pax_global_header00006660000000000000000000000064133031546320014512gustar00rootroot0000000000000052 comment=614ec6f326c15a3d00bed3069b454f03351fe1fe ExaML-3.0.21/000077500000000000000000000000001330315463200125435ustar00rootroot00000000000000ExaML-3.0.21/.gitignore000066400000000000000000000001351330315463200145320ustar00rootroot00000000000000*.o parser/parser parser/parse-examl examl-KNL examl-MIC examl-AVX examl-OMP-AVX examl-OMP ExaML-3.0.21/README.md000066400000000000000000000022321330315463200140210ustar00rootroot00000000000000ExaML ===== Exascale Maximum Likelihood (ExaML) code for phylogenetic inference using MPI. This code implements the popular RAxML search algorithm for maximum likelihood based inference of phylogenetic trees. It uses a radically new MPI parallelization approach that yields improved parallel efficiency, in particular on partitioned multi-gene or whole-genome datasets. When using ExaML please cite the following paper: Alexey M. Kozlov, Andre J. Aberer, Alexandros Stamatakis: "ExaML Version 3: A Tool for Phylogenomic Analyses on Supercomputers." Bioinformatics (2015) 31 (15): 2577-2579. It is up to 4 times faster than RAxML-Light [1]. As RAxML-Light, ExaML also implements checkpointing, SSE3, AVX vectorization and memory saving techniques. [1] A. Stamatakis, A.J. Aberer, C. Goll, S.A. Smith, S.A. Berger, F. Izquierdo-Carrasco: "RAxML-Light: A Tool for computing TeraByte Phylogenies", Bioinformatics 2012; doi: 10.1093/bioinformatics/bts309. Intel Xeon Phi -------------- For details on running ExaML on Intel Xeon Phi, please refer to: - Knights Landing: [README_KNL.txt](README_KNL.txt) - Knights Corner: [README_MIC.txt](README_MIC.txt) ExaML-3.0.21/README_KNL.txt000066400000000000000000000057571330315463200147630ustar00rootroot00000000000000Using ExaML on the Intel Xeon Phi (Knights Landing) coprocessors Compiling under Linux --------------------- Please set your MPI/MIC environment (ask your sysadmin if unsure) and then run: cd examl make -f Makefile.KNL.icc This will create an executable named examl-KNL. Running ---------------------- 1. Use parse-examl to generate a binary alignment file as usual 2. You can run examl-KNL in OpenMP-only, MPI-only or in hybrid OpenMP/MPI mode (s. sample commands below). Unlike with KNC, I didn't notice any significant performance differences between those three configurations, so just choose whatever is easier/more convenient: OpenMP: OMP_NUM_THREADS=128 ./examl-KNL -s myTest.binary -m GAMMA -t myStart.tre -n myTest MPI: mpirun -n 128 -env OMP_NUM_THREADS 1 ./examl-KNL -s myTest.binary -m GAMMA -t myStart.tre -n myTest Hybrid: mpirun -n 8 -env OMP_NUM_THREADS 16 ./examl-KNL -s myTest.binary -m GAMMA -t myStart.tre -n myTest NOTE: although KNL has 4 logical threads/core, it usually doesn't make sense to use more then 2 threads/core with ExaML (since ExaML doesn't benefit for hyper-threading). Furthermore, please consider the general recommendations regarding number of alignment patterns per core given in the ExaML manual. 3. IMPORTANT: KNL on-card memory can be configured in one of two modes: "Flat" or "Cache". You should find out which one is set on you card(s), since it has important performance implications. - in "Cache" mode, no - in "Flat" mode, you should use numactl to explicitly bind ExaML process to the fast memory NUMA domain: numactl --membind=1 mpirun -n 128 -env OMP_NUM_THREADS 1 ./examl-KNL -s myTest.binary -m GAMMA -t myStart.tre -n myTest Obviously, it is not possible if memory requirements for your analysis exceed the on-card memory size (typically 16GB). In this case, you should either switch into "Cache" mode, or let ExaML run in the (slow) main memory. The latter option will typically induce a huge performance penalty (up to 5x), and thus is not recommended. Limitations & caveats --------------------- 1. Supported on the MIC: + DNA and AA alignments + GAMMA model of rate heterogeneity + multiple partitions + all AA substitution matrices supported by ExaML, including LG4 2. Currently NOT supported: - binary and generic multi-state alignments - PSR model - memory saving for gappy alignments (-S option) 3. Performance ExaML-MIC performs best on alignments with large number of sites and few taxa. The latter is due to the limited on-card memory of the MICs (s. above), so you might need to use multiple cards if the number of taxa is large. For details, please refer to: http://www.hicomb.org/papers/HICOMB2014-04.pdf and https://doi.org/10.1093/bioinformatics/btv184 Contact & Support -------------------- Please use RAxML google group to ask questions: https://groups.google.com/forum/?hl=en#!forum/raxml ExaML-3.0.21/README_MIC.txt000066400000000000000000000055211330315463200147340ustar00rootroot00000000000000Using ExaML on the Intel MIC/Intel Xeon Phi coprocessors Compiling under Linux --------------------- Please set your MPI/MIC environment (ask your sysadmin if unsure) and then run: make -f Makefile.AVX.gcc make -f Makefile.MIC.icc clean make -f Makefile.MIC.icc This will create two executables for both host(=CPU) and MIC - they will be named examl-AVX and examl-MIC, respectively. Running ---------------------- 1. Use parse-examl to generate a binary alignment file as usual. 2. You might want to allocate MPI ranks on both host CPUs and MICs (hybrid mode) or just on the MICs, depending on your configuration. Sample command line for running ExaML in hybrid mode (16 CPU core + 2 MIC cards): mpiexec -host myhost-ib -n 16 /scratch/examl-AVX -n mictest -s /scratch/mictest.binary -t /scratch/start.tre -m GAMMA -w /scratch : \ -host myhost-mic0 -n 30 -env OMP_NUM_THREADS 4 -env KMP_AFFINITY "granularity=fine,balanced" /scratch/examl-MIC -n mictest \ -s /scratch/mictest.binary -t /scratch/start.tre -m GAMMA -w /scratch : \ -host myhost-mic1 -n 30 -env OMP_NUM_THREADS 4 -env KMP_AFFINITY "granularity=fine,balanced" /scratch/examl-MIC -n mictest \ -s /scratch/mictest.binary -t /scratch/start.tre -m GAMMA -w /scratch Here, we use 1 MPI rank per core on the host CPUs. On each MIC, we start 30 ranks x 4 OpenMP threads, which gives 120 threads in total or 2 threads per MIC core. Changing the ratio of CPU:MIC ranks allows to fine-tune load balance for the specific hardware configuration at hand. Limitations & caveats --------------------- 1. Supported on the MIC: + DNA and AA alignments + GAMMA model of rate heterogeneity + multiple partitions + all AA substitution matrices supported by ExaML, including LG4 2. Currently NOT supported: - binary and generic multi-state alignments - PSR model - memory saving for gappy alignments (-S option) 3. Memory Compared to traditional CPUs, MIC cards have significantly lower memory-per-core value, which poses a problem for memory-intensive ML computations. Thus you should plan carefully and split your run over multiple cards, if needed. To estimate memory requirements for your dataset, you can use the web-calculator here: http://sco.h-its.org/exelixis/web/software/raxml/index.html#memcalc A similar tool tailored for MICs is coming soon, stay tuned :) 4. Performance ExaML-MIC performs best on alignments with large number of sites and few taxa. The latter is due to the limited on-card memory of the MICs (s. above), so you might need to use multiple cards if the number of taxa is large. For details, please refer to: http://www.hicomb.org/papers/HICOMB2014-04.pdf Contact & Support -------------------- Please use RAxML google group to ask questions: https://groups.google.com/forum/?hl=en#!forum/raxml ExaML-3.0.21/codeDocumentation/000077500000000000000000000000001330315463200162075ustar00rootroot00000000000000ExaML-3.0.21/codeDocumentation/PSR.txt000066400000000000000000000002551330315463200174160ustar00rootroot00000000000000To disable per-site rate category scaling in ExaML it suffices to comment out the function invocations for: updatePerSiteRates() and checkPerSiteRates() in the source code. 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read header, taxa, partitions into ByteFile struct(use seekPos() to navigate in bytefile) PartitionAssignment *pAss 2. every process computes partition assignment partitions[0].yVectorpartitions[0].wgtpartitions[4].yVectorpartitions[4].wgt 3. process only reads data assigned to it (exa_fread/exa_fseek) 4. tree struct is initialized; bFile and pAss are deleted tree *tr ....pInfo *partitionData....Assign* assignments X X ExaML-3.0.21/examl/000077500000000000000000000000001330315463200136515ustar00rootroot00000000000000ExaML-3.0.21/examl/Makefile.AVX.gcc000066400000000000000000000050101330315463200164750ustar00rootroot00000000000000# Makefile August 2006 by Alexandros Stamatakis # Makefile cleanup October 2006, Courtesy of Peter Cordes CC = mpicc COMMON_FLAGS = -D__SIM_SSE3 -D__AVX -D_OPTIMIZED_FUNCTIONS -msse3 -D_GNU_SOURCE -fomit-frame-pointer -funroll-loops -D_USE_ALLREDUCE #-Wall -Wredundant-decls -Wreturn-type -Wswitch-default -Wunused-value -Wimplicit -Wimplicit-function-declaration -Wimplicit-int -Wimport -Wunused -Wunused-function -Wunused-label -Wno-int-to-pointer-cast -Wbad-function-cast -Wmissing-declarations -Wmissing-prototypes -Wnested-externs -Wold-style-definition -Wstrict-prototypes -Wpointer-sign -Wextra -Wredundant-decls -Wunused -Wunused-function -Wunused-parameter -Wunused-value -Wunused-variable -Wformat -Wformat-nonliteral -Wparentheses -Wsequence-point -Wuninitialized -Wundef -Wbad-function-cast -Wno-unused-parameter OPT_FLAG_1 = -O1 OPT_FLAG_2 = -O2 CFLAGS = $(COMMON_FLAGS) $(OPT_FLAG_2) LIBRARIES = -lm -mavx RM = rm -f objs = axml.o optimizeModel.o trash.o searchAlgo.o topologies.o treeIO.o models.o evaluatePartialGenericSpecial.o evaluateGenericSpecial.o newviewGenericSpecial.o makenewzGenericSpecial.o bipartitionList.o restartHashTable.o avxLikelihood.o byteFile.o partitionAssignment.o communication.o quartets.o all : clean examl-AVX GLOBAL_DEPS = axml.h globalVariables.h ../versionHeader/version.h examl-AVX : $(objs) $(CC) -o examl-AVX $(objs) $(LIBRARIES) avxLikelihood.o : avxLikelihood.c $(GLOBAL_DEPS) $(CC) $(CFLAGS) -mavx -c -o avxLikelihood.o avxLikelihood.c models.o : models.c $(GLOBAL_DEPS) $(CC) $(COMMON_FLAGS) $(OPT_FLAG_1) -c -o models.o models.c bipartitionList.o : bipartitionList.c $(GLOBAL_DEPS) evaluatePartialSpecialGeneric.o : evaluatePartialSpecialGeneric.c $(GLOBAL_DEPS) optimizeModel.o : optimizeModel.c $(GLOBAL_DEPS) trash.o : trash.c $(GLOBAL_DEPS) axml.o : axml.c $(GLOBAL_DEPS) searchAlgo.o : searchAlgo.c $(GLOBAL_DEPS) topologies.o : topologies.c $(GLOBAL_DEPS) treeIO.o : treeIO.c $(GLOBAL_DEPS) quartets.o : quartets.c $(GLOBAL_DEPS) evaluatePartialGenericSpecial.o : evaluatePartialGenericSpecial.c $(GLOBAL_DEPS) evaluateGenericSpecial.o : evaluateGenericSpecial.c $(GLOBAL_DEPS) newviewGenericSpecial.o : newviewGenericSpecial.c $(GLOBAL_DEPS) makenewzGenericSpecial.o : makenewzGenericSpecial.c $(GLOBAL_DEPS) restartHashTable.o : restartHashTable.c $(GLOBAL_DEPS) byteFile.o : byteFile.c partitionAssignment.o : partitionAssignment.c $(GLOBAL_DEPS) communication.o : communication.c $(GLOBAL_DEPS) clean : $(RM) *.o examl-AVX dev : examl-AVX ExaML-3.0.21/examl/Makefile.KNL.icc000066400000000000000000000054231330315463200164750ustar00rootroot00000000000000# Makefile August 2006 by Alexandros Stamatakis # Makefile cleanup October 2006, Courtesy of Peter Cordes CC = mpicc #MICFLAGS = -D__MIC_NATIVE -mmic -opt-streaming-cache-evict=0 -openmp -D_USE_OMP #-D_PROFILE_MPI KNLFLAGS=-xMIC-AVX512 -fma -align -finline-functions -D__MIC_NATIVE -qopenmp -D_USE_OMP COMMON_FLAGS = -std=c99 -D__SIM_SSE3 -D_OPTIMIZED_FUNCTIONS -D_GNU_SOURCE -fomit-frame-pointer -funroll-loops -D_USE_ALLREDUCE $(KNLFLAGS) # -Wall -Wredundant-decls -Wreturn-type -Wswitch-default -Wunused-value -Wimplicit -Wimplicit-function-declaration -Wimplicit-int -Wimport -Wunused -Wunused-function -Wunused-label -Wno-int-to-pointer-cast -Wbad-function-cast -Wmissing-declarations -Wmissing-prototypes -Wnested-externs -Wold-style-definition -Wstrict-prototypes -Wpointer-sign -Wextra -Wredundant-decls -Wunused -Wunused-function -Wunused-parameter -Wunused-value -Wunused-variable -Wformat -Wformat-nonliteral -Wparentheses -Wsequence-point -Wuninitialized -Wundef -Wbad-function-cast -Wno-unused-parameter OPT_FLAG_1 = -O1 OPT_FLAG_2 = -O2 CFLAGS = $(COMMON_FLAGS) $(OPT_FLAG_2) LIBRARIES = -lm -qopenmp #-mmic RM = rm -f objs = axml.o optimizeModel.o trash.o searchAlgo.o topologies.o treeIO.o models.o evaluatePartialGenericSpecial.o evaluateGenericSpecial.o newviewGenericSpecial.o makenewzGenericSpecial.o bipartitionList.o restartHashTable.o byteFile.o partitionAssignment.o communication.o mic_native_dna.o mic_native_aa.o quartets.o all : clean examl-KNL GLOBAL_DEPS = axml.h globalVariables.h examl-KNL : $(objs) $(CC) -o examl-KNL $(objs) $(LIBRARIES) models.o : models.c $(GLOBAL_DEPS) $(CC) $(COMMON_FLAGS) $(OPT_FLAG_1) -c -o models.o models.c partitionAssignment.o: partitionAssignment.o $(GLOBAL_DEPS) $(CC) $(COMMON_FLAGS) $(OPT_FLAG_1) -c -o partitionAssignment.o partitionAssignment.c bipartitionList.o : bipartitionList.c $(GLOBAL_DEPS) evaluatePartialSpecialGeneric.o : evaluatePartialSpecialGeneric.c $(GLOBAL_DEPS) optimizeModel.o : optimizeModel.c $(GLOBAL_DEPS) trash.o : trash.c $(GLOBAL_DEPS) axml.o : axml.c $(GLOBAL_DEPS) searchAlgo.o : searchAlgo.c $(GLOBAL_DEPS) topologies.o : topologies.c $(GLOBAL_DEPS) treeIO.o : treeIO.c $(GLOBAL_DEPS) evaluatePartialGenericSpecial.o : evaluatePartialGenericSpecial.c $(GLOBAL_DEPS) evaluateGenericSpecial.o : evaluateGenericSpecial.c $(GLOBAL_DEPS) newviewGenericSpecial.o : newviewGenericSpecial.c $(GLOBAL_DEPS) makenewzGenericSpecial.o : makenewzGenericSpecial.c $(GLOBAL_DEPS) restartHashTable.o : restartHashTable.c $(GLOBAL_DEPS) byteFile.o : byteFile.c communication.o : communication.c $(GLOBAL_DEPS) mic_native_dna.o : mic_native_dna.c $(GLOBAL_DEPS) mic_native_aa.o : mic_native_aa.c $(GLOBAL_DEPS) quartets.o : quartets.c $(GLOBAL_DEPS) clean : $(RM) *.o examl-KNL dev : examl-KNL ExaML-3.0.21/examl/Makefile.MIC.icc000066400000000000000000000052701330315463200164610ustar00rootroot00000000000000# Makefile August 2006 by Alexandros Stamatakis # Makefile cleanup October 2006, Courtesy of Peter Cordes CC = mpicc MICFLAGS = -D__MIC_NATIVE -mmic -qopt-streaming-cache-evict=0 -qopenmp -D_USE_OMP #-D_PROFILE_MPI COMMON_FLAGS = -std=c99 -D__SIM_SSE3 -D_OPTIMIZED_FUNCTIONS -D_GNU_SOURCE -fomit-frame-pointer -funroll-loops -D_USE_ALLREDUCE $(MICFLAGS) # -Wall -Wredundant-decls -Wreturn-type -Wswitch-default -Wunused-value -Wimplicit -Wimplicit-function-declaration -Wimplicit-int -Wimport -Wunused -Wunused-function -Wunused-label -Wno-int-to-pointer-cast -Wbad-function-cast -Wmissing-declarations -Wmissing-prototypes -Wnested-externs -Wold-style-definition -Wstrict-prototypes -Wpointer-sign -Wextra -Wredundant-decls -Wunused -Wunused-function -Wunused-parameter -Wunused-value -Wunused-variable -Wformat -Wformat-nonliteral -Wparentheses -Wsequence-point -Wuninitialized -Wundef -Wbad-function-cast -Wno-unused-parameter OPT_FLAG_1 = -O1 OPT_FLAG_2 = -O2 CFLAGS = $(COMMON_FLAGS) $(OPT_FLAG_2) LIBRARIES = -lm -mmic -openmp RM = rm -f objs = axml.o optimizeModel.o trash.o searchAlgo.o topologies.o treeIO.o models.o evaluatePartialGenericSpecial.o evaluateGenericSpecial.o newviewGenericSpecial.o makenewzGenericSpecial.o bipartitionList.o restartHashTable.o byteFile.o partitionAssignment.o communication.o mic_native_dna.o mic_native_aa.o quartets.o all : clean examl-MIC GLOBAL_DEPS = axml.h globalVariables.h examl-MIC : $(objs) $(CC) -o examl-MIC $(objs) $(LIBRARIES) models.o : models.c $(GLOBAL_DEPS) $(CC) $(COMMON_FLAGS) $(OPT_FLAG_1) -c -o models.o models.c partitionAssignment.o: partitionAssignment.o $(GLOBAL_DEPS) $(CC) $(COMMON_FLAGS) $(OPT_FLAG_1) -c -o partitionAssignment.o partitionAssignment.c bipartitionList.o : bipartitionList.c $(GLOBAL_DEPS) evaluatePartialSpecialGeneric.o : evaluatePartialSpecialGeneric.c $(GLOBAL_DEPS) optimizeModel.o : optimizeModel.c $(GLOBAL_DEPS) trash.o : trash.c $(GLOBAL_DEPS) axml.o : axml.c $(GLOBAL_DEPS) searchAlgo.o : searchAlgo.c $(GLOBAL_DEPS) topologies.o : topologies.c $(GLOBAL_DEPS) treeIO.o : treeIO.c $(GLOBAL_DEPS) evaluatePartialGenericSpecial.o : evaluatePartialGenericSpecial.c $(GLOBAL_DEPS) evaluateGenericSpecial.o : evaluateGenericSpecial.c $(GLOBAL_DEPS) newviewGenericSpecial.o : newviewGenericSpecial.c $(GLOBAL_DEPS) makenewzGenericSpecial.o : makenewzGenericSpecial.c $(GLOBAL_DEPS) restartHashTable.o : restartHashTable.c $(GLOBAL_DEPS) byteFile.o : byteFile.c communication.o : communication.c $(GLOBAL_DEPS) mic_native_dna.o : mic_native_dna.c $(GLOBAL_DEPS) mic_native_aa.o : mic_native_aa.c $(GLOBAL_DEPS) quartets.o : quartets.c $(GLOBAL_DEPS) clean : $(RM) *.o examl-MIC dev : examl-MIC ExaML-3.0.21/examl/Makefile.OMP.AVX.gcc000066400000000000000000000050721330315463200171370ustar00rootroot00000000000000# Makefile August 2006 by Alexandros Stamatakis # Makefile cleanup October 2006, Courtesy of Peter Cordes CC = mpicc COMMON_FLAGS = -D__SIM_SSE3 -D__AVX -D_USE_OMP -fopenmp -D_OPTIMIZED_FUNCTIONS -msse3 -D_GNU_SOURCE -fomit-frame-pointer -funroll-loops -D_USE_ALLREDUCE -Wall # -Wredundant-decls -Wreturn-type -Wswitch-default -Wunused-value -Wimplicit -Wimplicit-function-declaration -Wimplicit-int -Wimport -Wunused -Wunused-function -Wunused-label -Wno-int-to-pointer-cast -Wbad-function-cast -Wmissing-declarations -Wmissing-prototypes -Wnested-externs -Wold-style-definition -Wstrict-prototypes -Wpointer-sign -Wextra -Wredundant-decls -Wunused -Wunused-function -Wunused-parameter -Wunused-value -Wunused-variable -Wformat -Wformat-nonliteral -Wparentheses -Wsequence-point -Wuninitialized -Wundef -Wbad-function-cast -Wno-unused-parameter OPT_FLAG_1 = -O1 OPT_FLAG_2 = -O2 CFLAGS = $(COMMON_FLAGS) $(OPT_FLAG_2) LIBRARIES = -lm -mavx -fopenmp RM = rm -f objs = axml.o optimizeModel.o trash.o searchAlgo.o topologies.o treeIO.o models.o evaluatePartialGenericSpecial.o evaluateGenericSpecial.o newviewGenericSpecial.o makenewzGenericSpecial.o bipartitionList.o restartHashTable.o avxLikelihood.o byteFile.o partitionAssignment.o communication.o quartets.o all : clean examl-OMP-AVX GLOBAL_DEPS = axml.h globalVariables.h ../versionHeader/version.h examl-OMP-AVX : $(objs) $(CC) -o examl-OMP-AVX $(objs) $(LIBRARIES) avxLikelihood.o : avxLikelihood.c $(GLOBAL_DEPS) $(CC) $(CFLAGS) -mavx -c -o avxLikelihood.o avxLikelihood.c models.o : models.c $(GLOBAL_DEPS) $(CC) $(COMMON_FLAGS) $(OPT_FLAG_1) -c -o models.o models.c bipartitionList.o : bipartitionList.c $(GLOBAL_DEPS) evaluatePartialSpecialGeneric.o : evaluatePartialSpecialGeneric.c $(GLOBAL_DEPS) optimizeModel.o : optimizeModel.c $(GLOBAL_DEPS) trash.o : trash.c $(GLOBAL_DEPS) axml.o : axml.c $(GLOBAL_DEPS) searchAlgo.o : searchAlgo.c $(GLOBAL_DEPS) topologies.o : topologies.c $(GLOBAL_DEPS) treeIO.o : treeIO.c $(GLOBAL_DEPS) quartets.o : quartets.c $(GLOBAL_DEPS) evaluatePartialGenericSpecial.o : evaluatePartialGenericSpecial.c $(GLOBAL_DEPS) evaluateGenericSpecial.o : evaluateGenericSpecial.c $(GLOBAL_DEPS) newviewGenericSpecial.o : newviewGenericSpecial.c $(GLOBAL_DEPS) makenewzGenericSpecial.o : makenewzGenericSpecial.c $(GLOBAL_DEPS) restartHashTable.o : restartHashTable.c $(GLOBAL_DEPS) byteFile.o : byteFile.c partitionAssignment.o : partitionAssignment.c $(GLOBAL_DEPS) communication.o : communication.c $(GLOBAL_DEPS) clean : $(RM) *.o examl-OMP-AVX dev : examl-OMP-AVX ExaML-3.0.21/examl/Makefile.OMP.SSE3.gcc000066400000000000000000000047141330315463200172200ustar00rootroot00000000000000# Makefile August 2006 by Alexandros Stamatakis # Makefile cleanup October 2006, Courtesy of Peter Cordes CC = mpicc COMMON_FLAGS = -D_USE_OMP -fopenmp -D_GNU_SOURCE -D__SIM_SSE3 -msse3 -fomit-frame-pointer -funroll-loops -D_OPTIMIZED_FUNCTIONS -D_USE_ALLREDUCE -Wall #-Wunused-parameter -Wredundant-decls -Wreturn-type -Wswitch-default -Wunused-value -Wimplicit -Wimplicit-function-declaration -Wimplicit-int -Wimport -Wunused -Wunused-function -Wunused-label -Wno-int-to-pointer-cast -Wbad-function-cast -Wmissing-declarations -Wmissing-prototypes -Wnested-externs -Wold-style-definition -Wstrict-prototypes -Wpointer-sign -Wextra -Wredundant-decls -Wunused -Wunused-function -Wunused-parameter -Wunused-value -Wunused-variable -Wformat -Wformat-nonliteral -Wparentheses -Wsequence-point -Wuninitialized -Wundef -Wbad-function-cast -Wno-unused-parameter OPT_FLAG_1 = -O1 OPT_FLAG_2 = -O2 CFLAGS = $(COMMON_FLAGS) $(OPT_FLAG_2) LIBRARIES = -lm -fopenmp RM = rm -f objs = axml.o optimizeModel.o trash.o searchAlgo.o topologies.o treeIO.o models.o evaluatePartialGenericSpecial.o evaluateGenericSpecial.o newviewGenericSpecial.o makenewzGenericSpecial.o bipartitionList.o restartHashTable.o byteFile.o partitionAssignment.o communication.o quartets.o all : clean examl-OMP GLOBAL_DEPS = axml.h globalVariables.h ../versionHeader/version.h examl-OMP : $(objs) $(CC) -o examl-OMP $(objs) $(LIBRARIES) models.o : models.c $(GLOBAL_DEPS) $(CC) $(COMMON_FLAGS) $(OPT_FLAG_1) -c -o models.o models.c bipartitionList.o : bipartitionList.c $(GLOBAL_DEPS) evaluatePartialSpecialGeneric.o : evaluatePartialSpecialGeneric.c $(GLOBAL_DEPS) optimizeModel.o : optimizeModel.c $(GLOBAL_DEPS) trash.o : trash.c $(GLOBAL_DEPS) axml.o : axml.c $(GLOBAL_DEPS) searchAlgo.o : searchAlgo.c $(GLOBAL_DEPS) topologies.o : topologies.c $(GLOBAL_DEPS) treeIO.o : treeIO.c $(GLOBAL_DEPS) models.o : models.c $(GLOBAL_DEPS) evaluatePartialGenericSpecial.o : evaluatePartialGenericSpecial.c $(GLOBAL_DEPS) evaluateGenericSpecial.o : evaluateGenericSpecial.c $(GLOBAL_DEPS) newviewGenericSpecial.o : newviewGenericSpecial.c $(GLOBAL_DEPS) makenewzGenericSpecial.o : makenewzGenericSpecial.c $(GLOBAL_DEPS) restartHashTable.o : restartHashTable.c $(GLOBAL_DEPS) byteFile.o : byteFile.c partitionAssignment.o : partitionAssignment.c $(GLOBAL_DEPS) communication.o : communication.c $(GLOBAL_DEPS) quartets.o : quartets.c $(GLOBAL_DEPS) clean : $(RM) *.o examl-OMP dev : examl-OMP ExaML-3.0.21/examl/Makefile.SSE3.gcc000066400000000000000000000046341330315463200165670ustar00rootroot00000000000000# Makefile August 2006 by Alexandros Stamatakis # Makefile cleanup October 2006, Courtesy of Peter Cordes CC = mpicc COMMON_FLAGS = -D_GNU_SOURCE -D__SIM_SSE3 -msse3 -fomit-frame-pointer -funroll-loops -D_OPTIMIZED_FUNCTIONS -D_USE_ALLREDUCE #-Wall -Wunused-parameter -Wredundant-decls -Wreturn-type -Wswitch-default -Wunused-value -Wimplicit -Wimplicit-function-declaration -Wimplicit-int -Wimport -Wunused -Wunused-function -Wunused-label -Wno-int-to-pointer-cast -Wbad-function-cast -Wmissing-declarations -Wmissing-prototypes -Wnested-externs -Wold-style-definition -Wstrict-prototypes -Wpointer-sign -Wextra -Wredundant-decls -Wunused -Wunused-function -Wunused-parameter -Wunused-value -Wunused-variable -Wformat -Wformat-nonliteral -Wparentheses -Wsequence-point -Wuninitialized -Wundef -Wbad-function-cast -Wno-unused-parameter OPT_FLAG_1 = -O1 OPT_FLAG_2 = -O2 CFLAGS = $(COMMON_FLAGS) $(OPT_FLAG_2) LIBRARIES = -lm RM = rm -f objs = axml.o optimizeModel.o trash.o searchAlgo.o topologies.o treeIO.o models.o evaluatePartialGenericSpecial.o evaluateGenericSpecial.o newviewGenericSpecial.o makenewzGenericSpecial.o bipartitionList.o restartHashTable.o byteFile.o partitionAssignment.o communication.o quartets.o all : clean examl GLOBAL_DEPS = axml.h globalVariables.h ../versionHeader/version.h examl : $(objs) $(CC) -o examl $(objs) $(LIBRARIES) models.o : models.c $(GLOBAL_DEPS) $(CC) $(COMMON_FLAGS) $(OPT_FLAG_1) -c -o models.o models.c bipartitionList.o : bipartitionList.c $(GLOBAL_DEPS) evaluatePartialSpecialGeneric.o : evaluatePartialSpecialGeneric.c $(GLOBAL_DEPS) optimizeModel.o : optimizeModel.c $(GLOBAL_DEPS) trash.o : trash.c $(GLOBAL_DEPS) axml.o : axml.c $(GLOBAL_DEPS) searchAlgo.o : searchAlgo.c $(GLOBAL_DEPS) topologies.o : topologies.c $(GLOBAL_DEPS) treeIO.o : treeIO.c $(GLOBAL_DEPS) models.o : models.c $(GLOBAL_DEPS) evaluatePartialGenericSpecial.o : evaluatePartialGenericSpecial.c $(GLOBAL_DEPS) evaluateGenericSpecial.o : evaluateGenericSpecial.c $(GLOBAL_DEPS) newviewGenericSpecial.o : newviewGenericSpecial.c $(GLOBAL_DEPS) makenewzGenericSpecial.o : makenewzGenericSpecial.c $(GLOBAL_DEPS) restartHashTable.o : restartHashTable.c $(GLOBAL_DEPS) byteFile.o : byteFile.c partitionAssignment.o : partitionAssignment.c $(GLOBAL_DEPS) communication.o : communication.c $(GLOBAL_DEPS) quartets.o : quartets.c $(GLOBAL_DEPS) clean : $(RM) *.o examl dev : examl ExaML-3.0.21/examl/avxLikelihood.c000066400000000000000000003174201330315463200166260ustar00rootroot00000000000000#include #include #include #include #include #include #include #include #include #include "axml.h" #include #include #include #include #ifdef _FMA #include #define FMAMACC(a,b,c) _mm256_macc_pd(b,c,a) #endif extern const unsigned int mask32[32]; const union __attribute__ ((aligned (BYTE_ALIGNMENT))) { uint64_t i[4]; __m256d m; } absMask_AVX = {{0x7fffffffffffffffULL, 0x7fffffffffffffffULL, 0x7fffffffffffffffULL, 0x7fffffffffffffffULL}}; static inline __m256d hadd4(__m256d v, __m256d u) { __m256d a, b; v = _mm256_hadd_pd(v, v); a = _mm256_permute2f128_pd(v, v, 1); v = _mm256_add_pd(a, v); u = _mm256_hadd_pd(u, u); b = _mm256_permute2f128_pd(u, u, 1); u = _mm256_add_pd(b, u); v = _mm256_mul_pd(v, u); return v; } static inline __m256d hadd3(__m256d v) { __m256d a; v = _mm256_hadd_pd(v, v); a = _mm256_permute2f128_pd(v, v, 1); v = _mm256_add_pd(a, v); return v; } void newviewGTRGAMMA_AVX(int tipCase, double *x1, double *x2, double *x3, double *extEV, double *tipVector, unsigned char *tipX1, unsigned char *tipX2, const int n, double *left, double *right, int *wgt, int *scalerIncrement ) { int i, k, scale, addScale = 0; __m256d minlikelihood_avx = _mm256_set1_pd( minlikelihood ), twoto = _mm256_set1_pd(twotothe256); switch(tipCase) { case TIP_TIP: { double *uX1, umpX1[1024] __attribute__ ((aligned (BYTE_ALIGNMENT))), *uX2, umpX2[1024] __attribute__ ((aligned (BYTE_ALIGNMENT))); for (i = 1; i < 16; i++) { __m256d tv = _mm256_load_pd(&(tipVector[i * 4])); int j; for (j = 0; j < 4; j++) for (k = 0; k < 4; k++) { __m256d left1 = _mm256_load_pd(&left[j * 16 + k * 4]); left1 = _mm256_mul_pd(left1, tv); left1 = hadd3(left1); _mm256_store_pd(&umpX1[i * 64 + j * 16 + k * 4], left1); } for (j = 0; j < 4; j++) for (k = 0; k < 4; k++) { __m256d left1 = _mm256_load_pd(&right[j * 16 + k * 4]); left1 = _mm256_mul_pd(left1, tv); left1 = hadd3(left1); _mm256_store_pd(&umpX2[i * 64 + j * 16 + k * 4], left1); } } for(i = 0; i < n; i++) { uX1 = &umpX1[64 * tipX1[i]]; uX2 = &umpX2[64 * tipX2[i]]; for(k = 0; k < 4; k++) { __m256d xv = _mm256_setzero_pd(); int l; for(l = 0; l < 4; l++) { __m256d x1v = _mm256_mul_pd(_mm256_load_pd(&uX1[k * 16 + l * 4]), _mm256_load_pd(&uX2[k * 16 + l * 4])); __m256d evv = _mm256_load_pd(&extEV[l * 4]); #ifdef _FMA xv = FMAMACC(xv,x1v,evv); #else xv = _mm256_add_pd(xv, _mm256_mul_pd(x1v, evv)); #endif } _mm256_store_pd(&x3[16 * i + 4 * k], xv); } } } break; case TIP_INNER: { double *uX1, umpX1[1024] __attribute__ ((aligned (BYTE_ALIGNMENT))); for (i = 1; i < 16; i++) { __m256d tv = _mm256_load_pd(&(tipVector[i*4])); int j; for (j = 0; j < 4; j++) for (k = 0; k < 4; k++) { __m256d left1 = _mm256_load_pd(&left[j * 16 + k * 4]); left1 = _mm256_mul_pd(left1, tv); left1 = hadd3(left1); _mm256_store_pd(&umpX1[i * 64 + j * 16 + k * 4], left1); } } for(i = 0; i < n; i++) { __m256d xv[4]; scale = 1; uX1 = &umpX1[64 * tipX1[i]]; for(k = 0; k < 4; k++) { __m256d xvr = _mm256_load_pd(&(x2[i * 16 + k * 4])); int l; xv[k] = _mm256_setzero_pd(); for(l = 0; l < 4; l++) { __m256d x1v = _mm256_load_pd(&uX1[k * 16 + l * 4]), x2v = _mm256_mul_pd(xvr, _mm256_load_pd(&right[k * 16 + l * 4])); x2v = hadd3(x2v); x1v = _mm256_mul_pd(x1v, x2v); __m256d evv = _mm256_load_pd(&extEV[l * 4]); #ifdef _FMA xv[k] = FMAMACC(xv[k],x1v,evv); #else xv[k] = _mm256_add_pd(xv[k], _mm256_mul_pd(x1v, evv)); #endif } if(scale) { __m256d v1 = _mm256_and_pd(xv[k], absMask_AVX.m); v1 = _mm256_cmp_pd(v1, minlikelihood_avx, _CMP_LT_OS); if(_mm256_movemask_pd( v1 ) != 15) scale = 0; } } if(scale) { xv[0] = _mm256_mul_pd(xv[0], twoto); xv[1] = _mm256_mul_pd(xv[1], twoto); xv[2] = _mm256_mul_pd(xv[2], twoto); xv[3] = _mm256_mul_pd(xv[3], twoto); addScale += wgt[i]; } _mm256_store_pd(&x3[16 * i], xv[0]); _mm256_store_pd(&x3[16 * i + 4], xv[1]); _mm256_store_pd(&x3[16 * i + 8], xv[2]); _mm256_store_pd(&x3[16 * i + 12], xv[3]); } } break; case INNER_INNER: { for(i = 0; i < n; i++) { __m256d xv[4]; scale = 1; for(k = 0; k < 4; k++) { __m256d xvl = _mm256_load_pd(&(x1[i * 16 + k * 4])), xvr = _mm256_load_pd(&(x2[i * 16 + k * 4])); int l; xv[k] = _mm256_setzero_pd(); for(l = 0; l < 4; l++) { __m256d x1v = _mm256_mul_pd(xvl, _mm256_load_pd(&left[k * 16 + l * 4])), x2v = _mm256_mul_pd(xvr, _mm256_load_pd(&right[k * 16 + l * 4])); x1v = hadd4(x1v, x2v); __m256d evv = _mm256_load_pd(&extEV[l * 4]); xv[k] = _mm256_add_pd(xv[k], _mm256_mul_pd(x1v, evv)); } if(scale) { __m256d v1 = _mm256_and_pd(xv[k], absMask_AVX.m); v1 = _mm256_cmp_pd(v1, minlikelihood_avx, _CMP_LT_OS); if(_mm256_movemask_pd( v1 ) != 15) scale = 0; } } if(scale) { xv[0] = _mm256_mul_pd(xv[0], twoto); xv[1] = _mm256_mul_pd(xv[1], twoto); xv[2] = _mm256_mul_pd(xv[2], twoto); xv[3] = _mm256_mul_pd(xv[3], twoto); addScale += wgt[i]; } _mm256_store_pd(&x3[16 * i], xv[0]); _mm256_store_pd(&x3[16 * i + 4], xv[1]); _mm256_store_pd(&x3[16 * i + 8], xv[2]); _mm256_store_pd(&x3[16 * i + 12], xv[3]); } } break; default: assert(0); } *scalerIncrement = addScale; } void newviewGTRCAT_AVX(int tipCase, double *EV, int *cptr, double *x1_start, double *x2_start, double *x3_start, double *tipVector, unsigned char *tipX1, unsigned char *tipX2, int n, double *left, double *right, int *wgt, int *scalerIncrement) { double *le, *ri, *x1, *x2; int i, addScale = 0; __m256d minlikelihood_avx = _mm256_set1_pd( minlikelihood ), twoto = _mm256_set1_pd(twotothe256); switch(tipCase) { case TIP_TIP: for (i = 0; i < n; i++) { int l; le = &left[cptr[i] * 16]; ri = &right[cptr[i] * 16]; x1 = &(tipVector[4 * tipX1[i]]); x2 = &(tipVector[4 * tipX2[i]]); __m256d vv = _mm256_setzero_pd(); for(l = 0; l < 4; l++) { __m256d x1v = _mm256_mul_pd(_mm256_load_pd(x1), _mm256_load_pd(&le[l * 4])), x2v = _mm256_mul_pd(_mm256_load_pd(x2), _mm256_load_pd(&ri[l * 4])); x1v = hadd4(x1v, x2v); __m256d evv = _mm256_load_pd(&EV[l * 4]); #ifdef _FMA vv = FMAMACC(vv,x1v,evv); #else vv = _mm256_add_pd(vv, _mm256_mul_pd(x1v, evv)); #endif } _mm256_store_pd(&x3_start[4 * i], vv); } break; case TIP_INNER: for (i = 0; i < n; i++) { int l; x1 = &(tipVector[4 * tipX1[i]]); x2 = &x2_start[4 * i]; le = &left[cptr[i] * 16]; ri = &right[cptr[i] * 16]; __m256d vv = _mm256_setzero_pd(); for(l = 0; l < 4; l++) { __m256d x1v = _mm256_mul_pd(_mm256_load_pd(x1), _mm256_load_pd(&le[l * 4])), x2v = _mm256_mul_pd(_mm256_load_pd(x2), _mm256_load_pd(&ri[l * 4])); x1v = hadd4(x1v, x2v); __m256d evv = _mm256_load_pd(&EV[l * 4]); #ifdef _FMA vv = FMAMACC(vv,x1v,evv); #else vv = _mm256_add_pd(vv, _mm256_mul_pd(x1v, evv)); #endif } __m256d v1 = _mm256_and_pd(vv, absMask_AVX.m); v1 = _mm256_cmp_pd(v1, minlikelihood_avx, _CMP_LT_OS); if(_mm256_movemask_pd( v1 ) == 15) { vv = _mm256_mul_pd(vv, twoto); addScale += wgt[i]; } _mm256_store_pd(&x3_start[4 * i], vv); } break; case INNER_INNER: for (i = 0; i < n; i++) { int l; x1 = &x1_start[4 * i]; x2 = &x2_start[4 * i]; le = &left[cptr[i] * 16]; ri = &right[cptr[i] * 16]; __m256d vv = _mm256_setzero_pd(); for(l = 0; l < 4; l++) { __m256d x1v = _mm256_mul_pd(_mm256_load_pd(x1), _mm256_load_pd(&le[l * 4])), x2v = _mm256_mul_pd(_mm256_load_pd(x2), _mm256_load_pd(&ri[l * 4])); x1v = hadd4(x1v, x2v); __m256d evv = _mm256_load_pd(&EV[l * 4]); #ifdef _FMA vv = FMAMACC(vv,x1v,evv); #else vv = _mm256_add_pd(vv, _mm256_mul_pd(x1v, evv)); #endif } __m256d v1 = _mm256_and_pd(vv, absMask_AVX.m); v1 = _mm256_cmp_pd(v1, minlikelihood_avx, _CMP_LT_OS); if(_mm256_movemask_pd( v1 ) == 15) { vv = _mm256_mul_pd(vv, twoto); addScale += wgt[i]; } _mm256_store_pd(&x3_start[4 * i], vv); } break; default: assert(0); } *scalerIncrement = addScale; } void newviewGTRCATPROT_AVX(int tipCase, double *extEV, int *cptr, double *x1, double *x2, double *x3, double *tipVector, unsigned char *tipX1, unsigned char *tipX2, int n, double *left, double *right, int *wgt, int *scalerIncrement) { double *le, *ri, *v, *vl, *vr; int i, l, scale, addScale = 0; #ifdef _FMA int k; #endif switch(tipCase) { case TIP_TIP: { for (i = 0; i < n; i++) { le = &left[cptr[i] * 400]; ri = &right[cptr[i] * 400]; vl = &(tipVector[20 * tipX1[i]]); vr = &(tipVector[20 * tipX2[i]]); v = &x3[20 * i]; __m256d vv[5]; vv[0] = _mm256_setzero_pd(); vv[1] = _mm256_setzero_pd(); vv[2] = _mm256_setzero_pd(); vv[3] = _mm256_setzero_pd(); vv[4] = _mm256_setzero_pd(); for(l = 0; l < 20; l++) { __m256d x1v = _mm256_setzero_pd(), x2v = _mm256_setzero_pd(); double *ev = &extEV[l * 20], *lv = &le[l * 20], *rv = &ri[l * 20]; #ifdef _FMA for(k = 0; k < 20; k += 4) { __m256d vlv = _mm256_load_pd(&vl[k]); __m256d lvv = _mm256_load_pd(&lv[k]); x1v = FMAMACC(x1v,vlv,lvv); __m256d vrv = _mm256_load_pd(&vr[k]); __m256d rvv = _mm256_load_pd(&rv[k]); x2v = FMAMACC(x2v,vrv,rvv); } #else x1v = _mm256_add_pd(x1v, _mm256_mul_pd(_mm256_load_pd(&vl[0]), _mm256_load_pd(&lv[0]))); x1v = _mm256_add_pd(x1v, _mm256_mul_pd(_mm256_load_pd(&vl[4]), _mm256_load_pd(&lv[4]))); x1v = _mm256_add_pd(x1v, _mm256_mul_pd(_mm256_load_pd(&vl[8]), _mm256_load_pd(&lv[8]))); x1v = _mm256_add_pd(x1v, _mm256_mul_pd(_mm256_load_pd(&vl[12]), _mm256_load_pd(&lv[12]))); x1v = _mm256_add_pd(x1v, _mm256_mul_pd(_mm256_load_pd(&vl[16]), _mm256_load_pd(&lv[16]))); x2v = _mm256_add_pd(x2v, _mm256_mul_pd(_mm256_load_pd(&vr[0]), _mm256_load_pd(&rv[0]))); x2v = _mm256_add_pd(x2v, _mm256_mul_pd(_mm256_load_pd(&vr[4]), _mm256_load_pd(&rv[4]))); x2v = _mm256_add_pd(x2v, _mm256_mul_pd(_mm256_load_pd(&vr[8]), _mm256_load_pd(&rv[8]))); x2v = _mm256_add_pd(x2v, _mm256_mul_pd(_mm256_load_pd(&vr[12]), _mm256_load_pd(&rv[12]))); x2v = _mm256_add_pd(x2v, _mm256_mul_pd(_mm256_load_pd(&vr[16]), _mm256_load_pd(&rv[16]))); #endif x1v = hadd4(x1v, x2v); #ifdef _FMA for(k = 0; k < 5; k++) { __m256d evv = _mm256_load_pd(&ev[k*4]); vv[k] = FMAMACC(vv[k],x1v,evv); } #else __m256d evv[5]; evv[0] = _mm256_load_pd(&ev[0]); evv[1] = _mm256_load_pd(&ev[4]); evv[2] = _mm256_load_pd(&ev[8]); evv[3] = _mm256_load_pd(&ev[12]); evv[4] = _mm256_load_pd(&ev[16]); vv[0] = _mm256_add_pd(vv[0], _mm256_mul_pd(x1v, evv[0])); vv[1] = _mm256_add_pd(vv[1], _mm256_mul_pd(x1v, evv[1])); vv[2] = _mm256_add_pd(vv[2], _mm256_mul_pd(x1v, evv[2])); vv[3] = _mm256_add_pd(vv[3], _mm256_mul_pd(x1v, evv[3])); vv[4] = _mm256_add_pd(vv[4], _mm256_mul_pd(x1v, evv[4])); #endif } _mm256_store_pd(&v[0], vv[0]); _mm256_store_pd(&v[4], vv[1]); _mm256_store_pd(&v[8], vv[2]); _mm256_store_pd(&v[12], vv[3]); _mm256_store_pd(&v[16], vv[4]); } } break; case TIP_INNER: for (i = 0; i < n; i++) { le = &left[cptr[i] * 400]; ri = &right[cptr[i] * 400]; vl = &(tipVector[20 * tipX1[i]]); vr = &x2[20 * i]; v = &x3[20 * i]; __m256d vv[5]; vv[0] = _mm256_setzero_pd(); vv[1] = _mm256_setzero_pd(); vv[2] = _mm256_setzero_pd(); vv[3] = _mm256_setzero_pd(); vv[4] = _mm256_setzero_pd(); for(l = 0; l < 20; l++) { __m256d x1v = _mm256_setzero_pd(), x2v = _mm256_setzero_pd(); double *ev = &extEV[l * 20], *lv = &le[l * 20], *rv = &ri[l * 20]; #ifdef _FMA for(k = 0; k < 20; k += 4) { __m256d vlv = _mm256_load_pd(&vl[k]); __m256d lvv = _mm256_load_pd(&lv[k]); x1v = FMAMACC(x1v,vlv,lvv); __m256d vrv = _mm256_load_pd(&vr[k]); __m256d rvv = _mm256_load_pd(&rv[k]); x2v = FMAMACC(x2v,vrv,rvv); } #else x1v = _mm256_add_pd(x1v, _mm256_mul_pd(_mm256_load_pd(&vl[0]), _mm256_load_pd(&lv[0]))); x1v = _mm256_add_pd(x1v, _mm256_mul_pd(_mm256_load_pd(&vl[4]), _mm256_load_pd(&lv[4]))); x1v = _mm256_add_pd(x1v, _mm256_mul_pd(_mm256_load_pd(&vl[8]), _mm256_load_pd(&lv[8]))); x1v = _mm256_add_pd(x1v, _mm256_mul_pd(_mm256_load_pd(&vl[12]), _mm256_load_pd(&lv[12]))); x1v = _mm256_add_pd(x1v, _mm256_mul_pd(_mm256_load_pd(&vl[16]), _mm256_load_pd(&lv[16]))); x2v = _mm256_add_pd(x2v, _mm256_mul_pd(_mm256_load_pd(&vr[0]), _mm256_load_pd(&rv[0]))); x2v = _mm256_add_pd(x2v, _mm256_mul_pd(_mm256_load_pd(&vr[4]), _mm256_load_pd(&rv[4]))); x2v = _mm256_add_pd(x2v, _mm256_mul_pd(_mm256_load_pd(&vr[8]), _mm256_load_pd(&rv[8]))); x2v = _mm256_add_pd(x2v, _mm256_mul_pd(_mm256_load_pd(&vr[12]), _mm256_load_pd(&rv[12]))); x2v = _mm256_add_pd(x2v, _mm256_mul_pd(_mm256_load_pd(&vr[16]), _mm256_load_pd(&rv[16]))); #endif x1v = hadd4(x1v, x2v); __m256d evv[5]; evv[0] = _mm256_load_pd(&ev[0]); evv[1] = _mm256_load_pd(&ev[4]); evv[2] = _mm256_load_pd(&ev[8]); evv[3] = _mm256_load_pd(&ev[12]); evv[4] = _mm256_load_pd(&ev[16]); #ifdef _FMA for(k = 0; k < 5; k++) vv[k] = FMAMACC(vv[k],x1v,evv[k]); #else vv[0] = _mm256_add_pd(vv[0], _mm256_mul_pd(x1v, evv[0])); vv[1] = _mm256_add_pd(vv[1], _mm256_mul_pd(x1v, evv[1])); vv[2] = _mm256_add_pd(vv[2], _mm256_mul_pd(x1v, evv[2])); vv[3] = _mm256_add_pd(vv[3], _mm256_mul_pd(x1v, evv[3])); vv[4] = _mm256_add_pd(vv[4], _mm256_mul_pd(x1v, evv[4])); #endif } __m256d minlikelihood_avx = _mm256_set1_pd( minlikelihood ); scale = 1; for(l = 0; scale && (l < 20); l += 4) { __m256d v1 = _mm256_and_pd(vv[l / 4], absMask_AVX.m); v1 = _mm256_cmp_pd(v1, minlikelihood_avx, _CMP_LT_OS); if(_mm256_movemask_pd( v1 ) != 15) scale = 0; } if(scale) { __m256d twoto = _mm256_set1_pd(twotothe256); for(l = 0; l < 20; l += 4) vv[l / 4] = _mm256_mul_pd(vv[l / 4] , twoto); addScale += wgt[i]; } _mm256_store_pd(&v[0], vv[0]); _mm256_store_pd(&v[4], vv[1]); _mm256_store_pd(&v[8], vv[2]); _mm256_store_pd(&v[12], vv[3]); _mm256_store_pd(&v[16], vv[4]); } break; case INNER_INNER: for(i = 0; i < n; i++) { le = &left[cptr[i] * 400]; ri = &right[cptr[i] * 400]; vl = &x1[20 * i]; vr = &x2[20 * i]; v = &x3[20 * i]; __m256d vv[5]; vv[0] = _mm256_setzero_pd(); vv[1] = _mm256_setzero_pd(); vv[2] = _mm256_setzero_pd(); vv[3] = _mm256_setzero_pd(); vv[4] = _mm256_setzero_pd(); for(l = 0; l < 20; l++) { __m256d x1v = _mm256_setzero_pd(), x2v = _mm256_setzero_pd(); double *ev = &extEV[l * 20], *lv = &le[l * 20], *rv = &ri[l * 20]; x1v = _mm256_add_pd(x1v, _mm256_mul_pd(_mm256_load_pd(&vl[0]), _mm256_load_pd(&lv[0]))); x1v = _mm256_add_pd(x1v, _mm256_mul_pd(_mm256_load_pd(&vl[4]), _mm256_load_pd(&lv[4]))); x1v = _mm256_add_pd(x1v, _mm256_mul_pd(_mm256_load_pd(&vl[8]), _mm256_load_pd(&lv[8]))); x1v = _mm256_add_pd(x1v, _mm256_mul_pd(_mm256_load_pd(&vl[12]), _mm256_load_pd(&lv[12]))); x1v = _mm256_add_pd(x1v, _mm256_mul_pd(_mm256_load_pd(&vl[16]), _mm256_load_pd(&lv[16]))); x2v = _mm256_add_pd(x2v, _mm256_mul_pd(_mm256_load_pd(&vr[0]), _mm256_load_pd(&rv[0]))); x2v = _mm256_add_pd(x2v, _mm256_mul_pd(_mm256_load_pd(&vr[4]), _mm256_load_pd(&rv[4]))); x2v = _mm256_add_pd(x2v, _mm256_mul_pd(_mm256_load_pd(&vr[8]), _mm256_load_pd(&rv[8]))); x2v = _mm256_add_pd(x2v, _mm256_mul_pd(_mm256_load_pd(&vr[12]), _mm256_load_pd(&rv[12]))); x2v = _mm256_add_pd(x2v, _mm256_mul_pd(_mm256_load_pd(&vr[16]), _mm256_load_pd(&rv[16]))); x1v = hadd4(x1v, x2v); #ifdef _FMA for(k = 0; k < 5; k++) { __m256d evv = _mm256_load_pd(&ev[k*4]); vv[k] = FMAMACC(vv[k],x1v,evv); } #else __m256d evv[5]; evv[0] = _mm256_load_pd(&ev[0]); evv[1] = _mm256_load_pd(&ev[4]); evv[2] = _mm256_load_pd(&ev[8]); evv[3] = _mm256_load_pd(&ev[12]); evv[4] = _mm256_load_pd(&ev[16]); vv[0] = _mm256_add_pd(vv[0], _mm256_mul_pd(x1v, evv[0])); vv[1] = _mm256_add_pd(vv[1], _mm256_mul_pd(x1v, evv[1])); vv[2] = _mm256_add_pd(vv[2], _mm256_mul_pd(x1v, evv[2])); vv[3] = _mm256_add_pd(vv[3], _mm256_mul_pd(x1v, evv[3])); vv[4] = _mm256_add_pd(vv[4], _mm256_mul_pd(x1v, evv[4])); #endif } __m256d minlikelihood_avx = _mm256_set1_pd( minlikelihood ); scale = 1; for(l = 0; scale && (l < 20); l += 4) { __m256d v1 = _mm256_and_pd(vv[l / 4], absMask_AVX.m); v1 = _mm256_cmp_pd(v1, minlikelihood_avx, _CMP_LT_OS); if(_mm256_movemask_pd( v1 ) != 15) scale = 0; } if(scale) { __m256d twoto = _mm256_set1_pd(twotothe256); for(l = 0; l < 20; l += 4) vv[l / 4] = _mm256_mul_pd(vv[l / 4] , twoto); addScale += wgt[i]; } _mm256_store_pd(&v[0], vv[0]); _mm256_store_pd(&v[4], vv[1]); _mm256_store_pd(&v[8], vv[2]); _mm256_store_pd(&v[12], vv[3]); _mm256_store_pd(&v[16], vv[4]); } break; default: assert(0); } *scalerIncrement = addScale; } void newviewGTRGAMMAPROT_AVX_LG4(int tipCase, double *x1, double *x2, double *x3, double *extEV[4], double *tipVector[4], int *ex3, unsigned char *tipX1, unsigned char *tipX2, int n, double *left, double *right, int *wgt, int *scalerIncrement, const boolean useFastScaling) { double *uX1, *uX2, *v, x1px2, *vl, *vr; int i, j, l, k, scale, addScale = 0; #ifndef GCC_VERSION #define GCC_VERSION (__GNUC__ * 10000 + __GNUC_MINOR__ * 100 + __GNUC_PATCHLEVEL__) #endif #if GCC_VERSION < 40500 __m256d bitmask = _mm256_set_pd(0,0,0,-1); #else __m256i bitmask = _mm256_set_epi32(0, 0, 0, 0, 0, 0, -1, -1); #endif switch(tipCase) { case TIP_TIP: { double umpX1[1840] __attribute__ ((aligned (BYTE_ALIGNMENT))), umpX2[1840] __attribute__ ((aligned (BYTE_ALIGNMENT))); for(i = 0; i < 23; i++) { for(k = 0; k < 80; k++) { double *ll = &left[k * 20], *rr = &right[k * 20]; __m256d umpX1v = _mm256_setzero_pd(), umpX2v = _mm256_setzero_pd(); v = &(tipVector[k / 20][20 * i]); for(l = 0; l < 20; l+=4) { __m256d vv = _mm256_load_pd(&v[l]); #ifdef _FMA __m256d llv = _mm256_load_pd(&ll[l]); umpX1v = FMAMACC(umpX1v,vv,llv); __m256d rrv = _mm256_load_pd(&rr[l]); umpX2v = FMAMACC(umpX2v,vv,rrv); #else umpX1v = _mm256_add_pd(umpX1v,_mm256_mul_pd(vv,_mm256_load_pd(&ll[l]))); umpX2v = _mm256_add_pd(umpX2v,_mm256_mul_pd(vv,_mm256_load_pd(&rr[l]))); #endif } umpX1v = hadd3(umpX1v); umpX2v = hadd3(umpX2v); _mm256_maskstore_pd(&umpX1[80 * i + k], bitmask, umpX1v); _mm256_maskstore_pd(&umpX2[80 * i + k], bitmask, umpX2v); } } for(i = 0; i < n; i++) { uX1 = &umpX1[80 * tipX1[i]]; uX2 = &umpX2[80 * tipX2[i]]; for(j = 0; j < 4; j++) { __m256d vv[5]; v = &x3[i * 80 + j * 20]; vv[0] = _mm256_setzero_pd(); vv[1] = _mm256_setzero_pd(); vv[2] = _mm256_setzero_pd(); vv[3] = _mm256_setzero_pd(); vv[4] = _mm256_setzero_pd(); for(k = 0; k < 20; k++) { x1px2 = uX1[j * 20 + k] * uX2[j * 20 + k]; __m256d x1px2v = _mm256_set1_pd(x1px2); __m256d extEvv = _mm256_load_pd(&extEV[j][20 * k]); #ifdef _FMA vv[0] = FMAMACC(vv[0],x1px2v,extEvv); #else vv[0] = _mm256_add_pd(vv[0],_mm256_mul_pd(x1px2v,extEvv)); #endif _mm256_store_pd(&v[0],vv[0]); extEvv = _mm256_load_pd(&extEV[j][20 * k + 4]); #ifdef _FMA vv[1] = FMAMACC(vv[1],x1px2v,extEvv); #else vv[1] = _mm256_add_pd(vv[1],_mm256_mul_pd(x1px2v,extEvv)); #endif _mm256_store_pd(&v[4],vv[1]); extEvv = _mm256_load_pd(&extEV[j][20 * k + 8]); #ifdef _FMA vv[2] = FMAMACC(vv[2],x1px2v,extEvv); #else vv[2] = _mm256_add_pd(vv[2],_mm256_mul_pd(x1px2v,extEvv)); #endif _mm256_store_pd(&v[8],vv[2]); extEvv = _mm256_load_pd(&extEV[j][20 * k + 12]); #ifdef _FMA vv[3] = FMAMACC(vv[3],x1px2v,extEvv); #else vv[3] = _mm256_add_pd(vv[3],_mm256_mul_pd(x1px2v,extEvv)); #endif _mm256_store_pd(&v[12],vv[3]); extEvv = _mm256_load_pd(&extEV[j][20 * k + 16]); #ifdef _FMA vv[4] = FMAMACC(vv[4],x1px2v,extEvv); #else vv[4] = _mm256_add_pd(vv[4],_mm256_mul_pd(x1px2v,extEvv)); #endif _mm256_store_pd(&v[16],vv[4]); } } } } break; case TIP_INNER: { double umpX1[1840] __attribute__ ((aligned (BYTE_ALIGNMENT))), ump_x2[20] __attribute__ ((aligned (BYTE_ALIGNMENT))); for(i = 0; i < 23; i++) { for(k = 0; k < 80; k++) { __m256d umpX1v = _mm256_setzero_pd(); v = &(tipVector[k / 20][20 * i]); for(l = 0; l < 20; l+=4) { __m256d vv = _mm256_load_pd(&v[l]); __m256d leftv = _mm256_load_pd(&left[k * 20 + l]); #ifdef _FMA umpX1v = FMAMACC(umpX1v, vv, leftv); #else umpX1v = _mm256_add_pd(umpX1v, _mm256_mul_pd(vv, leftv)); #endif } umpX1v = hadd3(umpX1v); _mm256_maskstore_pd(&umpX1[80 * i + k], bitmask, umpX1v); } } for (i = 0; i < n; i++) { uX1 = &umpX1[80 * tipX1[i]]; for(k = 0; k < 4; k++) { v = &(x2[80 * i + k * 20]); for(l = 0; l < 20; l++) { __m256d ump_x2v = _mm256_setzero_pd(); __m256d vv = _mm256_load_pd(&v[0]); __m256d rightv = _mm256_load_pd(&right[k*400+l*20+0]); #ifdef _FMA ump_x2v = FMAMACC(ump_x2v,vv,rightv); #else ump_x2v = _mm256_add_pd(ump_x2v, _mm256_mul_pd(vv, rightv)); #endif vv = _mm256_load_pd(&v[4]); rightv = _mm256_load_pd(&right[k*400+l*20+4]); #ifdef _FMA ump_x2v = FMAMACC(ump_x2v,vv,rightv); #else ump_x2v = _mm256_add_pd(ump_x2v, _mm256_mul_pd(vv, rightv)); #endif vv = _mm256_load_pd(&v[8]); rightv = _mm256_load_pd(&right[k*400+l*20+8]); #ifdef _FMA ump_x2v = FMAMACC(ump_x2v,vv,rightv); #else ump_x2v = _mm256_add_pd(ump_x2v, _mm256_mul_pd(vv, rightv)); #endif vv = _mm256_load_pd(&v[12]); rightv = _mm256_load_pd(&right[k*400+l*20+12]); #ifdef _FMA ump_x2v = FMAMACC(ump_x2v,vv,rightv); #else ump_x2v = _mm256_add_pd(ump_x2v, _mm256_mul_pd(vv, rightv)); #endif vv = _mm256_load_pd(&v[16]); rightv = _mm256_load_pd(&right[k*400+l*20+16]); #ifdef _FMA ump_x2v = FMAMACC(ump_x2v,vv,rightv); #else ump_x2v = _mm256_add_pd(ump_x2v, _mm256_mul_pd(vv, rightv)); #endif ump_x2v = hadd3(ump_x2v); _mm256_maskstore_pd(&ump_x2[l], bitmask, ump_x2v); } v = &(x3[80 * i + 20 * k]); __m256d vv[5]; vv[0] = _mm256_setzero_pd(); vv[1] = _mm256_setzero_pd(); vv[2] = _mm256_setzero_pd(); vv[3] = _mm256_setzero_pd(); vv[4] = _mm256_setzero_pd(); for(l = 0; l < 20; l++) { x1px2 = uX1[k * 20 + l] * ump_x2[l]; __m256d x1px2v = _mm256_set1_pd(x1px2); #ifdef _FMA __m256d ev = _mm256_load_pd(&extEV[l * 20 + 0]); vv[0] = FMAMACC(vv[0],x1px2v, ev); #else vv[0] = _mm256_add_pd(vv[0],_mm256_mul_pd(x1px2v, _mm256_load_pd(&extEV[k][l * 20 + 0]))); #endif _mm256_store_pd(&v[0],vv[0]); #ifdef _FMA ev = _mm256_load_pd(&extEV[l * 20 + 4]); vv[1] = FMAMACC(vv[1],x1px2v, ev); #else vv[1] = _mm256_add_pd(vv[1],_mm256_mul_pd(x1px2v, _mm256_load_pd(&extEV[k][l * 20 + 4]))); #endif _mm256_store_pd(&v[4],vv[1]); #ifdef _FMA ev = _mm256_load_pd(&extEV[l * 20 + 8]); vv[2] = FMAMACC(vv[2],x1px2v, ev); #else vv[2] = _mm256_add_pd(vv[2],_mm256_mul_pd(x1px2v, _mm256_load_pd(&extEV[k][l * 20 + 8]))); #endif _mm256_store_pd(&v[8],vv[2]); #ifdef _FMA ev = _mm256_load_pd(&extEV[l * 20 + 12]); vv[3] = FMAMACC(vv[3],x1px2v, ev); #else vv[3] = _mm256_add_pd(vv[3],_mm256_mul_pd(x1px2v, _mm256_load_pd(&extEV[k][l * 20 + 12]))); #endif _mm256_store_pd(&v[12],vv[3]); #ifdef _FMA ev = _mm256_load_pd(&extEV[l * 20 + 16]); vv[4] = FMAMACC(vv[4],x1px2v, ev); #else vv[4] = _mm256_add_pd(vv[4],_mm256_mul_pd(x1px2v, _mm256_load_pd(&extEV[k][l * 20 + 16]))); #endif _mm256_store_pd(&v[16],vv[4]); } } v = &x3[80 * i]; __m256d minlikelihood_avx = _mm256_set1_pd(minlikelihood); scale = 1; for(l = 0; scale && (l < 80); l += 4) { __m256d vv = _mm256_load_pd(&v[l]); __m256d vv_abs = _mm256_and_pd(vv,absMask_AVX.m); vv_abs = _mm256_cmp_pd(vv_abs,minlikelihood_avx,_CMP_LT_OS); if(_mm256_movemask_pd(vv_abs) != 15) scale = 0; } if(scale) { __m256d twotothe256v = _mm256_set_pd(twotothe256,twotothe256,twotothe256,twotothe256); for(l = 0; l < 80; l += 4) { __m256d vv = _mm256_load_pd(&v[l]); _mm256_store_pd(&v[l],_mm256_mul_pd(vv,twotothe256v)); } if(useFastScaling) addScale += wgt[i]; else ex3[i] += 1; } } } break; case INNER_INNER: for(i = 0; i < n; i++) { scale = 1; for(k = 0; k < 4; k++) { vl = &(x1[80 * i + 20 * k]); vr = &(x2[80 * i + 20 * k]); v = &(x3[80 * i + 20 * k]); __m256d vv[5]; vv[0] = _mm256_setzero_pd(); vv[1] = _mm256_setzero_pd(); vv[2] = _mm256_setzero_pd(); vv[3] = _mm256_setzero_pd(); vv[4] = _mm256_setzero_pd(); for(l = 0; l < 20; l++) { __m256d al = _mm256_setzero_pd(); __m256d ar = _mm256_setzero_pd(); __m256d leftv = _mm256_load_pd(&left[k * 400 + l * 20 + 0]); __m256d rightv = _mm256_load_pd(&right[k * 400 + l * 20 + 0]); __m256d vlv = _mm256_load_pd(&vl[0]); __m256d vrv = _mm256_load_pd(&vr[0]); #ifdef _FMA al = FMAMACC(al, vlv, leftv); ar = FMAMACC(ar, vrv, rightv); #else al = _mm256_add_pd(al,_mm256_mul_pd(vlv,leftv)); ar = _mm256_add_pd(ar,_mm256_mul_pd(vrv,rightv)); #endif leftv = _mm256_load_pd(&left[k * 400 + l * 20 + 4]); rightv = _mm256_load_pd(&right[k * 400 + l * 20 + 4]); vlv = _mm256_load_pd(&vl[4]); vrv = _mm256_load_pd(&vr[4]); #ifdef _FMA al = FMAMACC(al, vlv, leftv); ar = FMAMACC(ar, vrv, rightv); #else al = _mm256_add_pd(al,_mm256_mul_pd(vlv,leftv)); ar = _mm256_add_pd(ar,_mm256_mul_pd(vrv,rightv)); #endif leftv = _mm256_load_pd(&left[k * 400 + l * 20 + 8]); rightv = _mm256_load_pd(&right[k * 400 + l * 20 + 8]); vlv = _mm256_load_pd(&vl[8]); vrv = _mm256_load_pd(&vr[8]); #ifdef _FMA al = FMAMACC(al, vlv, leftv); ar = FMAMACC(ar, vrv, rightv); #else al = _mm256_add_pd(al,_mm256_mul_pd(vlv,leftv)); ar = _mm256_add_pd(ar,_mm256_mul_pd(vrv,rightv)); #endif leftv = _mm256_load_pd(&left[k * 400 + l * 20 + 12]); rightv = _mm256_load_pd(&right[k * 400 + l * 20 + 12]); vlv = _mm256_load_pd(&vl[12]); vrv = _mm256_load_pd(&vr[12]); #ifdef _FMA al = FMAMACC(al, vlv, leftv); ar = FMAMACC(ar, vrv, rightv); #else al = _mm256_add_pd(al,_mm256_mul_pd(vlv,leftv)); ar = _mm256_add_pd(ar,_mm256_mul_pd(vrv,rightv)); #endif leftv = _mm256_load_pd(&left[k * 400 + l * 20 + 16]); rightv = _mm256_load_pd(&right[k * 400 + l * 20 + 16]); vlv = _mm256_load_pd(&vl[16]); vrv = _mm256_load_pd(&vr[16]); #ifdef _FMA al = FMAMACC(al, vlv, leftv); ar = FMAMACC(ar, vrv, rightv); #else al = _mm256_add_pd(al,_mm256_mul_pd(vlv,leftv)); ar = _mm256_add_pd(ar,_mm256_mul_pd(vrv,rightv)); #endif /**************************************************************************************************************/ al = hadd3(al); ar = hadd3(ar); al = _mm256_mul_pd(ar,al); /************************************************************************************************************/ #ifdef _FMA __m256d ev = _mm256_load_pd(&extEV[20 * l + 0]); vv[0] = FMAMACC(vv[0], al, ev); #else vv[0] = _mm256_add_pd(vv[0],_mm256_mul_pd(al, _mm256_load_pd(&extEV[k][20 * l + 0]))); #endif _mm256_store_pd(&v[0],vv[0]); #ifdef _FMA ev = _mm256_load_pd(&extEV[20 * l + 4]); vv[1] = FMAMACC(vv[1], al, ev); #else vv[1] = _mm256_add_pd(vv[1],_mm256_mul_pd(al, _mm256_load_pd(&extEV[k][20 * l + 4]))); #endif _mm256_store_pd(&v[4],vv[1]); #ifdef _FMA ev = _mm256_load_pd(&extEV[20 * l + 8]); vv[2] = FMAMACC(vv[2], al, ev); #else vv[2] = _mm256_add_pd(vv[2],_mm256_mul_pd(al, _mm256_load_pd(&extEV[k][20 * l + 8]))); #endif _mm256_store_pd(&v[8],vv[2]); #ifdef _FMA ev = _mm256_load_pd(&extEV[20 * l + 12]); vv[3] = FMAMACC(vv[3], al, ev); #else vv[3] = _mm256_add_pd(vv[3],_mm256_mul_pd(al, _mm256_load_pd(&extEV[k][20 * l + 12]))); #endif _mm256_store_pd(&v[12],vv[3]); #ifdef _FMA ev = _mm256_load_pd(&extEV[20 * l + 16]); vv[4] = FMAMACC(vv[4], al, ev); #else vv[4] = _mm256_add_pd(vv[4],_mm256_mul_pd(al, _mm256_load_pd(&extEV[k][20 * l + 16]))); #endif _mm256_store_pd(&v[16],vv[4]); } } v = &(x3[80 * i]); scale = 1; __m256d minlikelihood_avx = _mm256_set1_pd(minlikelihood); for(l = 0; scale && (l < 80); l += 4) { __m256d vv = _mm256_load_pd(&v[l]); __m256d vv_abs = _mm256_and_pd(vv,absMask_AVX.m); vv_abs = _mm256_cmp_pd(vv_abs,minlikelihood_avx,_CMP_LT_OS); if(_mm256_movemask_pd(vv_abs) != 15) scale = 0; } if(scale) { __m256d twotothe256v = _mm256_set_pd(twotothe256,twotothe256,twotothe256,twotothe256); for(l = 0; l < 80; l += 4) { __m256d vv = _mm256_load_pd(&v[l]); _mm256_store_pd(&v[l],_mm256_mul_pd(vv,twotothe256v)); } if(useFastScaling) addScale += wgt[i]; else ex3[i] += 1; } } break; default: assert(0); } if(useFastScaling) *scalerIncrement = addScale; } void newviewGTRGAMMAPROT_AVX(int tipCase, double *x1, double *x2, double *x3, double *extEV, double *tipVector, unsigned char *tipX1, unsigned char *tipX2, int n, double *left, double *right, int *wgt, int *scalerIncrement) { double *uX1, *uX2, *v, x1px2, *vl, *vr; int i, j, l, k, scale, addScale = 0; #ifndef GCC_VERSION #define GCC_VERSION (__GNUC__ * 10000 + __GNUC_MINOR__ * 100 + __GNUC_PATCHLEVEL__) #endif #if GCC_VERSION < 40500 __m256d bitmask = _mm256_set_pd(0,0,0,-1); #else __m256i bitmask = _mm256_set_epi32(0, 0, 0, 0, 0, 0, -1, -1); #endif switch(tipCase) { case TIP_TIP: { double umpX1[1840] __attribute__ ((aligned (BYTE_ALIGNMENT))), umpX2[1840] __attribute__ ((aligned (BYTE_ALIGNMENT))); for(i = 0; i < 23; i++) { v = &(tipVector[20 * i]); for(k = 0; k < 80; k++) { double *ll = &left[k * 20], *rr = &right[k * 20]; __m256d umpX1v = _mm256_setzero_pd(), umpX2v = _mm256_setzero_pd(); for(l = 0; l < 20; l+=4) { __m256d vv = _mm256_load_pd(&v[l]); #ifdef _FMA __m256d llv = _mm256_load_pd(&ll[l]); umpX1v = FMAMACC(umpX1v,vv,llv); __m256d rrv = _mm256_load_pd(&rr[l]); umpX2v = FMAMACC(umpX2v,vv,rrv); #else umpX1v = _mm256_add_pd(umpX1v,_mm256_mul_pd(vv,_mm256_load_pd(&ll[l]))); umpX2v = _mm256_add_pd(umpX2v,_mm256_mul_pd(vv,_mm256_load_pd(&rr[l]))); #endif } umpX1v = hadd3(umpX1v); umpX2v = hadd3(umpX2v); _mm256_maskstore_pd(&umpX1[80 * i + k], bitmask, umpX1v); _mm256_maskstore_pd(&umpX2[80 * i + k], bitmask, umpX2v); } } for(i = 0; i < n; i++) { uX1 = &umpX1[80 * tipX1[i]]; uX2 = &umpX2[80 * tipX2[i]]; for(j = 0; j < 4; j++) { __m256d vv[5]; v = &x3[i * 80 + j * 20]; vv[0] = _mm256_setzero_pd(); vv[1] = _mm256_setzero_pd(); vv[2] = _mm256_setzero_pd(); vv[3] = _mm256_setzero_pd(); vv[4] = _mm256_setzero_pd(); for(k = 0; k < 20; k++) { x1px2 = uX1[j * 20 + k] * uX2[j * 20 + k]; __m256d x1px2v = _mm256_set1_pd(x1px2); __m256d extEvv = _mm256_load_pd(&extEV[20 * k]); #ifdef _FMA vv[0] = FMAMACC(vv[0],x1px2v,extEvv); #else vv[0] = _mm256_add_pd(vv[0],_mm256_mul_pd(x1px2v,extEvv)); #endif _mm256_store_pd(&v[0],vv[0]); extEvv = _mm256_load_pd(&extEV[20 * k + 4]); #ifdef _FMA vv[1] = FMAMACC(vv[1],x1px2v,extEvv); #else vv[1] = _mm256_add_pd(vv[1],_mm256_mul_pd(x1px2v,extEvv)); #endif _mm256_store_pd(&v[4],vv[1]); extEvv = _mm256_load_pd(&extEV[20 * k + 8]); #ifdef _FMA vv[2] = FMAMACC(vv[2],x1px2v,extEvv); #else vv[2] = _mm256_add_pd(vv[2],_mm256_mul_pd(x1px2v,extEvv)); #endif _mm256_store_pd(&v[8],vv[2]); extEvv = _mm256_load_pd(&extEV[20 * k + 12]); #ifdef _FMA vv[3] = FMAMACC(vv[3],x1px2v,extEvv); #else vv[3] = _mm256_add_pd(vv[3],_mm256_mul_pd(x1px2v,extEvv)); #endif _mm256_store_pd(&v[12],vv[3]); extEvv = _mm256_load_pd(&extEV[20 * k + 16]); #ifdef _FMA vv[4] = FMAMACC(vv[4],x1px2v,extEvv); #else vv[4] = _mm256_add_pd(vv[4],_mm256_mul_pd(x1px2v,extEvv)); #endif _mm256_store_pd(&v[16],vv[4]); } } } } break; case TIP_INNER: { double umpX1[1840] __attribute__ ((aligned (BYTE_ALIGNMENT))), ump_x2[20] __attribute__ ((aligned (BYTE_ALIGNMENT))); for(i = 0; i < 23; i++) { v = &(tipVector[20 * i]); for(k = 0; k < 80; k++) { __m256d umpX1v = _mm256_setzero_pd(); for(l = 0; l < 20; l+=4) { __m256d vv = _mm256_load_pd(&v[l]); __m256d leftv = _mm256_load_pd(&left[k * 20 + l]); #ifdef _FMA umpX1v = FMAMACC(umpX1v, vv, leftv); #else umpX1v = _mm256_add_pd(umpX1v, _mm256_mul_pd(vv, leftv)); #endif } umpX1v = hadd3(umpX1v); _mm256_maskstore_pd(&umpX1[80 * i + k], bitmask, umpX1v); } } for (i = 0; i < n; i++) { uX1 = &umpX1[80 * tipX1[i]]; for(k = 0; k < 4; k++) { v = &(x2[80 * i + k * 20]); for(l = 0; l < 20; l++) { __m256d ump_x2v = _mm256_setzero_pd(); __m256d vv = _mm256_load_pd(&v[0]); __m256d rightv = _mm256_load_pd(&right[k*400+l*20+0]); #ifdef _FMA ump_x2v = FMAMACC(ump_x2v,vv,rightv); #else ump_x2v = _mm256_add_pd(ump_x2v, _mm256_mul_pd(vv, rightv)); #endif vv = _mm256_load_pd(&v[4]); rightv = _mm256_load_pd(&right[k*400+l*20+4]); #ifdef _FMA ump_x2v = FMAMACC(ump_x2v,vv,rightv); #else ump_x2v = _mm256_add_pd(ump_x2v, _mm256_mul_pd(vv, rightv)); #endif vv = _mm256_load_pd(&v[8]); rightv = _mm256_load_pd(&right[k*400+l*20+8]); #ifdef _FMA ump_x2v = FMAMACC(ump_x2v,vv,rightv); #else ump_x2v = _mm256_add_pd(ump_x2v, _mm256_mul_pd(vv, rightv)); #endif vv = _mm256_load_pd(&v[12]); rightv = _mm256_load_pd(&right[k*400+l*20+12]); #ifdef _FMA ump_x2v = FMAMACC(ump_x2v,vv,rightv); #else ump_x2v = _mm256_add_pd(ump_x2v, _mm256_mul_pd(vv, rightv)); #endif vv = _mm256_load_pd(&v[16]); rightv = _mm256_load_pd(&right[k*400+l*20+16]); #ifdef _FMA ump_x2v = FMAMACC(ump_x2v,vv,rightv); #else ump_x2v = _mm256_add_pd(ump_x2v, _mm256_mul_pd(vv, rightv)); #endif ump_x2v = hadd3(ump_x2v); _mm256_maskstore_pd(&ump_x2[l], bitmask, ump_x2v); } v = &(x3[80 * i + 20 * k]); __m256d vv[5]; vv[0] = _mm256_setzero_pd(); vv[1] = _mm256_setzero_pd(); vv[2] = _mm256_setzero_pd(); vv[3] = _mm256_setzero_pd(); vv[4] = _mm256_setzero_pd(); for(l = 0; l < 20; l++) { x1px2 = uX1[k * 20 + l] * ump_x2[l]; __m256d x1px2v = _mm256_set1_pd(x1px2); #ifdef _FMA __m256d ev = _mm256_load_pd(&extEV[l * 20 + 0]); vv[0] = FMAMACC(vv[0],x1px2v, ev); #else vv[0] = _mm256_add_pd(vv[0],_mm256_mul_pd(x1px2v, _mm256_load_pd(&extEV[l * 20 + 0]))); #endif _mm256_store_pd(&v[0],vv[0]); #ifdef _FMA ev = _mm256_load_pd(&extEV[l * 20 + 4]); vv[1] = FMAMACC(vv[1],x1px2v, ev); #else vv[1] = _mm256_add_pd(vv[1],_mm256_mul_pd(x1px2v, _mm256_load_pd(&extEV[l * 20 + 4]))); #endif _mm256_store_pd(&v[4],vv[1]); #ifdef _FMA ev = _mm256_load_pd(&extEV[l * 20 + 8]); vv[2] = FMAMACC(vv[2],x1px2v, ev); #else vv[2] = _mm256_add_pd(vv[2],_mm256_mul_pd(x1px2v, _mm256_load_pd(&extEV[l * 20 + 8]))); #endif _mm256_store_pd(&v[8],vv[2]); #ifdef _FMA ev = _mm256_load_pd(&extEV[l * 20 + 12]); vv[3] = FMAMACC(vv[3],x1px2v, ev); #else vv[3] = _mm256_add_pd(vv[3],_mm256_mul_pd(x1px2v, _mm256_load_pd(&extEV[l * 20 + 12]))); #endif _mm256_store_pd(&v[12],vv[3]); #ifdef _FMA ev = _mm256_load_pd(&extEV[l * 20 + 16]); vv[4] = FMAMACC(vv[4],x1px2v, ev); #else vv[4] = _mm256_add_pd(vv[4],_mm256_mul_pd(x1px2v, _mm256_load_pd(&extEV[l * 20 + 16]))); #endif _mm256_store_pd(&v[16],vv[4]); } } v = &x3[80 * i]; __m256d minlikelihood_avx = _mm256_set1_pd(minlikelihood); scale = 1; for(l = 0; scale && (l < 80); l += 4) { __m256d vv = _mm256_load_pd(&v[l]); __m256d vv_abs = _mm256_and_pd(vv,absMask_AVX.m); vv_abs = _mm256_cmp_pd(vv_abs,minlikelihood_avx,_CMP_LT_OS); if(_mm256_movemask_pd(vv_abs) != 15) scale = 0; } if(scale) { __m256d twotothe256v = _mm256_set_pd(twotothe256,twotothe256,twotothe256,twotothe256); for(l = 0; l < 80; l += 4) { __m256d vv = _mm256_load_pd(&v[l]); _mm256_store_pd(&v[l],_mm256_mul_pd(vv,twotothe256v)); } addScale += wgt[i]; } } } break; case INNER_INNER: for(i = 0; i < n; i++) { scale = 1; for(k = 0; k < 4; k++) { vl = &(x1[80 * i + 20 * k]); vr = &(x2[80 * i + 20 * k]); v = &(x3[80 * i + 20 * k]); __m256d vv[5]; vv[0] = _mm256_setzero_pd(); vv[1] = _mm256_setzero_pd(); vv[2] = _mm256_setzero_pd(); vv[3] = _mm256_setzero_pd(); vv[4] = _mm256_setzero_pd(); for(l = 0; l < 20; l++) { __m256d al = _mm256_setzero_pd(); __m256d ar = _mm256_setzero_pd(); __m256d leftv = _mm256_load_pd(&left[k * 400 + l * 20 + 0]); __m256d rightv = _mm256_load_pd(&right[k * 400 + l * 20 + 0]); __m256d vlv = _mm256_load_pd(&vl[0]); __m256d vrv = _mm256_load_pd(&vr[0]); #ifdef _FMA al = FMAMACC(al, vlv, leftv); ar = FMAMACC(ar, vrv, rightv); #else al = _mm256_add_pd(al,_mm256_mul_pd(vlv,leftv)); ar = _mm256_add_pd(ar,_mm256_mul_pd(vrv,rightv)); #endif leftv = _mm256_load_pd(&left[k * 400 + l * 20 + 4]); rightv = _mm256_load_pd(&right[k * 400 + l * 20 + 4]); vlv = _mm256_load_pd(&vl[4]); vrv = _mm256_load_pd(&vr[4]); #ifdef _FMA al = FMAMACC(al, vlv, leftv); ar = FMAMACC(ar, vrv, rightv); #else al = _mm256_add_pd(al,_mm256_mul_pd(vlv,leftv)); ar = _mm256_add_pd(ar,_mm256_mul_pd(vrv,rightv)); #endif leftv = _mm256_load_pd(&left[k * 400 + l * 20 + 8]); rightv = _mm256_load_pd(&right[k * 400 + l * 20 + 8]); vlv = _mm256_load_pd(&vl[8]); vrv = _mm256_load_pd(&vr[8]); #ifdef _FMA al = FMAMACC(al, vlv, leftv); ar = FMAMACC(ar, vrv, rightv); #else al = _mm256_add_pd(al,_mm256_mul_pd(vlv,leftv)); ar = _mm256_add_pd(ar,_mm256_mul_pd(vrv,rightv)); #endif leftv = _mm256_load_pd(&left[k * 400 + l * 20 + 12]); rightv = _mm256_load_pd(&right[k * 400 + l * 20 + 12]); vlv = _mm256_load_pd(&vl[12]); vrv = _mm256_load_pd(&vr[12]); #ifdef _FMA al = FMAMACC(al, vlv, leftv); ar = FMAMACC(ar, vrv, rightv); #else al = _mm256_add_pd(al,_mm256_mul_pd(vlv,leftv)); ar = _mm256_add_pd(ar,_mm256_mul_pd(vrv,rightv)); #endif leftv = _mm256_load_pd(&left[k * 400 + l * 20 + 16]); rightv = _mm256_load_pd(&right[k * 400 + l * 20 + 16]); vlv = _mm256_load_pd(&vl[16]); vrv = _mm256_load_pd(&vr[16]); #ifdef _FMA al = FMAMACC(al, vlv, leftv); ar = FMAMACC(ar, vrv, rightv); #else al = _mm256_add_pd(al,_mm256_mul_pd(vlv,leftv)); ar = _mm256_add_pd(ar,_mm256_mul_pd(vrv,rightv)); #endif /**************************************************************************************************************/ al = hadd3(al); ar = hadd3(ar); al = _mm256_mul_pd(ar,al); /************************************************************************************************************/ #ifdef _FMA __m256d ev = _mm256_load_pd(&extEV[20 * l + 0]); vv[0] = FMAMACC(vv[0], al, ev); #else vv[0] = _mm256_add_pd(vv[0],_mm256_mul_pd(al, _mm256_load_pd(&extEV[20 * l + 0]))); #endif _mm256_store_pd(&v[0],vv[0]); #ifdef _FMA ev = _mm256_load_pd(&extEV[20 * l + 4]); vv[1] = FMAMACC(vv[1], al, ev); #else vv[1] = _mm256_add_pd(vv[1],_mm256_mul_pd(al, _mm256_load_pd(&extEV[20 * l + 4]))); #endif _mm256_store_pd(&v[4],vv[1]); #ifdef _FMA ev = _mm256_load_pd(&extEV[20 * l + 8]); vv[2] = FMAMACC(vv[2], al, ev); #else vv[2] = _mm256_add_pd(vv[2],_mm256_mul_pd(al, _mm256_load_pd(&extEV[20 * l + 8]))); #endif _mm256_store_pd(&v[8],vv[2]); #ifdef _FMA ev = _mm256_load_pd(&extEV[20 * l + 12]); vv[3] = FMAMACC(vv[3], al, ev); #else vv[3] = _mm256_add_pd(vv[3],_mm256_mul_pd(al, _mm256_load_pd(&extEV[20 * l + 12]))); #endif _mm256_store_pd(&v[12],vv[3]); #ifdef _FMA ev = _mm256_load_pd(&extEV[20 * l + 16]); vv[4] = FMAMACC(vv[4], al, ev); #else vv[4] = _mm256_add_pd(vv[4],_mm256_mul_pd(al, _mm256_load_pd(&extEV[20 * l + 16]))); #endif _mm256_store_pd(&v[16],vv[4]); } } v = &(x3[80 * i]); scale = 1; __m256d minlikelihood_avx = _mm256_set1_pd(minlikelihood); for(l = 0; scale && (l < 80); l += 4) { __m256d vv = _mm256_load_pd(&v[l]); __m256d vv_abs = _mm256_and_pd(vv,absMask_AVX.m); vv_abs = _mm256_cmp_pd(vv_abs,minlikelihood_avx,_CMP_LT_OS); if(_mm256_movemask_pd(vv_abs) != 15) scale = 0; } if(scale) { __m256d twotothe256v = _mm256_set_pd(twotothe256,twotothe256,twotothe256,twotothe256); for(l = 0; l < 80; l += 4) { __m256d vv = _mm256_load_pd(&v[l]); _mm256_store_pd(&v[l],_mm256_mul_pd(vv,twotothe256v)); } addScale += wgt[i]; } } break; default: assert(0); } *scalerIncrement = addScale; } /***** functions with memory saving ******************************/ void newviewGTRGAMMA_AVX_GAPPED_SAVE(int tipCase, double *x1_start, double *x2_start, double *x3_start, double *extEV, double *tipVector, int *ex3, unsigned char *tipX1, unsigned char *tipX2, const int n, double *left, double *right, int *wgt, int *scalerIncrement, const boolean useFastScaling, unsigned int *x1_gap, unsigned int *x2_gap, unsigned int *x3_gap, double *x1_gapColumn, double *x2_gapColumn, double *x3_gapColumn ) { int i, k, scale, scaleGap, addScale = 0; __m256d minlikelihood_avx = _mm256_set1_pd( minlikelihood ), twoto = _mm256_set1_pd(twotothe256); double *x1, *x2, *x3, *x1_ptr = x1_start, *x2_ptr = x2_start; switch(tipCase) { case TIP_TIP: { double *uX1, umpX1[1024] __attribute__ ((aligned (BYTE_ALIGNMENT))), *uX2, umpX2[1024] __attribute__ ((aligned (BYTE_ALIGNMENT))); for (i = 1; i < 16; i++) { __m256d tv = _mm256_load_pd(&(tipVector[i * 4])); int j; for (j = 0; j < 4; j++) for (k = 0; k < 4; k++) { __m256d left1 = _mm256_load_pd(&left[j * 16 + k * 4]); left1 = _mm256_mul_pd(left1, tv); left1 = hadd3(left1); _mm256_store_pd(&umpX1[i * 64 + j * 16 + k * 4], left1); } for (j = 0; j < 4; j++) for (k = 0; k < 4; k++) { __m256d left1 = _mm256_load_pd(&right[j * 16 + k * 4]); left1 = _mm256_mul_pd(left1, tv); left1 = hadd3(left1); _mm256_store_pd(&umpX2[i * 64 + j * 16 + k * 4], left1); } } x3 = x3_gapColumn; { uX1 = &umpX1[960]; uX2 = &umpX2[960]; for(k = 0; k < 4; k++) { __m256d xv = _mm256_setzero_pd(); int l; for(l = 0; l < 4; l++) { __m256d x1v = _mm256_mul_pd(_mm256_load_pd(&uX1[k * 16 + l * 4]), _mm256_load_pd(&uX2[k * 16 + l * 4])); __m256d evv = _mm256_load_pd(&extEV[l * 4]); #ifdef _FMA xv = FMAMACC(xv,x1v,evv); #else xv = _mm256_add_pd(xv, _mm256_mul_pd(x1v, evv)); #endif } _mm256_store_pd(&x3[4 * k], xv); } } x3 = x3_start; for(i = 0; i < n; i++) { if(!(x3_gap[i / 32] & mask32[i % 32])) { uX1 = &umpX1[64 * tipX1[i]]; uX2 = &umpX2[64 * tipX2[i]]; for(k = 0; k < 4; k++) { __m256d xv = _mm256_setzero_pd(); int l; for(l = 0; l < 4; l++) { __m256d x1v = _mm256_mul_pd(_mm256_load_pd(&uX1[k * 16 + l * 4]), _mm256_load_pd(&uX2[k * 16 + l * 4])); __m256d evv = _mm256_load_pd(&extEV[l * 4]); #ifdef _FMA xv = FMAMACC(xv,x1v,evv); #else xv = _mm256_add_pd(xv, _mm256_mul_pd(x1v, evv)); #endif } _mm256_store_pd(&x3[4 * k], xv); } x3 += 16; } } } break; case TIP_INNER: { double *uX1, umpX1[1024] __attribute__ ((aligned (BYTE_ALIGNMENT))); for (i = 1; i < 16; i++) { __m256d tv = _mm256_load_pd(&(tipVector[i*4])); int j; for (j = 0; j < 4; j++) for (k = 0; k < 4; k++) { __m256d left1 = _mm256_load_pd(&left[j * 16 + k * 4]); left1 = _mm256_mul_pd(left1, tv); left1 = hadd3(left1); _mm256_store_pd(&umpX1[i * 64 + j * 16 + k * 4], left1); } } { __m256d xv[4]; scaleGap = 1; uX1 = &umpX1[960]; x2 = x2_gapColumn; x3 = x3_gapColumn; for(k = 0; k < 4; k++) { __m256d xvr = _mm256_load_pd(&(x2[k * 4])); int l; xv[k] = _mm256_setzero_pd(); for(l = 0; l < 4; l++) { __m256d x1v = _mm256_load_pd(&uX1[k * 16 + l * 4]), x2v = _mm256_mul_pd(xvr, _mm256_load_pd(&right[k * 16 + l * 4])); x2v = hadd3(x2v); x1v = _mm256_mul_pd(x1v, x2v); __m256d evv = _mm256_load_pd(&extEV[l * 4]); #ifdef _FMA xv[k] = FMAMACC(xv[k],x1v,evv); #else xv[k] = _mm256_add_pd(xv[k], _mm256_mul_pd(x1v, evv)); #endif } if(scaleGap) { __m256d v1 = _mm256_and_pd(xv[k], absMask_AVX.m); v1 = _mm256_cmp_pd(v1, minlikelihood_avx, _CMP_LT_OS); if(_mm256_movemask_pd( v1 ) != 15) scaleGap = 0; } } if(scaleGap) { xv[0] = _mm256_mul_pd(xv[0], twoto); xv[1] = _mm256_mul_pd(xv[1], twoto); xv[2] = _mm256_mul_pd(xv[2], twoto); xv[3] = _mm256_mul_pd(xv[3], twoto); } _mm256_store_pd(&x3[0], xv[0]); _mm256_store_pd(&x3[4], xv[1]); _mm256_store_pd(&x3[8], xv[2]); _mm256_store_pd(&x3[12], xv[3]); } x3 = x3_start; for(i = 0; i < n; i++) { if((x3_gap[i / 32] & mask32[i % 32])) { if(scaleGap) { if(useFastScaling) addScale += wgt[i]; else ex3[i] += 1; } } else { if(x2_gap[i / 32] & mask32[i % 32]) x2 = x2_gapColumn; else { x2 = x2_ptr; x2_ptr += 16; } __m256d xv[4]; scale = 1; uX1 = &umpX1[64 * tipX1[i]]; for(k = 0; k < 4; k++) { __m256d xvr = _mm256_load_pd(&(x2[k * 4])); int l; xv[k] = _mm256_setzero_pd(); for(l = 0; l < 4; l++) { __m256d x1v = _mm256_load_pd(&uX1[k * 16 + l * 4]), x2v = _mm256_mul_pd(xvr, _mm256_load_pd(&right[k * 16 + l * 4])); x2v = hadd3(x2v); x1v = _mm256_mul_pd(x1v, x2v); __m256d evv = _mm256_load_pd(&extEV[l * 4]); #ifdef _FMA xv[k] = FMAMACC(xv[k],x1v,evv); #else xv[k] = _mm256_add_pd(xv[k], _mm256_mul_pd(x1v, evv)); #endif } if(scale) { __m256d v1 = _mm256_and_pd(xv[k], absMask_AVX.m); v1 = _mm256_cmp_pd(v1, minlikelihood_avx, _CMP_LT_OS); if(_mm256_movemask_pd( v1 ) != 15) scale = 0; } } if(scale) { xv[0] = _mm256_mul_pd(xv[0], twoto); xv[1] = _mm256_mul_pd(xv[1], twoto); xv[2] = _mm256_mul_pd(xv[2], twoto); xv[3] = _mm256_mul_pd(xv[3], twoto); if(useFastScaling) addScale += wgt[i]; else ex3[i] += 1; } _mm256_store_pd(&x3[0], xv[0]); _mm256_store_pd(&x3[4], xv[1]); _mm256_store_pd(&x3[8], xv[2]); _mm256_store_pd(&x3[12], xv[3]); x3 += 16; } } } break; case INNER_INNER: { { x1 = x1_gapColumn; x2 = x2_gapColumn; x3 = x3_gapColumn; __m256d xv[4]; scaleGap = 1; for(k = 0; k < 4; k++) { __m256d xvl = _mm256_load_pd(&(x1[k * 4])), xvr = _mm256_load_pd(&(x2[k * 4])); int l; xv[k] = _mm256_setzero_pd(); for(l = 0; l < 4; l++) { __m256d x1v = _mm256_mul_pd(xvl, _mm256_load_pd(&left[k * 16 + l * 4])), x2v = _mm256_mul_pd(xvr, _mm256_load_pd(&right[k * 16 + l * 4])); x1v = hadd4(x1v, x2v); __m256d evv = _mm256_load_pd(&extEV[l * 4]); xv[k] = _mm256_add_pd(xv[k], _mm256_mul_pd(x1v, evv)); } if(scaleGap) { __m256d v1 = _mm256_and_pd(xv[k], absMask_AVX.m); v1 = _mm256_cmp_pd(v1, minlikelihood_avx, _CMP_LT_OS); if(_mm256_movemask_pd( v1 ) != 15) scaleGap = 0; } } if(scaleGap) { xv[0] = _mm256_mul_pd(xv[0], twoto); xv[1] = _mm256_mul_pd(xv[1], twoto); xv[2] = _mm256_mul_pd(xv[2], twoto); xv[3] = _mm256_mul_pd(xv[3], twoto); } _mm256_store_pd(&x3[0], xv[0]); _mm256_store_pd(&x3[4], xv[1]); _mm256_store_pd(&x3[8], xv[2]); _mm256_store_pd(&x3[12], xv[3]); } x3 = x3_start; for(i = 0; i < n; i++) { if(x3_gap[i / 32] & mask32[i % 32]) { if(scaleGap) { if(useFastScaling) addScale += wgt[i]; else ex3[i] += 1; } } else { if(x1_gap[i / 32] & mask32[i % 32]) x1 = x1_gapColumn; else { x1 = x1_ptr; x1_ptr += 16; } if(x2_gap[i / 32] & mask32[i % 32]) x2 = x2_gapColumn; else { x2 = x2_ptr; x2_ptr += 16; } __m256d xv[4]; scale = 1; for(k = 0; k < 4; k++) { __m256d xvl = _mm256_load_pd(&(x1[k * 4])), xvr = _mm256_load_pd(&(x2[k * 4])); int l; xv[k] = _mm256_setzero_pd(); for(l = 0; l < 4; l++) { __m256d x1v = _mm256_mul_pd(xvl, _mm256_load_pd(&left[k * 16 + l * 4])), x2v = _mm256_mul_pd(xvr, _mm256_load_pd(&right[k * 16 + l * 4])); x1v = hadd4(x1v, x2v); __m256d evv = _mm256_load_pd(&extEV[l * 4]); xv[k] = _mm256_add_pd(xv[k], _mm256_mul_pd(x1v, evv)); } if(scale) { __m256d v1 = _mm256_and_pd(xv[k], absMask_AVX.m); v1 = _mm256_cmp_pd(v1, minlikelihood_avx, _CMP_LT_OS); if(_mm256_movemask_pd( v1 ) != 15) scale = 0; } } if(scale) { xv[0] = _mm256_mul_pd(xv[0], twoto); xv[1] = _mm256_mul_pd(xv[1], twoto); xv[2] = _mm256_mul_pd(xv[2], twoto); xv[3] = _mm256_mul_pd(xv[3], twoto); if(useFastScaling) addScale += wgt[i]; else ex3[i] += 1; } _mm256_store_pd(&x3[0], xv[0]); _mm256_store_pd(&x3[4], xv[1]); _mm256_store_pd(&x3[8], xv[2]); _mm256_store_pd(&x3[12], xv[3]); x3 += 16; } } } break; default: assert(0); } if(useFastScaling) *scalerIncrement = addScale; } void newviewGTRCAT_AVX_GAPPED_SAVE(int tipCase, double *EV, int *cptr, double *x1_start, double *x2_start, double *x3_start, double *tipVector, int *ex3, unsigned char *tipX1, unsigned char *tipX2, int n, double *left, double *right, int *wgt, int *scalerIncrement, const boolean useFastScaling, unsigned int *x1_gap, unsigned int *x2_gap, unsigned int *x3_gap, double *x1_gapColumn, double *x2_gapColumn, double *x3_gapColumn, const int maxCats) { double *le, *ri, *x1, *x2, *x3, *x1_ptr = x1_start, *x2_ptr = x2_start, *x3_ptr = x3_start; int i, scaleGap = 0, addScale = 0; __m256d minlikelihood_avx = _mm256_set1_pd( minlikelihood ), twoto = _mm256_set1_pd(twotothe256); { int l; x1 = x1_gapColumn; x2 = x2_gapColumn; x3 = x3_gapColumn; le = &left[maxCats * 16]; ri = &right[maxCats * 16]; __m256d vv = _mm256_setzero_pd(); for(l = 0; l < 4; l++) { __m256d x1v = _mm256_mul_pd(_mm256_load_pd(x1), _mm256_load_pd(&le[l * 4])), x2v = _mm256_mul_pd(_mm256_load_pd(x2), _mm256_load_pd(&ri[l * 4])); x1v = hadd4(x1v, x2v); __m256d evv = _mm256_load_pd(&EV[l * 4]); #ifdef _FMA vv = FMAMACC(vv,x1v,evv); #else vv = _mm256_add_pd(vv, _mm256_mul_pd(x1v, evv)); #endif } if(tipCase != TIP_TIP) { __m256d v1 = _mm256_and_pd(vv, absMask_AVX.m); v1 = _mm256_cmp_pd(v1, minlikelihood_avx, _CMP_LT_OS); if(_mm256_movemask_pd( v1 ) == 15) { vv = _mm256_mul_pd(vv, twoto); scaleGap = 1; } } _mm256_store_pd(x3, vv); } switch(tipCase) { case TIP_TIP: for (i = 0; i < n; i++) { if(noGap(x3_gap, i)) { int l; x1 = &(tipVector[4 * tipX1[i]]); x2 = &(tipVector[4 * tipX2[i]]); x3 = x3_ptr; if(isGap(x1_gap, i)) le = &left[maxCats * 16]; else le = &left[cptr[i] * 16]; if(isGap(x2_gap, i)) ri = &right[maxCats * 16]; else ri = &right[cptr[i] * 16]; __m256d vv = _mm256_setzero_pd(); for(l = 0; l < 4; l++) { __m256d x1v = _mm256_mul_pd(_mm256_load_pd(x1), _mm256_load_pd(&le[l * 4])), x2v = _mm256_mul_pd(_mm256_load_pd(x2), _mm256_load_pd(&ri[l * 4])); x1v = hadd4(x1v, x2v); __m256d evv = _mm256_load_pd(&EV[l * 4]); #ifdef _FMA vv = FMAMACC(vv,x1v,evv); #else vv = _mm256_add_pd(vv, _mm256_mul_pd(x1v, evv)); #endif } _mm256_store_pd(x3, vv); x3_ptr += 4; } } break; case TIP_INNER: for (i = 0; i < n; i++) { if(isGap(x3_gap, i)) { if(scaleGap) { if(useFastScaling) addScale += wgt[i]; else ex3[i] += 1; } } else { int l; x1 = &(tipVector[4 * tipX1[i]]); x3 = x3_ptr; if(isGap(x1_gap, i)) le = &left[maxCats * 16]; else le = &left[cptr[i] * 16]; if(isGap(x2_gap, i)) { ri = &right[maxCats * 16]; x2 = x2_gapColumn; } else { ri = &right[cptr[i] * 16]; x2 = x2_ptr; x2_ptr += 4; } __m256d vv = _mm256_setzero_pd(); for(l = 0; l < 4; l++) { __m256d x1v = _mm256_mul_pd(_mm256_load_pd(x1), _mm256_load_pd(&le[l * 4])), x2v = _mm256_mul_pd(_mm256_load_pd(x2), _mm256_load_pd(&ri[l * 4])); x1v = hadd4(x1v, x2v); __m256d evv = _mm256_load_pd(&EV[l * 4]); #ifdef _FMA vv = FMAMACC(vv,x1v,evv); #else vv = _mm256_add_pd(vv, _mm256_mul_pd(x1v, evv)); #endif } __m256d v1 = _mm256_and_pd(vv, absMask_AVX.m); v1 = _mm256_cmp_pd(v1, minlikelihood_avx, _CMP_LT_OS); if(_mm256_movemask_pd( v1 ) == 15) { vv = _mm256_mul_pd(vv, twoto); if(useFastScaling) addScale += wgt[i]; else ex3[i] += 1; } _mm256_store_pd(x3, vv); x3_ptr += 4; } } break; case INNER_INNER: for (i = 0; i < n; i++) { if(isGap(x3_gap, i)) { if(scaleGap) { if(useFastScaling) addScale += wgt[i]; else ex3[i] += 1; } } else { int l; x3 = x3_ptr; if(isGap(x1_gap, i)) { x1 = x1_gapColumn; le = &left[maxCats * 16]; } else { le = &left[cptr[i] * 16]; x1 = x1_ptr; x1_ptr += 4; } if(isGap(x2_gap, i)) { x2 = x2_gapColumn; ri = &right[maxCats * 16]; } else { ri = &right[cptr[i] * 16]; x2 = x2_ptr; x2_ptr += 4; } __m256d vv = _mm256_setzero_pd(); for(l = 0; l < 4; l++) { __m256d x1v = _mm256_mul_pd(_mm256_load_pd(x1), _mm256_load_pd(&le[l * 4])), x2v = _mm256_mul_pd(_mm256_load_pd(x2), _mm256_load_pd(&ri[l * 4])); x1v = hadd4(x1v, x2v); __m256d evv = _mm256_load_pd(&EV[l * 4]); #ifdef _FMA vv = FMAMACC(vv,x1v,evv); #else vv = _mm256_add_pd(vv, _mm256_mul_pd(x1v, evv)); #endif } __m256d v1 = _mm256_and_pd(vv, absMask_AVX.m); v1 = _mm256_cmp_pd(v1, minlikelihood_avx, _CMP_LT_OS); if(_mm256_movemask_pd( v1 ) == 15) { vv = _mm256_mul_pd(vv, twoto); if(useFastScaling) addScale += wgt[i]; else ex3[i] += 1; } _mm256_store_pd(x3, vv); x3_ptr += 4; } } break; default: assert(0); } if(useFastScaling) *scalerIncrement = addScale; } void newviewGTRCATPROT_AVX_GAPPED_SAVE(int tipCase, double *extEV, int *cptr, double *x1, double *x2, double *x3, double *tipVector, int *ex3, unsigned char *tipX1, unsigned char *tipX2, int n, double *left, double *right, int *wgt, int *scalerIncrement, const boolean useFastScaling, unsigned int *x1_gap, unsigned int *x2_gap, unsigned int *x3_gap, double *x1_gapColumn, double *x2_gapColumn, double *x3_gapColumn, const int maxCats) { double *le, *ri, *v, *vl, *vr, *x1_ptr = x1, *x2_ptr = x2, *x3_ptr = x3; int i, l, scale, addScale = 0, scaleGap = 0; #ifdef _FMA int k; #endif { le = &left[maxCats * 400]; ri = &right[maxCats * 400]; vl = x1_gapColumn; vr = x2_gapColumn; v = x3_gapColumn; __m256d vv[5]; vv[0] = _mm256_setzero_pd(); vv[1] = _mm256_setzero_pd(); vv[2] = _mm256_setzero_pd(); vv[3] = _mm256_setzero_pd(); vv[4] = _mm256_setzero_pd(); for(l = 0; l < 20; l++) { __m256d x1v = _mm256_setzero_pd(), x2v = _mm256_setzero_pd(); double *ev = &extEV[l * 20], *lv = &le[l * 20], *rv = &ri[l * 20]; x1v = _mm256_add_pd(x1v, _mm256_mul_pd(_mm256_load_pd(&vl[0]), _mm256_load_pd(&lv[0]))); x1v = _mm256_add_pd(x1v, _mm256_mul_pd(_mm256_load_pd(&vl[4]), _mm256_load_pd(&lv[4]))); x1v = _mm256_add_pd(x1v, _mm256_mul_pd(_mm256_load_pd(&vl[8]), _mm256_load_pd(&lv[8]))); x1v = _mm256_add_pd(x1v, _mm256_mul_pd(_mm256_load_pd(&vl[12]), _mm256_load_pd(&lv[12]))); x1v = _mm256_add_pd(x1v, _mm256_mul_pd(_mm256_load_pd(&vl[16]), _mm256_load_pd(&lv[16]))); x2v = _mm256_add_pd(x2v, _mm256_mul_pd(_mm256_load_pd(&vr[0]), _mm256_load_pd(&rv[0]))); x2v = _mm256_add_pd(x2v, _mm256_mul_pd(_mm256_load_pd(&vr[4]), _mm256_load_pd(&rv[4]))); x2v = _mm256_add_pd(x2v, _mm256_mul_pd(_mm256_load_pd(&vr[8]), _mm256_load_pd(&rv[8]))); x2v = _mm256_add_pd(x2v, _mm256_mul_pd(_mm256_load_pd(&vr[12]), _mm256_load_pd(&rv[12]))); x2v = _mm256_add_pd(x2v, _mm256_mul_pd(_mm256_load_pd(&vr[16]), _mm256_load_pd(&rv[16]))); x1v = hadd4(x1v, x2v); #ifdef _FMA for(k = 0; k < 5; k++) { __m256d evv = _mm256_load_pd(&ev[k*4]); vv[k] = FMAMACC(vv[k],x1v,evv); } #else __m256d evv[5]; evv[0] = _mm256_load_pd(&ev[0]); evv[1] = _mm256_load_pd(&ev[4]); evv[2] = _mm256_load_pd(&ev[8]); evv[3] = _mm256_load_pd(&ev[12]); evv[4] = _mm256_load_pd(&ev[16]); vv[0] = _mm256_add_pd(vv[0], _mm256_mul_pd(x1v, evv[0])); vv[1] = _mm256_add_pd(vv[1], _mm256_mul_pd(x1v, evv[1])); vv[2] = _mm256_add_pd(vv[2], _mm256_mul_pd(x1v, evv[2])); vv[3] = _mm256_add_pd(vv[3], _mm256_mul_pd(x1v, evv[3])); vv[4] = _mm256_add_pd(vv[4], _mm256_mul_pd(x1v, evv[4])); #endif } if(tipCase != TIP_TIP) { __m256d minlikelihood_avx = _mm256_set1_pd( minlikelihood ); scale = 1; for(l = 0; scale && (l < 20); l += 4) { __m256d v1 = _mm256_and_pd(vv[l / 4], absMask_AVX.m); v1 = _mm256_cmp_pd(v1, minlikelihood_avx, _CMP_LT_OS); if(_mm256_movemask_pd( v1 ) != 15) scale = 0; } if(scale) { __m256d twoto = _mm256_set1_pd(twotothe256); for(l = 0; l < 20; l += 4) vv[l / 4] = _mm256_mul_pd(vv[l / 4] , twoto); scaleGap = 1; } } _mm256_store_pd(&v[0], vv[0]); _mm256_store_pd(&v[4], vv[1]); _mm256_store_pd(&v[8], vv[2]); _mm256_store_pd(&v[12], vv[3]); _mm256_store_pd(&v[16], vv[4]); } switch(tipCase) { case TIP_TIP: { for (i = 0; i < n; i++) { if(noGap(x3_gap, i)) { vl = &(tipVector[20 * tipX1[i]]); vr = &(tipVector[20 * tipX2[i]]); v = x3_ptr; if(isGap(x1_gap, i)) le = &left[maxCats * 400]; else le = &left[cptr[i] * 400]; if(isGap(x2_gap, i)) ri = &right[maxCats * 400]; else ri = &right[cptr[i] * 400]; __m256d vv[5]; vv[0] = _mm256_setzero_pd(); vv[1] = _mm256_setzero_pd(); vv[2] = _mm256_setzero_pd(); vv[3] = _mm256_setzero_pd(); vv[4] = _mm256_setzero_pd(); for(l = 0; l < 20; l++) { __m256d x1v = _mm256_setzero_pd(), x2v = _mm256_setzero_pd(); double *ev = &extEV[l * 20], *lv = &le[l * 20], *rv = &ri[l * 20]; #ifdef _FMA for(k = 0; k < 20; k += 4) { __m256d vlv = _mm256_load_pd(&vl[k]); __m256d lvv = _mm256_load_pd(&lv[k]); x1v = FMAMACC(x1v,vlv,lvv); __m256d vrv = _mm256_load_pd(&vr[k]); __m256d rvv = _mm256_load_pd(&rv[k]); x2v = FMAMACC(x2v,vrv,rvv); } #else x1v = _mm256_add_pd(x1v, _mm256_mul_pd(_mm256_load_pd(&vl[0]), _mm256_load_pd(&lv[0]))); x1v = _mm256_add_pd(x1v, _mm256_mul_pd(_mm256_load_pd(&vl[4]), _mm256_load_pd(&lv[4]))); x1v = _mm256_add_pd(x1v, _mm256_mul_pd(_mm256_load_pd(&vl[8]), _mm256_load_pd(&lv[8]))); x1v = _mm256_add_pd(x1v, _mm256_mul_pd(_mm256_load_pd(&vl[12]), _mm256_load_pd(&lv[12]))); x1v = _mm256_add_pd(x1v, _mm256_mul_pd(_mm256_load_pd(&vl[16]), _mm256_load_pd(&lv[16]))); x2v = _mm256_add_pd(x2v, _mm256_mul_pd(_mm256_load_pd(&vr[0]), _mm256_load_pd(&rv[0]))); x2v = _mm256_add_pd(x2v, _mm256_mul_pd(_mm256_load_pd(&vr[4]), _mm256_load_pd(&rv[4]))); x2v = _mm256_add_pd(x2v, _mm256_mul_pd(_mm256_load_pd(&vr[8]), _mm256_load_pd(&rv[8]))); x2v = _mm256_add_pd(x2v, _mm256_mul_pd(_mm256_load_pd(&vr[12]), _mm256_load_pd(&rv[12]))); x2v = _mm256_add_pd(x2v, _mm256_mul_pd(_mm256_load_pd(&vr[16]), _mm256_load_pd(&rv[16]))); #endif x1v = hadd4(x1v, x2v); #ifdef _FMA for(k = 0; k < 5; k++) { __m256d evv = _mm256_load_pd(&ev[k*4]); vv[k] = FMAMACC(vv[k],x1v,evv); } #else __m256d evv[5]; evv[0] = _mm256_load_pd(&ev[0]); evv[1] = _mm256_load_pd(&ev[4]); evv[2] = _mm256_load_pd(&ev[8]); evv[3] = _mm256_load_pd(&ev[12]); evv[4] = _mm256_load_pd(&ev[16]); vv[0] = _mm256_add_pd(vv[0], _mm256_mul_pd(x1v, evv[0])); vv[1] = _mm256_add_pd(vv[1], _mm256_mul_pd(x1v, evv[1])); vv[2] = _mm256_add_pd(vv[2], _mm256_mul_pd(x1v, evv[2])); vv[3] = _mm256_add_pd(vv[3], _mm256_mul_pd(x1v, evv[3])); vv[4] = _mm256_add_pd(vv[4], _mm256_mul_pd(x1v, evv[4])); #endif } _mm256_store_pd(&v[0], vv[0]); _mm256_store_pd(&v[4], vv[1]); _mm256_store_pd(&v[8], vv[2]); _mm256_store_pd(&v[12], vv[3]); _mm256_store_pd(&v[16], vv[4]); x3_ptr += 20; } } } break; case TIP_INNER: for (i = 0; i < n; i++) { if(isGap(x3_gap, i)) { if(scaleGap) { if(useFastScaling) addScale += wgt[i]; else ex3[i] += 1; } } else { vl = &(tipVector[20 * tipX1[i]]); vr = x2_ptr; v = x3_ptr; if(isGap(x1_gap, i)) le = &left[maxCats * 400]; else le = &left[cptr[i] * 400]; if(isGap(x2_gap, i)) { ri = &right[maxCats * 400]; vr = x2_gapColumn; } else { ri = &right[cptr[i] * 400]; vr = x2_ptr; x2_ptr += 20; } __m256d vv[5]; vv[0] = _mm256_setzero_pd(); vv[1] = _mm256_setzero_pd(); vv[2] = _mm256_setzero_pd(); vv[3] = _mm256_setzero_pd(); vv[4] = _mm256_setzero_pd(); for(l = 0; l < 20; l++) { __m256d x1v = _mm256_setzero_pd(), x2v = _mm256_setzero_pd(); double *ev = &extEV[l * 20], *lv = &le[l * 20], *rv = &ri[l * 20]; #ifdef _FMA for(k = 0; k < 20; k += 4) { __m256d vlv = _mm256_load_pd(&vl[k]); __m256d lvv = _mm256_load_pd(&lv[k]); x1v = FMAMACC(x1v,vlv,lvv); __m256d vrv = _mm256_load_pd(&vr[k]); __m256d rvv = _mm256_load_pd(&rv[k]); x2v = FMAMACC(x2v,vrv,rvv); } #else x1v = _mm256_add_pd(x1v, _mm256_mul_pd(_mm256_load_pd(&vl[0]), _mm256_load_pd(&lv[0]))); x1v = _mm256_add_pd(x1v, _mm256_mul_pd(_mm256_load_pd(&vl[4]), _mm256_load_pd(&lv[4]))); x1v = _mm256_add_pd(x1v, _mm256_mul_pd(_mm256_load_pd(&vl[8]), _mm256_load_pd(&lv[8]))); x1v = _mm256_add_pd(x1v, _mm256_mul_pd(_mm256_load_pd(&vl[12]), _mm256_load_pd(&lv[12]))); x1v = _mm256_add_pd(x1v, _mm256_mul_pd(_mm256_load_pd(&vl[16]), _mm256_load_pd(&lv[16]))); x2v = _mm256_add_pd(x2v, _mm256_mul_pd(_mm256_load_pd(&vr[0]), _mm256_load_pd(&rv[0]))); x2v = _mm256_add_pd(x2v, _mm256_mul_pd(_mm256_load_pd(&vr[4]), _mm256_load_pd(&rv[4]))); x2v = _mm256_add_pd(x2v, _mm256_mul_pd(_mm256_load_pd(&vr[8]), _mm256_load_pd(&rv[8]))); x2v = _mm256_add_pd(x2v, _mm256_mul_pd(_mm256_load_pd(&vr[12]), _mm256_load_pd(&rv[12]))); x2v = _mm256_add_pd(x2v, _mm256_mul_pd(_mm256_load_pd(&vr[16]), _mm256_load_pd(&rv[16]))); #endif x1v = hadd4(x1v, x2v); __m256d evv[5]; evv[0] = _mm256_load_pd(&ev[0]); evv[1] = _mm256_load_pd(&ev[4]); evv[2] = _mm256_load_pd(&ev[8]); evv[3] = _mm256_load_pd(&ev[12]); evv[4] = _mm256_load_pd(&ev[16]); #ifdef _FMA for(k = 0; k < 5; k++) vv[k] = FMAMACC(vv[k],x1v,evv[k]); #else vv[0] = _mm256_add_pd(vv[0], _mm256_mul_pd(x1v, evv[0])); vv[1] = _mm256_add_pd(vv[1], _mm256_mul_pd(x1v, evv[1])); vv[2] = _mm256_add_pd(vv[2], _mm256_mul_pd(x1v, evv[2])); vv[3] = _mm256_add_pd(vv[3], _mm256_mul_pd(x1v, evv[3])); vv[4] = _mm256_add_pd(vv[4], _mm256_mul_pd(x1v, evv[4])); #endif } __m256d minlikelihood_avx = _mm256_set1_pd( minlikelihood ); scale = 1; for(l = 0; scale && (l < 20); l += 4) { __m256d v1 = _mm256_and_pd(vv[l / 4], absMask_AVX.m); v1 = _mm256_cmp_pd(v1, minlikelihood_avx, _CMP_LT_OS); if(_mm256_movemask_pd( v1 ) != 15) scale = 0; } if(scale) { __m256d twoto = _mm256_set1_pd(twotothe256); for(l = 0; l < 20; l += 4) vv[l / 4] = _mm256_mul_pd(vv[l / 4] , twoto); if(useFastScaling) addScale += wgt[i]; else ex3[i] += 1; } _mm256_store_pd(&v[0], vv[0]); _mm256_store_pd(&v[4], vv[1]); _mm256_store_pd(&v[8], vv[2]); _mm256_store_pd(&v[12], vv[3]); _mm256_store_pd(&v[16], vv[4]); x3_ptr += 20; } } break; case INNER_INNER: for(i = 0; i < n; i++) { if(isGap(x3_gap, i)) { if(scaleGap) { if(useFastScaling) addScale += wgt[i]; else ex3[i] += 1; } } else { v = x3_ptr; if(isGap(x1_gap, i)) { vl = x1_gapColumn; le = &left[maxCats * 400]; } else { le = &left[cptr[i] * 400]; vl = x1_ptr; x1_ptr += 20; } if(isGap(x2_gap, i)) { vr = x2_gapColumn; ri = &right[maxCats * 400]; } else { ri = &right[cptr[i] * 400]; vr = x2_ptr; x2_ptr += 20; } __m256d vv[5]; vv[0] = _mm256_setzero_pd(); vv[1] = _mm256_setzero_pd(); vv[2] = _mm256_setzero_pd(); vv[3] = _mm256_setzero_pd(); vv[4] = _mm256_setzero_pd(); for(l = 0; l < 20; l++) { __m256d x1v = _mm256_setzero_pd(), x2v = _mm256_setzero_pd(); double *ev = &extEV[l * 20], *lv = &le[l * 20], *rv = &ri[l * 20]; x1v = _mm256_add_pd(x1v, _mm256_mul_pd(_mm256_load_pd(&vl[0]), _mm256_load_pd(&lv[0]))); x1v = _mm256_add_pd(x1v, _mm256_mul_pd(_mm256_load_pd(&vl[4]), _mm256_load_pd(&lv[4]))); x1v = _mm256_add_pd(x1v, _mm256_mul_pd(_mm256_load_pd(&vl[8]), _mm256_load_pd(&lv[8]))); x1v = _mm256_add_pd(x1v, _mm256_mul_pd(_mm256_load_pd(&vl[12]), _mm256_load_pd(&lv[12]))); x1v = _mm256_add_pd(x1v, _mm256_mul_pd(_mm256_load_pd(&vl[16]), _mm256_load_pd(&lv[16]))); x2v = _mm256_add_pd(x2v, _mm256_mul_pd(_mm256_load_pd(&vr[0]), _mm256_load_pd(&rv[0]))); x2v = _mm256_add_pd(x2v, _mm256_mul_pd(_mm256_load_pd(&vr[4]), _mm256_load_pd(&rv[4]))); x2v = _mm256_add_pd(x2v, _mm256_mul_pd(_mm256_load_pd(&vr[8]), _mm256_load_pd(&rv[8]))); x2v = _mm256_add_pd(x2v, _mm256_mul_pd(_mm256_load_pd(&vr[12]), _mm256_load_pd(&rv[12]))); x2v = _mm256_add_pd(x2v, _mm256_mul_pd(_mm256_load_pd(&vr[16]), _mm256_load_pd(&rv[16]))); x1v = hadd4(x1v, x2v); #ifdef _FMA for(k = 0; k < 5; k++) { __m256d evv = _mm256_load_pd(&ev[k*4]); vv[k] = FMAMACC(vv[k],x1v,evv); } #else __m256d evv[5]; evv[0] = _mm256_load_pd(&ev[0]); evv[1] = _mm256_load_pd(&ev[4]); evv[2] = _mm256_load_pd(&ev[8]); evv[3] = _mm256_load_pd(&ev[12]); evv[4] = _mm256_load_pd(&ev[16]); vv[0] = _mm256_add_pd(vv[0], _mm256_mul_pd(x1v, evv[0])); vv[1] = _mm256_add_pd(vv[1], _mm256_mul_pd(x1v, evv[1])); vv[2] = _mm256_add_pd(vv[2], _mm256_mul_pd(x1v, evv[2])); vv[3] = _mm256_add_pd(vv[3], _mm256_mul_pd(x1v, evv[3])); vv[4] = _mm256_add_pd(vv[4], _mm256_mul_pd(x1v, evv[4])); #endif } __m256d minlikelihood_avx = _mm256_set1_pd( minlikelihood ); scale = 1; for(l = 0; scale && (l < 20); l += 4) { __m256d v1 = _mm256_and_pd(vv[l / 4], absMask_AVX.m); v1 = _mm256_cmp_pd(v1, minlikelihood_avx, _CMP_LT_OS); if(_mm256_movemask_pd( v1 ) != 15) scale = 0; } if(scale) { __m256d twoto = _mm256_set1_pd(twotothe256); for(l = 0; l < 20; l += 4) vv[l / 4] = _mm256_mul_pd(vv[l / 4] , twoto); if(useFastScaling) addScale += wgt[i]; else ex3[i] += 1; } _mm256_store_pd(&v[0], vv[0]); _mm256_store_pd(&v[4], vv[1]); _mm256_store_pd(&v[8], vv[2]); _mm256_store_pd(&v[12], vv[3]); _mm256_store_pd(&v[16], vv[4]); x3_ptr += 20; } } break; default: assert(0); } if(useFastScaling) *scalerIncrement = addScale; } void newviewGTRGAMMAPROT_AVX_GAPPED_SAVE(int tipCase, double *x1_start, double *x2_start, double *x3_start, double *extEV, double *tipVector, int *ex3, unsigned char *tipX1, unsigned char *tipX2, int n, double *left, double *right, int *wgt, int *scalerIncrement, const boolean useFastScaling, unsigned int *x1_gap, unsigned int *x2_gap, unsigned int *x3_gap, double *x1_gapColumn, double *x2_gapColumn, double *x3_gapColumn) { double *x1 = x1_start, *x2 = x2_start, *x3_ptr = x3_start, *x2_ptr = x2_start, *x1_ptr = x1_start, *uX1, *uX2, *v, x1px2, *vl, *vr; int i, j, l, k, gapScaling = 0, scale, addScale = 0; #ifndef GCC_VERSION #define GCC_VERSION (__GNUC__ * 10000 + __GNUC_MINOR__ * 100 + __GNUC_PATCHLEVEL__) #endif #if GCC_VERSION < 40500 __m256d bitmask = _mm256_set_pd(0,0,0,-1); #else __m256i bitmask = _mm256_set_epi32(0, 0, 0, 0, 0, 0, -1, -1); #endif switch(tipCase) { case TIP_TIP: { double umpX1[1840] __attribute__ ((aligned (BYTE_ALIGNMENT))), umpX2[1840] __attribute__ ((aligned (BYTE_ALIGNMENT))); for(i = 0; i < 23; i++) { v = &(tipVector[20 * i]); for(k = 0; k < 80; k++) { double *ll = &left[k * 20], *rr = &right[k * 20]; __m256d umpX1v = _mm256_setzero_pd(), umpX2v = _mm256_setzero_pd(); for(l = 0; l < 20; l+=4) { __m256d vv = _mm256_load_pd(&v[l]); #ifdef _FMA __m256d llv = _mm256_load_pd(&ll[l]); umpX1v = FMAMACC(umpX1v,vv,llv); __m256d rrv = _mm256_load_pd(&rr[l]); umpX2v = FMAMACC(umpX2v,vv,rrv); #else umpX1v = _mm256_add_pd(umpX1v,_mm256_mul_pd(vv,_mm256_load_pd(&ll[l]))); umpX2v = _mm256_add_pd(umpX2v,_mm256_mul_pd(vv,_mm256_load_pd(&rr[l]))); #endif } umpX1v = hadd3(umpX1v); umpX2v = hadd3(umpX2v); _mm256_maskstore_pd(&umpX1[80 * i + k], bitmask, umpX1v); _mm256_maskstore_pd(&umpX2[80 * i + k], bitmask, umpX2v); } } { uX1 = &umpX1[1760]; uX2 = &umpX2[1760]; for(j = 0; j < 4; j++) { __m256d vv[5]; v = &x3_gapColumn[j * 20]; vv[0] = _mm256_setzero_pd(); vv[1] = _mm256_setzero_pd(); vv[2] = _mm256_setzero_pd(); vv[3] = _mm256_setzero_pd(); vv[4] = _mm256_setzero_pd(); for(k = 0; k < 20; k++) { x1px2 = uX1[j * 20 + k] * uX2[j * 20 + k]; __m256d x1px2v = _mm256_set1_pd(x1px2); __m256d extEvv = _mm256_load_pd(&extEV[20 * k]); #ifdef _FMA vv[0] = FMAMACC(vv[0],x1px2v,extEvv); #else vv[0] = _mm256_add_pd(vv[0],_mm256_mul_pd(x1px2v,extEvv)); #endif _mm256_store_pd(&v[0],vv[0]); extEvv = _mm256_load_pd(&extEV[20 * k + 4]); #ifdef _FMA vv[1] = FMAMACC(vv[1],x1px2v,extEvv); #else vv[1] = _mm256_add_pd(vv[1],_mm256_mul_pd(x1px2v,extEvv)); #endif _mm256_store_pd(&v[4],vv[1]); extEvv = _mm256_load_pd(&extEV[20 * k + 8]); #ifdef _FMA vv[2] = FMAMACC(vv[2],x1px2v,extEvv); #else vv[2] = _mm256_add_pd(vv[2],_mm256_mul_pd(x1px2v,extEvv)); #endif _mm256_store_pd(&v[8],vv[2]); extEvv = _mm256_load_pd(&extEV[20 * k + 12]); #ifdef _FMA vv[3] = FMAMACC(vv[3],x1px2v,extEvv); #else vv[3] = _mm256_add_pd(vv[3],_mm256_mul_pd(x1px2v,extEvv)); #endif _mm256_store_pd(&v[12],vv[3]); extEvv = _mm256_load_pd(&extEV[20 * k + 16]); #ifdef _FMA vv[4] = FMAMACC(vv[4],x1px2v,extEvv); #else vv[4] = _mm256_add_pd(vv[4],_mm256_mul_pd(x1px2v,extEvv)); #endif _mm256_store_pd(&v[16],vv[4]); } } } for(i = 0; i < n; i++) { if(!(x3_gap[i / 32] & mask32[i % 32])) { uX1 = &umpX1[80 * tipX1[i]]; uX2 = &umpX2[80 * tipX2[i]]; for(j = 0; j < 4; j++) { __m256d vv[5]; v = &x3_ptr[j * 20]; vv[0] = _mm256_setzero_pd(); vv[1] = _mm256_setzero_pd(); vv[2] = _mm256_setzero_pd(); vv[3] = _mm256_setzero_pd(); vv[4] = _mm256_setzero_pd(); for(k = 0; k < 20; k++) { x1px2 = uX1[j * 20 + k] * uX2[j * 20 + k]; __m256d x1px2v = _mm256_set1_pd(x1px2); __m256d extEvv = _mm256_load_pd(&extEV[20 * k]); #ifdef _FMA vv[0] = FMAMACC(vv[0],x1px2v,extEvv); #else vv[0] = _mm256_add_pd(vv[0],_mm256_mul_pd(x1px2v,extEvv)); #endif _mm256_store_pd(&v[0],vv[0]); extEvv = _mm256_load_pd(&extEV[20 * k + 4]); #ifdef _FMA vv[1] = FMAMACC(vv[1],x1px2v,extEvv); #else vv[1] = _mm256_add_pd(vv[1],_mm256_mul_pd(x1px2v,extEvv)); #endif _mm256_store_pd(&v[4],vv[1]); extEvv = _mm256_load_pd(&extEV[20 * k + 8]); #ifdef _FMA vv[2] = FMAMACC(vv[2],x1px2v,extEvv); #else vv[2] = _mm256_add_pd(vv[2],_mm256_mul_pd(x1px2v,extEvv)); #endif _mm256_store_pd(&v[8],vv[2]); extEvv = _mm256_load_pd(&extEV[20 * k + 12]); #ifdef _FMA vv[3] = FMAMACC(vv[3],x1px2v,extEvv); #else vv[3] = _mm256_add_pd(vv[3],_mm256_mul_pd(x1px2v,extEvv)); #endif _mm256_store_pd(&v[12],vv[3]); extEvv = _mm256_load_pd(&extEV[20 * k + 16]); #ifdef _FMA vv[4] = FMAMACC(vv[4],x1px2v,extEvv); #else vv[4] = _mm256_add_pd(vv[4],_mm256_mul_pd(x1px2v,extEvv)); #endif _mm256_store_pd(&v[16],vv[4]); } } x3_ptr += 80; } } } break; case TIP_INNER: { double umpX1[1840] __attribute__ ((aligned (BYTE_ALIGNMENT))), ump_x2[20] __attribute__ ((aligned (BYTE_ALIGNMENT))); for(i = 0; i < 23; i++) { v = &(tipVector[20 * i]); for(k = 0; k < 80; k++) { __m256d umpX1v = _mm256_setzero_pd(); for(l = 0; l < 20; l+=4) { __m256d vv = _mm256_load_pd(&v[l]); __m256d leftv = _mm256_load_pd(&left[k * 20 + l]); #ifdef _FMA umpX1v = FMAMACC(umpX1v, vv, leftv); #else umpX1v = _mm256_add_pd(umpX1v, _mm256_mul_pd(vv, leftv)); #endif } umpX1v = hadd3(umpX1v); _mm256_maskstore_pd(&umpX1[80 * i + k], bitmask, umpX1v); } } { uX1 = &umpX1[1760]; for(k = 0; k < 4; k++) { v = &(x2_gapColumn[k * 20]); for(l = 0; l < 20; l++) { __m256d ump_x2v = _mm256_setzero_pd(); __m256d vv = _mm256_load_pd(&v[0]); __m256d rightv = _mm256_load_pd(&right[k*400+l*20+0]); #ifdef _FMA ump_x2v = FMAMACC(ump_x2v,vv,rightv); #else ump_x2v = _mm256_add_pd(ump_x2v, _mm256_mul_pd(vv, rightv)); #endif vv = _mm256_load_pd(&v[4]); rightv = _mm256_load_pd(&right[k*400+l*20+4]); #ifdef _FMA ump_x2v = FMAMACC(ump_x2v,vv,rightv); #else ump_x2v = _mm256_add_pd(ump_x2v, _mm256_mul_pd(vv, rightv)); #endif vv = _mm256_load_pd(&v[8]); rightv = _mm256_load_pd(&right[k*400+l*20+8]); #ifdef _FMA ump_x2v = FMAMACC(ump_x2v,vv,rightv); #else ump_x2v = _mm256_add_pd(ump_x2v, _mm256_mul_pd(vv, rightv)); #endif vv = _mm256_load_pd(&v[12]); rightv = _mm256_load_pd(&right[k*400+l*20+12]); #ifdef _FMA ump_x2v = FMAMACC(ump_x2v,vv,rightv); #else ump_x2v = _mm256_add_pd(ump_x2v, _mm256_mul_pd(vv, rightv)); #endif vv = _mm256_load_pd(&v[16]); rightv = _mm256_load_pd(&right[k*400+l*20+16]); #ifdef _FMA ump_x2v = FMAMACC(ump_x2v,vv,rightv); #else ump_x2v = _mm256_add_pd(ump_x2v, _mm256_mul_pd(vv, rightv)); #endif ump_x2v = hadd3(ump_x2v); _mm256_maskstore_pd(&ump_x2[l], bitmask, ump_x2v); } v = &x3_gapColumn[20 * k]; __m256d vv[5]; vv[0] = _mm256_setzero_pd(); vv[1] = _mm256_setzero_pd(); vv[2] = _mm256_setzero_pd(); vv[3] = _mm256_setzero_pd(); vv[4] = _mm256_setzero_pd(); for(l = 0; l < 20; l++) { x1px2 = uX1[k * 20 + l] * ump_x2[l]; __m256d x1px2v = _mm256_set1_pd(x1px2); #ifdef _FMA __m256d ev = _mm256_load_pd(&extEV[l * 20 + 0]); vv[0] = FMAMACC(vv[0],x1px2v, ev); #else vv[0] = _mm256_add_pd(vv[0],_mm256_mul_pd(x1px2v, _mm256_load_pd(&extEV[l * 20 + 0]))); #endif _mm256_store_pd(&v[0],vv[0]); #ifdef _FMA ev = _mm256_load_pd(&extEV[l * 20 + 4]); vv[1] = FMAMACC(vv[1],x1px2v, ev); #else vv[1] = _mm256_add_pd(vv[1],_mm256_mul_pd(x1px2v, _mm256_load_pd(&extEV[l * 20 + 4]))); #endif _mm256_store_pd(&v[4],vv[1]); #ifdef _FMA ev = _mm256_load_pd(&extEV[l * 20 + 8]); vv[2] = FMAMACC(vv[2],x1px2v, ev); #else vv[2] = _mm256_add_pd(vv[2],_mm256_mul_pd(x1px2v, _mm256_load_pd(&extEV[l * 20 + 8]))); #endif _mm256_store_pd(&v[8],vv[2]); #ifdef _FMA ev = _mm256_load_pd(&extEV[l * 20 + 12]); vv[3] = FMAMACC(vv[3],x1px2v, ev); #else vv[3] = _mm256_add_pd(vv[3],_mm256_mul_pd(x1px2v, _mm256_load_pd(&extEV[l * 20 + 12]))); #endif _mm256_store_pd(&v[12],vv[3]); #ifdef _FMA ev = _mm256_load_pd(&extEV[l * 20 + 16]); vv[4] = FMAMACC(vv[4],x1px2v, ev); #else vv[4] = _mm256_add_pd(vv[4],_mm256_mul_pd(x1px2v, _mm256_load_pd(&extEV[l * 20 + 16]))); #endif _mm256_store_pd(&v[16],vv[4]); } } v = x3_gapColumn; __m256d minlikelihood_avx = _mm256_set1_pd(minlikelihood); scale = 1; for(l = 0; scale && (l < 80); l += 4) { __m256d vv = _mm256_load_pd(&v[l]); __m256d vv_abs = _mm256_and_pd(vv,absMask_AVX.m); vv_abs = _mm256_cmp_pd(vv_abs,minlikelihood_avx,_CMP_LT_OS); if(_mm256_movemask_pd(vv_abs) != 15) scale = 0; } if(scale) { __m256d twotothe256v = _mm256_set_pd(twotothe256,twotothe256,twotothe256,twotothe256); gapScaling = 1; for(l = 0; l < 80; l += 4) { __m256d vv = _mm256_load_pd(&v[l]); _mm256_store_pd(&v[l],_mm256_mul_pd(vv,twotothe256v)); } } } for (i = 0; i < n; i++) { if((x3_gap[i / 32] & mask32[i % 32])) { if(gapScaling) { if(useFastScaling) addScale += wgt[i]; else ex3[i] += 1; } } else { uX1 = &umpX1[80 * tipX1[i]]; if(x2_gap[i / 32] & mask32[i % 32]) x2 = x2_gapColumn; else { x2 = x2_ptr; x2_ptr += 80; } for(k = 0; k < 4; k++) { v = &(x2[k * 20]); for(l = 0; l < 20; l++) { __m256d ump_x2v = _mm256_setzero_pd(); __m256d vv = _mm256_load_pd(&v[0]); __m256d rightv = _mm256_load_pd(&right[k*400+l*20+0]); #ifdef _FMA ump_x2v = FMAMACC(ump_x2v,vv,rightv); #else ump_x2v = _mm256_add_pd(ump_x2v, _mm256_mul_pd(vv, rightv)); #endif vv = _mm256_load_pd(&v[4]); rightv = _mm256_load_pd(&right[k*400+l*20+4]); #ifdef _FMA ump_x2v = FMAMACC(ump_x2v,vv,rightv); #else ump_x2v = _mm256_add_pd(ump_x2v, _mm256_mul_pd(vv, rightv)); #endif vv = _mm256_load_pd(&v[8]); rightv = _mm256_load_pd(&right[k*400+l*20+8]); #ifdef _FMA ump_x2v = FMAMACC(ump_x2v,vv,rightv); #else ump_x2v = _mm256_add_pd(ump_x2v, _mm256_mul_pd(vv, rightv)); #endif vv = _mm256_load_pd(&v[12]); rightv = _mm256_load_pd(&right[k*400+l*20+12]); #ifdef _FMA ump_x2v = FMAMACC(ump_x2v,vv,rightv); #else ump_x2v = _mm256_add_pd(ump_x2v, _mm256_mul_pd(vv, rightv)); #endif vv = _mm256_load_pd(&v[16]); rightv = _mm256_load_pd(&right[k*400+l*20+16]); #ifdef _FMA ump_x2v = FMAMACC(ump_x2v,vv,rightv); #else ump_x2v = _mm256_add_pd(ump_x2v, _mm256_mul_pd(vv, rightv)); #endif ump_x2v = hadd3(ump_x2v); _mm256_maskstore_pd(&ump_x2[l], bitmask, ump_x2v); } v = &x3_ptr[k * 20]; __m256d vv[5]; vv[0] = _mm256_setzero_pd(); vv[1] = _mm256_setzero_pd(); vv[2] = _mm256_setzero_pd(); vv[3] = _mm256_setzero_pd(); vv[4] = _mm256_setzero_pd(); for(l = 0; l < 20; l++) { x1px2 = uX1[k * 20 + l] * ump_x2[l]; __m256d x1px2v = _mm256_set1_pd(x1px2); #ifdef _FMA __m256d ev = _mm256_load_pd(&extEV[l * 20 + 0]); vv[0] = FMAMACC(vv[0],x1px2v, ev); #else vv[0] = _mm256_add_pd(vv[0],_mm256_mul_pd(x1px2v, _mm256_load_pd(&extEV[l * 20 + 0]))); #endif _mm256_store_pd(&v[0],vv[0]); #ifdef _FMA ev = _mm256_load_pd(&extEV[l * 20 + 4]); vv[1] = FMAMACC(vv[1],x1px2v, ev); #else vv[1] = _mm256_add_pd(vv[1],_mm256_mul_pd(x1px2v, _mm256_load_pd(&extEV[l * 20 + 4]))); #endif _mm256_store_pd(&v[4],vv[1]); #ifdef _FMA ev = _mm256_load_pd(&extEV[l * 20 + 8]); vv[2] = FMAMACC(vv[2],x1px2v, ev); #else vv[2] = _mm256_add_pd(vv[2],_mm256_mul_pd(x1px2v, _mm256_load_pd(&extEV[l * 20 + 8]))); #endif _mm256_store_pd(&v[8],vv[2]); #ifdef _FMA ev = _mm256_load_pd(&extEV[l * 20 + 12]); vv[3] = FMAMACC(vv[3],x1px2v, ev); #else vv[3] = _mm256_add_pd(vv[3],_mm256_mul_pd(x1px2v, _mm256_load_pd(&extEV[l * 20 + 12]))); #endif _mm256_store_pd(&v[12],vv[3]); #ifdef _FMA ev = _mm256_load_pd(&extEV[l * 20 + 16]); vv[4] = FMAMACC(vv[4],x1px2v, ev); #else vv[4] = _mm256_add_pd(vv[4],_mm256_mul_pd(x1px2v, _mm256_load_pd(&extEV[l * 20 + 16]))); #endif _mm256_store_pd(&v[16],vv[4]); } } v = x3_ptr; __m256d minlikelihood_avx = _mm256_set1_pd(minlikelihood); scale = 1; for(l = 0; scale && (l < 80); l += 4) { __m256d vv = _mm256_load_pd(&v[l]); __m256d vv_abs = _mm256_and_pd(vv,absMask_AVX.m); vv_abs = _mm256_cmp_pd(vv_abs,minlikelihood_avx,_CMP_LT_OS); if(_mm256_movemask_pd(vv_abs) != 15) scale = 0; } if(scale) { __m256d twotothe256v = _mm256_set_pd(twotothe256,twotothe256,twotothe256,twotothe256); for(l = 0; l < 80; l += 4) { __m256d vv = _mm256_load_pd(&v[l]); _mm256_store_pd(&v[l],_mm256_mul_pd(vv,twotothe256v)); } if(useFastScaling) addScale += wgt[i]; else ex3[i] += 1; } x3_ptr += 80; } } } break; case INNER_INNER: for(k = 0; k < 4; k++) { vl = &(x1_gapColumn[20 * k]); vr = &(x2_gapColumn[20 * k]); v = &(x3_gapColumn[20 * k]); __m256d vv[5]; vv[0] = _mm256_setzero_pd(); vv[1] = _mm256_setzero_pd(); vv[2] = _mm256_setzero_pd(); vv[3] = _mm256_setzero_pd(); vv[4] = _mm256_setzero_pd(); for(l = 0; l < 20; l++) { __m256d al = _mm256_setzero_pd(); __m256d ar = _mm256_setzero_pd(); __m256d leftv = _mm256_load_pd(&left[k * 400 + l * 20 + 0]); __m256d rightv = _mm256_load_pd(&right[k * 400 + l * 20 + 0]); __m256d vlv = _mm256_load_pd(&vl[0]); __m256d vrv = _mm256_load_pd(&vr[0]); #ifdef _FMA al = FMAMACC(al, vlv, leftv); ar = FMAMACC(ar, vrv, rightv); #else al = _mm256_add_pd(al,_mm256_mul_pd(vlv,leftv)); ar = _mm256_add_pd(ar,_mm256_mul_pd(vrv,rightv)); #endif leftv = _mm256_load_pd(&left[k * 400 + l * 20 + 4]); rightv = _mm256_load_pd(&right[k * 400 + l * 20 + 4]); vlv = _mm256_load_pd(&vl[4]); vrv = _mm256_load_pd(&vr[4]); #ifdef _FMA al = FMAMACC(al, vlv, leftv); ar = FMAMACC(ar, vrv, rightv); #else al = _mm256_add_pd(al,_mm256_mul_pd(vlv,leftv)); ar = _mm256_add_pd(ar,_mm256_mul_pd(vrv,rightv)); #endif leftv = _mm256_load_pd(&left[k * 400 + l * 20 + 8]); rightv = _mm256_load_pd(&right[k * 400 + l * 20 + 8]); vlv = _mm256_load_pd(&vl[8]); vrv = _mm256_load_pd(&vr[8]); #ifdef _FMA al = FMAMACC(al, vlv, leftv); ar = FMAMACC(ar, vrv, rightv); #else al = _mm256_add_pd(al,_mm256_mul_pd(vlv,leftv)); ar = _mm256_add_pd(ar,_mm256_mul_pd(vrv,rightv)); #endif leftv = _mm256_load_pd(&left[k * 400 + l * 20 + 12]); rightv = _mm256_load_pd(&right[k * 400 + l * 20 + 12]); vlv = _mm256_load_pd(&vl[12]); vrv = _mm256_load_pd(&vr[12]); #ifdef _FMA al = FMAMACC(al, vlv, leftv); ar = FMAMACC(ar, vrv, rightv); #else al = _mm256_add_pd(al,_mm256_mul_pd(vlv,leftv)); ar = _mm256_add_pd(ar,_mm256_mul_pd(vrv,rightv)); #endif leftv = _mm256_load_pd(&left[k * 400 + l * 20 + 16]); rightv = _mm256_load_pd(&right[k * 400 + l * 20 + 16]); vlv = _mm256_load_pd(&vl[16]); vrv = _mm256_load_pd(&vr[16]); #ifdef _FMA al = FMAMACC(al, vlv, leftv); ar = FMAMACC(ar, vrv, rightv); #else al = _mm256_add_pd(al,_mm256_mul_pd(vlv,leftv)); ar = _mm256_add_pd(ar,_mm256_mul_pd(vrv,rightv)); #endif /**************************************************************************************************************/ al = hadd3(al); ar = hadd3(ar); al = _mm256_mul_pd(ar,al); /************************************************************************************************************/ #ifdef _FMA __m256d ev = _mm256_load_pd(&extEV[20 * l + 0]); vv[0] = FMAMACC(vv[0], al, ev); #else vv[0] = _mm256_add_pd(vv[0],_mm256_mul_pd(al, _mm256_load_pd(&extEV[20 * l + 0]))); #endif _mm256_store_pd(&v[0],vv[0]); #ifdef _FMA ev = _mm256_load_pd(&extEV[20 * l + 4]); vv[1] = FMAMACC(vv[1], al, ev); #else vv[1] = _mm256_add_pd(vv[1],_mm256_mul_pd(al, _mm256_load_pd(&extEV[20 * l + 4]))); #endif _mm256_store_pd(&v[4],vv[1]); #ifdef _FMA ev = _mm256_load_pd(&extEV[20 * l + 8]); vv[2] = FMAMACC(vv[2], al, ev); #else vv[2] = _mm256_add_pd(vv[2],_mm256_mul_pd(al, _mm256_load_pd(&extEV[20 * l + 8]))); #endif _mm256_store_pd(&v[8],vv[2]); #ifdef _FMA ev = _mm256_load_pd(&extEV[20 * l + 12]); vv[3] = FMAMACC(vv[3], al, ev); #else vv[3] = _mm256_add_pd(vv[3],_mm256_mul_pd(al, _mm256_load_pd(&extEV[20 * l + 12]))); #endif _mm256_store_pd(&v[12],vv[3]); #ifdef _FMA ev = _mm256_load_pd(&extEV[20 * l + 16]); vv[4] = FMAMACC(vv[4], al, ev); #else vv[4] = _mm256_add_pd(vv[4],_mm256_mul_pd(al, _mm256_load_pd(&extEV[20 * l + 16]))); #endif _mm256_store_pd(&v[16],vv[4]); } } v = x3_gapColumn; scale = 1; __m256d minlikelihood_avx = _mm256_set1_pd(minlikelihood); for(l = 0; scale && (l < 80); l += 4) { __m256d vv = _mm256_load_pd(&v[l]); __m256d vv_abs = _mm256_and_pd(vv,absMask_AVX.m); vv_abs = _mm256_cmp_pd(vv_abs,minlikelihood_avx,_CMP_LT_OS); if(_mm256_movemask_pd(vv_abs) != 15) scale = 0; } if(scale) { __m256d twotothe256v = _mm256_set_pd(twotothe256,twotothe256,twotothe256,twotothe256); gapScaling = 1; for(l = 0; l < 80; l += 4) { __m256d vv = _mm256_load_pd(&v[l]); _mm256_store_pd(&v[l],_mm256_mul_pd(vv,twotothe256v)); } } for(i = 0; i < n; i++) { if(x3_gap[i / 32] & mask32[i % 32]) { if(gapScaling) { if(useFastScaling) addScale += wgt[i]; else ex3[i] += 1; } } else { if(x1_gap[i / 32] & mask32[i % 32]) x1 = x1_gapColumn; else { x1 = x1_ptr; x1_ptr += 80; } if(x2_gap[i / 32] & mask32[i % 32]) x2 = x2_gapColumn; else { x2 = x2_ptr; x2_ptr += 80; } for(k = 0; k < 4; k++) { vl = &(x1[20 * k]); vr = &(x2[20 * k]); v = &(x3_ptr[20 * k]); __m256d vv[5]; vv[0] = _mm256_setzero_pd(); vv[1] = _mm256_setzero_pd(); vv[2] = _mm256_setzero_pd(); vv[3] = _mm256_setzero_pd(); vv[4] = _mm256_setzero_pd(); for(l = 0; l < 20; l++) { __m256d al = _mm256_setzero_pd(); __m256d ar = _mm256_setzero_pd(); __m256d leftv = _mm256_load_pd(&left[k * 400 + l * 20 + 0]); __m256d rightv = _mm256_load_pd(&right[k * 400 + l * 20 + 0]); __m256d vlv = _mm256_load_pd(&vl[0]); __m256d vrv = _mm256_load_pd(&vr[0]); #ifdef _FMA al = FMAMACC(al, vlv, leftv); ar = FMAMACC(ar, vrv, rightv); #else al = _mm256_add_pd(al,_mm256_mul_pd(vlv,leftv)); ar = _mm256_add_pd(ar,_mm256_mul_pd(vrv,rightv)); #endif leftv = _mm256_load_pd(&left[k * 400 + l * 20 + 4]); rightv = _mm256_load_pd(&right[k * 400 + l * 20 + 4]); vlv = _mm256_load_pd(&vl[4]); vrv = _mm256_load_pd(&vr[4]); #ifdef _FMA al = FMAMACC(al, vlv, leftv); ar = FMAMACC(ar, vrv, rightv); #else al = _mm256_add_pd(al,_mm256_mul_pd(vlv,leftv)); ar = _mm256_add_pd(ar,_mm256_mul_pd(vrv,rightv)); #endif leftv = _mm256_load_pd(&left[k * 400 + l * 20 + 8]); rightv = _mm256_load_pd(&right[k * 400 + l * 20 + 8]); vlv = _mm256_load_pd(&vl[8]); vrv = _mm256_load_pd(&vr[8]); #ifdef _FMA al = FMAMACC(al, vlv, leftv); ar = FMAMACC(ar, vrv, rightv); #else al = _mm256_add_pd(al,_mm256_mul_pd(vlv,leftv)); ar = _mm256_add_pd(ar,_mm256_mul_pd(vrv,rightv)); #endif leftv = _mm256_load_pd(&left[k * 400 + l * 20 + 12]); rightv = _mm256_load_pd(&right[k * 400 + l * 20 + 12]); vlv = _mm256_load_pd(&vl[12]); vrv = _mm256_load_pd(&vr[12]); #ifdef _FMA al = FMAMACC(al, vlv, leftv); ar = FMAMACC(ar, vrv, rightv); #else al = _mm256_add_pd(al,_mm256_mul_pd(vlv,leftv)); ar = _mm256_add_pd(ar,_mm256_mul_pd(vrv,rightv)); #endif leftv = _mm256_load_pd(&left[k * 400 + l * 20 + 16]); rightv = _mm256_load_pd(&right[k * 400 + l * 20 + 16]); vlv = _mm256_load_pd(&vl[16]); vrv = _mm256_load_pd(&vr[16]); #ifdef _FMA al = FMAMACC(al, vlv, leftv); ar = FMAMACC(ar, vrv, rightv); #else al = _mm256_add_pd(al,_mm256_mul_pd(vlv,leftv)); ar = _mm256_add_pd(ar,_mm256_mul_pd(vrv,rightv)); #endif /**************************************************************************************************************/ al = hadd3(al); ar = hadd3(ar); al = _mm256_mul_pd(ar,al); /************************************************************************************************************/ #ifdef _FMA __m256d ev = _mm256_load_pd(&extEV[20 * l + 0]); vv[0] = FMAMACC(vv[0], al, ev); #else vv[0] = _mm256_add_pd(vv[0],_mm256_mul_pd(al, _mm256_load_pd(&extEV[20 * l + 0]))); #endif _mm256_store_pd(&v[0],vv[0]); #ifdef _FMA ev = _mm256_load_pd(&extEV[20 * l + 4]); vv[1] = FMAMACC(vv[1], al, ev); #else vv[1] = _mm256_add_pd(vv[1],_mm256_mul_pd(al, _mm256_load_pd(&extEV[20 * l + 4]))); #endif _mm256_store_pd(&v[4],vv[1]); #ifdef _FMA ev = _mm256_load_pd(&extEV[20 * l + 8]); vv[2] = FMAMACC(vv[2], al, ev); #else vv[2] = _mm256_add_pd(vv[2],_mm256_mul_pd(al, _mm256_load_pd(&extEV[20 * l + 8]))); #endif _mm256_store_pd(&v[8],vv[2]); #ifdef _FMA ev = _mm256_load_pd(&extEV[20 * l + 12]); vv[3] = FMAMACC(vv[3], al, ev); #else vv[3] = _mm256_add_pd(vv[3],_mm256_mul_pd(al, _mm256_load_pd(&extEV[20 * l + 12]))); #endif _mm256_store_pd(&v[12],vv[3]); #ifdef _FMA ev = _mm256_load_pd(&extEV[20 * l + 16]); vv[4] = FMAMACC(vv[4], al, ev); #else vv[4] = _mm256_add_pd(vv[4],_mm256_mul_pd(al, _mm256_load_pd(&extEV[20 * l + 16]))); #endif _mm256_store_pd(&v[16],vv[4]); } } v = x3_ptr; scale = 1; __m256d minlikelihood_avx = _mm256_set1_pd(minlikelihood); for(l = 0; scale && (l < 80); l += 4) { __m256d vv = _mm256_load_pd(&v[l]); __m256d vv_abs = _mm256_and_pd(vv,absMask_AVX.m); vv_abs = _mm256_cmp_pd(vv_abs,minlikelihood_avx,_CMP_LT_OS); if(_mm256_movemask_pd(vv_abs) != 15) scale = 0; } if(scale) { __m256d twotothe256v = _mm256_set_pd(twotothe256,twotothe256,twotothe256,twotothe256); for(l = 0; l < 80; l += 4) { __m256d vv = _mm256_load_pd(&v[l]); _mm256_store_pd(&v[l],_mm256_mul_pd(vv,twotothe256v)); } if(useFastScaling) addScale += wgt[i]; else ex3[i] += 1; } x3_ptr += 80; } } break; default: assert(0); } if(useFastScaling) *scalerIncrement = addScale; } ExaML-3.0.21/examl/axml.c000066400000000000000000002174621330315463200147720ustar00rootroot00000000000000/* RAxML-VI-HPC (version 2.2) a program for sequential and parallel estimation of phylogenetic trees * Copyright August 2006 by Alexandros Stamatakis * * Partially derived from * fastDNAml, a program for estimation of phylogenetic trees from sequences by Gary J. Olsen * * and * * Programs of the PHYLIP package by Joe Felsenstein. * * This program is free software; you may redistribute it and/or modify its * 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. * * * For any other enquiries send an Email to Alexandros Stamatakis * Alexandros.Stamatakis@epfl.ch * * When publishing work that is based on the results from RAxML-VI-HPC please cite: * * Alexandros Stamatakis:"RAxML-VI-HPC: maximum likelihood-based phylogenetic analyses with thousands of taxa and mixed models". * Bioinformatics 2006; doi: 10.1093/bioinformatics/btl446 */ #ifdef WIN32 #include #endif #ifndef WIN32 #include #include #include #include #endif #include #include #include #include #include #include #include #include #include #include #include #if ! (defined(__ppc) || defined(__powerpc__) || defined(PPC)) #include /* special bug fix, enforces denormalized numbers to be flushed to zero, without this program is a tiny bit faster though. #include #define MM_DAZ_MASK 0x0040 #define MM_DAZ_ON 0x0040 #define MM_DAZ_OFF 0x0000 */ #endif #include "axml.h" #include "globalVariables.h" #include "byteFile.h" #include "partitionAssignment.h" #ifdef __MIC_NATIVE #include "mic_native.h" #endif /***************** UTILITY FUNCTIONS **************************/ /*pInfo *cleanPinfoInit() { pInfo *p = (pInfo*)malloc(sizeof(pInfo)); return p; }*/ void storeExecuteMaskInTraversalDescriptor(tree *tr) { int model; for(model = 0; model < tr->NumberOfModels; model++) tr->td[0].executeModel[model] = tr->executeModel[model]; } void storeValuesInTraversalDescriptor(tree *tr, double *value) { int model; for(model = 0; model < tr->NumberOfModels; model++) tr->td[0].parameterValues[model] = value[model]; } void myBinFwrite(void *ptr, size_t size, size_t nmemb, FILE *byteFile) { size_t bytes_read; bytes_read = fwrite(ptr, size, nmemb, byteFile); assert(bytes_read == nmemb); } void myBinFread(void *ptr, size_t size, size_t nmemb, FILE *byteFile) { size_t bytes_read; bytes_read = fread(ptr, size, nmemb, byteFile); assert(bytes_read == nmemb); } static void outOfMemory(void) { printf("ExaML process %d was not able to allocate enough memory.\n", processID); printf("Please check the approximate memory consumption of your dataset using\n"); printf("the memory calculator at http://www.exelixis-lab.org/web/software/raxml/index.html.\n"); printf("ExaML will exit now\n"); MPI_Abort(MPI_COMM_WORLD, -1); exit(-1); } void *malloc_aligned(size_t size) { void *ptr = (void *)NULL; int res; #ifdef WIN32 ptr = _aligned_malloc(size, BYTE_ALIGNMENT);; #else res = posix_memalign( &ptr, BYTE_ALIGNMENT, size ); if(res != 0) { outOfMemory(); assert(0); } #endif return ptr; } static void printBoth(FILE *f, const char* format, ... ) { if(processID == 0) { va_list args; va_start(args, format); vfprintf(f, format, args ); va_end(args); va_start(args, format); vprintf(format, args ); va_end(args); } } void printBothOpen(const char* format, ... ) { if(processID == 0) { FILE *f = myfopen(infoFileName, "ab"); va_list args; va_start(args, format); vfprintf(f, format, args ); va_end(args); va_start(args, format); vprintf(format, args ); va_end(args); fclose(f); } } static void printBothOpenDifferentFile(char *fileName, const char* format, ... ) { if(processID == 0) { FILE *f = myfopen(fileName, "ab"); va_list args; va_start(args, format); vfprintf(f, format, args ); va_end(args); fclose(f); } } boolean getSmoothFreqs(int dataType) { assert(MIN_MODEL < dataType && dataType < MAX_MODEL); return pLengths[dataType].smoothFrequencies; } const unsigned int *getBitVector(int dataType) { assert(MIN_MODEL < dataType && dataType < MAX_MODEL); return pLengths[dataType].bitVector; } int getStates(int dataType) { assert(MIN_MODEL < dataType && dataType < MAX_MODEL); return pLengths[dataType].states; } int getUndetermined(int dataType) { assert(MIN_MODEL < dataType && dataType < MAX_MODEL); return pLengths[dataType].undetermined; } char getInverseMeaning(int dataType, unsigned char state) { assert(MIN_MODEL < dataType && dataType < MAX_MODEL); return pLengths[dataType].inverseMeaning[state]; } partitionLengths *getPartitionLengths(pInfo *p) { int dataType = p->dataType, states = p->states, tipLength = p->maxTipStates; assert(states != -1 && tipLength != -1); assert(MIN_MODEL < dataType && dataType < MAX_MODEL); pLength.leftLength = pLength.rightLength = states * states; pLength.eignLength = states; pLength.evLength = states * states; pLength.eiLength = states * states; pLength.substRatesLength = (states * states - states) / 2; pLength.frequenciesLength = states; pLength.tipVectorLength = tipLength * states; pLength.symmetryVectorLength = (states * states - states) / 2; pLength.frequencyGroupingLength = states; pLength.nonGTR = FALSE; return (&pLengths[dataType]); } size_t discreteRateCategories(int rateHetModel) { size_t result; switch(rateHetModel) { case CAT: result = 1; break; case GAMMA: result = 4; break; default: assert(0); } return result; } double gettime(void) { #ifdef WIN32 time_t tp; struct tm localtm; tp = time(NULL); localtm = *localtime(&tp); return 60.0*localtm.tm_min + localtm.tm_sec; #else struct timeval ttime; gettimeofday(&ttime , NULL); return ttime.tv_sec + ttime.tv_usec * 0.000001; #endif } int gettimeSrand(void) { #ifdef WIN32 time_t tp; struct tm localtm; tp = time(NULL); localtm = *localtime(&tp); return 24*60*60*localtm.tm_yday + 60*60*localtm.tm_hour + 60*localtm.tm_min + localtm.tm_sec; #else struct timeval ttime; gettimeofday(&ttime , NULL); return ttime.tv_sec + ttime.tv_usec; #endif } double randum (long *seed) { long sum, mult0, mult1, seed0, seed1, seed2, newseed0, newseed1, newseed2; double res; mult0 = 1549; seed0 = *seed & 4095; sum = mult0 * seed0; newseed0 = sum & 4095; sum >>= 12; seed1 = (*seed >> 12) & 4095; mult1 = 406; sum += mult0 * seed1 + mult1 * seed0; newseed1 = sum & 4095; sum >>= 12; seed2 = (*seed >> 24) & 255; sum += mult0 * seed2 + mult1 * seed1; newseed2 = sum & 255; *seed = newseed2 << 24 | newseed1 << 12 | newseed0; res = 0.00390625 * (newseed2 + 0.000244140625 * (newseed1 + 0.000244140625 * newseed0)); return res; } static int filexists(char *filename) { FILE *fp; int res; fp = fopen(filename,"rb"); if(fp) { res = 1; fclose(fp); } else res = 0; return res; } FILE *myfopen(const char *path, const char *mode) { FILE *fp = fopen(path, mode); if(strcmp(mode,"r") == 0 || strcmp(mode,"rb") == 0) { if(fp) return fp; else { if(processID == 0) printf("The file %s you want to open for reading does not exist, exiting ...\n", path); errorExit(-1); return (FILE *)NULL; } } else { if(fp) return fp; else { if(processID == 0) printf("The file %s ExaML wants to open for writing or appending can not be opened [mode: %s], exiting ...\n", path, mode); errorExit(-1); return (FILE *)NULL; } } } /********************* END UTILITY FUNCTIONS ********************/ /******************************some functions for the likelihood computation ****************************/ boolean isTip(int number, int maxTips) { assert(number > 0); if(number <= maxTips) return TRUE; else return FALSE; } void getxnode (nodeptr p) { nodeptr s; if ((s = p->next)->x || (s = s->next)->x) { p->x = s->x; s->x = 0; } assert(p->x); } void hookup (nodeptr p, nodeptr q, double *z, int numBranches) { int i; p->back = q; q->back = p; for(i = 0; i < numBranches; i++) p->z[i] = q->z[i] = z[i]; } void hookupDefault (nodeptr p, nodeptr q, int numBranches) { int i; p->back = q; q->back = p; for(i = 0; i < numBranches; i++) p->z[i] = q->z[i] = defaultz; } /***********************reading and initializing input ******************/ boolean whitechar (int ch) { return (ch == ' ' || ch == '\n' || ch == '\t' || ch == '\r'); } static unsigned int KISS32(void) { static unsigned int x = 123456789, y = 362436069, z = 21288629, w = 14921776, c = 0; unsigned int t; x += 545925293; y ^= (y<<13); y ^= (y>>17); y ^= (y<<5); t = z + w + c; z = w; c = (t>>31); w = t & 2147483647; return (x+y+w); } static boolean setupTree (tree *tr) { nodeptr p0, p, q; int i, j, tips, inter; tr->bigCutoff = FALSE; tr->maxCategories = MAX(4, tr->categories); tr->partitionContributions = (double *)malloc(sizeof(double) * tr->NumberOfModels); tr->partitionWeights = (double *)malloc(sizeof(double) * tr->NumberOfModels); for(i = 0; i < tr->NumberOfModels; i++) { tr->partitionContributions[i] = -1.0; tr->partitionWeights[i] = -1.0; } tr->perPartitionLH = (double *)malloc(sizeof(double) * tr->NumberOfModels); for(i = 0; i < tr->NumberOfModels; i++) tr->perPartitionLH[i] = 0.0; tips = tr->mxtips; inter = tr->mxtips - 1; /* printf("%d tips\t%d inner\n", tips, inter); */ tr->treeStringLength = tr->mxtips * (nmlngth+128) + 256 + tr->mxtips * 2; tr->tree_string = (char*)calloc(tr->treeStringLength, sizeof(char)); tr->tree0 = (char*)calloc(tr->treeStringLength, sizeof(char)); tr->tree1 = (char*)calloc(tr->treeStringLength, sizeof(char)); /* TODO, must that be so long ? */ /* assert(0); */ tr->td[0].count = 0; tr->td[0].ti = (traversalInfo *)malloc(sizeof(traversalInfo) * tr->mxtips); tr->td[0].executeModel = (boolean *)malloc(sizeof(boolean) * tr->NumberOfModels); tr->td[0].parameterValues = (double *)malloc(sizeof(double) * tr->NumberOfModels); tr->constraintVector = (int *)malloc((2 * tr->mxtips) * sizeof(int)); if (!(p0 = (nodeptr) malloc((tips + 3*inter) * sizeof(node)))) { printf("ERROR: Unable to obtain sufficient tree memory\n"); return FALSE; } tr->nodeBaseAddress = p0; if (!(tr->nodep = (nodeptr *) malloc((2*tr->mxtips) * sizeof(nodeptr)))) { printf("ERROR: Unable to obtain sufficient tree memory, too\n"); return FALSE; } tr->nodep[0] = (node *) NULL; /* Use as 1-based array */ for (i = 1; i <= tips; i++) { p = p0++; p->hash = KISS32(); /* hast table stuff */ p->x = 0; p->xBips = 0; p->number = i; p->next = p; p->back = (node *)NULL; tr->nodep[i] = p; } for (i = tips + 1; i <= tips + inter; i++) { q = (node *) NULL; for (j = 1; j <= 3; j++) { p = p0++; if(j == 1) { p->xBips = 1; p->x = 1; } else { p->xBips = 0; p->x = 0; } p->number = i; p->next = q; p->back = (node *) NULL; p->hash = 0; q = p; } p->next->next->next = p; tr->nodep[i] = p; } tr->likelihood = unlikely; tr->start = (node *) NULL; tr->ntips = 0; tr->nextnode = 0; for(i = 0; i < tr->numBranches; i++) tr->partitionSmoothed[i] = FALSE; tr->bitVectors = (unsigned int **)NULL; tr->vLength = 0; tr->h = (hashtable*)NULL; tr->nameHash = initStringHashTable(10 * tr->mxtips); return TRUE; } static void initAdef(analdef *adef) { adef->max_rearrange = 21; adef->stepwidth = 5; adef->initial = 10; adef->bestTrav = 10; adef->initialSet = FALSE; adef->mode = BIG_RAPID_MODE; adef->likelihoodEpsilon = 0.1; adef->permuteTreeoptimize = FALSE; adef->perGeneBranchLengths = FALSE; adef->useCheckpoint = FALSE; adef->useQuartetGrouping = FALSE; adef->numberRandomQuartets = 0; adef->quartetCkpInterval = 1000; #ifdef _BAYESIAN adef->bayesian = FALSE; #endif } static int modelExists(char *model, tree *tr) { if(strcmp(model, "PSR\0") == 0) { tr->rateHetModel = CAT; return 1; } if(strcmp(model, "GAMMA\0") == 0) { tr->rateHetModel = GAMMA; return 1; } return 0; } /*********************************** *********************************************************/ static void printVersionInfo(void) { if(processID == 0) printf("\n\nThis is %s version %s released by Alexandros Stamatakis, Andre J. Aberer, and Alexey Kozlov on %s.\n\n", programName, programVersion, programDate); } static void printMinusFUsage(void) { printf("\n"); printf(" \"-f d\": new rapid hill-climbing \n"); printf(" DEFAULT: ON\n"); printf("\n"); printf(" \"-f e\": compute the likelihood of a bunch of trees passed via -t\n"); printf(" this option will do a quick and dirty optimization without re-optimizng\n"); printf(" the model parameters for each tree\n"); printf("\n"); printf(" \"-f E\": compute the likelihood of a bunch of trees passed via -t\n"); printf(" this option will do a thorough optimization that re-optimizes\n"); printf(" the model parameters for each tree\n"); printf("\n"); printf(" \"-f o\": old and slower rapid hill-climbing without heuristic cutoff\n"); printf("\n"); printf(" \"-f q\": fast quartet calculator\n"); printf("\n"); printf(" DEFAULT for \"-f\": new rapid hill climbing\n"); printf("\n"); } static void printREADME(void) { if(processID == 0) { printVersionInfo(); printf("\n"); printf("\nTo report bugs use the RAxML google group\n"); printf("Please send me all input files, the exact invocation, details of the HW and operating system,\n"); printf("as well as all error messages printed to screen.\n\n\n"); printf("examl|examl-AVX\n"); printf(" -s binarySequenceFileName\n"); printf(" -n outputFileNames\n"); printf(" -m rateHeterogeneityModel\n"); printf(" -t userStartingTree|-R binaryCheckpointFile|-g constraintTree -p randomNumberSeed\n"); printf(" [-a]\n"); printf(" [-B numberOfMLtreesToSave]\n"); printf(" [-c numberOfCategories]\n"); printf(" [-D]\n"); printf(" [-e likelihoodEpsilon] \n"); printf(" [-f d|e|E|o|q]\n"); printf(" [-h] \n"); printf(" [-i initialRearrangementSetting] \n"); printf(" [-I quartetCheckpointInterval] \n"); printf(" [-M]\n"); printf(" [-r randomQuartetNumber] \n"); printf(" [-S]\n"); printf(" [-v]\n"); printf(" [-w outputDirectory] \n"); printf(" [-Y quartetGroupingFileName]\n"); printf(" [--auto-prot=ml|bic|aic|aicc]\n"); printf("\n"); printf(" -a use the median for the discrete approximation of the GAMMA model of rate heterogeneity\n"); printf("\n"); printf(" DEFAULT: OFF\n"); printf("\n"); printf(" -B specify the number of best ML trees to save and print to file\n"); printf("\n"); printf(" -c Specify number of distinct rate catgories for ExaML when modelOfEvolution\n"); printf(" is set to GTRPSR\n"); printf(" Individual per-site rates are categorized into numberOfCategories rate \n"); printf(" categories to accelerate computations. \n"); printf("\n"); printf(" DEFAULT: 25\n"); printf("\n"); printf(" -D ML search convergence criterion. This will break off ML searches if the relative \n"); printf(" Robinson-Foulds distance between the trees obtained from two consecutive lazy SPR cycles\n"); printf(" is smaller or equal to 1%s. Usage recommended for very large datasets in terms of taxa.\n", "%"); printf(" On trees with more than 500 taxa this will yield execution time improvements of approximately 50%s\n", "%"); printf(" While yielding only slightly worse trees.\n"); printf("\n"); printf(" DEFAULT: OFF\n"); printf("\n"); printf(" -e set model optimization precision in log likelihood units for final\n"); printf(" optimization of model parameters\n"); printf("\n"); printf(" DEFAULT: 0.1 \n"); printf("\n"); printf(" -f select algorithm:\n"); printMinusFUsage(); printf("\n"); printf(" -g Pass a multi-furcating constraint tree to ExaML. The tree needs to contain all taxa of the alignment!\n"); printf(" When using this option you also need to specify a random number seed via \"-p\"\n"); printf("\n"); printf(" -h Display this help message.\n"); printf("\n"); printf(" -i Initial rearrangement setting for the subsequent application of topological \n"); printf(" changes phase\n"); printf("\n"); printf(" -I Set after how many quartet evaluations a new checkpoint will be printed.\n"); printf("\n"); printf(" DEFAULT: 1000\n"); printf("\n"); printf(" -m Model of rate heterogeneity\n"); printf("\n"); printf(" select \"-m PSR\" for the per-site rate category model (this used to be called CAT in RAxML)\n"); printf(" select \"-m GAMMA\" for the gamma model of rate heterogeneity with 4 discrete rates\n"); printf("\n"); printf(" -M Switch on estimation of individual per-partition branch lengths. Only has effect when used in combination with \"-q\"\n"); printf(" Branch lengths for individual partitions will be printed to separate files\n"); printf(" A weighted average of the branch lengths is computed by using the respective partition lengths\n"); printf("\n"); printf(" DEFAULT: OFF\n"); printf("\n"); printf(" -n Specifies the name of the output file.\n"); printf("\n"); printf(" -p Specify a random number seed, required in conjunction with the \"-g\" option for constraint trees\n"); printf("\n"); printf(" -R read in a binary checkpoint file called ExaML_binaryCheckpoint.RUN_ID_number\n"); printf("\n"); printf(" -r Pass the number of quartets to randomly sub-sample from the possible number of quartets for the given taxon set.\n"); printf(" Only works in combination with -f q !\n"); printf("\n"); printf(" -s Specify the name of the BINARY alignment data file generated by the parser component\n"); printf("\n"); printf(" -S turn on memory saving option for gappy multi-gene alignments. For large and gappy datasets specify -S to save memory\n"); printf(" This will produce slightly different likelihood values, may be a bit slower but can reduce memory consumption\n"); printf(" from 70GB to 19GB on very large and gappy datasets\n"); printf("\n"); printf(" -t Specify a user starting tree file name in Newick format\n"); printf("\n"); printf(" -v Display version information\n"); printf("\n"); printf(" -w FULL (!) path to the directory into which ExaML shall write its output files\n"); printf("\n"); printf(" DEFAULT: current directory\n"); printf("\n"); printf(" -Y Pass a quartet grouping file name defining four groups from which to draw quartets\n"); printf(" The file input format must contain 4 groups in the following form:\n"); printf(" (Chicken, Human, Loach), (Cow, Carp), (Mouse, Rat, Seal), (Whale, Frog);\n"); printf(" Only works in combination with -f q !\n"); printf("\n"); printf("\n"); printf(" --auto-prot=ml|bic|aic|aicc When using automatic protein model selection you can chose the criterion for selecting these models.\n"); printf(" RAxML will test all available prot subst. models except for LG4M, LG4X and GTR-based models, with and without empirical base frequencies.\n"); printf(" You can chose between ML score based selection and the BIC, AIC, and AICc criteria.\n"); printf("\n"); printf(" DEFAULT: ml\n"); printf("\n\n\n\n"); } } static void analyzeRunId(char id[128]) { int i = 0; while(id[i] != '\0') { if(i >= 128) { printf("Error: run id after \"-n\" is too long, it has %d characters please use a shorter one\n", i); assert(0); } if(id[i] == '/') { printf("Error character %c not allowed in run ID\n", id[i]); assert(0); } i++; } if(i == 0) { printf("Error: please provide a string for the run id after \"-n\" \n"); assert(0); } } static void get_args(int argc, char *argv[], analdef *adef, tree *tr) { boolean resultDirSet = FALSE; char resultDir[1024] = "", //*optarg, model[1024] = "", modelChar; double likelihoodEpsilon; int fOptionCount = 0, c, nameSet = 0, treeSet = 0, modelSet = 0, byteFileSet = 0, seedSet = 0; /*********** tr inits **************/ tr->doCutoff = TRUE; tr->secondaryStructureModel = SEC_16; /* default setting */ tr->searchConvergenceCriterion = FALSE; tr->rateHetModel = GAMMA; tr->multiStateModel = GTR_MULTI_STATE; tr->useGappedImplementation = FALSE; tr->saveMemory = FALSE; tr->constraintTree = FALSE; tr->fastTreeEvaluation = FALSE; /* tr->manyPartitions = FALSE; */ tr->categories = 25; tr->gapyness = 0.0; tr->saveBestTrees = 0; tr->useMedian = FALSE; tr->autoProteinSelectionType = AUTO_ML; /********* tr inits end*************/ //while(!bad_opt && ((c = mygetopt(argc,argv,"R:B:e:c:f:i:m:t:g:w:n:s:p:vhMSDa", &optind, &optarg))!=-1)) static int flag; while(1) { static struct option long_options[2] = { {"auto-prot", required_argument, &flag, 1}, {0, 0, 0, 0} }; int option_index; flag = 0; c = getopt_long(argc, argv, "R:B:Y:I:e:c:f:i:m:t:g:w:n:s:p:r:vhMSDa", long_options, &option_index); if(c == -1) break; if(flag > 0) { switch(option_index) { case 0: { char *autoModels[4] = {"ml", "bic", "aic", "aicc"}; int k; for(k = 0; k < 4; k++) if(strcmp(optarg, autoModels[k]) == 0) break; if(k == 4) { printf("\nError, unknown protein model selection type, you can specify one of the following selection criteria:\n\n"); for(k = 0; k < 4; k++) printf("--auto-prot=%s\n", autoModels[k]); printf("\n"); errorExit(-1); } else { switch(k) { case 0: tr->autoProteinSelectionType = AUTO_ML; break; case 1: tr->autoProteinSelectionType = AUTO_BIC; break; case 2: tr->autoProteinSelectionType = AUTO_AIC; break; case 3: tr->autoProteinSelectionType = AUTO_AICC; break; default: assert(0); } } } break; default: assert(0); } } else switch(c) { case 'Y': adef->useQuartetGrouping = TRUE; strcpy(quartetGroupingFileName, optarg); break; case 'r': sscanf(optarg, "%lu", &(adef->numberRandomQuartets)); assert(adef->numberRandomQuartets > 0); break; case 'p': sscanf(optarg,"%u", &(tr->randomSeed)); seedSet = 1; break; case 'a': tr->useMedian = TRUE; break; case 'B': sscanf(optarg,"%d", &(tr->saveBestTrees)); if(tr->saveBestTrees < 0) { printf("Number of best trees to save must be greater than 0!\n"); errorExit(-1); } break; case 's': strcpy(byteFileName, optarg); byteFileSet = TRUE; /*printf("%s \n", byteFileName);*/ break; case 'S': tr->saveMemory = TRUE; break; case 'D': tr->searchConvergenceCriterion = TRUE; break; case 'R': adef->useCheckpoint = TRUE; strcpy(binaryCheckpointInputName, optarg); break; case 'I': sscanf(optarg, "%lu", &(adef->quartetCkpInterval)); break; case 'M': adef->perGeneBranchLengths = TRUE; break; case 'e': sscanf(optarg,"%lf", &likelihoodEpsilon); adef->likelihoodEpsilon = likelihoodEpsilon; break; case 'v': printVersionInfo(); errorExit(0); case 'h': printREADME(); errorExit(0); case 'c': sscanf(optarg, "%d", &tr->categories); break; case 'f': sscanf(optarg, "%c", &modelChar); fOptionCount++; if(fOptionCount > 1) { printf("\nError: only one of the various \"-f \" options can be used per ExaML run!\n"); printf("They are mutually exclusive! exiting ...\n\n"); errorExit(-1); } switch(modelChar) { case 'e': adef->mode = TREE_EVALUATION; tr->fastTreeEvaluation = TRUE; break; case 'E': adef->mode = TREE_EVALUATION; tr->fastTreeEvaluation = FALSE; break; case 'd': adef->mode = BIG_RAPID_MODE; tr->doCutoff = TRUE; break; case 'o': adef->mode = BIG_RAPID_MODE; tr->doCutoff = FALSE; break; case 'q': adef->mode = QUARTET_CALCULATION; break; default: { if(processID == 0) { printf("Error select one of the following algorithms via -f :\n"); printMinusFUsage(); } errorExit(-1); } } break; case 'i': sscanf(optarg, "%d", &adef->initial); adef->initialSet = TRUE; break; case 'n': strcpy(run_id,optarg); analyzeRunId(run_id); nameSet = 1; break; case 'w': strcpy(resultDir, optarg); resultDirSet = TRUE; break; case 't': strcpy(tree_file, optarg); treeSet = 1; break; case 'g': strcpy(tree_file, optarg); treeSet = 1; tr->constraintTree = TRUE; break; case 'm': strcpy(model,optarg); if(modelExists(model, tr) == 0) { if(processID == 0) { printf("Rate heterogeneity Model %s does not exist\n\n", model); printf("For per site rates (called CAT in previous versions) use: PSR\n"); printf("For GAMMA use: GAMMA\n"); } errorExit(-1); } else modelSet = 1; break; default: errorExit(-1); } } if(adef->useQuartetGrouping && adef->mode != QUARTET_CALCULATION) { if(processID == 0) printf("\nError, you must specify \"-Y quartetGroupingFileName\" in combination with \"-f q\"\n"); errorExit(-1); } if(adef->numberRandomQuartets > 0 && adef->mode != QUARTET_CALCULATION) { if(processID == 0) printf("\nError, you must specify \"-r randomQuartetNumber\" in combination with \"-f q\"\n"); errorExit(-1); } if((adef->numberRandomQuartets > 0) && (adef->useQuartetGrouping)) { if(processID == 0) printf("\nError, you must specify either \"-r randomQuartetNumber\" or \"-Y quartetGroupingFileName\"\n"); errorExit(-1); } if(tr->constraintTree) { if(!seedSet && processID == 0) { printf("\nError, you must specify a random number seed via \"-p\" when using a constraint\n"); printf("tree via \"-g\" \n"); errorExit(-1); } } if(!byteFileSet) { if(processID == 0) printf("\nError, you must specify a binary format data file with the \"-s\" option\n"); errorExit(-1); } if(!modelSet) { if(processID == 0) printf("\nError, you must specify a model of rate heterogeneity with the \"-m\" option\n"); errorExit(-1); } if(!nameSet) { if(processID == 0) printf("\nError: please specify a name for this run with -n\n"); errorExit(-1); } if(!treeSet && !adef->useCheckpoint) { if(processID == 0) { printf("\nError: please either specify a starting tree for this run with -t\n"); printf("or re-start the run from a checkpoint with -R\n"); } errorExit(-1); } { const char *separator = "/"; if(resultDirSet) { char dir[1024] = ""; if(resultDir[0] != separator[0]) strcat(dir, separator); strcat(dir, resultDir); if(dir[strlen(dir) - 1] != separator[0]) strcat(dir, separator); strcpy(workdir, dir); } else { char dir[1024] = "", *result = getcwd(dir, sizeof(dir)); assert(result != (char*)NULL); if(dir[strlen(dir) - 1] != separator[0]) strcat(dir, separator); strcpy(workdir, dir); } } return; } void errorExit(int e) { MPI_Finalize(); exit(e); } static void makeFileNames(void) { int infoFileExists = 0; strcpy(resultFileName, workdir); strcpy(logFileName, workdir); strcpy(infoFileName, workdir); strcpy(treeFileName, workdir); strcpy(binaryCheckpointName, workdir); strcpy(modelFileName, workdir); strcpy(quartetFileName, workdir); strcat(resultFileName, "ExaML_result."); strcat(logFileName, "ExaML_log."); strcat(infoFileName, "ExaML_info."); strcat(binaryCheckpointName, "ExaML_binaryCheckpoint."); strcat(modelFileName, "ExaML_modelFile."); strcat(treeFileName, "ExaML_TreeFile."); strcat(quartetFileName, "ExaML_quartets."); strcat(resultFileName, run_id); strcat(logFileName, run_id); strcat(infoFileName, run_id); strcat(binaryCheckpointName, run_id); strcat(modelFileName, run_id); strcat(treeFileName, run_id); strcat(quartetFileName, run_id); infoFileExists = filexists(infoFileName); if(infoFileExists) { if(processID == 0) { printf("ExaML output files with the run ID <%s> already exist \n", run_id); printf("in directory %s ...... exiting\n", workdir); } errorExit(-1); } } /***********************reading and initializing input ******************/ /********************PRINTING various INFO **************************************/ static void printModelAndProgramInfo(tree *tr, analdef *adef, int argc, char *argv[]) { if(processID == 0) { int i, model; FILE *infoFile = myfopen(infoFileName, "ab"); char modelType[128]; if(tr->useMedian) strcpy(modelType, "GAMMA with Median"); else strcpy(modelType, "GAMMA"); printBoth(infoFile, "\n\nThis is %s version %s released by Alexandros Stamatakis, Andre Aberer, and Alexey Kozlov in %s.\n\n", programName, programVersion, programDate); printBoth(infoFile, "\nAlignment has %zu distinct alignment patterns\n\n", tr->originalCrunchedLength); printBoth(infoFile, "Proportion of gaps and completely undetermined characters in this alignment: %3.2f%s\n", 100.0 * tr->gapyness, "%"); switch(adef->mode) { case BIG_RAPID_MODE: printBoth(infoFile, "\nExaML rapid hill-climbing mode\n\n"); break; case TREE_EVALUATION: printBoth(infoFile, "\nExaML %s tree evaluation mode\n\n", (tr->fastTreeEvaluation)?"fast":"slow"); break; case QUARTET_CALCULATION: printBoth(infoFile, "\nExaML quartet evaluation mode\n\n"); break; default: assert(0); } if(adef->perGeneBranchLengths) printBoth(infoFile, "Using %d distinct models/data partitions with individual per partition branch length optimization\n\n\n", tr->NumberOfModels); else printBoth(infoFile, "Using %d distinct models/data partitions with joint branch length optimization\n\n\n", tr->NumberOfModels); printBoth(infoFile, "All free model parameters will be estimated by ExaML\n"); if(tr->rateHetModel == GAMMA || tr->rateHetModel == GAMMA_I) printBoth(infoFile, "%s model of rate heteorgeneity, ML estimate of alpha-parameter\n\n", modelType); else { printBoth(infoFile, "ML estimate of %d per site rate categories\n\n", tr->categories); } for(model = 0; model < tr->NumberOfModels; model++) { printBoth(infoFile, "Partition: %d\n", model); printBoth(infoFile, "Alignment Patterns: %d\n", tr->partitionData[model].upper - tr->partitionData[model].lower); printBoth(infoFile, "Name: %s\n", tr->partitionData[model].partitionName); switch(tr->partitionData[model].dataType) { case DNA_DATA: printBoth(infoFile, "DataType: DNA\n"); printBoth(infoFile, "Substitution Matrix: GTR\n"); if(tr->partitionData[model].optimizeBaseFrequencies) printBoth(infoFile, "ML optimization of base frequencies\n"); break; case AA_DATA: assert(tr->partitionData[model].protModels >= 0 && tr->partitionData[model].protModels < NUM_PROT_MODELS); printBoth(infoFile, "DataType: AA\n"); printBoth(infoFile, "Substitution Matrix: %s\n", protModels[tr->partitionData[model].protModels]); if(!tr->partitionData[model].optimizeBaseFrequencies) printBoth(infoFile, "Using %s Base Frequencies\n", (tr->partitionData[model].protFreqs == 1)?"empirical":"fixed"); else printBoth(infoFile, "ML optimization of base frequencies\n"); break; case BINARY_DATA: printBoth(infoFile, "DataType: BINARY/MORPHOLOGICAL\n"); printBoth(infoFile, "Substitution Matrix: Uncorrected\n"); break; /* case SECONDARY_DATA: printBoth(infoFile, "DataType: SECONDARY STRUCTURE\n"); printBoth(infoFile, "Substitution Matrix: %s\n", secondaryModelList[tr->secondaryStructureModel]); break; case SECONDARY_DATA_6: printBoth(infoFile, "DataType: SECONDARY STRUCTURE 6 STATE\n"); printBoth(infoFile, "Substitution Matrix: %s\n", secondaryModelList[tr->secondaryStructureModel]); break; case SECONDARY_DATA_7: printBoth(infoFile, "DataType: SECONDARY STRUCTURE 7 STATE\n"); printBoth(infoFile, "Substitution Matrix: %s\n", secondaryModelList[tr->secondaryStructureModel]); break; case GENERIC_32: printBoth(infoFile, "DataType: Multi-State with %d distinct states in use (maximum 32)\n",tr->partitionData[model].states); switch(tr->multiStateModel) { case ORDERED_MULTI_STATE: printBoth(infoFile, "Substitution Matrix: Ordered Likelihood\n"); break; case MK_MULTI_STATE: printBoth(infoFile, "Substitution Matrix: MK model\n"); break; case GTR_MULTI_STATE: printBoth(infoFile, "Substitution Matrix: GTR\n"); break; default: assert(0); } break; case GENERIC_64: printBoth(infoFile, "DataType: Codon\n"); break; */ default: assert(0); } printBoth(infoFile, "\n\n\n"); } printBoth(infoFile, "\n"); printBoth(infoFile, "ExaML was called as follows:\n\n"); for(i = 0; i < argc; i++) printBoth(infoFile,"%s ", argv[i]); printBoth(infoFile,"\n\n\n"); fclose(infoFile); } } void printResult(tree *tr, analdef *adef, boolean finalPrint) { if(processID == 0) { FILE *logFile; char temporaryFileName[1024] = ""; strcpy(temporaryFileName, resultFileName); switch(adef->mode) { case TREE_EVALUATION: Tree2String(tr->tree_string, tr, tr->start->back, TRUE, TRUE, FALSE, FALSE, finalPrint, SUMMARIZE_LH, FALSE, FALSE); logFile = myfopen(temporaryFileName, "wb"); fprintf(logFile, "%s", tr->tree_string); fclose(logFile); if(adef->perGeneBranchLengths) printTreePerGene(tr, adef, temporaryFileName, "wb"); break; case BIG_RAPID_MODE: if(finalPrint) { switch(tr->rateHetModel) { case GAMMA: case GAMMA_I: Tree2String(tr->tree_string, tr, tr->start->back, TRUE, TRUE, FALSE, FALSE, finalPrint, SUMMARIZE_LH, FALSE, FALSE); logFile = myfopen(temporaryFileName, "wb"); fprintf(logFile, "%s", tr->tree_string); fclose(logFile); if(adef->perGeneBranchLengths) printTreePerGene(tr, adef, temporaryFileName, "wb"); break; case CAT: /*Tree2String(tr->tree_string, tr, tr->start->back, FALSE, TRUE, FALSE, FALSE, finalPrint, adef, NO_BRANCHES, FALSE, FALSE);*/ Tree2String(tr->tree_string, tr, tr->start->back, TRUE, TRUE, FALSE, FALSE, TRUE, SUMMARIZE_LH, FALSE, FALSE); logFile = myfopen(temporaryFileName, "wb"); fprintf(logFile, "%s", tr->tree_string); fclose(logFile); break; default: assert(0); } } else { Tree2String(tr->tree_string, tr, tr->start->back, FALSE, TRUE, FALSE, FALSE, finalPrint, NO_BRANCHES, FALSE, FALSE); logFile = myfopen(temporaryFileName, "wb"); fprintf(logFile, "%s", tr->tree_string); fclose(logFile); } break; default: printf("FATAL ERROR call to printResult from undefined STATE %d\n", adef->mode); exit(-1); break; } } } void printLog(tree *tr) { if(processID == 0) { FILE *logFile; double t; t = gettime() - masterTime; logFile = myfopen(logFileName, "ab"); /* printf("%f %1.40f\n", t, tr->likelihood); */ fprintf(logFile, "%f %f\n", t, tr->likelihood); fclose(logFile); } } void getDataTypeString(tree *tr, int model, char typeOfData[1024]) { switch(tr->partitionData[model].dataType) { case AA_DATA: strcpy(typeOfData,"AA"); break; case DNA_DATA: strcpy(typeOfData,"DNA"); break; case BINARY_DATA: strcpy(typeOfData,"BINARY/MORPHOLOGICAL"); break; case SECONDARY_DATA: strcpy(typeOfData,"SECONDARY 16 STATE MODEL USING "); strcat(typeOfData, secondaryModelList[tr->secondaryStructureModel]); break; case SECONDARY_DATA_6: strcpy(typeOfData,"SECONDARY 6 STATE MODEL USING "); strcat(typeOfData, secondaryModelList[tr->secondaryStructureModel]); break; case SECONDARY_DATA_7: strcpy(typeOfData,"SECONDARY 7 STATE MODEL USING "); strcat(typeOfData, secondaryModelList[tr->secondaryStructureModel]); break; case GENERIC_32: strcpy(typeOfData,"Multi-State"); break; case GENERIC_64: strcpy(typeOfData,"Codon"); break; default: assert(0); } } static void printRatesDNA_BIN(int n, double *r, char **names, char *fileName) { int i, j, c; for(i = 0, c = 0; i < n; i++) { for(j = i + 1; j < n; j++) { if(i == n - 2 && j == n - 1) printBothOpenDifferentFile(fileName, "rate %s <-> %s: %f\n", names[i], names[j], 1.0); else printBothOpenDifferentFile(fileName, "rate %s <-> %s: %f\n", names[i], names[j], r[c]); c++; } } } static void printRatesRest(int n, double *r, char **names, char *fileName) { int i, j, c; for(i = 0, c = 0; i < n; i++) { for(j = i + 1; j < n; j++) { printBothOpenDifferentFile(fileName, "rate %s <-> %s: %f\n", names[i], names[j], r[c]); c++; } } } static double branchLength(int model, double *z, tree *tr) { double x; x = z[model]; assert(x > 0); if (x < zmin) x = zmin; assert(x <= zmax); x = -log(x); return x; } static double treeLengthRec(nodeptr p, tree *tr, int model) { double x = branchLength(model, p->z, tr); if(isTip(p->number, tr->mxtips)) return x; else { double acc = 0; nodeptr q; q = p->next; while(q != p) { acc += treeLengthRec(q->back, tr, model); q = q->next; } return acc + x; } } static double treeLength(tree *tr, int model) { return treeLengthRec(tr->start->back, tr, model); } static void printFreqs(int n, double *f, char **names, char *fileName) { int k; for(k = 0; k < n; k++) printBothOpenDifferentFile(fileName, "freq pi(%s): %f\n", names[k], f[k]); } static void printModelParams(tree *tr, analdef *adef, int treeIteration) { int model; double *f = (double*)NULL, *r = (double*)NULL; char fileName[2048], buf[64]; strcpy(fileName, modelFileName); if(treeIteration >= 0) { strcat(fileName, "."); sprintf(buf, "%d", treeIteration); strcat(fileName, buf); } for(model = 0; model < tr->NumberOfModels; model++) { double tl; char typeOfData[1024]; getDataTypeString(tr, model, typeOfData); printBothOpenDifferentFile(fileName, "\n\n"); printBothOpenDifferentFile(fileName, "Model Parameters of Partition %d, Name: %s, Type of Data: %s\n", model, tr->partitionData[model].partitionName, typeOfData); if(tr->rateHetModel == GAMMA) printBothOpenDifferentFile(fileName, "alpha: %f\n", tr->partitionData[model].alpha); if(adef->perGeneBranchLengths) tl = treeLength(tr, model); else tl = treeLength(tr, 0); printBothOpenDifferentFile(fileName, "Tree-Length: %f\n", tl); f = tr->partitionData[model].frequencies; r = tr->partitionData[model].substRates; switch(tr->partitionData[model].dataType) { case AA_DATA: { char *freqNames[20] = {"A", "R", "N ","D", "C", "Q", "E", "G", "H", "I", "L", "K", "M", "F", "P", "S", "T", "W", "Y", "V"}; if(tr->partitionData[model].protModels == LG4M || tr->partitionData[model].protModels == LG4X) { int k; for(k = 0; k < 4; k++) { printBothOpenDifferentFile(fileName, "LGM %d\n", k); printRatesRest(20, tr->partitionData[model].substRates_LG4[k], freqNames, fileName); printBothOpenDifferentFile(fileName, "\n"); printFreqs(20, tr->partitionData[model].frequencies_LG4[k], freqNames, fileName); } } printRatesRest(20, r, freqNames, fileName); printBothOpenDifferentFile(fileName, "\n"); printFreqs(20, f, freqNames, fileName); } break; case DNA_DATA: { char *freqNames[4] = {"A", "C", "G", "T"}; printRatesDNA_BIN(4, r, freqNames, fileName); printBothOpenDifferentFile(fileName, "\n"); printFreqs(4, f, freqNames, fileName); } break; case BINARY_DATA: { char *freqNames[2] = {"0", "1"}; printRatesDNA_BIN(2, r, freqNames, fileName); printBothOpenDifferentFile(fileName, "\n"); printFreqs(2, f, freqNames, fileName); } break; default: assert(0); } printBothOpenDifferentFile(fileName, "\n"); } printBothOpenDifferentFile(fileName, "\n"); } static void finalizeInfoFile(tree *tr, analdef *adef) { if(processID == 0) { double t; t = gettime() - masterTime; accumulatedTime = accumulatedTime + t; switch(adef->mode) { case BIG_RAPID_MODE: printBothOpen("\n\nOverall Time for 1 Inference %f\n", t); printBothOpen("\nOverall accumulated Time (in case of restarts): %f\n\n", accumulatedTime); printBothOpen("Likelihood : %f\n", tr->likelihood); printBothOpen("\n\n"); printBothOpen("Model parameters written to: %s\n", modelFileName); printBothOpen("Final tree written to: %s\n", resultFileName); printBothOpen("Execution Log File written to: %s\n", logFileName); printBothOpen("Execution information file written to: %s\n",infoFileName); break; case TREE_EVALUATION: printBothOpen("\n\nOverall Time for evaluating the likelihood of %d trees: %f secs\n\n", tr->numberOfTrees, t); printBothOpen("\n\nThe model parameters of the trees have been written to files called %s.i\n", modelFileName); printBothOpen("where i is the number of the tree\n\n"); printBothOpen("Note that, in case of a restart from a checkpoint, some tree model files will have been produced by previous runs!