PyWavelets-0.3.0/0000775000175000017500000000000012556460303015333 5ustar rgommersrgommers00000000000000PyWavelets-0.3.0/runtests.py0000664000175000017500000002124212556460247017604 0ustar rgommersrgommers00000000000000#!/usr/bin/env python """ runtests.py [OPTIONS] [-- ARGS] Run tests, building the project first. Examples:: $ python runtests.py $ python runtests.py -s {SAMPLE_SUBMODULE} $ python runtests.py -t {SAMPLE_TEST} $ python runtests.py --ipython $ python runtests.py --python somescript.py """ # # This is a generic test runner script for projects using Numpy's test # framework. Change the following values to adapt to your project: # PROJECT_MODULE = "pywt" PROJECT_ROOT_FILES = ['pywt', 'COPYING.txt', 'setup.py'] SAMPLE_TEST = "pywt/tests/test_modes.py:test_default_mode" SAMPLE_SUBMODULE = "_tools" EXTRA_PATH = ['/usr/lib/ccache', '/usr/lib/f90cache', '/usr/local/lib/ccache', '/usr/local/lib/f90cache'] # --------------------------------------------------------------------- if __doc__ is None: __doc__ = "Run without -OO if you want usage info" else: __doc__ = __doc__.format(**globals()) import sys import os # In case we are run from the source directory, we don't want to import the # project from there: sys.path.pop(0) import shutil import subprocess import time import imp from argparse import ArgumentParser, REMAINDER def main(argv): parser = ArgumentParser(usage=__doc__.lstrip()) parser.add_argument("--verbose", "-v", action="count", default=1, help="more verbosity") parser.add_argument("--no-build", "-n", action="store_true", default=False, help="do not build the project (use system installed version)") parser.add_argument("--build-only", "-b", action="store_true", default=False, help="just build, do not run any tests") parser.add_argument("--doctests", action="store_true", default=False, help="Run doctests in module") parser.add_argument("--coverage", action="store_true", default=False, help=("report coverage of project code. HTML output goes " "under build/coverage")) parser.add_argument("--mode", "-m", default="fast", help="'fast', 'full', or something that could be " "passed to nosetests -A [default: fast]") parser.add_argument("--submodule", "-s", default=None, help="Submodule whose tests to run (cluster, constants, ...)") parser.add_argument("--pythonpath", "-p", default=None, help="Paths to prepend to PYTHONPATH") parser.add_argument("--tests", "-t", action='append', help="Specify tests to run") parser.add_argument("--python", action="store_true", help="Start a Python shell with PYTHONPATH set") parser.add_argument("--ipython", "-i", action="store_true", help="Start IPython shell with PYTHONPATH set") parser.add_argument("--shell", action="store_true", help="Start Unix shell with PYTHONPATH set") parser.add_argument("--debug", "-g", action="store_true", help="Debug build") parser.add_argument("--show-build-log", action="store_true", help="Show build output rather than using a log file") parser.add_argument("args", metavar="ARGS", default=[], nargs=REMAINDER, help="Arguments to pass to Nose, Python or shell") args = parser.parse_args(argv) if args.pythonpath: for p in reversed(args.pythonpath.split(os.pathsep)): sys.path.insert(0, p) if not args.no_build: site_dir = build_project(args) sys.path.insert(0, site_dir) os.environ['PYTHONPATH'] = site_dir extra_argv = args.args[:] if extra_argv and extra_argv[0] == '--': extra_argv = extra_argv[1:] if args.python: if extra_argv: # Don't use subprocess, since we don't want to include the # current path in PYTHONPATH. sys.argv = extra_argv with open(extra_argv[0], 'r') as f: script = f.read() sys.modules['__main__'] = imp.new_module('__main__') ns = dict(__name__='__main__', __file__=extra_argv[0]) exec_(script, ns) sys.exit(0) else: import code code.interact() sys.exit(0) if args.ipython: import IPython IPython.embed(user_ns={}) sys.exit(0) if args.shell: shell = os.environ.get('SHELL', 'sh') print("Spawning a Unix shell...") os.execv(shell, [shell] + extra_argv) sys.exit(1) if args.coverage: dst_dir = os.path.join('build', 'coverage') fn = os.path.join(dst_dir, 'coverage_html.js') if os.path.isdir(dst_dir) and os.path.isfile(fn): shutil.rmtree(dst_dir) extra_argv += ['--cover-html', '--cover-html-dir='+dst_dir] if args.build_only: sys.exit(0) elif args.submodule: modname = PROJECT_MODULE + '.' + args.submodule try: __import__(modname) test = sys.modules[modname].test except (ImportError, KeyError, AttributeError): print("Cannot run tests for %s" % modname) sys.exit(2) elif args.tests: def test(*a, **kw): extra_argv = kw.pop('extra_argv', ()) extra_argv = extra_argv + args.tests[1:] kw['extra_argv'] = extra_argv from numpy.testing import Tester return Tester(args.tests[0]).test(*a, **kw) else: __import__(PROJECT_MODULE) test = sys.modules[PROJECT_MODULE].test result = test(args.mode, verbose=args.verbose, extra_argv=extra_argv, doctests=args.doctests, coverage=args.coverage) if result.wasSuccessful(): sys.exit(0) else: sys.exit(1) def build_project(args): """ Build a dev version of the project. Returns ------- site_dir site-packages directory where it was installed """ root_dir = os.path.abspath(os.path.join(os.path.dirname(__file__))) root_ok = [os.path.exists(os.path.join(root_dir, fn)) for fn in PROJECT_ROOT_FILES] if not all(root_ok): print("To build the project, run runtests.py in " "git checkout or unpacked source") sys.exit(1) dst_dir = os.path.join(root_dir, 'build', 'testenv') env = dict(os.environ) cmd = [sys.executable, 'setup.py'] # Always use ccache, if installed env['PATH'] = os.pathsep.join(EXTRA_PATH + env.get('PATH', '').split(os.pathsep)) if args.debug: # assume everyone uses gcc/gfortran env['OPT'] = '-O0 -ggdb' env['FOPT'] = '-O0 -ggdb' cmd += ["build", "--debug"] cmd += ['install', '--prefix=' + dst_dir] if args.show_build_log: ret = subprocess.call(cmd, env=env, cwd=root_dir) else: print("Building, see build.log...") with open('build.log', 'w') as log: p = subprocess.Popen(cmd, env=env, stdout=log, stderr=log, cwd=root_dir) # Wait for it to finish, and print something to indicate the # process is alive, but only if the log file has grown (to # allow continuous integration environments kill a hanging # process accurately if it produces no output) last_blip = time.time() last_log_size = os.stat('build.log').st_size while p.poll() is None: time.sleep(0.5) if time.time() - last_blip > 60: log_size = os.stat('build.log').st_size if log_size > last_log_size: print(" ... build in progress") last_blip = time.time() last_log_size = log_size ret = p.wait() if ret == 0: print("Build OK") else: if not args.show_build_log: with open('build.log', 'r') as f: print(f.read()) print("Build failed!") sys.exit(1) from distutils.sysconfig import get_python_lib site_dir = get_python_lib(prefix=dst_dir, plat_specific=True) return site_dir if sys.version_info[0] >= 3: import builtins exec_ = getattr(builtins, "exec") else: def exec_(code, globs=None, locs=None): """Execute code in a namespace.""" if globs is None: frame = sys._getframe(1) globs = frame.f_globals if locs is None: locs = frame.f_locals del frame elif locs is None: locs = globs exec("""exec code in globs, locs""") if __name__ == "__main__": main(argv=sys.argv[1:]) PyWavelets-0.3.0/util/0000775000175000017500000000000012556460303016310 5ustar rgommersrgommers00000000000000PyWavelets-0.3.0/util/cythonize.py0000775000175000017500000001314412556460247020713 0ustar rgommersrgommers00000000000000#!/usr/bin/env python """ cythonize Cythonize pyx files into C files as needed. Usage: cythonize [root_dir] Default [root_dir] is 'pywt'. Checks pyx files to see if they have been changed relative to their corresponding C files. If they have, then runs cython on these files to recreate the C files. The script thinks that the pyx files have changed relative to the C files by comparing hashes stored in a database file. Simple script to invoke Cython (and Tempita) on all .pyx (.pyx.in) files; while waiting for a proper build system. Uses file hashes to figure out if rebuild is needed. For now, this script should be run by developers when changing Cython files only, and the resulting C files checked in, so that end-users (and Python-only developers) do not get the Cython/Tempita dependencies. Originally written by Dag Sverre Seljebotn, and copied here from: https://raw.github.com/dagss/private-scipy-refactor/cythonize/cythonize.py Note: this script does not check any of the dependent C libraries; it only operates on the Cython .pyx files. """ from __future__ import division, print_function, absolute_import import os import re import sys import hashlib import subprocess HASH_FILE = 'cythonize.dat' DEFAULT_ROOT = 'pywt' # WindowsError is not defined on unix systems try: WindowsError except NameError: WindowsError = None # # Rules # def process_pyx(fromfile, tofile): try: from Cython.Compiler.Version import version as cython_version from distutils.version import LooseVersion if LooseVersion(cython_version) < LooseVersion('0.17'): raise Exception('Building PyWavelets requires Cython >= 0.17') except ImportError: pass flags = ['--fast-fail'] if tofile.endswith('.cxx'): flags += ['--cplus'] try: try: r = subprocess.call(['cython'] + flags + ["-o", tofile, fromfile]) if r != 0: raise Exception('Cython failed') except OSError: # There are ways of installing Cython that don't result in a cython # executable on the path, see gh-2397. r = subprocess.call([sys.executable, '-c', 'import sys; from Cython.Compiler.Main import ' 'setuptools_main as main; sys.exit(main())'] + flags + ["-o", tofile, fromfile]) if r != 0: raise Exception('Cython failed') except OSError: raise OSError('Cython needs to be installed') def process_tempita_pyx(fromfile, tofile): import tempita with open(fromfile, "rb") as f: tmpl = f.read() pyxcontent = tempita.sub(tmpl) assert fromfile.endswith('.pyx.in') pyxfile = fromfile[:-len('.pyx.in')] + '.pyx' with open(pyxfile, "wb") as f: f.write(pyxcontent) process_pyx(pyxfile, tofile) rules = { # fromext : function '.pyx' : process_pyx, '.pyx.in' : process_tempita_pyx } # # Hash db # def load_hashes(filename): # Return { filename : (sha1 of input, sha1 of output) } if os.path.isfile(filename): hashes = {} with open(filename, 'r') as f: for line in f: filename, inhash, outhash = line.split() hashes[filename] = (inhash, outhash) else: hashes = {} return hashes def save_hashes(hash_db, filename): with open(filename, 'w') as f: for key, value in sorted(hash_db.items()): f.write("%s %s %s\n" % (key, value[0], value[1])) def sha1_of_file(filename): h = hashlib.sha1() with open(filename, "rb") as f: h.update(f.read()) return h.hexdigest() # # Main program # def normpath(path): path = path.replace(os.sep, '/') if path.startswith('./'): path = path[2:] return path def get_hash(frompath, topath): from_hash = sha1_of_file(frompath) to_hash = sha1_of_file(topath) if os.path.exists(topath) else None return (from_hash, to_hash) def process(path, fromfile, tofile, processor_function, hash_db): fullfrompath = os.path.join(path, fromfile) fulltopath = os.path.join(path, tofile) current_hash = get_hash(fullfrompath, fulltopath) if current_hash == hash_db.get(normpath(fullfrompath), None): print('%s has not changed' % fullfrompath) return orig_cwd = os.getcwd() try: os.chdir(path) print('Processing %s' % fullfrompath) processor_function(fromfile, tofile) finally: os.chdir(orig_cwd) # changed target file, recompute hash current_hash = get_hash(fullfrompath, fulltopath) # store hash in db hash_db[normpath(fullfrompath)] = current_hash def find_process_files(root_dir): hash_db = load_hashes(HASH_FILE) for cur_dir, dirs, files in os.walk(root_dir): for filename in files: for fromext, function in rules.items(): if filename.endswith(fromext): toext = ".c" with open(os.path.join(cur_dir, filename), 'rb') as f: data = f.read() m = re.search(br"^\s*#\s*distutils:\s*language\s*=\s*c\+\+\s*$", data, re.I|re.M) if m: toext = ".cxx" fromfile = filename tofile = filename[:-len(fromext)] + toext process(cur_dir, fromfile, tofile, function, hash_db) save_hashes(hash_db, HASH_FILE) def main(): try: root_dir = sys.argv[1] except IndexError: root_dir = DEFAULT_ROOT find_process_files(root_dir) if __name__ == '__main__': main() PyWavelets-0.3.0/util/__init__.py0000664000175000017500000000000012556460247020416 0ustar rgommersrgommers00000000000000PyWavelets-0.3.0/util/setenv_build32.bat0000664000175000017500000000041212556460247021634 0ustar rgommersrgommers00000000000000rem Configure the environment for 32-bit builds. rem Use "vcvars32.bat" for a 32-bit build. "C:\Program Files (x86)\Microsoft Visual Studio 9.0\VC\bin\vcvars32.bat" setenv /x86 /release rem Convince setup.py to use the SDK tools. set MSSdk=1 set DISTUTILS_USE_SDK=1 PyWavelets-0.3.0/util/appveyor/0000775000175000017500000000000012556460303020155 5ustar rgommersrgommers00000000000000PyWavelets-0.3.0/util/appveyor/install.ps10000664000175000017500000000635612556460247022271 0ustar rgommersrgommers00000000000000# Sample script to install Python and pip under Windows # Authors: Olivier Grisel and Kyle Kastner # License: BSD 3 clause $BASE_URL = "https://www.python.org/ftp/python/" $GET_PIP_URL = "https://bootstrap.pypa.io/get-pip.py" $GET_PIP_PATH = "C:\get-pip.py" function DownloadPython ($python_version, $platform_suffix) { $webclient = New-Object System.Net.WebClient $filename = "python-" + $python_version + $platform_suffix + ".msi" $url = $BASE_URL + $python_version + "/" + $filename $basedir = $pwd.Path + "\" $filepath = $basedir + $filename if (Test-Path $filename) { Write-Host "Reusing" $filepath return $filepath } # Download and retry up to 3 times in case of network transient errors. Write-Host "Downloading" $filename "from" $url $retry_attempts = 2 for($i=0; $i -lt $retry_attempts; $i++){ try { $webclient.DownloadFile($url, $filepath) break } Catch [Exception]{ Start-Sleep 1 } } if (Test-Path $filepath) { Write-Host "File saved at" $filepath } else { # Retry once to get the error message if any at the last try $webclient.DownloadFile($url, $filepath) } return $filepath } function InstallPython ($python_version, $architecture, $python_home) { Write-Host "Installing Python" $python_version "for" $architecture "bit architecture to" $python_home if (Test-Path $python_home) { Write-Host $python_home "already exists, skipping." return $false } if ($architecture -eq "32") { $platform_suffix = "" } else { $platform_suffix = ".amd64" } $msipath = DownloadPython $python_version $platform_suffix Write-Host "Installing" $msipath "to" $python_home $install_log = $python_home + ".log" $install_args = "/qn /log $install_log /i $msipath TARGETDIR=$python_home" $uninstall_args = "/qn /x $msipath" RunCommand "msiexec.exe" $install_args if (-not(Test-Path $python_home)) { Write-Host "Python seems to be installed else-where, reinstalling." RunCommand "msiexec.exe" $uninstall_args RunCommand "msiexec.exe" $install_args } if (Test-Path $python_home) { Write-Host "Python $python_version ($architecture) installation complete" } else { Write-Host "Failed to install Python in $python_home" Get-Content -Path $install_log Exit 1 } } function RunCommand ($command, $command_args) { Write-Host $command $command_args Start-Process -FilePath $command -ArgumentList $command_args -Wait -Passthru } function InstallPip ($python_home) { $pip_path = $python_home + "\Scripts\pip.exe" $python_path = $python_home + "\python.exe" if (-not(Test-Path $pip_path)) { Write-Host "Installing pip..." $webclient = New-Object System.Net.WebClient $webclient.DownloadFile($GET_PIP_URL, $GET_PIP_PATH) Write-Host "Executing:" $python_path $GET_PIP_PATH Start-Process -FilePath "$python_path" -ArgumentList "$GET_PIP_PATH" -Wait -Passthru } else { Write-Host "pip already installed." } } function main () { InstallPython $env:PYTHON_VERSION $env:PYTHON_ARCH $env:PYTHON InstallPip $env:PYTHON } main PyWavelets-0.3.0/util/appveyor/requirements.txt0000664000175000017500000000116512556460247023453 0ustar rgommersrgommers00000000000000# Fetch a numpy wheel from the sklearn rackspace wheelhouse. # That wheel was generated by @ogrisel by calling `wheel convert` on # the binaries from http://www.lfd.uci.edu/~gohlke/pythonlibs/ # This is a temporary solution. As soon as numpy provides an official # wheel for windows we ca delete this --find-links line. --find-links http://28daf2247a33ed269873-7b1aad3fab3cc330e1fd9d109892382a.r6.cf2.rackcdn.com # fix the versions of numpy to force the use of numpy to use the whl # of the rackspace folder instead of trying to install from more recent # source tarball published on PyPI numpy==1.8.1 Cython==0.20.2 nose wheel PyWavelets-0.3.0/util/appveyor/run_with_env.cmd0000664000175000017500000000337212556460247023365 0ustar rgommersrgommers00000000000000:: To build extensions for 64 bit Python 3, we need to configure environment :: variables to use the MSVC 2010 C++ compilers from GRMSDKX_EN_DVD.iso of: :: MS Windows SDK for Windows 7 and .NET Framework 4 (SDK v7.1) :: :: To build extensions for 64 bit Python 2, we need to configure environment :: variables to use the MSVC 2008 C++ compilers from GRMSDKX_EN_DVD.iso of: :: MS Windows SDK for Windows 7 and .NET Framework 3.5 (SDK v7.0) :: :: 32 bit builds do not require specific environment configurations. :: :: Note: this script needs to be run with the /E:ON and /V:ON flags for the :: cmd interpreter, at least for (SDK v7.0) :: :: More details at: :: https://github.com/cython/cython/wiki/64BitCythonExtensionsOnWindows :: http://stackoverflow.com/a/13751649/163740 :: :: Author: Olivier Grisel :: License: BSD 3 clause @ECHO OFF SET COMMAND_TO_RUN=%* SET WIN_SDK_ROOT=C:\Program Files\Microsoft SDKs\Windows SET MAJOR_PYTHON_VERSION="%PYTHON_VERSION:~0,1%" IF %MAJOR_PYTHON_VERSION% == "2" ( SET WINDOWS_SDK_VERSION="v7.0" ) ELSE IF %MAJOR_PYTHON_VERSION% == "3" ( SET WINDOWS_SDK_VERSION="v7.1" ) ELSE ( ECHO Unsupported Python version: "%MAJOR_PYTHON_VERSION%" EXIT 1 ) IF "%PYTHON_ARCH%"=="64" ( ECHO Configuring Windows SDK %WINDOWS_SDK_VERSION% for Python %MAJOR_PYTHON_VERSION% on a 64 bit architecture SET DISTUTILS_USE_SDK=1 SET MSSdk=1 "%WIN_SDK_ROOT%\%WINDOWS_SDK_VERSION%\Setup\WindowsSdkVer.exe" -q -version:%WINDOWS_SDK_VERSION% "%WIN_SDK_ROOT%\%WINDOWS_SDK_VERSION%\Bin\SetEnv.cmd" /x64 /release ECHO Executing: %COMMAND_TO_RUN% call %COMMAND_TO_RUN% || EXIT 1 ) ELSE ( ECHO Using default MSVC build environment for 32 bit architecture ECHO Executing: %COMMAND_TO_RUN% call %COMMAND_TO_RUN% || EXIT 1 ) PyWavelets-0.3.0/util/authors.py0000775000175000017500000001343712556460247020371 0ustar rgommersrgommers00000000000000#!/usr/bin/env python # -*- encoding:utf-8 -*- """ git-authors [OPTIONS] REV1..REV2 List the authors who contributed within a given revision interval. """ # Author: Pauli Virtanen . This script is in the public domain. from __future__ import division, print_function, absolute_import import optparse import re import sys import os import subprocess from scipy._lib.six import u, PY3 if PY3: stdout_b = sys.stdout.buffer else: stdout_b = sys.stdout NAME_MAP = { u('Helder'): u('Helder Oliveira'), } def main(): p = optparse.OptionParser(__doc__.strip()) p.add_option("-d", "--debug", action="store_true", help="print debug output") options, args = p.parse_args() if len(args) != 1: p.error("invalid number of arguments") try: rev1, rev2 = args[0].split('..') except ValueError: p.error("argument is not a revision range") # Analyze log data all_authors = set() authors = set() def analyze_line(line, names, disp=False): line = line.strip().decode('utf-8') # Check the commit author name m = re.match(u('^@@@([^@]*)@@@'), line) if m: name = m.group(1) line = line[m.end():] name = NAME_MAP.get(name, name) if disp: if name not in names: stdout_b.write((" - Author: %s\n" % name).encode('utf-8')) names.add(name) # Look for "thanks to" messages in the commit log m = re.search(u(r'([Tt]hanks to|[Cc]ourtesy of) ([A-Z][A-Za-z]*? [A-Z][A-Za-z]*? [A-Z][A-Za-z]*|[A-Z][A-Za-z]*? [A-Z]\. [A-Z][A-Za-z]*|[A-Z][A-Za-z ]*? [A-Z][A-Za-z]*|[a-z0-9]+)($|\.| )'), line) if m: name = m.group(2) if name not in (u('this'),): if disp: stdout_b.write(" - Log : %s\n" % line.strip().encode('utf-8')) name = NAME_MAP.get(name, name) names.add(name) line = line[m.end():].strip() line = re.sub(u(r'^(and|, and|, ) '), u('Thanks to '), line) analyze_line(line.encode('utf-8'), names) # Find all authors before the named range for line in git.pipe('log', '--pretty=@@@%an@@@%n@@@%cn@@@%n%b', '%s' % (rev1,)): analyze_line(line, all_authors) # Find authors in the named range for line in git.pipe('log', '--pretty=@@@%an@@@%n@@@%cn@@@%n%b', '%s..%s' % (rev1, rev2)): analyze_line(line, authors, disp=options.debug) # Sort def name_key(fullname): m = re.search(u(' [a-z ]*[A-Za-z-]+$'), fullname) if m: forename = fullname[:m.start()].strip() surname = fullname[m.start():].strip() else: forename = "" surname = fullname.strip() if surname.startswith(u('van der ')): surname = surname[8:] if surname.startswith(u('de ')): surname = surname[3:] if surname.startswith(u('von ')): surname = surname[4:] return (surname.lower(), forename.lower()) authors = list(authors) authors.sort(key=name_key) # Print stdout_b.write(b""" Authors ======= """) for author in authors: if author in all_authors: stdout_b.write(("* %s\n" % author).encode('utf-8')) else: stdout_b.write(("* %s +\n" % author).encode('utf-8')) stdout_b.write((""" A total of %(count)d people contributed to this release. People with a "+" by their names contributed a patch for the first time. This list of names is automatically generated, and may not be fully complete. """ % dict(count=len(authors))).encode('utf-8')) stdout_b.write(("\nNOTE: Check this list manually! It is automatically generated " "and some names\n may be missing.\n").encode('utf-8')) #------------------------------------------------------------------------------ # Communicating with Git #------------------------------------------------------------------------------ class Cmd(object): executable = None def __init__(self, executable): self.executable = executable def _call(self, command, args, kw, repository=None, call=False): cmd = [self.executable, command] + list(args) cwd = None if repository is not None: cwd = os.getcwd() os.chdir(repository) try: if call: return subprocess.call(cmd, **kw) else: return subprocess.Popen(cmd, **kw) finally: if cwd is not None: os.chdir(cwd) def __call__(self, command, *a, **kw): ret = self._call(command, a, {}, call=True, **kw) if ret != 0: raise RuntimeError("%s failed" % self.executable) def pipe(self, command, *a, **kw): stdin = kw.pop('stdin', None) p = self._call(command, a, dict(stdin=stdin, stdout=subprocess.PIPE), call=False, **kw) return p.stdout def read(self, command, *a, **kw): p = self._call(command, a, dict(stdout=subprocess.PIPE), call=False, **kw) out, err = p.communicate() if p.returncode != 0: raise RuntimeError("%s failed" % self.executable) return out def readlines(self, command, *a, **kw): out = self.read(command, *a, **kw) return out.rstrip("\n").split("\n") def test(self, command, *a, **kw): ret = self._call(command, a, dict(stdout=subprocess.PIPE, stderr=subprocess.PIPE), call=True, **kw) return (ret == 0) git = Cmd("git") #------------------------------------------------------------------------------ if __name__ == "__main__": main() PyWavelets-0.3.0/util/templating_src.py0000664000175000017500000000211312556460247021701 0ustar rgommersrgommers00000000000000#!/usr/bin/env python # -*- coding: utf-8 -*- # Author: Ralf Gommers # Date: 1 Oct 2013 import glob import os from numpy.distutils.conv_template import process_str def needs_update(src_path, dst_path): # No update if .c file exists and is newer than last template change. if not os.path.exists(dst_path): return True if os.path.getmtime(dst_path) < os.path.getmtime(src_path): return True return False def expand_files(glob_pattern): files = glob.glob(glob_pattern) for src_path in files: dst_path = os.path.splitext(src_path)[0] if needs_update(src_path, dst_path): print("expanding template: %s -> %s" % (src_path, dst_path)) content = process_str(open(src_path, "rb").read().decode('utf-8')) new_file = open(dst_path, "wb") new_file.write(content.encode('utf-8')) new_file.close() if __name__ == '__main__': cwd = os.path.abspath(os.path.dirname(__file__)) templates_glob = os.path.join(cwd, '..', 'pywt', "src", "*.[ch].src") expand_files(templates_glob) PyWavelets-0.3.0/util/setenv_build64.bat0000664000175000017500000000041212556460247021641 0ustar rgommersrgommers00000000000000rem Configure the environment for 64-bit builds. rem Use "vcvars32.bat" for a 32-bit build. "C:\Program Files (x86)\Microsoft Visual Studio 9.0\VC\bin\vcvars64.bat" setenv /x64 /release rem Convince setup.py to use the SDK tools. set MSSdk=1 set DISTUTILS_USE_SDK=1 PyWavelets-0.3.0/util/gh_lists.py0000775000175000017500000001014312556460247020507 0ustar rgommersrgommers00000000000000#!/usr/bin/env python # -*- encoding:utf-8 -*- """ gh_lists.py MILESTONE Functions for Github API requests. """ from __future__ import print_function, division, absolute_import import os import re import sys import json import collections import argparse from urllib2 import urlopen Issue = collections.namedtuple('Issue', ('id', 'title', 'url')) def main(): p = argparse.ArgumentParser(usage=__doc__.lstrip()) p.add_argument('--project', default='PyWavelets/pywt') p.add_argument('milestone') args = p.parse_args() getter = CachedGet('gh_cache.json') try: milestones = get_milestones(getter, args.project) if args.milestone not in milestones: msg = "Milestone {0} not available. Available milestones: {1}" msg = msg.format(args.milestone, u", ".join(sorted(milestones))) p.error(msg) issues = get_issues(getter, args.project, args.milestone) issues.sort() finally: getter.save() prs = [x for x in issues if u'/pull/' in x.url] issues = [x for x in issues if x not in prs] def print_list(title, items): print() print(title) print("-"*len(title)) print() for issue in items: msg = u"- `#{0} <{1}>`__: {2}" title = re.sub(u"\s+", u" ", issue.title.strip()) if len(title) > 60: remainder = re.sub(u"\s.*$", u"...", title[60:]) if len(remainder) > 20: remainder = title[:80] + u"..." else: title = title[:60] + remainder msg = msg.format(issue.id, issue.url, title) print(msg) print() msg = u"Issues closed for {0}".format(args.milestone) print_list(msg, issues) msg = u"Pull requests for {0}".format(args.milestone) print_list(msg, prs) return 0 def get_milestones(getter, project): url = "https://api.github.com/repos/{project}/milestones".format(project=project) raw_data, info = getter.get(url) data = json.loads(raw_data) milestones = {} for ms in data: milestones[ms[u'title']] = ms[u'number'] return milestones def get_issues(getter, project, milestone): milestones = get_milestones(getter, project) mid = milestones[milestone] url = "https://api.github.com/repos/{project}/issues?milestone={mid}&state=closed&sort=created&direction=asc" url = url.format(project=project, mid=mid) raw_datas = [] while True: raw_data, info = getter.get(url) raw_datas.append(raw_data) if 'link' not in info: break m = re.search('<(.*?)>; rel="next"', info['link']) if m: url = m.group(1) continue break issues = [] for raw_data in raw_datas: data = json.loads(raw_data) for issue_data in data: issues.append(Issue(issue_data[u'number'], issue_data[u'title'], issue_data[u'html_url'])) return issues class CachedGet(object): def __init__(self, filename): self.filename = filename if os.path.isfile(filename): print("[gh_lists] using {0} as cache (remove it if you want fresh data)".format(filename), file=sys.stderr) with open(filename, 'rb') as f: self.cache = json.load(f) else: self.cache = {} def get(self, url): url = unicode(url) if url not in self.cache: print("[gh_lists] get:", url, file=sys.stderr) req = urlopen(url) if req.getcode() != 200: raise RuntimeError() data = req.read() info = dict(req.info()) self.cache[url] = (data, info) req.close() else: print("[gh_lists] get (cached):", url, file=sys.stderr) return self.cache[url] def save(self): tmp = self.filename + ".new" with open(tmp, 'wb') as f: json.dump(self.cache, f) os.rename(tmp, self.filename) if __name__ == "__main__": sys.exit(main()) PyWavelets-0.3.0/pywt/0000775000175000017500000000000012556460303016336 5ustar rgommersrgommers00000000000000PyWavelets-0.3.0/pywt/tests/0000775000175000017500000000000012556460303017500 5ustar rgommersrgommers00000000000000PyWavelets-0.3.0/pywt/tests/test_functions.py0000664000175000017500000000267412556460247023141 0ustar rgommersrgommers00000000000000#!/usr/bin/env python from __future__ import division, print_function, absolute_import from numpy.testing import (run_module_suite, assert_almost_equal, assert_allclose) import pywt def test_centrfreq(): # db1 is Haar function, frequency=1 w = pywt.Wavelet('db1') expected = 1 result = pywt.centfrq(w, precision=12) assert_almost_equal(result, expected, decimal=3) # db2, frequency=2/3 w = pywt.Wavelet('db2') expected = 2/3. result = pywt.centfrq(w, precision=12) assert_almost_equal(result, expected) def test_scal2frq_scale(): scale = 2 delta = 1 w = pywt.Wavelet('db1') expected = 1. / scale result = pywt.scal2frq(w, scale, delta, precision=12) assert_almost_equal(result, expected, decimal=3) def test_scal2frq_delta(): scale = 1 delta = 2 w = pywt.Wavelet('db1') expected = 1. / delta result = pywt.scal2frq(w, scale, delta, precision=12) assert_almost_equal(result, expected, decimal=3) def test_intwave_orthogonal(): w = pywt.Wavelet('db1') int_psi, x = pywt.intwave(w, precision=12) ix = x < 0.5 # For x < 0.5, the integral is equal to x assert_allclose(int_psi[ix], x[ix]) # For x > 0.5, the integral is equal to (1 - x) # Ignore last point here, there x > 1 and something goes wrong assert_allclose(int_psi[~ix][:-1], 1 - x[~ix][:-1], atol=1e-10) if __name__ == '__main__': run_module_suite() PyWavelets-0.3.0/pywt/tests/test_wp2d.py0000664000175000017500000001303412556460247021775 0ustar rgommersrgommers00000000000000#!/usr/bin/env python from __future__ import division, print_function, absolute_import import numpy as np from numpy.testing import (run_module_suite, assert_allclose, assert_, assert_raises) import pywt def test_traversing_tree_2d(): x = np.array([[1, 2, 3, 4, 5, 6, 7, 8]] * 8, dtype=np.float64) wp = pywt.WaveletPacket2D(data=x, wavelet='db1', mode='sym') assert_(np.all(wp.data == x)) assert_(wp.path == '') assert_(wp.level == 0) assert_(wp.maxlevel == 3) assert_allclose(wp['a'].data, np.array([[3., 7., 11., 15.]] * 4), rtol=1e-12) assert_allclose(wp['h'].data, np.zeros((4, 4)), rtol=1e-12, atol=1e-14) assert_allclose(wp['v'].data, -np.ones((4, 4)), rtol=1e-12, atol=1e-14) assert_allclose(wp['d'].data, np.zeros((4, 4)), rtol=1e-12, atol=1e-14) assert_allclose(wp['aa'].data, np.array([[10., 26.]] * 2), rtol=1e-12) assert_(wp['a']['a'].data is wp['aa'].data) assert_allclose(wp['aaa'].data, np.array([[36.]]), rtol=1e-12) assert_raises(IndexError, lambda: wp['aaaa']) assert_raises(ValueError, lambda: wp['f']) def test_accessing_node_atributes_2d(): x = np.array([[1, 2, 3, 4, 5, 6, 7, 8]] * 8, dtype=np.float64) wp = pywt.WaveletPacket2D(data=x, wavelet='db1', mode='sym') assert_allclose(wp['av'].data, np.zeros((2, 2)) - 4, rtol=1e-12) assert_(wp['av'].path == 'av') assert_(wp['av'].node_name == 'v') assert_(wp['av'].parent.path == 'a') assert_allclose(wp['av'].parent.data, np.array([[3., 7., 11., 15.]] * 4), rtol=1e-12) assert_(wp['av'].level == 2) assert_(wp['av'].maxlevel == 3) assert_(wp['av'].mode == 'sym') def test_collecting_nodes_2d(): x = np.array([[1, 2, 3, 4, 5, 6, 7, 8]] * 8, dtype=np.float64) wp = pywt.WaveletPacket2D(data=x, wavelet='db1', mode='sym') assert_(len(wp.get_level(0)) == 1) assert_(wp.get_level(0)[0].path == '') # First level assert_(len(wp.get_level(1)) == 4) assert_([node.path for node in wp.get_level(1)] == ['a', 'h', 'v', 'd']) # Second level assert_(len(wp.get_level(2)) == 16) paths = [node.path for node in wp.get_level(2)] expected_paths = ['aa', 'ah', 'av', 'ad', 'ha', 'hh', 'hv', 'hd', 'va', 'vh', 'vv', 'vd', 'da', 'dh', 'dv', 'dd'] assert_(paths == expected_paths) # Third level. assert_(len(wp.get_level(3)) == 64) paths = [node.path for node in wp.get_level(3)] expected_paths = ['aaa', 'aah', 'aav', 'aad', 'aha', 'ahh', 'ahv', 'ahd', 'ava', 'avh', 'avv', 'avd', 'ada', 'adh', 'adv', 'add', 'haa', 'hah', 'hav', 'had', 'hha', 'hhh', 'hhv', 'hhd', 'hva', 'hvh', 'hvv', 'hvd', 'hda', 'hdh', 'hdv', 'hdd', 'vaa', 'vah', 'vav', 'vad', 'vha', 'vhh', 'vhv', 'vhd', 'vva', 'vvh', 'vvv', 'vvd', 'vda', 'vdh', 'vdv', 'vdd', 'daa', 'dah', 'dav', 'dad', 'dha', 'dhh', 'dhv', 'dhd', 'dva', 'dvh', 'dvv', 'dvd', 'dda', 'ddh', 'ddv', 'ddd'] assert_(paths == expected_paths) def test_data_reconstruction_2d(): x = np.array([[1, 2, 3, 4, 5, 6, 7, 8]] * 8, dtype=np.float64) wp = pywt.WaveletPacket2D(data=x, wavelet='db1', mode='sym') new_wp = pywt.WaveletPacket2D(data=None, wavelet='db1', mode='sym') new_wp['vh'] = wp['vh'].data new_wp['vv'] = wp['vh'].data new_wp['vd'] = np.zeros((2, 2), dtype=np.float64) new_wp['a'] = [[3.0, 7.0, 11.0, 15.0]] * 4 new_wp['d'] = np.zeros((4, 4), dtype=np.float64) new_wp['h'] = wp['h'] # all zeros assert_allclose(new_wp.reconstruct(update=False), np.array([[1.5, 1.5, 3.5, 3.5, 5.5, 5.5, 7.5, 7.5]] * 8), rtol=1e-12) assert_allclose(wp['va'].data, np.zeros((2, 2)) - 2, rtol=1e-12) new_wp['va'] = wp['va'].data assert_allclose(new_wp.reconstruct(update=False), x, rtol=1e-12) def test_data_reconstruction_delete_nodes_2d(): x = np.array([[1, 2, 3, 4, 5, 6, 7, 8]] * 8, dtype=np.float64) wp = pywt.WaveletPacket2D(data=x, wavelet='db1', mode='sym') new_wp = pywt.WaveletPacket2D(data=None, wavelet='db1', mode='sym') new_wp['vh'] = wp['vh'].data new_wp['vv'] = wp['vh'].data new_wp['vd'] = np.zeros((2, 2), dtype=np.float64) new_wp['a'] = [[3.0, 7.0, 11.0, 15.0]] * 4 new_wp['d'] = np.zeros((4, 4), dtype=np.float64) new_wp['h'] = wp['h'] # all zeros assert_allclose(new_wp.reconstruct(update=False), np.array([[1.5, 1.5, 3.5, 3.5, 5.5, 5.5, 7.5, 7.5]] * 8), rtol=1e-12) new_wp['va'] = wp['va'].data assert_allclose(new_wp.reconstruct(update=False), x, rtol=1e-12) del(new_wp['va']) new_wp['va'] = wp['va'].data assert_(new_wp.data is None) assert_allclose(new_wp.reconstruct(update=True), x, rtol=1e-12) assert_allclose(new_wp.data, x, rtol=1e-12) # TODO: decompose=True def test_lazy_evaluation_2D(): # Note: internal implementation detail not to be relied on. Testing for # now for backwards compatibility, but this test may be broken in needed. x = np.array([[1, 2, 3, 4, 5, 6, 7, 8]] * 8) wp = pywt.WaveletPacket2D(data=x, wavelet='db1', mode='sym') assert_(wp.a is None) assert_allclose(wp['a'].data, np.array([[3., 7., 11., 15.]] * 4), rtol=1e-12) assert_allclose(wp.a.data, np.array([[3., 7., 11., 15.]] * 4), rtol=1e-12) assert_allclose(wp.d.data, np.zeros((4, 4)), rtol=1e-12, atol=1e-12) if __name__ == '__main__': run_module_suite() PyWavelets-0.3.0/pywt/tests/test_dwt_idwt.py0000664000175000017500000000561212556460247022751 0ustar rgommersrgommers00000000000000#!/usr/bin/env python from __future__ import division, print_function, absolute_import import numpy as np from numpy.testing import (run_module_suite, assert_allclose, assert_, assert_raises) import pywt def test_dwt_idwt_basic(): x = [3, 7, 1, 1, -2, 5, 4, 6] cA, cD = pywt.dwt(x, 'db2') cA_expect = [5.65685425, 7.39923721, 0.22414387, 3.33677403, 7.77817459] cD_expect = [-2.44948974, -1.60368225, -4.44140056, -0.41361256, 1.22474487] assert_allclose(cA, cA_expect) assert_allclose(cD, cD_expect) x_roundtrip = pywt.idwt(cA, cD, 'db2') assert_allclose(x_roundtrip, x, rtol=1e-10) def test_dwt_input_error(): data = np.ones((16, 1)) assert_raises(ValueError, pywt.dwt, data, 'haar') cA, cD = pywt.dwt(data[:, 0], 'haar') assert_raises(ValueError, pywt.idwt, cA[:, np.newaxis], cD, 'haar') def test_dwt_wavelet_kwd(): x = np.array([3, 7, 1, 1, -2, 5, 4, 6]) w = pywt.Wavelet('sym3') cA, cD = pywt.dwt(x, wavelet=w, mode='cpd') cA_expect = [4.38354585, 3.80302657, 7.31813271, -0.58565539, 4.09727044, 7.81994027] cD_expect = [-1.33068221, -2.78795192, -3.16825651, -0.67715519, -0.09722957, -0.07045258] assert_allclose(cA, cA_expect) assert_allclose(cD, cD_expect) def test_dwt_coeff_len(): x = np.array([3, 7, 1, 1, -2, 5, 4, 6]) w = pywt.Wavelet('sym3') ln = pywt.dwt_coeff_len(data_len=len(x), filter_len=w.dec_len, mode='sym') assert_(ln == 6) ln_modes = [pywt.dwt_coeff_len(len(x), w.dec_len, mode) for mode in pywt.MODES.modes] assert_allclose(ln_modes, [6, 6, 6, 6, 6, 4]) def test_idwt_none_input(): # None input equals arrays of zeros of the right length res1 = pywt.idwt([1, 2, 0, 1], None, 'db2', 'sym') res2 = pywt.idwt([1, 2, 0, 1], [0, 0, 0, 0], 'db2', 'sym') assert_allclose(res1, res2, rtol=1e-15, atol=1e-15) res1 = pywt.idwt(None, [1, 2, 0, 1], 'db2', 'sym') res2 = pywt.idwt([0, 0, 0, 0], [1, 2, 0, 1], 'db2', 'sym') assert_allclose(res1, res2, rtol=1e-15, atol=1e-15) # Only one argument at a time can be None assert_raises(ValueError, pywt.idwt, None, None, 'db2', 'sym') def test_idwt_correct_size_kw(): res = pywt.idwt([1, 2, 3, 4, 5], [1, 2, 3, 4], 'db2', 'sym', correct_size=True) expected = [1.76776695, 0.61237244, 3.18198052, 0.61237244, 4.59619408, 0.61237244] assert_allclose(res, expected) assert_raises(ValueError, pywt.idwt, [1, 2, 3, 4, 5], [1, 2, 3, 4], 'db2', 'sym') assert_raises(ValueError, pywt.idwt, [1, 2, 3, 4], [1, 2, 3, 4, 5], 'db2', 'sym', correct_size=True) def test_idwt_invalid_input(): # Too short, min length is 4 for 'db4': assert_raises(ValueError, pywt.idwt, [1, 2, 4], [4, 1, 3], 'db4', 'sym') if __name__ == '__main__': run_module_suite() PyWavelets-0.3.0/pywt/tests/test_multidim.py0000664000175000017500000000731312556460247022750 0ustar rgommersrgommers00000000000000#!/usr/bin/env python from __future__ import division, print_function, absolute_import import numpy as np from numpy.testing import (run_module_suite, assert_allclose, assert_, assert_raises, assert_equal) import pywt def test_dwtn_input_error(): data = dict() assert_raises(ValueError, pywt.dwtn, data, 'haar') def test_3D_reconstruct(): # All dimensions even length so `take` does not need to be specified data = np.array([ [[0, 4, 1, 5, 1, 4], [0, 5, 26, 3, 2, 1], [5, 8, 2, 33, 4, 9], [2, 5, 19, 4, 19, 1]], [[1, 5, 1, 2, 3, 4], [7, 12, 6, 52, 7, 8], [2, 12, 3, 52, 6, 8], [5, 2, 6, 78, 12, 2]]]) wavelet = pywt.Wavelet('haar') d = pywt.dwtn(data, wavelet) # idwtn creates even-length shapes (2x dwtn size) original_shape = [slice(None, s) for s in data.shape] assert_allclose(data, pywt.idwtn(d, wavelet)[original_shape], rtol=1e-13, atol=1e-13) def test_idwtn_idwt2(): data = np.array([ [0, 4, 1, 5, 1, 4], [0, 5, 6, 3, 2, 1], [2, 5, 19, 4, 19, 1]]) wavelet = pywt.Wavelet('haar') LL, (HL, LH, HH) = pywt.dwt2(data, wavelet) d = {'aa': LL, 'da': HL, 'ad': LH, 'dd': HH} for mode in pywt.MODES.modes: assert_allclose(pywt.idwt2((LL, (HL, LH, HH)), wavelet, mode=mode), pywt.idwtn(d, wavelet, mode=mode), rtol=1e-14, atol=1e-14) def test_idwtn_missing(): # Test to confirm missing data behave as zeroes data = np.array([ [0, 4, 1, 5, 1, 4], [0, 5, 6, 3, 2, 1], [2, 5, 19, 4, 19, 1]]) wavelet = pywt.Wavelet('haar') LL, (HL, _, HH) = pywt.dwt2(data, wavelet) d = {'aa': LL, 'da': HL, 'dd': HH} assert_allclose(pywt.idwt2((LL, (HL, None, HH)), wavelet), pywt.idwtn(d, 'haar'), atol=1e-15) def test_idwtn_take(): data = np.array([ [[1, 4, 1, 5, 1, 4], [0, 5, 6, 3, 2, 1], [2, 5, 19, 4, 19, 1]], [[1, 5, 1, 2, 3, 4], [7, 12, 6, 52, 7, 8], [5, 2, 6, 78, 12, 2]]]) wavelet = pywt.Wavelet('haar') d = pywt.dwtn(data, wavelet) assert_(data.shape != pywt.idwtn(d, wavelet).shape) assert_allclose(data, pywt.idwtn(d, wavelet, take=data.shape), atol=1e-15) # Check shape for take not equal to data.shape data = np.random.randn(51, 17, 68) d = pywt.dwtn(data, wavelet) assert_equal((2, 2, 2), pywt.idwtn(d, wavelet, take=2).shape) assert_equal((52, 18, 68), pywt.idwtn(d, wavelet, take=0).shape) def test_ignore_invalid_keys(): data = np.array([ [0, 4, 1, 5, 1, 4], [0, 5, 6, 3, 2, 1], [2, 5, 19, 4, 19, 1]]) wavelet = pywt.Wavelet('haar') LL, (HL, LH, HH) = pywt.dwt2(data, wavelet) d = {'aa': LL, 'da': HL, 'ad': LH, 'dd': HH, 'foo': LH, 'a': HH} assert_allclose(pywt.idwt2((LL, (HL, LH, HH)), wavelet), pywt.idwtn(d, wavelet), atol=1e-15) def test_error_mismatched_size(): data = np.array([ [0, 4, 1, 5, 1, 4], [0, 5, 6, 3, 2, 1], [2, 5, 19, 4, 19, 1]]) wavelet = pywt.Wavelet('haar') LL, (HL, LH, HH) = pywt.dwt2(data, wavelet) # Pass/fail depends on first element being shorter than remaining ones so # set 3/4 to an incorrect size to maximize chances. Order of dict items # is random so may not trigger on every test run. Dict is constructed # inside idwtn function so no use using an OrderedDict here. LL = LL[:, :-1] LH = LH[:, :-1] HH = HH[:, :-1] d = {'aa': LL, 'da': HL, 'ad': LH, 'dd': HH} assert_raises(ValueError, pywt.idwtn, d, wavelet) if __name__ == '__main__': run_module_suite() PyWavelets-0.3.0/pywt/tests/test_wavelet.py0000664000175000017500000001510312556460247022567 0ustar rgommersrgommers00000000000000#!/usr/bin/env python from __future__ import division, print_function, absolute_import import numpy as np from numpy.testing import run_module_suite, assert_allclose, assert_ import pywt def test_wavelet_properties(): w = pywt.Wavelet('db3') # Name assert_(w.name == 'db3') assert_(w.short_family_name == 'db') assert_(w.family_name, 'Daubechies') # String representation fields = ('Family name', 'Short name', 'Filters length', 'Orthogonal', 'Biorthogonal', 'Symmetry') for field in fields: assert_(field in str(w)) # Filter coefficients dec_lo = [0.03522629188210, -0.08544127388224, -0.13501102001039, 0.45987750211933, 0.80689150931334, 0.33267055295096] dec_hi = [-0.33267055295096, 0.80689150931334, -0.45987750211933, -0.13501102001039, 0.08544127388224, 0.03522629188210] rec_lo = [0.33267055295096, 0.80689150931334, 0.45987750211933, -0.13501102001039, -0.08544127388224, 0.03522629188210] rec_hi = [0.03522629188210, 0.08544127388224, -0.13501102001039, -0.45987750211933, 0.80689150931334, -0.33267055295096] assert_allclose(w.dec_lo, dec_lo) assert_allclose(w.dec_hi, dec_hi) assert_allclose(w.rec_lo, rec_lo) assert_allclose(w.rec_hi, rec_hi) assert_(len(w.filter_bank) == 4) # Orthogonality assert_(w.orthogonal) assert_(w.biorthogonal) # Symmetry assert_(w.symmetry) # Vanishing moments assert_(w.vanishing_moments_phi == 0) assert_(w.vanishing_moments_psi == 3) class _CustomHaarFilterBank(object): @property def filter_bank(self): val = np.sqrt(2) / 2 return ([val]*2, [-val, val], [val]*2, [val, -val]) def test_custom_wavelet(): haar_custom1 = pywt.Wavelet('Custom Haar Wavelet', filter_bank=_CustomHaarFilterBank()) haar_custom1.orthogonal = True haar_custom1.biorthogonal = True val = np.sqrt(2) / 2 filter_bank = ([val]*2, [-val, val], [val]*2, [val, -val]) haar_custom2 = pywt.Wavelet('Custom Haar Wavelet', filter_bank=filter_bank) haar_custom2.orthogonal = True haar_custom2.biorthogonal = True def test_wavefun_sym3(): w = pywt.Wavelet('sym3') # sym3 is an orthogonal wavelet, so 3 outputs from wavefun phi, psi, x = w.wavefun(level=3) assert_(phi.size == 41) assert_(psi.size == 41) assert_(x.size == 41) assert_allclose(x, np.linspace(0, 5, num=x.size)) phi_expect = np.array([0.00000000e+00, 1.04132926e-01, 2.52574126e-01, 3.96525521e-01, 5.70356539e-01, 7.18934305e-01, 8.70293448e-01, 1.05363620e+00, 1.24921722e+00, 1.15296888e+00, 9.41669683e-01, 7.55875887e-01, 4.96118565e-01, 3.28293151e-01, 1.67624969e-01, -7.33690312e-02, -3.35452855e-01, -3.31221131e-01, -2.32061503e-01, -1.66854239e-01, -4.34091324e-02, -2.86152390e-02, -3.63563035e-02, 2.06034491e-02, 8.30280254e-02, 7.17779073e-02, 3.85914311e-02, 1.47527100e-02, -2.31896077e-02, -1.86122172e-02, -1.56211329e-03, -8.70615088e-04, 3.20760857e-03, 2.34142153e-03, -7.73737194e-04, -2.99879354e-04, 1.23636238e-04, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00]) psi_expect = np.array([0.00000000e+00, 1.10265752e-02, 2.67449277e-02, 4.19878574e-02, 6.03947231e-02, 7.61275365e-02, 9.21548684e-02, 1.11568926e-01, 1.32278887e-01, 6.45829680e-02, -3.97635130e-02, -1.38929884e-01, -2.62428322e-01, -3.62246804e-01, -4.62843343e-01, -5.89607507e-01, -7.25363076e-01, -3.36865858e-01, 2.67715108e-01, 8.40176767e-01, 1.55574430e+00, 1.18688954e+00, 4.20276324e-01, -1.51697311e-01, -9.42076108e-01, -7.93172332e-01, -3.26343710e-01, -1.24552779e-01, 2.12909254e-01, 1.75770320e-01, 1.47523075e-02, 8.22192707e-03, -3.02920592e-02, -2.21119497e-02, 7.30703025e-03, 2.83200488e-03, -1.16759765e-03, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00]) assert_allclose(phi, phi_expect) assert_allclose(psi, psi_expect) def test_wavefun_bior13(): w = pywt.Wavelet('bior1.3') # bior1.3 is not an orthogonal wavelet, so 5 outputs from wavefun phi_d, psi_d, phi_r, psi_r, x = w.wavefun(level=3) for arr in [phi_d, psi_d, phi_r, psi_r]: assert_(arr.size == 40) phi_d_expect = np.array([0., -0.00195313, 0.00195313, 0.01757813, 0.01367188, 0.00390625, -0.03515625, -0.12890625, -0.15234375, -0.125, -0.09375, -0.0625, 0.03125, 0.15234375, 0.37890625, 0.78515625, 0.99609375, 1.08203125, 1.13671875, 1.13671875, 1.08203125, 0.99609375, 0.78515625, 0.37890625, 0.15234375, 0.03125, -0.0625, -0.09375, -0.125, -0.15234375, -0.12890625, -0.03515625, 0.00390625, 0.01367188, 0.01757813, 0.00195313, -0.00195313, 0., 0., 0.]) phi_r_expect = np.zeros(x.size, dtype=np.float) phi_r_expect[15:23] = 1 psi_d_expect = np.array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0.015625, -0.015625, -0.140625, -0.109375, -0.03125, 0.28125, 1.03125, 1.21875, 1.125, 0.625, -0.625, -1.125, -1.21875, -1.03125, -0.28125, 0.03125, 0.109375, 0.140625, 0.015625, -0.015625, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]) psi_r_expect = np.zeros(x.size, dtype=np.float) psi_r_expect[7:15] = -0.125 psi_r_expect[15:19] = 1 psi_r_expect[19:23] = -1 psi_r_expect[23:31] = 0.125 assert_allclose(x, np.linspace(0, 5, x.size, endpoint=False)) assert_allclose(phi_d, phi_d_expect, rtol=1e-5, atol=1e-9) assert_allclose(phi_r, phi_r_expect, rtol=1e-10, atol=1e-12) assert_allclose(psi_d, psi_d_expect, rtol=1e-10, atol=1e-12) assert_allclose(psi_r, psi_r_expect, rtol=1e-10, atol=1e-12) if __name__ == '__main__': run_module_suite() PyWavelets-0.3.0/pywt/tests/test_matlab_compatibility.py0000664000175000017500000000375612556460247025324 0ustar rgommersrgommers00000000000000""" Test used to verify PyWavelets Discrete Wavelet Transform computation accuracy against MathWorks Wavelet Toolbox. """ from __future__ import division, print_function, absolute_import import numpy as np from numpy.testing import assert_, dec, run_module_suite import pywt _has_matlab = False try: from mlabwrap import mlab except ImportError: print("To run Matlab compatibility tests you need to have MathWorks " "MATLAB, MathWorks Wavelet Toolbox and mlabwrap Python extension " "installed.") _has_matlab = True @dec.skipif(_has_matlab) def test_accuracy(): # list of mode names in pywt and matlab modes = [('zpd', 'zpd'), ('cpd', 'sp0'), ('sym', 'sym'), ('ppd', 'ppd'), ('sp1', 'sp1'), ('per', 'per')] families = ('db', 'sym', 'coif', 'bior', 'rbio') wavelets = sum([pywt.wavelist(name) for name in families], []) for pmode, mmode in modes: for wavelet in wavelets: yield check_accuracy, pmode, mmode, wavelet def check_accuracy(pmode, mmode, wavelet): # max RMSE epsilon = 1.0e-10 w = pywt.Wavelet(wavelet) data_size = list(range(w.dec_len, 40)) + [100, 200, 500, 1000, 50000] np.random.seed(1234) for N in data_size: data = np.random.random(N) # PyWavelets result pa, pd = pywt.dwt(data, wavelet, pmode) # Matlab result ma, md = mlab.dwt(data, wavelet, 'mode', mmode, nout=2) ma = ma.flat md = md.flat # calculate error measures rms_a = np.sqrt(np.mean((pa-ma)**2)) rms_d = np.sqrt(np.mean((pd-md)**2)) msg = ('[RMS_A > EPSILON] for Mode: %s, Wavelet: %s, ' 'Length: %d, rms=%.3g' % (pmode, wavelet, len(data), rms_a)) assert_(rms_a < epsilon, msg=msg) msg = ('[RMS_D > EPSILON] for Mode: %s, Wavelet: %s, ' 'Length: %d, rms=%.3g' % (pmode, wavelet, len(data), rms_d)) assert_(rms_d < epsilon, msg=msg) if __name__ == '__main__': run_module_suite() PyWavelets-0.3.0/pywt/tests/test_perfect_reconstruction.py0000664000175000017500000000334212556460247025713 0ustar rgommersrgommers00000000000000#!/usr/bin/env python """ Verify DWT perfect reconstruction. """ from __future__ import division, print_function, absolute_import import numpy as np from numpy.testing import assert_, run_module_suite import pywt def test_perfect_reconstruction(): families = ('db', 'sym', 'coif', 'bior', 'rbio') wavelets = sum([pywt.wavelist(name) for name in families], []) # list of mode names in pywt and matlab modes = [('zpd', 'zpd'), ('cpd', 'sp0'), ('sym', 'sym'), ('ppd', 'ppd'), ('sp1', 'sp1'), ('per', 'per')] dtypes = (np.float32, np.float64) for wavelet in wavelets: for pmode, mmode in modes: for dt in dtypes: yield check_reconstruction, pmode, mmode, wavelet, dt def check_reconstruction(pmode, mmode, wavelet, dtype): data_size = list(range(2, 40)) + [100, 200, 500, 1000, 2000, 10000, 50000, 100000] np.random.seed(12345) # TODO: smoke testing - more failures for different seeds if dtype == np.float32: epsilon = 3e-7 else: # FIXME: limit was 5e-11, but gave failures. Investigate epsilon = 1e-8 for N in data_size: data = np.asarray(np.random.random(N), dtype) # compute dwt coefficients pa, pd = pywt.dwt(data, wavelet, pmode) # compute reconstruction rec = pywt.idwt(pa, pd, wavelet, pmode) if len(data) % 2: rec = rec[:len(data)] rms_rec = np.sqrt(np.mean((data-rec)**2)) msg = ('[RMS_REC > EPSILON] for Mode: %s, Wavelet: %s, ' 'Length: %d, rms=%.3g' % (pmode, wavelet, len(data), rms_rec)) assert_(rms_rec < epsilon, msg=msg) if __name__ == '__main__': run_module_suite() PyWavelets-0.3.0/pywt/tests/test_multilevel.py0000664000175000017500000000377012556460247023311 0ustar rgommersrgommers00000000000000#!/usr/bin/env python from __future__ import division, print_function, absolute_import import numpy as np from numpy.testing import (run_module_suite, assert_almost_equal, assert_allclose, assert_) import pywt def test_wavedec(): x = [3, 7, 1, 1, -2, 5, 4, 6] db1 = pywt.Wavelet('db1') cA3, cD3, cD2, cD1 = pywt.wavedec(x, db1) assert_almost_equal(cA3, [8.83883476]) assert_almost_equal(cD3, [-0.35355339]) assert_allclose(cD2, [4., -3.5]) assert_allclose(cD1, [-2.82842712, 0, -4.94974747, -1.41421356]) assert_(pywt.dwt_max_level(len(x), db1) == 3) def test_waverec(): x = [3, 7, 1, 1, -2, 5, 4, 6] coeffs = pywt.wavedec(x, 'db1') assert_allclose(pywt.waverec(coeffs, 'db1'), x, rtol=1e-12) def test_swt_decomposition(): x = [3, 7, 1, 3, -2, 6, 4, 6] db1 = pywt.Wavelet('db1') (cA2, cD2), (cA1, cD1) = pywt.swt(x, db1, level=2) assert_allclose(cA1, [7.07106781, 5.65685425, 2.82842712, 0.70710678, 2.82842712, 7.07106781, 7.07106781, 6.36396103]) assert_allclose(cD1, [-2.82842712, 4.24264069, -1.41421356, 3.53553391, -5.65685425, 1.41421356, -1.41421356, 2.12132034]) expected_cA2 = [7, 4.5, 4, 5.5, 7, 9.5, 10, 8.5] assert_allclose(cA2, expected_cA2, rtol=1e-12) expected_cD2 = [3, 3.5, 0, -4.5, -3, 0.5, 0, 0.5] assert_allclose(cD2, expected_cD2, rtol=1e-12, atol=1e-14) # level=1, start_level=1 decomposition should match level=2 res = pywt.swt(cA1, db1, level=1, start_level=1) cA2, cD2 = res[0] assert_allclose(cA2, expected_cA2, rtol=1e-12) assert_allclose(cD2, expected_cD2, rtol=1e-12, atol=1e-14) coeffs = pywt.swt(x, db1) assert_(len(coeffs) == 3) assert_(pywt.swt_max_level(len(x)) == 3) def test_wavedec2(): coeffs = pywt.wavedec2(np.ones((4, 4)), 'db1') assert_(len(coeffs) == 3) assert_allclose(pywt.waverec2(coeffs, 'db1'), np.ones((4, 4)), rtol=1e-12) if __name__ == '__main__': run_module_suite() PyWavelets-0.3.0/pywt/tests/test_doc.py0000664000175000017500000000102012556460247021656 0ustar rgommersrgommers00000000000000from __future__ import division, print_function, absolute_import import doctest import glob import os import unittest pdir = os.path.pardir docs_base = os.path.abspath(os.path.join(os.path.dirname(__file__), pdir, pdir, "doc", "source")) files = glob.glob(os.path.join(docs_base, "*.rst")) + \ glob.glob(os.path.join(docs_base, "*", "*.rst")) suite = doctest.DocFileSuite(*files, module_relative=False, encoding="utf-8") if __name__ == "__main__": unittest.TextTestRunner().run(suite) PyWavelets-0.3.0/pywt/tests/test_thresholding.py0000664000175000017500000000401712556460247023614 0ustar rgommersrgommers00000000000000from __future__ import division, print_function, absolute_import import numpy as np from numpy.testing import assert_allclose, run_module_suite import pywt def test_threshold(): # soft data = np.linspace(1, 4, 7) soft_result = [0., 0., 0., 0.5, 1., 1.5, 2.] assert_allclose(pywt.threshold(data, 2, 'soft'), np.array(soft_result), rtol=1e-12) assert_allclose(pywt.threshold(-data, 2, 'soft'), -np.array(soft_result), rtol=1e-12) assert_allclose(pywt.threshold([[1, 2]] * 2, 1, 'soft'), [[0, 1]] * 2, rtol=1e-12) assert_allclose(pywt.threshold([[1, 2]] * 2, 2, 'soft'), [[0, 0]] * 2, rtol=1e-12) # hard data = np.linspace(1, 4, 7) hard_result = [0., 0., 2., 2.5, 3., 3.5, 4.] assert_allclose(pywt.threshold(data, 2, 'hard'), np.array(hard_result), rtol=1e-12) assert_allclose(pywt.threshold(-data, 2, 'hard'), -np.array(hard_result), rtol=1e-12) assert_allclose(pywt.threshold([[1, 2]] * 2, 1, 'hard'), [[1, 2]] * 2, rtol=1e-12) assert_allclose(pywt.threshold([[1, 2]] * 2, 2, 'hard'), [[0, 2]] * 2, rtol=1e-12) # greater data = np.linspace(1, 4, 7) assert_allclose(pywt.threshold(data, 2, 'greater'), np.array([0., 0., 2., 2.5, 3., 3.5, 4.]), rtol=1e-12) assert_allclose(pywt.threshold([[1, 2]] * 2, 1, 'greater'), [[1, 2]] * 2, rtol=1e-12) assert_allclose(pywt.threshold([[1, 2]] * 2, 2, 'greater'), [[0, 2]] * 2, rtol=1e-12) # less data = np.linspace(1, 4, 7) assert_allclose(pywt.threshold(data, 2, 'less'), np.array([1., 1.5, 2., 0., 0., 0., 0.]), rtol=1e-12) assert_allclose(pywt.threshold([[1, 2]] * 2, 1, 'less'), [[1, 0]] * 2, rtol=1e-12) assert_allclose(pywt.threshold([[1, 2]] * 2, 2, 'less'), [[1, 2]] * 2, rtol=1e-12) if __name__ == '__main__': run_module_suite() PyWavelets-0.3.0/pywt/tests/test__pywt.py0000664000175000017500000000310212556460247022256 0ustar rgommersrgommers00000000000000#!/usr/bin/env python from __future__ import division, print_function, absolute_import import numpy as np from numpy.testing import run_module_suite, assert_allclose import pywt def test_upcoef_docstring(): data = [1, 2, 3, 4, 5, 6] (cA, cD) = pywt.dwt(data, 'db2', 'sp1') rec = pywt.upcoef('a', cA, 'db2') + pywt.upcoef('d', cD, 'db2') expect = [-0.25, -0.4330127, 1., 2., 3., 4., 5., 6., 1.78589838, -1.03108891] assert_allclose(rec, expect) n = len(data) rec = (pywt.upcoef('a', cA, 'db2', take=n) + pywt.upcoef('d', cD, 'db2', take=n)) assert_allclose(rec, data) def test_upcoef_reconstruct(): data = np.arange(3) a = pywt.downcoef('a', data, 'haar') d = pywt.downcoef('d', data, 'haar') rec = (pywt.upcoef('a', a, 'haar', take=3) + pywt.upcoef('d', d, 'haar', take=3)) assert_allclose(rec, data) def test_downcoef_multilevel(): r = np.random.randn(16) nlevels = 3 # calling with level=1 nlevels times a1 = r.copy() for i in range(nlevels): a1 = pywt.downcoef('a', a1, 'haar', level=1) # call with level=nlevels once a3 = pywt.downcoef('a', r, 'haar', level=3) assert_allclose(a1, a3) def test_upcoef_multilevel(): r = np.random.randn(4) nlevels = 3 # calling with level=1 nlevels times a1 = r.copy() for i in range(nlevels): a1 = pywt.upcoef('a', a1, 'haar', level=1) # call with level=nlevels once a3 = pywt.upcoef('a', r, 'haar', level=3) assert_allclose(a1, a3) if __name__ == '__main__': run_module_suite() PyWavelets-0.3.0/pywt/tests/test_wp.py0000664000175000017500000001067212556460247021554 0ustar rgommersrgommers00000000000000#!/usr/bin/env python from __future__ import division, print_function, absolute_import import numpy as np from numpy.testing import (run_module_suite, assert_allclose, assert_, assert_raises) import pywt def test_wavelet_packet_structure(): x = [1, 2, 3, 4, 5, 6, 7, 8] wp = pywt.WaveletPacket(data=x, wavelet='db1', mode='sym') assert_(wp.data == [1, 2, 3, 4, 5, 6, 7, 8]) assert_(wp.path == '') assert_(wp.level == 0) assert_(wp['ad'].maxlevel == 3) def test_traversing_wp_tree(): x = [1, 2, 3, 4, 5, 6, 7, 8] wp = pywt.WaveletPacket(data=x, wavelet='db1', mode='sym') assert_(wp.maxlevel == 3) # First level assert_allclose(wp['a'].data, np.array([2.12132034356, 4.949747468306, 7.778174593052, 10.606601717798]), rtol=1e-12) # Second level assert_allclose(wp['aa'].data, np.array([5., 13.]), rtol=1e-12) # Third level assert_allclose(wp['aaa'].data, np.array([12.727922061358]), rtol=1e-12) def test_acess_path(): x = [1, 2, 3, 4, 5, 6, 7, 8] wp = pywt.WaveletPacket(data=x, wavelet='db1', mode='sym') assert_(wp['a'].path == 'a') assert_(wp['aa'].path == 'aa') assert_(wp['aaa'].path == 'aaa') # Maximum level reached: assert_raises(IndexError, lambda: wp['aaaa'].path) # Wrong path assert_raises(ValueError, lambda: wp['ac'].path) def test_access_node_atributes(): x = [1, 2, 3, 4, 5, 6, 7, 8] wp = pywt.WaveletPacket(data=x, wavelet='db1', mode='sym') assert_allclose(wp['ad'].data, np.array([-2., -2.]), rtol=1e-12) assert_(wp['ad'].path == 'ad') assert_(wp['ad'].node_name == 'd') assert_(wp['ad'].parent.path == 'a') assert_(wp['ad'].level == 2) assert_(wp['ad'].maxlevel == 3) assert_(wp['ad'].mode == 'sym') def test_collecting_nodes(): x = [1, 2, 3, 4, 5, 6, 7, 8] wp = pywt.WaveletPacket(data=x, wavelet='db1', mode='sym') # All nodes in natural order assert_([node.path for node in wp.get_level(3, 'natural')] == ['aaa', 'aad', 'ada', 'add', 'daa', 'dad', 'dda', 'ddd']) # and in frequency order. assert_([node.path for node in wp.get_level(3, 'freq')] == ['aaa', 'aad', 'add', 'ada', 'dda', 'ddd', 'dad', 'daa']) def test_reconstructing_data(): x = [1, 2, 3, 4, 5, 6, 7, 8] wp = pywt.WaveletPacket(data=x, wavelet='db1', mode='sym') # Create another Wavelet Packet and feed it with some data. new_wp = pywt.WaveletPacket(data=None, wavelet='db1', mode='sym') new_wp['aa'] = wp['aa'].data new_wp['ad'] = [-2., -2.] # For convenience, :attr:`Node.data` gets automatically extracted # from the :class:`Node` object: new_wp['d'] = wp['d'] # Reconstruct data from aa, ad, and d packets. assert_allclose(new_wp.reconstruct(update=False), x, rtol=1e-12) # The node's :attr:`~Node.data` will not be updated assert_(new_wp.data is None) # When `update` is True: assert_allclose(new_wp.reconstruct(update=True), x, rtol=1e-12) assert_allclose(new_wp.data, np.arange(1, 9), rtol=1e-12) assert_([n.path for n in new_wp.get_leaf_nodes(False)] == ['aa', 'ad', 'd']) assert_([n.path for n in new_wp.get_leaf_nodes(True)] == ['aaa', 'aad', 'ada', 'add', 'daa', 'dad', 'dda', 'ddd']) def test_removing_nodes(): x = [1, 2, 3, 4, 5, 6, 7, 8] wp = pywt.WaveletPacket(data=x, wavelet='db1', mode='sym') wp.get_level(2) dataleafs = [n.data for n in wp.get_leaf_nodes(False)] expected = np.array([[5., 13.], [-2, -2], [-1, -1], [0, 0]]) for i in range(4): assert_allclose(dataleafs[i], expected[i, :], atol=1e-12) node = wp['ad'] del(wp['ad']) dataleafs = [n.data for n in wp.get_leaf_nodes(False)] expected = np.array([[5., 13.], [-1, -1], [0, 0]]) for i in range(3): assert_allclose(dataleafs[i], expected[i, :], atol=1e-12) wp.reconstruct() # The reconstruction is: assert_allclose(wp.reconstruct(), np.array([2., 3., 2., 3., 6., 7., 6., 7.]), rtol=1e-12) # Restore the data wp['ad'].data = node.data dataleafs = [n.data for n in wp.get_leaf_nodes(False)] expected = np.array([[5., 13.], [-2, -2], [-1, -1], [0, 0]]) for i in range(4): assert_allclose(dataleafs[i], expected[i, :], atol=1e-12) assert_allclose(wp.reconstruct(), np.arange(1, 9), rtol=1e-12) if __name__ == '__main__': run_module_suite() PyWavelets-0.3.0/pywt/tests/test_modes.py0000664000175000017500000000524512556460247022235 0ustar rgommersrgommers00000000000000#!/usr/bin/env python from __future__ import division, print_function, absolute_import import numpy as np from numpy.testing import (assert_raises, run_module_suite, assert_equal, assert_allclose) import pywt def test_available_modes(): modes = ['zpd', 'cpd', 'sym', 'ppd', 'sp1', 'per'] assert_equal(pywt.MODES.modes, modes) assert_equal(pywt.MODES.from_object('cpd'), 2) def test_invalid_modes(): x = np.arange(4) assert_raises(ValueError, pywt.dwt, x, 'db2', 'unknown') assert_raises(TypeError, pywt.dwt, x, 'db2', -1) assert_raises(TypeError, pywt.dwt, x, 'db2', 7) assert_raises(TypeError, pywt.dwt, x, 'db2', None) assert_raises(ValueError, pywt.MODES.from_object, 'unknown') assert_raises(ValueError, pywt.MODES.from_object, -1) assert_raises(ValueError, pywt.MODES.from_object, 7) assert_raises(TypeError, pywt.MODES.from_object, None) def test_dwt_idwt_allmodes(): # Test that :func:`dwt` and :func:`idwt` can be performed using every mode x = [1, 2, 1, 5, -1, 8, 4, 6] dwt_result_modes = { 'zpd': ([-0.03467518, 1.73309178, 3.40612438, 6.32928585, 6.95094948], [-0.12940952, -2.15599552, -5.95034847, -1.21545369, -1.8625013]), 'cpd': ([1.28480404, 1.73309178, 3.40612438, 6.32928585, 7.51935555], [-0.48296291, -2.15599552, -5.95034847, -1.21545369, 0.25881905]), 'sym': ([1.76776695, 1.73309178, 3.40612438, 6.32928585, 7.77817459], [-0.61237244, -2.15599552, -5.95034847, -1.21545369, 1.22474487]), 'ppd': ([6.9162743, 1.73309178, 3.40612438, 6.32928585, 6.9162743], [-1.99191082, -2.15599552, -5.95034847, -1.21545369, -1.99191082]), 'sp1': ([-0.51763809, 1.73309178, 3.40612438, 6.32928585, 7.45000519], [0, -2.15599552, -5.95034847, -1.21545369, 0]), 'per': ([4.053172, 3.05257099, 2.85381112, 8.42522221], [0.18946869, 4.18258152, 4.33737503, 2.60428326]) } for mode in pywt.MODES.modes: cA, cD = pywt.dwt(x, 'db2', mode) assert_allclose(cA, dwt_result_modes[mode][0], rtol=1e-7, atol=1e-8) assert_allclose(cD, dwt_result_modes[mode][1], rtol=1e-7, atol=1e-8) assert_allclose(pywt.idwt(cA, cD, 'db2', mode), x, rtol=1e-10) def test_default_mode(): # The default mode should be 'sym' x = [1, 2, 1, 5, -1, 8, 4, 6] cA, cD = pywt.dwt(x, 'db2') cA2, cD2 = pywt.dwt(x, 'db2', mode='sym') assert_allclose(cA, cA2) assert_allclose(cD, cD2) assert_allclose(pywt.idwt(cA, cD, 'db2'), x) if __name__ == '__main__': run_module_suite() PyWavelets-0.3.0/pywt/__init__.py0000664000175000017500000000123712556460247020461 0ustar rgommersrgommers00000000000000# -*- coding: utf-8 -*- # flake8: noqa # Copyright (c) 2006-2012 Filip Wasilewski # See COPYING for license details. """ Discrete forward and inverse wavelet transform, stationary wavelet transform, wavelet packets signal decomposition and reconstruction module. """ from __future__ import division, print_function, absolute_import from ._pywt import * from .functions import * from .multilevel import * from .multidim import * from .thresholding import * from .wavelet_packets import * __all__ = [s for s in dir() if not s.startswith('_')] from pywt.version import version as __version__ from numpy.testing import Tester test = Tester().test PyWavelets-0.3.0/pywt/setup.py0000664000175000017500000000141512556460247020060 0ustar rgommersrgommers00000000000000#!/usr/bin/env python from __future__ import division, print_function, absolute_import def configuration(parent_package='', top_path=None): from numpy.distutils.misc_util import Configuration import numpy as np config = Configuration('pywt', parent_package, top_path) config.add_data_dir('tests') # add main PyWavelets module config.add_extension( '_pywt', sources=["src/_pywt.c", "src/common.c", "src/convolution.c", "src/wavelets.c", "src/wt.c"], include_dirs=["src", np.get_include()], define_macros=[("PY_EXTENSION", None)], ) config.make_config_py() return config if __name__ == '__main__': from numpy.distutils.core import setup setup(**configuration(top_path='').todict()) PyWavelets-0.3.0/pywt/functions.py0000664000175000017500000001541512556460247020735 0ustar rgommersrgommers00000000000000# -*- coding: utf-8 -*- # Copyright (c) 2006-2012 Filip Wasilewski # See COPYING for license details. """ Other wavelet related functions. """ from __future__ import division, print_function, absolute_import import numpy as np from numpy.fft import fft from ._pywt import Wavelet __all__ = ["intwave", "centfrq", "scal2frq", "qmf", "orthfilt"] WAVELET_CLASSES = (Wavelet) def wavelet_for_name(name): if not isinstance(name, str): raise TypeError( "Wavelet name must be of string type, not %s" % type(name)) try: wavelet = Wavelet(name) except ValueError: raise ValueError("Invalid wavelet name - %s." % name) return wavelet def _integrate(arr, step): integral = np.cumsum(arr) integral *= step return integral def intwave(wavelet, precision=8): """ Integrate `psi` wavelet function from -Inf to x using the rectangle integration method. Parameters ---------- wavelet : Wavelet instance, str or tuple Wavelet to integrate. If a string, should be the name of a wavelet. If a tuple, should contain ``(wavelet function approx., x grid)``. precision : int, optional Precision that will be used for wavelet function approximation computed with the wavefun(level=precision) Wavelet's method (default: 8). Returns ------- [int_psi, x] : for orthogonal wavelets [int_psi_d, int_psi_r, x] : for other wavelets [int_function, x] : for (function approx., x grid) pair Notes ----- (function_approx, x) : Function to integrate on the x grid. Used instead of Wavelet object to allow custom wavelet functions. Examples -------- >>> import pywt >>> wavelet1 = pywt.Wavelet('db2') >>> [int_psi, x] = pywt.intwave(wavelet1, precision=5) >>> wavelet2 = pywt.Wavelet('bior1.3') >>> [int_psi_d, int_psi_r, x] = pywt.intwave(wavelet2, precision=5) """ # FIXME: this function should really use scipy.integrate.quad if isinstance(wavelet, tuple): psi, x = np.asarray(wavelet[0]), np.asarray(wavelet[1]) step = x[1] - x[0] return _integrate(psi, step), x else: if not isinstance(wavelet, WAVELET_CLASSES): wavelet = wavelet_for_name(wavelet) functions_approximations = wavelet.wavefun(precision) if len(functions_approximations) == 2: # continuous wavelet psi, x = functions_approximations step = x[1] - x[0] return _integrate(psi, step), x elif len(functions_approximations) == 3: # orthogonal wavelet phi, psi, x = functions_approximations step = x[1] - x[0] return _integrate(psi, step), x else: # biorthogonal wavelet phi_d, psi_d, phi_r, psi_r, x = functions_approximations step = x[1] - x[0] return _integrate(psi_d, step), _integrate(psi_r, step), x def centfrq(wavelet, precision=8): """ Computes the central frequency of the `psi` wavelet function. Parameters ---------- wavelet : Wavelet instance, str or tuple Wavelet to integrate. If a string, should be the name of a wavelet. If a tuple, should contain ``(wavelet function approx., x grid)``. precision : int, optional Precision that will be used for wavelet function approximation computed with the wavefun(level=precision) Wavelet's method (default: 8). Returns ------- scalar Notes ----- (function_approx, xgrid) : Function defined on xgrid. Used instead of Wavelet object to allow custom wavelet functions. """ # FIXME: `wavelet` handling should be identical to intwave, factor out if isinstance(wavelet, tuple): psi, x = np.asarray(wavelet[0]), np.asarray(wavelet[1]) else: if not isinstance(wavelet, WAVELET_CLASSES): wavelet = wavelet_for_name(wavelet) functions_approximations = wavelet.wavefun(precision) if len(functions_approximations) == 2: psi, x = functions_approximations else: # (psi, x) for (phi, psi, x) # (psi_d, x) for (phi_d, psi_d, phi_r, psi_r, x) psi, x = functions_approximations[1], functions_approximations[-1] domain = float(x[-1] - x[0]) assert domain > 0 index = np.argmax(abs(fft(psi)[1:])) + 2 if index > len(psi) / 2: index = len(psi) - index + 2 return 1.0 / (domain / (index - 1)) def scal2frq(wavelet, scale, delta, precision=8): """ Parameters ---------- wavelet : Wavelet instance, str or tuple Wavelet to integrate. If a string, should be the name of a wavelet. If a tuple, should contain ``(wavelet function approx., x grid)``. scale : scalar delta : scalar sampling precision : int, optional Precision that will be used for wavelet function approximation computed with ``wavelet.wavefun(level=precision)``. Default is 8. Returns ------- freq : scalar Notes ----- (function_approx, xgrid) : Function defined on xgrid. Used instead of Wavelet object to allow custom wavelet functions. """ return centfrq(wavelet, precision=precision) / (scale * delta) def qmf(filter): """ Returns the Quadrature Mirror Filter(QMF). The magnitude response of QMF is mirror image about `pi/2` of that of the input filter. Parameters ---------- filter : array_like Input filter for which QMF needs to be computed. Returns ------- qm_filter : ndarray Quadrature mirror of the input filter. """ qm_filter = np.array(filter)[::-1] qm_filter[1::2] = -qm_filter[1::2] return qm_filter def orthfilt(scaling_filter): """ Returns the orthogonal filter bank. The orthogonal filter bank consists of the HPFs and LPFs at decomposition and reconstruction stage for the input scaling filter. Parameters ---------- scaling_filter : array_like Input scaling filter (father wavelet). Returns ------- orth_filt_bank : tuple of 4 ndarrays The orthogonal filter bank of the input scaling filter in the order : 1] Decomposition LPF 2] Decomposition HPF 3] Reconstruction LPF 4] Reconstruction HPF """ if not (len(scaling_filter) % 2 == 0): raise ValueError("`scaling_filter` length has to be even.") scaling_filter = np.asarray(scaling_filter, dtype=np.float64) rec_lo = np.sqrt(2) * scaling_filter / np.sum(scaling_filter) dec_lo = rec_lo[::-1] rec_hi = qmf(rec_lo) dec_hi = rec_hi[::-1] orth_filt_bank = (dec_lo, dec_hi, rec_lo, rec_hi) return orth_filt_bank PyWavelets-0.3.0/pywt/multilevel.py0000664000175000017500000001314712556460247021107 0ustar rgommersrgommers00000000000000# -*- coding: utf-8 -*- # Copyright (c) 2006-2012 Filip Wasilewski # See COPYING for license details. """ Multilevel 1D and 2D Discrete Wavelet Transform and Inverse Discrete Wavelet Transform. """ from __future__ import division, print_function, absolute_import __all__ = ['wavedec', 'waverec', 'wavedec2', 'waverec2'] import numpy as np from ._pywt import Wavelet from ._pywt import dwt, idwt, dwt_max_level from .multidim import dwt2, idwt2 def wavedec(data, wavelet, mode='sym', level=None): """ Multilevel 1D Discrete Wavelet Transform of data. Parameters ---------- data: array_like Input data wavelet : Wavelet object or name string Wavelet to use mode : str, optional Signal extension mode, see MODES (default: 'sym') level : int, optional Decomposition level. If level is None (default) then it will be calculated using `dwt_max_level` function. Returns ------- [cA_n, cD_n, cD_n-1, ..., cD2, cD1] : list Ordered list of coefficients arrays where `n` denotes the level of decomposition. The first element (`cA_n`) of the result is approximation coefficients array and the following elements (`cD_n` - `cD_1`) are details coefficients arrays. Examples -------- >>> from pywt import multilevel >>> coeffs = multilevel.wavedec([1,2,3,4,5,6,7,8], 'db1', level=2) >>> cA2, cD2, cD1 = coeffs >>> cD1 array([-0.70710678, -0.70710678, -0.70710678, -0.70710678]) >>> cD2 array([-2., -2.]) >>> cA2 array([ 5., 13.]) """ if not isinstance(wavelet, Wavelet): wavelet = Wavelet(wavelet) if level is None: level = dwt_max_level(len(data), wavelet.dec_len) elif level < 0: raise ValueError( "Level value of %d is too low . Minimum level is 0." % level) coeffs_list = [] a = data for i in range(level): a, d = dwt(a, wavelet, mode) coeffs_list.append(d) coeffs_list.append(a) coeffs_list.reverse() return coeffs_list def waverec(coeffs, wavelet, mode='sym'): """ Multilevel 1D Inverse Discrete Wavelet Transform. Parameters ---------- coeffs : array_like Coefficients list [cAn, cDn, cDn-1, ..., cD2, cD1] wavelet : Wavelet object or name string Wavelet to use mode : str, optional Signal extension mode, see MODES (default: 'sym') Examples -------- >>> from pywt import multilevel >>> coeffs = multilevel.wavedec([1,2,3,4,5,6,7,8], 'db2', level=2) >>> multilevel.waverec(coeffs, 'db2') array([ 1., 2., 3., 4., 5., 6., 7., 8.]) """ if not isinstance(coeffs, (list, tuple)): raise ValueError("Expected sequence of coefficient arrays.") if len(coeffs) < 2: raise ValueError( "Coefficient list too short (minimum 2 arrays required).") a, ds = coeffs[0], coeffs[1:] for d in ds: a = idwt(a, d, wavelet, mode, 1) return a def wavedec2(data, wavelet, mode='sym', level=None): """ Multilevel 2D Discrete Wavelet Transform. Parameters ---------- data : ndarray 2D input data wavelet : Wavelet object or name string Wavelet to use mode : str, optional Signal extension mode, see MODES (default: 'sym') level : int, optional Decomposition level. If level is None (default) then it will be calculated using `dwt_max_level` function. Returns ------- [cAn, (cHn, cVn, cDn), ... (cH1, cV1, cD1)] : list Coefficients list Examples -------- >>> from pywt import multilevel >>> coeffs = multilevel.wavedec2(np.ones((4,4)), 'db1') >>> # Levels: >>> len(coeffs)-1 2 >>> multilevel.waverec2(coeffs, 'db1') array([[ 1., 1., 1., 1.], [ 1., 1., 1., 1.], [ 1., 1., 1., 1.], [ 1., 1., 1., 1.]]) """ data = np.asarray(data, np.float64) if data.ndim != 2: raise ValueError("Expected 2D input data.") if not isinstance(wavelet, Wavelet): wavelet = Wavelet(wavelet) if level is None: size = min(data.shape) level = dwt_max_level(size, wavelet.dec_len) elif level < 0: raise ValueError( "Level value of %d is too low . Minimum level is 0." % level) coeffs_list = [] a = data for i in range(level): a, ds = dwt2(a, wavelet, mode) coeffs_list.append(ds) coeffs_list.append(a) coeffs_list.reverse() return coeffs_list def waverec2(coeffs, wavelet, mode='sym'): """ Multilevel 2D Inverse Discrete Wavelet Transform. coeffs : array_like Coefficients list [cAn, (cHn, cVn, cDn), ... (cH1, cV1, cD1)] wavelet : Wavelet object or name string Wavelet to use mode : str, optional Signal extension mode, see MODES (default: 'sym') Returns ------- 2D array of reconstructed data. Examples -------- >>> from pywt import multilevel >>> coeffs = multilevel.wavedec2(np.ones((4,4)), 'db1') >>> # Levels: >>> len(coeffs)-1 2 >>> multilevel.waverec2(coeffs, 'db1') array([[ 1., 1., 1., 1.], [ 1., 1., 1., 1.], [ 1., 1., 1., 1.], [ 1., 1., 1., 1.]]) """ if not isinstance(coeffs, (list, tuple)): raise ValueError("Expected sequence of coefficient arrays.") if len(coeffs) < 2: raise ValueError( "Coefficient list too short (minimum 2 arrays required).") a, ds = coeffs[0], coeffs[1:] for d in ds: a = idwt2((a, d), wavelet, mode) return a PyWavelets-0.3.0/pywt/version.py0000664000175000017500000000035112556460270020377 0ustar rgommersrgommers00000000000000 # THIS FILE IS GENERATED FROM PYWAVELETS SETUP.PY short_version = '0.3.0' version = '0.3.0' full_version = '0.3.0' git_revision = 'd15317facfad1c567cbfd02e4cbb1de934a1d9ec' release = True if not release: version = full_version PyWavelets-0.3.0/pywt/multidim.py0000664000175000017500000002754012556460247020553 0ustar rgommersrgommers00000000000000# -*- coding: utf-8 -*- # Copyright (c) 2006-2012 Filip Wasilewski # See COPYING for license details. """ 2D Discrete Wavelet Transform and Inverse Discrete Wavelet Transform. """ from __future__ import division, print_function, absolute_import __all__ = ['dwt2', 'idwt2', 'swt2', 'dwtn', 'idwtn'] from itertools import cycle, product, repeat, islice import numpy as np from ._pywt import Wavelet, MODES from ._pywt import dwt, idwt, swt, downcoef, upcoef def dwt2(data, wavelet, mode='sym'): """ 2D Discrete Wavelet Transform. Parameters ---------- data : ndarray 2D array with input data wavelet : Wavelet object or name string Wavelet to use mode : str, optional Signal extension mode, see MODES (default: 'sym') Returns ------- (cA, (cH, cV, cD)) : tuple Approximation, horizontal detail, vertical detail and diagonal detail coefficients respectively. Examples -------- >>> import pywt >>> data = np.ones((4,4), dtype=np.float64) >>> coeffs = pywt.dwt2(data, 'haar') >>> cA, (cH, cV, cD) = coeffs >>> cA array([[ 2., 2.], [ 2., 2.]]) >>> cV array([[ 0., 0.], [ 0., 0.]]) """ data = np.asarray(data) if data.ndim != 2: raise ValueError("Expected 2-D data array") if not isinstance(wavelet, Wavelet): wavelet = Wavelet(wavelet) mode = MODES.from_object(mode) # filter rows H, L = [], [] for row in data: cA, cD = dwt(row, wavelet, mode) L.append(cA) H.append(cD) # filter columns H = np.transpose(H) L = np.transpose(L) LL, HL = [], [] for row in L: cA, cD = dwt(np.array(row, np.float64), wavelet, mode) LL.append(cA) HL.append(cD) LH, HH = [], [] for row in H: cA, cD = dwt(np.array(row, np.float64), wavelet, mode) LH.append(cA) HH.append(cD) # build result structure: (approx, # (horizontal, vertical, diagonal)) ret = (np.transpose(LL), (np.transpose(HL), np.transpose(LH), np.transpose(HH))) return ret def idwt2(coeffs, wavelet, mode='sym'): """ 2-D Inverse Discrete Wavelet Transform. Reconstructs data from coefficient arrays. Parameters ---------- coeffs : tuple (cA, (cH, cV, cD)) A tuple with approximation coefficients and three details coefficients 2D arrays like from `dwt2()` wavelet : Wavelet object or name string Wavelet to use mode : str, optional Signal extension mode, see MODES (default: 'sym') Examples -------- >>> import pywt >>> data = np.array([[1,2], [3,4]], dtype=np.float64) >>> coeffs = pywt.dwt2(data, 'haar') >>> pywt.idwt2(coeffs, 'haar') array([[ 1., 2.], [ 3., 4.]]) """ if len(coeffs) != 2 or len(coeffs[1]) != 3: raise ValueError("Invalid coeffs param") # L -low-pass data, H - high-pass data LL, (LH, HL, HH) = coeffs if LL is not None: LL = np.transpose(LL) if LH is not None: LH = np.transpose(LH) if HL is not None: HL = np.transpose(HL) if HH is not None: HH = np.transpose(HH) all_none = True for arr in (LL, LH, HL, HH): if arr is not None: all_none = False if arr.ndim != 2: raise TypeError("All input coefficients arrays must be 2D.") if all_none: raise ValueError( "At least one input coefficients array must not be None.") if not isinstance(wavelet, Wavelet): wavelet = Wavelet(wavelet) mode = MODES.from_object(mode) # idwt columns L = [] if LL is None and LH is None: L = None else: if LL is None: # IDWT can handle None input values - equals to zero-array LL = cycle([None]) if LH is None: # IDWT can handle None input values - equals to zero-array LH = cycle([None]) for rowL, rowH in zip(LL, LH): L.append(idwt(rowL, rowH, wavelet, mode, 1)) H = [] if HL is None and HH is None: H = None else: if HL is None: # IDWT can handle None input values - equals to zero-array HL = cycle([None]) if HH is None: # IDWT can handle None input values - equals to zero-array HH = cycle([None]) for rowL, rowH in zip(HL, HH): H.append(idwt(rowL, rowH, wavelet, mode, 1)) if L is not None: L = np.transpose(L) if H is not None: H = np.transpose(H) # idwt rows data = [] if L is None: # IDWT can handle None input values - equals to zero-array L = cycle([None]) if H is None: # IDWT can handle None input values - equals to zero-array H = cycle([None]) for rowL, rowH in zip(L, H): data.append(idwt(rowL, rowH, wavelet, mode, 1)) return np.array(data, np.float64) def dwtn(data, wavelet, mode='sym'): """ Single-level n-dimensional Discrete Wavelet Transform. Parameters ---------- data : ndarray n-dimensional array with input data. wavelet : Wavelet object or name string Wavelet to use. mode : str, optional Signal extension mode, see `MODES`. Default is 'sym'. Returns ------- coeffs : dict Results are arranged in a dictionary, where key specifies the transform type on each dimension and value is a n-dimensional coefficients array. For example, for a 2D case the result will look something like this:: {'aa': # A(LL) - approx. on 1st dim, approx. on 2nd dim 'ad': # V(LH) - approx. on 1st dim, det. on 2nd dim 'da': # H(HL) - det. on 1st dim, approx. on 2nd dim 'dd': # D(HH) - det. on 1st dim, det. on 2nd dim } """ data = np.asarray(data) dim = data.ndim if dim < 1: raise ValueError("Input data must be at least 1D") coeffs = [('', data)] def _downcoef(data, wavelet, mode, type): """Adapts pywt.downcoef call for apply_along_axis""" return downcoef(type, data, wavelet, mode, level=1) for axis in range(dim): new_coeffs = [] for subband, x in coeffs: new_coeffs.extend([ (subband + 'a', np.apply_along_axis(_downcoef, axis, x, wavelet, mode, 'a')), (subband + 'd', np.apply_along_axis(_downcoef, axis, x, wavelet, mode, 'd'))]) coeffs = new_coeffs return dict(coeffs) def idwtn(coeffs, wavelet, mode='sym', take=None): """ Single-level n-dimensional Discrete Wavelet Transform. Parameters ---------- coeffs: dict Dictionary as in output of `dwtn`. Missing or None items will be treated as zeroes. wavelet : Wavelet object or name string Wavelet to use mode : str, optional Signal extension mode used in the decomposition, see MODES (default: 'sym'). Overridden by `take`. take : int or iterable of int or None, optional Number of values to take from the center of the idwtn for each axis. If 0, the entire reverse transformation will be used, including parts generated from padding in the forward transform. If None (default), will be calculated from `mode` to be the size of the original data, rounded up to the nearest multiple of 2. Passed to `upcoef`. Returns ------- data: ndarray Original signal reconstructed from input data. """ if not isinstance(wavelet, Wavelet): wavelet = Wavelet(wavelet) mode = MODES.from_object(mode) # Ignore any invalid keys coeffs = dict((k, v) for k, v in coeffs.items() if set(k) <= set('ad')) dims = max(len(key) for key in coeffs.keys()) try: coeff_shapes = (v.shape for k, v in coeffs.items() if v is not None and len(k) == dims) coeff_shape = next(coeff_shapes) except StopIteration: raise ValueError("`coeffs` must contain at least one non-null wavelet " "band") if any(s != coeff_shape for s in coeff_shapes): raise ValueError("`coeffs` must all be of equal size (or None)") if take is not None: try: takes = list(islice(take, dims)) takes.reverse() except TypeError: takes = repeat(take, dims) else: # As in src/common.c if mode == MODES.per: takes = [2*s for s in reversed(coeff_shape)] else: takes = [2*s - wavelet.rec_len + 2 for s in reversed(coeff_shape)] def _upcoef(coeffs, wavelet, take, type): """Adapts pywt.upcoef call for apply_along_axis""" return upcoef(type, coeffs, wavelet, level=1, take=take) for axis, take in zip(reversed(range(dims)), takes): new_coeffs = {} new_keys = [''.join(coeff) for coeff in product('ad', repeat=axis)] for key in new_keys: L = coeffs.get(key + 'a') H = coeffs.get(key + 'd') if L is not None: L = np.apply_along_axis(_upcoef, axis, L, wavelet, take, 'a') if H is not None: H = np.apply_along_axis(_upcoef, axis, H, wavelet, take, 'd') if H is None and L is None: new_coeffs[key] = None elif H is None: new_coeffs[key] = L elif L is None: new_coeffs[key] = H else: new_coeffs[key] = L + H coeffs = new_coeffs return coeffs[''] def swt2(data, wavelet, level, start_level=0): """ 2D Stationary Wavelet Transform. Parameters ---------- data : ndarray 2D array with input data wavelet : Wavelet object or name string Wavelet to use level : int How many decomposition steps to perform start_level : int, optional The level at which the decomposition will start (default: 0) Returns ------- coeffs : list Approximation and details coefficients:: [ (cA_n, (cH_n, cV_n, cD_n) ), (cA_n+1, (cH_n+1, cV_n+1, cD_n+1) ), ..., (cA_n+level, (cH_n+level, cV_n+level, cD_n+level) ) ] where cA is approximation, cH is horizontal details, cV is vertical details, cD is diagonal details and n is start_level. """ data = np.asarray(data) if data.ndim != 2: raise ValueError("Expected 2D data array") if not isinstance(wavelet, Wavelet): wavelet = Wavelet(wavelet) ret = [] for i in range(start_level, start_level + level): # filter rows H, L = [], [] for row in data: cA, cD = swt(row, wavelet, level=1, start_level=i)[0] L.append(cA) H.append(cD) # filter columns H = np.transpose(H) L = np.transpose(L) LL, LH = [], [] for row in L: cA, cD = swt( np.array(row, np.float64), wavelet, level=1, start_level=i )[0] LL.append(cA) LH.append(cD) HL, HH = [], [] for row in H: cA, cD = swt( np.array(row, np.float64), wavelet, level=1, start_level=i )[0] HL.append(cA) HH.append(cD) # build result structure: (approx, (horizontal, vertical, diagonal)) approx = np.transpose(LL) ret.append((approx, (np.transpose(LH), np.transpose(HL), np.transpose(HH)))) # for next iteration data = approx return ret PyWavelets-0.3.0/pywt/thresholding.py0000664000175000017500000000657512556460247021426 0ustar rgommersrgommers00000000000000# -*- coding: utf-8 -*- # Copyright (c) 2006-2012 Filip Wasilewski # See COPYING for license details. """ The thresholding helper module implements the most popular signal thresholding functions. """ from __future__ import division, print_function, absolute_import __all__ = ['threshold'] import numpy as np def soft(data, value, substitute=0): data = np.asarray(data) magnitude = np.absolute(data) sign = np.sign(data) thresholded = (magnitude - value).clip(0) * sign cond = np.less(magnitude, value) return np.where(cond, substitute, thresholded) def hard(data, value, substitute=0): data = np.asarray(data) cond = np.less(np.absolute(data), value) return np.where(cond, substitute, data) def greater(data, value, substitute=0): data = np.asarray(data) return np.where(np.less(data, value), substitute, data) def less(data, value, substitute=0): data = np.asarray(data) return np.where(np.greater(data, value), substitute, data) thresholding_options = {'soft': soft, 'hard': hard, 'greater': greater, 'less': less} def threshold(data, value, mode='soft', substitute=0): """ Thresholds the input data depending on the mode argument. In ``soft`` thresholding, the data values where their absolute value is less than the value param are replaced with substitute. From the data values with absolute value greater or equal to the thresholding value, a quantity of ``(signum * value)`` is subtracted. In ``hard`` thresholding, the data values where their absolute value is less than the value param are replaced with substitute. Data values with absolute value greater or equal to the thresholding value stay untouched. In ``greater`` thresholding, the data is replaced with substitute where data is below the thresholding value. Greater data values pass untouched. In ``less`` thresholding, the data is replaced with substitute where data is above the thresholding value. Less data values pass untouched. Parameters ---------- data : array_like Numeric data. value : scalar Thresholding value. mode : {'soft', 'hard', 'greater', 'less'} Decides the type of thresholding to be applied on input data. Default is 'soft'. substitute : float, optional Substitute value (default: 0). Returns ------- output : array Thresholded array. Examples -------- >>> import pywt >>> data = np.linspace(1, 4, 7) >>> data array([ 1. , 1.5, 2. , 2.5, 3. , 3.5, 4. ]) >>> pywt.threshold(data, 2, 'soft') array([ 0. , 0. , 0. , 0.5, 1. , 1.5, 2. ]) >>> pywt.threshold(data, 2, 'hard') array([ 0. , 0. , 2. , 2.5, 3. , 3.5, 4. ]) >>> pywt.threshold(data, 2, 'greater') array([ 0. , 0. , 2. , 2.5, 3. , 3.5, 4. ]) >>> pywt.threshold(data, 2, 'less') array([ 1. , 1.5, 2. , 0. , 0. , 0. , 0. ]) """ try: return thresholding_options[mode](data, value, substitute) except KeyError: # Make sure error is always identical by sorting keys keys = ("'{0}'".format(key) for key in sorted(thresholding_options.keys())) raise ValueError("The mode parameter only takes values from: {0}." .format(', '.join(keys))) PyWavelets-0.3.0/pywt/src/0000775000175000017500000000000012556460303017125 5ustar rgommersrgommers00000000000000PyWavelets-0.3.0/pywt/src/wavelets.h0000664000175000017500000000365212556460247021145 0ustar rgommersrgommers00000000000000/* Copyright (c) 2006-2012 Filip Wasilewski */ /* See COPYING for license details. */ /* Wavelet struct */ #ifndef _WAVELETS_H_ #define _WAVELETS_H_ #include "common.h" /* Wavelet symmetry properties */ typedef enum { UNKNOWN = -1, ASYMMETRIC = 0, NEAR_SYMMETRIC = 1, SYMMETRIC = 2 } SYMMETRY; /* Wavelet structure holding pointers to filter arrays and property attributes */ typedef struct { double* dec_hi_double; /* highpass decomposition */ double* dec_lo_double; /* lowpass decomposition */ double* rec_hi_double; /* highpass reconstruction */ double* rec_lo_double; /* lowpass reconstruction */ float* dec_hi_float; float* dec_lo_float; float* rec_hi_float; float* rec_lo_float; index_t dec_len; /* length of decomposition filter */ index_t rec_len; /* length of reconstruction filter */ /* Wavelet properties */ int vanishing_moments_psi; int vanishing_moments_phi; index_t support_width; SYMMETRY symmetry; int orthogonal:1; int biorthogonal:1; int compact_support:1; /* * Set if filters arrays shouldn't be deallocated by * free_wavelet(Wavelet) func */ int _builtin:1; char* family_name; char* short_name; } Wavelet; /* * Allocate Wavelet struct and set its attributes * name - (currently) a character codename of a wavelet family * order - order of the wavelet (ie. coif3 has order 3) * * _builtin field is set to 1 */ Wavelet* wavelet(char name, int order); /* * Allocate blank Wavelet with zero-filled filters of given length * _builtin field is set to 0 */ Wavelet* blank_wavelet(index_t filters_length); /* Deep copy Wavelet */ Wavelet* copy_wavelet(Wavelet* base); /* * Free wavelet struct. Use this to free Wavelet allocated with * wavelet(...) or blank_wavelet(...) functions. */ void free_wavelet(Wavelet *wavelet); #endif PyWavelets-0.3.0/pywt/src/common.h0000664000175000017500000000527212556460247020603 0ustar rgommersrgommers00000000000000/* Copyright (c) 2006-2012 Filip Wasilewski */ /* See COPYING for license details. */ /* Common constants, typedefs and functions */ #ifndef _COMMON_H_ #define _COMMON_H_ #include #include #include /* ##### Typedefs ##### */ #ifdef PY_EXTENSION /* another declaration is in .c file generated by Pyrex */ #ifndef PY_SSIZE_T_CLEAN #define PY_SSIZE_T_CLEAN #include "Python.h" #include "structmember.h" #ifndef PY_LONG_LONG #define PY_LONG_LONG LONG_LONG #endif #else #include "Python.h" #endif typedef Py_ssize_t index_t; /* using Python's memory manager */ #define wtmalloc(size) PyMem_Malloc(size) #define wtfree(ptr) PyMem_Free(ptr) void *wtcalloc(size_t, size_t); #else typedef int index_t; /* standard c memory management */ #define wtmalloc(size) malloc(size) #define wtfree(ptr) free(ptr) #define wtcalloc(len, size) calloc(len, size) #endif typedef const index_t const_index_t; /* Signal extension modes */ typedef enum { MODE_INVALID = -1, MODE_ZEROPAD = 0, /* default, signal extended with zeros */ MODE_SYMMETRIC, /* signal extended symmetrically (mirror) */ MODE_CONSTANT_EDGE, /* signal extended with the border value */ MODE_SMOOTH, /* linear extrapolation (first derivative) */ MODE_PERIODIC, /* signal is treated as being periodic */ MODE_PERIODIZATION, /* signal is treated as being periodic, minimal output lenght */ MODE_MAX, MODE_ASYMMETRIC /* TODO */ } MODE; /* ##### Calculating buffer lengths for various operations ##### */ /* * Length of DWT coeffs for specified input data length, filter length and * signal extension mode. */ index_t dwt_buffer_length(index_t input_len, index_t filter_len, MODE mode); /* * Length of reconstructed signal for specified input coeffs length and filter * length. It is used for direct reconstruction from coefficients (normal * convolution of upsampled coeffs with filter). */ index_t reconstruction_buffer_length(index_t coeffs_len, index_t filter_len); /* * Length of IDWT reconstructed signal for specified input coeffs length, filter * length and extension mode. */ index_t idwt_buffer_length(index_t coeffs_len, index_t filter_len, MODE mode); /* Length of SWT coefficients for specified input signal length (== input_len) */ index_t swt_buffer_length(index_t input_len); /* Maximum useful level of DWT decomposition. */ int dwt_max_level(index_t input_len, index_t filter_len); /* Maximum useful level of SWT decomposition. */ int swt_max_level(index_t input_len); #endif PyWavelets-0.3.0/pywt/src/wavelets_coeffs.h0000664000175000017500000063733012556460270022474 0ustar rgommersrgommers00000000000000 /* ***************************************************************************** ** This file was autogenerated from a template DO NOT EDIT!!!! ** ** Changes should be made to the original source (.src) file ** ***************************************************************************** */ #line 1 #ifndef _WAVELETS_COEFFS_H_ #define _WAVELETS_COEFFS_H_ /* * Filters coefficients for selected wavelets * * Daubechies: db1 - db20 * Symlets: sym2 - sym20 * Coiflets: coif1 - coif5 * Biorthogonal: bior 1.1, 1.3, 1.5, * 2.2, 2.4, 2.6, 2.8, * 3.1, 3.3, 3.5, 3.7, 3.9, * 4.4, 5.5, 6.8 * Discrete Meyer wavelet *approximation*: dmey */ /* ignore warning about initializing floats from double values */ #ifdef _MSC_VER #pragma warning (disable:4305) #endif #line 26 static double db1_double[][2] = { {0.70710678118654757, 0.70710678118654757}, {-0.70710678118654757, 0.70710678118654757}, {0.70710678118654757, 0.70710678118654757}, {0.70710678118654757, -0.70710678118654757} }; static double db2_double[][4] = { {-0.12940952255092145, 0.22414386804185735, 0.83651630373746899, 0.48296291314469025}, {-0.48296291314469025, 0.83651630373746899, -0.22414386804185735, -0.12940952255092145}, {0.48296291314469025, 0.83651630373746899, 0.22414386804185735, -0.12940952255092145}, {-0.12940952255092145, -0.22414386804185735, 0.83651630373746899, -0.48296291314469025} }; static double db3_double[][6] = { {0.035226291882100656, -0.085441273882241486, -0.13501102001039084, 0.45987750211933132, 0.80689150931333875, 0.33267055295095688}, {-0.33267055295095688, 0.80689150931333875, -0.45987750211933132, -0.13501102001039084, 0.085441273882241486, 0.035226291882100656}, {0.33267055295095688, 0.80689150931333875, 0.45987750211933132, -0.13501102001039084, -0.085441273882241486, 0.035226291882100656}, {0.035226291882100656, 0.085441273882241486, -0.13501102001039084, -0.45987750211933132, 0.80689150931333875, -0.33267055295095688} }; static double db4_double[][8] = { {-0.010597401784997278, 0.032883011666982945, 0.030841381835986965, -0.18703481171888114, -0.027983769416983849, 0.63088076792959036, 0.71484657055254153, 0.23037781330885523}, {-0.23037781330885523, 0.71484657055254153, -0.63088076792959036, -0.027983769416983849, 0.18703481171888114, 0.030841381835986965, -0.032883011666982945, -0.010597401784997278}, {0.23037781330885523, 0.71484657055254153, 0.63088076792959036, -0.027983769416983849, -0.18703481171888114, 0.030841381835986965, 0.032883011666982945, -0.010597401784997278}, {-0.010597401784997278, -0.032883011666982945, 0.030841381835986965, 0.18703481171888114, -0.027983769416983849, -0.63088076792959036, 0.71484657055254153, -0.23037781330885523} }; static double db5_double[][10] = { {0.0033357252850015492, -0.012580751999015526, -0.0062414902130117052, 0.077571493840065148, -0.03224486958502952, -0.24229488706619015, 0.13842814590110342, 0.72430852843857441, 0.60382926979747287, 0.16010239797412501}, {-0.16010239797412501, 0.60382926979747287, -0.72430852843857441, 0.13842814590110342, 0.24229488706619015, -0.03224486958502952, -0.077571493840065148, -0.0062414902130117052, 0.012580751999015526, 0.0033357252850015492}, {0.16010239797412501, 0.60382926979747287, 0.72430852843857441, 0.13842814590110342, -0.24229488706619015, -0.03224486958502952, 0.077571493840065148, -0.0062414902130117052, -0.012580751999015526, 0.0033357252850015492}, {0.0033357252850015492, 0.012580751999015526, -0.0062414902130117052, -0.077571493840065148, -0.03224486958502952, 0.24229488706619015, 0.13842814590110342, -0.72430852843857441, 