVIGNLVVLNLFLALLLSSFSGDNLSIGEDDGEMNNLQIAIGRITRGGNWLKTLVIRTVLQLLGREQKTADGIANCLVINVPIALGESDSEGESSVCSTADYRPPEPEEEEEPEPLEPEACFTDNCVKHWPCLNVDVTQGQGKKWWNLRKTCFTIVEHDWFETFIIFMILLSSGALAFEDIYIERRRTVKIILEFADKVFTFIFVLEMVLKWVAYGFKTYFTNAWCWLDFFIVDISLISLSANLMGLSDLGPIKSLRTLRALRPLRALSRFEGMRVVVNALIGAIPSIFNVLLVCLIFWLIFSIMGVNLFAGKFYHCINTTTQELFPISVVNNKSDCMAVQATQEARWVNVKVNYDNVGKGYLSLLQIATFKGWTAIMYAAVDSREVEEQPSYEINLYMYIYFVIFIIFGAFFTLNLFIGVIIDNFNQQKRKI-NKDIFMTEEQKKYYEAMKKLGSKKPQKPIPRPTNLIQGMVFDFISQQFFDIFIMVLICLNMVTMMVETDDQSPEKEDFLFKVNVAFIVVFTGECMLKLIALRQYFFT- ; END; BEGIN ASSUMPTIONS; TYPESET * UNTITLED = unord: 1 - 727; wtset * equal = 1: 1-.; END; BEGIN CODONS; CODESET * UNTITLED = universal: 1 - 727; END; BEGIN MESQUITECHARMODELS; ProbModelSet * UNTITLED (CHARACTERS = Protein_translation_of_Character_Matrix) = 'Mk1 (est.)': 1 - 727; END; garli-2.1-release/tests/data/z.11x2178.nex000066400000000000000000000575431241236125200200760ustar00rootroot00000000000000#NEXUS [ This dataset is from: Zakon, Lu, Zwickl and Hillis. 2006. Sodium channel genes and the evolution of diversity in communication signals of electric fishes: Convergent molecular evolution. Proc. Natl. Acad. Sci. USA. 103(10):3675-80. ] begin data; dimensions ntax=11 nchar=2178; format datatype=dna missing=? gap=-; matrix MorNa6 CCTGTGACTCCACATTTTGAGCACGTACTCAGTGTGGGAAACCTGGTTTTCTCAGGGATATTTGCTGGTGAAATGGTCTTGAAAATTATTGCTATGGACCCCTACTACTACTTCCAGGTTGGATGGAACGTGTTTGACAGCATCATTGTTACCATGAGTATGGTGGAGATGGTACTGGCTGATGTAGAGGGTCTGTCGGTTCTGCGGTCCTTTCGTTTGCTACGTGTCTTCAAGCTTGCCAAATCATGGCCTACCCTCAACATGCTGCTAACGATCATCGGAAACTCAGTGGGTGCTCTGGGGAACCTCACCGTGGTGCTGGCCATCATCGTTTTCATCTTCGCTGTGGTTGGAATGCAGCTGTTTGCCAAAAACTACAAGGACTGCGTCTGCAAGATCGCCGAGGATTGTGAGCTGCCCCGGTGGCACATGCATGACTTCTTCCACTCTTTCCTCATCGTGTTCCGCATCCTCTGTGGAGAGTGGATTGAGACCATGTGGGACTGCATGGAAGTGGCCAACAGAAACATGTGTTTGGTCCTCTTCTTAATGGTCATGATAATTGGGAACCTGGTGGTTCTGAACCTTTTCCTGGCCTTGCTGCTTAGCTCATTCAGCGGGGACAATCTGCAAATGGCAGATGACGACGGCGAGCTGAACAATCTGCAGCTTTCCGCACTCAGGATCACCAGAGCCATTGATTGGGTGAAGGCCTACGTTAGAGGGCTGATCTGGAAGATCCTGGGCAAGCAGCCAAGAGTGCTGGATGGTTTATCTCACTGGGCAACCTTCACCGTACCCATTGCCCAGGAAGAGTCTGATTTAGAAGATGGTGTGTCTGAGTGCAGCACAGTGGACTACGTGCCCCCTCCGCCGGATGAAGTGGAGGAACCGGAGCCTGTGGAACCTGAGGCCTGTTACACTGACAACTGCCTTAGACGGTGTCCTTGTCTGGTGCTGGACACCTCAGAGGGCAGAGGGAAGACCTGGTGGAACCTCAGGAGAACCTGCTACACCATTGTGGAGCATGACTACTTTGAGTCCTCCATAATCTTCATGATCCTTCTCAGCAGTGGTGCCTTGGCCTTTGAAGACATATATCTTGAAAGACGCAGAACGATAAAAATCCTGCTGGAATATGCAGATAAAGTCTTCAGCTATGTATTTGTTATTGAGATGCTCCTTAAGTGGGTGGCTTATGGTTACAAAGTATACTTTACCAATGCCTGGTGCTGGCTGGACTTCTTGATTGTTGATGTTTCCTTGGTCAGTTTGGCAGCAAGCATAATGGGCTATTCTGAACTAGGACCCATAAAGTCTTTGAGAACTCTTAGGGCTCTGAGGCCTCTAAGAGCCCTTTCCAGGTTTGAGGGGATGCGGGTTGTGGTGAACGCCCTTGTGGGGGCCGTCCCCGCCATCTTCAATGTGATGCTGGTCTGTCTCATCTTCTGGCTCATCTTCAGCATCATGGGGGTTAACCTGTTTGCCGGGACATTCTACCACTGCCTCAACACCACAACTGGGGAGATGTTTACCATTGATGTTGTAAACAACTATAGTGAGTGTTTGGCCCTCATGCACACAAACGAGGTGCGCTGGGCCAACGTCAGGGTCAACTATGACAACGTTGGGATGGGTTACCTGTCTCTGTTGCAAGTGTCAACATTCAAAGGCTGGATGGAAATTATGTATGCGGCTGTCGACTCACGTAAGGTGGGTCAACAGCCCTCATATGAGGCCAACCTTTACATGTACGTGTACTTTGTCATCTTCATCATCTTTGGGTCCTTCTTTACACTCAACCTCTTCATTGGTGTCATCATTGACAACTTCAATCAACAAAAGAATAAGATGGGAGGA---GATTGCTTTATGACTGAGGAGCAGAAGAAATATTACGACGCTATGAAAAAGCTAGGCAACAAGAAGCCAGCGAAGCCCATTCCAAGACCAACGGGCAAAATACCAGGCCTAGTATATGACTTCATCAGTCAGCAGGCCTTTGACATCTTTATCATGGTACTGATTTGCCTGAACATGGTGACCATGATGGTGGAGGAAGATGACCAAAGTGAACAGAAGACAGACATGCTGGGCAAAATCAATGCAGTCTTCATTGTGGTCTTCAGCAGTGAATGTTTGCTGAAGATGATTGCACTGAGACAATACTTCTTTACC ClownNa6 CCCATGAGCCCTGAGTTTGACCACATGCTCTCTGTGGGAAACCTGGTTTTCACTGGAATCTTCACAGCTGAAATGGTCCTAAAACTCATTGCTATGGACCCCTACTACTACTTCCAGGTTGGATGGAACATATTTGACAGCATCATTGTCACTCTAAGCCTAGTGGAACTGGGGCTCGCTAATGTTCAGGGTCTGTCAGTCCTGCGATCCTTTCGTTTGTTGCGAGTGTTCAAGCTGGCAAAGTCTTGGCCCACCCTCAACATGCTGATCAAGATCATCGGGAATTCCGTGGGCGCCCTGGGCAACCTGACCCTGGTGCTGGCCATCATCGTCTTCATCTTCGCCGTGGTGGGCATGCAGCTCTTTGGGAAGACCTACAAGGACTGCGTGTGCAAGATTGCCAGTGACTGCGAGCTTCCCCGCTGGCACATGAATGACTTCTTCCACTCGTTCCTTATCGTGTTCCGCATCCTCTGCGGGGAGTGGATCGAGACCATGTGGGACTGCATGGAGGTGGCTGGTGCAGGCATGTGCCTCGTGGTCTTCATGATGGTCATGGTCATTGGGAACCTAGTGGTGCTGAATCTCTTCCTGGCTTTGCTGCTCAGTTCATTCAGTGGAGACAACCTAGCAGGCGGTGATGAGGATGGCGAGATGAACAACTTGCAGATTGCTATCGGAAGGATCACCCGAGGCATTGACTGGGTGAAGGCATTTGTCATGGGACTGGTGTGGCGGGTGATGGGCAAAAAGCCTAAAATGCTGGATGGTTTATCTCACTGGGTAACCCTCAGTGTGCCCATGGCACAGGAGGAATCCGACTTAGAAGACGACTCCTCTGAATGCAGCACTGTGGACTATAGGCCTCCAGAGCCAGTGGAGGAGGAAGAACCAGAACAGGTGGAGCCTGTGGAGTGTTTTACTGATGACTGTGTCAGACGTTGCCCTTGTCTGACGGTGGACATCACGCAGGGCAAAGGAAGGACCTGGTGGAATCTCAGGAAAACATGTTACACCATCGTGGAGCATGACTACTTTGAGACCTTCATCATCTTCATGATCCTGCTTAGCAGTGGGGCCTTGGCCTTTGAAGATATATACATTGAAAGGCGCAGAACAATAAAAATCATTCTGGAATATGCAGACAAAGTATTTACATACGTATTTGTTGTTGAAATGCTCTTGAAGTGGGTTGCTTATGGTTTCAAGACATACTTCACTAATGCCTGGTGCTGGCTGGACTTTTTAATTGTGGATGTGTCCTTGATCAGTTTGACAGCAAACCTCATGGGCTACTCAGAGCTGGGGCCTATCAAATCCCTGAGAACCCTGAGGGCCCTGAGGCCACTACGAGCCCTGTCTAGGTTTGAGGGCATGAGAGTGGTGGTAAATGCATTGGTAGGGGCCATCCTTTCCATCTTCAACGTACTGCTGGTCTGTCTCATTTTCTGGCTTATCTTCAGCATTATGGGTGTCAACCTTTTTGCTGGAAAGTTCTACCGCTGTATCAACACCACCACAGAGGAGCTATTACCTGTCGAGATTGTGAACAATAAGAGTGACTGCTTGAATCTCATGCACACAAATGAAGTGCGCTGGGTCAATGTGAAGGTCAACTATGACAACGTTGGCCTTGGTTACCTCTCTCTACTCCAAGTTGCAACATTTAAAGGGTGGATGGACATTATGTATGCAGCTGTGGACTCTCGTGAGGTGGAAGAGCAGCCCTTGTATGAGGAAAACCTCTATATGTACTTATACTTCGTCATCTTCATCATTTTTGGGTCATTCTTTACACTCAACCTTTTCATTGGTGCCATCATCGACAACTTTAATCAGCAAAAGAAAAAGCTTGGTGGGAAGGATATCTTCATGACCGAGGAGCAAAAGAAGTACTACAATGCCATGAAAAAGCTTGGTTCCAAAAAGCCAGTGAAGCCTATTCCAAGACCTACGAACAAAATACAAGGTGTGGTATTTGACTTCATCAGTCAGCAGTTCTTTGACATCTTTATCATGGTATTGATCTGCCTCAACATGGTGACCATGATGGTGGAGACAGATGACCAAAGTCAGGAAAAAGAGAATATACTGAACCAAATCAATCTGGTATTCATTGTGATCTTCACCAGCGAATGCGTCTTGAAGATGTTTGCACTTAGACATTATTTCTTCACC AraNa6 CCAATGAGTCCCGCGTTTGACCATATGCTGACCGTGGGAAACCTCGTTTTTACGGGGATCTTTACAGCTGAGATGGTATTCAAGCTCATCGCCATGGATCCATACCACTACTTCCAGGTTGGATGGAACATTTTTGACAGCATCATTGTCACACTTAGCCTGGTGGAGCTGGGTCTCGCGAATGTTCAGGGCCTTTCGGTCTTGCGCTCCTTCCGCTTGCTGCGGGTCTTCAAGCTGGCCAAGTCTTGGCCTACCCTGAACATGCTCATCAAGATCATTGGAAACTCAGTGGGTGCCCTAGGGAACCTCACACTGGTGCTGGCCATCATCGTCTTCATCTTCGCTGTCGTGGGCATGCAGCTGTTCGGTAAGAGCTACAAGGACTGTGTGTGTAAGATTGCAGAGGACTGTGAGCTACCCCGCTGGCACATGAATGACTTCTTCCACTCCTTCCTCATCGTGTTCCGCATCTTGTGTGGCGAGTGGATCGAGACCATGTGGGACTGCATGGAGGTGGCGGGCGCTGGCATGTGTCTCGTTGTCTTCATGATGGTCATGGTCATCGGCAACCTGGTGGTCCTGAACCTCTTCCTGGCTTTGCTGCTGAGCTCGTTCAGTGGAGACAACCTGGCTGGAGGAGACGATGATGGCGAGATGAACAACCTGCAGATTGCCATTGGCAGGATCACCAGAGGCATTGACTGGATAAAAGCCTTTGCCATGGGCTTCATATGGAAGTTACTTGGAAAGAAGGCCAAGATGCTGGATGGTTTATCCCACTGGGTGACCCTGAGTGTTCCCATTGCCCAGGGAGAGTCTGATTTGGAGGATGACTCCTCTGAATGCAGCACGGTGGACTACAGACCCCCAGAACCAGAGGAGGAGGAGGAGCCTGAGCAGCAGGAGCCTGAGGCCTGTTTTACTGAGGATTGCTTCCGGCGTATGCCATGTTTGATGGTGGACATCACGCAGGGGAAGGGCAAGACCTGGTGGAAACTACGGAAAACCTGTTTTACCATTGTGGAGCATGGCTATTTTGAGACCTTCATCATTTTCATGATCCTTCTCAGCAGTGGAGCTCTGGCTTTTGAAGACATATACATTGAAAAGCGCAGAGTTATCAAAATCATCCTGGAATATGCGGACAAAGTCTTCACCTATGTATTTGTTATTGAAATGGTCCTCAAGTGGGTGGCTTATGGGTTCAAAGTATACTTCACAAACGCCTGGTGCTGGCTGGACTTCCTCATCGTTGATGTGTCCTTGATCAGTCTGACCGCTAACCTCATGGGCTACTCTGAGCTGGGGCCCATTAAGTCTCTGAGAACACTTAGGGCCCTTAGGCCCCTGAGGGCCCTCTCCAGGTTTGAGGGGATGAGGGTGGTGGTAAATGCGCTTGTGGGAGCCATCCTCTCCATTTTCAACGTTCTGCTCGTGTGCCTCATCTTCTGGCTCATCTTCAGCATCATGGGCGTTAACCTGTTTGCTGGGAAGTTCTACTACTGCATTAACACCACCTCAGAGGAGCGCTTACCCATTGATGTTGTGAATAACAAGAGCGACTGCATGGCCCTAATGCACACCAATGAGGTGCGCTGGGTCAACGTCAAGGTGAACTATGACAATGTCGGCTTGGGCTATCTCTCTCTGCTGCAGGTGGCTACTTTTAAAGGTTGGATGGATATAATGTATGCTGCCGTGGACTCACGGGAGGTGGGGGAGCAACCCTCCTATGAGGTCAACATCTACATGTACTTGTACTTTGTCATCTTCATCATCTTCGGGTCCTTCTTCACGCTCAACCTCTTCATTGGTGTCATCATTGACAACTTCAATCAGCAAAAGAAAAAGTTAGGAGGAAAAGACATATTCATGACTGAGGAACAGAAGAAGTATTACAATGCCATGAAGAAACTTGGCTCCAAGAAGCCAGTGAAGCCCATCCCACGACCTTCGAATAAAATTCAAGGCATGGTGTTTGACTTCATTACGCAGCAGTTTTTTGATATTTTCATCATGGTACTGATCTGCCTCAACATGGTGACCATGATGGTGGAGACGGATGATCAAAGCGAGGACAAAGAAAATGTCCTCTACCAGATTAACCTGGTCTTCATTGTGATCTTCACCTGCGAGTGCGTCCTCAAAATGTTTGCGCTTAGACAGTACTTCTTCACC puffNa6 CCCATGACCGAAGAGTTCGACTACATGCTTTCAGTGGGAAATCTGGTTTTCACAGGAATCTTCGCGGCGGAAATGTTCTTCAAATTGATCGCCATGGATCCGTACTACTATTTCCAAGTTGGCTGGAACATTTTTGACAGCATCATCGTCACGCTCAGTCTGGTGGAGTTAGGGCTTGCAAACGTCCAGGGGCTGTCCGTCCTCAGGTCCTTCCGTCTGCTTCGGGTCTTCAAACTTGCCAAGTCCTGGCCCACGCTCAACATGCTGATCAAGATTATCGGTAATTCAGTTGGAGCTTTAGGGAATCTGACTTTGGTGCTGGCCATCATCGTCTTCATCTTCGCTGTGGTGGGGATGCAGCTCTTCGGCAAAAGCTACAAGGACTGTGTGTGCAAGATTTCCTCCGACTGCGAGCTGCCACGCTGGCACATGAATGACTTCTTCCACTCCTTCCTCATCGTGTTCCGCATCCTGTGCGGCGAGTGGATCGAGACCATGTGGGACTGCATGGAGGTGGCTGGAGCCGGGATGTGCTTGGTTGTCTTCATGATGGTCATGGTCATCGGGAACCTCGTGGTGTTGAATCTCTTCCTGGCCCTGCTGCTCAGCTCATTCAGCGGAGACAACCTCTCGGTCGGAGACGACGATGGAGAGCTGAACAATCTTCAGATCGCCATCGGAAGGATCACACGAGGCGGCAACTGGCTCAAAGCCTTCTTCATCGGAACGCTTCAACGGGTTCTTGGAAGGGAACCAAAATTGGCAGACGGGATCGCCAACTGTCTTAGTATCACCGTCCCCATCGCCCTGGGAGAGTCGGACTCTGAAGGCGATTCTTCAGTGTGCAGCACAGTGGACTATCAGCCCCCAGAGCCTGAGGAAGAGGAAGAGCCGGACCTGGTGGAGCCAGAGGCCTGCTTCACTGACAACTGTGTGAAGCGCTGGCCTTGTCTGAACGTGGACATCAGCCAGGGGAAAGGAAAGAAGTGGTGGAACCTCCGCAAGACCTGCTTCACTATTGTGGAGCATGACTGGTTTGAGACCTTCATCATTTTCATGATCCTCCTCAGCAGCGGAGCTCTGGCCTTTGAAGACATATACATCGAAAGACGAAGAACCGTGAAAATTGTCCTGGAGTTTGCTGACAAAGTTTTCACCTTCATCTTTGTCATCGAGATGCTCCTGAAATGGGTCGCCTATGGCTTCAAGACCTACTTCACCAATGCCTGGTGCTGGCTGGACTTTTTCATCGTGGACATTTCCCTGATCAGTCTATCTGCCAACTTGATGGGCTTCTCTGACCTCGGACCAATCAAATCGCTCAGAACTCTCAGGGCTCTGCGGCCTCTTCGGGCGCTGTCCAGATTTGAAGGGATGAGGGTGGTGGTGAACGCTCTCATCGGAGCCATTCCCTCCATCTTCAACGTGCTCCTGGTGTGCCTGATCTTCTGGCTCATCTTCAGCATCATGGGAGTGAACCTGTTTGCGGGGAAGTTCTACCGCTGCATCAACACCACCACGGCGGAGCTCTTCCCCATCTCTGTGGTCAACAACAAGAGCGACTGCGTGGCGCTGCAGGCCACGCAGGAGGCCCGCTGGGTCAACGTCAAAGTCAACTACGACAACGTGGCAAAAGGCTACCTGTCGCTGCTTCAAATCGCAACTTTTAAAGGCTGGATGGATATTATGTATCCTGCGGTTGACTCAAGAGAGGTGGAAGAGCAACCTTCTTATGAGATCAACCTCTACATGTACATCTACTTTGTCATCTTTATCATCTTTGGCTCTTTCTTCACGCTGAACCTCTTCATCGGCGTCATCATCGACAATTTCAACCAGCAGAAGAAAAAGTTAGGAGATAAAGACATCTTCATGACAGAGGAGCAGAAAAAGTACTACGAAGCCATGAAGAAACTCGGCTCAAAGAAGCCGCAGAAGCCGATCCCACGTCCAGCTAACCTAATCCAGGGGCTAGTGTTTGACTTCATCAGTCAGCAGTTCTTTGACATCTTCATCATGGTGCTCATCTGTCTCAACATGGTCACCATGATGGTGGAGACGGACGACCAGAGTCCGGCGAAGGAGGACTTCCTCTTCAAAGTGAACGTGGCTTTTATTGTGGTCTTCACCGGGGAGTGCACGTTAAAGCTCATCGCCCTGCGACATTACTTCTTCACC NewZebra CCTATGAGTCCACATTTTGAACATGTCCTCTCAGTGGGCAACTTGGTGTTCACAGGAATCTTCACAGCTGAAATGGTGTTCAAGCTTATAGCTATGGACCCTTACTACTACTTCCAGGTGGGCTGGAACATTTTTGACAGCATCATTGTCACACTCAGCCTGGTGGAGTTGGGACTGGCCAACGTTCAGGGATTGTCCGTTCTAAGGTCCTTTCGTTTGCTACGTGTCTTCAAACTGGCTAAATCTTGGCCCACCCTTAACATGCTGATCAAGATCATCGGCAACTCAGTGGGTGCTCTAGGGAACCTAACACTTGTTCTGGCCATCATTGTCTTCATCTTTGCCGTGGTGGGCATGCAGCTTTTTGGAAAAAGCTACAAGGACTGCGTTTGTAAGATCTCTGAGGATTGCGAGCTGCCCCGCTGGCACATGAACGACTTCTTCCACTCATTCCTCATCGTCTTTCGGATCTTATGTGGAGAGTGGATTGAGACCATGTGGGACTGCATGGAGGTGGCAGGAGCTAGCATGTGTTTGATAGTCTTCATGATGGTCATGGTCATCGGAAACCTTGTGGTGCTGAATCTGTTTCTGGCCCTGCTGCTTAGCTCCTTCAGTGGAGATAACCTGTCTGGAGGTGATGATGATGGAGAGATGAACAACCTTCAGATTGCCATTGGCCGCATCACCAGGGGTATCGATTGGGTTAAAGCCTTAGTTGCCAGTATGGTGCAACGGATTCTGGGAAAGAAACCTAAAATGGCAGATGGTCTGACCAACTGTTTGACATTGACTGTACCTATTGCTCGTTGTGAGTCTGATGTGGAGGGTGACTCTTCGGTTTGTAGCACAGTGGACTACCAGCCTCCAGAACCTGTAGAAGAAGAGGAACCAGAACCTGAAGAACCAGAGGCCTGTTTCACAGAGGGCTGTATTAGGCGATGTGCATGTTTGAGTGTTGACATCACAGAAGGATGGGGTAAAAAATGGTGGAACCTCAGAAGGACATGCTTCACCATCGTTGAGCATGATTACTTTGAGACCTTCATCATCTTTATGATCCTCCTTAGCAGTGGAGCACTGGCTTTTGAGGATATCAACATTGAGAGGCGCAGAGTGATCAAGATCATTCTGGAGTATGCTGATAAAGTCTTTACATATATTTTTATAGTGGAGATGTTACTGAAGTGGGTGGCATATGGCTTCAAGACCTACTTCACTAATGCATGGTGCTGGCTGGACTTCCTCATTGTGGATGTGTCTCTGGTCAGTTTAACGGCTAATTTAATGGGCTATTCTGAGCTGGGGGCAATCAAATCTCTCAGGACACTTAGAGCTCTTCGTCCACTTCGAGCCCTATCCAGATTTGAAGGAATGAGGGTGGTAGTGAATGCACTTGTAGGTGCCATTCCCTCTATTTTTAACGTGCTCCTGGTGTGTCTGATATTCTGGCTCATCTTCAGCATTATGGGGGTCAATCTGTTTGCCGGAAAATTCTACCACTGCATCAACACCACCACAGAGGAACGGATCCCCATGGATGTAGTCAACAACAAGAGTGACTGCATGGCACTGATGTACACCAACGAGGTGCGATGGGTCAATGTCAAGGTCAACTACGACAACGTGGGACTCGGCTACCTCTCTCTGCTGCAGATTGCCACATTCAAAGGCTGGATGGATATCATGTATGCTGCAGTGGATTCTAGAGAGGTGGATGAGCAGCCATCATATGAAATCAACCTTTACATGTACCTTTATTTTGTTATTTTCATCATTTTTGGCTCCTTTTTTACTCTCAACCTCTTTATTGGTGTCATCATTGACAACTTCAATCAGCAAAAATCAAAGTTTGGAGGGAAAGACATTTTCATGACTGAGGAACAGAAAAAGTACTACAATGCCATGAAGAAGCTGGGTGCAAAGAAACGTCCAAAACCTATACCTCGACCATCAAATATTATCCAGGGTTTGGTGTTTGACTTCATATCAAAACAGTTCTTTGACATTTTTATCATGGTGCTAATCTGCCTCAACATGGTGACCATGATGATAGAGACGGATGATCAGAGTGCTGAGAAAGAATATGTCCTGTACCAGATCAATCTGGTCTTCATCGTCGTCTTCACAAGCGAATGTGTACTTAAATTATTTGCACTCAGACAGTACTTTTTCACT SterNa6 CCCATGAGCGAAACCTTTCAACACGTGCTCACCATAGGGAACCTGGTGTTTACTACCATCTTTACGGCTGAAATGGTGTCGAAGATCATCGCCCTGGACCCTTACTACTACTTCCAGGTGGGCTGGAACATCTTCGACTGCATCATCGTCACTCTCAGTCTGGTGGAGCTAAGCCTATCCAACATGCCGGGCCTGTCTGTGCTCAGATCCTTTCGTTTGATGCGTATTTTCAAGCTGGCCAAGTCCTGGCCCACGCTCAACATGCTGATCAAGATCATCGGCAACTCAATGGGCGCCCTGGGGAACCTGACCTTCGTGTTGGCCATCGTCATCTTCATCTTCGCCGTGGTGGGCTTCCAGCTGTTCGGGAAGAGCTACAAGGACAACGTGTGCAAGGTCAGCGCGGACTGCACGCTGCCTCGCTGGCACATGAACGACTTCTTCCACTCCTTCCTGATCGTGTTTCGCATCCTGTGCGGCGAGTGGATCGAGACCATGTGGGACTGCATGGAGGTGGACGGAGTGCCCATGTGCCTCACCGTCTTCATGATGGTCATGGTCATCGGAAACCTGGTGATGCTGAACCTGTTCCTTGCCTTGCTTCTCAGCTCATTCAGCTGCGACAATCTTGCCGCGCCAGACGATGACAGTGAAGTTACCAACATCCAGATCTCCATTGTGCGCATCAGCAGAGGGATAAGCTGGGTGAAGAAATTCATTGTAGGCACAGCCTGGTGGATCATGGGCAGGAAGCCCAAGATTGTAGATGGGATTACCAACTATGTTGTTCTGAATGTGCCTATTGCCAAGGGGGAGTCTGAGGTTGAGGATGACTCTTCGATTTGCAGTTCAGTGGACTACGAGCTTCTACAACCCGAGGAGGAAAAGGAA---GAGCCTGTTGATCCAGAAGCCTGTTTTACAGAAAACTGTGTGAGGTACTTTCCATGTCTGGATGTGGACATCACACAGGGGAAAGGGAAGATCTGGTGGAACCTCCGCTGCACCTGCTACAACATCGTGGAACATCACTATTTTGAAAACTTTCTCATCTTCATGATTCTCCTCAGTAGTGGAGTACTGGCATTCGAGGATGTTAATATCGAACGCCGCAGGGTCATTAAGACCATGTTGGAGTATGCAGACATAGTCTTCACATATATTTTCGTGGTGGAGATGTTTCTGAAGTGGACTGCATATGGGTTTAAAGCGTACTTCACCAGTGCCTGGTGCTGGCTGGATTTTTTTATTGTTGATGTGTCAGTTATTAGCTTAGTAGCCAATGTGTTGGGCTATGCAGAGCTGGGACCAGTCAGATCGCTCAGAACTTTCAGGGCTCTTCGACCTTTACGTGCCCTTTCCAGATTTGAAGGAATGAGGGTGGTAGTGAATGCATTGCTCGGTGCCATCCCCTCCATCATGAACGTCCTATTGGTGTGTCTGATCTTCTGGCTGATCTTCAGCATCATGGGGGTCAACTTGTTTGCGGGAAAGTTCTACCGCTGCATTAACACCACCACAGATGAGGTTCTGTCCACAGAGCAAGTGAACAACAGGAGTGAATGCATGGCACTAATGCACACTAATGAGGTGCGTTGGGTCAACCTTAAGGTCAACTACGACAATGTGGGCCAGGGATATCTCTCCTTGCTTCAAGTGGCCACATTTAAAGGGTGGATGGGCATCATGTATGGTGCAGTGGACTCTAGAGAGGTAGAGGATCAGCCATCATATGAGATTAACCTCTACATGTACCTGTACTTTGTCATCTTCATCACATTTGGATCCTTTTTTATCCTCAACCTTTTCATTGGTGTCATCATTGACAATTTTAACCGGCAAAAACAAAAGTTAGGAGGAGATGACCTCTTTATGACAGATGAACAAAAAAAGTATTATGCTGCCATGAAGAAGCTGGGTTCCAAGAAACCACTCAAACCTATACCCCGTCCTTCGAATATGGTTCAAGGGGTGGTGTTCGACTTCATCTCCCAAAAGTTCTTTGACATTTCCATCATGGTTCTCATCTGCCTCAACATGGTGATCATGATGGTGGAGGCGGACGACCAGAGTGAAGAGAAAGAGAATGTCCTCTATCAGATCAATATCATATTTATTGTCNTCTTCACCGGAGAGAGTTTACTCAAGTTGTTTGGACTTAGACATTACTTCTTCACT eelNa6 CCCATGAACGAAAGCTTTCAGAGTCTGCTCAGTGCAGGAAACCTGGTGTTTACCACTATCTTTGCGGCTGAAATGGTGTTGAAGATCATTGCCTTGGACCCCTACTACTACTTCCAGCAGACGTGGAACATATTTGACAGCATCATTGTCAGTCTCAGTCTGTTGGAGCTTGGACTATCCAATATGCAAGGAATGTCTGTGCTCAGATCCTTACGTTTGCTGCGTATCTTCAAATTGGCCAAGTCCTGGCCCACGCTCAACATTCTGATCAAGATAATCTGCAACTCGGTGGGCGCTCTGGGCAACCTGACCATTGTGCTGGCCATTATCGTCTTCATCTTCGCCTTGGTGGGCTTTCAGCTGTTCGGAAAGAACTACAAGGAGTACGTGTGCAAGATCTCTGATGACTGTGAGCTGCCCCGCTGGCACATGAATGACTTCTTCCACTCCTTCCTGATTGTGTTCCGTGCCTTGTGTGGCGAGTGGATTGAGACCATGTGGGACTGCATGGAGGTGGGCGGAGTTCCTATGTGCCTCGCCGTCTACATGATGGTCATAATCATTGGGAACCTGGTGATGCTGAACCTTTTCCTTGCCTTGCTTCTAAGCTCATTCAGCAGCGACAATCTCAGTTCAATTGAAGAAGATGATGAAGTTAACAGCCTCCAGGTTGCCTCTGAGCGCATTAGTAGGGCAAAAAACTGGGTGAAGATCTTCATCACTGGCACAGTCCTGTGGATCCAGGGCAAGAAGCCCAAGATTGTAGATGGGATAACCAACTGTGTAACTCTGAATCTACCCATTGTAAAGGGGGAGTCAGAGATCGAAGAAGACTCTTCAGTTTGTAGTACAGTGGACTATAGTCCTTCAGAACAAGAGGAGCCAGAGGAACTAGAGTCCAAAGATCCAGAAGCATGTTTTACAGAAAAATGTATATGGCGATTTCCTTTTCTGGATGTGGACATCACACAGGGGAAAGGGAAGATCTGGTGGAACCTACGTAGGACCTGCTACACCATCGTGGAGCATGACTACTTTGAAACCTTCATCATATTCATGATTCTCCTCAGTAGTGGAGTTCTGGCCTTTGAGGACATTTATATTTGGCGTCGCAGGGTGATTAAGGTCATCTTGGAGTATGCAGACAAAGTCTTCACATATGTCTTCATAGTAGAGATGTTACTTAAGTGGGTTGCATATGGGTTTAAAAGATATTTCACTGATGCCTGGTGCTGGCTCGACTTTGTAATTGTTGGTGCATCAATAATGGGCATAACATCCAGTTTGTTGGGCTATGAAGAGCTGGGAGCAATCAAAAATCTCAGAACTATCAGGGCTCTTCGCCCTTTACGTGCCCTTTCCAGATTTGAAGGAATGAAGGTGGTAGTGAGAGCATTGCTTGGTGCCATCCCCTCCATCATGAACGTGCTGCTGGTGTGTCTGATGTTCTGGCTCATCTTCAGCATTATGGGGGTCAATTTGTTTGCTGGAAAGTTCTACCGCTGCATTAACACCACCACAGATGAGATTCTGCCCGTGGAGGAAGTGAACAACCGGAGTGACTGCATGGCACTAATGTACACTAACGAGGTGCGCTGGGTCAACCTTAAGGTCAACTATGACAATGCGGGCATGGGATACCTCTCCCTGCTACAAGTGTCTACATTTAAAGGCTGGATGGACATCATGTATGCTGCAGTGGACTCCAGAGAGGTAGAGGATCAGCCAATCTATGAGATTAATGTCTACATGTACCTGTATTTTGTCATCTTCATCGTATTTGGAGCCTTCTTTACCCTCAACCTTTTCATTGGTGTCATCATAGACAACTTCAACCGTCAAAAGCAAAAGTTAGGAGGAGAAGATCTCTTTATGACAGAAGAGCAGAAGAAGTACTACAATGCCATGAAGAAGCTGGGTTCGAAGAAAGCTGCCAAATGTATACCCCGCCCTTCGAATGTGGTTCAAGGTGTGGTGTACGACATAGTCACCCAACCATTCACTGATATTTTCATCATGGCTCTCATTTGCATCAACATGGTGGCTATGATGGTCGAGTCGGAGGACCAGAGTCAAGTGAAGAAGGACATTCTCTCTCAGATCAATGTCATATTCGTTATCATCTTCACTGTAGAGTGCTTGTTAAAGCTACTTGCACTTAGACAGTACTTCTTCACT catNa6 CCCATGAGTTCGAACTTTGAACACGTGCTCAGTGTTGGCAATTTGGTGTTCACTGGTATTTTCACGGCTGAAATGGTGTTCAAGCTCATTGCCTTGGACCCCTTCTACTACTTCCAGGTTGGCTGGAACATATTTGACAGCATCATCGTCACTCTTAGCCTGGTGGAGTTAGGCCTGGCCAATGTGCAGGGTCTGTCTGTACTCAGATCCTTTCGTTTGCTGCGAGTCTTTAAGCTGGCTAAATCCTGGCCCACGCTCAACATGCTGATCAAAATCATTGGAAACTCTGTGGGTGCTCTGGGGAACCTGACTCTGGTGCTGGCCATCGTCGTCTTCATCTTCGCCGTCGTAGGCATGCAACTTTTTGGCAAGAGCTACAAGGACTGCGTGTGTAAGATTGCAGAGGACTGCGAACTGCCCCGCTGGCACATGAACGATTTTTTCCATTCGTTTCTCATTGTCTTCCGCATCCTTTGTGGTGAATGGATTGAAACCATGTGGGACTGCATGGAGGTGGCTGGAGCAGGCATGTGCCTTGTGGTTTTCCTTATGGTCATGGTCATAGGAAACCTGGTGGTGCTGAACCTGTTCCTTGCCTTGTTGCTCAGCTCTTTCAGCGGGGACAATCTCTCAGCAGGTGATGAAGATGGTGAAATGAACAATCTCCAGATTGCCATCGGCCGCATCACCAGGGGCATTGACTGGGTCAAATCCTTCATCATTGGCCTTGTACAGCAGATACTTTGCAGGAAGCCTAAGATGGCAGATAGGTTGACCAACTGTCTGACCCTGAATGTACCAATTGCCAAAGCTGAGTCTGATGTTGAAGAAGACTCTTCAATGTGTAGCACAGTGGACTATAGACCTCCAGAATCCGAGGAGGAAGAGGAACCAGAACCTGTTGAGCCAGAAGCCTGTTTTACTGAAAACTGTGTGAGACGATGTCCATGTCTGAATTTGGACATCACTCAGGGGAGGGGAAAGAGTTGGTGGAATCTGCGCAGAACTTGCTACACCATAGTGGAGCATGATTACTTTGAAACCTTCATCATCTTCATGATTCTCCTCAGTAGTGGTGCACTGGCCTTTGACGACATTTACATTGAGCGTCGCAGGGTGATTAAGATTATCTTGGAATATGCAGACCAAGTCTTCACATATATTTTTGTCATAGAGATGTTACTGAAATGGGTTGCGTATGGCTTCAAGACATACTTCACCAATGCCTGGTGCTGGCTGGACTTTTTCATCGTTGATGTGTCACTTATCGGTTTAACGGCAAATCTGTTGGGCTATTCAGAGCTGGGACCAATAAAATCTCTCAGAACTCTTAGGGCGCTTCGACCTTTACGTGCCCTGTCCAGATTTGAAGGAATGAGGGTGGTAGTGAACGCATTGCTGGGTGCCATTCCTTCCATCATGAATGTACTCCTGGTGTGTCTAATATTCTGGCTGATCTTCAGTATTATGGGGGTCAACCTGTTTGCTGGGAAATACTACCGCTGCATTAATACCACCACAGAAGAACTTTTACCCATCGAGCAAGTGAACAACATGAGTGATTGCATAGCACTAATGCACACTAAAGAAGCACGCTGGGTCAATGTCAAGGTCAACTTTGACAATGTGGGCTTGGGTTACCTTTCCCTGCTACAAGAGGCTACATTTAAAGGCTGGATGGACATTATGTATGCTGCAGTGGATTCCAGAGAGGTGGAAGAACAGCCATCATATGAGATTAACATATATATGTATCTGTATTTTGTCATCTTCATCATCTTTGGCTCCTCCTTCACCCTCAACCTCTTCATTGGTGTCATCATTGACAACTTTAATCAGCAAAAGCAAAAGTTTGGTGGGGAAGATCTCTTCATGACAGAGGAGCAGAAAAAGTACTACAATGCCATGAAAAAGCTTGGTTCCAAGAAGCCCGTCAAACCCATACCTCGCCCTGCGAATATGATCCAGGGCATAGTGTTTGACTTCATCTCTCAGCAGTTCTTTGACATTTTCATCATGGTGCTCATTTGCCTCAACAAGGTTACCATGATGATTGAGACAGATGACCAAAGTGCAGAGAAAGAATATGTTCTCTATCAGATCAACTTAATCTTCATTGTTGTCTTCACTGGGGAGTGCATCCTCAAAATGTTTGCACTGAGACAATACTTTTTCACT AptNa6 ---------------------------CTCACTGTGGGGAACCTGGTGTTTACTGGCATCTTTACGGCTGAAATGGTGTTTAAGCTCATTGCCATGGACCCCTACTACTACTTCCAGGTGGGCTGGAACATCTTCGACAGCATCATCGTCACCCTCAGTCTGGTGGAGCTGGGGCTAGCCAACGTGCAGGGTCTGTCTGTGCTCAGGTCCTTCCGTTTGCTGCGTGTCTTCAAGTTGGCCAAGTCCTGGCCAACGCTCAATATGCTCATCAAGATCATTGGCAACTCGGTGGGAGCCCTGGGCAACCTGACACTGGTGCTGGCCATTATTGTCTTCATCTTTGCCGTGGTGGGCATGCAGCTATTTGGGAAGAGCTACAAGGACTGCGTGTGCAAGATTGCGCTGGACTGCGAGCTTCCCCGCTGGCACATGACGGACTTCTTCCACTCCTTCCTGATCGTGTTCCGCATCCTATGCGGCGAGTGGATTGAGACCATGTGGGACTGCATGGAGGTGGCTGGACCGTCCATGTGCCTCATCGTCTTCATGTTGGTCATGGTCATTGGGAACCTGGTGGTGCTGAACCTGTTCCTTGCATTGCTTCTCAGCTCATTCAGCGGTGACAATCTCTCGGCAAGCGACGATGACAGTGAGATTAACAACCTCCAGATCGCCACAGGGCGCATCAGCAGAGCGATTGGCTGGGTGAAGAACTTTATCATCAGCACAGTCCAGTGGGTTCTGGGCAGAAAGCCCAAGATGGTGGATGGCATGACCAACTGCGTAGTCCTGAATGTGCCCATTGCCAAGGGGGAATCTGAGATTGAAGGAGACTATTCAGTTTGCAGTACAGCAGACTACAGACCTCCAGAACCCGAGGAGGAAAAGGTACCAGAGACCAATGATCCAGAAGCCTGCTTTACAGAAAATTGTGTGAGGCGATTTCCTTGTCTCAATGTGGACATCACCCAGGGGAAAGGGAAGAGCTGGTGGAACCTACGCAGAACCTGCTACATCATCGTGGAGCATGACTACTTTGAGACCTTCATCATCTTCATGATTCTCCTCAGTAGCGGAGCACTGGCTTTCGAGGACATTTATATAGAGCGTCGCAAGATGATTAAGATCATCTTGGAGTACGCAGACAAAATCTTCACCTATGTTTTCATAATGGAGATGTTACTGAAGTGGGTTGCTTATGGGTTTAAAACGTACTTCACCAATGCCTGGTGCTGGCTGGACTTTCTTATTGTTGATGTGTCAATTATTAGCTTAACAGCCAATCTGTTGGGCTATTCAGAGCTGGGACCAATCAAATCTCTCAGAACACTCAGGGCTCTTCGACCGCTACGTGCCCTTTCCAGATTTGAAGGAATGAGGGTGGTAGTGAATGCGTTGGTTGGCGCCATCCCCTCCATCATGAACGTGCTGCTGGTTTGTCTGATCTTCTGGCTCATCTTCAGTATCATGGGGGTCAACTTGTTTGCTGGAAAGTTCTACCGCTGCATTAACACTACCACCGAGGAGCTTCTGCCCATGGAGGAAGTGAACAACAGGAGTGATTGCATGGCGCTAATGCACACTAATGAGGTGCGCTGGGTCAATGTCAAGGTGAACTACGACAACGTCGCCCTGGGATACCTTTCCCTGCTGCAAGTGGCTACATTTAAAGGCTGGATGGACATCATGTATGCTGCAGTGGACTCCAGAGAGGTAGAGGAGCAGCCATCATATGAGATTAACCTCTACATGTACCTGTACTTTGTCATCTTCATCATATTGGGATCCTTTTTTACCCTCAACCTTTTCATTGGTGTCATCATAGACAACTTCAACAGGCAGAAGCAAAAGTTTGGAGGAGAAGATCTCTTTATGACGGAGGAGCAGAAGAAGTACTACAATGCCATGAAGAAGCTGGGATCCAAGAAGCCTGTCAAACCTATACCCCGTCCTACGAATGTTATTCAAGGTGTGGTGTTCGACCTCATTTCCCAGCAGTTCTTTGATATTTTCATCATGGTTCTCATTTGCCTCAACATGGTGACCATGATGGTGGAGACTGATGACCAGAGCAAAGAGAAAGAGCACATCCTCTATCAAATCAACGTCATATTCATTGTCGTCTTCACTGGAGAGTGTTTGCTCAAGATGTTTGCACTGAGGCAGTACTTCTTCACT PinniNa6 