\n\n"); printBothOpen("The trees with branch lengths have been written to file: %s\n", treeFileName); printBothOpen("They are in the same order as in the input file!\n\n"); break; case QUARTET_CALCULATION: printBothOpen("\n\nOverall quartet computation time: %f secs\n", t); printBothOpen("\nAll quartets and corresponding likelihoods written to file %s\n", quartetFileName); break; default: assert(0); } } } /************************************************************************************/ static int iterated_bitcount(unsigned int n) { int count=0; while(n) { count += n & 0x1u ; n >>= 1 ; } return count; } /*static char bits_in_16bits [0x1u << 16];*/ static void compute_bits_in_16bits(char *bits_in_16bits) { unsigned int i; /* size is 65536 */ for (i = 0; i < (0x1u<<16); i++) bits_in_16bits[i] = iterated_bitcount(i); return ; } unsigned int precomputed16_bitcount (unsigned int n, char *bits_in_16bits) { /* works only for 32-bit int*/ return bits_in_16bits [n & 0xffffu] + bits_in_16bits [(n >> 16) & 0xffffu] ; } static void clean_MPI_Exit(void) { MPI_Barrier(MPI_COMM_WORLD); MPI_Finalize(); } static void error_MPI_Exit(void) { MPI_Barrier(MPI_COMM_WORLD); MPI_Finalize(); exit(1); } static void initializePartitions(tree *tr) { size_t i, len, j, width; int model, maxCategories; compute_bits_in_16bits(tr->bits_in_16bits); maxCategories = tr->maxCategories; for(model = 0; model < tr->NumberOfModels; model++) { const partitionLengths *pl = getPartitionLengths(&(tr->partitionData[model])); //must already be set as a consequence of alloc in function readPartitions //and the subsequent copy of bf->partitions into tr->partitions! assert(tr->partitionData[model].partitionName != (char*)NULL); //printf("Partition name %s\n", tr->partitionData[model].partitionName); width = tr->partitionData[model].width; /* globalScaler needs to be 2 * tr->mxtips such that scalers of inner AND tip nodes can be added without a case switch to this end, it must also be initialized with zeros -> calloc */ len = 2 * tr->mxtips; tr->partitionData[model].globalScaler = (unsigned int *)calloc(len, sizeof(unsigned int)); #ifdef _USE_OMP tr->partitionData[model].threadGlobalScaler = (unsigned int**) calloc(tr->nThreads, sizeof(unsigned int*)); tr->partitionData[model].reductionBuffer = (double*) calloc(tr->nThreads, sizeof(double)); tr->partitionData[model].reductionBuffer2 = (double*) calloc(tr->nThreads, sizeof(double)); int t; for (t = 0; t < tr->maxThreadsPerModel; ++t) { Assign* pAss = tr->partThreadAssigns[model * tr->maxThreadsPerModel + t]; if (pAss) { int tid = pAss->procId; tr->partitionData[model].threadGlobalScaler[tid] = (unsigned int *)calloc(len, sizeof(unsigned int)); } } #endif tr->partitionData[model].left = (double *)malloc_aligned(pl->leftLength * (maxCategories + 1) * sizeof(double)); tr->partitionData[model].right = (double *)malloc_aligned(pl->rightLength * (maxCategories + 1) * sizeof(double)); tr->partitionData[model].EIGN = (double*)malloc(pl->eignLength * sizeof(double)); tr->partitionData[model].EV = (double*)malloc_aligned(pl->evLength * sizeof(double)); tr->partitionData[model].EI = (double*)malloc(pl->eiLength * sizeof(double)); tr->partitionData[model].substRates = (double *)malloc(pl->substRatesLength * sizeof(double)); //must already be set as a consequence of alloc in function readPartitions //and the subsequent copy of bf->partitions into tr->partitions! assert(tr->partitionData[model].frequencies != (double*)NULL); //tr->partitionData[model].frequencies = (double*)malloc(pl->frequenciesLength * sizeof(double)); tr->partitionData[model].freqExponents = (double*)malloc(pl->frequenciesLength * sizeof(double)); tr->partitionData[model].empiricalFrequencies = (double*)malloc(pl->frequenciesLength * sizeof(double)); tr->partitionData[model].tipVector = (double *)malloc_aligned(pl->tipVectorLength * sizeof(double)); if(tr->partitionData[model].protModels == LG4M || tr->partitionData[model].protModels == LG4X) { int k; for(k = 0; k < 4; k++) { tr->partitionData[model].rawEIGN_LG4[k] = (double*)malloc(pl->eignLength * sizeof(double)); tr->partitionData[model].EIGN_LG4[k] = (double*)malloc(pl->eignLength * sizeof(double)); tr->partitionData[model].EV_LG4[k] = (double*)malloc_aligned(pl->evLength * sizeof(double)); tr->partitionData[model].EI_LG4[k] = (double*)malloc(pl->eiLength * sizeof(double)); tr->partitionData[model].substRates_LG4[k] = (double *)malloc(pl->substRatesLength * sizeof(double)); tr->partitionData[model].frequencies_LG4[k] = (double*)malloc(pl->frequenciesLength * sizeof(double)); tr->partitionData[model].tipVector_LG4[k] = (double *)malloc_aligned(pl->tipVectorLength * sizeof(double)); } } tr->partitionData[model].symmetryVector = (int *)malloc(pl->symmetryVectorLength * sizeof(int)); tr->partitionData[model].frequencyGrouping = (int *)malloc(pl->frequencyGroupingLength * sizeof(int)); tr->partitionData[model].perSiteRates = (double *)malloc(sizeof(double) * tr->maxCategories); // tr->partitionData[model].nonGTR = FALSE; // tr->partitionData[model].optimizeBaseFrequencies = FALSE; //tr->partitionData[model].gammaRates = (double*)malloc(sizeof(double) * 4); tr->partitionData[model].xVector = (double **)malloc(sizeof(double*) * tr->mxtips); for(j = 0; j < (size_t)tr->mxtips; j++) tr->partitionData[model].xVector[j] = (double*)NULL; tr->partitionData[model].xSpaceVector = (size_t *)calloc(tr->mxtips, sizeof(size_t)); #ifdef __MIC_NATIVE tr->partitionData[model].mic_EV = (double*)malloc_aligned(4 * pl->evLength * sizeof(double)); tr->partitionData[model].mic_tipVector = (double*)malloc_aligned(4 * pl->tipVectorLength * sizeof(double)); tr->partitionData[model].mic_umpLeft = (double*)malloc_aligned(4 * pl->tipVectorLength * sizeof(double)); tr->partitionData[model].mic_umpRight = (double*)malloc_aligned(4 * pl->tipVectorLength * sizeof(double)); /* for Xeon Phi, sumBuffer must be padded to the multiple of 8 (because of site blocking in kernels) */ const int padded_width = GET_PADDED_WIDTH(width); const int span = (size_t)(tr->partitionData[model].states) * discreteRateCategories(tr->rateHetModel); tr->partitionData[model].sumBuffer = (double *)malloc_aligned(padded_width * span * sizeof(double)); /* fill padding entries with 1. (will be corrected for with zero site weights in wgt) */ { int k; for (k = width*span; k < padded_width*span; ++k) tr->partitionData[model].sumBuffer[k] = 1.; } #else tr->partitionData[model].sumBuffer = (double *)malloc_aligned(width * (size_t)(tr->partitionData[model].states) * discreteRateCategories(tr->rateHetModel) * sizeof(double)); #endif /* tr->partitionData[model].wgt = (int *)malloc_aligned(width * sizeof(int)); */ /* rateCategory must be assigned using calloc() at start up there is only one rate category 0 for all sites */ if(width > 0 && tr->saveMemory) { tr->partitionData[model].gapVectorLength = ((int)width / 32) + 1; len = tr->partitionData[model].gapVectorLength * 2 * tr->mxtips; tr->partitionData[model].gapVector = (unsigned int*)calloc(len, sizeof(unsigned int)); tr->partitionData[model].gapColumn = (double *)malloc_aligned(((size_t)tr->mxtips) * ((size_t)(tr->partitionData[model].states)) * discreteRateCategories(tr->rateHetModel) * sizeof(double)); } else { tr->partitionData[model].gapVectorLength = 0; tr->partitionData[model].gapVector = (unsigned int*)NULL; tr->partitionData[model].gapColumn = (double*)NULL; } } /* set up the averaged frac changes per partition such that no further reading accesses to aliaswgt are necessary and we can free the array for the GAMMA model */ { /* definitions: sizeof(short) <= sizeof(int) <= sizeof(long) size_t defined by address space (here: 64 bit). size_t + MPI is a bad idea: in the mpi2.2 standard, they do not mention it once. */ unsigned long *modelWeights = (unsigned long*) calloc(tr->NumberOfModels, sizeof(unsigned long)); size_t wgtsum = 0; /* determine my weights per partition */ for(model = 0; model < tr->NumberOfModels; model++) { const pInfo partition = tr->partitionData[model] ; size_t i = 0; for(i = 0; i < partition.width; ++i) modelWeights[model] += (long) partition.wgt[i]; } MPI_Allreduce(MPI_IN_PLACE, modelWeights, tr->NumberOfModels, MPI_UNSIGNED_LONG, MPI_SUM, MPI_COMM_WORLD); /* determine sum */ for(model = 0; model < tr->NumberOfModels; ++model) wgtsum += modelWeights[model]; for(model = 0; model < tr->NumberOfModels; model++) { tr->partitionWeights[model] = (double)modelWeights[model]; tr->partitionContributions[model] = ((double)modelWeights[model]) / ((double)wgtsum); } free(modelWeights); } /* initialize gap bit vectors at tips when memory saving option is enabled */ if(tr->saveMemory) { for(model = 0; model NumberOfModels; model++) { int undetermined = getUndetermined(tr->partitionData[model].dataType); width = tr->partitionData[model].width; if(width > 0) { for(j = 1; j <= (size_t)(tr->mxtips); j++) for(i = 0; i < width; i++) if(tr->partitionData[model].yVector[j][i] == undetermined) tr->partitionData[model].gapVector[tr->partitionData[model].gapVectorLength * j + i / 32] |= mask32[i % 32]; } } } } static void initializeTree(tree *tr, analdef *adef) { size_t i ; if(adef->perGeneBranchLengths) tr->numBranches = tr->NumberOfModels; else tr->numBranches = 1; if(NUM_BRANCHES < tr->numBranches) { if(processID == 0 ) printf("You have specified per-partition branch lengths (-M option) with %d models. \n\ Please set #define NUM_BRANCHES in axml.h to %d (or higher) and recompile %s\n", tr->NumberOfModels,tr->NumberOfModels, programName ); error_MPI_Exit(); } /* If we use the RF-based convergence criterion we will need to allocate some hash tables. let's not worry about this right now, because it is indeed ExaML-specific */ tr->executeModel = (boolean *)calloc( tr->NumberOfModels, sizeof(boolean)); for(i = 0; i < (size_t)tr->NumberOfModels; i++) tr->executeModel[i] = TRUE; setupTree(tr); if(tr->searchConvergenceCriterion && processID == 0) { tr->bitVectors = initBitVector(tr->mxtips, &(tr->vLength)); tr->h = initHashTable(tr->mxtips * 4); } for(i = 1; i <= (size_t)tr->mxtips; i++) addword(tr->nameList[i], tr->nameHash, i); initializePartitions(tr); initModel(tr); } static int getNumberOfTrees(char *fileName, boolean getOffsets, exa_off_t *treeOffsets) { FILE *f = myfopen(fileName, "r"); int trees = 0, ch; if(getOffsets) treeOffsets[trees] = 0; while((ch = fgetc(f)) != EOF) { if(ch == ';') { trees++; if(getOffsets) treeOffsets[trees] = exa_ftell(f) + 1; } } assert(trees > 0); fclose(f); return trees; } static void optimizeTrees(tree *tr, analdef *adef) { exa_off_t *treeOffsets; int i; tr->numberOfTrees = getNumberOfTrees(tree_file, FALSE, (exa_off_t *)NULL); if(processID == 0) accumulatedTime = 0.0; treeOffsets = (exa_off_t *)malloc(sizeof(exa_off_t) * (tr->numberOfTrees + 1)); tr->likelihoods = (double *)malloc(sizeof(double) * tr->numberOfTrees); tr->treeStrings = (char *)malloc(sizeof(char) * (size_t)tr->treeStringLength * (size_t)tr->numberOfTrees); getNumberOfTrees(tree_file, TRUE, treeOffsets); if(processID == 0) printBothOpen("\n\nFound %d trees to evaluate\n\n", tr->numberOfTrees); i = 0; if(adef->useCheckpoint) { restart(tr, adef); i = ckp.treeIteration; if(tr->fastTreeEvaluation && i > 0) treeEvaluate(tr, 2); else modOpt(tr, 0.1, adef, i); tr->likelihoods[i] = tr->likelihood; Tree2String(tr->tree_string, tr, tr->start->back, TRUE, TRUE, FALSE, FALSE, FALSE, SUMMARIZE_LH, FALSE, FALSE); memcpy(&(tr->treeStrings[(size_t)tr->treeStringLength * (size_t)i]), tr->tree_string, sizeof(char) * tr->treeStringLength); if(processID == 0) printModelParams(tr, adef, i); i++; } for(; i < tr->numberOfTrees; i++) { FILE *treeFile = myfopen(tree_file, "rb"); if(exa_fseek(treeFile, treeOffsets[i], SEEK_SET) != 0) assert(0); tr->likelihood = unlikely; treeReadLen(treeFile, tr, FALSE, FALSE, FALSE); fclose(treeFile); tr->start = tr->nodep[1]; if(i > 0) resetBranches(tr); evaluateGeneric(tr, tr->start, TRUE); if(tr->fastTreeEvaluation && i > 0) { ckp.state = MOD_OPT; ckp.treeIteration = i; writeCheckpoint(tr, adef); treeEvaluate(tr, 2); } else { treeEvaluate(tr, 1); modOpt(tr, 0.1, adef, i); } tr->likelihoods[i] = tr->likelihood; Tree2String(tr->tree_string, tr, tr->start->back, TRUE, TRUE, FALSE, FALSE, FALSE, SUMMARIZE_LH, FALSE, FALSE); memcpy(&(tr->treeStrings[(size_t)tr->treeStringLength * (size_t)i]), tr->tree_string, sizeof(char) * tr->treeStringLength); if(processID == 0) printModelParams(tr, adef, i); } if(processID == 0) { FILE *f = myfopen(treeFileName, "w"); for(i = 0; i < tr->numberOfTrees; i++) { printBothOpen("Likelihood tree %d: %f \n", i, tr->likelihoods[i]); fprintf(f, "%s", &(tr->treeStrings[(size_t)tr->treeStringLength * (size_t)i])); } fclose(f); } } static void readByteFile (tree *tr, int commRank, int commSize ) { /* read stuff that is cheap; do not change the order! */ ByteFile *bFile = NULL; initializeByteFile(&bFile, byteFileName); readHeader(bFile); readTaxa(bFile); readPartitions(bFile); /* calculate optimal distribution of data */ PartitionAssignment *pAss = NULL; initializePartitionAssignment(&pAss, bFile->partitions, bFile->numPartitions, commSize); assign(pAss); if(commRank == 0 ) { printf("\n"); printAssignments(pAss); printf("\n"); printLoad(pAss); printf("\n"); } /* now the data of this process is in this struct */ readMyData(bFile,pAss, commRank ); /* carry over the information to the tree */ initializeTreeFromByteFile(bFile, tr); /* just fills up tr->partAssigns that contains the representation of the assignment that we will need */ copyAssignmentInfoToTree(pAss, tr); deletePartitionAssignment(pAss); deleteByteFile(bFile); } #ifdef _USE_OMP void allocateXVectors(tree* tr) { nodeptr p = tr->start, q = p->back; tr->td[0].ti[0].pNumber = p->number; tr->td[0].ti[0].qNumber = q->number; tr->td[0].count = 1; computeTraversalInfo(q, &(tr->td[0].ti[0]), &(tr->td[0].count), tr->mxtips, tr->numBranches, FALSE); traversalInfo *ti = tr->td[0].ti; int i, model; for(i = 1; i < tr->td[0].count; i++) { traversalInfo *tInfo = &ti[i]; /* now loop over all partitions for nodes p, q, and r of the current traversal vector entry */ for(model = 0; model < tr->NumberOfModels; model++) { /* printf("new view on model %d with width %d\n", model, width); */ size_t width = (size_t)tr->partitionData[model].width; double *x3_start = tr->partitionData[model].xVector[tInfo->pNumber - tr->mxtips - 1]; size_t rateHet = discreteRateCategories(tr->rateHetModel), /* get the number of states in the data stored in partition model */ states = (size_t)tr->partitionData[model].states, /* get the length of the current likelihood array stored at node p. This is important mainly for the SEV-based memory saving option described in here: F. Izquierdo-Carrasco, S.A. Smith, A. Stamatakis: "Algorithms, Data Structures, and Numerics for Likelihood-based Phylogenetic Inference of Huge Trees". So tr->partitionData[model].xSpaceVector[i] provides the length of the allocated conditional array of partition model and node i */ availableLength = tr->partitionData[model].xSpaceVector[(tInfo->pNumber - tr->mxtips - 1)], requiredLength = 0; /* memory saving stuff, not important right now, but if you are interested ask Fernando */ if(tr->saveMemory) { size_t j, setBits = 0; unsigned int *x1_gap = &(tr->partitionData[model].gapVector[tInfo->qNumber * tr->partitionData[model].gapVectorLength]), *x2_gap = &(tr->partitionData[model].gapVector[tInfo->rNumber * tr->partitionData[model].gapVectorLength]), *x3_gap = &(tr->partitionData[model].gapVector[tInfo->pNumber * tr->partitionData[model].gapVectorLength]); for(j = 0; j < (size_t)tr->partitionData[model].gapVectorLength; j++) { x3_gap[j] = x1_gap[j] & x2_gap[j]; setBits += (size_t)(precomputed16_bitcount(x3_gap[j], tr->bits_in_16bits)); } requiredLength = (width - setBits) * rateHet * states * sizeof(double); } else /* if we are not trying to save memory the space required to store an inner likelihood array is the number of sites in the partition times the number of states of the data type in the partition times the number of discrete GAMMA rates (1 for CAT essentially) times 8 bytes */ requiredLength = width * rateHet * states * sizeof(double); /* Initially, even when not using memory saving no space is allocated for inner likelihood arrats hence availableLength will be zero at the very first time we traverse the tree. Hence we need to allocate something here */ if(requiredLength != availableLength) { /* if there is a vector of incorrect length assigned here i.e., x3 != NULL we must free it first */ if(x3_start) free(x3_start); /* allocate memory: note that here we use a byte-boundary aligned malloc, because we need the vectors to be aligned at 16 BYTE (SSE3) or 32 BYTE (AVX) boundaries! */ x3_start = (double*)malloc_aligned(requiredLength); /* update the data structures for consistent bookkeeping */ tr->partitionData[model].xVector[tInfo->pNumber - tr->mxtips - 1] = x3_start; tr->partitionData[model].xSpaceVector[(tInfo->pNumber - tr->mxtips - 1)] = requiredLength; } } // for model } // for traversal } void assignPartitionsToThreads(tree *tr, int commRank) { pInfo** rankPartitions = (pInfo **)calloc(tr->NumberOfModels, sizeof(pInfo*) ); int i; for (i = 0; i < tr->NumberOfModels; ++i) { rankPartitions[i] = (pInfo *)calloc(1, sizeof(pInfo)); rankPartitions[i]->lower = 0; rankPartitions[i]->upper = tr->partitionData[i].width; rankPartitions[i]->width = rankPartitions[i]->upper; rankPartitions[i]->states = tr->partitionData[i].states; } PartitionAssignment *pAss = NULL; initializePartitionAssignment(&pAss, rankPartitions, tr->NumberOfModels, tr->nThreads); /* */ for(i = 0; i < pAss->numPartitions; ++i) { Partition *p = pAss->partitions + i; p->width = (int) ceil((float) p->width / (float) VECTOR_PADDING); } assign(pAss); /* Align partition sizes to the boundary (needed for site-blocking on the MIC) */ int j; for(i = 0; i < pAss->numProc; ++i) { for(j = 0; j < pAss->numAssignPerProc[i] ; ++j) { Assignment *a = &pAss->assignPerProc[i][j]; a->offset *= VECTOR_PADDING; a->width *= VECTOR_PADDING; /* adjust width of last chunk -> must NOT include padding */ size_t realWidth = rankPartitions[a->partId]->width; if (a->offset + a->width > realWidth) a->width = realWidth - a->offset; } } printf("Partition assignments to threads: \n"); printAssignments(pAss); printf("\n"); printLoad(pAss); printf("\n"); copyThreadAssignmentInfoToTree(pAss, tr); deletePartitionAssignment(pAss); for (i = 0; i < tr->NumberOfModels; ++i) free(rankPartitions[i]); free(rankPartitions); } #endif int main (int argc, char *argv[]) { MPI_Init(&argc, &argv); MPI_Comm_rank(MPI_COMM_WORLD, &processID); MPI_Comm_size(MPI_COMM_WORLD, &processes); printf("\nThis is ExaML FINE-GRAIN MPI Process Number: %d\n", processID); MPI_Barrier(MPI_COMM_WORLD); { tree *tr = (tree*)malloc(sizeof(tree)); analdef *adef = (analdef*)malloc(sizeof(analdef)); /* tell the CPU to ignore exceptions generated by denormalized floating point values. If this is not done, depending on the input data, the likelihood functions can exhibit substantial run-time differences for vectors of equal length. */ #if ! (defined(__ppc) || defined(__powerpc__) || defined(PPC)) _mm_setcsr( _mm_getcsr() | _MM_FLUSH_ZERO_ON); #endif /* get the start time */ masterTime = gettime(); /* initialize the analysis parameters in struct adef to default values */ initAdef(adef); /* parse command line arguments: this has a side effect on tr struct and adef struct variables */ get_args(argc, argv, adef, tr); /* generate the ExaML output file names and store them in strings */ makeFileNames(); #ifdef _USE_OMP if(tr->saveMemory) { printBothOpen("\nError: Memory saving option \"-S\" is not supported by the OpenMP version of ExaML!\n\n"); error_MPI_Exit(); } #endif readByteFile(tr, processID, processes ); #ifdef _USE_OMP tr->nThreads = omp_get_max_threads(); assignPartitionsToThreads(tr, processID); #endif initializeTree(tr, adef); if(processID == 0) { printModelAndProgramInfo(tr, adef, argc, argv); printBothOpen("Memory Saving Option: %s\n", (tr->saveMemory == TRUE)?"ENABLED":"DISABLED"); } /* do some error checks for the LG4 model and the binary models and the MIC and exit gracefully */ { int countBinary = 0, countLG4 = 0, model; #ifdef __MIC_NATIVE if(tr->saveMemory) { printBothOpen("Error: There is no MIC support yet for the memory saving option \"-S\"!\n\n"); error_MPI_Exit(); } if(tr->rateHetModel == CAT) { printBothOpen("Error: There is no MIC support yet for the PSR model!\n\n"); error_MPI_Exit(); } #endif for(model = 0; model < tr->NumberOfModels; model++) { if(tr->partitionData[model].protModels == LG4M || tr->partitionData[model].protModels == LG4X) countLG4++; if(tr->partitionData[model].states == 2) countBinary++; } if(countLG4 > 0) { if(tr->saveMemory == TRUE) { printBothOpen("Error: the LG4 substitution model does not work in combination with the \"-S\" memory saving flag!\n\n"); error_MPI_Exit(); } if(tr->rateHetModel == CAT) { printBothOpen("Error: the LG4 substitution model does not work for proportion of invariavble sites estimates!\n\n"); error_MPI_Exit(); } } if(countBinary > 0) { if(tr->saveMemory == TRUE) { printBothOpen("Error: Binary data partitions can not be used in combination with the \"-S\" memory saving flag!\n\n"); error_MPI_Exit(); } #ifdef __MIC_NATIVE printBothOpen("Error: There is no MIC support yet for binary data partitions!\n\n"); error_MPI_Exit(); #endif } if(countBinary > 0) { if(tr->saveMemory == TRUE) { printBothOpen("Error: Binary data partitions can not be used in combination with the \"-S\" memory saving flag!\n\n"); error_MPI_Exit(); } #ifdef __MIC_NATIVE printBothOpen("Error: There is no MIC support yet for binary data partitions!\n\n"); error_MPI_Exit(); #endif } } /* this will re-start ExaML exactly where it has left off from a checkpoint file, while checkpointing is important and has to be implemented for the library we should not worry about this right now */ switch(adef->mode) { case TREE_EVALUATION: optimizeTrees(tr, adef); break; case BIG_RAPID_MODE: if(adef->useCheckpoint) { /* read checkpoint file */ restart(tr, adef); /* continue tree search where we left it off */ computeBIGRAPID(tr, adef, TRUE); /* now print the model parameters to file */ if(processID == 0) printModelParams(tr, adef, -1); } else { /* not important, only used to keep track of total accumulated exec time when checkpointing and restarts were used */ if(processID == 0) accumulatedTime = 0.0; /* get the starting tree: here we just parse the tree passed via the command line and do an initial likelihood computation traversal which we maybe should skip, TODO */ getStartingTree(tr); #ifdef _USE_OMP allocateXVectors(tr); #endif /* here we do an initial full tree traversal on the starting tree using the Felsenstein pruning algorithm This should basically be the first call to the library that actually computes something :-) */ evaluateGeneric(tr, tr->start, TRUE); /* the treeEvaluate() function repeatedly iterates over the entire tree to optimize branch lengths until convergence */ treeEvaluate(tr, 1); /* now start the ML search algorithm */ computeBIGRAPID(tr, adef, TRUE); /* now print the model parameters to file */ if(processID == 0) printModelParams(tr, adef, -1); } break; case QUARTET_CALCULATION: computeQuartets(tr, adef); break; default: assert(0); } /* print some more nonsense into the ExaML_info file */ if(processID == 0) finalizeInfoFile(tr, adef); } /* return 0 which means that our unix program terminated correctly, the return value is not 1 here */ clean_MPI_Exit(); return 0; } ExaML-3.0.21/examl/axml.h000066400000000000000000001142051330315463200147660ustar00rootroot00000000000000/* RAxML-VI-HPC (version 2.2) a program for sequential and parallel estimation of phylogenetic trees * Copyright August 2006 by Alexandros Stamatakis * * Partially derived from * fastDNAml, a program for estimation of phylogenetic trees from sequences by Gary J. Olsen * * and * * Programs of the PHYLIP package by Joe Felsenstein. * * This program is free software; you may redistribute it and/or modify its * 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. * * * For any other enquiries send an Email to Alexandros Stamatakis * Alexandros.Stamatakis@epfl.ch * * When publishing work that is based on the results from RAxML-VI-HPC please cite: * * Alexandros Stamatakis:"RAxML-VI-HPC: maximum likelihood-based phylogenetic analyses * with thousands of taxa and mixed models". * Bioinformatics 2006; doi: 10.1093/bioinformatics/btl446 */ #ifndef _AXML_H #define _AXML_H #include #include #include #include #include "../versionHeader/version.h" #ifdef __MIC_NATIVE #define BYTE_ALIGNMENT 64 #define VECTOR_PADDING 8 #elif defined __AVX #define BYTE_ALIGNMENT 32 #define VECTOR_PADDING 1 #else #define BYTE_ALIGNMENT 16 #define VECTOR_PADDING 1 #endif #define GET_PADDED_WIDTH(w) w % VECTOR_PADDING == 0 ? w : w + (VECTOR_PADDING - (w % VECTOR_PADDING)) #include #ifdef _USE_OMP #include "omp.h" #endif /* BEGIN: file streams */ #ifdef _GNU_SOURCE /* notice, that the gnu source macro implies posix compliance */ /* these are posix compliant functions. They potentially work on files larger than 2 GB (for gcc this can be ensured using the following macro) */ #define exa_fseek fseeko #define exa_ftell ftello #define exa_off_t off_t /* only usefull for ftello/fseeko: ensure that we are using 64-bit types for representing an offset */ #define _FILE_OFFSET_BITS 64 #else #define exa_fseek fseek #define exa_ftell ftell #define exa_off_t long #endif /* END: file streams */ #define MAX_TIP_EV 0.999999999 /* max tip vector value, sum of EVs needs to be smaller than 1.0, otherwise the numerics break down */ #define smoothings 32 /* maximum smoothing passes through tree */ #define iterations 10 /* maximum iterations of iterations per insert */ #define newzpercycle 1 /* iterations of makenewz per tree traversal */ #define nmlngth 256 /* number of characters in species name */ #define deltaz 0.00001 /* test of net branch length change in update */ #define defaultz 0.9 /* value of z assigned as starting point */ #define unlikely -1.0E300 /* low likelihood for initialization */ #define AUTO_ML 0 #define AUTO_BIC 1 #define AUTO_AIC 2 #define AUTO_AICC 3 #define SUMMARIZE_LENGTH -3 #define SUMMARIZE_LH -2 #define NO_BRANCHES -1 #define MASK_LENGTH 32 #define GET_BITVECTOR_LENGTH(x) ((x % MASK_LENGTH) ? (x / MASK_LENGTH + 1) : (x / MASK_LENGTH)) #define zmin 1.0E-15 /* max branch prop. to -log(zmin) (= 34) */ #define zmax (1.0 - 1.0E-6) /* min branch prop. to 1.0-zmax (= 1.0E-6) */ #define twotothe256 \ 115792089237316195423570985008687907853269984665640564039457584007913129639936.0 /* 2**256 (exactly) */ #define minlikelihood (1.0/twotothe256) #define minusminlikelihood -minlikelihood /* 18446744073709551616.0 */ /*4294967296.0*/ /* 18446744073709551616.0 */ /* 2**64 (exactly) */ /* 4294967296 2**32 */ #define badRear -1 #define NUM_BRANCHES 256 #define TRUE 1 #define FALSE 0 #define LIKELIHOOD_EPSILON 0.0000001 #define AA_SCALE 10.0 #define AA_SCALE_PLUS_EPSILON 10.001 /* ALPHA_MIN is critical -> numerical instability, eg for 4 discrete rate cats */ /* and alpha = 0.01 the lowest rate r_0 is */ /* 0.00000000000000000000000000000000000000000000000000000000000034878079110511010487 */ /* which leads to numerical problems Table for alpha settings below: */ /* */ /* 0.010000 0.00000000000000000000000000000000000000000000000000000000000034878079110511010487 */ /* 0.010000 yielded nasty numerical bugs in at least one case ! */ /* 0.020000 0.00000000000000000000000000000044136090435925743185910935350715027016962154188875 */ /* 0.030000 0.00000000000000000000476844846859006690412039180149775802624789852441798419292220 */ /* 0.040000 0.00000000000000049522423236954066431210260930029681736928018820007024736185030633 */ /* 0.050000 0.00000000000050625351310359203371872643495343928538368616365517027588794007897377 */ /* 0.060000 0.00000000005134625283884191118711474021861409372524676086868566926568746566772461 */ /* 0.070000 0.00000000139080650074206434685544624965062437960128249869740102440118789672851562 */ /* 0.080000 0.00000001650681201563587066858709818343436959153791576682124286890029907226562500 */ /* 0.090000 0.00000011301977332931251259273962858978301859735893231118097901344299316406250000 */ /* 0.100000 0.00000052651925834844387815526344648331402709118265192955732345581054687500000000 */ #define ALPHA_MIN 0.02 #define ALPHA_MAX 1000.0 #define RATE_MIN 0.0000001 #define RATE_MAX 1000000.0 #define INVAR_MIN 0.0001 #define INVAR_MAX 0.9999 #define TT_MIN 0.0000001 #define TT_MAX 1000000.0 #define FREQ_MIN 0.001 #define LG4X_RATE_MIN 0.0000001 #define LG4X_RATE_MAX 1000.0 /* previous values between 0.001 and 0.000001 TO AVOID NUMERICAL PROBLEMS WHEN FREQ == 0 IN PARTITIONED MODELS, ESPECIALLY WITH AA previous value of FREQ_MIN was: 0.000001, but this seemed to cause problems with some of the 7-state secondary structure models with some rather exotic small toy test datasets, on the other hand 0.001 caused problems with some of the 16-state secondary structure models For some reason the frequency settings seem to be repeatedly causing numerical problems */ #define ITMAX 100 #define SHFT(a,b,c,d) (a)=(b);(b)=(c);(c)=(d); #define SIGN(a,b) ((b) > 0.0 ? fabs(a) : -fabs(a)) #define ABS(x) (((x)<0) ? (-(x)) : (x)) #define MIN(x,y) (((x)<(y)) ? (x) : (y)) #define MAX(x,y) (((x)>(y)) ? (x) : (y)) #define NINT(x) ((int) ((x)>0 ? ((x)+0.5) : ((x)-0.5))) #define LOG(x) log(x) #define FABS(x) fabs(x) #define EXP(x) exp(x) #define PointGamma(prob,alpha,beta) PointChi2(prob,2.0*(alpha))/(2.0*(beta)) //#define programName "ExaML" //#define programVersion "2.0.3" //#define programDate "June 25 2014" #define TREE_EVALUATION 0 #define BIG_RAPID_MODE 1 #define QUARTET_CALCULATION 2 #define M_GTRCAT 1 #define M_GTRGAMMA 2 #define M_BINCAT 3 #define M_BINGAMMA 4 #define M_PROTCAT 5 #define M_PROTGAMMA 6 #define M_32CAT 7 #define M_32GAMMA 8 #define M_64CAT 9 #define M_64GAMMA 10 #define DAYHOFF 0 #define DCMUT 1 #define JTT 2 #define MTREV 3 #define WAG 4 #define RTREV 5 #define CPREV 6 #define VT 7 #define BLOSUM62 8 #define MTMAM 9 #define LG 10 #define MTART 11 #define MTZOA 12 #define PMB 13 #define HIVB 14 #define HIVW 15 #define JTTDCMUT 16 #define FLU 17 #define STMTREV 18 #define AUTO 19 #define LG4M 20 #define LG4X 21 #define GTR 22 /* GTR always needs to be the last one */ #define NUM_PROT_MODELS 23 /* bipartition stuff */ #define BIPARTITIONS_ALL 0 #define GET_BIPARTITIONS_BEST 1 #define DRAW_BIPARTITIONS_BEST 2 #define BIPARTITIONS_BOOTSTOP 3 #define BIPARTITIONS_RF 4 /* bootstopping stuff */ #define BOOTSTOP_PERMUTATIONS 100 #define START_BSTOP_TEST 10 #define FC_THRESHOLD 99 #define FC_SPACING 50 #define FC_LOWER 0.99 #define FC_INIT 20 #define FREQUENCY_STOP 0 #define MR_STOP 1 #define MRE_STOP 2 #define MRE_IGN_STOP 3 #define MR_CONSENSUS 0 #define MRE_CONSENSUS 1 #define STRICT_CONSENSUS 2 /* bootstopping stuff end */ #define TIP_TIP 0 #define TIP_INNER 1 #define INNER_INNER 2 #define MIN_MODEL -1 #define BINARY_DATA 0 #define DNA_DATA 1 #define AA_DATA 2 #define SECONDARY_DATA 3 #define SECONDARY_DATA_6 4 #define SECONDARY_DATA_7 5 #define GENERIC_32 6 #define GENERIC_64 7 #define MAX_MODEL 8 #define SEC_6_A 0 #define SEC_6_B 1 #define SEC_6_C 2 #define SEC_6_D 3 #define SEC_6_E 4 #define SEC_7_A 5 #define SEC_7_B 6 #define SEC_7_C 7 #define SEC_7_D 8 #define SEC_7_E 9 #define SEC_7_F 10 #define SEC_16 11 #define SEC_16_A 12 #define SEC_16_B 13 #define SEC_16_C 14 #define SEC_16_D 15 #define SEC_16_E 16 #define SEC_16_F 17 #define SEC_16_I 18 #define SEC_16_J 19 #define SEC_16_K 20 #define ORDERED_MULTI_STATE 0 #define MK_MULTI_STATE 1 #define GTR_MULTI_STATE 2 #define CAT 0 #define GAMMA 1 #define GAMMA_I 2 typedef int boolean; typedef struct { double lh; int tree; double weight; } elw; struct ent { unsigned int *bitVector; unsigned int *treeVector; unsigned int amountTips; int *supportVector; unsigned int bipNumber; unsigned int bipNumber2; unsigned int supportFromTreeset[2]; struct ent *next; }; typedef struct ent entry; typedef unsigned int hashNumberType; /*typedef uint_fast32_t parsimonyNumber;*/ #define PCF 32 /* typedef uint64_t parsimonyNumber; #define PCF 16 typedef unsigned char parsimonyNumber; #define PCF 2 */ typedef struct { hashNumberType tableSize; entry **table; hashNumberType entryCount; } hashtable; struct stringEnt { int nodeNumber; char *word; struct stringEnt *next; }; typedef struct stringEnt stringEntry; typedef struct { hashNumberType tableSize; stringEntry **table; } stringHashtable; typedef struct ratec { double accumulatedSiteLikelihood; double rate; } rateCategorize; typedef struct { int tipCase; int pNumber; int qNumber; int rNumber; double qz[NUM_BRANCHES]; double rz[NUM_BRANCHES]; } traversalInfo; typedef struct { traversalInfo *ti; int count; int functionType; boolean traversalHasChanged; boolean *executeModel; double *parameterValues; } traversalData; struct noderec; typedef struct { unsigned int *vector; int support; struct noderec *oP; struct noderec *oQ; } branchInfo; typedef struct { boolean valid; int partitions; int *partitionList; } linkageData; typedef struct { int entries; linkageData* ld; } linkageList; typedef struct noderec { double z[NUM_BRANCHES]; #ifdef _BAYESIAN double z_tmp[NUM_BRANCHES]; #endif struct noderec *next; struct noderec *back; hashNumberType hash; int number; char x; char xPars; char xBips; } node, *nodeptr; typedef struct { double lh; int number; } info; typedef struct bInf { double likelihood; nodeptr node; } bestInfo; typedef struct iL { bestInfo *list; int n; int valid; } infoList; typedef unsigned int parsimonyNumber; typedef struct { int states; int maxTipStates; size_t lower; size_t upper; size_t width; size_t offset; /* NEW: makes the data assigned to this process identifiable (since we now, that all data from one partition must be in one contiguous chunk). */ int dataType; int protModels; int autoProtModels; int protFreqs; boolean nonGTR; boolean optimizeBaseFrequencies; int numberOfCategories; char *partitionName; int *symmetryVector; int *frequencyGrouping; double *sumBuffer; double gammaRates[4]; double *EIGN; double *EV; double *EI; double *left; double *right; /* LG4 */ double *rawEIGN_LG4[4]; double *EIGN_LG4[4]; double *EV_LG4[4]; double *EI_LG4[4]; double *frequencies_LG4[4]; double *tipVector_LG4[4]; double *substRates_LG4[4]; /* LG4X */ double weights[4]; double weightExponents[4]; double weightsBuffer[4]; double weightExponentsBuffer[4]; /* LG4 */ double *frequencies; double *freqExponents; double *empiricalFrequencies; double *tipVector; double *substRates; double *perSiteRates; int *wgt; int *rateCategory; double alpha; double **xVector; size_t *xSpaceVector; unsigned char **yVector; unsigned char *yResource; /* contains the entire array, that is referenced in yVector */ unsigned int *globalScaler; int gapVectorLength; unsigned int *gapVector; double *gapColumn; size_t parsimonyLength; parsimonyNumber *parsVect; double *lhs; double *patrat; #ifdef _USE_OMP /* thread-private data for OMP version */ unsigned int **threadGlobalScaler; double *reductionBuffer; double *reductionBuffer2; #endif #ifdef __MIC_NATIVE double *mic_EV; double *mic_tipVector; /* these arrays will store the precomputed product of tipVector and left/right P-matrix */ double *mic_umpLeft; double *mic_umpRight; #endif } pInfo; typedef struct { int left; int right; double likelihood; } lhEntry; typedef struct { int count; int size; lhEntry *entries; } lhList; typedef struct List_{ void *value; struct List_ *next; } List; #define REARR_SETTING 1 #define FAST_SPRS 2 #define SLOW_SPRS 3 #define MOD_OPT 4 #define QUARTETS 5 typedef struct { boolean useMedian; int saveBestTrees; boolean saveMemory; boolean searchConvergenceCriterion; boolean perGeneBranchLengths; //adef double likelihoodEpsilon; //adef int categories; int mode; //adef int fastTreeEvaluation; boolean initialSet;//adef int initial;//adef int rateHetModel; int autoProteinSelectionType; //quartets boolean useQuartetGrouping;//adef unsigned long int numberRandomQuartets;//adef } commandLine; typedef struct { int state; /* search algorithm */ unsigned int vLength; boolean constraintTree; int rearrangementsMax; int rearrangementsMin; int thoroughIterations; int fastIterations; int treeVectorLength; int mintrav; int maxtrav; int bestTrav; int Thorough; int optimizeRateCategoryInvocations; double accumulatedTime; double startLH; double lh; double previousLh; double difference; double epsilon; boolean impr; boolean cutoff; double tr_startLH; double tr_endLH; double tr_likelihood; double tr_bestOfNode; double tr_lhCutoff; double tr_lhAVG; double tr_lhDEC; int tr_NumberOfCategories; int tr_itCount; int tr_doCutoff; /* modOpt */ int catOpt; int treeIteration; /* quartets */ long seed; int flavor; uint64_t quartetCounter; long filePosition; char quartetFileName[1024]; //FILE NAME??? /* command line settings */ commandLine cmd; } checkPointState; typedef struct { double EIGN[19] __attribute__ ((aligned (BYTE_ALIGNMENT))); double EV[400] __attribute__ ((aligned (BYTE_ALIGNMENT))); double EI[380] __attribute__ ((aligned (BYTE_ALIGNMENT))); double substRates[190]; double frequencies[20] ; double tipVector[460] __attribute__ ((aligned (BYTE_ALIGNMENT))); double left[1600] __attribute__ ((aligned (BYTE_ALIGNMENT))); double right[1600] __attribute__ ((aligned (BYTE_ALIGNMENT))); } siteAAModels; typedef struct assign { int partitionId; int procId; /* to which process is the partition assigned */ size_t offset; /* what is the offset of this assignment */ size_t width; } Assign ; typedef struct { int *ti; unsigned int randomSeed; boolean constraintTree; boolean useGappedImplementation; boolean saveMemory; int saveBestTrees; stringHashtable *nameHash; pInfo *partitionData; char *secondaryStructureInput; boolean *executeModel; double *perPartitionLH; traversalData td[1]; int maxCategories; int categories; double coreLZ[NUM_BRANCHES]; int numBranches; branchInfo *bInf; int multiStateModel; boolean curvatOK[NUM_BRANCHES]; /* the stuff below is shared among DNA and AA, span does not change depending on datatype */ /* model stuff end */ unsigned char **yVector; int secondaryStructureModel; size_t originalCrunchedLength; int *secondaryStructurePairs; double *partitionContributions; double *partitionWeights; double lhCutoff; double lhAVG; unsigned long lhDEC; unsigned long itCount; int numberOfInvariableColumns; int weightOfInvariableColumns; int rateHetModel; double startLH; double endLH; double likelihood; node **nodep; nodeptr nodeBaseAddress; node *start; int mxtips; int *constraintVector; int numberOfSecondaryColumns; boolean searchConvergenceCriterion; int ntips; int nextnode; int NumberOfModels; boolean bigCutoff; boolean partitionSmoothed[NUM_BRANCHES]; boolean partitionConverged[NUM_BRANCHES]; boolean rooted; boolean doCutoff; double gapyness; char **nameList; char *tree_string; char *treeStrings; char *tree0; char *tree1; int treeStringLength; unsigned int bestParsimony; unsigned int *parsimonyScore; double bestOfNode; nodeptr removeNode; nodeptr insertNode; double zqr[NUM_BRANCHES]; double currentZQR[NUM_BRANCHES]; double currentLZR[NUM_BRANCHES]; double currentLZQ[NUM_BRANCHES]; double currentLZS[NUM_BRANCHES]; double currentLZI[NUM_BRANCHES]; double lzs[NUM_BRANCHES]; double lzq[NUM_BRANCHES]; double lzr[NUM_BRANCHES]; double lzi[NUM_BRANCHES]; unsigned int **bitVectors; unsigned int vLength; hashtable *h; char bits_in_16bits [0x1u << 16]; boolean useMedian; int autoProteinSelectionType; int numberOfTrees; double *likelihoods; boolean fastTreeEvaluation; int numAssignments; Assign *partAssigns; /** IMPORTANT: introducing a few resource pointers. All memeory needed for example for per-partition patrats is owned by these basepointers, the per-partition pointer just points at the contiguous block of memory. The big advantage, why I really think, this is worth it is, that these base pointers can be used to conveniently gather/scatter all data at a master. The master still has to reorder the gathered data, but less copying is necessary at the workers REQUIREMENTS: * all memory necessary for a partition must be in a contiguous block, * memory for partitions is ordered by partition id (first * partition 1, then partition 2,... ) */ double *patrat_basePtr; int *rateCategory_basePtr; double *lhs_basePtr; #ifdef _USE_OMP /* number of OMP threads*/ int nThreads; /* maximum number of partitions assigned to a single thread */ int maxModelsPerThread; /* maximum number of threads assigned to a single partition */ int maxThreadsPerModel; /* partition-to-threads assignments: indexed by thread */ Assign **threadPartAssigns; /* partition-to-threads assignments: indexed by partition id */ Assign **partThreadAssigns; #endif } tree; /***************************************************************/ typedef struct { int partitionNumber; int partitionLength; } partitionType; typedef struct { double z[NUM_BRANCHES]; nodeptr p, q; int cp, cq; } connectRELL, *connptrRELL; typedef struct { connectRELL *connect; int start; double likelihood; } topolRELL; typedef struct { int max; topolRELL **t; } topolRELL_LIST; /**************************************************************/ typedef struct conntyp { double z[NUM_BRANCHES]; /* branch length */ node *p, *q; /* parent and child sectors */ void *valptr; /* pointer to value of subtree */ int descend; /* pointer to first connect of child */ int sibling; /* next connect from same parent */ } connect, *connptr; typedef struct { double likelihood; int initialTreeNumber; connect *links; /* pointer to first connect (start) */ node *start; int nextlink; /* index of next available connect */ /* tr->start = tpl->links->p */ int ntips; int nextnode; int scrNum; /* position in sorted list of scores */ int tplNum; /* position in sorted list of trees */ } topol; typedef struct { double best; /* highest score saved */ double worst; /* lowest score saved */ topol *start; /* starting tree for optimization */ topol **byScore; topol **byTopol; int nkeep; /* maximum topologies to save */ int nvalid; /* number of topologies saved */ int ninit; /* number of topologies initialized */ int numtrees; /* number of alternatives tested */ boolean improved; } bestlist; #define randomTree 0 #define givenTree 1 #define parsimonyTree 2 typedef struct { int bestTrav; int max_rearrange; int stepwidth; int initial; boolean initialSet; int mode; boolean perGeneBranchLengths; boolean permuteTreeoptimize; boolean compressPatterns; double likelihoodEpsilon; boolean useCheckpoint; boolean useQuartetGrouping; unsigned long int numberRandomQuartets; unsigned long int quartetCkpInterval; #ifdef _BAYESIAN boolean bayesian; #endif } analdef; typedef struct { int leftLength; int rightLength; int eignLength; int evLength; int eiLength; int substRatesLength; int frequenciesLength; int tipVectorLength; int symmetryVectorLength; int frequencyGroupingLength; boolean nonGTR; int undetermined; const char *inverseMeaning; int states; boolean smoothFrequencies; const unsigned int *bitVector; } partitionLengths; /****************************** FUNCTIONS ****************************************************/ #ifdef _BAYESIAN extern void mcmc(tree *tr, analdef *adef); #endif boolean isThisMyPartition(tree *localTree, int tid, int model); extern boolean allSmoothed(tree *tr); extern int treeFindTipName(FILE *fp, tree *tr, boolean check); extern void computePlacementBias(tree *tr, analdef *adef); extern int lookupWord(char *s, stringHashtable *h); extern void getDataTypeString(tree *tr, int model, char typeOfData[1024]); extern unsigned int genericBitCount(unsigned int* bitVector, unsigned int bitVectorLength); extern int countTips(nodeptr p, int numsp); extern entry *initEntry(void); extern void computeRogueTaxa(tree *tr, char* treeSetFileName, analdef *adef); extern unsigned int precomputed16_bitcount(unsigned int n, char *bits_in_16bits); extern size_t discreteRateCategories(int rateHetModel); extern partitionLengths * getPartitionLengths(pInfo *p); extern boolean getSmoothFreqs(int dataType); extern const unsigned int *getBitVector(int dataType); extern int getUndetermined(int dataType); extern int getStates(int dataType); extern char getInverseMeaning(int dataType, unsigned char state); extern double gettime ( void ); extern int gettimeSrand ( void ); extern double randum ( long *seed ); extern void getxnode ( nodeptr p ); extern void hookup ( nodeptr p, nodeptr q, double *z, int numBranches); extern void hookupDefault ( nodeptr p, nodeptr q, int numBranches); extern boolean whitechar ( int ch ); extern void errorExit ( int e ); extern void printResult ( tree *tr, analdef *adef, boolean finalPrint ); extern void printBootstrapResult ( tree *tr, analdef *adef, boolean finalPrint ); extern void printBipartitionResult ( tree *tr, analdef *adef, boolean finalPrint ); extern void printLog ( tree *tr); extern void printStartingTree ( tree *tr, analdef *adef, boolean finalPrint ); extern void writeInfoFile ( analdef *adef, tree *tr, double t ); extern int main ( int argc, char *argv[] ); extern void calcBipartitions ( tree *tr, analdef *adef, char *bestTreeFileName, char *bootStrapFileName ); extern void initReversibleGTR (tree *tr, int model); extern double LnGamma ( double alpha ); extern double IncompleteGamma ( double x, double alpha, double ln_gamma_alpha ); extern double PointNormal ( double prob ); extern double PointChi2 ( double prob, double v ); extern void makeGammaCats (double alpha, double *gammaRates, int K, boolean useMedian); extern void initModel ( tree *tr); extern void doAllInOne ( tree *tr, analdef *adef ); extern void classifyML(tree *tr, analdef *adef); extern void resetBranches ( tree *tr ); extern void modOpt ( tree *tr, double likelihoodEpsilon, analdef *adef, int treeIteration); extern void computeBOOTRAPID (tree *tr, analdef *adef, long *radiusSeed); extern void optimizeRAPID ( tree *tr, analdef *adef ); extern void thoroughOptimization ( tree *tr, analdef *adef, topolRELL_LIST *rl, int index ); extern int treeOptimizeThorough ( tree *tr, int mintrav, int maxtrav); extern void computeQuartets(tree *tr, analdef *adef); extern void makeRandomTree ( tree *tr); extern void nodeRectifier ( tree *tr ); extern void makeParsimonyTreeFast(tree *tr); extern void allocateParsimonyDataStructures(tree *tr); extern void freeParsimonyDataStructures(tree *tr); extern void parsimonySPR(nodeptr p, tree *tr); extern FILE *myfopen(const char *path, const char *mode); extern boolean initrav ( tree *tr, nodeptr p ); extern void initravPartition ( tree *tr, nodeptr p, int model ); extern boolean update ( tree *tr, nodeptr p ); extern boolean smooth ( tree *tr, nodeptr p ); extern boolean smoothTree ( tree *tr, int maxtimes ); extern boolean localSmooth ( tree *tr, nodeptr p, int maxtimes ); extern boolean localSmoothMulti(tree *tr, nodeptr p, int maxtimes, int model); extern void initInfoList ( int n ); extern void freeInfoList ( void ); extern void insertInfoList ( nodeptr node, double likelihood ); extern boolean smoothRegion ( tree *tr, nodeptr p, int region ); extern boolean regionalSmooth ( tree *tr, nodeptr p, int maxtimes, int region ); extern nodeptr removeNodeBIG ( tree *tr, nodeptr p, int numBranches); extern nodeptr removeNodeRestoreBIG ( tree *tr, nodeptr p ); extern boolean insertBIG ( tree *tr, nodeptr p, nodeptr q, int numBranches); extern boolean insertRestoreBIG ( tree *tr, nodeptr p, nodeptr q ); extern boolean testInsertBIG ( tree *tr, nodeptr p, nodeptr q ); extern void addTraverseBIG ( tree *tr, nodeptr p, nodeptr q, int mintrav, int maxtrav ); extern int rearrangeBIG ( tree *tr, nodeptr p, int mintrav, int maxtrav ); extern void traversalOrder ( nodeptr p, int *count, nodeptr *nodeArray ); extern double treeOptimizeRapid ( tree *tr, int mintrav, int maxtrav, analdef *adef, bestlist *bt, bestlist *bestML); extern boolean testInsertRestoreBIG ( tree *tr, nodeptr p, nodeptr q ); extern void restoreTreeFast ( tree *tr ); extern int determineRearrangementSetting ( tree *tr, analdef *adef, bestlist *bestT, bestlist *bt, bestlist *bestML); extern void computeBIGRAPID ( tree *tr, analdef *adef, boolean estimateModel); extern boolean treeEvaluate ( tree *tr, double smoothFactor ); extern boolean treeEvaluatePartition ( tree *tr, double smoothFactor, int model ); extern void meshTreeSearch(tree *tr, analdef *adef, int thorough); extern void initTL ( topolRELL_LIST *rl, tree *tr, int n ); extern void freeTL ( topolRELL_LIST *rl); extern void restoreTL ( topolRELL_LIST *rl, tree *tr, int n ); extern void resetTL ( topolRELL_LIST *rl ); extern void saveTL ( topolRELL_LIST *rl, tree *tr, int index ); extern int saveBestTree (bestlist *bt, tree *tr, boolean keepIdenticalTrees); extern int recallBestTree (bestlist *bt, int rank, tree *tr); extern int initBestTree ( bestlist *bt, int newkeep, int numsp ); extern void resetBestTree ( bestlist *bt ); extern boolean freeBestTree ( bestlist *bt ); extern char *Tree2String ( char *treestr, tree *tr, nodeptr p, boolean printBranchLengths, boolean printNames, boolean printLikelihood, boolean rellTree, boolean finalPrint, int perGene, boolean branchLabelSupport, boolean printSHSupport); extern void printTreePerGene(tree *tr, analdef *adef, char *fileName, char *permission); extern int treeReadLen (FILE *fp, tree *tr, boolean readBranches, boolean readNodeLabels, boolean topologyOnly); extern void treeReadTopologyString(char *treeString, tree *tr); extern boolean treeReadLenMULT ( FILE *fp, tree *tr, int *partCount); extern void getStartingTree ( tree *tr); extern void computeBootStopOnly(tree *tr, char *bootStrapFileName, analdef *adef); extern boolean bootStop(tree *tr, hashtable *h, int numberOfTrees, double *pearsonAverage, unsigned int **bitVectors, int treeVectorLength, unsigned int vectorLength); extern void computeConsensusOnly(tree *tr, char* treeSetFileName, analdef *adef); extern double evaluatePartialGeneric (tree *, int i, double ki, int _model); extern void evaluateGeneric (tree *tr, nodeptr p, boolean fullTraversal); extern void newviewGeneric (tree *tr, nodeptr p, boolean masked); extern void newviewGenericMulti (tree *tr, nodeptr p, int model); extern void makenewzGeneric(tree *tr, nodeptr p, nodeptr q, double *z0, int maxiter, double *result, boolean mask); extern void makenewzGenericDistance(tree *tr, int maxiter, double *z0, double *result, int taxon1, int taxon2); extern double evaluatePartitionGeneric (tree *tr, nodeptr p, int model); extern void newviewPartitionGeneric (tree *tr, nodeptr p, int model); extern double evaluateGenericVector (tree *tr, nodeptr p); extern void categorizeGeneric (tree *tr, nodeptr p); extern double makenewzPartitionGeneric(tree *tr, nodeptr p, nodeptr q, double z0, int maxiter, int model); extern boolean isTip(int number, int maxTips); extern void computeTraversalInfo(nodeptr p, traversalInfo *ti, int *counter, int maxTips, int numBranches, boolean partialTraversal); extern void newviewIterative(tree *tr, int startIndex); extern void evaluateIterative(tree *); extern void *malloc_aligned( size_t size); extern void storeExecuteMaskInTraversalDescriptor(tree *tr); extern void storeValuesInTraversalDescriptor(tree *tr, double *value); extern void makenewzIterative(tree *); extern void execCore(tree *, volatile double *dlnLdlz, volatile double *d2lnLdlz2); extern void determineFullTraversal(nodeptr p, tree *tr); /*extern void optRateCat(tree *, int i, double lower_spacing, double upper_spacing, double *lhs);*/ extern double evaluateGenericInitravPartition(tree *tr, nodeptr p, int model); extern void evaluateGenericVectorIterative(tree *, int startIndex, int endIndex); extern void categorizeIterative(tree *, int startIndex, int endIndex); extern void fixModelIndices(tree *tr, int endsite, boolean fixRates); extern void calculateModelOffsets(tree *tr); extern void gammaToCat(tree *tr); extern void catToGamma(tree *tr, analdef *adef); extern nodeptr findAnyTip(nodeptr p, int numsp); extern void parseProteinModel(analdef *adef); extern void computeNextReplicate(tree *tr, long *seed, int *originalRateCategories, int *originalInvariant, boolean isRapid, boolean fixRates); /*extern void computeNextReplicate(tree *tr, analdef *adef, int *originalRateCategories, int *originalInvariant);*/ extern void putWAG(double *ext_initialRates); extern void reductionCleanup(tree *tr, int *originalRateCategories, int *originalInvariant); extern void parseSecondaryStructure(tree *tr, analdef *adef, int sites); extern void printPartitions(tree *tr); extern void compareBips(tree *tr, char *bootStrapFileName, analdef *adef); extern void computeRF(tree *tr, char *bootStrapFileName, analdef *adef); extern unsigned int **initBitVector(int mxtips, unsigned int *vectorLength); extern hashtable *copyHashTable(hashtable *src, unsigned int vectorLength); extern hashtable *initHashTable(unsigned int n); extern void cleanupHashTable(hashtable *h, int state); extern double convergenceCriterion(hashtable *h, int mxtips); extern void freeBitVectors(unsigned int **v, int n); extern void freeHashTable(hashtable *h); extern stringHashtable *initStringHashTable(hashNumberType n); extern void addword(char *s, stringHashtable *h, int nodeNumber); extern void printBothOpen(const char* format, ... ); extern void initRateMatrix(tree *tr); extern void bitVectorInitravSpecial(unsigned int **bitVectors, nodeptr p, int numsp, unsigned int vectorLength, hashtable *h, int treeNumber, int function, branchInfo *bInf, int *countBranches, int treeVectorLength, boolean traverseOnly, boolean computeWRF); extern int getIncrement(tree *tr, int model); extern void writeBinaryModel(tree *tr); extern void readBinaryModel(tree *tr); extern void treeEvaluateRandom (tree *tr, double smoothFactor); extern void treeEvaluateProgressive(tree *tr); extern void testGapped(tree *tr); extern boolean issubset(unsigned int* bipA, unsigned int* bipB, unsigned int vectorLen); extern boolean compatible(entry* e1, entry* e2, unsigned int bvlen); extern int *permutationSH(tree *tr, int nBootstrap, long _randomSeed); extern void checkPerSiteRates(const tree * const tr ); extern void restart(tree *tr, analdef *adef); extern void writeCheckpoint(tree *tr, analdef *adef); extern boolean isGap(unsigned int *x, int pos); extern boolean noGap(unsigned int *x, int pos); extern void scaleLG4X_EIGN(tree *tr, int model); extern void myBinFwrite(void *ptr, size_t size, size_t nmemb, FILE *byteFile); extern void myBinFread(void *ptr, size_t size, size_t nmemb, FILE *byteFile); #ifdef __AVX extern void newviewGTRGAMMAPROT_AVX_LG4(int tipCase, double *x1, double *x2, double *x3, double *extEV[4], double *tipVector[4], int *ex3, unsigned char *tipX1, unsigned char *tipX2, int n, double *left, double *right, int *wgt, int *scalerIncrement, const boolean useFastScaling); extern void newviewGTRCAT_AVX(int tipCase, double *EV, int *cptr, double *x1_start, double *x2_start, double *x3_start, double *tipVector, unsigned char *tipX1, unsigned char *tipX2, int n, double *left, double *right, int *wgt, int *scalerIncrement); extern void newviewGTRCATPROT_AVX(int tipCase, double *extEV, int *cptr, double *x1, double *x2, double *x3, double *tipVector, unsigned char *tipX1, unsigned char *tipX2, int n, double *left, double *right, int *wgt, int *scalerIncrement); extern void newviewGTRGAMMA_AVX(int tipCase, double *x1_start, double *x2_start, double *x3_start, double *EV, double *tipVector, unsigned char *tipX1, unsigned char *tipX2, const int n, double *left, double *right, int *wgt, int *scalerIncrement ); extern void newviewGTRGAMMAPROT_AVX(int tipCase, double *x1, double *x2, double *x3, double *extEV, double *tipVector, unsigned char *tipX1, unsigned char *tipX2, int n, double *left, double *right, int *wgt, int *scalerIncrement); /* memory saving functions */ void newviewGTRCAT_AVX_GAPPED_SAVE(int tipCase, double *EV, int *cptr, double *x1_start, double *x2_start, double *x3_start, double *tipVector, int *ex3, unsigned char *tipX1, unsigned char *tipX2, int n, double *left, double *right, int *wgt, int *scalerIncrement, const boolean useFastScaling, unsigned int *x1_gap, unsigned int *x2_gap, unsigned int *x3_gap, double *x1_gapColumn, double *x2_gapColumn, double *x3_gapColumn, const int maxCats); void newviewGTRCATPROT_AVX_GAPPED_SAVE(int tipCase, double *extEV, int *cptr, double *x1, double *x2, double *x3, double *tipVector, int *ex3, unsigned char *tipX1, unsigned char *tipX2, int n, double *left, double *right, int *wgt, int *scalerIncrement, const boolean useFastScaling, unsigned int *x1_gap, unsigned int *x2_gap, unsigned int *x3_gap, double *x1_gapColumn, double *x2_gapColumn, double *x3_gapColumn, const int maxCats); void newviewGTRGAMMA_AVX_GAPPED_SAVE(int tipCase, double *x1_start, double *x2_start, double *x3_start, double *extEV, double *tipVector, int *ex3, unsigned char *tipX1, unsigned char *tipX2, const int n, double *left, double *right, int *wgt, int *scalerIncrement, const boolean useFastScaling, unsigned int *x1_gap, unsigned int *x2_gap, unsigned int *x3_gap, double *x1_gapColumn, double *x2_gapColumn, double *x3_gapColumn ); void newviewGTRGAMMAPROT_AVX_GAPPED_SAVE(int tipCase, double *x1_start, double *x2_start, double *x3_start, double *extEV, double *tipVector, int *ex3, unsigned char *tipX1, unsigned char *tipX2, int n, double *left, double *right, int *wgt, int *scalerIncrement, const boolean useFastScaling, unsigned int *x1_gap, unsigned int *x2_gap, unsigned int *x3_gap, double *x1_gapColumn, double *x2_gapColumn, double *x3_gapColumn); #endif /* from communication.c */ void calculateLengthAndDisplPerProcess(tree *tr, int **length_result, int **disp_result); void scatterDistrbutedArray(tree *tr, void *src, void *destination, MPI_Datatype type, int *countPerProc, int *displPerProc); void gatherDistributedArray(tree *tr, void **destination, void *src, MPI_Datatype type, int* countPerProc, int *displPerProc); #endif # ExaML-3.0.21/examl/bipartitionList.c000066400000000000000000000301711330315463200171770ustar00rootroot00000000000000/* RAxML-HPC, a program for sequential and parallel estimation of phylogenetic trees * Copyright March 2006 by Alexandros Stamatakis * * Partially derived from * fastDNAml, a program for estimation of phylogenetic trees from sequences by Gary J. Olsen * * and * * Programs of the PHYLIP package by Joe Felsenstein. * * This program is free software; you may redistribute it and/or modify its * 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. * * * For any other enquiries send an Email to Alexandros Stamatakis * stamatak@ics.forth.gr * * When publishing work that is based on the results from RAxML-VI-HPC please cite: * * Alexandros Stamatakis: "An Efficient Program for phylogenetic Inference Using Simulated Annealing". * Proceedings of IPDPS2005, Denver, Colorado, April 2005. * * AND * * Alexandros Stamatakis:"RAxML-VI-HPC: maximum likelihood-based phylogenetic analyses with thousands of taxa and mixed models". * Bioinformatics 2006; doi: 10.1093/bioinformatics/btl446 */ #ifndef WIN32 #include #include #include #include #endif #include #include #include #include #include #include #include #include #include "axml.h" extern const unsigned int mask32[32]; extern int processID; static void getxnodeBips (nodeptr p) { nodeptr s; if ((s = p->next)->xBips || (s = s->next)->xBips) { p->xBips = s->xBips; s->xBips = 0; } assert(p->xBips); } entry *initEntry(void) { entry *e = (entry*)malloc(sizeof(entry)); e->bitVector = (unsigned int*)NULL; e->treeVector = (unsigned int*)NULL; e->supportVector = (int*)NULL; e->bipNumber = 0; e->bipNumber2 = 0; e->supportFromTreeset[0] = 0; e->supportFromTreeset[1] = 0; e->next = (entry*)NULL; return e; } hashtable *initHashTable(hashNumberType n) { /* init with primes static const hashNumberType initTable[] = {53, 97, 193, 389, 769, 1543, 3079, 6151, 12289, 24593, 49157, 98317, 196613, 393241, 786433, 1572869, 3145739, 6291469, 12582917, 25165843, 50331653, 100663319, 201326611, 402653189, 805306457, 1610612741}; */ /* init with powers of two */ static const hashNumberType initTable[] = {64, 128, 256, 512, 1024, 2048, 4096, 8192, 16384, 32768, 65536, 131072, 262144, 524288, 1048576, 2097152, 4194304, 8388608, 16777216, 33554432, 67108864, 134217728, 268435456, 536870912, 1073741824, 2147483648U}; hashtable *h = (hashtable*)malloc(sizeof(hashtable)); hashNumberType tableSize, i, primeTableLength = sizeof(initTable)/sizeof(initTable[0]), maxSize = (hashNumberType)-1; assert(n <= maxSize); i = 0; while(initTable[i] < n && i < primeTableLength) i++; assert(i < primeTableLength); tableSize = initTable[i]; h->table = (entry**)calloc(tableSize, sizeof(entry*)); h->tableSize = tableSize; h->entryCount = 0; return h; } void freeHashTable(hashtable *h) { hashNumberType i, entryCount = 0; for(i = 0; i < h->tableSize; i++) { if(h->table[i] != NULL) { entry *e = h->table[i]; entry *previous; do { previous = e; e = e->next; if(previous->bitVector) free(previous->bitVector); if(previous->treeVector) free(previous->treeVector); if(previous->supportVector) free(previous->supportVector); free(previous); entryCount++; } while(e != NULL); } } assert(entryCount == h->entryCount); free(h->table); } void cleanupHashTable(hashtable *h, int state) { hashNumberType k, entryCount = 0, removeCount = 0; assert(state == 1 || state == 0); for(k = 0, entryCount = 0; k < h->tableSize; k++) { if(h->table[k] != NULL) { entry *e = h->table[k]; entry *start = (entry*)NULL; entry *lastValid = (entry*)NULL; do { if(state == 0) { e->treeVector[0] = e->treeVector[0] & 2; assert(!(e->treeVector[0] & 1)); } else { e->treeVector[0] = e->treeVector[0] & 1; assert(!(e->treeVector[0] & 2)); } if(e->treeVector[0] != 0) { if(!start) start = e; lastValid = e; e = e->next; } else { entry *remove = e; e = e->next; removeCount++; if(lastValid) lastValid->next = remove->next; if(remove->bitVector) free(remove->bitVector); if(remove->treeVector) free(remove->treeVector); if(remove->supportVector) free(remove->supportVector); free(remove); } entryCount++; } while(e != NULL); if(!start) { assert(!lastValid); h->table[k] = NULL; } else { h->table[k] = start; } } } assert(entryCount == h->entryCount); h->entryCount -= removeCount; } unsigned int **initBitVector(int mxtips, unsigned int *vectorLength) { unsigned int **bitVectors = (unsigned int **)malloc(sizeof(unsigned int*) * 2 * mxtips); int i; if(mxtips % MASK_LENGTH == 0) *vectorLength = mxtips / MASK_LENGTH; else *vectorLength = 1 + (mxtips / MASK_LENGTH); for(i = 1; i <= mxtips; i++) { bitVectors[i] = (unsigned int *)calloc(*vectorLength, sizeof(unsigned int)); assert(bitVectors[i]); bitVectors[i][(i - 1) / MASK_LENGTH] |= mask32[(i - 1) % MASK_LENGTH]; } for(i = mxtips + 1; i < 2 * mxtips; i++) { bitVectors[i] = (unsigned int *)malloc(sizeof(unsigned int) * *vectorLength); assert(bitVectors[i]); } return bitVectors; } void freeBitVectors(unsigned int **v, int n) { int i; for(i = 1; i < n; i++) free(v[i]); } static void newviewBipartitions(unsigned int **bitVectors, nodeptr p, int numsp, unsigned int vectorLength) { if(isTip(p->number, numsp)) return; { nodeptr q = p->next->back, r = p->next->next->back; unsigned int *vector = bitVectors[p->number], *left = bitVectors[q->number], *right = bitVectors[r->number]; unsigned int i; assert(processID == 0); while(!p->xBips) { if(!p->xBips) getxnodeBips(p); } p->hash = q->hash ^ r->hash; if(isTip(q->number, numsp) && isTip(r->number, numsp)) { for(i = 0; i < vectorLength; i++) vector[i] = left[i] | right[i]; } else { if(isTip(q->number, numsp) || isTip(r->number, numsp)) { if(isTip(r->number, numsp)) { nodeptr tmp = r; r = q; q = tmp; } while(!r->xBips) { if(!r->xBips) newviewBipartitions(bitVectors, r, numsp, vectorLength); } for(i = 0; i < vectorLength; i++) vector[i] = left[i] | right[i]; } else { while((!r->xBips) || (!q->xBips)) { if(!q->xBips) newviewBipartitions(bitVectors, q, numsp, vectorLength); if(!r->xBips) newviewBipartitions(bitVectors, r, numsp, vectorLength); } for(i = 0; i < vectorLength; i++) vector[i] = left[i] | right[i]; } } } } static void insertHashRF(unsigned int *bitVector, hashtable *h, unsigned int vectorLength, int treeNumber, int treeVectorLength, hashNumberType position, int support, boolean computeWRF) { if(h->table[position] != NULL) { entry *e = h->table[position]; do { unsigned int i; for(i = 0; i < vectorLength; i++) if(bitVector[i] != e->bitVector[i]) break; if(i == vectorLength) { e->treeVector[treeNumber / MASK_LENGTH] |= mask32[treeNumber % MASK_LENGTH]; if(computeWRF) { e->supportVector[treeNumber] = support; assert(0 <= treeNumber && treeNumber < treeVectorLength * MASK_LENGTH); } return; } e = e->next; } while(e != (entry*)NULL); e = initEntry(); /*e->bitVector = (unsigned int*)calloc(vectorLength, sizeof(unsigned int));*/ e->bitVector = (unsigned int*)malloc_aligned(vectorLength * sizeof(unsigned int)); memset(e->bitVector, 0, vectorLength * sizeof(unsigned int)); e->treeVector = (unsigned int*)calloc(treeVectorLength, sizeof(unsigned int)); if(computeWRF) e->supportVector = (int*)calloc(treeVectorLength * MASK_LENGTH, sizeof(int)); e->treeVector[treeNumber / MASK_LENGTH] |= mask32[treeNumber % MASK_LENGTH]; if(computeWRF) { e->supportVector[treeNumber] = support; assert(0 <= treeNumber && treeNumber < treeVectorLength * MASK_LENGTH); } memcpy(e->bitVector, bitVector, sizeof(unsigned int) * vectorLength); e->next = h->table[position]; h->table[position] = e; } else { entry *e = initEntry(); /*e->bitVector = (unsigned int*)calloc(vectorLength, sizeof(unsigned int)); */ e->bitVector = (unsigned int*)malloc_aligned(vectorLength * sizeof(unsigned int)); memset(e->bitVector, 0, vectorLength * sizeof(unsigned int)); e->treeVector = (unsigned int*)calloc(treeVectorLength, sizeof(unsigned int)); if(computeWRF) e->supportVector = (int*)calloc(treeVectorLength * MASK_LENGTH, sizeof(int)); e->treeVector[treeNumber / MASK_LENGTH] |= mask32[treeNumber % MASK_LENGTH]; if(computeWRF) { e->supportVector[treeNumber] = support; assert(0 <= treeNumber && treeNumber < treeVectorLength * MASK_LENGTH); } memcpy(e->bitVector, bitVector, sizeof(unsigned int) * vectorLength); h->table[position] = e; } h->entryCount = h->entryCount + 1; } void bitVectorInitravSpecial(unsigned int **bitVectors, nodeptr p, int numsp, unsigned int vectorLength, hashtable *h, int treeNumber, int function, branchInfo *bInf, int *countBranches, int treeVectorLength, boolean traverseOnly, boolean computeWRF) { if(isTip(p->number, numsp)) return; else { nodeptr q = p->next; do { bitVectorInitravSpecial(bitVectors, q->back, numsp, vectorLength, h, treeNumber, function, bInf, countBranches, treeVectorLength, traverseOnly, computeWRF); q = q->next; } while(q != p); newviewBipartitions(bitVectors, p, numsp, vectorLength); assert(p->xBips); assert(!traverseOnly); if(!(isTip(p->back->number, numsp))) { unsigned int *toInsert = bitVectors[p->number]; hashNumberType position = p->hash % h->tableSize; assert(!(toInsert[0] & 1)); assert(!computeWRF); switch(function) { case BIPARTITIONS_RF: insertHashRF(toInsert, h, vectorLength, treeNumber, treeVectorLength, position, 0, computeWRF); *countBranches = *countBranches + 1; break; default: assert(0); } } } } double convergenceCriterion(hashtable *h, int mxtips) { int rf = 0; unsigned int collisions = 0, k = 0, entryCount = 0; double rrf; for(k = 0, entryCount = 0; k < h->tableSize; k++) { if(h->table[k] != NULL) { entry *e = h->table[k]; unsigned int slotCollisions = 0; do { unsigned int *vector = e->treeVector; if(((vector[0] & 1) > 0) + ((vector[0] & 2) > 0) == 1) rf++; entryCount++; slotCollisions++; e = e->next; } while(e != NULL); collisions += (slotCollisions - 1); } } assert(entryCount == h->entryCount); rrf = (double)rf/((double)(2 * (mxtips - 3))); #ifdef _DEBUG_CHECKPOINTING printf("Collisions: %u\n", collisions); #endif return rrf; } ExaML-3.0.21/examl/byteFile.c000066400000000000000000000275741330315463200155770ustar00rootroot00000000000000#include #if defined(__APPLE__) #include #else #include #endif #include "byteFile.h" #include #ifdef __MIC_NATIVE #include "mic_native.h" #endif #define READ_VAR(file,var) assert( fread(&var, sizeof(var),1, file ) == 1 ) #define READ_ARRAY(file, arrPtr, numElem, size) assert( fread(arrPtr, size, numElem, file) == (unsigned int) numElem) extern int processID; /** seekPos finds the position in the byte file where a certain type of information is stored. See byteFile.h for possible values of "pos" . Notice, that this is a "fall-through" switch statement: if -- for instance -- we want to get to the position of the taxa, we have to skip everything that comes prior to the taxa in the file (but naturally not the taxa themselves). */ static void seekPos(ByteFile *bf, int pos) { exa_off_t toSkip = 0; int i; switch(pos) { case ALN_ALIGNMENT: /* skips partitions */ { assert(bf->hasRead & ALN_PARTITIONS); pInfo p ; toSkip += bf->numPartitions * ( sizeof(p.states) + sizeof(p.maxTipStates) + sizeof(p.lower) + sizeof(p.upper) + sizeof(p.width) + sizeof(p.dataType) + sizeof(p.protModels) + sizeof(p.protFreqs) + sizeof(p.nonGTR) + sizeof(p.optimizeBaseFrequencies)); /* skip the names and their lengths */ for( i = 0 ; i < bf->numPartitions; ++i) { pInfo *p = bf->partitions[i]; toSkip += (strlen(p->partitionName)+1 ) * sizeof(char) + sizeof(int); toSkip += sizeof(double) * p->states; /* also skip frequncies */ } } case ALN_PARTITIONS: /* skips taxa */ { assert(bf->hasRead & ALN_TAXA); for(i = 0; i < bf->numTax; ++i) toSkip += (strlen(bf->taxaNames[i]) + 1) * sizeof(char) + sizeof(int); } case ALN_TAXA: /* skips weights */ { assert(bf->hasRead & ALN_HEAD); toSkip += bf->numPattern * sizeof(int); } case ALN_WEIGHTS: /* skips header */ { toSkip += sizeof(bf->numTax) + sizeof(bf->numPattern) + sizeof(bf->numPartitions) + sizeof(bf->gappyness); } case ALN_HEAD : toSkip += (3 * sizeof(int)); /* skips the initial int that tells us how many bytes a size_t has as well as the integer for the version number and the magic integer number */ break; default : assert(0); } exa_fseek(bf->fh, toSkip, SEEK_SET); } /** initializes ByteFile **bf */ void initializeByteFile(ByteFile **bf, char *name) { *bf = (ByteFile *)calloc(1,sizeof(ByteFile)); ByteFile *result = *bf; result->fh = myfopen(name, "rb"); int sizeOfSizeT = 0, version = 0, magicNumber = 0; READ_VAR(result->fh, sizeOfSizeT); if(sizeOfSizeT != sizeof(size_t)) { if(processID == 0) { printf("\nError: the address data type has a size of %d bits on the current system while on the system on which you created the binary alignment file using the parser the address size is %d bits!\n", 8 * (int)sizeof(size_t), 8 * sizeOfSizeT); printf("Usually this indicates that the parser was executed on a 32-bit system while you are trying to run ExaML on a 64-bit system.\n"); printf("Please parse the binary alignment file on the same hardware on which you intend to run ExaML.\n\n\n"); } MPI_Barrier(MPI_COMM_WORLD); MPI_Finalize(); exit(-1); } //check that version numbers of parser and ExaML match READ_VAR(result->fh, version); if(version != (int)programVersionInt) { if(processID == 0) { printf("\nError: Version number %d of ExaML parser and version number %d of ExaML don't match.\n", version, (int)programVersionInt); printf("You are either using an outdated version of the parser or of ExaML.\n"); printf("Hasta siempre comandante.\n\n\n"); } MPI_Barrier(MPI_COMM_WORLD); MPI_Finalize(); exit(-1); } READ_VAR(result->fh, magicNumber); if(magicNumber != 6517718) { if(processID == 0) { printf("\nError: The magic number %d of ExaML parser and magic number %d of ExaML don't match.\n", magicNumber, 6517718); printf("Something went terribly wrong here.\n"); printf("Hasta la victoria siempre.\n\n\n"); } MPI_Barrier(MPI_COMM_WORLD); MPI_Finalize(); exit(-1); } } /** a shallow cleanup of ByteFile *bf. Notice, that various data may have been copied (by pointer value) to our tree instance and therefore should not be clean up. */ void deleteByteFile(ByteFile *bf) { /* only a shallow free! pointers inside the pInfo must persist */ int i; if(bf->partitions) { for( i = 0; i < bf->numPartitions; ++i) free(bf->partitions[i]); free(bf->partitions); } if(bf->fh) fclose(bf->fh); if(bf->taxaNames ) { for(i = 0; i < bf->numTax; ++i) free(bf->taxaNames[i] ); } free(bf->taxaNames); free(bf); } /** only reads initial header information */ void readHeader(ByteFile* bf) { seekPos(bf, ALN_HEAD); READ_VAR(bf->fh, bf->numTax); READ_VAR(bf->fh, bf->numPattern); READ_VAR(bf->fh, bf->numPartitions); READ_VAR(bf->fh, bf->gappyness) ; bf->hasRead |= ALN_HEAD; } /** reads partition information from the byte file. */ void readPartitions(ByteFile *bf) { int i ; seekPos(bf, ALN_PARTITIONS); assert(bf->partitions == (pInfo **)NULL); bf->partitions = (pInfo **)calloc(bf->numPartitions, sizeof(pInfo*) ); for(i = 0; i < bf->numPartitions; ++i) { bf->partitions[i] = (pInfo*)calloc(1,sizeof(pInfo)); pInfo* p = bf->partitions[i]; p->frequencies = (double*)NULL; p->partitionName = (char *)NULL; READ_VAR(bf->fh, p->states); READ_VAR(bf->fh, p->maxTipStates); READ_VAR(bf->fh, p->lower); READ_VAR(bf->fh, p->upper); /* DONT use this value! */ READ_VAR(bf->fh, p->width); p->width = 0; READ_VAR(bf->fh, p->dataType); READ_VAR(bf->fh, p->protModels); //READ_VAR(bf->fh, p->autoProtModels); READ_VAR(bf->fh, p->protFreqs); READ_VAR(bf->fh, p->nonGTR); READ_VAR(bf->fh, p->optimizeBaseFrequencies); // READ_VAR(bf->fh, p->numberOfCategories); /* read string */ unsigned int len = 0; READ_VAR(bf->fh, len); p->partitionName = (char*)calloc(len,sizeof(char)); READ_ARRAY(bf->fh, p->partitionName, len, sizeof(char)); p->frequencies = (double*)calloc(p->states, sizeof(double)); READ_ARRAY(bf->fh, p->frequencies, p->states , sizeof(double)); } bf->hasRead |= ALN_PARTITIONS; } /** reads the taxon names from the byte file */ void readTaxa(ByteFile *bf) { int i; assert(bf->taxaNames == (char **)NULL); seekPos(bf, ALN_TAXA); bf->taxaNames = (char **)calloc(bf->numTax, sizeof(char*)); for(i = 0; i < bf->numTax; ++i) { int len = 0; READ_VAR(bf->fh, len ); bf->taxaNames[i] = (char*)calloc(len, sizeof(char)); READ_ARRAY(bf->fh, bf->taxaNames[i], len, sizeof(char)); } bf->hasRead |= ALN_TAXA; } // #define OLD_LAYOUT /** uses the information in the PartitionAssignment to only extract data relevant to this process (weights and alignment characters). */ void readMyData(ByteFile *bf, PartitionAssignment *pa, int procId) { seekPos(bf, ALN_ALIGNMENT); exa_off_t alnPos = exa_ftell(bf->fh); size_t len; int numAssign = pa->numAssignPerProc[procId]; Assignment *myAssigns = pa->assignPerProc[procId]; /* first read aln characters */ int i,j ; for(i = 0; i < numAssign; ++i ) { Assignment a = myAssigns[i]; /* printf("reading for: ") ; */ /* printAssignment(a, procId); */ pInfo *partition = bf->partitions[a.partId]; partition->width = a.width; partition->offset = a.offset; len = bf->numTax * a.width; partition->yResource = (unsigned char*)malloc_aligned( len * sizeof(unsigned char)); memset(partition->yResource,0,len * sizeof(unsigned char)); partition->yVector = (unsigned char**) calloc(bf->numTax + 1 , sizeof(unsigned char*)); for(j = 1; j <= bf->numTax; ++j) partition->yVector[j] = partition->yResource + (j-1) * a.width; #ifdef OLD_LAYOUT for(j = 1; j <= bf->numTax; ++j ) { exa_off_t pos = alnPos + ( bf->numPattern * (j-1) + partition->lower + a.offset ) * sizeof(unsigned char); assert(alnPos <= pos); exa_fseek(bf->fh, pos, SEEK_SET); READ_ARRAY(bf->fh, partition->yVector[j], a.width, sizeof(unsigned char)); } #else /* if the entire partition is assigned to this process, read it in one go. Otherwise, several seeks are necessary. */ if( a.width == (partition->upper - partition->lower ) ) { exa_off_t pos = alnPos + (partition->lower * bf->numTax) * sizeof(unsigned char); assert(alnPos <= pos); exa_fseek(bf->fh, pos, SEEK_SET); READ_ARRAY(bf->fh, partition->yResource, a.width * bf->numTax, sizeof(unsigned char)); } else { for(j = 1; j <= bf->numTax; ++j ) { exa_off_t pos = alnPos + sizeof(unsigned char) * ( (partition->lower * bf->numTax ) /* until start of partition */ + ((j-1) * (partition->upper - partition->lower) ) /* until start of sequence of taxon within partition */ + a.offset ) ; assert(alnPos <= pos); exa_fseek(bf->fh, pos, SEEK_SET); READ_ARRAY(bf->fh, partition->yVector[j], a.width, sizeof(unsigned char)); } } #endif } /* now read weights */ seekPos(bf, ALN_WEIGHTS); exa_off_t wgtPos = exa_ftell(bf->fh); assert( ! (wgtPos < 0) ); for(i = 0; i < numAssign; ++i) { Assignment a = myAssigns[i]; pInfo *partition = bf->partitions[a.partId]; #ifdef __MIC_NATIVE /* for Xeon Phi, wgt must be padded to the multiple of 8 (because of site blocking in kernels) */ const int padded_width = GET_PADDED_WIDTH(a.width); len = padded_width * sizeof(int); #else len = a.width * sizeof(int); #endif partition->wgt = (int*)malloc_aligned( len); memset(partition->wgt, 0, len); exa_off_t pos = wgtPos + (partition->lower + a.offset) * sizeof(int); assert(wgtPos <= pos ); exa_fseek(bf->fh, pos, SEEK_SET); READ_ARRAY(bf->fh, partition->wgt, a.width, sizeof(int)); } bf->hasRead |= ALN_ALIGNMENT; bf->hasRead |= ALN_WEIGHTS; } /** copies all relevant information from our byte file to the tree instance. */ void initializeTreeFromByteFile(ByteFile *bf, tree *tr) { assert( ( bf->hasRead & ALN_HEAD ) && (bf->hasRead & ALN_WEIGHTS) && (bf->hasRead & ALN_TAXA) && (bf->hasRead & ALN_PARTITIONS) && (bf->hasRead & ALN_ALIGNMENT ) ); /* some additional stuff we read */ tr->mxtips = bf->numTax; tr->originalCrunchedLength = bf->numPattern; tr->NumberOfModels = bf->numPartitions; tr->gapyness = bf->gappyness; /* deep copy of taxa */ int i ; tr->nameList = (char **)calloc((size_t)(tr->mxtips + 1), sizeof(char *) ); tr->nameList[0] = (char *)NULL; for(i = 1; i <= bf->numTax; ++i) { tr->nameList[i] = (char*)calloc(strlen(bf->taxaNames[i-1]) + 1, sizeof(char)); strcpy(tr->nameList[i], bf->taxaNames[i-1]); } /* * shallow copy of partitions * * partition contains only shallow copies of a few data arrays that * needed to be initialized at this point */ int myLength = 0; tr->partitionData = (pInfo*)calloc(tr->NumberOfModels, sizeof(pInfo)); for(i = 0; i < tr->NumberOfModels; ++i) { tr->partitionData[i] = *(bf->partitions[i]); myLength += tr->partitionData[i].width; assert( bf->partitions[i]->wgt != (int*)NULL || bf->partitions[i]->width == 0); assert( ( tr->partitionData[i].wgt != (int*)NULL) || ( tr->partitionData[i].width == 0 ) ); } } ExaML-3.0.21/examl/byteFile.h000066400000000000000000000022061330315463200155650ustar00rootroot00000000000000#ifndef _BYTE_FILE #define _BYTE_FILE #include "axml.h" #include "partitionAssignment.h" #define ALN_HEAD 1 #define ALN_WEIGHTS 2 #define ALN_TAXA 4 #define ALN_PARTITIONS 8 #define ALN_ALIGNMENT 16 typedef struct { int numTax; size_t numPattern; int numPartitions; double gappyness; pInfo **partitions; char **taxaNames; FILE *fh; char hasRead ; } ByteFile; /* constructor */ void initializeByteFile(ByteFile **bf, char *name); /* deletor */ void deleteByteFile(ByteFile *bf) ; /* reads the header of a byte file */ void readHeader(ByteFile* bf); /* reads partition information in a byte file */ void readPartitions(ByteFile *bf); /* reads the taxon names in a byte file */ void readTaxa(ByteFile *bf); /* reads weights and alignment characters in a byte file */ void readMyData(ByteFile *bf, PartitionAssignment *pa, int procId); /* initializes a tree from a byte file. @notice Since shallow copies are involved, you cannot copy the information from a byte file into multiple tree instances. */ void initializeTreeFromByteFile(ByteFile *bf, tree *tr); #endif ExaML-3.0.21/examl/communication.c000066400000000000000000000121461330315463200166660ustar00rootroot00000000000000#include #include #include #include #include "axml.h" extern int processes; extern int processID; /** computes the count and displacement for gatherv/scatterv, assuming that the new partition assignment algorithm was used */ void calculateLengthAndDisplPerProcess(tree *tr, int **length_result, int **disp_result) { int i; *length_result = (int*) calloc((size_t) processes , sizeof(int)); *disp_result = (int*) calloc((size_t) processes, sizeof(int)); int* numPerProc = *length_result; int* displPerProc= *disp_result; for(i = 0; i < tr->numAssignments; ++i) { Assign* ass = &(tr->partAssigns[i]); numPerProc[ass->procId] += ass->width; } displPerProc[0] = 0; for(i = 1; i < processes ; ++i) displPerProc[i] = displPerProc[i-1] + numPerProc[i-1]; } static size_t mapMpiTypeToSize(MPI_Datatype type) { if(type == MPI_INT) return sizeof(int); else if(type == MPI_DOUBLE) return sizeof(double); else { assert(0); return 0; } } /** scatters a distributed array (e.g., what used to be tr->rateCategory) to partition-specfic arrays (e.g., tr->partitionData[i].rateCategory). This works, because tr->partitionData[i].rateCategory is a non-owning pointer to a position in the global resource array (e.g., tr->rateCategory_basePtr). */ void scatterDistrbutedArray(tree *tr, void *src, void *destination, MPI_Datatype type, int *countPerProc, int *displPerProc) { int i; size_t typeLen = mapMpiTypeToSize(type); char *srcReordered = (char *)NULL; /* master must reorder the data */ if(processID == 0) { srcReordered = (char *)malloc(tr->originalCrunchedLength * typeLen); int *seenPerProcesses = (int *)calloc((size_t) processes, sizeof(int)); Assign *aIter = tr->partAssigns; Assign *aEnd = &(tr->partAssigns[ tr->numAssignments ] ); while(aIter != aEnd) { pInfo *partition = &(tr->partitionData[ aIter->partitionId ]) ; memcpy( srcReordered + ( (size_t) displPerProc[aIter->procId] + (size_t) seenPerProcesses[aIter->procId] ) * typeLen , ((char*) src) + (partition->lower + aIter->offset) * typeLen, aIter->width * typeLen); seenPerProcesses[aIter->procId] += aIter->width; ++aIter; } for(i = 0; i < processes; ++i) assert(seenPerProcesses[i] == countPerProc[i]) ; free(seenPerProcesses); } MPI_Scatterv(srcReordered, countPerProc, displPerProc, type, destination, countPerProc[processID], type, 0, MPI_COMM_WORLD); /* after this scatter, every process already has the data correctly ordered at its repective base pointer */ if(processID == 0) free(srcReordered); } /** gathers a distributed array (e.g., what used to be tr->rateCategory) to partition-specfic arrays (e.g., tr->partitionData[i].rateCategory). This works, because tr->partitionData[i].rateCategory is a non-owning pointer to a position in the global resource array (e.g., tr->rateCategory_basePtr). */ void gatherDistributedArray(tree *tr, void **destinationPtr, void *src, MPI_Datatype type, int* countPerProc, int *displPerProc) { /* this is the raw array that the master will obtain from his peers. Data in this arrays are ordered per process */ char *destinationUnordered = (char*)NULL; char *destination = (char*)NULL; size_t typeLen = mapMpiTypeToSize(type); if(processID == 0) { //TODO one pointer is of type void the other of type char, not really nice *destinationPtr = (void *)malloc( tr->originalCrunchedLength * typeLen); destinationUnordered = (char *)malloc( tr->originalCrunchedLength * typeLen); destination = *destinationPtr; } MPI_Gatherv(src, countPerProc[processID], type, destinationUnordered, countPerProc, displPerProc, type,0 , MPI_COMM_WORLD ); /* here the master reorders the array it has obtained. Afterwards, destinationPtr is a pointer to the array that contains the global array that can be indexed by alignment position (i.e., if we have gathered tr->partitionData[i].lhs, then *destinationPtr corresponds to what previously was tr->lhs). This strongly couples the respective distributed array to tr->partAssigns. */ if(processID == 0) { int i, *seenPerProcesses = (int*) calloc(processes, sizeof(int)); Assign *aIter = tr->partAssigns; Assign *aEnd = tr->partAssigns + tr->numAssignments; while(aIter != aEnd) { pInfo *partition = &(tr->partitionData[aIter->partitionId]); memcpy(destination + (size_t) (partition->lower + aIter->offset) * typeLen, destinationUnordered + (size_t) (displPerProc[aIter->procId] + seenPerProcesses[aIter->procId]) * typeLen , typeLen * aIter->width); seenPerProcesses[aIter->procId] += aIter->width; ++aIter ; } /* check, if everything has been reordered */ for(i = 0; i < processes; ++i) assert(seenPerProcesses[i] == countPerProc[i]); free(seenPerProcesses); free(destinationUnordered); } } ExaML-3.0.21/examl/evaluateGenericSpecial.c000066400000000000000000001564661330315463200204430ustar00rootroot00000000000000/* RAxML-VI-HPC (version 2.2) a program for sequential and parallel estimation of phylogenetic trees * Copyright August 2006 by Alexandros Stamatakis * * Partially derived from * fastDNAml, a program for estimation of phylogenetic trees from sequences by Gary J. Olsen * * and * * Programs of the PHYLIP package by Joe Felsenstein. * This program is free software; you may redistribute it and/or modify its * 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. * * * For any other enquiries send an Email to Alexandros Stamatakis * Alexandros.Stamatakis@epfl.ch * * When publishing work that is based on the results from RAxML-VI-HPC please cite: * * Alexandros Stamatakis:"RAxML-VI-HPC: maximum likelihood-based phylogenetic analyses with thousands of taxa and mixed models". * Bioinformatics 2006; doi: 10.1093/bioinformatics/btl446 */ #ifndef WIN32 #include #endif #include #include #include #include #include #include #include "axml.h" /* the set of functions in here computes the log likelihood at a given branch (the virtual root of a tree) */ /* includes for using SSE3 intrinsics */ #ifdef __SIM_SSE3 #include #include /*#include */ #endif #ifdef __MIC_NATIVE #include "mic_native.h" #endif /* global variables of pthreads version, reductionBuffer is the global array that is used for implementing deterministic reduction operations, that is, the total log likelihood over the partial log lieklihoods for the sites that each thread has computed NumberOfThreads is just the number of threads. Note the volatile modifier here, that guarantees that the compiler will not do weird optimizations rearraengements of the code accessing those variables, because it does not know that several concurrent threads will access those variables simulatenously */ extern const char inverseMeaningDNA[16]; extern int processID; /* a pre-computed 32-bit integer mask */ extern const unsigned int mask32[32]; /* the function below computes the P matrix from the decomposition of the Q matrix and the respective rate categories for a single partition */ static void calcDiagptable(const double z, const int states, const int numberOfCategories, const double *rptr, const double *EIGN, double *diagptable) { int i, l; double lz, *lza = (double *)malloc(sizeof(double) * states); /* transform the root branch length to the log and check if it is not too small */ if (z < zmin) lz = log(zmin); else lz = log(z); /* do some pre-computations to avoid redundant computations further below */ for(i = 0; i < states; i++) lza[i] = EIGN[i] * lz; /* loop over the number of per-site or discrete gamma rate categories */ for(i = 0; i < numberOfCategories; i++) { /* diagptable is a pre-allocated array of doubles that stores the P-Matrix the first entry is always 1.0 */ diagptable[i * states] = 1.0; /* compute the P matrix for all remaining states of the model */ for(l = 1; l < states; l++) diagptable[i * states + l] = EXP(rptr[i] * lza[l]); } free(lza); } static void calcDiagptableFlex_LG4(double z, int numberOfCategories, double *rptr, double *EIGN[4], double *diagptable, const int numStates) { int i, l; double lz; assert(numStates <= 64); if (z < zmin) lz = log(zmin); else lz = log(z); for(i = 0; i < numberOfCategories; i++) { diagptable[i * numStates + 0] = 1.0; for(l = 1; l < numStates; l++) diagptable[i * numStates + l] = EXP(rptr[i] * EIGN[i][l] * lz); } } #ifndef _OPTIMIZED_FUNCTIONS /* below a a slow generic implementation of the likelihood computation at the root under the GAMMA model */ static double evaluateGAMMA_FLEX(int *wptr, double *x1_start, double *x2_start, double *tipVector, unsigned char *tipX1, const int n, double *diagptable, const int states) { double sum = 0.0, term, *x1, *x2; int i, j, k; /* span is the offset within the likelihood array at an inner node that gets us from the values of site i to the values of site i + 1 */ const int span = states * 4; /* we distingusih between two cases here: one node of the two nodes defining the branch at which we put the virtual root is a tip. Both nodes can not be tips because we do not allow for two-taxon trees ;-) Nota that, if a node is a tip, this will always be tipX1. This is done for code simplicity and the flipping of the nodes is done before when we compute the traversal descriptor. */ /* the left node is a tip */ if(tipX1) { /* loop over the sites of this partition */ for (i = 0; i < n; i++) { /* access pre-computed tip vector values via a lookup table */ x1 = &(tipVector[states * tipX1[i]]); /* access the other(inner) node at the other end of the branch */ x2 = &(x2_start[span * i]); /* loop over GAMMA rate categories, hard-coded as 4 in RAxML */ for(j = 0, term = 0.0; j < 4; j++) /* loop over states and multiply them with the P matrix */ for(k = 0; k < states; k++) term += x1[k] * x2[j * states + k] * diagptable[j * states + k]; /* take the log of the likelihood and multiply the per-gamma rate likelihood by 1/4. Under the GAMMA model the 4 discrete GAMMA rates all have the same probability of 0.25 */ term = LOG(0.25 * FABS(term)); sum += wptr[i] * term; } } else { for (i = 0; i < n; i++) { /* same as before, only that now we access two inner likelihood vectors x1 and x2 */ x1 = &(x1_start[span * i]); x2 = &(x2_start[span * i]); for(j = 0, term = 0.0; j < 4; j++) for(k = 0; k < states; k++) term += x1[j * states + k] * x2[j * states + k] * diagptable[j * states + k]; term = LOG(0.25 * FABS(term)); sum += wptr[i] * term; } } return sum; } /* a generic and slow implementation of the CAT model of rate heterogeneity */ static double evaluateCAT_FLEX (int *cptr, int *wptr, double *x1, double *x2, double *tipVector, unsigned char *tipX1, int n, double *diagptable_start, const int states) { double sum = 0.0, term, *diagptable, *left, *right; int i, l; /* chosing between tip vectors and non tip vectors is identical in all flavors of this function ,regardless of whether we are using CAT, GAMMA, DNA or protein data etc */ if(tipX1) { for (i = 0; i < n; i++) { /* same as in the GAMMA implementation */ left = &(tipVector[states * tipX1[i]]); right = &(x2[states * i]); /* important difference here, we do not have, as for GAMMA 4 P matrices assigned to each site, but just one. However those P-Matrices can be different for the sites. Hence we index into the precalculated P-matrices for individual sites via the category pointer cptr[i] */ diagptable = &diagptable_start[states * cptr[i]]; /* similar to gamma, with the only difference that we do not integrate (sum) over the discrete gamma rates, but simply compute the likelihood of the site and the given P-matrix */ for(l = 0, term = 0.0; l < states; l++) term += left[l] * right[l] * diagptable[l]; /* take the log */ term = LOG(FABS(term)); /* multiply the log with the pattern weight of this site. The site pattern for which we just computed the likelihood may represent several alignment columns sites that have been compressed into one site pattern if they are exactly identical AND evolve under the same model, i.e., form part of the same partition. */ sum += wptr[i] * term; } } else { for (i = 0; i < n; i++) { /* as before we now access the likelihood arrayes of two inner nodes */ left = &x1[states * i]; right = &x2[states * i]; diagptable = &diagptable_start[states * cptr[i]]; for(l = 0, term = 0.0; l < states; l++) term += left[l] * right[l] * diagptable[l]; term = LOG(FABS(term)); sum += wptr[i] * term; } } return sum; } #endif /* below are the function headers for unreadeble highly optimized versions of the above functions for DNA and protein data that also use SSE3 intrinsics and implement some memory saving tricks. The actual functions can be found at the end of this source file. All other likelihood function implementation files: newviewGenericSpacial.c makenewzSpecial.c evaluatePartialGenericSpecial.c are also structured like this To decide which set of function implementations to use you will have to undefine or define _OPTIMIZED_FUNCTIONS in the Makefile */ #ifdef _OPTIMIZED_FUNCTIONS static double evaluateGTRGAMMA_BINARY(int *ex1, int *ex2, int *wptr, double *x1_start, double *x2_start, double *tipVector, unsigned char *tipX1, const int n, double *diagptable, const boolean fastScaling); static double evaluateGTRCAT_BINARY (int *ex1, int *ex2, int *cptr, int *wptr, double *x1_start, double *x2_start, double *tipVector, unsigned char *tipX1, int n, double *diagptable_start, const boolean fastScaling); static double evaluateGTRGAMMAPROT_LG4(int *ex1, int *ex2, int *wptr, double *x1, double *x2, double *tipVector[4], unsigned char *tipX1, int n, double *diagptable, const boolean fastScaling, double *weights); /* GAMMA for proteins with memory saving */ static double evaluateGTRGAMMAPROT_GAPPED_SAVE (int *wptr, double *x1, double *x2, double *tipVector, unsigned char *tipX1, int n, double *diagptable, double *x1_gapColumn, double *x2_gapColumn, unsigned int *x1_gap, unsigned int *x2_gap); /* GAMMA for proteins */ static double evaluateGTRGAMMAPROT (int *wptr, double *x1, double *x2, double *tipVector, unsigned char *tipX1, int n, double *diagptable); /* CAT for proteins */ static double evaluateGTRCATPROT (int *cptr, int *wptr, double *x1, double *x2, double *tipVector, unsigned char *tipX1, int n, double *diagptable_start); /* CAT for proteins with memory saving */ static double evaluateGTRCATPROT_SAVE (int *cptr, int *wptr, double *x1, double *x2, double *tipVector, unsigned char *tipX1, int n, double *diagptable_start, double *x1_gapColumn, double *x2_gapColumn, unsigned int *x1_gap, unsigned int *x2_gap); /* analogous DNA fuctions */ static double evaluateGTRCAT_SAVE (int *cptr, int *wptr, double *x1_start, double *x2_start, double *tipVector, unsigned char *tipX1, int n, double *diagptable_start, double *x1_gapColumn, double *x2_gapColumn, unsigned int *x1_gap, unsigned int *x2_gap); static double evaluateGTRGAMMA_GAPPED_SAVE(int *wptr, double *x1_start, double *x2_start, double *tipVector, unsigned char *tipX1, const int n, double *diagptable, double *x1_gapColumn, double *x2_gapColumn, unsigned int *x1_gap, unsigned int *x2_gap); static double evaluateGTRGAMMA(int *wptr, double *x1_start, double *x2_start, double *tipVector, unsigned char *tipX1, const int n, double *diagptable); static double evaluateGTRCAT (int *cptr, int *wptr, double *x1_start, double *x2_start, double *tipVector, unsigned char *tipX1, int n, double *diagptable_start); #endif /* This is the core function for computing the log likelihood at a branch */ void evaluateIterative(tree *tr) { /* the branch lengths and node indices of the virtual root branch are always the first one that are stored in the very important traversal array data structure that describes a partial or full tree traversal */ /* get the branch length at the root */ double *pz = tr->td[0].ti[0].qz; /* get the node number of the node to the left and right of the branch that defines the virtual rooting */ int pNumber = tr->td[0].ti[0].pNumber, qNumber = tr->td[0].ti[0].qNumber; /* before we can compute the likelihood at the virtual root, we need to do a partial or full tree traversal to compute the conditional likelihoods of the vectors as specified in the traversal descriptor. Maintaining this tarversal descriptor consistent will unfortunately be the responsibility of users. This is tricky, if as planned for here, we use a rooted view (described somewhere in Felsenstein's book) for the conditional vectors with respect to the tree */ /* iterate over all valid entries in the traversal descriptor */ newviewIterative(tr, 1); int m; #ifdef _USE_OMP #pragma omp parallel for #endif for(m = 0; m < tr->NumberOfModels; m++) { /* check if this partition has to be processed now - otherwise no need to compute P matrix */ if(!tr->td[0].executeModel[m] || tr->partitionData[m].width == 0) continue; int categories, states = tr->partitionData[m].states; double z, *rateCategories, *diagptable = tr->partitionData[m].left; /* if we are using a per-partition branch length estimate, the branch has an index, otherwise, for a joint branch length estimate over all partitions we just use the branch length value with index 0 */ if(tr->numBranches > 1) z = pz[m]; else z = pz[0]; /* figure out if we are using the CAT or GAMMA model of rate heterogeneity and set pointers to the rate heterogeneity rate arrays and also set the number of distinct rate categories appropriately. Under GAMMA this is constant and hard-coded as 4, weheras under CAT the number of site-wise rate categories can vary in the course of computations up to a user defined maximum value of site categories (default: 25) */ if(tr->rateHetModel == CAT) { rateCategories = tr->partitionData[m].perSiteRates; categories = tr->partitionData[m].numberOfCategories; } else { rateCategories = tr->partitionData[m].gammaRates; categories = 4; } if(tr->partitionData[m].protModels == LG4M || tr->partitionData[m].protModels == LG4X) calcDiagptableFlex_LG4(z, 4, tr->partitionData[m].gammaRates, tr->partitionData[m].EIGN_LG4, diagptable, 20); else calcDiagptable(z, states, categories, rateCategories, tr->partitionData[m].EIGN, diagptable); } /* after the above call we are sure that we have properly and consistently computed the conditionals to the right and left of the virtual root and we can now invoke the the log likelihood computation */ /* we need to loop over all partitions. Note that we may have a mix of DNA, protein binary data etc partitions */ #ifdef _USE_OMP #pragma omp parallel #endif { int m, model, maxModel; #ifdef _USE_OMP maxModel = tr->maxModelsPerThread; #else maxModel = tr->NumberOfModels; #endif for(m = 0; m < maxModel; m++) { /* just defaults -> if partion wasn't assigned to this thread, it will be ignored later on */ size_t width = 0, offset = 0; double *diagptable = (double*)NULL, *perPartitionLH = (double*)NULL; unsigned int *globalScaler = (unsigned int*)NULL; #ifdef _USE_OMP int tid = omp_get_thread_num(); /* check if this thread should process this partition */ Assign* pAss = tr->threadPartAssigns[tid * tr->maxModelsPerThread + m]; if(pAss) { model = pAss->partitionId; width = pAss->width; offset = pAss->offset; assert(model < tr->NumberOfModels); diagptable = tr->partitionData[model].left; globalScaler = tr->partitionData[model].threadGlobalScaler[tid]; perPartitionLH = &tr->partitionData[model].reductionBuffer[tid]; } else break; #else model = m; /* number of sites in this partition */ width = (size_t)tr->partitionData[model].width; offset = 0; /* set this pointer to the memory area where space has been reserved a priori for storing the P matrix at the root */ diagptable = tr->partitionData[model].left; globalScaler = tr->partitionData[model].globalScaler; perPartitionLH = &tr->perPartitionLH[model]; #endif /* Important part of the tarversal descriptor: figure out if we need to recalculate the likelihood of this partition: The reasons why this is important in terms of performance are given in this paper here which you should actually read: A. Stamatakis, M. Ott: "Load Balance in the Phylogenetic Likelihood Kernel". Proceedings of ICPP 2009, accepted for publication, Vienna, Austria, September 2009 The width > 0 check is for checking if under the cyclic data distribution of per-partition sites to threads this thread does indeed have a site of the current partition. */ if(tr->td[0].executeModel[model] && width > 0) { int rateHet = (int)discreteRateCategories(tr->rateHetModel), /* get the number of states in the partition, e.g.: 4 = DNA, 20 = Protein */ states = tr->partitionData[model].states, /* span for single alignment site (in doubles!) */ span = rateHet * states; size_t /* offset for current thread's data in global xVector (in doubles!) */ x_offset = offset * (size_t)span; int /* integer weight vector with pattern compression weights */ *wgt = tr->partitionData[model].wgt + offset, /* integer rate category vector (for each pattern, _number_ of PSR category assigned to it, NOT actual rate!) */ *rateCategory = tr->partitionData[model].rateCategory + offset; double partitionLikelihood = 0.0, *weights = tr->partitionData[model].weights, *x1_start = (double*)NULL, *x2_start = (double*)NULL, *x1_gapColumn = (double*)NULL, *x2_gapColumn = (double*)NULL; unsigned int *x1_gap = (unsigned int*)NULL, *x2_gap = (unsigned int*)NULL; unsigned char *tip = (unsigned char*)NULL; /* figure out if we need to address tip vectors (a char array that indexes into a precomputed tip likelihood value array or if we need to address inner vectors */ /* either node p or node q is a tip */ if(isTip(pNumber, tr->mxtips) || isTip(qNumber, tr->mxtips)) { /* q is a tip */ if(isTip(qNumber, tr->mxtips)) { /* get the start address of the inner likelihood vector x2 for partition model, note that inner nodes are enumerated/indexed starting at 0 to save allocating some space for additional pointers */ x2_start = tr->partitionData[model].xVector[pNumber - tr->mxtips -1] + x_offset; /* get the corresponding tip vector */ tip = tr->partitionData[model].yVector[qNumber] + offset; /* memory saving stuff, let's deal with this later or ask Fernando ;-) */ if(tr->saveMemory) { x2_gap = &(tr->partitionData[model].gapVector[pNumber * tr->partitionData[model].gapVectorLength]); x2_gapColumn = &(tr->partitionData[model].gapColumn[(pNumber - tr->mxtips - 1) * states * rateHet]); } } else { /* p is a tip, same as above */ x2_start = tr->partitionData[model].xVector[qNumber - tr->mxtips - 1] + x_offset; tip = tr->partitionData[model].yVector[pNumber] + offset; if(tr->saveMemory) { x2_gap = &(tr->partitionData[model].gapVector[qNumber * tr->partitionData[model].gapVectorLength]); x2_gapColumn = &(tr->partitionData[model].gapColumn[(qNumber - tr->mxtips - 1) * states * rateHet]); } } } else { /* neither p nor q are tips, hence we need to get the addresses of two inner vectors */ x1_start = tr->partitionData[model].xVector[pNumber - tr->mxtips - 1] + x_offset; x2_start = tr->partitionData[model].xVector[qNumber - tr->mxtips - 1] + x_offset; /* memory saving option */ if(tr->saveMemory) { x1_gap = &(tr->partitionData[model].gapVector[pNumber * tr->partitionData[model].gapVectorLength]); x2_gap = &(tr->partitionData[model].gapVector[qNumber * tr->partitionData[model].gapVectorLength]); x1_gapColumn = &tr->partitionData[model].gapColumn[(pNumber - tr->mxtips - 1) * states * rateHet]; x2_gapColumn = &tr->partitionData[model].gapColumn[(qNumber - tr->mxtips - 1) * states * rateHet]; } } #ifndef _OPTIMIZED_FUNCTIONS /* generic slow functions, memory saving option is not implemented for these */ assert(!tr->saveMemory); /* decide wheter CAT or GAMMA is used and compute log like */ if(tr->rateHetModel == CAT) partitionLikelihood = evaluateCAT_FLEX(tr->partitionData[model].rateCategory, wgt, x1_start, x2_start, tr->partitionData[model].tipVector, tip, width, diagptable, states); else partitionLikelihood = evaluateGAMMA_FLEX(wgt, x1_start, x2_start, tr->partitionData[model].tipVector, tip, width, diagptable, states); #else /* for the optimized functions we have a dedicated, optimized function implementation for each rate heterogeneity and data type combination, we switch over the number of states and the rate heterogeneity model */ switch(states) { case 2: #ifdef __MIC_NATIVE assert(0 && "Binary data model is not implemented on Intel MIC"); #else assert(!tr->saveMemory); if(tr->rateHetModel == CAT) partitionLikelihood = evaluateGTRCAT_BINARY((int *)NULL, (int *)NULL, rateCategory, wgt, x1_start, x2_start, tr->partitionData[model].tipVector, tip, width, diagptable, TRUE); else partitionLikelihood = evaluateGTRGAMMA_BINARY((int *)NULL, (int *)NULL, wgt, x1_start, x2_start, tr->partitionData[model].tipVector, tip, width, diagptable, TRUE); #endif break; case 4: /* DNA */ { if(tr->rateHetModel == CAT) { if(tr->saveMemory) #ifdef __MIC_NATIVE assert(0 && "Neither CAT model of rate heterogeneity nor memory saving are implemented on Intel MIC"); #else partitionLikelihood = evaluateGTRCAT_SAVE(rateCategory, wgt, x1_start, x2_start, tr->partitionData[model].tipVector, tip, width, diagptable, x1_gapColumn, x2_gapColumn, x1_gap, x2_gap); #endif else #ifdef __MIC_NATIVE assert(0 && "CAT model of rate heterogeneity is not implemented on Intel MIC"); #else partitionLikelihood = evaluateGTRCAT(rateCategory, wgt, x1_start, x2_start, tr->partitionData[model].tipVector, tip, width, diagptable); #endif } else { if(tr->saveMemory) #ifdef __MIC_NATIVE assert(0 && "Memory saving is not implemented on Intel MIC"); #else partitionLikelihood = evaluateGTRGAMMA_GAPPED_SAVE(wgt, x1_start, x2_start, tr->partitionData[model].tipVector, tip, width, diagptable, x1_gapColumn, x2_gapColumn, x1_gap, x2_gap); #endif else #ifdef __MIC_NATIVE partitionLikelihood = evaluateGAMMA_MIC(wgt, x1_start, x2_start, tr->partitionData[model].mic_tipVector, tip, width, diagptable); #else partitionLikelihood = evaluateGTRGAMMA(wgt, x1_start, x2_start, tr->partitionData[model].tipVector, tip, width, diagptable); #endif } } break; case 20: /* proteins */ { if(tr->rateHetModel == CAT) { if(tr->saveMemory) #ifdef __MIC_NATIVE assert(0 && "Neither CAT model of rate heterogeneity nor memory saving are implemented on Intel MIC"); #else partitionLikelihood = evaluateGTRCATPROT_SAVE(rateCategory, wgt, x1_start, x2_start, tr->partitionData[model].tipVector, tip, width, diagptable, x1_gapColumn, x2_gapColumn, x1_gap, x2_gap); #endif else #ifdef __MIC_NATIVE assert(0 && "CAT model of rate heterogeneity is not implemented on Intel MIC"); #else partitionLikelihood = evaluateGTRCATPROT(rateCategory, wgt, x1_start, x2_start, tr->partitionData[model].tipVector, tip, width, diagptable); #endif } else { if(tr->saveMemory) #ifdef __MIC_NATIVE assert(0 && "Memory saving is not implemented on Intel MIC"); #else partitionLikelihood = evaluateGTRGAMMAPROT_GAPPED_SAVE(wgt, x1_start, x2_start, tr->partitionData[model].tipVector, tip, width, diagptable, x1_gapColumn, x2_gapColumn, x1_gap, x2_gap); #endif else { if(tr->partitionData[model].protModels == LG4M || tr->partitionData[model].protModels == LG4X) #ifdef __MIC_NATIVE partitionLikelihood = evaluateGAMMAPROT_LG4_MIC(wgt, x1_start, x2_start, tr->partitionData[model].mic_tipVector, tip, width, diagptable, weights); #else partitionLikelihood = evaluateGTRGAMMAPROT_LG4((int *)NULL, (int *)NULL, wgt, x1_start, x2_start, tr->partitionData[model].tipVector_LG4, tip, width, diagptable, TRUE, weights); #endif else #ifdef __MIC_NATIVE partitionLikelihood = evaluateGAMMAPROT_MIC(wgt, x1_start, x2_start, tr->partitionData[model].mic_tipVector, tip, width, diagptable); #else partitionLikelihood = evaluateGTRGAMMAPROT(wgt, x1_start, x2_start, tr->partitionData[model].tipVector, tip, width, diagptable); #endif } } } break; default: assert(0); } #endif /* now here is a nasty part, for each partition and each node we maintain an integer counter to count how often how many entries per node were scaled by a constant factor. Here we use this information generated during Felsenstein's pruning algorithm by the newview() functions to undo the preceding scaling multiplications at the root, for mathematical details you should actually read: A. Stamatakis: "Orchestrating the Phylogenetic Likelihood Function on Emerging Parallel Architectures". In B. Schmidt, editor, Bioinformatics: High Performance Parallel Computer Architectures, 85-115, CRC Press, Taylor & Francis, 2010. There's a copy of this book in my office */ partitionLikelihood += (globalScaler[pNumber] + globalScaler[qNumber]) * LOG(minlikelihood); /* check that there was no major numerical screw-up, the log likelihood should be < 0.0 always */ assert(partitionLikelihood < 0.0); /* now we have the correct log likelihood for the current partition after undoing scaling multiplications */ /* finally, we also store the per partition log likelihood which is important for optimizing the alpha parameter of this partition for example */ *perPartitionLH = partitionLikelihood; } else { /* if the current thread does not have a single site of this partition it is important to set the per partition log like to 0.0 because of the reduction operation that will take place later-on. That is, the values of tr->perPartitionLH across all threads need to be in a consistent state, always ! */ if(width == 0) *perPartitionLH = 0.0; else { assert(tr->td[0].executeModel[model] == FALSE && *perPartitionLH < 0.0); } } } /* for model */ } /* OMP parallel */ #ifdef _USE_OMP /* perform reduction of per-partition LH scores */ int model, t; for(model = 0; model < tr->NumberOfModels; model++) { if (!tr->td[0].executeModel[model]) continue; tr->perPartitionLH[model] = 0.0; for(t = 0; t < tr->maxThreadsPerModel; t++) { Assign* pAss = tr->partThreadAssigns[model * tr->maxThreadsPerModel + t]; if (pAss) { int tid = pAss->procId; tr->perPartitionLH[model] += tr->partitionData[model].reductionBuffer[tid]; } } } #endif } void evaluateGeneric (tree *tr, nodeptr p, boolean fullTraversal) { /* now this may be the entry point of the library to compute the log like at a branch defined by p and p->back == q */ volatile double result = 0.0; nodeptr q = p->back; int i, model; /* set the first entry of the traversal descriptor to contain the indices of nodes p and q */ tr->td[0].ti[0].pNumber = p->number; tr->td[0].ti[0].qNumber = q->number; /* copy the branch lengths of the tree into the first entry of the traversal descriptor. if -M is not used tr->numBranches must be 1 */ for(i = 0; i < tr->numBranches; i++) tr->td[0].ti[0].qz[i] = q->z[i]; /* now compute how many conditionals must be re-computed/re-oriented by newview to be able to calculate the likelihood at the root defined by p and q. */ /* one entry in the traversal descriptor is already used, hence set the tarversal length counter to 1 */ tr->td[0].count = 1; /* do we need to recompute any of the vectors at or below p ? */ if(fullTraversal) { assert(isTip(p->number, tr->mxtips)); computeTraversalInfo(q, &(tr->td[0].ti[0]), &(tr->td[0].count), tr->mxtips, tr->numBranches, FALSE); } else { if(!p->x) computeTraversalInfo(p, &(tr->td[0].ti[0]), &(tr->td[0].count), tr->mxtips, tr->numBranches, TRUE); /* recompute/reorient any descriptors at or below q ? computeTraversalInfo computes and stores the newview() to be executed for the traversal descriptor */ if(!q->x) computeTraversalInfo(q, &(tr->td[0].ti[0]), &(tr->td[0].count), tr->mxtips, tr->numBranches, TRUE); } /* now we copy this partition execute mask into the traversal descriptor which must come from the calling program, the logic of this should not form part of the library */ storeExecuteMaskInTraversalDescriptor(tr); /* also store in the traversal descriptor that something has changed i.e., in the parallel case that the traversal descriptor list of nodes needs to be broadcast once again */ tr->td[0].traversalHasChanged = TRUE; evaluateIterative(tr); { double *recv = (double *)malloc(sizeof(double) * tr->NumberOfModels); #ifdef _USE_ALLREDUCE MPI_Allreduce(tr->perPartitionLH, recv, tr->NumberOfModels, MPI_DOUBLE, MPI_SUM, MPI_COMM_WORLD); #else MPI_Reduce(tr->perPartitionLH, recv, tr->NumberOfModels, MPI_DOUBLE, MPI_SUM, 0, MPI_COMM_WORLD); MPI_Bcast(recv, tr->NumberOfModels, MPI_DOUBLE, 0, MPI_COMM_WORLD); #endif memcpy(tr->perPartitionLH, recv, tr->NumberOfModels * sizeof(double)); for(model = 0; model < tr->NumberOfModels; model++) result += tr->perPartitionLH[model]; free(recv); } /* set the tree data structure likelihood value to the total likelihood */ tr->likelihood = result; /* MPI_Barrier(MPI_COMM_WORLD); printf("Process %d likelihood: %f\n", processID, tr->likelihood); MPI_Barrier(MPI_COMM_WORLD); */ /* do some bookkeeping to have traversalHasChanged in a consistent state */ tr->td[0].traversalHasChanged = FALSE; } /* below are the optimized function versions with geeky intrinsics */ #ifdef _OPTIMIZED_FUNCTIONS /* binary data */ static double evaluateGTRCAT_BINARY (int *ex1, int *ex2, int *cptr, int *wptr, double *x1_start, double *x2_start, double *tipVector, unsigned char *tipX1, int n, double *diagptable_start, const boolean fastScaling) { double sum = 0.0, term; int i; double *diagptable, *x1, *x2; if(tipX1) { for (i = 0; i < n; i++) { double t[2] __attribute__ ((aligned (BYTE_ALIGNMENT))); x1 = &(tipVector[2 * tipX1[i]]); x2 = &(x2_start[2 * i]); diagptable = &(diagptable_start[2 * cptr[i]]); _mm_store_pd(t, _mm_mul_pd(_mm_load_pd(x1), _mm_mul_pd(_mm_load_pd(x2), _mm_load_pd(diagptable)))); if(fastScaling) term = log(fabs(t[0] + t[1])); else term = log(fabs(t[0] + t[1])) + (ex2[i] * log(minlikelihood)); sum += wptr[i] * term; } } else { for (i = 0; i < n; i++) { double t[2] __attribute__ ((aligned (BYTE_ALIGNMENT))); x1 = &x1_start[2 * i]; x2 = &x2_start[2 * i]; diagptable = &diagptable_start[2 * cptr[i]]; _mm_store_pd(t, _mm_mul_pd(_mm_load_pd(x1), _mm_mul_pd(_mm_load_pd(x2), _mm_load_pd(diagptable)))); if(fastScaling) term = log(fabs(t[0] + t[1])); else term = log(fabs(t[0] + t[1])) + ((ex1[i] + ex2[i]) * log(minlikelihood)); sum += wptr[i] * term; } } return sum; } static double evaluateGTRGAMMA_BINARY(int *ex1, int *ex2, int *wptr, double *x1_start, double *x2_start, double *tipVector, unsigned char *tipX1, const int n, double *diagptable, const boolean fastScaling) { double sum = 0.0, term; int i, j; double *x1, *x2; if(tipX1) { for (i = 0; i < n; i++) { double t[2] __attribute__ ((aligned (BYTE_ALIGNMENT))); __m128d termv, x1v, x2v, dv; x1 = &(tipVector[2 * tipX1[i]]); x2 = &x2_start[8 * i]; termv = _mm_set1_pd(0.0); for(j = 0; j < 4; j++) { x1v = _mm_load_pd(&x1[0]); x2v = _mm_load_pd(&x2[j * 2]); dv = _mm_load_pd(&diagptable[j * 2]); x1v = _mm_mul_pd(x1v, x2v); x1v = _mm_mul_pd(x1v, dv); termv = _mm_add_pd(termv, x1v); } _mm_store_pd(t, termv); if(fastScaling) term = log(0.25 * (fabs(t[0] + t[1]))); else term = log(0.25 * (fabs(t[0] + t[1]))) + (ex2[i] * log(minlikelihood)); sum += wptr[i] * term; } } else { for (i = 0; i < n; i++) { double t[2] __attribute__ ((aligned (BYTE_ALIGNMENT))); __m128d termv, x1v, x2v, dv; x1 = &x1_start[8 * i]; x2 = &x2_start[8 * i]; termv = _mm_set1_pd(0.0); for(j = 0; j < 4; j++) { x1v = _mm_load_pd(&x1[j * 2]); x2v = _mm_load_pd(&x2[j * 2]); dv = _mm_load_pd(&diagptable[j * 2]); x1v = _mm_mul_pd(x1v, x2v); x1v = _mm_mul_pd(x1v, dv); termv = _mm_add_pd(termv, x1v); } _mm_store_pd(t, termv); if(fastScaling) term = log(0.25 * (fabs(t[0] + t[1]))); else term = log(0.25 * (fabs(t[0] + t[1]))) + ((ex1[i] +ex2[i]) * log(minlikelihood)); sum += wptr[i] * term; } } return sum; } /* binary data end */ static double evaluateGTRGAMMAPROT_LG4(int *ex1, int *ex2, int *wptr, double *x1, double *x2, double *tipVector[4], unsigned char *tipX1, int n, double *diagptable, const boolean fastScaling, double *weights) { double sum = 0.0, term; int i, j, l; double *left, *right; if(tipX1) { for (i = 0; i < n; i++) { #ifdef __SIM_SSE3 __m128d tv = _mm_setzero_pd(); for(j = 0, term = 0.0; j < 4; j++) { double *d = &diagptable[j * 20]; __m128d t = _mm_setzero_pd(), w = _mm_set1_pd(weights[j]); left = &(tipVector[j][20 * tipX1[i]]); right = &(x2[80 * i + 20 * j]); for(l = 0; l < 20; l+=2) { __m128d mul = _mm_mul_pd(_mm_load_pd(&left[l]), _mm_load_pd(&right[l])); t = _mm_add_pd(t, _mm_mul_pd(mul, _mm_load_pd(&d[l]))); } tv = _mm_add_pd(tv, _mm_mul_pd(t, w)); } tv = _mm_hadd_pd(tv, tv); _mm_storel_pd(&term, tv); #else for(j = 0, term = 0.0; j < 4; j++) { double t = 0.0; left = &(tipVector[j][20 * tipX1[i]]); right = &(x2[80 * i + 20 * j]); for(l = 0; l < 20; l++) t += left[l] * right[l] * diagptable[j * 20 + l]; term += weights[j] * t; } #endif if(fastScaling) term = LOG(FABS(term)); else term = LOG(FABS(term)) + (ex2[i] * LOG(minlikelihood)); sum += wptr[i] * term; } } else { for (i = 0; i < n; i++) { #ifdef __SIM_SSE3 __m128d tv = _mm_setzero_pd(); for(j = 0, term = 0.0; j < 4; j++) { double *d = &diagptable[j * 20]; __m128d t = _mm_setzero_pd(), w = _mm_set1_pd(weights[j]); left = &(x1[80 * i + 20 * j]); right = &(x2[80 * i + 20 * j]); for(l = 0; l < 20; l+=2) { __m128d mul = _mm_mul_pd(_mm_load_pd(&left[l]), _mm_load_pd(&right[l])); t = _mm_add_pd(t, _mm_mul_pd(mul, _mm_load_pd(&d[l]))); } tv = _mm_add_pd(tv, _mm_mul_pd(t, w)); } tv = _mm_hadd_pd(tv, tv); _mm_storel_pd(&term, tv); #else for(j = 0, term = 0.0; j < 4; j++) { double t = 0.0; left = &(x1[80 * i + 20 * j]); right = &(x2[80 * i + 20 * j]); for(l = 0; l < 20; l++) t += left[l] * right[l] * diagptable[j * 20 + l]; term += weights[j] * t; } #endif if(fastScaling) term = LOG(FABS(term)); else term = LOG(FABS(term)) + ((ex1[i] + ex2[i])*LOG(minlikelihood)); sum += wptr[i] * term; } } return sum; } static double evaluateGTRGAMMAPROT_GAPPED_SAVE (int *wptr, double *x1, double *x2, double *tipVector, unsigned char *tipX1, int n, double *diagptable, double *x1_gapColumn, double *x2_gapColumn, unsigned int *x1_gap, unsigned int *x2_gap) { double sum = 0.0, term; int i, j, l; double *left, *right, *x1_ptr = x1, *x2_ptr = x2, *x1v, *x2v; if(tipX1) { for (i = 0; i < n; i++) { if(x2_gap[i / 32] & mask32[i % 32]) x2v = x2_gapColumn; else { x2v = x2_ptr; x2_ptr += 80; } __m128d tv = _mm_setzero_pd(); left = &(tipVector[20 * tipX1[i]]); for(j = 0, term = 0.0; j < 4; j++) { double *d = &diagptable[j * 20]; right = &(x2v[20 * j]); for(l = 0; l < 20; l+=2) { __m128d mul = _mm_mul_pd(_mm_load_pd(&left[l]), _mm_load_pd(&right[l])); tv = _mm_add_pd(tv, _mm_mul_pd(mul, _mm_load_pd(&d[l]))); } } tv = _mm_hadd_pd(tv, tv); _mm_storel_pd(&term, tv); term = LOG(0.25 * FABS(term)); sum += wptr[i] * term; } } else { for (i = 0; i < n; i++) { if(x1_gap[i / 32] & mask32[i % 32]) x1v = x1_gapColumn; else { x1v = x1_ptr; x1_ptr += 80; } if(x2_gap[i / 32] & mask32[i % 32]) x2v = x2_gapColumn; else { x2v = x2_ptr; x2_ptr += 80; } __m128d tv = _mm_setzero_pd(); for(j = 0, term = 0.0; j < 4; j++) { double *d = &diagptable[j * 20]; left = &(x1v[20 * j]); right = &(x2v[20 * j]); for(l = 0; l < 20; l+=2) { __m128d mul = _mm_mul_pd(_mm_load_pd(&left[l]), _mm_load_pd(&right[l])); tv = _mm_add_pd(tv, _mm_mul_pd(mul, _mm_load_pd(&d[l]))); } } tv = _mm_hadd_pd(tv, tv); _mm_storel_pd(&term, tv); term = LOG(0.25 * FABS(term)); sum += wptr[i] * term; } } return sum; } static double evaluateGTRGAMMAPROT (int *wptr, double *x1, double *x2, double *tipVector, unsigned char *tipX1, int n, double *diagptable) { double sum = 0.0, term; int i, j, l; double *left, *right; if(tipX1) { for (i = 0; i < n; i++) { __m128d tv = _mm_setzero_pd(); left = &(tipVector[20 * tipX1[i]]); for(j = 0, term = 0.0; j < 4; j++) { double *d = &diagptable[j * 20]; right = &(x2[80 * i + 20 * j]); for(l = 0; l < 20; l+=2) { __m128d mul = _mm_mul_pd(_mm_load_pd(&left[l]), _mm_load_pd(&right[l])); tv = _mm_add_pd(tv, _mm_mul_pd(mul, _mm_load_pd(&d[l]))); } } tv = _mm_hadd_pd(tv, tv); _mm_storel_pd(&term, tv); term = LOG(0.25 * FABS(term)); sum += wptr[i] * term; } } else { for (i = 0; i < n; i++) { __m128d tv = _mm_setzero_pd(); for(j = 0, term = 0.0; j < 4; j++) { double *d = &diagptable[j * 20]; left = &(x1[80 * i + 20 * j]); right = &(x2[80 * i + 20 * j]); for(l = 0; l < 20; l+=2) { __m128d mul = _mm_mul_pd(_mm_load_pd(&left[l]), _mm_load_pd(&right[l])); tv = _mm_add_pd(tv, _mm_mul_pd(mul, _mm_load_pd(&d[l]))); } } tv = _mm_hadd_pd(tv, tv); _mm_storel_pd(&term, tv); term = LOG(0.25 * FABS(term)); sum += wptr[i] * term; } } return sum; } static double evaluateGTRCATPROT (int *cptr, int *wptr, double *x1, double *x2, double *tipVector, unsigned char *tipX1, int n, double *diagptable_start) { double sum = 0.0, term; double *diagptable, *left, *right; int i, l; if(tipX1) { for (i = 0; i < n; i++) { left = &(tipVector[20 * tipX1[i]]); right = &(x2[20 * i]); diagptable = &diagptable_start[20 * cptr[i]]; __m128d tv = _mm_setzero_pd(); for(l = 0; l < 20; l+=2) { __m128d lv = _mm_load_pd(&left[l]); __m128d rv = _mm_load_pd(&right[l]); __m128d mul = _mm_mul_pd(lv, rv); __m128d dv = _mm_load_pd(&diagptable[l]); tv = _mm_add_pd(tv, _mm_mul_pd(mul, dv)); } tv = _mm_hadd_pd(tv, tv); _mm_storel_pd(&term, tv); term = LOG(FABS(term)); sum += wptr[i] * term; } } else { for (i = 0; i < n; i++) { left = &x1[20 * i]; right = &x2[20 * i]; diagptable = &diagptable_start[20 * cptr[i]]; __m128d tv = _mm_setzero_pd(); for(l = 0; l < 20; l+=2) { __m128d lv = _mm_load_pd(&left[l]); __m128d rv = _mm_load_pd(&right[l]); __m128d mul = _mm_mul_pd(lv, rv); __m128d dv = _mm_load_pd(&diagptable[l]); tv = _mm_add_pd(tv, _mm_mul_pd(mul, dv)); } tv = _mm_hadd_pd(tv, tv); _mm_storel_pd(&term, tv); term = LOG(FABS(term)); sum += wptr[i] * term; } } return sum; } static double evaluateGTRCATPROT_SAVE (int *cptr, int *wptr, double *x1, double *x2, double *tipVector, unsigned char *tipX1, int n, double *diagptable_start, double *x1_gapColumn, double *x2_gapColumn, unsigned int *x1_gap, unsigned int *x2_gap) { double sum = 0.0, term, *diagptable, *left, *right, *left_ptr = x1, *right_ptr = x2; int i, l; if(tipX1) { for (i = 0; i < n; i++) { left = &(tipVector[20 * tipX1[i]]); if(isGap(x2_gap, i)) right = x2_gapColumn; else { right = right_ptr; right_ptr += 20; } diagptable = &diagptable_start[20 * cptr[i]]; __m128d tv = _mm_setzero_pd(); for(l = 0; l < 20; l+=2) { __m128d lv = _mm_load_pd(&left[l]); __m128d rv = _mm_load_pd(&right[l]); __m128d mul = _mm_mul_pd(lv, rv); __m128d dv = _mm_load_pd(&diagptable[l]); tv = _mm_add_pd(tv, _mm_mul_pd(mul, dv)); } tv = _mm_hadd_pd(tv, tv); _mm_storel_pd(&term, tv); term = LOG(FABS(term)); sum += wptr[i] * term; } } else { for (i = 0; i < n; i++) { if(isGap(x1_gap, i)) left = x1_gapColumn; else { left = left_ptr; left_ptr += 20; } if(isGap(x2_gap, i)) right = x2_gapColumn; else { right = right_ptr; right_ptr += 20; } diagptable = &diagptable_start[20 * cptr[i]]; __m128d tv = _mm_setzero_pd(); for(l = 0; l < 20; l+=2) { __m128d lv = _mm_load_pd(&left[l]); __m128d rv = _mm_load_pd(&right[l]); __m128d mul = _mm_mul_pd(lv, rv); __m128d dv = _mm_load_pd(&diagptable[l]); tv = _mm_add_pd(tv, _mm_mul_pd(mul, dv)); } tv = _mm_hadd_pd(tv, tv); _mm_storel_pd(&term, tv); term = LOG(FABS(term)); sum += wptr[i] * term; } } return sum; } static double evaluateGTRCAT_SAVE (int *cptr, int *wptr, double *x1_start, double *x2_start, double *tipVector, unsigned char *tipX1, int n, double *diagptable_start, double *x1_gapColumn, double *x2_gapColumn, unsigned int *x1_gap, unsigned int *x2_gap) { double sum = 0.0, term; int i; double *diagptable, *x1, *x2, *x1_ptr = x1_start, *x2_ptr = x2_start; if(tipX1) { for (i = 0; i < n; i++) { double t[2] __attribute__ ((aligned (BYTE_ALIGNMENT))); __m128d x1v1, x1v2, x2v1, x2v2, dv1, dv2; x1 = &(tipVector[4 * tipX1[i]]); if(isGap(x2_gap, i)) x2 = x2_gapColumn; else { x2 = x2_ptr; x2_ptr += 4; } diagptable = &diagptable_start[4 * cptr[i]]; x1v1 = _mm_load_pd(&x1[0]); x1v2 = _mm_load_pd(&x1[2]); x2v1 = _mm_load_pd(&x2[0]); x2v2 = _mm_load_pd(&x2[2]); dv1 = _mm_load_pd(&diagptable[0]); dv2 = _mm_load_pd(&diagptable[2]); x1v1 = _mm_mul_pd(x1v1, x2v1); x1v1 = _mm_mul_pd(x1v1, dv1); x1v2 = _mm_mul_pd(x1v2, x2v2); x1v2 = _mm_mul_pd(x1v2, dv2); x1v1 = _mm_add_pd(x1v1, x1v2); _mm_store_pd(t, x1v1); term = LOG(FABS(t[0] + t[1])); sum += wptr[i] * term; } } else { for (i = 0; i < n; i++) { double t[2] __attribute__ ((aligned (BYTE_ALIGNMENT))); __m128d x1v1, x1v2, x2v1, x2v2, dv1, dv2; if(isGap(x1_gap, i)) x1 = x1_gapColumn; else { x1 = x1_ptr; x1_ptr += 4; } if(isGap(x2_gap, i)) x2 = x2_gapColumn; else { x2 = x2_ptr; x2_ptr += 4; } diagptable = &diagptable_start[4 * cptr[i]]; x1v1 = _mm_load_pd(&x1[0]); x1v2 = _mm_load_pd(&x1[2]); x2v1 = _mm_load_pd(&x2[0]); x2v2 = _mm_load_pd(&x2[2]); dv1 = _mm_load_pd(&diagptable[0]); dv2 = _mm_load_pd(&diagptable[2]); x1v1 = _mm_mul_pd(x1v1, x2v1); x1v1 = _mm_mul_pd(x1v1, dv1); x1v2 = _mm_mul_pd(x1v2, x2v2); x1v2 = _mm_mul_pd(x1v2, dv2); x1v1 = _mm_add_pd(x1v1, x1v2); _mm_store_pd(t, x1v1); term = LOG(FABS(t[0] + t[1])); sum += wptr[i] * term; } } return sum; } static double evaluateGTRGAMMA_GAPPED_SAVE(int *wptr, double *x1_start, double *x2_start, double *tipVector, unsigned char *tipX1, const int n, double *diagptable, double *x1_gapColumn, double *x2_gapColumn, unsigned int *x1_gap, unsigned int *x2_gap) { double sum = 0.0, term; int i, j; double *x1, *x2, *x1_ptr = x1_start, *x2_ptr = x2_start; if(tipX1) { for (i = 0; i < n; i++) { double t[2] __attribute__ ((aligned (BYTE_ALIGNMENT))); __m128d termv, x1v, x2v, dv; x1 = &(tipVector[4 * tipX1[i]]); if(x2_gap[i / 32] & mask32[i % 32]) x2 = x2_gapColumn; else { x2 = x2_ptr; x2_ptr += 16; } termv = _mm_set1_pd(0.0); for(j = 0; j < 4; j++) { x1v = _mm_load_pd(&x1[0]); x2v = _mm_load_pd(&x2[j * 4]); dv = _mm_load_pd(&diagptable[j * 4]); x1v = _mm_mul_pd(x1v, x2v); x1v = _mm_mul_pd(x1v, dv); termv = _mm_add_pd(termv, x1v); x1v = _mm_load_pd(&x1[2]); x2v = _mm_load_pd(&x2[j * 4 + 2]); dv = _mm_load_pd(&diagptable[j * 4 + 2]); x1v = _mm_mul_pd(x1v, x2v); x1v = _mm_mul_pd(x1v, dv); termv = _mm_add_pd(termv, x1v); } _mm_store_pd(t, termv); term = LOG(0.25 * FABS(t[0] + t[1])); sum += wptr[i] * term; } } else { for (i = 0; i < n; i++) { double t[2] __attribute__ ((aligned (BYTE_ALIGNMENT))); __m128d termv, x1v, x2v, dv; if(x1_gap[i / 32] & mask32[i % 32]) x1 = x1_gapColumn; else { x1 = x1_ptr; x1_ptr += 16; } if(x2_gap[i / 32] & mask32[i % 32]) x2 = x2_gapColumn; else { x2 = x2_ptr; x2_ptr += 16; } termv = _mm_set1_pd(0.0); for(j = 0; j < 4; j++) { x1v = _mm_load_pd(&x1[j * 4]); x2v = _mm_load_pd(&x2[j * 4]); dv = _mm_load_pd(&diagptable[j * 4]); x1v = _mm_mul_pd(x1v, x2v); x1v = _mm_mul_pd(x1v, dv); termv = _mm_add_pd(termv, x1v); x1v = _mm_load_pd(&x1[j * 4 + 2]); x2v = _mm_load_pd(&x2[j * 4 + 2]); dv = _mm_load_pd(&diagptable[j * 4 + 2]); x1v = _mm_mul_pd(x1v, x2v); x1v = _mm_mul_pd(x1v, dv); termv = _mm_add_pd(termv, x1v); } _mm_store_pd(t, termv); term = LOG(0.25 * FABS(t[0] + t[1])); sum += wptr[i] * term; } } return sum; } static double evaluateGTRGAMMA(int *wptr, double *x1_start, double *x2_start, double *tipVector, unsigned char *tipX1, const int n, double *diagptable) { double sum = 0.0, term; int i, j; double *x1, *x2; if(tipX1) { for (i = 0; i < n; i++) { double t[2] __attribute__ ((aligned (BYTE_ALIGNMENT))); __m128d termv, x1v, x2v, dv; x1 = &(tipVector[4 * tipX1[i]]); x2 = &x2_start[16 * i]; termv = _mm_set1_pd(0.0); for(j = 0; j < 4; j++) { x1v = _mm_load_pd(&x1[0]); x2v = _mm_load_pd(&x2[j * 4]); dv = _mm_load_pd(&diagptable[j * 4]); x1v = _mm_mul_pd(x1v, x2v); x1v = _mm_mul_pd(x1v, dv); termv = _mm_add_pd(termv, x1v); x1v = _mm_load_pd(&x1[2]); x2v = _mm_load_pd(&x2[j * 4 + 2]); dv = _mm_load_pd(&diagptable[j * 4 + 2]); x1v = _mm_mul_pd(x1v, x2v); x1v = _mm_mul_pd(x1v, dv); termv = _mm_add_pd(termv, x1v); } _mm_store_pd(t, termv); term = LOG(0.25 * FABS(t[0] + t[1])); sum += wptr[i] * term; } } else { for (i = 0; i < n; i++) { double t[2] __attribute__ ((aligned (BYTE_ALIGNMENT))); __m128d termv, x1v, x2v, dv; x1 = &x1_start[16 * i]; x2 = &x2_start[16 * i]; termv = _mm_set1_pd(0.0); for(j = 0; j < 4; j++) { x1v = _mm_load_pd(&x1[j * 4]); x2v = _mm_load_pd(&x2[j * 4]); dv = _mm_load_pd(&diagptable[j * 4]); x1v = _mm_mul_pd(x1v, x2v); x1v = _mm_mul_pd(x1v, dv); termv = _mm_add_pd(termv, x1v); x1v = _mm_load_pd(&x1[j * 4 + 2]); x2v = _mm_load_pd(&x2[j * 4 + 2]); dv = _mm_load_pd(&diagptable[j * 4 + 2]); x1v = _mm_mul_pd(x1v, x2v); x1v = _mm_mul_pd(x1v, dv); termv = _mm_add_pd(termv, x1v); } _mm_store_pd(t, termv); term = LOG(0.25 * FABS(t[0] + t[1])); sum += wptr[i] * term; } } return sum; } static double evaluateGTRCAT (int *cptr, int *wptr, double *x1_start, double *x2_start, double *tipVector, unsigned char *tipX1, int n, double *diagptable_start) { double sum = 0.0, term; int i; double *diagptable, *x1, *x2; if(tipX1) { for (i = 0; i < n; i++) { double t[2] __attribute__ ((aligned (BYTE_ALIGNMENT))); __m128d x1v1, x1v2, x2v1, x2v2, dv1, dv2; x1 = &(tipVector[4 * tipX1[i]]); x2 = &x2_start[4 * i]; diagptable = &diagptable_start[4 * cptr[i]]; x1v1 = _mm_load_pd(&x1[0]); x1v2 = _mm_load_pd(&x1[2]); x2v1 = _mm_load_pd(&x2[0]); x2v2 = _mm_load_pd(&x2[2]); dv1 = _mm_load_pd(&diagptable[0]); dv2 = _mm_load_pd(&diagptable[2]); x1v1 = _mm_mul_pd(x1v1, x2v1); x1v1 = _mm_mul_pd(x1v1, dv1); x1v2 = _mm_mul_pd(x1v2, x2v2); x1v2 = _mm_mul_pd(x1v2, dv2); x1v1 = _mm_add_pd(x1v1, x1v2); _mm_store_pd(t, x1v1); term = LOG(FABS(t[0] + t[1])); sum += wptr[i] * term; } } else { for (i = 0; i < n; i++) { double t[2] __attribute__ ((aligned (BYTE_ALIGNMENT))); __m128d x1v1, x1v2, x2v1, x2v2, dv1, dv2; x1 = &x1_start[4 * i]; x2 = &x2_start[4 * i]; diagptable = &diagptable_start[4 * cptr[i]]; x1v1 = _mm_load_pd(&x1[0]); x1v2 = _mm_load_pd(&x1[2]); x2v1 = _mm_load_pd(&x2[0]); x2v2 = _mm_load_pd(&x2[2]); dv1 = _mm_load_pd(&diagptable[0]); dv2 = _mm_load_pd(&diagptable[2]); x1v1 = _mm_mul_pd(x1v1, x2v1); x1v1 = _mm_mul_pd(x1v1, dv1); x1v2 = _mm_mul_pd(x1v2, x2v2); x1v2 = _mm_mul_pd(x1v2, dv2); x1v1 = _mm_add_pd(x1v1, x1v2); _mm_store_pd(t, x1v1); term = LOG(FABS(t[0] + t[1])); sum += wptr[i] * term; } } return sum; } #endif ExaML-3.0.21/examl/evaluatePartialGenericSpecial.c000066400000000000000000000660051330315463200217450ustar00rootroot00000000000000/* RAxML-VI-HPC (version 2.2) a program for sequential and parallel estimation of phylogenetic trees * Copyright August 2006 by Alexandros Stamatakis * * Partially derived from * fastDNAml, a program for estimation of phylogenetic trees from sequences by Gary J. Olsen * * and * * Programs of the PHYLIP package by Joe Felsenstein. * This program is free software; you may redistribute it and/or modify its * 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. * * * For any other enquiries send an Email to Alexandros Stamatakis * Alexandros.Stamatakis@epfl.ch * * When publishing work that is based on the results from RAxML-VI-HPC please cite: * * Alexandros Stamatakis:"RAxML-VI-HPC: maximum likelihood-based phylogenetic analyses with thousands of taxa and mixed models". * Bioinformatics 2006; doi: 10.1093/bioinformatics/btl446 */ #ifndef WIN32 #include #endif #include #include #include #include #include #include #include "axml.h" #ifdef __SIM_SSE3 #include #include #endif #if defined(_OPTIMIZED_FUNCTIONS) && !defined(__MIC_NATIVE) static inline void computeVectorGTRCATPROT(double *lVector, int *eVector, double ki, int i, double qz, double rz, traversalInfo *ti, double *EIGN, double *EI, double *EV, double *tipVector, unsigned char **yVector, int mxtips); static double evaluatePartialGTRCATPROT(int i, double ki, int counter, traversalInfo *ti, double qz, int w, double *EIGN, double *EI, double *EV, double *tipVector, unsigned char **yVector, int branchReference, int mxtips); static inline void computeVectorGTRGAMMAPROT(double *lVector, int *eVector, double *gammaRates, int i, double qz, double rz, traversalInfo *ti, double *EIGN, double *EI, double *EV, double *tipVector, unsigned char **yVector, int mxtips); static double evaluatePartialGTRGAMMAPROT(int i, int counter, traversalInfo *ti, double qz, int w, double *EIGN, double *EI, double *EV, double *tipVector, unsigned char **yVector, double *gammaRates, int branchReference, int mxtips); static inline void computeVectorGTRCAT(double *lVector, int *eVector, double ki, int i, double qz, double rz, traversalInfo *ti, double *EIGN, double *EI, double *EV, double *tipVector, unsigned char **yVector, int mxtips); static double evaluatePartialGTRCAT(int i, double ki, int counter, traversalInfo *ti, double qz, int w, double *EIGN, double *EI, double *EV, double *tipVector, unsigned char **yVector, int branchReference, int mxtips); static inline void computeVectorGTRCAT_BINARY(double *lVector, int *eVector, double ki, int i, double qz, double rz, traversalInfo *ti, double *EIGN, double *EI, double *EV, double *tipVector, unsigned char **yVector, int mxtips); static double evaluatePartialGTRCAT_BINARY(int i, double ki, int counter, traversalInfo *ti, double qz, int w, double *EIGN, double *EI, double *EV, double *tipVector, unsigned char **yVector, int branchReference, int mxtips); #else static inline void computeVectorCAT_FLEX(double *lVector, int *eVector, double ki, int i, double qz, double rz, traversalInfo *ti, double *EIGN, double *EI, double *EV, double *tipVector, unsigned char **yVector, int mxtips, const int states) { double *d1 = (double *)malloc(sizeof(double) * states), *d2 = (double *)malloc(sizeof(double) * states), *x1px2 = (double *)malloc(sizeof(double) * states), ump_x1, ump_x2, lz1, lz2, *x1, *x2, *x3; int scale, j, k, pNumber = ti->pNumber, rNumber = ti->rNumber, qNumber = ti->qNumber; x3 = &lVector[states * (pNumber - mxtips)]; switch(ti->tipCase) { case TIP_TIP: x1 = &(tipVector[states * yVector[qNumber][i]]); x2 = &(tipVector[states * yVector[rNumber][i]]); break; case TIP_INNER: x1 = &(tipVector[states * yVector[qNumber][i]]); x2 = &(lVector[states * (rNumber - mxtips)]); break; case INNER_INNER: x1 = &(lVector[states * (qNumber - mxtips)]); x2 = &(lVector[states * (rNumber - mxtips)]); break; default: assert(0); } lz1 = qz * ki; lz2 = rz * ki; d1[0] = x1[0]; d2[0] = x2[0]; for(j = 1; j < states; j++) { d1[j] = x1[j] * EXP(EIGN[j] * lz1); d2[j] = x2[j] * EXP(EIGN[j] * lz2); } for(j = 0; j < states; j++) { ump_x1 = 0.0; ump_x2 = 0.0; for(k = 0; k < states; k++) { ump_x1 += d1[k] * EI[j * states + k]; ump_x2 += d2[k] * EI[j * states + k]; } x1px2[j] = ump_x1 * ump_x2; } for(j = 0; j < states; j++) x3[j] = 0.0; for(j = 0; j < states; j++) for(k = 0; k < states; k++) x3[k] += x1px2[j] * EV[states * j + k]; scale = 1; for(j = 0; scale && (j < states); j++) scale = ((x3[j] < minlikelihood) && (x3[j] > minusminlikelihood)); if(scale) { for(j = 0; j < states; j++) x3[j] *= twotothe256; *eVector = *eVector + 1; } free(d1); free(d2); free(x1px2); return; } static double evaluatePartialCAT_FLEX(int i, double ki, int counter, traversalInfo *ti, double qz, int w, double *EIGN, double *EI, double *EV, double *tipVector, unsigned char **yVector, int branchReference, int mxtips, const int states) { int scale = 0, k; double *lVector = (double *)malloc_aligned(sizeof(double) * states * mxtips), *d = (double *)malloc_aligned(sizeof(double) * states), lz, term, *x1, *x2; traversalInfo *trav = &ti[0]; assert(isTip(trav->pNumber, mxtips)); x1 = &(tipVector[states * yVector[trav->pNumber][i]]); for(k = 1; k < counter; k++) { double qz = ti[k].qz[branchReference], rz = ti[k].rz[branchReference]; qz = (qz > zmin) ? log(qz) : log(zmin); rz = (rz > zmin) ? log(rz) : log(zmin); computeVectorCAT_FLEX(lVector, &scale, ki, i, qz, rz, &ti[k], EIGN, EI, EV, tipVector, yVector, mxtips, states); } x2 = &lVector[states * (trav->qNumber - mxtips)]; assert(0 <= (trav->qNumber - mxtips) && (trav->qNumber - mxtips) < mxtips); if(qz < zmin) lz = zmin; lz = log(qz); lz *= ki; d[0] = 1.0; for(k = 1; k < states; k++) d[k] = EXP (EIGN[k] * lz); term = 0.0; for(k = 0; k < states; k++) term += x1[k] * x2[k] * d[k]; term = LOG(FABS(term)) + (scale * LOG(minlikelihood)); term = term * w; free(lVector); free(d); return term; } #endif double evaluatePartialGeneric (tree *tr, int i, double ki, int _model) { double result; int branchReference, states = tr->partitionData[_model].states; int index; index = i; if(tr->numBranches > 1) branchReference = _model; else branchReference = 0; #ifndef _OPTIMIZED_FUNCTIONS if(tr->rateHetModel == CAT) result = evaluatePartialCAT_FLEX(index, ki, tr->td[0].count, tr->td[0].ti, tr->td[0].ti[0].qz[branchReference], tr->partitionData[_model].wgt[index], tr->partitionData[_model].EIGN, tr->partitionData[_model].EI, tr->partitionData[_model].EV, tr->partitionData[_model].tipVector, tr->partitionData[_model].yVector, branchReference, tr->mxtips, states); else /* the per-site site likelihood function should only be called for the CAT model under the GAMMA model this is required only for estimating per-site protein models which has however been removed in this version of the code */ assert(0); #elif defined(__MIC_NATIVE) if (tr->rateHetModel == CAT) result = evaluatePartialCAT_FLEX(index, ki, tr->td[0].count, tr->td[0].ti, tr->td[0].ti[0].qz[branchReference], tr->partitionData[_model].wgt[index], tr->partitionData[_model].EIGN, tr->partitionData[_model].EI, tr->partitionData[_model].EV, tr->partitionData[_model].tipVector, tr->partitionData[_model].yVector, branchReference, tr->mxtips, states); else assert(0); #else switch(states) { case 2: assert(!tr->saveMemory); assert(tr->rateHetModel == CAT); result = evaluatePartialGTRCAT_BINARY(index, ki, tr->td[0].count, tr->td[0].ti, tr->td[0].ti[0].qz[branchReference], tr->partitionData[_model].wgt[index], tr->partitionData[_model].EIGN, tr->partitionData[_model].EI, tr->partitionData[_model].EV, tr->partitionData[_model].tipVector, tr->partitionData[_model].yVector, branchReference, tr->mxtips); break; case 4: /* DNA */ assert(tr->rateHetModel == CAT); result = evaluatePartialGTRCAT(index, ki, tr->td[0].count, tr->td[0].ti, tr->td[0].ti[0].qz[branchReference], tr->partitionData[_model].wgt[index], tr->partitionData[_model].EIGN, tr->partitionData[_model].EI, tr->partitionData[_model].EV, tr->partitionData[_model].tipVector, tr->partitionData[_model].yVector, branchReference, tr->mxtips); break; case 20: /* proteins */ if(tr->rateHetModel == CAT) result = evaluatePartialGTRCATPROT(index, ki, tr->td[0].count, tr->td[0].ti, tr->td[0].ti[0].qz[branchReference], tr->partitionData[_model].wgt[index], tr->partitionData[_model].EIGN, tr->partitionData[_model].EI, tr->partitionData[_model].EV, tr->partitionData[_model].tipVector, tr->partitionData[_model].yVector, branchReference, tr->mxtips); else result = evaluatePartialGTRGAMMAPROT(index, tr->td[0].count, tr->td[0].ti, tr->td[0].ti[0].qz[branchReference], tr->partitionData[_model].wgt[index], tr->partitionData[_model].EIGN, tr->partitionData[_model].EI, tr->partitionData[_model].EV, tr->partitionData[_model].tipVector, tr->partitionData[_model].yVector, tr->partitionData[_model].gammaRates, branchReference, tr->mxtips); break; default: assert(0); } #endif return result; } #ifdef _OPTIMIZED_FUNCTIONS static inline void computeVectorGTRCAT_BINARY(double *lVector, int *eVector, double ki, int i, double qz, double rz, traversalInfo *ti, double *EIGN, double *EI, double *EV, double *tipVector, unsigned char **yVector, int mxtips) { double d1, d2, ump_x1, ump_x2, x1px2[2], lz1, lz2; double *x1, *x2, *x3; int j, k, pNumber = ti->pNumber, rNumber = ti->rNumber, qNumber = ti->qNumber; x3 = &lVector[2 * (pNumber - mxtips)]; switch(ti->tipCase) { case TIP_TIP: x1 = &(tipVector[2 * yVector[qNumber][i]]); x2 = &(tipVector[2 * yVector[rNumber][i]]); break; case TIP_INNER: x1 = &(tipVector[2 * yVector[qNumber][i]]); x2 = &lVector[2 * (rNumber - mxtips)]; break; case INNER_INNER: x1 = &lVector[2 * (qNumber - mxtips)]; x2 = &lVector[2 * (rNumber - mxtips)]; break; default: assert(0); } lz1 = qz * ki; lz2 = rz * ki; d1 = x1[1] * EXP(EIGN[1] * lz1); d2 = x2[1] * EXP(EIGN[1] * lz2); for(j = 0; j < 2; j++) { ump_x1 = x1[0]; ump_x2 = x2[0]; ump_x1 += d1 * EI[j * 2 + 1]; ump_x2 += d2 * EI[j * 2 + 1]; x1px2[j] = ump_x1 * ump_x2; } for(j = 0; j < 2; j++) x3[j] = 0.0; for(j = 0; j < 2; j++) for(k = 0; k < 2; k++) x3[k] += x1px2[j] * EV[2 * j + k]; if (x3[0] < minlikelihood && x3[0] > minusminlikelihood && x3[1] < minlikelihood && x3[1] > minusminlikelihood ) { x3[0] *= twotothe256; x3[1] *= twotothe256; *eVector = *eVector + 1; } return; } static double evaluatePartialGTRCAT_BINARY(int i, double ki, int counter, traversalInfo *ti, double qz, int w, double *EIGN, double *EI, double *EV, double *tipVector, unsigned char **yVector, int branchReference, int mxtips) { double lz, term; double d; double *x1, *x2; int scale = 0, k; double *lVector = (double *)malloc(sizeof(double) * 2 * mxtips); traversalInfo *trav = &ti[0]; assert(isTip(trav->pNumber, mxtips)); x1 = &(tipVector[2 * yVector[trav->pNumber][i]]); for(k = 1; k < counter; k++) { double qz = ti[k].qz[branchReference], rz = ti[k].rz[branchReference]; qz = (qz > zmin) ? log(qz) : log(zmin); rz = (rz > zmin) ? log(rz) : log(zmin); computeVectorGTRCAT_BINARY(lVector, &scale, ki, i, qz, rz, &ti[k], EIGN, EI, EV, tipVector, yVector, mxtips); } x2 = &lVector[2 * (trav->qNumber - mxtips)]; assert(0 <= (trav->qNumber - mxtips) && (trav->qNumber - mxtips) < mxtips); if(qz < zmin) lz = zmin; lz = log(qz); lz *= ki; d = EXP(EIGN[1] * lz); term = x1[0] * x2[0]; term += x1[1] * x2[1] * d; term = LOG(FABS(term)) + (scale * LOG(minlikelihood)); term = term * w; free(lVector); return term; } static inline void computeVectorGTRCATPROT(double *lVector, int *eVector, double ki, int i, double qz, double rz, traversalInfo *ti, double *EIGN, double *EI, double *EV, double *tipVector, unsigned char **yVector, int mxtips) { double *x1, *x2, *x3; int pNumber = ti->pNumber, rNumber = ti->rNumber, qNumber = ti->qNumber; x3 = &(lVector[20 * (pNumber - mxtips)]); switch(ti->tipCase) { case TIP_TIP: x1 = &(tipVector[20 * yVector[qNumber][i]]); x2 = &(tipVector[20 * yVector[rNumber][i]]); break; case TIP_INNER: x1 = &(tipVector[20 * yVector[qNumber][i]]); x2 = &( lVector[20 * (rNumber - mxtips)]); break; case INNER_INNER: x1 = &(lVector[20 * (qNumber - mxtips)]); x2 = &(lVector[20 * (rNumber - mxtips)]); break; default: assert(0); } { double e1[20] __attribute__ ((aligned (BYTE_ALIGNMENT))), e2[20] __attribute__ ((aligned (BYTE_ALIGNMENT))), d1[20] __attribute__ ((aligned (BYTE_ALIGNMENT))), d2[20] __attribute__ ((aligned (BYTE_ALIGNMENT))), lz1, lz2; int l, k, scale; lz1 = qz * ki; lz2 = rz * ki; e1[0] = 1.0; e2[0] = 1.0; for(l = 1; l < 20; l++) { e1[l] = EXP(EIGN[l] * lz1); e2[l] = EXP(EIGN[l] * lz2); } for(l = 0; l < 20; l+=2) { __m128d d1v = _mm_mul_pd(_mm_load_pd(&x1[l]), _mm_load_pd(&e1[l])); __m128d d2v = _mm_mul_pd(_mm_load_pd(&x2[l]), _mm_load_pd(&e2[l])); _mm_store_pd(&d1[l], d1v); _mm_store_pd(&d2[l], d2v); } __m128d zero = _mm_setzero_pd(); for(l = 0; l < 20; l+=2) _mm_store_pd(&x3[l], zero); for(l = 0; l < 20; l++) { double *ev = &EV[l * 20]; __m128d ump_x1v = _mm_setzero_pd(); __m128d ump_x2v = _mm_setzero_pd(); __m128d x1px2v; for(k = 0; k < 20; k+=2) { __m128d eiv = _mm_load_pd(&EI[20 * l + k]); __m128d d1v = _mm_load_pd(&d1[k]); __m128d d2v = _mm_load_pd(&d2[k]); ump_x1v = _mm_add_pd(ump_x1v, _mm_mul_pd(d1v, eiv)); ump_x2v = _mm_add_pd(ump_x2v, _mm_mul_pd(d2v, eiv)); } ump_x1v = _mm_hadd_pd(ump_x1v, ump_x1v); ump_x2v = _mm_hadd_pd(ump_x2v, ump_x2v); x1px2v = _mm_mul_pd(ump_x1v, ump_x2v); for(k = 0; k < 20; k+=2) { __m128d ex3v = _mm_load_pd(&x3[k]); __m128d EVV = _mm_load_pd(&ev[k]); ex3v = _mm_add_pd(ex3v, _mm_mul_pd(x1px2v, EVV)); _mm_store_pd(&x3[k], ex3v); } } scale = 1; for(l = 0; scale && (l < 20); l++) scale = ((x3[l] < minlikelihood) && (x3[l] > minusminlikelihood)); if(scale) { __m128d twoto = _mm_set_pd(twotothe256, twotothe256); for(l = 0; l < 20; l+=2) { __m128d ex3v = _mm_mul_pd(_mm_load_pd(&x3[l]),twoto); _mm_store_pd(&x3[l], ex3v); } *eVector = *eVector + 1; } return; } } static double evaluatePartialGTRCATPROT(int i, double ki, int counter, traversalInfo *ti, double qz, int w, double *EIGN, double *EI, double *EV, double *tipVector, unsigned char **yVector, int branchReference, int mxtips) { double lz, term; double d[20]; double *x1, *x2; int scale = 0, k, l; double *lVector = (double *)malloc_aligned(sizeof(double) * 20 * mxtips), myEI[400] __attribute__ ((aligned (BYTE_ALIGNMENT))); traversalInfo *trav = &ti[0]; for(k = 0; k < 20; k++) { for(l = 0; l < 20; l++) myEI[k * 20 + l] = EI[k * 20 + l]; } assert(isTip(trav->pNumber, mxtips)); x1 = &(tipVector[20 * yVector[trav->pNumber][i]]); for(k = 1; k < counter; k++) { double qz = ti[k].qz[branchReference], rz = ti[k].rz[branchReference]; qz = (qz > zmin) ? log(qz) : log(zmin); rz = (rz > zmin) ? log(rz) : log(zmin); computeVectorGTRCATPROT(lVector, &scale, ki, i, qz, rz, &ti[k], EIGN, myEI, EV, tipVector, yVector, mxtips); } x2 = &lVector[20 * (trav->qNumber - mxtips)]; assert(0 <= (trav->qNumber - mxtips) && (trav->qNumber - mxtips) < mxtips); if(qz < zmin) lz = zmin; lz = log(qz); lz *= ki; d[0] = 1.0; for(l = 1; l < 20; l++) d[l] = EXP (EIGN[l] * lz); term = 0.0; for(l = 0; l < 20; l++) term += x1[l] * x2[l] * d[l]; term = LOG(FABS(term)) + (scale * LOG(minlikelihood)); term = term * w; free(lVector); return term; } static inline void computeVectorGTRGAMMAPROT(double *lVector, int *eVector, double *gammaRates, int i, double qz, double rz, traversalInfo *ti, double *EIGN, double *EI, double *EV, double *tipVector, unsigned char **yVector, int mxtips) { double *x1, *x2, *x3; int s, pNumber = ti->pNumber, rNumber = ti->rNumber, qNumber = ti->qNumber, index1[4], index2[4]; x3 = &(lVector[80 * (pNumber - mxtips)]); switch(ti->tipCase) { case TIP_TIP: x1 = &(tipVector[20 * yVector[qNumber][i]]); x2 = &(tipVector[20 * yVector[rNumber][i]]); for(s = 0; s < 4; s++) { index1[s] = 0; index2[s] = 0; } break; case TIP_INNER: x1 = &(tipVector[20 * yVector[qNumber][i]]); x2 = &( lVector[80 * (rNumber - mxtips)]); for(s = 0; s < 4; s++) index1[s] = 0; for(s = 0; s < 4; s++) index2[s] = s; break; case INNER_INNER: x1 = &(lVector[80 * (qNumber - mxtips)]); x2 = &(lVector[80 * (rNumber - mxtips)]); for(s = 0; s < 4; s++) { index1[s] = s; index2[s] = s; } break; default: assert(0); } { double e1[20] __attribute__ ((aligned (BYTE_ALIGNMENT))), e2[20] __attribute__ ((aligned (BYTE_ALIGNMENT))), d1[20] __attribute__ ((aligned (BYTE_ALIGNMENT))), d2[20] __attribute__ ((aligned (BYTE_ALIGNMENT))), lz1, lz2; int l, k, scale, j; for(j = 0; j < 4; j++) { lz1 = qz * gammaRates[j]; lz2 = rz * gammaRates[j]; e1[0] = 1.0; e2[0] = 1.0; for(l = 1; l < 20; l++) { e1[l] = EXP(EIGN[l] * lz1); e2[l] = EXP(EIGN[l] * lz2); } for(l = 0; l < 20; l+=2) { __m128d d1v = _mm_mul_pd(_mm_load_pd(&x1[20 * index1[j] + l]), _mm_load_pd(&e1[l])); __m128d d2v = _mm_mul_pd(_mm_load_pd(&x2[20 * index2[j] + l]), _mm_load_pd(&e2[l])); _mm_store_pd(&d1[l], d1v); _mm_store_pd(&d2[l], d2v); } __m128d zero = _mm_setzero_pd(); for(l = 0; l < 20; l+=2) _mm_store_pd(&x3[j * 20 + l], zero); for(l = 0; l < 20; l++) { double *ev = &EV[l * 20]; __m128d ump_x1v = _mm_setzero_pd(); __m128d ump_x2v = _mm_setzero_pd(); __m128d x1px2v; for(k = 0; k < 20; k+=2) { __m128d eiv = _mm_load_pd(&EI[20 * l + k]); __m128d d1v = _mm_load_pd(&d1[k]); __m128d d2v = _mm_load_pd(&d2[k]); ump_x1v = _mm_add_pd(ump_x1v, _mm_mul_pd(d1v, eiv)); ump_x2v = _mm_add_pd(ump_x2v, _mm_mul_pd(d2v, eiv)); } ump_x1v = _mm_hadd_pd(ump_x1v, ump_x1v); ump_x2v = _mm_hadd_pd(ump_x2v, ump_x2v); x1px2v = _mm_mul_pd(ump_x1v, ump_x2v); for(k = 0; k < 20; k+=2) { __m128d ex3v = _mm_load_pd(&x3[j * 20 + k]); __m128d EVV = _mm_load_pd(&ev[k]); ex3v = _mm_add_pd(ex3v, _mm_mul_pd(x1px2v, EVV)); _mm_store_pd(&x3[j * 20 + k], ex3v); } } } scale = 1; for(l = 0; scale && (l < 80); l++) scale = ((x3[l] < minlikelihood) && (x3[l] > minusminlikelihood)); if(scale) { __m128d twoto = _mm_set_pd(twotothe256, twotothe256); for(l = 0; l < 80; l+=2) { __m128d ex3v = _mm_mul_pd(_mm_load_pd(&x3[l]),twoto); _mm_store_pd(&x3[l], ex3v); } *eVector = *eVector + 1; } return; } } static double evaluatePartialGTRGAMMAPROT(int i, int counter, traversalInfo *ti, double qz, int w, double *EIGN, double *EI, double *EV, double *tipVector, unsigned char **yVector, double *gammaRates, int branchReference, int mxtips) { double lz, term; double d[80]; double *x1, *x2; int scale = 0, k, l, j; double *lVector = (double *)malloc_aligned(sizeof(double) * 80 * mxtips), myEI[400] __attribute__ ((aligned (BYTE_ALIGNMENT))); traversalInfo *trav = &ti[0]; for(k = 0; k < 20; k++) { for(l = 0; l < 20; l++) myEI[k * 20 + l] = EI[k * 20 + l]; } assert(isTip(trav->pNumber, mxtips)); x1 = &(tipVector[20 * yVector[trav->pNumber][i]]); for(k = 1; k < counter; k++) { double qz = ti[k].qz[branchReference], rz = ti[k].rz[branchReference]; qz = (qz > zmin) ? log(qz) : log(zmin); rz = (rz > zmin) ? log(rz) : log(zmin); computeVectorGTRGAMMAPROT(lVector, &scale, gammaRates, i, qz, rz, &ti[k], EIGN, myEI, EV, tipVector, yVector, mxtips); } x2 = &lVector[80 * (trav->qNumber - mxtips)]; assert(0 <= (trav->qNumber - mxtips) && (trav->qNumber - mxtips) < mxtips); if(qz < zmin) lz = zmin; lz = log(qz); for(j = 0; j < 4; j++) { d[20 * j] = 1.0; for(l = 1; l < 20; l++) d[20 * j + l] = EXP(EIGN[l] * lz * gammaRates[j]); } for(j = 0, term = 0.0; j < 4; j++) { for(l = 0; l < 20; l++) term += x1[l] * x2[20 * j + l] * d[j * 20 + l]; } term = LOG(0.25 * FABS(term)) + (scale * LOG(minlikelihood)); term = term * w; free(lVector); return term; } static inline void computeVectorGTRCAT(double *lVector, int *eVector, double ki, int i, double qz, double rz, traversalInfo *ti, double *EIGN, double *EI, double *EV, double *tipVector, unsigned char **yVector, int mxtips) { double d1[3], d2[3], ump_x1, ump_x2, x1px2[4], lz1, lz2; double *x1, *x2, *x3; int j, k, pNumber = ti->pNumber, rNumber = ti->rNumber, qNumber = ti->qNumber; x3 = &lVector[4 * (pNumber - mxtips)]; switch(ti->tipCase) { case TIP_TIP: x1 = &(tipVector[4 * yVector[qNumber][i]]); x2 = &(tipVector[4 * yVector[rNumber][i]]); break; case TIP_INNER: x1 = &(tipVector[4 * yVector[qNumber][i]]); x2 = &lVector[4 * (rNumber - mxtips)]; break; case INNER_INNER: x1 = &lVector[4 * (qNumber - mxtips)]; x2 = &lVector[4 * (rNumber - mxtips)]; break; default: assert(0); } lz1 = qz * ki; lz2 = rz * ki; for(j = 0; j < 3; j++) { d1[j] = x1[j + 1] * EXP(EIGN[j + 1] * lz1); d2[j] = x2[j + 1] * EXP(EIGN[j + 1] * lz2); } for(j = 0; j < 4; j++) { ump_x1 = x1[0]; ump_x2 = x2[0]; for(k = 0; k < 3; k++) { ump_x1 += d1[k] * EI[j * 4 + k + 1]; ump_x2 += d2[k] * EI[j * 4 + k + 1]; } x1px2[j] = ump_x1 * ump_x2; } for(j = 0; j < 4; j++) x3[j] = 0.0; for(j = 0; j < 4; j++) for(k = 0; k < 4; k++) x3[k] += x1px2[j] * EV[4 * j + k]; if (x3[0] < minlikelihood && x3[0] > minusminlikelihood && x3[1] < minlikelihood && x3[1] > minusminlikelihood && x3[2] < minlikelihood && x3[2] > minusminlikelihood && x3[3] < minlikelihood && x3[3] > minusminlikelihood) { x3[0] *= twotothe256; x3[1] *= twotothe256; x3[2] *= twotothe256; x3[3] *= twotothe256; *eVector = *eVector + 1; } return; } static double evaluatePartialGTRCAT(int i, double ki, int counter, traversalInfo *ti, double qz, int w, double *EIGN, double *EI, double *EV, double *tipVector, unsigned char **yVector, int branchReference, int mxtips) { double lz, term; double d[3]; double *x1, *x2; int scale = 0, k; double *lVector = (double *)malloc_aligned(sizeof(double) * 4 * mxtips); traversalInfo *trav = &ti[0]; assert(isTip(trav->pNumber, mxtips)); x1 = &(tipVector[4 * yVector[trav->pNumber][i]]); for(k = 1; k < counter; k++) { double qz = ti[k].qz[branchReference], rz = ti[k].rz[branchReference]; qz = (qz > zmin) ? log(qz) : log(zmin); rz = (rz > zmin) ? log(rz) : log(zmin); computeVectorGTRCAT(lVector, &scale, ki, i, qz, rz, &ti[k], EIGN, EI, EV, tipVector, yVector, mxtips); } x2 = &lVector[4 * (trav->qNumber - mxtips)]; assert(0 <= (trav->qNumber - mxtips) && (trav->qNumber - mxtips) < mxtips); if(qz < zmin) lz = zmin; lz = log(qz); lz *= ki; d[0] = EXP (EIGN[1] * lz); d[1] = EXP (EIGN[2] * lz); d[2] = EXP (EIGN[3] * lz); term = x1[0] * x2[0]; term += x1[1] * x2[1] * d[0]; term += x1[2] * x2[2] * d[1]; term += x1[3] * x2[3] * d[2]; term = LOG(FABS(term)) + (scale * LOG(minlikelihood)); term = term * w; free(lVector); return term; } #endif ExaML-3.0.21/examl/globalVariables.h000066400000000000000000000204771330315463200171250ustar00rootroot00000000000000/* RAxML-VI-HPC (version 2.2) a program for sequential and parallel estimation of phylogenetic trees * Copyright August 2006 by Alexandros Stamatakis * * Partially derived from * fastDNAml, a program for estimation of phylogenetic trees from sequences by Gary J. Olsen * * and * * Programs of the PHYLIP package by Joe Felsenstein. * * This program is free software; you may redistribute it and/or modify its * 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. * * * For any other enquiries send an Email to Alexandros Stamatakis * Alexandros.Stamatakis@epfl.ch * * When publishing work that is based on the results from RAxML-VI-HPC please cite: * * Alexandros Stamatakis:"RAxML-VI-HPC: maximum likelihood-based phylogenetic analyses with thousands of taxa and mixed models". * Bioinformatics 2006; doi: 10.1093/bioinformatics/btl446 */ int processes; double *globalResult; int processID; infoList iList; int Thorough = 0; checkPointState ckp; char run_id[128] = "", workdir[1024] = "", seq_file[1024] = "", tree_file[1024]="", weightFileName[1024] = "", resultFileName[1024] = "", logFileName[1024] = "", infoFileName[1024] = "", randomFileName[1024] = "", proteinModelFileName[1024] = "", binaryCheckpointName[1024] = "", binaryCheckpointInputName[1024] = "", byteFileName[1024] = "", modelFileName[1024] = "", treeFileName[1024] = "", quartetGroupingFileName[1024], quartetFileName[1024]; char *protModels[NUM_PROT_MODELS] = {"DAYHOFF", "DCMUT", "JTT", "MTREV", "WAG", "RTREV", "CPREV", "VT", "BLOSUM62", "MTMAM", "LG", "MTART", "MTZOA", "PMB", "HIVB", "HIVW", "JTTDCMUT", "FLU", "STMTREV", "AUTO", "LG4M", "LG4X", "GTR"}; const char inverseMeaningBINARY[4] = {'_', '0', '1', '-'}; const char inverseMeaningDNA[16] = {'_', 'A', 'C', 'M', 'G', 'R', 'S', 'V', 'T', 'W', 'Y', 'H', 'K', 'D', 'B', '-'}; const char inverseMeaningPROT[23] = {'A','R', 'N', 'D', 'C', 'Q', 'E', 'G', 'H', 'I', 'L', 'K', 'M', 'F', 'P', 'S', 'T', 'W', 'Y', 'V', 'B', 'Z', '-'}; const char inverseMeaningGeneric32[33] = {'0', '1', '2', '3', '4', '5', '6', '7', '8', '9', 'A', 'B', 'C', 'D', 'E', 'F', 'G', 'H', 'I', 'J', 'K', 'L', 'M', 'N', 'O', 'P', 'Q', 'R', 'S', 'T', 'U', 'V', '-'}; const char inverseMeaningGeneric64[33] = {'0', '1', '2', '3', '4', '5', '6', '7', '8', '9', 'A', 'B', 'C', 'D', 'E', 'F', 'G', 'H', 'I', 'J', 'K', 'L', 'M', 'N', 'O', 'P', 'Q', 'R', 'S', 'T', 'U', 'V', '-'}; const unsigned int bitVectorIdentity[256] = {0 ,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 ,65 ,66 ,67 ,68 ,69 ,70 ,71 ,72 ,73 ,74 ,75 ,76 , 77 ,78 ,79 ,80 ,81 ,82 ,83 ,84 ,85 ,86 ,87 ,88 ,89 ,90 ,91 ,92 ,93 ,94 ,95 ,96 ,97 ,98 ,99 ,100 ,101 , 102 ,103 ,104 ,105 ,106 ,107 ,108 ,109 ,110 ,111 ,112 ,113 ,114 ,115 ,116 ,117 ,118 ,119 ,120 ,121 ,122 , 123 ,124 ,125 ,126 ,127 ,128 ,129 ,130 ,131 ,132 ,133 ,134 ,135 ,136 ,137 ,138 ,139 ,140 ,141 ,142 ,143 , 144 ,145 ,146 ,147 ,148 ,149 ,150 ,151 ,152 ,153 ,154 ,155 ,156 ,157 ,158 ,159 ,160 ,161 ,162 ,163 ,164 , 165 ,166 ,167 ,168 ,169 ,170 ,171 ,172 ,173 ,174 ,175 ,176 ,177 ,178 ,179 ,180 ,181 ,182 ,183 ,184 ,185 , 186 ,187 ,188 ,189 ,190 ,191 ,192 ,193 ,194 ,195 ,196 ,197 ,198 ,199 ,200 ,201 ,202 ,203 ,204 ,205 ,206 , 207 ,208 ,209 ,210 ,211 ,212 ,213 ,214 ,215 ,216 ,217 ,218 ,219 ,220 ,221 ,222 ,223 ,224 ,225 ,226 ,227 , 228 ,229 ,230 ,231 ,232 ,233 ,234 ,235 ,236 ,237 ,238 ,239 ,240 ,241 ,242 ,243 ,244 ,245 ,246 ,247 ,248 , 249 ,250 ,251 ,252 ,253 ,254 ,255}; const unsigned int bitVectorAA[23] = {1, 2, 4, 8, 16, 32, 64, 128, 256, 512, 1024, 2048, 4096, 8192, 16384, 32768, 65536, 131072, 262144, 524288, 12 /* N | D */, 96 /*Q | E*/, 1048575 /* - */}; const unsigned int bitVectorSecondary[256] = {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 0, 16, 32, 48, 64, 80, 96, 112, 128, 144, 160, 176, 192, 208, 224, 240, 0, 17, 34, 51, 68, 85, 102, 119, 136, 153, 170, 187, 204, 221, 238, 255, 0, 256, 512, 768, 1024, 1280, 1536, 1792, 2048, 2304, 2560, 2816, 3072, 3328, 3584, 3840, 0, 257, 514, 771, 1028, 1285, 1542, 1799, 2056, 2313, 2570, 2827, 3084, 3341, 3598, 3855, 0, 272, 544, 816, 1088, 1360, 1632, 1904, 2176, 2448, 2720, 2992, 3264, 3536, 3808, 4080, 0, 273, 546, 819, 1092, 1365, 1638, 1911, 2184, 2457, 2730, 3003, 3276, 3549, 3822, 4095, 0, 4096, 8192, 12288, 16384, 20480, 24576, 28672, 32768, 36864, 40960, 45056, 49152, 53248, 57344, 61440, 0, 4097, 8194, 12291, 16388, 20485, 24582, 28679, 32776, 36873, 40970, 45067, 49164, 53261, 57358, 61455, 0, 4112, 8224, 12336, 16448, 20560, 24672, 28784, 32896, 37008, 41120, 45232, 49344, 53456, 57568, 61680, 0, 4113, 8226, 12339, 16452, 20565, 24678, 28791, 32904, 37017, 41130, 45243, 49356, 53469, 57582, 61695, 0, 4352, 8704, 13056, 17408, 21760, 26112, 30464, 34816, 39168, 43520, 47872, 52224, 56576, 60928, 65280, 0, 4353, 8706, 13059, 17412, 21765, 26118, 30471, 34824, 39177, 43530, 47883, 52236, 56589, 60942, 65295, 0, 4368, 8736, 13104, 17472, 21840, 26208, 30576, 34944, 39312, 43680, 48048, 52416, 56784, 61152, 65520, 0, 4369, 8738, 13107, 17476, 21845, 26214, 30583, 34952, 39321, 43690, 48059, 52428, 56797, 61166, 65535}; const unsigned int bitVector32[33] = {1, 2, 4, 8, 16, 32, 64, 128, 256, 512, 1024, 2048, 4096, 8192, 16384, 32768, 65536, 131072, 262144, 524288, 1048576, 2097152, 4194304, 8388608, 16777216, 33554432, 67108864, 134217728, 268435456, 536870912, 1073741824, 2147483648u, 4294967295u}; /*const unsigned int bitVector64[65] = {};*/ const unsigned int mask32[32] = {1, 2, 4, 8, 16, 32, 64, 128, 256, 512, 1024, 2048, 4096, 8192, 16384, 32768, 65536, 131072, 262144, 524288, 1048576, 2097152, 4194304, 8388608, 16777216, 33554432, 67108864, 134217728, 268435456, 536870912, 1073741824, 2147483648U}; const char *secondaryModelList[21] = { "S6A (GTR)", "S6B", "S6C", "S6D", "S6E", "S7A (GTR)", "S7B", "S7C", "S7D", "S7E", "S7F", "S16 (GTR)", "S16A", "S16B", "S16C", "S16D", "S16E", "S16F", "S16I", "S16J", "S16K"}; double masterTime; double accumulatedTime; int optimizeRateCategoryInvocations = 1; partitionLengths pLengths[MAX_MODEL] = { /* BINARY */ //{4, 4, 2, 4, 2, 1, 2, 8, 2, 2, FALSE, 3, inverseMeaningBINARY, 2, FALSE, bitVectorIdentity}, //eiLength changed from 2 -> 4 {4, 4, 2, 4, 4, 1, 2, 8, 2, 2, FALSE, 3, inverseMeaningBINARY, 2, FALSE, bitVectorIdentity}, /* DNA */ {16, 16, 4, 16, 16, 6, 4, 64, 6, 4, FALSE, 15, inverseMeaningDNA, 4, FALSE, bitVectorIdentity}, /* AA */ {400, 400, 20, 400, 400, 190, 20, 460, 190, 20, FALSE, 22, inverseMeaningPROT, 20, TRUE, bitVectorAA}, /* SECONDARY_DATA */ {256, 256, 16, 256, 256, 120, 16, 4096, 120, 16, FALSE, 255, (char*)NULL, 16, TRUE, bitVectorSecondary}, /* SECONDARY_DATA_6 */ {36, 36, 6, 36, 36, 15, 6, 384, 15, 6, FALSE, 63, (char*)NULL, 6, TRUE, bitVectorIdentity}, /* SECONDARY_DATA_7 */ {49, 49, 7, 49, 49, 21, 7, 896, 21, 7, FALSE, 127, (char*)NULL, 7, TRUE, bitVectorIdentity}, /* 32 states */ {1024, 1024, 32, 1024, 1024, 496, 32, 1056, 496, 32, FALSE, 32, inverseMeaningGeneric32, 32, TRUE, bitVector32}, /* 64 states */ {4096, 4096, 64, 4096, 4096, 2016, 64, 4160, 64, 2016, FALSE, 64, (char*)NULL, 64, TRUE, (unsigned int*)NULL} }; partitionLengths pLength; ExaML-3.0.21/examl/makenewzGenericSpecial.c000066400000000000000000002146041330315463200204430ustar00rootroot00000000000000/* RAxML-VI-HPC (version 2.2) a program for sequential and parallel estimation of phylogenetic trees * Copyright August 2006 by Alexandros Stamatakis * * Partially derived from * fastDNAml, a program for estimation of phylogenetic trees from sequences by Gary J. Olsen * * and * * Programs of the PHYLIP package by Joe Felsenstein. * * This program is free software; you may redistribute it and/or modify its * 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. * * * For any other enquiries send an Email to Alexandros Stamatakis * Alexandros.Stamatakis@epfl.ch * * When publishing work that is based on the results from RAxML-VI-HPC please cite: * * Alexandros Stamatakis:"RAxML-VI-HPC: maximum likelihood-based phylogenetic analyses with * thousands of taxa and mixed models". * Bioinformatics 2006; doi: 10.1093/bioinformatics/btl446 */ #ifndef WIN32 #include #endif #include #include #include #include #include #include #include "axml.h" #ifdef __SIM_SSE3 #include #include /*#include */ #endif /* includes MIC-optimized functions */ #ifdef __MIC_NATIVE #include "mic_native.h" #endif /* pointers to reduction buffers for storing and gathering the first and second derivative of the likelihood in Pthreads and MPI */ extern int processID; extern const unsigned int mask32[32]; /*******************/ /* generic function to get the required pointers to the data associated with the left and right node that define a branch */ static void getVects(tree *tr, unsigned char **tipX1, unsigned char **tipX2, double **x1_start, double **x2_start, int *tipCase, int model, double **x1_gapColumn, double **x2_gapColumn, unsigned int **x1_gap, unsigned int **x2_gap, size_t offset) { int rateHet = (int)discreteRateCategories(tr->rateHetModel), states = tr->partitionData[model].states, span = rateHet * states; size_t x_offset = offset * (size_t)span; int pNumber, qNumber; /* get the left and right node number of the nodes defining the branch we want to optimize */ pNumber = tr->td[0].ti[0].pNumber; qNumber = tr->td[0].ti[0].qNumber; /* initialize to NULL */ *x1_start = (double*)NULL, *x2_start = (double*)NULL; *tipX1 = (unsigned char*)NULL, *tipX2 = (unsigned char*)NULL; /* switch over the different tip cases again here */ if(isTip(pNumber, tr->mxtips) || isTip(qNumber, tr->mxtips)) { if(!( isTip(pNumber, tr->mxtips) && isTip(qNumber, tr->mxtips)) ) { *tipCase = TIP_INNER; if(isTip(qNumber, tr->mxtips)) { *tipX1 = tr->partitionData[model].yVector[qNumber] + offset; *x2_start = tr->partitionData[model].xVector[pNumber - tr->mxtips - 1] + x_offset; if(tr->saveMemory) { *x2_gap = &(tr->partitionData[model].gapVector[pNumber * tr->partitionData[model].gapVectorLength]); *x2_gapColumn = &tr->partitionData[model].gapColumn[(pNumber - tr->mxtips - 1) * states * rateHet]; } } else { *tipX1 = tr->partitionData[model].yVector[pNumber] + offset; *x2_start = tr->partitionData[model].xVector[qNumber - tr->mxtips - 1] + x_offset; if(tr->saveMemory) { *x2_gap = &(tr->partitionData[model].gapVector[qNumber * tr->partitionData[model].gapVectorLength]); *x2_gapColumn = &tr->partitionData[model].gapColumn[(qNumber - tr->mxtips - 1) * states * rateHet]; } } } else { /* note that tip tip should normally not occur since this means that we are trying to optimize a branch in a two-taxon tree. However, this has been inherited be some RAxML function that optimized pair-wise distances between all taxa in a tree */ *tipCase = TIP_TIP; *tipX1 = tr->partitionData[model].yVector[pNumber] + offset; *tipX2 = tr->partitionData[model].yVector[qNumber] + offset; } } else { *tipCase = INNER_INNER; *x1_start = tr->partitionData[model].xVector[pNumber - tr->mxtips - 1] + x_offset; *x2_start = tr->partitionData[model].xVector[qNumber - tr->mxtips - 1] + x_offset; if(tr->saveMemory) { *x1_gap = &(tr->partitionData[model].gapVector[pNumber * tr->partitionData[model].gapVectorLength]); *x1_gapColumn = &tr->partitionData[model].gapColumn[(pNumber - tr->mxtips - 1) * states * rateHet]; *x2_gap = &(tr->partitionData[model].gapVector[qNumber * tr->partitionData[model].gapVectorLength]); *x2_gapColumn = &tr->partitionData[model].gapColumn[(qNumber - tr->mxtips - 1) * states * rateHet]; } } } /* this is actually a pre-computation and storage of values that remain constant while we change the value of the branch length we want to adapt. the target pointer sumtable is a single pre-allocated array that has the same size as a conditional likelihood vector at an inner node. So if we want to do a Newton-Rpahson optimization we only execute this function once in the beginning for each new branch we are considering ! */ #ifndef _OPTIMIZED_FUNCTIONS static void sumCAT_FLEX(int tipCase, double *sumtable, double *x1, double *x2, double *tipVector, unsigned char *tipX1, unsigned char *tipX2, int n, const int states) { int i, l; double *sum, *left, *right; switch(tipCase) { /* switch over possible configurations of the nodes p and q defining the branch */ case TIP_TIP: for (i = 0; i < n; i++) { left = &(tipVector[states * tipX1[i]]); right = &(tipVector[states * tipX2[i]]); sum = &sumtable[states * i]; /* just multiply the values with each other for each site, note the similarity with evaluate() we precompute the product which will remain constant and then just multiply this pre-computed product with the changing P matrix exponentaions that depend on the branch lengths */ for(l = 0; l < states; l++) sum[l] = left[l] * right[l]; } break; case TIP_INNER: /* same as for TIP_TIP only that we now access on tip vector and one inner vector. You may also observe that we do not consider using scaling vectors anywhere here. This is because we are interested in the first and second derivatives of the likelihood and hence the addition of the log() of the scaling factor times the number of scaling events becomes obsolete through the derivative */ for (i = 0; i < n; i++) { left = &(tipVector[states * tipX1[i]]); right = &x2[states * i]; sum = &sumtable[states * i]; for(l = 0; l < states; l++) sum[l] = left[l] * right[l]; } break; case INNER_INNER: for (i = 0; i < n; i++) { left = &x1[states * i]; right = &x2[states * i]; sum = &sumtable[states * i]; for(l = 0; l < states; l++) sum[l] = left[l] * right[l]; } break; default: assert(0); } } /* same thing for GAMMA models. The only noteworthy thing here is that we have an additional inner loop over the number of discrete gamma rates. The data access pattern is also different since for tip vector accesses through our lookup table, we do not distnguish between rates Note the different access pattern in TIP_INNER: left = &(tipVector[states * tipX1[i]]); right = &(x2[span * i + l * states]); */ static void sumGAMMA_FLEX(int tipCase, double *sumtable, double *x1, double *x2, double *tipVector, unsigned char *tipX1, unsigned char *tipX2, int n, const int states) { int i, l, k; const int span = 4 * states; double *left, *right, *sum; switch(tipCase) { case TIP_TIP: for(i = 0; i < n; i++) { left = &(tipVector[states * tipX1[i]]); right = &(tipVector[states * tipX2[i]]); for(l = 0; l < 4; l++) { sum = &sumtable[i * span + l * states]; for(k = 0; k < states; k++) sum[k] = left[k] * right[k]; } } break; case TIP_INNER: for(i = 0; i < n; i++) { left = &(tipVector[states * tipX1[i]]); for(l = 0; l < 4; l++) { right = &(x2[span * i + l * states]); sum = &sumtable[i * span + l * states]; for(k = 0; k < states; k++) sum[k] = left[k] * right[k]; } } break; case INNER_INNER: for(i = 0; i < n; i++) { for(l = 0; l < 4; l++) { left = &(x1[span * i + l * states]); right = &(x2[span * i + l * states]); sum = &(sumtable[i * span + l * states]); for(k = 0; k < states; k++) sum[k] = left[k] * right[k]; } } break; default: assert(0); } } #endif /* optimized functions for branch length optimization */ #ifdef _OPTIMIZED_FUNCTIONS static void sumGAMMA_BINARY(int tipCase, double *sumtable, double *x1_start, double *x2_start, double *tipVector, unsigned char *tipX1, unsigned char *tipX2, int n); static void coreGTRGAMMA_BINARY(const int upper, double *sumtable, volatile double *d1, volatile double *d2, double *EIGN, double *gammaRates, double lz, int *wrptr); static void coreGTRCAT_BINARY(int upper, int numberOfCategories, double *sum, volatile double *d1, volatile double *d2, double *rptr, double *EIGN, int *cptr, double lz, int *wgt); static void sumCAT_BINARY(int tipCase, double *sum, double *x1_start, double *x2_start, double *tipVector, unsigned char *tipX1, unsigned char *tipX2, int n); static void sumCAT_SAVE(int tipCase, double *sum, double *x1_start, double *x2_start, double *tipVector, unsigned char *tipX1, unsigned char *tipX2, int n, double *x1_gapColumn, double *x2_gapColumn, unsigned int *x1_gap, unsigned int *x2_gap); static void sumGAMMA_GAPPED_SAVE(int tipCase, double *sumtable, double *x1_start, double *x2_start, double *tipVector, unsigned char *tipX1, unsigned char *tipX2, int n, double *x1_gapColumn, double *x2_gapColumn, unsigned int *x1_gap, unsigned int *x2_gap); static void sumGAMMA(int tipCase, double *sumtable, double *x1_start, double *x2_start, double *tipVector, unsigned char *tipX1, unsigned char *tipX2, int n); static void sumCAT(int tipCase, double *sum, double *x1_start, double *x2_start, double *tipVector, unsigned char *tipX1, unsigned char *tipX2, int n); static void sumGAMMAPROT_GAPPED_SAVE(int tipCase, double *sumtable, double *x1, double *x2, double *tipVector, unsigned char *tipX1, unsigned char *tipX2, int n, double *x1_gapColumn, double *x2_gapColumn, unsigned int *x1_gap, unsigned int *x2_gap); static void sumGAMMAPROT_LG4(int tipCase, double *sumtable, double *x1, double *x2, double *tipVector[4], unsigned char *tipX1, unsigned char *tipX2, int n); static void sumGAMMAPROT(int tipCase, double *sumtable, double *x1, double *x2, double *tipVector, unsigned char *tipX1, unsigned char *tipX2, int n); static void sumGTRCATPROT(int tipCase, double *sumtable, double *x1, double *x2, double *tipVector, unsigned char *tipX1, unsigned char *tipX2, int n); static void sumGTRCATPROT_SAVE(int tipCase, double *sumtable, double *x1, double *x2, double *tipVector, unsigned char *tipX1, unsigned char *tipX2, int n, double *x1_gapColumn, double *x2_gapColumn, unsigned int *x1_gap, unsigned int *x2_gap); static void coreGTRGAMMAPROT_LG4(double *gammaRates, double *EIGN[4], double *sumtable, int upper, int *wrptr, volatile double *ext_dlnLdlz, volatile double *ext_d2lnLdlz2, double lz, double *weights); static void coreGTRGAMMA(const int upper, double *sumtable, volatile double *ext_dlnLdlz, volatile double *ext_d2lnLdlz2, double *EIGN, double *gammaRates, double lz, int *wgt); static void coreGTRCAT(int upper, int numberOfCategories, double *sum, volatile double *d1, volatile double *d2, int *wgt, double *rptr, double *EIGN, int *cptr, double lz); static void coreGTRGAMMAPROT(double *gammaRates, double *EIGN, double *sumtable, int upper, int *wgt, volatile double *ext_dlnLdlz, volatile double *ext_d2lnLdlz2, double lz); static void coreGTRCATPROT(double *EIGN, double lz, int numberOfCategories, double *rptr, int *cptr, int upper, int *wgt, volatile double *ext_dlnLdlz, volatile double *ext_d2lnLdlz2, double *sumtable); #endif #ifndef _OPTIMIZED_FUNCTIONS /* now this is the core function of the newton-Raphson based branch length optimization that actually computes the first and second derivative of the likelihood given a new proposed branch length lz */ static void coreCAT_FLEX(int upper, int numberOfCategories, double *sum, volatile double *d1, volatile double *d2, int *wgt, double *rptr, double *EIGN, int *cptr, double lz, const int states) { int i, l; double *d, /* arrays to store stuff we can pre-compute */ *d_start = (double *)malloc_aligned(numberOfCategories * states * sizeof(double)), *e =(double *)malloc_aligned(states * sizeof(double)), *s = (double *)malloc_aligned(states * sizeof(double)), *dd = (double *)malloc_aligned(states * sizeof(double)), inv_Li, dlnLidlz, d2lnLidlz2, dlnLdlz = 0.0, d2lnLdlz2 = 0.0; d = d_start; e[0] = 0.0; s[0] = 0.0; dd[0] = 0.0; /* we are pre-computing values for computing the first and second derivative of P(lz) since this requires an exponetial that the only thing we really have to derive here */ for(l = 1; l < states; l++) { s[l] = EIGN[l]; e[l] = EIGN[l] * EIGN[l]; dd[l] = s[l] * lz; } /* compute the P matrices and their derivatives for all per-site rate categories */ for(i = 0; i < numberOfCategories; i++) { d[states * i] = 1.0; for(l = 1; l < states; l++) d[states * i + l] = EXP(dd[l] * rptr[i]); } /* now loop over the sites in this partition to obtain the per-site 1st and 2nd derivatives */ for (i = 0; i < upper; i++) { double r = rptr[cptr[i]], wr1 = r * wgt[i], wr2 = r * r * wgt[i]; /* get the correct p matrix for the rate at the current site i */ d = &d_start[states * cptr[i]]; /* this is the likelihood at site i, NOT the log likelihood, we don't need the log likelihood to compute derivatives ! */ inv_Li = sum[states * i]; /* those are for storing the first and second derivative of the Likelihood at site i */ dlnLidlz = 0.0; d2lnLidlz2 = 0.0; /* now multiply the likelihood and the first and second derivative with the appropriate derivatives of P(lz) */ for(l = 1; l < states; l++) { double tmpv = d[l] * sum[states * i + l]; inv_Li += tmpv; dlnLidlz += tmpv * s[l]; d2lnLidlz2 += tmpv * e[l]; } /* below we are implementing the other mathematical operations that are required to obtain the deirivatives */ inv_Li = 1.0/ FABS(inv_Li); dlnLidlz *= inv_Li; d2lnLidlz2 *= inv_Li; /* under the CAT model, wrptr[] and wr2ptr[] are pre-computed extension sof the weight pointer: wrptr[i] = wgt[i] * rptr[cptr[i]]. and wr2ptr[i] = wgt[i] * rptr[cptr[i]] * rptr[cptr[i]] this is also something that is required for the derivatives because when computing the derivative of the exponential() the rate must be multiplied with the exponential wgt is just the pattern site wieght */ /* compute the accumulated first and second derivatives of this site */ dlnLdlz += wr1 * dlnLidlz; d2lnLdlz2 += wr2 * (d2lnLidlz2 - dlnLidlz * dlnLidlz); } /* set the result values, i.e., the sum of the per-site first and second derivatives of the likelihood function for this partition. */ *d1 = dlnLdlz; *d2 = d2lnLdlz2; /* free the temporary arrays */ free(d_start); free(e); free(s); free(dd); } static void coreGAMMA_FLEX(int upper, double *sumtable, volatile double *ext_dlnLdlz, volatile double *ext_d2lnLdlz2, double *EIGN, double *gammaRates, double lz, int *wgt, const int states) { double *sum, diagptable[1024], /* TODO make this dynamic */ dlnLdlz = 0.0, d2lnLdlz2 = 0.0, ki, kisqr, tmp, inv_Li, dlnLidlz, d2lnLidlz2; int i, j, l; const int gammaStates = 4 * states; /* pre-compute the derivatives of the P matrix for all discrete GAMMA rates */ for(i = 0; i < 4; i++) { ki = gammaRates[i]; kisqr = ki * ki; for(l = 1; l < states; l++) { diagptable[i * gammaStates + l * 4] = EXP(EIGN[l] * ki * lz); diagptable[i * gammaStates + l * 4 + 1] = EIGN[l] * ki; diagptable[i * gammaStates + l * 4 + 2] = EIGN[l] * EIGN[l] * kisqr; } } /* loop over sites in this partition */ for (i = 0; i < upper; i++) { double r = rptr[cptr[i]], wr1 = r * wgt[i], wr2 = r * r * wgt[i]; /* access the array with pre-computed values */ sum = &sumtable[i * gammaStates]; /* initial per-site likelihood and 1st and 2nd derivatives */ inv_Li = 0.0; dlnLidlz = 0.0; d2lnLidlz2 = 0.0; /* loop over discrete GAMMA rates */ for(j = 0; j < 4; j++) { inv_Li += sum[j * states]; for(l = 1; l < states; l++) { inv_Li += (tmp = diagptable[j * gammaStates + l * 4] * sum[j * states + l]); dlnLidlz += tmp * diagptable[j * gammaStates + l * 4 + 1]; d2lnLidlz2 += tmp * diagptable[j * gammaStates + l * 4 + 2]; } } /* finalize derivative computation */ /* note that wrptr[] here unlike in CAT above is the integer weight vector of the current site The operations: EIGN[l] * ki; EIGN[l] * EIGN[l] * kisqr; that are hidden in CAT in wrptr (at least the * ki and * ki *ki part of them are done explicitely here */ inv_Li = 1.0 / FABS(inv_Li); dlnLidlz *= inv_Li; d2lnLidlz2 *= inv_Li; dlnLdlz += wr1 * dlnLidlz; d2lnLdlz2 += wr2 * (d2lnLidlz2 - dlnLidlz * dlnLidlz); } *ext_dlnLdlz = dlnLdlz; *ext_d2lnLdlz2 = d2lnLdlz2; } #endif /* the function below is called only once at the very beginning of each Newton-Raphson procedure for optimizing barnch lengths. It initially invokes an iterative newview call to get a consistent pair of vectors at the left and the right end of the branch and thereafter invokes the one-time only precomputation of values (sumtable) that can be re-used in each Newton-Raphson iteration. Once this function has been called we can execute the actual NR procedure */ void makenewzIterative(tree *tr) { /* call newvieIterative to get the likelihood arrays to the left and right of the branch */ newviewIterative(tr, 1); /* loop over all partoitions to do the precomputation of the sumTable buffer This is analogous to the newviewIterative() and evaluateIterative() implementations. */ #ifdef _USE_OMP #pragma omp parallel #endif { int m, model, maxModel, tipCase; #ifdef _USE_OMP maxModel = tr->maxModelsPerThread; #else maxModel = tr->NumberOfModels; #endif double *x1_start = (double*)NULL, *x2_start = (double*)NULL; unsigned char *tipX1, *tipX2; double *x1_gapColumn = (double*)NULL, *x2_gapColumn = (double*)NULL; unsigned int *x1_gap = (unsigned int*)NULL, *x2_gap = (unsigned int*)NULL; for(m = 0; m < maxModel; m++) { size_t width = 0, offset = 0; #ifdef _USE_OMP int tid = omp_get_thread_num(); /* check if this thread should process this partition */ Assign* pAss = tr->threadPartAssigns[tid * tr->maxModelsPerThread + m]; if(pAss) { model = pAss->partitionId; width = pAss->width; offset = pAss->offset; } else break; #else model = m; /* number of sites in this partition */ width = (size_t)tr->partitionData[model].width; offset = 0; #endif if(tr->td[0].executeModel[model] && width > 0) { int rateHet = (int)discreteRateCategories(tr->rateHetModel), /* get the number of states in the partition, e.g.: 4 = DNA, 20 = Protein */ states = tr->partitionData[model].states, /* span for single alignment site (in doubles!) */ span = rateHet * states; size_t /* offset for current thread's data in global xVector (in doubles!) */ x_offset = offset * (size_t)span; getVects(tr, &tipX1, &tipX2, &x1_start, &x2_start, &tipCase, model, &x1_gapColumn, &x2_gapColumn, &x1_gap, &x2_gap, offset); double *sumBuffer = tr->partitionData[model].sumBuffer + x_offset; #ifndef _OPTIMIZED_FUNCTIONS assert(!tr->saveMemory); if(tr->rateHetModel == CAT) sumCAT_FLEX(tipCase, sumBuffer, x1_start, x2_start, tr->partitionData[model].tipVector, tipX1, tipX2, width, states); else sumGAMMA_FLEX(tipCase, sumBuffer, x1_start, x2_start, tr->partitionData[model].tipVector, tipX1, tipX2, width, states); #else switch(states) { case 2: #ifdef __MIC_NATIVE assert(0 && "Binary data model is not implemented on Intel MIC"); #else assert(!tr->saveMemory); if(tr->rateHetModel == CAT) sumCAT_BINARY(tipCase, sumBuffer, x1_start, x2_start, tr->partitionData[model].tipVector, tipX1, tipX2, width); else sumGAMMA_BINARY(tipCase, sumBuffer, x1_start, x2_start, tr->partitionData[model].tipVector, tipX1, tipX2, width); #endif break; case 4: /* DNA */ if(tr->rateHetModel == CAT) { if(tr->saveMemory) #ifdef __MIC_NATIVE assert(0 && "Neither CAT model of rate heterogeneity nor memory saving are implemented on Intel MIC"); #else sumCAT_SAVE(tipCase, sumBuffer, x1_start, x2_start, tr->partitionData[model].tipVector, tipX1, tipX2, width, x1_gapColumn, x2_gapColumn, x1_gap, x2_gap); #endif else #ifdef __MIC_NATIVE assert(0 && "CAT model of rate heterogeneity is not implemented on Intel MIC"); #else sumCAT(tipCase, sumBuffer, x1_start, x2_start, tr->partitionData[model].tipVector, tipX1, tipX2, width); #endif } else { if(tr->saveMemory) #ifdef __MIC_NATIVE assert(0 && "Memory saving is not implemented on Intel MIC"); #else sumGAMMA_GAPPED_SAVE(tipCase, sumBuffer, x1_start, x2_start, tr->partitionData[model].tipVector, tipX1, tipX2, width, x1_gapColumn, x2_gapColumn, x1_gap, x2_gap); #endif else #ifdef __MIC_NATIVE sumGAMMA_MIC(tipCase, sumBuffer, x1_start, x2_start, tr->partitionData[model].mic_tipVector, tipX1, tipX2, width); #else sumGAMMA(tipCase, sumBuffer, x1_start, x2_start, tr->partitionData[model].tipVector, tipX1, tipX2, width); #endif } break; case 20: /* proteins */ if(tr->rateHetModel == CAT) { if(tr->saveMemory) #ifdef __MIC_NATIVE assert(0 && "Neither CAT model of rate heterogeneity nor memory saving are implemented on Intel MIC"); #else sumGTRCATPROT_SAVE(tipCase, sumBuffer, x1_start, x2_start, tr->partitionData[model].tipVector, tipX1, tipX2, width, x1_gapColumn, x2_gapColumn, x1_gap, x2_gap); #endif else #ifdef __MIC_NATIVE assert(0 && "CAT model of rate heterogeneity is not implemented on Intel MIC"); #else sumGTRCATPROT(tipCase, sumBuffer, x1_start, x2_start, tr->partitionData[model].tipVector, tipX1, tipX2, width); #endif } else { if(tr->saveMemory) #ifdef __MIC_NATIVE assert(0 && "Memory saving is not implemented on Intel MIC"); #else sumGAMMAPROT_GAPPED_SAVE(tipCase, sumBuffer, x1_start, x2_start, tr->partitionData[model].tipVector, tipX1, tipX2, width, x1_gapColumn, x2_gapColumn, x1_gap, x2_gap); #endif else { if(tr->partitionData[model].protModels == LG4M || tr->partitionData[model].protModels == LG4X) #ifdef __MIC_NATIVE sumGAMMAPROT_LG4_MIC(tipCase, sumBuffer, x1_start, x2_start, tr->partitionData[model].mic_tipVector, tipX1, tipX2, width); #else sumGAMMAPROT_LG4(tipCase, sumBuffer, x1_start, x2_start, tr->partitionData[model].tipVector_LG4, tipX1, tipX2, width); #endif else #ifdef __MIC_NATIVE sumGAMMAPROT_MIC(tipCase, sumBuffer, x1_start, x2_start, tr->partitionData[model].mic_tipVector, tipX1, tipX2, width); #else sumGAMMAPROT(tipCase, sumBuffer, x1_start, x2_start, tr->partitionData[model].tipVector, tipX1, tipX2, width); #endif } } break; default: assert(0); } #endif } } // for model } // omp parallel region } /* this function actually computes the first and second derivatives of the likelihood for a given branch stored in tr->coreLZ[model] Note that in the parallel case coreLZ must always be broadcasted together with the traversal descriptor, at least for optimizing branch lengths */ void execCore(tree *tr, volatile double *_dlnLdlz, volatile double *_d2lnLdlz2) { #ifdef _USE_OMP #pragma omp parallel #endif { int m, model, maxModel, branchIndex; #ifdef _USE_OMP int tid = omp_get_thread_num(), nModels = (tr->numBranches > 1) ? tr->NumberOfModels : 1, p; /* Clear reduction buffers: since in OMP version each thread works only on a subset of partitions, * and their order is arbitrary, it's easier to perform this initialization before the main loop, * just to be on the safe side. */ for(p = 0; p < nModels; p++) { tr->partitionData[p].reductionBuffer[tid] = 0.; tr->partitionData[p].reductionBuffer2[tid] = 0.; } maxModel = tr->maxModelsPerThread; #else maxModel = tr->NumberOfModels; #endif double lz; /* double buffer_dlnLdlz[NUM_BRANCHES], buffer_d2lnLdlz2[NUM_BRANCHES];*/ /* loop over partitions */ for(m = 0; m < maxModel; m++) { size_t width = 0, offset = 0; #ifdef _USE_OMP /* check if this thread should process this partition */ Assign* pAss = tr->threadPartAssigns[tid * tr->maxModelsPerThread + m]; if (pAss) { model = pAss->partitionId; width = GET_PADDED_WIDTH(pAss->width); offset = pAss->offset; } else break; #else model = m; /* number of sites in this partition */ width = (size_t)tr->partitionData[model].width; offset = 0; #endif volatile double *d1acc = (double*) NULL, *d2acc = (double*) NULL; /* figure out if we are optimizing branch lengths individually per partition or jointly across all partitions. If we do this on a per partition basis, we also need to compute and store the per-partition derivatives of the likelihood separately, otherwise not */ if(tr->numBranches > 1) { branchIndex = model; lz = tr->td[0].parameterValues[model]; } else { branchIndex = 0; lz = tr->td[0].parameterValues[0]; } #ifdef _USE_OMP d1acc = &tr->partitionData[branchIndex].reductionBuffer[tid]; d2acc = &tr->partitionData[branchIndex].reductionBuffer2[tid]; #else d1acc = &_dlnLdlz[branchIndex]; d2acc = &_d2lnLdlz2[branchIndex]; /* We need to reset accumulated derivative values in two cases: a) per-partition derivatives or * b) joint derivatives AND we're processing the first partition */ if (branchIndex == model) { *d1acc = 0.0; *d2acc = 0.0; } #endif /* check if we (the present thread for instance) needs to compute something at all for the present partition */ if(tr->td[0].executeModel[model] && width > 0) { int rateHet = (int)discreteRateCategories(tr->rateHetModel), /* get the number of states in the partition, e.g.: 4 = DNA, 20 = Protein */ states = tr->partitionData[model].states, /* span for single alignment site (in doubles!) */ span = rateHet * states, /* offset for current thread's data in global xVector (in doubles!) */ x_offset = offset * span, /* integer weight vector with pattern compression weights */ *wgt = tr->partitionData[model].wgt + offset, /* integer rate category vector (for each pattern, _number_ of PSR category assigned to it, NOT actual rate!) */ *rateCategory = tr->partitionData[model].rateCategory + offset; /* set a pointer to the part of the pre-computed sumBuffer we are going to access */ double *weights = tr->partitionData[model].weights, *sumBuffer = tr->partitionData[model].sumBuffer + x_offset; volatile double dlnLdlz = 0.0, d2lnLdlz2 = 0.0; #ifndef _OPTIMIZED_FUNCTIONS /* compute first and second derivatives with the slow generic functions */ if(tr->rateHetModel == CAT) coreCAT_FLEX(width, tr->partitionData[model].numberOfCategories, sumBuffer, &dlnLdlz, &d2lnLdlz2, wgt, tr->partitionData[model].perSiteRates, tr->partitionData[model].EIGN, rateCategory, lz, states); else coreGAMMA_FLEX(width, sumBuffer, &dlnLdlz, &d2lnLdlz2, tr->partitionData[model].EIGN, tr->partitionData[model].gammaRates, lz, wgt, states); #else switch(states) { case 2: #ifdef __MIC_NATIVE assert(0 && "Binary data model is not implemented on Intel MIC"); #else assert(!tr->saveMemory); if(tr->rateHetModel == CAT) coreGTRCAT_BINARY(width, tr->partitionData[model].numberOfCategories, sumBuffer, &dlnLdlz, &d2lnLdlz2, tr->partitionData[model].perSiteRates, tr->partitionData[model].EIGN, rateCategory, lz, wgt); else coreGTRGAMMA_BINARY(width, sumBuffer, &dlnLdlz, &d2lnLdlz2, tr->partitionData[model].EIGN, tr->partitionData[model].gammaRates, lz, wgt); #endif break; case 4: /* DNA */ if(tr->rateHetModel == CAT) #ifdef __MIC_NATIVE assert(0 && "CAT model of rate heterogeneity is not implemented on Intel MIC"); #else coreGTRCAT(width, tr->partitionData[model].numberOfCategories, sumBuffer, &dlnLdlz, &d2lnLdlz2, wgt, tr->partitionData[model].perSiteRates, tr->partitionData[model].EIGN, rateCategory, lz); #endif else #ifdef __MIC_NATIVE coreGTRGAMMA_MIC(width, sumBuffer, &dlnLdlz, &d2lnLdlz2, tr->partitionData[model].EIGN, tr->partitionData[model].gammaRates, lz, wgt); #else coreGTRGAMMA(width, sumBuffer, &dlnLdlz, &d2lnLdlz2, tr->partitionData[model].EIGN, tr->partitionData[model].gammaRates, lz, wgt); #endif break; case 20: /* proteins */ if(tr->rateHetModel == CAT) #ifdef __MIC_NATIVE assert(0 && "CAT model of rate heterogeneity is not implemented on Intel MIC"); #else coreGTRCATPROT(tr->partitionData[model].EIGN, lz, tr->partitionData[model].numberOfCategories, tr->partitionData[model].perSiteRates, rateCategory, width, wgt, &dlnLdlz, &d2lnLdlz2, sumBuffer); #endif else { if(tr->partitionData[model].protModels == LG4M || tr->partitionData[model].protModels == LG4X) #ifdef __MIC_NATIVE coreGTRGAMMAPROT_LG4_MIC(width, sumBuffer, &dlnLdlz, &d2lnLdlz2, tr->partitionData[model].EIGN_LG4, tr->partitionData[model].gammaRates, lz, wgt, weights); #else { //printf("model %d weights %f %f %f %f\n", model, weights[0], weights[1], weights[2], weights[3]); coreGTRGAMMAPROT_LG4(tr->partitionData[model].gammaRates, tr->partitionData[model].EIGN_LG4, sumBuffer, width, wgt, &dlnLdlz, &d2lnLdlz2, lz, weights); } #endif else #ifdef __MIC_NATIVE coreGTRGAMMAPROT_MIC(width, sumBuffer, &dlnLdlz, &d2lnLdlz2, tr->partitionData[model].EIGN, tr->partitionData[model].gammaRates, lz, wgt); #else coreGTRGAMMAPROT(tr->partitionData[model].gammaRates, tr->partitionData[model].EIGN, sumBuffer, width, wgt, &dlnLdlz, &d2lnLdlz2, lz); #endif } break; default: assert(0); } #endif /* store first and second derivative */ *d1acc += dlnLdlz; *d2acc += d2lnLdlz2; } else { /* set to 0 to make the reduction operation consistent */ if(width == 0 && (tr->numBranches > 1)) { *d1acc = 0.0; *d2acc = 0.0; } if(width > 0 && (tr->numBranches > 1)) { assert(tr->td[0].executeModel[model] == FALSE); /* _dlnLdlz[model] = 0.0; _d2lnLdlz2[model] = 0.0;*/ } } } // for model } // omp parallel section #ifdef _USE_OMP /* perform reduction of 1st and 2nd derivative values */ int model, tid; int nModels = (tr->numBranches > 1) ? tr->NumberOfModels : 1; for(model = 0; model < nModels; model++) { _dlnLdlz[model] = 0.0; _d2lnLdlz2[model] = 0.0; for(tid = 0; tid < tr->nThreads; tid++) { _dlnLdlz[model] += tr->partitionData[model].reductionBuffer[tid]; _d2lnLdlz2[model] += tr->partitionData[model].reductionBuffer2[tid]; } } #endif } /* the function below actually implements the iterative Newton-Raphson procedure. It is particularly messy and hard to read because for the case of per-partition branch length estimates it needs to keep track of whetehr the Newton Raphson procedure has converged for each partition individually. The rational efor doing it like this is also provided in: A. Stamatakis, M. Ott: "Load Balance in the Phylogenetic Likelihood Kernel". Proceedings of ICPP 2009, */ static void topLevelMakenewz(tree *tr, double *z0, int _maxiter, double *result) { double z[NUM_BRANCHES], zprev[NUM_BRANCHES], zstep[NUM_BRANCHES]; double dlnLdlz[NUM_BRANCHES], d2lnLdlz2[NUM_BRANCHES]; int i, maxiter[NUM_BRANCHES], model; boolean firstIteration = TRUE; boolean outerConverged[NUM_BRANCHES]; boolean loopConverged; /* figure out if this is on a per partition basis or jointly across all partitions */ /* initialize loop convergence variables etc. maxiter is the maximum number of NR iterations we are going to do before giving up */ for(i = 0; i < tr->numBranches; i++) { z[i] = z0[i]; maxiter[i] = _maxiter; outerConverged[i] = FALSE; tr->curvatOK[i] = TRUE; } /* nested do while loops of Newton-Raphson */ do { /* check if we ar done for partition i or if we need to adapt the branch length again */ for(i = 0; i < tr->numBranches; i++) { if(outerConverged[i] == FALSE && tr->curvatOK[i] == TRUE) { tr->curvatOK[i] = FALSE; zprev[i] = z[i]; zstep[i] = (1.0 - zmax) * z[i] + zmin; } } for(i = 0; i < tr->numBranches; i++) { /* other case, the outer loop hasn't converged but we are trying to approach the maximum from the wrong side */ if(outerConverged[i] == FALSE && tr->curvatOK[i] == FALSE) { double lz; if (z[i] < zmin) z[i] = zmin; else if (z[i] > zmax) z[i] = zmax; lz = log(z[i]); tr->coreLZ[i] = lz; } } /* set the execution mask */ if(tr->numBranches > 1) { assert(tr->numBranches == tr->NumberOfModels); for(model = 0; model < tr->NumberOfModels; model++) { if(tr->executeModel[model]) tr->executeModel[model] = !tr->curvatOK[model]; } } else { for(model = 0; model < tr->NumberOfModels; model++) tr->executeModel[model] = !tr->curvatOK[0]; } /* store it in traversal descriptor */ storeExecuteMaskInTraversalDescriptor(tr); /* store the new branch length values to be tested in traversal descriptor */ storeValuesInTraversalDescriptor(tr, &(tr->coreLZ[0])); /* sequential part, if this is the first newton-raphson implementation, do the precomputations as well, otherwise just execute the computation of the derivatives */ if(firstIteration) { makenewzIterative(tr); firstIteration = FALSE; } execCore(tr, dlnLdlz, d2lnLdlz2); { double *send = (double *)malloc(sizeof(double) * tr->numBranches * 2), *recv = (double *)malloc(sizeof(double) * tr->numBranches * 2); memcpy(&send[0], dlnLdlz, sizeof(double) * tr->numBranches); memcpy(&send[tr->numBranches], d2lnLdlz2, sizeof(double) * tr->numBranches); #ifdef _USE_ALLREDUCE /* the MPI_Allreduce implementation is apparently sometimes not deterministic */ MPI_Allreduce(send, recv, tr->numBranches * 2, MPI_DOUBLE, MPI_SUM, MPI_COMM_WORLD); #else MPI_Reduce(send, recv, tr->numBranches * 2, MPI_DOUBLE, MPI_SUM, 0, MPI_COMM_WORLD); MPI_Bcast(recv, tr->numBranches * 2, MPI_DOUBLE, 0, MPI_COMM_WORLD); #endif memcpy(dlnLdlz, &recv[0], sizeof(double) * tr->numBranches); memcpy(d2lnLdlz2, &recv[tr->numBranches], sizeof(double) * tr->numBranches); free(send); free(recv); } /* do a NR step, if we are on the correct side of the maximum that's okay, otherwise shorten branch */ for(i = 0; i < tr->numBranches; i++) { if(outerConverged[i] == FALSE && tr->curvatOK[i] == FALSE) { if ((d2lnLdlz2[i] >= 0.0) && (z[i] < zmax)) zprev[i] = z[i] = 0.37 * z[i] + 0.63; /* Bad curvature, shorten branch */ else tr->curvatOK[i] = TRUE; } } /* do the standard NR step to obrain the next value, depending on the state for each partition */ for(i = 0; i < tr->numBranches; i++) { if(tr->curvatOK[i] == TRUE && outerConverged[i] == FALSE) { if (d2lnLdlz2[i] < 0.0) { double tantmp = -dlnLdlz[i] / d2lnLdlz2[i]; if (tantmp < 100) { z[i] *= EXP(tantmp); if (z[i] < zmin) z[i] = zmin; if (z[i] > 0.25 * zprev[i] + 0.75) z[i] = 0.25 * zprev[i] + 0.75; } else z[i] = 0.25 * zprev[i] + 0.75; } if (z[i] > zmax) z[i] = zmax; /* decrement the maximum number of itarations */ maxiter[i] = maxiter[i] - 1; /* check if the outer loop has converged */ //old code below commented out, integrated new PRELIMINARY BUG FIX ! //this needs further work at some point! /* if(maxiter[i] > 0 && (ABS(z[i] - zprev[i]) > zstep[i])) outerConverged[i] = FALSE; else outerConverged[i] = TRUE; */ if((ABS(z[i] - zprev[i]) > zstep[i])) { /* We should make a more informed decision here, based on the log like improvement */ if(maxiter[i] < -20) { z[i] = z0[i]; outerConverged[i] = TRUE; } else outerConverged[i] = FALSE; } else outerConverged[i] = TRUE; } } /* check if the loop has converged for all partitions */ loopConverged = TRUE; for(i = 0; i < tr->numBranches; i++) loopConverged = loopConverged && outerConverged[i]; } while (!loopConverged); /* reset partition execution mask */ for(model = 0; model < tr->NumberOfModels; model++) tr->executeModel[model] = TRUE; /* copy the new branches in the result array of branches. if we don't do a per partition estimate of branches this will only set result[0] */ for(i = 0; i < tr->numBranches; i++) result[i] = z[i]; } /* function called from RAxML to optimize a given branch with current branch lengths z0 between nodes p and q. The new branch lengths will be stored in result */ void makenewzGeneric(tree *tr, nodeptr p, nodeptr q, double *z0, int maxiter, double *result, boolean mask) { int i; /* the first entry of the traversal descriptor stores the node pair that defines the branch */ tr->td[0].ti[0].pNumber = p->number; tr->td[0].ti[0].qNumber = q->number; for(i = 0; i < tr->numBranches; i++) { tr->td[0].ti[0].qz[i] = z0[i]; if(mask) { if(tr->partitionConverged[i]) tr->executeModel[i] = FALSE; else tr->executeModel[i] = TRUE; } else assert(tr->executeModel[i]); } /* compute the traversal descriptor of the likelihood vectors that need to be re-computed first in makenewzIterative */ tr->td[0].count = 1; if(!p->x) computeTraversalInfo(p, &(tr->td[0].ti[0]), &(tr->td[0].count), tr->mxtips, tr->numBranches, TRUE); if(!q->x) computeTraversalInfo(q, &(tr->td[0].ti[0]), &(tr->td[0].count), tr->mxtips, tr->numBranches, TRUE); /* call the Newton-Raphson procedure */ topLevelMakenewz(tr, z0, maxiter, result); /* fix eceuteModel this seems to be a bit redundant with topLevelMakenewz */ for(i = 0; i < tr->numBranches; i++) tr->executeModel[i] = TRUE; } /* below are, once again the optimized functions */ #ifdef _OPTIMIZED_FUNCTIONS /**** binary ***/ static void coreGTRCAT_BINARY(int upper, int numberOfCategories, double *sum, volatile double *d1, volatile double *d2, double *rptr, double *EIGN, int *cptr, double lz, int *wgt) { int i; double *d, *d_start, tmp_0, inv_Li, dlnLidlz, d2lnLidlz2, dlnLdlz = 0.0, d2lnLdlz2 = 0.0; double e[2]; double dd1; /*e[0] = EIGN[0]; e[1] = EIGN[0] * EIGN[0];*/ e[0] = EIGN[1]; e[1] = EIGN[1] * EIGN[1]; d = d_start = (double *)malloc((size_t)numberOfCategories * sizeof(double)); dd1 = e[0] * lz; for(i = 0; i < numberOfCategories; i++) d[i] = exp(dd1 * rptr[i]); for (i = 0; i < upper; i++) { double r = rptr[cptr[i]], wr1 = r * wgt[i], wr2 = r * r * wgt[i]; d = &d_start[cptr[i]]; inv_Li = sum[2 * i]; inv_Li += (tmp_0 = d[0] * sum[2 * i + 1]); inv_Li = 1.0/fabs(inv_Li); dlnLidlz = tmp_0 * e[0]; d2lnLidlz2 = tmp_0 * e[1]; dlnLidlz *= inv_Li; d2lnLidlz2 *= inv_Li; dlnLdlz += wr1 * dlnLidlz; d2lnLdlz2 += wr2 * (d2lnLidlz2 - dlnLidlz * dlnLidlz); } *d1 = dlnLdlz; *d2 = d2lnLdlz2; free(d_start); } static void coreGTRGAMMA_BINARY(const int upper, double *sumtable, volatile double *d1, volatile double *d2, double *EIGN, double *gammaRates, double lz, int *wrptr) { double dlnLdlz = 0.0, d2lnLdlz2 = 0.0, ki, kisqr, inv_Li, dlnLidlz, d2lnLidlz2, *sum, diagptable0[8] __attribute__ ((aligned (BYTE_ALIGNMENT))), diagptable1[8] __attribute__ ((aligned (BYTE_ALIGNMENT))), diagptable2[8] __attribute__ ((aligned (BYTE_ALIGNMENT))); int i, j; for(i = 0; i < 4; i++) { ki = gammaRates[i]; kisqr = ki * ki; diagptable0[i * 2] = 1.0; diagptable1[i * 2] = 0.0; diagptable2[i * 2] = 0.0; diagptable0[i * 2 + 1] = exp(EIGN[1] * ki * lz); diagptable1[i * 2 + 1] = EIGN[1] * ki; diagptable2[i * 2 + 1] = EIGN[1] * EIGN[1] * kisqr; } for (i = 0; i < upper; i++) { __m128d a0 = _mm_setzero_pd(); __m128d a1 = _mm_setzero_pd(); __m128d a2 = _mm_setzero_pd(); sum = &sumtable[i * 8]; for(j = 0; j < 4; j++) { double *d0 = &diagptable0[j * 2], *d1 = &diagptable1[j * 2], *d2 = &diagptable2[j * 2]; __m128d tmpv = _mm_mul_pd(_mm_load_pd(d0), _mm_load_pd(&sum[j * 2])); a0 = _mm_add_pd(a0, tmpv); a1 = _mm_add_pd(a1, _mm_mul_pd(tmpv, _mm_load_pd(d1))); a2 = _mm_add_pd(a2, _mm_mul_pd(tmpv, _mm_load_pd(d2))); } a0 = _mm_hadd_pd(a0, a0); a1 = _mm_hadd_pd(a1, a1); a2 = _mm_hadd_pd(a2, a2); _mm_storel_pd(&inv_Li, a0); _mm_storel_pd(&dlnLidlz, a1); _mm_storel_pd(&d2lnLidlz2, a2); inv_Li = 1.0 / fabs(inv_Li); dlnLidlz *= inv_Li; d2lnLidlz2 *= inv_Li; dlnLdlz += wrptr[i] * dlnLidlz; d2lnLdlz2 += wrptr[i] * (d2lnLidlz2 - dlnLidlz * dlnLidlz); } *d1 = dlnLdlz; *d2 = d2lnLdlz2; } static void sumGAMMA_BINARY(int tipCase, double *sumtable, double *x1_start, double *x2_start, double *tipVector, unsigned char *tipX1, unsigned char *tipX2, int n) { double *x1, *x2, *sum; int i, j; /* C-OPT once again switch over possible configurations at inner node */ switch(tipCase) { case TIP_TIP: /* C-OPT main for loop overt alignment length */ for (i = 0; i < n; i++) { x1 = &(tipVector[2 * tipX1[i]]); x2 = &(tipVector[2 * tipX2[i]]); sum = &sumtable[i * 8]; for(j = 0; j < 4; j++) _mm_store_pd( &sum[j*2], _mm_mul_pd( _mm_load_pd( &x1[0] ), _mm_load_pd( &x2[0] ))); } break; case TIP_INNER: for (i = 0; i < n; i++) { x1 = &(tipVector[2 * tipX1[i]]); x2 = &x2_start[8 * i]; sum = &sumtable[8 * i]; for(j = 0; j < 4; j++) _mm_store_pd( &sum[j*2], _mm_mul_pd( _mm_load_pd( &x1[0] ), _mm_load_pd( &x2[j * 2] ))); } break; case INNER_INNER: for (i = 0; i < n; i++) { x1 = &x1_start[8 * i]; x2 = &x2_start[8 * i]; sum = &sumtable[8 * i]; for(j = 0; j < 4; j++) _mm_store_pd( &sum[j*2], _mm_mul_pd( _mm_load_pd( &x1[j * 2] ), _mm_load_pd( &x2[j * 2] ))); } break; default: assert(0); } } static void sumCAT_BINARY(int tipCase, double *sum, double *x1_start, double *x2_start, double *tipVector, unsigned char *tipX1, unsigned char *tipX2, int n) { int i; double *x1, *x2; switch(tipCase) { case TIP_TIP: for (i = 0; i < n; i++) { x1 = &(tipVector[2 * tipX1[i]]); x2 = &(tipVector[2 * tipX2[i]]); _mm_store_pd(&sum[i * 2], _mm_mul_pd( _mm_load_pd(x1), _mm_load_pd(x2))); } break; case TIP_INNER: for (i = 0; i < n; i++) { x1 = &(tipVector[2 * tipX1[i]]); x2 = &x2_start[2 * i]; _mm_store_pd(&sum[i * 2], _mm_mul_pd( _mm_load_pd(x1), _mm_load_pd(x2))); } break; case INNER_INNER: for (i = 0; i < n; i++) { x1 = &x1_start[2 * i]; x2 = &x2_start[2 * i]; _mm_store_pd(&sum[i * 2], _mm_mul_pd( _mm_load_pd(x1), _mm_load_pd(x2))); } break; default: assert(0); } } /*** binary end ****/ static void sumCAT_SAVE(int tipCase, double *sum, double *x1_start, double *x2_start, double *tipVector, unsigned char *tipX1, unsigned char *tipX2, int n, double *x1_gapColumn, double *x2_gapColumn, unsigned int *x1_gap, unsigned int *x2_gap) { int i; double *x1, *x2, *x1_ptr = x1_start, *x2_ptr = x2_start; switch(tipCase) { case TIP_TIP: for (i = 0; i < n; i++) { x1 = &(tipVector[4 * tipX1[i]]); x2 = &(tipVector[4 * tipX2[i]]); _mm_store_pd( &sum[i*4 + 0], _mm_mul_pd( _mm_load_pd( &x1[0] ), _mm_load_pd( &x2[0] ))); _mm_store_pd( &sum[i*4 + 2], _mm_mul_pd( _mm_load_pd( &x1[2] ), _mm_load_pd( &x2[2] ))); } break; case TIP_INNER: for (i = 0; i < n; i++) { x1 = &(tipVector[4 * tipX1[i]]); if(isGap(x2_gap, i)) x2 = x2_gapColumn; else { x2 = x2_ptr; x2_ptr += 4; } _mm_store_pd( &sum[i*4 + 0], _mm_mul_pd( _mm_load_pd( &x1[0] ), _mm_load_pd( &x2[0] ))); _mm_store_pd( &sum[i*4 + 2], _mm_mul_pd( _mm_load_pd( &x1[2] ), _mm_load_pd( &x2[2] ))); } break; case INNER_INNER: for (i = 0; i < n; i++) { if(isGap(x1_gap, i)) x1 = x1_gapColumn; else { x1 = x1_ptr; x1_ptr += 4; } if(isGap(x2_gap, i)) x2 = x2_gapColumn; else { x2 = x2_ptr; x2_ptr += 4; } _mm_store_pd( &sum[i*4 + 0], _mm_mul_pd( _mm_load_pd( &x1[0] ), _mm_load_pd( &x2[0] ))); _mm_store_pd( &sum[i*4 + 2], _mm_mul_pd( _mm_load_pd( &x1[2] ), _mm_load_pd( &x2[2] ))); } break; default: assert(0); } } static void sumGAMMA_GAPPED_SAVE(int tipCase, double *sumtable, double *x1_start, double *x2_start, double *tipVector, unsigned char *tipX1, unsigned char *tipX2, int n, double *x1_gapColumn, double *x2_gapColumn, unsigned int *x1_gap, unsigned int *x2_gap) { double *x1, *x2, *sum, *x1_ptr = x1_start, *x2_ptr = x2_start; int i, j, k; switch(tipCase) { case TIP_TIP: for (i = 0; i < n; i++) { x1 = &(tipVector[4 * tipX1[i]]); x2 = &(tipVector[4 * tipX2[i]]); sum = &sumtable[i * 16]; for(j = 0; j < 4; j++) for(k = 0; k < 4; k+=2) _mm_store_pd( &sum[j*4 + k], _mm_mul_pd( _mm_load_pd( &x1[k] ), _mm_load_pd( &x2[k] ))); } break; case TIP_INNER: for (i = 0; i < n; i++) { x1 = &(tipVector[4 * tipX1[i]]); if(x2_gap[i / 32] & mask32[i % 32]) x2 = x2_gapColumn; else { x2 = x2_ptr; x2_ptr += 16; } sum = &sumtable[16 * i]; for(j = 0; j < 4; j++) for(k = 0; k < 4; k+=2) _mm_store_pd( &sum[j*4 + k], _mm_mul_pd( _mm_load_pd( &x1[k] ), _mm_load_pd( &x2[j * 4 + k] ))); } break; case INNER_INNER: for (i = 0; i < n; i++) { if(x1_gap[i / 32] & mask32[i % 32]) x1 = x1_gapColumn; else { x1 = x1_ptr; x1_ptr += 16; } if(x2_gap[i / 32] & mask32[i % 32]) x2 = x2_gapColumn; else { x2 = x2_ptr; x2_ptr += 16; } sum = &sumtable[16 * i]; for(j = 0; j < 4; j++) for(k = 0; k < 4; k+=2) _mm_store_pd( &sum[j*4 + k], _mm_mul_pd( _mm_load_pd( &x1[j * 4 + k] ), _mm_load_pd( &x2[j * 4 + k] ))); } break; default: assert(0); } } static void sumGAMMA(int tipCase, double *sumtable, double *x1_start, double *x2_start, double *tipVector, unsigned char *tipX1, unsigned char *tipX2, int n) { double *x1, *x2, *sum; int i, j, k; /* C-OPT once again switch over possible configurations at inner node */ switch(tipCase) { case TIP_TIP: /* C-OPT main for loop overt alignment length */ for (i = 0; i < n; i++) { x1 = &(tipVector[4 * tipX1[i]]); x2 = &(tipVector[4 * tipX2[i]]); sum = &sumtable[i * 16]; for(j = 0; j < 4; j++) for(k = 0; k < 4; k+=2) _mm_store_pd( &sum[j*4 + k], _mm_mul_pd( _mm_load_pd( &x1[k] ), _mm_load_pd( &x2[k] ))); } break; case TIP_INNER: for (i = 0; i < n; i++) { x1 = &(tipVector[4 * tipX1[i]]); x2 = &x2_start[16 * i]; sum = &sumtable[16 * i]; for(j = 0; j < 4; j++) for(k = 0; k < 4; k+=2) _mm_store_pd( &sum[j*4 + k], _mm_mul_pd( _mm_load_pd( &x1[k] ), _mm_load_pd( &x2[j * 4 + k] ))); } break; case INNER_INNER: for (i = 0; i < n; i++) { x1 = &x1_start[16 * i]; x2 = &x2_start[16 * i]; sum = &sumtable[16 * i]; for(j = 0; j < 4; j++) for(k = 0; k < 4; k+=2) _mm_store_pd( &sum[j*4 + k], _mm_mul_pd( _mm_load_pd( &x1[j * 4 + k] ), _mm_load_pd( &x2[j * 4 + k] ))); } break; default: assert(0); } } static void sumCAT(int tipCase, double *sum, double *x1_start, double *x2_start, double *tipVector, unsigned char *tipX1, unsigned char *tipX2, int n) { int i; double *x1, *x2; switch(tipCase) { case TIP_TIP: for (i = 0; i < n; i++) { x1 = &(tipVector[4 * tipX1[i]]); x2 = &(tipVector[4 * tipX2[i]]); _mm_store_pd( &sum[i*4 + 0], _mm_mul_pd( _mm_load_pd( &x1[0] ), _mm_load_pd( &x2[0] ))); _mm_store_pd( &sum[i*4 + 2], _mm_mul_pd( _mm_load_pd( &x1[2] ), _mm_load_pd( &x2[2] ))); } break; case TIP_INNER: for (i = 0; i < n; i++) { x1 = &(tipVector[4 * tipX1[i]]); x2 = &x2_start[4 * i]; _mm_store_pd( &sum[i*4 + 0], _mm_mul_pd( _mm_load_pd( &x1[0] ), _mm_load_pd( &x2[0] ))); _mm_store_pd( &sum[i*4 + 2], _mm_mul_pd( _mm_load_pd( &x1[2] ), _mm_load_pd( &x2[2] ))); } break; case INNER_INNER: for (i = 0; i < n; i++) { x1 = &x1_start[4 * i]; x2 = &x2_start[4 * i]; _mm_store_pd( &sum[i*4 + 0], _mm_mul_pd( _mm_load_pd( &x1[0] ), _mm_load_pd( &x2[0] ))); _mm_store_pd( &sum[i*4 + 2], _mm_mul_pd( _mm_load_pd( &x1[2] ), _mm_load_pd( &x2[2] ))); } break; default: assert(0); } } static void sumGAMMAPROT_GAPPED_SAVE(int tipCase, double *sumtable, double *x1, double *x2, double *tipVector, unsigned char *tipX1, unsigned char *tipX2, int n, double *x1_gapColumn, double *x2_gapColumn, unsigned int *x1_gap, unsigned int *x2_gap) { int i, l, k; double *left, *right, *sum, *x1_ptr = x1, *x2_ptr = x2, *x1v, *x2v; switch(tipCase) { case TIP_TIP: for(i = 0; i < n; i++) { left = &(tipVector[20 * tipX1[i]]); right = &(tipVector[20 * tipX2[i]]); for(l = 0; l < 4; l++) { sum = &sumtable[i * 80 + l * 20]; for(k = 0; k < 20; k+=2) { __m128d sumv = _mm_mul_pd(_mm_load_pd(&left[k]), _mm_load_pd(&right[k])); _mm_store_pd(&sum[k], sumv); } } } break; case TIP_INNER: for(i = 0; i < n; i++) { left = &(tipVector[20 * tipX1[i]]); if(x2_gap[i / 32] & mask32[i % 32]) x2v = x2_gapColumn; else { x2v = x2_ptr; x2_ptr += 80; } for(l = 0; l < 4; l++) { right = &(x2v[l * 20]); sum = &sumtable[i * 80 + l * 20]; for(k = 0; k < 20; k+=2) { __m128d sumv = _mm_mul_pd(_mm_load_pd(&left[k]), _mm_load_pd(&right[k])); _mm_store_pd(&sum[k], sumv); } } } break; case INNER_INNER: for(i = 0; i < n; i++) { if(x1_gap[i / 32] & mask32[i % 32]) x1v = x1_gapColumn; else { x1v = x1_ptr; x1_ptr += 80; } if(x2_gap[i / 32] & mask32[i % 32]) x2v = x2_gapColumn; else { x2v = x2_ptr; x2_ptr += 80; } for(l = 0; l < 4; l++) { left = &(x1v[l * 20]); right = &(x2v[l * 20]); sum = &(sumtable[i * 80 + l * 20]); for(k = 0; k < 20; k+=2) { __m128d sumv = _mm_mul_pd(_mm_load_pd(&left[k]), _mm_load_pd(&right[k])); _mm_store_pd(&sum[k], sumv); } } } break; default: assert(0); } } static void sumGAMMAPROT_LG4(int tipCase, double *sumtable, double *x1, double *x2, double *tipVector[4], unsigned char *tipX1, unsigned char *tipX2, int n) { int i, l, k; double *left, *right, *sum; switch(tipCase) { case TIP_TIP: for(i = 0; i < n; i++) { for(l = 0; l < 4; l++) { left = &(tipVector[l][20 * tipX1[i]]); right = &(tipVector[l][20 * tipX2[i]]); sum = &sumtable[i * 80 + l * 20]; #ifdef __SIM_SSE3 for(k = 0; k < 20; k+=2) { __m128d sumv = _mm_mul_pd(_mm_load_pd(&left[k]), _mm_load_pd(&right[k])); _mm_store_pd(&sum[k], sumv); } #else for(k = 0; k < 20; k++) sum[k] = left[k] * right[k]; #endif } } break; case TIP_INNER: for(i = 0; i < n; i++) { for(l = 0; l < 4; l++) { left = &(tipVector[l][20 * tipX1[i]]); right = &(x2[80 * i + l * 20]); sum = &sumtable[i * 80 + l * 20]; #ifdef __SIM_SSE3 for(k = 0; k < 20; k+=2) { __m128d sumv = _mm_mul_pd(_mm_load_pd(&left[k]), _mm_load_pd(&right[k])); _mm_store_pd(&sum[k], sumv); } #else for(k = 0; k < 20; k++) sum[k] = left[k] * right[k]; #endif } } break; case INNER_INNER: for(i = 0; i < n; i++) { for(l = 0; l < 4; l++) { left = &(x1[80 * i + l * 20]); right = &(x2[80 * i + l * 20]); sum = &(sumtable[i * 80 + l * 20]); #ifdef __SIM_SSE3 for(k = 0; k < 20; k+=2) { __m128d sumv = _mm_mul_pd(_mm_load_pd(&left[k]), _mm_load_pd(&right[k])); _mm_store_pd(&sum[k], sumv); } #else for(k = 0; k < 20; k++) sum[k] = left[k] * right[k]; #endif } } break; default: assert(0); } } static void sumGAMMAPROT(int tipCase, double *sumtable, double *x1, double *x2, double *tipVector, unsigned char *tipX1, unsigned char *tipX2, int n) { int i, l, k; double *left, *right, *sum; switch(tipCase) { case TIP_TIP: for(i = 0; i < n; i++) { left = &(tipVector[20 * tipX1[i]]); right = &(tipVector[20 * tipX2[i]]); for(l = 0; l < 4; l++) { sum = &sumtable[i * 80 + l * 20]; for(k = 0; k < 20; k+=2) { __m128d sumv = _mm_mul_pd(_mm_load_pd(&left[k]), _mm_load_pd(&right[k])); _mm_store_pd(&sum[k], sumv); } } } break; case TIP_INNER: for(i = 0; i < n; i++) { left = &(tipVector[20 * tipX1[i]]); for(l = 0; l < 4; l++) { right = &(x2[80 * i + l * 20]); sum = &sumtable[i * 80 + l * 20]; for(k = 0; k < 20; k+=2) { __m128d sumv = _mm_mul_pd(_mm_load_pd(&left[k]), _mm_load_pd(&right[k])); _mm_store_pd(&sum[k], sumv); } } } break; case INNER_INNER: for(i = 0; i < n; i++) { for(l = 0; l < 4; l++) { left = &(x1[80 * i + l * 20]); right = &(x2[80 * i + l * 20]); sum = &(sumtable[i * 80 + l * 20]); for(k = 0; k < 20; k+=2) { __m128d sumv = _mm_mul_pd(_mm_load_pd(&left[k]), _mm_load_pd(&right[k])); _mm_store_pd(&sum[k], sumv); } } } break; default: assert(0); } } static void sumGTRCATPROT(int tipCase, double *sumtable, double *x1, double *x2, double *tipVector, unsigned char *tipX1, unsigned char *tipX2, int n) { int i, l; double *sum, *left, *right; switch(tipCase) { case TIP_TIP: for (i = 0; i < n; i++) { left = &(tipVector[20 * tipX1[i]]); right = &(tipVector[20 * tipX2[i]]); sum = &sumtable[20 * i]; for(l = 0; l < 20; l+=2) { __m128d sumv = _mm_mul_pd(_mm_load_pd(&left[l]), _mm_load_pd(&right[l])); _mm_store_pd(&sum[l], sumv); } } break; case TIP_INNER: for (i = 0; i < n; i++) { left = &(tipVector[20 * tipX1[i]]); right = &x2[20 * i]; sum = &sumtable[20 * i]; for(l = 0; l < 20; l+=2) { __m128d sumv = _mm_mul_pd(_mm_load_pd(&left[l]), _mm_load_pd(&right[l])); _mm_store_pd(&sum[l], sumv); } } break; case INNER_INNER: for (i = 0; i < n; i++) { left = &x1[20 * i]; right = &x2[20 * i]; sum = &sumtable[20 * i]; for(l = 0; l < 20; l+=2) { __m128d sumv = _mm_mul_pd(_mm_load_pd(&left[l]), _mm_load_pd(&right[l])); _mm_store_pd(&sum[l], sumv); } } break; default: assert(0); } } static void sumGTRCATPROT_SAVE(int tipCase, double *sumtable, double *x1, double *x2, double *tipVector, unsigned char *tipX1, unsigned char *tipX2, int n, double *x1_gapColumn, double *x2_gapColumn, unsigned int *x1_gap, unsigned int *x2_gap) { int i, l; double *sum, *left, *right, *left_ptr = x1, *right_ptr = x2; switch(tipCase) { case TIP_TIP: for (i = 0; i < n; i++) { left = &(tipVector[20 * tipX1[i]]); right = &(tipVector[20 * tipX2[i]]); sum = &sumtable[20 * i]; for(l = 0; l < 20; l+=2) { __m128d sumv = _mm_mul_pd(_mm_load_pd(&left[l]), _mm_load_pd(&right[l])); _mm_store_pd(&sum[l], sumv); } } break; case TIP_INNER: for (i = 0; i < n; i++) { left = &(tipVector[20 * tipX1[i]]); if(isGap(x2_gap, i)) right = x2_gapColumn; else { right = right_ptr; right_ptr += 20; } sum = &sumtable[20 * i]; for(l = 0; l < 20; l+=2) { __m128d sumv = _mm_mul_pd(_mm_load_pd(&left[l]), _mm_load_pd(&right[l])); _mm_store_pd(&sum[l], sumv); } } break; case INNER_INNER: for (i = 0; i < n; i++) { if(isGap(x1_gap, i)) left = x1_gapColumn; else { left = left_ptr; left_ptr += 20; } if(isGap(x2_gap, i)) right = x2_gapColumn; else { right = right_ptr; right_ptr += 20; } sum = &sumtable[20 * i]; for(l = 0; l < 20; l+=2) { __m128d sumv = _mm_mul_pd(_mm_load_pd(&left[l]), _mm_load_pd(&right[l])); _mm_store_pd(&sum[l], sumv); } } break; default: assert(0); } } static void coreGTRGAMMA(const int upper, double *sumtable, volatile double *ext_dlnLdlz, volatile double *ext_d2lnLdlz2, double *EIGN, double *gammaRates, double lz, int *wgt) { double dlnLdlz = 0.0, d2lnLdlz2 = 0.0, ki, kisqr, inv_Li, dlnLidlz, d2lnLidlz2, *sum, diagptable0[16] __attribute__ ((aligned (BYTE_ALIGNMENT))), diagptable1[16] __attribute__ ((aligned (BYTE_ALIGNMENT))), diagptable2[16] __attribute__ ((aligned (BYTE_ALIGNMENT))); int i, j, l; for(i = 0; i < 4; i++) { ki = gammaRates[i]; kisqr = ki * ki; diagptable0[i * 4] = 1.0; diagptable1[i * 4] = 0.0; diagptable2[i * 4] = 0.0; for(l = 1; l < 4; l++) { diagptable0[i * 4 + l] = EXP(EIGN[l] * ki * lz); diagptable1[i * 4 + l] = EIGN[l] * ki; diagptable2[i * 4 + l] = EIGN[l] * EIGN[l] * kisqr; } } for (i = 0; i < upper; i++) { __m128d a0 = _mm_setzero_pd(); __m128d a1 = _mm_setzero_pd(); __m128d a2 = _mm_setzero_pd(); sum = &sumtable[i * 16]; for(j = 0; j < 4; j++) { double *d0 = &diagptable0[j * 4], *d1 = &diagptable1[j * 4], *d2 = &diagptable2[j * 4]; for(l = 0; l < 4; l+=2) { __m128d tmpv = _mm_mul_pd(_mm_load_pd(&d0[l]), _mm_load_pd(&sum[j * 4 + l])); a0 = _mm_add_pd(a0, tmpv); a1 = _mm_add_pd(a1, _mm_mul_pd(tmpv, _mm_load_pd(&d1[l]))); a2 = _mm_add_pd(a2, _mm_mul_pd(tmpv, _mm_load_pd(&d2[l]))); } } a0 = _mm_hadd_pd(a0, a0); a1 = _mm_hadd_pd(a1, a1); a2 = _mm_hadd_pd(a2, a2); _mm_storel_pd(&inv_Li, a0); _mm_storel_pd(&dlnLidlz, a1); _mm_storel_pd(&d2lnLidlz2, a2); inv_Li = 1.0 / FABS(inv_Li); dlnLidlz *= inv_Li; d2lnLidlz2 *= inv_Li; dlnLdlz += wgt[i] * dlnLidlz; d2lnLdlz2 += wgt[i] * (d2lnLidlz2 - dlnLidlz * dlnLidlz); } *ext_dlnLdlz = dlnLdlz; *ext_d2lnLdlz2 = d2lnLdlz2; } static void coreGTRCAT(int upper, int numberOfCategories, double *sum, volatile double *d1, volatile double *d2, int *wgt, double *rptr, double *EIGN, int *cptr, double lz) { int i; double *d, *d_start, inv_Li, dlnLidlz, d2lnLidlz2, dlnLdlz = 0.0, d2lnLdlz2 = 0.0; double e1[4] __attribute__ ((aligned (BYTE_ALIGNMENT))); double e2[4] __attribute__ ((aligned (BYTE_ALIGNMENT))); double dd1, dd2, dd3; __m128d e1v[2], e2v[2]; e1[0] = 0.0; e2[0] = 0.0; e1[1] = EIGN[1]; e2[1] = EIGN[1] * EIGN[1]; e1[2] = EIGN[2]; e2[2] = EIGN[2] * EIGN[2]; e1[3] = EIGN[3]; e2[3] = EIGN[3] * EIGN[3]; e1v[0]= _mm_load_pd(&e1[0]); e1v[1]= _mm_load_pd(&e1[2]); e2v[0]= _mm_load_pd(&e2[0]); e2v[1]= _mm_load_pd(&e2[2]); d = d_start = (double *)malloc_aligned(numberOfCategories * 4 * sizeof(double)); dd1 = EIGN[1] * lz; dd2 = EIGN[2] * lz; dd3 = EIGN[3] * lz; for(i = 0; i < numberOfCategories; i++) { d[i * 4 + 0] = 1.0; d[i * 4 + 1] = EXP(dd1 * rptr[i]); d[i * 4 + 2] = EXP(dd2 * rptr[i]); d[i * 4 + 3] = EXP(dd3 * rptr[i]); } for (i = 0; i < upper; i++) { double *s = &sum[4 * i]; double r = rptr[cptr[i]], wr1 = r * wgt[i], wr2 = r * r * wgt[i]; d = &d_start[4 * cptr[i]]; __m128d tmp_0v =_mm_mul_pd(_mm_load_pd(&d[0]),_mm_load_pd(&s[0])); __m128d tmp_1v =_mm_mul_pd(_mm_load_pd(&d[2]),_mm_load_pd(&s[2])); __m128d inv_Liv = _mm_add_pd(tmp_0v, tmp_1v); __m128d dlnLidlzv = _mm_add_pd(_mm_mul_pd(tmp_0v, e1v[0]), _mm_mul_pd(tmp_1v, e1v[1])); __m128d d2lnLidlz2v = _mm_add_pd(_mm_mul_pd(tmp_0v, e2v[0]), _mm_mul_pd(tmp_1v, e2v[1])); inv_Liv = _mm_hadd_pd(inv_Liv, inv_Liv); dlnLidlzv = _mm_hadd_pd(dlnLidlzv, dlnLidlzv); d2lnLidlz2v = _mm_hadd_pd(d2lnLidlz2v, d2lnLidlz2v); _mm_storel_pd(&inv_Li, inv_Liv); _mm_storel_pd(&dlnLidlz, dlnLidlzv); _mm_storel_pd(&d2lnLidlz2, d2lnLidlz2v); inv_Li = 1.0/FABS(inv_Li); dlnLidlz *= inv_Li; d2lnLidlz2 *= inv_Li; dlnLdlz += wr1 * dlnLidlz; d2lnLdlz2 += wr2 * (d2lnLidlz2 - dlnLidlz * dlnLidlz); } *d1 = dlnLdlz; *d2 = d2lnLdlz2; free(d_start); } static void coreGTRGAMMAPROT_LG4(double *gammaRates, double *EIGN[4], double *sumtable, int upper, int *wrptr, volatile double *ext_dlnLdlz, volatile double *ext_d2lnLdlz2, double lz, double *weights) { double *sum, diagptable0[80] __attribute__ ((aligned (BYTE_ALIGNMENT))), diagptable1[80] __attribute__ ((aligned (BYTE_ALIGNMENT))), diagptable2[80] __attribute__ ((aligned (BYTE_ALIGNMENT))); int i, j, l; double dlnLdlz = 0; double d2lnLdlz2 = 0; double ki, kisqr; for(i = 0; i < 4; i++) { ki = gammaRates[i]; kisqr = ki * ki; diagptable0[i * 20] = 1.0; diagptable1[i * 20] = 0.0; diagptable2[i * 20] = 0.0; for(l = 1; l < 20; l++) { diagptable0[i * 20 + l] = EXP(EIGN[i][l] * ki * lz); diagptable1[i * 20 + l] = EIGN[i][l] * ki; diagptable2[i * 20 + l] = EIGN[i][l] * EIGN[i][l] * kisqr; } } for (i = 0; i < upper; i++) { double inv_Li = 0.0, dlnLidlz = 0.0, d2lnLidlz2 = 0.0; sum = &sumtable[i * 80]; for(j = 0; j < 4; j++) { double l0, l1, l2, *d0 = &diagptable0[j * 20], *d1 = &diagptable1[j * 20], *d2 = &diagptable2[j * 20]; __m128d a0 = _mm_setzero_pd(), a1 = _mm_setzero_pd(), a2 = _mm_setzero_pd(); for(l = 0; l < 20; l+=2) { __m128d tmpv = _mm_mul_pd(_mm_load_pd(&d0[l]), _mm_load_pd(&sum[j * 20 +l])); a0 = _mm_add_pd(a0, tmpv); a1 = _mm_add_pd(a1, _mm_mul_pd(tmpv, _mm_load_pd(&d1[l]))); a2 = _mm_add_pd(a2, _mm_mul_pd(tmpv, _mm_load_pd(&d2[l]))); } a0 = _mm_hadd_pd(a0, a0); a1 = _mm_hadd_pd(a1, a1); a2 = _mm_hadd_pd(a2, a2); _mm_storel_pd(&l0, a0); _mm_storel_pd(&l1, a1); _mm_storel_pd(&l2, a2); inv_Li += weights[j] * l0; dlnLidlz += weights[j] * l1; d2lnLidlz2 += weights[j] * l2; } inv_Li = 1.0 / FABS(inv_Li); dlnLidlz *= inv_Li; d2lnLidlz2 *= inv_Li; dlnLdlz += wrptr[i] * dlnLidlz; d2lnLdlz2 += wrptr[i] * (d2lnLidlz2 - dlnLidlz * dlnLidlz); } *ext_dlnLdlz = dlnLdlz; *ext_d2lnLdlz2 = d2lnLdlz2; } static void coreGTRGAMMAPROT(double *gammaRates, double *EIGN, double *sumtable, int upper, int *wgt, volatile double *ext_dlnLdlz, volatile double *ext_d2lnLdlz2, double lz) { double *sum, diagptable0[80] __attribute__ ((aligned (BYTE_ALIGNMENT))), diagptable1[80] __attribute__ ((aligned (BYTE_ALIGNMENT))), diagptable2[80] __attribute__ ((aligned (BYTE_ALIGNMENT))); int i, j, l; double dlnLdlz = 0; double d2lnLdlz2 = 0; double ki, kisqr; double inv_Li, dlnLidlz, d2lnLidlz2; for(i = 0; i < 4; i++) { ki = gammaRates[i]; kisqr = ki * ki; diagptable0[i * 20] = 1.0; diagptable1[i * 20] = 0.0; diagptable2[i * 20] = 0.0; for(l = 1; l < 20; l++) { diagptable0[i * 20 + l] = EXP(EIGN[l] * ki * lz); diagptable1[i * 20 + l] = EIGN[l] * ki; diagptable2[i * 20 + l] = EIGN[l] * EIGN[l] * kisqr; } } for (i = 0; i < upper; i++) { __m128d a0 = _mm_setzero_pd(); __m128d a1 = _mm_setzero_pd(); __m128d a2 = _mm_setzero_pd(); sum = &sumtable[i * 80]; for(j = 0; j < 4; j++) { double *d0 = &diagptable0[j * 20], *d1 = &diagptable1[j * 20], *d2 = &diagptable2[j * 20]; for(l = 0; l < 20; l+=2) { __m128d tmpv = _mm_mul_pd(_mm_load_pd(&d0[l]), _mm_load_pd(&sum[j * 20 +l])); a0 = _mm_add_pd(a0, tmpv); a1 = _mm_add_pd(a1, _mm_mul_pd(tmpv, _mm_load_pd(&d1[l]))); a2 = _mm_add_pd(a2, _mm_mul_pd(tmpv, _mm_load_pd(&d2[l]))); } } a0 = _mm_hadd_pd(a0, a0); a1 = _mm_hadd_pd(a1, a1); a2 = _mm_hadd_pd(a2, a2); _mm_storel_pd(&inv_Li, a0); _mm_storel_pd(&dlnLidlz, a1); _mm_storel_pd(&d2lnLidlz2, a2); inv_Li = 1.0 / FABS(inv_Li); dlnLidlz *= inv_Li; d2lnLidlz2 *= inv_Li; dlnLdlz += wgt[i] * dlnLidlz; d2lnLdlz2 += wgt[i] * (d2lnLidlz2 - dlnLidlz * dlnLidlz); } *ext_dlnLdlz = dlnLdlz; *ext_d2lnLdlz2 = d2lnLdlz2; } static void coreGTRCATPROT(double *EIGN, double lz, int numberOfCategories, double *rptr, int *cptr, int upper, int *wgt, volatile double *ext_dlnLdlz, volatile double *ext_d2lnLdlz2, double *sumtable) { int i, l; double *d1, *d_start, *sum; double e[20] __attribute__ ((aligned (BYTE_ALIGNMENT))), s[20] __attribute__ ((aligned (BYTE_ALIGNMENT))), dd[20] __attribute__ ((aligned (BYTE_ALIGNMENT))); double inv_Li, dlnLidlz, d2lnLidlz2; double dlnLdlz = 0.0; double d2lnLdlz2 = 0.0; d1 = d_start = (double *)malloc_aligned(numberOfCategories * 20 * sizeof(double)); e[0] = 0.0; s[0] = 0.0; for(l = 1; l < 20; l++) { e[l] = EIGN[l] * EIGN[l]; s[l] = EIGN[l]; dd[l] = s[l] * lz; } for(i = 0; i < numberOfCategories; i++) { d1[20 * i] = 1.0; for(l = 1; l < 20; l++) d1[20 * i + l] = EXP(dd[l] * rptr[i]); } for (i = 0; i < upper; i++) { __m128d a0 = _mm_setzero_pd(); __m128d a1 = _mm_setzero_pd(); __m128d a2 = _mm_setzero_pd(); double r = rptr[cptr[i]], wr1 = r * wgt[i], wr2 = r * r * wgt[i]; d1 = &d_start[20 * cptr[i]]; sum = &sumtable[20 * i]; for(l = 0; l < 20; l+=2) { __m128d tmpv = _mm_mul_pd(_mm_load_pd(&d1[l]), _mm_load_pd(&sum[l])); a0 = _mm_add_pd(a0, tmpv); __m128d sv = _mm_load_pd(&s[l]); a1 = _mm_add_pd(a1, _mm_mul_pd(tmpv, sv)); __m128d ev = _mm_load_pd(&e[l]); a2 = _mm_add_pd(a2, _mm_mul_pd(tmpv, ev)); } a0 = _mm_hadd_pd(a0, a0); a1 = _mm_hadd_pd(a1, a1); a2 = _mm_hadd_pd(a2, a2); _mm_storel_pd(&inv_Li, a0); _mm_storel_pd(&dlnLidlz, a1); _mm_storel_pd(&d2lnLidlz2, a2); inv_Li = 1.0/FABS(inv_Li); dlnLidlz *= inv_Li; d2lnLidlz2 *= inv_Li; dlnLdlz += wr1 * dlnLidlz; d2lnLdlz2 += wr2 * (d2lnLidlz2 - dlnLidlz * dlnLidlz); } *ext_dlnLdlz = dlnLdlz; *ext_d2lnLdlz2 = d2lnLdlz2; free(d_start); } #endif ExaML-3.0.21/examl/mic_native.h000066400000000000000000000105261330315463200161440ustar00rootroot00000000000000#ifndef MIC_NATIVE_H_ #define MIC_NATIVE_H_ //#define VECTOR_PADDING 8 //#define GET_PADDED_WIDTH(w) w % VECTOR_PADDING == 0 ? w : w + (VECTOR_PADDING - (w % VECTOR_PADDING)) // general functions void updateModel_MIC(pInfo* part); // DNA data void makeP_DNA_MIC(double z1, double z2, double *rptr, double *EI, double *EIGN, int numberOfCategories, double *left, double *right, boolean saveMem, int maxCat); void precomputeTips_DNA_MIC(int tipCase, double *tipVector, double *left, double *right, double *umpLeft, double *umpRight, int numberOfCategories); void newviewGTRGAMMA_MIC(int tipCase, double *x1, double *x2, double *x3, double *extEV, double *tipVector, unsigned char *tipX1, unsigned char *tipX2, int n, double *left, double *right, int *wgt, int *scalerIncrement, double *umpLeft, double *umpRight); double evaluateGAMMA_MIC(int *wptr, double *x1_start, double *x2_start, double *tipVector, unsigned char *tipX1, const int n, double *diagptable); void sumGAMMA_MIC(int tipCase, double *sumtable, double *x1_start, double *x2_start, double *tipVector, unsigned char *tipX1, unsigned char *tipX2, int n); void coreGTRGAMMA_MIC(const int upper, double *sumtable, volatile double *ext_dlnLdlz, volatile double *ext_d2lnLdlz2, double *EIGN, double *gammaRates, double lz, int *wrptr); void sumcoreGTRGAMMA_MIC(int tipCase, double *x1_start, double *x2_start, double *tipVector, unsigned char *tipX1, unsigned char *tipX2, const int n, volatile double *ext_dlnLdlz, volatile double *ext_d2lnLdlz2, double *EIGN, double *gammaRates, double lz, int *wgt); // protein data - single matrix void makeP_PROT_MIC(double z1, double z2, double *rptr, double *EI, double *EIGN, int numberOfCategories, double *left, double *right, boolean saveMem, int maxCat); void precomputeTips_PROT_MIC(int tipCase, double *tipVector, double *left, double *right, double *umpLeft, double *umpRight, int numberOfCategories); void newviewGTRGAMMAPROT_MIC(int tipCase, double *x1, double *x2, double *x3, double *extEV, double *tipVector, unsigned char *tipX1, unsigned char *tipX2, int n, double *left, double *right, int *wgt, int *scalerIncrement, double *umpLeft, double *umpRight); double evaluateGAMMAPROT_MIC(int *wptr, double *x1_start, double *x2_start, double *tipVector, unsigned char *tipX1, const int n, double *diagptable); void sumGAMMAPROT_MIC(int tipCase, double *sumtable, double *x1_start, double *x2_start, double *tipVector, unsigned char *tipX1, unsigned char *tipX2, int n); void coreGTRGAMMAPROT_MIC(const int upper, double *sumtable, volatile double *ext_dlnLdlz, volatile double *ext_d2lnLdlz2, double *EIGN, double *gammaRates, double lz, int *wrptr); // protein data - LG4 void updateModel_LG4_MIC(pInfo* part); void makeP_PROT_LG4_MIC(double z1, double z2, double *rptr, double *EI[4], double *EIGN[4], int numberOfCategories, double *left, double *right); void precomputeTips_PROT_LG4_MIC(int tipCase, double *tipVector[4], double *left, double *right, double *umpLeft, double *umpRight, int numberOfCategories); void newviewGTRGAMMAPROT_LG4_MIC(int tipCase, double *x1, double *x2, double *x3, double *extEV, double *tipVector, unsigned char *tipX1, unsigned char *tipX2, int n, double *left, double *right, int *wgt, int *scalerIncrement, double *umpLeft, double *umpRight); double evaluateGAMMAPROT_LG4_MIC(int *wptr, double *x1_start, double *x2_start, double *tipVector, unsigned char *tipX1, const int n, double *diagptable, double* weights); void sumGAMMAPROT_LG4_MIC(int tipCase, double *sumtable, double *x1_start, double *x2_start, double *tipVector, unsigned char *tipX1, unsigned char *tipX2, int n); void coreGTRGAMMAPROT_LG4_MIC(const int upper, double *sumtable, volatile double *ext_dlnLdlz, volatile double *ext_d2lnLdlz2, double *EIGN[4], double *gammaRates, double lz, int *wrptr, double* weights); #endif /* MIC_NATIVE_H_ */ ExaML-3.0.21/examl/mic_native_aa.c000066400000000000000000001147721330315463200166100ustar00rootroot00000000000000#include #include #include #include "axml.h" #include "mic_native.h" static const int states = 20; static const int statesSquare = 20 * 20; static const int span = 20 * 4; static const int maxStateValue = 23; void makeP_PROT_MIC(double z1, double z2, double *rptr, double *EI, double *EIGN, int numberOfCategories, double *left, double *right, boolean saveMem, int maxCat) { int i, j, k, span = states * numberOfCategories; /* assign some space for pre-computing and later re-using functions */ double lz1[20] __attribute__((aligned(BYTE_ALIGNMENT))); double lz2[20] __attribute__((aligned(BYTE_ALIGNMENT))); double d1[20] __attribute__((aligned(BYTE_ALIGNMENT))); double d2[20] __attribute__((aligned(BYTE_ALIGNMENT))); /* multiply branch lengths with eigenvalues */ for(i = 1; i < states; i++) { lz1[i] = EIGN[i] * z1; lz2[i] = EIGN[i] * z2; } /* loop over the number of rate categories, this will be 4 for the GAMMA model and variable for the CAT model */ for(i = 0; i < numberOfCategories; i++) { /* exponentiate the rate multiplied by the branch */ for(j = 1; j < states; j++) { d1[j] = EXP(rptr[i] * lz1[j]); d2[j] = EXP(rptr[i] * lz2[j]); } /* now fill the P matrices for the two branch length values */ for(j = 0; j < states; j++) { /* left and right are pre-allocated arrays */ left[i * states + j] = 1.0; right[i * states + j] = 1.0; for(k = 1; k < states; k++) { left[k * span + i * states + j] = d1[k] * EI[states * j + k]; right[k * span + i * states + j] = d2[k] * EI[states * j + k]; } } } /* if memory saving is enabled and we are using CAT we need to do one additional P matrix calculation for a rate of 1.0 to compute the entries of a column/tree site comprising only gaps */ if(saveMem) { i = maxCat; for(j = 1; j < states; j++) { d1[j] = EXP (lz1[j]); d2[j] = EXP (lz2[j]); } for(j = 0; j < states; j++) { left[statesSquare * i + states * j] = 1.0; right[statesSquare * i + states * j] = 1.0; for(k = 1; k < states; k++) { left[statesSquare * i + states * j + k] = d1[k] * EI[states * j + k]; right[statesSquare * i + states * j + k] = d2[k] * EI[states * j + k]; } } } } void precomputeTips_PROT_MIC(int tipCase, double *tipVector, double *left, double *right, double *umpLeft, double *umpRight, int numberOfCategories) { /* no precomputation needed if both children are inner nodes */ if (tipCase == INNER_INNER) return; const int span = states * numberOfCategories, umpSize = span * maxStateValue; for(int k = 0; k < umpSize; ++k) { umpLeft[k] = 0.0; umpRight[k] = 0.0; } for(int i = 0; i < maxStateValue; ++i) { for(int l = 0; l < states; ++l) { #pragma ivdep #pragma vector aligned for(int k = 0; k < span; ++k) { umpLeft[span * i + k] += tipVector[i * states + l] * left[l * span + k]; if (tipCase == TIP_TIP) umpRight[span * i + k] += tipVector[i * states + l] * right[l * span + k]; } } } } inline void mic_fma4x80(const double* inv, double* outv, double* mulv) { __mmask8 k1 = _mm512_int2mask(0x0F); __mmask8 k2 = _mm512_int2mask(0xF0); for(int l = 0; l < 80; l += 40) { __m512d t = _mm512_setzero_pd(); t = _mm512_extload_pd(&inv[l], _MM_UPCONV_PD_NONE, _MM_BROADCAST_1X8, _MM_HINT_NONE); __m512d m = _mm512_load_pd(&mulv[l]); __m512d acc = _mm512_load_pd(&outv[l]); __m512d r = _mm512_fmadd_pd(t, m, acc); _mm512_store_pd(&outv[l], r); m = _mm512_load_pd(&mulv[l + 8]); acc = _mm512_load_pd(&outv[l + 8]); r = _mm512_fmadd_pd(t, m, acc); _mm512_store_pd(&outv[l + 8], r); t = _mm512_mask_extload_pd(t, k1, &inv[l], _MM_UPCONV_PD_NONE, _MM_BROADCAST_1X8, _MM_HINT_NONE); t = _mm512_mask_extload_pd(t, k2, &inv[l+20], _MM_UPCONV_PD_NONE, _MM_BROADCAST_1X8, _MM_HINT_NONE); m = _mm512_load_pd(&mulv[l + 16]); acc = _mm512_load_pd(&outv[l + 16]); r = _mm512_fmadd_pd(t, m, acc); _mm512_store_pd(&outv[l + 16], r); t = _mm512_extload_pd(&inv[l+20], _MM_UPCONV_PD_NONE, _MM_BROADCAST_1X8, _MM_HINT_NONE); m = _mm512_load_pd(&mulv[l + 24]); acc = _mm512_load_pd(&outv[l + 24]); r = _mm512_fmadd_pd(t, m, acc); _mm512_store_pd(&outv[l + 24], r); m = _mm512_load_pd(&mulv[l + 32]); acc = _mm512_load_pd(&outv[l + 32]); r = _mm512_fmadd_pd(t, m, acc); _mm512_store_pd(&outv[l + 32], r); } } void newviewGTRGAMMAPROT_MIC(int tipCase, double *x1, double *x2, double *x3, double *extEV, double *tipVector, unsigned char *tipX1, unsigned char *tipX2, int n, double *left, double *right, int *wgt, int *scalerIncrement, double *umpLeft, double *umpRight) { __m512d minlikelihood_MIC = _mm512_set1_pd(minlikelihood); __m512d twotothe256_MIC = _mm512_set1_pd(twotothe256); __m512i absMask_MIC = _mm512_set1_epi64(0x7fffffffffffffffULL); int addScale = 0; double *aEV = extEV, *aRight = right, *aLeft = left, *umpX1 = umpLeft, *umpX2 = umpRight; switch(tipCase) { case TIP_TIP: { /* multiply all possible tip state vectors with the respective P-matrices */ for (int i = 0; i < n; i++) { const double *uX1 = &umpX1[span * tipX1[i]]; const double *uX2 = &umpX2[span * tipX2[i]]; double uX[span] __attribute__((aligned(BYTE_ALIGNMENT))); double* v3 = &x3[i * span]; #pragma ivdep #pragma vector aligned for(int l = 0; l < span; ++l) { uX[l] = uX1[l] * uX2[l]; v3[l] = 0.; } for(int k = 0; k < states; ++k) { for (int j = 0; j < span; j += 8) { _mm_prefetch((const char *)&aEV[span*(k+1) + j], _MM_HINT_T0); } mic_fma4x80(&uX[k], v3, &aEV[k * span]); } } // sites loop } break; case TIP_INNER: { for (int i = 0; i < n; i++) { #pragma unroll(10) for (int j = 0; j < span; j += 8) { _mm_prefetch((const char *)&x2[span*(i+1) + j], _MM_HINT_T1); // _mm_prefetch((const char *)&x2[span*(i+1) + j], _MM_HINT_T0); } /* access pre-computed value based on the raw sequence data tipX1 that is used as an index */ double* uX1 = &umpX1[span * tipX1[i]]; double uX2[span] __attribute__((aligned(BYTE_ALIGNMENT))); double uX[span] __attribute__((aligned(BYTE_ALIGNMENT))); double* v3 = &(x3[span * i]); const double* v2 = &(x2[span * i]); #pragma vector aligned for(int l = 0; l < span; ++l) { uX2[l] = 0.; } for(int k = 0; k < states; ++k) { #pragma unroll(10) for (int j = 0; j < span; j += 8) { _mm_prefetch((const char *)&aRight[span*(k+1) + j], _MM_HINT_T0); } mic_fma4x80(&v2[k], uX2, &aRight[k * span]); } #pragma ivdep #pragma vector aligned for(int l = 0; l < span; ++l) { uX[l] = uX1[l] * uX2[l]; v3[l] = 0.; } for(int k = 0; k < states; ++k) { #pragma unroll(10) for (int j = 0; j < span; j += 8) { _mm_prefetch((const char *)&aEV[span*(k+1) + j], _MM_HINT_T0); } mic_fma4x80(&uX[k], v3, &aEV[k * span]); } __m512d t1 = _mm512_load_pd(&v3[0]); t1 = _mm512_castsi512_pd(_mm512_and_epi64(_mm512_castpd_si512(t1), absMask_MIC)); double vmax = _mm512_reduce_max_pd(t1); for (int l = 8; l < span; l += 8) { __m512d t = _mm512_load_pd(&v3[l]); t = _mm512_castsi512_pd(_mm512_and_epi64(_mm512_castpd_si512(t), absMask_MIC)); double vmax2 = _mm512_reduce_max_pd(t); vmax = MAX(vmax, vmax2); } if (vmax < minlikelihood) { #pragma vector aligned nontemporal for(int l = 0; l < span; l++) v3[l] *= twotothe256; addScale += wgt[i]; } } // site loop } break; case INNER_INNER: { /* same as above, without pre-computations */ for (int i = 0; i < n; i++) { #pragma unroll(10) for (int j = 0; j < span; j += 8) { _mm_prefetch((const char *)&x1[span*(i+1) + j], _MM_HINT_T1); _mm_prefetch((const char *)&x2[span*(i+1) + j], _MM_HINT_T1); // _mm_prefetch((const char *)&x1[span*(i+1) + j], _MM_HINT_T0); // _mm_prefetch((const char *)&x2[span*(i+1) + j], _MM_HINT_T0); } double uX1[span] __attribute__((aligned(BYTE_ALIGNMENT))); double uX2[span] __attribute__((aligned(BYTE_ALIGNMENT))); double uX[span] __attribute__((aligned(BYTE_ALIGNMENT))); double* v3 = &(x3[span * i]); const double* v1 = &(x1[span * i]); const double* v2 = &(x2[span * i]); #pragma vector aligned for(int l = 0; l < span; ++l) { uX1[l] = 0.; uX2[l] = 0.; } for(int k = 0; k < states; ++k) { #pragma unroll(10) for (int j = 0; j < span; j += 8) { _mm_prefetch((const char *)&aRight[span*(k+1) + j], _MM_HINT_T0); _mm_prefetch((const char *)&aLeft[span*(k+1) + j], _MM_HINT_T0); } mic_fma4x80(&v1[k], uX1, &aLeft[k * span]); mic_fma4x80(&v2[k], uX2, &aRight[k * span]); } #pragma ivdep #pragma vector aligned for(int l = 0; l < span; ++l) { uX[l] = uX1[l] * uX2[l]; v3[l] = 0.; } for(int k = 0; k < states; ++k) { #pragma unroll(10) for (int j = 0; j < span; j += 8) { _mm_prefetch((const char *)&aEV[span*(k+1) + j], _MM_HINT_T0); } mic_fma4x80(&uX[k], v3, &aEV[k * span]); } __m512d t1 = _mm512_load_pd(&v3[0]); t1 = _mm512_castsi512_pd(_mm512_and_epi64(_mm512_castpd_si512(t1), absMask_MIC)); double vmax = _mm512_reduce_max_pd(t1); for (int l = 8; l < span; l += 8) { __m512d t = _mm512_load_pd(&v3[l]); t = _mm512_castsi512_pd(_mm512_and_epi64(_mm512_castpd_si512(t), absMask_MIC)); double vmax2 = _mm512_reduce_max_pd(t); vmax = MAX(vmax, vmax2); } if (vmax < minlikelihood) { #pragma vector aligned nontemporal for(int l = 0; l < span; l++) v3[l] *= twotothe256; addScale += wgt[i]; } } } break; default: // assert(0); break; } *scalerIncrement = addScale; } double evaluateGAMMAPROT_MIC(int *wgt, double *x1_start, double *x2_start, double *tipVector, unsigned char *tipX1, const int n, double *diagptable) { double sum = 0.0; /* the left node is a tip */ if(tipX1) { double *aTipVec = tipVector; /* loop over the sites of this partition */ for (int i = 0; i < n; i++) { /* access pre-computed tip vector values via a lookup table */ const double *x1 = &(aTipVec[span * tipX1[i]]); /* access the other(inner) node at the other end of the branch */ const double *x2 = &(x2_start[span * i]); #pragma unroll(10) for (int k = 0; k < span; k += 8) { _mm_prefetch((const char *) &x2_start[span*(i+2) + k], _MM_HINT_T1); _mm_prefetch((const char *) &x2_start[span*(i+1) + k], _MM_HINT_T0); } double term = 0.; #pragma ivdep #pragma vector aligned #pragma noprefetch x2 for(int j = 0; j < span; j++) { term += x1[j] * x2[j] * diagptable[j]; } term = log(0.25 * fabs(term)); sum += wgt[i] * term; } } else { for (int i = 0; i < n; i++) { #pragma unroll(10) for (int k = 0; k < span; k += 8) { _mm_prefetch((const char *) &x1_start[span*(i+2) + k], _MM_HINT_T1); _mm_prefetch((const char *) &x1_start[span*(i+1) + k], _MM_HINT_T0); _mm_prefetch((const char *) &x2_start[span*(i+2) + k], _MM_HINT_T1); _mm_prefetch((const char *) &x2_start[span*(i+1) + k], _MM_HINT_T0); } const double *x1 = &(x1_start[span * i]); const double *x2 = &(x2_start[span * i]); double term = 0.; #pragma ivdep #pragma vector aligned #pragma noprefetch x1 x2 for(int j = 0; j < span; j++) term += x1[j] * x2[j] * diagptable[j]; term = log(0.25 * fabs(term)); sum += wgt[i] * term; } } return sum; } void sumGAMMAPROT_MIC(int tipCase, double *sumtable, double *x1_start, double *x2_start, double *tipVector, unsigned char *tipX1, unsigned char *tipX2, int n) { double *aTipVec = tipVector; switch(tipCase) { case TIP_TIP: { for(int i = 0; i < n; i++) { const double *left = &(aTipVec[span * tipX1[i]]); const double *right = &(aTipVec[span * tipX2[i]]); #pragma ivdep #pragma vector aligned nontemporal for(int l = 0; l < span; l++) { sumtable[i * span + l] = left[l] * right[l]; } } } break; case TIP_INNER: { for(int i = 0; i < n; i++) { #pragma unroll(10) for (int k = 0; k < span; k += 8) { _mm_prefetch((const char *) &x2_start[span*(i+2) + k], _MM_HINT_T1); _mm_prefetch((const char *) &x2_start[span*(i+1) + k], _MM_HINT_T0); } const double *left = &(aTipVec[span * tipX1[i]]); const double *right = &(x2_start[span * i]); #pragma ivdep #pragma vector aligned nontemporal #pragma noprefetch right for(int l = 0; l < span; l++) { sumtable[i * span + l] = left[l] * right[l]; } } } break; case INNER_INNER: { for(int i = 0; i < n; i++) { #pragma unroll(10) for (int k = 0; k < span; k += 8) { _mm_prefetch((const char *) &x1_start[span*(i+2) + k], _MM_HINT_T1); _mm_prefetch((const char *) &x1_start[span*(i+1) + k], _MM_HINT_T0); _mm_prefetch((const char *) &x2_start[span*(i+2) + k], _MM_HINT_T1); _mm_prefetch((const char *) &x2_start[span*(i+1) + k], _MM_HINT_T0); } const double *left = &(x1_start[span * i]); const double *right = &(x2_start[span * i]); #pragma ivdep #pragma vector aligned nontemporal #pragma noprefetch left right for(int l = 0; l < span; l++) { sumtable[i * span + l] = left[l] * right[l]; } } } break; // default: // assert(0); } } void coreGTRGAMMAPROT_MIC(const int upper, double *sumtable, volatile double *ext_dlnLdlz, volatile double *ext_d2lnLdlz2, double *EIGN, double *gammaRates, double lz, int *wgt) { static const int states = 20; static const int span = 20 * 4; double diagptable0[span] __attribute__((aligned(BYTE_ALIGNMENT))); double diagptable1[span] __attribute__((aligned(BYTE_ALIGNMENT))); double diagptable2[span] __attribute__((aligned(BYTE_ALIGNMENT))); double diagptable01[span] __attribute__((aligned(BYTE_ALIGNMENT))); double diagptable02[span] __attribute__((aligned(BYTE_ALIGNMENT))); /* pre-compute the derivatives of the P matrix for all discrete GAMMA rates */ for(int i = 0; i < 4; i++) { const double ki = gammaRates[i]; const double kisqr = ki * ki; diagptable0[i*states] = 1.; diagptable1[i*states] = 0.; diagptable2[i*states] = 0.; for(int l = 1; l < states; l++) { diagptable0[i * states + l] = exp(EIGN[l] * ki * lz); diagptable1[i * states + l] = EIGN[l] * ki; diagptable2[i * states + l] = EIGN[l] * EIGN[l] * kisqr; } } #pragma ivdep for(int i = 0; i < span; i++) { diagptable01[i] = diagptable0[i] * diagptable1[i]; diagptable02[i] = diagptable0[i] * diagptable2[i]; } /* loop over sites in this partition */ const int aligned_width = upper % 8 == 0 ? upper / 8 : upper / 8 + 1; double dlnLBuf[8] __attribute__((aligned(BYTE_ALIGNMENT))); double d2lnLBuf[8] __attribute__((aligned(BYTE_ALIGNMENT))); for (int j = 0; j < 8; ++j) { dlnLBuf[j] = 0.; d2lnLBuf[j] = 0.; } __mmask16 k1 = _mm512_int2mask(0x000000FF); for (int i = 0; i < aligned_width; i++) { /* access the array with pre-computed values */ const double *sum = &sumtable[i * span * 8]; /* initial per-site likelihood and 1st and 2nd derivatives */ double invBuf[8] __attribute__((aligned(BYTE_ALIGNMENT))); double d1Buf[8] __attribute__((aligned(BYTE_ALIGNMENT))); double d2Buf[8] __attribute__((aligned(BYTE_ALIGNMENT))); __m512d invVec; __m512d d1Vec; __m512d d2Vec; int mask = 0x01; #pragma noprefetch sum #pragma unroll(8) for(int j = 0; j < 8; j++) { #pragma unroll(10) for (int k = 0; k < span; k += 8) { _mm_prefetch((const char *) &sum[span*(j+2) + k], _MM_HINT_T1); _mm_prefetch((const char *) &sum[span*(j+1) + k], _MM_HINT_T0); } __m512d inv_1 = _mm512_setzero_pd(); __m512d d1_1 = _mm512_setzero_pd(); __m512d d2_1 = _mm512_setzero_pd(); for (int offset = 0; offset < span; offset += 8) { __m512d d0_1 = _mm512_load_pd(&diagptable0[offset]); __m512d d01_1 = _mm512_load_pd(&diagptable01[offset]); __m512d d02_1 = _mm512_load_pd(&diagptable02[offset]); __m512d s_1 = _mm512_load_pd(&sum[j*span + offset]); inv_1 = _mm512_fmadd_pd(d0_1, s_1, inv_1); d1_1 = _mm512_fmadd_pd(d01_1, s_1, d1_1); d2_1 = _mm512_fmadd_pd(d02_1, s_1, d2_1); } __mmask8 k1 = _mm512_int2mask(mask); mask <<= 1; // reduce inv_1 = _mm512_add_pd (inv_1, _mm512_swizzle_pd(inv_1, _MM_SWIZ_REG_CDAB)); inv_1 = _mm512_add_pd (inv_1, _mm512_swizzle_pd(inv_1, _MM_SWIZ_REG_BADC)); inv_1 = _mm512_add_pd (inv_1, _mm512_castsi512_pd(_mm512_permute4f128_epi32(_mm512_castpd_si512(inv_1), _MM_PERM_BADC))); invVec = _mm512_mask_mov_pd(invVec, k1, inv_1); d1_1 = _mm512_add_pd (d1_1, _mm512_swizzle_pd(d1_1, _MM_SWIZ_REG_CDAB)); d1_1 = _mm512_add_pd (d1_1, _mm512_swizzle_pd(d1_1, _MM_SWIZ_REG_BADC)); d1_1 = _mm512_add_pd (d1_1, _mm512_castsi512_pd(_mm512_permute4f128_epi32(_mm512_castpd_si512(d1_1), _MM_PERM_BADC))); d1Vec = _mm512_mask_mov_pd(d1Vec, k1, d1_1); d2_1 = _mm512_add_pd (d2_1, _mm512_swizzle_pd(d2_1, _MM_SWIZ_REG_CDAB)); d2_1 = _mm512_add_pd (d2_1, _mm512_swizzle_pd(d2_1, _MM_SWIZ_REG_BADC)); d2_1 = _mm512_add_pd (d2_1, _mm512_castsi512_pd(_mm512_permute4f128_epi32(_mm512_castpd_si512(d2_1), _MM_PERM_BADC))); d2Vec = _mm512_mask_mov_pd(d2Vec, k1, d2_1); } _mm512_store_pd(&invBuf[0], invVec); _mm512_store_pd(&d1Buf[0], d1Vec); _mm512_store_pd(&d2Buf[0], d2Vec); #pragma ivdep #pragma vector aligned for (int j = 0; j < 8; ++j) { const double inv_Li = 1.0 / invBuf[j]; const double d1 = d1Buf[j] * inv_Li; const double d2 = d2Buf[j] * inv_Li; dlnLBuf[j] += wgt[i * 8 + j] * d1; d2lnLBuf[j] += wgt[i * 8 + j] * (d2 - d1 * d1); } } // site loop double dlnLdlz = 0.; double d2lnLdlz2 = 0.; for (int j = 0; j < 8; ++j) { dlnLdlz += dlnLBuf[j]; d2lnLdlz2 += d2lnLBuf[j]; } *ext_dlnLdlz = dlnLdlz; *ext_d2lnLdlz2 = d2lnLdlz2; } /**** * PROTEIN - LG4 */ void updateModel_LG4_MIC(pInfo* part) { double **EV = part->EV_LG4, **tipVector = part->tipVector_LG4, *aEV = part->mic_EV, *aTipVector = part->mic_tipVector; const int states = part->states, span = 4 * states, maxState = getUndetermined(part->dataType) + 1; int k, l; #pragma ivdep for (l = 0; l < 4 * states * states; ++l) { aEV[l] = EV[(l % span) / states][(l / span) * states + (l % states)]; } for(int k = 0; k < maxState; k++) { for(int j = 0; j < 4; j++) { for(int l = 0; l < states; l++) { aTipVector[k*span + j*states + l] = tipVector[j][k*states + l]; } } } } void makeP_PROT_LG4_MIC(double z1, double z2, double *rptr, double *EI[4], double *EIGN[4], int numberOfCategories, double *left, double *right) { int i, j, k; double d1[64], d2[64]; for(i = 0; i < numberOfCategories; i++) { for(j = 1; j < states; j++) { d1[j] = EXP (rptr[i] * EIGN[i][j] * z1); d2[j] = EXP (rptr[i] * EIGN[i][j] * z2); } for(j = 0; j < states; j++) { left[i * states + j] = 1.0; right[i * states + j] = 1.0; for(k = 1; k < states; k++) { left[k * span + i * states + j] = d1[k] * EI[i][states * j + k]; right[k * span + i * states + j] = d2[k] * EI[i][states * j + k]; } } } } void precomputeTips_PROT_LG4_MIC(int tipCase, double *tipVector[4], double *left, double *right, double *umpLeft, double *umpRight, int numberOfCategories) { /* no precomputation needed if both children are inner nodes */ if (tipCase == INNER_INNER) return; const int span = states * numberOfCategories, umpSize = span * maxStateValue; for(int k = 0; k < umpSize; ++k) { umpLeft[k] = 0.0; umpRight[k] = 0.0; } for(int i = 0; i < maxStateValue; ++i) { for(int l = 0; l < states; ++l) { #pragma ivdep #pragma vector aligned for(int k = 0; k < span; ++k) { umpLeft[span * i + k] += tipVector[k/20][i * states + l] * left[l * span + k]; if (tipCase == TIP_TIP) umpRight[span * i + k] += tipVector[k/20][i * states + l] * right[l * span + k]; } } } } void newviewGTRGAMMAPROT_LG4_MIC(int tipCase, double *x1, double *x2, double *x3, double *extEV, double *tipVector, unsigned char *tipX1, unsigned char *tipX2, int n, double *left, double *right, int *wgt, int *scalerIncrement, double *umpLeft, double *umpRight) { __m512d minlikelihood_MIC = _mm512_set1_pd(minlikelihood); __m512d twotothe256_MIC = _mm512_set1_pd(twotothe256); __m512i absMask_MIC = _mm512_set1_epi64(0x7fffffffffffffffULL); int addScale = 0; /* we assume that P-matrix and eigenvectors are in correct layout already */ double *aEV = extEV, *aRight = right, *aLeft = left, *umpX1 = umpLeft, *umpX2 = umpRight; switch(tipCase) { case TIP_TIP: { for (int i = 0; i < n; i++) { const double *uX1 = &umpX1[span * tipX1[i]]; const double *uX2 = &umpX2[span * tipX2[i]]; double uX[span] __attribute__((aligned(BYTE_ALIGNMENT))); double* v3 = &x3[i * span]; #pragma ivdep #pragma vector aligned for(int l = 0; l < span; ++l) { uX[l] = uX1[l] * uX2[l]; v3[l] = 0.; } for(int k = 0; k < states; ++k) { for (int j = 0; j < span; j += 8) { _mm_prefetch((const char *)&aEV[span*(k+1) + j], _MM_HINT_T0); } mic_fma4x80(&uX[k], v3, &aEV[k * span]); } } // sites loop } break; case TIP_INNER: { for (int i = 0; i < n; i++) { #pragma unroll(10) for (int j = 0; j < span; j += 8) { _mm_prefetch((const char *)&x2[span*(i+1) + j], _MM_HINT_T1); // _mm_prefetch((const char *)&x2[span*(i+1) + j], _MM_HINT_T0); } /* access pre-computed value based on the raw sequence data tipX1 that is used as an index */ double* uX1 = &umpX1[span * tipX1[i]]; double uX2[span] __attribute__((aligned(BYTE_ALIGNMENT))); double uX[span] __attribute__((aligned(BYTE_ALIGNMENT))); double* v3 = &(x3[span * i]); const double* v2 = &(x2[span * i]); #pragma vector aligned for(int l = 0; l < span; ++l) { uX2[l] = 0.; } for(int k = 0; k < states; ++k) { #pragma unroll(10) for (int j = 0; j < span; j += 8) { _mm_prefetch((const char *)&aRight[span*(k+1) + j], _MM_HINT_T0); } mic_fma4x80(&v2[k], uX2, &aRight[k * span]); } #pragma ivdep #pragma vector aligned for(int l = 0; l < span; ++l) { uX[l] = uX1[l] * uX2[l]; v3[l] = 0.; } for(int k = 0; k < states; ++k) { #pragma unroll(10) for (int j = 0; j < span; j += 8) { _mm_prefetch((const char *)&aEV[span*(k+1) + j], _MM_HINT_T0); } mic_fma4x80(&uX[k], v3, &aEV[k * span]); } __m512d t1 = _mm512_load_pd(&v3[0]); t1 = _mm512_castsi512_pd(_mm512_and_epi64(_mm512_castpd_si512(t1), absMask_MIC)); double vmax = _mm512_reduce_max_pd(t1); for (int l = 8; l < span; l += 8) { __m512d t = _mm512_load_pd(&v3[l]); t = _mm512_castsi512_pd(_mm512_and_epi64(_mm512_castpd_si512(t), absMask_MIC)); double vmax2 = _mm512_reduce_max_pd(t); vmax = MAX(vmax, vmax2); } if (vmax < minlikelihood) { #pragma vector aligned nontemporal for(int l = 0; l < span; l++) v3[l] *= twotothe256; addScale += wgt[i]; } } // site loop } break; case INNER_INNER: { for (int i = 0; i < n; i++) { #pragma unroll(10) for (int j = 0; j < span; j += 8) { _mm_prefetch((const char *)&x1[span*(i+1) + j], _MM_HINT_T1); _mm_prefetch((const char *)&x2[span*(i+1) + j], _MM_HINT_T1); // _mm_prefetch((const char *)&x1[span*(i+1) + j], _MM_HINT_T0); // _mm_prefetch((const char *)&x2[span*(i+1) + j], _MM_HINT_T0); } double uX1[span] __attribute__((aligned(BYTE_ALIGNMENT))); double uX2[span] __attribute__((aligned(BYTE_ALIGNMENT))); double uX[span] __attribute__((aligned(BYTE_ALIGNMENT))); double* v3 = &(x3[span * i]); const double* v1 = &(x1[span * i]); const double* v2 = &(x2[span * i]); #pragma vector aligned for(int l = 0; l < span; ++l) { uX1[l] = 0.; uX2[l] = 0.; } for(int k = 0; k < states; ++k) { #pragma unroll(10) for (int j = 0; j < span; j += 8) { _mm_prefetch((const char *)&aRight[span*(k+1) + j], _MM_HINT_T0); _mm_prefetch((const char *)&aLeft[span*(k+1) + j], _MM_HINT_T0); } mic_fma4x80(&v1[k], uX1, &aLeft[k * span]); mic_fma4x80(&v2[k], uX2, &aRight[k * span]); } #pragma ivdep #pragma vector aligned for(int l = 0; l < span; ++l) { uX[l] = uX1[l] * uX2[l]; v3[l] = 0.; } for(int k = 0; k < states; ++k) { #pragma unroll(10) for (int j = 0; j < span; j += 8) { _mm_prefetch((const char *)&aEV[span*(k+1) + j], _MM_HINT_T0); } mic_fma4x80(&uX[k], v3, &aEV[k * span]); } __m512d t1 = _mm512_load_pd(&v3[0]); t1 = _mm512_castsi512_pd(_mm512_and_epi64(_mm512_castpd_si512(t1), absMask_MIC)); double vmax = _mm512_reduce_max_pd(t1); for (int l = 8; l < span; l += 8) { __m512d t = _mm512_load_pd(&v3[l]); t = _mm512_castsi512_pd(_mm512_and_epi64(_mm512_castpd_si512(t), absMask_MIC)); // double vmax2 = _mm512_reduce_max_pd(t); double vmax2 = _mm512_reduce_max_pd(t); vmax = MAX(vmax, vmax2); } if (vmax < minlikelihood) { #pragma vector aligned nontemporal for(int l = 0; l < span; l++) v3[l] *= twotothe256; addScale += wgt[i]; } } } break; default: // assert(0); break; } *scalerIncrement = addScale; } double evaluateGAMMAPROT_LG4_MIC(int *wgt, double *x1_start, double *x2_start, double *tipVector, unsigned char *tipX1, const int n, double *diagptable, double *weights) { double wtable[span] __attribute__((aligned(BYTE_ALIGNMENT))); /* pre-multiply diagptable entries with the corresponding weights */ for(int j = 0; j < 4; j++) for(int k = 0; k < states; k++) { wtable[j * states + k] = diagptable[j * states + k] * weights[j]; } double sum = 0.0; /* the left node is a tip */ if(tipX1) { /* loop over the sites of this partition */ for (int i = 0; i < n; i++) { const double *aTipVec = tipVector; /* access pre-computed tip vector values via a lookup table */ const double *x1 = &(aTipVec[span * tipX1[i]]); /* access the other(inner) node at the other end of the branch */ const double *x2 = &(x2_start[span * i]); #pragma unroll(10) for (int k = 0; k < span; k += 8) { _mm_prefetch((const char *) &x2_start[span*(i+2) + k], _MM_HINT_T1); _mm_prefetch((const char *) &x2_start[span*(i+1) + k], _MM_HINT_T0); } double term = 0.; #pragma ivdep #pragma vector aligned #pragma noprefetch x2 for(int j = 0; j < span; j++) { term += x1[j] * x2[j] * wtable[j]; } term = log(fabs(term)); sum += wgt[i] * term; } } else { for (int i = 0; i < n; i++) { #pragma unroll(10) for (int k = 0; k < span; k += 8) { _mm_prefetch((const char *) &x1_start[span*(i+2) + k], _MM_HINT_T1); _mm_prefetch((const char *) &x1_start[span*(i+1) + k], _MM_HINT_T0); _mm_prefetch((const char *) &x2_start[span*(i+2) + k], _MM_HINT_T1); _mm_prefetch((const char *) &x2_start[span*(i+1) + k], _MM_HINT_T0); } const double *x1 = &(x1_start[span * i]); const double *x2 = &(x2_start[span * i]); double term = 0.; #pragma ivdep #pragma vector aligned #pragma noprefetch x1 x2 for(int j = 0; j < span; j++) term += x1[j] * x2[j] * wtable[j]; term = log(fabs(term)); sum += wgt[i] * term; } } return sum; } void sumGAMMAPROT_LG4_MIC(int tipCase, double *sumtable, double *x1_start, double *x2_start, double *tipVector, unsigned char *tipX1, unsigned char *tipX2, int n) { const double *aTipVec = tipVector; switch(tipCase) { case TIP_TIP: { for(int i = 0; i < n; i++) { const double *left = &(aTipVec[span * tipX1[i]]); const double *right = &(aTipVec[span * tipX2[i]]); #pragma ivdep #pragma vector aligned nontemporal for(int l = 0; l < span; l++) { sumtable[i * span + l] = left[l] * right[l]; } } } break; case TIP_INNER: { for(int i = 0; i < n; i++) { #pragma unroll(10) for (int k = 0; k < span; k += 8) { _mm_prefetch((const char *) &x2_start[span*(i+2) + k], _MM_HINT_T1); _mm_prefetch((const char *) &x2_start[span*(i+1) + k], _MM_HINT_T0); } const double *left = &(aTipVec[span * tipX1[i]]); const double *right = &(x2_start[span * i]); #pragma ivdep #pragma vector aligned nontemporal #pragma noprefetch right for(int l = 0; l < span; l++) { sumtable[i * span + l] = left[l] * right[l]; } } } break; case INNER_INNER: { for(int i = 0; i < n; i++) { #pragma unroll(10) for (int k = 0; k < span; k += 8) { _mm_prefetch((const char *) &x1_start[span*(i+2) + k], _MM_HINT_T1); _mm_prefetch((const char *) &x1_start[span*(i+1) + k], _MM_HINT_T0); _mm_prefetch((const char *) &x2_start[span*(i+2) + k], _MM_HINT_T1); _mm_prefetch((const char *) &x2_start[span*(i+1) + k], _MM_HINT_T0); } const double *left = &(x1_start[span * i]); const double *right = &(x2_start[span * i]); #pragma ivdep #pragma vector aligned nontemporal #pragma noprefetch left right for(int l = 0; l < span; l++) { sumtable[i * span + l] = left[l] * right[l]; } } } break; // default: // assert(0); } } void coreGTRGAMMAPROT_LG4_MIC(const int upper, double *sumtable, volatile double *ext_dlnLdlz, volatile double *ext_d2lnLdlz2, double *EIGN[4], double *gammaRates, double lz, int *wgt, double *weights) { double diagptable0[span] __attribute__((aligned(BYTE_ALIGNMENT))); double diagptable1[span] __attribute__((aligned(BYTE_ALIGNMENT))); double diagptable2[span] __attribute__((aligned(BYTE_ALIGNMENT))); double diagptable01[span] __attribute__((aligned(BYTE_ALIGNMENT))); double diagptable02[span] __attribute__((aligned(BYTE_ALIGNMENT))); /* pre-compute the derivatives of the P matrix for all discrete GAMMA rates */ for(int i = 0; i < 4; i++) { const double ki = gammaRates[i]; const double kisqr = ki * ki; diagptable0[i*states] = 1. * weights[i]; diagptable1[i*states] = 0.; diagptable2[i*states] = 0.; for(int l = 1; l < states; l++) { diagptable0[i * states + l] = exp(EIGN[i][l] * ki * lz) * weights[i]; diagptable1[i * states + l] = EIGN[i][l] * ki; diagptable2[i * states + l] = EIGN[i][l] * EIGN[i][l] * kisqr; } } #pragma ivdep for(int i = 0; i < span; i++) { diagptable01[i] = diagptable0[i] * diagptable1[i]; diagptable02[i] = diagptable0[i] * diagptable2[i]; } /* loop over sites in this partition */ const int aligned_width = upper % 8 == 0 ? upper / 8 : upper / 8 + 1; double dlnLdlz = 0.; double d2lnLdlz2 = 0.; __mmask16 k1 = _mm512_int2mask(0x000000FF); for (int i = 0; i < aligned_width; i++) { /* access the array with pre-computed values */ const double *sum = &sumtable[i * span * 8]; /* initial per-site likelihood and 1st and 2nd derivatives */ double invBuf[8] __attribute__((aligned(BYTE_ALIGNMENT))); double d1Buf[8] __attribute__((aligned(BYTE_ALIGNMENT))); double d2Buf[8] __attribute__((aligned(BYTE_ALIGNMENT))); __m512d invVec; __m512d d1Vec; __m512d d2Vec; int mask = 0x01; #pragma noprefetch sum #pragma unroll(8) for(int j = 0; j < 8; j++) { #pragma unroll(10) for (int k = 0; k < span; k += 8) { _mm_prefetch((const char *) &sum[span*(j+2) + k], _MM_HINT_T1); _mm_prefetch((const char *) &sum[span*(j+1) + k], _MM_HINT_T0); } __m512d inv_1 = _mm512_setzero_pd(); __m512d d1_1 = _mm512_setzero_pd(); __m512d d2_1 = _mm512_setzero_pd(); for (int offset = 0; offset < span; offset += 8) { __m512d d0_1 = _mm512_load_pd(&diagptable0[offset]); __m512d d01_1 = _mm512_load_pd(&diagptable01[offset]); __m512d d02_1 = _mm512_load_pd(&diagptable02[offset]); __m512d s_1 = _mm512_load_pd(&sum[j*span + offset]); inv_1 = _mm512_fmadd_pd(d0_1, s_1, inv_1); d1_1 = _mm512_fmadd_pd(d01_1, s_1, d1_1); d2_1 = _mm512_fmadd_pd(d02_1, s_1, d2_1); } __mmask8 k1 = _mm512_int2mask(mask); mask <<= 1; // reduce inv_1 = _mm512_add_pd (inv_1, _mm512_swizzle_pd(inv_1, _MM_SWIZ_REG_CDAB)); inv_1 = _mm512_add_pd (inv_1, _mm512_swizzle_pd(inv_1, _MM_SWIZ_REG_BADC)); inv_1 = _mm512_add_pd (inv_1, _mm512_castsi512_pd(_mm512_permute4f128_epi32(_mm512_castpd_si512(inv_1), _MM_PERM_BADC))); invVec = _mm512_mask_mov_pd(invVec, k1, inv_1); d1_1 = _mm512_add_pd (d1_1, _mm512_swizzle_pd(d1_1, _MM_SWIZ_REG_CDAB)); d1_1 = _mm512_add_pd (d1_1, _mm512_swizzle_pd(d1_1, _MM_SWIZ_REG_BADC)); d1_1 = _mm512_add_pd (d1_1, _mm512_castsi512_pd(_mm512_permute4f128_epi32(_mm512_castpd_si512(d1_1), _MM_PERM_BADC))); d1Vec = _mm512_mask_mov_pd(d1Vec, k1, d1_1); d2_1 = _mm512_add_pd (d2_1, _mm512_swizzle_pd(d2_1, _MM_SWIZ_REG_CDAB)); d2_1 = _mm512_add_pd (d2_1, _mm512_swizzle_pd(d2_1, _MM_SWIZ_REG_BADC)); d2_1 = _mm512_add_pd (d2_1, _mm512_castsi512_pd(_mm512_permute4f128_epi32(_mm512_castpd_si512(d2_1), _MM_PERM_BADC))); d2Vec = _mm512_mask_mov_pd(d2Vec, k1, d2_1); } _mm512_store_pd(&invBuf[0], invVec); _mm512_store_pd(&d1Buf[0], d1Vec); _mm512_store_pd(&d2Buf[0], d2Vec); #pragma ivdep #pragma vector aligned for (int j = 0; j < 8; ++j) { const double inv_Li = 1.0 / invBuf[j]; const double d1 = d1Buf[j] * inv_Li; const double d2 = d2Buf[j] * inv_Li; dlnLdlz += wgt[i * 8 + j] * d1; d2lnLdlz2 += wgt[i * 8 + j] * (d2 - d1 * d1); } } // site loop *ext_dlnLdlz = dlnLdlz; *ext_d2lnLdlz2 = d2lnLdlz2; } ExaML-3.0.21/examl/mic_native_dna.c000066400000000000000000000510721330315463200167620ustar00rootroot00000000000000#include #include #include #include "axml.h" #include "mic_native.h" static const int states = 4; static const int statesSquare = 16; static const int span = 4 * 4; static const int maxStateValue = 16; /* Common functions */ void updateModel_MIC(pInfo* part) { double *EV = part->EV, *tipVector = part->tipVector, *aEV = part->mic_EV, *aTipVector = part->mic_tipVector; const int states = part->states, span = 4 * states, maxState = getUndetermined(part->dataType) + 1; int k, l; #pragma ivdep for (l = 0; l < 4 * states * states; ++l) { aEV[l] = EV[(l / span) * states + (l % states)]; } for(int k = 0; k < maxState; k++) { #pragma ivdep for(int l = 0; l < states; l++) { aTipVector[k*span + l] = aTipVector[k*span + states + l] = aTipVector[k*span + 2*states + l] = aTipVector[k*span + 3*states + l] = tipVector[k*states + l]; } } } /* DNA */ void makeP_DNA_MIC(double z1, double z2, double *rptr, double *EI, double *EIGN, int numberOfCategories, double *left, double *right, boolean saveMem, int maxCat) { int i, j, k, span = states * numberOfCategories; /* assign some space for pre-computing and later re-using functions */ double lz1[4] __attribute__((aligned(BYTE_ALIGNMENT))); double lz2[4] __attribute__((aligned(BYTE_ALIGNMENT))); double d1[4] __attribute__((aligned(BYTE_ALIGNMENT))); double d2[4] __attribute__((aligned(BYTE_ALIGNMENT))); /* multiply branch lengths with eigenvalues */ for(i = 1; i < states; i++) { lz1[i] = EIGN[i] * z1; lz2[i] = EIGN[i] * z2; } /* loop over the number of rate categories, this will be 4 for the GAMMA model and variable for the CAT model */ for(i = 0; i < numberOfCategories; i++) { /* exponentiate the rate multiplied by the branch */ for(j = 1; j < states; j++) { d1[j] = EXP(rptr[i] * lz1[j]); d2[j] = EXP(rptr[i] * lz2[j]); } /* now fill the P matrices for the two branch length values */ for(j = 0; j < states; j++) { /* left and right are pre-allocated arrays */ left[i * states + j] = 1.0; right[i * states + j] = 1.0; for(k = 1; k < states; k++) { left[k * span + i * states + j] = d1[k] * EI[states * j + k]; right[k * span + i * states + j] = d2[k] * EI[states * j + k]; } } } /* if memory saving is enabled and we are using CAT we need to do one additional P matrix calculation for a rate of 1.0 to compute the entries of a column/tree site comprising only gaps */ if(saveMem) { i = maxCat; for(j = 1; j < states; j++) { d1[j] = EXP (lz1[j]); d2[j] = EXP (lz2[j]); } for(j = 0; j < states; j++) { left[statesSquare * i + states * j] = 1.0; right[statesSquare * i + states * j] = 1.0; for(k = 1; k < states; k++) { left[statesSquare * i + states * j + k] = d1[k] * EI[states * j + k]; right[statesSquare * i + states * j + k] = d2[k] * EI[states * j + k]; } } } } void precomputeTips_DNA_MIC(int tipCase, double *tipVector, double *left, double *right, double *umpLeft, double *umpRight, int numberOfCategories) { /* no precomputation needed if both children are inner nodes */ if (tipCase == INNER_INNER) return; const int span = states * 4, umpSize = span * 16; for(int k = 0; k < umpSize; ++k) { umpLeft[k] = 0.0; umpRight[k] = 0.0; } for(int i = 0; i < maxStateValue; ++i) { for(int l = 0; l < states; ++l) { #pragma ivdep #pragma vector aligned for(int k = 0; k < span; ++k) { umpLeft[span * i + k] += tipVector[i * states + l] * left[l * span + k]; if (tipCase == TIP_TIP) umpRight[span * i + k] += tipVector[i * states + l] * right[l * span + k]; } } } } inline void mic_fma4x16(const double* inv, double* outv, double* mulv) { __mmask8 k1 = _mm512_int2mask(0x0F); __mmask8 k2 = _mm512_int2mask(0xF0); __m512d acc1 = _mm512_setzero_pd(); __m512d acc2 = _mm512_setzero_pd(); __m512d t; for(int k = 0; k < 4; k++) { t = _mm512_mask_extload_pd(t, k1, &inv[0 + k], _MM_UPCONV_PD_NONE, _MM_BROADCAST_1X8, _MM_HINT_NONE); t = _mm512_mask_extload_pd(t, k2, &inv[4 + k], _MM_UPCONV_PD_NONE, _MM_BROADCAST_1X8, _MM_HINT_NONE); __m512d m = _mm512_load_pd(&mulv[k * 16]); acc1 = _mm512_fmadd_pd(t, m, acc1); t = _mm512_mask_extload_pd(t, k1, &inv[8 + k], _MM_UPCONV_PD_NONE, _MM_BROADCAST_1X8, _MM_HINT_NONE); t = _mm512_mask_extload_pd(t, k2, &inv[12 + k], _MM_UPCONV_PD_NONE, _MM_BROADCAST_1X8, _MM_HINT_NONE); m = _mm512_load_pd(&mulv[k * 16 + 8]); acc2 = _mm512_fmadd_pd(t, m, acc2); } _mm512_store_pd(&outv[0], acc1); _mm512_store_pd(&outv[8], acc2); } void newviewGTRGAMMA_MIC(int tipCase, double *x1, double *x2, double *x3, double *extEV, double *tipVector, unsigned char *tipX1, unsigned char *tipX2, int n, double *left, double *right, int *wgt, int *scalerIncrement, double *umpLeft, double *umpRight) { __m512d minlikelihood_MIC = _mm512_set1_pd(minlikelihood); __m512d twotothe256_MIC = _mm512_set1_pd(twotothe256); __m512i absMask_MIC = _mm512_set1_epi64(0x7fffffffffffffffULL); int addScale = 0; /* we assume that P-matrix and eigenvectors are in correct layout already */ double *aEV = extEV, *aRight = right, *aLeft = left, *umpX1 = umpLeft, *umpX2 = umpRight; switch(tipCase) { case TIP_TIP: { #pragma noprefetch umpX1,umpX2 for (int i = 0; i < n; i++) { _mm_prefetch((const char *)&x3[span*(i+8)], _MM_HINT_ET1); _mm_prefetch((const char *)&x3[span*(i+8) + 8], _MM_HINT_ET1); _mm_prefetch((const char *)&x3[span*(i+1)], _MM_HINT_ET0); _mm_prefetch((const char *)&x3[span*(i+1) + 8], _MM_HINT_ET0); const double *uX1 = &umpX1[16 * tipX1[i]]; const double *uX2 = &umpX2[16 * tipX2[i]]; double uX[16] __attribute__((aligned(64))); #pragma ivdep #pragma vector aligned for(int l = 0; l < 16; ++l) { uX[l] = uX1[l] * uX2[l]; } double* v3 = &x3[i * 16]; mic_fma4x16(uX, v3, aEV); } // sites loop } break; case TIP_INNER: { #pragma noprefetch umpX1 for (int i = 0; i < n; i++) { _mm_prefetch((const char *)&x2[span*(i+16)], _MM_HINT_T1); _mm_prefetch((const char *)&x2[span*(i+16) + 8], _MM_HINT_T1); _mm_prefetch((const char *)&x3[span*(i+16)], _MM_HINT_ET1); _mm_prefetch((const char *)&x3[span*(i+16) + 8], _MM_HINT_ET1); _mm_prefetch((const char *)&x2[span*(i+1)], _MM_HINT_T0); _mm_prefetch((const char *)&x2[span*(i+1) + 8], _MM_HINT_T0); _mm_prefetch((const char *)&x3[span*(i+1)], _MM_HINT_ET0); _mm_prefetch((const char *)&x3[span*(i+1) + 8], _MM_HINT_ET0); /* access pre-computed value based on the raw sequence data tipX1 that is used as an index */ double* uX1 = &umpX1[span * tipX1[i]]; double uX2[16] __attribute__((aligned(64))); double uX[16] __attribute__((aligned(64))); const double* v2 = &(x2[16 * i]); mic_fma4x16(v2, uX2, aRight); #pragma ivdep #pragma vector aligned for(int l = 0; l < 16; ++l) { uX[l] = uX1[l] * uX2[l]; } double* v3 = &(x3[span * i]); mic_fma4x16(uX, v3, aEV); __m512d t1 = _mm512_load_pd(&v3[0]); t1 = _mm512_castsi512_pd(_mm512_and_epi64(_mm512_castpd_si512(t1), absMask_MIC)); double vmax1 = _mm512_reduce_max_pd(t1); __m512d t2 = _mm512_load_pd(&v3[8]); t2 = _mm512_castsi512_pd(_mm512_and_epi64(_mm512_castpd_si512(t2), absMask_MIC)); double vmax2 = _mm512_reduce_max_pd(t2); if(vmax1 < minlikelihood && vmax2 < minlikelihood) { /* t1 = _mm512_mul_pd(t1, twotothe256_MIC); _mm512_store_pd(&v3[0], t1); t2 = _mm512_mul_pd(t2, twotothe256_MIC); _mm512_store_pd(&v3[8], t2);*/ #pragma vector aligned nontemporal for(int l = 0; l < span; l++) v3[l] *= twotothe256; addScale += wgt[i]; } } // site loop } break; case INNER_INNER: { for (int i = 0; i < n; i++) { _mm_prefetch((const char *) &x1[span*(i+8)], _MM_HINT_T1); _mm_prefetch((const char *) &x1[span*(i+8) + 8], _MM_HINT_T1); _mm_prefetch((const char *) &x2[span*(i+8)], _MM_HINT_T1); _mm_prefetch((const char *) &x2[span*(i+8) + 8], _MM_HINT_T1); _mm_prefetch((const char *) &x3[span*(i+8)], _MM_HINT_ET1); _mm_prefetch((const char *) &x3[span*(i+8) + 8], _MM_HINT_ET1); _mm_prefetch((const char *) &x1[span*(i+1)], _MM_HINT_T0); _mm_prefetch((const char *) &x1[span*(i+1) + 8], _MM_HINT_T0); _mm_prefetch((const char *) &x2[span*(i+1)], _MM_HINT_T0); _mm_prefetch((const char *) &x2[span*(i+1) + 8], _MM_HINT_T0); _mm_prefetch((const char *) &x3[span*(i+1)], _MM_HINT_ET0); _mm_prefetch((const char *) &x3[span*(i+1) + 8], _MM_HINT_ET0); double uX1[16] __attribute__((aligned(64))); double uX2[16] __attribute__((aligned(64))); double uX[16] __attribute__((aligned(64))); const double* v1 = &(x1[span * i]); const double* v2 = &(x2[span * i]); mic_fma4x16(v1, uX1, aLeft); mic_fma4x16(v2, uX2, aRight); #pragma ivdep #pragma vector aligned for(int l = 0; l < 16; ++l) { uX[l] = uX1[l] * uX2[l]; } double* v3 = &(x3[span * i]); mic_fma4x16(uX, v3, aEV); __m512d t1 = _mm512_load_pd(&v3[0]); t1 = _mm512_castsi512_pd(_mm512_and_epi64(_mm512_castpd_si512(t1), absMask_MIC)); double vmax1 = _mm512_reduce_max_pd(t1); __m512d t2 = _mm512_load_pd(&v3[8]); t2 = _mm512_castsi512_pd(_mm512_and_epi64(_mm512_castpd_si512(t2), absMask_MIC)); double vmax2 = _mm512_reduce_max_pd(t2); if(vmax1 < minlikelihood && vmax2 < minlikelihood) { /* t1 = _mm512_mul_pd(t1, twotothe256_MIC); _mm512_store_pd(&v3[0], t1); t2 = _mm512_mul_pd(t2, twotothe256_MIC); _mm512_store_pd(&v3[8], t2); */ #pragma vector aligned nontemporal for(int l = 0; l < span; l++) v3[l] *= twotothe256; addScale += wgt[i]; } } } break; default: // assert(0); break; } *scalerIncrement = addScale; } double evaluateGAMMA_MIC(int *wgt, double *x1_start, double *x2_start, double *tipVector, unsigned char *tipX1, const int n, double *diagptable) { double sum = 0.0; /* the left node is a tip */ if(tipX1) { double *aTipVec = tipVector; /* loop over the sites of this partition */ for (int i = 0; i < n; i++) { /* access pre-computed tip vector values via a lookup table */ const double *x1 = &(aTipVec[16 * tipX1[i]]); /* access the other(inner) node at the other end of the branch */ const double *x2 = &(x2_start[span * i]); double term = 0.; #pragma ivdep #pragma vector aligned for(int j = 0; j < 16; j++) term += x1[j] * x2[j] * diagptable[j]; term = log(0.25 * fabs(term)); sum += wgt[i] * term; } } else { for (int i = 0; i < n; i++) { _mm_prefetch((const char *) &x1_start[span*(i+8)], _MM_HINT_T1); _mm_prefetch((const char *) &x1_start[span*(i+8) + 8], _MM_HINT_T1); _mm_prefetch((const char *) &x2_start[span*(i+8)], _MM_HINT_T1); _mm_prefetch((const char *) &x2_start[span*(i+8) + 8], _MM_HINT_T1); _mm_prefetch((const char *) &x1_start[span*(i+1)], _MM_HINT_T0); _mm_prefetch((const char *) &x1_start[span*(i+1) + 8], _MM_HINT_T0); _mm_prefetch((const char *) &x2_start[span*(i+1)], _MM_HINT_T0); _mm_prefetch((const char *) &x2_start[span*(i+1) + 8], _MM_HINT_T0); const double *x1 = &(x1_start[span * i]); const double *x2 = &(x2_start[span * i]); double term = 0.; #pragma ivdep #pragma vector aligned for(int j = 0; j < 16; j++) term += x1[j] * x2[j] * diagptable[j]; term = log(0.25 * fabs(term)); sum += wgt[i] * term; } } return sum; } void sumGAMMA_MIC(int tipCase, double *sumtable, double *x1_start, double *x2_start, double *tipVector, unsigned char *tipX1, unsigned char *tipX2, int n) { const double *aTipVec = tipVector; switch(tipCase) { case TIP_TIP: { #pragma unroll(8) for(int i = 0; i < n; i++) { const double *left = &(aTipVec[16 * tipX1[i]]); const double *right = &(aTipVec[16 * tipX2[i]]); #pragma ivdep #pragma vector aligned nontemporal for(int l = 0; l < 16; l++) { sumtable[i * span + l] = left[l] * right[l]; } } } break; case TIP_INNER: { #pragma unroll(8) for(int i = 0; i < n; i++) { _mm_prefetch((const char *) &x2_start[span*(i+32)], _MM_HINT_T1); _mm_prefetch((const char *) &x2_start[span*(i+32) + 8], _MM_HINT_T1); _mm_prefetch((const char *) &x2_start[span*(i+8)], _MM_HINT_T0); _mm_prefetch((const char *) &x2_start[span*(i+8) + 8], _MM_HINT_T0); const double *left = &(aTipVec[16 * tipX1[i]]); const double *right = &(x2_start[span * i]); #pragma ivdep #pragma vector aligned nontemporal for(int l = 0; l < 16; l++) { sumtable[i * span + l] = left[l] * right[l]; } } } break; case INNER_INNER: { #pragma unroll(8) for(int i = 0; i < n; i++) { _mm_prefetch((const char *) &x1_start[span*(i+16)], _MM_HINT_T1); _mm_prefetch((const char *) &x1_start[span*(i+16) + 8], _MM_HINT_T1); _mm_prefetch((const char *) &x2_start[span*(i+16)], _MM_HINT_T1); _mm_prefetch((const char *) &x2_start[span*(i+16) + 8], _MM_HINT_T1); _mm_prefetch((const char *) &x1_start[span*(i+4)], _MM_HINT_T0); _mm_prefetch((const char *) &x1_start[span*(i+4) + 8], _MM_HINT_T0); _mm_prefetch((const char *) &x2_start[span*(i+4)], _MM_HINT_T0); _mm_prefetch((const char *) &x2_start[span*(i+4) + 8], _MM_HINT_T0); const double *left = &(x1_start[span * i]); const double *right = &(x2_start[span * i]); #pragma ivdep #pragma vector aligned nontemporal for(int l = 0; l < 16; l++) { sumtable[i * span + l] = left[l] * right[l]; } } } break; // default: // assert(0); } } void coreGTRGAMMA_MIC(const int upper, double *sumtable, volatile double *ext_dlnLdlz, volatile double *ext_d2lnLdlz2, double *EIGN, double *gammaRates, double lz, int *wgt) { double diagptable0[16] __attribute__((aligned(64))); double diagptable1[16] __attribute__((aligned(64))); double diagptable2[16] __attribute__((aligned(64))); double diagptable01[16] __attribute__((aligned(64))); double diagptable02[16] __attribute__((aligned(64))); /* pre-compute the derivatives of the P matrix for all discrete GAMMA rates */ for(int i = 0; i < 4; i++) { const double ki = gammaRates[i]; const double kisqr = ki * ki; diagptable0[i*4] = 1.; diagptable1[i*4] = 0.; diagptable2[i*4] = 0.; for(int l = 1; l < 4; l++) { diagptable0[i * 4 + l] = exp(EIGN[l] * ki * lz); diagptable1[i * 4 + l] = EIGN[l] * ki; diagptable2[i * 4 + l] = EIGN[l] * EIGN[l] * kisqr; } } #pragma ivdep for(int i = 0; i < 16; i++) { diagptable01[i] = diagptable0[i] * diagptable1[i]; diagptable02[i] = diagptable0[i] * diagptable2[i]; } /* loop over sites in this partition */ const int aligned_width = upper % 8 == 0 ? upper / 8 : upper / 8 + 1; double dlnLBuf[8] __attribute__((aligned(64))); double d2lnLBuf[8] __attribute__((aligned(64))); for (int j = 0; j < 8; ++j) { dlnLBuf[j] = 0.; d2lnLBuf[j] = 0.; } __mmask16 k1 = _mm512_int2mask(0x000000FF); for (int i = 0; i < aligned_width; i++) { _mm_prefetch((const char *) &sumtable[i * span * 8], _MM_HINT_T0); _mm_prefetch((const char *) &sumtable[i * span * 8 + 8], _MM_HINT_T0); /* access the array with pre-computed values */ const double *sum = &sumtable[i * span * 8]; /* initial per-site likelihood and 1st and 2nd derivatives */ double invBuf[8] __attribute__((aligned(64))); double d1Buf[8] __attribute__((aligned(64))); double d2Buf[8] __attribute__((aligned(64))); __m512d invVec; __m512d d1Vec; __m512d d2Vec; int mask = 0x01; #pragma noprefetch sum #pragma unroll(8) for(int j = 0; j < 8; j++) { _mm_prefetch((const char *) &sum[span*(j+8)], _MM_HINT_T1); _mm_prefetch((const char *) &sum[span*(j+8) + 8], _MM_HINT_T1); _mm_prefetch((const char *) &sum[span*(j+1)], _MM_HINT_T0); _mm_prefetch((const char *) &sum[span*(j+1) + 8], _MM_HINT_T0); __m512d d0_1 = _mm512_load_pd(&diagptable0[0]); __m512d d0_2 = _mm512_load_pd(&diagptable0[8]); __m512d d01_1 = _mm512_load_pd(&diagptable01[0]); __m512d d01_2 = _mm512_load_pd(&diagptable01[8]); __m512d d02_1 = _mm512_load_pd(&diagptable02[0]); __m512d d02_2 = _mm512_load_pd(&diagptable02[8]); __m512d s_1 = _mm512_load_pd(&sum[j*16]); __m512d s_2 = _mm512_load_pd(&sum[j*16 + 8]); __m512d inv_1 = _mm512_mul_pd(d0_1, s_1); __m512d d1_1 = _mm512_mul_pd(d01_1, s_1); __m512d d2_1 = _mm512_mul_pd(d02_1, s_1); __m512d inv_2 = _mm512_fmadd_pd(d0_2, s_2, inv_1); __m512d d1_2 = _mm512_fmadd_pd(d01_2, s_2, d1_1); __m512d d2_2 = _mm512_fmadd_pd(d02_2, s_2, d2_1); __mmask8 k1 = _mm512_int2mask(mask); mask <<= 1; // reduce inv_2 = _mm512_add_pd (inv_2, _mm512_swizzle_pd(inv_2, _MM_SWIZ_REG_CDAB)); inv_2 = _mm512_add_pd (inv_2, _mm512_swizzle_pd(inv_2, _MM_SWIZ_REG_BADC)); inv_2 = _mm512_add_pd (inv_2, _mm512_castsi512_pd(_mm512_permute4f128_epi32(_mm512_castpd_si512(inv_2), _MM_PERM_BADC))); invVec = _mm512_mask_mov_pd(invVec, k1, inv_2); d1_2 = _mm512_add_pd (d1_2, _mm512_swizzle_pd(d1_2, _MM_SWIZ_REG_CDAB)); d1_2 = _mm512_add_pd (d1_2, _mm512_swizzle_pd(d1_2, _MM_SWIZ_REG_BADC)); d1_2 = _mm512_add_pd (d1_2, _mm512_castsi512_pd(_mm512_permute4f128_epi32(_mm512_castpd_si512(d1_2), _MM_PERM_BADC))); d1Vec = _mm512_mask_mov_pd(d1Vec, k1, d1_2); d2_2 = _mm512_add_pd (d2_2, _mm512_swizzle_pd(d2_2, _MM_SWIZ_REG_CDAB)); d2_2 = _mm512_add_pd (d2_2, _mm512_swizzle_pd(d2_2, _MM_SWIZ_REG_BADC)); d2_2 = _mm512_add_pd (d2_2, _mm512_castsi512_pd(_mm512_permute4f128_epi32(_mm512_castpd_si512(d2_2), _MM_PERM_BADC))); d2Vec = _mm512_mask_mov_pd(d2Vec, k1, d2_2); } _mm512_store_pd(&invBuf[0], invVec); _mm512_store_pd(&d1Buf[0], d1Vec); _mm512_store_pd(&d2Buf[0], d2Vec); #pragma ivdep #pragma vector aligned for (int j = 0; j < 8; ++j) { const double inv_Li = 1.0 / invBuf[j]; const double d1 = d1Buf[j] * inv_Li; const double d2 = d2Buf[j] * inv_Li; dlnLBuf[j] += wgt[i * 8 + j] * d1; d2lnLBuf[j] += wgt[i * 8 + j] * (d2 - d1 * d1); } } // site loop double dlnLdlz = 0.; double d2lnLdlz2 = 0.; for (int j = 0; j < 8; ++j) { dlnLdlz += dlnLBuf[j]; d2lnLdlz2 += d2lnLBuf[j]; } *ext_dlnLdlz = dlnLdlz; *ext_d2lnLdlz2 = d2lnLdlz2; } ExaML-3.0.21/examl/models.c000066400000000000000000005526421330315463200153160ustar00rootroot00000000000000/* RAxML-VI-HPC (version 2.2) a program for sequential and parallel estimation of phylogenetic trees * Copyright August 2006 by Alexandros Stamatakis * * Partially derived from * fastDNAml, a program for estimation of phylogenetic trees from sequences by Gary J. Olsen * * and * * Programs of the PHYLIP package by Joe Felsenstein. * * This program is free software; you may redistribute it and/or modify its * 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. * * * For any other enquiries send an Email to Alexandros Stamatakis * Alexandros.Stamatakis@epfl.ch * * When publishing work that is based on the results from RAxML-VI-HPC please cite: * * Alexandros Stamatakis:"RAxML-VI-HPC: maximum likelihood-based phylogenetic analyses with thousands * of taxa and mixed models". * Bioinformatics 2006; doi: 10.1093/bioinformatics/btl446 */ #ifndef WIN32 #include #include #include #include #endif #include #include #include #include #include #include #include "axml.h" #ifdef __MIC_NATIVE #include "mic_native.h" #endif extern int optimizeRatesInvocations; extern int optimizeRateCategoryInvocations; extern int optimizeAlphaInvocations; extern int optimizeTTRatioInvocations; extern int optimizeInvarInvocations; extern const unsigned int bitVectorSecondary[256]; extern const unsigned int bitVector32[33]; extern const unsigned int bitVectorAA[23]; extern const unsigned int bitVectorIdentity[256]; extern const partitionLengths pLengths[MAX_MODEL]; //extern FILE *byteFile; void putWAG(double *ext_initialRates) { double scaler, q[20][20], daa[400]; int i, j, r; daa[ 1*20+ 0] = 55.15710; daa[ 2*20+ 0] = 50.98480; daa[ 2*20+ 1] = 63.53460; daa[ 3*20+ 0] = 73.89980; daa[ 3*20+ 1] = 14.73040; daa[ 3*20+ 2] = 542.94200; daa[ 4*20+ 0] = 102.70400; daa[ 4*20+ 1] = 52.81910; daa[ 4*20+ 2] = 26.52560; daa[ 4*20+ 3] = 3.02949; daa[ 5*20+ 0] = 90.85980; daa[ 5*20+ 1] = 303.55000; daa[ 5*20+ 2] = 154.36400; daa[ 5*20+ 3] = 61.67830; daa[ 5*20+ 4] = 9.88179; daa[ 6*20+ 0] = 158.28500; daa[ 6*20+ 1] = 43.91570; daa[ 6*20+ 2] = 94.71980; daa[ 6*20+ 3] = 617.41600; daa[ 6*20+ 4] = 2.13520; daa[ 6*20+ 5] = 546.94700; daa[ 7*20+ 0] = 141.67200; daa[ 7*20+ 1] = 58.46650; daa[ 7*20+ 2] = 112.55600; daa[ 7*20+ 3] = 86.55840; daa[ 7*20+ 4] = 30.66740; daa[ 7*20+ 5] = 33.00520; daa[ 7*20+ 6] = 56.77170; daa[ 8*20+ 0] = 31.69540; daa[ 8*20+ 1] = 213.71500; daa[ 8*20+ 2] = 395.62900; daa[ 8*20+ 3] = 93.06760; daa[ 8*20+ 4] = 24.89720; daa[ 8*20+ 5] = 429.41100; daa[ 8*20+ 6] = 57.00250; daa[ 8*20+ 7] = 24.94100; daa[ 9*20+ 0] = 19.33350; daa[ 9*20+ 1] = 18.69790; daa[ 9*20+ 2] = 55.42360; daa[ 9*20+ 3] = 3.94370; daa[ 9*20+ 4] = 17.01350; daa[ 9*20+ 5] = 11.39170; daa[ 9*20+ 6] = 12.73950; daa[ 9*20+ 7] = 3.04501; daa[ 9*20+ 8] = 13.81900; daa[10*20+ 0] = 39.79150; daa[10*20+ 1] = 49.76710; daa[10*20+ 2] = 13.15280; daa[10*20+ 3] = 8.48047; daa[10*20+ 4] = 38.42870; daa[10*20+ 5] = 86.94890; daa[10*20+ 6] = 15.42630; daa[10*20+ 7] = 6.13037; daa[10*20+ 8] = 49.94620; daa[10*20+ 9] = 317.09700; daa[11*20+ 0] = 90.62650; daa[11*20+ 1] = 535.14200; daa[11*20+ 2] = 301.20100; daa[11*20+ 3] = 47.98550; daa[11*20+ 4] = 7.40339; daa[11*20+ 5] = 389.49000; daa[11*20+ 6] = 258.44300; daa[11*20+ 7] = 37.35580; daa[11*20+ 8] = 89.04320; daa[11*20+ 9] = 32.38320; daa[11*20+10] = 25.75550; daa[12*20+ 0] = 89.34960; daa[12*20+ 1] = 68.31620; daa[12*20+ 2] = 19.82210; daa[12*20+ 3] = 10.37540; daa[12*20+ 4] = 39.04820; daa[12*20+ 5] = 154.52600; daa[12*20+ 6] = 31.51240; daa[12*20+ 7] = 17.41000; daa[12*20+ 8] = 40.41410; daa[12*20+ 9] = 425.74600; daa[12*20+10] = 485.40200; daa[12*20+11] = 93.42760; daa[13*20+ 0] = 21.04940; daa[13*20+ 1] = 10.27110; daa[13*20+ 2] = 9.61621; daa[13*20+ 3] = 4.67304; daa[13*20+ 4] = 39.80200; daa[13*20+ 5] = 9.99208; daa[13*20+ 6] = 8.11339; daa[13*20+ 7] = 4.99310; daa[13*20+ 8] = 67.93710; daa[13*20+ 9] = 105.94700; daa[13*20+10] = 211.51700; daa[13*20+11] = 8.88360; daa[13*20+12] = 119.06300; daa[14*20+ 0] = 143.85500; daa[14*20+ 1] = 67.94890; daa[14*20+ 2] = 19.50810; daa[14*20+ 3] = 42.39840; daa[14*20+ 4] = 10.94040; daa[14*20+ 5] = 93.33720; daa[14*20+ 6] = 68.23550; daa[14*20+ 7] = 24.35700; daa[14*20+ 8] = 69.61980; daa[14*20+ 9] = 9.99288; daa[14*20+10] = 41.58440; daa[14*20+11] = 55.68960; daa[14*20+12] = 17.13290; daa[14*20+13] = 16.14440; daa[15*20+ 0] = 337.07900; daa[15*20+ 1] = 122.41900; daa[15*20+ 2] = 397.42300; daa[15*20+ 3] = 107.17600; daa[15*20+ 4] = 140.76600; daa[15*20+ 5] = 102.88700; daa[15*20+ 6] = 70.49390; daa[15*20+ 7] = 134.18200; daa[15*20+ 8] = 74.01690; daa[15*20+ 9] = 31.94400; daa[15*20+10] = 34.47390; daa[15*20+11] = 96.71300; daa[15*20+12] = 49.39050; daa[15*20+13] = 54.59310; daa[15*20+14] = 161.32800; daa[16*20+ 0] = 212.11100; daa[16*20+ 1] = 55.44130; daa[16*20+ 2] = 203.00600; daa[16*20+ 3] = 37.48660; daa[16*20+ 4] = 51.29840; daa[16*20+ 5] = 85.79280; daa[16*20+ 6] = 82.27650; daa[16*20+ 7] = 22.58330; daa[16*20+ 8] = 47.33070; daa[16*20+ 9] = 145.81600; daa[16*20+10] = 32.66220; daa[16*20+11] = 138.69800; daa[16*20+12] = 151.61200; daa[16*20+13] = 17.19030; daa[16*20+14] = 79.53840; daa[16*20+15] = 437.80200; daa[17*20+ 0] = 11.31330; daa[17*20+ 1] = 116.39200; daa[17*20+ 2] = 7.19167; daa[17*20+ 3] = 12.97670; daa[17*20+ 4] = 71.70700; daa[17*20+ 5] = 21.57370; daa[17*20+ 6] = 15.65570; daa[17*20+ 7] = 33.69830; daa[17*20+ 8] = 26.25690; daa[17*20+ 9] = 21.24830; daa[17*20+10] = 66.53090; daa[17*20+11] = 13.75050; daa[17*20+12] = 51.57060; daa[17*20+13] = 152.96400; daa[17*20+14] = 13.94050; daa[17*20+15] = 52.37420; daa[17*20+16] = 11.08640; daa[18*20+ 0] = 24.07350; daa[18*20+ 1] = 38.15330; daa[18*20+ 2] = 108.60000; daa[18*20+ 3] = 32.57110; daa[18*20+ 4] = 54.38330; daa[18*20+ 5] = 22.77100; daa[18*20+ 6] = 19.63030; daa[18*20+ 7] = 10.36040; daa[18*20+ 8] = 387.34400; daa[18*20+ 9] = 42.01700; daa[18*20+10] = 39.86180; daa[18*20+11] = 13.32640; daa[18*20+12] = 42.84370; daa[18*20+13] = 645.42800; daa[18*20+14] = 21.60460; daa[18*20+15] = 78.69930; daa[18*20+16] = 29.11480; daa[18*20+17] = 248.53900; daa[19*20+ 0] = 200.60100; daa[19*20+ 1] = 25.18490; daa[19*20+ 2] = 19.62460; daa[19*20+ 3] = 15.23350; daa[19*20+ 4] = 100.21400; daa[19*20+ 5] = 30.12810; daa[19*20+ 6] = 58.87310; daa[19*20+ 7] = 18.72470; daa[19*20+ 8] = 11.83580; daa[19*20+ 9] = 782.13000; daa[19*20+10] = 180.03400; daa[19*20+11] = 30.54340; daa[19*20+12] = 205.84500; daa[19*20+13] = 64.98920; daa[19*20+14] = 31.48870; daa[19*20+15] = 23.27390; daa[19*20+16] = 138.82300; daa[19*20+17] = 36.53690; daa[19*20+18] = 31.47300; for(i = 0; i < 20; i++) for(j = 0; j < 20; j++) q[i][j] = 0.0; for (i=0; i<20; i++) for (j=0; j max) max = temp; } scaler = AA_SCALE / max; /* SCALING HAS BEEN RE-INTRODUCED TO RESOLVE NUMERICAL PROBLEMS */ r = 0; for(i = 0; i < 19; i++) { for(j = i + 1; j < 20; j++) { q[i][j] *= scaler; assert(q[i][j] <= AA_SCALE_PLUS_EPSILON); initialRates[r++] = q[i][j]; } } } static void mytred2(double **a, const int n, double *d, double *e) { int l, k, j, i; double scale, hh, h, g, f; for (i = n; i > 1; i--) { l = i - 1; h = 0.0; scale = 0.0; if (l > 1) { for (k = 1; k <= l; k++) scale += fabs(a[k - 1][i - 1]); if (scale == 0.0) e[i - 1] = a[l - 1][i - 1]; else { for (k = 1; k <= l; k++) { a[k - 1][i - 1] /= scale; h += a[k - 1][i - 1] * a[k - 1][i - 1]; } f = a[l - 1][i - 1]; g = ((f > 0) ? -sqrt(h) : sqrt(h)); /* diff */ e[i - 1] = scale * g; h -= f * g; a[l - 1][i - 1] = f - g; f = 0.0; for (j = 1; j <= l; j++) { a[i - 1][j - 1] = a[j - 1][i - 1] / h; g = 0.0; for (k = 1; k <= j; k++) g += a[k - 1][j - 1] * a[k - 1][i - 1]; for (k = j + 1; k <= l; k++) g += a[j - 1][k - 1] * a[k - 1][i - 1]; e[j - 1] = g / h; f += e[j - 1] * a[j - 1][i - 1]; } hh = f / (h + h); for (j = 1; j <= l; j++) { f = a[j - 1][i - 1]; g = e[j - 1] - hh * f; e[j - 1] = g; for (k = 1; k <= j; k++) a[k - 1][j - 1] -= (f * e[k - 1] + g * a[k - 1][i - 1]); } } } else e[i - 1] = a[l - 1][i - 1]; d[i - 1] = h; } d[0] = 0.0; e[0] = 0.0; for (i = 1; i <= n; i++) { l = i - 1; if (d[i - 1] != 0.0) { for (j = 1; j <= l; j++) { g = 0.0; for (k = 1; k <= l; k++) g += a[k - 1][i - 1] * a[j - 1][k - 1]; for(k = 1; k <= l; k++) a[j - 1][k - 1] -= g * a[i - 1][k - 1]; } } d[i - 1] = a[i - 1][i - 1]; a[i - 1][i - 1] = 1.0; for (j = 1; j <= l; j++) a[i - 1][j - 1] = a[j - 1][i - 1] = 0.0; } } /*#define MYSIGN(a,b) ((b)<0 ? -fabs(a) : fabs(a))*/ static int mytqli(double *d, double *e, const int n, double **z) { int m, l, iter, i, k; double s, r, p, g, f, dd, c, b; for (i = 2; i <= n; i++) e[i - 2] = e[i - 1]; e[n - 1] = 0.0; for (l = 1; l <= n; l++) { iter = 0; do { for (m = l; m <= n - 1; m++) { dd = fabs(d[m - 1]) + fabs(d[m]); if (fabs(e[m - 1]) + dd == dd) break; } if (m != l) { assert(iter < 30); g = (d[l] - d[l - 1]) / (2.0 * e[l - 1]); r = sqrt((g * g) + 1.0); g = d[m - 1] - d[l - 1] + e[l - 1] / (g + ((g < 0)?