0.60382926979747287, -0.16010239797412501} }; static double db6_double[][12] = { {-0.0010773010849955799, 0.0047772575110106514, 0.0005538422009938016, -0.031582039318031156, 0.027522865530016288, 0.097501605587079362, -0.12976686756709563, -0.22626469396516913, 0.3152503517092432, 0.75113390802157753, 0.49462389039838539, 0.11154074335008017}, {-0.11154074335008017, 0.49462389039838539, -0.75113390802157753, 0.3152503517092432, 0.22626469396516913, -0.12976686756709563, -0.097501605587079362, 0.027522865530016288, 0.031582039318031156, 0.0005538422009938016, -0.0047772575110106514, -0.0010773010849955799}, {0.11154074335008017, 0.49462389039838539, 0.75113390802157753, 0.3152503517092432, -0.22626469396516913, -0.12976686756709563, 0.097501605587079362, 0.027522865530016288, -0.031582039318031156, 0.0005538422009938016, 0.0047772575110106514, -0.0010773010849955799}, {-0.0010773010849955799, -0.0047772575110106514, 0.0005538422009938016, 0.031582039318031156, 0.027522865530016288, -0.097501605587079362, -0.12976686756709563, 0.22626469396516913, 0.3152503517092432, -0.75113390802157753, 0.49462389039838539, -0.11154074335008017} }; static double db7_double[][14] = { {0.00035371380000103988, -0.0018016407039998328, 0.00042957797300470274, 0.012550998556013784, -0.01657454163101562, -0.038029936935034633, 0.080612609151065898, 0.071309219267050042, -0.22403618499416572, -0.14390600392910627, 0.4697822874053586, 0.72913209084655506, 0.39653931948230575, 0.077852054085062364}, {-0.077852054085062364, 0.39653931948230575, -0.72913209084655506, 0.4697822874053586, 0.14390600392910627, -0.22403618499416572, -0.071309219267050042, 0.080612609151065898, 0.038029936935034633, -0.01657454163101562, -0.012550998556013784, 0.00042957797300470274, 0.0018016407039998328, 0.00035371380000103988}, {0.077852054085062364, 0.39653931948230575, 0.72913209084655506, 0.4697822874053586, -0.14390600392910627, -0.22403618499416572, 0.071309219267050042, 0.080612609151065898, -0.038029936935034633, -0.01657454163101562, 0.012550998556013784, 0.00042957797300470274, -0.0018016407039998328, 0.00035371380000103988}, {0.00035371380000103988, 0.0018016407039998328, 0.00042957797300470274, -0.012550998556013784, -0.01657454163101562, 0.038029936935034633, 0.080612609151065898, -0.071309219267050042, -0.22403618499416572, 0.14390600392910627, 0.4697822874053586, -0.72913209084655506, 0.39653931948230575, -0.077852054085062364} }; static double db8_double[][16] = { {-0.00011747678400228192, 0.00067544940599855677, -0.00039174037299597711, -0.0048703529930106603, 0.0087460940470156547, 0.013981027917015516, -0.044088253931064719, -0.017369301002022108, 0.12874742662018601, 0.00047248457399797254, -0.28401554296242809, -0.015829105256023893, 0.58535468365486909, 0.67563073629801285, 0.31287159091446592, 0.054415842243081609}, {-0.054415842243081609, 0.31287159091446592, -0.67563073629801285, 0.58535468365486909, 0.015829105256023893, -0.28401554296242809, -0.00047248457399797254, 0.12874742662018601, 0.017369301002022108, -0.044088253931064719, -0.013981027917015516, 0.0087460940470156547, 0.0048703529930106603, -0.00039174037299597711, -0.00067544940599855677, -0.00011747678400228192}, {0.054415842243081609, 0.31287159091446592, 0.67563073629801285, 0.58535468365486909, -0.015829105256023893, -0.28401554296242809, 0.00047248457399797254, 0.12874742662018601, -0.017369301002022108, -0.044088253931064719, 0.013981027917015516, 0.0087460940470156547, -0.0048703529930106603, -0.00039174037299597711, 0.00067544940599855677, -0.00011747678400228192}, {-0.00011747678400228192, -0.00067544940599855677, -0.00039174037299597711, 0.0048703529930106603, 0.0087460940470156547, -0.013981027917015516, -0.044088253931064719, 0.017369301002022108, 0.12874742662018601, -0.00047248457399797254, -0.28401554296242809, 0.015829105256023893, 0.58535468365486909, -0.67563073629801285, 0.31287159091446592, -0.054415842243081609} }; static double db9_double[][18] = { {3.9347319995026124e-005, -0.00025196318899817888, 0.00023038576399541288, 0.0018476468829611268, -0.0042815036819047227, -0.004723204757894831, 0.022361662123515244, 0.00025094711499193845, -0.067632829059523988, 0.030725681478322865, 0.14854074933476008, -0.096840783220879037, -0.29327378327258685, 0.13319738582208895, 0.65728807803663891, 0.6048231236767786, 0.24383467463766728, 0.038077947363167282}, {-0.038077947363167282, 0.24383467463766728, -0.6048231236767786, 0.65728807803663891, -0.13319738582208895, -0.29327378327258685, 0.096840783220879037, 0.14854074933476008, -0.030725681478322865, -0.067632829059523988, -0.00025094711499193845, 0.022361662123515244, 0.004723204757894831, -0.0042815036819047227, -0.0018476468829611268, 0.00023038576399541288, 0.00025196318899817888, 3.9347319995026124e-005}, {0.038077947363167282, 0.24383467463766728, 0.6048231236767786, 0.65728807803663891, 0.13319738582208895, -0.29327378327258685, -0.096840783220879037, 0.14854074933476008, 0.030725681478322865, -0.067632829059523988, 0.00025094711499193845, 0.022361662123515244, -0.004723204757894831, -0.0042815036819047227, 0.0018476468829611268, 0.00023038576399541288, -0.00025196318899817888, 3.9347319995026124e-005}, {3.9347319995026124e-005, 0.00025196318899817888, 0.00023038576399541288, -0.0018476468829611268, -0.0042815036819047227, 0.004723204757894831, 0.022361662123515244, -0.00025094711499193845, -0.067632829059523988, -0.030725681478322865, 0.14854074933476008, 0.096840783220879037, -0.29327378327258685, -0.13319738582208895, 0.65728807803663891, -0.6048231236767786, 0.24383467463766728, -0.038077947363167282} }; static double db10_double[][20] = { {-1.3264203002354869e-005, 9.3588670001089845e-005, -0.0001164668549943862, -0.00068585669500468248, 0.0019924052949908499, 0.0013953517469940798, -0.010733175482979604, 0.0036065535669883944, 0.033212674058933238, -0.029457536821945671, -0.071394147165860775, 0.093057364603806592, 0.12736934033574265, -0.19594627437659665, -0.24984642432648865, 0.28117234366042648, 0.68845903945259213, 0.52720118893091983, 0.18817680007762133, 0.026670057900950818}, {-0.026670057900950818, 0.18817680007762133, -0.52720118893091983, 0.68845903945259213, -0.28117234366042648, -0.24984642432648865, 0.19594627437659665, 0.12736934033574265, -0.093057364603806592, -0.071394147165860775, 0.029457536821945671, 0.033212674058933238, -0.0036065535669883944, -0.010733175482979604, -0.0013953517469940798, 0.0019924052949908499, 0.00068585669500468248, -0.0001164668549943862, -9.3588670001089845e-005, -1.3264203002354869e-005}, {0.026670057900950818, 0.18817680007762133, 0.52720118893091983, 0.68845903945259213, 0.28117234366042648, -0.24984642432648865, -0.19594627437659665, 0.12736934033574265, 0.093057364603806592, -0.071394147165860775, -0.029457536821945671, 0.033212674058933238, 0.0036065535669883944, -0.010733175482979604, 0.0013953517469940798, 0.0019924052949908499, -0.00068585669500468248, -0.0001164668549943862, 9.3588670001089845e-005, -1.3264203002354869e-005}, {-1.3264203002354869e-005, -9.3588670001089845e-005, -0.0001164668549943862, 0.00068585669500468248, 0.0019924052949908499, -0.0013953517469940798, -0.010733175482979604, -0.0036065535669883944, 0.033212674058933238, 0.029457536821945671, -0.071394147165860775, -0.093057364603806592, 0.12736934033574265, 0.19594627437659665, -0.24984642432648865, -0.28117234366042648, 0.68845903945259213, -0.52720118893091983, 0.18817680007762133, -0.026670057900950818} }; static double db11_double[][22] = { {4.4942742772363519e-006, -3.4634984186983789e-005, 5.4439074699366381e-005, 0.00024915252355281426, -0.00089302325066623663, -0.00030859285881515924, 0.0049284176560587777, -0.0033408588730145018, -0.015364820906201324, 0.020840904360180039, 0.031335090219045313, -0.066438785695020222, -0.04647995511667613, 0.14981201246638268, 0.066043588196690886, -0.27423084681792875, -0.16227524502747828, 0.41196436894789695, 0.68568677491617847, 0.44989976435603013, 0.14406702115061959, 0.018694297761470441}, {-0.018694297761470441, 0.14406702115061959, -0.44989976435603013, 0.68568677491617847, -0.41196436894789695, -0.16227524502747828, 0.27423084681792875, 0.066043588196690886, -0.14981201246638268, -0.04647995511667613, 0.066438785695020222, 0.031335090219045313, -0.020840904360180039, -0.015364820906201324, 0.0033408588730145018, 0.0049284176560587777, 0.00030859285881515924, -0.00089302325066623663, -0.00024915252355281426, 5.4439074699366381e-005, 3.4634984186983789e-005, 4.4942742772363519e-006}, {0.018694297761470441, 0.14406702115061959, 0.44989976435603013, 0.68568677491617847, 0.41196436894789695, -0.16227524502747828, -0.27423084681792875, 0.066043588196690886, 0.14981201246638268, -0.04647995511667613, -0.066438785695020222, 0.031335090219045313, 0.020840904360180039, -0.015364820906201324, -0.0033408588730145018, 0.0049284176560587777, -0.00030859285881515924, -0.00089302325066623663, 0.00024915252355281426, 5.4439074699366381e-005, -3.4634984186983789e-005, 4.4942742772363519e-006}, {4.4942742772363519e-006, 3.4634984186983789e-005, 5.4439074699366381e-005, -0.00024915252355281426, -0.00089302325066623663, 0.00030859285881515924, 0.0049284176560587777, 0.0033408588730145018, -0.015364820906201324, -0.020840904360180039, 0.031335090219045313, 0.066438785695020222, -0.04647995511667613, -0.14981201246638268, 0.066043588196690886, 0.27423084681792875, -0.16227524502747828, -0.41196436894789695, 0.68568677491617847, -0.44989976435603013, 0.14406702115061959, -0.018694297761470441} }; static double db12_double[][24] = { {-1.5290717580684923e-006, 1.2776952219379579e-005, -2.4241545757030318e-005, -8.8504109208203182e-005, 0.00038865306282092672, 6.5451282125215034e-006, -0.0021795036186277044, 0.0022486072409952287, 0.0067114990087955486, -0.012840825198299882, -0.01221864906974642, 0.041546277495087637, 0.010849130255828966, -0.09643212009649671, 0.0053595696743599965, 0.18247860592758275, -0.023779257256064865, -0.31617845375277914, -0.044763885653777619, 0.51588647842780067, 0.65719872257929113, 0.37735513521420411, 0.10956627282118277, 0.013112257957229239}, {-0.013112257957229239, 0.10956627282118277, -0.37735513521420411, 0.65719872257929113, -0.51588647842780067, -0.044763885653777619, 0.31617845375277914, -0.023779257256064865, -0.18247860592758275, 0.0053595696743599965, 0.09643212009649671, 0.010849130255828966, -0.041546277495087637, -0.01221864906974642, 0.012840825198299882, 0.0067114990087955486, -0.0022486072409952287, -0.0021795036186277044, -6.5451282125215034e-006, 0.00038865306282092672, 8.8504109208203182e-005, -2.4241545757030318e-005, -1.2776952219379579e-005, -1.5290717580684923e-006}, {0.013112257957229239, 0.10956627282118277, 0.37735513521420411, 0.65719872257929113, 0.51588647842780067, -0.044763885653777619, -0.31617845375277914, -0.023779257256064865, 0.18247860592758275, 0.0053595696743599965, -0.09643212009649671, 0.010849130255828966, 0.041546277495087637, -0.01221864906974642, -0.012840825198299882, 0.0067114990087955486, 0.0022486072409952287, -0.0021795036186277044, 6.5451282125215034e-006, 0.00038865306282092672, -8.8504109208203182e-005, -2.4241545757030318e-005, 1.2776952219379579e-005, -1.5290717580684923e-006}, {-1.5290717580684923e-006, -1.2776952219379579e-005, -2.4241545757030318e-005, 8.8504109208203182e-005, 0.00038865306282092672, -6.5451282125215034e-006, -0.0021795036186277044, -0.0022486072409952287, 0.0067114990087955486, 0.012840825198299882, -0.01221864906974642, -0.041546277495087637, 0.010849130255828966, 0.09643212009649671, 0.0053595696743599965, -0.18247860592758275, -0.023779257256064865, 0.31617845375277914, -0.044763885653777619, -0.51588647842780067, 0.65719872257929113, -0.37735513521420411, 0.10956627282118277, -0.013112257957229239} }; static double db13_double[][26] = { {5.2200350984547998e-007, -4.7004164793608082e-006, 1.0441930571407941e-005, 3.0678537579324358e-005, -0.00016512898855650571, 4.9251525126285676e-005, 0.00093232613086724904, -0.0013156739118922766, -0.002761911234656831, 0.0072555894016171187, 0.0039239414487955773, -0.023831420710327809, 0.0023799722540522269, 0.056139477100276156, -0.026488406475345658, -0.10580761818792761, 0.072948933656788742, 0.17947607942935084, -0.12457673075080665, -0.31497290771138414, 0.086985726179645007, 0.58888957043121193, 0.61105585115878114, 0.31199632216043488, 0.082861243872901946, 0.0092021335389622788}, {-0.0092021335389622788, 0.082861243872901946, -0.31199632216043488, 0.61105585115878114, -0.58888957043121193, 0.086985726179645007, 0.31497290771138414, -0.12457673075080665, -0.17947607942935084, 0.072948933656788742, 0.10580761818792761, -0.026488406475345658, -0.056139477100276156, 0.0023799722540522269, 0.023831420710327809, 0.0039239414487955773, -0.0072555894016171187, -0.002761911234656831, 0.0013156739118922766, 0.00093232613086724904, -4.9251525126285676e-005, -0.00016512898855650571, -3.0678537579324358e-005, 1.0441930571407941e-005, 4.7004164793608082e-006, 5.2200350984547998e-007}, {0.0092021335389622788, 0.082861243872901946, 0.31199632216043488, 0.61105585115878114, 0.58888957043121193, 0.086985726179645007, -0.31497290771138414, -0.12457673075080665, 0.17947607942935084, 0.072948933656788742, -0.10580761818792761, -0.026488406475345658, 0.056139477100276156, 0.0023799722540522269, -0.023831420710327809, 0.0039239414487955773, 0.0072555894016171187, -0.002761911234656831, -0.0013156739118922766, 0.00093232613086724904, 4.9251525126285676e-005, -0.00016512898855650571, 3.0678537579324358e-005, 1.0441930571407941e-005, -4.7004164793608082e-006, 5.2200350984547998e-007}, {5.2200350984547998e-007, 4.7004164793608082e-006, 1.0441930571407941e-005, -3.0678537579324358e-005, -0.00016512898855650571, -4.9251525126285676e-005, 0.00093232613086724904, 0.0013156739118922766, -0.002761911234656831, -0.0072555894016171187, 0.0039239414487955773, 0.023831420710327809, 0.0023799722540522269, -0.056139477100276156, -0.026488406475345658, 0.10580761818792761, 0.072948933656788742, -0.17947607942935084, -0.12457673075080665, 0.31497290771138414, 0.086985726179645007, -0.58888957043121193, 0.61105585115878114, -0.31199632216043488, 0.082861243872901946, -0.0092021335389622788} }; static double db14_double[][28] = { {-1.7871399683109222e-007, 1.7249946753674012e-006, -4.3897049017804176e-006, -1.0337209184568496e-005, 6.875504252695734e-005, -4.1777245770370672e-005, -0.00038683194731287514, 0.00070802115423540481, 0.001061691085606874, -0.003849638868019787, -0.00074621898926387534, 0.012789493266340071, -0.0056150495303375755, -0.030185351540353976, 0.026981408307947971, 0.05523712625925082, -0.071548955503983505, -0.086748411568110598, 0.13998901658445695, 0.13839521386479153, -0.21803352999321651, -0.27168855227867705, 0.21867068775886594, 0.63118784910471981, 0.55430561794077093, 0.25485026779256437, 0.062364758849384874, 0.0064611534600864905}, {-0.0064611534600864905, 0.062364758849384874, -0.25485026779256437, 0.55430561794077093, -0.63118784910471981, 0.21867068775886594, 0.27168855227867705, -0.21803352999321651, -0.13839521386479153, 0.13998901658445695, 0.086748411568110598, -0.071548955503983505, -0.05523712625925082, 0.026981408307947971, 0.030185351540353976, -0.0056150495303375755, -0.012789493266340071, -0.00074621898926387534, 0.003849638868019787, 0.001061691085606874, -0.00070802115423540481, -0.00038683194731287514, 4.1777245770370672e-005, 6.875504252695734e-005, 1.0337209184568496e-005, -4.3897049017804176e-006, -1.7249946753674012e-006, -1.7871399683109222e-007}, {0.0064611534600864905, 0.062364758849384874, 0.25485026779256437, 0.55430561794077093, 0.63118784910471981, 0.21867068775886594, -0.27168855227867705, -0.21803352999321651, 0.13839521386479153, 0.13998901658445695, -0.086748411568110598, -0.071548955503983505, 0.05523712625925082, 0.026981408307947971, -0.030185351540353976, -0.0056150495303375755, 0.012789493266340071, -0.00074621898926387534, -0.003849638868019787, 0.001061691085606874, 0.00070802115423540481, -0.00038683194731287514, -4.1777245770370672e-005, 6.875504252695734e-005, -1.0337209184568496e-005, -4.3897049017804176e-006, 1.7249946753674012e-006, -1.7871399683109222e-007}, {-1.7871399683109222e-007, -1.7249946753674012e-006, -4.3897049017804176e-006, 1.0337209184568496e-005, 6.875504252695734e-005, 4.1777245770370672e-005, -0.00038683194731287514, -0.00070802115423540481, 0.001061691085606874, 0.003849638868019787, -0.00074621898926387534, -0.012789493266340071, -0.0056150495303375755, 0.030185351540353976, 0.026981408307947971, -0.05523712625925082, -0.071548955503983505, 0.086748411568110598, 0.13998901658445695, -0.13839521386479153, -0.21803352999321651, 0.27168855227867705, 0.21867068775886594, -0.63118784910471981, 0.55430561794077093, -0.25485026779256437, 0.062364758849384874, -0.0064611534600864905} }; static double db15_double[][30] = { {6.1333599133037138e-008, -6.3168823258794506e-007, 1.8112704079399406e-006, 3.3629871817363823e-006, -2.8133296266037558e-005, 2.579269915531323e-005, 0.00015589648992055726, -0.00035956524436229364, -0.00037348235413726472, 0.0019433239803823459, -0.00024175649075894543, -0.0064877345603061454, 0.0051010003604228726, 0.015083918027862582, -0.020810050169636805, -0.025767007328366939, 0.054780550584559995, 0.033877143923563204, -0.11112093603713753, -0.039666176555733602, 0.19014671400708816, 0.065282952848765688, -0.28888259656686216, -0.19320413960907623, 0.33900253545462167, 0.64581314035721027, 0.49263177170797529, 0.20602386398692688, 0.046743394892750617, 0.0045385373615773762}, {-0.0045385373615773762, 0.046743394892750617, -0.20602386398692688, 0.49263177170797529, -0.64581314035721027, 0.33900253545462167, 0.19320413960907623, -0.28888259656686216, -0.065282952848765688, 0.19014671400708816, 0.039666176555733602, -0.11112093603713753, -0.033877143923563204, 0.054780550584559995, 0.025767007328366939, -0.020810050169636805, -0.015083918027862582, 0.0051010003604228726, 0.0064877345603061454, -0.00024175649075894543, -0.0019433239803823459, -0.00037348235413726472, 0.00035956524436229364, 0.00015589648992055726, -2.579269915531323e-005, -2.8133296266037558e-005, -3.3629871817363823e-006, 1.8112704079399406e-006, 6.3168823258794506e-007, 6.1333599133037138e-008}, {0.0045385373615773762, 0.046743394892750617, 0.20602386398692688, 0.49263177170797529, 0.64581314035721027, 0.33900253545462167, -0.19320413960907623, -0.28888259656686216, 0.065282952848765688, 0.19014671400708816, -0.039666176555733602, -0.11112093603713753, 0.033877143923563204, 0.054780550584559995, -0.025767007328366939, -0.020810050169636805, 0.015083918027862582, 0.0051010003604228726, -0.0064877345603061454, -0.00024175649075894543, 0.0019433239803823459, -0.00037348235413726472, -0.00035956524436229364, 0.00015589648992055726, 2.579269915531323e-005, -2.8133296266037558e-005, 3.3629871817363823e-006, 1.8112704079399406e-006, -6.3168823258794506e-007, 6.1333599133037138e-008}, {6.1333599133037138e-008, 6.3168823258794506e-007, 1.8112704079399406e-006, -3.3629871817363823e-006, -2.8133296266037558e-005, -2.579269915531323e-005, 0.00015589648992055726, 0.00035956524436229364, -0.00037348235413726472, -0.0019433239803823459, -0.00024175649075894543, 0.0064877345603061454, 0.0051010003604228726, -0.015083918027862582, -0.020810050169636805, 0.025767007328366939, 0.054780550584559995, -0.033877143923563204, -0.11112093603713753, 0.039666176555733602, 0.19014671400708816, -0.065282952848765688, -0.28888259656686216, 0.19320413960907623, 0.33900253545462167, -0.64581314035721027, 0.49263177170797529, -0.20602386398692688, 0.046743394892750617, -0.0045385373615773762} }; static double db16_double[][32] = { {-2.1093396300980412e-008, 2.3087840868545578e-007, -7.3636567854418147e-007, -1.0435713423102517e-006, 1.133660866126152e-005, -1.394566898819319e-005, -6.103596621404321e-005, 0.00017478724522506327, 0.00011424152003843815, -0.00094102174935854332, 0.00040789698084934395, 0.00312802338120381, -0.0036442796214883506, -0.0069900145633907508, 0.013993768859843242, 0.010297659641009963, -0.036888397691556774, -0.0075889743686425939, 0.075924236044457791, -0.0062397227521562536, -0.13238830556335474, 0.027340263752899923, 0.21119069394696974, -0.02791820813292813, -0.32706331052747578, -0.089751089402363524, 0.44029025688580486, 0.63735633208298326, 0.43031272284545874, 0.1650642834886438, 0.034907714323629047, 0.0031892209253436892}, {-0.0031892209253436892, 0.034907714323629047, -0.1650642834886438, 0.43031272284545874, -0.63735633208298326, 0.44029025688580486, 0.089751089402363524, -0.32706331052747578, 0.02791820813292813, 0.21119069394696974, -0.027340263752899923, -0.13238830556335474, 0.0062397227521562536, 0.075924236044457791, 0.0075889743686425939, -0.036888397691556774, -0.010297659641009963, 0.013993768859843242, 0.0069900145633907508, -0.0036442796214883506, -0.00312802338120381, 0.00040789698084934395, 0.00094102174935854332, 0.00011424152003843815, -0.00017478724522506327, -6.103596621404321e-005, 1.394566898819319e-005, 1.133660866126152e-005, 1.0435713423102517e-006, -7.3636567854418147e-007, -2.3087840868545578e-007, -2.1093396300980412e-008}, {0.0031892209253436892, 0.034907714323629047, 0.1650642834886438, 0.43031272284545874, 0.63735633208298326, 0.44029025688580486, -0.089751089402363524, -0.32706331052747578, -0.02791820813292813, 0.21119069394696974, 0.027340263752899923, -0.13238830556335474, -0.0062397227521562536, 0.075924236044457791, -0.0075889743686425939, -0.036888397691556774, 0.010297659641009963, 0.013993768859843242, -0.0069900145633907508, -0.0036442796214883506, 0.00312802338120381, 0.00040789698084934395, -0.00094102174935854332, 0.00011424152003843815, 0.00017478724522506327, -6.103596621404321e-005, -1.394566898819319e-005, 1.133660866126152e-005, -1.0435713423102517e-006, -7.3636567854418147e-007, 2.3087840868545578e-007, -2.1093396300980412e-008}, {-2.1093396300980412e-008, -2.3087840868545578e-007, -7.3636567854418147e-007, 1.0435713423102517e-006, 1.133660866126152e-005, 1.394566898819319e-005, -6.103596621404321e-005, -0.00017478724522506327, 0.00011424152003843815, 0.00094102174935854332, 0.00040789698084934395, -0.00312802338120381, -0.0036442796214883506, 0.0069900145633907508, 0.013993768859843242, -0.010297659641009963, -0.036888397691556774, 0.0075889743686425939, 0.075924236044457791, 0.0062397227521562536, -0.13238830556335474, -0.027340263752899923, 0.21119069394696974, 0.02791820813292813, -0.32706331052747578, 0.089751089402363524, 0.44029025688580486, -0.63735633208298326, 0.43031272284545874, -0.1650642834886438, 0.034907714323629047, -0.0031892209253436892} }; static double db17_double[][34] = { {7.2674929685663697e-009, -8.4239484460081536e-008, 2.9577009333187617e-007, 3.0165496099963414e-007, -4.5059424772259631e-006, 6.9906009850812941e-006, 2.3186813798761639e-005, -8.2048032024582121e-005, -2.5610109566546042e-005, 0.00043946542776894542, -0.00032813251941022427, -0.001436845304805, 0.0023012052421511474, 0.0029679966915180638, -0.0086029215203478147, -0.0030429899813869555, 0.022733676583919053, -0.0032709555358783646, -0.046922438389378908, 0.022312336178011833, 0.081105986654080822, -0.057091419631858077, -0.12681569177849797, 0.10113548917744287, 0.19731058956508457, -0.12659975221599248, -0.32832074836418546, 0.027314970403312946, 0.5183157640572823, 0.61099661568502728, 0.37035072415288578, 0.13121490330791097, 0.025985393703623173, 0.0022418070010387899}, {-0.0022418070010387899, 0.025985393703623173, -0.13121490330791097, 0.37035072415288578, -0.61099661568502728, 0.5183157640572823, -0.027314970403312946, -0.32832074836418546, 0.12659975221599248, 0.19731058956508457, -0.10113548917744287, -0.12681569177849797, 0.057091419631858077, 0.081105986654080822, -0.022312336178011833, -0.046922438389378908, 0.0032709555358783646, 0.022733676583919053, 0.0030429899813869555, -0.0086029215203478147, -0.0029679966915180638, 0.0023012052421511474, 0.001436845304805, -0.00032813251941022427, -0.00043946542776894542, -2.5610109566546042e-005, 8.2048032024582121e-005, 2.3186813798761639e-005, -6.9906009850812941e-006, -4.5059424772259631e-006, -3.0165496099963414e-007, 2.9577009333187617e-007, 8.4239484460081536e-008, 7.2674929685663697e-009}, {0.0022418070010387899, 0.025985393703623173, 0.13121490330791097, 0.37035072415288578, 0.61099661568502728, 0.5183157640572823, 0.027314970403312946, -0.32832074836418546, -0.12659975221599248, 0.19731058956508457, 0.10113548917744287, -0.12681569177849797, -0.057091419631858077, 0.081105986654080822, 0.022312336178011833, -0.046922438389378908, -0.0032709555358783646, 0.022733676583919053, -0.0030429899813869555, -0.0086029215203478147, 0.0029679966915180638, 0.0023012052421511474, -0.001436845304805, -0.00032813251941022427, 0.00043946542776894542, -2.5610109566546042e-005, -8.2048032024582121e-005, 2.3186813798761639e-005, 6.9906009850812941e-006, -4.5059424772259631e-006, 3.0165496099963414e-007, 2.9577009333187617e-007, -8.4239484460081536e-008, 7.2674929685663697e-009}, {7.2674929685663697e-009, 8.4239484460081536e-008, 2.9577009333187617e-007, -3.0165496099963414e-007, -4.5059424772259631e-006, -6.9906009850812941e-006, 2.3186813798761639e-005, 8.2048032024582121e-005, -2.5610109566546042e-005, -0.00043946542776894542, -0.00032813251941022427, 0.001436845304805, 0.0023012052421511474, -0.0029679966915180638, -0.0086029215203478147, 0.0030429899813869555, 0.022733676583919053, 0.0032709555358783646, -0.046922438389378908, -0.022312336178011833, 0.081105986654080822, 0.057091419631858077, -0.12681569177849797, -0.10113548917744287, 0.19731058956508457, 0.12659975221599248, -0.32832074836418546, -0.027314970403312946, 0.5183157640572823, -0.61099661568502728, 0.37035072415288578, -0.13121490330791097, 0.025985393703623173, -0.0022418070010387899} }; static double db18_double[][36] = { {-2.5079344549419292e-009, 3.0688358630370302e-008, -1.1760987670250871e-007, -7.691632689865049e-008, 1.7687129836228861e-006, -3.3326344788769603e-006, -8.5206025374234635e-006, 3.7412378807308472e-005, -1.5359171230213409e-007, -0.00019864855231101547, 0.0002135815619103188, 0.00062846568296447147, -0.0013405962983313922, -0.0011187326669886426, 0.0049433436054565939, 0.00011863003387493042, -0.013051480946517112, 0.0062621679544386608, 0.026670705926689853, -0.023733210395336858, -0.04452614190225633, 0.057051247739058272, 0.064887216212358198, -0.10675224665906288, -0.092331884150304119, 0.16708131276294505, 0.14953397556500755, -0.21648093400458224, -0.29365404073579809, 0.14722311196952223, 0.57180165488712198, 0.57182680776508177, 0.31467894133619284, 0.10358846582214751, 0.019288531724094969, 0.0015763102184365595}, {-0.0015763102184365595, 0.019288531724094969, -0.10358846582214751, 0.31467894133619284, -0.57182680776508177, 0.57180165488712198, -0.14722311196952223, -0.29365404073579809, 0.21648093400458224, 0.14953397556500755, -0.16708131276294505, -0.092331884150304119, 0.10675224665906288, 0.064887216212358198, -0.057051247739058272, -0.04452614190225633, 0.023733210395336858, 0.026670705926689853, -0.0062621679544386608, -0.013051480946517112, -0.00011863003387493042, 0.0049433436054565939, 0.0011187326669886426, -0.0013405962983313922, -0.00062846568296447147, 0.0002135815619103188, 0.00019864855231101547, -1.5359171230213409e-007, -3.7412378807308472e-005, -8.5206025374234635e-006, 3.3326344788769603e-006, 1.7687129836228861e-006, 7.691632689865049e-008, -1.1760987670250871e-007, -3.0688358630370302e-008, -2.5079344549419292e-009}, {0.0015763102184365595, 0.019288531724094969, 0.10358846582214751, 0.31467894133619284, 0.57182680776508177, 0.57180165488712198, 0.14722311196952223, -0.29365404073579809, -0.21648093400458224, 0.14953397556500755, 0.16708131276294505, -0.092331884150304119, -0.10675224665906288, 0.064887216212358198, 0.057051247739058272, -0.04452614190225633, -0.023733210395336858, 0.026670705926689853, 0.0062621679544386608, -0.013051480946517112, 0.00011863003387493042, 0.0049433436054565939, -0.0011187326669886426, -0.0013405962983313922, 0.00062846568296447147, 0.0002135815619103188, -0.00019864855231101547, -1.5359171230213409e-007, 3.7412378807308472e-005, -8.5206025374234635e-006, -3.3326344788769603e-006, 1.7687129836228861e-006, -7.691632689865049e-008, -1.1760987670250871e-007, 3.0688358630370302e-008, -2.5079344549419292e-009}, {-2.5079344549419292e-009, -3.0688358630370302e-008, -1.1760987670250871e-007, 7.691632689865049e-008, 1.7687129836228861e-006, 3.3326344788769603e-006, -8.5206025374234635e-006, -3.7412378807308472e-005, -1.5359171230213409e-007, 0.00019864855231101547, 0.0002135815619103188, -0.00062846568296447147, -0.0013405962983313922, 0.0011187326669886426, 0.0049433436054565939, -0.00011863003387493042, -0.013051480946517112, -0.0062621679544386608, 0.026670705926689853, 0.023733210395336858, -0.04452614190225633, -0.057051247739058272, 0.064887216212358198, 0.10675224665906288, -0.092331884150304119, -0.16708131276294505, 0.14953397556500755, 0.21648093400458224, -0.29365404073579809, -0.14722311196952223, 0.57180165488712198, -0.57182680776508177, 0.31467894133619284, -0.10358846582214751, 0.019288531724094969, -0.0015763102184365595} }; static double db19_double[][38] = { {8.6668488390344833e-010, -1.1164020670405678e-008, 4.6369377758023682e-008, 1.4470882988040879e-008, -6.8627556577981102e-007, 1.5319314766978769e-006, 3.0109643163099385e-006, -1.6640176297224622e-005, 5.1059504870906939e-006, 8.7112704672504432e-005, -0.00012460079173506306, -0.00026067613568119951, 0.0007358025205041731, 0.00034180865344939543, -0.0026875518007344408, 0.00076895435922424884, 0.0070407473670804953, -0.0058669222811121953, -0.013988388678695632, 0.019375549889114482, 0.021623767409452484, -0.045674226277784918, -0.026501236250778635, 0.086906755555450702, 0.027584350624887129, -0.14278569504021468, -0.033518541903202262, 0.21234974330662043, 0.074652269708066474, -0.28583863175723145, -0.22809139421653665, 0.26089495265212009, 0.60170454913009164, 0.52443637746688621, 0.26438843174202237, 0.08127811326580564, 0.01428109845082521, 0.0011086697631864314}, {-0.0011086697631864314, 0.01428109845082521, -0.08127811326580564, 0.26438843174202237, -0.52443637746688621, 0.60170454913009164, -0.26089495265212009, -0.22809139421653665, 0.28583863175723145, 0.074652269708066474, -0.21234974330662043, -0.033518541903202262, 0.14278569504021468, 0.027584350624887129, -0.086906755555450702, -0.026501236250778635, 0.045674226277784918, 0.021623767409452484, -0.019375549889114482, -0.013988388678695632, 0.0058669222811121953, 0.0070407473670804953, -0.00076895435922424884, -0.0026875518007344408, -0.00034180865344939543, 0.0007358025205041731, 0.00026067613568119951, -0.00012460079173506306, -8.7112704672504432e-005, 5.1059504870906939e-006, 1.6640176297224622e-005, 3.0109643163099385e-006, -1.5319314766978769e-006, -6.8627556577981102e-007, -1.4470882988040879e-008, 4.6369377758023682e-008, 1.1164020670405678e-008, 8.6668488390344833e-010}, {0.0011086697631864314, 0.01428109845082521, 0.08127811326580564, 0.26438843174202237, 0.52443637746688621, 0.60170454913009164, 0.26089495265212009, -0.22809139421653665, -0.28583863175723145, 0.074652269708066474, 0.21234974330662043, -0.033518541903202262, -0.14278569504021468, 0.027584350624887129, 0.086906755555450702, -0.026501236250778635, -0.045674226277784918, 0.021623767409452484, 0.019375549889114482, -0.013988388678695632, -0.0058669222811121953, 0.0070407473670804953, 0.00076895435922424884, -0.0026875518007344408, 0.00034180865344939543, 0.0007358025205041731, -0.00026067613568119951, -0.00012460079173506306, 8.7112704672504432e-005, 5.1059504870906939e-006, -1.6640176297224622e-005, 3.0109643163099385e-006, 1.5319314766978769e-006, -6.8627556577981102e-007, 1.4470882988040879e-008, 4.6369377758023682e-008, -1.1164020670405678e-008, 8.6668488390344833e-010}, {8.6668488390344833e-010, 1.1164020670405678e-008, 4.6369377758023682e-008, -1.4470882988040879e-008, -6.8627556577981102e-007, -1.5319314766978769e-006, 3.0109643163099385e-006, 1.6640176297224622e-005, 5.1059504870906939e-006, -8.7112704672504432e-005, -0.00012460079173506306, 0.00026067613568119951, 0.0007358025205041731, -0.00034180865344939543, -0.0026875518007344408, -0.00076895435922424884, 0.0070407473670804953, 0.0058669222811121953, -0.013988388678695632, -0.019375549889114482, 0.021623767409452484, 0.045674226277784918, -0.026501236250778635, -0.086906755555450702, 0.027584350624887129, 0.14278569504021468, -0.033518541903202262, -0.21234974330662043, 0.074652269708066474, 0.28583863175723145, -0.22809139421653665, -0.26089495265212009, 0.60170454913009164, -0.52443637746688621, 0.26438843174202237, -0.08127811326580564, 0.01428109845082521, -0.0011086697631864314} }; static double db20_double[][40] = { {-2.9988364896157532e-010, 4.05612705554717e-009, -1.8148432482976221e-008, 2.0143220235374613e-010, 2.633924226266962e-007, -6.847079596993149e-007, -1.0119940100181473e-006, 7.2412482876637907e-006, -4.3761438621821972e-006, -3.7105861833906152e-005, 6.7742808283730477e-005, 0.00010153288973669777, -0.0003851047486990061, -5.3497598443404532e-005, 0.0013925596193045254, -0.00083156217287724745, -0.003581494259744107, 0.0044205423867663502, 0.0067216273018096935, -0.013810526137727442, -0.0087893249245557647, 0.032294299530119162, 0.0058746818113949465, -0.061722899624668884, 0.0056322468576854544, 0.10229171917513397, -0.024716827337521424, -0.15545875070604531, 0.039850246458519104, 0.22829105082013823, -0.016727088308801888, -0.32678680043353758, -0.13921208801128787, 0.36150229873889705, 0.61049323893785579, 0.47269618531033147, 0.21994211355113222, 0.063423780459005291, 0.010549394624937735, 0.00077995361366591117}, {-0.00077995361366591117, 0.010549394624937735, -0.063423780459005291, 0.21994211355113222, -0.47269618531033147, 0.61049323893785579, -0.36150229873889705, -0.13921208801128787, 0.32678680043353758, -0.016727088308801888, -0.22829105082013823, 0.039850246458519104, 0.15545875070604531, -0.024716827337521424, -0.10229171917513397, 0.0056322468576854544, 0.061722899624668884, 0.0058746818113949465, -0.032294299530119162, -0.0087893249245557647, 0.013810526137727442, 0.0067216273018096935, -0.0044205423867663502, -0.003581494259744107, 0.00083156217287724745, 0.0013925596193045254, 5.3497598443404532e-005, -0.0003851047486990061, -0.00010153288973669777, 6.7742808283730477e-005, 3.7105861833906152e-005, -4.3761438621821972e-006, -7.2412482876637907e-006, -1.0119940100181473e-006, 6.847079596993149e-007, 2.633924226266962e-007, -2.0143220235374613e-010, -1.8148432482976221e-008, -4.05612705554717e-009, -2.9988364896157532e-010}, {0.00077995361366591117, 0.010549394624937735, 0.063423780459005291, 0.21994211355113222, 0.47269618531033147, 0.61049323893785579, 0.36150229873889705, -0.13921208801128787, -0.32678680043353758, -0.016727088308801888, 0.22829105082013823, 0.039850246458519104, -0.15545875070604531, -0.024716827337521424, 0.10229171917513397, 0.0056322468576854544, -0.061722899624668884, 0.0058746818113949465, 0.032294299530119162, -0.0087893249245557647, -0.013810526137727442, 0.0067216273018096935, 0.0044205423867663502, -0.003581494259744107, -0.00083156217287724745, 0.0013925596193045254, -5.3497598443404532e-005, -0.0003851047486990061, 0.00010153288973669777, 6.7742808283730477e-005, -3.7105861833906152e-005, -4.3761438621821972e-006, 7.2412482876637907e-006, -1.0119940100181473e-006, -6.847079596993149e-007, 2.633924226266962e-007, 2.0143220235374613e-010, -1.8148432482976221e-008, 4.05612705554717e-009, -2.9988364896157532e-010}, {-2.9988364896157532e-010, -4.05612705554717e-009, -1.8148432482976221e-008, -2.0143220235374613e-010, 2.633924226266962e-007, 6.847079596993149e-007, -1.0119940100181473e-006, -7.2412482876637907e-006, -4.3761438621821972e-006, 3.7105861833906152e-005, 6.7742808283730477e-005, -0.00010153288973669777, -0.0003851047486990061, 5.3497598443404532e-005, 0.0013925596193045254, 0.00083156217287724745, -0.003581494259744107, -0.0044205423867663502, 0.0067216273018096935, 0.013810526137727442, -0.0087893249245557647, -0.032294299530119162, 0.0058746818113949465, 0.061722899624668884, 0.0056322468576854544, -0.10229171917513397, -0.024716827337521424, 0.15545875070604531, 0.039850246458519104, -0.22829105082013823, -0.016727088308801888, 0.32678680043353758, -0.13921208801128787, -0.36150229873889705, 0.61049323893785579, -0.47269618531033147, 0.21994211355113222, -0.063423780459005291, 0.010549394624937735, -0.00077995361366591117} }; static double sym2_double[][4] = { {-0.12940952255092145, 0.22414386804185735, 0.83651630373746899, 0.48296291314469025}, {-0.48296291314469025, 0.83651630373746899, -0.22414386804185735, -0.12940952255092145}, {0.48296291314469025, 0.83651630373746899, 0.22414386804185735, -0.12940952255092145}, {-0.12940952255092145, -0.22414386804185735, 0.83651630373746899, -0.48296291314469025} }; static double sym3_double[][6] = { {0.035226291882100656, -0.085441273882241486, -0.13501102001039084, 0.45987750211933132, 0.80689150931333875, 0.33267055295095688}, {-0.33267055295095688, 0.80689150931333875, -0.45987750211933132, -0.13501102001039084, 0.085441273882241486, 0.035226291882100656}, {0.33267055295095688, 0.80689150931333875, 0.45987750211933132, -0.13501102001039084, -0.085441273882241486, 0.035226291882100656}, {0.035226291882100656, 0.085441273882241486, -0.13501102001039084, -0.45987750211933132, 0.80689150931333875, -0.33267055295095688} }; static double sym4_double[][8] = { {-0.075765714789273325, -0.02963552764599851, 0.49761866763201545, 0.80373875180591614, 0.29785779560527736, -0.099219543576847216, -0.012603967262037833, 0.032223100604042702}, {-0.032223100604042702, -0.012603967262037833, 0.099219543576847216, 0.29785779560527736, -0.80373875180591614, 0.49761866763201545, 0.02963552764599851, -0.075765714789273325}, {0.032223100604042702, -0.012603967262037833, -0.099219543576847216, 0.29785779560527736, 0.80373875180591614, 0.49761866763201545, -0.02963552764599851, -0.075765714789273325}, {-0.075765714789273325, 0.02963552764599851, 0.49761866763201545, -0.80373875180591614, 0.29785779560527736, 0.099219543576847216, -0.012603967262037833, -0.032223100604042702} }; static double sym5_double[][10] = { {0.027333068345077982, 0.029519490925774643, -0.039134249302383094, 0.1993975339773936, 0.72340769040242059, 0.63397896345821192, 0.016602105764522319, -0.17532808990845047, -0.021101834024758855, 0.019538882735286728}, {-0.019538882735286728, -0.021101834024758855, 0.17532808990845047, 0.016602105764522319, -0.63397896345821192, 0.72340769040242059, -0.1993975339773936, -0.039134249302383094, -0.029519490925774643, 0.027333068345077982}, {0.019538882735286728, -0.021101834024758855, -0.17532808990845047, 0.016602105764522319, 0.63397896345821192, 0.72340769040242059, 0.1993975339773936, -0.039134249302383094, 0.029519490925774643, 0.027333068345077982}, {0.027333068345077982, -0.029519490925774643, -0.039134249302383094, -0.1993975339773936, 0.72340769040242059, -0.63397896345821192, 0.016602105764522319, 0.17532808990845047, -0.021101834024758855, -0.019538882735286728} }; static double sym6_double[][12] = { {0.015404109327027373, 0.0034907120842174702, -0.11799011114819057, -0.048311742585632998, 0.49105594192674662, 0.787641141030194, 0.3379294217276218, -0.072637522786462516, -0.021060292512300564, 0.044724901770665779, 0.0017677118642428036, -0.007800708325034148}, {0.007800708325034148, 0.0017677118642428036, -0.044724901770665779, -0.021060292512300564, 0.072637522786462516, 0.3379294217276218, -0.787641141030194, 0.49105594192674662, 0.048311742585632998, -0.11799011114819057, -0.0034907120842174702, 0.015404109327027373}, {-0.007800708325034148, 0.0017677118642428036, 0.044724901770665779, -0.021060292512300564, -0.072637522786462516, 0.3379294217276218, 0.787641141030194, 0.49105594192674662, -0.048311742585632998, -0.11799011114819057, 0.0034907120842174702, 0.015404109327027373}, {0.015404109327027373, -0.0034907120842174702, -0.11799011114819057, 0.048311742585632998, 0.49105594192674662, -0.787641141030194, 0.3379294217276218, 0.072637522786462516, -0.021060292512300564, -0.044724901770665779, 0.0017677118642428036, 0.007800708325034148} }; static double sym7_double[][14] = { {0.0026818145682578781, -0.0010473848886829163, -0.01263630340325193, 0.03051551316596357, 0.067892693501372697, -0.049552834937127255, 0.017441255086855827, 0.5361019170917628, 0.76776431700316405, 0.28862963175151463, -0.14004724044296152, -0.10780823770381774, 0.0040102448715336634, 0.010268176708511255}, {-0.010268176708511255, 0.0040102448715336634, 0.10780823770381774, -0.14004724044296152, -0.28862963175151463, 0.76776431700316405, -0.5361019170917628, 0.017441255086855827, 0.049552834937127255, 0.067892693501372697, -0.03051551316596357, -0.01263630340325193, 0.0010473848886829163, 0.0026818145682578781}, {0.010268176708511255, 0.0040102448715336634, -0.10780823770381774, -0.14004724044296152, 0.28862963175151463, 0.76776431700316405, 0.5361019170917628, 0.017441255086855827, -0.049552834937127255, 0.067892693501372697, 0.03051551316596357, -0.01263630340325193, -0.0010473848886829163, 0.0026818145682578781}, {0.0026818145682578781, 0.0010473848886829163, -0.01263630340325193, -0.03051551316596357, 0.067892693501372697, 0.049552834937127255, 0.017441255086855827, -0.5361019170917628, 0.76776431700316405, -0.28862963175151463, -0.14004724044296152, 0.10780823770381774, 0.0040102448715336634, -0.010268176708511255} }; static double sym8_double[][16] = { {-0.0033824159510061256, -0.00054213233179114812, 0.031695087811492981, 0.0076074873249176054, -0.14329423835080971, -0.061273359067658524, 0.48135965125837221, 0.77718575170052351, 0.3644418948353314, -0.051945838107709037, -0.027219029917056003, 0.049137179673607506, 0.0038087520138906151, -0.014952258337048231, -0.0003029205147213668, 0.0018899503327594609}, {-0.0018899503327594609, -0.0003029205147213668, 0.014952258337048231, 0.0038087520138906151, -0.049137179673607506, -0.027219029917056003, 0.051945838107709037, 0.3644418948353314, -0.77718575170052351, 0.48135965125837221, 0.061273359067658524, -0.14329423835080971, -0.0076074873249176054, 0.031695087811492981, 0.00054213233179114812, -0.0033824159510061256}, {0.0018899503327594609, -0.0003029205147213668, -0.014952258337048231, 0.0038087520138906151, 0.049137179673607506, -0.027219029917056003, -0.051945838107709037, 0.3644418948353314, 0.77718575170052351, 0.48135965125837221, -0.061273359067658524, -0.14329423835080971, 0.0076074873249176054, 0.031695087811492981, -0.00054213233179114812, -0.0033824159510061256}, {-0.0033824159510061256, 0.00054213233179114812, 0.031695087811492981, -0.0076074873249176054, -0.14329423835080971, 0.061273359067658524, 0.48135965125837221, -0.77718575170052351, 0.3644418948353314, 0.051945838107709037, -0.027219029917056003, -0.049137179673607506, 0.0038087520138906151, 0.014952258337048231, -0.0003029205147213668, -0.0018899503327594609} }; static double sym9_double[][18] = { {0.0014009155259146807, 0.00061978088898558676, -0.013271967781817119, -0.01152821020767923, 0.03022487885827568, 0.00058346274612580684, -0.054568958430834071, 0.238760914607303, 0.717897082764412, 0.61733844914093583, 0.035272488035271894, -0.19155083129728512, -0.018233770779395985, 0.06207778930288603, 0.0088592674934004842, -0.010264064027633142, -0.00047315449868008311, 0.0010694900329086053}, {-0.0010694900329086053, -0.00047315449868008311, 0.010264064027633142, 0.0088592674934004842, -0.06207778930288603, -0.018233770779395985, 0.19155083129728512, 0.035272488035271894, -0.61733844914093583, 0.717897082764412, -0.238760914607303, -0.054568958430834071, -0.00058346274612580684, 0.03022487885827568, 0.01152821020767923, -0.013271967781817119, -0.00061978088898558676, 0.0014009155259146807}, {0.0010694900329086053, -0.00047315449868008311, -0.010264064027633142, 0.0088592674934004842, 0.06207778930288603, -0.018233770779395985, -0.19155083129728512, 0.035272488035271894, 0.61733844914093583, 0.717897082764412, 0.238760914607303, -0.054568958430834071, 0.00058346274612580684, 0.03022487885827568, -0.01152821020767923, -0.013271967781817119, 0.00061978088898558676, 0.0014009155259146807}, {0.0014009155259146807, -0.00061978088898558676, -0.013271967781817119, 0.01152821020767923, 0.03022487885827568, -0.00058346274612580684, -0.054568958430834071, -0.238760914607303, 0.717897082764412, -0.61733844914093583, 0.035272488035271894, 0.19155083129728512, -0.018233770779395985, -0.06207778930288603, 0.0088592674934004842, 0.010264064027633142, -0.00047315449868008311, -0.0010694900329086053} }; static double sym10_double[][20] = { {0.00077015980911449011, 9.5632670722894754e-005, -0.0086412992770224222, -0.0014653825813050513, 0.045927239231092203, 0.011609893903711381, -0.15949427888491757, -0.070880535783243853, 0.47169066693843925, 0.7695100370211071, 0.38382676106708546, -0.035536740473817552, -0.0319900568824278, 0.049994972077376687, 0.0057649120335819086, -0.02035493981231129, -0.00080435893201654491, 0.0045931735853118284, 5.7036083618494284e-005, -0.00045932942100465878}, {0.00045932942100465878, 5.7036083618494284e-005, -0.0045931735853118284, -0.00080435893201654491, 0.02035493981231129, 0.0057649120335819086, -0.049994972077376687, -0.0319900568824278, 0.035536740473817552, 0.38382676106708546, -0.7695100370211071, 0.47169066693843925, 0.070880535783243853, -0.15949427888491757, -0.011609893903711381, 0.045927239231092203, 0.0014653825813050513, -0.0086412992770224222, -9.5632670722894754e-005, 0.00077015980911449011}, {-0.00045932942100465878, 5.7036083618494284e-005, 0.0045931735853118284, -0.00080435893201654491, -0.02035493981231129, 0.0057649120335819086, 0.049994972077376687, -0.0319900568824278, -0.035536740473817552, 0.38382676106708546, 0.7695100370211071, 0.47169066693843925, -0.070880535783243853, -0.15949427888491757, 0.011609893903711381, 0.045927239231092203, -0.0014653825813050513, -0.0086412992770224222, 9.5632670722894754e-005, 0.00077015980911449011}, {0.00077015980911449011, -9.5632670722894754e-005, -0.0086412992770224222, 0.0014653825813050513, 0.045927239231092203, -0.011609893903711381, -0.15949427888491757, 0.070880535783243853, 0.47169066693843925, -0.7695100370211071, 0.38382676106708546, 0.035536740473817552, -0.0319900568824278, -0.049994972077376687, 0.0057649120335819086, 0.02035493981231129, -0.00080435893201654491, -0.0045931735853118284, 5.7036083618494284e-005, 0.00045932942100465878} }; static double sym11_double[][22] = { {0.00017172195069934854, -3.8795655736158566e-005, -0.0017343662672978692, 0.00058835273539699145, 0.0065124956747714497, -0.0098579348287897942, -0.024080841595864003, 0.0370374159788594, 0.069976799610734136, -0.022832651022562687, 0.097198394458909473, 0.57202297801008706, 0.73034354908839572, 0.23768990904924897, -0.2046547944958006, -0.14460234370531561, 0.035266759564466552, 0.043000190681552281, -0.0020034719001093887, -0.0063896036664548919, 0.00011053509764272153, 0.00048926361026192387}, {-0.00048926361026192387, 0.00011053509764272153, 0.0063896036664548919, -0.0020034719001093887, -0.043000190681552281, 0.035266759564466552, 0.14460234370531561, -0.2046547944958006, -0.23768990904924897, 0.73034354908839572, -0.57202297801008706, 0.097198394458909473, 0.022832651022562687, 0.069976799610734136, -0.0370374159788594, -0.024080841595864003, 0.0098579348287897942, 0.0065124956747714497, -0.00058835273539699145, -0.0017343662672978692, 3.8795655736158566e-005, 0.00017172195069934854}, {0.00048926361026192387, 0.00011053509764272153, -0.0063896036664548919, -0.0020034719001093887, 0.043000190681552281, 0.035266759564466552, -0.14460234370531561, -0.2046547944958006, 0.23768990904924897, 0.73034354908839572, 0.57202297801008706, 0.097198394458909473, -0.022832651022562687, 0.069976799610734136, 0.0370374159788594, -0.024080841595864003, -0.0098579348287897942, 0.0065124956747714497, 0.00058835273539699145, -0.0017343662672978692, -3.8795655736158566e-005, 0.00017172195069934854}, {0.00017172195069934854, 3.8795655736158566e-005, -0.0017343662672978692, -0.00058835273539699145, 0.0065124956747714497, 0.0098579348287897942, -0.024080841595864003, -0.0370374159788594, 0.069976799610734136, 0.022832651022562687, 0.097198394458909473, -0.57202297801008706, 0.73034354908839572, -0.23768990904924897, -0.2046547944958006, 0.14460234370531561, 0.035266759564466552, -0.043000190681552281, -0.0020034719001093887, 0.0063896036664548919, 0.00011053509764272153, -0.00048926361026192387} }; static double sym12_double[][24] = { {0.00011196719424656033, -1.1353928041541452e-005, -0.0013497557555715387, 0.00018021409008538188, 0.007414965517654251, -0.0014089092443297553, -0.024220722675013445, 0.0075537806116804775, 0.049179318299660837, -0.035848830736954392, -0.022162306170337816, 0.39888597239022, 0.76347909778365719, 0.46274103121927235, -0.07833262231634322, -0.17037069723886492, 0.01530174062247884, 0.057804179445505657, -0.0026043910313322326, -0.014589836449234145, 0.00030764779631059454, 0.0023502976141834648, -1.8158078862617515e-005, -0.00017906658697508691}, {0.00017906658697508691, -1.8158078862617515e-005, -0.0023502976141834648, 0.00030764779631059454, 0.014589836449234145, -0.0026043910313322326, -0.057804179445505657, 0.01530174062247884, 0.17037069723886492, -0.07833262231634322, -0.46274103121927235, 0.76347909778365719, -0.39888597239022, -0.022162306170337816, 0.035848830736954392, 0.049179318299660837, -0.0075537806116804775, -0.024220722675013445, 0.0014089092443297553, 0.007414965517654251, -0.00018021409008538188, -0.0013497557555715387, 1.1353928041541452e-005, 0.00011196719424656033}, {-0.00017906658697508691, -1.8158078862617515e-005, 0.0023502976141834648, 0.00030764779631059454, -0.014589836449234145, -0.0026043910313322326, 0.057804179445505657, 0.01530174062247884, -0.17037069723886492, -0.07833262231634322, 0.46274103121927235, 0.76347909778365719, 0.39888597239022, -0.022162306170337816, -0.035848830736954392, 0.049179318299660837, 0.0075537806116804775, -0.024220722675013445, -0.0014089092443297553, 0.007414965517654251, 0.00018021409008538188, -0.0013497557555715387, -1.1353928041541452e-005, 0.00011196719424656033}, {0.00011196719424656033, 1.1353928041541452e-005, -0.0013497557555715387, -0.00018021409008538188, 0.007414965517654251, 0.0014089092443297553, -0.024220722675013445, -0.0075537806116804775, 0.049179318299660837, 0.035848830736954392, -0.022162306170337816, -0.39888597239022, 0.76347909778365719, -0.46274103121927235, -0.07833262231634322, 0.17037069723886492, 0.01530174062247884, -0.057804179445505657, -0.0026043910313322326, 0.014589836449234145, 0.00030764779631059454, -0.0023502976141834648, -1.8158078862617515e-005, 0.00017906658697508691} }; static double sym13_double[][26] = { {6.8203252630753188e-005, -3.5738623648689009e-005, -0.0011360634389281183, -0.00017094285853022211, 0.0075262253899680996, 0.0052963597387250252, -0.02021676813338983, -0.017211642726299048, 0.013862497435849205, -0.059750627717943698, -0.12436246075153011, 0.19770481877117801, 0.69573915056149638, 0.64456438390118564, 0.11023022302137217, -0.14049009311363403, 0.0088197576704205465, 0.092926030899137119, 0.017618296880653084, -0.020749686325515677, -0.0014924472742598532, 0.0056748537601224395, 0.00041326119884196064, -0.0007213643851362283, 3.6905373423196241e-005, 7.0429866906944016e-005}, {-7.0429866906944016e-005, 3.6905373423196241e-005, 0.0007213643851362283, 0.00041326119884196064, -0.0056748537601224395, -0.0014924472742598532, 0.020749686325515677, 0.017618296880653084, -0.092926030899137119, 0.0088197576704205465, 0.14049009311363403, 0.11023022302137217, -0.64456438390118564, 0.69573915056149638, -0.19770481877117801, -0.12436246075153011, 0.059750627717943698, 0.013862497435849205, 0.017211642726299048, -0.02021676813338983, -0.0052963597387250252, 0.0075262253899680996, 0.00017094285853022211, -0.0011360634389281183, 3.5738623648689009e-005, 6.8203252630753188e-005}, {7.0429866906944016e-005, 3.6905373423196241e-005, -0.0007213643851362283, 0.00041326119884196064, 0.0056748537601224395, -0.0014924472742598532, -0.020749686325515677, 0.017618296880653084, 0.092926030899137119, 0.0088197576704205465, -0.14049009311363403, 0.11023022302137217, 0.64456438390118564, 0.69573915056149638, 0.19770481877117801, -0.12436246075153011, -0.059750627717943698, 0.013862497435849205, -0.017211642726299048, -0.02021676813338983, 0.0052963597387250252, 0.0075262253899680996, -0.00017094285853022211, -0.0011360634389281183, -3.5738623648689009e-005, 6.8203252630753188e-005}, {6.8203252630753188e-005, 3.5738623648689009e-005, -0.0011360634389281183, 0.00017094285853022211, 0.0075262253899680996, -0.0052963597387250252, -0.02021676813338983, 0.017211642726299048, 0.013862497435849205, 0.059750627717943698, -0.12436246075153011, -0.19770481877117801, 0.69573915056149638, -0.64456438390118564, 0.11023022302137217, 0.14049009311363403, 0.0088197576704205465, -0.092926030899137119, 0.017618296880653084, 0.020749686325515677, -0.0014924472742598532, -0.0056748537601224395, 0.00041326119884196064, 0.0007213643851362283, 3.6905373423196241e-005, -7.0429866906944016e-005} }; static double sym14_double[][28] = { {-2.5879090265397886e-005, 1.1210865808890361e-005, 0.00039843567297594335, -6.2865424814776362e-005, -0.002579441725933078, 0.00036647657366011829, 0.010037693717672269, -0.0027537747912240711, -0.029196217764038187, 0.0042805204990193782, 0.037433088362853452, -0.057634498351326995, -0.035318112114979733, 0.39320152196208885, 0.75997624196109093, 0.47533576263420663, -0.058111823317717831, -0.15999741114652205, 0.025898587531046669, 0.069827616361807551, -0.0023650488367403851, -0.019439314263626713, 0.0010131419871842082, 0.0045326774719456481, -7.3214213567023991e-005, -0.00060576018246643346, 1.9329016965523917e-005, 4.4618977991475265e-005}, {-4.4618977991475265e-005, 1.9329016965523917e-005, 0.00060576018246643346, -7.3214213567023991e-005, -0.0045326774719456481, 0.0010131419871842082, 0.019439314263626713, -0.0023650488367403851, -0.069827616361807551, 0.025898587531046669, 0.15999741114652205, -0.058111823317717831, -0.47533576263420663, 0.75997624196109093, -0.39320152196208885, -0.035318112114979733, 0.057634498351326995, 0.037433088362853452, -0.0042805204990193782, -0.029196217764038187, 0.0027537747912240711, 0.010037693717672269, -0.00036647657366011829, -0.002579441725933078, 6.2865424814776362e-005, 0.00039843567297594335, -1.1210865808890361e-005, -2.5879090265397886e-005}, {4.4618977991475265e-005, 1.9329016965523917e-005, -0.00060576018246643346, -7.3214213567023991e-005, 0.0045326774719456481, 0.0010131419871842082, -0.019439314263626713, -0.0023650488367403851, 0.069827616361807551, 0.025898587531046669, -0.15999741114652205, -0.058111823317717831, 0.47533576263420663, 0.75997624196109093, 0.39320152196208885, -0.035318112114979733, -0.057634498351326995, 0.037433088362853452, 0.0042805204990193782, -0.029196217764038187, -0.0027537747912240711, 0.010037693717672269, 0.00036647657366011829, -0.002579441725933078, -6.2865424814776362e-005, 0.00039843567297594335, 1.1210865808890361e-005, -2.5879090265397886e-005}, {-2.5879090265397886e-005, -1.1210865808890361e-005, 0.00039843567297594335, 6.2865424814776362e-005, -0.002579441725933078, -0.00036647657366011829, 0.010037693717672269, 0.0027537747912240711, -0.029196217764038187, -0.0042805204990193782, 0.037433088362853452, 0.057634498351326995, -0.035318112114979733, -0.39320152196208885, 0.75997624196109093, -0.47533576263420663, -0.058111823317717831, 0.15999741114652205, 0.025898587531046669, -0.069827616361807551, -0.0023650488367403851, 0.019439314263626713, 0.0010131419871842082, -0.0045326774719456481, -7.3214213567023991e-005, 0.00060576018246643346, 1.9329016965523917e-005, -4.4618977991475265e-005} }; static double sym15_double[][30] = { {9.7124197379633478e-006, -7.3596667989194696e-006, -0.00016066186637495343, 5.5122547855586653e-005, 0.0010705672194623959, -0.00026731644647180568, -0.0035901654473726417, 0.003423450736351241, 0.010079977087905669, -0.019405011430934468, -0.038876716876833493, 0.021937642719753955, 0.040735479696810677, -0.04108266663538248, 0.11153369514261872, 0.57864041521503451, 0.72184302963618119, 0.2439627054321663, -0.1966263587662373, -0.13405629845625389, 0.068393310060480245, 0.067969829044879179, -0.0087447888864779517, -0.017171252781638731, 0.0015261382781819983, 0.003481028737064895, -0.00010815440168545525, -0.00040216853760293483, 2.1717890150778919e-005, 2.8660708525318081e-005}, {-2.8660708525318081e-005, 2.1717890150778919e-005, 0.00040216853760293483, -0.00010815440168545525, -0.003481028737064895, 0.0015261382781819983, 0.017171252781638731, -0.0087447888864779517, -0.067969829044879179, 0.068393310060480245, 0.13405629845625389, -0.1966263587662373, -0.2439627054321663, 0.72184302963618119, -0.57864041521503451, 0.11153369514261872, 0.04108266663538248, 0.040735479696810677, -0.021937642719753955, -0.038876716876833493, 0.019405011430934468, 0.010079977087905669, -0.003423450736351241, -0.0035901654473726417, 0.00026731644647180568, 0.0010705672194623959, -5.5122547855586653e-005, -0.00016066186637495343, 7.3596667989194696e-006, 9.7124197379633478e-006}, {2.8660708525318081e-005, 2.1717890150778919e-005, -0.00040216853760293483, -0.00010815440168545525, 0.003481028737064895, 0.0015261382781819983, -0.017171252781638731, -0.0087447888864779517, 0.067969829044879179, 0.068393310060480245, -0.13405629845625389, -0.1966263587662373, 0.2439627054321663, 0.72184302963618119, 0.57864041521503451, 0.11153369514261872, -0.04108266663538248, 0.040735479696810677, 0.021937642719753955, -0.038876716876833493, -0.019405011430934468, 0.010079977087905669, 0.003423450736351241, -0.0035901654473726417, -0.00026731644647180568, 0.0010705672194623959, 5.5122547855586653e-005, -0.00016066186637495343, -7.3596667989194696e-006, 9.7124197379633478e-006}, {9.7124197379633478e-006, 7.3596667989194696e-006, -0.00016066186637495343, -5.5122547855586653e-005, 0.0010705672194623959, 0.00026731644647180568, -0.0035901654473726417, -0.003423450736351241, 0.010079977087905669, 0.019405011430934468, -0.038876716876833493, -0.021937642719753955, 0.040735479696810677, 0.04108266663538248, 0.11153369514261872, -0.57864041521503451, 0.72184302963618119, -0.2439627054321663, -0.1966263587662373, 0.13405629845625389, 0.068393310060480245, -0.067969829044879179, -0.0087447888864779517, 0.017171252781638731, 0.0015261382781819983, -0.003481028737064895, -0.00010815440168545525, 0.00040216853760293483, 2.1717890150778919e-005, -2.8660708525318081e-005} }; static double sym16_double[][32] = { {6.2300067012207606e-006, -3.1135564076219692e-006, -0.00010943147929529757, 2.8078582128442894e-005, 0.00085235471080470952, -0.0001084456223089688, -0.0038809122526038786, 0.00071821197883178923, 0.012666731659857348, -0.0031265171722710075, -0.031051202843553064, 0.0048692744049046071, 0.032333091610663785, -0.066983049070217779, -0.034574228416972504, 0.39712293362064416, 0.75652498787569711, 0.47534280601152273, -0.054040601387606135, -0.15959219218520598, 0.03072113906330156, 0.078037852903419913, -0.0035102750683740089, -0.024952758046290123, 0.001359844742484172, 0.0069377611308027096, -0.00022211647621176323, -0.0013387206066921965, 3.656592483348223e-005, 0.00016545679579108483, -5.3964831793152419e-006, -1.0797982104319795e-005}, {1.0797982104319795e-005, -5.3964831793152419e-006, -0.00016545679579108483, 3.656592483348223e-005, 0.0013387206066921965, -0.00022211647621176323, -0.0069377611308027096, 0.001359844742484172, 0.024952758046290123, -0.0035102750683740089, -0.078037852903419913, 0.03072113906330156, 0.15959219218520598, -0.054040601387606135, -0.47534280601152273, 0.75652498787569711, -0.39712293362064416, -0.034574228416972504, 0.066983049070217779, 0.032333091610663785, -0.0048692744049046071, -0.031051202843553064, 0.0031265171722710075, 0.012666731659857348, -0.00071821197883178923, -0.0038809122526038786, 0.0001084456223089688, 0.00085235471080470952, -2.8078582128442894e-005, -0.00010943147929529757, 3.1135564076219692e-006, 6.2300067012207606e-006}, {-1.0797982104319795e-005, -5.3964831793152419e-006, 0.00016545679579108483, 3.656592483348223e-005, -0.0013387206066921965, -0.00022211647621176323, 0.0069377611308027096, 0.001359844742484172, -0.024952758046290123, -0.0035102750683740089, 0.078037852903419913, 0.03072113906330156, -0.15959219218520598, -0.054040601387606135, 0.47534280601152273, 0.75652498787569711, 0.39712293362064416, -0.034574228416972504, -0.066983049070217779, 0.032333091610663785, 0.0048692744049046071, -0.031051202843553064, -0.0031265171722710075, 0.012666731659857348, 0.00071821197883178923, -0.0038809122526038786, -0.0001084456223089688, 0.00085235471080470952, 2.8078582128442894e-005, -0.00010943147929529757, -3.1135564076219692e-006, 6.2300067012207606e-006}, {6.2300067012207606e-006, 3.1135564076219692e-006, -0.00010943147929529757, -2.8078582128442894e-005, 0.00085235471080470952, 0.0001084456223089688, -0.0038809122526038786, -0.00071821197883178923, 0.012666731659857348, 0.0031265171722710075, -0.031051202843553064, -0.0048692744049046071, 0.032333091610663785, 0.066983049070217779, -0.034574228416972504, -0.39712293362064416, 0.75652498787569711, -0.47534280601152273, -0.054040601387606135, 0.15959219218520598, 0.03072113906330156, -0.078037852903419913, -0.0035102750683740089, 0.024952758046290123, 0.001359844742484172, -0.0069377611308027096, -0.00022211647621176323, 0.0013387206066921965, 3.656592483348223e-005, -0.00016545679579108483, -5.3964831793152419e-006, 1.0797982104319795e-005} }; static double sym17_double[][34] = { {4.297343327345983e-006, 2.7801266938414138e-006, -6.2937025975541919e-005, -1.3506383399901165e-005, 0.0004759963802638669, -0.00013864230268045499, -0.0027416759756816018, 0.0008567700701915741, 0.010482366933031529, -0.0048192128031761478, -0.033291383492359328, 0.017903952214341119, 0.10475461484223211, 0.0172711782105185, -0.11856693261143636, 0.14239835041467819, 0.65071662920454565, 0.68148899534492502, 0.18053958458111286, -0.15507600534974825, -0.086070874720733381, 0.016158808725919346, -0.0072616347509287674, -0.01803889724191924, 0.0099529825235095976, 0.012396988366648726, -0.0019054076898526659, -0.0039323252797979023, 5.8400428694052584e-005, 0.0007198270642148971, 2.5207933140828779e-005, -7.6071244056051285e-005, -2.4527163425832999e-006, 3.7912531943321266e-006}, {-3.7912531943321266e-006, -2.4527163425832999e-006, 7.6071244056051285e-005, 2.5207933140828779e-005, -0.0007198270642148971, 5.8400428694052584e-005, 0.0039323252797979023, -0.0019054076898526659, -0.012396988366648726, 0.0099529825235095976, 0.01803889724191924, -0.0072616347509287674, -0.016158808725919346, -0.086070874720733381, 0.15507600534974825, 0.18053958458111286, -0.68148899534492502, 0.65071662920454565, -0.14239835041467819, -0.11856693261143636, -0.0172711782105185, 0.10475461484223211, -0.017903952214341119, -0.033291383492359328, 0.0048192128031761478, 0.010482366933031529, -0.0008567700701915741, -0.0027416759756816018, 0.00013864230268045499, 0.0004759963802638669, 1.3506383399901165e-005, -6.2937025975541919e-005, -2.7801266938414138e-006, 4.297343327345983e-006}, {3.7912531943321266e-006, -2.4527163425832999e-006, -7.6071244056051285e-005, 2.5207933140828779e-005, 0.0007198270642148971, 5.8400428694052584e-005, -0.0039323252797979023, -0.0019054076898526659, 0.012396988366648726, 0.0099529825235095976, -0.01803889724191924, -0.0072616347509287674, 0.016158808725919346, -0.086070874720733381, -0.15507600534974825, 0.18053958458111286, 0.68148899534492502, 0.65071662920454565, 0.14239835041467819, -0.11856693261143636, 0.0172711782105185, 0.10475461484223211, 0.017903952214341119, -0.033291383492359328, -0.0048192128031761478, 0.010482366933031529, 0.0008567700701915741, -0.0027416759756816018, -0.00013864230268045499, 0.0004759963802638669, -1.3506383399901165e-005, -6.2937025975541919e-005, 2.7801266938414138e-006, 4.297343327345983e-006}, {4.297343327345983e-006, -2.7801266938414138e-006, -6.2937025975541919e-005, 1.3506383399901165e-005, 0.0004759963802638669, 0.00013864230268045499, -0.0027416759756816018, -0.0008567700701915741, 0.010482366933031529, 0.0048192128031761478, -0.033291383492359328, -0.017903952214341119, 0.10475461484223211, -0.0172711782105185, -0.11856693261143636, -0.14239835041467819, 0.65071662920454565, -0.68148899534492502, 0.18053958458111286, 0.15507600534974825, -0.086070874720733381, -0.016158808725919346, -0.0072616347509287674, 0.01803889724191924, 0.0099529825235095976, -0.012396988366648726, -0.0019054076898526659, 0.0039323252797979023, 5.8400428694052584e-005, -0.0007198270642148971, 2.5207933140828779e-005, 7.6071244056051285e-005, -2.4527163425832999e-006, -3.7912531943321266e-006} }; static double sym18_double[][36] = { {2.6126125564836423e-006, 1.354915761832114e-006, -4.5246757874949856e-005, -1.4020992577726755e-005, 0.00039616840638254753, 7.0212734590362685e-005, -0.0023138718145060992, -0.00041152110923597756, 0.0095021643909623654, 0.0016429863972782159, -0.030325091089369604, -0.0050770851607570529, 0.084219929970386548, 0.033995667103947358, -0.15993814866932407, -0.052029158983952786, 0.47396905989393956, 0.75362914010179283, 0.40148386057061813, -0.032480573290138676, -0.073799207290607169, 0.028529597039037808, 0.0062779445543116943, -0.031712684731814537, -0.0032607442000749834, 0.015012356344250213, 0.0010877847895956929, -0.0052397896830266083, -0.00018877623940755607, 0.0014280863270832796, 4.7416145183736671e-005, -0.00026583011024241041, -9.858816030140058e-006, 2.9557437620930811e-005, 7.8472980558317646e-007, -1.5131530692371587e-006}, {1.5131530692371587e-006, 7.8472980558317646e-007, -2.9557437620930811e-005, -9.858816030140058e-006, 0.00026583011024241041, 4.7416145183736671e-005, -0.0014280863270832796, -0.00018877623940755607, 0.0052397896830266083, 0.0010877847895956929, -0.015012356344250213, -0.0032607442000749834, 0.031712684731814537, 0.0062779445543116943, -0.028529597039037808, -0.073799207290607169, 0.032480573290138676, 0.40148386057061813, -0.75362914010179283, 0.47396905989393956, 0.052029158983952786, -0.15993814866932407, -0.033995667103947358, 0.084219929970386548, 0.0050770851607570529, -0.030325091089369604, -0.0016429863972782159, 0.0095021643909623654, 0.00041152110923597756, -0.0023138718145060992, -7.0212734590362685e-005, 0.00039616840638254753, 1.4020992577726755e-005, -4.5246757874949856e-005, -1.354915761832114e-006, 2.6126125564836423e-006}, {-1.5131530692371587e-006, 7.8472980558317646e-007, 2.9557437620930811e-005, -9.858816030140058e-006, -0.00026583011024241041, 4.7416145183736671e-005, 0.0014280863270832796, -0.00018877623940755607, -0.0052397896830266083, 0.0010877847895956929, 0.015012356344250213, -0.0032607442000749834, -0.031712684731814537, 0.0062779445543116943, 0.028529597039037808, -0.073799207290607169, -0.032480573290138676, 0.40148386057061813, 0.75362914010179283, 0.47396905989393956, -0.052029158983952786, -0.15993814866932407, 0.033995667103947358, 0.084219929970386548, -0.0050770851607570529, -0.030325091089369604, 0.0016429863972782159, 0.0095021643909623654, -0.00041152110923597756, -0.0023138718145060992, 7.0212734590362685e-005, 0.00039616840638254753, -1.4020992577726755e-005, -4.5246757874949856e-005, 1.354915761832114e-006, 2.6126125564836423e-006}, {2.6126125564836423e-006, -1.354915761832114e-006, -4.5246757874949856e-005, 1.4020992577726755e-005, 0.00039616840638254753, -7.0212734590362685e-005, -0.0023138718145060992, 0.00041152110923597756, 0.0095021643909623654, -0.0016429863972782159, -0.030325091089369604, 0.0050770851607570529, 0.084219929970386548, -0.033995667103947358, -0.15993814866932407, 0.052029158983952786, 0.47396905989393956, -0.75362914010179283, 0.40148386057061813, 0.032480573290138676, -0.073799207290607169, -0.028529597039037808, 0.0062779445543116943, 0.031712684731814537, -0.0032607442000749834, -0.015012356344250213, 0.0010877847895956929, 0.0052397896830266083, -0.00018877623940755607, -0.0014280863270832796, 4.7416145183736671e-005, 0.00026583011024241041, -9.858816030140058e-006, -2.9557437620930811e-005, 7.8472980558317646e-007, 1.5131530692371587e-006} }; static double sym19_double[][38] = { {5.4877327682158382e-007, -6.4636513033459633e-007, -1.1880518269823984e-005, 8.8733121737292863e-006, 0.0001155392333357879, -4.6120396002105868e-005, -0.00063576451500433403, 0.00015915804768084938, 0.0021214250281823303, -0.0011607032572062486, -0.005122205002583014, 0.0079684383206133063, 0.015797439295674631, -0.022651993378245951, -0.046635983534938946, 0.0070155738571741596, 0.0089545911730436242, -0.067525058040294086, 0.10902582508127781, 0.57814494533860505, 0.71955552571639425, 0.25826616923728363, -0.17659686625203097, -0.11624173010739675, 0.093630843415897141, 0.084072676279245043, -0.016908234861345205, -0.027709896931311252, 0.0043193518748949689, 0.0082622369555282547, -0.00061792232779831076, -0.0017049602611649971, 0.00012930767650701415, 0.00027621877685734072, -1.6821387029373716e-005, -2.8151138661550245e-005, 2.0623170632395688e-006, 1.7509367995348687e-006}, {-1.7509367995348687e-006, 2.0623170632395688e-006, 2.8151138661550245e-005, -1.6821387029373716e-005, -0.00027621877685734072, 0.00012930767650701415, 0.0017049602611649971, -0.00061792232779831076, -0.0082622369555282547, 0.0043193518748949689, 0.027709896931311252, -0.016908234861345205, -0.084072676279245043, 0.093630843415897141, 0.11624173010739675, -0.17659686625203097, -0.25826616923728363, 0.71955552571639425, -0.57814494533860505, 0.10902582508127781, 0.067525058040294086, 0.0089545911730436242, -0.0070155738571741596, -0.046635983534938946, 0.022651993378245951, 0.015797439295674631, -0.0079684383206133063, -0.005122205002583014, 0.0011607032572062486, 0.0021214250281823303, -0.00015915804768084938, -0.00063576451500433403, 4.6120396002105868e-005, 0.0001155392333357879, -8.8733121737292863e-006, -1.1880518269823984e-005, 6.4636513033459633e-007, 5.4877327682158382e-007}, {1.7509367995348687e-006, 2.0623170632395688e-006, -2.8151138661550245e-005, -1.6821387029373716e-005, 0.00027621877685734072, 0.00012930767650701415, -0.0017049602611649971, -0.00061792232779831076, 0.0082622369555282547, 0.0043193518748949689, -0.027709896931311252, -0.016908234861345205, 0.084072676279245043, 0.093630843415897141, -0.11624173010739675, -0.17659686625203097, 0.25826616923728363, 0.71955552571639425, 0.57814494533860505, 0.10902582508127781, -0.067525058040294086, 0.0089545911730436242, 0.0070155738571741596, -0.046635983534938946, -0.022651993378245951, 0.015797439295674631, 0.0079684383206133063, -0.005122205002583014, -0.0011607032572062486, 0.0021214250281823303, 0.00015915804768084938, -0.00063576451500433403, -4.6120396002105868e-005, 0.0001155392333357879, 8.8733121737292863e-006, -1.1880518269823984e-005, -6.4636513033459633e-007, 5.4877327682158382e-007}, {5.4877327682158382e-007, 6.4636513033459633e-007, -1.1880518269823984e-005, -8.8733121737292863e-006, 0.0001155392333357879, 4.6120396002105868e-005, -0.00063576451500433403, -0.00015915804768084938, 0.0021214250281823303, 0.0011607032572062486, -0.005122205002583014, -0.0079684383206133063, 0.015797439295674631, 0.022651993378245951, -0.046635983534938946, -0.0070155738571741596, 0.0089545911730436242, 0.067525058040294086, 0.10902582508127781, -0.57814494533860505, 0.71955552571639425, -0.25826616923728363, -0.17659686625203097, 0.11624173010739675, 0.093630843415897141, -0.084072676279245043, -0.016908234861345205, 0.027709896931311252, 0.0043193518748949689, -0.0082622369555282547, -0.00061792232779831076, 0.0017049602611649971, 0.00012930767650701415, -0.00027621877685734072, -1.6821387029373716e-005, 2.8151138661550245e-005, 2.0623170632395688e-006, -1.7509367995348687e-006} }; static double sym20_double[][40] = { {3.695537474835221e-007, -1.9015675890554106e-007, -7.919361411976999e-006, 3.0256660627369661e-006, 7.992967835772481e-005, -1.928412300645204e-005, -0.00049473109156726548, 7.2159911880740349e-005, 0.0020889947081901982, -0.0003052628317957281, -0.0066065857990888609, 0.0014230873594621453, 0.017004049023390339, -0.0033138573836233591, -0.031629437144957966, 0.0081232283560096815, 0.025579349509413946, -0.078994344928398158, -0.029819368880333728, 0.40583144434845059, 0.75116272842273002, 0.47199147510148703, -0.051088342921067398, -0.16057829841525254, 0.036250951653933078, 0.088919668028199561, -0.0068437019650692274, -0.035373336756604236, 0.0019385970672402002, 0.012157040948785737, -0.0006111263857992088, -0.0034716478028440734, 0.00012544091723067259, 0.00074761085978205719, -2.6615550335516086e-005, -0.00011739133516291466, 4.5254222091516362e-006, 1.22872527779612e-005, -3.2567026420174407e-007, -6.3291290447763946e-007}, {6.3291290447763946e-007, -3.2567026420174407e-007, -1.22872527779612e-005, 4.5254222091516362e-006, 0.00011739133516291466, -2.6615550335516086e-005, -0.00074761085978205719, 0.00012544091723067259, 0.0034716478028440734, -0.0006111263857992088, -0.012157040948785737, 0.0019385970672402002, 0.035373336756604236, -0.0068437019650692274, -0.088919668028199561, 0.036250951653933078, 0.16057829841525254, -0.051088342921067398, -0.47199147510148703, 0.75116272842273002, -0.40583144434845059, -0.029819368880333728, 0.078994344928398158, 0.025579349509413946, -0.0081232283560096815, -0.031629437144957966, 0.0033138573836233591, 0.017004049023390339, -0.0014230873594621453, -0.0066065857990888609, 0.0003052628317957281, 0.0020889947081901982, -7.2159911880740349e-005, -0.00049473109156726548, 1.928412300645204e-005, 7.992967835772481e-005, -3.0256660627369661e-006, -7.919361411976999e-006, 1.9015675890554106e-007, 3.695537474835221e-007}, {-6.3291290447763946e-007, -3.2567026420174407e-007, 1.22872527779612e-005, 4.5254222091516362e-006, -0.00011739133516291466, -2.6615550335516086e-005, 0.00074761085978205719, 0.00012544091723067259, -0.0034716478028440734, -0.0006111263857992088, 0.012157040948785737, 0.0019385970672402002, -0.035373336756604236, -0.0068437019650692274, 0.088919668028199561, 0.036250951653933078, -0.16057829841525254, -0.051088342921067398, 0.47199147510148703, 0.75116272842273002, 0.40583144434845059, -0.029819368880333728, -0.078994344928398158, 0.025579349509413946, 0.0081232283560096815, -0.031629437144957966, -0.0033138573836233591, 0.017004049023390339, 0.0014230873594621453, -0.0066065857990888609, -0.0003052628317957281, 0.0020889947081901982, 7.2159911880740349e-005, -0.00049473109156726548, -1.928412300645204e-005, 7.992967835772481e-005, 3.0256660627369661e-006, -7.919361411976999e-006, -1.9015675890554106e-007, 3.695537474835221e-007}, {3.695537474835221e-007, 1.9015675890554106e-007, -7.919361411976999e-006, -3.0256660627369661e-006, 7.992967835772481e-005, 1.928412300645204e-005, -0.00049473109156726548, -7.2159911880740349e-005, 0.0020889947081901982, 0.0003052628317957281, -0.0066065857990888609, -0.0014230873594621453, 0.017004049023390339, 0.0033138573836233591, -0.031629437144957966, -0.0081232283560096815, 0.025579349509413946, 0.078994344928398158, -0.029819368880333728, -0.40583144434845059, 0.75116272842273002, -0.47199147510148703, -0.051088342921067398, 0.16057829841525254, 0.036250951653933078, -0.088919668028199561, -0.0068437019650692274, 0.035373336756604236, 0.0019385970672402002, -0.012157040948785737, -0.0006111263857992088, 0.0034716478028440734, 0.00012544091723067259, -0.00074761085978205719, -2.6615550335516086e-005, 0.00011739133516291466, 4.5254222091516362e-006, -1.22872527779612e-005, -3.2567026420174407e-007, 6.3291290447763946e-007} }; static double coif1_double[][6] = { {-0.01565572813546454, -0.072732619512853897, 0.38486484686420286, 0.85257202021225542, 0.33789766245780922, -0.072732619512853897}, {0.072732619512853897, 0.33789766245780922, -0.85257202021225542, 0.38486484686420286, 0.072732619512853897, -0.01565572813546454}, {-0.072732619512853897, 0.33789766245780922, 0.85257202021225542, 0.38486484686420286, -0.072732619512853897, -0.01565572813546454}, {-0.01565572813546454, 0.072732619512853897, 0.38486484686420286, -0.85257202021225542, 0.33789766245780922, 0.072732619512853897} }; static double coif2_double[][12] = { {-0.00072054944536451221, -0.0018232088707029932, 0.0056114348193944995, 0.023680171946334084, -0.059434418646456898, -0.076488599078306393, 0.41700518442169254, 0.81272363544554227, 0.38611006682116222, -0.067372554721963018, -0.041464936781759151, 0.016387336463522112}, {-0.016387336463522112, -0.041464936781759151, 0.067372554721963018, 0.38611006682116222, -0.81272363544554227, 0.41700518442169254, 0.076488599078306393, -0.059434418646456898, -0.023680171946334084, 0.0056114348193944995, 0.0018232088707029932, -0.00072054944536451221}, {0.016387336463522112, -0.041464936781759151, -0.067372554721963018, 0.38611006682116222, 0.81272363544554227, 0.41700518442169254, -0.076488599078306393, -0.059434418646456898, 0.023680171946334084, 0.0056114348193944995, -0.0018232088707029932, -0.00072054944536451221}, {-0.00072054944536451221, 0.0018232088707029932, 0.0056114348193944995, -0.023680171946334084, -0.059434418646456898, 0.076488599078306393, 0.41700518442169254, -0.81272363544554227, 0.38611006682116222, 0.067372554721963018, -0.041464936781759151, -0.016387336463522112} }; static double coif3_double[][18] = { {-3.4599772836212559e-005, -7.0983303138141252e-005, 0.00046621696011288631, 0.0011175187708906016, -0.0025745176887502236, -0.0090079761366615805, 0.015880544863615904, 0.034555027573061628, -0.082301927106885983, -0.071799821619312018, 0.42848347637761874, 0.79377722262562056, 0.4051769024096169, -0.061123390002672869, -0.0657719112818555, 0.023452696141836267, 0.0077825964273254182, -0.0037935128644910141}, {0.0037935128644910141, 0.0077825964273254182, -0.023452696141836267, -0.0657719112818555, 0.061123390002672869, 0.4051769024096169, -0.79377722262562056, 0.42848347637761874, 0.071799821619312018, -0.082301927106885983, -0.034555027573061628, 0.015880544863615904, 0.0090079761366615805, -0.0025745176887502236, -0.0011175187708906016, 0.00046621696011288631, 7.0983303138141252e-005, -3.4599772836212559e-005}, {-0.0037935128644910141, 0.0077825964273254182, 0.023452696141836267, -0.0657719112818555, -0.061123390002672869, 0.4051769024096169, 0.79377722262562056, 0.42848347637761874, -0.071799821619312018, -0.082301927106885983, 0.034555027573061628, 0.015880544863615904, -0.0090079761366615805, -0.0025745176887502236, 0.0011175187708906016, 0.00046621696011288631, -7.0983303138141252e-005, -3.4599772836212559e-005}, {-3.4599772836212559e-005, 7.0983303138141252e-005, 0.00046621696011288631, -0.0011175187708906016, -0.0025745176887502236, 0.0090079761366615805, 0.015880544863615904, -0.034555027573061628, -0.082301927106885983, 0.071799821619312018, 0.42848347637761874, -0.79377722262562056, 0.4051769024096169, 0.061123390002672869, -0.0657719112818555, -0.023452696141836267, 0.0077825964273254182, 0.0037935128644910141} }; static double coif4_double[][24] = { {-1.7849850030882614e-006, -3.2596802368833675e-006, 3.1229875865345646e-005, 6.2339034461007128e-005, -0.00025997455248771324, -0.00058902075624433831, 0.0012665619292989445, 0.0037514361572784571, -0.0056582866866107199, -0.015211731527946259, 0.025082261844864097, 0.039334427123337491, -0.096220442033987982, -0.066627474263425038, 0.4343860564914685, 0.78223893092049901, 0.41530840703043026, -0.056077313316754807, -0.081266699680878754, 0.026682300156053072, 0.016068943964776348, -0.0073461663276420935, -0.0016294920126017326, 0.00089231366858231456}, {-0.00089231366858231456, -0.0016294920126017326, 0.0073461663276420935, 0.016068943964776348, -0.026682300156053072, -0.081266699680878754, 0.056077313316754807, 0.41530840703043026, -0.78223893092049901, 0.4343860564914685, 0.066627474263425038, -0.096220442033987982, -0.039334427123337491, 0.025082261844864097, 0.015211731527946259, -0.0056582866866107199, -0.0037514361572784571, 0.0012665619292989445, 0.00058902075624433831, -0.00025997455248771324, -6.2339034461007128e-005, 3.1229875865345646e-005, 3.2596802368833675e-006, -1.7849850030882614e-006}, {0.00089231366858231456, -0.0016294920126017326, -0.0073461663276420935, 0.016068943964776348, 0.026682300156053072, -0.081266699680878754, -0.056077313316754807, 0.41530840703043026, 0.78223893092049901, 0.4343860564914685, -0.066627474263425038, -0.096220442033987982, 0.039334427123337491, 0.025082261844864097, -0.015211731527946259, -0.0056582866866107199, 0.0037514361572784571, 0.0012665619292989445, -0.00058902075624433831, -0.00025997455248771324, 6.2339034461007128e-005, 3.1229875865345646e-005, -3.2596802368833675e-006, -1.7849850030882614e-006}, {-1.7849850030882614e-006, 3.2596802368833675e-006, 3.1229875865345646e-005, -6.2339034461007128e-005, -0.00025997455248771324, 0.00058902075624433831, 0.0012665619292989445, -0.0037514361572784571, -0.0056582866866107199, 0.015211731527946259, 0.025082261844864097, -0.039334427123337491, -0.096220442033987982, 0.066627474263425038, 0.4343860564914685, -0.78223893092049901, 0.41530840703043026, 0.056077313316754807, -0.081266699680878754, -0.026682300156053072, 0.016068943964776348, 0.0073461663276420935, -0.0016294920126017326, -0.00089231366858231456} }; static double coif5_double[][30] = { {-9.517657273819165e-008, -1.6744288576823017e-007, 2.0637618513646814e-006, 3.7346551751414047e-006, -2.1315026809955787e-005, -4.1340432272512511e-005, 0.00014054114970203437, 0.00030225958181306315, -0.00063813134304511142, -0.0016628637020130838, 0.0024333732126576722, 0.0067641854480530832, -0.0091642311624818458, -0.019761778942572639, 0.032683574267111833, 0.041289208750181702, -0.10557420870333893, -0.062035963962903569, 0.43799162617183712, 0.77428960365295618, 0.42156620669085149, -0.052043163176243773, -0.091920010559696244, 0.02816802897093635, 0.023408156785839195, -0.010131117519849788, -0.004159358781386048, 0.0021782363581090178, 0.00035858968789573785, -0.00021208083980379827}, {0.00021208083980379827, 0.00035858968789573785, -0.0021782363581090178, -0.004159358781386048, 0.010131117519849788, 0.023408156785839195, -0.02816802897093635, -0.091920010559696244, 0.052043163176243773, 0.42156620669085149, -0.77428960365295618, 0.43799162617183712, 0.062035963962903569, -0.10557420870333893, -0.041289208750181702, 0.032683574267111833, 0.019761778942572639, -0.0091642311624818458, -0.0067641854480530832, 0.0024333732126576722, 0.0016628637020130838, -0.00063813134304511142, -0.00030225958181306315, 0.00014054114970203437, 4.1340432272512511e-005, -2.1315026809955787e-005, -3.7346551751414047e-006, 2.0637618513646814e-006, 1.6744288576823017e-007, -9.517657273819165e-008}, {-0.00021208083980379827, 0.00035858968789573785, 0.0021782363581090178, -0.004159358781386048, -0.010131117519849788, 0.023408156785839195, 0.02816802897093635, -0.091920010559696244, -0.052043163176243773, 0.42156620669085149, 0.77428960365295618, 0.43799162617183712, -0.062035963962903569, -0.10557420870333893, 0.041289208750181702, 0.032683574267111833, -0.019761778942572639, -0.0091642311624818458, 0.0067641854480530832, 0.0024333732126576722, -0.0016628637020130838, -0.00063813134304511142, 0.00030225958181306315, 0.00014054114970203437, -4.1340432272512511e-005, -2.1315026809955787e-005, 3.7346551751414047e-006, 2.0637618513646814e-006, -1.6744288576823017e-007, -9.517657273819165e-008}, {-9.517657273819165e-008, 1.6744288576823017e-007, 2.0637618513646814e-006, -3.7346551751414047e-006, -2.1315026809955787e-005, 4.1340432272512511e-005, 0.00014054114970203437, -0.00030225958181306315, -0.00063813134304511142, 0.0016628637020130838, 0.0024333732126576722, -0.0067641854480530832, -0.0091642311624818458, 0.019761778942572639, 0.032683574267111833, -0.041289208750181702, -0.10557420870333893, 0.062035963962903569, 0.43799162617183712, -0.77428960365295618, 0.42156620669085149, 0.052043163176243773, -0.091920010559696244, -0.02816802897093635, 0.023408156785839195, 0.010131117519849788, -0.004159358781386048, -0.0021782363581090178, 0.00035858968789573785, 0.00021208083980379827} }; static double bior1_1_double[][2] = { {0.70710678118654757, 0.70710678118654757}, {-0.70710678118654757, 0.70710678118654757}, {0.70710678118654757, 0.70710678118654757}, {0.70710678118654757, -0.70710678118654757} }; static double bior1_3_double[][6] = { {-0.088388347648318447, 0.088388347648318447, 0.70710678118654757, 0.70710678118654757, 0.088388347648318447, -0.088388347648318447}, {0.0, 0.0, -0.70710678118654757, 0.70710678118654757, 0.0, 0.0}, {0.0, 0.0, 0.70710678118654757, 0.70710678118654757, 0.0, 0.0}, {-0.088388347648318447, -0.088388347648318447, 0.70710678118654757, -0.70710678118654757, 0.088388347648318447, 0.088388347648318447} }; static double bior1_5_double[][10] = { {0.01657281518405971, -0.01657281518405971, -0.12153397801643787, 0.12153397801643787, 0.70710678118654757, 0.70710678118654757, 0.12153397801643787, -0.12153397801643787, -0.01657281518405971, 0.01657281518405971}, {0.0, 0.0, 0.0, 0.0, -0.70710678118654757, 0.70710678118654757, 0.0, 0.0, 0.0, 0.0}, {0.0, 0.0, 0.0, 0.0, 0.70710678118654757, 0.70710678118654757, 0.0, 0.0, 0.0, 0.0}, {0.01657281518405971, 0.01657281518405971, -0.12153397801643787, -0.12153397801643787, 0.70710678118654757, -0.70710678118654757, 0.12153397801643787, 0.12153397801643787, -0.01657281518405971, -0.01657281518405971} }; static double bior2_2_double[][6] = { {0.0, -0.17677669529663689, 0.35355339059327379, 1.0606601717798214, 0.35355339059327379, -0.17677669529663689}, {0.0, 0.35355339059327379, -0.70710678118654757, 0.35355339059327379, 0.0, 0.0}, {0.0, 0.35355339059327379, 0.70710678118654757, 0.35355339059327379, 0.0, 0.0}, {0.0, 0.17677669529663689, 0.35355339059327379, -1.0606601717798214, 0.35355339059327379, 0.17677669529663689} }; static double bior2_4_double[][10] = { {0.0, 0.033145630368119419, -0.066291260736238838, -0.17677669529663689, 0.4198446513295126, 0.99436891104358249, 0.4198446513295126, -0.17677669529663689, -0.066291260736238838, 0.033145630368119419}, {0.0, 0.0, 0.0, 0.35355339059327379, -0.70710678118654757, 0.35355339059327379, 0.0, 0.0, 0.0, 0.0}, {0.0, 0.0, 0.0, 0.35355339059327379, 0.70710678118654757, 0.35355339059327379, 0.0, 0.0, 0.0, 0.0}, {0.0, -0.033145630368119419, -0.066291260736238838, 0.17677669529663689, 0.4198446513295126, -0.99436891104358249, 0.4198446513295126, 0.17677669529663689, -0.066291260736238838, -0.033145630368119419} }; static double bior2_6_double[][14] = { {0.0, -0.0069053396600248784, 0.013810679320049757, 0.046956309688169176, -0.10772329869638811, -0.16987135563661201, 0.44746600996961211, 0.96674755240348298, 0.44746600996961211, -0.16987135563661201, -0.10772329869638811, 0.046956309688169176, 0.013810679320049757, -0.0069053396600248784}, {0.0, 0.0, 0.0, 0.0, 0.0, 0.35355339059327379, -0.70710678118654757, 0.35355339059327379, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0}, {0.0, 0.0, 0.0, 0.0, 0.0, 0.35355339059327379, 0.70710678118654757, 0.35355339059327379, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0}, {0.0, 0.0069053396600248784, 0.013810679320049757, -0.046956309688169176, -0.10772329869638811, 0.16987135563661201, 0.44746600996961211, -0.96674755240348298, 0.44746600996961211, 0.16987135563661201, -0.10772329869638811, -0.046956309688169176, 0.013810679320049757, 0.0069053396600248784} }; static double bior2_8_double[][18] = { {0.0, 0.0015105430506304422, -0.0030210861012608843, -0.012947511862546647, 0.028916109826354178, 0.052998481890690945, -0.13491307360773608, -0.16382918343409025, 0.46257144047591658, 0.95164212189717856, 0.46257144047591658, -0.16382918343409025, -0.13491307360773608, 0.052998481890690945, 0.028916109826354178, -0.012947511862546647, -0.0030210861012608843, 0.0015105430506304422}, {0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.35355339059327379, -0.70710678118654757, 0.35355339059327379, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0}, {0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.35355339059327379, 0.70710678118654757, 0.35355339059327379, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0}, {0.0, -0.0015105430506304422, -0.0030210861012608843, 0.012947511862546647, 0.028916109826354178, -0.052998481890690945, -0.13491307360773608, 0.16382918343409025, 0.46257144047591658, -0.95164212189717856, 0.46257144047591658, 0.16382918343409025, -0.13491307360773608, -0.052998481890690945, 0.028916109826354178, 0.012947511862546647, -0.0030210861012608843, -0.0015105430506304422} }; static double bior3_1_double[][4] = { {-0.35355339059327379, 1.0606601717798214, 1.0606601717798214, -0.35355339059327379}, {-0.17677669529663689, 0.53033008588991071, -0.53033008588991071, 0.17677669529663689}, {0.17677669529663689, 0.53033008588991071, 0.53033008588991071, 0.17677669529663689}, {-0.35355339059327379, -1.0606601717798214, 1.0606601717798214, 0.35355339059327379} }; static double bior3_3_double[][8] = { {0.066291260736238838, -0.19887378220871652, -0.15467960838455727, 0.99436891104358249, 0.99436891104358249, -0.15467960838455727, -0.19887378220871652, 0.066291260736238838}, {0.0, 0.0, -0.17677669529663689, 0.53033008588991071, -0.53033008588991071, 0.17677669529663689, 0.0, 0.0}, {0.0, 0.0, 0.17677669529663689, 0.53033008588991071, 0.53033008588991071, 0.17677669529663689, 0.0, 0.0}, {0.066291260736238838, 0.19887378220871652, -0.15467960838455727, -0.99436891104358249, 0.99436891104358249, 0.15467960838455727, -0.19887378220871652, -0.066291260736238838} }; static double bior3_5_double[][12] = { {-0.013810679320049757, 0.041432037960149271, 0.052480581416189075, -0.26792717880896527, -0.071815532464258744, 0.96674755240348298, 0.96674755240348298, -0.071815532464258744, -0.26792717880896527, 0.052480581416189075, 0.041432037960149271, -0.013810679320049757}, {0.0, 0.0, 0.0, 0.0, -0.17677669529663689, 0.53033008588991071, -0.53033008588991071, 0.17677669529663689, 0.0, 0.0, 0.0, 0.0}, {0.0, 0.0, 0.0, 0.0, 0.17677669529663689, 0.53033008588991071, 0.53033008588991071, 0.17677669529663689, 0.0, 0.0, 0.0, 0.0}, {-0.013810679320049757, -0.041432037960149271, 0.052480581416189075, 0.26792717880896527, -0.071815532464258744, -0.96674755240348298, 0.96674755240348298, 0.071815532464258744, -0.26792717880896527, -0.052480581416189075, 0.041432037960149271, 0.013810679320049757} }; static double bior3_7_double[][16] = { {0.0030210861012608843, -0.0090632583037826529, -0.016831765421310641, 0.074663985074019001, 0.031332978707362888, -0.301159125922835, -0.026499240945345472, 0.95164212189717856, 0.95164212189717856, -0.026499240945345472, -0.301159125922835, 0.031332978707362888, 0.074663985074019001, -0.016831765421310641, -0.0090632583037826529, 0.0030210861012608843}, {0.0, 0.0, 0.0, 0.0, 0.0, 0.0, -0.17677669529663689, 0.53033008588991071, -0.53033008588991071, 0.17677669529663689, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0}, {0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.17677669529663689, 0.53033008588991071, 0.53033008588991071, 0.17677669529663689, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0}, {0.0030210861012608843, 0.0090632583037826529, -0.016831765421310641, -0.074663985074019001, 0.031332978707362888, 0.301159125922835, -0.026499240945345472, -0.95164212189717856, 0.95164212189717856, 0.026499240945345472, -0.301159125922835, -0.031332978707362888, 0.074663985074019001, 0.016831765421310641, -0.0090632583037826529, -0.0030210861012608843} }; static double bior3_9_double[][20] = { {-0.00067974437278369901, 0.0020392331183510968, 0.0050603192196119811, -0.020618912641105536, -0.014112787930175846, 0.09913478249423216, 0.012300136269419315, -0.32019196836077857, 0.0020500227115698858, 0.94212570067820678, 0.94212570067820678, 0.0020500227115698858, -0.32019196836077857, 0.012300136269419315, 0.09913478249423216, -0.014112787930175846, -0.020618912641105536, 0.0050603192196119811, 0.0020392331183510968, -0.00067974437278369901}, {0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, -0.17677669529663689, 0.53033008588991071, -0.53033008588991071, 0.17677669529663689, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0}, {0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.17677669529663689, 0.53033008588991071, 0.53033008588991071, 0.17677669529663689, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0}, {-0.00067974437278369901, -0.0020392331183510968, 0.0050603192196119811, 0.020618912641105536, -0.014112787930175846, -0.09913478249423216, 0.012300136269419315, 0.32019196836077857, 0.0020500227115698858, -0.94212570067820678, 0.94212570067820678, -0.0020500227115698858, -0.32019196836077857, -0.012300136269419315, 0.09913478249423216, 0.014112787930175846, -0.020618912641105536, -0.0050603192196119811, 0.0020392331183510968, 0.00067974437278369901} }; static double bior4_4_double[][10] = { {0.0, 0.03782845550726404, -0.023849465019556843, -0.11062440441843718, 0.37740285561283066, 0.85269867900889385, 0.37740285561283066, -0.11062440441843718, -0.023849465019556843, 0.03782845550726404}, {0.0, -0.064538882628697058, 0.040689417609164058, 0.41809227322161724, -0.7884856164055829, 0.41809227322161724, 0.040689417609164058, -0.064538882628697058, 0.0, 0.0}, {0.0, -0.064538882628697058, -0.040689417609164058, 0.41809227322161724, 0.7884856164055829, 0.41809227322161724, -0.040689417609164058, -0.064538882628697058, 0.0, 0.0}, {0.0, -0.03782845550726404, -0.023849465019556843, 0.11062440441843718, 0.37740285561283066, -0.85269867900889385, 0.37740285561283066, 0.11062440441843718, -0.023849465019556843, -0.03782845550726404} }; static double bior5_5_double[][12] = { {0.0, 0.0, 0.03968708834740544, 0.0079481086372403219, -0.054463788468236907, 0.34560528195603346, 0.73666018142821055, 0.34560528195603346, -0.054463788468236907, 0.0079481086372403219, 0.03968708834740544, 0.0}, {-0.013456709459118716, -0.0026949668801115071, 0.13670658466432914, -0.093504697400938863, -0.47680326579848425, 0.89950610974864842, -0.47680326579848425, -0.093504697400938863, 0.13670658466432914, -0.0026949668801115071, -0.013456709459118716, 0.0}, {0.013456709459118716, -0.0026949668801115071, -0.13670658466432914, -0.093504697400938863, 0.47680326579848425, 0.89950610974864842, 0.47680326579848425, -0.093504697400938863, -0.13670658466432914, -0.0026949668801115071, 0.013456709459118716, 0.0}, {0.0, 0.0, 0.03968708834740544, -0.0079481086372403219, -0.054463788468236907, -0.34560528195603346, 0.73666018142821055, -0.34560528195603346, -0.054463788468236907, -0.0079481086372403219, 0.03968708834740544, 0.0} }; static double bior6_8_double[][18] = { {0.0, 0.0019088317364812906, -0.0019142861290887667, -0.016990639867602342, 0.01193456527972926, 0.04973290349094079, -0.077263173167204144, -0.09405920349573646, 0.42079628460982682, 0.82592299745840225, 0.42079628460982682, -0.09405920349573646, -0.077263173167204144, 0.04973290349094079, 0.01193456527972926, -0.016990639867602342, -0.0019142861290887667, 0.0019088317364812906}, {0.0, 0.0, 0.0, 0.014426282505624435, -0.014467504896790148, -0.078722001062628819, 0.040367979030339923, 0.41784910915027457, -0.75890772945365415, 0.41784910915027457, 0.040367979030339923, -0.078722001062628819, -0.014467504896790148, 0.014426282505624435, 0.0, 0.0, 0.0, 0.0}, {0.0, 0.0, 0.0, 0.014426282505624435, 0.014467504896790148, -0.078722001062628819, -0.040367979030339923, 0.41784910915027457, 0.75890772945365415, 