CCCATGAGTGAAACGTTTGATTACGTCCTCAGCACAGGGAACCTGGTGTTTACCATCATCTTTGCAGCTGAAATGGTCTTGAAGCTCATTGCCATGGACCCCTACTACTACTTCCAGCAGACGTGGAACATCTTTGACTTTTTCATTGTCTCACTCAGTCTGGTGGAGATGGGACTGGCTAACATGCAGGGGCTGTCAGTGCTTAGGTCCTTTCGACTGCTGCGTATCTTTAAGTTGGCCAAGTCCTGGCCCACGCTCAATATTCTGATCAAGATCATCTGCAACTCGGTGGGCGCCCTGGGAAACCTGACCATCGTGCTGGCCATTATCGTCTTCATCTTCGCCTTGGTGGGCATGCAGCTGTTCGGGAAGAATTACAAAGAGTTTGTGTGCAAGATCAGTGCAGACTGTACGCTGCCTCGCTGGCATATGAATGACTTCTTCCATTCCTTCCTGATTGTGTTCCGCTGCCTGTGCGGCGAGTGGATTGAGACTATGTGGGACTGTATGGAGGTGGGCGGTGTGCCCATGTGCCTCAGCGTTTACATGATGGTCATAATCATCGGGAACCTGGTGGTGCTGAACCTGTTCCTTGCCTTACTGCTAAGCTCATTCAGTGGTGACAATCTCACTGCAAACGATGATGACCAAGAGGATAACAACATCCTGATTGCAGCTGAGCGGATCAGCAGGGCAAAACTCTGGGTGAAGGGGTTCATAATACGGACGGTCTTGGGGATGCTGGGCAAGGAGCCAAAGATTGTGAATGGGCTAGCCAACGGTGTAGTTCTGAATGTGCCCATTGCCAAGGGCGAGTCTGAGACTGAAGATGACTCTTCAGTCTGCAGTACAGTGGACTACAGTCCTCCAAATCCAGAGGAACCCGAGGAACCAGAACCCGATAATCCAGAAGATTGTTTAACGGAAGAATGTGTGTCACGATTTCCTTGGCTGAATGTGGACATAACACAGCCAAAAGGGAAGAGTTGGTGGAACCTTCGTAGGACATGCTACGTCATCGTAGAGCATGACTACTTTGAGACTTTCATCATCTTCATGATTCTCCTCAGTAGTGGAGCACTGGCTTTCGAGGACATTTATATTGAGCGTCGCAGGGTGATTAAGATCATCTTGGAGTATGCGGACAAAGTCTTCACATATATTTTCATAGCAGAGATGTTACTGAAGTGGGTTGCATATGGGTTTAAAAAGTACTTCTCCGACGCCTGGTGCTGGTTAGACTTTCTAATTGTTGATGTGTCAATAATTAGCTTAACAGCCAATTTGTTGGGCTATTCAGAGTTGGGACCAATCAAATCTCTCAGAACTCTCAGGGCTCTTCGACCTTTACGTGCACTTTCCAGATTTGAAGGAATGAGGGTGGTAGTCAAAGCATTGGTTGGCGCCATCCCCTCCATCGTGAACGTGCTGCTGGTATGTCTCATGTTCTGGCTCATCTTCAGCATTATGGGAGTCAACTTGTTTGCTGGAAAGTTCTACCGCTGCATTAACACTACCACAGAAGAGACCATGCCCYTGGAAGAAGTCAACAACCGCAGTGACTGCAATGCACTTATGTACACTAATGAGGTGCGATGGGTCAACCTTAAGGTCAACTATGACAATGCAGGCATGGGATACCTCTCCCTGCTACAAGTGGCAACATTTAAAGGTTGGATGGACATCATGTATGCTGCAGTGGACTCCAGAGGGGTAGAGGATCAGCCGATATACGAGATTAACGTCTACATGTACCTGTATTTTGTCATCTTCATCGTATTTGGATCCTTTTTCACCCTAAACCTCTTCATTGGTGTCATCATAGACAACTTCAACCGTCAAAAGCAAAAGTTAGGAGGAGATGATCTCTTTATGACAGAAGAACAGAAAAAGTATTATGATGCCATGAAGAAGCTGGGTTCCAAGAAACCTGTCAARGTTATACCACGCCCTTCGAACAAGATTCTGGGTGTGTTGTATGACATAGTCAACCAACGGGTCACTGATATTTTCATCATGTCTCTCATTTGGCTAAACATGGTTACCATGATGGTGGAGACAGATGACCAGAGCGAAGAAAAGAAGAATGTTCTCTATCAGATCAATTTAATATTCATTATCATCTTCACTGGAGAATGTCTGCTCAAGTTGCTTGCACTAAGACATTACTTCTTCACT tetra CCCATGACCCAGGAGTTCGACTACATGCTTTCAGTGGGAAATCTGGTTTTCACAGGAATTTTTGCAGCAGAAATGTTCTTCAAGCTGATCGCCATGGATCCGTACTACTATTTCCAAGTTGGCTGGAACATTTTTGACAGCATCATTGTCACCCTCAGCCTGGTAGAGTTGGGGCTTGCGAACGTCCAGGGCCTGTCTGTCCTCAGGTCCTTCCGCCTGCTCCGTGTCTTCAAACTTGCCAAATCCTGGCCCACACTCAACATGCTGATCAAGATTATTGGGAGCTCAGTTGGAGCGCTAGGGAATCTGACGTTGGTGCTGGCCATCATCGTCTTCATCTTCGCTGTGGTGGGGATGCAGCTCTTTGGCAAAAGCTACAAGGACTGCGTGTGCAAGATTTCCACGGAGTGCGAGCTGCCGCGCTGGCACATGAATGACTTCTTCCACTCCTTCCTCATCGTCTTCCGCATCCTGTGTGGCGAATGGATCGAGAACATGTGGGCCTGCATGGAAGTGGCTGGAGCTGGGATGTGCTTAGTTGTCTTCATGATGGTCATGGTGATTGGAAACCTCGTGGTGTTGAACCTCTTCCTGGCCCTGCTGCTCAGCTCGTTCAGCGGGGACAATCTGTCCATCGGAGAGGACGATGGAGAGATGAACAATCTTCAGATTGCCATCGGCAGAATCACACGAGGTGGAAACTGGCTCAAGACCCTTGTCATCAGAACGGTCCTGCAGCTTCTCGGTAGGGAGCAGAAAACGGCAGATGGGATAGCTAACTGTCTTGTTATCAACGTCCCCATCGCCTTGGGGGAGTCAGACTCTGAAGGCGAGTCTTCAGTGTGCAGCACAGCAGACTATCGGCCCCCCGAGCCTGAGGAAGAGGAAGAGCCGGAACCACTGGAGCCAGAGGCCTGCTTTACTGACAACTGCGTCAAACACTGGCCTTGTCTGAACGTGGACGTCACCCAAGGTCAAGGGAAGAAGTGGTGGAACCTCCGCAAGACCTGCTTCACAATCGTAGAGCATGACTGGTTTGAGACCTTCATCATCTTCATGATCCTCCTCAGCAGCGGAGCCCTGGCCTTTGAAGATATATACATCGAAAGACGAAGAACCGTCAAAATTATCCTGGAGTTTGCCGACAAAGTTTTCACCTTCATCTTTGTCCTTGAGATGGTGCTGAAATGGGTGGCCTATGGCTTCAAGACCTACTTCACCAACGCCTGGTGCTGGTTGGACTTTTTCATTGTAGACATTTCCCTGATCAGTTTATCGGCCAACCTGATGGGCCTCTCTGACCTGGGACCAATCAAATCTCTCAGAACACTCCGGGCACTGAGGCCTCTTCGAGCTCTGTCCAGATTTGAAGGGATGAGGGTGGTGGTGAACGCTCTTATCGGAGCCATTCCCTCCATCTTCAACGTGCTGCTGGTGTGCCTGATCTTCTGGCTCATCTTCAGCATCATGGGAGTGAACCTGTTTGCGGGGAAGTTCTACCACTGCATCAACACCACCACACAGGAGCTCTTCCCCATCTCTGTGGTCAACAACAAGAGCGACTGCATGGCCGTCCAGGCCACGCAGGAGGCCCGCTGGGTCAACGTCAAGGTCAACTACGACAACGTGGGAAAAGGCTACCTGTCGCTGCTTCAAATCGCCACTTTTAAAGGCTGGACGGCCATTATGTATGCTGCAGTAGATTCAAGAGAGGTGGAAGAGCAACCTTCCTATGAGATCAACCTGTACATGTACATCTACTTTGTCATCTTCATCATCTTTGGCGCTTTCTTCACGCTCAACCTGTTCATCGGCGTCATCATCGATAACTTCAACCAGCAGAAGAGAAAGATA---AACAAAGACATCTTCATGACGGAGGAGCAGAAAAAGTACTACGAAGCCATGAAGAAACTCGGCTCCAAGAAGCCGCAGAAGCCGATCCCACGTCCGACCAACCTCATCCAGGGAATGGTGTTTGACTTCATCAGTCAGCAGTTCTTTGACATCTTCATCATGGTGCTCATCTGCCTCAACATGGTGACCATGATGGTGGAGACGGACGACCAGAGCCCCGAGAAGGAGGATTTCCTCTTCAAAGTGAACGTGGCTTTTATCGTGGTCTTCACGGGGGAGTGCATGCTGAAGCTCATCGCCCTGCGACAGTACTTCTTCACC ; end; garli-2.1-release/tests/data/z.11x2178.wtset.nex000066400000000000000000000606631241236125200212400ustar00rootroot00000000000000#NEXUS [ This dataset is from: Zakon, Lu, Zwickl and Hillis. 2006. Sodium channel genes and the evolution of diversity in communication signals of electric fishes: Convergent molecular evolution. Proc. Natl. Acad. Sci. USA. 103(10):3675-80. ] begin data; dimensions ntax=11 nchar=2180; format datatype=dna missing=? gap=-; matrix MorNa6 CCTGTGACTCCACATTTTGAGCACGTACTCAGTGTGGGAAACCTGGTTTTCTCAGGGATATTTGCTGGTGAAATGGTCTTGAAAATTATTGCTATGGACCCCTACTACTACTTCCAGGTTGGATGGAACGTGTTTGACAGCATCATTGTTACCATGAGTATGGTGGAGATGGTACTGGCTGATGTAGAGGGTCTGTCGGTTCTGCGGTCCTTTCGTTTGCTACGTGTCTTCAAGCTTGCCAAATCATGGCCTACCCTCAACATGCTGCTAACGATCATCGGAAACTCAGTGGGTGCTCTGGGGAACCTCACCGTGGTGCTGGCCATCATCGTTTTCATCTTCGCTGTGGTTGGAATGCAGCTGTTTGCCAAAAACTACAAGGACTGCGTCTGCAAGATCGCCGAGGATTGTGAGCTGCCCCGGTGGCACATGCATGACTTCTTCCACTCTTTCCTCATCGTGTTCCGCATCCTCTGTGGAGAGTGGATTGAGACCATGTGGGACTGCATGGAAGTGGCCAACAGAAACATGTGTTTGGTCCTCTTCTTAATGGTCATGATAATTGGGAACCTGGTGGTTCTGAACCTTTTCCTGGCCTTGCTGCTTAGCTCATTCAGCGGGGACAATCTGCAAATGGCAGATGACGACGGCGAGCTGAACAATCTGCAGCTTTCCGCACTCAGGATCACCAGAGCCATTGATTGGGTGAAGGCCTACGTTAGAGGGCTGATCTGGAAGATCCTGGGCAAGCAGCCAAGAGTGCTGGATGGTTTATCTCACTGGGCAACCTTCACCGTACCCATTGCCCAGGAAGAGTCTGATTTAGAAGATGGTGTGTCTGAGTGCAGCACAGTGGACTACGTGCCCCCTCCGCCGGATGAAGTGGAGGAACCGGAGCCTGTGGAACCTGAGGCCTGTTACACTGACAACTGCCTTAGACGGTGTCCTTGTCTGGTGCTGGACACCTCAGAGGGCAGAGGGAAGACCTGGTGGAACCTCAGGAGAACCTGCTACACCATTGTGGAGCATGACTACTTTGAGTCCTCCATAATCTTCATGATCCTTCTCAGCAGTGGTGCCTTGGCCTTTGAAGACATATATCTTGAAAGACGCAGAACGATAAAAATCCTGCTGGAATATGCAGATAAAGTCTTCAGCTATGTATTTGTTATTGAGATGCTCCTTAAGTGGGTGGCTTATGGTTACAAAGTATACTTTACCAATGCCTGGTGCTGGCTGGACTTCTTGATTGTTGATGTTTCCTTGGTCAGTTTGGCAGCAAGCATAATGGGCTATTCTGAACTAGGACCCATAAAGTCTTTGAGAACTCTTAGGGCTCTGAGGCCTCTAAGAGCCCTTTCCAGGTTTGAGGGGATGCGGGTTGTGGTGAACGCCCTTGTGGGGGCCGTCCCCGCCATCTTCAATGTGATGCTGGTCTGTCTCATCTTCTGGCTCATCTTCAGCATCATGGGGGTTAACCTGTTTGCCGGGACATTCTACCACTGCCTCAACACCACAACTGGGGAGATGTTTACCATTGATGTTGTAAACAACTATAGTGAGTGTTTGGCCCTCATGCACACAAACGAGGTGCGCTGGGCCAACGTCAGGGTCAACTATGACAACGTTGGGATGGGTTACCTGTCTCTGTTGCAAGTGTCAACATTCAAAGGCTGGATGGAAATTATGTATGCGGCTGTCGACTCACGTAAGGTGGGTCAACAGCCCTCATATGAGGCCAACCTTTACATGTACGTGTACTTTGTCATCTTCATCATCTTTGGGTCCTTCTTTACACTCAACCTCTTCATTGGTGTCATCATTGACAACTTCAATCAACAAAAGAATAAGATGGGAGGA---GATTGCTTTATGACTGAGGAGCAGAAGAAATATTACGACGCTATGAAAAAGCTAGGCAACAAGAAGCCAGCGAAGCCCATTCCAAGACCAACGGGCAAAATACCAGGCCTAGTATATGACTTCATCAGTCAGCAGGCCTTTGACATCTTTATCATGGTACTGATTTGCCTGAACATGGTGACCATGATGGTGGAGGAAGATGACCAAAGTGAACAGAAGACAGACATGCTGGGCAAAATCAATGCAGTCTTCATTGTGGTCTTCAGCAGTGAATGTTTGCTGAAGATGATTGCACTGAGACAATACTTCTTTACCN- ClownNa6 CCCATGAGCCCTGAGTTTGACCACATGCTCTCTGTGGGAAACCTGGTTTTCACTGGAATCTTCACAGCTGAAATGGTCCTAAAACTCATTGCTATGGACCCCTACTACTACTTCCAGGTTGGATGGAACATATTTGACAGCATCATTGTCACTCTAAGCCTAGTGGAACTGGGGCTCGCTAATGTTCAGGGTCTGTCAGTCCTGCGATCCTTTCGTTTGTTGCGAGTGTTCAAGCTGGCAAAGTCTTGGCCCACCCTCAACATGCTGATCAAGATCATCGGGAATTCCGTGGGCGCCCTGGGCAACCTGACCCTGGTGCTGGCCATCATCGTCTTCATCTTCGCCGTGGTGGGCATGCAGCTCTTTGGGAAGACCTACAAGGACTGCGTGTGCAAGATTGCCAGTGACTGCGAGCTTCCCCGCTGGCACATGAATGACTTCTTCCACTCGTTCCTTATCGTGTTCCGCATCCTCTGCGGGGAGTGGATCGAGACCATGTGGGACTGCATGGAGGTGGCTGGTGCAGGCATGTGCCTCGTGGTCTTCATGATGGTCATGGTCATTGGGAACCTAGTGGTGCTGAATCTCTTCCTGGCTTTGCTGCTCAGTTCATTCAGTGGAGACAACCTAGCAGGCGGTGATGAGGATGGCGAGATGAACAACTTGCAGATTGCTATCGGAAGGATCACCCGAGGCATTGACTGGGTGAAGGCATTTGTCATGGGACTGGTGTGGCGGGTGATGGGCAAAAAGCCTAAAATGCTGGATGGTTTATCTCACTGGGTAACCCTCAGTGTGCCCATGGCACAGGAGGAATCCGACTTAGAAGACGACTCCTCTGAATGCAGCACTGTGGACTATAGGCCTCCAGAGCCAGTGGAGGAGGAAGAACCAGAACAGGTGGAGCCTGTGGAGTGTTTTACTGATGACTGTGTCAGACGTTGCCCTTGTCTGACGGTGGACATCACGCAGGGCAAAGGAAGGACCTGGTGGAATCTCAGGAAAACATGTTACACCATCGTGGAGCATGACTACTTTGAGACCTTCATCATCTTCATGATCCTGCTTAGCAGTGGGGCCTTGGCCTTTGAAGATATATACATTGAAAGGCGCAGAACAATAAAAATCATTCTGGAATATGCAGACAAAGTATTTACATACGTATTTGTTGTTGAAATGCTCTTGAAGTGGGTTGCTTATGGTTTCAAGACATACTTCACTAATGCCTGGTGCTGGCTGGACTTTTTAATTGTGGATGTGTCCTTGATCAGTTTGACAGCAAACCTCATGGGCTACTCAGAGCTGGGGCCTATCAAATCCCTGAGAACCCTGAGGGCCCTGAGGCCACTACGAGCCCTGTCTAGGTTTGAGGGCATGAGAGTGGTGGTAAATGCATTGGTAGGGGCCATCCTTTCCATCTTCAACGTACTGCTGGTCTGTCTCATTTTCTGGCTTATCTTCAGCATTATGGGTGTCAACCTTTTTGCTGGAAAGTTCTACCGCTGTATCAACACCACCACAGAGGAGCTATTACCTGTCGAGATTGTGAACAATAAGAGTGACTGCTTGAATCTCATGCACACAAATGAAGTGCGCTGGGTCAATGTGAAGGTCAACTATGACAACGTTGGCCTTGGTTACCTCTCTCTACTCCAAGTTGCAACATTTAAAGGGTGGATGGACATTATGTATGCAGCTGTGGACTCTCGTGAGGTGGAAGAGCAGCCCTTGTATGAGGAAAACCTCTATATGTACTTATACTTCGTCATCTTCATCATTTTTGGGTCATTCTTTACACTCAACCTTTTCATTGGTGCCATCATCGACAACTTTAATCAGCAAAAGAAAAAGCTTGGTGGGAAGGATATCTTCATGACCGAGGAGCAAAAGAAGTACTACAATGCCATGAAAAAGCTTGGTTCCAAAAAGCCAGTGAAGCCTATTCCAAGACCTACGAACAAAATACAAGGTGTGGTATTTGACTTCATCAGTCAGCAGTTCTTTGACATCTTTATCATGGTATTGATCTGCCTCAACATGGTGACCATGATGGTGGAGACAGATGACCAAAGTCAGGAAAAAGAGAATATACTGAACCAAATCAATCTGGTATTCATTGTGATCTTCACCAGCGAATGCGTCTTGAAGATGTTTGCACTTAGACATTATTTCTTCACCN- AraNa6 CCAATGAGTCCCGCGTTTGACCATATGCTGACCGTGGGAAACCTCGTTTTTACGGGGATCTTTACAGCTGAGATGGTATTCAAGCTCATCGCCATGGATCCATACCACTACTTCCAGGTTGGATGGAACATTTTTGACAGCATCATTGTCACACTTAGCCTGGTGGAGCTGGGTCTCGCGAATGTTCAGGGCCTTTCGGTCTTGCGCTCCTTCCGCTTGCTGCGGGTCTTCAAGCTGGCCAAGTCTTGGCCTACCCTGAACATGCTCATCAAGATCATTGGAAACTCAGTGGGTGCCCTAGGGAACCTCACACTGGTGCTGGCCATCATCGTCTTCATCTTCGCTGTCGTGGGCATGCAGCTGTTCGGTAAGAGCTACAAGGACTGTGTGTGTAAGATTGCAGAGGACTGTGAGCTACCCCGCTGGCACATGAATGACTTCTTCCACTCCTTCCTCATCGTGTTCCGCATCTTGTGTGGCGAGTGGATCGAGACCATGTGGGACTGCATGGAGGTGGCGGGCGCTGGCATGTGTCTCGTTGTCTTCATGATGGTCATGGTCATCGGCAACCTGGTGGTCCTGAACCTCTTCCTGGCTTTGCTGCTGAGCTCGTTCAGTGGAGACAACCTGGCTGGAGGAGACGATGATGGCGAGATGAACAACCTGCAGATTGCCATTGGCAGGATCACCAGAGGCATTGACTGGATAAAAGCCTTTGCCATGGGCTTCATATGGAAGTTACTTGGAAAGAAGGCCAAGATGCTGGATGGTTTATCCCACTGGGTGACCCTGAGTGTTCCCATTGCCCAGGGAGAGTCTGATTTGGAGGATGACTCCTCTGAATGCAGCACGGTGGACTACAGACCCCCAGAACCAGAGGAGGAGGAGGAGCCTGAGCAGCAGGAGCCTGAGGCCTGTTTTACTGAGGATTGCTTCCGGCGTATGCCATGTTTGATGGTGGACATCACGCAGGGGAAGGGCAAGACCTGGTGGAAACTACGGAAAACCTGTTTTACCATTGTGGAGCATGGCTATTTTGAGACCTTCATCATTTTCATGATCCTTCTCAGCAGTGGAGCTCTGGCTTTTGAAGACATATACATTGAAAAGCGCAGAGTTATCAAAATCATCCTGGAATATGCGGACAAAGTCTTCACCTATGTATTTGTTATTGAAATGGTCCTCAAGTGGGTGGCTTATGGGTTCAAAGTATACTTCACAAACGCCTGGTGCTGGCTGGACTTCCTCATCGTTGATGTGTCCTTGATCAGTCTGACCGCTAACCTCATGGGCTACTCTGAGCTGGGGCCCATTAAGTCTCTGAGAACACTTAGGGCCCTTAGGCCCCTGAGGGCCCTCTCCAGGTTTGAGGGGATGAGGGTGGTGGTAAATGCGCTTGTGGGAGCCATCCTCTCCATTTTCAACGTTCTGCTCGTGTGCCTCATCTTCTGGCTCATCTTCAGCATCATGGGCGTTAACCTGTTTGCTGGGAAGTTCTACTACTGCATTAACACCACCTCAGAGGAGCGCTTACCCATTGATGTTGTGAATAACAAGAGCGACTGCATGGCCCTAATGCACACCAATGAGGTGCGCTGGGTCAACGTCAAGGTGAACTATGACAATGTCGGCTTGGGCTATCTCTCTCTGCTGCAGGTGGCTACTTTTAAAGGTTGGATGGATATAATGTATGCTGCCGTGGACTCACGGGAGGTGGGGGAGCAACCCTCCTATGAGGTCAACATCTACATGTACTTGTACTTTGTCATCTTCATCATCTTCGGGTCCTTCTTCACGCTCAACCTCTTCATTGGTGTCATCATTGACAACTTCAATCAGCAAAAGAAAAAGTTAGGAGGAAAAGACATATTCATGACTGAGGAACAGAAGAAGTATTACAATGCCATGAAGAAACTTGGCTCCAAGAAGCCAGTGAAGCCCATCCCACGACCTTCGAATAAAATTCAAGGCATGGTGTTTGACTTCATTACGCAGCAGTTTTTTGATATTTTCATCATGGTACTGATCTGCCTCAACATGGTGACCATGATGGTGGAGACGGATGATCAAAGCGAGGACAAAGAAAATGTCCTCTACCAGATTAACCTGGTCTTCATTGTGATCTTCACCTGCGAGTGCGTCCTCAAAATGTTTGCGCTTAGACAGTACTTCTTCACCN- puffNa6 CCCATGACCGAAGAGTTCGACTACATGCTTTCAGTGGGAAATCTGGTTTTCACAGGAATCTTCGCGGCGGAAATGTTCTTCAAATTGATCGCCATGGATCCGTACTACTATTTCCAAGTTGGCTGGAACATTTTTGACAGCATCATCGTCACGCTCAGTCTGGTGGAGTTAGGGCTTGCAAACGTCCAGGGGCTGTCCGTCCTCAGGTCCTTCCGTCTGCTTCGGGTCTTCAAACTTGCCAAGTCCTGGCCCACGCTCAACATGCTGATCAAGATTATCGGTAATTCAGTTGGAGCTTTAGGGAATCTGACTTTGGTGCTGGCCATCATCGTCTTCATCTTCGCTGTGGTGGGGATGCAGCTCTTCGGCAAAAGCTACAAGGACTGTGTGTGCAAGATTTCCTCCGACTGCGAGCTGCCACGCTGGCACATGAATGACTTCTTCCACTCCTTCCTCATCGTGTTCCGCATCCTGTGCGGCGAGTGGATCGAGACCATGTGGGACTGCATGGAGGTGGCTGGAGCCGGGATGTGCTTGGTTGTCTTCATGATGGTCATGGTCATCGGGAACCTCGTGGTGTTGAATCTCTTCCTGGCCCTGCTGCTCAGCTCATTCAGCGGAGACAACCTCTCGGTCGGAGACGACGATGGAGAGCTGAACAATCTTCAGATCGCCATCGGAAGGATCACACGAGGCGGCAACTGGCTCAAAGCCTTCTTCATCGGAACGCTTCAACGGGTTCTTGGAAGGGAACCAAAATTGGCAGACGGGATCGCCAACTGTCTTAGTATCACCGTCCCCATCGCCCTGGGAGAGTCGGACTCTGAAGGCGATTCTTCAGTGTGCAGCACAGTGGACTATCAGCCCCCAGAGCCTGAGGAAGAGGAAGAGCCGGACCTGGTGGAGCCAGAGGCCTGCTTCACTGACAACTGTGTGAAGCGCTGGCCTTGTCTGAACGTGGACATCAGCCAGGGGAAAGGAAAGAAGTGGTGGAACCTCCGCAAGACCTGCTTCACTATTGTGGAGCATGACTGGTTTGAGACCTTCATCATTTTCATGATCCTCCTCAGCAGCGGAGCTCTGGCCTTTGAAGACATATACATCGAAAGACGAAGAACCGTGAAAATTGTCCTGGAGTTTGCTGACAAAGTTTTCACCTTCATCTTTGTCATCGAGATGCTCCTGAAATGGGTCGCCTATGGCTTCAAGACCTACTTCACCAATGCCTGGTGCTGGCTGGACTTTTTCATCGTGGACATTTCCCTGATCAGTCTATCTGCCAACTTGATGGGCTTCTCTGACCTCGGACCAATCAAATCGCTCAGAACTCTCAGGGCTCTGCGGCCTCTTCGGGCGCTGTCCAGATTTGAAGGGATGAGGGTGGTGGTGAACGCTCTCATCGGAGCCATTCCCTCCATCTTCAACGTGCTCCTGGTGTGCCTGATCTTCTGGCTCATCTTCAGCATCATGGGAGTGAACCTGTTTGCGGGGAAGTTCTACCGCTGCATCAACACCACCACGGCGGAGCTCTTCCCCATCTCTGTGGTCAACAACAAGAGCGACTGCGTGGCGCTGCAGGCCACGCAGGAGGCCCGCTGGGTCAACGTCAAAGTCAACTACGACAACGTGGCAAAAGGCTACCTGTCGCTGCTTCAAATCGCAACTTTTAAAGGCTGGATGGATATTATGTATCCTGCGGTTGACTCAAGAGAGGTGGAAGAGCAACCTTCTTATGAGATCAACCTCTACATGTACATCTACTTTGTCATCTTTATCATCTTTGGCTCTTTCTTCACGCTGAACCTCTTCATCGGCGTCATCATCGACAATTTCAACCAGCAGAAGAAAAAGTTAGGAGATAAAGACATCTTCATGACAGAGGAGCAGAAAAAGTACTACGAAGCCATGAAGAAACTCGGCTCAAAGAAGCCGCAGAAGCCGATCCCACGTCCAGCTAACCTAATCCAGGGGCTAGTGTTTGACTTCATCAGTCAGCAGTTCTTTGACATCTTCATCATGGTGCTCATCTGTCTCAACATGGTCACCATGATGGTGGAGACGGACGACCAGAGTCCGGCGAAGGAGGACTTCCTCTTCAAAGTGAACGTGGCTTTTATTGTGGTCTTCACCGGGGAGTGCACGTTAAAGCTCATCGCCCTGCGACATTACTTCTTCACCN- NewZebra CCTATGAGTCCACATTTTGAACATGTCCTCTCAGTGGGCAACTTGGTGTTCACAGGAATCTTCACAGCTGAAATGGTGTTCAAGCTTATAGCTATGGACCCTTACTACTACTTCCAGGTGGGCTGGAACATTTTTGACAGCATCATTGTCACACTCAGCCTGGTGGAGTTGGGACTGGCCAACGTTCAGGGATTGTCCGTTCTAAGGTCCTTTCGTTTGCTACGTGTCTTCAAACTGGCTAAATCTTGGCCCACCCTTAACATGCTGATCAAGATCATCGGCAACTCAGTGGGTGCTCTAGGGAACCTAACACTTGTTCTGGCCATCATTGTCTTCATCTTTGCCGTGGTGGGCATGCAGCTTTTTGGAAAAAGCTACAAGGACTGCGTTTGTAAGATCTCTGAGGATTGCGAGCTGCCCCGCTGGCACATGAACGACTTCTTCCACTCATTCCTCATCGTCTTTCGGATCTTATGTGGAGAGTGGATTGAGACCATGTGGGACTGCATGGAGGTGGCAGGAGCTAGCATGTGTTTGATAGTCTTCATGATGGTCATGGTCATCGGAAACCTTGTGGTGCTGAATCTGTTTCTGGCCCTGCTGCTTAGCTCCTTCAGTGGAGATAACCTGTCTGGAGGTGATGATGATGGAGAGATGAACAACCTTCAGATTGCCATTGGCCGCATCACCAGGGGTATCGATTGGGTTAAAGCCTTAGTTGCCAGTATGGTGCAACGGATTCTGGGAAAGAAACCTAAAATGGCAGATGGTCTGACCAACTGTTTGACATTGACTGTACCTATTGCTCGTTGTGAGTCTGATGTGGAGGGTGACTCTTCGGTTTGTAGCACAGTGGACTACCAGCCTCCAGAACCTGTAGAAGAAGAGGAACCAGAACCTGAAGAACCAGAGGCCTGTTTCACAGAGGGCTGTATTAGGCGATGTGCATGTTTGAGTGTTGACATCACAGAAGGATGGGGTAAAAAATGGTGGAACCTCAGAAGGACATGCTTCACCATCGTTGAGCATGATTACTTTGAGACCTTCATCATCTTTATGATCCTCCTTAGCAGTGGAGCACTGGCTTTTGAGGATATCAACATTGAGAGGCGCAGAGTGATCAAGATCATTCTGGAGTATGCTGATAAAGTCTTTACATATATTTTTATAGTGGAGATGTTACTGAAGTGGGTGGCATATGGCTTCAAGACCTACTTCACTAATGCATGGTGCTGGCTGGACTTCCTCATTGTGGATGTGTCTCTGGTCAGTTTAACGGCTAATTTAATGGGCTATTCTGAGCTGGGGGCAATCAAATCTCTCAGGACACTTAGAGCTCTTCGTCCACTTCGAGCCCTATCCAGATTTGAAGGAATGAGGGTGGTAGTGAATGCACTTGTAGGTGCCATTCCCTCTATTTTTAACGTGCTCCTGGTGTGTCTGATATTCTGGCTCATCTTCAGCATTATGGGGGTCAATCTGTTTGCCGGAAAATTCTACCACTGCATCAACACCACCACAGAGGAACGGATCCCCATGGATGTAGTCAACAACAAGAGTGACTGCATGGCACTGATGTACACCAACGAGGTGCGATGGGTCAATGTCAAGGTCAACTACGACAACGTGGGACTCGGCTACCTCTCTCTGCTGCAGATTGCCACATTCAAAGGCTGGATGGATATCATGTATGCTGCAGTGGATTCTAGAGAGGTGGATGAGCAGCCATCATATGAAATCAACCTTTACATGTACCTTTATTTTGTTATTTTCATCATTTTTGGCTCCTTTTTTACTCTCAACCTCTTTATTGGTGTCATCATTGACAACTTCAATCAGCAAAAATCAAAGTTTGGAGGGAAAGACATTTTCATGACTGAGGAACAGAAAAAGTACTACAATGCCATGAAGAAGCTGGGTGCAAAGAAACGTCCAAAACCTATACCTCGACCATCAAATATTATCCAGGGTTTGGTGTTTGACTTCATATCAAAACAGTTCTTTGACATTTTTATCATGGTGCTAATCTGCCTCAACATGGTGACCATGATGATAGAGACGGATGATCAGAGTGCTGAGAAAGAATATGTCCTGTACCAGATCAATCTGGTCTTCATCGTCGTCTTCACAAGCGAATGTGTACTTAAATTATTTGCACTCAGACAGTACTTTTTCACTN- SterNa6 CCCATGAGCGAAACCTTTCAACACGTGCTCACCATAGGGAACCTGGTGTTTACTACCATCTTTACGGCTGAAATGGTGTCGAAGATCATCGCCCTGGACCCTTACTACTACTTCCAGGTGGGCTGGAACATCTTCGACTGCATCATCGTCACTCTCAGTCTGGTGGAGCTAAGCCTATCCAACATGCCGGGCCTGTCTGTGCTCAGATCCTTTCGTTTGATGCGTATTTTCAAGCTGGCCAAGTCCTGGCCCACGCTCAACATGCTGATCAAGATCATCGGCAACTCAATGGGCGCCCTGGGGAACCTGACCTTCGTGTTGGCCATCGTCATCTTCATCTTCGCCGTGGTGGGCTTCCAGCTGTTCGGGAAGAGCTACAAGGACAACGTGTGCAAGGTCAGCGCGGACTGCACGCTGCCTCGCTGGCACATGAACGACTTCTTCCACTCCTTCCTGATCGTGTTTCGCATCCTGTGCGGCGAGTGGATCGAGACCATGTGGGACTGCATGGAGGTGGACGGAGTGCCCATGTGCCTCACCGTCTTCATGATGGTCATGGTCATCGGAAACCTGGTGATGCTGAACCTGTTCCTTGCCTTGCTTCTCAGCTCATTCAGCTGCGACAATCTTGCCGCGCCAGACGATGACAGTGAAGTTACCAACATCCAGATCTCCATTGTGCGCATCAGCAGAGGGATAAGCTGGGTGAAGAAATTCATTGTAGGCACAGCCTGGTGGATCATGGGCAGGAAGCCCAAGATTGTAGATGGGATTACCAACTATGTTGTTCTGAATGTGCCTATTGCCAAGGGGGAGTCTGAGGTTGAGGATGACTCTTCGATTTGCAGTTCAGTGGACTACGAGCTTCTACAACCCGAGGAGGAAAAGGAA---GAGCCTGTTGATCCAGAAGCCTGTTTTACAGAAAACTGTGTGAGGTACTTTCCATGTCTGGATGTGGACATCACACAGGGGAAAGGGAAGATCTGGTGGAACCTCCGCTGCACCTGCTACAACATCGTGGAACATCACTATTTTGAAAACTTTCTCATCTTCATGATTCTCCTCAGTAGTGGAGTACTGGCATTCGAGGATGTTAATATCGAACGCCGCAGGGTCATTAAGACCATGTTGGAGTATGCAGACATAGTCTTCACATATATTTTCGTGGTGGAGATGTTTCTGAAGTGGACTGCATATGGGTTTAAAGCGTACTTCACCAGTGCCTGGTGCTGGCTGGATTTTTTTATTGTTGATGTGTCAGTTATTAGCTTAGTAGCCAATGTGTTGGGCTATGCAGAGCTGGGACCAGTCAGATCGCTCAGAACTTTCAGGGCTCTTCGACCTTTACGTGCCCTTTCCAGATTTGAAGGAATGAGGGTGGTAGTGAATGCATTGCTCGGTGCCATCCCCTCCATCATGAACGTCCTATTGGTGTGTCTGATCTTCTGGCTGATCTTCAGCATCATGGGGGTCAACTTGTTTGCGGGAAAGTTCTACCGCTGCATTAACACCACCACAGATGAGGTTCTGTCCACAGAGCAAGTGAACAACAGGAGTGAATGCATGGCACTAATGCACACTAATGAGGTGCGTTGGGTCAACCTTAAGGTCAACTACGACAATGTGGGCCAGGGATATCTCTCCTTGCTTCAAGTGGCCACATTTAAAGGGTGGATGGGCATCATGTATGGTGCAGTGGACTCTAGAGAGGTAGAGGATCAGCCATCATATGAGATTAACCTCTACATGTACCTGTACTTTGTCATCTTCATCACATTTGGATCCTTTTTTATCCTCAACCTTTTCATTGGTGTCATCATTGACAATTTTAACCGGCAAAAACAAAAGTTAGGAGGAGATGACCTCTTTATGACAGATGAACAAAAAAAGTATTATGCTGCCATGAAGAAGCTGGGTTCCAAGAAACCACTCAAACCTATACCCCGTCCTTCGAATATGGTTCAAGGGGTGGTGTTCGACTTCATCTCCCAAAAGTTCTTTGACATTTCCATCATGGTTCTCATCTGCCTCAACATGGTGATCATGATGGTGGAGGCGGACGACCAGAGTGAAGAGAAAGAGAATGTCCTCTATCAGATCAATATCATATTTATTGTCNTCTTCACCGGAGAGAGTTTACTCAAGTTGTTTGGACTTAGACATTACTTCTTCACTN- eelNa6 CCCATGAACGAAAGCTTTCAGAGTCTGCTCAGTGCAGGAAACCTGGTGTTTACCACTATCTTTGCGGCTGAAATGGTGTTGAAGATCATTGCCTTGGACCCCTACTACTACTTCCAGCAGACGTGGAACATATTTGACAGCATCATTGTCAGTCTCAGTCTGTTGGAGCTTGGACTATCCAATATGCAAGGAATGTCTGTGCTCAGATCCTTACGTTTGCTGCGTATCTTCAAATTGGCCAAGTCCTGGCCCACGCTCAACATTCTGATCAAGATAATCTGCAACTCGGTGGGCGCTCTGGGCAACCTGACCATTGTGCTGGCCATTATCGTCTTCATCTTCGCCTTGGTGGGCTTTCAGCTGTTCGGAAAGAACTACAAGGAGTACGTGTGCAAGATCTCTGATGACTGTGAGCTGCCCCGCTGGCACATGAATGACTTCTTCCACTCCTTCCTGATTGTGTTCCGTGCCTTGTGTGGCGAGTGGATTGAGACCATGTGGGACTGCATGGAGGTGGGCGGAGTTCCTATGTGCCTCGCCGTCTACATGATGGTCATAATCATTGGGAACCTGGTGATGCTGAACCTTTTCCTTGCCTTGCTTCTAAGCTCATTCAGCAGCGACAATCTCAGTTCAATTGAAGAAGATGATGAAGTTAACAGCCTCCAGGTTGCCTCTGAGCGCATTAGTAGGGCAAAAAACTGGGTGAAGATCTTCATCACTGGCACAGTCCTGTGGATCCAGGGCAAGAAGCCCAAGATTGTAGATGGGATAACCAACTGTGTAACTCTGAATCTACCCATTGTAAAGGGGGAGTCAGAGATCGAAGAAGACTCTTCAGTTTGTAGTACAGTGGACTATAGTCCTTCAGAACAAGAGGAGCCAGAGGAACTAGAGTCCAAAGATCCAGAAGCATGTTTTACAGAAAAATGTATATGGCGATTTCCTTTTCTGGATGTGGACATCACACAGGGGAAAGGGAAGATCTGGTGGAACCTACGTAGGACCTGCTACACCATCGTGGAGCATGACTACTTTGAAACCTTCATCATATTCATGATTCTCCTCAGTAGTGGAGTTCTGGCCTTTGAGGACATTTATATTTGGCGTCGCAGGGTGATTAAGGTCATCTTGGAGTATGCAGACAAAGTCTTCACATATGTCTTCATAGTAGAGATGTTACTTAAGTGGGTTGCATATGGGTTTAAAAGATATTTCACTGATGCCTGGTGCTGGCTCGACTTTGTAATTGTTGGTGCATCAATAATGGGCATAACATCCAGTTTGTTGGGCTATGAAGAGCTGGGAGCAATCAAAAATCTCAGAACTATCAGGGCTCTTCGCCCTTTACGTGCCCTTTCCAGATTTGAAGGAATGAAGGTGGTAGTGAGAGCATTGCTTGGTGCCATCCCCTCCATCATGAACGTGCTGCTGGTGTGTCTGATGTTCTGGCTCATCTTCAGCATTATGGGGGTCAATTTGTTTGCTGGAAAGTTCTACCGCTGCATTAACACCACCACAGATGAGATTCTGCCCGTGGAGGAAGTGAACAACCGGAGTGACTGCATGGCACTAATGTACACTAACGAGGTGCGCTGGGTCAACCTTAAGGTCAACTATGACAATGCGGGCATGGGATACCTCTCCCTGCTACAAGTGTCTACATTTAAAGGCTGGATGGACATCATGTATGCTGCAGTGGACTCCAGAGAGGTAGAGGATCAGCCAATCTATGAGATTAATGTCTACATGTACCTGTATTTTGTCATCTTCATCGTATTTGGAGCCTTCTTTACCCTCAACCTTTTCATTGGTGTCATCATAGACAACTTCAACCGTCAAAAGCAAAAGTTAGGAGGAGAAGATCTCTTTATGACAGAAGAGCAGAAGAAGTACTACAATGCCATGAAGAAGCTGGGTTCGAAGAAAGCTGCCAAATGTATACCCCGCCCTTCGAATGTGGTTCAAGGTGTGGTGTACGACATAGTCACCCAACCATTCACTGATATTTTCATCATGGCTCTCATTTGCATCAACATGGTGGCTATGATGGTCGAGTCGGAGGACCAGAGTCAAGTGAAGAAGGACATTCTCTCTCAGATCAATGTCATATTCGTTATCATCTTCACTGTAGAGTGCTTGTTAAAGCTACTTGCACTTAGACAGTACTTCTTCACTN- catNa6 CCCATGAGTTCGAACTTTGAACACGTGCTCAGTGTTGGCAATTTGGTGTTCACTGGTATTTTCACGGCTGAAATGGTGTTCAAGCTCATTGCCTTGGACCCCTTCTACTACTTCCAGGTTGGCTGGAACATATTTGACAGCATCATCGTCACTCTTAGCCTGGTGGAGTTAGGCCTGGCCAATGTGCAGGGTCTGTCTGTACTCAGATCCTTTCGTTTGCTGCGAGTCTTTAAGCTGGCTAAATCCTGGCCCACGCTCAACATGCTGATCAAAATCATTGGAAACTCTGTGGGTGCTCTGGGGAACCTGACTCTGGTGCTGGCCATCGTCGTCTTCATCTTCGCCGTCGTAGGCATGCAACTTTTTGGCAAGAGCTACAAGGACTGCGTGTGTAAGATTGCAGAGGACTGCGAACTGCCCCGCTGGCACATGAACGATTTTTTCCATTCGTTTCTCATTGTCTTCCGCATCCTTTGTGGTGAATGGATTGAAACCATGTGGGACTGCATGGAGGTGGCTGGAGCAGGCATGTGCCTTGTGGTTTTCCTTATGGTCATGGTCATAGGAAACCTGGTGGTGCTGAACCTGTTCCTTGCCTTGTTGCTCAGCTCTTTCAGCGGGGACAATCTCTCAGCAGGTGATGAAGATGGTGAAATGAACAATCTCCAGATTGCCATCGGCCGCATCACCAGGGGCATTGACTGGGTCAAATCCTTCATCATTGGCCTTGTACAGCAGATACTTTGCAGGAAGCCTAAGATGGCAGATAGGTTGACCAACTGTCTGACCCTGAATGTACCAATTGCCAAAGCTGAGTCTGATGTTGAAGAAGACTCTTCAATGTGTAGCACAGTGGACTATAGACCTCCAGAATCCGAGGAGGAAGAGGAACCAGAACCTGTTGAGCCAGAAGCCTGTTTTACTGAAAACTGTGTGAGACGATGTCCATGTCTGAATTTGGACATCACTCAGGGGAGGGGAAAGAGTTGGTGGAATCTGCGCAGAACTTGCTACACCATAGTGGAGCATGATTACTTTGAAACCTTCATCATCTTCATGATTCTCCTCAGTAGTGGTGCACTGGCCTTTGACGACATTTACATTGAGCGTCGCAGGGTGATTAAGATTATCTTGGAATATGCAGACCAAGTCTTCACATATATTTTTGTCATAGAGATGTTACTGAAATGGGTTGCGTATGGCTTCAAGACATACTTCACCAATGCCTGGTGCTGGCTGGACTTTTTCATCGTTGATGTGTCACTTATCGGTTTAACGGCAAATCTGTTGGGCTATTCAGAGCTGGGACCAATAAAATCTCTCAGAACTCTTAGGGCGCTTCGACCTTTACGTGCCCTGTCCAGATTTGAAGGAATGAGGGTGGTAGTGAACGCATTGCTGGGTGCCATTCCTTCCATCATGAATGTACTCCTGGTGTGTCTAATATTCTGGCTGATCTTCAGTATTATGGGGGTCAACCTGTTTGCTGGGAAATACTACCGCTGCATTAATACCACCACAGAAGAACTTTTACCCATCGAGCAAGTGAACAACATGAGTGATTGCATAGCACTAATGCACACTAAAGAAGCACGCTGGGTCAATGTCAAGGTCAACTTTGACAATGTGGGCTTGGGTTACCTTTCCCTGCTACAAGAGGCTACATTTAAAGGCTGGATGGACATTATGTATGCTGCAGTGGATTCCAGAGAGGTGGAAGAACAGCCATCATATGAGATTAACATATATATGTATCTGTATTTTGTCATCTTCATCATCTTTGGCTCCTCCTTCACCCTCAACCTCTTCATTGGTGTCATCATTGACAACTTTAATCAGCAAAAGCAAAAGTTTGGTGGGGAAGATCTCTTCATGACAGAGGAGCAGAAAAAGTACTACAATGCCATGAAAAAGCTTGGTTCCAAGAAGCCCGTCAAACCCATACCTCGCCCTGCGAATATGATCCAGGGCATAGTGTTTGACTTCATCTCTCAGCAGTTCTTTGACATTTTCATCATGGTGCTCATTTGCCTCAACAAGGTTACCATGATGATTGAGACAGATGACCAAAGTGCAGAGAAAGAATATGTTCTCTATCAGATCAACTTAATCTTCATTGTTGTCTTCACTGGGGAGTGCATCCTCAAAATGTTTGCACTGAGACAATACTTTTTCACTN- AptNa6 ---------------------------CTCACTGTGGGGAACCTGGTGTTTACTGGCATCTTTACGGCTGAAATGGTGTTTAAGCTCATTGCCATGGACCCCTACTACTACTTCCAGGTGGGCTGGAACATCTTCGACAGCATCATCGTCACCCTCAGTCTGGTGGAGCTGGGGCTAGCCAACGTGCAGGGTCTGTCTGTGCTCAGGTCCTTCCGTTTGCTGCGTGTCTTCAAGTTGGCCAAGTCCTGGCCAACGCTCAATATGCTCATCAAGATCATTGGCAACTCGGTGGGAGCCCTGGGCAACCTGACACTGGTGCTGGCCATTATTGTCTTCATCTTTGCCGTGGTGGGCATGCAGCTATTTGGGAAGAGCTACAAGGACTGCGTGTGCAAGATTGCGCTGGACTGCGAGCTTCCCCGCTGGCACATGACGGACTTCTTCCACTCCTTCCTGATCGTGTTCCGCATCCTATGCGGCGAGTGGATTGAGACCATGTGGGACTGCATGGAGGTGGCTGGACCGTCCATGTGCCTCATCGTCTTCATGTTGGTCATGGTCATTGGGAACCTGGTGGTGCTGAACCTGTTCCTTGCATTGCTTCTCAGCTCATTCAGCGGTGACAATCTCTCGGCAAGCGACGATGACAGTGAGATTAACAACCTCCAGATCGCCACAGGGCGCATCAGCAGAGCGATTGGCTGGGTGAAGAACTTTATCATCAGCACAGTCCAGTGGGTTCTGGGCAGAAAGCCCAAGATGGTGGATGGCATGACCAACTGCGTAGTCCTGAATGTGCCCATTGCCAAGGGGGAATCTGAGATTGAAGGAGACTATTCAGTTTGCAGTACAGCAGACTACAGACCTCCAGAACCCGAGGAGGAAAAGGTACCAGAGACCAATGATCCAGAAGCCTGCTTTACAGAAAATTGTGTGAGGCGATTTCCTTGTCTCAATGTGGACATCACCCAGGGGAAAGGGAAGAGCTGGTGGAACCTACGCAGAACCTGCTACATCATCGTGGAGCATGACTACTTTGAGACCTTCATCATCTTCATGATTCTCCTCAGTAGCGGAGCACTGGCTTTCGAGGACATTTATATAGAGCGTCGCAAGATGATTAAGATCATCTTGGAGTACGCAGACAAAATCTTCACCTATGTTTTCATAATGGAGATGTTACTGAAGTGGGTTGCTTATGGGTTTAAAACGTACTTCACCAATGCCTGGTGCTGGCTGGACTTTCTTATTGTTGATGTGTCAATTATTAGCTTAACAGCCAATCTGTTGGGCTATTCAGAGCTGGGACCAATCAAATCTCTCAGAACACTCAGGGCTCTTCGACCGCTACGTGCCCTTTCCAGATTTGAAGGAATGAGGGTGGTAGTGAATGCGTTGGTTGGCGCCATCCCCTCCATCATGAACGTGCTGCTGGTTTGTCTGATCTTCTGGCTCATCTTCAGTATCATGGGGGTCAACTTGTTTGCTGGAAAGTTCTACCGCTGCATTAACACTACCACCGAGGAGCTTCTGCCCATGGAGGAAGTGAACAACAGGAGTGATTGCATGGCGCTAATGCACACTAATGAGGTGCGCTGGGTCAATGTCAAGGTGAACTACGACAACGTCGCCCTGGGATACCTTTCCCTGCTGCAAGTGGCTACATTTAAAGGCTGGATGGACATCATGTATGCTGCAGTGGACTCCAGAGAGGTAGAGGAGCAGCCATCATATGAGATTAACCTCTACATGTACCTGTACTTTGTCATCTTCATCATATTGGGATCCTTTTTTACCCTCAACCTTTTCATTGGTGTCATCATAGACAACTTCAACAGGCAGAAGCAAAAGTTTGGAGGAGAAGATCTCTTTATGACGGAGGAGCAGAAGAAGTACTACAATGCCATGAAGAAGCTGGGATCCAAGAAGCCTGTCAAACCTATACCCCGTCCTACGAATGTTATTCAAGGTGTGGTGTTCGACCTCATTTCCCAGCAGTTCTTTGATATTTTCATCATGGTTCTCATTTGCCTCAACATGGTGACCATGATGGTGGAGACTGATGACCAGAGCAAAGAGAAAGAGCACATCCTCTATCAAATCAACGTCATATTCATTGTCGTCTTCACTGGAGAGTGTTTGCTCAAGATGTTTGCACTGAGGCAGTACTTCTTCACTN- PinniNa6 CCCATGAGTGAAACGTTTGATTACGTCCTCAGCACAGGGAACCTGGTGTTTACCATCATCTTTGCAGCTGAAATGGTCTTGAAGCTCATTGCCATGGACCCCTACTACTACTTCCAGCAGACGTGGAACATCTTTGACTTTTTCATTGTCTCACTCAGTCTGGTGGAGATGGGACTGGCTAACATGCAGGGGCTGTCAGTGCTTAGGTCCTTTCGACTGCTGCGTATCTTTAAGTTGGCCAAGTCCTGGCCCACGCTCAATATTCTGATCAAGATCATCTGCAACTCGGTGGGCGCCCTGGGAAACCTGACCATCGTGCTGGCCATTATCGTCTTCATCTTCGCCTTGGTGGGCATGCAGCTGTTCGGGAAGAATTACAAAGAGTTTGTGTGCAAGATCAGTGCAGACTGTACGCTGCCTCGCTGGCATATGAATGACTTCTTCCATTCCTTCCTGATTGTGTTCCGCTGCCTGTGCGGCGAGTGGATTGAGACTATGTGGGACTGTATGGAGGTGGGCGGTGTGCCCATGTGCCTCAGCGTTTACATGATGGTCATAATCATCGGGAACCTGGTGGTGCTGAACCTGTTCCTTGCCTTACTGCTAAGCTCATTCAGTGGTGACAATCTCACTGCAAACGATGATGACCAAGAGGATAACAACATCCTGATTGCAGCTGAGCGGATCAGCAGGGCAAAACTCTGGGTGAAGGGGTTCATAATACGGACGGTCTTGGGGATGCTGGGCAAGGAGCCAAAGATTGTGAATGGGCTAGCCAACGGTGTAGTTCTGAATGTGCCCATTGCCAAGGGCGAGTCTGAGACTGAAGATGACTCTTCAGTCTGCAGTACAGTGGACTACAGTCCTCCAAATCCAGAGGAACCCGAGGAACCAGAACCCGATAATCCAGAAGATTGTTTAACGGAAGAATGTGTGTCACGATTTCCTTGGCTGAATGTGGACATAACACAGCCAAAAGGGAAGAGTTGGTGGAACCTTCGTAGGACATGCTACGTCATCGTAGAGCATGACTACTTTGAGACTTTCATCATCTTCATGATTCTCCTCAGTAGTGGAGCACTGGCTTTCGAGGACATTTATATTGAGCGTCGCAGGGTGATTAAGATCATCTTGGAGTATGCGGACAAAGTCTTCACATATATTTTCATAGCAGAGATGTTACTGAAGTGGGTTGCATATGGGTTTAAAAAGTACTTCTCCGACGCCTGGTGCTGGTTAGACTTTCTAATTGTTGATGTGTCAATAATTAGCTTAACAGCCAATTTGTTGGGCTATTCAGAGTTGGGACCAATCAAATCTCTCAGAACTCTCAGGGCTCTTCGACCTTTACGTGCACTTTCCAGATTTGAAGGAATGAGGGTGGTAGTCAAAGCATTGGTTGGCGCCATCCCCTCCATCGTGAACGTGCTGCTGGTATGTCTCATGTTCTGGCTCATCTTCAGCATTATGGGAGTCAACTTGTTTGCTGGAAAGTTCTACCGCTGCATTAACACTACCACAGAAGAGACCATGCCCYTGGAAGAAGTCAACAACCGCAGTGACTGCAATGCACTTATGTACACTAATGAGGTGCGATGGGTCAACCTTAAGGTCAACTATGACAATGCAGGCATGGGATACCTCTCCCTGCTACAAGTGGCAACATTTAAAGGTTGGATGGACATCATGTATGCTGCAGTGGACTCCAGAGGGGTAGAGGATCAGCCGATATACGAGATTAACGTCTACATGTACCTGTATTTTGTCATCTTCATCGTATTTGGATCCTTTTTCACCCTAAACCTCTTCATTGGTGTCATCATAGACAACTTCAACCGTCAAAAGCAAAAGTTAGGAGGAGATGATCTCTTTATGACAGAAGAACAGAAAAAGTATTATGATGCCATGAAGAAGCTGGGTTCCAAGAAACCTGTCAARGTTATACCACGCCCTTCGAACAAGATTCTGGGTGTGTTGTATGACATAGTCAACCAACGGGTCACTGATATTTTCATCATGTCTCTCATTTGGCTAAACATGGTTACCATGATGGTGGAGACAGATGACCAGAGCGAAGAAAAGAAGAATGTTCTCTATCAGATCAATTTAATATTCATTATCATCTTCACTGGAGAATGTCTGCTCAAGTTGCTTGCACTAAGACATTACTTCTTCACTN- tetra CCCATGACCCAGGAGTTCGACTACATGCTTTCAGTGGGAAATCTGGTTTTCACAGGAATTTTTGCAGCAGAAATGTTCTTCAAGCTGATCGCCATGGATCCGTACTACTATTTCCAAGTTGGCTGGAACATTTTTGACAGCATCATTGTCACCCTCAGCCTGGTAGAGTTGGGGCTTGCGAACGTCCAGGGCCTGTCTGTCCTCAGGTCCTTCCGCCTGCTCCGTGTCTTCAAACTTGCCAAATCCTGGCCCACACTCAACATGCTGATCAAGATTATTGGGAGCTCAGTTGGAGCGCTAGGGAATCTGACGTTGGTGCTGGCCATCATCGTCTTCATCTTCGCTGTGGTGGGGATGCAGCTCTTTGGCAAAAGCTACAAGGACTGCGTGTGCAAGATTTCCACGGAGTGCGAGCTGCCGCGCTGGCACATGAATGACTTCTTCCACTCCTTCCTCATCGTCTTCCGCATCCTGTGTGGCGAATGGATCGAGAACATGTGGGCCTGCATGGAAGTGGCTGGAGCTGGGATGTGCTTAGTTGTCTTCATGATGGTCATGGTGATTGGAAACCTCGTGGTGTTGAACCTCTTCCTGGCCCTGCTGCTCAGCTCGTTCAGCGGGGACAATCTGTCCATCGGAGAGGACGATGGAGAGATGAACAATCTTCAGATTGCCATCGGCAGAATCACACGAGGTGGAAACTGGCTCAAGACCCTTGTCATCAGAACGGTCCTGCAGCTTCTCGGTAGGGAGCAGAAAACGGCAGATGGGATAGCTAACTGTCTTGTTATCAACGTCCCCATCGCCTTGGGGGAGTCAGACTCTGAAGGCGAGTCTTCAGTGTGCAGCACAGCAGACTATCGGCCCCCCGAGCCTGAGGAAGAGGAAGAGCCGGAACCACTGGAGCCAGAGGCCTGCTTTACTGACAACTGCGTCAAACACTGGCCTTGTCTGAACGTGGACGTCACCCAAGGTCAAGGGAAGAAGTGGTGGAACCTCCGCAAGACCTGCTTCACAATCGTAGAGCATGACTGGTTTGAGACCTTCATCATCTTCATGATCCTCCTCAGCAGCGGAGCCCTGGCCTTTGAAGATATATACATCGAAAGACGAAGAACCGTCAAAATTATCCTGGAGTTTGCCGACAAAGTTTTCACCTTCATCTTTGTCCTTGAGATGGTGCTGAAATGGGTGGCCTATGGCTTCAAGACCTACTTCACCAACGCCTGGTGCTGGTTGGACTTTTTCATTGTAGACATTTCCCTGATCAGTTTATCGGCCAACCTGATGGGCCTCTCTGACCTGGGACCAATCAAATCTCTCAGAACACTCCGGGCACTGAGGCCTCTTCGAGCTCTGTCCAGATTTGAAGGGATGAGGGTGGTGGTGAACGCTCTTATCGGAGCCATTCCCTCCATCTTCAACGTGCTGCTGGTGTGCCTGATCTTCTGGCTCATCTTCAGCATCATGGGAGTGAACCTGTTTGCGGGGAAGTTCTACCACTGCATCAACACCACCACACAGGAGCTCTTCCCCATCTCTGTGGTCAACAACAAGAGCGACTGCATGGCCGTCCAGGCCACGCAGGAGGCCCGCTGGGTCAACGTCAAGGTCAACTACGACAACGTGGGAAAAGGCTACCTGTCGCTGCTTCAAATCGCCACTTTTAAAGGCTGGACGGCCATTATGTATGCTGCAGTAGATTCAAGAGAGGTGGAAGAGCAACCTTCCTATGAGATCAACCTGTACATGTACATCTACTTTGTCATCTTCATCATCTTTGGCGCTTTCTTCACGCTCAACCTGTTCATCGGCGTCATCATCGATAACTTCAACCAGCAGAAGAGAAAGATA---AACAAAGACATCTTCATGACGGAGGAGCAGAAAAAGTACTACGAAGCCATGAAGAAACTCGGCTCCAAGAAGCCGCAGAAGCCGATCCCACGTCCGACCAACCTCATCCAGGGAATGGTGTTTGACTTCATCAGTCAGCAGTTCTTTGACATCTTCATCATGGTGCTCATCTGCCTCAACATGGTGACCATGATGGTGGAGACGGACGACCAGAGCCCCGAGAAGGAGGATTTCCTCTTCAAAGTGAACGTGGCTTTTATCGTGGTCTTCACGGGGGAGTGCATGCTGAAGCTCATCGCCCTGCGACAGTACTTCTTCACCN- ; end; begin assumptions; [These sites are identical: 1929, 1995 (inform) 750, 816 (inform) 1009, 1012, 1019, 1022 (const) last two chars of all taxa are missing, the last of which is excluded the wtset exactly counteracts the exset ] exset * zeroWeight = 1995 816 1012 1019 1022 2180; wtset * compensate = 1:1-749 751-1008 1010-1928 1930-., 2:1929 750, 4:1009; [exset * zeroWeight = 162 167 234;] [wtset * equal = 1: 1-141, 4:142, 0:162 167 234, 1:144-161, 1:163-166 168-233 235-.;] [wtset * equal = 1: 1-141, 4:142, 1:162 167 234, 1:143-161, 1:163-166 168-233 235-.;] end; garli-2.1-release/tests/data/z.11x30.AA.fas000077500000000000000000000004361241236125200201460ustar00rootroot00000000000000>MorNa6 PVTPHFEHVL- >ClownNa6 PMSPEFDHML- >AraNa6 PMSPAFDHML- >puffNa6 PMTEEFDYML- >NewZebra PMSPHFEHVL- >SterNa6 PMSETFQHVL- >eelNa6 PMNESFQSLL- >catNa6 PMSSNFEHVL- >AptNa6 ---------L- >PinniNa6 PMSETFDYVL- >tetra PMTQEFDYML- garli-2.1-release/tests/data/z.11x30.phy000066400000000000000000000006761241236125200177200ustar00rootroot0000000000000011 30 MorNa6 CCTGTGACTCCACATTTTGAGCACGTACTC ClownNa6 CCCATGAGCCCTGAGTTTGACCACATGCTC AraNa6 CCAATGAGTCCCGCGTTTGACCATATGCTG puffNa6 CCCATGACCGAAGAGTTCGACTACATGCTT NewZebra CCTATGAGTCCACATTTTGAACATGTCCTC SterNa6 CCCATGAGCGAAACCTTTCAACACGTGCTC eelNa6 CCCATGAACGAAAGCTTTCAGAGTCTGCTC catNa6 CCCATGAGTTCGAACTTTGAACACGTGCTC AptNa6 ---------------------------CTC PinniNa6 CCCATGAGTGAAACGTTTGATTACGTCCTC tetra CCCATGACCCAGGAGTTCGACTACATGCTT garli-2.1-release/tests/data/z.11x30.stop.nex000066400000000000000000000010701241236125200206630ustar00rootroot00000000000000#NEXUS Begin data; Dimensions ntax=11 nchar=33; Format datatype=dna gap=-; Matrix MorNa6 CCTGTGACTCCACATTTTGAGCACGTACTCTAA ClownNa6 CCCATGAGCCCTGAGTTTGACCACATGCTCTAA AraNa6 CCAATGAGTCCCGCGTTTGACCATATGCTG--- puffNa6 CCCATGACCGAAGAGTTCGACTACATGCTT--- NewZebra CCTATGAGTCCACATTTTGAACATGTCCTC--- SterNa6 CCCATGAGCGAAACCTTTCAACACGTGCTC--- eelNa6 CCCATGAACGAAAGCTTTCAGAGTCTGCTC--- catNa6 CCCATGAGTTCGAACTTTGAACACGTGCTC--- AptNa6 ---------------------------CTC--- PinniNa6 CCCATGAGTGAAACGTTTGATTACGTCCTC--- tetra CCCATGACCCAGGAGTTCGACTACATGCTT--- ; End; garli-2.1-release/tests/data/z.11x30.wackyNames.nex000066400000000000000000000010511241236125200217770ustar00rootroot00000000000000#NEXUS Begin data; Dimensions ntax=11 nchar=30; Format datatype=dna gap=-; Matrix Mor_Na6 CCTGTGACTCCACATTTTGAGCACGTACTC 'Clown Na6' CCCATGAGCCCTGAGTTTGACCACATGCTC 'Ara-Na6' CCAATGAGTCCCGCGTTTGACCATATGCTG 'puff -Na6' CCCATGACCGAAGAGTTCGACTACATGCTT 'New_Zebra' CCTATGAGTCCACATTTTGAACATGTCCTC 'Ster_-Na6' CCCATGAGCGAAACCTTTCAACACGTGCTC eelNa6 CCCATGAACGAAAGCTTTCAGAGTCTGCTC catNa6 CCCATGAGTTCGAACTTTGAACACGTGCTC AptNa6 ---------------------------CTC PinniNa6 CCCATGAGTGAAACGTTTGATTACGTCCTC tetra CCCATGACCCAGGAGTTCGACTACATGCTT ; End; garli-2.1-release/tests/data/z.byPos.11x2178.nex000066400000000000000000000600461241236125200211610ustar00rootroot00000000000000#NEXUS [ This dataset is from: Zakon, Lu, Zwickl and Hillis. 2006. Sodium channel genes and the evolution of diversity in communication signals of electric fishes: Convergent molecular evolution. Proc. Natl. Acad. Sci. USA. 103(10):3675-80. ] begin data; dimensions ntax=11 nchar=2178; format datatype=dna missing=? gap=-; matrix MorNa6 CCTGTGACTCCACATTTTGAGCACGTACTCAGTGTGGGAAACCTGGTTTTCTCAGGGATATTTGCTGGTGAAATGGTCTTGAAAATTATTGCTATGGACCCCTACTACTACTTCCAGGTTGGATGGAACGTGTTTGACAGCATCATTGTTACCATGAGTATGGTGGAGATGGTACTGGCTGATGTAGAGGGTCTGTCGGTTCTGCGGTCCTTTCGTTTGCTACGTGTCTTCAAGCTTGCCAAATCATGGCCTACCCTCAACATGCTGCTAACGATCATCGGAAACTCAGTGGGTGCTCTGGGGAACCTCACCGTGGTGCTGGCCATCATCGTTTTCATCTTCGCTGTGGTTGGAATGCAGCTGTTTGCCAAAAACTACAAGGACTGCGTCTGCAAGATCGCCGAGGATTGTGAGCTGCCCCGGTGGCACATGCATGACTTCTTCCACTCTTTCCTCATCGTGTTCCGCATCCTCTGTGGAGAGTGGATTGAGACCATGTGGGACTGCATGGAAGTGGCCAACAGAAACATGTGTTTGGTCCTCTTCTTAATGGTCATGATAATTGGGAACCTGGTGGTTCTGAACCTTTTCCTGGCCTTGCTGCTTAGCTCATTCAGCGGGGACAATCTGCAAATGGCAGATGACGACGGCGAGCTGAACAATCTGCAGCTTTCCGCACTCAGGATCACCAGAGCCATTGATTGGGTGAAGGCCTACGTTAGAGGGCTGATCTGGAAGATCCTGGGCAAGCAGCCAAGAGTGCTGGATGGTTTATCTCACTGGGCAACCTTCACCGTACCCATTGCCCAGGAAGAGTCTGATTTAGAAGATGGTGTGTCTGAGTGCAGCACAGTGGACTACGTGCCCCCTCCGCCGGATGAAGTGGAGGAACCGGAGCCTGTGGAACCTGAGGCCTGTTACACTGACAACTGCCTTAGACGGTGTCCTTGTCTGGTGCTGGACACCTCAGAGGGCAGAGGGAAGACCTGGTGGAACCTCAGGAGAACCTGCTACACCATTGTGGAGCATGACTACTTTGAGTCCTCCATAATCTTCATGATCCTTCTCAGCAGTGGTGCCTTGGCCTTTGAAGACATATATCTTGAAAGACGCAGAACGATAAAAATCCTGCTGGAATATGCAGATAAAGTCTTCAGCTATGTATTTGTTATTGAGATGCTCCTTAAGTGGGTGGCTTATGGTTACAAAGTATACTTTACCAATGCCTGGTGCTGGCTGGACTTCTTGATTGTTGATGTTTCCTTGGTCAGTTTGGCAGCAAGCATAATGGGCTATTCTGAACTAGGACCCATAAAGTCTTTGAGAACTCTTAGGGCTCTGAGGCCTCTAAGAGCCCTTTCCAGGTTTGAGGGGATGCGGGTTGTGGTGAACGCCCTTGTGGGGGCCGTCCCCGCCATCTTCAATGTGATGCTGGTCTGTCTCATCTTCTGGCTCATCTTCAGCATCATGGGGGTTAACCTGTTTGCCGGGACATTCTACCACTGCCTCAACACCACAACTGGGGAGATGTTTACCATTGATGTTGTAAACAACTATAGTGAGTGTTTGGCCCTCATGCACACAAACGAGGTGCGCTGGGCCAACGTCAGGGTCAACTATGACAACGTTGGGATGGGTTACCTGTCTCTGTTGCAAGTGTCAACATTCAAAGGCTGGATGGAAATTATGTATGCGGCTGTCGACTCACGTAAGGTGGGTCAACAGCCCTCATATGAGGCCAACCTTTACATGTACGTGTACTTTGTCATCTTCATCATCTTTGGGTCCTTCTTTACACTCAACCTCTTCATTGGTGTCATCATTGACAACTTCAATCAACAAAAGAATAAGATGGGAGGA---GATTGCTTTATGACTGAGGAGCAGAAGAAATATTACGACGCTATGAAAAAGCTAGGCAACAAGAAGCCAGCGAAGCCCATTCCAAGACCAACGGGCAAAATACCAGGCCTAGTATATGACTTCATCAGTCAGCAGGCCTTTGACATCTTTATCATGGTACTGATTTGCCTGAACATGGTGACCATGATGGTGGAGGAAGATGACCAAAGTGAACAGAAGACAGACATGCTGGGCAAAATCAATGCAGTCTTCATTGTGGTCTTCAGCAGTGAATGTTTGCTGAAGATGATTGCACTGAGACAATACTTCTTTACC ClownNa6 CCCATGAGCCCTGAGTTTGACCACATGCTCTCTGTGGGAAACCTGGTTTTCACTGGAATCTTCACAGCTGAAATGGTCCTAAAACTCATTGCTATGGACCCCTACTACTACTTCCAGGTTGGATGGAACATATTTGACAGCATCATTGTCACTCTAAGCCTAGTGGAACTGGGGCTCGCTAATGTTCAGGGTCTGTCAGTCCTGCGATCCTTTCGTTTGTTGCGAGTGTTCAAGCTGGCAAAGTCTTGGCCCACCCTCAACATGCTGATCAAGATCATCGGGAATTCCGTGGGCGCCCTGGGCAACCTGACCCTGGTGCTGGCCATCATCGTCTTCATCTTCGCCGTGGTGGGCATGCAGCTCTTTGGGAAGACCTACAAGGACTGCGTGTGCAAGATTGCCAGTGACTGCGAGCTTCCCCGCTGGCACATGAATGACTTCTTCCACTCGTTCCTTATCGTGTTCCGCATCCTCTGCGGGGAGTGGATCGAGACCATGTGGGACTGCATGGAGGTGGCTGGTGCAGGCATGTGCCTCGTGGTCTTCATGATGGTCATGGTCATTGGGAACCTAGTGGTGCTGAATCTCTTCCTGGCTTTGCTGCTCAGTTCATTCAGTGGAGACAACCTAGCAGGCGGTGATGAGGATGGCGAGATGAACAACTTGCAGATTGCTATCGGAAGGATCACCCGAGGCATTGACTGGGTGAAGGCATTTGTCATGGGACTGGTGTGGCGGGTGATGGGCAAAAAGCCTAAAATGCTGGATGGTTTATCTCACTGGGTAACCCTCAGTGTGCCCATGGCACAGGAGGAATCCGACTTAGAAGACGACTCCTCTGAATGCAGCACTGTGGACTATAGGCCTCCAGAGCCAGTGGAGGAGGAAGAACCAGAACAGGTGGAGCCTGTGGAGTGTTTTACTGATGACTGTGTCAGACGTTGCCCTTGTCTGACGGTGGACATCACGCAGGGCAAAGGAAGGACCTGGTGGAATCTCAGGAAAACATGTTACACCATCGTGGAGCATGACTACTTTGAGACCTTCATCATCTTCATGATCCTGCTTAGCAGTGGGGCCTTGGCCTTTGAAGATATATACATTGAAAGGCGCAGAACAATAAAAATCATTCTGGAATATGCAGACAAAGTATTTACATACGTATTTGTTGTTGAAATGCTCTTGAAGTGGGTTGCTTATGGTTTCAAGACATACTTCACTAATGCCTGGTGCTGGCTGGACTTTTTAATTGTGGATGTGTCCTTGATCAGTTTGACAGCAAACCTCATGGGCTACTCAGAGCTGGGGCCTATCAAATCCCTGAGAACCCTGAGGGCCCTGAGGCCACTACGAGCCCTGTCTAGGTTTGAGGGCATGAGAGTGGTGGTAAATGCATTGGTAGGGGCCATCCTTTCCATCTTCAACGTACTGCTGGTCTGTCTCATTTTCTGGCTTATCTTCAGCATTATGGGTGTCAACCTTTTTGCTGGAAAGTTCTACCGCTGTATCAACACCACCACAGAGGAGCTATTACCTGTCGAGATTGTGAACAATAAGAGTGACTGCTTGAATCTCATGCACACAAATGAAGTGCGCTGGGTCAATGTGAAGGTCAACTATGACAACGTTGGCCTTGGTTACCTCTCTCTACTCCAAGTTGCAACATTTAAAGGGTGGATGGACATTATGTATGCAGCTGTGGACTCTCGTGAGGTGGAAGAGCAGCCCTTGTATGAGGAAAACCTCTATATGTACTTATACTTCGTCATCTTCATCATTTTTGGGTCATTCTTTACACTCAACCTTTTCATTGGTGCCATCATCGACAACTTTAATCAGCAAAAGAAAAAGCTTGGTGGGAAGGATATCTTCATGACCGAGGAGCAAAAGAAGTACTACAATGCCATGAAAAAGCTTGGTTCCAAAAAGCCAGTGAAGCCTATTCCAAGACCTACGAACAAAATACAAGGTGTGGTATTTGACTTCATCAGTCAGCAGTTCTTTGACATCTTTATCATGGTATTGATCTGCCTCAACATGGTGACCATGATGGTGGAGACAGATGACCAAAGTCAGGAAAAAGAGAATATACTGAACCAAATCAATCTGGTATTCATTGTGATCTTCACCAGCGAATGCGTCTTGAAGATGTTTGCACTTAGACATTATTTCTTCACC AraNa6 CCAATGAGTCCCGCGTTTGACCATATGCTGACCGTGGGAAACCTCGTTTTTACGGGGATCTTTACAGCTGAGATGGTATTCAAGCTCATCGCCATGGATCCATACCACTACTTCCAGGTTGGATGGAACATTTTTGACAGCATCATTGTCACACTTAGCCTGGTGGAGCTGGGTCTCGCGAATGTTCAGGGCCTTTCGGTCTTGCGCTCCTTCCGCTTGCTGCGGGTCTTCAAGCTGGCCAAGTCTTGGCCTACCCTGAACATGCTCATCAAGATCATTGGAAACTCAGTGGGTGCCCTAGGGAACCTCACACTGGTGCTGGCCATCATCGTCTTCATCTTCGCTGTCGTGGGCATGCAGCTGTTCGGTAAGAGCTACAAGGACTGTGTGTGTAAGATTGCAGAGGACTGTGAGCTACCCCGCTGGCACATGAATGACTTCTTCCACTCCTTCCTCATCGTGTTCCGCATCTTGTGTGGCGAGTGGATCGAGACCATGTGGGACTGCATGGAGGTGGCGGGCGCTGGCATGTGTCTCGTTGTCTTCATGATGGTCATGGTCATCGGCAACCTGGTGGTCCTGAACCTCTTCCTGGCTTTGCTGCTGAGCTCGTTCAGTGGAGACAACCTGGCTGGAGGAGACGATGATGGCGAGATGAACAACCTGCAGATTGCCATTGGCAGGATCACCAGAGGCATTGACTGGATAAAAGCCTTTGCCATGGGCTTCATATGGAAGTTACTTGGAAAGAAGGCCAAGATGCTGGATGGTTTATCCCACTGGGTGACCCTGAGTGTTCCCATTGCCCAGGGAGAGTCTGATTTGGAGGATGACTCCTCTGAATGCAGCACGGTGGACTACAGACCCCCAGAACCAGAGGAGGAGGAGGAGCCTGAGCAGCAGGAGCCTGAGGCCTGTTTTACTGAGGATTGCTTCCGGCGTATGCCATGTTTGATGGTGGACATCACGCAGGGGAAGGGCAAGACCTGGTGGAAACTACGGAAAACCTGTTTTACCATTGTGGAGCATGGCTATTTTGAGACCTTCATCATTTTCATGATCCTTCTCAGCAGTGGAGCTCTGGCTTTTGAAGACATATACATTGAAAAGCGCAGAGTTATCAAAATCATCCTGGAATATGCGGACAAAGTCTTCACCTATGTATTTGTTATTGAAATGGTCCTCAAGTGGGTGGCTTATGGGTTCAAAGTATACTTCACAAACGCCTGGTGCTGGCTGGACTTCCTCATCGTTGATGTGTCCTTGATCAGTCTGACCGCTAACCTCATGGGCTACTCTGAGCTGGGGCCCATTAAGTCTCTGAGAACACTTAGGGCCCTTAGGCCCCTGAGGGCCCTCTCCAGGTTTGAGGGGATGAGGGTGGTGGTAAATGCGCTTGTGGGAGCCATCCTCTCCATTTTCAACGTTCTGCTCGTGTGCCTCATCTTCTGGCTCATCTTCAGCATCATGGGCGTTAACCTGTTTGCTGGGAAGTTCTACTACTGCATTAACACCACCTCAGAGGAGCGCTTACCCATTGATGTTGTGAATAACAAGAGCGACTGCATGGCCCTAATGCACACCAATGAGGTGCGCTGGGTCAACGTCAAGGTGAACTATGACAATGTCGGCTTGGGCTATCTCTCTCTGCTGCAGGTGGCTACTTTTAAAGGTTGGATGGATATAATGTATGCTGCCGTGGACTCACGGGAGGTGGGGGAGCAACCCTCCTATGAGGTCAACATCTACATGTACTTGTACTTTGTCATCTTCATCATCTTCGGGTCCTTCTTCACGCTCAACCTCTTCATTGGTGTCATCATTGACAACTTCAATCAGCAAAAGAAAAAGTTAGGAGGAAAAGACATATTCATGACTGAGGAACAGAAGAAGTATTACAATGCCATGAAGAAACTTGGCTCCAAGAAGCCAGTGAAGCCCATCCCACGACCTTCGAATAAAATTCAAGGCATGGTGTTTGACTTCATTACGCAGCAGTTTTTTGATATTTTCATCATGGTACTGATCTGCCTCAACATGGTGACCATGATGGTGGAGACGGATGATCAAAGCGAGGACAAAGAAAATGTCCTCTACCAGATTAACCTGGTCTTCATTGTGATCTTCACCTGCGAGTGCGTCCTCAAAATGTTTGCGCTTAGACAGTACTTCTTCACC puffNa6 CCCATGACCGAAGAGTTCGACTACATGCTTTCAGTGGGAAATCTGGTTTTCACAGGAATCTTCGCGGCGGAAATGTTCTTCAAATTGATCGCCATGGATCCGTACTACTATTTCCAAGTTGGCTGGAACATTTTTGACAGCATCATCGTCACGCTCAGTCTGGTGGAGTTAGGGCTTGCAAACGTCCAGGGGCTGTCCGTCCTCAGGTCCTTCCGTCTGCTTCGGGTCTTCAAACTTGCCAAGTCCTGGCCCACGCTCAACATGCTGATCAAGATTATCGGTAATTCAGTTGGAGCTTTAGGGAATCTGACTTTGGTGCTGGCCATCATCGTCTTCATCTTCGCTGTGGTGGGGATGCAGCTCTTCGGCAAAAGCTACAAGGACTGTGTGTGCAAGATTTCCTCCGACTGCGAGCTGCCACGCTGGCACATGAATGACTTCTTCCACTCCTTCCTCATCGTGTTCCGCATCCTGTGCGGCGAGTGGATCGAGACCATGTGGGACTGCATGGAGGTGGCTGGAGCCGGGATGTGCTTGGTTGTCTTCATGATGGTCATGGTCATCGGGAACCTCGTGGTGTTGAATCTCTTCCTGGCCCTGCTGCTCAGCTCATTCAGCGGAGACAACCTCTCGGTCGGAGACGACGATGGAGAGCTGAACAATCTTCAGATCGCCATCGGAAGGATCACACGAGGCGGCAACTGGCTCAAAGCCTTCTTCATCGGAACGCTTCAACGGGTTCTTGGAAGGGAACCAAAATTGGCAGACGGGATCGCCAACTGTCTTAGTATCACCGTCCCCATCGCCCTGGGAGAGTCGGACTCTGAAGGCGATTCTTCAGTGTGCAGCACAGTGGACTATCAGCCCCCAGAGCCTGAGGAAGAGGAAGAGCCGGACCTGGTGGAGCCAGAGGCCTGCTTCACTGACAACTGTGTGAAGCGCTGGCCTTGTCTGAACGTGGACATCAGCCAGGGGAAAGGAAAGAAGTGGTGGAACCTCCGCAAGACCTGCTTCACTATTGTGGAGCATGACTGGTTTGAGACCTTCATCATTTTCATGATCCTCCTCAGCAGCGGAGCTCTGGCCTTTGAAGACATATACATCGAAAGACGAAGAACCGTGAAAATTGTCCTGGAGTTTGCTGACAAAGTTTTCACCTTCATCTTTGTCATCGAGATGCTCCTGAAATGGGTCGCCTATGGCTTCAAGACCTACTTCACCAATGCCTGGTGCTGGCTGGACTTTTTCATCGTGGACATTTCCCTGATCAGTCTATCTGCCAACTTGATGGGCTTCTCTGACCTCGGACCAATCAAATCGCTCAGAACTCTCAGGGCTCTGCGGCCTCTTCGGGCGCTGTCCAGATTTGAAGGGATGAGGGTGGTGGTGAACGCTCTCATCGGAGCCATTCCCTCCATCTTCAACGTGCTCCTGGTGTGCCTGATCTTCTGGCTCATCTTCAGCATCATGGGAGTGAACCTGTTTGCGGGGAAGTTCTACCGCTGCATCAACACCACCACGGCGGAGCTCTTCCCCATCTCTGTGGTCAACAACAAGAGCGACTGCGTGGCGCTGCAGGCCACGCAGGAGGCCCGCTGGGTCAACGTCAAAGTCAACTACGACAACGTGGCAAAAGGCTACCTGTCGCTGCTTCAAATCGCAACTTTTAAAGGCTGGATGGATATTATGTATCCTGCGGTTGACTCAAGAGAGGTGGAAGAGCAACCTTCTTATGAGATCAACCTCTACATGTACATCTACTTTGTCATCTTTATCATCTTTGGCTCTTTCTTCACGCTGAACCTCTTCATCGGCGTCATCATCGACAATTTCAACCAGCAGAAGAAAAAGTTAGGAGATAAAGACATCTTCATGACAGAGGAGCAGAAAAAGTACTACGAAGCCATGAAGAAACTCGGCTCAAAGAAGCCGCAGAAGCCGATCCCACGTCCAGCTAACCTAATCCAGGGGCTAGTGTTTGACTTCATCAGTCAGCAGTTCTTTGACATCTTCATCATGGTGCTCATCTGTCTCAACATGGTCACCATGATGGTGGAGACGGACGACCAGAGTCCGGCGAAGGAGGACTTCCTCTTCAAAGTGAACGTGGCTTTTATTGTGGTCTTCACCGGGGAGTGCACGTTAAAGCTCATCGCCCTGCGACATTACTTCTTCACC NewZebra CCTATGAGTCCACATTTTGAACATGTCCTCTCAGTGGGCAACTTGGTGTTCACAGGAATCTTCACAGCTGAAATGGTGTTCAAGCTTATAGCTATGGACCCTTACTACTACTTCCAGGTGGGCTGGAACATTTTTGACAGCATCATTGTCACACTCAGCCTGGTGGAGTTGGGACTGGCCAACGTTCAGGGATTGTCCGTTCTAAGGTCCTTTCGTTTGCTACGTGTCTTCAAACTGGCTAAATCTTGGCCCACCCTTAACATGCTGATCAAGATCATCGGCAACTCAGTGGGTGCTCTAGGGAACCTAACACTTGTTCTGGCCATCATTGTCTTCATCTTTGCCGTGGTGGGCATGCAGCTTTTTGGAAAAAGCTACAAGGACTGCGTTTGTAAGATCTCTGAGGATTGCGAGCTGCCCCGCTGGCACATGAACGACTTCTTCCACTCATTCCTCATCGTCTTTCGGATCTTATGTGGAGAGTGGATTGAGACCATGTGGGACTGCATGGAGGTGGCAGGAGCTAGCATGTGTTTGATAGTCTTCATGATGGTCATGGTCATCGGAAACCTTGTGGTGCTGAATCTGTTTCTGGCCCTGCTGCTTAGCTCCTTCAGTGGAGATAACCTGTCTGGAGGTGATGATGATGGAGAGATGAACAACCTTCAGATTGCCATTGGCCGCATCACCAGGGGTATCGATTGGGTTAAAGCCTTAGTTGCCAGTATGGTGCAACGGATTCTGGGAAAGAAACCTAAAATGGCAGATGGTCTGACCAACTGTTTGACATTGACTGTACCTATTGCTCGTTGTGAGTCTGATGTGGAGGGTGACTCTTCGGTTTGTAGCACAGTGGACTACCAGCCTCCAGAACCTGTAGAAGAAGAGGAACCAGAACCTGAAGAACCAGAGGCCTGTTTCACAGAGGGCTGTATTAGGCGATGTGCATGTTTGAGTGTTGACATCACAGAAGGATGGGGTAAAAAATGGTGGAACCTCAGAAGGACATGCTTCACCATCGTTGAGCATGATTACTTTGAGACCTTCATCATCTTTATGATCCTCCTTAGCAGTGGAGCACTGGCTTTTGAGGATATCAACATTGAGAGGCGCAGAGTGATCAAGATCATTCTGGAGTATGCTGATAAAGTCTTTACATATATTTTTATAGTGGAGATGTTACTGAAGTGGGTGGCATATGGCTTCAAGACCTACTTCACTAATGCATGGTGCTGGCTGGACTTCCTCATTGTGGATGTGTCTCTGGTCAGTTTAACGGCTAATTTAATGGGCTATTCTGAGCTGGGGGCAATCAAATCTCTCAGGACACTTAGAGCTCTTCGTCCACTTCGAGCCCTATCCAGATTTGAAGGAATGAGGGTGGTAGTGAATGCACTTGTAGGTGCCATTCCCTCTATTTTTAACGTGCTCCTGGTGTGTCTGATATTCTGGCTCATCTTCAGCATTATGGGGGTCAATCTGTTTGCCGGAAAATTCTACCACTGCATCAACACCACCACAGAGGAACGGATCCCCATGGATGTAGTCAACAACAAGAGTGACTGCATGGCACTGATGTACACCAACGAGGTGCGATGGGTCAATGTCAAGGTCAACTACGACAACGTGGGACTCGGCTACCTCTCTCTGCTGCAGATTGCCACATTCAAAGGCTGGATGGATATCATGTATGCTGCAGTGGATTCTAGAGAGGTGGATGAGCAGCCATCATATGAAATCAACCTTTACATGTACCTTTATTTTGTTATTTTCATCATTTTTGGCTCCTTTTTTACTCTCAACCTCTTTATTGGTGTCATCATTGACAACTTCAATCAGCAAAAATCAAAGTTTGGAGGGAAAGACATTTTCATGACTGAGGAACAGAAAAAGTACTACAATGCCATGAAGAAGCTGGGTGCAAAGAAACGTCCAAAACCTATACCTCGACCATCAAATATTATCCAGGGTTTGGTGTTTGACTTCATATCAAAACAGTTCTTTGACATTTTTATCATGGTGCTAATCTGCCTCAACATGGTGACCATGATGATAGAGACGGATGATCAGAGTGCTGAGAAAGAATATGTCCTGTACCAGATCAATCTGGTCTTCATCGTCGTCTTCACAAGCGAATGTGTACTTAAATTATTTGCACTCAGACAGTACTTTTTCACT SterNa6 CCCATGAGCGAAACCTTTCAACACGTGCTCACCATAGGGAACCTGGTGTTTACTACCATCTTTACGGCTGAAATGGTGTCGAAGATCATCGCCCTGGACCCTTACTACTACTTCCAGGTGGGCTGGAACATCTTCGACTGCATCATCGTCACTCTCAGTCTGGTGGAGCTAAGCCTATCCAACATGCCGGGCCTGTCTGTGCTCAGATCCTTTCGTTTGATGCGTATTTTCAAGCTGGCCAAGTCCTGGCCCACGCTCAACATGCTGATCAAGATCATCGGCAACTCAATGGGCGCCCTGGGGAACCTGACCTTCGTGTTGGCCATCGTCATCTTCATCTTCGCCGTGGTGGGCTTCCAGCTGTTCGGGAAGAGCTACAAGGACAACGTGTGCAAGGTCAGCGCGGACTGCACGCTGCCTCGCTGGCACATGAACGACTTCTTCCACTCCTTCCTGATCGTGTTTCGCATCCTGTGCGGCGAGTGGATCGAGACCATGTGGGACTGCATGGAGGTGGACGGAGTGCCCATGTGCCTCACCGTCTTCATGATGGTCATGGTCATCGGAAACCTGGTGATGCTGAACCTGTTCCTTGCCTTGCTTCTCAGCTCATTCAGCTGCGACAATCTTGCCGCGCCAGACGATGACAGTGAAGTTACCAACATCCAGATCTCCATTGTGCGCATCAGCAGAGGGATAAGCTGGGTGAAGAAATTCATTGTAGGCACAGCCTGGTGGATCATGGGCAGGAAGCCCAAGATTGTAGATGGGATTACCAACTATGTTGTTCTGAATGTGCCTATTGCCAAGGGGGAGTCTGAGGTTGAGGATGACTCTTCGATTTGCAGTTCAGTGGACTACGAGCTTCTACAACCCGAGGAGGAAAAGGAA---GAGCCTGTTGATCCAGAAGCCTGTTTTACAGAAAACTGTGTGAGGTACTTTCCATGTCTGGATGTGGACATCACACAGGGGAAAGGGAAGATCTGGTGGAACCTCCGCTGCACCTGCTACAACATCGTGGAACATCACTATTTTGAAAACTTTCTCATCTTCATGATTCTCCTCAGTAGTGGAGTACTGGCATTCGAGGATGTTAATATCGAACGCCGCAGGGTCATTAAGACCATGTTGGAGTATGCAGACATAGTCTTCACATATATTTTCGTGGTGGAGATGTTTCTGAAGTGGACTGCATATGGGTTTAAAGCGTACTTCACCAGTGCCTGGTGCTGGCTGGATTTTTTTATTGTTGATGTGTCAGTTATTAGCTTAGTAGCCAATGTGTTGGGCTATGCAGAGCTGGGACCAGTCAGATCGCTCAGAACTTTCAGGGCTCTTCGACCTTTACGTGCCCTTTCCAGATTTGAAGGAATGAGGGTGGTAGTGAATGCATTGCTCGGTGCCATCCCCTCCATCATGAACGTCCTATTGGTGTGTCTGATCTTCTGGCTGATCTTCAGCATCATGGGGGTCAACTTGTTTGCGGGAAAGTTCTACCGCTGCATTAACACCACCACAGATGAGGTTCTGTCCACAGAGCAAGTGAACAACAGGAGTGAATGCATGGCACTAATGCACACTAATGAGGTGCGTTGGGTCAACCTTAAGGTCAACTACGACAATGTGGGCCAGGGATATCTCTCCTTGCTTCAAGTGGCCACATTTAAAGGGTGGATGGGCATCATGTATGGTGCAGTGGACTCTAGAGAGGTAGAGGATCAGCCATCATATGAGATTAACCTCTACATGTACCTGTACTTTGTCATCTTCATCACATTTGGATCCTTTTTTATCCTCAACCTTTTCATTGGTGTCATCATTGACAATTTTAACCGGCAAAAACAAAAGTTAGGAGGAGATGACCTCTTTATGACAGATGAACAAAAAAAGTATTATGCTGCCATGAAGAAGCTGGGTTCCAAGAAACCACTCAAACCTATACCCCGTCCTTCGAATATGGTTCAAGGGGTGGTGTTCGACTTCATCTCCCAAAAGTTCTTTGACATTTCCATCATGGTTCTCATCTGCCTCAACATGGTGATCATGATGGTGGAGGCGGACGACCAGAGTGAAGAGAAAGAGAATGTCCTCTATCAGATCAATATCATATTTATTGTCNTCTTCACCGGAGAGAGTTTACTCAAGTTGTTTGGACTTAGACATTACTTCTTCACT