-fabs(r):fabs(r)));/*MYSIGN(r, g));*/ s = c = 1.0; p = 0.0; for (i = m - 1; i >= l; i--) { f = s * e[i - 1]; b = c * e[i - 1]; if (fabs(f) >= fabs(g)) { c = g / f; r = sqrt((c * c) + 1.0); e[i] = f * r; c *= (s = 1.0 / r); } else { s = f / g; r = sqrt((s * s) + 1.0); e[i] = g * r; s *= (c = 1.0 / r); } g = d[i] - p; r = (d[i - 1] - g) * s + 2.0 * c * b; p = s * r; d[i] = g + p; g = c * r - b; for (k = 1; k <= n; k++) { f = z[i][k-1]; z[i][k-1] = s * z[i - 1][k - 1] + c * f; z[i - 1][k - 1] = c * z[i - 1][k - 1] - s * f; } } d[l - 1] = d[l - 1] - p; e[l - 1] = g; e[m - 1] = 0.0; } } while (m != l); } return (1); } static void makeEigen(double **_a, const int n, double *d, double *e) { mytred2(_a, n, d, e); mytqli(d, e, n, _a); } static void initGeneric(const int n, const unsigned int *valueVector, int valueVectorLength, double *ext_EIGN, double *EV, double *EI, double *frequencies, double *ext_initialRates, double *tipVector, int model) { double fracchange = 0.0, **r, **a, **EIGV, *initialRates = ext_initialRates, *f, *e, *d, *invfreq, *EIGN, *eptr; int i, j, k, m, l; r = (double **)malloc(n * sizeof(double *)); EIGV = (double **)malloc(n * sizeof(double *)); a = (double **)malloc(n * sizeof(double *)); for(i = 0; i < n; i++) { a[i] = (double*)malloc(n * sizeof(double)); EIGV[i] = (double*)malloc(n * sizeof(double)); r[i] = (double*)malloc(n * sizeof(double)); } f = (double*)malloc(n * sizeof(double)); e = (double*)malloc(n * sizeof(double)); d = (double*)malloc(n * sizeof(double)); invfreq = (double*)malloc(n * sizeof(double)); EIGN = (double*)malloc(n * sizeof(double)); for(l = 0; l < n; l++) f[l] = frequencies[l]; i = 0; for(j = 0; j < n; j++) for(k = 0; k < n; k++) r[j][k] = 0.0; for(j = 0; j < n - 1; j++) for (k = j+1; k < n; k++) r[j][k] = initialRates[i++]; for (j = 0; j < n; j++) { r[j][j] = 0.0; for (k = 0; k < j; k++) r[j][k] = r[k][j]; } for (j = 0; j< n; j++) for (k = 0; k< n; k++) fracchange += f[j] * r[j][k] * f[k]; m = 0; for(i=0; i< n; i++) a[i][i] = 0; /* assert(r[n - 2][n - 1] == 1.0);*/ for(i=0; i < n; i++) { for(j=i+1; j < n; j++) { double factor = initialRates[m++]; a[i][j] = a[j][i] = factor * sqrt( f[i] * f[j]); a[i][i] -= factor * f[j]; a[j][j] -= factor * f[i]; } } makeEigen(a, n, d, e); for(i=0; i -1e-8) { if (i != 0) { double tmp = d[i], sum=0; d[i] = d[0]; d[0] = tmp; for (j=0; j < n; j++) { tmp = a[i][j]; a[i][j] = a[0][j]; sum += (a[0][j] = tmp); } for (j=0; j < n; j++) a[0][j] /= sum; } break; } } for (i=0; i< n; i++) { EIGN[i] = -d[i]; for (j=0; j 0.0); } eptr = EV; for(i = 0; i < n; i++) for(j = 0; j < n; j++) { *eptr++ = EIGV[i][j]; } for(i = 0; i < n; i++) for(j = 0; j < n; j++) { if(j == 0) EI[i * n + j] = 1.0; else EI[i * n + j] = EV[i * n + j] * invfreq[i]; } /* printf("EIGN\n"); for(i = 0; i < n; i++) printf("%f ", ext_EIGN[i]); printf("\n"); printf("EI\n"); for(i = 0; i < n; i++) { for(j = 0; j < n; j++) { printf("%f ", EI[i * n + j]); } printf("\n"); } */ for(i=0; i < valueVectorLength; i++) { unsigned int value = valueVector[i]; for(j = 0; j < n; j++) tipVector[i * n + j] = 0; if(value > 0) { for (j = 0; j < n; j++) { if ((value >> j) & 1) { int l; for(l = 0; l < n; l++) tipVector[i * n + l] += EIGV[j][l]; } } } } for(i = 0; i < valueVectorLength; i++) { for(j = 0; j < n; j++) if(tipVector[i * n + j] > MAX_TIP_EV) tipVector[i * n + j] = MAX_TIP_EV; } for(i = 0; i < n; i++) { free(EIGV[i]); free(a[i]); free(r[i]); } free(r); free(a); free(EIGV); free(f); free(e); free(d); free(invfreq); free(EIGN); } void initReversibleGTR(tree *tr, int model) { double *ext_EIGN = tr->partitionData[model].EIGN, *EV = tr->partitionData[model].EV, *EI = tr->partitionData[model].EI, *frequencies = tr->partitionData[model].frequencies, *ext_initialRates = tr->partitionData[model].substRates, *tipVector = tr->partitionData[model].tipVector; int states = tr->partitionData[model].states; switch(tr->partitionData[model].dataType) { case GENERIC_32: case GENERIC_64: case SECONDARY_DATA_6: case SECONDARY_DATA_7: case SECONDARY_DATA: case DNA_DATA: case BINARY_DATA: initGeneric(states, getBitVector(tr->partitionData[model].dataType), getUndetermined(tr->partitionData[model].dataType) + 1, ext_EIGN, EV, EI, frequencies, ext_initialRates, tipVector, model); break; case AA_DATA: if(tr->partitionData[model].protModels != GTR) { double f[20]; if(tr->partitionData[model].protModels == LG4M || tr->partitionData[model].protModels == LG4X) { int i; for(i = 0; i < 4; i++) { initProtMat(f, tr->partitionData[model].protModels, &(tr->partitionData[model].substRates_LG4[i][0]), i); if(!tr->partitionData[model].protFreqs && !tr->partitionData[model].optimizeBaseFrequencies) memcpy(tr->partitionData[model].frequencies_LG4[i], f, 20 * sizeof(double)); //for(l = 0; l < 20; l++) // tr->partitionData[model].frequencies_LG4[i][l] = f[l]; else memcpy(tr->partitionData[model].frequencies_LG4[i], frequencies, 20 * sizeof(double)); } } else { if(tr->partitionData[model].protModels == AUTO) initProtMat(f, tr->partitionData[model].autoProtModels, ext_initialRates, 0); else initProtMat(f, tr->partitionData[model].protModels, ext_initialRates, 0); /*if(adef->protEmpiricalFreqs && tr->NumberOfModels == 1) assert(tr->partitionData[model].protFreqs);*/ if(tr->partitionData[model].protModels == AUTO) { if(tr->partitionData[model].protFreqs) memcpy(frequencies, f, 20 * sizeof(double)); else memcpy(frequencies, tr->partitionData[model].empiricalFrequencies, 20 * sizeof(double)); } else { if(!tr->partitionData[model].optimizeBaseFrequencies) { if(!tr->partitionData[model].protFreqs) { memcpy(frequencies, f, 20 * sizeof(double)); /*for(l = 0; l < 20; l++) frequencies[l] = f[l]; */ } else { memcpy(frequencies, tr->partitionData[model].empiricalFrequencies, 20 * sizeof(double)); /*for(l = 0; l < 20; l++) frequencies[l] = tr->partitionData[model].empiricalFrequencies[l]; */ } } } } } if(tr->partitionData[model].protModels == LG4M || tr->partitionData[model].protModels == LG4X) { int i; for(i = 0; i < 4; i++) initGeneric(states, bitVectorAA, 23, tr->partitionData[model].rawEIGN_LG4[i], tr->partitionData[model].EV_LG4[i], tr->partitionData[model].EI_LG4[i], tr->partitionData[model].frequencies_LG4[i], tr->partitionData[model].substRates_LG4[i], tr->partitionData[model].tipVector_LG4[i], model); scaleLG4X_EIGN(tr, model); } else initGeneric(states, bitVectorAA, 23, ext_EIGN, EV, EI, frequencies, ext_initialRates, tipVector, model); break; default: assert(0); } #ifdef __MIC_NATIVE if(tr->partitionData[model].protModels == LG4M || tr->partitionData[model].protModels == LG4X) updateModel_LG4_MIC(&tr->partitionData[model]); else updateModel_MIC(&tr->partitionData[model]); #endif } double LnGamma (double alpha) { /* returns ln(gamma(alpha)) for alpha>0, accurate to 10 decimal places. Stirling's formula is used for the central polynomial part of the procedure. Pike MC & Hill ID (1966) Algorithm 291: Logarithm of the gamma function. Communications of the Association for Computing Machinery, 9:684 */ double x, f, z, result; x = alpha; f = 0.0; if ( x < 7.0) { f = 1.0; z = alpha - 1.0; while ((z = z + 1.0) < 7.0) { f *= z; } x = z; assert(f != 0.0); f=-log(f); } z = 1/(x*x); result = f + (x-0.5)*log(x) - x + .918938533204673 + (((-.000595238095238*z+.000793650793651)*z-.002777777777778)*z +.083333333333333)/x; return result; } double IncompleteGamma (double x, double alpha, double ln_gamma_alpha) { /* returns the incomplete gamma ratio I(x,alpha) where x is the upper limit of the integration and alpha is the shape parameter. returns (-1) if in error ln_gamma_alpha = ln(Gamma(alpha)), is almost redundant. (1) series expansion if (alpha>x || x<=1) (2) continued fraction otherwise RATNEST FORTRAN by Bhattacharjee GP (1970) The incomplete gamma integral. Applied Statistics, 19: 285-287 (AS32) */ int i; double p=alpha, g=ln_gamma_alpha; double accurate=1e-8, overflow=1e30; double factor, gin=0, rn=0, a=0,b=0,an=0,dif=0, term=0, pn[6]; if (x==0) return (0); if (x<0 || p<=0) return (-1); factor=exp(p*log(x)-x-g); if (x>1 && x>=p) goto l30; /* (1) series expansion */ gin=1; term=1; rn=p; l20: rn++; term*=x/rn; gin+=term; if (term > accurate) goto l20; gin*=factor/p; goto l50; l30: /* (2) continued fraction */ a=1-p; b=a+x+1; term=0; pn[0]=1; pn[1]=x; pn[2]=x+1; pn[3]=x*b; gin=pn[2]/pn[3]; l32: a++; b+=2; 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=1-factor*gin; l50: return (gin); } double PointNormal (double prob) { /* returns z so that Prob{x.999998 || v<=0) return (-1); g = LnGamma(v/2); xx=v/2; c=xx-1; if (v >= -1.24*log(p)) goto l1; ch=pow((p*xx*exp(g+xx*aa)), 1/xx); if (ch-e<0) return (ch); goto l4; l1: if (v>.32) goto l3; ch=0.4; a=log(1-p); l2: q=ch; p1=1+ch*(4.67+ch); p2=ch*(6.73+ch*(6.66+ch)); t=-0.5+(4.67+2*ch)/p1 - (6.73+ch*(13.32+3*ch))/p2; ch-=(1-exp(a+g+.5*ch+c*aa)*p2/p1)/t; if (fabs(q/ch-1)-.01 <= 0) goto l4; else goto l2; l3: x=PointNormal (p); p1=0.222222/v; ch=v*pow((x*sqrt(p1)+1-p1), 3.0); if (ch>2.2*v+6) ch=-2*(log(1-p)-c*log(.5*ch)+g); l4: q=ch; p1=.5*ch; if ((t=IncompleteGamma (p1, xx, g))< 0.0) { printf ("IncompleteGamma \n"); return (-1); } p2=p-t; t=p2*exp(xx*aa+g+p1-c*log(ch)); b=t/ch; a=0.5*t-b*c; s1=(210+a*(140+a*(105+a*(84+a*(70+60*a))))) / 420; s2=(420+a*(735+a*(966+a*(1141+1278*a))))/2520; s3=(210+a*(462+a*(707+932*a)))/2520; s4=(252+a*(672+1182*a)+c*(294+a*(889+1740*a)))/5040; s5=(84+264*a+c*(175+606*a))/2520; s6=(120+c*(346+127*c))/5040; ch+=t*(1+0.5*t*s1-b*c*(s1-b*(s2-b*(s3-b*(s4-b*(s5-b*s6)))))); if (fabs(q/ch-1) > e) goto l4; return (ch); } void makeGammaCats(double alpha, double *gammaRates, int K, boolean useMedian) { int i; double factor = alpha / alpha * K, lnga1, alfa = alpha, beta = alpha, *gammaProbs = (double *)malloc(K * sizeof(double)); /* Note that ALPHA_MIN setting is somewhat critical due to */ /* numerical instability caused by very small rate[0] values */ /* induced by low alpha values around 0.01 */ assert(alfa >= ALPHA_MIN); if(useMedian) { double middle = 1.0 / (2.0*K), t = 0.0; for(i = 0; i < K; i++) gammaRates[i] = PointGamma((double)(i * 2 + 1) * middle, alfa, beta); for (i = 0; i < K; i++) t += gammaRates[i]; for( i = 0; i < K; i++) gammaRates[i] *= factor / t; } else { lnga1 = LnGamma(alfa + 1); for (i = 0; i < K - 1; i++) gammaProbs[i] = PointGamma((i + 1.0) / K, alfa, beta); for (i = 0; i < K - 1; i++) gammaProbs[i] = IncompleteGamma(gammaProbs[i] * beta, alfa + 1, lnga1); gammaRates[0] = gammaProbs[0] * factor; gammaRates[K - 1] = (1 - gammaProbs[K - 2]) * factor; for (i= 1; i < K - 1; i++) gammaRates[i] = (gammaProbs[i] - gammaProbs[i - 1]) * factor; } /* assert(gammaRates[0] >= 0.00000000000000000000000000000044136090435925743185910935350715027016962154188875); */ free(gammaProbs); return; } static void setRates(double *r, int rates) { int i; //changed to 1.0 instead of 0.5 for making the //implementation of an interface function to set other models //than GTR easier for(i = 0; i < rates - 1; i++) r[i] = 1.0; r[rates - 1] = 1.0; } void initRateMatrix(tree *tr) { int model; for(model = 0; model < tr->NumberOfModels; model++) { int i, states = tr->partitionData[model].states, rates = (states * states - states) / 2; switch(tr->partitionData[model].dataType) { case BINARY_DATA: case DNA_DATA: case SECONDARY_DATA: case SECONDARY_DATA_6: case SECONDARY_DATA_7: setRates(tr->partitionData[model].substRates, rates); break; case GENERIC_32: case GENERIC_64: switch(tr->multiStateModel) { case ORDERED_MULTI_STATE: { int j, k, i = 0; for(j = 0; j < states; j++) for(k = j + 1; k < states; k++) tr->partitionData[model].substRates[i++] = (double)(k - j); assert(i == rates); } break; case MK_MULTI_STATE: for(i = 0; i < rates; i++) tr->partitionData[model].substRates[i] = 1.0; break; case GTR_MULTI_STATE: setRates(tr->partitionData[model].substRates, rates); break; default: assert(0); } break; case AA_DATA: if(tr->partitionData[model].protModels == GTR) putWAG(tr->partitionData[model].substRates); break; default: assert(0); } if(tr->partitionData[model].nonGTR) { assert(tr->partitionData[model].dataType == SECONDARY_DATA || tr->partitionData[model].dataType == SECONDARY_DATA_6 || tr->partitionData[model].dataType == SECONDARY_DATA_7); for(i = 0; i < rates; i++) { if(tr->partitionData[model].symmetryVector[i] == -1) tr->partitionData[model].substRates[i] = 0.0; else { if(tr->partitionData[model].symmetryVector[i] == tr->partitionData[model].symmetryVector[rates - 1]) tr->partitionData[model].substRates[i] = 1.0; } } } } } static void setSymmetry(int *s, int *sDest, const int sCount, int *f, int *fDest, const int fCount) { int i; for(i = 0; i < sCount; i++) sDest[i] = s[i]; for(i = 0; i < fCount; i++) fDest[i] = f[i]; } static void setupSecondaryStructureSymmetries(tree *tr) { int model; for(model = 0; model < tr->NumberOfModels; model++) { if(tr->partitionData[model].dataType == SECONDARY_DATA || tr->partitionData[model].dataType == SECONDARY_DATA_6 || tr->partitionData[model].dataType == SECONDARY_DATA_7) { switch(tr->secondaryStructureModel) { case SEC_6_A: tr->partitionData[model].nonGTR = FALSE; break; case SEC_6_B: { int f[6] = {0, 1, 2, 3, 4, 5}; int s[15] = {2, 0, 1, 2, 2, 2, 2, 0, 1, 1, 2, 2, 2, 2, 1}; setSymmetry(s, tr->partitionData[model].symmetryVector, 15, f, tr->partitionData[model].frequencyGrouping, 6); tr->partitionData[model].nonGTR = TRUE; } break; case SEC_6_C: { int f[6] = {0, 2, 2, 1, 0, 1}; int s[15] = {2, 0, 1, 2, 2, 2, 2, 0, 1, 1, 2, 2, 2, 2, 1}; setSymmetry(s, tr->partitionData[model].symmetryVector, 15, f, tr->partitionData[model].frequencyGrouping, 6); tr->partitionData[model].nonGTR = TRUE; } break; case SEC_6_D: { int f[6] = {0, 2, 2, 1, 0, 1}; int s[15] = {2, -1, 1, 2, 2, 2, 2, -1, 1, 1, 2, 2, 2, 2, 1}; setSymmetry(s, tr->partitionData[model].symmetryVector, 15, f, tr->partitionData[model].frequencyGrouping, 6); tr->partitionData[model].nonGTR = TRUE; } break; case SEC_6_E: { int f[6] = {0, 1, 2, 3, 4, 5}; int s[15] = {2, -1, 1, 2, 2, 2, 2, -1, 1, 1, 2, 2, 2, 2, 1}; setSymmetry(s, tr->partitionData[model].symmetryVector, 15, f, tr->partitionData[model].frequencyGrouping, 6); tr->partitionData[model].nonGTR = TRUE; } break; case SEC_7_A: tr->partitionData[model].nonGTR = FALSE; break; case SEC_7_B: { int f[7] = {0, 2, 2, 1, 0, 1, 3}; int s[21] = {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20}; setSymmetry(s, tr->partitionData[model].symmetryVector, 21, f, tr->partitionData[model].frequencyGrouping, 7); tr->partitionData[model].nonGTR = TRUE; } break; case SEC_7_C: { int f[7] = {0, 1, 2, 3, 4, 5, 6}; int s[21] = {-1, -1, 0, -1, -1, 4, -1, -1, -1, 3, 5, 1, -1, -1, 6, -1, -1, 7, 2, 8, 9}; setSymmetry(s, tr->partitionData[model].symmetryVector, 21, f, tr->partitionData[model].frequencyGrouping, 7); tr->partitionData[model].nonGTR = TRUE; } break; case SEC_7_D: { int f[7] = {0, 1, 2, 3, 4, 5, 6}; int s[21] = {2, 0, 1, 2, 2, 3, 2, 2, 0, 1, 3, 1, 2, 2, 3, 2, 2, 3, 1, 3, 3}; setSymmetry(s, tr->partitionData[model].symmetryVector, 21, f, tr->partitionData[model].frequencyGrouping, 7); tr->partitionData[model].nonGTR = TRUE; } break; case SEC_7_E: { int f[7] = {0, 1, 2, 3, 4, 5, 6}; int s[21] = {-1, -1, 0, -1, -1, 1, -1, -1, -1, 0, 1, 0, -1, -1, 1, -1, -1, 1, 0, 1, 1}; setSymmetry(s, tr->partitionData[model].symmetryVector, 21, f, tr->partitionData[model].frequencyGrouping, 7); tr->partitionData[model].nonGTR = TRUE; } break; case SEC_7_F: { int f[7] = {0, 2, 2, 1, 0, 1, 3}; int s[21] = {2, 0, 1, 2, 2, 3, 2, 2, 0, 1, 3, 1, 2, 2, 3, 2, 2, 3, 1, 3, 3}; setSymmetry(s, tr->partitionData[model].symmetryVector, 21, f, tr->partitionData[model].frequencyGrouping, 7); tr->partitionData[model].nonGTR = TRUE; } break; case SEC_16: tr->partitionData[1].nonGTR = FALSE; break; case SEC_16_A: { int f[16] = {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15}; int s[120] = {/* AA */ 4, 4, 3, 4, -1, -1, -1, 4, -1, -1, -1, 3, -1, -1, -1, /* AC */ 4, 3, -1, 4, -1, -1, -1, 3, -1, -1, -1, 4, -1, -1, /* AG */ 3, -1, -1, 3, -1, -1, -1, 4, -1, -1, -1, 3, -1, /* AU */ -1, -1, 2, 3, -1, 0, -1, 1, 2, -1, 2, 3, /* CA */ 4, 3, 4, 4, -1, -1, -1, 3, -1, -1, -1, /* CC */ 3, 4, -1, 3, -1, -1, -1, 4, -1, -1, /* CG */ 3, -1, 2, 3, 2, 0, -1, 1, -1, /* CU */ -1, -1, -1, 3, -1, -1, -1, 4, /* GA */ 3, 4, 3, 3, -1, -1, -1, /* GC */ 3, 1, 2, 3, 2, -1, /* GG */ 3, -1, -1, 3, -1, /* GU */ 2, -1, 2, 3, /* UA */ 3, 1, 3, /* UC */ 3, 4, /* UG */ 3}; setSymmetry(s, tr->partitionData[model].symmetryVector, 120, f, tr->partitionData[model].frequencyGrouping, 16); tr->partitionData[model].nonGTR = TRUE; } break; case SEC_16_B: { int f[16] = {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15}; int s[120] = {/* AA */ 0, 0, 0, 0, -1, -1, -1, 0, -1, -1, -1, 0, -1, -1, -1, /* AC */ 0, 0, -1, 0, -1, -1, -1, 0, -1, -1, -1, 0, -1, -1, /* AG */ 0, -1, -1, 0, -1, -1, -1, 0, -1, -1, -1, 0, -1, /* AU */ -1, -1, 0, 0, -1, 0, -1, 0, 0, -1, 0, 0, /* CA */ 0, 0, 0, 0, -1, -1, -1, 0, -1, -1, -1, /* CC */ 0, 0, -1, 0, -1, -1, -1, 0, -1, -1, /* CG */ 0, -1, 0, 0, 0, 0, -1, 0, -1, /* CU */ -1, -1, -1, 0, -1, -1, -1, 0, /* GA */ 0, 0, 0, 0, -1, -1, -1, /* GC */ 0, 0, 0, 0, 0, -1, /* GG */ 0, -1, -1, 0, -1, /* GU */ 0, -1, 0, 0, /* UA */ 0, 0, 0, /* UC */ 0, 0, /* UG */ 0}; setSymmetry(s, tr->partitionData[model].symmetryVector, 120, f, tr->partitionData[model].frequencyGrouping, 16); tr->partitionData[model].nonGTR = TRUE; } break; case SEC_16_C: case SEC_16_D: case SEC_16_E: case SEC_16_F: case SEC_16_I: case SEC_16_J: case SEC_16_K: assert(0); default: assert(0); } } } } /* this function is only called once at program start-up ! */ static void initializeBaseFreqs(tree *tr) { size_t model; for(model = 0; model < (size_t)tr->NumberOfModels; model++) { if(tr->partitionData[model].optimizeBaseFrequencies) { //set all base frequencies to identical starting values 1.0 / numberOfDataStates //if we want to optimize base freqeuncies for the current partition int l, numFreqs = tr->partitionData[model].states; double f = 1.0 / ((double)numFreqs); for(l = 0; l < numFreqs; l++) { tr->partitionData[model].frequencies[l] = f; tr->partitionData[model].empiricalFrequencies[l] = f; } } else { //otherwise, at startup examl reads and stores the empirical base frequencies as determined by the //parser code in .frequencies, now we just store them in .empiricalFrequencies such that we can //overwrite .frequencies without losing the empirical base freqs memcpy(tr->partitionData[model].empiricalFrequencies, tr->partitionData[model].frequencies, sizeof(double) * tr->partitionData[model].states); } } } /* this function is only called once at program start-up ! */ void initModel(tree *tr) { int model; optimizeRateCategoryInvocations = 1; tr->numberOfInvariableColumns = 0; tr->weightOfInvariableColumns = 0; if(tr->rateHetModel == CAT) { for(model = 0; model < tr->NumberOfModels; model++) { tr->partitionData[model].numberOfCategories = 1; tr->partitionData[model].perSiteRates[0] = 1.0; size_t i; for(i = 0; i < tr->partitionData[model].width; ++i) { tr->partitionData[model].rateCategory[i] = 0; tr->partitionData[model].patrat[i] = 1.; } } checkPerSiteRates(tr); } setupSecondaryStructureSymmetries(tr); initRateMatrix(tr); initializeBaseFreqs(tr); for(model = 0; model < tr->NumberOfModels; model++) { int k; tr->partitionData[model].alpha = 1.0; if(tr->partitionData[model].protModels == AUTO) tr->partitionData[model].autoProtModels = WAG; /* initialize by WAG per default when AUTO is used */ makeGammaCats(tr->partitionData[model].alpha, tr->partitionData[model].gammaRates, 4, tr->useMedian); for(k = 0; k < tr->partitionData[model].states; k++) tr->partitionData[model].freqExponents[k] = 0.0; //LG4X inits for(k = 0; k < 4; k++) { tr->partitionData[model].weights[k] = 0.25; tr->partitionData[model].weightExponents[k] = 0.0; } initReversibleGTR(tr, model); } } ExaML-3.0.21/examl/newviewGenericSpecial.c000066400000000000000000005266751330315463200203240ustar00rootroot00000000000000/* RAxML-VI-HPC (version 2.2) a program for sequential and parallel estimation of phylogenetic trees * Copyright August 2006 by Alexandros Stamatakis * * Partially derived from * fastDNAml, a program for estimation of phylogenetic trees from sequences by Gary J. Olsen * * and * * Programs of the PHYLIP package by Joe Felsenstein. * This program is free software; you may redistribute it and/or modify its * 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. * * * For any other enquiries send an Email to Alexandros Stamatakis * Alexandros.Stamatakis@epfl.ch * * When publishing work that is based on the results from RAxML-VI-HPC please cite: * * Alexandros Stamatakis:"RAxML-VI-HPC: maximum likelihood-based phylogenetic analyses with thousands of taxa and mixed models". * Bioinformatics 2006; doi: 10.1093/bioinformatics/btl446 */ #ifndef WIN32 #include #endif #include #include #include #include #include #include #include #include #include "axml.h" #ifdef __SIM_SSE3 #include #include #include /* required to compute the absoliute values of double precision numbers with SSE3 */ const union __attribute__ ((aligned (BYTE_ALIGNMENT))) { uint64_t i[2]; __m128d m; } absMask = {{0x7fffffffffffffffULL , 0x7fffffffffffffffULL }}; #endif /* includes MIC-optimized functions */ #ifdef __MIC_NATIVE #include "mic_native.h" #endif extern int processID; /* bit mask */ extern const unsigned int mask32[32]; /* generic function for computing the P matrices, for computing the conditional likelihood at a node p, given child nodes q and r we compute P(z1) and P(z2) here */ static void makeP(double z1, double z2, double *rptr, double *EI, double *EIGN, int numberOfCategories, double *left, double *right, boolean saveMem, int maxCat, const int states) { int i, j, k, /* square of the number of states = P-matrix size */ statesSquare = states * states; /* assign some space for pre-computing and later re-using functions */ double *lz1 = (double*)malloc(sizeof(double) * states), *lz2 = (double*)malloc(sizeof(double) * states), *d1 = (double*)malloc(sizeof(double) * states), *d2 = (double*)malloc(sizeof(double) * states); /* multiply branch lengths with eigenvalues */ for(i = 1; i < states; i++) { lz1[i] = EIGN[i] * z1; lz2[i] = EIGN[i] * z2; } /* loop over the number of rate categories, this will be 4 for the GAMMA model and variable for the CAT model */ for(i = 0; i < numberOfCategories; i++) { /* exponentiate the rate multiplied by the branch */ for(j = 1; j < states; j++) { d1[j] = EXP(rptr[i] * lz1[j]); d2[j] = EXP(rptr[i] * lz2[j]); } /* now fill the P matrices for the two branch length values */ for(j = 0; j < states; j++) { /* left and right are pre-allocated arrays */ left[statesSquare * i + states * j] = 1.0; right[statesSquare * i + states * j] = 1.0; for(k = 1; k < states; k++) { left[statesSquare * i + states * j + k] = d1[k] * EI[states * j + k]; right[statesSquare * i + states * j + k] = d2[k] * EI[states * j + k]; } } } /* if memory saving is enabled and we are using CAT we need to do one additional P matrix calculation for a rate of 1.0 to compute the entries of a column/tree site comprising only gaps */ if(saveMem) { i = maxCat; for(j = 1; j < states; j++) { d1[j] = EXP (lz1[j]); d2[j] = EXP (lz2[j]); } for(j = 0; j < states; j++) { left[statesSquare * i + states * j] = 1.0; right[statesSquare * i + states * j] = 1.0; for(k = 1; k < states; k++) { left[statesSquare * i + states * j + k] = d1[k] * EI[states * j + k]; right[statesSquare * i + states * j + k] = d2[k] * EI[states * j + k]; } } } /* free the temporary buffers */ free(lz1); free(lz2); free(d1); free(d2); } static void makeP_FlexLG4(double z1, double z2, double *rptr, double *EI[4], double *EIGN[4], int numberOfCategories, double *left, double *right, const int numStates) { int i, j, k; const int statesSquare = numStates * numStates; double d1[64], d2[64]; assert(numStates <= 64); for(i = 0; i < numberOfCategories; i++) { for(j = 1; j < numStates; j++) { d1[j] = EXP (rptr[i] * EIGN[i][j] * z1); d2[j] = EXP (rptr[i] * EIGN[i][j] * z2); } for(j = 0; j < numStates; j++) { left[statesSquare * i + numStates * j] = 1.0; right[statesSquare * i + numStates * j] = 1.0; for(k = 1; k < numStates; k++) { left[statesSquare * i + numStates * j + k] = d1[k] * EI[i][numStates * j + k]; right[statesSquare * i + numStates * j + k] = d2[k] * EI[i][numStates * j + k]; } } } } /* The functions here are organized in a similar way as in evaluateGenericSpecial.c I provide generic, slow but readable function implementations for computing the conditional likelihood arrays at p, given child nodes q and r. Once again we need two generic function implementations, one for CAT and one for GAMMA */ #ifndef _OPTIMIZED_FUNCTIONS static void newviewCAT_FLEX(int tipCase, double *extEV, int *cptr, double *x1, double *x2, double *x3, double *tipVector, unsigned char *tipX1, unsigned char *tipX2, int n, double *left, double *right, int *wgt, int *scalerIncrement, const int states) { double *le, *ri, *v, *vl, *vr, ump_x1, ump_x2, x1px2; int i, l, j, scale, addScale = 0; const int statesSquare = states * states; /* here we switch over the different cases for efficiency, but also because each case accesses different data types. We consider three cases: either q and r are both tips, q or r are tips, and q and r are inner nodes. */ switch(tipCase) { /* both child nodes of p weher we want to update the conditional likelihood are tips */ case TIP_TIP: /* loop over sites */ for (i = 0; i < n; i++) { /* set a pointer to the P-Matrices for the rate category of this site */ le = &left[cptr[i] * statesSquare]; ri = &right[cptr[i] * statesSquare]; /* pointers to the likelihood entries of the tips q (vl) and r (vr) We will do reading accesses to these values only. */ vl = &(tipVector[states * tipX1[i]]); vr = &(tipVector[states * tipX2[i]]); /* address of the conditional likelihood array entres at site i. This is a writing access to v */ v = &x3[states * i]; /* initialize v */ for(l = 0; l < states; l++) v[l] = 0.0; /* loop over states to compute the cond likelihoods at p (v) */ for(l = 0; l < states; l++) { ump_x1 = 0.0; ump_x2 = 0.0; /* le and ri are the P-matrices */ for(j = 0; j < states; j++) { ump_x1 += vl[j] * le[l * states + j]; ump_x2 += vr[j] * ri[l * states + j]; } x1px2 = ump_x1 * ump_x2; /* multiply with matrix of eigenvectors extEV */ for(j = 0; j < states; j++) v[j] += x1px2 * extEV[l * states + j]; } } break; case TIP_INNER: /* same as above, only that now vl is a tip and vr is the conditional probability vector at an inner node. Note that, if we have the case that either q or r is a tip, the nodes will be flipped to ensure that tipX1 always points to the sequence at the tip. */ for (i = 0; i < n; i++) { le = &left[cptr[i] * statesSquare]; ri = &right[cptr[i] * statesSquare]; /* access tip vector lookup table */ vl = &(tipVector[states * tipX1[i]]); /* access conditional likelihoo arrays */ /* again, vl and vr are reading accesses, while v is a writing access */ vr = &x2[states * i]; v = &x3[states * i]; /* same as in the loop above */ for(l = 0; l < states; l++) v[l] = 0.0; for(l = 0; l < states; l++) { ump_x1 = 0.0; ump_x2 = 0.0; for(j = 0; j < states; j++) { ump_x1 += vl[j] * le[l * states + j]; ump_x2 += vr[j] * ri[l * states + j]; } x1px2 = ump_x1 * ump_x2; for(j = 0; j < states; j++) v[j] += x1px2 * extEV[l * states + j]; } /* now let's check for numerical scaling. The maths in RAxML are a bit non-standard to avoid/economize on arithmetic operations at the virtual root and for branch length optimization and hence values stored in the conditional likelihood vectors can become negative. Below we check if all absolute values stored at position i of v are smaller than a pre-defined value in axml.h. If they are all smaller we can then safely multiply them by a large, constant number twotothe256 (without numerical overflow) that is also speced in axml.h */ scale = 1; for(l = 0; scale && (l < states); l++) scale = ((v[l] < minlikelihood) && (v[l] > minusminlikelihood)); if(scale) { for(l = 0; l < states; l++) v[l] *= twotothe256; /* if we have scaled the entries to prevent underflow, we need to keep track of how many scaling multiplications we did per node such as to undo them at the virtual root, e.g., in evaluateGeneric() Note here, that, if we scaled the site we need to increment the scaling counter by the wieght, i.e., the number of sites this potentially compressed pattern represents ! */ addScale += wgt[i]; } } break; case INNER_INNER: /* same as above, only that the two child nodes q and r are now inner nodes */ for(i = 0; i < n; i++) { le = &left[cptr[i] * statesSquare]; ri = &right[cptr[i] * statesSquare]; /* index conditional likelihood vectors of inner nodes */ vl = &x1[states * i]; vr = &x2[states * i]; v = &x3[states * i]; for(l = 0; l < states; l++) v[l] = 0.0; for(l = 0; l < states; l++) { ump_x1 = 0.0; ump_x2 = 0.0; for(j = 0; j < states; j++) { ump_x1 += vl[j] * le[l * states + j]; ump_x2 += vr[j] * ri[l * states + j]; } x1px2 = ump_x1 * ump_x2; for(j = 0; j < states; j++) v[j] += x1px2 * extEV[l * states + j]; } scale = 1; for(l = 0; scale && (l < states); l++) scale = ((v[l] < minlikelihood) && (v[l] > minusminlikelihood)); if(scale) { for(l = 0; l < states; l++) v[l] *= twotothe256; addScale += wgt[i]; } } break; default: assert(0); } /* increment the scaling counter by the additional scalings done at node p */ *scalerIncrement = addScale; } static void newviewGAMMA_FLEX(int tipCase, double *x1, double *x2, double *x3, double *extEV, double *tipVector, unsigned char *tipX1, unsigned char *tipX2, int n, double *left, double *right, int *wgt, int *scalerIncrement, const int states, const int maxStateValue) { double *uX1, *uX2, *v, x1px2, *vl, *vr, al, ar; int i, j, l, k, scale, addScale = 0; const int statesSquare = states * states, span = states * 4, /* this is required for doing some pre-computations that help to save numerical operations. What we are actually computing here are additional lookup tables for each possible state a certain data-type can assume. for DNA with ambuguity coding this is 15, for proteins this is 22 or 23, since there also exist one or two amibguity codes for protein data. Essentially this is very similar to the tip vectors which we also use as lookup tables */ precomputeLength = maxStateValue * span; switch(tipCase) { case TIP_TIP: { /* allocate pre-compute memory space */ double *umpX1 = (double*)malloc(sizeof(double) * precomputeLength), *umpX2 = (double*)malloc(sizeof(double) * precomputeLength); /* multiply all possible tip state vectors with the respective P-matrices */ for(i = 0; i < maxStateValue; i++) { v = &(tipVector[states * i]); for(k = 0; k < span; k++) { umpX1[span * i + k] = 0.0; umpX2[span * i + k] = 0.0; for(l = 0; l < states; l++) { umpX1[span * i + k] += v[l] * left[k * states + l]; umpX2[span * i + k] += v[l] * right[k * states + l]; } } } for(i = 0; i < n; i++) { /* access the precomputed arrays (pre-computed multiplication of conditional with the tip state) */ uX1 = &umpX1[span * tipX1[i]]; uX2 = &umpX2[span * tipX2[i]]; /* loop over discrete GAMMA rates */ for(j = 0; j < 4; j++) { /* the rest is the same as for CAT */ v = &x3[i * span + j * states]; for(k = 0; k < states; k++) v[k] = 0.0; for(k = 0; k < states; k++) { x1px2 = uX1[j * states + k] * uX2[j * states + k]; for(l = 0; l < states; l++) v[l] += x1px2 * extEV[states * k + l]; } } } /* free precomputed vectors */ free(umpX1); free(umpX2); } break; case TIP_INNER: { /* we do analogous pre-computations as above, with the only difference that we now do them only for one tip vector */ double *umpX1 = (double*)malloc(sizeof(double) * precomputeLength), *ump_x2 = (double*)malloc(sizeof(double) * states); /* precompute P and left tip vector product */ for(i = 0; i < maxStateValue; i++) { v = &(tipVector[states * i]); for(k = 0; k < span; k++) { umpX1[span * i + k] = 0.0; for(l = 0; l < states; l++) umpX1[span * i + k] += v[l] * left[k * states + l]; } } for (i = 0; i < n; i++) { /* access pre-computed value based on the raw sequence data tipX1 that is used as an index */ uX1 = &umpX1[span * tipX1[i]]; /* loop over discrete GAMMA rates */ for(k = 0; k < 4; k++) { v = &(x2[span * i + k * states]); for(l = 0; l < states; l++) { ump_x2[l] = 0.0; for(j = 0; j < states; j++) ump_x2[l] += v[j] * right[k * statesSquare + l * states + j]; } v = &(x3[span * i + states * k]); for(l = 0; l < states; l++) v[l] = 0; for(l = 0; l < states; l++) { x1px2 = uX1[k * states + l] * ump_x2[l]; for(j = 0; j < states; j++) v[j] += x1px2 * extEV[l * states + j]; } } /* also do numerical scaling as above. Note that here we need to scale 4 * 4 values for DNA or 4 * 20 values for protein data. If they are ALL smaller than our threshold, we scale. Note that, this can cause numerical problems with GAMMA, if the values generated by the four discrete GAMMA rates are too different. For details, see: F. Izquierdo-Carrasco, S.A. Smith, A. Stamatakis: "Algorithms, Data Structures, and Numerics for Likelihood-based Phylogenetic Inference of Huge Trees" */ v = &x3[span * i]; scale = 1; for(l = 0; scale && (l < span); l++) scale = (ABS(v[l]) < minlikelihood); if (scale) { for(l = 0; l < span; l++) v[l] *= twotothe256; addScale += wgt[i]; } } free(umpX1); free(ump_x2); } break; case INNER_INNER: /* same as above, without pre-computations */ for (i = 0; i < n; i++) { for(k = 0; k < 4; k++) { vl = &(x1[span * i + states * k]); vr = &(x2[span * i + states * k]); v = &(x3[span * i + states * k]); for(l = 0; l < states; l++) v[l] = 0; for(l = 0; l < states; l++) { al = 0.0; ar = 0.0; for(j = 0; j < states; j++) { al += vl[j] * left[k * statesSquare + l * states + j]; ar += vr[j] * right[k * statesSquare + l * states + j]; } x1px2 = al * ar; for(j = 0; j < states; j++) v[j] += x1px2 * extEV[states * l + j]; } } v = &(x3[span * i]); scale = 1; for(l = 0; scale && (l < span); l++) scale = ((ABS(v[l]) < minlikelihood)); if(scale) { for(l = 0; l < span; l++) v[l] *= twotothe256; addScale += wgt[i]; } } break; default: assert(0); } /* as above, increment the global counter that counts scaling multiplications by the scaling multiplications carried out for computing the likelihood array at node p */ *scalerIncrement = addScale; } #endif /* The function below computes partial traversals only down to the point/node in the tree where the conditional likelihhod vector summarizing a subtree is already oriented in the correct direction */ void computeTraversalInfo(nodeptr p, traversalInfo *ti, int *counter, int maxTips, int numBranches, boolean partialTraversal) { /* if it's a tip we don't do anything */ if(isTip(p->number, maxTips)) return; { int i; /* get the left and right descendants */ nodeptr q = p->next->back, r = p->next->next->back; /* if the left and right children are tips there is not that much to do */ if(isTip(r->number, maxTips) && isTip(q->number, maxTips)) { /* fix the orientation of p->x */ if (! p->x) getxnode(p); assert(p->x); /* add the current node triplet p,q,r to the traversal descriptor */ ti[*counter].tipCase = TIP_TIP; ti[*counter].pNumber = p->number; ti[*counter].qNumber = q->number; ti[*counter].rNumber = r->number; /* copy branches to traversal descriptor */ for(i = 0; i < numBranches; i++) { ti[*counter].qz[i] = q->z[i]; ti[*counter].rz[i] = r->z[i]; } /* increment length counter */ *counter = *counter + 1; } else { /* if either r or q are tips, flip them to make sure that the tip data is stored for q */ if(isTip(r->number, maxTips) || isTip(q->number, maxTips)) { if(isTip(r->number, maxTips)) { nodeptr tmp = r; r = q; q = tmp; } /* if the orientation of the liklihood vector at r is not correct we need to re-compute it and descend into its subtree to figure out if there are more vrctors in there to re-compute and re-orient */ if(!r->x || !partialTraversal) computeTraversalInfo(r, ti, counter, maxTips, numBranches, partialTraversal); if(! p->x) getxnode(p); /* make sure that everything is consistent now */ assert(p->x && r->x); /* store data for p, q, r in the traversal descriptor */ ti[*counter].tipCase = TIP_INNER; ti[*counter].pNumber = p->number; ti[*counter].qNumber = q->number; ti[*counter].rNumber = r->number; for(i = 0; i < numBranches; i++) { ti[*counter].qz[i] = q->z[i]; ti[*counter].rz[i] = r->z[i]; } *counter = *counter + 1; } else { /* same as above, only now q and r are inner nodes. Hence if they are not oriented correctly they will need to be recomputed and we need to descend into the respective subtrees to check if everything is consistent in there, potentially expanding the traversal descriptor */ if(! q->x || !partialTraversal) computeTraversalInfo(q, ti, counter, maxTips, numBranches, partialTraversal); if(! r->x || !partialTraversal) computeTraversalInfo(r, ti, counter, maxTips, numBranches, partialTraversal); if(! p->x) getxnode(p); /* check that the vector orientations are consistent now */ assert(p->x && r->x && q->x); ti[*counter].tipCase = INNER_INNER; ti[*counter].pNumber = p->number; ti[*counter].qNumber = q->number; ti[*counter].rNumber = r->number; for(i = 0; i < numBranches; i++) { ti[*counter].qz[i] = q->z[i]; ti[*counter].rz[i] = r->z[i]; } *counter = *counter + 1; } } } } /* below are the optimized unrolled, and vectorized versions of the above generi cfunctions for computing the conditional likelihood at p given child nodes q and r. The actual implementation is located at the end/bottom of this file. */ #if (defined(_OPTIMIZED_FUNCTIONS) && !defined(__AVX)) static void newviewGTRGAMMAPROT_LG4(int tipCase, double *x1, double *x2, double *x3, double *extEV[4], double *tipVector[4], int *ex3, unsigned char *tipX1, unsigned char *tipX2, int n, double *left, double *right, int *wgt, int *scalerIncrement, const boolean useFastScaling); static void newviewGTRGAMMA_GAPPED_SAVE(int tipCase, double *x1_start, double *x2_start, double *x3_start, double *EV, double *tipVector, unsigned char *tipX1, unsigned char *tipX2, const int n, double *left, double *right, int *wgt, int *scalerIncrement, unsigned int *x1_gap, unsigned int *x2_gap, unsigned int *x3_gap, double *x1_gapColumn, double *x2_gapColumn, double *x3_gapColumn); static void newviewGTRGAMMA(int tipCase, double *x1_start, double *x2_start, double *x3_start, double *EV, double *tipVector, unsigned char *tipX1, unsigned char *tipX2, const int n, double *left, double *right, int *wgt, int *scalerIncrement ); static void newviewGTRCAT( int tipCase, double *EV, int *cptr, double *x1_start, double *x2_start, double *x3_start, double *tipVector, unsigned char *tipX1, unsigned char *tipX2, int n, double *left, double *right, int *wgt, int *scalerIncrement); static void newviewGTRCAT_SAVE( int tipCase, double *EV, int *cptr, double *x1_start, double *x2_start, double *x3_start, double *tipVector, unsigned char *tipX1, unsigned char *tipX2, int n, double *left, double *right, int *wgt, int *scalerIncrement, unsigned int *x1_gap, unsigned int *x2_gap, unsigned int *x3_gap, double *x1_gapColumn, double *x2_gapColumn, double *x3_gapColumn, const int maxCats); static void newviewGTRGAMMAPROT_GAPPED_SAVE(int tipCase, double *x1, double *x2, double *x3, double *extEV, double *tipVector, unsigned char *tipX1, unsigned char *tipX2, int n, double *left, double *right, int *wgt, int *scalerIncrement, unsigned int *x1_gap, unsigned int *x2_gap, unsigned int *x3_gap, double *x1_gapColumn, double *x2_gapColumn, double *x3_gapColumn ); static void newviewGTRGAMMAPROT(int tipCase, double *x1, double *x2, double *x3, double *extEV, double *tipVector, unsigned char *tipX1, unsigned char *tipX2, int n, double *left, double *right, int *wgt, int *scalerIncrement); static void newviewGTRCATPROT(int tipCase, double *extEV, int *cptr, double *x1, double *x2, double *x3, double *tipVector, unsigned char *tipX1, unsigned char *tipX2, int n, double *left, double *right, int *wgt, int *scalerIncrement ); static void newviewGTRCATPROT_SAVE(int tipCase, double *extEV, int *cptr, double *x1, double *x2, double *x3, double *tipVector, unsigned char *tipX1, unsigned char *tipX2, int n, double *left, double *right, int *wgt, int *scalerIncrement, unsigned int *x1_gap, unsigned int *x2_gap, unsigned int *x3_gap, double *x1_gapColumn, double *x2_gapColumn, double *x3_gapColumn, const int maxCats); #endif #ifdef _OPTIMIZED_FUNCTIONS static void newviewGTRCAT_BINARY( int tipCase, double *EV, int *cptr, double *x1_start, double *x2_start, double *x3_start, double *tipVector, int *ex3, unsigned char *tipX1, unsigned char *tipX2, int n, double *left, double *right, int *wgt, int *scalerIncrement, const boolean useFastScaling); static void newviewGTRGAMMA_BINARY(int tipCase, double *x1_start, double *x2_start, double *x3_start, double *EV, double *tipVector, int *ex3, unsigned char *tipX1, unsigned char *tipX2, const int n, double *left, double *right, int *wgt, int *scalerIncrement, const boolean useFastScaling ); #endif boolean isGap(unsigned int *x, int pos) { return (x[pos / 32] & mask32[pos % 32]); } boolean noGap(unsigned int *x, int pos) { return (!(x[pos / 32] & mask32[pos % 32])); } /* now this is the function that just iterates over the length of the traversal descriptor and just computes the conditional likelihhod arrays in the order given by the descriptor. So in a sense, this function has no clue that there is any tree-like structure in the traversal descriptor, it just operates on an array of structs of given length */ extern const char inverseMeaningDNA[16]; void newviewIterative (tree *tr, int startIndex) { traversalInfo *ti = tr->td[0].ti; int i; /* loop over traversal descriptor length. Note that on average we only re-compute the conditionals on 3 -4 nodes in RAxML */ for(i = startIndex; i < tr->td[0].count; i++) { traversalInfo *tInfo = &ti[i]; int model; #ifdef _USE_OMP #pragma omp parallel for #endif for(model = 0; model < tr->NumberOfModels; model++) { /* check if this partition has to be processed now - otherwise no need to compute P matrix */ if(!tr->td[0].executeModel[model] || tr->partitionData[model].width == 0) continue; int categories, states = tr->partitionData[model].states; double qz, rz, *rateCategories, *left = tr->partitionData[model].left, *right = tr->partitionData[model].right; /* figure out what kind of rate heterogeneity approach we are using */ if(tr->rateHetModel == CAT) { rateCategories = tr->partitionData[model].perSiteRates; categories = tr->partitionData[model].numberOfCategories; } else { rateCategories = tr->partitionData[model].gammaRates; categories = 4; } /* if we use per-partition branch length optimization get the branch length of partition model and take the log otherwise use the joint branch length among all partitions that is always stored at index [0] */ if(tr->numBranches > 1) { qz = tInfo->qz[model]; rz = tInfo->rz[model]; } else { qz = tInfo->qz[0]; rz = tInfo->rz[0]; } qz = (qz > zmin) ? log(qz) : log(zmin); rz = (rz > zmin) ? log(rz) : log(zmin); /* compute the left and right P matrices */ #ifdef __MIC_NATIVE switch (tr->partitionData[model].states) { case 2: /* BINARY data */ assert(0 && "Binary data model is not implemented on Intel MIC"); break; case 4: /* DNA data */ { makeP_DNA_MIC(qz, rz, rateCategories, tr->partitionData[model].EI, tr->partitionData[model].EIGN, categories, left, right, tr->saveMemory, tr->maxCategories); precomputeTips_DNA_MIC(tInfo->tipCase, tr->partitionData[model].tipVector, left, right, tr->partitionData[model].mic_umpLeft, tr->partitionData[model].mic_umpRight, categories); } break; case 20: /* AA data */ { if(tr->partitionData[model].protModels == LG4M || tr->partitionData[model].protModels == LG4X) { makeP_PROT_LG4_MIC(qz, rz, tr->partitionData[model].gammaRates, tr->partitionData[model].EI_LG4, tr->partitionData[model].EIGN_LG4, 4, left, right); precomputeTips_PROT_LG4_MIC(tInfo->tipCase, tr->partitionData[model].tipVector_LG4, left, right, tr->partitionData[model].mic_umpLeft, tr->partitionData[model].mic_umpRight, categories); } else { makeP_PROT_MIC(qz, rz, rateCategories, tr->partitionData[model].EI, tr->partitionData[model].EIGN, categories, left, right, tr->saveMemory, tr->maxCategories); precomputeTips_PROT_MIC(tInfo->tipCase, tr->partitionData[model].tipVector, left, right, tr->partitionData[model].mic_umpLeft, tr->partitionData[model].mic_umpRight, categories); } } break; default: assert(0); } #else if(tr->partitionData[model].protModels == LG4M || tr->partitionData[model].protModels == LG4X) makeP_FlexLG4(qz, rz, tr->partitionData[model].gammaRates, tr->partitionData[model].EI_LG4, tr->partitionData[model].EIGN_LG4, 4, left, right, 20); else makeP(qz, rz, rateCategories, tr->partitionData[model].EI, tr->partitionData[model].EIGN, categories, left, right, tr->saveMemory, tr->maxCategories, states); #endif } // for model /* now loop over all partitions for nodes p, q, and r of the current traversal vector entry */ #ifdef _USE_OMP #pragma omp parallel #endif { int m, model, maxModel; #ifdef _USE_OMP maxModel = tr->maxModelsPerThread; #else maxModel = tr->NumberOfModels; #endif for(m = 0; m < maxModel; m++) { size_t width = 0, offset = 0; double *left = (double*)NULL, *right = (double*)NULL; unsigned int *globalScaler = (unsigned int*)NULL; #ifdef _USE_OMP int tid = omp_get_thread_num(); /* check if this thread should process this partition */ Assign* pAss = tr->threadPartAssigns[tid * tr->maxModelsPerThread + m]; if(pAss) { assert(tid == pAss->procId); model = pAss->partitionId; width = pAss->width; offset = pAss->offset; left = tr->partitionData[model].left; right = tr->partitionData[model].right; globalScaler = tr->partitionData[model].threadGlobalScaler[tid]; } else break; #else model = m; /* number of sites in this partition */ width = (size_t)tr->partitionData[model].width; offset = 0; /* set the pointers to the left and right P matrices to the pre-allocated memory space for storing them */ left = tr->partitionData[model].left; right = tr->partitionData[model].right; globalScaler = tr->partitionData[model].globalScaler; #endif /* this conditional statement is exactly identical to what we do in evaluateIterative */ if(tr->td[0].executeModel[model] && width > 0) { double *x1_start = (double*)NULL, *x2_start = (double*)NULL, *x3_start = (double*)NULL, //tr->partitionData[model].xVector[tInfo->pNumber - tr->mxtips - 1], *x1_gapColumn = (double*)NULL, *x2_gapColumn = (double*)NULL, *x3_gapColumn = (double*)NULL; int scalerIncrement = 0, /* integer wieght vector with pattern compression weights */ *wgt = tr->partitionData[model].wgt + offset, /* integer rate category vector (for each pattern, _number_ of PSR category assigned to it, NOT actual rate!) */ *rateCategory = tr->partitionData[model].rateCategory + offset; unsigned int *x1_gap = (unsigned int*)NULL, *x2_gap = (unsigned int*)NULL, *x3_gap = (unsigned int*)NULL; unsigned char *tipX1 = (unsigned char *)NULL, *tipX2 = (unsigned char *)NULL; size_t gapOffset = 0, rateHet = discreteRateCategories(tr->rateHetModel), /* get the number of states in the data stored in partition model */ states = (size_t)tr->partitionData[model].states, /* span for single alignment site (in doubles!) */ span = rateHet * states, x_offset = offset * (size_t)span, /* get the length of the current likelihood array stored at node p. This is important mainly for the SEV-based memory saving option described in here: F. Izquierdo-Carrasco, S.A. Smith, A. Stamatakis: "Algorithms, Data Structures, and Numerics for Likelihood-based Phylogenetic Inference of Huge Trees". So tr->partitionData[model].xSpaceVector[i] provides the length of the allocated conditional array of partition model and node i */ availableLength = tr->partitionData[model].xSpaceVector[(tInfo->pNumber - tr->mxtips - 1)], requiredLength = 0; x3_start = tr->partitionData[model].xVector[tInfo->pNumber - tr->mxtips - 1] + x_offset; /* memory saving stuff, not important right now, but if you are interested ask Fernando */ if(tr->saveMemory) { size_t j, setBits = 0; gapOffset = states * (size_t)getUndetermined(tr->partitionData[model].dataType); x1_gap = &(tr->partitionData[model].gapVector[tInfo->qNumber * tr->partitionData[model].gapVectorLength]); x2_gap = &(tr->partitionData[model].gapVector[tInfo->rNumber * tr->partitionData[model].gapVectorLength]); x3_gap = &(tr->partitionData[model].gapVector[tInfo->pNumber * tr->partitionData[model].gapVectorLength]); for(j = 0; j < (size_t)tr->partitionData[model].gapVectorLength; j++) { x3_gap[j] = x1_gap[j] & x2_gap[j]; setBits += (size_t)(precomputed16_bitcount(x3_gap[j], tr->bits_in_16bits)); } requiredLength = (width - setBits) * rateHet * states * sizeof(double); } else /* if we are not trying to save memory the space required to store an inner likelihood array is the number of sites in the partition times the number of states of the data type in the partition times the number of discrete GAMMA rates (1 for CAT essentially) times 8 bytes */ requiredLength = width * rateHet * states * sizeof(double); /* Initially, even when not using memory saving no space is allocated for inner likelihood arrats hence availableLength will be zero at the very first time we traverse the tree. Hence we need to allocate something here */ #ifndef _USE_OMP if(requiredLength != availableLength) { /* if there is a vector of incorrect length assigned here i.e., x3 != NULL we must free it first */ if(x3_start) free(x3_start); /* allocate memory: note that here we use a byte-boundary aligned malloc, because we need the vectors to be aligned at 16 BYTE (SSE3) or 32 BYTE (AVX) boundaries! */ x3_start = (double*)malloc_aligned(requiredLength); /* update the data structures for consistent bookkeeping */ tr->partitionData[model].xVector[tInfo->pNumber - tr->mxtips - 1] = x3_start; tr->partitionData[model].xSpaceVector[(tInfo->pNumber - tr->mxtips - 1)] = requiredLength; } #endif /* now just set the pointers for data accesses in the newview() implementations above to the corresponding values according to the tip case */ switch(tInfo->tipCase) { case TIP_TIP: tipX1 = tr->partitionData[model].yVector[tInfo->qNumber] + offset; tipX2 = tr->partitionData[model].yVector[tInfo->rNumber] + offset; if(tr->saveMemory) { x1_gapColumn = &(tr->partitionData[model].tipVector[gapOffset]); x2_gapColumn = &(tr->partitionData[model].tipVector[gapOffset]); x3_gapColumn = &tr->partitionData[model].gapColumn[(tInfo->pNumber - tr->mxtips - 1) * states * rateHet]; } break; case TIP_INNER: tipX1 = tr->partitionData[model].yVector[tInfo->qNumber] + offset; x2_start = tr->partitionData[model].xVector[tInfo->rNumber - tr->mxtips - 1] + x_offset; if(tr->saveMemory) { x1_gapColumn = &(tr->partitionData[model].tipVector[gapOffset]); x2_gapColumn = &tr->partitionData[model].gapColumn[(tInfo->rNumber - tr->mxtips - 1) * states * rateHet]; x3_gapColumn = &tr->partitionData[model].gapColumn[(tInfo->pNumber - tr->mxtips - 1) * states * rateHet]; } break; case INNER_INNER: x1_start = tr->partitionData[model].xVector[tInfo->qNumber - tr->mxtips - 1] + x_offset; x2_start = tr->partitionData[model].xVector[tInfo->rNumber - tr->mxtips - 1] + x_offset; if(tr->saveMemory) { x1_gapColumn = &tr->partitionData[model].gapColumn[(tInfo->qNumber - tr->mxtips - 1) * states * rateHet]; x2_gapColumn = &tr->partitionData[model].gapColumn[(tInfo->rNumber - tr->mxtips - 1) * states * rateHet]; x3_gapColumn = &tr->partitionData[model].gapColumn[(tInfo->pNumber - tr->mxtips - 1) * states * rateHet]; } break; default: assert(0); } #ifndef _OPTIMIZED_FUNCTIONS /* memory saving not implemented */ assert(!tr->saveMemory); /* figure out if we need to compute the CAT or GAMMA model of rate heterogeneity */ if(tr->rateHetModel == CAT) newviewCAT_FLEX(tInfo->tipCase, tr->partitionData[model].EV, rateCategory, x1_start, x2_start, x3_start, tr->partitionData[model].tipVector, tipX1, tipX2, width, left, right, wgt, &scalerIncrement, states); else newviewGAMMA_FLEX(tInfo->tipCase, x1_start, x2_start, x3_start, tr->partitionData[model].EV, tr->partitionData[model].tipVector, tipX1, tipX2, width, left, right, wgt, &scalerIncrement, states, getUndetermined(tr->partitionData[model].dataType) + 1); #else /* dedicated highly optimized functions. Analogously to the functions in evaluateGeneric() we also siwtch over the state number */ switch(states) { case 2: #ifdef __MIC_NATIVE assert(0 && "Binary data model is not implemented on Intel MIC"); #else assert(!tr->saveMemory); if(tr->rateHetModel == CAT) newviewGTRCAT_BINARY(tInfo->tipCase, tr->partitionData[model].EV, rateCategory, x1_start, x2_start, x3_start, tr->partitionData[model].tipVector, (int*)NULL, tipX1, tipX2, width, left, right, wgt, &scalerIncrement, TRUE); else newviewGTRGAMMA_BINARY(tInfo->tipCase, x1_start, x2_start, x3_start, tr->partitionData[model].EV, tr->partitionData[model].tipVector, (int *)NULL, tipX1, tipX2, width, left, right, wgt, &scalerIncrement, TRUE); #endif break; case 4: /* DNA */ if(tr->rateHetModel == CAT) { if(tr->saveMemory) #ifdef __MIC_NATIVE assert(0 && "Neither CAT model of rate heterogeneity nor memory saving are implemented on Intel MIC"); #elif __AVX newviewGTRCAT_AVX_GAPPED_SAVE(tInfo->tipCase, tr->partitionData[model].EV, rateCategory, x1_start, x2_start, x3_start, tr->partitionData[model].tipVector, (int*)NULL, tipX1, tipX2, width, left, right, wgt, &scalerIncrement, TRUE, x1_gap, x2_gap, x3_gap, x1_gapColumn, x2_gapColumn, x3_gapColumn, tr->maxCategories); #else newviewGTRCAT_SAVE(tInfo->tipCase, tr->partitionData[model].EV, rateCategory, x1_start, x2_start, x3_start, tr->partitionData[model].tipVector, tipX1, tipX2, width, left, right, wgt, &scalerIncrement, x1_gap, x2_gap, x3_gap, x1_gapColumn, x2_gapColumn, x3_gapColumn, tr->maxCategories); #endif else #ifdef __MIC_NATIVE assert(0 && "CAT model of rate heterogeneity is not implemented on Intel MIC"); #elif __AVX newviewGTRCAT_AVX(tInfo->tipCase, tr->partitionData[model].EV, rateCategory, x1_start, x2_start, x3_start, tr->partitionData[model].tipVector, tipX1, tipX2, width, left, right, wgt, &scalerIncrement); #else newviewGTRCAT(tInfo->tipCase, tr->partitionData[model].EV, rateCategory, x1_start, x2_start, x3_start, tr->partitionData[model].tipVector, tipX1, tipX2, width, left, right, wgt, &scalerIncrement); #endif } else { if(tr->saveMemory) #ifdef __MIC_NATIVE assert(0 && "Memory saving is not implemented on Intel MIC"); #elif __AVX newviewGTRGAMMA_AVX_GAPPED_SAVE(tInfo->tipCase, x1_start, x2_start, x3_start, tr->partitionData[model].EV, tr->partitionData[model].tipVector, (int*)NULL, tipX1, tipX2, width, left, right, wgt, &scalerIncrement, TRUE, x1_gap, x2_gap, x3_gap, x1_gapColumn, x2_gapColumn, x3_gapColumn); #else newviewGTRGAMMA_GAPPED_SAVE(tInfo->tipCase, x1_start, x2_start, x3_start, tr->partitionData[model].EV, tr->partitionData[model].tipVector, tipX1, tipX2, width, left, right, wgt, &scalerIncrement, x1_gap, x2_gap, x3_gap, x1_gapColumn, x2_gapColumn, x3_gapColumn); #endif else #ifdef __MIC_NATIVE newviewGTRGAMMA_MIC(tInfo->tipCase, x1_start, x2_start, x3_start, tr->partitionData[model].mic_EV, tr->partitionData[model].tipVector, tipX1, tipX2, width, left, right, wgt, &scalerIncrement, tr->partitionData[model].mic_umpLeft, tr->partitionData[model].mic_umpRight); #elif __AVX newviewGTRGAMMA_AVX(tInfo->tipCase, x1_start, x2_start, x3_start, tr->partitionData[model].EV, tr->partitionData[model].tipVector, tipX1, tipX2, width, left, right, wgt, &scalerIncrement); #else newviewGTRGAMMA(tInfo->tipCase, x1_start, x2_start, x3_start, tr->partitionData[model].EV, tr->partitionData[model].tipVector, tipX1, tipX2, width, left, right, wgt, &scalerIncrement); #endif } break; case 20: /* proteins */ if(tr->rateHetModel == CAT) { if(tr->saveMemory) { #ifdef __MIC_NATIVE assert(0 && "Neither CAT model of rate heterogeneity nor memory saving are implemented on Intel MIC"); #elif __AVX newviewGTRCATPROT_AVX_GAPPED_SAVE(tInfo->tipCase, tr->partitionData[model].EV, rateCategory, x1_start, x2_start, x3_start, tr->partitionData[model].tipVector, (int*)NULL, tipX1, tipX2, width, left, right, wgt, &scalerIncrement, TRUE, x1_gap, x2_gap, x3_gap, x1_gapColumn, x2_gapColumn, x3_gapColumn, tr->maxCategories); #else newviewGTRCATPROT_SAVE(tInfo->tipCase, tr->partitionData[model].EV, rateCategory, x1_start, x2_start, x3_start, tr->partitionData[model].tipVector, tipX1, tipX2, width, left, right, wgt, &scalerIncrement, x1_gap, x2_gap, x3_gap, x1_gapColumn, x2_gapColumn, x3_gapColumn, tr->maxCategories); #endif } else { #ifdef __MIC_NATIVE assert(0 && "CAT model of rate heterogeneity is not implemented on Intel MIC"); #elif __AVX newviewGTRCATPROT_AVX(tInfo->tipCase, tr->partitionData[model].EV, rateCategory, x1_start, x2_start, x3_start, tr->partitionData[model].tipVector, tipX1, tipX2, width, left, right, wgt, &scalerIncrement); #else newviewGTRCATPROT(tInfo->tipCase, tr->partitionData[model].EV, rateCategory, x1_start, x2_start, x3_start, tr->partitionData[model].tipVector, tipX1, tipX2, width, left, right, wgt, &scalerIncrement); #endif } } else { if(tr->saveMemory) { #ifdef __MIC_NATIVE assert(0 && "Memory saving is not implemented on Intel MIC"); #elif __AVX newviewGTRGAMMAPROT_AVX_GAPPED_SAVE(tInfo->tipCase, x1_start, x2_start, x3_start, tr->partitionData[model].EV, tr->partitionData[model].tipVector, (int*)NULL, tipX1, tipX2, width, left, right, wgt, &scalerIncrement, TRUE, x1_gap, x2_gap, x3_gap, x1_gapColumn, x2_gapColumn, x3_gapColumn); #else newviewGTRGAMMAPROT_GAPPED_SAVE(tInfo->tipCase, x1_start, x2_start, x3_start, tr->partitionData[model].EV, tr->partitionData[model].tipVector, tipX1, tipX2, width, left, right, wgt, &scalerIncrement, x1_gap, x2_gap, x3_gap, x1_gapColumn, x2_gapColumn, x3_gapColumn); #endif } else { if(tr->partitionData[model].protModels == LG4M || tr->partitionData[model].protModels == LG4X) { #ifdef __MIC_NATIVE newviewGTRGAMMAPROT_LG4_MIC(tInfo->tipCase, x1_start, x2_start, x3_start, tr->partitionData[model].mic_EV, tr->partitionData[model].mic_tipVector, tipX1, tipX2, width, left, right, wgt, &scalerIncrement, tr->partitionData[model].mic_umpLeft, tr->partitionData[model].mic_umpRight); #elif __AVX newviewGTRGAMMAPROT_AVX_LG4(tInfo->tipCase, x1_start, x2_start, x3_start, tr->partitionData[model].EV_LG4, tr->partitionData[model].tipVector_LG4, (int*)NULL, tipX1, tipX2, width, left, right, wgt, &scalerIncrement, TRUE); #else newviewGTRGAMMAPROT_LG4(tInfo->tipCase, x1_start, x2_start, x3_start, tr->partitionData[model].EV_LG4, tr->partitionData[model].tipVector_LG4, (int*)NULL, tipX1, tipX2, width, left, right, wgt, &scalerIncrement, TRUE); #endif } else { #ifdef __MIC_NATIVE newviewGTRGAMMAPROT_MIC(tInfo->tipCase, x1_start, x2_start, x3_start, tr->partitionData[model].mic_EV, tr->partitionData[model].mic_tipVector, tipX1, tipX2, width, left, right, wgt, &scalerIncrement, tr->partitionData[model].mic_umpLeft, tr->partitionData[model].mic_umpRight); #elif __AVX newviewGTRGAMMAPROT_AVX(tInfo->tipCase, x1_start, x2_start, x3_start, tr->partitionData[model].EV, tr->partitionData[model].tipVector, tipX1, tipX2, width, left, right, wgt, &scalerIncrement); #else newviewGTRGAMMAPROT(tInfo->tipCase, x1_start, x2_start, x3_start, tr->partitionData[model].EV, tr->partitionData[model].tipVector, tipX1, tipX2, width, left, right, wgt, &scalerIncrement); #endif } } } break; default: assert(0); } #endif /* important step, here we essentiallt recursively compute the number of scaling multiplications at node p: it's the sum of the number of scaling multiplications already conducted for computing nodes q and r plus the scaling multiplications done at node p */ globalScaler[tInfo->pNumber] = globalScaler[tInfo->qNumber] + globalScaler[tInfo->rNumber] + (unsigned int)scalerIncrement; /* check that we are not getting an integer overflow ! */ assert(globalScaler[tInfo->pNumber] < INT_MAX); } } // for model } // omp parallel block } // for traversal } /* here is the generic function that could be called from the user program it re-computes the vector at node p (regardless of whether it's orientation is correct and then it also re-computes reciursively the likelihood arrays in the subtrees of p as needed and if needed */ void newviewGeneric (tree *tr, nodeptr p, boolean masked) { /* if it's a tip there is nothing to do */ if(isTip(p->number, tr->mxtips)) return; /* the first entry of the traversal descriptor is always reserved for evaluate or branch length optimization calls, hence we start filling the array at the second entry with index one. This is not very nice and should be fixed at some point */ tr->td[0].count = 0; /* compute the traversal descriptor */ computeTraversalInfo(p, &(tr->td[0].ti[0]), &(tr->td[0].count), tr->mxtips, tr->numBranches, TRUE); /* the traversal descriptor has been recomputed -> not sure if it really always changes, something to optimize in the future */ tr->td[0].traversalHasChanged = TRUE; /* We do a masked newview, i.e., do not execute newvies for each partition, when for example doing a branch length optimization on the entire tree when branches are estimated on a per partition basis. you may imagine that for partition 5 the branch length optimization has already converged whereas for partition 6 we still need to go over the tree again. This is explained in more detail in: A. Stamatakis, M. Ott: "Load Balance in the Phylogenetic Likelihood Kernel". Proceedings of ICPP 2009 The external boolean array tr->partitionConverged[] contains exactly that information and is copied to executeModel and subsequently to the executeMask of the traversal descriptor */ if(masked) { int model; for(model = 0; model < tr->NumberOfModels; model++) { if(tr->partitionConverged[model]) tr->executeModel[model] = FALSE; else tr->executeModel[model] = TRUE; } } /* if there is something to re-compute */ if(tr->td[0].count > 0) { /* store execute mask in traversal descriptor */ storeExecuteMaskInTraversalDescriptor(tr); newviewIterative(tr, 0); } /* clean up */ if(masked) { int model; for(model = 0; model < tr->NumberOfModels; model++) tr->executeModel[model] = TRUE; } tr->td[0].traversalHasChanged = FALSE; } /* optimized function implementations */ #if (defined(_OPTIMIZED_FUNCTIONS) && !defined(__AVX)) static void newviewGTRGAMMA_GAPPED_SAVE(int tipCase, double *x1_start, double *x2_start, double *x3_start, double *EV, double *tipVector, unsigned char *tipX1, unsigned char *tipX2, const int n, double *left, double *right, int *wgt, int *scalerIncrement, unsigned int *x1_gap, unsigned int *x2_gap, unsigned int *x3_gap, double *x1_gapColumn, double *x2_gapColumn, double *x3_gapColumn) { int i, j, k, l, addScale = 0, scaleGap = 0; double *x1, *x2, *x3, *x1_ptr = x1_start, *x2_ptr = x2_start, max, maxima[2] __attribute__ ((aligned (BYTE_ALIGNMENT))), EV_t[16] __attribute__ ((aligned (BYTE_ALIGNMENT))); __m128d values[8], EVV[8]; for(k = 0; k < 4; k++) for (l=0; l < 4; l++) EV_t[4 * l + k] = EV[4 * k + l]; for(k = 0; k < 8; k++) EVV[k] = _mm_load_pd(&EV_t[k * 2]); switch(tipCase) { case TIP_TIP: { double *uX1, umpX1[256] __attribute__ ((aligned (BYTE_ALIGNMENT))), *uX2, umpX2[256] __attribute__ ((aligned (BYTE_ALIGNMENT))); for (i = 1; i < 16; i++) { __m128d x1_1 = _mm_load_pd(&(tipVector[i*4])); __m128d x1_2 = _mm_load_pd(&(tipVector[i*4 + 2])); for (j = 0; j < 4; j++) for (k = 0; k < 4; k++) { __m128d left1 = _mm_load_pd(&left[j*16 + k*4]); __m128d left2 = _mm_load_pd(&left[j*16 + k*4 + 2]); __m128d acc = _mm_setzero_pd(); acc = _mm_add_pd(acc, _mm_mul_pd(left1, x1_1)); acc = _mm_add_pd(acc, _mm_mul_pd(left2, x1_2)); acc = _mm_hadd_pd(acc, acc); _mm_storel_pd(&umpX1[i*16 + j*4 + k], acc); } for (j = 0; j < 4; j++) for (k = 0; k < 4; k++) { __m128d left1 = _mm_load_pd(&right[j*16 + k*4]); __m128d left2 = _mm_load_pd(&right[j*16 + k*4 + 2]); __m128d acc = _mm_setzero_pd(); acc = _mm_add_pd(acc, _mm_mul_pd(left1, x1_1)); acc = _mm_add_pd(acc, _mm_mul_pd(left2, x1_2)); acc = _mm_hadd_pd(acc, acc); _mm_storel_pd(&umpX2[i*16 + j*4 + k], acc); } } uX1 = &umpX1[240]; uX2 = &umpX2[240]; for (j = 0; j < 4; j++) { __m128d uX1_k0_sse = _mm_load_pd( &uX1[j * 4] ); __m128d uX1_k2_sse = _mm_load_pd( &uX1[j * 4 + 2] ); __m128d uX2_k0_sse = _mm_load_pd( &uX2[j * 4] ); __m128d uX2_k2_sse = _mm_load_pd( &uX2[j * 4 + 2] ); __m128d x1px2_k0 = _mm_mul_pd( uX1_k0_sse, uX2_k0_sse ); __m128d x1px2_k2 = _mm_mul_pd( uX1_k2_sse, uX2_k2_sse ); __m128d EV_t_l0_k0 = EVV[0]; __m128d EV_t_l0_k2 = EVV[1]; __m128d EV_t_l1_k0 = EVV[2]; __m128d EV_t_l1_k2 = EVV[3]; __m128d EV_t_l2_k0 = EVV[4]; __m128d EV_t_l2_k2 = EVV[5]; __m128d EV_t_l3_k0 = EVV[6]; __m128d EV_t_l3_k2 = EVV[7]; EV_t_l0_k0 = _mm_mul_pd( x1px2_k0, EV_t_l0_k0 ); EV_t_l0_k2 = _mm_mul_pd( x1px2_k2, EV_t_l0_k2 ); EV_t_l0_k0 = _mm_hadd_pd( EV_t_l0_k0, EV_t_l0_k2 ); EV_t_l1_k0 = _mm_mul_pd( x1px2_k0, EV_t_l1_k0 ); EV_t_l1_k2 = _mm_mul_pd( x1px2_k2, EV_t_l1_k2 ); EV_t_l1_k0 = _mm_hadd_pd( EV_t_l1_k0, EV_t_l1_k2 ); EV_t_l0_k0 = _mm_hadd_pd( EV_t_l0_k0, EV_t_l1_k0 ); EV_t_l2_k0 = _mm_mul_pd( x1px2_k0, EV_t_l2_k0 ); EV_t_l2_k2 = _mm_mul_pd( x1px2_k2, EV_t_l2_k2 ); EV_t_l2_k0 = _mm_hadd_pd( EV_t_l2_k0, EV_t_l2_k2 ); EV_t_l3_k0 = _mm_mul_pd( x1px2_k0, EV_t_l3_k0 ); EV_t_l3_k2 = _mm_mul_pd( x1px2_k2, EV_t_l3_k2 ); EV_t_l3_k0 = _mm_hadd_pd( EV_t_l3_k0, EV_t_l3_k2 ); EV_t_l2_k0 = _mm_hadd_pd( EV_t_l2_k0, EV_t_l3_k0 ); _mm_store_pd( &x3_gapColumn[j * 4 + 0], EV_t_l0_k0 ); _mm_store_pd( &x3_gapColumn[j * 4 + 2], EV_t_l2_k0 ); } x3 = x3_start; for (i = 0; i < n; i++) { if(!(x3_gap[i / 32] & mask32[i % 32])) { uX1 = &umpX1[16 * tipX1[i]]; uX2 = &umpX2[16 * tipX2[i]]; for (j = 0; j < 4; j++) { __m128d uX1_k0_sse = _mm_load_pd( &uX1[j * 4] ); __m128d uX1_k2_sse = _mm_load_pd( &uX1[j * 4 + 2] ); __m128d uX2_k0_sse = _mm_load_pd( &uX2[j * 4] ); __m128d uX2_k2_sse = _mm_load_pd( &uX2[j * 4 + 2] ); // // multiply left * right // __m128d x1px2_k0 = _mm_mul_pd( uX1_k0_sse, uX2_k0_sse ); __m128d x1px2_k2 = _mm_mul_pd( uX1_k2_sse, uX2_k2_sse ); // // multiply with EV matrix (!?) // __m128d EV_t_l0_k0 = EVV[0]; __m128d EV_t_l0_k2 = EVV[1]; __m128d EV_t_l1_k0 = EVV[2]; __m128d EV_t_l1_k2 = EVV[3]; __m128d EV_t_l2_k0 = EVV[4]; __m128d EV_t_l2_k2 = EVV[5]; __m128d EV_t_l3_k0 = EVV[6]; __m128d EV_t_l3_k2 = EVV[7]; EV_t_l0_k0 = _mm_mul_pd( x1px2_k0, EV_t_l0_k0 ); EV_t_l0_k2 = _mm_mul_pd( x1px2_k2, EV_t_l0_k2 ); EV_t_l0_k0 = _mm_hadd_pd( EV_t_l0_k0, EV_t_l0_k2 ); EV_t_l1_k0 = _mm_mul_pd( x1px2_k0, EV_t_l1_k0 ); EV_t_l1_k2 = _mm_mul_pd( x1px2_k2, EV_t_l1_k2 ); EV_t_l1_k0 = _mm_hadd_pd( EV_t_l1_k0, EV_t_l1_k2 ); EV_t_l0_k0 = _mm_hadd_pd( EV_t_l0_k0, EV_t_l1_k0 ); EV_t_l2_k0 = _mm_mul_pd( x1px2_k0, EV_t_l2_k0 ); EV_t_l2_k2 = _mm_mul_pd( x1px2_k2, EV_t_l2_k2 ); EV_t_l2_k0 = _mm_hadd_pd( EV_t_l2_k0, EV_t_l2_k2 ); EV_t_l3_k0 = _mm_mul_pd( x1px2_k0, EV_t_l3_k0 ); EV_t_l3_k2 = _mm_mul_pd( x1px2_k2, EV_t_l3_k2 ); EV_t_l3_k0 = _mm_hadd_pd( EV_t_l3_k0, EV_t_l3_k2 ); EV_t_l2_k0 = _mm_hadd_pd( EV_t_l2_k0, EV_t_l3_k0 ); _mm_store_pd( &x3[j * 4 + 0], EV_t_l0_k0 ); _mm_store_pd( &x3[j * 4 + 2], EV_t_l2_k0 ); } x3 += 16; } } } break; case TIP_INNER: { double *uX1, umpX1[256] __attribute__ ((aligned (BYTE_ALIGNMENT))); for (i = 1; i < 16; i++) { __m128d x1_1 = _mm_load_pd(&(tipVector[i*4])); __m128d x1_2 = _mm_load_pd(&(tipVector[i*4 + 2])); for (j = 0; j < 4; j++) for (k = 0; k < 4; k++) { __m128d left1 = _mm_load_pd(&left[j*16 + k*4]); __m128d left2 = _mm_load_pd(&left[j*16 + k*4 + 2]); __m128d acc = _mm_setzero_pd(); acc = _mm_add_pd(acc, _mm_mul_pd(left1, x1_1)); acc = _mm_add_pd(acc, _mm_mul_pd(left2, x1_2)); acc = _mm_hadd_pd(acc, acc); _mm_storel_pd(&umpX1[i*16 + j*4 + k], acc); } } { __m128d maxv =_mm_setzero_pd(); scaleGap = 0; x2 = x2_gapColumn; x3 = x3_gapColumn; uX1 = &umpX1[240]; for (j = 0; j < 4; j++) { double *x2_p = &x2[j*4]; double *right_k0_p = &right[j*16]; double *right_k1_p = &right[j*16 + 1*4]; double *right_k2_p = &right[j*16 + 2*4]; double *right_k3_p = &right[j*16 + 3*4]; __m128d x2_0 = _mm_load_pd( &x2_p[0] ); __m128d x2_2 = _mm_load_pd( &x2_p[2] ); __m128d right_k0_0 = _mm_load_pd( &right_k0_p[0] ); __m128d right_k0_2 = _mm_load_pd( &right_k0_p[2] ); __m128d right_k1_0 = _mm_load_pd( &right_k1_p[0] ); __m128d right_k1_2 = _mm_load_pd( &right_k1_p[2] ); __m128d right_k2_0 = _mm_load_pd( &right_k2_p[0] ); __m128d right_k2_2 = _mm_load_pd( &right_k2_p[2] ); __m128d right_k3_0 = _mm_load_pd( &right_k3_p[0] ); __m128d right_k3_2 = _mm_load_pd( &right_k3_p[2] ); right_k0_0 = _mm_mul_pd( x2_0, right_k0_0); right_k0_2 = _mm_mul_pd( x2_2, right_k0_2); right_k1_0 = _mm_mul_pd( x2_0, right_k1_0); right_k1_2 = _mm_mul_pd( x2_2, right_k1_2); right_k0_0 = _mm_hadd_pd( right_k0_0, right_k0_2); right_k1_0 = _mm_hadd_pd( right_k1_0, right_k1_2); right_k0_0 = _mm_hadd_pd( right_k0_0, right_k1_0); right_k2_0 = _mm_mul_pd( x2_0, right_k2_0); right_k2_2 = _mm_mul_pd( x2_2, right_k2_2); right_k3_0 = _mm_mul_pd( x2_0, right_k3_0); right_k3_2 = _mm_mul_pd( x2_2, right_k3_2); right_k2_0 = _mm_hadd_pd( right_k2_0, right_k2_2); right_k3_0 = _mm_hadd_pd( right_k3_0, right_k3_2); right_k2_0 = _mm_hadd_pd( right_k2_0, right_k3_0); __m128d uX1_k0_sse = _mm_load_pd( &uX1[j * 4] ); __m128d uX1_k2_sse = _mm_load_pd( &uX1[j * 4 + 2] ); __m128d x1px2_k0 = _mm_mul_pd( uX1_k0_sse, right_k0_0 ); __m128d x1px2_k2 = _mm_mul_pd( uX1_k2_sse, right_k2_0 ); __m128d EV_t_l0_k0 = EVV[0]; __m128d EV_t_l0_k2 = EVV[1]; __m128d EV_t_l1_k0 = EVV[2]; __m128d EV_t_l1_k2 = EVV[3]; __m128d EV_t_l2_k0 = EVV[4]; __m128d EV_t_l2_k2 = EVV[5]; __m128d EV_t_l3_k0 = EVV[6]; __m128d EV_t_l3_k2 = EVV[7]; EV_t_l0_k0 = _mm_mul_pd( x1px2_k0, EV_t_l0_k0 ); EV_t_l0_k2 = _mm_mul_pd( x1px2_k2, EV_t_l0_k2 ); EV_t_l0_k0 = _mm_hadd_pd( EV_t_l0_k0, EV_t_l0_k2 ); EV_t_l1_k0 = _mm_mul_pd( x1px2_k0, EV_t_l1_k0 ); EV_t_l1_k2 = _mm_mul_pd( x1px2_k2, EV_t_l1_k2 ); EV_t_l1_k0 = _mm_hadd_pd( EV_t_l1_k0, EV_t_l1_k2 ); EV_t_l0_k0 = _mm_hadd_pd( EV_t_l0_k0, EV_t_l1_k0 ); EV_t_l2_k0 = _mm_mul_pd( x1px2_k0, EV_t_l2_k0 ); EV_t_l2_k2 = _mm_mul_pd( x1px2_k2, EV_t_l2_k2 ); EV_t_l2_k0 = _mm_hadd_pd( EV_t_l2_k0, EV_t_l2_k2 ); EV_t_l3_k0 = _mm_mul_pd( x1px2_k0, EV_t_l3_k0 ); EV_t_l3_k2 = _mm_mul_pd( x1px2_k2, EV_t_l3_k2 ); EV_t_l3_k0 = _mm_hadd_pd( EV_t_l3_k0, EV_t_l3_k2 ); EV_t_l2_k0 = _mm_hadd_pd( EV_t_l2_k0, EV_t_l3_k0 ); values[j * 2] = EV_t_l0_k0; values[j * 2 + 1] = EV_t_l2_k0; maxv = _mm_max_pd(maxv, _mm_and_pd(EV_t_l0_k0, absMask.m)); maxv = _mm_max_pd(maxv, _mm_and_pd(EV_t_l2_k0, absMask.m)); } _mm_store_pd(maxima, maxv); max = MAX(maxima[0], maxima[1]); if(max < minlikelihood) { scaleGap = 1; __m128d sv = _mm_set1_pd(twotothe256); _mm_store_pd(&x3[0], _mm_mul_pd(values[0], sv)); _mm_store_pd(&x3[2], _mm_mul_pd(values[1], sv)); _mm_store_pd(&x3[4], _mm_mul_pd(values[2], sv)); _mm_store_pd(&x3[6], _mm_mul_pd(values[3], sv)); _mm_store_pd(&x3[8], _mm_mul_pd(values[4], sv)); _mm_store_pd(&x3[10], _mm_mul_pd(values[5], sv)); _mm_store_pd(&x3[12], _mm_mul_pd(values[6], sv)); _mm_store_pd(&x3[14], _mm_mul_pd(values[7], sv)); } else { _mm_store_pd(&x3[0], values[0]); _mm_store_pd(&x3[2], values[1]); _mm_store_pd(&x3[4], values[2]); _mm_store_pd(&x3[6], values[3]); _mm_store_pd(&x3[8], values[4]); _mm_store_pd(&x3[10], values[5]); _mm_store_pd(&x3[12], values[6]); _mm_store_pd(&x3[14], values[7]); } } x3 = x3_start; for (i = 0; i < n; i++) { if((x3_gap[i / 32] & mask32[i % 32])) { if(scaleGap) { addScale += wgt[i]; } } else { __m128d maxv =_mm_setzero_pd(); if(x2_gap[i / 32] & mask32[i % 32]) x2 = x2_gapColumn; else { x2 = x2_ptr; x2_ptr += 16; } uX1 = &umpX1[16 * tipX1[i]]; for (j = 0; j < 4; j++) { double *x2_p = &x2[j*4]; double *right_k0_p = &right[j*16]; double *right_k1_p = &right[j*16 + 1*4]; double *right_k2_p = &right[j*16 + 2*4]; double *right_k3_p = &right[j*16 + 3*4]; __m128d x2_0 = _mm_load_pd( &x2_p[0] ); __m128d x2_2 = _mm_load_pd( &x2_p[2] ); __m128d right_k0_0 = _mm_load_pd( &right_k0_p[0] ); __m128d right_k0_2 = _mm_load_pd( &right_k0_p[2] ); __m128d right_k1_0 = _mm_load_pd( &right_k1_p[0] ); __m128d right_k1_2 = _mm_load_pd( &right_k1_p[2] ); __m128d right_k2_0 = _mm_load_pd( &right_k2_p[0] ); __m128d right_k2_2 = _mm_load_pd( &right_k2_p[2] ); __m128d right_k3_0 = _mm_load_pd( &right_k3_p[0] ); __m128d right_k3_2 = _mm_load_pd( &right_k3_p[2] ); right_k0_0 = _mm_mul_pd( x2_0, right_k0_0); right_k0_2 = _mm_mul_pd( x2_2, right_k0_2); right_k1_0 = _mm_mul_pd( x2_0, right_k1_0); right_k1_2 = _mm_mul_pd( x2_2, right_k1_2); right_k0_0 = _mm_hadd_pd( right_k0_0, right_k0_2); right_k1_0 = _mm_hadd_pd( right_k1_0, right_k1_2); right_k0_0 = _mm_hadd_pd( right_k0_0, right_k1_0); right_k2_0 = _mm_mul_pd( x2_0, right_k2_0); right_k2_2 = _mm_mul_pd( x2_2, right_k2_2); right_k3_0 = _mm_mul_pd( x2_0, right_k3_0); right_k3_2 = _mm_mul_pd( x2_2, right_k3_2); right_k2_0 = _mm_hadd_pd( right_k2_0, right_k2_2); right_k3_0 = _mm_hadd_pd( right_k3_0, right_k3_2); right_k2_0 = _mm_hadd_pd( right_k2_0, right_k3_0); { // // load left side from tip vector // __m128d uX1_k0_sse = _mm_load_pd( &uX1[j * 4] ); __m128d uX1_k2_sse = _mm_load_pd( &uX1[j * 4 + 2] ); // // multiply left * right // __m128d x1px2_k0 = _mm_mul_pd( uX1_k0_sse, right_k0_0 ); __m128d x1px2_k2 = _mm_mul_pd( uX1_k2_sse, right_k2_0 ); // // multiply with EV matrix (!?) // __m128d EV_t_l0_k0 = EVV[0]; __m128d EV_t_l0_k2 = EVV[1]; __m128d EV_t_l1_k0 = EVV[2]; __m128d EV_t_l1_k2 = EVV[3]; __m128d EV_t_l2_k0 = EVV[4]; __m128d EV_t_l2_k2 = EVV[5]; __m128d EV_t_l3_k0 = EVV[6]; __m128d EV_t_l3_k2 = EVV[7]; EV_t_l0_k0 = _mm_mul_pd( x1px2_k0, EV_t_l0_k0 ); EV_t_l0_k2 = _mm_mul_pd( x1px2_k2, EV_t_l0_k2 ); EV_t_l0_k0 = _mm_hadd_pd( EV_t_l0_k0, EV_t_l0_k2 ); EV_t_l1_k0 = _mm_mul_pd( x1px2_k0, EV_t_l1_k0 ); EV_t_l1_k2 = _mm_mul_pd( x1px2_k2, EV_t_l1_k2 ); EV_t_l1_k0 = _mm_hadd_pd( EV_t_l1_k0, EV_t_l1_k2 ); EV_t_l0_k0 = _mm_hadd_pd( EV_t_l0_k0, EV_t_l1_k0 ); EV_t_l2_k0 = _mm_mul_pd( x1px2_k0, EV_t_l2_k0 ); EV_t_l2_k2 = _mm_mul_pd( x1px2_k2, EV_t_l2_k2 ); EV_t_l2_k0 = _mm_hadd_pd( EV_t_l2_k0, EV_t_l2_k2 ); EV_t_l3_k0 = _mm_mul_pd( x1px2_k0, EV_t_l3_k0 ); EV_t_l3_k2 = _mm_mul_pd( x1px2_k2, EV_t_l3_k2 ); EV_t_l3_k0 = _mm_hadd_pd( EV_t_l3_k0, EV_t_l3_k2 ); EV_t_l2_k0 = _mm_hadd_pd( EV_t_l2_k0, EV_t_l3_k0 ); values[j * 2] = EV_t_l0_k0; values[j * 2 + 1] = EV_t_l2_k0; maxv = _mm_max_pd(maxv, _mm_and_pd(EV_t_l0_k0, absMask.m)); maxv = _mm_max_pd(maxv, _mm_and_pd(EV_t_l2_k0, absMask.m)); } } _mm_store_pd(maxima, maxv); max = MAX(maxima[0], maxima[1]); if(max < minlikelihood) { __m128d sv = _mm_set1_pd(twotothe256); _mm_store_pd(&x3[0], _mm_mul_pd(values[0], sv)); _mm_store_pd(&x3[2], _mm_mul_pd(values[1], sv)); _mm_store_pd(&x3[4], _mm_mul_pd(values[2], sv)); _mm_store_pd(&x3[6], _mm_mul_pd(values[3], sv)); _mm_store_pd(&x3[8], _mm_mul_pd(values[4], sv)); _mm_store_pd(&x3[10], _mm_mul_pd(values[5], sv)); _mm_store_pd(&x3[12], _mm_mul_pd(values[6], sv)); _mm_store_pd(&x3[14], _mm_mul_pd(values[7], sv)); addScale += wgt[i]; } else { _mm_store_pd(&x3[0], values[0]); _mm_store_pd(&x3[2], values[1]); _mm_store_pd(&x3[4], values[2]); _mm_store_pd(&x3[6], values[3]); _mm_store_pd(&x3[8], values[4]); _mm_store_pd(&x3[10], values[5]); _mm_store_pd(&x3[12], values[6]); _mm_store_pd(&x3[14], values[7]); } x3 += 16; } } } break; case INNER_INNER: { __m128d maxv =_mm_setzero_pd(); scaleGap = 0; x1 = x1_gapColumn; x2 = x2_gapColumn; x3 = x3_gapColumn; for (j = 0; j < 4; j++) { double *x1_p = &x1[j*4]; double *left_k0_p = &left[j*16]; double *left_k1_p = &left[j*16 + 1*4]; double *left_k2_p = &left[j*16 + 2*4]; double *left_k3_p = &left[j*16 + 3*4]; __m128d x1_0 = _mm_load_pd( &x1_p[0] ); __m128d x1_2 = _mm_load_pd( &x1_p[2] ); __m128d left_k0_0 = _mm_load_pd( &left_k0_p[0] ); __m128d left_k0_2 = _mm_load_pd( &left_k0_p[2] ); __m128d left_k1_0 = _mm_load_pd( &left_k1_p[0] ); __m128d left_k1_2 = _mm_load_pd( &left_k1_p[2] ); __m128d left_k2_0 = _mm_load_pd( &left_k2_p[0] ); __m128d left_k2_2 = _mm_load_pd( &left_k2_p[2] ); __m128d left_k3_0 = _mm_load_pd( &left_k3_p[0] ); __m128d left_k3_2 = _mm_load_pd( &left_k3_p[2] ); left_k0_0 = _mm_mul_pd(x1_0, left_k0_0); left_k0_2 = _mm_mul_pd(x1_2, left_k0_2); left_k1_0 = _mm_mul_pd(x1_0, left_k1_0); left_k1_2 = _mm_mul_pd(x1_2, left_k1_2); left_k0_0 = _mm_hadd_pd( left_k0_0, left_k0_2 ); left_k1_0 = _mm_hadd_pd( left_k1_0, left_k1_2); left_k0_0 = _mm_hadd_pd( left_k0_0, left_k1_0); left_k2_0 = _mm_mul_pd(x1_0, left_k2_0); left_k2_2 = _mm_mul_pd(x1_2, left_k2_2); left_k3_0 = _mm_mul_pd(x1_0, left_k3_0); left_k3_2 = _mm_mul_pd(x1_2, left_k3_2); left_k2_0 = _mm_hadd_pd( left_k2_0, left_k2_2); left_k3_0 = _mm_hadd_pd( left_k3_0, left_k3_2); left_k2_0 = _mm_hadd_pd( left_k2_0, left_k3_0); double *x2_p = &x2[j*4]; double *right_k0_p = &right[j*16]; double *right_k1_p = &right[j*16 + 1*4]; double *right_k2_p = &right[j*16 + 2*4]; double *right_k3_p = &right[j*16 + 3*4]; __m128d x2_0 = _mm_load_pd( &x2_p[0] ); __m128d x2_2 = _mm_load_pd( &x2_p[2] ); __m128d right_k0_0 = _mm_load_pd( &right_k0_p[0] ); __m128d right_k0_2 = _mm_load_pd( &right_k0_p[2] ); __m128d right_k1_0 = _mm_load_pd( &right_k1_p[0] ); __m128d right_k1_2 = _mm_load_pd( &right_k1_p[2] ); __m128d right_k2_0 = _mm_load_pd( &right_k2_p[0] ); __m128d right_k2_2 = _mm_load_pd( &right_k2_p[2] ); __m128d right_k3_0 = _mm_load_pd( &right_k3_p[0] ); __m128d right_k3_2 = _mm_load_pd( &right_k3_p[2] ); right_k0_0 = _mm_mul_pd( x2_0, right_k0_0); right_k0_2 = _mm_mul_pd( x2_2, right_k0_2); right_k1_0 = _mm_mul_pd( x2_0, right_k1_0); right_k1_2 = _mm_mul_pd( x2_2, right_k1_2); right_k0_0 = _mm_hadd_pd( right_k0_0, right_k0_2); right_k1_0 = _mm_hadd_pd( right_k1_0, right_k1_2); right_k0_0 = _mm_hadd_pd( right_k0_0, right_k1_0); right_k2_0 = _mm_mul_pd( x2_0, right_k2_0); right_k2_2 = _mm_mul_pd( x2_2, right_k2_2); right_k3_0 = _mm_mul_pd( x2_0, right_k3_0); right_k3_2 = _mm_mul_pd( x2_2, right_k3_2); right_k2_0 = _mm_hadd_pd( right_k2_0, right_k2_2); right_k3_0 = _mm_hadd_pd( right_k3_0, right_k3_2); right_k2_0 = _mm_hadd_pd( right_k2_0, right_k3_0); __m128d x1px2_k0 = _mm_mul_pd( left_k0_0, right_k0_0 ); __m128d x1px2_k2 = _mm_mul_pd( left_k2_0, right_k2_0 ); __m128d EV_t_l0_k0 = EVV[0]; __m128d EV_t_l0_k2 = EVV[1]; __m128d EV_t_l1_k0 = EVV[2]; __m128d EV_t_l1_k2 = EVV[3]; __m128d EV_t_l2_k0 = EVV[4]; __m128d EV_t_l2_k2 = EVV[5]; __m128d EV_t_l3_k0 = EVV[6]; __m128d EV_t_l3_k2 = EVV[7]; EV_t_l0_k0 = _mm_mul_pd( x1px2_k0, EV_t_l0_k0 ); EV_t_l0_k2 = _mm_mul_pd( x1px2_k2, EV_t_l0_k2 ); EV_t_l0_k0 = _mm_hadd_pd( EV_t_l0_k0, EV_t_l0_k2 ); EV_t_l1_k0 = _mm_mul_pd( x1px2_k0, EV_t_l1_k0 ); EV_t_l1_k2 = _mm_mul_pd( x1px2_k2, EV_t_l1_k2 ); EV_t_l1_k0 = _mm_hadd_pd( EV_t_l1_k0, EV_t_l1_k2 ); EV_t_l0_k0 = _mm_hadd_pd( EV_t_l0_k0, EV_t_l1_k0 ); EV_t_l2_k0 = _mm_mul_pd( x1px2_k0, EV_t_l2_k0 ); EV_t_l2_k2 = _mm_mul_pd( x1px2_k2, EV_t_l2_k2 ); EV_t_l2_k0 = _mm_hadd_pd( EV_t_l2_k0, EV_t_l2_k2 ); EV_t_l3_k0 = _mm_mul_pd( x1px2_k0, EV_t_l3_k0 ); EV_t_l3_k2 = _mm_mul_pd( x1px2_k2, EV_t_l3_k2 ); EV_t_l3_k0 = _mm_hadd_pd( EV_t_l3_k0, EV_t_l3_k2 ); EV_t_l2_k0 = _mm_hadd_pd( EV_t_l2_k0, EV_t_l3_k0 ); values[j * 2] = EV_t_l0_k0; values[j * 2 + 1] = EV_t_l2_k0; maxv = _mm_max_pd(maxv, _mm_and_pd(EV_t_l0_k0, absMask.m)); maxv = _mm_max_pd(maxv, _mm_and_pd(EV_t_l2_k0, absMask.m)); } _mm_store_pd(maxima, maxv); max = MAX(maxima[0], maxima[1]); if(max < minlikelihood) { __m128d sv = _mm_set1_pd(twotothe256); scaleGap = 1; _mm_store_pd(&x3[0], _mm_mul_pd(values[0], sv)); _mm_store_pd(&x3[2], _mm_mul_pd(values[1], sv)); _mm_store_pd(&x3[4], _mm_mul_pd(values[2], sv)); _mm_store_pd(&x3[6], _mm_mul_pd(values[3], sv)); _mm_store_pd(&x3[8], _mm_mul_pd(values[4], sv)); _mm_store_pd(&x3[10], _mm_mul_pd(values[5], sv)); _mm_store_pd(&x3[12], _mm_mul_pd(values[6], sv)); _mm_store_pd(&x3[14], _mm_mul_pd(values[7], sv)); } else { _mm_store_pd(&x3[0], values[0]); _mm_store_pd(&x3[2], values[1]); _mm_store_pd(&x3[4], values[2]); _mm_store_pd(&x3[6], values[3]); _mm_store_pd(&x3[8], values[4]); _mm_store_pd(&x3[10], values[5]); _mm_store_pd(&x3[12], values[6]); _mm_store_pd(&x3[14], values[7]); } } x3 = x3_start; for (i = 0; i < n; i++) { if(x3_gap[i / 32] & mask32[i % 32]) { if(scaleGap) { addScale += wgt[i]; } } else { __m128d maxv =_mm_setzero_pd(); if(x1_gap[i / 32] & mask32[i % 32]) x1 = x1_gapColumn; else { x1 = x1_ptr; x1_ptr += 16; } if(x2_gap[i / 32] & mask32[i % 32]) x2 = x2_gapColumn; else { x2 = x2_ptr; x2_ptr += 16; } for (j = 0; j < 4; j++) { double *x1_p = &x1[j*4]; double *left_k0_p = &left[j*16]; double *left_k1_p = &left[j*16 + 1*4]; double *left_k2_p = &left[j*16 + 2*4]; double *left_k3_p = &left[j*16 + 3*4]; __m128d x1_0 = _mm_load_pd( &x1_p[0] ); __m128d x1_2 = _mm_load_pd( &x1_p[2] ); __m128d left_k0_0 = _mm_load_pd( &left_k0_p[0] ); __m128d left_k0_2 = _mm_load_pd( &left_k0_p[2] ); __m128d left_k1_0 = _mm_load_pd( &left_k1_p[0] ); __m128d left_k1_2 = _mm_load_pd( &left_k1_p[2] ); __m128d left_k2_0 = _mm_load_pd( &left_k2_p[0] ); __m128d left_k2_2 = _mm_load_pd( &left_k2_p[2] ); __m128d left_k3_0 = _mm_load_pd( &left_k3_p[0] ); __m128d left_k3_2 = _mm_load_pd( &left_k3_p[2] ); left_k0_0 = _mm_mul_pd(x1_0, left_k0_0); left_k0_2 = _mm_mul_pd(x1_2, left_k0_2); left_k1_0 = _mm_mul_pd(x1_0, left_k1_0); left_k1_2 = _mm_mul_pd(x1_2, left_k1_2); left_k0_0 = _mm_hadd_pd( left_k0_0, left_k0_2 ); left_k1_0 = _mm_hadd_pd( left_k1_0, left_k1_2); left_k0_0 = _mm_hadd_pd( left_k0_0, left_k1_0); left_k2_0 = _mm_mul_pd(x1_0, left_k2_0); left_k2_2 = _mm_mul_pd(x1_2, left_k2_2); left_k3_0 = _mm_mul_pd(x1_0, left_k3_0); left_k3_2 = _mm_mul_pd(x1_2, left_k3_2); left_k2_0 = _mm_hadd_pd( left_k2_0, left_k2_2); left_k3_0 = _mm_hadd_pd( left_k3_0, left_k3_2); left_k2_0 = _mm_hadd_pd( left_k2_0, left_k3_0); // // multiply/add right side // double *x2_p = &x2[j*4]; double *right_k0_p = &right[j*16]; double *right_k1_p = &right[j*16 + 1*4]; double *right_k2_p = &right[j*16 + 2*4]; double *right_k3_p = &right[j*16 + 3*4]; __m128d x2_0 = _mm_load_pd( &x2_p[0] ); __m128d x2_2 = _mm_load_pd( &x2_p[2] ); __m128d right_k0_0 = _mm_load_pd( &right_k0_p[0] ); __m128d right_k0_2 = _mm_load_pd( &right_k0_p[2] ); __m128d right_k1_0 = _mm_load_pd( &right_k1_p[0] ); __m128d right_k1_2 = _mm_load_pd( &right_k1_p[2] ); __m128d right_k2_0 = _mm_load_pd( &right_k2_p[0] ); __m128d right_k2_2 = _mm_load_pd( &right_k2_p[2] ); __m128d right_k3_0 = _mm_load_pd( &right_k3_p[0] ); __m128d right_k3_2 = _mm_load_pd( &right_k3_p[2] ); right_k0_0 = _mm_mul_pd( x2_0, right_k0_0); right_k0_2 = _mm_mul_pd( x2_2, right_k0_2); right_k1_0 = _mm_mul_pd( x2_0, right_k1_0); right_k1_2 = _mm_mul_pd( x2_2, right_k1_2); right_k0_0 = _mm_hadd_pd( right_k0_0, right_k0_2); right_k1_0 = _mm_hadd_pd( right_k1_0, right_k1_2); right_k0_0 = _mm_hadd_pd( right_k0_0, right_k1_0); right_k2_0 = _mm_mul_pd( x2_0, right_k2_0); right_k2_2 = _mm_mul_pd( x2_2, right_k2_2); right_k3_0 = _mm_mul_pd( x2_0, right_k3_0); right_k3_2 = _mm_mul_pd( x2_2, right_k3_2); right_k2_0 = _mm_hadd_pd( right_k2_0, right_k2_2); right_k3_0 = _mm_hadd_pd( right_k3_0, right_k3_2); right_k2_0 = _mm_hadd_pd( right_k2_0, right_k3_0); // // multiply left * right // __m128d x1px2_k0 = _mm_mul_pd( left_k0_0, right_k0_0 ); __m128d x1px2_k2 = _mm_mul_pd( left_k2_0, right_k2_0 ); // // multiply with EV matrix (!?) // __m128d EV_t_l0_k0 = EVV[0]; __m128d EV_t_l0_k2 = EVV[1]; __m128d EV_t_l1_k0 = EVV[2]; __m128d EV_t_l1_k2 = EVV[3]; __m128d EV_t_l2_k0 = EVV[4]; __m128d EV_t_l2_k2 = EVV[5]; __m128d EV_t_l3_k0 = EVV[6]; __m128d EV_t_l3_k2 = EVV[7]; EV_t_l0_k0 = _mm_mul_pd( x1px2_k0, EV_t_l0_k0 ); EV_t_l0_k2 = _mm_mul_pd( x1px2_k2, EV_t_l0_k2 ); EV_t_l0_k0 = _mm_hadd_pd( EV_t_l0_k0, EV_t_l0_k2 ); EV_t_l1_k0 = _mm_mul_pd( x1px2_k0, EV_t_l1_k0 ); EV_t_l1_k2 = _mm_mul_pd( x1px2_k2, EV_t_l1_k2 ); EV_t_l1_k0 = _mm_hadd_pd( EV_t_l1_k0, EV_t_l1_k2 ); EV_t_l0_k0 = _mm_hadd_pd( EV_t_l0_k0, EV_t_l1_k0 ); EV_t_l2_k0 = _mm_mul_pd( x1px2_k0, EV_t_l2_k0 ); EV_t_l2_k2 = _mm_mul_pd( x1px2_k2, EV_t_l2_k2 ); EV_t_l2_k0 = _mm_hadd_pd( EV_t_l2_k0, EV_t_l2_k2 ); EV_t_l3_k0 = _mm_mul_pd( x1px2_k0, EV_t_l3_k0 ); EV_t_l3_k2 = _mm_mul_pd( x1px2_k2, EV_t_l3_k2 ); EV_t_l3_k0 = _mm_hadd_pd( EV_t_l3_k0, EV_t_l3_k2 ); EV_t_l2_k0 = _mm_hadd_pd( EV_t_l2_k0, EV_t_l3_k0 ); values[j * 2] = EV_t_l0_k0; values[j * 2 + 1] = EV_t_l2_k0; maxv = _mm_max_pd(maxv, _mm_and_pd(EV_t_l0_k0, absMask.m)); maxv = _mm_max_pd(maxv, _mm_and_pd(EV_t_l2_k0, absMask.m)); } _mm_store_pd(maxima, maxv); max = MAX(maxima[0], maxima[1]); if(max < minlikelihood) { __m128d sv = _mm_set1_pd(twotothe256); _mm_store_pd(&x3[0], _mm_mul_pd(values[0], sv)); _mm_store_pd(&x3[2], _mm_mul_pd(values[1], sv)); _mm_store_pd(&x3[4], _mm_mul_pd(values[2], sv)); _mm_store_pd(&x3[6], _mm_mul_pd(values[3], sv)); _mm_store_pd(&x3[8], _mm_mul_pd(values[4], sv)); _mm_store_pd(&x3[10], _mm_mul_pd(values[5], sv)); _mm_store_pd(&x3[12], _mm_mul_pd(values[6], sv)); _mm_store_pd(&x3[14], _mm_mul_pd(values[7], sv)); addScale += wgt[i]; } else { _mm_store_pd(&x3[0], values[0]); _mm_store_pd(&x3[2], values[1]); _mm_store_pd(&x3[4], values[2]); _mm_store_pd(&x3[6], values[3]); _mm_store_pd(&x3[8], values[4]); _mm_store_pd(&x3[10], values[5]); _mm_store_pd(&x3[12], values[6]); _mm_store_pd(&x3[14], values[7]); } x3 += 16; } } break; default: assert(0); } *scalerIncrement = addScale; } static void newviewGTRGAMMA(int tipCase, double *x1_start, double *x2_start, double *x3_start, double *EV, double *tipVector, unsigned char *tipX1, unsigned char *tipX2, const int n, double *left, double *right, int *wgt, int *scalerIncrement ) { int i, j, k, l, addScale = 0; double *x1, *x2, *x3, max, maxima[2] __attribute__ ((aligned (BYTE_ALIGNMENT))), EV_t[16] __attribute__ ((aligned (BYTE_ALIGNMENT))); __m128d values[8], EVV[8]; for(k = 0; k < 4; k++) for (l=0; l < 4; l++) EV_t[4 * l + k] = EV[4 * k + l]; for(k = 0; k < 8; k++) EVV[k] = _mm_load_pd(&EV_t[k * 2]); switch(tipCase) { case TIP_TIP: { double *uX1, umpX1[256] __attribute__ ((aligned (BYTE_ALIGNMENT))), *uX2, umpX2[256] __attribute__ ((aligned (BYTE_ALIGNMENT))); for (i = 1; i < 16; i++) { __m128d x1_1 = _mm_load_pd(&(tipVector[i*4])); __m128d x1_2 = _mm_load_pd(&(tipVector[i*4 + 2])); for (j = 0; j < 4; j++) for (k = 0; k < 4; k++) { __m128d left1 = _mm_load_pd(&left[j*16 + k*4]); __m128d left2 = _mm_load_pd(&left[j*16 + k*4 + 2]); __m128d acc = _mm_setzero_pd(); acc = _mm_add_pd(acc, _mm_mul_pd(left1, x1_1)); acc = _mm_add_pd(acc, _mm_mul_pd(left2, x1_2)); acc = _mm_hadd_pd(acc, acc); _mm_storel_pd(&umpX1[i*16 + j*4 + k], acc); } for (j = 0; j < 4; j++) for (k = 0; k < 4; k++) { __m128d left1 = _mm_load_pd(&right[j*16 + k*4]); __m128d left2 = _mm_load_pd(&right[j*16 + k*4 + 2]); __m128d acc = _mm_setzero_pd(); acc = _mm_add_pd(acc, _mm_mul_pd(left1, x1_1)); acc = _mm_add_pd(acc, _mm_mul_pd(left2, x1_2)); acc = _mm_hadd_pd(acc, acc); _mm_storel_pd(&umpX2[i*16 + j*4 + k], acc); } } for (i = 0; i < n; i++) { x3 = &x3_start[i * 16]; uX1 = &umpX1[16 * tipX1[i]]; uX2 = &umpX2[16 * tipX2[i]]; for (j = 0; j < 4; j++) { __m128d uX1_k0_sse = _mm_load_pd( &uX1[j * 4] ); __m128d uX1_k2_sse = _mm_load_pd( &uX1[j * 4 + 2] ); __m128d uX2_k0_sse = _mm_load_pd( &uX2[j * 4] ); __m128d uX2_k2_sse = _mm_load_pd( &uX2[j * 4 + 2] ); // // multiply left * right // __m128d x1px2_k0 = _mm_mul_pd( uX1_k0_sse, uX2_k0_sse ); __m128d x1px2_k2 = _mm_mul_pd( uX1_k2_sse, uX2_k2_sse ); // // multiply with EV matrix (!?) // __m128d EV_t_l0_k0 = EVV[0]; __m128d EV_t_l0_k2 = EVV[1]; __m128d EV_t_l1_k0 = EVV[2]; __m128d EV_t_l1_k2 = EVV[3]; __m128d EV_t_l2_k0 = EVV[4]; __m128d EV_t_l2_k2 = EVV[5]; __m128d EV_t_l3_k0 = EVV[6]; __m128d EV_t_l3_k2 = EVV[7]; EV_t_l0_k0 = _mm_mul_pd( x1px2_k0, EV_t_l0_k0 ); EV_t_l0_k2 = _mm_mul_pd( x1px2_k2, EV_t_l0_k2 ); EV_t_l0_k0 = _mm_hadd_pd( EV_t_l0_k0, EV_t_l0_k2 ); EV_t_l1_k0 = _mm_mul_pd( x1px2_k0, EV_t_l1_k0 ); EV_t_l1_k2 = _mm_mul_pd( x1px2_k2, EV_t_l1_k2 ); EV_t_l1_k0 = _mm_hadd_pd( EV_t_l1_k0, EV_t_l1_k2 ); EV_t_l0_k0 = _mm_hadd_pd( EV_t_l0_k0, EV_t_l1_k0 ); EV_t_l2_k0 = _mm_mul_pd( x1px2_k0, EV_t_l2_k0 ); EV_t_l2_k2 = _mm_mul_pd( x1px2_k2, EV_t_l2_k2 ); EV_t_l2_k0 = _mm_hadd_pd( EV_t_l2_k0, EV_t_l2_k2 ); EV_t_l3_k0 = _mm_mul_pd( x1px2_k0, EV_t_l3_k0 ); EV_t_l3_k2 = _mm_mul_pd( x1px2_k2, EV_t_l3_k2 ); EV_t_l3_k0 = _mm_hadd_pd( EV_t_l3_k0, EV_t_l3_k2 ); EV_t_l2_k0 = _mm_hadd_pd( EV_t_l2_k0, EV_t_l3_k0 ); _mm_store_pd( &x3[j * 4 + 0], EV_t_l0_k0 ); _mm_store_pd( &x3[j * 4 + 2], EV_t_l2_k0 ); } } } break; case TIP_INNER: { double *uX1, umpX1[256] __attribute__ ((aligned (BYTE_ALIGNMENT))); for (i = 1; i < 16; i++) { __m128d x1_1 = _mm_load_pd(&(tipVector[i*4])); __m128d x1_2 = _mm_load_pd(&(tipVector[i*4 + 2])); for (j = 0; j < 4; j++) for (k = 0; k < 4; k++) { __m128d left1 = _mm_load_pd(&left[j*16 + k*4]); __m128d left2 = _mm_load_pd(&left[j*16 + k*4 + 2]); __m128d acc = _mm_setzero_pd(); acc = _mm_add_pd(acc, _mm_mul_pd(left1, x1_1)); acc = _mm_add_pd(acc, _mm_mul_pd(left2, x1_2)); acc = _mm_hadd_pd(acc, acc); _mm_storel_pd(&umpX1[i*16 + j*4 + k], acc); } } for (i = 0; i < n; i++) { __m128d maxv =_mm_setzero_pd(); x2 = &x2_start[i * 16]; x3 = &x3_start[i * 16]; uX1 = &umpX1[16 * tipX1[i]]; for (j = 0; j < 4; j++) { // // multiply/add right side // double *x2_p = &x2[j*4]; double *right_k0_p = &right[j*16]; double *right_k1_p = &right[j*16 + 1*4]; double *right_k2_p = &right[j*16 + 2*4]; double *right_k3_p = &right[j*16 + 3*4]; __m128d x2_0 = _mm_load_pd( &x2_p[0] ); __m128d x2_2 = _mm_load_pd( &x2_p[2] ); __m128d right_k0_0 = _mm_load_pd( &right_k0_p[0] ); __m128d right_k0_2 = _mm_load_pd( &right_k0_p[2] ); __m128d right_k1_0 = _mm_load_pd( &right_k1_p[0] ); __m128d right_k1_2 = _mm_load_pd( &right_k1_p[2] ); __m128d right_k2_0 = _mm_load_pd( &right_k2_p[0] ); __m128d right_k2_2 = _mm_load_pd( &right_k2_p[2] ); __m128d right_k3_0 = _mm_load_pd( &right_k3_p[0] ); __m128d right_k3_2 = _mm_load_pd( &right_k3_p[2] ); right_k0_0 = _mm_mul_pd( x2_0, right_k0_0); right_k0_2 = _mm_mul_pd( x2_2, right_k0_2); right_k1_0 = _mm_mul_pd( x2_0, right_k1_0); right_k1_2 = _mm_mul_pd( x2_2, right_k1_2); right_k0_0 = _mm_hadd_pd( right_k0_0, right_k0_2); right_k1_0 = _mm_hadd_pd( right_k1_0, right_k1_2); right_k0_0 = _mm_hadd_pd( right_k0_0, right_k1_0); right_k2_0 = _mm_mul_pd( x2_0, right_k2_0); right_k2_2 = _mm_mul_pd( x2_2, right_k2_2); right_k3_0 = _mm_mul_pd( x2_0, right_k3_0); right_k3_2 = _mm_mul_pd( x2_2, right_k3_2); right_k2_0 = _mm_hadd_pd( right_k2_0, right_k2_2); right_k3_0 = _mm_hadd_pd( right_k3_0, right_k3_2); right_k2_0 = _mm_hadd_pd( right_k2_0, right_k3_0); { // // load left side from tip vector // __m128d uX1_k0_sse = _mm_load_pd( &uX1[j * 4] ); __m128d uX1_k2_sse = _mm_load_pd( &uX1[j * 4 + 2] ); // // multiply left * right // __m128d x1px2_k0 = _mm_mul_pd( uX1_k0_sse, right_k0_0 ); __m128d x1px2_k2 = _mm_mul_pd( uX1_k2_sse, right_k2_0 ); // // multiply with EV matrix (!?) // __m128d EV_t_l0_k0 = EVV[0]; __m128d EV_t_l0_k2 = EVV[1]; __m128d EV_t_l1_k0 = EVV[2]; __m128d EV_t_l1_k2 = EVV[3]; __m128d EV_t_l2_k0 = EVV[4]; __m128d EV_t_l2_k2 = EVV[5]; __m128d EV_t_l3_k0 = EVV[6]; __m128d EV_t_l3_k2 = EVV[7]; EV_t_l0_k0 = _mm_mul_pd( x1px2_k0, EV_t_l0_k0 ); EV_t_l0_k2 = _mm_mul_pd( x1px2_k2, EV_t_l0_k2 ); EV_t_l0_k0 = _mm_hadd_pd( EV_t_l0_k0, EV_t_l0_k2 ); EV_t_l1_k0 = _mm_mul_pd( x1px2_k0, EV_t_l1_k0 ); EV_t_l1_k2 = _mm_mul_pd( x1px2_k2, EV_t_l1_k2 ); EV_t_l1_k0 = _mm_hadd_pd( EV_t_l1_k0, EV_t_l1_k2 ); EV_t_l0_k0 = _mm_hadd_pd( EV_t_l0_k0, EV_t_l1_k0 ); EV_t_l2_k0 = _mm_mul_pd( x1px2_k0, EV_t_l2_k0 ); EV_t_l2_k2 = _mm_mul_pd( x1px2_k2, EV_t_l2_k2 ); EV_t_l2_k0 = _mm_hadd_pd( EV_t_l2_k0, EV_t_l2_k2 ); EV_t_l3_k0 = _mm_mul_pd( x1px2_k0, EV_t_l3_k0 ); EV_t_l3_k2 = _mm_mul_pd( x1px2_k2, EV_t_l3_k2 ); EV_t_l3_k0 = _mm_hadd_pd( EV_t_l3_k0, EV_t_l3_k2 ); EV_t_l2_k0 = _mm_hadd_pd( EV_t_l2_k0, EV_t_l3_k0 ); values[j * 2] = EV_t_l0_k0; values[j * 2 + 1] = EV_t_l2_k0; maxv = _mm_max_pd(maxv, _mm_and_pd(EV_t_l0_k0, absMask.m)); maxv = _mm_max_pd(maxv, _mm_and_pd(EV_t_l2_k0, absMask.m)); } } _mm_store_pd(maxima, maxv); max = MAX(maxima[0], maxima[1]); if(max < minlikelihood) { __m128d sv = _mm_set1_pd(twotothe256); _mm_store_pd(&x3[0], _mm_mul_pd(values[0], sv)); _mm_store_pd(&x3[2], _mm_mul_pd(values[1], sv)); _mm_store_pd(&x3[4], _mm_mul_pd(values[2], sv)); _mm_store_pd(&x3[6], _mm_mul_pd(values[3], sv)); _mm_store_pd(&x3[8], _mm_mul_pd(values[4], sv)); _mm_store_pd(&x3[10], _mm_mul_pd(values[5], sv)); _mm_store_pd(&x3[12], _mm_mul_pd(values[6], sv)); _mm_store_pd(&x3[14], _mm_mul_pd(values[7], sv)); addScale += wgt[i]; } else { _mm_store_pd(&x3[0], values[0]); _mm_store_pd(&x3[2], values[1]); _mm_store_pd(&x3[4], values[2]); _mm_store_pd(&x3[6], values[3]); _mm_store_pd(&x3[8], values[4]); _mm_store_pd(&x3[10], values[5]); _mm_store_pd(&x3[12], values[6]); _mm_store_pd(&x3[14], values[7]); } } } break; case INNER_INNER: for (i = 0; i < n; i++) { __m128d maxv =_mm_setzero_pd(); x1 = &x1_start[i * 16]; x2 = &x2_start[i * 16]; x3 = &x3_start[i * 16]; for (j = 0; j < 4; j++) { double *x1_p = &x1[j*4]; double *left_k0_p = &left[j*16]; double *left_k1_p = &left[j*16 + 1*4]; double *left_k2_p = &left[j*16 + 2*4]; double *left_k3_p = &left[j*16 + 3*4]; __m128d x1_0 = _mm_load_pd( &x1_p[0] ); __m128d x1_2 = _mm_load_pd( &x1_p[2] ); __m128d left_k0_0 = _mm_load_pd( &left_k0_p[0] ); __m128d left_k0_2 = _mm_load_pd( &left_k0_p[2] ); __m128d left_k1_0 = _mm_load_pd( &left_k1_p[0] ); __m128d left_k1_2 = _mm_load_pd( &left_k1_p[2] ); __m128d left_k2_0 = _mm_load_pd( &left_k2_p[0] ); __m128d left_k2_2 = _mm_load_pd( &left_k2_p[2] ); __m128d left_k3_0 = _mm_load_pd( &left_k3_p[0] ); __m128d left_k3_2 = _mm_load_pd( &left_k3_p[2] ); left_k0_0 = _mm_mul_pd(x1_0, left_k0_0); left_k0_2 = _mm_mul_pd(x1_2, left_k0_2); left_k1_0 = _mm_mul_pd(x1_0, left_k1_0); left_k1_2 = _mm_mul_pd(x1_2, left_k1_2); left_k0_0 = _mm_hadd_pd( left_k0_0, left_k0_2 ); left_k1_0 = _mm_hadd_pd( left_k1_0, left_k1_2); left_k0_0 = _mm_hadd_pd( left_k0_0, left_k1_0); left_k2_0 = _mm_mul_pd(x1_0, left_k2_0); left_k2_2 = _mm_mul_pd(x1_2, left_k2_2); left_k3_0 = _mm_mul_pd(x1_0, left_k3_0); left_k3_2 = _mm_mul_pd(x1_2, left_k3_2); left_k2_0 = _mm_hadd_pd( left_k2_0, left_k2_2); left_k3_0 = _mm_hadd_pd( left_k3_0, left_k3_2); left_k2_0 = _mm_hadd_pd( left_k2_0, left_k3_0); // // multiply/add right side // double *x2_p = &x2[j*4]; double *right_k0_p = &right[j*16]; double *right_k1_p = &right[j*16 + 1*4]; double *right_k2_p = &right[j*16 + 2*4]; double *right_k3_p = &right[j*16 + 3*4]; __m128d x2_0 = _mm_load_pd( &x2_p[0] ); __m128d x2_2 = _mm_load_pd( &x2_p[2] ); __m128d right_k0_0 = _mm_load_pd( &right_k0_p[0] ); __m128d right_k0_2 = _mm_load_pd( &right_k0_p[2] ); __m128d right_k1_0 = _mm_load_pd( &right_k1_p[0] ); __m128d right_k1_2 = _mm_load_pd( &right_k1_p[2] ); __m128d right_k2_0 = _mm_load_pd( &right_k2_p[0] ); __m128d right_k2_2 = _mm_load_pd( &right_k2_p[2] ); __m128d right_k3_0 = _mm_load_pd( &right_k3_p[0] ); __m128d right_k3_2 = _mm_load_pd( &right_k3_p[2] ); right_k0_0 = _mm_mul_pd( x2_0, right_k0_0); right_k0_2 = _mm_mul_pd( x2_2, right_k0_2); right_k1_0 = _mm_mul_pd( x2_0, right_k1_0); right_k1_2 = _mm_mul_pd( x2_2, right_k1_2); right_k0_0 = _mm_hadd_pd( right_k0_0, right_k0_2); right_k1_0 = _mm_hadd_pd( right_k1_0, right_k1_2); right_k0_0 = _mm_hadd_pd( right_k0_0, right_k1_0); right_k2_0 = _mm_mul_pd( x2_0, right_k2_0); right_k2_2 = _mm_mul_pd( x2_2, right_k2_2); right_k3_0 = _mm_mul_pd( x2_0, right_k3_0); right_k3_2 = _mm_mul_pd( x2_2, right_k3_2); right_k2_0 = _mm_hadd_pd( right_k2_0, right_k2_2); right_k3_0 = _mm_hadd_pd( right_k3_0, right_k3_2); right_k2_0 = _mm_hadd_pd( right_k2_0, right_k3_0); // // multiply left * right // __m128d x1px2_k0 = _mm_mul_pd( left_k0_0, right_k0_0 ); __m128d x1px2_k2 = _mm_mul_pd( left_k2_0, right_k2_0 ); // // multiply with EV matrix (!?) // __m128d EV_t_l0_k0 = EVV[0]; __m128d EV_t_l0_k2 = EVV[1]; __m128d EV_t_l1_k0 = EVV[2]; __m128d EV_t_l1_k2 = EVV[3]; __m128d EV_t_l2_k0 = EVV[4]; __m128d EV_t_l2_k2 = EVV[5]; __m128d EV_t_l3_k0 = EVV[6]; __m128d EV_t_l3_k2 = EVV[7]; EV_t_l0_k0 = _mm_mul_pd( x1px2_k0, EV_t_l0_k0 ); EV_t_l0_k2 = _mm_mul_pd( x1px2_k2, EV_t_l0_k2 ); EV_t_l0_k0 = _mm_hadd_pd( EV_t_l0_k0, EV_t_l0_k2 ); EV_t_l1_k0 = _mm_mul_pd( x1px2_k0, EV_t_l1_k0 ); EV_t_l1_k2 = _mm_mul_pd( x1px2_k2, EV_t_l1_k2 ); EV_t_l1_k0 = _mm_hadd_pd( EV_t_l1_k0, EV_t_l1_k2 ); EV_t_l0_k0 = _mm_hadd_pd( EV_t_l0_k0, EV_t_l1_k0 ); EV_t_l2_k0 = _mm_mul_pd( x1px2_k0, EV_t_l2_k0 ); EV_t_l2_k2 = _mm_mul_pd( x1px2_k2, EV_t_l2_k2 ); EV_t_l2_k0 = _mm_hadd_pd( EV_t_l2_k0, EV_t_l2_k2 ); EV_t_l3_k0 = _mm_mul_pd( x1px2_k0, EV_t_l3_k0 ); EV_t_l3_k2 = _mm_mul_pd( x1px2_k2, EV_t_l3_k2 ); EV_t_l3_k0 = _mm_hadd_pd( EV_t_l3_k0, EV_t_l3_k2 ); EV_t_l2_k0 = _mm_hadd_pd( EV_t_l2_k0, EV_t_l3_k0 ); values[j * 2] = EV_t_l0_k0; values[j * 2 + 1] = EV_t_l2_k0; maxv = _mm_max_pd(maxv, _mm_and_pd(EV_t_l0_k0, absMask.m)); maxv = _mm_max_pd(maxv, _mm_and_pd(EV_t_l2_k0, absMask.m)); } _mm_store_pd(maxima, maxv); max = MAX(maxima[0], maxima[1]); if(max < minlikelihood) { __m128d sv = _mm_set1_pd(twotothe256); _mm_store_pd(&x3[0], _mm_mul_pd(values[0], sv)); _mm_store_pd(&x3[2], _mm_mul_pd(values[1], sv)); _mm_store_pd(&x3[4], _mm_mul_pd(values[2], sv)); _mm_store_pd(&x3[6], _mm_mul_pd(values[3], sv)); _mm_store_pd(&x3[8], _mm_mul_pd(values[4], sv)); _mm_store_pd(&x3[10], _mm_mul_pd(values[5], sv)); _mm_store_pd(&x3[12], _mm_mul_pd(values[6], sv)); _mm_store_pd(&x3[14], _mm_mul_pd(values[7], sv)); addScale += wgt[i]; } else { _mm_store_pd(&x3[0], values[0]); _mm_store_pd(&x3[2], values[1]); _mm_store_pd(&x3[4], values[2]); _mm_store_pd(&x3[6], values[3]); _mm_store_pd(&x3[8], values[4]); _mm_store_pd(&x3[10], values[5]); _mm_store_pd(&x3[12], values[6]); _mm_store_pd(&x3[14], values[7]); } } break; default: assert(0); } *scalerIncrement = addScale; } static void newviewGTRCAT( int tipCase, double *EV, int *cptr, double *x1_start, double *x2_start, double *x3_start, double *tipVector, unsigned char *tipX1, unsigned char *tipX2, int n, double *left, double *right, int *wgt, int *scalerIncrement) { double *le, *ri, *x1, *x2, *x3, EV_t[16] __attribute__ ((aligned (BYTE_ALIGNMENT))); int i, j, scale, addScale = 0; __m128d minlikelihood_sse = _mm_set1_pd( minlikelihood ), sc = _mm_set1_pd(twotothe256), EVV[8]; for(i = 0; i < 4; i++) for (j=0; j < 4; j++) EV_t[4 * j + i] = EV[4 * i + j]; for(i = 0; i < 8; i++) EVV[i] = _mm_load_pd(&EV_t[i * 2]); switch(tipCase) { case TIP_TIP: for (i = 0; i < n; i++) { x1 = &(tipVector[4 * tipX1[i]]); x2 = &(tipVector[4 * tipX2[i]]); x3 = &x3_start[i * 4]; le = &left[cptr[i] * 16]; ri = &right[cptr[i] * 16]; __m128d x1_0 = _mm_load_pd( &x1[0] ); __m128d x1_2 = _mm_load_pd( &x1[2] ); __m128d left_k0_0 = _mm_load_pd( &le[0] ); __m128d left_k0_2 = _mm_load_pd( &le[2] ); __m128d left_k1_0 = _mm_load_pd( &le[4] ); __m128d left_k1_2 = _mm_load_pd( &le[6] ); __m128d left_k2_0 = _mm_load_pd( &le[8] ); __m128d left_k2_2 = _mm_load_pd( &le[10] ); __m128d left_k3_0 = _mm_load_pd( &le[12] ); __m128d left_k3_2 = _mm_load_pd( &le[14] ); left_k0_0 = _mm_mul_pd(x1_0, left_k0_0); left_k0_2 = _mm_mul_pd(x1_2, left_k0_2); left_k1_0 = _mm_mul_pd(x1_0, left_k1_0); left_k1_2 = _mm_mul_pd(x1_2, left_k1_2); left_k0_0 = _mm_hadd_pd( left_k0_0, left_k0_2 ); left_k1_0 = _mm_hadd_pd( left_k1_0, left_k1_2); left_k0_0 = _mm_hadd_pd( left_k0_0, left_k1_0); left_k2_0 = _mm_mul_pd(x1_0, left_k2_0); left_k2_2 = _mm_mul_pd(x1_2, left_k2_2); left_k3_0 = _mm_mul_pd(x1_0, left_k3_0); left_k3_2 = _mm_mul_pd(x1_2, left_k3_2); left_k2_0 = _mm_hadd_pd( left_k2_0, left_k2_2); left_k3_0 = _mm_hadd_pd( left_k3_0, left_k3_2); left_k2_0 = _mm_hadd_pd( left_k2_0, left_k3_0); __m128d x2_0 = _mm_load_pd( &x2[0] ); __m128d x2_2 = _mm_load_pd( &x2[2] ); __m128d right_k0_0 = _mm_load_pd( &ri[0] ); __m128d right_k0_2 = _mm_load_pd( &ri[2] ); __m128d right_k1_0 = _mm_load_pd( &ri[4] ); __m128d right_k1_2 = _mm_load_pd( &ri[6] ); __m128d right_k2_0 = _mm_load_pd( &ri[8] ); __m128d right_k2_2 = _mm_load_pd( &ri[10] ); __m128d right_k3_0 = _mm_load_pd( &ri[12] ); __m128d right_k3_2 = _mm_load_pd( &ri[14] ); right_k0_0 = _mm_mul_pd( x2_0, right_k0_0); right_k0_2 = _mm_mul_pd( x2_2, right_k0_2); right_k1_0 = _mm_mul_pd( x2_0, right_k1_0); right_k1_2 = _mm_mul_pd( x2_2, right_k1_2); right_k0_0 = _mm_hadd_pd( right_k0_0, right_k0_2); right_k1_0 = _mm_hadd_pd( right_k1_0, right_k1_2); right_k0_0 = _mm_hadd_pd( right_k0_0, right_k1_0); right_k2_0 = _mm_mul_pd( x2_0, right_k2_0); right_k2_2 = _mm_mul_pd( x2_2, right_k2_2); right_k3_0 = _mm_mul_pd( x2_0, right_k3_0); right_k3_2 = _mm_mul_pd( x2_2, right_k3_2); right_k2_0 = _mm_hadd_pd( right_k2_0, right_k2_2); right_k3_0 = _mm_hadd_pd( right_k3_0, right_k3_2); right_k2_0 = _mm_hadd_pd( right_k2_0, right_k3_0); __m128d x1px2_k0 = _mm_mul_pd( left_k0_0, right_k0_0 ); __m128d x1px2_k2 = _mm_mul_pd( left_k2_0, right_k2_0 ); __m128d EV_t_l0_k0 = EVV[0]; __m128d EV_t_l0_k2 = EVV[1]; __m128d EV_t_l1_k0 = EVV[2]; __m128d EV_t_l1_k2 = EVV[3]; __m128d EV_t_l2_k0 = EVV[4]; __m128d EV_t_l2_k2 = EVV[5]; __m128d EV_t_l3_k0 = EVV[6]; __m128d EV_t_l3_k2 = EVV[7]; EV_t_l0_k0 = _mm_mul_pd( x1px2_k0, EV_t_l0_k0 ); EV_t_l0_k2 = _mm_mul_pd( x1px2_k2, EV_t_l0_k2 ); EV_t_l0_k0 = _mm_hadd_pd( EV_t_l0_k0, EV_t_l0_k2 ); EV_t_l1_k0 = _mm_mul_pd( x1px2_k0, EV_t_l1_k0 ); EV_t_l1_k2 = _mm_mul_pd( x1px2_k2, EV_t_l1_k2 ); EV_t_l1_k0 = _mm_hadd_pd( EV_t_l1_k0, EV_t_l1_k2 ); EV_t_l0_k0 = _mm_hadd_pd( EV_t_l0_k0, EV_t_l1_k0 ); EV_t_l2_k0 = _mm_mul_pd( x1px2_k0, EV_t_l2_k0 ); EV_t_l2_k2 = _mm_mul_pd( x1px2_k2, EV_t_l2_k2 ); EV_t_l2_k0 = _mm_hadd_pd( EV_t_l2_k0, EV_t_l2_k2 ); EV_t_l3_k0 = _mm_mul_pd( x1px2_k0, EV_t_l3_k0 ); EV_t_l3_k2 = _mm_mul_pd( x1px2_k2, EV_t_l3_k2 ); EV_t_l3_k0 = _mm_hadd_pd( EV_t_l3_k0, EV_t_l3_k2 ); EV_t_l2_k0 = _mm_hadd_pd( EV_t_l2_k0, EV_t_l3_k0 ); _mm_store_pd(x3, EV_t_l0_k0); _mm_store_pd(&x3[2], EV_t_l2_k0); } break; case TIP_INNER: for (i = 0; i < n; i++) { x1 = &(tipVector[4 * tipX1[i]]); x2 = &x2_start[4 * i]; x3 = &x3_start[4 * i]; le = &left[cptr[i] * 16]; ri = &right[cptr[i] * 16]; __m128d x1_0 = _mm_load_pd( &x1[0] ); __m128d x1_2 = _mm_load_pd( &x1[2] ); __m128d left_k0_0 = _mm_load_pd( &le[0] ); __m128d left_k0_2 = _mm_load_pd( &le[2] ); __m128d left_k1_0 = _mm_load_pd( &le[4] ); __m128d left_k1_2 = _mm_load_pd( &le[6] ); __m128d left_k2_0 = _mm_load_pd( &le[8] ); __m128d left_k2_2 = _mm_load_pd( &le[10] ); __m128d left_k3_0 = _mm_load_pd( &le[12] ); __m128d left_k3_2 = _mm_load_pd( &le[14] ); left_k0_0 = _mm_mul_pd(x1_0, left_k0_0); left_k0_2 = _mm_mul_pd(x1_2, left_k0_2); left_k1_0 = _mm_mul_pd(x1_0, left_k1_0); left_k1_2 = _mm_mul_pd(x1_2, left_k1_2); left_k0_0 = _mm_hadd_pd( left_k0_0, left_k0_2 ); left_k1_0 = _mm_hadd_pd( left_k1_0, left_k1_2); left_k0_0 = _mm_hadd_pd( left_k0_0, left_k1_0); left_k2_0 = _mm_mul_pd(x1_0, left_k2_0); left_k2_2 = _mm_mul_pd(x1_2, left_k2_2); left_k3_0 = _mm_mul_pd(x1_0, left_k3_0); left_k3_2 = _mm_mul_pd(x1_2, left_k3_2); left_k2_0 = _mm_hadd_pd( left_k2_0, left_k2_2); left_k3_0 = _mm_hadd_pd( left_k3_0, left_k3_2); left_k2_0 = _mm_hadd_pd( left_k2_0, left_k3_0); __m128d x2_0 = _mm_load_pd( &x2[0] ); __m128d x2_2 = _mm_load_pd( &x2[2] ); __m128d right_k0_0 = _mm_load_pd( &ri[0] ); __m128d right_k0_2 = _mm_load_pd( &ri[2] ); __m128d right_k1_0 = _mm_load_pd( &ri[4] ); __m128d right_k1_2 = _mm_load_pd( &ri[6] ); __m128d right_k2_0 = _mm_load_pd( &ri[8] ); __m128d right_k2_2 = _mm_load_pd( &ri[10] ); __m128d right_k3_0 = _mm_load_pd( &ri[12] ); __m128d right_k3_2 = _mm_load_pd( &ri[14] ); right_k0_0 = _mm_mul_pd( x2_0, right_k0_0); right_k0_2 = _mm_mul_pd( x2_2, right_k0_2); right_k1_0 = _mm_mul_pd( x2_0, right_k1_0); right_k1_2 = _mm_mul_pd( x2_2, right_k1_2); right_k0_0 = _mm_hadd_pd( right_k0_0, right_k0_2); right_k1_0 = _mm_hadd_pd( right_k1_0, right_k1_2); right_k0_0 = _mm_hadd_pd( right_k0_0, right_k1_0); right_k2_0 = _mm_mul_pd( x2_0, right_k2_0); right_k2_2 = _mm_mul_pd( x2_2, right_k2_2); right_k3_0 = _mm_mul_pd( x2_0, right_k3_0); right_k3_2 = _mm_mul_pd( x2_2, right_k3_2); right_k2_0 = _mm_hadd_pd( right_k2_0, right_k2_2); right_k3_0 = _mm_hadd_pd( right_k3_0, right_k3_2); right_k2_0 = _mm_hadd_pd( right_k2_0, right_k3_0); __m128d x1px2_k0 = _mm_mul_pd( left_k0_0, right_k0_0 ); __m128d x1px2_k2 = _mm_mul_pd( left_k2_0, right_k2_0 ); __m128d EV_t_l0_k0 = EVV[0]; __m128d EV_t_l0_k2 = EVV[1]; __m128d EV_t_l1_k0 = EVV[2]; __m128d EV_t_l1_k2 = EVV[3]; __m128d EV_t_l2_k0 = EVV[4]; __m128d EV_t_l2_k2 = EVV[5]; __m128d EV_t_l3_k0 = EVV[6]; __m128d EV_t_l3_k2 = EVV[7]; EV_t_l0_k0 = _mm_mul_pd( x1px2_k0, EV_t_l0_k0 ); EV_t_l0_k2 = _mm_mul_pd( x1px2_k2, EV_t_l0_k2 ); EV_t_l0_k0 = _mm_hadd_pd( EV_t_l0_k0, EV_t_l0_k2 ); EV_t_l1_k0 = _mm_mul_pd( x1px2_k0, EV_t_l1_k0 ); EV_t_l1_k2 = _mm_mul_pd( x1px2_k2, EV_t_l1_k2 ); EV_t_l1_k0 = _mm_hadd_pd( EV_t_l1_k0, EV_t_l1_k2 ); EV_t_l0_k0 = _mm_hadd_pd( EV_t_l0_k0, EV_t_l1_k0 ); EV_t_l2_k0 = _mm_mul_pd( x1px2_k0, EV_t_l2_k0 ); EV_t_l2_k2 = _mm_mul_pd( x1px2_k2, EV_t_l2_k2 ); EV_t_l2_k0 = _mm_hadd_pd( EV_t_l2_k0, EV_t_l2_k2 ); EV_t_l3_k0 = _mm_mul_pd( x1px2_k0, EV_t_l3_k0 ); EV_t_l3_k2 = _mm_mul_pd( x1px2_k2, EV_t_l3_k2 ); EV_t_l3_k0 = _mm_hadd_pd( EV_t_l3_k0, EV_t_l3_k2 ); EV_t_l2_k0 = _mm_hadd_pd( EV_t_l2_k0, EV_t_l3_k0 ); scale = 1; __m128d v1 = _mm_and_pd(EV_t_l0_k0, absMask.m); v1 = _mm_cmplt_pd(v1, minlikelihood_sse); if(_mm_movemask_pd( v1 ) != 3) scale = 0; else { v1 = _mm_and_pd(EV_t_l2_k0, absMask.m); v1 = _mm_cmplt_pd(v1, minlikelihood_sse); if(_mm_movemask_pd( v1 ) != 3) scale = 0; } if(scale) { _mm_store_pd(&x3[0], _mm_mul_pd(EV_t_l0_k0, sc)); _mm_store_pd(&x3[2], _mm_mul_pd(EV_t_l2_k0, sc)); addScale += wgt[i]; } else { _mm_store_pd(x3, EV_t_l0_k0); _mm_store_pd(&x3[2], EV_t_l2_k0); } } break; case INNER_INNER: for (i = 0; i < n; i++) { x1 = &x1_start[4 * i]; x2 = &x2_start[4 * i]; x3 = &x3_start[4 * i]; le = &left[cptr[i] * 16]; ri = &right[cptr[i] * 16]; __m128d x1_0 = _mm_load_pd( &x1[0] ); __m128d x1_2 = _mm_load_pd( &x1[2] ); __m128d left_k0_0 = _mm_load_pd( &le[0] ); __m128d left_k0_2 = _mm_load_pd( &le[2] ); __m128d left_k1_0 = _mm_load_pd( &le[4] ); __m128d left_k1_2 = _mm_load_pd( &le[6] ); __m128d left_k2_0 = _mm_load_pd( &le[8] ); __m128d left_k2_2 = _mm_load_pd( &le[10] ); __m128d left_k3_0 = _mm_load_pd( &le[12] ); __m128d left_k3_2 = _mm_load_pd( &le[14] ); left_k0_0 = _mm_mul_pd(x1_0, left_k0_0); left_k0_2 = _mm_mul_pd(x1_2, left_k0_2); left_k1_0 = _mm_mul_pd(x1_0, left_k1_0); left_k1_2 = _mm_mul_pd(x1_2, left_k1_2); left_k0_0 = _mm_hadd_pd( left_k0_0, left_k0_2 ); left_k1_0 = _mm_hadd_pd( left_k1_0, left_k1_2); left_k0_0 = _mm_hadd_pd( left_k0_0, left_k1_0); left_k2_0 = _mm_mul_pd(x1_0, left_k2_0); left_k2_2 = _mm_mul_pd(x1_2, left_k2_2); left_k3_0 = _mm_mul_pd(x1_0, left_k3_0); left_k3_2 = _mm_mul_pd(x1_2, left_k3_2); left_k2_0 = _mm_hadd_pd( left_k2_0, left_k2_2); left_k3_0 = _mm_hadd_pd( left_k3_0, left_k3_2); left_k2_0 = _mm_hadd_pd( left_k2_0, left_k3_0); __m128d x2_0 = _mm_load_pd( &x2[0] ); __m128d x2_2 = _mm_load_pd( &x2[2] ); __m128d right_k0_0 = _mm_load_pd( &ri[0] ); __m128d right_k0_2 = _mm_load_pd( &ri[2] ); __m128d right_k1_0 = _mm_load_pd( &ri[4] ); __m128d right_k1_2 = _mm_load_pd( &ri[6] ); __m128d right_k2_0 = _mm_load_pd( &ri[8] ); __m128d right_k2_2 = _mm_load_pd( &ri[10] ); __m128d right_k3_0 = _mm_load_pd( &ri[12] ); __m128d right_k3_2 = _mm_load_pd( &ri[14] ); right_k0_0 = _mm_mul_pd( x2_0, right_k0_0); right_k0_2 = _mm_mul_pd( x2_2, right_k0_2); right_k1_0 = _mm_mul_pd( x2_0, right_k1_0); right_k1_2 = _mm_mul_pd( x2_2, right_k1_2); right_k0_0 = _mm_hadd_pd( right_k0_0, right_k0_2); right_k1_0 = _mm_hadd_pd( right_k1_0, right_k1_2); right_k0_0 = _mm_hadd_pd( right_k0_0, right_k1_0); right_k2_0 = _mm_mul_pd( x2_0, right_k2_0); right_k2_2 = _mm_mul_pd( x2_2, right_k2_2); right_k3_0 = _mm_mul_pd( x2_0, right_k3_0); right_k3_2 = _mm_mul_pd( x2_2, right_k3_2); right_k2_0 = _mm_hadd_pd( right_k2_0, right_k2_2); right_k3_0 = _mm_hadd_pd( right_k3_0, right_k3_2); right_k2_0 = _mm_hadd_pd( right_k2_0, right_k3_0); __m128d x1px2_k0 = _mm_mul_pd( left_k0_0, right_k0_0 ); __m128d x1px2_k2 = _mm_mul_pd( left_k2_0, right_k2_0 ); __m128d EV_t_l0_k0 = EVV[0]; __m128d EV_t_l0_k2 = EVV[1]; __m128d EV_t_l1_k0 = EVV[2]; __m128d EV_t_l1_k2 = EVV[3]; __m128d EV_t_l2_k0 = EVV[4]; __m128d EV_t_l2_k2 = EVV[5]; __m128d EV_t_l3_k0 = EVV[6]; __m128d EV_t_l3_k2 = EVV[7]; EV_t_l0_k0 = _mm_mul_pd( x1px2_k0, EV_t_l0_k0 ); EV_t_l0_k2 = _mm_mul_pd( x1px2_k2, EV_t_l0_k2 ); EV_t_l0_k0 = _mm_hadd_pd( EV_t_l0_k0, EV_t_l0_k2 ); EV_t_l1_k0 = _mm_mul_pd( x1px2_k0, EV_t_l1_k0 ); EV_t_l1_k2 = _mm_mul_pd( x1px2_k2, EV_t_l1_k2 ); EV_t_l1_k0 = _mm_hadd_pd( EV_t_l1_k0, EV_t_l1_k2 ); EV_t_l0_k0 = _mm_hadd_pd( EV_t_l0_k0, EV_t_l1_k0 ); EV_t_l2_k0 = _mm_mul_pd( x1px2_k0, EV_t_l2_k0 ); EV_t_l2_k2 = _mm_mul_pd( x1px2_k2, EV_t_l2_k2 ); EV_t_l2_k0 = _mm_hadd_pd( EV_t_l2_k0, EV_t_l2_k2 ); EV_t_l3_k0 = _mm_mul_pd( x1px2_k0, EV_t_l3_k0 ); EV_t_l3_k2 = _mm_mul_pd( x1px2_k2, EV_t_l3_k2 ); EV_t_l3_k0 = _mm_hadd_pd( EV_t_l3_k0, EV_t_l3_k2 ); EV_t_l2_k0 = _mm_hadd_pd( EV_t_l2_k0, EV_t_l3_k0 ); scale = 1; __m128d v1 = _mm_and_pd(EV_t_l0_k0, absMask.m); v1 = _mm_cmplt_pd(v1, minlikelihood_sse); if(_mm_movemask_pd( v1 ) != 3) scale = 0; else { v1 = _mm_and_pd(EV_t_l2_k0, absMask.m); v1 = _mm_cmplt_pd(v1, minlikelihood_sse); if(_mm_movemask_pd( v1 ) != 3) scale = 0; } if(scale) { _mm_store_pd(&x3[0], _mm_mul_pd(EV_t_l0_k0, sc)); _mm_store_pd(&x3[2], _mm_mul_pd(EV_t_l2_k0, sc)); addScale += wgt[i]; } else { _mm_store_pd(x3, EV_t_l0_k0); _mm_store_pd(&x3[2], EV_t_l2_k0); } } break; default: assert(0); } *scalerIncrement = addScale; } static void newviewGTRCAT_SAVE( int tipCase, double *EV, int *cptr, double *x1_start, double *x2_start, double *x3_start, double *tipVector, unsigned char *tipX1, unsigned char *tipX2, int n, double *left, double *right, int *wgt, int *scalerIncrement, unsigned int *x1_gap, unsigned int *x2_gap, unsigned int *x3_gap, double *x1_gapColumn, double *x2_gapColumn, double *x3_gapColumn, const int maxCats) { double *le, *ri, *x1, *x2, *x3, *x1_ptr = x1_start, *x2_ptr = x2_start, *x3_ptr = x3_start, EV_t[16] __attribute__ ((aligned (BYTE_ALIGNMENT))); int i, j, scale, scaleGap = 0, addScale = 0; __m128d minlikelihood_sse = _mm_set1_pd( minlikelihood ), sc = _mm_set1_pd(twotothe256), EVV[8]; for(i = 0; i < 4; i++) for (j=0; j < 4; j++) EV_t[4 * j + i] = EV[4 * i + j]; for(i = 0; i < 8; i++) EVV[i] = _mm_load_pd(&EV_t[i * 2]); { x1 = x1_gapColumn; x2 = x2_gapColumn; x3 = x3_gapColumn; le = &left[maxCats * 16]; ri = &right[maxCats * 16]; __m128d x1_0 = _mm_load_pd( &x1[0] ); __m128d x1_2 = _mm_load_pd( &x1[2] ); __m128d left_k0_0 = _mm_load_pd( &le[0] ); __m128d left_k0_2 = _mm_load_pd( &le[2] ); __m128d left_k1_0 = _mm_load_pd( &le[4] ); __m128d left_k1_2 = _mm_load_pd( &le[6] ); __m128d left_k2_0 = _mm_load_pd( &le[8] ); __m128d left_k2_2 = _mm_load_pd( &le[10] ); __m128d left_k3_0 = _mm_load_pd( &le[12] ); __m128d left_k3_2 = _mm_load_pd( &le[14] ); left_k0_0 = _mm_mul_pd(x1_0, left_k0_0); left_k0_2 = _mm_mul_pd(x1_2, left_k0_2); left_k1_0 = _mm_mul_pd(x1_0, left_k1_0); left_k1_2 = _mm_mul_pd(x1_2, left_k1_2); left_k0_0 = _mm_hadd_pd( left_k0_0, left_k0_2 ); left_k1_0 = _mm_hadd_pd( left_k1_0, left_k1_2); left_k0_0 = _mm_hadd_pd( left_k0_0, left_k1_0); left_k2_0 = _mm_mul_pd(x1_0, left_k2_0); left_k2_2 = _mm_mul_pd(x1_2, left_k2_2); left_k3_0 = _mm_mul_pd(x1_0, left_k3_0); left_k3_2 = _mm_mul_pd(x1_2, left_k3_2); left_k2_0 = _mm_hadd_pd( left_k2_0, left_k2_2); left_k3_0 = _mm_hadd_pd( left_k3_0, left_k3_2); left_k2_0 = _mm_hadd_pd( left_k2_0, left_k3_0); __m128d x2_0 = _mm_load_pd( &x2[0] ); __m128d x2_2 = _mm_load_pd( &x2[2] ); __m128d right_k0_0 = _mm_load_pd( &ri[0] ); __m128d right_k0_2 = _mm_load_pd( &ri[2] ); __m128d right_k1_0 = _mm_load_pd( &ri[4] ); __m128d right_k1_2 = _mm_load_pd( &ri[6] ); __m128d right_k2_0 = _mm_load_pd( &ri[8] ); __m128d right_k2_2 = _mm_load_pd( &ri[10] ); __m128d right_k3_0 = _mm_load_pd( &ri[12] ); __m128d right_k3_2 = _mm_load_pd( &ri[14] ); right_k0_0 = _mm_mul_pd( x2_0, right_k0_0); right_k0_2 = _mm_mul_pd( x2_2, right_k0_2); right_k1_0 = _mm_mul_pd( x2_0, right_k1_0); right_k1_2 = _mm_mul_pd( x2_2, right_k1_2); right_k0_0 = _mm_hadd_pd( right_k0_0, right_k0_2); right_k1_0 = _mm_hadd_pd( right_k1_0, right_k1_2); right_k0_0 = _mm_hadd_pd( right_k0_0, right_k1_0); right_k2_0 = _mm_mul_pd( x2_0, right_k2_0); right_k2_2 = _mm_mul_pd( x2_2, right_k2_2); right_k3_0 = _mm_mul_pd( x2_0, right_k3_0); right_k3_2 = _mm_mul_pd( x2_2, right_k3_2); right_k2_0 = _mm_hadd_pd( right_k2_0, right_k2_2); right_k3_0 = _mm_hadd_pd( right_k3_0, right_k3_2); right_k2_0 = _mm_hadd_pd( right_k2_0, right_k3_0); __m128d x1px2_k0 = _mm_mul_pd( left_k0_0, right_k0_0 ); __m128d x1px2_k2 = _mm_mul_pd( left_k2_0, right_k2_0 ); __m128d EV_t_l0_k0 = EVV[0]; __m128d EV_t_l0_k2 = EVV[1]; __m128d EV_t_l1_k0 = EVV[2]; __m128d EV_t_l1_k2 = EVV[3]; __m128d EV_t_l2_k0 = EVV[4]; __m128d EV_t_l2_k2 = EVV[5]; __m128d EV_t_l3_k0 = EVV[6]; __m128d EV_t_l3_k2 = EVV[7]; EV_t_l0_k0 = _mm_mul_pd( x1px2_k0, EV_t_l0_k0 ); EV_t_l0_k2 = _mm_mul_pd( x1px2_k2, EV_t_l0_k2 ); EV_t_l0_k0 = _mm_hadd_pd( EV_t_l0_k0, EV_t_l0_k2 ); EV_t_l1_k0 = _mm_mul_pd( x1px2_k0, EV_t_l1_k0 ); EV_t_l1_k2 = _mm_mul_pd( x1px2_k2, EV_t_l1_k2 ); EV_t_l1_k0 = _mm_hadd_pd( EV_t_l1_k0, EV_t_l1_k2 ); EV_t_l0_k0 = _mm_hadd_pd( EV_t_l0_k0, EV_t_l1_k0 ); EV_t_l2_k0 = _mm_mul_pd( x1px2_k0, EV_t_l2_k0 ); EV_t_l2_k2 = _mm_mul_pd( x1px2_k2, EV_t_l2_k2 ); EV_t_l2_k0 = _mm_hadd_pd( EV_t_l2_k0, EV_t_l2_k2 ); EV_t_l3_k0 = _mm_mul_pd( x1px2_k0, EV_t_l3_k0 ); EV_t_l3_k2 = _mm_mul_pd( x1px2_k2, EV_t_l3_k2 ); EV_t_l3_k0 = _mm_hadd_pd( EV_t_l3_k0, EV_t_l3_k2 ); EV_t_l2_k0 = _mm_hadd_pd( EV_t_l2_k0, EV_t_l3_k0 ); if(tipCase != TIP_TIP) { scale = 1; __m128d v1 = _mm_and_pd(EV_t_l0_k0, absMask.m); v1 = _mm_cmplt_pd(v1, minlikelihood_sse); if(_mm_movemask_pd( v1 ) != 3) scale = 0; else { v1 = _mm_and_pd(EV_t_l2_k0, absMask.m); v1 = _mm_cmplt_pd(v1, minlikelihood_sse); if(_mm_movemask_pd( v1 ) != 3) scale = 0; } if(scale) { _mm_store_pd(&x3[0], _mm_mul_pd(EV_t_l0_k0, sc)); _mm_store_pd(&x3[2], _mm_mul_pd(EV_t_l2_k0, sc)); scaleGap = TRUE; } else { _mm_store_pd(x3, EV_t_l0_k0); _mm_store_pd(&x3[2], EV_t_l2_k0); } } else { _mm_store_pd(x3, EV_t_l0_k0); _mm_store_pd(&x3[2], EV_t_l2_k0); } } switch(tipCase) { case TIP_TIP: for (i = 0; i < n; i++) { if(noGap(x3_gap, i)) { x1 = &(tipVector[4 * tipX1[i]]); x2 = &(tipVector[4 * tipX2[i]]); x3 = x3_ptr; if(isGap(x1_gap, i)) le = &left[maxCats * 16]; else le = &left[cptr[i] * 16]; if(isGap(x2_gap, i)) ri = &right[maxCats * 16]; else ri = &right[cptr[i] * 16]; __m128d x1_0 = _mm_load_pd( &x1[0] ); __m128d x1_2 = _mm_load_pd( &x1[2] ); __m128d left_k0_0 = _mm_load_pd( &le[0] ); __m128d left_k0_2 = _mm_load_pd( &le[2] ); __m128d left_k1_0 = _mm_load_pd( &le[4] ); __m128d left_k1_2 = _mm_load_pd( &le[6] ); __m128d left_k2_0 = _mm_load_pd( &le[8] ); __m128d left_k2_2 = _mm_load_pd( &le[10] ); __m128d left_k3_0 = _mm_load_pd( &le[12] ); __m128d left_k3_2 = _mm_load_pd( &le[14] ); left_k0_0 = _mm_mul_pd(x1_0, left_k0_0); left_k0_2 = _mm_mul_pd(x1_2, left_k0_2); left_k1_0 = _mm_mul_pd(x1_0, left_k1_0); left_k1_2 = _mm_mul_pd(x1_2, left_k1_2); left_k0_0 = _mm_hadd_pd( left_k0_0, left_k0_2 ); left_k1_0 = _mm_hadd_pd( left_k1_0, left_k1_2); left_k0_0 = _mm_hadd_pd( left_k0_0, left_k1_0); left_k2_0 = _mm_mul_pd(x1_0, left_k2_0); left_k2_2 = _mm_mul_pd(x1_2, left_k2_2); left_k3_0 = _mm_mul_pd(x1_0, left_k3_0); left_k3_2 = _mm_mul_pd(x1_2, left_k3_2); left_k2_0 = _mm_hadd_pd( left_k2_0, left_k2_2); left_k3_0 = _mm_hadd_pd( left_k3_0, left_k3_2); left_k2_0 = _mm_hadd_pd( left_k2_0, left_k3_0); __m128d x2_0 = _mm_load_pd( &x2[0] ); __m128d x2_2 = _mm_load_pd( &x2[2] ); __m128d right_k0_0 = _mm_load_pd( &ri[0] ); __m128d right_k0_2 = _mm_load_pd( &ri[2] ); __m128d right_k1_0 = _mm_load_pd( &ri[4] ); __m128d right_k1_2 = _mm_load_pd( &ri[6] ); __m128d right_k2_0 = _mm_load_pd( &ri[8] ); __m128d right_k2_2 = _mm_load_pd( &ri[10] ); __m128d right_k3_0 = _mm_load_pd( &ri[12] ); __m128d right_k3_2 = _mm_load_pd( &ri[14] ); right_k0_0 = _mm_mul_pd( x2_0, right_k0_0); right_k0_2 = _mm_mul_pd( x2_2, right_k0_2); right_k1_0 = _mm_mul_pd( x2_0, right_k1_0); right_k1_2 = _mm_mul_pd( x2_2, right_k1_2); right_k0_0 = _mm_hadd_pd( right_k0_0, right_k0_2); right_k1_0 = _mm_hadd_pd( right_k1_0, right_k1_2); right_k0_0 = _mm_hadd_pd( right_k0_0, right_k1_0); right_k2_0 = _mm_mul_pd( x2_0, right_k2_0); right_k2_2 = _mm_mul_pd( x2_2, right_k2_2); right_k3_0 = _mm_mul_pd( x2_0, right_k3_0); right_k3_2 = _mm_mul_pd( x2_2, right_k3_2); right_k2_0 = _mm_hadd_pd( right_k2_0, right_k2_2); right_k3_0 = _mm_hadd_pd( right_k3_0, right_k3_2); right_k2_0 = _mm_hadd_pd( right_k2_0, right_k3_0); __m128d x1px2_k0 = _mm_mul_pd( left_k0_0, right_k0_0 ); __m128d x1px2_k2 = _mm_mul_pd( left_k2_0, right_k2_0 ); __m128d EV_t_l0_k0 = EVV[0]; __m128d EV_t_l0_k2 = EVV[1]; __m128d EV_t_l1_k0 = EVV[2]; __m128d EV_t_l1_k2 = EVV[3]; __m128d EV_t_l2_k0 = EVV[4]; __m128d EV_t_l2_k2 = EVV[5]; __m128d EV_t_l3_k0 = EVV[6]; __m128d EV_t_l3_k2 = EVV[7]; EV_t_l0_k0 = _mm_mul_pd( x1px2_k0, EV_t_l0_k0 ); EV_t_l0_k2 = _mm_mul_pd( x1px2_k2, EV_t_l0_k2 ); EV_t_l0_k0 = _mm_hadd_pd( EV_t_l0_k0, EV_t_l0_k2 ); EV_t_l1_k0 = _mm_mul_pd( x1px2_k0, EV_t_l1_k0 ); EV_t_l1_k2 = _mm_mul_pd( x1px2_k2, EV_t_l1_k2 ); EV_t_l1_k0 = _mm_hadd_pd( EV_t_l1_k0, EV_t_l1_k2 ); EV_t_l0_k0 = _mm_hadd_pd( EV_t_l0_k0, EV_t_l1_k0 ); EV_t_l2_k0 = _mm_mul_pd( x1px2_k0, EV_t_l2_k0 ); EV_t_l2_k2 = _mm_mul_pd( x1px2_k2, EV_t_l2_k2 ); EV_t_l2_k0 = _mm_hadd_pd( EV_t_l2_k0, EV_t_l2_k2 ); EV_t_l3_k0 = _mm_mul_pd( x1px2_k0, EV_t_l3_k0 ); EV_t_l3_k2 = _mm_mul_pd( x1px2_k2, EV_t_l3_k2 ); EV_t_l3_k0 = _mm_hadd_pd( EV_t_l3_k0, EV_t_l3_k2 ); EV_t_l2_k0 = _mm_hadd_pd( EV_t_l2_k0, EV_t_l3_k0 ); _mm_store_pd(x3, EV_t_l0_k0); _mm_store_pd(&x3[2], EV_t_l2_k0); x3_ptr += 4; } } break; case TIP_INNER: for (i = 0; i < n; i++) { if(isGap(x3_gap, i)) { if(scaleGap) addScale += wgt[i]; } else { x1 = &(tipVector[4 * tipX1[i]]); x2 = x2_ptr; x3 = x3_ptr; if(isGap(x1_gap, i)) le = &left[maxCats * 16]; else le = &left[cptr[i] * 16]; if(isGap(x2_gap, i)) { ri = &right[maxCats * 16]; x2 = x2_gapColumn; } else { ri = &right[cptr[i] * 16]; x2 = x2_ptr; x2_ptr += 4; } __m128d x1_0 = _mm_load_pd( &x1[0] ); __m128d x1_2 = _mm_load_pd( &x1[2] ); __m128d left_k0_0 = _mm_load_pd( &le[0] ); __m128d left_k0_2 = _mm_load_pd( &le[2] ); __m128d left_k1_0 = _mm_load_pd( &le[4] ); __m128d left_k1_2 = _mm_load_pd( &le[6] ); __m128d left_k2_0 = _mm_load_pd( &le[8] ); __m128d left_k2_2 = _mm_load_pd( &le[10] ); __m128d left_k3_0 = _mm_load_pd( &le[12] ); __m128d left_k3_2 = _mm_load_pd( &le[14] ); left_k0_0 = _mm_mul_pd(x1_0, left_k0_0); left_k0_2 = _mm_mul_pd(x1_2, left_k0_2); left_k1_0 = _mm_mul_pd(x1_0, left_k1_0); left_k1_2 = _mm_mul_pd(x1_2, left_k1_2); left_k0_0 = _mm_hadd_pd( left_k0_0, left_k0_2 ); left_k1_0 = _mm_hadd_pd( left_k1_0, left_k1_2); left_k0_0 = _mm_hadd_pd( left_k0_0, left_k1_0); left_k2_0 = _mm_mul_pd(x1_0, left_k2_0); left_k2_2 = _mm_mul_pd(x1_2, left_k2_2); left_k3_0 = _mm_mul_pd(x1_0, left_k3_0); left_k3_2 = _mm_mul_pd(x1_2, left_k3_2); left_k2_0 = _mm_hadd_pd( left_k2_0, left_k2_2); left_k3_0 = _mm_hadd_pd( left_k3_0, left_k3_2); left_k2_0 = _mm_hadd_pd( left_k2_0, left_k3_0); __m128d x2_0 = _mm_load_pd( &x2[0] ); __m128d x2_2 = _mm_load_pd( &x2[2] ); __m128d right_k0_0 = _mm_load_pd( &ri[0] ); __m128d right_k0_2 = _mm_load_pd( &ri[2] ); __m128d right_k1_0 = _mm_load_pd( &ri[4] ); __m128d right_k1_2 = _mm_load_pd( &ri[6] ); __m128d right_k2_0 = _mm_load_pd( &ri[8] ); __m128d right_k2_2 = _mm_load_pd( &ri[10] ); __m128d right_k3_0 = _mm_load_pd( &ri[12] ); __m128d right_k3_2 = _mm_load_pd( &ri[14] ); right_k0_0 = _mm_mul_pd( x2_0, right_k0_0); right_k0_2 = _mm_mul_pd( x2_2, right_k0_2); right_k1_0 = _mm_mul_pd( x2_0, right_k1_0); right_k1_2 = _mm_mul_pd( x2_2, right_k1_2); right_k0_0 = _mm_hadd_pd( right_k0_0, right_k0_2); right_k1_0 = _mm_hadd_pd( right_k1_0, right_k1_2); right_k0_0 = _mm_hadd_pd( right_k0_0, right_k1_0); right_k2_0 = _mm_mul_pd( x2_0, right_k2_0); right_k2_2 = _mm_mul_pd( x2_2, right_k2_2); right_k3_0 = _mm_mul_pd( x2_0, right_k3_0); right_k3_2 = _mm_mul_pd( x2_2, right_k3_2); right_k2_0 = _mm_hadd_pd( right_k2_0, right_k2_2); right_k3_0 = _mm_hadd_pd( right_k3_0, right_k3_2); right_k2_0 = _mm_hadd_pd( right_k2_0, right_k3_0); __m128d x1px2_k0 = _mm_mul_pd( left_k0_0, right_k0_0 ); __m128d x1px2_k2 = _mm_mul_pd( left_k2_0, right_k2_0 ); __m128d EV_t_l0_k0 = EVV[0]; __m128d EV_t_l0_k2 = EVV[1]; __m128d EV_t_l1_k0 = EVV[2]; __m128d EV_t_l1_k2 = EVV[3]; __m128d EV_t_l2_k0 = EVV[4]; __m128d EV_t_l2_k2 = EVV[5]; __m128d EV_t_l3_k0 = EVV[6]; __m128d EV_t_l3_k2 = EVV[7]; EV_t_l0_k0 = _mm_mul_pd( x1px2_k0, EV_t_l0_k0 ); EV_t_l0_k2 = _mm_mul_pd( x1px2_k2, EV_t_l0_k2 ); EV_t_l0_k0 = _mm_hadd_pd( EV_t_l0_k0, EV_t_l0_k2 ); EV_t_l1_k0 = _mm_mul_pd( x1px2_k0, EV_t_l1_k0 ); EV_t_l1_k2 = _mm_mul_pd( x1px2_k2, EV_t_l1_k2 ); EV_t_l1_k0 = _mm_hadd_pd( EV_t_l1_k0, EV_t_l1_k2 ); EV_t_l0_k0 = _mm_hadd_pd( EV_t_l0_k0, EV_t_l1_k0 ); EV_t_l2_k0 = _mm_mul_pd( x1px2_k0, EV_t_l2_k0 ); EV_t_l2_k2 = _mm_mul_pd( x1px2_k2, EV_t_l2_k2 ); EV_t_l2_k0 = _mm_hadd_pd( EV_t_l2_k0, EV_t_l2_k2 ); EV_t_l3_k0 = _mm_mul_pd( x1px2_k0, EV_t_l3_k0 ); EV_t_l3_k2 = _mm_mul_pd( x1px2_k2, EV_t_l3_k2 ); EV_t_l3_k0 = _mm_hadd_pd( EV_t_l3_k0, EV_t_l3_k2 ); EV_t_l2_k0 = _mm_hadd_pd( EV_t_l2_k0, EV_t_l3_k0 ); scale = 1; __m128d v1 = _mm_and_pd(EV_t_l0_k0, absMask.m); v1 = _mm_cmplt_pd(v1, minlikelihood_sse); if(_mm_movemask_pd( v1 ) != 3) scale = 0; else { v1 = _mm_and_pd(EV_t_l2_k0, absMask.m); v1 = _mm_cmplt_pd(v1, minlikelihood_sse); if(_mm_movemask_pd( v1 ) != 3) scale = 0; } if(scale) { _mm_store_pd(&x3[0], _mm_mul_pd(EV_t_l0_k0, sc)); _mm_store_pd(&x3[2], _mm_mul_pd(EV_t_l2_k0, sc)); addScale += wgt[i]; } else { _mm_store_pd(x3, EV_t_l0_k0); _mm_store_pd(&x3[2], EV_t_l2_k0); } x3_ptr += 4; } } break; case INNER_INNER: for (i = 0; i < n; i++) { if(isGap(x3_gap, i)) { if(scaleGap) addScale += wgt[i]; } else { x3 = x3_ptr; if(isGap(x1_gap, i)) { x1 = x1_gapColumn; le = &left[maxCats * 16]; } else { le = &left[cptr[i] * 16]; x1 = x1_ptr; x1_ptr += 4; } if(isGap(x2_gap, i)) { x2 = x2_gapColumn; ri = &right[maxCats * 16]; } else { ri = &right[cptr[i] * 16]; x2 = x2_ptr; x2_ptr += 4; } __m128d x1_0 = _mm_load_pd( &x1[0] ); __m128d x1_2 = _mm_load_pd( &x1[2] ); __m128d left_k0_0 = _mm_load_pd( &le[0] ); __m128d left_k0_2 = _mm_load_pd( &le[2] ); __m128d left_k1_0 = _mm_load_pd( &le[4] ); __m128d left_k1_2 = _mm_load_pd( &le[6] ); __m128d left_k2_0 = _mm_load_pd( &le[8] ); __m128d left_k2_2 = _mm_load_pd( &le[10] ); __m128d left_k3_0 = _mm_load_pd( &le[12] ); __m128d left_k3_2 = _mm_load_pd( &le[14] ); left_k0_0 = _mm_mul_pd(x1_0, left_k0_0); left_k0_2 = _mm_mul_pd(x1_2, left_k0_2); left_k1_0 = _mm_mul_pd(x1_0, left_k1_0); left_k1_2 = _mm_mul_pd(x1_2, left_k1_2); left_k0_0 = _mm_hadd_pd( left_k0_0, left_k0_2 ); left_k1_0 = _mm_hadd_pd( left_k1_0, left_k1_2); left_k0_0 = _mm_hadd_pd( left_k0_0, left_k1_0); left_k2_0 = _mm_mul_pd(x1_0, left_k2_0); left_k2_2 = _mm_mul_pd(x1_2, left_k2_2); left_k3_0 = _mm_mul_pd(x1_0, left_k3_0); left_k3_2 = _mm_mul_pd(x1_2, left_k3_2); left_k2_0 = _mm_hadd_pd( left_k2_0, left_k2_2); left_k3_0 = _mm_hadd_pd( left_k3_0, left_k3_2); left_k2_0 = _mm_hadd_pd( left_k2_0, left_k3_0); __m128d x2_0 = _mm_load_pd( &x2[0] ); __m128d x2_2 = _mm_load_pd( &x2[2] ); __m128d right_k0_0 = _mm_load_pd( &ri[0] ); __m128d right_k0_2 = _mm_load_pd( &ri[2] ); __m128d right_k1_0 = _mm_load_pd( &ri[4] ); __m128d right_k1_2 = _mm_load_pd( &ri[6] ); __m128d right_k2_0 = _mm_load_pd( &ri[8] ); __m128d right_k2_2 = _mm_load_pd( &ri[10] ); __m128d right_k3_0 = _mm_load_pd( &ri[12] ); __m128d right_k3_2 = _mm_load_pd( &ri[14] ); right_k0_0 = _mm_mul_pd( x2_0, right_k0_0); right_k0_2 = _mm_mul_pd( x2_2, right_k0_2); right_k1_0 = _mm_mul_pd( x2_0, right_k1_0); right_k1_2 = _mm_mul_pd( x2_2, right_k1_2); right_k0_0 = _mm_hadd_pd( right_k0_0, right_k0_2); right_k1_0 = _mm_hadd_pd( right_k1_0, right_k1_2); right_k0_0 = _mm_hadd_pd( right_k0_0, right_k1_0); right_k2_0 = _mm_mul_pd( x2_0, right_k2_0); right_k2_2 = _mm_mul_pd( x2_2, right_k2_2); right_k3_0 = _mm_mul_pd( x2_0, right_k3_0); right_k3_2 = _mm_mul_pd( x2_2, right_k3_2); right_k2_0 = _mm_hadd_pd( right_k2_0, right_k2_2); right_k3_0 = _mm_hadd_pd( right_k3_0, right_k3_2); right_k2_0 = _mm_hadd_pd( right_k2_0, right_k3_0); __m128d x1px2_k0 = _mm_mul_pd( left_k0_0, right_k0_0 ); __m128d x1px2_k2 = _mm_mul_pd( left_k2_0, right_k2_0 ); __m128d EV_t_l0_k0 = EVV[0]; __m128d EV_t_l0_k2 = EVV[1]; __m128d EV_t_l1_k0 = EVV[2]; __m128d EV_t_l1_k2 = EVV[3]; __m128d EV_t_l2_k0 = EVV[4]; __m128d EV_t_l2_k2 = EVV[5]; __m128d EV_t_l3_k0 = EVV[6]; __m128d EV_t_l3_k2 = EVV[7]; EV_t_l0_k0 = _mm_mul_pd( x1px2_k0, EV_t_l0_k0 ); EV_t_l0_k2 = _mm_mul_pd( x1px2_k2, EV_t_l0_k2 ); EV_t_l0_k0 = _mm_hadd_pd( EV_t_l0_k0, EV_t_l0_k2 ); EV_t_l1_k0 = _mm_mul_pd( x1px2_k0, EV_t_l1_k0 ); EV_t_l1_k2 = _mm_mul_pd( x1px2_k2, EV_t_l1_k2 ); EV_t_l1_k0 = _mm_hadd_pd( EV_t_l1_k0, EV_t_l1_k2 ); EV_t_l0_k0 = _mm_hadd_pd( EV_t_l0_k0, EV_t_l1_k0 ); EV_t_l2_k0 = _mm_mul_pd( x1px2_k0, EV_t_l2_k0 ); EV_t_l2_k2 = _mm_mul_pd( x1px2_k2, EV_t_l2_k2 ); EV_t_l2_k0 = _mm_hadd_pd( EV_t_l2_k0, EV_t_l2_k2 ); EV_t_l3_k0 = _mm_mul_pd( x1px2_k0, EV_t_l3_k0 ); EV_t_l3_k2 = _mm_mul_pd( x1px2_k2, EV_t_l3_k2 ); EV_t_l3_k0 = _mm_hadd_pd( EV_t_l3_k0, EV_t_l3_k2 ); EV_t_l2_k0 = _mm_hadd_pd( EV_t_l2_k0, EV_t_l3_k0 ); scale = 1; __m128d v1 = _mm_and_pd(EV_t_l0_k0, absMask.m); v1 = _mm_cmplt_pd(v1, minlikelihood_sse); if(_mm_movemask_pd( v1 ) != 3) scale = 0; else { v1 = _mm_and_pd(EV_t_l2_k0, absMask.m); v1 = _mm_cmplt_pd(v1, minlikelihood_sse); if(_mm_movemask_pd( v1 ) != 3) scale = 0; } if(scale) { _mm_store_pd(&x3[0], _mm_mul_pd(EV_t_l0_k0, sc)); _mm_store_pd(&x3[2], _mm_mul_pd(EV_t_l2_k0, sc)); addScale += wgt[i]; } else { _mm_store_pd(x3, EV_t_l0_k0); _mm_store_pd(&x3[2], EV_t_l2_k0); } x3_ptr += 4; } } break; default: assert(0); } *scalerIncrement = addScale; } static void newviewGTRGAMMAPROT_GAPPED_SAVE(int tipCase, double *x1, double *x2, double *x3, double *extEV, double *tipVector, unsigned char *tipX1, unsigned char *tipX2, int n, double *left, double *right, int *wgt, int *scalerIncrement, unsigned int *x1_gap, unsigned int *x2_gap, unsigned int *x3_gap, double *x1_gapColumn, double *x2_gapColumn, double *x3_gapColumn ) { double *uX1, *uX2, *v; double x1px2; int i, j, l, k, scale, addScale = 0, gapScaling = 0; double *vl, *vr, *x1v, *x2v, *x1_ptr = x1, *x2_ptr = x2, *x3_ptr = x3; switch(tipCase) { case TIP_TIP: { double umpX1[1840], umpX2[1840]; for(i = 0; i < 23; i++) { v = &(tipVector[20 * i]); for(k = 0; k < 80; k++) { double *ll = &left[k * 20]; double *rr = &right[k * 20]; __m128d umpX1v = _mm_setzero_pd(); __m128d umpX2v = _mm_setzero_pd(); for(l = 0; l < 20; l+=2) { __m128d vv = _mm_load_pd(&v[l]); umpX1v = _mm_add_pd(umpX1v, _mm_mul_pd(vv, _mm_load_pd(&ll[l]))); umpX2v = _mm_add_pd(umpX2v, _mm_mul_pd(vv, _mm_load_pd(&rr[l]))); } umpX1v = _mm_hadd_pd(umpX1v, umpX1v); umpX2v = _mm_hadd_pd(umpX2v, umpX2v); _mm_storel_pd(&umpX1[80 * i + k], umpX1v); _mm_storel_pd(&umpX2[80 * i + k], umpX2v); } } { uX1 = &umpX1[1760]; uX2 = &umpX2[1760]; for(j = 0; j < 4; j++) { v = &x3_gapColumn[j * 20]; __m128d zero = _mm_setzero_pd(); for(k = 0; k < 20; k+=2) _mm_store_pd(&v[k], zero); for(k = 0; k < 20; k++) { double *eev = &extEV[k * 20]; x1px2 = uX1[j * 20 + k] * uX2[j * 20 + k]; __m128d x1px2v = _mm_set1_pd(x1px2); for(l = 0; l < 20; l+=2) { __m128d vv = _mm_load_pd(&v[l]); __m128d ee = _mm_load_pd(&eev[l]); vv = _mm_add_pd(vv, _mm_mul_pd(x1px2v,ee)); _mm_store_pd(&v[l], vv); } } } } for(i = 0; i < n; i++) { if(!