0.41784910915027457, -0.040367979030339923, -0.078722001062628819, 0.014467504896790148, 0.014426282505624435, 0.0, 0.0, 0.0, 0.0}, {0.0, -0.0019088317364812906, -0.0019142861290887667, 0.016990639867602342, 0.01193456527972926, -0.04973290349094079, -0.077263173167204144, 0.09405920349573646, 0.42079628460982682, -0.82592299745840225, 0.42079628460982682, 0.09405920349573646, -0.077263173167204144, -0.04973290349094079, 0.01193456527972926, 0.016990639867602342, -0.0019142861290887667, -0.0019088317364812906} }; static double dmey_double[][62] = { {0.0, -1.0099999569414229e-012, 8.519459636796214e-009, -1.111944952595278e-008, -1.0798819539621958e-008, 6.0669757413511352e-008, -1.0866516536735883e-007, 8.2006806503864813e-008, 1.1783004497663934e-007, -5.5063405652522782e-007, 1.1307947017916706e-006, -1.4895492164971559e-006, 7.367572885903746e-007, 3.2054419133447798e-006, -1.6312699734552807e-005, 6.5543059305751491e-005, -0.00060115023435160925, -0.002704672124643725, 0.0022025341009110021, 0.006045814097323304, -0.0063877183184971563, -0.011061496392513451, 0.015270015130934803, 0.017423434103729693, -0.032130793990211758, -0.024348745906078023, 0.063739024322801596, 0.030655091960824263, -0.13284520043622938, -0.035087555656258346, 0.44459300275757724, 0.74458559231880628, 0.44459300275757724, -0.035087555656258346, -0.13284520043622938, 0.030655091960824263, 0.063739024322801596, -0.024348745906078023, -0.032130793990211758, 0.017423434103729693, 0.015270015130934803, -0.011061496392513451, -0.0063877183184971563, 0.006045814097323304, 0.0022025341009110021, -0.002704672124643725, -0.00060115023435160925, 6.5543059305751491e-005, -1.6312699734552807e-005, 3.2054419133447798e-006, 7.367572885903746e-007, -1.4895492164971559e-006, 1.1307947017916706e-006, -5.5063405652522782e-007, 1.1783004497663934e-007, 8.2006806503864813e-008, -1.0866516536735883e-007, 6.0669757413511352e-008, -1.0798819539621958e-008, -1.111944952595278e-008, 8.519459636796214e-009, -1.0099999569414229e-012}, {1.0099999569414229e-012, 8.519459636796214e-009, 1.111944952595278e-008, -1.0798819539621958e-008, -6.0669757413511352e-008, -1.0866516536735883e-007, -8.2006806503864813e-008, 1.1783004497663934e-007, 5.5063405652522782e-007, 1.1307947017916706e-006, 1.4895492164971559e-006, 7.367572885903746e-007, -3.2054419133447798e-006, -1.6312699734552807e-005, -6.5543059305751491e-005, -0.00060115023435160925, 0.002704672124643725, 0.0022025341009110021, -0.006045814097323304, -0.0063877183184971563, 0.011061496392513451, 0.015270015130934803, -0.017423434103729693, -0.032130793990211758, 0.024348745906078023, 0.063739024322801596, -0.030655091960824263, -0.13284520043622938, 0.035087555656258346, 0.44459300275757724, -0.74458559231880628, 0.44459300275757724, 0.035087555656258346, -0.13284520043622938, -0.030655091960824263, 0.063739024322801596, 0.024348745906078023, -0.032130793990211758, -0.017423434103729693, 0.015270015130934803, 0.011061496392513451, -0.0063877183184971563, -0.006045814097323304, 0.0022025341009110021, 0.002704672124643725, -0.00060115023435160925, -6.5543059305751491e-005, -1.6312699734552807e-005, -3.2054419133447798e-006, 7.367572885903746e-007, 1.4895492164971559e-006, 1.1307947017916706e-006, 5.5063405652522782e-007, 1.1783004497663934e-007, -8.2006806503864813e-008, -1.0866516536735883e-007, -6.0669757413511352e-008, -1.0798819539621958e-008, 1.111944952595278e-008, 8.519459636796214e-009, 1.0099999569414229e-012, 0.0}, {-1.0099999569414229e-012, 8.519459636796214e-009, -1.111944952595278e-008, -1.0798819539621958e-008, 6.0669757413511352e-008, -1.0866516536735883e-007, 8.2006806503864813e-008, 1.1783004497663934e-007, -5.5063405652522782e-007, 1.1307947017916706e-006, -1.4895492164971559e-006, 7.367572885903746e-007, 3.2054419133447798e-006, -1.6312699734552807e-005, 6.5543059305751491e-005, -0.00060115023435160925, -0.002704672124643725, 0.0022025341009110021, 0.006045814097323304, -0.0063877183184971563, -0.011061496392513451, 0.015270015130934803, 0.017423434103729693, -0.032130793990211758, -0.024348745906078023, 0.063739024322801596, 0.030655091960824263, -0.13284520043622938, -0.035087555656258346, 0.44459300275757724, 0.74458559231880628, 0.44459300275757724, -0.035087555656258346, -0.13284520043622938, 0.030655091960824263, 0.063739024322801596, -0.024348745906078023, -0.032130793990211758, 0.017423434103729693, 0.015270015130934803, -0.011061496392513451, -0.0063877183184971563, 0.006045814097323304, 0.0022025341009110021, -0.002704672124643725, -0.00060115023435160925, 6.5543059305751491e-005, -1.6312699734552807e-005, 3.2054419133447798e-006, 7.367572885903746e-007, -1.4895492164971559e-006, 1.1307947017916706e-006, -5.5063405652522782e-007, 1.1783004497663934e-007, 8.2006806503864813e-008, -1.0866516536735883e-007, 6.0669757413511352e-008, -1.0798819539621958e-008, -1.111944952595278e-008, 8.519459636796214e-009, -1.0099999569414229e-012, 0.0}, {0.0, 1.0099999569414229e-012, 8.519459636796214e-009, 1.111944952595278e-008, -1.0798819539621958e-008, -6.0669757413511352e-008, -1.0866516536735883e-007, -8.2006806503864813e-008, 1.1783004497663934e-007, 5.5063405652522782e-007, 1.1307947017916706e-006, 1.4895492164971559e-006, 7.367572885903746e-007, -3.2054419133447798e-006, -1.6312699734552807e-005, -6.5543059305751491e-005, -0.00060115023435160925, 0.002704672124643725, 0.0022025341009110021, -0.006045814097323304, -0.0063877183184971563, 0.011061496392513451, 0.015270015130934803, -0.017423434103729693, -0.032130793990211758, 0.024348745906078023, 0.063739024322801596, -0.030655091960824263, -0.13284520043622938, 0.035087555656258346, 0.44459300275757724, -0.74458559231880628, 0.44459300275757724, 0.035087555656258346, -0.13284520043622938, -0.030655091960824263, 0.063739024322801596, 0.024348745906078023, -0.032130793990211758, -0.017423434103729693, 0.015270015130934803, 0.011061496392513451, -0.0063877183184971563, -0.006045814097323304, 0.0022025341009110021, 0.002704672124643725, -0.00060115023435160925, -6.5543059305751491e-005, -1.6312699734552807e-005, -3.2054419133447798e-006, 7.367572885903746e-007, 1.4895492164971559e-006, 1.1307947017916706e-006, 5.5063405652522782e-007, 1.1783004497663934e-007, -8.2006806503864813e-008, -1.0866516536735883e-007, -6.0669757413511352e-008, -1.0798819539621958e-008, 1.111944952595278e-008, 8.519459636796214e-009, 1.0099999569414229e-012} }; #line 26 static float db1_float[][2] = { {0.70710678118654757, 0.70710678118654757}, {-0.70710678118654757, 0.70710678118654757}, {0.70710678118654757, 0.70710678118654757}, {0.70710678118654757, -0.70710678118654757} }; static float db2_float[][4] = { {-0.12940952255092145, 0.22414386804185735, 0.83651630373746899, 0.48296291314469025}, {-0.48296291314469025, 0.83651630373746899, -0.22414386804185735, -0.12940952255092145}, {0.48296291314469025, 0.83651630373746899, 0.22414386804185735, -0.12940952255092145}, {-0.12940952255092145, -0.22414386804185735, 0.83651630373746899, -0.48296291314469025} }; static float db3_float[][6] = { {0.035226291882100656, -0.085441273882241486, -0.13501102001039084, 0.45987750211933132, 0.80689150931333875, 0.33267055295095688}, {-0.33267055295095688, 0.80689150931333875, -0.45987750211933132, -0.13501102001039084, 0.085441273882241486, 0.035226291882100656}, {0.33267055295095688, 0.80689150931333875, 0.45987750211933132, -0.13501102001039084, -0.085441273882241486, 0.035226291882100656}, {0.035226291882100656, 0.085441273882241486, -0.13501102001039084, -0.45987750211933132, 0.80689150931333875, -0.33267055295095688} }; static float db4_float[][8] = { {-0.010597401784997278, 0.032883011666982945, 0.030841381835986965, -0.18703481171888114, -0.027983769416983849, 0.63088076792959036, 0.71484657055254153, 0.23037781330885523}, {-0.23037781330885523, 0.71484657055254153, -0.63088076792959036, -0.027983769416983849, 0.18703481171888114, 0.030841381835986965, -0.032883011666982945, -0.010597401784997278}, {0.23037781330885523, 0.71484657055254153, 0.63088076792959036, -0.027983769416983849, -0.18703481171888114, 0.030841381835986965, 0.032883011666982945, -0.010597401784997278}, {-0.010597401784997278, -0.032883011666982945, 0.030841381835986965, 0.18703481171888114, -0.027983769416983849, -0.63088076792959036, 0.71484657055254153, -0.23037781330885523} }; static float db5_float[][10] = { {0.0033357252850015492, -0.012580751999015526, -0.0062414902130117052, 0.077571493840065148, -0.03224486958502952, -0.24229488706619015, 0.13842814590110342, 0.72430852843857441, 0.60382926979747287, 0.16010239797412501}, {-0.16010239797412501, 0.60382926979747287, -0.72430852843857441, 0.13842814590110342, 0.24229488706619015, -0.03224486958502952, -0.077571493840065148, -0.0062414902130117052, 0.012580751999015526, 0.0033357252850015492}, {0.16010239797412501, 0.60382926979747287, 0.72430852843857441, 0.13842814590110342, -0.24229488706619015, -0.03224486958502952, 0.077571493840065148, -0.0062414902130117052, -0.012580751999015526, 0.0033357252850015492}, {0.0033357252850015492, 0.012580751999015526, -0.0062414902130117052, -0.077571493840065148, -0.03224486958502952, 0.24229488706619015, 0.13842814590110342, -0.72430852843857441, 0.60382926979747287, -0.16010239797412501} }; static float db6_float[][12] = { {-0.0010773010849955799, 0.0047772575110106514, 0.0005538422009938016, -0.031582039318031156, 0.027522865530016288, 0.097501605587079362, -0.12976686756709563, -0.22626469396516913, 0.3152503517092432, 0.75113390802157753, 0.49462389039838539, 0.11154074335008017}, {-0.11154074335008017, 0.49462389039838539, -0.75113390802157753, 0.3152503517092432, 0.22626469396516913, -0.12976686756709563, -0.097501605587079362, 0.027522865530016288, 0.031582039318031156, 0.0005538422009938016, -0.0047772575110106514, -0.0010773010849955799}, {0.11154074335008017, 0.49462389039838539, 0.75113390802157753, 0.3152503517092432, -0.22626469396516913, -0.12976686756709563, 0.097501605587079362, 0.027522865530016288, -0.031582039318031156, 0.0005538422009938016, 0.0047772575110106514, -0.0010773010849955799}, {-0.0010773010849955799, -0.0047772575110106514, 0.0005538422009938016, 0.031582039318031156, 0.027522865530016288, -0.097501605587079362, -0.12976686756709563, 0.22626469396516913, 0.3152503517092432, -0.75113390802157753, 0.49462389039838539, -0.11154074335008017} }; static float db7_float[][14] = { {0.00035371380000103988, -0.0018016407039998328, 0.00042957797300470274, 0.012550998556013784, -0.01657454163101562, -0.038029936935034633, 0.080612609151065898, 0.071309219267050042, -0.22403618499416572, -0.14390600392910627, 0.4697822874053586, 0.72913209084655506, 0.39653931948230575, 0.077852054085062364}, {-0.077852054085062364, 0.39653931948230575, -0.72913209084655506, 0.4697822874053586, 0.14390600392910627, -0.22403618499416572, -0.071309219267050042, 0.080612609151065898, 0.038029936935034633, -0.01657454163101562, -0.012550998556013784, 0.00042957797300470274, 0.0018016407039998328, 0.00035371380000103988}, {0.077852054085062364, 0.39653931948230575, 0.72913209084655506, 0.4697822874053586, -0.14390600392910627, -0.22403618499416572, 0.071309219267050042, 0.080612609151065898, -0.038029936935034633, -0.01657454163101562, 0.012550998556013784, 0.00042957797300470274, -0.0018016407039998328, 0.00035371380000103988}, {0.00035371380000103988, 0.0018016407039998328, 0.00042957797300470274, -0.012550998556013784, -0.01657454163101562, 0.038029936935034633, 0.080612609151065898, -0.071309219267050042, -0.22403618499416572, 0.14390600392910627, 0.4697822874053586, -0.72913209084655506, 0.39653931948230575, -0.077852054085062364} }; static float db8_float[][16] = { {-0.00011747678400228192, 0.00067544940599855677, -0.00039174037299597711, -0.0048703529930106603, 0.0087460940470156547, 0.013981027917015516, -0.044088253931064719, -0.017369301002022108, 0.12874742662018601, 0.00047248457399797254, -0.28401554296242809, -0.015829105256023893, 0.58535468365486909, 0.67563073629801285, 0.31287159091446592, 0.054415842243081609}, {-0.054415842243081609, 0.31287159091446592, -0.67563073629801285, 0.58535468365486909, 0.015829105256023893, -0.28401554296242809, -0.00047248457399797254, 0.12874742662018601, 0.017369301002022108, -0.044088253931064719, -0.013981027917015516, 0.0087460940470156547, 0.0048703529930106603, -0.00039174037299597711, -0.00067544940599855677, -0.00011747678400228192}, {0.054415842243081609, 0.31287159091446592, 0.67563073629801285, 0.58535468365486909, -0.015829105256023893, -0.28401554296242809, 0.00047248457399797254, 0.12874742662018601, -0.017369301002022108, -0.044088253931064719, 0.013981027917015516, 0.0087460940470156547, -0.0048703529930106603, -0.00039174037299597711, 0.00067544940599855677, -0.00011747678400228192}, {-0.00011747678400228192, -0.00067544940599855677, -0.00039174037299597711, 0.0048703529930106603, 0.0087460940470156547, -0.013981027917015516, -0.044088253931064719, 0.017369301002022108, 0.12874742662018601, -0.00047248457399797254, -0.28401554296242809, 0.015829105256023893, 0.58535468365486909, -0.67563073629801285, 0.31287159091446592, -0.054415842243081609} }; static float db9_float[][18] = { {3.9347319995026124e-005, -0.00025196318899817888, 0.00023038576399541288, 0.0018476468829611268, -0.0042815036819047227, -0.004723204757894831, 0.022361662123515244, 0.00025094711499193845, -0.067632829059523988, 0.030725681478322865, 0.14854074933476008, -0.096840783220879037, -0.29327378327258685, 0.13319738582208895, 0.65728807803663891, 0.6048231236767786, 0.24383467463766728, 0.038077947363167282}, {-0.038077947363167282, 0.24383467463766728, -0.6048231236767786, 0.65728807803663891, -0.13319738582208895, -0.29327378327258685, 0.096840783220879037, 0.14854074933476008, -0.030725681478322865, -0.067632829059523988, -0.00025094711499193845, 0.022361662123515244, 0.004723204757894831, -0.0042815036819047227, -0.0018476468829611268, 0.00023038576399541288, 0.00025196318899817888, 3.9347319995026124e-005}, {0.038077947363167282, 0.24383467463766728, 0.6048231236767786, 0.65728807803663891, 0.13319738582208895, -0.29327378327258685, -0.096840783220879037, 0.14854074933476008, 0.030725681478322865, -0.067632829059523988, 0.00025094711499193845, 0.022361662123515244, -0.004723204757894831, -0.0042815036819047227, 0.0018476468829611268, 0.00023038576399541288, -0.00025196318899817888, 3.9347319995026124e-005}, {3.9347319995026124e-005, 0.00025196318899817888, 0.00023038576399541288, -0.0018476468829611268, -0.0042815036819047227, 0.004723204757894831, 0.022361662123515244, -0.00025094711499193845, -0.067632829059523988, -0.030725681478322865, 0.14854074933476008, 0.096840783220879037, -0.29327378327258685, -0.13319738582208895, 0.65728807803663891, -0.6048231236767786, 0.24383467463766728, -0.038077947363167282} }; static float db10_float[][20] = { {-1.3264203002354869e-005, 9.3588670001089845e-005, -0.0001164668549943862, -0.00068585669500468248, 0.0019924052949908499, 0.0013953517469940798, -0.010733175482979604, 0.0036065535669883944, 0.033212674058933238, -0.029457536821945671, -0.071394147165860775, 0.093057364603806592, 0.12736934033574265, -0.19594627437659665, -0.24984642432648865, 0.28117234366042648, 0.68845903945259213, 0.52720118893091983, 0.18817680007762133, 0.026670057900950818}, {-0.026670057900950818, 0.18817680007762133, -0.52720118893091983, 0.68845903945259213, -0.28117234366042648, -0.24984642432648865, 0.19594627437659665, 0.12736934033574265, -0.093057364603806592, -0.071394147165860775, 0.029457536821945671, 0.033212674058933238, -0.0036065535669883944, -0.010733175482979604, -0.0013953517469940798, 0.0019924052949908499, 0.00068585669500468248, -0.0001164668549943862, -9.3588670001089845e-005, -1.3264203002354869e-005}, {0.026670057900950818, 0.18817680007762133, 0.52720118893091983, 0.68845903945259213, 0.28117234366042648, -0.24984642432648865, -0.19594627437659665, 0.12736934033574265, 0.093057364603806592, -0.071394147165860775, -0.029457536821945671, 0.033212674058933238, 0.0036065535669883944, -0.010733175482979604, 0.0013953517469940798, 0.0019924052949908499, -0.00068585669500468248, -0.0001164668549943862, 9.3588670001089845e-005, -1.3264203002354869e-005}, {-1.3264203002354869e-005, -9.3588670001089845e-005, -0.0001164668549943862, 0.00068585669500468248, 0.0019924052949908499, -0.0013953517469940798, -0.010733175482979604, -0.0036065535669883944, 0.033212674058933238, 0.029457536821945671, -0.071394147165860775, -0.093057364603806592, 0.12736934033574265, 0.19594627437659665, -0.24984642432648865, -0.28117234366042648, 0.68845903945259213, -0.52720118893091983, 0.18817680007762133, -0.026670057900950818} }; static float db11_float[][22] = { {4.4942742772363519e-006, -3.4634984186983789e-005, 5.4439074699366381e-005, 0.00024915252355281426, -0.00089302325066623663, -0.00030859285881515924, 0.0049284176560587777, -0.0033408588730145018, -0.015364820906201324, 0.020840904360180039, 0.031335090219045313, -0.066438785695020222, -0.04647995511667613, 0.14981201246638268, 0.066043588196690886, -0.27423084681792875, -0.16227524502747828, 0.41196436894789695, 0.68568677491617847, 0.44989976435603013, 0.14406702115061959, 0.018694297761470441}, {-0.018694297761470441, 0.14406702115061959, -0.44989976435603013, 0.68568677491617847, -0.41196436894789695, -0.16227524502747828, 0.27423084681792875, 0.066043588196690886, -0.14981201246638268, -0.04647995511667613, 0.066438785695020222, 0.031335090219045313, -0.020840904360180039, -0.015364820906201324, 0.0033408588730145018, 0.0049284176560587777, 0.00030859285881515924, -0.00089302325066623663, -0.00024915252355281426, 5.4439074699366381e-005, 3.4634984186983789e-005, 4.4942742772363519e-006}, {0.018694297761470441, 0.14406702115061959, 0.44989976435603013, 0.68568677491617847, 0.41196436894789695, -0.16227524502747828, -0.27423084681792875, 0.066043588196690886, 0.14981201246638268, -0.04647995511667613, -0.066438785695020222, 0.031335090219045313, 0.020840904360180039, -0.015364820906201324, -0.0033408588730145018, 0.0049284176560587777, -0.00030859285881515924, -0.00089302325066623663, 0.00024915252355281426, 5.4439074699366381e-005, -3.4634984186983789e-005, 4.4942742772363519e-006}, {4.4942742772363519e-006, 3.4634984186983789e-005, 5.4439074699366381e-005, -0.00024915252355281426, -0.00089302325066623663, 0.00030859285881515924, 0.0049284176560587777, 0.0033408588730145018, -0.015364820906201324, -0.020840904360180039, 0.031335090219045313, 0.066438785695020222, -0.04647995511667613, -0.14981201246638268, 0.066043588196690886, 0.27423084681792875, -0.16227524502747828, -0.41196436894789695, 0.68568677491617847, -0.44989976435603013, 0.14406702115061959, -0.018694297761470441} }; static float db12_float[][24] = { {-1.5290717580684923e-006, 1.2776952219379579e-005, -2.4241545757030318e-005, -8.8504109208203182e-005, 0.00038865306282092672, 6.5451282125215034e-006, -0.0021795036186277044, 0.0022486072409952287, 0.0067114990087955486, -0.012840825198299882, -0.01221864906974642, 0.041546277495087637, 0.010849130255828966, -0.09643212009649671, 0.0053595696743599965, 0.18247860592758275, -0.023779257256064865, -0.31617845375277914, -0.044763885653777619, 0.51588647842780067, 0.65719872257929113, 0.37735513521420411, 0.10956627282118277, 0.013112257957229239}, {-0.013112257957229239, 0.10956627282118277, -0.37735513521420411, 0.65719872257929113, -0.51588647842780067, -0.044763885653777619, 0.31617845375277914, -0.023779257256064865, -0.18247860592758275, 0.0053595696743599965, 0.09643212009649671, 0.010849130255828966, -0.041546277495087637, -0.01221864906974642, 0.012840825198299882, 0.0067114990087955486, -0.0022486072409952287, -0.0021795036186277044, -6.5451282125215034e-006, 0.00038865306282092672, 8.8504109208203182e-005, -2.4241545757030318e-005, -1.2776952219379579e-005, -1.5290717580684923e-006}, {0.013112257957229239, 0.10956627282118277, 0.37735513521420411, 0.65719872257929113, 0.51588647842780067, -0.044763885653777619, -0.31617845375277914, -0.023779257256064865, 0.18247860592758275, 0.0053595696743599965, -0.09643212009649671, 0.010849130255828966, 0.041546277495087637, -0.01221864906974642, -0.012840825198299882, 0.0067114990087955486, 0.0022486072409952287, -0.0021795036186277044, 6.5451282125215034e-006, 0.00038865306282092672, -8.8504109208203182e-005, -2.4241545757030318e-005, 1.2776952219379579e-005, -1.5290717580684923e-006}, {-1.5290717580684923e-006, -1.2776952219379579e-005, -2.4241545757030318e-005, 8.8504109208203182e-005, 0.00038865306282092672, -6.5451282125215034e-006, -0.0021795036186277044, -0.0022486072409952287, 0.0067114990087955486, 0.012840825198299882, -0.01221864906974642, -0.041546277495087637, 0.010849130255828966, 0.09643212009649671, 0.0053595696743599965, -0.18247860592758275, -0.023779257256064865, 0.31617845375277914, -0.044763885653777619, -0.51588647842780067, 0.65719872257929113, -0.37735513521420411, 0.10956627282118277, -0.013112257957229239} }; static float db13_float[][26] = { {5.2200350984547998e-007, -4.7004164793608082e-006, 1.0441930571407941e-005, 3.0678537579324358e-005, -0.00016512898855650571, 4.9251525126285676e-005, 0.00093232613086724904, -0.0013156739118922766, -0.002761911234656831, 0.0072555894016171187, 0.0039239414487955773, -0.023831420710327809, 0.0023799722540522269, 0.056139477100276156, -0.026488406475345658, -0.10580761818792761, 0.072948933656788742, 0.17947607942935084, -0.12457673075080665, -0.31497290771138414, 0.086985726179645007, 0.58888957043121193, 0.61105585115878114, 0.31199632216043488, 0.082861243872901946, 0.0092021335389622788}, {-0.0092021335389622788, 0.082861243872901946, -0.31199632216043488, 0.61105585115878114, -0.58888957043121193, 0.086985726179645007, 0.31497290771138414, -0.12457673075080665, -0.17947607942935084, 0.072948933656788742, 0.10580761818792761, -0.026488406475345658, -0.056139477100276156, 0.0023799722540522269, 0.023831420710327809, 0.0039239414487955773, -0.0072555894016171187, -0.002761911234656831, 0.0013156739118922766, 0.00093232613086724904, -4.9251525126285676e-005, -0.00016512898855650571, -3.0678537579324358e-005, 1.0441930571407941e-005, 4.7004164793608082e-006, 5.2200350984547998e-007}, {0.0092021335389622788, 0.082861243872901946, 0.31199632216043488, 0.61105585115878114, 0.58888957043121193, 0.086985726179645007, -0.31497290771138414, -0.12457673075080665, 0.17947607942935084, 0.072948933656788742, -0.10580761818792761, -0.026488406475345658, 0.056139477100276156, 0.0023799722540522269, -0.023831420710327809, 0.0039239414487955773, 0.0072555894016171187, -0.002761911234656831, -0.0013156739118922766, 0.00093232613086724904, 4.9251525126285676e-005, -0.00016512898855650571, 3.0678537579324358e-005, 1.0441930571407941e-005, -4.7004164793608082e-006, 5.2200350984547998e-007}, {5.2200350984547998e-007, 4.7004164793608082e-006, 1.0441930571407941e-005, -3.0678537579324358e-005, -0.00016512898855650571, -4.9251525126285676e-005, 0.00093232613086724904, 0.0013156739118922766, -0.002761911234656831, -0.0072555894016171187, 0.0039239414487955773, 0.023831420710327809, 0.0023799722540522269, -0.056139477100276156, -0.026488406475345658, 0.10580761818792761, 0.072948933656788742, -0.17947607942935084, -0.12457673075080665, 0.31497290771138414, 0.086985726179645007, -0.58888957043121193, 0.61105585115878114, -0.31199632216043488, 0.082861243872901946, -0.0092021335389622788} }; static float db14_float[][28] = { {-1.7871399683109222e-007, 1.7249946753674012e-006, -4.3897049017804176e-006, -1.0337209184568496e-005, 6.875504252695734e-005, -4.1777245770370672e-005, -0.00038683194731287514, 0.00070802115423540481, 0.001061691085606874, -0.003849638868019787, -0.00074621898926387534, 0.012789493266340071, -0.0056150495303375755, -0.030185351540353976, 0.026981408307947971, 0.05523712625925082, -0.071548955503983505, -0.086748411568110598, 0.13998901658445695, 0.13839521386479153, -0.21803352999321651, -0.27168855227867705, 0.21867068775886594, 0.63118784910471981, 0.55430561794077093, 0.25485026779256437, 0.062364758849384874, 0.0064611534600864905}, {-0.0064611534600864905, 0.062364758849384874, -0.25485026779256437, 0.55430561794077093, -0.63118784910471981, 0.21867068775886594, 0.27168855227867705, -0.21803352999321651, -0.13839521386479153, 0.13998901658445695, 0.086748411568110598, -0.071548955503983505, -0.05523712625925082, 0.026981408307947971, 0.030185351540353976, -0.0056150495303375755, -0.012789493266340071, -0.00074621898926387534, 0.003849638868019787, 0.001061691085606874, -0.00070802115423540481, -0.00038683194731287514, 4.1777245770370672e-005, 6.875504252695734e-005, 1.0337209184568496e-005, -4.3897049017804176e-006, -1.7249946753674012e-006, -1.7871399683109222e-007}, {0.0064611534600864905, 0.062364758849384874, 0.25485026779256437, 0.55430561794077093, 0.63118784910471981, 0.21867068775886594, -0.27168855227867705, -0.21803352999321651, 0.13839521386479153, 0.13998901658445695, -0.086748411568110598, -0.071548955503983505, 0.05523712625925082, 0.026981408307947971, -0.030185351540353976, -0.0056150495303375755, 0.012789493266340071, -0.00074621898926387534, -0.003849638868019787, 0.001061691085606874, 0.00070802115423540481, -0.00038683194731287514, -4.1777245770370672e-005, 6.875504252695734e-005, -1.0337209184568496e-005, -4.3897049017804176e-006, 1.7249946753674012e-006, -1.7871399683109222e-007}, {-1.7871399683109222e-007, -1.7249946753674012e-006, -4.3897049017804176e-006, 1.0337209184568496e-005, 6.875504252695734e-005, 4.1777245770370672e-005, -0.00038683194731287514, -0.00070802115423540481, 0.001061691085606874, 0.003849638868019787, -0.00074621898926387534, -0.012789493266340071, -0.0056150495303375755, 0.030185351540353976, 0.026981408307947971, -0.05523712625925082, -0.071548955503983505, 0.086748411568110598, 0.13998901658445695, -0.13839521386479153, -0.21803352999321651, 0.27168855227867705, 0.21867068775886594, -0.63118784910471981, 0.55430561794077093, -0.25485026779256437, 0.062364758849384874, -0.0064611534600864905} }; static float db15_float[][30] = { {6.1333599133037138e-008, -6.3168823258794506e-007, 1.8112704079399406e-006, 3.3629871817363823e-006, -2.8133296266037558e-005, 2.579269915531323e-005, 0.00015589648992055726, -0.00035956524436229364, -0.00037348235413726472, 0.0019433239803823459, -0.00024175649075894543, -0.0064877345603061454, 0.0051010003604228726, 0.015083918027862582, -0.020810050169636805, -0.025767007328366939, 0.054780550584559995, 0.033877143923563204, -0.11112093603713753, -0.039666176555733602, 0.19014671400708816, 0.065282952848765688, -0.28888259656686216, -0.19320413960907623, 0.33900253545462167, 0.64581314035721027, 0.49263177170797529, 0.20602386398692688, 0.046743394892750617, 0.0045385373615773762}, {-0.0045385373615773762, 0.046743394892750617, -0.20602386398692688, 0.49263177170797529, -0.64581314035721027, 0.33900253545462167, 0.19320413960907623, -0.28888259656686216, -0.065282952848765688, 0.19014671400708816, 0.039666176555733602, -0.11112093603713753, -0.033877143923563204, 0.054780550584559995, 0.025767007328366939, -0.020810050169636805, -0.015083918027862582, 0.0051010003604228726, 0.0064877345603061454, -0.00024175649075894543, -0.0019433239803823459, -0.00037348235413726472, 0.00035956524436229364, 0.00015589648992055726, -2.579269915531323e-005, -2.8133296266037558e-005, -3.3629871817363823e-006, 1.8112704079399406e-006, 6.3168823258794506e-007, 6.1333599133037138e-008}, {0.0045385373615773762, 0.046743394892750617, 0.20602386398692688, 0.49263177170797529, 0.64581314035721027, 0.33900253545462167, -0.19320413960907623, -0.28888259656686216, 0.065282952848765688, 0.19014671400708816, -0.039666176555733602, -0.11112093603713753, 0.033877143923563204, 0.054780550584559995, -0.025767007328366939, -0.020810050169636805, 0.015083918027862582, 0.0051010003604228726, -0.0064877345603061454, -0.00024175649075894543, 0.0019433239803823459, -0.00037348235413726472, -0.00035956524436229364, 0.00015589648992055726, 2.579269915531323e-005, -2.8133296266037558e-005, 3.3629871817363823e-006, 1.8112704079399406e-006, -6.3168823258794506e-007, 6.1333599133037138e-008}, {6.1333599133037138e-008, 6.3168823258794506e-007, 1.8112704079399406e-006, -3.3629871817363823e-006, -2.8133296266037558e-005, -2.579269915531323e-005, 0.00015589648992055726, 0.00035956524436229364, -0.00037348235413726472, -0.0019433239803823459, -0.00024175649075894543, 0.0064877345603061454, 0.0051010003604228726, -0.015083918027862582, -0.020810050169636805, 0.025767007328366939, 0.054780550584559995, -0.033877143923563204, -0.11112093603713753, 0.039666176555733602, 0.19014671400708816, -0.065282952848765688, -0.28888259656686216, 0.19320413960907623, 0.33900253545462167, -0.64581314035721027, 0.49263177170797529, -0.20602386398692688, 0.046743394892750617, -0.0045385373615773762} }; static float db16_float[][32] = { {-2.1093396300980412e-008, 2.3087840868545578e-007, -7.3636567854418147e-007, -1.0435713423102517e-006, 1.133660866126152e-005, -1.394566898819319e-005, -6.103596621404321e-005, 0.00017478724522506327, 0.00011424152003843815, -0.00094102174935854332, 0.00040789698084934395, 0.00312802338120381, -0.0036442796214883506, -0.0069900145633907508, 0.013993768859843242, 0.010297659641009963, -0.036888397691556774, -0.0075889743686425939, 0.075924236044457791, -0.0062397227521562536, -0.13238830556335474, 0.027340263752899923, 0.21119069394696974, -0.02791820813292813, -0.32706331052747578, -0.089751089402363524, 0.44029025688580486, 0.63735633208298326, 0.43031272284545874, 0.1650642834886438, 0.034907714323629047, 0.0031892209253436892}, {-0.0031892209253436892, 0.034907714323629047, -0.1650642834886438, 0.43031272284545874, -0.63735633208298326, 0.44029025688580486, 0.089751089402363524, -0.32706331052747578, 0.02791820813292813, 0.21119069394696974, -0.027340263752899923, -0.13238830556335474, 0.0062397227521562536, 0.075924236044457791, 0.0075889743686425939, -0.036888397691556774, -0.010297659641009963, 0.013993768859843242, 0.0069900145633907508, -0.0036442796214883506, -0.00312802338120381, 0.00040789698084934395, 0.00094102174935854332, 0.00011424152003843815, -0.00017478724522506327, -6.103596621404321e-005, 1.394566898819319e-005, 1.133660866126152e-005, 1.0435713423102517e-006, -7.3636567854418147e-007, -2.3087840868545578e-007, -2.1093396300980412e-008}, {0.0031892209253436892, 0.034907714323629047, 0.1650642834886438, 0.43031272284545874, 0.63735633208298326, 0.44029025688580486, -0.089751089402363524, -0.32706331052747578, -0.02791820813292813, 0.21119069394696974, 0.027340263752899923, -0.13238830556335474, -0.0062397227521562536, 0.075924236044457791, -0.0075889743686425939, -0.036888397691556774, 0.010297659641009963, 0.013993768859843242, -0.0069900145633907508, -0.0036442796214883506, 0.00312802338120381, 0.00040789698084934395, -0.00094102174935854332, 0.00011424152003843815, 0.00017478724522506327, -6.103596621404321e-005, -1.394566898819319e-005, 1.133660866126152e-005, -1.0435713423102517e-006, -7.3636567854418147e-007, 2.3087840868545578e-007, -2.1093396300980412e-008}, {-2.1093396300980412e-008, -2.3087840868545578e-007, -7.3636567854418147e-007, 1.0435713423102517e-006, 1.133660866126152e-005, 1.394566898819319e-005, -6.103596621404321e-005, -0.00017478724522506327, 0.00011424152003843815, 0.00094102174935854332, 0.00040789698084934395, -0.00312802338120381, -0.0036442796214883506, 0.0069900145633907508, 0.013993768859843242, -0.010297659641009963, -0.036888397691556774, 0.0075889743686425939, 0.075924236044457791, 0.0062397227521562536, -0.13238830556335474, -0.027340263752899923, 0.21119069394696974, 0.02791820813292813, -0.32706331052747578, 0.089751089402363524, 0.44029025688580486, -0.63735633208298326, 0.43031272284545874, -0.1650642834886438, 0.034907714323629047, -0.0031892209253436892} }; static float db17_float[][34] = { {7.2674929685663697e-009, -8.4239484460081536e-008, 2.9577009333187617e-007, 3.0165496099963414e-007, -4.5059424772259631e-006, 6.9906009850812941e-006, 2.3186813798761639e-005, -8.2048032024582121e-005, -2.5610109566546042e-005, 0.00043946542776894542, -0.00032813251941022427, -0.001436845304805, 0.0023012052421511474, 0.0029679966915180638, -0.0086029215203478147, -0.0030429899813869555, 0.022733676583919053, -0.0032709555358783646, -0.046922438389378908, 0.022312336178011833, 0.081105986654080822, -0.057091419631858077, -0.12681569177849797, 0.10113548917744287, 0.19731058956508457, -0.12659975221599248, -0.32832074836418546, 0.027314970403312946, 0.5183157640572823, 0.61099661568502728, 0.37035072415288578, 0.13121490330791097, 0.025985393703623173, 0.0022418070010387899}, {-0.0022418070010387899, 0.025985393703623173, -0.13121490330791097, 0.37035072415288578, -0.61099661568502728, 0.5183157640572823, -0.027314970403312946, -0.32832074836418546, 0.12659975221599248, 0.19731058956508457, -0.10113548917744287, -0.12681569177849797, 0.057091419631858077, 0.081105986654080822, -0.022312336178011833, -0.046922438389378908, 0.0032709555358783646, 0.022733676583919053, 0.0030429899813869555, -0.0086029215203478147, -0.0029679966915180638, 0.0023012052421511474, 0.001436845304805, -0.00032813251941022427, -0.00043946542776894542, -2.5610109566546042e-005, 8.2048032024582121e-005, 2.3186813798761639e-005, -6.9906009850812941e-006, -4.5059424772259631e-006, -3.0165496099963414e-007, 2.9577009333187617e-007, 8.4239484460081536e-008, 7.2674929685663697e-009}, {0.0022418070010387899, 0.025985393703623173, 0.13121490330791097, 0.37035072415288578, 0.61099661568502728, 0.5183157640572823, 0.027314970403312946, -0.32832074836418546, -0.12659975221599248, 0.19731058956508457, 0.10113548917744287, -0.12681569177849797, -0.057091419631858077, 0.081105986654080822, 0.022312336178011833, -0.046922438389378908, -0.0032709555358783646, 0.022733676583919053, -0.0030429899813869555, -0.0086029215203478147, 0.0029679966915180638, 0.0023012052421511474, -0.001436845304805, -0.00032813251941022427, 0.00043946542776894542, -2.5610109566546042e-005, -8.2048032024582121e-005, 2.3186813798761639e-005, 6.9906009850812941e-006, -4.5059424772259631e-006, 3.0165496099963414e-007, 2.9577009333187617e-007, -8.4239484460081536e-008, 7.2674929685663697e-009}, {7.2674929685663697e-009, 8.4239484460081536e-008, 2.9577009333187617e-007, -3.0165496099963414e-007, -4.5059424772259631e-006, -6.9906009850812941e-006, 2.3186813798761639e-005, 8.2048032024582121e-005, -2.5610109566546042e-005, -0.00043946542776894542, -0.00032813251941022427, 0.001436845304805, 0.0023012052421511474, -0.0029679966915180638, -0.0086029215203478147, 0.0030429899813869555, 0.022733676583919053, 0.0032709555358783646, -0.046922438389378908, -0.022312336178011833, 0.081105986654080822, 0.057091419631858077, -0.12681569177849797, -0.10113548917744287, 0.19731058956508457, 0.12659975221599248, -0.32832074836418546, -0.027314970403312946, 0.5183157640572823, -0.61099661568502728, 0.37035072415288578, -0.13121490330791097, 0.025985393703623173, -0.0022418070010387899} }; static float db18_float[][36] = { {-2.5079344549419292e-009, 3.0688358630370302e-008, -1.1760987670250871e-007, -7.691632689865049e-008, 1.7687129836228861e-006, -3.3326344788769603e-006, -8.5206025374234635e-006, 3.7412378807308472e-005, -1.5359171230213409e-007, -0.00019864855231101547, 0.0002135815619103188, 0.00062846568296447147, -0.0013405962983313922, -0.0011187326669886426, 0.0049433436054565939, 0.00011863003387493042, -0.013051480946517112, 0.0062621679544386608, 0.026670705926689853, -0.023733210395336858, -0.04452614190225633, 0.057051247739058272, 0.064887216212358198, -0.10675224665906288, -0.092331884150304119, 0.16708131276294505, 0.14953397556500755, -0.21648093400458224, -0.29365404073579809, 0.14722311196952223, 0.57180165488712198, 0.57182680776508177, 0.31467894133619284, 0.10358846582214751, 0.019288531724094969, 0.0015763102184365595}, {-0.0015763102184365595, 0.019288531724094969, -0.10358846582214751, 0.31467894133619284, -0.57182680776508177, 0.57180165488712198, -0.14722311196952223, -0.29365404073579809, 0.21648093400458224, 0.14953397556500755, -0.16708131276294505, -0.092331884150304119, 0.10675224665906288, 0.064887216212358198, -0.057051247739058272, -0.04452614190225633, 0.023733210395336858, 0.026670705926689853, -0.0062621679544386608, -0.013051480946517112, -0.00011863003387493042, 0.0049433436054565939, 0.0011187326669886426, -0.0013405962983313922, -0.00062846568296447147, 0.0002135815619103188, 0.00019864855231101547, -1.5359171230213409e-007, -3.7412378807308472e-005, -8.5206025374234635e-006, 3.3326344788769603e-006, 1.7687129836228861e-006, 7.691632689865049e-008, -1.1760987670250871e-007, -3.0688358630370302e-008, -2.5079344549419292e-009}, {0.0015763102184365595, 0.019288531724094969, 0.10358846582214751, 0.31467894133619284, 0.57182680776508177, 0.57180165488712198, 0.14722311196952223, -0.29365404073579809, -0.21648093400458224, 0.14953397556500755, 0.16708131276294505, -0.092331884150304119, -0.10675224665906288, 0.064887216212358198, 0.057051247739058272, -0.04452614190225633, -0.023733210395336858, 0.026670705926689853, 0.0062621679544386608, -0.013051480946517112, 0.00011863003387493042, 0.0049433436054565939, -0.0011187326669886426, -0.0013405962983313922, 0.00062846568296447147, 0.0002135815619103188, -0.00019864855231101547, -1.5359171230213409e-007, 3.7412378807308472e-005, -8.5206025374234635e-006, -3.3326344788769603e-006, 1.7687129836228861e-006, -7.691632689865049e-008, -1.1760987670250871e-007, 3.0688358630370302e-008, -2.5079344549419292e-009}, {-2.5079344549419292e-009, -3.0688358630370302e-008, -1.1760987670250871e-007, 7.691632689865049e-008, 1.7687129836228861e-006, 3.3326344788769603e-006, -8.5206025374234635e-006, -3.7412378807308472e-005, -1.5359171230213409e-007, 0.00019864855231101547, 0.0002135815619103188, -0.00062846568296447147, -0.0013405962983313922, 0.0011187326669886426, 0.0049433436054565939, -0.00011863003387493042, -0.013051480946517112, -0.0062621679544386608, 0.026670705926689853, 0.023733210395336858, -0.04452614190225633, -0.057051247739058272, 0.064887216212358198, 0.10675224665906288, -0.092331884150304119, -0.16708131276294505, 0.14953397556500755, 0.21648093400458224, -0.29365404073579809, -0.14722311196952223, 0.57180165488712198, -0.57182680776508177, 0.31467894133619284, -0.10358846582214751, 0.019288531724094969, -0.0015763102184365595} }; static float db19_float[][38] = { {8.6668488390344833e-010, -1.1164020670405678e-008, 4.6369377758023682e-008, 1.4470882988040879e-008, -6.8627556577981102e-007, 1.5319314766978769e-006, 3.0109643163099385e-006, -1.6640176297224622e-005, 5.1059504870906939e-006, 8.7112704672504432e-005, -0.00012460079173506306, -0.00026067613568119951, 0.0007358025205041731, 0.00034180865344939543, -0.0026875518007344408, 0.00076895435922424884, 0.0070407473670804953, -0.0058669222811121953, -0.013988388678695632, 0.019375549889114482, 0.021623767409452484, -0.045674226277784918, -0.026501236250778635, 0.086906755555450702, 0.027584350624887129, -0.14278569504021468, -0.033518541903202262, 0.21234974330662043, 0.074652269708066474, -0.28583863175723145, -0.22809139421653665, 0.26089495265212009, 0.60170454913009164, 0.52443637746688621, 0.26438843174202237, 0.08127811326580564, 0.01428109845082521, 0.0011086697631864314}, {-0.0011086697631864314, 0.01428109845082521, -0.08127811326580564, 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-0.28583863175723145, 0.074652269708066474, 0.21234974330662043, -0.033518541903202262, -0.14278569504021468, 0.027584350624887129, 0.086906755555450702, -0.026501236250778635, -0.045674226277784918, 0.021623767409452484, 0.019375549889114482, -0.013988388678695632, -0.0058669222811121953, 0.0070407473670804953, 0.00076895435922424884, -0.0026875518007344408, 0.00034180865344939543, 0.0007358025205041731, -0.00026067613568119951, -0.00012460079173506306, 8.7112704672504432e-005, 5.1059504870906939e-006, -1.6640176297224622e-005, 3.0109643163099385e-006, 1.5319314766978769e-006, -6.8627556577981102e-007, 1.4470882988040879e-008, 4.6369377758023682e-008, -1.1164020670405678e-008, 8.6668488390344833e-010}, {8.6668488390344833e-010, 1.1164020670405678e-008, 4.6369377758023682e-008, -1.4470882988040879e-008, -6.8627556577981102e-007, -1.5319314766978769e-006, 3.0109643163099385e-006, 1.6640176297224622e-005, 5.1059504870906939e-006, -8.7112704672504432e-005, -0.00012460079173506306, 0.00026067613568119951, 0.0007358025205041731, -0.00034180865344939543, -0.0026875518007344408, -0.00076895435922424884, 0.0070407473670804953, 0.0058669222811121953, -0.013988388678695632, -0.019375549889114482, 0.021623767409452484, 0.045674226277784918, -0.026501236250778635, -0.086906755555450702, 0.027584350624887129, 0.14278569504021468, -0.033518541903202262, -0.21234974330662043, 0.074652269708066474, 0.28583863175723145, -0.22809139421653665, -0.26089495265212009, 0.60170454913009164, -0.52443637746688621, 0.26438843174202237, -0.08127811326580564, 0.01428109845082521, -0.0011086697631864314} }; static float db20_float[][40] = { {-2.9988364896157532e-010, 4.05612705554717e-009, -1.8148432482976221e-008, 2.0143220235374613e-010, 2.633924226266962e-007, -6.847079596993149e-007, -1.0119940100181473e-006, 7.2412482876637907e-006, -4.3761438621821972e-006, -3.7105861833906152e-005, 6.7742808283730477e-005, 0.00010153288973669777, -0.0003851047486990061, -5.3497598443404532e-005, 0.0013925596193045254, -0.00083156217287724745, -0.003581494259744107, 0.0044205423867663502, 0.0067216273018096935, -0.013810526137727442, -0.0087893249245557647, 0.032294299530119162, 0.0058746818113949465, -0.061722899624668884, 0.0056322468576854544, 0.10229171917513397, -0.024716827337521424, -0.15545875070604531, 0.039850246458519104, 0.22829105082013823, -0.016727088308801888, -0.32678680043353758, -0.13921208801128787, 0.36150229873889705, 0.61049323893785579, 0.47269618531033147, 0.21994211355113222, 0.063423780459005291, 0.010549394624937735, 0.00077995361366591117}, {-0.00077995361366591117, 0.010549394624937735, -0.063423780459005291, 0.21994211355113222, -0.47269618531033147, 0.61049323893785579, -0.36150229873889705, -0.13921208801128787, 0.32678680043353758, -0.016727088308801888, -0.22829105082013823, 0.039850246458519104, 0.15545875070604531, -0.024716827337521424, -0.10229171917513397, 0.0056322468576854544, 0.061722899624668884, 0.0058746818113949465, -0.032294299530119162, -0.0087893249245557647, 0.013810526137727442, 0.0067216273018096935, -0.0044205423867663502, -0.003581494259744107, 0.00083156217287724745, 0.0013925596193045254, 5.3497598443404532e-005, -0.0003851047486990061, -0.00010153288973669777, 6.7742808283730477e-005, 3.7105861833906152e-005, -4.3761438621821972e-006, -7.2412482876637907e-006, -1.0119940100181473e-006, 6.847079596993149e-007, 2.633924226266962e-007, -2.0143220235374613e-010, -1.8148432482976221e-008, -4.05612705554717e-009, -2.9988364896157532e-010}, {0.00077995361366591117, 0.010549394624937735, 0.063423780459005291, 0.21994211355113222, 0.47269618531033147, 0.61049323893785579, 0.36150229873889705, -0.13921208801128787, -0.32678680043353758, -0.016727088308801888, 0.22829105082013823, 0.039850246458519104, -0.15545875070604531, -0.024716827337521424, 0.10229171917513397, 0.0056322468576854544, -0.061722899624668884, 0.0058746818113949465, 0.032294299530119162, -0.0087893249245557647, -0.013810526137727442, 0.0067216273018096935, 0.0044205423867663502, -0.003581494259744107, -0.00083156217287724745, 0.0013925596193045254, -5.3497598443404532e-005, -0.0003851047486990061, 0.00010153288973669777, 6.7742808283730477e-005, -3.7105861833906152e-005, -4.3761438621821972e-006, 7.2412482876637907e-006, -1.0119940100181473e-006, -6.847079596993149e-007, 2.633924226266962e-007, 2.0143220235374613e-010, -1.8148432482976221e-008, 4.05612705554717e-009, -2.9988364896157532e-010}, {-2.9988364896157532e-010, -4.05612705554717e-009, -1.8148432482976221e-008, -2.0143220235374613e-010, 2.633924226266962e-007, 6.847079596993149e-007, -1.0119940100181473e-006, -7.2412482876637907e-006, -4.3761438621821972e-006, 3.7105861833906152e-005, 6.7742808283730477e-005, -0.00010153288973669777, -0.0003851047486990061, 5.3497598443404532e-005, 0.0013925596193045254, 0.00083156217287724745, -0.003581494259744107, -0.0044205423867663502, 0.0067216273018096935, 0.013810526137727442, -0.0087893249245557647, -0.032294299530119162, 0.0058746818113949465, 0.061722899624668884, 0.0056322468576854544, -0.10229171917513397, -0.024716827337521424, 0.15545875070604531, 0.039850246458519104, -0.22829105082013823, -0.016727088308801888, 0.32678680043353758, -0.13921208801128787, -0.36150229873889705, 0.61049323893785579, -0.47269618531033147, 0.21994211355113222, -0.063423780459005291, 0.010549394624937735, -0.00077995361366591117} }; static float sym2_float[][4] = { {-0.12940952255092145, 0.22414386804185735, 0.83651630373746899, 0.48296291314469025}, {-0.48296291314469025, 0.83651630373746899, -0.22414386804185735, -0.12940952255092145}, {0.48296291314469025, 0.83651630373746899, 0.22414386804185735, -0.12940952255092145}, {-0.12940952255092145, -0.22414386804185735, 0.83651630373746899, -0.48296291314469025} }; static float sym3_float[][6] = { {0.035226291882100656, -0.085441273882241486, -0.13501102001039084, 0.45987750211933132, 0.80689150931333875, 0.33267055295095688}, {-0.33267055295095688, 0.80689150931333875, -0.45987750211933132, -0.13501102001039084, 0.085441273882241486, 0.035226291882100656}, {0.33267055295095688, 0.80689150931333875, 0.45987750211933132, -0.13501102001039084, -0.085441273882241486, 0.035226291882100656}, {0.035226291882100656, 0.085441273882241486, -0.13501102001039084, -0.45987750211933132, 0.80689150931333875, -0.33267055295095688} }; static float sym4_float[][8] = { {-0.075765714789273325, -0.02963552764599851, 0.49761866763201545, 0.80373875180591614, 0.29785779560527736, -0.099219543576847216, -0.012603967262037833, 0.032223100604042702}, {-0.032223100604042702, -0.012603967262037833, 0.099219543576847216, 0.29785779560527736, -0.80373875180591614, 0.49761866763201545, 0.02963552764599851, -0.075765714789273325}, {0.032223100604042702, -0.012603967262037833, -0.099219543576847216, 0.29785779560527736, 0.80373875180591614, 0.49761866763201545, -0.02963552764599851, -0.075765714789273325}, {-0.075765714789273325, 0.02963552764599851, 0.49761866763201545, -0.80373875180591614, 0.29785779560527736, 0.099219543576847216, -0.012603967262037833, -0.032223100604042702} }; static float sym5_float[][10] = { {0.027333068345077982, 0.029519490925774643, -0.039134249302383094, 0.1993975339773936, 0.72340769040242059, 0.63397896345821192, 0.016602105764522319, -0.17532808990845047, -0.021101834024758855, 0.019538882735286728}, {-0.019538882735286728, -0.021101834024758855, 0.17532808990845047, 0.016602105764522319, -0.63397896345821192, 0.72340769040242059, -0.1993975339773936, -0.039134249302383094, -0.029519490925774643, 0.027333068345077982}, {0.019538882735286728, -0.021101834024758855, -0.17532808990845047, 0.016602105764522319, 0.63397896345821192, 0.72340769040242059, 0.1993975339773936, -0.039134249302383094, 0.029519490925774643, 0.027333068345077982}, {0.027333068345077982, -0.029519490925774643, -0.039134249302383094, -0.1993975339773936, 0.72340769040242059, -0.63397896345821192, 0.016602105764522319, 0.17532808990845047, -0.021101834024758855, -0.019538882735286728} }; static float sym6_float[][12] = { {0.015404109327027373, 0.0034907120842174702, -0.11799011114819057, -0.048311742585632998, 0.49105594192674662, 0.787641141030194, 0.3379294217276218, -0.072637522786462516, -0.021060292512300564, 0.044724901770665779, 0.0017677118642428036, -0.007800708325034148}, {0.007800708325034148, 0.0017677118642428036, -0.044724901770665779, -0.021060292512300564, 0.072637522786462516, 0.3379294217276218, -0.787641141030194, 0.49105594192674662, 0.048311742585632998, -0.11799011114819057, -0.0034907120842174702, 0.015404109327027373}, {-0.007800708325034148, 0.0017677118642428036, 0.044724901770665779, -0.021060292512300564, -0.072637522786462516, 0.3379294217276218, 0.787641141030194, 0.49105594192674662, -0.048311742585632998, -0.11799011114819057, 0.0034907120842174702, 0.015404109327027373}, {0.015404109327027373, -0.0034907120842174702, -0.11799011114819057, 0.048311742585632998, 0.49105594192674662, -0.787641141030194, 0.3379294217276218, 0.072637522786462516, -0.021060292512300564, -0.044724901770665779, 0.0017677118642428036, 0.007800708325034148} }; static float sym7_float[][14] = { {0.0026818145682578781, -0.0010473848886829163, -0.01263630340325193, 0.03051551316596357, 0.067892693501372697, -0.049552834937127255, 0.017441255086855827, 0.5361019170917628, 0.76776431700316405, 0.28862963175151463, -0.14004724044296152, -0.10780823770381774, 0.0040102448715336634, 0.010268176708511255}, {-0.010268176708511255, 0.0040102448715336634, 0.10780823770381774, -0.14004724044296152, -0.28862963175151463, 0.76776431700316405, -0.5361019170917628, 0.017441255086855827, 0.049552834937127255, 0.067892693501372697, -0.03051551316596357, -0.01263630340325193, 0.0010473848886829163, 0.0026818145682578781}, {0.010268176708511255, 0.0040102448715336634, -0.10780823770381774, -0.14004724044296152, 0.28862963175151463, 0.76776431700316405, 0.5361019170917628, 0.017441255086855827, -0.049552834937127255, 0.067892693501372697, 0.03051551316596357, -0.01263630340325193, -0.0010473848886829163, 0.0026818145682578781}, {0.0026818145682578781, 0.0010473848886829163, -0.01263630340325193, -0.03051551316596357, 0.067892693501372697, 0.049552834937127255, 0.017441255086855827, -0.5361019170917628, 0.76776431700316405, -0.28862963175151463, -0.14004724044296152, 0.10780823770381774, 0.0040102448715336634, -0.010268176708511255} }; static float sym8_float[][16] = { {-0.0033824159510061256, -0.00054213233179114812, 0.031695087811492981, 0.0076074873249176054, -0.14329423835080971, -0.061273359067658524, 0.48135965125837221, 0.77718575170052351, 0.3644418948353314, -0.051945838107709037, -0.027219029917056003, 0.049137179673607506, 0.0038087520138906151, -0.014952258337048231, -0.0003029205147213668, 0.0018899503327594609}, {-0.0018899503327594609, -0.0003029205147213668, 0.014952258337048231, 0.0038087520138906151, -0.049137179673607506, -0.027219029917056003, 0.051945838107709037, 0.3644418948353314, -0.77718575170052351, 0.48135965125837221, 0.061273359067658524, -0.14329423835080971, -0.0076074873249176054, 0.031695087811492981, 0.00054213233179114812, -0.0033824159510061256}, {0.0018899503327594609, -0.0003029205147213668, -0.014952258337048231, 0.0038087520138906151, 0.049137179673607506, -0.027219029917056003, -0.051945838107709037, 0.3644418948353314, 0.77718575170052351, 0.48135965125837221, -0.061273359067658524, -0.14329423835080971, 0.0076074873249176054, 0.031695087811492981, -0.00054213233179114812, -0.0033824159510061256}, {-0.0033824159510061256, 0.00054213233179114812, 0.031695087811492981, -0.0076074873249176054, -0.14329423835080971, 0.061273359067658524, 0.48135965125837221, -0.77718575170052351, 0.3644418948353314, 0.051945838107709037, -0.027219029917056003, -0.049137179673607506, 0.0038087520138906151, 0.014952258337048231, -0.0003029205147213668, -0.0018899503327594609} }; static float sym9_float[][18] = { {0.0014009155259146807, 0.00061978088898558676, -0.013271967781817119, -0.01152821020767923, 0.03022487885827568, 0.00058346274612580684, -0.054568958430834071, 0.238760914607303, 0.717897082764412, 0.61733844914093583, 0.035272488035271894, -0.19155083129728512, -0.018233770779395985, 0.06207778930288603, 0.0088592674934004842, -0.010264064027633142, -0.00047315449868008311, 0.0010694900329086053}, {-0.0010694900329086053, -0.00047315449868008311, 0.010264064027633142, 0.0088592674934004842, -0.06207778930288603, -0.018233770779395985, 0.19155083129728512, 0.035272488035271894, -0.61733844914093583, 0.717897082764412, -0.238760914607303, -0.054568958430834071, -0.00058346274612580684, 0.03022487885827568, 0.01152821020767923, -0.013271967781817119, -0.00061978088898558676, 0.0014009155259146807}, {0.0010694900329086053, -0.00047315449868008311, -0.010264064027633142, 0.0088592674934004842, 0.06207778930288603, -0.018233770779395985, -0.19155083129728512, 0.035272488035271894, 0.61733844914093583, 0.717897082764412, 0.238760914607303, -0.054568958430834071, 0.00058346274612580684, 0.03022487885827568, -0.01152821020767923, -0.013271967781817119, 0.00061978088898558676, 0.0014009155259146807}, {0.0014009155259146807, -0.00061978088898558676, -0.013271967781817119, 0.01152821020767923, 0.03022487885827568, -0.00058346274612580684, -0.054568958430834071, -0.238760914607303, 0.717897082764412, -0.61733844914093583, 0.035272488035271894, 0.19155083129728512, -0.018233770779395985, -0.06207778930288603, 0.0088592674934004842, 0.010264064027633142, -0.00047315449868008311, -0.0010694900329086053} }; static float sym10_float[][20] = { {0.00077015980911449011, 9.5632670722894754e-005, -0.0086412992770224222, -0.0014653825813050513, 0.045927239231092203, 0.011609893903711381, -0.15949427888491757, -0.070880535783243853, 0.47169066693843925, 0.7695100370211071, 0.38382676106708546, -0.035536740473817552, -0.0319900568824278, 0.049994972077376687, 0.0057649120335819086, -0.02035493981231129, -0.00080435893201654491, 0.0045931735853118284, 5.7036083618494284e-005, -0.00045932942100465878}, {0.00045932942100465878, 5.7036083618494284e-005, -0.0045931735853118284, -0.00080435893201654491, 0.02035493981231129, 0.0057649120335819086, -0.049994972077376687, -0.0319900568824278, 0.035536740473817552, 0.38382676106708546, -0.7695100370211071, 0.47169066693843925, 0.070880535783243853, -0.15949427888491757, -0.011609893903711381, 0.045927239231092203, 0.0014653825813050513, -0.0086412992770224222, -9.5632670722894754e-005, 0.00077015980911449011}, {-0.00045932942100465878, 5.7036083618494284e-005, 0.0045931735853118284, -0.00080435893201654491, -0.02035493981231129, 0.0057649120335819086, 0.049994972077376687, -0.0319900568824278, -0.035536740473817552, 0.38382676106708546, 0.7695100370211071, 0.47169066693843925, -0.070880535783243853, -0.15949427888491757, 0.011609893903711381, 0.045927239231092203, -0.0014653825813050513, -0.0086412992770224222, 9.5632670722894754e-005, 0.00077015980911449011}, {0.00077015980911449011, -9.5632670722894754e-005, -0.0086412992770224222, 0.0014653825813050513, 0.045927239231092203, -0.011609893903711381, -0.15949427888491757, 0.070880535783243853, 0.47169066693843925, -0.7695100370211071, 0.38382676106708546, 0.035536740473817552, -0.0319900568824278, -0.049994972077376687, 0.0057649120335819086, 0.02035493981231129, -0.00080435893201654491, -0.0045931735853118284, 5.7036083618494284e-005, 0.00045932942100465878} }; static float sym11_float[][22] = { {0.00017172195069934854, -3.8795655736158566e-005, -0.0017343662672978692, 0.00058835273539699145, 0.0065124956747714497, -0.0098579348287897942, -0.024080841595864003, 0.0370374159788594, 0.069976799610734136, -0.022832651022562687, 0.097198394458909473, 0.57202297801008706, 0.73034354908839572, 0.23768990904924897, -0.2046547944958006, -0.14460234370531561, 0.035266759564466552, 0.043000190681552281, -0.0020034719001093887, -0.0063896036664548919, 0.00011053509764272153, 0.00048926361026192387}, {-0.00048926361026192387, 0.00011053509764272153, 0.0063896036664548919, -0.0020034719001093887, -0.043000190681552281, 0.035266759564466552, 0.14460234370531561, -0.2046547944958006, -0.23768990904924897, 0.73034354908839572, -0.57202297801008706, 0.097198394458909473, 0.022832651022562687, 0.069976799610734136, -0.0370374159788594, -0.024080841595864003, 0.0098579348287897942, 0.0065124956747714497, -0.00058835273539699145, -0.0017343662672978692, 3.8795655736158566e-005, 0.00017172195069934854}, {0.00048926361026192387, 0.00011053509764272153, -0.0063896036664548919, -0.0020034719001093887, 0.043000190681552281, 0.035266759564466552, -0.14460234370531561, -0.2046547944958006, 0.23768990904924897, 0.73034354908839572, 0.57202297801008706, 0.097198394458909473, -0.022832651022562687, 0.069976799610734136, 0.0370374159788594, -0.024080841595864003, -0.0098579348287897942, 0.0065124956747714497, 0.00058835273539699145, -0.0017343662672978692, -3.8795655736158566e-005, 0.00017172195069934854}, {0.00017172195069934854, 3.8795655736158566e-005, -0.0017343662672978692, -0.00058835273539699145, 0.0065124956747714497, 0.0098579348287897942, -0.024080841595864003, -0.0370374159788594, 0.069976799610734136, 0.022832651022562687, 0.097198394458909473, -0.57202297801008706, 0.73034354908839572, -0.23768990904924897, -0.2046547944958006, 0.14460234370531561, 0.035266759564466552, -0.043000190681552281, -0.0020034719001093887, 0.0063896036664548919, 0.00011053509764272153, -0.00048926361026192387} }; static float sym12_float[][24] = { {0.00011196719424656033, -1.1353928041541452e-005, -0.0013497557555715387, 0.00018021409008538188, 0.007414965517654251, -0.0014089092443297553, -0.024220722675013445, 0.0075537806116804775, 0.049179318299660837, -0.035848830736954392, -0.022162306170337816, 0.39888597239022, 0.76347909778365719, 0.46274103121927235, -0.07833262231634322, -0.17037069723886492, 0.01530174062247884, 0.057804179445505657, -0.0026043910313322326, -0.014589836449234145, 0.00030764779631059454, 0.0023502976141834648, -1.8158078862617515e-005, -0.00017906658697508691}, {0.00017906658697508691, -1.8158078862617515e-005, -0.0023502976141834648, 0.00030764779631059454, 0.014589836449234145, -0.0026043910313322326, -0.057804179445505657, 0.01530174062247884, 0.17037069723886492, -0.07833262231634322, -0.46274103121927235, 0.76347909778365719, -0.39888597239022, -0.022162306170337816, 0.035848830736954392, 0.049179318299660837, -0.0075537806116804775, -0.024220722675013445, 0.0014089092443297553, 0.007414965517654251, -0.00018021409008538188, -0.0013497557555715387, 1.1353928041541452e-005, 0.00011196719424656033}, {-0.00017906658697508691, -1.8158078862617515e-005, 0.0023502976141834648, 0.00030764779631059454, -0.014589836449234145, -0.0026043910313322326, 0.057804179445505657, 0.01530174062247884, -0.17037069723886492, -0.07833262231634322, 0.46274103121927235, 0.76347909778365719, 0.39888597239022, -0.022162306170337816, -0.035848830736954392, 0.049179318299660837, 0.0075537806116804775, -0.024220722675013445, -0.0014089092443297553, 0.007414965517654251, 0.00018021409008538188, -0.0013497557555715387, -1.1353928041541452e-005, 0.00011196719424656033}, {0.00011196719424656033, 1.1353928041541452e-005, -0.0013497557555715387, -0.00018021409008538188, 0.007414965517654251, 0.0014089092443297553, -0.024220722675013445, -0.0075537806116804775, 0.049179318299660837, 0.035848830736954392, -0.022162306170337816, -0.39888597239022, 0.76347909778365719, -0.46274103121927235, -0.07833262231634322, 0.17037069723886492, 0.01530174062247884, -0.057804179445505657, -0.0026043910313322326, 0.014589836449234145, 0.00030764779631059454, -0.0023502976141834648, -1.8158078862617515e-005, 0.00017906658697508691} }; static float sym13_float[][26] = { {6.8203252630753188e-005, -3.5738623648689009e-005, -0.0011360634389281183, -0.00017094285853022211, 0.0075262253899680996, 0.0052963597387250252, -0.02021676813338983, -0.017211642726299048, 0.013862497435849205, -0.059750627717943698, -0.12436246075153011, 0.19770481877117801, 0.69573915056149638, 0.64456438390118564, 0.11023022302137217, -0.14049009311363403, 0.0088197576704205465, 0.092926030899137119, 0.017618296880653084, -0.020749686325515677, -0.0014924472742598532, 0.0056748537601224395, 0.00041326119884196064, -0.0007213643851362283, 3.6905373423196241e-005, 7.0429866906944016e-005}, {-7.0429866906944016e-005, 3.6905373423196241e-005, 0.0007213643851362283, 0.00041326119884196064, -0.0056748537601224395, -0.0014924472742598532, 0.020749686325515677, 0.017618296880653084, -0.092926030899137119, 0.0088197576704205465, 0.14049009311363403, 0.11023022302137217, -0.64456438390118564, 0.69573915056149638, -0.19770481877117801, -0.12436246075153011, 0.059750627717943698, 0.013862497435849205, 0.017211642726299048, -0.02021676813338983, -0.0052963597387250252, 0.0075262253899680996, 0.00017094285853022211, -0.0011360634389281183, 3.5738623648689009e-005, 6.8203252630753188e-005}, {7.0429866906944016e-005, 3.6905373423196241e-005, -0.0007213643851362283, 0.00041326119884196064, 0.0056748537601224395, -0.0014924472742598532, -0.020749686325515677, 0.017618296880653084, 0.092926030899137119, 0.0088197576704205465, -0.14049009311363403, 0.11023022302137217, 0.64456438390118564, 0.69573915056149638, 0.19770481877117801, -0.12436246075153011, -0.059750627717943698, 0.013862497435849205, -0.017211642726299048, -0.02021676813338983, 0.0052963597387250252, 0.0075262253899680996, -0.00017094285853022211, -0.0011360634389281183, -3.5738623648689009e-005, 6.8203252630753188e-005}, {6.8203252630753188e-005, 3.5738623648689009e-005, -0.0011360634389281183, 0.00017094285853022211, 0.0075262253899680996, -0.0052963597387250252, -0.02021676813338983, 0.017211642726299048, 0.013862497435849205, 0.059750627717943698, -0.12436246075153011, -0.19770481877117801, 0.69573915056149638, -0.64456438390118564, 0.11023022302137217, 0.14049009311363403, 0.0088197576704205465, -0.092926030899137119, 0.017618296880653084, 0.020749686325515677, -0.0014924472742598532, -0.0056748537601224395, 0.00041326119884196064, 0.0007213643851362283, 3.6905373423196241e-005, -7.0429866906944016e-005} }; static float sym14_float[][28] = { {-2.5879090265397886e-005, 1.1210865808890361e-005, 0.00039843567297594335, -6.2865424814776362e-005, -0.002579441725933078, 0.00036647657366011829, 0.010037693717672269, -0.0027537747912240711, -0.029196217764038187, 0.0042805204990193782, 0.037433088362853452, -0.057634498351326995, -0.035318112114979733, 0.39320152196208885, 0.75997624196109093, 0.47533576263420663, -0.058111823317717831, -0.15999741114652205, 0.025898587531046669, 0.069827616361807551, -0.0023650488367403851, -0.019439314263626713, 0.0010131419871842082, 0.0045326774719456481, -7.3214213567023991e-005, -0.00060576018246643346, 1.9329016965523917e-005, 4.4618977991475265e-005}, {-4.4618977991475265e-005, 1.9329016965523917e-005, 0.00060576018246643346, -7.3214213567023991e-005, -0.0045326774719456481, 0.0010131419871842082, 0.019439314263626713, -0.0023650488367403851, -0.069827616361807551, 0.025898587531046669, 0.15999741114652205, -0.058111823317717831, -0.47533576263420663, 0.75997624196109093, -0.39320152196208885, -0.035318112114979733, 0.057634498351326995, 0.037433088362853452, -0.0042805204990193782, -0.029196217764038187, 0.0027537747912240711, 0.010037693717672269, -0.00036647657366011829, -0.002579441725933078, 6.2865424814776362e-005, 0.00039843567297594335, -1.1210865808890361e-005, -2.5879090265397886e-005}, {4.4618977991475265e-005, 1.9329016965523917e-005, -0.00060576018246643346, -7.3214213567023991e-005, 0.0045326774719456481, 0.0010131419871842082, -0.019439314263626713, -0.0023650488367403851, 0.069827616361807551, 0.025898587531046669, 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0.00060576018246643346, 1.9329016965523917e-005, -4.4618977991475265e-005} }; static float sym15_float[][30] = { {9.7124197379633478e-006, -7.3596667989194696e-006, -0.00016066186637495343, 5.5122547855586653e-005, 0.0010705672194623959, -0.00026731644647180568, -0.0035901654473726417, 0.003423450736351241, 0.010079977087905669, -0.019405011430934468, -0.038876716876833493, 0.021937642719753955, 0.040735479696810677, -0.04108266663538248, 0.11153369514261872, 0.57864041521503451, 0.72184302963618119, 0.2439627054321663, -0.1966263587662373, -0.13405629845625389, 0.068393310060480245, 0.067969829044879179, -0.0087447888864779517, -0.017171252781638731, 0.0015261382781819983, 0.003481028737064895, -0.00010815440168545525, -0.00040216853760293483, 2.1717890150778919e-005, 2.8660708525318081e-005}, {-2.8660708525318081e-005, 2.1717890150778919e-005, 0.00040216853760293483, -0.00010815440168545525, -0.003481028737064895, 0.0015261382781819983, 0.017171252781638731, -0.0087447888864779517, 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0.003423450736351241, -0.0035901654473726417, -0.00026731644647180568, 0.0010705672194623959, 5.5122547855586653e-005, -0.00016066186637495343, -7.3596667989194696e-006, 9.7124197379633478e-006}, {9.7124197379633478e-006, 7.3596667989194696e-006, -0.00016066186637495343, -5.5122547855586653e-005, 0.0010705672194623959, 0.00026731644647180568, -0.0035901654473726417, -0.003423450736351241, 0.010079977087905669, 0.019405011430934468, -0.038876716876833493, -0.021937642719753955, 0.040735479696810677, 0.04108266663538248, 0.11153369514261872, -0.57864041521503451, 0.72184302963618119, -0.2439627054321663, -0.1966263587662373, 0.13405629845625389, 0.068393310060480245, -0.067969829044879179, -0.0087447888864779517, 0.017171252781638731, 0.0015261382781819983, -0.003481028737064895, -0.00010815440168545525, 0.00040216853760293483, 2.1717890150778919e-005, -2.8660708525318081e-005} }; static float sym16_float[][32] = { {6.2300067012207606e-006, -3.1135564076219692e-006, 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0.15959219218520598, -0.054040601387606135, -0.47534280601152273, 0.75652498787569711, -0.39712293362064416, -0.034574228416972504, 0.066983049070217779, 0.032333091610663785, -0.0048692744049046071, -0.031051202843553064, 0.0031265171722710075, 0.012666731659857348, -0.00071821197883178923, -0.0038809122526038786, 0.0001084456223089688, 0.00085235471080470952, -2.8078582128442894e-005, -0.00010943147929529757, 3.1135564076219692e-006, 6.2300067012207606e-006}, {-1.0797982104319795e-005, -5.3964831793152419e-006, 0.00016545679579108483, 3.656592483348223e-005, -0.0013387206066921965, -0.00022211647621176323, 0.0069377611308027096, 0.001359844742484172, -0.024952758046290123, -0.0035102750683740089, 0.078037852903419913, 0.03072113906330156, -0.15959219218520598, -0.054040601387606135, 0.47534280601152273, 0.75652498787569711, 0.39712293362064416, -0.034574228416972504, -0.066983049070217779, 0.032333091610663785, 0.0048692744049046071, -0.031051202843553064, -0.0031265171722710075, 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sym17_float[][34] = { {4.297343327345983e-006, 2.7801266938414138e-006, -6.2937025975541919e-005, -1.3506383399901165e-005, 0.0004759963802638669, -0.00013864230268045499, -0.0027416759756816018, 0.0008567700701915741, 0.010482366933031529, -0.0048192128031761478, -0.033291383492359328, 0.017903952214341119, 0.10475461484223211, 0.0172711782105185, -0.11856693261143636, 0.14239835041467819, 0.65071662920454565, 0.68148899534492502, 0.18053958458111286, -0.15507600534974825, -0.086070874720733381, 0.016158808725919346, -0.0072616347509287674, -0.01803889724191924, 0.0099529825235095976, 0.012396988366648726, -0.0019054076898526659, -0.0039323252797979023, 5.8400428694052584e-005, 0.0007198270642148971, 2.5207933140828779e-005, -7.6071244056051285e-005, -2.4527163425832999e-006, 3.7912531943321266e-006}, {-3.7912531943321266e-006, -2.4527163425832999e-006, 7.6071244056051285e-005, 2.5207933140828779e-005, -0.0007198270642148971, 5.8400428694052584e-005, 0.0039323252797979023, -0.0019054076898526659, -0.012396988366648726, 0.0099529825235095976, 0.01803889724191924, -0.0072616347509287674, -0.016158808725919346, -0.086070874720733381, 0.15507600534974825, 0.18053958458111286, -0.68148899534492502, 0.65071662920454565, -0.14239835041467819, -0.11856693261143636, -0.0172711782105185, 0.10475461484223211, -0.017903952214341119, -0.033291383492359328, 0.0048192128031761478, 0.010482366933031529, -0.0008567700701915741, -0.0027416759756816018, 0.00013864230268045499, 0.0004759963802638669, 1.3506383399901165e-005, -6.2937025975541919e-005, -2.7801266938414138e-006, 4.297343327345983e-006}, {3.7912531943321266e-006, -2.4527163425832999e-006, -7.6071244056051285e-005, 2.5207933140828779e-005, 0.0007198270642148971, 5.8400428694052584e-005, -0.0039323252797979023, -0.0019054076898526659, 0.012396988366648726, 0.0099529825235095976, -0.01803889724191924, -0.0072616347509287674, 0.016158808725919346, -0.086070874720733381, -0.15507600534974825, 0.18053958458111286, 0.68148899534492502, 0.65071662920454565, 0.14239835041467819, -0.11856693261143636, 0.0172711782105185, 0.10475461484223211, 0.017903952214341119, -0.033291383492359328, -0.0048192128031761478, 0.010482366933031529, 0.0008567700701915741, -0.0027416759756816018, -0.00013864230268045499, 0.0004759963802638669, -1.3506383399901165e-005, -6.2937025975541919e-005, 2.7801266938414138e-006, 4.297343327345983e-006}, {4.297343327345983e-006, -2.7801266938414138e-006, -6.2937025975541919e-005, 1.3506383399901165e-005, 0.0004759963802638669, 0.00013864230268045499, -0.0027416759756816018, -0.0008567700701915741, 0.010482366933031529, 0.0048192128031761478, -0.033291383492359328, -0.017903952214341119, 0.10475461484223211, -0.0172711782105185, -0.11856693261143636, -0.14239835041467819, 0.65071662920454565, -0.68148899534492502, 0.18053958458111286, 0.15507600534974825, -0.086070874720733381, -0.016158808725919346, -0.0072616347509287674, 0.01803889724191924, 0.0099529825235095976, -0.012396988366648726, -0.0019054076898526659, 0.0039323252797979023, 5.8400428694052584e-005, -0.0007198270642148971, 2.5207933140828779e-005, 7.6071244056051285e-005, -2.4527163425832999e-006, -3.7912531943321266e-006} }; static float sym18_float[][36] = { {2.6126125564836423e-006, 1.354915761832114e-006, -4.5246757874949856e-005, -1.4020992577726755e-005, 0.00039616840638254753, 7.0212734590362685e-005, -0.0023138718145060992, -0.00041152110923597756, 0.0095021643909623654, 0.0016429863972782159, -0.030325091089369604, -0.0050770851607570529, 0.084219929970386548, 0.033995667103947358, -0.15993814866932407, -0.052029158983952786, 0.47396905989393956, 0.75362914010179283, 0.40148386057061813, -0.032480573290138676, -0.073799207290607169, 0.028529597039037808, 0.0062779445543116943, -0.031712684731814537, -0.0032607442000749834, 0.015012356344250213, 0.0010877847895956929, -0.0052397896830266083, -0.00018877623940755607, 0.0014280863270832796, 4.7416145183736671e-005, -0.00026583011024241041, -9.858816030140058e-006, 2.9557437620930811e-005, 7.8472980558317646e-007, -1.5131530692371587e-006}, {1.5131530692371587e-006, 7.8472980558317646e-007, -2.9557437620930811e-005, -9.858816030140058e-006, 0.00026583011024241041, 4.7416145183736671e-005, -0.0014280863270832796, -0.00018877623940755607, 0.0052397896830266083, 0.0010877847895956929, -0.015012356344250213, -0.0032607442000749834, 0.031712684731814537, 0.0062779445543116943, -0.028529597039037808, -0.073799207290607169, 0.032480573290138676, 0.40148386057061813, -0.75362914010179283, 0.47396905989393956, 0.052029158983952786, -0.15993814866932407, -0.033995667103947358, 0.084219929970386548, 0.0050770851607570529, -0.030325091089369604, -0.0016429863972782159, 0.0095021643909623654, 0.00041152110923597756, -0.0023138718145060992, -7.0212734590362685e-005, 0.00039616840638254753, 1.4020992577726755e-005, -4.5246757874949856e-005, -1.354915761832114e-006, 2.6126125564836423e-006}, {-1.5131530692371587e-006, 7.8472980558317646e-007, 2.9557437620930811e-005, -9.858816030140058e-006, -0.00026583011024241041, 4.7416145183736671e-005, 0.0014280863270832796, -0.00018877623940755607, -0.0052397896830266083, 0.0010877847895956929, 0.015012356344250213, -0.0032607442000749834, -0.031712684731814537, 0.0062779445543116943, 0.028529597039037808, -0.073799207290607169, -0.032480573290138676, 0.40148386057061813, 0.75362914010179283, 0.47396905989393956, -0.052029158983952786, -0.15993814866932407, 0.033995667103947358, 0.084219929970386548, -0.0050770851607570529, -0.030325091089369604, 0.0016429863972782159, 0.0095021643909623654, -0.00041152110923597756, -0.0023138718145060992, 7.0212734590362685e-005, 0.00039616840638254753, -1.4020992577726755e-005, -4.5246757874949856e-005, 1.354915761832114e-006, 2.6126125564836423e-006}, {2.6126125564836423e-006, -1.354915761832114e-006, -4.5246757874949856e-005, 1.4020992577726755e-005, 0.00039616840638254753, -7.0212734590362685e-005, -0.0023138718145060992, 0.00041152110923597756, 0.0095021643909623654, -0.0016429863972782159, -0.030325091089369604, 0.0050770851607570529, 0.084219929970386548, -0.033995667103947358, -0.15993814866932407, 0.052029158983952786, 0.47396905989393956, -0.75362914010179283, 0.40148386057061813, 0.032480573290138676, -0.073799207290607169, -0.028529597039037808, 0.0062779445543116943, 0.031712684731814537, -0.0032607442000749834, -0.015012356344250213, 0.0010877847895956929, 0.0052397896830266083, -0.00018877623940755607, -0.0014280863270832796, 4.7416145183736671e-005, 0.00026583011024241041, -9.858816030140058e-006, -2.9557437620930811e-005, 7.8472980558317646e-007, 1.5131530692371587e-006} }; static float sym19_float[][38] = { {5.4877327682158382e-007, -6.4636513033459633e-007, -1.1880518269823984e-005, 8.8733121737292863e-006, 0.0001155392333357879, -4.6120396002105868e-005, -0.00063576451500433403, 0.00015915804768084938, 0.0021214250281823303, -0.0011607032572062486, -0.005122205002583014, 0.0079684383206133063, 0.015797439295674631, -0.022651993378245951, -0.046635983534938946, 0.0070155738571741596, 0.0089545911730436242, -0.067525058040294086, 0.10902582508127781, 0.57814494533860505, 0.71955552571639425, 0.25826616923728363, -0.17659686625203097, -0.11624173010739675, 0.093630843415897141, 0.084072676279245043, -0.016908234861345205, -0.027709896931311252, 0.0043193518748949689, 0.0082622369555282547, -0.00061792232779831076, -0.0017049602611649971, 0.00012930767650701415, 0.00027621877685734072, -1.6821387029373716e-005, -2.8151138661550245e-005, 2.0623170632395688e-006, 1.7509367995348687e-006}, {-1.7509367995348687e-006, 2.0623170632395688e-006, 2.8151138661550245e-005, -1.6821387029373716e-005, -0.00027621877685734072, 0.00012930767650701415, 0.0017049602611649971, -0.00061792232779831076, -0.0082622369555282547, 0.0043193518748949689, 0.027709896931311252, -0.016908234861345205, -0.084072676279245043, 0.093630843415897141, 0.11624173010739675, -0.17659686625203097, -0.25826616923728363, 0.71955552571639425, -0.57814494533860505, 0.10902582508127781, 0.067525058040294086, 0.0089545911730436242, -0.0070155738571741596, -0.046635983534938946, 0.022651993378245951, 0.015797439295674631, -0.0079684383206133063, -0.005122205002583014, 0.0011607032572062486, 0.0021214250281823303, -0.00015915804768084938, -0.00063576451500433403, 4.6120396002105868e-005, 0.0001155392333357879, -8.8733121737292863e-006, -1.1880518269823984e-005, 6.4636513033459633e-007, 5.4877327682158382e-007}, {1.7509367995348687e-006, 2.0623170632395688e-006, -2.8151138661550245e-005, -1.6821387029373716e-005, 0.00027621877685734072, 0.00012930767650701415, -0.0017049602611649971, -0.00061792232779831076, 0.0082622369555282547, 0.0043193518748949689, -0.027709896931311252, -0.016908234861345205, 0.084072676279245043, 0.093630843415897141, -0.11624173010739675, -0.17659686625203097, 0.25826616923728363, 0.71955552571639425, 0.57814494533860505, 0.10902582508127781, -0.067525058040294086, 0.0089545911730436242, 0.0070155738571741596, -0.046635983534938946, -0.022651993378245951, 0.015797439295674631, 0.0079684383206133063, -0.005122205002583014, -0.0011607032572062486, 0.0021214250281823303, 0.00015915804768084938, -0.00063576451500433403, -4.6120396002105868e-005, 0.0001155392333357879, 8.8733121737292863e-006, -1.1880518269823984e-005, -6.4636513033459633e-007, 5.4877327682158382e-007}, {5.4877327682158382e-007, 6.4636513033459633e-007, -1.1880518269823984e-005, -8.8733121737292863e-006, 0.0001155392333357879, 4.6120396002105868e-005, -0.00063576451500433403, -0.00015915804768084938, 0.0021214250281823303, 0.0011607032572062486, -0.005122205002583014, -0.0079684383206133063, 0.015797439295674631, 0.022651993378245951, -0.046635983534938946, -0.0070155738571741596, 0.0089545911730436242, 0.067525058040294086, 0.10902582508127781, -0.57814494533860505, 0.71955552571639425, -0.25826616923728363, -0.17659686625203097, 0.11624173010739675, 0.093630843415897141, -0.084072676279245043, -0.016908234861345205, 0.027709896931311252, 0.0043193518748949689, -0.0082622369555282547, -0.00061792232779831076, 0.0017049602611649971, 0.00012930767650701415, -0.00027621877685734072, -1.6821387029373716e-005, 2.8151138661550245e-005, 2.0623170632395688e-006, -1.7509367995348687e-006} }; static float sym20_float[][40] = { {3.695537474835221e-007, -1.9015675890554106e-007, -7.919361411976999e-006, 3.0256660627369661e-006, 7.992967835772481e-005, -1.928412300645204e-005, -0.00049473109156726548, 7.2159911880740349e-005, 0.0020889947081901982, -0.0003052628317957281, -0.0066065857990888609, 0.0014230873594621453, 0.017004049023390339, -0.0033138573836233591, -0.031629437144957966, 0.0081232283560096815, 0.025579349509413946, -0.078994344928398158, -0.029819368880333728, 0.40583144434845059, 0.75116272842273002, 0.47199147510148703, -0.051088342921067398, -0.16057829841525254, 0.036250951653933078, 0.088919668028199561, -0.0068437019650692274, -0.035373336756604236, 0.0019385970672402002, 0.012157040948785737, -0.0006111263857992088, -0.0034716478028440734, 0.00012544091723067259, 0.00074761085978205719, -2.6615550335516086e-005, -0.00011739133516291466, 4.5254222091516362e-006, 1.22872527779612e-005, -3.2567026420174407e-007, -6.3291290447763946e-007}, {6.3291290447763946e-007, -3.2567026420174407e-007, -1.22872527779612e-005, 4.5254222091516362e-006, 0.00011739133516291466, -2.6615550335516086e-005, -0.00074761085978205719, 0.00012544091723067259, 0.0034716478028440734, -0.0006111263857992088, -0.012157040948785737, 0.0019385970672402002, 0.035373336756604236, -0.0068437019650692274, -0.088919668028199561, 0.036250951653933078, 0.16057829841525254, -0.051088342921067398, -0.47199147510148703, 0.75116272842273002, -0.40583144434845059, -0.029819368880333728, 0.078994344928398158, 0.025579349509413946, -0.0081232283560096815, -0.031629437144957966, 0.0033138573836233591, 0.017004049023390339, -0.0014230873594621453, -0.0066065857990888609, 0.0003052628317957281, 0.0020889947081901982, -7.2159911880740349e-005, -0.00049473109156726548, 1.928412300645204e-005, 7.992967835772481e-005, -3.0256660627369661e-006, -7.919361411976999e-006, 1.9015675890554106e-007, 3.695537474835221e-007}, {-6.3291290447763946e-007, -3.2567026420174407e-007, 1.22872527779612e-005, 4.5254222091516362e-006, -0.00011739133516291466, -2.6615550335516086e-005, 0.00074761085978205719, 0.00012544091723067259, -0.0034716478028440734, -0.0006111263857992088, 0.012157040948785737, 0.0019385970672402002, -0.035373336756604236, -0.0068437019650692274, 0.088919668028199561, 0.036250951653933078, -0.16057829841525254, -0.051088342921067398, 0.47199147510148703, 0.75116272842273002, 0.40583144434845059, -0.029819368880333728, -0.078994344928398158, 0.025579349509413946, 0.0081232283560096815, -0.031629437144957966, -0.0033138573836233591, 0.017004049023390339, 0.0014230873594621453, -0.0066065857990888609, -0.0003052628317957281, 0.0020889947081901982, 7.2159911880740349e-005, -0.00049473109156726548, -1.928412300645204e-005, 7.992967835772481e-005, 3.0256660627369661e-006, -7.919361411976999e-006, -1.9015675890554106e-007, 3.695537474835221e-007}, {3.695537474835221e-007, 1.9015675890554106e-007, -7.919361411976999e-006, -3.0256660627369661e-006, 7.992967835772481e-005, 1.928412300645204e-005, -0.00049473109156726548, -7.2159911880740349e-005, 0.0020889947081901982, 0.0003052628317957281, -0.0066065857990888609, -0.0014230873594621453, 0.017004049023390339, 0.0033138573836233591, -0.031629437144957966, -0.0081232283560096815, 0.025579349509413946, 0.078994344928398158, -0.029819368880333728, -0.40583144434845059, 0.75116272842273002, -0.47199147510148703, -0.051088342921067398, 0.16057829841525254, 0.036250951653933078, -0.088919668028199561, -0.0068437019650692274, 0.035373336756604236, 0.0019385970672402002, -0.012157040948785737, -0.0006111263857992088, 0.0034716478028440734, 0.00012544091723067259, -0.00074761085978205719, -2.6615550335516086e-005, 0.00011739133516291466, 4.5254222091516362e-006, -1.22872527779612e-005, -3.2567026420174407e-007, 6.3291290447763946e-007} }; static float coif1_float[][6] = { {-0.01565572813546454, -0.072732619512853897, 0.38486484686420286, 0.85257202021225542, 0.33789766245780922, -0.072732619512853897}, {0.072732619512853897, 0.33789766245780922, -0.85257202021225542, 0.38486484686420286, 0.072732619512853897, -0.01565572813546454}, {-0.072732619512853897, 0.33789766245780922, 0.85257202021225542, 0.38486484686420286, -0.072732619512853897, -0.01565572813546454}, {-0.01565572813546454, 0.072732619512853897, 0.38486484686420286, -0.85257202021225542, 0.33789766245780922, 0.072732619512853897} }; static float coif2_float[][12] = { {-0.00072054944536451221, -0.0018232088707029932, 0.0056114348193944995, 0.023680171946334084, -0.059434418646456898, -0.076488599078306393, 0.41700518442169254, 0.81272363544554227, 0.38611006682116222, -0.067372554721963018, -0.041464936781759151, 0.016387336463522112}, {-0.016387336463522112, -0.041464936781759151, 0.067372554721963018, 0.38611006682116222, -0.81272363544554227, 0.41700518442169254, 0.076488599078306393, -0.059434418646456898, -0.023680171946334084, 0.0056114348193944995, 0.0018232088707029932, -0.00072054944536451221}, {0.016387336463522112, -0.041464936781759151, -0.067372554721963018, 0.38611006682116222, 0.81272363544554227, 0.41700518442169254, -0.076488599078306393, -0.059434418646456898, 0.023680171946334084, 0.0056114348193944995, -0.0018232088707029932, -0.00072054944536451221}, {-0.00072054944536451221, 0.0018232088707029932, 0.0056114348193944995, -0.023680171946334084, -0.059434418646456898, 0.076488599078306393, 0.41700518442169254, -0.81272363544554227, 0.38611006682116222, 0.067372554721963018, -0.041464936781759151, -0.016387336463522112} }; static float coif3_float[][18] = { {-3.4599772836212559e-005, -7.0983303138141252e-005, 0.00046621696011288631, 0.0011175187708906016, -0.0025745176887502236, -0.0090079761366615805, 0.015880544863615904, 0.034555027573061628, -0.082301927106885983, -0.071799821619312018, 0.42848347637761874, 0.79377722262562056, 0.4051769024096169, -0.061123390002672869, -0.0657719112818555, 0.023452696141836267, 0.0077825964273254182, -0.0037935128644910141}, {0.0037935128644910141, 0.0077825964273254182, -0.023452696141836267, -0.0657719112818555, 0.061123390002672869, 0.4051769024096169, -0.79377722262562056, 0.42848347637761874, 0.071799821619312018, -0.082301927106885983, -0.034555027573061628, 0.015880544863615904, 0.0090079761366615805, -0.0025745176887502236, -0.0011175187708906016, 0.00046621696011288631, 7.0983303138141252e-005, -3.4599772836212559e-005}, {-0.0037935128644910141, 0.0077825964273254182, 0.023452696141836267, -0.0657719112818555, -0.061123390002672869, 0.4051769024096169, 0.79377722262562056, 0.42848347637761874, -0.071799821619312018, -0.082301927106885983, 0.034555027573061628, 0.015880544863615904, -0.0090079761366615805, -0.0025745176887502236, 0.0011175187708906016, 0.00046621696011288631, -7.0983303138141252e-005, -3.4599772836212559e-005}, {-3.4599772836212559e-005, 7.0983303138141252e-005, 0.00046621696011288631, -0.0011175187708906016, -0.0025745176887502236, 0.0090079761366615805, 0.015880544863615904, -0.034555027573061628, -0.082301927106885983, 0.071799821619312018, 0.42848347637761874, -0.79377722262562056, 0.4051769024096169, 0.061123390002672869, -0.0657719112818555, -0.023452696141836267, 0.0077825964273254182, 0.0037935128644910141} }; static float coif4_float[][24] = { {-1.7849850030882614e-006, -3.2596802368833675e-006, 3.1229875865345646e-005, 6.2339034461007128e-005, -0.00025997455248771324, -0.00058902075624433831, 0.0012665619292989445, 0.0037514361572784571, -0.0056582866866107199, -0.015211731527946259, 0.025082261844864097, 0.039334427123337491, -0.096220442033987982, -0.066627474263425038, 0.4343860564914685, 0.78223893092049901, 0.41530840703043026, -0.056077313316754807, -0.081266699680878754, 0.026682300156053072, 0.016068943964776348, -0.0073461663276420935, -0.0016294920126017326, 0.00089231366858231456}, {-0.00089231366858231456, -0.0016294920126017326, 0.0073461663276420935, 0.016068943964776348, -0.026682300156053072, -0.081266699680878754, 0.056077313316754807, 0.41530840703043026, -0.78223893092049901, 0.4343860564914685, 0.066627474263425038, -0.096220442033987982, -0.039334427123337491, 0.025082261844864097, 0.015211731527946259, -0.0056582866866107199, -0.0037514361572784571, 0.0012665619292989445, 0.00058902075624433831, -0.00025997455248771324, -6.2339034461007128e-005, 3.1229875865345646e-005, 3.2596802368833675e-006, -1.7849850030882614e-006}, {0.00089231366858231456, -0.0016294920126017326, -0.0073461663276420935, 0.016068943964776348, 0.026682300156053072, -0.081266699680878754, -0.056077313316754807, 0.41530840703043026, 0.78223893092049901, 0.4343860564914685, -0.066627474263425038, -0.096220442033987982, 0.039334427123337491, 0.025082261844864097, -0.015211731527946259, -0.0056582866866107199, 0.0037514361572784571, 0.0012665619292989445, -0.00058902075624433831, -0.00025997455248771324, 6.2339034461007128e-005, 3.1229875865345646e-005, -3.2596802368833675e-006, -1.7849850030882614e-006}, {-1.7849850030882614e-006, 3.2596802368833675e-006, 3.1229875865345646e-005, -6.2339034461007128e-005, -0.00025997455248771324, 0.00058902075624433831, 0.0012665619292989445, -0.0037514361572784571, -0.0056582866866107199, 0.015211731527946259, 0.025082261844864097, -0.039334427123337491, -0.096220442033987982, 0.066627474263425038, 0.4343860564914685, -0.78223893092049901, 0.41530840703043026, 0.056077313316754807, -0.081266699680878754, -0.026682300156053072, 0.016068943964776348, 0.0073461663276420935, -0.0016294920126017326, -0.00089231366858231456} }; static float coif5_float[][30] = { {-9.517657273819165e-008, -1.6744288576823017e-007, 2.0637618513646814e-006, 3.7346551751414047e-006, -2.1315026809955787e-005, -4.1340432272512511e-005, 0.00014054114970203437, 0.00030225958181306315, -0.00063813134304511142, -0.0016628637020130838, 0.0024333732126576722, 0.0067641854480530832, -0.0091642311624818458, -0.019761778942572639, 0.032683574267111833, 0.041289208750181702, -0.10557420870333893, -0.062035963962903569, 0.43799162617183712, 0.77428960365295618, 0.42156620669085149, -0.052043163176243773, -0.091920010559696244, 0.02816802897093635, 0.023408156785839195, -0.010131117519849788, -0.004159358781386048, 0.0021782363581090178, 0.00035858968789573785, -0.00021208083980379827}, {0.00021208083980379827, 0.00035858968789573785, -0.0021782363581090178, -0.004159358781386048, 0.010131117519849788, 0.023408156785839195, -0.02816802897093635, -0.091920010559696244, 0.052043163176243773, 0.42156620669085149, -0.77428960365295618, 0.43799162617183712, 0.062035963962903569, -0.10557420870333893, -0.041289208750181702, 0.032683574267111833, 0.019761778942572639, -0.0091642311624818458, -0.0067641854480530832, 0.0024333732126576722, 0.0016628637020130838, -0.00063813134304511142, -0.00030225958181306315, 0.00014054114970203437, 4.1340432272512511e-005, -2.1315026809955787e-005, -3.7346551751414047e-006, 2.0637618513646814e-006, 1.6744288576823017e-007, -9.517657273819165e-008}, {-0.00021208083980379827, 0.00035858968789573785, 0.0021782363581090178, -0.004159358781386048, -0.010131117519849788, 0.023408156785839195, 0.02816802897093635, -0.091920010559696244, -0.052043163176243773, 0.42156620669085149, 0.77428960365295618, 0.43799162617183712, -0.062035963962903569, -0.10557420870333893, 0.041289208750181702, 0.032683574267111833, -0.019761778942572639, -0.0091642311624818458, 0.0067641854480530832, 0.0024333732126576722, -0.0016628637020130838, -0.00063813134304511142, 0.00030225958181306315, 0.00014054114970203437, -4.1340432272512511e-005, -2.1315026809955787e-005, 3.7346551751414047e-006, 2.0637618513646814e-006, -1.6744288576823017e-007, -9.517657273819165e-008}, {-9.517657273819165e-008, 1.6744288576823017e-007, 2.0637618513646814e-006, -3.7346551751414047e-006, -2.1315026809955787e-005, 4.1340432272512511e-005, 0.00014054114970203437, -0.00030225958181306315, -0.00063813134304511142, 0.0016628637020130838, 0.0024333732126576722, -0.0067641854480530832, -0.0091642311624818458, 0.019761778942572639, 0.032683574267111833, -0.041289208750181702, -0.10557420870333893, 0.062035963962903569, 0.43799162617183712, -0.77428960365295618, 0.42156620669085149, 0.052043163176243773, -0.091920010559696244, -0.02816802897093635, 0.023408156785839195, 0.010131117519849788, -0.004159358781386048, -0.0021782363581090178, 0.00035858968789573785, 0.00021208083980379827} }; static float bior1_1_float[][2] = { {0.70710678118654757, 0.70710678118654757}, {-0.70710678118654757, 0.70710678118654757}, {0.70710678118654757, 0.70710678118654757}, {0.70710678118654757, -0.70710678118654757} }; static float bior1_3_float[][6] = { {-0.088388347648318447, 0.088388347648318447, 0.70710678118654757, 0.70710678118654757, 0.088388347648318447, -0.088388347648318447}, {0.0, 0.0, -0.70710678118654757, 0.70710678118654757, 0.0, 0.0}, {0.0, 0.0, 0.70710678118654757, 0.70710678118654757, 0.0, 0.0}, {-0.088388347648318447, -0.088388347648318447, 0.70710678118654757, -0.70710678118654757, 0.088388347648318447, 0.088388347648318447} }; static float bior1_5_float[][10] = { {0.01657281518405971, -0.01657281518405971, -0.12153397801643787, 0.12153397801643787, 0.70710678118654757, 0.70710678118654757, 0.12153397801643787, -0.12153397801643787, -0.01657281518405971, 0.01657281518405971}, {0.0, 0.0, 0.0, 0.0, -0.70710678118654757, 0.70710678118654757, 0.0, 0.0, 0.0, 0.0}, {0.0, 0.0, 0.0, 0.0, 0.70710678118654757, 0.70710678118654757, 0.0, 0.0, 0.0, 0.0}, {0.01657281518405971, 0.01657281518405971, -0.12153397801643787, -0.12153397801643787, 0.70710678118654757, -0.70710678118654757, 0.12153397801643787, 0.12153397801643787, -0.01657281518405971, -0.01657281518405971} }; static float bior2_2_float[][6] = { {0.0, -0.17677669529663689, 0.35355339059327379, 1.0606601717798214, 0.35355339059327379, -0.17677669529663689}, {0.0, 0.35355339059327379, -0.70710678118654757, 0.35355339059327379, 0.0, 0.0}, {0.0, 0.35355339059327379, 0.70710678118654757, 0.35355339059327379, 0.0, 0.0}, {0.0, 0.17677669529663689, 0.35355339059327379, -1.0606601717798214, 0.35355339059327379, 0.17677669529663689} }; static float bior2_4_float[][10] = { {0.0, 0.033145630368119419, -0.066291260736238838, -0.17677669529663689, 0.4198446513295126, 0.99436891104358249, 0.4198446513295126, -0.17677669529663689, -0.066291260736238838, 0.033145630368119419}, {0.0, 0.0, 0.0, 0.35355339059327379, -0.70710678118654757, 0.35355339059327379, 0.0, 0.0, 0.0, 0.0}, {0.0, 0.0, 0.0, 0.35355339059327379, 0.70710678118654757, 0.35355339059327379, 0.0, 0.0, 0.0, 0.0}, {0.0, -0.033145630368119419, -0.066291260736238838, 0.17677669529663689, 0.4198446513295126, -0.99436891104358249, 0.4198446513295126, 0.17677669529663689, -0.066291260736238838, -0.033145630368119419} }; static float bior2_6_float[][14] = { {0.0, -0.0069053396600248784, 0.013810679320049757, 0.046956309688169176, -0.10772329869638811, -0.16987135563661201, 0.44746600996961211, 0.96674755240348298, 0.44746600996961211, -0.16987135563661201, -0.10772329869638811, 0.046956309688169176, 0.013810679320049757, -0.0069053396600248784}, {0.0, 0.0, 0.0, 0.0, 0.0, 0.35355339059327379, -0.70710678118654757, 0.35355339059327379, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0}, {0.0, 0.0, 0.0, 0.0, 0.0, 0.35355339059327379, 0.70710678118654757, 0.35355339059327379, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0}, {0.0, 0.0069053396600248784, 0.013810679320049757, -0.046956309688169176, -0.10772329869638811, 0.16987135563661201, 0.44746600996961211, -0.96674755240348298, 0.44746600996961211, 0.16987135563661201, -0.10772329869638811, -0.046956309688169176, 0.013810679320049757, 0.0069053396600248784} }; static float bior2_8_float[][18] = { {0.0, 0.0015105430506304422, -0.0030210861012608843, -0.012947511862546647, 0.028916109826354178, 0.052998481890690945, -0.13491307360773608, -0.16382918343409025, 0.46257144047591658, 0.95164212189717856, 0.46257144047591658, -0.16382918343409025, -0.13491307360773608, 0.052998481890690945, 0.028916109826354178, -0.012947511862546647, -0.0030210861012608843, 0.0015105430506304422}, {0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.35355339059327379, -0.70710678118654757, 0.35355339059327379, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0}, {0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.35355339059327379, 0.70710678118654757, 0.35355339059327379, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0}, {0.0, -0.0015105430506304422, -0.0030210861012608843, 0.012947511862546647, 0.028916109826354178, -0.052998481890690945, -0.13491307360773608, 0.16382918343409025, 0.46257144047591658, -0.95164212189717856, 0.46257144047591658, 0.16382918343409025, -0.13491307360773608, -0.052998481890690945, 0.028916109826354178, 0.012947511862546647, -0.0030210861012608843, -0.0015105430506304422} }; static float bior3_1_float[][4] = { {-0.35355339059327379, 1.0606601717798214, 1.0606601717798214, -0.35355339059327379}, {-0.17677669529663689, 0.53033008588991071, -0.53033008588991071, 0.17677669529663689}, {0.17677669529663689, 0.53033008588991071, 0.53033008588991071, 0.17677669529663689}, {-0.35355339059327379, -1.0606601717798214, 1.0606601717798214, 0.35355339059327379} }; static float bior3_3_float[][8] = { {0.066291260736238838, -0.19887378220871652, -0.15467960838455727, 0.99436891104358249, 0.99436891104358249, -0.15467960838455727, -0.19887378220871652, 0.066291260736238838}, {0.0, 0.0, -0.17677669529663689, 0.53033008588991071, -0.53033008588991071, 0.17677669529663689, 0.0, 0.0}, {0.0, 0.0, 0.17677669529663689, 0.53033008588991071, 0.53033008588991071, 0.17677669529663689, 0.0, 0.0}, {0.066291260736238838, 0.19887378220871652, -0.15467960838455727, -0.99436891104358249, 0.99436891104358249, 0.15467960838455727, -0.19887378220871652, -0.066291260736238838} }; static float bior3_5_float[][12] = { {-0.013810679320049757, 0.041432037960149271, 0.052480581416189075, -0.26792717880896527, -0.071815532464258744, 0.96674755240348298, 0.96674755240348298, -0.071815532464258744, -0.26792717880896527, 0.052480581416189075, 0.041432037960149271, -0.013810679320049757}, {0.0, 0.0, 0.0, 0.0, -0.17677669529663689, 0.53033008588991071, -0.53033008588991071, 0.17677669529663689, 0.0, 0.0, 0.0, 0.0}, {0.0, 0.0, 0.0, 0.0, 0.17677669529663689, 0.53033008588991071, 0.53033008588991071, 0.17677669529663689, 0.0, 0.0, 0.0, 0.0}, {-0.013810679320049757, -0.041432037960149271, 0.052480581416189075, 0.26792717880896527, -0.071815532464258744, -0.96674755240348298, 0.96674755240348298, 0.071815532464258744, -0.26792717880896527, -0.052480581416189075, 0.041432037960149271, 0.013810679320049757} }; static float bior3_7_float[][16] = { {0.0030210861012608843, -0.0090632583037826529, -0.016831765421310641, 0.074663985074019001, 0.031332978707362888, -0.301159125922835, -0.026499240945345472, 0.95164212189717856, 0.95164212189717856, -0.026499240945345472, -0.301159125922835, 0.031332978707362888, 0.074663985074019001, -0.016831765421310641, -0.0090632583037826529, 0.0030210861012608843}, {0.0, 0.0, 0.0, 0.0, 0.0, 0.0, -0.17677669529663689, 0.53033008588991071, -0.53033008588991071, 0.17677669529663689, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0}, {0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.17677669529663689, 0.53033008588991071, 0.53033008588991071, 