eelNa6 CCCATGAACGAAAGCTTTCAGAGTCTGCTCAGTGCAGGAAACCTGGTGTTTACCACTATCTTTGCGGCTGAAATGGTGTTGAAGATCATTGCCTTGGACCCCTACTACTACTTCCAGCAGACGTGGAACATATTTGACAGCATCATTGTCAGTCTCAGTCTGTTGGAGCTTGGACTATCCAATATGCAAGGAATGTCTGTGCTCAGATCCTTACGTTTGCTGCGTATCTTCAAATTGGCCAAGTCCTGGCCCACGCTCAACATTCTGATCAAGATAATCTGCAACTCGGTGGGCGCTCTGGGCAACCTGACCATTGTGCTGGCCATTATCGTCTTCATCTTCGCCTTGGTGGGCTTTCAGCTGTTCGGAAAGAACTACAAGGAGTACGTGTGCAAGATCTCTGATGACTGTGAGCTGCCCCGCTGGCACATGAATGACTTCTTCCACTCCTTCCTGATTGTGTTCCGTGCCTTGTGTGGCGAGTGGATTGAGACCATGTGGGACTGCATGGAGGTGGGCGGAGTTCCTATGTGCCTCGCCGTCTACATGATGGTCATAATCATTGGGAACCTGGTGATGCTGAACCTTTTCCTTGCCTTGCTTCTAAGCTCATTCAGCAGCGACAATCTCAGTTCAATTGAAGAAGATGATGAAGTTAACAGCCTCCAGGTTGCCTCTGAGCGCATTAGTAGGGCAAAAAACTGGGTGAAGATCTTCATCACTGGCACAGTCCTGTGGATCCAGGGCAAGAAGCCCAAGATTGTAGATGGGATAACCAACTGTGTAACTCTGAATCTACCCATTGTAAAGGGGGAGTCAGAGATCGAAGAAGACTCTTCAGTTTGTAGTACAGTGGACTATAGTCCTTCAGAACAAGAGGAGCCAGAGGAACTAGAGTCCAAAGATCCAGAAGCATGTTTTACAGAAAAATGTATATGGCGATTTCCTTTTCTGGATGTGGACATCACACAGGGGAAAGGGAAGATCTGGTGGAACCTACGTAGGACCTGCTACACCATCGTGGAGCATGACTACTTTGAAACCTTCATCATATTCATGATTCTCCTCAGTAGTGGAGTTCTGGCCTTTGAGGACATTTATATTTGGCGTCGCAGGGTGATTAAGGTCATCTTGGAGTATGCAGACAAAGTCTTCACATATGTCTTCATAGTAGAGATGTTACTTAAGTGGGTTGCATATGGGTTTAAAAGATATTTCACTGATGCCTGGTGCTGGCTCGACTTTGTAATTGTTGGTGCATCAATAATGGGCATAACATCCAGTTTGTTGGGCTATGAAGAGCTGGGAGCAATCAAAAATCTCAGAACTATCAGGGCTCTTCGCCCTTTACGTGCCCTTTCCAGATTTGAAGGAATGAAGGTGGTAGTGAGAGCATTGCTTGGTGCCATCCCCTCCATCATGAACGTGCTGCTGGTGTGTCTGATGTTCTGGCTCATCTTCAGCATTATGGGGGTCAATTTGTTTGCTGGAAAGTTCTACCGCTGCATTAACACCACCACAGATGAGATTCTGCCCGTGGAGGAAGTGAACAACCGGAGTGACTGCATGGCACTAATGTACACTAACGAGGTGCGCTGGGTCAACCTTAAGGTCAACTATGACAATGCGGGCATGGGATACCTCTCCCTGCTACAAGTGTCTACATTTAAAGGCTGGATGGACATCATGTATGCTGCAGTGGACTCCAGAGAGGTAGAGGATCAGCCAATCTATGAGATTAATGTCTACATGTACCTGTATTTTGTCATCTTCATCGTATTTGGAGCCTTCTTTACCCTCAACCTTTTCATTGGTGTCATCATAGACAACTTCAACCGTCAAAAGCAAAAGTTAGGAGGAGAAGATCTCTTTATGACAGAAGAGCAGAAGAAGTACTACAATGCCATGAAGAAGCTGGGTTCGAAGAAAGCTGCCAAATGTATACCCCGCCCTTCGAATGTGGTTCAAGGTGTGGTGTACGACATAGTCACCCAACCATTCACTGATATTTTCATCATGGCTCTCATTTGCATCAACATGGTGGCTATGATGGTCGAGTCGGAGGACCAGAGTCAAGTGAAGAAGGACATTCTCTCTCAGATCAATGTCATATTCGTTATCATCTTCACTGTAGAGTGCTTGTTAAAGCTACTTGCACTTAGACAGTACTTCTTCACT catNa6 CCCATGAGTTCGAACTTTGAACACGTGCTCAGTGTTGGCAATTTGGTGTTCACTGGTATTTTCACGGCTGAAATGGTGTTCAAGCTCATTGCCTTGGACCCCTTCTACTACTTCCAGGTTGGCTGGAACATATTTGACAGCATCATCGTCACTCTTAGCCTGGTGGAGTTAGGCCTGGCCAATGTGCAGGGTCTGTCTGTACTCAGATCCTTTCGTTTGCTGCGAGTCTTTAAGCTGGCTAAATCCTGGCCCACGCTCAACATGCTGATCAAAATCATTGGAAACTCTGTGGGTGCTCTGGGGAACCTGACTCTGGTGCTGGCCATCGTCGTCTTCATCTTCGCCGTCGTAGGCATGCAACTTTTTGGCAAGAGCTACAAGGACTGCGTGTGTAAGATTGCAGAGGACTGCGAACTGCCCCGCTGGCACATGAACGATTTTTTCCATTCGTTTCTCATTGTCTTCCGCATCCTTTGTGGTGAATGGATTGAAACCATGTGGGACTGCATGGAGGTGGCTGGAGCAGGCATGTGCCTTGTGGTTTTCCTTATGGTCATGGTCATAGGAAACCTGGTGGTGCTGAACCTGTTCCTTGCCTTGTTGCTCAGCTCTTTCAGCGGGGACAATCTCTCAGCAGGTGATGAAGATGGTGAAATGAACAATCTCCAGATTGCCATCGGCCGCATCACCAGGGGCATTGACTGGGTCAAATCCTTCATCATTGGCCTTGTACAGCAGATACTTTGCAGGAAGCCTAAGATGGCAGATAGGTTGACCAACTGTCTGACCCTGAATGTACCAATTGCCAAAGCTGAGTCTGATGTTGAAGAAGACTCTTCAATGTGTAGCACAGTGGACTATAGACCTCCAGAATCCGAGGAGGAAGAGGAACCAGAACCTGTTGAGCCAGAAGCCTGTTTTACTGAAAACTGTGTGAGACGATGTCCATGTCTGAATTTGGACATCACTCAGGGGAGGGGAAAGAGTTGGTGGAATCTGCGCAGAACTTGCTACACCATAGTGGAGCATGATTACTTTGAAACCTTCATCATCTTCATGATTCTCCTCAGTAGTGGTGCACTGGCCTTTGACGACATTTACATTGAGCGTCGCAGGGTGATTAAGATTATCTTGGAATATGCAGACCAAGTCTTCACATATATTTTTGTCATAGAGATGTTACTGAAATGGGTTGCGTATGGCTTCAAGACATACTTCACCAATGCCTGGTGCTGGCTGGACTTTTTCATCGTTGATGTGTCACTTATCGGTTTAACGGCAAATCTGTTGGGCTATTCAGAGCTGGGACCAATAAAATCTCTCAGAACTCTTAGGGCGCTTCGACCTTTACGTGCCCTGTCCAGATTTGAAGGAATGAGGGTGGTAGTGAACGCATTGCTGGGTGCCATTCCTTCCATCATGAATGTACTCCTGGTGTGTCTAATATTCTGGCTGATCTTCAGTATTATGGGGGTCAACCTGTTTGCTGGGAAATACTACCGCTGCATTAATACCACCACAGAAGAACTTTTACCCATCGAGCAAGTGAACAACATGAGTGATTGCATAGCACTAATGCACACTAAAGAAGCACGCTGGGTCAATGTCAAGGTCAACTTTGACAATGTGGGCTTGGGTTACCTTTCCCTGCTACAAGAGGCTACATTTAAAGGCTGGATGGACATTATGTATGCTGCAGTGGATTCCAGAGAGGTGGAAGAACAGCCATCATATGAGATTAACATATATATGTATCTGTATTTTGTCATCTTCATCATCTTTGGCTCCTCCTTCACCCTCAACCTCTTCATTGGTGTCATCATTGACAACTTTAATCAGCAAAAGCAAAAGTTTGGTGGGGAAGATCTCTTCATGACAGAGGAGCAGAAAAAGTACTACAATGCCATGAAAAAGCTTGGTTCCAAGAAGCCCGTCAAACCCATACCTCGCCCTGCGAATATGATCCAGGGCATAGTGTTTGACTTCATCTCTCAGCAGTTCTTTGACATTTTCATCATGGTGCTCATTTGCCTCAACAAGGTTACCATGATGATTGAGACAGATGACCAAAGTGCAGAGAAAGAATATGTTCTCTATCAGATCAACTTAATCTTCATTGTTGTCTTCACTGGGGAGTGCATCCTCAAAATGTTTGCACTGAGACAATACTTTTTCACT AptNa6 ---------------------------CTCACTGTGGGGAACCTGGTGTTTACTGGCATCTTTACGGCTGAAATGGTGTTTAAGCTCATTGCCATGGACCCCTACTACTACTTCCAGGTGGGCTGGAACATCTTCGACAGCATCATCGTCACCCTCAGTCTGGTGGAGCTGGGGCTAGCCAACGTGCAGGGTCTGTCTGTGCTCAGGTCCTTCCGTTTGCTGCGTGTCTTCAAGTTGGCCAAGTCCTGGCCAACGCTCAATATGCTCATCAAGATCATTGGCAACTCGGTGGGAGCCCTGGGCAACCTGACACTGGTGCTGGCCATTATTGTCTTCATCTTTGCCGTGGTGGGCATGCAGCTATTTGGGAAGAGCTACAAGGACTGCGTGTGCAAGATTGCGCTGGACTGCGAGCTTCCCCGCTGGCACATGACGGACTTCTTCCACTCCTTCCTGATCGTGTTCCGCATCCTATGCGGCGAGTGGATTGAGACCATGTGGGACTGCATGGAGGTGGCTGGACCGTCCATGTGCCTCATCGTCTTCATGTTGGTCATGGTCATTGGGAACCTGGTGGTGCTGAACCTGTTCCTTGCATTGCTTCTCAGCTCATTCAGCGGTGACAATCTCTCGGCAAGCGACGATGACAGTGAGATTAACAACCTCCAGATCGCCACAGGGCGCATCAGCAGAGCGATTGGCTGGGTGAAGAACTTTATCATCAGCACAGTCCAGTGGGTTCTGGGCAGAAAGCCCAAGATGGTGGATGGCATGACCAACTGCGTAGTCCTGAATGTGCCCATTGCCAAGGGGGAATCTGAGATTGAAGGAGACTATTCAGTTTGCAGTACAGCAGACTACAGACCTCCAGAACCCGAGGAGGAAAAGGTACCAGAGACCAATGATCCAGAAGCCTGCTTTACAGAAAATTGTGTGAGGCGATTTCCTTGTCTCAATGTGGACATCACCCAGGGGAAAGGGAAGAGCTGGTGGAACCTACGCAGAACCTGCTACATCATCGTGGAGCATGACTACTTTGAGACCTTCATCATCTTCATGATTCTCCTCAGTAGCGGAGCACTGGCTTTCGAGGACATTTATATAGAGCGTCGCAAGATGATTAAGATCATCTTGGAGTACGCAGACAAAATCTTCACCTATGTTTTCATAATGGAGATGTTACTGAAGTGGGTTGCTTATGGGTTTAAAACGTACTTCACCAATGCCTGGTGCTGGCTGGACTTTCTTATTGTTGATGTGTCAATTATTAGCTTAACAGCCAATCTGTTGGGCTATTCAGAGCTGGGACCAATCAAATCTCTCAGAACACTCAGGGCTCTTCGACCGCTACGTGCCCTTTCCAGATTTGAAGGAATGAGGGTGGTAGTGAATGCGTTGGTTGGCGCCATCCCCTCCATCATGAACGTGCTGCTGGTTTGTCTGATCTTCTGGCTCATCTTCAGTATCATGGGGGTCAACTTGTTTGCTGGAAAGTTCTACCGCTGCATTAACACTACCACCGAGGAGCTTCTGCCCATGGAGGAAGTGAACAACAGGAGTGATTGCATGGCGCTAATGCACACTAATGAGGTGCGCTGGGTCAATGTCAAGGTGAACTACGACAACGTCGCCCTGGGATACCTTTCCCTGCTGCAAGTGGCTACATTTAAAGGCTGGATGGACATCATGTATGCTGCAGTGGACTCCAGAGAGGTAGAGGAGCAGCCATCATATGAGATTAACCTCTACATGTACCTGTACTTTGTCATCTTCATCATATTGGGATCCTTTTTTACCCTCAACCTTTTCATTGGTGTCATCATAGACAACTTCAACAGGCAGAAGCAAAAGTTTGGAGGAGAAGATCTCTTTATGACGGAGGAGCAGAAGAAGTACTACAATGCCATGAAGAAGCTGGGATCCAAGAAGCCTGTCAAACCTATACCCCGTCCTACGAATGTTATTCAAGGTGTGGTGTTCGACCTCATTTCCCAGCAGTTCTTTGATATTTTCATCATGGTTCTCATTTGCCTCAACATGGTGACCATGATGGTGGAGACTGATGACCAGAGCAAAGAGAAAGAGCACATCCTCTATCAAATCAACGTCATATTCATTGTCGTCTTCACTGGAGAGTGTTTGCTCAAGATGTTTGCACTGAGGCAGTACTTCTTCACT PinniNa6 CCCATGAGTGAAACGTTTGATTACGTCCTCAGCACAGGGAACCTGGTGTTTACCATCATCTTTGCAGCTGAAATGGTCTTGAAGCTCATTGCCATGGACCCCTACTACTACTTCCAGCAGACGTGGAACATCTTTGACTTTTTCATTGTCTCACTCAGTCTGGTGGAGATGGGACTGGCTAACATGCAGGGGCTGTCAGTGCTTAGGTCCTTTCGACTGCTGCGTATCTTTAAGTTGGCCAAGTCCTGGCCCACGCTCAATATTCTGATCAAGATCATCTGCAACTCGGTGGGCGCCCTGGGAAACCTGACCATCGTGCTGGCCATTATCGTCTTCATCTTCGCCTTGGTGGGCATGCAGCTGTTCGGGAAGAATTACAAAGAGTTTGTGTGCAAGATCAGTGCAGACTGTACGCTGCCTCGCTGGCATATGAATGACTTCTTCCATTCCTTCCTGATTGTGTTCCGCTGCCTGTGCGGCGAGTGGATTGAGACTATGTGGGACTGTATGGAGGTGGGCGGTGTGCCCATGTGCCTCAGCGTTTACATGATGGTCATAATCATCGGGAACCTGGTGGTGCTGAACCTGTTCCTTGCCTTACTGCTAAGCTCATTCAGTGGTGACAATCTCACTGCAAACGATGATGACCAAGAGGATAACAACATCCTGATTGCAGCTGAGCGGATCAGCAGGGCAAAACTCTGGGTGAAGGGGTTCATAATACGGACGGTCTTGGGGATGCTGGGCAAGGAGCCAAAGATTGTGAATGGGCTAGCCAACGGTGTAGTTCTGAATGTGCCCATTGCCAAGGGCGAGTCTGAGACTGAAGATGACTCTTCAGTCTGCAGTACAGTGGACTACAGTCCTCCAAATCCAGAGGAACCCGAGGAACCAGAACCCGATAATCCAGAAGATTGTTTAACGGAAGAATGTGTGTCACGATTTCCTTGGCTGAATGTGGACATAACACAGCCAAAAGGGAAGAGTTGGTGGAACCTTCGTAGGACATGCTACGTCATCGTAGAGCATGACTACTTTGAGACTTTCATCATCTTCATGATTCTCCTCAGTAGTGGAGCACTGGCTTTCGAGGACATTTATATTGAGCGTCGCAGGGTGATTAAGATCATCTTGGAGTATGCGGACAAAGTCTTCACATATATTTTCATAGCAGAGATGTTACTGAAGTGGGTTGCATATGGGTTTAAAAAGTACTTCTCCGACGCCTGGTGCTGGTTAGACTTTCTAATTGTTGATGTGTCAATAATTAGCTTAACAGCCAATTTGTTGGGCTATTCAGAGTTGGGACCAATCAAATCTCTCAGAACTCTCAGGGCTCTTCGACCTTTACGTGCACTTTCCAGATTTGAAGGAATGAGGGTGGTAGTCAAAGCATTGGTTGGCGCCATCCCCTCCATCGTGAACGTGCTGCTGGTATGTCTCATGTTCTGGCTCATCTTCAGCATTATGGGAGTCAACTTGTTTGCTGGAAAGTTCTACCGCTGCATTAACACTACCACAGAAGAGACCATGCCCYTGGAAGAAGTCAACAACCGCAGTGACTGCAATGCACTTATGTACACTAATGAGGTGCGATGGGTCAACCTTAAGGTCAACTATGACAATGCAGGCATGGGATACCTCTCCCTGCTACAAGTGGCAACATTTAAAGGTTGGATGGACATCATGTATGCTGCAGTGGACTCCAGAGGGGTAGAGGATCAGCCGATATACGAGATTAACGTCTACATGTACCTGTATTTTGTCATCTTCATCGTATTTGGATCCTTTTTCACCCTAAACCTCTTCATTGGTGTCATCATAGACAACTTCAACCGTCAAAAGCAAAAGTTAGGAGGAGATGATCTCTTTATGACAGAAGAACAGAAAAAGTATTATGATGCCATGAAGAAGCTGGGTTCCAAGAAACCTGTCAARGTTATACCACGCCCTTCGAACAAGATTCTGGGTGTGTTGTATGACATAGTCAACCAACGGGTCACTGATATTTTCATCATGTCTCTCATTTGGCTAAACATGGTTACCATGATGGTGGAGACAGATGACCAGAGCGAAGAAAAGAAGAATGTTCTCTATCAGATCAATTTAATATTCATTATCATCTTCACTGGAGAATGTCTGCTCAAGTTGCTTGCACTAAGACATTACTTCTTCACT tetra CCCATGACCCAGGAGTTCGACTACATGCTTTCAGTGGGAAATCTGGTTTTCACAGGAATTTTTGCAGCAGAAATGTTCTTCAAGCTGATCGCCATGGATCCGTACTACTATTTCCAAGTTGGCTGGAACATTTTTGACAGCATCATTGTCACCCTCAGCCTGGTAGAGTTGGGGCTTGCGAACGTCCAGGGCCTGTCTGTCCTCAGGTCCTTCCGCCTGCTCCGTGTCTTCAAACTTGCCAAATCCTGGCCCACACTCAACATGCTGATCAAGATTATTGGGAGCTCAGTTGGAGCGCTAGGGAATCTGACGTTGGTGCTGGCCATCATCGTCTTCATCTTCGCTGTGGTGGGGATGCAGCTCTTTGGCAAAAGCTACAAGGACTGCGTGTGCAAGATTTCCACGGAGTGCGAGCTGCCGCGCTGGCACATGAATGACTTCTTCCACTCCTTCCTCATCGTCTTCCGCATCCTGTGTGGCGAATGGATCGAGAACATGTGGGCCTGCATGGAAGTGGCTGGAGCTGGGATGTGCTTAGTTGTCTTCATGATGGTCATGGTGATTGGAAACCTCGTGGTGTTGAACCTCTTCCTGGCCCTGCTGCTCAGCTCGTTCAGCGGGGACAATCTGTCCATCGGAGAGGACGATGGAGAGATGAACAATCTTCAGATTGCCATCGGCAGAATCACACGAGGTGGAAACTGGCTCAAGACCCTTGTCATCAGAACGGTCCTGCAGCTTCTCGGTAGGGAGCAGAAAACGGCAGATGGGATAGCTAACTGTCTTGTTATCAACGTCCCCATCGCCTTGGGGGAGTCAGACTCTGAAGGCGAGTCTTCAGTGTGCAGCACAGCAGACTATCGGCCCCCCGAGCCTGAGGAAGAGGAAGAGCCGGAACCACTGGAGCCAGAGGCCTGCTTTACTGACAACTGCGTCAAACACTGGCCTTGTCTGAACGTGGACGTCACCCAAGGTCAAGGGAAGAAGTGGTGGAACCTCCGCAAGACCTGCTTCACAATCGTAGAGCATGACTGGTTTGAGACCTTCATCATCTTCATGATCCTCCTCAGCAGCGGAGCCCTGGCCTTTGAAGATATATACATCGAAAGACGAAGAACCGTCAAAATTATCCTGGAGTTTGCCGACAAAGTTTTCACCTTCATCTTTGTCCTTGAGATGGTGCTGAAATGGGTGGCCTATGGCTTCAAGACCTACTTCACCAACGCCTGGTGCTGGTTGGACTTTTTCATTGTAGACATTTCCCTGATCAGTTTATCGGCCAACCTGATGGGCCTCTCTGACCTGGGACCAATCAAATCTCTCAGAACACTCCGGGCACTGAGGCCTCTTCGAGCTCTGTCCAGATTTGAAGGGATGAGGGTGGTGGTGAACGCTCTTATCGGAGCCATTCCCTCCATCTTCAACGTGCTGCTGGTGTGCCTGATCTTCTGGCTCATCTTCAGCATCATGGGAGTGAACCTGTTTGCGGGGAAGTTCTACCACTGCATCAACACCACCACACAGGAGCTCTTCCCCATCTCTGTGGTCAACAACAAGAGCGACTGCATGGCCGTCCAGGCCACGCAGGAGGCCCGCTGGGTCAACGTCAAGGTCAACTACGACAACGTGGGAAAAGGCTACCTGTCGCTGCTTCAAATCGCCACTTTTAAAGGCTGGACGGCCATTATGTATGCTGCAGTAGATTCAAGAGAGGTGGAAGAGCAACCTTCCTATGAGATCAACCTGTACATGTACATCTACTTTGTCATCTTCATCATCTTTGGCGCTTTCTTCACGCTCAACCTGTTCATCGGCGTCATCATCGATAACTTCAACCAGCAGAAGAGAAAGATA---AACAAAGACATCTTCATGACGGAGGAGCAGAAAAAGTACTACGAAGCCATGAAGAAACTCGGCTCCAAGAAGCCGCAGAAGCCGATCCCACGTCCGACCAACCTCATCCAGGGAATGGTGTTTGACTTCATCAGTCAGCAGTTCTTTGACATCTTCATCATGGTGCTCATCTGCCTCAACATGGTGACCATGATGGTGGAGACGGACGACCAGAGCCCCGAGAAGGAGGATTTCCTCTTCAAAGTGAACGTGGCTTTTATCGTGGTCTTCACGGGGGAGTGCATGCTGAAGCTCATCGCCCTGCGACAGTACTTCTTCACC ; end; begin sets; charset 1st = 1-2178\3; charset 2nd = 2-2178\3; charset 3rd = 3-2178\3; charpartition byPos = 1stpos:1st, 2ndpos:2nd, 3rdpos:3rd; end; begin assumptions; exset * test = 4 5 6; end; garli-2.1-release/tests/data/z.neg.const.tre000066400000000000000000000001321241236125200210170ustar00rootroot00000000000000-(MorNa6,ClownNa6,puffNa6,tetra,AraNa6,(SterNa6,eelNa6,PinniNa6,catNa6,AptNa6),NewZebra); garli-2.1-release/tests/data/z.negBack.const.tre000066400000000000000000000001131241236125200215770ustar00rootroot00000000000000-(MorNa6,ClownNa6,(puffNa6,tetra),AraNa6,PinniNa6,catNa6,AptNa6,NewZebra); garli-2.1-release/tests/data/z.pos.const.tre000066400000000000000000000001441241236125200210520ustar00rootroot00000000000000+(MorNa6,(((ClownNa6,(puffNa6,tetra)),AraNa6),((SterNa6,eelNa6),PinniNa6),catNa6,AptNa6),NewZebra); garli-2.1-release/tests/data/z.posBack.const.tre000066400000000000000000000001211241236125200216260ustar00rootroot00000000000000+(MorNa6,(((ClownNa6,(puffNa6,tetra)),AraNa6),PinniNa6,catNa6,AptNa6),NewZebra); garli-2.1-release/tests/internal/000077500000000000000000000000001241236125200170445ustar00rootroot00000000000000garli-2.1-release/tests/internal/a.G3.conf000066400000000000000000000023611241236125200204050ustar00rootroot00000000000000[general] datafname = data/moore.matK90-120.nex constraintfile = none streefname = data/moore.start attachmentspertaxon = 50 ofprefix = int.a.G3 randseed = -1 availablememory = 512 logevery = 10 saveevery = 100 refinestart = 1 outputeachbettertopology = 0 outputcurrentbesttopology = 0 enforcetermconditions = 1 genthreshfortopoterm = 2000 scorethreshforterm = 0.05 significanttopochange = 0.01 outputphyliptree = 0 outputmostlyuselessfiles = 0 writecheckpoints = 0 restart = 0 outputsitelikelihoods = 0 collapsebranches = 1 usepatternmanager = 1 searchreps = 1 datatype = codon-aminoacid ratematrix = jones statefrequencies = empirical ratehetmodel = gamma numratecats = 3 invariantsites = estimate [master] nindivs = 4 holdover = 1 selectionintensity = 0.5 holdoverpenalty = 0 stopgen = 5000000 stoptime = 5000000 startoptprec = 0.5 minoptprec = 0.01 numberofprecreductions = 1 treerejectionthreshold = 50.0 topoweight = 1.0 modweight = 0.05 brlenweight = 0.2 randnniweight = 0.1 randsprweight = 0.3 limsprweight = 0.6 intervallength = 100 intervalstostore = 5 uniqueswapbias = 0.1 distanceswapbias = 1.0 limsprrange = 6 meanbrlenmuts = 5 gammashapebrlen = 1000 gammashapemodel = 1000 bootstrapreps = 0 resampleproportion = 1.0 inferinternalstateprobs = 0 garli-2.1-release/tests/internal/a.G4.conf000066400000000000000000000023611241236125200204060ustar00rootroot00000000000000[general] datafname = data/moore.matK90-120.nex constraintfile = none streefname = data/moore.start attachmentspertaxon = 50 ofprefix = int.a.G4 randseed = -1 availablememory = 512 logevery = 10 saveevery = 100 refinestart = 1 outputeachbettertopology = 0 outputcurrentbesttopology = 0 enforcetermconditions = 1 genthreshfortopoterm = 2000 scorethreshforterm = 0.05 significanttopochange = 0.01 outputphyliptree = 0 outputmostlyuselessfiles = 0 writecheckpoints = 0 restart = 0 outputsitelikelihoods = 0 collapsebranches = 1 usepatternmanager = 1 searchreps = 1 datatype = codon-aminoacid ratematrix = jones statefrequencies = empirical ratehetmodel = gamma numratecats = 4 invariantsites = estimate [master] nindivs = 4 holdover = 1 selectionintensity = 0.5 holdoverpenalty = 0 stopgen = 5000000 stoptime = 5000000 startoptprec = 0.5 minoptprec = 0.01 numberofprecreductions = 1 treerejectionthreshold = 50.0 topoweight = 1.0 modweight = 0.05 brlenweight = 0.2 randnniweight = 0.1 randsprweight = 0.3 limsprweight = 0.6 intervallength = 100 intervalstostore = 5 uniqueswapbias = 0.1 distanceswapbias = 1.0 limsprrange = 6 meanbrlenmuts = 5 gammashapebrlen = 1000 gammashapemodel = 1000 bootstrapreps = 0 resampleproportion = 1.0 inferinternalstateprobs = 0 garli-2.1-release/tests/internal/a.conf000066400000000000000000000023511241236125200201340ustar00rootroot00000000000000[general] datafname = data/moore.matK90-120.nex constraintfile = none streefname = data/moore.start attachmentspertaxon = 50 ofprefix = int.a randseed = -1 availablememory = 512 logevery = 10 saveevery = 100 refinestart = 1 outputeachbettertopology = 0 outputcurrentbesttopology = 0 enforcetermconditions = 1 genthreshfortopoterm = 2000 scorethreshforterm = 0.05 significanttopochange = 0.01 outputphyliptree = 0 outputmostlyuselessfiles = 0 writecheckpoints = 0 restart = 0 outputsitelikelihoods = 2 collapsebranches = 1 usepatternmanager = 1 searchreps = 1 datatype = codon-aminoacid ratematrix = jones statefrequencies = empirical ratehetmodel = none numratecats = 1 invariantsites = none [master] nindivs = 4 holdover = 1 selectionintensity = 0.5 holdoverpenalty = 0 stopgen = 5000000 stoptime = 5000000 startoptprec = 0.5 minoptprec = 0.01 numberofprecreductions = 1 treerejectionthreshold = 50.0 topoweight = 1.0 modweight = 0.05 brlenweight = 0.2 randnniweight = 0.1 randsprweight = 0.3 limsprweight = 0.6 intervallength = 100 intervalstostore = 5 uniqueswapbias = 0.1 distanceswapbias = 1.0 limsprrange = 6 meanbrlenmuts = 5 gammashapebrlen = 1000 gammashapemodel = 1000 bootstrapreps = 0 resampleproportion = 1.0 inferinternalstateprobs = 0 garli-2.1-release/tests/internal/c.M3x2.conf000066400000000000000000000024101241236125200206620ustar00rootroot00000000000000[general] datafname = data/z.11x30.stop.nex constraintfile = none streefname = stepwise attachmentspertaxon = 50 ofprefix = int.c.M3x2 randseed = -1 availablememory = 512 logevery = 10 saveevery = 100 refinestart = 1 outputeachbettertopology = 0 outputcurrentbesttopology = 0 enforcetermconditions = 1 genthreshfortopoterm = 2000 scorethreshforterm = 0.05 significanttopochange = 0.01 outputphyliptree = 0 outputmostlyuselessfiles = 0 writecheckpoints = 0 restart = 0 outputsitelikelihoods = 0 collapsebranches = 1 usepatternmanager = 1 searchreps = 1 ignorestopcodons = 1 datatype = codon geneticcode = standard ratematrix = 6rate statefrequencies = f3x4 ratehetmodel = nonsynonymous numratecats = 2 invariantsites = none [master] nindivs = 4 holdover = 1 selectionintensity = 0.5 holdoverpenalty = 0 stopgen = 5000000 stoptime = 5000000 startoptprec = 0.5 minoptprec = 0.01 numberofprecreductions = 1 treerejectionthreshold = 50.0 topoweight = 1.0 modweight = 0.05 brlenweight = 0.2 randnniweight = 0.1 randsprweight = 0.3 limsprweight = 0.6 intervallength = 100 intervalstostore = 5 uniqueswapbias = 0.1 distanceswapbias = 1.0 limsprrange = 6 meanbrlenmuts = 5 gammashapebrlen = 1000 gammashapemodel = 1000 bootstrapreps = 0 resampleproportion = 1.0 inferinternalstateprobs = 0 garli-2.1-release/tests/internal/c.conf000066400000000000000000000023121241236125200201330ustar00rootroot00000000000000[general] datafname = data/z.11x30.phy constraintfile = none streefname = stepwise attachmentspertaxon = 50 ofprefix = int.c randseed = -1 availablememory = 512 logevery = 10 saveevery = 100 refinestart = 1 outputeachbettertopology = 0 outputcurrentbesttopology = 0 enforcetermconditions = 1 genthreshfortopoterm = 2000 scorethreshforterm = 0.05 significanttopochange = 0.01 outputphyliptree = 0 outputmostlyuselessfiles = 0 writecheckpoints = 0 restart = 0 outputsitelikelihoods = 0 collapsebranches = 1 searchreps = 1 datatype = codon geneticcode = standard ratematrix = 6rate statefrequencies = f3x4 ratehetmodel = none numratecats = 1 invariantsites = none [master] nindivs = 4 holdover = 1 selectionintensity = 0.5 holdoverpenalty = 0 stopgen = 5000000 stoptime = 5000000 startoptprec = 0.5 minoptprec = 0.01 numberofprecreductions = 1 treerejectionthreshold = 50.0 topoweight = 1.0 modweight = 0.05 brlenweight = 0.2 randnniweight = 0.1 randsprweight = 0.3 limsprweight = 0.6 intervallength = 100 intervalstostore = 5 uniqueswapbias = 0.1 distanceswapbias = 1.0 limsprrange = 6 meanbrlenmuts = 5 gammashapebrlen = 1000 gammashapemodel = 1000 bootstrapreps = 0 resampleproportion = 1.0 inferinternalstateprobs = 0 garli-2.1-release/tests/internal/n.G3.conf000066400000000000000000000023571241236125200204270ustar00rootroot00000000000000[general] datafname = data/moore.matK90-120.nex constraintfile = none streefname = stepwise attachmentspertaxon = 50 ofprefix = int.n.G3 randseed = -1 availablememory = 512 logevery = 10 saveevery = 100 refinestart = 1 outputeachbettertopology = 0 outputcurrentbesttopology = 0 enforcetermconditions = 1 genthreshfortopoterm = 2000 scorethreshforterm = 0.05 significanttopochange = 0.01 outputphyliptree = 0 outputmostlyuselessfiles = 0 writecheckpoints = 0 restart = 0 outgroup = 1 outputsitelikelihoods = 0 collapsebranches = 1 usepatternmanager = 1 searchreps = 1 datatype = nucleotide ratematrix = 6rate statefrequencies = estimate ratehetmodel = gamma numratecats = 3 invariantsites = estimate [master] nindivs = 4 holdover = 1 selectionintensity = 0.5 holdoverpenalty = 0 stopgen = 5000000 stoptime = 5000000 startoptprec = 0.5 minoptprec = 0.01 numberofprecreductions = 1 treerejectionthreshold = 50.0 topoweight = 1.0 modweight = 0.05 brlenweight = 0.2 randnniweight = 0.1 randsprweight = 0.3 limsprweight = 0.6 intervallength = 100 intervalstostore = 5 limsprrange = 6 meanbrlenmuts = 5 gammashapebrlen = 1000 gammashapemodel = 1000 uniqueswapbias = 0.1 distanceswapbias = 1.0 bootstrapreps = 0 resampleproportion = 1.0 inferinternalstateprobs = 0 garli-2.1-release/tests/internal/n.G4.conf000066400000000000000000000023311241236125200204200ustar00rootroot00000000000000[general] datafname = data/moore.matK90-120.nex constraintfile = none streefname = stepwise attachmentspertaxon = 50 ofprefix = int.n.G4 randseed = -1 availablememory = 512 logevery = 10 saveevery = 100 refinestart = 1 outputeachbettertopology = 0 outputcurrentbesttopology = 0 enforcetermconditions = 1 genthreshfortopoterm = 2000 scorethreshforterm = 0.05 significanttopochange = 0.01 outputphyliptree = 0 outputmostlyuselessfiles = 0 writecheckpoints = 0 restart = 0 outgroup = 1 outputsitelikelihoods = 0 collapsebranches = 1 searchreps = 1 datatype = nucleotide ratematrix = 6rate statefrequencies = estimate ratehetmodel = gamma numratecats = 