(x3_gap[i / 32] & mask32[i % 32])) { uX1 = &umpX1[80 * tipX1[i]]; uX2 = &umpX2[80 * tipX2[i]]; for(j = 0; j < 4; j++) { v = &x3_ptr[j * 20]; __m128d zero = _mm_setzero_pd(); for(k = 0; k < 20; k+=2) _mm_store_pd(&v[k], zero); for(k = 0; k < 20; k++) { double *eev = &extEV[k * 20]; x1px2 = uX1[j * 20 + k] * uX2[j * 20 + k]; __m128d x1px2v = _mm_set1_pd(x1px2); for(l = 0; l < 20; l+=2) { __m128d vv = _mm_load_pd(&v[l]); __m128d ee = _mm_load_pd(&eev[l]); vv = _mm_add_pd(vv, _mm_mul_pd(x1px2v,ee)); _mm_store_pd(&v[l], vv); } } } x3_ptr += 80; } } } break; case TIP_INNER: { double umpX1[1840], ump_x2[20]; for(i = 0; i < 23; i++) { v = &(tipVector[20 * i]); for(k = 0; k < 80; k++) { double *ll = &left[k * 20]; __m128d umpX1v = _mm_setzero_pd(); for(l = 0; l < 20; l+=2) { __m128d vv = _mm_load_pd(&v[l]); umpX1v = _mm_add_pd(umpX1v, _mm_mul_pd(vv, _mm_load_pd(&ll[l]))); } umpX1v = _mm_hadd_pd(umpX1v, umpX1v); _mm_storel_pd(&umpX1[80 * i + k], umpX1v); } } { uX1 = &umpX1[1760]; for(k = 0; k < 4; k++) { v = &(x2_gapColumn[k * 20]); for(l = 0; l < 20; l++) { double *r = &right[k * 400 + l * 20]; __m128d ump_x2v = _mm_setzero_pd(); for(j = 0; j < 20; j+= 2) { __m128d vv = _mm_load_pd(&v[j]); __m128d rr = _mm_load_pd(&r[j]); ump_x2v = _mm_add_pd(ump_x2v, _mm_mul_pd(vv, rr)); } ump_x2v = _mm_hadd_pd(ump_x2v, ump_x2v); _mm_storel_pd(&ump_x2[l], ump_x2v); } v = &(x3_gapColumn[20 * k]); __m128d zero = _mm_setzero_pd(); for(l = 0; l < 20; l+=2) _mm_store_pd(&v[l], zero); for(l = 0; l < 20; l++) { double *eev = &extEV[l * 20]; x1px2 = uX1[k * 20 + l] * ump_x2[l]; __m128d x1px2v = _mm_set1_pd(x1px2); for(j = 0; j < 20; j+=2) { __m128d vv = _mm_load_pd(&v[j]); __m128d ee = _mm_load_pd(&eev[j]); vv = _mm_add_pd(vv, _mm_mul_pd(x1px2v,ee)); _mm_store_pd(&v[j], vv); } } } { v = x3_gapColumn; __m128d minlikelihood_sse = _mm_set1_pd( minlikelihood ); scale = 1; for(l = 0; scale && (l < 80); l += 2) { __m128d vv = _mm_load_pd(&v[l]); __m128d v1 = _mm_and_pd(vv, absMask.m); v1 = _mm_cmplt_pd(v1, minlikelihood_sse); if(_mm_movemask_pd( v1 ) != 3) scale = 0; } } if (scale) { gapScaling = 1; __m128d twoto = _mm_set_pd(twotothe256, twotothe256); for(l = 0; l < 80; l+=2) { __m128d ex3v = _mm_load_pd(&v[l]); _mm_store_pd(&v[l], _mm_mul_pd(ex3v,twoto)); } } } for (i = 0; i < n; i++) { if((x3_gap[i / 32] & mask32[i % 32])) { if(gapScaling) { addScale += wgt[i]; } } else { uX1 = &umpX1[80 * tipX1[i]]; if(x2_gap[i / 32] & mask32[i % 32]) x2v = x2_gapColumn; else { x2v = x2_ptr; x2_ptr += 80; } for(k = 0; k < 4; k++) { v = &(x2v[k * 20]); for(l = 0; l < 20; l++) { double *r = &right[k * 400 + l * 20]; __m128d ump_x2v = _mm_setzero_pd(); for(j = 0; j < 20; j+= 2) { __m128d vv = _mm_load_pd(&v[j]); __m128d rr = _mm_load_pd(&r[j]); ump_x2v = _mm_add_pd(ump_x2v, _mm_mul_pd(vv, rr)); } ump_x2v = _mm_hadd_pd(ump_x2v, ump_x2v); _mm_storel_pd(&ump_x2[l], ump_x2v); } v = &x3_ptr[20 * k]; __m128d zero = _mm_setzero_pd(); for(l = 0; l < 20; l+=2) _mm_store_pd(&v[l], zero); for(l = 0; l < 20; l++) { double *eev = &extEV[l * 20]; x1px2 = uX1[k * 20 + l] * ump_x2[l]; __m128d x1px2v = _mm_set1_pd(x1px2); for(j = 0; j < 20; j+=2) { __m128d vv = _mm_load_pd(&v[j]); __m128d ee = _mm_load_pd(&eev[j]); vv = _mm_add_pd(vv, _mm_mul_pd(x1px2v,ee)); _mm_store_pd(&v[j], vv); } } } { v = x3_ptr; __m128d minlikelihood_sse = _mm_set1_pd( minlikelihood ); scale = 1; for(l = 0; scale && (l < 80); l += 2) { __m128d vv = _mm_load_pd(&v[l]); __m128d v1 = _mm_and_pd(vv, absMask.m); v1 = _mm_cmplt_pd(v1, minlikelihood_sse); if(_mm_movemask_pd( v1 ) != 3) scale = 0; } } if (scale) { __m128d twoto = _mm_set_pd(twotothe256, twotothe256); for(l = 0; l < 80; l+=2) { __m128d ex3v = _mm_load_pd(&v[l]); _mm_store_pd(&v[l], _mm_mul_pd(ex3v,twoto)); } addScale += wgt[i]; } x3_ptr += 80; } } } break; case INNER_INNER: { for(k = 0; k < 4; k++) { vl = &(x1_gapColumn[20 * k]); vr = &(x2_gapColumn[20 * k]); v = &(x3_gapColumn[20 * k]); __m128d zero = _mm_setzero_pd(); for(l = 0; l < 20; l+=2) _mm_store_pd(&v[l], zero); for(l = 0; l < 20; l++) { { __m128d al = _mm_setzero_pd(); __m128d ar = _mm_setzero_pd(); double *ll = &left[k * 400 + l * 20]; double *rr = &right[k * 400 + l * 20]; double *EVEV = &extEV[20 * l]; for(j = 0; j < 20; j+=2) { __m128d lv = _mm_load_pd(&ll[j]); __m128d rv = _mm_load_pd(&rr[j]); __m128d vll = _mm_load_pd(&vl[j]); __m128d vrr = _mm_load_pd(&vr[j]); al = _mm_add_pd(al, _mm_mul_pd(vll, lv)); ar = _mm_add_pd(ar, _mm_mul_pd(vrr, rv)); } al = _mm_hadd_pd(al, al); ar = _mm_hadd_pd(ar, ar); al = _mm_mul_pd(al, ar); for(j = 0; j < 20; j+=2) { __m128d vv = _mm_load_pd(&v[j]); __m128d EVV = _mm_load_pd(&EVEV[j]); vv = _mm_add_pd(vv, _mm_mul_pd(al, EVV)); _mm_store_pd(&v[j], vv); } } } } { v = x3_gapColumn; __m128d minlikelihood_sse = _mm_set1_pd( minlikelihood ); scale = 1; for(l = 0; scale && (l < 80); l += 2) { __m128d vv = _mm_load_pd(&v[l]); __m128d v1 = _mm_and_pd(vv, absMask.m); v1 = _mm_cmplt_pd(v1, minlikelihood_sse); if(_mm_movemask_pd( v1 ) != 3) scale = 0; } } if (scale) { gapScaling = 1; __m128d twoto = _mm_set_pd(twotothe256, twotothe256); for(l = 0; l < 80; l+=2) { __m128d ex3v = _mm_load_pd(&v[l]); _mm_store_pd(&v[l], _mm_mul_pd(ex3v,twoto)); } } } for (i = 0; i < n; i++) { if(x3_gap[i / 32] & mask32[i % 32]) { if(gapScaling) { addScale += wgt[i]; } } else { if(x1_gap[i / 32] & mask32[i % 32]) x1v = x1_gapColumn; else { x1v = x1_ptr; x1_ptr += 80; } if(x2_gap[i / 32] & mask32[i % 32]) x2v = x2_gapColumn; else { x2v = x2_ptr; x2_ptr += 80; } for(k = 0; k < 4; k++) { vl = &(x1v[20 * k]); vr = &(x2v[20 * k]); v = &x3_ptr[20 * k]; __m128d zero = _mm_setzero_pd(); for(l = 0; l < 20; l+=2) _mm_store_pd(&v[l], zero); for(l = 0; l < 20; l++) { { __m128d al = _mm_setzero_pd(); __m128d ar = _mm_setzero_pd(); double *ll = &left[k * 400 + l * 20]; double *rr = &right[k * 400 + l * 20]; double *EVEV = &extEV[20 * l]; for(j = 0; j < 20; j+=2) { __m128d lv = _mm_load_pd(&ll[j]); __m128d rv = _mm_load_pd(&rr[j]); __m128d vll = _mm_load_pd(&vl[j]); __m128d vrr = _mm_load_pd(&vr[j]); al = _mm_add_pd(al, _mm_mul_pd(vll, lv)); ar = _mm_add_pd(ar, _mm_mul_pd(vrr, rv)); } al = _mm_hadd_pd(al, al); ar = _mm_hadd_pd(ar, ar); al = _mm_mul_pd(al, ar); for(j = 0; j < 20; j+=2) { __m128d vv = _mm_load_pd(&v[j]); __m128d EVV = _mm_load_pd(&EVEV[j]); vv = _mm_add_pd(vv, _mm_mul_pd(al, EVV)); _mm_store_pd(&v[j], vv); } } } } { v = x3_ptr; __m128d minlikelihood_sse = _mm_set1_pd( minlikelihood ); scale = 1; for(l = 0; scale && (l < 80); l += 2) { __m128d vv = _mm_load_pd(&v[l]); __m128d v1 = _mm_and_pd(vv, absMask.m); v1 = _mm_cmplt_pd(v1, minlikelihood_sse); if(_mm_movemask_pd( v1 ) != 3) scale = 0; } } if (scale) { __m128d twoto = _mm_set_pd(twotothe256, twotothe256); for(l = 0; l < 80; l+=2) { __m128d ex3v = _mm_load_pd(&v[l]); _mm_store_pd(&v[l], _mm_mul_pd(ex3v,twoto)); } addScale += wgt[i]; } x3_ptr += 80; } } break; default: assert(0); } *scalerIncrement = addScale; } static void newviewGTRGAMMAPROT(int tipCase, double *x1, double *x2, double *x3, double *extEV, double *tipVector, unsigned char *tipX1, unsigned char *tipX2, int n, double *left, double *right, int *wgt, int *scalerIncrement) { double *uX1, *uX2, *v; double x1px2; int i, j, l, k, scale, addScale = 0; double *vl, *vr; switch(tipCase) { case TIP_TIP: { double umpX1[1840], umpX2[1840]; for(i = 0; i < 23; i++) { v = &(tipVector[20 * i]); for(k = 0; k < 80; k++) { double *ll = &left[k * 20]; double *rr = &right[k * 20]; __m128d umpX1v = _mm_setzero_pd(); __m128d umpX2v = _mm_setzero_pd(); for(l = 0; l < 20; l+=2) { __m128d vv = _mm_load_pd(&v[l]); umpX1v = _mm_add_pd(umpX1v, _mm_mul_pd(vv, _mm_load_pd(&ll[l]))); umpX2v = _mm_add_pd(umpX2v, _mm_mul_pd(vv, _mm_load_pd(&rr[l]))); } umpX1v = _mm_hadd_pd(umpX1v, umpX1v); umpX2v = _mm_hadd_pd(umpX2v, umpX2v); _mm_storel_pd(&umpX1[80 * i + k], umpX1v); _mm_storel_pd(&umpX2[80 * i + k], umpX2v); } } for(i = 0; i < n; i++) { uX1 = &umpX1[80 * tipX1[i]]; uX2 = &umpX2[80 * tipX2[i]]; for(j = 0; j < 4; j++) { v = &x3[i * 80 + j * 20]; __m128d zero = _mm_setzero_pd(); for(k = 0; k < 20; k+=2) _mm_store_pd(&v[k], zero); for(k = 0; k < 20; k++) { double *eev = &extEV[k * 20]; x1px2 = uX1[j * 20 + k] * uX2[j * 20 + k]; __m128d x1px2v = _mm_set1_pd(x1px2); for(l = 0; l < 20; l+=2) { __m128d vv = _mm_load_pd(&v[l]); __m128d ee = _mm_load_pd(&eev[l]); vv = _mm_add_pd(vv, _mm_mul_pd(x1px2v,ee)); _mm_store_pd(&v[l], vv); } } } } } break; case TIP_INNER: { double umpX1[1840], ump_x2[20]; for(i = 0; i < 23; i++) { v = &(tipVector[20 * i]); for(k = 0; k < 80; k++) { double *ll = &left[k * 20]; __m128d umpX1v = _mm_setzero_pd(); for(l = 0; l < 20; l+=2) { __m128d vv = _mm_load_pd(&v[l]); umpX1v = _mm_add_pd(umpX1v, _mm_mul_pd(vv, _mm_load_pd(&ll[l]))); } umpX1v = _mm_hadd_pd(umpX1v, umpX1v); _mm_storel_pd(&umpX1[80 * i + k], umpX1v); } } for (i = 0; i < n; i++) { uX1 = &umpX1[80 * tipX1[i]]; for(k = 0; k < 4; k++) { v = &(x2[80 * i + k * 20]); for(l = 0; l < 20; l++) { double *r = &right[k * 400 + l * 20]; __m128d ump_x2v = _mm_setzero_pd(); for(j = 0; j < 20; j+= 2) { __m128d vv = _mm_load_pd(&v[j]); __m128d rr = _mm_load_pd(&r[j]); ump_x2v = _mm_add_pd(ump_x2v, _mm_mul_pd(vv, rr)); } ump_x2v = _mm_hadd_pd(ump_x2v, ump_x2v); _mm_storel_pd(&ump_x2[l], ump_x2v); } v = &(x3[80 * i + 20 * k]); __m128d zero = _mm_setzero_pd(); for(l = 0; l < 20; l+=2) _mm_store_pd(&v[l], zero); for(l = 0; l < 20; l++) { double *eev = &extEV[l * 20]; x1px2 = uX1[k * 20 + l] * ump_x2[l]; __m128d x1px2v = _mm_set1_pd(x1px2); for(j = 0; j < 20; j+=2) { __m128d vv = _mm_load_pd(&v[j]); __m128d ee = _mm_load_pd(&eev[j]); vv = _mm_add_pd(vv, _mm_mul_pd(x1px2v,ee)); _mm_store_pd(&v[j], vv); } } } { v = &(x3[80 * i]); __m128d minlikelihood_sse = _mm_set1_pd( minlikelihood ); scale = 1; for(l = 0; scale && (l < 80); l += 2) { __m128d vv = _mm_load_pd(&v[l]); __m128d v1 = _mm_and_pd(vv, absMask.m); v1 = _mm_cmplt_pd(v1, minlikelihood_sse); if(_mm_movemask_pd( v1 ) != 3) scale = 0; } } if (scale) { __m128d twoto = _mm_set_pd(twotothe256, twotothe256); for(l = 0; l < 80; l+=2) { __m128d ex3v = _mm_load_pd(&v[l]); _mm_store_pd(&v[l], _mm_mul_pd(ex3v,twoto)); } addScale += wgt[i]; } } } break; case INNER_INNER: for (i = 0; i < n; i++) { for(k = 0; k < 4; k++) { vl = &(x1[80 * i + 20 * k]); vr = &(x2[80 * i + 20 * k]); v = &(x3[80 * i + 20 * k]); __m128d zero = _mm_setzero_pd(); for(l = 0; l < 20; l+=2) _mm_store_pd(&v[l], zero); for(l = 0; l < 20; l++) { { __m128d al = _mm_setzero_pd(); __m128d ar = _mm_setzero_pd(); double *ll = &left[k * 400 + l * 20]; double *rr = &right[k * 400 + l * 20]; double *EVEV = &extEV[20 * l]; for(j = 0; j < 20; j+=2) { __m128d lv = _mm_load_pd(&ll[j]); __m128d rv = _mm_load_pd(&rr[j]); __m128d vll = _mm_load_pd(&vl[j]); __m128d vrr = _mm_load_pd(&vr[j]); al = _mm_add_pd(al, _mm_mul_pd(vll, lv)); ar = _mm_add_pd(ar, _mm_mul_pd(vrr, rv)); } al = _mm_hadd_pd(al, al); ar = _mm_hadd_pd(ar, ar); al = _mm_mul_pd(al, ar); for(j = 0; j < 20; j+=2) { __m128d vv = _mm_load_pd(&v[j]); __m128d EVV = _mm_load_pd(&EVEV[j]); vv = _mm_add_pd(vv, _mm_mul_pd(al, EVV)); _mm_store_pd(&v[j], vv); } } } } { v = &(x3[80 * i]); __m128d minlikelihood_sse = _mm_set1_pd( minlikelihood ); scale = 1; for(l = 0; scale && (l < 80); l += 2) { __m128d vv = _mm_load_pd(&v[l]); __m128d v1 = _mm_and_pd(vv, absMask.m); v1 = _mm_cmplt_pd(v1, minlikelihood_sse); if(_mm_movemask_pd( v1 ) != 3) scale = 0; } } if (scale) { __m128d twoto = _mm_set_pd(twotothe256, twotothe256); for(l = 0; l < 80; l+=2) { __m128d ex3v = _mm_load_pd(&v[l]); _mm_store_pd(&v[l], _mm_mul_pd(ex3v,twoto)); } addScale += wgt[i]; } } break; default: assert(0); } *scalerIncrement = addScale; } static void newviewGTRCATPROT(int tipCase, double *extEV, int *cptr, double *x1, double *x2, double *x3, double *tipVector, unsigned char *tipX1, unsigned char *tipX2, int n, double *left, double *right, int *wgt, int *scalerIncrement ) { double *le, *ri, *v, *vl, *vr; int i, l, j, scale, addScale = 0; switch(tipCase) { case TIP_TIP: { for (i = 0; i < n; i++) { le = &left[cptr[i] * 400]; ri = &right[cptr[i] * 400]; vl = &(tipVector[20 * tipX1[i]]); vr = &(tipVector[20 * tipX2[i]]); v = &x3[20 * i]; for(l = 0; l < 20; l+=2) _mm_store_pd(&v[l], _mm_setzero_pd()); for(l = 0; l < 20; l++) { __m128d x1v = _mm_setzero_pd(); __m128d x2v = _mm_setzero_pd(); double *ev = &extEV[l * 20], *lv = &le[l * 20], *rv = &ri[l * 20]; for(j = 0; j < 20; j+=2) { x1v = _mm_add_pd(x1v, _mm_mul_pd(_mm_load_pd(&vl[j]), _mm_load_pd(&lv[j]))); x2v = _mm_add_pd(x2v, _mm_mul_pd(_mm_load_pd(&vr[j]), _mm_load_pd(&rv[j]))); } x1v = _mm_hadd_pd(x1v, x1v); x2v = _mm_hadd_pd(x2v, x2v); x1v = _mm_mul_pd(x1v, x2v); for(j = 0; j < 20; j+=2) { __m128d vv = _mm_load_pd(&v[j]); vv = _mm_add_pd(vv, _mm_mul_pd(x1v, _mm_load_pd(&ev[j]))); _mm_store_pd(&v[j], vv); } } } } break; case TIP_INNER: { for (i = 0; i < n; i++) { le = &left[cptr[i] * 400]; ri = &right[cptr[i] * 400]; vl = &(tipVector[20 * tipX1[i]]); vr = &x2[20 * i]; v = &x3[20 * i]; for(l = 0; l < 20; l+=2) _mm_store_pd(&v[l], _mm_setzero_pd()); for(l = 0; l < 20; l++) { __m128d x1v = _mm_setzero_pd(); __m128d x2v = _mm_setzero_pd(); double *ev = &extEV[l * 20], *lv = &le[l * 20], *rv = &ri[l * 20]; for(j = 0; j < 20; j+=2) { x1v = _mm_add_pd(x1v, _mm_mul_pd(_mm_load_pd(&vl[j]), _mm_load_pd(&lv[j]))); x2v = _mm_add_pd(x2v, _mm_mul_pd(_mm_load_pd(&vr[j]), _mm_load_pd(&rv[j]))); } x1v = _mm_hadd_pd(x1v, x1v); x2v = _mm_hadd_pd(x2v, x2v); x1v = _mm_mul_pd(x1v, x2v); for(j = 0; j < 20; j+=2) { __m128d vv = _mm_load_pd(&v[j]); vv = _mm_add_pd(vv, _mm_mul_pd(x1v, _mm_load_pd(&ev[j]))); _mm_store_pd(&v[j], vv); } } { __m128d minlikelihood_sse = _mm_set1_pd( minlikelihood ); scale = 1; for(l = 0; scale && (l < 20); l += 2) { __m128d vv = _mm_load_pd(&v[l]); __m128d v1 = _mm_and_pd(vv, absMask.m); v1 = _mm_cmplt_pd(v1, minlikelihood_sse); if(_mm_movemask_pd( v1 ) != 3) scale = 0; } } if(scale) { __m128d twoto = _mm_set_pd(twotothe256, twotothe256); for(l = 0; l < 20; l+=2) { __m128d ex3v = _mm_load_pd(&v[l]); _mm_store_pd(&v[l], _mm_mul_pd(ex3v,twoto)); } addScale += wgt[i]; } } } break; case INNER_INNER: for(i = 0; i < n; i++) { le = &left[cptr[i] * 400]; ri = &right[cptr[i] * 400]; vl = &x1[20 * i]; vr = &x2[20 * i]; v = &x3[20 * i]; for(l = 0; l < 20; l+=2) _mm_store_pd(&v[l], _mm_setzero_pd()); for(l = 0; l < 20; l++) { __m128d x1v = _mm_setzero_pd(); __m128d x2v = _mm_setzero_pd(); double *ev = &extEV[l * 20], *lv = &le[l * 20], *rv = &ri[l * 20]; for(j = 0; j < 20; j+=2) { x1v = _mm_add_pd(x1v, _mm_mul_pd(_mm_load_pd(&vl[j]), _mm_load_pd(&lv[j]))); x2v = _mm_add_pd(x2v, _mm_mul_pd(_mm_load_pd(&vr[j]), _mm_load_pd(&rv[j]))); } x1v = _mm_hadd_pd(x1v, x1v); x2v = _mm_hadd_pd(x2v, x2v); x1v = _mm_mul_pd(x1v, x2v); for(j = 0; j < 20; j+=2) { __m128d vv = _mm_load_pd(&v[j]); vv = _mm_add_pd(vv, _mm_mul_pd(x1v, _mm_load_pd(&ev[j]))); _mm_store_pd(&v[j], vv); } } { __m128d minlikelihood_sse = _mm_set1_pd( minlikelihood ); scale = 1; for(l = 0; scale && (l < 20); l += 2) { __m128d vv = _mm_load_pd(&v[l]); __m128d v1 = _mm_and_pd(vv, absMask.m); v1 = _mm_cmplt_pd(v1, minlikelihood_sse); if(_mm_movemask_pd( v1 ) != 3) scale = 0; } } if(scale) { __m128d twoto = _mm_set_pd(twotothe256, twotothe256); for(l = 0; l < 20; l+=2) { __m128d ex3v = _mm_load_pd(&v[l]); _mm_store_pd(&v[l], _mm_mul_pd(ex3v,twoto)); } addScale += wgt[i]; } } break; default: assert(0); } *scalerIncrement = addScale; } static void newviewGTRCATPROT_SAVE(int tipCase, double *extEV, int *cptr, double *x1, double *x2, double *x3, double *tipVector, unsigned char *tipX1, unsigned char *tipX2, int n, double *left, double *right, int *wgt, int *scalerIncrement, unsigned int *x1_gap, unsigned int *x2_gap, unsigned int *x3_gap, double *x1_gapColumn, double *x2_gapColumn, double *x3_gapColumn, const int maxCats) { double *le, *ri, *v, *vl, *vr, *x1_ptr = x1, *x2_ptr = x2, *x3_ptr = x3; int i, l, j, scale, scaleGap = 0, addScale = 0; { vl = x1_gapColumn; vr = x2_gapColumn; v = x3_gapColumn; le = &left[maxCats * 400]; ri = &right[maxCats * 400]; for(l = 0; l < 20; l+=2) _mm_store_pd(&v[l], _mm_setzero_pd()); for(l = 0; l < 20; l++) { __m128d x1v = _mm_setzero_pd(); __m128d x2v = _mm_setzero_pd(); double *ev = &extEV[l * 20], *lv = &le[l * 20], *rv = &ri[l * 20]; for(j = 0; j < 20; j+=2) { x1v = _mm_add_pd(x1v, _mm_mul_pd(_mm_load_pd(&vl[j]), _mm_load_pd(&lv[j]))); x2v = _mm_add_pd(x2v, _mm_mul_pd(_mm_load_pd(&vr[j]), _mm_load_pd(&rv[j]))); } x1v = _mm_hadd_pd(x1v, x1v); x2v = _mm_hadd_pd(x2v, x2v); x1v = _mm_mul_pd(x1v, x2v); for(j = 0; j < 20; j+=2) { __m128d vv = _mm_load_pd(&v[j]); vv = _mm_add_pd(vv, _mm_mul_pd(x1v, _mm_load_pd(&ev[j]))); _mm_store_pd(&v[j], vv); } } if(tipCase != TIP_TIP) { __m128d minlikelihood_sse = _mm_set1_pd( minlikelihood ); scale = 1; for(l = 0; scale && (l < 20); l += 2) { __m128d vv = _mm_load_pd(&v[l]); __m128d v1 = _mm_and_pd(vv, absMask.m); v1 = _mm_cmplt_pd(v1, minlikelihood_sse); if(_mm_movemask_pd( v1 ) != 3) scale = 0; } if(scale) { __m128d twoto = _mm_set_pd(twotothe256, twotothe256); for(l = 0; l < 20; l+=2) { __m128d ex3v = _mm_load_pd(&v[l]); _mm_store_pd(&v[l], _mm_mul_pd(ex3v,twoto)); } scaleGap = TRUE; } } } switch(tipCase) { case TIP_TIP: { for (i = 0; i < n; i++) { if(noGap(x3_gap, i)) { vl = &(tipVector[20 * tipX1[i]]); vr = &(tipVector[20 * tipX2[i]]); v = x3_ptr; if(isGap(x1_gap, i)) le = &left[maxCats * 400]; else le = &left[cptr[i] * 400]; if(isGap(x2_gap, i)) ri = &right[maxCats * 400]; else ri = &right[cptr[i] * 400]; for(l = 0; l < 20; l+=2) _mm_store_pd(&v[l], _mm_setzero_pd()); for(l = 0; l < 20; l++) { __m128d x1v = _mm_setzero_pd(); __m128d x2v = _mm_setzero_pd(); double *ev = &extEV[l * 20], *lv = &le[l * 20], *rv = &ri[l * 20]; for(j = 0; j < 20; j+=2) { x1v = _mm_add_pd(x1v, _mm_mul_pd(_mm_load_pd(&vl[j]), _mm_load_pd(&lv[j]))); x2v = _mm_add_pd(x2v, _mm_mul_pd(_mm_load_pd(&vr[j]), _mm_load_pd(&rv[j]))); } x1v = _mm_hadd_pd(x1v, x1v); x2v = _mm_hadd_pd(x2v, x2v); x1v = _mm_mul_pd(x1v, x2v); for(j = 0; j < 20; j+=2) { __m128d vv = _mm_load_pd(&v[j]); vv = _mm_add_pd(vv, _mm_mul_pd(x1v, _mm_load_pd(&ev[j]))); _mm_store_pd(&v[j], vv); } } x3_ptr += 20; } } } break; case TIP_INNER: { for (i = 0; i < n; i++) { if(isGap(x3_gap, i)) { if(scaleGap) addScale += wgt[i]; } else { vl = &(tipVector[20 * tipX1[i]]); vr = x2_ptr; v = x3_ptr; if(isGap(x1_gap, i)) le = &left[maxCats * 400]; else le = &left[cptr[i] * 400]; if(isGap(x2_gap, i)) { ri = &right[maxCats * 400]; vr = x2_gapColumn; } else { ri = &right[cptr[i] * 400]; vr = x2_ptr; x2_ptr += 20; } for(l = 0; l < 20; l+=2) _mm_store_pd(&v[l], _mm_setzero_pd()); for(l = 0; l < 20; l++) { __m128d x1v = _mm_setzero_pd(); __m128d x2v = _mm_setzero_pd(); double *ev = &extEV[l * 20], *lv = &le[l * 20], *rv = &ri[l * 20]; for(j = 0; j < 20; j+=2) { x1v = _mm_add_pd(x1v, _mm_mul_pd(_mm_load_pd(&vl[j]), _mm_load_pd(&lv[j]))); x2v = _mm_add_pd(x2v, _mm_mul_pd(_mm_load_pd(&vr[j]), _mm_load_pd(&rv[j]))); } x1v = _mm_hadd_pd(x1v, x1v); x2v = _mm_hadd_pd(x2v, x2v); x1v = _mm_mul_pd(x1v, x2v); for(j = 0; j < 20; j+=2) { __m128d vv = _mm_load_pd(&v[j]); vv = _mm_add_pd(vv, _mm_mul_pd(x1v, _mm_load_pd(&ev[j]))); _mm_store_pd(&v[j], vv); } } { __m128d minlikelihood_sse = _mm_set1_pd( minlikelihood ); scale = 1; for(l = 0; scale && (l < 20); l += 2) { __m128d vv = _mm_load_pd(&v[l]); __m128d v1 = _mm_and_pd(vv, absMask.m); v1 = _mm_cmplt_pd(v1, minlikelihood_sse); if(_mm_movemask_pd( v1 ) != 3) scale = 0; } } if(scale) { __m128d twoto = _mm_set_pd(twotothe256, twotothe256); for(l = 0; l < 20; l+=2) { __m128d ex3v = _mm_load_pd(&v[l]); _mm_store_pd(&v[l], _mm_mul_pd(ex3v,twoto)); } addScale += wgt[i]; } x3_ptr += 20; } } } break; case INNER_INNER: for(i = 0; i < n; i++) { if(isGap(x3_gap, i)) { if(scaleGap) addScale += wgt[i]; } else { v = x3_ptr; if(isGap(x1_gap, i)) { vl = x1_gapColumn; le = &left[maxCats * 400]; } else { le = &left[cptr[i] * 400]; vl = x1_ptr; x1_ptr += 20; } if(isGap(x2_gap, i)) { vr = x2_gapColumn; ri = &right[maxCats * 400]; } else { ri = &right[cptr[i] * 400]; vr = x2_ptr; x2_ptr += 20; } for(l = 0; l < 20; l+=2) _mm_store_pd(&v[l], _mm_setzero_pd()); for(l = 0; l < 20; l++) { __m128d x1v = _mm_setzero_pd(); __m128d x2v = _mm_setzero_pd(); double *ev = &extEV[l * 20], *lv = &le[l * 20], *rv = &ri[l * 20]; for(j = 0; j < 20; j+=2) { x1v = _mm_add_pd(x1v, _mm_mul_pd(_mm_load_pd(&vl[j]), _mm_load_pd(&lv[j]))); x2v = _mm_add_pd(x2v, _mm_mul_pd(_mm_load_pd(&vr[j]), _mm_load_pd(&rv[j]))); } x1v = _mm_hadd_pd(x1v, x1v); x2v = _mm_hadd_pd(x2v, x2v); x1v = _mm_mul_pd(x1v, x2v); for(j = 0; j < 20; j+=2) { __m128d vv = _mm_load_pd(&v[j]); vv = _mm_add_pd(vv, _mm_mul_pd(x1v, _mm_load_pd(&ev[j]))); _mm_store_pd(&v[j], vv); } } { __m128d minlikelihood_sse = _mm_set1_pd( minlikelihood ); scale = 1; for(l = 0; scale && (l < 20); l += 2) { __m128d vv = _mm_load_pd(&v[l]); __m128d v1 = _mm_and_pd(vv, absMask.m); v1 = _mm_cmplt_pd(v1, minlikelihood_sse); if(_mm_movemask_pd( v1 ) != 3) scale = 0; } } if(scale) { __m128d twoto = _mm_set_pd(twotothe256, twotothe256); for(l = 0; l < 20; l+=2) { __m128d ex3v = _mm_load_pd(&v[l]); _mm_store_pd(&v[l], _mm_mul_pd(ex3v,twoto)); } addScale += wgt[i]; } x3_ptr += 20; } } break; default: assert(0); } *scalerIncrement = addScale; } static void newviewGTRGAMMAPROT_LG4(int tipCase, double *x1, double *x2, double *x3, double *extEV[4], double *tipVector[4], int *ex3, unsigned char *tipX1, unsigned char *tipX2, int n, double *left, double *right, int *wgt, int *scalerIncrement, const boolean useFastScaling) { double *uX1, *uX2, *v; double x1px2; int i, j, l, k, scale, addScale = 0; double *vl, *vr; #ifndef __SIM_SSE3 double al, ar; #endif switch(tipCase) { case TIP_TIP: { double umpX1[1840], umpX2[1840]; for(i = 0; i < 23; i++) { for(k = 0; k < 80; k++) { v = &(tipVector[k / 20][20 * i]); #ifdef __SIM_SSE3 double *ll = &left[k * 20]; double *rr = &right[k * 20]; __m128d umpX1v = _mm_setzero_pd(); __m128d umpX2v = _mm_setzero_pd(); for(l = 0; l < 20; l+=2) { __m128d vv = _mm_load_pd(&v[l]); umpX1v = _mm_add_pd(umpX1v, _mm_mul_pd(vv, _mm_load_pd(&ll[l]))); umpX2v = _mm_add_pd(umpX2v, _mm_mul_pd(vv, _mm_load_pd(&rr[l]))); } umpX1v = _mm_hadd_pd(umpX1v, umpX1v); umpX2v = _mm_hadd_pd(umpX2v, umpX2v); _mm_storel_pd(&umpX1[80 * i + k], umpX1v); _mm_storel_pd(&umpX2[80 * i + k], umpX2v); #else umpX1[80 * i + k] = 0.0; umpX2[80 * i + k] = 0.0; for(l = 0; l < 20; l++) { umpX1[80 * i + k] += v[l] * left[k * 20 + l]; umpX2[80 * i + k] += v[l] * right[k * 20 + l]; } #endif } } for(i = 0; i < n; i++) { uX1 = &umpX1[80 * tipX1[i]]; uX2 = &umpX2[80 * tipX2[i]]; for(j = 0; j < 4; j++) { v = &x3[i * 80 + j * 20]; #ifdef __SIM_SSE3 __m128d zero = _mm_setzero_pd(); for(k = 0; k < 20; k+=2) _mm_store_pd(&v[k], zero); for(k = 0; k < 20; k++) { double *eev = &extEV[j][k * 20]; x1px2 = uX1[j * 20 + k] * uX2[j * 20 + k]; __m128d x1px2v = _mm_set1_pd(x1px2); for(l = 0; l < 20; l+=2) { __m128d vv = _mm_load_pd(&v[l]); __m128d ee = _mm_load_pd(&eev[l]); vv = _mm_add_pd(vv, _mm_mul_pd(x1px2v,ee)); _mm_store_pd(&v[l], vv); } } #else for(k = 0; k < 20; k++) v[k] = 0.0; for(k = 0; k < 20; k++) { x1px2 = uX1[j * 20 + k] * uX2[j * 20 + k]; for(l = 0; l < 20; l++) v[l] += x1px2 * extEV[j][20 * k + l]; } #endif } } } break; case TIP_INNER: { double umpX1[1840], ump_x2[20]; for(i = 0; i < 23; i++) { for(k = 0; k < 80; k++) { v = &(tipVector[k / 20][20 * i]); #ifdef __SIM_SSE3 double *ll = &left[k * 20]; __m128d umpX1v = _mm_setzero_pd(); for(l = 0; l < 20; l+=2) { __m128d vv = _mm_load_pd(&v[l]); umpX1v = _mm_add_pd(umpX1v, _mm_mul_pd(vv, _mm_load_pd(&ll[l]))); } umpX1v = _mm_hadd_pd(umpX1v, umpX1v); _mm_storel_pd(&umpX1[80 * i + k], umpX1v); #else umpX1[80 * i + k] = 0.0; for(l = 0; l < 20; l++) umpX1[80 * i + k] += v[l] * left[k * 20 + l]; #endif } } for (i = 0; i < n; i++) { uX1 = &umpX1[80 * tipX1[i]]; for(k = 0; k < 4; k++) { v = &(x2[80 * i + k * 20]); #ifdef __SIM_SSE3 for(l = 0; l < 20; l++) { double *r = &right[k * 400 + l * 20]; __m128d ump_x2v = _mm_setzero_pd(); for(j = 0; j < 20; j+= 2) { __m128d vv = _mm_load_pd(&v[j]); __m128d rr = _mm_load_pd(&r[j]); ump_x2v = _mm_add_pd(ump_x2v, _mm_mul_pd(vv, rr)); } ump_x2v = _mm_hadd_pd(ump_x2v, ump_x2v); _mm_storel_pd(&ump_x2[l], ump_x2v); } v = &(x3[80 * i + 20 * k]); __m128d zero = _mm_setzero_pd(); for(l = 0; l < 20; l+=2) _mm_store_pd(&v[l], zero); for(l = 0; l < 20; l++) { double *eev = &extEV[k][l * 20]; x1px2 = uX1[k * 20 + l] * ump_x2[l]; __m128d x1px2v = _mm_set1_pd(x1px2); for(j = 0; j < 20; j+=2) { __m128d vv = _mm_load_pd(&v[j]); __m128d ee = _mm_load_pd(&eev[j]); vv = _mm_add_pd(vv, _mm_mul_pd(x1px2v,ee)); _mm_store_pd(&v[j], vv); } } #else for(l = 0; l < 20; l++) { ump_x2[l] = 0.0; for(j = 0; j < 20; j++) ump_x2[l] += v[j] * right[k * 400 + l * 20 + j]; } v = &(x3[80 * i + 20 * k]); for(l = 0; l < 20; l++) v[l] = 0; for(l = 0; l < 20; l++) { x1px2 = uX1[k * 20 + l] * ump_x2[l]; for(j = 0; j < 20; j++) v[j] += x1px2 * extEV[k][l * 20 + j]; } #endif } #ifdef __SIM_SSE3 { v = &(x3[80 * i]); __m128d minlikelihood_sse = _mm_set1_pd( minlikelihood ); scale = 1; for(l = 0; scale && (l < 80); l += 2) { __m128d vv = _mm_load_pd(&v[l]); __m128d v1 = _mm_and_pd(vv, absMask.m); v1 = _mm_cmplt_pd(v1, minlikelihood_sse); if(_mm_movemask_pd( v1 ) != 3) scale = 0; } } #else v = &x3[80 * i]; scale = 1; for(l = 0; scale && (l < 80); l++) scale = (ABS(v[l]) < minlikelihood); #endif if (scale) { #ifdef __SIM_SSE3 __m128d twoto = _mm_set_pd(twotothe256, twotothe256); for(l = 0; l < 80; l+=2) { __m128d ex3v = _mm_load_pd(&v[l]); _mm_store_pd(&v[l], _mm_mul_pd(ex3v,twoto)); } #else for(l = 0; l < 80; l++) v[l] *= twotothe256; #endif if(useFastScaling) addScale += wgt[i]; else ex3[i] += 1; } } } break; case INNER_INNER: for (i = 0; i < n; i++) { for(k = 0; k < 4; k++) { vl = &(x1[80 * i + 20 * k]); vr = &(x2[80 * i + 20 * k]); v = &(x3[80 * i + 20 * k]); #ifdef __SIM_SSE3 __m128d zero = _mm_setzero_pd(); for(l = 0; l < 20; l+=2) _mm_store_pd(&v[l], zero); #else for(l = 0; l < 20; l++) v[l] = 0; #endif for(l = 0; l < 20; l++) { #ifdef __SIM_SSE3 { __m128d al = _mm_setzero_pd(); __m128d ar = _mm_setzero_pd(); double *ll = &left[k * 400 + l * 20]; double *rr = &right[k * 400 + l * 20]; double *EVEV = &extEV[k][20 * l]; for(j = 0; j < 20; j+=2) { __m128d lv = _mm_load_pd(&ll[j]); __m128d rv = _mm_load_pd(&rr[j]); __m128d vll = _mm_load_pd(&vl[j]); __m128d vrr = _mm_load_pd(&vr[j]); al = _mm_add_pd(al, _mm_mul_pd(vll, lv)); ar = _mm_add_pd(ar, _mm_mul_pd(vrr, rv)); } al = _mm_hadd_pd(al, al); ar = _mm_hadd_pd(ar, ar); al = _mm_mul_pd(al, ar); for(j = 0; j < 20; j+=2) { __m128d vv = _mm_load_pd(&v[j]); __m128d EVV = _mm_load_pd(&EVEV[j]); vv = _mm_add_pd(vv, _mm_mul_pd(al, EVV)); _mm_store_pd(&v[j], vv); } } #else al = 0.0; ar = 0.0; for(j = 0; j < 20; j++) { al += vl[j] * left[k * 400 + l * 20 + j]; ar += vr[j] * right[k * 400 + l * 20 + j]; } x1px2 = al * ar; for(j = 0; j < 20; j++) v[j] += x1px2 * extEV[k][20 * l + j]; #endif } } #ifdef __SIM_SSE3 { v = &(x3[80 * i]); __m128d minlikelihood_sse = _mm_set1_pd( minlikelihood ); scale = 1; for(l = 0; scale && (l < 80); l += 2) { __m128d vv = _mm_load_pd(&v[l]); __m128d v1 = _mm_and_pd(vv, absMask.m); v1 = _mm_cmplt_pd(v1, minlikelihood_sse); if(_mm_movemask_pd( v1 ) != 3) scale = 0; } } #else v = &(x3[80 * i]); scale = 1; for(l = 0; scale && (l < 80); l++) scale = ((ABS(v[l]) < minlikelihood)); #endif if (scale) { #ifdef __SIM_SSE3 __m128d twoto = _mm_set_pd(twotothe256, twotothe256); for(l = 0; l < 80; l+=2) { __m128d ex3v = _mm_load_pd(&v[l]); _mm_store_pd(&v[l], _mm_mul_pd(ex3v,twoto)); } #else for(l = 0; l < 80; l++) v[l] *= twotothe256; #endif if(useFastScaling) addScale += wgt[i]; else ex3[i] += 1; } } break; default: assert(0); } if(useFastScaling) *scalerIncrement = addScale; } #endif #ifdef _OPTIMIZED_FUNCTIONS /*** BINARY DATA functions *****/ static void newviewGTRCAT_BINARY( int tipCase, double *EV, int *cptr, double *x1_start, double *x2_start, double *x3_start, double *tipVector, int *ex3, unsigned char *tipX1, unsigned char *tipX2, int n, double *left, double *right, int *wgt, int *scalerIncrement, const boolean useFastScaling) { double *le, *ri, *x1, *x2, *x3; int i, l, scale, addScale = 0; switch(tipCase) { case TIP_TIP: { for(i = 0; i < n; i++) { x1 = &(tipVector[2 * tipX1[i]]); x2 = &(tipVector[2 * tipX2[i]]); x3 = &x3_start[2 * i]; le = &left[cptr[i] * 4]; ri = &right[cptr[i] * 4]; _mm_store_pd(x3, _mm_setzero_pd()); for(l = 0; l < 2; l++) { __m128d al = _mm_mul_pd(_mm_load_pd(x1), _mm_load_pd(&le[l * 2])); __m128d ar = _mm_mul_pd(_mm_load_pd(x2), _mm_load_pd(&ri[l * 2])); al = _mm_hadd_pd(al, al); ar = _mm_hadd_pd(ar, ar); al = _mm_mul_pd(al, ar); __m128d vv = _mm_load_pd(x3); __m128d EVV = _mm_load_pd(&EV[2 * l]); vv = _mm_add_pd(vv, _mm_mul_pd(al, EVV)); _mm_store_pd(x3, vv); } } } break; case TIP_INNER: { for (i = 0; i < n; i++) { x1 = &(tipVector[2 * tipX1[i]]); x2 = &x2_start[2 * i]; x3 = &x3_start[2 * i]; le = &left[cptr[i] * 4]; ri = &right[cptr[i] * 4]; _mm_store_pd(x3, _mm_setzero_pd()); for(l = 0; l < 2; l++) { __m128d al = _mm_mul_pd(_mm_load_pd(x1), _mm_load_pd(&le[l * 2])); __m128d ar = _mm_mul_pd(_mm_load_pd(x2), _mm_load_pd(&ri[l * 2])); al = _mm_hadd_pd(al, al); ar = _mm_hadd_pd(ar, ar); al = _mm_mul_pd(al, ar); __m128d vv = _mm_load_pd(x3); __m128d EVV = _mm_load_pd(&EV[2 * l]); vv = _mm_add_pd(vv, _mm_mul_pd(al, EVV)); _mm_store_pd(x3, vv); } __m128d minlikelihood_sse = _mm_set1_pd(minlikelihood); scale = 1; __m128d v1 = _mm_and_pd(_mm_load_pd(x3), absMask.m); v1 = _mm_cmplt_pd(v1, minlikelihood_sse); if(_mm_movemask_pd( v1 ) != 3) scale = 0; if(scale) { __m128d twoto = _mm_set_pd(twotothe256, twotothe256); __m128d ex3v = _mm_load_pd(x3); _mm_store_pd(x3, _mm_mul_pd(ex3v,twoto)); if(useFastScaling) addScale += wgt[i]; else ex3[i] += 1; } } } break; case INNER_INNER: for (i = 0; i < n; i++) { x1 = &x1_start[2 * i]; x2 = &x2_start[2 * i]; x3 = &x3_start[2 * i]; le = &left[cptr[i] * 4]; ri = &right[cptr[i] * 4]; _mm_store_pd(x3, _mm_setzero_pd()); for(l = 0; l < 2; l++) { __m128d al = _mm_mul_pd(_mm_load_pd(x1), _mm_load_pd(&le[l * 2])); __m128d ar = _mm_mul_pd(_mm_load_pd(x2), _mm_load_pd(&ri[l * 2])); al = _mm_hadd_pd(al, al); ar = _mm_hadd_pd(ar, ar); al = _mm_mul_pd(al, ar); __m128d vv = _mm_load_pd(x3); __m128d EVV = _mm_load_pd(&EV[2 * l]); vv = _mm_add_pd(vv, _mm_mul_pd(al, EVV)); _mm_store_pd(x3, vv); } __m128d minlikelihood_sse = _mm_set1_pd(minlikelihood); scale = 1; __m128d v1 = _mm_and_pd(_mm_load_pd(x3), absMask.m); v1 = _mm_cmplt_pd(v1, minlikelihood_sse); if(_mm_movemask_pd( v1 ) != 3) scale = 0; if(scale) { __m128d twoto = _mm_set_pd(twotothe256, twotothe256); __m128d ex3v = _mm_load_pd(x3); _mm_store_pd(x3, _mm_mul_pd(ex3v,twoto)); if(useFastScaling) addScale += wgt[i]; else ex3[i] += 1; } } break; default: assert(0); } if(useFastScaling) *scalerIncrement = addScale; } static void newviewGTRGAMMA_BINARY(int tipCase, double *x1_start, double *x2_start, double *x3_start, double *EV, double *tipVector, int *ex3, unsigned char *tipX1, unsigned char *tipX2, const int n, double *left, double *right, int *wgt, int *scalerIncrement, const boolean useFastScaling ) { double *x1, *x2, *x3; int i, k, l, scale, addScale = 0; switch(tipCase) { case TIP_TIP: for (i = 0; i < n; i++) { x1 = &(tipVector[2 * tipX1[i]]); x2 = &(tipVector[2 * tipX2[i]]); for(k = 0; k < 4; k++) { x3 = &(x3_start[8 * i + 2 * k]); _mm_store_pd(x3, _mm_setzero_pd()); for(l = 0; l < 2; l++) { __m128d al = _mm_mul_pd(_mm_load_pd(x1), _mm_load_pd(&left[k * 4 + l * 2])); __m128d ar = _mm_mul_pd(_mm_load_pd(x2), _mm_load_pd(&right[k * 4 + l * 2])); al = _mm_hadd_pd(al, al); ar = _mm_hadd_pd(ar, ar); al = _mm_mul_pd(al, ar); __m128d vv = _mm_load_pd(x3); __m128d EVV = _mm_load_pd(&EV[2 * l]); vv = _mm_add_pd(vv, _mm_mul_pd(al, EVV)); _mm_store_pd(x3, vv); } } } break; case TIP_INNER: for (i = 0; i < n; i++) { x1 = &(tipVector[2 * tipX1[i]]); for(k = 0; k < 4; k++) { x2 = &(x2_start[8 * i + 2 * k]); x3 = &(x3_start[8 * i + 2 * k]); _mm_store_pd(x3, _mm_setzero_pd()); for(l = 0; l < 2; l++) { __m128d al = _mm_mul_pd(_mm_load_pd(x1), _mm_load_pd(&left[k * 4 + l * 2])); __m128d ar = _mm_mul_pd(_mm_load_pd(x2), _mm_load_pd(&right[k * 4 + l * 2])); al = _mm_hadd_pd(al, al); ar = _mm_hadd_pd(ar, ar); al = _mm_mul_pd(al, ar); __m128d vv = _mm_load_pd(x3); __m128d EVV = _mm_load_pd(&EV[2 * l]); vv = _mm_add_pd(vv, _mm_mul_pd(al, EVV)); _mm_store_pd(x3, vv); } } x3 = &(x3_start[8 * i]); __m128d minlikelihood_sse = _mm_set1_pd( minlikelihood ); scale = 1; for(l = 0; scale && (l < 8); l += 2) { __m128d vv = _mm_load_pd(&x3[l]); __m128d v1 = _mm_and_pd(vv, absMask.m); v1 = _mm_cmplt_pd(v1, minlikelihood_sse); if(_mm_movemask_pd( v1 ) != 3) scale = 0; } if(scale) { __m128d twoto = _mm_set_pd(twotothe256, twotothe256); for(l = 0; l < 8; l+=2) { __m128d ex3v = _mm_load_pd(&x3[l]); _mm_store_pd(&x3[l], _mm_mul_pd(ex3v,twoto)); } if(useFastScaling) addScale += wgt[i]; else ex3[i] += 1; } } break; case INNER_INNER: for (i = 0; i < n; i++) { for(k = 0; k < 4; k++) { x1 = &(x1_start[8 * i + 2 * k]); x2 = &(x2_start[8 * i + 2 * k]); x3 = &(x3_start[8 * i + 2 * k]); _mm_store_pd(x3, _mm_setzero_pd()); for(l = 0; l < 2; l++) { __m128d al = _mm_mul_pd(_mm_load_pd(x1), _mm_load_pd(&left[k * 4 + l * 2])); __m128d ar = _mm_mul_pd(_mm_load_pd(x2), _mm_load_pd(&right[k * 4 + l * 2])); al = _mm_hadd_pd(al, al); ar = _mm_hadd_pd(ar, ar); al = _mm_mul_pd(al, ar); __m128d vv = _mm_load_pd(x3); __m128d EVV = _mm_load_pd(&EV[2 * l]); vv = _mm_add_pd(vv, _mm_mul_pd(al, EVV)); _mm_store_pd(x3, vv); } } x3 = &(x3_start[8 * i]); __m128d minlikelihood_sse = _mm_set1_pd( minlikelihood ); scale = 1; for(l = 0; scale && (l < 8); l += 2) { __m128d vv = _mm_load_pd(&x3[l]); __m128d v1 = _mm_and_pd(vv, absMask.m); v1 = _mm_cmplt_pd(v1, minlikelihood_sse); if(_mm_movemask_pd( v1 ) != 3) scale = 0; } if(scale) { __m128d twoto = _mm_set_pd(twotothe256, twotothe256); for(l = 0; l < 8; l+=2) { __m128d ex3v = _mm_load_pd(&x3[l]); _mm_store_pd(&x3[l], _mm_mul_pd(ex3v,twoto)); } if(useFastScaling) addScale += wgt[i]; else ex3[i] += 1; } } break; default: assert(0); } if(useFastScaling) *scalerIncrement = addScale; } /**** BINARY DATA functions end ****/ #endif ExaML-3.0.21/examl/optimizeModel.c000066400000000000000000002257411330315463200166510ustar00rootroot00000000000000/* RAxML-VI-HPC (version 2.2) a program for sequential and parallel estimation of phylogenetic trees * Copyright August 2006 by Alexandros Stamatakis * * Partially derived from * fastDNAml, a program for estimation of phylogenetic trees from sequences by Gary J. Olsen * * and * * Programs of the PHYLIP package by Joe Felsenstein. * * This program is free software; you may redistribute it and/or modify its * 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. * * * For any other enquiries send an Email to Alexandros Stamatakis * Alexandros.Stamatakis@epfl.ch * * When publishing work that is based on the results from RAxML-VI-HPC please cite: * * Alexandros Stamatakis:"RAxML-VI-HPC: maximum likelihood-based phylogenetic analyses with thousands * of taxa and mixed models". * Bioinformatics 2006; doi: 10.1093/bioinformatics/btl446 */ #ifndef WIN32 #include #endif #include #include #include #include #include #include #include "axml.h" static const double MNBRAK_GOLD = 1.618034; static const double MNBRAK_TINY = 1.e-20; static const double MNBRAK_GLIMIT = 100.0; static const double BRENT_ZEPS = 1.e-5; static const double BRENT_CGOLD = 0.3819660; extern int optimizeRatesInvocations; extern int optimizeRateCategoryInvocations; extern int optimizeAlphaInvocations; extern int optimizeInvarInvocations; extern double masterTime; extern char ratesFileName[1024]; extern char workdir[1024]; extern char run_id[128]; extern char lengthFileName[1024]; extern char lengthFileNameModel[1024]; extern char *protModels[NUM_PROT_MODELS]; extern checkPointState ckp; extern int processes; extern int processID; static void optParamGeneric(tree *tr, double modelEpsilon, linkageList *ll, int numberOfModels, int rateNumber, double lim_inf, double lim_sup, int whichParameterType); // FLAG for easier debugging of model parameter optimization routines //#define _DEBUG_MOD_OPT /*********************FUNCTIONS FOOR EXACT MODEL OPTIMIZATION UNDER GTRGAMMA ***************************************/ static void setRateModel(tree *tr, int model, double rate, int position) { int states = tr->partitionData[model].states, numRates = (states * states - states) / 2; if(tr->partitionData[model].dataType == DNA_DATA) assert(position >= 0 && position < (numRates - 1)); else assert(position >= 0 && position < numRates); assert(tr->partitionData[model].dataType != BINARY_DATA); assert(rate >= RATE_MIN && rate <= RATE_MAX); if(tr->partitionData[model].nonGTR) { int i, index = tr->partitionData[model].symmetryVector[position], lastRate = tr->partitionData[model].symmetryVector[numRates - 1]; for(i = 0; i < numRates; i++) { if(tr->partitionData[model].symmetryVector[i] == index) { if(index == lastRate) tr->partitionData[model].substRates[i] = 1.0; else tr->partitionData[model].substRates[i] = rate; } //printf("%f ", tr->partitionData[model].substRates[i]); } //printf("\n"); } else tr->partitionData[model].substRates[position] = rate; } //LIBRARY: the only thing that we will need to do here is to //replace linkList by a string and also add some error correction //code static linkageList* initLinkageList(int *linkList, tree *tr) { int k, partitions, numberOfModels = 0, i, pos; linkageList *ll = (linkageList*)malloc(sizeof(linkageList)); for(i = 0; i < tr->NumberOfModels; i++) { assert(linkList[i] >= 0 && linkList[i] < tr->NumberOfModels); if(linkList[i] > numberOfModels) numberOfModels = linkList[i]; } numberOfModels++; ll->entries = numberOfModels; ll->ld = (linkageData*)malloc(sizeof(linkageData) * numberOfModels); for(i = 0; i < numberOfModels; i++) { ll->ld[i].valid = TRUE; partitions = 0; for(k = 0; k < tr->NumberOfModels; k++) if(linkList[k] == i) partitions++; ll->ld[i].partitions = partitions; ll->ld[i].partitionList = (int*)malloc(sizeof(int) * partitions); for(k = 0, pos = 0; k < tr->NumberOfModels; k++) if(linkList[k] == i) ll->ld[i].partitionList[pos++] = k; } return ll; } static linkageList* initLinkageListString(char *linkageString, tree *tr) { int *list = (int*)malloc(sizeof(int) * tr->NumberOfModels), j; linkageList *l; char *str1, *saveptr, *ch = (char *)calloc(strlen(linkageString), sizeof(char)), *token; strncpy(ch, linkageString, strlen(linkageString)); for(j = 0, str1 = ch; ;j++, str1 = (char *)NULL) { token = strtok_r(str1, ",", &saveptr); if(token == (char *)NULL) break; assert(j < tr->NumberOfModels); list[j] = atoi(token); //printf("%d: %s\n", j, token); } free(ch); l = initLinkageList(list, tr); free(list); return l; } static void init_Q_MatrixSymmetries(char *linkageString, tree *tr, int model) { int states = tr->partitionData[model].states, numberOfRates = ((states * states - states) / 2), *list = (int *)malloc(sizeof(int) * numberOfRates), j, max = -1; char *str1, *saveptr, *ch = (char*)calloc(strlen(linkageString), sizeof(char)), *token; strncpy(ch, linkageString, strlen(linkageString)); for(j = 0, str1 = ch; ;j++, str1 = (char *)NULL) { token = strtok_r(str1, ",", &saveptr); if(token == (char *)NULL) break; assert(j < numberOfRates); list[j] = atoi(token); } free(ch); for(j = 0; j < numberOfRates; j++) { assert(list[j] <= j); assert(list[j] <= max + 1); if(list[j] > max) max = list[j]; } assert(numberOfRates == 6); for(j = 0; j < numberOfRates; j++) tr->partitionData[model].symmetryVector[j] = list[j]; //less than the maximum possible number of rate parameters if(max < numberOfRates - 1) tr->partitionData[model].nonGTR = TRUE; free(list); } static linkageList* initLinkageListGTR(tree *tr) { int i, *links = (int*)malloc(sizeof(int) * tr->NumberOfModels), firstAA = tr->NumberOfModels + 2, countGTR = 0, countOtherModel = 0; linkageList* ll; for(i = 0; i < tr->NumberOfModels; i++) { if(tr->partitionData[i].dataType == AA_DATA) { if(tr->partitionData[i].protModels == GTR) { if(i < firstAA) firstAA = i; countGTR++; } else countOtherModel++; } } assert((countGTR > 0 && countOtherModel == 0) || (countGTR == 0 && countOtherModel > 0) || (countGTR == 0 && countOtherModel == 0)); if(countGTR == 0) { for(i = 0; i < tr->NumberOfModels; i++) links[i] = i; } else { for(i = 0; i < tr->NumberOfModels; i++) { switch(tr->partitionData[i].dataType) { case DNA_DATA: case BINARY_DATA: case GENERIC_32: case GENERIC_64: case SECONDARY_DATA: case SECONDARY_DATA_6: case SECONDARY_DATA_7: links[i] = i; break; case AA_DATA: links[i] = firstAA; break; default: assert(0); } } } ll = initLinkageList(links, tr); free(links); return ll; } static void freeLinkageList( linkageList* ll) { int i; for(i = 0; i < ll->entries; i++) free(ll->ld[i].partitionList); free(ll->ld); free(ll); } #define ALPHA_F 0 #define RATE_F 1 #define FREQ_F 2 #define LXRATE_F 3 #define LXWEIGHT_F 4 void scaleLG4X_EIGN(tree *tr, int model) { double acc = 0.0; int i, l; for(i = 0; i < 4; i++) acc += tr->partitionData[model].weights[i] * tr->partitionData[model].gammaRates[i]; acc = 1.0 / acc; /* printf("update %f %f %f %f %f\n", acc, tr->partitionData[model].gammaRates[0], tr->partitionData[model].gammaRates[1], tr->partitionData[model].gammaRates[2], tr->partitionData[model].gammaRates[3]); printf("weigths: %f %f %f %f\n", tr->partitionData[model].weights[0], tr->partitionData[model].weights[1], tr->partitionData[model].weights[2], tr->partitionData[model].weights[3]); */ for(i = 0; i < 4; i++) for(l = 0; l < 20; l++) tr->partitionData[model].EIGN_LG4[i][l] = tr->partitionData[model].rawEIGN_LG4[i][l] * acc; } static void updateWeights(tree *tr, int model, int rate, double value) { int j; double w = 0.0; assert(rate >= 0 && rate < 4); tr->partitionData[model].weightExponents[rate] = value; for(j = 0; j < 4; j++) w += exp(tr->partitionData[model].weightExponents[j]); for(j = 0; j < 4; j++) tr->partitionData[model].weights[j] = exp(tr->partitionData[model].weightExponents[j]) / w; } static void optimizeWeights(tree *tr, double modelEpsilon, linkageList *ll, int numberOfModels) { int i; double initialLH = 0.0, finalLH = 0.0; evaluateGeneric(tr, tr->start, TRUE); initialLH = tr->likelihood; //printf("W: %f %f [%f] ->", tr->perPartitionLH[0], tr->perPartitionLH[1], initialLH); for(i = 0; i < 4; i++) optParamGeneric(tr, modelEpsilon, ll, numberOfModels, i, -1000000.0, 200.0, LXWEIGHT_F); //optLG4X_Weights(tr, ll, numberOfModels, i, modelEpsilon); evaluateGeneric(tr, tr->start, TRUE); finalLH = tr->likelihood; if(finalLH < initialLH) printf("Final: %f initial: %f\n", finalLH, initialLH); assert(finalLH >= initialLH); //printf("%f %f [%f]\n", tr->perPartitionLH[0], tr->perPartitionLH[1], finalLH); } static void changeModelParameters(int index, int rateNumber, double value, int whichParameterType, tree *tr) { switch(whichParameterType) { case RATE_F: setRateModel(tr, index, value, rateNumber); initReversibleGTR(tr, index); break; case ALPHA_F: tr->partitionData[index].alpha = value; makeGammaCats(tr->partitionData[index].alpha, tr->partitionData[index].gammaRates, 4, tr->useMedian); break; case FREQ_F: { int states = tr->partitionData[index].states, j; double w = 0.0; tr->partitionData[index].freqExponents[rateNumber] = value; for(j = 0; j < states; j++) w += exp(tr->partitionData[index].freqExponents[j]); for(j = 0; j < states; j++) tr->partitionData[index].frequencies[j] = exp(tr->partitionData[index].freqExponents[j]) / w; initReversibleGTR(tr, index); } break; case LXRATE_F: tr->partitionData[index].gammaRates[rateNumber] = value; scaleLG4X_EIGN(tr, index); break; case LXWEIGHT_F: updateWeights(tr, index, rateNumber, value); scaleLG4X_EIGN(tr, index); break; default: assert(0); } } static void evaluateChange(tree *tr, int rateNumber, double *value, double *result, boolean* converged, int whichFunction, int numberOfModels, linkageList *ll, double modelEpsilon) { int i, k, pos; for(i = 0, pos = 0; i < ll->entries; i++) { if(ll->ld[i].valid) { if(converged[pos]) { //if parameter optimizations for this specific model have converged //set executeModel to FALSE for(k = 0; k < ll->ld[i].partitions; k++) tr->executeModel[ll->ld[i].partitionList[k]] = FALSE; } else { for(k = 0; k < ll->ld[i].partitions; k++) { int index = ll->ld[i].partitionList[k]; changeModelParameters(index, rateNumber, value[pos], whichFunction, tr); } } pos++; } else { // if this partition is not being optimized anyway (e.g., we may be optimizing GTR rates for all DNA partitions, // but there are also a couple of Protein partitions with fixed models like WAG, JTT, etc.) set executeModel to FALSE for(k = 0; k < ll->ld[i].partitions; k++) tr->executeModel[ll->ld[i].partitionList[k]] = FALSE; } } assert(pos == numberOfModels); //some error checks for individual model parameters switch(whichFunction) { case RATE_F: assert(rateNumber != -1); break; case ALPHA_F: break; case LXRATE_F: assert(rateNumber != -1); case LXWEIGHT_F: assert(rateNumber != -1); break; case FREQ_F: break; default: assert(0); } switch(whichFunction) { case RATE_F: case ALPHA_F: case LXRATE_F: case FREQ_F: case LXWEIGHT_F: evaluateGeneric(tr, tr->start, TRUE); break; default: assert(0); } //LIBRARY: need to switch over parallel regions here either call //the one for the rates or for alpha! //commented out evaluate below in the course of the LG4X integration //evaluateGeneric(tr, tr->start, TRUE); for(i = 0, pos = 0; i < ll->entries; i++) { if(ll->ld[i].valid) { result[pos] = 0.0; for(k = 0; k < ll->ld[i].partitions; k++) { int index = ll->ld[i].partitionList[k]; assert(tr->perPartitionLH[index] <= 0.0); result[pos] -= tr->perPartitionLH[index]; } pos++; } //set execute model for ALL partitions to true again //for consistency for(k = 0; k < ll->ld[i].partitions; k++) { int index = ll->ld[i].partitionList[k]; tr->executeModel[index] = TRUE; } } assert(pos == numberOfModels); } static void brentGeneric(double *ax, double *bx, double *cx, double *fb, double tol, double *xmin, double *result, int numberOfModels, int whichFunction, int rateNumber, tree *tr, linkageList *ll, double *lim_inf, double *lim_sup) { int iter, i; double *a = (double *)malloc(sizeof(double) * numberOfModels), *b = (double *)malloc(sizeof(double) * numberOfModels), *d = (double *)malloc(sizeof(double) * numberOfModels), *etemp = (double *)malloc(sizeof(double) * numberOfModels), *fu = (double *)malloc(sizeof(double) * numberOfModels), *fv = (double *)malloc(sizeof(double) * numberOfModels), *fw = (double *)malloc(sizeof(double) * numberOfModels), *fx = (double *)malloc(sizeof(double) * numberOfModels), *p = (double *)malloc(sizeof(double) * numberOfModels), *q = (double *)malloc(sizeof(double) * numberOfModels), *r = (double *)malloc(sizeof(double) * numberOfModels), *tol1 = (double *)malloc(sizeof(double) * numberOfModels), *tol2 = (double *)malloc(sizeof(double) * numberOfModels), *u = (double *)malloc(sizeof(double) * numberOfModels), *v = (double *)malloc(sizeof(double) * numberOfModels), *w = (double *)malloc(sizeof(double) * numberOfModels), *x = (double *)malloc(sizeof(double) * numberOfModels), *xm = (double *)malloc(sizeof(double) * numberOfModels), *e = (double *)malloc(sizeof(double) * numberOfModels); boolean *converged = (boolean *)malloc(sizeof(boolean) * numberOfModels); boolean allConverged; for(i = 0; i < numberOfModels; i++) converged[i] = FALSE; for(i = 0; i < numberOfModels; i++) { e[i] = 0.0; d[i] = 0.0; } for(i = 0; i < numberOfModels; i++) { a[i]=((ax[i] < cx[i]) ? ax[i] : cx[i]); b[i]=((ax[i] > cx[i]) ? ax[i] : cx[i]); x[i] = w[i] = v[i] = bx[i]; fw[i] = fv[i] = fx[i] = fb[i]; } for(i = 0; i < numberOfModels; i++) { assert(a[i] >= lim_inf[i] && a[i] <= lim_sup[i]); assert(b[i] >= lim_inf[i] && b[i] <= lim_sup[i]); assert(x[i] >= lim_inf[i] && x[i] <= lim_sup[i]); assert(v[i] >= lim_inf[i] && v[i] <= lim_sup[i]); assert(w[i] >= lim_inf[i] && w[i] <= lim_sup[i]); } for(iter = 1; iter <= ITMAX; iter++) { allConverged = TRUE; for(i = 0; i < numberOfModels && allConverged; i++) allConverged = allConverged && converged[i]; if(allConverged) { free(converged); free(a); free(b); free(d); free(etemp); free(fu); free(fv); free(fw); free(fx); free(p); free(q); free(r); free(tol1); free(tol2); free(u); free(v); free(w); free(x); free(xm); free(e); return; } for(i = 0; i < numberOfModels; i++) { if(!converged[i]) { assert(a[i] >= lim_inf[i] && a[i] <= lim_sup[i]); assert(b[i] >= lim_inf[i] && b[i] <= lim_sup[i]); assert(x[i] >= lim_inf[i] && x[i] <= lim_sup[i]); assert(v[i] >= lim_inf[i] && v[i] <= lim_sup[i]); assert(w[i] >= lim_inf[i] && w[i] <= lim_sup[i]); xm[i] = 0.5 * (a[i] + b[i]); tol2[i] = 2.0 * (tol1[i] = tol * fabs(x[i]) + BRENT_ZEPS); if(fabs(x[i] - xm[i]) <= (tol2[i] - 0.5 * (b[i] - a[i]))) { result[i] = -fx[i]; xmin[i] = x[i]; converged[i] = TRUE; } else { if(fabs(e[i]) > tol1[i]) { r[i] = (x[i] - w[i]) * (fx[i] - fv[i]); q[i] = (x[i] - v[i]) * (fx[i] - fw[i]); p[i] = (x[i] - v[i]) * q[i] - (x[i] - w[i]) * r[i]; q[i] = 2.0 * (q[i] - r[i]); if(q[i] > 0.0) p[i] = -p[i]; q[i] = fabs(q[i]); etemp[i] = e[i]; e[i] = d[i]; if((fabs(p[i]) >= fabs(0.5 * q[i] * etemp[i])) || (p[i] <= q[i] * (a[i]-x[i])) || (p[i] >= q[i] * (b[i] - x[i]))) d[i] = BRENT_CGOLD * (e[i] = (x[i] >= xm[i] ? a[i] - x[i] : b[i] - x[i])); else { d[i] = p[i] / q[i]; u[i] = x[i] + d[i]; if( u[i] - a[i] < tol2[i] || b[i] - u[i] < tol2[i]) d[i] = SIGN(tol1[i], xm[i] - x[i]); } } else { d[i] = BRENT_CGOLD * (e[i] = (x[i] >= xm[i] ? a[i] - x[i]: b[i] - x[i])); } u[i] = ((fabs(d[i]) >= tol1[i]) ? (x[i] + d[i]): (x[i] +SIGN(tol1[i], d[i]))); } if(!converged[i]) assert(u[i] >= lim_inf[i] && u[i] <= lim_sup[i]); } } evaluateChange(tr, rateNumber, u, fu, converged, whichFunction, numberOfModels, ll, tol); for(i = 0; i < numberOfModels; i++) { if(!converged[i]) { if(fu[i] <= fx[i]) { if(u[i] >= x[i]) a[i] = x[i]; else b[i] = x[i]; SHFT(v[i],w[i],x[i],u[i]); SHFT(fv[i],fw[i],fx[i],fu[i]); } else { if(u[i] < x[i]) a[i] = u[i]; else b[i] = u[i]; if(fu[i] <= fw[i] || w[i] == x[i]) { v[i] = w[i]; w[i] = u[i]; fv[i] = fw[i]; fw[i] = fu[i]; } else { if(fu[i] <= fv[i] || v[i] == x[i] || v[i] == w[i]) { v[i] = u[i]; fv[i] = fu[i]; } } } assert(a[i] >= lim_inf[i] && a[i] <= lim_sup[i]); assert(b[i] >= lim_inf[i] && b[i] <= lim_sup[i]); assert(x[i] >= lim_inf[i] && x[i] <= lim_sup[i]); assert(v[i] >= lim_inf[i] && v[i] <= lim_sup[i]); assert(w[i] >= lim_inf[i] && w[i] <= lim_sup[i]); assert(u[i] >= lim_inf[i] && u[i] <= lim_sup[i]); } } } free(converged); free(a); free(b); free(d); free(etemp); free(fu); free(fv); free(fw); free(fx); free(p); free(q); free(r); free(tol1); free(tol2); free(u); free(v); free(w); free(x); free(xm); free(e); printf("\n. Too many iterations in BRENT !"); assert(0); } static int brakGeneric(double *param, double *ax, double *bx, double *cx, double *fa, double *fb, double *fc, double *lim_inf, double *lim_sup, int numberOfModels, int rateNumber, int whichFunction, tree *tr, linkageList *ll, double modelEpsilon) { double *ulim = (double *)malloc(sizeof(double) * numberOfModels), *u = (double *)malloc(sizeof(double) * numberOfModels), *r = (double *)malloc(sizeof(double) * numberOfModels), *q = (double *)malloc(sizeof(double) * numberOfModels), *fu = (double *)malloc(sizeof(double) * numberOfModels), *dum = (double *)malloc(sizeof(double) * numberOfModels), *temp = (double *)malloc(sizeof(double) * numberOfModels); int i, *state = (int *)malloc(sizeof(int) * numberOfModels), *endState = (int *)malloc(sizeof(int) * numberOfModels); boolean *converged = (boolean *)malloc(sizeof(boolean) * numberOfModels); boolean allConverged; for(i = 0; i < numberOfModels; i++) converged[i] = FALSE; for(i = 0; i < numberOfModels; i++) { state[i] = 0; endState[i] = 0; u[i] = 0.0; param[i] = ax[i]; if(param[i] > lim_sup[i]) param[i] = ax[i] = lim_sup[i]; if(param[i] < lim_inf[i]) param[i] = ax[i] = lim_inf[i]; assert(param[i] >= lim_inf[i] && param[i] <= lim_sup[i]); } evaluateChange(tr, rateNumber, param, fa, converged, whichFunction, numberOfModels, ll, modelEpsilon); for(i = 0; i < numberOfModels; i++) { param[i] = bx[i]; if(param[i] > lim_sup[i]) param[i] = bx[i] = lim_sup[i]; if(param[i] < lim_inf[i]) param[i] = bx[i] = lim_inf[i]; assert(param[i] >= lim_inf[i] && param[i] <= lim_sup[i]); } evaluateChange(tr, rateNumber, param, fb, converged, whichFunction, numberOfModels, ll, modelEpsilon); for(i = 0; i < numberOfModels; i++) { if (fb[i] > fa[i]) { SHFT(dum[i],ax[i],bx[i],dum[i]); SHFT(dum[i],fa[i],fb[i],dum[i]); } cx[i] = bx[i] + MNBRAK_GOLD * (bx[i] - ax[i]); param[i] = cx[i]; if(param[i] > lim_sup[i]) param[i] = cx[i] = lim_sup[i]; if(param[i] < lim_inf[i]) param[i] = cx[i] = lim_inf[i]; assert(param[i] >= lim_inf[i] && param[i] <= lim_sup[i]); } evaluateChange(tr, rateNumber, param, fc, converged, whichFunction, numberOfModels, ll, modelEpsilon); while(1) { allConverged = TRUE; for(i = 0; i < numberOfModels && allConverged; i++) allConverged = allConverged && converged[i]; if(allConverged) { for(i = 0; i < numberOfModels; i++) { if(ax[i] > lim_sup[i]) ax[i] = lim_sup[i]; if(ax[i] < lim_inf[i]) ax[i] = lim_inf[i]; if(bx[i] > lim_sup[i]) bx[i] = lim_sup[i]; if(bx[i] < lim_inf[i]) bx[i] = lim_inf[i]; if(cx[i] > lim_sup[i]) cx[i] = lim_sup[i]; if(cx[i] < lim_inf[i]) cx[i] = lim_inf[i]; } free(converged); free(ulim); free(u); free(r); free(q); free(fu); free(dum); free(temp); free(state); free(endState); return 0; } for(i = 0; i < numberOfModels; i++) { if(!converged[i]) { switch(state[i]) { case 0: endState[i] = 0; if(!(fb[i] > fc[i])) converged[i] = TRUE; else { if(ax[i] > lim_sup[i]) ax[i] = lim_sup[i]; if(ax[i] < lim_inf[i]) ax[i] = lim_inf[i]; if(bx[i] > lim_sup[i]) bx[i] = lim_sup[i]; if(bx[i] < lim_inf[i]) bx[i] = lim_inf[i]; if(cx[i] > lim_sup[i]) cx[i] = lim_sup[i]; if(cx[i] < lim_inf[i]) cx[i] = lim_inf[i]; r[i]=(bx[i]-ax[i])*(fb[i]-fc[i]); q[i]=(bx[i]-cx[i])*(fb[i]-fa[i]); u[i]=(bx[i])-((bx[i]-cx[i])*q[i]-(bx[i]-ax[i])*r[i])/ (2.0*SIGN(MAX(fabs(q[i]-r[i]),MNBRAK_TINY),q[i]-r[i])); ulim[i]=(bx[i])+MNBRAK_GLIMIT*(cx[i]-bx[i]); if(u[i] > lim_sup[i]) u[i] = lim_sup[i]; if(u[i] < lim_inf[i]) u[i] = lim_inf[i]; if(ulim[i] > lim_sup[i]) ulim[i] = lim_sup[i]; if(ulim[i] < lim_inf[i]) ulim[i] = lim_inf[i]; if ((bx[i]-u[i])*(u[i]-cx[i]) > 0.0) { param[i] = u[i]; if(param[i] > lim_sup[i]) param[i] = u[i] = lim_sup[i]; if(param[i] < lim_inf[i]) param[i] = u[i] = lim_inf[i]; endState[i] = 1; } else { if ((cx[i]-u[i])*(u[i]-ulim[i]) > 0.0) { param[i] = u[i]; if(param[i] > lim_sup[i]) param[i] = u[i] = lim_sup[i]; if(param[i] < lim_inf[i]) param[i] = u[i] = lim_inf[i]; endState[i] = 2; } else { if ((u[i]-ulim[i])*(ulim[i]-cx[i]) >= 0.0) { u[i] = ulim[i]; param[i] = u[i]; if(param[i] > lim_sup[i]) param[i] = u[i] = ulim[i] = lim_sup[i]; if(param[i] < lim_inf[i]) param[i] = u[i] = ulim[i] = lim_inf[i]; endState[i] = 0; } else { u[i]=(cx[i])+MNBRAK_GOLD*(cx[i]-bx[i]); param[i] = u[i]; endState[i] = 0; if(param[i] > lim_sup[i]) param[i] = u[i] = lim_sup[i]; if(param[i] < lim_inf[i]) param[i] = u[i] = lim_inf[i]; } } } } break; case 1: endState[i] = 0; break; case 2: endState[i] = 3; break; default: assert(0); } assert(param[i] >= lim_inf[i] && param[i] <= lim_sup[i]); } } evaluateChange(tr, rateNumber, param, temp, converged, whichFunction, numberOfModels, ll, modelEpsilon); for(i = 0; i < numberOfModels; i++) { if(!converged[i]) { switch(endState[i]) { case 0: fu[i] = temp[i]; SHFT(ax[i],bx[i],cx[i],u[i]); SHFT(fa[i],fb[i],fc[i],fu[i]); state[i] = 0; break; case 1: fu[i] = temp[i]; if (fu[i] < fc[i]) { ax[i]=(bx[i]); bx[i]=u[i]; fa[i]=(fb[i]); fb[i]=fu[i]; converged[i] = TRUE; } else { if (fu[i] > fb[i]) { assert(u[i] >= lim_inf[i] && u[i] <= lim_sup[i]); cx[i]=u[i]; fc[i]=fu[i]; converged[i] = TRUE; } else { u[i]=(cx[i])+MNBRAK_GOLD*(cx[i]-bx[i]); param[i] = u[i]; if(param[i] > lim_sup[i]) {param[i] = u[i] = lim_sup[i];} if(param[i] < lim_inf[i]) {param[i] = u[i] = lim_inf[i];} state[i] = 1; } } break; case 2: fu[i] = temp[i]; if (fu[i] < fc[i]) { SHFT(bx[i],cx[i],u[i], cx[i]+MNBRAK_GOLD*(cx[i]-bx[i])); state[i] = 2; } else { state[i] = 0; SHFT(ax[i],bx[i],cx[i],u[i]); SHFT(fa[i],fb[i],fc[i],fu[i]); } break; case 3: SHFT(fb[i],fc[i],fu[i], temp[i]); SHFT(ax[i],bx[i],cx[i],u[i]); SHFT(fa[i],fb[i],fc[i],fu[i]); state[i] = 0; break; default: assert(0); } } } } assert(0); free(converged); free(ulim); free(u); free(r); free(q); free(fu); free(dum); free(temp); free(state); free(endState); return(0); } /*******************************************************************************************************/ /******** LG4X ***************************************************************************************/ static void optLG4X(tree *tr, double modelEpsilon, linkageList *ll, int numberOfModels) { int i; for(i = 0; i < 4; i++) { optParamGeneric(tr, modelEpsilon, ll, numberOfModels, i, LG4X_RATE_MIN, LG4X_RATE_MAX, LXRATE_F); optimizeWeights(tr, modelEpsilon, ll, numberOfModels); } } /**********************************************************************************************************/ /* ALPHA PARAM ********************************************************************************************/ //this function is required for implementing the LG4X model later-on static void optAlphasGeneric(tree *tr, double modelEpsilon, linkageList *ll) { int i, non_LG4X_Partitions = 0, LG4X_Partitions = 0; /* assumes homogeneous super-partitions, that either contain DNA or AA partitions !*/ /* does not check whether AA are all linked */ /* first do non-LG4X partitions */ for(i = 0; i < ll->entries; i++) { switch(tr->partitionData[ll->ld[i].partitionList[0]].dataType) { case DNA_DATA: case BINARY_DATA: case SECONDARY_DATA: case SECONDARY_DATA_6: case SECONDARY_DATA_7: case GENERIC_32: case GENERIC_64: ll->ld[i].valid = TRUE; non_LG4X_Partitions++; break; case AA_DATA: //to be implemented later-on if(tr->partitionData[ll->ld[i].partitionList[0]].protModels == LG4X) { LG4X_Partitions++; ll->ld[i].valid = FALSE; } else { ll->ld[i].valid = TRUE; non_LG4X_Partitions++; } break; default: assert(0); } } if(non_LG4X_Partitions > 0) optParamGeneric(tr, modelEpsilon, ll, non_LG4X_Partitions, -1, ALPHA_MIN, ALPHA_MAX, ALPHA_F); /* then LG4x partitions */ for(i = 0; i < ll->entries; i++) { switch(tr->partitionData[ll->ld[i].partitionList[0]].dataType) { case DNA_DATA: case BINARY_DATA: case SECONDARY_DATA: case SECONDARY_DATA_6: case SECONDARY_DATA_7: case GENERIC_32: case GENERIC_64: ll->ld[i].valid = FALSE; break; case AA_DATA: if(tr->partitionData[ll->ld[i].partitionList[0]].protModels == LG4X) ll->ld[i].valid = TRUE; else ll->ld[i].valid = FALSE; break; default: assert(0); } } if(LG4X_Partitions > 0) optLG4X(tr, modelEpsilon, ll, LG4X_Partitions); for(i = 0; i < ll->entries; i++) ll->ld[i].valid = TRUE; } static double minFreq(int index, int whichFreq, tree *tr, double absoluteMin) { double min = 0.0, *w = tr->partitionData[index].freqExponents, c = 0.0; int states = tr->partitionData[index].states, i; for(i = 0; i < states; i++) if(i != whichFreq) c += exp(w[i]); min = log(FREQ_MIN) + log(c) - log (1.0 - FREQ_MIN); if(0) { double check = exp(min) / (exp(min) + c); printf("check %f\n", check); printf("min: %f \n", min); } return MAX(min, absoluteMin); } static double maxFreq(int index, int whichFreq, tree *tr, double absoluteMax) { double max = 0.0, *w = tr->partitionData[index].freqExponents, c = 0.0; int states = tr->partitionData[index].states, i; for(i = 0; i < states; i++) if(i != whichFreq) c += exp(w[i]); max = log(1.0 - ((double)(states - 1) * FREQ_MIN)) + log(c) - log ((double)(states - 1) * FREQ_MIN); if(0) { double check = exp(max) / (exp(max) + c); printf("check max %f\n", check); printf("max: %f \n", max); } return MIN(max, absoluteMax); } static void optParamGeneric(tree *tr, double modelEpsilon, linkageList *ll, int numberOfModels, int rateNumber, double _lim_inf, double _lim_sup, int whichParameterType) { int l, k, j, pos; double *startRates = (double *)malloc(sizeof(double) * numberOfModels * 4), *startWeights = (double *)malloc(sizeof(double) * numberOfModels * 4), *startExponents = (double *)malloc(sizeof(double) * numberOfModels * 4), *startValues = (double *)malloc(sizeof(double) * numberOfModels), *startLH = (double *)malloc(sizeof(double) * numberOfModels), *endLH = (double *)malloc(sizeof(double) * numberOfModels), *_a = (double *)malloc(sizeof(double) * numberOfModels), *_b = (double *)malloc(sizeof(double) * numberOfModels), *_c = (double *)malloc(sizeof(double) * numberOfModels), *_fa = (double *)malloc(sizeof(double) * numberOfModels), *_fb = (double *)malloc(sizeof(double) * numberOfModels), *_fc = (double *)malloc(sizeof(double) * numberOfModels), *_param = (double *)malloc(sizeof(double) * numberOfModels), *_x = (double *)malloc(sizeof(double) * numberOfModels), *lim_inf = (double *)malloc(sizeof(double) * numberOfModels), *lim_sup = (double *)malloc(sizeof(double) * numberOfModels); evaluateGeneric(tr, tr->start, TRUE); #ifdef _DEBUG_MOD_OPT double initialLH = tr->likelihood; #endif /* at this point here every worker has the traversal data it needs for the search */ for(l = 0, pos = 0; l < ll->entries; l++) { if(ll->ld[l].valid) { endLH[pos] = unlikely; startLH[pos] = 0.0; for(j = 0; j < ll->ld[l].partitions; j++) { int index = ll->ld[l].partitionList[j]; startLH[pos] += tr->perPartitionLH[index]; switch(whichParameterType) { case ALPHA_F: lim_inf[pos] = _lim_inf; lim_sup[pos] = _lim_sup; startValues[pos] = tr->partitionData[index].alpha; break; case RATE_F: lim_inf[pos] = _lim_inf; lim_sup[pos] = _lim_sup; startValues[pos] = tr->partitionData[index].substRates[rateNumber]; break; case FREQ_F: lim_inf[pos] = minFreq(index, rateNumber, tr, _lim_inf); lim_sup[pos] = maxFreq(index, rateNumber, tr, _lim_sup); startValues[pos] = tr->partitionData[index].freqExponents[rateNumber]; break; case LXRATE_F: lim_inf[pos] = _lim_inf; lim_sup[pos] = _lim_sup; assert(rateNumber >= 0 && rateNumber < 4); startValues[pos] = tr->partitionData[index].gammaRates[rateNumber]; memcpy(&startRates[pos * 4], tr->partitionData[index].gammaRates, 4 * sizeof(double)); memcpy(&startExponents[pos * 4], tr->partitionData[index].weightExponents, 4 * sizeof(double)); memcpy(&startWeights[pos * 4], tr->partitionData[index].weights, 4 * sizeof(double)); break; case LXWEIGHT_F: lim_inf[pos] = _lim_inf; lim_sup[pos] = _lim_sup; assert(rateNumber >= 0 && rateNumber < 4); startValues[pos] = tr->partitionData[index].weightExponents[rateNumber]; break; default: assert(0); } } pos++; } } assert(pos == numberOfModels); for(k = 0, pos = 0; k < ll->entries; k++) { if(ll->ld[k].valid) { _a[pos] = startValues[pos] + 0.1; _b[pos] = startValues[pos] - 0.1; if(_a[pos] < lim_inf[pos]) _a[pos] = lim_inf[pos]; if(_a[pos] > lim_sup[pos]) _a[pos] = lim_sup[pos]; if(_b[pos] < lim_inf[pos]) _b[pos] = lim_inf[pos]; if(_b[pos] > lim_sup[pos]) _b[pos] = lim_sup[pos]; pos++; } } assert(pos == numberOfModels); brakGeneric(_param, _a, _b, _c, _fa, _fb, _fc, lim_inf, lim_sup, numberOfModels, rateNumber, whichParameterType, tr, ll, modelEpsilon); for(k = 0; k < numberOfModels; k++) { assert(_a[k] >= lim_inf[k] && _a[k] <= lim_sup[k]); assert(_b[k] >= lim_inf[k] && _b[k] <= lim_sup[k]); assert(_c[k] >= lim_inf[k] && _c[k] <= lim_sup[k]); } brentGeneric(_a, _b, _c, _fb, modelEpsilon, _x, endLH, numberOfModels, whichParameterType, rateNumber, tr, ll, lim_inf, lim_sup); for(k = 0, pos = 0; k < ll->entries; k++) { if(ll->ld[k].valid) { if(startLH[pos] > endLH[pos]) { //if the initial likelihood was better than the likelihodo after optimization, we set the values back //to their original values for(j = 0; j < ll->ld[k].partitions; j++) { int index = ll->ld[k].partitionList[j]; changeModelParameters(index, rateNumber, startValues[pos], whichParameterType, tr); } } else { //otherwise we set the value to the optimized value //this used to be a bug in standard RAxML, before I fixed it //I was not using _x[pos] as value that needs to be set for(j = 0; j < ll->ld[k].partitions; j++) { int index = ll->ld[k].partitionList[j]; changeModelParameters(index, rateNumber, _x[pos], whichParameterType, tr); } } pos++; } } //LIBRARY call the barrier here in the LIBRARY to update model params at all threads/processes ! assert(pos == numberOfModels); free(startLH); free(endLH); free(_a); free(_b); free(_c); free(_fa); free(_fb); free(_fc); free(_param); free(_x); free(startValues); free(startRates); free(startWeights); free(startExponents); free(lim_inf); free(lim_sup); #ifdef _DEBUG_MOD_OPT evaluateGeneric(tr, tr->start, TRUE); if(tr->likelihood < initialLH) printf("%f %f\n", tr->likelihood, initialLH); assert(tr->likelihood >= initialLH); #endif } //******************** rate optimization functions ***************************************************/ static void optFreqs(tree *tr, double modelEpsilon, linkageList *ll, int numberOfModels, int states) { int rateNumber; double freqMin = -1000000.0, freqMax = 200.0; for(rateNumber = 0; rateNumber < states; rateNumber++) optParamGeneric(tr, modelEpsilon, ll, numberOfModels, rateNumber, freqMin, freqMax, FREQ_F); } static void optBaseFreqs(tree *tr, double modelEpsilon, linkageList *ll) { int i, states, dnaPartitions = 0, aaPartitions = 0, binaryPartitions = 0; /* first do DNA */ for(i = 0; i < ll->entries; i++) { switch(tr->partitionData[ll->ld[i].partitionList[0]].dataType) { case DNA_DATA: states = tr->partitionData[ll->ld[i].partitionList[0]].states; if(tr->partitionData[ll->ld[i].partitionList[0]].optimizeBaseFrequencies) { ll->ld[i].valid = TRUE; dnaPartitions++; } else ll->ld[i].valid = FALSE; break; case AA_DATA: case BINARY_DATA: ll->ld[i].valid = FALSE; break; default: assert(0); } } if(dnaPartitions > 0) optFreqs(tr, modelEpsilon, ll, dnaPartitions, states); /* then AA */ for(i = 0; i < ll->entries; i++) { switch(tr->partitionData[ll->ld[i].partitionList[0]].dataType) { case AA_DATA: states = tr->partitionData[ll->ld[i].partitionList[0]].states; if(tr->partitionData[ll->ld[i].partitionList[0]].optimizeBaseFrequencies) { ll->ld[i].valid = TRUE; aaPartitions++; } else ll->ld[i].valid = FALSE; break; case DNA_DATA: case BINARY_DATA: ll->ld[i].valid = FALSE; break; default: assert(0); } } if(aaPartitions > 0) optFreqs(tr, modelEpsilon, ll, aaPartitions, states); //then binary for(i = 0; i < ll->entries; i++) { switch(tr->partitionData[ll->ld[i].partitionList[0]].dataType) { case BINARY_DATA: states = tr->partitionData[ll->ld[i].partitionList[0]].states; if(tr->partitionData[ll->ld[i].partitionList[0]].optimizeBaseFrequencies) { ll->ld[i].valid = TRUE; binaryPartitions++; } else ll->ld[i].valid = FALSE; break; case DNA_DATA: case AA_DATA: ll->ld[i].valid = FALSE; break; default: assert(0); } } if(binaryPartitions > 0) optFreqs(tr, modelEpsilon, ll, binaryPartitions, states); for(i = 0; i < ll->entries; i++) ll->ld[i].valid = TRUE; } //new version for optimizing rates, an external loop that iterates over the rates static void optRates(tree *tr, double modelEpsilon, linkageList *ll, int numberOfModels, int states) { int rateNumber, numberOfRates = ((states * states - states) / 2) - 1; for(rateNumber = 0; rateNumber < numberOfRates; rateNumber++) optParamGeneric(tr, modelEpsilon, ll, numberOfModels, rateNumber, RATE_MIN, RATE_MAX, RATE_F); } static boolean AAisGTR(tree *tr) { int i, count = 0; for(i = 0; i < tr->NumberOfModels; i++) { if(tr->partitionData[i].dataType == AA_DATA) { count++; if(tr->partitionData[i].protModels != GTR) return FALSE; } } if(count == 0) return FALSE; return TRUE; } static void optRatesGeneric(tree *tr, double modelEpsilon, linkageList *ll) { int i, dnaPartitions = 0, aaPartitions = 0, states = -1; /* assumes homogeneous super-partitions, that either contain DNA or AA partitions !*/ /* does not check whether AA are all linked */ /* first do DNA */ for(i = 0; i < ll->entries; i++) { switch(tr->partitionData[ll->ld[i].partitionList[0]].dataType) { case DNA_DATA: states = tr->partitionData[ll->ld[i].partitionList[0]].states; ll->ld[i].valid = TRUE; dnaPartitions++; break; case BINARY_DATA: case AA_DATA: case SECONDARY_DATA: case SECONDARY_DATA_6: case SECONDARY_DATA_7: case GENERIC_32: case GENERIC_64: ll->ld[i].valid = FALSE; break; default: assert(0); } } if(dnaPartitions > 0) optRates(tr, modelEpsilon, ll, dnaPartitions, states); /* then AA for GTR */ if(AAisGTR(tr)) { for(i = 0; i < ll->entries; i++) { switch(tr->partitionData[ll->ld[i].partitionList[0]].dataType) { case AA_DATA: states = tr->partitionData[ll->ld[i].partitionList[0]].states; ll->ld[i].valid = TRUE; aaPartitions++; break; case DNA_DATA: case BINARY_DATA: case SECONDARY_DATA: case SECONDARY_DATA_6: case SECONDARY_DATA_7: ll->ld[i].valid = FALSE; break; default: assert(0); } } assert(aaPartitions == 1); optRates(tr, modelEpsilon, ll, aaPartitions, states); } for(i = 0; i < ll->entries; i++) ll->ld[i].valid = TRUE; } /*********************FUNCTIONS FOR APPROXIMATE MODEL OPTIMIZATION ***************************************/ static int catCompare(const void *p1, const void *p2) { rateCategorize *rc1 = (rateCategorize *)p1; rateCategorize *rc2 = (rateCategorize *)p2; double i = rc1->accumulatedSiteLikelihood; double j = rc2->accumulatedSiteLikelihood; if (i > j) return (1); if (i < j) return (-1); return (0); } static void categorizePartition(tree *tr, rateCategorize *rc, int model, int lower, int upper, double *patrat, int *rateCategory /* temporary; used to be tr->rateCategory */ ) { int zeroCounter, i, k; double diff, min; for (i = lower, zeroCounter = 0; i < upper; i++, zeroCounter++) { double temp = patrat[i]; int found = 0; for(k = 0; k < tr->partitionData[model].numberOfCategories; k++) { if(temp == rc[k].rate || (fabs(temp - rc[k].rate) < 0.001)) { found = 1; rateCategory[i] = k; break; } } if(!found) { min = fabs(temp - rc[0].rate); rateCategory[i] = 0; for(k = 1; k < tr->partitionData[model].numberOfCategories; k++) { diff = fabs(temp - rc[k].rate); if(diff < min) { min = diff; rateCategory[i] = k; } } } } for(k = 0; k < tr->partitionData[model].numberOfCategories; k++) tr->partitionData[model].perSiteRates[k] = rc[k].rate; } static void optRateCatPthreads(tree *tr, double lower_spacing, double upper_spacing) { #ifdef _USE_OMP #pragma omp parallel #endif { int m, model, maxModel; #ifdef _USE_OMP maxModel = tr->maxModelsPerThread; #else maxModel = tr->NumberOfModels; #endif for(m = 0; m < maxModel; m++) { /* just defaults -> if partion wasn't assigned to this thread, it will be ignored later on */ size_t width = 0, offset = 0; #ifdef _USE_OMP int tid = omp_get_thread_num(); /* check if this thread should process this partition */ Assign* pAss = tr->threadPartAssigns[tid * tr->maxModelsPerThread + m]; if(pAss) { model = pAss->partitionId; width = pAss->width; offset = pAss->offset; assert(model < tr->NumberOfModels); } else break; #else model = m; /* number of sites in this partition */ width = (size_t)tr->partitionData[model].width; offset = 0; #endif size_t i; pInfo *partition = &(tr->partitionData[model]); for( i = offset; i < offset + width; ++i) { double initialRate, initialLikelihood, leftLH, rightLH, leftRate, rightRate, v; const double epsilon = 0.00001; int k; initialRate = partition->patrat[i]; initialLikelihood = evaluatePartialGeneric(tr, i, initialRate, model); /* i is real i ??? */ leftLH = rightLH = initialLikelihood; leftRate = rightRate = initialRate; k = 1; while((initialRate - k * lower_spacing > 0.0001) && ((v = evaluatePartialGeneric(tr, i, initialRate - k * lower_spacing, model)) > leftLH) && (fabs(leftLH - v) > epsilon)) { #ifndef WIN32 if(isnan(v)) assert(0); #endif leftLH = v; leftRate = initialRate - k * lower_spacing; k++; } k = 1; while(((v = evaluatePartialGeneric(tr, i, initialRate + k * upper_spacing, model)) > rightLH) && (fabs(rightLH - v) > epsilon)) { #ifndef WIN32 if(isnan(v)) assert(0); #endif rightLH = v; rightRate = initialRate + k * upper_spacing; k++; } if(rightLH > initialLikelihood || leftLH > initialLikelihood) { if(rightLH > leftLH) { partition->patrat[i] = rightRate; partition->lhs[i] = rightLH; } else { partition->patrat[i] = leftRate; partition->lhs[i] = leftLH; } } else partition->lhs[i] = initialLikelihood; } } } } /** determines the weighted rates for each partition. Intended for use with normalization of the CAT model rates. Since information about rates and weights is distributed (each process only has the respective info for the data assigned to it), we have to communicate with peer processes. Notice, that weightPerPart could actually be stored in a variable, since the result does not change... output: weightPerPart_result -- the sum of weights per partition weightedRates_result -- sum of rates per partition weighted by site weight */ static void getWeightsAndWeightedRates(const tree * const tr, int **weightPerPart_result, double **weightedRates_result ) { int i, *weightPerPart = (int *)NULL; double *weightedRates = (double *)NULL; *weightPerPart_result = (int*)calloc((size_t)tr->NumberOfModels, sizeof(int)); *weightedRates_result = (double*) calloc((size_t)tr->NumberOfModels, sizeof(double)); weightedRates = *weightedRates_result; weightPerPart = *weightPerPart_result; for(i = 0; i < tr->NumberOfModels; ++i) { size_t j; pInfo *partition = &(tr->partitionData[i]); for(j = 0; j < partition->width; ++j) { int c = partition->rateCategory[j]; weightPerPart[i] += partition->wgt[j]; assert(0 <= c && c < tr->maxCategories); weightedRates[i] += ((double)partition->wgt[j]) * partition->perSiteRates[c]; } } MPI_Allreduce(MPI_IN_PLACE, weightPerPart, tr->NumberOfModels, MPI_INT, MPI_SUM, MPI_COMM_WORLD); MPI_Allreduce(MPI_IN_PLACE, weightedRates, tr->NumberOfModels, MPI_DOUBLE, MPI_SUM, MPI_COMM_WORLD); for( i = 0; i < tr->NumberOfModels; ++i) { assert(weightPerPart[i] > 0 ); assert(weightedRates[i] > 0.0 ); } } /* this used to be updatePerSiteRates without scaling. Previously, updatePerSiteRates without scaling only conducted a check about whether sites are scaled correctly. */ //Andre but isn't this checking that the rates have been scaled correctly? //shouldn't the assertions fail in this case, i.e., without scaling ? void checkPerSiteRates(const tree *const tr ) { int i, *weightPerPart = (int *)NULL; double *weightedRates = (double *)NULL; /* determine the sum of weights (weightPerPart) and the sum of all rates of a partition weighted by site weights */ getWeightsAndWeightedRates(tr, &weightPerPart, &weightedRates); if(tr->numBranches > 1 ) { /* check if the mean of rates of each partition is 1 */ for(i = 0; i < tr->NumberOfModels; ++i) { double accRat = weightedRates[i] / (double)weightPerPart[i]; assert(fabs(accRat - 1.0) < 1e-5); } } else { /* check, if the overall mean of rates is 1 */ double accRat = 0.0, accWgt = 0.0; for(i = 0; i < tr->NumberOfModels; ++i) { accRat += weightedRates[i]; accWgt += weightPerPart[i]; } accRat /= (double)accWgt; assert(fabs(accRat - 1.0) < 1e-5); } free(weightedRates); free(weightPerPart); } /** updatePerSiteRates is called after the master has categorized rates into several categories and every process has obtained the categorization for only the data assigned to it. Now, we still have to scale the rates, s.t. they are 1 on average. Thus, some communication is still needed to determine the total weight and the weighted rates (because this information is destributed). Notice that this function previously had two modes (scaleRates = {TRUE,FALSE}). Previously, scaleRates = FALSE, only performed a check on whether rates are scaled correctly such that the average rate is 1. For clarity, this functionality is now in a separate function called checkPerSiteRates. */ static void updatePerSiteRates(tree *tr) { int i, *weightPerPart = (int *)NULL; double *weightedRates = (double *)NULL; getWeightsAndWeightedRates(tr, &weightPerPart, &weightedRates); if(tr->numBranches > 1 ) { /* scale each partition, s.t. average rate within the partition is 1 */ for(i = 0; i < tr->NumberOfModels; ++i) { int j; double scaler = weightedRates[i] / (double)weightPerPart[i]; scaler = 1.0 / scaler; for(j = 0; j < tr->partitionData[i].numberOfCategories; ++j) tr->partitionData[i].perSiteRates[j] *= scaler; } } else { /* scale, s.t. average rate is 1 */ double scaler = 0.0, accWgt = 0.0; for(i = 0; i < tr->NumberOfModels; ++i) { scaler += weightedRates[i]; accWgt += weightPerPart[i]; } scaler /= (double)accWgt; scaler = 1.0 / scaler; for(i = 0; i < tr->NumberOfModels; ++i) { pInfo *partition = &(tr->partitionData[i]); int j; for(j = 0; j < partition->numberOfCategories; ++j) partition->perSiteRates[j] *= scaler; } } free(weightedRates); free(weightPerPart); /* finally check, whether the rates are scaled correctly, s.t. their mean is 1 */ checkPerSiteRates(tr); } /* gathers optimized rates and the associated persite-lnls from all processes at the master. Notice that for instance tr->patrat_basePtr already contain all rate data of a single process. Output: optRates_result -- (only at master) a pointer to an array of optimized rates (corresponds to what used to be tr->patratStored) lnls_result -- (only at master) a pointer to an array with persite-lnls that correspond to the newly proposed rate (used to be tr->lhs) */ static void gatherOptimizedRates(tree *tr, double **optRates_result, double **lnls_result) { /* determine counts and displacement for data for each processor */ int *numPerProc = (int *)NULL, *displPerProc = (int *)NULL; calculateLengthAndDisplPerProcess(tr, &numPerProc, &displPerProc); gatherDistributedArray( tr, (void**) optRates_result, tr->patrat_basePtr, MPI_DOUBLE, numPerProc, displPerProc); gatherDistributedArray(tr , (void**) lnls_result, tr->lhs_basePtr, MPI_DOUBLE, numPerProc, displPerProc); free(numPerProc); free(displPerProc); } /* The master creates rate categories and assigns the rate categories to processes. Only executed by the master to assure consistent categorization. This code used to be the first part of of optimizeRateCategories() and has only slightly been modified. Input: patrat -- a global array of optimized rates (used to be tr->patratStored) lnls -- a global array of per-site lnls (used to be tr->lhs) Output: rateCategory_result -- a pointer to a global array of rate categories (used to be tr->rateCategory) side effect: tr->partitionData[i].perSiteRates gets computed in categorizePartition. */ static void categorizeTheRates(tree *tr, double *patrat, double *lnls, int maxCategories, int **rateCategory_result) { int model, i; *rateCategory_result = (int*) calloc((size_t)tr->originalCrunchedLength, sizeof(int)); for(model = 0; model < tr->NumberOfModels; model++) { double temp = 0.0; int where = 1, found = 0, width = tr->partitionData[model].upper - tr->partitionData[model].lower, upper = tr->partitionData[model].upper, lower = tr->partitionData[model].lower; rateCategorize *rc = (rateCategorize *)malloc(sizeof(rateCategorize) * width); for (i = 0; i < width; i++) { rc[i].accumulatedSiteLikelihood = 0.0; rc[i].rate = 0.0; } rc[0].accumulatedSiteLikelihood = lnls[lower]; rc[0].rate = patrat[lower]; for (i = lower + 1; i < upper; i++) { int k; temp = patrat[i]; found = 0; for(k = 0; k < where; k++) { if(temp == rc[k].rate || (fabs(temp - rc[k].rate) < 0.001)) { found = 1; rc[k].accumulatedSiteLikelihood += lnls[i]; break; } } if(!found) { rc[where].rate = temp; rc[where].accumulatedSiteLikelihood += lnls[i]; where++; } } qsort(rc, where, sizeof(rateCategorize), catCompare); if(where < maxCategories) { tr->partitionData[model].numberOfCategories = where; categorizePartition(tr, rc, model, lower, upper, patrat, *rateCategory_result); } else { tr->partitionData[model].numberOfCategories = maxCategories; categorizePartition(tr, rc, model, lower, upper, patrat, *rateCategory_result); } free(rc); } } /* #define PRINT_RAT_CAT */ /** informs all peer processes about * rateCategory * numberOfCategories * perSiteRates of their data */ static void scatterProcessedRates(tree *tr, int *rateCategory) { int i, *countPerProc = (int *)NULL, *displPerProc = (int *)NULL, *numCatPerPart = (int*) calloc((size_t)tr->NumberOfModels, sizeof(int)); if(processID == 0) { for(i = 0; i < tr->NumberOfModels; ++i) numCatPerPart[i] = tr->partitionData[i].numberOfCategories; } MPI_Bcast(numCatPerPart, tr->NumberOfModels, MPI_INT, 0,MPI_COMM_WORLD); for(i = 0; i < tr->NumberOfModels; ++i) tr->partitionData[i].numberOfCategories = numCatPerPart[i]; free(numCatPerPart); /* for simplicity, broad cast all peSiteRates */ for(i = 0; i < tr->NumberOfModels; ++i) MPI_Bcast(tr->partitionData[i].perSiteRates, tr->maxCategories, MPI_DOUBLE, 0, MPI_COMM_WORLD); /* prepare for scattering */ calculateLengthAndDisplPerProcess(tr, &countPerProc, &displPerProc); #ifdef PRINT_RAT_CAT if(processID == 0) { printf("rates BEFORE: "); for(i = 0; i < tr->originalCrunchedLength; ++i) printf("%d,", rateCategory[i]); printf("\n"); } #endif scatterDistrbutedArray(tr, rateCategory, tr->rateCategory_basePtr, MPI_INT, countPerProc, displPerProc); #ifdef PRINT_RAT_CAT int len = getMyCharacterLength(tr); printf("basepointer AFTER: "); for(i = 0; i < len ; ++i) printf("%d,", tr->rateCategory_basePtr[i]); printf("\n"); #endif free(countPerProc); free(displPerProc); } /* backup for one partition */ typedef struct { double *patrat; int *rateCategory; double *perSiteRates; int numberOfCategories; } RateBackup; /** This function creates a backup of all data relevant for CAT-rate assignment. Previously, the backup info has been stored in patratStored. Or maybe it is the otherway around and patrat was the backup, while patratStored contained the actual optimized rates. Output: resultPtr -- contains the backup */ static void backupRates(tree *tr, RateBackup** resultPtr) { int i, numCat = tr->maxCategories; RateBackup *backup; *resultPtr = (RateBackup* ) calloc((size_t)tr->NumberOfModels, sizeof(RateBackup)); backup = *resultPtr; for(i = 0; i < tr->NumberOfModels; ++i) { pInfo *partition = &(tr->partitionData[i]); RateBackup *bk = backup + i; bk->patrat = (double*)calloc((size_t)partition->width, sizeof(double)); bk->perSiteRates = (double*) calloc((size_t)numCat, sizeof(double)); bk->rateCategory = (int*) calloc((size_t)partition->width, sizeof(int)) ; bk->numberOfCategories = partition->numberOfCategories; memcpy(bk->patrat, partition->patrat, sizeof(double) * (size_t)partition->width); memcpy(bk->perSiteRates, partition->perSiteRates, sizeof(double) * (size_t)numCat); memcpy(bk->rateCategory, partition->rateCategory, sizeof(int) * (size_t)partition->width); } } static void restoreBackupRates(tree *tr , RateBackup *rb) { int numCat = tr->maxCategories, i; for(i = 0; i < tr->NumberOfModels; ++i) { pInfo *partition = &(tr->partitionData[i]); RateBackup *bk = rb + i; partition->numberOfCategories = bk->numberOfCategories; memcpy(partition->patrat, bk->patrat, sizeof(double) * (size_t)partition->width); memcpy(partition->perSiteRates, bk->perSiteRates, sizeof(double) * (size_t)numCat); memcpy(partition->rateCategory, bk->rateCategory, sizeof(int) * (size_t)partition->width); } } static void deleteBackupRates(tree *tr, RateBackup** rbPtr) { int i ; for(i = 0; i< tr->NumberOfModels; ++i) { RateBackup *rb = &((*rbPtr)[i]); free(rb->patrat); free(rb->perSiteRates); free(rb->rateCategory); } free(*rbPtr); rbPtr = (RateBackup **)NULL; } static void optimizeRateCategories(tree *tr, int _maxCategories) { assert(_maxCategories > 0); if(_maxCategories == 1) return; double lower_spacing, upper_spacing, initialLH = tr->likelihood, *optRates = (double*)NULL, *lnls = (double*)NULL; int *rateCategory = (int *)NULL, maxCategories = _maxCategories ; RateBackup *rateBackup = (RateBackup *)NULL; assert(isTip(tr->start->number, tr->mxtips)); evaluateGeneric(tr, tr->start, TRUE); if(optimizeRateCategoryInvocations == 1) { lower_spacing = 0.5 / ((double)optimizeRateCategoryInvocations); upper_spacing = 1.0 / ((double)optimizeRateCategoryInvocations); } else { lower_spacing = 0.05 / ((double)optimizeRateCategoryInvocations); upper_spacing = 0.1 / ((double)optimizeRateCategoryInvocations); } if(lower_spacing < 0.001) lower_spacing = 0.001; if(upper_spacing < 0.001) upper_spacing = 0.001; optimizeRateCategoryInvocations++; //store old rate category assignment backupRates(tr, &rateBackup); /* process specific: each process optimizes rates for data assigned to it */ optRateCatPthreads(tr, lower_spacing, upper_spacing); /* gather rates and lnls at the master */ gatherOptimizedRates(tr, &optRates, &lnls); /* master has all necessary info now and can categorize the rates */ if(processID == 0) { categorizeTheRates(tr, optRates, lnls, maxCategories, &rateCategory ); /* only allocated at master */ free(optRates); free(lnls); } scatterProcessedRates(tr, rateCategory ); if(processID == 0) free(rateCategory); /* every process has now new rates and a new category assignment. However, we still have to scale the rates, such their weighted mean rate is 1. */ updatePerSiteRates(tr); evaluateGeneric(tr, tr->start, TRUE); if(tr->likelihood < initialLH) { restoreBackupRates(tr, rateBackup); //Andre I don't understand the comment below ... //can per-site rate scaling still be dis-abled in this version of the code? /* => Andre: I am afraid neither do I. Comparing it to the original code, I think everything should be fine: we restore the previous state and check, whether rates are scaled correctly. */ /* cannot do that any more here */ checkPerSiteRates(tr); evaluateGeneric(tr, tr->start, TRUE); assert(initialLH == tr->likelihood); } deleteBackupRates(tr,&rateBackup); } /*****************************************************************************************************/ void resetBranches(tree *tr) { nodeptr p, q; int nodes, i; nodes = tr->mxtips + 3 * (tr->mxtips - 2); p = tr->nodep[1]; while (nodes-- > 0) { for(i = 0; i < tr->numBranches; i++) p->z[i] = defaultz; q = p->next; while(q != p) { for(i = 0; i < tr->numBranches; i++) q->z[i] = defaultz; q = q->next; } p++; } } static void printAAmatrix(tree *tr, double epsilon) { if(AAisGTR(tr)) { int model; for(model = 0; model < tr->NumberOfModels; model++) { if(tr->partitionData[model].dataType == AA_DATA) { char gtrFileName[1024]; char epsilonStr[1024]; FILE *gtrFile; double *rates = tr->partitionData[model].substRates; double *f = tr->partitionData[model].frequencies; double q[20][20]; int r = 0; int i, j; assert(tr->partitionData[model].protModels == GTR); sprintf(epsilonStr, "%f", epsilon); strcpy(gtrFileName, workdir); strcat(gtrFileName, "RAxML_proteinGTRmodel."); strcat(gtrFileName, run_id); strcat(gtrFileName, "_"); strcat(gtrFileName, epsilonStr); gtrFile = myfopen(gtrFileName, "wb"); for(i = 0; i < 20; i++) for(j = 0; j < 20; j++) q[i][j] = 0.0; for(i = 0; i < 19; i++) for(j = i + 1; j < 20; j++) q[i][j] = rates[r++]; for(i = 0; i < 20; i++) for(j = 0; j <= i; j++) { if(i == j) q[i][j] = 0.0; else q[i][j] = q[j][i]; } for(i = 0; i < 20; i++) { for(j = 0; j < 20; j++) fprintf(gtrFile, "%1.80f ", q[i][j]); fprintf(gtrFile, "\n"); } for(i = 0; i < 20; i++) fprintf(gtrFile, "%1.80f ", f[i]); fprintf(gtrFile, "\n"); fclose(gtrFile); printBothOpen("\nPrinted intermediate AA substitution matrix to file %s\n\n", gtrFileName); break; } } } } static void optModel(tree *tr, int numProteinModels, int *bestIndex, double *bestScores, boolean empiricalFreqs) { int i, model; for(model = 0; model < tr->NumberOfModels; model++) { bestIndex[model] = -1; bestScores[model] = unlikely; } for(i = 0; i < numProteinModels; i++) { for(model = 0; model < tr->NumberOfModels; model++) { if(tr->partitionData[model].protModels == AUTO) { if(empiricalFreqs) tr->partitionData[model].protFreqs = 0; else tr->partitionData[model].protFreqs = 1; assert(!tr->partitionData[model].optimizeBaseFrequencies); tr->partitionData[model].autoProtModels = i; initReversibleGTR(tr, model); } } resetBranches(tr); evaluateGeneric(tr, tr->start, TRUE); treeEvaluate(tr, 0.5); //if(processID == 0) //printf("Subst Model %d Freqs: %s like %f %f\n", i, (empiricalFreqs == TRUE)?"empirical":"fixed", tr->likelihood, tr->perPartitionLH[0]); for(model = 0; model < tr->NumberOfModels; model++) { if(tr->partitionData[model].protModels == AUTO) { /* if(processID == 0) { int k; for(k = 0; k < 20; k++) printf("%f ", tr->partitionData[model].frequencies[k]); printf("\n"); } */ if(tr->perPartitionLH[model] > bestScores[model]) { bestScores[model] = tr->perPartitionLH[model]; bestIndex[model] = i; } } } } } static void autoProtein(tree *tr, analdef *adef) { int countAutos = 0, model; for(model = 0; model < tr->NumberOfModels; model++) if(tr->partitionData[model].protModels == AUTO) countAutos++; if(countAutos > 0) { int numProteinModels = AUTO, *bestIndex = (int*)malloc(sizeof(int) * tr->NumberOfModels), *oldIndex = (int*)malloc(sizeof(int) * tr->NumberOfModels), *bestIndexEmpFreqs = (int*)malloc(sizeof(int) * tr->NumberOfModels); boolean *oldFreqs = (boolean*)malloc(sizeof(boolean) * tr->NumberOfModels); double startLH, *bestScores = (double*)malloc(sizeof(double) * tr->NumberOfModels), *bestScoresEmpFreqs = (double*)malloc(sizeof(double) * tr->NumberOfModels); topolRELL_LIST *rl = (topolRELL_LIST *)malloc(sizeof(topolRELL_LIST)); char *autoModels[4] = {"ML", "BIC", "AIC", "AICc"}; initTL(rl, tr, 1); saveTL(rl, tr, 0); evaluateGeneric(tr, tr->start, TRUE); startLH = tr->likelihood; for(model = 0; model < tr->NumberOfModels; model++) { oldIndex[model] = tr->partitionData[model].autoProtModels; oldFreqs[model] = tr->partitionData[model].protFreqs; } optModel(tr, numProteinModels, bestIndex, bestScores, FALSE); optModel(tr, numProteinModels, bestIndexEmpFreqs, bestScoresEmpFreqs, TRUE); printBothOpen("Automatic protein model assignment algorithm using %s criterion:\n\n", autoModels[tr->autoProteinSelectionType]); for(model = 0; model < tr->NumberOfModels; model++) { if(tr->partitionData[model].protModels == AUTO) { int bestIndexFixed = bestIndex[model], bestIndexEmp = bestIndexEmpFreqs[model]; double bestLhFixed = bestScores[model], bestLhEmp = bestScoresEmpFreqs[model], samples = 0.0, freeParamsFixed = 0.0, freeParamsEmp = 0.0; samples = tr->partitionWeights[model]; //printf("Sample size %f\n", samples); assert(samples != -1.0 && samples > 0.0); //we always deal with comprehensive trees in ExaML assert(tr->ntips == tr->mxtips); freeParamsFixed = freeParamsEmp = (2 * tr->ntips - 3); freeParamsEmp += 19.0; switch(tr->rateHetModel) { case CAT: freeParamsFixed += (double)tr->partitionData[model].numberOfCategories; freeParamsEmp += (double)tr->partitionData[model].numberOfCategories; break; case GAMMA: freeParamsFixed += 1.0; freeParamsEmp += 1.0; break; case GAMMA_I: freeParamsFixed += 2.0; freeParamsEmp += 2.0; break; default: assert(0); } switch(tr->autoProteinSelectionType) { case AUTO_ML: if(bestLhFixed > bestLhEmp) { tr->partitionData[model].autoProtModels = bestIndexFixed; tr->partitionData[model].protFreqs = 1; } else { tr->partitionData[model].autoProtModels = bestIndexEmp; tr->partitionData[model].protFreqs = 0; } break; case AUTO_BIC: { //BIC: -2 * lnL + k * ln(n) double bicFixed = -2.0 * bestLhFixed + freeParamsFixed * log(samples), bicEmp = -2.0 * bestLhEmp + freeParamsEmp * log(samples); if(bicFixed < bicEmp) { tr->partitionData[model].autoProtModels = bestIndexFixed; tr->partitionData[model].protFreqs = 1; } else { tr->partitionData[model].autoProtModels = bestIndexEmp; tr->partitionData[model].protFreqs = 0; } } break; case AUTO_AIC: { //AIC: 2 * (k - lnL) double aicFixed = 2.0 * (freeParamsFixed - bestLhFixed), aicEmp = 2.0 * (freeParamsEmp - bestLhEmp); if(aicFixed < aicEmp) { tr->partitionData[model].autoProtModels = bestIndexFixed; tr->partitionData[model].protFreqs = 1; } else { tr->partitionData[model].autoProtModels = bestIndexEmp; tr->partitionData[model].protFreqs = 0; } } break; case AUTO_AICC: { //AICc: AIC + (2 * k * (k + 1))/(n - k - 1) double aiccFixed, aiccEmp; /* * Even though samples and freeParamsFixed are fp variables, they are actually integers. * That's why we are comparing with a 0.5 threshold. */ if(fabs(samples - freeParamsFixed - 1.0) < 0.5) aiccFixed = 0.0; else aiccFixed = (2.0 * (freeParamsFixed - bestLhFixed)) + ((2.0 * freeParamsFixed * (freeParamsFixed + 1.0)) / (samples - freeParamsFixed - 1.0)); if(fabs(samples - freeParamsEmp - 1.0) < 0.5) aiccEmp = 0.0; else aiccEmp = (2.0 * (freeParamsEmp - bestLhEmp)) + ((2.0 * freeParamsEmp * (freeParamsEmp + 1.0)) / (samples - freeParamsEmp - 1.0)); if(aiccFixed < aiccEmp) { tr->partitionData[model].autoProtModels = bestIndexFixed; tr->partitionData[model].protFreqs = 1; } else { tr->partitionData[model].autoProtModels = bestIndexEmp; tr->partitionData[model].protFreqs = 0; } } break; default: assert(0); } initReversibleGTR(tr, model); printBothOpen("\tPartition: %d best-scoring AA model: %s likelihood %f with %s base frequencies\n", model, protModels[tr->partitionData[model].autoProtModels], (tr->partitionData[model].protFreqs == 1)?bestLhFixed:bestLhEmp, (tr->partitionData[model].protFreqs == 1)?"fixed":"empirical"); } } printBothOpen("\n\n"); resetBranches(tr); evaluateGeneric(tr, tr->start, TRUE); treeEvaluate(tr, 2.0); //printf("exit %f\n", tr->likelihood); if(tr->likelihood < startLH) { for(model = 0; model < tr->NumberOfModels; model++) { if(tr->partitionData[model].protModels == AUTO) { tr->partitionData[model].autoProtModels = oldIndex[model]; tr->partitionData[model].protFreqs = oldFreqs[model] ; initReversibleGTR(tr, model); } } restoreTL(rl, tr, 0); evaluateGeneric(tr, tr->start, TRUE); } assert(tr->likelihood >= startLH); freeTL(rl); free(rl); free(oldIndex); free(bestIndex); free(bestScores); free(bestIndexEmpFreqs); free(bestScoresEmpFreqs); free(oldFreqs); } } static void checkMatrixSymnmetriesAndLinkage(tree *tr, linkageList *ll) { int i; for(i = 0; i < ll->entries; i++) { int partitions = ll->ld[i].partitions; if(partitions > 1) { int k, reference = ll->ld[i].partitionList[0]; for(k = 1; k < partitions; k++) { int index = ll->ld[i].partitionList[k]; int states = tr->partitionData[index].states, rates = ((states * states - states) / 2); if(tr->partitionData[reference].nonGTR != tr->partitionData[index].nonGTR) assert(0); if(tr->partitionData[reference].nonGTR) { int j; for(j = 0; j < rates; j++) { if(tr->partitionData[reference].symmetryVector[j] != tr->partitionData[index].symmetryVector[j]) assert(0); } } } } } } static void checkTolerance(double l1, double l2) { if(l1 < l2) { double tolerance = fabs(MAX(l1, l2) * 0.000000000001); if(fabs(l1 - l2) > MIN(0.1, tolerance)) { printf("Likelihood problem in model optimization l1: %1.40f l2: %1.40f tolerance: %1.40f\n", l1, l2, tolerance); assert(0); } } } void modOpt(tree *tr, double likelihoodEpsilon, analdef *adef, int treeIteration) { int i, catOpt = 0, *unlinked = (int *)malloc(sizeof(int) * tr->NumberOfModels); double inputLikelihood, currentLikelihood, modelEpsilon = 0.0001; linkageList *alphaList, *rateList, *freqList; for(i = 0; i < tr->NumberOfModels; i++) unlinked[i] = i; //test code for library if(0) { //assuming that we have three partitions for testing here alphaList = initLinkageListString("0,1,2", tr); rateList = initLinkageListString("0,1,1", tr); init_Q_MatrixSymmetries("0,1,2,3,4,5", tr, 0); init_Q_MatrixSymmetries("0,1,2,3,4,4", tr, 1); init_Q_MatrixSymmetries("0,1,1,2,3,4", tr, 2); //function that checks that partitions that have linked Q matrices as in our example above //will not have different configurations of the Q matrix as set by the init_Q_MatrixSymmetries() function //e.g., on would have HKY and one would have GTR, while the user claimes that they are linked //in our example, the Q matrices of partitions 1 and 2 are linked //but we set different matrix symmetries via // init_Q_MatrixSymmetries("0,1,2,3,4,4", tr, 1); // and // init_Q_MatrixSymmetries("0,1,1,2,3,4", tr, 2); // //the function just let's assertions fail for the time being ..... checkMatrixSymnmetriesAndLinkage(tr, rateList); } else { alphaList = initLinkageList(unlinked, tr); freqList = initLinkageList(unlinked, tr); rateList = initLinkageListGTR(tr); } tr->start = tr->nodep[1]; if(adef->useCheckpoint && adef->mode == TREE_EVALUATION) { assert(ckp.state == MOD_OPT); catOpt = ckp.catOpt; } inputLikelihood = tr->likelihood; evaluateGeneric(tr, tr->start, TRUE); assert(inputLikelihood == tr->likelihood); do { if(adef->mode == TREE_EVALUATION) { ckp.state = MOD_OPT; ckp.catOpt = catOpt; ckp.treeIteration = treeIteration; writeCheckpoint(tr, adef); } currentLikelihood = tr->likelihood; #ifdef _DEBUG_MOD_OPT printf("start: %f\n", currentLikelihood); #endif optRatesGeneric(tr, modelEpsilon, rateList); evaluateGeneric(tr, tr->start, TRUE); #ifdef _DEBUG_MOD_OPT printf("after rates %f\n", tr->likelihood); #endif autoProtein(tr, adef); treeEvaluate(tr, 0.0625); #ifdef _DEBUG_MOD_OPT evaluateGeneric(tr, tr->start, TRUE); printf("after br-len 1 %f\n", tr->likelihood); #endif evaluateGeneric(tr, tr->start, TRUE); optBaseFreqs(tr, modelEpsilon, freqList); evaluateGeneric(tr, tr->start, TRUE); treeEvaluate(tr, 0.0625); #ifdef _DEBUG_MOD_OPT evaluateGeneric(tr, tr->start, TRUE); printf("after optBaseFreqs 1 %f\n", tr->likelihood); #endif switch(tr->rateHetModel) { case GAMMA: optAlphasGeneric(tr, modelEpsilon, alphaList); evaluateGeneric(tr, tr->start, TRUE); #ifdef _DEBUG_MOD_OPT printf("after alphas %f\n", tr->likelihood); #endif treeEvaluate(tr, 0.1); #ifdef _DEBUG_MOD_OPT evaluateGeneric(tr, tr->start, TRUE); printf("after br-len 2 %f\n", tr->likelihood); #endif break; case CAT: if(catOpt < 3) { evaluateGeneric(tr, tr->start, TRUE); optimizeRateCategories(tr, tr->categories); #ifdef _DEBUG_MOD_OPT evaluateGeneric(tr, tr->start, TRUE); printf("after cat-opt %f\n", tr->likelihood); #endif catOpt++; } break; default: assert(0); } checkTolerance(tr->likelihood, currentLikelihood); /* if(tr->likelihood < currentLikelihood) printf("%f %f\n", tr->likelihood, currentLikelihood); assert(tr->likelihood >= currentLikelihood); */ printAAmatrix(tr, fabs(currentLikelihood - tr->likelihood)); } while(fabs(currentLikelihood - tr->likelihood) > likelihoodEpsilon); free(unlinked); freeLinkageList(freqList); freeLinkageList(alphaList); freeLinkageList(rateList); } ExaML-3.0.21/examl/partitionAssignment.c000066400000000000000000000422551330315463200200670ustar00rootroot00000000000000#include #include #include #include "partitionAssignment.h" extern int processID; void initializePartitionAssignment( PartitionAssignment **pAssPtr, pInfo **partitions, int numPart, int numProc) { int i; PartitionAssignment *pAss; *pAssPtr = (PartitionAssignment*)calloc(1, sizeof(PartitionAssignment)); pAss = *pAssPtr; pAss->numProc = numProc; pAss->numPartitions = numPart; pAss->partitions = (Partition *)calloc((size_t)pAss->numPartitions, sizeof(Partition)); for(i = 0; i < numPart; ++i) { Partition *p = pAss->partitions + i; p->id = i; p->width = partitions[i]->upper - partitions[i]->lower; p->type = partitions[i]->states; } pAss->assignPerProc = (Assignment **)calloc((size_t)pAss->numProc , sizeof(Assignment*)); pAss->numAssignPerProc = (int *)calloc((size_t)pAss->numProc, sizeof(int)); } void deletePartitionAssignment(PartitionAssignment *pAss) { int i; free(pAss->partitions); for(i = 0; i < pAss->numProc; ++i) free(pAss->assignPerProc[i]); free(pAss->assignPerProc); free(pAss); } static int partSort(const void *a, const void *b ) { return ((const Partition*)a)->width - ((const Partition*) b)->width ; } /** helper function that executes the assignment of a partial assignment (only numElem character are assigned) */ static void assignPartitionPartial(PartitionAssignment *pa, Partition* p, int procId, int *numAssigned, size_t *sizeAssigned, size_t offset, size_t numElem) { Assignment *a; int newArrayLen; ++numAssigned[procId]; ++pa->numAssignPerProc[procId]; newArrayLen = pa->numAssignPerProc[procId]; pa->assignPerProc[procId] = (Assignment*)realloc(pa->assignPerProc[procId], newArrayLen * sizeof(Assignment)); a = pa->assignPerProc[procId] + (newArrayLen-1); a->offset = offset; a->partId = p->id; a->width = numElem; sizeAssigned[procId] += numElem; } /** helper function that executes the assignment of a full partition */ static void assignPartitionFull(PartitionAssignment* pa, Partition* p, int procId, int *numAssigned, size_t *sizeAssigned) { assignPartitionPartial(pa, p, procId, numAssigned, sizeAssigned, 0, p->width); } /** Request a process to which a part of the partition should be assigned to. At this stage there are processes that have one more partition assignment than others. These have been categorized into two stacks (high and low). For each stack, we have in iter variable (of type int**) that gets decremented, if an element is removed from the stack and a the start of the array that allows us to determine when the stack is empty. popAndYield tries to satisfy the request for a process with more or less assignments (see wantLow), but will return any process, if the request cannot be fulfilled. If both queues are empty, it will return -1. */ static int popAndYield(int **procsHighIter, int *procsHighStart, int **procsLowIter, int *procsLowStart, boolean wantLow) { boolean fromHigh = FALSE, fromLow = FALSE; int result = -1; if(wantLow) { if(*procsLowIter - procsLowStart > 0) fromLow = TRUE; else if(*procsHighIter - procsHighStart > 0) fromHigh = TRUE; } else { if(*procsHighIter - procsHighStart > 0 ) fromHigh = TRUE; else if(*procsLowIter - procsLowStart > 0) fromLow = TRUE; } if(fromHigh) { result = **procsHighIter; --(*procsHighIter); } else if(fromLow) { result = **procsLowIter; --(*procsLowIter); } return result; } static void assignThesePartitions(PartitionAssignment* pa, Partition *partitions, int numCur) { int proc, remainder, /* number of processes that receive 1 character less than other s */ i, numFull = 0, /* number of processes that cannot take any more */ numLow = 0, /* */ *numAssigned = (int *)NULL, /* number of characters assigned to a process */ *procsHighIter = (int *)NULL, /* stack of processes that have one assignment more than others */ *procsLowIter = (int *)NULL, /* stack of processes that have one assignment less than others */ *procsLowStart = (int *)NULL, /* start of stack */ *procsHighStart = (int *)NULL, /* start of stack */ highProc, /* id of a process that potentially has more partitions assigned than others */ lowProc; /* id of a process that potentially has less partitions assigned than others */ size_t totalElems = 0, *sizeAssigned = (size_t *)NULL, toAdd, cap; /* defines a cap: once we have assigned this many characters, the remaining processes will get one character less */ boolean iterate = TRUE; Partition *partIter = partitions, *partEnd = partitions +numCur; /* The following implements Kassian's algorithm. Originally, his algorithm consists of 5 phases */ /* Sorts partitions according to their size. According to Kassians algorithm, this step is NOT obligatory. However, if we do it, then phase 3 (called "top-up" phase) is not necessary (this has been clarified with Kassian). */ qsort(partitions, numCur, sizeof(Partition), partSort); for(i = 0; i < numCur; ++i) totalElems += partitions[i].width; cap = ceil( (double)totalElems / (double)pa->numProc ); remainder = cap * pa->numProc - totalElems; assert(remainder >= 0 ); numAssigned = (int *)calloc((size_t)pa->numProc, sizeof(int)); sizeAssigned = (size_t *)calloc((size_t)pa->numProc, sizeof(size_t)); /* phase 2: initial distribution of full partitions to procesess. We distribute full partitions until for the first time, we cannot assign an entire partition any more, because this would exceed the number of characters we want to assign to this process */ while(iterate) { for(proc = 0; proc < pa->numProc;++proc) { if(partIter < partEnd && sizeAssigned[proc] + partIter->width <= cap) { assignPartitionFull(pa, partIter, proc, numAssigned, sizeAssigned); if(sizeAssigned[proc] == cap) { ++numFull; if(numFull == pa->numProc - remainder) --cap; } ++partIter; } else { numLow = numAssigned[proc]; iterate = FALSE; break; } } } /* phase 3: top-up => not necessary because of previous sorting */ /* phase 4: stick breaking Here we partially assign the remaining partitions to processes until every process has as many characters as it can take. */ /* first categorize processes into two stacks, dependent on whether they have gotton one more partition than others */ procsHighIter = (int*)calloc((size_t)pa->numProc + 1, sizeof(int)); procsLowIter = (int *)calloc((size_t)pa->numProc + 1, sizeof(int)); procsLowStart = procsLowIter; procsHighStart = procsHighIter; numFull = 0; for(proc = 0; proc < pa->numProc; ++proc) { if(sizeAssigned[proc] < cap) { if(numAssigned[proc] == numLow) { ++procsLowIter; *procsLowIter = proc; } else { ++procsHighIter; *procsHighIter = proc; } } else ++numFull; } assert((procsHighIter - procsHighStart) + (procsLowIter - procsLowStart) + numFull == pa->numProc); toAdd = (partIter < partEnd) ? partIter->width : 0 ; highProc = popAndYield(&procsHighIter, procsHighStart, &procsLowIter, procsLowStart, FALSE); lowProc = popAndYield(&procsHighIter, procsHighStart, &procsLowIter, procsLowStart, TRUE); /* now assign as long as there is something to assign. Once both stacks are empty, popAndYield yields -1. This then breaks the loop condition here. */ while( ! (highProc == -1 && lowProc == -1 && (procsHighIter - procsHighStart <= 0 ) && (procsLowIter - procsLowStart <= 0 )) ) { /* try to finish a assignments for a process that has many partitions */ if(highProc != -1 && sizeAssigned[highProc] + toAdd >= cap) { size_t toTransfer = cap - sizeAssigned[highProc], offset = partIter->width - toAdd; assignPartitionPartial( pa, partIter, highProc, numAssigned, sizeAssigned, offset, toTransfer); toAdd -= toTransfer; if(toAdd == 0 && partIter < partEnd ) { ++partIter; toAdd = partIter < partEnd ? partIter->width : 0; } ++numFull; if(numFull == pa->numProc - remainder) --cap; highProc = popAndYield(&procsHighIter, procsHighStart, &procsLowIter, procsLowStart, FALSE); } else if(lowProc != -1) { /* assign the enitre remaining portion to a process that still has fewer partitions */ if(sizeAssigned[lowProc] + toAdd < cap) { size_t offset = partIter->width - toAdd; assignPartitionPartial(pa, partIter, lowProc, numAssigned, sizeAssigned, offset, toAdd); if(highProc != -1 ) { ++procsHighIter; *procsHighIter = highProc; } highProc = lowProc; toAdd = 0; if( partIter != partEnd ) { ++partIter; toAdd = partIter < partEnd ? partIter->width : 0 ; } lowProc = popAndYield(&procsHighIter, procsHighStart, &procsLowIter, procsLowStart, TRUE); } else { /* assign as much as possible to a process with less partitions (the rest probably needs to be assigned to the next process) */ size_t toTransfer = cap - sizeAssigned[lowProc], offset = partIter->width - toAdd; assignPartitionPartial(pa, partIter, lowProc, numAssigned, sizeAssigned, offset, toTransfer); toAdd -= toTransfer; if(toAdd == 0 && partIter < partEnd) { ++partIter; toAdd = partIter < partEnd ? partIter->width : 0; } ++numFull; if(numFull == pa->numProc - remainder) --cap ; lowProc = popAndYield(&procsHighIter, procsHighStart, &procsLowIter, procsLowStart, FALSE); } } else { /* should not occurr, but I am not entirely happly with this assert. */ assert(0); } } assert(toAdd == 0 ); assert(partIter == partEnd); free(numAssigned); free(sizeAssigned); } /** Assigns all partitions. Notice that for each data type (currently only AA and DNA), we execute the algorithm separately. Thus, in the worst case imbalances for AA and DNA could hit the same processes (but this is probably not worth bothering). */ void assign(PartitionAssignment *pa) { int partitionsHandled = 0, curType = -1, j, i; /* only handling 3 types (BIN, DNA, AA) at the moment. Please adapt, when the number of types increases. */ int types[3] = { 2, 4, 20 }; for(j = 0; j < 3 ; ++j) { size_t cnt; Partition *curPartitions = (Partition *)NULL; curType = types[j]; /* count number of type */ cnt = 0; for(i = 0; i < pa->numPartitions; ++i) { if(pa->partitions[i].type == curType) ++cnt; } if(cnt == 0 ) continue; curPartitions = (Partition*)calloc((size_t)cnt, sizeof(Partition)); cnt = 0; for(i = 0; i< pa->numPartitions; ++i) { if(pa->partitions[i].type == curType) curPartitions[cnt++] = pa->partitions[i]; } assignThesePartitions(pa, curPartitions, cnt); free(curPartitions); partitionsHandled += cnt; } assert(partitionsHandled == pa->numPartitions); } void printAssignment(Assignment a, int procid) { printf("p: %d\t(%lu,%lu) -> proc %d\n", a.partId, a.offset, a.width , procid); } void printAssignments(PartitionAssignment *pa) { int i,j; printf("proc\toffset\tlength\tpart\n"); for(i = 0; i < pa->numProc; ++i) { for(j = 0; j < pa->numAssignPerProc[i] ; ++j) { Assignment a = pa->assignPerProc[i][j]; printf("%d\t%lu\t%lu\t%d\n", i,a.offset, a.width, a.partId); } } } void printLoad(PartitionAssignment *pa) { int i, j, *numsPerProc = (int *)calloc((size_t)pa->numProc, sizeof(int)); size_t *sitesPerProc = (size_t *)calloc((size_t)pa->numProc, sizeof(size_t)); for(i = 0; i< pa->numProc; ++i) { for(j = 0; j < pa->numAssignPerProc[i]; ++j) { Assignment a = pa->assignPerProc[i][j]; sitesPerProc[i] += a.width; ++numsPerProc[i]; } } printf("#proc\t#part\t#sites\n"); for( i = 0; i < pa->numProc ; ++i) printf("%d\t%d\t%lu\n", i, numsPerProc[i], sitesPerProc[i]); free(numsPerProc); free(sitesPerProc); } /** allocates global arrays for CAT and sets the pointers in each pInfo instance. */ static void setupBasePointersInTree(tree *tr) { size_t len = 0; int i; for(i = 0; i < tr->NumberOfModels; ++i) len += tr->partitionData[i].width ; tr->patrat_basePtr = (double*) calloc((size_t)len, sizeof(double)); tr->rateCategory_basePtr = (int*) calloc((size_t)len, sizeof(int)); tr->lhs_basePtr = (double*) calloc((size_t)len, sizeof(double)); len = 0; for(i = 0; i < tr->NumberOfModels; ++i) { if(tr->partitionData[i].width > 0) { tr->partitionData[i].rateCategory = tr->rateCategory_basePtr + len; tr->partitionData[i].patrat = tr->patrat_basePtr + len; tr->partitionData[i].lhs = tr->lhs_basePtr + len ; } else { tr->partitionData[i].rateCategory = (int *)NULL; tr->partitionData[i].patrat = (double*)NULL; tr->partitionData[i].lhs = (double*)NULL; } len += tr->partitionData[i].width; } } static int sortById(const void *a, const void *b) { return ((Assign*) a)->partitionId - ((Assign*) b)->partitionId ; } void copyAssignmentInfoToTree(PartitionAssignment *pa, tree *tr) { int i, numAssign = 0; Assign *assIter; for(i = 0; i < pa->numProc; ++i) numAssign += pa->numAssignPerProc[i]; /* copy the partition assignment to the tree structure */ tr->numAssignments = numAssign; tr->partAssigns = (Assign *)calloc((size_t)numAssign, sizeof(Assign)); assIter = tr->partAssigns; for(i = 0; i < pa->numProc; ++i) { int j; for( j = 0 ; j < pa->numAssignPerProc[i]; ++j) { Assignment *ass = pa->assignPerProc[i] + j; assIter->procId = i; assIter->offset = ass->offset; assIter->width = ass->width; assIter->partitionId = ass->partId; ++assIter; } } /* the sorting makes it easier to deal with the assignments later in case of a gather/scatter at the master. Thus, we do not need to jump around in the array that be obtained or send to a process, because we are sure that the data is ordered the same we as we obtained. */ qsort(tr->partAssigns, tr->numAssignments, sizeof(Assign), sortById); if(tr->rateHetModel == CAT) setupBasePointersInTree( tr); } #ifdef _USE_OMP void copyThreadAssignmentInfoToTree(PartitionAssignment *pa, tree *tr) { int i, j; /* we want to know max number of partitions assigned to a single thread -> mainly for memory allocation */ int *numsPerProc = (int *)calloc((size_t)pa->numProc, sizeof(int)), *numsPerPart = (int *)calloc((size_t)pa->numPartitions, sizeof(int)); for(i = 0; i< pa->numProc; ++i) { for(j = 0; j < pa->numAssignPerProc[i]; ++j) { Assignment *a = &pa->assignPerProc[i][j]; ++numsPerProc[i]; ++numsPerPart[a->partId]; } } int pmax = 0; for(i = 1; i< pa->numProc; ++i) { if (numsPerProc[i] > numsPerProc[pmax]) pmax = i; } /* save max partition count to the tree structure */ tr->maxModelsPerThread = numsPerProc[pmax]; assert(tr->maxModelsPerThread > 0 && tr->maxModelsPerThread <= pa->numPartitions); pmax = 0; for(i = 1; i< pa->numPartitions; ++i) { if (numsPerPart[i] > numsPerPart[pmax]) pmax = i; } /* save max threads count to the tree structure */ tr->maxThreadsPerModel = numsPerPart[pmax]; assert(tr->maxThreadsPerModel > 0 && tr->maxThreadsPerModel <= pa->numProc); free(numsPerProc); free(numsPerPart); printf("\n maxModelsPerThread: %d, maxThreadsPerModel: %d\n", tr->maxModelsPerThread, tr->maxThreadsPerModel); /* copy the partition assignment to the tree structure */ int threadPartSize = pa->numProc * tr->maxModelsPerThread, partThreadSize = pa->numPartitions * tr->maxThreadsPerModel; tr->threadPartAssigns = (Assign **)calloc((size_t)threadPartSize, sizeof(Assign*)); tr->partThreadAssigns = (Assign **)calloc((size_t)partThreadSize, sizeof(Assign*)); for(i = 0; i < pa->numProc; ++i) { int partCount = 0; for( j = 0 ; j < pa->numAssignPerProc[i]; ++j) { Assignment *ass = pa->assignPerProc[i] + j; Assign* pTreeAss = (Assign *)calloc(1, sizeof(Assign)); pTreeAss->procId = i; pTreeAss->offset = ass->offset; pTreeAss->width = ass->width; pTreeAss->partitionId = ass->partId; size_t ind = i * tr->maxModelsPerThread + partCount; assert(ind < (i+1) * tr->maxModelsPerThread); tr->threadPartAssigns[ind] = pTreeAss; ++partCount; ind = ass->partId * tr->maxThreadsPerModel; while (tr->partThreadAssigns[ind]) ++ind; assert( ind < (ass->partId+1) * tr->maxThreadsPerModel); tr->partThreadAssigns[ind] = pTreeAss; } } } #endif ExaML-3.0.21/examl/partitionAssignment.h000066400000000000000000000023471330315463200200720ustar00rootroot00000000000000#ifndef _PARTITIION_ASSIGNMENT #define _PARTITIION_ASSIGNMENT #include "axml.h" #define not ! typedef struct { int partId; size_t width; size_t offset; } Assignment; typedef struct { int id; size_t width; int type; } Partition; typedef struct { int numProc; int numPartitions; Partition *partitions; Assignment **assignPerProc; /* procid -> array of assignments */ int *numAssignPerProc; /* procid -> size of above array */ } PartitionAssignment; /* constructor */ void initializePartitionAssignment( PartitionAssignment **pAssPtr, pInfo **partitions, int numPart, int numProc); /* deletor */ void deletePartitionAssignment(PartitionAssignment *pAss); /* assign partitions to all proceses */ void assign(PartitionAssignment *pa); /* prints a single assignment */ void printAssignment(Assignment a, int procid); /* calculates and prints the load (number of partitions and number of sites) for each process */ void printLoad(PartitionAssignment *pa); void copyAssignmentInfoToTree(PartitionAssignment *pa, tree *tr); #ifdef _USE_OMP void copyThreadAssignmentInfoToTree(PartitionAssignment *pa, tree *tr); #endif void printAssignments(PartitionAssignment *pa); #endif ExaML-3.0.21/examl/quartets.c000066400000000000000000000417451330315463200157000ustar00rootroot00000000000000/* RAxML-HPC, a program for sequential and parallel estimation of phylogenetic trees * Copyright March 2006 by Alexandros Stamatakis * * Partially derived from * fastDNAml, a program for estimation of phylogenetic trees from sequences by Gary J. Olsen * * and * * Programs of the PHYLIP package by Joe Felsenstein. * * This program is free software; you may redistribute it and/or modify its * 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. * * * For any other enquiries send an Email to Alexandros Stamatakis * stamatak@ics.forth.gr * * When publishing work that is based on the results from RAxML-VI-HPC please cite: * * Alexandros Stamatakis: "An Efficient Program for phylogenetic Inference Using Simulated Annealing". * Proceedings of IPDPS2005, Denver, Colorado, April 2005. * * AND * * Alexandros Stamatakis:"RAxML-VI-HPC: maximum likelihood-based phylogenetic analyses with thousands of taxa and mixed models". * Bioinformatics 2006; doi: 10.1093/bioinformatics/btl446 */ #include #include #include #include #include #include #include #include #include #include #include #include "axml.h" extern double masterTime; extern char workdir[1024]; extern char run_id[128]; extern char quartetGroupingFileName[1024]; extern char quartetFileName[1024]; extern checkPointState ckp; extern int processID; /* a parser error function */ static void parseError(int c) { printBothOpen("Quartet grouping parser expecting symbol: %c\n", c); assert(0); } /* parser for the taxon grouping format, one has to specify 4 groups in a newick-like format from which quartets (a substantially smaller number compared to ungrouped quartets) will be drawn */ static void groupingParser(char *quartetGroupFileName, int *groups[4], int groupSize[4], tree *tr) { FILE *f = myfopen(quartetGroupFileName, "r"); int taxonCounter = 0, n, state = 0, groupCounter = 0, ch, i; printBothOpen("%s\n", quartetGroupFileName); for(i = 0; i < 4; i++) { groups[i] = (int*)malloc(sizeof(int) * (tr->mxtips + 1)); groupSize[i] = 0; } while((ch = getc(f)) != EOF) { if(!whitechar(ch)) { switch(state) { case 0: if(ch != '(') parseError('('); state = 1; break; case 1: ungetc(ch, f); n = treeFindTipName(f, tr, FALSE); if(n <= 0 || n > tr->mxtips) printBothOpen("parsing error, raxml is expecting to read a taxon name, found \"%c\" instead\n", ch); assert(n > 0 && n <= tr->mxtips); taxonCounter++; groups[groupCounter][groupSize[groupCounter]] = n; groupSize[groupCounter] = groupSize[groupCounter] + 1; state = 2; break; case 2: if(ch == ',') state = 1; else { if(ch == ')') { groupCounter++; state = 3; } else parseError('?'); } break; case 3: if(groupCounter == 4) { if(ch == ';') state = 4; else parseError(';'); } else { if(ch != ',') parseError(','); state = 0; } break; case 4: printBothOpen("Error: extra char after ; %c\n", ch); assert(0); default: assert(0); } } } assert(state == 4); assert(groupCounter == 4); assert(taxonCounter == tr->mxtips); printBothOpen("Successfully parsed quartet groups\n\n"); /* print out the taxa that have been assigned to the 4 groups */ for(i = 0; i < 4; i++) { int j; printBothOpen("group %d has %d members\n", i, groupSize[i]); for(j = 0; j < groupSize[i]; j++) printBothOpen("%s\n", tr->nameList[groups[i][j]]); printBothOpen("\n"); } fclose(f); } /*****************************/ static void nniSmooth(tree *tr, nodeptr p, int maxtimes) { int i; for(i = 0; i < tr->numBranches; i++) tr->partitionConverged[i] = FALSE; while (--maxtimes >= 0) { for(i = 0; i < tr->numBranches; i++) tr->partitionSmoothed[i] = TRUE; assert(!isTip(p->number, tr->mxtips)); assert(!isTip(p->back->number, tr->mxtips)); update(tr, p); update(tr, p->next); update(tr, p->next->next); update(tr, p->back->next); update(tr, p->back->next->next); if (allSmoothed(tr)) break; } for(i = 0; i < tr->numBranches; i++) { tr->partitionSmoothed[i] = FALSE; tr->partitionConverged[i] = FALSE; } } static double quartetLikelihood(tree *tr, nodeptr p1, nodeptr p2, nodeptr p3, nodeptr p4, nodeptr q1, nodeptr q2, analdef *adef, boolean firstQuartet) { /* build a quartet tree, where q1 and q2 are the inner nodes and p1, p2, p3, p4 are the tips of the quartet where the sequence data is located. initially set all branch lengths to the default value. */ /* for the tree and node data structure used, please see one of the last chapter's of Joe Felsensteins book. */ hookupDefault(q1, q2, tr->numBranches); hookupDefault(q1->next, p1, tr->numBranches); hookupDefault(q1->next->next, p2, tr->numBranches); hookupDefault(q2->next, p3, tr->numBranches); hookupDefault(q2->next->next, p4, tr->numBranches); /* now compute the likelihood vectors at the two inner nodes of the tree, here the virtual root is located between the two inner nodes q1 and q2. */ newviewGeneric(tr, q1, FALSE); newviewGeneric(tr, q2, FALSE); /* call a function that is also used for NNIs that iteratively optimizes all 5 branch lengths in the tree. Note that 16 is an important tuning parameter, this integer value determines how many times we visit all branches until we give up further optimizing the branch length configuration. */ nniSmooth(tr, q1, 16); /* now compute the log likelihood of the tree for the virtual root located between inner nodes q1 and q2 */ /* debugging code { double l; */ evaluateGeneric(tr, q1->back->next->next, FALSE); /* debugging code l = tr->likelihood; newviewGeneric(tr, q1); newviewGeneric(tr, q2); evaluateGeneric(tr, q1); assert(ABS(l - tr->likelihood) < 0.00001); } */ return (tr->likelihood); } static void computeAllThreeQuartets(tree *tr, nodeptr q1, nodeptr q2, int t1, int t2, int t3, int t4, FILE *f, analdef *adef) { /* set the tip nodes to different sequences with the tip indices t1, t2, t3, t4 */ nodeptr p1 = tr->nodep[t1], p2 = tr->nodep[t2], p3 = tr->nodep[t3], p4 = tr->nodep[t4]; double l; /* first quartet */ /* compute the likelihood of tree ((p1, p2), (p3, p4)) */ l = quartetLikelihood(tr, p1, p2, p3, p4, q1, q2, adef, TRUE); if(processID == 0) fprintf(f, "%d %d | %d %d: %f\n", p1->number, p2->number, p3->number, p4->number, l); /* second quartet */ /* compute the likelihood of tree ((p1, p3), (p2, p4)) */ l = quartetLikelihood(tr, p1, p3, p2, p4, q1, q2, adef, FALSE); if(processID == 0) fprintf(f, "%d %d | %d %d: %f\n", p1->number, p3->number, p2->number, p4->number, l); /* third quartet */ /* compute the likelihood of tree ((p1, p4), (p2, p3)) */ l = quartetLikelihood(tr, p1, p4, p2, p3, q1, q2, adef, FALSE); if(processID == 0) fprintf(f, "%d %d | %d %d: %f\n", p1->number, p4->number, p2->number, p3->number, l); } /* the three quartet options: all quartets, randomly sub-sample a certain number n of quartets, subsample all quartets from 4 pre-defined groups of quartets */ static void writeQuartetCheckpoint(uint64_t quartetCounter, FILE *f, tree *tr, analdef *adef) { if(quartetCounter % adef->quartetCkpInterval == 0) { ckp.quartetCounter = quartetCounter; if(processID == 0) { fflush(f); ckp.filePosition = ftell(f); } printBothOpen("\nPrinting checkpoint after %f seconds of run-time\n", gettime() - masterTime); writeCheckpoint(tr, adef); } } #define ALL_QUARTETS 0 #define RANDOM_QUARTETS 1 #define GROUPED_QUARTETS 2 void computeQuartets(tree *tr, analdef *adef) { /* some indices for generating quartets in an arbitrary way */ int flavor = ALL_QUARTETS, //type of quartet calculation i, t1, t2, t3, t4, *groups[4], groupSize[4]; double fraction = 0.0; uint64_t randomQuartets = (uint64_t)(adef->numberRandomQuartets), //number of random quartets to compute quartetCounter = 0, //total number of possible quartets, note that we count the following ((A,B),(C,D)), ((A,C),(B,D)), ((A,D),(B,C)) as one quartet here numberOfQuartets = ((uint64_t)tr->mxtips * ((uint64_t)tr->mxtips - 1) * ((uint64_t)tr->mxtips - 2) * ((uint64_t)tr->mxtips - 3)) / 24; /* use two inner tree nodes for building quartet trees */ nodeptr q1 = tr->nodep[tr->mxtips + 1], q2 = tr->nodep[tr->mxtips + 2]; FILE *f; long seed = (long)(tr->randomSeed); /***********************************/ /* get a starting tree on which we optimize the likelihood model parameters: either reads in a tree or computes a randomized stepwise addition parsimony tree */ if(adef->useCheckpoint) { /* read checkpoint file */ restart(tr, adef); strcpy(quartetFileName, workdir); strcat(quartetFileName, basename(ckp.quartetFileName)); printBothOpen("Time for reading checkpoint file: %f\n\n", gettime() - masterTime); seed = ckp.seed; if(processID == 0) { f = myfopen(quartetFileName, "r+"); fseek(f, ckp.filePosition, SEEK_SET); if(ftruncate(fileno(f), ckp.filePosition) != 0) assert(0); } } else { getStartingTree(tr); evaluateGeneric(tr, tr->start, TRUE); treeEvaluate(tr, 1); /* optimize model parameters on that comprehensive tree that can subsequently be used for evaluation of quartet likelihoods */ modOpt(tr, adef->likelihoodEpsilon, adef, 0); printBothOpen("Time for parsing input tree or building parsimony tree and optimizing model parameters: %f\n\n", gettime() - masterTime); printBothOpen("Tree likelihood: %f\n\n", tr->likelihood); if(processID == 0) f = myfopen(quartetFileName, "w"); } /* figure out which flavor of quartets we want to compute */ if(adef->useQuartetGrouping) { //quartet grouping evaluates all possible quartets from four disjoint //sets of user-specified taxon names flavor = GROUPED_QUARTETS; //parse the four disjoint sets of taxon names specified by the user from file groupingParser(quartetGroupingFileName, groups, groupSize, tr); } else { //if the user specified more random quartets to sample than there actually //exist for the number of taxa, then fix this. if(randomQuartets == 0 || randomQuartets >= numberOfQuartets) //TODO add usre warning? if second case above true? flavor = ALL_QUARTETS; else { //compute the fraction of random quartets to sample //there may be an issue here with the unit64_t <-> double cast fraction = (double)randomQuartets / (double)numberOfQuartets; assert(fraction < 1.0); flavor = RANDOM_QUARTETS; } } ckp.state = QUARTETS; ckp.seed = seed; strncpy(ckp.quartetFileName, quartetFileName, 1024); /* print some output on what we are doing*/ switch(flavor) { case ALL_QUARTETS: printBothOpen("There are %" PRIu64 " quartet sets for which RAxML will evaluate all %" PRIu64 " quartet trees\n", numberOfQuartets, numberOfQuartets * 3); break; case RANDOM_QUARTETS: printBothOpen("There are %" PRIu64 " quartet sets for which RAxML will randomly sub-sambple %" PRIu64 " sets (%f per cent), i.e., compute %" PRIu64 " quartet trees\n", numberOfQuartets, randomQuartets, 100 * fraction, randomQuartets * 3); break; case GROUPED_QUARTETS: printBothOpen("There are 4 quartet groups from which RAxML will evaluate all %u quartet trees\n", (unsigned int)groupSize[0] * (unsigned int)groupSize[1] * (unsigned int)groupSize[2] * (unsigned int)groupSize[3] * 3); break; default: assert(0); } /* print taxon name to taxon number correspondance table to output file */ if(!adef->useCheckpoint) { if(processID == 0) fprintf(f, "Taxon names and indices:\n\n"); for(i = 1; i <= tr->mxtips; i++) { if(processID == 0) fprintf(f, "%s %d\n", tr->nameList[i], i); assert(tr->nodep[i]->number == i); } if(processID == 0) { fprintf(f, "\n\n"); fflush(f); } } /* do a loop to generate some quartets to test. note that tip nodes/sequences in RAxML are indexed from 1,...,n and not from 0,...,n-1 as one might expect tr->mxtips is the maximum number of tips in the alignment/tree */ //now do the respective quartet evaluations by switching over the three distinct flavors switch(flavor) { case ALL_QUARTETS: { /* compute all possible quartets */ for(t1 = 1; t1 <= tr->mxtips; t1++) for(t2 = t1 + 1; t2 <= tr->mxtips; t2++) for(t3 = t2 + 1; t3 <= tr->mxtips; t3++) for(t4 = t3 + 1; t4 <= tr->mxtips; t4++) { if((adef->useCheckpoint && quartetCounter >= ckp.quartetCounter) || !adef->useCheckpoint) { writeQuartetCheckpoint(quartetCounter, f, tr, adef); computeAllThreeQuartets(tr, q1, q2, t1, t2, t3, t4, f, adef); } quartetCounter++; } assert(quartetCounter == numberOfQuartets); } break; case RANDOM_QUARTETS: { //endless loop ta make sure we randomly sub-sample exactly as many quartets as the user specified //This is not very elegant, but it works, note however, that especially when the number of //random quartets to be sampled is large, that is, close to the total number of quartets //some quartets may be sampled twice by pure chance. To randomly sample unique quartets //using hashes or bitmaps to store which quartets have already been sampled is not memory efficient. //Insetad, we need to use a random number generator that can generate a unique series of random numbers //and then have a function f() that maps those random numbers to the corresponding index quartet (t1, t2, t3, t4). do { //loop over all quartets for(t1 = 1; t1 <= tr->mxtips; t1++) for(t2 = t1 + 1; t2 <= tr->mxtips; t2++) for(t3 = t2 + 1; t3 <= tr->mxtips; t3++) for(t4 = t3 + 1; t4 <= tr->mxtips; t4++) { //chose a random number double r = randum(&seed); //if the random number is smaller than the fraction of quartets to subsample //evaluate the likelihood of the current quartet if(r < fraction) { if((adef->useCheckpoint && quartetCounter >= ckp.quartetCounter) || !adef->useCheckpoint) { writeQuartetCheckpoint(quartetCounter, f, tr, adef); //function that computes the likelihood for all three possible unrooted trees //defined by the given quartet of taxa computeAllThreeQuartets(tr, q1, q2, t1, t2, t3, t4, f, adef); } //increment quartet counter that counts how many quartets we have evaluated quartetCounter++; } //exit endless loop if we have randomly sub-sampled as many quartets as the user specified if(quartetCounter == randomQuartets) goto DONE; } } while(1); DONE: assert(quartetCounter == randomQuartets); } break; case GROUPED_QUARTETS: { /* compute all quartets that can be built out of the four pre-defined groups */ for(t1 = 0; t1 < groupSize[0]; t1++) for(t2 = 0; t2 < groupSize[1]; t2++) for(t3 = 0; t3 < groupSize[2]; t3++) for(t4 = 0; t4 < groupSize[3]; t4++) { int i1 = groups[0][t1], i2 = groups[1][t2], i3 = groups[2][t3], i4 = groups[3][t4]; if((adef->useCheckpoint && quartetCounter >= ckp.quartetCounter) || !adef->useCheckpoint) { writeQuartetCheckpoint(quartetCounter, f, tr, adef); computeAllThreeQuartets(tr, q1, q2, i1, i2, i3, i4, f, adef); } quartetCounter++; } printBothOpen("\nComputed all %" PRIu64 " possible grouped quartets\n", quartetCounter); } break; default: assert(0); } if(processID == 0) fclose(f); } ExaML-3.0.21/examl/restartHashTable.c000066400000000000000000000162461330315463200172660ustar00rootroot00000000000000/* RAxML-VI-HPC (version 2.2) a program for sequential and parallel estimation of phylogenetic trees * Copyright August 2006 by Alexandros Stamatakis * * Partially derived from * fastDNAml, a program for estimation of phylogenetic trees from sequences by Gary J. Olsen * * and * * Programs of the PHYLIP package by Joe Felsenstein. * * This program is free software; you may redistribute it and/or modify its * 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. * * * For any other enquiries send an Email to Alexandros Stamatakis * Alexandros.Stamatakis@epfl.ch * * When publishing work that is based on the results from RAxML-VI-HPC please cite: * * Alexandros Stamatakis:"RAxML-VI-HPC: maximum likelihood-based phylogenetic analyses with thousands of taxa and mixed models". * Bioinformatics 2006; doi: 10.1093/bioinformatics/btl446 */ #ifndef WIN32 #include #include #include #include #endif #include #include #include #include #include #include #include "axml.h" static boolean treeNeedString(const char *fp, char c1, int *position) { char c2 = fp[(*position)++]; if(c2 == c1) return TRUE; else { int lower = MAX(0, *position - 20), upper = *position + 20; printf("Tree Parsing ERROR: Expecting '%c', found: '%c'\n", c1, c2); printf("Context: \n"); while(lower < upper && fp[lower]) printf("%c", fp[lower++]); printf("\n"); return FALSE; } } static boolean treeLabelEndString (char ch) { switch(ch) { case '\0': case '\t': case '\n': case '\r': case ' ': case ':': case ',': case '(': case ')': case ';': return TRUE; default: break; } return FALSE; } static boolean treeGetLabelString (const char *fp, char *lblPtr, int maxlen, int *position) { char ch; boolean done, lblfound; if (--maxlen < 0) lblPtr = (char *)NULL; else if(lblPtr == NULL) maxlen = 0; ch = fp[(*position)++]; done = treeLabelEndString(ch); lblfound = !done; while(!done) { if(treeLabelEndString(ch)) break; if(--maxlen >= 0) *lblPtr++ = ch; ch = fp[(*position)++]; } (*position)--; if (lblPtr != NULL) *lblPtr = '\0'; return lblfound; } static boolean treeFlushLabelString(const char *fp, int *position) { return treeGetLabelString(fp, (char *) NULL, (int) 0, position); } static boolean treeProcessLengthString (const char *fp, double *dptr, int *position) { (*position)++; if(sscanf(&fp[*position], "%lf", dptr) != 1) { printf("ERROR: treeProcessLength: Problem reading branch length\n"); assert(0); } while(fp[*position] != ',' && fp[*position] != ')' && fp[*position] != ';') *position = *position + 1; return TRUE; } static int treeFlushLenString (const char *fp, int *position) { double dummy; char ch; ch = fp[(*position)++]; if(ch == ':') { if(!treeProcessLengthString(fp, &dummy, position)) return 0; return 1; } (*position)--; return 1; } static int treeFindTipByLabelString(char *str, tree *tr) { int lookup = lookupWord(str, tr->nameHash); if(lookup > 0) { assert(! tr->nodep[lookup]->back); return lookup; } else { printf("ERROR: Cannot find tree species: %s\n", str); return 0; } } static int treeFindTipNameString (const char *fp, tree *tr, int *position) { char str[nmlngth+2]; int n; if(treeGetLabelString(fp, str, nmlngth+2, position)) n = treeFindTipByLabelString(str, tr); else n = 0; return n; } static boolean addElementLenString(const char *fp, tree *tr, nodeptr p, int *position) { nodeptr q; int n, fres; char ch; if ((ch = fp[(*position)++]) == '(') { n = (tr->nextnode)++; if (n > 2*(tr->mxtips) - 2) { if (tr->rooted || n > 2*(tr->mxtips) - 1) { printf("ERROR: Too many internal nodes. Is tree rooted?\n"); printf(" Deepest splitting should be a trifurcation.\n"); return FALSE; } else { tr->rooted = TRUE; } } q = tr->nodep[n]; if (!addElementLenString(fp, tr, q->next, position)) return FALSE; if (!treeNeedString(fp, ',', position)) return FALSE; if (!addElementLenString(fp, tr, q->next->next, position)) return FALSE; if (!treeNeedString(fp, ')', position)) return FALSE; treeFlushLabelString(fp, position); } else { (*position)--; if ((n = treeFindTipNameString(fp, tr, position)) <= 0) return FALSE; q = tr->nodep[n]; if (tr->start->number > n) tr->start = q; (tr->ntips)++; } fres = treeFlushLenString(fp, position); if(!fres) return FALSE; hookupDefault(p, q, tr->numBranches); return TRUE; } void treeReadTopologyString(char *treeString, tree *tr) { char *fp = treeString; nodeptr p; int position = 0, i; char ch; for(i = 1; i <= tr->mxtips; i++) tr->nodep[i]->back = (node *)NULL; for(i = tr->mxtips + 1; i < 2 * tr->mxtips; i++) { tr->nodep[i]->back = (nodeptr)NULL; tr->nodep[i]->next->back = (nodeptr)NULL; tr->nodep[i]->next->next->back = (nodeptr)NULL; tr->nodep[i]->number = i; tr->nodep[i]->next->number = i; tr->nodep[i]->next->next->number = i; } tr->start = tr->nodep[1]; tr->ntips = 0; tr->nextnode = tr->mxtips + 1; tr->rooted = FALSE; p = tr->nodep[(tr->nextnode)++]; assert(fp[position++] == '('); if (! addElementLenString(fp, tr, p, &position)) assert(0); if (! treeNeedString(fp, ',', &position)) assert(0); if (! addElementLenString(fp, tr, p->next, &position)) assert(0); if(!tr->rooted) { if ((ch = fp[position++]) == ',') { if (! addElementLenString(fp, tr, p->next->next, &position)) assert(0); } else assert(0); } else assert(0); if (! treeNeedString(fp, ')', &position)) assert(0); treeFlushLabelString(fp, &position); if (!treeFlushLenString(fp, &position)) assert(0); if (!treeNeedString(fp, ';', &position)) assert(0); if(tr->rooted) assert(0); else tr->start = tr->nodep[1]; printf("Tree parsed\n"); } ExaML-3.0.21/examl/searchAlgo.c000066400000000000000000002043511330315463200160720ustar00rootroot00000000000000/* RAxML-VI-HPC (version 2.2) a program for sequential and parallel estimation of phylogenetic trees * Copyright August 2006 by Alexandros Stamatakis * * Partially derived from * fastDNAml, a program for estimation of phylogenetic trees from sequences by Gary J. Olsen * * and * * Programs of the PHYLIP package by Joe Felsenstein. * * This program is free software; you may redistribute it and/or modify its * 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. * * * For any other enquiries send an Email to Alexandros Stamatakis * Alexandros.Stamatakis@epfl.ch * * When publishing work that is based on the results from RAxML-VI-HPC please cite: * * Alexandros Stamatakis:"RAxML-VI-HPC: maximum likelihood-based phylogenetic analyses with thousands of taxa and mixed models". * Bioinformatics 2006; doi: 10.1093/bioinformatics/btl446 */ #ifndef WIN32 #include #include #include #include #endif #include #include #include #include #include #include #include "axml.h" extern int processes; extern int Thorough; extern int optimizeRateCategoryInvocations; extern infoList iList; extern char seq_file[1024]; extern char resultFileName[1024]; extern char tree_file[1024]; extern char workdir[1024]; extern char run_id[128]; extern double masterTime; extern double accumulatedTime; extern checkPointState ckp; extern partitionLengths pLengths[MAX_MODEL]; extern char binaryCheckpointName[1024]; extern char binaryCheckpointInputName[1024]; extern int processID; static int checker(tree *tr, nodeptr p) { int group = tr->constraintVector[p->number]; if(isTip(p->number, tr->mxtips)) { group = tr->constraintVector[p->number]; return group; } else { if(group != -9) return group; group = checker(tr, p->next->back); if(group != -9) return group; group = checker(tr, p->next->next->back); if(group != -9) return group; return -9; } } boolean initrav (tree *tr, nodeptr p) { nodeptr q; if (!isTip(p->number, tr->mxtips)) { q = p->next; do { if (! initrav(tr, q->back)) return FALSE; q = q->next; } while (q != p); newviewGeneric(tr, p, FALSE); } return TRUE; } /* #define _DEBUG_UPDATE */ boolean update(tree *tr, nodeptr p) { nodeptr q; boolean smoothedPartitions[NUM_BRANCHES]; int i; double z[NUM_BRANCHES], z0[NUM_BRANCHES]; double _deltaz; #ifdef _DEBUG_UPDATE double startLH; evaluateGeneric(tr, p, FALSE); startLH = tr->likelihood; #endif q = p->back; for(i = 0; i < tr->numBranches; i++) z0[i] = q->z[i]; if(tr->numBranches > 1) makenewzGeneric(tr, p, q, z0, newzpercycle, z, TRUE); else makenewzGeneric(tr, p, q, z0, newzpercycle, z, FALSE); for(i = 0; i < tr->numBranches; i++) smoothedPartitions[i] = tr->partitionSmoothed[i]; for(i = 0; i < tr->numBranches; i++) { if(!tr->partitionConverged[i]) { _deltaz = deltaz; if(ABS(z[i] - z0[i]) > _deltaz) { smoothedPartitions[i] = FALSE; } p->z[i] = q->z[i] = z[i]; } } #ifdef _DEBUG_UPDATE evaluateGeneric(tr, p, FALSE); if(tr->likelihood <= startLH) { if(fabs(tr->likelihood - startLH) > 0.01) { printf("%f %f\n", startLH, tr->likelihood); assert(0); } } #endif for(i = 0; i < tr->numBranches; i++) tr->partitionSmoothed[i] = smoothedPartitions[i]; return TRUE; } boolean smooth (tree *tr, nodeptr p) { nodeptr q; if (! update(tr, p)) return FALSE; /* Adjust branch */ if (! isTip(p->number, tr->mxtips)) { /* Adjust descendants */ q = p->next; while (q != p) { if (! smooth(tr, q->back)) return FALSE; q = q->next; } if(tr->numBranches > 1) newviewGeneric(tr, p, TRUE); else newviewGeneric(tr, p, FALSE); } return TRUE; } boolean allSmoothed(tree *tr) { int i; boolean result = TRUE; for(i = 0; i < tr->numBranches; i++) { if(tr->partitionSmoothed[i] == FALSE) result = FALSE; else tr->partitionConverged[i] = TRUE; } return result; } boolean smoothTree (tree *tr, int maxtimes) { nodeptr p, q; int i, count = 0; p = tr->start; for(i = 0; i < tr->numBranches; i++) tr->partitionConverged[i] = FALSE; while (--maxtimes >= 0) { for(i = 0; i < tr->numBranches; i++) tr->partitionSmoothed[i] = TRUE; if (! smooth(tr, p->back)) return FALSE; if (!isTip(p->number, tr->mxtips)) { q = p->next; while (q != p) { if (! smooth(tr, q->back)) return FALSE; q = q->next; } } count++; if (allSmoothed(tr)) break; } for(i = 0; i < tr->numBranches; i++) tr->partitionConverged[i] = FALSE; return TRUE; } boolean localSmooth (tree *tr, nodeptr p, int maxtimes) { nodeptr q; int i; if (isTip(p->number, tr->mxtips)) return FALSE; for(i = 0; i < tr->numBranches; i++) tr->partitionConverged[i] = FALSE; while (--maxtimes >= 0) { for(i = 0; i < tr->numBranches; i++) tr->partitionSmoothed[i] = TRUE; q = p; do { if (! update(tr, q)) return FALSE; q = q->next; } while (q != p); if (allSmoothed(tr)) break; } for(i = 0; i < tr->numBranches; i++) { tr->partitionSmoothed[i] = FALSE; tr->partitionConverged[i] = FALSE; } return TRUE; } static void resetInfoList(void) { int i; iList.valid = 0; for(i = 0; i < iList.n; i++) { iList.list[i].node = (nodeptr)NULL; iList.list[i].likelihood = unlikely; } } void initInfoList(int n) { int i; iList.n = n; iList.valid = 0; iList.list = (bestInfo *)malloc(sizeof(bestInfo) * n); for(i = 0; i < n; i++) { iList.list[i].node = (nodeptr)NULL; iList.list[i].likelihood = unlikely; } } void freeInfoList(void) { free(iList.list); } void insertInfoList(nodeptr node, double likelihood) { int i; int min = 0; double min_l = iList.list[0].likelihood; for(i = 1; i < iList.n; i++) { if(iList.list[i].likelihood < min_l) { min = i; min_l = iList.list[i].likelihood; } } if(likelihood > min_l) { iList.list[min].likelihood = likelihood; iList.list[min].node = node; iList.valid += 1; } if(iList.valid > iList.n) iList.valid = iList.n; } boolean smoothRegion (tree *tr, nodeptr p, int region) { nodeptr q; if (! update(tr, p)) return FALSE; /* Adjust branch */ if(region > 0) { if (!isTip(p->number, tr->mxtips)) { q = p->next; while (q != p) { if (! smoothRegion(tr, q->back, --region)) return FALSE; q = q->next; } newviewGeneric(tr, p, FALSE); } } return TRUE; } boolean regionalSmooth (tree *tr, nodeptr p, int maxtimes, int region) { nodeptr q; int i; if (isTip(p->number, tr->mxtips)) return FALSE; /* Should be an error */ for(i = 0; i < tr->numBranches; i++) tr->partitionConverged[i] = FALSE; while (--maxtimes >= 0) { for(i = 0; i < tr->numBranches; i++) tr->partitionSmoothed[i] = TRUE; q = p; do { if (! smoothRegion(tr, q, region)) return FALSE; q = q->next; } while (q != p); if (allSmoothed(tr)) break; } for(i = 0; i < tr->numBranches; i++) tr->partitionSmoothed[i] = FALSE; for(i = 0; i < tr->numBranches; i++) tr->partitionConverged[i] = FALSE; return TRUE; } /* localSmooth */ nodeptr removeNodeBIG (tree *tr, nodeptr p, int numBranches) { double zqr[NUM_BRANCHES], result[NUM_BRANCHES]; nodeptr q, r; int i; q = p->next->back; r = p->next->next->back; for(i = 0; i < numBranches; i++) zqr[i] = q->z[i] * r->z[i]; makenewzGeneric(tr, q, r, zqr, iterations, result, FALSE); for(i = 0; i < numBranches; i++) tr->zqr[i] = result[i]; hookup(q, r, result, numBranches); p->next->next->back = p->next->back = (node *) NULL; return q; } nodeptr removeNodeRestoreBIG (tree *tr, nodeptr p) { nodeptr q, r; q = p->next->back; r = p->next->next->back; newviewGeneric(tr, q, FALSE); newviewGeneric(tr, r, FALSE); hookup(q, r, tr->currentZQR, tr->numBranches); p->next->next->back = p->next->back = (node *) NULL; return q; } boolean insertBIG (tree *tr, nodeptr p, nodeptr q, int numBranches) { nodeptr r, s; int i; r = q->back; s = p->back; for(i = 0; i < numBranches; i++) tr->lzi[i] = q->z[i]; if(Thorough) { double zqr[NUM_BRANCHES], zqs[NUM_BRANCHES], zrs[NUM_BRANCHES], lzqr, lzqs, lzrs, lzsum, lzq, lzr, lzs, lzmax; double defaultArray[NUM_BRANCHES]; double e1[NUM_BRANCHES], e2[NUM_BRANCHES], e3[NUM_BRANCHES]; double *qz; qz = q->z; for(i = 0; i < numBranches; i++) defaultArray[i] = defaultz; makenewzGeneric(tr, q, r, qz, iterations, zqr, FALSE); makenewzGeneric(tr, q, s, defaultArray, iterations, zqs, FALSE); makenewzGeneric(tr, r, s, defaultArray, iterations, zrs, FALSE); for(i = 0; i < numBranches; i++) { lzqr = (zqr[i] > zmin) ? log(zqr[i]) : log(zmin); lzqs = (zqs[i] > zmin) ? log(zqs[i]) : log(zmin); lzrs = (zrs[i] > zmin) ? log(zrs[i]) : log(zmin); lzsum = 0.5 * (lzqr + lzqs + lzrs); lzq = lzsum - lzrs; lzr = lzsum - lzqs; lzs = lzsum - lzqr; lzmax = log(zmax); if (lzq > lzmax) {lzq = lzmax; lzr = lzqr; lzs = lzqs;} else if (lzr > lzmax) {lzr = lzmax; lzq = lzqr; lzs = lzrs;} else if (lzs > lzmax) {lzs = lzmax; lzq = lzqs; lzr = lzrs;} e1[i] = exp(lzq); e2[i] = exp(lzr); e3[i] = exp(lzs); } hookup(p->next, q, e1, numBranches); hookup(p->next->next, r, e2, numBranches); hookup(p, s, e3, numBranches); } else { double z[NUM_BRANCHES]; for(i = 0; i < numBranches; i++) { z[i] = sqrt(q->z[i]); if(z[i] < zmin) z[i] = zmin; if(z[i] > zmax) z[i] = zmax; } hookup(p->next, q, z, tr->numBranches); hookup(p->next->next, r, z, tr->numBranches); } newviewGeneric(tr, p, FALSE); if(Thorough) { localSmooth(tr, p, smoothings); for(i = 0; i < numBranches; i++) { tr->lzq[i] = p->next->z[i]; tr->lzr[i] = p->next->next->z[i]; tr->lzs[i] = p->z[i]; } } return TRUE; } boolean insertRestoreBIG (tree *tr, nodeptr p, nodeptr q) { nodeptr r, s; r = q->back; s = p->back; if(Thorough) { hookup(p->next, q, tr->currentLZQ, tr->numBranches); hookup(p->next->next, r, tr->currentLZR, tr->numBranches); hookup(p, s, tr->currentLZS, tr->numBranches); } else { double z[NUM_BRANCHES]; int i; for(i = 0; i < tr->numBranches; i++) { double zz; zz = sqrt(q->z[i]); if(zz < zmin) zz = zmin; if(zz > zmax) zz = zmax; z[i] = zz; } hookup(p->next, q, z, tr->numBranches); hookup(p->next->next, r, z, tr->numBranches); } newviewGeneric(tr, p, FALSE); return TRUE; } static void restoreTopologyOnly(tree *tr, bestlist *bt, bestlist *bestML) { nodeptr p = tr->removeNode; nodeptr q = tr->insertNode; double qz[NUM_BRANCHES], pz[NUM_BRANCHES], p1z[NUM_BRANCHES], p2z[NUM_BRANCHES]; nodeptr p1, p2, r, s; double currentLH = tr->likelihood; int i; p1 = p->next->back; p2 = p->next->next->back; for(i = 0; i < tr->numBranches; i++) { p1z[i] = p1->z[i]; p2z[i] = p2->z[i]; } hookup(p1, p2, tr->currentZQR, tr->numBranches); p->next->next->back = p->next->back = (node *) NULL; for(i = 0; i < tr->numBranches; i++) { qz[i] = q->z[i]; pz[i] = p->z[i]; } r = q->back; s = p->back; if(Thorough) { hookup(p->next, q, tr->currentLZQ, tr->numBranches); hookup(p->next->next, r, tr->currentLZR, tr->numBranches); hookup(p, s, tr->currentLZS, tr->numBranches); } else { double z[NUM_BRANCHES]; for(i = 0; i < tr->numBranches; i++) { z[i] = sqrt(q->z[i]); if(z[i] < zmin) z[i] = zmin; if(z[i] > zmax) z[i] = zmax; } hookup(p->next, q, z, tr->numBranches); hookup(p->next->next, r, z, tr->numBranches); } tr->likelihood = tr->bestOfNode; saveBestTree(bt, tr, TRUE); if(tr->saveBestTrees) saveBestTree(bestML, tr, FALSE); tr->likelihood = currentLH; hookup(q, r, qz, tr->numBranches); p->next->next->back = p->next->back = (nodeptr) NULL; if(Thorough) hookup(p, s, pz, tr->numBranches); hookup(p->next, p1, p1z, tr->numBranches); hookup(p->next->next, p2, p2z, tr->numBranches); } boolean testInsertBIG (tree *tr, nodeptr p, nodeptr q) { double qz[NUM_BRANCHES], pz[NUM_BRANCHES]; nodeptr r; boolean doIt = TRUE; double startLH = tr->endLH; int i; r = q->back; for(i = 0; i < tr->numBranches; i++) { qz[i] = q->z[i]; pz[i] = p->z[i]; } if(tr->constraintTree) { int rNumber, qNumber, pNumber; doIt = FALSE; rNumber = tr->constraintVector[r->number]; qNumber = tr->constraintVector[q->number]; pNumber = tr->constraintVector[p->number]; if(pNumber == -9) pNumber = checker(tr, p->back); if(pNumber == -9) doIt = TRUE; else { if(qNumber == -9) qNumber = checker(tr, q); if(rNumber == -9) rNumber = checker(tr, r); if(pNumber == rNumber || pNumber == qNumber) doIt = TRUE; } } if(doIt) { if (! insertBIG(tr, p, q, tr->numBranches)) return FALSE; evaluateGeneric(tr, p->next->next, FALSE); if(tr->likelihood > tr->bestOfNode) { tr->bestOfNode = tr->likelihood; tr->insertNode = q; tr->removeNode = p; for(i = 0; i < tr->numBranches; i++) { tr->currentZQR[i] = tr->zqr[i]; tr->currentLZR[i] = tr->lzr[i]; tr->currentLZQ[i] = tr->lzq[i]; tr->currentLZS[i] = tr->lzs[i]; } } if(tr->likelihood > tr->endLH) { tr->insertNode = q; tr->removeNode = p; for(i = 0; i < tr->numBranches; i++) tr->currentZQR[i] = tr->zqr[i]; tr->endLH = tr->likelihood; } hookup(q, r, qz, tr->numBranches); p->next->next->back = p->next->back = (nodeptr) NULL; if(Thorough) { nodeptr s = p->back; hookup(p, s, pz, tr->numBranches); } if((tr->doCutoff) && (tr->likelihood < startLH)) { tr->lhAVG += (startLH - tr->likelihood); tr->lhDEC++; if((startLH - tr->likelihood) >= tr->lhCutoff) return FALSE; else return TRUE; } else return TRUE; } else return TRUE; } void addTraverseBIG(tree *tr, nodeptr p, nodeptr q, int mintrav, int maxtrav) { if (--mintrav <= 0) { if (! testInsertBIG(tr, p, q)) return; } if ((!isTip(q->number, tr->mxtips)) && (--maxtrav > 0)) { addTraverseBIG(tr, p, q->next->back, mintrav, maxtrav); addTraverseBIG(tr, p, q->next->next->back, mintrav, maxtrav); } } int rearrangeBIG(tree *tr, nodeptr p, int mintrav, int maxtrav) { double p1z[NUM_BRANCHES], p2z[NUM_BRANCHES], q1z[NUM_BRANCHES], q2z[NUM_BRANCHES]; nodeptr p1, p2, q, q1, q2; int mintrav2, i; boolean doP = TRUE, doQ = TRUE; if (maxtrav < 1 || mintrav > maxtrav) return 0; q = p->back; if (!isTip(p->number, tr->mxtips) && doP) { p1 = p->next->back; p2 = p->next->next->back; if(!isTip(p1->number, tr->mxtips) || !isTip(p2->number, tr->mxtips)) { for(i = 0; i < tr->numBranches; i++) { p1z[i] = p1->z[i]; p2z[i] = p2->z[i]; } if (! removeNodeBIG(tr, p, tr->numBranches)) return badRear; if (!isTip(p1->number, tr->mxtips)) { addTraverseBIG(tr, p, p1->next->back, mintrav, maxtrav); addTraverseBIG(tr, p, p1->next->next->back, mintrav, maxtrav); } if (!isTip(p2->number, tr->mxtips)) { addTraverseBIG(tr, p, p2->next->back, mintrav, maxtrav); addTraverseBIG(tr, p, p2->next->next->back, mintrav, maxtrav); } hookup(p->next, p1, p1z, tr->numBranches); hookup(p->next->next, p2, p2z, tr->numBranches); newviewGeneric(tr, p, FALSE); } } if (!isTip(q->number, tr->mxtips) && maxtrav > 0 && doQ) { q1 = q->next->back; q2 = q->next->next->back; /*if (((!q1->tip) && (!q1->next->back->tip || !q1->next->next->back->tip)) || ((!q2->tip) && (!q2->next->back->tip || !q2->next->next->back->tip))) */ if ( ( ! isTip(q1->number, tr->mxtips) && (! isTip(q1->next->back->number, tr->mxtips) || ! isTip(q1->next->next->back->number, tr->mxtips)) ) || ( ! isTip(q2->number, tr->mxtips) && (! isTip(q2->next->back->number, tr->mxtips) || ! isTip(q2->next->next->back->number, tr->mxtips)) ) ) { for(i = 0; i < tr->numBranches; i++) { q1z[i] = q1->z[i]; q2z[i] = q2->z[i]; } if (! removeNodeBIG(tr, q, tr->numBranches)) return badRear; mintrav2 = mintrav > 2 ? mintrav : 2; if (/*! q1->tip*/ !isTip(q1->number, tr->mxtips)) { addTraverseBIG(tr, q, q1->next->back, mintrav2 , maxtrav); addTraverseBIG(tr, q, q1->next->next->back, mintrav2 , maxtrav); } if (/*! q2->tip*/ ! isTip(q2->number, tr->mxtips)) { addTraverseBIG(tr, q, q2->next->back, mintrav2 , maxtrav); addTraverseBIG(tr, q, q2->next->next->back, mintrav2 , maxtrav); } hookup(q->next, q1, q1z, tr->numBranches); hookup(q->next->next, q2, q2z, tr->numBranches); newviewGeneric(tr, q, FALSE); } } return 1; } double treeOptimizeRapid(tree *tr, int mintrav, int maxtrav, analdef *adef, bestlist *bt, bestlist *bestML) { int i, index, *perm = (int*)NULL; nodeRectifier(tr); if (maxtrav > tr->mxtips - 3) maxtrav = tr->mxtips - 3; resetInfoList(); resetBestTree(bt); tr->startLH = tr->endLH = tr->likelihood; if(tr->doCutoff) { if(tr->bigCutoff) { if(tr->itCount == 0) tr->lhCutoff = 0.5 * (tr->likelihood / -1000.0); else tr->lhCutoff = 0.5 * ((tr->lhAVG) / ((double)(tr->lhDEC))); } else { if(tr->itCount == 0) tr->lhCutoff = tr->likelihood / -1000.0; else tr->lhCutoff = (tr->lhAVG) / ((double)(tr->lhDEC)); } tr->itCount = tr->itCount + 1; tr->lhAVG = 0; tr->lhDEC = 0; } /* printf("DoCutoff: %d\n", tr->doCutoff); printf("%d %f %f %f\n", tr->itCount, tr->lhAVG, tr->lhDEC, tr->lhCutoff); printf("%d %d\n", mintrav, maxtrav); */ for(i = 1; i <= tr->mxtips + tr->mxtips - 2; i++) { tr->bestOfNode = unlikely; if(adef->permuteTreeoptimize) index = perm[i]; else index = i; if(rearrangeBIG(tr, tr->nodep[index], mintrav, maxtrav)) { if(Thorough) { if(tr->endLH > tr->startLH) { restoreTreeFast(tr); tr->startLH = tr->endLH = tr->likelihood; saveBestTree(bt, tr, TRUE); if(tr->saveBestTrees) saveBestTree(bestML, tr, FALSE); } else { if(tr->bestOfNode != unlikely) restoreTopologyOnly(tr, bt, bestML); } } else { insertInfoList(tr->nodep[index], tr->bestOfNode); if(tr->endLH > tr->startLH) { restoreTreeFast(tr); tr->startLH = tr->endLH = tr->likelihood; } } } } if(!Thorough) { Thorough = 1; for(i = 0; i < iList.valid; i++) { tr->bestOfNode = unlikely; if(rearrangeBIG(tr, iList.list[i].node, mintrav, maxtrav)) { if(tr->endLH > tr->startLH) { restoreTreeFast(tr); tr->startLH = tr->endLH = tr->likelihood; saveBestTree(bt, tr, TRUE); if(tr->saveBestTrees) saveBestTree(bestML, tr, FALSE); } else { if(tr->bestOfNode != unlikely) { restoreTopologyOnly(tr, bt, bestML); } } } } Thorough = 0; } if(adef->permuteTreeoptimize) free(perm); return tr->startLH; } boolean testInsertRestoreBIG (tree *tr, nodeptr p, nodeptr q) { if(Thorough) { if (! insertBIG(tr, p, q, tr->numBranches)) return FALSE; evaluateGeneric(tr, p->next->next, FALSE); } else { if (! insertRestoreBIG(tr, p, q)) return FALSE; { nodeptr x, y; x = p->next->next; y = p->back; if(! isTip(x->number, tr->mxtips) && isTip(y->number, tr->mxtips)) { while ((! x->x)) { if (! (x->x)) newviewGeneric(tr, x, FALSE); } } if(isTip(x->number, tr->mxtips) && !isTip(y->number, tr->mxtips)) { while ((! y->x)) { if (! (y->x)) newviewGeneric(tr, y, FALSE); } } if(!isTip(x->number, tr->mxtips) && !isTip(y->number, tr->mxtips)) { while ((! x->x) || (! y->x)) { if (! (x->x)) newviewGeneric(tr, x, FALSE); if (! (y->x)) newviewGeneric(tr, y, FALSE); } } } tr->likelihood = tr->endLH; } return TRUE; } void restoreTreeFast(tree *tr) { removeNodeRestoreBIG(tr, tr->removeNode); testInsertRestoreBIG(tr, tr->removeNode, tr->insertNode); } static void writeTree(tree *tr, FILE *f) { int x = tr->mxtips + 3 * (tr->mxtips - 1); nodeptr base = tr->nodeBaseAddress; myBinFwrite(&(tr->start->number), sizeof(int), 1, f); myBinFwrite(&base, sizeof(nodeptr), 1, f); myBinFwrite(tr->nodeBaseAddress, sizeof(node), x, f); } int ckpCount = 0; /** gathers patrat and rateCategory */ static void gatherDistributedCatInfos(tree *tr, int **rateCategory_result, double **patrat_result) { /* countPerProc and displPerProc must be int, since the MPI functino signatures demand so */ int *countPerProc = (int*)NULL, *displPerProc = (int*)NULL; calculateLengthAndDisplPerProcess(tr, &countPerProc, &displPerProc); if(processID == 0) { *rateCategory_result = (int*)calloc((size_t)tr->originalCrunchedLength , sizeof(int)); *patrat_result = (double*)calloc((size_t)tr->originalCrunchedLength, sizeof(double)); } gatherDistributedArray(tr, (void**) patrat_result, tr->patrat_basePtr, MPI_DOUBLE , countPerProc, displPerProc); gatherDistributedArray(tr, (void**) rateCategory_result, tr->rateCategory_basePtr, MPI_INT, countPerProc, displPerProc ); free(countPerProc); free(displPerProc); } /** added parameters patrat and rateCategory. The checkpoint writer has to gather this distributed information first. */ static void writeCheckpointInner(tree *tr, int *rateCategory, double *patrat, analdef *adef) { int model; char extendedName[2048], buf[64]; FILE *f; /* only master should write the checkpoint */ assert(processID == 0); strcpy(extendedName, binaryCheckpointName); strcat(extendedName, "_"); sprintf(buf, "%d", ckpCount); strcat(extendedName, buf); ckpCount++; f = myfopen(extendedName, "w"); ckp.cmd.useMedian = tr->useMedian; ckp.cmd.saveBestTrees = tr->saveBestTrees; ckp.cmd.saveMemory = tr->saveMemory; ckp.cmd.searchConvergenceCriterion = tr->searchConvergenceCriterion; ckp.cmd.perGeneBranchLengths = adef->perGeneBranchLengths; //adef ckp.cmd.likelihoodEpsilon = adef->likelihoodEpsilon; //adef ckp.cmd.categories = tr->categories; ckp.cmd.mode = adef->mode; //adef ckp.cmd.fastTreeEvaluation = tr->fastTreeEvaluation; ckp.cmd.initialSet = adef->initialSet;//adef ckp.cmd.initial = adef->initial;//adef ckp.cmd.rateHetModel = tr->rateHetModel; ckp.cmd.autoProteinSelectionType = tr->autoProteinSelectionType; ckp.cmd.useQuartetGrouping = adef->useQuartetGrouping; ckp.cmd.numberRandomQuartets = adef->numberRandomQuartets; /* cdta */ ckp.accumulatedTime = accumulatedTime + (gettime() - masterTime); ckp.constraintTree = tr->constraintTree; /* printf("Acc time: %f\n", ckp.accumulatedTime); */ myBinFwrite(&ckp, sizeof(checkPointState), 1, f); if(tr->constraintTree) myBinFwrite(tr->constraintVector, sizeof(int), 2 * tr->mxtips, f); myBinFwrite(tr->tree0, sizeof(char), tr->treeStringLength, f); myBinFwrite(tr->tree1, sizeof(char), tr->treeStringLength, f); if(tr->rateHetModel == CAT) { myBinFwrite(rateCategory, sizeof(int), tr->originalCrunchedLength, f); myBinFwrite(patrat, sizeof(double), tr->originalCrunchedLength, f); } //end for(model = 0; model < tr->NumberOfModels; model++) { int dataType = tr->partitionData[model].dataType; myBinFwrite(&(tr->partitionData[model].numberOfCategories), sizeof(int), 1, f); myBinFwrite(tr->partitionData[model].perSiteRates, sizeof(double), tr->maxCategories, f); myBinFwrite(tr->partitionData[model].EIGN, sizeof(double), pLengths[dataType].eignLength, f); myBinFwrite(tr->partitionData[model].EV, sizeof(double), pLengths[dataType].evLength, f); myBinFwrite(tr->partitionData[model].EI, sizeof(double), pLengths[dataType].eiLength, f); myBinFwrite(tr->partitionData[model].freqExponents, sizeof(double), pLengths[dataType].frequenciesLength, f); myBinFwrite(tr->partitionData[model].frequencies, sizeof(double), pLengths[dataType].frequenciesLength, f); myBinFwrite(tr->partitionData[model].tipVector, sizeof(double), pLengths[dataType].tipVectorLength, f); myBinFwrite(tr->partitionData[model].substRates, sizeof(double), pLengths[dataType].substRatesLength, f); //LG4X related variables myBinFwrite(tr->partitionData[model].weights , sizeof(double), 4, f); myBinFwrite(tr->partitionData[model].weightExponents , sizeof(double), 4, f); //myBinFwrite(tr->partitionData[model].weightsBuffer , sizeof(double), 4, f); //myBinFwrite(tr->partitionData[model].weightExponentsBuffer , sizeof(double), 4, f); //LG4X end if(tr->partitionData[model].protModels == LG4M || tr->partitionData[model].protModels == LG4X) { int k; for(k = 0; k < 4; k++) { myBinFwrite(tr->partitionData[model].rawEIGN_LG4[k], sizeof(double), pLengths[dataType].eignLength, f); myBinFwrite(tr->partitionData[model].EIGN_LG4[k], sizeof(double), pLengths[dataType].eignLength, f); myBinFwrite(tr->partitionData[model].EV_LG4[k], sizeof(double), pLengths[dataType].evLength, f); myBinFwrite(tr->partitionData[model].EI_LG4[k], sizeof(double), pLengths[dataType].eiLength, f); myBinFwrite(tr->partitionData[model].frequencies_LG4[k], sizeof(double), pLengths[dataType].frequenciesLength, f); myBinFwrite(tr->partitionData[model].tipVector_LG4[k], sizeof(double), pLengths[dataType].tipVectorLength, f); myBinFwrite(tr->partitionData[model].substRates_LG4[k], sizeof(double), pLengths[dataType].substRatesLength, f); } } myBinFwrite(&(tr->partitionData[model].alpha), sizeof(double), 1, f); myBinFwrite(&(tr->partitionData[model].gammaRates), sizeof(double), 4, f); myBinFwrite(&(tr->partitionData[model].protModels), sizeof(int), 1, f); myBinFwrite(&(tr->partitionData[model].autoProtModels), sizeof(int), 1, f); } if(ckp.state == MOD_OPT) { myBinFwrite(tr->likelihoods, sizeof(double), tr->numberOfTrees, f); myBinFwrite(tr->treeStrings, sizeof(char), (size_t)tr->treeStringLength * (size_t)tr->numberOfTrees, f); } writeTree(tr, f); fclose(f); /* printBothOpen("\nCheckpoint written to: %s likelihood: %f\n", extendedName, tr->likelihood); */ } void writeCheckpoint(tree *tr, analdef *adef) { int *rateCategory = (int *)NULL; double *patrat = (double *)NULL; if(tr->rateHetModel == CAT) gatherDistributedCatInfos(tr, &rateCategory, &patrat); if(processID == 0) { writeCheckpointInner(tr, rateCategory, patrat, adef); if(tr->rateHetModel == CAT) { free(rateCategory); free(patrat); } } } static void readTree(tree *tr, FILE *f) { int nodeNumber, x = tr->mxtips + 3 * (tr->mxtips - 1); nodeptr startAddress; myBinFread(&nodeNumber, sizeof(int), 1, f); tr->start = tr->nodep[nodeNumber]; /*printf("Start: %d %d\n", tr->start->number, nodeNumber);*/ myBinFread(&startAddress, sizeof(nodeptr), 1, f); /*printf("%u %u\n", (size_t)startAddress, (size_t)tr->nodeBaseAddress);*/ myBinFread(tr->nodeBaseAddress, sizeof(node), x, f); { int i; size_t offset; boolean addIt; if(startAddress > tr->nodeBaseAddress) { addIt = FALSE; offset = (size_t)startAddress - (size_t)tr->nodeBaseAddress; } else { addIt = TRUE; offset = (size_t)tr->nodeBaseAddress - (size_t)startAddress; } for(i = 0; i < x; i++) { if(addIt) { tr->nodeBaseAddress[i].next = (nodeptr)((size_t)tr->nodeBaseAddress[i].next + offset); tr->nodeBaseAddress[i].back = (nodeptr)((size_t)tr->nodeBaseAddress[i].back + offset); } else { tr->nodeBaseAddress[i].next = (nodeptr)((size_t)tr->nodeBaseAddress[i].next - offset); tr->nodeBaseAddress[i].back = (nodeptr)((size_t)tr->nodeBaseAddress[i].back - offset); } } } evaluateGeneric(tr, tr->start, TRUE); if(ckp.state != QUARTETS) printBothOpen("ExaML Restart with likelihood: %1.50f\n", tr->likelihood); } static void genericError(void) { printBothOpen("\nError: command lines used in initial run and re-start from checkpoint do not match!