0.17677669529663689, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0}, {0.0030210861012608843, 0.0090632583037826529, -0.016831765421310641, -0.074663985074019001, 0.031332978707362888, 0.301159125922835, -0.026499240945345472, -0.95164212189717856, 0.95164212189717856, 0.026499240945345472, -0.301159125922835, -0.031332978707362888, 0.074663985074019001, 0.016831765421310641, -0.0090632583037826529, -0.0030210861012608843} }; static float bior3_9_float[][20] = { {-0.00067974437278369901, 0.0020392331183510968, 0.0050603192196119811, -0.020618912641105536, -0.014112787930175846, 0.09913478249423216, 0.012300136269419315, -0.32019196836077857, 0.0020500227115698858, 0.94212570067820678, 0.94212570067820678, 0.0020500227115698858, -0.32019196836077857, 0.012300136269419315, 0.09913478249423216, -0.014112787930175846, -0.020618912641105536, 0.0050603192196119811, 0.0020392331183510968, -0.00067974437278369901}, {0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, -0.17677669529663689, 0.53033008588991071, -0.53033008588991071, 0.17677669529663689, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0}, {0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.17677669529663689, 0.53033008588991071, 0.53033008588991071, 0.17677669529663689, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0}, {-0.00067974437278369901, -0.0020392331183510968, 0.0050603192196119811, 0.020618912641105536, -0.014112787930175846, -0.09913478249423216, 0.012300136269419315, 0.32019196836077857, 0.0020500227115698858, -0.94212570067820678, 0.94212570067820678, -0.0020500227115698858, -0.32019196836077857, -0.012300136269419315, 0.09913478249423216, 0.014112787930175846, -0.020618912641105536, -0.0050603192196119811, 0.0020392331183510968, 0.00067974437278369901} }; static float bior4_4_float[][10] = { {0.0, 0.03782845550726404, -0.023849465019556843, -0.11062440441843718, 0.37740285561283066, 0.85269867900889385, 0.37740285561283066, -0.11062440441843718, -0.023849465019556843, 0.03782845550726404}, {0.0, -0.064538882628697058, 0.040689417609164058, 0.41809227322161724, -0.7884856164055829, 0.41809227322161724, 0.040689417609164058, -0.064538882628697058, 0.0, 0.0}, {0.0, -0.064538882628697058, -0.040689417609164058, 0.41809227322161724, 0.7884856164055829, 0.41809227322161724, -0.040689417609164058, -0.064538882628697058, 0.0, 0.0}, {0.0, -0.03782845550726404, -0.023849465019556843, 0.11062440441843718, 0.37740285561283066, -0.85269867900889385, 0.37740285561283066, 0.11062440441843718, -0.023849465019556843, -0.03782845550726404} }; static float bior5_5_float[][12] = { {0.0, 0.0, 0.03968708834740544, 0.0079481086372403219, -0.054463788468236907, 0.34560528195603346, 0.73666018142821055, 0.34560528195603346, -0.054463788468236907, 0.0079481086372403219, 0.03968708834740544, 0.0}, {-0.013456709459118716, -0.0026949668801115071, 0.13670658466432914, -0.093504697400938863, -0.47680326579848425, 0.89950610974864842, -0.47680326579848425, -0.093504697400938863, 0.13670658466432914, -0.0026949668801115071, -0.013456709459118716, 0.0}, {0.013456709459118716, -0.0026949668801115071, -0.13670658466432914, -0.093504697400938863, 0.47680326579848425, 0.89950610974864842, 0.47680326579848425, -0.093504697400938863, -0.13670658466432914, -0.0026949668801115071, 0.013456709459118716, 0.0}, {0.0, 0.0, 0.03968708834740544, -0.0079481086372403219, -0.054463788468236907, -0.34560528195603346, 0.73666018142821055, -0.34560528195603346, -0.054463788468236907, -0.0079481086372403219, 0.03968708834740544, 0.0} }; static float bior6_8_float[][18] = { {0.0, 0.0019088317364812906, -0.0019142861290887667, -0.016990639867602342, 0.01193456527972926, 0.04973290349094079, -0.077263173167204144, -0.09405920349573646, 0.42079628460982682, 0.82592299745840225, 0.42079628460982682, -0.09405920349573646, -0.077263173167204144, 0.04973290349094079, 0.01193456527972926, -0.016990639867602342, -0.0019142861290887667, 0.0019088317364812906}, {0.0, 0.0, 0.0, 0.014426282505624435, -0.014467504896790148, -0.078722001062628819, 0.040367979030339923, 0.41784910915027457, -0.75890772945365415, 0.41784910915027457, 0.040367979030339923, -0.078722001062628819, -0.014467504896790148, 0.014426282505624435, 0.0, 0.0, 0.0, 0.0}, {0.0, 0.0, 0.0, 0.014426282505624435, 0.014467504896790148, -0.078722001062628819, -0.040367979030339923, 0.41784910915027457, 0.75890772945365415, 0.41784910915027457, -0.040367979030339923, -0.078722001062628819, 0.014467504896790148, 0.014426282505624435, 0.0, 0.0, 0.0, 0.0}, {0.0, -0.0019088317364812906, -0.0019142861290887667, 0.016990639867602342, 0.01193456527972926, -0.04973290349094079, -0.077263173167204144, 0.09405920349573646, 0.42079628460982682, -0.82592299745840225, 0.42079628460982682, 0.09405920349573646, -0.077263173167204144, -0.04973290349094079, 0.01193456527972926, 0.016990639867602342, -0.0019142861290887667, -0.0019088317364812906} }; static float dmey_float[][62] = { {0.0, -1.0099999569414229e-012, 8.519459636796214e-009, -1.111944952595278e-008, -1.0798819539621958e-008, 6.0669757413511352e-008, -1.0866516536735883e-007, 8.2006806503864813e-008, 1.1783004497663934e-007, -5.5063405652522782e-007, 1.1307947017916706e-006, -1.4895492164971559e-006, 7.367572885903746e-007, 3.2054419133447798e-006, -1.6312699734552807e-005, 6.5543059305751491e-005, -0.00060115023435160925, -0.002704672124643725, 0.0022025341009110021, 0.006045814097323304, -0.0063877183184971563, -0.011061496392513451, 0.015270015130934803, 0.017423434103729693, -0.032130793990211758, -0.024348745906078023, 0.063739024322801596, 0.030655091960824263, -0.13284520043622938, -0.035087555656258346, 0.44459300275757724, 0.74458559231880628, 0.44459300275757724, -0.035087555656258346, -0.13284520043622938, 0.030655091960824263, 0.063739024322801596, -0.024348745906078023, -0.032130793990211758, 0.017423434103729693, 0.015270015130934803, -0.011061496392513451, -0.0063877183184971563, 0.006045814097323304, 0.0022025341009110021, -0.002704672124643725, -0.00060115023435160925, 6.5543059305751491e-005, -1.6312699734552807e-005, 3.2054419133447798e-006, 7.367572885903746e-007, -1.4895492164971559e-006, 1.1307947017916706e-006, -5.5063405652522782e-007, 1.1783004497663934e-007, 8.2006806503864813e-008, -1.0866516536735883e-007, 6.0669757413511352e-008, -1.0798819539621958e-008, -1.111944952595278e-008, 8.519459636796214e-009, -1.0099999569414229e-012}, {1.0099999569414229e-012, 8.519459636796214e-009, 1.111944952595278e-008, -1.0798819539621958e-008, -6.0669757413511352e-008, -1.0866516536735883e-007, -8.2006806503864813e-008, 1.1783004497663934e-007, 5.5063405652522782e-007, 1.1307947017916706e-006, 1.4895492164971559e-006, 7.367572885903746e-007, -3.2054419133447798e-006, -1.6312699734552807e-005, -6.5543059305751491e-005, -0.00060115023435160925, 0.002704672124643725, 0.0022025341009110021, -0.006045814097323304, -0.0063877183184971563, 0.011061496392513451, 0.015270015130934803, -0.017423434103729693, -0.032130793990211758, 0.024348745906078023, 0.063739024322801596, -0.030655091960824263, -0.13284520043622938, 0.035087555656258346, 0.44459300275757724, -0.74458559231880628, 0.44459300275757724, 0.035087555656258346, -0.13284520043622938, -0.030655091960824263, 0.063739024322801596, 0.024348745906078023, -0.032130793990211758, -0.017423434103729693, 0.015270015130934803, 0.011061496392513451, -0.0063877183184971563, -0.006045814097323304, 0.0022025341009110021, 0.002704672124643725, -0.00060115023435160925, -6.5543059305751491e-005, -1.6312699734552807e-005, -3.2054419133447798e-006, 7.367572885903746e-007, 1.4895492164971559e-006, 1.1307947017916706e-006, 5.5063405652522782e-007, 1.1783004497663934e-007, -8.2006806503864813e-008, -1.0866516536735883e-007, -6.0669757413511352e-008, -1.0798819539621958e-008, 1.111944952595278e-008, 8.519459636796214e-009, 1.0099999569414229e-012, 0.0}, {-1.0099999569414229e-012, 8.519459636796214e-009, -1.111944952595278e-008, -1.0798819539621958e-008, 6.0669757413511352e-008, -1.0866516536735883e-007, 8.2006806503864813e-008, 1.1783004497663934e-007, -5.5063405652522782e-007, 1.1307947017916706e-006, -1.4895492164971559e-006, 7.367572885903746e-007, 3.2054419133447798e-006, -1.6312699734552807e-005, 6.5543059305751491e-005, -0.00060115023435160925, -0.002704672124643725, 0.0022025341009110021, 0.006045814097323304, -0.0063877183184971563, -0.011061496392513451, 0.015270015130934803, 0.017423434103729693, -0.032130793990211758, -0.024348745906078023, 0.063739024322801596, 0.030655091960824263, -0.13284520043622938, -0.035087555656258346, 0.44459300275757724, 0.74458559231880628, 0.44459300275757724, -0.035087555656258346, -0.13284520043622938, 0.030655091960824263, 0.063739024322801596, -0.024348745906078023, -0.032130793990211758, 0.017423434103729693, 0.015270015130934803, -0.011061496392513451, -0.0063877183184971563, 0.006045814097323304, 0.0022025341009110021, -0.002704672124643725, -0.00060115023435160925, 6.5543059305751491e-005, -1.6312699734552807e-005, 3.2054419133447798e-006, 7.367572885903746e-007, -1.4895492164971559e-006, 1.1307947017916706e-006, -5.5063405652522782e-007, 1.1783004497663934e-007, 8.2006806503864813e-008, -1.0866516536735883e-007, 6.0669757413511352e-008, -1.0798819539621958e-008, -1.111944952595278e-008, 8.519459636796214e-009, -1.0099999569414229e-012, 0.0}, {0.0, 1.0099999569414229e-012, 8.519459636796214e-009, 1.111944952595278e-008, -1.0798819539621958e-008, -6.0669757413511352e-008, -1.0866516536735883e-007, -8.2006806503864813e-008, 1.1783004497663934e-007, 5.5063405652522782e-007, 1.1307947017916706e-006, 1.4895492164971559e-006, 7.367572885903746e-007, -3.2054419133447798e-006, -1.6312699734552807e-005, -6.5543059305751491e-005, -0.00060115023435160925, 0.002704672124643725, 0.0022025341009110021, -0.006045814097323304, -0.0063877183184971563, 0.011061496392513451, 0.015270015130934803, -0.017423434103729693, -0.032130793990211758, 0.024348745906078023, 0.063739024322801596, -0.030655091960824263, -0.13284520043622938, 0.035087555656258346, 0.44459300275757724, -0.74458559231880628, 0.44459300275757724, 0.035087555656258346, -0.13284520043622938, -0.030655091960824263, 0.063739024322801596, 0.024348745906078023, -0.032130793990211758, -0.017423434103729693, 0.015270015130934803, 0.011061496392513451, -0.0063877183184971563, -0.006045814097323304, 0.0022025341009110021, 0.002704672124643725, -0.00060115023435160925, -6.5543059305751491e-005, -1.6312699734552807e-005, -3.2054419133447798e-006, 7.367572885903746e-007, 1.4895492164971559e-006, 1.1307947017916706e-006, 5.5063405652522782e-007, 1.1783004497663934e-007, -8.2006806503864813e-008, -1.0866516536735883e-007, -6.0669757413511352e-008, -1.0798819539621958e-008, 1.111944952595278e-008, 8.519459636796214e-009, 1.0099999569414229e-012} }; #endif PyWavelets-0.3.0/pywt/src/convolution.h0000664000175000017500000001262512556460270021666 0ustar rgommersrgommers00000000000000 /* ***************************************************************************** ** This file was autogenerated from a template DO NOT EDIT!!!! ** ** Changes should be made to the original source (.src) file ** ***************************************************************************** */ #line 1 /* Copyright (c) 2006-2012 Filip Wasilewski */ /* See COPYING for license details. */ #ifndef _CONVOLUTION_H_ #define _CONVOLUTION_H_ #include #include "common.h" #line 13 /* * Performs convolution of input with filter and downsamples by taking every * step-th element from the result. * * input - input data * N - input data length * filter - filter data * F - filter data length * output - output data * step - decimation step * mode - signal extension mode */ /* memory efficient version */ int double_downsampling_convolution(const double* input, const_index_t N, const double* filter, const_index_t F, double* output, const_index_t step, MODE mode); /* * Straightforward implementation with memory reallocation - for very short * signals (shorter than filter). This id called from downsampling_convolution */ int double_allocating_downsampling_convolution(const double* input, const_index_t N, const double* filter, const_index_t F, double* output, const_index_t step, MODE mode); /* * Performs normal (full) convolution of "upsampled" input coeffs array with * filter Requires zero-filled output buffer (adds values instead of * overwriting - can be called many times with the same output). * * input - input data * N - input data length * filter - filter data * F - filter data length * output - output data * O - output lenght (currently not used) * mode - signal extension mode */ int double_upsampling_convolution_full(const double* input, const_index_t N, const double* filter, const_index_t F, double* output, const_index_t O); /* * Performs valid convolution (signals must overlap) * Extends (virtually) input for MODE_PERIODIZATION. */ int double_upsampling_convolution_valid_sf(const double* input, const_index_t N, const double* filter, const_index_t F, double* output, const_index_t O, MODE mode); /* * TODO * for SWT * int upsampled_filter_convolution(const double* input, const int N, * const double* filter, const int F, * double* output, int step, int mode); */ #line 13 /* * Performs convolution of input with filter and downsamples by taking every * step-th element from the result. * * input - input data * N - input data length * filter - filter data * F - filter data length * output - output data * step - decimation step * mode - signal extension mode */ /* memory efficient version */ int float_downsampling_convolution(const float* input, const_index_t N, const float* filter, const_index_t F, float* output, const_index_t step, MODE mode); /* * Straightforward implementation with memory reallocation - for very short * signals (shorter than filter). This id called from downsampling_convolution */ int float_allocating_downsampling_convolution(const float* input, const_index_t N, const float* filter, const_index_t F, float* output, const_index_t step, MODE mode); /* * Performs normal (full) convolution of "upsampled" input coeffs array with * filter Requires zero-filled output buffer (adds values instead of * overwriting - can be called many times with the same output). * * input - input data * N - input data length * filter - filter data * F - filter data length * output - output data * O - output lenght (currently not used) * mode - signal extension mode */ int float_upsampling_convolution_full(const float* input, const_index_t N, const float* filter, const_index_t F, float* output, const_index_t O); /* * Performs valid convolution (signals must overlap) * Extends (virtually) input for MODE_PERIODIZATION. */ int float_upsampling_convolution_valid_sf(const float* input, const_index_t N, const float* filter, const_index_t F, float* output, const_index_t O, MODE mode); /* * TODO * for SWT * int upsampled_filter_convolution(const float* input, const int N, * const float* filter, const int F, * float* output, int step, int mode); */ #endif PyWavelets-0.3.0/pywt/src/_pywt.c0000664000175000017500000752537012556460302020455 0ustar rgommersrgommers00000000000000/* Generated by Cython 0.22 */ #define PY_SSIZE_T_CLEAN #ifndef CYTHON_USE_PYLONG_INTERNALS #ifdef PYLONG_BITS_IN_DIGIT #define CYTHON_USE_PYLONG_INTERNALS 0 #else #include "pyconfig.h" #ifdef PYLONG_BITS_IN_DIGIT #define CYTHON_USE_PYLONG_INTERNALS 1 #else #define CYTHON_USE_PYLONG_INTERNALS 0 #endif #endif #endif #include "Python.h" #ifndef Py_PYTHON_H #error Python headers needed to compile C extensions, please install development version of Python. #elif PY_VERSION_HEX < 0x02060000 || (0x03000000 <= PY_VERSION_HEX && PY_VERSION_HEX < 0x03020000) #error Cython requires Python 2.6+ or Python 3.2+. #else #define CYTHON_ABI "0_22" #include #ifndef offsetof #define offsetof(type, member) ( (size_t) & ((type*)0) -> member ) #endif #if !defined(WIN32) && !defined(MS_WINDOWS) #ifndef __stdcall #define __stdcall #endif #ifndef __cdecl #define __cdecl #endif #ifndef __fastcall #define __fastcall #endif #endif #ifndef DL_IMPORT #define DL_IMPORT(t) t #endif #ifndef DL_EXPORT #define DL_EXPORT(t) t #endif #ifndef PY_LONG_LONG #define PY_LONG_LONG LONG_LONG #endif #ifndef Py_HUGE_VAL #define Py_HUGE_VAL HUGE_VAL #endif #ifdef PYPY_VERSION #define CYTHON_COMPILING_IN_PYPY 1 #define CYTHON_COMPILING_IN_CPYTHON 0 #else #define CYTHON_COMPILING_IN_PYPY 0 #define CYTHON_COMPILING_IN_CPYTHON 1 #endif #if CYTHON_COMPILING_IN_PYPY && PY_VERSION_HEX < 0x02070600 && !defined(Py_OptimizeFlag) #define Py_OptimizeFlag 0 #endif #define __PYX_BUILD_PY_SSIZE_T "n" #define CYTHON_FORMAT_SSIZE_T "z" #if PY_MAJOR_VERSION < 3 #define __Pyx_BUILTIN_MODULE_NAME "__builtin__" #define __Pyx_PyCode_New(a, k, l, s, f, code, c, n, v, fv, cell, fn, name, fline, lnos) \ PyCode_New(a+k, l, s, f, code, c, n, v, fv, cell, fn, name, fline, lnos) #define __Pyx_DefaultClassType PyClass_Type #else #define __Pyx_BUILTIN_MODULE_NAME "builtins" #define __Pyx_PyCode_New(a, k, l, s, f, code, c, n, v, fv, cell, fn, name, fline, lnos) \ PyCode_New(a, k, l, s, f, code, c, n, v, fv, cell, fn, name, fline, lnos) #define __Pyx_DefaultClassType PyType_Type #endif #if PY_MAJOR_VERSION >= 3 #define Py_TPFLAGS_CHECKTYPES 0 #define Py_TPFLAGS_HAVE_INDEX 0 #define Py_TPFLAGS_HAVE_NEWBUFFER 0 #endif #if PY_VERSION_HEX < 0x030400a1 && !defined(Py_TPFLAGS_HAVE_FINALIZE) #define Py_TPFLAGS_HAVE_FINALIZE 0 #endif #if PY_VERSION_HEX > 0x03030000 && defined(PyUnicode_KIND) #define CYTHON_PEP393_ENABLED 1 #define __Pyx_PyUnicode_READY(op) (likely(PyUnicode_IS_READY(op)) ? 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PyMethod_New(func, self) : PyInstanceMethod_New(func)) #else #define __Pyx_PyMethod_New(func, self, klass) PyMethod_New(func, self, klass) #endif #ifndef CYTHON_INLINE #if defined(__GNUC__) #define CYTHON_INLINE __inline__ #elif defined(_MSC_VER) #define CYTHON_INLINE __inline #elif defined (__STDC_VERSION__) && __STDC_VERSION__ >= 199901L #define CYTHON_INLINE inline #else #define CYTHON_INLINE #endif #endif #ifndef CYTHON_RESTRICT #if defined(__GNUC__) #define CYTHON_RESTRICT __restrict__ #elif defined(_MSC_VER) && _MSC_VER >= 1400 #define CYTHON_RESTRICT __restrict #elif defined (__STDC_VERSION__) && __STDC_VERSION__ >= 199901L #define CYTHON_RESTRICT restrict #else #define CYTHON_RESTRICT #endif #endif #ifdef NAN #define __PYX_NAN() ((float) NAN) #else static CYTHON_INLINE float __PYX_NAN() { /* Initialize NaN. The sign is irrelevant, an exponent with all bits 1 and a nonzero mantissa means NaN. If the first bit in the mantissa is 1, it is a quiet NaN. */ float value; memset(&value, 0xFF, sizeof(value)); return value; } #endif #define __Pyx_void_to_None(void_result) (void_result, Py_INCREF(Py_None), Py_None) #ifdef __cplusplus template void __Pyx_call_destructor(T* x) { x->~T(); } template class __Pyx_FakeReference { public: __Pyx_FakeReference() : ptr(NULL) { } __Pyx_FakeReference(T& ref) : ptr(&ref) { } T *operator->() { return ptr; } operator T&() { return *ptr; } private: T *ptr; }; #endif #if PY_MAJOR_VERSION >= 3 #define __Pyx_PyNumber_Divide(x,y) PyNumber_TrueDivide(x,y) #define __Pyx_PyNumber_InPlaceDivide(x,y) PyNumber_InPlaceTrueDivide(x,y) #else #define __Pyx_PyNumber_Divide(x,y) PyNumber_Divide(x,y) #define __Pyx_PyNumber_InPlaceDivide(x,y) PyNumber_InPlaceDivide(x,y) #endif #ifndef __PYX_EXTERN_C #ifdef __cplusplus #define __PYX_EXTERN_C extern "C" #else #define __PYX_EXTERN_C extern #endif #endif #if defined(WIN32) || defined(MS_WINDOWS) #define _USE_MATH_DEFINES #endif #include #define __PYX_HAVE___pywt #define __PYX_HAVE_API___pywt #include "common.h" #include "wavelets.h" #include "wt.h" #include "math.h" #include "string.h" #include "stdio.h" #include "stdlib.h" #include "numpy/arrayobject.h" #include "numpy/ufuncobject.h" #include "pythread.h" #include "pystate.h" #ifdef _OPENMP #include #endif /* _OPENMP */ #ifdef PYREX_WITHOUT_ASSERTIONS #define CYTHON_WITHOUT_ASSERTIONS #endif #ifndef CYTHON_UNUSED # if defined(__GNUC__) # if !(defined(__cplusplus)) || (__GNUC__ > 3 || (__GNUC__ == 3 && __GNUC_MINOR__ >= 4)) # define CYTHON_UNUSED __attribute__ ((__unused__)) # else # define CYTHON_UNUSED # endif # elif defined(__ICC) || (defined(__INTEL_COMPILER) && !defined(_MSC_VER)) # define CYTHON_UNUSED __attribute__ ((__unused__)) # else # define CYTHON_UNUSED # endif #endif typedef struct {PyObject **p; char *s; const Py_ssize_t n; const char* encoding; const char is_unicode; const char is_str; const char intern; } __Pyx_StringTabEntry; #define __PYX_DEFAULT_STRING_ENCODING_IS_ASCII 0 #define __PYX_DEFAULT_STRING_ENCODING_IS_DEFAULT 0 #define __PYX_DEFAULT_STRING_ENCODING "" #define __Pyx_PyObject_FromString __Pyx_PyBytes_FromString #define __Pyx_PyObject_FromStringAndSize __Pyx_PyBytes_FromStringAndSize #define __Pyx_fits_Py_ssize_t(v, type, is_signed) ( \ (sizeof(type) < sizeof(Py_ssize_t)) || \ (sizeof(type) > sizeof(Py_ssize_t) && \ likely(v < (type)PY_SSIZE_T_MAX || \ v == (type)PY_SSIZE_T_MAX) && \ (!is_signed || likely(v > (type)PY_SSIZE_T_MIN || \ v == (type)PY_SSIZE_T_MIN))) || \ (sizeof(type) == sizeof(Py_ssize_t) && \ (is_signed || likely(v < (type)PY_SSIZE_T_MAX || \ v == (type)PY_SSIZE_T_MAX))) ) static CYTHON_INLINE char* __Pyx_PyObject_AsString(PyObject*); static CYTHON_INLINE char* __Pyx_PyObject_AsStringAndSize(PyObject*, Py_ssize_t* length); #define __Pyx_PyByteArray_FromString(s) PyByteArray_FromStringAndSize((const char*)s, strlen((const char*)s)) #define __Pyx_PyByteArray_FromStringAndSize(s, l) PyByteArray_FromStringAndSize((const char*)s, l) #define __Pyx_PyBytes_FromString PyBytes_FromString #define __Pyx_PyBytes_FromStringAndSize PyBytes_FromStringAndSize static CYTHON_INLINE PyObject* __Pyx_PyUnicode_FromString(const char*); #if PY_MAJOR_VERSION < 3 #define __Pyx_PyStr_FromString __Pyx_PyBytes_FromString #define __Pyx_PyStr_FromStringAndSize __Pyx_PyBytes_FromStringAndSize #else #define __Pyx_PyStr_FromString __Pyx_PyUnicode_FromString #define __Pyx_PyStr_FromStringAndSize __Pyx_PyUnicode_FromStringAndSize #endif #define __Pyx_PyObject_AsSString(s) ((signed char*) __Pyx_PyObject_AsString(s)) #define __Pyx_PyObject_AsUString(s) ((unsigned char*) __Pyx_PyObject_AsString(s)) #define __Pyx_PyObject_FromCString(s) __Pyx_PyObject_FromString((const char*)s) #define __Pyx_PyBytes_FromCString(s) __Pyx_PyBytes_FromString((const char*)s) #define __Pyx_PyByteArray_FromCString(s) __Pyx_PyByteArray_FromString((const char*)s) #define __Pyx_PyStr_FromCString(s) __Pyx_PyStr_FromString((const char*)s) #define __Pyx_PyUnicode_FromCString(s) __Pyx_PyUnicode_FromString((const char*)s) #if PY_MAJOR_VERSION < 3 static CYTHON_INLINE size_t __Pyx_Py_UNICODE_strlen(const Py_UNICODE *u) { const Py_UNICODE *u_end = u; while (*u_end++) ; return (size_t)(u_end - u - 1); } #else #define __Pyx_Py_UNICODE_strlen Py_UNICODE_strlen #endif #define __Pyx_PyUnicode_FromUnicode(u) PyUnicode_FromUnicode(u, __Pyx_Py_UNICODE_strlen(u)) #define __Pyx_PyUnicode_FromUnicodeAndLength PyUnicode_FromUnicode #define __Pyx_PyUnicode_AsUnicode PyUnicode_AsUnicode #define __Pyx_Owned_Py_None(b) (Py_INCREF(Py_None), Py_None) #define __Pyx_PyBool_FromLong(b) ((b) ? (Py_INCREF(Py_True), Py_True) : (Py_INCREF(Py_False), Py_False)) static CYTHON_INLINE int __Pyx_PyObject_IsTrue(PyObject*); static CYTHON_INLINE PyObject* __Pyx_PyNumber_Int(PyObject* x); static CYTHON_INLINE Py_ssize_t __Pyx_PyIndex_AsSsize_t(PyObject*); static CYTHON_INLINE PyObject * __Pyx_PyInt_FromSize_t(size_t); #if CYTHON_COMPILING_IN_CPYTHON #define __pyx_PyFloat_AsDouble(x) (PyFloat_CheckExact(x) ? PyFloat_AS_DOUBLE(x) : PyFloat_AsDouble(x)) #else #define __pyx_PyFloat_AsDouble(x) PyFloat_AsDouble(x) #endif #define __pyx_PyFloat_AsFloat(x) ((float) __pyx_PyFloat_AsDouble(x)) #if PY_MAJOR_VERSION < 3 && __PYX_DEFAULT_STRING_ENCODING_IS_ASCII static int __Pyx_sys_getdefaultencoding_not_ascii; static int __Pyx_init_sys_getdefaultencoding_params(void) { PyObject* sys; PyObject* default_encoding = NULL; PyObject* ascii_chars_u = NULL; PyObject* ascii_chars_b = NULL; const char* default_encoding_c; sys = PyImport_ImportModule("sys"); if (!sys) goto bad; default_encoding = PyObject_CallMethod(sys, (char*) "getdefaultencoding", NULL); Py_DECREF(sys); if (!default_encoding) goto bad; default_encoding_c = PyBytes_AsString(default_encoding); if (!default_encoding_c) goto bad; if (strcmp(default_encoding_c, "ascii") == 0) { __Pyx_sys_getdefaultencoding_not_ascii = 0; } else { char ascii_chars[128]; int c; for (c = 0; c < 128; c++) { ascii_chars[c] = c; } __Pyx_sys_getdefaultencoding_not_ascii = 1; ascii_chars_u = PyUnicode_DecodeASCII(ascii_chars, 128, NULL); if (!ascii_chars_u) goto bad; ascii_chars_b = PyUnicode_AsEncodedString(ascii_chars_u, default_encoding_c, NULL); if (!ascii_chars_b || !PyBytes_Check(ascii_chars_b) || memcmp(ascii_chars, PyBytes_AS_STRING(ascii_chars_b), 128) != 0) { PyErr_Format( PyExc_ValueError, "This module compiled with c_string_encoding=ascii, but default encoding '%.200s' is not a superset of ascii.", default_encoding_c); goto bad; } Py_DECREF(ascii_chars_u); Py_DECREF(ascii_chars_b); } Py_DECREF(default_encoding); return 0; bad: Py_XDECREF(default_encoding); Py_XDECREF(ascii_chars_u); Py_XDECREF(ascii_chars_b); return -1; } #endif #if __PYX_DEFAULT_STRING_ENCODING_IS_DEFAULT && PY_MAJOR_VERSION >= 3 #define __Pyx_PyUnicode_FromStringAndSize(c_str, size) PyUnicode_DecodeUTF8(c_str, size, NULL) #else #define __Pyx_PyUnicode_FromStringAndSize(c_str, size) PyUnicode_Decode(c_str, size, __PYX_DEFAULT_STRING_ENCODING, NULL) #if __PYX_DEFAULT_STRING_ENCODING_IS_DEFAULT static char* __PYX_DEFAULT_STRING_ENCODING; static int __Pyx_init_sys_getdefaultencoding_params(void) { PyObject* sys; PyObject* default_encoding = NULL; char* default_encoding_c; sys = PyImport_ImportModule("sys"); if (!sys) goto bad; default_encoding = PyObject_CallMethod(sys, (char*) (const char*) "getdefaultencoding", NULL); Py_DECREF(sys); if (!default_encoding) goto bad; default_encoding_c = PyBytes_AsString(default_encoding); if (!default_encoding_c) goto bad; __PYX_DEFAULT_STRING_ENCODING = (char*) malloc(strlen(default_encoding_c)); if (!__PYX_DEFAULT_STRING_ENCODING) goto bad; strcpy(__PYX_DEFAULT_STRING_ENCODING, default_encoding_c); Py_DECREF(default_encoding); return 0; bad: Py_XDECREF(default_encoding); return -1; } #endif #endif /* Test for GCC > 2.95 */ #if defined(__GNUC__) && (__GNUC__ > 2 || (__GNUC__ == 2 && (__GNUC_MINOR__ > 95))) #define likely(x) __builtin_expect(!!(x), 1) #define unlikely(x) __builtin_expect(!!(x), 0) #else /* !__GNUC__ or GCC < 2.95 */ #define likely(x) (x) #define unlikely(x) (x) #endif /* __GNUC__ */ static PyObject *__pyx_m; static PyObject *__pyx_d; static PyObject *__pyx_b; static PyObject *__pyx_empty_tuple; static PyObject *__pyx_empty_bytes; static int __pyx_lineno; static int __pyx_clineno = 0; static const char * __pyx_cfilenm= __FILE__; static const char *__pyx_filename; #if !defined(CYTHON_CCOMPLEX) #if defined(__cplusplus) #define CYTHON_CCOMPLEX 1 #elif defined(_Complex_I) #define CYTHON_CCOMPLEX 1 #else #define CYTHON_CCOMPLEX 0 #endif #endif #if CYTHON_CCOMPLEX #ifdef __cplusplus #include #else #include #endif #endif #if CYTHON_CCOMPLEX && !defined(__cplusplus) && defined(__sun__) && defined(__GNUC__) #undef _Complex_I #define _Complex_I 1.0fj #endif static const char *__pyx_f[] = { "_pywt.pyx", "__init__.pxd", "stringsource", "type.pxd", "wavelets_list.pxi", }; #define IS_UNSIGNED(type) (((type) -1) > 0) struct __Pyx_StructField_; #define __PYX_BUF_FLAGS_PACKED_STRUCT (1 << 0) typedef struct { const char* name; struct __Pyx_StructField_* fields; size_t size; size_t arraysize[8]; int ndim; char typegroup; char is_unsigned; int flags; } __Pyx_TypeInfo; typedef struct __Pyx_StructField_ { __Pyx_TypeInfo* type; const char* name; size_t offset; } __Pyx_StructField; typedef struct { __Pyx_StructField* field; size_t parent_offset; } __Pyx_BufFmt_StackElem; typedef struct { __Pyx_StructField root; __Pyx_BufFmt_StackElem* head; size_t fmt_offset; size_t new_count, enc_count; size_t struct_alignment; int is_complex; char enc_type; char new_packmode; char enc_packmode; char is_valid_array; } __Pyx_BufFmt_Context; struct __pyx_memoryview_obj; typedef struct { struct __pyx_memoryview_obj *memview; char *data; Py_ssize_t shape[8]; Py_ssize_t strides[8]; Py_ssize_t suboffsets[8]; } __Pyx_memviewslice; #include #ifndef CYTHON_ATOMICS #define CYTHON_ATOMICS 1 #endif #define __pyx_atomic_int_type int #if CYTHON_ATOMICS && __GNUC__ >= 4 && (__GNUC_MINOR__ > 1 || \ (__GNUC_MINOR__ == 1 && __GNUC_PATCHLEVEL >= 2)) && \ !defined(__i386__) #define __pyx_atomic_incr_aligned(value, lock) __sync_fetch_and_add(value, 1) #define __pyx_atomic_decr_aligned(value, lock) __sync_fetch_and_sub(value, 1) #ifdef __PYX_DEBUG_ATOMICS #warning "Using GNU atomics" #endif #elif CYTHON_ATOMICS && MSC_VER #include #define __pyx_atomic_int_type LONG #define __pyx_atomic_incr_aligned(value, lock) InterlockedIncrement(value) #define __pyx_atomic_decr_aligned(value, lock) InterlockedDecrement(value) #ifdef __PYX_DEBUG_ATOMICS #warning "Using MSVC atomics" #endif #elif CYTHON_ATOMICS && (defined(__ICC) || defined(__INTEL_COMPILER)) && 0 #define __pyx_atomic_incr_aligned(value, lock) _InterlockedIncrement(value) #define __pyx_atomic_decr_aligned(value, lock) _InterlockedDecrement(value) #ifdef __PYX_DEBUG_ATOMICS #warning "Using Intel atomics" #endif #else #undef CYTHON_ATOMICS #define CYTHON_ATOMICS 0 #ifdef __PYX_DEBUG_ATOMICS #warning "Not using atomics" #endif #endif typedef volatile __pyx_atomic_int_type __pyx_atomic_int; #if CYTHON_ATOMICS #define __pyx_add_acquisition_count(memview) \ __pyx_atomic_incr_aligned(__pyx_get_slice_count_pointer(memview), memview->lock) #define __pyx_sub_acquisition_count(memview) \ __pyx_atomic_decr_aligned(__pyx_get_slice_count_pointer(memview), memview->lock) #else #define __pyx_add_acquisition_count(memview) \ __pyx_add_acquisition_count_locked(__pyx_get_slice_count_pointer(memview), memview->lock) #define __pyx_sub_acquisition_count(memview) \ __pyx_sub_acquisition_count_locked(__pyx_get_slice_count_pointer(memview), memview->lock) #endif /* "../../../../../.local/lib/python2.6/site-packages/Cython/Includes/numpy/__init__.pxd":726 * # in Cython to enable them only on the right systems. * * ctypedef npy_int8 int8_t # <<<<<<<<<<<<<< * ctypedef npy_int16 int16_t * ctypedef npy_int32 int32_t */ typedef npy_int8 __pyx_t_5numpy_int8_t; /* "../../../../../.local/lib/python2.6/site-packages/Cython/Includes/numpy/__init__.pxd":727 * * ctypedef npy_int8 int8_t * ctypedef npy_int16 int16_t # <<<<<<<<<<<<<< * ctypedef npy_int32 int32_t * ctypedef npy_int64 int64_t */ typedef npy_int16 __pyx_t_5numpy_int16_t; /* "../../../../../.local/lib/python2.6/site-packages/Cython/Includes/numpy/__init__.pxd":728 * ctypedef npy_int8 int8_t * ctypedef npy_int16 int16_t * ctypedef npy_int32 int32_t # <<<<<<<<<<<<<< * ctypedef npy_int64 int64_t * #ctypedef npy_int96 int96_t */ typedef npy_int32 __pyx_t_5numpy_int32_t; /* "../../../../../.local/lib/python2.6/site-packages/Cython/Includes/numpy/__init__.pxd":729 * ctypedef npy_int16 int16_t * ctypedef npy_int32 int32_t * ctypedef npy_int64 int64_t # <<<<<<<<<<<<<< * #ctypedef npy_int96 int96_t * #ctypedef npy_int128 int128_t */ typedef npy_int64 __pyx_t_5numpy_int64_t; /* "../../../../../.local/lib/python2.6/site-packages/Cython/Includes/numpy/__init__.pxd":733 * #ctypedef npy_int128 int128_t * * ctypedef npy_uint8 uint8_t # <<<<<<<<<<<<<< * ctypedef npy_uint16 uint16_t * ctypedef npy_uint32 uint32_t */ typedef npy_uint8 __pyx_t_5numpy_uint8_t; /* "../../../../../.local/lib/python2.6/site-packages/Cython/Includes/numpy/__init__.pxd":734 * * ctypedef npy_uint8 uint8_t * ctypedef npy_uint16 uint16_t # <<<<<<<<<<<<<< * ctypedef npy_uint32 uint32_t * ctypedef npy_uint64 uint64_t */ typedef npy_uint16 __pyx_t_5numpy_uint16_t; /* "../../../../../.local/lib/python2.6/site-packages/Cython/Includes/numpy/__init__.pxd":735 * ctypedef npy_uint8 uint8_t * ctypedef npy_uint16 uint16_t * ctypedef npy_uint32 uint32_t # <<<<<<<<<<<<<< * ctypedef npy_uint64 uint64_t * #ctypedef npy_uint96 uint96_t */ typedef npy_uint32 __pyx_t_5numpy_uint32_t; /* "../../../../../.local/lib/python2.6/site-packages/Cython/Includes/numpy/__init__.pxd":736 * ctypedef npy_uint16 uint16_t * ctypedef npy_uint32 uint32_t * ctypedef npy_uint64 uint64_t # <<<<<<<<<<<<<< * #ctypedef npy_uint96 uint96_t * #ctypedef npy_uint128 uint128_t */ typedef npy_uint64 __pyx_t_5numpy_uint64_t; /* "../../../../../.local/lib/python2.6/site-packages/Cython/Includes/numpy/__init__.pxd":740 * #ctypedef npy_uint128 uint128_t * * ctypedef npy_float32 float32_t # <<<<<<<<<<<<<< * ctypedef npy_float64 float64_t * #ctypedef npy_float80 float80_t */ typedef npy_float32 __pyx_t_5numpy_float32_t; /* "../../../../../.local/lib/python2.6/site-packages/Cython/Includes/numpy/__init__.pxd":741 * * ctypedef npy_float32 float32_t * ctypedef npy_float64 float64_t # <<<<<<<<<<<<<< * #ctypedef npy_float80 float80_t * #ctypedef npy_float128 float128_t */ typedef npy_float64 __pyx_t_5numpy_float64_t; /* "../../../../../.local/lib/python2.6/site-packages/Cython/Includes/numpy/__init__.pxd":750 * # The int types are mapped a bit surprising -- * # numpy.int corresponds to 'l' and numpy.long to 'q' * ctypedef npy_long int_t # <<<<<<<<<<<<<< * ctypedef npy_longlong long_t * ctypedef npy_longlong longlong_t */ typedef npy_long __pyx_t_5numpy_int_t; /* "../../../../../.local/lib/python2.6/site-packages/Cython/Includes/numpy/__init__.pxd":751 * # numpy.int corresponds to 'l' and numpy.long to 'q' * ctypedef npy_long int_t * ctypedef npy_longlong long_t # <<<<<<<<<<<<<< * ctypedef npy_longlong longlong_t * */ typedef npy_longlong __pyx_t_5numpy_long_t; /* "../../../../../.local/lib/python2.6/site-packages/Cython/Includes/numpy/__init__.pxd":752 * ctypedef npy_long int_t * ctypedef npy_longlong long_t * ctypedef npy_longlong longlong_t # <<<<<<<<<<<<<< * * ctypedef npy_ulong uint_t */ typedef npy_longlong __pyx_t_5numpy_longlong_t; /* "../../../../../.local/lib/python2.6/site-packages/Cython/Includes/numpy/__init__.pxd":754 * ctypedef npy_longlong longlong_t * * ctypedef npy_ulong uint_t # <<<<<<<<<<<<<< * ctypedef npy_ulonglong ulong_t * ctypedef npy_ulonglong ulonglong_t */ typedef npy_ulong __pyx_t_5numpy_uint_t; /* "../../../../../.local/lib/python2.6/site-packages/Cython/Includes/numpy/__init__.pxd":755 * * ctypedef npy_ulong uint_t * ctypedef npy_ulonglong ulong_t # <<<<<<<<<<<<<< * ctypedef npy_ulonglong ulonglong_t * */ typedef npy_ulonglong __pyx_t_5numpy_ulong_t; /* "../../../../../.local/lib/python2.6/site-packages/Cython/Includes/numpy/__init__.pxd":756 * ctypedef npy_ulong uint_t * ctypedef npy_ulonglong ulong_t * ctypedef npy_ulonglong ulonglong_t # <<<<<<<<<<<<<< * * ctypedef npy_intp intp_t */ typedef npy_ulonglong __pyx_t_5numpy_ulonglong_t; /* "../../../../../.local/lib/python2.6/site-packages/Cython/Includes/numpy/__init__.pxd":758 * ctypedef npy_ulonglong ulonglong_t * * ctypedef npy_intp intp_t # <<<<<<<<<<<<<< * ctypedef npy_uintp uintp_t * */ typedef npy_intp __pyx_t_5numpy_intp_t; /* "../../../../../.local/lib/python2.6/site-packages/Cython/Includes/numpy/__init__.pxd":759 * * ctypedef npy_intp intp_t * ctypedef npy_uintp uintp_t # <<<<<<<<<<<<<< * * ctypedef npy_double float_t */ typedef npy_uintp __pyx_t_5numpy_uintp_t; /* "../../../../../.local/lib/python2.6/site-packages/Cython/Includes/numpy/__init__.pxd":761 * ctypedef npy_uintp uintp_t * * ctypedef npy_double float_t # <<<<<<<<<<<<<< * ctypedef npy_double double_t * ctypedef npy_longdouble longdouble_t */ typedef npy_double __pyx_t_5numpy_float_t; /* "../../../../../.local/lib/python2.6/site-packages/Cython/Includes/numpy/__init__.pxd":762 * * ctypedef npy_double float_t * ctypedef npy_double double_t # <<<<<<<<<<<<<< * ctypedef npy_longdouble longdouble_t * */ typedef npy_double __pyx_t_5numpy_double_t; /* "../../../../../.local/lib/python2.6/site-packages/Cython/Includes/numpy/__init__.pxd":763 * ctypedef npy_double float_t * ctypedef npy_double double_t * ctypedef npy_longdouble longdouble_t # <<<<<<<<<<<<<< * * ctypedef npy_cfloat cfloat_t */ typedef npy_longdouble __pyx_t_5numpy_longdouble_t; /* "_pywt.pyx":16 * from libc.math cimport pow, sqrt * * ctypedef Py_ssize_t index_t # <<<<<<<<<<<<<< * * import warnings */ typedef Py_ssize_t __pyx_t_5_pywt_index_t; #if CYTHON_CCOMPLEX #ifdef __cplusplus typedef ::std::complex< float > __pyx_t_float_complex; #else typedef float _Complex __pyx_t_float_complex; #endif #else typedef struct { float real, imag; } __pyx_t_float_complex; #endif #if CYTHON_CCOMPLEX #ifdef __cplusplus typedef ::std::complex< double > __pyx_t_double_complex; #else typedef double _Complex __pyx_t_double_complex; #endif #else typedef struct { double real, imag; } __pyx_t_double_complex; #endif /*--- Type declarations ---*/ struct WaveletObject; struct __pyx_array_obj; struct __pyx_MemviewEnum_obj; struct __pyx_memoryview_obj; struct __pyx_memoryviewslice_obj; /* "../../../../../.local/lib/python2.6/site-packages/Cython/Includes/numpy/__init__.pxd":765 * ctypedef npy_longdouble longdouble_t * * ctypedef npy_cfloat cfloat_t # <<<<<<<<<<<<<< * ctypedef npy_cdouble cdouble_t * ctypedef npy_clongdouble clongdouble_t */ typedef npy_cfloat __pyx_t_5numpy_cfloat_t; /* "../../../../../.local/lib/python2.6/site-packages/Cython/Includes/numpy/__init__.pxd":766 * * ctypedef npy_cfloat cfloat_t * ctypedef npy_cdouble cdouble_t # <<<<<<<<<<<<<< * ctypedef npy_clongdouble clongdouble_t * */ typedef npy_cdouble __pyx_t_5numpy_cdouble_t; /* "../../../../../.local/lib/python2.6/site-packages/Cython/Includes/numpy/__init__.pxd":767 * ctypedef npy_cfloat cfloat_t * ctypedef npy_cdouble cdouble_t * ctypedef npy_clongdouble clongdouble_t # <<<<<<<<<<<<<< * * ctypedef npy_cdouble complex_t */ typedef npy_clongdouble __pyx_t_5numpy_clongdouble_t; /* "../../../../../.local/lib/python2.6/site-packages/Cython/Includes/numpy/__init__.pxd":769 * ctypedef npy_clongdouble clongdouble_t * * ctypedef npy_cdouble complex_t # <<<<<<<<<<<<<< * * cdef inline object PyArray_MultiIterNew1(a): */ typedef npy_cdouble __pyx_t_5numpy_complex_t; struct __pyx_defaults; typedef struct __pyx_defaults __pyx_defaults; struct __pyx_defaults1; typedef struct __pyx_defaults1 __pyx_defaults1; struct __pyx_defaults2; typedef struct __pyx_defaults2 __pyx_defaults2; struct __pyx_defaults3; typedef struct __pyx_defaults3 __pyx_defaults3; struct __pyx_defaults4; typedef struct __pyx_defaults4 __pyx_defaults4; struct __pyx_defaults5; typedef struct __pyx_defaults5 __pyx_defaults5; struct __pyx_defaults6; typedef struct __pyx_defaults6 __pyx_defaults6; struct __pyx_defaults7; typedef struct __pyx_defaults7 __pyx_defaults7; struct __pyx_defaults8; typedef struct __pyx_defaults8 __pyx_defaults8; struct __pyx_defaults9; typedef struct __pyx_defaults9 __pyx_defaults9; struct __pyx_defaults10; typedef struct __pyx_defaults10 __pyx_defaults10; struct __pyx_defaults11; typedef struct __pyx_defaults11 __pyx_defaults11; struct __pyx_defaults12; typedef struct __pyx_defaults12 __pyx_defaults12; struct __pyx_defaults13; typedef struct __pyx_defaults13 __pyx_defaults13; struct __pyx_defaults14; typedef struct __pyx_defaults14 __pyx_defaults14; struct __pyx_defaults15; typedef struct __pyx_defaults15 __pyx_defaults15; struct __pyx_defaults16; typedef struct __pyx_defaults16 __pyx_defaults16; struct __pyx_defaults17; typedef struct __pyx_defaults17 __pyx_defaults17; struct __pyx_defaults18; typedef struct __pyx_defaults18 __pyx_defaults18; struct __pyx_defaults19; typedef struct __pyx_defaults19 __pyx_defaults19; 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static void __Pyx_RaiseDoubleKeywordsError(const char* func_name, PyObject* kw_name); static int __Pyx_ParseOptionalKeywords(PyObject *kwds, PyObject **argnames[], \ PyObject *kwds2, PyObject *values[], Py_ssize_t num_pos_args, \ const char* function_name); #if CYTHON_COMPILING_IN_CPYTHON static CYTHON_INLINE PyObject* __Pyx_PyObject_Call(PyObject *func, PyObject *arg, PyObject *kw); #else #define __Pyx_PyObject_Call(func, arg, kw) PyObject_Call(func, arg, kw) #endif static CYTHON_INLINE void __Pyx_ErrRestore(PyObject *type, PyObject *value, PyObject *tb); static CYTHON_INLINE void __Pyx_ErrFetch(PyObject **type, PyObject **value, PyObject **tb); static void __Pyx_Raise(PyObject *type, PyObject *value, PyObject *tb, PyObject *cause); static CYTHON_INLINE PyObject *__Pyx_GetModuleGlobalName(PyObject *name); static CYTHON_INLINE PyObject *__Pyx_GetAttr(PyObject *, PyObject *); static CYTHON_INLINE void __Pyx_ExceptionSave(PyObject **type, PyObject **value, PyObject **tb); static void __Pyx_ExceptionReset(PyObject *type, PyObject *value, PyObject *tb); static int __Pyx_GetException(PyObject **type, PyObject **value, PyObject **tb); #define __Pyx_GetItemInt(o, i, type, is_signed, to_py_func, is_list, wraparound, boundscheck) \ (__Pyx_fits_Py_ssize_t(i, type, is_signed) ? \ __Pyx_GetItemInt_Fast(o, (Py_ssize_t)i, is_list, wraparound, boundscheck) : \ (is_list ? (PyErr_SetString(PyExc_IndexError, "list index out of range"), (PyObject*)NULL) : \ __Pyx_GetItemInt_Generic(o, to_py_func(i)))) #define __Pyx_GetItemInt_List(o, i, type, is_signed, to_py_func, is_list, wraparound, boundscheck) \ (__Pyx_fits_Py_ssize_t(i, type, is_signed) ? \ __Pyx_GetItemInt_List_Fast(o, (Py_ssize_t)i, wraparound, boundscheck) : \ (PyErr_SetString(PyExc_IndexError, "list index out of range"), (PyObject*)NULL)) static CYTHON_INLINE PyObject *__Pyx_GetItemInt_List_Fast(PyObject *o, Py_ssize_t i, int wraparound, int boundscheck); #define __Pyx_GetItemInt_Tuple(o, i, type, is_signed, to_py_func, is_list, wraparound, boundscheck) \ (__Pyx_fits_Py_ssize_t(i, type, is_signed) ? \ __Pyx_GetItemInt_Tuple_Fast(o, (Py_ssize_t)i, wraparound, boundscheck) : \ (PyErr_SetString(PyExc_IndexError, "tuple index out of range"), (PyObject*)NULL)) static CYTHON_INLINE PyObject *__Pyx_GetItemInt_Tuple_Fast(PyObject *o, Py_ssize_t i, int wraparound, int boundscheck); static CYTHON_INLINE PyObject *__Pyx_GetItemInt_Generic(PyObject *o, PyObject* j); static CYTHON_INLINE PyObject *__Pyx_GetItemInt_Fast(PyObject *o, Py_ssize_t i, int is_list, int wraparound, int boundscheck); #if CYTHON_COMPILING_IN_CPYTHON static CYTHON_INLINE int __Pyx_PyList_Append(PyObject* list, PyObject* x) { PyListObject* L = (PyListObject*) list; Py_ssize_t len = Py_SIZE(list); if (likely(L->allocated > len) & likely(len > (L->allocated >> 1))) { Py_INCREF(x); PyList_SET_ITEM(list, len, x); Py_SIZE(list) = len+1; return 0; } return PyList_Append(list, x); } #else #define __Pyx_PyList_Append(L,x) PyList_Append(L,x) #endif #if CYTHON_COMPILING_IN_CPYTHON static CYTHON_INLINE PyObject* __Pyx_PyObject_CallMethO(PyObject *func, PyObject *arg); #endif static CYTHON_INLINE PyObject* __Pyx_PyObject_CallOneArg(PyObject *func, PyObject *arg); static PyObject* __Pyx_PyObject_CallMethod1(PyObject* obj, PyObject* method_name, PyObject* arg); static CYTHON_INLINE int __Pyx_PyObject_Append(PyObject* L, PyObject* x); static CYTHON_INLINE PyObject* __Pyx_PyObject_GetSlice( PyObject* obj, Py_ssize_t cstart, Py_ssize_t cstop, PyObject** py_start, PyObject** py_stop, PyObject** py_slice, int has_cstart, int has_cstop, int wraparound); static CYTHON_INLINE int __Pyx_PySequence_Contains(PyObject* item, PyObject* seq, int eq) { int result = PySequence_Contains(seq, item); return unlikely(result < 0) ? result : (result == (eq == Py_EQ)); } #if CYTHON_COMPILING_IN_CPYTHON static CYTHON_INLINE PyObject* __Pyx_PyObject_CallNoArg(PyObject *func); #else #define __Pyx_PyObject_CallNoArg(func) __Pyx_PyObject_Call(func, __pyx_empty_tuple, NULL) #endif static CYTHON_INLINE void __Pyx_RaiseTooManyValuesError(Py_ssize_t expected); static CYTHON_INLINE void __Pyx_RaiseNeedMoreValuesError(Py_ssize_t index); static CYTHON_INLINE int __Pyx_IterFinish(void); static int __Pyx_IternextUnpackEndCheck(PyObject *retval, Py_ssize_t expected); #include static CYTHON_INLINE PyObject* __Pyx_decode_c_string( const char* cstring, Py_ssize_t start, Py_ssize_t stop, const char* encoding, const char* errors, PyObject* (*decode_func)(const char *s, Py_ssize_t size, const char *errors)); #ifndef __PYX_FORCE_INIT_THREADS #define __PYX_FORCE_INIT_THREADS 0 #endif static CYTHON_INLINE long __Pyx_mod_long(long, long); /* proto */ static CYTHON_INLINE int __Pyx_PyDict_Contains(PyObject* item, PyObject* dict, int eq) { int result = PyDict_Contains(dict, item); return unlikely(result < 0) ? result : (result == (eq == Py_EQ)); } #if PY_MAJOR_VERSION >= 3 static PyObject *__Pyx_PyDict_GetItem(PyObject *d, PyObject* key) { PyObject *value; value = PyDict_GetItemWithError(d, key); if (unlikely(!value)) { if (!PyErr_Occurred()) { PyObject* args = PyTuple_Pack(1, key); if (likely(args)) PyErr_SetObject(PyExc_KeyError, args); Py_XDECREF(args); } return NULL; } Py_INCREF(value); return value; } #else #define __Pyx_PyDict_GetItem(d, key) PyObject_GetItem(d, key) #endif #define __Pyx_SetItemInt(o, i, v, type, is_signed, to_py_func, is_list, wraparound, boundscheck) \ (__Pyx_fits_Py_ssize_t(i, type, is_signed) ? \ __Pyx_SetItemInt_Fast(o, (Py_ssize_t)i, v, is_list, wraparound, boundscheck) : \ (is_list ? (PyErr_SetString(PyExc_IndexError, "list assignment index out of range"), -1) : \ __Pyx_SetItemInt_Generic(o, to_py_func(i), v))) static CYTHON_INLINE int __Pyx_SetItemInt_Generic(PyObject *o, PyObject *j, PyObject *v); static CYTHON_INLINE int __Pyx_SetItemInt_Fast(PyObject *o, Py_ssize_t i, PyObject *v, int is_list, int wraparound, int boundscheck); static PyObject* __Pyx_PyObject_CallMethod0(PyObject* obj, PyObject* method_name); static CYTHON_INLINE void __Pyx_RaiseNoneNotIterableError(void); static void __Pyx_UnpackTupleError(PyObject *, Py_ssize_t index); static CYTHON_INLINE int __Pyx_unpack_tuple2(PyObject* tuple, PyObject** value1, PyObject** value2, int is_tuple, int has_known_size, int decref_tuple); static CYTHON_INLINE PyObject* __Pyx_dict_iterator(PyObject* dict, int is_dict, PyObject* method_name, Py_ssize_t* p_orig_length, int* p_is_dict); static CYTHON_INLINE int __Pyx_dict_iter_next(PyObject* dict_or_iter, Py_ssize_t orig_length, Py_ssize_t* ppos, PyObject** pkey, PyObject** pvalue, PyObject** pitem, int is_dict); static CYTHON_INLINE int __Pyx_ArgTypeTest(PyObject *obj, PyTypeObject *type, int none_allowed, const char *name, int exact); static CYTHON_INLINE int __Pyx_GetBufferAndValidate(Py_buffer* buf, PyObject* obj, __Pyx_TypeInfo* dtype, int flags, int nd, int cast, __Pyx_BufFmt_StackElem* stack); static CYTHON_INLINE void __Pyx_SafeReleaseBuffer(Py_buffer* info); static CYTHON_INLINE int __Pyx_TypeTest(PyObject *obj, PyTypeObject *type); static void __Pyx_RaiseBufferFallbackError(void); static void __Pyx_RaiseBufferIndexError(int axis); #define __Pyx_BufPtrStrided1d(type, buf, i0, s0) (type)((char*)buf + i0 * s0) #define __Pyx_BufPtrCContig1d(type, buf, i0, s0) ((type)buf + i0) static CYTHON_INLINE int __Pyx_PyBytes_Equals(PyObject* s1, PyObject* s2, int equals); static CYTHON_INLINE int __Pyx_PyUnicode_Equals(PyObject* s1, PyObject* s2, int equals); #if PY_MAJOR_VERSION >= 3 #define __Pyx_PyString_Equals __Pyx_PyUnicode_Equals #else #define __Pyx_PyString_Equals __Pyx_PyBytes_Equals #endif static CYTHON_INLINE __pyx_t_5_pywt_index_t __Pyx_div___pyx_t_5_pywt_index_t(__pyx_t_5_pywt_index_t, __pyx_t_5_pywt_index_t); /* proto */ static CYTHON_INLINE __pyx_t_5_pywt_index_t __Pyx_mod___pyx_t_5_pywt_index_t(__pyx_t_5_pywt_index_t, __pyx_t_5_pywt_index_t); /* proto */ static CYTHON_INLINE Py_ssize_t __Pyx_div_Py_ssize_t(Py_ssize_t, Py_ssize_t); /* proto */ #define UNARY_NEG_WOULD_OVERFLOW(x) (((x) < 0) & ((unsigned long)(x) == 0-(unsigned long)(x))) static CYTHON_UNUSED int __pyx_array_getbuffer(PyObject *__pyx_v_self, Py_buffer *__pyx_v_info, int __pyx_v_flags); /*proto*/ static PyObject *get_memview(PyObject *__pyx_v_self); /*proto*/ static CYTHON_INLINE void __Pyx_ExceptionSwap(PyObject **type, PyObject **value, PyObject **tb); static CYTHON_UNUSED int __pyx_memoryview_getbuffer(PyObject *__pyx_v_self, Py_buffer *__pyx_v_info, int __pyx_v_flags); /*proto*/ static PyObject *__pyx_memoryview_transpose(PyObject *__pyx_v_self); /*proto*/ static PyObject *__pyx_memoryview__get__base(PyObject *__pyx_v_self); /*proto*/ static PyObject *__pyx_memoryview_get_shape(PyObject *__pyx_v_self); /*proto*/ #if CYTHON_COMPILING_IN_CPYTHON static CYTHON_INLINE int __Pyx_ListComp_Append(PyObject* list, PyObject* x) { PyListObject* L = (PyListObject*) list; Py_ssize_t len = Py_SIZE(list); if (likely(L->allocated > len)) { Py_INCREF(x); PyList_SET_ITEM(list, len, x); Py_SIZE(list) = len+1; return 0; } return PyList_Append(list, x); } #else #define __Pyx_ListComp_Append(L,x) PyList_Append(L,x) #endif static PyObject *__pyx_memoryview_get_strides(PyObject *__pyx_v_self); /*proto*/ static PyObject *__pyx_memoryview_get_suboffsets(PyObject *__pyx_v_self); /*proto*/ static PyObject *__pyx_memoryview_get_ndim(PyObject *__pyx_v_self); /*proto*/ static PyObject *__pyx_memoryview_get_itemsize(PyObject *__pyx_v_self); /*proto*/ static PyObject *__pyx_memoryview_get_nbytes(PyObject *__pyx_v_self); /*proto*/ static PyObject *__pyx_memoryview_get_size(PyObject *__pyx_v_self); /*proto*/ static CYTHON_INLINE int __Pyx_PyList_Extend(PyObject* L, PyObject* v) { #if CYTHON_COMPILING_IN_CPYTHON PyObject* none = _PyList_Extend((PyListObject*)L, v); if (unlikely(!none)) return -1; Py_DECREF(none); return 0; #else return PyList_SetSlice(L, PY_SSIZE_T_MAX, PY_SSIZE_T_MAX, v); #endif } static CYTHON_INLINE void __Pyx_RaiseUnboundLocalError(const char *varname); static CYTHON_INLINE long __Pyx_div_long(long, long); /* proto */ static PyObject *__pyx_memoryviewslice__get__base(PyObject *__pyx_v_self); /*proto*/ static void __Pyx_WriteUnraisable(const char *name, int clineno, int lineno, const char *filename, int full_traceback); static int __Pyx_SetVtable(PyObject *dict, void *vtable); static PyObject *__Pyx_CalculateMetaclass(PyTypeObject *metaclass, PyObject *bases); static PyTypeObject* __Pyx_FetchCommonType(PyTypeObject* type); #define __Pyx_CyFunction_USED 1 #include #define __Pyx_CYFUNCTION_STATICMETHOD 0x01 #define __Pyx_CYFUNCTION_CLASSMETHOD 0x02 #define __Pyx_CYFUNCTION_CCLASS 0x04 #define __Pyx_CyFunction_GetClosure(f) \ (((__pyx_CyFunctionObject *) (f))->func_closure) #define __Pyx_CyFunction_GetClassObj(f) \ (((__pyx_CyFunctionObject *) (f))->func_classobj) #define __Pyx_CyFunction_Defaults(type, f) \ ((type *)(((__pyx_CyFunctionObject *) (f))->defaults)) #define __Pyx_CyFunction_SetDefaultsGetter(f, g) \ ((__pyx_CyFunctionObject *) (f))->defaults_getter = (g) typedef struct { PyCFunctionObject func; #if PY_VERSION_HEX < 0x030500A0 PyObject *func_weakreflist; #endif PyObject *func_dict; PyObject *func_name; PyObject *func_qualname; PyObject *func_doc; PyObject *func_globals; PyObject *func_code; PyObject *func_closure; PyObject *func_classobj; void *defaults; int defaults_pyobjects; int flags; PyObject *defaults_tuple; PyObject *defaults_kwdict; PyObject *(*defaults_getter)(PyObject *); PyObject *func_annotations; } __pyx_CyFunctionObject; static PyTypeObject *__pyx_CyFunctionType = 0; #define __Pyx_CyFunction_NewEx(ml, flags, qualname, self, module, globals, code) \ __Pyx_CyFunction_New(__pyx_CyFunctionType, ml, flags, qualname, self, module, globals, code) static PyObject *__Pyx_CyFunction_New(PyTypeObject *, PyMethodDef *ml, int flags, PyObject* qualname, PyObject *self, PyObject *module, PyObject *globals, PyObject* code); static CYTHON_INLINE void *__Pyx_CyFunction_InitDefaults(PyObject *m, size_t size, int pyobjects); static CYTHON_INLINE void __Pyx_CyFunction_SetDefaultsTuple(PyObject *m, PyObject *tuple); static CYTHON_INLINE void __Pyx_CyFunction_SetDefaultsKwDict(PyObject *m, PyObject *dict); static CYTHON_INLINE void __Pyx_CyFunction_SetAnnotationsDict(PyObject *m, PyObject *dict); static int __Pyx_CyFunction_init(void); static PyObject *__Pyx_Py3MetaclassPrepare(PyObject *metaclass, PyObject *bases, PyObject *name, PyObject *qualname, PyObject *mkw, PyObject *modname, PyObject *doc); static PyObject *__Pyx_Py3ClassCreate(PyObject *metaclass, PyObject *name, PyObject *bases, PyObject *dict, PyObject *mkw, int calculate_metaclass, int allow_py2_metaclass); typedef struct { __pyx_CyFunctionObject func; PyObject *__signatures__; PyObject *type; PyObject *self; } __pyx_FusedFunctionObject; #define __pyx_FusedFunction_NewEx(ml, flags, qualname, self, module, globals, code) \ __pyx_FusedFunction_New(__pyx_FusedFunctionType, ml, flags, qualname, self, module, globals, code) static PyObject *__pyx_FusedFunction_New(PyTypeObject *type, PyMethodDef *ml, int flags, PyObject *qualname, PyObject *self, PyObject *module, PyObject *globals, PyObject *code); static int __pyx_FusedFunction_clear(__pyx_FusedFunctionObject *self); static PyTypeObject *__pyx_FusedFunctionType = NULL; static int __pyx_FusedFunction_init(void); #define __Pyx_FusedFunction_USED typedef struct { int code_line; PyCodeObject* code_object; } __Pyx_CodeObjectCacheEntry; struct __Pyx_CodeObjectCache { int count; int max_count; __Pyx_CodeObjectCacheEntry* entries; }; static struct __Pyx_CodeObjectCache __pyx_code_cache = {0,0,NULL}; static int __pyx_bisect_code_objects(__Pyx_CodeObjectCacheEntry* entries, int count, int code_line); static PyCodeObject *__pyx_find_code_object(int code_line); static void __pyx_insert_code_object(int code_line, PyCodeObject* code_object); static void __Pyx_AddTraceback(const char *funcname, int c_line, int py_line, const char *filename); #define __Pyx_BUF_MAX_NDIMS %(BUF_MAX_NDIMS)d #define __Pyx_MEMVIEW_DIRECT 1 #define __Pyx_MEMVIEW_PTR 2 #define __Pyx_MEMVIEW_FULL 4 #define __Pyx_MEMVIEW_CONTIG 8 #define __Pyx_MEMVIEW_STRIDED 16 #define __Pyx_MEMVIEW_FOLLOW 32 #define __Pyx_IS_C_CONTIG 1 #define __Pyx_IS_F_CONTIG 2 static int __Pyx_init_memviewslice( struct __pyx_memoryview_obj *memview, int ndim, __Pyx_memviewslice *memviewslice, int memview_is_new_reference); static CYTHON_INLINE int __pyx_add_acquisition_count_locked( __pyx_atomic_int *acquisition_count, PyThread_type_lock lock); static CYTHON_INLINE int __pyx_sub_acquisition_count_locked( __pyx_atomic_int *acquisition_count, PyThread_type_lock lock); #define __pyx_get_slice_count_pointer(memview) (memview->acquisition_count_aligned_p) #define __pyx_get_slice_count(memview) (*__pyx_get_slice_count_pointer(memview)) #define __PYX_INC_MEMVIEW(slice, have_gil) __Pyx_INC_MEMVIEW(slice, have_gil, __LINE__) #define __PYX_XDEC_MEMVIEW(slice, have_gil) __Pyx_XDEC_MEMVIEW(slice, have_gil, __LINE__) static CYTHON_INLINE void __Pyx_INC_MEMVIEW(__Pyx_memviewslice *, int, int); static CYTHON_INLINE void __Pyx_XDEC_MEMVIEW(__Pyx_memviewslice *, int, int); static int __pyx_typeinfo_cmp(__Pyx_TypeInfo *a, __Pyx_TypeInfo *b); static int __Pyx_ValidateAndInit_memviewslice( int *axes_specs, int c_or_f_flag, int buf_flags, int ndim, __Pyx_TypeInfo *dtype, __Pyx_BufFmt_StackElem stack[], __Pyx_memviewslice *memviewslice, PyObject *original_obj); static CYTHON_INLINE __Pyx_memviewslice __Pyx_PyObject_to_MemoryviewSlice_ds_nn___pyx_t_5numpy_float32_t(PyObject *); static CYTHON_INLINE __Pyx_memviewslice __Pyx_PyObject_to_MemoryviewSlice_ds_nn___pyx_t_5numpy_float64_t(PyObject *); static PyObject *__Pyx_Import(PyObject *name, PyObject *from_list, int level); typedef struct { Py_ssize_t shape, strides, suboffsets; } __Pyx_Buf_DimInfo; typedef struct { size_t refcount; Py_buffer pybuffer; } __Pyx_Buffer; typedef struct { __Pyx_Buffer *rcbuffer; char *data; __Pyx_Buf_DimInfo diminfo[8]; } __Pyx_LocalBuf_ND; #if PY_MAJOR_VERSION < 3 static int __Pyx_GetBuffer(PyObject *obj, Py_buffer *view, int flags); static void __Pyx_ReleaseBuffer(Py_buffer *view); #else #define __Pyx_GetBuffer PyObject_GetBuffer #define __Pyx_ReleaseBuffer PyBuffer_Release #endif static Py_ssize_t __Pyx_zeros[] = {0, 0, 0, 0, 0, 0, 0, 0}; static Py_ssize_t __Pyx_minusones[] = {-1, -1, -1, -1, -1, -1, -1, -1}; static CYTHON_INLINE PyObject* __Pyx_PyInt_From_char(char value); static CYTHON_INLINE int __Pyx_PyInt_As_int(PyObject *); static CYTHON_INLINE PyObject* __Pyx_PyInt_From_long(long value); static CYTHON_INLINE PyObject* __Pyx_PyInt_From_int(int value); static CYTHON_INLINE char __Pyx_PyInt_As_char(PyObject *); static CYTHON_INLINE PyObject* __Pyx_PyInt_From_index_t(index_t value); static CYTHON_INLINE index_t __Pyx_PyInt_As_index_t(PyObject *); static CYTHON_INLINE int __Pyx_BytesContains(PyObject* bytes, char character); #if CYTHON_CCOMPLEX #ifdef __cplusplus #define __Pyx_CREAL(z) ((z).real()) #define __Pyx_CIMAG(z) ((z).imag()) #else #define __Pyx_CREAL(z) (__real__(z)) #define __Pyx_CIMAG(z) (__imag__(z)) #endif #else #define __Pyx_CREAL(z) ((z).real) #define __Pyx_CIMAG(z) ((z).imag) #endif #if (defined(_WIN32) || defined(__clang__)) && defined(__cplusplus) && CYTHON_CCOMPLEX #define __Pyx_SET_CREAL(z,x) ((z).real(x)) #define __Pyx_SET_CIMAG(z,y) ((z).imag(y)) #else #define __Pyx_SET_CREAL(z,x) __Pyx_CREAL(z) = (x) #define __Pyx_SET_CIMAG(z,y) __Pyx_CIMAG(z) = (y) #endif static CYTHON_INLINE __pyx_t_float_complex __pyx_t_float_complex_from_parts(float, float); #if CYTHON_CCOMPLEX #define __Pyx_c_eqf(a, b) ((a)==(b)) #define __Pyx_c_sumf(a, b) ((a)+(b)) #define __Pyx_c_difff(a, b) ((a)-(b)) #define __Pyx_c_prodf(a, b) ((a)*(b)) #define __Pyx_c_quotf(a, b) ((a)/(b)) #define __Pyx_c_negf(a) (-(a)) #ifdef __cplusplus #define __Pyx_c_is_zerof(z) ((z)==(float)0) #define __Pyx_c_conjf(z) (::std::conj(z)) #if 1 #define __Pyx_c_absf(z) (::std::abs(z)) #define __Pyx_c_powf(a, b) (::std::pow(a, b)) #endif #else #define __Pyx_c_is_zerof(z) ((z)==0) #define __Pyx_c_conjf(z) (conjf(z)) #if 1 #define __Pyx_c_absf(z) (cabsf(z)) #define __Pyx_c_powf(a, b) (cpowf(a, b)) #endif #endif #else static CYTHON_INLINE int __Pyx_c_eqf(__pyx_t_float_complex, __pyx_t_float_complex); static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_sumf(__pyx_t_float_complex, __pyx_t_float_complex); static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_difff(__pyx_t_float_complex, __pyx_t_float_complex); static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_prodf(__pyx_t_float_complex, __pyx_t_float_complex); static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_quotf(__pyx_t_float_complex, __pyx_t_float_complex); static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_negf(__pyx_t_float_complex); static CYTHON_INLINE int __Pyx_c_is_zerof(__pyx_t_float_complex); static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_conjf(__pyx_t_float_complex); #if 1 static CYTHON_INLINE float __Pyx_c_absf(__pyx_t_float_complex); static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_powf(__pyx_t_float_complex, __pyx_t_float_complex); #endif #endif static CYTHON_INLINE __pyx_t_double_complex __pyx_t_double_complex_from_parts(double, double); #if CYTHON_CCOMPLEX #define __Pyx_c_eq(a, b) ((a)==(b)) #define __Pyx_c_sum(a, b) ((a)+(b)) #define __Pyx_c_diff(a, b) ((a)-(b)) #define __Pyx_c_prod(a, b) ((a)*(b)) #define __Pyx_c_quot(a, b) ((a)/(b)) #define __Pyx_c_neg(a) (-(a)) #ifdef __cplusplus #define __Pyx_c_is_zero(z) ((z)==(double)0) #define __Pyx_c_conj(z) (::std::conj(z)) #if 1 #define __Pyx_c_abs(z) (::std::abs(z)) #define __Pyx_c_pow(a, b) (::std::pow(a, b)) #endif #else #define __Pyx_c_is_zero(z) ((z)==0) #define __Pyx_c_conj(z) (conj(z)) #if 1 #define __Pyx_c_abs(z) (cabs(z)) #define __Pyx_c_pow(a, b) (cpow(a, b)) #endif #endif #else static CYTHON_INLINE int __Pyx_c_eq(__pyx_t_double_complex, __pyx_t_double_complex); static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_sum(__pyx_t_double_complex, __pyx_t_double_complex); static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_diff(__pyx_t_double_complex, __pyx_t_double_complex); static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_prod(__pyx_t_double_complex, __pyx_t_double_complex); static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_quot(__pyx_t_double_complex, __pyx_t_double_complex); static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_neg(__pyx_t_double_complex); static CYTHON_INLINE int __Pyx_c_is_zero(__pyx_t_double_complex); static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_conj(__pyx_t_double_complex); #if 1 static CYTHON_INLINE double __Pyx_c_abs(__pyx_t_double_complex); static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_pow(__pyx_t_double_complex, __pyx_t_double_complex); #endif #endif static int __pyx_memviewslice_is_contig(const __Pyx_memviewslice *mvs, char order, int ndim); static int __pyx_slices_overlap(__Pyx_memviewslice *slice1, __Pyx_memviewslice *slice2, int ndim, size_t itemsize); static __Pyx_memviewslice __pyx_memoryview_copy_new_contig(const __Pyx_memviewslice *from_mvs, const char *mode, int ndim, size_t sizeof_dtype, int contig_flag, int dtype_is_object); static CYTHON_INLINE PyObject *__pyx_capsule_create(void *p, const char *sig); static CYTHON_INLINE long __Pyx_PyInt_As_long(PyObject *); static int __Pyx_check_binary_version(void); #if !defined(__Pyx_PyIdentifier_FromString) #if PY_MAJOR_VERSION < 3 #define __Pyx_PyIdentifier_FromString(s) PyString_FromString(s) #else #define __Pyx_PyIdentifier_FromString(s) PyUnicode_FromString(s) #endif #endif static PyObject *__Pyx_ImportModule(const char *name); static PyTypeObject *__Pyx_ImportType(const char *module_name, const char *class_name, size_t size, int strict); static int __Pyx_InitStrings(__Pyx_StringTabEntry *t); static char *__pyx_memoryview_get_item_pointer(struct __pyx_memoryview_obj *__pyx_v_self, PyObject *__pyx_v_index); /* proto*/ static PyObject *__pyx_memoryview_is_slice(struct __pyx_memoryview_obj *__pyx_v_self, PyObject *__pyx_v_obj); /* proto*/ static PyObject *__pyx_memoryview_setitem_slice_assignment(struct __pyx_memoryview_obj *__pyx_v_self, PyObject *__pyx_v_dst, PyObject *__pyx_v_src); /* proto*/ static PyObject *__pyx_memoryview_setitem_slice_assign_scalar(struct __pyx_memoryview_obj *__pyx_v_self, struct __pyx_memoryview_obj *__pyx_v_dst, PyObject *__pyx_v_value); /* proto*/ static PyObject *__pyx_memoryview_setitem_indexed(struct __pyx_memoryview_obj *__pyx_v_self, PyObject *__pyx_v_index, PyObject *__pyx_v_value); /* proto*/ static PyObject *__pyx_memoryview_convert_item_to_object(struct __pyx_memoryview_obj *__pyx_v_self, char *__pyx_v_itemp); /* proto*/ static PyObject *__pyx_memoryview_assign_item_from_object(struct __pyx_memoryview_obj *__pyx_v_self, char *__pyx_v_itemp, PyObject *__pyx_v_value); /* proto*/ static PyObject *__pyx_memoryviewslice_convert_item_to_object(struct __pyx_memoryviewslice_obj *__pyx_v_self, char *__pyx_v_itemp); /* proto*/ static PyObject *__pyx_memoryviewslice_assign_item_from_object(struct __pyx_memoryviewslice_obj *__pyx_v_self, char *__pyx_v_itemp, PyObject *__pyx_v_value); /* proto*/ /* Module declarations from 'c_wt' */ /* Module declarations from 'libc.math' */ /* Module declarations from 'cpython.buffer' */ /* Module declarations from 'cpython.ref' */ /* Module declarations from 'libc.string' */ /* Module declarations from 'libc.stdio' */ /* Module declarations from 'cpython.object' */ /* Module declarations from '__builtin__' */ /* Module declarations from 'cpython.type' */ static PyTypeObject *__pyx_ptype_7cpython_4type_type = 0; /* Module declarations from 'libc.stdlib' */ /* Module declarations from 'numpy' */ /* Module declarations from 'numpy' */ static PyTypeObject *__pyx_ptype_5numpy_dtype = 0; static PyTypeObject *__pyx_ptype_5numpy_flatiter = 0; static PyTypeObject *__pyx_ptype_5numpy_broadcast = 0; static PyTypeObject *__pyx_ptype_5numpy_ndarray = 0; static PyTypeObject *__pyx_ptype_5numpy_ufunc = 0; static CYTHON_INLINE char *__pyx_f_5numpy__util_dtypestring(PyArray_Descr *, char *, char *, int *); /*proto*/ /* Module declarations from '_pywt' */ static PyTypeObject *__pyx_ptype_5_pywt_Wavelet = 0; static PyTypeObject *__pyx_array_type = 0; static PyTypeObject *__pyx_MemviewEnum_type = 0; static PyTypeObject *__pyx_memoryview_type = 0; static PyTypeObject *__pyx_memoryviewslice_type = 0; static PyObject *__pyx_v_5_pywt___wname_to_code = 0; static PyObject *__pyx_v_5_pywt___wfamily_list_short = 0; static PyObject *__pyx_v_5_pywt___wfamily_list_long = 0; static PyObject *generic = 0; static PyObject *strided = 0; static PyObject *indirect = 0; static PyObject *contiguous = 0; static PyObject *indirect_contiguous = 0; static PyObject *__pyx_f_5_pywt_wname_to_code(PyObject *); /*proto*/ static __pyx_t_5_pywt_index_t __pyx_f_5_pywt_get_keep_length(__pyx_t_5_pywt_index_t, int, __pyx_t_5_pywt_index_t); /*proto*/ static __pyx_t_5_pywt_index_t __pyx_f_5_pywt_fix_output_length(__pyx_t_5_pywt_index_t, __pyx_t_5_pywt_index_t); /*proto*/ static __pyx_t_5_pywt_index_t __pyx_f_5_pywt_get_right_extent_length(__pyx_t_5_pywt_index_t, __pyx_t_5_pywt_index_t); /*proto*/ static PyObject *__pyx_f_5_pywt_c_wavelet_from_object(PyObject *); /*proto*/ static PyObject *__pyx_f_5_pywt_float64_array_to_list(double *, __pyx_t_5_pywt_index_t); /*proto*/ static void __pyx_f_5_pywt_copy_object_to_float64_array(PyObject *, double *); /*proto*/ static void __pyx_f_5_pywt_copy_object_to_float32_array(PyObject *, float *); /*proto*/ static struct __pyx_array_obj *__pyx_array_new(PyObject *, Py_ssize_t, char *, char *, char *); /*proto*/ static void *__pyx_align_pointer(void *, size_t); /*proto*/ static PyObject *__pyx_memoryview_new(PyObject *, int, int, __Pyx_TypeInfo *); /*proto*/ static CYTHON_INLINE int __pyx_memoryview_check(PyObject *); /*proto*/ static PyObject *_unellipsify(PyObject *, int); /*proto*/ static PyObject *assert_direct_dimensions(Py_ssize_t *, int); /*proto*/ static struct __pyx_memoryview_obj *__pyx_memview_slice(struct __pyx_memoryview_obj *, PyObject *); /*proto*/ static int __pyx_memoryview_slice_memviewslice(__Pyx_memviewslice *, Py_ssize_t, Py_ssize_t, Py_ssize_t, int, int, int *, Py_ssize_t, Py_ssize_t, Py_ssize_t, int, int, int, int); /*proto*/ static char *__pyx_pybuffer_index(Py_buffer *, char *, Py_ssize_t, Py_ssize_t); /*proto*/ static int __pyx_memslice_transpose(__Pyx_memviewslice *); /*proto*/ static PyObject *__pyx_memoryview_fromslice(__Pyx_memviewslice, int, PyObject *(*)(char *), int (*)(char *, PyObject *), int); /*proto*/ static __Pyx_memviewslice *__pyx_memoryview_get_slice_from_memoryview(struct __pyx_memoryview_obj *, __Pyx_memviewslice *); /*proto*/ static void __pyx_memoryview_slice_copy(struct __pyx_memoryview_obj *, __Pyx_memviewslice *); /*proto*/ static PyObject *__pyx_memoryview_copy_object(struct __pyx_memoryview_obj *); /*proto*/ static PyObject *__pyx_memoryview_copy_object_from_slice(struct __pyx_memoryview_obj *, __Pyx_memviewslice *); /*proto*/ static Py_ssize_t abs_py_ssize_t(Py_ssize_t); /*proto*/ static char __pyx_get_best_slice_order(__Pyx_memviewslice *, int); /*proto*/ static void _copy_strided_to_strided(char *, Py_ssize_t *, char *, Py_ssize_t *, Py_ssize_t *, Py_ssize_t *, int, size_t); /*proto*/ static void copy_strided_to_strided(__Pyx_memviewslice *, __Pyx_memviewslice *, int, size_t); /*proto*/ static Py_ssize_t __pyx_memoryview_slice_get_size(__Pyx_memviewslice *, int); /*proto*/ static Py_ssize_t __pyx_fill_contig_strides_array(Py_ssize_t *, Py_ssize_t *, Py_ssize_t, int, char); /*proto*/ static void *__pyx_memoryview_copy_data_to_temp(__Pyx_memviewslice *, __Pyx_memviewslice *, char, int); /*proto*/ static int __pyx_memoryview_err_extents(int, Py_ssize_t, Py_ssize_t); /*proto*/ static int __pyx_memoryview_err_dim(PyObject *, char *, int); /*proto*/ static int __pyx_memoryview_err(PyObject *, char *); /*proto*/ static int __pyx_memoryview_copy_contents(__Pyx_memviewslice, __Pyx_memviewslice, int, int, int); /*proto*/ static void __pyx_memoryview_broadcast_leading(__Pyx_memviewslice *, int, int); /*proto*/ static void __pyx_memoryview_refcount_copying(__Pyx_memviewslice *, int, int, int); /*proto*/ static void __pyx_memoryview_refcount_objects_in_slice_with_gil(char *, Py_ssize_t *, Py_ssize_t *, int, int); /*proto*/ static void __pyx_memoryview_refcount_objects_in_slice(char *, Py_ssize_t *, Py_ssize_t *, int, int); /*proto*/ static void __pyx_memoryview_slice_assign_scalar(__Pyx_memviewslice *, int, size_t, void *, int); /*proto*/ static void __pyx_memoryview__slice_assign_scalar(char *, Py_ssize_t *, Py_ssize_t *, int, size_t, void *); /*proto*/ static __Pyx_TypeInfo __Pyx_TypeInfo_nn___pyx_t_5numpy_float32_t = { "float32_t", NULL, sizeof(__pyx_t_5numpy_float32_t), { 0 }, 0, 'R', 0, 0 }; static __Pyx_TypeInfo __Pyx_TypeInfo_nn___pyx_t_5numpy_float64_t = { "float64_t", NULL, sizeof(__pyx_t_5numpy_float64_t), { 0 }, 0, 'R', 0, 0 }; #define __Pyx_MODULE_NAME "_pywt" int __pyx_module_is_main__pywt = 0; /* Implementation of '_pywt' */ static PyObject *__pyx_builtin_object; static PyObject *__pyx_builtin_ValueError; static PyObject *__pyx_builtin_AttributeError; static PyObject *__pyx_builtin_KeyError; static PyObject *__pyx_builtin_TypeError; static PyObject *__pyx_builtin_DeprecationWarning; static PyObject *__pyx_builtin_MemoryError; static PyObject *__pyx_builtin_ImportError; static PyObject *__pyx_builtin_ord; static PyObject *__pyx_builtin_zip; static PyObject *__pyx_builtin_RuntimeError; static PyObject *__pyx_builtin_range; static PyObject *__pyx_builtin_enumerate; static PyObject *__pyx_builtin_Ellipsis; static PyObject *__pyx_builtin_id; static PyObject *__pyx_builtin_IndexError; static PyObject *__pyx_pf_5_pywt_6_Modes_from_object(CYTHON_UNUSED PyObject *__pyx_self, CYTHON_UNUSED PyObject *__pyx_v_self, PyObject *__pyx_v_mode); /* proto */ static PyObject *__pyx_pf_5_pywt_wavelist(CYTHON_UNUSED PyObject *__pyx_self, PyObject *__pyx_v_family); /* proto */ static PyObject *__pyx_pf_5_pywt_2families(CYTHON_UNUSED PyObject *__pyx_self, int __pyx_v_short); /* proto */ static int __pyx_pf_5_pywt_7Wavelet___cinit__(struct WaveletObject *__pyx_v_self, PyObject *__pyx_v_name, PyObject *__pyx_v_filter_bank); /* proto */ static void __pyx_pf_5_pywt_7Wavelet_2__dealloc__(struct WaveletObject *__pyx_v_self); /* proto */ static Py_ssize_t __pyx_pf_5_pywt_7Wavelet_4__len__(struct WaveletObject *__pyx_v_self); /* proto */ static PyObject *__pyx_pf_5_pywt_7Wavelet_6dec_lo___get__(struct WaveletObject *__pyx_v_self); /* proto */ static PyObject *__pyx_pf_5_pywt_7Wavelet_6dec_hi___get__(struct WaveletObject *__pyx_v_self); /* proto */ static PyObject *__pyx_pf_5_pywt_7Wavelet_6rec_lo___get__(struct WaveletObject *__pyx_v_self); /* proto */ static PyObject *__pyx_pf_5_pywt_7Wavelet_6rec_hi___get__(struct WaveletObject *__pyx_v_self); /* proto */ static PyObject *__pyx_pf_5_pywt_7Wavelet_7rec_len___get__(struct WaveletObject *__pyx_v_self); /* proto */ static PyObject *__pyx_pf_5_pywt_7Wavelet_7dec_len___get__(struct WaveletObject *__pyx_v_self); /* proto */ static PyObject *__pyx_pf_5_pywt_7Wavelet_11family_name___get__(struct WaveletObject *__pyx_v_self); /* proto */ static PyObject *__pyx_pf_5_pywt_7Wavelet_17short_family_name___get__(struct WaveletObject *__pyx_v_self); /* proto */ static PyObject *__pyx_pf_5_pywt_7Wavelet_10orthogonal___get__(struct WaveletObject *__pyx_v_self); /* proto */ static int __pyx_pf_5_pywt_7Wavelet_10orthogonal_2__set__(struct WaveletObject *__pyx_v_self, int __pyx_v_value); /* proto */ static PyObject *__pyx_pf_5_pywt_7Wavelet_12biorthogonal___get__(struct WaveletObject *__pyx_v_self); /* proto */ static int __pyx_pf_5_pywt_7Wavelet_12biorthogonal_2__set__(struct WaveletObject *__pyx_v_self, int __pyx_v_value); /* proto */ static PyObject *__pyx_pf_5_pywt_7Wavelet_8symmetry___get__(struct WaveletObject *__pyx_v_self); /* proto */ static PyObject *__pyx_pf_5_pywt_7Wavelet_21vanishing_moments_psi___get__(struct WaveletObject *__pyx_v_self); /* proto */ static PyObject *__pyx_pf_5_pywt_7Wavelet_21vanishing_moments_phi___get__(struct WaveletObject *__pyx_v_self); /* proto */ static PyObject *__pyx_pf_5_pywt_7Wavelet_8_builtin___get__(struct WaveletObject *__pyx_v_self); /* proto */ static PyObject *__pyx_pf_5_pywt_7Wavelet_11filter_bank___get__(struct WaveletObject *__pyx_v_self); /* proto */ static PyObject *__pyx_pf_5_pywt_7Wavelet_6get_filters_coeffs(struct WaveletObject *__pyx_v_self); /* proto */ static PyObject *__pyx_pf_5_pywt_7Wavelet_19inverse_filter_bank___get__(struct WaveletObject *__pyx_v_self); /* proto */ static PyObject *__pyx_pf_5_pywt_7Wavelet_8get_reverse_filters_coeffs(struct WaveletObject *__pyx_v_self); /* proto */ static PyObject *__pyx_pf_5_pywt_7Wavelet_10wavefun(struct WaveletObject *__pyx_v_self, int __pyx_v_level); /* proto */ static PyObject *__pyx_pf_5_pywt_7Wavelet_12__str__(struct WaveletObject *__pyx_v_self); /* proto */ static PyObject *__pyx_pf_5_pywt_7Wavelet_4name___get__(struct WaveletObject *__pyx_v_self); /* proto */ static PyObject *__pyx_pf_5_pywt_7Wavelet_6number___get__(struct WaveletObject *__pyx_v_self); /* proto */ static PyObject *__pyx_pf_5_pywt_4wavelet_from_object(CYTHON_UNUSED PyObject *__pyx_self, PyObject *__pyx_v_wavelet); /* proto */ static PyObject *__pyx_pf_5_pywt_6dwt_max_level(CYTHON_UNUSED PyObject *__pyx_self, PyObject *__pyx_v_data_len, PyObject *__pyx_v_filter_len); /* proto */ static PyObject *__pyx_pf_5_pywt_8dwt(CYTHON_UNUSED PyObject *__pyx_self, PyObject *__pyx_v_data, PyObject *__pyx_v_wavelet, PyObject *__pyx_v_mode); /* proto */ static PyObject *__pyx_pf_5_pywt_10_dwt(CYTHON_UNUSED PyObject *__pyx_self, PyObject *__pyx_v_signatures, PyObject *__pyx_v_args, PyObject *__pyx_v_kwargs, CYTHON_UNUSED PyObject *__pyx_v_defaults); /* proto */ static PyObject *__pyx_pf_5_pywt_72__defaults__(CYTHON_UNUSED PyObject *__pyx_self); /* proto */ static PyObject *__pyx_pf_5_pywt_38_dwt(CYTHON_UNUSED PyObject *__pyx_self, PyArrayObject *__pyx_v_data, PyObject *__pyx_v_wavelet, PyObject *__pyx_v_mode); /* proto */ static PyObject *__pyx_pf_5_pywt_74__defaults__(CYTHON_UNUSED PyObject *__pyx_self); /* proto */ static PyObject *__pyx_pf_5_pywt_40_dwt(CYTHON_UNUSED PyObject *__pyx_self, PyArrayObject *__pyx_v_data, PyObject *__pyx_v_wavelet, PyObject *__pyx_v_mode); /* proto */ static PyObject *__pyx_pf_5_pywt_12dwt_coeff_len(CYTHON_UNUSED PyObject *__pyx_self, PyObject *__pyx_v_data_len, PyObject *__pyx_v_filter_len, PyObject *__pyx_v_mode); /* proto */ static PyObject *__pyx_pf_5_pywt_14_try_mode(CYTHON_UNUSED PyObject *__pyx_self, PyObject *__pyx_v_mode); /* proto */ static PyObject *__pyx_pf_5_pywt_16_check_dtype(CYTHON_UNUSED PyObject *__pyx_self, PyObject *__pyx_v_data); /* proto */ static PyObject *__pyx_pf_5_pywt_18idwt(CYTHON_UNUSED PyObject *__pyx_self, PyObject *__pyx_v_cA, PyObject *__pyx_v_cD, PyObject *__pyx_v_wavelet, PyObject *__pyx_v_mode, int __pyx_v_correct_size); /* proto */ static PyObject *__pyx_pf_5_pywt_20_idwt(CYTHON_UNUSED PyObject *__pyx_self, PyObject *__pyx_v_signatures, PyObject *__pyx_v_args, PyObject *__pyx_v_kwargs, CYTHON_UNUSED PyObject *__pyx_v_defaults); /* proto */ static PyObject *__pyx_pf_5_pywt_80__defaults__(CYTHON_UNUSED PyObject *__pyx_self); /* proto */ static PyObject *__pyx_pf_5_pywt_44_idwt(CYTHON_UNUSED PyObject *__pyx_self, PyArrayObject *__pyx_v_cA, PyArrayObject *__pyx_v_cD, PyObject *__pyx_v_wavelet, PyObject *__pyx_v_mode, int __pyx_v_correct_size); /* proto */ static PyObject *__pyx_pf_5_pywt_82__defaults__(CYTHON_UNUSED PyObject *__pyx_self); /* proto */ static PyObject *__pyx_pf_5_pywt_46_idwt(CYTHON_UNUSED PyObject *__pyx_self, PyArrayObject *__pyx_v_cA, PyArrayObject *__pyx_v_cD, PyObject *__pyx_v_wavelet, PyObject *__pyx_v_mode, int __pyx_v_correct_size); /* proto */ static PyObject *__pyx_pf_5_pywt_22upcoef(CYTHON_UNUSED PyObject *__pyx_self, PyObject *__pyx_v_part, PyObject *__pyx_v_coeffs, PyObject *__pyx_v_wavelet, PyObject *__pyx_v_level, PyObject *__pyx_v_take); /* proto */ static PyObject *__pyx_pf_5_pywt_24_upcoef(CYTHON_UNUSED PyObject *__pyx_self, PyObject *__pyx_v_signatures, PyObject *__pyx_v_args, PyObject *__pyx_v_kwargs, CYTHON_UNUSED PyObject *__pyx_v_defaults); /* proto */ static PyObject *__pyx_pf_5_pywt_88__defaults__(CYTHON_UNUSED PyObject *__pyx_self); /* proto */ static PyObject *__pyx_pf_5_pywt_50_upcoef(CYTHON_UNUSED PyObject *__pyx_self, PyObject *__pyx_v_part, PyArrayObject *__pyx_v_coeffs, PyObject *__pyx_v_wavelet, int __pyx_v_level, int __pyx_v_take); /* proto */ static PyObject *__pyx_pf_5_pywt_90__defaults__(CYTHON_UNUSED PyObject *__pyx_self); /* proto */ static PyObject *__pyx_pf_5_pywt_52_upcoef(CYTHON_UNUSED PyObject *__pyx_self, PyObject *__pyx_v_part, PyArrayObject *__pyx_v_coeffs, PyObject *__pyx_v_wavelet, int __pyx_v_level, int __pyx_v_take); /* proto */ static PyObject *__pyx_pf_5_pywt_26downcoef(CYTHON_UNUSED PyObject *__pyx_self, PyObject *__pyx_v_part, PyObject *__pyx_v_data, PyObject *__pyx_v_wavelet, PyObject *__pyx_v_mode, PyObject *__pyx_v_level); /* proto */ static PyObject *__pyx_pf_5_pywt_28_downcoef(CYTHON_UNUSED PyObject *__pyx_self, PyObject *__pyx_v_signatures, PyObject *__pyx_v_args, PyObject *__pyx_v_kwargs, CYTHON_UNUSED PyObject *__pyx_v_defaults); /* proto */ static PyObject *__pyx_pf_5_pywt_96__defaults__(CYTHON_UNUSED PyObject *__pyx_self); /* proto */ static PyObject *__pyx_pf_5_pywt_56_downcoef(CYTHON_UNUSED PyObject *__pyx_self, PyObject *__pyx_v_part, PyArrayObject *__pyx_v_data, PyObject *__pyx_v_wavelet, PyObject *__pyx_v_mode, int __pyx_v_level); /* proto */ static PyObject *__pyx_pf_5_pywt_98__defaults__(CYTHON_UNUSED PyObject *__pyx_self); /* proto */ static PyObject *__pyx_pf_5_pywt_58_downcoef(CYTHON_UNUSED PyObject *__pyx_self, PyObject *__pyx_v_part, PyArrayObject *__pyx_v_data, PyObject *__pyx_v_wavelet, PyObject *__pyx_v_mode, int __pyx_v_level); /* proto */ static PyObject *__pyx_pf_5_pywt_30swt_max_level(CYTHON_UNUSED PyObject *__pyx_self, PyObject *__pyx_v_input_len); /* proto */ static PyObject *__pyx_pf_5_pywt_32swt(CYTHON_UNUSED PyObject *__pyx_self, PyObject *__pyx_v_data, PyObject *__pyx_v_wavelet, PyObject *__pyx_v_level, int __pyx_v_start_level); /* proto */ static PyObject *__pyx_pf_5_pywt_34_swt(CYTHON_UNUSED PyObject *__pyx_self, PyObject *__pyx_v_signatures, PyObject *__pyx_v_args, PyObject *__pyx_v_kwargs, CYTHON_UNUSED PyObject *__pyx_v_defaults); /* proto */ static PyObject *__pyx_pf_5_pywt_104__defaults__(CYTHON_UNUSED PyObject *__pyx_self); /* proto */ static PyObject *__pyx_pf_5_pywt_62_swt(CYTHON_UNUSED PyObject *__pyx_self, PyArrayObject *__pyx_v_data, PyObject *__pyx_v_wavelet, PyObject *__pyx_v_level, int __pyx_v_start_level); /* proto */ static PyObject *__pyx_pf_5_pywt_106__defaults__(CYTHON_UNUSED PyObject *__pyx_self); /* proto */ static PyObject *__pyx_pf_5_pywt_64_swt(CYTHON_UNUSED PyObject *__pyx_self, PyArrayObject *__pyx_v_data, PyObject *__pyx_v_wavelet, PyObject *__pyx_v_level, int __pyx_v_start_level); /* proto */ static PyObject *__pyx_pf_5_pywt_36keep(CYTHON_UNUSED PyObject *__pyx_self, PyObject *__pyx_v_arr, PyObject *__pyx_v_keep_length); /* proto */ static int __pyx_pf_5numpy_7ndarray___getbuffer__(PyArrayObject *__pyx_v_self, Py_buffer *__pyx_v_info, int __pyx_v_flags); /* proto */ static void __pyx_pf_5numpy_7ndarray_2__releasebuffer__(PyArrayObject *__pyx_v_self, Py_buffer *__pyx_v_info); /* proto */ static int __pyx_array___pyx_pf_15View_dot_MemoryView_5array___cinit__(struct __pyx_array_obj *__pyx_v_self, PyObject *__pyx_v_shape, Py_ssize_t __pyx_v_itemsize, PyObject *__pyx_v_format, PyObject *__pyx_v_mode, int __pyx_v_allocate_buffer); /* proto */ static int __pyx_array___pyx_pf_15View_dot_MemoryView_5array_2__getbuffer__(struct __pyx_array_obj *__pyx_v_self, Py_buffer *__pyx_v_info, int __pyx_v_flags); /* proto */ static void __pyx_array___pyx_pf_15View_dot_MemoryView_5array_4__dealloc__(struct __pyx_array_obj *__pyx_v_self); /* proto */ static PyObject *__pyx_pf_15View_dot_MemoryView_5array_7memview___get__(struct __pyx_array_obj *__pyx_v_self); /* proto */ static PyObject *__pyx_array___pyx_pf_15View_dot_MemoryView_5array_6__getattr__(struct __pyx_array_obj *__pyx_v_self, PyObject *__pyx_v_attr); /* proto */ static PyObject *__pyx_array___pyx_pf_15View_dot_MemoryView_5array_8__getitem__(struct __pyx_array_obj *__pyx_v_self, PyObject *__pyx_v_item); /* proto */ static int __pyx_array___pyx_pf_15View_dot_MemoryView_5array_10__setitem__(struct __pyx_array_obj *__pyx_v_self, PyObject *__pyx_v_item, PyObject *__pyx_v_value); /* proto */ static int __pyx_MemviewEnum___pyx_pf_15View_dot_MemoryView_4Enum___init__(struct __pyx_MemviewEnum_obj *__pyx_v_self, PyObject *__pyx_v_name); /* proto */ static PyObject *__pyx_MemviewEnum___pyx_pf_15View_dot_MemoryView_4Enum_2__repr__(struct __pyx_MemviewEnum_obj *__pyx_v_self); /* proto */ static int __pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview___cinit__(struct __pyx_memoryview_obj *__pyx_v_self, PyObject *__pyx_v_obj, int __pyx_v_flags, int __pyx_v_dtype_is_object); /* proto */ static void __pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview_2__dealloc__(struct __pyx_memoryview_obj *__pyx_v_self); /* proto */ static PyObject *__pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview_4__getitem__(struct __pyx_memoryview_obj *__pyx_v_self, PyObject *__pyx_v_index); /* proto */ static int __pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview_6__setitem__(struct __pyx_memoryview_obj *__pyx_v_self, PyObject *__pyx_v_index, PyObject *__pyx_v_value); /* proto */ static int __pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview_8__getbuffer__(struct __pyx_memoryview_obj *__pyx_v_self, Py_buffer *__pyx_v_info, int __pyx_v_flags); /* proto */ static PyObject *__pyx_pf_15View_dot_MemoryView_10memoryview_1T___get__(struct __pyx_memoryview_obj *__pyx_v_self); /* proto */ static PyObject *__pyx_pf_15View_dot_MemoryView_10memoryview_4base___get__(struct __pyx_memoryview_obj *__pyx_v_self); /* proto */ static PyObject *__pyx_pf_15View_dot_MemoryView_10memoryview_5shape___get__(struct __pyx_memoryview_obj *__pyx_v_self); /* proto */ static PyObject *__pyx_pf_15View_dot_MemoryView_10memoryview_7strides___get__(struct __pyx_memoryview_obj *__pyx_v_self); /* proto */ static PyObject *__pyx_pf_15View_dot_MemoryView_10memoryview_10suboffsets___get__(struct __pyx_memoryview_obj *__pyx_v_self); /* proto */ static PyObject *__pyx_pf_15View_dot_MemoryView_10memoryview_4ndim___get__(struct __pyx_memoryview_obj *__pyx_v_self); /* proto */ static PyObject *__pyx_pf_15View_dot_MemoryView_10memoryview_8itemsize___get__(struct __pyx_memoryview_obj *__pyx_v_self); /* proto */ static PyObject *__pyx_pf_15View_dot_MemoryView_10memoryview_6nbytes___get__(struct __pyx_memoryview_obj *__pyx_v_self); /* proto */ static PyObject *__pyx_pf_15View_dot_MemoryView_10memoryview_4size___get__(struct __pyx_memoryview_obj *__pyx_v_self); /* proto */ static Py_ssize_t __pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview_10__len__(struct __pyx_memoryview_obj *__pyx_v_self); /* proto */ static PyObject *__pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview_12__repr__(struct __pyx_memoryview_obj *__pyx_v_self); /* proto */ static PyObject *__pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview_14__str__(struct __pyx_memoryview_obj *__pyx_v_self); /* proto */ static PyObject *__pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview_16is_c_contig(struct __pyx_memoryview_obj *__pyx_v_self); /* proto */ static PyObject *__pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview_18is_f_contig(struct __pyx_memoryview_obj *__pyx_v_self); /* proto */ static PyObject *__pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview_20copy(struct __pyx_memoryview_obj *__pyx_v_self); /* proto */ static PyObject *__pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview_22copy_fortran(struct __pyx_memoryview_obj *__pyx_v_self); /* proto */ static void __pyx_memoryviewslice___pyx_pf_15View_dot_MemoryView_16_memoryviewslice___dealloc__(struct __pyx_memoryviewslice_obj *__pyx_v_self); /* proto */ static PyObject *__pyx_pf_15View_dot_MemoryView_16_memoryviewslice_4base___get__(struct __pyx_memoryviewslice_obj *__pyx_v_self); /* proto */ static PyObject *__pyx_tp_new_5_pywt_Wavelet(PyTypeObject *t, PyObject *a, PyObject *k); /*proto*/ static PyObject *__pyx_tp_new_array(PyTypeObject *t, PyObject *a, PyObject *k); /*proto*/ static PyObject *__pyx_tp_new_Enum(PyTypeObject *t, PyObject *a, PyObject *k); /*proto*/ static PyObject *__pyx_tp_new_memoryview(PyTypeObject *t, PyObject *a, PyObject *k); /*proto*/ static PyObject *__pyx_tp_new__memoryviewslice(PyTypeObject *t, PyObject *a, PyObject *k); /*proto*/ static char __pyx_k_B[] = "B"; static char __pyx_k_H[] = "H"; static char __pyx_k_I[] = "I"; static char __pyx_k_L[] = "L"; static char __pyx_k_O[] = "O"; static char __pyx_k_Q[] = "Q"; static char __pyx_k_a[] = "a"; static char __pyx_k_b[] = "b"; static char __pyx_k_c[] = "c"; static char __pyx_k_d[] = "d"; static char __pyx_k_e[] = "e"; static char __pyx_k_f[] = "f"; static char __pyx_k_g[] = "g"; static char __pyx_k_h[] = "h"; static char __pyx_k_i[] = "i"; static char __pyx_k_l[] = "l"; static char __pyx_k_m[] = "m"; static char __pyx_k_q[] = "q"; static char __pyx_k_w[] = "w"; static char __pyx_k_x[] = "x"; static char __pyx_k_Zd[] = "Zd"; static char __pyx_k_Zf[] = "Zf"; static char __pyx_k_Zg[] = "Zg"; static char __pyx_k__6[] = ""; static char __pyx_k_cA[] = "cA"; static char __pyx_k_cD[] = "cD"; static char __pyx_k_db[] = "db"; static char __pyx_k_dt[] = "dt"; static char __pyx_k_id[] = "id"; static char __pyx_k_np[] = "np"; static char __pyx_k__17[] = "\n"; static char __pyx_k__20[] = "()"; static char __pyx_k__22[] = "|"; static char __pyx_k_all[] = "__all__"; static char __pyx_k_arr[] = "arr"; static char __pyx_k_cpd[] = "cpd"; static char __pyx_k_db1[] = "db1"; static char __pyx_k_db2[] = "db2"; static char __pyx_k_db3[] = "db3"; static char __pyx_k_db4[] = "db4"; static char __pyx_k_db5[] = "db5"; static char __pyx_k_db6[] = "db6"; static char __pyx_k_db7[] = "db7"; static char __pyx_k_db8[] = "db8"; static char __pyx_k_db9[] = "db9"; static char __pyx_k_doc[] = "__doc__"; static char __pyx_k_dwt[] = "_dwt"; static char __pyx_k_msg[] = "msg"; static char __pyx_k_obj[] = "obj"; static char __pyx_k_ord[] = "ord"; static char __pyx_k_per[] = "per"; static char __pyx_k_ppd[] = "ppd"; static char __pyx_k_rec[] = "rec"; static char __pyx_k_ret[] = "ret"; static char __pyx_k_sp1[] = "sp1"; static char __pyx_k_swt[] = "_swt"; static char __pyx_k_sym[] = "sym"; static char __pyx_k_zip[] = "zip"; static char __pyx_k_zpd[] = "zpd"; static char __pyx_k_Haar[] = "Haar"; static char __pyx_k_args[] = "args"; static char __pyx_k_asym[] = "_asym"; static char __pyx_k_base[] = "base"; static char __pyx_k_bior[] = "bior"; static char __pyx_k_coif[] = "coif"; static char __pyx_k_data[] = "data"; static char __pyx_k_db10[] = "db10"; static char __pyx_k_db11[] = "db11"; static char __pyx_k_db12[] = "db12"; static char __pyx_k_db13[] = "db13"; static char __pyx_k_db14[] = "db14"; static char __pyx_k_db15[] = "db15"; static char __pyx_k_db16[] = "db16"; static char __pyx_k_db17[] = "db17"; static char __pyx_k_db18[] = "db18"; static char __pyx_k_db19[] = "db19"; static char __pyx_k_db20[] = "db20"; static char __pyx_k_dmey[] = "dmey"; static char __pyx_k_haar[] = "haar"; static char __pyx_k_idwt[] = "_idwt"; static char __pyx_k_keep[] = "keep"; static char __pyx_k_kind[] = "kind"; static char __pyx_k_main[] = "__main__"; static char __pyx_k_mode[] = "mode"; static char __pyx_k_name[] = "name"; static char __pyx_k_ndim[] = "ndim"; static char __pyx_k_pack[] = "pack"; static char __pyx_k_part[] = "part"; static char __pyx_k_pywt[] = "_pywt"; static char __pyx_k_rbio[] = "rbio"; static char __pyx_k_self[] = "self"; static char __pyx_k_size[] = "size"; static char __pyx_k_sort[] = "sort"; static char __pyx_k_step[] = "step"; static char __pyx_k_stop[] = "stop"; static char __pyx_k_sym2[] = "sym2"; static char __pyx_k_sym3[] = "sym3"; static char __pyx_k_sym4[] = "sym4"; static char __pyx_k_sym5[] = "sym5"; static char __pyx_k_sym6[] = "sym6"; static char __pyx_k_sym7[] = "sym7"; static char __pyx_k_sym8[] = "sym8"; static char __pyx_k_sym9[] = "sym9"; static char __pyx_k_take[] = "take"; static char __pyx_k_test[] = "__test__"; static char __pyx_k_warn[] = "warn"; static char __pyx_k_MODES[] = "MODES"; static char __pyx_k_Modes[] = "_Modes"; static char __pyx_k_array[] = "array"; static char __pyx_k_class[] = "__class__"; static char __pyx_k_coif1[] = "coif1"; static char __pyx_k_coif2[] = "coif2"; static char __pyx_k_coif3[] = "coif3"; static char __pyx_k_coif4[] = "coif4"; static char __pyx_k_coif5[] = "coif5"; static char __pyx_k_dtype[] = "dtype"; static char __pyx_k_dwt_2[] = "dwt"; static char __pyx_k_error[] = "error"; static char __pyx_k_flags[] = "flags"; static char __pyx_k_level[] = "level"; static char __pyx_k_lower[] = "lower"; static char __pyx_k_modes[] = "modes"; static char __pyx_k_numpy[] = "numpy"; static char __pyx_k_range[] = "range"; static char __pyx_k_shape[] = "shape"; static char __pyx_k_short[] = "short"; static char __pyx_k_split[] = "split"; static char __pyx_k_start[] = "start"; static char __pyx_k_strip[] = "strip"; static char __pyx_k_swt_2[] = "swt"; static char __pyx_k_sym10[] = "sym10"; static char __pyx_k_sym11[] = "sym11"; static char __pyx_k_sym12[] = "sym12"; static char __pyx_k_sym13[] = "sym13"; static char __pyx_k_sym14[] = "sym14"; static char __pyx_k_sym15[] = "sym15"; static char __pyx_k_sym16[] = "sym16"; static char __pyx_k_sym17[] = "sym17"; static char __pyx_k_sym18[] = "sym18"; static char __pyx_k_sym19[] = "sym19"; static char __pyx_k_sym20[] = "sym20"; static char __pyx_k_zeros[] = "zeros"; static char __pyx_k_append[] = "append"; static char __pyx_k_astype[] = "astype"; static char __pyx_k_coeffs[] = "coeffs"; static char __pyx_k_dec_hi[] = "dec_hi"; static char __pyx_k_dec_lo[] = "dec_lo"; static char __pyx_k_family[] = "family"; static char __pyx_k_format[] = "format"; static char __pyx_k_idwt_2[] = "idwt"; static char __pyx_k_import[] = "__import__"; static char __pyx_k_kwargs[] = "kwargs"; static char __pyx_k_length[] = "length"; static char __pyx_k_mode_2[] = "mode_"; static char __pyx_k_module[] = "__module__"; static char __pyx_k_name_2[] = "__name__"; static char __pyx_k_object[] = "object"; static char __pyx_k_rec_hi[] = "rec_hi"; static char __pyx_k_rec_lo[] = "rec_lo"; static char __pyx_k_rstrip[] = "rstrip"; static char __pyx_k_struct[] = "struct"; static char __pyx_k_unpack[] = "unpack"; static char __pyx_k_upcoef[] = "upcoef"; static char __pyx_k_Symlets[] = "Symlets"; static char __pyx_k_Wavelet[] = "Wavelet"; static char __pyx_k_asarray[] = "asarray"; static char __pyx_k_bior1_1[] = "bior1.1"; static char __pyx_k_bior1_3[] = "bior1.3"; static char __pyx_k_bior1_5[] = "bior1.5"; static char __pyx_k_bior2_2[] = "bior2.2"; static char __pyx_k_bior2_4[] = "bior2.4"; static char __pyx_k_bior2_6[] = "bior2.6"; static char __pyx_k_bior2_8[] = "bior2.8"; static char __pyx_k_bior3_1[] = "bior3.1"; static char __pyx_k_bior3_3[] = "bior3.3"; static char __pyx_k_bior3_5[] = "bior3.5"; static char __pyx_k_bior3_7[] = "bior3.7"; static char __pyx_k_bior3_9[] = "bior3.9"; static char __pyx_k_bior4_4[] = "bior4.4"; static char __pyx_k_bior5_5[] = "bior5.5"; static char __pyx_k_bior6_8[] = "bior6.8"; static char __pyx_k_dec_len[] = "dec_len"; static char __pyx_k_float32[] = "float32"; static char __pyx_k_float64[] = "float64"; static char __pyx_k_fortran[] = "fortran"; static char __pyx_k_level_2[] = "level_"; static char __pyx_k_memview[] = "memview"; static char __pyx_k_ndarray[] = "ndarray"; static char __pyx_k_prepare[] = "__prepare__"; static char __pyx_k_rbio1_1[] = "rbio1.1"; static char __pyx_k_rbio1_3[] = "rbio1.3"; static char __pyx_k_rbio1_5[] = "rbio1.5"; static char __pyx_k_rbio2_2[] = "rbio2.2"; static char __pyx_k_rbio2_4[] = "rbio2.4"; static char __pyx_k_rbio2_6[] = "rbio2.6"; static char __pyx_k_rbio2_8[] = "rbio2.8"; static char __pyx_k_rbio3_1[] = "rbio3.1"; static char __pyx_k_rbio3_3[] = "rbio3.3"; static char __pyx_k_rbio3_5[] = "rbio3.5"; static char __pyx_k_rbio3_7[] = "rbio3.7"; static char __pyx_k_rbio3_9[] = "rbio3.9"; static char __pyx_k_rbio4_4[] = "rbio4.4"; static char __pyx_k_rbio5_5[] = "rbio5.5"; static char __pyx_k_rbio6_8[] = "rbio6.8"; static char __pyx_k_rec_len[] = "rec_len"; static char __pyx_k_unknown[] = "unknown"; static char __pyx_k_wavelet[] = "wavelet"; static char __pyx_k_Coiflets[] = "Coiflets"; static char __pyx_k_Ellipsis[] = "Ellipsis"; static char __pyx_k_KeyError[] = "KeyError"; static char __pyx_k_data_len[] = "data_len"; static char __pyx_k_defaults[] = "defaults"; static char __pyx_k_do_dec_a[] = "do_dec_a"; static char __pyx_k_do_rec_a[] = "do_rec_a"; static char __pyx_k_downcoef[] = "_downcoef"; static char __pyx_k_families[] = "families"; static char __pyx_k_itemsize[] = "itemsize"; static char __pyx_k_linspace[] = "linspace"; static char __pyx_k_qualname[] = "__qualname__"; static char __pyx_k_symmetry[] = "symmetry"; static char __pyx_k_try_mode[] = "_try_mode"; static char __pyx_k_upcoef_2[] = "_upcoef"; static char __pyx_k_warnings[] = "warnings"; static char __pyx_k_wavelets[] = "wavelets"; static char __pyx_k_wavelist[] = "wavelist"; static char __pyx_k_TypeError[] = "TypeError"; static char __pyx_k_Wavelet_s[] = "Wavelet %s"; static char __pyx_k_end_level[] = "end_level"; static char __pyx_k_enumerate[] = "enumerate"; static char __pyx_k_float32_t[] = "float32_t"; static char __pyx_k_float64_t[] = "float64_t"; static char __pyx_k_input_len[] = "input_len"; static char __pyx_k_metaclass[] = "__metaclass__"; static char __pyx_k_size_diff[] = "size_diff"; static char __pyx_k_symmetric[] = "symmetric"; static char __pyx_k_Daubechies[] = "Daubechies"; static char __pyx_k_IndexError[] = "IndexError"; static char __pyx_k_Symmetry_s[] = " Symmetry: %s"; static char __pyx_k_ValueError[] = "ValueError"; static char __pyx_k_asymmetric[] = "asymmetric"; static char __pyx_k_downcoef_2[] = "downcoef"; static char __pyx_k_filter_len[] = "filter_len"; static char __pyx_k_left_bound[] = "left_bound"; static char __pyx_k_orthogonal[] = "orthogonal"; static char __pyx_k_output_len[] = "output_len"; static char __pyx_k_pyx_vtable[] = "__pyx_vtable__"; static char __pyx_k_signatures[] = "signatures"; static char __pyx_k_startswith[] = "startswith"; static char __pyx_k_ImportError[] = "ImportError"; static char __pyx_k_MemoryError[] = "MemoryError"; static char __pyx_k_check_dtype[] = "_check_dtype"; static char __pyx_k_concatenate[] = "concatenate"; static char __pyx_k_family_name[] = "family_name"; static char __pyx_k_filter_bank[] = "filter_bank"; static char __pyx_k_from_object[] = "from_object"; static char __pyx_k_keep_length[] = "keep_length"; static char __pyx_k_right_bound[] = "right_bound"; static char __pyx_k_start_level[] = "start_level"; static char __pyx_k_Biorthogonal[] = "Biorthogonal"; static char __pyx_k_C_dwt_failed[] = "C dwt failed."; static char __pyx_k_C_swt_failed[] = "C swt failed."; static char __pyx_k_Invalid_mode[] = "Invalid mode."; static char __pyx_k_Orthogonal_s[] = " Orthogonal: %s"; static char __pyx_k_RuntimeError[] = "RuntimeError"; static char __pyx_k_Short_name_s[] = " Short name: %s"; static char __pyx_k_biorthogonal[] = "biorthogonal"; static char __pyx_k_correct_size[] = "correct_size"; static char __pyx_k_dwt_line_605[] = "dwt (line 605)"; static char __pyx_k_filter_len_2[] = "filter_len_"; static char __pyx_k_sorting_list[] = "sorting_list"; static char __pyx_k_C_idwt_failed[] = "C idwt failed."; static char __pyx_k_Family_name_s[] = " Family name: %s"; static char __pyx_k_Is_orthogonal[] = "Is orthogonal"; static char __pyx_k_dwt_coeff_len[] = "dwt_coeff_len"; static char __pyx_k_dwt_max_level[] = "dwt_max_level"; static char __pyx_k_pyx_getbuffer[] = "__pyx_getbuffer"; static char __pyx_k_swt_max_level[] = "swt_max_level"; static char __pyx_k_AttributeError[] = "AttributeError"; static char __pyx_k_Biorthogonal_s[] = " Biorthogonal: %s"; static char __pyx_k_C_dec_a_failed[] = "C dec_a failed."; static char __pyx_k_C_rec_a_failed[] = "C rec_a failed."; static char __pyx_k_Invalid_mode_0[] = "Invalid mode: {0}"; static char __pyx_k_near_symmetric[] = "near symmetric"; static char __pyx_k_Is_biorthogonal[] = "Is biorthogonal"; static char __pyx_k_allocate_buffer[] = "allocate_buffer"; static char __pyx_k_dtype_is_object[] = "dtype_is_object"; static char __pyx_k_upcoef_line_890[] = "upcoef (line 890)"; static char __pyx_k_Filters_length_d[] = " Filters length: %d"; static char __pyx_k_Wavelet_symmetry[] = "Wavelet symmetry"; static char __pyx_k_Modes_from_object[] = "_Modes.from_object"; static char __pyx_k_Unknown_mode_name[] = "Unknown mode name"; static char __pyx_k_families_line_171[] = "families (line 171)"; static char __pyx_k_short_family_name[] = "short_family_name"; static char __pyx_k_wavelist_line_126[] = "wavelist (line 126)"; static char __pyx_k_DeprecationWarning[] = "DeprecationWarning"; static char __pyx_k_get_filters_coeffs[] = "get_filters_coeffs"; static char __pyx_k_strided_and_direct[] = ""; static char __pyx_k_Unknown_mode_name_s[] = "Unknown mode name '%s'."; static char __pyx_k_Wavelet_family_name[] = "Wavelet family name"; static char __pyx_k_inverse_filter_bank[] = "inverse_filter_bank"; static char __pyx_k_wavelet_from_object[] = "wavelet_from_object"; static char __pyx_k_Invalid_wavelet_name[] = "Invalid wavelet name."; static char __pyx_k_Reverse_biorthogonal[] = "Reverse biorthogonal"; static char __pyx_k_strided_and_indirect[] = ""; static char __pyx_k_Invalid_output_length[] = "Invalid output length."; static char __pyx_k_contiguous_and_direct[] = ""; static char __pyx_k_MemoryView_of_r_object[] = ""; static char __pyx_k_dwt_max_level_line_572[] = "dwt_max_level (line 572)"; static char __pyx_k_MemoryView_of_r_at_0x_x[] = ""; static char __pyx_k_contiguous_and_indirect[] = ""; static char __pyx_k_Cannot_index_with_type_s[] = "Cannot index with type '%s'"; static char __pyx_k_Wavelet_wavefun_line_428[] = "Wavelet.wavefun (line 428)"; static char __pyx_k_getbuffer_obj_view_flags[] = "getbuffer(obj, view, flags)"; static char __pyx_k_Dimension_d_is_not_direct[] = "Dimension %d is not direct"; static char __pyx_k_Invalid_shape_in_axis_d_d[] = "Invalid shape in axis %d: %d."; static char __pyx_k_Short_wavelet_family_name[] = "Short wavelet family name"; static char __pyx_k_Index_out_of_bounds_axis_d[] = "Index out of bounds (axis %d)"; static char __pyx_k_Invalid_short_family_name_s[] = "Invalid short family name '%s'."; static char __pyx_k_Length_of_data_must_be_even[] = "Length of data must be even."; static char __pyx_k_No_matching_signature_found[] = "No matching signature found"; static char __pyx_k_Step_may_not_be_zero_axis_d[] = "Step may not be zero (axis %d)"; static char __pyx_k_itemsize_0_for_cython_array[] = "itemsize <= 0 for cython.array"; static char __pyx_k_ndarray_is_not_C_contiguous[] = "ndarray is not C contiguous"; static char __pyx_k_Decomposition_filters_length[] = "Decomposition filters length"; static char __pyx_k_Lowpass_decomposition_filter[] = "Lowpass decomposition filter"; static char __pyx_k_dwt_requires_a_1D_data_array[] = "dwt requires a 1D data array."; static char __pyx_k_Expected_at_least_d_arguments[] = "Expected at least %d arguments"; static char __pyx_k_Highpass_decomposition_filter[] = "Highpass decomposition filter"; static char __pyx_k_Lowpass_reconstruction_filter[] = "Lowpass reconstruction filter"; static char __pyx_k_Reconstruction_filters_length[] = "Reconstruction filters length"; static char __pyx_k_unable_to_allocate_array_data[] = "unable to allocate array data."; static char __pyx_k_Highpass_reconstruction_filter[] = "Highpass reconstruction filter"; static char __pyx_k_strided_and_direct_or_indirect[] = ""; static char __pyx_k_All_filters_in_filter_bank_must[] = "All filters in filter bank must be 1D."; static char __pyx_k_Argument_1_must_be_a_or_d_not_s[] = "Argument 1 must be 'a' or 'd', not '%s'."; static char __pyx_k_Because_the_most_common_and_pra[] = "\n Because the most common and practical way of representing digital signals\n in computer science is with finite arrays of values, some extrapolation\n of the input data has to be performed in order to extend the signal before\n computing the :ref:`Discrete Wavelet Transform ` using the cascading\n filter banks algorithm.