4 invariantsites = estimate [master] nindivs = 4 holdover = 1 selectionintensity = 0.5 holdoverpenalty = 0 stopgen = 5000000 stoptime = 5000000 startoptprec = 0.5 minoptprec = 0.01 numberofprecreductions = 1 treerejectionthreshold = 50.0 topoweight = 1.0 modweight = 0.05 brlenweight = 0.2 randnniweight = 0.1 randsprweight = 0.3 limsprweight = 0.6 intervallength = 100 intervalstostore = 5 limsprrange = 6 meanbrlenmuts = 5 gammashapebrlen = 1000 gammashapemodel = 1000 uniqueswapbias = 0.1 distanceswapbias = 1.0 bootstrapreps = 0 resampleproportion = 1.0 inferinternalstateprobs = 0 garli-2.1-release/tests/internal/n.conf000066400000000000000000000023471241236125200201560ustar00rootroot00000000000000[general] datafname = data/moore.matK90-120.nex constraintfile = none streefname = stepwise attachmentspertaxon = 50 ofprefix = int.n randseed = -1 availablememory = 512 logevery = 10 saveevery = 100 refinestart = 1 outputeachbettertopology = 0 outputcurrentbesttopology = 0 enforcetermconditions = 1 genthreshfortopoterm = 2000 scorethreshforterm = 0.05 significanttopochange = 0.01 outputphyliptree = 0 outputmostlyuselessfiles = 0 writecheckpoints = 0 restart = 0 outgroup = 1 outputsitelikelihoods = 0 collapsebranches = 1 usepatternmanager = 1 searchreps = 1 datatype = nucleotide ratematrix = 1rate statefrequencies = estimate ratehetmodel = none numratecats = 1 invariantsites = none [master] nindivs = 4 holdover = 1 selectionintensity = 0.5 holdoverpenalty = 0 stopgen = 5000000 stoptime = 5000000 startoptprec = 0.5 minoptprec = 0.01 numberofprecreductions = 1 treerejectionthreshold = 50.0 topoweight = 1.0 modweight = 0.05 brlenweight = 0.2 randnniweight = 0.1 randsprweight = 0.3 limsprweight = 0.6 intervallength = 100 intervalstostore = 5 limsprrange = 6 meanbrlenmuts = 5 gammashapebrlen = 1000 gammashapemodel = 1000 uniqueswapbias = 0.1 distanceswapbias = 1.0 bootstrapreps = 0 resampleproportion = 1.0 inferinternalstateprobs = 0 garli-2.1-release/tests/internal/p.3diff.conf000066400000000000000000000030341241236125200211440ustar00rootroot00000000000000[general] datafname = data/z.byPos.11x2178.nex constraintfile = none streefname = stepwise attachmentspertaxon = 50 ofprefix = int.p.3diff randseed = -1 availablememory = 512 logevery = 10 saveevery = 100 refinestart = 1 outputeachbettertopology = 0 outputcurrentbesttopology = 0 enforcetermconditions = 1 genthreshfortopoterm = 5000 scorethreshforterm = 0.001 significanttopochange = 0.01 outputphyliptree = 0 outputmostlyuselessfiles = 0 writecheckpoints = 0 restart = 0 searchreps = 1 collapsebranches = 1 linkmodels = 0 subsetspecificrates = 1 [model1] datatype = nucleotide ratematrix = ( 0 1 2 2 3 4 ) statefrequencies = estimate ratehetmodel = gamma numratecats = 4 invariantsites = none [model2] datatype = nucleotide ratematrix = ( 0 1 2 1 0 3 ) statefrequencies = estimate ratehetmodel = gamma numratecats = 4 invariantsites = none [model3] datatype = nucleotide ratematrix = ( 0 1 2 3 1 0 ) statefrequencies = estimate ratehetmodel = gamma numratecats = 4 invariantsites = estimate [master] nindivs = 4 holdover = 1 selectionintensity = 0.5 holdoverpenalty = 0 stopgen = 5000000 stoptime = 5000000 startoptprec = 0.5 minoptprec = 0.01 numberofprecreductions = 5 treerejectionthreshold = 50.0 topoweight = 0.01 modweight = 0.002 brlenweight = 0.002 randnniweight = 0.1 randsprweight = 0.3 limsprweight = 0.6 intervallength = 100 intervalstostore = 5 limsprrange = 6 meanbrlenmuts = 5 gammashapebrlen = 1000 gammashapemodel = 1000 uniqueswapbias = 0.1 distanceswapbias = 1.0 bootstrapreps = 0 resampleproportion = 1.0 inferinternalstateprobs = 0 garli-2.1-release/tests/internal/p.mk.conf000066400000000000000000000024111241236125200205560ustar00rootroot00000000000000[general] datafname = data/L2001.30x52.nex constraintfile = none streefname = data/L.start attachmentspertaxon = 100 ofprefix = int.mk randseed = -1 availablememory = 512 logevery = 10 saveevery = 100 refinestart = 1 outputeachbettertopology = 0 outputcurrentbesttopology = 0 enforcetermconditions = 1 genthreshfortopoterm = 100 scorethreshforterm = 0.001 significanttopochange = 0.01 outputphyliptree = 0 outputmostlyuselessfiles = 0 writecheckpoints = 0 restart = 0 usepatternmanager = 1 searchreps = 5 collapsebranches = 1 linkmodels = 0 subsetspecificrates = 0 optimizeinputonly = 1 [model1] datatype = standard ratematrix = 1rate statefrequencies = equal ratehetmodel = none numratecats = 1 invariantsites = none [master] nindivs = 4 holdover = 1 selectionintensity = 0.5 holdoverpenalty = 0 stopgen = 5000000 stoptime = 5000000 startoptprec = 0.01 minoptprec = 0.01 numberofprecreductions = 10 treerejectionthreshold = 50.0 topoweight = 0.01 modweight = 0.002 brlenweight = 0.002 randnniweight = 0.1 randsprweight = 0.3 limsprweight = 0.6 intervallength = 100 intervalstostore = 5 limsprrange = 6 meanbrlenmuts = 5 gammashapebrlen = 1000 gammashapemodel = 1000 uniqueswapbias = 0.1 distanceswapbias = 1.0 bootstrapreps = 0 resampleproportion = 1.0 inferinternalstateprobs = 0 garli-2.1-release/tests/internal/p.mk.ssr.conf000066400000000000000000000024151241236125200213700ustar00rootroot00000000000000[general] datafname = data/L2001.30x52.nex constraintfile = none streefname = data/L.start attachmentspertaxon = 100 ofprefix = int.mk.ssr randseed = -1 availablememory = 512 logevery = 10 saveevery = 100 refinestart = 1 outputeachbettertopology = 0 outputcurrentbesttopology = 0 enforcetermconditions = 1 genthreshfortopoterm = 100 scorethreshforterm = 0.001 significanttopochange = 0.01 outputphyliptree = 0 outputmostlyuselessfiles = 0 writecheckpoints = 0 restart = 0 usepatternmanager = 1 searchreps = 5 collapsebranches = 1 linkmodels = 0 subsetspecificrates = 1 optimizeinputonly = 1 [model1] datatype = standard ratematrix = 1rate statefrequencies = equal ratehetmodel = none numratecats = 1 invariantsites = none [master] nindivs = 4 holdover = 1 selectionintensity = 0.5 holdoverpenalty = 0 stopgen = 5000000 stoptime = 5000000 startoptprec = 0.01 minoptprec = 0.01 numberofprecreductions = 10 treerejectionthreshold = 50.0 topoweight = 0.01 modweight = 0.002 brlenweight = 0.002 randnniweight = 0.1 randsprweight = 0.3 limsprweight = 0.6 intervallength = 100 intervalstostore = 5 limsprrange = 6 meanbrlenmuts = 5 gammashapebrlen = 1000 gammashapemodel = 1000 uniqueswapbias = 0.1 distanceswapbias = 1.0 bootstrapreps = 0 resampleproportion = 1.0 inferinternalstateprobs = 0 garli-2.1-release/tests/internal/p.mkO.conf000066400000000000000000000024211241236125200206760ustar00rootroot00000000000000[general] datafname = data/L2001.30x52.nex constraintfile = none streefname = data/L.start attachmentspertaxon = 100 ofprefix = int.mkO randseed = -1 availablememory = 512 logevery = 10 saveevery = 100 refinestart = 1 outputeachbettertopology = 0 outputcurrentbesttopology = 0 enforcetermconditions = 1 genthreshfortopoterm = 100 scorethreshforterm = 0.001 significanttopochange = 0.01 outputphyliptree = 0 outputmostlyuselessfiles = 0 writecheckpoints = 0 restart = 0 usepatternmanager = 1 searchreps = 5 collapsebranches = 1 linkmodels = 0 subsetspecificrates = 0 optimizeinputonly = 1 [model1] datatype = standardordered ratematrix = 1rate statefrequencies = equal ratehetmodel = none numratecats = 1 invariantsites = none [master] nindivs = 4 holdover = 1 selectionintensity = 0.5 holdoverpenalty = 0 stopgen = 5000000 stoptime = 5000000 startoptprec = 0.01 minoptprec = 0.01 numberofprecreductions = 10 treerejectionthreshold = 50.0 topoweight = 0.01 modweight = 0.002 brlenweight = 0.002 randnniweight = 0.1 randsprweight = 0.3 limsprweight = 0.6 intervallength = 100 intervalstostore = 5 limsprrange = 6 meanbrlenmuts = 5 gammashapebrlen = 1000 gammashapemodel = 1000 uniqueswapbias = 0.1 distanceswapbias = 1.0 bootstrapreps = 0 resampleproportion = 1.0 inferinternalstateprobs = 0 garli-2.1-release/tests/internal/p.mkO.ssr.conf000066400000000000000000000024251241236125200215100ustar00rootroot00000000000000[general] datafname = data/L2001.30x52.nex constraintfile = none streefname = data/L.start attachmentspertaxon = 100 ofprefix = int.mkO.ssr randseed = -1 availablememory = 512 logevery = 10 saveevery = 100 refinestart = 1 outputeachbettertopology = 0 outputcurrentbesttopology = 0 enforcetermconditions = 1 genthreshfortopoterm = 100 scorethreshforterm = 0.001 significanttopochange = 0.01 outputphyliptree = 0 outputmostlyuselessfiles = 0 writecheckpoints = 0 restart = 0 usepatternmanager = 1 searchreps = 5 collapsebranches = 1 linkmodels = 0 subsetspecificrates = 1 optimizeinputonly = 1 [model1] datatype = standardordered ratematrix = 1rate statefrequencies = equal ratehetmodel = none numratecats = 1 invariantsites = none [master] nindivs = 4 holdover = 1 selectionintensity = 0.5 holdoverpenalty = 0 stopgen = 5000000 stoptime = 5000000 startoptprec = 0.01 minoptprec = 0.01 numberofprecreductions = 10 treerejectionthreshold = 50.0 topoweight = 0.01 modweight = 0.002 brlenweight = 0.002 randnniweight = 0.1 randsprweight = 0.3 limsprweight = 0.6 intervallength = 100 intervalstostore = 5 limsprrange = 6 meanbrlenmuts = 5 gammashapebrlen = 1000 gammashapemodel = 1000 uniqueswapbias = 0.1 distanceswapbias = 1.0 bootstrapreps = 0 resampleproportion = 1.0 inferinternalstateprobs = 0 garli-2.1-release/tests/internal/p.mkv.conf000066400000000000000000000024221241236125200207460ustar00rootroot00000000000000[general] datafname = data/L2001.30x52.nex constraintfile = none streefname = data/L.start attachmentspertaxon = 100 ofprefix = int.mkv randseed = -1 availablememory = 512 logevery = 10 saveevery = 100 refinestart = 1 outputeachbettertopology = 0 outputcurrentbesttopology = 0 enforcetermconditions = 1 genthreshfortopoterm = 100 scorethreshforterm = 0.001 significanttopochange = 0.01 outputphyliptree = 0 outputmostlyuselessfiles = 0 writecheckpoints = 0 restart = 0 usepatternmanager = 1 searchreps = 5 collapsebranches = 1 linkmodels = 0 subsetspecificrates = 0 optimizeinputonly = 1 [model1] datatype = standardvariable ratematrix = 1rate statefrequencies = equal ratehetmodel = none numratecats = 1 invariantsites = none [master] nindivs = 4 holdover = 1 selectionintensity = 0.5 holdoverpenalty = 0 stopgen = 5000000 stoptime = 5000000 startoptprec = 0.01 minoptprec = 0.01 numberofprecreductions = 10 treerejectionthreshold = 50.0 topoweight = 0.01 modweight = 0.002 brlenweight = 0.002 randnniweight = 0.1 randsprweight = 0.3 limsprweight = 0.6 intervallength = 100 intervalstostore = 5 limsprrange = 6 meanbrlenmuts = 5 gammashapebrlen = 1000 gammashapemodel = 1000 uniqueswapbias = 0.1 distanceswapbias = 1.0 bootstrapreps = 0 resampleproportion = 1.0 inferinternalstateprobs = 0 garli-2.1-release/tests/internal/p.mkv.ssr.conf000066400000000000000000000024261241236125200215600ustar00rootroot00000000000000[general] datafname = data/L2001.30x52.nex constraintfile = none streefname = data/L.start attachmentspertaxon = 100 ofprefix = int.mkv.ssr randseed = -1 availablememory = 512 logevery = 10 saveevery = 100 refinestart = 1 outputeachbettertopology = 0 outputcurrentbesttopology = 0 enforcetermconditions = 1 genthreshfortopoterm = 100 scorethreshforterm = 0.001 significanttopochange = 0.01 outputphyliptree = 0 outputmostlyuselessfiles = 0 writecheckpoints = 0 restart = 0 usepatternmanager = 1 searchreps = 5 collapsebranches = 1 linkmodels = 0 subsetspecificrates = 1 optimizeinputonly = 1 [model1] datatype = standardvariable ratematrix = 1rate statefrequencies = equal ratehetmodel = none numratecats = 1 invariantsites = none [master] nindivs = 4 holdover = 1 selectionintensity = 0.5 holdoverpenalty = 0 stopgen = 5000000 stoptime = 5000000 startoptprec = 0.01 minoptprec = 0.01 numberofprecreductions = 10 treerejectionthreshold = 50.0 topoweight = 0.01 modweight = 0.002 brlenweight = 0.002 randnniweight = 0.1 randsprweight = 0.3 limsprweight = 0.6 intervallength = 100 intervalstostore = 5 limsprrange = 6 meanbrlenmuts = 5 gammashapebrlen = 1000 gammashapemodel = 1000 uniqueswapbias = 0.1 distanceswapbias = 1.0 bootstrapreps = 0 resampleproportion = 1.0 inferinternalstateprobs = 0 garli-2.1-release/tests/internal/p.mkvO.conf000066400000000000000000000024041241236125200210650ustar00rootroot00000000000000[general] datafname = data/L2001.30x52.nex constraintfile = none streefname = data/L.start attachmentspertaxon = 100 ofprefix = int.mkvO randseed = -1 availablememory = 512 logevery = 10 saveevery = 100 refinestart = 1 outputeachbettertopology = 0 outputcurrentbesttopology = 0 enforcetermconditions = 1 genthreshfortopoterm = 100 scorethreshforterm = 0.001 significanttopochange = 0.01 outputphyliptree = 0 outputmostlyuselessfiles = 0 writecheckpoints = 0 restart = 0 searchreps = 5 collapsebranches = 1 linkmodels = 0 subsetspecificrates = 0 optimizeinputonly = 1 [model1] datatype = standardorderedvariable ratematrix = 1rate statefrequencies = equal ratehetmodel = none numratecats = 1 invariantsites = none [master] nindivs = 4 holdover = 1 selectionintensity = 0.5 holdoverpenalty = 0 stopgen = 5000000 stoptime = 5000000 startoptprec = 0.01 minoptprec = 0.01 numberofprecreductions = 10 treerejectionthreshold = 50.0 topoweight = 0.01 modweight = 0.002 brlenweight = 0.002 randnniweight = 0.1 randsprweight = 0.3 limsprweight = 0.6 intervallength = 100 intervalstostore = 5 limsprrange = 6 meanbrlenmuts = 5 gammashapebrlen = 1000 gammashapemodel = 1000 uniqueswapbias = 0.1 distanceswapbias = 1.0 bootstrapreps = 0 resampleproportion = 1.0 inferinternalstateprobs = 0 garli-2.1-release/tests/internal/p.mkvO.ssr.conf000066400000000000000000000024361241236125200217000ustar00rootroot00000000000000[general] datafname = data/L2001.30x52.nex constraintfile = none streefname = data/L.start attachmentspertaxon = 100 ofprefix = int.mkvO.ssr randseed = -1 availablememory = 512 logevery = 10 saveevery = 100 refinestart = 1 outputeachbettertopology = 0 outputcurrentbesttopology = 0 enforcetermconditions = 1 genthreshfortopoterm = 100 scorethreshforterm = 0.001 significanttopochange = 0.01 outputphyliptree = 0 outputmostlyuselessfiles = 0 writecheckpoints = 0 restart = 0 usepatternmanager = 1 searchreps = 5 collapsebranches = 1 linkmodels = 0 subsetspecificrates = 1 optimizeinputonly = 1 [model1] datatype = standardorderedvariable ratematrix = 1rate statefrequencies = equal ratehetmodel = none numratecats = 1 invariantsites = none [master] nindivs = 4 holdover = 1 selectionintensity = 0.5 holdoverpenalty = 0 stopgen = 5000000 stoptime = 5000000 startoptprec = 0.01 minoptprec = 0.01 numberofprecreductions = 10 treerejectionthreshold = 50.0 topoweight = 0.01 modweight = 0.002 brlenweight = 0.002 randnniweight = 0.1 randsprweight = 0.3 limsprweight = 0.6 intervallength = 100 intervalstostore = 5 limsprrange = 6 meanbrlenmuts = 5 gammashapebrlen = 1000 gammashapemodel = 1000 uniqueswapbias = 0.1 distanceswapbias = 1.0 bootstrapreps = 0 resampleproportion = 1.0 inferinternalstateprobs = 0 garli-2.1-release/tests/output/000077500000000000000000000000001241236125200165705ustar00rootroot00000000000000garli-2.1-release/tests/output/a.G3.conf000066400000000000000000000023651241236125200201350ustar00rootroot00000000000000[general] datafname = data/z.11x30.phy constraintfile = none streefname = stepwise attachmentspertaxon = 50 ofprefix = out.a.G3 randseed = -1 availablememory = 512 logevery = 10 saveevery = 100 refineend = 0 refinestart = 1 outputeachbettertopology = 0 outputcurrentbesttopology = 0 enforcetermconditions = 1 genthreshfortopoterm = 2000 scorethreshforterm = 0.05 significanttopochange = 0.01 outputphyliptree = 1 outputmostlyuselessfiles = 1 writecheckpoints = 0 restart = 0 outgroup = 1 outputsitelikelihoods = 1 collapsebranches = 1 usepatternmanager = 1 searchreps = 1 datatype = codon-aminoacid ratematrix = jones statefrequencies = empirical ratehetmodel = gamma numratecats = 3 invariantsites = estimate [master] nindivs = 4 holdover = 1 selectionintensity = 0.5 holdoverpenalty = 0 stopgen = 5 stoptime = 5000000 startoptprec = 0.5 minoptprec = 0.01 numberofprecreductions = 1 treerejectionthreshold = 50.0 topoweight = 1.0 modweight = 0.05 brlenweight = 0.2 randnniweight = 0.1 randsprweight = 0.3 limsprweight = 0.6 intervallength = 100 intervalstostore = 5 uniqueswapbias = 0.1 distanceswapbias = 1.0 limsprrange = 6 meanbrlenmuts = 5 gammashapebrlen = 1000 gammashapemodel = 1000 bootstrapreps = 0 resampleproportion = 1.0 inferinternalstateprobs = 1 garli-2.1-release/tests/output/a.G4.conf000066400000000000000000000023671241236125200201400ustar00rootroot00000000000000[general] datafname = data/z.11x30.phy constraintfile = none streefname = stepwise attachmentspertaxon = 50 ofprefix = out.a.G4 randseed = -1 availablememory = 512 logevery = 10 saveevery = 100 refineend = 0 refinestart = 1 outputeachbettertopology = 0 outputcurrentbesttopology = 0 enforcetermconditions = 1 genthreshfortopoterm = 2000 scorethreshforterm = 0.05 significanttopochange = 0.01 outputphyliptree = 1 outputmostlyuselessfiles = 1 writecheckpoints = 0 restart = 0 outgroup = 1-2 outputsitelikelihoods = 1 collapsebranches = 1 usepatternmanager = 1 searchreps = 1 datatype = codon-aminoacid ratematrix = jones statefrequencies = empirical ratehetmodel = gamma numratecats = 4 invariantsites = estimate [master] nindivs = 4 holdover = 1 selectionintensity = 0.5 holdoverpenalty = 0 stopgen = 5 stoptime = 5000000 startoptprec = 0.5 minoptprec = 0.01 numberofprecreductions = 1 treerejectionthreshold = 50.0 topoweight = 1.0 modweight = 0.05 brlenweight = 0.2 randnniweight = 0.1 randsprweight = 0.3 limsprweight = 0.6 intervallength = 100 intervalstostore = 5 uniqueswapbias = 0.1 distanceswapbias = 1.0 limsprrange = 6 meanbrlenmuts = 5 gammashapebrlen = 1000 gammashapemodel = 1000 bootstrapreps = 0 resampleproportion = 1.0 inferinternalstateprobs = 1 garli-2.1-release/tests/output/a.conf000066400000000000000000000023541241236125200176630ustar00rootroot00000000000000[general] datafname = data/z.11x30.AA.fas constraintfile = none streefname = stepwise attachmentspertaxon = 50 ofprefix = out.a randseed = -1 availablememory = 512 logevery = 10 saveevery = 100 refineend = 0 refinestart = 1 outputeachbettertopology = 0 outputcurrentbesttopology = 0 enforcetermconditions = 1 genthreshfortopoterm = 2000 scorethreshforterm = 0.05 significanttopochange = 0.01 outputphyliptree = 1 outputmostlyuselessfiles = 1 writecheckpoints = 0 restart = 0 outgroup = 1-3 outputsitelikelihoods = 1 collapsebranches = 1 usepatternmanager = 1 searchreps = 1 datatype = aminoacid ratematrix = jones statefrequencies = empirical ratehetmodel = none numratecats = 1 invariantsites = none [master] nindivs = 4 holdover = 1 selectionintensity = 0.5 holdoverpenalty = 0 stopgen = 5 stoptime = 5000000 startoptprec = 0.5 minoptprec = 0.01 numberofprecreductions = 1 treerejectionthreshold = 50.0 topoweight = 1.0 modweight = 0.05 brlenweight = 0.2 randnniweight = 0.1 randsprweight = 0.3 limsprweight = 0.6 intervallength = 100 intervalstostore = 5 uniqueswapbias = 0.1 distanceswapbias = 1.0 limsprrange = 6 meanbrlenmuts = 5 gammashapebrlen = 1000 gammashapemodel = 1000 bootstrapreps = 0 resampleproportion = 1.0 inferinternalstateprobs = 1 garli-2.1-release/tests/output/c.M3x2.conf000066400000000000000000000023571241236125200204200ustar00rootroot00000000000000[general] datafname = data/z.11x30.phy constraintfile = none streefname = stepwise attachmentspertaxon = 50 ofprefix = out.c.M3x2 randseed = -1 availablememory = 512 logevery = 10 saveevery = 100 refineend = 0 refinestart = 1 outputeachbettertopology = 0 outputcurrentbesttopology = 0 enforcetermconditions = 1 genthreshfortopoterm = 2000 scorethreshforterm = 0.05 significanttopochange = 0.01 outputphyliptree = 1 outputmostlyuselessfiles = 1 writecheckpoints = 0 restart = 0 outgroup = 1-5 outputsitelikelihoods = 1 collapsebranches = 1 searchreps = 1 datatype = codon geneticcode = standard ratematrix = 6rate statefrequencies = f3x4 ratehetmodel = nonsynonymous numratecats = 2 invariantsites = none [master] nindivs = 4 holdover = 1 selectionintensity = 0.5 holdoverpenalty = 0 stopgen = 5 stoptime = 5000000 startoptprec = 0.5 minoptprec = 0.01 numberofprecreductions = 1 treerejectionthreshold = 50.0 topoweight = 1.0 modweight = 0.05 brlenweight = 0.2 randnniweight = 0.1 randsprweight = 0.3 limsprweight = 0.6 intervallength = 100 intervalstostore = 5 uniqueswapbias = 0.1 distanceswapbias = 1.0 limsprrange = 6 meanbrlenmuts = 5 gammashapebrlen = 1000 gammashapemodel = 1000 bootstrapreps = 0 resampleproportion = 1.0 inferinternalstateprobs = 1 garli-2.1-release/tests/output/c.conf000066400000000000000000000023371241236125200176660ustar00rootroot00000000000000[general] datafname = data/z.11x30.phy constraintfile = none streefname = stepwise attachmentspertaxon = 50 ofprefix = out.c randseed = -1 availablememory = 512 logevery = 10 saveevery = 100 refineend = 0 refinestart = 1 outputeachbettertopology = 0 outputcurrentbesttopology = 0 enforcetermconditions = 1 genthreshfortopoterm = 2000 scorethreshforterm = 0.05 significanttopochange = 0.01 outputphyliptree = 1 outputmostlyuselessfiles = 1 writecheckpoints = 0 restart = 0 outgroup = 5 outputsitelikelihoods = 1 collapsebranches = 1 searchreps = 1 datatype = codon geneticcode = standard ratematrix = 6rate statefrequencies = f3x4 ratehetmodel = none numratecats = 1 invariantsites = none [master] nindivs = 4 holdover = 1 selectionintensity = 0.5 holdoverpenalty = 0 stopgen = 5 stoptime = 5000000 startoptprec = 0.5 minoptprec = 0.01 numberofprecreductions = 1 treerejectionthreshold = 50.0 topoweight = 1.0 modweight = 0.05 brlenweight = 0.2 randnniweight = 0.1 randsprweight = 0.3 limsprweight = 0.6 intervallength = 100 intervalstostore = 5 uniqueswapbias = 0.1 distanceswapbias = 1.0 limsprrange = 6 meanbrlenmuts = 5 gammashapebrlen = 1000 