\n"); } static void checkCommandLineArguments(tree *tr, analdef *adef) { boolean match = TRUE; if(ckp.cmd.useMedian != tr->useMedian) { genericError(); printBothOpen("\nDisagreement in median for gamma option: -a\n"); match = FALSE; } if(ckp.cmd.saveBestTrees != tr->saveBestTrees) { genericError(); printBothOpen("\nDisagreement in tree saving option: -B\n"); match = FALSE; } if(ckp.cmd.saveMemory != tr->saveMemory) { genericError(); printBothOpen("\nDisagreement in memory saving option: -S\n"); match = FALSE; } if(ckp.cmd.searchConvergenceCriterion != tr->searchConvergenceCriterion) { genericError(); printBothOpen("\nDisagreement in search convergence criterion: -D\n"); match = FALSE; } if(ckp.cmd.perGeneBranchLengths != adef->perGeneBranchLengths) { genericError(); printBothOpen("\nDisagreement in using per-partition branch lengths: -M\n"); match = FALSE; } if(ckp.cmd.likelihoodEpsilon != adef->likelihoodEpsilon) { genericError(); printBothOpen("\nDisagreement in likelihood epsilon value: -e\n"); match = FALSE; } if(ckp.cmd.categories != tr->categories) { genericError(); printBothOpen("\nDisagreement in number of PSR rate categories: -c\n"); match = FALSE; } if(ckp.cmd.mode != adef->mode) { genericError(); printBothOpen("\nDisagreement in tree search or evaluation mode\n"); match = FALSE; } if(ckp.cmd.fastTreeEvaluation != tr->fastTreeEvaluation) { genericError(); printBothOpen("\nDisagreement in fast tree evaluation: -e|-E\n"); match = FALSE; } if(ckp.cmd.initialSet != adef->initialSet) { genericError(); printBothOpen("\nDisagreement in rearrangement radius limitation setting: -i\n"); match = FALSE; } if(ckp.cmd.initial != adef->initial) { genericError(); printBothOpen("\nDisagreement in rearrangement radius value: -i\n"); match = FALSE; } if(ckp.cmd.rateHetModel != tr->rateHetModel) { genericError(); printBothOpen("\nDisagreement in rate heterogeneity model: -m\n"); match = FALSE; } if(ckp.cmd.autoProteinSelectionType != tr->autoProteinSelectionType) { genericError(); printBothOpen("\nDisagreement in protein model selection criterion: --auto-prot\n"); match = FALSE; } if(ckp.cmd.useQuartetGrouping != adef->useQuartetGrouping) { genericError(); printBothOpen("\nDisagreement in quartet grouping option: -Y\n"); match = FALSE; } if(ckp.cmd.numberRandomQuartets != adef->numberRandomQuartets) { genericError(); printBothOpen("\nDisagreement in number of random quartet subsamples: -r\n"); match = FALSE; } if(!match) { printBothOpen("\nExaML will exit now ...\n\n"); errorExit(-1); } } static void readCheckpoint(tree *tr, analdef *adef) { int model; FILE *f = myfopen(binaryCheckpointInputName, "rb"); /* cdta */ myBinFread(&ckp, sizeof(checkPointState), 1, f); checkCommandLineArguments(tr, adef); tr->constraintTree = ckp.constraintTree; if(tr->constraintTree) myBinFread(tr->constraintVector, sizeof(int), 2 * tr->mxtips, f); tr->ntips = tr->mxtips; tr->startLH = ckp.tr_startLH; tr->endLH = ckp.tr_endLH; tr->likelihood = ckp.tr_likelihood; tr->bestOfNode = ckp.tr_bestOfNode; tr->lhCutoff = ckp.tr_lhCutoff; tr->lhAVG = ckp.tr_lhAVG; tr->lhDEC = ckp.tr_lhDEC; tr->itCount = ckp.tr_itCount; Thorough = ckp.Thorough; accumulatedTime = ckp.accumulatedTime; /* printf("Accumulated time so far: %f\n", accumulatedTime); */ optimizeRateCategoryInvocations = ckp.optimizeRateCategoryInvocations; myBinFread(tr->tree0, sizeof(char), tr->treeStringLength, f); myBinFread(tr->tree1, sizeof(char), tr->treeStringLength, f); if(tr->searchConvergenceCriterion && processID == 0) { int bCounter = 0; if((ckp.state == FAST_SPRS && ckp.fastIterations > 0) || (ckp.state == SLOW_SPRS && ckp.thoroughIterations > 0)) { #ifdef _DEBUG_CHECKPOINTING printf("parsing Tree 0\n"); #endif treeReadTopologyString(tr->tree0, tr); bitVectorInitravSpecial(tr->bitVectors, tr->nodep[1]->back, tr->mxtips, tr->vLength, tr->h, 0, BIPARTITIONS_RF, (branchInfo *)NULL, &bCounter, 1, FALSE, FALSE); assert(bCounter == tr->mxtips - 3); } bCounter = 0; if((ckp.state == FAST_SPRS && ckp.fastIterations > 1) || (ckp.state == SLOW_SPRS && ckp.thoroughIterations > 1)) { #ifdef _DEBUG_CHECKPOINTING printf("parsing Tree 1\n"); #endif treeReadTopologyString(tr->tree1, tr); bitVectorInitravSpecial(tr->bitVectors, tr->nodep[1]->back, tr->mxtips, tr->vLength, tr->h, 1, BIPARTITIONS_RF, (branchInfo *)NULL, &bCounter, 1, FALSE, FALSE); assert(bCounter == tr->mxtips - 3); } } if(tr->rateHetModel == CAT ) { /* every process reads its data */ /* Andre will this also work if we re-start with a different number of processors? have you tested? => Andre: yes that works: before writing the checkpoint, we gather all lhs/patrat with gatherDistributedCatInfos. This function calls gatherDisributedArray in communication.c. gatherDistributedArray takes care of reordering the data it obtained from the various processes, such the correct global array (i.e., indexing consistent with character position) is obtained. Thus, the indexing below (for reading in the patrat/lhs again) works correctly. */ /* Andre I think tr->originalCrunchedLength is of type size_t??? -> casting required ? Andre: in the very worst case, pPos overflows. There is not much one can do here. See explanation about fseek/fseeko at other location. But I have added an assert, in case something goes wrong */ exa_off_t rPos = exa_ftell(f), pPos = rPos + sizeof(int) * tr->originalCrunchedLength; /* fails, in case reading failed (ftello returns -1) or an overflow happened */ assert( ! ( rPos < 0 || pPos < 0 ) && rPos <= pPos ) ; /* first patrat then rateCategory */ Assign *aIter = tr->partAssigns, *aEnd = &(tr->partAssigns [ tr->numAssignments ]) ; /* Andre coould you maybe add a drawing (scanned drawn by hand if you like) documenting this layout ? => Andre: TODO */ while(aIter != aEnd) { if(aIter->procId == processID) { pInfo *partition = &(tr->partitionData[aIter->partitionId]); exa_off_t theOffset = pPos + (partition->lower + aIter->offset) * sizeof(double); assert(pPos <= theOffset); exa_fseek(f, theOffset, SEEK_SET); myBinFread(partition->patrat, sizeof(double), aIter->width, f); theOffset = rPos + (partition->lower + aIter->offset) * sizeof(int); assert(rPos <= theOffset); exa_fseek(f, theOffset, SEEK_SET); myBinFread(partition->rateCategory, sizeof(int), aIter->width, f); } ++aIter; } /* Set file pointer to the end of both of the arrays */ exa_fseek(f, pPos + tr->originalCrunchedLength * sizeof(double) , SEEK_SET); } //end for(model = 0; model < tr->NumberOfModels; model++) { int dataType = tr->partitionData[model].dataType; myBinFread(&(tr->partitionData[model].numberOfCategories), sizeof(int), 1, f); myBinFread(tr->partitionData[model].perSiteRates, sizeof(double), tr->maxCategories, f); myBinFread(tr->partitionData[model].EIGN, sizeof(double), pLengths[dataType].eignLength, f); myBinFread(tr->partitionData[model].EV, sizeof(double), pLengths[dataType].evLength, f); myBinFread(tr->partitionData[model].EI, sizeof(double), pLengths[dataType].eiLength, f); myBinFread(tr->partitionData[model].freqExponents, sizeof(double), pLengths[dataType].frequenciesLength, f); myBinFread(tr->partitionData[model].frequencies, sizeof(double), pLengths[dataType].frequenciesLength, f); myBinFread(tr->partitionData[model].tipVector, sizeof(double), pLengths[dataType].tipVectorLength, f); myBinFread(tr->partitionData[model].substRates, sizeof(double), pLengths[dataType].substRatesLength, f); //LG4X related variables myBinFread(tr->partitionData[model].weights , sizeof(double), 4, f); myBinFread(tr->partitionData[model].weightExponents , sizeof(double), 4, f); //myBinFread(tr->partitionData[model].weightsBuffer , sizeof(double), 4, f); //myBinFread(tr->partitionData[model].weightExponentsBuffer , sizeof(double), 4, f); //LG4X end if(tr->partitionData[model].protModels == LG4X || tr->partitionData[model].protModels == LG4M) { int k; for(k = 0; k < 4; k++) { myBinFread(tr->partitionData[model].rawEIGN_LG4[k], sizeof(double), pLengths[dataType].eignLength, f); myBinFread(tr->partitionData[model].EIGN_LG4[k], sizeof(double), pLengths[dataType].eignLength, f); myBinFread(tr->partitionData[model].EV_LG4[k], sizeof(double), pLengths[dataType].evLength, f); myBinFread(tr->partitionData[model].EI_LG4[k], sizeof(double), pLengths[dataType].eiLength, f); myBinFread(tr->partitionData[model].frequencies_LG4[k], sizeof(double), pLengths[dataType].frequenciesLength, f); myBinFread(tr->partitionData[model].tipVector_LG4[k], sizeof(double), pLengths[dataType].tipVectorLength, f); myBinFread(tr->partitionData[model].substRates_LG4[k], sizeof(double), pLengths[dataType].substRatesLength, f); } } myBinFread(&(tr->partitionData[model].alpha), sizeof(double), 1, f); myBinFread(&(tr->partitionData[model].gammaRates), sizeof(double), 4, f); //conditional added by Andre modified by me //only overwrite values of discrete gamma cats by calling makeGammaCats if not using //LG4X! if(tr->rateHetModel != CAT && !(tr->partitionData[model].protModels == LG4X)) makeGammaCats(tr->partitionData[model].alpha, tr->partitionData[model].gammaRates, 4, tr->useMedian); myBinFread(&(tr->partitionData[model].protModels), sizeof(int), 1, f); myBinFread(&(tr->partitionData[model].autoProtModels), sizeof(int), 1, f); } if(ckp.state == MOD_OPT) { myBinFread(tr->likelihoods, sizeof(double), tr->numberOfTrees, f); myBinFread(tr->treeStrings, sizeof(char), (size_t)tr->treeStringLength * (size_t)tr->numberOfTrees, f); } if(tr->rateHetModel == CAT) checkPerSiteRates(tr); readTree(tr, f); fclose(f); } void restart(tree *tr, analdef *adef) { readCheckpoint(tr, adef); switch(ckp.state) { case REARR_SETTING: assert(adef->mode == BIG_RAPID_MODE); break; case FAST_SPRS: assert(adef->mode == BIG_RAPID_MODE); break; case SLOW_SPRS: assert(adef->mode == BIG_RAPID_MODE); break; case MOD_OPT: assert(adef->mode == TREE_EVALUATION); break; case QUARTETS: assert(adef->mode == QUARTET_CALCULATION); break; default: assert(0); } } int determineRearrangementSetting(tree *tr, analdef *adef, bestlist *bestT, bestlist *bt, bestlist *bestML) { const int MaxFast = 26; int i, maxtrav = 5, bestTrav = 5; double startLH = tr->likelihood; boolean impr = TRUE, cutoff = tr->doCutoff; if(adef->useCheckpoint) { assert(ckp.state == REARR_SETTING); maxtrav = ckp.maxtrav; bestTrav = ckp.bestTrav; startLH = ckp.startLH; impr = ckp.impr; cutoff = ckp.cutoff; adef->useCheckpoint = FALSE; } tr->doCutoff = FALSE; resetBestTree(bt); #ifdef _DEBUG_CHECKPOINTING printBothOpen("MAXTRAV: %d\n", maxtrav); #endif assert(Thorough == 0); while(impr && maxtrav < MaxFast) { recallBestTree(bestT, 1, tr); nodeRectifier(tr); /* Andre I believe that the code below, except for writeCheckpoint cann still only be executed by process 0 => Andre: all other processes need to enter writeCheckpoint, because of the gather that happens there. But the assignments to the checkpoint state are not necessary for all processes; does it matter? */ { ckp.optimizeRateCategoryInvocations = optimizeRateCategoryInvocations; ckp.cutoff = cutoff; ckp.state = REARR_SETTING; ckp.maxtrav = maxtrav; ckp.bestTrav = bestTrav; ckp.startLH = startLH; ckp.impr = impr; ckp.tr_startLH = tr->startLH; ckp.tr_endLH = tr->endLH; ckp.tr_likelihood = tr->likelihood; ckp.tr_bestOfNode = tr->bestOfNode; ckp.tr_lhCutoff = tr->lhCutoff; ckp.tr_lhAVG = tr->lhAVG; ckp.tr_lhDEC = tr->lhDEC; ckp.tr_itCount = tr->itCount; writeCheckpoint(tr, adef); } if (maxtrav > tr->mxtips - 3) maxtrav = tr->mxtips - 3; tr->startLH = tr->endLH = tr->likelihood; /* printBothOpen("TRAV: %d lh %f MNZC %d\n", maxtrav, tr->likelihood, mnzc); */ { int changes = 0; for(i = 1; i <= tr->mxtips + tr->mxtips - 2; i++) { tr->bestOfNode = unlikely; if(rearrangeBIG(tr, tr->nodep[i], 1, maxtrav)) { if(tr->endLH > tr->startLH) { restoreTreeFast(tr); tr->startLH = tr->endLH = tr->likelihood; changes++; } } } /* evaluateGeneric(tr, tr->start, TRUE); printBothOpen("Changes: %d TRAV: %d lh %f MNZC %d\n", changes, maxtrav, tr->likelihood, mnzc); */ } treeEvaluate(tr, 0.25); /* printBothOpen("TRAV: %d lh %f MNZC %d\n", maxtrav, tr->likelihood, mnzc); */ saveBestTree(bt, tr, TRUE); if(tr->saveBestTrees) saveBestTree(bestML, tr, FALSE); #ifdef _DEBUG_CHECKPOINTING printBothOpen("TRAV: %d lh %f MNZC %d\n", maxtrav, tr->likelihood, mnzc); #endif if(tr->likelihood > startLH) { startLH = tr->likelihood; printLog(tr); bestTrav = maxtrav; impr = TRUE; } else impr = FALSE; if(tr->doCutoff) { tr->lhCutoff = (tr->lhAVG) / ((double)(tr->lhDEC)); tr->itCount = tr->itCount + 1; tr->lhAVG = 0; tr->lhDEC = 0; } maxtrav += 5; } recallBestTree(bt, 1, tr); tr->doCutoff = cutoff; #ifdef _DEBUG_CHECKPOINTING printBothOpen("BestTrav %d\n", bestTrav); #endif return bestTrav; } void computeBIGRAPID (tree *tr, analdef *adef, boolean estimateModel) { int i, impr, bestTrav = 0, treeVectorLength = 0, rearrangementsMax = 0, rearrangementsMin = 0, thoroughIterations = 0, fastIterations = 0; double lh = unlikely, previousLh = unlikely, difference, epsilon; bestlist *bestML, *bestT, *bt; /* now here is the RAxML hill climbing search algorithm */ tr->lhAVG = 0.0; tr->lhDEC = 0.0; /* initialization for the hash table to compute RF distances */ if(tr->searchConvergenceCriterion && processID == 0) treeVectorLength = 1; /* initialize two lists of size 1 and size 20 that will keep track of the best and 20 best tree topologies respectively */ bestT = (bestlist *) malloc(sizeof(bestlist)); bestT->ninit = 0; initBestTree(bestT, 1, tr->mxtips); bt = (bestlist *) malloc(sizeof(bestlist)); bt->ninit = 0; initBestTree(bt, 20, tr->mxtips); if(tr->saveBestTrees > 0) { bestML = (bestlist *) malloc(sizeof(bestlist)); bestML->ninit = 0; initBestTree(bestML, tr->saveBestTrees, tr->mxtips); } else bestML = (bestlist *)NULL; /* initialize an additional data structure used by the search algo, all of this is pretty RAxML-specific and should probably not be in the library */ initInfoList(50); /* some pretty atbitrary thresholds */ difference = 10.0; epsilon = 0.01; /* Thorough = 0 means that we will do fast SPR inbsertions without optimizing the three branches adjacent to the subtree insertion position via Newton-Raphson */ Thorough = 0; /* if we are not using a checkpoint and estimateModel is set to TRUE we call the function that optimizes model parameters, such as the CAT model assignment, the alpha paremeter or the rates in the GTR matrix. Otherwise we just optimize the branch lengths. Note that the second parameter of treeEvaluate() controls how many times we will iterate over all branches of the tree until we give up, provided that, the br-len opt. has not converged before. */ if(!adef->useCheckpoint) { if(estimateModel) modOpt(tr, 10.0, adef, 0); else treeEvaluate(tr, 2); } /* print some stuff to the RAxML_log file */ printLog(tr); /* save the current tree (which is the input tree parsed via -t in the bestT list */ saveBestTree(bestT, tr, TRUE); /* if the rearrangmenet radius has been set by the user ie. adef->initailSet == TRUE then just set the apppropriate parameter. Otherwise, call the function determineRearrangementSetting() that seeks for the best radius by executing SPR moves on the initial tree with different radii and returns the smallest radius that yields the best log likelihood score after applying one cycle of SPR moves to the tree */ if(!adef->initialSet) { if((!adef->useCheckpoint) || (adef->useCheckpoint && ckp.state == REARR_SETTING)) { bestTrav = adef->bestTrav = determineRearrangementSetting(tr, adef, bestT, bt, bestML); printBothOpen("\nBest rearrangement radius: %d\n", bestTrav); } } else { bestTrav = adef->bestTrav = adef->initial; printBothOpen("\nUser-defined rearrangement radius: %d\n", bestTrav); } /* some checkpointing noise */ if(!(adef->useCheckpoint && (ckp.state == FAST_SPRS || ckp.state == SLOW_SPRS))) { /* optimize model params more thoroughly or just optimize branch lengths */ if(estimateModel) modOpt(tr, 5.0, adef, 0); else treeEvaluate(tr, 1); } /* save the current tree again, while the topology has not changed, the branch lengths have changed in the meantime, hence we need to store them again */ saveBestTree(bestT, tr, TRUE); /* set the loop variable to TRUE */ impr = 1; /* this is for the additional RAxML heuristics described imn this paper here: A. Stamatakis, F. Blagojevic, C.D. Antonopoulos, D.S. Nikolopoulos: "Exploring new Search Algorithms and Hardware for Phylogenetics: RAxML meets the IBM Cell". In Journal of VLSI Signal Processing Systems, 48(3):271-286, 2007. This is turned on by default */ if(tr->doCutoff) tr->itCount = 0; /* figure out where to continue computations if we restarted from a checkpoint */ if(adef->useCheckpoint && ckp.state == FAST_SPRS) goto START_FAST_SPRS; if(adef->useCheckpoint && ckp.state == SLOW_SPRS) goto START_SLOW_SPRS; while(impr) { START_FAST_SPRS: /* if re-starting from checkpoint set the required variable values to the values that they had when the checkpoint was written */ if(adef->useCheckpoint && ckp.state == FAST_SPRS) { optimizeRateCategoryInvocations = ckp.optimizeRateCategoryInvocations; impr = ckp.impr; Thorough = ckp.Thorough; bestTrav = ckp.bestTrav; treeVectorLength = ckp.treeVectorLength; rearrangementsMax = ckp.rearrangementsMax; rearrangementsMin = ckp.rearrangementsMin; thoroughIterations = ckp.thoroughIterations; fastIterations = ckp.fastIterations; lh = ckp.lh; previousLh = ckp.previousLh; difference = ckp.difference; epsilon = ckp.epsilon; tr->likelihood = ckp.tr_likelihood; tr->lhCutoff = ckp.tr_lhCutoff; tr->lhAVG = ckp.tr_lhAVG; tr->lhDEC = ckp.tr_lhDEC; tr->itCount = ckp.tr_itCount; adef->useCheckpoint = FALSE; } else /* otherwise, restore the currently best tree */ recallBestTree(bestT, 1, tr); /* save states of algorithmic/heuristic variables for printing the next checkpoint */ /* Andre I believe that the code below, except for writeCheckpoint cann still only be executed by process 0 => Andre: see above */ { ckp.state = FAST_SPRS; ckp.optimizeRateCategoryInvocations = optimizeRateCategoryInvocations; ckp.impr = impr; ckp.Thorough = Thorough; ckp.bestTrav = bestTrav; ckp.treeVectorLength = treeVectorLength; ckp.rearrangementsMax = rearrangementsMax; ckp.rearrangementsMin = rearrangementsMin; ckp.thoroughIterations = thoroughIterations; ckp.fastIterations = fastIterations; ckp.lh = lh; ckp.previousLh = previousLh; ckp.difference = difference; ckp.epsilon = epsilon; ckp.bestTrav = bestTrav; ckp.impr = impr; ckp.tr_startLH = tr->startLH; ckp.tr_endLH = tr->endLH; ckp.tr_likelihood = tr->likelihood; ckp.tr_bestOfNode = tr->bestOfNode; ckp.tr_lhCutoff = tr->lhCutoff; ckp.tr_lhAVG = tr->lhAVG; ckp.tr_lhDEC = tr->lhDEC; ckp.tr_itCount = tr->itCount; /* write a binary checkpoint */ writeCheckpoint(tr, adef); } /* this is the aforementioned convergence criterion that requires computing the RF, let's not worry about this right now */ if(tr->searchConvergenceCriterion && processID == 0) { int bCounter = 0; char *buffer = (char*)calloc(tr->treeStringLength, sizeof(char)); if(fastIterations > 1) cleanupHashTable(tr->h, (fastIterations % 2)); bitVectorInitravSpecial(tr->bitVectors, tr->nodep[1]->back, tr->mxtips, tr->vLength, tr->h, fastIterations % 2, BIPARTITIONS_RF, (branchInfo *)NULL, &bCounter, 1, FALSE, FALSE); #ifdef _DEBUG_CHECKPOINTING printf("Storing tree in slot %d\n", fastIterations % 2); #endif Tree2String(buffer, tr, tr->start->back, FALSE, TRUE, FALSE, FALSE, FALSE, SUMMARIZE_LH, FALSE, FALSE); if(fastIterations % 2 == 0) memcpy(tr->tree0, buffer, tr->treeStringLength * sizeof(char)); else memcpy(tr->tree1, buffer, tr->treeStringLength * sizeof(char)); free(buffer); assert(bCounter == tr->mxtips - 3); if(fastIterations > 0) { double rrf = convergenceCriterion(tr->h, tr->mxtips); MPI_Bcast(&rrf, 1, MPI_DOUBLE, 0, MPI_COMM_WORLD); if(rrf <= 0.01) /* 1% cutoff */ { printBothOpen("ML fast search converged at fast SPR cycle %d with stopping criterion\n", fastIterations); printBothOpen("Relative Robinson-Foulds (RF) distance between respective best trees after one succseful SPR cycle: %f%s\n", rrf, "%"); cleanupHashTable(tr->h, 0); cleanupHashTable(tr->h, 1); goto cleanup_fast; } else printBothOpen("ML search convergence criterion fast cycle %d->%d Relative Robinson-Foulds %f\n", fastIterations - 1, fastIterations, rrf); } } if(tr->searchConvergenceCriterion && processID != 0 && fastIterations > 0) { double rrf; MPI_Bcast(&rrf, 1, MPI_DOUBLE, 0, MPI_COMM_WORLD); if(rrf <= 0.01) /* 1% cutoff */ goto cleanup_fast; } /* count how many fast iterations with so-called fast SPR moves we have executed */ fastIterations++; /* optimize branch lengths */ treeEvaluate(tr, 1.0); /* save the tree with those branch lengths again */ saveBestTree(bestT, tr, TRUE); /* print the log likelihood */ printLog(tr); /* print this intermediate tree to file */ printResult(tr, adef, FALSE); /* update the current best likelihood */ lh = previousLh = tr->likelihood; /* in here we actually do a cycle of SPR moves */ treeOptimizeRapid(tr, 1, bestTrav, adef, bt, bestML); /* set impr to 0 since in the immediately following for loop we check if the SPR moves above have generated a better tree */ impr = 0; /* loop over the 20 best trees generated by the fast SPR moves, and check if they improve the likelihood after all of their branch lengths have been optimized */ for(i = 1; i <= bt->nvalid; i++) { /* restore tree i from list generated by treeOptimizeRapid */ recallBestTree(bt, i, tr); /* optimize branch lengths of this tree */ treeEvaluate(tr, 0.25); /* calc. the likelihood improvement */ difference = ((tr->likelihood > previousLh)? tr->likelihood - previousLh: previousLh - tr->likelihood); /* if the likelihood has improved save the current tree as best tree and continue */ /* note that we always compre this tree to the likelihood of the previous best tree */ if(tr->likelihood > lh && difference > epsilon) { impr = 1; lh = tr->likelihood; saveBestTree(bestT, tr, TRUE); } } #ifdef _DEBUG_CHECKPOINTING printBothOpen("FAST LH: %f\n", lh); #endif } /* needed for this RF-based convergence criterion that I actually describe in here: A. Stamatakis: "Phylogenetic Search Algorithms for Maximum Likelihood". In M. Elloumi, A.Y. Zomaya, editors. Algorithms in Computational Biology: techniques, Approaches and Applications, John Wiley and Sons a copy of this book is in my office */ if(tr->searchConvergenceCriterion && processID == 0) { cleanupHashTable(tr->h, 0); cleanupHashTable(tr->h, 1); } cleanup_fast: /* now we have jumped out of the loop that executes fast SPRs, and next we will execute a loop that executes throough SPR cycles (with SPR moves that optimize via newton-Raphson all adjacent branches to the insertion point) until no through SPR move can be found that improves the likelihood further. A classic hill climbing algo. */ Thorough = 1; impr = 1; /* restore the currently best tree. this si actually required, because we do not know which tree is actually stored in the tree data structure when the above loop exits */ recallBestTree(bestT, 1, tr); /* RE-TRAVERSE THE ENTIRE TREE */ evaluateGeneric(tr, tr->start, TRUE); #ifdef _DEBUG_CHECKPOINTING printBothOpen("After Fast SPRs Final %f\n", tr->likelihood); #endif /* optimize model params (including branch lengths) or just optimize branch lengths and leave the other model parameters (GTR rates, alhpa) alone */ if(estimateModel) modOpt(tr, 1.0, adef, 0); else treeEvaluate(tr, 1.0); /* start loop that executes thorough SPR cycles */ while(1) { /* once again if we want to restart from a checkpoint that was written during this loop we need to restore the values of the variables appropriately */ START_SLOW_SPRS: if(adef->useCheckpoint && ckp.state == SLOW_SPRS) { optimizeRateCategoryInvocations = ckp.optimizeRateCategoryInvocations; impr = ckp.impr; Thorough = ckp.Thorough; bestTrav = ckp.bestTrav; treeVectorLength = ckp.treeVectorLength; rearrangementsMax = ckp.rearrangementsMax; rearrangementsMin = ckp.rearrangementsMin; thoroughIterations = ckp.thoroughIterations; fastIterations = ckp.fastIterations; lh = ckp.lh; previousLh = ckp.previousLh; difference = ckp.difference; epsilon = ckp.epsilon; tr->likelihood = ckp.tr_likelihood; tr->lhCutoff = ckp.tr_lhCutoff; tr->lhAVG = ckp.tr_lhAVG; tr->lhDEC = ckp.tr_lhDEC; tr->itCount = ckp.tr_itCount; adef->useCheckpoint = FALSE; } else /* otherwise we restore the currently best tree and load it from bestT into our tree data structuire tr */ recallBestTree(bestT, 1, tr); /* now, we write a checkpoint */ /* Andre I believe that the code below, except for writeCheckpoint cann still only be executed by process 0 => Andre: see above */ { ckp.state = SLOW_SPRS; ckp.optimizeRateCategoryInvocations = optimizeRateCategoryInvocations; ckp.impr = impr; ckp.Thorough = Thorough; ckp.bestTrav = bestTrav; ckp.treeVectorLength = treeVectorLength; ckp.rearrangementsMax = rearrangementsMax; ckp.rearrangementsMin = rearrangementsMin; ckp.thoroughIterations = thoroughIterations; ckp.fastIterations = fastIterations; ckp.lh = lh; ckp.previousLh = previousLh; ckp.difference = difference; ckp.epsilon = epsilon; ckp.bestTrav = bestTrav; ckp.impr = impr; ckp.tr_startLH = tr->startLH; ckp.tr_endLH = tr->endLH; ckp.tr_likelihood = tr->likelihood; ckp.tr_bestOfNode = tr->bestOfNode; ckp.tr_lhCutoff = tr->lhCutoff; ckp.tr_lhAVG = tr->lhAVG; ckp.tr_lhDEC = tr->lhDEC; ckp.tr_itCount = tr->itCount; /* write binary checkpoint to file */ writeCheckpoint(tr, adef); } if(impr) { /* if the logl has improved write out some stuff and adapt the rearrangement radii */ printResult(tr, adef, FALSE); /* minimum rearrangement radius */ rearrangementsMin = 1; /* max radius, this is probably something I need to explain at the whiteboard */ rearrangementsMax = adef->stepwidth; /* once again the convergence criterion */ if(tr->searchConvergenceCriterion && processID == 0) { int bCounter = 0; char *buffer = (char*)calloc(tr->treeStringLength, sizeof(char)); if(thoroughIterations > 1) cleanupHashTable(tr->h, (thoroughIterations % 2)); bitVectorInitravSpecial(tr->bitVectors, tr->nodep[1]->back, tr->mxtips, tr->vLength, tr->h, thoroughIterations % 2, BIPARTITIONS_RF, (branchInfo *)NULL, &bCounter, 1, FALSE, FALSE); #ifdef _DEBUG_CHECKPOINTING printf("Storing tree in slot %d\n", thoroughIterations % 2); #endif Tree2String(buffer, tr, tr->start->back, FALSE, TRUE, FALSE, FALSE, FALSE, SUMMARIZE_LH, FALSE, FALSE); if(thoroughIterations % 2 == 0) memcpy(tr->tree0, buffer, tr->treeStringLength * sizeof(char)); else memcpy(tr->tree1, buffer, tr->treeStringLength * sizeof(char)); free(buffer); assert(bCounter == tr->mxtips - 3); if(thoroughIterations > 0) { double rrf = convergenceCriterion(tr->h, tr->mxtips); MPI_Bcast(&rrf, 1, MPI_DOUBLE, 0, MPI_COMM_WORLD); if(rrf <= 0.01) /* 1% cutoff */ { printBothOpen("ML search converged at thorough SPR cycle %d with stopping criterion\n", thoroughIterations); printBothOpen("Relative Robinson-Foulds (RF) distance between respective best trees after one succseful SPR cycle: %f%s\n", rrf, "%"); goto cleanup; } else printBothOpen("ML search convergence criterion thorough cycle %d->%d Relative Robinson-Foulds %f\n", thoroughIterations - 1, thoroughIterations, rrf); } } if(tr->searchConvergenceCriterion && processID != 0 && thoroughIterations > 0) { double rrf; MPI_Bcast(&rrf, 1, MPI_DOUBLE, 0, MPI_COMM_WORLD); if(rrf <= 0.01) /* 1% cutoff */ goto cleanup; } thoroughIterations++; } else { /* if the lnl has not imrpved by the current SPR cycle adapt the min and max rearrangemnt radii and try again */ rearrangementsMax += adef->stepwidth; rearrangementsMin += adef->stepwidth; /* if we have already tried them then abandon this loop, the search has converged */ if(rearrangementsMax > adef->max_rearrange) goto cleanup; } /* optimize branch lengths of best tree */ treeEvaluate(tr, 1.0); /* do some bokkeeping and printouts again */ previousLh = lh = tr->likelihood; saveBestTree(bestT, tr, TRUE); printLog(tr); /* do a cycle of thorough SPR moves with the minimum and maximum rearrangement radii */ treeOptimizeRapid(tr, rearrangementsMin, rearrangementsMax, adef, bt, bestML); impr = 0; /* once again get the best 20 trees produced by the SPR cycle, load them from the bt tree list into tr optimize their branch lengths and figure out if the LnL of the tree has improved */ for(i = 1; i <= bt->nvalid; i++) { recallBestTree(bt, i, tr); treeEvaluate(tr, 0.25); difference = ((tr->likelihood > previousLh)? tr->likelihood - previousLh: previousLh - tr->likelihood); if(tr->likelihood > lh && difference > epsilon) { impr = 1; lh = tr->likelihood; saveBestTree(bestT, tr, TRUE); } } #ifdef _DEBUG_CHECKPOINTING printBothOpen("SLOW LH: %f\n", lh); #endif } cleanup: /* do a final full tree traversal, not sure if this is required here */ evaluateGeneric(tr, tr->start, TRUE); #ifdef _DEBUG_CHECKPOINTING printBothOpen("After SLOW SPRs Final %f\n", tr->likelihood); #endif printBothOpen("\nLikelihood of best tree: %f\n", tr->likelihood); /* print the absolut best tree */ printLog(tr); printResult(tr, adef, TRUE); /* print other good trees encountered during the search */ if(tr->saveBestTrees > 0) { char fileName[2048] = "", buf[64] = ""; printBothOpen("\n\nEvaluating %d other good ML trees\n\n", bestML->nvalid); for(i = 1; i <= bestML->nvalid; i++) { recallBestTree(bestML, i, tr); /*treeEvaluate(tr, 0.25);*/ printBothOpen("tree %d likelihood %1.80f\n", i, tr->likelihood); if(processID == 0) { FILE *treeFile; strcpy(fileName, workdir); strcat(fileName, "RAxML_"); sprintf(buf, "%d", bestML->nvalid); strcat(fileName, buf); strcat(fileName, "_goodTrees."); strcat(fileName, run_id); treeFile = myfopen(fileName, "a"); Tree2String(tr->tree_string, tr, tr->start->back, TRUE, TRUE, FALSE, FALSE, TRUE, SUMMARIZE_LH, FALSE, FALSE); fprintf(treeFile, "%s", tr->tree_string); fclose(treeFile); } } printBothOpen("\n\nOther good trees written to file %s\n", fileName); } /* free data structures */ if(tr->searchConvergenceCriterion && processID == 0) { freeBitVectors(tr->bitVectors, 2 * tr->mxtips); free(tr->bitVectors); freeHashTable(tr->h); free(tr->h); } freeBestTree(bestT); free(bestT); freeBestTree(bt); free(bt); freeInfoList(); /* and we are done, return to main() in axml.c */ } boolean treeEvaluate (tree *tr, double smoothFactor) /* Evaluate a user tree */ { boolean result; result = smoothTree(tr, (int)((double)smoothings * smoothFactor)); assert(result); //make sure that all vectors are oriented correctly ! evaluateGeneric(tr, tr->start, TRUE); return TRUE; } ExaML-3.0.21/examl/topologies.c000066400000000000000000000357721330315463200162170ustar00rootroot00000000000000 /* RAxML-VI-HPC (version 2.2) a program for sequential and parallel estimation of phylogenetic trees * Copyright August 2006 by Alexandros Stamatakis * * Partially derived from * fastDNAml, a program for estimation of phylogenetic trees from sequences by Gary J. Olsen * * and * * Programs of the PHYLIP package by Joe Felsenstein. * * This program is free software; you may redistribute it and/or modify its * 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. * * * For any other enquiries send an Email to Alexandros Stamatakis * Alexandros.Stamatakis@epfl.ch * * When publishing work that is based on the results from RAxML-VI-HPC please cite: * * Alexandros Stamatakis:"RAxML-VI-HPC: maximum likelihood-based phylogenetic analyses with thousands of taxa and mixed models". * Bioinformatics 2006; doi: 10.1093/bioinformatics/btl446 */ #ifndef WIN32 #include #include #include #include #endif #include #include #include #include #include #include #include "axml.h" static void saveTopolRELLRec(tree *tr, nodeptr p, topolRELL *tpl, int *i, int numsp, int numBranches) { int k; if(isTip(p->number, numsp)) return; else { nodeptr q = p->next; while(q != p) { tpl->connect[*i].p = q; tpl->connect[*i].q = q->back; if(tr->constraintTree) { tpl->connect[*i].cp = tr->constraintVector[q->number]; tpl->connect[*i].cq = tr->constraintVector[q->back->number]; } for(k = 0; k < numBranches; k++) tpl->connect[*i].z[k] = q->z[k]; *i = *i + 1; saveTopolRELLRec(tr, q->back, tpl, i, numsp, numBranches); q = q->next; } } } static void saveTopolRELL(tree *tr, topolRELL *tpl) { nodeptr p = tr->start; int k, i = 0; tpl->likelihood = tr->likelihood; tpl->start = 1; tpl->connect[i].p = p; tpl->connect[i].q = p->back; if(tr->constraintTree) { tpl->connect[i].cp = tr->constraintVector[p->number]; tpl->connect[i].cq = tr->constraintVector[p->back->number]; } for(k = 0; k < tr->numBranches; k++) tpl->connect[i].z[k] = p->z[k]; i++; saveTopolRELLRec(tr, p->back, tpl, &i, tr->mxtips, tr->numBranches); assert(i == 2 * tr->mxtips - 3); } static void restoreTopolRELL(tree *tr, topolRELL *tpl) { int i; for (i = 0; i < 2 * tr->mxtips - 3; i++) { hookup(tpl->connect[i].p, tpl->connect[i].q, tpl->connect[i].z, tr->numBranches); tr->constraintVector[tpl->connect[i].p->number] = tpl->connect[i].cp; tr->constraintVector[tpl->connect[i].q->number] = tpl->connect[i].cq; } tr->likelihood = tpl->likelihood; tr->start = tr->nodep[tpl->start]; /* TODO */ } void initTL(topolRELL_LIST *rl, tree *tr, int n) { int i; rl->max = n; rl->t = (topolRELL **)malloc(sizeof(topolRELL *) * n); for(i = 0; i < n; i++) { rl->t[i] = (topolRELL *)malloc(sizeof(topolRELL)); rl->t[i]->connect = (connectRELL *)malloc((2 * tr->mxtips - 3) * sizeof(connectRELL)); rl->t[i]->likelihood = unlikely; } } void freeTL(topolRELL_LIST *rl) { int i; for(i = 0; i < rl->max; i++) { free(rl->t[i]->connect); free(rl->t[i]); } free(rl->t); } void restoreTL(topolRELL_LIST *rl, tree *tr, int n) { assert(n >= 0 && n < rl->max); restoreTopolRELL(tr, rl->t[n]); } void resetTL(topolRELL_LIST *rl) { int i; for(i = 0; i < rl->max; i++) rl->t[i]->likelihood = unlikely; } void saveTL(topolRELL_LIST *rl, tree *tr, int index) { assert(index >= 0 && index < rl->max); if(tr->likelihood > rl->t[index]->likelihood) saveTopolRELL(tr, rl->t[index]); } static void *tipValPtr (nodeptr p) { return (void *) & p->number; } static int cmpTipVal (void *v1, void *v2) { int i1, i2; i1 = *((int *) v1); i2 = *((int *) v2); return (i1 < i2) ? -1 : ((i1 == i2) ? 0 : 1); } /* These are the only routines that need to UNDERSTAND topologies */ static topol *setupTopol (int maxtips) { topol *tpl; if (! (tpl = (topol *) malloc(sizeof(topol))) || ! (tpl->links = (connptr) malloc((2*maxtips-3) * sizeof(connect)))) { printf("ERROR: Unable to get topology memory"); tpl = (topol *) NULL; } else { tpl->likelihood = unlikely; tpl->start = (node *) NULL; tpl->nextlink = 0; tpl->ntips = 0; tpl->nextnode = 0; tpl->scrNum = 0; /* position in sorted list of scores */ tpl->tplNum = 0; /* position in sorted list of trees */ } return tpl; } static void freeTopol (topol *tpl) { free(tpl->links); free(tpl); } static int saveSubtree (nodeptr p, topol *tpl, int numsp, int numBranches) { connptr r, r0; nodeptr q, s; int t, t0, t1, k; r0 = tpl->links; r = r0 + (tpl->nextlink)++; r->p = p; r->q = q = p->back; for(k = 0; k < numBranches; k++) r->z[k] = p->z[k]; r->descend = 0; /* No children (yet) */ if (isTip(q->number, numsp)) { r->valptr = tipValPtr(q); /* Assign value */ } else { /* Internal node, look at children */ s = q->next; /* First child */ do { t = saveSubtree(s, tpl, numsp, numBranches); /* Generate child's subtree */ t0 = 0; /* Merge child into list */ t1 = r->descend; while (t1 && (cmpTipVal(r0[t1].valptr, r0[t].valptr) < 0)) { t0 = t1; t1 = r0[t1].sibling; } if (t0) r0[t0].sibling = t; else r->descend = t; r0[t].sibling = t1; s = s->next; /* Next child */ } while (s != q); r->valptr = r0[r->descend].valptr; /* Inherit first child's value */ } /* End of internal node processing */ return (r - r0); } static nodeptr minSubtreeTip (nodeptr p0, int numsp) { nodeptr minTip, p, testTip; if (isTip(p0->number, numsp)) return p0; p = p0->next; minTip = minSubtreeTip(p->back, numsp); while ((p = p->next) != p0) { testTip = minSubtreeTip(p->back, numsp); if (cmpTipVal(tipValPtr(testTip), tipValPtr(minTip)) < 0) minTip = testTip; } return minTip; } static nodeptr minTreeTip (nodeptr p, int numsp) { nodeptr minp, minpb; minp = minSubtreeTip(p, numsp); minpb = minSubtreeTip(p->back, numsp); return (cmpTipVal(tipValPtr(minp), tipValPtr(minpb)) < 0 ? minp : minpb); } static void saveTree (tree *tr, topol *tpl) /* Save a tree topology in a standard order so that first branches * from a node contain lower value tips than do second branches from * the node. The root tip should have the lowest value of all. */ { connptr r; tpl->nextlink = 0; /* Reset link pointer */ r = tpl->links + saveSubtree(minTreeTip(tr->start, tr->mxtips), tpl, tr->mxtips, tr->numBranches); /* Save tree */ r->sibling = 0; tpl->likelihood = tr->likelihood; tpl->start = tr->start; tpl->ntips = tr->ntips; tpl->nextnode = tr->nextnode; } /* saveTree */ static boolean restoreTree (topol *tpl, tree *tr) { connptr r; nodeptr p, p0; int i; for (i = 1; i <= 2*(tr->mxtips) - 2; i++) { /* Uses p = p->next at tip */ p0 = p = tr->nodep[i]; do { p->back = (nodeptr) NULL; p = p->next; } while (p != p0); } /* Copy connections from topology */ for (r = tpl->links, i = 0; i < tpl->nextlink; r++, i++) hookup(r->p, r->q, r->z, tr->numBranches); tr->likelihood = tpl->likelihood; tr->start = tpl->start; tr->ntips = tpl->ntips; tr->nextnode = tpl->nextnode; evaluateGeneric(tr, tr->start, TRUE); return TRUE; } int initBestTree (bestlist *bt, int newkeep, int numsp) { /* initBestTree */ int i; bt->nkeep = 0; if (bt->ninit <= 0) { if (! (bt->start = setupTopol(numsp))) return 0; bt->ninit = -1; bt->nvalid = 0; bt->numtrees = 0; bt->best = unlikely; bt->improved = FALSE; bt->byScore = (topol **) malloc((newkeep+1) * sizeof(topol *)); bt->byTopol = (topol **) malloc((newkeep+1) * sizeof(topol *)); if (! bt->byScore || ! bt->byTopol) { printf( "initBestTree: malloc failure\n"); return 0; } } else if (ABS(newkeep) > bt->ninit) { if (newkeep < 0) newkeep = -(bt->ninit); else newkeep = bt->ninit; } if (newkeep < 1) { /* Use negative newkeep to clear list */ newkeep = -newkeep; if (newkeep < 1) newkeep = 1; bt->nvalid = 0; bt->best = unlikely; } if (bt->nvalid >= newkeep) { bt->nvalid = newkeep; bt->worst = bt->byScore[newkeep]->likelihood; } else { bt->worst = unlikely; } for (i = bt->ninit + 1; i <= newkeep; i++) { if (! (bt->byScore[i] = setupTopol(numsp))) break; bt->byTopol[i] = bt->byScore[i]; bt->ninit = i; } return (bt->nkeep = MIN(newkeep, bt->ninit)); } /* initBestTree */ void resetBestTree (bestlist *bt) { /* resetBestTree */ bt->best = unlikely; bt->worst = unlikely; bt->nvalid = 0; bt->improved = FALSE; } /* resetBestTree */ boolean freeBestTree(bestlist *bt) { /* freeBestTree */ while (bt->ninit >= 0) freeTopol(bt->byScore[(bt->ninit)--]); /* VALGRIND */ free(bt->byScore); free(bt->byTopol); /* VALGRIND END */ freeTopol(bt->start); return TRUE; } /* freeBestTree */ /* Compare two trees, assuming that each is in standard order. Return * -1 if first preceeds second, 0 if they are identical, or +1 if first * follows second in standard order. Lower number tips preceed higher * number tips. A tip preceeds a corresponding internal node. Internal * nodes are ranked by their lowest number tip. */ static int cmpSubtopol (connptr p10, connptr p1, connptr p20, connptr p2) { connptr p1d, p2d; int cmp; if (! p1->descend && ! p2->descend) /* Two tips */ return cmpTipVal(p1->valptr, p2->valptr); if (! p1->descend) return -1; /* p1 = tip, p2 = node */ if (! p2->descend) return 1; /* p2 = tip, p1 = node */ p1d = p10 + p1->descend; p2d = p20 + p2->descend; while (1) { /* Two nodes */ if ((cmp = cmpSubtopol(p10, p1d, p20, p2d))) return cmp; /* Subtrees */ if (! p1d->sibling && ! p2d->sibling) return 0; /* Lists done */ if (! p1d->sibling) return -1; /* One done, other not */ if (! p2d->sibling) return 1; /* One done, other not */ p1d = p10 + p1d->sibling; /* Neither done */ p2d = p20 + p2d->sibling; } } static int cmpTopol (void *tpl1, void *tpl2) { connptr r1, r2; int cmp; r1 = ((topol *) tpl1)->links; r2 = ((topol *) tpl2)->links; cmp = cmpTipVal(tipValPtr(r1->p), tipValPtr(r2->p)); if (cmp) return cmp; return cmpSubtopol(r1, r1, r2, r2); } static int cmpTplScore (void *tpl1, void *tpl2) { double l1, l2; l1 = ((topol *) tpl1)->likelihood; l2 = ((topol *) tpl2)->likelihood; return (l1 > l2) ? -1 : ((l1 == l2) ? 0 : 1); } /* Find an item in a sorted list of n items. If the item is in the list, * return its index. If it is not in the list, return the negative of the * position into which it should be inserted. */ static int findInList (void *item, void *list[], int n, int (* cmpFunc)(void *, void *)) { int mid, hi, lo, cmp = 0; if (n < 1) return -1; /* No match; first index */ lo = 1; mid = 0; hi = n; while (lo < hi) { mid = (lo + hi) >> 1; cmp = (* cmpFunc)(item, list[mid-1]); if (cmp) { if (cmp < 0) hi = mid; else lo = mid + 1; } else return mid; /* Exact match */ } if (lo != mid) { cmp = (* cmpFunc)(item, list[lo-1]); if (cmp == 0) return lo; } if (cmp > 0) lo++; /* Result of step = 0 test */ return -lo; } static int findTreeInList (bestlist *bt, tree *tr) { topol *tpl; tpl = bt->byScore[0]; saveTree(tr, tpl); return findInList((void *) tpl, (void **) (& (bt->byTopol[1])), bt->nvalid, cmpTopol); } int saveBestTree (bestlist *bt, tree *tr, boolean keepIdenticalTrees) { topol *tpl, *reuse; int tplNum, scrNum, reuseScrNum, reuseTplNum, i, oldValid, newValid; tplNum = findTreeInList(bt, tr); tpl = bt->byScore[0]; oldValid = newValid = bt->nvalid; if(tplNum > 0) { /* Topology is in list */ if(!keepIdenticalTrees) return 0; reuse = bt->byTopol[tplNum]; /* Matching topol */ reuseScrNum = reuse->scrNum; reuseTplNum = reuse->tplNum; } /* Good enough to keep? */ else { if(tr->likelihood < bt->worst) return 0; else { /* Topology is not in list */ tplNum = -tplNum; /* Add to list (not replace) */ if (newValid < bt->nkeep) bt->nvalid = ++newValid; reuseScrNum = newValid; /* Take worst tree */ reuse = bt->byScore[reuseScrNum]; reuseTplNum = (newValid > oldValid) ? newValid : reuse->tplNum; if (tr->likelihood > bt->start->likelihood) bt->improved = TRUE; } } scrNum = findInList((void *) tpl, (void **) (& (bt->byScore[1])), oldValid, cmpTplScore); scrNum = ABS(scrNum); if (scrNum < reuseScrNum) { for (i = reuseScrNum; i > scrNum; i--) (bt->byScore[i] = bt->byScore[i-1])->scrNum = i; } else { if (scrNum > reuseScrNum) { scrNum--; for (i = reuseScrNum; i < scrNum; i++) (bt->byScore[i] = bt->byScore[i+1])->scrNum = i; } } if(tplNum < reuseTplNum) for (i = reuseTplNum; i > tplNum; i--) (bt->byTopol[i] = bt->byTopol[i-1])->tplNum = i; else { if (tplNum > reuseTplNum) { tplNum--; for (i = reuseTplNum; i < tplNum; i++) (bt->byTopol[i] = bt->byTopol[i+1])->tplNum = i; } } tpl->scrNum = scrNum; tpl->tplNum = tplNum; bt->byTopol[tplNum] = bt->byScore[scrNum] = tpl; bt->byScore[0] = reuse; if (scrNum == 1) bt->best = tr->likelihood; if (newValid == bt->nkeep) bt->worst = bt->byScore[newValid]->likelihood; return scrNum; } int recallBestTree (bestlist *bt, int rank, tree *tr) { if (rank < 1) rank = 1; if (rank > bt->nvalid) rank = bt->nvalid; if (rank > 0) if (! restoreTree(bt->byScore[rank], tr)) return FALSE; return rank; } ExaML-3.0.21/examl/trash.c000066400000000000000000000032221330315463200151350ustar00rootroot00000000000000#ifndef WIN32 #include #include #include #include #endif #include #include #include #include #include #include #include #include "axml.h" static void reorderNodes(tree *tr, nodeptr *np, nodeptr p, int *count) { int i, found = 0; if(isTip(p->number, tr->mxtips)) return; else { for(i = tr->mxtips + 1; (i <= (tr->mxtips + tr->mxtips - 1)) && (found == 0); i++) { if (p == np[i] || p == np[i]->next || p == np[i]->next->next) { if(p == np[i]) tr->nodep[*count + tr->mxtips + 1] = np[i]; else { if(p == np[i]->next) tr->nodep[*count + tr->mxtips + 1] = np[i]->next; else tr->nodep[*count + tr->mxtips + 1] = np[i]->next->next; } found = 1; *count = *count + 1; } } assert(found != 0); reorderNodes(tr, np, p->next->back, count); reorderNodes(tr, np, p->next->next->back, count); } } void nodeRectifier(tree *tr) { nodeptr *np = (nodeptr *)malloc(2 * tr->mxtips * sizeof(nodeptr)); int i; int count = 0; tr->start = tr->nodep[1]; tr->rooted = FALSE; /* TODO why is tr->rooted set to FALSE here ?*/ for(i = tr->mxtips + 1; i <= (tr->mxtips + tr->mxtips - 1); i++) np[i] = tr->nodep[i]; reorderNodes(tr, np, tr->start->back, &count); free(np); } nodeptr findAnyTip(nodeptr p, int numsp) { return isTip(p->number, numsp) ? p : findAnyTip(p->next->back, numsp); } ExaML-3.0.21/examl/treeIO.c000066400000000000000000000643721330315463200152200ustar00rootroot00000000000000/* RAxML-VI-HPC (version 2.2) a program for sequential and parallel estimation of phylogenetic trees * Copyright August 2006 by Alexandros Stamatakis * * Partially derived from * fastDNAml, a program for estimation of phylogenetic trees from sequences by Gary J. Olsen * * and * * Programs of the PHYLIP package by Joe Felsenstein. * * This program is free software; you may redistribute it and/or modify its * 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. * * * For any other enquiries send an Email to Alexandros Stamatakis * Alexandros.Stamatakis@epfl.ch * * When publishing work that is based on the results from RAxML-VI-HPC please cite: * * Alexandros Stamatakis:"RAxML-VI-HPC: maximum likelihood-based phylogenetic analyses with thousands of taxa and mixed models". * Bioinformatics 2006; doi: 10.1093/bioinformatics/btl446 */ #ifndef WIN32 #include #include #include #include #endif #include #include #include #include #include #include #include "axml.h" extern char infoFileName[1024]; extern char tree_file[1024]; extern char *likelihood_key; extern char *ntaxa_key; extern char *smoothed_key; extern double masterTime; stringHashtable *initStringHashTable(hashNumberType n) { /* init with primes */ static const hashNumberType initTable[] = {53, 97, 193, 389, 769, 1543, 3079, 6151, 12289, 24593, 49157, 98317, 196613, 393241, 786433, 1572869, 3145739, 6291469, 12582917, 25165843, 50331653, 100663319, 201326611, 402653189, 805306457, 1610612741}; /* init with powers of two static const hashNumberType initTable[] = {64, 128, 256, 512, 1024, 2048, 4096, 8192, 16384, 32768, 65536, 131072, 262144, 524288, 1048576, 2097152, 4194304, 8388608, 16777216, 33554432, 67108864, 134217728, 268435456, 536870912, 1073741824, 2147483648U}; */ stringHashtable *h = (stringHashtable*)malloc(sizeof(stringHashtable)); hashNumberType tableSize, i, primeTableLength = sizeof(initTable)/sizeof(initTable[0]), maxSize = (hashNumberType)-1; assert(n <= maxSize); i = 0; while(initTable[i] < n && i < primeTableLength) i++; assert(i < primeTableLength); tableSize = initTable[i]; h->table = (stringEntry**)calloc(tableSize, sizeof(stringEntry*)); h->tableSize = tableSize; return h; } static hashNumberType hashString(char *p, hashNumberType tableSize) { hashNumberType h = 0; for(; *p; p++) h = 31 * h + *p; return (h % tableSize); } void addword(char *s, stringHashtable *h, int nodeNumber) { hashNumberType position = hashString(s, h->tableSize); stringEntry *p = h->table[position]; for(; p!= NULL; p = p->next) { if(strcmp(s, p->word) == 0) return; } p = (stringEntry *)malloc(sizeof(stringEntry)); assert(p); p->nodeNumber = nodeNumber; p->word = (char*)calloc(strlen(s) + 1, sizeof(char)); strcpy(p->word, s); p->next = h->table[position]; h->table[position] = p; } int lookupWord(char *s, stringHashtable *h) { hashNumberType position = hashString(s, h->tableSize); stringEntry *p = h->table[position]; for(; p!= NULL; p = p->next) { if(strcmp(s, p->word) == 0) return p->nodeNumber; } return -1; } int countTips(nodeptr p, int numsp) { if(isTip(p->number, numsp)) return 1; { nodeptr q; int tips = 0; q = p->next; while(q != p) { tips += countTips(q->back, numsp); q = q->next; } return tips; } } static double getBranchLength(tree *tr, int perGene, nodeptr p) { double z = 0.0, x = 0.0; assert(perGene != NO_BRANCHES); if(tr->numBranches == 1) { z = p->z[0]; if (z < zmin) z = zmin; x = -log(z); } else { if(perGene == SUMMARIZE_LH) { int i; double avgX = 0.0; for(i = 0; i < tr->numBranches; i++) { z = p->z[i]; if(z < zmin) z = zmin; x = -log(z); avgX += x * tr->partitionContributions[i]; } x = avgX; } else { assert(perGene >= 0 && perGene < tr->numBranches); z = p->z[perGene]; if(z < zmin) z = zmin; x = -log(z); } } return x; } static char *Tree2StringREC(char *treestr, tree *tr, nodeptr p, boolean printBranchLengths, boolean printNames, boolean printLikelihood, boolean rellTree, boolean finalPrint, int perGene, boolean branchLabelSupport, boolean printSHSupport) { char *nameptr; if(isTip(p->number, tr->mxtips)) { if(printNames) { nameptr = tr->nameList[p->number]; sprintf(treestr, "%s", nameptr); } else sprintf(treestr, "%d", p->number); while (*treestr) treestr++; } else { *treestr++ = '('; treestr = Tree2StringREC(treestr, tr, p->next->back, printBranchLengths, printNames, printLikelihood, rellTree, finalPrint, perGene, branchLabelSupport, printSHSupport); *treestr++ = ','; treestr = Tree2StringREC(treestr, tr, p->next->next->back, printBranchLengths, printNames, printLikelihood, rellTree, finalPrint, perGene, branchLabelSupport, printSHSupport); if(p == tr->start->back) { *treestr++ = ','; treestr = Tree2StringREC(treestr, tr, p->back, printBranchLengths, printNames, printLikelihood, rellTree, finalPrint, perGene, branchLabelSupport, printSHSupport); } *treestr++ = ')'; } if(p == tr->start->back) { if(printBranchLengths && !rellTree) sprintf(treestr, ":0.0;\n"); else sprintf(treestr, ";\n"); } else { if(rellTree || branchLabelSupport || printSHSupport) { if(( !isTip(p->number, tr->mxtips)) && ( !isTip(p->back->number, tr->mxtips))) { assert(0); /*assert(p->bInf != (branchInfo *)NULL);*/ /*if(rellTree) sprintf(treestr, "%d:%8.20f", p->bInf->support, p->z[0]); if(branchLabelSupport) sprintf(treestr, ":%8.20f[%d]", p->z[0], p->bInf->support); if(printSHSupport) sprintf(treestr, ":%8.20f[%d]", getBranchLength(tr, perGene, p), p->bInf->support); */ } else { if(rellTree || branchLabelSupport) sprintf(treestr, ":%8.20f", p->z[0]); if(printSHSupport) sprintf(treestr, ":%8.20f", getBranchLength(tr, perGene, p)); } } else { if(printBranchLengths) sprintf(treestr, ":%8.20f", getBranchLength(tr, perGene, p)); else sprintf(treestr, "%s", "\0"); } } while (*treestr) treestr++; return treestr; } char *Tree2String(char *treestr, tree *tr, nodeptr p, boolean printBranchLengths, boolean printNames, boolean printLikelihood, boolean rellTree, boolean finalPrint, int perGene, boolean branchLabelSupport, boolean printSHSupport) { if(rellTree) assert(!branchLabelSupport && !printSHSupport); if(branchLabelSupport) assert(!rellTree && !printSHSupport); if(printSHSupport) assert(!branchLabelSupport && !rellTree); Tree2StringREC(treestr, tr, p, printBranchLengths, printNames, printLikelihood, rellTree, finalPrint, perGene, branchLabelSupport, printSHSupport); while (*treestr) treestr++; return treestr; } void printTreePerGene(tree *tr, analdef *adef, char *fileName, char *permission) { FILE *treeFile; char extendedTreeFileName[1024]; char buf[16]; int i; assert(adef->perGeneBranchLengths); for(i = 0; i < tr->numBranches; i++) { strcpy(extendedTreeFileName, fileName); sprintf(buf,"%d", i); strcat(extendedTreeFileName, ".PARTITION."); strcat(extendedTreeFileName, buf); /*printf("Partitiuon %d file %s\n", i, extendedTreeFileName);*/ Tree2String(tr->tree_string, tr, tr->start->back, TRUE, TRUE, FALSE, FALSE, TRUE, i, FALSE, FALSE); treeFile = myfopen(extendedTreeFileName, permission); fprintf(treeFile, "%s", tr->tree_string); fclose(treeFile); } } /*=======================================================================*/ /* Read a tree from a file */ /*=======================================================================*/ /* 1.0.A Processing of quotation marks in comment removed */ static int treeFinishCom (FILE *fp, char **strp) { int ch; while ((ch = getc(fp)) != EOF && ch != ']') { if (strp != NULL) *(*strp)++ = ch; /* save character */ if (ch == '[') { /* nested comment; find its end */ if ((ch = treeFinishCom(fp, strp)) == EOF) break; if (strp != NULL) *(*strp)++ = ch; /* save closing ] */ } } if (strp != NULL) **strp = '\0'; /* terminate string */ return ch; } /* treeFinishCom */ static int treeGetCh (FILE *fp) /* get next nonblank, noncomment character */ { /* treeGetCh */ int ch; while ((ch = getc(fp)) != EOF) { if (whitechar(ch)) ; else if (ch == '[') { /* comment; find its end */ if ((ch = treeFinishCom(fp, (char **) NULL)) == EOF) break; } else break; } return ch; } /* treeGetCh */ static boolean treeLabelEnd (int ch) { switch (ch) { case EOF: case '\0': case '\t': case '\n': case '\r': case ' ': case ':': case ',': case '(': case ')': case ';': return TRUE; default: break; } return FALSE; } static boolean treeGetLabel (FILE *fp, char *lblPtr, int maxlen) { int ch; boolean done, quoted, lblfound; if (--maxlen < 0) lblPtr = (char *) NULL; else if (lblPtr == NULL) maxlen = 0; ch = getc(fp); done = treeLabelEnd(ch); lblfound = ! done; quoted = (ch == '\''); if (quoted && ! done) { ch = getc(fp); done = (ch == EOF); } while (! done) { if (quoted) { if (ch == '\'') { ch = getc(fp); if (ch != '\'') break; } } else if (treeLabelEnd(ch)) break; if (--maxlen >= 0) *lblPtr++ = ch; ch = getc(fp); if (ch == EOF) break; } if (ch != EOF) (void) ungetc(ch, fp); if (lblPtr != NULL) *lblPtr = '\0'; return lblfound; } static boolean treeFlushLabel (FILE *fp) { return treeGetLabel(fp, (char *) NULL, (int) 0); } static int treeFindTipByLabelString(char *str, tree *tr, boolean check) { int lookup = lookupWord(str, tr->nameHash); if(lookup > 0) { if(check) assert(! tr->nodep[lookup]->back); return lookup; } else { printf("ERROR: Cannot find tree species: %s\n", str); return 0; } } int treeFindTipName(FILE *fp, tree *tr, boolean check) { char str[nmlngth+2]; int n; if(treeGetLabel(fp, str, nmlngth+2)) n = treeFindTipByLabelString(str, tr, check); else n = 0; return n; } static void treeEchoContext (FILE *fp1, FILE *fp2, int n) { /* treeEchoContext */ int ch; boolean waswhite; waswhite = TRUE; while (n > 0 && ((ch = getc(fp1)) != EOF)) { if (whitechar(ch)) { ch = waswhite ? '\0' : ' '; waswhite = TRUE; } else { waswhite = FALSE; } if (ch > '\0') {putc(ch, fp2); n--;} } } /* treeEchoContext */ static boolean treeProcessLength (FILE *fp, double *dptr) { int ch; if ((ch = treeGetCh(fp)) == EOF) return FALSE; /* Skip comments */ (void) ungetc(ch, fp); if (fscanf(fp, "%lf", dptr) != 1) { printf("ERROR: treeProcessLength: Problem reading branch length\n"); treeEchoContext(fp, stdout, 40); printf("\n"); return FALSE; } return TRUE; } static int treeFlushLen (FILE *fp) { double dummy; int ch; ch = treeGetCh(fp); if (ch == ':') { ch = treeGetCh(fp); ungetc(ch, fp); if(!treeProcessLength(fp, & dummy)) return 0; return 1; } if (ch != EOF) (void) ungetc(ch, fp); return 1; } static boolean treeNeedCh (FILE *fp, int c1, char *where) { int c2; if ((c2 = treeGetCh(fp)) == c1) return TRUE; printf("ERROR: Expecting '%c' %s tree; found:", c1, where); if (c2 == EOF) { printf("End-of-File"); } else { ungetc(c2, fp); treeEchoContext(fp, stdout, 40); } putchar('\n'); if(c1 == ':') printf("RAxML may be expecting to read a tree that contains branch lengths\n"); return FALSE; } static boolean addElementLen (FILE *fp, tree *tr, nodeptr p, boolean readBranchLengths, boolean readNodeLabels, int *lcount) { nodeptr q; int n, ch, fres; if ((ch = treeGetCh(fp)) == '(') { n = (tr->nextnode)++; if (n > 2*(tr->mxtips) - 2) { if (tr->rooted || n > 2*(tr->mxtips) - 1) { printf("ERROR: Too many internal nodes. Is tree rooted?\n"); printf(" Deepest splitting should be a trifurcation.\n"); return FALSE; } else { assert(!readNodeLabels); tr->rooted = TRUE; } } q = tr->nodep[n]; if (! addElementLen(fp, tr, q->next, readBranchLengths, readNodeLabels, lcount)) return FALSE; if (! treeNeedCh(fp, ',', "in")) return FALSE; if (! addElementLen(fp, tr, q->next->next, readBranchLengths, readNodeLabels, lcount)) return FALSE; if (! treeNeedCh(fp, ')', "in")) return FALSE; if(readNodeLabels) { char label[64]; int support; if(treeGetLabel (fp, label, 10)) { int val = sscanf(label, "%d", &support); assert(val == 1); /*printf("LABEL %s Number %d\n", label, support);*/ /*p->support = q->support = support;*/ /*printf("%d %d %d %d\n", p->support, q->support, p->number, q->number);*/ assert(p->number > tr->mxtips && q->number > tr->mxtips); *lcount = *lcount + 1; } } else (void) treeFlushLabel(fp); } else { ungetc(ch, fp); if ((n = treeFindTipName(fp, tr, TRUE)) <= 0) return FALSE; q = tr->nodep[n]; if (tr->start->number > n) tr->start = q; (tr->ntips)++; } if(readBranchLengths) { double branch; if (! treeNeedCh(fp, ':', "in")) return FALSE; if (! treeProcessLength(fp, &branch)) return FALSE; /*printf("Branch %8.20f %d\n", branch, tr->numBranches);*/ hookup(p, q, &branch, tr->numBranches); } else { fres = treeFlushLen(fp); if(!fres) return FALSE; hookupDefault(p, q, tr->numBranches); } return TRUE; } static nodeptr uprootTree (tree *tr, nodeptr p, boolean readBranchLengths) { nodeptr q, r, s, start; int n, i; for(i = tr->mxtips + 1; i < 2 * tr->mxtips - 1; i++) assert(i == tr->nodep[i]->number); if(isTip(p->number, tr->mxtips) || p->back) { printf("ERROR: Unable to uproot tree.\n"); printf(" Inappropriate node marked for removal.\n"); assert(0); } assert(p->back == (nodeptr)NULL); tr->nextnode = tr->nextnode - 1; assert(tr->nextnode < 2 * tr->mxtips); n = tr->nextnode; assert(tr->nodep[tr->nextnode]); if (n != tr->mxtips + tr->ntips - 1) { printf("ERROR: Unable to uproot tree. Inconsistent\n"); printf(" number of tips and nodes for rooted tree.\n"); assert(0); } q = p->next->back; /* remove p from tree */ r = p->next->next->back; assert(p->back == (nodeptr)NULL); if(readBranchLengths) { double b[NUM_BRANCHES]; int i; for(i = 0; i < tr->numBranches; i++) b[i] = (r->z[i] + q->z[i]); hookup (q, r, b, tr->numBranches); } else hookupDefault(q, r, tr->numBranches); if(tr->constraintTree) { if(tr->constraintVector[p->number] != 0) { printf("Root node to remove should have top-level grouping of 0\n"); assert(0); } } assert(!(isTip(r->number, tr->mxtips) && isTip(q->number, tr->mxtips))); assert(p->number > tr->mxtips); if(tr->ntips > 2 && p->number != n) { q = tr->nodep[n]; /* transfer last node's conections to p */ r = q->next; s = q->next->next; if(tr->constraintTree) tr->constraintVector[p->number] = tr->constraintVector[q->number]; hookup(p, q->back, q->z, tr->numBranches); /* move connections to p */ hookup(p->next, r->back, r->z, tr->numBranches); hookup(p->next->next, s->back, s->z, tr->numBranches); q->back = q->next->back = q->next->next->back = (nodeptr) NULL; } else p->back = p->next->back = p->next->next->back = (nodeptr) NULL; assert(tr->ntips > 2); start = findAnyTip(tr->nodep[tr->mxtips + 1], tr->mxtips); assert(isTip(start->number, tr->mxtips)); tr->rooted = FALSE; return start; } int treeReadLen (FILE *fp, tree *tr, boolean readBranches, boolean readNodeLabels, boolean topologyOnly) { nodeptr p; int i, ch, lcount = 0; for (i = 1; i <= tr->mxtips; i++) { tr->nodep[i]->back = (node *) NULL; /*if(topologyOnly) tr->nodep[i]->support = -1;*/ } for(i = tr->mxtips + 1; i < 2 * tr->mxtips; i++) { tr->nodep[i]->back = (nodeptr)NULL; tr->nodep[i]->next->back = (nodeptr)NULL; tr->nodep[i]->next->next->back = (nodeptr)NULL; tr->nodep[i]->number = i; tr->nodep[i]->next->number = i; tr->nodep[i]->next->next->number = i; /*if(topologyOnly) { tr->nodep[i]->support = -2; tr->nodep[i]->next->support = -2; tr->nodep[i]->next->next->support = -2; }*/ } if(topologyOnly) tr->start = tr->nodep[tr->mxtips]; else tr->start = tr->nodep[1]; tr->ntips = 0; tr->nextnode = tr->mxtips + 1; for(i = 0; i < tr->numBranches; i++) tr->partitionSmoothed[i] = FALSE; tr->rooted = FALSE; p = tr->nodep[(tr->nextnode)++]; while((ch = treeGetCh(fp)) != '('); if(!topologyOnly) assert(readBranches == FALSE && readNodeLabels == FALSE); if (! addElementLen(fp, tr, p, readBranches, readNodeLabels, &lcount)) assert(0); if (! treeNeedCh(fp, ',', "in")) assert(0); if (! addElementLen(fp, tr, p->next, readBranches, readNodeLabels, &lcount)) assert(0); if (! tr->rooted) { if ((ch = treeGetCh(fp)) == ',') { if (! addElementLen(fp, tr, p->next->next, readBranches, readNodeLabels, &lcount)) assert(0); } else { /* A rooted format */ tr->rooted = TRUE; if (ch != EOF) (void) ungetc(ch, fp); } } else { p->next->next->back = (nodeptr) NULL; } if (! treeNeedCh(fp, ')', "in")) assert(0); if(topologyOnly) assert(!(tr->rooted && readNodeLabels)); (void) treeFlushLabel(fp); if (! treeFlushLen(fp)) assert(0); if (! treeNeedCh(fp, ';', "at end of")) assert(0); if (tr->rooted) { assert(!readNodeLabels); p->next->next->back = (nodeptr) NULL; tr->start = uprootTree(tr, p->next->next, FALSE); if (! tr->start) { printf("FATAL ERROR UPROOTING TREE\n"); assert(0); } } else tr->start = findAnyTip(p, tr->mxtips); assert(tr->ntips == tr->mxtips); return lcount; } static int randomInt(int n) { return rand() %n; } static boolean addElementLenMULT (FILE *fp, tree *tr, nodeptr p, int partitionCounter, int *partCount) { nodeptr q, r, s; int n, ch, fres, rn; double randomResolution; int old; tr->constraintVector[p->number] = partitionCounter; if ((ch = treeGetCh(fp)) == '(') { *partCount = *partCount + 1; old = *partCount; n = (tr->nextnode)++; if (n > 2*(tr->mxtips) - 2) { if (tr->rooted || n > 2*(tr->mxtips) - 1) { printf("ERROR: Too many internal nodes. Is tree rooted?\n"); printf(" Deepest splitting should be a trifurcation.\n"); return FALSE; } else { tr->rooted = TRUE; } } q = tr->nodep[n]; tr->constraintVector[q->number] = *partCount; if (! addElementLenMULT(fp, tr, q->next, old, partCount)) return FALSE; if (! treeNeedCh(fp, ',', "in")) return FALSE; if (! addElementLenMULT(fp, tr, q->next->next, old, partCount)) return FALSE; hookupDefault(p, q, tr->numBranches); while((ch = treeGetCh(fp)) == ',') { n = (tr->nextnode)++; if (n > 2*(tr->mxtips) - 2) { if (tr->rooted || n > 2*(tr->mxtips) - 1) { printf("ERROR: Too many internal nodes. Is tree rooted?\n"); printf(" Deepest splitting should be a trifurcation.\n"); return FALSE; } else { tr->rooted = TRUE; } } r = tr->nodep[n]; tr->constraintVector[r->number] = *partCount; rn = randomInt(10000); if(rn == 0) randomResolution = 0; else randomResolution = ((double)rn)/10000.0; if(randomResolution < 0.5) { s = q->next->back; r->back = q->next; q->next->back = r; r->next->back = s; s->back = r->next; addElementLenMULT(fp, tr, r->next->next, old, partCount); } else { s = q->next->next->back; r->back = q->next->next; q->next->next->back = r; r->next->back = s; s->back = r->next; addElementLenMULT(fp, tr, r->next->next, old, partCount); } } if(ch != ')') { printf("Missing /) in treeReadLenMULT\n"); exit(-1); } (void) treeFlushLabel(fp); } else { ungetc(ch, fp); if ((n = treeFindTipName(fp, tr, TRUE)) <= 0) return FALSE; q = tr->nodep[n]; tr->constraintVector[q->number] = partitionCounter; if (tr->start->number > n) tr->start = q; (tr->ntips)++; hookupDefault(p, q, tr->numBranches); } fres = treeFlushLen(fp); if(!fres) return FALSE; return TRUE; } boolean treeReadLenMULT (FILE *fp, tree *tr, int *partCount) { nodeptr p, r, s; int i, ch, n, rn; int partitionCounter = 0; double randomResolution; srand(tr->randomSeed); for(i = 0; i < 2 * tr->mxtips; i++) tr->constraintVector[i] = -1; for (i = 1; i <= tr->mxtips; i++) tr->nodep[i]->back = (node *) NULL; for(i = tr->mxtips + 1; i < 2 * tr->mxtips; i++) { tr->nodep[i]->back = (nodeptr)NULL; tr->nodep[i]->next->back = (nodeptr)NULL; tr->nodep[i]->next->next->back = (nodeptr)NULL; tr->nodep[i]->number = i; tr->nodep[i]->next->number = i; tr->nodep[i]->next->next->number = i; } tr->start = tr->nodep[tr->mxtips]; tr->ntips = 0; tr->nextnode = tr->mxtips + 1; for(i = 0; i < tr->numBranches; i++) tr->partitionSmoothed[i] = FALSE; tr->rooted = FALSE; p = tr->nodep[(tr->nextnode)++]; while((ch = treeGetCh(fp)) != '('); if (! addElementLenMULT(fp, tr, p, partitionCounter, partCount)) return FALSE; if (! treeNeedCh(fp, ',', "in")) return FALSE; if (! addElementLenMULT(fp, tr, p->next, partitionCounter, partCount)) return FALSE; if (! tr->rooted) { if ((ch = treeGetCh(fp)) == ',') { if (! addElementLenMULT(fp, tr, p->next->next, partitionCounter, partCount)) return FALSE; while((ch = treeGetCh(fp)) == ',') { n = (tr->nextnode)++; assert(n <= 2*(tr->mxtips) - 2); r = tr->nodep[n]; tr->constraintVector[r->number] = partitionCounter; rn = randomInt(10000); if(rn == 0) randomResolution = 0; else randomResolution = ((double)rn)/10000.0; if(randomResolution < 0.5) { s = p->next->next->back; r->back = p->next->next; p->next->next->back = r; r->next->back = s; s->back = r->next; addElementLenMULT(fp, tr, r->next->next, partitionCounter, partCount); } else { s = p->next->back; r->back = p->next; p->next->back = r; r->next->back = s; s->back = r->next; addElementLenMULT(fp, tr, r->next->next, partitionCounter, partCount); } } if(ch != ')') { printf("Missing /) in treeReadLenMULT\n"); exit(-1); } else ungetc(ch, fp); } else { tr->rooted = TRUE; if (ch != EOF) (void) ungetc(ch, fp); } } else { p->next->next->back = (nodeptr) NULL; } if (! treeNeedCh(fp, ')', "in")) return FALSE; (void) treeFlushLabel(fp); if (! treeFlushLen(fp)) return FALSE; if (! treeNeedCh(fp, ';', "at end of")) return FALSE; if (tr->rooted) { p->next->next->back = (nodeptr) NULL; tr->start = uprootTree(tr, p->next->next, FALSE); if (! tr->start) return FALSE; } else { tr->start = findAnyTip(p, tr->mxtips); } assert(tr->ntips == tr->mxtips); return TRUE; } void getStartingTree(tree *tr) { FILE *treeFile = myfopen(tree_file, "rb"); tr->likelihood = unlikely; if(tr->constraintTree) { int partCount = 0; if (! treeReadLenMULT(treeFile, tr, &partCount)) exit(-1); } else treeReadLen(treeFile, tr, FALSE, FALSE, FALSE); fclose(treeFile); tr->start = tr->nodep[1]; } ExaML-3.0.21/gpl-3.0.txt000066400000000000000000001045131330315463200143700ustar00rootroot00000000000000 GNU GENERAL PUBLIC LICENSE Version 3, 29 June 2007 Copyright (C) 2007 Free Software Foundation, Inc. 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The terms of this License will continue to apply to the part which is the covered work, but the special requirements of the GNU Affero General Public License, section 13, concerning interaction through a network will apply to the combination as such. 14. Revised Versions of this License. The Free Software Foundation may publish revised and/or new versions of the GNU General Public License from time to time. Such new versions will be similar in spirit to the present version, but may differ in detail to address new problems or concerns. Each version is given a distinguishing version number. If the Program specifies that a certain numbered version of the GNU General Public License "or any later version" applies to it, you have the option of following the terms and conditions either of that numbered version or of any later version published by the Free Software Foundation. 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IN NO EVENT UNLESS REQUIRED BY APPLICABLE LAW OR AGREED TO IN WRITING WILL ANY COPYRIGHT HOLDER, OR ANY OTHER PARTY WHO MODIFIES AND/OR CONVEYS THE PROGRAM AS PERMITTED ABOVE, BE LIABLE TO YOU FOR DAMAGES, INCLUDING ANY GENERAL, SPECIAL, INCIDENTAL OR CONSEQUENTIAL DAMAGES ARISING OUT OF THE USE OR INABILITY TO USE THE PROGRAM (INCLUDING BUT NOT LIMITED TO LOSS OF DATA OR DATA BEING RENDERED INACCURATE OR LOSSES SUSTAINED BY YOU OR THIRD PARTIES OR A FAILURE OF THE PROGRAM TO OPERATE WITH ANY OTHER PROGRAMS), EVEN IF SUCH HOLDER OR OTHER PARTY HAS BEEN ADVISED OF THE POSSIBILITY OF SUCH DAMAGES. 17. Interpretation of Sections 15 and 16. 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 How to Apply These Terms to Your New Programs If you develop a new program, and you want it to be of the greatest possible use to the public, the best way to achieve this is to make it free software which everyone can redistribute and change under these terms. To do so, attach the following notices to the program. It is safest to attach them to the start of each source file to most effectively state the exclusion of warranty; and each file should have at least the "copyright" line and a pointer to where the full notice is found. Copyright (C) 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 . Also add information on how to contact you by electronic and paper mail. If the program does terminal interaction, make it output a short notice like this when it starts in an interactive mode: Copyright (C) This program comes with ABSOLUTELY NO WARRANTY; for details type `show w'. This is free software, and you are welcome to redistribute it under certain conditions; type `show c' for details. The hypothetical commands `show w' and `show c' should show the appropriate parts of the General Public License. Of course, your program's commands might be different; for a GUI interface, you would use an "about box". You should also get your employer (if you work as a programmer) or school, if any, to sign a "copyright disclaimer" for the program, if necessary. For more information on this, and how to apply and follow the GNU GPL, see . The GNU General Public License does not permit incorporating your program into proprietary programs. If your program is a subroutine library, you may consider it more useful to permit linking proprietary applications with the library. If this is what you want to do, use the GNU Lesser General Public License instead of this License. But first, please read . 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/AdditionalStreams [/application#2Fvnd#2Eoasis#2Eopendocument#2Etext 127 0 R ] >> startxref 378290 %%EOF ExaML-3.0.21/parser/000077500000000000000000000000001330315463200140375ustar00rootroot00000000000000ExaML-3.0.21/parser/Makefile.SSE3.gcc000066400000000000000000000022041330315463200167440ustar00rootroot00000000000000# Makefile August 2006 by Alexandros Stamatakis # Makefile cleanup October 2006, Courtesy of Peter Cordes CC = gcc CFLAGS = -fomit-frame-pointer -O2 -D_GNU_SOURCE -msse -funroll-loops #-Wall -Wunused-parameter -Wredundant-decls -Wreturn-type -Wswitch-default -Wunused-value -Wimplicit -Wimplicit-function-declaration -Wimplicit-int -Wimport -Wunused -Wunused-function -Wunused-label -Wno-int-to-pointer-cast -Wbad-function-cast -Wmissing-declarations -Wmissing-prototypes -Wnested-externs -Wold-style-definition -Wstrict-prototypes -Wpointer-sign -Wextra -Wredundant-decls -Wunused -Wunused-function -Wunused-parameter -Wunused-value -Wunused-variable -Wformat -Wformat-nonliteral -Wparentheses -Wsequence-point -Wuninitialized -Wundef -Wbad-function-cast LIBRARIES = -lm RM = rm -f objs = axml.o parsePartitions.o all : clean parse-examl GLOBAL_DEPS = axml.h globalVariables.h ../versionHeader/version.h parse-examl : $(objs) $(CC) -o parse-examl $(objs) $(LIBRARIES) axml.o : axml.c $(GLOBAL_DEPS) parsePartitions.o : parsePartitions.c $(GLOBAL_DEPS) clean : $(RM) *.o parse-examl dev : parse-examl ExaML-3.0.21/parser/Makefile.check.warnings000066400000000000000000000022041330315463200204000ustar00rootroot00000000000000# Makefile August 2006 by Alexandros Stamatakis # Makefile cleanup October 2006, Courtesy of Peter Cordes CC = clang CFLAGS = -fomit-frame-pointer -O2 -D_GNU_SOURCE -msse -funroll-loops -Weverything -Wno-padded #-Wall -Wunused-parameter -Wredundant-decls -Wreturn-type -Wswitch-default -Wunused-value -Wimplicit -Wimplicit-function-declaration -Wimplicit-int -Wimport -Wunused -Wunused-function -Wunused-label -Wno-int-to-pointer-cast -Wbad-function-cast -Wmissing-declarations -Wmissing-prototypes -Wnested-externs -Wold-style-definition -Wstrict-prototypes -Wpointer-sign -Wextra -Wredundant-decls -Wunused -Wunused-function -Wunused-parameter -Wunused-value -Wunused-variable -Wformat -Wformat-nonliteral -Wparentheses -Wsequence-point -Wuninitialized -Wundef -Wbad-function-cast LIBRARIES = -lm RM = rm -f objs = axml.o parsePartitions.o all : parse-examl GLOBAL_DEPS = axml.h globalVariables.h ../versionHeader/version.h parse-examl : $(objs) $(CC) -o parse-examl $(objs) $(LIBRARIES) axml.o : axml.c $(GLOBAL_DEPS) parsePartitions.o : parsePartitions.c $(GLOBAL_DEPS) clean : $(RM) *.o parse-examl ExaML-3.0.21/parser/USAGE000066400000000000000000000001001330315463200146150ustar00rootroot00000000000000./parser -m DNA -s ../testdata/49 -q ../testdata/49.model -n 49 ExaML-3.0.21/parser/axml.c000066400000000000000000001746331330315463200151620ustar00rootroot00000000000000/* RAxML-VI-HPC (version 2.2) a program for sequential and parallel estimation of phylogenetic trees * Copyright August 2006 by Alexandros Stamatakis * * Partially derived from * fastDNAml, a program for estimation of phylogenetic trees from sequences by Gary J. Olsen * * and * * Programs of the PHYLIP package by Joe Felsenstein. * * This program is free software; you may redistribute it and/or modify its * 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. * * * For any other enquiries send an Email to Alexandros Stamatakis * Alexandros.Stamatakis@epfl.ch * * When publishing work that is based on the results from RAxML-VI-HPC please cite: * * Alexandros Stamatakis:"RAxML-VI-HPC: maximum likelihood-based phylogenetic analyses with thousands of taxa and mixed models". * Bioinformatics 2006; doi: 10.1093/bioinformatics/btl446 */ #ifdef WIN32 #include #endif #ifndef WIN32 #include #include #include #include #endif #include #include #include #include #include #include #include #include #ifdef _FINE_GRAIN_MPI #include #endif #ifdef _USE_PTHREADS #include #endif #if ! (defined(__ppc) || defined(__powerpc__) || defined(PPC)) #include /* special bug fix, enforces denormalized numbers to be flushed to zero, without this program is a tiny bit faster though. #include #define MM_DAZ_MASK 0x0040 #define MM_DAZ_ON 0x0040 #define MM_DAZ_OFF 0x0000 */ #endif #include "axml.h" #include "globalVariables.h" #define _PORTABLE_PTHREADS /***************** UTILITY FUNCTIONS **************************/ void myBinFwrite(const void *ptr, size_t size, size_t nmemb) { size_t bytes_written = fwrite(ptr, size, nmemb, byteFile); assert(bytes_written == nmemb); } void *malloc_aligned(size_t size) { void *ptr = (void *)NULL; int res; #if defined (__APPLE__) /* presumably malloc on MACs always returns a 16-byte aligned pointer */ ptr = malloc(size); if(ptr == (void*)NULL) assert(0); #ifdef __AVX assert(0); #endif #else res = posix_memalign( &ptr, BYTE_ALIGNMENT, size ); if(res != 0) assert(0); #endif return ptr; } void printBothOpen(const char* format, ... ) { FILE *f = myfopen(infoFileName, "ab"); va_list args; va_start(args, format); vfprintf(f, format, args ); va_end(args); va_start(args, format); vprintf(format, args ); va_end(args); fclose(f); } void printBothOpenMPI(const char* format, ... ) { #ifdef _WAYNE_MPI if(processID == 0) #endif { FILE *f = myfopen(infoFileName, "ab"); va_list args; va_start(args, format); vfprintf(f, format, args ); va_end(args); va_start(args, format); vprintf(format, args ); va_end(args); fclose(f); } } boolean getSmoothFreqs(int dataType) { assert(MIN_MODEL < dataType && dataType < MAX_MODEL); return pLengths[dataType].smoothFrequencies; } const unsigned int *getBitVector(int dataType) { assert(MIN_MODEL < dataType && dataType < MAX_MODEL); return pLengths[dataType].bitVector; } int getStates(int dataType) { assert(MIN_MODEL < dataType && dataType < MAX_MODEL); return pLengths[dataType].states; } unsigned char getUndetermined(int dataType) { assert(MIN_MODEL < dataType && dataType < MAX_MODEL); return pLengths[dataType].undetermined; } char getInverseMeaning(int dataType, unsigned char state) { assert(MIN_MODEL < dataType && dataType < MAX_MODEL); return pLengths[dataType].inverseMeaning[state]; } partitionLengths *getPartitionLengths(pInfo *p) { int dataType = p->dataType, states = p->states, tipLength = p->maxTipStates; assert(states != -1 && tipLength != -1); assert(MIN_MODEL < dataType && dataType < MAX_MODEL); pLength.leftLength = pLength.rightLength = states * states; pLength.eignLength = states; pLength.evLength = states * states; pLength.eiLength = states * states; pLength.substRatesLength = (states * states - states) / 2; pLength.frequenciesLength = states; pLength.tipVectorLength = tipLength * states; pLength.symmetryVectorLength = (states * states - states) / 2; pLength.frequencyGroupingLength = states; pLength.nonGTR = FALSE; pLength.optimizeBaseFrequencies = FALSE; return (&pLengths[dataType]); } double gettime(void) { #ifdef WIN32 time_t tp; struct tm localtm; tp = time(NULL); localtm = *localtime(&tp); return 60.0*localtm.tm_min + localtm.tm_sec; #else struct timeval ttime; gettimeofday(&ttime , NULL); return ttime.tv_sec + ttime.tv_usec * 0.000001; #endif } double randum (long *seed) { long sum, mult0, mult1, seed0, seed1, seed2, newseed0, newseed1, newseed2; double res; mult0 = 1549; seed0 = *seed & 4095; sum = mult0 * seed0; newseed0 = sum & 4095; sum >>= 12; seed1 = (*seed >> 12) & 4095; mult1 = 406; sum += mult0 * seed1 + mult1 * seed0; newseed1 = sum & 4095; sum >>= 12; seed2 = (*seed >> 24) & 255; sum += mult0 * seed2 + mult1 * seed1; newseed2 = sum & 255; *seed = newseed2 << 24 | newseed1 << 12 | newseed0; res = 0.00390625 * (newseed2 + 0.000244140625 * (newseed1 + 0.000244140625 * newseed0)); return res; } static int filexists(char *filename) { FILE *fp; int res; fp = fopen(filename,"rb"); if(fp) { res = 1; fclose(fp); } else res = 0; return res; } FILE *myfopen(const char *path, const char *mode) { FILE *fp = fopen(path, mode); if(strcmp(mode,"r") == 0 || strcmp(mode,"rb") == 0) { if(fp) return fp; else { if(processID == 0) printf("\n Error: the file %s you want to open for reading does not exist, exiting ...\n\n", path); errorExit(-1); return (FILE *)NULL; } } else { if(fp) return fp; else { if(processID == 0) printf("\n Error: the file %s you want to open for writing or appending can not be opened [mode: %s], exiting ...\n\n", path, mode); errorExit(-1); return (FILE *)NULL; } } } /********************* END UTILITY FUNCTIONS ********************/ /******************************some functions for the likelihood computation ****************************/ /***********************reading and initializing input ******************/ static void getnums (rawdata *rdta) { if (fscanf(INFILE, "%d %d", & rdta->numsp, & rdta->sites) != 2) { if(processID == 0) printf("\n Error: problem reading number of species and sites\n\n"); errorExit(-1); } if (rdta->numsp < 4) { if(processID == 0) printf("\n Error: too few species\n\n"); errorExit(-1); } if (rdta->sites < 1) { if(processID == 0) printf("\n Error: too few sites\n\n"); errorExit(-1); } return; } boolean whitechar (int ch) { return (ch == ' ' || ch == '\n' || ch == '\t' || ch == '\r'); } static void uppercase (int *chptr) { int ch; ch = *chptr; if ((ch >= 'a' && ch <= 'i') || (ch >= 'j' && ch <= 'r') || (ch >= 's' && ch <= 'z')) *chptr = ch + 'A' - 'a'; } static void getyspace (rawdata *rdta) { size_t size = 4 * ((size_t)(rdta->sites / 4 + 1)); int i; unsigned char *y0; rdta->y = (unsigned char **) malloc(((size_t)rdta->numsp + 1) * sizeof(unsigned char *)); assert(rdta->y); y0 = (unsigned char *)calloc(((size_t)(rdta->numsp + 1)) * size, sizeof(unsigned char)); /* printf("Raw alignment data Assigning %Zu bytes\n", ((size_t)(rdta->numsp + 1)) * size * sizeof(unsigned char)); */ assert(y0); rdta->y0 = y0; for (i = 0; i <= rdta->numsp; i++) { rdta->y[i] = y0; y0 += size; } return; } static boolean setupTree (tree *tr, analdef *adef) { nodeptr p0; int tips, inter; if(!adef->readTaxaOnly) { /*tr->bigCutoff = FALSE;*/ tr->patternPosition = (int*)NULL; tr->columnPosition = (int*)NULL; /*tr->maxCategories = MAX(4, adef->categories);*/ /*tr->partitionContributions = (double *)malloc(sizeof(double) * tr->NumberOfModels); for(i = 0; i < tr->NumberOfModels; i++) tr->partitionContributions[i] = -1.0; tr->perPartitionLH = (double *)malloc(sizeof(double) * tr->NumberOfModels); for(i = 0; i < tr->NumberOfModels; i++) { tr->perPartitionLH[i] = 0.0; } if(adef->grouping) tr->grouped = TRUE; else tr->grouped = FALSE; if(adef->constraint) tr->constrained = TRUE; else tr->constrained = FALSE; tr->treeID = 0;*/ } tips = tr->mxtips; inter = tr->mxtips - 1; if(!adef->readTaxaOnly) { tr->yVector = (unsigned char **) malloc(((size_t)tr->mxtips + 1) * sizeof(unsigned char *)); /* tr->fracchanges = (double *)malloc(tr->NumberOfModels * sizeof(double)); tr->likelihoods = (double *)malloc(adef->multipleRuns * sizeof(double));*/ } /*tr->numberOfTrees = -1; tr->treeStringLength = tr->mxtips * (nmlngth+128) + 256 + tr->mxtips * 2; tr->tree_string = (char*)calloc(tr->treeStringLength, sizeof(char)); tr->tree0 = (char*)calloc(tr->treeStringLength, sizeof(char)); tr->tree1 = (char*)calloc(tr->treeStringLength, sizeof(char));*/ /*TODO, must that be so long ?*/ if(!adef->readTaxaOnly) { /*tr->td[0].count = 0; tr->td[0].ti = (traversalInfo *)malloc(sizeof(traversalInfo) * tr->mxtips); tr->td[0].executeModel = (boolean *)malloc(sizeof(boolean) * tr->NumberOfModels); tr->td[0].parameterValues = (double *)malloc(sizeof(double) * tr->NumberOfModels); for(i = 0; i < tr->NumberOfModels; i++) tr->fracchanges[i] = -1.0; tr->fracchange = -1.0; tr->constraintVector = (int *)malloc((2 * tr->mxtips) * sizeof(int));*/ tr->nameList = (char **)malloc(sizeof(char *) * ((size_t)tips + 1)); } if (!(p0 = (nodeptr) malloc(((size_t)tips + 3 * (size_t)inter) * sizeof(node)))) { printf("\n Error: unable to obtain sufficient tree memory\n\n"); return FALSE; } tr->vLength = 0; tr->h = (hashtable*)NULL; return TRUE; } static void checkTaxonName(char *buffer, int len) { int i; for(i = 0; i < len - 1; i++) { boolean valid; switch(buffer[i]) { case '\0': case '\t': case '\n': case '\r': case ' ': case ':': case ',': case '(': case ')': case ';': case '[': case ']': valid = FALSE; break; default: valid = TRUE; } if(!valid) { printf("\n Error: Taxon Name \"%s\" is invalid at position %d, it contains illegal character %c\n\n", buffer, i, buffer[i]); printf(" Illegal characters in taxon-names are: tabulators, carriage returns, spaces, \":\", \",\", \")\", \"(\", \";\", \"]\", \"[\"\n"); printf(" Exiting\n"); exit(-1); } } assert(buffer[len - 1] == '\0'); } static boolean getdata(analdef *adef, rawdata *rdta, tree *tr) { int i, j, basesread, basesnew, ch, my_i, meaning, len, meaningAA[256], meaningDNA[256], meaningBINARY[256], meaningGeneric32[256], meaningGeneric64[256]; boolean allread, firstpass; char buffer[nmlngth + 2]; unsigned char genericChars32[32] = {'0', '1', '2', '3', '4', '5', '6', '7', '8', '9', 'A', 'B', 'C', 'D', 'E', 'F', 'G', 'H', 'I', 'J', 'K', 'L', 'M', 'N', 'O', 'P', 'Q', 'R', 'S', 'T', 'U', 'V'}; unsigned long total = 0, gaps = 0; for (i = 0; i < 256; i++) { meaningAA[i] = -1; meaningDNA[i] = -1; meaningBINARY[i] = -1; meaningGeneric32[i] = -1; meaningGeneric64[i] = -1; } /* generic 32 data */ for(i = 0; i < 32; i++) meaningGeneric32[genericChars32[i]] = i; meaningGeneric32['-'] = getUndetermined(GENERIC_32); meaningGeneric32['?'] = getUndetermined(GENERIC_32); /* AA data */ meaningAA['A'] = 0; /* alanine */ meaningAA['R'] = 1; /* arginine */ meaningAA['N'] = 2; /* asparagine*/ meaningAA['D'] = 3; /* aspartic */ meaningAA['C'] = 4; /* cysteine */ meaningAA['Q'] = 5; /* glutamine */ meaningAA['E'] = 6; /* glutamic */ meaningAA['G'] = 7; /* glycine */ meaningAA['H'] = 8; /* histidine */ meaningAA['I'] = 9; /* isoleucine */ meaningAA['L'] = 10; /* leucine */ meaningAA['K'] = 11; /* lysine */ meaningAA['M'] = 12; /* methionine */ meaningAA['F'] = 13; /* phenylalanine */ meaningAA['P'] = 14; /* proline */ meaningAA['S'] = 15; /* serine */ meaningAA['T'] = 16; /* threonine */ meaningAA['W'] = 17; /* tryptophan */ meaningAA['Y'] = 18; /* tyrosine */ meaningAA['V'] = 19; /* valine */ meaningAA['B'] = 20; /* asparagine, aspartic 2 and 3*/ meaningAA['Z'] = 21; /*21 glutamine glutamic 5 and 6*/ meaningAA['X'] = meaningAA['?'] = meaningAA['*'] = meaningAA['-'] = getUndetermined(AA_DATA); /* DNA data */ meaningDNA['A'] = 1; meaningDNA['B'] = 14; meaningDNA['C'] = 2; meaningDNA['D'] = 13; meaningDNA['G'] = 4; meaningDNA['H'] = 11; meaningDNA['K'] = 12; meaningDNA['M'] = 3; meaningDNA['R'] = 5; meaningDNA['S'] = 6; meaningDNA['T'] = 8; meaningDNA['U'] = 8; meaningDNA['V'] = 7; meaningDNA['W'] = 9; meaningDNA['Y'] = 10; meaningDNA['N'] = meaningDNA['O'] = meaningDNA['X'] = meaningDNA['-'] = meaningDNA['?'] = getUndetermined(DNA_DATA); /* BINARY DATA */ meaningBINARY['0'] = 1; meaningBINARY['1'] = 2; meaningBINARY['-'] = meaningBINARY['?'] = getUndetermined(BINARY_DATA); /*******************************************************************/ basesread = basesnew = 0; allread = FALSE; firstpass = TRUE; ch = ' '; while (! allread) { for (i = 1; i <= tr->mxtips; i++) { if (firstpass) { ch = getc(INFILE); while(ch == ' ' || ch == '\n' || ch == '\t' || ch == '\r') ch = getc(INFILE); my_i = 0; do { buffer[my_i] = (char)ch; ch = getc(INFILE); my_i++; if(my_i >= nmlngth) { if(processID == 0) { printf("Taxon Name to long at taxon %d, adapt constant nmlngth in\n", i); printf("axml.h, current setting %d\n", nmlngth); } errorExit(-1); } } while(ch != ' ' && ch != '\n' && ch != '\t' && ch != '\r'); while(ch == ' ' || ch == '\n' || ch == '\t' || ch == '\r') ch = getc(INFILE); ungetc(ch, INFILE); buffer[my_i] = '\0'; len = (int)strlen(buffer) + 1; checkTaxonName(buffer, len); tr->nameList[i] = (char *)malloc(sizeof(char) * (size_t)len); strcpy(tr->nameList[i], buffer); } j = basesread; while ((j < rdta->sites) && ((ch = getc(INFILE)) != EOF) && (ch != '\n') && (ch != '\r')) { uppercase(& ch); assert(tr->dataVector[j + 1] != -1); switch(tr->dataVector[j + 1]) { case BINARY_DATA: meaning = meaningBINARY[ch]; break; case DNA_DATA: case SECONDARY_DATA: case SECONDARY_DATA_6: case SECONDARY_DATA_7: /* still dealing with DNA/RNA here, hence just act if as they where DNA characters corresponding column merging for sec struct models will take place later */ meaning = meaningDNA[ch]; break; case AA_DATA: meaning = meaningAA[ch]; break; case GENERIC_32: meaning = meaningGeneric32[ch]; break; case GENERIC_64: meaning = meaningGeneric64[ch]; break; default: assert(0); } if (meaning != -1) { j++; rdta->y[i][j] = (unsigned char)ch; } else { if(!whitechar(ch)) { printf("\n Error: bad base (%c) at site %d of sequence %d\n\n", ch, j + 1, i); return FALSE; } } } if (ch == EOF) { printf("\n Error: end-of-file at site %d of sequence %d\n\n", j + 1, i); return FALSE; } if (! firstpass && (j == basesread)) i--; else { if (i == 1) basesnew = j; else if (j != basesnew) { printf("\n Error: sequences out of alignment\n"); printf("%d (instead of %d) residues read in sequence %d %s\n", j - basesread, basesnew - basesread, i, tr->nameList[i]); return FALSE; } } while (ch != '\n' && ch != EOF && ch != '\r') ch = getc(INFILE); /* flush line *//* PC-LINEBREAK*/ } firstpass = FALSE; basesread = basesnew; allread = (basesread >= rdta->sites); } for(j = 1; j <= tr->mxtips; j++) for(i = 1; i <= rdta->sites; i++) { assert(tr->dataVector[i] != -1); switch(tr->dataVector[i]) { case BINARY_DATA: meaning = meaningBINARY[rdta->y[j][i]]; if(meaning == getUndetermined(BINARY_DATA)) gaps++; break; case SECONDARY_DATA: case SECONDARY_DATA_6: case SECONDARY_DATA_7: assert(tr->secondaryStructurePairs[i - 1] != -1); assert(i - 1 == tr->secondaryStructurePairs[tr->secondaryStructurePairs[i - 1]]); /* don't worry too much about undetermined column count here for sec-struct, just count DNA/RNA gaps here and worry about the rest later-on, falling through to DNA again :-) */ case DNA_DATA: meaning = meaningDNA[rdta->y[j][i]]; if(meaning == getUndetermined(DNA_DATA)) gaps++; break; case AA_DATA: meaning = meaningAA[rdta->y[j][i]]; if(meaning == getUndetermined(AA_DATA)) gaps++; break; case GENERIC_32: meaning = meaningGeneric32[rdta->y[j][i]]; if(meaning == getUndetermined(GENERIC_32)) gaps++; break; case GENERIC_64: meaning = meaningGeneric64[rdta->y[j][i]]; if(meaning == getUndetermined(GENERIC_64)) gaps++; break; default: assert(0); } total++; rdta->y[j][i] = (unsigned char)meaning; } adef->gapyness = (double)gaps / (double)total; /*myBinFwrite(&(adef->gapyness), sizeof(double), 1);*/ printf("gappyness: %f\n", adef->gapyness); /*for(i = 1; i <= tr->mxtips; i++) { int len = strlen(tr->nameList[i]) + 1; myBinFwrite(&len, sizeof(int), 1); myBinFwrite(tr->nameList[i], sizeof(char), len); printf("%d %s\n", len, tr->nameList[i]); } */ return TRUE; } static void inputweights (rawdata *rdta) { int i, w, fres; FILE *weightFile; int *wv = (int *)malloc(sizeof(int) * (size_t)rdta->sites); weightFile = myfopen(weightFileName, "rb"); i = 0; while((fres = fscanf(weightFile,"%d", &w)) != EOF) { if(!fres) { if(processID == 0) printf("error reading weight file probably encountered a non-integer weight value\n"); errorExit(-1); } wv[i] = w; i++; } if(i != rdta->sites) { if(processID == 0) printf("number %d of weights not equal to number %d of alignment columns\n", i, rdta->sites); errorExit(-1); } for(i = 1; i <= rdta->sites; i++) rdta->wgt[i] = wv[i - 1]; fclose(weightFile); free(wv); } static hashNumberType hashString(char *p, hashNumberType tableSize) { hashNumberType h = 0; for(; *p; p++) h = 31 * h + (hashNumberType)*p; return (h % tableSize); } static void addword(char *s, stringHashtable *h, int nodeNumber) { hashNumberType position = hashString(s, h->tableSize); stringEntry *p = h->table[position]; for(; p!= NULL; p = p->next) { if(strcmp(s, p->word) == 0) return; } p = (stringEntry *)malloc(sizeof(stringEntry)); assert(p); p->nodeNumber = nodeNumber; p->word = (char *)malloc((strlen(s) + 1) * sizeof(char)); strcpy(p->word, s); p->next = h->table[position]; h->table[position] = p; } static stringHashtable *initStringHashTable(hashNumberType n) { /* init with primes */ static const hashNumberType initTable[] = {53, 97, 193, 389, 769, 1543, 3079, 6151, 12289, 24593, 49157, 98317, 196613, 393241, 786433, 1572869, 3145739, 6291469, 12582917, 25165843, 50331653, 100663319, 201326611, 402653189, 805306457, 1610612741}; /* init with powers of two static const hashNumberType initTable[] = {64, 128, 256, 512, 1024, 2048, 4096, 8192, 16384, 32768, 65536, 131072, 262144, 524288, 1048576, 2097152, 4194304, 8388608, 16777216, 33554432, 67108864, 134217728, 268435456, 536870912, 1073741824, 2147483648U}; */ stringHashtable *h = (stringHashtable*)malloc(sizeof(stringHashtable)); hashNumberType tableSize, i, primeTableLength = sizeof(initTable)/sizeof(initTable[0]), maxSize = (hashNumberType)-1; assert(n <= maxSize); i = 0; while(initTable[i] < n && i < primeTableLength) i++; assert(i < primeTableLength); tableSize = initTable[i]; h->table = (stringEntry**)calloc(tableSize, sizeof(stringEntry*)); h->tableSize = tableSize; return h; } static void getinput(analdef *adef, rawdata *rdta, cruncheddata *cdta, tree *tr) { int i; INFILE = myfopen(seq_file, "rb"); getnums(rdta); /*myBinFwrite(&(rdta->sites), sizeof(int), 1); myBinFwrite(&(rdta->numsp), sizeof(int), 1); printf("%d %d\n", rdta->sites, rdta->numsp);*/ tr->mxtips = rdta->numsp; rdta->wgt = (int *) malloc(((size_t)rdta->sites + 1) * sizeof(int)); cdta->alias = (int *) malloc(((size_t)rdta->sites + 1) * sizeof(int)); cdta->aliaswgt = (int *) malloc(((size_t)rdta->sites + 1) * sizeof(int)); tr->model = (int *) calloc(((size_t)rdta->sites + 1), sizeof(int)); tr->initialDataVector = (int *) malloc(((size_t)rdta->sites + 1) * sizeof(int)); tr->extendedDataVector = (int *) malloc(((size_t)rdta->sites + 1) * sizeof(int)); if(!adef->useWeightFile) { for (i = 1; i <= rdta->sites; i++) rdta->wgt[i] = 1; } else { assert(!adef->useSecondaryStructure); inputweights(rdta); } if(adef->useMultipleModel) { int ref; parsePartitions(adef, rdta, tr); for(i = 1; i <= rdta->sites; i++) { ref = tr->model[i]; tr->initialDataVector[i] = tr->initialPartitionData[ref].dataType; } } else { int dataType = -1; tr->initialPartitionData = (pInfo*)malloc(sizeof(pInfo)); tr->initialPartitionData->optimizeBaseFrequencies = FALSE; tr->initialPartitionData[0].partitionName = (char*)malloc(128 * sizeof(char)); strcpy(tr->initialPartitionData[0].partitionName, "No Name Provided"); tr->initialPartitionData[0].protModels = adef->proteinMatrix; tr->initialPartitionData[0].protFreqs = adef->protEmpiricalFreqs; tr->NumberOfModels = 1; if(adef->model == M_PROTCAT || adef->model == M_PROTGAMMA) dataType = AA_DATA; if(adef->model == M_GTRCAT || adef->model == M_GTRGAMMA) dataType = DNA_DATA; if(adef->model == M_BINCAT || adef->model == M_BINGAMMA) dataType = BINARY_DATA; if(adef->model == M_32CAT || adef->model == M_32GAMMA) dataType = GENERIC_32; if(adef->model == M_64CAT || adef->model == M_64GAMMA) dataType = GENERIC_64; assert(dataType == BINARY_DATA || dataType == DNA_DATA || dataType == AA_DATA || dataType == GENERIC_32 || dataType == GENERIC_64); tr->initialPartitionData[0].dataType = dataType; for(i = 0; i <= rdta->sites; i++) { tr->initialDataVector[i] = dataType; tr->model[i] = 0; } } if(adef->useSecondaryStructure) { memcpy(tr->extendedDataVector, tr->initialDataVector, ((size_t)rdta->sites + 1) * sizeof(int)); tr->extendedPartitionData =(pInfo*)malloc(sizeof(pInfo) * (size_t)tr->NumberOfModels); for(i = 0; i < tr->NumberOfModels; i++) { tr->extendedPartitionData[i].partitionName = (char*)malloc((strlen(tr->initialPartitionData[i].partitionName) + 1) * sizeof(char)); strcpy(tr->extendedPartitionData[i].partitionName, tr->initialPartitionData[i].partitionName); tr->extendedPartitionData[i].dataType = tr->initialPartitionData[i].dataType; tr->extendedPartitionData[i].protModels = tr->initialPartitionData[i].protModels; tr->extendedPartitionData[i].protFreqs = tr->initialPartitionData[i].protFreqs; } parseSecondaryStructure(tr, adef, rdta->sites); tr->dataVector = tr->extendedDataVector; tr->partitionData = tr->extendedPartitionData; } else { tr->dataVector = tr->initialDataVector; tr->partitionData = tr->initialPartitionData; } getyspace(rdta); setupTree(tr, adef); if(!getdata(adef, rdta, tr)) { printf("Problem reading alignment file \n"); errorExit(1); } tr->nameHash = initStringHashTable(10 * (size_t)tr->mxtips); for(i = 1; i <= tr->mxtips; i++) addword(tr->nameList[i], tr->nameHash, i); fclose(INFILE); } static unsigned char buildStates(int secModel, unsigned char v1, unsigned char v2) { unsigned char new = 0; switch(secModel) { case SECONDARY_DATA: new = v1; new = new << 4; new = new | v2; break; case SECONDARY_DATA_6: { int meaningDNA[256], i; const unsigned char allowedStates[6][2] = {{'A','T'}, {'C', 'G'}, {'G', 'C'}, {'G','T'}, {'T', 'A'}, {'T', 'G'}}; const unsigned char finalBinaryStates[6] = {1, 2, 4, 8, 16, 32}; unsigned char intermediateBinaryStates[6]; int length = 6; for(i = 0; i < 256; i++) meaningDNA[i] = -1; meaningDNA['A'] = 1; meaningDNA['B'] = 14; meaningDNA['C'] = 2; meaningDNA['D'] = 13; meaningDNA['G'] = 4; meaningDNA['H'] = 11; meaningDNA['K'] = 12; meaningDNA['M'] = 3; meaningDNA['N'] = 15; meaningDNA['O'] = 15; meaningDNA['R'] = 5; meaningDNA['S'] = 6; meaningDNA['T'] = 8; meaningDNA['U'] = 8; meaningDNA['V'] = 7; meaningDNA['W'] = 9; meaningDNA['X'] = 15; meaningDNA['Y'] = 10; meaningDNA['-'] = 15; meaningDNA['?'] = 15; for(i = 0; i < length; i++) { unsigned char n1 = meaningDNA[allowedStates[i][0]]; unsigned char n2 = meaningDNA[allowedStates[i][1]]; new = n1; new = new << 4; new = new | n2; intermediateBinaryStates[i] = new; } new = v1; new = new << 4; new = new | v2; for(i = 0; i < length; i++) { if(new == intermediateBinaryStates[i]) break; } if(i < length) new = finalBinaryStates[i]; else { new = 0; for(i = 0; i < length; i++) { if(v1 & meaningDNA[allowedStates[i][0]]) { /*printf("Adding %c%c\n", allowedStates[i][0], allowedStates[i][1]);*/ new |= finalBinaryStates[i]; } if(v2 & meaningDNA[allowedStates[i][1]]) { /*printf("Adding %c%c\n", allowedStates[i][0], allowedStates[i][1]);*/ new |= finalBinaryStates[i]; } } } } break; case SECONDARY_DATA_7: { int meaningDNA[256], i; const unsigned char allowedStates[6][2] = {{'A','T'}, {'C', 'G'}, {'G', 'C'}, {'G','T'}, {'T', 'A'}, {'T', 'G'}}; const unsigned char finalBinaryStates[7] = {1, 2, 4, 8, 16, 32, 64}; unsigned char intermediateBinaryStates[7]; for(i = 0; i < 256; i++) meaningDNA[i] = -1; meaningDNA['A'] = 1; meaningDNA['B'] = 14; meaningDNA['C'] = 2; meaningDNA['D'] = 13; meaningDNA['G'] = 4; meaningDNA['H'] = 11; meaningDNA['K'] = 12; meaningDNA['M'] = 3; meaningDNA['N'] = 15; meaningDNA['O'] = 15; meaningDNA['R'] = 5; meaningDNA['S'] = 6; meaningDNA['T'] = 8; meaningDNA['U'] = 8; meaningDNA['V'] = 7; meaningDNA['W'] = 9; meaningDNA['X'] = 15; meaningDNA['Y'] = 10; meaningDNA['-'] = 15; meaningDNA['?'] = 15; for(i = 0; i < 6; i++) { unsigned char n1 = meaningDNA[allowedStates[i][0]]; unsigned char n2 = meaningDNA[allowedStates[i][1]]; new = n1; new = new << 4; new = new | n2; intermediateBinaryStates[i] = new; } new = v1; new = new << 4; new = new | v2; for(i = 0; i < 6; i++) { /* exact match */ if(new == intermediateBinaryStates[i]) break; } if(i < 6) new = finalBinaryStates[i]; else { /* distinguish between exact mismatches and partial mismatches */ for(i = 0; i < 6; i++) if((v1 & meaningDNA[allowedStates[i][0]]) && (v2 & meaningDNA[allowedStates[i][1]])) break; if(i < 6) { /* printf("partial mismatch\n"); */ new = 0; for(i = 0; i < 6; i++) { if((v1 & meaningDNA[allowedStates[i][0]]) && (v2 & meaningDNA[allowedStates[i][1]])) { /*printf("Adding %c%c\n", allowedStates[i][0], allowedStates[i][1]);*/ new |= finalBinaryStates[i]; } else new |= finalBinaryStates[6]; } } else new = finalBinaryStates[6]; } } break; default: assert(0); } return new; } static void adaptRdataToSecondary(tree *tr, rawdata *rdta) { int *alias = (int*)calloc((size_t)rdta->sites, sizeof(int)); int i, j, realPosition; for(i = 0; i < rdta->sites; i++) alias[i] = -1; for(i = 0, realPosition = 0; i < rdta->sites; i++) { int partner = tr->secondaryStructurePairs[i]; if(partner != -1) { assert(tr->dataVector[i+1] == SECONDARY_DATA || tr->dataVector[i+1] == SECONDARY_DATA_6 || tr->dataVector[i+1] == SECONDARY_DATA_7); if(i < partner) { for(j = 1; j <= rdta->numsp; j++) { unsigned char v1 = rdta->y[j][i+1]; unsigned char v2 = rdta->y[j][partner+1]; assert(i+1 < partner+1); rdta->y[j][i+1] = buildStates(tr->dataVector[i+1], v1, v2); } alias[realPosition] = i; realPosition++; } } else { alias[realPosition] = i; realPosition++; } } assert(rdta->sites - realPosition == tr->numberOfSecondaryColumns / 2); rdta->sites = realPosition; for(i = 0; i < rdta->sites; i++) { assert(alias[i] != -1); tr->model[i+1] = tr->model[alias[i]+1]; tr->dataVector[i+1] = tr->dataVector[alias[i]+1]; rdta->wgt[i+1] = rdta->wgt[alias[i]+1]; for(j = 1; j <= rdta->numsp; j++) rdta->y[j][i+1] = rdta->y[j][alias[i]+1]; } free(alias); } static void sitesort(rawdata *rdta, cruncheddata *cdta, tree *tr, analdef *adef) { int gap, i, j, jj, jg, k, n, nsp; int *index, *category = (int*)NULL; boolean flip, tied; unsigned char **data; if(adef->useSecondaryStructure) { assert(tr->NumberOfModels > 1 && adef->useMultipleModel); adaptRdataToSecondary(tr, rdta); } if(adef->useMultipleModel) category = tr->model; index = cdta->alias; data = rdta->y; n = rdta->sites; nsp = rdta->numsp; index[0] = -1; if(adef->compressPatterns) { for (gap = n / 2; gap > 0; gap /= 2) { for (i = gap + 1; i <= n; i++) { j = i - gap; do { jj = index[j]; jg = index[j+gap]; if(adef->useMultipleModel) { assert(category[jj] != -1 && category[jg] != -1); flip = (category[jj] > category[jg]); tied = (category[jj] == category[jg]); } else { flip = 0; tied = 1; } for (k = 1; (k <= nsp) && tied; k++) { flip = (data[k][jj] > data[k][jg]); tied = (data[k][jj] == data[k][jg]); } if (flip) { index[j] = jg; index[j+gap] = jj; j -= gap; } } while (flip && (j > 0)); } } } } static void sitecombcrunch (rawdata *rdta, cruncheddata *cdta, tree *tr, analdef *adef) { boolean tied; int i, sitei, j, sitej, k; int *aliasModel = (int*)NULL, *aliasSuperModel = (int*)NULL, undeterminedSites = 0; if(adef->useMultipleModel) { aliasSuperModel = (int*)malloc(sizeof(int) * ((size_t)rdta->sites + 1)); aliasModel = (int*)malloc(sizeof(int) * ((size_t)rdta->sites + 1)); } i = 0; cdta->alias[0] = cdta->alias[1]; cdta->aliaswgt[0] = 0; if(adef->mode == PER_SITE_LL) { assert(0); /* tr->patternPosition = (int*)malloc(sizeof(int) * rdta->sites); tr->columnPosition = (int*)malloc(sizeof(int) * rdta->sites); for(i = 0; i < rdta->sites; i++) { tr->patternPosition[i] = -1; tr->columnPosition[i] = -1; } */ } i = 0; for (j = 1; j <= rdta->sites; j++) { int allGap = TRUE; unsigned char undetermined; sitei = cdta->alias[i]; sitej = cdta->alias[j]; undetermined = getUndetermined(tr->dataVector[sitej]); for(k = 1; k <= rdta->numsp; k++) { if(rdta->y[k][sitej] != undetermined) { allGap = FALSE; break; } } if(allGap) undeterminedSites++; #ifdef _DEBUG_UNDET_REMOVAL if(allGap) printf("Skipping gap site %d\n", sitej); #endif if(!adef->compressPatterns) tied = 0; else { if(adef->useMultipleModel) { tied = (tr->model[sitei] == tr->model[sitej]); if(tied) assert(tr->dataVector[sitei] == tr->dataVector[sitej]); } else tied = 1; } for (k = 1; tied && (k <= rdta->numsp); k++) tied = (rdta->y[k][sitei] == rdta->y[k][sitej]); assert(!(tied && allGap)); if(tied && !allGap) { if(adef->mode == PER_SITE_LL) { tr->patternPosition[j - 1] = i; tr->columnPosition[j - 1] = sitej; /*printf("Pattern %d from column %d also at site %d\n", i, sitei, sitej);*/ } cdta->aliaswgt[i] += rdta->wgt[sitej]; if(adef->useMultipleModel) { aliasModel[i] = tr->model[sitej]; aliasSuperModel[i] = tr->dataVector[sitej]; } } else { if(!allGap) { if(cdta->aliaswgt[i] > 0) i++; if(adef->mode == PER_SITE_LL) { tr->patternPosition[j - 1] = i; tr->columnPosition[j - 1] = sitej; /*printf("Pattern %d is from cloumn %d\n", i, sitej);*/ } cdta->aliaswgt[i] = rdta->wgt[sitej]; cdta->alias[i] = sitej; if(adef->useMultipleModel) { aliasModel[i] = tr->model[sitej]; aliasSuperModel[i] = tr->dataVector[sitej]; } } } } cdta->endsite = (size_t)i; if (cdta->aliaswgt[i] > 0) cdta->endsite++; #ifdef _DEBUG_UNDET_REMOVAL printf("included sites: %d\n", cdta->endsite); #endif if(adef->mode == PER_SITE_LL) { assert(0); for(i = 0; i < rdta->sites; i++) { int p = tr->patternPosition[i]; int c = tr->columnPosition[i]; assert(p >= 0 && (size_t) p < cdta->endsite); assert(c >= 1 && c <= rdta->sites); } } if(adef->useMultipleModel) { for(i = 0; i <= rdta->sites; i++) { tr->model[i] = aliasModel[i]; tr->dataVector[i] = aliasSuperModel[i]; } } if(adef->useMultipleModel) { free(aliasModel); free(aliasSuperModel); } if(undeterminedSites > 0) printBothOpen("\nAlignment has %d completely undetermined sites that will be automatically removed from the binary alignment file\n\n", undeterminedSites); } static boolean makeweights (analdef *adef, rawdata *rdta, cruncheddata *cdta, tree *tr) { int i; for (i = 1; i <= rdta->sites; i++) cdta->alias[i] = i; sitesort(rdta, cdta, tr, adef); sitecombcrunch(rdta, cdta, tr, adef); return TRUE; } static boolean makevalues(rawdata *rdta, cruncheddata *cdta, tree *tr, analdef *adef) { int i, model, modelCounter; size_t j; unsigned char *y = (unsigned char *)malloc(((size_t)rdta->numsp) * ((size_t)cdta->endsite) * sizeof(unsigned char)); /* printf("compressed data Assigning %Zu bytes\n", ((size_t)rdta->numsp) * ((size_t)cdta->endsite) * sizeof(unsigned char)); */ { for (i = 1; i <= rdta->numsp; i++) for (j = 0; j < cdta->endsite; j++) y[(((size_t)(i - 1)) * ((size_t)cdta->endsite)) + j] = rdta->y[i][cdta->alias[j]]; /* printf("Free on raw data\n"); */ free(rdta->y0); free(rdta->y); } rdta->y0 = y; if(!adef->useMultipleModel) tr->NumberOfModels = 1; #ifdef _DEBUG_UNDET_REMOVAL for(i = 0; i < cdta->endsite; i++) printf("%d ", tr->model[i]); printf("\n"); #endif if(adef->useMultipleModel) { tr->partitionData[0].lower = 0; model = tr->model[0]; modelCounter = 0; i = 1; while((size_t) i < cdta->endsite) { if(tr->model[i] != model) { tr->partitionData[modelCounter].upper = (size_t)i; tr->partitionData[modelCounter + 1].lower = (size_t)i; model = tr->model[i]; modelCounter++; } i++; } if(modelCounter < tr->NumberOfModels - 1) { printf("\nYou specified %d partitions, but after parsing and pre-processing ExaML only found %d partitions\n", tr->NumberOfModels, modelCounter + 1); printf("Presumably one or more partitions vanished because they consisted entirely of undetermined characters.\n"); printf("Please fix your data!\n\n"); exit(-1); } tr->partitionData[tr->NumberOfModels - 1].upper = (size_t)cdta->endsite; for(i = 0; i < tr->NumberOfModels; i++) tr->partitionData[i].width = tr->partitionData[i].upper - tr->partitionData[i].lower; model = tr->model[0]; modelCounter = 0; tr->model[0] = modelCounter; i = 1; while((size_t) i < cdta->endsite) { if(tr->model[i] != model) { model = tr->model[i]; modelCounter++; tr->model[i] = modelCounter; } else tr->model[i] = modelCounter; i++; } } else { tr->partitionData[0].lower = 0; tr->partitionData[0].upper = (size_t)cdta->endsite; tr->partitionData[0].width = tr->partitionData[0].upper - tr->partitionData[0].lower; } tr->rdta = rdta; tr->cdta = cdta; tr->originalCrunchedLength = tr->cdta->endsite; for(i = 0; i < rdta->numsp; i++) tr->yVector[i + 1] = &(rdta->y0[(tr->originalCrunchedLength) * ((size_t)i)]); return TRUE; } static void initAdef(analdef *adef) { adef->useSecondaryStructure = FALSE; adef->bootstrapBranchLengths = FALSE; adef->model = M_GTRCAT; adef->max_rearrange = 21; adef->stepwidth = 5; adef->initial = adef->bestTrav = 10; adef->initialSet = FALSE; adef->restart = FALSE; adef->mode = BIG_RAPID_MODE; adef->categories = 25; adef->boot = 0; adef->rapidBoot = 0; adef->useWeightFile = FALSE; adef->checkpoints = 0; adef->startingTreeOnly = 0; adef->multipleRuns = 1; adef->useMultipleModel = FALSE; adef->likelihoodEpsilon = 0.1; adef->constraint = FALSE; adef->grouping = FALSE; adef->randomStartingTree = FALSE; adef->parsimonySeed = 0; adef->proteinMatrix = JTT; adef->protEmpiricalFreqs = 0; adef->useInvariant = FALSE; adef->permuteTreeoptimize = FALSE; adef->useInvariant = FALSE; adef->allInOne = FALSE; adef->likelihoodTest = FALSE; adef->perGeneBranchLengths = FALSE; adef->generateBS = FALSE; adef->bootStopping = FALSE; adef->gapyness = 0.0; adef->similarityFilterMode = 0; adef->useExcludeFile = FALSE; adef->userProteinModel = FALSE; adef->externalAAMatrix = (double*)NULL; adef->computeELW = FALSE; adef->computeDistance = FALSE; adef->thoroughInsertion = FALSE; adef->compressPatterns = TRUE; adef->readTaxaOnly = FALSE; adef->meshSearch = 0; adef->useCheckpoint = FALSE; adef->leaveDropMode = FALSE; adef->slidingWindowSize = 100; #ifdef _BAYESIAN adef->bayesian = FALSE; #endif } static int dataExists(char *model, analdef *adef) { /********** BINARY ********************/ if(strcmp(model, "BIN\0") == 0) { adef->model = M_BINGAMMA; return 1; } /*********** DNA **********************/ if(strcmp(model, "DNA\0") == 0) { adef->model = M_GTRGAMMA; return 1; } /*************** AA GTR ********************/ if(strcmp(model, "PROT\0") == 0) { adef->model = M_PROTGAMMA; return 1; } return 0; } /*********************************************************************************************/ static void printVersionInfo(void) { printf("\n\nThis is the parse-examl version %s released by Alexandros Stamatakis, Andre J. Aberer, and Alexey Kozlov in %s.\n\n", programVersion, programDate); } static void printREADME(void) { printVersionInfo(); printf("\n"); printf("\nTo report bugs use the RAxML google group\n"); printf("Please send us all input files, the exact invocation, details of the HW and operating system,\n"); printf("as well as all error messages printed to screen.\n\n\n"); printf("parse-examl\n"); printf(" -s sequenceFileName\n"); printf(" -n outputFileName\n"); printf(" -m substitutionModel\n"); printf(" [-c]\n"); printf(" [-q]\n"); printf(" [-h]\n"); printf("\n"); printf(" -m Model of Nucleotide or Amino Acid Substitution:\n"); printf("\n"); printf(" For Binary data use: BIN\n"); printf(" For DNA data use: DNA\n"); printf(" For AA data use: PROT\n"); printf("\n"); printf(" -c disable site pattern compression\n"); printf("\n"); printf(" -q Specify the file name which contains the assignment of models to alignment\n"); printf(" partitions for multiple models of substitution. For the syntax of this file\n"); printf(" please consult the manual.\n"); printf("\n"); printf(" -h Display this help message.\n"); printf("\n"); printf("\n\n\n\n"); } static int mygetopt(int argc, char **argv, char *opts, int *optind, char **optarg) { static int sp = 1; register int c; register char *cp; if(sp == 1) { if(*optind >= argc || argv[*optind][0] != '-' || argv[*optind][1] == '\0') { return -1; } } else { if(strcmp(argv[*optind], "--") == 0) { *optind = *optind + 1; return -1; } } c = argv[*optind][sp]; if(c == ':' || (cp=strchr(opts, c)) == 0) { if(argv[*optind][++sp] == '\0') { *optind = *optind + 1; sp = 1; } printf("\n Error: illegal option -- %c\n\n", c); return('?'); } if(*++cp == ':') { if(argv[*optind][sp+1] != '\0') { *optarg = &argv[*optind][sp+1]; *optind = *optind + 1; } else { *optind = *optind + 1; if(*optind >= argc) { if ( c != 'h') { sp = 1; printf("\n Error: option -- %c requires an argument\n\n", c); return('?'); } else return ( c ); } else { *optarg = argv[*optind]; *optind = *optind + 1; } } sp = 1; } else { if(argv[*optind][++sp] == '\0') { sp = 1; *optind = *optind + 1; } *optarg = 0; } return(c); } /*********************************************************************************************/ static void analyzeRunId(char id[128]) { int i = 0; while(id[i] != '\0') { if(i >= 128) { printf("\n Error: run id after \"-n\" is too long, it has %d characters please use a shorter one\n\n", i); assert(0); } if(id[i] == '/') { printf("\n Error character %c not allowed in run ID\n\n", id[i]); assert(0); } i++; } if(i == 0) { printf("\n Error: please provide a string for the run id after \"-n\" \n\n"); assert(0); } } static void get_args(int argc, char *argv[], analdef *adef, tree *tr) { boolean bad_opt =FALSE; char *optarg = (char*)NULL, model[2048] = ""; int optind = 1, c, nameSet = 0, alignmentSet = 0, modelSet = 0; run_id[0] = 0; seq_file[0] = 0; model[0] = 0; weightFileName[0] = 0; modelFileName[0] = 0; /*********** tr inits **************/ #ifdef _USE_PTHREADS NumberOfThreads = 0; #endif tr->bootStopCriterion = -1; tr->wcThreshold = 0.03; tr->doCutoff = TRUE; tr->secondaryStructureModel = SEC_16; /* default setting */ tr->searchConvergenceCriterion = FALSE; tr->catOnly = FALSE; tr->multiStateModel = GTR_MULTI_STATE; tr->useGappedImplementation = FALSE; tr->saveMemory = FALSE; /********* tr inits end*************/ while( !bad_opt && ( ( c = mygetopt(argc,argv,"q:s:n:m:hc", &optind, &optarg ) ) != -1 ) ) { switch(c) { case 'c': adef->compressPatterns = FALSE; break; case 'h': printREADME(); errorExit(-1); break; case 'q': strcpy(modelFileName,optarg); adef->useMultipleModel = TRUE; break; case 'n': strcpy(run_id,optarg); analyzeRunId(run_id); nameSet = 1; break; case 's': strcpy(seq_file, optarg); alignmentSet = 1; break; case 'm': strcpy(model,optarg); if(dataExists(model, adef) == 0) { printf("\n Error: model %s does not exist\n\n", model); errorExit(-1); } else modelSet = 1; break; default: errorExit(-1); } } if(!adef->useMultipleModel && !modelSet) { if(processID == 0) { printREADME(); printf("\n Error, you must specify a data type for unpartitioned alignment with the \"-m\" option\n\n"); } errorExit(-1); } if(!nameSet) { if(processID == 0) { printREADME(); printf("\n Error: please specify a name for this run with -n\n\n"); } errorExit(-1); } if(!alignmentSet) { if(processID == 0) { printREADME(); printf("\n Error: please specify an alignment for this run with -s\n\n"); } errorExit(-1); } strcat(infoFileName, "RAxML_info."); strcat(infoFileName, run_id); if(processID == 0) { int infoFileExists = 0; infoFileExists = filexists(infoFileName); if(infoFileExists) { printf("\n Error: output files with the run ID <%s> already exist... exiting\n\n", run_id); exit(-1); } } strcat(byteFileName, run_id); strcat(byteFileName, ".binary"); if(filexists(byteFileName)) { printf("\n Error: binary compressed file %s you want to generate already exists... exiting\n\n", byteFileName); exit(0); } byteFile = fopen(byteFileName, "wb"); if ( !byteFile ) printf("%s\n", byteFileName); return; } void errorExit(int e) { #ifdef _WAYNE_MPI MPI_Finalize(); #endif exit(e); } /***********************reading and initializing input ******************/ /********************PRINTING various INFO **************************************/ void getDataTypeString(tree *tr, int model, char typeOfData[1024]) { switch(tr->partitionData[model].dataType) { case AA_DATA: strcpy(typeOfData,"AA"); break; case DNA_DATA: strcpy(typeOfData,"DNA"); break; case BINARY_DATA: strcpy(typeOfData,"BINARY/MORPHOLOGICAL"); break; case SECONDARY_DATA: strcpy(typeOfData,"SECONDARY 16 STATE MODEL USING "); strcat(typeOfData, secondaryModelList[tr->secondaryStructureModel]); break; case SECONDARY_DATA_6: strcpy(typeOfData,"SECONDARY 6 STATE MODEL USING "); strcat(typeOfData, secondaryModelList[tr->secondaryStructureModel]); break; case SECONDARY_DATA_7: strcpy(typeOfData,"SECONDARY 7 STATE MODEL USING "); strcat(typeOfData, secondaryModelList[tr->secondaryStructureModel]); break; case GENERIC_32: strcpy(typeOfData,"Multi-State"); break; case GENERIC_64: strcpy(typeOfData,"Codon"); break; default: assert(0); } } /************************************************************************************/ static int iterated_bitcount(unsigned int n) { int count=0; while(n) { count += n & 0x1u ; n >>= 1 ; } return count; } static char bits_in_16bits [0x1u << 16]; static void compute_bits_in_16bits(void) { unsigned int i; for (i = 0; i < (0x1u<<16); i++) bits_in_16bits[i] = iterated_bitcount(i); return ; } unsigned int precomputed16_bitcount (unsigned int n) { /* works only for 32-bit int*/ return bits_in_16bits [n & 0xffffu] + bits_in_16bits [(n >> 16) & 0xffffu] ; } static void smoothFreqs(const int n, double *pfreqs, double *dst, pInfo *partitionData) { int countScale = 0, l, loopCounter = 0; for(l = 0; l < n; l++) if(pfreqs[l] < FREQ_MIN) countScale++; /* for(l = 0; l < n; l++) if(pfreqs[l] == 0.0) countScale++;*/ if(countScale > 0) { while(countScale > 0) { double correction = 0.0; double factor = 1.0; for(l = 0; l < n; l++) { if(pfreqs[l] == 0.0) correction += FREQ_MIN; else if(pfreqs[l] < FREQ_MIN) { correction += (FREQ_MIN - pfreqs[l]); factor -= (FREQ_MIN - pfreqs[l]); } } countScale = 0; for(l = 0; l < n; l++) { if(pfreqs[l] >= FREQ_MIN) pfreqs[l] = pfreqs[l] - (pfreqs[l] * correction * factor); else pfreqs[l] = FREQ_MIN; if(pfreqs[l] < FREQ_MIN) countScale++; } assert(loopCounter < 100); loopCounter++; } } for(l = 0; l < n; l++) dst[l] = pfreqs[l]; if(partitionData->nonGTR) { int k; assert(partitionData->dataType == SECONDARY_DATA_7 || partitionData->dataType == SECONDARY_DATA_6 || partitionData->dataType == SECONDARY_DATA); for(l = 0; l < n; l++) { int count = 1; for(k = 0; k < n; k++) { if(k != l && partitionData->frequencyGrouping[l] == partitionData->frequencyGrouping[k]) { count++; dst[l] += pfreqs[k]; } } dst[l] /= ((double)count); } } } static void genericBaseFrequencies(tree *tr, const int numFreqs, rawdata *rdta, cruncheddata *cdta, int lower, int upper, int model, boolean smoothFrequencies, const unsigned int *bitMask) { double wj, acc, pfreqs[64], sumf[64], temp[64]; int countStatesPresent = 0, statesPresent[64], i, j, k, l; unsigned char *yptr; for(l = 0; l < numFreqs; l++) { pfreqs[l] = 1.0 / ((double)numFreqs); statesPresent[l] = 0; } #ifdef _DEBUG_UNDET_REMOVAL printf("bounds %d %d\n", lower, upper); for(j = lower; j < upper; j++) { for(i = 0; i < rdta->numsp; i++) { unsigned int code; yptr = &(rdta->y0[((size_t)i) * (tr->originalCrunchedLength)]); code = yptr[j]; printf("%c", inverseMeaningDNA[code]); } printf("\n"); } printf("\n\n"); #endif for(i = 0; i < rdta->numsp; i++) { yptr = &(rdta->y0[((size_t)i) * (tr->originalCrunchedLength)]); for(j = lower; j < upper; j++) { unsigned int code = bitMask[yptr[j]]; switch(numFreqs) { case 2: switch(code) { case 1: statesPresent[0] = 1; break; case 2: statesPresent[1] = 1; break; default: ; } break; case 4: switch(code) { case 1: statesPresent[0] = 1; break; case 2: statesPresent[1] = 1; break; case 4: statesPresent[2] = 1; break; case 8: statesPresent[3] = 1; break; default: ; } break; case 20: if(yptr[j] >= 0 && yptr[j] < 20) statesPresent[yptr[j]] = 1; break; default: assert(0); } } } for(i = 0, countStatesPresent = 0; i < numFreqs; i++) if(statesPresent[i] == 1) countStatesPresent++; for (k = 1; k <= 8; k++) { for(l = 0; l < numFreqs; l++) sumf[l] = 0.0; for(i = 0; i < rdta->numsp; i++) { yptr = &(rdta->y0[((size_t)i) * (tr->originalCrunchedLength)]); for(j = lower; j < upper; j++) { unsigned int code = bitMask[yptr[j]]; assert(code >= 1); for(l = 0; l < numFreqs; l++) { if((code >> l) & 1) temp[l] = pfreqs[l]; else temp[l] = 0.0; } for(l = 0, acc = 0.0; l < numFreqs; l++) { if(temp[l] != 0.0) acc += temp[l]; } wj = ((double)cdta->aliaswgt[j]) / acc; for(l = 0; l < numFreqs; l++) { if(temp[l] != 0.0) sumf[l] += wj * temp[l]; } } } for(l = 0, acc = 0.0; l < numFreqs; l++) { if(sumf[l] != 0.0) acc += sumf[l]; } for(l = 0; l < numFreqs; l++) pfreqs[l] = sumf[l] / acc; } if(countStatesPresent < numFreqs) { printf("Partition %s number %d has a problem, the number of expected states is %d the number of states that are present is %d.\n", tr->partitionData[model].partitionName, model, numFreqs, countStatesPresent); printf("Please go and fix your data!\n\n"); } if(smoothFrequencies) { smoothFreqs(numFreqs, pfreqs, tr->partitionData[model].frequencies, &(tr->partitionData[model])); } else { boolean zeroFreq = FALSE; char typeOfData[1024]; getDataTypeString(tr, model, typeOfData); for(l = 0; l < numFreqs; l++) { if(pfreqs[l] == 0.0) { printBothOpen("Empirical base frequency for state number %d is equal to zero in %s data partition %s\n", l, typeOfData, tr->partitionData[model].partitionName); printBothOpen("Since this is probably not what you want to do, RAxML will soon exit.\n\n"); zeroFreq = TRUE; } } if(zeroFreq) exit(-1); for(l = 0; l < numFreqs; l++) { assert(pfreqs[l] > 0.0); tr->partitionData[model].frequencies[l] = pfreqs[l]; } } } static void baseFrequenciesGTR(rawdata *rdta, cruncheddata *cdta, tree *tr) { int model; size_t lower, upper; int states; for(model = 0; model < tr->NumberOfModels; model++) { lower = tr->partitionData[model].lower; upper = tr->partitionData[model].upper; states = tr->partitionData[model].states; switch(tr->partitionData[model].dataType) { case GENERIC_32: switch(tr->multiStateModel) { case ORDERED_MULTI_STATE: case MK_MULTI_STATE: { int i; double freq = 1.0 / (double)states, acc = 0.0; for(i = 0; i < states; i++) { acc += freq; tr->partitionData[model].frequencies[i] = freq; /*printf("%f \n", freq);*/ } /*printf("Frequency Deviation: %1.60f\n", acc);*/ } break; case GTR_MULTI_STATE: genericBaseFrequencies(tr, states, rdta, cdta, lower, upper, model, TRUE, bitVector32); break; default: assert(0); } break; case GENERIC_64: assert(0); break; case SECONDARY_DATA_6: case SECONDARY_DATA_7: case SECONDARY_DATA: case AA_DATA: case DNA_DATA: case BINARY_DATA: genericBaseFrequencies(tr, states, rdta, cdta, lower, upper, model, getSmoothFreqs(tr->partitionData[model].dataType), getBitVector(tr->partitionData[model].dataType)); break; default: assert(0); } } return; } // #define OLD_LAYOUT int main (int argc, char *argv[]) { int model; rawdata *rdta; cruncheddata *cdta; tree *tr; analdef *adef; /* get the start time */ masterTime = gettime(); /* get some memory for the basic data structures */ adef = (analdef *)malloc(sizeof(analdef)); rdta = (rawdata *)malloc(sizeof(rawdata)); cdta = (cruncheddata *)malloc(sizeof(cruncheddata)); tr = (tree *)malloc(sizeof(tree)); /* the initialization below is required for the hash tables that are used */ compute_bits_in_16bits(); /* initialize the analysis parameters in struct adef to default values */ initAdef(adef); /* parse command line arguments: this has a side effect on tr struct and adef struct variables */ get_args(argc,argv, adef, tr); /* parse the phylip file: this should probably be re-done, perhaps using the relatively flexible parser written in C++ by Marc Holder */ getinput(adef, rdta, cdta, tr); printBothOpen("Pattern compression: %s\n", (adef->compressPatterns)?"ON":"OFF"); makeweights(adef, rdta, cdta, tr); makevalues(rdta, cdta, tr, adef); for(model = 0; model < tr->NumberOfModels; model++) { tr->partitionData[model].states = getStates(tr->partitionData[model].dataType); tr->partitionData[model].maxTipStates = getUndetermined(tr->partitionData[model].dataType) + 1; tr->partitionData[model].nonGTR = FALSE; partitionLengths *pl = getPartitionLengths(&(tr->partitionData[model])); tr->partitionData[model].frequencies = (double*)malloc(pl->frequenciesLength * sizeof(double)); } baseFrequenciesGTR(tr->rdta, tr->cdta, tr); { int sizeOfSizeT = sizeof(size_t), version = (int)programVersionInt, magicNumber = 6517718; size_t i, model; /* NEW, we firstly write, how many bytes size_t comprises */ myBinFwrite(&(sizeOfSizeT), sizeof(sizeOfSizeT), 1); //error checking for parser! myBinFwrite(&version, sizeof(int), 1); myBinFwrite(&magicNumber, sizeof(int), 1); //error checking for correct parser end myBinFwrite(&(tr->mxtips), sizeof(int), 1); myBinFwrite(&(tr->originalCrunchedLength), sizeof(size_t), 1); myBinFwrite(&(tr->NumberOfModels), sizeof(int), 1); myBinFwrite(&(adef->gapyness), sizeof(double), 1); myBinFwrite(tr->cdta->aliaswgt, sizeof(int), tr->originalCrunchedLength); for(i = 1; i <= (size_t)tr->mxtips; i++) { int len = strlen(tr->nameList[i]) + 1; myBinFwrite(&len, sizeof(int), 1); myBinFwrite(tr->nameList[i], sizeof(char), len); } for(model = 0; model < (size_t)tr->NumberOfModels; model++) { int len; pInfo *p = &(tr->partitionData[model]); myBinFwrite(&(p->states), sizeof(int), 1); myBinFwrite(&(p->maxTipStates), sizeof(int), 1); myBinFwrite(&(p->lower), sizeof(size_t), 1); myBinFwrite(&(p->upper), sizeof(size_t), 1); myBinFwrite(&(p->width), sizeof(size_t), 1); myBinFwrite(&(p->dataType), sizeof(int), 1); myBinFwrite(&(p->protModels), sizeof(int), 1); myBinFwrite(&(p->protFreqs), sizeof(int), 1); myBinFwrite(&(p->nonGTR), sizeof(boolean), 1); myBinFwrite(&(p->optimizeBaseFrequencies), sizeof(boolean), 1); /* later on if adding secondary structure data int *symmetryVector; int *frequencyGrouping; */ len = strlen(p->partitionName) + 1; myBinFwrite(&len, sizeof(int), 1); myBinFwrite(p->partitionName, sizeof(char), len); myBinFwrite(tr->partitionData[model].frequencies, sizeof(double), tr->partitionData[model].states); } #ifdef OLD_LAYOUT myBinFwrite(rdta->y0, sizeof(unsigned char), (tr->originalCrunchedLength) * ((size_t)tr->mxtips)); #else /* Write each partition, taxon by taxon. Thus, if unpartitioned, nothing changes. */ size_t mem_reqs_cat = 0, mem_reqs_gamma = 0, unique_patterns = 0; for(model = 0; model < (size_t) tr->NumberOfModels; ++model ) { pInfo *p = &(tr->partitionData[model]); size_t width = p->upper - p->lower; unique_patterns += width; //multiply partition width with number of states we need to store in each CLV entry mem_reqs_cat += (size_t)tr->partitionData[model].states * width; for(i = 0; i < (size_t)tr->mxtips; ++i) { myBinFwrite(rdta->y0 + sizeof(unsigned char) * ( (i * tr->originalCrunchedLength) + p->lower ) , sizeof(unsigned char), width); } } printBothOpen("\n\nYour alignment has %zu %s\n", unique_patterns, (adef->compressPatterns == TRUE)?"unique patterns":"sites"); //multiply CLV vector length with number of tips and 8, since b bytes are needed to store an inner conditional probability vector mem_reqs_cat *= (size_t)tr->mxtips * sizeof(double); //mem reqs for gamma are 4 times higher than for CAT mem_reqs_gamma = mem_reqs_cat * 4; //now add the space for storing the tips: mem_reqs_cat += (size_t)tr->mxtips * unique_patterns * sizeof(unsigned char); mem_reqs_gamma += (size_t)tr->mxtips * unique_patterns * sizeof(unsigned char); printBothOpen("\n\nUnder CAT the memory required by ExaML for storing CLVs and tip vectors will be\n%zu bytes\n%zu kiloBytes\n%zu MegaBytes\n%zu GigaBytes\n", mem_reqs_cat, mem_reqs_cat / 1024 , mem_reqs_cat / (1024 * 1024), mem_reqs_cat / (1024 * 1024 * 1024)); printBothOpen("\n\nUnder GAMMA the memory required by ExaML for storing CLVs and tip vectors will be\n%zu bytes\n%zu kiloBytes\n%zu MegaBytes\n%zu GigaBytes\n", mem_reqs_gamma, mem_reqs_gamma / 1024 , mem_reqs_gamma / (1024 * 1024), mem_reqs_gamma / (1024 * 1024 * 1024)); printBothOpen("\nPlease note that, these are just the memory requirements for doing likelihood calculations!\n"); printBothOpen("To be on the safe side, we recommend that you execute ExaML on a system with twice that memory.