\n\n Depending on the extrapolation method, significant artifacts at the signal's\n borders can be introduced during that process, which in turn may lead to\n inaccurate computations of the :ref:`DWT ` at the signal's ends.\n\n PyWavelets provides several methods of signal extrapolation that can be used to\n minimize this negative effect:\n\n zpd - zero-padding 0 0 | x1 x2 ... xn | 0 0\n cpd - constant-padding x1 x1 | x1 x2 ... xn | xn xn\n sym - symmetric-padding x2 x1 | x1 x2 ... xn | xn xn-1\n ppd - periodic-padding xn-1 xn | x1 x2 ... xn | x1 x2\n sp1 - smooth-padding (1st derivative interpolation)\n\n DWT performed for these extension modes is slightly redundant, but ensure\n a perfect reconstruction for IDWT. To receive the smallest possible number of coefficients,\n computations can be performed with the periodization mode:\n\n per - periodization - like periodic-padding but gives the smallest possible number\n of decomposition coefficients. IDWT must be performed with the same mode.\n\n Examples\n --------\n >>> import pywt\n >>> pywt.MODES.modes\n ['zpd', 'cpd', 'sym', 'ppd', 'sp1', 'per']\n >>> # The different ways of passing wavelet and mode parameters\n >>> (a, d) = pywt.dwt([1,2,3,4,5,6], 'db2', 'sp1')\n >>> (a, d) = pywt.dwt([1,2,3,4,5,6], pywt.Wavelet('db2'), pywt.MODES.sp1)\n\n Notes\n -----\n Extending data in context of PyWavelets does not mean reallocation of the data\n in computer's physical memory and copying values, but rather computing""\n the extra values only when they are needed.\n This feature saves extra memory and CPU resources and helps to avoid page\n swapping when handling relatively big data arrays on computers with low\n physical memory.\n\n "; static char __pyx_k_Filter_bank_with_numeric_values[] = "Filter bank with numeric values required."; static char __pyx_k_Number_of_vanishing_moments_for[] = "Number of vanishing moments for wavelet function"; static char __pyx_k_Value_of_data_len_value_must_be[] = "Value of data_len value must be greater than zero."; static char __pyx_k_cA_cD_dwt_data_wavelet_mode_sym[] = "\n (cA, cD) = dwt(data, wavelet, mode='sym')\n\n Single level Discrete Wavelet Transform.\n\n Parameters\n ----------\n data : array_like\n Input signal\n wavelet : Wavelet object or name\n Wavelet to use\n mode : str, optional (default: 'sym')\n Signal extension mode, see MODES\n\n Returns\n -------\n (cA, cD) : tuple\n Approximation and detail coefficients.\n\n Notes\n -----\n Length of coefficients arrays depends on the selected mode:\n for all modes except periodization:\n len(cA) == len(cD) == floor((len(data) + wavelet.dec_len - 1) / 2)\n for periodization mode (\"per\"):\n len(cA) == len(cD) == ceil(len(data) / 2)\n\n Examples\n --------\n >>> import pywt\n >>> (cA, cD) = pywt.dwt([1, 2, 3, 4, 5, 6], 'db1')\n >>> cA\n [ 2.12132034 4.94974747 7.77817459]\n >>> cD\n [-0.70710678 -0.70710678 -0.70710678]\n\n "; static char __pyx_k_dwt_max_level_data_len_filter_l[] = "\n dwt_max_level(data_len, filter_len)\n\n Compute the maximum useful level of decomposition.\n\n Parameters\n ----------\n data_len : int\n Input data length.\n filter_len : int\n Wavelet filter length.\n\n Returns\n -------\n max_level : int\n Maximum level.\n\n Examples\n --------\n >>> import pywt\n >>> w = pywt.Wavelet('sym5')\n >>> pywt.dwt_max_level(data_len=1000, filter_len=w.dec_len)\n 6\n >>> pywt.dwt_max_level(1000, w)\n 6\n "; static char __pyx_k_families_short_True_Returns_a_l[] = "\n families(short=True)\n\n Returns a list of available built-in wavelet families.\n\n Currently the built-in families are:\n\n * Haar (``haar``)\n * Daubechies (``db``)\n * Symlets (``sym``)\n * Coiflets (``coif``)\n * Biorthogonal (``bior``)\n * Reverse biorthogonal (``rbio``)\n * `\"Discrete\"` FIR approximation of Meyer wavelet (``dmey``)\n\n Parameters\n ----------\n short : bool, optional\n Use short names (default: True).\n\n Returns\n -------\n families : list\n List of available wavelet families.\n\n Examples\n --------\n >>> import pywt\n >>> pywt.families()\n ['haar', 'db', 'sym', 'coif', 'bior', 'rbio', 'dmey']\n >>> pywt.families(short=False)\n ['Haar', 'Daubechies', 'Symlets', 'Coiflets', 'Biorthogonal',\n 'Reverse biorthogonal', 'Discrete Meyer (FIR Approximation)']\n\n "; static char __pyx_k_home_rgommers_Code_tmp_pywt_pyw[] = "/home/rgommers/Code/tmp/pywt/pywt/src/_pywt.pyx"; static char __pyx_k_start_level_must_be_less_than_d[] = "start_level must be less than %d."; static char __pyx_k_unknown_dtype_code_in_numpy_pxd[] = "unknown dtype code in numpy.pxd (%d)"; static char __pyx_k_upcoef_part_coeffs_wavelet_leve[] = "\n upcoef(part, coeffs, wavelet, level=1, take=0)\n\n Direct reconstruction from coefficients.\n\n Parameters\n ----------\n part : str\n Coefficients type:\n * 'a' - approximations reconstruction is performed\n * 'd' - details reconstruction is performed\n coeffs : array_like\n Coefficients array to recontruct\n wavelet : Wavelet object or name\n Wavelet to use\n level : int, optional\n Multilevel reconstruction level. Default is 1.\n take : int, optional\n Take central part of length equal to 'take' from the result.\n Default is 0.\n\n Returns\n -------\n rec : ndarray\n 1-D array with reconstructed data from coefficients.\n\n See Also\n --------\n downcoef\n\n Examples\n --------\n >>> import pywt\n >>> data = [1,2,3,4,5,6]\n >>> (cA, cD) = pywt.dwt(data, 'db2', 'sp1')\n >>> pywt.upcoef('a', cA, 'db2') + pywt.upcoef('d', cD, 'db2')\n [-0.25 -0.4330127 1. 2. 3. 4. 5.\n 6. 1.78589838 -1.03108891]\n >>> n = len(data)\n >>> pywt.upcoef('a', cA, 'db2', take=n) + pywt.upcoef('d', cD, 'db2', take=n)\n [ 1. 2. 3. 4. 5. 6.]\n\n "; static char __pyx_k_wavefun_self_level_8_Calculates[] = "\n wavefun(self, level=8)\n\n Calculates approximations of scaling function (`phi`) and wavelet\n function (`psi`) on xgrid (`x`) at a given level of refinement.\n\n Parameters\n ----------\n level : int, optional\n Level of refinement (default: 8).\n\n Returns\n -------\n [phi, psi, x] : array_like\n For orthogonal wavelets returns scaling function, wavelet function\n and xgrid - [phi, psi, x].\n\n [phi_d, psi_d, phi_r, psi_r, x] : array_like\n For biorthogonal wavelets returns scaling and wavelet function both\n for decomposition and reconstruction and xgrid\n\n Examples\n --------\n >>> import pywt\n >>> # Orthogonal\n >>> wavelet = pywt.Wavelet('db2')\n >>> phi, psi, x = wavelet.wavefun(level=5)\n >>> # Biorthogonal\n >>> wavelet = pywt.Wavelet('bior3.5')\n >>> phi_d, psi_d, phi_r, psi_r, x = wavelet.wavefun(level=5)\n\n "; static char __pyx_k_wavelist_family_None_Returns_li[] = "\n wavelist(family=None)\n\n Returns list of available wavelet names for the given family name.\n\n Parameters\n ----------\n family : {'haar', 'db', 'sym', 'coif', 'bior', 'rbio', 'dmey'}\n Short family name. If the family name is None (default) then names\n of all the built-in wavelets are returned. Otherwise the function\n returns names of wavelets that belong to the given family.\n\n Returns\n -------\n wavelist : list\n List of available wavelet names\n\n Examples\n --------\n >>> import pywt\n >>> pywt.wavelist('coif')\n ['coif1', 'coif2', 'coif3', 'coif4', 'coif5']\n\n "; static char __pyx_k_All_dimensions_preceding_dimensi[] = "All dimensions preceding dimension %d must be indexed and not sliced"; static char __pyx_k_At_least_one_coefficient_paramet[] = "At least one coefficient parameter must be specified."; static char __pyx_k_Buffer_view_does_not_expose_stri[] = "Buffer view does not expose strides"; static char __pyx_k_Can_only_create_a_buffer_that_is[] = "Can only create a buffer that is contiguous in memory."; static char __pyx_k_Cannot_transpose_memoryview_with[] = "Cannot transpose memoryview with indirect dimensions"; static char __pyx_k_Coefficients_arrays_must_have_th[] = "Coefficients arrays must have the same size."; static char __pyx_k_Coefficients_arrays_must_satisfy[] = "Coefficients arrays must satisfy (0 <= len(cA) - len(cD) <= 1)."; static char __pyx_k_Could_not_allocate_memory_for_gi[] = "Could not allocate memory for given filter bank."; static char __pyx_k_Creating_custom_Wavelets_using_o[] = "Creating custom Wavelets using objects that define `get_filters_coeffs` method is deprecated. The `filter_bank` parameter should define a `filter_bank` attribute instead of `get_filters_coeffs` method."; static char __pyx_k_Discrete_Meyer_FIR_Approximation[] = "Discrete Meyer (FIR Approximation)"; static char __pyx_k_Empty_shape_tuple_for_cython_arr[] = "Empty shape tuple for cython.array"; static char __pyx_k_Expected_filter_bank_with_4_filt[] = "Expected filter bank with 4 filters, got filter bank with %d filters."; static char __pyx_k_Expected_list_of_4_filters_coeff[] = "Expected list of 4 filters coefficients, got %d filters."; static char __pyx_k_Format_string_allocated_too_shor[] = "Format string allocated too short, see comment in numpy.pxd"; static char __pyx_k_Function_call_with_ambiguous_arg[] = "Function call with ambiguous argument types"; static char __pyx_k_Indirect_dimensions_not_supporte[] = "Indirect dimensions not supported"; static char __pyx_k_Invalid_coefficient_arrays_lengt[] = "Invalid coefficient arrays length for specified wavelet. Wavelet and mode must be the same as used for decomposition."; static char __pyx_k_Invalid_mode_expected_c_or_fortr[] = "Invalid mode, expected 'c' or 'fortran', got %s"; static char __pyx_k_Level_value_must_be_greater_than[] = "Level value must be greater than zero."; static char __pyx_k_Level_value_too_high_max_level_f[] = "Level value too high (max level for current data size and start_level is %d)."; static char __pyx_k_Non_native_byte_order_not_suppor[] = "Non-native byte order not supported"; static char __pyx_k_Out_of_bounds_on_buffer_access_a[] = "Out of bounds on buffer access (axis %d)"; static char __pyx_k_Pyrex_wrapper_for_low_level_C_wa[] = "Pyrex wrapper for low-level C wavelet transform implementation."; static char __pyx_k_Returns_True_if_the_wavelet_is_b[] = "Returns True if the wavelet is built-in one (not created with\n custom filter bank).\n "; static char __pyx_k_Returns_tuple_of_wavelet_filters[] = "Returns tuple of wavelet filters coefficients\n (dec_lo, dec_hi, rec_lo, rec_hi)\n "; static char __pyx_k_The_get_filters_coeffs_method_is[] = "The `get_filters_coeffs` method is deprecated. Use `filter_bank` attribute instead."; static char __pyx_k_The_get_reverse_filters_coeffs_m[] = "The `get_reverse_filters_coeffs` method is deprecated. Use `inverse_filter_bank` attribute instead."; static char __pyx_k_Tuple_of_inverse_wavelet_filters[] = "Tuple of inverse wavelet filters coefficients\n (rec_lo[::-1], rec_hi[::-1], dec_lo[::-1], dec_hi[::-1])\n "; static char __pyx_k_Unable_to_convert_item_to_object[] = "Unable to convert item to object"; static char __pyx_k_Unknown_wavelet_name_s_check_wav[] = "Unknown wavelet name '%s', check wavelist() for the list of available builtin wavelets."; static char __pyx_k_Value_of_filter_len_must_be_grea[] = "Value of filter_len must be greater than zero."; static char __pyx_k_Value_of_level_must_be_greater_t[] = "Value of level must be greater than 0."; static char __pyx_k_Wavelet_name_or_filter_bank_must[] = "Wavelet name or filter bank must be specified."; static char __pyx_k_got_differing_extents_in_dimensi[] = "got differing extents in dimension %d (got %d and %d)"; static char __pyx_k_idwt_requires_1D_coefficient_arr[] = "idwt requires 1D coefficient arrays."; static char __pyx_k_ndarray_is_not_Fortran_contiguou[] = "ndarray is not Fortran contiguous"; static char __pyx_k_start_level_must_be_greater_than[] = "start_level must be greater than zero."; static char __pyx_k_unable_to_allocate_shape_and_str[] = "unable to allocate shape and strides."; static char __pyx_k_All_filters_in_filter_bank_must_2[] = "All filters in filter bank must have length greater than 0."; static char __pyx_k_Number_of_vanishing_moments_for_2[] = "Number of vanishing moments for scaling function"; static char __pyx_k_Format_string_allocated_too_shor_2[] = "Format string allocated too short."; static PyObject *__pyx_kp_s_All_filters_in_filter_bank_must; static PyObject *__pyx_kp_s_All_filters_in_filter_bank_must_2; static PyObject *__pyx_kp_s_Argument_1_must_be_a_or_d_not_s; static PyObject *__pyx_kp_s_At_least_one_coefficient_paramet; static PyObject *__pyx_n_s_AttributeError; static PyObject *__pyx_kp_s_Because_the_most_common_and_pra; static PyObject *__pyx_n_s_Biorthogonal; static PyObject *__pyx_kp_u_Biorthogonal_s; static PyObject *__pyx_kp_s_Buffer_view_does_not_expose_stri; static PyObject *__pyx_kp_s_C_dec_a_failed; static PyObject *__pyx_kp_s_C_dwt_failed; static PyObject *__pyx_kp_s_C_idwt_failed; static PyObject *__pyx_kp_s_C_rec_a_failed; static PyObject *__pyx_kp_s_C_swt_failed; static PyObject *__pyx_kp_s_Can_only_create_a_buffer_that_is; static PyObject *__pyx_kp_s_Cannot_index_with_type_s; static PyObject *__pyx_kp_s_Coefficients_arrays_must_have_th; static PyObject *__pyx_kp_s_Coefficients_arrays_must_satisfy; static PyObject *__pyx_n_s_Coiflets; static PyObject *__pyx_kp_s_Could_not_allocate_memory_for_gi; static PyObject *__pyx_kp_s_Creating_custom_Wavelets_using_o; static PyObject *__pyx_n_s_Daubechies; static PyObject *__pyx_n_s_DeprecationWarning; static PyObject *__pyx_kp_s_Discrete_Meyer_FIR_Approximation; static PyObject *__pyx_n_s_Ellipsis; static PyObject *__pyx_kp_s_Empty_shape_tuple_for_cython_arr; static PyObject *__pyx_kp_s_Expected_at_least_d_arguments; static PyObject *__pyx_kp_s_Expected_filter_bank_with_4_filt; static PyObject *__pyx_kp_s_Expected_list_of_4_filters_coeff; static PyObject *__pyx_kp_u_Family_name_s; static PyObject *__pyx_kp_s_Filter_bank_with_numeric_values; static PyObject *__pyx_kp_u_Filters_length_d; static PyObject *__pyx_kp_u_Format_string_allocated_too_shor; static PyObject *__pyx_kp_u_Format_string_allocated_too_shor_2; static PyObject *__pyx_kp_s_Function_call_with_ambiguous_arg; static PyObject *__pyx_n_s_Haar; static PyObject *__pyx_n_s_ImportError; static PyObject *__pyx_n_s_IndexError; static PyObject *__pyx_kp_s_Indirect_dimensions_not_supporte; static PyObject *__pyx_kp_s_Invalid_coefficient_arrays_lengt; static PyObject *__pyx_kp_s_Invalid_mode; static PyObject *__pyx_kp_s_Invalid_mode_0; static PyObject *__pyx_kp_s_Invalid_mode_expected_c_or_fortr; static PyObject *__pyx_kp_s_Invalid_output_length; static PyObject *__pyx_kp_s_Invalid_shape_in_axis_d_d; static PyObject *__pyx_kp_s_Invalid_short_family_name_s; static PyObject *__pyx_kp_s_Invalid_wavelet_name; static PyObject *__pyx_n_s_KeyError; static PyObject *__pyx_kp_s_Length_of_data_must_be_even; static PyObject *__pyx_kp_s_Level_value_must_be_greater_than; static PyObject *__pyx_kp_s_Level_value_too_high_max_level_f; static PyObject *__pyx_n_s_MODES; static PyObject *__pyx_n_s_MemoryError; static PyObject *__pyx_kp_s_MemoryView_of_r_at_0x_x; static PyObject *__pyx_kp_s_MemoryView_of_r_object; static PyObject *__pyx_n_s_Modes; static PyObject *__pyx_n_s_Modes_from_object; static PyObject *__pyx_kp_s_No_matching_signature_found; static PyObject *__pyx_kp_u_Non_native_byte_order_not_suppor; static PyObject *__pyx_n_b_O; static PyObject *__pyx_kp_u_Orthogonal_s; static PyObject *__pyx_kp_s_Out_of_bounds_on_buffer_access_a; static PyObject *__pyx_kp_s_Pyrex_wrapper_for_low_level_C_wa; static PyObject *__pyx_kp_s_Reverse_biorthogonal; static PyObject *__pyx_n_s_RuntimeError; static PyObject *__pyx_kp_u_Short_name_s; static PyObject *__pyx_n_s_Symlets; static PyObject *__pyx_kp_u_Symmetry_s; static PyObject *__pyx_kp_s_The_get_filters_coeffs_method_is; static PyObject *__pyx_kp_s_The_get_reverse_filters_coeffs_m; static PyObject *__pyx_n_s_TypeError; static PyObject *__pyx_kp_s_Unable_to_convert_item_to_object; static PyObject *__pyx_kp_s_Unknown_mode_name; static PyObject *__pyx_kp_s_Unknown_mode_name_s; static PyObject *__pyx_kp_s_Unknown_wavelet_name_s_check_wav; static PyObject *__pyx_n_s_ValueError; static PyObject *__pyx_kp_s_Value_of_data_len_value_must_be; static PyObject *__pyx_kp_s_Value_of_filter_len_must_be_grea; static PyObject *__pyx_kp_s_Value_of_level_must_be_greater_t; static PyObject *__pyx_n_s_Wavelet; static PyObject *__pyx_kp_s_Wavelet_name_or_filter_bank_must; static PyObject *__pyx_kp_u_Wavelet_s; static PyObject *__pyx_kp_u_Wavelet_wavefun_line_428; static PyObject *__pyx_kp_u__17; static PyObject *__pyx_kp_s__20; static PyObject *__pyx_kp_s__22; static PyObject *__pyx_kp_u__6; static PyObject *__pyx_n_s_a; static PyObject *__pyx_n_s_all; static PyObject *__pyx_n_s_allocate_buffer; static PyObject *__pyx_n_s_append; static PyObject *__pyx_n_s_args; static PyObject *__pyx_n_s_arr; static PyObject *__pyx_n_s_array; static PyObject *__pyx_n_s_asarray; static PyObject *__pyx_n_s_astype; static PyObject *__pyx_n_s_asym; static PyObject *__pyx_n_s_asymmetric; static PyObject *__pyx_n_s_base; static PyObject *__pyx_n_s_bior; static PyObject *__pyx_kp_s_bior1_1; static PyObject *__pyx_kp_s_bior1_3; static PyObject *__pyx_kp_s_bior1_5; static PyObject *__pyx_kp_s_bior2_2; static PyObject *__pyx_kp_s_bior2_4; static PyObject *__pyx_kp_s_bior2_6; static PyObject *__pyx_kp_s_bior2_8; static PyObject *__pyx_kp_s_bior3_1; static PyObject *__pyx_kp_s_bior3_3; static PyObject *__pyx_kp_s_bior3_5; static PyObject *__pyx_kp_s_bior3_7; static PyObject *__pyx_kp_s_bior3_9; static PyObject *__pyx_kp_s_bior4_4; static PyObject *__pyx_kp_s_bior5_5; static PyObject *__pyx_kp_s_bior6_8; static PyObject *__pyx_n_s_biorthogonal; static PyObject *__pyx_n_s_c; static PyObject *__pyx_n_u_c; static PyObject *__pyx_n_s_cA; static PyObject *__pyx_kp_u_cA_cD_dwt_data_wavelet_mode_sym; static PyObject *__pyx_n_s_cD; static PyObject *__pyx_n_s_check_dtype; static PyObject *__pyx_n_s_class; static PyObject *__pyx_n_s_coeffs; static PyObject *__pyx_n_s_coif; static PyObject *__pyx_n_s_coif1; static PyObject *__pyx_n_s_coif2; static PyObject *__pyx_n_s_coif3; static PyObject *__pyx_n_s_coif4; static PyObject *__pyx_n_s_coif5; static PyObject *__pyx_n_s_concatenate; static PyObject *__pyx_kp_s_contiguous_and_direct; static PyObject *__pyx_kp_s_contiguous_and_indirect; static PyObject *__pyx_n_s_correct_size; static PyObject *__pyx_n_s_cpd; static PyObject *__pyx_n_s_d; static PyObject *__pyx_n_s_data; static PyObject *__pyx_n_s_data_len; static PyObject *__pyx_n_s_db; static PyObject *__pyx_n_s_db1; static PyObject *__pyx_n_s_db10; static PyObject *__pyx_n_s_db11; static PyObject *__pyx_n_s_db12; static PyObject *__pyx_n_s_db13; static PyObject *__pyx_n_s_db14; static PyObject *__pyx_n_s_db15; static PyObject *__pyx_n_s_db16; static PyObject *__pyx_n_s_db17; static PyObject *__pyx_n_s_db18; static PyObject *__pyx_n_s_db19; static PyObject *__pyx_n_s_db2; static PyObject *__pyx_n_s_db20; static PyObject *__pyx_n_s_db3; static PyObject *__pyx_n_s_db4; static PyObject *__pyx_n_s_db5; static PyObject *__pyx_n_s_db6; static PyObject *__pyx_n_s_db7; static PyObject *__pyx_n_s_db8; static PyObject *__pyx_n_s_db9; static PyObject *__pyx_n_s_dec_hi; static PyObject *__pyx_n_s_dec_len; static PyObject *__pyx_n_s_dec_lo; static PyObject *__pyx_n_s_defaults; static PyObject *__pyx_n_s_dmey; static PyObject *__pyx_n_s_do_dec_a; static PyObject *__pyx_n_s_do_rec_a; static PyObject *__pyx_n_s_doc; static PyObject *__pyx_n_s_downcoef; static PyObject *__pyx_n_s_downcoef_2; static PyObject *__pyx_n_s_dt; static PyObject *__pyx_n_s_dtype; static PyObject *__pyx_n_s_dtype_is_object; static PyObject *__pyx_n_s_dwt; static PyObject *__pyx_n_s_dwt_2; static PyObject *__pyx_n_s_dwt_coeff_len; static PyObject *__pyx_kp_u_dwt_line_605; static PyObject *__pyx_n_s_dwt_max_level; static PyObject *__pyx_kp_u_dwt_max_level_data_len_filter_l; static PyObject *__pyx_kp_u_dwt_max_level_line_572; static PyObject *__pyx_kp_s_dwt_requires_a_1D_data_array; static PyObject *__pyx_n_s_e; static PyObject *__pyx_n_s_end_level; static PyObject *__pyx_n_s_enumerate; static PyObject *__pyx_n_s_error; static PyObject *__pyx_n_s_families; static PyObject *__pyx_kp_u_families_line_171; static PyObject *__pyx_kp_u_families_short_True_Returns_a_l; static PyObject *__pyx_n_s_family; static PyObject *__pyx_n_s_family_name; static PyObject *__pyx_n_s_filter_bank; static PyObject *__pyx_n_s_filter_len; static PyObject *__pyx_n_s_filter_len_2; static PyObject *__pyx_n_s_flags; static PyObject *__pyx_n_s_float32; static PyObject *__pyx_n_s_float32_t; static PyObject *__pyx_n_s_float64; static PyObject *__pyx_n_s_float64_t; static PyObject *__pyx_n_s_format; static PyObject *__pyx_n_s_fortran; static PyObject *__pyx_n_u_fortran; static PyObject *__pyx_n_s_from_object; static PyObject *__pyx_n_s_get_filters_coeffs; static PyObject *__pyx_kp_s_got_differing_extents_in_dimensi; static PyObject *__pyx_n_s_haar; static PyObject *__pyx_kp_s_home_rgommers_Code_tmp_pywt_pyw; static PyObject *__pyx_n_s_i; static PyObject *__pyx_n_s_id; static PyObject *__pyx_n_s_idwt; static PyObject *__pyx_n_s_idwt_2; static PyObject *__pyx_kp_s_idwt_requires_1D_coefficient_arr; static PyObject *__pyx_n_s_import; static PyObject *__pyx_n_s_input_len; static PyObject *__pyx_n_s_inverse_filter_bank; static PyObject *__pyx_n_s_itemsize; static PyObject *__pyx_kp_s_itemsize_0_for_cython_array; static PyObject *__pyx_n_s_keep; static PyObject *__pyx_n_s_keep_length; static PyObject *__pyx_n_s_kind; static PyObject *__pyx_n_s_kwargs; static PyObject *__pyx_n_s_left_bound; static PyObject *__pyx_n_s_length; static PyObject *__pyx_n_s_level; static PyObject *__pyx_n_s_level_2; static PyObject *__pyx_n_s_linspace; static PyObject *__pyx_n_s_lower; static PyObject *__pyx_n_s_m; static PyObject *__pyx_n_s_main; static PyObject *__pyx_n_s_memview; static PyObject *__pyx_n_s_metaclass; static PyObject *__pyx_n_s_mode; static PyObject *__pyx_n_s_mode_2; static PyObject *__pyx_n_s_modes; static PyObject *__pyx_n_s_module; static PyObject *__pyx_n_s_msg; static PyObject *__pyx_n_s_name; static PyObject *__pyx_n_s_name_2; static PyObject *__pyx_n_s_ndarray; static PyObject *__pyx_kp_u_ndarray_is_not_C_contiguous; static PyObject *__pyx_kp_u_ndarray_is_not_Fortran_contiguou; static PyObject *__pyx_n_s_ndim; static PyObject *__pyx_kp_s_near_symmetric; static PyObject *__pyx_n_s_np; static PyObject *__pyx_n_s_numpy; static PyObject *__pyx_n_s_obj; static PyObject *__pyx_n_s_object; static PyObject *__pyx_n_s_ord; static PyObject *__pyx_n_s_orthogonal; static PyObject *__pyx_n_s_output_len; static PyObject *__pyx_n_s_pack; static PyObject *__pyx_n_s_part; static PyObject *__pyx_n_s_per; static PyObject *__pyx_n_s_ppd; static PyObject *__pyx_n_s_prepare; static PyObject *__pyx_n_s_pywt; static PyObject *__pyx_n_s_pyx_getbuffer; static PyObject *__pyx_n_s_pyx_vtable; static PyObject *__pyx_n_s_qualname; static PyObject *__pyx_n_s_range; static PyObject *__pyx_n_s_rbio; static PyObject *__pyx_kp_s_rbio1_1; static PyObject *__pyx_kp_s_rbio1_3; static PyObject *__pyx_kp_s_rbio1_5; static PyObject *__pyx_kp_s_rbio2_2; static PyObject *__pyx_kp_s_rbio2_4; static PyObject *__pyx_kp_s_rbio2_6; static PyObject *__pyx_kp_s_rbio2_8; static PyObject *__pyx_kp_s_rbio3_1; static PyObject *__pyx_kp_s_rbio3_3; static PyObject *__pyx_kp_s_rbio3_5; static PyObject *__pyx_kp_s_rbio3_7; static PyObject *__pyx_kp_s_rbio3_9; static PyObject *__pyx_kp_s_rbio4_4; static PyObject *__pyx_kp_s_rbio5_5; static PyObject *__pyx_kp_s_rbio6_8; static PyObject *__pyx_n_s_rec; static PyObject *__pyx_n_s_rec_hi; static PyObject *__pyx_n_s_rec_len; static PyObject *__pyx_n_s_rec_lo; static PyObject *__pyx_n_s_ret; static PyObject *__pyx_n_s_right_bound; static PyObject *__pyx_n_s_rstrip; static PyObject *__pyx_n_s_self; static PyObject *__pyx_n_s_shape; static PyObject *__pyx_n_s_short; static PyObject *__pyx_n_s_short_family_name; static PyObject *__pyx_n_s_signatures; static PyObject *__pyx_n_s_size; static PyObject *__pyx_n_s_size_diff; static PyObject *__pyx_n_s_sort; static PyObject *__pyx_n_s_sorting_list; static PyObject *__pyx_n_s_sp1; static PyObject *__pyx_n_s_split; static PyObject *__pyx_n_s_start; static PyObject *__pyx_n_s_start_level; static PyObject *__pyx_kp_s_start_level_must_be_greater_than; static PyObject *__pyx_kp_s_start_level_must_be_less_than_d; static PyObject *__pyx_n_s_startswith; static PyObject *__pyx_n_s_step; static PyObject *__pyx_n_s_stop; static PyObject *__pyx_kp_s_strided_and_direct; static PyObject *__pyx_kp_s_strided_and_direct_or_indirect; static PyObject *__pyx_kp_s_strided_and_indirect; static PyObject *__pyx_n_s_strip; static PyObject *__pyx_n_s_struct; static PyObject *__pyx_n_s_swt; static PyObject *__pyx_n_s_swt_2; static PyObject *__pyx_n_s_swt_max_level; static PyObject *__pyx_n_s_sym; static PyObject *__pyx_n_s_sym10; static PyObject *__pyx_n_s_sym11; static PyObject *__pyx_n_s_sym12; static PyObject *__pyx_n_s_sym13; static PyObject *__pyx_n_s_sym14; static PyObject *__pyx_n_s_sym15; static PyObject *__pyx_n_s_sym16; static PyObject *__pyx_n_s_sym17; static PyObject *__pyx_n_s_sym18; static PyObject *__pyx_n_s_sym19; static PyObject *__pyx_n_s_sym2; static PyObject *__pyx_n_s_sym20; static PyObject *__pyx_n_s_sym3; static PyObject *__pyx_n_s_sym4; static PyObject *__pyx_n_s_sym5; static PyObject *__pyx_n_s_sym6; static PyObject *__pyx_n_s_sym7; static PyObject *__pyx_n_s_sym8; static PyObject *__pyx_n_s_sym9; static PyObject *__pyx_n_s_symmetric; static PyObject *__pyx_n_s_symmetry; static PyObject *__pyx_n_s_take; static PyObject *__pyx_n_s_test; static PyObject *__pyx_n_s_try_mode; static PyObject *__pyx_kp_s_unable_to_allocate_array_data; static PyObject *__pyx_kp_s_unable_to_allocate_shape_and_str; static PyObject *__pyx_n_s_unknown; static PyObject *__pyx_kp_u_unknown_dtype_code_in_numpy_pxd; static PyObject *__pyx_n_s_unpack; static PyObject *__pyx_n_s_upcoef; static PyObject *__pyx_n_s_upcoef_2; static PyObject *__pyx_kp_u_upcoef_line_890; static PyObject *__pyx_kp_u_upcoef_part_coeffs_wavelet_leve; static PyObject *__pyx_n_s_w; static PyObject *__pyx_n_s_warn; static PyObject *__pyx_n_s_warnings; static PyObject *__pyx_kp_u_wavefun_self_level_8_Calculates; static PyObject *__pyx_n_s_wavelet; static PyObject *__pyx_n_s_wavelet_from_object; static PyObject *__pyx_n_s_wavelets; static PyObject *__pyx_n_s_wavelist; static PyObject *__pyx_kp_u_wavelist_family_None_Returns_li; static PyObject *__pyx_kp_u_wavelist_line_126; static PyObject *__pyx_n_s_x; static PyObject *__pyx_n_s_zeros; static PyObject *__pyx_n_s_zip; static PyObject *__pyx_n_s_zpd; static PyObject *__pyx_float_0_; static PyObject *__pyx_float_0_0; static PyObject *__pyx_int_0; static PyObject *__pyx_int_1; static PyObject *__pyx_int_2; static PyObject *__pyx_int_3; static PyObject *__pyx_int_4; static PyObject *__pyx_int_5; static PyObject *__pyx_int_6; static PyObject *__pyx_int_7; static PyObject *__pyx_int_8; static PyObject *__pyx_int_9; static PyObject *__pyx_int_10; static PyObject *__pyx_int_11; static PyObject *__pyx_int_12; static PyObject *__pyx_int_13; static PyObject *__pyx_int_14; static PyObject *__pyx_int_15; static PyObject *__pyx_int_16; static PyObject *__pyx_int_17; static PyObject *__pyx_int_18; static PyObject *__pyx_int_19; static PyObject *__pyx_int_20; static PyObject *__pyx_int_22; static PyObject *__pyx_int_24; static PyObject *__pyx_int_26; static PyObject *__pyx_int_28; static PyObject *__pyx_int_31; static PyObject *__pyx_int_33; static PyObject *__pyx_int_35; static PyObject *__pyx_int_37; static PyObject *__pyx_int_39; static PyObject *__pyx_int_44; static PyObject *__pyx_int_55; static PyObject *__pyx_int_68; static PyObject *__pyx_int_neg_1; static PyObject *__pyx_k__19; static PyObject *__pyx_k__35; static PyObject *__pyx_k__42; static PyObject *__pyx_k__55; static PyObject *__pyx_k__68; static PyObject *__pyx_k__69; static PyObject *__pyx_tuple_; static PyObject *__pyx_slice__2; static PyObject *__pyx_slice__3; static PyObject *__pyx_slice__4; static PyObject *__pyx_slice__5; static PyObject *__pyx_tuple__7; static PyObject *__pyx_tuple__8; static PyObject *__pyx_tuple__9; static PyObject *__pyx_slice__13; static PyObject *__pyx_slice__14; static PyObject *__pyx_slice__15; static PyObject *__pyx_slice__16; static PyObject *__pyx_tuple__10; static PyObject *__pyx_tuple__11; static PyObject *__pyx_tuple__12; static PyObject *__pyx_tuple__18; static PyObject *__pyx_tuple__21; static PyObject *__pyx_tuple__23; static PyObject *__pyx_tuple__24; static PyObject *__pyx_tuple__25; static PyObject *__pyx_tuple__26; static PyObject *__pyx_tuple__27; static PyObject *__pyx_tuple__28; static PyObject *__pyx_tuple__29; static PyObject *__pyx_tuple__30; static PyObject *__pyx_tuple__31; static PyObject *__pyx_tuple__32; static PyObject *__pyx_tuple__33; static PyObject *__pyx_tuple__34; static PyObject *__pyx_tuple__36; static PyObject *__pyx_tuple__37; static PyObject *__pyx_tuple__38; static PyObject *__pyx_tuple__39; static PyObject *__pyx_tuple__40; static PyObject *__pyx_tuple__41; static PyObject *__pyx_tuple__43; static PyObject *__pyx_tuple__44; static PyObject *__pyx_tuple__45; static PyObject *__pyx_tuple__46; static PyObject *__pyx_tuple__47; static PyObject *__pyx_tuple__48; static PyObject *__pyx_tuple__49; static PyObject *__pyx_tuple__50; static PyObject *__pyx_tuple__51; static PyObject *__pyx_tuple__52; static PyObject *__pyx_tuple__53; static PyObject *__pyx_tuple__54; static PyObject *__pyx_tuple__56; static PyObject *__pyx_tuple__57; static PyObject *__pyx_tuple__58; static PyObject *__pyx_tuple__59; static PyObject *__pyx_tuple__60; static PyObject *__pyx_tuple__61; static PyObject *__pyx_tuple__62; static PyObject *__pyx_tuple__63; static PyObject *__pyx_tuple__64; static PyObject *__pyx_tuple__65; static PyObject *__pyx_tuple__66; static PyObject *__pyx_tuple__67; static PyObject *__pyx_tuple__70; static PyObject *__pyx_tuple__71; static PyObject *__pyx_tuple__72; static PyObject *__pyx_tuple__73; static PyObject *__pyx_tuple__74; static PyObject *__pyx_tuple__75; static PyObject *__pyx_tuple__76; static PyObject *__pyx_tuple__77; static PyObject *__pyx_tuple__78; static PyObject *__pyx_tuple__79; static PyObject *__pyx_tuple__80; static PyObject *__pyx_tuple__81; static PyObject *__pyx_tuple__82; static PyObject *__pyx_tuple__83; static PyObject *__pyx_tuple__84; static PyObject *__pyx_tuple__85; static PyObject *__pyx_tuple__86; static PyObject *__pyx_tuple__87; static PyObject *__pyx_tuple__88; static PyObject *__pyx_tuple__89; static PyObject *__pyx_tuple__90; static PyObject *__pyx_tuple__91; static PyObject *__pyx_tuple__92; static PyObject *__pyx_tuple__93; static PyObject *__pyx_tuple__94; static PyObject *__pyx_tuple__95; static PyObject *__pyx_tuple__96; static PyObject *__pyx_tuple__97; static PyObject *__pyx_tuple__98; static PyObject *__pyx_tuple__99; static PyObject *__pyx_slice__100; static PyObject *__pyx_slice__101; static PyObject *__pyx_slice__102; static PyObject *__pyx_tuple__103; static PyObject *__pyx_tuple__104; static PyObject *__pyx_tuple__106; static PyObject *__pyx_tuple__108; static PyObject *__pyx_tuple__110; static PyObject *__pyx_tuple__112; static PyObject *__pyx_tuple__114; static PyObject *__pyx_tuple__116; static PyObject *__pyx_tuple__118; static PyObject *__pyx_tuple__120; static PyObject *__pyx_tuple__122; static PyObject *__pyx_tuple__124; static PyObject *__pyx_tuple__126; static PyObject *__pyx_tuple__128; static PyObject *__pyx_tuple__130; static PyObject *__pyx_tuple__132; static PyObject *__pyx_tuple__134; static PyObject *__pyx_tuple__136; static PyObject *__pyx_tuple__138; static PyObject *__pyx_tuple__140; static PyObject *__pyx_tuple__142; static PyObject *__pyx_tuple__144; static PyObject *__pyx_tuple__145; static PyObject *__pyx_tuple__146; static PyObject *__pyx_tuple__147; static PyObject *__pyx_tuple__148; static PyObject *__pyx_codeobj__105; static PyObject *__pyx_codeobj__107; static PyObject *__pyx_codeobj__109; static PyObject *__pyx_codeobj__111; 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__pyx_lineno = 738; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __pyx_r = ((struct __pyx_memoryview_obj *)__pyx_t_3); __pyx_t_3 = 0; goto __pyx_L0; } /* "View.MemoryView":666 * * @cname('__pyx_memview_slice') * cdef memoryview memview_slice(memoryview memview, object indices): # <<<<<<<<<<<<<< * cdef int new_ndim = 0, suboffset_dim = -1, dim * cdef bint negative_step */ /* function exit code */ __pyx_L1_error:; __Pyx_XDECREF(__pyx_t_3); __Pyx_XDECREF(__pyx_t_9); __Pyx_AddTraceback("View.MemoryView.memview_slice", __pyx_clineno, __pyx_lineno, __pyx_filename); __pyx_r = 0; __pyx_L0:; __Pyx_XDECREF((PyObject *)__pyx_v_memviewsliceobj); __Pyx_XDECREF(__pyx_v_index); __Pyx_XGIVEREF((PyObject *)__pyx_r); __Pyx_RefNannyFinishContext(); return __pyx_r; } /* "View.MemoryView":763 * * @cname('__pyx_memoryview_slice_memviewslice') * cdef int slice_memviewslice( # <<<<<<<<<<<<<< * __Pyx_memviewslice *dst, * Py_ssize_t shape, Py_ssize_t stride, Py_ssize_t suboffset, */ static int __pyx_memoryview_slice_memviewslice(__Pyx_memviewslice *__pyx_v_dst, Py_ssize_t __pyx_v_shape, Py_ssize_t __pyx_v_stride, Py_ssize_t __pyx_v_suboffset, int __pyx_v_dim, int __pyx_v_new_ndim, int *__pyx_v_suboffset_dim, Py_ssize_t __pyx_v_start, Py_ssize_t __pyx_v_stop, Py_ssize_t __pyx_v_step, int __pyx_v_have_start, int __pyx_v_have_stop, int __pyx_v_have_step, int __pyx_v_is_slice) { Py_ssize_t __pyx_v_new_shape; int __pyx_v_negative_step; int __pyx_r; int __pyx_t_1; int __pyx_t_2; int __pyx_t_3; int __pyx_lineno = 0; const char *__pyx_filename = NULL; int __pyx_clineno = 0; /* "View.MemoryView":783 * cdef bint negative_step * * if not is_slice: # <<<<<<<<<<<<<< * * if start < 0: */ __pyx_t_1 = ((!(__pyx_v_is_slice != 0)) != 0); if (__pyx_t_1) { /* "View.MemoryView":785 * if not is_slice: * * if start < 0: # <<<<<<<<<<<<<< * start += shape * if not 0 <= start < shape: */ __pyx_t_1 = ((__pyx_v_start < 0) != 0); if (__pyx_t_1) { /* "View.MemoryView":786 * * if start < 0: * start += shape # <<<<<<<<<<<<<< * if not 0 <= start < shape: * _err_dim(IndexError, "Index out of bounds (axis %d)", dim) */ __pyx_v_start = (__pyx_v_start + __pyx_v_shape); goto __pyx_L4; } __pyx_L4:; /* "View.MemoryView":787 * if start < 0: * start += shape * if not 0 <= start < shape: # <<<<<<<<<<<<<< * _err_dim(IndexError, "Index out of bounds (axis %d)", dim) * else: */ __pyx_t_1 = (0 <= __pyx_v_start); if (__pyx_t_1) { __pyx_t_1 = (__pyx_v_start < __pyx_v_shape); } __pyx_t_2 = ((!(__pyx_t_1 != 0)) != 0); if (__pyx_t_2) { /* "View.MemoryView":788 * start += shape * if not 0 <= start < shape: * _err_dim(IndexError, "Index out of bounds (axis %d)", dim) # <<<<<<<<<<<<<< * else: * */ __pyx_t_3 = __pyx_memoryview_err_dim(__pyx_builtin_IndexError, __pyx_k_Index_out_of_bounds_axis_d, __pyx_v_dim); if (unlikely(__pyx_t_3 == -1)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 788; __pyx_clineno = __LINE__; goto __pyx_L1_error;} goto __pyx_L5; } __pyx_L5:; goto __pyx_L3; } /*else*/ { /* "View.MemoryView":791 * else: * * negative_step = have_step != 0 and step < 0 # <<<<<<<<<<<<<< * * if have_step and step == 0: */ __pyx_t_1 = ((__pyx_v_have_step != 0) != 0); if (__pyx_t_1) { } else { __pyx_t_2 = __pyx_t_1; goto __pyx_L6_bool_binop_done; } __pyx_t_1 = ((__pyx_v_step < 0) != 0); __pyx_t_2 = __pyx_t_1; __pyx_L6_bool_binop_done:; __pyx_v_negative_step = __pyx_t_2; /* "View.MemoryView":793 * negative_step = have_step != 0 and step < 0 * * if have_step and step == 0: # <<<<<<<<<<<<<< * _err_dim(ValueError, "Step may not be zero (axis %d)", dim) * */ __pyx_t_1 = (__pyx_v_have_step != 0); if (__pyx_t_1) { } else { __pyx_t_2 = __pyx_t_1; goto __pyx_L9_bool_binop_done; } __pyx_t_1 = ((__pyx_v_step == 0) != 0); __pyx_t_2 = __pyx_t_1; __pyx_L9_bool_binop_done:; if (__pyx_t_2) { /* "View.MemoryView":794 * * if have_step and step == 0: * _err_dim(ValueError, "Step may not be zero (axis %d)", dim) # <<<<<<<<<<<<<< * * */ __pyx_t_3 = __pyx_memoryview_err_dim(__pyx_builtin_ValueError, __pyx_k_Step_may_not_be_zero_axis_d, __pyx_v_dim); if (unlikely(__pyx_t_3 == -1)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 794; __pyx_clineno = __LINE__; goto __pyx_L1_error;} goto __pyx_L8; } __pyx_L8:; /* "View.MemoryView":797 * * * if have_start: # <<<<<<<<<<<<<< * if start < 0: * start += shape */ __pyx_t_2 = (__pyx_v_have_start != 0); if (__pyx_t_2) { /* "View.MemoryView":798 * * if have_start: * if start < 0: # <<<<<<<<<<<<<< * start += shape * if start < 0: */ __pyx_t_2 = ((__pyx_v_start < 0) != 0); if (__pyx_t_2) { /* "View.MemoryView":799 * if have_start: * if start < 0: * start += shape # <<<<<<<<<<<<<< * if start < 0: * start = 0 */ __pyx_v_start = (__pyx_v_start + __pyx_v_shape); /* "View.MemoryView":800 * if start < 0: * start += shape * if start < 0: # <<<<<<<<<<<<<< * start = 0 * elif start >= shape: */ __pyx_t_2 = ((__pyx_v_start < 0) != 0); if (__pyx_t_2) { /* "View.MemoryView":801 * start += shape * if start < 0: * start = 0 # <<<<<<<<<<<<<< * elif start >= shape: * if negative_step: */ __pyx_v_start = 0; goto __pyx_L13; } __pyx_L13:; goto __pyx_L12; } /* "View.MemoryView":802 * if start < 0: * start = 0 * elif start >= shape: # <<<<<<<<<<<<<< * if negative_step: * start = shape - 1 */ __pyx_t_2 = ((__pyx_v_start >= __pyx_v_shape) != 0); if (__pyx_t_2) { /* "View.MemoryView":803 * start = 0 * elif start >= shape: * if negative_step: # <<<<<<<<<<<<<< * start = shape - 1 * else: */ __pyx_t_2 = (__pyx_v_negative_step != 0); if (__pyx_t_2) { /* "View.MemoryView":804 * elif start >= shape: * if negative_step: * start = shape - 1 # <<<<<<<<<<<<<< * else: * start = shape */ __pyx_v_start = (__pyx_v_shape - 1); goto __pyx_L14; } /*else*/ { /* "View.MemoryView":806 * start = shape - 1 * else: * start = shape # <<<<<<<<<<<<<< * else: * if negative_step: */ __pyx_v_start = __pyx_v_shape; } __pyx_L14:; goto __pyx_L12; } __pyx_L12:; goto __pyx_L11; } /*else*/ { /* "View.MemoryView":808 * start = shape * else: * if negative_step: # <<<<<<<<<<<<<< * start = shape - 1 * else: */ __pyx_t_2 = (__pyx_v_negative_step != 0); if (__pyx_t_2) { /* "View.MemoryView":809 * else: * if negative_step: * start = shape - 1 # <<<<<<<<<<<<<< * else: * start = 0 */ __pyx_v_start = (__pyx_v_shape - 1); goto __pyx_L15; } /*else*/ { /* "View.MemoryView":811 * start = shape - 1 * else: * start = 0 # <<<<<<<<<<<<<< * * if have_stop: */ __pyx_v_start = 0; } __pyx_L15:; } __pyx_L11:; /* "View.MemoryView":813 * start = 0 * * if have_stop: # <<<<<<<<<<<<<< * if stop < 0: * stop += shape */ __pyx_t_2 = (__pyx_v_have_stop != 0); if (__pyx_t_2) { /* "View.MemoryView":814 * * if have_stop: * if stop < 0: # <<<<<<<<<<<<<< * stop += shape * if stop < 0: */ __pyx_t_2 = ((__pyx_v_stop < 0) != 0); if (__pyx_t_2) { /* "View.MemoryView":815 * if have_stop: * if stop < 0: * stop += shape # <<<<<<<<<<<<<< * if stop < 0: * stop = 0 */ __pyx_v_stop = (__pyx_v_stop + __pyx_v_shape); /* "View.MemoryView":816 * if stop < 0: * stop += shape * if stop < 0: # <<<<<<<<<<<<<< * stop = 0 * elif stop > shape: */ __pyx_t_2 = ((__pyx_v_stop < 0) != 0); if (__pyx_t_2) { /* "View.MemoryView":817 * stop += shape * if stop < 0: * stop = 0 # <<<<<<<<<<<<<< * elif stop > shape: * stop = shape */ __pyx_v_stop = 0; goto __pyx_L18; } __pyx_L18:; goto __pyx_L17; } /* "View.MemoryView":818 * if stop < 0: * stop = 0 * elif stop > shape: # <<<<<<<<<<<<<< * stop = shape * else: */ __pyx_t_2 = ((__pyx_v_stop > __pyx_v_shape) != 0); if (__pyx_t_2) { /* "View.MemoryView":819 * stop = 0 * elif stop > shape: * stop = shape # <<<<<<<<<<<<<< * else: * if negative_step: */ __pyx_v_stop = __pyx_v_shape; goto __pyx_L17; } __pyx_L17:; goto __pyx_L16; } /*else*/ { /* "View.MemoryView":821 * stop = shape * else: * if negative_step: # <<<<<<<<<<<<<< * stop = -1 * else: */ __pyx_t_2 = (__pyx_v_negative_step != 0); if (__pyx_t_2) { /* "View.MemoryView":822 * else: * if negative_step: * stop = -1 # <<<<<<<<<<<<<< * else: * stop = shape */ __pyx_v_stop = -1; goto __pyx_L19; } /*else*/ { /* "View.MemoryView":824 * stop = -1 * else: * stop = shape # <<<<<<<<<<<<<< * * if not have_step: */ __pyx_v_stop = __pyx_v_shape; } __pyx_L19:; } __pyx_L16:; /* "View.MemoryView":826 * stop = shape * * if not have_step: # <<<<<<<<<<<<<< * step = 1 * */ __pyx_t_2 = ((!(__pyx_v_have_step != 0)) != 0); if (__pyx_t_2) { /* "View.MemoryView":827 * * if not have_step: * step = 1 # <<<<<<<<<<<<<< * * */ __pyx_v_step = 1; goto __pyx_L20; } __pyx_L20:; /* "View.MemoryView":831 * * with cython.cdivision(True): * new_shape = (stop - start) // step # <<<<<<<<<<<<<< * * if (stop - start) - step * new_shape: */ __pyx_v_new_shape = ((__pyx_v_stop - __pyx_v_start) / __pyx_v_step); /* "View.MemoryView":833 * new_shape = (stop - start) // step * * if (stop - start) - step * new_shape: # <<<<<<<<<<<<<< * new_shape += 1 * */ __pyx_t_2 = (((__pyx_v_stop - __pyx_v_start) - (__pyx_v_step * __pyx_v_new_shape)) != 0); if (__pyx_t_2) { /* "View.MemoryView":834 * * if (stop - start) - step * new_shape: * new_shape += 1 # <<<<<<<<<<<<<< * * if new_shape < 0: */ __pyx_v_new_shape = (__pyx_v_new_shape + 1); goto __pyx_L21; } __pyx_L21:; /* "View.MemoryView":836 * new_shape += 1 * * if new_shape < 0: # <<<<<<<<<<<<<< * new_shape = 0 * */ __pyx_t_2 = ((__pyx_v_new_shape < 0) != 0); if (__pyx_t_2) { /* "View.MemoryView":837 * * if new_shape < 0: * new_shape = 0 # <<<<<<<<<<<<<< * * */ __pyx_v_new_shape = 0; goto __pyx_L22; } __pyx_L22:; /* "View.MemoryView":840 * * * dst.strides[new_ndim] = stride * step # <<<<<<<<<<<<<< * dst.shape[new_ndim] = new_shape * dst.suboffsets[new_ndim] = suboffset */ (__pyx_v_dst->strides[__pyx_v_new_ndim]) = (__pyx_v_stride * __pyx_v_step); /* "View.MemoryView":841 * * dst.strides[new_ndim] = stride * step * dst.shape[new_ndim] = new_shape # <<<<<<<<<<<<<< * dst.suboffsets[new_ndim] = suboffset * */ (__pyx_v_dst->shape[__pyx_v_new_ndim]) = __pyx_v_new_shape; /* "View.MemoryView":842 * dst.strides[new_ndim] = stride * step * dst.shape[new_ndim] = new_shape * dst.suboffsets[new_ndim] = suboffset # <<<<<<<<<<<<<< * * */ (__pyx_v_dst->suboffsets[__pyx_v_new_ndim]) = __pyx_v_suboffset; 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__pyx_t_1 > -1; __pyx_t_1-=1) { __pyx_v_i = __pyx_t_1; /* "View.MemoryView":1079 * * for i in range(ndim - 1, -1, -1): * if mslice.shape[i] > 1: # <<<<<<<<<<<<<< * c_stride = mslice.strides[i] * break */ __pyx_t_2 = (((__pyx_v_mslice->shape[__pyx_v_i]) > 1) != 0); if (__pyx_t_2) { /* "View.MemoryView":1080 * for i in range(ndim - 1, -1, -1): * if mslice.shape[i] > 1: * c_stride = mslice.strides[i] # <<<<<<<<<<<<<< * break * */ __pyx_v_c_stride = (__pyx_v_mslice->strides[__pyx_v_i]); /* "View.MemoryView":1081 * if mslice.shape[i] > 1: * c_stride = mslice.strides[i] * break # <<<<<<<<<<<<<< * * for i in range(ndim): */ goto __pyx_L4_break; } } __pyx_L4_break:; /* "View.MemoryView":1083 * break * * for i in range(ndim): # <<<<<<<<<<<<<< * if mslice.shape[i] > 1: * f_stride = mslice.strides[i] */ __pyx_t_1 = __pyx_v_ndim; for (__pyx_t_3 = 0; __pyx_t_3 < __pyx_t_1; __pyx_t_3+=1) { __pyx_v_i = __pyx_t_3; /* "View.MemoryView":1084 * * for i in range(ndim): * if mslice.shape[i] > 1: # <<<<<<<<<<<<<< * f_stride = mslice.strides[i] * break */ __pyx_t_2 = (((__pyx_v_mslice->shape[__pyx_v_i]) > 1) != 0); if (__pyx_t_2) { /* "View.MemoryView":1085 * for i in range(ndim): * if mslice.shape[i] > 1: * f_stride = mslice.strides[i] # <<<<<<<<<<<<<< * break * */ __pyx_v_f_stride = (__pyx_v_mslice->strides[__pyx_v_i]); /* "View.MemoryView":1086 * if mslice.shape[i] > 1: * f_stride = mslice.strides[i] * break # <<<<<<<<<<<<<< * * if abs_py_ssize_t(c_stride) <= abs_py_ssize_t(f_stride): */ goto __pyx_L7_break; } } __pyx_L7_break:; /* "View.MemoryView":1088 * break * * if abs_py_ssize_t(c_stride) <= abs_py_ssize_t(f_stride): # <<<<<<<<<<<<<< * return 'C' * else: */ __pyx_t_2 = ((abs_py_ssize_t(__pyx_v_c_stride) <= abs_py_ssize_t(__pyx_v_f_stride)) != 0); if (__pyx_t_2) { /* "View.MemoryView":1089 * * if abs_py_ssize_t(c_stride) <= abs_py_ssize_t(f_stride): * return 'C' # <<<<<<<<<<<<<< * else: * return 'F' */ __pyx_r = 'C'; goto __pyx_L0; } /*else*/ { /* "View.MemoryView":1091 * return 'C' * else: * return 'F' # <<<<<<<<<<<<<< * * @cython.cdivision(True) */ __pyx_r = 'F'; goto __pyx_L0; } /* "View.MemoryView":1070 * * @cname('__pyx_get_best_slice_order') * cdef char get_best_order(__Pyx_memviewslice *mslice, int ndim) nogil: # <<<<<<<<<<<<<< * """ * Figure out the best memory access order for a given slice. */ /* function exit code */ __pyx_L0:; return __pyx_r; } /* "View.MemoryView":1094 * * @cython.cdivision(True) * cdef void _copy_strided_to_strided(char *src_data, Py_ssize_t *src_strides, # <<<<<<<<<<<<<< * char *dst_data, Py_ssize_t *dst_strides, * Py_ssize_t *src_shape, Py_ssize_t *dst_shape, */ static void _copy_strided_to_strided(char *__pyx_v_src_data, Py_ssize_t *__pyx_v_src_strides, char *__pyx_v_dst_data, Py_ssize_t *__pyx_v_dst_strides, Py_ssize_t *__pyx_v_src_shape, Py_ssize_t *__pyx_v_dst_shape, int __pyx_v_ndim, size_t __pyx_v_itemsize) { CYTHON_UNUSED Py_ssize_t __pyx_v_i; CYTHON_UNUSED Py_ssize_t __pyx_v_src_extent; Py_ssize_t __pyx_v_dst_extent; Py_ssize_t __pyx_v_src_stride; Py_ssize_t __pyx_v_dst_stride; int __pyx_t_1; int __pyx_t_2; int __pyx_t_3; Py_ssize_t __pyx_t_4; Py_ssize_t __pyx_t_5; /* "View.MemoryView":1101 * * cdef Py_ssize_t i * cdef Py_ssize_t src_extent = src_shape[0] # <<<<<<<<<<<<<< * cdef Py_ssize_t dst_extent = dst_shape[0] * cdef Py_ssize_t src_stride = src_strides[0] */ __pyx_v_src_extent = (__pyx_v_src_shape[0]); /* "View.MemoryView":1102 * cdef Py_ssize_t i * cdef Py_ssize_t src_extent = src_shape[0] * cdef Py_ssize_t dst_extent = dst_shape[0] # <<<<<<<<<<<<<< * cdef Py_ssize_t src_stride = src_strides[0] * cdef Py_ssize_t dst_stride = dst_strides[0] */ __pyx_v_dst_extent = (__pyx_v_dst_shape[0]); /* "View.MemoryView":1103 * cdef Py_ssize_t src_extent = src_shape[0] * cdef Py_ssize_t dst_extent = dst_shape[0] * cdef Py_ssize_t src_stride = src_strides[0] # <<<<<<<<<<<<<< * cdef Py_ssize_t dst_stride = dst_strides[0] * */ __pyx_v_src_stride = (__pyx_v_src_strides[0]); /* "View.MemoryView":1104 * cdef Py_ssize_t dst_extent = dst_shape[0] * cdef Py_ssize_t src_stride = src_strides[0] * cdef Py_ssize_t dst_stride = dst_strides[0] # <<<<<<<<<<<<<< * * if ndim == 1: */ __pyx_v_dst_stride = (__pyx_v_dst_strides[0]); /* "View.MemoryView":1106 * cdef Py_ssize_t dst_stride = dst_strides[0] * * if ndim == 1: # <<<<<<<<<<<<<< * if (src_stride > 0 and dst_stride > 0 and * src_stride == itemsize == dst_stride): */ __pyx_t_1 = ((__pyx_v_ndim == 1) != 0); if (__pyx_t_1) { /* "View.MemoryView":1107 * * if ndim == 1: * if (src_stride > 0 and dst_stride > 0 and # <<<<<<<<<<<<<< * src_stride == itemsize == dst_stride): * memcpy(dst_data, src_data, itemsize * dst_extent) */ __pyx_t_2 = ((__pyx_v_src_stride > 0) != 0); if (__pyx_t_2) { } else { __pyx_t_1 = __pyx_t_2; goto __pyx_L5_bool_binop_done; } __pyx_t_2 = ((__pyx_v_dst_stride > 0) != 0); if (__pyx_t_2) { } else { __pyx_t_1 = __pyx_t_2; goto __pyx_L5_bool_binop_done; } /* "View.MemoryView":1108 * if ndim == 1: * if (src_stride > 0 and dst_stride > 0 and * src_stride == itemsize == dst_stride): # <<<<<<<<<<<<<< * memcpy(dst_data, src_data, itemsize * dst_extent) * else: */ __pyx_t_2 = (((size_t)__pyx_v_src_stride) == __pyx_v_itemsize); if (__pyx_t_2) { __pyx_t_2 = (__pyx_v_itemsize == ((size_t)__pyx_v_dst_stride)); } __pyx_t_3 = (__pyx_t_2 != 0); __pyx_t_1 = __pyx_t_3; __pyx_L5_bool_binop_done:; if (__pyx_t_1) { /* "View.MemoryView":1109 * if (src_stride > 0 and dst_stride > 0 and * src_stride == itemsize == dst_stride): * memcpy(dst_data, src_data, itemsize * dst_extent) # <<<<<<<<<<<<<< * else: * for i in range(dst_extent): */ memcpy(__pyx_v_dst_data, __pyx_v_src_data, (__pyx_v_itemsize * __pyx_v_dst_extent)); goto __pyx_L4; } /*else*/ { /* "View.MemoryView":1111 * memcpy(dst_data, src_data, itemsize * dst_extent) * else: * for i in range(dst_extent): # <<<<<<<<<<<<<< * memcpy(dst_data, src_data, itemsize) * src_data += src_stride */ __pyx_t_4 = __pyx_v_dst_extent; for (__pyx_t_5 = 0; __pyx_t_5 < __pyx_t_4; __pyx_t_5+=1) { __pyx_v_i = __pyx_t_5; /* "View.MemoryView":1112 * else: * for i in range(dst_extent): * memcpy(dst_data, src_data, itemsize) # <<<<<<<<<<<<<< * src_data += src_stride * dst_data += dst_stride */ memcpy(__pyx_v_dst_data, __pyx_v_src_data, __pyx_v_itemsize); /* "View.MemoryView":1113 * for i in range(dst_extent): * memcpy(dst_data, src_data, itemsize) * src_data += src_stride # <<<<<<<<<<<<<< * dst_data += dst_stride * else: */ __pyx_v_src_data = (__pyx_v_src_data + __pyx_v_src_stride); /* "View.MemoryView":1114 * memcpy(dst_data, src_data, itemsize) * src_data += src_stride * dst_data += dst_stride # <<<<<<<<<<<<<< * else: * for i in range(dst_extent): */ __pyx_v_dst_data = (__pyx_v_dst_data + __pyx_v_dst_stride); } } __pyx_L4:; goto __pyx_L3; } /*else*/ { /* "View.MemoryView":1116 * dst_data += dst_stride * else: * for i in range(dst_extent): # <<<<<<<<<<<<<< * _copy_strided_to_strided(src_data, src_strides + 1, * dst_data, dst_strides + 1, */ __pyx_t_4 = __pyx_v_dst_extent; for (__pyx_t_5 = 0; __pyx_t_5 < __pyx_t_4; __pyx_t_5+=1) { __pyx_v_i = __pyx_t_5; /* "View.MemoryView":1117 * else: * for i in range(dst_extent): * _copy_strided_to_strided(src_data, src_strides + 1, # <<<<<<<<<<<<<< * dst_data, dst_strides + 1, * src_shape + 1, dst_shape + 1, */ _copy_strided_to_strided(__pyx_v_src_data, (__pyx_v_src_strides + 1), __pyx_v_dst_data, (__pyx_v_dst_strides + 1), (__pyx_v_src_shape + 1), (__pyx_v_dst_shape + 1), (__pyx_v_ndim - 1), __pyx_v_itemsize); /* "View.MemoryView":1121 * src_shape + 1, dst_shape + 1, * ndim - 1, itemsize) * src_data += src_stride # <<<<<<<<<<<<<< * dst_data += dst_stride * */ __pyx_v_src_data = (__pyx_v_src_data + __pyx_v_src_stride); /* "View.MemoryView":1122 * ndim - 1, itemsize) * src_data += src_stride * dst_data += dst_stride # <<<<<<<<<<<<<< * * cdef void copy_strided_to_strided(__Pyx_memviewslice *src, */ __pyx_v_dst_data = (__pyx_v_dst_data + __pyx_v_dst_stride); } } __pyx_L3:; /* "View.MemoryView":1094 * * @cython.cdivision(True) * cdef void _copy_strided_to_strided(char *src_data, Py_ssize_t *src_strides, # <<<<<<<<<<<<<< * char *dst_data, Py_ssize_t *dst_strides, * Py_ssize_t *src_shape, Py_ssize_t *dst_shape, */ /* function exit code */ } /* "View.MemoryView":1124 * dst_data += dst_stride * * cdef void copy_strided_to_strided(__Pyx_memviewslice *src, # <<<<<<<<<<<<<< * __Pyx_memviewslice *dst, * int ndim, size_t itemsize) nogil: */ static void copy_strided_to_strided(__Pyx_memviewslice *__pyx_v_src, __Pyx_memviewslice *__pyx_v_dst, int __pyx_v_ndim, size_t __pyx_v_itemsize) { /* "View.MemoryView":1127 * __Pyx_memviewslice *dst, * int ndim, size_t itemsize) nogil: * _copy_strided_to_strided(src.data, src.strides, dst.data, dst.strides, # <<<<<<<<<<<<<< * src.shape, dst.shape, ndim, itemsize) * */ _copy_strided_to_strided(__pyx_v_src->data, __pyx_v_src->strides, __pyx_v_dst->data, __pyx_v_dst->strides, __pyx_v_src->shape, __pyx_v_dst->shape, __pyx_v_ndim, __pyx_v_itemsize); /* "View.MemoryView":1124 * dst_data += dst_stride * * cdef void copy_strided_to_strided(__Pyx_memviewslice *src, # <<<<<<<<<<<<<< * __Pyx_memviewslice *dst, * int ndim, size_t itemsize) nogil: */ /* function exit code */ } /* "View.MemoryView":1131 * * @cname('__pyx_memoryview_slice_get_size') * cdef Py_ssize_t slice_get_size(__Pyx_memviewslice *src, int ndim) nogil: # <<<<<<<<<<<<<< * "Return the size of the memory occupied by the slice in number of bytes" * cdef int i */ static Py_ssize_t __pyx_memoryview_slice_get_size(__Pyx_memviewslice *__pyx_v_src, int __pyx_v_ndim) { int __pyx_v_i; Py_ssize_t __pyx_v_size; Py_ssize_t __pyx_r; Py_ssize_t __pyx_t_1; int __pyx_t_2; int __pyx_t_3; /* "View.MemoryView":1134 * "Return the size of the memory occupied by the slice in number of bytes" * cdef int i * cdef Py_ssize_t size = src.memview.view.itemsize # <<<<<<<<<<<<<< * * for i in range(ndim): */ __pyx_t_1 = __pyx_v_src->memview->view.itemsize; __pyx_v_size = __pyx_t_1; /* "View.MemoryView":1136 * cdef Py_ssize_t size = src.memview.view.itemsize * * for i in range(ndim): # <<<<<<<<<<<<<< * size *= src.shape[i] * */ __pyx_t_2 = __pyx_v_ndim; for (__pyx_t_3 = 0; __pyx_t_3 < __pyx_t_2; __pyx_t_3+=1) { __pyx_v_i = __pyx_t_3; /* "View.MemoryView":1137 * * for i in range(ndim): * size *= src.shape[i] # <<<<<<<<<<<<<< * * return size */ __pyx_v_size = (__pyx_v_size * (__pyx_v_src->shape[__pyx_v_i])); } /* "View.MemoryView":1139 * size *= src.shape[i] * * return size # <<<<<<<<<<<<<< * * @cname('__pyx_fill_contig_strides_array') */ __pyx_r = __pyx_v_size; goto __pyx_L0; /* "View.MemoryView":1131 * * @cname('__pyx_memoryview_slice_get_size') * cdef Py_ssize_t slice_get_size(__Pyx_memviewslice *src, int ndim) nogil: # <<<<<<<<<<<<<< * "Return the size of the memory occupied by the slice in number of bytes" * cdef int i */ /* function exit code */ __pyx_L0:; return __pyx_r; } /* "View.MemoryView":1142 * * @cname('__pyx_fill_contig_strides_array') * cdef Py_ssize_t fill_contig_strides_array( # <<<<<<<<<<<<<< * Py_ssize_t *shape, Py_ssize_t *strides, Py_ssize_t stride, * int ndim, char order) nogil: */ static Py_ssize_t __pyx_fill_contig_strides_array(Py_ssize_t *__pyx_v_shape, Py_ssize_t *__pyx_v_strides, Py_ssize_t __pyx_v_stride, int __pyx_v_ndim, char __pyx_v_order) { int __pyx_v_idx; Py_ssize_t __pyx_r; int __pyx_t_1; int __pyx_t_2; int __pyx_t_3; /* "View.MemoryView":1151 * cdef int idx * * if order == 'F': # <<<<<<<<<<<<<< * for idx in range(ndim): * strides[idx] = stride */ __pyx_t_1 = ((__pyx_v_order == 'F') != 0); if (__pyx_t_1) { /* "View.MemoryView":1152 * * if order == 'F': * for idx in range(ndim): # <<<<<<<<<<<<<< * strides[idx] = stride * stride = stride * shape[idx] */ __pyx_t_2 = __pyx_v_ndim; for (__pyx_t_3 = 0; __pyx_t_3 < __pyx_t_2; __pyx_t_3+=1) { __pyx_v_idx = __pyx_t_3; /* "View.MemoryView":1153 * if order == 'F': * for idx in range(ndim): * strides[idx] = stride # <<<<<<<<<<<<<< * stride = stride * shape[idx] * else: */ (__pyx_v_strides[__pyx_v_idx]) = __pyx_v_stride; /* "View.MemoryView":1154 * for idx in range(ndim): * strides[idx] = stride * stride = stride * shape[idx] # <<<<<<<<<<<<<< * else: * for idx in range(ndim - 1, -1, -1): */ __pyx_v_stride = (__pyx_v_stride * (__pyx_v_shape[__pyx_v_idx])); } goto __pyx_L3; } /*else*/ { /* "View.MemoryView":1156 * stride = stride * shape[idx] * else: * for idx in range(ndim - 1, -1, -1): # <<<<<<<<<<<<<< * strides[idx] = stride * stride = stride * shape[idx] */ for (__pyx_t_2 = (__pyx_v_ndim - 1); __pyx_t_2 > -1; __pyx_t_2-=1) { __pyx_v_idx = __pyx_t_2; /* "View.MemoryView":1157 * else: * for idx in range(ndim - 1, -1, -1): * strides[idx] = stride # <<<<<<<<<<<<<< * stride = stride * shape[idx] * */ (__pyx_v_strides[__pyx_v_idx]) = __pyx_v_stride; /* "View.MemoryView":1158 * for idx in range(ndim - 1, -1, -1): * strides[idx] = stride * stride = stride * shape[idx] # <<<<<<<<<<<<<< * * return stride */ __pyx_v_stride = (__pyx_v_stride * (__pyx_v_shape[__pyx_v_idx])); } } __pyx_L3:; /* "View.MemoryView":1160 * stride = stride * shape[idx] * * return stride # <<<<<<<<<<<<<< * * @cname('__pyx_memoryview_copy_data_to_temp') */ __pyx_r = __pyx_v_stride; goto __pyx_L0; /* "View.MemoryView":1142 * * @cname('__pyx_fill_contig_strides_array') * cdef Py_ssize_t fill_contig_strides_array( # <<<<<<<<<<<<<< * Py_ssize_t *shape, Py_ssize_t *strides, Py_ssize_t stride, * int ndim, char order) nogil: */ /* function exit code */ __pyx_L0:; return __pyx_r; } /* "View.MemoryView":1163 * * @cname('__pyx_memoryview_copy_data_to_temp') * cdef void *copy_data_to_temp(__Pyx_memviewslice *src, # <<<<<<<<<<<<<< * __Pyx_memviewslice *tmpslice, * char order, */ static void *__pyx_memoryview_copy_data_to_temp(__Pyx_memviewslice *__pyx_v_src, __Pyx_memviewslice *__pyx_v_tmpslice, char __pyx_v_order, int __pyx_v_ndim) { int __pyx_v_i; void *__pyx_v_result; size_t __pyx_v_itemsize; size_t __pyx_v_size; void *__pyx_r; Py_ssize_t __pyx_t_1; int __pyx_t_2; int __pyx_t_3; struct __pyx_memoryview_obj *__pyx_t_4; int __pyx_t_5; int __pyx_lineno = 0; const char *__pyx_filename = NULL; int __pyx_clineno = 0; /* "View.MemoryView":1174 * cdef void *result * * cdef size_t itemsize = src.memview.view.itemsize # <<<<<<<<<<<<<< * cdef size_t size = slice_get_size(src, ndim) * */ __pyx_t_1 = __pyx_v_src->memview->view.itemsize; __pyx_v_itemsize = __pyx_t_1; /* "View.MemoryView":1175 * * cdef size_t itemsize = src.memview.view.itemsize * cdef size_t size = slice_get_size(src, ndim) # <<<<<<<<<<<<<< * * result = malloc(size) */ __pyx_v_size = __pyx_memoryview_slice_get_size(__pyx_v_src, __pyx_v_ndim); /* "View.MemoryView":1177 * cdef size_t size = slice_get_size(src, ndim) * * result = malloc(size) # <<<<<<<<<<<<<< * if not result: * _err(MemoryError, NULL) */ __pyx_v_result = malloc(__pyx_v_size); /* "View.MemoryView":1178 * * result = malloc(size) * if not result: # <<<<<<<<<<<<<< * _err(MemoryError, NULL) * */ __pyx_t_2 = ((!(__pyx_v_result != 0)) != 0); if (__pyx_t_2) { /* "View.MemoryView":1179 * result = malloc(size) * if not result: * _err(MemoryError, NULL) # <<<<<<<<<<<<<< * * */ __pyx_t_3 = __pyx_memoryview_err(__pyx_builtin_MemoryError, NULL); if (unlikely(__pyx_t_3 == -1)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 1179; __pyx_clineno = __LINE__; goto __pyx_L1_error;} goto __pyx_L3; } __pyx_L3:; /* "View.MemoryView":1182 * * * tmpslice.data = result # <<<<<<<<<<<<<< * tmpslice.memview = src.memview * for i in range(ndim): */ __pyx_v_tmpslice->data = ((char *)__pyx_v_result); /* "View.MemoryView":1183 * * tmpslice.data = result * tmpslice.memview = src.memview # <<<<<<<<<<<<<< * for i in range(ndim): * tmpslice.shape[i] = src.shape[i] */ __pyx_t_4 = __pyx_v_src->memview; __pyx_v_tmpslice->memview = __pyx_t_4; /* "View.MemoryView":1184 * tmpslice.data = result * tmpslice.memview = src.memview * for i in range(ndim): # <<<<<<<<<<<<<< * tmpslice.shape[i] = src.shape[i] * tmpslice.suboffsets[i] = -1 */ __pyx_t_3 = __pyx_v_ndim; for (__pyx_t_5 = 0; __pyx_t_5 < __pyx_t_3; __pyx_t_5+=1) { __pyx_v_i = __pyx_t_5; /* "View.MemoryView":1185 * tmpslice.memview = src.memview * for i in range(ndim): * tmpslice.shape[i] = src.shape[i] # <<<<<<<<<<<<<< * tmpslice.suboffsets[i] = -1 * */ (__pyx_v_tmpslice->shape[__pyx_v_i]) = (__pyx_v_src->shape[__pyx_v_i]); /* "View.MemoryView":1186 * for i in range(ndim): * tmpslice.shape[i] = src.shape[i] * tmpslice.suboffsets[i] = -1 # <<<<<<<<<<<<<< * * fill_contig_strides_array(&tmpslice.shape[0], &tmpslice.strides[0], itemsize, */ (__pyx_v_tmpslice->suboffsets[__pyx_v_i]) = -1; } /* "View.MemoryView":1188 * tmpslice.suboffsets[i] = -1 * * fill_contig_strides_array(&tmpslice.shape[0], &tmpslice.strides[0], itemsize, # <<<<<<<<<<<<<< * ndim, order) * */ __pyx_fill_contig_strides_array((&(__pyx_v_tmpslice->shape[0])), (&(__pyx_v_tmpslice->strides[0])), __pyx_v_itemsize, __pyx_v_ndim, __pyx_v_order); /* "View.MemoryView":1192 * * * for i in range(ndim): # <<<<<<<<<<<<<< * if tmpslice.shape[i] == 1: * tmpslice.strides[i] = 0 */ __pyx_t_3 = __pyx_v_ndim; for (__pyx_t_5 = 0; __pyx_t_5 < __pyx_t_3; __pyx_t_5+=1) { __pyx_v_i = __pyx_t_5; /* "View.MemoryView":1193 * * for i in range(ndim): * if tmpslice.shape[i] == 1: # <<<<<<<<<<<<<< * tmpslice.strides[i] = 0 * */ __pyx_t_2 = (((__pyx_v_tmpslice->shape[__pyx_v_i]) == 1) != 0); if (__pyx_t_2) { /* "View.MemoryView":1194 * for i in range(ndim): * if tmpslice.shape[i] == 1: * tmpslice.strides[i] = 0 # <<<<<<<<<<<<<< * * if slice_is_contig(src, order, ndim): */ (__pyx_v_tmpslice->strides[__pyx_v_i]) = 0; goto __pyx_L8; } __pyx_L8:; } /* "View.MemoryView":1196 * tmpslice.strides[i] = 0 * * if slice_is_contig(src, order, ndim): # <<<<<<<<<<<<<< * memcpy(result, src.data, size) * else: */ __pyx_t_2 = (__pyx_memviewslice_is_contig(__pyx_v_src, __pyx_v_order, __pyx_v_ndim) != 0); if (__pyx_t_2) { /* "View.MemoryView":1197 * * if slice_is_contig(src, order, ndim): * memcpy(result, src.data, size) # <<<<<<<<<<<<<< * else: * copy_strided_to_strided(src, tmpslice, ndim, itemsize) */ memcpy(__pyx_v_result, __pyx_v_src->data, __pyx_v_size); 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int __pyx_t_3; int __pyx_t_4; int __pyx_t_5; void *__pyx_t_6; int __pyx_t_7; int __pyx_lineno = 0; const char *__pyx_filename = NULL; int __pyx_clineno = 0; /* "View.MemoryView":1231 * Check for overlapping memory and verify the shapes. * """ * cdef void *tmpdata = NULL # <<<<<<<<<<<<<< * cdef size_t itemsize = src.memview.view.itemsize * cdef int i */ __pyx_v_tmpdata = NULL; /* "View.MemoryView":1232 * """ * cdef void *tmpdata = NULL * cdef size_t itemsize = src.memview.view.itemsize # <<<<<<<<<<<<<< * cdef int i * cdef char order = get_best_order(&src, src_ndim) */ __pyx_t_1 = __pyx_v_src.memview->view.itemsize; __pyx_v_itemsize = __pyx_t_1; /* "View.MemoryView":1234 * cdef size_t itemsize = src.memview.view.itemsize * cdef int i * cdef char order = get_best_order(&src, src_ndim) # <<<<<<<<<<<<<< * cdef bint broadcasting = False * cdef bint direct_copy = False */ __pyx_v_order = __pyx_get_best_slice_order((&__pyx_v_src), __pyx_v_src_ndim); /* "View.MemoryView":1235 * cdef int i * cdef char order = get_best_order(&src, src_ndim) * cdef bint broadcasting = False # <<<<<<<<<<<<<< * cdef bint direct_copy = False * cdef __Pyx_memviewslice tmp */ __pyx_v_broadcasting = 0; /* "View.MemoryView":1236 * cdef char order = get_best_order(&src, src_ndim) * cdef bint broadcasting = False * cdef bint direct_copy = False # <<<<<<<<<<<<<< * cdef __Pyx_memviewslice tmp * */ __pyx_v_direct_copy = 0; /* "View.MemoryView":1239 * cdef __Pyx_memviewslice tmp * * if src_ndim < dst_ndim: # <<<<<<<<<<<<<< * broadcast_leading(&src, src_ndim, dst_ndim) * elif dst_ndim < src_ndim: */ __pyx_t_2 = ((__pyx_v_src_ndim < __pyx_v_dst_ndim) != 0); if (__pyx_t_2) { /* "View.MemoryView":1240 * * if src_ndim < dst_ndim: * broadcast_leading(&src, src_ndim, dst_ndim) # <<<<<<<<<<<<<< * elif dst_ndim < src_ndim: * broadcast_leading(&dst, dst_ndim, src_ndim) */ __pyx_memoryview_broadcast_leading((&__pyx_v_src), __pyx_v_src_ndim, __pyx_v_dst_ndim); goto __pyx_L3; } /* "View.MemoryView":1241 * if src_ndim < dst_ndim: * broadcast_leading(&src, src_ndim, dst_ndim) * elif dst_ndim < src_ndim: # <<<<<<<<<<<<<< * broadcast_leading(&dst, dst_ndim, src_ndim) * */ __pyx_t_2 = ((__pyx_v_dst_ndim < __pyx_v_src_ndim) != 0); if (__pyx_t_2) { /* "View.MemoryView":1242 * broadcast_leading(&src, src_ndim, dst_ndim) * elif dst_ndim < src_ndim: * broadcast_leading(&dst, dst_ndim, src_ndim) # <<<<<<<<<<<<<< * * cdef int ndim = max(src_ndim, dst_ndim) */ __pyx_memoryview_broadcast_leading((&__pyx_v_dst), __pyx_v_dst_ndim, __pyx_v_src_ndim); goto __pyx_L3; } __pyx_L3:; /* "View.MemoryView":1244 * broadcast_leading(&dst, dst_ndim, src_ndim) * * cdef int ndim = max(src_ndim, dst_ndim) # <<<<<<<<<<<<<< * * for i in range(ndim): */ __pyx_t_3 = __pyx_v_dst_ndim; __pyx_t_4 = __pyx_v_src_ndim; if (((__pyx_t_3 > __pyx_t_4) != 0)) { __pyx_t_5 = __pyx_t_3; } else { __pyx_t_5 = __pyx_t_4; } __pyx_v_ndim = __pyx_t_5; /* "View.MemoryView":1246 * cdef int ndim = max(src_ndim, dst_ndim) * * for i in range(ndim): # <<<<<<<<<<<<<< * if src.shape[i] != dst.shape[i]: * if src.shape[i] == 1: */ __pyx_t_5 = __pyx_v_ndim; for (__pyx_t_3 = 0; __pyx_t_3 < __pyx_t_5; __pyx_t_3+=1) { __pyx_v_i = __pyx_t_3; /* "View.MemoryView":1247 * * for i in range(ndim): * if src.shape[i] != dst.shape[i]: # <<<<<<<<<<<<<< * if src.shape[i] == 1: * broadcasting = True */ __pyx_t_2 = (((__pyx_v_src.shape[__pyx_v_i]) != (__pyx_v_dst.shape[__pyx_v_i])) != 0); if (__pyx_t_2) { /* "View.MemoryView":1248 * for i in range(ndim): * if src.shape[i] != dst.shape[i]: * if src.shape[i] == 1: # <<<<<<<<<<<<<< * broadcasting = True * src.strides[i] = 0 */ __pyx_t_2 = (((__pyx_v_src.shape[__pyx_v_i]) == 1) != 0); if (__pyx_t_2) { /* "View.MemoryView":1249 * if src.shape[i] != dst.shape[i]: * if src.shape[i] == 1: * broadcasting = True # <<<<<<<<<<<<<< * src.strides[i] = 0 * else: */ __pyx_v_broadcasting = 1; /* "View.MemoryView":1250 * if src.shape[i] == 1: * broadcasting = True * src.strides[i] = 0 # <<<<<<<<<<<<<< * else: * _err_extents(i, dst.shape[i], src.shape[i]) */ (__pyx_v_src.strides[__pyx_v_i]) = 0; goto __pyx_L7; } /*else*/ { /* "View.MemoryView":1252 * src.strides[i] = 0 * else: * _err_extents(i, dst.shape[i], src.shape[i]) # <<<<<<<<<<<<<< * * if src.suboffsets[i] >= 0: */ __pyx_t_4 = __pyx_memoryview_err_extents(__pyx_v_i, (__pyx_v_dst.shape[__pyx_v_i]), (__pyx_v_src.shape[__pyx_v_i])); if (unlikely(__pyx_t_4 == -1)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 1252; __pyx_clineno = __LINE__; goto __pyx_L1_error;} } __pyx_L7:; goto __pyx_L6; } __pyx_L6:; /* "View.MemoryView":1254 * _err_extents(i, dst.shape[i], src.shape[i]) * * if src.suboffsets[i] >= 0: # <<<<<<<<<<<<<< * _err_dim(ValueError, "Dimension %d is not direct", i) * */ __pyx_t_2 = (((__pyx_v_src.suboffsets[__pyx_v_i]) >= 0) != 0); if (__pyx_t_2) { /* "View.MemoryView":1255 * * if src.suboffsets[i] >= 0: * _err_dim(ValueError, "Dimension %d is not direct", i) # <<<<<<<<<<<<<< * * if slices_overlap(&src, &dst, ndim, itemsize): */ __pyx_t_4 = __pyx_memoryview_err_dim(__pyx_builtin_ValueError, __pyx_k_Dimension_d_is_not_direct, __pyx_v_i); if (unlikely(__pyx_t_4 == -1)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 1255; __pyx_clineno = __LINE__; goto __pyx_L1_error;} goto __pyx_L8; } __pyx_L8:; } /* "View.MemoryView":1257 * _err_dim(ValueError, "Dimension %d is not direct", i) * * if slices_overlap(&src, &dst, ndim, itemsize): # <<<<<<<<<<<<<< * * if not slice_is_contig(&src, order, ndim): */ __pyx_t_2 = (__pyx_slices_overlap((&__pyx_v_src), (&__pyx_v_dst), __pyx_v_ndim, __pyx_v_itemsize) != 0); if (__pyx_t_2) { /* "View.MemoryView":1259 * if slices_overlap(&src, &dst, ndim, itemsize): * * if not slice_is_contig(&src, order, ndim): # <<<<<<<<<<<<<< * order = get_best_order(&dst, ndim) * */ __pyx_t_2 = ((!(__pyx_memviewslice_is_contig((&__pyx_v_src), __pyx_v_order, __pyx_v_ndim) != 0)) != 0); if (__pyx_t_2) { /* "View.MemoryView":1260 * * if not slice_is_contig(&src, order, ndim): * order = get_best_order(&dst, ndim) # <<<<<<<<<<<<<< * * tmpdata = copy_data_to_temp(&src, &tmp, order, ndim) */ __pyx_v_order = __pyx_get_best_slice_order((&__pyx_v_dst), __pyx_v_ndim); goto __pyx_L10; } __pyx_L10:; /* "View.MemoryView":1262 * order = get_best_order(&dst, ndim) * * tmpdata = copy_data_to_temp(&src, &tmp, order, ndim) # <<<<<<<<<<<<<< * src = tmp * */ __pyx_t_6 = __pyx_memoryview_copy_data_to_temp((&__pyx_v_src), (&__pyx_v_tmp), __pyx_v_order, __pyx_v_ndim); if (unlikely(__pyx_t_6 == NULL)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 1262; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __pyx_v_tmpdata = __pyx_t_6; /* "View.MemoryView":1263 * * tmpdata = copy_data_to_temp(&src, &tmp, order, ndim) * src = tmp # <<<<<<<<<<<<<< * * if not broadcasting: */ __pyx_v_src = __pyx_v_tmp; goto __pyx_L9; } __pyx_L9:; /* "View.MemoryView":1265 * src = tmp * * if not broadcasting: # <<<<<<<<<<<<<< * * */ __pyx_t_2 = ((!(__pyx_v_broadcasting != 0)) != 0); if (__pyx_t_2) { /* "View.MemoryView":1268 * * * if slice_is_contig(&src, 'C', ndim): # <<<<<<<<<<<<<< * direct_copy = slice_is_contig(&dst, 'C', ndim) * elif slice_is_contig(&src, 'F', ndim): */ __pyx_t_2 = (__pyx_memviewslice_is_contig((&__pyx_v_src), 'C', __pyx_v_ndim) != 0); if (__pyx_t_2) { /* "View.MemoryView":1269 * * if slice_is_contig(&src, 'C', ndim): * direct_copy = slice_is_contig(&dst, 'C', ndim) # <<<<<<<<<<<<<< * elif slice_is_contig(&src, 'F', ndim): * direct_copy = slice_is_contig(&dst, 'F', ndim) */ __pyx_v_direct_copy = __pyx_memviewslice_is_contig((&__pyx_v_dst), 'C', __pyx_v_ndim); goto __pyx_L12; } /* "View.MemoryView":1270 * if slice_is_contig(&src, 'C', ndim): * direct_copy = slice_is_contig(&dst, 'C', ndim) * elif slice_is_contig(&src, 'F', ndim): # <<<<<<<<<<<<<< * direct_copy = slice_is_contig(&dst, 'F', ndim) * */ __pyx_t_2 = (__pyx_memviewslice_is_contig((&__pyx_v_src), 'F', __pyx_v_ndim) != 0); if (__pyx_t_2) { /* "View.MemoryView":1271 * direct_copy = slice_is_contig(&dst, 'C', ndim) * elif slice_is_contig(&src, 'F', ndim): * direct_copy = slice_is_contig(&dst, 'F', ndim) # <<<<<<<<<<<<<< * * if direct_copy: */ __pyx_v_direct_copy = __pyx_memviewslice_is_contig((&__pyx_v_dst), 'F', __pyx_v_ndim); goto __pyx_L12; } __pyx_L12:; /* "View.MemoryView":1273 * direct_copy = slice_is_contig(&dst, 'F', ndim) * * if direct_copy: # <<<<<<<<<<<<<< * * refcount_copying(&dst, dtype_is_object, ndim, False) */ __pyx_t_2 = (__pyx_v_direct_copy != 0); if (__pyx_t_2) { /* "View.MemoryView":1275 * if direct_copy: * * refcount_copying(&dst, dtype_is_object, ndim, False) # <<<<<<<<<<<<<< * memcpy(dst.data, src.data, slice_get_size(&src, ndim)) * refcount_copying(&dst, dtype_is_object, ndim, True) */ __pyx_memoryview_refcount_copying((&__pyx_v_dst), __pyx_v_dtype_is_object, __pyx_v_ndim, 0); /* "View.MemoryView":1276 * * refcount_copying(&dst, dtype_is_object, ndim, False) * memcpy(dst.data, src.data, slice_get_size(&src, ndim)) # <<<<<<<<<<<<<< * refcount_copying(&dst, dtype_is_object, ndim, True) * free(tmpdata) */ memcpy(__pyx_v_dst.data, __pyx_v_src.data, __pyx_memoryview_slice_get_size((&__pyx_v_src), __pyx_v_ndim)); /* "View.MemoryView":1277 * refcount_copying(&dst, dtype_is_object, ndim, False) * memcpy(dst.data, src.data, slice_get_size(&src, ndim)) * refcount_copying(&dst, dtype_is_object, ndim, True) # <<<<<<<<<<<<<< * free(tmpdata) * return 0 */ __pyx_memoryview_refcount_copying((&__pyx_v_dst), __pyx_v_dtype_is_object, __pyx_v_ndim, 1); /* "View.MemoryView":1278 * memcpy(dst.data, src.data, slice_get_size(&src, ndim)) * refcount_copying(&dst, dtype_is_object, ndim, True) * free(tmpdata) # <<<<<<<<<<<<<< * return 0 * */ free(__pyx_v_tmpdata); /* "View.MemoryView":1279 * refcount_copying(&dst, dtype_is_object, ndim, True) * free(tmpdata) * return 0 # <<<<<<<<<<<<<< * * if order == 'F' == get_best_order(&dst, ndim): */ __pyx_r = 0; goto __pyx_L0; } goto __pyx_L11; } __pyx_L11:; /* "View.MemoryView":1281 * return 0 * * if order == 'F' == get_best_order(&dst, ndim): # <<<<<<<<<<<<<< * * */ __pyx_t_2 = (__pyx_v_order == 'F'); if (__pyx_t_2) { __pyx_t_2 = ('F' == __pyx_get_best_slice_order((&__pyx_v_dst), __pyx_v_ndim)); } __pyx_t_7 = (__pyx_t_2 != 0); if (__pyx_t_7) { /* "View.MemoryView":1284 * * * transpose_memslice(&src) # <<<<<<<<<<<<<< * transpose_memslice(&dst) * */ __pyx_t_5 = __pyx_memslice_transpose((&__pyx_v_src)); if (unlikely(__pyx_t_5 == 0)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 1284; __pyx_clineno = __LINE__; goto __pyx_L1_error;} /* "View.MemoryView":1285 * * transpose_memslice(&src) * transpose_memslice(&dst) # <<<<<<<<<<<<<< * * refcount_copying(&dst, dtype_is_object, ndim, False) */ __pyx_t_5 = __pyx_memslice_transpose((&__pyx_v_dst)); if (unlikely(__pyx_t_5 == 0)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 1285; __pyx_clineno = __LINE__; goto __pyx_L1_error;} goto __pyx_L14; } __pyx_L14:; /* "View.MemoryView":1287 * transpose_memslice(&dst) * * refcount_copying(&dst, dtype_is_object, ndim, False) # <<<<<<<<<<<<<< * copy_strided_to_strided(&src, &dst, ndim, itemsize) * refcount_copying(&dst, dtype_is_object, ndim, True) */ __pyx_memoryview_refcount_copying((&__pyx_v_dst), __pyx_v_dtype_is_object, __pyx_v_ndim, 0); /* "View.MemoryView":1288 * * refcount_copying(&dst, dtype_is_object, ndim, False) * copy_strided_to_strided(&src, &dst, ndim, itemsize) # <<<<<<<<<<<<<< * refcount_copying(&dst, dtype_is_object, ndim, True) * */ copy_strided_to_strided((&__pyx_v_src), (&__pyx_v_dst), __pyx_v_ndim, __pyx_v_itemsize); /* "View.MemoryView":1289 * refcount_copying(&dst, dtype_is_object, ndim, False) * copy_strided_to_strided(&src, &dst, ndim, itemsize) * refcount_copying(&dst, dtype_is_object, ndim, True) # <<<<<<<<<<<<<< * * free(tmpdata) */ __pyx_memoryview_refcount_copying((&__pyx_v_dst), __pyx_v_dtype_is_object, __pyx_v_ndim, 1); /* "View.MemoryView":1291 * refcount_copying(&dst, dtype_is_object, ndim, True) * * free(tmpdata) # <<<<<<<<<<<<<< * return 0 * */ free(__pyx_v_tmpdata); /* "View.MemoryView":1292 * * free(tmpdata) * return 0 # <<<<<<<<<<<<<< * * @cname('__pyx_memoryview_broadcast_leading') */ __pyx_r = 0; goto __pyx_L0; /* "View.MemoryView":1223 * * @cname('__pyx_memoryview_copy_contents') * cdef int memoryview_copy_contents(__Pyx_memviewslice src, # <<<<<<<<<<<<<< * __Pyx_memviewslice dst, * int src_ndim, int dst_ndim, */ /* function exit code */ __pyx_L1_error:; { #ifdef WITH_THREAD PyGILState_STATE __pyx_gilstate_save = PyGILState_Ensure(); #endif __Pyx_AddTraceback("View.MemoryView.memoryview_copy_contents", __pyx_clineno, __pyx_lineno, __pyx_filename); #ifdef WITH_THREAD PyGILState_Release(__pyx_gilstate_save); #endif } __pyx_r = -1; __pyx_L0:; return __pyx_r; } /* "View.MemoryView":1295 * * @cname('__pyx_memoryview_broadcast_leading') * cdef void broadcast_leading(__Pyx_memviewslice *mslice, # <<<<<<<<<<<<<< * int ndim, * int ndim_other) nogil: */ static void __pyx_memoryview_broadcast_leading(__Pyx_memviewslice *__pyx_v_mslice, int __pyx_v_ndim, int __pyx_v_ndim_other) { int __pyx_v_i; int __pyx_v_offset; int __pyx_t_1; int __pyx_t_2; /* "View.MemoryView":1299 * int ndim_other) nogil: * cdef int i * cdef int offset = ndim_other - ndim # <<<<<<<<<<<<<< * * for i in range(ndim - 1, -1, -1): */ __pyx_v_offset = (__pyx_v_ndim_other - __pyx_v_ndim); /* "View.MemoryView":1301 * cdef int offset = ndim_other - ndim * * for i in range(ndim - 1, -1, -1): # <<<<<<<<<<<<<< * mslice.shape[i + offset] = mslice.shape[i] * mslice.strides[i + offset] = mslice.strides[i] */ for (__pyx_t_1 = (__pyx_v_ndim - 1); __pyx_t_1 > -1; __pyx_t_1-=1) { __pyx_v_i = __pyx_t_1; /* "View.MemoryView":1302 * * for i in range(ndim - 1, -1, -1): * mslice.shape[i + offset] = mslice.shape[i] # <<<<<<<<<<<<<< * mslice.strides[i + offset] = mslice.strides[i] * mslice.suboffsets[i + offset] = mslice.suboffsets[i] */ (__pyx_v_mslice->shape[(__pyx_v_i + __pyx_v_offset)]) = (__pyx_v_mslice->shape[__pyx_v_i]); /* "View.MemoryView":1303 * for i in range(ndim - 1, -1, -1): * mslice.shape[i + offset] = mslice.shape[i] * mslice.strides[i + offset] = mslice.strides[i] # <<<<<<<<<<<<<< * mslice.suboffsets[i + offset] = mslice.suboffsets[i] * */ (__pyx_v_mslice->strides[(__pyx_v_i + __pyx_v_offset)]) = (__pyx_v_mslice->strides[__pyx_v_i]); /* "View.MemoryView":1304 * mslice.shape[i + offset] = mslice.shape[i] * mslice.strides[i + offset] = mslice.strides[i] * mslice.suboffsets[i + offset] = mslice.suboffsets[i] # <<<<<<<<<<<<<< * * for i in range(offset): */ (__pyx_v_mslice->suboffsets[(__pyx_v_i + __pyx_v_offset)]) = (__pyx_v_mslice->suboffsets[__pyx_v_i]); } /* "View.MemoryView":1306 * mslice.suboffsets[i + offset] = mslice.suboffsets[i] * * for i in range(offset): # <<<<<<<<<<<<<< * mslice.shape[i] = 1 * mslice.strides[i] = mslice.strides[0] */ __pyx_t_1 = __pyx_v_offset; for (__pyx_t_2 = 0; __pyx_t_2 < __pyx_t_1; __pyx_t_2+=1) { __pyx_v_i = __pyx_t_2; /* "View.MemoryView":1307 * * for i in range(offset): * mslice.shape[i] = 1 # <<<<<<<<<<<<<< * mslice.strides[i] = mslice.strides[0] * mslice.suboffsets[i] = -1 */ (__pyx_v_mslice->shape[__pyx_v_i]) = 1; /* "View.MemoryView":1308 * for i in range(offset): * mslice.shape[i] = 1 * mslice.strides[i] = mslice.strides[0] # <<<<<<<<<<<<<< * mslice.suboffsets[i] = -1 * */ (__pyx_v_mslice->strides[__pyx_v_i]) = (__pyx_v_mslice->strides[0]); /* "View.MemoryView":1309 * mslice.shape[i] = 1 * mslice.strides[i] = mslice.strides[0] * mslice.suboffsets[i] = -1 # <<<<<<<<<<<<<< * * */ (__pyx_v_mslice->suboffsets[__pyx_v_i]) = -1; } /* "View.MemoryView":1295 * * @cname('__pyx_memoryview_broadcast_leading') * cdef void broadcast_leading(__Pyx_memviewslice *mslice, # <<<<<<<<<<<<<< * int ndim, * int ndim_other) nogil: */ /* function exit code */ } /* "View.MemoryView":1317 * * @cname('__pyx_memoryview_refcount_copying') * cdef void refcount_copying(__Pyx_memviewslice *dst, bint dtype_is_object, # <<<<<<<<<<<<<< * int ndim, bint inc) nogil: * */ static void __pyx_memoryview_refcount_copying(__Pyx_memviewslice *__pyx_v_dst, int __pyx_v_dtype_is_object, int __pyx_v_ndim, int __pyx_v_inc) { int __pyx_t_1; 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} } __pyx_L3:; /* "View.MemoryView":1362 * * @cname('__pyx_memoryview__slice_assign_scalar') * cdef void _slice_assign_scalar(char *data, Py_ssize_t *shape, # <<<<<<<<<<<<<< * Py_ssize_t *strides, int ndim, * size_t itemsize, void *item) nogil: */ /* function exit code */ } static PyObject *__pyx_tp_new_5_pywt_Wavelet(PyTypeObject *t, PyObject *a, PyObject *k) { struct WaveletObject *p; PyObject *o; if (likely((t->tp_flags & Py_TPFLAGS_IS_ABSTRACT) == 0)) { o = (*t->tp_alloc)(t, 0); } else { o = (PyObject *) PyBaseObject_Type.tp_new(t, __pyx_empty_tuple, 0); } if (unlikely(!o)) return 0; p = ((struct WaveletObject *)o); p->name = Py_None; Py_INCREF(Py_None); p->number = Py_None; Py_INCREF(Py_None); if (unlikely(__pyx_pw_5_pywt_7Wavelet_1__cinit__(o, a, k) < 0)) { Py_DECREF(o); o = 0; } return o; } static void __pyx_tp_dealloc_5_pywt_Wavelet(PyObject *o) { struct WaveletObject *p = (struct WaveletObject *)o; #if PY_VERSION_HEX >= 0x030400a1 if (unlikely(Py_TYPE(o)->tp_finalize) && !_PyGC_FINALIZED(o)) { if (PyObject_CallFinalizerFromDealloc(o)) return; } #endif PyObject_GC_UnTrack(o); { PyObject *etype, *eval, *etb; PyErr_Fetch(&etype, &eval, &etb); ++Py_REFCNT(o); __pyx_pw_5_pywt_7Wavelet_3__dealloc__(o); --Py_REFCNT(o); PyErr_Restore(etype, eval, etb); } Py_CLEAR(p->name); Py_CLEAR(p->number); (*Py_TYPE(o)->tp_free)(o); } static int __pyx_tp_traverse_5_pywt_Wavelet(PyObject *o, visitproc v, void *a) { int e; struct WaveletObject *p = (struct WaveletObject *)o; if (p->name) { e = (*v)(p->name, a); if (e) return e; } if (p->number) { e = (*v)(p->number, a); if (e) return e; } return 0; } static int __pyx_tp_clear_5_pywt_Wavelet(PyObject *o) { PyObject* tmp; struct WaveletObject *p = (struct WaveletObject *)o; tmp = ((PyObject*)p->name); p->name = Py_None; Py_INCREF(Py_None); Py_XDECREF(tmp); tmp = ((PyObject*)p->number); p->number = Py_None; Py_INCREF(Py_None); Py_XDECREF(tmp); return 0; } static PyObject *__pyx_getprop_5_pywt_7Wavelet_dec_lo(PyObject *o, CYTHON_UNUSED void *x) { return __pyx_pw_5_pywt_7Wavelet_6dec_lo_1__get__(o); } static PyObject *__pyx_getprop_5_pywt_7Wavelet_dec_hi(PyObject *o, CYTHON_UNUSED void *x) { return __pyx_pw_5_pywt_7Wavelet_6dec_hi_1__get__(o); } static PyObject *__pyx_getprop_5_pywt_7Wavelet_rec_lo(PyObject *o, CYTHON_UNUSED void *x) { return __pyx_pw_5_pywt_7Wavelet_6rec_lo_1__get__(o); } static PyObject *__pyx_getprop_5_pywt_7Wavelet_rec_hi(PyObject *o, CYTHON_UNUSED void *x) { return __pyx_pw_5_pywt_7Wavelet_6rec_hi_1__get__(o); } static PyObject *__pyx_getprop_5_pywt_7Wavelet_rec_len(PyObject *o, CYTHON_UNUSED void *x) { return __pyx_pw_5_pywt_7Wavelet_7rec_len_1__get__(o); } static PyObject *__pyx_getprop_5_pywt_7Wavelet_dec_len(PyObject *o, CYTHON_UNUSED void *x) { return __pyx_pw_5_pywt_7Wavelet_7dec_len_1__get__(o); } static PyObject *__pyx_getprop_5_pywt_7Wavelet_family_name(PyObject *o, CYTHON_UNUSED void *x) { return __pyx_pw_5_pywt_7Wavelet_11family_name_1__get__(o); } static PyObject *__pyx_getprop_5_pywt_7Wavelet_short_family_name(PyObject *o, CYTHON_UNUSED void *x) { return __pyx_pw_5_pywt_7Wavelet_17short_family_name_1__get__(o); } static PyObject *__pyx_getprop_5_pywt_7Wavelet_orthogonal(PyObject *o, CYTHON_UNUSED void *x) { return __pyx_pw_5_pywt_7Wavelet_10orthogonal_1__get__(o); } static int __pyx_setprop_5_pywt_7Wavelet_orthogonal(PyObject *o, PyObject *v, CYTHON_UNUSED void *x) { if (v) { return __pyx_pw_5_pywt_7Wavelet_10orthogonal_3__set__(o, v); } else { PyErr_SetString(PyExc_NotImplementedError, "__del__"); return -1; } } static PyObject *__pyx_getprop_5_pywt_7Wavelet_biorthogonal(PyObject *o, CYTHON_UNUSED void *x) { return __pyx_pw_5_pywt_7Wavelet_12biorthogonal_1__get__(o); } static int __pyx_setprop_5_pywt_7Wavelet_biorthogonal(PyObject *o, PyObject *v, CYTHON_UNUSED void *x) { if (v) { return __pyx_pw_5_pywt_7Wavelet_12biorthogonal_3__set__(o, v); } else { PyErr_SetString(PyExc_NotImplementedError, "__del__"); return -1; } } static PyObject *__pyx_getprop_5_pywt_7Wavelet_symmetry(PyObject *o, CYTHON_UNUSED void *x) { return __pyx_pw_5_pywt_7Wavelet_8symmetry_1__get__(o); } static PyObject *__pyx_getprop_5_pywt_7Wavelet_vanishing_moments_psi(PyObject *o, CYTHON_UNUSED void *x) { return __pyx_pw_5_pywt_7Wavelet_21vanishing_moments_psi_1__get__(o); } static PyObject *__pyx_getprop_5_pywt_7Wavelet_vanishing_moments_phi(PyObject *o, CYTHON_UNUSED void *x) { return __pyx_pw_5_pywt_7Wavelet_21vanishing_moments_phi_1__get__(o); } static PyObject *__pyx_getprop_5_pywt_7Wavelet__builtin(PyObject *o, CYTHON_UNUSED void *x) { return __pyx_pw_5_pywt_7Wavelet_8_builtin_1__get__(o); } static PyObject *__pyx_getprop_5_pywt_7Wavelet_filter_bank(PyObject *o, CYTHON_UNUSED void *x) { return __pyx_pw_5_pywt_7Wavelet_11filter_bank_1__get__(o); } static PyObject *__pyx_getprop_5_pywt_7Wavelet_inverse_filter_bank(PyObject *o, CYTHON_UNUSED void *x) { return __pyx_pw_5_pywt_7Wavelet_19inverse_filter_bank_1__get__(o); } static PyObject *__pyx_getprop_5_pywt_7Wavelet_name(PyObject *o, CYTHON_UNUSED void *x) { return __pyx_pw_5_pywt_7Wavelet_4name_1__get__(o); } static PyObject *__pyx_getprop_5_pywt_7Wavelet_number(PyObject *o, CYTHON_UNUSED void *x) { return __pyx_pw_5_pywt_7Wavelet_6number_1__get__(o); } static PyMethodDef __pyx_methods_5_pywt_Wavelet[] = { {"get_filters_coeffs", (PyCFunction)__pyx_pw_5_pywt_7Wavelet_7get_filters_coeffs, METH_NOARGS, 0}, {"get_reverse_filters_coeffs", (PyCFunction)__pyx_pw_5_pywt_7Wavelet_9get_reverse_filters_coeffs, METH_NOARGS, 0}, {"wavefun", (PyCFunction)__pyx_pw_5_pywt_7Wavelet_11wavefun, METH_VARARGS|METH_KEYWORDS, __pyx_doc_5_pywt_7Wavelet_10wavefun}, {0, 0, 0, 0} }; static struct PyGetSetDef __pyx_getsets_5_pywt_Wavelet[] = { {(char *)"dec_lo", __pyx_getprop_5_pywt_7Wavelet_dec_lo, 0, __pyx_k_Lowpass_decomposition_filter, 0}, {(char *)"dec_hi", __pyx_getprop_5_pywt_7Wavelet_dec_hi, 0, __pyx_k_Highpass_decomposition_filter, 0}, {(char *)"rec_lo", __pyx_getprop_5_pywt_7Wavelet_rec_lo, 0, __pyx_k_Lowpass_reconstruction_filter, 0}, {(char *)"rec_hi", __pyx_getprop_5_pywt_7Wavelet_rec_hi, 0, __pyx_k_Highpass_reconstruction_filter, 0}, {(char *)"rec_len", __pyx_getprop_5_pywt_7Wavelet_rec_len, 0, __pyx_k_Reconstruction_filters_length, 0}, {(char *)"dec_len", __pyx_getprop_5_pywt_7Wavelet_dec_len, 0, __pyx_k_Decomposition_filters_length, 0}, {(char *)"family_name", __pyx_getprop_5_pywt_7Wavelet_family_name, 0, __pyx_k_Wavelet_family_name, 0}, {(char *)"short_family_name", __pyx_getprop_5_pywt_7Wavelet_short_family_name, 0, __pyx_k_Short_wavelet_family_name, 0}, {(char *)"orthogonal", __pyx_getprop_5_pywt_7Wavelet_orthogonal, __pyx_setprop_5_pywt_7Wavelet_orthogonal, __pyx_k_Is_orthogonal, 0}, {(char *)"biorthogonal", __pyx_getprop_5_pywt_7Wavelet_biorthogonal, __pyx_setprop_5_pywt_7Wavelet_biorthogonal, __pyx_k_Is_biorthogonal, 0}, {(char *)"symmetry", __pyx_getprop_5_pywt_7Wavelet_symmetry, 0, __pyx_k_Wavelet_symmetry, 0}, {(char *)"vanishing_moments_psi", __pyx_getprop_5_pywt_7Wavelet_vanishing_moments_psi, 0, __pyx_k_Number_of_vanishing_moments_for, 0}, {(char *)"vanishing_moments_phi", __pyx_getprop_5_pywt_7Wavelet_vanishing_moments_phi, 0, __pyx_k_Number_of_vanishing_moments_for_2, 0}, {(char *)"_builtin", __pyx_getprop_5_pywt_7Wavelet__builtin, 0, __pyx_k_Returns_True_if_the_wavelet_is_b, 0}, {(char *)"filter_bank", __pyx_getprop_5_pywt_7Wavelet_filter_bank, 0, __pyx_k_Returns_tuple_of_wavelet_filters, 0}, {(char *)"inverse_filter_bank", __pyx_getprop_5_pywt_7Wavelet_inverse_filter_bank, 0, __pyx_k_Tuple_of_inverse_wavelet_filters, 0}, {(char *)"name", __pyx_getprop_5_pywt_7Wavelet_name, 0, 0, 0}, {(char *)"number", __pyx_getprop_5_pywt_7Wavelet_number, 0, 0, 0}, {0, 0, 0, 0, 0} }; static PySequenceMethods __pyx_tp_as_sequence_Wavelet = { __pyx_pw_5_pywt_7Wavelet_5__len__, /*sq_length*/ 0, /*sq_concat*/ 0, /*sq_repeat*/ 0, /*sq_item*/ 0, /*sq_slice*/ 0, /*sq_ass_item*/ 0, /*sq_ass_slice*/ 0, /*sq_contains*/ 0, /*sq_inplace_concat*/ 0, /*sq_inplace_repeat*/ }; static PyMappingMethods __pyx_tp_as_mapping_Wavelet = { __pyx_pw_5_pywt_7Wavelet_5__len__, /*mp_length*/ 0, /*mp_subscript*/ 0, /*mp_ass_subscript*/ }; DL_EXPORT(PyTypeObject) WaveletType = { PyVarObject_HEAD_INIT(0, 0) "_pywt.Wavelet", /*tp_name*/ sizeof(struct WaveletObject), /*tp_basicsize*/ 0, /*tp_itemsize*/ __pyx_tp_dealloc_5_pywt_Wavelet, /*tp_dealloc*/ 0, /*tp_print*/ 0, /*tp_getattr*/ 0, /*tp_setattr*/ #if PY_MAJOR_VERSION < 3 0, /*tp_compare*/ #else 0, /*reserved*/ #endif 0, /*tp_repr*/ 0, /*tp_as_number*/ &__pyx_tp_as_sequence_Wavelet, /*tp_as_sequence*/ &__pyx_tp_as_mapping_Wavelet, /*tp_as_mapping*/ 0, /*tp_hash*/ 0, /*tp_call*/ __pyx_pw_5_pywt_7Wavelet_13__str__, /*tp_str*/ 0, /*tp_getattro*/ 0, /*tp_setattro*/ 0, /*tp_as_buffer*/ Py_TPFLAGS_DEFAULT|Py_TPFLAGS_HAVE_VERSION_TAG|Py_TPFLAGS_CHECKTYPES|Py_TPFLAGS_HAVE_NEWBUFFER|Py_TPFLAGS_BASETYPE|Py_TPFLAGS_HAVE_GC, /*tp_flags*/ "\n Wavelet(name, filter_bank=None) object describe properties of\n a wavelet identified by name.\n\n In order to use a built-in wavelet the parameter name must be\n a valid name from the wavelist() list.\n To create a custom wavelet object, filter_bank parameter must\n be specified. It can be either a list of four filters or an object\n that a `filter_bank` attribute which returns a list of four\n filters - just like the Wavelet instance itself.\n\n ", /*tp_doc*/ __pyx_tp_traverse_5_pywt_Wavelet, /*tp_traverse*/ __pyx_tp_clear_5_pywt_Wavelet, /*tp_clear*/ 0, /*tp_richcompare*/ 0, /*tp_weaklistoffset*/ 0, /*tp_iter*/ 0, /*tp_iternext*/ __pyx_methods_5_pywt_Wavelet, /*tp_methods*/ 0, /*tp_members*/ __pyx_getsets_5_pywt_Wavelet, /*tp_getset*/ 0, /*tp_base*/ 0, /*tp_dict*/ 0, /*tp_descr_get*/ 0, /*tp_descr_set*/ 0, /*tp_dictoffset*/ 0, /*tp_init*/ 0, /*tp_alloc*/ __pyx_tp_new_5_pywt_Wavelet, /*tp_new*/ 0, /*tp_free*/ 0, /*tp_is_gc*/ 0, /*tp_bases*/ 0, /*tp_mro*/ 0, /*tp_cache*/ 0, /*tp_subclasses*/ 0, /*tp_weaklist*/ 0, /*tp_del*/ 0, /*tp_version_tag*/ #if PY_VERSION_HEX >= 0x030400a1 0, /*tp_finalize*/ #endif }; static PyObject *__pyx_tp_new_array(PyTypeObject *t, PyObject *a, PyObject *k) { struct __pyx_array_obj *p; PyObject *o; if (likely((t->tp_flags & Py_TPFLAGS_IS_ABSTRACT) == 0)) { o = (*t->tp_alloc)(t, 0); } else { o = (PyObject *) PyBaseObject_Type.tp_new(t, __pyx_empty_tuple, 0); } if (unlikely(!o)) return 0; p = ((struct __pyx_array_obj *)o); p->mode = ((PyObject*)Py_None); Py_INCREF(Py_None); p->_format = ((PyObject*)Py_None); Py_INCREF(Py_None); if (unlikely(__pyx_array___cinit__(o, a, k) < 0)) { Py_DECREF(o); o = 0; } return o; } static void __pyx_tp_dealloc_array(PyObject *o) { struct __pyx_array_obj *p = (struct __pyx_array_obj *)o; #if PY_VERSION_HEX >= 0x030400a1 if (unlikely(Py_TYPE(o)->tp_finalize) && (!PyType_IS_GC(Py_TYPE(o)) || !_PyGC_FINALIZED(o))) { if (PyObject_CallFinalizerFromDealloc(o)) return; } #endif { PyObject *etype, *eval, *etb; PyErr_Fetch(&etype, &eval, &etb); ++Py_REFCNT(o); __pyx_array___dealloc__(o); --Py_REFCNT(o); PyErr_Restore(etype, eval, etb); } Py_CLEAR(p->mode); Py_CLEAR(p->_format); (*Py_TYPE(o)->tp_free)(o); } static PyObject *__pyx_sq_item_array(PyObject *o, Py_ssize_t i) { PyObject *r; PyObject *x = PyInt_FromSsize_t(i); if(!x) return 0; r = Py_TYPE(o)->tp_as_mapping->mp_subscript(o, x); Py_DECREF(x); return r; } static int __pyx_mp_ass_subscript_array(PyObject *o, PyObject *i, PyObject *v) { if (v) { return __pyx_array___setitem__(o, i, v); } else { PyErr_Format(PyExc_NotImplementedError, "Subscript deletion not supported by %.200s", Py_TYPE(o)->tp_name); return -1; } } static PyObject *__pyx_tp_getattro_array(PyObject *o, PyObject *n) { PyObject *v = PyObject_GenericGetAttr(o, n); if (!v && PyErr_ExceptionMatches(PyExc_AttributeError)) { PyErr_Clear(); v = __pyx_array___getattr__(o, n); } return v; } static PyObject *__pyx_getprop___pyx_array_memview(PyObject *o, CYTHON_UNUSED void *x) { return get_memview(o); } static PyMethodDef __pyx_methods_array[] = { {"__getattr__", (PyCFunction)__pyx_array___getattr__, METH_O|METH_COEXIST, 0}, {0, 0, 0, 0} }; static struct PyGetSetDef __pyx_getsets_array[] = { {(char *)"memview", __pyx_getprop___pyx_array_memview, 0, 0, 0}, {0, 0, 0, 0, 0} }; static PySequenceMethods __pyx_tp_as_sequence_array = { 0, /*sq_length*/ 0, /*sq_concat*/ 0, /*sq_repeat*/ __pyx_sq_item_array, /*sq_item*/ 0, /*sq_slice*/ 0, /*sq_ass_item*/ 0, /*sq_ass_slice*/ 0, /*sq_contains*/ 0, /*sq_inplace_concat*/ 0, /*sq_inplace_repeat*/ }; static PyMappingMethods __pyx_tp_as_mapping_array = { 0, /*mp_length*/ __pyx_array___getitem__, /*mp_subscript*/ __pyx_mp_ass_subscript_array, /*mp_ass_subscript*/ }; static PyBufferProcs __pyx_tp_as_buffer_array = { #if PY_MAJOR_VERSION < 3 0, /*bf_getreadbuffer*/ #endif #if PY_MAJOR_VERSION < 3 0, /*bf_getwritebuffer*/ #endif #if PY_MAJOR_VERSION < 3 0, /*bf_getsegcount*/ #endif #if PY_MAJOR_VERSION < 3 0, /*bf_getcharbuffer*/ #endif __pyx_array_getbuffer, /*bf_getbuffer*/ 0, /*bf_releasebuffer*/ }; static PyTypeObject __pyx_type___pyx_array = { PyVarObject_HEAD_INIT(0, 0) "_pywt.array", /*tp_name*/ sizeof(struct __pyx_array_obj), /*tp_basicsize*/ 0, /*tp_itemsize*/ __pyx_tp_dealloc_array, /*tp_dealloc*/ 0, /*tp_print*/ 0, /*tp_getattr*/ 0, /*tp_setattr*/ #if PY_MAJOR_VERSION < 3 0, /*tp_compare*/ #else 0, /*reserved*/ #endif 0, /*tp_repr*/ 0, /*tp_as_number*/ &__pyx_tp_as_sequence_array, /*tp_as_sequence*/ &__pyx_tp_as_mapping_array, /*tp_as_mapping*/ 0, /*tp_hash*/ 0, /*tp_call*/ 0, /*tp_str*/ __pyx_tp_getattro_array, /*tp_getattro*/ 0, /*tp_setattro*/ &__pyx_tp_as_buffer_array, /*tp_as_buffer*/ Py_TPFLAGS_DEFAULT|Py_TPFLAGS_HAVE_VERSION_TAG|Py_TPFLAGS_CHECKTYPES|Py_TPFLAGS_HAVE_NEWBUFFER|Py_TPFLAGS_BASETYPE, /*tp_flags*/ 0, /*tp_doc*/ 0, /*tp_traverse*/ 0, /*tp_clear*/ 0, /*tp_richcompare*/ 0, /*tp_weaklistoffset*/ 0, /*tp_iter*/ 0, /*tp_iternext*/ __pyx_methods_array, /*tp_methods*/ 0, /*tp_members*/ __pyx_getsets_array, /*tp_getset*/ 0, /*tp_base*/ 0, /*tp_dict*/ 0, /*tp_descr_get*/ 0, /*tp_descr_set*/ 0, /*tp_dictoffset*/ 0, /*tp_init*/ 0, /*tp_alloc*/ __pyx_tp_new_array, /*tp_new*/ 0, /*tp_free*/ 0, /*tp_is_gc*/ 0, /*tp_bases*/ 0, /*tp_mro*/ 0, /*tp_cache*/ 0, /*tp_subclasses*/ 0, /*tp_weaklist*/ 0, /*tp_del*/ 0, /*tp_version_tag*/ #if PY_VERSION_HEX >= 0x030400a1 0, /*tp_finalize*/ #endif }; static PyObject *__pyx_tp_new_Enum(PyTypeObject *t, CYTHON_UNUSED PyObject *a, CYTHON_UNUSED PyObject *k) { struct __pyx_MemviewEnum_obj *p; PyObject *o; if (likely((t->tp_flags & Py_TPFLAGS_IS_ABSTRACT) == 0)) { o = (*t->tp_alloc)(t, 0); } else { o = (PyObject *) PyBaseObject_Type.tp_new(t, __pyx_empty_tuple, 0); } if (unlikely(!o)) return 0; p = ((struct __pyx_MemviewEnum_obj *)o); p->name = Py_None; Py_INCREF(Py_None); return o; } static void __pyx_tp_dealloc_Enum(PyObject *o) { struct __pyx_MemviewEnum_obj *p = (struct __pyx_MemviewEnum_obj *)o; #if PY_VERSION_HEX >= 0x030400a1 if (unlikely(Py_TYPE(o)->tp_finalize) && !