gammashapemodel = 1000 bootstrapreps = 0 resampleproportion = 1.0 inferinternalstateprobs = 1 garli-2.1-release/tests/output/n.G3.conf000066400000000000000000000023561241236125200201520ustar00rootroot00000000000000[general] datafname = data/z.11x30.phy constraintfile = none streefname = stepwise attachmentspertaxon = 50 ofprefix = out.n.G3 randseed = -1 availablememory = 512 logevery = 10 saveevery = 500 refineend = 0 refinestart = 1 outputeachbettertopology = 0 outputcurrentbesttopology = 0 enforcetermconditions = 1 genthreshfortopoterm = 2000 scorethreshforterm = 0.05 significanttopochange = 0.01 outputphyliptree = 1 outputmostlyuselessfiles = 1 writecheckpoints = 0 restart = 0 outgroup = 1 outputsitelikelihoods = 1 collapsebranches = 1 usepatternmanager = 1 searchreps = 1 datatype = nucleotide ratematrix = 6rate statefrequencies = estimate ratehetmodel = gamma numratecats = 3 invariantsites = estimate [master] nindivs = 4 holdover = 1 selectionintensity = 0.5 holdoverpenalty = 0 stopgen = 5 stoptime = 5000000 startoptprec = 0.5 minoptprec = 0.01 numberofprecreductions = 1 treerejectionthreshold = 50.0 topoweight = 1.0 modweight = 0.05 brlenweight = 0.2 randnniweight = 0.1 randsprweight = 0.3 limsprweight = 0.6 intervallength = 100 intervalstostore = 5 limsprrange = 6 meanbrlenmuts = 5 gammashapebrlen = 1000 gammashapemodel = 1000 uniqueswapbias = 0.1 distanceswapbias = 1.0 bootstrapreps = 0 resampleproportion = 1.0 inferinternalstateprobs = 1 garli-2.1-release/tests/output/n.G4.conf000066400000000000000000000023601241236125200201460ustar00rootroot00000000000000[general] datafname = data/z.11x30.phy constraintfile = none streefname = stepwise attachmentspertaxon = 50 ofprefix = out.n.G4 randseed = -1 availablememory = 512 logevery = 10 saveevery = 500 refineend = 0 refinestart = 1 outputeachbettertopology = 0 outputcurrentbesttopology = 0 enforcetermconditions = 1 genthreshfortopoterm = 2000 scorethreshforterm = 0.05 significanttopochange = 0.01 outputphyliptree = 1 outputmostlyuselessfiles = 1 writecheckpoints = 0 restart = 0 outgroup = 1-4 outputsitelikelihoods = 1 collapsebranches = 1 usepatternmanager = 1 searchreps = 1 datatype = nucleotide ratematrix = 6rate statefrequencies = estimate ratehetmodel = gamma numratecats = 4 invariantsites = estimate [master] nindivs = 4 holdover = 1 selectionintensity = 0.5 holdoverpenalty = 0 stopgen = 5 stoptime = 5000000 startoptprec = 0.5 minoptprec = 0.01 numberofprecreductions = 1 treerejectionthreshold = 50.0 topoweight = 1.0 modweight = 0.05 brlenweight = 0.2 randnniweight = 0.1 randsprweight = 0.3 limsprweight = 0.6 intervallength = 100 intervalstostore = 5 limsprrange = 6 meanbrlenmuts = 5 gammashapebrlen = 1000 gammashapemodel = 1000 uniqueswapbias = 0.1 distanceswapbias = 1.0 bootstrapreps = 0 resampleproportion = 1.0 inferinternalstateprobs = 1 garli-2.1-release/tests/output/n.conf000066400000000000000000000023501241236125200176740ustar00rootroot00000000000000[general] datafname = data/z.11x30.phy constraintfile = none streefname = stepwise attachmentspertaxon = 50 ofprefix = out.n randseed = -1 availablememory = 512 logevery = 10 saveevery = 500 refineend = 0 refinestart = 1 outputeachbettertopology = 0 outputcurrentbesttopology = 0 enforcetermconditions = 1 genthreshfortopoterm = 2000 scorethreshforterm = 0.05 significanttopochange = 0.01 outputphyliptree = 1 outputmostlyuselessfiles = 1 writecheckpoints = 0 restart = 0 outgroup = 2-3 outputsitelikelihoods = 1 collapsebranches = 1 usepatternmanager = 1 searchreps = 1 datatype = nucleotide ratematrix = 6rate statefrequencies = estimate ratehetmodel = none numratecats = 1 invariantsites = none [master] nindivs = 4 holdover = 1 selectionintensity = 0.5 holdoverpenalty = 0 stopgen = 5 stoptime = 5000000 startoptprec = 0.5 minoptprec = 0.01 numberofprecreductions = 1 treerejectionthreshold = 50.0 topoweight = 1.0 modweight = 0.05 brlenweight = 0.2 randnniweight = 0.1 randsprweight = 0.3 limsprweight = 0.6 intervallength = 100 intervalstostore = 5 limsprrange = 6 meanbrlenmuts = 5 gammashapebrlen = 1000 gammashapemodel = 1000 uniqueswapbias = 0.1 distanceswapbias = 1.0 bootstrapreps = 0 resampleproportion = 1.0 inferinternalstateprobs = 1 garli-2.1-release/tests/output/n.wackyNames.conf000066400000000000000000000023611241236125200217770ustar00rootroot00000000000000[general] datafname = data/z.11x30.wackyNames.nex constraintfile = none streefname = stepwise attachmentspertaxon = 50 ofprefix = out.n randseed = -1 availablememory = 512 logevery = 10 saveevery = 500 refineend = 0 refinestart = 1 outputeachbettertopology = 0 outputcurrentbesttopology = 0 enforcetermconditions = 1 genthreshfortopoterm = 2000 scorethreshforterm = 0.05 significanttopochange = 0.01 outputphyliptree = 1 outputmostlyuselessfiles = 1 writecheckpoints = 0 restart = 0 outgroup = 1 outputsitelikelihoods = 1 collapsebranches = 1 usepatternmanager = 1 searchreps = 1 datatype = nucleotide ratematrix = 6rate statefrequencies = estimate ratehetmodel = none numratecats = 1 invariantsites = none [master] nindivs = 4 holdover = 1 selectionintensity = 0.5 holdoverpenalty = 0 stopgen = 5 stoptime = 5000000 startoptprec = 0.5 minoptprec = 0.01 numberofprecreductions = 1 treerejectionthreshold = 50.0 topoweight = 1.0 modweight = 0.05 brlenweight = 0.2 randnniweight = 0.1 randsprweight = 0.3 limsprweight = 0.6 intervallength = 100 intervalstostore = 5 limsprrange = 6 meanbrlenmuts = 5 gammashapebrlen = 1000 gammashapemodel = 1000 uniqueswapbias = 0.1 distanceswapbias = 1.0 bootstrapreps = 0 resampleproportion = 1.0 inferinternalstateprobs = 0 garli-2.1-release/tests/output/p.3diff.conf000066400000000000000000000031431241236125200206710ustar00rootroot00000000000000[general] datafname = data/z.byPos.11x2178.nex constraintfile = none streefname = stepwise attachmentspertaxon = 50 ofprefix = out.p.3diff randseed = -1 availablememory = 512 logevery = 10 saveevery = 100 refinestart = 1 refineend = 0 outputeachbettertopology = 1 outputcurrentbesttopology = 0 enforcetermconditions = 1 genthreshfortopoterm = 5000 scorethreshforterm = 0.001 significanttopochange = 0.01 outputphyliptree = 1 outputmostlyuselessfiles = 1 writecheckpoints = 0 restart = 0 outgroup = 1-2 usepatternmanager = 1 searchreps = 1 collapsebranches = 1 outputsitelikelihoods = 1 linkmodels = 0 subsetspecificrates = 1 [model1] datatype = nucleotide ratematrix = ( 0 1 2 2 3 4 ) statefrequencies = estimate ratehetmodel = gamma numratecats = 4 invariantsites = none [model2] datatype = nucleotide ratematrix = ( 0 1 2 1 0 3 ) statefrequencies = estimate ratehetmodel = gamma numratecats = 4 invariantsites = none [model3] datatype = nucleotide ratematrix = ( 0 1 2 3 1 0 ) statefrequencies = estimate ratehetmodel = gamma numratecats = 4 invariantsites = estimate [master] nindivs = 4 holdover = 1 selectionintensity = 0.5 holdoverpenalty = 0 stopgen = 5 stoptime = 5000000 startoptprec = 0.5 minoptprec = 0.01 numberofprecreductions = 5 treerejectionthreshold = 50.0 topoweight = 0.01 modweight = 0.002 brlenweight = 0.002 randnniweight = 0.1 randsprweight = 0.3 limsprweight = 0.6 intervallength = 100 intervalstostore = 5 limsprrange = 6 meanbrlenmuts = 5 gammashapebrlen = 1000 gammashapemodel = 1000 uniqueswapbias = 0.1 distanceswapbias = 1.0 bootstrapreps = 0 resampleproportion = 1.0 inferinternalstateprobs = 1 garli-2.1-release/tests/output/p.mkvO.ssr.conf000066400000000000000000000024371241236125200214250ustar00rootroot00000000000000[general] datafname = data/L2001.30x52.nex constraintfile = none streefname = data/L.start attachmentspertaxon = 100 ofprefix = out.p.mkvO.ssr randseed = -1 availablememory = 512 logevery = 10 saveevery = 100 refinestart = 1 refineend = 0 outputeachbettertopology = 0 outputcurrentbesttopology = 0 enforcetermconditions = 1 genthreshfortopoterm = 100 scorethreshforterm = 0.001 significanttopochange = 0.01 outputphyliptree = 0 outputmostlyuselessfiles = 0 writecheckpoints = 0 restart = 0 outgroup = 5 usepatternmanager = 1 searchreps = 5 collapsebranches = 1 linkmodels = 0 subsetspecificrates = 1 [model1] datatype = standardorderedvariable ratematrix = 1rate statefrequencies = equal ratehetmodel = none numratecats = 1 invariantsites = none [master] nindivs = 4 holdover = 1 selectionintensity = 0.5 holdoverpenalty = 0 stopgen = 5 stoptime = 5000000 startoptprec = 0.01 minoptprec = 0.01 numberofprecreductions = 10 treerejectionthreshold = 50.0 topoweight = 0.01 modweight = 0.002 brlenweight = 0.002 randnniweight = 0.1 randsprweight = 0.3 limsprweight = 0.6 intervallength = 100 intervalstostore = 5 limsprrange = 6 meanbrlenmuts = 5 gammashapebrlen = 1000 gammashapemodel = 1000 uniqueswapbias = 0.1 distanceswapbias = 1.0 bootstrapreps = 0 resampleproportion = 1.0 inferinternalstateprobs = 0 garli-2.1-release/tests/restart/000077500000000000000000000000001241236125200167145ustar00rootroot00000000000000garli-2.1-release/tests/restart/a.G3.conf000066400000000000000000000024231241236125200202540ustar00rootroot00000000000000[general] datafname = data/z.11x30.stop.nex constraintfile = none streefname = stepwise attachmentspertaxon = 50 ofprefix = ch.a.G3 randseed = -1 availablememory = 512 logevery = 10 saveevery = 100 refineend = 0 refinestart = 1 outputeachbettertopology = 0 outputcurrentbesttopology = 0 enforcetermconditions = 1 genthreshfortopoterm = 2000 scorethreshforterm = 0.05 significanttopochange = 0.01 outputphyliptree = 0 outputmostlyuselessfiles = 1 writecheckpoints = 0 restart = 1 outgroup = 1-2 outputsitelikelihoods = 0 collapsebranches = 1 usepatternmanager = 1 searchreps = 1 ignorestopcodons = 1 datatype = codon-aminoacid ratematrix = jones statefrequencies = empirical ratehetmodel = gamma numratecats = 3 invariantsites = estimate [master] nindivs = 4 holdover = 1 selectionintensity = 0.5 holdoverpenalty = 0 stopgen = 1000 stoptime = 5000000 startoptprec = 0.5 minoptprec = 0.01 numberofprecreductions = 1 treerejectionthreshold = 50.0 topoweight = 1.0 modweight = 0.05 brlenweight = 0.2 randnniweight = 0.1 randsprweight = 0.3 limsprweight = 0.6 intervallength = 100 intervalstostore = 5 uniqueswapbias = 0.1 distanceswapbias = 1.0 limsprrange = 6 meanbrlenmuts = 5 gammashapebrlen = 1000 gammashapemodel = 1000 bootstrapreps = 1 resampleproportion = 1.0 inferinternalstateprobs = 0 garli-2.1-release/tests/restart/a.G4.conf000077500000000000000000000023641241236125200202640ustar00rootroot00000000000000[general] datafname = data/z.11x30.AA.fas constraintfile = none streefname = stepwise attachmentspertaxon = 50 ofprefix = ch.a.G4 randseed = -1 availablememory = 512 logevery = 10 saveevery = 100 refineend = 0 refinestart = 1 outputeachbettertopology = 0 outputcurrentbesttopology = 0 enforcetermconditions = 1 genthreshfortopoterm = 2000 scorethreshforterm = 0.05 significanttopochange = 0.01 outputphyliptree = 0 outputmostlyuselessfiles = 1 writecheckpoints = 0 restart = 1 outgroup = 2 outputsitelikelihoods = 1 collapsebranches = 1 usepatternmanager = 1 searchreps = 1 datatype = aminoacid ratematrix = jones statefrequencies = empirical ratehetmodel = gamma numratecats = 4 invariantsites = estimate [master] nindivs = 4 holdover = 1 selectionintensity = 0.5 holdoverpenalty = 0 stopgen = 1000 stoptime = 5000000 startoptprec = 0.5 minoptprec = 0.01 numberofprecreductions = 1 treerejectionthreshold = 50.0 topoweight = 1.0 modweight = 0.05 brlenweight = 0.2 randnniweight = 0.1 randsprweight = 0.3 limsprweight = 0.6 intervallength = 100 intervalstostore = 5 uniqueswapbias = 0.1 distanceswapbias = 1.0 limsprrange = 6 meanbrlenmuts = 5 gammashapebrlen = 1000 gammashapemodel = 1000 bootstrapreps = 0 resampleproportion = 1.0 inferinternalstateprobs = 1 garli-2.1-release/tests/restart/a.conf000077500000000000000000000023571241236125200200150ustar00rootroot00000000000000[general] datafname = data/z.11x30.phy constraintfile = none streefname = stepwise attachmentspertaxon = 50 ofprefix = ch.a randseed = -1 availablememory = 512 logevery = 10 saveevery = 100 refineend = 0 refinestart = 1 outputeachbettertopology = 0 outputcurrentbesttopology = 0 enforcetermconditions = 1 genthreshfortopoterm = 2000 scorethreshforterm = 0.05 significanttopochange = 0.01 outputphyliptree = 0 outputmostlyuselessfiles = 1 writecheckpoints = 0 restart = 1 outgroup = 2 outputsitelikelihoods = 1 collapsebranches = 1 usepatternmanager = 1 searchreps = 1 datatype = codon-aminoacid ratematrix = jones statefrequencies = empirical ratehetmodel = none numratecats = 1 invariantsites = none [master] nindivs = 4 holdover = 1 selectionintensity = 0.5 holdoverpenalty = 0 stopgen = 1000 stoptime = 5000000 startoptprec = 0.5 minoptprec = 0.01 numberofprecreductions = 1 treerejectionthreshold = 50.0 topoweight = 1.0 modweight = 0.05 brlenweight = 0.2 randnniweight = 0.1 randsprweight = 0.3 limsprweight = 0.6 intervallength = 100 intervalstostore = 5 uniqueswapbias = 0.1 distanceswapbias = 1.0 limsprrange = 6 meanbrlenmuts = 5 gammashapebrlen = 1000 gammashapemodel = 1000 bootstrapreps = 0 resampleproportion = 1.0 inferinternalstateprobs = 1 garli-2.1-release/tests/restart/c.M3x2.conf000077500000000000000000000024031241236125200205370ustar00rootroot00000000000000[general] datafname = data/z.11x30.phy constraintfile = none streefname = stepwise attachmentspertaxon = 50 ofprefix = ch.c.M3x2 randseed = -1 availablememory = 512 logevery = 10 saveevery = 10 refineend = 0 refinestart = 1 outputeachbettertopology = 0 outputcurrentbesttopology = 0 enforcetermconditions = 1 genthreshfortopoterm = 2000 scorethreshforterm = 0.05 significanttopochange = 0.01 outputphyliptree = 0 outputmostlyuselessfiles = 1 writecheckpoints = 0 restart = 1 outgroup = 1 outputsitelikelihoods = 1 collapsebranches = 1 usepatternmanager = 1 searchreps = 1 datatype = codon geneticcode = standard ratematrix = 6rate statefrequencies = f3x4 ratehetmodel = nonsynonymous numratecats = 2 invariantsites = none [master] nindivs = 4 holdover = 1 selectionintensity = 0.5 holdoverpenalty = 0 stopgen = 100 stoptime = 5000000 startoptprec = 0.5 minoptprec = 0.01 numberofprecreductions = 1 treerejectionthreshold = 50.0 topoweight = 1.0 modweight = 0.05 brlenweight = 0.2 randnniweight = 0.1 randsprweight = 0.3 limsprweight = 0.6 intervallength = 100 intervalstostore = 5 uniqueswapbias = 0.1 distanceswapbias = 1.0 limsprrange = 6 meanbrlenmuts = 5 gammashapebrlen = 1000 gammashapemodel = 1000 bootstrapreps = 0 resampleproportion = 1.0 inferinternalstateprobs = 1 garli-2.1-release/tests/restart/c.conf000066400000000000000000000023671241236125200200150ustar00rootroot00000000000000[general] datafname = data/z.11x30.phy constraintfile = none streefname = stepwise attachmentspertaxon = 50 ofprefix = ch.c randseed = -1 availablememory = 512 logevery = 10 saveevery = 10 refineend = 0 refinestart = 1 outputeachbettertopology = 0 outputcurrentbesttopology = 0 enforcetermconditions = 1 genthreshfortopoterm = 2000 scorethreshforterm = 0.05 significanttopochange = 0.01 outputphyliptree = 0 outputmostlyuselessfiles = 1 writecheckpoints = 0 restart = 1 outgroup = 1-2 outputsitelikelihoods = 0 collapsebranches = 1 usepatternmanager = 1 searchreps = 1 datatype = codon geneticcode = standard ratematrix = 6rate statefrequencies = f3x4 ratehetmodel = none numratecats = 1 invariantsites = none [master] nindivs = 4 holdover = 1 selectionintensity = 0.5 holdoverpenalty = 0 stopgen = 100 stoptime = 5000000 startoptprec = 0.5 minoptprec = 0.01 numberofprecreductions = 1 treerejectionthreshold = 50.0 topoweight = 1.0 modweight = 0.05 brlenweight = 0.2 randnniweight = 0.1 randsprweight = 0.3 limsprweight = 0.6 intervallength = 100 intervalstostore = 5 uniqueswapbias = 0.1 distanceswapbias = 1.0 limsprrange = 6 meanbrlenmuts = 5 gammashapebrlen = 1000 gammashapemodel = 1000 bootstrapreps = 1 resampleproportion = 1.0 inferinternalstateprobs = 0 garli-2.1-release/tests/restart/g.dnaBnoZ.conf000077500000000000000000000026441241236125200213540ustar00rootroot00000000000000[general] datafname = data/dnaGap.8x1K.nex constraintfile = none streefname = data/dnaGap.8x1K.nex attachmentspertaxon = 50 ofprefix = ch.g.dnaBnoZ randseed = -1 availablememory = 512 logevery = 10 saveevery = 100 refinestart = 1 outputeachbettertopology = 0 outputcurrentbesttopology = 0 enforcetermconditions = 1 genthreshfortopoterm = 10000 scorethreshforterm = 0.05 significanttopochange = 0.01 outputphyliptree = 0 outputmostlyuselessfiles = 0 writecheckpoints = 0 restart = 1 outgroup = 1 subsetspecificrates = 1 collapsebranches = 1 usepatternmanager = 1 searchreps = 2 outputsitelikelihoods = 0 [model1] datatype = nucleotide ratematrix = 6rate statefrequencies = estimate ratehetmodel = gamma numratecats = 4 invariantsites = estimate [model2] datatype = binaryNotAllZeros ratematrix = 1rate statefrequencies = equal ratehetmodel = none numratecats = 1 invariantsites = none [master] nindivs = 4 holdover = 1 selectionintensity = 0.5 holdoverpenalty = 0 stopgen = 5000000 stoptime = 5 startoptprec = 0.5 minoptprec = 0.01 numberofprecreductions = 2 treerejectionthreshold = 50.0 topoweight = 1.0 modweight = 0.05 brlenweight = 0.2 randnniweight = 0.1 randsprweight = 0.3 limsprweight = 0.6 intervallength = 100 intervalstostore = 5 limsprrange = 6 meanbrlenmuts = 5 gammashapebrlen = 1000 gammashapemodel = 1000 uniqueswapbias = 0.1 distanceswapbias = 1.0 bootstrapreps = 0 resampleproportion = 1.0 inferinternalstateprobs = 0 garli-2.1-release/tests/restart/g.dnaMix.conf000077500000000000000000000026411241236125200212360ustar00rootroot00000000000000[general] datafname = data/dnaGap.8x1K.nex constraintfile = none streefname = data/dnaGap.8x1K.nex attachmentspertaxon = 50 ofprefix = ch.g.dnaMix randseed = -1 availablememory = 512 logevery = 10 saveevery = 100 refinestart = 1 outputeachbettertopology = 0 outputcurrentbesttopology = 0 enforcetermconditions = 1 genthreshfortopoterm = 10000 scorethreshforterm = 0.05 significanttopochange = 0.01 outputphyliptree = 0 outputmostlyuselessfiles = 0 writecheckpoints = 0 restart = 1 outgroup = 1 subsetspecificrates = 0 collapsebranches = 1 usepatternmanager = 1 searchreps = 2 outputsitelikelihoods = 0 [model1] datatype = nucleotide ratematrix = 6rate statefrequencies = estimate ratehetmodel = gamma numratecats = 4 invariantsites = estimate [model2] datatype = gapmixturemodel ratematrix = 1rate statefrequencies = equal ratehetmodel = none numratecats = 1 invariantsites = none [master] nindivs = 4 holdover = 1 selectionintensity = 0.5 holdoverpenalty = 0 stopgen = 5000000 stoptime = 5 startoptprec = 0.5 minoptprec = 0.01 numberofprecreductions = 2 treerejectionthreshold = 50.0 topoweight = 1.0 modweight = 0.05 brlenweight = 0.2 randnniweight = 0.1 randsprweight = 0.3 limsprweight = 0.6 intervallength = 100 intervalstostore = 5 limsprrange = 6 meanbrlenmuts = 5 gammashapebrlen = 1000 gammashapemodel = 1000 uniqueswapbias = 0.1 distanceswapbias = 1.0 bootstrapreps = 0 resampleproportion = 1.0 inferinternalstateprobs = 0 garli-2.1-release/tests/restart/n.G3.conf000077500000000000000000000023531241236125200202760ustar00rootroot00000000000000[general] datafname = data/z.11x30.phy constraintfile = none streefname = stepwise attachmentspertaxon = 50 ofprefix = ch.n.G3 randseed = -1 availablememory = 512 logevery = 10 saveevery = 100 refineend = 0 refinestart = 1 outputeachbettertopology = 0 outputcurrentbesttopology = 0 enforcetermconditions = 1 genthreshfortopoterm = 2000 scorethreshforterm = 0.05 significanttopochange = 0.01 outputphyliptree = 0 outputmostlyuselessfiles = 1 writecheckpoints = 0 restart = 1 outgroup = 1 outputsitelikelihoods = 1 collapsebranches = 1 usepatternmanager = 1 searchreps = 1 datatype = nucleotide ratematrix = 6rate statefrequencies = estimate ratehetmodel = gamma numratecats = 3 invariantsites = estimate [master] nindivs = 4 holdover = 1 selectionintensity = 0.5 holdoverpenalty = 0 stopgen = 10000 stoptime = 5 startoptprec = 0.5 minoptprec = 0.01 numberofprecreductions = 1 treerejectionthreshold = 50.0 topoweight = 1.0 modweight = 0.05 brlenweight = 0.2 randnniweight = 0.1 randsprweight = 0.3 limsprweight = 0.6 intervallength = 100 intervalstostore = 5 limsprrange = 6 meanbrlenmuts = 5 gammashapebrlen = 1000 gammashapemodel = 1000 uniqueswapbias = 0.1 distanceswapbias = 1.0 bootstrapreps = 0 resampleproportion = 1.0 inferinternalstateprobs = 1 garli-2.1-release/tests/restart/n.G4.conf000066400000000000000000000023531241236125200202740ustar00rootroot00000000000000[general] datafname = data/z.11x30.phy constraintfile = none streefname = stepwise attachmentspertaxon = 50 ofprefix = ch.n.G4 randseed = -1 availablememory = 512 logevery = 10 saveevery = 100 refineend = 0 refinestart = 1 outputeachbettertopology = 0 outputcurrentbesttopology = 0 enforcetermconditions = 1 genthreshfortopoterm = 2000 scorethreshforterm = 0.05 significanttopochange = 0.01 outputphyliptree = 0 outputmostlyuselessfiles = 1 writecheckpoints = 0 restart = 1 outgroup = 1 outputsitelikelihoods = 0 collapsebranches = 1 usepatternmanager = 1 searchreps = 1 datatype = nucleotide ratematrix = 6rate statefrequencies = estimate ratehetmodel = gamma numratecats = 4 invariantsites = estimate [master] nindivs = 4 holdover = 1 selectionintensity = 0.5 holdoverpenalty = 0 stopgen = 10000 stoptime = 5 startoptprec = 0.5 minoptprec = 0.01 numberofprecreductions = 1 treerejectionthreshold = 50.0 topoweight = 1.0 modweight = 0.05 brlenweight = 0.2 randnniweight = 0.1 randsprweight = 0.3 limsprweight = 0.6 intervallength = 100 intervalstostore = 5 limsprrange = 6 meanbrlenmuts = 5 gammashapebrlen = 1000 gammashapemodel = 1000 uniqueswapbias = 0.1 distanceswapbias = 1.0 bootstrapreps = 1 resampleproportion = 1.0 inferinternalstateprobs = 0 garli-2.1-release/tests/restart/n.conf000077500000000000000000000023511241236125200200240ustar00rootroot00000000000000[general] datafname = data/z.11x30.phy constraintfile = none streefname = stepwise attachmentspertaxon = 50 ofprefix = ch.n randseed = -1 availablememory = 512 logevery = 10 saveevery = 100 refineend = 0 refinestart = 1 outputeachbettertopology = 0 outputcurrentbesttopology = 0 enforcetermconditions = 1 genthreshfortopoterm = 2000 scorethreshforterm = 0.05 significanttopochange = 0.01 outputphyliptree = 0 outputmostlyuselessfiles = 1 writecheckpoints = 0 restart = 1 outgroup = 1 outputsitelikelihoods = 1 collapsebranches = 1 usepatternmanager = 1 searchreps = 1 datatype = nucleotide ratematrix = 6rate statefrequencies = estimate ratehetmodel = none numratecats = 1 invariantsites = none [master] nindivs = 4 holdover = 1 selectionintensity = 0.5 holdoverpenalty = 0 stopgen = 10000 stoptime = 5000000 startoptprec = 0.5 minoptprec = 0.01 numberofprecreductions = 1 treerejectionthreshold = 50.0 topoweight = 1.0 modweight = 0.05 brlenweight = 0.2 randnniweight = 0.1 randsprweight = 0.3 limsprweight = 0.6 intervallength = 100 intervalstostore = 5 limsprrange = 6 meanbrlenmuts = 5 gammashapebrlen = 1000 gammashapemodel = 1000 uniqueswapbias = 0.1 distanceswapbias = 1.0 bootstrapreps = 0 resampleproportion = 1.0 inferinternalstateprobs = 1 garli-2.1-release/tests/restart/n.const.conf000077500000000000000000000024041241236125200211500ustar00rootroot00000000000000[general] datafname = data/z.11x30.phy constraintfile = data/z.pos.const.tre streefname = stepwise attachmentspertaxon = 50 ofprefix = ch.n.const randseed = -1 availablememory = 512 logevery = 10 saveevery = 100 refineend = 0 refinestart = 1 outputeachbettertopology = 0 outputcurrentbesttopology = 0 enforcetermconditions = 1 genthreshfortopoterm = 2000 scorethreshforterm = 0.05 significanttopochange = 0.01 outputphyliptree = 0 outputmostlyuselessfiles = 1 writecheckpoints = 0 restart = 1 outgroup = 1 outputsitelikelihoods = 1 collapsebranches = 1 usepatternmanager = 1 searchreps = 1 datatype = nucleotide ratematrix = 6rate statefrequencies = estimate ratehetmodel = gamma numratecats = 4 invariantsites = estimate [master] nindivs = 4 holdover = 1 selectionintensity = 0.5 holdoverpenalty = 0 stopgen = 10000 stoptime = 5000000 startoptprec = 0.5 minoptprec = 0.01 numberofprecreductions = 1 treerejectionthreshold = 50.0 topoweight = 1.0 modweight = 0.05 brlenweight = 0.2 randnniweight = 0.1 randsprweight = 0.3 limsprweight = 0.6 intervallength = 100 intervalstostore = 5 limsprrange = 6 meanbrlenmuts = 5 gammashapebrlen = 1000 gammashapemodel = 1000 uniqueswapbias = 0.1 distanceswapbias = 1.0 bootstrapreps = 0 resampleproportion = 1.0 inferinternalstateprobs = 1 garli-2.1-release/tests/restart/p.3diff.conf000066400000000000000000000031001241236125200210060ustar00rootroot00000000000000[general] datafname = data/z.byPos.11x2178.nex constraintfile = none streefname = data/p.3diff.start attachmentspertaxon = 50 ofprefix = ch.p.3diff randseed = -1 availablememory = 512 logevery = 10 saveevery = 100 refinestart = 1 refineend = 0 outputeachbettertopology = 0 outputcurrentbesttopology = 0 enforcetermconditions = 1 genthreshfortopoterm = 5000 scorethreshforterm = 0.001 significanttopochange = 0.01 outputphyliptree = 0 outputmostlyuselessfiles = 0 writecheckpoints = 0 restart = 1 usepatternmanager = 1 searchreps = 1 collapsebranches = 1 linkmodels = 0 subsetspecificrates = 1 [model1] datatype = nucleotide ratematrix = ( 0 1 2 2 3 4 ) statefrequencies = estimate ratehetmodel = gamma numratecats = 4 invariantsites = none [model2] datatype = nucleotide ratematrix = ( 0 1 2 1 0 3 ) statefrequencies = estimate ratehetmodel = gamma numratecats = 4 invariantsites = none [model3] datatype = nucleotide ratematrix = ( 0 1 2 3 1 0 ) statefrequencies = estimate ratehetmodel = gamma numratecats = 4 invariantsites = estimate [master] nindivs = 4 holdover = 1 selectionintensity = 0.5 holdoverpenalty = 0 stopgen = 2000 stoptime = 1 startoptprec = 0.5 minoptprec = 0.01 numberofprecreductions = 5 treerejectionthreshold = 50.0 topoweight = 0.01 modweight = 0.002 brlenweight = 0.002 randnniweight = 0.1 randsprweight = 0.3 limsprweight = 0.6 intervallength = 100 intervalstostore = 5 limsprrange = 6 meanbrlenmuts = 5 gammashapebrlen = 1000 gammashapemodel = 1000 uniqueswapbias = 0.1 distanceswapbias = 1.0 bootstrapreps = 1 resampleproportion = 1.0 inferinternalstateprobs = 0 garli-2.1-release/tests/restart/p.mk.ssr.conf000077500000000000000000000023631241236125200212450ustar00rootroot00000000000000[general] datafname = data/L2001.30x52.nex constraintfile = none streefname = data/L.start attachmentspertaxon = 100 ofprefix = ch.p.mk.ssr randseed = -1 availablememory = 512 logevery = 10 saveevery = 100 refinestart = 1 outputeachbettertopology = 0 outputcurrentbesttopology = 0 enforcetermconditions = 1 genthreshfortopoterm = 100 scorethreshforterm = 0.001 significanttopochange = 0.01 outputphyliptree = 0 outputmostlyuselessfiles = 0 writecheckpoints = 0 restart = 1 usepatternmanager = 1 searchreps = 5 collapsebranches = 1 linkmodels = 0 subsetspecificrates = 1 [model1] datatype = standard ratematrix = 1rate statefrequencies = equal ratehetmodel = none numratecats = 1 invariantsites = none [master] nindivs = 4 holdover = 1 selectionintensity = 0.5 holdoverpenalty = 0 stopgen = 5000000 stoptime = 5 startoptprec = 0.01 minoptprec = 0.01 numberofprecreductions = 10 treerejectionthreshold = 50.0 topoweight = 0.01 modweight = 0.002 brlenweight = 0.002 randnniweight = 0.1 randsprweight = 0.3 limsprweight = 0.6 intervallength = 100 intervalstostore = 5 limsprrange = 6 meanbrlenmuts = 5 gammashapebrlen = 1000 gammashapemodel = 1000 uniqueswapbias = 0.1 distanceswapbias = 1.0 bootstrapreps = 0 resampleproportion = 1.0 inferinternalstateprobs = 0 garli-2.1-release/tests/restart/p.mkO.ssr.conf000077500000000000000000000023731241236125200213650ustar00rootroot00000000000000[general] datafname = data/L2001.30x52.nex constraintfile = none streefname = data/L.start attachmentspertaxon = 100 ofprefix = ch.p.mkO.ssr randseed = -1 availablememory = 512 logevery = 10 saveevery = 100 refinestart = 1 outputeachbettertopology = 0 outputcurrentbesttopology = 0 enforcetermconditions = 1 genthreshfortopoterm = 100 scorethreshforterm = 0.001 significanttopochange = 0.01 outputphyliptree = 0 outputmostlyuselessfiles = 0 writecheckpoints = 0 restart = 1 usepatternmanager = 1 searchreps = 5 collapsebranches = 1 linkmodels = 0 subsetspecificrates = 1 [model1] datatype = standardordered ratematrix = 1rate statefrequencies = equal ratehetmodel = none numratecats = 1 invariantsites = none [master] nindivs = 4 holdover = 1 selectionintensity = 0.5 holdoverpenalty = 0 stopgen = 5000000 stoptime = 5 startoptprec = 0.01 minoptprec = 0.01 numberofprecreductions = 10 treerejectionthreshold = 50.0 topoweight = 0.01 modweight = 0.002 brlenweight = 0.002 randnniweight = 0.1 randsprweight = 0.3 limsprweight = 0.6 intervallength = 100 intervalstostore = 5 limsprrange = 6 meanbrlenmuts = 5 gammashapebrlen = 1000 gammashapemodel = 1000 uniqueswapbias = 0.1 distanceswapbias = 1.0 bootstrapreps = 0 resampleproportion = 1.0 inferinternalstateprobs = 0 garli-2.1-release/tests/restart/p.mkv.ssr.conf000077500000000000000000000023741241236125200214350ustar00rootroot00000000000000[general] datafname = data/L2001.30x52.nex constraintfile = none streefname = data/L.start attachmentspertaxon = 100 ofprefix = ch.p.mkv.ssr randseed = -1 availablememory = 512 logevery = 10 saveevery = 100 refinestart = 1 outputeachbettertopology = 0 outputcurrentbesttopology = 0 enforcetermconditions = 1 genthreshfortopoterm = 100 scorethreshforterm = 0.001 significanttopochange = 0.01 outputphyliptree = 0 outputmostlyuselessfiles = 0 writecheckpoints = 0 restart = 1 usepatternmanager = 1 searchreps = 5 collapsebranches = 1 linkmodels = 0 subsetspecificrates = 1 [model1] datatype = standardvariable ratematrix = 1rate statefrequencies = equal ratehetmodel = none numratecats = 1 invariantsites = none [master] nindivs = 4 holdover = 1 selectionintensity = 0.5 holdoverpenalty = 0 stopgen = 5000000 stoptime = 5 startoptprec = 0.01 minoptprec = 0.01 numberofprecreductions = 10 treerejectionthreshold = 50.0 topoweight = 0.01 modweight = 0.002 brlenweight = 0.002 randnniweight = 0.1 randsprweight = 0.3 limsprweight = 0.6 intervallength = 100 intervalstostore = 5 limsprrange = 6 meanbrlenmuts = 5 gammashapebrlen = 1000 gammashapemodel = 1000 uniqueswapbias = 0.1 distanceswapbias = 1.0 bootstrapreps = 1 resampleproportion = 1.0 inferinternalstateprobs = 0 garli-2.1-release/tests/restart/p.mkvO.ssr.conf000077500000000000000000000024041241236125200215460ustar00rootroot00000000000000[general] datafname = data/L2001.30x52.nex constraintfile = none streefname = data/L.start attachmentspertaxon = 100 ofprefix = ch.p.mkvO.ssr randseed = -1 availablememory = 512 logevery = 10 saveevery = 100 refinestart = 1 outputeachbettertopology = 0 outputcurrentbesttopology = 0 enforcetermconditions = 1 genthreshfortopoterm = 100 scorethreshforterm = 0.001 significanttopochange = 0.01 outputphyliptree = 0 outputmostlyuselessfiles = 0 writecheckpoints = 0 restart = 1 usepatternmanager = 1 searchreps = 5 collapsebranches = 1 linkmodels = 0 subsetspecificrates = 1 [model1] datatype = standardorderedvariable ratematrix = 1rate statefrequencies = equal ratehetmodel = none numratecats = 1 invariantsites = none [master] nindivs = 4 holdover = 1 selectionintensity = 0.5 holdoverpenalty = 0 stopgen = 5000000 stoptime = 5 startoptprec = 0.01 minoptprec = 0.01 numberofprecreductions = 10 treerejectionthreshold = 50.0 topoweight = 0.01 modweight = 0.002 brlenweight = 0.002 randnniweight = 0.1 randsprweight = 0.3 limsprweight = 0.6 intervallength = 100 intervalstostore = 5 limsprrange = 6 meanbrlenmuts = 5 gammashapebrlen = 1000 gammashapemodel = 1000 uniqueswapbias = 0.1 distanceswapbias = 1.0 bootstrapreps = 0 resampleproportion = 1.0 inferinternalstateprobs = 0 garli-2.1-release/tests/runtests.sh000077500000000000000000000135031241236125200174600ustar00rootroot00000000000000#!/bin/bash if [ $# -lt 3 ]; then echo Usage: pass three, or \(optionally\) more arguments: echo '$0 [optional: GARLI command-line arguments]' exit 1 fi TESTS_DIR=$1 GARLI_BIN=$2 NEXUS_VAL=$3 if [ $# -gt 3 ] then shift; shift; shift; GARLI_ARGS=$@ fi #set this to move on to the next test after failing one #NO_EXIT_ON_ERR=1 rm -f *.log00.log *.screen.log *.best*.tre *.best*.tre.phy *.boot.tre *.boot.phy *treelog00.tre *treelog00.log *problog00.log *fate00.log .*lock* *swaplog* *.check out.* qout.* mpi_m* *SiteLikes.log *sitelikes.log *best.all.phy *best.phy *current.phy *internalstates.log echo "Linking to data ...." if [ -d data ];then echo "data folder already exists" else ln -sf $TESTS_DIR/data | exit 1 fi echo "**************************" echo "Running internal tests ..." echo "**************************" if [ -d $TESTS_DIR/internal ];then for i in $TESTS_DIR/internal/*.conf do base=${i/*\/} base=${base/.conf/} echo "Running internal test $i" echo "Running internal test $i" >&2 $GARLI_BIN -t $i $GARLI_ARGS if [[ ! $? -eq 0 && ! -n "$NO_EXIT_ON_ERR" ]];then exit 1 fi done else echo "No internal tests found ..." fi echo "**************************" echo "Running scoring tests ..." echo "**************************" if [ -d $TESTS_DIR/scoring ];then for i in $TESTS_DIR/scoring/*.conf do if [ -f $i ];then base=${i/*\/} base=${base/.conf/} echo "Running scoring test $i" echo "Running scoring test $i" >&2 $GARLI_BIN $i $GARLI_ARGS if [[ ! $? -eq 0 && ! -n "$NO_EXIT_ON_ERR" ]];then exit 1 fi #figure out what precision we can expect if [ ! `grep "likelihood precision" scr.$base.screen.log | wc -l` -eq 0 ] then allowed=`grep "likelihood precision" scr.$base.screen.log | awk '{print $6}'` else #for partitioned models allow a bit more scoring leeway if test "${base:0:2}" = "p." then allowed=0.05 else allowed=0.01 fi fi #sum up the individual site likes and the full likelihood, both appearing #in column 2. Divide by 2 for the full like. Thus, this tests that both #general scoring and sitelike output are correct. sum=`awk '{sum+=$2}END{printf("%.5f", sum)}' scr.$base.sitelikes.log` score=`echo "scale=5; $sum / 2.0" | bc` #score=`tail -1 scr.$base.sitelikes.log | awk '{print $2}'` expect=`grep $base.conf data/expected.scr | awk '{print $2}'` diff=`echo \($score\) - \($expect\) | bc` echo ***********TEST************** echo ***Score is $score echo ***Expected is $expect echo ***SCORE DIFFERENCE IS $diff echo ***ALLOWED ERROR IS $allowed OK=`echo "$diff < $allowed && $diff > -$allowed" | bc` if (( $OK )) then echo "***Scoring OK for $i ***" else echo "***Scoring test failed for $i ***" if [[ ! -n "$NO_EXIT_ON_ERR" ]];then exit 1 fi fi fi done else echo "No scoring tests found ..." fi echo "**************************" echo "Running constraint tests ..." echo "**************************" if [ -d $TESTS_DIR/const ];then for i in $TESTS_DIR/const/*.conf do base=${i/*\/} base=${base/.conf/} echo "Running constraint test $base" echo "Running constraint test $base" >&2 $GARLI_BIN $i $GARLI_ARGS if [[ ! $? -eq 0 && ! -n "$NO_EXIT_ON_ERR" ]];then exit 1 fi #NEXUSvalidator gives a warning every time it reads a tree file #without a taxa block. So, shut it up initially and then if it #fails let it output whatever error $NEXUS_VAL con.$base*.tre 2> /dev/null if [ $? -eq 0 ] then echo TREEFILES PASS else $NEXUS_VAL con.$base*.tre if [[ ! $? -eq 0 && ! -n "$NO_EXIT_ON_ERR" ]];then exit 1 fi fi done else echo "No constraint tests found ..." fi echo "**************************" echo "Running output tests ..." echo "**************************" if [ -d $TESTS_DIR/output ];then for i in $TESTS_DIR/output/*.conf do base=${i/*\/} base=${base/.conf/} echo "Running output test $base" echo "Running output test $base" >&2 $GARLI_BIN $i $GARLI_ARGS if [[ ! $? -eq 0 && ! -n "$NO_EXIT_ON_ERR" ]];then exit 1 fi #NEXUSvalidator gives a warning every time it reads a tree file #without a taxa block. So, shut it up initially and then if it #fails let it output whatever error data=`grep datafname $i | grep -o " data.*$"` TESTNEX=test.out.$base.nex cp $data $TESTNEX cat out.$base.best.tre | grep -iv nexus >> $TESTNEX #$NEXUS_VAL out.$base.best.tre 2> /dev/null #$NEXUS_VAL $TESTNEX 2> /dev/null $NEXUS_VAL $TESTNEX if [ $? -eq 0 ] then echo "TREEFILES PASS" else #$NEXUS_VAL out.$base*.tre $NEXUS_VAL $TESTNEX 2> /dev/null if [[ ! $? -eq 0 && ! -n "$NO_EXIT_ON_ERR" ]];then exit 1 fi fi done else echo "No output tests found ..." fi echo "**************************" echo "Running checkpoint tests ..." echo "**************************" if [ -d $TESTS_DIR/check ];then for i in $TESTS_DIR/check/*.conf do base=${i/*\/} base=${base/.conf/} echo "Running checkpoint test $i" echo "Running checkpoint test $i" >&2 $GARLI_BIN $i $GARLI_ARGS if [[ ! $? -eq 0 && ! -n "$NO_EXIT_ON_ERR" ]];then exit 1 fi $GARLI_BIN $TESTS_DIR/restart/$base.conf $GARLI_ARGS if [[ ! $? -eq 0 && ! -n "$NO_EXIT_ON_ERR" ]];then exit 1 fi #NEXUSvalidator gives a warning every time it reads a tree file #without a taxa block. So, shut it up initially and then if it #fails let it output whatever error $NEXUS_VAL ch.$base*.tre 2> /dev/null if [ $? -eq 0 ];then echo "TREEFILES PASS" else $NEXUS_VAL ch.$base*.tre if [[ ! $? -eq 0 && ! -n "$NO_EXIT_ON_ERR" ]];then exit 1 fi fi done else echo "No checkpoint tests found ..." fi echo "ALL TESTS COMPLETED SUCCESSFULLY" garli-2.1-release/tests/scoring/000077500000000000000000000000001241236125200166745ustar00rootroot00000000000000garli-2.1-release/tests/scoring/a.G3.conf000066400000000000000000000024521241236125200202360ustar00rootroot00000000000000[general] datafname = data/z.11x2178.AA.nex constraintfile = none streefname = data/a.G3.start attachmentspertaxon = 50 ofprefix = scr.a.G3 randseed = -1 availablememory = 512 logevery = 10 saveevery = 100 refinestart = 1 outputeachbettertopology = 0 outputcurrentbesttopology = 0 enforcetermconditions = 1 genthreshfortopoterm = 2000 scorethreshforterm = 0.05 significanttopochange = 0.01 outputphyliptree = 0 outputmostlyuselessfiles = 0 writecheckpoints = 0 restart = 0 outgroup = 1-2 outputsitelikelihoods = 0 collapsebranches = 1 optimizeinputonly = 1 usepatternmanager = 1 searchreps = 1 parametervaluestring = M1 a 0.43677 datatype = aminoacid ratematrix = wag statefrequencies = empirical ratehetmodel = gamma numratecats = 3 invariantsites = none [master] nindivs = 4 holdover = 1 selectionintensity = 0.5 holdoverpenalty = 0 stopgen = 5000000 stoptime = 5000000 startoptprec = 0.5 minoptprec = 0.01 numberofprecreductions = 1 treerejectionthreshold = 50.0 topoweight = 1.0 modweight = 0.05 brlenweight = 0.2 randnniweight = 0.1 randsprweight = 0.3 limsprweight = 0.6 intervallength = 100 intervalstostore = 5 uniqueswapbias = 0.1 distanceswapbias = 1.0 limsprrange = 6 meanbrlenmuts = 5 gammashapebrlen = 1000 gammashapemodel = 1000 bootstrapreps = 0 resampleproportion = 1.0 inferinternalstateprobs = 0 garli-2.1-release/tests/scoring/a.G4.conf000066400000000000000000000024121241236125200202330ustar00rootroot00000000000000[general] datafname = data/z.11x2178.nex constraintfile = none streefname = data/a.G4.start attachmentspertaxon = 50 ofprefix = scr.a.G4 randseed = -1 availablememory = 512 logevery = 10 saveevery = 100 refinestart = 1 outputeachbettertopology = 0 outputcurrentbesttopology = 0 enforcetermconditions = 1 genthreshfortopoterm = 2000 scorethreshforterm = 0.05 significanttopochange = 0.01 outputphyliptree = 0 outputmostlyuselessfiles = 0 writecheckpoints = 0 restart = 0 outgroup = 1-5 outputsitelikelihoods = 0 collapsebranches = 1 optimizeinputonly = 1 usepatternmanager = 1 searchreps = 1 datatype = codon-aminoacid ratematrix = fixed statefrequencies = empirical ratehetmodel = gamma numratecats = 4 invariantsites = none [master] nindivs = 4 holdover = 1 selectionintensity = 0.5 holdoverpenalty = 0 stopgen = 5000000 stoptime = 5000000 startoptprec = 0.5 minoptprec = 0.01 numberofprecreductions = 1 treerejectionthreshold = 50.0 topoweight = 1.0 modweight = 0.05 brlenweight = 0.2 randnniweight = 0.1 randsprweight = 0.3 limsprweight = 0.6 intervallength = 100 intervalstostore = 5 uniqueswapbias = 0.1 distanceswapbias = 1.0 limsprrange = 6 meanbrlenmuts = 5 gammashapebrlen = 1000 gammashapemodel = 1000 bootstrapreps = 0 resampleproportion = 1.0 inferinternalstateprobs = 0 garli-2.1-release/tests/scoring/a.conf000066400000000000000000000023771241236125200177740ustar00rootroot00000000000000[general] datafname = data/z.11x2178.nex constraintfile = none streefname = data/a.start attachmentspertaxon = 50 ofprefix = scr.a randseed = -1 availablememory = 512 logevery = 10 saveevery = 100 refinestart = 1 outputeachbettertopology = 0 outputcurrentbesttopology = 0 enforcetermconditions = 1 genthreshfortopoterm = 2000 scorethreshforterm = 0.05 significanttopochange = 0.01 outputphyliptree = 0 outputmostlyuselessfiles = 0 writecheckpoints = 0 restart = 0 outgroup = 1 outputsitelikelihoods = 0 collapsebranches = 1 optimizeinputonly = 1 usepatternmanager = 1 searchreps = 1 datatype = codon-aminoacid ratematrix = wag statefrequencies = empirical ratehetmodel = none numratecats = 1 invariantsites = none [master] nindivs = 4 holdover = 1 selectionintensity = 0.5 holdoverpenalty = 0 stopgen = 5000000 stoptime = 5000000 startoptprec = 0.5 minoptprec = 0.01 numberofprecreductions = 1 treerejectionthreshold = 50.0 topoweight = 1.0 modweight = 0.05 brlenweight = 0.2 randnniweight = 0.1 randsprweight = 0.3 limsprweight = 0.6 intervallength = 100 intervalstostore = 5 uniqueswapbias = 0.1 distanceswapbias = 1.0 limsprrange = 6 meanbrlenmuts = 5 gammashapebrlen = 1000 gammashapemodel = 1000 bootstrapreps = 0 resampleproportion = 1.0 inferinternalstateprobs = 0 garli-2.1-release/tests/scoring/c.M3x2.conf000066400000000000000000000024431241236125200205200ustar00rootroot00000000000000[general] datafname = data/z.11x2178.nex constraintfile = none streefname = data/c.M3x2.start attachmentspertaxon = 50 ofprefix = scr.c.M3x2 randseed = -1 availablememory = 512 logevery = 10 saveevery = 100 refinestart = 0 outputeachbettertopology = 0 outputcurrentbesttopology = 0 enforcetermconditions = 1 genthreshfortopoterm = 2000 scorethreshforterm = 0.05 significanttopochange = 0.01 outputphyliptree = 0 outputmostlyuselessfiles = 0 writecheckpoints = 0 restart = 0 outgroup = 1-2 outputsitelikelihoods = 0 collapsebranches = 1 optimizeinputonly = 1 usepatternmanager = 1 searchreps = 1 datatype = codon geneticcode = standard ratematrix = fixed statefrequencies = empirical ratehetmodel = nonsynonymous numratecats = 2 invariantsites = none [master] nindivs = 4 holdover = 1 selectionintensity = 0.5 holdoverpenalty = 0 stopgen = 5000000 stoptime = 5000000 startoptprec = 1.0 minoptprec = 0.01 numberofprecreductions = 1 treerejectionthreshold = 50.0 topoweight = 1.0 modweight = 0.05 brlenweight = 0.2 randnniweight = 0.1 randsprweight = 0.3 limsprweight = 0.6 intervallength = 100 intervalstostore = 5 uniqueswapbias = 0.1 distanceswapbias = 1.0 limsprrange = 6 meanbrlenmuts = 5 gammashapebrlen = 1000 gammashapemodel = 1000 bootstrapreps = 0 resampleproportion = 1.0 inferinternalstateprobs = 0 garli-2.1-release/tests/scoring/c.conf000066400000000000000000000024361241236125200177720ustar00rootroot00000000000000[general] datafname = data/z.11x2178.nex constraintfile = none streefname = data/c.start attachmentspertaxon = 50 ofprefix = scr.c randseed = -1 availablememory = 512 logevery = 10 saveevery = 100 refinestart = 0 outputeachbettertopology = 0 outputcurrentbesttopology = 0 enforcetermconditions = 1 genthreshfortopoterm = 2000 scorethreshforterm = 0.05 significanttopochange = 0.01 outputphyliptree = 0 outputmostlyuselessfiles = 0 writecheckpoints = 0 restart = 0 outgroup = 1 2 outputsitelikelihoods = 0 collapsebranches = 1 optimizeinputonly = 1 