\n"); #endif } fclose(byteFile); printBothOpen("\n\nBinary and compressed alignment file written to file %s\n\n", byteFileName); printBothOpen("Parsing completed, exiting now ... \n\n"); return 0; } ExaML-3.0.21/parser/axml.h000066400000000000000000001057511330315463200151620ustar00rootroot00000000000000/* RAxML-VI-HPC (version 2.2) a program for sequential and parallel estimation of phylogenetic trees * Copyright August 2006 by Alexandros Stamatakis * * Partially derived from * fastDNAml, a program for estimation of phylogenetic trees from sequences by Gary J. Olsen * * and * * Programs of the PHYLIP package by Joe Felsenstein. * * This program is free software; you may redistribute it and/or modify its * 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. * * * For any other enquiries send an Email to Alexandros Stamatakis * Alexandros.Stamatakis@epfl.ch * * When publishing work that is based on the results from RAxML-VI-HPC please cite: * * Alexandros Stamatakis:"RAxML-VI-HPC: maximum likelihood-based phylogenetic analyses * with thousands of taxa and mixed models". * Bioinformatics 2006; doi: 10.1093/bioinformatics/btl446 */ #include #include #include "../versionHeader/version.h" #ifdef __AVX #define BYTE_ALIGNMENT 32 #else #define BYTE_ALIGNMENT 16 #endif #define MAX_TIP_EV 0.999999999 /* max tip vector value, sum of EVs needs to be smaller than 1.0, otherwise the numerics break down */ #define smoothings 32 /* maximum smoothing passes through tree */ #define iterations 10 /* maximum iterations of iterations per insert */ #define newzpercycle 1 /* iterations of makenewz per tree traversal */ #define nmlngth 256 /* number of characters in species name */ #define deltaz 0.00001 /* test of net branch length change in update */ #define defaultz 0.9 /* value of z assigned as starting point */ #define unlikely -1.0E300 /* low likelihood for initialization */ #define SUMMARIZE_LENGTH -3 #define SUMMARIZE_LH -2 #define NO_BRANCHES -1 #define MASK_LENGTH 32 #define GET_BITVECTOR_LENGTH(x) ((x % MASK_LENGTH) ? (x / MASK_LENGTH + 1) : (x / MASK_LENGTH)) #define zmin 1.0E-15 /* max branch prop. to -log(zmin) (= 34) */ #define zmax (1.0 - 1.0E-6) /* min branch prop. to 1.0-zmax (= 1.0E-6) */ #define twotothe256 \ 115792089237316195423570985008687907853269984665640564039457584007913129639936.0 /* 2**256 (exactly) */ #define minlikelihood (1.0/twotothe256) #define minusminlikelihood -minlikelihood /* 18446744073709551616.0 */ /*4294967296.0*/ /* 18446744073709551616.0 */ /* 2**64 (exactly) */ /* 4294967296 2**32 */ #define badRear -1 //#define NUM_BRANCHES 1 #define TRUE 1 #define FALSE 0 #define LIKELIHOOD_EPSILON 0.0000001 #define AA_SCALE 10.0 #define AA_SCALE_PLUS_EPSILON 10.001 /* ALPHA_MIN is critical -> numerical instability, eg for 4 discrete rate cats */ /* and alpha = 0.01 the lowest rate r_0 is */ /* 0.00000000000000000000000000000000000000000000000000000000000034878079110511010487 */ /* which leads to numerical problems Table for alpha settings below: */ /* */ /* 0.010000 0.00000000000000000000000000000000000000000000000000000000000034878079110511010487 */ /* 0.010000 yielded nasty numerical bugs in at least one case ! */ /* 0.020000 0.00000000000000000000000000000044136090435925743185910935350715027016962154188875 */ /* 0.030000 0.00000000000000000000476844846859006690412039180149775802624789852441798419292220 */ /* 0.040000 0.00000000000000049522423236954066431210260930029681736928018820007024736185030633 */ /* 0.050000 0.00000000000050625351310359203371872643495343928538368616365517027588794007897377 */ /* 0.060000 0.00000000005134625283884191118711474021861409372524676086868566926568746566772461 */ /* 0.070000 0.00000000139080650074206434685544624965062437960128249869740102440118789672851562 */ /* 0.080000 0.00000001650681201563587066858709818343436959153791576682124286890029907226562500 */ /* 0.090000 0.00000011301977332931251259273962858978301859735893231118097901344299316406250000 */ /* 0.100000 0.00000052651925834844387815526344648331402709118265192955732345581054687500000000 */ #define ALPHA_MIN 0.02 #define ALPHA_MAX 1000.0 #define RATE_MIN 0.0000001 #define RATE_MAX 1000000.0 #define INVAR_MIN 0.0001 #define INVAR_MAX 0.9999 #define TT_MIN 0.0000001 #define TT_MAX 1000000.0 #define FREQ_MIN 0.001 /* previous values between 0.001 and 0.000001 TO AVOID NUMERICAL PROBLEMS WHEN FREQ == 0 IN PARTITIONED MODELS, ESPECIALLY WITH AA previous value of FREQ_MIN was: 0.000001, but this seemed to cause problems with some of the 7-state secondary structure models with some rather exotic small toy test datasets, on the other hand 0.001 caused problems with some of the 16-state secondary structure models For some reason the frequency settings seem to be repeatedly causing numerical problems */ #define ITMAX 100 #define SHFT(a,b,c,d) (a)=(b);(b)=(c);(c)=(d); #define SIGN(a,b) ((b) > 0.0 ? fabs(a) : -fabs(a)) #define ABS(x) (((x)<0) ? (-(x)) : (x)) #define MIN(x,y) (((x)<(y)) ? (x) : (y)) #define MAX(x,y) (((x)>(y)) ? (x) : (y)) #define NINT(x) ((int) ((x)>0 ? ((x)+0.5) : ((x)-0.5))) #ifdef _USE_FPGA_LOG extern double log_approx (double input); #define LOG(x) log_approx(x) #else #define LOG(x) log(x) #endif #ifdef _USE_FPGA_EXP extern double exp_approx (double x); #define EXP(x) exp_approx(x) #else #define EXP(x) exp(x) #endif #define LOGF(x) logf(x) #define PointGamma(prob,alpha,beta) PointChi2(prob,2.0*(alpha))/(2.0*(beta)) //#define programName "the phylip file parser for ExaML" //#define programVersion "2.0.1" //#define programDate "June 3 2014" #define TREE_EVALUATION 0 #define BIG_RAPID_MODE 1 #define CALC_BIPARTITIONS 3 #define SPLIT_MULTI_GENE 4 #define CHECK_ALIGNMENT 5 #define PER_SITE_LL 6 #define PARSIMONY_ADDITION 7 #define CLASSIFY_ML 9 #define DISTANCE_MODE 11 #define GENERATE_BS 12 #define COMPUTE_ELW 13 #define BOOTSTOP_ONLY 14 #define COMPUTE_LHS 17 #define COMPUTE_BIPARTITION_CORRELATION 18 #define THOROUGH_PARSIMONY 19 #define COMPUTE_RF_DISTANCE 20 #define MORPH_CALIBRATOR 21 #define CONSENSUS_ONLY 22 #define MESH_TREE_SEARCH 23 #define FAST_SEARCH 24 #define MORPH_CALIBRATOR_PARSIMONY 25 #define SH_LIKE_SUPPORTS 28 #define M_GTRCAT 1 #define M_GTRGAMMA 2 #define M_BINCAT 3 #define M_BINGAMMA 4 #define M_PROTCAT 5 #define M_PROTGAMMA 6 #define M_32CAT 7 #define M_32GAMMA 8 #define M_64CAT 9 #define M_64GAMMA 10 #define DAYHOFF 0 #define DCMUT 1 #define JTT 2 #define MTREV 3 #define WAG 4 #define RTREV 5 #define CPREV 6 #define VT 7 #define BLOSUM62 8 #define MTMAM 9 #define LG 10 #define MTART 11 #define MTZOA 12 #define PMB 13 #define HIVB 14 #define HIVW 15 #define JTTDCMUT 16 #define FLU 17 #define STMTREV 18 #define AUTO 19 #define LG4M 20 #define LG4X 21 #define GTR 22 /* GTR always needs to be the last one */ #define NUM_PROT_MODELS 23 /* bipartition stuff */ #define BIPARTITIONS_ALL 0 #define GET_BIPARTITIONS_BEST 1 #define DRAW_BIPARTITIONS_BEST 2 #define BIPARTITIONS_BOOTSTOP 3 #define BIPARTITIONS_RF 4 /* bootstopping stuff */ #define BOOTSTOP_PERMUTATIONS 100 #define START_BSTOP_TEST 10 #define FC_THRESHOLD 99 #define FC_SPACING 50 #define FC_LOWER 0.99 #define FC_INIT 20 #define FREQUENCY_STOP 0 #define MR_STOP 1 #define MRE_STOP 2 #define MRE_IGN_STOP 3 #define MR_CONSENSUS 0 #define MRE_CONSENSUS 1 #define STRICT_CONSENSUS 2 /* bootstopping stuff end */ #define TIP_TIP 0 #define TIP_INNER 1 #define INNER_INNER 2 #define MIN_MODEL -1 #define BINARY_DATA 0 #define DNA_DATA 1 #define AA_DATA 2 #define SECONDARY_DATA 3 #define SECONDARY_DATA_6 4 #define SECONDARY_DATA_7 5 #define GENERIC_32 6 #define GENERIC_64 7 #define MAX_MODEL 8 #define SEC_6_A 0 #define SEC_6_B 1 #define SEC_6_C 2 #define SEC_6_D 3 #define SEC_6_E 4 #define SEC_7_A 5 #define SEC_7_B 6 #define SEC_7_C 7 #define SEC_7_D 8 #define SEC_7_E 9 #define SEC_7_F 10 #define SEC_16 11 #define SEC_16_A 12 #define SEC_16_B 13 #define SEC_16_C 14 #define SEC_16_D 15 #define SEC_16_E 16 #define SEC_16_F 17 #define SEC_16_I 18 #define SEC_16_J 19 #define SEC_16_K 20 #define ORDERED_MULTI_STATE 0 #define MK_MULTI_STATE 1 #define GTR_MULTI_STATE 2 #define CAT 0 #define GAMMA 1 #define GAMMA_I 2 typedef int boolean; typedef struct { double lh; int tree; double weight; } elw; struct ent { unsigned int *bitVector; unsigned int *treeVector; unsigned int amountTips; int *supportVector; unsigned int bipNumber; unsigned int bipNumber2; unsigned int supportFromTreeset[2]; struct ent *next; }; typedef struct ent entry; typedef unsigned int hashNumberType; typedef unsigned int parsimonyNumber; /*typedef uint_fast32_t parsimonyNumber;*/ #define PCF 32 /* typedef uint64_t parsimonyNumber; #define PCF 16 typedef unsigned char parsimonyNumber; #define PCF 2 */ typedef struct { hashNumberType tableSize; entry **table; hashNumberType entryCount; } hashtable; struct stringEnt { int nodeNumber; char *word; struct stringEnt *next; }; typedef struct stringEnt stringEntry; typedef struct { hashNumberType tableSize; stringEntry **table; } stringHashtable; typedef struct { unsigned int parsimonyScore; unsigned int parsimonyState; } parsimonyVector; typedef struct ratec { double accumulatedSiteLikelihood; double rate; } rateCategorize; typedef struct { int tipCase; int pNumber; int qNumber; int rNumber; //double qz[NUM_BRANCHES]; //double rz[NUM_BRANCHES]; } traversalInfo; typedef struct { traversalInfo *ti; int count; int functionType; boolean traversalHasChanged; boolean *executeModel; double *parameterValues; } traversalData; struct noderec; typedef struct epBrData { int *countThem; int *executeThem; unsigned int *parsimonyScores; double *branches; double *bootstrapBranches; double *likelihoods; double originalBranchLength; char branchLabel[64]; int leftNodeNumber; int rightNodeNumber; int *leftScaling; int *rightScaling; parsimonyVector *leftParsimony; parsimonyVector *rightParsimony; //double branchLengths[NUM_BRANCHES]; double *left; double *right; int branchNumber; } epaBranchData; typedef struct { epaBranchData *epa; unsigned int *vector; int support; struct noderec *oP; struct noderec *oQ; } branchInfo; typedef struct { boolean valid; int partitions; int *partitionList; } linkageData; typedef struct { int entries; linkageData* ld; } linkageList; typedef struct noderec { branchInfo *bInf; // double z[NUM_BRANCHES]; #ifdef _BAYESIAN //double z_tmp[NUM_BRANCHES]; #endif struct noderec *next; struct noderec *back; hashNumberType hash; int support; int number; char x; } node, *nodeptr; typedef struct { double lh; int number; } info; typedef struct bInf { double likelihood; nodeptr node; } bestInfo; typedef struct iL { bestInfo *list; int n; int valid; } infoList; typedef struct { int numsp; int sites; unsigned char **y; unsigned char *y0; int *wgt; } rawdata; typedef struct { int *alias; /* site representing a pattern */ int *aliaswgt; /* weight by pattern */ int *rateCategory; size_t endsite; /* # of sequence patterns */ double *patrat; /* rates per pattern */ double *patratStored; } cruncheddata; typedef struct { int states; int maxTipStates; size_t lower; size_t upper; size_t width; int dataType; int protModels; int autoProtModels; int protFreqs; int **expVector; double **xVector; size_t *xSpaceVector; unsigned char **yVector; char *partitionName; double *sumBuffer; double *gammaRates; double *EIGN; double *EV; double *EI; double *left; double *right; double *frequencies; double *tipVector; double *substRates; double *perSiteRates; double *wr; double *wr2; unsigned int *globalScaler; double *globalScalerDouble; int *wgt; int *rateCategory; int *symmetryVector; int *frequencyGrouping; boolean nonGTR; boolean optimizeBaseFrequencies; double alpha; int gapVectorLength; unsigned int *gapVector; double *gapColumn; int numberOfCategories; } pInfo; typedef struct { int left; int right; double likelihood; } lhEntry; typedef struct { int count; int size; lhEntry *entries; } lhList; typedef struct List_{ void *value; struct List_ *next; } List; #define REARR_SETTING 1 #define FAST_SPRS 2 #define SLOW_SPRS 3 typedef struct { int state; unsigned int vLength; int rearrangementsMax; int rearrangementsMin; int thoroughIterations; int fastIterations; int treeVectorLength; int mintrav; int maxtrav; int bestTrav; int Thorough; int optimizeRateCategoryInvocations; double accumulatedTime; double startLH; double lh; double previousLh; double difference; double epsilon; boolean impr; boolean cutoff; double tr_startLH; double tr_endLH; double tr_likelihood; double tr_bestOfNode; double tr_lhCutoff; double tr_lhAVG; double tr_lhDEC; int tr_NumberOfCategories; int tr_itCount; int tr_doCutoff; } checkPointState; typedef struct { double EIGN[19] __attribute__ ((aligned (BYTE_ALIGNMENT))); double EV[400] __attribute__ ((aligned (BYTE_ALIGNMENT))); double EI[380] __attribute__ ((aligned (BYTE_ALIGNMENT))); double substRates[190]; double frequencies[20] ; double tipVector[460] __attribute__ ((aligned (BYTE_ALIGNMENT))); double fracchange[1]; double left[1600] __attribute__ ((aligned (BYTE_ALIGNMENT))); double right[1600] __attribute__ ((aligned (BYTE_ALIGNMENT))); } siteAAModels; typedef struct { boolean useGappedImplementation; boolean saveMemory; siteAAModels siteProtModel[2 * (NUM_PROT_MODELS - 2)]; boolean estimatePerSiteAA; int *resample; int numberOfBranches; int numberOfTipsForInsertion; int *inserts; int branchCounter; parsimonyNumber **parsimonyState_A; parsimonyNumber **parsimonyState_C; parsimonyNumber **parsimonyState_G; parsimonyNumber **parsimonyState_T; unsigned int *parsimonyScore; int *ti; unsigned int compressedWidth; int numberOfTrees; stringHashtable *nameHash; pInfo *partitionData; pInfo *initialPartitionData; pInfo *extendedPartitionData; int *dataVector; int *initialDataVector; int *extendedDataVector; int *patternPosition; int *columnPosition; char *secondaryStructureInput; boolean *executeModel; double *perPartitionLH; traversalData td[1]; int maxCategories; double *wr; double *wr2; // double coreLZ[NUM_BRANCHES]; int modelNumber; int numBranches; int bootStopCriterion; int consensusType; double wcThreshold; branchInfo *bInf; int multiStateModel; // boolean curvatOK[NUM_BRANCHES]; /* the stuff below is shared among DNA and AA, span does not change depending on datatype */ double *fracchanges; /* model stuff end */ unsigned char **yVector; int secondaryStructureModel; size_t originalCrunchedLength; int fullSites; int *originalModel; int *originalDataVector; int *originalWeights; int *secondaryStructurePairs; double *partitionContributions; double fracchange; double lhCutoff; double lhAVG; unsigned long lhDEC; unsigned long itCount; int numberOfInvariableColumns; int weightOfInvariableColumns; int rateHetModel; double startLH; double endLH; double likelihood; double *likelihoods; node **nodep; nodeptr nodeBaseAddress; node *start; int mxtips; int *model; int *constraintVector; int numberOfSecondaryColumns; boolean searchConvergenceCriterion; int ntips; int nextnode; int NumberOfModels; int parsimonyLength; int checkPointCounter; int treeID; boolean bigCutoff; // boolean partitionSmoothed[NUM_BRANCHES]; // boolean partitionConverged[NUM_BRANCHES]; boolean rooted; boolean grouped; boolean constrained; boolean doCutoff; boolean catOnly; rawdata *rdta; cruncheddata *cdta; char **nameList; char *tree_string; char *tree0; char *tree1; int treeStringLength; unsigned int bestParsimony; double bestOfNode; nodeptr removeNode; nodeptr insertNode; /* double zqr[NUM_BRANCHES]; double currentZQR[NUM_BRANCHES]; double currentLZR[NUM_BRANCHES]; double currentLZQ[NUM_BRANCHES]; double currentLZS[NUM_BRANCHES]; double currentLZI[NUM_BRANCHES]; double lzs[NUM_BRANCHES]; double lzq[NUM_BRANCHES]; double lzr[NUM_BRANCHES]; double lzi[NUM_BRANCHES]; */ int mr_thresh; unsigned int **bitVectors; unsigned int vLength; hashtable *h; } tree; /***************************************************************/ typedef struct { int partitionNumber; int partitionLength; } partitionType; typedef struct { // double z[NUM_BRANCHES]; nodeptr p, q; int cp, cq; } connectRELL, *connptrRELL; typedef struct { connectRELL *connect; int start; double likelihood; } topolRELL; typedef struct { int max; topolRELL **t; } topolRELL_LIST; /**************************************************************/ typedef struct conntyp { // double z[NUM_BRANCHES]; /* branch length */ node *p, *q; /* parent and child sectors */ void *valptr; /* pointer to value of subtree */ int descend; /* pointer to first connect of child */ int sibling; /* next connect from same parent */ } connect, *connptr; typedef struct { double likelihood; int initialTreeNumber; connect *links; /* pointer to first connect (start) */ node *start; int nextlink; /* index of next available connect */ /* tr->start = tpl->links->p */ int ntips; int nextnode; int scrNum; /* position in sorted list of scores */ int tplNum; /* position in sorted list of trees */ } topol; typedef struct { double best; /* highest score saved */ double worst; /* lowest score saved */ topol *start; /* starting tree for optimization */ topol **byScore; topol **byTopol; int nkeep; /* maximum topologies to save */ int nvalid; /* number of topologies saved */ int ninit; /* number of topologies initialized */ int numtrees; /* number of alternatives tested */ boolean improved; } bestlist; typedef struct { int categories; int model; int bestTrav; int max_rearrange; int stepwidth; int initial; boolean initialSet; int mode; long boot; long rapidBoot; boolean bootstrapBranchLengths; boolean restart; boolean useWeightFile; boolean useMultipleModel; boolean constraint; boolean grouping; boolean randomStartingTree; boolean useInvariant; int protEmpiricalFreqs; int proteinMatrix; int checkpoints; int startingTreeOnly; int multipleRuns; long parsimonySeed; boolean perGeneBranchLengths; boolean likelihoodTest; boolean permuteTreeoptimize; boolean allInOne; boolean generateBS; boolean bootStopping; boolean useExcludeFile; boolean userProteinModel; boolean computeELW; boolean computeDistance; boolean thoroughInsertion; boolean compressPatterns; boolean useSecondaryStructure; double likelihoodEpsilon; double gapyness; int similarityFilterMode; double *externalAAMatrix; boolean readTaxaOnly; int meshSearch; boolean veryFast; boolean useCheckpoint; boolean leaveDropMode; int slidingWindowSize; boolean writeBinaryFile; boolean readBinaryFile; #ifdef _BAYESIAN boolean bayesian; #endif } analdef; typedef struct { int leftLength; int rightLength; int eignLength; int evLength; int eiLength; int substRatesLength; int frequenciesLength; int tipVectorLength; int symmetryVectorLength; int frequencyGroupingLength; boolean nonGTR; boolean optimizeBaseFrequencies; unsigned char undetermined; const char *inverseMeaning; int states; boolean smoothFrequencies; const unsigned int *bitVector; } partitionLengths; /****************************** FUNCTIONS ****************************************************/ extern void computePlacementBias(tree *tr, analdef *adef); extern int lookupWord(char *s, stringHashtable *h); extern void getDataTypeString(tree *tr, int model, char typeOfData[1024]); extern unsigned int genericBitCount(unsigned int* bitVector, unsigned int bitVectorLength); extern int countTips(nodeptr p, int numsp); extern entry *initEntry(void); extern void computeRogueTaxa(tree *tr, char* treeSetFileName, analdef *adef); extern unsigned int precomputed16_bitcount(unsigned int n); extern size_t discreteRateCategories(int rateHetModel); extern partitionLengths * getPartitionLengths(pInfo *p); extern boolean getSmoothFreqs(int dataType); extern const unsigned int *getBitVector(int dataType); extern unsigned char getUndetermined(int dataType); extern int getStates(int dataType); extern char getInverseMeaning(int dataType, unsigned char state); extern double gettime ( void ); extern int gettimeSrand ( void ); extern double randum ( long *seed ); extern void getxnode ( nodeptr p ); extern void hookup ( nodeptr p, nodeptr q, double *z, int numBranches); extern void hookupDefault ( nodeptr p, nodeptr q, int numBranches); extern boolean whitechar ( int ch ); extern void errorExit ( int e ); extern void printResult ( tree *tr, analdef *adef, boolean finalPrint ); extern void printBootstrapResult ( tree *tr, analdef *adef, boolean finalPrint ); extern void printBipartitionResult ( tree *tr, analdef *adef, boolean finalPrint ); extern void printLog ( tree *tr, analdef *adef, boolean finalPrint ); extern void printStartingTree ( tree *tr, analdef *adef, boolean finalPrint ); extern void writeInfoFile ( analdef *adef, tree *tr, double t ); extern int main ( int argc, char *argv[] ); extern void calcBipartitions ( tree *tr, analdef *adef, char *bestTreeFileName, char *bootStrapFileName ); extern void initReversibleGTR (tree *tr, int model); extern double LnGamma ( double alpha ); extern double IncompleteGamma ( double x, double alpha, double ln_gamma_alpha ); extern double PointNormal ( double prob ); extern double PointChi2 ( double prob, double v ); extern void makeGammaCats (double alpha, double *gammaRates, int K); extern void initModel ( tree *tr, rawdata *rdta, cruncheddata *cdta, analdef *adef ); extern void doAllInOne ( tree *tr, analdef *adef ); extern void classifyML(tree *tr, analdef *adef); extern void doBootstrap ( tree *tr, analdef *adef, rawdata *rdta, cruncheddata *cdta ); extern void doInference ( tree *tr, analdef *adef, rawdata *rdta, cruncheddata *cdta ); extern void resetBranches ( tree *tr ); extern void modOpt ( tree *tr, analdef *adef , double likelihoodEpsilon); extern void parsePartitions ( analdef *adef, rawdata *rdta, tree *tr); extern void computeBOOTRAPID (tree *tr, analdef *adef, long *radiusSeed); extern void optimizeRAPID ( tree *tr, analdef *adef ); extern void thoroughOptimization ( tree *tr, analdef *adef, topolRELL_LIST *rl, int index ); extern int treeOptimizeThorough ( tree *tr, int mintrav, int maxtrav); extern int checker ( tree *tr, nodeptr p ); extern int randomInt ( int n ); extern void makePermutation ( int *perm, int n, analdef *adef ); extern boolean tipHomogeneityChecker ( tree *tr, nodeptr p, int grouping ); extern void makeRandomTree ( tree *tr, analdef *adef ); extern void nodeRectifier ( tree *tr ); extern void makeParsimonyTreeThorough(tree *tr, analdef *adef); extern void makeParsimonyTree ( tree *tr, analdef *adef ); extern void makeParsimonyTreeFastDNA(tree *tr, analdef *adef); extern void makeParsimonyTreeIncomplete ( tree *tr, analdef *adef ); extern void makeParsimonyInsertions(tree *tr, nodeptr startNodeQ, nodeptr startNodeR); extern FILE *myfopen(const char *path, const char *mode); extern boolean initrav ( tree *tr, nodeptr p ); extern void initravPartition ( tree *tr, nodeptr p, int model ); extern boolean update ( tree *tr, nodeptr p ); extern boolean smooth ( tree *tr, nodeptr p ); extern boolean smoothTree ( tree *tr, int maxtimes ); extern boolean localSmooth ( tree *tr, nodeptr p, int maxtimes ); extern boolean localSmoothMulti(tree *tr, nodeptr p, int maxtimes, int model); extern void initInfoList ( int n ); extern void freeInfoList ( void ); extern void insertInfoList ( nodeptr node, double likelihood ); extern boolean smoothRegion ( tree *tr, nodeptr p, int region ); extern boolean regionalSmooth ( tree *tr, nodeptr p, int maxtimes, int region ); extern nodeptr removeNodeBIG ( tree *tr, nodeptr p, int numBranches); extern nodeptr removeNodeRestoreBIG ( tree *tr, nodeptr p ); extern boolean insertBIG ( tree *tr, nodeptr p, nodeptr q, int numBranches); extern boolean insertRestoreBIG ( tree *tr, nodeptr p, nodeptr q ); extern boolean testInsertBIG ( tree *tr, nodeptr p, nodeptr q ); extern void addTraverseBIG ( tree *tr, nodeptr p, nodeptr q, int mintrav, int maxtrav ); extern int rearrangeBIG ( tree *tr, nodeptr p, int mintrav, int maxtrav ); extern void traversalOrder ( nodeptr p, int *count, nodeptr *nodeArray ); extern double treeOptimizeRapid ( tree *tr, int mintrav, int maxtrav, analdef *adef, bestlist *bt); extern boolean testInsertRestoreBIG ( tree *tr, nodeptr p, nodeptr q ); extern void restoreTreeFast ( tree *tr ); extern int determineRearrangementSetting ( tree *tr, analdef *adef, bestlist *bestT, bestlist *bt ); extern void computeBIGRAPID ( tree *tr, analdef *adef, boolean estimateModel); extern boolean treeEvaluate ( tree *tr, double smoothFactor ); extern boolean treeEvaluatePartition ( tree *tr, double smoothFactor, int model ); extern void meshTreeSearch(tree *tr, analdef *adef, int thorough); extern void initTL ( topolRELL_LIST *rl, tree *tr, int n ); extern void freeTL ( topolRELL_LIST *rl); extern void restoreTL ( topolRELL_LIST *rl, tree *tr, int n ); extern void resetTL ( topolRELL_LIST *rl ); extern void saveTL ( topolRELL_LIST *rl, tree *tr, int index ); extern int saveBestTree (bestlist *bt, tree *tr); extern int recallBestTree (bestlist *bt, int rank, tree *tr); extern int initBestTree ( bestlist *bt, int newkeep, int numsp ); extern void resetBestTree ( bestlist *bt ); extern boolean freeBestTree ( bestlist *bt ); extern char *Tree2String ( char *treestr, tree *tr, nodeptr p, boolean printBranchLengths, boolean printNames, boolean printLikelihood, boolean rellTree, boolean finalPrint, int perGene, boolean branchLabelSupport, boolean printSHSupport); extern void printTreePerGene(tree *tr, analdef *adef, char *fileName, char *permission); extern int treeReadLen (FILE *fp, tree *tr, boolean readBranches, boolean readNodeLabels, boolean topologyOnly); extern void treeReadTopologyString(char *treeString, tree *tr); extern boolean treeReadLenMULT ( FILE *fp, tree *tr, analdef *adef ); extern void getStartingTree ( tree *tr); extern double treeLength(tree *tr, int model); extern void computeBootStopOnly(tree *tr, char *bootStrapFileName, analdef *adef); extern boolean bootStop(tree *tr, hashtable *h, int numberOfTrees, double *pearsonAverage, unsigned int **bitVectors, int treeVectorLength, unsigned int vectorLength); extern void computeConsensusOnly(tree *tr, char* treeSetFileName, analdef *adef); extern double evaluatePartialGeneric (tree *, int i, double ki, int _model); extern void evaluateGeneric (tree *tr, nodeptr p, boolean fullTraversal); extern void newviewGeneric (tree *tr, nodeptr p, boolean masked); extern void newviewGenericMulti (tree *tr, nodeptr p, int model); extern void makenewzGeneric(tree *tr, nodeptr p, nodeptr q, double *z0, int maxiter, double *result, boolean mask); extern void makenewzGenericDistance(tree *tr, int maxiter, double *z0, double *result, int taxon1, int taxon2); extern double evaluatePartitionGeneric (tree *tr, nodeptr p, int model); extern void newviewPartitionGeneric (tree *tr, nodeptr p, int model); extern double evaluateGenericVector (tree *tr, nodeptr p); extern void categorizeGeneric (tree *tr, nodeptr p); extern double makenewzPartitionGeneric(tree *tr, nodeptr p, nodeptr q, double z0, int maxiter, int model); extern boolean isTip(int number, int maxTips); extern void computeTraversalInfo(nodeptr p, traversalInfo *ti, int *counter, int maxTips, int numBranches, boolean partialTraversal); extern void newviewIterative(tree *tr, int startIndex); extern void evaluateIterative(tree *); extern void *malloc_aligned( size_t size); extern void storeExecuteMaskInTraversalDescriptor(tree *tr); extern void storeValuesInTraversalDescriptor(tree *tr, double *value); extern void myBinFwrite(const void *ptr, size_t size, size_t nmemb); extern void myBinFread(void *ptr, size_t size, size_t nmemb); extern void makenewzIterative(tree *); extern void execCore(tree *, volatile double *dlnLdlz, volatile double *d2lnLdlz2); extern void determineFullTraversal(nodeptr p, tree *tr); /*extern void optRateCat(tree *, int i, double lower_spacing, double upper_spacing, double *lhs);*/ extern unsigned int evaluateParsimonyIterative(tree *); extern void newviewParsimonyIterative(tree *); extern unsigned int evaluateParsimonyIterativeFast(tree *); extern void newviewParsimonyIterativeFast(tree *); extern unsigned int evaluatePerSiteParsimony(tree *tr, nodeptr p, unsigned int *siteParsimony); extern void initravParsimonyNormal(tree *tr, nodeptr p); extern double evaluateGenericInitravPartition(tree *tr, nodeptr p, int model); extern void evaluateGenericVectorIterative(tree *, int startIndex, int endIndex); extern void categorizeIterative(tree *, int startIndex, int endIndex); extern void fixModelIndices(tree *tr, int endsite, boolean fixRates); extern void calculateModelOffsets(tree *tr); extern void gammaToCat(tree *tr); extern void catToGamma(tree *tr, analdef *adef); extern void handleExcludeFile(tree *tr, analdef *adef, rawdata *rdta); extern nodeptr findAnyTip(nodeptr p, int numsp); extern void parseProteinModel(analdef *adef); extern void computeNextReplicate(tree *tr, long *seed, int *originalRateCategories, int *originalInvariant, boolean isRapid, boolean fixRates); /*extern void computeNextReplicate(tree *tr, analdef *adef, int *originalRateCategories, int *originalInvariant);*/ extern void putWAG(double *ext_initialRates); extern void reductionCleanup(tree *tr, int *originalRateCategories, int *originalInvariant); extern void parseSecondaryStructure(tree *tr, analdef *adef, int sites); extern void printPartitions(tree *tr); extern void compareBips(tree *tr, char *bootStrapFileName, analdef *adef); extern void computeRF(tree *tr, char *bootStrapFileName, analdef *adef); extern unsigned int **initBitVector(tree *tr, unsigned int *vectorLength); extern hashtable *copyHashTable(hashtable *src, unsigned int vectorLength); extern hashtable *initHashTable(unsigned int n); extern void cleanupHashTable(hashtable *h, int state); extern double convergenceCriterion(hashtable *h, int mxtips); extern void freeBitVectors(unsigned int **v, int n); extern void freeHashTable(hashtable *h); extern void printBothOpen(const char* format, ... ); extern void printBothOpenMPI(const char* format, ... ); extern void initRateMatrix(tree *tr); extern void bitVectorInitravSpecial(unsigned int **bitVectors, nodeptr p, int numsp, unsigned int vectorLength, hashtable *h, int treeNumber, int function, branchInfo *bInf, int *countBranches, int treeVectorLength, boolean traverseOnly, boolean computeWRF); extern int getIncrement(tree *tr, int model); extern void fastSearch(tree *tr, analdef *adef, rawdata *rdta, cruncheddata *cdta); extern void shSupports(tree *tr, analdef *adef, rawdata *rdta, cruncheddata *cdta); extern FILE *getNumberOfTrees(tree *tr, char *fileName, analdef *adef); extern void writeBinaryModel(tree *tr); extern void readBinaryModel(tree *tr); extern void treeEvaluateRandom (tree *tr, double smoothFactor); extern void treeEvaluateProgressive(tree *tr); extern void testGapped(tree *tr); extern boolean issubset(unsigned int* bipA, unsigned int* bipB, unsigned int vectorLen); extern boolean compatible(entry* e1, entry* e2, unsigned int bvlen); extern int *permutationSH(tree *tr, int nBootstrap, long _randomSeed); extern void updatePerSiteRates(tree *tr, boolean scaleRates); extern void restart(tree *tr); ExaML-3.0.21/parser/globalVariables.h000066400000000000000000000211201330315463200172750ustar00rootroot00000000000000/* RAxML-VI-HPC (version 2.2) a program for sequential and parallel estimation of phylogenetic trees * Copyright August 2006 by Alexandros Stamatakis * * Partially derived from * fastDNAml, a program for estimation of phylogenetic trees from sequences by Gary J. Olsen * * and * * Programs of the PHYLIP package by Joe Felsenstein. * * This program is free software; you may redistribute it and/or modify its * 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. * * * For any other enquiries send an Email to Alexandros Stamatakis * Alexandros.Stamatakis@epfl.ch * * When publishing work that is based on the results from RAxML-VI-HPC please cite: * * Alexandros Stamatakis:"RAxML-VI-HPC: maximum likelihood-based phylogenetic analyses with thousands of taxa and mixed models". * Bioinformatics 2006; doi: 10.1093/bioinformatics/btl446 */ #ifdef _USE_ZLIB #include #endif #ifdef _FINE_GRAIN_MPI int processes; double *globalResult; #endif int processID; infoList iList; FILE *INFILE; #ifdef _USE_ZLIB gzFile byteFile; #else FILE *byteFile; #endif char run_id[128] = "", seq_file[1024] = "", weightFileName[1024] = "", modelFileName[1024] = "", byteFileName[1024] = "", infoFileName[1024] = "", secondaryStructureFileName[1024] = "", excludeFileName[1024], proteinModelFileName[1024]; char *protModels[NUM_PROT_MODELS] = {"DAYHOFF", "DCMUT", "JTT", "MTREV", "WAG", "RTREV", "CPREV", "VT", "BLOSUM62", "MTMAM", "LG", "MTART", "MTZOA", "PMB", "HIVB", "HIVW", "JTTDCMUT", "FLU", "STMTREV", "AUTO", "LG4M", "LG4X", "GTR"}; const char inverseMeaningBINARY[4] = {'_', '0', '1', '-'}; const char inverseMeaningDNA[16] = {'_', 'A', 'C', 'M', 'G', 'R', 'S', 'V', 'T', 'W', 'Y', 'H', 'K', 'D', 'B', '-'}; const char inverseMeaningPROT[23] = {'A','R', 'N', 'D', 'C', 'Q', 'E', 'G', 'H', 'I', 'L', 'K', 'M', 'F', 'P', 'S', 'T', 'W', 'Y', 'V', 'B', 'Z', '-'}; const char inverseMeaningGeneric32[33] = {'0', '1', '2', '3', '4', '5', '6', '7', '8', '9', 'A', 'B', 'C', 'D', 'E', 'F', 'G', 'H', 'I', 'J', 'K', 'L', 'M', 'N', 'O', 'P', 'Q', 'R', 'S', 'T', 'U', 'V', '-'}; const char inverseMeaningGeneric64[33] = {'0', '1', '2', '3', '4', '5', '6', '7', '8', '9', 'A', 'B', 'C', 'D', 'E', 'F', 'G', 'H', 'I', 'J', 'K', 'L', 'M', 'N', 'O', 'P', 'Q', 'R', 'S', 'T', 'U', 'V', '-'}; const unsigned int bitVectorIdentity[256] = {0 ,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 ,65 ,66 ,67 ,68 ,69 ,70 ,71 ,72 ,73 ,74 ,75 ,76 , 77 ,78 ,79 ,80 ,81 ,82 ,83 ,84 ,85 ,86 ,87 ,88 ,89 ,90 ,91 ,92 ,93 ,94 ,95 ,96 ,97 ,98 ,99 ,100 ,101 , 102 ,103 ,104 ,105 ,106 ,107 ,108 ,109 ,110 ,111 ,112 ,113 ,114 ,115 ,116 ,117 ,118 ,119 ,120 ,121 ,122 , 123 ,124 ,125 ,126 ,127 ,128 ,129 ,130 ,131 ,132 ,133 ,134 ,135 ,136 ,137 ,138 ,139 ,140 ,141 ,142 ,143 , 144 ,145 ,146 ,147 ,148 ,149 ,150 ,151 ,152 ,153 ,154 ,155 ,156 ,157 ,158 ,159 ,160 ,161 ,162 ,163 ,164 , 165 ,166 ,167 ,168 ,169 ,170 ,171 ,172 ,173 ,174 ,175 ,176 ,177 ,178 ,179 ,180 ,181 ,182 ,183 ,184 ,185 , 186 ,187 ,188 ,189 ,190 ,191 ,192 ,193 ,194 ,195 ,196 ,197 ,198 ,199 ,200 ,201 ,202 ,203 ,204 ,205 ,206 , 207 ,208 ,209 ,210 ,211 ,212 ,213 ,214 ,215 ,216 ,217 ,218 ,219 ,220 ,221 ,222 ,223 ,224 ,225 ,226 ,227 , 228 ,229 ,230 ,231 ,232 ,233 ,234 ,235 ,236 ,237 ,238 ,239 ,240 ,241 ,242 ,243 ,244 ,245 ,246 ,247 ,248 , 249 ,250 ,251 ,252 ,253 ,254 ,255}; const unsigned int bitVectorAA[23] = {1, 2, 4, 8, 16, 32, 64, 128, 256, 512, 1024, 2048, 4096, 8192, 16384, 32768, 65536, 131072, 262144, 524288, 12 /* N | D */, 96 /*Q | E*/, 1048575 /* - */}; const unsigned int bitVectorSecondary[256] = {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 0, 16, 32, 48, 64, 80, 96, 112, 128, 144, 160, 176, 192, 208, 224, 240, 0, 17, 34, 51, 68, 85, 102, 119, 136, 153, 170, 187, 204, 221, 238, 255, 0, 256, 512, 768, 1024, 1280, 1536, 1792, 2048, 2304, 2560, 2816, 3072, 3328, 3584, 3840, 0, 257, 514, 771, 1028, 1285, 1542, 1799, 2056, 2313, 2570, 2827, 3084, 3341, 3598, 3855, 0, 272, 544, 816, 1088, 1360, 1632, 1904, 2176, 2448, 2720, 2992, 3264, 3536, 3808, 4080, 0, 273, 546, 819, 1092, 1365, 1638, 1911, 2184, 2457, 2730, 3003, 3276, 3549, 3822, 4095, 0, 4096, 8192, 12288, 16384, 20480, 24576, 28672, 32768, 36864, 40960, 45056, 49152, 53248, 57344, 61440, 0, 4097, 8194, 12291, 16388, 20485, 24582, 28679, 32776, 36873, 40970, 45067, 49164, 53261, 57358, 61455, 0, 4112, 8224, 12336, 16448, 20560, 24672, 28784, 32896, 37008, 41120, 45232, 49344, 53456, 57568, 61680, 0, 4113, 8226, 12339, 16452, 20565, 24678, 28791, 32904, 37017, 41130, 45243, 49356, 53469, 57582, 61695, 0, 4352, 8704, 13056, 17408, 21760, 26112, 30464, 34816, 39168, 43520, 47872, 52224, 56576, 60928, 65280, 0, 4353, 8706, 13059, 17412, 21765, 26118, 30471, 34824, 39177, 43530, 47883, 52236, 56589, 60942, 65295, 0, 4368, 8736, 13104, 17472, 21840, 26208, 30576, 34944, 39312, 43680, 48048, 52416, 56784, 61152, 65520, 0, 4369, 8738, 13107, 17476, 21845, 26214, 30583, 34952, 39321, 43690, 48059, 52428, 56797, 61166, 65535}; const unsigned int bitVector32[33] = {1, 2, 4, 8, 16, 32, 64, 128, 256, 512, 1024, 2048, 4096, 8192, 16384, 32768, 65536, 131072, 262144, 524288, 1048576, 2097152, 4194304, 8388608, 16777216, 33554432, 67108864, 134217728, 268435456, 536870912, 1073741824, 2147483648u, 4294967295u}; /*const unsigned int bitVector64[65] = {};*/ const unsigned int mask32[32] = {1, 2, 4, 8, 16, 32, 64, 128, 256, 512, 1024, 2048, 4096, 8192, 16384, 32768, 65536, 131072, 262144, 524288, 1048576, 2097152, 4194304, 8388608, 16777216, 33554432, 67108864, 134217728, 268435456, 536870912, 1073741824, 2147483648U}; const char *secondaryModelList[21] = { "S6A (GTR)", "S6B", "S6C", "S6D", "S6E", "S7A (GTR)", "S7B", "S7C", "S7D", "S7E", "S7F", "S16 (GTR)", "S16A", "S16B", "S16C", "S16D", "S16E", "S16F", "S16I", "S16J", "S16K"}; double masterTime; double accumulatedTime; int partCount = 0; int optimizeRateCategoryInvocations = 1; partitionLengths pLengths[MAX_MODEL] = { /* BINARY */ // {4, 4, 2, 4, 2, 1, 2, 8, 2, 2, FALSE, FALSE, 3, inverseMeaningBINARY, 2, FALSE, bitVectorIdentity}, //eiLength changed from 2 -> 4 {4, 4, 2, 4, 4, 1, 2, 8, 2, 2, FALSE, FALSE, 3, inverseMeaningBINARY, 2, FALSE, bitVectorIdentity}, /* DNA */ {16, 16, 4, 16, 16, 6, 4, 64, 6, 4, FALSE, FALSE, 15, inverseMeaningDNA, 4, FALSE, bitVectorIdentity}, /* AA */ {400, 400, 20, 400, 400, 190, 20, 460, 190, 20, FALSE, FALSE, 22, inverseMeaningPROT, 20, TRUE, bitVectorAA}, /* SECONDARY_DATA */ {256, 256, 16, 256, 256, 120, 16, 4096, 120, 16, FALSE, FALSE, 255, (char*)NULL, 16, TRUE, bitVectorSecondary}, /* SECONDARY_DATA_6 */ {36, 36, 6, 36, 36, 15, 6, 384, 15, 6, FALSE, FALSE, 63, (char*)NULL, 6, TRUE, bitVectorIdentity}, /* SECONDARY_DATA_7 */ {49, 49, 7, 49, 49, 21, 7, 896, 21, 7, FALSE, FALSE, 127, (char*)NULL, 7, TRUE, bitVectorIdentity}, /* 32 states */ {1024, 1024, 32, 1024, 1024, 496, 32, 1056, 496, 32, FALSE, FALSE, 32, inverseMeaningGeneric32, 32, TRUE, bitVector32}, /* 64 states */ {4096, 4096, 64, 4096, 4096, 2016, 64, 4160, 64, 2016, FALSE, FALSE, 64, (char*)NULL, 64, TRUE, (unsigned int*)NULL} }; partitionLengths pLength; #ifdef _USE_PTHREADS volatile int NumberOfJobs; volatile int jobCycle = 0; volatile int threadJob = 0; volatile int NumberOfThreads; volatile double *reductionBuffer; volatile double *reductionBufferTwo; volatile char *barrierBuffer; #endif ExaML-3.0.21/parser/parsePartitions.c000066400000000000000000001033271330315463200174000ustar00rootroot00000000000000/* RAxML-VI-HPC (version 2.2) a program for sequential and parallel estimation of phylogenetic trees * Copyright August 2006 by Alexandros Stamatakis * * Partially derived from * fastDNAml, a program for estimation of phylogenetic trees from sequences by Gary J. Olsen * * and * * Programs of the PHYLIP package by Joe Felsenstein. * * This program is free software; you may redistribute it and/or modify its * 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. * * * For any other enquiries send an Email to Alexandros Stamatakis * Alexandros.Stamatakis@epfl.ch * * When publishing work that is based on the results from RAxML-VI-HPC please cite: * * Alexandros Stamatakis:"RAxML-VI-HPC: maximum likelihood-based phylogenetic analyses with thousands of taxa and mixed models". * Bioinformatics 2006; doi: 10.1093/bioinformatics/btl446 */ #ifndef WIN32 #include #include #include #include #endif #include #include #include #include #include #include #include #include "axml.h" /*****************************FUNCTIONS FOR READING MULTIPLE MODEL SPECIFICATIONS************************************************/ extern char modelFileName[1024]; extern char excludeFileName[1024]; extern char proteinModelFileName[1024]; extern char secondaryStructureFileName[1024]; extern char seq_file[1024]; extern char *protModels[NUM_PROT_MODELS]; static boolean lineContainsOnlyWhiteChars(char *line) { int i, n = strlen(line); if(n == 0) return TRUE; for(i = 0; i < n; i++) { if(!whitechar(line[i])) return FALSE; } return TRUE; } static int isNum(char c) { return (c == '0' || c == '1' || c == '2' || c == '3' || c == '4' || c == '5' || c == '6' || c == '7' || c == '8' || c == '9'); } static void skipWhites(char **ch) { while(**ch == ' ' || **ch == '\t') *ch = *ch + 1; } static void analyzeIdentifier(char **ch, int modelNumber, tree *tr) { char *start = *ch, ident[2048] = ""; char model[128] = ""; char thisModel[1024]; int i = 0, n, j; int containsComma = 0; while(**ch != '=') { if(**ch == '\n' || **ch == '\r') { printf("\nPartition file parsing error!\n"); printf("Each line must contain a \"=\" character\n"); printf("Offending line: %s\n", start); printf("ExaML will exit now.\n\n"); exit(-1); } if(**ch != ' ' && **ch != '\t') { ident[i] = **ch; i++; } *ch = *ch + 1; } n = i; i = 0; for(i = 0; i < n; i++) if(ident[i] == ',') containsComma = 1; if(!containsComma) { printf("Error, model file must have format: DNA or AA model, then a comma, and then the partition name\n"); exit(-1); } else { boolean found = FALSE; i = 0; while(ident[i] != ',') { model[i] = ident[i]; i++; } /* AA */ for(i = 0; i < NUM_PROT_MODELS && !found; i++) { strcpy(thisModel, protModels[i]); if(strcasecmp(model, thisModel) == 0) { tr->initialPartitionData[modelNumber].protModels = i; tr->initialPartitionData[modelNumber].protFreqs = 0; tr->initialPartitionData[modelNumber].dataType = AA_DATA; found = TRUE; } strcpy(thisModel, protModels[i]); strcat(thisModel, "F"); if(strcasecmp(model, thisModel) == 0) { tr->initialPartitionData[modelNumber].protModels = i; tr->initialPartitionData[modelNumber].protFreqs = 1; tr->initialPartitionData[modelNumber].dataType = AA_DATA; found = TRUE; if(tr->initialPartitionData[modelNumber].protModels == AUTO) { printf("\nError: Option AUTOF has been deprecated, exiting\n\n"); errorExit(-1); } if(tr->initialPartitionData[modelNumber].protModels == LG4M || tr->initialPartitionData[modelNumber].protModels == LG4X) { printf("\nError: Options LG4MF and LG4XF have been deprecated.\n"); printf("They shall only be used with the given base frequencies of the model, exiting\n\n"); errorExit(-1); } } strcpy(thisModel, protModels[i]); strcat(thisModel, "X"); if(strcasecmp(model, thisModel) == 0) { tr->initialPartitionData[modelNumber].protModels = i; tr->initialPartitionData[modelNumber].protFreqs = 0; tr->initialPartitionData[modelNumber].optimizeBaseFrequencies = TRUE; tr->initialPartitionData[modelNumber].dataType = AA_DATA; found = TRUE; if(tr->initialPartitionData[modelNumber].protModels == AUTO) { printf("\nError: Option AUTOX has been deprecated, exiting\n\n"); errorExit(-1); } if(tr->initialPartitionData[modelNumber].protModels == LG4M || tr->initialPartitionData[modelNumber].protModels == LG4X) { printf("\nError: Options LG4MX and LG4XX have been deprecated.\n"); printf("They shall only be used with the given base frequencies of the model, exiting\n\n"); errorExit(-1); } } /*if(found) printf("%s %d\n", model, i);*/ } if(!found) { if(strcasecmp(model, "DNA") == 0) { tr->initialPartitionData[modelNumber].protModels = -1; tr->initialPartitionData[modelNumber].protFreqs = -1; tr->initialPartitionData[modelNumber].dataType = DNA_DATA; tr->initialPartitionData[modelNumber].optimizeBaseFrequencies = FALSE; found = TRUE; } else { if(strcasecmp(model, "DNAX") == 0) { tr->initialPartitionData[modelNumber].protModels = -1; tr->initialPartitionData[modelNumber].protFreqs = -1; tr->initialPartitionData[modelNumber].dataType = DNA_DATA; tr->initialPartitionData[modelNumber].optimizeBaseFrequencies = TRUE; found = TRUE; } else { if(strcasecmp(model, "BIN") == 0) { tr->initialPartitionData[modelNumber].protModels = -1; tr->initialPartitionData[modelNumber].protFreqs = -1; tr->initialPartitionData[modelNumber].dataType = BINARY_DATA; tr->initialPartitionData[modelNumber].optimizeBaseFrequencies = FALSE; found = TRUE; } else { if(strcasecmp(model, "BINX") == 0) { tr->initialPartitionData[modelNumber].protModels = -1; tr->initialPartitionData[modelNumber].protFreqs = -1; tr->initialPartitionData[modelNumber].dataType = BINARY_DATA; tr->initialPartitionData[modelNumber].optimizeBaseFrequencies = TRUE; found = TRUE; } else { if(strcasecmp(model, "MULTI") == 0) { tr->initialPartitionData[modelNumber].protModels = -1; tr->initialPartitionData[modelNumber].protFreqs = -1; tr->initialPartitionData[modelNumber].dataType = GENERIC_32; found = TRUE; } else { if(strcasecmp(model, "CODON") == 0) { tr->initialPartitionData[modelNumber].protModels = -1; tr->initialPartitionData[modelNumber].protFreqs = -1; tr->initialPartitionData[modelNumber].dataType = GENERIC_64; found = TRUE; } } } } } } } if(!found) { printf("ERROR: you specified the unknown model %s for partition %d\n", model, modelNumber); exit(-1); } i = 0; while(ident[i++] != ','); tr->initialPartitionData[modelNumber].partitionName = (char*)malloc((n - i + 1) * sizeof(char)); j = 0; while(i < n) tr->initialPartitionData[modelNumber].partitionName[j++] = ident[i++]; tr->initialPartitionData[modelNumber].partitionName[j] = '\0'; } } static void setModel(int model, int position, int *a) { if(a[position] == -1) a[position] = model; else { printf("ERROR trying to assign model %d to position %d \n", model, position); printf("while already model %d has been assigned to this position\n", a[position]); exit(-1); } } static int myGetline(char **lineptr, int *n, FILE *stream) { char *line, *p; int size, copy, len; int chunkSize = 256 * sizeof(char); if (*lineptr == NULL || *n < 2) { line = (char *)realloc(*lineptr, chunkSize); if (line == NULL) return -1; *lineptr = line; *n = chunkSize; } line = *lineptr; size = *n; copy = size; p = line; while(1) { while (--copy > 0) { register int c = getc(stream); if (c == EOF) goto lose; else { *p++ = c; if(c == '\n' || c == '\r') goto win; } } /* Need to enlarge the line buffer. */ len = p - line; size *= 2; line = realloc (line, size); if (line == NULL) goto lose; *lineptr = line; *n = size; p = line + len; copy = size - len; } lose: if (p == *lineptr) return -1; /* Return a partial line since we got an error in the middle. */ win: *p = '\0'; return p - *lineptr; } static void nonContiguousError(analdef *adef) { if(adef->compressPatterns == FALSE) { printf("\nError: You are not allowed to use interleaved partitions, that is, assign non-contiguous sites\n"); printf("to the same partition model, when pattern compression is disabled via the -c flag!\n\n"); exit(-1); } } void parsePartitions(analdef *adef, rawdata *rdta, tree *tr) { FILE *f; int numberOfModels = 0; int nbytes = 0; char *ch; char *cc = (char *)NULL; char **p_names; int n, i, l; int lower, upper, modulo; char buf[256]; int **partitions; int pairsCount; int as, j; int k; f = myfopen(modelFileName, "rb"); while(myGetline(&cc, &nbytes, f) > -1) { if(!lineContainsOnlyWhiteChars(cc)) { numberOfModels++; } if(cc) free(cc); cc = (char *)NULL; } rewind(f); p_names = (char **)malloc(sizeof(char *) * numberOfModels); partitions = (int **)malloc(sizeof(int *) * numberOfModels); tr->initialPartitionData = (pInfo*)malloc(sizeof(pInfo) * numberOfModels); for(i = 0; i < numberOfModels; i++) { tr->initialPartitionData[i].protModels = adef->proteinMatrix; tr->initialPartitionData[i].protFreqs = adef->protEmpiricalFreqs; tr->initialPartitionData[i].dataType = -1; } for(i = 0; i < numberOfModels; i++) partitions[i] = (int *)NULL; i = 0; while(myGetline(&cc, &nbytes, f) > -1) { if(!lineContainsOnlyWhiteChars(cc)) { n = strlen(cc); p_names[i] = (char *)malloc(sizeof(char) * (n + 1)); strcpy(&(p_names[i][0]), cc); i++; } if(cc) free(cc); cc = (char *)NULL; } for(i = 0; i < numberOfModels; i++) { ch = p_names[i]; pairsCount = 0; skipWhites(&ch); if(*ch == '=') { printf("Identifier missing prior to '=' in %s\n", p_names[i]); exit(-1); } analyzeIdentifier(&ch, i, tr); ch++; numberPairs: pairsCount++; partitions[i] = (int *)realloc((void *)partitions[i], (1 + 3 * pairsCount) * sizeof(int)); partitions[i][0] = pairsCount; partitions[i][3 + 3 * (pairsCount - 1)] = -1; skipWhites(&ch); if(!isNum(*ch)) { printf("%c Number expected in %s\n", *ch, p_names[i]); exit(-1); } l = 0; while(isNum(*ch)) { /*printf("%c", *ch);*/ buf[l] = *ch; ch++; l++; } buf[l] = '\0'; lower = atoi(buf); partitions[i][1 + 3 * (pairsCount - 1)] = lower; skipWhites(&ch); /* NEW */ if((*ch != '-') && (*ch != ',')) { if(*ch == '\0' || *ch == '\n' || *ch == '\r') { upper = lower; goto SINGLE_NUMBER; } else { printf("'-' or ',' expected in %s\n", p_names[i]); exit(-1); } } if(*ch == ',') { upper = lower; nonContiguousError(adef); goto SINGLE_NUMBER; } /* END NEW */ ch++; skipWhites(&ch); if(!isNum(*ch)) { printf("%c Number expected in %s\n", *ch, p_names[i]); exit(-1); } l = 0; while(isNum(*ch)) { buf[l] = *ch; ch++; l++; } buf[l] = '\0'; upper = atoi(buf); SINGLE_NUMBER: partitions[i][2 + 3 * (pairsCount - 1)] = upper; if(upper < lower) { printf("Upper bound %d smaller than lower bound %d for this partition: %s\n", upper, lower, p_names[i]); exit(-1); } skipWhites(&ch); if(*ch == '\0' || *ch == '\n' || *ch == '\r') /* PC-LINEBREAK*/ { goto parsed; } if(*ch == ',') { ch++; nonContiguousError(adef); goto numberPairs; } if(*ch == '\\') { ch++; skipWhites(&ch); if(!isNum(*ch)) { printf("%c Number expected in %s\n", *ch, p_names[i]); exit(-1); } nonContiguousError(adef); l = 0; while(isNum(*ch)) { buf[l] = *ch; ch++; l++; } buf[l] = '\0'; modulo = atoi(buf); partitions[i][3 + 3 * (pairsCount - 1)] = modulo; skipWhites(&ch); if(*ch == '\0' || *ch == '\n' || *ch == '\r') { goto parsed; } if(*ch == ',') { ch++; nonContiguousError(adef); goto numberPairs; } } printf("\nError: You may be using \"/\" for specifying interleaved partitions in the model file, while it should be \"\\\" !\n\n"); assert(0); parsed: i = i; } fclose(f); /*********************************************************************************************************************/ for(i = 0; i <= rdta->sites; i++) tr->model[i] = -1; for(i = 0; i < numberOfModels; i++) { as = partitions[i][0]; for(j = 0; j < as; j++) { lower = partitions[i][1 + j * 3]; upper = partitions[i][2 + j * 3]; modulo = partitions[i][3 + j * 3]; if(modulo == -1) { for(k = lower; k <= upper; k++) setModel(i, k, tr->model); } else { for(k = lower; k <= upper; k += modulo) { if(k <= rdta->sites) setModel(i, k, tr->model); } } } } for(i = 1; i < rdta->sites + 1; i++) { if(tr->model[i] == -1) { printf("ERROR: Alignment Position %d has not been assigned any model\n", i); exit(-1); } } for(i = 0; i < numberOfModels; i++) { free(partitions[i]); free(p_names[i]); } free(partitions); free(p_names); tr->NumberOfModels = numberOfModels; } /*******************************************************************************************************************************/ void handleExcludeFile(tree *tr, analdef *adef, rawdata *rdta) { FILE *f; char buf[256]; int ch, j, value, i, state = 0, numberOfModels = 0, l = -1, excludeRegion = 0, excludedColumns = 0, modelCounter = 1; int *excludeArray, *countArray, *modelList; int **partitions; printf("\n\n"); f = myfopen(excludeFileName, "rb"); while((ch = getc(f)) != EOF) { if(ch == '-') numberOfModels++; } excludeArray = (int*)malloc(sizeof(int) * (rdta->sites + 1)); countArray = (int*)malloc(sizeof(int) * (rdta->sites + 1)); modelList = (int *)malloc((rdta->sites + 1)* sizeof(int)); partitions = (int **)malloc(sizeof(int *) * numberOfModels); for(i = 0; i < numberOfModels; i++) partitions[i] = (int *)malloc(sizeof(int) * 2); rewind(f); while((ch = getc(f)) != EOF) { switch(state) { case 0: /* get first number */ if(!whitechar(ch)) { if(!isNum(ch)) { printf("exclude file must have format: number-number [number-number]*\n"); exit(-1); } l = 0; buf[l++] = ch; state = 1; } break; case 1: /*get the number or detect - */ if(!isNum(ch) && ch != '-') { printf("exclude file must have format: number-number [number-number]*\n"); exit(-1); } if(isNum(ch)) { buf[l++] = ch; } else { buf[l++] = '\0'; value = atoi(buf); partitions[excludeRegion][0] = value; state = 2; } break; case 2: /*get second number */ if(!isNum(ch)) { printf("exclude file must have format: number-number [number-number]*\n"); exit(-1); } l = 0; buf[l++] = ch; state = 3; break; case 3: /* continue second number or find end */ if(!isNum(ch) && !whitechar(ch)) { printf("exclude file must have format: number-number [number-number]*\n"); exit(-1); } if(isNum(ch)) { buf[l++] = ch; } else { buf[l++] = '\0'; value = atoi(buf); partitions[excludeRegion][1] = value; excludeRegion++; state = 0; } break; default: assert(0); } } if(state == 3) { buf[l++] = '\0'; value = atoi(buf); partitions[excludeRegion][1] = value; excludeRegion++; } assert(excludeRegion == numberOfModels); for(i = 0; i <= rdta->sites; i++) { excludeArray[i] = -1; countArray[i] = 0; modelList[i] = -1; } for(i = 0; i < numberOfModels; i++) { int lower = partitions[i][0]; int upper = partitions[i][1]; if(lower > upper) { printf("Misspecified exclude region %d\n", i); printf("lower bound %d is greater than upper bound %d\n", lower, upper); exit(-1); } if(lower == 0) { printf("Misspecified exclude region %d\n", i); printf("lower bound must be greater than 0\n"); exit(-1); } if(upper > rdta->sites) { printf("Misspecified exclude region %d\n", i); printf("upper bound %d must be smaller than %d\n", upper, (rdta->sites + 1)); exit(-1); } for(j = lower; j <= upper; j++) { if(excludeArray[j] != -1) { printf("WARNING: Exclude regions %d and %d overlap at position %d (already excluded %d times)\n", excludeArray[j], i, j, countArray[j]); } excludeArray[j] = i; countArray[j] = countArray[j] + 1; } } for(i = 1; i <= rdta->sites; i++) { if(excludeArray[i] != -1) excludedColumns++; else { modelList[modelCounter] = tr->model[i]; modelCounter++; } } printf("You have excluded %d out of %d columns\n", excludedColumns, rdta->sites); if(excludedColumns == rdta->sites) { printf("Error: You have excluded all sites\n"); exit(-1); } if(adef->useSecondaryStructure && (excludedColumns > 0)) { char mfn[2048]; int countColumns; FILE *newFile; assert(adef->useMultipleModel); strcpy(mfn, secondaryStructureFileName); strcat(mfn, "."); strcat(mfn, excludeFileName); newFile = myfopen(mfn, "wb"); printBothOpen("\nA secondary structure file with analogous structure assignments for non-excluded columns is printed to file %s\n", mfn); for(i = 1, countColumns = 0; i <= rdta->sites; i++) { if(excludeArray[i] == -1) fprintf(newFile, "%c", tr->secondaryStructureInput[i - 1]); else countColumns++; } assert(countColumns == excludedColumns); fprintf(newFile,"\n"); fclose(newFile); } if(adef->useMultipleModel && (excludedColumns > 0)) { char mfn[2048]; FILE *newFile; strcpy(mfn, modelFileName); strcat(mfn, "."); strcat(mfn, excludeFileName); newFile = myfopen(mfn, "wb"); printf("\nA partition file with analogous model assignments for non-excluded columns is printed to file %s\n", mfn); for(i = 0; i < tr->NumberOfModels; i++) { boolean modelStillExists = FALSE; for(j = 1; (j <= rdta->sites) && (!modelStillExists); j++) { if(modelList[j] == i) modelStillExists = TRUE; } if(modelStillExists) { int k = 1; int lower, upper; int parts = 0; switch(tr->partitionData[i].dataType) { case AA_DATA: { char AAmodel[1024]; strcpy(AAmodel, protModels[tr->partitionData[i].protModels]); if(tr->partitionData[i].protFreqs) strcat(AAmodel, "F"); fprintf(newFile, "%s, ", AAmodel); } break; case DNA_DATA: fprintf(newFile, "DNA, "); break; case BINARY_DATA: fprintf(newFile, "BIN, "); break; case GENERIC_32: fprintf(newFile, "MULTI, "); break; case GENERIC_64: fprintf(newFile, "CODON, "); break; default: assert(0); } fprintf(newFile, "%s = ", tr->partitionData[i].partitionName); while(k <= rdta->sites) { if(modelList[k] == i) { lower = k; while((modelList[k + 1] == i) && (k <= rdta->sites)) k++; upper = k; if(lower == upper) { if(parts == 0) fprintf(newFile, "%d", lower); else fprintf(newFile, ",%d", lower); } else { if(parts == 0) fprintf(newFile, "%d-%d", lower, upper); else fprintf(newFile, ",%d-%d", lower, upper); } parts++; } k++; } fprintf(newFile, "\n"); } } fclose(newFile); } { FILE *newFile; char mfn[2048]; strcpy(mfn, seq_file); strcat(mfn, "."); strcat(mfn, excludeFileName); newFile = myfopen(mfn, "wb"); printf("\nAn alignment file with excluded columns is printed to file %s\n\n\n", mfn); fprintf(newFile, "%d %d\n", tr->mxtips, rdta->sites - excludedColumns); for(i = 1; i <= tr->mxtips; i++) { unsigned char *tipI = &(rdta->y[i][1]); fprintf(newFile, "%s ", tr->nameList[i]); for(j = 0; j < rdta->sites; j++) { if(excludeArray[j + 1] == -1) fprintf(newFile, "%c", getInverseMeaning(tr->dataVector[j + 1], tipI[j])); } fprintf(newFile, "\n"); } fclose(newFile); } fclose(f); for(i = 0; i < numberOfModels; i++) free(partitions[i]); free(partitions); free(excludeArray); free(countArray); free(modelList); } void parseProteinModel(analdef *adef) { FILE *f; int doublesRead = 0; int result = 0; int i, j; double acc = 0.0; assert(adef->userProteinModel); printf("User-defined prot mod %s\n", proteinModelFileName); adef->externalAAMatrix = (double*)malloc(420 * sizeof(double)); f = myfopen(proteinModelFileName, "rb"); while(doublesRead < 420) { result = fscanf(f, "%lf", &(adef->externalAAMatrix[doublesRead++])); if(result == EOF) { printf("Error protein model file must consist of exactly 420 entries \n"); printf("The first 400 entries are for the rates of the AA matrix, while the\n"); printf("last 20 should contain the empirical base frequencies\n"); printf("Reached End of File after %d entries\n", (doublesRead - 1)); exit(-1); } } fclose(f); /* CHECKS */ for(i = 0; i < 20; i++) for(j = 0; j < 20; j++) { if(i != j) { if(adef->externalAAMatrix[i * 20 + j] != adef->externalAAMatrix[j * 20 + i]) { printf("Error user-defined Protein model matrix must be symmetric\n"); printf("Entry P[%d][%d]=%f at position %d is not equal to P[%d][%d]=%f at position %d\n", i, j, adef->externalAAMatrix[i * 20 + j], (i * 20 + j), j, i, adef->externalAAMatrix[j * 20 + i], (j * 20 + i)); exit(-1); } } } acc = 0.0; for(i = 400; i < 420; i++) acc += adef->externalAAMatrix[i]; if((acc > 1.0 + 1.0E-6) || (acc < 1.0 - 1.0E-6)) { printf("Base frequencies in user-defined AA substitution matrix do not sum to 1.0\n"); printf("the sum is %1.80f\n", acc); exit(-1); } } void parseSecondaryStructure(tree *tr, analdef *adef, int sites) { if(adef->useSecondaryStructure) { FILE *f = myfopen(secondaryStructureFileName, "rb"); int i, k, countCharacters = 0, ch, *characters, **brackets, opening, closing, depth, numberOfSymbols, numSecondaryColumns; unsigned char bracketTypes[4][2] = {{'(', ')'}, {'<', '>'}, {'[', ']'}, {'{', '}'}}; numberOfSymbols = 4; tr->secondaryStructureInput = (char*)malloc(sizeof(char) * sites); while((ch = fgetc(f)) != EOF) { if(ch == '(' || ch == ')' || ch == '<' || ch == '>' || ch == '[' || ch == ']' || ch == '{' || ch == '}' || ch == '.') countCharacters++; else { if(!whitechar(ch)) { printf("Secondary Structure file %s contains character %c at position %d\n", secondaryStructureFileName, ch, countCharacters + 1); printf("Allowed Characters are \"( ) < > [ ] { } \" and \".\" \n"); errorExit(-1); } } } if(countCharacters != sites) { printf("Error: Alignment length is: %d, secondary structure file has length %d\n", sites, countCharacters); errorExit(-1); } characters = (int*)malloc(sizeof(int) * countCharacters); brackets = (int **)malloc(sizeof(int*) * numberOfSymbols); for(k = 0; k < numberOfSymbols; k++) brackets[k] = (int*)calloc(countCharacters, sizeof(int)); rewind(f); countCharacters = 0; while((ch = fgetc(f)) != EOF) { if(!whitechar(ch)) { tr->secondaryStructureInput[countCharacters] = ch; characters[countCharacters++] = ch; } } assert(countCharacters == sites); for(k = 0; k < numberOfSymbols; k++) { for(i = 0, opening = 0, closing = 0, depth = 0; i < countCharacters; i++) { if((characters[i] == bracketTypes[k][0] || characters[i] == bracketTypes[k][1]) && (tr->extendedDataVector[i+1] == AA_DATA || tr->extendedDataVector[i+1] == BINARY_DATA || tr->extendedDataVector[i+1] == GENERIC_32 || tr->extendedDataVector[i+1] == GENERIC_64)) { printf("Secondary Structure only for DNA character positions \n"); printf("I am at position %d of the secondary structure file and this is not part of a DNA partition\n", i+1); errorExit(-1); } if(characters[i] == bracketTypes[k][0]) { depth++; /*printf("%d %d\n", depth, i);*/ brackets[k][i] = depth; opening++; } if(characters[i] == bracketTypes[k][1]) { brackets[k][i] = depth; /*printf("%d %d\n", depth, i); */ depth--; closing++; } if(closing > opening) { printf("at position %d there is a closing bracket too much\n", i+1); errorExit(-1); } } assert(depth == 0 && countCharacters == sites); if(closing != opening) { printf("Number of opening brackets %d should be equal to number of closing brackets %d\n", opening, closing); errorExit(-1); } } for(i = 0, numSecondaryColumns = 0; i < countCharacters; i++) { int checkSum = 0; for(k = 0; k < numberOfSymbols; k++) { if(brackets[k][i] > 0) { checkSum++; switch(tr->secondaryStructureModel) { case SEC_16: case SEC_16_A: case SEC_16_B: case SEC_16_C: case SEC_16_D: case SEC_16_E: case SEC_16_F: case SEC_16_I: case SEC_16_J: case SEC_16_K: tr->extendedDataVector[i+1] = SECONDARY_DATA; break; case SEC_6_A: case SEC_6_B: case SEC_6_C: case SEC_6_D: case SEC_6_E: tr->extendedDataVector[i+1] = SECONDARY_DATA_6; break; case SEC_7_A: case SEC_7_B: case SEC_7_C: case SEC_7_D: case SEC_7_E: case SEC_7_F: tr->extendedDataVector[i+1] = SECONDARY_DATA_7; break; default: assert(0); } numSecondaryColumns++; } } assert(checkSum <= 1); } assert(numSecondaryColumns % 2 == 0); /*printf("Number of secondary columns: %d merged columns: %d\n", numSecondaryColumns, numSecondaryColumns / 2);*/ tr->numberOfSecondaryColumns = numSecondaryColumns; if(numSecondaryColumns > 0) { int model = tr->NumberOfModels; int countPairs; pInfo *partBuffer = (pInfo*)malloc(sizeof(pInfo) * tr->NumberOfModels); for(i = 1; i <= sites; i++) { for(k = 0; k < numberOfSymbols; k++) { if(brackets[k][i-1] > 0) tr->model[i] = model; } } /* now make a copy of partition data */ for(i = 0; i < tr->NumberOfModels; i++) { partBuffer[i].partitionName = (char*)malloc((strlen(tr->extendedPartitionData[i].partitionName) + 1) * sizeof(char)); strcpy(partBuffer[i].partitionName, tr->extendedPartitionData[i].partitionName); partBuffer[i].dataType = tr->extendedPartitionData[i].dataType; partBuffer[i].protModels= tr->extendedPartitionData[i].protModels; partBuffer[i].protFreqs= tr->extendedPartitionData[i].protFreqs; } for(i = 0; i < tr->NumberOfModels; i++) free(tr->extendedPartitionData[i].partitionName); free(tr->extendedPartitionData); tr->extendedPartitionData = (pInfo*)malloc(sizeof(pInfo) * (tr->NumberOfModels + 1)); for(i = 0; i < tr->NumberOfModels; i++) { tr->extendedPartitionData[i].partitionName = (char*)malloc((strlen(partBuffer[i].partitionName) + 1) * sizeof(char)); strcpy(tr->extendedPartitionData[i].partitionName, partBuffer[i].partitionName); tr->extendedPartitionData[i].dataType = partBuffer[i].dataType; tr->extendedPartitionData[i].protModels= partBuffer[i].protModels; tr->extendedPartitionData[i].protFreqs= partBuffer[i].protFreqs; free(partBuffer[i].partitionName); } free(partBuffer); tr->extendedPartitionData[i].partitionName = (char*)malloc(64 * sizeof(char)); switch(tr->secondaryStructureModel) { case SEC_16: case SEC_16_A: case SEC_16_B: case SEC_16_C: case SEC_16_D: case SEC_16_E: case SEC_16_F: case SEC_16_I: case SEC_16_J: case SEC_16_K: strcpy(tr->extendedPartitionData[i].partitionName, "SECONDARY STRUCTURE 16 STATE MODEL"); tr->extendedPartitionData[i].dataType = SECONDARY_DATA; break; case SEC_6_A: case SEC_6_B: case SEC_6_C: case SEC_6_D: case SEC_6_E: strcpy(tr->extendedPartitionData[i].partitionName, "SECONDARY STRUCTURE 6 STATE MODEL"); tr->extendedPartitionData[i].dataType = SECONDARY_DATA_6; break; case SEC_7_A: case SEC_7_B: case SEC_7_C: case SEC_7_D: case SEC_7_E: case SEC_7_F: strcpy(tr->extendedPartitionData[i].partitionName, "SECONDARY STRUCTURE 7 STATE MODEL"); tr->extendedPartitionData[i].dataType = SECONDARY_DATA_7; break; default: assert(0); } tr->extendedPartitionData[i].protModels= -1; tr->extendedPartitionData[i].protFreqs= -1; tr->NumberOfModels++; if(adef->perGeneBranchLengths) { /*if(tr->NumberOfModels > NUM_BRANCHES) { printf("You are trying to use %d partitioned models for an individual per-gene branch length estimate.\n", tr->NumberOfModels); printf("Currently only %d are allowed to improve efficiency.\n", NUM_BRANCHES); printf("Note that the number of partitions has automatically been incremented by one to accomodate secondary structure models\n"); printf("\n"); printf("In order to change this please replace the line \"#define NUM_BRANCHES %d\" in file \"axml.h\" \n", NUM_BRANCHES); printf("by \"#define NUM_BRANCHES %d\" and then re-compile RAxML.\n", tr->NumberOfModels); exit(-1); } else*/ { tr->numBranches = tr->NumberOfModels; } } assert(countCharacters == sites); tr->secondaryStructurePairs = (int*)malloc(sizeof(int) * countCharacters); for(i = 0; i < countCharacters; i++) tr->secondaryStructurePairs[i] = -1; /* for(i = 0; i < countCharacters; i++) printf("%d", brackets[i]); printf("\n"); */ countPairs = 0; for(k = 0; k < numberOfSymbols; k++) { i = 0; while(i < countCharacters) { int j = i, bracket = 0, openBracket, closeBracket; while(j < countCharacters && ((bracket = brackets[k][j]) == 0)) { i++; j++; } if(j == countCharacters) { assert(bracket == 0); break; } openBracket = j; j++; while(bracket != brackets[k][j] && j < countCharacters) j++; assert(j < countCharacters); closeBracket = j; assert(closeBracket < countCharacters && openBracket < countCharacters); assert(brackets[k][closeBracket] > 0 && brackets[k][openBracket] > 0); /*printf("%d %d %d\n", openBracket, closeBracket, bracket);*/ brackets[k][closeBracket] = 0; brackets[k][openBracket] = 0; countPairs++; tr->secondaryStructurePairs[closeBracket] = openBracket; tr->secondaryStructurePairs[openBracket] = closeBracket; } assert(i == countCharacters); } assert(countPairs == numSecondaryColumns / 2); /*for(i = 0; i < countCharacters; i++) printf("%d ", tr->secondaryStructurePairs[i]); printf("\n");*/ adef->useMultipleModel = TRUE; } for(k = 0; k < numberOfSymbols; k++) free(brackets[k]); free(brackets); free(characters); fclose(f); } } ExaML-3.0.21/testData/000077500000000000000000000000001330315463200143145ustar00rootroot00000000000000ExaML-3.0.21/testData/140000066400000000000000000004577141330315463200145650ustar00rootroot00000000000000 140 1104 Seq1 MDFIDTESSSESDSELHRQLLL-----QQRDETDTVLRELVQGGCNRKRRASSTCTVPQTVPKAMRLYSSNFFCLTSKNRKLSALAAFKKMYNASYYEVAREYKSDKTQSYEWVLGCSPERVSALNCLKGVTEFILYD-YSALFYLEFYCSKNREGVRRLLNVDTDSILLLNPPNKRSVLAALFYQKLVLAHGD--FPDWCR-D-ILSNFELSQMIQWALDNKHHDEGSIAYHYAIHAEQDNNAKLWLQSNQQAKYVRDAATMVRHFVKGRLHSITMSEHIAAQDGWKKILVFLTFQHINFKEFISILCMWLKGPKKSCITIAGVPDSGKSMFAYSLIKFLNGSVLSFANSKSHFWLQPLTECKAALIDDVTLPCWDYVDTFLRNALDGNAICDCKHRAPVQTKCPPLLLTSNYDPRLHYLNSRIQFLLFNRVIPLYG-TQPRFYIEPADWRSFFQKYSEDLQLYDGEGELLQKLLQLQERESQLLEETHTLKEMEEYWRKQQIILTYAATKGLKYLGLTRVPSQVVSEHMAKDAILMQMLITSISKSRFSDLDFNLDFMPQTVKKVPKLGAKTVRVTYGGDPDTETEYVYWG-RLLKH-DPLTDRWEECSGVDLAGIYYKSYTSVWSNEAALHRTAVERLIDDAVDPVGLCFEGYTHQVKTIRRRLYATNLR-YQRVSSTWHWFDSKEDRDNFAKNFTGA---NVPPLYIPSQVTVSPYSTDDYVTPTEHLYHLQTRLITVGNPYFVIAVPKVSANQYRCFRLQLPDPNGFPLPSNNLYDPEHRLVWQLVGLEIDRGQPLGVGLVAAPAFNRGRDAEANSRADNRISCAEPKHNQMLIVGCAPAVGQHWGLGTKCTKCPPIELVNSNIQDGDMSDIGFGAMDFNALGLNRSDVPMELAASVAKYPDWIKMHTDAHGDSCFYYVTREQIYARRYWQQSGYMACPSGSVITSDTNLFNRPYWLSRAQGTNNGILWNENLFVTVLDNSRNVIMKIYDWKNYYECVRHVEEYGISAIVRLCRVTLTAENLGSIYRMNPDILKKWGIQ-EAPLAEDKYSQAITCPPNNAPDPYKTENYWTVDCRERLSDDLLRYPLGRKFLQLPRRKR Seq2 MDFITIESSQCEEEESHVQLL-------QDFEIIPKR--VAGHYTRKRRRTRSPGDPKKTVPKTIRQYNRPHKCLVSKNKTLTALAVFKELYTASFTEVTRTFKSDKTQSYEWVLGCSHIALEAVKVLIHNTEHVILD-HLGVYYVGFTVSKSREGLLRFLNIFTENVVLSNPPNKRSVLSALFFDKLVQVSGD--KPQWMI-D-IITSFELSKMIQWALDNNMYDEGAIAYNYALLADTDLNAQLWLKHNSQAKYVRDAATMCRHYRRGQMQAIGVMEHLATRG-WKRIIVFLRYQHVDHHTFINDLKYWIVNPKRSTIAIVGIPDSGKSMFGMSLIQFLDGRVLSFSNHKSHFWLQPLSETRYALVDDVTWPAWDYMDVYMRNALDGNPICDCKHRAPIQTKCPPLLLTSNYDPRERYLLSRITFMSFNRSIPCIG-GQPRFLISPADWRSFMLKFRKELDI---TGELRESLERLQRREAEILEDPTDLQTITEYWKKQHLLLYAAGQKGYKQLGLQRVPPLHVSEQEARDGILMVVLLRSLLGTPHALRTWSLEWGPRLFRTPPKFGPHTVRVFYCNDPSTETEYPYWD-SYLFY-DPTTGEWTEGIGYDNVGIWHRGYHMIWRDEARRVGTRLDRLIAEAKDPPGLCFVGRTGQLKTIRYRVQTGPYN-VTRISTTWHWFNDSHQREVFARAFVV-APALPSALYIPNAAPLPLFTTDDFVSPTDYVYHVNTRLLMVGNPYFRAAVPKVSGNQYRVFRLKLPDPNDQFDLPDGLFDPEFRYVWQLVGLEVCRGQPLGVGISAAPAFNKGRDVESPARLDNRVSVGDPKQNQMLIVGCAPAYGQHWGKATPCTQCPPIELISSTLQDGDMCDIGFGCMDFAALAANTSDIPLELINTVSKYPDWIRMHNDPKGDCCFFLMRREQLYARHMWQHSGYMCVPSGSVYTSDTQLFNRPYWLSKAQGPNNGVCWGDDLFITVLDNTRGGVMNIYKPSDFREYVRHVEEYELSCVLRLCKVPLSPDVLASLYRAVPHVLGRWGISEYPQAPEDKYSQATRCDTPTPVDPWADMTFWTVDCTSRISPELPRFPLGRKFLALPGKRR Seq3 MTDPNSKGFGDWCLLDISDLLDQGN-SLELFHQQECEQSEEQLQKLKRKY-LSPSPRLESIKSKRRLFDSGLELLRSSNKKATLMAKFKESFGVGFNELTRQFKSHKTCCKDWVYAVHDDLFESSKLLQQHCDYIWVR--MSLYLLCFKAGKNRGTVHKLILNVHEQQILSEPPKLRNTAAALFWYKGCMGSGAGPYPDWIAQQTILGHFDFSAMVQWAFHNHLLDEADIAYQYARLAPEDANAVAWLAHNNQAKFVRECAYMVRFYKKGQMRDMSISEWIYTKGHWSDIVKFIRYQNINFIVFLTALKEFLHSPKKNCILIYGPPNSGKSSFAMSLIRVLKGRVLSFVNSKSQFWLQPLSECKIALLDDVTDPCWIYMDTYLRNGLDGHYVSDCKYRAPTQMKFPPLLLTSNINVHGEYLHTTIKGFEFPNPFPMKADNTPQFELTDQSWKSFFTRLWTQLDLEEEFQCLSERFNALQDQLMNIYEAEQTLQAQIKHWRKEPVLLYYAREKGVTRLGYQPVPVKAVSETKAKEAIAMVLQLESLQTSDFAHEPWTLDTSIETFRSAPHFKKGPLPVEVIYDNDPDNANLYTMWTYVYYMDAD-DKWHKARSVNHIGIYYKNYYVLFADDAKRYTGRLGRLLEEARDPPVIIVKGAANTLKNVRNRAKIKYMGLFRSFSTTWSWFSSYTQRRDFDEAVRYPWHSANGKVYLPPSTPVRVQSTDEYIQRTNIYYHAFSRLLTVGHPYFKLEVPKVSGNQHRVFRLKLPDPNRFALPDMSVYNPDERLVWACRGLEIGRGQPLGVRSTGHPYFNKVKDTENSNAYDDRQDTSDPKQIQMFIVGCTPCIGEHWDKAVPCGLCPPIELKNTYIQDGDMADIGFGNMNFKALQDSRSDVSLDIVNETCKYPDFLKMQNDIYGDACFFYARREQCYARHFFVRGGYFPTVSGSLVSSDAQLFNRPFWLQRAQGHNNGILWANQMFITVVDNTRNTNFSIYNADQFREYQRHVEEYEISLILQLCKVPLKAQVLAQINAMNSSLLEDWQLGFVPTPIQDTYSLATRCPPKEKEDPYKGLHFWDVDLTERLSLDLDQYSLGRKFLFQAGKRK Seq4 MADPKGSGFGDWCILDISDLIDQGN-SLELFHQQECKQSEEQLQKLKRKC-LSPSPRLQSIKSKRRLFDSGVELLRSSNKKATLMAKFKAAFGVGFNELTRQFKSHKTCCNHWVYAVHDDLFESSKLLQQHCDYLWVR--MSLYLLCFKAGKNRGTVHKLMLNVHEQQILSEPPKLRNTAAALFWYKGCMGSGVGPYPDWIAQQTILGHFDFSQMVQWAFDNQLVDEGDIAYRYARLAPEDANAVAWLAHNSQAKFVRECAAMVRFYKKGQMRDMSMSEWIYTKGHWSDIVKFLRYQEVNFIMFLAAFKDFLHSPKKNCILIHGPPNSGKSSFAMSLIRVLKGRVLSFVNSKSQFWLQPLSECKIALIDDVTDPCWLYMDNYLRNGLDGHYVSDCKYKAPMQTKFPPLLLTSNINVHEEYLHSRIKGFAFPNPFPMKSDDTPQFELTDQSWKSFFERLWTQLELEDEFQCLSERFNALQDLLMNIYEAEQTLEAQIKHWRQEAVLLYFARQRGVTRLGYQPVPVKAVSEAKAKEAIAMVLQLQSLQTSEYASETWTLDTSIETFRSAPHFKKGPVPVEVIYDNDADNANLYTMWTYVYYME-D-DVWHKARSVNETGIYYKYYYVLFADDARKYTGRLGRLLEEALDPPVILVRGEANTLKCFRNRAKIKYMGLYRSFSTTWSWFSSYNQRRDFDDVVRYPWHSANGKVYLPPSTPVRVQSTDEYIQRTNIYYHAFSRLLTVGHPYFKLEVPKVSGNQHRVFRLKLPDPNRFALADMSVYNPDERLVWSCRGLEIGRGQPLGVGSTGHPLFNKLKDTENSNSYDDRQDTSDPKQIQMFIVGCTPCIGEHWDKAIPCRLCPPIELKTTYIEDGDMADIGFGNLNFKNLQESRSDVSLDIVNETCKYPDFLKMQNDVYGDACFFYARREQCYARHFFVRGGYYPTVSGSLVSSDAQLFNRPFWLQRAQGHNNGILWANQMFITVVDNTRNTNFSIYTAEQFREYQRHVEEYEISLILQLCKVPLKAEVLAQINAMNSSLLEDWQLGFVPTPIQDTYSLATRCPPKEKEDPYKGLKFWDVDLTERLSLDLDQYSLGRKFLFQAGKRK Seq5 MADSKGSGFGDWCILDISDLLDQGN-SRELFHQQECKQSEEQLQKLKRKY-LSPSPRLESIKSKRRLFDSGLELLRSSNQKATLLAKFKQAFGVGFNELTRQFKSYKTCCNHWVYAVHDDLFESSKLLQQHCDYIWVR--MSLYLLCFKAGKNRGTVHKLILNVHEQQILSEPPKLRNTAAALFWYKGCMGPGVGPYPEWIAQLTILGHFDLSVMVQWAFDNNLFEEADIAYGYARLAPEDSNAVAWLAHNNQAKYVRECAMMVRYYKKGQMRDMSMSEWIYTRGQWSSIVKFLRYQEINFISFLAALKDLLHSPKRNCILFHGPPNTGKSSFGMSLIKVLRGRVLSFVNSKSQFWLQPLGECKIALLDDVTDPCWVYMDQYLRNGLDGHFVSDCKYRAPMQTKFPPLILTSNINVHAEYLHSRIKGFEFKNPFPMKADNTPQFELTDQSWKSFFTRLWTHLDLEDEFQCLSERFNALQEQLMNIYEAEQTLKAQILHWRKEAVTLYFARQKGINRLGYQPVPALAISEARAKEAIYMVLQLESLQKSAFALEPWTLDTSTETFKSAPHFKKGPVPVEVIYDKDEANANLYTMWTFVYYMDSD-DVWHKTTSVNQTGIYYKHYYVLFADDAKRYSGRLGRLLEEARDPPVILVRGDANTLKCFRNRARNKYRGLFRSFSTTFSWFSCLTQRRDFDDAVKYPWHSANGKVYLPPSTPVRVQSTDEYIQRTNIYYHANTRLLTVGHPYFTLEVPKVSGNQHRVFRLKLPDPNRFALADMSVYNPDERLVWACRGLEIGRGQPLGVGSTGHPYFNKVKDTENSNSYDDRQDTSDPKQIQMFIVGCTPCIGEHWDKAEPCGLCPPIELKNTYIQDGDMADIGFGNINFKALQHSRSDVSLDIVNETCKYPDFLKMQNDVYGDACFFYARREQCYARHFFVRGGYFPTVSGSLVSSDAQLFNRPFWLQRAQGHNNGILWANQMFVTVVDNTRNTNFSIYNADNFREYQRHVEEYEISVILQLCKVPLKAEVLAQINAMNSSLLEEWQLGFVPTPIQDTYSLATRCPPKEKVDPYKGLNFWDVDMTERLSLDLDQYSLGRKFLFQAGKRK Seq6 MADHKGSGLSEWCILDISDLLDQGN-SLELFHQQECEQSEEQLQKLKRKY-LSPSPRLQSIKSKRRLFDSGVELLRSSNTKATLMAKFKEAFGDGFNELTRQFKSYKTCCNYWVYAVHD-VYESSKLLQQHCDYIWVR--ITLYLLSFKAGKNRGTVHKLMLNVQEQQILSEPPKLRHTAAALFWYKGGMGTGTGSYPDWIAHQTILGHFDFSVMVQWAFDNNHFEEADIAYGYAKLAPEDANAVAWLAHNSQAKFVRECAAMVRFYKRGQMREMTMSEWIYTRGHWSSIVKFVRYQGINFITFLAALKDFLHSPKRNCLLIYGPPNTGKSTFAMSLIQVLKGRVLSYVNSKSQFWLQPLGDCKIALLDDVTDPCWLYMDTFLRNGLDGHVVSDCKYKAPMQIKFPPLLLTSNINLHEEYLHSRVRGFEFPNPFPMKPDNTPEFELTDQSWKSFFARLWTQLELEDEFQCLSERFNVLQDQLMNIYEAEQTLEAQIAHWRKEAVLLYFARQKGITRIGYQPVPPLAVSEAKAKQAIGIMLQLQSLQKSEFADEPWTLDTSIETYKNAPHFKKGATPVEVIYDKQPDNANVYTMWKHIYYTDAD-DKWHKTTSVNQTGIYYRHYYVVFADDARRYTGRLGRLLDEAIDPPVILVRGEANTLKCFRNRARVRYRGLFKYFSTTWSWFTSAGQRKDFDETVKYPWQSATGKVYLPPSTPVRVQSTDEYIQRTNIYYHANTRLLTVGHPYFTLQVPKVSGNQHRVFRLKLPDPNRFALADMSVYNPDERLVWACRGLEISRGQPLGVGSTGHPYFNKVKDTENSNSYDDRQNTSDPKQIQMFIVGCTPCIGEHWEKAIPCGLCPPIELKNTVIEDGDMADIGFGNMNFKTLQQNRSDVSLDIVNEICKYPDFLKMQNDVYGDACFFYARREQCYARHFFVRGGYFPTVSGSLVSSDAQLFNRPFWLQRAQGHNNGILWANQMFVTVVDNTRNTNFSIYKSTQFREYLRHVEEYEISLILQLCKIPLKADVLAQINAMNSSLLEEWQLGFVPTPIHDTYSLATRCPPKEK-DPYAKFNFWNVDLTERLSLDLDQYSLGRKFLFQAGKRK Seq7 MADSKGSGLSDWCILDVSDLLDQGN-SLELFHQQECEQSEEQLQILKRKY-LSPSPRLESIKSKRRLFDSGLELLRSSNIKATLMAKFKESFGVGFNELTRQFKSYKTCCNDWVYAVHDDLFESSKLLQQHCDYIWVR--MTLYLLCFKAGKNRGTVHKLMLNVQEQQILSEPPKLRNTAAALFWYKGGMGSGAGTYPDWIAHQTILGHFDFSAMVQWAFDNNYLEEPDIAYQYAKLAPEDSNAVAWLAHNQQAKFVRECAAMVRFYKKGQMKEMSMSEWIHTKGHWSDIVKFLRYQDVNFITFLAAFKNFLHAPKHNCILIYGPPNSGKSSFAMSLIKVLKGRVLSFVNSKSQFWLQPLGESKIALLDDVTDPCWVYIDTYLRNGLDGHFVSDCKYKAPVQIKFPPLLLTSNINVHGEYLHSRIKGFEFPHPFPMKPDNTPQFQLTDQSWKSFFERLWTQLDLEEEFQCLSERFNVLQDQLMNIYEAEHTLETQIAHWRREAVLLYYARQKGITRLGYQPVPTLAVSEAKAKEAIGIMLQLQSLQKSEYASENWTLDTSAETYNNVPHFKKGPVPVEVIYDKEPENANVYTMWKYVYYMDPE-DVWHKTTSVNQTGIYYKHYYVLFADGARMYRGRLGRLLEEALDPPVIICKGGANTLKCFRNRARHKYTGLFKAFSTTWSWFTSTNQRKDFDETVKYPWQAAHGKVYLPPSTPVRVQSTDEYIQRTNIYYHANTRLLTVGHPYFKLEVPKVSGNQHRVFRLKLPDPNRFALADMSVYNPDERLVWACRGLEISRGQPLGVGSTGHPYFNKIKDTENSNNYDDRQNTSDPKQIQMFIVGCTPCVGEHWEKALPCGVCPPIELKNTFIEDGDMADIGFGNMNFKTLQQNRSDVSLDIVNETCKYPDFLKMQNDVYGDACFFYARREQCYARHFFVRGGYFPTVSGSLVSSDAQLFNRPFWLQRAQGHNNGILWANQMFVTVVDNTRNTNFSIYDSQKFREYLRHVEEYEISLILQLCKVPLKAEVLAQINAMNSSLLEDWQLGFVPTPIHDTYSLATRCPPKEK-DPYDGLSFWTVDMTERLSLDLDQYSLGRKFLFQAGKRK Seq8 MADPKGSGLGDWCILDVSDLLDQGN-SLELFHQQECKQSEEQLQILKRKY-LSPSPRLELMKSKRRLFDSGLELLRSSNRKATLMAKFKDAFGVGFNELTRQFKSYKTCCNHRVYAVHDDLFESSKLLQQHCDYIWVR--MTLYLLCFKAGKNRGTVHKLLLNVQEQQILSEPPKLRNTAAALFWYKGGMGSGAGKYPDWIAQQTVLGHFDFSVMVQWAFDNNHVDEADIAYQYARLAPEDSNAVAWLAHNSQAKFVRDCAAMVRFYKNLQMREMSMSEWIYTRGHWSSIVKFLGYQGVNFIMFLAALKNFLHAPKQNCILIHGPPNSGKSSFAMSLIKVLKGRVLSFVNSRSQFWLQPLGECKIALIDDVTDPCWLYMDTYLRNGLDGHFVSDCKYKAPVQTKFLPLLLTSNINVHEEYLHSRIKGFEFPNPFPMKSDNTPQFELTDQSWKSFFERLWTQLELEEEFQCLSERFNVLQDQLMNIYEAEHTLETQIAHWRREAVLLYVARQKGITRLGYQPVPPLAVSEGKAKEAIGIMLQLQSLQKSEYASEPWTLDTSAETYRNAPHFKKGPIPVEVIYDKESGNANVYTMWKFVYYMDPE-DVWHKTTSVNHNGIHYKHYYVLFADGARTYRGRLGRLLEEALDPPVILCKGGANTLKCFRNRTRGKYTGLFRAISTTWSWFSSDGQRKHFDETVKYPWHSANGKVYLPPSTPVRVQSTDEYIQRTNIYYHANTRLLTVGHPYFKLEVPKVSGNQHRVFRLKLPDPNRFALADKSVYNPDERLVWGCRGLEISRGQPLGVGSTGHPYFNKVKDTENTNNYDDRQNTSDPKQIQMFIVGCTPCVGEHWEKALPCGICPPIELKNTFIEDGDMADIGFGNMNFKTLQQNRADVSLDIVNETCKYPDFLKMQNDVYGDACFFYARREQCYARHFFVRGRYFPTVSGSLVSSDAQLFNRPFWLQRAQGHNNGILWANQMFVTVVDNTRNTNFSIYDSQKFREYLRHVEEYEISIILQLCKIPLQAEVLAQINAMNPSLLEDWQLGFVPTPIQDTYSLATRCPPKEK-DPYEKLTFWNVDLTERLSLDLDQYSLGRKFLFQAGKRK Seq9 MADPKGSGLDDWCIVDISELLDQGN-SRELFHQQESKESEEHLQKLKRKY-LSPSPRLESIKSKRRLFDSGLELLRASNNKAILMAKFKEAFGVGFNDLTRQFKSYKTCCNHWVYAVHDDLLESSKLLQQHCDYVWIR--MSLFLLCFKVGKNRGTVHKLMLNVHEKQILSEPPKLRNVAAALFWYKGAMGSGTGPYPDWMAHQTIVGHFDMSVMVQWAFDNNYLDEADIAYQYAKLAPEDSNAVAWLAHNNQARFVRECASMVRFYKKGQMKEMSMSEWIHTRGHWSTIAKFLRYQQVNFIMFLAALKDMLHSPKRNCILIYGPPNTGKSAFTMSLIRVLRGRVLSFVNSKSQFWLQPMSECKIALIDDVTDPCWLYMDTYLRNGLDGHYVSDCKHKAPIQTKFPALLLTSNINVHNEYLHSRIKGFEFPNPFPMKADNTPEFELTDQSWKSFFTRLWNQLELEDEFQCLSDRFNALQDQLMNIYEAANTLESQIEHWRKEAVLLYFARQNGVTRLGYQVVPTLAISEAKAKQAIGMVLQLQSLQKSQFGSEPWSLDTSGETFRSAPHFKKGPVSVEVIYDNDKDNANAYTMWKHIYYQD-DDEQWHKSASVNHTGIYYRNYYVLFADDATRYTGRLGRLLDEALDPPVILVRGDPNTLRCFRNRAKQKFTGLYRAFSTAWSWFFSFNQRRDFDQTVKYPWQAASGKVYLPPSTPVRVQSTDEYVQRTNIYYHAYSRLLTVGHPYFKIKVPKVSGNQHRVFRLKLPDPNRFALADMSVYNPDERLVWACRGIEIGRGQPLGVGSVGHPLFNKVGDTENPNSYDDRQNVSDPKQLQMFIIGCAPCMGEHWDRALPCGSCPPIELKNTVIEDGDMADIGYGNLNFKALQENRSDVSLDIVNEICKYPDFLKMQNDVYGDSCFFYARREQCYARHFFVRGGYFPTVSGSLVSSDAQLFNRPFWLQRAQGHNNGICWFNQLFVTVVDNTRNTNFSIYTSQKFQEYLRHVEEYEMSLILQLCKIPLTAEVLAQINAMNSNILEEWQLGFVPAPIHDTYSAATRCPPKEREDPYKNFNFWNVDLTERLSLDLDQYSLGRKFLFQAGKRK Seq10 MADPKGSGLEDWCIVDISELLDQGN-SRELFHQQESKESEEQLQKLKRKY-LSPSPRLESIKSKRRLFDSGLELLRASNNKAILMAKFKEFFGVGFNDLTRQFKSYKTCCNAWVYAVHDDLLESSKLLQQHCDYIWIR--MSLFLLCFKVGKNRGTVHKLMLNVHEKQIISEPPKLRNVAAALFWYKGAMGSGAGPYPDWIAQQTIVGHFDMSAMVQWAFDNNYLDEADIAYQYAKLAPEDSNAVAWLAHNNQARYVREVASMVRFYKKGQMKEMSMSEWIHTRGHWSTIAKFLRYQQVNFIMFLAALKDMLHSPKRNCILIYGPPNTGKSAFTMSLIHVLRGRVLSFVNSKSQFWLQPMSECKIALIDDVTDPCWIYMDTYLRNGLDGHVVSDCKHKAPMQTKFPALLLTSNINVHNEYLHSRIKGFEFPNPFPMKADNTPEFELTDQSWKSFFTRLWNQLELEDEFQCLSDRFNVLQDQLMNIYEAANTIESQIEHWRKEAVLLYFARQKGVTRLGYQYVPPLAVSESRAKQAIGMMLQLQSLQKSEYAKEPWSLDTSAETFRSPPHFKKGPVSVEVIYDNDKDNANAYTMWRYVYYVD-DDDQWHKSPSVNHTGIYFRHYYVLFADDASRYTGRLGRLLDEALDPPVILVRGEPNTLKCFRNRAKLKYAGLYKAFSTAWSWFFSFEQRKDFDKTVKYPWQAASGKVYLPPSTPVRVQSTDEYVQRTNIYYHAYSRLLTVGHPYFKIKVPKVSGNQHRVFRLKLPDPNRFALADMSVYNPDERLVWACRGIEIGRGQPLGVGSVGHPLFNKVGDTENPSSYDDRQNVSDPKQLQMFIIGCAPCLGEHWDKAIPCGSCPPIELINSAIQDGDMADIGYGNLNFKALQQNRSDVSLDIVNETCKYPDFLKMQNDVYGDSCFFYARREQCYARHFFVRGGYFPTVSGSLVSSDAQLFNRPFWLQRAQGHNNGICWFNQLFVTVVDNTRNTNFSIYKAESFQEYLRHVEEYELSLILQLCKVPLTAEVLAQINAMNANILEEWQLGFVPAPIHDTYSAATRCPPKEREDPYKNMKFWDVDLTERLSLDLDQYSLGRKFLFQAGKRK Seq11 MADPKGSGLDDWCIVDISELLDQGN-SRELFHQQECKDSEEQLQKLKRKY-ISPSPRLESIKSKRRLFDSGLELLRASNHKAILLAKFKEAFGIGFNDLTRQFKSYKTCCNDWVYAVHEDLLESSKLLQQHCDYIWIR--MSLFLLCFKAGKNRGTVHKLMLNVHEKQILSEPPKLRNVAAALFWYKGAMGSGAGPYPNWMAQQTIVGHFDLSEMIQWAFDHNYLDEADIAFQYAKLAPENSNAVAWLAHNNQARFVRECASMVRFYKKGQMKEMSMSEWIYARGHWSSIAKFLRYQQVNVIMFLAALKDMLHSPKHNCILIHGPPNTGKSAFTMSLIHVLKGRVLSFVNSKSQFWLQPMSETKIALIDDVTDPCWVYMDTYLRNGLDGHYVSDCKHKAPIQTKFPALLLTSNINVHNEYLHSRIKGFEFPNPFPMKPDNTPEFELTDQSWKSFFTRLWKQLELEDEFQCLSKRFNALQDQLMNIYEAPDTLESQIEHWRKEAVLLYFARQHGISRVGYQPVPVLAVSEAKAKQAIGMVLRLQSLQKSEYGSEPWSLDASAETFRSPPHFKKGPISVEVIYDKDKDNANAYTMWRFVYYQD-DDDKWHKSASVNQTGIYFRHYYVLFADDASRYTGRLGRLLDEALDPPVILVRGEPNTLKCFRNRAKQRYTGLYKSFSTAWSWFISFSQRKDFDETVKYPWQAASGKVYLPPSTPVRVQSTDEYVQRTNIYYHAYSRLLTVGHPYFKIKVPKVSGNQHRVFRLKLPDPNRFALADMSVYNPDERLVWGCRGIEIGRGQPLGVGSVGHPLFNKLGDTENPNSYDDRQNVSDPKQLQMFIIGCAPCLGEHWDRALPCGSCPPIELKNTAIQDGDMADIGYGNLNFKALQENRADVSLDIVNETCKYPDFLKMQNDVYGDSCFFYARREQCYARHFFVRGGYFPTVSGSLVSSDAQLFNRPFWLQRAQGHNNGICWFNQLFVTVVDNTRNTNFSIYDSQKFQEYLRHVEEYEISLILQLCKVPLTAEVLAQINAMNSNILEEWQLGFVPAPIHDTYSAATRCPPKEREDPYKDLNFWNVDLSERLSLELDQYSLGRKFLFQAGKRK Seq12 MAESKGSGFGDWCILDISDLLDQGN-SRELFHQQECQESEEHLQKLKRKY-LSPSPRFESIKSKRRLFDSGLELLRANNNRAILMAKFKEAFGVGFYDLTRQFKSYKTCCNAWVYAVHDDLLESSKLLQQHCDYVWIR--MSLFLLCFKVGKNRGTVHKLMLNVHEKQILSEPPKLRNTAAALFWYKGCMGSGGGPYPDWIAQQTILGHFDLSEMIQWAFDNNHMDESDIAYQYAKLAPENSNAVAWLAHNNQARFVRECAAMVRFYKKGQMKEMSMSEWIYARGHWSTIAKFLRYQQVNFIMFLAALKDLLHAPKRNCILIYGPPNTGKSAFTMSLIRVLKGRVISFVNSKSQFWLQPLSECKIALLDDVTDPCWIYMDTYLRNGLDGHVVSDCKHKAPIQTKFPALLLTSNINVHNEYLHSRIQGFEFPNPFPMKADNTPQFELTDQSWKSFFTRLWQQLELEEEFQCLSERFNVLQDQLMNIYEAAETLESQIEHWRKEAVLLYFARRKGVTRIGYQPVPTLAVSEAKAKEAIGMVLQLQSLQKSEYGTEPWSLDTSAETYRSAPYFKKGPMPIEVIYDKDADNANLYTMWKFVYYVD-EDDNWHKSESVNHTGLYFRHYYVLFADDARKYTGRLGRLLEEALDPPVILVRGEPNTLRSFRNRAKHMYRGLFSSFSTAWSWFVSFNQRKHFDDTVRYPWQAASGKVYLPPSTPVRVQSTDEYVQRTNIYYHAYSRLLTVGHPYFKLEIPKVSGNQHRVFRLKLPDPNRFALADMSVYNPDERLVWGCRGIEIGRGQPLGVGSVGHPLFNKVGDTENPNSYDDRQNVSDPKQLQMFIIGCAPCIGEHWDKALPCGSCPPIELINSVIEDGDMADIGYGNLNFKALQQNRSDVSLDIVNETCKYPDFLKMQNDVYGDSCFFYARREQCYARHFFVRGGYFPTVSGSLVSSDAQLFNRPFWLQRAQGHNNGICWFNQLFVTVVDNTRNTNFSIYNSANFKEYLRHVEEYEISLILQLCKIPLKAEVLAQINAMNSNILEEWQLGFVPAPIQDTYSLATRCPPKEKVDPYKNLHFWNVDLSERLSLDLDQYALGRKFLFQAGKRK Seq13 MADPKGSGFNDWCILDISDLLDQGN-SRELFHLQECQESEEQLQKLKRKY-LSPSPRFESIKSKRRLFDSGLELLRASNNKAILMAKFKEAFGVGFNDLTRQFKSYKTCCNAWVYAVHDDLIESSKLLQQHCDYVWIR--MSLFLVCFKAGKNRGTVHKLMLNVHEKQILSEPPKLRNVAAALFWYKGSMGSGVGSYPDWIAHQTILGHFDLSDMVQWAFDNNYLDEADIAYQYAKLAPDNSNAVAWLAHNNQAKFVRECASMVRFYKKGQMKEMSMSEWIYTKGQWSTIVQFLRYQQVNFIMFLAALKDLLHSPKRNCILFYGPPNTGKSAFTMSLIKVLKGRVLSFCNSKSQFWLQPLSECKIALLDDVTDPCWVYMDTYLRNGLDGHYVSDCKHKAPMQTKFPALLLTSNINVHNEYLHSRIKGFEFPNPFPMKADNTPQFDLTDQSWKSFFTRLWHQLDLEDEFQCLSERFNVLQDQLMNIYEAAQTLEAQIEHWRREAVLLYFARQKGVTRLGYQPVPALMVSEAKAKEAIGMVLQLQSLQKSEFGKEPWSLDTSTETYKSPPHFKKGPMPIEVIYDKDADNANAYTMWRYIYYVD-DDDKWHKSASVNHTGIYFRHYYVLFADDARRYTGRLGRLLEEALDPPVILVRGDANTLRSFRNRAKHMYTGLFSSFSTAWSWFISNSQRKHFDDAVRYPWQAASGKVYLPPSTPVRVQSTDEYVQRTNIYYHAYSRLLTVGHPYFKLQIPKVSGNQHRVFRLKLPDPNRFALADMSVYNPDERLVWACRGIEIGRGQPLGVGSVGHPLFNKVGDTENPNSYDDRQNVSDPKQLQMFIIGCAPCIGEHWDKALPCGSCPPIELINSVIEDGDMADIGYGNLNFKALQQNRADVSLDIVNETCKYPDFLKMQNDVYGDSCFFYARREQCYARHFFVRGGYFPTVSGSLVSSDAQLFNRPFWLQRAQGHNNGICWFNQLFVTVVDNTRNTNFSIYNSQKFTEYLRHVEEYELSLILQLCKVPLKAEILAQINAMNSNILEEWQLGFVPAPIQDTYSLATRCPPKEKVDPYKNLHFWDVDLTERLSLDLDQYSLGRKFLFQAGKRK Seq14 MADNKGSGLHEWCLLDVSDLISQGN-SRELFQQQELEESNALLQSLKRKY-ISPSPQLESIKTKRKLFDSGVELMRCSNLKATLLSKFKNAFGVSFVELTRQFRSNKTCCNDWVYGVNYDLFESSKLLQQHCDYIWVT--MFLYLLCFKAGKNRQTVIRLLLYVAEEQILSEPPKLRSTVSALFWYKGSSNAATGSYPKWIIEQTLIGHFDMSTMVQWAFDNDLTEEADIAFQYAKLAPDDVNATAWLAHNNQARFVRECANMVRYYKKGQMREMSMSAWIHFKGQWSTIVKFIRYQGINFISFLSALKDFLHGPKKNCLLIYGPPNTGKSAFTMSLIKVLHGRVISFVNSKSHFWLQPMSEAKIALLDDATDPCWIYMDTYLRNGLDGHLVSDCKHKAPIQIRFPPLLITSNINAMAEYLHSRLVAFEFPNPFPMKDDDTPEFELTDQSWKSFFKRLWRQLDLEDEFRCLKKRFDVLQDLLMNIYEGSDTLESQIEHWRREAVLLYYARQNGVLRLGYLPVPPLATSEAKAKQAISMVLQLQSLQQSPYGTEKWTLDTSIETFKNTPHFKKGPINVEVIYDGDPDNANLYTMWKYVYYMD-DNDQWQKTESANHTGIYYKHYYVLFADDANRYSGRLGRLLEEALDPPVILLRGGANTLKCFRNRAKLRYRGHYKAFSTSWSWFTSFKQRSGFLDLVRFPWLPASGKVYLPPSTPVKVQSTDEYIQRTNIFYHAYSRLLTVGHPYFVLEVPKVSGNQHRVFRLKLPDPNRFALADMSVYNPEERLVWGCRGVEIGRGQPLGVGTSGHPLFNKVNDTENPVSYDDRQNTSDPKQIQMFIIGCAPCIGEHWEVAERCGRCPPIKLVNSVIQDGDMADIGYGNLNFRTLQQSRSDVSLDIVNETCKYPDFLKMQNDVYGDSCFFFARREQCYARHFFVRGGYFPTVSGSLVSSDAQLFNRPFWLQRAQGHNNGICWANQLFITVVDNTRNTNFSIYDANKFREYQRHVEEYEISLILQLCKIPLKADVLAQINAMNPSLLEEWQLGFVPAPLQSTYSLATPCAPKEREDPYAPYTFWNVDLSERLSLELDQYSLGRKFLFQAGKRK Seq15 MADNKGSGLSDWCLLDVSDLLNQGN-SRELFQQQELEDSETLLQSLKRKY-ISPSPQLESIKSKRKLFDSGVELMRCSNLKATLLAKFKSAFGVSFAELTRQYKSNKTCCNDWVYGVNNDLFEGSKLLQQHCDYIWLT--MYLYLLCFKAGKNRHTVIRLLLHVAEEQILSEPPKLRSTVAALFWYKGSSNSGTGSYPKWIVEQTLIGHFDMSTMVQWAFDNNLTEEADIAFQYAKLAPDDVNATAWLAHNNQARFVREVAAMVRFYKKGQMREMSMSAWIHFRGHWSSIVKFIRYQGINFISFLSALKDFLHAPKKNCLLIYGPPNTGKSAFTMSLIKVLNGRVISFVNSKSHFWLQPMSECKIALLDDATDPCWVYMDTYLRNGLDGHLVSDCKHRAPMQIKFPPLLITSNINAMAEYLHSRLVAFEFPNPFPMKDDDTPEFELTDQSWKSFFTRLWTQLELEDEFRCLRERFDVLQDQLMNIYEASDTLEAQIEHWRKEAVLLYFARQNGVLRLGYQPVPPLATSEAKAKEAIGMMLQLQSLQKSAYASEKWTLDTSIETFKNTPHFKKGPVNVEVIYDGDPDNANLYTMWKYVYYLDSE-DRWQKTESANHTGIYYKHYYTLFADDAKKYTGRLGRLLEEARDPPVILIRGEANTVKCFRNRAKQKYKGLCKAFSTTWSWFTSHTQRSSFLKVVKFPWLPASGKVYLPPSTPVRVQSTDEYVQRTNIFYHAYSRLLTVGHPYYVLEVPKVSGNQHRVFRLKLPDPNRFALADMSVYNPEERLVWACRGLEIGRGQPLGVGTTGHPLFNKVNDTENPLNYDDRQNLSDPKQIQMFIIGCTPCVGEHWDVAPKCGICPPIQLVNTVIQDGDMADIGYGNLNFKALQENRSDVSLDIVNETCEYPDFLKMQNDVYGDSCFFFARREQCYARHFFVRGGYFPTVSGSLVSSDAQLFNRPFWLQRAQGHNNGICWANQLFITVVDNTRNTNFSIYDANKFREYQRHVEEYEISLILQLCKIPLKSEVLAQINAMNPSLLEEWQLGFVPTPLQSTYSLATPCPPKEKEDPYAPYTFWNVDLTERLSLDLDQYSLGRKFLYQANKRK Seq16 MADDKGSENDNWCLLDISDLVDQGN-SRELLHQQQCNDSELQVQKLKRKY-LSPSPRLESIKSKRKLFDSGLELMRCSNVKATLLCKFKLAFGVSFSELTRQYKSNKTCCNDWVYGIRDELYEGSKLLQQHCDYIWVY--MSLFLLCFKAGKNRTTVHRLLLDVQEQQILSEPPKLRSTVAALFWYKGSFGSKAGAYPQWIVQQTMVGHFELSTMVQWAFDNNLTDEADIAYKYANMAFEDVNAAAWLAHNNQARFVRECASMVRFYKRGQMREMSISEWIHHKGHWSSIVKFIRYQEINFICFLAALKDFLHSPKRNCLLIYGPPNTGKSAFTMSLIKVLGGRVISFVNSRSQFWLQPLSECKIALLDDATDPCWTYMDTYLRNGLDGHMVSDCKHKAPMQTKFPPMLVTSNINVLEEYLHSRIVGFKFPNPFPLKPDNTPEFELTDQSWKSFFERLWSQLDLEEEFQCLSERFNALQDELMNIYEAKETLQAQIDHWRQEAVMMYYGRKKGYMRLGYQPLPPLAVSEHKAKEAIAMVLHLQSLQHSPFSEEKWTLDASAETFRNAPYFKKGATNVEVIYDNNEENANLYTLWKYVYYLDGD-DMWQKTTSVNHTGIYFRHYYVRFADDAARYTGRLGRLLAEAVDPPVILVRGDANTLKCFRNRAKQRYWGLYKSFSTTWSWFLSTTQRSHFEKTVRYPWLPASGKVYLPPSTPVRVQSTDEYVERTNIYYHAYSRLLTVGHPYFKVEVPKVSGNQHRVFRLRLPDPNRFALADMSVYNPDERLVWACRGLEIGRGQPLGVGSSGHPLFNKLGDTENPNNYDDRQNVSDPKQLQMFVIGCAPCIGEHWDRAIACGSCPPIELKNTYIEDGDMADIGYGNLNFQTLQQSRADVSLDIVHETCKYPDFLKMQNDVYGDSCFFYARREQCYARHFFVRGGYFPTVSGSLVSTDAQLFNRPFWLQRAQGHNNGICWANQLFLTVVDNTRNTNFSIYDPNKFREYQRHVEEYEISLILQLCKIPLTAEVLAQINAMNPALLEDWQLGFVPAPIHDTYSLATRCPAKEKEDPYGKYTFWEVDLTERLSLDLDQYSLGRKFLFQAGKRK Seq17 MEDNKGTGCSDWFLVDISDLIDQGN-SRELLCQQETEESEQQVQLLKRKY-FSPSPRLQSIKSKKRLFDSGLELLKSSNVKATLMGKFKDAFGVGFNELTRQYKSNKTCCKDWVYCVQDDLLEASKLLQKHCNYIWMH--MTLYLLCFNAGKSRETVCRLLLQIDDMQALLEPPRLRSVLSALFWYKGSMNPNVGTYPDWIVAQTMISHFSLSRMVQWAFDNEHLEEADIAYNYAKLAETDSNAKAFLDSNSQANFVRQCALMVRHYKRGQMRDMSMSCWIHTRGHWSEIVKFIRYQNLNFIMFLDKFRTFLKNPKRNCMCFYGPPDSGKSMFTMSLINVLKGRVLSFANSRSQFWLQPLSETKLALLDDATQECWNYIDTFLRNGVDGNYVSDIKHRAPLQIKFPPLMITTNMNILKEYLHTRIEFFEFPNKFPFDNNNKPQFHLTDQSWKSFFERLWTQLELEDEFHCLSERFTALQDKLMDIYEGLETLEVQIEHWRQEQVLLNFARRNGILRLGYQPVPSLATSETKAKDAIGMVLMLQSLQNSKYAEETWSLQTSLETVRSPPCFKKEPQTIEVVFDGDPENRMIYTVWNWIYFQNTE-DIWQKVKGVDYEGAFYKHYYIKFENDANRFHGRLGRLLDEAKDPPVILLRGEANQLKCYRFRARKKHKGNYKYFSSTWSWFTSTHQREMFLDTMKLPWLPATGKVYLPPSTPVRVLSTDEYVERTNIFYHATSRLLTVGHPYFKVEVPKVSGNQFRAFRMLFPDPNRFALADMSVYNPDERLVWACAGIEINRGQPLGVGTTGHPLFNKVKDTENTNSYDDRQNISDPKQVQMFVIGCTPCLGEYWDRALPCGKCPPIELKNSFIEDGDMIDIGFGNINNKTLSVNKSDVSLDLVNEIAKYPDFLTMANDVYGDSCFFFARREQCYARHYFTRGGYYPTVSGSLVSSDAQLFNRPFWLKRAQGHNNGILWGNQMFVTVADNTRNTNFTIYDANNIREFLRHVEEYQISVILQLCKVSLQPDVLAQINAMNSGILEDWQLGFVPTPVHDTYSAATKCAPKEKEDPFAQYFFWRVDMTEKLSLDLDQYPLGRKFLFQTTKRK Seq18 MSDNKGTCCSAWLSLDISDLIDQGN-SRELFCQQESEESEQQTQLLKRKY-ISPSPQLESIKPKRRLFDSGLELLKHSNVKAVLMAKFKEAFGVGFAELTRQYKSNKTCCRDWVYAVNDDLIESSKLLLQHCAYIWLH--MCLYLLCFNVGKSRETVCRLLLQVSEVQLLSEPPKLRSVCAALFWYKGSMNPNVGAYPEWILTQTLINHFDLSTMIQFAYDHEYFDEATIAYQYAKLAETDANARAFLQSNSQARLVKECATMVRHYMRGEMKEMSMSTWIHRKGQWSDIVRFIRYQDINFIEFLTVFKAFLQNPKQNCLLFHGPPDTGKSMFTMSLISVLKGKVLSFANCKSTFWLQPIADTKLALIDDVTHVCWEYIDQYLRNGLDGNYVCDMKHRAPCQMKFPPLMLTSNIDITKDYLHSRVKSFAFNNKFPLDANHKPQFELTDQSWKSFFKRLWTQLDLEDEFQCLSARFNALQETLMDLYEGREDLQSQIDHWRQEQILLHYARKNGVMRLGYQPVPPLATSEQKAKDAIGMVLLLQSLQRSAYGQEPWTLQTSLEAVRSPPAFKKGPQNIEVVYDGDPDNVMSYTIWNFIYYQTVN-D-WEKVQGVDYFGAYYKTYYINFDKDAARYHGRLARLLAEAKDPPLMLLRGDANVLKCYRFRERKKKRGLVKYYSTTWSWFDTYEHRQQFIRTMKLPWLPASGKVYLPPATPVRVQSTDEYVERTNIFYHAISRLLTVGHPYYRIEVPKVSGNQYRAFRISLPDPNRFALADMSVYNPDERLVWACRGIEIGRGQPLGVGTSGHPLFNKVRDTENSSNYDDRQNTSDPKQVQMFIIGCIPCLGEHWDKAKVCGLCPPIELRNTVIEDGDMFDIGFGNINNKELSFNKSDVSLDIVDETCKYPDFLTMANDVYGDACFFFARREQCYARHYYVRGGYYPTVSGSLVTSDAQLFNRPFWLQRAQGHNNGILWGNQIFVTVADNTRNTNFTIYNANNIREYLRHVEEYQISLILQLCKVPLVAEVLSQINAMNSGILEDWQLGFVPTPVHDIYSKATKCEPTEKEDPFAKYSFWKVDLTERLSLDLDQYPLGRKFLFQAGKRR Seq19 MSDDKGTGCSDWFVLDISDLIDQGN-SRELLCQQESEESEQQIHWLKRKY-ISSSPRLQCIKSKRRLFDSGLELLKCNNVKAMLLAKFKEAFGVGFMELTRQYKSSKTCCRDWVYAVQDELLESSKLLIQHCAYIWLH--MCLYLLCFNVGKSRETVLRLLLQVSEIQIIAEPPKLRSTLSALFWYKGSMNPNVGEYPEWIMTQTMINHFDLSTMVQYAYDNELSEEAEIAWHYAKLADTDANARAFLQHNSQARLVKDCAIMVRHYRRGEMKEMSMSSWIHKKGHWSDIVKFVRYQDINFIQFLDSFKSFLHNPKKSCMLIYGPPDTGKSMFTMSLIKVLKGKVLSFANYKSTFWLQPVADTKIALIDDVTYVCWDYIDQYLRNALDGGVVCDMKHRAPCQIRFPPLMLTSNIDIMKEYLRSRVQAFAFPHKFPFDSDNNPQFKLTDQSWKSFFERLWRQLELEDEFQCLSERFNALQENLMDIYEGRDDIETQILHWRQEQVLFYFARKHGVMRL?YQPVPPLATSETKAKDAIGMVILLQSLQKSAYGKEPWTLQTSLETVRSAPCFKKGPQNIEVMFDKDPENIMVYTVWTYIYYQTLD-DTWNKVEGIDYHGAYYKVYYIQFEVDAARFTGRLERLLEEARDPPVILLRGDANKLKCFRFRAKKKYQDLVKYYSTTWSWFSSNTERELFIKIMKLPWLPTTGKVYLPPTPPVRVQSTDEYVERTNVFYHAMSRLLTVGHPYFSIEVPKVSGNQYRAFRVTFPDPNRFALADMSVYNPEERLVWACVGLEIGRGQPLGVGTSGHPLFNKVKDTENNSNYDDRQNTSDPKQVQMFVVGCVPCLGEHWDRALVCGKCPPLELKNTVIEDGDMFDIGFGNINNKALSVTKSDVSLDIVNETCKYPDFLTMANDVYGDACFFFARREQCYARHYFVRGGYFPTVSGSLVTSDAQLFNRPFWLRRAQGHNNGILWGNQMFITVADNTRNTNFTIYNSQNIREFLRHVEEYQLSIILQLCKIPLKAEVLTQINAMNSGILEDWQLGFVPTPVQDIYSKATKCQPKDKEDPFGKYTFWNVDLTEKLSLDLDQYPLGRKFIFQAGKRK Seq20 MSDEKGTGCSEWFDLDISDLIDQGN-SRELLCQQESEESEQQIHWLKRKY-ISSSPRLQSIKSKRRLFDSGLELLKCSNVKAMLLAKFKEAFGVGYMELTRQYRSSKTCCRDWVYAVQDELLESSKLLIQHCAYIWLH--MCLYLLCFNVGKSRETVLRLLLQVSEVQIIAEPPKLRSTLSALFWYKGSMNPNVGEYPEWIMTQTMISHFDLSTMVQYAYDNELTDEAEIAYHYAKLADTDANARAFLQHNSQARLVKDCAIMVRHYRRGEMKEMSMSAWIHKKGHWSDIVKFIRYQEINFIQFLNAFKLFLHNPKKSCLLFYGPPDTGKSMFTMSLIKLLKGKVLSFANYKSTFWLQPVADTKVALIDDVTYVCWDYIEQYLRNALDGNTVCDMKHRAPCQIRFPPLMLTSNIDIMKEYLYSRIQAFAFPHKFPFDSDNKPQFKLTDQSWKSFFERLWRQLDLEDEFQCLSERFNALQENLMDIYEGRDDIETQIMHWRQEQVLFYFARQHGVMRLGYQPVPPLATSEAKAKDAIGMVILLQSLQKSQYGKEPWTLQTSLETVRSAPCFKKGPQNIEVMFDNDPENLMVYTVWSHIYYQTLD-DTWKKVEGVDYHGAYYKNYYIQFEIDAARFNGRLERLLEDARDPPVIVLRGDANKLKCFRFRAKKKYRDLVKYYSTTWSWFTSNTERQLFINIMKLPWLPATGKVYLPPTPPVRVQSTDEYVERTNVFYHAMSRLLTVGHPYFKIDVPKVSGNQYRAFRVTFPDPNRFALADMSVYNPEERLVWACAGLEIGRGQPLGVGTSGHPLFNKVKDTENNSGYDDRQNTSDPKQVQMFIVGCVPCLGEHWDRAPVCGKCPPLELKNTVIEDGDMFDIGFGNINNKVLSVTKSDVSLDIVNETCKYPDFLTMANDVYGDACFFFARREQCYARHYFVRGGYFPTVSGSLVTSDAQLFNRPFWLRRAQGHNNGILWGNQMFITVADNTRNTNFTIYNTQNIREFLRHVEEYQLAIILQLCKVPLKAEVLTQINAMNAGILEEWQLGFVPTPVHDIYSKATKCAPKDKEDPFGKYTFWNVDLTEKLSLDLDQYPLGRKFIFQAGKRK Seq21 MTDDNKGGCSQWCILEISDLIDQGN-SRELLCQQESEESEQQIQLLKRKY-LSSSPRLQSIKSKRRLFDSGLELLKCSNVKAMLLAKFKEAFGVGYMDLTRQYKSSKTCCRDWVYAVQDELIESSKLLLQHCAYIWLQ--MCLYLLCFNVGKSRETVSRLLLQVAEVQMLAEPPKLRSMLSALFWYKGSMNPNVGEYPEWILTQTMINHFDLSTMIQFAYDNEYLQEDEIAYHYAKLADTDANARAFLQHNSQARFVKECAIMVRHYKRGEMKEMSISTWVHRKGHWSDIVKFIRYQDINFIRFLDIFKSFLHNPKKNCILIHGPPDTGKSMFTMSLIKVLKGKVLSFANCRSNFWLQPLADTKLALIDDVTFVCWDYIDQYLRNGLDGNVVCDLKHRAPCQIKFPPLLLTSNIDVMKEYLHSRIQSFAFPNKFPFDNNNMPQFRLTDQSWKSFFERLWHQLDLEEEFQCLSERFNVLQENLMDIYEGQEDIETQIKHWRQEQVLFYYARKNGVMRVGYQPVPPLATSEAKAKDAIGMVLLLQSLQKSPYGKEPWTLQTSLETVRSPPCFKKGPQNIEVMFDNDPENLMSYTVWSFIYYQNLD-DTWNKVEGVDYHGAYYKVYYIQFEVDAARFGGRLTRLLEEARDPPVILLRGEANKLKCFRYRAKKRYGSLVKYYSTTWSWFNTYDERELFIQKMKLPWLPTTGKVYLPPTPPVRVQSTDEYVERTNIFYHAMSRLLTVGHPFYRIEVPKVSGNQYRAFRVTLPDPNKFALADMSVYNPEERLVWACAGLEIGRGQPLGVGTTGHPLFNKLRDTENNSSYDDRQNTSDPKQVQMFVVGCVPCIGEHWDRAPVCGLCPPLELKNTVIEDGDMVDIGFGNINNKVLSFNKSDVSLDIVNETCKYPDFLSMANDVYGDACFFFARREQCYARHYFVRGGYFPTVSGSLVTSDAQLFNRPFWLRRAQGHNNGILWGNQIFVTVADNTRNTNFSIYNSQNIREYLRHVEEYQLSFILQLCKIPLKAEVLTQINAMNSGILEDWQLGFVPTPVHDIYSKATKCVEKEKEDPFAKYTFWNVNLTEKLSLDLDQYPLGRKFIFQSGKRK Seq22 MTDDTKGGCSDWFVLDISDLIDQGN-SRELLCQQQSEESEQQIHLLKRKY-FSSSPRLQSIKSKRRLFDSGLELLKCSNVKAMLLAKFKEAFGVGFMELTRQYKSCKTCCRDWVYAVQDELIESSKLLLQHCAYIWLQ--MCLYLLCFNVGKSRETVFRLLLQVAEVQILAEPPKLRSTLSALFWYKGSMNPNVGEYPEWIMTQTMINHFDLSTMIQYAYDNDLINEDEIAYNYAKLADTDANARAFLQHNSQARFVRECALMVRYYKRGEMKDMSISAWIHNKGHWSDIVKFVRFQDINFIRFLDVFKSFLHNPKKNCLLFYGPPDTGKSMFTMSLIKVLKGKVLSFANYKSNFWLQPLADTKIALIDDVTHVCWDYIDQYLRNGLDGNFVCDLKHRAPCQIKFPPLLLTSNMDIMKEYLHSRVHAFAFPNKFPFDSNNKPQFRLTDQSWKSFFERLWKQLDLEDEFQCLSDRFNALQENLMDIYEGRDDLETQIMHWRQEQILFHYARKNGVMRLGYQPVPPLATSEAKAKDAIGMVILLESLQQSAYGKESWTLQTSLETVRSPPCFKKGPQNIEVMFDNDPENLMVYTAWSFIYYQTVD-DTWNKVEGVDYYGAYYKVYYIQFEGDAARFSGRLKRLLDEARDPPVIVLRGDANKLKCYRYRAKKKHGNLVKYYSTTWSWFQSNTERELFLKTMKLPWLPATGKVYLPPTPPVRVQSTDDYVERTNVFYHAMSRLLTVGHPYYKIEVPKVSGNQYRAFRVRLPDPNKFALADMSVYNPEERLVWACAGLEIGRGQPLGVGTTGHPLFNKLRDTENNSNYDDRQNTSDPKQVQMFVVGCVPCMGEHWDKAPVCGQCPPLELKNTVIEDGDMFDIGFGNINNKVLSTNKSDVSLDIVNEICKYPDFLTMANDVYGDACFFFARREQCYARHYFVRGGYFPTVSGSLVTSDAQLFNRPFWLRRAQGHNNGILWGNQMFITVADNTRNTNFSIYNSQTLREYLRHVEEYQLSIILQLCKVPLKAEVLTQINAMNSGILEEWQLGFVPTPVHDLYSKATKCVEKEKEDPFAKYTFWNVDLTEKLSLDLDQYPLGRKFIFQSGKRK Seq23 MDDDKGTGCSGWFMLDVSDLINQGN-SRELLCQQQSEECEQQIQYLKRKY-FSPSPRLQSMKSKRRLFDSGLELLRCNNVKAVLLGKFKDAFGVSYNELTRQFRSNKTCCKHWVYAAKDELIDASKLLQQHCTYLWLQ--MSLYLCCFNVGKSRETVMRLLLQVNENHILSEPPKIRSMIAALFWYKGSMNPNVGEYPEWIMTQTMIHHFDLSEMIQWAYDQDYVDECTIAYQYARLADSNSNARAFLAHNSQAKYVRECAQMVRYYKRGEMRDMSISAWIHHCGHWQDIVKFLRYQGLNFIVFLDKFRTFLKNPKKNCLLICGPPDTGKSMFSMSLMKALRGQVVSFANSKSHFWLQPLADAKLALLDDATEVCWQYIDAFLRNGLDGNMVSDMKHRAPCQMKFPPLIITSNISLKKEYLHSRIYEFEFPNKFPFDANDTPLFKLTDQSWASFFKRLWTQLELEEEFQCLSERFSALQEKLMDLYEGVEDLETQIQHWRQEQVLFYYARRHGILRLGYQPVPTLATSESKAKDAIAMGLLLESLQKSQYAEEPWTLETSLETVKSPPCFKKGPKSVEVYFDGDPENVMSYTVWSYIYYQT-DDESWEKVEGVDYTGAYYKTYYIKFETDAKRYSGRLAQLLDAAKDPPVILLRGAANTLKCYRYRFRKKHAGSFQFISTTWSWFHTDREREKCLQQMKLPWLPTSGKIYLPPTPPVRVQNTDEYVERTDIYYHAISRLLTVGHPYFKIEVPKVSGNQFRAFRVTFPDPNKFALGDMTIHDPEYRLVWACKGLEIGRGQPLGVGTTGHPLFNKLHDTENPTERDDRRNVSDPKQVQMFIIGCIPCLGEYWDKAPVCGLCPPLELKNGVIEDGDMFDIGFGNINNKTLSFNRSDVSLDIVNEICKYPDFLTMSNDVYGDSCFFCARREQCYARHNFVRGGYFPTVSGSLVTSDAQLFNRPFWLKRAQGHNNGILWNNQMFVTVADNTRNTNFSIYDAKKIREFMRHVEEYQLSFILQLCRIPLEAEVLTQINAMNHGILENWQLGFVPTPVHDTYSKATKCPDTQKEDPFGQYTFWNVDMSEKLSLDLDQYPLGRKFLFQSGKRR Seq24 MDDDKGTGCSTWCLLDVSDLINQGN-SRELLCQQESEECEQQIQYLKRKYNISPSPRLQSLKSKRRLFDSGLELLRCKNAKAVLLHKFKEGFGISYNELTRQFKSNKTCCKHWVYGAKEELIDASKLLQQHCSYIWLQ--MSLYLCCFNVAKSRETVVKLLLQIHENHILSEPPKNRSVPVALFWYKGSMNPNVGEYPEWIVTQTMIQHFDLSRMIQWAYDNDHLDECSIAYNYAKLADTDSNARAFLAQNSQAKHVRDCAQMVKHYKRGEMREMTISAWVHHCGQWQDIVKFLRYQGLNFIVFLDKFRTFLQNPKKNCLLIYGPPDTGKSMFTMSLMKALRGQVISFANSKSQFWLQPLADAKIALLDDATEVCWQYIDMFLRNGLDGNVVSDMKHRAPCQMKFPPLIITSNISLKKEYLHSRIYEFEFPNRFPFDSDDKPLFKLTDQSWASFFKRLWIQLGLEDEFQCLSERFSALQDKLMDLYEGLEDLETQIQHWRQEQILLYYARKRGIMRLGYQPVPPLATSEIKAKDAIAIGILLESLQKSKYADEPWTLETSLETIRSPPCFKKGPKTVEVYFDGDPENVMPYTVWSYIYYQT-DEDTWEKVEGVDYTGAYFKNYYIKFEADAKRFSGRLAQLLDAAKDPPVILLRGGANTLKCYRYRFRKKHAGKFYYVSTTWSWFHSNHEREKCIQEMKLPWLPASGKIYLPPTPPVRVQSTDEYVERTDIYYHATSRLLTVGHPYFKIDVPKVSGNQFRAFRVTFPDPNKFALADMTIYDPDYRLVWACAGLEIGRGQPLGVGSTGHPLFNKLRDAENSSERDDRRNISDPKQVQMFIIGCTPCLGEYWDTAPVCGLCPPLELKNSVIEDGDMFDIGFGNINNKTLSFNRSDVSLDLVNEVCKYPDFLTMSNDVYGDACFFCARREQCYARHYFVRGGYFPTVSGSLVTSDSQLFNRPFWLKRAQGHNNGILWNNQMFVTVADNTRNTNFSIYDATKIREFTRHVEEYQLSFILQLCRIPLKAEVLTQINAMNSDILENWQLGFVPTPVHDTYSKATKCPDTQKEDPFGKYSFWNVDMTEKLSLDLDQYPLGRKFLFQIGKRK Seq25 MADDKGTGCSDFIY-DISDLINQGN-SRELLCQQEREESELQVQYLKRKC-FSPSPRLQSMKSKRRLFDSGLELLRSSNSRATLLSKFKDSFGVSFTELTRQYKSNKTCCHHWVYAAKDDLIDASKLLQQHCFYIWLQ--MSLYLCCFNVGKSRDTVVRLILQVHENHILSEPPKNRSIPAALFWYKGSLNSNVGEAPDWILSQTMIQHFDLSRMIQWAYDNDHIDESIIAYQYAKLADIDSNAKAFLAHNSQVKYVKECALMVRYYKRGEMKEMSISAWIHHCGNWQHIVRFIRYQNLNFIMFLDKFRTFLKNPKKNCLLIYGPPDTGKSMFAMSLIKLLKGSVVSFANSKSQFWLQPLADGKIGLLDDATDVCWQYIDSFLRNGLDGNLVSDIKHKAPCQMKFPPLIITSNINLLKEFLHSRVTQIDFPNKFPFDSDNKPLFELTDQSWASFFKRLWTQLELEDEFHCLSARFTVLQEKLMDIYEGVEDLDTQIQHWRQEQIIYHYARRHGVTRLGYQPVPSLASSEAKAKDAISMVLLLESLKKSKYADEQWTLQTSLEAVRSPPCFKKGPKNIEVVFDGDPENLMSYTVWTYIYYLT-DEDIWEKVEGVDYTGAYYKVYYLKFENDAKRYGGRLTRLLADAADPPVILLRGDANTLKCYRYRFRKKHAGGFRFVSTTWSWFYSESQREKFIQTMKLPWLPASGKIYLPPTPPVRVQSTDEYVERTDIYYHATSRLLTVGHPYFKIEVPKVSGNQYRSFRVTFPDPNKFALADMSVYDPDYRLVWACKGLEIGRGQPLGVGTTGHPLFNKVRDTENSSNYDDRQNTSDPKQVQMFIIGCTPCLGEYWDKAPVCGLCPPLELKNSVIEDGDMFDIGFGNINNKTLSFNRSDVSLDIVNETCKYPDFLTMSNDVYGDSCFFFVRREQCYARHYFVRGGYFPTVSGSLVTSDAQLFNRPFWLQRAQGHNNGILWGNQMFVTVADNTRNTNFTIYDSANIREYLRHVEEYQLSFILQLCKVPLNAEVLTQINAMNSGILENWQLGFVPTPVHDTYSKATKCPETEKEDPFGQYTFWNVDMTEKLSLDLDQYPLGRKFLFQAGKRK Seq26 MADDKGTGCSEWFIDNISNLLNQGN-SRDLLRQQEFEESAEQVQKLKRKY-FSPSPRLQSMKSKRRLFDSGLELMRCNNSRAKLLSKVKEYFGVGFYELARQYKSDKTCCKDWVYGVREELVESAKLLLNHCSYVWIN--MTLYLLCFNHAKSRETVGRLLLDVQLLQLICEPPKLRSVVSALYWYKGSMDSSVGAYPDWIVNQTMISHFDLSEMIQWAYDSDLTDEADIAYLYAKMANSDSNARAWLAHNNQARYLRECAQMVRHYRRGEMRDMSMSEWIHHRGHWSEIVKFIRFQEINFIIFLDAFKQFIHGPKKSCLLIHGPPDCGKSMFAMSLLKVLKGKVISFVNAKSQFWLSPLSECKIGLLDDATDPCWQYIDTYLRNGLDGNVVSDCKHKTPMQIRFPPLLITSNYNIKANFLYSRIAIFEFKHKFPFKEDGTPVFQLTDQSWKSFFERLWTQLELEDEFQCLNARFNVLQEMLMDIYEGKEDLETQIEHWRQEQALLFFARKHSIMRLGYQPVPPMAVSETKAKQAIGMMLTLQSLQKSPFGKEKWTLNTSLETYNAPPCFKKGPYNIEVIFDGDPENLMVYTAWKEIYFVDSD-DMWQKVQGVDYAGAYYKQYYVTFADDAVRYLGRLGRLLEEASDPPVILLRGDPNILKCYRYRDKKRKLGLVKHYSTTWSWFTSNSTRSQYVKIMKLPWLPATGKVYLPPSTPVRVQSTDEYIQRTDIYYHANSRLLTVGHPYFKILVPKVSGNQYRAFRLLLPDPNRFALVDMNIYNPEERLVWACRGLEIGRGQPLGVGTTGHPLFNKVKDTENANNYDDRQDTSDPKQVQMFIIGCTPCMGEYWDAAKPCGKCPPLELINSVIQDGDMIDIGFGNINNKTLSVNRSDVSLDIVNDICKYPDFLKMANDIYGDACFFYARREQCYARHFFVRGGYFPTVSGSLVSTDAQLFNRPFWLQRAQGHNNGICWENQLFITVADNTRNTNFTIYDSTKVREFLRHVEEYEISIILQLCKVPLEPEVLAQINAMNSSILENWQLGFVPTPIHDTYSQATRCPAPER-DPYEQYNFWTVDLTEKLSLDLDQYSLGRKFLFQAGKRK Seq27 MADDKGTGCSEWFLDNISELIDQGN-SRDLFRLQEFEESAEQVQMLKRKY-FSPSPRLQSLKSKRRLFDSGLELMRCSNSRARLLSKVKEYFGVGFYELARQYKSNKTCCRDWVYGVREELLEGAKLLLNHCSYVWIN--MSLFLLCFNNAKSRETVGRLLLDVQLLQLICEPPKLRSVVSALYWYKGSMDSSVGTYPDWIVNQTMLTHFDLSQMIQWAYDTDLTDEADIAYGYAKMAESDSNARAWLAHNSQAKFVRECAQMVRHYRRGEMRDMSISEWIHYRGHWSEIVKFIRFQEINFILFLDAFKQFLHGPKKSCLLIYGPPDCGKSMFAMSLIRVLKGRVISFVNAKSQFWLSPLAECKIGLLDDATDPCWQYIDAYLRNGIDGNIVSDCKHKTPLQIRFPPLLITSNYNIKDNYLYSRIVIFEFKHKFPFKEDGSPEFLLTDQSWKSFFKRLWSQLELEDEFQCLNDRFNALQDKLMDIYEGKDDLETQIEHWRQEQVLYFYARKKGILRIGYQPVPTLAVSETKAKEAIGMMLTLESLQKSPFGKESWTLNTSLETFRTPPCFKKGPINIEVRFDGDPENIMLYTAWTHIYFLDAE-EHWQKVKGVDYLGAFYKHYYIHFNDDALRYLGRLGRLLAEASDPPVILLRGDPNILKCFRYRDKKNRKGLVKCYSTTWSWFTSEQNRAEYINSMKLPWLPTTGKVYLPPSTPVRVQSTDEYIQRTDIYYHANSRLLTVGHPYFKILVPKVSGNQFRAFRLLLPDPNRFALVDMNVYNPEERLVWACRGLEIGRGQPLGVGSTGHPLFNKVKDTENSNNYDDRQDTSDPKQVQMFIIGCAPCIGEHWDAAKPCGKCPPLELVNTVIEDGDMVDIGFGNINNKTLSANKSDVSLDIVNNICKYPDFLKMANDIYGDSCFFYARREQCYARHFFVRGGYVPTVSGSLVSTDAQLFNRPFWLQRAQGHNNGICWENQLFVTVVDNTRNTNFTIYDSGKIREFLRHVEEYELSVILQLCRVPLEPEVLAQINAMNPNILENWQLGFVPSPIHDTYSQATRCPAPEK-DPYEQYTFWPVDLTEKLSLDLDQYSLGRKFLFQAGKRK Seq28 MADDKGTGCSEWFLDNISELIDQGN-SRDLLRQQEFEESAEQVQKLKRKY-FSPSPRLQSLKSKRRLFDSGLELMRCSNSRARLLSKVKEYFGVGFYELARQYKSDKTCCRDWVYGVREELLEGAKLLINHCSYVWIN--MSLFLLCFNNAKSRETVGRLLLDVQLLQLICEPPKLRSVVSALYWYKGSMDSSVGTYPDWIVNQTMLTHFDLSEMIQWAYDTDLTDEADIAYGYAKMAESDSNARAWLAHNSQAKFVRECAQMVRHYRRGEMRDMSISEWIHYRGHWSEIVKFIRYQGINFILFLDAFKQFLHGPKKSCLLIYGPPDCGKSMFAMSLIRVLRGRVISFVNAKSQFWLSPLAECKIGLLDDATDPCWQYIDTYLRNGIDGNIVSDCKHKTPLQIRFPPLLITSNYNIKDNYLYSRIVIFEFKHKFPFKEDGSPEFLLTDQSWKSFFKRLWNQLDLEDEFQCLNDRFNALQDKLMDIYEGKDDLETQIEHWRQEQVLYYYARKKGVLRLGYQPIPTLATSETKAKEAIGMMLTLESLQKSPFGKEPWTLNTSLETYRTPPCFKKGPVNIEVRFDEDPENIMLYTSWTYIYFLDAEDQ-WQKVKGVDYLGAFYKHYYINFNDDALRYLGRLGRLLAEASDPPVILLRGDPNVLKCFRYRDKKNKKGLVKCYSTTWSWFTSQQHRAQYISSMKLPWLPATGKVYLPPSTPVRVQSTDEYIERTDIYYHANSRLLTVGHPYFKIQVPKVSGNQFRAFRLLLPDPNRFALVDMNVYNPEERLVWACRGLEIGRGQPLGVGSTGHPLFNKVKDTENANNYDDRQDTSDPKQVQMFIIGCAPCIGEHWDAAKPCGKCPPLELVNTVIEDGDMVDIGFGNINNKTLSANKSDVSLDIVNNICKYPDFLKMANDIYGDSCFFYARREQCYARHFFVRGGYVPTVSGSLVSTDAQLFNRPFWLQRAQGHNNGICWENQLFVTVVDNTRNTNFTIYDSTKIREFLRHVEEYELSVILQLCKVPLVPEVLAQINAMNSNILENWQLGFVPTPIHDTYSQATRCPAPEK-DPYEQYNFWQVDLTEKLSLDLDQYSLGRKFLFQAGKRK Seq29 MAD-KGIGCSTWCLIDISDLLDELGNPQELLCLQEREESDLQLQQLKRKY-FSPSPQLESIKSKRRLFDSGLELLQCSNARATLLSKFKAAFGVSFTELTRRYKSDNTCCRDWAYGL-QDIIEGSKLFQQHCEYIWLH--ISLYLLCFKTGKSRNTVKNLLLNVGDAQLIADPPQIRSVVAALFWYKESMNKNVGEYPEWIANQTLLSHFDLSRMIQWAYDNEYTEDSDIAYHYAKLADEDSNARAFLAHNSQAKFVRECGQMVRHYKRGEMKNMSMSAWIYTRGHWSDIVKFIRFQQINFIMFLDVFKQFLASPKRNCLLIYGAPDCGKSMFCMSLIKALKGKVISFVNARSQFWLSPLVESKIALLDDATECCWNYIDNYLRNGIDGNMVSDCKHKNPVQIRFPPLLITSNNNIMSDYLHSRIKAFEFVNKFPFKDDGSPLFELTDQSWKSFFQRLWRQLDLEDEFQCLSDRFNALQDKLMTIYEASESLKDQIEHWRQEQVLFHYARQRGVLRLGYQPVPALTISEAKAKEAIAMVLHLEALQRSPYKNEKWTLNTSVETFRTPPCFKKGPKTIEIVYDGNPENTMLYTIWTHIYFED-DEGNWQKTEGLDYHGAYFKQYYIRFAQDARRFSGRLARLLDDARDPPVILLKGQANTLKCYRYRAKEKYKGYYDCFSTTWSWFTSKSQRQMFLSIMKLPWLPANGKVYLPPSTPVRVQSTDEFVQRTNIFYHANSRLLTVGHPYFDVLVPKVSGNQFRAFRLKLPDPNRFALADMSVYNPDERLVWGCRGLEIGRGQPLGVGTTGHPLFNKVLDTENPNKYDDRVNTSDPKQIQLFVLGCTPCLGEHWDTALPCGGCPPLELKNTVISDGDMVDIGFGNMNFKALSVTKSDVSLDIVDSTCKYPDFLKMSNDVYGNSCFFYGRREQCYVRHMFVRGGYFPTVSGSLVSTDAQLFNRPYWLQRAQGHNNGICWSNELFLTVCDNTRNTNFNIYDSQKIREYTRHVEEYEISLILQLCKIPLEAEILAQINAMNPNILEDWQLGFVPTPIQDAYSKATPCQPKEREDPWAPYTFWVVDLTEKLSLDLDQYSLGRKFLFQAGKRK Seq30 MADNRGIGCSNWF-SDLSDLIDEQGISRDLFRQQGSEEFEQQIQDLKRKY-FSPSPRLEAIKCKKRLFDSGLELLKCSNLRATMLSKFKNSFGVGFMELCRKFNSNKTCCRDWVYGVKEELLEGCKLLQEHCGYIWLH--MSLFLLCFKTGKSRDTVVRLLLSIHKEQLLTEPPKLRSVMAVLYWYKGSMNPNIGEYPDWIVQQTMISHFELSPMVQWAYDNDYIEDSDIAYNYAKLADEDINARAFLAHNNQAKIVRDCAWMVRHYKRGEMRYMSISKWIWYKGHWSNIVKFVRFQGINFIMFLDAFKHFLLSTKKNCILFYGPSDCGKTMFCMSLIKALGGRVISFANAKSQFWLQPLTESKIAMLDDATEACWNYLDTYLRNGLDGNWVSDCKHKAPIQIRFPPLLITSNYDILKNFLVSRIKIFEFKNKFPFNEDGTPMFELTDQSWKSFFQRLWKQLDLEDEFQCLNQRFNALEDQLMDIYEGSDKLEDQIKHWRKEQILLHYARRRGIMRLGYTPVPSLAASETKAKDVIYMVLQLEKLRDSPYKDEPWTLNTSLETYRAPPCFKKGPQNVEVIFDADPENIMLYTSWQFIYFEDTD-GHWQKTEGIDYAGLYYRHYYVEFKVDARRFSSRLKRLLDEALDPPVILLRGEPNTLKCYRYRVKDKLRGSFDKISTTWSWFVSEDQREKFINVMKLPWLSTTGKVYLPPSTPVRVQSTDSYIQRTNIYYHANTRLLTVGHPYFEVLVPKVSGNQYRAFRVHLPDPNRFALADMSVVNPDERLVWAVRGMEIGRGQPLGVGTSGHPLFNKVKDTENPNGYDDRVNTSDPKQIQMFVLGCIPCLGEHWDKALPCGACPPLELKNTIIEDGDMGDIGFGNLNFKTLSVTKSDVSLDIVNEICKYPDFLKMANDVYGNACFFYARREQCYARHMFCRGGYTPTVSGSLVSTDAQIFNRPFWLQRAQGHNNGICWGNQIFLTVIDNTRNTNFCIYNTANFREYLRHVEEYELSFILQLCKVPLEPEVLAQINAMNADILEDWQLGFVPSPINDTYSAATRCTPKEKADPFAGYHFWDVDLSEKLSLDLDQYSLGRKFLFQANKRK Seq31 M-EGK-KSFTSPFIIDIS-FIDEGN-TAQLFAQHQALDAAQEISAVKRKL-PLTGS-------KKGKLDSGYAWVNASSEKGAKLAIFKQTYGVTFASLTRVFKSDKTCCHNWVFSASEEVIEGSKQLRQYCDFYYASGHCVLYLLDFKASKNRETVIRLFLAVPDHCILSDPPKLKHVPAALFWLKTSNQPHVGQLPNWICQQTMLNYFELRKMVQWALDHNLTDDSMIAYNYAQLAEEDENANAWLNSNSQARYLKECALMTRHFLRAQRLEMTMAKWLTRCGDWKAIIKFLKYQNVNIVNFLSMFRDFMNSPKKNCLVICGAPNTGKSIFAMSLMQFLQGKTISFANHKSHFWLQPLADCKFAVLDDATLPCWSYLDIYCRNALDGNYVCDSKHKNPVQIKIPPLLITTNYNILQEYLHSRLLFLEFNNAFPLDEEGNPQFDLNDQNWKSFFIKLRRQLDLEEEV*RLEARFEEVQEKLLELLENPETLDEQIEYWKWENIYYNAARRKGIHKLGFSPVPSLQASEHGAKEAIGMKLQLTNLKSSGFGSEKWTLDTSSMTYLAPPTFKKLSKPVTVTFDSNSQNAVRYTCWSHVYYLN-DNDMYVKSYSVDVHGVYYKQYYVNFEKDARKYRSHERLHSAVKKDPYVILVRGKPNQLKCWRWRLHNRARRVFRCFSTTFSWFDTRQDRELFLHRV-TFWTPSTGRVYLPPSTPLKVLHTDDFVRRTSLFYHVKSRLLTVGNPYYKILVPKVNASQYRVFRVRLPDPNKFALSEPHIYDPDERLVWKLLGMQIGRGQPLGISSTGHPFFNKFLDTENPMAQDDRQNTSDPKQIQMFIVGCTPCNGEYWEKALPCGACPPIELKSVLIQDGFMCDIGLGAMNFKTLQDTKSGGSLDIVAEPCYYPDFLKMTDDSYGNSCFFYGKREQLYSRHYFVRGGYFTTPSGSLVTSESQILNRPYWLQRAQGTNNGICWNNQVFVTVVDTTRGTNFTIYDQNKFKQYSRHVEEFELSFIFELGVVVLSPEVLAHLHNMDKNILDDWNLGVVAPPLEDQYSQATKCPPTEKEDPYKGLTFWNVDLTDKFSSDLSQHSLGRKFLFQSNKKR Seq32 M-DPNLK-GQ-SFLDDLSDLIDQGN-SAELFAQQEAFAFQEHIRTTKRKLKLSFTSQ--SNAPKRRVLDSGYNLLAASSHRAVQLAIFKEKFGISLNSLTRIFKNDKTCCSNWVFGAREELLAASQILQRVCDSIMLLGFMGLYLLEFKNAKSRDTVRHLFLQVENNDMLLEPPKIKSLPAATFWWKLRHSSAAGNLPDWIARQTSITHFDLSAMVQWAYDHNFVDEAQIAYYYARLASEDSNAAAFLRCNNQVKHVKECAQMTRYYKTAEMREMSMSKWIKKCGDWKQIINFIKYQNINFLSFLACFRDLLHSPKRNCLVIVGPPNTGKSMFVMSLMRTLKGRVLSFVNSKSHFWLQPLNAAKIAILDDATRPTWSYIDTYLRNGLDGTPVSDMKHRAPMQICFPPLIITTNVDVAKDYLHSRLMSFEFANAFPLDENGKPALILNELSWKSFFERLWNQLDLEDEFRCLQARFDAVQEQLLEIYEDTSSLDVQITYWRHEQALYYYARRQGILRLGIYQVPPTGVSEKKAKDAIKMTLFLKSLQKSEFAAQPWSLETSLERFLAAPTFKKRGQHVTVIYDKDSMNAMEYTLWTEIYAVD-DNDQWHKYTSVDYDGIYFKNYYVSFADDAALYKGRLAELISAAYDPPVLLLQGCANTLKSFRRRTTQSYPHTFLCMSTSWTWFVNSEQRTLFLATVKIPWLPSQGKLYLPPPTAVQYLDTDDFVTRTDTFYHTNTRLLFVGHPYFKVLVPKVSGSQFRVFRLKFPDPNKFSFPDPNVYNSDQRLVWALRGIEICRGQPLGIGVTGHPSFNKFKDAENNNNKDDRVNMADPKQVQMFIVGCTPCDGEHWDVAQACGACPPIELKNTIIEDGEMCDTGFGNMNFQKLQASKSGAPLDIVNQIVKYPDFLKMGSDPHGNSMFFYAKREQMYVRHLWSRGGYFGSPSGSLVSSDQQIYNRPFWIQRAQGRNNGICWNNQLFVTAVDSTRGTNFTIYTAAEFKHYLRHVEEWEVSLVLQLCIVDLTPEALAHINGMDPRIIESWNLGFIHAPIEDQYSIATRCDAAATEDPYAKYTFWDVDLTERFSMNLDQYSLGRKFLFQIGKRR Seq33 M-DPNEK--VLSFIDDLSDLIDQGN-SAELFAQQEALAVQEHIRASKRKLKLSFTSH--SNAPKRRKLDSGYNFLRAGSRRATQLAIFKDKFNISFNSLTRPFKNDKTCCNNWVFGARDELLEASKLLQRHCDYLMLLGFMALYLLEFKHAKSRETIRHLFLQIEKEEMFLEPPKLKSLPAATFWWKISHSASAGELPDWIARQTSLSHFDLSQMVQWAYDHNYTDEPTIAYNYARMASEDSNAAAFLRCNSQVKFVKECAQMTKYYKTAEMREMSMGKWIKRAGDWKDIINFLKYQGINFLSFLASFKDLLHGPKRNCLVIVGPPNTGKSMFVMSLMKALKGRVLSFVNSKSHFWLQPLNAAKIAVLDDATKATWSYIDTYLRNGLDGTPVSDMKHRAPIQICFPPLLITTNVQVMKDYLHSRLMCFEFPNPFPLNEAGQPALILNELSWKSFFARLWRQLDL-EDFRCLQARFDAVQDQLIDIYEDTRSLEVQVTYWRREQALFYYARQQGITRLGLYQVPLTSVSERKAKEAIKMSMYLTSLQKSPFAQLSWSLETSPEMFFAPPTFKKKGSHVTVVYDKDSNNAMEYAVWGEVFYVD-ETDTWHKAQSVDYDGVYYKVYYVHFADDAAHYSRRLAQLIESANDPPVLLLQGCANTLKCFRRRTSHAHPHKFLCMSTSWTWFSDYEQRRCFLAGVKLPWLPSSGKLFLPPPTPVNILNTDDFVTRTDTFYHASSRLLNVGHPYFEVIVPKVSGSQFRVFRLQLPDPNKFTFQTPNVYNPEQRLVWALKGIEICRGQPLGIGVTGHPSFNKFRDAENLNNNDDRVNTADPKQVQLFIVGCTPCEGEHWDVAESCGACPPLQLVNTLIQDGEMCDIGFGNINNKALQATKSDAPLDIVDQIVKYPDFLKMSSDLQGNSMFFYAKREQLYLRHLWARGGYFGGPSGSLVSSDQQIFNRPFWIQRAQGNNNGVCWHNQLFVTAVDSTRGTNFTIYKSTDFNHYLRHVEEWELSCIMQLCIVDLKPETLAHLHNMDPRILETWNLGFIQPPIEDQYSLATKCETAEKEDPYAKYKFWDVNLTERFSSNLEQYSLGRKFLFQIGKRR Seq34 M-DP--K-TVLDFIEDISDLIDQGN-SAELFAQQQAFDFHKDICTTKRNLKRSLTSQ--SNAPKRRLLDSGYNLLRAGSRRAAYLGVFKEKFTISFTALTRIFKNDKTCCRNWVYRAREELLEASKILQKCCDFILLLGFLALFLLEFKTAKSRETVQRLFLQVEKEDMLLEPPKLKSLPAATFWWKIQHSNNSGTLPDWIARQTMISHFSLSVMVQWAYDHNYTEESTIAYHYAKLASEDSNAAAFLKCNNQVKHVKECAQMTRYYKTAEMTEMSMGQWIKKCGDWKQICKFLKFQNVNFLSFMSALKDLLHRPKRNCMVICGPPNTGKSMFVMSFMKALQGKVLSFVNSKSHFWLQPLRGAKVAVLDDATRATWTYFDTYLRNGLDGTPVSDMKHRAPLQICFPPLVITTNVNVMQDYLHSRIVCFEFPNTFPLDEAGNPLLLIDELSWKSFFERLWTQLDLEEDFRCLEARFDAVQDQLLQVYEDSNTLELCLQYWRRENALYYYARQQGKTRLGLYTVPPTRVSEQKAKDAIKMYLCLESLQKSEFANQRWSLDTSIETFKAPPTLKKRGQHVTVIYDQNAMNSMVYTLWKEVYYVD-ETETWHKTSSLDYDGIFYKIYYVNFQDDAALYSSRLAQLIDAAYDPPVLLLQGAANTLKCFRRRATQAHPHKFLCMSTSWTWFSNSEQRNCFLASVRLPWVPNSAKLYLPPPTPVQFLDTDEFVTRTDIFYHTSTRLLFVGHPYFTVVVPKVSGSQFRVFRLKFPDPNKFSFPTQDIYNPEQRLVWAVRGIEICRGQPLGVGVTGHPSFNKFKDAENLNQNDDRVNVCDPKQVQLFIVGCVPCDGEHWDKAQACGDCPPIELKNTKIQDGEMCDTGFGNMNFAALQASKSGAPLDIVDQIVKYPDFLKMGNDPYGNSMFFYAKREQMYVRHLWARAGYIGSPSGSLVSSDQQIYNRPFWIQRAQGSNNGMCWNNELFVTAVDSTRGTNFSIYTATKFKHYLRHVEEWDLSLIMQLCIVNLTPESIAYLHNMNESIIENWNLGFIQPPIEDHYSLATRCDTQVKEDPYKDLKFWDVDLTEKFTTNLNQHSLGRKFLFQIGKRR Seq35 MAEDKGTVSGSWYLDDDPEFISEGNSSELLHNNHMLAKDGEQIQLLKRKY-MSPSPRLALVSSKRRLFETKDKILKSKNQKATALAQFKEAFGVSFTDLTRSFISNKTCTQHWVFGPNSDILDGTGLLEPHCTFLLKCGPIILLLIEFKASKCRDTVQNLLMRVEHHQMLLEPPKIRSQLTAFFFYKKTMAGGCGKLPDWLTRLTVLSHFELSRMVQWAYDNDMLEDSEIAYYYAQHADVDSNAAAWLKTNNQAKYVRDCGNMVRLYKQQEMKNLTMSEYIYKRGDWKHIFKLLRYQDVNMIQFLTSFRDLLSCPKRQCLVIYGPPDTGKSYFLYSLISFLKGKVISFTNSKSHFWLQPLLNAKVALLDDATKACWNYMDCYMRTALDGNAVSDSKFKAPVQVRLPPLLISTNVELPLLYLHSRTMCYCFAKPCLYDDEGNPLFNLTDRHWKGFFLHLEQQLGL-SEFRCLAEHLDACQEQMLELIEDSQELEDQIDYWKRENLLLFAAKEAGLSRLGYEPVPPTKVSEGKAKNAIMMSISLQSLQSSEFGRDPWTLQTSLEVFMSNPCFKKNGEHVEVLFDGDKNKAVIFVKWGEVYVQDLLG-AWHKCPSVVYEGIYYRTFYLRFEEEAAKYAEGGGHTSTAPGHYPVLIGKGRPNCLKCWRNRFGVSHKGLFLDCSSTFSWFETEQQLQTFVDTVRPTWNNSS-KVYLPPTQPIRVLSTKEYVQTTGYYYHGQSRLITVGHPFYKIVVPQVSANQLRAFRIKLPDPNKFVFADPNFYNPEHRLVWLLKAIEIGRGGPLGVGCTGHPFFNKI-DTENPNKYDDRMHTSDPKHCQMFVVGCKPCIGSHWGLAKSCEHCPPIQLVNSFIEDGDMGDIGLGNMDFLSLQEDRSCAPLEIVTKKCKFPDFLKMQAEASGDSMFFYGRKESLYARHMFSRVGYFATPSGSLNASDSQLFNRAYFLRNSQGPNNGVLWNNEMFVTTMDNSRNTNFTIYDATRIKYYMRHVEIYELMFVLEVGKIELNGTVLAHINAMNPSVIDSWNLGFVPMPTEDTYSLATKCVP-EK-DPYDGYSFWEVDCTEKMTMELDQYPLGRKFLAQR-KKR Seq36 MEDKDNKYNAIDFIDDISDLIDEGN-SLALLNKQQLEDDTQQLKILKRKYFSPSSPRLQQLK-RRLVFDSGLGLLHSSNREATAYAKFKATFDVGYKELTRPFISNKSCCCSWIFGVVAEILEAAKLLQPHCEYLQIIGVTVMMLFQFYAAKCRDTVINLLLHVREWQIITNPPKHRSVAVALYFYKTSMSNVSGAMPEWIKKQTLVNHFEFATMVQWAYDNNIRDEAEVAYGYASLADDDTNAAAWLKCNNQFKYVKDCVQMVAMYKRYEMRNMTIGQWLVKCGNWKNIINFLKYQEISIVAFLTTLRYFLQGPKKNCLAIWGPPDTGKSMFCYSLIKYTQGKVVSFVNSRSQFWLQPLVDGKIGLIDDATFACYQYMDVYMRNGLDGNAVSDVKHKAPIQLKLPPLLLTSNIEVHAEYLHSRIQEYKFPNKLRLDANGNPIITITDADWKSFFSKLWKQLDL-DDFRCLVDRFDAVQDRLLGIYEGRTDIDGQIVHWRQEYVLLHYARKNGISNLGIQKVPSLATSESKAKQAILMGIVLKSLKNSAFGNEQWTLDTSYELFMTPPTFKKQGKTIEVWYGNNEDNAVPYTLWTAIYYQDAQ-DNWYKARGVSADGLYYKHFYVRFADEATQFRSRLARLQDEARDPPVILIKGPANTTKCWRYRMKQNYRALFESISTAFSWFRNNSQRERFLHTVKLPWLPQTGKIYLPPAPPVRSLTTDEYVVETSVFFHASSRLVTVGHPYFTVEVPKVSGNQFRAFRLRLPDPNKFALIDKQLYNVEERLVWRLRGIDIGRGGPLGIGSTGHPLFNKLKDTENPNGYDARQNVSDPKQTQLFVVGCTPCKGEHWDVAPACGTCPPLQLVSSVIQDGDMCDIGFGAMNFAALQADHSGVPLDIVADTCKWPDFLGMANDRFGDGMFFFGRREQVYARHYFTHNGYFATPSGSLASSDSQLFSRPYWIERAQGANNAVLWNNNVFVTLVDNTHNVNFTIYDSAKYKHYLRHVEEFEISLIVQLCKVSLTPDVLAHIHLMNPSILDEWQLGI-AAPIETTYSLATMCPPKEKEDPYDKMSFWVVDLSDRMSSELGAYPLGRRFLHQTGKRR Seq37 MEDKDTKANACEFIEDISELIERGN-PQALLNRQQLEEDSQLLTVLKRKYVSPSSPRLEAL-SKRRLFDSGLGILHSSNRQATALTKFKNVFGVGYKEITRPFQSNKSCCHSWVFGVVAEMLEAAKLFKVHCDYLQIIGVTVLCLFEFSSSKCRDTVQKLILNVQEHQIITDPPRHRSVPVALFFYKQSMSNTSGTMPDWLKRQTMLNHFELSHMVQWAYDNNIWDEAELAYQYACLADVEPNAAAWLKSNQQYKYVSDCAKMVRMYKKYEMQQMSMAQWIKKCGDWKKIINLLKYQEISVIAFLTSFRMFLKGPKKNCIALWGPPDTGKSMFCYSLIRYVKGKVVSYANSKSQFWLQPLTDAKLGLIDDATFPCFQFMDVYMRSALDGNEVSDCKHRVPVQIKLPPLLVTSNIDMHSEYLQSRITSFKFPHKLPLDTNGNPIFIITDTDWKCFFSKLEQQLDL-EDFRCLVERFDAVQEKLLGLYEGRSDLESQIQHWRQEHVLLNYARRQGIQTLGLQKVPPLAASESKAKTAIMMQLVLKSLKKSQYGQEPWTLDTSHELFTTAPTFKKGGVTVEVLFDNEEEKAMPYTLWRLIYYQDAD-ELWHKTHGVDADGLYFKHYYVHFTKEAVKYPSRLARLQAEAWDPPILLVKGPSNTLKCWRYRCRIKYRGLFQCISTAFTWFTNTSQREKFIQTVKLPWLPQSGKLYLPPSQPVKILPTDEYVVETSIFFHASSRLLTVGHPYFTIVVPKVSGNQFRAFHILLPDPNKLALVDSNIYNAEERLVWKLRGLDIGRGGPLSVGSSGHPLFNKLKDTENPNSYENRQSVSDPKQTQLFVVGCTPCLGEHWDAAKPCGACPPLELVSSIIEDGDMCDIGFGAMNFATLQEDHSGVPLDIVASKCKWPDFLRMANDTYGNQMFFFGRREQLYARHFFARDGYYATPSGSLVSSDSQLFGRPYWIQKAQGPNNSVCWRNNLFITLVDNTHNVNFTIYAATSYKHYLRHVEEFEIALILQLCKVPLTADVLAHIHVMDPAIIEDWQLGFVPPPIGDTYSLATRCPPKEKQDPYDKLSFWKVDLTERLSSDLDRYPLGRRFLSQTGKKR Seq38 MAEGGERLDAGWFVVNVSDLIDN-EGHAGVLNQQLLEESEQQTAYLKRKYCTPSSPRLQAVHSKRRLFDSGI-LQGSNQEATIL-AKFKGCFGVSLKELTRPYKSSKTCCNEWVFGIREELLTASKLLQPHCDFFLADGYVCLYLITFKAAKNRETITKLFLNCYDYQLRADPPKNRSVAVALFFYKLGLSGGCGDFPPWLAKQLLVSHFELSKMVQWAYDNDHTDESEIAFHYACLADEDSNAAAWLKSNAQAKYVADCSKMVRHYKKQEMRNMSMSQWIYRCGDWTVVAKYLKYQGVSFLGFLTALRHLFEGPKKQCLLIYGPPDTGKSWFCFSLLNFLRGKVVSYQNSRSHFWLSPLADCKVGMLDDATHACFQFIDVNMRSAFDGNYVSDCKHKAPIQIKLPPMLVTSNVNLPGEYLHSRVTGFEFPRKFPIDQDGSPVFSLTHSVWKAFFKRLHHQLGLEDEFRCLARRFDVLQEVMLHHYEGSKKLTDQCNYWRRESALLHYARKNRITRLGITPVPSLATSENSAKKAIRMSLLLSSLCNSPYKDEPWTLDTSIELWECPPCFKKGGFTVDVLFDNDPGNVFPYTAWAYIYYQN-QNDEWIKVPGVDYDGLFYKRYYQTFAKDALRFEPEPGRSQEQNRPFPVILVKGDGNASKCWRRRLRLRYSGLFESISTGFSWFNSDGQRDHFLKNIKIPWIQSQGKLYLPPPKPVKVIHTDDFVRPTAIYYYGSTRLLTVGNPYFTIIVPKVSGNQYRAFRLKLPDPNKFALTDPTVYNPDERLVWRLTGIEIGRGGPLGFGTTGNFLFDRLQDTENPNNTDDRQNVSDPKQTQLFVVGCTPCKGEHWDQAPRCGDCPPLKLVSSIIEDGQMCDVGFGAMNFKVLQFNKSGVPIDIVDTVCKWPDFLQMNNEKYGNSCFFFGRREQVYCRHMFVRGGYVGTPSGSLVTSDSQIFNRPYWIRQAQGHNNGVIWNNNLFVTVVDNTRNTNFTIYKSEEMRNYVRHVEEYELTIIVQLCIVPLEPEVLTHLNTMDPTILENWNLGVIPPPLESNYSLATMCPPKKKEDPYDALNFWTIDMTEKLSLDLEQFSLGRRFLYQYTKKR Seq39 M-DAVNK-G-WCFIE----FLDDRGNHLALFTQQLFSEDDQHIAALKRKYAATPSPRLHSCQSRRRLFDSGIGLLRMSNRVAASLARFKDAFSVSFSDLTRSFKSDKTCSVNWVFGAREPLLEALLVLKPQCDYFQTVRRVDIILFEFKVGKSRNTLRKQMLGLDEKLIMADPPNHRSTLAALFFYKKVLFGAAGQTPAWIAQQTILEHFDFSKMVQWAYDNQLIEESEIAYRYACEAETDANAQAWLKCTNQVKHVRDCCAMVRLYKRQEMRDMTMAQWVRKCGDWKTIAGFLRYQEVNMVLFLTALRHMFKGPKKHCLVISGPPDTGKSYFCNSLNTFLHGRVISFMNSKSQFWLQPLVDAKMGFLDDATNACWTFMDIYMRNALDGNPMQDMKHRAPLQLKLPPLLITTNVDVMHNYLHSRLQCFAFEKPMPLNNDGHPQFPLHAANWKSFFTRLAKQLGIEEEFRCLEARFDAVQEQILSLYEGSTDLADHVKYWRAEGALEYCARQSGRNKLGLHSVPSSVGAESKAKKAIHMQMVLSSLQDSSFGSEPWTMDTSLEMYERTSTFKKKGSEAEVTFGSDPDNSVSYMVWGCVYTQD-EDGQWHKHMSVDYYGVYYRVYYHQFDEDSKTFSCQRRVPNPVHCDPPVILLKGPTNTLKCWRNRLRRRHFRPFIKISTAFSWFLDSQQRDVFLKTVPIPWTPQNGKLYLPPTAPVRVQNTDEYVVPTGLYCHAMTRLLTVGHPYFKVTVPKVSGNQYRVFRLKFPDPNRFALPRDDFYNPEERLVWRLRGLEISRGGPLGIGTTGHPLFNKLGDTENPNKYDNRQNTSDPKQTQMFIVGCVPCTGEHWDVARACGDCPPIQLVNSTIEDGDMCDIGFGNMNFKALQQDKSGAPLDIVATKCKWPDFLKMTNEVYGNSLFFFGRREQLYARHYFTRNGYFGTPSGSLVSSDGQLFNRPFWLQRAQGNNNGVCWSNELFVTMVDNTRNTNFTIYDSQNFKQYLRHVEQFELSLILQVCKVPLQPNVLAHINTMNPDILEDWNLGFVPPPITDDYSLATRCPPKEKEDPYKGLVFWEVDLTERFSQDLDQFPLGRKFLFQTGKRR Seq40 M-D-NDK-YRWAFLD----FLDQGN-SLALLTSQLFEQDEQHITALKRKYVTTPSPRLNAVTSRRRLFDSGVGLLRANNVYNACLARFKEAFGVGFTDLTRSFKSNKTCSQHWVFGAPETLIEAAKQLGEQSLFLQHQKRVDLFLFQFKAEKCRLTLTKQVLGVAERLVLAEPPNCRSNLAAFYFYKKTLGKEPGSSPEWIVKQVLIEHFDFSKMVQWAYDNNYVEESEIAYNYALEAETDSNAESWLKTTSQVKYVKDCAQMVRMYKRQQMREMSNTQWIRKCGDWKVIAAFLRYQEVNLVMFLSALRNMLKGPKRHCLVITGPPDTGKSYFCTTLVSFLKGRVISFMNSKSQFWLQPLADAKIGFLDDATHTCWTYMDTYLRNALDGNPVQDMKHRASIQLKLPPLLITSNIDVMNMYLHSRLQSFEFTKRMPLDSKGQPEFVLSAANWASFFTRLAKQLGLEEEFRCLQDRFDALQEQILNLYEGSSLLQDQITFWRKEGALEFFARKQGFTRLGLHVVPSQASAEAKAKGAISMSLYLTSLQESPYGSEKWTMETSREMFEQTETFKKGGVHVQVYYDCDKKNEVSYVLWTSVYVQN-EEGFWQKHSSVDYYGVYFKSYYHNFEKDSVKYLGRYGRLIADARDPPIVLVKGPTNALKCWRNRLRRRFHKPFQNVSTAFSWFLDEHQRSVFLETVPLPWLP-NGKLYLPPTAPVKVQSTDEYVIPTTLYCHANTRLLTVGHPYFKVTVPKVSGNQYRVFRLKFPDPNKFALQQLDIYNPEERLVWRLRGIEIGRGGPLGIGTTGHPLFNKLGDTENPTKYDNRQNTSDPKQTQMFIVGCVPCTGEHWDVARPCGDCPPIQLVNSVIQDGDMCDIGFGNMNFKTLQEDKSGATLDIVSTMCKWPDFLRMTNEIYGDQMFFFGRREQVYARHYFTRNGYFGTPSGSLVSSDGQLFNRPFWLQRAQGNNNGICWNNELFVTLVDNTRNVNFTIYDSQNFKQYLRHVEQFELSVIVQLCKVPLEPSVLAHINVMNPNILEDWNLGFVPPVISDDYSLATRCPPKEKEDPYKDLSFWEVDLTERFSQDLDQFPLGRKFLFQTGKRK Seq41 MAD-KGTGCSGWYIVETNSFIDQGN-SLSLFHEQLFLSSEEQIACLKRKYAATPSPRLESVSSRRKLFDSGIGLLKSSNIYATCLSKFKTAYGCSFAELTRQFKSDKTCSPHWVFGAPEQLVEASKLLPQHCEYAQLSSKVLLFLFEFKASKNRETVRKLLLGVQECLIIAEPPKERSVLSALFFYKKVMFQGSGQLPEWVAKQTLVEHFDLSRMIQWAYDNDYAEESAIAYNYALYAEADANAEAFLKSNCQAKYVKDCATMVRLYKRQEMRDMSMSQWVKKCGDWKVIAAFLRYQEVNVVLFLAALRHLFLGPKKHCLVIYGPPDTGKSYFCTTLVGFLKGKMISFMNSKSQFWLQPLVDSKIGFLDDATTACWQYMDVFMRNALDGNPISDMKHRAPTQIKLPPLLITTNVNVQANFLHSRLQFFAFNKPMLFDDSGNPQYPLSKANWRSFFTRLGKQLGIDDEFRCLTERFDAVQDQILNLIEGSTALADHVLYWRKEGVLQYYARKHNMNRLGLHALPCQLGAETKAKKAIQMGMLLRSLLDSKYGSEPWTVETSLELYEQTDTFKKGGQTIEVHYDNDPENSVAYTLWKYLYKQD-EAGQWHKLEGVDYAGLYYHVYYEEFCEDSDRYFGGYPEVLGRSGDPPILLLTGPANCLKCWRNRVKRRPHRLYKRMSTCFSWFTDALQREQFLRTVPLPWMQPSGKLYLPPAKPIRVQSTDEYLQPTSFYCHASTRLLTVGHPYFKVVVPKVSGNQYRVFRLKLPDPNKFALAQQDLYNPDERLVWRLRGLEIGRGGPLGVGTTGSPLFNKLGDTENPNKYDNRQNLSDPKQTQMFIVGCAPCTGEHWDIAKPCGDCPPIQLVNTVIQDGDMCDIGFGAMNFKTLHDDKSGVPLDIVNSACKWPDFLKMTNEVYGDRMFFFGRREQVYARHYFVRNGYFATPSGSLLSSDGQLFNRPFWLQRAQGNNNGICWGNDLFITLVDNTRNVNFTIYAATNFSQYLRHVEQFEISCIFQICKVPLDADVLAHLNVMNPDILEDWNLGFVPPPIHDEYSLATRCPPKEKDDPYKDYTFWTVDLTERFSQELDQFPLGRKYLYQSGKRR Seq42 MADKGTD-GNNWYIVNISNLIDQGN-SLALYNAQINEDCDNALAHLKRKYNKSPSPQLQAVHSKRRLFDSGIFLLQSSNRRATMLAKFKEWYGVSYNEITRIYKSDKSCSDNWVFRAAVEVLESSKVLKQHCTYIQVK--SALYLVQFKSAKSRETVQKLMLNIQEYQMLCDPPKLRSVPTALYFYKHAMLTESGQTPDWIAKQTLVSHFELSRMVQWAYDNNYVDECDIAYHYAMYAEEDANAAAYLKSNNQVKHVRDCSTMVRMYKRYEMRDMSMSEWIYKCGDWKPISQFLKYQGVNILSFLIVLKSFLKGPKKNCIVIHGPPDTGKSLFCYSFIKFLKGKVVSYVNRSSHFWLQPLMDCKVGFMDDATYVCWTYIDQNLRNALDGNPMCDAKHRAPQQLKLPPMLITSNIDIKQEYLHSRIQCFNFPNKMPILDDGSPMYTFTDGTWKSFFQKLGRQLELEEEFRCLVARFDALQEAILTHIEQESTLESQIQYWRKENAIMHYARKQGLTKLGLQPLPTLAVTEYNAKQAIQIHLTLQSLLKSPFASERWTLDVSAELINTSPCLKKGGYDVAVWFDNDRQNAMLYTNWDFLYYQDMN-EQWHKVKGVDYDGLYFRAYFTLFSSDAQRFLSRLGLLQAEARDPPMILLKGTANSLKCWRYRKVNSNCCNFLFMSTVWNWFDSTDQRDAFVKHNLFPWLSTTGKVYLPPAQPVRVLETDEYITGTSLYFHAGTRLLTVGHPYFKVLVPKVSGSQFRVFRFNLPDPNRFALIDNGFYDSDERLVWKLRGIEIGRGGPLGIGTTGHPLYNKFGDTENPNGYDNRQDVSDPKQTQMFIIGCTPAIGEHWDKAEPCGDCPPIELVNSYIQDGDMCDIGFGAFNFKALQADKSSAPLDVIATVCKWPDFLKMGKDIYGDSLFFFGRREQLYARHFFVRAGYVGTPSGSLVSSESQLFNRPYWLNRAQGTNNGICWDNQLFVTLVDNTHNTNFTIYKASDFKQYLRHIEEFEMEFIFQLCKVPLTADVMAHLNVMNPNILDNWQLNFVPPPIEDQYSRATRCPATEKEDPYKDLSFWVVDLSERFSSELSQFSLGRRFLYQSGKRK Seq43 MADKGTE-GSSWYIVNVSNLIDQGN-SLALYNAKITDDCDNAIAHLKRKYNKSPSPQLQAVNSKRRLFDSGIFLLQTSNRRATMLAKFKDWYGVSYNEITRVYKSDKSCSDNWVFRAAVEVLESSKVLQQHCTYIQVK--SALYLLQFKSAKSRETVQKLMLNIQEFQILTDPPKLRSVPTALYFYKQAMLTESGQTPDWIAKQTLVSHFELSKMVQWAYDNNLLEECDIAYHYAMYADEDANAAAYLKSNNQVKHVRDCSTMVRMYKRYEMRDMSMSEWIYKCGDWKPISQFLKYQGVNILSFLIVLKSFLKGPKKNCIVIHGPPDTGKSLFCYSLVKFLKGKVVSYVNRSSHFWLQPLMDCKVGFMDDATYVCWTYIDQNLRNALDGNPMCDAKHRAPQQLKLPPMLITSNIDVKQEYLHSRVQCFSFPNKMPFLDDGSPMYTFTDATWKSFFQKLGRQLELEEEFRCLVARFDALQEAILTHIEQDDTLESQIRYWRKENAIMHFARKQGLTKLGLQPLPTLAVTEYNAKQAIQIHLTLQSLLKSPYGSERWTLEVSAELINTAPCLKKGGYDVSVWFDNDRYNAMVYTNWDYLYYQDVN-EIWHKVKGVDYDGLYFRAYFTLFSTDAHRFLSRLGQLQAEARDPPMILLKGTANSLKCWRYRKQNSSNCGFLFMSTVWNWFKSPGQRDSFVKHNLFPWLSAKGKVYLPPAQPVRVLETDEYITGTSLYFHAGTRLLTVGHPYFKVLVPKVSGSQYRVFRFYLPDPNRFALIDNGFYDSDERLVWRLRGIEIGRGGPLGIGTTGHPLYNKFGDSENPNGYDNRQDVSDPKQTQMFIIGCTPAIGEHWDKAEPCGDCPPIELVNTYIEDGDMCDIGFGAFNFKALQQDKSSAPLDVVATMCKWPDFLKMSKDVYGDSLFFYGRREQLYARHFFVRAGYIGTPSGSLVSSESQLFNRPYWLNRAQGTNNGICWDNQLFVTLVDNTHNTNFTIYKAGDFKQYLRHIEEFEMEFIFQLCTVPLTADVMAHLNVMNPNILDNWQLNFVPPPIEDQYSRATRCPSTEKEDPYKDLSFWGVDLTERFSSELSQFSLGRRFLYQSGKRK Seq44 MADKGTE-GSSWYFVNISNLIDQGN-SLALYNTQITDACENAIAALKRKYTKSPSPQLQAVKSKRRLFDSGICLLQDNNRRATMLAKFKDWYGVSYTEITRLYKSDKSCSDNWVFKAPVEVLESSKVLQQHCQYIQVK--SALYLLQFKSSKSRETVYKLLLNIQEFQILADPPKLRSVPAALYFYKHALLTECGQTPDWIAKQTIVSHFELSRMVQWAYDNNHLEECDIAYHYALYADEDANAAAYLKSNNQVKHVKDCSTMVRMYKRYEMREMSMSEWIHKCGDWKPISHFLKYQGVNILSFLIVLKSFLKGPKKNCILIHGPPDTGKSLFCYSLIKFLRGKVVSYVNRTSQFWLQPLMDGKIGFLDDATYVCWTYIDQNLRNALDGNPMSDAKHRAPQQLKLPPMLITSNINVKQEYLHSRVQSFEFPNKMPFLDDGSPLYTFTDATWKSFFEKLGRQLDLEEEFRCLVARFDALQEEILTHIEGNTTLESQIKYWRKENAIMHYARKQGLTKLGLQPLPSLLASEYNAKQAIQIQLTLLSLLKSPYASEPWTLEVSAELINTPPVLKKGGYDVTVWFDDDRNNTMVYTNWTALYYQDAN-EIWHKVKGVDYNGLFFRAYFTLFSTDAERYLSRLGQLQADARDPPMIMLKGHANSLKCWRYRKLTSNSCGFLYMSTVWNWFTSTDQRDYFVKHHFFPWLSTTGKVYLPPAQPVRVLETDEYITGTSLYFHAGTRLLTVGHPYFKVLVPKVSGSQYRVFRFQLPDPNRFALIDNGFYDSDERLVWRLRGIEIGRGGPLGIGTTGHPLFNKYGDTENPNAYDNRQDVSDPKQTQMFIIGCIPAIGEHWDKAEACGDCPPIELVNTYIEDGDMCDIGFGAFNFKALQDDKSSAPLDVVGTLCKWPDFLKMSKDIYGDSLFFFGRREQLYARHFFVRAGYFGTPSGSLVSSESQLFNRPYWLNRAQGTNNGICWDNQLFVTLVDNTHNTNFTIYKANDFKQYLRHIEEFEMEFIFQLCRVPLTADVMAHLNVMNPNILDNWQLNFVPPPIEDQYSRATRCPPAEREDPFKDLSFWTVDLSERFSSELSQFSLGRRFLYQSGKRK Seq45 MADPNKGH-SEWYVVIVSNLIDEGN-SLALYNEQLTEDCNRAILALKRKLTKTPSPRLEAVQSKRRLFDSGLGIFRSTNRKATLLAKFKEYFGVAYGDLTRPFKSDRSCCENWVCAAAEEVIEASKVMQQHCDFLQVI--YALYLVKFKTAKSRDTIMKLFLNVQEQQLMCDPPKSRSTPTALYFYRRSFGNASGPFPDWLAKLTMLDHFELAQMIQFAYDNNLTTESEIAYKYALLADSDANAAAFLKSNQQVKYVRDCYAMLRYYKRQEMKDMSISEWIWKCGNWKLIAQFLRYQEVNFISFLCALKTLFKGPKRNCLVFWGPPDTGKSYICSSLTRFMQGKVVSFMNRHSQFWLQPLQDCKLGFLDDATFQCWQYMDVNMRNALDGNHISDLKHKAPLQIKLPPLLITTNVDVENEYLKSRLVFFKFPNKLPLKENDEVLYEITDASWKCFFIKFASHLELGDEFRCERS-DALQEQI-LNLYEDSKDIQAQIQYWRKLYVTYYYARKEGYSHLGLQPLPALQVSEYKAKQAIEMGLLLTSLSKSQYASELWGLDTSAELLLTPPTFKKKGYTVNVWFDNNENNTFPYTNWEYIYYQD-DIEQWHRTRGVDYNGLYFRAYFLLFDSDAQTYISRLARLQGEARDPPILLIKGLANSLKCWRYRLKKY-TRYFKCMSTVFRWFNDTTQRDVFMKLVTLPWLQTAGQLYLPPSKPVRVLSTDEYVQPTNLVFHTGTRMLIVGHPYFNITVPKCSGNQFRVMRLLFPDPNKFAMIDRAVFNPEERLVWRLEGLEIGRGGPLGIGTSGHPLFNKYGDTENPAAYDNRMDVSDPKQMQLFIVGCKPATGEHWDIAKPCGSCPPIKLTQSIIQDGEMCDTGFGNANFITLQEDKSGVPLDITNEICKYPDLLKMTKDIYGDAVFFFGKREQIYSRHYFVRGGYFPTPSGSVASSDNQLFNRPYWLHRAQGANNGICWGNQLFITIVDNTRNTNLSIYKQEDFRQYLRHTEEYEVELILRLCKVPLNPDVLAHLNVMDKNILEDWQLSFVPPPIEDAYSQATMCPNTEREDPYKQYTFWTIDLQERFSNELSQFSLGKRYLYQYGKRK Seq46 MAELKGTI-NELFDNTISNLIDQGN-SHALLNAQLSEEYDKDLVTVKRKFYATPSPRLSAVQSKRRLFDSGI-LLHSNNRRAALLCKFKEKYGIPFNEITRTFKSNKSCTQNWIFACAEDLIEASKTMQNHVSYLQMI--SALYIICFKAAKSRETVVKLILNTKEEQVLCDPPKIKSMAAALYCYKKVIADTCGDFPDWIATHTVINHFKFSDMVQWAYDNDMLDEAAIAYNYACYASENENAAAFLQTNSQLKYVKECCAMVRLYKKQEMRNMTMPEWIKSCDDWKVIVRYLKYQNINFLEFLLALKLLLKGPKKMCLVIYGPPDTGKSYFCYQFIQFMRGKVVSFMNKNSHFWLMPLLDSKIGFLDDATQCCWMYLDTHMRNAFDGNAVSDVKHKNLQQIVLPPMLITTNCDVCRDYLRSRLTCFNFPNKLPLYENGEPKFKFTDNCWTSFFSKFWKHLDLDPDFSCLSARFLAQQDIQLNLIEDSKNLKDHIDYWRKEQVLAFYAKKENMSRLGLQPLPPAKVSEQKAKDAIRIQLLLQSLYKSDFGSEPWTLECSLEMLNAPPCFKKQPFTVTVQFDNDPKNVYPYICYEYIYYQD-DRDKWHKVKGVDHNGLYFSVYFKLFQPDATVYLSRLRRLQEEARDPPVLIITGQQNNLKCWRYRFSQKYADLYECCSSAWKWFKNPEQRLSFLNTVGLPWSSTSGKLYLPPSTPVRVLSTDEYVKETDVYFHARSRLLIVGHPYYDIKVPKVSANQYRVFRCELPDPNKFALIDTTLYNSDERLVWKLVGIEVGRGGPLGVGSTGHPLFNKVGDTENPSFYDERQNLSDPKQTQLLIVGCKPAVGEYWDLAEPCGKCPPIQLVNSYIQDGDMGDIGFGNANFPKLQQDRAGVPLDIVDSISLWPDLLKMTKDVYGDHVFFYAKQEQLYARHLFTHAGYFTTPSGSLNTSSSQLFNRPYWIRRAQGTNNCICWGNEVFVTVFDNTRNINFNIYSKDDFNQYSRHTEEYELEFIFQLCKVGLDADILAHLNVMDPRILENWQLAYVPPAIGDTYSDATKCSSAEVVDPYKEYTFWNVNLTEKFSSELDQYALGRKFLFQTGKRK Seq47 MADHKGTLDGSWCLIVVSNLLD------SIIQGN-SEESDRCIQELKRKLNVTPSPRLSAVASKRRLFDSGVVILRSNNIRATVLCKFKDKFGVSFNELTRSFKSDKTCTPNWVIGIREDLRDACKLLQQHVEFLEMI--SVLLLVEFKVTKNRETVLKLMLNAKEEQILCEPPKLKSTAAALYFYKKIITDTCGTLPSWVSRLTIVEHFSLSEMVQWAYDNDFTEEASVAYNYACYATENTNAAAFLASNMQVKYVKDCVAMVRMYKRQEMKSMTMSEWISKCEEWKEIVQFLKYQGVNFLEFLIALKQFFKCPKKMCIVIYGPPDTGKSMFCFKLVQFLKGQVVSYINKSSQFWLMPLQDAKIGLLDDATHNCWIYLDTYLRNAFDGNTFCDIKHKNLQQTKLPPMIITTNVNVTTDYLRSRLTCFNFPNKLPMSDKDEPLFTISDKSWTCFFRKFWNQLELDA-FCCLSTRFAAQQEIQLTLIEESYDLKDHLAYWRLENVIAYYARKEHITKLGLQPLPTLAVTEYKAKEAINIQLLIQSLLKSEFALERWTLETSAETINSSPCFKKVPFIVNVWFDNDERNSFPYTCWDFIYYQD-DQNKWHKTEGVDHNGCYYFVYFTLFQPDAVKYYSRLGRLQEEARDPPLVLFTGQQNNLKCWRNRCTTKYASLFLCFSSVWKWFKSDAQRQVFLNTVHIPWSAVPGKVYLPPSAPVRVLRTDEYVQETDVYFYTSTRLLIVGNPYFTITVPKVSANQYRVFRCKLPDPNKFALVDKNLYNSDERLVWKLVGLEVGRGGPLGVGSTGHPLLNKIGDTENPSFYDERQNVSDPKQSQILIVGCAPATGEYWDLAKPCGAAPPIQLVNTVIQDGDMGDIGFGAANFPKLMQDRAGVPLELIDSISIWPDFLKMTKDIYGDSVFFFGKREQCYARHLFARAGYFTTPSGSLNTSDSQLFNRPYWLRRAQGTNNGICWGNELFITVFDNTHNVNFTIYNNADFKQYLRHTEEYEIELVFQLCKVNLTADVLAHLHVMNPRILEEWQLAFVPPAIEDTYSMATKC-P-EEDDPYKAYSFWTLDMTERFSSDLSQFSLGRKFLYQTGKRR Seq48 MTD-RGT-NDDWYIVDISDLLDQGN-SLELFHLQEHLQNEQDLNTLKRKYLNSPSPRLESIKARKQLFDSGIEILKCSNTRSALLAKFKDTVGVSFTDLTRAYKNNKTCCSYWVWGVTSTSVDVVKVFQVQCNYMHVENKFLIVLAGFKAQKSRETVLNLVLNVQSNYIMAEPPKNRSMAAALYWYRRSMSPAVGEMPDWMAQQTLLNHFELSQMVQWAYDNGYTDESDIAYYYAILAEEDENAKAFLASNAQAKYVKDCARMVSHYKRAEMSSMSMSAWIYKRGDWKHIVKFLRFQEVEFISFMIAFKELLSGPKKNCLVIYGPPNTGKSMFCMSLLRVLKGKVISYVNSKSQFWLQPLASTKIALLDDATKPAWDYIDLFLRNALDGNPICDLKHKAPQQIKCPPLMITSNINVKADYLHSRITCFEFKQPFPFDENGQPAFSLTDINWKSFFERFWSQLDLEDELRLLNNRLDWLQEQLLTLYEDSKDIEDQIMQWRQEQVLFHYARKKGIMRLGLQVVPSLAASQDKAKTAIEMTLYLSGLRDSQYGSEQWSLDTSREIFLAPPTFKKGGQTIEVIYDEDPNNSTRHTVWRHIYYQNGD-NRWRKAASVDVHGVFYKNYYVDFQEEANRYQSRLRRLIQEARDPPIICLKGGPNQLKCLRYRIKASNSSDFESISTTWHWFISTEQRDRFLDKVVVPWLPAQNKFYLPT-QPIKILSSDDYVSRTNIFYHATSRLLIVGHPLYTMTVPKVSPNQYRVFRVRFPDPNRFAFGDKDIFDPEERLVWGLRGIEIGRGQPLGVGISGNPLLNRFDDAENPSRYDNRQNVADAKQTQMFLIGCTPATGEHWSIARRCGDCPPIELVNTVIEDGDMFDIGLGAMDFGSLQANKADAPLDIAGTVCKYPDYIKMGQEVHGNSLFFFARREQMYLRHVFTHAGYFVAPSGSLVSSDVQIFNRPYWLQRSQGQNNGICWRNELFVTVADNTRGTTMNIYDSADYNEYTRHVEEYELSFIVQLCKVKLTPENLAFLHNMDPTIIDSWQLTV-SQPIEDKYSLATKCPPPTP-DPYKDLRFWDVDLSERMSEQLDQFPLGRKFLYQSGKRR Seq49 MADNKGT--NDWFLVDLSDLLDQGN-SLELFHKQESLESEQELNALKRKLLYSPSPRLETIRYRRQLFDSGLEILKASNIRAALLSRFKDTAGVSFTDLTRSYKSNKTCCGDWVWGVRENLIDSVKLLQTHCVYIQLENRFLFLLVRFKAQKSRETVIKLILPVDASYILSEPPKSRSVAAALFWYKRSMSSTVGTTLEWIAQQTLINHFELCKMVQWAYDNGHTEECKIAYYYAVLADEDENARAFLSSNSQAKYVKDCAQMVRHYLRAEMAQMSMSEWIFRKGNWKEIVRFLRFQEVEFISFMIAFKDLLCGPKKNCLLIFGPPNTGKSMFCTSLLKLLGGKVISYCNSKSQFWLQPLADAKIGLLDDATKPCWDYMDIYMRNALDGNTICDLKHRAPQQIKCPPLLITSNIDVKSDYLHSRISAFKFAHEFPFKDNGDPGFSLTDENWKSFFERFWQQLELEDELRLLSSRLDLLQEQLMNLYEDSKLIEDQIKQWRQEQVLFHFARKNGVMRIGLQAVPSLASSQEKAKTAIEMVLHLESLKDSPYGTEDWSLDTSRELFLAPPTFKKSGSTLEVTYDNNPDNQTRHTIWNHVYYQNGD-DVWRKVSSVDAVGVYYKNYYVLFAEEASKYSSRLRRLLQEAWDPPVVCVKGGANQLKCLRYRLKASTQVDFDSISTTWHWFIDEAQREKFLERVALPWLPAQNKFYLPP-QPIRILSTDEYVTRTNLFYHATSRLLLVGHPLFTVTIPKVSPNAFRVFRVRFADPNRFAFGDKAIFNPEERLVWGLRGIEIGRGQPLGIGITGHPLLNKLDDAENPTNYDSRQNTADAKQTQMFLVGCTPASGEHWTSSR-CGDCPRVQMIESVIEDGDMMDIGFGAMDFAALQQDKSDVPLDVVQATCKYPDYIRMNHEAYGNSMFFFARREQMYTRHFFTRGGYVGTPSGSMVSSDVQLFNRSYWLQRCQGQNNGICWRNQLFITVGDNTRGTSLSIYSNANFNDFLRHTEEFDLSFIVQLCKVKLTPENLAYIHTMDPNILEDWQLSV-SQPLEDQYSLAAKCPPEP-QDPYSQYKFWEVDLTERMSEQLDQFPLGRKFLYQSGKRR Seq50 MAE--GTGSSGWFLVTIGDLIDQGN-SLELFHQQETAEVLAEIAQLKRKYCDSPSPRMQSVSVKKRLFNEAVSVFTQSSSRIAQLAIFKEAHTVSFAELTRPFKSDKTVCGDWVSGVHCALGDSLKSLRSHCMFFLYDSTSILLLLRFKSQKSRDTVTSLLLGVDHIQVMLDPPKTRSVPAALFWYKRAMVTAVGPFPEWITQQTQVNHFELSTMIQWAYDNHITEESKIAYQYALLADSDENAKAFLASNAQAKYVKDCAAMVRLYFRAEMQEMSISAWIHYRGDWKEIVRFLRFQGIEYIPFMISMKKFLKGPKKNCIVIYGPPNSGKSYFCMSLLRLMGGKVISFANSKSHFWLQPLADAKIGLLDDATKPCWDFIDTYLRNALDGNPISDCKHRAPTELKCPPLLVTTNVDVMGDYLHSRIVFLRFMNKMPLKSDGTPGYNLDDKNWKSFFTRFWETLELEEELRLLSQRLDSVQEQLLNLYEESQSLQDQIAHWRKEQVILHFARKHGIRRLGMTYVPTLAATQHNAKQAIEIVLFLESLQRSQYGQEPWTLDTSKERFKSPPCFKKGAQTVEVIYDGNKDNNFRYTVWKFIYFWD-ESGDWHKVPSVDEKGVYYNIYYVDFETDAARFGGRVRQLISEARDPPVICLKGGNNQLKCLRYRLKAKHRTLFDCISTTWSWFKDEAQRERFLEEVPIPWLSQQSKFYVPP-QPIKILSTDEYVSRTNIFYHASTRLLTVGHPYYTVVVPKVSPNQYRVFRVRLPDPNKFAFNDKQLYDPEERLVWAVRGVEVGRGQPLGVNVTGNPLFNRYDDVENSSRYDNRQNIADPKQTQLFILGCVPATGEHWTQAQRCGDCPPIELINTVIEDGDMSDIGLGAMDHRLLQVSKAEVPMELVNSVSKYPDYIKMLKDPFGDSLFFYARGEQMYARHFFSRAGYMVTPSGSLVSSDSQVFNRAYWLQRAQGMNNGICWNNQMFVTIVDNTRGTVMNIWANNAFKSYLRHVEEFELQFIVQLCKVRLSPENLAFLHKMQPSIIDNWQLSITA-PLEDQYSLATKCPPQE-DDPYKNYKFWDVDLSEKMSDQLDQFPLGRKFLNQSGKRK Seq51 MAE--GT-DCGGFLDTVSSLLDQGN-SLEPFQHHEATETLKSIEHLKRKYVDSPSPRLQAFAVKKRLFDEAASANTARVKH-LLL--FRQAHSVSFSELTRTFQSDKTMSWDWVADIHVSVLESLQSLRSHCVYVQYDASSLLLLLRFKAQKCRDGVKALLLGVQDLKVLLEPPKTRSVAVALFWYKRAMVSGVGPMPEWITQQTNVNHFQLSVMVQWAYDNHLQDESSIAYKYAMLAETDENARAFLASNSQAKYVRDCCNMVRLYLRAEMRQMTMSAWINYRGDWKVVVHFLRHQRVEFIPFMVKLKAFLRGPKKNCMVFYGPPNSGKSYFCMSLIRLLAGRVLSFANSRSHFWLQPLADAKLALVDDATSACWDFIDTYLRNALDGNPISDLKHKAPIEIKCPPLLITTNVDVKSDYLFSRICVFNFLQELPIR-NGTPVYELNDANWKSFFKRFWSTLELEDELRLLSQRLDSIQEELLSLYEESTSLESQLQHWRKEQVLLHFCKKHGIRQLGYTPVPSLLTSQECAKQAIEMVLYIESLLRSPYSDEPWTLDTSRERFESPPTFKKNPAIVEVYYDGDRGNNNEYTLWGIFIIGNAD-GEWVKTESVDYRGIYYYVYYVDFSTDAGRFNRRLRELITEASDPPVICLKGGHNQLKCLRYRLKSKHSSLFDCISTTWSWFADSEQRDKFLSRVPLPWLSTQNKFYLPP-QPVKIPSTDEYVTRTNVFYYASSRLLTVGHPYYTMLVPKVSPNQYRVFRIKLPDPNKFAFGDKQLYDPEERLVWCLRGIEVNRGQPLGVSVTGNPIFNKFDDVENPTKYDYRKSMADPKQVQLLMLGCVPATGEHWAQAKQCTDCPPIELVNTVIEDGDMCEIGFGAMDHKTLQASLSEVPLELAQSISKYPDYLKMQKDQFGDSMFFYARREQMYARHFFSRAGYCITPSGSLVSSDSQVFNRAYWLQKAQGMNNGVCWDNQIFVTVVDNTRGTILSLGKTVHFSSYLRHVEEYELQFVLQLCKVKLTPENLSYLHSMHPTIIDNWQLSVSAQPLEDQYSIATKCPPKE-NDPYKNYKFWEVDLSEKLSDQLDQYPLGRKFLNQSGKRK Seq52 MAARKGTTEDGGWVLNVSDLVDQG-LSLQLFQQQELTECEEQLQQLKRKFVQSPSPQLASIKVKKQLFDSGIQLFKVRDKRAFLYSKFKSSFGISFTDLTRVYNSDKTCSSDWVYHVSDDRREAGKLLQDHCEYFFLH--CTLLLLCLFVPKCRNTLFKLCFHISNVQMLADPPKTRSPAVALYWYKKGFASGTGELPSWIAQQTLITHFDLSEMVQWAYDNDLKDESEIAYKYAALAETDENALAFLKSNNQPKHVKDCATMCRYYKKAEMKRLSMSQWIDERGDWKEVVKFLRHQGIEFILFLADFKRFLRGPKKNCLVFWGPPNTGKSMFCMSLLSFLHGVVISYVNSKSHFWLQPLTEGKMGLLDDATRPCWLYIDTYLRNALDGNTFSDCKHKAPLQLKCPPLLITTNVNVCGDYLRSRCSFFHFPQEFPLDDNGNPGFQLNDQSWASFFKRFWKHLDLED-LRLLSEALDLLQEELLSLYENSQSLADQSRHWRKEQVLLYYARGKGIMRIGMQPVPPQSVSQAKAKQAIEQSLYIDSLLHSKYANEPWTLDTSRERLVAEPTFKKGGKQIDVRYGDSEENIVRYVLWLDIYYQD-EFDTWEKAHGLDHKGLSYQVYYVDFEEEANKYGGRLGRLLQEAYDPPVLVLAGDPNSLKCIRYRLSHKHRGLYLGASTTWKWFLSDQQREDFMDRVTFPWLPAQNKFYLPP-QPSKVLSTDEYVSRTNIFYHASSRLLTVGHPFYEVIVPKVSPNQYRVFRLLLPDPNNFAFGDKSLFDPEERLVWGLRGLEIGRGQPLGISVTGHPTFDRYNDVENPNKNDSRVNMGDPKQTQMFMIGCKPALGEHWSLTRWCGQCPPIELRNTTIEDGDMVDIGFGAMDFKALQHYKSGVPIDIVNSACKYPDYLKMANEPYGDRCFFFVRREQLYARHIMSRSGYFSTPSGSLVSSEGQLFNRPYWIQRSQGKNNGIAWGNQLFLTVVDNTRGTPLTIYVPSSYRTYLRHVEEYEVSIIVQLCKVKLSPENLAIIHTMDPNIIEDWHLNVTP-PLDDTYSLATKCPPKT-NDPFADFKFWEVDLKDKMTEQLDQTPLGRKFLFQTNKRK Seq53 MADKKGT-DLSDWVLDISDLVDQG-LSLQLFRLQEQKESDEQLQQLKRKYIASPSPQLEAVKAKKQLFDSGIDLFRAKNSRVFALGKFKETYGLSFMDLVRVFQSDKTCSLDWVLYMNPERSEAAKLLQDHCAYIFFT--AALMLLCFKYQKSRETVMKLLFDCSAQQILAEPPKTRSTAAALYWYKKSLIAGAGAFPEWIAKQTLINHFDLSAMVQWAYDNDLYEECEIAYQYASLADTEENAAAFLKSNSQAKHVRDCATMCRYYKRAEMQRMTMSEWISRQGDWKDIVKFLRYQDLEFTSFLSAFTKFLKGPKKNCLVFWGPPNTGKSMFCMSLMQFLKGKVLSFVNSKSQFWLQPLADAKVALLDDATAPCWTYFDTFLRNALDGNPICDAKHKAPYQVKCPPLMVTTNVDVIGDYLRSRLSAFCFATEFPFKEDGSPGFCLSDQSWASFFTRFWSRLELEDELRLISKALDSIQEQLLTLYEDSADLTDQIEHWRREQTLYHYARRQGWMRIGMNPVPSLAASQSKAKSAIEQELLLQSMQKSAFAREPWTLDTSRERLLTEPCFKKGGRQVEVRYDNDRDNTSRHVLWDFIYFQ-GDNDEWHKTPGLDARGLYYKTYYVDFEEEAKKYTTRLGRLLLDARDPPLLVLKGDPNSLKCVRYRLKGKYSSLFCWVSTTWSWFKDLEQREVFVKTVKLPWLPAQNKFYLPP-QPTRVLHTDEYVTRTRIFYHASSRLLTVGHPFYEVIVPKVSPNQYRVFRLRLPDPNNFAFGDKSLFNPEERLVWALRGLEIGRGQPLGVGVSGNPTFDRYADVENPNKNDPRVNMADPKQTQMFMVGCKPALGEHWIKARWCQQCPPIELKNSTIEDGDMVDIGFGAMDFRNLQQNRSAVPLDIADTQCKYPDYIKMANEPYGDRCFFFVRREQLYARHLSTRSGYFSTPSGSLVSSEAQLFNRPYWIQRSQGQNNGIAWENQLFITVADNTRGTPLTIYKAGSYKTYLRHVEEFDISVILQLCKVHLTPENLATIHTMDPNIIESWHLNVNP-PLDDTYSLATKCPPKEREDPYANMKFWEVDLRDKLTEQLDQTPLGRKFLFQTNKRR Seq54 MADKKGT-DLSDWVLDISDLVDQG-LSLQLFRLQEQTESDEQLQQLKRKYFHSPSPRLQAVKAKKQLLDSGIDLFRAKNRRLFSLGKFKETYGLSFMDLVRVFQSDKTCSLDWVLYLHEERSEAAKLLQDHCSYVFCN--TTLMLLSFKSQKSRETVLKLLFDCKGEQFLAEPPKTRSTAAALYWYKKSVVSGTGILPEWIARQTLINHFDLSAMVQWAYDNDVYEECEIAYRYACLGETEENAAAFLKSNNQAKHVRDCATMCRYYKRAEMQRMSISEWIHRQGDWKDVVRFLRYQGLEFMEFLGAFTKFLKGPKRNCLVFWGPPNTGKSMFCMSLLRFLRGKVISYVNSKSQFWLQPLADAKVALLDDATVPCWNYFDVYLRNALDGNPVCDAKHKAPYQIKCPPLMVTTNVDVLADYLRSRLSAFCFATEFPFKEDGSPGFLLNDQSWASFFTRFWLRLELEDELRVISKALESVQEQLLTLYEDSTDLTDQIKHWRREQALYYCARRQGITRVGMNVVPSLAASQQRAKSAIEQELLLQSLLDSDFADEPWSLDTSRERLLADPCFKKGGQQVELRYDRDRDNTSRHVLWKDIYFQ-GEEDTWRKTHGVDEKGLFYKTYYVEFEKEAAKFGTRLGRLLHDARDPPVLVLRGDANSLKCIRYRLKGKYSDLFCRVSTTWTWFRDLSQRDTFERTVRLPWLPAQNKFYLPP-QPTRVLHTDEYVTRTRIFYHASSRLLTVGHPFFEIIVPKVSPNQYRVFRLRLPDPNNFAFGDKSLFNPEERLVWALRGLEIGRGQPLGVGVSGNPTFDRYTDVENPNKNDPRVNMADPKQTQMFMVGCKPALGEHWVKARWCQQCPPIELTNTPIEDGDMVDIGFGAMDFRTLQQNRSAVPLDIIDTQCKYPDYIKMANEPYGDRCFFFVRREQLYARHLLTRSGYFSSPSGSLVSSEAQLFNRPYWIQRSQGQNNGIAWENQLFITVADNTRGTPMTIYKAGSYKTYLRHVEEFDISVILQLCKVHLTPENLATIHTMDPNIIESWHLNVNP-PLDDTYSLATKCPPKEKEDPYAHLKFWEVDLRDKLTEQLDQTPLGRKFLFQTNKRR Seq55 MADKKGT-DLSDWVLDISDLVDQG-LSLQLFRLQEQTESDEQLQQLKRKYLASPSPRLQSVKAKKKLWDSGIELLRSKNRRLFSLGKFKETYGLSFLDLVRVFQSDKTCSMDWVLYLNEERAEAAKLLQDHCSYVFLT--VSLMLLSFKSQKSRETVSKLLFDCRGEQFLAEPPKTRSTAAALYWYKKSTVSGAGMLPDWIAKQTLINHFDLSAMVQWAYDNDLYEECEIAYQYACCAETDENAAAFLKSNSQAKHVRDCATMCRYYKRAEMQRMSISEWIHRQGDWKEVVKFLRHQGLEFIEFLSAFTKFLKGPKKNCIVFWGPPNTGKSMFCMSLLNFLKGKVISYANSKSHFWLQPLADAKLALLDDATAPCWNYIDTFMRNALDGNPVCDAKHKAPFQIKCPPLLVTTNVDVLGDYLRSRLSAFCFAAEFPFNEDGSPGFHLNDQSWASFFERFWPRLELEDELRVISLALESVQEQLLTLYEDSPDLRDQIKHWRREQVLYYCARRRGITRVGMAVVPPLAAAQQRAKAAIEQELLLQSLLESDFAGEPWTLDTSRERLLTEPCFKKDGEQIEVRYDNDRENMSSHVLWTAIYYQ-SDDDKWRKTQGVDARGLFYKTYYVDFEQEAKKYG--AGPEGSLQQDPPVLVLAGDANSLKCVRFRLKGKHSSLFCRVSTTWTWFRDNNQREIFEKTVRLPWLPAQNKFYLPP-QPTRVLHTDEYVTRTRIFYHASSRLLTVGHPFFEIIVPKVSPNQYRVFRLRLPDPNNFAFGDKSLFNPEERLVWALRGLEIGRGQPLGVGVSGNPTFDRYSDVENANKNDPRVNMADPKQTQMFMVGCKPALGEHWVKARWCQQCPPIELKNTPIEDGDMVDIGFGAMDFKNLQQNRSAVPLDIIDTHCKYPDYIKMANDPYGDTCFFFVRREQLYARHLLARSGYFSSPSGSLVSSEAQLFNRPYWIQRSQGQNNGIAWENQLFLTVADNTRGTPLTIYKAGSYKTYLRHVEEFDISVILQLCKVQLTPENLATIHTMNPDIIESWHLNVNP-PLDDTYSLATKCPPKEREDPYAHLKFWEVDLRDKLTEQLDQTPLGRKFLFQTNKRR Seq56 MADKKGT-DLSDWVLDISDLVDQG-LSLQLFRLQEQTESDDQLQQLKRKYIASPSPRLQAVKAKKQLVDSGVDLFKSKNRRLFALGKFKENFGISYMDLVRVFQSHKTCSMDWVLYLHDERSEAAKLLQDHCAYIFFT--VTLMLLAFTSQKSRETVFKLLFDCKEEQFLAEPPKTRSTAAALYWYKKSLLAGAGIFPEWIAKQTLINHFDLSVMIQWAYDNNITEESEIAYQYAMFADTDENAAAFLNSNNQAKHVRDCATMCRYYRRAEMQRMTMSEWIHKQGDWKEVVKFLRYQGLEFVEFLSAFTKFLKGPKRNCLVFWGPPNTGKSLFCMTLLKFLRGKVISYVNSRSQFWLQPLADAKVALLDDATVPCWNYFDTFLRNALDGNPVCDCKHKAPFQIKCPPLLVTTNLNVKGDYLHSRLSAFCFANEFPFKEDGSPGFNLNDQSWASFFKRFWLRLELEDELRVVSKALECVQEQLLHLYEDSAELRDQIMHWRREQALYYCARKQGITRIGMNPVPTLAAAQQKAKSAIEQELLLQSLLDSKYADEPWTLDTSRERLLVEPCFKKGGQQVEVRFDCDRENVSRYVLWTDIYHQ-TEQDQWRKTKGADNRGLFYKVYYVDFEDEAKKFGDRLGRLLLDARDPPVLVLKGDPNSLKCTRYRLKGKYSHLFCRASTTWQWFTDTAQRDLFEKAVKLPWLPAQNKFYLPP-QPSRVLHTDEYVTRTRIFYHASSRLLTVGHPFFEIIVPKVSPNQYRVFRLRLPDPNNFAFGDKSLFNPEERLVWALRGLEIGRGQPLGVGVSGNPTFDKYNDVENPNKVDPRMNMADPKQTQMFMVGCKPALGEHWVKAKWCQQCPPIELKNSPIEDGDMVDIGFGAMDFRNLQQNRSAVPLDIVDTQCKYPDYIKMANEPYGDRCFFFVRREQLYARHLLTRSGYFSSPSGSLVSSEAQLFNRPYWIQRSQGQNNGIAWENQLFVTVADNTRGTPLTIYTANSYKTYLRHVEEFEISVILQLCKVQLTPENLATIHTMDPAIIESWHLNVNP-PLDDTYSLATKCPPKEKEDPFKGLKFWEVDLRDKLTEQLDQTPLGRKFLFQTNKRR Seq57 MVD-KGT-EESDWVLSLADFVDEG-LSLELFRQQEADREEEHLLQLKRKYIRSPSPRLESIKAKKQLKDSGLGLFKSKNQRAVLFAKFKECFGLSFTDLTRNFKSDKTCTADWVIYISEARAEAGKLLQDHCEYVFVS--CALCLLSFKAQKNRETVLKLLFGVRDCQLMAEPPRTRSAAAALYWYKRGMSNCAGQLPEWIAKQTLLGHFDLSQMVQWAYDNDLVEESEIAYQYALLGEEDENAAAFLNSNNQTKHVKDCAVMCRYYKKAERESMSMSEWIHRSELWREIVKFLRHQTVSFVSFIAAFKRFLRGPKSNCIVIWGPPNTGKSLFCMTLLKFLKGRVISFVNSKSQFWLQPLADAKIGLLDDATRPCWDYFDAYMRNALDGNPICDCKHKAPSQIKCPPLLITTNLNVMGDYLRSRLSSFCFPTEFPFHDDGSPGFILNDESWASFFARFWTHLELEDELRLLRQALDSVQEELLNLYEDSSDLTDQVTHWRREQVILHYARKNAITRVGMTFVPPQNVSQQKAKEAIEQELYLQSLLGSDYSNERWTLDTSREVLLAPPCFKKGGRPVDVRFDGDPENVTQYTLWGHIYYQNAE-DVWQKTKGVDDTGLYYRVYYVVFKDEAKRYGTRLERLVLEARDPPVVVFKGEPNSLKCLRFRLRKGYFHLFQKVSTTWSWFDSSEQRESFLRQAPIPWLPAQNRFYLPP-QPSKVLNTDDYVTRTSIFYHAGSRLLTVGHPLYHVIVPKVSANQYRVFRIRLPDPNNFAFGDKAIFDPEERLVWAVRGLEIGRGQPLGVCVSGNPLFDKNNDVENPTKYDNRVNVADPKQTQLFMIGCKPAIGEHWGQARRCGHCPPIELKNTTIEDGDMIDIGLGAMDFRVLQQNKAGVPLDISNSECKYPDYIKMANDPYGDNLFFYVRREQLYARHMFTRSGYFSTPSGSLVSSEAQLFNRPYWIQRSQGQNNGIAWQNQLFITVVDNTRGTSLNIFNPADYRCYMRHVEEYEISLILQLCKVKLTPENLAFIHTMNPDIIEDWHLNVNP-PIDDVYSLATKCPPKTREDPYGLYRFWEVDLKDKMTEQLDQTPLGRKFLFQTGKRK Seq58 ?ETKIKTLG-CSYIV-TEDFVDAGE-HLSLLQTQMRASDAQQIASLKRKYVKSPSPKLEQCKARKQLFDSGISDERSRVLYMYRR--FNDMYGVKYTDLIRAFKSDKTMSANWVYVPLLEDGKAAATLQQQCSWYFME--IQLFNVEFNAQKCRATVIKLFFNFSSKRLMADPPKLRSAPACLFWYQKVLKKVGGELPDYIHTQCALGSFELTKMVQWALDNNLTEESSIALKYAMLAEEDENAQAFLKSNNQPKLVKDCCTMVRMFQTALMRDMSISQYVDHR--WRSIVHFLRYQGVQFLSFMIDLKNLLHHPKKCTIVVCGPPNTGKSYFVLSFVKFMNGCVISFVNYGSHFWLTPLRTARIGMIDDATNSFWKYCDTYMRTLLDGNDVSDCKHRNPIQLRCPPLVITTNEDIKNDYLQTRLRFLYFNKPFPLHDRGDPVYKIESLQWASFFRKFWRHLDLLEELRLLQHRLDYTQEKILTLYEDSTDINDHISLWRHENVLFHTMRKRGIVRVMGRSIPSLQASEKSAKHAIMMQLQLEDLAKSPYGLEPWSLETTLERYLAPPTFKKGGVHVNVTFDNEESNSAEYVMWTYIYVTD-ENGDWHKTQGVDRTGIYYHIYYVYFEDEAAQHHRSSAERPGPTPQFYLIGCRGPVNTLRCYRFKLKKRPNPDITYISTTFNWFTSKEHRDEFYVAARPPWLPAQNRFYLPPA-PVRILSTEDYVHRTDVYVHASTRLLTVGHPYYTVKVPKVSSSQYRAFRCRFPDPNNFAFGDTSIFNAEERLCWALRGIEVGRGQPLGIGATGHPYTNRFQDVENPFNPDDRLNVADPKQTQLIIVGCRPAQGEHWDAAPWCNRCPPLELRSTVIEDSDMVDIGLGNLNFESLQDNKADAPLDIVRSICKYPDFLKMSEDPYGDEMFFFTRREALYARHMFQRSGYYISPSGSLVTSEQQLFNRPYWLRSAQGHNNGILWHNEAFVTVVDTTRGTNFNVFKNGKFSEFLRHCEEYQLSFILQLCKVSLTPENLAYIHTMDQSIIEAWHLSVNA-PLEDKYSLATKCTTSES-DPYSKYRFWDIDLTDRITEQLDQTPLGRKFRFQGGLKR Seq59 MAMKTKREARCSYIL-VEDLVDQGN-SLSLFHAQTVEEYEGEIQSLKRKFILSPSPRLAGVKARKSLFDSGIDLFQSRQRCTHMYSKFKAVYGVSFTDITRPFKSDKTTSQHWVYYLAFDSEISAMLLRQQCQFLYID--IILFFLEYNVQKSRTTVYNWFFHYNENRMLANPPRTRNMPAALFFYHRFMGTGGGAMPEIIVNQCVVSNFELSRMVQWALDNDLQDEHMLALEYALLAESDGNARAFLKQNNQPMIVKNCSIMVRHYKTALVAKMSISQYVNKRNSWRGIVHFLRYQGQEFLPFMCKMHNFLHHPKKSTLVLCGPSDTGKSYFANGLNKFLDGHVLSFVSNGSHFWLSPLRGARCCLIDDATLTFWRYADQNMRALLDGYEISDAKHRNPMQTRAPPLIITTNEDIMRLYLQTRTMYVYFNKPFPLKGNGQPLYYIDGYTWNSFFRKFWRHLNL-EDIRLLLERLDYIQEQILTLYEDSVDLEDHIRLWRRENAIWYVLRQEGHARVGGRAVPAMTVSEANAKFAIEMQIKLESLKASPYAAEGWSLETTKERYLAEPTFKKLGQPVTLMFDNDPENLTEVVLWKWVYYITP-TDEWYKARGIDDTGIYYKMYYVRFDMEAENFKKTAGVAPAEGH-Y-LVGAKGPVNSLRCLRYKWKNKYSGDIMYLGTTFTWFSNETKREKFLKSVKIPWLPGPNRFYLPP-QPIRTLNTEEYVRRTSTFLHAATRLLTVGHPFYKEVVPKVSSNQFRAFRVRFPNPNTFAFCDKSLFNPDERLVWGIRGIEVSRGQPLGIGVTGNPFFNKFDDAENPYNGDSRLSIADPKQTQLLIVGAKPAKGEYWDVAATCDKCPALELKSSYIEDADMSDIGLGNLNFSTLQRNKSDAPLDIVDSICKYPDYLQMIEELYGDHMFFYVRREALYARHIMQHAGYFMTPSGSLVATEQQLFNRPFWLQRSQGHNNGILWHNEAFVTLVDTTRGTNFTIYNNSKFFEFLRHTEEFQLAFILQLCKVDLTPENLAYIHTMDPSIIEDWHLAVTS-PLEDHYSIATKCADDTS-DPYKDLKFWEVDLRDRMTEQLDQTPLGRKFLFQTGKRR Seq60 MEDLEEGGCSGWF-DSIADMFDQGN-SLELFHTQEKEETRTQIQALKRKYI-PSSPRLRAIPSRRLFEDSG-NLLQSHNRVARLLAVFKEAYGVSYKELTREYKSDKTCNPDWVYSLSEPILNAARTLQGICEYVFMQATVALLTVRFKCSKSRETVRKQMFHSDPLLCLCDPPKVQSVPAALYWYKSSMYSGTGEAPEWIKRQTMITCFDLSEMVQWAYDNNYEDESQIAFEYARTATESPNANAWLASNAQAKHVRDCATMVRHYKRAEMKAMSMSQWVWKCGTWTPISLYLASEGVEVIRFLSAMKSWLRGPKKNCLVFYGPPNTGKSLFTMSLIKFLRGRVISFANSKSHFWMQPLAEAKVVLLDDATRATWDYVDTYMRNAMDGNPLSDCKYRTPVQVKCPPMLVTTNEDVHLNYLHSRIQVFHLKEPMPIDTAGNPEYSFSNRHWKAFFEKLQKPLDLEGDFSCIHSRLAAVQEELMCMYEGEETLEAQLKHWRKEQVLLHAARQHGHNKIGLQAVPPLSVTQQNAKNAIEMHLLLQSLAETPYAREAWTLQTSREMYMAGPTFKKDGTIVEVIFDGDKTNMMTYTKWGKIYFADPN-GNWSRTTSTDINGIYFKEYYVRFKEEAKRYSRPSRGHCRDPRTACLLIIKGSSNQVKCLRFRLKSWHHSLFSYISTTWQWCEDSAQMDRFLCTVKIPWLPNNQKLYLPPA-PVRILSTDEFTTRTDIYYYASSRLLTVGNPYYTVTVPKVSPNQYRVFRCKLPDPNRFAFGEKSVYDPEQRLAWCIRGVEIARGQPLGIGITGHPLYNRLEDVENPGKYDNRQNVGDPKQTQMFIVGCVPAQGEHWSRALTCGDCPPIQRMSGMIEDGDMGDIGYGNLDFRVLQENKSEVPLEVVDSICKYPDYLGMSKETHGNSCFFYARQARLYSRHFFNRAGYSGTPSGSLVSSDAVLFNRPYWLERAQGQNNGILWNNDLFVTVLDNTRGTHFSIYTASNYKQYTRHVEEFELEFIFQLVKINLSTEVLAYLHGMDPSILDNWNLTL-G-PLADKYSLATKCEVTKP-DPYKGRIFWNIDLTERLTADLDQFSLGRKFLYQHAKRR Seq61 MASQRGTLGGIDFIDSVSNLFDPGN-PLQLLQQQEAAEDERLVALIKRKHLTTPSPKLDAM-AKKKLFDSGVGILRAANRRVCMLARFKEVYGVSVTDLTRQFKSDKTCCKNWVFGLCEPYYITLTVLPDHCCYSHMQGGIALMLCDFKAMKNRDTVIKLIVPVSDDLIMVQPPNVRSPAAALYWVQRAQSNASGEYPSWITKQTMLSHFDFSNMVQWAYDQGYTEESKIAYHYAQLAEEDKNAMAWLSSAAQAQHVKHCAQMVRYYMQAQTAEMTMAQYIHERGDWKHIIAFLRHQDIEIIPWLRTTRDWLKGPKRNCVCYHGPPDTGKSMFGMSLMRFMRGAIISYVNSRSHFWLQPLVSAKVAMLDDATDACWQYIDTNLRNLLDGNPLSDLKHRAPIQATCPPLLITSNIDITQDYLRSRVKCFAFHCPLPVGEDGMPTLVLTEASWKCFFRKFASTLEV-EEFRCLRDRLDVLQDQILGHYEQSTCLQDHVQYWRRESSLLFYARGKGVKTLGYLPVPVQQVSQDRARQAIQLHLATQSLAKSKYKDEPWTLDVSRENYMSEPTFKKGGTQVEVIYDGDEDNVMLYTSWEHIYIQD-DDGSWYKRKGVCVEGLYYKTFYVNFEEELGKYGAGFRPQPPTAVVAPVIIIAGPPNALKCQRYRLQNQHYRHFAQVTTTYHWFVDTAQRETFLNTGALSWIPSPQAVYVAPA-PVTIPSTEDFITRTPYFYHANSRLLTVGNPFYKILVPKVSGNQYRVFRIRFPDPNKFALPDPNVFNPDERLVWGLRGIEVGRGGPLGMEVTGNLGFGRNADVENPNQATKRFNVGEPKQNQLLIVGCSPAWGEFWDSTPACGDCPALELKSTRLQDGTMTDIGFGHMNFKSLQDDKSGVPLEIVNSVCVYPDFYKMSKDPYGNSCFFSVRKEQMYIRHYFSRVGYMGTPSGSIVSTEGQVLNRPYWLLKAQGRNNGMLWGNQCFVTVVDNTRSLNFLIFQADEFANYLRHTEEYEIACIVQLCKVRLDPETLSILNTMDPEILEEWQIGVNP-PVNDRYSLATHCKAKEKEDPYAGLAFWNLDFTESLSPDLDQFPLGRRFLTQAGKRR Seq62 MTDKSG---E-YFLLNGVDFIDG-NTLAEYNRKEADRHKRDLEQLKRRHVR-RPVGGSPSSSSKRRCL-GL-ELLRSANRQATFLGKFKDTYGISFTELTRPFKSDKTCCEDWVYGISGPLYEGAKLLEGHVIYMQLTGLLLLMLLRLKHAKSRATLRRLLFNISEMQLLAEPPKTRSVPVALFWYKGTLSSLSGTCPEWIHRQTLINHFDLSSMIQWAYDYDYDDECTIAYQYARLAETDANANAWLNSPAQARYVKDTATMVKYYKRAQMREMTMGEWIKHRGDWKKIVQFIRFQGIEFPLFFGALKKFLHGPKHNCIVIWGPPDTGKSMFCMNLIKLLGGKIISFANSKSQFWLQPLADAKVGLLDDATGVCWDYIDQYLRNALDGNPISDLKHRAPTQMKCPPLLITTNLDITANYLVSRVACFKFSEPFPFTDRDTPTYPLTECNWKALLERLWKQLDFQEEFKCLLDRLNAVQSKILDLYEAENTLSSQQEHWRQEQILYYACRSKGILRLGYQPIPTTSVSQTKAKAAISMCLFLAKLKDSAFASEAWTFETSQERLAAPPCFKKGGYTVEVTFDEEIENKMLYTAWKYIYYLDMC-DNWCKSEGLDINGLYYKIYYVNFADEAQLYTQSSVVVPSISGAYPVTVFRGNVNQLKCWRGRIRDKYHMYFKDISTTWKWFHSAVQRERFRCFVPVPWLPNNDKVYLPPT-PARILNTEDYVTRTSLYYHANSRLLTIGHPYFKLKVPKVSGNQYRVFRIQLPDPNRFALTDKSIFDPEERLVWGCRGLEVSRGLPLGVGLSGHPYFNRKADNENPDPIDTRQNMADVKQSQVIMVGCVPQMGEHWQAALPCGECPPLELVSTIIEDGDMADLGFGAMDFKNLQHTKADAPLDLVDTISKYPDYIKMNQDIFGDMLFFYARREQCFTRHFMSRTGYYGTPSGSLVSSDAQLFNRPYWLQRAQGHNNGIAWQNQLFVTILDNTRGTNMTVYDDNDYAQYLRHAEEFELSFVFQLCKVQLTTEALAHIHTMNPKILEDWHIGLRPPPVEDQYSLATRCVPAENDDPYKTKKFWVVDLSTRFSTELDQFPLGRRFLFQSGKRK Seq63 MEGDIDSSRGGGFILDLINDSLQGN-SQALLHQQIMREDNRQVQDLKRKYVSPKSPRLRAISAKRRLFDSGLELMRASNQRATQLALFKKGYGISLTELTRVFKSNKTCNPDWVFGVHHNTYSDLVRLEKHCEYVQCSGYIVLMLLRFTAHKNRNTLIKLMLSVSDIQILADPPKIRSVPAALYWYRNSMSTAVGPLPDWVARQTLVQHFVLSTMVQWAYDNDHTEESDIAYHYALLADEDTNAAAWLGTNSQAKHVRDCAVMVKHYRRAIMSAMSMSEWI-NRGDWKNIGNFLRYQGIEVITFIGALRDMLKGPKRTCMCIVGPPDTGKSAFCLSLLDFFGGRVLSFTNYKSQFWLQPLADTRIALIDDATKSTWDYIDEYMRNALDGNAICDLKHKNPLQIRCPPLLITSNINIKHNYLYSRIHIVEFKHAFPFNEEGEPVYQLTKGNWKSFFKRLWLRLDLEDEFRCLKKRLDAIQDELLTIYEGSELISVQVQHWRKENVLMHYAKKSGVTRLGLQPVPSLRVSAEKAKQAIEMQLTLQTLQDSPYATEQWTLDTSYERWTTEPCFKKGPTTVEVYFDGDRANAMHYTMWSKVYYTDVN-EKWQKTKSVDNDGLWFKRYYVRFSDEAAKYGPQPFLREAGDSSPSVIVLAGPCNTLKCFRYRAKNLHRGLFERISTTWYWFSGNSQRAEFLNRVRLPWVSNSQKLFLPP-NPVKVLHTDTYIKRTSVFCHAASRLLTVGNPFFTVKIPKVSSNQFRVFRVTLPDPNQFAFAERPAHDPEERLVWGLRGMDVGRGQPLGIGVTGHPLFNRYTDNENPGKYDSRQNVADAKQTQVLIVGCSPALGEHWSAARPCGECPPLELKNTYIQDGDMMEIGYGAMDHRGLSQNKSDAPLDTVFSITKYPDYLKMSKEASGDSMFFFARREQEYIRHFFSREGYYGAPSGSLTSTDSQIFNRPYWLQRSQGLNNGICWLNQLFVTVGDTTRGTNMTIYDSSKFTTYLRHTEEFELSFIFQLCIVPLTPDVLAHLHSLNSHILDKWNIGLQA-PLEDRYSLATRCPPS-PEDPYAGYSFWLVDLSEKFSQDLDQFPLGRKFLAQSSKRK Seq64 MDAEEAGEGSSWFLQE--DLIDQGN-SLLLFQQQEAQADEQHLSVFKRKYC-SPSPRLGAIQVKRRLFDSGLDILRSANRKATMLGLFKDAFGVPYGELTRQFRSDKTGCFDWVYAVREPFFESGKQLRQHCRYTHVTGTVLLMLVSFNNQKCRDTVNKLIFNVHELLLMLEPPKIRSVAAAMYWYKQSLTNATGELPEWIKKLILINHFDFSQFVQWAYDNEYQEEHEIAYNYASIADEDSNAAAWLGLTGQAKVVKDVATMVRYYRRAEMNRMSMSNWIHNRGQWQPIVNFLKYQGVAMVTFINALKSFLKGPKKNCLVIWGPPNTGKSWFCMSLMHFLGGRVLSHVNSNSHFWLQPLGDAKVALLDDATTVVWDYFDRYMRNACDGNPISDMKHKAPVQIKCPPLLITSNIDVKADYLHSRLVTFHFPNLFPFEDDGSPVYQFNDENWNSLFTRLWRALDLEDEFRCLRQRLDAVQEKLMNLLEGSSDLSSQICYWRKENVLLYYAREKGLSRLGLQMVPHKAVSQSQAKQAIHMELILLSLQGSSYEQEPWTLDCSWERWLQAPCLKKDPVIVEVVYDGNSENANWYTLWGLIYYQ-TFEGDWMCTRGCDHSGLYYKRYYVHFIDDAARYGNDPAPVPLEAAFAPIVIFQGGTNQCKCYRWRLKKRHRSLFVAITTTYFWFSSDLQRRLLLATVPPPWSSNSQKVYLPPT-TLKAVSTDTYVTRLGIYYHGHSRLLTVGHPFYTMRVPKVSANQFRVFRVILPNPNKFALPDSNVFDPDERLVWAVKAMEICRGQPIGPQVTGHPLFNRFEDVENPAVYDKRQNMADYKQIQMVVLGCRPALGEHWGKTRSIGDCPAIELFHTTIEDGDMVDIGLGNLDFAQLQADKSGAPLDIVQSICKYPDTLKMAQEITGDTMFFSARREQSYLRHMMTRAGYCGVVSGSLFSSDAQIFNRPFWLNQAQGLNNGIAWNNQLFVTAVDNTRATNFTIYDAGSFNAYLRHVESYELQFVFELCKVKLTPENLTILHQQDPGILKGWELGVTP-PLEDTYSVATKCPEEVQEDPWGRFAFWRVDLSERFSLDLDQFPLGRRFLALSAKQR Seq65 MDEKPGS-GGTSFILSDEDLVDRGN-HLELFQTQEKEAGEKQISILKRKFCLSPPGLAGIRVVRRRLFTGGPDVVQDFNMAATIQKLFKTLYVATFGEITRIFQSNKTNNQQWVYGVPELLYTASFLLNNHCNYLLANGSLSLYLAVFNVGKSRDTVCKLVLNTTNQNLLLQPPKVRGLCSALFWYKLSLSPATGSTPDWIQQQTNVANFDFGTMVQWAYDHHLTEECKIAYQYAKCAGSDVNAKAFLASTNQARLVKDCCTMVRHYLRAEEQALSISAYIKKRGSWLSIMNLLKFHGIEPIHFVNALNPWLKGPKHNCIAIVGPPNSGKSLLCNSLITFLGGKVLTFANHSSHFWLAPLSDCRVALIDDATHACWRYFDTYLRNVLDGYPVCDRKHKSAVQMKAPPLLLTSNIDVHADYLQSRVKSFYFTEPCCASDNGEPLFVITDADWRNFFERLWERLDLEDELTCASEHLLAAQETQMTLIEDSTSLRDHETYWRREHGLLYAARKKGLTRLGCVPVPPCSVSAEKAKQAICMQLITRELINSQWANEPWSLDLSWDRYQANPCLKKGARVVEVEYDGNSTNKTWYTTWTVVLTRKTEEEGWEAATGADEKGLYYRVYFETFDIDASRWTTPRFSLLTKTGGQPCLILSGNGNQTKCFRFRCKKHFRRHYEHVTTTW-WFKNSCQRQDFLKTVPLPW-QPGQRLYLPPT-PVKVLCSEQYIRRRDVFYHGETRMLSVGHPFFGKSIPKVSPNQYRVFRVALPDPNQFALPDRALYDPSERMVWAVVGLQVSRGQPLGGSVAGHSYFNAFLDAENVSKKDDRKQAGDVKQQQVLLLGCTPAVGEYWTEARPCGSCPPIELKNKPIEDGDMMDIGFGAANFKELNATKSDLPLDIANDICLYPDYLKMTEEAAGNSMFFFARKEQAYVRHIWSRGGFFPVPSGSLVSTDGQLFNRPYWLFRAQGMNNGICWNNQLFVTVGDNTRGTTLTIYDTSKFNVYQRHVEEYKLAFVFQLCSVELTADTISHLQGLMPSILETWDINLQP-PLEDTYSPATKCSPSKPEDPYSGLKFWEVNLKEKLSLDLDQFPLGRRFLAQQGKKR Seq66 MAETAGS-GGGAYICSDEDLVDPGN-HLELFQTQEKEAGERQISLLKRKFCLSPPGLAGIRVVRRRLFDAGGRPASDGNMAAVMHKLFKTLYIAGFGEITRVFQSDKTNNNQWVHGASEVLYAASFILSKHCSYLQASGSMSLFLAVFNVGKSRETVRKLILNTPCSRLLLQPPKIRGLCPALFWFKLGLSPATGTTPDWIKQQTNVAYFDFGTMVQWAYDHRLTEECKIAYQYAKCAGTDLNAKAFLASTNQARLVKDCCTMVKHYLRAEEQSLTISAFIKRRGSWLSIMNLLKFQGIEPINFVNALKPWLKGPKHNCIAIVGPPNSGKSLLCNTLMSFLGGKVLTFANHSSHFWLAPLTDCRVALIDDATHACWRYFDTYLRNVLDGYPVCDRKHKSAVQLKAPPLLLTSNIDVHADYLQSRVKTFYFKEPCPASDTGEPLFFITDADWKNFFERLWERLDLEDEFTCASDHLLAAQETQMQCIEDSRLLQDHACYWRREKLLLYAARTKGLKTIGCVPVPPCSVTAEQAKQAICMQLIVEELLHSPWAKEPWSLDLSWERYQAAPCLKKGARVVEVEYDGNSSNKTWYTAWSTVYVRGTEEEGWETAVCADGQGIYY-VYFETFETDARRWEPERFSILSKPGGQPCLILSGNGNQAKCYRFRCKRYFREHYQHITTTW-WFKDSSQRGVFLKRVPLPW-QPSQRLYLPPT-PVKVLCSEQYIRRKDVFYHGETRMLTVGHPYYGKTIPKVSPNQYRVFRILLPDPNQFALPDKAMYDPSERLVWAVVGVQVSRGQPLGGSVSGHSYQNTLIDAENVSKKDDRKQGGDVKQQQILLLGCTPAIGEYWTTARPCGSCPPIELKNKPIEDGDMMDIGFGAANFKELNATKSDLPLDIAKDICLYPDYLKMTEEAAGNSMFFFARKEQVYVRHIWSRGGLFGVPSGSLVSTDGQLFNRPYWLFRAQGMNNGICWLNQLFVTVGDNTRGTTLTIYDTSKFNVFQRHVEEYKLAFVFQLCSVTLSPETVSHLQGLMPSILEHWDINMQP-PLEDTYSPATKCTPMGPEDPYAGLKFWEVNLKERLSLDLDQFPLGRRFLAQQGRKR Seq67 MDKENAG-GGDSFILD-EDLLDPGN-HLELFQTQEKEAGERQISILKRKLCLSPSWACCHKVVRRRLFDPGGASSAEPNMAACIQKLFKTLYIASHGEITRVFQSNKTVNHQWVYGVSEVLYSASFLFGKQCNCLQTSGSISVYRCMFNVAKSRDTVQKLMLNVTAGNLLLQPPKIRGLGPALFWFKLTLSPATGTTPEWIQQATNVASFDLGTMVQWAYDHGFTEESKIAYEYALCAGSDCNAKAFLASTSQARLVKDCCTMVRHYLRAEVQALTMSGYIKRRGSWLSIMNLLKYHGIEHIQFVNALKPWLKGPKYNCITIVGPPNSGKSLLCNSLIAFLGGKVLTFANHHSHFWLAPLADCRVALIDDATTACWRYFDTHLRNVLDGYPFGDRKHNTAVQMKAPPLLVTSNIDVHAEYSHSRVKPFYFKEPCPASDNGEPMFSITDADWKHFFERLWGRLDLEDEVTCAKEQLLAAQETQMTLIEDSTDLKDHIDSWRREHGLLYAARHKGLIWLGLNPVPPCSVKCLEARQAIEMQLLGNSLKESPWCNEPWSLDLSWGRYQAPPTLKKGARLVEVEYDGSSTNKTWYTAWNSLYLRKPDEEGWETATGADADGLFYRVYYELFERDAARYKEPRFSLFGSSGGLPCLLISGTGNQVKCYSFRVKRWHRDKYHHCTTTW-WFKDQSQRSMFLQQVPLPW-QPGQALYLPPT-PVKVLCSEQYINVRDIFYHGETRMLTSG-SILHTTVPKVSPNQYRVFRVALPDPNQFALPDKALHNPSERLVWAVVGVQVSRGQPLGGEVRGHSYFNTFLDAENVSKKDDRKQAGDTKQQQVLMLGCTPAIGEYWTKARPCGSCPPIELKLSFIEDGDMMDIGFGAANFKELNATKSDLPLDIANSICLYPDYLKMTEEAAGNSMFFFARKEQVYVRHIWTPWGFFASPSGSLVSTDGQLFNRPYWILRAQGMNNGVCWNNTLFVTVGDNTRGSTLTIYDTSKFNVYQRHVEEFKLAFILELCSVELTPETVSSLQGSMPSILENWEINLQP-PLEDIYSPATKCSPSKPEDPYSAHKFWEVNLKEKLSLDLDQFPLGRLVLQFDCICF Seq68 MSDEPGSGKGSEFILDLEDFVDQGN-HRELFQTQEKEAGEKAIQKLKRKLALSPPGLAAISLVKRRLFIGPKAILKSKNSAACKLKLFKTIFACSFCDLTRVFQSNKTTNLQWVYGPSETMYEASFLLKKACSYVLAVGTIALILACFNNAKSRDTVQKLFLNVHHEQLLMQPPKIRGVCAALFWFRLTFSPATGTLPQWIRTQTIAAEFDFGTMVQWAYDNSYCEESKIAYEYAMLANCDTNAKAFLASNNQAKMVKDCATMVRHYKRAEVQAMTMSEYIKRRGSWLPIMNLFKFQGIEPIRFVNSMRQWLRGPKKNCICIVGPPNSGKSLLCNSLISFLGGRVLTFAMHKSHFWLAPLSEARVALIDDATYACWKYFDTYLRNALDGYPICDRKHKTAVQMKAPPLLVTSNIDVHADYLHSRIVSFYFKETCT-TANGEPMFSITNADWKIFFERLWGRLELEEEFACASERLRAAQEQQMLLIEDSTKLCDHILFWRTENGLLYAARQKGITTLNGFPVPALAVSSSKAKQAIEMQLLLTELLNTTWGRDAWTLDTSYERYLLEPTLKKGPRVVEVMYDNDPANRMCYTAWSLLYMRTPA--GWLFATGADECGLYYRHYYDFFGRDAARYDPNKFSLFKTSGGQQCLLLSGSGNQAKCFRFRMKKGHRHKYVHCTTTW-WFDSPTQRELFLKTVPIPW-QAGQKLYLPPT-PVKTLCSETYVSRRDIYYHAETRLLTVGHPFYK-TVPKVSPNQYRAFMIQLPDPNQFALPDRTLYDPGEKLVWGLIGLQVSRGQPLGGAISGHTTFNALLDAENVSRRDDRKPAGDAKQQQVLLVGCTPAVGEYWAKARPCGSCPPLELKHKPIEDGDMMDIGFGAANFKELNSSRSDLPLDIVNEICLYPDYLRMTEETAGNSMFFFARKEQVYVRHVWARGGFIGAPSGSLLSTDGLIFNRPYWLYRAQGMNNGICWNNTVFVTVGDNTRGTNLTIYETGKINVYHRHVEEYKLAFIFQLCSVQLTPETVSSLQGLMPSILENWEINVQP-PLEDTYSPATKCPAS-K-DPYEGLRFWKIDLKEKFSLDIDQFPLGRRFLAQQGKRR Seq69 MSDEPGSGKGSEFILDLEDFVDQGN-HRELFQIQEKEAGDKAIQKLKRKLALSPPGLAAITLVKRRLFIGPKAILKSKNSAACKLKLFKTIFACSYSDLTRVFQSNKTTNLQWVYGPSETMFEASFLLKKACSYLLSVGTVALFLACFNNAKSRDTVRKLFLNVHPEQLLMQPPKIRGVCAALFWFRLTFSPATGTLPQWIRTQTIAAEFDFGTMVQWAYDNSYCEESKIAYEYAMLANCDSNAKAFLASNNQAKMVKDCATMVRHYKRAEVQAMSISEYIKRRGSWLPIMNLFKFQGIEPIRFVNSMRQWLRGPKKNCICIVGPPNSGKSLLCNSLISFLGGRVLTFAMHKSHFWLAPLSEARVALIDDATYACWKYFDTYLRNALDGYPICDRKHKTAVQMKAPPLLVTSNIDVHADYLHSRIVSFYFKETCT-TANGEPMFSITNADWKIFFERLWGRLELEEEFTCASDRLRAAQEQQMLLIEDSTKLSDHVLFWRTENGLLYAARQKGITSMNGFPVPALAVSSTKAKQAIEMQLLLTELMHTTWGRDAWTLDVSYERYLMEPTLKKGPRVVEVMYDNDPANRTWYTAWSLLYMRTPA--GWLFATGADECGLYYRHYYDFFGRDAARYDPNQFSLFKSSGGQQCVLLSGSGNQAKCFRFRLKKGHRDKYLHCTTTW-WFDSPTQRELFLKTVPIPW-HAGQKLYLPPT-PVKTLCSESYVYRKDIYYHAETRLLTVGHPFYK-TVPKVSPNQYRAFMVQLPDPNQFALPDRTIYDPTEKLVWGLIGLQVSRGQPLGGAISGHTSFNALLDAENVGRRDDRKAAGDAKQQQVLLVGCTPALGEYWDTARPCGSCPALELKHKPIEDGDMMDIGFGAANFKELNSSKSDLPLDITNEICLYPDYLRMTEETAGNSMFFFARKEQVYVRHVWSRGGFIGAPSGSLLSTDGLIFNRPYWLYRAQGMNNGICWNNTVFVTVGDNTRGTNLTIYDSAKINVYHRHVEEYKLAFIFQLCSVQLTPETVSSLQGLMPSILQNWEVNVQP-PLEDTYSPATKCSPT-K-DPYDGLKFWKIDLKEKFSLDIDQFPLGRRFLAQQGKRR Seq70 MANDKGSALGCSYLLQDEDFLDQGN-HLEVFQALEKKAGEEQLLNLKRKV-LGSSEASETPGAKRRLFENEANLVKSKNATVFKLGLFKSLFLCSFHDLTRLFKNDKTTNQQWVFGIAEVFFEASLLLKKQCSFVQMQGTCAVYLLCFNTAKSRETVRNLMLNVREECLLMQPPKIRGLSAALFWFKSSLSPATGALPEWIRAQTTL-HFDFGTMVQWAYDHKYAEESKIAYEYALAAGSDSNARAFLATNSQAKHVKDCATMVRHYLRAETQALSMPAYIKTRGSWKSILTFFNYQNIELITFINALKLWLNGPKKNCLAFIGPPKTGKSMLCNSLIHFLGGSVLSFANHKSHFWLASLADARAALVDDATHACWRYFDTYLRNALDGYPVSDRKHKAAVQIKAPPLLVTSNIDVQAEYLHSRVQTFRFEQPCT-DESGEQPFTITDADWKSFFVRLWGRLDLEEDFTCACERLHVAQETQMQLIEASDKLQDHILYWRTENTLLYAARKKGVTVLGHCRVPHSVVCQERAKQAIEMQLSLQELSKTEFGNEPWCLDTSWDRYMSEPCFKKGARVVEVEFDGNASNTNWYTVYSKLYMR-TE-DGWQLAKAADGTGLYYRIYYSRFGEEAARFDADGFHLL-KAGQSCFALISGSANQVKCYRFRVKKNHRHRYENCTTTS-FFGSPGQRQDFLKHVPLPW-QQGQKLYLPPT-PVKVLCSETYVQRKSIFYHAETRLLTVGHPYYK-TVPKVSANQFRVFKIQLPDPNQFALPDRTVHNPSERLVWAVIGVQVSRGQPLGGTVTGHPTFNALLDAENVNRKDDRKQTGDAKQQQILLLGCTPAEGEYWTTARPCGACPPLELKNKHIEDGDMMEIGFGAADFKTLNASKSDLPLDIQNEICLYPDYLKMAEDAAGNSMFFFARKEQVYVRHIWTRGGHFGSPSGSLVSTDNQIFNRPYWLFRAQGMNNGIAWNNLLFLTVGDNTRGTNLSIYDTGKFNLYHRHMEEYKLAFILELCSVEITAQTLSHLQGLMPSVLQNWEIGVQP-PLEDTYSPATKCIPP-KEDPYAGLKFWSIDLKEKLSLDLDQFPLGRRFLAQQGKRK Seq71 MANDKGSGLGCSYLLQDEDFVDQGN-HLEVFQALEKKAGEEQILNLKRKV-LGSSEASETPGAKRRLFE?EANLVKSKNATVFKLGLFKSLFLCSFHDITRLFKNDKTTNQQWVFGLAEVFFEASFLLKKQCSFLQMQGTCAVYLICFNTAKSRETVRNLMLNVREECLMLQPAKIRGLSAALFWFKSSLSPATGALPEWIRAQTTL-NFDFGTMVQWAYDHKYAEESKIAYEYALAAGSDSNARAFLATNSQAKHVKDCATMVRHYLRAETQALSMPAYIKARGSWKSILTFFNYQNIELITFINALKLWLKGPKKNCLAFIGPPNTGKSMLCNSLIHFLGGSVLSFANHKSHFWLASLADTRAALVDDATHACWRYFDTYLRNALDGYPVSDRKHKAAVQIKAPPLLVTSNIDVQAEYLHSRVQTFRFEQPCT-DESGEQPFNITDADWKSFFVRLWGRLDLEEDFTCACERLHVAQETQMQLIESSDKLQDHILYWRTENTLLYAARKKGVTVLGHCRVPHSVVCQERAKQAIEMQLSLQELSKTEFGDEPWSLDTSWDRYMSEPCFKKGARVVEVEFDGNASNTNWYTVYSNLYMR-TE-DGWQLAKAADGTGLYYRIYYSRFGDEAARFN-DGFHLL-KAGGSCFALISGTANQVKCYRFRVKKNHRHRYENCTTTW-FFGSPSQRQDFLKHVPLPW-QQGQKLYLPPT-PVKVLCSETYVQRKSIFYHAETRLLTIGHPYYK-TVPKVSANQYRVFKIQLPDPNQFALPDRTVHNPSERLVWAVIGVQVSRGQPLGGTVTGHPTFNALLDAENVNRKDDRKQTGDAKQQQILLLGCTPAEGEYWTTARPCGACPPLELKNKHIEDGDMMEIGFGAANFKEINASKSDLPLDIQNEICLYPDYLKMAEDAAGNSMFFFARKEQVYVRHIWTRGGHFGSPSGSLVSTDNQIFNRPYWLFRAQGMNNGIAWNNLLFLTVGDNTRGTNLTIYDSSKFNVYHRHMEEYKLAFILELCSVEITAQTVSHLQGLMPSVLENWEIGVQP-PLEDTYSPATKCIPA-KEDPYAGFKFWNIDLKEKLSLDLDQFPLGRRFLAQQGKKK Seq72 MADKSGRGGCSFVLDFDAEFIDQGN-TLALFQSQVAQAGKQKVNYLKRKLHLESRAVLQPVAAKRRLFCSSSEILKSKNSAACKLAVFKFVYAASFCDLTRPFKNDKTTNYQWVFGVSEELFEASKLLGRSCTYLHATGSVALLLLSFHVAKSRETVTNLLLNLRAEHMMLQPPKLRGVTSAMFWYKMTLSPNTGQLPRWIEQQILITEFDFSHMVQWALDNEMMDESSIAFHYAQMADHDSNARAWLGLSNQAKIVKDVCTMVHHYQRAIMRSMTMSAYVHKMGSWLVIMQFLKFHGIEPIRFVNALRPWLQGPKKNCLAFIGPPDTGKSLFTNSLMSFLKGKVLNFANSASHFWLAPLTEAKVALIDDATHACLKYCDTYLRNFFDGYSVCDRKHKNAVQIKAPPMLLTSNIDIQAEYLKSRVTCFYFNDKCPLNEDGKPLFQITDPDWKSFFERLWQRLELEEEFICAAERLSAAQETQMTLLEPSFDLKDHISYYRTENTIFYAARKKGLTSLGHCPVPTLATAAANAKAAIEMQLLLKDLLRSPFAKNDWSLDVSHERYKAPPTLKRKPRIVEVIFDKDPQNKTWYTLWDEVYVC-TV-DGWTLTTSADATGIFVRQYYELFGEDAQRFQRAGEFDLLKGGCRPCCLIEGNGNKVKCLRFRLKKSHRSRFLDITTTF-WFTDTTQRDLFLGSVSIPWQQQGQRLYFPP-NPVKVLCTESYVKRTSIFYHGETRLLTVGHPYWQ-NIPKVSGNQYRVFRVQLPDPNQFALPDKNLHNPAERLVWAILGLQVSRGQPLGAPVTGNQLFNVWTDAENVTAKDDRKQLGDVKQTQMLLIGCTPAIGEYWGKAIPCGDCPPIELKNKPIEDGDMMDIGFGACDWKDFSQNLSDVPLDLINSKSLYPDYLKMAEDSLGNSCFFYARREQVYVRHVYSRGGFMGTPSGSLVSTDGQLFNRPYWLYQAQGLNNGVCWDNELFITVGDNSRGGVFTIYNSANMNIYCRHVEEYKLAVILELCSVELTSETVAYLQTVNPSVLEKWEVGVNP-PLEDTYSKAIKCAAA-K-DKYENLSFWNIDLREKLSADLDQYPLGRRFLAQNGNKK Seq73 MDNTPGTGSSDWVLLDLVDFIDDSDFYRRLQVEQQREDDQRAAHVLKRKFLDSPSPRLEAIRARRKLYDSGHGLMQAGKPRNVLLALCKDAYGCSFSDLTRSYKSDKTVCGDWVAGVPCSLEEAITLLKPHSDYTHVNGLLLLLLVRWKTAKCRETVQKLLMSVEKHQMVLEPPKIRHPATAMFWYKRTLANASGETPEWILKQVSLQEFSLSAMVQWAYDNGLEGESEIAYGYAQLAEEDTNAEAFLRSNAQAKHVKDCAIMVRHYRRAEMCKMNIAQWIKLRGDWRPIMKFLKFQKVEILAFLTFMRHFLRGPKRNCMVLLGPPNTGKSLFGMSLMHFLGGKIISHVNSGSHFWLQPLLECKVAMLDDATTSTWDYMDIYLRNMLDGNTVCDAKHKAPMQLKCPPLIVTTNVDVTANYLHSRLKVFTFPNLCPLNCRGDPEFQLTPENWKAFLEKCWTSLGLDLLLRCLCSRLDVLQEQQMELIDDSGCLKDIISYYRREAVLLFAANVRDIKKVGLTVVPPKQVCEANAKQAIEMHLVLCSLSESTYGQEPWYLQVSHDMYMLRPTFKKNGKRVLVTFDGDESNLMEYMCWEAVYKQRQNGQ-WSCVKSVSHEGIYYRDMYVDFAREAAKYDNWGSKRPGTCPDIPTLLISGGPNQVKCLRYRLRRHHRKAYRSCSTTWSWFYSEAQRVNFQKTVRLPWLPSTDKLFLPPPAPVKILSTDAFVTRLDIFYHAGTRQLLVGHPYFKLIAKKVSGNQYRAARFTLPDPNRFALQDPTIYDPDERLVWACRGLQVGRGLPLGGGTTGHPYYNKAKDTENPNSGDNRQNVSDPKQVQMVFVGCSPCVGEHWDKVTSTGDCPAIELVSSHIQDGDMCDIGFGAINNKTLQESRSEVPLDIVSSICKHPDILQMSNDPFGNSMWFFAKREQMYVRHMWARRGYSATPSGSLITSDGQLFNRPYWIQTAQGKNNGICWGNEVFVTVADNTRSTNITIYEADHFKIYTRHMEEYEFSFIFQLCKVPLTPEVLAQLNNMNSKIIEKWNVGFATAALAEHYSLATKCEDTEE-DPYEGESYWNIDLSEAFSSELDSFPLGRKFLYQASKRA