_PyGC_FINALIZED(o)) { if (PyObject_CallFinalizerFromDealloc(o)) return; } #endif PyObject_GC_UnTrack(o); Py_CLEAR(p->name); (*Py_TYPE(o)->tp_free)(o); } static int __pyx_tp_traverse_Enum(PyObject *o, visitproc v, void *a) { int e; struct __pyx_MemviewEnum_obj *p = (struct __pyx_MemviewEnum_obj *)o; if (p->name) { e = (*v)(p->name, a); if (e) return e; } return 0; } static int __pyx_tp_clear_Enum(PyObject *o) { PyObject* tmp; struct __pyx_MemviewEnum_obj *p = (struct __pyx_MemviewEnum_obj *)o; tmp = ((PyObject*)p->name); p->name = Py_None; Py_INCREF(Py_None); Py_XDECREF(tmp); return 0; } static PyMethodDef __pyx_methods_Enum[] = { {0, 0, 0, 0} }; static PyTypeObject __pyx_type___pyx_MemviewEnum = { PyVarObject_HEAD_INIT(0, 0) "_pywt.Enum", /*tp_name*/ sizeof(struct __pyx_MemviewEnum_obj), /*tp_basicsize*/ 0, /*tp_itemsize*/ __pyx_tp_dealloc_Enum, /*tp_dealloc*/ 0, /*tp_print*/ 0, /*tp_getattr*/ 0, /*tp_setattr*/ #if PY_MAJOR_VERSION < 3 0, /*tp_compare*/ #else 0, /*reserved*/ #endif __pyx_MemviewEnum___repr__, /*tp_repr*/ 0, /*tp_as_number*/ 0, /*tp_as_sequence*/ 0, /*tp_as_mapping*/ 0, /*tp_hash*/ 0, /*tp_call*/ 0, /*tp_str*/ 0, /*tp_getattro*/ 0, /*tp_setattro*/ 0, /*tp_as_buffer*/ Py_TPFLAGS_DEFAULT|Py_TPFLAGS_HAVE_VERSION_TAG|Py_TPFLAGS_CHECKTYPES|Py_TPFLAGS_HAVE_NEWBUFFER|Py_TPFLAGS_BASETYPE|Py_TPFLAGS_HAVE_GC, /*tp_flags*/ 0, /*tp_doc*/ __pyx_tp_traverse_Enum, /*tp_traverse*/ __pyx_tp_clear_Enum, /*tp_clear*/ 0, /*tp_richcompare*/ 0, /*tp_weaklistoffset*/ 0, /*tp_iter*/ 0, /*tp_iternext*/ __pyx_methods_Enum, /*tp_methods*/ 0, /*tp_members*/ 0, /*tp_getset*/ 0, /*tp_base*/ 0, /*tp_dict*/ 0, /*tp_descr_get*/ 0, /*tp_descr_set*/ 0, /*tp_dictoffset*/ __pyx_MemviewEnum___init__, /*tp_init*/ 0, /*tp_alloc*/ __pyx_tp_new_Enum, /*tp_new*/ 0, /*tp_free*/ 0, /*tp_is_gc*/ 0, /*tp_bases*/ 0, /*tp_mro*/ 0, /*tp_cache*/ 0, /*tp_subclasses*/ 0, /*tp_weaklist*/ 0, /*tp_del*/ 0, /*tp_version_tag*/ #if PY_VERSION_HEX >= 0x030400a1 0, /*tp_finalize*/ #endif }; static struct __pyx_vtabstruct_memoryview __pyx_vtable_memoryview; static PyObject *__pyx_tp_new_memoryview(PyTypeObject *t, PyObject *a, PyObject *k) { struct __pyx_memoryview_obj *p; PyObject *o; if (likely((t->tp_flags & Py_TPFLAGS_IS_ABSTRACT) == 0)) { o = (*t->tp_alloc)(t, 0); } else { o = (PyObject *) PyBaseObject_Type.tp_new(t, __pyx_empty_tuple, 0); } if (unlikely(!o)) return 0; p = ((struct __pyx_memoryview_obj *)o); p->__pyx_vtab = __pyx_vtabptr_memoryview; p->obj = Py_None; Py_INCREF(Py_None); p->_size = Py_None; Py_INCREF(Py_None); p->_array_interface = Py_None; Py_INCREF(Py_None); p->view.obj = NULL; if (unlikely(__pyx_memoryview___cinit__(o, a, k) < 0)) { Py_DECREF(o); o = 0; } return o; } static void __pyx_tp_dealloc_memoryview(PyObject *o) { struct __pyx_memoryview_obj *p = (struct __pyx_memoryview_obj *)o; #if PY_VERSION_HEX >= 0x030400a1 if (unlikely(Py_TYPE(o)->tp_finalize) && !_PyGC_FINALIZED(o)) { if (PyObject_CallFinalizerFromDealloc(o)) return; } #endif PyObject_GC_UnTrack(o); { PyObject *etype, *eval, *etb; PyErr_Fetch(&etype, &eval, &etb); ++Py_REFCNT(o); __pyx_memoryview___dealloc__(o); --Py_REFCNT(o); PyErr_Restore(etype, eval, etb); } Py_CLEAR(p->obj); Py_CLEAR(p->_size); Py_CLEAR(p->_array_interface); (*Py_TYPE(o)->tp_free)(o); } static int __pyx_tp_traverse_memoryview(PyObject *o, visitproc v, void *a) { int e; struct __pyx_memoryview_obj *p = (struct __pyx_memoryview_obj *)o; if (p->obj) { e = (*v)(p->obj, a); if (e) return e; } if (p->_size) { e = (*v)(p->_size, a); if (e) return e; } if (p->_array_interface) { e = (*v)(p->_array_interface, a); if (e) return e; } if (p->view.obj) { e = (*v)(p->view.obj, a); if (e) return e; } return 0; } static int __pyx_tp_clear_memoryview(PyObject *o) { PyObject* tmp; struct __pyx_memoryview_obj *p = (struct __pyx_memoryview_obj *)o; tmp = ((PyObject*)p->obj); p->obj = Py_None; Py_INCREF(Py_None); Py_XDECREF(tmp); tmp = ((PyObject*)p->_size); p->_size = Py_None; Py_INCREF(Py_None); Py_XDECREF(tmp); tmp = ((PyObject*)p->_array_interface); p->_array_interface = Py_None; Py_INCREF(Py_None); Py_XDECREF(tmp); Py_CLEAR(p->view.obj); return 0; } static PyObject *__pyx_sq_item_memoryview(PyObject *o, Py_ssize_t i) { PyObject *r; PyObject *x = PyInt_FromSsize_t(i); if(!x) return 0; r = Py_TYPE(o)->tp_as_mapping->mp_subscript(o, x); Py_DECREF(x); return r; } static int __pyx_mp_ass_subscript_memoryview(PyObject *o, PyObject *i, PyObject *v) { if (v) { return __pyx_memoryview___setitem__(o, i, v); } else { PyErr_Format(PyExc_NotImplementedError, "Subscript deletion not supported by %.200s", Py_TYPE(o)->tp_name); return -1; } } static PyObject *__pyx_getprop___pyx_memoryview_T(PyObject *o, CYTHON_UNUSED void *x) { return __pyx_memoryview_transpose(o); } static PyObject *__pyx_getprop___pyx_memoryview_base(PyObject *o, CYTHON_UNUSED void *x) { return __pyx_memoryview__get__base(o); } static PyObject *__pyx_getprop___pyx_memoryview_shape(PyObject *o, CYTHON_UNUSED void *x) { return __pyx_memoryview_get_shape(o); } static PyObject *__pyx_getprop___pyx_memoryview_strides(PyObject *o, CYTHON_UNUSED void *x) { return __pyx_memoryview_get_strides(o); } static PyObject *__pyx_getprop___pyx_memoryview_suboffsets(PyObject *o, CYTHON_UNUSED void *x) { return __pyx_memoryview_get_suboffsets(o); } static PyObject *__pyx_getprop___pyx_memoryview_ndim(PyObject *o, CYTHON_UNUSED void *x) { return __pyx_memoryview_get_ndim(o); } static PyObject *__pyx_getprop___pyx_memoryview_itemsize(PyObject *o, CYTHON_UNUSED void *x) { return __pyx_memoryview_get_itemsize(o); } static PyObject *__pyx_getprop___pyx_memoryview_nbytes(PyObject *o, CYTHON_UNUSED void *x) { return __pyx_memoryview_get_nbytes(o); } static PyObject *__pyx_getprop___pyx_memoryview_size(PyObject *o, CYTHON_UNUSED void *x) { return __pyx_memoryview_get_size(o); } static PyMethodDef __pyx_methods_memoryview[] = { {"is_c_contig", (PyCFunction)__pyx_memoryview_is_c_contig, METH_NOARGS, 0}, {"is_f_contig", (PyCFunction)__pyx_memoryview_is_f_contig, METH_NOARGS, 0}, {"copy", (PyCFunction)__pyx_memoryview_copy, METH_NOARGS, 0}, {"copy_fortran", (PyCFunction)__pyx_memoryview_copy_fortran, METH_NOARGS, 0}, {0, 0, 0, 0} }; static struct PyGetSetDef __pyx_getsets_memoryview[] = { {(char *)"T", __pyx_getprop___pyx_memoryview_T, 0, 0, 0}, {(char *)"base", __pyx_getprop___pyx_memoryview_base, 0, 0, 0}, {(char *)"shape", __pyx_getprop___pyx_memoryview_shape, 0, 0, 0}, {(char *)"strides", __pyx_getprop___pyx_memoryview_strides, 0, 0, 0}, {(char *)"suboffsets", __pyx_getprop___pyx_memoryview_suboffsets, 0, 0, 0}, {(char *)"ndim", __pyx_getprop___pyx_memoryview_ndim, 0, 0, 0}, {(char *)"itemsize", __pyx_getprop___pyx_memoryview_itemsize, 0, 0, 0}, {(char *)"nbytes", __pyx_getprop___pyx_memoryview_nbytes, 0, 0, 0}, {(char *)"size", __pyx_getprop___pyx_memoryview_size, 0, 0, 0}, {0, 0, 0, 0, 0} }; static PySequenceMethods __pyx_tp_as_sequence_memoryview = { __pyx_memoryview___len__, /*sq_length*/ 0, /*sq_concat*/ 0, /*sq_repeat*/ __pyx_sq_item_memoryview, /*sq_item*/ 0, /*sq_slice*/ 0, /*sq_ass_item*/ 0, /*sq_ass_slice*/ 0, /*sq_contains*/ 0, /*sq_inplace_concat*/ 0, /*sq_inplace_repeat*/ }; static PyMappingMethods __pyx_tp_as_mapping_memoryview = { __pyx_memoryview___len__, /*mp_length*/ __pyx_memoryview___getitem__, /*mp_subscript*/ __pyx_mp_ass_subscript_memoryview, /*mp_ass_subscript*/ }; static PyBufferProcs __pyx_tp_as_buffer_memoryview = { #if PY_MAJOR_VERSION < 3 0, /*bf_getreadbuffer*/ #endif #if PY_MAJOR_VERSION < 3 0, /*bf_getwritebuffer*/ #endif #if PY_MAJOR_VERSION < 3 0, /*bf_getsegcount*/ #endif #if PY_MAJOR_VERSION < 3 0, /*bf_getcharbuffer*/ #endif __pyx_memoryview_getbuffer, /*bf_getbuffer*/ 0, /*bf_releasebuffer*/ }; static PyTypeObject __pyx_type___pyx_memoryview = { PyVarObject_HEAD_INIT(0, 0) "_pywt.memoryview", /*tp_name*/ sizeof(struct __pyx_memoryview_obj), /*tp_basicsize*/ 0, /*tp_itemsize*/ __pyx_tp_dealloc_memoryview, /*tp_dealloc*/ 0, /*tp_print*/ 0, /*tp_getattr*/ 0, /*tp_setattr*/ #if PY_MAJOR_VERSION < 3 0, /*tp_compare*/ #else 0, /*reserved*/ #endif __pyx_memoryview___repr__, /*tp_repr*/ 0, /*tp_as_number*/ &__pyx_tp_as_sequence_memoryview, /*tp_as_sequence*/ &__pyx_tp_as_mapping_memoryview, /*tp_as_mapping*/ 0, /*tp_hash*/ 0, /*tp_call*/ __pyx_memoryview___str__, /*tp_str*/ 0, /*tp_getattro*/ 0, /*tp_setattro*/ &__pyx_tp_as_buffer_memoryview, /*tp_as_buffer*/ Py_TPFLAGS_DEFAULT|Py_TPFLAGS_HAVE_VERSION_TAG|Py_TPFLAGS_CHECKTYPES|Py_TPFLAGS_HAVE_NEWBUFFER|Py_TPFLAGS_BASETYPE|Py_TPFLAGS_HAVE_GC, /*tp_flags*/ 0, /*tp_doc*/ __pyx_tp_traverse_memoryview, /*tp_traverse*/ __pyx_tp_clear_memoryview, /*tp_clear*/ 0, /*tp_richcompare*/ 0, /*tp_weaklistoffset*/ 0, /*tp_iter*/ 0, /*tp_iternext*/ __pyx_methods_memoryview, /*tp_methods*/ 0, /*tp_members*/ __pyx_getsets_memoryview, /*tp_getset*/ 0, /*tp_base*/ 0, /*tp_dict*/ 0, /*tp_descr_get*/ 0, /*tp_descr_set*/ 0, /*tp_dictoffset*/ 0, /*tp_init*/ 0, /*tp_alloc*/ __pyx_tp_new_memoryview, /*tp_new*/ 0, /*tp_free*/ 0, /*tp_is_gc*/ 0, /*tp_bases*/ 0, /*tp_mro*/ 0, /*tp_cache*/ 0, /*tp_subclasses*/ 0, /*tp_weaklist*/ 0, /*tp_del*/ 0, /*tp_version_tag*/ #if PY_VERSION_HEX >= 0x030400a1 0, /*tp_finalize*/ #endif }; static struct __pyx_vtabstruct__memoryviewslice __pyx_vtable__memoryviewslice; static PyObject *__pyx_tp_new__memoryviewslice(PyTypeObject *t, PyObject *a, PyObject *k) { struct __pyx_memoryviewslice_obj *p; PyObject *o = __pyx_tp_new_memoryview(t, a, k); if (unlikely(!o)) return 0; p = ((struct __pyx_memoryviewslice_obj *)o); p->__pyx_base.__pyx_vtab = (struct __pyx_vtabstruct_memoryview*)__pyx_vtabptr__memoryviewslice; p->from_object = Py_None; Py_INCREF(Py_None); p->from_slice.memview = NULL; return o; } static void __pyx_tp_dealloc__memoryviewslice(PyObject *o) { struct __pyx_memoryviewslice_obj *p = (struct __pyx_memoryviewslice_obj *)o; #if PY_VERSION_HEX >= 0x030400a1 if (unlikely(Py_TYPE(o)->tp_finalize) && !_PyGC_FINALIZED(o)) { if (PyObject_CallFinalizerFromDealloc(o)) return; } #endif PyObject_GC_UnTrack(o); { PyObject *etype, *eval, *etb; PyErr_Fetch(&etype, &eval, &etb); ++Py_REFCNT(o); __pyx_memoryviewslice___dealloc__(o); --Py_REFCNT(o); PyErr_Restore(etype, eval, etb); } Py_CLEAR(p->from_object); PyObject_GC_Track(o); __pyx_tp_dealloc_memoryview(o); } static int __pyx_tp_traverse__memoryviewslice(PyObject *o, visitproc v, void *a) { int e; struct __pyx_memoryviewslice_obj *p = (struct __pyx_memoryviewslice_obj *)o; e = __pyx_tp_traverse_memoryview(o, v, a); if (e) return e; if (p->from_object) { e = (*v)(p->from_object, a); if (e) return e; } return 0; } static int __pyx_tp_clear__memoryviewslice(PyObject *o) { PyObject* tmp; struct __pyx_memoryviewslice_obj *p = (struct __pyx_memoryviewslice_obj *)o; __pyx_tp_clear_memoryview(o); tmp = ((PyObject*)p->from_object); p->from_object = Py_None; Py_INCREF(Py_None); Py_XDECREF(tmp); __PYX_XDEC_MEMVIEW(&p->from_slice, 1); return 0; } static PyObject *__pyx_getprop___pyx_memoryviewslice_base(PyObject *o, CYTHON_UNUSED void *x) { return __pyx_memoryviewslice__get__base(o); } static PyMethodDef __pyx_methods__memoryviewslice[] = { {0, 0, 0, 0} }; static struct PyGetSetDef 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__pyx_kp_s_All_filters_in_filter_bank_must_2); if (unlikely(!__pyx_tuple__11)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 287; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __Pyx_GOTREF(__pyx_tuple__11); __Pyx_GIVEREF(__pyx_tuple__11); /* "_pywt.pyx":292 * self.w = c_wt.blank_wavelet(filter_length) * if self.w is NULL: * raise MemoryError("Could not allocate memory for given " # <<<<<<<<<<<<<< * "filter bank.") * */ __pyx_tuple__12 = PyTuple_Pack(1, __pyx_kp_s_Could_not_allocate_memory_for_gi); if (unlikely(!__pyx_tuple__12)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 292; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __Pyx_GOTREF(__pyx_tuple__12); __Pyx_GIVEREF(__pyx_tuple__12); /* "_pywt.pyx":419 * """ * def __get__(self): * return (self.rec_lo[::-1], self.rec_hi[::-1], self.dec_lo[::-1], # <<<<<<<<<<<<<< * self.dec_hi[::-1]) * */ __pyx_slice__13 = PySlice_New(Py_None, Py_None, __pyx_int_neg_1); if (unlikely(!__pyx_slice__13)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 419; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __Pyx_GOTREF(__pyx_slice__13); __Pyx_GIVEREF(__pyx_slice__13); __pyx_slice__14 = PySlice_New(Py_None, Py_None, __pyx_int_neg_1); if (unlikely(!__pyx_slice__14)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 419; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __Pyx_GOTREF(__pyx_slice__14); __Pyx_GIVEREF(__pyx_slice__14); __pyx_slice__15 = PySlice_New(Py_None, Py_None, __pyx_int_neg_1); if (unlikely(!__pyx_slice__15)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 419; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __Pyx_GOTREF(__pyx_slice__15); __Pyx_GIVEREF(__pyx_slice__15); /* "_pywt.pyx":420 * def __get__(self): * return (self.rec_lo[::-1], self.rec_hi[::-1], self.dec_lo[::-1], * self.dec_hi[::-1]) # <<<<<<<<<<<<<< * * def get_reverse_filters_coeffs(self): */ __pyx_slice__16 = PySlice_New(Py_None, Py_None, __pyx_int_neg_1); if (unlikely(!__pyx_slice__16)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 420; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __Pyx_GOTREF(__pyx_slice__16); __Pyx_GIVEREF(__pyx_slice__16); /* "_pywt.pyx":647 * data = np.array(data, dtype=dt) * if data.ndim != 1: * raise ValueError("dwt requires a 1D data array.") # <<<<<<<<<<<<<< * return _dwt(data, wavelet, mode) * */ __pyx_tuple__18 = PyTuple_Pack(1, __pyx_kp_s_dwt_requires_a_1D_data_array); if (unlikely(!__pyx_tuple__18)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 647; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __Pyx_GOTREF(__pyx_tuple__18); __Pyx_GIVEREF(__pyx_tuple__18); /* "_pywt.pyx":651 * * * def _dwt(np.ndarray[data_t, ndim=1] data, object wavelet, object mode='sym'): # <<<<<<<<<<<<<< * """See `dwt` docstring for details.""" * cdef np.ndarray[data_t, ndim=1, mode="c"] cA, cD */ __pyx_tuple__21 = PyTuple_Pack(1, __pyx_kp_s__20); if (unlikely(!__pyx_tuple__21)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 651; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __Pyx_GOTREF(__pyx_tuple__21); __Pyx_GIVEREF(__pyx_tuple__21); __pyx_tuple__23 = PyTuple_Pack(1, __pyx_kp_s__22); if (unlikely(!__pyx_tuple__23)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 651; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __Pyx_GOTREF(__pyx_tuple__23); __Pyx_GIVEREF(__pyx_tuple__23); __pyx_tuple__24 = PyTuple_Pack(1, __pyx_kp_s_No_matching_signature_found); if (unlikely(!__pyx_tuple__24)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 651; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __Pyx_GOTREF(__pyx_tuple__24); __Pyx_GIVEREF(__pyx_tuple__24); __pyx_tuple__25 = PyTuple_Pack(1, __pyx_kp_s_Function_call_with_ambiguous_arg); if (unlikely(!__pyx_tuple__25)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 651; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __Pyx_GOTREF(__pyx_tuple__25); __Pyx_GIVEREF(__pyx_tuple__25); /* "_pywt.pyx":663 * output_len = c_wt.dwt_buffer_length(data.size, w.dec_len, mode_) * if output_len < 1: * raise RuntimeError("Invalid output length.") # <<<<<<<<<<<<<< * * cA = np.zeros(output_len, data.dtype) */ __pyx_tuple__26 = PyTuple_Pack(1, __pyx_kp_s_Invalid_output_length); if (unlikely(!__pyx_tuple__26)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 663; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __Pyx_GOTREF(__pyx_tuple__26); __Pyx_GIVEREF(__pyx_tuple__26); /* "_pywt.pyx":681 * c_wt.float_dec_d(&data[0], data.size, w.w, * &cD[0], cD.size, mode_) < 0): * raise RuntimeError("C dwt failed.") # <<<<<<<<<<<<<< * else: * raise RuntimeError("Invalid data type.") */ __pyx_tuple__27 = PyTuple_Pack(1, __pyx_kp_s_C_dwt_failed); if (unlikely(!__pyx_tuple__27)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 681; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __Pyx_GOTREF(__pyx_tuple__27); __Pyx_GIVEREF(__pyx_tuple__27); /* "_pywt.pyx":663 * output_len = c_wt.dwt_buffer_length(data.size, w.dec_len, mode_) * if output_len < 1: * raise RuntimeError("Invalid output length.") # <<<<<<<<<<<<<< * * cA = np.zeros(output_len, data.dtype) */ __pyx_tuple__28 = PyTuple_Pack(1, __pyx_kp_s_Invalid_output_length); if (unlikely(!__pyx_tuple__28)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 663; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __Pyx_GOTREF(__pyx_tuple__28); __Pyx_GIVEREF(__pyx_tuple__28); /* "_pywt.pyx":674 * c_wt.double_dec_d(&data[0], data.size, w.w, * &cD[0], cD.size, mode_) < 0): * raise RuntimeError("C dwt failed.") # <<<<<<<<<<<<<< * elif data_t == np.float32_t: * if (c_wt.float_dec_a(&data[0], data.size, w.w, */ __pyx_tuple__29 = PyTuple_Pack(1, __pyx_kp_s_C_dwt_failed); if (unlikely(!__pyx_tuple__29)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 674; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __Pyx_GOTREF(__pyx_tuple__29); __Pyx_GIVEREF(__pyx_tuple__29); /* "_pywt.pyx":727 * * if data_len < 1: * raise ValueError("Value of data_len value must be greater than zero.") # <<<<<<<<<<<<<< * if filter_len_ < 1: * raise ValueError("Value of filter_len must be greater than zero.") */ __pyx_tuple__30 = PyTuple_Pack(1, __pyx_kp_s_Value_of_data_len_value_must_be); if (unlikely(!__pyx_tuple__30)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 727; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __Pyx_GOTREF(__pyx_tuple__30); __Pyx_GIVEREF(__pyx_tuple__30); /* "_pywt.pyx":729 * raise ValueError("Value of data_len value must be greater than zero.") * if filter_len_ < 1: * raise ValueError("Value of filter_len must be greater than zero.") # <<<<<<<<<<<<<< * * return c_wt.dwt_buffer_length(data_len, filter_len_, _try_mode(mode)) */ __pyx_tuple__31 = PyTuple_Pack(1, __pyx_kp_s_Value_of_filter_len_must_be_grea); if (unlikely(!__pyx_tuple__31)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 729; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __Pyx_GOTREF(__pyx_tuple__31); __Pyx_GIVEREF(__pyx_tuple__31); /* "_pywt.pyx":794 * * if cA is None and cD is None: * raise ValueError("At least one coefficient parameter must be " # <<<<<<<<<<<<<< * "specified.") * */ __pyx_tuple__32 = PyTuple_Pack(1, __pyx_kp_s_At_least_one_coefficient_paramet); if (unlikely(!__pyx_tuple__32)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 794; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __Pyx_GOTREF(__pyx_tuple__32); __Pyx_GIVEREF(__pyx_tuple__32); /* "_pywt.pyx":801 * cA = np.array(cA, dtype=dt) * if cA.ndim != 1: * raise ValueError("idwt requires 1D coefficient arrays.") # <<<<<<<<<<<<<< * if cD is not None: * dt = _check_dtype(cD) */ __pyx_tuple__33 = PyTuple_Pack(1, __pyx_kp_s_idwt_requires_1D_coefficient_arr); if (unlikely(!__pyx_tuple__33)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 801; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __Pyx_GOTREF(__pyx_tuple__33); __Pyx_GIVEREF(__pyx_tuple__33); /* "_pywt.pyx":806 * cD = np.array(cD, dtype=dt) * if cD.ndim != 1: * raise ValueError("idwt requires 1D coefficient arrays.") # <<<<<<<<<<<<<< * * if cA is not None and cD is not None: */ __pyx_tuple__34 = PyTuple_Pack(1, __pyx_kp_s_idwt_requires_1D_coefficient_arr); if (unlikely(!__pyx_tuple__34)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 806; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __Pyx_GOTREF(__pyx_tuple__34); __Pyx_GIVEREF(__pyx_tuple__34); /* "_pywt.pyx":821 * * * def _idwt(np.ndarray[data_t, ndim=1, mode="c"] cA, # <<<<<<<<<<<<<< * np.ndarray[data_t, ndim=1, mode="c"] cD, * object wavelet, object mode='sym', int correct_size=0): */ __pyx_tuple__36 = PyTuple_Pack(1, __pyx_kp_s__20); if (unlikely(!__pyx_tuple__36)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 821; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __Pyx_GOTREF(__pyx_tuple__36); __Pyx_GIVEREF(__pyx_tuple__36); __pyx_tuple__37 = PyTuple_Pack(1, __pyx_kp_s__22); if (unlikely(!__pyx_tuple__37)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 821; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __Pyx_GOTREF(__pyx_tuple__37); __Pyx_GIVEREF(__pyx_tuple__37); __pyx_tuple__38 = PyTuple_Pack(1, __pyx_kp_s_No_matching_signature_found); if (unlikely(!__pyx_tuple__38)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 821; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __Pyx_GOTREF(__pyx_tuple__38); __Pyx_GIVEREF(__pyx_tuple__38); __pyx_tuple__39 = PyTuple_Pack(1, __pyx_kp_s_Function_call_with_ambiguous_arg); if (unlikely(!__pyx_tuple__39)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 821; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __Pyx_GOTREF(__pyx_tuple__39); __Pyx_GIVEREF(__pyx_tuple__39); /* "_pywt.pyx":879 * &rec[0], rec.size, mode_, * correct_size) < 0: * raise RuntimeError("C idwt failed.") # <<<<<<<<<<<<<< * else: * raise RuntimeError("Invalid data type.") */ __pyx_tuple__40 = PyTuple_Pack(1, __pyx_kp_s_C_idwt_failed); if (unlikely(!__pyx_tuple__40)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 879; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __Pyx_GOTREF(__pyx_tuple__40); __Pyx_GIVEREF(__pyx_tuple__40); /* "_pywt.pyx":873 * &rec[0], rec.size, mode_, * correct_size) < 0: * raise RuntimeError("C idwt failed.") # <<<<<<<<<<<<<< * elif data_t == np.float32_t: * if c_wt.float_idwt(&cA[0], cA.size, */ __pyx_tuple__41 = PyTuple_Pack(1, __pyx_kp_s_C_idwt_failed); if (unlikely(!__pyx_tuple__41)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 873; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __Pyx_GOTREF(__pyx_tuple__41); __Pyx_GIVEREF(__pyx_tuple__41); /* "_pywt.pyx":940 * * * def _upcoef(part, np.ndarray[data_t, ndim=1, mode="c"] coeffs, wavelet, # <<<<<<<<<<<<<< * int level=1, int take=0): * cdef Wavelet w */ __pyx_tuple__43 = PyTuple_Pack(1, __pyx_kp_s__20); if (unlikely(!__pyx_tuple__43)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 940; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __Pyx_GOTREF(__pyx_tuple__43); __Pyx_GIVEREF(__pyx_tuple__43); __pyx_tuple__44 = PyTuple_Pack(1, __pyx_kp_s__22); if (unlikely(!__pyx_tuple__44)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 940; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __Pyx_GOTREF(__pyx_tuple__44); __Pyx_GIVEREF(__pyx_tuple__44); __pyx_tuple__45 = PyTuple_Pack(1, __pyx_kp_s_No_matching_signature_found); if (unlikely(!__pyx_tuple__45)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 940; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __Pyx_GOTREF(__pyx_tuple__45); __Pyx_GIVEREF(__pyx_tuple__45); __pyx_tuple__46 = PyTuple_Pack(1, __pyx_kp_s_Function_call_with_ambiguous_arg); if (unlikely(!__pyx_tuple__46)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 940; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __Pyx_GOTREF(__pyx_tuple__46); __Pyx_GIVEREF(__pyx_tuple__46); /* "_pywt.pyx":956 * * if level < 1: * raise ValueError("Value of level must be greater than 0.") # <<<<<<<<<<<<<< * * for i from 0 <= i < level: */ __pyx_tuple__47 = PyTuple_Pack(1, __pyx_kp_s_Value_of_level_must_be_greater_t); if (unlikely(!__pyx_tuple__47)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 956; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __Pyx_GOTREF(__pyx_tuple__47); __Pyx_GIVEREF(__pyx_tuple__47); /* "_pywt.pyx":962 * rec_len = c_wt.reconstruction_buffer_length(coeffs.size, w.dec_len) * if rec_len < 1: * raise RuntimeError("Invalid output length.") # <<<<<<<<<<<<<< * * # reconstruct */ __pyx_tuple__48 = PyTuple_Pack(1, __pyx_kp_s_Invalid_output_length); if (unlikely(!__pyx_tuple__48)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 962; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __Pyx_GOTREF(__pyx_tuple__48); __Pyx_GIVEREF(__pyx_tuple__48); /* "_pywt.pyx":975 * if c_wt.float_rec_a(&coeffs[0], coeffs.size, w.w, * &rec[0], rec.size) < 0: * raise RuntimeError("C rec_a failed.") # <<<<<<<<<<<<<< * else: * raise RuntimeError("Invalid data type.") */ __pyx_tuple__49 = PyTuple_Pack(1, __pyx_kp_s_C_rec_a_failed); if (unlikely(!__pyx_tuple__49)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 975; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __Pyx_GOTREF(__pyx_tuple__49); __Pyx_GIVEREF(__pyx_tuple__49); /* "_pywt.pyx":986 * if c_wt.float_rec_d(&coeffs[0], coeffs.size, w.w, * &rec[0], rec.size) < 0: * raise RuntimeError("C rec_a failed.") # <<<<<<<<<<<<<< * else: * raise RuntimeError("Invalid data type.") */ __pyx_tuple__50 = PyTuple_Pack(1, __pyx_kp_s_C_rec_a_failed); if (unlikely(!__pyx_tuple__50)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 986; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __Pyx_GOTREF(__pyx_tuple__50); __Pyx_GIVEREF(__pyx_tuple__50); /* "_pywt.pyx":956 * * if level < 1: * raise ValueError("Value of level must be greater than 0.") # <<<<<<<<<<<<<< * * for i from 0 <= i < level: */ __pyx_tuple__51 = PyTuple_Pack(1, __pyx_kp_s_Value_of_level_must_be_greater_t); if (unlikely(!__pyx_tuple__51)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 956; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __Pyx_GOTREF(__pyx_tuple__51); __Pyx_GIVEREF(__pyx_tuple__51); /* "_pywt.pyx":962 * rec_len = c_wt.reconstruction_buffer_length(coeffs.size, w.dec_len) * if rec_len < 1: * raise RuntimeError("Invalid output length.") # <<<<<<<<<<<<<< * * # reconstruct */ __pyx_tuple__52 = PyTuple_Pack(1, __pyx_kp_s_Invalid_output_length); if (unlikely(!__pyx_tuple__52)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 962; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __Pyx_GOTREF(__pyx_tuple__52); __Pyx_GIVEREF(__pyx_tuple__52); /* "_pywt.pyx":971 * if c_wt.double_rec_a(&coeffs[0], coeffs.size, w.w, * &rec[0], rec.size) < 0: * raise RuntimeError("C rec_a failed.") # <<<<<<<<<<<<<< * elif data_t is np.float32_t: * if c_wt.float_rec_a(&coeffs[0], coeffs.size, w.w, */ __pyx_tuple__53 = PyTuple_Pack(1, __pyx_kp_s_C_rec_a_failed); if (unlikely(!__pyx_tuple__53)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 971; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __Pyx_GOTREF(__pyx_tuple__53); __Pyx_GIVEREF(__pyx_tuple__53); /* "_pywt.pyx":982 * if c_wt.double_rec_d(&coeffs[0], coeffs.size, w.w, * &rec[0], rec.size) < 0: * raise RuntimeError("C rec_a failed.") # <<<<<<<<<<<<<< * elif data_t is np.float32_t: * if c_wt.float_rec_d(&coeffs[0], coeffs.size, w.w, */ __pyx_tuple__54 = PyTuple_Pack(1, __pyx_kp_s_C_rec_a_failed); if (unlikely(!__pyx_tuple__54)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 982; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __Pyx_GOTREF(__pyx_tuple__54); __Pyx_GIVEREF(__pyx_tuple__54); /* "_pywt.pyx":1047 * * * def _downcoef(part, np.ndarray[data_t, ndim=1, mode="c"] data, # <<<<<<<<<<<<<< * object wavelet, object mode='sym', int level=1): * cdef np.ndarray[data_t, ndim=1, mode="c"] coeffs */ __pyx_tuple__56 = PyTuple_Pack(1, __pyx_kp_s__20); if (unlikely(!__pyx_tuple__56)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1047; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __Pyx_GOTREF(__pyx_tuple__56); __Pyx_GIVEREF(__pyx_tuple__56); __pyx_tuple__57 = PyTuple_Pack(1, __pyx_kp_s__22); if (unlikely(!__pyx_tuple__57)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1047; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __Pyx_GOTREF(__pyx_tuple__57); __Pyx_GIVEREF(__pyx_tuple__57); __pyx_tuple__58 = PyTuple_Pack(1, __pyx_kp_s_No_matching_signature_found); if (unlikely(!__pyx_tuple__58)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1047; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __Pyx_GOTREF(__pyx_tuple__58); __Pyx_GIVEREF(__pyx_tuple__58); __pyx_tuple__59 = PyTuple_Pack(1, __pyx_kp_s_Function_call_with_ambiguous_arg); if (unlikely(!__pyx_tuple__59)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1047; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __Pyx_GOTREF(__pyx_tuple__59); __Pyx_GIVEREF(__pyx_tuple__59); /* "_pywt.pyx":1063 * * if level < 1: * raise ValueError("Value of level must be greater than 0.") # <<<<<<<<<<<<<< * * for i from 0 <= i < level: */ __pyx_tuple__60 = PyTuple_Pack(1, __pyx_kp_s_Value_of_level_must_be_greater_t); if (unlikely(!__pyx_tuple__60)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1063; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __Pyx_GOTREF(__pyx_tuple__60); __Pyx_GIVEREF(__pyx_tuple__60); /* "_pywt.pyx":1068 * output_len = c_wt.dwt_buffer_length(data.size, w.dec_len, mode_) * if output_len < 1: * raise RuntimeError("Invalid output length.") # <<<<<<<<<<<<<< * coeffs = np.zeros(output_len, dtype=data.dtype) * */ __pyx_tuple__61 = PyTuple_Pack(1, __pyx_kp_s_Invalid_output_length); if (unlikely(!__pyx_tuple__61)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1068; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __Pyx_GOTREF(__pyx_tuple__61); __Pyx_GIVEREF(__pyx_tuple__61); /* "_pywt.pyx":1079 * if c_wt.float_dec_a(&data[0], data.size, w.w, * &coeffs[0], coeffs.size, mode_) < 0: * raise RuntimeError("C dec_a failed.") # <<<<<<<<<<<<<< * else: * raise RuntimeError("Invalid data type.") */ __pyx_tuple__62 = PyTuple_Pack(1, __pyx_kp_s_C_dec_a_failed); if (unlikely(!__pyx_tuple__62)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1079; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __Pyx_GOTREF(__pyx_tuple__62); __Pyx_GIVEREF(__pyx_tuple__62); /* "_pywt.pyx":1090 * if c_wt.float_dec_d(&data[0], data.size, w.w, * &coeffs[0], coeffs.size, mode_) < 0: * raise RuntimeError("C dec_a failed.") # <<<<<<<<<<<<<< * else: * raise RuntimeError("Invalid data type.") */ __pyx_tuple__63 = PyTuple_Pack(1, __pyx_kp_s_C_dec_a_failed); if (unlikely(!__pyx_tuple__63)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1090; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __Pyx_GOTREF(__pyx_tuple__63); __Pyx_GIVEREF(__pyx_tuple__63); /* "_pywt.pyx":1063 * * if level < 1: * raise ValueError("Value of level must be greater than 0.") # <<<<<<<<<<<<<< * * for i from 0 <= i < level: */ __pyx_tuple__64 = PyTuple_Pack(1, __pyx_kp_s_Value_of_level_must_be_greater_t); if (unlikely(!__pyx_tuple__64)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1063; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __Pyx_GOTREF(__pyx_tuple__64); __Pyx_GIVEREF(__pyx_tuple__64); /* "_pywt.pyx":1068 * output_len = c_wt.dwt_buffer_length(data.size, w.dec_len, mode_) * if output_len < 1: * raise RuntimeError("Invalid output length.") # <<<<<<<<<<<<<< * coeffs = np.zeros(output_len, dtype=data.dtype) * */ __pyx_tuple__65 = PyTuple_Pack(1, __pyx_kp_s_Invalid_output_length); if (unlikely(!__pyx_tuple__65)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1068; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __Pyx_GOTREF(__pyx_tuple__65); __Pyx_GIVEREF(__pyx_tuple__65); /* "_pywt.pyx":1075 * if c_wt.double_dec_a(&data[0], data.size, w.w, * &coeffs[0], coeffs.size, mode_) < 0: * raise RuntimeError("C dec_a failed.") # <<<<<<<<<<<<<< * elif data_t is np.float32_t: * if c_wt.float_dec_a(&data[0], data.size, w.w, */ __pyx_tuple__66 = PyTuple_Pack(1, __pyx_kp_s_C_dec_a_failed); if (unlikely(!__pyx_tuple__66)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1075; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __Pyx_GOTREF(__pyx_tuple__66); __Pyx_GIVEREF(__pyx_tuple__66); /* "_pywt.pyx":1086 * if c_wt.double_dec_d(&data[0], data.size, w.w, * &coeffs[0], coeffs.size, mode_) < 0: * raise RuntimeError("C dec_a failed.") # <<<<<<<<<<<<<< * elif data_t is np.float32_t: * if c_wt.float_dec_d(&data[0], data.size, w.w, */ __pyx_tuple__67 = PyTuple_Pack(1, __pyx_kp_s_C_dec_a_failed); if (unlikely(!__pyx_tuple__67)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1086; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __Pyx_GOTREF(__pyx_tuple__67); __Pyx_GIVEREF(__pyx_tuple__67); /* "_pywt.pyx":1162 * * * def _swt(np.ndarray[data_t, ndim=1, mode="c"] data, object wavelet, # <<<<<<<<<<<<<< * object level=None, int start_level=0): * """See `swt` for details.""" */ __pyx_tuple__70 = PyTuple_Pack(1, __pyx_kp_s__20); if (unlikely(!__pyx_tuple__70)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1162; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __Pyx_GOTREF(__pyx_tuple__70); __Pyx_GIVEREF(__pyx_tuple__70); __pyx_tuple__71 = PyTuple_Pack(1, __pyx_kp_s__22); if (unlikely(!__pyx_tuple__71)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1162; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __Pyx_GOTREF(__pyx_tuple__71); __Pyx_GIVEREF(__pyx_tuple__71); __pyx_tuple__72 = PyTuple_Pack(1, __pyx_kp_s_No_matching_signature_found); if (unlikely(!__pyx_tuple__72)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1162; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __Pyx_GOTREF(__pyx_tuple__72); __Pyx_GIVEREF(__pyx_tuple__72); __pyx_tuple__73 = PyTuple_Pack(1, __pyx_kp_s_Function_call_with_ambiguous_arg); if (unlikely(!__pyx_tuple__73)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1162; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __Pyx_GOTREF(__pyx_tuple__73); __Pyx_GIVEREF(__pyx_tuple__73); /* "_pywt.pyx":1170 * * if data.size % 2: * raise ValueError("Length of data must be even.") # <<<<<<<<<<<<<< * * w = c_wavelet_from_object(wavelet) */ __pyx_tuple__74 = PyTuple_Pack(1, __pyx_kp_s_Length_of_data_must_be_even); if (unlikely(!__pyx_tuple__74)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1170; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __Pyx_GOTREF(__pyx_tuple__74); __Pyx_GIVEREF(__pyx_tuple__74); /* "_pywt.pyx":1182 * * if level_ < 1: * raise ValueError("Level value must be greater than zero.") # <<<<<<<<<<<<<< * if start_level < 0: * raise ValueError("start_level must be greater than zero.") */ __pyx_tuple__75 = PyTuple_Pack(1, __pyx_kp_s_Level_value_must_be_greater_than); if (unlikely(!__pyx_tuple__75)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1182; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __Pyx_GOTREF(__pyx_tuple__75); __Pyx_GIVEREF(__pyx_tuple__75); /* "_pywt.pyx":1184 * raise ValueError("Level value must be greater than zero.") * if start_level < 0: * raise ValueError("start_level must be greater than zero.") # <<<<<<<<<<<<<< * if start_level >= c_wt.swt_max_level(data.size): * raise ValueError("start_level must be less than %d." % */ __pyx_tuple__76 = PyTuple_Pack(1, __pyx_kp_s_start_level_must_be_greater_than); if (unlikely(!__pyx_tuple__76)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1184; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __Pyx_GOTREF(__pyx_tuple__76); __Pyx_GIVEREF(__pyx_tuple__76); /* "_pywt.pyx":1198 * output_len = c_wt.swt_buffer_length(data.size) * if output_len < 1: * raise RuntimeError("Invalid output length.") # <<<<<<<<<<<<<< * * ret = [] */ __pyx_tuple__77 = PyTuple_Pack(1, __pyx_kp_s_Invalid_output_length); if (unlikely(!__pyx_tuple__77)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1198; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __Pyx_GOTREF(__pyx_tuple__77); __Pyx_GIVEREF(__pyx_tuple__77); /* "_pywt.pyx":1212 * if c_wt.float_swt_d(&data[0], data.size, w.w, * &cD[0], cD.size, i) < 0: * raise RuntimeError("C swt failed.") # <<<<<<<<<<<<<< * else: * raise RuntimeError("Invalid data type.") */ __pyx_tuple__78 = PyTuple_Pack(1, __pyx_kp_s_C_swt_failed); if (unlikely(!__pyx_tuple__78)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1212; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __Pyx_GOTREF(__pyx_tuple__78); __Pyx_GIVEREF(__pyx_tuple__78); /* "_pywt.pyx":1226 * if c_wt.float_swt_a(&data[0], data.size, w.w, * &cA[0], cA.size, i) < 0: * raise RuntimeError("C swt failed.") # <<<<<<<<<<<<<< * else: * raise RuntimeError("Invalid data type.") */ __pyx_tuple__79 = PyTuple_Pack(1, __pyx_kp_s_C_swt_failed); if (unlikely(!__pyx_tuple__79)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1226; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __Pyx_GOTREF(__pyx_tuple__79); __Pyx_GIVEREF(__pyx_tuple__79); /* "_pywt.pyx":1170 * * if data.size % 2: * raise ValueError("Length of data must be even.") # <<<<<<<<<<<<<< * * w = c_wavelet_from_object(wavelet) */ __pyx_tuple__80 = PyTuple_Pack(1, __pyx_kp_s_Length_of_data_must_be_even); if (unlikely(!__pyx_tuple__80)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1170; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __Pyx_GOTREF(__pyx_tuple__80); __Pyx_GIVEREF(__pyx_tuple__80); /* "_pywt.pyx":1182 * * if level_ < 1: * raise ValueError("Level value must be greater than zero.") # <<<<<<<<<<<<<< * if start_level < 0: * raise ValueError("start_level must be greater than zero.") */ __pyx_tuple__81 = PyTuple_Pack(1, __pyx_kp_s_Level_value_must_be_greater_than); if (unlikely(!__pyx_tuple__81)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1182; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __Pyx_GOTREF(__pyx_tuple__81); __Pyx_GIVEREF(__pyx_tuple__81); /* "_pywt.pyx":1184 * raise ValueError("Level value must be greater than zero.") * if start_level < 0: * raise ValueError("start_level must be greater than zero.") # <<<<<<<<<<<<<< * if start_level >= c_wt.swt_max_level(data.size): * raise ValueError("start_level must be less than %d." % */ __pyx_tuple__82 = PyTuple_Pack(1, __pyx_kp_s_start_level_must_be_greater_than); if (unlikely(!__pyx_tuple__82)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1184; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __Pyx_GOTREF(__pyx_tuple__82); __Pyx_GIVEREF(__pyx_tuple__82); /* "_pywt.pyx":1198 * output_len = c_wt.swt_buffer_length(data.size) * if output_len < 1: * raise RuntimeError("Invalid output length.") # <<<<<<<<<<<<<< * * ret = [] */ __pyx_tuple__83 = PyTuple_Pack(1, __pyx_kp_s_Invalid_output_length); if (unlikely(!__pyx_tuple__83)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1198; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __Pyx_GOTREF(__pyx_tuple__83); __Pyx_GIVEREF(__pyx_tuple__83); /* "_pywt.pyx":1208 * if c_wt.double_swt_d(&data[0], data.size, w.w, * &cD[0], cD.size, i) < 0: * raise RuntimeError("C swt failed.") # <<<<<<<<<<<<<< * elif data_t is np.float32_t: * if c_wt.float_swt_d(&data[0], data.size, w.w, */ __pyx_tuple__84 = PyTuple_Pack(1, __pyx_kp_s_C_swt_failed); if (unlikely(!__pyx_tuple__84)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1208; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __Pyx_GOTREF(__pyx_tuple__84); __Pyx_GIVEREF(__pyx_tuple__84); /* "_pywt.pyx":1222 * if c_wt.double_swt_a(&data[0], data.size, w.w, * &cA[0], cA.size, i) < 0: * raise RuntimeError("C swt failed.") # <<<<<<<<<<<<<< * elif data_t is np.float32_t: * if c_wt.float_swt_a(&data[0], data.size, w.w, */ __pyx_tuple__85 = PyTuple_Pack(1, __pyx_kp_s_C_swt_failed); if (unlikely(!__pyx_tuple__85)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1222; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __Pyx_GOTREF(__pyx_tuple__85); __Pyx_GIVEREF(__pyx_tuple__85); /* "../../../../../.local/lib/python2.6/site-packages/Cython/Includes/numpy/__init__.pxd":218 * if ((flags & pybuf.PyBUF_C_CONTIGUOUS == pybuf.PyBUF_C_CONTIGUOUS) * and not PyArray_CHKFLAGS(self, NPY_C_CONTIGUOUS)): * raise ValueError(u"ndarray is not C contiguous") # <<<<<<<<<<<<<< * * if ((flags & pybuf.PyBUF_F_CONTIGUOUS == pybuf.PyBUF_F_CONTIGUOUS) */ __pyx_tuple__86 = PyTuple_Pack(1, __pyx_kp_u_ndarray_is_not_C_contiguous); if (unlikely(!__pyx_tuple__86)) {__pyx_filename = __pyx_f[1]; __pyx_lineno = 218; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __Pyx_GOTREF(__pyx_tuple__86); __Pyx_GIVEREF(__pyx_tuple__86); /* "../../../../../.local/lib/python2.6/site-packages/Cython/Includes/numpy/__init__.pxd":222 * if ((flags & pybuf.PyBUF_F_CONTIGUOUS == pybuf.PyBUF_F_CONTIGUOUS) * and not PyArray_CHKFLAGS(self, NPY_F_CONTIGUOUS)): * raise ValueError(u"ndarray is not Fortran contiguous") # <<<<<<<<<<<<<< * * info.buf = PyArray_DATA(self) */ __pyx_tuple__87 = PyTuple_Pack(1, __pyx_kp_u_ndarray_is_not_Fortran_contiguou); if (unlikely(!__pyx_tuple__87)) {__pyx_filename = __pyx_f[1]; __pyx_lineno = 222; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __Pyx_GOTREF(__pyx_tuple__87); __Pyx_GIVEREF(__pyx_tuple__87); /* "../../../../../.local/lib/python2.6/site-packages/Cython/Includes/numpy/__init__.pxd":260 * if ((descr.byteorder == c'>' and little_endian) or * (descr.byteorder == c'<' and not little_endian)): * raise ValueError(u"Non-native byte order not supported") # <<<<<<<<<<<<<< * if t == NPY_BYTE: f = "b" * elif t == NPY_UBYTE: f = "B" */ __pyx_tuple__88 = PyTuple_Pack(1, __pyx_kp_u_Non_native_byte_order_not_suppor); if (unlikely(!__pyx_tuple__88)) {__pyx_filename = __pyx_f[1]; __pyx_lineno = 260; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __Pyx_GOTREF(__pyx_tuple__88); __Pyx_GIVEREF(__pyx_tuple__88); /* "../../../../../.local/lib/python2.6/site-packages/Cython/Includes/numpy/__init__.pxd":802 * * if (end - f) - (new_offset - offset[0]) < 15: * raise RuntimeError(u"Format string allocated too short, see comment in numpy.pxd") # <<<<<<<<<<<<<< * * if ((child.byteorder == c'>' and little_endian) or */ __pyx_tuple__89 = PyTuple_Pack(1, __pyx_kp_u_Format_string_allocated_too_shor); if (unlikely(!__pyx_tuple__89)) {__pyx_filename = __pyx_f[1]; __pyx_lineno = 802; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __Pyx_GOTREF(__pyx_tuple__89); __Pyx_GIVEREF(__pyx_tuple__89); /* "../../../../../.local/lib/python2.6/site-packages/Cython/Includes/numpy/__init__.pxd":806 * if ((child.byteorder == c'>' and little_endian) or * (child.byteorder == c'<' and not little_endian)): * raise ValueError(u"Non-native byte order not supported") # <<<<<<<<<<<<<< * # One could encode it in the format 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goto raise_error; } } __Pyx_ErrRestore(type, value, tb); return; raise_error: Py_XDECREF(value); Py_XDECREF(type); Py_XDECREF(tb); return; } #else static void __Pyx_Raise(PyObject *type, PyObject *value, PyObject *tb, PyObject *cause) { PyObject* owned_instance = NULL; if (tb == Py_None) { tb = 0; } else if (tb && !PyTraceBack_Check(tb)) { PyErr_SetString(PyExc_TypeError, "raise: arg 3 must be a traceback or None"); goto bad; } if (value == Py_None) value = 0; if (PyExceptionInstance_Check(type)) { if (value) { PyErr_SetString(PyExc_TypeError, "instance exception may not have a separate value"); goto bad; } value = type; type = (PyObject*) Py_TYPE(value); } else if (PyExceptionClass_Check(type)) { PyObject *instance_class = NULL; if (value && PyExceptionInstance_Check(value)) { instance_class = (PyObject*) Py_TYPE(value); if (instance_class != type) { if (PyObject_IsSubclass(instance_class, type)) { type = instance_class; } else { instance_class = NULL; } } } if (!instance_class) { PyObject *args; if (!value) args = PyTuple_New(0); else if (PyTuple_Check(value)) { Py_INCREF(value); args = value; } else args = PyTuple_Pack(1, value); if (!args) goto bad; owned_instance = PyObject_Call(type, args, NULL); Py_DECREF(args); if (!owned_instance) goto bad; value = owned_instance; if (!PyExceptionInstance_Check(value)) { PyErr_Format(PyExc_TypeError, "calling %R should have returned an instance of " "BaseException, not %R", type, Py_TYPE(value)); goto bad; } } } else { PyErr_SetString(PyExc_TypeError, "raise: exception class must be a subclass of BaseException"); goto bad; } #if PY_VERSION_HEX >= 0x03030000 if (cause) { #else if (cause && cause != Py_None) { #endif PyObject *fixed_cause; if (cause == Py_None) { fixed_cause = NULL; } else if (PyExceptionClass_Check(cause)) { fixed_cause = PyObject_CallObject(cause, NULL); if (fixed_cause == NULL) goto bad; } else if (PyExceptionInstance_Check(cause)) { fixed_cause = cause; Py_INCREF(fixed_cause); } else { PyErr_SetString(PyExc_TypeError, "exception causes must derive from " "BaseException"); goto bad; } PyException_SetCause(value, fixed_cause); } PyErr_SetObject(type, value); if (tb) { #if CYTHON_COMPILING_IN_PYPY PyObject *tmp_type, *tmp_value, *tmp_tb; PyErr_Fetch(tmp_type, tmp_value, tmp_tb); Py_INCREF(tb); PyErr_Restore(tmp_type, tmp_value, tb); Py_XDECREF(tmp_tb); #else PyThreadState *tstate = PyThreadState_GET(); PyObject* tmp_tb = tstate->curexc_traceback; if (tb != tmp_tb) { Py_INCREF(tb); tstate->curexc_traceback = tb; Py_XDECREF(tmp_tb); } #endif } bad: Py_XDECREF(owned_instance); return; } #endif static CYTHON_INLINE PyObject *__Pyx_GetModuleGlobalName(PyObject *name) { PyObject *result; #if CYTHON_COMPILING_IN_CPYTHON result = PyDict_GetItem(__pyx_d, name); if (likely(result)) { Py_INCREF(result); } else { #else result = PyObject_GetItem(__pyx_d, name); if (!result) { PyErr_Clear(); #endif result = __Pyx_GetBuiltinName(name); } return result; } static CYTHON_INLINE PyObject *__Pyx_GetAttr(PyObject *o, PyObject *n) { #if CYTHON_COMPILING_IN_CPYTHON #if PY_MAJOR_VERSION >= 3 if (likely(PyUnicode_Check(n))) #else if (likely(PyString_Check(n))) #endif return __Pyx_PyObject_GetAttrStr(o, n); #endif return PyObject_GetAttr(o, n); } static CYTHON_INLINE void __Pyx_ExceptionSave(PyObject **type, PyObject **value, PyObject **tb) { #if CYTHON_COMPILING_IN_CPYTHON PyThreadState *tstate = PyThreadState_GET(); *type = tstate->exc_type; *value = tstate->exc_value; *tb = tstate->exc_traceback; Py_XINCREF(*type); Py_XINCREF(*value); Py_XINCREF(*tb); #else PyErr_GetExcInfo(type, value, tb); #endif } static void __Pyx_ExceptionReset(PyObject *type, PyObject *value, PyObject *tb) { #if CYTHON_COMPILING_IN_CPYTHON PyObject *tmp_type, *tmp_value, *tmp_tb; PyThreadState *tstate = PyThreadState_GET(); tmp_type = tstate->exc_type; tmp_value = tstate->exc_value; tmp_tb = tstate->exc_traceback; tstate->exc_type = type; tstate->exc_value = value; tstate->exc_traceback = tb; Py_XDECREF(tmp_type); Py_XDECREF(tmp_value); Py_XDECREF(tmp_tb); #else PyErr_SetExcInfo(type, value, tb); #endif } static int __Pyx_GetException(PyObject **type, PyObject **value, PyObject **tb) { PyObject *local_type, *local_value, *local_tb; #if CYTHON_COMPILING_IN_CPYTHON PyObject *tmp_type, *tmp_value, *tmp_tb; PyThreadState *tstate = PyThreadState_GET(); local_type = tstate->curexc_type; local_value = tstate->curexc_value; local_tb = tstate->curexc_traceback; tstate->curexc_type = 0; tstate->curexc_value = 0; tstate->curexc_traceback = 0; #else PyErr_Fetch(&local_type, &local_value, &local_tb); #endif PyErr_NormalizeException(&local_type, &local_value, &local_tb); #if CYTHON_COMPILING_IN_CPYTHON if (unlikely(tstate->curexc_type)) #else if (unlikely(PyErr_Occurred())) #endif goto bad; #if PY_MAJOR_VERSION >= 3 if (local_tb) { if (unlikely(PyException_SetTraceback(local_value, local_tb) < 0)) goto bad; } #endif Py_XINCREF(local_tb); Py_XINCREF(local_type); Py_XINCREF(local_value); *type = local_type; *value = local_value; *tb = local_tb; #if CYTHON_COMPILING_IN_CPYTHON tmp_type = tstate->exc_type; tmp_value = tstate->exc_value; tmp_tb = tstate->exc_traceback; tstate->exc_type = local_type; tstate->exc_value = local_value; tstate->exc_traceback = local_tb; Py_XDECREF(tmp_type); Py_XDECREF(tmp_value); Py_XDECREF(tmp_tb); #else PyErr_SetExcInfo(local_type, local_value, local_tb); #endif return 0; bad: *type = 0; *value = 0; *tb = 0; Py_XDECREF(local_type); Py_XDECREF(local_value); Py_XDECREF(local_tb); return -1; } static CYTHON_INLINE PyObject *__Pyx_GetItemInt_Generic(PyObject *o, PyObject* j) { PyObject *r; if (!j) return NULL; r = PyObject_GetItem(o, j); Py_DECREF(j); return r; } static CYTHON_INLINE PyObject *__Pyx_GetItemInt_List_Fast(PyObject *o, Py_ssize_t i, int wraparound, int boundscheck) { #if CYTHON_COMPILING_IN_CPYTHON if (wraparound & unlikely(i < 0)) i += PyList_GET_SIZE(o); if ((!boundscheck) || likely((0 <= i) & (i < PyList_GET_SIZE(o)))) { PyObject *r = PyList_GET_ITEM(o, i); Py_INCREF(r); return r; } return __Pyx_GetItemInt_Generic(o, PyInt_FromSsize_t(i)); #else return PySequence_GetItem(o, i); #endif } static CYTHON_INLINE PyObject *__Pyx_GetItemInt_Tuple_Fast(PyObject *o, Py_ssize_t i, int wraparound, int boundscheck) { #if CYTHON_COMPILING_IN_CPYTHON if (wraparound & unlikely(i < 0)) i += PyTuple_GET_SIZE(o); if ((!boundscheck) || likely((0 <= i) & (i < PyTuple_GET_SIZE(o)))) { PyObject *r = PyTuple_GET_ITEM(o, i); Py_INCREF(r); return r; } return __Pyx_GetItemInt_Generic(o, PyInt_FromSsize_t(i)); #else return PySequence_GetItem(o, i); #endif } static CYTHON_INLINE PyObject *__Pyx_GetItemInt_Fast(PyObject *o, Py_ssize_t i, int is_list, int wraparound, int boundscheck) { #if CYTHON_COMPILING_IN_CPYTHON if (is_list || PyList_CheckExact(o)) { Py_ssize_t n = ((!wraparound) | likely(i >= 0)) ? i : i + PyList_GET_SIZE(o); if ((!boundscheck) || (likely((n >= 0) & (n < PyList_GET_SIZE(o))))) { PyObject *r = PyList_GET_ITEM(o, n); Py_INCREF(r); return r; } } else if (PyTuple_CheckExact(o)) { Py_ssize_t n = ((!wraparound) | likely(i >= 0)) ? i : i + PyTuple_GET_SIZE(o); if ((!boundscheck) || likely((n >= 0) & (n < PyTuple_GET_SIZE(o)))) { PyObject *r = PyTuple_GET_ITEM(o, n); Py_INCREF(r); return r; } } else { PySequenceMethods *m = Py_TYPE(o)->tp_as_sequence; if (likely(m && m->sq_item)) { if (wraparound && unlikely(i < 0) && likely(m->sq_length)) { Py_ssize_t l = m->sq_length(o); if (likely(l >= 0)) { i += l; } else { if (PyErr_ExceptionMatches(PyExc_OverflowError)) PyErr_Clear(); else return NULL; } } return m->sq_item(o, i); } } #else if (is_list || PySequence_Check(o)) { return PySequence_GetItem(o, i); } #endif return __Pyx_GetItemInt_Generic(o, PyInt_FromSsize_t(i)); } #if CYTHON_COMPILING_IN_CPYTHON static CYTHON_INLINE PyObject* __Pyx_PyObject_CallMethO(PyObject *func, PyObject *arg) { PyObject *self, *result; PyCFunction cfunc; cfunc = PyCFunction_GET_FUNCTION(func); self = PyCFunction_GET_SELF(func); if (unlikely(Py_EnterRecursiveCall((char*)" while calling a Python object"))) return NULL; result = cfunc(self, arg); Py_LeaveRecursiveCall(); if (unlikely(!result) && unlikely(!PyErr_Occurred())) { PyErr_SetString( PyExc_SystemError, "NULL result without error in PyObject_Call"); } return result; } #endif #if CYTHON_COMPILING_IN_CPYTHON static PyObject* __Pyx__PyObject_CallOneArg(PyObject *func, PyObject *arg) { PyObject *result; PyObject *args = PyTuple_New(1); if (unlikely(!args)) return NULL; Py_INCREF(arg); PyTuple_SET_ITEM(args, 0, arg); result = __Pyx_PyObject_Call(func, args, NULL); Py_DECREF(args); return result; } static CYTHON_INLINE PyObject* __Pyx_PyObject_CallOneArg(PyObject *func, PyObject *arg) { #ifdef __Pyx_CyFunction_USED if (likely(PyCFunction_Check(func) || PyObject_TypeCheck(func, __pyx_CyFunctionType))) { #else if (likely(PyCFunction_Check(func))) { #endif if (likely(PyCFunction_GET_FLAGS(func) & METH_O)) { return __Pyx_PyObject_CallMethO(func, arg); } } return __Pyx__PyObject_CallOneArg(func, arg); } #else static CYTHON_INLINE PyObject* __Pyx_PyObject_CallOneArg(PyObject *func, PyObject *arg) { PyObject* args = PyTuple_Pack(1, arg); return (likely(args)) ? __Pyx_PyObject_Call(func, args, NULL) : NULL; } #endif static PyObject* __Pyx_PyObject_CallMethod1(PyObject* obj, PyObject* method_name, PyObject* arg) { PyObject *method, *result = NULL; method = __Pyx_PyObject_GetAttrStr(obj, method_name); if (unlikely(!method)) goto bad; #if CYTHON_COMPILING_IN_CPYTHON if (likely(PyMethod_Check(method))) { PyObject *self = PyMethod_GET_SELF(method); if (likely(self)) { PyObject *args; PyObject *function = PyMethod_GET_FUNCTION(method); args = PyTuple_New(2); if (unlikely(!args)) goto bad; Py_INCREF(self); PyTuple_SET_ITEM(args, 0, self); Py_INCREF(arg); PyTuple_SET_ITEM(args, 1, arg); Py_INCREF(function); Py_DECREF(method); method = NULL; result = __Pyx_PyObject_Call(function, args, NULL); Py_DECREF(args); Py_DECREF(function); return result; } } #endif result = __Pyx_PyObject_CallOneArg(method, arg); bad: Py_XDECREF(method); return result; } static CYTHON_INLINE int __Pyx_PyObject_Append(PyObject* L, PyObject* x) { if (likely(PyList_CheckExact(L))) { if (unlikely(__Pyx_PyList_Append(L, x) < 0)) return -1; } else { PyObject* retval = __Pyx_PyObject_CallMethod1(L, __pyx_n_s_append, x); if (unlikely(!retval)) return -1; Py_DECREF(retval); } return 0; } static CYTHON_INLINE PyObject* __Pyx_PyObject_GetSlice( PyObject* obj, Py_ssize_t cstart, Py_ssize_t cstop, PyObject** _py_start, PyObject** _py_stop, PyObject** _py_slice, int has_cstart, int has_cstop, CYTHON_UNUSED int wraparound) { #if CYTHON_COMPILING_IN_CPYTHON PyMappingMethods* mp; #if PY_MAJOR_VERSION < 3 PySequenceMethods* ms = Py_TYPE(obj)->tp_as_sequence; if (likely(ms && ms->sq_slice)) { if (!has_cstart) { if (_py_start && (*_py_start != Py_None)) { cstart = __Pyx_PyIndex_AsSsize_t(*_py_start); if ((cstart == (Py_ssize_t)-1) && PyErr_Occurred()) goto bad; } else cstart = 0; } if (!has_cstop) { if (_py_stop && (*_py_stop != Py_None)) { cstop = __Pyx_PyIndex_AsSsize_t(*_py_stop); if ((cstop == (Py_ssize_t)-1) && PyErr_Occurred()) goto bad; } else cstop = PY_SSIZE_T_MAX; } if (wraparound && unlikely((cstart < 0) | (cstop < 0)) && likely(ms->sq_length)) { Py_ssize_t l = ms->sq_length(obj); if (likely(l >= 0)) { if (cstop < 0) { cstop += l; if (cstop < 0) cstop = 0; } if (cstart < 0) { cstart += l; if (cstart < 0) cstart = 0; } } else { if (PyErr_ExceptionMatches(PyExc_OverflowError)) PyErr_Clear(); else goto bad; } } return ms->sq_slice(obj, cstart, cstop); } #endif mp = Py_TYPE(obj)->tp_as_mapping; if (likely(mp && mp->mp_subscript)) #endif { PyObject* result; PyObject *py_slice, *py_start, *py_stop; if (_py_slice) { py_slice = *_py_slice; } else { PyObject* owned_start = NULL; PyObject* owned_stop = NULL; if (_py_start) { py_start = *_py_start; } else { if (has_cstart) { owned_start = py_start = PyInt_FromSsize_t(cstart); if (unlikely(!py_start)) goto bad; } else py_start = Py_None; } if (_py_stop) { py_stop = *_py_stop; } else { if (has_cstop) { owned_stop = py_stop = PyInt_FromSsize_t(cstop); if (unlikely(!py_stop)) { Py_XDECREF(owned_start); goto bad; } } else py_stop = Py_None; } py_slice = PySlice_New(py_start, py_stop, Py_None); Py_XDECREF(owned_start); Py_XDECREF(owned_stop); if (unlikely(!py_slice)) goto bad; } #if CYTHON_COMPILING_IN_CPYTHON result = mp->mp_subscript(obj, py_slice); #else result = PyObject_GetItem(obj, py_slice); #endif if (!_py_slice) { Py_DECREF(py_slice); } return result; } PyErr_Format(PyExc_TypeError, "'%.200s' object is unsliceable", Py_TYPE(obj)->tp_name); bad: return NULL; } #if CYTHON_COMPILING_IN_CPYTHON static CYTHON_INLINE PyObject* __Pyx_PyObject_CallNoArg(PyObject *func) { #ifdef __Pyx_CyFunction_USED if (likely(PyCFunction_Check(func) || PyObject_TypeCheck(func, __pyx_CyFunctionType))) { #else if (likely(PyCFunction_Check(func))) { #endif if (likely(PyCFunction_GET_FLAGS(func) & METH_NOARGS)) { return __Pyx_PyObject_CallMethO(func, NULL); } } return __Pyx_PyObject_Call(func, __pyx_empty_tuple, NULL); } #endif static CYTHON_INLINE void __Pyx_RaiseTooManyValuesError(Py_ssize_t expected) { PyErr_Format(PyExc_ValueError, "too many values to unpack (expected %" CYTHON_FORMAT_SSIZE_T "d)", expected); } static CYTHON_INLINE void __Pyx_RaiseNeedMoreValuesError(Py_ssize_t index) { PyErr_Format(PyExc_ValueError, "need more than %" CYTHON_FORMAT_SSIZE_T "d value%.1s to unpack", index, (index == 1) ? "" : "s"); } static CYTHON_INLINE int __Pyx_IterFinish(void) { #if CYTHON_COMPILING_IN_CPYTHON PyThreadState *tstate = PyThreadState_GET(); PyObject* exc_type = tstate->curexc_type; if (unlikely(exc_type)) { if (likely(exc_type == PyExc_StopIteration) || PyErr_GivenExceptionMatches(exc_type, PyExc_StopIteration)) { PyObject *exc_value, *exc_tb; exc_value = tstate->curexc_value; exc_tb = tstate->curexc_traceback; tstate->curexc_type = 0; tstate->curexc_value = 0; tstate->curexc_traceback = 0; Py_DECREF(exc_type); Py_XDECREF(exc_value); Py_XDECREF(exc_tb); return 0; } else { return -1; } } return 0; #else if (unlikely(PyErr_Occurred())) { if (likely(PyErr_ExceptionMatches(PyExc_StopIteration))) { PyErr_Clear(); return 0; } else { return -1; } } return 0; #endif } static int __Pyx_IternextUnpackEndCheck(PyObject *retval, Py_ssize_t expected) { if (unlikely(retval)) { Py_DECREF(retval); __Pyx_RaiseTooManyValuesError(expected); return -1; } else { return __Pyx_IterFinish(); } return 0; } static CYTHON_INLINE PyObject* __Pyx_decode_c_string( const char* cstring, Py_ssize_t start, Py_ssize_t stop, const char* encoding, const char* errors, PyObject* (*decode_func)(const char *s, Py_ssize_t size, const char *errors)) { Py_ssize_t length; if (unlikely((start < 0) | (stop < 0))) { length = strlen(cstring); if (start < 0) { start += length; if (start < 0) start = 0; } if (stop < 0) stop += length; } length = stop - start; if (unlikely(length <= 0)) return PyUnicode_FromUnicode(NULL, 0); cstring += start; if (decode_func) { return decode_func(cstring, length, errors); } else { return PyUnicode_Decode(cstring, length, encoding, errors); } } static CYTHON_INLINE long __Pyx_mod_long(long a, long b) { long r = a % b; r += ((r != 0) & ((r ^ b) < 0)) * b; return r; } static CYTHON_INLINE int __Pyx_SetItemInt_Generic(PyObject *o, PyObject *j, PyObject *v) { int r; if (!j) return -1; r = PyObject_SetItem(o, j, v); Py_DECREF(j); return r; } static CYTHON_INLINE int __Pyx_SetItemInt_Fast(PyObject *o, Py_ssize_t i, PyObject *v, int is_list, int wraparound, int boundscheck) { #if CYTHON_COMPILING_IN_CPYTHON if (is_list || PyList_CheckExact(o)) { Py_ssize_t n = (!wraparound) ? i : ((likely(i >= 0)) ? i : i + PyList_GET_SIZE(o)); if ((!boundscheck) || likely((n >= 0) & (n < PyList_GET_SIZE(o)))) { PyObject* old = PyList_GET_ITEM(o, n); Py_INCREF(v); PyList_SET_ITEM(o, n, v); Py_DECREF(old); return 1; } } else { PySequenceMethods *m = Py_TYPE(o)->tp_as_sequence; if (likely(m && m->sq_ass_item)) { if (wraparound && unlikely(i < 0) && likely(m->sq_length)) { Py_ssize_t l = m->sq_length(o); if (likely(l >= 0)) { i += l; } else { if (PyErr_ExceptionMatches(PyExc_OverflowError)) PyErr_Clear(); else return -1; } } return m->sq_ass_item(o, i, v); } } #else #if CYTHON_COMPILING_IN_PYPY if (is_list || (PySequence_Check(o) && !PyDict_Check(o))) { #else if (is_list || PySequence_Check(o)) { #endif return PySequence_SetItem(o, i, v); } #endif return __Pyx_SetItemInt_Generic(o, PyInt_FromSsize_t(i), v); } static PyObject* __Pyx_PyObject_CallMethod0(PyObject* obj, PyObject* method_name) { PyObject *method, *result = NULL; method = __Pyx_PyObject_GetAttrStr(obj, method_name); if (unlikely(!method)) goto bad; #if CYTHON_COMPILING_IN_CPYTHON if (likely(PyMethod_Check(method))) { PyObject *self = PyMethod_GET_SELF(method); if (likely(self)) { PyObject *function = PyMethod_GET_FUNCTION(method); result = __Pyx_PyObject_CallOneArg(function, self); Py_DECREF(method); return result; } } #endif result = __Pyx_PyObject_CallNoArg(method); Py_DECREF(method); bad: return result; } static CYTHON_INLINE void __Pyx_RaiseNoneNotIterableError(void) { PyErr_SetString(PyExc_TypeError, "'NoneType' object is not iterable"); } static void __Pyx_UnpackTupleError(PyObject *t, Py_ssize_t index) { if (t == Py_None) { __Pyx_RaiseNoneNotIterableError(); } else if (PyTuple_GET_SIZE(t) < index) { __Pyx_RaiseNeedMoreValuesError(PyTuple_GET_SIZE(t)); } else { __Pyx_RaiseTooManyValuesError(index); } } static CYTHON_INLINE int __Pyx_unpack_tuple2(PyObject* tuple, PyObject** pvalue1, PyObject** pvalue2, int is_tuple, int has_known_size, int decref_tuple) { Py_ssize_t index; PyObject *value1 = NULL, *value2 = NULL, *iter = NULL; if (!is_tuple && unlikely(!PyTuple_Check(tuple))) { iternextfunc iternext; iter = PyObject_GetIter(tuple); if (unlikely(!iter)) goto bad; if (decref_tuple) { Py_DECREF(tuple); tuple = NULL; } iternext = Py_TYPE(iter)->tp_iternext; value1 = iternext(iter); if (unlikely(!value1)) { index = 0; goto unpacking_failed; } value2 = iternext(iter); if (unlikely(!value2)) { index = 1; goto unpacking_failed; } if (!has_known_size && unlikely(__Pyx_IternextUnpackEndCheck(iternext(iter), 2))) goto bad; Py_DECREF(iter); } else { if (!has_known_size && unlikely(PyTuple_GET_SIZE(tuple) != 2)) { __Pyx_UnpackTupleError(tuple, 2); goto bad; } #if CYTHON_COMPILING_IN_PYPY value1 = PySequence_ITEM(tuple, 0); if (unlikely(!value1)) goto bad; value2 = PySequence_ITEM(tuple, 1); if (unlikely(!value2)) goto bad; #else value1 = PyTuple_GET_ITEM(tuple, 0); value2 = PyTuple_GET_ITEM(tuple, 1); Py_INCREF(value1); Py_INCREF(value2); #endif if (decref_tuple) { Py_DECREF(tuple); } } *pvalue1 = value1; *pvalue2 = value2; return 0; unpacking_failed: if (!has_known_size && __Pyx_IterFinish() == 0) __Pyx_RaiseNeedMoreValuesError(index); bad: Py_XDECREF(iter); Py_XDECREF(value1); Py_XDECREF(value2); if (decref_tuple) { Py_XDECREF(tuple); } return -1; } static CYTHON_INLINE PyObject* __Pyx_dict_iterator(PyObject* iterable, int is_dict, PyObject* method_name, Py_ssize_t* p_orig_length, int* p_source_is_dict) { is_dict = is_dict || likely(PyDict_CheckExact(iterable)); *p_source_is_dict = is_dict; #if !CYTHON_COMPILING_IN_PYPY if (is_dict) { *p_orig_length = PyDict_Size(iterable); Py_INCREF(iterable); return iterable; } #endif *p_orig_length = 0; if (method_name) { PyObject* iter; iterable = __Pyx_PyObject_CallMethod0(iterable, method_name); if (!iterable) return NULL; #if !CYTHON_COMPILING_IN_PYPY if (PyTuple_CheckExact(iterable) || PyList_CheckExact(iterable)) return iterable; #endif iter = PyObject_GetIter(iterable); Py_DECREF(iterable); return iter; } return PyObject_GetIter(iterable); } static CYTHON_INLINE int __Pyx_dict_iter_next(PyObject* iter_obj, Py_ssize_t orig_length, Py_ssize_t* ppos, PyObject** pkey, PyObject** pvalue, PyObject** pitem, int source_is_dict) { PyObject* next_item; #if !CYTHON_COMPILING_IN_PYPY if (source_is_dict) { PyObject *key, *value; if (unlikely(orig_length != PyDict_Size(iter_obj))) { PyErr_SetString(PyExc_RuntimeError, "dictionary changed size during iteration"); return -1; } if (unlikely(!PyDict_Next(iter_obj, ppos, &key, &value))) { return 0; } if (pitem) { PyObject* tuple = PyTuple_New(2); if (unlikely(!tuple)) { return -1; } Py_INCREF(key); Py_INCREF(value); PyTuple_SET_ITEM(tuple, 0, key); PyTuple_SET_ITEM(tuple, 1, value); *pitem = tuple; } else { if (pkey) { Py_INCREF(key); *pkey = key; } if (pvalue) { Py_INCREF(value); *pvalue = value; } } return 1; } else if (PyTuple_CheckExact(iter_obj)) { Py_ssize_t pos = *ppos; if (unlikely(pos >= PyTuple_GET_SIZE(iter_obj))) return 0; *ppos = pos + 1; next_item = PyTuple_GET_ITEM(iter_obj, pos); Py_INCREF(next_item); } else if (PyList_CheckExact(iter_obj)) { Py_ssize_t pos = *ppos; if (unlikely(pos >= PyList_GET_SIZE(iter_obj))) return 0; *ppos = pos + 1; next_item = PyList_GET_ITEM(iter_obj, pos); Py_INCREF(next_item); } else #endif { next_item = PyIter_Next(iter_obj); if (unlikely(!next_item)) { return __Pyx_IterFinish(); } } if (pitem) { *pitem = next_item; } else if (pkey && pvalue) { if (__Pyx_unpack_tuple2(next_item, pkey, pvalue, source_is_dict, source_is_dict, 1)) return -1; } else if (pkey) { *pkey = next_item; } else { *pvalue = next_item; } return 1; } static void __Pyx_RaiseArgumentTypeInvalid(const char* name, PyObject *obj, PyTypeObject *type) { PyErr_Format(PyExc_TypeError, "Argument '%.200s' has incorrect type (expected %.200s, got %.200s)", name, type->tp_name, Py_TYPE(obj)->tp_name); } static CYTHON_INLINE int __Pyx_ArgTypeTest(PyObject *obj, PyTypeObject *type, int none_allowed, const char *name, int exact) { if (unlikely(!type)) { PyErr_SetString(PyExc_SystemError, "Missing type object"); return 0; } if (none_allowed && obj == Py_None) return 1; else if (exact) { if (likely(Py_TYPE(obj) == type)) return 1; #if PY_MAJOR_VERSION == 2 else if ((type == &PyBaseString_Type) && likely(__Pyx_PyBaseString_CheckExact(obj))) return 1; #endif } else { if (likely(PyObject_TypeCheck(obj, type))) return 1; } __Pyx_RaiseArgumentTypeInvalid(name, obj, type); return 0; } static CYTHON_INLINE int __Pyx_IsLittleEndian(void) { unsigned int n = 1; return *(unsigned char*)(&n) != 0; } static void __Pyx_BufFmt_Init(__Pyx_BufFmt_Context* ctx, __Pyx_BufFmt_StackElem* stack, __Pyx_TypeInfo* type) { stack[0].field = &ctx->root; stack[0].parent_offset = 0; ctx->root.type = type; ctx->root.name = "buffer dtype"; ctx->root.offset = 0; ctx->head = stack; ctx->head->field = &ctx->root; ctx->fmt_offset = 0; ctx->head->parent_offset = 0; ctx->new_packmode = '@'; ctx->enc_packmode = '@'; ctx->new_count = 1; ctx->enc_count = 0; ctx->enc_type = 0; ctx->is_complex = 0; ctx->is_valid_array = 0; ctx->struct_alignment = 0; while (type->typegroup == 'S') { ++ctx->head; ctx->head->field = type->fields; ctx->head->parent_offset = 0; type = type->fields->type; } } static int __Pyx_BufFmt_ParseNumber(const char** ts) { int count; const char* t = *ts; if (*t < '0' || *t > '9') { return -1; } else { count = *t++ - '0'; while (*t >= '0' && *t < '9') { count *= 10; count += *t++ - '0'; } } *ts = t; return count; } static int __Pyx_BufFmt_ExpectNumber(const char **ts) { int number = __Pyx_BufFmt_ParseNumber(ts); if (number == -1) PyErr_Format(PyExc_ValueError,\ "Does not understand character buffer dtype format string ('%c')", **ts); return number; } static void __Pyx_BufFmt_RaiseUnexpectedChar(char ch) { PyErr_Format(PyExc_ValueError, "Unexpected format string character: '%c'", ch); } static const char* __Pyx_BufFmt_DescribeTypeChar(char ch, int is_complex) { switch (ch) { case 'c': return "'char'"; case 'b': return "'signed char'"; case 'B': return "'unsigned char'"; case 'h': return "'short'"; case 'H': return "'unsigned short'"; case 'i': return "'int'"; case 'I': return "'unsigned int'"; case 'l': return "'long'"; case 'L': return "'unsigned long'"; case 'q': return "'long long'"; case 'Q': return "'unsigned long long'"; case 'f': return (is_complex ? "'complex float'" : "'float'"); case 'd': return (is_complex ? "'complex double'" : "'double'"); case 'g': return (is_complex ? "'complex long double'" : "'long double'"); case 'T': return "a struct"; case 'O': return "Python object"; case 'P': return "a pointer"; case 's': case 'p': return "a string"; case 0: return "end"; default: return "unparseable format string"; } } static size_t __Pyx_BufFmt_TypeCharToStandardSize(char ch, int is_complex) { switch (ch) { case '?': case 'c': case 'b': case 'B': case 's': case 'p': return 1; case 'h': case 'H': return 2; case 'i': case 'I': case 'l': case 'L': return 4; case 'q': case 'Q': return 8; case 'f': return (is_complex ? 8 : 4); case 'd': return (is_complex ? 16 : 8); case 'g': { PyErr_SetString(PyExc_ValueError, "Python does not define a standard format string size for long double ('g').."); return 0; } case 'O': case 'P': return sizeof(void*); default: __Pyx_BufFmt_RaiseUnexpectedChar(ch); return 0; } } static size_t __Pyx_BufFmt_TypeCharToNativeSize(char ch, int is_complex) { switch (ch) { case 'c': case 'b': case 'B': case 's': case 'p': return 1; case 'h': case 'H': return sizeof(short); case 'i': case 'I': return sizeof(int); case 'l': case 'L': return sizeof(long); #ifdef HAVE_LONG_LONG case 'q': case 'Q': return sizeof(PY_LONG_LONG); #endif case 'f': return sizeof(float) * (is_complex ? 2 : 1); case 'd': return sizeof(double) * (is_complex ? 2 : 1); case 'g': return sizeof(long double) * (is_complex ? 2 : 1); case 'O': case 'P': return sizeof(void*); default: { __Pyx_BufFmt_RaiseUnexpectedChar(ch); return 0; } } } typedef struct { char c; short x; } __Pyx_st_short; typedef struct { char c; int x; } __Pyx_st_int; typedef struct { char c; long x; } __Pyx_st_long; typedef struct { char c; float x; } __Pyx_st_float; typedef struct { char c; double x; } __Pyx_st_double; typedef struct { char c; long double x; } __Pyx_st_longdouble; typedef struct { char c; void *x; } __Pyx_st_void_p; #ifdef HAVE_LONG_LONG typedef struct { char c; PY_LONG_LONG x; } __Pyx_st_longlong; #endif static size_t __Pyx_BufFmt_TypeCharToAlignment(char ch, CYTHON_UNUSED int is_complex) { switch (ch) { case '?': case 'c': case 'b': case 'B': case 's': case 'p': return 1; case 'h': case 'H': return sizeof(__Pyx_st_short) - sizeof(short); case 'i': case 'I': return sizeof(__Pyx_st_int) - sizeof(int); case 'l': case 'L': return sizeof(__Pyx_st_long) - sizeof(long); #ifdef HAVE_LONG_LONG case 'q': case 'Q': return sizeof(__Pyx_st_longlong) - sizeof(PY_LONG_LONG); #endif case 'f': return sizeof(__Pyx_st_float) - sizeof(float); case 'd': return sizeof(__Pyx_st_double) - sizeof(double); case 'g': return sizeof(__Pyx_st_longdouble) - sizeof(long double); case 'P': case 'O': return sizeof(__Pyx_st_void_p) - sizeof(void*); default: __Pyx_BufFmt_RaiseUnexpectedChar(ch); return 0; } } /* These are for computing the padding at the end of the struct to align on the first member of the struct. This will probably the same as above, but we don't have any guarantees. */ typedef struct { short x; char c; } __Pyx_pad_short; typedef struct { int x; char c; } __Pyx_pad_int; typedef struct { long x; char c; } __Pyx_pad_long; typedef struct { float x; char c; } __Pyx_pad_float; typedef struct { double x; char c; } __Pyx_pad_double; typedef struct { long double x; char c; } __Pyx_pad_longdouble; typedef struct { void *x; char c; } __Pyx_pad_void_p; #ifdef HAVE_LONG_LONG typedef struct { PY_LONG_LONG x; char c; } __Pyx_pad_longlong; #endif static size_t __Pyx_BufFmt_TypeCharToPadding(char ch, CYTHON_UNUSED int is_complex) { switch (ch) { case '?': case 'c': case 'b': case 'B': case 's': case 'p': return 1; case 'h': case 'H': return sizeof(__Pyx_pad_short) - sizeof(short); case 'i': case 'I': return sizeof(__Pyx_pad_int) - sizeof(int); case 'l': case 'L': return sizeof(__Pyx_pad_long) - sizeof(long); #ifdef HAVE_LONG_LONG case 'q': case 'Q': return sizeof(__Pyx_pad_longlong) - sizeof(PY_LONG_LONG); #endif case 'f': return sizeof(__Pyx_pad_float) - sizeof(float); case 'd': return sizeof(__Pyx_pad_double) - sizeof(double); case 'g': return sizeof(__Pyx_pad_longdouble) - sizeof(long double); case 'P': case 'O': return sizeof(__Pyx_pad_void_p) - sizeof(void*); default: __Pyx_BufFmt_RaiseUnexpectedChar(ch); return 0; } } static char __Pyx_BufFmt_TypeCharToGroup(char ch, int is_complex) { switch (ch) { case 'c': return 'H'; case 'b': case 'h': case 'i': case 'l': case 'q': case 's': case 'p': return 'I'; case 'B': case 'H': case 'I': case 'L': case 'Q': return 'U'; case 'f': case 'd': case 'g': return (is_complex ? 'C' : 'R'); case 'O': return 'O'; case 'P': return 'P'; default: { __Pyx_BufFmt_RaiseUnexpectedChar(ch); return 0; } } } static void __Pyx_BufFmt_RaiseExpected(__Pyx_BufFmt_Context* ctx) { if (ctx->head == NULL || ctx->head->field == &ctx->root) { const char* expected; const char* quote; if (ctx->head == NULL) { expected = "end"; quote = ""; } else { expected = ctx->head->field->type->name; quote = "'"; } PyErr_Format(PyExc_ValueError, "Buffer dtype mismatch, expected %s%s%s but got %s", quote, expected, quote, __Pyx_BufFmt_DescribeTypeChar(ctx->enc_type, ctx->is_complex)); } else { __Pyx_StructField* field = ctx->head->field; __Pyx_StructField* parent = (ctx->head - 1)->field; PyErr_Format(PyExc_ValueError, "Buffer dtype mismatch, expected '%s' but got %s in '%s.%s'", field->type->name, __Pyx_BufFmt_DescribeTypeChar(ctx->enc_type, ctx->is_complex), parent->type->name, field->name); } } static int __Pyx_BufFmt_ProcessTypeChunk(__Pyx_BufFmt_Context* ctx) { char group; size_t size, offset, arraysize = 1; if (ctx->enc_type == 0) return 0; if (ctx->head->field->type->arraysize[0]) { int i, ndim = 0; if (ctx->enc_type == 's' || ctx->enc_type == 'p') { ctx->is_valid_array = ctx->head->field->type->ndim == 1; ndim = 1; if (ctx->enc_count != ctx->head->field->type->arraysize[0]) { PyErr_Format(PyExc_ValueError, "Expected a dimension of size %zu, got %zu", ctx->head->field->type->arraysize[0], ctx->enc_count); return -1; } } if (!ctx->is_valid_array) { PyErr_Format(PyExc_ValueError, "Expected %d dimensions, got %d", ctx->head->field->type->ndim, ndim); return -1; } for (i = 0; i < ctx->head->field->type->ndim; i++) { arraysize *= ctx->head->field->type->arraysize[i]; } ctx->is_valid_array = 0; ctx->enc_count = 1; } group = __Pyx_BufFmt_TypeCharToGroup(ctx->enc_type, ctx->is_complex); do { __Pyx_StructField* field = ctx->head->field; __Pyx_TypeInfo* type = field->type; if (ctx->enc_packmode == '@' || ctx->enc_packmode == '^') { size = __Pyx_BufFmt_TypeCharToNativeSize(ctx->enc_type, ctx->is_complex); } else { size = __Pyx_BufFmt_TypeCharToStandardSize(ctx->enc_type, ctx->is_complex); } if (ctx->enc_packmode == '@') { size_t align_at = __Pyx_BufFmt_TypeCharToAlignment(ctx->enc_type, ctx->is_complex); size_t align_mod_offset; if (align_at == 0) return -1; align_mod_offset = ctx->fmt_offset % align_at; if (align_mod_offset > 0) ctx->fmt_offset += align_at - align_mod_offset; if (ctx->struct_alignment == 0) ctx->struct_alignment = __Pyx_BufFmt_TypeCharToPadding(ctx->enc_type, ctx->is_complex); } if (type->size != size || type->typegroup != group) { if (type->typegroup == 'C' && type->fields != NULL) { size_t parent_offset = ctx->head->parent_offset + field->offset; ++ctx->head; ctx->head->field = type->fields; ctx->head->parent_offset = parent_offset; continue; } if ((type->typegroup == 'H' || group == 'H') && type->size == size) { } else { __Pyx_BufFmt_RaiseExpected(ctx); return -1; } } offset = ctx->head->parent_offset + field->offset; if (ctx->fmt_offset != offset) { PyErr_Format(PyExc_ValueError, "Buffer dtype mismatch; next field is at offset %" CYTHON_FORMAT_SSIZE_T "d but %" CYTHON_FORMAT_SSIZE_T "d expected", (Py_ssize_t)ctx->fmt_offset, (Py_ssize_t)offset); return -1; } ctx->fmt_offset += size; if (arraysize) ctx->fmt_offset += (arraysize - 1) * size; --ctx->enc_count; while (1) { if (field == &ctx->root) { ctx->head = NULL; if (ctx->enc_count != 0) { __Pyx_BufFmt_RaiseExpected(ctx); return -1; } break; } ctx->head->field = ++field; if (field->type == NULL) { --ctx->head; field = ctx->head->field; continue; } else if (field->type->typegroup == 'S') { size_t parent_offset = ctx->head->parent_offset + field->offset; if (field->type->fields->type == NULL) continue; field = field->type->fields; ++ctx->head; ctx->head->field = field; ctx->head->parent_offset = parent_offset; break; } else { break; } } } while (ctx->enc_count); ctx->enc_type = 0; ctx->is_complex = 0; return 0; } static CYTHON_INLINE PyObject * __pyx_buffmt_parse_array(__Pyx_BufFmt_Context* ctx, const char** tsp) { const char *ts = *tsp; int i = 0, number; int ndim = ctx->head->field->type->ndim; ; ++ts; if (ctx->new_count != 1) { PyErr_SetString(PyExc_ValueError, "Cannot handle repeated arrays in format string"); return NULL; } if (__Pyx_BufFmt_ProcessTypeChunk(ctx) == -1) return NULL; while (*ts && *ts != ')') { switch (*ts) { case ' ': case '\f': case '\r': case '\n': case '\t': case '\v': continue; default: break; } number = __Pyx_BufFmt_ExpectNumber(&ts); if (number == -1) return NULL; if (i < ndim && (size_t) number != ctx->head->field->type->arraysize[i]) return PyErr_Format(PyExc_ValueError, "Expected a dimension of size %zu, got %d", ctx->head->field->type->arraysize[i], number); if (*ts != ',' && *ts != ')') return PyErr_Format(PyExc_ValueError, "Expected a comma in format string, got '%c'", *ts); if (*ts == ',') ts++; i++; } if (i != ndim) return PyErr_Format(PyExc_ValueError, "Expected %d dimension(s), got %d", ctx->head->field->type->ndim, i); if (!*ts) { PyErr_SetString(PyExc_ValueError, "Unexpected end of format string, expected ')'"); return NULL; } ctx->is_valid_array = 1; ctx->new_count = 1; *tsp = ++ts; return Py_None; } static const char* __Pyx_BufFmt_CheckString(__Pyx_BufFmt_Context* ctx, const char* ts) { int got_Z = 0; while (1) { switch(*ts) { case 0: if (ctx->enc_type != 0 && ctx->head == NULL) { __Pyx_BufFmt_RaiseExpected(ctx); return NULL; } if (__Pyx_BufFmt_ProcessTypeChunk(ctx) == -1) return NULL; if (ctx->head != NULL) { __Pyx_BufFmt_RaiseExpected(ctx); return NULL; } return ts; case ' ': case '\r': case '\n': ++ts; break; case '<': if (!__Pyx_IsLittleEndian()) { PyErr_SetString(PyExc_ValueError, "Little-endian buffer not supported on big-endian compiler"); return NULL; } ctx->new_packmode = '='; ++ts; break; case '>': case '!': if (__Pyx_IsLittleEndian()) { PyErr_SetString(PyExc_ValueError, "Big-endian buffer not supported on little-endian compiler"); return NULL; } ctx->new_packmode = '='; ++ts; break; case '=': case '@': case '^': ctx->new_packmode = *ts++; break; case 'T': { const char* ts_after_sub; size_t i, struct_count = ctx->new_count; size_t struct_alignment = ctx->struct_alignment; ctx->new_count = 1; ++ts; if (*ts != '{') { PyErr_SetString(PyExc_ValueError, "Buffer acquisition: Expected '{' after 'T'"); return NULL; } if (__Pyx_BufFmt_ProcessTypeChunk(ctx) == -1) return NULL; ctx->enc_type = 0; ctx->enc_count = 0; ctx->struct_alignment = 0; ++ts; ts_after_sub = ts; for (i = 0; i != struct_count; ++i) { ts_after_sub = __Pyx_BufFmt_CheckString(ctx, ts); if (!ts_after_sub) return NULL; } ts = ts_after_sub; if (struct_alignment) ctx->struct_alignment = struct_alignment; } break; case '}': { size_t alignment = ctx->struct_alignment; ++ts; if (__Pyx_BufFmt_ProcessTypeChunk(ctx) == -1) return NULL; ctx->enc_type = 0; if (alignment && ctx->fmt_offset % alignment) { ctx->fmt_offset += alignment - (ctx->fmt_offset % alignment); } } return ts; case 'x': if (__Pyx_BufFmt_ProcessTypeChunk(ctx) == -1) return NULL; ctx->fmt_offset += ctx->new_count; ctx->new_count = 1; ctx->enc_count = 0; ctx->enc_type = 0; ctx->enc_packmode = ctx->new_packmode; ++ts; break; case 'Z': got_Z = 1; ++ts; if (*ts != 'f' && *ts != 'd' && *ts != 'g') { __Pyx_BufFmt_RaiseUnexpectedChar('Z'); return NULL; } case 'c': case 'b': case 'B': case 'h': case 'H': case 'i': case 'I': case 'l': case 'L': case 'q': case 'Q': case 'f': case 'd': case 'g': case 'O': case 'p': if (ctx->enc_type == *ts && got_Z == ctx->is_complex && ctx->enc_packmode == ctx->new_packmode) { ctx->enc_count += ctx->new_count; ctx->new_count = 1; got_Z = 0; ++ts; break; } case 's': if (__Pyx_BufFmt_ProcessTypeChunk(ctx) == -1) return NULL; ctx->enc_count = ctx->new_count; ctx->enc_packmode = ctx->new_packmode; ctx->enc_type = *ts; ctx->is_complex = got_Z; ++ts; ctx->new_count = 1; got_Z = 0; break; case ':': ++ts; while(*ts != ':') ++ts; ++ts; break; case '(': if (!__pyx_buffmt_parse_array(ctx, &ts)) return NULL; break; default: { int number = __Pyx_BufFmt_ExpectNumber(&ts); if (number == -1) return NULL; ctx->new_count = (size_t)number; } } } } static CYTHON_INLINE void __Pyx_ZeroBuffer(Py_buffer* buf) { buf->buf = NULL; buf->obj = NULL; buf->strides = __Pyx_zeros; buf->shape = __Pyx_zeros; buf->suboffsets = __Pyx_minusones; } static CYTHON_INLINE int __Pyx_GetBufferAndValidate( Py_buffer* buf, PyObject* obj, __Pyx_TypeInfo* dtype, int flags, int nd, int cast, __Pyx_BufFmt_StackElem* stack) { if (obj == Py_None || obj == NULL) { __Pyx_ZeroBuffer(buf); return 0; } buf->buf = NULL; if (__Pyx_GetBuffer(obj, buf, flags) == -1) goto fail; if (buf->ndim != nd) { PyErr_Format(PyExc_ValueError, "Buffer has wrong number of dimensions (expected %d, got %d)", nd, buf->ndim); goto fail; } if (!cast) { __Pyx_BufFmt_Context ctx; __Pyx_BufFmt_Init(&ctx, stack, dtype); if (!__Pyx_BufFmt_CheckString(&ctx, buf->format)) goto fail; } if ((unsigned)buf->itemsize != dtype->size) { PyErr_Format(PyExc_ValueError, "Item size of buffer (%" CYTHON_FORMAT_SSIZE_T "d byte%s) does not match size of '%s' (%" CYTHON_FORMAT_SSIZE_T "d byte%s)", buf->itemsize, (buf->itemsize > 1) ? "s" : "", dtype->name, (Py_ssize_t)dtype->size, (dtype->size > 1) ? "s" : ""); goto fail; } if (buf->suboffsets == NULL) buf->suboffsets = __Pyx_minusones; return 0; fail:; __Pyx_ZeroBuffer(buf); return -1; } static CYTHON_INLINE void __Pyx_SafeReleaseBuffer(Py_buffer* info) { if (info->buf == NULL) return; if (info->suboffsets == __Pyx_minusones) info->suboffsets = NULL; __Pyx_ReleaseBuffer(info); } static CYTHON_INLINE int __Pyx_TypeTest(PyObject *obj, PyTypeObject *type) { if (unlikely(!type)) { PyErr_SetString(PyExc_SystemError, "Missing type object"); return 0; } if (likely(PyObject_TypeCheck(obj, type))) return 1; PyErr_Format(PyExc_TypeError, "Cannot convert %.200s to %.200s", Py_TYPE(obj)->tp_name, type->tp_name); return 0; } static void __Pyx_RaiseBufferFallbackError(void) { PyErr_SetString(PyExc_ValueError, "Buffer acquisition failed on assignment; and then reacquiring the old buffer failed too!"); } static void __Pyx_RaiseBufferIndexError(int axis) { PyErr_Format(PyExc_IndexError, "Out of bounds on buffer access (axis %d)", axis); } static CYTHON_INLINE int __Pyx_PyBytes_Equals(PyObject* s1, PyObject* s2, int equals) { #if CYTHON_COMPILING_IN_PYPY return PyObject_RichCompareBool(s1, s2, equals); #else if (s1 == s2) { return (equals == Py_EQ); } else if (PyBytes_CheckExact(s1) & PyBytes_CheckExact(s2)) { const char *ps1, *ps2; Py_ssize_t length = PyBytes_GET_SIZE(s1); if (length != PyBytes_GET_SIZE(s2)) return (equals == Py_NE); ps1 = PyBytes_AS_STRING(s1); ps2 = PyBytes_AS_STRING(s2); if (ps1[0] != ps2[0]) { return (equals == Py_NE); } else if (length == 1) { return (equals == Py_EQ); } else { int result = memcmp(ps1, ps2, (size_t)length); return (equals == Py_EQ) ? (result == 0) : (result != 0); } } else if ((s1 == Py_None) & PyBytes_CheckExact(s2)) { return (equals == Py_NE); } else if ((s2 == Py_None) & PyBytes_CheckExact(s1)) { return (equals == Py_NE); } else { int result; PyObject* py_result = PyObject_RichCompare(s1, s2, equals); if (!py_result) return -1; result = __Pyx_PyObject_IsTrue(py_result); Py_DECREF(py_result); return result; } #endif } static CYTHON_INLINE int __Pyx_PyUnicode_Equals(PyObject* s1, PyObject* s2, int equals) { #if CYTHON_COMPILING_IN_PYPY return PyObject_RichCompareBool(s1, s2, equals); #else #if PY_MAJOR_VERSION < 3 PyObject* owned_ref = NULL; #endif int s1_is_unicode, s2_is_unicode; if (s1 == s2) { goto return_eq; } s1_is_unicode = PyUnicode_CheckExact(s1); s2_is_unicode = PyUnicode_CheckExact(s2); #if PY_MAJOR_VERSION < 3 if ((s1_is_unicode & (!s2_is_unicode)) && PyString_CheckExact(s2)) { owned_ref = PyUnicode_FromObject(s2); if (unlikely(!owned_ref)) return -1; s2 = owned_ref; s2_is_unicode = 1; } else if ((s2_is_unicode & (!s1_is_unicode)) && PyString_CheckExact(s1)) { owned_ref = PyUnicode_FromObject(s1); if (unlikely(!owned_ref)) return -1; s1 = owned_ref; s1_is_unicode = 1; } else if (((!s2_is_unicode) & (!s1_is_unicode))) { return __Pyx_PyBytes_Equals(s1, s2, equals); } #endif if (s1_is_unicode & s2_is_unicode) { Py_ssize_t length; int kind; void *data1, *data2; if (unlikely(__Pyx_PyUnicode_READY(s1) < 0) || unlikely(__Pyx_PyUnicode_READY(s2) < 0)) return -1; length = __Pyx_PyUnicode_GET_LENGTH(s1); if (length != __Pyx_PyUnicode_GET_LENGTH(s2)) { goto return_ne; } kind = __Pyx_PyUnicode_KIND(s1); if (kind != __Pyx_PyUnicode_KIND(s2)) { goto return_ne; } data1 = __Pyx_PyUnicode_DATA(s1); data2 = __Pyx_PyUnicode_DATA(s2); if (__Pyx_PyUnicode_READ(kind, data1, 0) != __Pyx_PyUnicode_READ(kind, data2, 0)) { goto return_ne; } else if (length == 1) { goto return_eq; } else { int result = memcmp(data1, data2, (size_t)(length * kind)); #if PY_MAJOR_VERSION < 3 Py_XDECREF(owned_ref); #endif return (equals == Py_EQ) ? (result == 0) : (result != 0); } } else if ((s1 == Py_None) & s2_is_unicode) { goto return_ne; } else if ((s2 == Py_None) & s1_is_unicode) { goto return_ne; } else { int result; PyObject* py_result = PyObject_RichCompare(s1, s2, equals); if (!py_result) return -1; result = __Pyx_PyObject_IsTrue(py_result); Py_DECREF(py_result); return result; } return_eq: #if PY_MAJOR_VERSION < 3 Py_XDECREF(owned_ref); #endif return (equals == Py_EQ); return_ne: #if PY_MAJOR_VERSION < 3 Py_XDECREF(owned_ref); #endif return (equals == Py_NE); #endif } static CYTHON_INLINE __pyx_t_5_pywt_index_t __Pyx_div___pyx_t_5_pywt_index_t(__pyx_t_5_pywt_index_t a, __pyx_t_5_pywt_index_t b) { __pyx_t_5_pywt_index_t q = a / b; __pyx_t_5_pywt_index_t r = a - q*b; q -= ((r != 0) & ((r ^ b) < 0)); return q; } static CYTHON_INLINE __pyx_t_5_pywt_index_t __Pyx_mod___pyx_t_5_pywt_index_t(__pyx_t_5_pywt_index_t a, __pyx_t_5_pywt_index_t b) { __pyx_t_5_pywt_index_t r = a % b; r += ((r != 0) & ((r ^ b) < 0)) * b; return r; } static CYTHON_INLINE Py_ssize_t __Pyx_div_Py_ssize_t(Py_ssize_t a, Py_ssize_t b) { Py_ssize_t q = a / b; Py_ssize_t r = a - q*b; q -= ((r != 0) & ((r ^ b) < 0)); return q; } static CYTHON_INLINE void __Pyx_ExceptionSwap(PyObject **type, PyObject **value, PyObject **tb) { PyObject *tmp_type, *tmp_value, *tmp_tb; #if CYTHON_COMPILING_IN_CPYTHON PyThreadState *tstate = PyThreadState_GET(); tmp_type = tstate->exc_type; tmp_value = tstate->exc_value; tmp_tb = tstate->exc_traceback; tstate->exc_type = *type; tstate->exc_value = *value; tstate->exc_traceback = *tb; #else PyErr_GetExcInfo(&tmp_type, &tmp_value, &tmp_tb); PyErr_SetExcInfo(*type, *value, *tb); #endif *type = tmp_type; *value = tmp_value; *tb = tmp_tb; } static CYTHON_INLINE void __Pyx_RaiseUnboundLocalError(const char *varname) { PyErr_Format(PyExc_UnboundLocalError, "local variable '%s' referenced before assignment", varname); } static CYTHON_INLINE long __Pyx_div_long(long a, long b) { long q = a / b; long r = a - q*b; q -= ((r != 0) & ((r ^ b) < 0)); return q; } static void __Pyx_WriteUnraisable(const char *name, CYTHON_UNUSED int clineno, CYTHON_UNUSED int lineno, CYTHON_UNUSED const char *filename, int full_traceback) { PyObject *old_exc, *old_val, *old_tb; PyObject *ctx; __Pyx_ErrFetch(&old_exc, &old_val, &old_tb); if (full_traceback) { Py_XINCREF(old_exc); Py_XINCREF(old_val); Py_XINCREF(old_tb); __Pyx_ErrRestore(old_exc, old_val, old_tb); PyErr_PrintEx(1); } #if PY_MAJOR_VERSION < 3 ctx = PyString_FromString(name); #else ctx = PyUnicode_FromString(name); #endif __Pyx_ErrRestore(old_exc, old_val, old_tb); if (!ctx) { PyErr_WriteUnraisable(Py_None); } else { PyErr_WriteUnraisable(ctx); Py_DECREF(ctx); } } static int __Pyx_SetVtable(PyObject *dict, void *vtable) { #if PY_VERSION_HEX >= 0x02070000 PyObject *ob = PyCapsule_New(vtable, 0, 0); #else PyObject *ob = PyCObject_FromVoidPtr(vtable, 0); #endif if (!ob) goto bad; if (PyDict_SetItem(dict, __pyx_n_s_pyx_vtable, ob) < 0) goto bad; Py_DECREF(ob); return 0; bad: Py_XDECREF(ob); return -1; } static PyObject *__Pyx_CalculateMetaclass(PyTypeObject *metaclass, PyObject *bases) { Py_ssize_t i, nbases = PyTuple_GET_SIZE(bases); for (i=0; i < nbases; i++) { PyTypeObject *tmptype; PyObject *tmp = PyTuple_GET_ITEM(bases, i); tmptype = Py_TYPE(tmp); #if PY_MAJOR_VERSION < 3 if (tmptype == &PyClass_Type) continue; #endif if (!metaclass) { metaclass = tmptype; continue; } if (PyType_IsSubtype(metaclass, tmptype)) continue; if (PyType_IsSubtype(tmptype, metaclass)) { metaclass = tmptype; continue; } PyErr_SetString(PyExc_TypeError, "metaclass conflict: " "the metaclass of a derived class " "must be a (non-strict) subclass " "of the metaclasses of all its bases"); return NULL; } if (!metaclass) { #if PY_MAJOR_VERSION < 3 metaclass = &PyClass_Type; #else metaclass = &PyType_Type; #endif } Py_INCREF((PyObject*) metaclass); return (PyObject*) metaclass; } static PyTypeObject* __Pyx_FetchCommonType(PyTypeObject* type) { PyObject* fake_module; PyTypeObject* cached_type = NULL; fake_module = PyImport_AddModule((char*) "_cython_" CYTHON_ABI); if (!fake_module) return NULL; Py_INCREF(fake_module); cached_type = (PyTypeObject*) PyObject_GetAttrString(fake_module, type->tp_name); if (cached_type) { if (!PyType_Check((PyObject*)cached_type)) { PyErr_Format(PyExc_TypeError, "Shared Cython type %.200s is not a type object", type->tp_name); goto bad; } if (cached_type->tp_basicsize != type->tp_basicsize) { PyErr_Format(PyExc_TypeError, "Shared Cython type %.200s has the wrong size, try recompiling", type->tp_name); goto bad; } } else { if (!PyErr_ExceptionMatches(PyExc_AttributeError)) goto bad; PyErr_Clear(); if (PyType_Ready(type) < 0) goto bad; if (PyObject_SetAttrString(fake_module, type->tp_name, (PyObject*) type) < 0) goto bad; Py_INCREF(type); cached_type = type; } done: Py_DECREF(fake_module); return cached_type; bad: Py_XDECREF(cached_type); cached_type = NULL; goto done; } static PyObject * __Pyx_CyFunction_get_doc(__pyx_CyFunctionObject *op, CYTHON_UNUSED void *closure) { if (unlikely(op->func_doc == NULL)) { if (op->func.m_ml->ml_doc) { #if PY_MAJOR_VERSION >= 3 op->func_doc = PyUnicode_FromString(op->func.m_ml->ml_doc); #else op->func_doc = PyString_FromString(op->func.m_ml->ml_doc); #endif if (unlikely(op->func_doc == NULL)) return NULL; } else { Py_INCREF(Py_None); return Py_None; } } Py_INCREF(op->func_doc); return op->func_doc; } static int __Pyx_CyFunction_set_doc(__pyx_CyFunctionObject *op, PyObject *value) { PyObject *tmp = op->func_doc; if (value == NULL) { value = Py_None; } Py_INCREF(value); op->func_doc = value; Py_XDECREF(tmp); return 0; } static PyObject * __Pyx_CyFunction_get_name(__pyx_CyFunctionObject *op) { if (unlikely(op->func_name == NULL)) { #if PY_MAJOR_VERSION >= 3 op->func_name = PyUnicode_InternFromString(op->func.m_ml->ml_name); #else op->func_name = PyString_InternFromString(op->func.m_ml->ml_name); #endif if (unlikely(op->func_name == NULL)) return NULL; } Py_INCREF(op->func_name); return op->func_name; } static int __Pyx_CyFunction_set_name(__pyx_CyFunctionObject *op, PyObject *value) { PyObject *tmp; #if PY_MAJOR_VERSION >= 3 if (unlikely(value == NULL || !PyUnicode_Check(value))) { #else if (unlikely(value == NULL || !PyString_Check(value))) { #endif PyErr_SetString(PyExc_TypeError, "__name__ must be set to a string object"); return -1; } tmp = op->func_name; Py_INCREF(value); op->func_name = value; Py_XDECREF(tmp); return 0; } static PyObject * __Pyx_CyFunction_get_qualname(__pyx_CyFunctionObject *op) { Py_INCREF(op->func_qualname); return op->func_qualname; } static int __Pyx_CyFunction_set_qualname(__pyx_CyFunctionObject *op, PyObject *value) { PyObject *tmp; #if PY_MAJOR_VERSION >= 3 if (unlikely(value == NULL || !PyUnicode_Check(value))) { #else if (unlikely(value == NULL || !PyString_Check(value))) { #endif PyErr_SetString(PyExc_TypeError, "__qualname__ must be set to a string object"); return -1; } tmp = op->func_qualname; Py_INCREF(value); op->func_qualname = value; Py_XDECREF(tmp); return 0; } static PyObject * __Pyx_CyFunction_get_self(__pyx_CyFunctionObject *m, CYTHON_UNUSED void *closure) { PyObject *self; self = m->func_closure; if (self == NULL) self = Py_None; Py_INCREF(self); return self; } static PyObject * __Pyx_CyFunction_get_dict(__pyx_CyFunctionObject *op) { if (unlikely(op->func_dict == NULL)) { op->func_dict = PyDict_New(); if (unlikely(op->func_dict == NULL)) return NULL; } Py_INCREF(op->func_dict); return op->func_dict; } static int __Pyx_CyFunction_set_dict(__pyx_CyFunctionObject *op, PyObject *value) { PyObject *tmp; if (unlikely(value == NULL)) { PyErr_SetString(PyExc_TypeError, "function's dictionary may not be deleted"); return -1; } if (unlikely(!PyDict_Check(value))) { PyErr_SetString(PyExc_TypeError, "setting function's dictionary to a non-dict"); return -1; } tmp = op->func_dict; Py_INCREF(value); op->func_dict = value; Py_XDECREF(tmp); return 0; } static PyObject * __Pyx_CyFunction_get_globals(__pyx_CyFunctionObject *op) { Py_INCREF(op->func_globals); return op->func_globals; } static PyObject * __Pyx_CyFunction_get_closure(CYTHON_UNUSED __pyx_CyFunctionObject *op) { Py_INCREF(Py_None); return Py_None; } static PyObject * __Pyx_CyFunction_get_code(__pyx_CyFunctionObject *op) { PyObject* result = (op->func_code) ? op->func_code : Py_None; Py_INCREF(result); return result; } static int __Pyx_CyFunction_init_defaults(__pyx_CyFunctionObject *op) { PyObject *res = op->defaults_getter((PyObject *) op); if (unlikely(!res)) return -1; op->defaults_tuple = PyTuple_GET_ITEM(res, 0); Py_INCREF(op->defaults_tuple); op->defaults_kwdict = PyTuple_GET_ITEM(res, 1); Py_INCREF(op->defaults_kwdict); Py_DECREF(res); return 0; } static int __Pyx_CyFunction_set_defaults(__pyx_CyFunctionObject *op, PyObject* value) { PyObject* tmp; if (!value) { value = Py_None; } else if (value != Py_None && !PyTuple_Check(value)) { PyErr_SetString(PyExc_TypeError, "__defaults__ must be set to a tuple object"); return -1; } Py_INCREF(value); tmp = op->defaults_tuple; op->defaults_tuple = value; Py_XDECREF(tmp); return 0; } static PyObject * __Pyx_CyFunction_get_defaults(__pyx_CyFunctionObject *op) { PyObject* result = op->defaults_tuple; if (unlikely(!result)) { if (op->defaults_getter) { if (__Pyx_CyFunction_init_defaults(op) < 0) return NULL; result = op->defaults_tuple; } else { result = Py_None; } } Py_INCREF(result); return result; } static int __Pyx_CyFunction_set_kwdefaults(__pyx_CyFunctionObject *op, PyObject* value) { PyObject* tmp; if (!value) { value = Py_None; } else if (value != Py_None && !PyDict_Check(value)) { PyErr_SetString(PyExc_TypeError, "__kwdefaults__ must be set to a dict object"); return -1; } Py_INCREF(value); tmp = op->defaults_kwdict; op->defaults_kwdict = value; Py_XDECREF(tmp); return 0; } static PyObject * __Pyx_CyFunction_get_kwdefaults(__pyx_CyFunctionObject *op) { PyObject* result = op->defaults_kwdict; if (unlikely(!result)) { if (op->defaults_getter) { if (__Pyx_CyFunction_init_defaults(op) < 0) return NULL; result = op->defaults_kwdict; } else { result = Py_None; } } Py_INCREF(result); return result; } static int __Pyx_CyFunction_set_annotations(__pyx_CyFunctionObject *op, PyObject* value) { PyObject* tmp; if (!value || value == Py_None) { value = NULL; } else if (!PyDict_Check(value)) { PyErr_SetString(PyExc_TypeError, "__annotations__ must be set to a dict object"); return -1; } Py_XINCREF(value); tmp = op->func_annotations; op->func_annotations = value; Py_XDECREF(tmp); return 0; } static PyObject * __Pyx_CyFunction_get_annotations(__pyx_CyFunctionObject *op) { PyObject* result = op->func_annotations; if (unlikely(!result)) { result = PyDict_New(); if (unlikely(!result)) return NULL; op->func_annotations = result; } Py_INCREF(result); return result; } static PyGetSetDef __pyx_CyFunction_getsets[] = { {(char *) "func_doc", (getter)__Pyx_CyFunction_get_doc, (setter)__Pyx_CyFunction_set_doc, 0, 0}, {(char *) "__doc__", (getter)__Pyx_CyFunction_get_doc, (setter)__Pyx_CyFunction_set_doc, 0, 0}, {(char *) "func_name", (getter)__Pyx_CyFunction_get_name, (setter)__Pyx_CyFunction_set_name, 0, 0}, {(char *) "__name__", (getter)__Pyx_CyFunction_get_name, (setter)__Pyx_CyFunction_set_name, 0, 0}, {(char *) "__qualname__", (getter)__Pyx_CyFunction_get_qualname, (setter)__Pyx_CyFunction_set_qualname, 0, 0}, {(char *) "__self__", (getter)__Pyx_CyFunction_get_self, 0, 0, 0}, {(char *) "func_dict", (getter)__Pyx_CyFunction_get_dict, (setter)__Pyx_CyFunction_set_dict, 0, 0}, {(char *) "__dict__", (getter)__Pyx_CyFunction_get_dict, (setter)__Pyx_CyFunction_set_dict, 0, 0}, {(char *) "func_globals", (getter)__Pyx_CyFunction_get_globals, 0, 0, 0}, {(char *) "__globals__", (getter)__Pyx_CyFunction_get_globals, 0, 0, 0}, {(char *) "func_closure", (getter)__Pyx_CyFunction_get_closure, 0, 0, 0}, {(char *) "__closure__", (getter)__Pyx_CyFunction_get_closure, 0, 0, 0}, {(char *) "func_code", (getter)__Pyx_CyFunction_get_code, 0, 0, 0}, {(char *) "__code__", (getter)__Pyx_CyFunction_get_code, 0, 0, 0}, {(char *) "func_defaults", (getter)__Pyx_CyFunction_get_defaults, (setter)__Pyx_CyFunction_set_defaults, 0, 0}, {(char *) "__defaults__", (getter)__Pyx_CyFunction_get_defaults, (setter)__Pyx_CyFunction_set_defaults, 0, 0}, {(char *) "__kwdefaults__", (getter)__Pyx_CyFunction_get_kwdefaults, (setter)__Pyx_CyFunction_set_kwdefaults, 0, 0}, {(char *) "__annotations__", (getter)__Pyx_CyFunction_get_annotations, (setter)__Pyx_CyFunction_set_annotations, 0, 0}, {0, 0, 0, 0, 0} }; static PyMemberDef __pyx_CyFunction_members[] = { {(char *) "__module__", T_OBJECT, offsetof(__pyx_CyFunctionObject, func.m_module), PY_WRITE_RESTRICTED, 0}, {0, 0, 0, 0, 0} }; static PyObject * __Pyx_CyFunction_reduce(__pyx_CyFunctionObject *m, CYTHON_UNUSED PyObject *args) { #if PY_MAJOR_VERSION >= 3 return PyUnicode_FromString(m->func.m_ml->ml_name); #else return PyString_FromString(m->func.m_ml->ml_name); #endif } static PyMethodDef __pyx_CyFunction_methods[] = { {"__reduce__", (PyCFunction)__Pyx_CyFunction_reduce, METH_VARARGS, 0}, {0, 0, 0, 0} }; #if PY_VERSION_HEX < 0x030500A0 #define __Pyx_CyFunction_weakreflist(cyfunc) ((cyfunc)->func_weakreflist) #else #define __Pyx_CyFunction_weakreflist(cyfunc) ((cyfunc)->func.m_weakreflist) #endif static PyObject *__Pyx_CyFunction_New(PyTypeObject *type, PyMethodDef *ml, int flags, PyObject* qualname, PyObject *closure, PyObject *module, PyObject* globals, PyObject* code) { __pyx_CyFunctionObject *op = PyObject_GC_New(__pyx_CyFunctionObject, type); if (op == NULL) return NULL; op->flags = flags; __Pyx_CyFunction_weakreflist(op) = NULL; op->func.m_ml = ml; op->func.m_self = (PyObject *) op; Py_XINCREF(closure); op->func_closure = closure; Py_XINCREF(module); op->func.m_module = module; op->func_dict = NULL; op->func_name = NULL; Py_INCREF(qualname); op->func_qualname = qualname; op->func_doc = NULL; op->func_classobj = NULL; op->func_globals = globals; Py_INCREF(op->func_globals); Py_XINCREF(code); op->func_code = code; op->defaults_pyobjects = 0; op->defaults = NULL; op->defaults_tuple = NULL; op->defaults_kwdict = NULL; op->defaults_getter = NULL; op->func_annotations = NULL; PyObject_GC_Track(op); return (PyObject *) op; } static int __Pyx_CyFunction_clear(__pyx_CyFunctionObject *m) { Py_CLEAR(m->func_closure); Py_CLEAR(m->func.m_module); Py_CLEAR(m->func_dict); Py_CLEAR(m->func_name); Py_CLEAR(m->func_qualname); Py_CLEAR(m->func_doc); Py_CLEAR(m->func_globals); Py_CLEAR(m->func_code); Py_CLEAR(m->func_classobj); Py_CLEAR(m->defaults_tuple); Py_CLEAR(m->defaults_kwdict); Py_CLEAR(m->func_annotations); if (m->defaults) { PyObject **pydefaults = __Pyx_CyFunction_Defaults(PyObject *, m); int i; for (i = 0; i < m->defaults_pyobjects; i++) Py_XDECREF(pydefaults[i]); PyMem_Free(m->defaults); m->defaults = NULL; } return 0; } static void __Pyx_CyFunction_dealloc(__pyx_CyFunctionObject *m) { PyObject_GC_UnTrack(m); if (__Pyx_CyFunction_weakreflist(m) != NULL) PyObject_ClearWeakRefs((PyObject *) m); __Pyx_CyFunction_clear(m); PyObject_GC_Del(m); } static int __Pyx_CyFunction_traverse(__pyx_CyFunctionObject *m, visitproc visit, void *arg) { Py_VISIT(m->func_closure); Py_VISIT(m->func.m_module); Py_VISIT(m->func_dict); Py_VISIT(m->func_name); Py_VISIT(m->func_qualname); Py_VISIT(m->func_doc); Py_VISIT(m->func_globals); Py_VISIT(m->func_code); Py_VISIT(m->func_classobj); Py_VISIT(m->defaults_tuple); Py_VISIT(m->defaults_kwdict); if (m->defaults) { PyObject **pydefaults = __Pyx_CyFunction_Defaults(PyObject *, m); int i; for (i = 0; i < m->defaults_pyobjects; i++) Py_VISIT(pydefaults[i]); } return 0; } static PyObject *__Pyx_CyFunction_descr_get(PyObject *func, PyObject *obj, PyObject *type) { __pyx_CyFunctionObject *m = (__pyx_CyFunctionObject *) func; if (m->flags & __Pyx_CYFUNCTION_STATICMETHOD) { Py_INCREF(func); return func; } if (m->flags & __Pyx_CYFUNCTION_CLASSMETHOD) { if (type == NULL) type = (PyObject *)(Py_TYPE(obj)); return __Pyx_PyMethod_New(func, type, (PyObject *)(Py_TYPE(type))); } if (obj == Py_None) obj = NULL; return __Pyx_PyMethod_New(func, obj, type); } static PyObject* __Pyx_CyFunction_repr(__pyx_CyFunctionObject *op) { #if PY_MAJOR_VERSION >= 3 return PyUnicode_FromFormat("", op->func_qualname, (void *)op); #else return PyString_FromFormat("", PyString_AsString(op->func_qualname), (void *)op); #endif } #if CYTHON_COMPILING_IN_PYPY static PyObject * __Pyx_CyFunction_Call(PyObject *func, PyObject *arg, PyObject *kw) { PyCFunctionObject* f = (PyCFunctionObject*)func; PyCFunction meth = PyCFunction_GET_FUNCTION(func); PyObject *self = PyCFunction_GET_SELF(func); Py_ssize_t size; switch (PyCFunction_GET_FLAGS(func) & ~(METH_CLASS | METH_STATIC | METH_COEXIST)) { case METH_VARARGS: if (likely(kw == NULL) || PyDict_Size(kw) == 0) return (*meth)(self, arg); break; case METH_VARARGS | METH_KEYWORDS: return (*(PyCFunctionWithKeywords)meth)(self, arg, kw); case METH_NOARGS: if (likely(kw == NULL) || PyDict_Size(kw) == 0) { size = PyTuple_GET_SIZE(arg); if (size == 0) return (*meth)(self, NULL); PyErr_Format(PyExc_TypeError, "%.200s() takes no arguments (%" CYTHON_FORMAT_SSIZE_T "d given)", f->m_ml->ml_name, size); return NULL; } break; case METH_O: if (likely(kw == NULL) || PyDict_Size(kw) == 0) { size = PyTuple_GET_SIZE(arg); if (size == 1) return (*meth)(self, PyTuple_GET_ITEM(arg, 0)); PyErr_Format(PyExc_TypeError, "%.200s() takes exactly one argument (%" CYTHON_FORMAT_SSIZE_T "d given)", f->m_ml->ml_name, size); return NULL; } break; default: PyErr_SetString(PyExc_SystemError, "Bad call flags in " "__Pyx_CyFunction_Call. METH_OLDARGS is no " "longer supported!"); return NULL; } PyErr_Format(PyExc_TypeError, "%.200s() takes no keyword arguments", f->m_ml->ml_name); return NULL; } #else static PyObject * __Pyx_CyFunction_Call(PyObject *func, PyObject *arg, PyObject *kw) { return PyCFunction_Call(func, arg, kw); } #endif static PyTypeObject __pyx_CyFunctionType_type = { PyVarObject_HEAD_INIT(0, 0) "cython_function_or_method", sizeof(__pyx_CyFunctionObject), 0, (destructor) __Pyx_CyFunction_dealloc, 0, 0, 0, #if PY_MAJOR_VERSION < 3 0, #else 0, #endif (reprfunc) __Pyx_CyFunction_repr, 0, 0, 0, 0, __Pyx_CyFunction_Call, 0, 0, 0, 0, Py_TPFLAGS_DEFAULT | Py_TPFLAGS_HAVE_GC, 0, (traverseproc) __Pyx_CyFunction_traverse, (inquiry) __Pyx_CyFunction_clear, 0, #if PY_VERSION_HEX < 0x030500A0 offsetof(__pyx_CyFunctionObject, func_weakreflist), #else offsetof(PyCFunctionObject, m_weakreflist), #endif 0, 0, __pyx_CyFunction_methods, __pyx_CyFunction_members, __pyx_CyFunction_getsets, 0, 0, __Pyx_CyFunction_descr_get, 0, offsetof(__pyx_CyFunctionObject, func_dict), 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, #if PY_VERSION_HEX >= 0x030400a1 0, #endif }; static int __Pyx_CyFunction_init(void) { #if !CYTHON_COMPILING_IN_PYPY __pyx_CyFunctionType_type.tp_call = PyCFunction_Call; #endif __pyx_CyFunctionType = __Pyx_FetchCommonType(&__pyx_CyFunctionType_type); if (__pyx_CyFunctionType == NULL) { return -1; } return 0; } static CYTHON_INLINE void *__Pyx_CyFunction_InitDefaults(PyObject *func, size_t size, int pyobjects) { __pyx_CyFunctionObject *m = (__pyx_CyFunctionObject *) func; m->defaults = PyMem_Malloc(size); if (!m->defaults) return PyErr_NoMemory(); memset(m->defaults, 0, size); m->defaults_pyobjects = pyobjects; return m->defaults; } static CYTHON_INLINE void __Pyx_CyFunction_SetDefaultsTuple(PyObject *func, PyObject *tuple) { __pyx_CyFunctionObject *m = (__pyx_CyFunctionObject *) func; m->defaults_tuple = tuple; Py_INCREF(tuple); } static CYTHON_INLINE void __Pyx_CyFunction_SetDefaultsKwDict(PyObject *func, PyObject *dict) { __pyx_CyFunctionObject *m = (__pyx_CyFunctionObject *) func; m->defaults_kwdict = dict; Py_INCREF(dict); } static CYTHON_INLINE void __Pyx_CyFunction_SetAnnotationsDict(PyObject *func, PyObject *dict) { __pyx_CyFunctionObject *m = (__pyx_CyFunctionObject *) func; m->func_annotations = dict; Py_INCREF(dict); } static PyObject *__Pyx_Py3MetaclassPrepare(PyObject *metaclass, PyObject *bases, PyObject *name, PyObject *qualname, PyObject *mkw, PyObject *modname, PyObject *doc) { PyObject *ns; if (metaclass) { PyObject *prep = __Pyx_PyObject_GetAttrStr(metaclass, __pyx_n_s_prepare); if (prep) { PyObject *pargs = PyTuple_Pack(2, name, bases); if (unlikely(!pargs)) { Py_DECREF(prep); return NULL; } ns = PyObject_Call(prep, pargs, mkw); Py_DECREF(prep); Py_DECREF(pargs); } else { if (unlikely(!PyErr_ExceptionMatches(PyExc_AttributeError))) return NULL; PyErr_Clear(); ns = PyDict_New(); } } else { ns = PyDict_New(); } if (unlikely(!ns)) return NULL; if (unlikely(PyObject_SetItem(ns, __pyx_n_s_module, modname) < 0)) goto bad; if (unlikely(PyObject_SetItem(ns, __pyx_n_s_qualname, qualname) < 0)) goto bad; if (unlikely(doc && PyObject_SetItem(ns, __pyx_n_s_doc, doc) < 0)) goto bad; return ns; bad: Py_DECREF(ns); return NULL; } static PyObject *__Pyx_Py3ClassCreate(PyObject *metaclass, PyObject *name, PyObject *bases, PyObject *dict, PyObject *mkw, int calculate_metaclass, int allow_py2_metaclass) { PyObject *result, *margs; PyObject *owned_metaclass = NULL; if (allow_py2_metaclass) { owned_metaclass = PyObject_GetItem(dict, __pyx_n_s_metaclass); if (owned_metaclass) { metaclass = owned_metaclass; } else if (likely(PyErr_ExceptionMatches(PyExc_KeyError))) { PyErr_Clear(); } else { return NULL; } } if (calculate_metaclass && (!metaclass || PyType_Check(metaclass))) { metaclass = __Pyx_CalculateMetaclass((PyTypeObject*) metaclass, bases); Py_XDECREF(owned_metaclass); if (unlikely(!metaclass)) return NULL; owned_metaclass = metaclass; } margs = PyTuple_Pack(3, name, bases, dict); if (unlikely(!margs)) { result = NULL; } else { result = PyObject_Call(metaclass, margs, mkw); Py_DECREF(margs); } Py_XDECREF(owned_metaclass); return result; } static PyObject * __pyx_FusedFunction_New(PyTypeObject *type, PyMethodDef *ml, int flags, PyObject *qualname, PyObject *self, PyObject *module, PyObject *globals, PyObject *code) { __pyx_FusedFunctionObject *fusedfunc = (__pyx_FusedFunctionObject *) __Pyx_CyFunction_New(type, ml, flags, qualname, self, module, globals, code); if (!fusedfunc) return NULL; fusedfunc->__signatures__ = NULL; fusedfunc->type = NULL; fusedfunc->self = NULL; return (PyObject *) fusedfunc; } static void __pyx_FusedFunction_dealloc(__pyx_FusedFunctionObject *self) { __pyx_FusedFunction_clear(self); __pyx_FusedFunctionType->tp_free((PyObject *) self); } static int __pyx_FusedFunction_traverse(__pyx_FusedFunctionObject *self, visitproc visit, void *arg) { Py_VISIT(self->self); Py_VISIT(self->type); Py_VISIT(self->__signatures__); return __Pyx_CyFunction_traverse((__pyx_CyFunctionObject *) self, visit, arg); } static int __pyx_FusedFunction_clear(__pyx_FusedFunctionObject *self) { Py_CLEAR(self->self); Py_CLEAR(self->type); Py_CLEAR(self->__signatures__); return __Pyx_CyFunction_clear((__pyx_CyFunctionObject *) self); } static PyObject * __pyx_FusedFunction_descr_get(PyObject *self, PyObject *obj, PyObject *type) { __pyx_FusedFunctionObject *func, *meth; func = (__pyx_FusedFunctionObject *) self; if (func->self || func->func.flags & __Pyx_CYFUNCTION_STATICMETHOD) { Py_INCREF(self); return self; } if (obj == Py_None) obj = NULL; meth = (__pyx_FusedFunctionObject *) __pyx_FusedFunction_NewEx( ((PyCFunctionObject *) func)->m_ml, ((__pyx_CyFunctionObject *) func)->flags, ((__pyx_CyFunctionObject *) func)->func_qualname, ((__pyx_CyFunctionObject *) func)->func_closure, ((PyCFunctionObject *) func)->m_module, ((__pyx_CyFunctionObject *) func)->func_globals, ((__pyx_CyFunctionObject *) func)->func_code); if (!meth) return NULL; Py_XINCREF(func->func.func_classobj); meth->func.func_classobj = func->func.func_classobj; Py_XINCREF(func->__signatures__); meth->__signatures__ = func->__signatures__; Py_XINCREF(type); meth->type = type; Py_XINCREF(func->func.defaults_tuple); meth->func.defaults_tuple = func->func.defaults_tuple; if (func->func.flags & __Pyx_CYFUNCTION_CLASSMETHOD) obj = type; Py_XINCREF(obj); meth->self = obj; return (PyObject *) meth; } static PyObject * _obj_to_str(PyObject *obj) { if (PyType_Check(obj)) return PyObject_GetAttr(obj, __pyx_n_s_name_2); else return PyObject_Str(obj); } static PyObject * __pyx_FusedFunction_getitem(__pyx_FusedFunctionObject *self, PyObject *idx) { PyObject *signature = NULL; PyObject *unbound_result_func; PyObject *result_func = NULL; if (self->__signatures__ == NULL) { PyErr_SetString(PyExc_TypeError, "Function is not fused"); return NULL; } if (PyTuple_Check(idx)) { PyObject *list = PyList_New(0); Py_ssize_t n = PyTuple_GET_SIZE(idx); PyObject *string = NULL; PyObject *sep = NULL; int i; if (!list) return NULL; for (i = 0; i < n; i++) { PyObject *item = PyTuple_GET_ITEM(idx, i); string = _obj_to_str(item); if (!string || PyList_Append(list, string) < 0) goto __pyx_err; Py_DECREF(string); } sep = PyUnicode_FromString("|"); if (sep) signature = PyUnicode_Join(sep, list); __pyx_err: ; Py_DECREF(list); Py_XDECREF(sep); } else { signature = _obj_to_str(idx); } if (!signature) return NULL; unbound_result_func = PyObject_GetItem(self->__signatures__, signature); if (unbound_result_func) { if (self->self || self->type) { __pyx_FusedFunctionObject *unbound = (__pyx_FusedFunctionObject *) unbound_result_func; Py_CLEAR(unbound->func.func_classobj); Py_XINCREF(self->func.func_classobj); unbound->func.func_classobj = self->func.func_classobj; result_func = __pyx_FusedFunction_descr_get(unbound_result_func, self->self, self->type); } else { result_func = unbound_result_func; Py_INCREF(result_func); } } Py_DECREF(signature); Py_XDECREF(unbound_result_func); return result_func; } static PyObject * __pyx_FusedFunction_callfunction(PyObject *func, PyObject *args, PyObject *kw) { __pyx_CyFunctionObject *cyfunc = (__pyx_CyFunctionObject *) func; PyObject *result; int static_specialized = (cyfunc->flags & __Pyx_CYFUNCTION_STATICMETHOD && !