usepatternmanager = 1 searchreps = 1 datatype = codon geneticcode = standard ratematrix = 6rate statefrequencies = empirical ratehetmodel = nonsynonymousfixed numratecats = 1 invariantsites = none [master] nindivs = 4 holdover = 1 selectionintensity = 0.5 holdoverpenalty = 0 stopgen = 5000000 stoptime = 5000000 startoptprec = 0.5 minoptprec = 0.01 numberofprecreductions = 1 treerejectionthreshold = 50.0 topoweight = 1.0 modweight = 0.05 brlenweight = 0.2 randnniweight = 0.1 randsprweight = 0.3 limsprweight = 0.6 intervallength = 100 intervalstostore = 5 uniqueswapbias = 0.1 distanceswapbias = 1.0 limsprrange = 6 meanbrlenmuts = 5 gammashapebrlen = 1000 gammashapemodel = 1000 bootstrapreps = 0 resampleproportion = 1.0 inferinternalstateprobs = 0 garli-2.1-release/tests/scoring/g.dnaBnoZ.conf000066400000000000000000000027001241236125200213220ustar00rootroot00000000000000[general] datafname = data/dnaGap.8x1K.nex constraintfile = none streefname = data/dnaGap.8x1K.nex attachmentspertaxon = 50 ofprefix = scr.g.dnaBnoZ randseed = -1 availablememory = 512 logevery = 10 saveevery = 100 refinestart = 1 outputeachbettertopology = 0 outputcurrentbesttopology = 0 enforcetermconditions = 1 genthreshfortopoterm = 10000 scorethreshforterm = 0.05 significanttopochange = 0.01 outputphyliptree = 0 outputmostlyuselessfiles = 0 writecheckpoints = 0 restart = 0 outgroup = 1 subsetspecificrates = 1 collapsebranches = 1 usepatternmanager = 1 searchreps = 2 outputsitelikelihoods = 0 optimizeinputonly = 1 [model1] datatype = nucleotide ratematrix = 6rate statefrequencies = estimate ratehetmodel = gamma numratecats = 4 invariantsites = estimate [model2] datatype = binaryNotAllZeros ratematrix = 1rate statefrequencies = equal ratehetmodel = none numratecats = 1 invariantsites = none [master] nindivs = 4 holdover = 1 selectionintensity = 0.5 holdoverpenalty = 0 stopgen = 5000000 stoptime = 5000000 startoptprec = 0.5 minoptprec = 0.01 numberofprecreductions = 2 treerejectionthreshold = 50.0 topoweight = 1.0 modweight = 0.05 brlenweight = 0.2 randnniweight = 0.1 randsprweight = 0.3 limsprweight = 0.6 intervallength = 100 intervalstostore = 5 limsprrange = 6 meanbrlenmuts = 5 gammashapebrlen = 1000 gammashapemodel = 1000 uniqueswapbias = 0.1 distanceswapbias = 1.0 bootstrapreps = 0 resampleproportion = 1.0 inferinternalstateprobs = 0 garli-2.1-release/tests/scoring/g.dnaMix.conf000066400000000000000000000026751241236125200212220ustar00rootroot00000000000000[general] datafname = data/dnaGap.8x1K.nex constraintfile = none streefname = data/dnaGap.8x1K.nex attachmentspertaxon = 50 ofprefix = scr.g.dnaMix randseed = -1 availablememory = 512 logevery = 10 saveevery = 100 refinestart = 1 outputeachbettertopology = 0 outputcurrentbesttopology = 0 enforcetermconditions = 1 genthreshfortopoterm = 10000 scorethreshforterm = 0.05 significanttopochange = 0.01 outputphyliptree = 0 outputmostlyuselessfiles = 0 writecheckpoints = 0 restart = 0 outgroup = 1 subsetspecificrates = 0 collapsebranches = 1 usepatternmanager = 1 searchreps = 2 outputsitelikelihoods = 0 optimizeinputonly = 1 [model1] datatype = nucleotide ratematrix = 6rate statefrequencies = estimate ratehetmodel = gamma numratecats = 4 invariantsites = estimate [model2] datatype = gapmixturemodel ratematrix = 1rate statefrequencies = equal ratehetmodel = none numratecats = 1 invariantsites = none [master] nindivs = 4 holdover = 1 selectionintensity = 0.5 holdoverpenalty = 0 stopgen = 5000000 stoptime = 5000000 startoptprec = 0.5 minoptprec = 0.01 numberofprecreductions = 2 treerejectionthreshold = 50.0 topoweight = 1.0 modweight = 0.05 brlenweight = 0.2 randnniweight = 0.1 randsprweight = 0.3 limsprweight = 0.6 intervallength = 100 intervalstostore = 5 limsprrange = 6 meanbrlenmuts = 5 gammashapebrlen = 1000 gammashapemodel = 1000 uniqueswapbias = 0.1 distanceswapbias = 1.0 bootstrapreps = 0 resampleproportion = 1.0 inferinternalstateprobs = 0 garli-2.1-release/tests/scoring/n.G4.conf000066400000000000000000000024131241236125200202510ustar00rootroot00000000000000[general] datafname = data/z.11x2178.wtset.nex constraintfile = none streefname = data/n.G4.start attachmentspertaxon = 50 ofprefix = scr.n.G4 randseed = -1 availablememory = 512 logevery = 10 saveevery = 100 refinestart = 1 outputeachbettertopology = 0 outputcurrentbesttopology = 0 enforcetermconditions = 1 genthreshfortopoterm = 2000 scorethreshforterm = 0.05 significanttopochange = 0.01 outputphyliptree = 0 outputmostlyuselessfiles = 0 writecheckpoints = 0 restart = 0 outgroup = 2 outputsitelikelihoods = 0 collapsebranches = 1 optimizeinputonly = 1 usepatternmanager = 1 searchreps = 1 datatype = nucleotide ratematrix = 6rate statefrequencies = estimate ratehetmodel = gamma numratecats = 4 invariantsites = estimate [master] nindivs = 4 holdover = 1 selectionintensity = 0.5 holdoverpenalty = 0 stopgen = 5000000 stoptime = 5000000 startoptprec = 0.5 minoptprec = 0.01 numberofprecreductions = 1 treerejectionthreshold = 50.0 topoweight = 1.0 modweight = 0.05 brlenweight = 0.2 randnniweight = 0.1 randsprweight = 0.3 limsprweight = 0.6 intervallength = 100 intervalstostore = 5 limsprrange = 6 meanbrlenmuts = 5 gammashapebrlen = 1000 gammashapemodel = 1000 uniqueswapbias = 0.1 distanceswapbias = 1.0 bootstrapreps = 0 resampleproportion = 1.0 inferinternalstateprobs = 0 garli-2.1-release/tests/scoring/n.G5.conf000066400000000000000000000024131241236125200202520ustar00rootroot00000000000000[general] datafname = data/z.11x2178.wtset.nex constraintfile = none streefname = data/n.G5.start attachmentspertaxon = 50 ofprefix = scr.n.G5 randseed = -1 availablememory = 512 logevery = 10 saveevery = 100 refinestart = 1 outputeachbettertopology = 0 outputcurrentbesttopology = 0 enforcetermconditions = 1 genthreshfortopoterm = 2000 scorethreshforterm = 0.05 significanttopochange = 0.01 outputphyliptree = 0 outputmostlyuselessfiles = 0 writecheckpoints = 0 restart = 0 outgroup = 1 outputsitelikelihoods = 0 collapsebranches = 1 optimizeinputonly = 1 usepatternmanager = 1 searchreps = 1 datatype = nucleotide ratematrix = 6rate statefrequencies = estimate ratehetmodel = gamma numratecats = 5 invariantsites = estimate [master] nindivs = 4 holdover = 1 selectionintensity = 0.5 holdoverpenalty = 0 stopgen = 5000000 stoptime = 5000000 startoptprec = 0.5 minoptprec = 0.01 numberofprecreductions = 1 treerejectionthreshold = 50.0 topoweight = 1.0 modweight = 0.05 brlenweight = 0.2 randnniweight = 0.1 randsprweight = 0.3 limsprweight = 0.6 intervallength = 100 intervalstostore = 5 limsprrange = 6 meanbrlenmuts = 5 gammashapebrlen = 1000 gammashapemodel = 1000 uniqueswapbias = 0.1 distanceswapbias = 1.0 bootstrapreps = 0 resampleproportion = 1.0 inferinternalstateprobs = 0 garli-2.1-release/tests/scoring/n.conf000066400000000000000000000024031241236125200177770ustar00rootroot00000000000000[general] datafname = data/z.11x2178.wtset.nex constraintfile = none streefname = data/n.start attachmentspertaxon = 50 ofprefix = scr.n randseed = -1 availablememory = 512 logevery = 10 saveevery = 100 refinestart = 1 outputeachbettertopology = 0 outputcurrentbesttopology = 0 enforcetermconditions = 1 genthreshfortopoterm = 1000 scorethreshforterm = 0.05 significanttopochange = 0.01 outputphyliptree = 0 outputmostlyuselessfiles = 0 writecheckpoints = 0 restart = 0 outgroup = 1-3 outputsitelikelihoods = 1 collapsebranches = 1 optimizeinputonly = 0 usepatternmanager = 1 searchreps = 1 datatype = nucleotide ratematrix = 6rate statefrequencies = estimate ratehetmodel = none numratecats = 1 invariantsites = none [master] nindivs = 4 holdover = 1 selectionintensity = 0.5 holdoverpenalty = 0 stopgen = 5000000 stoptime = 5000000 startoptprec = 0.01 minoptprec = 0.01 numberofprecreductions = 1 treerejectionthreshold = 50.0 topoweight = 1.0 modweight = 0.05 brlenweight = 0.2 randnniweight = 0.1 randsprweight = 0.3 limsprweight = 0.6 intervallength = 100 intervalstostore = 5 limsprrange = 6 meanbrlenmuts = 5 gammashapebrlen = 1000 gammashapemodel = 1000 uniqueswapbias = 0.1 distanceswapbias = 1.0 bootstrapreps = 0 resampleproportion = 1.0 inferinternalstateprobs = 0 garli-2.1-release/tests/scoring/p.3diff.conf000066400000000000000000000030741241236125200210000ustar00rootroot00000000000000[general] datafname = data/z.byPos.11x2178.nex constraintfile = none streefname = data/p.3diff.start attachmentspertaxon = 50 ofprefix = scr.p.3diff randseed = -1 availablememory = 512 logevery = 10 saveevery = 100 refinestart = 1 outputeachbettertopology = 0 outputcurrentbesttopology = 0 enforcetermconditions = 1 genthreshfortopoterm = 5000 scorethreshforterm = 0.001 significanttopochange = 0.01 outputphyliptree = 0 outputmostlyuselessfiles = 0 writecheckpoints = 0 restart = 0 searchreps = 5 collapsebranches = 1 optimizeinputonly = 1 linkmodels = 0 subsetspecificrates = 1 [model1] datatype = nucleotide ratematrix = ( 0 1 2 2 3 4 ) statefrequencies = estimate ratehetmodel = gamma numratecats = 4 invariantsites = none [model2] datatype = nucleotide ratematrix = ( 0 1 2 1 0 3 ) statefrequencies = estimate ratehetmodel = gamma numratecats = 4 invariantsites = none [model3] datatype = nucleotide ratematrix = ( 0 1 2 3 1 0 ) statefrequencies = estimate ratehetmodel = gamma numratecats = 4 invariantsites = estimate [master] nindivs = 4 holdover = 1 selectionintensity = 0.5 holdoverpenalty = 0 stopgen = 5000000 stoptime = 5000000 startoptprec = 0.5 minoptprec = 0.01 numberofprecreductions = 5 treerejectionthreshold = 50.0 topoweight = 0.01 modweight = 0.002 brlenweight = 0.002 randnniweight = 0.1 randsprweight = 0.3 limsprweight = 0.6 intervallength = 100 intervalstostore = 5 limsprrange = 6 meanbrlenmuts = 5 gammashapebrlen = 1000 gammashapemodel = 1000 uniqueswapbias = 0.1 distanceswapbias = 1.0 bootstrapreps = 0 resampleproportion = 1.0 inferinternalstateprobs = 0 garli-2.1-release/tests/scoring/p.mk.conf000066400000000000000000000024131241236125200204100ustar00rootroot00000000000000[general] datafname = data/L2001.30x52.nex constraintfile = none streefname = data/L.start attachmentspertaxon = 100 ofprefix = scr.p.mk randseed = -1 availablememory = 512 logevery = 10 saveevery = 100 refinestart = 1 outputeachbettertopology = 0 outputcurrentbesttopology = 0 enforcetermconditions = 1 genthreshfortopoterm = 100 scorethreshforterm = 0.001 significanttopochange = 0.01 outputphyliptree = 0 outputmostlyuselessfiles = 0 writecheckpoints = 0 restart = 0 usepatternmanager = 1 searchreps = 5 collapsebranches = 1 linkmodels = 0 subsetspecificrates = 0 optimizeinputonly = 1 [model1] datatype = standard ratematrix = 1rate statefrequencies = equal ratehetmodel = none numratecats = 1 invariantsites = none [master] nindivs = 4 holdover = 1 selectionintensity = 0.5 holdoverpenalty = 0 stopgen = 5000000 stoptime = 5000000 startoptprec = 0.01 minoptprec = 0.01 numberofprecreductions = 10 treerejectionthreshold = 50.0 topoweight = 0.01 modweight = 0.002 brlenweight = 0.002 randnniweight = 0.1 randsprweight = 0.3 limsprweight = 0.6 intervallength = 100 intervalstostore = 5 limsprrange = 6 meanbrlenmuts = 5 gammashapebrlen = 1000 gammashapemodel = 1000 uniqueswapbias = 0.1 distanceswapbias = 1.0 bootstrapreps = 0 resampleproportion = 1.0 inferinternalstateprobs = 0 garli-2.1-release/tests/scoring/p.mk.ssr.conf000066400000000000000000000024171241236125200212220ustar00rootroot00000000000000[general] datafname = data/L2001.30x52.nex constraintfile = none streefname = data/L.start attachmentspertaxon = 100 ofprefix = scr.p.mk.ssr randseed = -1 availablememory = 512 logevery = 10 saveevery = 100 refinestart = 1 outputeachbettertopology = 0 outputcurrentbesttopology = 0 enforcetermconditions = 1 genthreshfortopoterm = 100 scorethreshforterm = 0.001 significanttopochange = 0.01 outputphyliptree = 0 outputmostlyuselessfiles = 0 writecheckpoints = 0 restart = 0 usepatternmanager = 1 searchreps = 5 collapsebranches = 1 linkmodels = 0 subsetspecificrates = 1 optimizeinputonly = 1 [model1] datatype = standard ratematrix = 1rate statefrequencies = equal ratehetmodel = none numratecats = 1 invariantsites = none [master] nindivs = 4 holdover = 1 selectionintensity = 0.5 holdoverpenalty = 0 stopgen = 5000000 stoptime = 5000000 startoptprec = 0.01 minoptprec = 0.01 numberofprecreductions = 10 treerejectionthreshold = 50.0 topoweight = 0.01 modweight = 0.002 brlenweight = 0.002 randnniweight = 0.1 randsprweight = 0.3 limsprweight = 0.6 intervallength = 100 intervalstostore = 5 limsprrange = 6 meanbrlenmuts = 5 gammashapebrlen = 1000 gammashapemodel = 1000 uniqueswapbias = 0.1 distanceswapbias = 1.0 bootstrapreps = 0 resampleproportion = 1.0 inferinternalstateprobs = 0 garli-2.1-release/tests/scoring/p.mkO.conf000066400000000000000000000024231241236125200205300ustar00rootroot00000000000000[general] datafname = data/L2001.30x52.nex constraintfile = none streefname = data/L.start attachmentspertaxon = 100 ofprefix = scr.p.mkO randseed = -1 availablememory = 512 logevery = 10 saveevery = 100 refinestart = 1 outputeachbettertopology = 0 outputcurrentbesttopology = 0 enforcetermconditions = 1 genthreshfortopoterm = 100 scorethreshforterm = 0.001 significanttopochange = 0.01 outputphyliptree = 0 outputmostlyuselessfiles = 0 writecheckpoints = 0 restart = 0 usepatternmanager = 1 searchreps = 5 collapsebranches = 1 linkmodels = 0 subsetspecificrates = 0 optimizeinputonly = 1 [model1] datatype = standardordered ratematrix = 1rate statefrequencies = equal ratehetmodel = none numratecats = 1 invariantsites = none [master] nindivs = 4 holdover = 1 selectionintensity = 0.5 holdoverpenalty = 0 stopgen = 5000000 stoptime = 5000000 startoptprec = 0.01 minoptprec = 0.01 numberofprecreductions = 10 treerejectionthreshold = 50.0 topoweight = 0.01 modweight = 0.002 brlenweight = 0.002 randnniweight = 0.1 randsprweight = 0.3 limsprweight = 0.6 intervallength = 100 intervalstostore = 5 limsprrange = 6 meanbrlenmuts = 5 gammashapebrlen = 1000 gammashapemodel = 1000 uniqueswapbias = 0.1 distanceswapbias = 1.0 bootstrapreps = 0 resampleproportion = 1.0 inferinternalstateprobs = 0 garli-2.1-release/tests/scoring/p.mkO.ssr.conf000066400000000000000000000024271241236125200213420ustar00rootroot00000000000000[general] datafname = data/L2001.30x52.nex constraintfile = none streefname = data/L.start attachmentspertaxon = 100 ofprefix = scr.p.mkO.ssr randseed = -1 availablememory = 512 logevery = 10 saveevery = 100 refinestart = 1 outputeachbettertopology = 0 outputcurrentbesttopology = 0 enforcetermconditions = 1 genthreshfortopoterm = 100 scorethreshforterm = 0.001 significanttopochange = 0.01 outputphyliptree = 0 outputmostlyuselessfiles = 0 writecheckpoints = 0 restart = 0 usepatternmanager = 1 searchreps = 5 collapsebranches = 1 linkmodels = 0 subsetspecificrates = 1 optimizeinputonly = 1 [model1] datatype = standardordered ratematrix = 1rate statefrequencies = equal ratehetmodel = none numratecats = 1 invariantsites = none [master] nindivs = 4 holdover = 1 selectionintensity = 0.5 holdoverpenalty = 0 stopgen = 5000000 stoptime = 5000000 startoptprec = 0.01 minoptprec = 0.01 numberofprecreductions = 10 treerejectionthreshold = 50.0 topoweight = 0.01 modweight = 0.002 brlenweight = 0.002 randnniweight = 0.1 randsprweight = 0.3 limsprweight = 0.6 intervallength = 100 intervalstostore = 5 limsprrange = 6 meanbrlenmuts = 5 gammashapebrlen = 1000 gammashapemodel = 1000 uniqueswapbias = 0.1 distanceswapbias = 1.0 bootstrapreps = 0 resampleproportion = 1.0 inferinternalstateprobs = 0 garli-2.1-release/tests/scoring/p.mkv.conf000066400000000000000000000024241241236125200206000ustar00rootroot00000000000000[general] datafname = data/L2001.30x52.nex constraintfile = none streefname = data/L.start attachmentspertaxon = 100 ofprefix = scr.p.mkv randseed = -1 availablememory = 512 logevery = 10 saveevery = 100 refinestart = 1 outputeachbettertopology = 0 outputcurrentbesttopology = 0 enforcetermconditions = 1 genthreshfortopoterm = 100 scorethreshforterm = 0.001 significanttopochange = 0.01 outputphyliptree = 0 outputmostlyuselessfiles = 0 writecheckpoints = 0 restart = 0 usepatternmanager = 1 searchreps = 5 collapsebranches = 1 linkmodels = 0 subsetspecificrates = 0 optimizeinputonly = 1 [model1] datatype = standardvariable ratematrix = 1rate statefrequencies = equal ratehetmodel = none numratecats = 1 invariantsites = none [master] nindivs = 4 holdover = 1 selectionintensity = 0.5 holdoverpenalty = 0 stopgen = 5000000 stoptime = 5000000 startoptprec = 0.01 minoptprec = 0.01 numberofprecreductions = 10 treerejectionthreshold = 50.0 topoweight = 0.01 modweight = 0.002 brlenweight = 0.002 randnniweight = 0.1 randsprweight = 0.3 limsprweight = 0.6 intervallength = 100 intervalstostore = 5 limsprrange = 6 meanbrlenmuts = 5 gammashapebrlen = 1000 gammashapemodel = 1000 uniqueswapbias = 0.1 distanceswapbias = 1.0 bootstrapreps = 0 resampleproportion = 1.0 inferinternalstateprobs = 0 garli-2.1-release/tests/scoring/p.mkv.ssr.conf000066400000000000000000000024301241236125200214030ustar00rootroot00000000000000[general] datafname = data/L2001.30x52.nex constraintfile = none streefname = data/L.start attachmentspertaxon = 100 ofprefix = scr.p.mkv.ssr randseed = -1 availablememory = 512 logevery = 10 saveevery = 100 refinestart = 1 outputeachbettertopology = 0 outputcurrentbesttopology = 0 enforcetermconditions = 1 genthreshfortopoterm = 100 scorethreshforterm = 0.001 significanttopochange = 0.01 outputphyliptree = 0 outputmostlyuselessfiles = 0 writecheckpoints = 0 restart = 0 usepatternmanager = 1 searchreps = 5 collapsebranches = 1 linkmodels = 0 subsetspecificrates = 1 optimizeinputonly = 1 [model1] datatype = standardvariable ratematrix = 1rate statefrequencies = equal ratehetmodel = none numratecats = 1 invariantsites = none [master] nindivs = 4 holdover = 1 selectionintensity = 0.5 holdoverpenalty = 0 stopgen = 5000000 stoptime = 5000000 startoptprec = 0.01 minoptprec = 0.01 numberofprecreductions = 10 treerejectionthreshold = 50.0 topoweight = 0.01 modweight = 0.002 brlenweight = 0.002 randnniweight = 0.1 randsprweight = 0.3 limsprweight = 0.6 intervallength = 100 intervalstostore = 5 limsprrange = 6 meanbrlenmuts = 5 gammashapebrlen = 1000 gammashapemodel = 1000 uniqueswapbias = 0.1 distanceswapbias = 1.0 bootstrapreps = 0 resampleproportion = 1.0 inferinternalstateprobs = 0 garli-2.1-release/tests/scoring/p.mkvO.conf000066400000000000000000000024341241236125200207200ustar00rootroot00000000000000[general] datafname = data/L2001.30x52.nex constraintfile = none streefname = data/L.start attachmentspertaxon = 100 ofprefix = scr.p.mkvO randseed = -1 availablememory = 512 logevery = 10 saveevery = 100 refinestart = 1 outputeachbettertopology = 0 outputcurrentbesttopology = 0 enforcetermconditions = 1 genthreshfortopoterm = 100 scorethreshforterm = 0.001 significanttopochange = 0.01 outputphyliptree = 0 outputmostlyuselessfiles = 0 writecheckpoints = 0 restart = 0 usepatternmanager = 1 searchreps = 5 collapsebranches = 1 linkmodels = 0 subsetspecificrates = 0 optimizeinputonly = 1 [model1] datatype = standardorderedvariable ratematrix = 1rate statefrequencies = equal ratehetmodel = none numratecats = 1 invariantsites = none [master] nindivs = 4 holdover = 1 selectionintensity = 0.5 holdoverpenalty = 0 stopgen = 5000000 stoptime = 5000000 startoptprec = 0.01 minoptprec = 0.01 numberofprecreductions = 10 treerejectionthreshold = 50.0 topoweight = 0.01 modweight = 0.002 brlenweight = 0.002 randnniweight = 0.1 randsprweight = 0.3 limsprweight = 0.6 intervallength = 100 intervalstostore = 5 limsprrange = 6 meanbrlenmuts = 5 gammashapebrlen = 1000 gammashapemodel = 1000 uniqueswapbias = 0.1 distanceswapbias = 1.0 bootstrapreps = 0 resampleproportion = 1.0 inferinternalstateprobs = 0 garli-2.1-release/tests/scoring/p.mkvO.ssr.conf000066400000000000000000000024401241236125200215230ustar00rootroot00000000000000[general] datafname = data/L2001.30x52.nex constraintfile = none streefname = data/L.start attachmentspertaxon = 100 ofprefix = scr.p.mkvO.ssr randseed = -1 availablememory = 512 logevery = 10 saveevery = 100 refinestart = 1 outputeachbettertopology = 0 outputcurrentbesttopology = 0 enforcetermconditions = 1 genthreshfortopoterm = 100 scorethreshforterm = 0.001 significanttopochange = 0.01 outputphyliptree = 0 outputmostlyuselessfiles = 0 writecheckpoints = 0 restart = 0 usepatternmanager = 1 searchreps = 5 collapsebranches = 1 linkmodels = 0 subsetspecificrates = 1 optimizeinputonly = 1 [model1] datatype = standardorderedvariable ratematrix = 1rate statefrequencies = equal ratehetmodel = none numratecats = 1 invariantsites = none [master] nindivs = 4 holdover = 1 selectionintensity = 0.5 holdoverpenalty = 0 stopgen = 5000000 stoptime = 5000000 startoptprec = 0.01 minoptprec = 0.01 numberofprecreductions = 10 treerejectionthreshold = 50.0 topoweight = 0.01 modweight = 0.002 brlenweight = 0.002 randnniweight = 0.1 randsprweight = 0.3 limsprweight = 0.6 intervallength = 100 intervalstostore = 5 limsprrange = 6 meanbrlenmuts = 5 gammashapebrlen = 1000 gammashapemodel = 1000 uniqueswapbias = 0.1 distanceswapbias = 1.0 bootstrapreps = 0 resampleproportion = 1.0 inferinternalstateprobs = 0 garli-2.1-release/tests/scoring/runScoring.sh000077500000000000000000000010541241236125200213640ustar00rootroot00000000000000#!/bin/bash echo $score expect=14498.88532 echo $expect line=1 for i in *.conf do $TESTING_EXEC $i score=`tail -1 scr.${i%.conf}.sitelikes.log | awk '{print $2}'` expect=`head -n$line data/expected.scr | tail -n1` diff=`echo \($score\) - \($expect\) | bc` echo ***********TEST************** echo ***Score is $score echo ***Excpected is $expect echo ***SCORE DIFFERENCE IS $diff OK=`echo "$diff < 0.01 && $diff > -0.01" | bc` if (( $OK )) then echo ***Score OK*** else echo ***Score Not OK*** exit 1 fi line=`expr $line + 1` done