Seq74 MASQDSTGSGG-FILEYMDFIDDDGGRHLNALLAEDDARAVQAVMSKIGH-SREYSSGGKESSHRKRTRSPDSLIRSGKARAAMLGIFKDSFGVRFTDITRHFKSDKTVCRDWVVGVACSVSDAVPLVRPHTVYSHTTGNMALGLVRWKTAKCRDTCCKLLLTVENKQLLLEPPQTQNAGAALFWYKKSISRGSGETLEWIARQVSLSSFCLSRMVQWAYDCGYTNESTIAYEYAKLADDDSNAEAFLKCNNQARYVRDCCKMVTLYARAEMAKMSMNEWIGRRKEWRVIVQFLKVQKVEFIPFLMQFRKFLKGPKNNCLCFYGPSNTGKSMFCMSLLEFLKGRTISYVNSKSQFWLQPLGDSKIGLLDDATLPVWDYMDVYLRNLLDGNVFCDAKHKAPSQIKAPPLLVTSNYNIKEYYLVNRVHVITFPTVCQTDYKGDVSVKLESHHWKSFFRRWWPLLDSNDGFRCLTNHLDVLQETQMEIFENSGNISDAVEYWRKEHSLLFLARRNGHVKVGHQMVPPAHVSESSAKDAIGMQLLLEGLQKTEYSTLTWTLETSKDMYMSPPTFKKGGTTVTVTYDESGYNCMDYTYWKYVFFQ--DGDTWTRGQGCNEKGLAY-CYYVTFAEEAVKYASGRWKQSPYCTEVPALIIRGPPNNVKCYRYRLKQKYPDIFVSCSSTWTWFANTHKREYFLNGIHMP-------LQLPPPGALRVLHTDEFIVRTNAFYHAMTRLITIGNPYYSVIAEKVSPHQYRVFRIQLPDPNQLALVDASVYDPKERLVWLLRGFDIGRGGPLGVGATGHPLFDKLKDAENPNNADSRQNVCDPKSMQMIIVGCTPAVGHYWDKADACGACPALVLKNSPLEDGDMIDLGFGSMNNKALNESHSAVPLDIVNSITKHPDMLKMAAEPYGNSCWFCLVREQMFARHLWARNGYLCTPSGSLVSSDTQVFNRPFWLQRAQGRNNGACWHNELFVSIADNTRGTNFNIYKGDDFKQYVRHCEIFELTFIIQLGKVSLTAEAVAHLQGMDASILEEWNIGFQGTAAEEKYSLATKCPTPKVQDRYDGLSFWTVDVSKTLSKDLENYPLGRKFLYQAGKRK Seq75 MAHAEGTGAGGWFVVDLVDFI-Q-EVPLELFVQQTANDDAAAVQALKRKFVGSPSPRLDAIKARRRLFDSGYGLFKGSNVRAAILSKFKDLFGLSFYDLVRQFKSDKSICGDWVFGVYYAVAEAVKLIQPQCIYAHIQGMVVLLLVRFKCGKSRETVAKYMLNVPEKHMLIEPPKIRSGPCALYWYRTAMGNACGETPEWIVRQTVVGHFSLSVLVQWAYDNDIQDESDLAYEYAKLGNEDANAAAFLASNCQAKYIKDAMTMCRLYRRAEQARMSMAQWIVHRGDWKHIVKFLRYQRVEFITFISAFKLFLKGPKKSCLVFYGPSDTGKSLFCMSLLQYLGGAVISFVNSSSHFWLSPLADAKIGLLDDATGQCWTYIDVYLRSILDGNPISDRKHRTLTQLKCPPLMITTNVDPLADYLRSRITVFKFMNKCPVTASGEPVYTLNNETWKSFFQRSWARLELEEEFRCLADRLDACQEMLIDLYEDSNKLEDQLLHWRREHAILFRAREAGHTSVGHQVVPPLSVTKTKACQAIAVHLSLQSLNNSDFKYEPWTLDTSMDMWNTPPCWKKKGCTVTVKFDNSDCNEMDYVSWGHIYVNKEDTDTWYKVPGIDYAGLYYKHYYVAFAKEALKYCPKRPRDNSDSNNTPVIHLQGEANKLKCFRYRLHKFVPTLLVGATSTWHWYSSVEQRNLFLSRVTIPWRHGDGKTYLPPT-PVKVMSTDRYVTRTSLFYYGGSRLLTVGHPYYKANIPKVSGYQYRVFRVKLPDPNKFALPDTALYNPDERMVWACRGVEVGRGQPLGVGTSGHPLYNRLDDTENTPLLDRRDNVSDYKQTQLLIIGCKPPIGEHWAKGTVCGDCPPLQFVNSTIQDGDMVETGYGAMDFANLQESKSEVPIDICTGICKYPDYLQMAAEPYGDCMFFCLRREQMFARHFFNRHGYAPTPSGSMVTSDSQLFNKPYWLQRAQGHNNGICWFNEVFVTVVDTTRSTNITIYQASNFKEYLRHTEEYDLQFIFQLCKIRITPEIMAYLHGMNESLLDDWNFGVLPPPLDDTYSRAITCAPDPK-DPYDGLAFWEVDLKDKLSMDLDQFPLGRKFLLQLGKKK Seq76 MADTEGTGAGGWFMVDLVDFI-Q-EVPLELFVQQTAEDDAAIVQAVKRKFVCSPSPRLDAIKARRRLFDSGYGLFKCSNVRAAILSKFKDLFGLSFYDLVRQFKSDKSICGDWVFGVYYAVAEAVKLLQPQCLYAHIQGMVVLMLLRFKCGKSRETVAKYMLNVPEKHMLIEPPKIRSGPCALYWYRTAMGNASGETPEWIVRQTVVGHFSLSMLVQWAYDNDIQEESDLAYGYAQLGNTDPNAAAFLASNCQAKYIKDAMTMCRLYRRAEQSRMSMAQWIAHRGDWKHIVKFLKYQNVEFISFISAFKLFLKGPKKSCLVFYGPSDTGKSLFCMSLLQYLGGAVISYVNSSSHFWLSPLADAKIGLLDDATAQCWTYIDVYLRSILDGNPTSDRKHRTLTQLKCPPLMITTNVDPLADYQRSRITVFKFLNKCPVTNSGELVYTLNNETWKSFFQRSWARLELEEEFRCLADRLDACQETLIDLYEDSNKLEDQLLHWRLEHAILFRAKEAGHAKVGHQVVPPLHVTKSKACQAIAVHLSLQSLNDSDFKHEPWTLDTSMEMWNTPPCWKKKGRTVTVRFDKSDCNEMDYVSWGHIYVYTEETRTWHKVSGVDYAGLYYKQYYVEFVKEAQKYCTNATRDTVNRYHTPVIHLQGEPNKLKCFRYRLHKSVPTLFVRATSTWHWYASIEQRELFLTRVTIPWRPGDGKTYLPPT-PVKVISTDRYVTRTNLFYYGGSRLLTVGHPYYKATIPKVSGYQYRVFRIRLPDPNKFSLPDTSLYNPDERMVWACRGIEVGRGQPLGVGTSGHPLYNRLDDTENTPLLDRRDNVSDYKQTQLIIIGCKPPIGEHWAKGTVCGDCPPLQFVNSTIQDGDMVETGYGAMDFATLQESKSEVPIDICTATCKYPDYLQMAAEPYGDCMFFCLRREQMFARHFFNRHGYAPTPSGSLVSSDSQLFNKPYWLHRAQGHNNGICWFNELFVTVVDTTRSTNITIYTASNFKEYLRHTEEYDLQVILQLCKIHLTPEIMAYLHSMNEHLLDEWNFGVLPPPLDDTYSRAITCAPAPK-DPYDGLVFWEVDLKDKLSTDLDQFPLGRKFLLQLGKKK Seq77 MADHEGTRAGGWFLVDLVDFIVQ-EVPLALYVHQNAQDDAAAVQALKRKFTYSPSPRLDAIKARRRLFDSGYGLLKASNIRATILSKFKELFGLSYYDLVRQFKSDKSTCGDWVFGVYHAVAEAVKLLQPHCVYAHIQGMVVLALVRFKCGKNRESVAHCMLNIPDRHMLIEPPKIRSGPCALYWYRTAMGNASGETPEWIVRQTVIGEFSLSTLVQWAYDNDITDESQLAYEYALLGNEDPNAAAFLASNCQAKYIKDAITMCKHYKRAEQARMTMAQWIKYRGDWRHIVKFLRYQNVEFITFMSAFKHFLKGPKKSCMVFYGPSDTGKSLFCMSLLHYLGGAVISFVNSSSHFWLSPLVDAKVGLLDDATMQCWTYIDVYLRSILDGNAISDRKHRNLTQLKCPPLMITTNVDPLADYLKSRIVVFRFLNKCPMNANGEPVYTLNNETWKSFFQRSWARLDLEEEFRCLADRLDACQERLIDLYEDSNKLQDQLSHWRLEYAILYKARQAGLTSVGHQVVPPLNVTKEKASQAITIHLSLQSLNASAYKDEPWTLHTSLDMWNAPPCWKKKGHTVTVKYDGEDQKAMEYVSWGEIYIQCPETDMWYKVAGVNATGLYYRHYYVNFTKEAKHYGTERLGIAHCCNTAPVIHLQGEANKLKCFRYRLKQTVPDLYLRASSTWHWYSSAEQRAQFLARVHIPWRPGDGKVYLPPT-PVKVLSTDRYVQRTNLYYYGGSRLITVGHPYYKANIPKVSGYQYRVFRVRLPDPNKFGLPDASLYNPDERLVWACRGIEVGRGQPLGVGTSGHPLYNRLDDTENASLLDSRDNISDYKQTQLLIIGCKPPIGEHWAKGTVCGDCPPLQLLNTTIEDGDMVETGYGAMDFSKLQESKSEVPIDICDSTCKYPDYLQMAAEPYGDCMFFCLRREQMFARHFFNRQGYAPTPSGSMVTSDSQLFNKPYWLHRAQGHNNGICWFNELFVTVVDTTRSTNLTIYSSTRFKEYLRHTEEYDLQFIFQLCKIHLTPEIMSYLHNMNDTLLDEWNFGVIPPPLDDTYSRAITCAPEPK-DPYDKLSFWDVDLKERLSTDLDQFPLGRKFLLQLGKRK Seq78 MADAEGTGAGGWFMVDLVDFIDQ-EVPLELFVQQNARDDAAAVQALKRKYTYSPSPRLNAIRARRRLFDSGYGLLKTSNLRATLLSKFKELYGLAFGELVRQFKSDKSVCGDWVFGVYHAVAEAIKLIQPVCLYAHIQGMVILMLIRFKCSKSRETVAKCILNVPDKQMLIEPPKIRSAPCALYWFRTAMGNASGETPEWITRQTVVGHFSLSVLVQWAYDNDIVDESDLAYQYALLGNDDPNAAAFLASNCQAKYIKDAITMCKYYKRAEQKRMSMAQWIAHRGDWRPIVRFLRYQKIEFVTFMSALKMFLRNPKKSCIVIYGPSDTGKSLFCMSLLKFLGGAVISYVNSTSHFWLSPLTDAKVGLLDDATYPCWVYIDTHLRSVLDGNQISDRKHKNLTQIQCPPLFITTNINPLEDYLHSRIAVFHFMYKCPLDDKGDPVYQFNNENWKSFFQRSWAQIEGEEEFRCLADRLDACQEKLIDFYEDSDKLDDQVKHWRLEHAILFKARQAGLTQLGHQVVPALSVTKGKAHLAIEVHLALKSLYASVYKDEPWTLSTSLDMWNAKPCWKKKGRTVTVKFDCEDSKIMEYVSWGYIYVQ-TDTEQWCKVQGVSYEGLYYRHYYVVFADEAKKYSTK-GRHVCDGGAAPVIHLRGEPNKLKCLRYRLRGSVPHLFVKASSTWHWYANIEQRTEFLQRVTIPWRPGDGKVYLPPT-PVKVLSTDTYVTRTNLFYYGGSRLLTVGHPYCRATIPKVSGYQYRVFRVKLPDPNKFTLPDATLYNPDERMVWACRGIEVGRGQPLGVGTSGHPLYNRLDDTENTSLLDSRDNISDYKQTQLLIVGCKPPIGEHWTKGTLCTECPPLEFKNTTIQDGDMVETGYGAIDFKSLQESKSEVPLDICTSTCKYPDYLQMAAEPYGDCMFFCLRREQMFARHFFNRHGYAPTPSGSMVSSDSQLFNKPYWLQRAQGHNNGICWFNELFVTVVDTTRSTNFTIYKATNFREFLRHTEEFDLQFIFQLCKIQLTPEIMAYLHNMNKDLLDDWNFGVLPPPLDETYSRAITCSPSPKVDPYAQMTFWTVDLKDKLSTDLDQFPLGRKFLLQAGKRK Seq79 MADVEGTRAGGWFMVDLVDFI-Q-EVPLELFVQQNAQDDAAAVHALKRKYIHSPSPRLDAIRARRRLFDSGYGLLKARNLRATLLSKFKELYGLAFGELVRQFKSDKSTCTDWVFGVYYAVAEALKLIQPLCHYAHIQGMVQLMLIRFKCGKSRDTVAHCILNVSEKQMLIEPPKIKSTPCALYWYKTAMGNASGETPEWIVRQTVVGHFSLSVMVQWAYDHDITDESQLAYEYALLGHEDPNAAAFLASNCQARYIKDAITMCRHYKRAEQARMSMAQWIAHRGDWKPIVRFLRFQKIEFMTFMGAFKMWLKGPKRSCIVIHGPSDTGKSLFCMSLVQFLGGAVISYVNASSHFWLSPLADAKVGLLDDATHPCWVYIDTHLRSVVDGNLISDRKHRNLAQLKCPPLLITTNINPLEDYLHSRMAVFSFMYKCPLDDNGDPVYKFNNENWKSFFQRSWARLEVEEEFRCLADRLDACQETLIDLYEDSNKLEDQIKHWRLENAILFKARQAGLTHLGHQVVPTLNVTKGKAHQAIAVHLSLQSLYASEYKDEPWTLNTSLDMWNTQPCWKKKGRTVTVKYDCEDSKKMEYVNWGHIYVQSPHTEQWHRVHGVSYDGLYYKQYYVQFAQEARKYSTE-TRNFSDGDTAPVIHLRGDANKLKCLRYRLRSSVPHLFVNASSTWHWYANEDQRAKFLQRVTIPWRPGDGKVYLPPT-PVKVISTDAYVTRTNLFYYGGSRLLTVGHPYCRSTIPKVSGYQYRVFRVKLPDPNKFALPDGTLYNPDERMVWACRGIEVGRGQPLGVGTSGHPLYNRLDDTENTAYLDSRDNISDYKQTQLLIIGCKPPIGEHWARGTFCNECPPLEFKNTTIQDGDMVETGYGAIDFKALQENKSEVPLDICNTTCKYPDYLQMAAEPYGDCMFFCLRREQMFARHFFNRHGYAPTPSGSMVSSDSQLFNKPYWLQRAQGHNNGICWFNEMFVTVVDTTRSTNFTIYKASNFKEFLRHTEEYDLQFIFQLCKIQLTPEIMAYLHNMNKALLDDWNFGVLPPPLDDTYSRAITCAPAPKEDPYADMSFWTVDLKDKLSTDLDQFPLGRKFLLQSGKR- Seq80 MADSEGTRAGGWFLVDLVDFI-Q-EVPLALFVQQNAQDDAATVQALKRKYTCSPSPRLDAIRARRRLFDSGYGVFKVSNLKAKLLYKFKDLFGLAFGELVRNFKSDKSICGDWVFGVYHAVAEAVKLIQPICVYAHIQGMVILMLVRYKCGKSRETVAHSMLNIPERQMLIEPPKIRSAPCALYWYRTAMGNASGETPEWIVRQTVVGHFSLSMLVQWAYDNDITDESVLAYEYALLGNEDPNAAAFLASNCQAKYIKDAITMCKHYRRAEQAKMTMAQWITHRGDWKAIVKYLRYQQVEFVPFISALKLFLKGPKKSCMVFYGPSDTGKSLFCMSLLNFLGGAVISYVNSSSHFWLSPLADTKVGLLDDATYQCWQYIDTYLRTVLDGNAISDRKHRNLTQLKCPPLMITTNINPLEDYLHSRIVVFQFLHKCPLNSNGDPVYTLNNENWKSFFRRSWARIEGEEEFRCLADRLDACQEKLLDLYEDSNKLEDQILHWRLENAMLFKARQAGLTRVGHQMVPTLSVTKGKAHKAIEVHLSLQGLQTSAYAHEPWTLTTSLEMWNTQPCWKKKGRRLTVKFDGEDHKAVEYVSWGYIYVQSTETDLWYKVPGVSYKGLYYEHYYVTFAQEAQKYSTETG-DNCDSYSSPVIHLKGDPNKLKCFRYRLQHSVPELFDKASSTWHWYVNVEQRKQFLNRVTIPWRPGDGKVYLPPT-PVKVISTDRYVQRTNLFYYGGSRLLTVGHPYCKNTIPKVSGYQYRVFRVQLPDPNKFALPDGTLYNPDERMVWACRGIEVGRGQPLGVGTSGHPLYNRLDDTENTTLLDSRDNVSDYKQTQLLIVGCKPPIGEHWTKGTACTDCPPLEFTNTTIQDGDMVETGYGAIDFAALQENKSEVPLDICTTICKYPDYLQMAAEPYGDCMFFCLRREQMFARHFFNRQGYSPTPSGSMVSSDSQLFNKPYWLQRAQGHNNGICWFNELFVTVVDTTRSTNLTIYKATSFREYLRHTEEFDLQFIFQLCKIHLTPEIMAYLHNMNKALLDDWNFGVVPPPLEDTYSRAITCAPPPKEDRYAKLSFWTVDLRDKFSTDLDQFPLGRKFLLQAGKRK Seq81 MANCEGTRAGGWFLVDLVDFI-Q-EVPLDLFVQQNARDDAATVQALKRKYTCSPSPRLDAIRARRRLFDSGYGIFKVSNLRVTLLHKFKELFGLAYGDLVRQFKSDKSICGDWVFGVYHAVAEAVKLIQPICLYAHIQGMVILMLVRYKCGKSRETVAHSMLNIPEKQMLIEPPKIRSGPCALYWYRTAMGNGSGETPEWIVRQTVVGHFSLSTLVQWAYDNDITDESELAYDYAMLGNEDPNAAAFLASNCQAKYIKDAITMCKHYKRAEQARMSMTQWIAHRGD*E?IVKYLRYQRVEFVTFMGALKLFLKGPKKSCMVFYGPSDTGKSLFCMSLLKYLGGAVISYVNSGSHFWLSPLVDAKVGLLDDATYQCWQYIDTYLRTVLDGNAISDRKHRNLTQLKCPPLMITTNINPLEDYLHSRIVLFKFMHKCPLKSNGDPVYTLNNENWKSFFQRSWARIEGQEEFRCLADRLDACQEKLLDLYEDSDKLEDQILHWRLEHAMLFKARQAGLTHVGHQVVPTLSVTKGKAHQAIEVHLSLQGLQNSAYAQEPWTLNTSLEMWNAHPCWKKKGRTITVKFDCEDLKAVEYVSWGCIYVQSTEDEQWYKVQGVSYHGLYYKQYYVTFGHEARKYCTQQARDICNSHGAPIIHLKGEPNKLKCFRYRLQQSVPNLFLKASSTWHWYVDTDQRTQFLSRVNIPWRPGDGKVYLPP-NPVKVLSTDRYVQRTNLYYYGGSRLLTVGHPYCKNTIPKVSGYQYRVFRVKLPDPNKFALPDGTLYNPDERLVWACRGIEVGRGQPLGVGTSGHPLYNRLDDTENTSLLDSRDNVSDYKQTQLLIIGCKPPIGEHWTKGTPCTDCPPLEFTNSTIQDGDMVETGYGAIDFATLQENKSEVPLDICTTTCKYPDYLQMAAEPYGDCMFFCLRREQMFARHFFNRQGYSPTPSGSMVSSDAQLFNKPYWLQRAQGHNNGICWFNELFVTVVDTTRSTNVTIYTASNFREYLRHTEEFDLQFIFQLCKIHLTPEIMAYLHNMNKALLDDWNFGVVPPPLDDTYSRAITCTPPPKEDPYANLSFWTVDLKDKFSTDLDQFPLGRKFLLQVGKRK Seq82 MAESPEGGAGGWFVVDMVDFVDQ-EVPLGLYVQQTMQDDAATVQALKRKFMGSPSPRLDAIKARRRLFDSGYGLLQVSNLRVQLFTKFKELFGLSFKDLVRQFKSDRSTCAEWVFGVYYAVAEAAKLLQPVCEYAHIQGMVMLLLLRFKCNKSRETVAKCLLNIPEKRMLIEPPKQRSAPCALYWYKTAMGNASGDTPDWIVRQTVIGHFSLSVLVQWAYDNEITDDSELAYEYAKLGNEDPNAAAFLASNCQARYIKDAITMVRHYRRAEQARMSMSQWIAHRGDWRHIVKLLRFQGIEFISFMEALKQFLKGPKKSCLVFYGPSDTGKSLFCMSLLRYLGGAVISFVNSTSHFWLSPLVDAKIGLLDDATQQCWVYIDTYLRTVLDGNTMSDRKHKNLQQLKCPPLMITTNVNIAADYLRSRMVVFPFLQKCPLDSNGEPVYKLNNENWKSFFQRSWARLDLEEEFRCLADRLDACQDKLLDLYEDSNTLEDQLLHWRLEKAILFKAREAGLTHIGHQVVPASSVTKEKARQAITVHLSLQSLNNSEFKHEPWTLDTSLDMWNVPPCWKKGGRPIRVKYDGEDDKEMEYVSWGFIYIYCGSDETWHKVPGISHAGCYYKHYYVDFGKEAKQYIPQWSRDNSNSHGAPVIHLKGDPNRLKCLRYRLQQSVPQLFERASSTWRWYRDTEQRQQFLARVNIPWRPGDGKVYLPPT-PVKVLSTDRYVSRTNLFYYGGSRLLTVGHPYYKANIPKVSGYQYRVFRVRLPDPNKFGLPDAQLYNPDERLVWACRGLEVGRGQPLGVGASGHPLYNRLDDTENTSFLDSRDNVSDYKQTQVLIVGCKPSIGEHWGKGTVCGDCPPLQFTNSTIQDGDMVEAGYGAMDFDKLQENKSEVPLDICATVCKYPDYLQMAAEPYGDCMFFCLRREQMFARHFFNRQGYAPTPSGSMVSSDSQLFNKPYWLQKAQGHNNSICWFNQLFVTVVDTTRSTNFTIYDPSKFNEYLRHAEEYDLQFIFQLCKVRLTPEIMSYLHSMNESLLDEWNFGVVPPPLEDTYSRAITCAPQPKEDPYAGMVFWEVDLKDKLSTDLDQFPLGRKFLLQSAKRR Seq83 MAESPEGGQG?WFVVDMVDFIDQ-EVPLDLYVQQTMQDDAATVHALKRKYIGSPSPRLDAIKARRRLFDSGYGLLQVSNLRVKLLAKFKELFGLSFMDLVRQFKSNKSTCGDWVFGVYYAVAEAAKLLQPVCDYAHIQGMVMLLLLRFKCNKSRETVAHCILNIPEKRMLIEPPKQRSGPCALYWYKTAMSNASGETPDWIVRQTVIGHFSLSVLVQWAYDNEITEESELAYEYAQLGNEDANAAAFLASNCQARYIKDAITMVRHYRRAEQARMTMSQWIAYRGDWRHIVKLLRYQGIEFISFMEALKHFLKGPKKSCLVFYGPSDTGKSMFCMSLLKYLGGAVISFVNSTSHFWLSPLVDTKIGLLDDATQQCWVYMDTYLRTVLDGNTMSDRKHKTLQQLKCPPLMITTNVNIEADYLRSRMVVFPFLHKCPLDSNGDPVYKLNNENWKSFFQRSWARLDLEEEFRCLAARLDACQDKLIDLYEDSNKLEDQLLHWRLEKAMLFKARETGLTHIGHQVVPPLSVTKEKARQAITVHLSLQSLNNSEFRHEPWTLDTSLDMWNVSPCWKKQGRPIRVKYDGEDDKEMEYVSWGYIYIQCASTDTWLKVPGISHQGLYYKRYYVDFAKEAKHYNTQWTRDNCISNGAPVLHLKGDPNRLKCFRYRLQQSVPQLFDKASSTWRWYKDTEQRKQFLARVNIPWRPGDGKVYLPPT-PVKVISTDRYVSRTNLFYYGGSRLLTVGHPYYKSNIPKVSGYQYRVFRVRLPDPNKFGLPDAQLYNPDERLVWACRGLEVGRGQPLGVGTSGHPLYNRLDDTENTPFLDSRDNVSDYKQTQLLIVGCKPSIGEHWSKGTVCGDCPPLQFTNSTIQDGDMVEAGYGAIDFEKLQENKSEVPLDICTTVCKYPDYLQMAAEPYGDCMFFCLRREQMFARHYFNRQGYAPTPSGSMVSSDSQLFNKPYWLQRAQGHNNGICWFNQLFVTVVDTTRSTNFTIYDPTKFKEYLRHVEEYDLQFIFQLCKVRLTPEVMAYLHTMNESLLDEWNFGVVPPPLDDTYSRAITCATKPKEDPYTGMLFWEVDLKDKLSTDLDQFPLGRKFLLQSAKRR Seq84 MAESPEGGAGGWFVVDLVDFIDQ-EVPLDLYVQQTIQDDAATVQALKRKFMGSPSPRLNAIKARRRLFDSGYGLLQVSNLRVKLLGKFKELFGLSFMDLVRQFKSNKSTCGDWVFGVYHAVAEAAKLLQPVCEYAHIQGMVMLLLLRFKCNKSRETVAHCILNVPEKRMLIEPPKQRSGPCALYWYRTAMGNACGETPDWIVRQTVIGHFSLSKLVQWAYDNDITDESELAYEYAQLGTEEPNAAAFLASNCQARYIKDAMIMCRHYRRAEQTRMSMSQWITYRGDWRHIVKLLRYQGIEFISFMTALKQFLKGPKKGCLVFYGPSDTGKSLFCMSLINYLGGTVISFVNSTSHFWLSPLADAKIGLLDDATYQCWIYMDTYLRSVLDGNVISDRKHKNLVQLKCPPLLITTNINPETDYLRSRMVIFPFLNKCPLDANGDPVYQLNNENWKSFFRRSWARLDLEEEFRCLADRLDACQEKLIDLYEDSNKLEDQLMHWRLEQAMLFKAREAGLRHIGHQVVPTLSVTKEKARQAIMVHLSLQSLNNSAFKHEPWTLDTSLNMWTVAPCWKKHGQPIRVKYDGEDDKEMEYVNWGFIYVHCASEDTWYKVPGISNRGLYYKHYYVEFAKEAKQYSPKRPREHSNCDSAPVLHLKGQSNSLKCFRYRLHQSVPDLFERASSTWKWYKSTDQRKQLLARVHIPWRPGDGKVYLPPT-PVKVISTDRYVSRTNLFYYGGSRLLTVGHPYYKATIPKVSGYQYRVFRVHLPDPNKFGLPDAQLYNPDERLVWACRGVEVGRGQPLGVGTSGHPLYNRLDDTENTPFLDSRDNVSDYKQTQLLIIGCKPSIGEHWGKGTVCGDCPPLQFTNSTIEDGDMVEAGYGAIDFATLQESKSEVPLDLCTTTCKYPDYLQMAAEPYGDCMFFCLRREQMFARHFFNRQGYAPTPSGSMVSSDSQLFNKPYWLQKAQGHNNGICWFNQLFVTVVDTTRSTNFTIYKPTNFKEYLRHVEEYDLQFIFQLCKVRLTPEVMSYLHTMNDSLLDEWNFGVVPPPLDDTYSRAITCAAKPKDDPYAGMSFWDVDLKDKFSTDLDQYPLGRKFLLQSAKRR Seq85 MADGEGTSACGWFWVDMVDFIDQ-EVALELYRQQEAQDDEAFVQALKRKYLASPSPRLDAIKAKRRLFDSGYGLLKTSNLRATLLGKFKDIYGLSFMELARQFKSNRTTCLDWVFGVYCTVAEGVKLIQQHCQYAHIQGMVVLMLVRYNCAKNRDTVAKCMLNIPEQHMLIEPPKIRHPAAALYWYKAGMGNASGETPEWIVRQTVVGHFQLSVMVQWAYDHDITDESILAYEYAKLADVDGNAAAFLASNCQAKYVKDACTMCRHYKRAEQAQMTMSEWIRFRGDWRPIVRFLRHHDIEFITFVISLKNFLKGPKKCCIVIYGPADTGKSYFCMSLLRFLGGVVISYANSTSHFWLQPLCDAKIGLIDDVTPQCWSYIDTYLRNALDGNQVCDRKHRPLLQLKCPPLLMTTNTNPLEEFLRSRLQLFTFPNAFPVNQKGDPLYTLNDANWKCFFQRLWARLDLDDQFKCLASRLNACREKLLELYEDSDQLQDQIMHWRLENVIMYKAREAGMLHLNHQVVPVQLIAKAKACKAIEMQLALKTLLKSPYGTEPWTLDTSQEMWDAAPCWKKKGYTVDVKFDGEDTKTMCYTCWRYIYVQSGDDDTWTKVSGVDHAGLYFRVTYVRFAQEAQTYSAKQHGNIRVGDSAPVTHLRGDPNKLKCLRYRLHKHKSVLFSKVTSTWRWHDSAEQRATFLSTITLPWKPSDNKVYLPPA-PVKVLSTDAYVSRTNIYYYAGSRLLTVGHPYFPIVVPKVSGYQYRVFRVRLPDPNKFGLPDASLYNPDQRLVWACTGVEVGRGQPLGVGVSGHPLYNRLYDTENTNLYDTRDNLTDYKQTQLFIIGCKPPLGEHWAKGTPCGDCPPIELKSSTIQDGDMIDTGFGALDFAALQTNKSDVPLDIVTTTCKYPDFLQMAAEPYGDLMFFCLRREQMFIRHFYNRAGYGSTPSGSMVTSESQLFNKPYWLQRAQGPNNGICWGNQLFVTVVDTTRSTNMTIYKASNFKEYMRHGEEFDLQFIFQLCVINLTTEVMAYLHGMNATLLEDWNFGSLPPPLGDTYSQAISCVPTE-NDPYAGLTFWEVDLSERFSLELDQFPLGRKFLLQRGKRK Seq86 MADCEGTGACGWFWVDMVDFIDQ-EGAPELYRQQEVQDDEAIVQALKRKYIASPSPRLDAIRAKRRLFDSGYGLLKTSNLRATLLGKFKDLFGLSYMELVRQFKSNKTTCIDWVFGVYCTVAEGVKLIQQHCQYAHIQGMVVLMLVRYNCAKNRDTVAKCMLNIPEHHMLIEPPKIRYPPAALYWYKAGMGNASGETPEWIVRQTVVGHFQLSVMVQWAYDHDITDESILAYEYARLADVDGNAAAFLASNCQAKYVKDACTMCRHYKRAEQAQMTMSQWITFRGDWRPIVRYLRHQDIEFISFVIALKNLLKGPKKCCIVIYGPADTGKSYFCMSLLRFLGGVVISYANSSSHFWLQPLCDAKIGLIDDVTPQCWSYIDTYLRNALDGNQVCDRKHRPLLQLKCPPLLMTTNTNPLEEFLRSRLQMFTFKNAFPVNQKGDPLYILNDANWKCFFQRLWARLDLQDDFRCLASRLDVCQEKLLELYEDSDQLQDQILHWRWENVILYKAREAGHLHLNHQVVPVQLIAKAKACKAIEVQLALKTLLKSPYGTEPWTLDTSQEMWDTEPCWKKRGYTVDVKFDGEESKTMCYTCWRDIYVQSIADDTWDKVTGVDHAGLYYRVTYVKFSQEAQTYGANQHRNISVGHGAPVIHLRGDPNKLKCLRYRLKKHTPVLFTKASSTWRWHDSVEQRAKFLATVTLPWKPSDNKVYLPPA-PVKVLSTDDYVSRTNIYYYAGSRLLTVGHPYYPIVVPKVSGYQYRVFRVRLPDPNKFGLPDASLYNPDQRLVWACTGVEVGRGQPLGVGVSGHPLYNRLYDTENTNLFDNRDNMSDYKQTQLCIIGCKPPLGEHWAKGTPCGDCPPLELTSSTIQDGDMIDTGFGALDFASLQTNKSDVPLDIVTTTCKYPDFLQMAAEPYGDLMFFSLRREQMFVRHFYNRAGYGSTPSGSMVTSESQLFNKPYWLQRAQGRNNGICWGNQVFVTVVDTTRSTNMTIYKASNFKEYLRHGEEFDLQFIFQLCVINLTAEVMAYLHSMNAALLEDWNFGSLPPPLGDTYSQAITCAPA-KKDPYDGLTFWDVDLSERFSLELDQFPLGRKFLLQHRKRK Seq87 MADCEGTGAGGWFFVDMVDFINQ-EVAAEVYRQQEALDDEAIVQPLKRKFLASPSPRLDAIKAKRRLFDSGYGLLNSSNRRATLLGKFKDLYGLSYMELVRQFKSNKTTCLDWVFGVYCTVAEGVKLIQQHCEYAHIQGVVILMLLRYKCAKNRDTVAKGLLNIPETNMLIEPPKIRSTPAALYWFRASMGNASGETPEWIVRQTVVGHFQLSVMVQWAYDHDITDESILAYEYARLADVDSNAAAFLASNCQAKYVKDACTMCRHYKRAEQAQMSMSQWISFRGDWRTIVKYLRHQDIEFITFIIALKNFLKGPKKSCLVFYGPADTGKSYFCMSLLRFLGGVVISYANSSSHFWLQPLADAKLGLIDDVTPNCWSYIDVYLRNALDGNQICDRKHRPLLQLKCPPLLITTNTNPLEEFLRSRLQLFTFKNAFPLNSKGDPMYPLNDANWKCFFQRLWARLDLDEQFRCLASRLDACQETLLELYEDSDQLQDQIKHWRWENVLLFKAREAGITHLAHQVVPVLGIAKAKACKAIEIQLALKTLLNSPYSNERWTLDTSQEMWDAVPCWKKKGYTVEVRYDCKEEKTMCYTCWREIYVQNSTNETWEKVCGVDHAGIYYRVDCVLFSKEAVIYGAHEHGRTRDCNSAPVIHLRGEANKLKCLRYRLQKHKSVLFAKASSTWHWHDSVEQRAQFLATVHIPWRPSDSKVYLPPA-PRQVLSTDDYVTRTKLFYYAGSRLLTVGHPYFRIVVPKVSGYQYRVFRVRLPDPNKFGLPDASLYNPDQRLVWACKGLEVGRGQPLGIGVSGHPLFNKLNDTENATLFDTRDNVSDYKQTQLCIIGCKPPLGEHWAKGTPCGSCPPLELASTVIQDGDMVDTGFGAMDFAALQTNRSDVPLDIVTTTCKY?DYF?MAAEP?GDRMFFLR??E?MF?R?FYNRQAYASTPSGSMVTSESQLFNKPYWLQRAQGTNNGICW?NLLFVTV?DTSRSTNMSIYKASSFMEYLRHGEEFDLQFIFQLCVINLTAEIMAYLHGMDATLLEDWNFGSLPPPLGDTYSQAITCPPAEK-DPYADLTFWEVDLKERFSLELDQFPLGRKFLLQSGKRK Seq88 MEDSEGTRAGGWFHVEDLDFIDQ-EVPLQLYAQQIAQDDEATVQALKRKFVASPSPRLDAIKAKRRLFDSGYGLLKSSNLKATLLSKFKELYGVGYYELVRQFKSSRTACADWVFRVYYAVAEGIKLIQPHTQYAHIQGMVVFMLLRYNCAKNRDTVSKNMLNIPEKHMLIEPPKLRSTPAALYWYKTSMGNGSGETPEWIVRQTLIGHFKLSVMVQYAYDHDITDESALAFEYAQLADVDANAAAFLNSNCQAKYLKDAVTMCRHYKRAEREQMSMSQWITFRGDWKPIVKFLRHQGVEFVSFLAAFKSFLKGPKKNCIVFYGPADTGKSYFCMSLLQFLGGAVISYANSSSHFWLQPLADSKIGLLDDATAQCWTYIDTYLRNLLDGNPFSDRKHKTLLQIKCPPLMITTNINPLEEYLRSRVTLFKFTNPFPFASPGEPLYPINNANWKCFFQRSWSRLDLEDQFRCLASRLDACQETLLELYEDSNKLEDQIKHWRLENVMLFKARECGMTRVGCTTVPALTVSKAKACQAIEVQLALQTLMQSAYSTEAWTLDTCLEMWEAPPCWKKKGQSVLVKFDGSCDRDMIYTGWGHIYVQDINDDTWHKVPGVDELGLFYRVNYVDFGIEALTYSTDPRRGHSDLDAVPVIHLQGEANCLKCFRYRVQKHKDVLFVKASSTWHWYKSQEQRAEFLTRVHLPWRPNESKVYLPPT-PVKVLSTDVYVTRTNVYYHGGSRLLTVGHPYYKVSVPKVSGYQYRVFHVKLPDPNKFGLPDANLYDPDQRLLWACVGVEVGRGQPLGVGISGHPYYNKQDDTENS-HNDGREYISDYKQTQLFILGCKPPIGEHWSKGTTCGDCPPLQFTNTTIEDGDMVETGFGALDFAALQSNKSDVPLDICTNICKYPDYLKMAADPYGDSMFFSLRREQMFTRHFFNRGGYTSTPSGSMVSSEQQLFNKPYWLRRAQGHNNGMCWGNRIFLTVVDTTRSTNVSLYKASNYKEYLRHMEEYDLQFIFQLCKITLTPEIMAYIHNMDARLLEDWNFGVPPPPLQDTYSQAITCPKT-P-DPYATMTFWDVDLSESFSMDLDQFPLGRKFLLQRGKRK Seq89 MEDSEGTRAGGWFHVEDVDFIDQ-EIPLQLYTQQIAQDDEATVQALKRKFVASPSPRLDAIKAKRRLFDSGYGLLRSSNLKATLLSKFKELFGVGYYELVRQFKSSKTACADWVFGVYYAVAEGIKLIQPHTQYAHIQGMVVFMLLRYNCAKNRDTVSKNMLNIPEKHMLIEPPKLRSTPAALYWYKTAMGNGSGETPEWIVRQTLVGHFRLSVMVQFAYDHDIVEESVLAFEYAQLADVDANAAAFLNSNCQAKYVKDAVTMCRHYKRAERAQMSMSQWITFRGDWKPIVKFLRHQGVEFVSFLAAFKLFLKGPKKNCIVFYGPADTGKSYFCMSLLQFLGGAVISYANSSSHFWLQPLSDSKIGLLDDATPQCWSYIDTYLRNLLDGNPVSDRKHKTLLQLKCPPLMITTNINPLEEYLRSRLTLFTFNNPFPFASPGEPLYPINNANWKCFFQRSWSRLDLEEQFRCLANRLDACQETLLELYEDSNKLEDQIKHWRLENVMLFKARECGMTRVGCTTVPALTVSKAKACQAIEVQLALQTLMQSAYSTEAWTLDTCLEMWDAPPCWKKKGLSVLVKFDGSSDRDMIYTGWHHIYVQDANDDTWHKVPGVDELGLYYRVNYVDFGTESLTYSTAPRWDHGDTDTVPVIHLRGDANCLKCFRYRVQKHKDKLYDRVSSTWHWYTSVEQRKEFLTRVNIPWRPNESKVYLPPT-PVKVISTDVYVTRTNVYYHGGSRLLTVGHPYYRLAVPKVSGYQYRVFHVKLPDPNKFGLPDADLYDPDQRLLWACVGVEVGRGQPLGVGVSGHPYYNRQDDTENA-HTDGRENISDYKQTQLFILGCKPSIGEHWSKGTTCGDCPPLQFTNSTIEDGDMVETGFGALDFATLQSNRSDVPLDICTNVCKYPDYLKMAAEPYGDSMFFSLRREQMFTRHFFNRAGYTSTPSGSMVSSEQQLFNKPYWLRRAQGHNNGMCWGNRIFLTVVDTTRSTNVSLYKATNFKEYLRHMEEYDLQFIFQLCKITLTPEIMAYIHNMDPQLLEDWNFGVPPPPLQDTYSQAITCPKT-P-DPYANMTFWDVDLRESFSMDLDQFPLGRKFLLQRGKRK Seq90 MEDSEGTRAGGWFHVEDLDFIDQ-EIPLQLYAQQTAQDDEATVQALKRKFVASPSPRLDAIKAKRRLFDSGYGLLRSSNLKATLLSKFKDLFGVGFYELVRQFKSSKTACADWVYGVYYAVAEGLKLIQPHTQYAHIQGMVVFMLLRYNCAKNRDSVSKNMLNIPEKHMLIEPPKLRSTPAALYWYKTAMGNGSGETPEWIVRQTLVGHFRLSVMVQYAYDHDIVEESVLAFEYAQLADVDANAAAFLNSNCQAKYVKDAVTMCRHYKRAEREQMSMSQWITFRGDWKPIVRFLRHQGVEFVSFLAAFKLFLKGPKKNCIVFYGPADTGKSYFCMSLLQFLGGAVISYANSSSHFWLQPLSDSKIGLLDDATPQCWSYIDIYLRNLLDGHPVSDRKHKTLLQLKCPPLMITTNTNPLEEYLRSRLTVFTFKNPFPFASPGEPLYPINNANWKCFFQRSWSRLDLEEQFRCLANRLDACQETLLELYEDSNKLEDQIKHWRLENVMLFKARECGMTRVGCTAVPALTVSKAKACQAIEVQLALQTLMQSAYSTEAWTLDTCLEMWDAPPCWKKKGQSVLVKFDGSSDRDMIYTSWGFIYVQDTITDSWHKVPGVDELGLYYRVNYVDFGTESLTYRTDPRWGHGDTDSVPVIHLRGDANCLKCFRYRVQKHKDVLYARVSSTWHWYTSVEQRTEFLTRVSIPWRPNESKVYLPPT-PVKVISTDVYVTRTNVYYHGGSRLLTVGHPYYKVAVPKVSGYQYRVFHVKLPDPNKFGLPDADLYDPDQRLLWACVGVEVGRGQPLGVGVSGHPYYNRLDDTENA-HTDGRENISDYKQTQLFILGCKPPIGEHWSKGTTCGDCPPLQFTNTTIEDGDMVETGFGALDFATLQSNKSDVPLDICTNTCKYPDYLKMAAEPYGDSMFFSLRREQMFTRHFFNLGGYTSTPSGSMVSSEQQLFNKPYWLRRAQGHNNGMCWGNRVFLTVVDTTRSTNVSLYKATNFKEYLRHMEEYDLQFIFQLCKITLTPEIMAYIHNMDPQLLEDWNFGVPPPPLQDTYSQAITCPKT-P-DPYASLTFWDVDLSESFSMDLDQFPLGRKFLLQRGKRK Seq91 MADNSGTRAGGWFMVDMVDFIDQ-EVAQELLLQQAAADDDEAVHTVKRKFAPSPSPRLDAIKAKRRLFDSGYGILKASNQKATLLGKFKEQFGLGYNELVRHFKSSRTACVDWVFGVYCTVAEGIKLIQPLCEYAHIQGMTVLMLVRYKRAKNRETVAKGLLNVPESHMLIEPPKLRSSPAALYWYKTSMSNISGETPEWIVRQTMVGHFSLSEMVQWAYDHDITDEGTLAYEYALIADVDSNAAAFLASNCQAKYVKDACTMCRHYKRGEQARMSMSEWIRFRGDWKPIVHFLRYQNVEFIPFLCAFKLFLQGPKKSCLVFYGPADTGKSYFCMSLLKFMGGVVISYANSHSHFWLQPLSEAKMGLLDDATSQCWSYVDTYLRNALDGNVMCDRKHRSLLQLKCPPLLITTNVNPLEDYLRSRLQVFTFSNPCPLTSKGEPVYTLNDQNWKSFFQRLWARLSLDDEFRCLANRLDACQDKILELYEDSDKLEDQITHWRVESALYYKARECGMTRIGHQVVPTLSVAKAKACSAIEMHVALQQLQQSAYGKEPWTLDTSREMWDAVPCWKKRGVTVEVRYDGDETKAMCHVLWKDIIVQNLSDDQWVKVKGVSYEGLYYKVFYVKFHKDARVYSQNPYRHPSDPDCAPVIHLRGDPNSLKCFRYRLQNGKKGLYCKASSTWRWYTSVTQRAEFLANVKIPWRSSDNLVYLPPT-PVKVISTDDYVTRTNIYYYAGSRLLTVGHPHYKVDVPKVSAFQYRVFRVRLPDPNKFGLPDARIYNPEERLVWACTGVEVGRGQPLGVGLSGHPLYNKLNDTENSNIADSRDNIADYKQTQLCILGCTPPMGEHWGKGTVCGDCPPLELMTTHIEDGDMVDTGYGAMDFAALQVNKSDVPLDICQSTCKYPDYLGMAADPYGDSMFFFLRREQLFARHFFNRAGYSPTPSGSMVTSEAQIFNKPYWLQQAQGHNNGICWANQVFLTVVDTTRSTNMSLYDATKIKEYLRHGEEYDLQFIFQLCKVTLTPEIMAYLHTMNSALLEDWNFGLTLPPLEDTYSSAITCPT-EKQDPYAKLNFWDVDLKDRFTLDLSQFPLGRKFLLQIGKRK Seq92 MADNSGTRAGGWFIVDMVDFIDQ-EVAQELLLQQAAADDDVAVQAVKRKFTHSPSPRLDAIKAKRRLFDSGYGILKASNQRATLLGKFKEQFGLGYNELVRHFKSNRTACADWVFGVYCTVAEGIKLIQPLCDYAHIQGMTVLMLLRYKRAKNRETVAKGLLNVPESHMLIEPPKLRSGPAALYWYKTGMSNISGDTPDWIVRQTIVGHFRLSDMVQWAYDHDITDEGTLAYEYALIAEFDANAAAFLASNCQAKYVRDACTMCRHYKRGEQARMTMSEWIKFRGNWKPIVQYLRYQDVEFVPFLCALKSFLQGPKKSCLVFYGPADTGKSYFCMSLLRFMGGAVISYANSTSHFWLQPLSEAKMGLLDDATSQCWNYIDTYLRNALDGNVICDRKHRSLLQLKCPPLLITTNVNPLEDYLRSRLQVFTFKNKFPVTSSGDPLYTLNDQNWKSFFQRLWARLRLDDEFRCLASRLDACQDKMLELYEDSDKLEDQIMHWRLESVLLYKARECGMTHIGHQVVPTLSVAKAKACSAIEMHVALQQLQRSAYAHEPWTLDTSREMWDAAPCWKKRGVTVEVRYDGDETKAMCYVLWKELIVQNVSDDQWVKVAGVSYEGLYYKVFYVKFKTDARVYSQNSIRTPSDLDCAPVIHLRGDPNSLKCFRYRLKNGKKDLYCRASSTWRWYTSYTQRDEFLATVKIPWRSSDNLVYLPPT-PVKVISTDDYVTRTNVYYYAGSRLLTVGHPYFKVDVPKVSAFQYRVFRVRLPDPNKFGLPDARIYNPEERLVWACTGVEVGRGQPLGVGLSGHPLYNKLNDTENSNIADSRDNISDCKQTQLCILGCTPPMGEYWGKGTPCGDCPPLELMTSYIQDGDMVDTGYGAMDFTALQFNKSDVPLDICQSICKYPDYLGMAADPYGDSMFFFLRREQLFARHFFNRAGYSPTPSGSMVTSEAQIFNKPYWLQQAQGHNNGICWGNQVFLTVVDTTRSTNMSLYDATKIKEYLRHGEEYDLQFIFQLCKVTLTPEIMAYIHTMNTALLEDWNFGLTLPPLEDTYSSAITCPT-EKQDPYAKLNFWDVDLKDRFTLDLSQFPLGRKFLLQIGKRK Seq93 MDDTSGTRAGGWFMVDLVDFIDQ-EVAQELLLQQAAADDDVEVQTVKRKFAPSPSPRLDAIKAKRRLFDSGYGILKASNHKATLLGKFKEQFGLGFNELIRHFKSNKTVCSDWVFGVYCTLAESFKLIQPQCEYAHIQGMTVLTLVRFKRAKNRETVAKGFLNVPENHMLIEPPKLRSAPAALYWFKTSLSNCSGETPEWIVRQTVVGHFSLSEMVQYAYDHDITDESTLAYEYALQADTDANAAAFLASNCQAKYVKDACTMCRHYKRGEQARMNMSEWIKFRGDWKPIVQYLRYQDVEFIPFLCALKSFLQGPKKSCIVFYGPADTGKSYFCMSLLKFLGGVVISYANSSSHFWLQPLAEAKIGLLDDATSQCWCYIDTYLRNALDGNQVCDRKHRALLQLKCPPLLITTNINPLGDYLRSRLQVFTFNNKFPLTTQGEPLYTLNDQNWKSFFQRLWARLNLEDEFRCLANRLDVCQDKILELYEDSDKLEDQIMHWRLEQALLYKARECGLTHIGHQVVPPLSVTKAKARSAIEVHVSLQQLQHSAHAQDPWTLDTSREMWDTVPCWKKRGLTVEVRYDGDENKAMCYVQWREIIVQNYTDDNWVKVAGVSHEGLYYKTFYVKFKDDARVYCPHPIGHRSDPDCVPVIHLRGDPNCLKCFRYRLNKGKNKLYSRTSSTWRWYTSYGQREAFLSTVKVPWRSSDNLVYLPPT-PVKVLSTDDYVTRTNIYYYAGSRLLTVGHPYFKMDIPKVSAFQYRVFRVRLPDPNKFGLPDARIYNPDERLVWACTGVEVGRGLPLGVGLSGHPLYNKLDDTENSNIADSRDNISDNKQTQLCIVGCTPPMGEHWGKGTPCGDCPPLELITAPIQDGDMVDTGYGAMDFGNLQSNKSDVPLDICQTTCKYPDYLGMAAEPYGDSMFFYLRKEQLFARHFLNRAGYCPTPSGSMVTSETQLFNKPYWLRRAQGHNNGICWANQLFVTVVDTTRSTNMTLYDATKFKEYLRHGEEYDLQFIFQLCKVTLTPEIMAYLHTMNSTLLEDWNFGLTLPPLEDTYSSAITCPT-EKQDPYAKLNFWDVDLKDRFSLDLSQFPLGRKFLMQLGKRK Seq94 MDDTSGTRAGGWFMVDFVDFIDQ-EVAQELLLQQAAADDDVAVQAVKRKFAPSPSPRLDAITAKRRLFDSGYGILRASNQKATLLGKFKEQFGLGFNELIRHFKSSKTVCLDWVFGVYCTLAEGIKLIQPQCDYAHIQGMTVLMLVRYKRAKNRETVAKGLLNVPESHMLIEPPKLRSGPAALYWYKTAMSNCSGETPEWIVRQTMVGHFSLSEMVQYAYDHDITDESMLAFEYALLADTDANAAAFLSSNCQAKYVKDACTMCRHYKRGEQARMNMSEWIWFRGDWKPIVQFLRYHDVEFIPFLCAFKTFLQGPKKSCLVFYGPADTGKSYFCMSLLRFLGGVVISYANSNSHFWLQPLADAKIGLLDDATSQCWCYIDTYLRNALDGNQVCDRKHRALLQLKCPPLLITTNINPLEDYLRSRVQLFTFKNKFPLTTQGEPLYTLNDQNWKCFFRRLWARLSLDDEFRCLANRLDVCQDKMLELYEDSNKLEDQIMHWRVENALLYKARECGLTHIGHQVVPPLSVTKAKARSAIEVHVALLQLQESAYAQDSWTLDTSREMWDTVPCWKKRGVTVEVRYDGDETKSMCYVHWRDIFTQNYSDDKWVKVAGVSYEGLYYQTFYVKFKDDAYVYCPYTVGHPSDPDCAPVVHLKGDPNCLKCFRYRLHKGKRKLYCKTSSTWRWYTSYSQRNEFLSTVKVPWRSSDNLVYLPPT-PVKVLSTDDYVTRTNIYYYAGTRLLTVGHPYFKADVPKVSAFQYRVFRVRLPDPNKFGLPDARIYNPDERLVWACTGVEVGRGQPLGVGLSGHPLYNKLDDTENSNIADSRDNISDNKQTQLCIVGCTPPMGEHWGKGTPCGDCPPLELITAPIQDGDMVDTGFGAMDFAVLQANKSDVPLDICQSTCKYPDYLGMAAEPYGDSMFFYLRKEQLFARHFFNRAGYSPTPSGSMISSEAQLFNKPYWLRRAQGHNNGICWANQLFVTVVDTTRSTNMTLYDASKFKEYLRHGEEYDLQFIFQLCKVTLTPDIMAYLHTMNNSLLEDWNFGLTLPPLEDTYSSAITCPTT-KEDPYAKLNFWEVDLKDRFSLDLSQFPLGRKFLMQLGKRK Seq95 MDDNTGTRAGGWFIVDFVDFIDQ-EVAQELFQQQTAADDDVAVQTVKRKFAPSPSPRLDAIKAKRRLFDSGYGILRASNKKATLLGKFKEQFGLGYNELIRHFKSDRTSCADWVFGVFCTVAEGIKLIQPLCDYAHIQGMTVLMLVRYKRAKNRETVAKGLLNVPESQMLIEPPKLRSGPAALYWYKTSMSSCSGETPEWIVRQTMVGHFSLSEMVQWAYDHDITDESTLAYEYALIADTDSNAAAFLSSNCQAKYLKDACTMCRHYKRGEQARMSMSEWIWFRGDWKPIVQFLRYQDVEFIPFLCAFKTFLQGPKKSCLVFYGPADTGKSYFCMSLLRFLGGAVISYANSSSHFWLQPLSEAKIGLLDDATSQCWNYIDTYLRNALDGNQICDRKHRALLQLKCPPLLITTNINPLTDFLRSRLQLFTFKNPFPVTTQGEPMYTLNDQNWKCFFRRLWARLSLEDEFRCLANRLDACQDKMLELYEDSDKLEDQITHWRVENALLYKARECGLTHIGHQVVPPLSVTKAKARNAIEVHVALQQLQESAYAHEPWTLDTSREMWDTAPCWKKRGITVEVRYDGDESKAMCYVQWRELYVQNYSDDRWVKVPGVSYEGLYYNIYYVNFKDDACVYSAHPVRHPSDPDCAPVIHLRGDPNSLKCFRYRLHHGKRKLYSRSSSTWRWYTSDTQRTEFLNVVKVPWRSSDNLVYLPPT-PVKVLSTDDYVTRTNIYYYAGTRLLTVGHPYFKVDVPKVSAFQYRVFRVRLPDPNKFGLPDARIYNPDERLVWACTGVEVGRGQPLGVGLSGHPLYNKLEDTENSNIADSRDNISDNKQTQLCIIGCTPPMGEHWGKGTPCGDCPPLELITSPIQDGDMVDTGYGAMDFTALQLNKSDVPIDICQSTCKYPDYLGMAAEPYGDSMFFYLRREQLFARHFFNRASYSPTPSGSMVTSEAQLFNKPYWLRRAQGHNNGICWANQLFVTVVDTTRSTNMCLYDATKFKEYLRHGEEYDLQFIFQLCKVTLTPDIMAYLHTMNSSLLEDWNFGLTLPPLEDTYSSAITCPT-EKQDPYAKLNFWDVDLKDRFSLDLSQFPLGRKFLLQLGKRK Seq96 MDDNTGTRAGGWFIVDCVDFIDQ-EVARELFLQQAAADDDIAVQTVKRKFAPSPSPRLDAIKAKRRLFDSGYGILKASNHKATLLGKFKEQFGLGYNELIRHFKSDRTACVDWVFGVYCTVAEGIKLIQPLCDYAHIQGMTVFMLVRYKRAKNRETVAKGLLNVPESQMLIEPPKLRSGPAALYWYKTSMSSCSGETPEWIVRQTMVGHFTLSEMIQWAYDHDITDESTLAYEYALIADTDANAAAFLASNCQAKYLKDACTMCRHYKRGEQARMSMSEWIRFRGDWKPIVQFLRYQDVEFIPFLCAFKTFLQGPKKSCIVFYGPADTGKSYFCMSLLRFLGGAVISYANSSSHFWLQPLSEAKIGLLDDATTQCWNYVDTYLRNALDGNQVCDRKHRALLQLKCPPLLITTNVNPLADYLRSRLQLFTFKNPFPVTAQGEPLYTLNDQNWKCFFRRLWARLSLEDEFRCLANRLDACQDKMLELYEDSNKLEDQITHWRVENALLYKARECGLTHIGHQVVPPLSVTKAKARNAIEVHVALQQLQDSAYAHESWTLDTSREMWDAAPCWKKRGITVEVRYDGDESKAMCYVQWRDIFVQNYSDDRWVKVSGVSHEGLYYNIFYANFKDDAFVYRAFPVWPPSDPDCAPVIHLRGDPNCLKCFRYRLHHGKRKLYSRSTSTWRWYTSEAQRAEFLNVVKVPWRPSDNLVYLPPT-PVKVLSTDDYVTRTNIYYYAGTRLLTVGHPYFKVDVPKVSAYQYRVFRVRLPDPNKFGLPDARIYNPDERLVWACTGVEIGRGLPLGVGLSGHPLYNKLDDTENSNIADSRDNISDNKQTQLCILGCTPPMGEHWGKGTPCGDCPPLELVTSVIQDGDMVDTGYGAMDFTALQLNKSDVPIDICQSTCKYPDYLGMAAEPYGDSMFFYLRREQLFARHFFNRASYSPTPSGSMVTSEAQLFNKPYWLKRAQGHNNGICWGNQMFVTVVDTTRSTNLSLYDATKFKEFLRHGEEYDLQFIFQLCKVTLTPEIMAYLHTMNSSLLEDWNFGLTLPPLEDTYSSAITCPT-EKQDPYAKLNFWDVDLKDRFTLDLSQFPLGRKFLLQLGKRK Seq97 MADPEGTGCNGWFYVDMVDFIDELETAQALFHAQEVHNDAQVLHVLKRKFAGGSSPRLQEIKAKRRLFDSGYGLLKVNNKQGAMLAVFKDTYGLSFTDLVRNFKSDKTTCTDWVFGVNPTIAEGFKLIQPFILYAHIQGVLILALLRYKCGKSRLTVAKGLLHVPETCMLIQPPKLRSSVAALYWYRTGISNISGDTPEWIQRLTIIQHFDLSEMVQWAFDNELTDESDMAFEYALLADSNSNAAAFLKSNCQAKYLKDCATMCKHYRRAQKRQMNMSQWIRFRGDWRPIVQFLRYQQIEFITFLGALKSFLKGPKKNCLVFCGPANTGKSYFGMSFIHFIQGAVISFVNSTSHFWLEPLTDTKVAMLDDATTTCWTYFDTYMRNALDGNPISDRKHKPLIQLKCPPILLTTNIHPAKDYLESRITVFEFPNAFPFDKNGNPVYEINDKNWKCFFERTWSRLDLEEDFKLLSERLSCVQDKIIDHYEDSKDIDSQIQYWRWENAIFFAAREHGIQTLNHQVVPAYNISKSKAHKAIELQMALQGLAQSRYKTEDWTLDTCEELWNTEPCFKKGGQTVQVYFDGNKDNCMTYVAWDSVYYM-TDAGTWDKTATVSHRGLYYNTFYIEFKSECEKY-NNKRRKLCSGNTTPIIHLKGDRNSLKCLRYRLRKHSDH-YRDISSTWHWYHSETQRTKFLNTVAIPWRPSDNTVYLPPP-SVRVVNTDDYVTPTSIFYHAGSRLLTVGNPYFKQDIPKVSAYQYRVFRVQLPDPNKFGLPDTSIYNPEQRLVWACAGVEIGRGQPLGVGLSGHPFYNKLDDTESSHAADVRDNVSDYKQTQLCILGCAPAIGEHWAKGTACGDCPPLELKNTVLEDGDMVDTGYGAMDFSTLQDTKCEVPLDICQSICKYPDYLQMSADPYGDSMFFCLRREQLFARHFWNRAGYSPSPSGSIVTSDSQLFNKPYWLHKAQGHNNGVCWHNQLFVTVVDTTPSTNLTIYDATKFKQYSRHVEEYDLQFIFQLCTITLTADVMSYIHSMNSSILEDWNFGVPPPPLVDTYSVAITCPA-E-NDPYDKLKFWNVDLKEKFSLDLDQYPLGRKFLVQAGKRV Seq98 MADPEGTGCNGWFFVDMVDFIDEQETAQALFHAQEVQNDAQVLHLLKRKFAGGSSPRLQEIKAKRRLFDSGYGLLQASNKKAAMLAVFKDIYGLSFTDLVRNFKSDKTTCTDWVFGVNPTVAEGFKLIKPATLYAHIQGVLILALLRYKCGKNRLTVAKGLLHVPETCMLIEPPKLRSSVAALYWYRTGISNISGDTPEWIQRLTIIQHFDLSDMVQWAFDNDLTDESDMAFQYAQLADCNSNAAAFLKSNCQAKYLKDCAVMCRHYKRAQKRQMNMSQWIKYRGDWRPIVQFLRYQGVEFISFLRALKEFLKGPKKNCILLYGPANTGKSYFGMSFIHFLQGAIISFVNSNSHFWLEPLADTKVAMLDDATHTCWTYFDNYMRNALDGNPISDRKHKPLLQLKCPPILLTSNIDPAKDYLESRVTVFTFPHAFPFDKNGNPVYEINDKNWKCFFERTWSRLDLDEDFKCLSERLSALQDKILDHYEDSKDINSQISYWRLENAILFTAREHGITKLNHQVVPPINISKSKAHKAIELQMALKGLAQSKYNNEEWTLDTCEELWNTEPCFKKGGKTVHVYFDGNKDNCMNYVVWDSIYYI-TETGIWDKTAAVSYWGVYYTTYYVQFKSECEKY-NNKRRKVCSGNTTPIIHLKGDKNSLKCLRYRLRKYADH-YSEISSTWHWYNSEVQRNTFLDVVTIPWRPSDSTVYLPPP-SVRVVSTDDYVSRTSIFYHAGSRLLTVGNPYFKQAVPKVSAYQYRVFRVALPDPNKFGLPDSTIYNPEQRLVWACVGMEIGRGQPLGIGLSGHPFYNKLDDTESAHAADVRDNVSDYKQTQLCILGCVPAIGEHWAKGTLCGDCPPLELKNTIIEDGDMVDTGYGAMDFSTLQDTKCEVPLDICQSICKYPDYLQMSADPYGDSMFFCLRREQLFARHFWNRAGYSPSPSGSIITSDSQLFNKPYWLHKAQGHNNGICWHNQLFVTVVDTTRSTNLTLYDPTKFKQYSRHVEEYDLQFIFQLCTITLTAEVMSYIHSMNSSILENWNFGVPPPPLVDTYSVAVTCPP-EKQDPYDKLKFWTVDLKEKFSSDLDQYPLGRKFLVQAGKRV Seq99 MEDSQGTGCNGWFYVDMVDFIDELETAQALFHAQEVDNDAQVLHVLKRKYGTESSPRLQEIKAKRRLFDSGYGLLKANNKKAAMLAVFKETYGLSFADLVRTFKSDKTTCTDWVFGVNPTIAEGFKLIQPCTLYAHIQGVLILALLRYKCGKNRLTVAKGLLHVPETCMLIEPPKLRSSVAALYWYRTGISNISGDTPEWIQRLTIIQHFDLSEMIQWAFDNDFTDESDIAYEYAQLADCNSNAAAFLKSNCQAKYLRDCAVMCRHYKRAQKRQMNMSQWIKYRGDWRPIVQFLRFQGIEFITFLGALKAFLKGPKKNCIVIHGPANTGKSYFGMSFIHFIQGAIISFVNSNSHFWLEPLADAKVAMLDDATNTCWTYFDNYMRNALDGNPISDRKHKPLLQLKCPPILLTSNINPAIDYLESRVTVFTFPNAFPFDKNGNPVYEINDKNWKCFFERTWSRLDLDDDFKCLSERLSVLQDKILDHYEDSKDIHSQINYWRLENAIFFAAREHGIQTLNHQVVPAFNISKNKAYKAIELQMALKGLAQSQYNTEEWTLDTCEELWNTEPCFKKSGQTVEVYYDCNKDNCMSYVAWKCVYYM-TEAGTWDKTEAVSHWGLYYKTFYVQFEHESKKYGSNKRRKLCSGNTTPIIHLKGDKNSLKCLRYRFKKYNQ-YYSSISSTWHWYDSEHQRTKFLDSVPIPWRPSDSTVYLPPP-SVRVVNTDDYVSRTSIFYHAGSRLLAVGNPYFRQDVPKVSAYQYRVFRVTLPDPNKFGLPDSTIYNPEQRLVWACVGVEIGRGQPLGIGLSGHPLYNKLDDTESSHAADVRDNVSDYKQTQLCILGCVPAIGEHWAKGTPCGDCPPLELKNTVLEDGDMVDTGYGAMDFSTLQDTKCEVPLDICQSICKYPDYLQMSNDPYGDSMFFCLRREQLFARHFWNRAGYSPSPSGSIINTESQLFNKPYWLHRAQGHNNGICWHNQLFVTVVDTTRSTNLTLYDPTKYKQYSRHVEEYDLQFIFQLCTITLSADVMSYIHSMNSSILENWNFGVPPPPLVDTYSVAITCPA-EK-DPYDNLKFWNVDLKERFSLDLDQYPLGRKFLVQSGKRL Seq100 MADSEGTGCNGWFFVDLVDFIDERETAQALFNVQEAQRDAREMHVLKRKFGCS-SP-LQEIKVKRRLIDSGYGLLHSKNKKAAMYAKFKELYGLSFQDLVRTFKSDRTTCSDWVFGVNPTVAEGFKLIQPYVLYAHIQGVVILALLRYKCGKNRITVAKGLLHVPDTCMLIEPPKLRSGVAALYWYRTGMSNISGETPEWIQRLTIIQHFDLSEMIQWAFDNDLTDESDIAYEYALIADSNSNAAAFLKSNCQAKYLKDCAVMCRHYKRAQKRQMSMSQWIKWRGDWKPIVQFLRYQGVEFITFLCALKDFLKGPKRNCIVLCGPANTGKSYFGMSLLHFLQGTVISHVNSNSHFWLEPLTDRKLAMLDDATDSCWTYFDTYMRNALDGNPISDRKHRHLVQIKCPPMLITSNTNPVTDYLNSRLMVFKFPNKLPFDKNRNPVYTINDRNWKCFFERTWCRLDLEEDFKCLSQRLSVLQDQILEHYEDSKDINEHINYWRMENVILFAARENNIHTLNHQVVPTFLVSKNKACEAIELQSNRTSTVMPCFLKHFLGACHSSWHVSCIVSFLNSVCAKLSNAICSKENTMHYTSWTFIYYVN-DVGQWCKTTGVDFWGLYYQVYYVKFIHDAKKY-NNPRRHIPCSNTTPIIHLKGDKNGLKCLRYRLRKVHW-LFENISSTWHWYTSETQRNEFLDTVKIPWRSSDNKVYLPPP-SVKVVSTDEYVTRTSIFYHAGSRLLTVGHPYFRQDVPKVSAYQYRVFRVKLPDPNKFGLPDNTVYDPNQRLVWACVGVEIGRGQPLGVGLSGHPLYNKLDDTENSHVADTRDNVSDYKQTQLCIIGCVPAIGEHWTKGTACGDCPPLELINTPIEDGDMVDTGYGAMDFKLLQDNKSEVPLDICQSICKYPDYLQMSADAYGDSMFFCLRREQVFARHFWNRSGYSPSPSGSVVTSDSQLFNKPYWLHKAQGLNNGICWHNQLFLTVVDTTRSTNLSVYTPTSFKEYARHVEEFDLQFIFQLCKITLTTEVMSYIHNMNTTILEDWNFGVTPPPLVDTYSAAVTCPP-VKQDPYDKLKFWPVDLKERFSADLDQFPLGRKFLLQLGRVK Seq101 MANCEGTGCNGWFFVDMVDFIDERETAQVLLNMQEAQRDAQRVRALKRKYTDS-SP-LQELQARQPAYDSGYGLLQCNNKKAAMLTEFKKVYGLSFNDLVRTFKSDKTTCTDWVFGVNPTIAEGFKLIKQYALYTHIQGILILMLIRYKCGKNRITVGKGLLHVPDSCMLLQPPKLRSPVAALYWYRTGISNISGDTPEWIKRLTIIQHFDLSDMVQWAFDNELTDDSDIAFQYAMLADCNSNAAAFLKSNCQAKYVKDCATMCRHYKRAQKRQMTMPQWIKFRGDWRPIVQFLRYQGLEFITFLCALKDFLKGPKRNCIVIHGPPNTGKSYFCMSLIHFLQGTIISYVNSASHFWLEPLADAKIAMLDDATGTCWSYFDNYMRNALDGNPISDRKHRHLIQIKCPPMLITSNTNPVEDYLHSRLTVFKFPNAFPFDQNRNPVYTINDKNWKCFFEKTWCRLDLEDEFKCLSQRLNVLQEKILEHYEDSKCIQDHINYWRLENAIYYAARERGMHNIDHQVVPPVNISKTKAYQAIELQMALESLAKTAYSAEEWTLDTSNELWHTKPCFKKHGVTVEVWYDGDKSNSMHYVVWGTIYFKN-STDTWCKTQGVDYWGVYYKTYYERFMQDAQLYGQNKRRSLCCGDTTPIVHLKGDKNGLKCLRYRLQKHNA-LYDNISCTWHWYVSEAQRDKFLETVKLPWRASDNMVYLPPP-SVKVVNTDDYVTRTGMYYYAGTRLLTVGHPYFKQGIPKVSAYQYRVFRVTLPDPNKFSVPESTLYNPDQRMVWACVGVEIGRGQPLGVGLSGHPLYNRLDDTENSPFSDSRDNVADCKQTQLCIIGCVPAIGEHWAKGKSCGDCPPLELVNTPIEDGDMIDTGYGAMDFGTLQETKSEVPLDICQSVCKYPDYLQMSADVYGDSMFFCLRREQLFARHFWNRGGYAPSPSGSMVSSDSQLFNKPYWLHKAQGHNNGICWHNQLFLTVVDTTRSTNFTLYDSNKFKEYVRHVEEYDLQFIFQLCTITLSTDVMSYIHTMNPAILDDWNFGVAPPPLVDTYSAAITCAP-VK-DPYDGLNFWNVDLKEKFSSELDQFPLGRKFLLQAGHK- Seq102 MANCEGTGCNGWFLVDLADFIDERETAQVLYNMQEAQRDAQSVRALKRKYGGSNRVTLQELQARTNVYDSGYGVLQANNQKAILLSQFKHTYGLAFNDLVRTFKSDKTICTDWVCGVNPTIAEGFKLIQPYALYTHIQGVYILLLIRYKCGKNRITVGKGLLHVPESCMLIEPPKLRSPVAALYWYRTGMSNISGTTPEWIQRLTVIQHFDLSDMVQWAFDNDVTEDSDIAYGYALLADSNSNAAAFLKSNCQAKYVRDCATMCRHYKRAQKKQMTMAQWIRFRGDWRPIVQFLRYQGVEFITFLCAFKEFLKGPKKNCIVIQGPPNTGKSYFCMSLMHFLQGTVISYVNSTSHFWLEPLADAKVAMLDDATGTCWSYFDTYMRNALDGNPISDRKHRHLIQIKCPPILITSNTNPVEEYLTSRLTVFTFPNAFPFDQNRNPVYTINNKNWKSFFQKTWCKLDLEDEFKCLSQRLNALQEKILEHYEDSKLIYDQINYWRLENAIFYAARERGMHTIDHQVVPPGTTSKAKAYQAIELQMALESLAQTDFNKEEWTLDTSNEMWQTKPCFKKKGVTVEVWYDGNKDNSMHYVVWGAIYYK-THTDTWCKTEGVDYWGIYYKTYYEVFKQDAQMYGCNKRRHQCCGDTTPIVHLKGDKNGLKCLRYRLRKFNS-LYENISCTWHWYTTEAQRQKFLETVRIPWRSSDNTVYLPPP-SVKVVNTDDYVTRTGIYYYAGSRLLTVGHPYFKQEIPKVSAYQYRVFRVSLPDPNKFGLPDPSLYNPDQRLVWACIGVEIGRGQPLGVGVSGHPLYNRLDDTENSHFSDSRDNVSDYKQTQLCIIGCVPAMGEHWAKGKACGDCPPLELVNTAIEDGDMIDTGYGAMDFRTLQETKSEVPLDICQSVCKYPDYLQMSADVYGDSMFFCLRKEQLFARHFWNRGGYSPSPSGSMVSSDSQLFNKPYWLHKAQGHNNGICWHNQLFITVVDTTRSTNFTLYSPTKFKEYTRHVEEYDLQFIFQLCTITLTADVMAYIHTMNPAILDNWNIGVTPPPLVDTYSAAIACTP-EK-DPYDDLKFWNVDLKEKFSTELDQFPLGRKFLLQVGKHK Seq103 MANREGTGCNGWFLVDLADFIDERETAQVLLHMQEAQRDAQAVRALKRKYTDSSRGTLQEIQATQTVYDSGYGLLQSNNKKAAMLTQFKETYGLSFTDLVRTFKSDKTTCTDWVFGVHPTIAEGFKLINKYALYTHIQGVLILMLIRYTCGKNRVTVGKGLLHVPESCMLLEPPKLRSPVAALYWYRTGISNISGDTPEWIQRLTVIQHFDLSDMVQWAFDNEYTDESDIAFNYAMLADCNSNAAAFLKSNCQAKYVKDCATMCKHYKRAQKRQMSMSQWIKFRGDWRPIVQFLRYQGIEFISFLCALKEFLKGPKKNCIVIYGPANTGKSHFCMSLMHFLQGTVISYVNSTSHFWLEPLADAKLAMLDDATGTCWSYFDNYMRNALDGYAISDRKYKSLLQMKCPPLLITSNTNPVEDYLRSRLTVFKFPNAFPFDQNRNPVYTINDKNWKCFFEKTWCRLDLEDEFKCLSQRLNVLQDKILEYYEDSKSIYDQINYWRMENAIFYAARERGMHTIDHQVVPTINISKCKAYQAIELQMALESVAQTEYNTEEWTLDTSNELWHTQPCFKKQGTTVEVWYDGDKCNAMNYVLWGAIYYKN-NIDIWCKTEGVDYWGIYYKVYYEVFIQDAERYGHNTRRYLSCGNTTPIIHLKGDKNGLKCLRYRLQKYDT-LFENISCTWHWYATESQRQKFLDTVKIPWRSSDSMVYLPPP-SVKVVNTDDYVTRTGIYYYAGSRLLTVGHPYFKQDIPKVSAYQYRVFRVTLPDPNKFSIPDASLYNPEQRLVWACVGVEVGRGQPLGVGISGHPLYNRQDDTENSPFSDSRDNVSDYKQTQLCIIGCVPAIGEHWGKGKACGDCPPLELVNTPIEDGDMIDTGYGAMDFGALQETKSEVPLDICQSICKYPDYLQMSADVYGDSMFFCLRREQLFARHFWNRGGYCPSPSGSMVTSDSQLFNKPYWLHKAQGHNNGICWHNQLFLTVVDTTRSTNFTLYDPSKFKEYTRHVEEYDLQFIFQLCTVTLTTDVMSYIHTMNSSILDNWNFAVAPPPLVDTYSAAITCAP-EK-DPYDGLKFWNVDLREKFSLELDQFPLGRKFLLQARKHK Seq104 MADPEGTGCNGWFFVDLVDFIDDREAAQALLHAQEVETDTKLLHALKRKYGAHSSSPLQEITAKRRLCDSGYGLLKVNNKKAAILAKFKETYGLSFTDLVRTFKSDKTTCTDWVCGVNPNIAEGFKLIQPYVLYAHIQGVFILALLRYKCGKNRLTVAKGLLHVPDTHMLIEPPKLRSSCAALYWYRTGISNISGDTPEWIQRQTIIQHFDLSEMIQWAFDNDYIDESDIAYEYAQLADCNSNAAAFLKSNCQAKYLRDCAVMCRHYKRAQRKQMNMSQWISYRGDWKPIVQFLRFQGIEFITFLRAFKDFLKGPKKNCIVIYGPANTGKSYFCMSLIQFLHGTVLSFVNSNSHFWLEPLTDTKIAMVDDATPTCWSYFDNYMRNALDGNPISDRKHKHLIQMKCPPMLITSNTNPATDYLRSRVTVFTFPHTFPFDSNGNPVYDINDKNWKCFFKRTWSRLDLEEDFKCLSQRLNVLQEKILEHYEDSTDICDQIDYWRLENAIYYAAREHGLKTINHHVVPTFQISKSKAHEAIELQMALESLAKSEFKNELWTLDTCQELYQTPPCFKKQGQTVEVRYDGDKDNTMHYTSWDYIYYV-TEGDKWCKTKGVNYCGLYYQTYYVQFKCDAQQYGNNGFRKHNSGDTTPIVHLKGDKNRLKCLRYRLQKFHN-LYTQISCTWHWYANETQRQKFLDVVKIPWRSSDSKVYLPPP-SVKVVNTDDYVTRTSTFYHAGSRLLTVGHPYYKQDIPKVSAYQYRVFRVTLPDPNKFGLPDTNIYNPEQRLVWACVGMEVGRGQPLGVSLSGHPFYNKLDDTENSHVADTRDNVSDYKQTQLCIIGCVPAIGEHWAKGTACGDCPPLELVNTPIEDGDMIDTGYGAMDFSTLQDNKSEVPLDICQSICKYPDYLQMSADAYGDSMFFCLRREQLFARHFWNRGGYSPSPSGSMVSSDAQMFNKPYWLHKAQGQNNGICWHNQLFITVVDTTRSTNLTLYEPSKFKEYTRHVEEYDLQFIFQLCKITLTTDVMSYIHNMDPTILDSWNFGVSPPPLVDTYSSAITCVP-QK-DPYEKLKFWNVDLKEHFSSDLDQFPLGRKFLLQAGKRK Seq105 M-DCEGTGCTGWFSVDLIGFIDQ-EVTQALFQAQQKQANTKAVRNLKRKLLGS-SDSQQNT-AKRRAVDSGYGLLKCSNVKAALLSKFKTVYGVSFAELVRVFKSDKTCCSDWVFGVAGSVAESIKLIQQYCLYYHIQGVIVLMLVRFTCAKNRTTIKNCLLNVPETQLLIEPPKLRSTAVALYFYKTGLSNISGDTPEWIVRQTQLEHFDLSKMVQWAFDHDITDDSEIAFKYAQLADIDSNAAAFLKSNCQAKYVKDCATMTRHYKRAQKRSMCMSQWLQYRGSWKEIAKFLRFQHVNFIYFLQVLKQFLKGPKHNCIVIYGPPNTGKSQFAMSFIKFMQGSVISYVNSNSHFWLQPLEDAKVAVLDDATYSCWLYIDKYLRNFLDGNPCCDRKHRSLLQVTCPPLIITSNINPQEDYLHSRVTVIPFPNTFPFDSNGNPVYALTDVNWKSFFSTTWSRLDLDADFKCLCQRLNACQEKILDYYEDSNKLTDQIDYWRYECAIFYKAREGNMQCINHQVVPSTVVCKQKAWQAIEIHIALQSLINTDYNTEAWTMDTSYEMYMTEPCFKKEGTTVTVVFDCNKENTMDYIRWKYVYYK-TD-IGWCKGTGVDAKGIYYKQYYVDFKQEAEKYGHHPGRDTSSDQTVFIVHLKGDTNSLKCLRYRFKKHKG-LYCNVSSTWHWFNSITQRNNFLTTVKIPWRTSDSKVYLPPT-PVRVVNTDEYVTRTGIYYYAGSRLLTLGHPYFKAEIPKVSAYQYRVFRVHLPDPNKFGLPDPQLYNPDERLVWACVGVEVGRGQPLGIGLSGHPLFNKLDDTENSHLADNRDNVSDNKQTQLCIIGCTPPLGEHWGIGTICGDCPPLELISSIIEDGDMIDTGFGAMDFTALQATKSDVPIDISQSTCKYPDYLKMSADTYGNSMFFFLRREQLFARHFYNKAGYSATPSGSMVTSDAQLFNKPYWLQRAQGHNNGICWGNQLFVTCVDTTRSTNLTIFKPSDYKQFIRHGEEYELQFIFQLCKITLTTDVMAYIHLMNASILEDWNFGLTLPPLEDAYNSATTCPV-PKEDPFQKFKFWDVDLKEKFSIDLDQFPLGRKFMLQAGKRK Seq106 M-DCEGTGCTGWFSVDLIGFIDQ-QVAQALFQAQETQANKKAVRALKRKLLGS-SNSQQST-AKRRAVDSGYGLLKCSNVKAALLSKFKTVYGVSYTELVRVFKSDKTCCSDWVFGVAGSVAESLKLIQPYCLYYHIQGVLPLMLIRFTCAKNRATIKKCLLNVPDTQLLIEPPKLRSTAVALYFYKTGLSNISGDTPEWIVRQTQLEHFDLSKMVQWAFDHDITDDSEIAFKYAQLADIESNAAAFLKSNCQAKYVKDCATMTRHYKRAQKRSMGMSQWLQHRGTWKDIARFLRYQNVNFIYFLQVLKQFLKGPKHNCIVIYGPPNTGKSQFAMSFIKFVQGSVISYVNSNSHFWLQPLEDAKVALLDDATYGCWLYIDKYLRNFLDGNPCCDRKHRSLIQVRCPPLIITSNINPQDDYLHSRVTVIPFPNTFPFDSNGNPVYELTDVNWKSFFSTTWSRLDLDADFKCLCQRLNACQEKILDYYEDSDKLSDQIDYWRYECAIFYKAREGNMQCINHQVVPSTTVCKEKAWQAIELHIALQSLMNSEYGKEKWTMDTCYELYVTEPCFKKEGTTVTVVFDCNKENTMDYIRWTSVYYK-TD-MGWCKTNGVDYKGIYYKQYYVDFKQEALKYSNNPGRGTCSDQTVFIVHLKGDTNCLKCLRYRFKKHKG-LYCKVSSTWHWFDSETQRASFLTTVKIPRRTSDSKVYLPPT-PVRVVRRMNMYTHR-IYYYAGSRLLTLGHPYFTAEIPKVSAYQYRVFRVHLPDPNKFGLPDPQLYNPEERLVWACVGVEVGRGQPLGVGLSGHPLFNKLDDTENSHLADNRDNVCDNKQTQLCIIGCTPPLGEHWGVGTVCGDCPPLELISSVIEDGDMIDTGFGAMDFTALQATKCDVPLDINQSICKYPDYLKMSADTYGNSMFFFLRREQLFARHFFNKAGYSRTPSGSMVTSDAQLFNKPYWLQRAQGHNNGICWGNQLFVTCVDTTRSTNLTIFKPSDYKQFIRHGEEYELQFIFQLCKITLTTDVMAYIHTMNSTILENWNFGLTLPPLEDAYNSATTCAQ-PKEDPFSKLKFWDVDLKEKFSIDLDQFPLGRKFMLQAGTRK Seq107 M-DCEGTGCNGWFFVDLINFIDEQETARALFQAQELQANKEAVHQLKRKFLVSPNNTH-SH-VKRRLLDSGYGVLKSSNAKATLMAKFKELYGISYNELVRVFKSDKTCCIDWVFGVSPMVAENLKLIKPFCMYYHIQGTIVLMLIRFSCAKNRTTIAKCLVNIPQSQMFIEPPKLRSTPVALYFYRTGISNISGETPEWITRQTQLQHFELSQMVQWAFDHEVLDDSEIAFHYAQLADIDSNAAAFLKSNCQAKYVKDCGTMARHYKRAQRKSLSMSAWIRYRGNWREIAKFLRYQGVNFMSFIQMFKQFLKGPKHNCIVIYGPPNTGKSLFAMSLMKFMQGSIISYVNSGSHFWLQPLEDAKIALLDDATYGCWTYIDQYLRNFLDGNPCSDRKHRSLIQLVCPPLLITSNINPQEDYLHTRVTVLKFLNTFPFDNNGNAVYTLNDENWKNFFSTTWSRLDLEEDFKCLCHRLNVCQEKILDCYEDSDKLVDQINYWRYEAAMFYAARERNLRTINHQVVPATTVSKQKACQAIEMHMALQSLNKSDYNMEPWTMETCYELWCVAPCFKKGGITVTVIFDGNKDNAMDYTSWKFIYIYD-N-DKWVKTNGVDYTGIYYKEYYVQFKDEAKIYTNTGGHQSAT-QTAFIVHLKGDTNCLKCFRYRFTKHKG-LYKNVSSTWHWFDSAHQRETFIKTIKVPWRTNDSKVYLPPA-PVRIVNTEEYITRTGIYYYAGSRLITLGHPYFRAAIPKVSAFQYRVFRVQLPDPNKFGLPDPNLYNPDDRLVWGCVGVEVGRGQPLGVGLSGHPLFNKYDDTENSRIADVRDNTSDNKQTQLCIIGCAPPIGEHWGIGTTCGDCPPLELVSSVIQDGDMIDTGFGAMDFAALQATKSDVPLDISQSVCKYPDYLKMSADTYGNSMFFHLRREQIFARHYYNKLVYSATPSGSMITSDSQIFNKPYWLHRAQGHNNGICWNNQLFITCVDTTRSTNLTIFTPSNFKQYIRHGEEYELQFIFQLCKITLTTEVMAYLHTMDPTILEQWNFGLTLPPLEDAYNAATSCPQ-AK-DPLAKYKFWDVDLKERFSLDLDQFALGRKFLLQVGKRK Seq108 M-DSEGTGCTGWFYVDIIDFIDERETAQALLQVQETQAHKEAVQHLKRKFLGSPSNSQQQP-GKRRLLDSGYGVLKCSNAKAMFMAKFKELYGVSYNELVRVFKSDKTCCTDWVFGVSPMVAENLKLIQPFCMYYHIQGTIVLLLARFTCAKNRLTIAKCLVNIPQSQMFIEPPKLRSTAVALYFYRTGISNISGETPEWITRQTQLQHFELSQMVQWAFDHDVVDDSEIAFYYAQLADTDSNAAAFLKSNCQAKYVKDCGTMTRHYKRAQRKSLTMSAWIRYRGNWREIAKFLRYQGINFMYFIQTFKLFLKGPKHNCIVIQGPPNTGKSQFAMSLIRFLQGCVISYVNSGSHFWLQPLEDAKVALLDDATYGCWTYIDQYLRNFLNGNPCSDRKHRSLLQIVCPPLLITSNINPKEDYLHSRVTVFQFLNAFPFDPHGNPVYALNDVNWKNFFSTTWSRLDLEEDFKCLCHRLNVCQEKILDCYEDSDKLVDQINYWRYECAMFYTARERNMQTLNHQVVPASAVSKQKACQAIEMHMALESLNKSEYNMEPWTMDTCYELWGEAPCFKKGGKTVTVMFDGNKDNTMDYTCWTYVYIYK-E-DRWVKTQGVDYTGIYYKEYYVTFKDEAKKYPNTGGHLSAT-KTAFIVHLKGATNCLKCLRYRFAKHRN-LFKEVSSTWHWFDSAHQRQKFIDTVKVPWRTNDSKVYLPPA-PVRIVNTEEYITRTGIYYYAGSRLITLGHPYFRAEIPKVSAFQYRVFRVQLPDPNKFGLPDPNLFNPDDRLVWGCVGVEVGRGQPLGVGLSGHPLFNKYDDTENSRFADVRDNISDNKQTQLCIIGCAPPIGEHWATGTTCGDCPPLELVSTVIEDGDMVDTGFGAMDFANLQATKSDVPLDIAQSVCKYPDYLKMSADTYGNSMFFHLRREQIFARHYYNKAGYSATPSGSMITSDSQIFNKPYWLHRAQGHNNGICWNNQLFITCVDTTKSTNLTIFTPANFKQYIRHGEEYELQFIFQLCKITLTTEIMAYLHTMDSTILEQWNFGLTLPPLEDAYNAATSCPQ-AKEDPLAKYKFWNVDLKERFSLDLDQFALGRKFLLQIGKRK Seq109 MASPEGTGCRGWFHVDLDGFIDERETAQQLLH?QNTHADTQTLQKLKRKYLGSP------SAVKRRLIDSGYGILKCSNVQAKLYCKFKDIFGIPFSELVRTFKSDSTCCHDWIFGVN?TLAEALKIIKTQCIYYHMQGVVILLLIRYTCGKNRKTIVKSLLNVPTEQMLVQPPKIRSPAVALYFYKTSISNISGSTPEWIERQTQLQHFELSKMVQWAFDNEVTDDSQIAFHYAQLADVDSNAQAFLKSNMQAKYVKDCGIMCRHYKRAQQQQMNMKQWIKHVGDWKPIVQFLRYQGVEFISFLSYFKLFLQGPKHNCLVIYGPPNTGKSCFAMSLINFFHGSVISYVNSHSHFWLQPLDNTKLGMLDDATEACWKYIDEYLRNLLDGNPVSDRKHKQLVQIKCPPVLITTNINPMQDYLHSRIHVLQFLNPFPIDVNGNPVYQLNNANWKCFFERTWSRLDLDEDFRCLCQRLDACQEKILDCFEDSKNITDHIDYWRQENVIYYKARENNMTKLGHQVVPCLQVCKAKACVAIELQIALESLCKTEYNMEEWTLDVCESMWYTEPCFKKQGQHIEVWFDGSKDNRAEYVVWKWVYYCG-E-DGWCKVSSVSYEGIYYKTYYTNFKDEATKYSCNNTEIDSGYKTAPVVHIKGEANRLKCLRYRFQKHKQ-LFVTVSSTYHWYKDETQRQKFLDIVKIPWRPSDSKVYLPPT-PVKVITTDAYVKRTTIFYHAGSRLLTVGHPYYKADIPKVSAFQYRVFRVRLPDPNKFGLPDTNIFNPDERLVWACVGLEIGRGQPLGVGVSGHPLFNRLDDTESSSIADSRDNVSDPKQTQLCIIGCAPAIGEHWTKGTACGDCPPLELINSPIEDGDMVDTGFGALNFKALQESKSDVPLDIVQSTCKYPDYLKMSADAYGDSMWFYLRREQLFTRHFFNRAGYVATPSGSMITSEAQLFNKPYWLQRAQGHNNGICWNNQLFVTVVDTTRNTNMTLYNSKQIKQYVRHAEEYELQFVFQLCKISLSAEVMAYLHTMNSTLLEDWNIGLSPPVLEDKYSAAITCPP-EKQDPLSKYKFWEVNLQNSFSADLDQFPLGRKFLMQVGKRK Seq110 MASPEGTGCTGWFHVDLDGFLDDRETAQQLLHAQNTYADTQTLHNLKRKYLGSP------SGVKRRIIDSGYGLLKSSNVQAKLCYKFKELFGIPFSELVRTFKSDSTCCHDWIFGVNETLAEALKIIKSQCMYYHIQGVVILMLIRYTCGKNRKTIIKSLVNVPSEQMLVQPPKIRSPAVALYFYKTAMSNISGETPEWIQRQTQIQHFELSKMVQWAFDNDVTDDSDIAFYYAQLADVDSNAQAFLKSNMQAKYVKDCGIMCRHYKRAQQQQMNMKQWITHIGDWRPIVQFLRYQGVDFISFLSYFKLFLRGPKHNCLVLYGPPNTGKSCFAMSLIQFFQGSVISYVNSHSHFWLQPLDNAKLGMLDDATDACWRYIDEYMRNLLDGNPVSDRKHKQLVQIKCPPVIITTNINPLHDYLHSRIHVVPFLNPFPIDTNGNPVYQLNNVNWKCFFERTWSRLDLDEDFRCLCQRLDACQEKILDCFEDSKKIEDHIVYWRHENVVLYKARQNNITKLRHQVVPCLQVCKAKACVAIEIQMALESLYKTEYKVEEWTLDVCENMWHTAPCFKKSGKRIEVWFDGKKDNRTEYVVWQWVYYCG-D-NGWTKVPSVDYKGIYYKVYYTDFNDEAVKYTCYNTEVDGGYKTTPVVHLKGEPNRLKCLRYRCQKHK-HLFVNISSTYHWYKDETQRANFLNVVKIPWRPSETKVYLPPT-PVKVVPTDAYVKRTNIFYHAGSRLLAVGHPYYKTDVPKVSAFQYRVFRVRLPDPNKFGLPDTNVFNPEERLVWACVGLEIGRGQPLGVGVSGNPLFNKLDDTESSTIADSRDNISDPKQTQLCIIGCTPAIGEHWAKGTACGDCPPLELVNSPIQDGDMVDIGFGAMDFKTLQESKSDVPLDISQSTCKYPDYLKMSADAYGDSMWFYLRREQLFARHYFNRAGYVATPSGSMVTSEAQLFNKPYWLQRAQGHNNGICWGNQVFVTVVDTTRNTNMTIYNSSQIKQYVRHVEEYELQFVFQLCKISLSAETMAYLHTMNSTLLEGWNIGLSPPALEDKYSLAITCPA-EKEDPLAKYKFWDVNLQDSFSADLDQFPLGRKFLMQLGKRK Seq111 MASPEGTGCCGWFQVDVDGFIDDRETAQQLLQVQTAHADAQTLQKLKRKYIGSP------SEVKRRLIDSGYGLFKSSNVQGRLHFKFKEVYGVPYTELVRTFKSDSTCCNDWIFGVNETLAEALKILKPQCVYYHMQGVIVMMLIRYICGKNRKTITKSLLNVPQEQMLIQPPKLRSPAVALYFYKTAMSNISGETPEWIQRQTQLQHFELSKMVQWAFDNEVTDDSQIAFLYAQLADIDSNAQAFLKSNMQAKYVKDCGIMCRHYKRAQQQQMNMCQWIKHIGDWKPIVQFLRYQGVDFISFLSYFKLFLQGPKHNCLVLCGPPNTGKSCFAMSLINFFQGSVISFVNSQSHFWLQPLDNAKLGLLDDATDTCWRYIDDYLRNLLDGNPISDRKHKQLVQIKCPPVIITTNVNPMQDYLHSRISVFKFENPFPLDNNGNPVYELSNVNWKCFFERTWSRLNLDEDFRCLSQRLDACQNKILDCYEDSKCIIDHIDYWRHEYVLYYKARENDINVLNHQMVPSLQVCKAKACSAIELQIALEAISNTIYKNEEWTLDTCDELWRTEPCFKKEGQHIEVWFDGNKNNCMEYVVWKFIYYNG-E-CGWCKVSSVDYRGIYYKTYYTDFEQEAKKYPRINTQSHCGDKTTPVIHLKGEANRLKCCRYRFQKYKT-LFTDVTTTYHWYKDETQRDTFLNVVKIPWRPSDNKVYLPPT-PVKVVATDTYVKRTSIFYHAGSRLLAVGHPYYKTNIPKVSAYQYRVFRVRLPDPNKFGLPDPSFYNPDERLVWACVGLEVGRGQPLGAGLSGHPLFNRLDDTEVSNLADSRDNISDCKQTQLCIVGCAPALGEHWTKGAVCGDCPPLALVNTPIEDGDMVDTGFGAMDFKLLQESKAEVPLDIVQSTCKYPDYLKMSADAYGDSMWFYLRREQLFARHYFNRAGYVATPSGSMITSEAQLFNKPYWLQRAQGHNNGICWGNQVFVTVVDTTRSTNMTIYDAREINQYLRHVEEYELQFVFQLCKITLTAEVMAYLHNMNNTLLDDWNIGLSPPVLEDKYSTAITCPA-EKQDPLAKYKFWEVNLQDSFSADLDQFPLGRKFLMQLGKRK Seq112 MASPEGTGCCGWFEVDLDGFIDDAET?QQLLQVQTAHADKQTLQKLKRKYIASP------SGVKRRLIDSGYGLFKSSNLQGKLYYKFKEVYGIPFSELVRTFKSDSTCCNDWIFGVNETLAEALKIIKPHCMYYHMQGVIVMMLIRYTCGKNRKTIAKALLNVPQEQMLIQPPKIRSPAVALYFYKTAMSNISGDTPEWIQRQTQLQHFELSKMVQWAFDNEVTDDSQIAFQYAQLADVDSNAQAFLKSNMQAKYVKDCGIMCRHYKRAQQQQMNMCQWIKHIGDWKPIVQFLRYQGVDFISFLSYFKLFLQGPKHNCLVLCGPPNTGKSCFAMSLIKFFQGSVISFVNSQSHFWLQPLDNAKLGLLDDATEICWKYIDDYLRNLVDGNPISDRKHKQLVQIKCPPLLITTNINPMLDYLHSRMLVFQFQNPFPLDNNGNPVYELSNVNWKCFFTRTWSRLNLDEDFKCLSQRLNACQNKILDCFEDSRCIADHIEYWRHENVLYYKARENDITVLNHQMVPCLQVCKAKACSAIEVQIALESLSTTIYNNEEWTLDTCEELWLTEPCFKKEGQHIEVWFDGSKNNCMQYVAWKYIYYNG-D-CGWQKVCSVDYRGIYYKTYYTDFEQEAKKFRSINNNNHPGDKTTPVVHLKGEPNRLKCCRYRFQKYKT-LFVDVTSTYHWYKDETQRNSFLSHVKIPWRPSENKVYLPPT-PVKVVATDSYVKRTSIFYHAGSRLLAVGHPYYKTNIPKVSAYQYRVFRVRLPDPNKFGLPDTNIYNPDERLVWACVGLEVGRGQPLGAGLSGHPLFNRLDDTESSNLADSRDNISDGKQTQLCIVGCTPAMGEHWTKGAVCGDCPPLALINTPIEDGDMIDTGFGAMDFKVLQESKAEVPLDIVQSTCKYPDYLKMSADAYGDSMWFYLRREQLFARHYFNRAGYVATPSGSMITSEAQLFNKPYWLQRAQGHNNGICWGNQLFVTVVDTTRSTNMTIYDARKINQYLRHVEEYELQFVFQLCKITLSAEVMAYLHNMNANLLEDWNIGLSPPVLEDKYSTAITCPT-EKQDPLAKYKFWDVNLQDSFSTDLDQFPLGRKFLMQLGKRK Seq113 MDPEGTPGCTGWFNVDLVDFIDD-EAPGALLHAQETQAHAEAVQVLKRKFVGSPSPRLNEIQAKRRLFDSGYGLLRCSNLKATLLSKFKSVYGVSFSELVRSFKSDRTTCADWVAGVHHSVAEGLKLIQPFCSYAHIQGVYLLLLARFKCGKNRLTVSKCMLNVQETHMLIEPPKLRSAAAALYWYRTGISNVSGETPEWITRQTMFQHFDLSEMVQWAYDHDFTDDSVIAYEYAQLAGIDSNAAAFLKSNAQAKYVKDCATMCRHYKRAERQQMTMSQWIKQRGDWRPIVQFLRYQGVEFIAFLAALKLFLKGPKKNCIVLFGPPNTGKSYFGMSLIHFLQGSIISYVNSNSHFWLQPLADAKVAMLDDATPQCWSYIDNYLRNALDGNPISDRKHKNLVQMKCPPLLITSNTNAGQDYLHSRMVVFTFEQPFPFDQNGNPVYELNDKNWKSFFSRTWSRLDLEEEFKCLAERLSALQDRILELYEDSKDLKDQIEHWRQECAVLYKAREVGFSHLNHQVVPSLTVSRAKAHKAIEVQLALESLQNSEYNNEEWTLDASLEMWHTEPCFKKTGVPVTVLFDCDKDNTMEYVLWGHIYVWG-D-NGWVKTFGADNWGLHYKVYYVQFYEDAKKYHNNATGSSGDSDYTPIVHLKGESNCLKCLRFRL-GKHKHLYINISSTWRWFANELQRQQFLNTVKIPWRPSDSKVYLPP-VPVKVVSTDEYVSRTSIYYHAGSRLLAVGHPYYKVSVPKVSGLQYRVFRVRLPDPNKFGLPDANFYDPNQRLVWACLGVEVGRGQPLGVGTSGHPLLNKLDDTENGPKVDNRECVSDYKQTQLCMLGCKPPVGEHWGKGNPCGDCPALELVNSVIQDGDMVDTGYGAMDFNALQANKSDVPIDICTSVCKYPDYLKMASDPYGDSLFFYLRREQMFVRHLFNRAGFYPTPSGSMVTSDAQLFNKPYWLQKAQGHNNGICWGNQVFLTVVDTTRSTNMTLYNNESFKEYLRHVEEFDLQFIFQLCKVTLNTEVMAYIHSMDASILEDWNFGLQPPPLQDTYSAAITCPPKEKEDPLAKYTFWEVDLKEKFSADLDQFPLGRKFLLQAGRKR Seq114 MADPEGTGCTGWFEVDLLEFIDDTEAARALFNIQEGEDDLNAVCALKRKFAACSANPCRTSYRKRKIDDSGYGVLHSSNTKANILYKFKEAYGISFMELVRPFKSDKTSCTDWCYGISPSVAESLKLIKQHSLYTHLQGIIILLLIRFRCSKNRLTVAKLMLSIPETCMVIEPPKLRSQTCALYWFRTAMSNISGTTPEWIDRLTVLQHFDLSEMVQWAYDNELTDDSDIAYYYAQLADSNSNAAAFLKSNSQAKIVKDCGIMCRHYKKAEKRKMSIGQWIQSRGNWRPIVQLLRYQNIEFTAFLGAFKKFLKGPKKSCMLICGPANTGKSYFGMSLIQFLKGCVISCVNSKSHFWLQPLSDAKIGMIDDVTPISWTYIDDYMRNALDGNEISDVKHRALVQLKCPPLLLTSNTNAGTDYLHSRLTVFEFKNPFPFDENGNPVYAINDENWKSFFSRTWCKLDLEEDFKCISARLNAVQEKILDLYEDKTDLPSQIEHWRMECALLYTAKQMGFSHLCHQVVPSLLASKTKAFQVIELQMALETLSKSQYSTSQWTLQTSLEVWLCEPCFKKQGETVTVQYDNDKKNTMDYTNWGEIYIIE-E-DTCTMVTGVDYIGMYYKVYFKYFKEDAAKYCTNKQRTVCSSNVAPIVHLKGESNSLKCLRYRLKPYKE-LYSSMSSTWHWFVTEQQQQMFLGTVKIPWRPSEATVYLPP-VPVKVVSTDEYVSRTSIYYYAGSRLLAVGHPYFKLLVPKVSGLQYRVFRVRLPDPNKFGFPDTSFYNPDQRLVWACVGLEIGRGQPLGVGISGHPLLNKFDDTETGNKYDNRECLSDYKQTQLCLLGCKPPTGEHWGKGVACNDCPPLELINTIIEDGDMVDTGFGCMDFKTLQANKSDVPIDICGSTCKYPDYLKMTSEPYGDSLFFFLRREQMFVRHFFNRAGFFPTPSGSMVTSESQLFNKPYWLQRAQGHNNGICWGNQVFVTVVDTTRSTNMTLYKNENFKEYIRHVEEYDLQFVFQLCKVTLTAEVMTYIHAMNPDILEDWQFGLTPPPLQDTYSQAITCPPKEKEDPLGKYTFWEVDLKEKFSADLDQFPLGRKFLLQAGKRK Seq115 MDDPEGTGCTGWFEVDLIEFIDEAEAARALFNVQEGVDDINAVCALKRKFAACSANVCVSWHRKRKIIDSGYGILHNSNTKATLLYKFKEAYGVSFMELVRPFKSDKTSCTDWCYGISPSVAESLKLIKQHSIYTHLQGIILLLLIRFKCSKNRLTVAKLMLSIPETCMIIEPPKLRSQACALYWFRTAMSNISGTTPEWIDRLTVLQHFDLSEMIQWAYDNDITDDSDIAYKYAQLADVNSNAAAFLRSNAQAKIVKDCGVMCRHYKRAEKRGMTMGQWIQSRGNWRPIVQFLRYQNIEFTAFLVAFKQFLQGPKKSCMLLCGPANTGKSYFGMSLIHFLKGCIISYVNSKSHFWLQPLSDAKLGMIDDVTAISWTYIDDYMRNALDGNDISDVKHRALVQLKCPPLIITSNTNAGKDYLHSRLTVFEFNNPFPFDANGNPVYKINDENWKSFFSRTWCKLGLEEDFKCISARLSAVQDKILDIYEDKNDLTSQIEHWRMECAIMYTARQMGISHLCHQVVPSLVASKTKAFQVIELQMALETLNASPYKTDEWTLQTSLEVWLSEPCFKKKGITVTVQYDNDKANTMDYTNWSEIYIIE-E-TTCTLVAGVDYVGLYYKTYFKYFKEDAKKYCTYKGRNVCSSKVSPIVHLKGDPNSLKCLRYRLKPFKD-LYCNMSSTWHWYTTETQRQLFLNTVKIPWRPSEATVYLPP-VPVKVVSTDEYVSRTSIYYYAGSRLLAVGNPYFKVLVPKVSGLQYRVFRVRLPDPNKFGFPDTSFYNPDQRLVWACVGLEIGRGQPLGVGVSGHPYLNKFDDTETSNRYDNRECLSDYKQTQLCLIGCKPPTGEHWGKGVACTDCPPLELFNSIIEDGDMVDTGFGCMDFGTLQANKSDVPIDICNSTCKYPDYLKMASEPYGDSLFFFLRREQMFVRHFFNRAGFFPTPSGSIVTSESQLFNKPYWLQRAQGHNNGICWGNQLFVTVVDTTRSTNMTLYKNDNFKEYVRHVEEYDLQFVFQLCKITLTAEIMTYIHTMDSNILEDWQFGLTPPPLQDTYSQAITCPPKEKEDPLNKYTFWEVNLKEKFSADLDQFPLGRKFLLQSGKRK Seq116 MEDPEGTGCTGWFEVDLIDFIDEHEAARALFNAQEGEDDLHAVSAVKRKFTSSPSPRA-KHLPKRKPCDSGYGIMCENSIKTTVLFKFKETYGVSFMELVRPFKSNRSSCTDWCMGVTPSVAEGLKLIQPYSIYAHLQGVLILLLIRFKCGKNRLTVSKLMLNIPETHMVIEPPKLRSATCALYWYRTGLSNISGTTPEWIEQQTVLQHFDFGEMVQWAYDHDITDDSDIAYKYAQLADVNSNAAAFLKSNSQAKIVKDCATMCRHYKRAERKHMNIGQWIQYRGDWRPIVRFLRYQDIEFTAFLDAFKKFLKGPKKNCLVLYGPANTGKSYFGMSLIRFLSGCVISYVNSKSHFWLQPLTDAKVGMIDDVTPICWTYIDDYMRNALDGNDISDVKHRALVQIKCPPLILTTNTNAGTDYLHSRLVVFHFKNPFPFDENGNPIYEINNENWKSFFSRTWCKLDLEEDFKCIPARLNAVQEKILDLYEDSNDLNAQIEHWRMECVLFYKAKELGITHIGHQVVPPMAVSKAKACQAIELQLALEALNKTQYSTDGWTLQTSLEMWRAEPYFKKHGYTITVQYDNDKNNTMDYTNWKEIYLLG-E-CECTIVEGVDYYGLYYKIYFVKFSNDAKQYCTNKGRVAHTTCTAPIIHLKGDPNSLKCLRYRVKTHKS-LYVQISSTWHWYSDETQRQQFLKTVKIPWRPSEATVYLPP-VPVKVVSTDEYVSRTSIYYYAGSRLLTVGHPYFKVLVPKVSGLQYRVFRIKLPDPNKFGFPDTSFYNPEQRLVWACTGLEIGRGQPLGVGISGHPLLNKFDDTETSNKYDNRECLSDYKQTQLCILGCKPPIGEHWGKGTPCGDCPPLQLINSVIQDGDMVDTGFGCMDFNTLQASKSDVPIDICSSVCKYPDYLQMASEPYGDSLFFFLRREQMFVRHFFNRAGFFPTPSGSMVTSESQLFNKPYWLQRAQGHNNGICWGNQLFVTVVDTTRSTNMTLYKNENFKEYLRHGEEFDLQFIFQLCKITLTADVMTYIHKMDATILEDWQFGLTPPPLEDTYSTAITCPPKGKEDPLKDYMFWEVDLKEKFSADLDQFPLGRKFLLQAGKKK Seq117 MEDPEGTGCTGWFSVDLIGFIDDTQAARALFNLQEEEDDLNAVSALKRKF--TGGGNSN--AAKRRAYDSGYGIMHVNNIKATLMHKFKEAYGVTFTQLIRPFKSDRTSCTDWCFGITPSVAESLKLIKPQTLYTHLQGIIILLLVRFKCAKNRLTVSKLMLSIPETHMIIEPPKIRSTTCALYWFRTGMSNISGQTPEWIERLTVLQHFDLGEMVQWAYDNDITDDSEIAYQYAMLADVNSNAAAFLKSNSQAKIVKDCGTMCRHYKRAEKRKMTIGQWIQARGDWRTIVKLLRYQNVEFTQFLATFKKFLKGPKKSCMVICGPPNTGKTYFAMSLIHFLQGCVISYVNAKSHFWLQPLSDAKIGMIDDVTAICWTYIDDYLRNALDGNDISDVKHKALVQLKCPPLLLTSNIDVATDFLHSRVVVFRFNNPFPFDENGNPVYNLNDENWKSFFSRTWCQLDLEEDFKCIATRLNAVQEKILDVYENKKDLCTQIEHWRIECALYYKAKELGLARIGHQVVPPMSVTKAKACQIIELQMALQTLNETQYSNDEWTLSTSLELWLCEPCLKKKGETVTVQYDNDKENTMEYTSWNEIYIVK-D-SVWTVVVGVDYMGLYYKTYYVKFETDAKKYCTNKGRSVCAPNTAPIVHLKGDPNSLKCLRYRLKCNTD-LYCHMSSTWHWYASETQRQQFLNSVRIPWRPSEATVYLPP-VPVKVVSTDEYVSRTSIYYYAGSRLLAVGHPYFKVLVPKVSGLQYRVFRVRLPDPNKFGFPDTSFYNPDQRLVWACVGIEIGRGQPLGVGISGHPLLNKFDDTETNNKYDNRECLSDAKQTQLCIIGCKPPTGEHWGKGTPCGACPPLELMNTVIEDGDMIDTGFGCMDFKSLQANKSDVPLDICTSICKYPDYLGMASEAYGDSLFFFLRREQMFVRHLFNRAGFFPTPSGSMVSSESQLFNKPYWLQRAQGHNNGICWGNQIFVTVVDTTRSTNMTLYKNENFKEYLRHVEEYDLQFIFQLCKISLTANVMQYIHTMNPDILEDWQFGLTPPPLQDTYSQAITCPPTAKEDPLKKYSFWEINLKEKFSADLDQFPLGRKFLLQAGKRK Seq118 MADPAGTGCNGWFYVDMVDFIDEAETAQALFHAQEAEEHAEAVQVLKRKYVGSPSPRLKAITAKRRLFDSGYGVLKTSNGKAAMLGKFKELYGVSFMELIRPFQSNKSTCTDWCFGVTGTVAEGFKLLQPYCLYCHLQGMVMLMLVRFKCAKNRITIEKLLLCISTNCMLIQPPKLRSTAAALYWYRTGMSNISGETPEWIERQTVLQHFDLSQMVQWAYDNDVMDDSEIAYKYAQLADSDSNACAFLKSNSQAKIVKDCGTMCRHYKRAEKRQMSMGQWIKSRGDWRDIVKFLRYQQIEFVSFLSALKLFLKGPKKNCILIHGAPNTGKSYFGMSLISFLQGCIISYANSKSHFWLQPLADAKIGMLDDATTPCWHYIDNYLRNALDGNPVSDVKHKALMQLKCPPLLITSNINAGKDYLHSRLVVFTFPNPFPFDKNGNPVYELSDKNWKSFFSRTWCRLNLEEDFKCLSQRLNVCQDKILEHYEDSKRLCDHIDYWRLECVLMYKAREMGIHSINHQVVPALSVSKAKALQAIELQMMLETLNNTEYKNEDWTMQTSLELYLTAPCLKKHGYTVEVQFDGDVHNTMHYTNWKFIYLCI-D-GQCTVVEGVNCKGIYYITYFVNFTEEAKKYCTNQTRAVSCPATTPIIHLKGDANILKCLRYRL-SKYKQLYEQVSSTWHWYISTSQRDDFLNTVKIPWRPSEATVYLPP-VPVKVVSTDEYVTRTNIYYHAGSRLLTVGHPYYKIVVPKVSGLQYRVFRVRLPDPNKFGFPDTSFYNPEQRLVWACVGLEVGRGQPLGVGISGHPLLNKFDDTENSNRYDNRECISDYKQTQLCLLGCKPPIGEHWGKGSPCGDCPPLELKNSVIQDGDMVDTGFGAMDFTALQDTKSNVPLDICNSICKYPDYLKMVAEPYGDTLFFYLRREQMFVRHFFNRSGYFPTPSGSMVTSDAQIFNKPYWMQRAQGHNNGICWGNQLFVTVVDTTRSTNMSVFKSSNFKEYLRHGEEFDLQFIFQLCKITLSADIMTYIHSMNPAILEDWNFGLTTPPLEDTYSQAITCPQKPKEDPFKDYVFWEVNLKEKFSADLDQFPLGRKFLLQAGKRK Seq119 MADPAGTGCNGWFYVDLVDFIVETETAHALFTAQEAKQHRDAVQVLKRKYLGSPSPRLKAIAAKRRLFDSGYGVLKTSNAKAAMLAKFKELYGVSFSELVRPFKSNKSTCCDWCFGLTPSIADSIKLLQQYCLYLHIQGMVVLLLVRYKCGKNRETIEKLLLCVSPMCMMIEPPKLRSTAAALYWYKTGMSNISGDTPEWIQRQTVLQHFELSRMVQWAYDNDIVDDSEIAYKYAQLADTNSNASAFLKSNSQAKIVKDCATMCRHYKRAEKKQMSMSQWIKYRGDWKQIVMFLRYQGVEFMPFLTALKRFLQGPKKNCILLYGAANTGKSLFGMSLIKFLQGSVICFVNSKSHFWLQPLADAKIGMLDDATVPCWNYIDDNLRNALDGNLVSDVKHRPLVQLKCPPLLITSNINAGTDYLHNRLVVFTFPNEFPFDENGNPVYELNDKNWKSFFSRTWSRLSLDEDFKCLCQRLNVCQDKILTHYEDSTNLRDHIDYWRLECAIYYKAREMGFKHINHQVVPTLAVSKNKALQAIELQLTLETIYNPQYSNEKWTLDVSLEVYLTAPCIKKHGYTVEVQFDGDICNTMHYTNWKHIYICE-E-ASVTVVEGVDYYGLYYQTYFVQFKDDAEKYSSHKGRINCNSNTTPIVHLKGDANTLKCLRYRF-KKHCKLYTAVSSTWHWYDSEWQREQFLSQVKIPWLPSEATVYLPP-VPVKVVSTDEYVARTNIYYHAGTRLLAVGHPYFKILVPKVSGLQYRVFRIHLPDPNKFGFPDTSFYNPDQRLVWACVGVEVGRGQPLGVGISGHPLLNKLDDTENASAYDNRECISDYKQTQLCLIGCKPPIGEHWGKGSPCGDCPPLELINTVIQDGDMVDTGFGAMDFTTLQANKSEVPLDICTSICKYPDYIKMVSEPYGDSLFFYLRREQMFVRHLFNRAGYFPTPSGSMVTSDAQIFNKPYWLQRAQGHNNGICWGNQLFVTVVDTTRSTNMSLYKNTNFKEYLRHGEEYDLQFIFQLCKITLTADVMTYIHSMNSTILEDWNFGLQPPPLEDTYSQAIACPA-PKEDPLKKYTFWEVNLKEKFSADLDQFPLGRKFLLQAGKRK Seq120 MADPAGTGCNGWFYVDLVDFIVETETAHALFTAQEAKQHRDAVQVLKRKY?GSPSPRLKAIAAKRRLFDSGYGVLKTSNAKAAMLAKFKELYGVSFSELVRPFKSNKSTCCDWCFGLTPSIADSIKLLQQYCLYLHIQGMVVLLLVRYKCGKNRETIEKLLLCVSPMCMMIEPPKLRSTAAALYWYKTGISNISGDTPEWIQRQTVLQHFELSQMVQWAYDNDIVDDSEIAYKYAQLADTNSNASAFLKSNSQAKIVKDCATMCRHYKRAEKKQMSMSQWIKYRGDWKQIVMFLRYQGVEFMSFLTALKRFLQGPKKNCILLYGAANTGKSLFGMSLMKFLQGSVICFVNSKSHFWLQPLADAKIGMLDDATVPCWNYIDDNLRNALDGNLVSDVKHRPLVQLKCPPLLITSNINAGTDYLHNRLVVFTFPNEFPFDENGNPVYELNDKNWKSFFSRTWSRLSLDEDFKCLCQRLNVCQDKILTHYEDSTDLRDHIDYWRLECAIYYKAREMGFKHINHQVVPTLAVSKNKALQAIELQLTLETIYNSQYSNEKWTLDVSLEVYLTAPCIKKHGYTVEVQFDGDICNTMHYTNWTHIYICE-E-ASVTVVEGVDYYGLYYRTYFVQFKDDAEKYSSHKGRINCNSNTTPIVHLKGDANTLKCLRYRF-KKHCTLYTAVSSTWHWYDSEWQRDQFLSQVKIPWLPSEATVYLPP-VPVKVVSTDEYVARTNIYYHAGTRLLAVGHPYFKILVPKVSGLQYRVFRIHLPDPNKFGFPDTSFYNPDQRLVWACVGVEVGRGQPLGVGISGHPLLNKLDDTENASAYDNRECISDYKQTQLCLIGCKPPIGEHWGKGSPCGDCPPLELINTVIQDGDMVHTGFGAMDFTTLQANKSEVPLDICTSICKYPDYIKMVSEPYGDSLFFYLRREQMFVRHLFNRAGYFPTPSGSMVTSDAQIFNKPYWLQRAQGHNNGICWGNQLFVTVVDTTRSTNMSLYKNTNFKEYLRHGEEYDLQFIFQLCKITLTADVMTYIHSMNSTILEDWNFGLQPPPLEDTYTQAIACPA-PKEDPLKKYTFWEVNLKEKFSADLDQFPLGRKFLLQAGKRK Seq121 MADPAGTGCNGWFYVDLVDFIVETETAHALFTAQEAKEHRDAVQVLKRKYLGSPSPRLKAIAAKRRLFDSGYGVLKTSNAKAAMLAKFKELYGVSFSELVRPFKSNKSTCCDWCFGLTPSIADSIKLLQQYCLYLHIQGMVVLLLVRYKCGKNRETIEKLMLCVSPMCMMIEPPKLRSTAAALYWYKTGMSNISGDTPEWIQRQTVLQHFELSQMVQWAYDNDIVDDSEIAYKYAQLADTNSNASAFLKSNSQAKIVKDCATMCRHYKRAEKKQMSMSQWIKYRGDWKQIVMFLRYQGVDFMSFLSALKKFLQGPKKNCILLYGAANTGKSLFGMSLMKFLQGSVICFVNSKSHFWLQPLADAKIGMLDDATVPCWNYIDDNLRNALDGNLVSDVKHRPLVQLKCPPLLITSNINAGTDYLHNRLVVFTFPNEFPFDENGNPVYELNDKNWKSFFSRTWSRLSLDEDFKCLCQRLNVCQDKILTHYEDSTDLRDHIDYWRLECAIYYKAREMGFKHINHQVVPTLAVSKNKALQAIELQLTLETIYNSQYSNEKWTLDVSLEVYLTAPCIKKHGYTVEVQFDGDICNTMHYTNWKYIYICE-E-TSVTVVEGVDYYGLYYQTYFVQFKDDAEKYSSHKGRINCNSNTTPIVHLKGDANTLKCLRYRF-KKHCKLYTAVSSTWHWYDSECQREQFLSQVKIPWLPSEATVYLPP-VPVKVVSTDEYVARTNIYYHAGTRLLAVGHPYFKILVPKVSGLQYRVFRIYLPDPNKFGFPDTSFYNPDQRLVWACVGVEVGRGQPLGVGISGHPLLNKLDDTENASAYDNRECISDYKQTQLCLIGCKPPIGEHWGKGSPCGDCPPLELINTVIQDGDMVDTGFGAMDFTTLQANKSEVPLDICTSICKYPDYIKMVSEPYGDSLFFYLRREQMFVRHLFNRAGYFPTPSGSMVTSDAQIFNKPYWLQRAQGHNNGICWGNQLFVTVVDTTRSTNMSLYKNTNFKEYLRHGEEYDLQFIFQLCKITLTADVMTYIHSMNSTILEDWNFGLQPPPLEDTYSQAIACPA-PKEDPLKKYTFWEVNLKEKFSADLDQFPLGRKFLLQAGKRK Seq122 MADPAGTGCNGWFYVDLVDFIVETETAHALFTAQEAKEHRDAVQVLKRKYLGSPSPRLKAIAAKRRLFDSGYGVLKTSNAKAAMLAKFKELYGVSFTELVRPFKSNKSTCCDWCFGLTPSIADSIKLLQQYCLYLHIQGMVVLLLVRYKCGKNRETIEKLMLCVSPMCMMIEPPKLRSTAAALYWYKTGMSNISGDTPEWIQRQTVLQHFELSQMVQWAYDNDIVDDSEIAYKYAQLADTNSNASAFLKSNSQAKIVKDCATMCRHYKRAEKKQMSMSQWIKYRGDWKQIVMFLRYQGVDFMSFLSALKKFLQGPKKNCILLYGAANTGKSLFGMSLMKFLQGSVICFVNSKSHFWLQPLADAKIGMLDDATVPCWNYIDDNLRNALDGNLVSDVKHRPLVQLKCPPLLITSNINAGTDYLHNRLVVFTFPNEFPFDKNGNPVYELNDKNWKSFFSRTWSRLSLDEDFKCLCQRLNVCQDKILTHYEDSTDLRDHIDYWRLECAIYYKAREMGFKHINHQVVPTLAVSKNKALQAIELQLTLETIYNSQYSNEKWTLDVSLEVYLTAPCIKKHGYTVEVQFDGDICNTMHYTNWKYIYICE-E-TSVTVVEGVDYYGMYYQTYFVQFKDDAEKYSSHKGRINCNSNTTPIVHLKGDANTLKCLRYRF-KKHCKLYTAVSSTWHWYDSECQREQFLSQVKIPWLPSEATVYLPP-VPVKVVSTDEYVARTNIYYHAGTRLLAVGHPYFKILVPKVSGLQYRVFRIYLPDPNKFGFPDTSFYNPDQRLVWACVGVEVGRGQPLGVGISGHPLLNKLDDTENASAYDNRECISDYKQTQLCLIGCKPPIGEHWGKGSPCGDCPPLELINTVIQDGDMVDTGFGAMDFTTLQANKSEVPLDICTSICKYPDYIKMVSEPYGDSLFFYLRREQMFVRHLFNRAGYFPTPSGSMVTSDAQIFNKPYWLQRAQGHNNGICWGNQLFVTVVDTTRSTNMSLYKNTNFKEYLRHGEEYDLQFIFQLCKITLTADVMTYIHSMNSTILEDWNFGLQPPPLEDTYSQAIACPA-PKEDPLKKYTFWEVNLKEKFSADLDQFPLGRKFLLQAGKRK Seq123 MADPAGTGCNGWFYVDLVDFIVETETAHALFTAQEAKEHRDAVQVLKRKYLGSPSPRLKAIAAKRRLFDSGYGVLKTSNAKAAMLAKFKELYGVSFSELVRPFKSNKSTCCDWCFGLTPSIADSIKLLQQYCLYLHIQGMVVLLLVRYKCGKNRETIEKLLLCVSPMCMMIEPPKLRSTAAALYWYKTGMSNISGDTPEWIQRQTVLQHFELSQMVQWAYDNDIVDDSEIAYKYAQLADTNSNASAFLKSNSQAKIVKDCATMCRHYKRAEKKQMSMSQWIKYRGDWKQIVMFLRYQGVDFMSFLTALKRFLQGPKKNCILLYGAANTGKSLFGMSLMKFLQGSVICFVNSKSHFWLQPLADAKIGMLDDATVPCWNYIDDNLRNALDGNLVSDVKHRPLVQLKCPPLLITSNINAGTDYLHNRLVVFTFPNEFPFDENGNPVYELNDKNWKSFFSRTWSRLSLDEDFKCLCQRLNVCQDKILTHYEDSTDLRDHIDYWRLECAIYYKAREMGFKHINHQVVPTLAVSKNKALQAIELQLTLETIYNSQYSNEKWTLDVSLEVYLTAPCIKKHGYTVEVQFDGDICNTMHYTNWKYIYICE-E-TSVTVVEGVDYYGLYYQTYFVQFKDDAEKYSSHKGRINCNSNTTPIVHLKGDANILKCLRYRF-KKHCKLYTAVSSTWHWYDSECQREQFLSQVKIPWLPSEATVYLPP-VPVKVVSTDEYVARTNIYYHAGTRLLAVGHPYFKILVPKVSGLQYRVFRIYLPDPNKFGFPDTSFYNPDQRLVWACVGVEVGRGQPLGVGISGHPLLNKLDDTENASAYDNRECISDYKQTQLCLIGCKPPIGEHWGKGSPCGDCPPLELINTVIQDGDMVDTGFGAMDFTTLQANKSEVPLDICTSICKYPDYIKMVSEPYGDSLFFYLRREQMFVRHLFNRAGYFPTPSGSMVTSDAQIFNKPYWLQRAQGHNNGICWGNQLFVTVVDTTRSTNMSLYKNTNFKEYLRHGEEYDLQFIFQLCKITLTADVMTYIHSMNSTILEDWNFGLQPPPLEDTYSQAIACPA-PKEDPLKKYTFWEVNLKEKFSADLDQFPLGRKFLLQAGKRK Seq124 MADPAGTGCNGWFFVDMVDFINETETAQALFHAQEEQTHKEAVQVLKRKYASSPSPRLKAIAAKRRLFDSGYGILKCSNANAAMLAKFKELFGISFTELIRPFKSDKSTCTDWCFGIAPSVANFKH-----?ICIHIQAMVILALLRFKV?KTRTTIENY*LCISAASMLIQPPKLRSTPAALYWFKTAMSNISGETPEWIQRQTVLQHFDLSEMVQWAYDNDFIDDSDIAYKYAQLAETNSNACAFLKSNSQAKIVKDCATMCRHYKRAEKREMTMSQWIKRRGDWRDIVRFLRYQQVDFVAFLSALKNFLHGPKKNCILIYGAPNTGKSLFGMSLMHFLQGAIISYVNSKSHFWLQPLYDAKIAMLDDATSPC??YIDQYLRNALDGNPIFDVKH*A?VH??CPPLLIT?NINAGKDYLHSRVVVFTFHNEFPFDKNGNPEYGLNDKNWKSFFSRTWCRLNLEEVFKCLSQRLSVCQDKILEHYEDSTCLSDHIQYWRLECAVFYKAREMGIKTLNHQVVPTQAISKAKAMQAIELQLMLETLNTTEYSTEDWTLETSIELYTTVPCLKKDVYTVEAQFDGDKQNTMHYTNWTHIYILE-D-SICTVVKGVNYKGIYYETYYVTFREEAKKYCTNKDRCGSCSTTTPIVHLKGDANTLKCSRYRL-GKYKALYQDASSTWRWYTTEYQRDKFLTTVKIPWRSNEATVYLPP-VSVKVVSTDEYVTRTNIYYHAGSRLLAVGHPYYKIAVPKVSGLQYRVFRVKLPDPNKFGFPDTSFYDPCQRLVWACTGVEVGRGQPLGVGISGHPLLNKLDDTENLNKYDNRECISDYKQTQLCLIGCRPPIGEHWGKGTPCGECPPLELLNTVLQDGDMVDTGFGAMDFTTLQANKSDVPLDICSSICKYPDYLKMVSEPYGDMLFFYLRREQMFVRHLFNRAGYFPTPSGSMVTSDAQIFNKPYWLQRAQGHNNGICWSNQLFVTVVDTTRSTNMSVYKNDNFKEYLRHGEEYDLQFIFQLCKITLTADVMTYIHSMNPSILEDWNFGLTPPPLEDTYSQAVTCAPKPKDDPLKNYTFWEVDLKEKFSADLDQFPLGRKFLLQAGKRR Seq125 MADSGNWRCSGWFNVEMGDFIDQQEIAQALYHSQQVNADNEAIRVLKRKFAGSASPH--ILTSTHLLCDSGYGILKSSNVKATLLAKFKEVYGLSYMELVRPYKSDKTQCQDWVFGVAPSLAESLKLLTQYCLYIHLQGIIVLLLARFKCNKNRLTVQKLLLNVTQEYMLIEPPRLRSTPCALYWYRTSLSNISGEVPEWIKRQTVVQHFDLSQMVQWAFDNDITNDCEIAYKYALLASEDSNAAAFLKSNAQAKYVKDCGTMCRHYKAAERKQMTMSQWITHRGNWKHIVQFLRYQQVEFVPFLIALKQFLKGPKQNCIVIYGPPDTGKSHFGMSLMQFMQGVVISYVNSNSHFWLSPLADAKMALLDDATPACWTYIDRYLRNALDGNPMCDRKHKHLLQIKCPPLLITSNTNPKADYLHSRMKVFTFSNPFPFDSNGNPLYQLTNENWKAFFTKTWSKLDLDDDFKCLCKRLSACQDAILELYEDSIHLSDHIDHWRLENVLLHKAREMGLQSVNQQAVPSLAVSRSKGHNAIELQLALESLNESSYNTEEWTLQTSWEQWVTDPCFKKGGKTVEVRYDCDKDNTMQYVVWTFVYYW-LEG-KWYKVSSVDYNGIYYKVYYTQFDRDAKRYTTDSSTQCTLHNVAPIVHLKGDKNSLKCLRYRMHKGYSHLFNNVTTTWHWFSSTSQQKQFLQCAKIPWRPTEAKVYLPP-VSVKVVSTEEYVTRTNIYYYAGSRLLAVGHPYYKIAVPKVSGLQYRVFRIRLPDPNKFGFPDASFYNPDERLVWACAGVEVGRGQPLGIGTSGNPFMNKLEDTENAAKYDSRECMSDYKQTQLCIVGCKPPLGEHWGTGTPCGECPPLELKNTTIQDGDMIDVGFGAMDFKALQANKSDVPIDISNTICKYPDYLGMAADPYGDSMWFYIRREQMFVRHLFNRAGFYPTPSGSMVSSDAQIFNKPYWLQKAQGQNNGICWHNQLFLTVVDTTRSTNFSVYANSNFKEYLRHAEEYDLQFVFQLCKINLTTDVMTYIHSMSSSILEQWNFGLTPPPLEETYSQAITCPPKETEDPYGKMTFWEVDLKEKFSAELDQFALGRKFLLQLGKKK Seq126 MADSGNWRCTGWFNVEMGDFIDQQEIAQALYQSQQANADNEAIRVLKRKFTGSPSPQINVLTSKRRLFDSGYGLLQRNNAKAALLAKFKEVYGLSYMELVRPYKSDKTHCQDWVFGVIPSLAESLKLLTQYCMYIHLQGIIVLVLVRFKCNKNRLTVQKLLLNVTQERMLIEPPRLRSTPCALYWYRTSLSNISGDTPEWIKRQTLVQHFDLSQMIQWAFDNDITDDCEIAYKYALLGNVDSNAAAFLKSNAQAKYVKDCGTMCRHYKAAERKQMSMAQWIQHRGNWKDIVLFLRYQNVEFMPFLITLKQFLKGPKQNCIVLYGPPDTGKSHFGMSLIKFIQGVVISYVNSTSHFWLSPLADAKMALLDDATPGCWTYIDKYLRNALDGNPICDRKHKNLLQVKCPPLLITSNTNPKADYLHSRIKVFTFLNPFPFDSNGNPLYQLTNENWKAFFTKTWSKLDLDDDFKCLCKRLSACQDAILELYEDSVHLSDHIDHWRHENVLLHKAREMGLQTVNNQAVPSLAVSRSKGYNAIEMQIALESLNESLYNTEEWTLHTSWELWVTEPCFKKDGKTVEVRYDCEKDNSMQYVFWTHIYCWY-EG-GWAKVGSIDYNGIYYKVYYTRFDTDAKRYTTESCTQCTTHNVAPIVHLKGDKNSLKCFRYRLHKGYSHLFKNVTTTWHWFTTVLQQQHFLQHVKIPWRPTDAKVYLPP-VSVKVVSTDEYVTRTNIYYYAGSRLLAVGHPYFKTIVPKVSGLQYRVFRLRLPDPNKFGFPDASFYNPDERLVWACSGVEVGRGQPLGIGTSGNPFMNKLDDTENAPKYDGRECMSDYKQTQLCILGCRPPLGEHWGPGTPCGDCPPLELKNTPIQDGDMIDVGFGAMDFKALQANKSDVPIDISNTTCKYPDYLGMAADPYGDSMWFYLRREQMFVRHLFNRAGFYPTPSGSMVSSDAQLFNKPYWLQKAQGQNNGICWHNQLFLTVVDTTRSTNFSVYANSNFKEYLRHAEEFDLQFVFQLCKISLTTEVMTYIHSMNSTILEEWNFGLTPPPLEETYSQAISCPPKETEDPYAKLSFWDVDLKEKFSAELDQFPLGRKFLLQLGKKK Seq127 MADDTGTGCSGWFLVDMVDFID-LE-AQALLNEQEADAHYAAVQDLKRKYLGSPSPRLDAIKVKRRLFDSGYGLLKCKDVRATLHGKFKECYGLSFKDLTREFKSDKTTCGDWVFGVHHSVSEAFQLIQPLSTYSHIQGMVLLVLLRFKVNKNRCTVARTLLNIPEDHMLIEPPKIQSSVAALYWFRTSISNASGDTPEWIARQTIVEHFKLTEMVQWAYDNDYCDESDIAFEYAQRADFDSNAKAFLNSNCQAKYVKDCATMCKHYKNAEMKKMSIKQWIKYRGNWKPIVQFLRHQGIEFISFLSKLKLWLHGPKKNCIAIVGPPDTGKSAFCMSLIKFLGGTVISYVNSSSHFWLQPLCNAKVALLDDATQSCWGYMDTYMRNLLDGNPMSDRKHKSLALIKCPPLLVTSNIDITTEYLYSRVTLFKFPNPFPFDSNGNAVYELCDANWKCFFARLSASLDIE-DFRCLAKHLDACQEQLLELYENSNELKKHIQHWRYENVLLHKARQMGISHIGPQVVPPLQVSQTKGHEAIEMQMRIETLLKSQFGMEPWTLDTSFEMWLTPPCFKKQGKTVEVKYDCNAENTMHYVLWKYIYVYNTEKEIWLKVKGVDYKGLYYKTYYVDFEKEAKQYSSNNGRNNSYCTGTPIVQLQGDSNNLKCFRYRLHSNYKHLFFACISTWHWYVNEQQRQEFLNTVKIPWRPSDNKLYVPPPAPVKVLTTDAYVTRTKIFYHASSRLLAVGNPYFKTIVPKVSGFQFRVFKIVLPDPNKFALPDTSIFDSTQRLVWACIGLEVGRGQPLGVGYCGHPCLNKFDDVENSASYDNRVNVADYKQTQLCLVGCAPPLGEHWGKGKQCGDCPPLELVTSVIQDGDMVDTGFGAMDFAELQSNKSDVPLDICTSTCKYPDYLQMAADPYGDRLFFYLRKEQMFARHFFNRAGYFNTPSGSLVSSEAQLFNKPYWLHKAQGHNNGICWGNTLFVTVVDTTRSTNMTVYTASEYKQYMRHVEEFDLQFIFQLCTIKLTAELMAYIHTMNPTVLEEWNFGLSPPPLEDTYSQAITCPDKEKQDPYAGLSFWEVNLKEKFSSELEQYPLGRKFLLQTGRKK Seq128 MADNTGTGCSGWFLVDMVDFID-LE-AQALLNEQEADAHYAAVQDLKRKYLGSPSPRLNAIKVKRRLFDSGYGLLKCKDIRATLHGKFKQCYGLSFTDLIRQFKSNKTTCEDWVFGVHHSVSEAFELIQPLTIYRHIQGMLLLVLLRFKVNKNRCTVARTLLNIPEDHMLIEPPKIQSSVAALYWFRTSLSNASGETPEWIARQTIVEHFKLTEMVQWAYDNDYCDECDIAFEYAKRADFDSNAKAFLNSNCQAKYVKDCATMCKHYKNAEMKKMTMNQWIKHRGNWKPIVQFLRHQNIEFISFLSKLKLWLQGPKKNCIAIVGPPDTGKSMFCMSLIKFLGGTVISYVNSSSHFWLQPLCNTKVALLDDATHSCWGYMDTYMRNLLDGNPMSDRKHKSLALIKCPPLLVTSNIDITTEYLYSRVTVFKFPNPFPFDRNGNAVYELCDANWKCFFARLSASLDIE-DFRCLAKHLDACQEQLLELYENSNELTKHIQHWRHENVLLYKARQMGLSHIGLQVVPPLKVSQTKGHEAIEMQMTLETVLKSEYGTEPWTLETSFEMWLTPPCFKKQGQTVEVRYDCNAENSMHYVLWKYIYVC--ENDRWQKVKGVDIKGLYYKTYYIDFEKEAKQYNNNQQRNNSNSSGTPIVQLQGDSNNLKCFRYRLHDKYKHLFMLASSTWHWYVNEQQRQDFLNTVKIPWRPSDNKLYVPPPAPVKVITTDAYVTRTKIFYHASSRLLAVGNPYFKTIVPKVSGFQFRVFKIVLPDPNKFALPDTSIFDSTQRLVWACIGLEVGRGQPLGVGISGHPLLNKFDDVENSASYDNRVNVADYKQTQLCLVGCAPPLGEHWGKGTQCGDCPPLELVTSVIQDGDMVDTGFGAMDFAQLQSNKSDVPLDICTATCKYPDYLQMAADPYGDRLFFSLRKEQMFARHFFNRAGYFNTPSGSLVSSEAQLFNKPYWLHKAQGHNNGICWGNTLFVTVVDTTRSTNMTVYTASEYKQYMRHVEEFDLQFIFQLCSIKLTAEVMAYIHTMNPTVLEEWNFGLSPPPLEDTYSQAITCPDKEKQDPYAGLSFWEVNLKEKFSSELDQYPLGRKFLLQTGRKR Seq129 MAEDTGTGCSGWFLVDMVDFID-VE-AQALLNEQEADAHYAAVQDLKRKYLGSPSPRLDAIKVKRRLFDSGYGLLKCKDVRATLYGKFKDCYGLSFTDLIRPFKSDKTTCGDWVFGIHHSVSEAFELMQPLTTYMHIQGMVLLVLIRFKVNKSRCTVARTLLNIPEDHMLIEPPKIQSSVAALYWFRTGISNASGETPEWIKRQTIVEHFKLTEMVQWAYDNDFCDESEIAFEYAQRGDFDSNARAFLNSNCQAKYVKDCATMCKHYKNAEMKKMSMKQWITYRGNWKPIVQFLRHQNIEFIPFLSKLKLWLHGPKKNCIAIVGPPDTGKSCFCMSLIKFLGGTVISYVNSSSHFWLQPLCNAKVALLDDATQSCWVYMDTYMRNLLDGNPMSDRKHKSLALIKCPPLLVTSNVDITKDYLYSRVTTLTFPNPFPFDRNGNAVYELSDANWKCFFTRLSASLDIE-DFRCIAKHLDACQEQLLELYENSNELKKHIQHWRYESVLLHKARQMGLSHIGLQVVPPLTVSQAKGHEAIEMQMTLETLLESEFGMEPWTLDTSREMWLTPPCFKKQGQTVEVKYDCNTDNRMDYVSWTYIY-F--DTDKWTKVKGVDYKGLYYKTYYLEFEKEAKKYNNNKGRDNSYCAATPIVQLQGDSNCLKCFRYRLHEKYKDLFLLASSTWHWYVNEQQRQDFLKTVKIPWRPSDNKLYVPPPAPVKVITTDAYVTRTNIFYHASSRLLAVGNPYFKTVVPKVSGYQFRVFKVVLPDPNKFALPDTSIFDSTQRLVWACTGLEVGRGQPLGVGISGHPLLNKYDDVENSASYDNRVNVADYKQTQLCLVGCAPPLGEHWGQGKQCGDCPPLELISSVIQDGDMVDTGFGAMNFAELQSNKSDVPLDICTSTCKYPDYLQMAADPYGDRLFFYLRKEQMFARHFFNRAGYYNTPSGSLVSSEAQLFNKPYWLQKAQGHNNGICWGNHLFVTVVDTTRSTNMTVYKATEYKQYMRHVEEFDLQFIFQLCTIKLTAEVMSYIHTMNPTILEDWNFGLSPPPLEDTYSQAITCPDKEKQDPYAGLSFWEVNLKEKFSSELDQYPLGRKFLLQTGRKK Seq130 MADNTGTGCSGWFLVDMVDFID-ME-AQALLNEQEADAHYAAVQDLKRKYLGSPSPRLDAIKVKRRLFDSGYGLLKCKNIRATLLGKFKDCYGLSYTDLIRQFKSDKTTCGDWVFGVHHSVSEAFQLIQPVTTYSHIQGMVLLALVRFKVNKNRCTVARMMLNIPEDHMLIEPPKIQSGVAALYWFRSGISNASGETPEWITRQTIVEHFKLADMVQWAYDNDFCEESEIAFEYAQRADIDANARAFLNSNCQAKYVKDCATMCKHYKTAEMKKMNMKQWIKFRGNWKPIVQFLRHQNIEFIPFLTKLKMWLHGPKKNCIAIVGPPDTGKSCFCMSLIKFLGGTVISYVNSSSHFWLQPLCNAKVALLDDVTQSCWVYMDTYMRNLLDGNPMTDRKHKSLALIKCPPLIVTSNIDITKEYLCSRVTLFTFPNPFPFDRNGNALYDLCETNWKCFFARLSSSLDIE-DFRCIAKHLDVCQEQLLELYENSNKLTKHIQHWRYECVLLHKAKQMGLNHIGMQVVPALAVSQTKGHQAIEMQMTLETLLNSDYGTEPWTLETSREMWLTPPCFKKQGQTVEVKFDCNADNAMEYVWWKVIYVF--DTDKWVKVTGIDYKGLYYKTYYTNFEKEAEKYNSNKGRDNNYCTATPVVQLQGDANCLKCLRYRLHAKYKTLFVAASSTWRWYVDEQQRQQFLNTVKLPWRPSENQVYVPPPAPVKVIPTDAYVKRTNIYYHASSRLLAVGNPYFKTLVPKVSGFQYRVFKMVLPDPNKFALPDTSIYDPTQRLVWACIGLEVGRGQPLGVGISGHPLLNKLDDVENSASYDNRVNVADYKQTQLCLVGCAPPLGEHWGKGKQCGDCPPLELITSVIEDGDMVDTGFGAMNFAELQPNKSDVPLDICTATCKYPDYLQMAADPYGDRLFFYLRKEQMFARHFFNRAGYFNTPSGSLVSSETQLFNKPFWLQKAQGHNNGICWGNQLFVTVVDTTRSTNMTIYTSEQYKQYMRHVEEFDLQFMFQLCSITLTAEVMAYLHTMNAGILEQWNFGLSPPPLEDKYSQAITCPEKAKQDPYAKLSFWEVDLREKFSSELDQYPLGRKFLLQTGQKR Seq131 MAENTGTGCSGWFLVDMVDFID-LE-AQALLNEQEADAHYAAVQDLKRKYLGSPSPRLDAIKVKRRLFDSGYGLLKCKDVRATLLGKFKDCYGLSYTDLIRQFKSNKSTCGHWVFGVHHSVADAFQLIQPVTTYSHIQGMVLLALLTFKVNKNRCTVARMLLNIPEDHMLIEPPKIQSTVAALYWFRSSLSNASGDTPDWITRQTIVEHFKLADMVQWAYDNDLCDESEIAFDYAQRADIDANARAFLNSNCQAKYVKDCATMCKHYKNAEMKKMNMKQWIHYRGNWKPIVQFLKHQNIEFIPFLSKLKLWLHGPKKNCIAIVGPPDTGKSCFCMSLIKFLGGTVISYVNSSSHFWLQPLCNAKVALLDDATQSCWVYIDTYMRNLLDGNPMSDRKHKSLALIKCPPLLITSNIDITKDYLFSRVSVFTFPNPFPFDRNGNAVYDLCESNWKCFFTRLSASLDIE-DFRCIAKHLDVCQEQLLELYENSNNLEKHIQHWRHESVLLHKARQMGLHHIGMQVVPPLAVSQTKGHEAIEMQMTLETLLKSAYGMEPWTLDTSREMWLAPPCFKKQGQTVEVKYDCKADNIMEYVMWKFIYIL--DGDEWVKVEGVDYRGLYYKTYYTNFETEAKKYNNNKGRDNCYCRATPIVQLQGDPNCLKCFRYRLHDKHKHLFLSASSTWHWYANEQQRQQFLDTVRLPWRPSDNQVYVPPPAPVKVITTDAYVTRTNIFYHASSRLLAVGNPYFKTVVPKVSGYQFRVFKVLLPDPNKFALPDTSLFDPSQRLVWACTGLEVGRGQPLGIGISGHPLLNKLDDVENSASYDNRVNVADYKQTQLCMVGCAPPLGEHWGKGKQCGDCPALELITSVIQDGDMVDTGFGAMNFAELQPLKSDVPLDICNTTCKYPDYLQMAADPYGDRLFFYLRKEQMFARHFFNRAGYFNTPSGSLVSSETQLFNKPFWLQKAQGHNNGICWGNQLFVTVVDTTRSTNMTVYTSTDYKQYMRHVEEFDLQFIFQLCSIKLTAEVMAYIHTMNPTVLEDWNFGLTPPPLEDTYSQAIACPDKAK-NPYANLSFWEVNLKEKFSSELDQYPLGRKFLLQTGQKR Seq132 MADDSGTGCTGWFMVDMVDFID-LE-AQALFNRQEADTHYATVQDLKRKYLGSPSPRLDAIKVKRRLFDSGYGLLKCKDLRAALLGKFKECFGLSFIDLIRPFKSDKTTCLDWVFGIHHSISEAFQLIEPLSLYAHIQGMVLLVLLRFKVNKSRSTVARTLLNIPENQMLIEPPKIQSGVAALYWFRTGISNASGEAPEWITRQTVIEHFKLTEMVQWAYDNDICEESEIAFEYAQRGDFDSNARAFLNSNMQAKYVKDCATMCRHYKHAEMRKMSIKQWIKHRGNWKPIVQFLRHQNIEFIPFLTKFKLWLHGPKKNCIAIVGPPDTGKSYFCMSLISFLGGTVISHVNSSSHFWLQPLVDAKVALLDDATQPCWIYMDTYMRNLLDGNPMSDRKHKALTLIKCPPLLVTSNIDITKEYLHTRVTTFTFPNPFPFDRNGNAVYELSNTNWKCFFERLSSSLDIE-DFRCIAKRLDACQEQLLELYENSTDLHKHVLHWRHESVLLYKAKQMGLSHIGMQVVPPLKVSEAKGHNAIEMQMHLESLLRTEYSMEPWTLETSYEMWQTPPCFKKRGKTVEVKFDGCANNTMDYVVWTDVYVQ--DNDTWVKVHSVDAKGIYYKTYYVNFVKEAEKYDQHQRRNNSNSSATPIVQFQGESNCLKCFRYRLNDRHRHLFDLISSTWHWYDSEEQRQQFLDVVKIPWRPSDSTVYVPPPNPVKVVATDAYVTRTNIFYHASSRLLAVGHPYFKTVVPKVSGYQYRVFKVVLPDPNKFALPDSSLFDPTQRLVWACTGLEVGRGQPLGVGVSGHPFLNKYDDVENSGS-DNRVNVGDYKQTQLCMVGCAPPLGEHWGKGKQCGDCPPLELITSVIQDGDMVDTGFGAMNFADLQTNKSDVPIDICGTTCKYPDYLQMAADPYGDRLFFFLRKEQMFARHFFNRAGYVNTPSGSLVSSEAQLFNKPYWLQKAQGHNNGICWGNQLFVTVVDTTRSTNMTLYTNSDYKEYMRHVEEYDLQFIFQLCSITLSAEVMAYIHTMNPSVLEDWNFGLSPPPLEDTYSQAITCPEKEK-DPYKNLSFWEVNLKEKFSSELDQYPLGRKFLLQSGRKR Seq133 MADDSGTGCTGWFMVDMVDFID-VE-AQALFNRQEADAHYATVQDLKRKYLGSPSPRLDAIKVKRRLFDSGYGLLKCKDIRSTLHGKFKDCFGLSFVDLIRPFKSDRTTCADWVFGIHHSIADAFQLIEPLSLYAHIQGMVLLVLIRFKVNKSRCTVARTLLNIPENHMLIEPPKIQSGVRALYWFRTGISNASGEAPEWITRQTVIEHFKLTEMVQWAYDNDICEESEIAFEYAQRGDFDSNARAFLNSNMQAKYVKDCAIMCRHYKHAEMKKMSIKQWIKYRGNWKPIVQFLRHQNIEFIPFLSKLKLWLHGPKKNCIAIVGPPDTGKSCFCMSLIKFLGGTVISYVNSCSHFWLQPLTDAKVALLDDATQPCWTYMDTYMRNLLDGNPMSDRKHRALTLIKCPPLLVTSNIDISKEYLHSRVTTFTFPNPFPFDRNGNAVYELSDANWKCFFERLSSSLDIE-DFRCIAKRLDACQDQLLELYENSIDIHKHIMHWRLESVLLHKAKQMGLSHIGLQVVPPLTVSETKGHNAIEMQMHLESLAKTQYGVEPWTLDTSYEMWLTPPCFKKQGNTVEVKFDGCEDNVMEYVVWTHIYLQ--DNDSWVKVTSVDAKGIYYKTYYVNFNKEAQKYNKHQRRNNCHSAATPIVQLQGDSNCLKCFRYRLNDKYKHLFELASSTWHWYSSEEQRQQFLNSVKIPWRPSDSTVYVPPPNPVKVVATDAYVKRTNIFYHASSRLLAVGHPYYKTVVPKVSGYQYRVFKVVLPDPNKFALPDSSLFDPTQRLVWACTGLEVGRGQPLGVGVSGHPLLNKYDDVENSGGYDNRVNVGDYKQTQLCMVGCAPPLGEHWGKGTQCGDCPPLELITSVIQDGDMVDTGFGAMNFADLQTNKSDVPLDICGTVCKYPDYLQMAADPYGDRLFFYLRKEQMFARHFFNRAGYVHTPSGSLVSSEAQLFNKPYWLQKAQGHNNGICWGNHLFVTVVDTTRSTNMTLYTNSDYKEYMRHVEEFDLQFIFQLCSITLSAEVMAYIHTMNPSVLEDWNFGLSPPPLEDTYSQAITCPEKEKQDPYKDMSFWEVNLKEKFSSELDQFPLGRKFLLQSGRKR Seq134 MADDSGTGCSGWFLVDMVDFINL-SNAQALLHAQQTCADAVELCELKRKYISP-SPRLHAIKAKRRLFDSGYGLFKCKDLNAKLCGKFKELFGVGFHDLVRQFKSDKSTCTDWVFGVNPTIAEGFHLLKGQALYLHTQGMVLLALCRYKVAKNRETVVRQLLNVPDNQLMVQPPKLQSSAAALFWFRSGMGNGSGTTPEWIAKQTMLEHFSLTQMVQWAYDNGHTDECEIAYYYAQIADIDANAAAFLKSNNQAKYVRDCAAMCKHYRLAEMRRMSMADWIKHRGDWKPIVKLLRYQHIDIIVFLAALKKWLHGPKKNCICIVGPPDTGKSCFGMSLMHFLQGTIISFVNSCSHFWLQSLVDAKVAMLDDVTSACWAYMDTHMRNLLDGNPTSDRKHKSLAVIKCPPLLLTSNINIKHDYLQSRVTVFEFPNPFPFDSNGNAVYELSDANWNSFFKRLASSLELE-DF--LARRLDLCQEQLLELYEDSKQLQHHILHWRYESVIYYTARQMGIKRLGHQVVPSLDVSKAKAHAAIEMQMCLESLQTTEYNLEPWTLDTSQELWLAEPCFKKGGKTVEVRFDCNEHNAMHYTLWTAVYVQ-VE-DTWTKVEGVDHRGLFYTTYYVDFGKEAHTYSNNKIRNSGGGHSTPIIQLEGDANCLKCFRYRLTKVS-HLYTNSSTTWRWYSSVHQRSQFLALVKIPWQLNENQVYLPPPTPVTIVSTDEYVQRTSLYYHAGSRLLTIGHPYFDVSVPKVSGHQYRVFRVRLPDPNKFGLSDTSLFNSEQRLVWACVGVEVGRGQPLGVGISGHPYFNKDEDVENSSVYDSRENVADYKQTQLCIVGCTPPIGEYWGMGTPCGDCPVLELKSEVIEDGDMVDAGFGAMDFASLQANKSDVPLDLCTSISKYPDYLGMAAEPYGNSLFFFLRREQMFVRHFFNRAGYYSTPSGSLVTSDSQIFNKPLWIQKAQGHNNGICFGNQLFVTVVDTTRSTNLTLYDNSKFKEYLRHGEEFDLQFIFQLCVITLNAEVMTYIHAMDSSLLDDWNFKIGPPALEDTYNKAIACPPKEKEDPYKKYKFWEVNLTEKFSSQLDQFPLGRKFLMQAGKRK Seq135 MADSPGTGCSGWFVVDMIDFIDELSNAQALLHVQQTCADAADLCELKRKYISP-SPRLHAIKAKRRLFDSGYGLFKCKDLNAKLYGKFKELYGVGFGDLVRQFKSDKSTCTDWVFGVNPTIAEGFHLLKRQALYLHTQGMVLLALCRYKVGKNRETVVRQLLNVPDNQILVQPPKLQSPPAALFWFRAGMGNGSGTTPEWIAKQTMLEHFSLTDMVQWAYDNGHTDECEIAYYYAQRADVDANAAAFLKSNNQAKYVRDCASMCKHYRLAEMRRMSMAEWIKHRGDWKPIVKLLRYQHIDIIVFLAALKKWLQGPKKNCICIVGPPDTGKSCFGMSLMHFMQGTIISYVNSCSHFWLQSLADAKVAMLDDVTAACWGYMDTHMRNLLDGNPTSDRKHKPLAVIKCPPLLLTSNINITQDYLQSRVQVFEFPNPFPFDSNGNAVYELNDANWNSFFKRLASSLELG-DF--LARRLDLCQEQLLELYENSKELQQHILHWRYESAIYYTARQMGIKHLGHQVVPSLDVSKAKAHAAIEMQMCLESLQNTEYNVEPWTLDTSQELWLAEPCFKKGGKTVEVRFDCNETNAMHYTLWTTVYVQ-VD-DAWTKVKGVDYKGLSYTTYYVDFAKEAQTYSNNGHRNCGGGSSTPIIQLEGEANCLKCFRYRLGKVS-HLFCNSSTTWRWYSSVQQRSDFLAIVKIPWQLNENQVYLPPPTPVTIVSTDEYVQRTSLYYHAGSRLLTIGHPYFDISVPKVSGHQYRVFRVRLPDPNKFGLSDTSLFNSEQRLVWACVGVEVGRGQPLGVGVSGHPYFNKDEDVENSSAYDSRENVADYKQTQLCMLGCTPPIGEYWGKGTPCGDCPVLELKTEVIQDGDMVDTGFGAMDFASLQANKSDVPLDLCTSISKYPDYLGMAAEPYGNSLFFFLRREQMFVRHFFNRAGYYSTPSGSLVTSDSQIFNKPLWIQKAQGHNNGICFGNQLFVTVVDTTRSTNLTLYNNSNFKEYLRHGEEFDLQFIFQLCVITLNAEVMTYIHAMDPTLLEDWNFKIAPPALEDTYNKAIACPPKVREDPYKKYKFWDVNLTERFSSQLDQFPLGRKFLMQAGKRK Seq136 MADKQGTGCSGWFIVDMVDFINDHSSAQALLNAQQADADAAIVQELKRKYMSP-SPRLHAIKAKRRLFDSTNGLFKDKDVTVKLLGKFKELFGVGFNDLVRQFRSDKSTCTDWVFGVNPSISEGFHLLKEHTLYLHTQGMVLLALCRYKVAKNRSTIVRQLLNVPVQQILIQPPKLQSAPAALFWFRSSMGNGSGTTPEWISRQTMLEHFSLTDMVQWAYDNGYTEEYDIAYYYAQRGDIDANAAAFLKSNMQARYVRDCACMCKHYKLAEMKKMSMAEWIKHRGDWKPIVKFLKYQHIDIIAFLGALKKWLHGPKKNCICIIGPPDTGKSCFGMSLMKFLGGTILSYVNASSHFWLQPLVDAKVAMLDDVTAGCWTYMDMHMRNLLDGNPTSDRKHRALTVIKCPPLLLTSNLDISTEYLRSRITTFTFPNTFPFDTNGNAIYELNDENWNSFFKRLASSLELE-DY--LARRLDMCQEQLLELYEDSKTLEDHVLHWRYECALYYKAREAGIKHLGHQVVPQLEVSRQKAYLAIELQMSLEALLQTEYSLEPWTLDTSQEVWLTEPCFKKRGQTVEVRYDCNPANAMHYTLWSDIYV-PLN-STWLKVSGVDYEGLSYKHYYVEFHKEARTYDHHPRGHNRGSHTTPIIQLQGEANALKCFRYRLNKHK-HLFADVSSTWRWYISEQQRADFLSRVKIPWRLNDNKVYLPPPGPISIVSTDEYVQRTNLFYYAGSRLLAVGHPYFKITVPKVSGYQYRVFRVKLPDPNKFGFSETTLVTSDQRLVWGCVGVEIGRGQPLGVGISGHPYLNKYDDTENPSGYDNRENVADYKQTQLCIVGCTPPMGEYWGQGVPCGDCPVIELKSEVIQDGDMVDTGFGAMDFASLQASKSDVPLDLVNTKSKYPDYLGMAAEPYGNSLFFFLRREQMFLRHFFNKAGYFSTPSGSLVTSDSQLFNKPLWIQKAQGHNNGICFGNQLFVTVVDTTRSTNLTLYDNAKFKEYLRHVEEYDLQFIFQLCIITLNPEVMTYIHTMDPTLLEDWNFGVSPPALEDTYNKAIACPPKEREDPYKKYTFWDINLTEKFSAQLTQFPLGRKFVMQAGKRK Seq137 MADKQGTGCSGWFIVDMVDFINEQSCAQALLNKQQADADAAIVQELKRKYISP-SPRLHAIKAKRRLFHSNYGLFKDKDVTVKLLGKFKDLFGVGFNDLVRQFKSDKSTCTDWVFGVNPSIAEGFHLLKEQTLYLHTQGMVLLALCRYKVAKNRSTVGRQLLNVPVQQILIQPPKLQSAPAALFWFRAGMGNGSGTTPEWISRQTVLEHFSLTNMVQWAYDNGYTEECDIAYYYAQLGDTDANAAAFLKSNMQARYVRDCACMCKHYKLAEMKKMSMAEWIKHRGDWKPIIRFLRYQHIDIITFLAALKKWLHGPKKNCICIIGPPDTGKSSFGMSLMKFLGGTMLSYVNSSSHFWLQSLVDAKAAMLDDVTAACWNYMDMHMRNLLDGNLTSDRKHKALAVIKCPPLLLTSNMDINTDYLKSRITTFTFPNAFPFDTNGNAIYEFNDENWNPFFKRLASSLELE-DY--LARRLDMCQEQLLELYEDSRDLQDHVLHWRYECALYYKAREMGIKHLGHQVVPKLEVSRAKAHVAIEMQLSLESLLQTEYSIEPWTLDTCQELWHTEPCFKKRGQTVEVRFDCNPENTMQYTLWSEIYV-PVN-DTWVKVHGVDYKGLSYKTYYVDFIKEAQTYNHNQGGHSGGSNATPIVQLQGDANSLKCFRYRLGKYK-YLYVDVSSTWRWYASVQQREAFLANVKIPWRLNDNKVYLPPPGPISIVSTDEYVQRTNLFYHAGSRLLAVGHPFFKVIVPKVSGHQYRVFRVKLPDPNKFGFSETTLVTSDQRLVWGCVGVEIGRGQPLGVGISGHPYLNKYDDIENPSGYDNRENVADYKQTQLCIVGCTPPMGEYWGKGVPCGDCPVIELKSEVIEDGDMVDTGFGALDFASLQASKSDVPLDLCNTKSKYPDYLGMAAEPYGNSLFFFLRREQMFVRHFFNRAGYYSTPSGSLVTSDSQLFNKPLWMQKAQGHNNGICFGNQVFVTVVDTTRSTNLTLYDNTKFKEYLRHAEEFDLQFIFQLCIITLNPEVMTYIHTMDASLLEDWNFGVSPPALEDTYNKAIACPPKAKEDPYKNYTFWDVDLTERFSAQLTQFPLGRKFVMQAGKRK Seq138 MADDTGTGCSGWFSVDLVDFVDNQLKAQALLNRQQAHADKEAVQALKRKLLGSPSPRLGGLGAKRRLFDSGYGLLKCKNLQATLLGKFKELFGLSFGDLVRQFKSDKSSCTDWVFGVHHSIAEGFNLIKAEALYTHIQGMVLLMLIRFKCGKNRTTVSKGMLNIPANQLLIEPPRLQSVAAAIYWFRAGISNASGETPEWIQRQTIVEHFNLTEMVQWAYDNDLTEDSDIAYEYAQRADTDSNAAAFLKSNCQAKYVKDCGIMCRHYKKAQMKRMSMPQWIKHRGDWRPIVKFIRYQGIDFLTFMSAFKKFLHNPKKSCLVLIGPPNTGKSQFGMSLVKFLAGTVISFVNSHSHFWLQPLDSAKIAMLDDATPPCWTYLDTYLRNLLDGNPCSDRKHKALTVVKCPPLIITSNTDIRTEYLYSRISLFEFPNPFPLDKNGNPVYVLNDENWKSFFQRLWSSLEFEDEFRCLAKRLDACQEQLLELYEDSKHLEKHVQHWRIEAALLFKAREMGYAQVGHQIVPALEISRAKAHVAIEIQLALETLLQSTFGTEPWTLETSYEMWHAEPCLKKQGRTVEVVFDGNPENAMHYTAWTFIYVQTLD-GTWCKVYGVCYAGLYYKQFYCNFKNEAKKYNNQRRNPCGN-QTTPVIHLQGDPNCLKCLRWRLKKNCSHLFTQVSSTWHLYYNEEQRDKFLSTVKLPWRPSDNKVYLPPP-PVKVVSTDEYVQRTNYFYHASSRLLAVGHPYYRTSIPKVSGLQYRVFRVRLPDPNKFTLPETNLYNPEQRMVWACVGLEVGRGQPLGVGLSGHPLLNRLDDTENGPRYDNRENVSDCKQTQLCLVGCKPAIGEHWGKGAACGDCPPLELQNSVIQDGDMADVGFGAMDFSALQTSKAEVPLDIMNSISKYPDYLKMSAEAYGDNMFFFLRREQMFVRHLFNRAGYYPTPSGSMVTSDAQIFNKPYWLQQAQGHNNGICWGNQVFLTVVDTTRSTNMTVYKSTNFKEYLRHAEEYDIQFIFQLCKITLSVEVMSYIHTMNPDILDDWNVGVAPPPLEDSYSQAIRCAP-EK-DPFSDYSFWEVNLSEKFSSDLDQFPLGRKFLLQAGKRR Seq139 MADDTGTGCSGWFCVDLVDFVDQ-VHAQALLNKQQAHADQEAVQALKRKLLGSPSPRLGGLGAKRRLFDSGYGLLKCKNLHATLLGKFKELFGVSFGDLVRQFKSDKSSCTDWVFGVNHSIAEGFNLIKADSLYTHIQGMVLLMLIRFKCGKNRTTVSKGLLNIPTNQLLIEPPRLQSVAAAIYWFRSGISNASGDTPEWIQRQTILEHFNLTEMVQWAYDNDITEDSDIAYEYAQRADRDSNAAAFLKSNCQAKYVKDCGVMCRHYKKAQMRRMSMGAWIKHRGDWKPIVKFIRYQQIDFLAFMSAFKKFLHNPKKSCLVLIGPPNTGKSQFGMSLINFLAGTVISFVNSHSHFWLQPLDSAKIAMLDDATPPCWTYLDIYLRNLLDGNPCSDRKHKALTVVKCPPLLITSNTDIRTNYLYSRVSLFEFPNPFPLDTNGNPVYELNDKNWKSFFQRLWSSLEFEDEFRCLAKRLDACQEQLLELYENSRDLQKHIEHWRMEAVVLYKAREMGFANIGHQIVPTLETCRAKAHMAIEIHLALETLLQSSYGKEPWTLETSNELWLTNPCFKKQGRTVEVIFDGKQDNAMHYTAWTYIYIQTVQ-GTWCKVQGVCHAGLYYKQFYCNFKEEAKKYCNKRRNHCGS-QATPVIHLQGDPNCLKCLRFRLKRNCSHLFTQVSSTWHLYYDEAQRNLFLNTVKIPWRPSDNKVYLPPP-PVKVVSTDEYVQRTNYFYHASSRLLVVGHPYYKTSIPKVSGLQYRVFRVRLPDPNKFTLPETNLYNPEQRMVWACVGLEVGRGQPLGVGISGHPLLNKLDDTENAPTYDNRENVSDYKQTQLCLVGCKPAIGEHWGKGTACGDCPPLELKNSFIQDGDMVDVGFGALDFGALQSSKAEVPLDIVNSITKYPDYLKMSAEAYGDSMFFFLRREQMFVRHLFNRAGYYPTPSGSMVTSDAQLFNKPYWLQQAQGHNNGICWGNQLFLTVVDTTRSTNMTLYTAANFKEYLRHAEEYDVQFIFQLCKITLTVEVMSYIHNMNPNILEEWNVGVAPPPLEDSYSEAIRCTP-EK-DPYSDFWFWEVNLSEKFSTDLDQFPLGRKFLLQAGKRK Seq140 MADNQGTGCNGWFFVDMVDFIDQ-ENPQALLHAQQLQADVEAVQQLKRKYIGSPSPRLGAIKAKRRLFPPPNGLIHNTNIRVALFGMFKDLYGLSFMDLARPFKSDKTVCTDWVFGIYHGITDGFKLLEPHCLYGHIQGMVLLLLTRFKCGKNRLTVSKCLLNIPETQMLIDPPKLRTPAAALYWYRQGLSNASGTPPEWLARQTVIEYFDLSKMVQWAYDHNYIDDSIIALEYAKLADIDENAAAFLGSNCQAKYVKDCGTMCRHYIRAQKMQMTMSQWIKHRGEWKEIVRFLRYQHVDFISFMIALKQFLQGPKHNCILLYGPPDTGKSNFAMSLISFLGGVVLSYVNSSSHFWLEPLADAKIAMLDDATTQCWNYMDIYMRNALDGNPMCDRKHRAMVQTKCPPLIVTSNINASTDYLHSRVKCFCFPNRFPFDSNGNPVYDLSNKNWKSFFKRSWSRLALDNEFRCLATRLDVCQERLLDLYEDSNKLEDQIEHWRLECALQYKAREMGYKVLQHQALPALAVSKGKGHKAIELQLALETLQKTVYSTEPWTLDTCLERWNAPPCLKRRGQTVDVIFDGHQDNTMQYVMWGDIYYQNCDGEGWTKVCSIDAMGIYYKVYYVDFKKEASKYNKNQGRHTSSGDTTPIVHFKGEPNTLKCFRQRIQKYK-HLFEQASSTWHWYSSVEQRQQFLVTVRIPWRPSENKVYLPPT-PVKVVSTDEYVTRTSIYYHASSRLLAVGHPYFKQSIPKVSGYQYRVFRVQLPDPNKFGLPDPSLYNPEQRLVWACTGVEVGRGQPLGLGLSGHPLLNKLDDTENAPKYDNRENVSDYKQTQLCILGCTPPIGEHWAKGNLCGDCPPLELVNSYIQDGDMVDIGFGAMDFKTLQTSKSEVPLDVATSICKYPDYLKMAAEAYGDSLFFYLRREQMFVRHMLNRAGYAATPSGSMVTSEYQIFNKPYWLQRAQGQNNGICWGNQVFLTVVDTTRSTNLTLFNNSDFREYIRHVEEYDLQFIFQLCTITLTADVMAYIHGMNPTILEDWNFGITPPALEDTYSQAIACPAKEKEDPYSKFNFWTVDLKERFSSDLDQFPLGRKFLLQAGRKR Seq141 Seq142 ExaML-3.0.21/testData/140.model000066400000000000000000000000671330315463200156450ustar00rootroot00000000000000WAG, p0 = 1-399 AUTO, p1 = 400-699 AUTO, p2 = 700-1104 ExaML-3.0.21/testData/140.tree000066400000000000000000000021751330315463200155060ustar00rootroot00000000000000((((((Seq67,(Seq66,Seq65)),(Seq69,Seq68)),(Seq70,Seq71)),Seq72),(Seq63,((((((Seq38,(Seq37,Seq36)),(((Seq45,(Seq47,Seq46)),(Seq44,(Seq43,Seq42))),(Seq35,(Seq41,(Seq39,Seq40))))),((Seq31,(Seq34,(Seq32,Seq33))),(((Seq14,Seq15),(Seq16,((((Seq8,Seq7),Seq6),(Seq5,(Seq4,Seq3))),((Seq11,(Seq9,Seq10)),(Seq13,Seq12))))),((Seq17,(((Seq24,Seq23),Seq25),(Seq18,((Seq22,Seq21),(Seq19,Seq20))))),((Seq26,(Seq28,Seq27)),(Seq30,Seq29)))))),(((Seq49,Seq48),(Seq51,Seq50)),((Seq52,(((Seq55,(Seq53,Seq54)),Seq56),Seq57)),(Seq59,Seq58)))),(Seq64,(Seq62,Seq60))),(((Seq74,Seq73),Seq61),(((((Seq80,Seq81),(Seq78,Seq79)),((Seq84,(Seq83,Seq82)),(Seq77,(Seq75,Seq76)))),(((Seq86,Seq85),Seq87),(((Seq92,Seq91),((Seq93,Seq94),(Seq95,Seq96))),(Seq88,(Seq90,Seq89))))),((Seq140,((Seq138,Seq139),(((Seq132,Seq133),((Seq130,Seq131),((Seq127,Seq128),Seq129))),((Seq135,Seq134),(Seq137,Seq136))))),(((((Seq107,Seq108),(Seq106,Seq105)),((Seq111,Seq112),(Seq109,Seq110))),((Seq102,(Seq103,Seq101)),(((Seq99,(Seq97,Seq98)),Seq100),Seq104))),(((((Seq120,Seq119),((Seq121,Seq122),Seq123)),(Seq124,Seq118)),((Seq117,(Seq115,Seq114)),Seq116)),((Seq126,Seq125),Seq113))))))))),Seq2,Seq1); ExaML-3.0.21/testData/354.tree000066400000000000000000000127271330315463200155210ustar00rootroot00000000000000((((bn_001BGTue,((ac002MorArb,bn_002BGTue),((bf_005BGTue,(bf_002BGTue,st_001BGTue)),st_002BGTue))),((er101AA26384W,(er002MorArb,(er003MorArb,(er108AA26384W,er005MorArb)))),(ol037MorArb,(((si_006MorArb,ol111PRChina),si_003MorArb),(am111TS17259W,(((ja157TS17319W,ja117TS17319W),ja144TS17319W),((((ps209TS16075W,(ps202TS16075W,(ps117TS16020W,(ps120TS16020W,(ps211TS16075W,ps115TS16020W))))),ps121TS16020W),(pa005BGTue,pa006BGTue)),(((fl017MorArb,fl010MorArb),((wa125For29653E,(wa224Kin20879E,(wa112For29653E,am110TS17259W))),(ol039MorArb,(fl114PRChina,fl103PRChina)))),(((laevigatumSH,(cc009MorArb,cc004MorArb)),am104TS17259W),(sh109TS17342W,(sh_001aureum,(sh108TS17342W,sh106TS17342W)))))))))))),(((sterculiaceumSH,(db_038BGTue,(franchetiiSH,(db_021BGTue,db_025BGTue)))),(((cd018BGTue,(cd001BGTue,(cd127AragwiVGeo,(cd125AragwiVGeo,cd126AragwiVGeo)))),((my_005MorArb,my_009MorArb),((mo202ShenHubei,((mo203ShenHubei,(mo106ShenHubei,((mo324LongHubei,mo303LongHubei),mo103ShenHubei))),((pl1501SSweden,(pl_403Germany,pl1426SkeenS)),(ok103Sun4194W,(ok105Sun4194W,(tr_024BGTue,(ok112Sun4194W,(mo519TS16041W,(mo413TS16017W,(mo518TS16041W,(tr_002BGTue,(mo420TS16017W,(mo505TS16041W,mo415TS16017W))))))))))))),(ca1507EAustria,(ca1913QuilanF,(ca2111StAnastF,(ca2103StAnastF,(ca3102VdLlobrE,((ca30b10VdHeraultF,(ca2407VdCisseF,ca2905CsseNoirF)),((ca2835GdlJonteF,ca2837GdlJonteF),((((ca2804GdlJonteF,ca306PdCissaI),(ca1616EBulgaria,((ca301PdCissaI,(ca2912CsseNoirF,(ca2858GdlJonteF,(ca2114StAnastF,(ca2207BurgEltzD,(ca2212BurgEltzD,ca2102StAnastF)))))),ca1633EBulgaria))),ca2422VdlCisseF),(ca1407VeynesF,((ca27a66_2707loc1F,(ca2302MuehltLUX,(ca1801SWGer,(ca2303MuehltLUX,(ca30b04VdHeraultF,((ca3010VdHeraultF,ca2856GdlJonteF),((ca2807GdlJonteF,ca2808GdlJonteF),(ca3101VdLlobrE,ca3110VdLlobrE)))))))),ca2803GdlJonteF))))))))))))),(mp307SCalif,((mp103SequoiaNP,mp101SequoiaNP),mp001BGTue)))),((((gl_104BryceCyn,(gl_111BryceCyn,(gl_105BryceCyn,(gl_113BryceCyn,(gl_110BryceCyn,gl_114BryceCyn))))),(((((ts102TS17304W,(ko105TS16025W,(cb014MorArb,cb017MorArb))),(tegmentosumSH,((pe110NEStates,pe107NEStates),((((cp023BGTue,(cp021BGTue,(da121PRChina,(gs101BGTue,gs102BGTue)))),da220ZhejiangBG),(cr043BGTue,(cr101TS17261W,(cr041BGTue,(cr103TS17261W,(cr105TS17261W,caudatifoliumSH)))))),(cp107TS17302W,((cp101TS17302W,gs011BGTue),cp102TS17302W)))))),ds115TS17313W),(Di307XishTrBotG,(np102MtSankaiJ,(np107MtSankajJ,np101MtSankaiJ)))),((mt_105PRChina,((sp_001BGTue,(sp_005BGTue,(sp_003BGTue,(sp_002BGTue,sp_008BGTue)))),(uk_020MorArb,uk_004MorArb))),((he112BGTue,(he111BGTue,((cs103MorArb,cs009BGTue),he005BGTue))),((ne203NEStates,ne201NEStates),(ne1038ZionNP,(ne708NUtah,(ne704NUtah,(ne801EscalNM,(ne907ZionNP,ne806EscalNM)))))))))),((((tv101Georgia,(ve303SirinRudIRN,(ve202VazIRN,(tv112Georgia,(ve308SirinRudIRN,(ve150AssalamIRN,(tv111Georgia,(tv102Georgia,((ve112AssalamIRN,ve306MazanIRN),(ve240VazIRN,((ve115AssalamIRN,(ve209VazIRN,(ve153AssalamIRN,((ve207VazIRN,ve312SirinRudIRN),ve144AssalamIRN)))),((hd218CGreece,hd223CGreece),(tv110Georgia,((hd252CGreece,hd247CGreece),ve140AssalamIRN)))))))))))))),(((((gd116EmigUtah,(gd035MorArb,((gd241ZionNP,(gd126EmigUtah,gd114EmigUtah)),gd025MorArb))),(fd102FloridaCavSP,(fd119FloridaCavSP,(sc140RenssCoNY,(ni101AlbanyNY,(ni209AlbanyNY,ni206AlbanyNY)))))),((ms408BdlHerreriaE,(ms1908VaucluseF,((ms714VdCeleF,((ms903VdCeleF,(ms703VdCeleF,ms814VdCeleF)),ms718VdCeleF)),(ms2131GordesF,((ms1704GdlNesqueF,ms1705GdNesqueF),(ms1226GdlJonteF,((ms1315GdlJonteF,(ms1305GdlJonteF,(ms921VdCeleF,ms512VdCeleF))),ms411BdlHerreriaE))))))),((((((ib113AlpadGEO,hy102Iran),ib303NorawARM),ib402AlpadGEO),ib111AlpadGEO),(hy153Iran,hy133Iran)),(ib108AlpadGEO,ib403AlpadGEO)))),(((pp4004VdHeraultF,pp3012LdAydatF),(pp2804QuilanF,(pp3008LdAydatF,(pp3502GdJonteF,(pp2704Luxembg,((pp2209Hungary,pp2829QuilanF),(pp2247BudapestH,(pp3013LdAydatF,(pp2517Luxembg,(pp3405GdTarnF,(pp4007VdHeraultF,(pp3911cultSWGer,(pp3801CseSauvetF,(pp1702natBGTue,pp3009LdAydatF)))))))))))))),((us1104LovecBUL,us1109LovecBUL),(sv4a2DiktiCrete,(of110Cyprus,(((sv4a5DiktiCrete,(sv207Crete,(sv201Crete,sv204Crete))),sv102Crete),(sv4a3DiktiCrete,sv124Crete))))))),((ot103BGTue,(ot207AetnaSicily,(op1227GdJonteF,(ot202AetnaSicily,(op502CaussolsF,ot105BGTue))))),(op1201GdJonteF,(op702ArezzoI,(op509CaussolsF,(op1228GdJonteF,(op1607CseNoirF,(op816VdAudeF,((op1203GdJonteF,(op1374GdTarnF,op1432VdLotF)),((op1516CseNoirF,(op1334GdTarnF,op407StVallierF)),op404StVallierF))))))))))),(((se_025BGTue,se_021BGTue),((tt109NovosRus,tt_008BGTue),(tt107NovosRus,(us1201cultSENebr,(gi_021BGTue,(tt_014BGTue,(az123AA37692bW,(az112AA37692bW,(gi104ZhejiangBG,(us_903AlterBG,gi_022BGTue)))))))))),(cf_127BGTue,((cf_004BGTue,cf_001BGTue),cf_002BGTue)))),((cm001MorArb,(cm002MorArb,cm004MorArb)),(ga154ShenHubei,(ga122ShenHubei,ga131ShenHubei))))),((((la124Indonesia,(la107Indonesia,la102Indonesia)),la105Indonesia),((py002SeattleArb,py009SeattleArb),(ru_103NEStates,(ru_007BGTue,(ru_025BGTue,((sa_421SENebr,sa_424SENebr),(sa_406SENebr,(sa_109wieri,(sa_105wieri,sa_403SENebr))))))))),((pi108MtMNurs,pi105MtMNurs),((((pt109MunechMat,pt103MuenchMat),(ma102TS16051W,(ma103TS16051W,(ma104TS16051W,ma105TS16051W)))),((mx047MorArb,(mx053MorArb,((mx007MorArb,mx019MorArb),(mx017MorArb,mx001MorArb)))),((gr015BGTue,gr118PRChina),(tf_004MorArb,(tf_003MorArb,(gr128PRChina,(gr007BGTue,(gr001BGTue,gr002BGTue)))))))),((bu326ZhejiangBG,((bu301ZhejiangBG,(bu004BGTue,(bu324ZhejiangBG,bu328ZhejiangBG))),(bu015BGTue,(bu040BGTue,bu045BGTue)))),(ob114Taiwan,(co011ZhejiangBG,(co017ZhejiangBG,(co031ZhejiangBG,ob106Taiwan))))))))))),Di145BGTue,Di106BGTue); ExaML-3.0.21/testData/49000066400000000000000000001634061330315463200145050ustar00rootroot0000000000000049 1200 Seq1 ATGACCAACATTCGAAAATCACACCCCCTTATCAAAATCGTTAATCACTCATTCATCGATTTACCCACCCCACCTAACATTTCAGCATGATGAAACTTCGGCTCCCTACTAGGAGTCTGCCTAGTCCTACAGATCCTAACCGGCCTTTTCCTAGCCATACACTACACATCAGACACAATAACCGCCTTTTCATCAGTTACTCACATCTGCCGCGACGTCAACTACGGCTGAATTATTCGATACATGCACGCCAACGGAGCCTCTATATTCTTTATCTGCCTATACATGCATGTAGGACGAGGAATATACTACGGCTCCTACACCTTCTCAGAAACATGAAATATTGGAATCATACTACTACTCACAGTCATAGCCACAGCCTTCATAGGATATGTCTTACCATGAGGTCAAATATCTTTCTGAGGAGCAACTGTAATTACCAACCTCCTATCAGCAATTCCTTACATCGGCACTAATCTAGTAGAGTGAATTTGAGGAGGTTTCTCGGTAGACAAAGCTACCCTGACACGATTCTTTGCCTTCCACTTTATCCTTCCATTCATCATCTCAGCCCTAGCAGCAGTACACCTCTTATTTCTCCACGAGACAGGATCCAATAACCCCTCAGGAATCACATCCGACTCAGACAAAATTCCATTCCACCCATACTACATAATCAAGGACATTCTAGGTCTCCTAATACTAATTTTAATACTCACACTACTCGTCCTCTTCTCACCAGACCTGTTAGGAGATCCAGACAACTACATCCCCGCCAACCCCCTAAACACTCCTCCCCATATTAAACCCGAATGATATTTTTTATTCGCATACGCAATCCTCCGATCCATCCCTAACAAATTAGGAGGAGTCCTAGCCCTAATATTCTCCATCCTAATCCTAGCAATCATTCCAATTTTCCACACCTCCAAACAACGAGGAATAATATTTCGACCACTAAGCCAATGCTTATTCTGACTCCTAGTAGCAGACCTCCTCACCCTAACATGAATTGGCGGCCAACCTGTAGAACACCCCTTTATTACCATCGGCCAACTAGCCTCCATTCTATACTTCTCAACCCTCCTAGTTCTAATACCTATTTCAGGCATCATCGAAAACCGCCTCCTA------AAATGA------------------------------------------------------AGA Seq2 ATGAAAATTATACGAAAAACACACCCACTCCTAAAAATCATTAACCATGCATTCGTCGACCTCCCTGCACCCTCCAACATCTCATCATGATGAAACTTCGGCTCTCTATTAGGAGTATGCCTAATAATCCAAATCCTCACAGGACTGTTTCTAGCAATACACTACACCTCAGACACTATAACAGCATTCTCATCCGTAACCCACATCTGCCGAGACGTAAATTACGGTTGACTGATTCGATACCTCCATGCAAACGGAGCCTCCATGTTCTTCATGTGCTTATTCATACACGTAGGACGAGGCATCTACTATGGGTCCTACACCTTTATAGAGACATGAAACCTTGGTATTATTCTACTGTTTGCCGTAATAGCAACTGCATTTATAGGATATGTCCTCCCATGGGGGCAAATATCCTTCTGAGGGGCCACAGTCATCACAAACCTACTTTCAGCCATCCCCTACATCGGTACTAACCTAGTAGAATGAATCTGAGGCGGATTTTCAGTAGACAAAGCCACCCTTACACGCTTCTTCGCATTCCACTTTATCCTCCCCTTTATTATTGCAGCTCTGGCCATGGTACACCTACTTTTTCTCCACGAAACAGGCTCTAATAACCCCACAGGAATGAACTCCGATGCAGACAAAATCCCCTTCCACCCCTACTACACAATCAAAGACCTCTTAGGTGTATTCCTCATACTGTCAGTCCTCCTCCTATTAGTACTTTTCTCCCCAGACCTATTAGGAGACCCCGACAACTATACACCAGCTAACCCCCTAAACACCCCTCCTCACATTAAGCCAGAATGATACTTTCTATTTGCATATGCTATCCTACGCTCTATCCCCAACAAACTAGGAGGCGTATTAGCCCTAGTACTATCCATCCTAATCCTAGCTTTTATACCTCTCACCTACACCTTAAAACAACGAATCTTAATATTCCGCCCAATCAGCCAGACCATATTCTGAATCCTAGCAGCCAACCTACTTATCCTAACCTGAATCGGAGGACAACCCGTAGAACACCCCTTCATCATTATTGGCCAACTAGCTTCAATCAGCTACTTCTCCATCATTTTAATTCTAATACCAATCTCAGGACTAATCGAAAACAAAATACTAAAACTA------AAC---------------------------------------------------T-- Seq3 ATGAAAATTATACGAAAAACACACCCACTCCTAAAAATCATTAATCACGCATTCGTCGACCTCCCTGCACCCTCTAACATCTCATCATGATGAAACTTCGGCTCCCTATTAGGAGTATGCCTAATAATCCAAATCCTCACAGGACTATTTCTAGCAATACACTACACCTCCGACACTACAACAGCATTCTCATCCGTAACCCACATCTGCCGAGACGTAAACTACGGCTGATTAATTCGATACCTCCATGCAAATGGGGCTTCCATATTCTTCATGTGCTTATTCATACACGTAGGACGAGGCATTTATTATGGGTCTTACACCTTCACAGAGACATGAAACCTTGGTATCATTCTACTGTTTGCCGTAATAGCAACTGCATTTATAGGATATGTCCTTCCATGGGGACAAATATCCTTCTGAGGGGCCACAGTCATTACAAACCTACTCTCAGCCATCCCCTACATCGGCACTGACCTGGTAGAGTGAATCTGAGGCGGGTTTTCAGTAGACAAAGCCACCCTTACACGCTTCTTCGCATTCCACTTTATTCTCCCCTTTATCATTGCAGCCCTAGCCATAGTACACCTACTCTTTCTACACGAAACAGGCTCTAATAACCCCACAGGAGTGAACTCCGATGCAGACAAAATCCCCTTCCACCCCTACTACACAATCAAAGACCTTTTAGGTGTATTCCTCATACTATTAGTCCTCCTCCTATTAGTACTATTCTCCCCAGACCTACTAGGAGACCCCGACAACTACACACCAGCTAACCCCCTGAACACCCCTCCTCACATTAAACCAGAATGATATTTCCTATTCGCATATGCTATTCTACGCTCCATCCCCAACAAACTCGGAGGCGTACTAGCTCTAGTAATATCTATCCTAATCCTAATTTTCATACCCCTCACCCACACCTCAAAACAACGAAGCTTAATATTCCGCCCAATCAGTCAGGCTATATTCTGAATCCTAACAGCTAACCTACTTATCCTAACCTGAATCGGAGGACAACCCGTAGAACACCCCTTTATCATTATCGGCCAACTAGCTTCAATCAGCTATTTCTCCATCATTTTAATCCTAATACCAATCTCAGGACTAATTGAAAACAAAATACTAAAATTA------AAC---------------------------------------------------T-- Seq4 ATGAAAATTATACGAAAAACACACCCACTCATAAAAATTATCAACCACGCATTCATCGATCTCCCTGCACCCTCCAACATCTCATCATGATGAAACTTTGGTTCTCTATTAGGAGTATGCCTAATAGTCCAAATCCTCACAGGCCTATTCTTAGCAATACACTACACCTCCGACACTATAACAGCATTCACATCCGTAACCCACATCTGCCGAGACGTAAACTACGGCTGATTAATTCGATATCTCCATGCAAACGGAGCCTCCATATTCTTCGTATGCTTGTTTATACACGTAGGACGAGGAATCTACTATGGATCTTACACCTTTACAGAAACATGAAATCTTGGTGTTATTCTACTATTTGCCGTAATAGCAACTGCATTTATAGGATATGTACTTCCATGAGGACAAATATCCTTCTGAGGAGCCACAGTCATTACAAACCTTCTCTCAGCTATTCCCTACATCGGTACTAACCTAGTAGAATGAATTTGAGGGGGGTTCTCAGTAGACAAAGCCACCCTCACACGCTTCTTCGCATTCCACTTTATCCTTCCATTTATCATTGCAGCCCTAGCCATAGTACACCTACTATTCCTCCACGAAACAGGCTCTAATAACCCCACAGGAATGAACTCTGACGCAGATAAAATTCCCTTTCACCCCTACTATACAATCAAAGACCTCTTAGGCGTATTTCTCATACTACTAGTTCTCCTACTATTAGTACTATTTTCCCCAGACTTATTAGGAGACCCCGACAACTACACACCAGCCAACCCCCTAAATACCCCTCCCCACATTAAACCAGAGTGATATTTTCTATT-GCATACGCTATCCTACGCTCTATCCCCAATAAACTTGGAGGCGTACTAGCCCTAGTACTATCTATCCTAATCCTAATTCTCATACCCTTAATCCATACCTCAAAACAACGAAGCTTAATATTCCGTCCAATTAGCCAAATCATATTCTGAATCCTAACAGCTAACCTACTCATCCTGACCTGAATCGGAGGACAACCCGTAGAACATCCCTTTATCATTATCGGCCAACTAGCTTCAATCAGCTATTTCTCCATTATTCTAATCCTAATACCGATCTCAGGACTAATTGAAAATAAAATACTAAAATTA------AAC---------------------------------------------------T-- Seq5 ATGAAAAACATACGAAAAACGCAACCACTCCTAAAAATTATTAACCACGAATTCATTGA-TTTCCTGAAACATCCAA-ATCTCATCATGATGAAACTTTGGCTCTCTACTAGGCATCTGCCTAGTAATCCAGATCCTAACAGGCTTATTCCTAGCAATACACTATACCTCCGACACCACCACAGCATTTTCATCTGTAACCCACATTTGCCGAGACGTAAACTACGGCTGACTGATTCGTTACCTCCATGCAAATGGAGCCTCCATATTCTTCATGTGCCTGTTCATACATGTAGGACGGGGAATCTACTACGGATCTTATACCTTCATAGAAACCTGAAATCTCGGCATTATTCTACTGTTCGCCGTAATAGCAACTGCATTTATAGGATATGTACTCCCATGAGGACAGATATCCTTCTGAGGGGCCACAGTCATTACAAATCTACTCTCAGCTATCCCCTACATCGGAACTAATCTAGTAGAGTGAATTTGAGGAGGCTTCTCAGTAGACAAAGCCACTCTAACACGCTTTTTCGCATTCCACTTTATCCTACCCTTTATCATTGCAGCCCTAGCTATAGTCCACCTACTATTCCTCCACGAAACAGGATCTAACAA-CCCACCGGGATAAACTCTGACGCAGATAAAATCCCCTTTCATCCCTACTACACAATTAAAGATCTCCTAGGAGTATTCCTAATATTAACAGCCCTACTCTTATTAGTCCTCTTCTCCCCAGACTTATTAGGAGACCCCGACAACTACACACCAGCCAACCCCCTAAACACCCCTCCCCACATCAAACCAGAATGATATTTCCTATTTGCATACGCCATCCTACGCTCTATTCCTAACAAACTCGGAGGAGTACTAGCCCTAGTGCTATCAATCCTAATCCTAATTTTTTTACCCTTCATCCACACTTCCAAACAACGAAGCCTAATATTCCGCCCAATCAGCCAAACCATATTTTGAATCCTAGCAGCAAATCTTCTTGTCCTAACCTGAATCGGAGGACAACCTGTAGAATACCCCTTCACCATTATTGGCCAACTAGCCTCAATCAGTTATTTCTCTATTATCCTAATCCTACTACCAATCTCAGGTCTAATC-AAAATAAAATAATAAAAATA------AAC---------------------------------------------------T-- Seq6 ATGAAAATTATACGAAAAACACACCCACTCCTAAAAATCATTAATCACGCATTCGTCGACCTCCCTGCACCCTCTAACATCTCATCATGATGAAACTTCGGCTCCCTATTAGGAGTATGCCTAATAATCCAAATCCTCACAGGACTATTTCTAGCAATACACTACACCTCCGACACTACAACAGCATTCTCATCCGTAACCCACATCTGCCGAGACGTAAACTACGGCTGATTAATTCGATACCTCCATGCAAATGGGGCTTCCATATTCTTCATGTGCTTATTCATACACGTAGGACGAGGCATTTATTATGGGTCTTACACCTTCACAGAGACATGAAACCTTGGTATCATTCTACTGTTTGCCGTAATAGCAACTGCATTTATAGGATATGTCCTTCCATGGGGACAAATATCCTTCTGAGGGGCCACAGTCATTACAAACCTACTCTCAGCCATCCCCTACATCGGCACTGACCTGGTAGAGTGAATCTGAGGCGGGTTTTCAGTAGACAAAGCCACCCTTACACGCTTCTTCGCATTCCACTTTATTCTCCCCTTTATCATTGCAGCCCTAGCCATAGTACACCTACTCTTTCTACACGAAACAGGCTCTAATAACCCCACAGGAGTGAACTCCGATGCAGACAAAATCCCCTTCCACCCCTACTACACAATCAAAGACCTTTTAGGTGTATTCCTCATACTATTAGCCCTCCTCCTATTAGTACTATTCTCCCCAGACCTACTAGGAGACCCCGACAACTACACACCAGCTAACCCCCTGAACACCCCTCCTCACATTAAACCAGAATGATATTTCCTATTCGCATATGCTATTCTACGCTCCATCCCCAACAAACTCGGAGGCGTACTAGCTCTAGTAATATCTATCCTAATCCTAATTTTCATACCCCTCACCCACACCTCAAAACAACGAAGCTTAATATTCCGCCCAATCAGTCAGGCTATATTCTGAATCCTAACAGCTAACCTACTTATCCTAACCTGAATCGGAGGACAACCCGTAGAACACCCCTTTATCATTATCGGCCAACTAGCTTCAATCAGCTATTTCTCCATCATTTTAATCCTAATACCAATCTCAGGACTAATTGAAAACAAAATACTAAAATTA------AAC---------------------------------------------------T-- Seq7 ATGAAAATTATACGAAAAACACACCCACTCCTAAAAATCATTAATCACGCATTCGTCGACCTCCCTGCACCCTCTAATATCTCATCATGATGAAACTTCGGCTCCCTATTAGGAGTATGCCTAGTAATCCAAATCCTCACAGGATTATTTCTAGCAATACACTACACCTCCGACACTATAACAGCATTCTCATCCGTAACCCACATCTGCCGAGACGTAAACTACGGCTGATTAATTCGATACCTCCATGCAAATGGGGCTTCCATATTCTTCGTGTGCTTATTCATACACGTAGGACGAGGCATTTATTATGGATCTTACACCTTCACAGAGACATGAAACCTTGGTATCATTCTACTGTTTGCCGTAATAGCAACTGCATTTATAGGGTATGTCCTTCCATGGGGACAAATATCCTTCTGAGGGGCCACAGTCATTACAAACCTACTCTCAGCCATCCCCTACATCGGCACTGACCTGGTAGAGTGAATCTGAGGCGGGTTTTCAGTAGACAAAGCCACCCTTACACGCTTCTTCGCATTCCACTTTATTCTCCCCTTTATCATTGCAGCCCTAGCCATAGTACACCTACTCTTTCTACACGAAACAGGCTCTAATAACCCCACAGGAGTAAACTCCGATGCAGACAAAATCCCCTTCCACCCCTACTACACAATCAAAGACCTTTTAGGTGTTTTCCTCATACTATTAGTCCTCCTCCTACTAGTACTATTCTCCCCAGATCTACTAGGAGACCCCGACAACTACACACCAGCTAACCCCCTGAACACCCCTCCTCACATTAAACCAGAATGGTATTTCCTATTCGCATATGCTATTCTACGCTCCATCCCCAACAAACTCGGAGGTGTACTAGCTCTAGTAATATCTATCCTAATCCTAATTTTCATACCCCTCACCCACACCTCAAAACAACGAAGCTTAATATTCCGCCCAATCAGTCAGGCTATATTCTGAATCCTAACAGCTAACCTACTTATCCTAACCTGAATCGGAGGACAACCCGTAGAACACCCCTTTATCATTATCGGCCAACTAGCTTCAATCAGCTACTTCTCCATTATTTTAATCCTAATACCAATCTCAGGACTAATTGAAAACAAAATACTAAAATTA------AAC---------------------------------------------------T-- Seq8 ATGAAAAACATACGAAAATCACACCCACTACTAAAAATCATTAATCTCGCATTTATTGACCTACCCGCACCATCCAATATCTCATCATGATGAAACTTTGGGTCCCTTCTAGGAGTCTGTCTAGTAGTACAAATTATCACAGGACTATTTCTAGCAATACACTACACCTCTGATACCACAACAGCATTTTCATCTGTAACTCATATCTGCCGAGATGTAAACTACGGTTGATTGATTCGATATCTTCATGCAAACGGAGCCTCAATATTCTTCATGTGCCTATTTATACACGTAGGACGAGGAATCTACTACGGATCCTACACCTTTACAGAAACCTGAAATATTGGCATTATTCTACTGTTCGCCGTAATAGCAACTGCATTTATAGGATATGTGCTTCCCTGAGGACAAATATCCTTCTGAGGAGCCACAGTTATTACTAACCTTCTCTCAGCAGTTCCCTACATCGGTACTAATTTAGTAGAATGAATCTGAGGGGGGTTCTCAGTAGACAAAGCCACTCTTACACGCTTCTTCGCATTCCATTTTATCCTCCCATTCATCATTGCAGCCCTAGCTATAGTCCACCTACTTTTTCTCCACGAAACTGGATCCAATAACCCCACCGGAATCAACTCTGATGCAGACAAAATTCCATTCCATCCGTACTACACTATAAAAGACTTCCTAGGGGCCCTACTCATGTTAGTAGTTCTACTCCTACTTGTTTTATTTTCCCCAGATATTTTAGGCGACCCAGATAATTATACACCAGCTAACCCCCTAAACACACCTCCTCACATCAAACCAGAGTGATATTTCCTATTCGCCTACGCTATCCTACGTTCTATCCCCAATAAACTAGGAGGAGTGTTAGCTTTAGTACTATCCATCCTTGTCCTAGCCTTGCTACCTCTCATGCACACCTCAAAACAACGAAGCCTTATGTTCCGCCCAATTAGCCAAATCCTATTTTGAATACTAACAGCTAATCTTCTAATTCTAACCTGAATTGGGGGACAACCAGTAGAACACCCCTTCATTATCATTGGACAAGTAGCTTCAATCAGCTACTTCTCTATTATTTTAATCCTAATACCAATCTCAGGACTAATCGAAAACAAAATACTAAAACTT------------------------------------------------------------T-- Seq9 ATGACAAACATCCGAAAAACACACCCCCTACTTAAAATTATTAATAACGCATTCATTGACCTACCAGCCCCATCCAACATTTCATCATGATGAAACTTCGGGTCTTTACTAGGAATCTGCTTAATCATCCAAATCATCACAGGACTTTTCCTAGCCATACATTACACCTCAGACACCTCAACAGCATTCTCATCTGTTACCCATATTTGCCGAGATGTAAACTACGGTTGACTTATTCGCTATCTTCATGCAAACGGAGCCTCCATATTCTTTGTCTGCTTATTTATACATGTTGGACGAGGAATCTATTACGGATCTTATACCTACATAGAAACATGAAATATCGGCATCATTCTACTGTTCGCCGTAATAGCAACTGCATTTATAGGATATGTACTTCCATGAGGACAAATATCTTTTTGAGGGGCCACAGTCATTACCAACCTACTATCAGCTATCCCTTACATTGGCACTAACTTAGTAGAATGAATTTGAGGTGGATTTTCCGTAGATAAAGCCACCCTAACACGATTCTTCGCCTTCCACTTTATTCTCCCATTTATCATCGCTGCCCTAGCCATAGTTCATCTTCTTTTCCTTCACGAAACAGGATCTAATAATCCTACCGGAATTAACTCCGACACTGACAAAATTCCCTTTCATCCTTACTACACAACAAAAGACCTATTAGGAGCTCTAATACTCATTATAGTCCTACTAGCACTAGTATTATTTTCACCTGACCTCCTAGGAGACCCAGATAACTACACACCAGCTAACCCATTAAATACACCTCCCCATATCAAACCAGAATGATATTTCTTATTCGCCTACGCAATCCTCCGATCCATCCCTAACAAACTAGGAGGAGTCTTAGCCCTGGTCTTATCAATCCTAGTCCTAGCAATCCTCCCACTAATCCACACCTCAAAACAACGAAGCCTCATATTCCGCCCAATTAGCCAAACCCTATTCTGAATCCTAGTGGCCAACCTATTAATCCTTACCTGAATCGGAGGACAACCCGTAGAACACCCCTTCATCATCATCGGCCAACTAGCTTCCATCAGCTACTTCACAATCATCCTAACTCTCATTCCTATCTCAGGCCTAATCGAAAATAAAATAATG------AAATGAAAC---------------------------------------------------T-- Seq10 ATGACTAACATTCGAAAAACTCACCCACTGATAAAAATTGTAAACAACGCATTTATCGACCTCCCAGCCCCATCAAACATTTCATCATGATGAAACTTTGGCTCCCTACTAGGCATCTGCCTAATCCTGCAAATCTTAACAGGCCTATTCCTAGCGATACACTACACATCCGACACAACAACAGCATTCTCCTCTGTCGCCCACATTTGCCGAGACGTCAATTATGGCTGAATCATCCGATACATACACGCAAACGGAGCATCAATATTTTTTATCTGCCTATTCATACACGTAGGACGAGGCCTCTACTATGGGTCATATACCTTCCTAGAAACATGAAACGTCGGAGTAATCCTCCTATTTACAACAATAGCCACAGCATTTATAGGCTATGTCCTGCCATGAGGACAAATATCATTCTGAGGAGCAACAGTCATCACCAACCTTCTCTCAGCAATCCCATATATCGGCACAGACCTGGTCGAATGAATCTGAGGAGGATTCTCCGTAGACAAAGCAACCCTTACCCGATTTTTCGCCTTCCACTTTATTCTCCCCTTTATTATCGCTGCCCTTGCCATAGTCCATCTACTCTTTCTCCACGAAACAGGCTCCAACAACCCTACAGGAATCTCCTCAGACACAGACAAAATCCCATTCCACCCTTACTATACCATTAAAGACATCTTAGGCGCCCTACTACTAATTCTAGTCCTCATACTACTAGTATTATTCACACCCGACCTACTTGGAGACCCAGACAATTATACCCCAGCAAATCCACTTAGCACGCCCCCTCACATCAAACCTGAATGATATTTCCTATTTGCATACGCAATTCTACGATCAATCCCCAACAAACTAGGAGGAGTTCTAGCCTTAGTCCTCTCAATCCTAATCCTGGTACTCGTACCCGCACTCCACACATCCAAGCAACGAAGCATAATATTTCGACCAATCAGCCAATGCATTTTCTGAATCTTAGTAGCAGACCTATTAACACTCACATGAATCGGAGGACAACCAGTTGAACACCCATACATTATTATTGGACAATTAGCATCTATCACATACTTCCTACTTATCCTAGTACTTATACCAGTAGCCAGTACTATTGAAAATAACCTTCTA------AAATGA------------------------------------------------------AGA Seq11 ATGACAAACATCCGAAAAATTCATCCCCTAATAAAAACCATTAACCACTCCTTCATTGATCTCCCCGCACCATCCAACATCTCATCATGATGAAACTTCGGCTCTCTACTAGGAATTTGCTTAATAGTACAAATCATCACAGGTCTATTCTTAGCCATACATTATACATCAGACACAACAACAGCATTTTCATCAGTAACCCATATCTGCCGAGACGTAAATTATGGATGACTAATCCGATATATACATGCAAACGGAGCCTCAATGTTCTTCATCTGCTTATTCCTTCATGTAGGACGAGGAATATACTATGGATCTTATACATTCCTAGAAACATGAAACATCGGAGTGATTTTATTATTTACAGTCATAGCCACTGCATTCATAGGATATGTCCTTCCATGAGGACAAATATCATTCTGAGGGGCCACAGTAATTACAAACTTACTTTCAGCCATCCCATATATTGGCACAATCCTAGTAGAATGAATCTGAGGCGGATTCTCAGTAGACAAGGCTACTTTAACACGCTTTTTCGCATTTCACTTCATCTTACCATTCATTATTGCCGCCTTAGCAATCGTTCACCTACTATTTCTACATGAAACAGGCTCAAATAATCCAACAGGACTTAACTCAGATGCAGACAAAATCCCCTTCCACCCTTACTATACCATCAAAGATATTCTAGGAATCATAATTATACTTCTACTATTAATAATTTTAGTATTATTCTTTCCCGACTTACTAGGAGACCCTGACAATTATATGCCAGCTAACCCACTCAACACCCCTCCACACATCAAACCAGAATGATACTTCCTATTTGCTTACGCCATCCTACGCTCCATCCCTAATAAACTAGGAGGTGTACTTGCCCTAATTCTATCTATCCTTATTCTAGCCTTCCTACCCTTCCTTCACACCTCAAAACAACGCAGCCTAATTTTCCGCCCAATCACCCAAACCCTATACTGACTTCTAGTAGCAAATCTACTCATCCTAACCTGAATCGGAGGGCAACCAGTAGAACACCCATTCATCATTATTGGACAACTAGCCTCCATTAGTTACTTCTCCATTATCCTTATTCTAATACCAATCTCAGGTATAATCGAAAATAAAATACTA------AAATGAAAC------------------------------------------------------ Seq12 ATGACAAACATCCGAAAAACTCATCCCTTAATAAAAATTATTAATCATTCATTCATTGATCTTCCCGCACCATCCAACATCTCATCATGATGAAATTTCGGCTCCCTATTAGGAATCTGCCTAACAGTACAAATTGCCACAGGCCTATTTCTAGCCATACATTATACATCAGATACAACAACAGCATTCTCATCAGTAGCCCACATTTGCCGAGACGTAAATTACGGATGATTTATCCGATATATACATGCAAACGGAGCTTCCATATTTTTCATATGCCTATTCCTCCACGTAGGACGAGGAATATATTACGGATCCTACACATTTCTAGAAACATGAAATATCGGAGTAATTCTTCTATTTGCAGTTATAGCCACTGCATTCATAGGATATGTCCTTCCATGAGGACAAATATCATTTTGAGGAGCTACAGTAATTACAAATCTACTCTCAGCCATCCCATACATTGGCAGTACTTTAGTAGAGTGAATCTGAGGTGGATTCTCAGTAGACAAAGCTACTTTAACACGCTTTTTCGCATTTCACTTTATCTTACCATTCATCATTGCAGCCCTTGCAATCGTTCATTTATTATTCTTACATGAAACAGGCTCAAATAACCCAATAGGACTAAACTCAGACGCAGACAAAATTCCATTTCACCCCTACTATACTACCAAAGATATTCTAGGAATTTTAATTATACTAACACTACTAATAATCCTAGTATTATTCTTTCCAGACCTACTAGGAGACCCCGACAACTATATACCAGCCAATCCACTCAACACCCCTCCTCATATTAAACCAGAATGATATTTTCTATTTGCCTATGCCATCCTTCGTTCCATCCCTAACAAACTAGGAGGTGTAGTGGCCTTAATCCTATCTATCCTTATCCTAGCCCTCCTACCACTCCTACATACCTCAAAACAACGCAGCCTAATATTCCGCCCAATTACCCAAACTCTTTACTGAATACTAGTAGCAAACTTAATTATCTTAACCTGAATCGGAGGTCAACCCGTAGAACACCCATTCATCATTATTGGCCAACTAGCCTCCATCAGCTACTTCTCTATTATTCTCATTCTAATACCAATCTCAGGCATCATTGAAAACAAAATATTA------AAATGAAAC------------------------------------------------------ Seq13 ATGACAAACATCCGAAAAACCCATCCCCTATTCAAAATTATTAATCACTCATTCATTGACCTTCCAGCCCCATCCAACATCTCATCATGATGAAACTTCGGTTCACTTCTTGGAATCTGCTTAATAGTCCAAATTTTAACTGGCTTATTCTTAGCTATACACTACACCTCCGACACACTAACAGCATTCTCATCAGTAACCCACATCTGTCGAGACGTTAATTACGGCTGATTGGTACGATATATACACGCAAATGGAGCCTCAATATTCTTCATCTGCCTATTTATACACGTAGGACGAGGTATATACTACGGATCATACACATTTTTAGAAACATGAAACATCGGGGTAATTCTTTTATTTACAGTAATAGCTACCGCATTTATAGGTTATGTACTCCCATGAGGACAAATATCATTTTGAGGGGCAACAGTAATTACAAACTTACTATCTGCCATTCCATACATTGGAACTACTTTAGTAGAATGAATCTGAGGAGGATTCTCAGTAGATAAAGCCACCCTAACCCGATTCTTCGCATTTCACTTCATTTTACCATTCATCATTACAGCCCTCGTAATTGTTCACCTCTTATTCCTGCACGAAACAGGCTCCAACAACCCAACAGGACTAAATTCAGACGCAGACAAAATTCCATTTCATCCATATTATACCATCAAAGACATTCTAGGGATATTAATTATACTAGTACTCCTAATAATTCTAGTCTTATTCTTTCCAGACTCATTAGGAGACCCAGACAACTATATACCAGCCAACCCACTTAACACCCCCCCACATATCAAACCAGAATGATACTTCCTATTTGCCTACGCTATTCTACGGTCCATCCCTAATAAACTAGGAGGGGTACTAGCTCTAATTATATCCATTCTAATCCTAGTTTTTTTACCTTACCTTCATACCTCAAAACAACGCAGTCTAATATTCCGGCCAATTACTCAAATATTATACTGACTATTAGTAGCAAACTTACTAATTCTAACCTGAATCGGAGGTCAACCTGTAGAACACCCATTTATTATCATTGGTCAACTAGCCTCTATCAGCTATTTCTCCATTATTCTAATCCTCATACCAATTGCAGGCATCATCGAAGACAAAATACTA------AAATGAAAC------------------------------------------------------ Seq14 ATGATCAACATCCGAAAAACTCATCCATTAGTTAAAATTATCAACAACTCATTCATTGACCTTCCAACACCATCAAACATTTCAACATGATGGAACTTTGGGTCCCTGTTAGGAGTGTGTCTGATCTTGCAAATCTTAACAGGCTTATTTCTAGCCATACACTATACATCAGATACAGCTACAGCCTTTTCATCAGTCGCACACATTTGTCGAGACGTCAACTATGGGTGATTTATCCGATATATACATGCCAATGGGGCCTCTATATTTTTTATCTGCCTATTTATACACGTAGGGCGAGGCTTATACTATGGATCATACCTATTTCCAGAGACATGGAATATCGGAATTATTCTCCTACTTACAATTATAGCCACCGCATTTATAGGATACGTCCTACCCTGAGGCCAAATGTCCTTCTGAGGAGCGACTGTCATCACCAACCTACTATCGGCCATTCCCTACATCGGAACGAACCTAGTAGAATGAATCTGAGGGGGCTTTTCCGTAGATAAGGCGACCCTAACACGATTCTTTGCTTTCCACTTTATCCTTCCATTTATCATCTCAGCACTAGCAATAGTCCATCTATTATTCCTTCACGAAACAGGATCTAATAACCCCTCCGGAATTCCATCTGACCCAGACAAAATCCCATTTTACCCCTATCATACAATTAAAGACATCCTAGGCGTCCTATTTCTTGTCCTCGCCTTAATAACCCTGGCTTTATTCTCACCAGACCTGTTAGGAGACCCTGATAACTATACCCCTGCAAATCCACTAAGTACCCCGCCACATATTAAGCCTGAATGGTACTTTCTATTTGCCTACGCTATCCTGCGATCCATTCCTAATAAACTAGGAGGGGTGCTAGCTCTAATCTTCTCTATTCTAATTCTAACTATTATTCCACTATTACATACATCCAAACAACGAAGCATGATATTCCGACCTCTAAGTCAATGCTTATTCTGACTCCTAGTAGCAGACCTACTCACACTAACATGAATTGGAGGACAGCCAGTAGAACACCCCTTCATTATTATTGGGCAATTGGCCTCTATTCTCTACTTTACAATTCTTCTAGTACTTATACCTATCACTAGCATTATTGAGAATAGCCTCTCA------AAATGA------------------------------------------------------AGA Seq15 ATGAAAATTTTACGGAAAAATCACCCGCTACTTAAAATTGTTAATCATTCATTTATTGACCTCCCAACCCCATCTAACATCTCATCTTGATGGAATTTCGGGTCACTACTCGGTGTGTGCCTAGTAATCCAAATTCTGACCGGCCTATTCCTAGCCATACACTACACATCAGACACAACCACAGCATTCTCCTCAGTTGCCCACATTTGCCGAGATGTAAACTACGGATGATTAATTCGCTACCTTCACGCTAACGGAGCCTCCATATTCTTTATCTGCCTTTTCATCCACGTAGGCCGAGGAATCTACTACGGCTCCTATGTCCTCTCAGAAACCTGAAACATCGGTATCATCCTGTTCCTTACAACTATAGCAACAGCATTCGTAGGGTATGTTCTACCGTGGGGACAAATATCCTTCTGAGGAGCTACCGTAATCACAAACCTCCTCTCAGCAATCCCATACATCGGAAGCACCCTTGTTGAATGAATTTGAGGGGGCTTCTCAGTTGATAAAGCCACCCTAACCCGCTTCTTCGCCTTCCATTTTATTCTGCCTTTTATCATCACGGCCTTCGCCTTAGTCCACCTATTATTCCTTCACGAGACAGGATCCAACAACCCATCAGGCTTAAACTCAGACTCCGACAAAATCCCATTCCACCCCTACTACACAATCAAAGATCTCCTGGGCATCTTCCTCCTATTATTAATTCTCATAACTTTAGCCCTATTTTTCCCAGATGTCCTTGGAGACCCAGACAATTTTACCCCAGCAAACCCCCTCAACACTCCAGCACACATCAAGCCAGAATGATATTTCCTATTTGCTTACGCCATCCTTCGGTCTATCCCCAACAAACTTGGAGGTGTGTTAGCCCTTATTCTCTCTATTCTCATTTTAGCAACGTTCCCACTACTAAATACTTCCAAACAACACGGTTTAATTTTCCGACCTATCACACAAACCATTTACTGAACTTTTATCGCCAACCTTCTCGTACTTACATGAATTGGGGGGCAACCCGTTGAATACCCATTCACAACAATTGGACAAATCGCATCAATCACCTACTTCACTATTATTATCATTTTAATGCCAGTCTCTAATACCATCGAGAACAACATCATCAAACTT------------------------------------------------------------TAG Seq16 ATGAAAATTTTACGGAAAAACCACCCGCTACTTAAAATTGTTAATCACTCATTTATTGACCTCCCAACCCCATCCAACATCTCATCTTGATGAAATTTTGGGTCACTACTCGGTGTATGCCTAATAATTCAAATTCTGACTGGCCTATTCCTAGCCATACACTACACATCAGACACAACCACAGCATTCTCTTCAGTTGCCCACATTTGCCGAGACGTGAACTACGGATGATTAATCCGCTACCTCCACGCCAACGGAGCCTCCATATTCTTTATCTGCCTTTTTATCCACGTAGGTCGAGGAATCTACTACGGCTCCTATGTCCTCTCAGAAACCTGAAACATCGGCATCATCCTATTCCTTACAACTATGGCAACAGCATTCGTAGGGTATGTACTACCATGAGGACAAATATCTTTCTGAGGGGCTACTGTAATCACAAATCTCCTCTCAGCAATCCCCTACATCGGAAGCACCCTTGTTGAATGAATTTGAGGGGGCTTCTCAGTTGATAAAGCCACCCTGACCCGCTTCTTCGCCTTCCATTTTATTCTGCCTTTTATCATCACGGCCTTCGCTTTAGTCCACCTACTATTCCTCCACGAGACAGGATCTAACAATCCATCGGGCTTAAACTCAGACTCCGACAAAATCCCATTCCACCCCTACTACACAATCAAAGATCTCCTGGGCATCTTCCTCCTATTATTAGCTCTCATAATTTTAGCCCTATTTTTCCCAGATGTCCTCGGGGACCCAGACAATTTTACCCCAGCAAACCCTCTCAACACTCCAGCACACATTAAGCCAGAGTGGTATTTCCTATTTGCTTACGCCATCCTTCGATCTATCCCCAACAAGCTTGGAGGCGTATTAGCCCTTATCCTCTCTATTCTCATTTTAGCAATTTTCCCACTACTGAATGTTTCCAAACAACACGGTTTAATTTTCCGACCTATCACACAAACCATTTACTGAACTTTTATTGCCAACCTTCTCGTACTTACATGAATTGGGGGGCAACCCGTTGAGTACCCATTCACAACAATTGGACAAATCGCATCAATCACATACTTCACTAT-ATTATCATTTTAATGCCAGTCTCTAATACCATCGA-AACAATATCATCAAACTC------------------------------------------------------------TAG Seq17 ATGAAAATTTTACGGAAAAATCACCCACTACTTAAAATTGTTAATCACTCATTCATTGACCTACCAACCCCATCTAGCATCTCGTCTTGATGGAATTTTGGGTCACTACTTGGTGTGTGTCTGATAATTCAAATTCTGACCGGCCTATTCCTAGCCATACACTACACATCAGACACAACCACAGCATTCTCTTCAGTAGCTCACATCTGTCGAGATGTAAACTACGGATGATTAATCCGCTACCTACATGCTAACGGGGCTTCCATATTCTTTATCTGTCTCTTCATCCACGTAGGCCGAGGGATCTATTACGGTTCCTATGTCCTCTCAGAAACTTGAAACATCGGTATCATTCTATTTCTTACAACTATAGCAACAGCATTCGTAGGCTATGTGTTACCATGAGGACAAATATCTTTCTGAGGGGCCACTGTAATCACAAATCTCCTCTCAGCAATCCCCTACATCGGAAGCACCCTTGTTGAATGAATCTGAGGCGGCTTCTCAGTTGACAAAGCCACCTTAACCCGTTTCTTCGCCTTTCACTTTATCCTACCTTTTATCATCACAGCCTTGGTCCTAGTTCACCTACTGTTTCTTCACGAAACAGGATCCAACAACCCATCAGGTTTAAACTCAGACTCCGACAAAATCCCGTTCCACCCATACTACACAATCAAAGATCTCCTGGGCATCCTCCTTCTATTATTAGCTCTTATAATTCTAGCCTTATTTTTCCCCGATATCCTCGGAGACCCAGACAACTTTACCCCGGCAAACCCCCTCAACACTCCAGCACACATTAAGCCAGAATGGTATTTCCTATTTGCATACGCCATCCTCCGATCTATCCCCAATAAGCTTGGAGGTGTTCTAGCCCTCATCCTCTCTATTCTCATCCTAGCAACATTCCCATTATTAAATACTTCCAAACAACACGGTTTAATTTTCCGACCTATTACACAAACCATCTACTGAACTTTTATTGCTAACCTTCTTGTACTTACATGAATTGGGGGCCAACCTGTCGAATACCCATTCACAACAATTGGACAAATCGCATCAATCACCTACTTCACTATCATTATCATTTTAATGCCAGTCTCTAATACTATCGAAAATAATATCATCAAACTC------------------------------------------------------------TAA Seq18 ATGAAAATTTTACGTAAAAATCACCCACTACTCAAAATTATAAATCACTCATTCATTGATCTGCCAGCTCCATCTAACATCTCATCCTGATGGAACTTTGGATCCCTACTTGGTACATGCCTAGTAATCCAAATCCTAACAGGCCTATTCTTAGCTATACACTACACATCAGACACAACCACAGCATTCTCCTCAGTAGCTCATATCTGCCGAGACGTAAACTACGGATGATTAATCCGCTACTTACACGCTAATGGAGCCTCCATATTCTTCATCTGCCTCTTCATCCACGTAGGCCGAGGAATCTACTACGGCTCCTACGTCCTTTCAAAAACTTGAAATATCGGCATTATCTTATTCCTCACAACTATAGCAACAGCATTTGTGGGGTACGTACTTCCATGAGGACAAATATCCTTCTGAGGGGCCACTGTAATTACAAACCTCCTCTCAGCCATCCCCTACATCGGAAGCACCCTAGTTGAATGAATCTGAGGAGGCTTTTCAGTTGACAAAGCTACCCTCACCCGCTTCTTCGCCTTCCACTTCATTCTACCTTTTATCATTGCAGCCTTCACCCTAGTTCACCTATTATTTCTTCACGAAACAGGATCCAACAACCCCTCAGGCTTAAACTCAAACTCTGATAAAATTCCATTCCACCCCTACTACACAACCAAAGATCTTCTAGGCATCTTTCTGCTACTACTAGCTCTCATAATTTTAACCTTATTTTTCCCAGACATCTTAGGAGACCCAGACAATTACACTCCAGCAAACCCTCTCAACACTCCAGCACACATTAAACCAGAGTGATATTTCCTATTTGCCTACGC-ATCCTTCGATCAATTCCTAACAAACTAGGAGGCGTCTTAGCCCTCATCCTTTCCATTCTCATCCTAGCAACATTCCCACTACTGAATAATTCCAAACAACACGGTTTAATTTTCCGACCTATTACACAAACCATCTACTGAATCTTCATTGCTAACCTTCTTGTTCTTACATGAATTGGAGGCCAACCTGTCGAGCTCCCATTCACAACAATTGGACAAATCGCATCAATTACCTACTTCGCTATCATTATCATCCTAATTCCAATCTCTAATGCTATCGAGAACAATATCATCAAGCTT------------------------------------------------------------TAG Seq19 ATGAAAATTTTACGGAAAAATCACCCGCTACTCAAAATTGTTAATCACTCATTCATTGACCTACCAACTCCATCTAACATCTCATCCTGATGAAATTTTGGATCCCTACTAGGCATATGCCTAATAATCCAAATTTTAACAGGCCTATTCCTAGCCATACACTACACATCAGACACAACCACAGCATTCTCCTCAGTAGCACATATCTGCCGAGATGTAAACTACGGATGATTAATCCGCTACTTGCACGCTAATGGAGCCTCCATATTCTTTATCTGCCTCTTCATCCACGTAGGCCGAGGTATTTACTATGGTTCCTATACCCTCTCAGAAACCTGAAACATTGGCATCATCTTATTCCTCACAACTATAGCAACAGCATTTGTAGGATATGTACTCCCATGAGGACAAATATCCTTCTGGGGTGCCACCGTAATCACAAACCTCCTCTCAGCTATTCCCTACATCGGAAACACCCTAGTTGAATGAATCTGAGGTGGTTTTTCAGTTGACAAAGCCACCCTCACCCGCTTCTTCGCTTTTCACTTTATCTTACCCTTTATCATCACAGCCTTCGCCCTAGTTCACCTATTATTTCTTCACGAAACAGGGTCCAACAATCCATCAGGCCTAAATTCAGACTCTGACAAAATCCCGTTTCACCCCTACTACACAACCAAAGACCTCCTAGGCATCTTCCTTTTATTATTAGTTCTCATAATTTTAGCCTTATTTTTCCCAGACATTCTCGGAGACCCAGACAATTTTACCCCAGCAAATCCCCTCAACACCCCAGCACACATTAAACCAGAATGGTATTTTCTATTTGCCTATGCCATTCTCCGATCCATTCCCAACAAATTAGGGGGTGT-TTAGCCCTCGTCCTCTCCATTCTTATCCTAGCAGCATTCCCACTATTAAATACTTCCAAACAACACGGTTTAATTTTCCGACCTATCACACAAGTCATTTACTGAATTTTCATTG-TAACCTTCTCGTACTTACATGAATCGGAGGGCAGCCTGTCGAATATCCA-TCACAATAATTGGACAAATCGCATCAATTACATACTTCGCTATCATTATTATTCTTATCCCAGTCTCCAACACTATCGAGAACAATATTATTAAGCTC------------------------------------------------------------TAA Seq20 ATGAAAATCTTACGGAAAAATCATCCACTGCTTAAAATTGTTAATCACTCATTCATTGATCTACCAACTCCATCCAACATCTCATCCTGATGGAATTTTGGATCTCTTCTAGGAATATGCTTAGTAATCCAAATTCTAACAGGCCTATTCCTAGCCATACACTACACATCAGACACAACCACAGCATTCTCCTCAGTAGCCCATATCTGCCGAGATGTAAACTACGGATGGCTGATCCGCTACTTACACGCCAACGGGGCCTCCATATTCTTTATCTGCCTTTTCATCCACGTAGGCCGAGGGATTTACTACGGCTCCTACGTCCTCTCAGAAACCTGAAACATCGGCATCATCTTACTTCTCACAACCATAGCAACAGCATTTGTGGGATACGTACTCCCATGAGGACAAATATCTTTTTGAGGGGCTACCGTAATCACAAACCTTCTTTCAGCCATCCCATACATCGGAAACACCCTAGTTGAATGAATCTGAGGTGGCTTTTCAGTCGACAAAGCTACCCTCACTCGCTTTTTCGCTCTCCATTTCATCTTACCCTTTATCATTGCAGCCTTTGCCCTAGTTCACCTCCTGTTTCTCCACGAAACAGGGTCCAATAACCCATCAGGCTTAAACTCAGATTCTGACAAAATTCCATTTCACCCCTACTACACTACCAAAGATCTCTTAGGCATCTTCCTTTTATTATTAGTTCTCATAATTTTAGCCTTATTTTTCCCAGACGTCCTAGGAGACCCAGACAATTTTACCCCAGCAAATCCTCTCAACACCCCAGCACACATTAAACCGGAGTGATATTTTCTATTTGCCTACGCTATCCTCCGATCCATTCCCAACAAATTAGGGGGCGTTCTAGCCCTCGTCCTTTCCATCCTCATCCTAGCAGCATTCCCATTACTAAATACTTCCAAACAACACGGTTTAATTTTCCGACCTGTCACACAAGTCATTTACTGAATCTTTATCGCTAACCTTCTTGTACTTACATGAATCGGAGGCCAACCTGTCGAATACCCATTCACAACAATCGGACAAATCGCGTCAGTTACCTACTTCGCAACTATCATTATCCTAATCCCAGTCTCTAATACTATCGAAAATAATATTATCAAGCTC------------------------------------------------------------TAA Seq21 ATGAAAATTTTACGAAAAAATCACCCATTATTCAAAATTATTAATCACTCATTCATTGACCTACCAACCCCATCCAATATCTCATCCTGATGGAACTTTGGGTCTCTACTCGGTATGTGCTTAATAATCCAAATTCTAACTGGCTTATTCCTAGCCATACACTACACATCAGACACAACCACAGCATTCTCTTCAGTAGCCCACATCTGCCGAGACGTGAACTACGGATGACTAATCCGCTACTTACACGCTAATGGAGCCTCTATATTCTTCATCTGCCTTTTCATCCACGTAGGCCGAGGAATCTACTATGGTTCCTATGTCCTCTCAGAAACTTGAAACATTGGCATTATCTTATTCCTCACAACTATAGCTACAGCGTTCGTGGGGTATGTACTTCCATGGGGACAAATATCCTTCTGAGGAGCCACCGTAATTACAAATCTCCTCTCAGCAATCCCCTACATCGGAAGCACATTAGTTGAATGAATCTGAGGGGGCTTCTCAGTCGATAAAGCTACTCTTACCCGCTTTTTCGCTTTCCATTTCATTTTACCTTTTATCATCACAGCCTTCGTTCTAGTTCACCTACTTTTTCTCCACGAAACAGGATCCAACAACCCATCAGGCTTAAACTCAAACTCTGACAAAATTCCATTCCACCCCTACTATACAATTAAAGATCTCCTAGGCATTCTCTTCCTACTAACAGCTCTTATAATTTTAGCCCTATTTTTCCCAGATATCCTCGGAGATCCAGACAATTATACTCCAGCGAACCCACTCAACACCCCAGCACACATCAAACCAGAATGATACTTCCTATTTGCCTATGCTATTCTACGATCCATCCCCAACAAACTAGGGGGTGTGTTAGCCCTCCTTCTTTCTATCCTCATCTTAATAGCATTCCCATTACTAAATACTTCCAAACAACACGGTTTAATTTTCCGACCTATCACACAGACCATCTACTGAATTCTTATCGCTAATCTTCTTGTACTTACATGAATTGGGGGCCAACCTGTTGAATACCCATTCACAATAATTGGACAGATCGCATCAATCACCTACTTTGCTATCATCCTTATCCTAATACCAGTCTCTAATACTATCGAGAACAATATTATCAAGCTC------------------------------------------------------------TAA Seq22 ATGAAAATCTTACGAAAAAATCATCCACTACTCAAAATTATTAATCATTCATTTATTGACCTACCAGCCCCATCTAACATCTCATCCTGATGGAACTTTGGGTCTCTACTCGGTGTATGCCTAATAATCCAAATTCTAACCGGCCTATTCCTAGCCATACACTACACATCAGACACAACCACAGCATTCTCTTCAGTAGCCCATATCTGTCGAGACGTAAATTACGGGTGATTAATCCGTTACCTACACGCTAATGGTGCCTCTATATTCTTCATCTGCCTTTTCATTCATGTAGGTCGAGGGATCTACTATGGCTCTTATGTACTCTCAGAAACTTGAAATATCGGCATTATCCTATTCCTCACAACTATAGCCACAGCATTCGTAGGGTATGTTCTTCCATGAGGACAAATATCTTTCTGAGGGGCCACTGTAATCACAAATCTCCTCTCAGCAATCCCCTACATCGGAAACACCCTAGTTGAATGAATTTGAGGTGGCTTTTCAGTTGATAAAGCCACCCTCACCCGCTTTTTCGCCTTCCATTTTATCCTACCTTTTATTATCACCGCCTTCGTTCTAGTTCACTTACTATTCCTCCACGAAACAGGATCCAACAACCCATCAGGCTTAAACTCAGACTCCGACAAAATTCCATTCCACCCTTACTACACAATTAAAGATCTCCTAGGCATCCTCTTCTTACTAATAGCTCTTATAATTTTAGCCCTATTCTTCCCAGATATCCTCGGAGACCCAGACAATTACACCCCAGCAAACCCTCTCAACACCCCAGCACACATCAAACCAGAATGATACTTCCTATTTGCCTACGCCATTCTACGATCAATCCCTAACAAACTAGGAGGCGTCTTAGCCCTCCTTCTTTCTATCATCATCTTAGCGGCATTCCCATTACTAAATACTTCCAAACAACACGGTTTAATTTTCCGACCTGTCACACAAACCATTTACTGAATTTTTATTGCTAATCTTCTTGTACTTACATGAATTGGGGGTCAACCTGTCGAATACCCATTCACAATAATTGGACAAATCGCATCAGTTATTTACTTCGCTACCATCATCGTCCTAATACCAGTCTCTAATACTATCGAAAATAATATTATCAAGCTC------------------------------------------------------------TAA Seq23 ATGAAAGTCTTACGAAAAAATCACCCACTACTCAAAATTGTTAATCACTCATTTATCGATCTACCAACCCCATCTAACATCTCATCCTGATGGAATTTCGGGTCCCTACTAGGCACATGCCTAGTAATCCAAATTCTAACAGGCCTATTCCTAGCCATACACTACACGTCAGATACAACCACAGCATTCTCCTCAGTAGCCCACATCTGCCGAGATGTAAACTACGGATGATTAATCCGCTACTTACACGCTAACGGAGCCTCTATATTCTTTATCTGCCTCTTCATCCATGTAGGCCGAGGGATTTACTACGGCTCCTACATCCTCTCAGAAACCTGAAACATTGGCATCATCTTGTTTCTCACAACTATAGCAACAGCATTTGTAGGGTATGTACTTCCATGAGGACAAATATCTTTCTGAGGGGCCACTGTAATCACAAATCTCCTTTCAGCTATCCCCTACATTGGAAACACCTTAGTTGAATGAATCTGAGGTGGCTTTTCAGTTGACAAAGCCACCCTCACCCGCTTCTTCGCCTTCCATTTTATCTTACCCTTTATTATTACAGCCCTCGCCCTAGTTCACCTTCTGTTCCTCCACGAAACAGGATCCAATAACCCATCAGGCCTAAACTCAGACTCTGACAAAATCCCATTCCACCCCTACTATACAACCAAAGATCTCCTAGGTATTTTCCTTTTATTATTAGTTCTCATAATTTTAGCCTTATTTTTCCCAGACGTCCTCGGAGACCCAGACAATTTTACACCAGCAAATCCTCTTAACACCCCAGCACACATTAAACCAGAATGATATTTCCTATTTGCCTACGCCATTCTTCGATC-ATTCCCAACAAACTAGGAGGTGTTCTAGCCCTCATCCTCTCCATTCTCATCTTAGCAGCATTCCCACTATTAAATACTTCCAAACAACACGGTTTAATTTTCCGACCTGTCACACAAGTCATTTACTGAATTTTCATCGCCAACCTTCTTGTACTTACATGAATTGGAGGCCAACCCGTCGAATACCCATTCACAATAATCGGACAAATCGCATCAATTACTTACTTCACTATTATCATTATCCTTATACCAGTCTCCAATACCATCGAGAACAATATCATCAAACTC------------------------------------------------------------TAA Seq24 ATGAAAATTTTACGTAAAACTCACCCACTACTTAAAATTGTTAACCACTCATTCATTGACCTACCCACCCCATCTAACATCTCATCCTGATGAAACTTTGGATCCCTACTAGGCATGTGCCTAGTAATTCAAATTCTAACAGGCCTATTCCTAGCTATACACTACACATCAGACACAGCCACAGCATTTTCTTCAGTTGCCCACATCTGTCGAGATGTAAATTACGGATGATTAATCCGTTATCTACACGCCAACGGAGCTTCCATATTCTTCATCTGCCTTTTCATTCATGTAGGACGAGGAATCTACTATGGCTCCTATGTCCTTTCAGAAACCTGAAACATTGGAATTATCCTACTGCTAACTACTATAGCAACAGCATTTGTAGGATATGTTCTACCATGGGGACAAATATCATTCTGAGGCGCTACCGTAATCACAAACCTTCTCTCAGCAATTCCTTACATCGGAAATACCCTAGTTGAATGAATCTGAGGGGGGTTTTCAGTCGATAAAGCTACCCTAACCCGCTTCTTCGCCTTCCACTTCATCCTACCATTTATCATCACAGCTCTAGTCCTAGTTCACCTATTATTCTTACACGAAACAGGATCTAACAACCCATCAGGTCTAAACTCAGACTCAGACAAAATTCCATTCCACCCCTATTATACAATCAAAGACCTCCTAGGCATCTTCCTCCTACTAATAACTCTCATAATCTTAGCCTTATTTTTCCCAGATATCCTAGGAGACCCTGACAACTTCACCCCAGCAAATCCACTAAATACTCCAGCACATATTAAACCAGAGTGATACTTCTTATTCGCTTATGCTATCCTACGGTCTATTCCTAATAAACTAGGAGGAGTACTAGCTCTTGTCCTTTCCATCCTTATCCTAGCAACATTCCCATTACTGAATACTTCCAAACAACACGGTTTGATTTTCCGACCTATTACACAAACCATTTACTGAATTTTTATTGCTAACCTTCTAGTACTTACATGAATCGGGGGTCAACCTGTTGAATATCCATTCACAACAATTGGGCAAATCGCATCAATTACCTACTTCACAATCATCGTCATTATTATACCAGTCTCCAATACCATCGAGAACAACATCATCAAACTC------------------------------------------------------------TAG Seq25 ATGAAAATCTTACGAAAAAATCACCCACTACTCAAAATTATTAATCACTCATTCATTGATCTTCCAACTCCATCTAACATCTCATCCTGATGGAATTTCGGATCCCTACTAGGCATATGCCTAATGATCCAAATTCTAACAGGCCTATTCCTAGCCATACACTATACATCAGACACAACCACAGCATTCTCCTCAGTAGCCCACATTTGCCGAGATGTAAACTACGGATGATTAATCCGCTATCTACACGCTAACGGAGCCTCCATATTCTTTATTTGTCTCTTCATCCATGTAGGCCGAGGTATTTACTACGGCTCCTATGCCCTCTCAGAAACCTGAAACATCGGCATCATCTTATTTCTCATAACTATAGCAACAGCATTTGTAGGATATGTACTCCCATGAGGACAAATATCCTTCTGAGGGGCTACTGTAATCACAAATCTCCTTTCAGCTATCCCCTACATCGGAAGCACCTTAGTTGAATGAATCTGAGGTGGGTTTTCAGTCGACAAAGCCACCCTCACCCGCTTCTTCGCCTTCCATTTCATTTTACCTTTTATTATTGCAGCCTTCGCCCTAGTTCACCTACTATTTCTCCACGAAACAGGATCCAACAACCCATCAGGCTTAAACTCAGACTCTGACAAAATCCCATTTCACCCCTACTACACAACCAAAGACCTCCTAGGCATTTTCCTTTTATTATTAGTTCTCATAATTTTAGCCTTATTTTTCCCAGACGTCCTCGGAGACCCAGACAATTTTACTCCAGCTAATCCCCTCAACACTCCAGCACACATTAAACCAGAATGATACTTTCTATTTGCCTATGCCATTCTTCGATCCATTCCCAACAAACTAGGAGGTGTTTTAGCCCTCATCCTTTCCATTCTCATCCTAGCAGCATTCCCACTACTAAATAATTCCAAACAACACGGTTTAATTTTCCGACCTGTCACACAAGTCATTTACTGAATTTTCACTGCTAACCTTCTTGTTCTTACATGAATCGGAGGCCAACCTGTCGAATATCCATTCACAATAATTGGACAAATTGCATCAATTACATACTTAGCTATCATTATTATCCTCATACCAATTTCCAACACTATCGAGAACAATATTATCAAACTC------------------------------------------------------------TAA Seq26 ATGAAAATCTTACGGAAAAATCACCCACTACTCAAAATTGTTAATCACTCATTTATTGATCTACCAACTCCATCTAACATCTCATCCTGATGAAATTTTGGGTCTTTGCTAGGTATATGCCTAGTAATCCAAATTCTAACAGGCCTATTCCTAGCCATGCACTACACATCAGACACAGCCACAGCATTCTCTTCAGTAGCCCATATCTGCCGAGACGTAAACTATGGTTGACTAATCCGCTACCTACACGCTAATGGAGCCTCTATATTCTTTATCTGCCTTTTCATCCACGTAGGCCGAGGAATCTACTACGGCTCCTATGTCCTCTCAGAAACTTGAAACATCGGCATCATCTTACTTCTCACAACCATAGCAACAGCATTCGTAGGATACGTACTTCCATGAGGACAAATATCCTTTTGAGGGGCTACTGTAATCACAAATCTCCTTTCAGCCATCCCCTACATTGGAAGCACCCTAGTCGAATGAATCTGAGGCGGCTTTTCAGTTGACAAAGCCACCCTCACCCGCTTCTTCGCCTTCCATTTTATCTTGCCTTTCATCATCGCAGCCTTCGCCTTAGTTCACCTTCTATTTCTCCACGAAACAGGATCCAACAACCCATCCGGCCTAAACTCAGACTCTGACAAAATCCCATTTCACCCCTACTACACAATCAAAGATCTGCTAGGCATCTTTCTCTTATTATTAGTTCTCATAACTTTAGCTTTATTTTTCCCAGATGTCCTCGGAGACCCAGATAATTTTACCCCAGCAAACCCTCTCAACACTCCAGCACACATTAAACCAGAATGGTATTTTCTATTTGCCTATGCCATTCTTCGATCTATCCCTAACAAATTAGGAGGCGTCTTAGCCCTCGTCCTCTCCATCCTCATCTTAGCAGCATTCCCATTACTAAATACTTCCAAACAACACGGTTTAATTTTCCGACCTGTCACACAAACCATTTATTGAATTTTTATCGCTAACCTTCTTGTACTTACATGAATTGGGGGCCAACCTGTTGAATATCCATTCACAACAATTGGACAAATCGCATCAATCACCTACTTCACCATCATTATTATTCTGATCCCAGTCTCCAACACCATCGAGAACAATATCATCAAGCTC------------------------------------------------------------TAA Seq27 ATGACAATTATACGAAAAACCCACCCGCTACTTAAAATTATTAACCACTCATTTATTGATCTCCCTACCCCCTCCAACATTTCATCTTGATGGAACTTTGGCTCACTTTTAGGTATTTGCCTAATCATTCAAATTTTAACTGGCCTCTTCCTGGCCATACACTACACATCCGACACAGCCACAGCATTCTCCTCCGTCACCCACATCTGCCGAGACGTAAACTATGGCTGGCTCATCCGTTATATACACGCCAACGGAGCATCCATATTTTTTATTTGCCTATTCATTCACGTAGGACGAGGAATCTACTACGGCTCCTACATGCTCTCAGAAACCTGAAACATTGGCATCATCCTACTCCTAACCACAATAGCCACAGCATTCGTAGGCTATGTTCTCCCATGAGGGCAAATATCCTTCTGAGGCGCCACAGTAATCACAAATTTACTATCAGCAATCCCCTATATCGGAACAACTCTAGTTGAATGAATCTGAGGGGGGTTTTCAGTAGACAAAGCCACATTAACCCGCTTCTTCGCATTTCACTTTATTTTACCTTTCATTATCACAGCTTTTGTCCTGGTTCACCTCCTATTTCTACACGAAACAGGATCTAATAACCCCTCAGGCCTCAACTCAAATTCTGACAAAATCCCATTCCATCCCTACTACACAATCAAAGACTTGCTCGGAGTAATTCTTCTTTTAATAGTTCTCATAATTTTAGTATTATTTTTCCCAGATGTTCTCGGAGACCCCGATAATTATACCCCAGCAAATCCACTCAACACCCCAGCACACATTAAACCAGAATGGTATTT-CTTTTTGCCTACGCTATCCTCCG-TCCATCCCCAACAAACTAGGAGGAGTCCTAGCTCTAATCTTATCAATCTTAATTTTAGCAGCGTTCCCACTTCTTAATTCTTCCAAACAACACGGACTTATTTATCGCCCTATCTCACAAACCCTCTACTGAATCTTTGTTTCTAACCTTCTAATCCTCACATGAATTGGAGGACAACCTGTAGAATACCCATTTACAGTAATCGGTCAAATCGCATCAATCCTGTACTTCGCTATTATTATCATCCTTATACCTAT-GCCAACATAATCGAGAATAACATCCTAAAACTC---TAC------------------------------------------------------T-- Seq28 ATGAAAAT-TTACGAAAAAATCACCCATTATTCAAAATTATTAACCACTCATTCATTGACCTGCCAACCCCATCCAATATCTCATCCTGATGGAACTTTGGATCTCTACTCGGTATGTGCTTAATAATCCAAATTCTAACTGGCTTATTCCTAGCCATACACTACACATCAGACACAACCACAGCATTCTCTTCAGTAGCCCACATCTGCCGAGATGTAAACTACGGATGACTAATCCGCTACTTACACGCTAATGGAGCCTCTATATTCTTCATCTGCCTTTTCATCCACGTAGGCCGAGGAATCTACTATGGTTCCTATGTCCTCTCAGAAACTTGAAACATCGGCATTATCTTATTCCTCACAACTATAGCTACAGCATTCGTAGGGTATGTACTTCCATGGGGACAAATATCCTTCTGAGGAGCCACCGTAATTACAAACCTCCTCTCAGCAATCCCCTACATCGGAAGCACATTAGTTGAATGAATCTGAGG-GGCTTCTCAGTCGATAAAGCTACTCTTACCCGCTTTTT-GCTTTCCATTTCATTCTACCTTTTATCATCACAGCCTTCGT-CTAGTTCACCTACTTTTTCTCCACGAAACAGGATCCAACAACCCATCAGGCTTAAACTCAAACTCTGACAAAATTCCATTCCACCCCTACTATACAATTAAAGATCTCCTAGGCATTCTCTTCCTACTAACAGCTCTTATAATTTTAGCCCTATTTTTCCCAGATATCCTCGGAGATCCAGACAATTATACTCCAGCGAACCCACTCAACACCCCAGCACACATCAAACCAGAATGATACTTCCTATTTGCCTATGCTATTCTACGATCCATCCCCAACAAACTAGGGGGTGTGTTAGCCCTCCTTCTTTCTATCCTCATCTTAATAGCATTCCCATTACTAAATACTTCCAAACAACACGGTTTAATTTTCCGACCTATCACACAGACCATCTACTGAATTCTTATCGCTAATCTTCTTGTACTTACATGAATTGGGGGCCAACCTGTTGAATACCCATTCACAATAATTGGACAAATTGCATCAATCACCTACTTTGCTATTATCCTTATCCTAATACCAGTCTCTAATACTATCGAGAACAATATTATCAAGCTC------------------------------------------------------------TAA Seq29 ATGAAAATCTTACGGAAAAATCACCCGCTACTTAAAATTGTTAATCACTCATTTATTGATCTACCAACTCCATCCAACATCTCATCTTGATGGAACTTTGGGTCACTACTTGGTTTATGCCTAATAATCCAAATTCTGACCGGCCTATTCCTAGCTATACACTACACATCAGACACAACCACAGCATTCTCTTCAGTAGCCCACATTTGCCGAGATGTAAACTACGGATGATTAATCCGCTATCTACACGCTAACGGAGCTTCTATATTCTTTATCTGCCTTTTCATCCACGTAGGCCGAGGAATCTACTACGGCTCCTATGTCCTCTCAGAAACCTGAAACATCGGTATCATTCTATTCCTTACAACCATAGCAACAGCATTCGTAGGATATGTACTACCATGAGGACAAATATCTTTCTGAGGGGCTACTGTAATTACAAACCTCCTTTCAGCAATCCCCTACATCGGAAACACCCTTGTGGAATGAATTTGAGGAGGCTTCTCAGTTGATAAAGCCACCCTAACCCGCTTCTTCGCCTTCCATTTTATCCTACCCTTTATCATCACAGCCCTCGCCTTAGTCCACCTATTATTCCTCCACGAGACAGGATCTAATAACCCATCAGGCTTAAACTCAGACTCCGACAAGATCCCATTCCACCCCTACTACACAATCAAAGACCTCCTGGGCATCTTTCTCCTATTATTAGCTCTCATAATTTTAGCCTTATTCTTCCCAGACATCCTCGGAGACCCAGACAATTTTACCCCAGCAAACCCTCTCAACACTCCAGCACACATTAAACCAGAATGATATTTCCTATTTGCCTACGCCATTCTCCGATCTATCCCCAACAAACTTGGAGGTGTATTAGCCCTTATTCTCTCTATCCTCATTTTAGCAGCATTCCCATTATTAAATACTTCCAAACAACACGGTTTAATTTTCCGACCTATCACACAAACCATTTACTGAACTTTTATTGCTAACCTTCTCGTACTTACATGAATTGGAGGCCAACCTGTTGAATATCCGTTCACAACAATTGGACAAATCGCATCAATCACCTACTTCACTATCATCATTATCTTAATACCAGTCTCTAATACCATCGAGAACAATATCATCAAGCTC------------------------------------------------------------TAG Seq30 ATGAAAATTATACGAAAGAATCACCCCCTACTTAAAATTATTAACCACTCATTCATCGACCTACCAACCCCGTCCAACATCTCATCATGATGAAACTTTGGGTCCCTACTAGGTGCCTGCCTAATTATCCAAATCTTAACGGGCCTCTTTCTAGCCATACACTACACTTCAGATACAACCACAGCATTCTCCTCAGTAGCCCACATTTGCCGAGACGTAAATTACGGGTGATTAATTCGCTATCTACACGCCAACGGAGCCTCCATATTCTTCATCTGCCTATCCATCCACGCCGGCCGAGGAATTTACTACGGCTCCTACGTCCTTTCAGAAACCTGAAACATCGGTATCATCTTATTCCTTACAACCATAGCAACAGCATTTGTAGGTTATGTGCTTCCATGAGGACAAATATCCTTCTGAGGCGCTACCGTAATCACTAACCTTCTCTCAGCAATCCCCTACATCGGAAGCACTCTATTTGAATGAATCTGAGGAGGCTTTTCAGTTGATAAAGCTACCCTCACCCGTTTCTTCGCCTTCCACTTTATCTTACCCTTTATTATCACAGCCTTTGTCCTTGTACACCTCCTGTTCCTTCATGAAACAGGCTCTAACAACCCATCAGGCCTAAACTCAAACTCCGACAAAATCCCATTCCATCCCTACTACACAATCAAAGATCTCCTAGGAGTCTTCCTATTATTATTAGCTCTCATAATTTTAGCCTTATTTTTCCCAGATGTTCTCGGGGACCCAGATAATTTTACCCCAGCAAATCCTCTCAATACTCCAGCACACATTAAACCGGAATGATATTTTCTATTCGCCTACGCTATCCTTCGATCAATTCCCAACAAACTAGGGGGAGTCTTAGCCCTCATTCTATCTATCCTTATCCTAGCAGCATTCCCTCTATTAAATGTTTCCAAACAACACGGTTTAATTTTCCGTCCAATTACACAAACCATTTATTGAATCTTTATTGCTAACCTTCTAGTACTTACATGAATTGGAGGCCAACCCGTA-AATACCCATTCACAATAATTGGACAGATCGCATCAATCACCTACTTTGCTATCATCCTCATCCTAATACCAATCTCTAATACTATCGAAAACAATATTATTAAACTC------------------------------------------------------------TAA Seq31 ATGACAATCATACGAAAAAACCACCCTTTACTTAAAATCATTAATCACTCGTTTATTGACCTGCCCACCCCTTCCAACATTTCATCCTGATGGAACTTCGGCTCACTCCTTGGCATTTGCTTAATAATTCAAATTTTAACTGGCCTCTTCCTAGCCATACATTATACGTCCGATACAGCTACAGCATTTTCCTCCGTCACCCATATCTGCCGAGACGTAAATTACGGATGACTTATCCGCTACTTACATGCCAATGGGGCATCTATATTTTTTATCTGCCTATTTATTCATGTAGGACGAGGTATCTACTACGGCTCCTACATACTTTCAGAAACATGAAACATCGGAATTATCCTATTCCTAACCACAATAGCCACAGCATTTGTAGGCTATGTTCTTCCATGGGGACAGATATCTTTCTGAGGGGCCACAGTAATTACAAACTTACTCTCAGCAATTCCCTATATTGGAACCTCTCTAGTTGAATGAATCTGAGGGGGATTTTCAGTTGATAAAGCCACATTAACCCGCTTCTTTGCATTTCATTTCATTTTACCCTTCATCATCACAGCCTTTGTTTTAGTCCATCTTTTATTTCTACACGAAACAGGATCAAATAATCCCTCAGGCCTCAACTCAAATTCAGATAAAATTCCATTCCACCCTTATTACACAATCAAAGACCTGCTAGGGGTCATCCTTCCTTTAATAGTTCTCATAATTTTGGTTTTATTTTTCCCAGATGTTCTCGGAGACCCCGATAATTACACCCCAGCAAATCCACTTAACACCCCAGCACACATTAAGCCAGAGTGGTATTTCCTTTTCGCCTACGCTATTTTACGCTCCATCCCTAACAAACTAGGAGGAGTTTTAGCCCTAGTTTTATCTATTCTCATCTTAGCAGCATTCCCACTTCTCAATTCTTCCAAACAGCATGGACTAATATATCGTCCTATTACACAAACCCTCTACTGAATCTTTATTTCCAACCTCCTTATTCTCACATGAATTGGAGGCCAGCCAGTAGAATACCCATTTACAACAATTGGTCAAATTGCATCAATTTTATACTTTACTATTATTATCATCCTTATACCCATTGCCAACACGATCGAAAATAATATCCTAAAACTCCATTAC------------------------------------------------------T-- Seq32 ATGAAAATTCTACGGAAAAATCACCCACTACTTAAAATTGTTAATCACTCATTCATTGACCTACCAACCCCATCCAATATCTCATCCTGATGGAATTTTGGATCGCTACTCGGCGTATGCCTGATAATCCAAATTCTAACCGGTCTATTTCTAGCCATACACTACACATCAGACACAACCACAGCATTCTCTTCAGTAGCCCACATTTGCCGAGATGTAAACTACGGATGATTAATCCGCTATCTACACGCTAATGGAGCCTCCATATTCTTCATCTGTCTTTTTATTCATGTAGGTCGAGGAATCTACTACGGCTCTTATGTCCTCTCAGAAACCTGAAACATCGGCATCATTTTATTCCTCACAACTATAGCAACAGCATTCGTAGGATATGTATTACCATGAGGACAAATATCTTTCTGAGGAGCTACTGTAATCACAAATCTCCTTTCAGCGATTCCCTACATCGGAAGCACCCTTGTCGAATGAATCTGAGGAGGCTTCTCAGTTGATAAAGCCACCCTAACCCGCTTTTTCGCCTTCCATTTTATTTTACCTTTTATCATCACGGCCTTCGCCTTAGTCCATCTACTATTCCTCCACGAGACAGGATCCAACAACCCATCAGGCTTAAACTCAGACTCCGACAAAATCCCATTTCACCCCTACTACACAATTAAAGATCTCCTGGGCATCTTCCTTCTACTACTAGCTCTCATAATTTTAGCCCTATTTTTCCCAGATGTCCTCGGGGACCCAGACAATTTTACCCCAGCAAACCCTCTAAACACTCCAGCACACATTAAACCAGAATGGTATTTCTTATTTGCCTACGCCATCCTCCGATCTATCCCCAACAAACTTGGAGGAGTATTAGCCCTTATTCTCTCTATTCTCATTTTAGCAGCATTCCCATTATTAAATACTTCCAAACAACACGGTTTAATTTTCCGACCTGTCACACAAACCATTTATTGAGCTTTTATTGCCAACCTTCTCGTACTTACATGAATCGGAGGTCAACCTGTTGAATACCCATTCACAACAATCGGACAAATCGCATCAATTACCTACTTCACTATCATCATCATTTTAATGCCAGTCTCTAATACCATCGAGAACAATATCTTCAAGCTC------------------------------------------------------------TAG Seq33 ATGAAAATTTTACGGAAAAACCACCCACTACTCAAAATTATTAATCACTCATTTATTGACCTACCAACTCCATCTAACATCTCATCCTGGTGAAATTTTGGATCCCTACTAGGCATATGCCTAGTAATCCAAATTCTAACAGGCCTATTCCTAGCCATACACTATACATCAGACACAACCACAGCATTCTCCTCAGTAGCCCACATCTGCCGAGATGTAAATTACGGATGATTAATCCGCTATCTACACGCCAATGGAGCTTCTATATTCTTTATCTGCCTCTTCATCCATGTAGGCCGAGGTATTTACTACGGCTCCTATGTCCTCTCAGAAACCTGAAACATCGGCATCATCTTATTCCTCACAACTATAGCAACAGCATTCGTAGGATATGTACTACCATGAGGACAAATGTCTTTCTGAGGAGCCACTGTAATTACAAATCTCCTTTCAGCCATTCCCTACATCGGAAGCACCCTAGTTGAATGAATCTGAGGTGGCTTTTCAGTTGACAAAGCCACCCTCACCCGCTTCTTCGCTTTCCACTTTATTTTACCCTTTATTATCGCAGCTTTCGCCCTAGTTCACCTCTTATTTCTCCACGAAACAGGATCCAATAACCCATCAGGCTTAAATTCAGACTCCGACAAAATCCCATTCCACCCATACTACACAACCAAGGATCTCCTAGGTATCTTCCTTTTATTATTAGTTCTCATAATTTTAGCCTTATTTTTCCCAGACATCCTCGGAGACCCAGACAATTTTACCCCAGCAAACCCCCTCAACACCCCAGCACACAT-AA-CC-GA-TG-TA-TT-CT-TTTGC-TATGCCATTCTTCGATCTATCCCTAACAAACTAGGAGGCGT-TTAGCCCTCATCCTCTCCATCCTCATCCTAGCGGCATTCCCACTATTAAATACTTCCAAACAACACGGTTTAATTTTCCGACCTGTCACACAAGTCATTTACTGAAT-T-CATTGCTAACCTTCTTGTACTTACATGAATTGGAGGCCAACC-GTCGAATACCCATTCACAATAATTGGACAAATCGCATCAATTACATACTTCGCTATCAT-ATTATTCTTATCCCAGTCTCCAACACTATCGAGAACAATATTATTAAACTC------------------------------------------------------------TAA Seq34 ATGAAAATTTTACGAAAAAATCACCCCCTACTCAAAATTATTAATCACTCGTTCATCGACTTACCAACCCCATCCAACATCTCATCCTGATGAAATTTTGGATCCCTACTTGCCCTATGCCTAGCCATCCAAATCCTCACAGGCCTATTTCTAGCCATACATTACACATCAGACACAACCACAGCATTCTCCTCAGTAGCCCACATCTGTCGAGATGTAAATTACGGATGATTAATCCGCTATCTACATGCTAACGGAGCCTCCATATTCTTCATCTGCCTTTTCATCCACGTGGGCCGAGGGATTTATTACGGCTCATATATCCTCTCAGAAACCTGAAACATCGGTATCATTCTATTCCTTACAACTATAGCAACTGCATTCGTAGGATATGTCCTCCCATGGGGACAAATATCTTTCTGAGGAGCCACTGTAATTACTAATCTCCTCTCAGCTATTCCTTACATCGGAAATACCCTAGTAGAATGAATCTGAGGTGGATTCTCAGTTGACAAAGCCACTCTCACCCGATTCTTCGCCTTCCACTTTATTCTACCCTTTATCATTGCAGCCTTTGTCCTTGTCCACCTATTATTTCTACATGAAACCGGCTCAAACAACCCATCAGGCCTAAACTCAGACTCAGACAAAATCCCCTTCCACCCCTACTACACAATCAAAGACCTCTTAGGAGTTTTCCTTTTATTACTAGCTCTCATAACTTTAGCCTTATTTTTCCCTGATGTTCTTGGAGACCCCGATAATTTTACCCCAGCAAATCCCCTCAACACTCCAGCACACATTAAACCAGAGTGGTACTTCCTATTTGCCTACGCCATTCTTCGATCTATCCCAAACAAACTAGGAGGCGTACTAGCCCTTGTTCTTTCCATCCTAATTCTAGCAGCATTCCCGCTATTAAATACTTCCAAACAACACGGTTTAATTTTCCGACCTATTACACAAATCATTTTCTGAATTTTTATTGCTAACCTTCTGTTACTTACATGAATCGGAGGTCAACCAGTAGAATACCCATTCACAGCAATTGGCCAAATTGCATCAATTACTTACTTTATTATTATTATCATTTTAATGCCAGTCTCTAACACTATCGAAAACAATATTATCAAACTC------------------------------------------------------------TAA Seq35 ATGAAAATTTTACGGAAAAATCACCCGCTACTTAAAATTGTAAACCACTCATTTATTGACCTACCAACCCCATCTAATATTTCATCCTGATGAAATTTTGGGTCCCTACTCGGCGTATGCTTAATTATTCAAATCCTAACCGGTTTATTCCTAGCCATACACTATACATCAGACACAACCACAGCATTCTCTTCAGTAGCCCACATTTGCCGAGATGTAAACTACGGATGATTAATCCGCTATCTACACGCTAACGGAGCCTCCATATTCTTCATCTGTCTTTTCATTCACGTGGGTCGAGGAATCTATTATGGTTCCTATATCCTCTCAGAAACCTGAAACATCGGCATCATTCTATTCCTTACAACTATAGCAACAGCATTTGTAGGATATGTACTACCATGAGGACAGATATCCTTTTGAGGAGCTACCGTAATCACGAATCTTCTATCAGCAATTCCCTACATCGGAAACACCCTTGTTGAATGAATCTGAGGCGGCTTCTCAGTCGACAAAGCCACCCTAACCCGATTTTTCGCCTTCCATTTCATCCTACCTTTTATCATCACGGCCTTCGCCTTAGTTCACCTGCTATTTCTTCATGAGACAGGATCCAACAACCCATCAGGCCTAAACTCAGACTCCGACAAAATTCCATTCCACCCCTACTACACAATTAAAGATCTCCTGGGCATCTTCCTTCTATTGATGGTTCTCATAATTTTAGCCTTATTTTTCCCAGATGTGCTCGGGGACCCCGACAATTTCACCCCAGCAAATCCTCTCAACACTCCAGCACACATTAAGCCAGAATGATACTTCCTATTCGCTTACGCCATCCTTCGATCTATTCCCAACAAACTAGGAGGCGTTTTAGCCCTTATTCTGTCTATCCTCATTTTAGCAGCATTCCCATTATTAAATACTTCCAAACAACACGGTTTAATTTTCCGACCTGTCACACAAACCATTTACTGAACTTTTATTGCCAACCTTCTTGTACTTACATGAATTGGAGGTCAGCCCGTTGAGTATCCATTCACAACAATTGGGCAAATTGCATCAATTACTTACTTCACCATTATCATCATCCTAATGCCAGTCTCCAATACTATTGAAAATAATATTATCAAACTC------------------------------------------------------------TAG Seq36 ATGAAAATTTTACGGAAGAATCACCCGCTACTCAAAATTGTTAATCATTCATTTATCGACCTTCCAACTCCATCGAACATCTCATCCTGATGAAATTTTGGATCCCTACTAGGCATATGCCTAATAATCCAAATTCTAACAGGCCTATTCCTAGCCATACACTACACATCAGACACAACCACAGCATTCTCCTCAGTAGCCCATATCTGCCGAGATGTAAATTACGGATGATTAATCCGCTACTTGCACGCTAATGGAGCCTCCATATTCTTTATCTGCCTCTTCATCCACGTAGGCCGAGGTATTTACTATGGTTCCTATGTCCTCTCAGAAACCTGAAACATCGGCATCATCTTATTCCTCACAACTATAGCAACAGCATTCGTGGGGTATGTACTCCCATGAGGACAAATATCCTTCTGAGGTGCCACCGTAATCACAAACCTCCTCTCAGCCATCCCCTACATCGGAAACACCCTAGTTGAATGAATCTGAGGTGGCTTTTCAGTTGATAAGGCCACCCTCACCCGCTTCTTCGCTTTCCATTTTATCTTACCCTTTATCATCACAGCCTTCGCTTTAGTTCACCTATTATTTCTTCACGAAACAGGATCTAATAACCCATCAGGCCTAAATTCAGACTCTGACAAAATCCCATTCCACCCCTACTACACAACCAAGGACCTCCTAGGCATTTTCCTTTTATTATTAGTTCTTATAATTTTAGCCTTATTTTTCCCAGACATCCTCGGAGACCCAGACAATTTTACTCCAGCAAATCCCCTTAATACCCCAGCACACATTAAACCAGAATGGTATTTCCTATTTGCCTATGCCATTCTTCGATCCATCCCCAACAAATTAGGAGGTGTTTTAGCCCTCGTCCTTTCCATTCTTATTCTAGCAGCATTCCCACTACTAAATAATTCCAAACAACACGGTTTAATTTTCCGACCTATCACACAAGTCATCTACTGAATTTTCATTGCTAACCTTCTTGTACTTACATGAATCGGAGGTCAACCTGTCGAATACCCATTCACAATAATTGGACAAATCGCATCAATTACATACTTCGCTATCATCATTATTCTTATTCCAATCTCCAACACTATCGAGAACAATATTATCAAGCTC------------------------------------------------------------TAA Seq37 ATGAAAATTTTACGGAAAAACCACCCACTACTCAAAATTATTAATCACTCATTCATTGACTTACCAACTCCATCTAACATCTCATCCTGATGAAATTTCGGATCCCTACTAGGCATATGCTTAGTGATCCAAATTCTAACAGGCCTGTTCCTAGCCATACACTATACATCCGACACAACTACAGCATTCTCCTCAGTAGCCCATATCTGCCGAGATGTAAACTACGGATGACTAATCCGCTACTTACACGCTAACGGAGCCTCTATATTCTTCATCTGCCTCTTCATCCATGTAGGCCGAGGTATTTACTACGGCTCCTATGTCCTCTCAGAAACTTGAAACATCGGCATCATCTTATTCCTCACAACTATAGCAACAGCATTCGTAGGATATGTATTACCATGAGGACAAATGTCCTTCTGAGGGGCCACTGTAATCACAAACCTCCTTTCAGCCATCCCATACATCGGAACCACCCTAGTTGAATGAATCTGAGGAGGCTTTTCAGTTGACAAAGCCACCCTCACCCGCTTCTTCGCTTTCCATTTTATTTTACCCTTTATCATCGCAGCTTTCGCCCTAGTCCACCTCTTATTTCTCCACGAAACAGGATCCAATAATCCATCAGGCTTAAATTCAGATTCCGACAAAATCCCATTCCACCCCTACTACACAATCAAGGATCTCCTAGGCATCTTCCTTTTGTTATTAGTTCTCATAATTTTAACCCTATTTTTCCCAGACATCCTCGGAGACCCAGACAACTTTACTCCAGCAAACCCCCTCAACACCCCAGCACACATTAAACCAGAATGATACTTCCTATTTGCCTACGCTATCCTTCGATCCATCCCTAACAAGCTAGGAGGTGTTTTAGCCCTCATCCTCTCCATCCTCATTCTGGCAGCATTCCCACTATTAAATACTTCCAAACAATACGGTTTAATTTTCCGACCTGTCACACAAGTCATTTACTGAATTTTCATCGCCAACCTTCTTGTACTTACATGAATTGGGGGTCAACCAGTCGAATACCCATTTACAATAATTGGACAAATTGCATCAATTACATACTTCGCTATCATTATTATCCTTATCCCAGTCTCCAACACTATCGAGAACAATATTATTAAACTC------------------------------------------------------------TAA Seq38 ATGAAAATTTTACGAAAAAATCACCCATTACTCAAAATTATTAATCACTCATTCATTGACCTACCAACCCCATCCAATATCTCATCCTGATGGAACTTTGGGTCTCTACTCGGTATATGCTTAATAATTCAAATTCTAACTGGCTTATTCCTAGCCATACACTACACATCAGACACAACCACAGCATTCTCTTCAGTAGCCCACATCTGCCGAGATGTAAACTACGGATGACTAATCCGCTACTTACACGCTAATGGAGCCTCTATATTCTTCATCTGCCTTTTCATCCACGTAGGCCGAGGAATCTACTATGGTTCCTATGTCCTCTCAGAAACTTGAAACATCGGCATTATCTTATTCCTCACAACTATAGCTACAGCGTTCGTGGGGTATGTACTTCCATGAGGACAAATATCCTTCTGAGGAGCCACCGTAATTACAAATCTCCTCTCAGCAATCCCCTACATCGGAAGCACACTAGTCGAATGAATCTGAGGGGGCTTCTCAGTTGATAAAGCTACTCTTACCCGCTTCTTCGCCTTCCACTTCATTCTACCTTTTATCATCACAGCCTTCGTTCTAGTTCACCTACTTTTTCTCCACGAAACAGGATCCAACAACCCATCAGGCTTAAACTCAAACTCTGACAAAATTCCATTCCACCCCTACTACACAATTAAAGATCTCCTAGGCATTCTCTTCCTACTAACAGCCCTTATAATTCTAGCCCTATTTTTCCCCGATATCCTCGGAGATCCAGACAACTATACTCCAGCAAACCCACTCAACACCCCAGCACACATCAAACCAGAATGGTACTTCCTATTTGCCTACGCTATTCTACGATCTATCCCCAACAAACTAGGAGGCGTGTTAGCCCTCCTTCTTTCTATCCTCATCTTAATAGCATTCCCATTATTAAATACTTCCAAACAACACGGTTTAATTTTCCGACCTATCACACAGACCATCTACTGAATTCTTATTGCTAACCTTCTTGTACTTACATGAATTGGGGGCCAACCTGTTGAATACCCATTCACAATAATTGGACAGATCGCATCAATTACCTACTTTGCTATCATCCTTATTCTAAT-CCAGTCTCTAATACTATCGAGAACAATATTATCAAGCTC------------------------------------------------------------TAA Seq39 ATGAAAATTTTACGGAAAAATCACCCGCTACTTAAAATTGTAAATCACTCATTCATTGACTTACCAACCCCATCCAACATCTCATCTTGATGAAACTTTGGGTCACTACTCGGTGTATGCCTAATAATCCAAATTCTGACCGGCCTATTCCTAGCCATACACTACACATCAGACACAACCACAGCATTCTCTTCAGTAGCCCACATTTGCCGAGATGTAAACTACGGATGATTAATCCGCTATCTACACGCTAACGGAGCTTCCATATTCTTTATCTGCCTTTTCATCCATGTAGGCCGAGGAATCTATTACGGCTCCTATGTCCTCTCAGAAACCTGAAACGTCGGTATCATCCTATTCCTCACAACTATAGCAACAGCATTCGTAGGGTACGTGTTACCATGAGGACAAATATCTTTCTGAGGAGCTACCGTAATTACAAACCTCCTCTCAGCAATCCCCTACATCGGAAGCACCCTCGTCGAATGAATTTGAGGAGGCTTCTCAGTTGACAAAGCCACCCTAACCCGCTTTTTCGCCTTCCATTTTATTCTACCTTTTATCATTACGGCCTTCGCCTTAGTCCACCTACTATTCCTCCACGAGACAGGATCTAACAACCCATCAGGCTTAAACTCAGACTCCGACAAAATCCCATTTCACCCCTACTACACAATCAAAGATCTCCTGGGCATCTTCCTCCTATTATTAGTTCTCATAATTTTAGCTCTATTTTTCCCAGATGTCCTTGGAGACCCAGACAATTTTACTCCAGCAAACCCCCTCAACACCCCAGCACACATTAAGCCAGAATGATATTTCCTATTTGCCTATGCCATCCTTCGATCTATTCCAAACAAACTCGGAGG-GTATTAGCCCTCATTCTCTCTATCCTCATTTTAGCAGCATTCCCATTACTAAATACTTCCAAACAACACGGTTTAATTTTCCGACCTGTCACACAAACCATTTACTGAACCTTTATTGCCAACCTTCTCGTACTTACATGAATTGGAGGTCAACCTGTTGAATATCCATTCACAATAATTGGACAAATCGCATCAATTACCTACTTCACTATCATCATCATTTTAATGCCAGTCTCCAATACTATCGAGAACAATATCATCAAGCTC------------------------------------------------------------TAG Seq40 ATGAAAATTTTACGAAAAAATCACCCATTACTCAAAATTATTAATCACTCATTCATTGACCTGCCAACCCCATCCAATATCTCATCCTGATGGAACTTTGGGTCTCTACTCGGTATGTGCTTAATAATTCAAATTCTAACTGGCTTATTCCTAGCCATACACTACACATCAGACACAACCACAGCATTCTCTTCAGTAGCCCACATCTGCCGAGATGTAAACTACGGATGACTAATCCGCTACTTACACGCTAATGGAGCCTCTATATTCTTCATCTGCCTTTTCATCCACGTAGGCCGAGGAATCTACTATGGTTCCTATGTCCTCTCAGAAACTTGAAACATCGGCATTATCTTATTCCTCACAACTATAGCTACAGCGTTCGTGGGGTATGTACTTCCATGAGGACAAATATCCTTCTGAGGAGCCACCGTAATTACAAATCTCCTCTCAGCAATCCCCTACATCGGAAGCACACTAGTCGAATGAATCTGAGGGGGCTTCTCAGTTGATAAAGCTACTCTTACCCGCTTCTTCGCCTTCCATTTCATTCTACCTTTTATCATCACAGCCTTCGTTCTAGTTCACCTACTTTTTCTCCACGAAACAGGATCCAACAACCCATCAGGCTTAAACTCAAACTCTGACAAAATTCCATTCCACCCCTACTACACAATTAAAGATCTCCTAGGCATTCTCTTCCTACTAACAGCCCTTATAATTTTAGCCCTATTTTTCCCCGATATCCTCGGAGATCCAGACAACTATACTCCAGCAAACCCACTCAACACCCCAGCACACATCAAACCAGAATGGTACTTCCTATTTGCCTACGCTATTCTACGATCTATCCCCAACAAACTAGGGGGTGTGTTAGCCCTCCTTCTTTCTATCCTCATCTTAATAGCATTCCCATTATTAAATACTTCCAAACAACACGGTTTAATTTTCCGACCTATCACACAGACCATCTACTGAATTCTTATTGCTAACCTTCTTGTACTTACATGAATTGGGGGCCAACCTGTTGAATACCCATTCACAATAATTGGACAGATTGCATCAATTACCTACTTTGCTATCATCCTTATTCTAATACCAGTCTCTAATACTATCGAGAACAATATTATCAAGCTC------------------------------------------------------------TAA Seq41 ATGACAATCATACGAAAAAACCACCCTTTACTTAAAATCATTAATCACTCGTTTATTGACCTGCCCACCCCTTCCAACATTTCATCCTGATGGAACTTCGGCTCACTCCTTGGCATTTGCTTAATAATTCAAATTTTAACTGGCCTCTTCCTAGCCATACATTATACGTCCGATACAGCTACAGCATTCTCCTCCGTCACCCATATCTGCCGAGACGTAAATTACGGATGACTTATCCGCTACTTACATGCCAATGGGGCATCTATATTTTTTATCTGCCTATTTATTCATGTAGGACGAGGTATCTACTACGGCTCCTACATACTCTCAGAAACCTGAAACATCGGAATTATCCTATTCCTAACCACAATAGCCACAGCATTCGTAGGCTATGTTCTTCCATGAGGACAGATATCTTTCTGAGGAGCCACAGTAATTACAAACTTGCTCTCAGCAATTCCTTATATTGGAACCTCTCTAGTTGAATGAATCTGAGGGGGATTTTCAGTTGATAAAGCCACATTAACCCGCTTCTTTGCATTTCATTTCATTTTACCCTTCATTATCACAGCCTTTGTTTTAGTCCATCTTTTATTTCTACACGAAACAGGATCAAATAACCCCTCAGGCCTCAACTCAAATTCAGATAAAATTCCATTCCACCCTTATTACACAATCAAAGACCTGCTAGGAGTCATCCTTCTTTTAATAGTTCTCATAATTTTGGTTTTATTTTTCCCAGATGTTCTCGGAGACCCCGATAATTACACCCCAGCAAATCCACTTAACACCCCAGCACACATTAAGCCAGAATGGTATTTCCTTTTCGCCTACGCTATTTTACGCTCCATCCCTAACAAACTAGGAGGAGTTTTAGCCCTAGTTTTATCTATTCTCATTTTAGCAGCATTCCCACTTCTCAATTCTTCCAAACAGCATGGACTAATATATCGTCCTATTACACAAACCCTCTACTGAATCTTTATTTCCAACCTCCTTATTCTCACATGAATTGGAGGCCAGCCAGTAGAATACCCATTTACAATAATTGGTCAAATTGCATCAATTTTATACTTTACTATTATCGTCATCCTTATACCCATTGCCAACACGATCGAAAATAATATCCTAAAACTCCATTAC------------------------------------------------------T-- Seq42 ATGACCAACATCCGAAAGACCCACCCACTAATAAAAATTATTAACAATGCATTCATTGACCTCCCTGCCCCATCAAACATCTCATCGTGATGAAATTTTGGCTCCCTTCTAGGCATCTGCCTAATCCTACAGATCCTAACAGGACTATTTCTAGCAATACACTACACATCTGATACAACAACAGCATTCTCCTCTGTCACCCACATTTGCCGAGACGTCAACTATGGCTGAATCATCCGATATATACACGCAAACGGAGCCTCAATATTTTTTATCTGCCTATTCCTACATGTAGGACGAGGCCTATATTACGGATCCTACGCCTTCCTAGAAACATGAAACGTCGGAGTAATCCTTTTATTCGCAACAATGGCCACAGCATTTATGGGCTACGTTCTGCCATGAGGACAAATATCATTCTGAGGGGCAACAGTCATCACTAATCTCCTCTCAGCAATCCCATATATTGGCACAGACCTAGTAGAATGAATCTGAGGGGGATTCTCAGTAGACAAAGCAACCCTTACCCGATTTTTTGCCTTCCACTTCATTCTCCCATTCATCATTGCAGCCCTTGCCATAGTTCACCTCTTATTCCTCCACGAAACAGGATCTAACAACCCCACAGGAATCTCATCAGACGCGGATAAAATCCCATTCCACCCCTACTATACGAATAAGGACATTCTAGGCGCCCTATTACTAATCCTAGCCCTCATACTACTAGTACTATTCGCACCCGACCTGCTCGGAGACCCAGACAACTACACTCCCGCAAACCCACTTAACACACCCCCTCACATCAAACCCGAATGATATTTCCTATTTGCATACGCAATCCTACGATCAATCCCTAACGGACTAGGAGGGGTCCTAGCTTTGGCTCTCTCAATCCTAATCTTAGTACTAGTGCCACTACTCCATACATCCAAACAACGAAGCATAATATTCCGACCAATCAGCCAGTGTATATTCTGAATTCTAGTAGCAGACCTATTAACGCTCACATGAATCGGAGGACAGCCGGTCGAGCACCCATACATCATCATCGGGCAACTAGCATCCATCATATATTTCCTGCTAATCCTAGTACTAATGCCAATGGCCAGCACTATCGAAAATAACCTCCTA------AAATGA------------------------------------------------------AGA Seq43 ATGACCAACATACGAAAAACTCATCCTTTAATAAAAATCATTAACGAGTCTTTCATTGACCTTCCTACCCCATCTAACATCTCTGCATGATGGAACTTCGGCTCTCTTTTAGGATTATGCCTTGTAATTCAGATTCTCACAGGACTTTTCCTAGCCATACATTACACCTCCGACACCACAACAGCCTTCTCATCAGTCACCCATATCTGCCGAGACGTAAACTACGGATGATTAATCCGATACATACATGCTAACGGAGCTTCAATATTCTTCATCTGCCTCTTCCTCCACGTAGGACGAGGCCTGTACTATGGGTCATACACTTTCATTGAAACCTGAAACATTGGAGTACTATTATTATTCACTGTAATAGCAACAGCCTTTATAGGCTACGTCTTACCATGAGGACAAATATCCTTCTGAGGAGCCACAGTAATTACAAACCTCCTATCCGCTATCCCCTATATCGGCACAACCCTGGTAGAATGGACTTGAGGCGGCTTCTCAGTAGACAAAGCCACCCTGACTCGATTCTTCGCCTTTCACTTTATCCTTCCATTCATCGTAGCAGCCCTAGCTGTAGTACATCTTCTATTCCTCCACGAAACAGGATCCAACAACCCTATCGGACTAAACTCAGACGTGGACAAAATTCCATTCCATCCGTACTACACAATCAAAGATGCCCTGGGATTCCTTCTCCTAATCCTAGTTCTCCTTATACTAGTACTATTCTCCCCAGACCTCCTAGGAGACCCCGACAACTACACACCAGCTAACCCACTCAACACTCCACCCCATATCAAGCCTGAATGATACTTCCTATTTGCCTACGCCATCTTACGATCAATTCCAAATAAACTAGGAGGAGTAATTGCTCTAGTCCTATCAATCCTGATCCTAGCCCTCGTCCCATTCCTACATAATTCTAAACAACGAAGCATAATCTTCCGACCAATTAGCCAATGCCTGTACTGACTACTCGTAGCTGACCTGCTTACCCTAACTTGAATCGGAGGACAACCAGTGGAGCACCCTTTCATTATCATCGGACAAATCGCATCTATCCTATACTTCTCCATCATCCTCATCCTCATACCCCTAGCTAGCATACTAGAAAACAAGATATTA------AAATGA------------------------------------------------------AGA Seq44 ATGACAACCCCCCGCAAAACACATCCACTAGCAAAAATCATTAACAACTCATTCATTGATCTCCCCACACCATCCAACATCTCCGCCTGATGAAATTTCGGCTCACTCCTAGGTATTTGCCTGATTATCCAAATTACTACAGGTCTATTCTTAGCCATACACTACACACCAGACACCTCAACTGCCTTCTCCTCAGTCGCCCACATCACCCGAGACGTCAACTACGGCTGAATAATCCGCTACCTACACGCCAATGGCGCCTCCATATTCTTCATCTGCCTCTTCCTCCACATTGGCCGAGGCCTATACTATGGATCATTCCTTTTTCTGAAGACCTGAAACGTCGGTATTATCCTCCTACTCACAACCATAGCCACAGCATTCATAGGCTATGTCCTCCCATGGGGCCAAATATCATTCTGAGGGGCCACAGTAATTACAAACCTTCTATCAGCCATCCCATACATCGGATCTGACCTCGTACAATGAATCTGAGGCGGCTTCTCAGTAGATAAAGCCACCCTCACACGATTTTTCACCTTTCACTTTATTCTACCCTTTATCATCGCTGCCCTGGCAACCATCCACCTCTTGTTTCTGCATGAAACAGGATCAAGTAACCCATCAGGAGTAGCATCAGACCTCGATAAAATCACATTTCACCCCTATTACACAACCAAAGACATCCTAGGTCTAATTATTCTCCTCCTATGCCTAATAAGCCTAACCCTATTTTCACCTGACCTTCTAACTGACCCAGATAATTACACACTAGCTAATCCCCTCAACACTCCACCCCACATTAAACCAGAATGATACTTTCTATTCGCATACGCAATCCTACGATCTATCCCTAACAAATTAGGAGGCGTCCTAGCCCTAATACTCTCCATCCTAATCCTCATAATCATCCCTACTCTCCACCTATCAAAGCAACAAAGCATAGGATTCCGACCCATTACCCAAATCCTATTCTGAACCCTAGTAGCCGACCTATTTACGCTCACATGAATCGG---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- Seq45 ATGACTACCCCCCGCAAGACACATCCACTAACAAAAATCATTAACAACTCATTCATTGATCTCCCCACACCATCCAACATTTCCGCCTGATGAAATTTCGGCTCACTCCTAGGTATTTGCCTAATTATCCAAATCACTACAGGTCTATTCCTAGCCATACATTATACACCAGACACTTCAACTGCCTTCTCCTCGGTCGCCCACATCACCCGAGACGTCAACTACGGCTGAATAATCCGCTACCTACACGCCAACGGCGCTTCCATATTCTTCATCTGCCTATTCCTCCACATTGGCCGAGGCTTATATTACGGGTCATTCCTTTTTCTGAAGACCTGAAACGTCGGTATTATCCTCCTACTCACAACCATAGCCACAGCATTCATAGGCTACGTCCTCCCATGAGGCCAAATATCATTCTGAGGGGCCACAGTAATTACAAACCTTCTATCAGCCATCCCATACATCGGATCTGACCTCGTACAATGAATCTGAGGTGGGTTCTCAGTAGATAAAGCCACCCTCACACGATTTTTCACCTTTCACTTTATTTTACCCTTTATCATTGCTGCCCTAGCAACTATCCACCTCTTGTTTCTGCATGAGACAGGATCAAGTAACCCATCAGGAATGGCATCAGACCTCGACAAAATCACATTTCACCCCTACTATACAACCAAAGACATCCTAGGCCTAATTATTCTCCTCCTATGTCTAATAAGCCTAACCCTATTTTCACCTGATCTTCTAACCGACCCAGATAATTACACACTAGCTAACCCCCTCAACACCCCACCCCACATTAAACCAGAATGATACTTCCTATTTGCATACGCAATCCTACGATCTATCCCTAACAAATTAGGAGGCGTCCTAGCCCTAATACTCTCCATCCTAATTCTCATAATTATCCCCACCCTCCACCTATCAAAGCAACAAAGCATAGGATTCCGACCTATCACCCAAATCCTATTCTGAACCCTAGTAGCCGACCTATTCACACTCACATGAATCGG---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- Seq46 ATGACTACCCCCCGCAAAACTCACCCACTAGCAAAAATCATCAACAATTCATTCATTGACCTCCCTACACCATCCAACATCTCCGCCTGGTGAAATTTCGGCTCACTCCTAGGTATTTGCCTAATTATTCAAATCACTACAGGTCTATTCTTAGCCATACACTATACACCAGACACTTCAACCGCCTTCTCCTCAGTCGCCCACATCACCCGAGACGTCAACTATGGCTGAATAATCCGCTACCTACATGCCAACGGCGCCTCCATATTCTTTATCTGCCTCTTTCTCCACATTGGCCGAGGCTTATATTACGGATCATTCCTTTTTCTGGAGACCTGAAACGTCGGTATTATCCTCCTACTCACAACCATAGCCACAGCATTCATAGGCTATGTCCTCCCATGAGGCCAAATATCATTCTGAGGGGCCACAGTAATTACAAACCTTCTGTCAGCCATTCCATATATCGGGTCTGACCTCGTACAATGAATCTGAGGTGGCTTCTCAGTAGATAAAGCCACCCTCACACGATTTTTCACCTTTCACTTTATCTTGCCATTTATTATCGCTGCCCTAGCAATCATCCACCTCTTGTTTCTGCATGAAACAGGATCAAGTAACCCATCAGGAGTAGCATCGGACCTCGACAAAATTACATTTCACCCCTACTATACAACCAAAGATATCCTAGGCTTAATTATTCTCCTCCTATGCCTAATAAGCCTAACCCTATTTCTACCCGACCTTCTAACCGACCCAGACAATTATACACTAGCCAACCCCCTCAACACCCCACCTCA---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- Seq47 ATGACTACTCCCCGCAAAACACATCCACTAGCAAAAATCATCAACAACTCATTCATTGATCTCCCTACACCATCCAACATCTCCGCCTGATGAAATTTCGGCTCACTCCTAGGTATTTGCCTAATTATTCAAATCACTACAGGTCTATTCTTAGCCATACACTATACACCAGACACTTCAACTGCCTTCTCCTCAGTCGCCCACATCACCCGAGACGTCAACTACGGCTGAATAATCCGCTACCTACACGCCAATGGCGCCTCCATATTCTTCATCTGCCTCTTCCTTCACATTGGCCGAGGCCTATATTATGGATCATTCCTTTTTCTGAAGACCTGAAACGTCGGTATTATCCTTCTACTCACAACTATAGCCACAGCATTCATAGGCTATGTCCTCCCATGAGGCCAAATATCATTCTGAGGGGCCACAGTAATTACAAACCTCCTATCAGCCATCCCATACATCGGACCTGACCTCGTACAATGAATCTGAGGTGGTTTCTCAGTAGATAAAGCCACCCTCACACGATTTTTCACCTTTCACTTCATTCTACCCTTTATTATCGCTGCCCTAGCAACCATCCACCTCTTGTTTCTGCATGAAACAGGATCAAGTAACCCATCAGGAATGACATCGGACCTTGACAAAATCACATTTCACCCCTACTATACAACCAAAGATATCCTAGGCCTAACTATTCTCCTCCTATGTCTAATAAGCCTGACCCTATTTTCACCTGACCTTCTAACCGACCCAGATAATTATACACTAGCTAATCCCCTCAACACTCCACCCCACATTAAACCAGAGTGGTATTTCCTATTTGCATACGCAATCCTACGATCTATCCCTAACAAACTAGGAGGTGTCCTAGCCCTAGTACTCTCCATCCTGATCCTAATAATTATCCCTACCCTCCACCTATCAAAGCAACAAAGCATAGGATTCCGACCCATCACCCAAATCCTATTCTGAACTCTAGTAGCCGACCTATTCACACTCACATGAATCGG---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- Seq48 ATGACTACCCCCCGCAAAACTCACCCACTAGCAAAAATCATCAACAATTCATTCATTGACCTCCCTACACCATCCAACATCTCCGCCTGGTGAAATTTCGGCTCACTCCTAGGTATTTGCCTAATTATTCAAATCACTACAGGTCTATTCTTAGCCATACACTATACACCAGACACTTCAACCGCCTTCTCCTCAGTCGCCCACATCACCCGAGACGTCAACTATGGCTGAATAATCCGCTATCTACATGCCAACGGCGCCTCCATATTCTTTATCTGCCTCTTTCTCCACATTGGCCGAGGCTTATATTACGGATCATTCCTTTTTCTGGAGACCTGGAACGTCGGTATTATCCTCCTACTCACAACCATAGCCACAGCATTCATAGGCTATGTCCTCCCATGAGGCCAAATATCATTCTGAGGGGCCACAGTAATTACAAACCTTCTGTCAGCCATTCCATATATCGGGTCTGACCTCGTACAATGAATCTGAGGTGGCTTCTCAGTAGATAAAGCCACCCTCACACGATTTTTCACCTTTCACTTTATCTTGCCATTTATTATCGCTGCCCTAGCAACCATCCACCTCTTGTTTCTGCATGAAACAGGATCAAGTAACCCATCAGGAGTAGCATCGGACCTCGACAAAATTACATTTCACCCCTACTATACAACCAAAGATATCCTAGGCTTAATTATTCTCCTCCTATGCCTAATAAGCCTAACCCTATTTCTACCCGACCTTCTAACCGACCCAGACAATTATACACTAGCCAACCCTCTCAACACCCCACCTCA---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- Seq49 ATGACTACCCCCCGCAAAACTCACCCACTAGCAAAAATCATCAACAATTCATTCATTGACCTCCCTACACCATCCAACATCTCCGCCTGATGAAATTTCGGCTCACTCCTAGGCATTTGCCTCATTATTCAAATTACTACAGGCCTATTCTTAGCCATACACTATACACCAGATACTTCAACCGCCTTCTCTTCAGTCGCTCACATCACCCGAGACGTCAACTATGGCTGAATAATCCGCTACCTACACGCCAATGGCGCCTCCATATTCTTTATCTGTCTCTTTCTCCACATTGGCCGAGGCTTATATTACGGATCATTCCTTTTTCTGGAGACCTGAAACGTCGGTATTATCCTCCTACTCACAACCATAGCCACAGCATTCATAGGCTATGTCCTCCCATGAGGCCAAATATCATTCTGAGGCGCCACAGTAATTACAAACCTTCTGTCAGCCATCCCATATATCGGATCTGACCTTGTACAATGAATCTGAGGTGGCTTCTCAGTAGATAAAGCCACCCTCACACGATTTTTCACCTTTCACTTTATTTTACCATTTATTATCGCTGCCCTAGCAACCATCCATCTCTTGTTTCTGCATGAAACAGGATCAAGTAACCCGTCAGGAATGGCATCAGACCTCGACAAAATTACATTTCACCCCTACTATACAACCAAAGACATCCTAGGCTTAATTATTCTCCTTCTATGCCTAATGAGCCTAACTCTATTTTCACCCGACCTTTTAACCGACCCAGACAATTATACACTAGCTAATCCCCTCAACACCCCACCTCA---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- ExaML-3.0.21/testData/49.model000066400000000000000000000001251330315463200155700ustar00rootroot00000000000000DNA, gene1 = 1-300 DNA, gene2 = 301-900 DNA, gene3 = 901-1100 DNA, gene4 = 1101-1200 ExaML-3.0.21/testData/49.tree000066400000000000000000000005741330315463200154370ustar00rootroot00000000000000(Seq14,(((Seq10,Seq42),((Seq47,(Seq44,Seq45)),(Seq49,(Seq48,Seq46)))),(((((((Seq2,((Seq6,Seq3),Seq7)),Seq4),Seq5),(Seq9,Seq8)),(Seq13,(Seq11,Seq12))),((Seq27,(Seq31,Seq41)),((((Seq22,((Seq38,Seq40),(Seq28,Seq21))),(((Seq34,Seq30),((Seq20,Seq26),(((Seq19,Seq36),(Seq37,Seq33)),(Seq25,Seq23)))),(Seq17,(Seq29,(((Seq16,Seq15),Seq39),(Seq35,Seq32)))))),Seq18),Seq24))),Seq43)),Seq1); ExaML-3.0.21/versionHeader/000077500000000000000000000000001330315463200153415ustar00rootroot00000000000000ExaML-3.0.21/versionHeader/version.h000066400000000000000000000002201330315463200171710ustar00rootroot00000000000000#define programName "ExaML" #define programVersion "3.0.21" #define programVersionInt 3021 #define programDate "May 29 2018"