((__pyx_FusedFunctionObject *) func)->__signatures__); if (cyfunc->flags & __Pyx_CYFUNCTION_CCLASS && !static_specialized) { Py_ssize_t argc; PyObject *new_args; PyObject *self; PyObject *m_self; argc = PyTuple_GET_SIZE(args); new_args = PyTuple_GetSlice(args, 1, argc); if (!new_args) return NULL; self = PyTuple_GetItem(args, 0); if (!self) return NULL; m_self = cyfunc->func.m_self; cyfunc->func.m_self = self; result = __Pyx_CyFunction_Call(func, new_args, kw); cyfunc->func.m_self = m_self; Py_DECREF(new_args); } else { result = __Pyx_CyFunction_Call(func, args, kw); } return result; } static PyObject * __pyx_FusedFunction_call(PyObject *func, PyObject *args, PyObject *kw) { __pyx_FusedFunctionObject *binding_func = (__pyx_FusedFunctionObject *) func; Py_ssize_t argc = PyTuple_GET_SIZE(args); PyObject *new_args = NULL; __pyx_FusedFunctionObject *new_func = NULL; PyObject *result = NULL; PyObject *self = NULL; int is_staticmethod = binding_func->func.flags & __Pyx_CYFUNCTION_STATICMETHOD; int is_classmethod = binding_func->func.flags & __Pyx_CYFUNCTION_CLASSMETHOD; if (binding_func->self) { Py_ssize_t i; new_args = PyTuple_New(argc + 1); if (!new_args) return NULL; self = binding_func->self; Py_INCREF(self); PyTuple_SET_ITEM(new_args, 0, self); for (i = 0; i < argc; i++) { PyObject *item = PyTuple_GET_ITEM(args, i); Py_INCREF(item); PyTuple_SET_ITEM(new_args, i + 1, item); } args = new_args; } else if (binding_func->type) { if (argc < 1) { PyErr_SetString(PyExc_TypeError, "Need at least one argument, 0 given."); return NULL; } self = PyTuple_GET_ITEM(args, 0); } if (self && !is_classmethod && !is_staticmethod && !PyObject_IsInstance(self, binding_func->type)) { PyErr_Format(PyExc_TypeError, "First argument should be of type %.200s, got %.200s.", ((PyTypeObject *) binding_func->type)->tp_name, self->ob_type->tp_name); goto __pyx_err; } if (binding_func->__signatures__) { PyObject *tup = PyTuple_Pack(4, binding_func->__signatures__, args, kw == NULL ? Py_None : kw, binding_func->func.defaults_tuple); if (!tup) goto __pyx_err; new_func = (__pyx_FusedFunctionObject *) __pyx_FusedFunction_callfunction(func, tup, NULL); Py_DECREF(tup); if (!new_func) goto __pyx_err; Py_XINCREF(binding_func->func.func_classobj); Py_CLEAR(new_func->func.func_classobj); new_func->func.func_classobj = binding_func->func.func_classobj; func = (PyObject *) new_func; } result = __pyx_FusedFunction_callfunction(func, args, kw); __pyx_err: Py_XDECREF(new_args); Py_XDECREF((PyObject *) new_func); return result; } static PyMemberDef __pyx_FusedFunction_members[] = { {(char *) "__signatures__", T_OBJECT, offsetof(__pyx_FusedFunctionObject, __signatures__), READONLY, 0}, {0, 0, 0, 0, 0}, }; static PyMappingMethods __pyx_FusedFunction_mapping_methods = { 0, (binaryfunc) __pyx_FusedFunction_getitem, 0, }; static PyTypeObject __pyx_FusedFunctionType_type = { PyVarObject_HEAD_INIT(0, 0) "fused_cython_function", sizeof(__pyx_FusedFunctionObject), 0, (destructor) __pyx_FusedFunction_dealloc, 0, 0, 0, #if PY_MAJOR_VERSION < 3 0, #else 0, #endif 0, 0, 0, &__pyx_FusedFunction_mapping_methods, 0, (ternaryfunc) __pyx_FusedFunction_call, 0, 0, 0, 0, Py_TPFLAGS_DEFAULT | Py_TPFLAGS_HAVE_GC | Py_TPFLAGS_BASETYPE, 0, (traverseproc) __pyx_FusedFunction_traverse, (inquiry) __pyx_FusedFunction_clear, 0, 0, 0, 0, 0, __pyx_FusedFunction_members, __pyx_CyFunction_getsets, &__pyx_CyFunctionType_type, 0, __pyx_FusedFunction_descr_get, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, #if PY_VERSION_HEX >= 0x030400a1 0, #endif }; static int __pyx_FusedFunction_init(void) { __pyx_FusedFunctionType = __Pyx_FetchCommonType(&__pyx_FusedFunctionType_type); if (__pyx_FusedFunctionType == NULL) { return -1; } return 0; } static int __pyx_bisect_code_objects(__Pyx_CodeObjectCacheEntry* entries, int count, int code_line) { int start = 0, mid = 0, end = count - 1; if (end >= 0 && code_line > entries[end].code_line) { return count; } while (start < end) { mid = (start + end) / 2; if (code_line < entries[mid].code_line) { end = mid; } else if (code_line > entries[mid].code_line) { start = mid + 1; } else { return mid; } } if (code_line <= entries[mid].code_line) { return mid; } else { return mid + 1; } } static PyCodeObject *__pyx_find_code_object(int code_line) { PyCodeObject* code_object; int pos; if (unlikely(!code_line) || unlikely(!__pyx_code_cache.entries)) { return NULL; } pos = __pyx_bisect_code_objects(__pyx_code_cache.entries, __pyx_code_cache.count, code_line); if (unlikely(pos >= __pyx_code_cache.count) || unlikely(__pyx_code_cache.entries[pos].code_line != code_line)) { return NULL; } code_object = __pyx_code_cache.entries[pos].code_object; Py_INCREF(code_object); return code_object; } static void __pyx_insert_code_object(int code_line, PyCodeObject* code_object) { int pos, i; __Pyx_CodeObjectCacheEntry* entries = __pyx_code_cache.entries; if (unlikely(!code_line)) { return; } if (unlikely(!entries)) { entries = (__Pyx_CodeObjectCacheEntry*)PyMem_Malloc(64*sizeof(__Pyx_CodeObjectCacheEntry)); if (likely(entries)) { __pyx_code_cache.entries = entries; __pyx_code_cache.max_count = 64; __pyx_code_cache.count = 1; entries[0].code_line = code_line; entries[0].code_object = code_object; Py_INCREF(code_object); } return; } pos = __pyx_bisect_code_objects(__pyx_code_cache.entries, __pyx_code_cache.count, code_line); if ((pos < __pyx_code_cache.count) && unlikely(__pyx_code_cache.entries[pos].code_line == code_line)) { PyCodeObject* tmp = entries[pos].code_object; entries[pos].code_object = code_object; Py_DECREF(tmp); return; } if (__pyx_code_cache.count == __pyx_code_cache.max_count) { int new_max = __pyx_code_cache.max_count + 64; entries = (__Pyx_CodeObjectCacheEntry*)PyMem_Realloc( __pyx_code_cache.entries, (size_t)new_max*sizeof(__Pyx_CodeObjectCacheEntry)); if (unlikely(!entries)) { return; } __pyx_code_cache.entries = entries; __pyx_code_cache.max_count = new_max; } for (i=__pyx_code_cache.count; i>pos; i--) { entries[i] = entries[i-1]; } entries[pos].code_line = code_line; entries[pos].code_object = code_object; __pyx_code_cache.count++; Py_INCREF(code_object); } #include "compile.h" #include "frameobject.h" #include "traceback.h" static PyCodeObject* __Pyx_CreateCodeObjectForTraceback( const char *funcname, int c_line, int py_line, const char *filename) { PyCodeObject *py_code = 0; PyObject *py_srcfile = 0; PyObject *py_funcname = 0; #if PY_MAJOR_VERSION < 3 py_srcfile = PyString_FromString(filename); #else py_srcfile = PyUnicode_FromString(filename); #endif if (!py_srcfile) goto bad; if (c_line) { #if PY_MAJOR_VERSION < 3 py_funcname = PyString_FromFormat( "%s (%s:%d)", funcname, __pyx_cfilenm, c_line); #else py_funcname = PyUnicode_FromFormat( "%s (%s:%d)", funcname, __pyx_cfilenm, c_line); #endif } else { #if PY_MAJOR_VERSION < 3 py_funcname = PyString_FromString(funcname); #else py_funcname = PyUnicode_FromString(funcname); #endif } if (!py_funcname) goto bad; py_code = __Pyx_PyCode_New( 0, 0, 0, 0, 0, __pyx_empty_bytes, /*PyObject *code,*/ __pyx_empty_tuple, /*PyObject *consts,*/ __pyx_empty_tuple, /*PyObject *names,*/ __pyx_empty_tuple, /*PyObject *varnames,*/ __pyx_empty_tuple, /*PyObject *freevars,*/ __pyx_empty_tuple, /*PyObject *cellvars,*/ py_srcfile, /*PyObject *filename,*/ py_funcname, /*PyObject *name,*/ py_line, __pyx_empty_bytes /*PyObject *lnotab*/ ); Py_DECREF(py_srcfile); Py_DECREF(py_funcname); return py_code; bad: Py_XDECREF(py_srcfile); Py_XDECREF(py_funcname); return NULL; } static void __Pyx_AddTraceback(const char *funcname, int c_line, int py_line, const char *filename) { PyCodeObject *py_code = 0; PyFrameObject *py_frame = 0; py_code = __pyx_find_code_object(c_line ? c_line : py_line); if (!py_code) { py_code = __Pyx_CreateCodeObjectForTraceback( funcname, c_line, py_line, filename); if (!py_code) goto bad; __pyx_insert_code_object(c_line ? c_line : py_line, py_code); } py_frame = PyFrame_New( PyThreadState_GET(), /*PyThreadState *tstate,*/ py_code, /*PyCodeObject *code,*/ __pyx_d, /*PyObject *globals,*/ 0 /*PyObject *locals*/ ); if (!py_frame) goto bad; py_frame->f_lineno = py_line; PyTraceBack_Here(py_frame); bad: Py_XDECREF(py_code); Py_XDECREF(py_frame); } static int __Pyx_init_memviewslice(struct __pyx_memoryview_obj *memview, int ndim, __Pyx_memviewslice *memviewslice, int memview_is_new_reference) { __Pyx_RefNannyDeclarations int i, retval=-1; Py_buffer *buf = &memview->view; __Pyx_RefNannySetupContext("init_memviewslice", 0); if (!buf) { PyErr_SetString(PyExc_ValueError, "buf is NULL."); goto fail; } else if (memviewslice->memview || memviewslice->data) { PyErr_SetString(PyExc_ValueError, "memviewslice is already initialized!"); goto fail; } if (buf->strides) { for (i = 0; i < ndim; i++) { memviewslice->strides[i] = buf->strides[i]; } } else { Py_ssize_t stride = buf->itemsize; for (i = ndim - 1; i >= 0; i--) { memviewslice->strides[i] = stride; stride *= buf->shape[i]; } } for (i = 0; i < ndim; i++) { memviewslice->shape[i] = buf->shape[i]; if (buf->suboffsets) { memviewslice->suboffsets[i] = buf->suboffsets[i]; } else { memviewslice->suboffsets[i] = -1; } } memviewslice->memview = memview; memviewslice->data = (char *)buf->buf; if (__pyx_add_acquisition_count(memview) == 0 && !memview_is_new_reference) { Py_INCREF(memview); } retval = 0; goto no_fail; fail: memviewslice->memview = 0; memviewslice->data = 0; retval = -1; no_fail: __Pyx_RefNannyFinishContext(); return retval; } static CYTHON_INLINE void __pyx_fatalerror(const char *fmt, ...) { va_list vargs; char msg[200]; va_start(vargs, fmt); #ifdef HAVE_STDARG_PROTOTYPES va_start(vargs, fmt); #else va_start(vargs); #endif vsnprintf(msg, 200, fmt, vargs); Py_FatalError(msg); va_end(vargs); } static CYTHON_INLINE int __pyx_add_acquisition_count_locked(__pyx_atomic_int *acquisition_count, PyThread_type_lock lock) { int result; PyThread_acquire_lock(lock, 1); result = (*acquisition_count)++; PyThread_release_lock(lock); return result; } static CYTHON_INLINE int __pyx_sub_acquisition_count_locked(__pyx_atomic_int *acquisition_count, PyThread_type_lock lock) { int result; PyThread_acquire_lock(lock, 1); result = (*acquisition_count)--; PyThread_release_lock(lock); return result; } static CYTHON_INLINE void __Pyx_INC_MEMVIEW(__Pyx_memviewslice *memslice, int have_gil, int lineno) { int first_time; struct __pyx_memoryview_obj *memview = memslice->memview; if (!memview || (PyObject *) memview == Py_None) return; if (__pyx_get_slice_count(memview) < 0) __pyx_fatalerror("Acquisition count is %d (line %d)", __pyx_get_slice_count(memview), lineno); first_time = __pyx_add_acquisition_count(memview) == 0; if (first_time) { if (have_gil) { Py_INCREF((PyObject *) memview); } else { PyGILState_STATE _gilstate = PyGILState_Ensure(); Py_INCREF((PyObject *) memview); PyGILState_Release(_gilstate); } } } static CYTHON_INLINE void __Pyx_XDEC_MEMVIEW(__Pyx_memviewslice *memslice, int have_gil, int lineno) { int last_time; struct __pyx_memoryview_obj *memview = memslice->memview; if (!memview ) { return; } else if ((PyObject *) memview == Py_None) { memslice->memview = NULL; return; } if (__pyx_get_slice_count(memview) <= 0) __pyx_fatalerror("Acquisition count is %d (line %d)", __pyx_get_slice_count(memview), lineno); last_time = __pyx_sub_acquisition_count(memview) == 1; memslice->data = NULL; if (last_time) { if (have_gil) { Py_CLEAR(memslice->memview); } else { PyGILState_STATE _gilstate = PyGILState_Ensure(); Py_CLEAR(memslice->memview); PyGILState_Release(_gilstate); } } else { memslice->memview = NULL; } } static int __pyx_typeinfo_cmp(__Pyx_TypeInfo *a, __Pyx_TypeInfo *b) { int i; if (!a || !b) return 0; if (a == b) return 1; if (a->size != b->size || a->typegroup != b->typegroup || a->is_unsigned != b->is_unsigned || a->ndim != b->ndim) { if (a->typegroup == 'H' || b->typegroup == 'H') { return a->size == b->size; } else { return 0; } } if (a->ndim) { for (i = 0; i < a->ndim; i++) if (a->arraysize[i] != b->arraysize[i]) return 0; } if (a->typegroup == 'S') { if (a->flags != b->flags) return 0; if (a->fields || b->fields) { if (!(a->fields && b->fields)) return 0; for (i = 0; a->fields[i].type && b->fields[i].type; i++) { __Pyx_StructField *field_a = a->fields + i; __Pyx_StructField *field_b = b->fields + i; if (field_a->offset != field_b->offset || !__pyx_typeinfo_cmp(field_a->type, field_b->type)) return 0; } return !a->fields[i].type && !b->fields[i].type; } } return 1; } static int __pyx_check_strides(Py_buffer *buf, int dim, int ndim, int spec) { if (buf->shape[dim] <= 1) return 1; if (buf->strides) { if (spec & __Pyx_MEMVIEW_CONTIG) { if (spec & (__Pyx_MEMVIEW_PTR|__Pyx_MEMVIEW_FULL)) { if (buf->strides[dim] != sizeof(void *)) { PyErr_Format(PyExc_ValueError, "Buffer is not indirectly contiguous " "in dimension %d.", dim); goto fail; } } else if (buf->strides[dim] != buf->itemsize) { PyErr_SetString(PyExc_ValueError, "Buffer and memoryview are not contiguous " "in the same dimension."); goto fail; } } if (spec & __Pyx_MEMVIEW_FOLLOW) { Py_ssize_t stride = buf->strides[dim]; if (stride < 0) stride = -stride; if (stride < buf->itemsize) { PyErr_SetString(PyExc_ValueError, "Buffer and memoryview are not contiguous " "in the same dimension."); goto fail; } } } else { if (spec & __Pyx_MEMVIEW_CONTIG && dim != ndim - 1) { PyErr_Format(PyExc_ValueError, "C-contiguous buffer is not contiguous in " "dimension %d", dim); goto fail; } else if (spec & (__Pyx_MEMVIEW_PTR)) { PyErr_Format(PyExc_ValueError, "C-contiguous buffer is not indirect in " "dimension %d", dim); goto fail; } else if (buf->suboffsets) { PyErr_SetString(PyExc_ValueError, "Buffer exposes suboffsets but no strides"); goto fail; } } return 1; fail: return 0; } static int __pyx_check_suboffsets(Py_buffer *buf, int dim, CYTHON_UNUSED int ndim, int spec) { if (spec & __Pyx_MEMVIEW_DIRECT) { if (buf->suboffsets && buf->suboffsets[dim] >= 0) { PyErr_Format(PyExc_ValueError, "Buffer not compatible with direct access " "in dimension %d.", dim); goto fail; } } if (spec & __Pyx_MEMVIEW_PTR) { if (!buf->suboffsets || (buf->suboffsets && buf->suboffsets[dim] < 0)) { PyErr_Format(PyExc_ValueError, "Buffer is not indirectly accessible " "in dimension %d.", dim); goto fail; } } return 1; fail: return 0; } static int __pyx_verify_contig(Py_buffer *buf, int ndim, int c_or_f_flag) { int i; if (c_or_f_flag & __Pyx_IS_F_CONTIG) { Py_ssize_t stride = 1; for (i = 0; i < ndim; i++) { if (stride * buf->itemsize != buf->strides[i] && buf->shape[i] > 1) { PyErr_SetString(PyExc_ValueError, "Buffer not fortran contiguous."); goto fail; } stride = stride * buf->shape[i]; } } else if (c_or_f_flag & __Pyx_IS_C_CONTIG) { Py_ssize_t stride = 1; for (i = ndim - 1; i >- 1; i--) { if (stride * buf->itemsize != buf->strides[i] && buf->shape[i] > 1) { PyErr_SetString(PyExc_ValueError, "Buffer not C contiguous."); goto fail; } stride = stride * buf->shape[i]; } } return 1; fail: return 0; } static int __Pyx_ValidateAndInit_memviewslice( int *axes_specs, int c_or_f_flag, int buf_flags, int ndim, __Pyx_TypeInfo *dtype, __Pyx_BufFmt_StackElem stack[], __Pyx_memviewslice *memviewslice, PyObject *original_obj) { struct __pyx_memoryview_obj *memview, *new_memview; __Pyx_RefNannyDeclarations Py_buffer *buf; int i, spec = 0, retval = -1; __Pyx_BufFmt_Context ctx; int from_memoryview = __pyx_memoryview_check(original_obj); __Pyx_RefNannySetupContext("ValidateAndInit_memviewslice", 0); if (from_memoryview && __pyx_typeinfo_cmp(dtype, ((struct __pyx_memoryview_obj *) original_obj)->typeinfo)) { memview = (struct __pyx_memoryview_obj *) original_obj; new_memview = NULL; } else { memview = (struct __pyx_memoryview_obj *) __pyx_memoryview_new( original_obj, buf_flags, 0, dtype); new_memview = memview; if (unlikely(!memview)) goto fail; } buf = &memview->view; if (buf->ndim != ndim) { PyErr_Format(PyExc_ValueError, "Buffer has wrong number of dimensions (expected %d, got %d)", ndim, buf->ndim); goto fail; } if (new_memview) { __Pyx_BufFmt_Init(&ctx, stack, dtype); if (!__Pyx_BufFmt_CheckString(&ctx, buf->format)) goto fail; } if ((unsigned) buf->itemsize != dtype->size) { PyErr_Format(PyExc_ValueError, "Item size of buffer (%" CYTHON_FORMAT_SSIZE_T "u byte%s) " "does not match size of '%s' (%" CYTHON_FORMAT_SSIZE_T "u byte%s)", buf->itemsize, (buf->itemsize > 1) ? "s" : "", dtype->name, dtype->size, (dtype->size > 1) ? "s" : ""); goto fail; } for (i = 0; i < ndim; i++) { spec = axes_specs[i]; if (!__pyx_check_strides(buf, i, ndim, spec)) goto fail; if (!__pyx_check_suboffsets(buf, i, ndim, spec)) goto fail; } if (buf->strides && !__pyx_verify_contig(buf, ndim, c_or_f_flag)) goto fail; if (unlikely(__Pyx_init_memviewslice(memview, ndim, memviewslice, new_memview != NULL) == -1)) { goto fail; } retval = 0; goto no_fail; fail: Py_XDECREF(new_memview); retval = -1; no_fail: __Pyx_RefNannyFinishContext(); return retval; } static CYTHON_INLINE __Pyx_memviewslice __Pyx_PyObject_to_MemoryviewSlice_ds_nn___pyx_t_5numpy_float32_t(PyObject *obj) { __Pyx_memviewslice result = { 0, 0, { 0 }, { 0 }, { 0 } }; __Pyx_BufFmt_StackElem stack[1]; int axes_specs[] = { (__Pyx_MEMVIEW_DIRECT | __Pyx_MEMVIEW_STRIDED) }; int retcode; if (obj == Py_None) { result.memview = (struct __pyx_memoryview_obj *) Py_None; return result; } retcode = __Pyx_ValidateAndInit_memviewslice(axes_specs, 0, PyBUF_RECORDS, 1, &__Pyx_TypeInfo_nn___pyx_t_5numpy_float32_t, stack, &result, obj); if (unlikely(retcode == -1)) goto __pyx_fail; return result; __pyx_fail: result.memview = NULL; result.data = NULL; return result; } static CYTHON_INLINE __Pyx_memviewslice __Pyx_PyObject_to_MemoryviewSlice_ds_nn___pyx_t_5numpy_float64_t(PyObject *obj) { __Pyx_memviewslice result = { 0, 0, { 0 }, { 0 }, { 0 } }; __Pyx_BufFmt_StackElem stack[1]; int axes_specs[] = { (__Pyx_MEMVIEW_DIRECT | __Pyx_MEMVIEW_STRIDED) }; int retcode; if (obj == Py_None) { result.memview = (struct __pyx_memoryview_obj *) Py_None; return result; } retcode = __Pyx_ValidateAndInit_memviewslice(axes_specs, 0, PyBUF_RECORDS, 1, &__Pyx_TypeInfo_nn___pyx_t_5numpy_float64_t, stack, &result, obj); if (unlikely(retcode == -1)) goto __pyx_fail; return result; __pyx_fail: result.memview = NULL; result.data = NULL; return result; } static PyObject *__Pyx_Import(PyObject *name, PyObject *from_list, int level) { PyObject *empty_list = 0; PyObject *module = 0; PyObject *global_dict = 0; PyObject *empty_dict = 0; PyObject *list; #if PY_VERSION_HEX < 0x03030000 PyObject *py_import; py_import = __Pyx_PyObject_GetAttrStr(__pyx_b, __pyx_n_s_import); if (!py_import) goto bad; #endif if (from_list) list = from_list; else { empty_list = PyList_New(0); if (!empty_list) goto bad; list = empty_list; } global_dict = PyModule_GetDict(__pyx_m); if (!global_dict) goto bad; empty_dict = PyDict_New(); if (!empty_dict) goto bad; { #if PY_MAJOR_VERSION >= 3 if (level == -1) { if (strchr(__Pyx_MODULE_NAME, '.')) { #if PY_VERSION_HEX < 0x03030000 PyObject *py_level = PyInt_FromLong(1); if (!py_level) goto bad; module = PyObject_CallFunctionObjArgs(py_import, name, global_dict, empty_dict, list, py_level, NULL); Py_DECREF(py_level); #else module = PyImport_ImportModuleLevelObject( name, global_dict, empty_dict, list, 1); #endif if (!module) { if (!PyErr_ExceptionMatches(PyExc_ImportError)) goto bad; PyErr_Clear(); } } level = 0; } #endif if (!module) { #if PY_VERSION_HEX < 0x03030000 PyObject *py_level = PyInt_FromLong(level); if (!py_level) goto bad; module = PyObject_CallFunctionObjArgs(py_import, name, global_dict, empty_dict, list, py_level, NULL); Py_DECREF(py_level); #else module = PyImport_ImportModuleLevelObject( name, global_dict, empty_dict, list, level); #endif } } bad: #if PY_VERSION_HEX < 0x03030000 Py_XDECREF(py_import); #endif Py_XDECREF(empty_list); Py_XDECREF(empty_dict); return module; } #if PY_MAJOR_VERSION < 3 static int __Pyx_GetBuffer(PyObject *obj, Py_buffer *view, int flags) { if (PyObject_CheckBuffer(obj)) return PyObject_GetBuffer(obj, view, flags); if (PyObject_TypeCheck(obj, __pyx_ptype_5numpy_ndarray)) return __pyx_pw_5numpy_7ndarray_1__getbuffer__(obj, view, flags); if (PyObject_TypeCheck(obj, __pyx_array_type)) return __pyx_array_getbuffer(obj, view, flags); if (PyObject_TypeCheck(obj, __pyx_memoryview_type)) return __pyx_memoryview_getbuffer(obj, view, flags); PyErr_Format(PyExc_TypeError, "'%.200s' does not have the buffer interface", Py_TYPE(obj)->tp_name); return -1; } static void __Pyx_ReleaseBuffer(Py_buffer *view) { PyObject *obj = view->obj; if (!obj) return; if (PyObject_CheckBuffer(obj)) { PyBuffer_Release(view); return; } if (PyObject_TypeCheck(obj, __pyx_ptype_5numpy_ndarray)) { __pyx_pw_5numpy_7ndarray_3__releasebuffer__(obj, view); return; } Py_DECREF(obj); view->obj = NULL; } #endif static CYTHON_INLINE PyObject* __Pyx_PyInt_From_char(char value) { const char neg_one = (char) -1, const_zero = 0; const int is_unsigned = neg_one > const_zero; if (is_unsigned) { if (sizeof(char) < sizeof(long)) { return PyInt_FromLong((long) value); } else if (sizeof(char) <= sizeof(unsigned long)) { return PyLong_FromUnsignedLong((unsigned long) value); } else if (sizeof(char) <= sizeof(unsigned long long)) { return PyLong_FromUnsignedLongLong((unsigned long long) value); } } else { if (sizeof(char) <= sizeof(long)) { return PyInt_FromLong((long) value); } else if (sizeof(char) <= sizeof(long long)) { return PyLong_FromLongLong((long long) value); } } { int one = 1; int little = (int)*(unsigned char *)&one; unsigned char *bytes = (unsigned char *)&value; return _PyLong_FromByteArray(bytes, sizeof(char), little, !is_unsigned); } } #define __PYX_VERIFY_RETURN_INT(target_type, func_type, func_value) \ { \ func_type value = func_value; \ if (sizeof(target_type) < sizeof(func_type)) { \ if (unlikely(value != (func_type) (target_type) value)) { \ func_type zero = 0; \ if (is_unsigned && unlikely(value < zero)) \ goto raise_neg_overflow; \ else \ goto raise_overflow; \ } \ } \ return (target_type) value; \ } #if CYTHON_COMPILING_IN_CPYTHON && PY_MAJOR_VERSION >= 3 #if CYTHON_USE_PYLONG_INTERNALS #include "longintrepr.h" #endif #endif static CYTHON_INLINE int __Pyx_PyInt_As_int(PyObject *x) { const int neg_one = (int) -1, const_zero = 0; const int is_unsigned = neg_one > const_zero; #if PY_MAJOR_VERSION < 3 if (likely(PyInt_Check(x))) { if (sizeof(int) < sizeof(long)) { __PYX_VERIFY_RETURN_INT(int, long, PyInt_AS_LONG(x)) } else { long val = PyInt_AS_LONG(x); if (is_unsigned && unlikely(val < 0)) { goto raise_neg_overflow; } return (int) val; } } else #endif if (likely(PyLong_Check(x))) { if (is_unsigned) { #if CYTHON_COMPILING_IN_CPYTHON && PY_MAJOR_VERSION >= 3 #if CYTHON_USE_PYLONG_INTERNALS switch (Py_SIZE(x)) { case 0: return 0; case 1: __PYX_VERIFY_RETURN_INT(int, digit, ((PyLongObject*)x)->ob_digit[0]); } #endif #endif if (unlikely(Py_SIZE(x) < 0)) { goto raise_neg_overflow; } if (sizeof(int) <= sizeof(unsigned long)) { __PYX_VERIFY_RETURN_INT(int, unsigned long, PyLong_AsUnsignedLong(x)) } else if (sizeof(int) <= sizeof(unsigned long long)) { __PYX_VERIFY_RETURN_INT(int, unsigned long long, PyLong_AsUnsignedLongLong(x)) } } else { #if CYTHON_COMPILING_IN_CPYTHON && PY_MAJOR_VERSION >= 3 #if CYTHON_USE_PYLONG_INTERNALS switch (Py_SIZE(x)) { case 0: return 0; case 1: __PYX_VERIFY_RETURN_INT(int, digit, +(((PyLongObject*)x)->ob_digit[0])); case -1: __PYX_VERIFY_RETURN_INT(int, sdigit, -(sdigit) ((PyLongObject*)x)->ob_digit[0]); } #endif #endif if (sizeof(int) <= sizeof(long)) { __PYX_VERIFY_RETURN_INT(int, long, PyLong_AsLong(x)) } else if (sizeof(int) <= sizeof(long long)) { __PYX_VERIFY_RETURN_INT(int, long long, PyLong_AsLongLong(x)) } } { #if CYTHON_COMPILING_IN_PYPY && !defined(_PyLong_AsByteArray) PyErr_SetString(PyExc_RuntimeError, "_PyLong_AsByteArray() not available in PyPy, cannot convert large numbers"); #else int val; PyObject *v = __Pyx_PyNumber_Int(x); #if PY_MAJOR_VERSION < 3 if (likely(v) && !PyLong_Check(v)) { PyObject *tmp = v; v = PyNumber_Long(tmp); Py_DECREF(tmp); } #endif if (likely(v)) { int one = 1; int is_little = (int)*(unsigned char *)&one; unsigned char *bytes = (unsigned char *)&val; int ret = _PyLong_AsByteArray((PyLongObject *)v, bytes, sizeof(val), is_little, !is_unsigned); Py_DECREF(v); if (likely(!ret)) return val; } #endif return (int) -1; } } else { int val; PyObject *tmp = __Pyx_PyNumber_Int(x); if (!tmp) return (int) -1; val = __Pyx_PyInt_As_int(tmp); Py_DECREF(tmp); return val; } raise_overflow: PyErr_SetString(PyExc_OverflowError, "value too large to convert to int"); return (int) -1; raise_neg_overflow: PyErr_SetString(PyExc_OverflowError, "can't convert negative value to int"); return (int) -1; } static CYTHON_INLINE PyObject* __Pyx_PyInt_From_long(long value) { const long neg_one = (long) -1, const_zero = 0; const int is_unsigned = neg_one > const_zero; if (is_unsigned) { if (sizeof(long) < sizeof(long)) { return PyInt_FromLong((long) value); } else if (sizeof(long) <= sizeof(unsigned long)) { return PyLong_FromUnsignedLong((unsigned long) value); } else if (sizeof(long) <= sizeof(unsigned long long)) { return PyLong_FromUnsignedLongLong((unsigned long long) value); } } else { if (sizeof(long) <= sizeof(long)) { return PyInt_FromLong((long) value); } else if (sizeof(long) <= sizeof(long long)) { return PyLong_FromLongLong((long long) value); } } { int one = 1; int little = (int)*(unsigned char *)&one; unsigned char *bytes = (unsigned char *)&value; return _PyLong_FromByteArray(bytes, sizeof(long), little, !is_unsigned); } } static CYTHON_INLINE PyObject* __Pyx_PyInt_From_int(int value) { const int neg_one = (int) -1, const_zero = 0; const int is_unsigned = neg_one > const_zero; if (is_unsigned) { if (sizeof(int) < sizeof(long)) { return PyInt_FromLong((long) value); } else if (sizeof(int) <= sizeof(unsigned long)) { return PyLong_FromUnsignedLong((unsigned long) value); } else if (sizeof(int) <= sizeof(unsigned long long)) { return PyLong_FromUnsignedLongLong((unsigned long long) value); } } else { if (sizeof(int) <= sizeof(long)) { return PyInt_FromLong((long) value); } else if (sizeof(int) <= sizeof(long long)) { return PyLong_FromLongLong((long long) value); } } { int one = 1; int little = (int)*(unsigned char *)&one; unsigned char *bytes = (unsigned char *)&value; return _PyLong_FromByteArray(bytes, sizeof(int), little, !is_unsigned); } } static CYTHON_INLINE char __Pyx_PyInt_As_char(PyObject *x) { const char neg_one = (char) -1, const_zero = 0; const int is_unsigned = neg_one > const_zero; #if PY_MAJOR_VERSION < 3 if (likely(PyInt_Check(x))) { if (sizeof(char) < sizeof(long)) { __PYX_VERIFY_RETURN_INT(char, long, PyInt_AS_LONG(x)) } else { long val = PyInt_AS_LONG(x); if (is_unsigned && unlikely(val < 0)) { goto raise_neg_overflow; } return (char) val; } } else #endif if (likely(PyLong_Check(x))) { if (is_unsigned) { #if CYTHON_COMPILING_IN_CPYTHON && PY_MAJOR_VERSION >= 3 #if CYTHON_USE_PYLONG_INTERNALS switch (Py_SIZE(x)) { case 0: return 0; case 1: __PYX_VERIFY_RETURN_INT(char, digit, ((PyLongObject*)x)->ob_digit[0]); } #endif #endif if (unlikely(Py_SIZE(x) < 0)) { goto raise_neg_overflow; } if (sizeof(char) <= sizeof(unsigned long)) { __PYX_VERIFY_RETURN_INT(char, unsigned long, PyLong_AsUnsignedLong(x)) } else if (sizeof(char) <= sizeof(unsigned long long)) { __PYX_VERIFY_RETURN_INT(char, unsigned long long, PyLong_AsUnsignedLongLong(x)) } } else { #if CYTHON_COMPILING_IN_CPYTHON && PY_MAJOR_VERSION >= 3 #if CYTHON_USE_PYLONG_INTERNALS switch (Py_SIZE(x)) { case 0: return 0; case 1: __PYX_VERIFY_RETURN_INT(char, digit, +(((PyLongObject*)x)->ob_digit[0])); case -1: __PYX_VERIFY_RETURN_INT(char, sdigit, -(sdigit) ((PyLongObject*)x)->ob_digit[0]); } #endif #endif if (sizeof(char) <= sizeof(long)) { __PYX_VERIFY_RETURN_INT(char, long, PyLong_AsLong(x)) } else if (sizeof(char) <= sizeof(long long)) { __PYX_VERIFY_RETURN_INT(char, long long, PyLong_AsLongLong(x)) } } { #if CYTHON_COMPILING_IN_PYPY && !defined(_PyLong_AsByteArray) PyErr_SetString(PyExc_RuntimeError, "_PyLong_AsByteArray() not available in PyPy, cannot convert large numbers"); #else char val; PyObject *v = __Pyx_PyNumber_Int(x); #if PY_MAJOR_VERSION < 3 if (likely(v) && !PyLong_Check(v)) { PyObject *tmp = v; v = PyNumber_Long(tmp); Py_DECREF(tmp); } #endif if (likely(v)) { int one = 1; int is_little = (int)*(unsigned char *)&one; unsigned char *bytes = (unsigned char *)&val; int ret = _PyLong_AsByteArray((PyLongObject *)v, bytes, sizeof(val), is_little, !is_unsigned); Py_DECREF(v); if (likely(!ret)) return val; } #endif return (char) -1; } } else { char val; PyObject *tmp = __Pyx_PyNumber_Int(x); if (!tmp) return (char) -1; val = __Pyx_PyInt_As_char(tmp); Py_DECREF(tmp); return val; } raise_overflow: PyErr_SetString(PyExc_OverflowError, "value too large to convert to char"); return (char) -1; raise_neg_overflow: PyErr_SetString(PyExc_OverflowError, "can't convert negative value to char"); return (char) -1; } static CYTHON_INLINE PyObject* __Pyx_PyInt_From_index_t(index_t value) { const index_t neg_one = (index_t) -1, const_zero = 0; const int is_unsigned = neg_one > const_zero; if (is_unsigned) { if (sizeof(index_t) < sizeof(long)) { return PyInt_FromLong((long) value); } else if (sizeof(index_t) <= sizeof(unsigned long)) { return PyLong_FromUnsignedLong((unsigned long) value); } else if (sizeof(index_t) <= sizeof(unsigned long long)) { return PyLong_FromUnsignedLongLong((unsigned long long) value); } } else { if (sizeof(index_t) <= sizeof(long)) { return PyInt_FromLong((long) value); } else if (sizeof(index_t) <= sizeof(long long)) { return PyLong_FromLongLong((long long) value); } } { int one = 1; int little = (int)*(unsigned char *)&one; unsigned char *bytes = (unsigned char *)&value; return _PyLong_FromByteArray(bytes, sizeof(index_t), little, !is_unsigned); } } static CYTHON_INLINE index_t __Pyx_PyInt_As_index_t(PyObject *x) { const index_t neg_one = (index_t) -1, const_zero = 0; const int is_unsigned = neg_one > const_zero; #if PY_MAJOR_VERSION < 3 if (likely(PyInt_Check(x))) { if (sizeof(index_t) < sizeof(long)) { __PYX_VERIFY_RETURN_INT(index_t, long, PyInt_AS_LONG(x)) } else { long val = PyInt_AS_LONG(x); if (is_unsigned && unlikely(val < 0)) { goto raise_neg_overflow; } return (index_t) val; } } else #endif if (likely(PyLong_Check(x))) { if (is_unsigned) { #if CYTHON_COMPILING_IN_CPYTHON && PY_MAJOR_VERSION >= 3 #if CYTHON_USE_PYLONG_INTERNALS switch (Py_SIZE(x)) { case 0: return 0; case 1: __PYX_VERIFY_RETURN_INT(index_t, digit, ((PyLongObject*)x)->ob_digit[0]); } #endif #endif if (unlikely(Py_SIZE(x) < 0)) { goto raise_neg_overflow; } if (sizeof(index_t) <= sizeof(unsigned long)) { __PYX_VERIFY_RETURN_INT(index_t, unsigned long, PyLong_AsUnsignedLong(x)) } else if (sizeof(index_t) <= sizeof(unsigned long long)) { __PYX_VERIFY_RETURN_INT(index_t, unsigned long long, PyLong_AsUnsignedLongLong(x)) } } else { #if CYTHON_COMPILING_IN_CPYTHON && PY_MAJOR_VERSION >= 3 #if CYTHON_USE_PYLONG_INTERNALS switch (Py_SIZE(x)) { case 0: return 0; case 1: __PYX_VERIFY_RETURN_INT(index_t, digit, +(((PyLongObject*)x)->ob_digit[0])); case -1: __PYX_VERIFY_RETURN_INT(index_t, sdigit, -(sdigit) ((PyLongObject*)x)->ob_digit[0]); } #endif #endif if (sizeof(index_t) <= sizeof(long)) { __PYX_VERIFY_RETURN_INT(index_t, long, PyLong_AsLong(x)) } else if (sizeof(index_t) <= sizeof(long long)) { __PYX_VERIFY_RETURN_INT(index_t, long long, PyLong_AsLongLong(x)) } } { #if CYTHON_COMPILING_IN_PYPY && !defined(_PyLong_AsByteArray) PyErr_SetString(PyExc_RuntimeError, "_PyLong_AsByteArray() not available in PyPy, cannot convert large numbers"); #else index_t val; PyObject *v = __Pyx_PyNumber_Int(x); #if PY_MAJOR_VERSION < 3 if (likely(v) && !PyLong_Check(v)) { PyObject *tmp = v; v = PyNumber_Long(tmp); Py_DECREF(tmp); } #endif if (likely(v)) { int one = 1; int is_little = (int)*(unsigned char *)&one; unsigned char *bytes = (unsigned char *)&val; int ret = _PyLong_AsByteArray((PyLongObject *)v, bytes, sizeof(val), is_little, !is_unsigned); Py_DECREF(v); if (likely(!ret)) return val; } #endif return (index_t) -1; } } else { index_t val; PyObject *tmp = __Pyx_PyNumber_Int(x); if (!tmp) return (index_t) -1; val = __Pyx_PyInt_As_index_t(tmp); Py_DECREF(tmp); return val; } raise_overflow: PyErr_SetString(PyExc_OverflowError, "value too large to convert to index_t"); return (index_t) -1; raise_neg_overflow: PyErr_SetString(PyExc_OverflowError, "can't convert negative value to index_t"); return (index_t) -1; } static CYTHON_INLINE int __Pyx_BytesContains(PyObject* bytes, char character) { const Py_ssize_t length = PyBytes_GET_SIZE(bytes); char* char_start = PyBytes_AS_STRING(bytes); char* pos; for (pos=char_start; pos < char_start+length; pos++) { if (character == pos[0]) return 1; } return 0; } #if CYTHON_CCOMPLEX #ifdef __cplusplus static CYTHON_INLINE __pyx_t_float_complex __pyx_t_float_complex_from_parts(float x, float y) { return ::std::complex< float >(x, y); } #else static CYTHON_INLINE __pyx_t_float_complex __pyx_t_float_complex_from_parts(float x, float y) { return x + y*(__pyx_t_float_complex)_Complex_I; } #endif #else static CYTHON_INLINE __pyx_t_float_complex __pyx_t_float_complex_from_parts(float x, float y) { __pyx_t_float_complex z; z.real = x; z.imag = y; return z; } #endif #if CYTHON_CCOMPLEX #else static CYTHON_INLINE int __Pyx_c_eqf(__pyx_t_float_complex a, __pyx_t_float_complex b) { return (a.real == b.real) && (a.imag == b.imag); } static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_sumf(__pyx_t_float_complex a, __pyx_t_float_complex b) { __pyx_t_float_complex z; z.real = a.real + b.real; z.imag = a.imag + b.imag; return z; } static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_difff(__pyx_t_float_complex a, __pyx_t_float_complex b) { __pyx_t_float_complex z; z.real = a.real - b.real; z.imag = a.imag - b.imag; return z; } static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_prodf(__pyx_t_float_complex a, __pyx_t_float_complex b) { __pyx_t_float_complex z; z.real = a.real * b.real - a.imag * b.imag; z.imag = a.real * b.imag + a.imag * b.real; return z; } static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_quotf(__pyx_t_float_complex a, __pyx_t_float_complex b) { __pyx_t_float_complex z; float denom = b.real * b.real + b.imag * b.imag; z.real = (a.real * b.real + a.imag * b.imag) / denom; z.imag = (a.imag * b.real - a.real * b.imag) / denom; return z; } static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_negf(__pyx_t_float_complex a) { __pyx_t_float_complex z; z.real = -a.real; z.imag = -a.imag; return z; } static CYTHON_INLINE int __Pyx_c_is_zerof(__pyx_t_float_complex a) { return (a.real == 0) && (a.imag == 0); } static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_conjf(__pyx_t_float_complex a) { __pyx_t_float_complex z; z.real = a.real; z.imag = -a.imag; return z; } #if 1 static CYTHON_INLINE float __Pyx_c_absf(__pyx_t_float_complex z) { #if !defined(HAVE_HYPOT) || defined(_MSC_VER) return sqrtf(z.real*z.real + z.imag*z.imag); #else return hypotf(z.real, z.imag); #endif } static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_powf(__pyx_t_float_complex a, __pyx_t_float_complex b) { __pyx_t_float_complex z; float r, lnr, theta, z_r, z_theta; if (b.imag == 0 && b.real == (int)b.real) { if (b.real < 0) { float denom = a.real * a.real + a.imag * a.imag; a.real = a.real / denom; a.imag = -a.imag / denom; b.real = -b.real; } switch ((int)b.real) { case 0: z.real = 1; z.imag = 0; return z; case 1: return a; case 2: z = __Pyx_c_prodf(a, a); return __Pyx_c_prodf(a, a); case 3: z = __Pyx_c_prodf(a, a); return __Pyx_c_prodf(z, a); case 4: z = __Pyx_c_prodf(a, a); return __Pyx_c_prodf(z, z); } } if (a.imag == 0) { if (a.real == 0) { return a; } r = a.real; theta = 0; } else { r = __Pyx_c_absf(a); theta = atan2f(a.imag, a.real); } lnr = logf(r); z_r = expf(lnr * b.real - theta * b.imag); z_theta = theta * b.real + lnr * b.imag; z.real = z_r * cosf(z_theta); z.imag = z_r * sinf(z_theta); return z; } #endif #endif #if CYTHON_CCOMPLEX #ifdef __cplusplus static CYTHON_INLINE __pyx_t_double_complex __pyx_t_double_complex_from_parts(double x, double y) { return ::std::complex< double >(x, y); } #else static CYTHON_INLINE __pyx_t_double_complex __pyx_t_double_complex_from_parts(double x, double y) { return x + y*(__pyx_t_double_complex)_Complex_I; } #endif #else static CYTHON_INLINE __pyx_t_double_complex __pyx_t_double_complex_from_parts(double x, double y) { __pyx_t_double_complex z; z.real = x; z.imag = y; return z; } #endif #if CYTHON_CCOMPLEX #else static CYTHON_INLINE int __Pyx_c_eq(__pyx_t_double_complex a, __pyx_t_double_complex b) { return (a.real == b.real) && (a.imag == b.imag); } static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_sum(__pyx_t_double_complex a, __pyx_t_double_complex b) { __pyx_t_double_complex z; z.real = a.real + b.real; z.imag = a.imag + b.imag; return z; } static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_diff(__pyx_t_double_complex a, __pyx_t_double_complex b) { __pyx_t_double_complex z; z.real = a.real - b.real; z.imag = a.imag - b.imag; return z; } static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_prod(__pyx_t_double_complex a, __pyx_t_double_complex b) { __pyx_t_double_complex z; z.real = a.real * b.real - a.imag * b.imag; z.imag = a.real * b.imag + a.imag * b.real; return z; } static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_quot(__pyx_t_double_complex a, __pyx_t_double_complex b) { __pyx_t_double_complex z; double denom = b.real * b.real + b.imag * b.imag; z.real = (a.real * b.real + a.imag * b.imag) / denom; z.imag = (a.imag * b.real - a.real * b.imag) / denom; return z; } static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_neg(__pyx_t_double_complex a) { __pyx_t_double_complex z; z.real = -a.real; z.imag = -a.imag; return z; } static CYTHON_INLINE int __Pyx_c_is_zero(__pyx_t_double_complex a) { return (a.real == 0) && (a.imag == 0); } static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_conj(__pyx_t_double_complex a) { __pyx_t_double_complex z; z.real = a.real; z.imag = -a.imag; return z; } #if 1 static CYTHON_INLINE double __Pyx_c_abs(__pyx_t_double_complex z) { #if !defined(HAVE_HYPOT) || defined(_MSC_VER) return sqrt(z.real*z.real + z.imag*z.imag); #else return hypot(z.real, z.imag); #endif } static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_pow(__pyx_t_double_complex a, __pyx_t_double_complex b) { __pyx_t_double_complex z; double r, lnr, theta, z_r, z_theta; if (b.imag == 0 && b.real == (int)b.real) { if (b.real < 0) { double denom = a.real * a.real + a.imag * a.imag; a.real = a.real / denom; a.imag = -a.imag / denom; b.real = -b.real; } switch ((int)b.real) { case 0: z.real = 1; z.imag = 0; return z; case 1: return a; case 2: z = __Pyx_c_prod(a, a); return __Pyx_c_prod(a, a); case 3: z = __Pyx_c_prod(a, a); return __Pyx_c_prod(z, a); case 4: z = __Pyx_c_prod(a, a); return __Pyx_c_prod(z, z); } } if (a.imag == 0) { if (a.real == 0) { return a; } r = a.real; theta = 0; } else { r = __Pyx_c_abs(a); theta = atan2(a.imag, a.real); } lnr = log(r); z_r = exp(lnr * b.real - theta * b.imag); z_theta = theta * b.real + lnr * b.imag; z.real = z_r * cos(z_theta); z.imag = z_r * sin(z_theta); return z; } #endif #endif static int __pyx_memviewslice_is_contig(const __Pyx_memviewslice *mvs, char order, int ndim) { int i, index, step, start; Py_ssize_t itemsize = mvs->memview->view.itemsize; if (order == 'F') { step = 1; start = 0; } else { step = -1; start = ndim - 1; } for (i = 0; i < ndim; i++) { index = start + step * i; if (mvs->suboffsets[index] >= 0 || mvs->strides[index] != itemsize) return 0; itemsize *= mvs->shape[index]; } return 1; } static void __pyx_get_array_memory_extents(__Pyx_memviewslice *slice, void **out_start, void **out_end, int ndim, size_t itemsize) { char *start, *end; int i; start = end = slice->data; for (i = 0; i < ndim; i++) { Py_ssize_t stride = slice->strides[i]; Py_ssize_t extent = slice->shape[i]; if (extent == 0) { *out_start = *out_end = start; return; } else { if (stride > 0) end += stride * (extent - 1); else start += stride * (extent - 1); } } *out_start = start; *out_end = end + itemsize; } static int __pyx_slices_overlap(__Pyx_memviewslice *slice1, __Pyx_memviewslice *slice2, int ndim, size_t itemsize) { void *start1, *end1, *start2, *end2; __pyx_get_array_memory_extents(slice1, &start1, &end1, ndim, itemsize); __pyx_get_array_memory_extents(slice2, &start2, &end2, ndim, itemsize); return (start1 < end2) && (start2 < end1); } static __Pyx_memviewslice __pyx_memoryview_copy_new_contig(const __Pyx_memviewslice *from_mvs, const char *mode, int ndim, size_t sizeof_dtype, int contig_flag, int dtype_is_object) { __Pyx_RefNannyDeclarations int i; __Pyx_memviewslice new_mvs = { 0, 0, { 0 }, { 0 }, { 0 } }; struct __pyx_memoryview_obj *from_memview = from_mvs->memview; Py_buffer *buf = &from_memview->view; PyObject *shape_tuple = NULL; PyObject *temp_int = NULL; struct __pyx_array_obj *array_obj = NULL; struct __pyx_memoryview_obj *memview_obj = NULL; __Pyx_RefNannySetupContext("__pyx_memoryview_copy_new_contig", 0); for (i = 0; i < ndim; i++) { if (from_mvs->suboffsets[i] >= 0) { PyErr_Format(PyExc_ValueError, "Cannot copy memoryview slice with " "indirect dimensions (axis %d)", i); goto fail; } } shape_tuple = PyTuple_New(ndim); if (unlikely(!shape_tuple)) { goto fail; } __Pyx_GOTREF(shape_tuple); for(i = 0; i < ndim; i++) { temp_int = PyInt_FromSsize_t(from_mvs->shape[i]); if(unlikely(!temp_int)) { goto fail; } else { PyTuple_SET_ITEM(shape_tuple, i, temp_int); temp_int = NULL; } } array_obj = __pyx_array_new(shape_tuple, sizeof_dtype, buf->format, (char *) mode, NULL); if (unlikely(!array_obj)) { goto fail; } __Pyx_GOTREF(array_obj); memview_obj = (struct __pyx_memoryview_obj *) __pyx_memoryview_new( (PyObject *) array_obj, contig_flag, dtype_is_object, from_mvs->memview->typeinfo); if (unlikely(!memview_obj)) goto fail; if (unlikely(__Pyx_init_memviewslice(memview_obj, ndim, &new_mvs, 1) < 0)) goto fail; if (unlikely(__pyx_memoryview_copy_contents(*from_mvs, new_mvs, ndim, ndim, dtype_is_object) < 0)) goto fail; goto no_fail; fail: __Pyx_XDECREF(new_mvs.memview); new_mvs.memview = NULL; new_mvs.data = NULL; no_fail: __Pyx_XDECREF(shape_tuple); __Pyx_XDECREF(temp_int); __Pyx_XDECREF(array_obj); __Pyx_RefNannyFinishContext(); return new_mvs; } static CYTHON_INLINE PyObject * __pyx_capsule_create(void *p, CYTHON_UNUSED const char *sig) { PyObject *cobj; #if PY_VERSION_HEX >= 0x02070000 cobj = PyCapsule_New(p, sig, NULL); #else cobj = PyCObject_FromVoidPtr(p, NULL); #endif return cobj; } static CYTHON_INLINE long __Pyx_PyInt_As_long(PyObject *x) { const long neg_one = (long) -1, const_zero = 0; const int is_unsigned = neg_one > const_zero; #if PY_MAJOR_VERSION < 3 if (likely(PyInt_Check(x))) { if (sizeof(long) < sizeof(long)) { __PYX_VERIFY_RETURN_INT(long, long, PyInt_AS_LONG(x)) } else { long val = PyInt_AS_LONG(x); if (is_unsigned && unlikely(val < 0)) { goto raise_neg_overflow; } return (long) val; } } else #endif if (likely(PyLong_Check(x))) { if (is_unsigned) { #if CYTHON_COMPILING_IN_CPYTHON && PY_MAJOR_VERSION >= 3 #if CYTHON_USE_PYLONG_INTERNALS switch (Py_SIZE(x)) { case 0: return 0; case 1: __PYX_VERIFY_RETURN_INT(long, digit, ((PyLongObject*)x)->ob_digit[0]); } #endif #endif if (unlikely(Py_SIZE(x) < 0)) { goto raise_neg_overflow; } if (sizeof(long) <= sizeof(unsigned long)) { __PYX_VERIFY_RETURN_INT(long, unsigned long, PyLong_AsUnsignedLong(x)) } else if (sizeof(long) <= sizeof(unsigned long long)) { __PYX_VERIFY_RETURN_INT(long, unsigned long long, PyLong_AsUnsignedLongLong(x)) } } else { #if CYTHON_COMPILING_IN_CPYTHON && PY_MAJOR_VERSION >= 3 #if CYTHON_USE_PYLONG_INTERNALS switch (Py_SIZE(x)) { case 0: return 0; case 1: __PYX_VERIFY_RETURN_INT(long, digit, +(((PyLongObject*)x)->ob_digit[0])); case -1: __PYX_VERIFY_RETURN_INT(long, sdigit, -(sdigit) ((PyLongObject*)x)->ob_digit[0]); } #endif #endif if (sizeof(long) <= sizeof(long)) { __PYX_VERIFY_RETURN_INT(long, long, PyLong_AsLong(x)) } else if (sizeof(long) <= sizeof(long long)) { __PYX_VERIFY_RETURN_INT(long, long long, PyLong_AsLongLong(x)) } } { #if CYTHON_COMPILING_IN_PYPY && !defined(_PyLong_AsByteArray) PyErr_SetString(PyExc_RuntimeError, "_PyLong_AsByteArray() not available in PyPy, cannot convert large numbers"); #else long val; PyObject *v = __Pyx_PyNumber_Int(x); #if PY_MAJOR_VERSION < 3 if (likely(v) && !PyLong_Check(v)) { PyObject *tmp = v; v = PyNumber_Long(tmp); Py_DECREF(tmp); } #endif if (likely(v)) { int one = 1; int is_little = (int)*(unsigned char *)&one; unsigned char *bytes = (unsigned char *)&val; int ret = _PyLong_AsByteArray((PyLongObject *)v, bytes, sizeof(val), is_little, !is_unsigned); Py_DECREF(v); if (likely(!ret)) return val; } #endif return (long) -1; } } else { long val; PyObject *tmp = __Pyx_PyNumber_Int(x); if (!tmp) return (long) -1; val = __Pyx_PyInt_As_long(tmp); Py_DECREF(tmp); return val; } raise_overflow: PyErr_SetString(PyExc_OverflowError, "value too large to convert to long"); return (long) -1; raise_neg_overflow: PyErr_SetString(PyExc_OverflowError, "can't convert negative value to long"); return (long) -1; } static int __Pyx_check_binary_version(void) { char ctversion[4], rtversion[4]; PyOS_snprintf(ctversion, 4, "%d.%d", PY_MAJOR_VERSION, PY_MINOR_VERSION); PyOS_snprintf(rtversion, 4, "%s", Py_GetVersion()); if (ctversion[0] != rtversion[0] || ctversion[2] != rtversion[2]) { char message[200]; PyOS_snprintf(message, sizeof(message), "compiletime version %s of module '%.100s' " "does not match runtime version %s", ctversion, __Pyx_MODULE_NAME, rtversion); return PyErr_WarnEx(NULL, message, 1); } return 0; } #ifndef __PYX_HAVE_RT_ImportModule #define __PYX_HAVE_RT_ImportModule static PyObject *__Pyx_ImportModule(const char *name) { PyObject *py_name = 0; PyObject *py_module = 0; py_name = __Pyx_PyIdentifier_FromString(name); if (!py_name) goto bad; py_module = PyImport_Import(py_name); Py_DECREF(py_name); return py_module; bad: Py_XDECREF(py_name); return 0; } #endif #ifndef __PYX_HAVE_RT_ImportType #define __PYX_HAVE_RT_ImportType static PyTypeObject *__Pyx_ImportType(const char *module_name, const char *class_name, size_t size, int strict) { PyObject *py_module = 0; PyObject *result = 0; PyObject *py_name = 0; char warning[200]; Py_ssize_t basicsize; #ifdef Py_LIMITED_API PyObject *py_basicsize; #endif py_module = __Pyx_ImportModule(module_name); if (!py_module) goto bad; py_name = __Pyx_PyIdentifier_FromString(class_name); if (!py_name) goto bad; result = PyObject_GetAttr(py_module, py_name); Py_DECREF(py_name); py_name = 0; Py_DECREF(py_module); py_module = 0; if (!result) goto bad; if (!PyType_Check(result)) { PyErr_Format(PyExc_TypeError, "%.200s.%.200s is not a type object", module_name, class_name); goto bad; } #ifndef Py_LIMITED_API basicsize = ((PyTypeObject *)result)->tp_basicsize; #else py_basicsize = PyObject_GetAttrString(result, "__basicsize__"); if (!py_basicsize) goto bad; basicsize = PyLong_AsSsize_t(py_basicsize); Py_DECREF(py_basicsize); py_basicsize = 0; if (basicsize == (Py_ssize_t)-1 && PyErr_Occurred()) goto bad; #endif if (!strict && (size_t)basicsize > size) { PyOS_snprintf(warning, sizeof(warning), "%s.%s size changed, may indicate binary incompatibility", module_name, class_name); if (PyErr_WarnEx(NULL, warning, 0) < 0) goto bad; } else if ((size_t)basicsize != size) { PyErr_Format(PyExc_ValueError, "%.200s.%.200s has the wrong size, try recompiling", module_name, class_name); goto bad; } return (PyTypeObject *)result; bad: Py_XDECREF(py_module); Py_XDECREF(result); return NULL; } #endif static int __Pyx_InitStrings(__Pyx_StringTabEntry *t) { while (t->p) { #if PY_MAJOR_VERSION < 3 if (t->is_unicode) { *t->p = PyUnicode_DecodeUTF8(t->s, t->n - 1, NULL); } else if (t->intern) { *t->p = PyString_InternFromString(t->s); } else { *t->p = PyString_FromStringAndSize(t->s, t->n - 1); } #else if (t->is_unicode | t->is_str) { if (t->intern) { *t->p = PyUnicode_InternFromString(t->s); } else if (t->encoding) { *t->p = PyUnicode_Decode(t->s, t->n - 1, t->encoding, NULL); } else { *t->p = PyUnicode_FromStringAndSize(t->s, t->n - 1); } } else { *t->p = PyBytes_FromStringAndSize(t->s, t->n - 1); } #endif if (!*t->p) return -1; ++t; } return 0; } static CYTHON_INLINE PyObject* __Pyx_PyUnicode_FromString(const char* c_str) { return __Pyx_PyUnicode_FromStringAndSize(c_str, (Py_ssize_t)strlen(c_str)); } static CYTHON_INLINE char* __Pyx_PyObject_AsString(PyObject* o) { Py_ssize_t ignore; return __Pyx_PyObject_AsStringAndSize(o, &ignore); } static CYTHON_INLINE char* __Pyx_PyObject_AsStringAndSize(PyObject* o, Py_ssize_t *length) { #if __PYX_DEFAULT_STRING_ENCODING_IS_ASCII || __PYX_DEFAULT_STRING_ENCODING_IS_DEFAULT if ( #if PY_MAJOR_VERSION < 3 && __PYX_DEFAULT_STRING_ENCODING_IS_ASCII __Pyx_sys_getdefaultencoding_not_ascii && #endif PyUnicode_Check(o)) { #if PY_VERSION_HEX < 0x03030000 char* defenc_c; PyObject* defenc = _PyUnicode_AsDefaultEncodedString(o, NULL); if (!defenc) return NULL; defenc_c = PyBytes_AS_STRING(defenc); #if __PYX_DEFAULT_STRING_ENCODING_IS_ASCII { char* end = defenc_c + PyBytes_GET_SIZE(defenc); char* c; for (c = defenc_c; c < end; c++) { if ((unsigned char) (*c) >= 128) { PyUnicode_AsASCIIString(o); return NULL; } } } #endif *length = PyBytes_GET_SIZE(defenc); return defenc_c; #else if (__Pyx_PyUnicode_READY(o) == -1) return NULL; #if __PYX_DEFAULT_STRING_ENCODING_IS_ASCII if (PyUnicode_IS_ASCII(o)) { *length = PyUnicode_GET_LENGTH(o); return PyUnicode_AsUTF8(o); } else { PyUnicode_AsASCIIString(o); return NULL; } #else return PyUnicode_AsUTF8AndSize(o, length); #endif #endif } else #endif #if !CYTHON_COMPILING_IN_PYPY if (PyByteArray_Check(o)) { *length = PyByteArray_GET_SIZE(o); return PyByteArray_AS_STRING(o); } else #endif { char* result; int r = PyBytes_AsStringAndSize(o, &result, length); if (unlikely(r < 0)) { return NULL; } else { return result; } } } static CYTHON_INLINE int __Pyx_PyObject_IsTrue(PyObject* x) { int is_true = x == Py_True; if (is_true | (x == Py_False) | (x == Py_None)) return is_true; else return PyObject_IsTrue(x); } static CYTHON_INLINE PyObject* __Pyx_PyNumber_Int(PyObject* x) { PyNumberMethods *m; const char *name = NULL; PyObject *res = NULL; #if PY_MAJOR_VERSION < 3 if (PyInt_Check(x) || PyLong_Check(x)) #else if (PyLong_Check(x)) #endif return Py_INCREF(x), x; m = Py_TYPE(x)->tp_as_number; #if PY_MAJOR_VERSION < 3 if (m && m->nb_int) { name = "int"; res = PyNumber_Int(x); } else if (m && m->nb_long) { name = "long"; res = PyNumber_Long(x); } #else if (m && m->nb_int) { name = "int"; res = PyNumber_Long(x); } #endif if (res) { #if PY_MAJOR_VERSION < 3 if (!PyInt_Check(res) && !PyLong_Check(res)) { #else if (!PyLong_Check(res)) { #endif PyErr_Format(PyExc_TypeError, "__%.4s__ returned non-%.4s (type %.200s)", name, name, Py_TYPE(res)->tp_name); Py_DECREF(res); return NULL; } } else if (!PyErr_Occurred()) { PyErr_SetString(PyExc_TypeError, "an integer is required"); } return res; } static CYTHON_INLINE Py_ssize_t __Pyx_PyIndex_AsSsize_t(PyObject* b) { Py_ssize_t ival; PyObject *x; #if PY_MAJOR_VERSION < 3 if (likely(PyInt_CheckExact(b))) return PyInt_AS_LONG(b); #endif if (likely(PyLong_CheckExact(b))) { #if CYTHON_COMPILING_IN_CPYTHON && PY_MAJOR_VERSION >= 3 #if CYTHON_USE_PYLONG_INTERNALS switch (Py_SIZE(b)) { case -1: return -(sdigit)((PyLongObject*)b)->ob_digit[0]; case 0: return 0; case 1: return ((PyLongObject*)b)->ob_digit[0]; } #endif #endif return PyLong_AsSsize_t(b); } x = PyNumber_Index(b); if (!x) return -1; ival = PyInt_AsSsize_t(x); Py_DECREF(x); return ival; } static CYTHON_INLINE PyObject * __Pyx_PyInt_FromSize_t(size_t ival) { return PyInt_FromSize_t(ival); } #endif /* Py_PYTHON_H */ PyWavelets-0.3.0/pywt/src/common.c0000664000175000017500000000352112556460247020571 0ustar rgommersrgommers00000000000000/* Copyright (c) 2006-2012 Filip Wasilewski */ /* See COPYING for license details. */ #include "common.h" #ifdef PY_EXTENSION void *wtcalloc(size_t len, size_t size){ void *p = wtmalloc(len*size); if(p) memset(p, 0, len*size); return p; } #endif /* buffers and max levels params */ index_t dwt_buffer_length(index_t input_len, index_t filter_len, MODE mode){ if(input_len < 1 || filter_len < 1) return 0; switch(mode){ case MODE_PERIODIZATION: return (index_t) ceil(input_len / 2.0); default: return (index_t) floor((input_len + filter_len - 1) / 2.0); } } index_t reconstruction_buffer_length(index_t coeffs_len, index_t filter_len){ if(coeffs_len < 1 || filter_len < 1) return 0; return 2*coeffs_len+filter_len-2; } index_t idwt_buffer_length(index_t coeffs_len, index_t filter_len, MODE mode){ if(coeffs_len < 0 || filter_len < 0) return 0; switch(mode){ case MODE_PERIODIZATION: return 2*coeffs_len; default: return 2*coeffs_len-filter_len+2; } } index_t swt_buffer_length(index_t input_len){ if(input_len < 0) return 0; return input_len; } int dwt_max_level(index_t input_len, index_t filter_len){ int i; if(input_len < 1 || filter_len < 2) return 0; i = (int) floor(log((double)input_len/(double)(filter_len-1)) /log(2.0)); return (i > 0) ? i : 0; } int swt_max_level(index_t input_len){ int i, j; i = (int) floor(log((double) input_len)/log(2.0)); /* check how many times (maximum i times) input_len is divisible by 2 */ for(j=0; j <= i; ++j){ if((input_len & 0x1)==1) return j; input_len >>= 1; } return (i > 0) ? i : 0; } PyWavelets-0.3.0/pywt/src/_pywt.h0000664000175000017500000000154412556460302020443 0ustar rgommersrgommers00000000000000#ifndef __PYX_HAVE___pywt #define __PYX_HAVE___pywt struct WaveletObject; /* "_pywt.pyx":211 * return __wfamily_list_long[:] * * cdef public class Wavelet [type WaveletType, object WaveletObject]: # <<<<<<<<<<<<<< * """ * Wavelet(name, filter_bank=None) object describe properties of */ struct WaveletObject { PyObject_HEAD Wavelet *w; PyObject *name; PyObject *number; }; #ifndef __PYX_HAVE_API___pywt #ifndef __PYX_EXTERN_C #ifdef __cplusplus #define __PYX_EXTERN_C extern "C" #else #define __PYX_EXTERN_C extern #endif #endif #ifndef DL_IMPORT #define DL_IMPORT(_T) _T #endif __PYX_EXTERN_C DL_IMPORT(PyTypeObject) WaveletType; #endif /* !__PYX_HAVE_API___pywt */ #if PY_MAJOR_VERSION < 3 PyMODINIT_FUNC init_pywt(void); #else PyMODINIT_FUNC PyInit__pywt(void); #endif #endif /* !__PYX_HAVE___pywt */ PyWavelets-0.3.0/pywt/src/convolution.c.src0000664000175000017500000007705212556460247022460 0ustar rgommersrgommers00000000000000/* Copyright (c) 2006-2012 Filip Wasilewski */ /* See COPYING for license details. */ #include "convolution.h" /**begin repeat * #type = double, float# */ int @type@_downsampling_convolution_periodization(const @type@* input, const_index_t N, const @type@* filter, const_index_t F, @type@* output, const_index_t step) { index_t i, j, k, F_2, corr; index_t start; @type@ sum; @type@* ptr_w = output; i = step-1; /* first element taken from input is input[step-1] */ start = F_2 = F/2; /* extending by (F-2)/2 elements */ corr = 0; for(i=start; i < F; i+=step){ sum = 0; for(j = 0; j < i+1-corr; ++j) /* overlapping */ sum += filter[j]*input[i-j-corr]; if(N%2){ if(F-j){ /* if something to extend */ sum += filter[j] * input[N-1]; if(F-j){ for(k = 2-corr; k <= F-j; ++k) sum += filter[j-1+k] * input[N-k+1]; } } } else { /* extra element from input -> i0 i1 i2 [i2] */ for(k = 1; k <= F-j; ++k) sum += filter[j-1+k] * input[N-k]; } *(ptr_w++) = sum; } /* F - N-1 : filter in input range. Most time is spent in this loop */ for(; i < N; i+=step){ /* input elements, */ sum = 0; for(j = 0; j < F; ++j) sum += input[i-j]*filter[j]; *(ptr_w++) = sum; } for(; i < N-step + (F/2)+1 + N%2; i += step){ /* input elements */ sum = 0; k = i-N+1; for(j = k; j < F; ++j) /* overlapped elements */ sum += filter[j]*input[i-j]; if(N%2 == 0){ for(j = 0; j < k; ++j){ /* out of boundary (filter elements [0, k-1]) */ sum += filter[j]*input[k-1-j]; } } else { /* repeating extra element -> i0 i1 i2 [i2] */ for(j = 0; j < k-1; ++j) /* out of boundary (filter elements [0, k-1]) */ sum += filter[j]*input[k-2-j]; sum += filter[k-1] * input[N-1]; } *(ptr_w++) = sum; } return 0; } int @type@_downsampling_convolution(const @type@* input, const_index_t N, const @type@* filter, const_index_t F, @type@* output, const_index_t step, MODE mode) { /* * This convolution performs efficient downsampling by computing every * step'th element of normal convolution (currently tested only for step=1 * and step=2). * * It also implements several different strategies of dealing with border * distortion problem (the problem of computing convolution for not * existing elements of signal). To handle this the signal has to be * "extended" on both sides by computing the missing values. * * General schema is as follows: 1. Handle extended on the left, convolve * filter with samples computed for time < 0 2. Do the normal decimated * convolution of filter with signal samples 3. Handle extended on the * right, convolve filter with samples computed for time > n-1 */ index_t i, j, k; index_t start; @type@ sum, tmp; #ifdef OPT_UNROLL2 @type@ sum2; #endif #ifdef OPT_UNROLL4 #ifndef OPT_UNROLL2 @type@ sum2; #endif @type@ sum3, sum4; #endif @type@* ptr_w = output; i = start = step-1; /* first element taken from input is input[step-1] */ if(F <= N){ if(mode == MODE_PERIODIZATION){ return @type@_downsampling_convolution_periodization(input, N, filter, F, output, step); /* Other signal extension modes */ } else { /* 0 - F-1 : sliding in filter */ switch(mode) { case MODE_SYMMETRIC: for(i=start; i < F; i+=step){ sum = 0; for(j = 0; j <= i; ++j) sum += filter[j]*input[i-j]; k = i+1; for(j = i+1; j < F; ++j) sum += filter[j] * input[j-k]; *(ptr_w++) = sum; } break; case MODE_ASYMMETRIC: for(i=start; i < F; i+=step){ sum = 0; for(j = 0; j <= i; ++j) sum += filter[j]*input[i-j]; k = i+1; for(j = i+1; j < F; ++j) sum += filter[j] * (input[0] - input[j-k]); *(ptr_w++) = sum; } break; case MODE_CONSTANT_EDGE: for(i=start; i < F; i+=step){ sum = 0; for(j = 0; j <= i; ++j) sum += filter[j]*input[i-j]; k = i+1; for(j = i+1; j < F; ++j) sum += filter[j] * input[0]; *(ptr_w++) = sum; } break; case MODE_SMOOTH: for(i=start; i < F; i+=step){ sum = 0; for(j = 0; j <= i; ++j) sum += filter[j]*input[i-j]; tmp = input[0]-input[1]; for(j = i+1; j < F; ++j){ sum += filter[j] * (input[0] + tmp * (j-i)); } *(ptr_w++) = sum; } break; case MODE_PERIODIC: for(i=start; i < F; i+=step){ sum = 0; for(j = 0; j <= i; ++j) sum += filter[j]*input[i-j]; k = N+i; for(j = i+1; j < F; ++j) sum += filter[j] * input[k-j]; *(ptr_w++) = sum; } break; case MODE_ZEROPAD: default: for(i=start; i < F; i+=step){ sum = 0; for(j = 0; j <= i; ++j) sum += filter[j]*input[i-j]; *(ptr_w++) = sum; } break; } /* * F - N-1 : filter in input range - simple convolution * Most time is spent in this loop. */ #ifdef OPT_UNROLL4 /* manually unroll the loop a bit */ if((N - F)/step > 4) { for(; i < (N - (3*step)); i += 4*step){ /* input elements */ sum = input[i] * filter[0]; sum2 = input[i+step] * filter[0]; sum3 = input[i+(2*step)] * filter[0]; sum4 = input[i+(3*step)] * filter[0]; for(j = 1; j < F; ++j){ sum += input[i-j] * filter[j]; sum2 += input[(step+i)-j] * filter[j]; sum3 += input[(2*step+i)-j] * filter[j]; sum4 += input[(3*step+i)-j] * filter[j]; } *(ptr_w++) = sum; *(ptr_w++) = sum2; *(ptr_w++) = sum3; *(ptr_w++) = sum4; } } #endif #ifdef OPT_UNROLL2 if((N - F)/step > 2) { for(; i < (N - step); i += 2*step){ /* input elements, */ sum = input[i] * filter[0]; sum2 = input[i+step] * filter[0]; for(j = 1; j < F; ++j){ sum += input[i-j] * filter[j]; sum2 += input[(step+i)-j] * filter[j]; } *(ptr_w++) = sum; *(ptr_w++) = sum2; } } #endif for(; i < N; i+=step){ /* input elements, */ sum = input[i] * filter[0]; for(j = 1; j < F; ++j){ sum += input[i-j]*filter[j]; } *(ptr_w++) = sum; } /* N - N+F-1 : sliding out filter */ switch(mode) { case MODE_SYMMETRIC: for(; i < N+F-1; i += step){ /* input elements */ sum = 0; k = i-N+1; /* 1, 2, 3 : overlapped elements */ for(j = k; j < F; ++j) /*TODO: j < F-_offset */ sum += filter[j]*input[i-j]; for(j = 0; j < k; ++j) /* out of boundary (TODO: j = _offset) */ /* j-i-1 0*(N-1), 0*(N-2) 1*(N-1) */ sum += filter[j]*input[N-k+j]; *(ptr_w++) = sum; } break; case MODE_ASYMMETRIC: for(; i < N+F-1; i += step){ /* input elements */ sum = 0; k = i-N+1; for(j = k; j < F; ++j) /* overlapped elements */ sum += filter[j]*input[i-j]; for(j = 0; j < k; ++j) /* out of boundary */ sum += filter[j]*(input[N-1]-input[N-k-1+j]); /* -= j-i-1 */ *(ptr_w++) = sum; } break; case MODE_CONSTANT_EDGE: for(; i < N+F-1; i += step){ /* input elements */ sum = 0; k = i-N+1; for(j = k; j < F; ++j) /* overlapped elements */ sum += filter[j]*input[i-j]; for(j = 0; j < k; ++j) /* out of boundary (filter elements [0, k-1]) */ sum += filter[j]*input[N-1]; /* input[N-1] = const */ *(ptr_w++) = sum; } break; case MODE_SMOOTH: for(; i < N+F-1; i += step){ /* input elements */ sum = 0; k = i-N+1; for(j = k; j < F; ++j) /* overlapped elements */ sum += filter[j]*input[i-j]; tmp = input[N-1]-input[N-2]; for(j = 0; j < k; ++j) /* out of boundary (filter elements [0, k-1]) */ sum += filter[j] * (input[N-1] + tmp * (k-j)); *(ptr_w++) = sum; } break; case MODE_PERIODIC: for(; i < N+F-1; i += step){ /* input elements */ sum = 0; k = i-N+1; for(j = k; j < F; ++j) /* overlapped elements */ sum += filter[j]*input[i-j]; for(j = 0; j < k; ++j) /* out of boundary (filter elements [0, k-1]) */ sum += filter[j]*input[k-1-j]; *(ptr_w++) = sum; } break; case MODE_ZEROPAD: default: for(; i < N+F-1; i += step){ sum = 0; for(j = i-(N-1); j < F; ++j) sum += input[i-j]*filter[j]; *(ptr_w++) = sum; } break; } } return 0; } else { /* reallocating memory for short signals (shorter than filter) is cheap */ return @type@_allocating_downsampling_convolution(input, N, filter, F, output, step, mode); } } /* * ### like downsampling_convolution, but with memory allocation ### */ int @type@_allocating_downsampling_convolution(const @type@* input, const_index_t N, const @type@* filter, const_index_t F, @type@* output, const_index_t step, MODE mode) { index_t i, j, F_minus_1, N_extended_len, N_extended_right_start; index_t start, stop; @type@ sum, tmp; @type@ *buffer; @type@* ptr_w = output; F_minus_1 = F - 1; start = F_minus_1+step-1; /* allocate memory and copy input */ if(mode != MODE_PERIODIZATION){ N_extended_len = N + 2*F_minus_1; N_extended_right_start = N + F_minus_1; buffer = wtcalloc(N_extended_len, sizeof(@type@)); if(buffer == NULL) return -1; memcpy(buffer+F_minus_1, input, sizeof(@type@) * N); stop = N_extended_len; } else { N_extended_len = N + F-1; N_extended_right_start = N-1 + F/2; buffer = wtcalloc(N_extended_len, sizeof(@type@)); if(buffer == NULL) return -1; memcpy(buffer+F/2-1, input, sizeof(@type@) * N); start -= 1; if(step == 1) stop = N_extended_len-1; else /* step == 2 */ stop = N_extended_len; } /* copy extended signal elements */ switch(mode){ case MODE_PERIODIZATION: if(N%2){ /* odd - repeat last element */ buffer[N_extended_right_start] = input[N-1]; for(j = 1; j < F/2; ++j) buffer[N_extended_right_start+j] = buffer[F/2-2 + j]; /* copy from beginning of `input` to right */ for(j = 0; j < F/2-1; ++j) /* copy from 'buffer' to left */ buffer[F/2-2-j] = buffer[N_extended_right_start-j]; } else { for(j = 0; j < F/2; ++j) buffer[N_extended_right_start+j] = input[j%N]; /* copy from beginning of `input` to right */ for(j = 0; j < F/2-1; ++j) /* copy from 'buffer' to left */ buffer[F/2-2-j] = buffer[N_extended_right_start-1-j]; } break; case MODE_SYMMETRIC: for(j = 0; j < N; ++j){ buffer[F_minus_1-1-j] = input[j%N]; buffer[N_extended_right_start+j] = input[N-1-(j%N)]; } i=j; /* use `buffer` as source */ for(; j < F_minus_1; ++j){ buffer[F_minus_1-1-j] = buffer[N_extended_right_start-1+i-j]; buffer[N_extended_right_start+j] = buffer[F_minus_1+j-i]; } break; case MODE_ASYMMETRIC: for(j = 0; j < N; ++j){ buffer[F_minus_1-1-j] = input[0] - input[j%N]; buffer[N_extended_right_start+j] = (input[N-1] - input[N-1-(j%N)]); } i=j; /* use `buffer` as source */ for(; j < F_minus_1; ++j){ buffer[F_minus_1-1-j] = buffer[N_extended_right_start-1+i-j]; buffer[N_extended_right_start+j] = buffer[F_minus_1+j-i]; } break; case MODE_SMOOTH: if(N>1){ tmp = input[0]-input[1]; for(j = 0; j < F_minus_1; ++j) buffer[j] = input[0] + (tmp * (F_minus_1-j)); tmp = input[N-1]-input[N-2]; for(j = 0; j < F_minus_1; ++j) buffer[N_extended_right_start+j] = input[N-1] + (tmp*j); break; } case MODE_CONSTANT_EDGE: for(j = 0; j < F_minus_1; ++j){ buffer[j] = input[0]; buffer[N_extended_right_start+j] = input[N-1]; } break; case MODE_PERIODIC: for(j = 0; j < F_minus_1; ++j) buffer[N_extended_right_start+j] = input[j%N]; /* copy from beginning of `input` to right */ for(j = 0; j < F_minus_1; ++j) /* copy from 'buffer' to left */ buffer[F_minus_1-1-j] = buffer[N_extended_right_start-1-j]; break; case MODE_ZEROPAD: default: break; } /* * F - N-1 : filter in input range, perform convolution with decimation */ for(i=start; i < stop; i+=step){ /* input elements */ sum = 0; for(j = 0; j < F; ++j){ sum += buffer[i-j]*filter[j]; } *(ptr_w++) = sum; } /* free memory */ wtfree(buffer); return 0; } /* * Requires zero-filled output buffer output is larger than input * performs "normal" convolution of "upsampled" input coeffs array with filter */ int @type@_upsampling_convolution_full(const @type@* input, const_index_t N, const @type@* filter, const_index_t F, @type@* output, const_index_t O){ register index_t i; register index_t j; @type@ *ptr_out; if(F<2) return -1; ptr_out = output + ((N-1) << 1); for(i = N-1; i >= 0; --i){ /* * sliding in filter from the right (end of input) * i0 0 i1 0 i2 0 * f1 -> o1 * f1 f2 -> o2 * f1 f2 f3 -> o3 */ for(j = 0; j < F; ++j) ptr_out[j] += input[i] * filter[j]; /* input[i] - const in loop */ ptr_out -= 2; } return 0; } /* * performs IDWT for PERIODIZATION mode only * (refactored from the upsampling_convolution_valid_sf function) * * The upsampling is performed by splitting filters to even and odd elements * and performing 2 convolutions * * The input data has to be periodically extended for this mode. */ int @type@_upsampling_convolution_valid_sf_periodization(const @type@* input, const_index_t N, const @type@* filter, const_index_t F, @type@* output, const_index_t O) { @type@ *ptr_out = output; @type@ *filter_even, *filter_odd; @type@ *periodization_buf = NULL; @type@ *periodization_buf_rear = NULL; @type@ *ptr_base; @type@ sum_even, sum_odd; index_t i, j, k, N_p = 0; index_t F_2 = F/2; if(F%2) return -3; /* Filter must have even-length. */ /* * Handle special situation when input coeff data is shorter than half of * the filter's length. The coeff array has to be extended periodically. * This can be only valid for PERIODIZATION_MODE */ if(N < F_2) { /* Input data for periodization mode has to be periodically extended */ /* New length for temporary input */ N_p = F_2-1 +N; /* periodization_buf will hold periodically copied input coeffs values */ periodization_buf = wtcalloc(N_p, sizeof(@type@)); if(periodization_buf == NULL) return -1; /* Copy input data to its place in the periodization_buf */ /* -> [0 0 0 i1 i2 i3 0 0 0] */ k = (F_2-1)/2; for(i=k; i < k+N; ++i) periodization_buf[i] = input[(i-k)%N]; /* if(N%2) * periodization_buf[i++] = input[N-1]; * * [0 0 0 i1 i2 i3 0 0 0] * points here ^^ */ periodization_buf_rear = periodization_buf+i-1; /* copy cyclically () to right [0 0 0 i1 i2 i3 i1 i2 ...] */ j = i-k; for(; i < N_p; ++i) periodization_buf[i] = periodization_buf[i-j]; /* copy cyclically () to left [... i2 i3 i1 i2 i3 i1 i2 i3] */ j = 0; for(i=k-1; i >= 0; --i){ periodization_buf[i] = periodization_buf_rear[j]; --j; } /* Now perform the valid convolution */ if(F_2%2){ @type@_upsampling_convolution_valid_sf(periodization_buf, N_p, filter, F, output, O, MODE_ZEROPAD); /* The F_2%2==0 case needs special result fix (oh my, another one..) */ } else { /* * Cheap result fix for short inputs * Memory allocation for temporary output is done. * Computed temporary result is copied to output* */ ptr_out = wtcalloc(idwt_buffer_length(N, F, MODE_PERIODIZATION), sizeof(@type@)); if(ptr_out == NULL){ wtfree(periodization_buf); return -1; } /* Convolve here as for (F_2%2) branch above */ @type@_upsampling_convolution_valid_sf(periodization_buf, N_p, filter, F, ptr_out, O, MODE_ZEROPAD); /* rewrite result to output */ for(i=2*N-1; i > 0; --i){ output[i] += ptr_out[i-1]; } /* and the first element */ output[0] += ptr_out[2*N-1]; wtfree(ptr_out); /* and voila!, ugh */ } } else { /* Otherwise (N >= F_2) */ /* Allocate memory for even and odd elements of the filter */ filter_even = wtmalloc(F_2 * sizeof(@type@)); filter_odd = wtmalloc(F_2 * sizeof(@type@)); if(filter_odd == NULL || filter_odd == NULL){ if(filter_odd == NULL) wtfree(filter_odd); if(filter_even == NULL) wtfree(filter_even); return -1; } /* split filter to even and odd values */ for(i = 0; i < F_2; ++i){ filter_even[i] = filter[i << 1]; filter_odd[i] = filter[(i << 1) + 1]; } /* * ############################################################ * This part is quite complicated and has some wild checking to * get results similar to those from Matlab(TM) Wavelet Toolbox */ k = F_2-1; /* Check if extending is really needed */ /* split filter len correct + extra samples*/ N_p = F_2-1 + (index_t) ceil(k/2.); /* * ok, if is then do: * 1. Allocate buffers for front and rear parts of extended input * 2. Copy periodically appropriate elements from input to the buffers * 3. Convolve front buffer, input and rear buffer with even and odd * elements of the filter (this results in upsampling) * 4. Free memory */ if(N_p > 0){ /* * Allocate memory only for the front and rear extension parts, not * the whole input */ periodization_buf = wtcalloc(N_p, sizeof(@type@)); periodization_buf_rear = wtcalloc(N_p, sizeof(@type@)); /* Memory checking */ if(periodization_buf == NULL || periodization_buf_rear == NULL){ if(periodization_buf == NULL) wtfree(periodization_buf); if(periodization_buf_rear == NULL) wtfree(periodization_buf_rear); wtfree(filter_odd); wtfree(filter_even); return -1; } /* Fill buffers with appropriate elements */ /* copy from beginning of input to end of buffer */ memcpy(periodization_buf + N_p - k, input, k * sizeof(@type@)); for(i = 1; i <= (N_p - k); ++i) periodization_buf[(N_p - k) - i] = input[N - (i%N)]; /* copy from end of input to beginning of buffer */ memcpy(periodization_buf_rear, input + N - k, k * sizeof(@type@)); for(i = 0; i < (N_p - k); ++i) periodization_buf_rear[k + i] = input[i%N]; /* * Convolve filters with the (front) periodization_buf and compute * the first part of output */ ptr_base = periodization_buf + F_2 - 1; if(k%2 == 1){ sum_odd = 0; for(j = 0; j < F_2; ++j) sum_odd += filter_odd[j] * ptr_base[-j]; *(ptr_out++) += sum_odd; --k; if(k) @type@_upsampling_convolution_valid_sf(periodization_buf + 1, N_p-1, filter, F, ptr_out, O-1, MODE_ZEROPAD); ptr_out += k; /* k0 - 1, really move backward by 1 */ } else if(k){ @type@_upsampling_convolution_valid_sf(periodization_buf, N_p, filter, F, ptr_out, O, MODE_ZEROPAD); ptr_out += k; } } /* * Perform _valid_ convolution (only when all filter_even and * filter_odd elements are in range of input data). * * This part is simple, no extra hacks, just two convolutions in one * loop */ ptr_base = (@type@*)input + F_2 - 1; for(i = 0; i < N-(F_2-1); ++i){ /* sliding over signal from left to right */ sum_even = 0; sum_odd = 0; for(j = 0; j < F_2; ++j){ sum_even += filter_even[j] * ptr_base[i-j]; sum_odd += filter_odd[j] * ptr_base[i-j]; } *(ptr_out++) += sum_even; *(ptr_out++) += sum_odd; } if(N_p > 0){ k = F_2-1; if(k%2 == 1){ if(F/2 <= N_p - 1){ /* k > 1 ? */ @type@_upsampling_convolution_valid_sf(periodization_buf_rear , N_p-1, filter, F, ptr_out, O-1, MODE_ZEROPAD); } ptr_out += k; /* move forward anyway -> see lower */ if(F_2%2 == 0){ /* remaining one element */ ptr_base = periodization_buf_rear + N_p - 1; sum_even = 0; for(j = 0; j < F_2; ++j){ sum_even += filter_even[j] * ptr_base[-j]; } *(--ptr_out) += sum_even; /* move backward first */ } } else { if(k){ @type@_upsampling_convolution_valid_sf(periodization_buf_rear, N_p, filter, F, ptr_out, O, MODE_ZEROPAD); } } } if(periodization_buf != NULL) wtfree(periodization_buf); if(periodization_buf_rear != NULL) wtfree(periodization_buf_rear); wtfree(filter_even); wtfree(filter_odd); } return 0; } /* * performs IDWT for all modes * * The upsampling is performed by splitting filters to even and odd elements * and performing 2 convolutions. After refactoring the PERIODIZATION mode * case to separate function this looks much clearer now. */ int @type@_upsampling_convolution_valid_sf(const @type@* input, const_index_t N, const @type@* filter, const_index_t F, @type@* output, const_index_t O, MODE mode){ @type@ *ptr_out = output; @type@ *filter_even, *filter_odd; @type@ *ptr_base; @type@ sum_even, sum_odd; #ifdef OPT_UNROLL2 @type@ sum_even2, sum_odd2; #endif #ifdef OPT_UNROLL4 #ifndef OPT_UNROLL2 @type@ sum_even2, sum_odd2; #endif @type@ sum_even3, sum_odd3; @type@ sum_even4, sum_odd4; #endif index_t i, j; index_t F_2 = F/2; if(mode == MODE_PERIODIZATION) /* Special case */ return @type@_upsampling_convolution_valid_sf_periodization(input, N, filter, F, output, O); if((F%2) || (N < F_2)) /* Filter must have even length. */ return -1; /* Allocate memory for even and odd elements of the filter */ filter_even = wtmalloc(F_2 * sizeof(@type@)); filter_odd = wtmalloc(F_2 * sizeof(@type@)); if(filter_odd == NULL || filter_odd == NULL){ if(filter_odd == NULL) wtfree(filter_odd); if(filter_even == NULL) wtfree(filter_even); return -1; } /* split filter to even and odd values */ for(i = 0; i < F_2; ++i){ filter_even[i] = filter[i << 1]; filter_odd[i] = filter[(i << 1) + 1]; } /* * Perform _valid_ convolution (only when all filter_even and filter_odd elements * are in range of input data). * * This part is simple, no extra hacks, just two convolutions in one loop */ ptr_base = (@type@*)input + F_2 - 1; i = 0; #ifdef OPT_UNROLL4 /* manually unroll the loop a bit */ for(; i < N-(F_2-1+8); i+=4){ /* sliding over signal from left to right */ sum_even = filter_even[0] * ptr_base[i]; sum_even2 = filter_even[0] * ptr_base[i+1]; sum_even3 = filter_even[0] * ptr_base[i+2]; sum_even4 = filter_even[0] * ptr_base[i+3]; sum_odd = filter_odd[0] * ptr_base[i]; sum_odd2 = filter_odd[0] * ptr_base[i+1]; sum_odd3 = filter_odd[0] * ptr_base[i+2]; sum_odd4 = filter_odd[0] * ptr_base[i+3]; for(j = 1; j < F_2; ++j){ sum_even += filter_even[j] * ptr_base[i-j]; sum_even2 += filter_even[j] * ptr_base[(i+1)-j]; sum_even3 += filter_even[j] * ptr_base[(i+2)-j]; sum_even4 += filter_even[j] * ptr_base[(i+3)-j]; sum_odd += filter_odd[j] * ptr_base[i-j]; sum_odd2 += filter_odd[j] * ptr_base[(i+1)-j]; sum_odd3 += filter_odd[j] * ptr_base[(i+2)-j]; sum_odd4 += filter_odd[j] * ptr_base[(i+3)-j]; } *(ptr_out++) += sum_even; *(ptr_out++) += sum_odd; *(ptr_out++) += sum_even2; *(ptr_out++) += sum_odd2; *(ptr_out++) += sum_even3; *(ptr_out++) += sum_odd3; *(ptr_out++) += sum_even4; *(ptr_out++) += sum_odd4; } #endif #ifdef OPT_UNROLL2 /* manually unroll the loop a bit */ for(; i < N-(F_2+1); i+=2){ /* sliding over signal from left to right */ sum_even = filter_even[0] * ptr_base[i]; sum_even2 = filter_even[0] * ptr_base[i+1]; sum_odd = filter_odd[0] * ptr_base[i]; sum_odd2 = filter_odd[0] * ptr_base[i+1]; for(j = 1; j < F_2; ++j){ sum_even += filter_even[j] * ptr_base[i-j]; sum_even2 += filter_even[j] * ptr_base[(i+1)-j]; sum_odd += filter_odd[j] * ptr_base[i-j]; sum_odd2 += filter_odd[j] * ptr_base[(i+1)-j]; } *(ptr_out++) += sum_even; *(ptr_out++) += sum_odd; *(ptr_out++) += sum_even2; *(ptr_out++) += sum_odd2; } #endif for(; i < N-(F_2-1); ++i){ /* sliding over signal from left to right */ sum_even = filter_even[0] * ptr_base[i]; sum_odd = filter_odd[0] * ptr_base[i]; for(j = 1; j < F_2; ++j){ sum_even += filter_even[j] * ptr_base[i-j]; sum_odd += filter_odd[j] * ptr_base[i-j]; } *(ptr_out++) += sum_even; *(ptr_out++) += sum_odd; } wtfree(filter_even); wtfree(filter_odd); return 0; } /* -> swt - todo */ int @type@_upsampled_filter_convolution(const @type@* input, const_index_t N, const @type@* filter, const_index_t F, @type@* output, const_index_t step, MODE mode) { return -1; } /**end repeat**/ PyWavelets-0.3.0/pywt/src/c_wt.pxd0000664000175000017500000001114212556460247020604 0ustar rgommersrgommers00000000000000# Copyright (c) 2006-2012 Filip Wasilewski # See COPYING for license details. cdef extern from "common.h": ctypedef int index_t ctypedef int const_index_t cdef void* wtmalloc(long size) cdef void* wtcalloc(long len, long size) cdef void wtfree(void* ptr) ctypedef enum MODE: MODE_INVALID = -1 MODE_ZEROPAD = 0 MODE_SYMMETRIC MODE_ASYMMETRIC MODE_CONSTANT_EDGE MODE_SMOOTH MODE_PERIODIC MODE_PERIODIZATION MODE_MAX # buffers lengths cdef index_t dwt_buffer_length(index_t input_len, index_t filter_len, MODE mode) cdef index_t upsampling_buffer_length(index_t coeffs_len, index_t filter_len, MODE mode) cdef index_t idwt_buffer_length(index_t coeffs_len, index_t filter_len, MODE mode) cdef index_t swt_buffer_length(index_t coeffs_len) cdef index_t reconstruction_buffer_length(index_t coeffs_len, index_t filter_len) # max dec levels cdef int dwt_max_level(index_t input_len, index_t filter_len) cdef int swt_max_level(index_t input_len) cdef extern from "wavelets.h": ctypedef enum SYMMETRY: ASYMMETRIC NEAR_SYMMETRIC SYMMETRIC ctypedef struct Wavelet: double* dec_hi_double # highpass decomposition double* dec_lo_double # lowpass decomposition double* rec_hi_double # highpass reconstruction double* rec_lo_double # lowpass reconstruction float* dec_hi_float float* dec_lo_float float* rec_hi_float float* rec_lo_float index_t dec_len # length of decomposition filter index_t rec_len # length of reconstruction filter index_t dec_hi_offset index_t dec_lo_offset index_t rec_hi_offset index_t rec_lo_offset int vanishing_moments_psi int vanishing_moments_phi index_t support_width int orthogonal int biorthogonal int symmetry int compact_support int _builtin char* family_name char* short_name cdef Wavelet* wavelet(char name, int type) cdef Wavelet* blank_wavelet(index_t filter_length) cdef void free_wavelet(Wavelet* wavelet) cdef extern from "wt.h": cdef int double_dec_a(double input[], index_t input_len, Wavelet* wavelet, double output[], index_t output_len, MODE mode) cdef int double_dec_d(double input[], index_t input_len, Wavelet* wavelet, double output[], index_t output_len, MODE mode) cdef int double_rec_a(double coeffs_a[], index_t coeffs_len, Wavelet* wavelet, double output[], index_t output_len) cdef int double_rec_d(double coeffs_d[], index_t coeffs_len, Wavelet* wavelet, double output[], index_t output_len) cdef int double_idwt(double coeffs_a[], index_t coeffs_a_len, double coeffs_d[], index_t coeffs_d_len, Wavelet* wavelet, double output[], index_t output_len, MODE mode, int correct_size) cdef int double_swt_a(double input[], index_t input_len, Wavelet* wavelet, double output[], index_t output_len, int level) cdef int double_swt_d(double input[], index_t input_len, Wavelet* wavelet, double output[], index_t output_len, int level) cdef int float_dec_a(float input[], index_t input_len, Wavelet* wavelet, float output[], index_t output_len, MODE mode) cdef int float_dec_d(float input[], index_t input_len, Wavelet* wavelet, float output[], index_t output_len, MODE mode) cdef int float_rec_a(float coeffs_a[], index_t coeffs_len, Wavelet* wavelet, float output[], index_t output_len) cdef int float_rec_d(float coeffs_d[], index_t coeffs_len, Wavelet* wavelet, float output[], index_t output_len) cdef int float_idwt(float coeffs_a[], index_t coeffs_a_len, float coeffs_d[], index_t coeffs_d_len, Wavelet* wavelet, float output[], index_t output_len, MODE mode, int correct_size) cdef int float_swt_a(float input[], index_t input_len, Wavelet* wavelet, float output[], index_t output_len, int level) cdef int float_swt_d(float input[], index_t input_len, Wavelet* wavelet, float output[], index_t output_len, int level) PyWavelets-0.3.0/pywt/src/wavelets_coeffs.h.src0000664000175000017500000032011112556460247023250 0ustar rgommersrgommers00000000000000#ifndef _WAVELETS_COEFFS_H_ #define _WAVELETS_COEFFS_H_ /* * Filters coefficients for selected wavelets * * Daubechies: db1 - db20 * Symlets: sym2 - sym20 * Coiflets: coif1 - coif5 * Biorthogonal: bior 1.1, 1.3, 1.5, * 2.2, 2.4, 2.6, 2.8, * 3.1, 3.3, 3.5, 3.7, 3.9, * 4.4, 5.5, 6.8 * Discrete Meyer wavelet *approximation*: dmey */ /* ignore warning about initializing floats from double values */ #ifdef _MSC_VER #pragma warning (disable:4305) #endif /**begin repeat * #type = double, float# */ static @type@ db1_@type@[][2] = { {0.70710678118654757, 0.70710678118654757}, {-0.70710678118654757, 0.70710678118654757}, {0.70710678118654757, 0.70710678118654757}, {0.70710678118654757, -0.70710678118654757} }; static @type@ db2_@type@[][4] = { {-0.12940952255092145, 0.22414386804185735, 0.83651630373746899, 0.48296291314469025}, {-0.48296291314469025, 0.83651630373746899, -0.22414386804185735, -0.12940952255092145}, {0.48296291314469025, 0.83651630373746899, 0.22414386804185735, -0.12940952255092145}, {-0.12940952255092145, -0.22414386804185735, 0.83651630373746899, -0.48296291314469025} }; static @type@ db3_@type@[][6] = { {0.035226291882100656, -0.085441273882241486, -0.13501102001039084, 0.45987750211933132, 0.80689150931333875, 0.33267055295095688}, {-0.33267055295095688, 0.80689150931333875, -0.45987750211933132, -0.13501102001039084, 0.085441273882241486, 0.035226291882100656}, {0.33267055295095688, 0.80689150931333875, 0.45987750211933132, -0.13501102001039084, -0.085441273882241486, 0.035226291882100656}, {0.035226291882100656, 0.085441273882241486, -0.13501102001039084, -0.45987750211933132, 0.80689150931333875, -0.33267055295095688} }; static @type@ db4_@type@[][8] = { {-0.010597401784997278, 0.032883011666982945, 0.030841381835986965, -0.18703481171888114, -0.027983769416983849, 0.63088076792959036, 0.71484657055254153, 0.23037781330885523}, {-0.23037781330885523, 0.71484657055254153, -0.63088076792959036, -0.027983769416983849, 0.18703481171888114, 0.030841381835986965, -0.032883011666982945, -0.010597401784997278}, {0.23037781330885523, 0.71484657055254153, 0.63088076792959036, -0.027983769416983849, -0.18703481171888114, 0.030841381835986965, 0.032883011666982945, -0.010597401784997278}, {-0.010597401784997278, -0.032883011666982945, 0.030841381835986965, 0.18703481171888114, -0.027983769416983849, -0.63088076792959036, 0.71484657055254153, -0.23037781330885523} }; static @type@ db5_@type@[][10] = { {0.0033357252850015492, -0.012580751999015526, -0.0062414902130117052, 0.077571493840065148, -0.03224486958502952, -0.24229488706619015, 0.13842814590110342, 0.72430852843857441, 0.60382926979747287, 0.16010239797412501}, {-0.16010239797412501, 0.60382926979747287, -0.72430852843857441, 0.13842814590110342, 0.24229488706619015, -0.03224486958502952, -0.077571493840065148, -0.0062414902130117052, 0.012580751999015526, 0.0033357252850015492}, {0.16010239797412501, 0.60382926979747287, 0.72430852843857441, 0.13842814590110342, -0.24229488706619015, -0.03224486958502952, 0.077571493840065148, -0.0062414902130117052, -0.012580751999015526, 0.0033357252850015492}, {0.0033357252850015492, 0.012580751999015526, -0.0062414902130117052, -0.077571493840065148, -0.03224486958502952, 0.24229488706619015, 0.13842814590110342, -0.72430852843857441, 0.60382926979747287, -0.16010239797412501} }; static @type@ db6_@type@[][12] = { {-0.0010773010849955799, 0.0047772575110106514, 0.0005538422009938016, -0.031582039318031156, 0.027522865530016288, 0.097501605587079362, -0.12976686756709563, -0.22626469396516913, 0.3152503517092432, 0.75113390802157753, 0.49462389039838539, 0.11154074335008017}, {-0.11154074335008017, 0.49462389039838539, -0.75113390802157753, 0.3152503517092432, 0.22626469396516913, -0.12976686756709563, -0.097501605587079362, 0.027522865530016288, 0.031582039318031156, 0.0005538422009938016, -0.0047772575110106514, -0.0010773010849955799}, {0.11154074335008017, 0.49462389039838539, 0.75113390802157753, 0.3152503517092432, -0.22626469396516913, -0.12976686756709563, 0.097501605587079362, 0.027522865530016288, -0.031582039318031156, 0.0005538422009938016, 0.0047772575110106514, -0.0010773010849955799}, {-0.0010773010849955799, -0.0047772575110106514, 0.0005538422009938016, 0.031582039318031156, 0.027522865530016288, -0.097501605587079362, -0.12976686756709563, 0.22626469396516913, 0.3152503517092432, -0.75113390802157753, 0.49462389039838539, -0.11154074335008017} }; static @type@ db7_@type@[][14] = { {0.00035371380000103988, -0.0018016407039998328, 0.00042957797300470274, 0.012550998556013784, -0.01657454163101562, -0.038029936935034633, 0.080612609151065898, 0.071309219267050042, -0.22403618499416572, -0.14390600392910627, 0.4697822874053586, 0.72913209084655506, 0.39653931948230575, 0.077852054085062364}, {-0.077852054085062364, 0.39653931948230575, -0.72913209084655506, 0.4697822874053586, 0.14390600392910627, -0.22403618499416572, -0.071309219267050042, 0.080612609151065898, 0.038029936935034633, -0.01657454163101562, -0.012550998556013784, 0.00042957797300470274, 0.0018016407039998328, 0.00035371380000103988}, {0.077852054085062364, 0.39653931948230575, 0.72913209084655506, 0.4697822874053586, -0.14390600392910627, -0.22403618499416572, 0.071309219267050042, 0.080612609151065898, -0.038029936935034633, -0.01657454163101562, 0.012550998556013784, 0.00042957797300470274, -0.0018016407039998328, 0.00035371380000103988}, {0.00035371380000103988, 0.0018016407039998328, 0.00042957797300470274, -0.012550998556013784, -0.01657454163101562, 0.038029936935034633, 0.080612609151065898, -0.071309219267050042, -0.22403618499416572, 0.14390600392910627, 0.4697822874053586, -0.72913209084655506, 0.39653931948230575, -0.077852054085062364} }; static @type@ db8_@type@[][16] = { {-0.00011747678400228192, 0.00067544940599855677, -0.00039174037299597711, -0.0048703529930106603, 0.0087460940470156547, 0.013981027917015516, -0.044088253931064719, -0.017369301002022108, 0.12874742662018601, 0.00047248457399797254, -0.28401554296242809, -0.015829105256023893, 0.58535468365486909, 0.67563073629801285, 0.31287159091446592, 0.054415842243081609}, {-0.054415842243081609, 0.31287159091446592, -0.67563073629801285, 0.58535468365486909, 0.015829105256023893, -0.28401554296242809, -0.00047248457399797254, 0.12874742662018601, 0.017369301002022108, -0.044088253931064719, -0.013981027917015516, 0.0087460940470156547, 0.0048703529930106603, -0.00039174037299597711, -0.00067544940599855677, -0.00011747678400228192}, {0.054415842243081609, 0.31287159091446592, 0.67563073629801285, 0.58535468365486909, -0.015829105256023893, -0.28401554296242809, 0.00047248457399797254, 0.12874742662018601, -0.017369301002022108, -0.044088253931064719, 0.013981027917015516, 0.0087460940470156547, -0.0048703529930106603, -0.00039174037299597711, 0.00067544940599855677, -0.00011747678400228192}, {-0.00011747678400228192, -0.00067544940599855677, -0.00039174037299597711, 0.0048703529930106603, 0.0087460940470156547, -0.013981027917015516, -0.044088253931064719, 0.017369301002022108, 0.12874742662018601, -0.00047248457399797254, -0.28401554296242809, 0.015829105256023893, 0.58535468365486909, -0.67563073629801285, 0.31287159091446592, -0.054415842243081609} }; static @type@ db9_@type@[][18] = { {3.9347319995026124e-005, -0.00025196318899817888, 0.00023038576399541288, 0.0018476468829611268, -0.0042815036819047227, -0.004723204757894831, 0.022361662123515244, 0.00025094711499193845, -0.067632829059523988, 0.030725681478322865, 0.14854074933476008, -0.096840783220879037, -0.29327378327258685, 0.13319738582208895, 0.65728807803663891, 0.6048231236767786, 0.24383467463766728, 0.038077947363167282}, {-0.038077947363167282, 0.24383467463766728, -0.6048231236767786, 0.65728807803663891, -0.13319738582208895, -0.29327378327258685, 0.096840783220879037, 0.14854074933476008, -0.030725681478322865, -0.067632829059523988, -0.00025094711499193845, 0.022361662123515244, 0.004723204757894831, -0.0042815036819047227, -0.0018476468829611268, 0.00023038576399541288, 0.00025196318899817888, 3.9347319995026124e-005}, {0.038077947363167282, 0.24383467463766728, 0.6048231236767786, 0.65728807803663891, 0.13319738582208895, -0.29327378327258685, -0.096840783220879037, 0.14854074933476008, 0.030725681478322865, -0.067632829059523988, 0.00025094711499193845, 0.022361662123515244, -0.004723204757894831, -0.0042815036819047227, 0.0018476468829611268, 0.00023038576399541288, -0.00025196318899817888, 3.9347319995026124e-005}, {3.9347319995026124e-005, 0.00025196318899817888, 0.00023038576399541288, -0.0018476468829611268, -0.0042815036819047227, 0.004723204757894831, 0.022361662123515244, -0.00025094711499193845, -0.067632829059523988, -0.030725681478322865, 0.14854074933476008, 0.096840783220879037, -0.29327378327258685, -0.13319738582208895, 0.65728807803663891, -0.6048231236767786, 0.24383467463766728, -0.038077947363167282} }; static @type@ db10_@type@[][20] = { {-1.3264203002354869e-005, 9.3588670001089845e-005, -0.0001164668549943862, -0.00068585669500468248, 0.0019924052949908499, 0.0013953517469940798, -0.010733175482979604, 0.0036065535669883944, 0.033212674058933238, -0.029457536821945671, -0.071394147165860775, 0.093057364603806592, 0.12736934033574265, -0.19594627437659665, -0.24984642432648865, 0.28117234366042648, 0.68845903945259213, 0.52720118893091983, 0.18817680007762133, 0.026670057900950818}, {-0.026670057900950818, 0.18817680007762133, -0.52720118893091983, 0.68845903945259213, -0.28117234366042648, -0.24984642432648865, 0.19594627437659665, 0.12736934033574265, -0.093057364603806592, -0.071394147165860775, 0.029457536821945671, 0.033212674058933238, -0.0036065535669883944, -0.010733175482979604, -0.0013953517469940798, 0.0019924052949908499, 0.00068585669500468248, -0.0001164668549943862, -9.3588670001089845e-005, -1.3264203002354869e-005}, {0.026670057900950818, 0.18817680007762133, 0.52720118893091983, 0.68845903945259213, 0.28117234366042648, -0.24984642432648865, -0.19594627437659665, 0.12736934033574265, 0.093057364603806592, -0.071394147165860775, -0.029457536821945671, 0.033212674058933238, 0.0036065535669883944, -0.010733175482979604, 0.0013953517469940798, 0.0019924052949908499, -0.00068585669500468248, -0.0001164668549943862, 9.3588670001089845e-005, -1.3264203002354869e-005}, {-1.3264203002354869e-005, -9.3588670001089845e-005, -0.0001164668549943862, 0.00068585669500468248, 0.0019924052949908499, -0.0013953517469940798, -0.010733175482979604, -0.0036065535669883944, 0.033212674058933238, 0.029457536821945671, -0.071394147165860775, -0.093057364603806592, 0.12736934033574265, 0.19594627437659665, -0.24984642432648865, -0.28117234366042648, 0.68845903945259213, -0.52720118893091983, 0.18817680007762133, -0.026670057900950818} }; static @type@ db11_@type@[][22] = { {4.4942742772363519e-006, -3.4634984186983789e-005, 5.4439074699366381e-005, 0.00024915252355281426, -0.00089302325066623663, -0.00030859285881515924, 0.0049284176560587777, -0.0033408588730145018, -0.015364820906201324, 0.020840904360180039, 0.031335090219045313, -0.066438785695020222, -0.04647995511667613, 0.14981201246638268, 0.066043588196690886, -0.27423084681792875, -0.16227524502747828, 0.41196436894789695, 0.68568677491617847, 0.44989976435603013, 0.14406702115061959, 0.018694297761470441}, {-0.018694297761470441, 0.14406702115061959, -0.44989976435603013, 0.68568677491617847, -0.41196436894789695, -0.16227524502747828, 0.27423084681792875, 0.066043588196690886, -0.14981201246638268, -0.04647995511667613, 0.066438785695020222, 0.031335090219045313, -0.020840904360180039, -0.015364820906201324, 0.0033408588730145018, 0.0049284176560587777, 0.00030859285881515924, -0.00089302325066623663, -0.00024915252355281426, 5.4439074699366381e-005, 3.4634984186983789e-005, 4.4942742772363519e-006}, {0.018694297761470441, 0.14406702115061959, 0.44989976435603013, 0.68568677491617847, 0.41196436894789695, -0.16227524502747828, -0.27423084681792875, 0.066043588196690886, 0.14981201246638268, -0.04647995511667613, -0.066438785695020222, 0.031335090219045313, 0.020840904360180039, -0.015364820906201324, -0.0033408588730145018, 0.0049284176560587777, -0.00030859285881515924, -0.00089302325066623663, 0.00024915252355281426, 5.4439074699366381e-005, -3.4634984186983789e-005, 4.4942742772363519e-006}, {4.4942742772363519e-006, 3.4634984186983789e-005, 5.4439074699366381e-005, -0.00024915252355281426, -0.00089302325066623663, 0.00030859285881515924, 0.0049284176560587777, 0.0033408588730145018, -0.015364820906201324, -0.020840904360180039, 0.031335090219045313, 0.066438785695020222, -0.04647995511667613, -0.14981201246638268, 0.066043588196690886, 0.27423084681792875, -0.16227524502747828, -0.41196436894789695, 0.68568677491617847, -0.44989976435603013, 0.14406702115061959, -0.018694297761470441} }; static @type@ db12_@type@[][24] = { {-1.5290717580684923e-006, 1.2776952219379579e-005, -2.4241545757030318e-005, -8.8504109208203182e-005, 0.00038865306282092672, 6.5451282125215034e-006, -0.0021795036186277044, 0.0022486072409952287, 0.0067114990087955486, -0.012840825198299882, -0.01221864906974642, 0.041546277495087637, 0.010849130255828966, -0.09643212009649671, 0.0053595696743599965, 0.18247860592758275, -0.023779257256064865, -0.31617845375277914, -0.044763885653777619, 0.51588647842780067, 0.65719872257929113, 0.37735513521420411, 0.10956627282118277, 0.013112257957229239}, {-0.013112257957229239, 0.10956627282118277, -0.37735513521420411, 0.65719872257929113, -0.51588647842780067, -0.044763885653777619, 0.31617845375277914, -0.023779257256064865, -0.18247860592758275, 0.0053595696743599965, 0.09643212009649671, 0.010849130255828966, -0.041546277495087637, -0.01221864906974642, 0.012840825198299882, 0.0067114990087955486, -0.0022486072409952287, -0.0021795036186277044, -6.5451282125215034e-006, 0.00038865306282092672, 8.8504109208203182e-005, -2.4241545757030318e-005, -1.2776952219379579e-005, -1.5290717580684923e-006}, {0.013112257957229239, 0.10956627282118277, 0.37735513521420411, 0.65719872257929113, 0.51588647842780067, -0.044763885653777619, -0.31617845375277914, -0.023779257256064865, 0.18247860592758275, 0.0053595696743599965, -0.09643212009649671, 0.010849130255828966, 0.041546277495087637, -0.01221864906974642, -0.012840825198299882, 0.0067114990087955486, 0.0022486072409952287, -0.0021795036186277044, 6.5451282125215034e-006, 0.00038865306282092672, -8.8504109208203182e-005, -2.4241545757030318e-005, 1.2776952219379579e-005, -1.5290717580684923e-006}, {-1.5290717580684923e-006, -1.2776952219379579e-005, -2.4241545757030318e-005, 8.8504109208203182e-005, 0.00038865306282092672, -6.5451282125215034e-006, -0.0021795036186277044, -0.0022486072409952287, 0.0067114990087955486, 0.012840825198299882, -0.01221864906974642, -0.041546277495087637, 0.010849130255828966, 0.09643212009649671, 0.0053595696743599965, -0.18247860592758275, -0.023779257256064865, 0.31617845375277914, -0.044763885653777619, -0.51588647842780067, 0.65719872257929113, -0.37735513521420411, 0.10956627282118277, -0.013112257957229239} }; static @type@ db13_@type@[][26] = { {5.2200350984547998e-007, -4.7004164793608082e-006, 1.0441930571407941e-005, 3.0678537579324358e-005, -0.00016512898855650571, 4.9251525126285676e-005, 0.00093232613086724904, -0.0013156739118922766, -0.002761911234656831, 0.0072555894016171187, 0.0039239414487955773, -0.023831420710327809, 0.0023799722540522269, 0.056139477100276156, -0.026488406475345658, -0.10580761818792761, 0.072948933656788742, 0.17947607942935084, -0.12457673075080665, -0.31497290771138414, 0.086985726179645007, 0.58888957043121193, 0.61105585115878114, 0.31199632216043488, 0.082861243872901946, 0.0092021335389622788}, {-0.0092021335389622788, 0.082861243872901946, -0.31199632216043488, 0.61105585115878114, -0.58888957043121193, 0.086985726179645007, 0.31497290771138414, -0.12457673075080665, -0.17947607942935084, 0.072948933656788742, 0.10580761818792761, -0.026488406475345658, -0.056139477100276156, 0.0023799722540522269, 0.023831420710327809, 0.0039239414487955773, -0.0072555894016171187, -0.002761911234656831, 0.0013156739118922766, 0.00093232613086724904, -4.9251525126285676e-005, -0.00016512898855650571, -3.0678537579324358e-005, 1.0441930571407941e-005, 4.7004164793608082e-006, 5.2200350984547998e-007}, {0.0092021335389622788, 0.082861243872901946, 0.31199632216043488, 0.61105585115878114, 0.58888957043121193, 0.086985726179645007, -0.31497290771138414, -0.12457673075080665, 0.17947607942935084, 0.072948933656788742, -0.10580761818792761, -0.026488406475345658, 0.056139477100276156, 0.0023799722540522269, -0.023831420710327809, 0.0039239414487955773, 0.0072555894016171187, -0.002761911234656831, -0.0013156739118922766, 0.00093232613086724904, 4.9251525126285676e-005, -0.00016512898855650571, 3.0678537579324358e-005, 1.0441930571407941e-005, -4.7004164793608082e-006, 5.2200350984547998e-007}, {5.2200350984547998e-007, 4.7004164793608082e-006, 1.0441930571407941e-005, -3.0678537579324358e-005, -0.00016512898855650571, -4.9251525126285676e-005, 0.00093232613086724904, 0.0013156739118922766, -0.002761911234656831, -0.0072555894016171187, 0.0039239414487955773, 0.023831420710327809, 0.0023799722540522269, -0.056139477100276156, -0.026488406475345658, 0.10580761818792761, 0.072948933656788742, -0.17947607942935084, -0.12457673075080665, 0.31497290771138414, 0.086985726179645007, -0.58888957043121193, 0.61105585115878114, -0.31199632216043488, 0.082861243872901946, -0.0092021335389622788} }; static @type@ db14_@type@[][28] = { {-1.7871399683109222e-007, 1.7249946753674012e-006, -4.3897049017804176e-006, -1.0337209184568496e-005, 6.875504252695734e-005, -4.1777245770370672e-005, -0.00038683194731287514, 0.00070802115423540481, 0.001061691085606874, -0.003849638868019787, -0.00074621898926387534, 0.012789493266340071, -0.0056150495303375755, -0.030185351540353976, 0.026981408307947971, 0.05523712625925082, -0.071548955503983505, -0.086748411568110598, 0.13998901658445695, 0.13839521386479153, -0.21803352999321651, -0.27168855227867705, 0.21867068775886594, 0.63118784910471981, 0.55430561794077093, 0.25485026779256437, 0.062364758849384874, 0.0064611534600864905}, {-0.0064611534600864905, 0.062364758849384874, -0.25485026779256437, 0.55430561794077093, -0.63118784910471981, 0.21867068775886594, 0.27168855227867705, -0.21803352999321651, -0.13839521386479153, 0.13998901658445695, 0.086748411568110598, -0.071548955503983505, -0.05523712625925082, 0.026981408307947971, 0.030185351540353976, -0.0056150495303375755, -0.012789493266340071, -0.00074621898926387534, 0.003849638868019787, 0.001061691085606874, -0.00070802115423540481, -0.00038683194731287514, 4.1777245770370672e-005, 6.875504252695734e-005, 1.0337209184568496e-005, -4.3897049017804176e-006, -1.7249946753674012e-006, -1.7871399683109222e-007}, {0.0064611534600864905, 0.062364758849384874, 0.25485026779256437, 0.55430561794077093, 0.63118784910471981, 0.21867068775886594, -0.27168855227867705, -0.21803352999321651, 0.13839521386479153, 0.13998901658445695, -0.086748411568110598, -0.071548955503983505, 0.05523712625925082, 0.026981408307947971, -0.030185351540353976, -0.0056150495303375755, 0.012789493266340071, -0.00074621898926387534, -0.003849638868019787, 0.001061691085606874, 0.00070802115423540481, -0.00038683194731287514, -4.1777245770370672e-005, 6.875504252695734e-005, -1.0337209184568496e-005, -4.3897049017804176e-006, 1.7249946753674012e-006, -1.7871399683109222e-007}, {-1.7871399683109222e-007, -1.7249946753674012e-006, -4.3897049017804176e-006, 1.0337209184568496e-005, 6.875504252695734e-005, 4.1777245770370672e-005, -0.00038683194731287514, -0.00070802115423540481, 0.001061691085606874, 0.003849638868019787, -0.00074621898926387534, -0.012789493266340071, -0.0056150495303375755, 0.030185351540353976, 0.026981408307947971, -0.05523712625925082, -0.071548955503983505, 0.086748411568110598, 0.13998901658445695, -0.13839521386479153, -0.21803352999321651, 0.27168855227867705, 0.21867068775886594, -0.63118784910471981, 0.55430561794077093, -0.25485026779256437, 0.062364758849384874, -0.0064611534600864905} }; static @type@ db15_@type@[][30] = { {6.1333599133037138e-008, -6.3168823258794506e-007, 1.8112704079399406e-006, 3.3629871817363823e-006, -2.8133296266037558e-005, 2.579269915531323e-005, 0.00015589648992055726, -0.00035956524436229364, -0.00037348235413726472, 0.0019433239803823459, -0.00024175649075894543, -0.0064877345603061454, 0.0051010003604228726, 0.015083918027862582, -0.020810050169636805, -0.025767007328366939, 0.054780550584559995, 0.033877143923563204, -0.11112093603713753, -0.039666176555733602, 0.19014671400708816, 0.065282952848765688, -0.28888259656686216, -0.19320413960907623, 0.33900253545462167, 0.64581314035721027, 0.49263177170797529, 0.20602386398692688, 0.046743394892750617, 0.0045385373615773762}, {-0.0045385373615773762, 0.046743394892750617, -0.20602386398692688, 0.49263177170797529, -0.64581314035721027, 0.33900253545462167, 0.19320413960907623, -0.28888259656686216, -0.065282952848765688, 0.19014671400708816, 0.039666176555733602, -0.11112093603713753, -0.033877143923563204, 0.054780550584559995, 0.025767007328366939, -0.020810050169636805, -0.015083918027862582, 0.0051010003604228726, 0.0064877345603061454, -0.00024175649075894543, -0.0019433239803823459, -0.00037348235413726472, 0.00035956524436229364, 0.00015589648992055726, -2.579269915531323e-005, -2.8133296266037558e-005, -3.3629871817363823e-006, 1.8112704079399406e-006, 6.3168823258794506e-007, 6.1333599133037138e-008}, {0.0045385373615773762, 0.046743394892750617, 0.20602386398692688, 0.49263177170797529, 0.64581314035721027, 0.33900253545462167, -0.19320413960907623, -0.28888259656686216, 0.065282952848765688, 0.19014671400708816, -0.039666176555733602, -0.11112093603713753, 0.033877143923563204, 0.054780550584559995, -0.025767007328366939, -0.020810050169636805, 0.015083918027862582, 0.0051010003604228726, -0.0064877345603061454, -0.00024175649075894543, 0.0019433239803823459, -0.00037348235413726472, -0.00035956524436229364, 0.00015589648992055726, 2.579269915531323e-005, -2.8133296266037558e-005, 3.3629871817363823e-006, 1.8112704079399406e-006, -6.3168823258794506e-007, 6.1333599133037138e-008}, {6.1333599133037138e-008, 6.3168823258794506e-007, 1.8112704079399406e-006, -3.3629871817363823e-006, -2.8133296266037558e-005, -2.579269915531323e-005, 0.00015589648992055726, 0.00035956524436229364, -0.00037348235413726472, -0.0019433239803823459, -0.00024175649075894543, 0.0064877345603061454, 0.0051010003604228726, -0.015083918027862582, -0.020810050169636805, 0.025767007328366939, 0.054780550584559995, -0.033877143923563204, -0.11112093603713753, 0.039666176555733602, 0.19014671400708816, -0.065282952848765688, -0.28888259656686216, 0.19320413960907623, 0.33900253545462167, -0.64581314035721027, 0.49263177170797529, -0.20602386398692688, 0.046743394892750617, -0.0045385373615773762} }; static @type@ db16_@type@[][32] = { {-2.1093396300980412e-008, 2.3087840868545578e-007, -7.3636567854418147e-007, -1.0435713423102517e-006, 1.133660866126152e-005, -1.394566898819319e-005, -6.103596621404321e-005, 0.00017478724522506327, 0.00011424152003843815, -0.00094102174935854332, 0.00040789698084934395, 0.00312802338120381, -0.0036442796214883506, -0.0069900145633907508, 0.013993768859843242, 0.010297659641009963, -0.036888397691556774, -0.0075889743686425939, 0.075924236044457791, -0.0062397227521562536, -0.13238830556335474, 0.027340263752899923, 0.21119069394696974, -0.02791820813292813, -0.32706331052747578, -0.089751089402363524, 0.44029025688580486, 0.63735633208298326, 0.43031272284545874, 0.1650642834886438, 0.034907714323629047, 0.0031892209253436892}, {-0.0031892209253436892, 0.034907714323629047, -0.1650642834886438, 0.43031272284545874, -0.63735633208298326, 0.44029025688580486, 0.089751089402363524, -0.32706331052747578, 0.02791820813292813, 0.21119069394696974, -0.027340263752899923, -0.13238830556335474, 0.0062397227521562536, 0.075924236044457791, 0.0075889743686425939, -0.036888397691556774, -0.010297659641009963, 0.013993768859843242, 0.0069900145633907508, -0.0036442796214883506, -0.00312802338120381, 0.00040789698084934395, 0.00094102174935854332, 0.00011424152003843815, -0.00017478724522506327, -6.103596621404321e-005, 1.394566898819319e-005, 1.133660866126152e-005, 1.0435713423102517e-006, -7.3636567854418147e-007, -2.3087840868545578e-007, -2.1093396300980412e-008}, {0.0031892209253436892, 0.034907714323629047, 0.1650642834886438, 0.43031272284545874, 0.63735633208298326, 0.44029025688580486, -0.089751089402363524, -0.32706331052747578, -0.02791820813292813, 0.21119069394696974, 0.027340263752899923, -0.13238830556335474, -0.0062397227521562536, 0.075924236044457791, -0.0075889743686425939, -0.036888397691556774, 0.010297659641009963, 0.013993768859843242, -0.0069900145633907508, -0.0036442796214883506, 0.00312802338120381, 0.00040789698084934395, -0.00094102174935854332, 0.00011424152003843815, 0.00017478724522506327, -6.103596621404321e-005, -1.394566898819319e-005, 1.133660866126152e-005, -1.0435713423102517e-006, -7.3636567854418147e-007, 2.3087840868545578e-007, -2.1093396300980412e-008}, {-2.1093396300980412e-008, -2.3087840868545578e-007, -7.3636567854418147e-007, 1.0435713423102517e-006, 1.133660866126152e-005, 1.394566898819319e-005, -6.103596621404321e-005, -0.00017478724522506327, 0.00011424152003843815, 0.00094102174935854332, 0.00040789698084934395, -0.00312802338120381, -0.0036442796214883506, 0.0069900145633907508, 0.013993768859843242, -0.010297659641009963, -0.036888397691556774, 0.0075889743686425939, 0.075924236044457791, 0.0062397227521562536, -0.13238830556335474, -0.027340263752899923, 0.21119069394696974, 0.02791820813292813, -0.32706331052747578, 0.089751089402363524, 0.44029025688580486, -0.63735633208298326, 0.43031272284545874, -0.1650642834886438, 0.034907714323629047, -0.0031892209253436892} }; static @type@ db17_@type@[][34] = { {7.2674929685663697e-009, -8.4239484460081536e-008, 2.9577009333187617e-007, 3.0165496099963414e-007, -4.5059424772259631e-006, 6.9906009850812941e-006, 2.3186813798761639e-005, -8.2048032024582121e-005, -2.5610109566546042e-005, 0.00043946542776894542, -0.00032813251941022427, -0.001436845304805, 0.0023012052421511474, 0.0029679966915180638, -0.0086029215203478147, -0.0030429899813869555, 0.022733676583919053, -0.0032709555358783646, -0.046922438389378908, 0.022312336178011833, 0.081105986654080822, -0.057091419631858077, -0.12681569177849797, 0.10113548917744287, 0.19731058956508457, -0.12659975221599248, -0.32832074836418546, 0.027314970403312946, 0.5183157640572823, 0.61099661568502728, 0.37035072415288578, 0.13121490330791097, 0.025985393703623173, 0.0022418070010387899}, {-0.0022418070010387899, 0.025985393703623173, -0.13121490330791097, 0.37035072415288578, -0.61099661568502728, 0.5183157640572823, -0.027314970403312946, -0.32832074836418546, 0.12659975221599248, 0.19731058956508457, -0.10113548917744287, -0.12681569177849797, 0.057091419631858077, 0.081105986654080822, -0.022312336178011833, -0.046922438389378908, 0.0032709555358783646, 0.022733676583919053, 0.0030429899813869555, -0.0086029215203478147, -0.0029679966915180638, 0.0023012052421511474, 0.001436845304805, -0.00032813251941022427, -0.00043946542776894542, -2.5610109566546042e-005, 8.2048032024582121e-005, 2.3186813798761639e-005, -6.9906009850812941e-006, -4.5059424772259631e-006, -3.0165496099963414e-007, 2.9577009333187617e-007, 8.4239484460081536e-008, 7.2674929685663697e-009}, {0.0022418070010387899, 0.025985393703623173, 0.13121490330791097, 0.37035072415288578, 0.61099661568502728, 0.5183157640572823, 0.027314970403312946, -0.32832074836418546, -0.12659975221599248, 0.19731058956508457, 0.10113548917744287, -0.12681569177849797, -0.057091419631858077, 0.081105986654080822, 0.022312336178011833, -0.046922438389378908, -0.0032709555358783646, 0.022733676583919053, -0.0030429899813869555, -0.0086029215203478147, 0.0029679966915180638, 0.0023012052421511474, -0.001436845304805, -0.00032813251941022427, 0.00043946542776894542, -2.5610109566546042e-005, -8.2048032024582121e-005, 2.3186813798761639e-005, 6.9906009850812941e-006, -4.5059424772259631e-006, 3.0165496099963414e-007, 2.9577009333187617e-007, -8.4239484460081536e-008, 7.2674929685663697e-009}, {7.2674929685663697e-009, 8.4239484460081536e-008, 2.9577009333187617e-007, -3.0165496099963414e-007, -4.5059424772259631e-006, -6.9906009850812941e-006, 2.3186813798761639e-005, 8.2048032024582121e-005, -2.5610109566546042e-005, -0.00043946542776894542, -0.00032813251941022427, 0.001436845304805, 0.0023012052421511474, -0.0029679966915180638, -0.0086029215203478147, 0.0030429899813869555, 0.022733676583919053, 0.0032709555358783646, -0.046922438389378908, -0.022312336178011833, 0.081105986654080822, 0.057091419631858077, -0.12681569177849797, -0.10113548917744287, 0.19731058956508457, 0.12659975221599248, -0.32832074836418546, -0.027314970403312946, 0.5183157640572823, -0.61099661568502728, 0.37035072415288578, -0.13121490330791097, 0.025985393703623173, -0.0022418070010387899} }; static @type@ db18_@type@[][36] = { {-2.5079344549419292e-009, 3.0688358630370302e-008, -1.1760987670250871e-007, -7.691632689865049e-008, 1.7687129836228861e-006, -3.3326344788769603e-006, -8.5206025374234635e-006, 3.7412378807308472e-005, -1.5359171230213409e-007, -0.00019864855231101547, 0.0002135815619103188, 0.00062846568296447147, -0.0013405962983313922, -0.0011187326669886426, 0.0049433436054565939, 0.00011863003387493042, -0.013051480946517112, 0.0062621679544386608, 0.026670705926689853, -0.023733210395336858, -0.04452614190225633, 0.057051247739058272, 0.064887216212358198, -0.10675224665906288, -0.092331884150304119, 0.16708131276294505, 0.14953397556500755, -0.21648093400458224, -0.29365404073579809, 0.14722311196952223, 0.57180165488712198, 0.57182680776508177, 0.31467894133619284, 0.10358846582214751, 0.019288531724094969, 0.0015763102184365595}, {-0.0015763102184365595, 0.019288531724094969, -0.10358846582214751, 0.31467894133619284, -0.57182680776508177, 0.57180165488712198, -0.14722311196952223, -0.29365404073579809, 0.21648093400458224, 0.14953397556500755, -0.16708131276294505, -0.092331884150304119, 0.10675224665906288, 0.064887216212358198, -0.057051247739058272, -0.04452614190225633, 0.023733210395336858, 0.026670705926689853, -0.0062621679544386608, -0.013051480946517112, -0.00011863003387493042, 0.0049433436054565939, 0.0011187326669886426, -0.0013405962983313922, -0.00062846568296447147, 0.0002135815619103188, 0.00019864855231101547, -1.5359171230213409e-007, -3.7412378807308472e-005, -8.5206025374234635e-006, 3.3326344788769603e-006, 1.7687129836228861e-006, 7.691632689865049e-008, -1.1760987670250871e-007, -3.0688358630370302e-008, -2.5079344549419292e-009}, {0.0015763102184365595, 0.019288531724094969, 0.10358846582214751, 0.31467894133619284, 0.57182680776508177, 0.57180165488712198, 0.14722311196952223, -0.29365404073579809, -0.21648093400458224, 0.14953397556500755, 0.16708131276294505, -0.092331884150304119, -0.10675224665906288, 0.064887216212358198, 0.057051247739058272, -0.04452614190225633, -0.023733210395336858, 0.026670705926689853, 0.0062621679544386608, -0.013051480946517112, 0.00011863003387493042, 0.0049433436054565939, -0.0011187326669886426, -0.0013405962983313922, 0.00062846568296447147, 0.0002135815619103188, -0.00019864855231101547, -1.5359171230213409e-007, 3.7412378807308472e-005, -8.5206025374234635e-006, -3.3326344788769603e-006, 1.7687129836228861e-006, -7.691632689865049e-008, -1.1760987670250871e-007, 3.0688358630370302e-008, -2.5079344549419292e-009}, {-2.5079344549419292e-009, -3.0688358630370302e-008, -1.1760987670250871e-007, 7.691632689865049e-008, 1.7687129836228861e-006, 3.3326344788769603e-006, -8.5206025374234635e-006, -3.7412378807308472e-005, -1.5359171230213409e-007, 0.00019864855231101547, 0.0002135815619103188, -0.00062846568296447147, -0.0013405962983313922, 0.0011187326669886426, 0.0049433436054565939, -0.00011863003387493042, -0.013051480946517112, -0.0062621679544386608, 0.026670705926689853, 0.023733210395336858, -0.04452614190225633, -0.057051247739058272, 0.064887216212358198, 0.10675224665906288, -0.092331884150304119, -0.16708131276294505, 0.14953397556500755, 0.21648093400458224, -0.29365404073579809, -0.14722311196952223, 0.57180165488712198, -0.57182680776508177, 0.31467894133619284, -0.10358846582214751, 0.019288531724094969, -0.0015763102184365595} }; static @type@ db19_@type@[][38] = { {8.6668488390344833e-010, -1.1164020670405678e-008, 4.6369377758023682e-008, 1.4470882988040879e-008, -6.8627556577981102e-007, 1.5319314766978769e-006, 3.0109643163099385e-006, -1.6640176297224622e-005, 5.1059504870906939e-006, 8.7112704672504432e-005, -0.00012460079173506306, -0.00026067613568119951, 0.0007358025205041731, 0.00034180865344939543, -0.0026875518007344408, 0.00076895435922424884, 0.0070407473670804953, -0.0058669222811121953, -0.013988388678695632, 0.019375549889114482, 0.021623767409452484, -0.045674226277784918, -0.026501236250778635, 0.086906755555450702, 0.027584350624887129, -0.14278569504021468, -0.033518541903202262, 0.21234974330662043, 0.074652269708066474, -0.28583863175723145, -0.22809139421653665, 0.26089495265212009, 0.60170454913009164, 0.52443637746688621, 0.26438843174202237, 0.08127811326580564, 0.01428109845082521, 0.0011086697631864314}, {-0.0011086697631864314, 0.01428109845082521, -0.08127811326580564, 0.26438843174202237, -0.52443637746688621, 0.60170454913009164, -0.26089495265212009, -0.22809139421653665, 0.28583863175723145, 0.074652269708066474, -0.21234974330662043, -0.033518541903202262, 0.14278569504021468, 0.027584350624887129, -0.086906755555450702, -0.026501236250778635, 0.045674226277784918, 0.021623767409452484, -0.019375549889114482, -0.013988388678695632, 0.0058669222811121953, 0.0070407473670804953, -0.00076895435922424884, -0.0026875518007344408, -0.00034180865344939543, 0.0007358025205041731, 0.00026067613568119951, -0.00012460079173506306, -8.7112704672504432e-005, 5.1059504870906939e-006, 1.6640176297224622e-005, 3.0109643163099385e-006, -1.5319314766978769e-006, -6.8627556577981102e-007, -1.4470882988040879e-008, 4.6369377758023682e-008, 1.1164020670405678e-008, 8.6668488390344833e-010}, {0.0011086697631864314, 0.01428109845082521, 0.08127811326580564, 0.26438843174202237, 0.52443637746688621, 0.60170454913009164, 0.26089495265212009, -0.22809139421653665, -0.28583863175723145, 0.074652269708066474, 0.21234974330662043, -0.033518541903202262, -0.14278569504021468, 0.027584350624887129, 0.086906755555450702, -0.026501236250778635, -0.045674226277784918, 0.021623767409452484, 0.019375549889114482, -0.013988388678695632, -0.0058669222811121953, 0.0070407473670804953, 0.00076895435922424884, -0.0026875518007344408, 0.00034180865344939543, 0.0007358025205041731, -0.00026067613568119951, -0.00012460079173506306, 8.7112704672504432e-005, 5.1059504870906939e-006, -1.6640176297224622e-005, 3.0109643163099385e-006, 1.5319314766978769e-006, -6.8627556577981102e-007, 1.4470882988040879e-008, 4.6369377758023682e-008, -1.1164020670405678e-008, 8.6668488390344833e-010}, {8.6668488390344833e-010, 1.1164020670405678e-008, 4.6369377758023682e-008, -1.4470882988040879e-008, -6.8627556577981102e-007, -1.5319314766978769e-006, 3.0109643163099385e-006, 1.6640176297224622e-005, 5.1059504870906939e-006, -8.7112704672504432e-005, -0.00012460079173506306, 0.00026067613568119951, 0.0007358025205041731, -0.00034180865344939543, -0.0026875518007344408, -0.00076895435922424884, 0.0070407473670804953, 0.0058669222811121953, -0.013988388678695632, -0.019375549889114482, 0.021623767409452484, 0.045674226277784918, -0.026501236250778635, -0.086906755555450702, 0.027584350624887129, 0.14278569504021468, -0.033518541903202262, -0.21234974330662043, 0.074652269708066474, 0.28583863175723145, -0.22809139421653665, -0.26089495265212009, 0.60170454913009164, -0.52443637746688621, 0.26438843174202237, -0.08127811326580564, 0.01428109845082521, -0.0011086697631864314} }; static @type@ db20_@type@[][40] = { {-2.9988364896157532e-010, 4.05612705554717e-009, -1.8148432482976221e-008, 2.0143220235374613e-010, 2.633924226266962e-007, -6.847079596993149e-007, -1.0119940100181473e-006, 7.2412482876637907e-006, -4.3761438621821972e-006, -3.7105861833906152e-005, 6.7742808283730477e-005, 0.00010153288973669777, -0.0003851047486990061, -5.3497598443404532e-005, 0.0013925596193045254, -0.00083156217287724745, -0.003581494259744107, 0.0044205423867663502, 0.0067216273018096935, -0.013810526137727442, -0.0087893249245557647, 0.032294299530119162, 0.0058746818113949465, -0.061722899624668884, 0.0056322468576854544, 0.10229171917513397, -0.024716827337521424, -0.15545875070604531, 0.039850246458519104, 0.22829105082013823, -0.016727088308801888, -0.32678680043353758, -0.13921208801128787, 0.36150229873889705, 0.61049323893785579, 0.47269618531033147, 0.21994211355113222, 0.063423780459005291, 0.010549394624937735, 0.00077995361366591117}, {-0.00077995361366591117, 0.010549394624937735, -0.063423780459005291, 0.21994211355113222, -0.47269618531033147, 0.61049323893785579, -0.36150229873889705, -0.13921208801128787, 0.32678680043353758, -0.016727088308801888, -0.22829105082013823, 0.039850246458519104, 0.15545875070604531, -0.024716827337521424, -0.10229171917513397, 0.0056322468576854544, 0.061722899624668884, 0.0058746818113949465, -0.032294299530119162, -0.0087893249245557647, 0.013810526137727442, 0.0067216273018096935, -0.0044205423867663502, -0.003581494259744107, 0.00083156217287724745, 0.0013925596193045254, 5.3497598443404532e-005, -0.0003851047486990061, -0.00010153288973669777, 6.7742808283730477e-005, 3.7105861833906152e-005, -4.3761438621821972e-006, -7.2412482876637907e-006, -1.0119940100181473e-006, 6.847079596993149e-007, 2.633924226266962e-007, -2.0143220235374613e-010, -1.8148432482976221e-008, -4.05612705554717e-009, -2.9988364896157532e-010}, {0.00077995361366591117, 0.010549394624937735, 0.063423780459005291, 0.21994211355113222, 0.47269618531033147, 0.61049323893785579, 0.36150229873889705, -0.13921208801128787, -0.32678680043353758, -0.016727088308801888, 0.22829105082013823, 0.039850246458519104, -0.15545875070604531, -0.024716827337521424, 0.10229171917513397, 0.0056322468576854544, -0.061722899624668884, 0.0058746818113949465, 0.032294299530119162, -0.0087893249245557647, -0.013810526137727442, 0.0067216273018096935, 0.0044205423867663502, -0.003581494259744107, -0.00083156217287724745, 0.0013925596193045254, -5.3497598443404532e-005, -0.0003851047486990061, 0.00010153288973669777, 6.7742808283730477e-005, -3.7105861833906152e-005, -4.3761438621821972e-006, 7.2412482876637907e-006, -1.0119940100181473e-006, -6.847079596993149e-007, 2.633924226266962e-007, 2.0143220235374613e-010, -1.8148432482976221e-008, 4.05612705554717e-009, -2.9988364896157532e-010}, {-2.9988364896157532e-010, -4.05612705554717e-009, -1.8148432482976221e-008, -2.0143220235374613e-010, 2.633924226266962e-007, 6.847079596993149e-007, -1.0119940100181473e-006, -7.2412482876637907e-006, -4.3761438621821972e-006, 3.7105861833906152e-005, 6.7742808283730477e-005, -0.00010153288973669777, -0.0003851047486990061, 5.3497598443404532e-005, 0.0013925596193045254, 0.00083156217287724745, -0.003581494259744107, -0.0044205423867663502, 0.0067216273018096935, 0.013810526137727442, -0.0087893249245557647, -0.032294299530119162, 0.0058746818113949465, 0.061722899624668884, 0.0056322468576854544, -0.10229171917513397, -0.024716827337521424, 0.15545875070604531, 0.039850246458519104, -0.22829105082013823, -0.016727088308801888, 0.32678680043353758, -0.13921208801128787, -0.36150229873889705, 0.61049323893785579, -0.47269618531033147, 0.21994211355113222, -0.063423780459005291, 0.010549394624937735, -0.00077995361366591117} }; static @type@ sym2_@type@[][4] = { {-0.12940952255092145, 0.22414386804185735, 0.83651630373746899, 0.48296291314469025}, {-0.48296291314469025, 0.83651630373746899, -0.22414386804185735, -0.12940952255092145}, {0.48296291314469025, 0.83651630373746899, 0.22414386804185735, -0.12940952255092145}, {-0.12940952255092145, -0.22414386804185735, 0.83651630373746899, -0.48296291314469025} }; static @type@ sym3_@type@[][6] = { {0.035226291882100656, -0.085441273882241486, -0.13501102001039084, 0.45987750211933132, 0.80689150931333875, 0.33267055295095688}, {-0.33267055295095688, 0.80689150931333875, -0.45987750211933132, -0.13501102001039084, 0.085441273882241486, 0.035226291882100656}, {0.33267055295095688, 0.80689150931333875, 0.45987750211933132, -0.13501102001039084, -0.085441273882241486, 0.035226291882100656}, {0.035226291882100656, 0.085441273882241486, -0.13501102001039084, -0.45987750211933132, 0.80689150931333875, -0.33267055295095688} }; static @type@ sym4_@type@[][8] = { {-0.075765714789273325, -0.02963552764599851, 0.49761866763201545, 0.80373875180591614, 0.29785779560527736, -0.099219543576847216, -0.012603967262037833, 0.032223100604042702}, {-0.032223100604042702, -0.012603967262037833, 0.099219543576847216, 0.29785779560527736, -0.80373875180591614, 0.49761866763201545, 0.02963552764599851, -0.075765714789273325}, {0.032223100604042702, -0.012603967262037833, -0.099219543576847216, 0.29785779560527736, 0.80373875180591614, 0.49761866763201545, -0.02963552764599851, -0.075765714789273325}, {-0.075765714789273325, 0.02963552764599851, 0.49761866763201545, -0.80373875180591614, 0.29785779560527736, 0.099219543576847216, -0.012603967262037833, -0.032223100604042702} }; static @type@ sym5_@type@[][10] = { {0.027333068345077982, 0.029519490925774643, -0.039134249302383094, 0.1993975339773936, 0.72340769040242059, 0.63397896345821192, 0.016602105764522319, -0.17532808990845047, -0.021101834024758855, 0.019538882735286728}, {-0.019538882735286728, -0.021101834024758855, 0.17532808990845047, 0.016602105764522319, -0.63397896345821192, 0.72340769040242059, -0.1993975339773936, -0.039134249302383094, -0.029519490925774643, 0.027333068345077982}, {0.019538882735286728, -0.021101834024758855, -0.17532808990845047, 0.016602105764522319, 0.63397896345821192, 0.72340769040242059, 0.1993975339773936, -0.039134249302383094, 0.029519490925774643, 0.027333068345077982}, {0.027333068345077982, -0.029519490925774643, -0.039134249302383094, -0.1993975339773936, 0.72340769040242059, -0.63397896345821192, 0.016602105764522319, 0.17532808990845047, -0.021101834024758855, -0.019538882735286728} }; static @type@ sym6_@type@[][12] = { {0.015404109327027373, 0.0034907120842174702, -0.11799011114819057, -0.048311742585632998, 0.49105594192674662, 0.787641141030194, 0.3379294217276218, -0.072637522786462516, -0.021060292512300564, 0.044724901770665779, 0.0017677118642428036, -0.007800708325034148}, {0.007800708325034148, 0.0017677118642428036, -0.044724901770665779, -0.021060292512300564, 0.072637522786462516, 0.3379294217276218, -0.787641141030194, 0.49105594192674662, 0.048311742585632998, -0.11799011114819057, -0.0034907120842174702, 0.015404109327027373}, {-0.007800708325034148, 0.0017677118642428036, 0.044724901770665779, -0.021060292512300564, -0.072637522786462516, 0.3379294217276218, 0.787641141030194, 0.49105594192674662, -0.048311742585632998, -0.11799011114819057, 0.0034907120842174702, 0.015404109327027373}, {0.015404109327027373, -0.0034907120842174702, -0.11799011114819057, 0.048311742585632998, 0.49105594192674662, -0.787641141030194, 0.3379294217276218, 0.072637522786462516, -0.021060292512300564, -0.044724901770665779, 0.0017677118642428036, 0.007800708325034148} }; static @type@ sym7_@type@[][14] = { {0.0026818145682578781, -0.0010473848886829163, -0.01263630340325193, 0.03051551316596357, 0.067892693501372697, -0.049552834937127255, 0.017441255086855827, 0.5361019170917628, 0.76776431700316405, 0.28862963175151463, -0.14004724044296152, -0.10780823770381774, 0.0040102448715336634, 0.010268176708511255}, {-0.010268176708511255, 0.0040102448715336634, 0.10780823770381774, -0.14004724044296152, -0.28862963175151463, 0.76776431700316405, -0.5361019170917628, 0.017441255086855827, 0.049552834937127255, 0.067892693501372697, -0.03051551316596357, -0.01263630340325193, 0.0010473848886829163, 0.0026818145682578781}, {0.010268176708511255, 0.0040102448715336634, -0.10780823770381774, -0.14004724044296152, 0.28862963175151463, 0.76776431700316405, 0.5361019170917628, 0.017441255086855827, -0.049552834937127255, 0.067892693501372697, 0.03051551316596357, -0.01263630340325193, -0.0010473848886829163, 0.0026818145682578781}, {0.0026818145682578781, 0.0010473848886829163, -0.01263630340325193, -0.03051551316596357, 0.067892693501372697, 0.049552834937127255, 0.017441255086855827, -0.5361019170917628, 0.76776431700316405, -0.28862963175151463, -0.14004724044296152, 0.10780823770381774, 0.0040102448715336634, -0.010268176708511255} }; static @type@ sym8_@type@[][16] = { {-0.0033824159510061256, -0.00054213233179114812, 0.031695087811492981, 0.0076074873249176054, -0.14329423835080971, -0.061273359067658524, 0.48135965125837221, 0.77718575170052351, 0.3644418948353314, -0.051945838107709037, -0.027219029917056003, 0.049137179673607506, 0.0038087520138906151, -0.014952258337048231, -0.0003029205147213668, 0.0018899503327594609}, {-0.0018899503327594609, -0.0003029205147213668, 0.014952258337048231, 0.0038087520138906151, -0.049137179673607506, -0.027219029917056003, 0.051945838107709037, 0.3644418948353314, -0.77718575170052351, 0.48135965125837221, 0.061273359067658524, -0.14329423835080971, -0.0076074873249176054, 0.031695087811492981, 0.00054213233179114812, -0.0033824159510061256}, {0.0018899503327594609, -0.0003029205147213668, -0.014952258337048231, 0.0038087520138906151, 0.049137179673607506, -0.027219029917056003, -0.051945838107709037, 0.3644418948353314, 0.77718575170052351, 0.48135965125837221, -0.061273359067658524, -0.14329423835080971, 0.0076074873249176054, 0.031695087811492981, -0.00054213233179114812, -0.0033824159510061256}, {-0.0033824159510061256, 0.00054213233179114812, 0.031695087811492981, -0.0076074873249176054, -0.14329423835080971, 0.061273359067658524, 0.48135965125837221, -0.77718575170052351, 0.3644418948353314, 0.051945838107709037, -0.027219029917056003, -0.049137179673607506, 0.0038087520138906151, 0.014952258337048231, -0.0003029205147213668, -0.0018899503327594609} }; static @type@ sym9_@type@[][18] = { {0.0014009155259146807, 0.00061978088898558676, -0.013271967781817119, -0.01152821020767923, 0.03022487885827568, 0.00058346274612580684, -0.054568958430834071, 0.238760914607303, 0.717897082764412, 0.61733844914093583, 0.035272488035271894, -0.19155083129728512, -0.018233770779395985, 0.06207778930288603, 0.0088592674934004842, -0.010264064027633142, -0.00047315449868008311, 0.0010694900329086053}, {-0.0010694900329086053, -0.00047315449868008311, 0.010264064027633142, 0.0088592674934004842, -0.06207778930288603, -0.018233770779395985, 0.19155083129728512, 0.035272488035271894, -0.61733844914093583, 0.717897082764412, -0.238760914607303, -0.054568958430834071, -0.00058346274612580684, 0.03022487885827568, 0.01152821020767923, -0.013271967781817119, -0.00061978088898558676, 0.0014009155259146807}, {0.0010694900329086053, -0.00047315449868008311, -0.010264064027633142, 0.0088592674934004842, 0.06207778930288603, -0.018233770779395985, -0.19155083129728512, 0.035272488035271894, 0.61733844914093583, 0.717897082764412, 0.238760914607303, -0.054568958430834071, 0.00058346274612580684, 0.03022487885827568, -0.01152821020767923, -0.013271967781817119, 0.00061978088898558676, 0.0014009155259146807}, {0.0014009155259146807, -0.00061978088898558676, -0.013271967781817119, 0.01152821020767923, 0.03022487885827568, -0.00058346274612580684, -0.054568958430834071, -0.238760914607303, 0.717897082764412, -0.61733844914093583, 0.035272488035271894, 0.19155083129728512, -0.018233770779395985, -0.06207778930288603, 0.0088592674934004842, 0.010264064027633142, -0.00047315449868008311, -0.0010694900329086053} }; static @type@ sym10_@type@[][20] = { {0.00077015980911449011, 9.5632670722894754e-005, -0.0086412992770224222, -0.0014653825813050513, 0.045927239231092203, 0.011609893903711381, -0.15949427888491757, -0.070880535783243853, 0.47169066693843925, 0.7695100370211071, 0.38382676106708546, -0.035536740473817552, -0.0319900568824278, 0.049994972077376687, 0.0057649120335819086, -0.02035493981231129, -0.00080435893201654491, 0.0045931735853118284, 5.7036083618494284e-005, -0.00045932942100465878}, {0.00045932942100465878, 5.7036083618494284e-005, -0.0045931735853118284, -0.00080435893201654491, 0.02035493981231129, 0.0057649120335819086, -0.049994972077376687, -0.0319900568824278, 0.035536740473817552, 0.38382676106708546, -0.7695100370211071, 0.47169066693843925, 0.070880535783243853, -0.15949427888491757, -0.011609893903711381, 0.045927239231092203, 0.0014653825813050513, -0.0086412992770224222, -9.5632670722894754e-005, 0.00077015980911449011}, {-0.00045932942100465878, 5.7036083618494284e-005, 0.0045931735853118284, -0.00080435893201654491, -0.02035493981231129, 0.0057649120335819086, 0.049994972077376687, -0.0319900568824278, -0.035536740473817552, 0.38382676106708546, 0.7695100370211071, 0.47169066693843925, -0.070880535783243853, -0.15949427888491757, 0.011609893903711381, 0.045927239231092203, -0.0014653825813050513, -0.0086412992770224222, 9.5632670722894754e-005, 0.00077015980911449011}, {0.00077015980911449011, -9.5632670722894754e-005, -0.0086412992770224222, 0.0014653825813050513, 0.045927239231092203, -0.011609893903711381, -0.15949427888491757, 0.070880535783243853, 0.47169066693843925, -0.7695100370211071, 0.38382676106708546, 0.035536740473817552, -0.0319900568824278, -0.049994972077376687, 0.0057649120335819086, 0.02035493981231129, -0.00080435893201654491, -0.0045931735853118284, 5.7036083618494284e-005, 0.00045932942100465878} }; static @type@ sym11_@type@[][22] = { {0.00017172195069934854, -3.8795655736158566e-005, -0.0017343662672978692, 0.00058835273539699145, 0.0065124956747714497, -0.0098579348287897942, -0.024080841595864003, 0.0370374159788594, 0.069976799610734136, -0.022832651022562687, 0.097198394458909473, 0.57202297801008706, 0.73034354908839572, 0.23768990904924897, -0.2046547944958006, -0.14460234370531561, 0.035266759564466552, 0.043000190681552281, -0.0020034719001093887, -0.0063896036664548919, 0.00011053509764272153, 0.00048926361026192387}, {-0.00048926361026192387, 0.00011053509764272153, 0.0063896036664548919, -0.0020034719001093887, -0.043000190681552281, 0.035266759564466552, 0.14460234370531561, -0.2046547944958006, -0.23768990904924897, 0.73034354908839572, -0.57202297801008706, 0.097198394458909473, 0.022832651022562687, 0.069976799610734136, -0.0370374159788594, -0.024080841595864003, 0.0098579348287897942, 0.0065124956747714497, -0.00058835273539699145, -0.0017343662672978692, 3.8795655736158566e-005, 0.00017172195069934854}, {0.00048926361026192387, 0.00011053509764272153, -0.0063896036664548919, -0.0020034719001093887, 0.043000190681552281, 0.035266759564466552, -0.14460234370531561, -0.2046547944958006, 0.23768990904924897, 0.73034354908839572, 0.57202297801008706, 0.097198394458909473, -0.022832651022562687, 0.069976799610734136, 0.0370374159788594, -0.024080841595864003, -0.0098579348287897942, 0.0065124956747714497, 0.00058835273539699145, -0.0017343662672978692, -3.8795655736158566e-005, 0.00017172195069934854}, {0.00017172195069934854, 3.8795655736158566e-005, -0.0017343662672978692, -0.00058835273539699145, 0.0065124956747714497, 0.0098579348287897942, -0.024080841595864003, -0.0370374159788594, 0.069976799610734136, 0.022832651022562687, 0.097198394458909473, -0.57202297801008706, 0.73034354908839572, -0.23768990904924897, -0.2046547944958006, 0.14460234370531561, 0.035266759564466552, -0.043000190681552281, -0.0020034719001093887, 0.0063896036664548919, 0.00011053509764272153, -0.00048926361026192387} }; static @type@ sym12_@type@[][24] = { {0.00011196719424656033, -1.1353928041541452e-005, -0.0013497557555715387, 0.00018021409008538188, 0.007414965517654251, -0.0014089092443297553, -0.024220722675013445, 0.0075537806116804775, 0.049179318299660837, -0.035848830736954392, -0.022162306170337816, 0.39888597239022, 0.76347909778365719, 0.46274103121927235, -0.07833262231634322, -0.17037069723886492, 0.01530174062247884, 0.057804179445505657, -0.0026043910313322326, -0.014589836449234145, 0.00030764779631059454, 0.0023502976141834648, -1.8158078862617515e-005, -0.00017906658697508691}, {0.00017906658697508691, -1.8158078862617515e-005, -0.0023502976141834648, 0.00030764779631059454, 0.014589836449234145, -0.0026043910313322326, -0.057804179445505657, 0.01530174062247884, 0.17037069723886492, -0.07833262231634322, -0.46274103121927235, 0.76347909778365719, -0.39888597239022, -0.022162306170337816, 0.035848830736954392, 0.049179318299660837, -0.0075537806116804775, -0.024220722675013445, 0.0014089092443297553, 0.007414965517654251, -0.00018021409008538188, -0.0013497557555715387, 1.1353928041541452e-005, 0.00011196719424656033}, {-0.00017906658697508691, -1.8158078862617515e-005, 0.0023502976141834648, 0.00030764779631059454, -0.014589836449234145, -0.0026043910313322326, 0.057804179445505657, 0.01530174062247884, -0.17037069723886492, -0.07833262231634322, 0.46274103121927235, 0.76347909778365719, 0.39888597239022, -0.022162306170337816, -0.035848830736954392, 0.049179318299660837, 0.0075537806116804775, -0.024220722675013445, -0.0014089092443297553, 0.007414965517654251, 0.00018021409008538188, -0.0013497557555715387, -1.1353928041541452e-005, 0.00011196719424656033}, {0.00011196719424656033, 1.1353928041541452e-005, -0.0013497557555715387, -0.00018021409008538188, 0.007414965517654251, 0.0014089092443297553, -0.024220722675013445, -0.0075537806116804775, 0.049179318299660837, 0.035848830736954392, -0.022162306170337816, -0.39888597239022, 0.76347909778365719, -0.46274103121927235, -0.07833262231634322, 0.17037069723886492, 0.01530174062247884, -0.057804179445505657, -0.0026043910313322326, 0.014589836449234145, 0.00030764779631059454, -0.0023502976141834648, -1.8158078862617515e-005, 0.00017906658697508691} }; static @type@ sym13_@type@[][26] = { {6.8203252630753188e-005, -3.5738623648689009e-005, -0.0011360634389281183, -0.00017094285853022211, 0.0075262253899680996, 0.0052963597387250252, -0.02021676813338983, -0.017211642726299048, 0.013862497435849205, -0.059750627717943698, -0.12436246075153011, 0.19770481877117801, 0.69573915056149638, 0.64456438390118564, 0.11023022302137217, -0.14049009311363403, 0.0088197576704205465, 0.092926030899137119, 0.017618296880653084, -0.020749686325515677, -0.0014924472742598532, 0.0056748537601224395, 0.00041326119884196064, -0.0007213643851362283, 3.6905373423196241e-005, 7.0429866906944016e-005}, {-7.0429866906944016e-005, 3.6905373423196241e-005, 0.0007213643851362283, 0.00041326119884196064, -0.0056748537601224395, -0.0014924472742598532, 0.020749686325515677, 0.017618296880653084, -0.092926030899137119, 0.0088197576704205465, 0.14049009311363403, 0.11023022302137217, -0.64456438390118564, 0.69573915056149638, -0.19770481877117801, -0.12436246075153011, 0.059750627717943698, 0.013862497435849205, 0.017211642726299048, -0.02021676813338983, -0.0052963597387250252, 0.0075262253899680996, 0.00017094285853022211, -0.0011360634389281183, 3.5738623648689009e-005, 6.8203252630753188e-005}, {7.0429866906944016e-005, 3.6905373423196241e-005, -0.0007213643851362283, 0.00041326119884196064, 0.0056748537601224395, -0.0014924472742598532, -0.020749686325515677, 0.017618296880653084, 0.092926030899137119, 0.0088197576704205465, -0.14049009311363403, 0.11023022302137217, 0.64456438390118564, 0.69573915056149638, 0.19770481877117801, -0.12436246075153011, -0.059750627717943698, 0.013862497435849205, -0.017211642726299048, -0.02021676813338983, 0.0052963597387250252, 0.0075262253899680996, -0.00017094285853022211, -0.0011360634389281183, -3.5738623648689009e-005, 6.8203252630753188e-005}, {6.8203252630753188e-005, 3.5738623648689009e-005, -0.0011360634389281183, 0.00017094285853022211, 0.0075262253899680996, -0.0052963597387250252, -0.02021676813338983, 0.017211642726299048, 0.013862497435849205, 0.059750627717943698, -0.12436246075153011, -0.19770481877117801, 0.69573915056149638, -0.64456438390118564, 0.11023022302137217, 0.14049009311363403, 0.0088197576704205465, -0.092926030899137119, 0.017618296880653084, 0.020749686325515677, -0.0014924472742598532, -0.0056748537601224395, 0.00041326119884196064, 0.0007213643851362283, 3.6905373423196241e-005, -7.0429866906944016e-005} }; static @type@ sym14_@type@[][28] = { {-2.5879090265397886e-005, 1.1210865808890361e-005, 0.00039843567297594335, -6.2865424814776362e-005, -0.002579441725933078, 0.00036647657366011829, 0.010037693717672269, -0.0027537747912240711, -0.029196217764038187, 0.0042805204990193782, 0.037433088362853452, -0.057634498351326995, -0.035318112114979733, 0.39320152196208885, 0.75997624196109093, 0.47533576263420663, -0.058111823317717831, -0.15999741114652205, 0.025898587531046669, 0.069827616361807551, -0.0023650488367403851, -0.019439314263626713, 0.0010131419871842082, 0.0045326774719456481, -7.3214213567023991e-005, -0.00060576018246643346, 1.9329016965523917e-005, 4.4618977991475265e-005}, {-4.4618977991475265e-005, 1.9329016965523917e-005, 0.00060576018246643346, -7.3214213567023991e-005, -0.0045326774719456481, 0.0010131419871842082, 0.019439314263626713, -0.0023650488367403851, -0.069827616361807551, 0.025898587531046669, 0.15999741114652205, -0.058111823317717831, -0.47533576263420663, 0.75997624196109093, -0.39320152196208885, -0.035318112114979733, 0.057634498351326995, 0.037433088362853452, -0.0042805204990193782, -0.029196217764038187, 0.0027537747912240711, 0.010037693717672269, -0.00036647657366011829, -0.002579441725933078, 6.2865424814776362e-005, 0.00039843567297594335, -1.1210865808890361e-005, -2.5879090265397886e-005}, {4.4618977991475265e-005, 1.9329016965523917e-005, -0.00060576018246643346, -7.3214213567023991e-005, 0.0045326774719456481, 0.0010131419871842082, -0.019439314263626713, -0.0023650488367403851, 0.069827616361807551, 0.025898587531046669, -0.15999741114652205, -0.058111823317717831, 0.47533576263420663, 0.75997624196109093, 0.39320152196208885, -0.035318112114979733, -0.057634498351326995, 0.037433088362853452, 0.0042805204990193782, -0.029196217764038187, -0.0027537747912240711, 0.010037693717672269, 0.00036647657366011829, -0.002579441725933078, -6.2865424814776362e-005, 0.00039843567297594335, 1.1210865808890361e-005, -2.5879090265397886e-005}, {-2.5879090265397886e-005, -1.1210865808890361e-005, 0.00039843567297594335, 6.2865424814776362e-005, -0.002579441725933078, -0.00036647657366011829, 0.010037693717672269, 0.0027537747912240711, -0.029196217764038187, -0.0042805204990193782, 0.037433088362853452, 0.057634498351326995, -0.035318112114979733, -0.39320152196208885, 0.75997624196109093, -0.47533576263420663, -0.058111823317717831, 0.15999741114652205, 0.025898587531046669, -0.069827616361807551, -0.0023650488367403851, 0.019439314263626713, 0.0010131419871842082, -0.0045326774719456481, -7.3214213567023991e-005, 0.00060576018246643346, 1.9329016965523917e-005, -4.4618977991475265e-005} }; static @type@ sym15_@type@[][30] = { {9.7124197379633478e-006, -7.3596667989194696e-006, -0.00016066186637495343, 5.5122547855586653e-005, 0.0010705672194623959, -0.00026731644647180568, -0.0035901654473726417, 0.003423450736351241, 0.010079977087905669, -0.019405011430934468, -0.038876716876833493, 0.021937642719753955, 0.040735479696810677, -0.04108266663538248, 0.11153369514261872, 0.57864041521503451, 0.72184302963618119, 0.2439627054321663, -0.1966263587662373, -0.13405629845625389, 0.068393310060480245, 0.067969829044879179, -0.0087447888864779517, -0.017171252781638731, 0.0015261382781819983, 0.003481028737064895, -0.00010815440168545525, -0.00040216853760293483, 2.1717890150778919e-005, 2.8660708525318081e-005}, {-2.8660708525318081e-005, 2.1717890150778919e-005, 0.00040216853760293483, -0.00010815440168545525, -0.003481028737064895, 0.0015261382781819983, 0.017171252781638731, -0.0087447888864779517, -0.067969829044879179, 0.068393310060480245, 0.13405629845625389, -0.1966263587662373, -0.2439627054321663, 0.72184302963618119, -0.57864041521503451, 0.11153369514261872, 0.04108266663538248, 0.040735479696810677, -0.021937642719753955, -0.038876716876833493, 0.019405011430934468, 0.010079977087905669, -0.003423450736351241, -0.0035901654473726417, 0.00026731644647180568, 0.0010705672194623959, -5.5122547855586653e-005, -0.00016066186637495343, 7.3596667989194696e-006, 9.7124197379633478e-006}, {2.8660708525318081e-005, 2.1717890150778919e-005, -0.00040216853760293483, -0.00010815440168545525, 0.003481028737064895, 0.0015261382781819983, -0.017171252781638731, -0.0087447888864779517, 0.067969829044879179, 0.068393310060480245, -0.13405629845625389, -0.1966263587662373, 0.2439627054321663, 0.72184302963618119, 0.57864041521503451, 0.11153369514261872, -0.04108266663538248, 0.040735479696810677, 0.021937642719753955, -0.038876716876833493, -0.019405011430934468, 0.010079977087905669, 0.003423450736351241, -0.0035901654473726417, -0.00026731644647180568, 0.0010705672194623959, 5.5122547855586653e-005, -0.00016066186637495343, -7.3596667989194696e-006, 9.7124197379633478e-006}, {9.7124197379633478e-006, 7.3596667989194696e-006, -0.00016066186637495343, -5.5122547855586653e-005, 0.0010705672194623959, 0.00026731644647180568, -0.0035901654473726417, -0.003423450736351241, 0.010079977087905669, 0.019405011430934468, -0.038876716876833493, -0.021937642719753955, 0.040735479696810677, 0.04108266663538248, 0.11153369514261872, -0.57864041521503451, 0.72184302963618119, -0.2439627054321663, -0.1966263587662373, 0.13405629845625389, 0.068393310060480245, -0.067969829044879179, -0.0087447888864779517, 0.017171252781638731, 0.0015261382781819983, -0.003481028737064895, -0.00010815440168545525, 0.00040216853760293483, 2.1717890150778919e-005, -2.8660708525318081e-005} }; static @type@ sym16_@type@[][32] = { {6.2300067012207606e-006, -3.1135564076219692e-006, -0.00010943147929529757, 2.8078582128442894e-005, 0.00085235471080470952, -0.0001084456223089688, -0.0038809122526038786, 0.00071821197883178923, 0.012666731659857348, -0.0031265171722710075, -0.031051202843553064, 0.0048692744049046071, 0.032333091610663785, -0.066983049070217779, -0.034574228416972504, 0.39712293362064416, 0.75652498787569711, 0.47534280601152273, -0.054040601387606135, -0.15959219218520598, 0.03072113906330156, 0.078037852903419913, -0.0035102750683740089, -0.024952758046290123, 0.001359844742484172, 0.0069377611308027096, -0.00022211647621176323, -0.0013387206066921965, 3.656592483348223e-005, 0.00016545679579108483, -5.3964831793152419e-006, -1.0797982104319795e-005}, {1.0797982104319795e-005, -5.3964831793152419e-006, -0.00016545679579108483, 3.656592483348223e-005, 0.0013387206066921965, -0.00022211647621176323, -0.0069377611308027096, 0.001359844742484172, 0.024952758046290123, -0.0035102750683740089, -0.078037852903419913, 0.03072113906330156, 0.15959219218520598, -0.054040601387606135, -0.47534280601152273, 0.75652498787569711, -0.39712293362064416, -0.034574228416972504, 0.066983049070217779, 0.032333091610663785, -0.0048692744049046071, -0.031051202843553064, 0.0031265171722710075, 0.012666731659857348, -0.00071821197883178923, -0.0038809122526038786, 0.0001084456223089688, 0.00085235471080470952, -2.8078582128442894e-005, -0.00010943147929529757, 3.1135564076219692e-006, 6.2300067012207606e-006}, {-1.0797982104319795e-005, -5.3964831793152419e-006, 0.00016545679579108483, 3.656592483348223e-005, -0.0013387206066921965, -0.00022211647621176323, 0.0069377611308027096, 0.001359844742484172, -0.024952758046290123, -0.0035102750683740089, 0.078037852903419913, 0.03072113906330156, -0.15959219218520598, -0.054040601387606135, 0.47534280601152273, 0.75652498787569711, 0.39712293362064416, -0.034574228416972504, -0.066983049070217779, 0.032333091610663785, 0.0048692744049046071, -0.031051202843553064, -0.0031265171722710075, 0.012666731659857348, 0.00071821197883178923, -0.0038809122526038786, -0.0001084456223089688, 0.00085235471080470952, 2.8078582128442894e-005, -0.00010943147929529757, -3.1135564076219692e-006, 6.2300067012207606e-006}, {6.2300067012207606e-006, 3.1135564076219692e-006, -0.00010943147929529757, -2.8078582128442894e-005, 0.00085235471080470952, 0.0001084456223089688, -0.0038809122526038786, -0.00071821197883178923, 0.012666731659857348, 0.0031265171722710075, -0.031051202843553064, -0.0048692744049046071, 0.032333091610663785, 0.066983049070217779, -0.034574228416972504, -0.39712293362064416, 0.75652498787569711, -0.47534280601152273, -0.054040601387606135, 0.15959219218520598, 0.03072113906330156, -0.078037852903419913, -0.0035102750683740089, 0.024952758046290123, 0.001359844742484172, -0.0069377611308027096, -0.00022211647621176323, 0.0013387206066921965, 3.656592483348223e-005, -0.00016545679579108483, -5.3964831793152419e-006, 1.0797982104319795e-005} }; static @type@ sym17_@type@[][34] = { {4.297343327345983e-006, 2.7801266938414138e-006, -6.2937025975541919e-005, -1.3506383399901165e-005, 0.0004759963802638669, -0.00013864230268045499, -0.0027416759756816018, 0.0008567700701915741, 0.010482366933031529, -0.0048192128031761478, -0.033291383492359328, 0.017903952214341119, 0.10475461484223211, 0.0172711782105185, -0.11856693261143636, 0.14239835041467819, 0.65071662920454565, 0.68148899534492502, 0.18053958458111286, -0.15507600534974825, -0.086070874720733381, 0.016158808725919346, -0.0072616347509287674, -0.01803889724191924, 0.0099529825235095976, 0.012396988366648726, -0.0019054076898526659, -0.0039323252797979023, 5.8400428694052584e-005, 0.0007198270642148971, 2.5207933140828779e-005, -7.6071244056051285e-005, -2.4527163425832999e-006, 3.7912531943321266e-006}, {-3.7912531943321266e-006, -2.4527163425832999e-006, 7.6071244056051285e-005, 2.5207933140828779e-005, -0.0007198270642148971, 5.8400428694052584e-005, 0.0039323252797979023, -0.0019054076898526659, -0.012396988366648726, 0.0099529825235095976, 0.01803889724191924, -0.0072616347509287674, -0.016158808725919346, -0.086070874720733381, 0.15507600534974825, 0.18053958458111286, -0.68148899534492502, 0.65071662920454565, -0.14239835041467819, -0.11856693261143636, -0.0172711782105185, 0.10475461484223211, -0.017903952214341119, -0.033291383492359328, 0.0048192128031761478, 0.010482366933031529, -0.0008567700701915741, -0.0027416759756816018, 0.00013864230268045499, 0.0004759963802638669, 1.3506383399901165e-005, -6.2937025975541919e-005, -2.7801266938414138e-006, 4.297343327345983e-006}, {3.7912531943321266e-006, -2.4527163425832999e-006, -7.6071244056051285e-005, 2.5207933140828779e-005, 0.0007198270642148971, 5.8400428694052584e-005, -0.0039323252797979023, -0.0019054076898526659, 0.012396988366648726, 0.0099529825235095976, -0.01803889724191924, -0.0072616347509287674, 0.016158808725919346, -0.086070874720733381, -0.15507600534974825, 0.18053958458111286, 0.68148899534492502, 0.65071662920454565, 0.14239835041467819, -0.11856693261143636, 0.0172711782105185, 0.10475461484223211, 0.017903952214341119, -0.033291383492359328, -0.0048192128031761478, 0.010482366933031529, 0.0008567700701915741, -0.0027416759756816018, -0.00013864230268045499, 0.0004759963802638669, -1.3506383399901165e-005, -6.2937025975541919e-005, 2.7801266938414138e-006, 4.297343327345983e-006}, {4.297343327345983e-006, -2.7801266938414138e-006, -6.2937025975541919e-005, 1.3506383399901165e-005, 0.0004759963802638669, 0.00013864230268045499, -0.0027416759756816018, -0.0008567700701915741, 0.010482366933031529, 0.0048192128031761478, -0.033291383492359328, -0.017903952214341119, 0.10475461484223211, -0.0172711782105185, -0.11856693261143636, -0.14239835041467819, 0.65071662920454565, -0.68148899534492502, 0.18053958458111286, 0.15507600534974825, -0.086070874720733381, -0.016158808725919346, -0.0072616347509287674, 0.01803889724191924, 0.0099529825235095976, -0.012396988366648726, -0.0019054076898526659, 0.0039323252797979023, 5.8400428694052584e-005, -0.0007198270642148971, 2.5207933140828779e-005, 7.6071244056051285e-005, -2.4527163425832999e-006, -3.7912531943321266e-006} }; static @type@ sym18_@type@[][36] = { {2.6126125564836423e-006, 1.354915761832114e-006, -4.5246757874949856e-005, -1.4020992577726755e-005, 0.00039616840638254753, 7.0212734590362685e-005, -0.0023138718145060992, -0.00041152110923597756, 0.0095021643909623654, 0.0016429863972782159, -0.030325091089369604, -0.0050770851607570529, 0.084219929970386548, 0.033995667103947358, -0.15993814866932407, -0.052029158983952786, 0.47396905989393956, 0.75362914010179283, 0.40148386057061813, -0.032480573290138676, -0.073799207290607169, 0.028529597039037808, 0.0062779445543116943, -0.031712684731814537, -0.0032607442000749834, 0.015012356344250213, 0.0010877847895956929, -0.0052397896830266083, -0.00018877623940755607, 0.0014280863270832796, 4.7416145183736671e-005, -0.00026583011024241041, -9.858816030140058e-006, 2.9557437620930811e-005, 7.8472980558317646e-007, -1.5131530692371587e-006}, {1.5131530692371587e-006, 7.8472980558317646e-007, -2.9557437620930811e-005, -9.858816030140058e-006, 0.00026583011024241041, 4.7416145183736671e-005, -0.0014280863270832796, -0.00018877623940755607, 0.0052397896830266083, 0.0010877847895956929, -0.015012356344250213, -0.0032607442000749834, 0.031712684731814537, 0.0062779445543116943, -0.028529597039037808, -0.073799207290607169, 0.032480573290138676, 0.40148386057061813, -0.75362914010179283, 0.47396905989393956, 0.052029158983952786, -0.15993814866932407, -0.033995667103947358, 0.084219929970386548, 0.0050770851607570529, -0.030325091089369604, -0.0016429863972782159, 0.0095021643909623654, 0.00041152110923597756, -0.0023138718145060992, -7.0212734590362685e-005, 0.00039616840638254753, 1.4020992577726755e-005, -4.5246757874949856e-005, -1.354915761832114e-006, 2.6126125564836423e-006}, {-1.5131530692371587e-006, 7.8472980558317646e-007, 2.9557437620930811e-005, -9.858816030140058e-006, -0.00026583011024241041, 4.7416145183736671e-005, 0.0014280863270832796, -0.00018877623940755607, -0.0052397896830266083, 0.0010877847895956929, 0.015012356344250213, -0.0032607442000749834, -0.031712684731814537, 0.0062779445543116943, 0.028529597039037808, -0.073799207290607169, -0.032480573290138676, 0.40148386057061813, 0.75362914010179283, 0.47396905989393956, -0.052029158983952786, -0.15993814866932407, 0.033995667103947358, 0.084219929970386548, -0.0050770851607570529, -0.030325091089369604, 0.0016429863972782159, 0.0095021643909623654, -0.00041152110923597756, -0.0023138718145060992, 7.0212734590362685e-005, 0.00039616840638254753, -1.4020992577726755e-005, -4.5246757874949856e-005, 1.354915761832114e-006, 2.6126125564836423e-006}, {2.6126125564836423e-006, -1.354915761832114e-006, -4.5246757874949856e-005, 1.4020992577726755e-005, 0.00039616840638254753, -7.0212734590362685e-005, -0.0023138718145060992, 0.00041152110923597756, 0.0095021643909623654, -0.0016429863972782159, -0.030325091089369604, 0.0050770851607570529, 0.084219929970386548, -0.033995667103947358, -0.15993814866932407, 0.052029158983952786, 0.47396905989393956, -0.75362914010179283, 0.40148386057061813, 0.032480573290138676, -0.073799207290607169, -0.028529597039037808, 0.0062779445543116943, 0.031712684731814537, -0.0032607442000749834, -0.015012356344250213, 0.0010877847895956929, 0.0052397896830266083, -0.00018877623940755607, -0.0014280863270832796, 4.7416145183736671e-005, 0.00026583011024241041, -9.858816030140058e-006, -2.9557437620930811e-005, 7.8472980558317646e-007, 1.5131530692371587e-006} }; static @type@ sym19_@type@[][38] = { {5.4877327682158382e-007, -6.4636513033459633e-007, -1.1880518269823984e-005, 8.8733121737292863e-006, 0.0001155392333357879, -4.6120396002105868e-005, -0.00063576451500433403, 0.00015915804768084938, 0.0021214250281823303, -0.0011607032572062486, -0.005122205002583014, 0.0079684383206133063, 0.015797439295674631, -0.022651993378245951, -0.046635983534938946, 0.0070155738571741596, 0.0089545911730436242, -0.067525058040294086, 0.10902582508127781, 0.57814494533860505, 0.71955552571639425, 0.25826616923728363, -0.17659686625203097, -0.11624173010739675, 0.093630843415897141, 0.084072676279245043, -0.016908234861345205, -0.027709896931311252, 0.0043193518748949689, 0.0082622369555282547, -0.00061792232779831076, -0.0017049602611649971, 0.00012930767650701415, 0.00027621877685734072, -1.6821387029373716e-005, -2.8151138661550245e-005, 2.0623170632395688e-006, 1.7509367995348687e-006}, {-1.7509367995348687e-006, 2.0623170632395688e-006, 2.8151138661550245e-005, -1.6821387029373716e-005, -0.00027621877685734072, 0.00012930767650701415, 0.0017049602611649971, -0.00061792232779831076, -0.0082622369555282547, 0.0043193518748949689, 0.027709896931311252, -0.016908234861345205, -0.084072676279245043, 0.093630843415897141, 0.11624173010739675, -0.17659686625203097, -0.25826616923728363, 0.71955552571639425, -0.57814494533860505, 0.10902582508127781, 0.067525058040294086, 0.0089545911730436242, -0.0070155738571741596, -0.046635983534938946, 0.022651993378245951, 0.015797439295674631, -0.0079684383206133063, -0.005122205002583014, 0.0011607032572062486, 0.0021214250281823303, -0.00015915804768084938, -0.00063576451500433403, 4.6120396002105868e-005, 0.0001155392333357879, -8.8733121737292863e-006, -1.1880518269823984e-005, 6.4636513033459633e-007, 5.4877327682158382e-007}, {1.7509367995348687e-006, 2.0623170632395688e-006, -2.8151138661550245e-005, -1.6821387029373716e-005, 0.00027621877685734072, 0.00012930767650701415, -0.0017049602611649971, -0.00061792232779831076, 0.0082622369555282547, 0.0043193518748949689, -0.027709896931311252, -0.016908234861345205, 0.084072676279245043, 0.093630843415897141, -0.11624173010739675, -0.17659686625203097, 0.25826616923728363, 0.71955552571639425, 0.57814494533860505, 0.10902582508127781, -0.067525058040294086, 0.0089545911730436242, 0.0070155738571741596, -0.046635983534938946, -0.022651993378245951, 0.015797439295674631, 0.0079684383206133063, -0.005122205002583014, -0.0011607032572062486, 0.0021214250281823303, 0.00015915804768084938, -0.00063576451500433403, -4.6120396002105868e-005, 0.0001155392333357879, 8.8733121737292863e-006, -1.1880518269823984e-005, -6.4636513033459633e-007, 5.4877327682158382e-007}, {5.4877327682158382e-007, 6.4636513033459633e-007, -1.1880518269823984e-005, -8.8733121737292863e-006, 0.0001155392333357879, 4.6120396002105868e-005, -0.00063576451500433403, -0.00015915804768084938, 0.0021214250281823303, 0.0011607032572062486, -0.005122205002583014, -0.0079684383206133063, 0.015797439295674631, 0.022651993378245951, -0.046635983534938946, -0.0070155738571741596, 0.0089545911730436242, 0.067525058040294086, 0.10902582508127781, -0.57814494533860505, 0.71955552571639425, -0.25826616923728363, -0.17659686625203097, 0.11624173010739675, 0.093630843415897141, -0.084072676279245043, -0.016908234861345205, 0.027709896931311252, 0.0043193518748949689, -0.0082622369555282547, -0.00061792232779831076, 0.0017049602611649971, 0.00012930767650701415, -0.00027621877685734072, -1.6821387029373716e-005, 2.8151138661550245e-005, 2.0623170632395688e-006, -1.7509367995348687e-006} }; static @type@ sym20_@type@[][40] = { {3.695537474835221e-007, -1.9015675890554106e-007, -7.919361411976999e-006, 3.0256660627369661e-006, 7.992967835772481e-005, -1.928412300645204e-005, -0.00049473109156726548, 7.2159911880740349e-005, 0.0020889947081901982, -0.0003052628317957281, -0.0066065857990888609, 0.0014230873594621453, 0.017004049023390339, -0.0033138573836233591, -0.031629437144957966, 0.0081232283560096815, 0.025579349509413946, -0.078994344928398158, -0.029819368880333728, 0.40583144434845059, 0.75116272842273002, 0.47199147510148703, -0.051088342921067398, -0.16057829841525254, 0.036250951653933078, 0.088919668028199561, -0.0068437019650692274, -0.035373336756604236, 0.0019385970672402002, 0.012157040948785737, -0.0006111263857992088, -0.0034716478028440734, 0.00012544091723067259, 0.00074761085978205719, -2.6615550335516086e-005, -0.00011739133516291466, 4.5254222091516362e-006, 1.22872527779612e-005, -3.2567026420174407e-007, -6.3291290447763946e-007}, {6.3291290447763946e-007, -3.2567026420174407e-007, -1.22872527779612e-005, 4.5254222091516362e-006, 0.00011739133516291466, -2.6615550335516086e-005, -0.00074761085978205719, 0.00012544091723067259, 0.0034716478028440734, -0.0006111263857992088, -0.012157040948785737, 0.0019385970672402002, 0.035373336756604236, -0.0068437019650692274, -0.088919668028199561, 0.036250951653933078, 0.16057829841525254, -0.051088342921067398, -0.47199147510148703, 0.75116272842273002, -0.40583144434845059, -0.029819368880333728, 0.078994344928398158, 0.025579349509413946, -0.0081232283560096815, -0.031629437144957966, 0.0033138573836233591, 0.017004049023390339, -0.0014230873594621453, -0.0066065857990888609, 0.0003052628317957281, 0.0020889947081901982, -7.2159911880740349e-005, -0.00049473109156726548, 1.928412300645204e-005, 7.992967835772481e-005, -3.0256660627369661e-006, -7.919361411976999e-006, 1.9015675890554106e-007, 3.695537474835221e-007}, {-6.3291290447763946e-007, -3.2567026420174407e-007, 1.22872527779612e-005, 4.5254222091516362e-006, -0.00011739133516291466, -2.6615550335516086e-005, 0.00074761085978205719, 0.00012544091723067259, -0.0034716478028440734, -0.0006111263857992088, 0.012157040948785737, 0.0019385970672402002, -0.035373336756604236, -0.0068437019650692274, 0.088919668028199561, 0.036250951653933078, -0.16057829841525254, -0.051088342921067398, 0.47199147510148703, 0.75116272842273002, 0.40583144434845059, -0.029819368880333728, -0.078994344928398158, 0.025579349509413946, 0.0081232283560096815, -0.031629437144957966, -0.0033138573836233591, 0.017004049023390339, 0.0014230873594621453, -0.0066065857990888609, -0.0003052628317957281, 0.0020889947081901982, 7.2159911880740349e-005, -0.00049473109156726548, -1.928412300645204e-005, 7.992967835772481e-005, 3.0256660627369661e-006, -7.919361411976999e-006, -1.9015675890554106e-007, 3.695537474835221e-007}, {3.695537474835221e-007, 1.9015675890554106e-007, -7.919361411976999e-006, -3.0256660627369661e-006, 7.992967835772481e-005, 1.928412300645204e-005, -0.00049473109156726548, -7.2159911880740349e-005, 0.0020889947081901982, 0.0003052628317957281, -0.0066065857990888609, -0.0014230873594621453, 0.017004049023390339, 0.0033138573836233591, -0.031629437144957966, -0.0081232283560096815, 0.025579349509413946, 0.078994344928398158, -0.029819368880333728, -0.40583144434845059, 0.75116272842273002, -0.47199147510148703, -0.051088342921067398, 0.16057829841525254, 0.036250951653933078, -0.088919668028199561, -0.0068437019650692274, 0.035373336756604236, 0.0019385970672402002, -0.012157040948785737, -0.0006111263857992088, 0.0034716478028440734, 0.00012544091723067259, -0.00074761085978205719, -2.6615550335516086e-005, 0.00011739133516291466, 4.5254222091516362e-006, -1.22872527779612e-005, -3.2567026420174407e-007, 6.3291290447763946e-007} }; static @type@ coif1_@type@[][6] = { {-0.01565572813546454, -0.072732619512853897, 0.38486484686420286, 0.85257202021225542, 0.33789766245780922, -0.072732619512853897}, {0.072732619512853897, 0.33789766245780922, -0.85257202021225542, 0.38486484686420286, 0.072732619512853897, -0.01565572813546454}, {-0.072732619512853897, 0.33789766245780922, 0.85257202021225542, 0.38486484686420286, -0.072732619512853897, -0.01565572813546454}, {-0.01565572813546454, 0.072732619512853897, 0.38486484686420286, -0.85257202021225542, 0.33789766245780922, 0.072732619512853897} }; static @type@ coif2_@type@[][12] = { {-0.00072054944536451221, -0.0018232088707029932, 0.0056114348193944995, 0.023680171946334084, -0.059434418646456898, -0.076488599078306393, 0.41700518442169254, 0.81272363544554227, 0.38611006682116222, -0.067372554721963018, -0.041464936781759151, 0.016387336463522112}, {-0.016387336463522112, -0.041464936781759151, 0.067372554721963018, 0.38611006682116222, -0.81272363544554227, 0.41700518442169254, 0.076488599078306393, -0.059434418646456898, -0.023680171946334084, 0.0056114348193944995, 0.0018232088707029932, -0.00072054944536451221}, {0.016387336463522112, -0.041464936781759151, -0.067372554721963018, 0.38611006682116222, 0.81272363544554227, 0.41700518442169254, -0.076488599078306393, -0.059434418646456898, 0.023680171946334084, 0.0056114348193944995, -0.0018232088707029932, -0.00072054944536451221}, {-0.00072054944536451221, 0.0018232088707029932, 0.0056114348193944995, -0.023680171946334084, -0.059434418646456898, 0.076488599078306393, 0.41700518442169254, -0.81272363544554227, 0.38611006682116222, 0.067372554721963018, -0.041464936781759151, -0.016387336463522112} }; static @type@ coif3_@type@[][18] = { {-3.4599772836212559e-005, -7.0983303138141252e-005, 0.00046621696011288631, 0.0011175187708906016, -0.0025745176887502236, -0.0090079761366615805, 0.015880544863615904, 0.034555027573061628, -0.082301927106885983, -0.071799821619312018, 0.42848347637761874, 0.79377722262562056, 0.4051769024096169, -0.061123390002672869, -0.0657719112818555, 0.023452696141836267, 0.0077825964273254182, -0.0037935128644910141}, {0.0037935128644910141, 0.0077825964273254182, -0.023452696141836267, -0.0657719112818555, 0.061123390002672869, 0.4051769024096169, -0.79377722262562056, 0.42848347637761874, 0.071799821619312018, -0.082301927106885983, -0.034555027573061628, 0.015880544863615904, 0.0090079761366615805, -0.0025745176887502236, -0.0011175187708906016, 0.00046621696011288631, 7.0983303138141252e-005, -3.4599772836212559e-005}, {-0.0037935128644910141, 0.0077825964273254182, 0.023452696141836267, -0.0657719112818555, -0.061123390002672869, 0.4051769024096169, 0.79377722262562056, 0.42848347637761874, -0.071799821619312018, -0.082301927106885983, 0.034555027573061628, 0.015880544863615904, -0.0090079761366615805, -0.0025745176887502236, 0.0011175187708906016, 0.00046621696011288631, -7.0983303138141252e-005, -3.4599772836212559e-005}, {-3.4599772836212559e-005, 7.0983303138141252e-005, 0.00046621696011288631, -0.0011175187708906016, -0.0025745176887502236, 0.0090079761366615805, 0.015880544863615904, -0.034555027573061628, -0.082301927106885983, 0.071799821619312018, 0.42848347637761874, -0.79377722262562056, 0.4051769024096169, 0.061123390002672869, -0.0657719112818555, -0.023452696141836267, 0.0077825964273254182, 0.0037935128644910141} }; static @type@ coif4_@type@[][24] = { {-1.7849850030882614e-006, -3.2596802368833675e-006, 3.1229875865345646e-005, 6.2339034461007128e-005, -0.00025997455248771324, -0.00058902075624433831, 0.0012665619292989445, 0.0037514361572784571, -0.0056582866866107199, -0.015211731527946259, 0.025082261844864097, 0.039334427123337491, -0.096220442033987982, -0.066627474263425038, 0.4343860564914685, 0.78223893092049901, 0.41530840703043026, -0.056077313316754807, -0.081266699680878754, 0.026682300156053072, 0.016068943964776348, -0.0073461663276420935, -0.0016294920126017326, 0.00089231366858231456}, {-0.00089231366858231456, -0.0016294920126017326, 0.0073461663276420935, 0.016068943964776348, -0.026682300156053072, -0.081266699680878754, 0.056077313316754807, 0.41530840703043026, -0.78223893092049901, 0.4343860564914685, 0.066627474263425038, -0.096220442033987982, -0.039334427123337491, 0.025082261844864097, 0.015211731527946259, -0.0056582866866107199, -0.0037514361572784571, 0.0012665619292989445, 0.00058902075624433831, -0.00025997455248771324, -6.2339034461007128e-005, 3.1229875865345646e-005, 3.2596802368833675e-006, -1.7849850030882614e-006}, {0.00089231366858231456, -0.0016294920126017326, -0.0073461663276420935, 0.016068943964776348, 0.026682300156053072, -0.081266699680878754, -0.056077313316754807, 0.41530840703043026, 0.78223893092049901, 0.4343860564914685, -0.066627474263425038, -0.096220442033987982, 0.039334427123337491, 0.025082261844864097, -0.015211731527946259, -0.0056582866866107199, 0.0037514361572784571, 0.0012665619292989445, -0.00058902075624433831, -0.00025997455248771324, 6.2339034461007128e-005, 3.1229875865345646e-005, -3.2596802368833675e-006, -1.7849850030882614e-006}, {-1.7849850030882614e-006, 3.2596802368833675e-006, 3.1229875865345646e-005, -6.2339034461007128e-005, -0.00025997455248771324, 0.00058902075624433831, 0.0012665619292989445, -0.0037514361572784571, -0.0056582866866107199, 0.015211731527946259, 0.025082261844864097, -0.039334427123337491, -0.096220442033987982, 0.066627474263425038, 0.4343860564914685, -0.78223893092049901, 0.41530840703043026, 0.056077313316754807, -0.081266699680878754, -0.026682300156053072, 0.016068943964776348, 0.0073461663276420935, -0.0016294920126017326, -0.00089231366858231456} }; static @type@ coif5_@type@[][30] = { {-9.517657273819165e-008, -1.6744288576823017e-007, 2.0637618513646814e-006, 3.7346551751414047e-006, -2.1315026809955787e-005, -4.1340432272512511e-005, 0.00014054114970203437, 0.00030225958181306315, -0.00063813134304511142, -0.0016628637020130838, 0.0024333732126576722, 0.0067641854480530832, -0.0091642311624818458, -0.019761778942572639, 0.032683574267111833, 0.041289208750181702, -0.10557420870333893, -0.062035963962903569, 0.43799162617183712, 0.77428960365295618, 0.42156620669085149, -0.052043163176243773, -0.091920010559696244, 0.02816802897093635, 0.023408156785839195, -0.010131117519849788, -0.004159358781386048, 0.0021782363581090178, 0.00035858968789573785, -0.00021208083980379827}, {0.00021208083980379827, 0.00035858968789573785, -0.0021782363581090178, -0.004159358781386048, 0.010131117519849788, 0.023408156785839195, -0.02816802897093635, -0.091920010559696244, 0.052043163176243773, 0.42156620669085149, -0.77428960365295618, 0.43799162617183712, 0.062035963962903569, -0.10557420870333893, -0.041289208750181702, 0.032683574267111833, 0.019761778942572639, -0.0091642311624818458, -0.0067641854480530832, 0.0024333732126576722, 0.0016628637020130838, -0.00063813134304511142, -0.00030225958181306315, 0.00014054114970203437, 4.1340432272512511e-005, -2.1315026809955787e-005, -3.7346551751414047e-006, 2.0637618513646814e-006, 1.6744288576823017e-007, -9.517657273819165e-008}, {-0.00021208083980379827, 0.00035858968789573785, 0.0021782363581090178, -0.004159358781386048, -0.010131117519849788, 0.023408156785839195, 0.02816802897093635, -0.091920010559696244, -0.052043163176243773, 0.42156620669085149, 0.77428960365295618, 0.43799162617183712, -0.062035963962903569, -0.10557420870333893, 0.041289208750181702, 0.032683574267111833, -0.019761778942572639, -0.0091642311624818458, 0.0067641854480530832, 0.0024333732126576722, -0.0016628637020130838, -0.00063813134304511142, 0.00030225958181306315, 0.00014054114970203437, -4.1340432272512511e-005, -2.1315026809955787e-005, 3.7346551751414047e-006, 2.0637618513646814e-006, -1.6744288576823017e-007, -9.517657273819165e-008}, {-9.517657273819165e-008, 1.6744288576823017e-007, 2.0637618513646814e-006, -3.7346551751414047e-006, -2.1315026809955787e-005, 4.1340432272512511e-005, 0.00014054114970203437, -0.00030225958181306315, -0.00063813134304511142, 0.0016628637020130838, 0.0024333732126576722, -0.0067641854480530832, -0.0091642311624818458, 0.019761778942572639, 0.032683574267111833, -0.041289208750181702, -0.10557420870333893, 0.062035963962903569, 0.43799162617183712, -0.77428960365295618, 0.42156620669085149, 0.052043163176243773, -0.091920010559696244, -0.02816802897093635, 0.023408156785839195, 0.010131117519849788, -0.004159358781386048, -0.0021782363581090178, 0.00035858968789573785, 0.00021208083980379827} }; static @type@ bior1_1_@type@[][2] = { {0.70710678118654757, 0.70710678118654757}, {-0.70710678118654757, 0.70710678118654757}, {0.70710678118654757, 0.70710678118654757}, {0.70710678118654757, -0.70710678118654757} }; static @type@ bior1_3_@type@[][6] = { {-0.088388347648318447, 0.088388347648318447, 0.70710678118654757, 0.70710678118654757, 0.088388347648318447, -0.088388347648318447}, {0.0, 0.0, -0.70710678118654757, 0.70710678118654757, 0.0, 0.0}, {0.0, 0.0, 0.70710678118654757, 0.70710678118654757, 0.0, 0.0}, {-0.088388347648318447, -0.088388347648318447, 0.70710678118654757, -0.70710678118654757, 0.088388347648318447, 0.088388347648318447} }; static @type@ bior1_5_@type@[][10] = { {0.01657281518405971, -0.01657281518405971, -0.12153397801643787, 0.12153397801643787, 0.70710678118654757, 0.70710678118654757, 0.12153397801643787, -0.12153397801643787, -0.01657281518405971, 0.01657281518405971}, {0.0, 0.0, 0.0, 0.0, -0.70710678118654757, 0.70710678118654757, 0.0, 0.0, 0.0, 0.0}, {0.0, 0.0, 0.0, 0.0, 0.70710678118654757, 0.70710678118654757, 0.0, 0.0, 0.0, 0.0}, {0.01657281518405971, 0.01657281518405971, -0.12153397801643787, -0.12153397801643787, 0.70710678118654757, -0.70710678118654757, 0.12153397801643787, 0.12153397801643787, -0.01657281518405971, -0.01657281518405971} }; static @type@ bior2_2_@type@[][6] = { {0.0, -0.17677669529663689, 0.35355339059327379, 1.0606601717798214, 0.35355339059327379, -0.17677669529663689}, {0.0, 0.35355339059327379, -0.70710678118654757, 0.35355339059327379, 0.0, 0.0}, {0.0, 0.35355339059327379, 0.70710678118654757, 0.35355339059327379, 0.0, 0.0}, {0.0, 0.17677669529663689, 0.35355339059327379, -1.0606601717798214, 0.35355339059327379, 0.17677669529663689} }; static @type@ bior2_4_@type@[][10] = { {0.0, 0.033145630368119419, -0.066291260736238838, -0.17677669529663689, 0.4198446513295126, 0.99436891104358249, 0.4198446513295126, -0.17677669529663689, -0.066291260736238838, 0.033145630368119419}, {0.0, 0.0, 0.0, 0.35355339059327379, -0.70710678118654757, 0.35355339059327379, 0.0, 0.0, 0.0, 0.0}, {0.0, 0.0, 0.0, 0.35355339059327379, 0.70710678118654757, 0.35355339059327379, 0.0, 0.0, 0.0, 0.0}, {0.0, -0.033145630368119419, -0.066291260736238838, 0.17677669529663689, 0.4198446513295126, -0.99436891104358249, 0.4198446513295126, 0.17677669529663689, -0.066291260736238838, -0.033145630368119419} }; static @type@ bior2_6_@type@[][14] = { {0.0, -0.0069053396600248784, 0.013810679320049757, 0.046956309688169176, -0.10772329869638811, -0.16987135563661201, 0.44746600996961211, 0.96674755240348298, 0.44746600996961211, -0.16987135563661201, -0.10772329869638811, 0.046956309688169176, 0.013810679320049757, -0.0069053396600248784}, {0.0, 0.0, 0.0, 0.0, 0.0, 0.35355339059327379, -0.70710678118654757, 0.35355339059327379, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0}, {0.0, 0.0, 0.0, 0.0, 0.0, 0.35355339059327379, 0.70710678118654757, 0.35355339059327379, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0}, {0.0, 0.0069053396600248784, 0.013810679320049757, -0.046956309688169176, -0.10772329869638811, 0.16987135563661201, 0.44746600996961211, -0.96674755240348298, 0.44746600996961211, 0.16987135563661201, -0.10772329869638811, -0.046956309688169176, 0.013810679320049757, 0.0069053396600248784} }; static @type@ bior2_8_@type@[][18] = { {0.0, 0.0015105430506304422, -0.0030210861012608843, -0.012947511862546647, 0.028916109826354178, 0.052998481890690945, -0.13491307360773608, -0.16382918343409025, 0.46257144047591658, 0.95164212189717856, 0.46257144047591658, -0.16382918343409025, -0.13491307360773608, 0.052998481890690945, 0.028916109826354178, -0.012947511862546647, -0.0030210861012608843, 0.0015105430506304422}, {0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.35355339059327379, -0.70710678118654757, 0.35355339059327379, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0}, {0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.35355339059327379, 0.70710678118654757, 0.35355339059327379, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0}, {0.0, -0.0015105430506304422, -0.0030210861012608843, 0.012947511862546647, 0.028916109826354178, -0.052998481890690945, -0.13491307360773608, 0.16382918343409025, 0.46257144047591658, -0.95164212189717856, 0.46257144047591658, 0.16382918343409025, -0.13491307360773608, -0.052998481890690945, 0.028916109826354178, 0.012947511862546647, -0.0030210861012608843, -0.0015105430506304422} }; static @type@ bior3_1_@type@[][4] = { {-0.35355339059327379, 1.0606601717798214, 1.0606601717798214, -0.35355339059327379}, {-0.17677669529663689, 0.53033008588991071, -0.53033008588991071, 0.17677669529663689}, {0.17677669529663689, 0.53033008588991071, 0.53033008588991071, 0.17677669529663689}, {-0.35355339059327379, -1.0606601717798214, 1.0606601717798214, 0.35355339059327379} }; static @type@ bior3_3_@type@[][8] = { {0.066291260736238838, -0.19887378220871652, -0.15467960838455727, 0.99436891104358249, 0.99436891104358249, -0.15467960838455727, -0.19887378220871652, 0.066291260736238838}, {0.0, 0.0, -0.17677669529663689, 0.53033008588991071, -0.53033008588991071, 0.17677669529663689, 0.0, 0.0}, {0.0, 0.0, 0.17677669529663689, 0.53033008588991071, 0.53033008588991071, 0.17677669529663689, 0.0, 0.0}, {0.066291260736238838, 0.19887378220871652, -0.15467960838455727, -0.99436891104358249, 0.99436891104358249, 0.15467960838455727, -0.19887378220871652, -0.066291260736238838} }; static @type@ bior3_5_@type@[][12] = { {-0.013810679320049757, 0.041432037960149271, 0.052480581416189075, -0.26792717880896527, -0.071815532464258744, 0.96674755240348298, 0.96674755240348298, -0.071815532464258744, -0.26792717880896527, 0.052480581416189075, 0.041432037960149271, -0.013810679320049757}, {0.0, 0.0, 0.0, 0.0, -0.17677669529663689, 0.53033008588991071, -0.53033008588991071, 0.17677669529663689, 0.0, 0.0, 0.0, 0.0}, {0.0, 0.0, 0.0, 0.0, 0.17677669529663689, 0.53033008588991071, 0.53033008588991071, 0.17677669529663689, 0.0, 0.0, 0.0, 0.0}, {-0.013810679320049757, -0.041432037960149271, 0.052480581416189075, 0.26792717880896527, -0.071815532464258744, -0.96674755240348298, 0.96674755240348298, 0.071815532464258744, -0.26792717880896527, -0.052480581416189075, 0.041432037960149271, 0.013810679320049757} }; static @type@ bior3_7_@type@[][16] = { {0.0030210861012608843, -0.0090632583037826529, -0.016831765421310641, 0.074663985074019001, 0.031332978707362888, -0.301159125922835, -0.026499240945345472, 0.95164212189717856, 0.95164212189717856, -0.026499240945345472, -0.301159125922835, 0.031332978707362888, 0.074663985074019001, -0.016831765421310641, -0.0090632583037826529, 0.0030210861012608843}, {0.0, 0.0, 0.0, 0.0, 0.0, 0.0, -0.17677669529663689, 0.53033008588991071, -0.53033008588991071, 0.17677669529663689, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0}, {0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.17677669529663689, 0.53033008588991071, 0.53033008588991071, 0.17677669529663689, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0}, {0.0030210861012608843, 0.0090632583037826529, -0.016831765421310641, -0.074663985074019001, 0.031332978707362888, 0.301159125922835, -0.026499240945345472, -0.95164212189717856, 0.95164212189717856, 0.026499240945345472, -0.301159125922835, -0.031332978707362888, 0.074663985074019001, 0.016831765421310641, -0.0090632583037826529, -0.0030210861012608843} }; static @type@ bior3_9_@type@[][20] = { {-0.00067974437278369901, 0.0020392331183510968, 0.0050603192196119811, -0.020618912641105536, -0.014112787930175846, 0.09913478249423216, 0.012300136269419315, -0.32019196836077857, 0.0020500227115698858, 0.94212570067820678, 0.94212570067820678, 0.0020500227115698858, -0.32019196836077857, 0.012300136269419315, 0.09913478249423216, -0.014112787930175846, -0.020618912641105536, 0.0050603192196119811, 0.0020392331183510968, -0.00067974437278369901}, {0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, -0.17677669529663689, 0.53033008588991071, -0.53033008588991071, 0.17677669529663689, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0}, {0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.17677669529663689, 0.53033008588991071, 0.53033008588991071, 0.17677669529663689, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0}, {-0.00067974437278369901, -0.0020392331183510968, 0.0050603192196119811, 0.020618912641105536, -0.014112787930175846, -0.09913478249423216, 0.012300136269419315, 0.32019196836077857, 0.0020500227115698858, -0.94212570067820678, 0.94212570067820678, -0.0020500227115698858, -0.32019196836077857, -0.012300136269419315, 0.09913478249423216, 0.014112787930175846, -0.020618912641105536, -0.0050603192196119811, 0.0020392331183510968, 0.00067974437278369901} }; static @type@ bior4_4_@type@[][10] = { {0.0, 0.03782845550726404, -0.023849465019556843, -0.11062440441843718, 0.37740285561283066, 0.85269867900889385, 0.37740285561283066, -0.11062440441843718, -0.023849465019556843, 0.03782845550726404}, {0.0, -0.064538882628697058, 0.040689417609164058, 0.41809227322161724, -0.7884856164055829, 0.41809227322161724, 0.040689417609164058, -0.064538882628697058, 0.0, 0.0}, {0.0, -0.064538882628697058, -0.040689417609164058, 0.41809227322161724, 0.7884856164055829, 0.41809227322161724, -0.040689417609164058, -0.064538882628697058, 0.0, 0.0}, {0.0, -0.03782845550726404, -0.023849465019556843, 0.11062440441843718, 0.37740285561283066, -0.85269867900889385, 0.37740285561283066, 0.11062440441843718, -0.023849465019556843, -0.03782845550726404} }; static @type@ bior5_5_@type@[][12] = { {0.0, 0.0, 0.03968708834740544, 0.0079481086372403219, -0.054463788468236907, 0.34560528195603346, 0.73666018142821055, 0.34560528195603346, -0.054463788468236907, 0.0079481086372403219, 0.03968708834740544, 0.0}, {-0.013456709459118716, -0.0026949668801115071, 0.13670658466432914, -0.093504697400938863, -0.47680326579848425, 0.89950610974864842, -0.47680326579848425, -0.093504697400938863, 0.13670658466432914, -0.0026949668801115071, -0.013456709459118716, 0.0}, {0.013456709459118716, -0.0026949668801115071, -0.13670658466432914, -0.093504697400938863, 0.47680326579848425, 0.89950610974864842, 0.47680326579848425, -0.093504697400938863, -0.13670658466432914, -0.0026949668801115071, 0.013456709459118716, 0.0}, {0.0, 0.0, 0.03968708834740544, -0.0079481086372403219, -0.054463788468236907, -0.34560528195603346, 0.73666018142821055, -0.34560528195603346, -0.054463788468236907, -0.0079481086372403219, 0.03968708834740544, 0.0} }; static @type@ bior6_8_@type@[][18] = { {0.0, 0.0019088317364812906, -0.0019142861290887667, -0.016990639867602342, 0.01193456527972926, 0.04973290349094079, -0.077263173167204144, -0.09405920349573646, 0.42079628460982682, 0.82592299745840225, 0.42079628460982682, -0.09405920349573646, -0.077263173167204144, 0.04973290349094079, 0.01193456527972926, -0.016990639867602342, -0.0019142861290887667, 0.0019088317364812906}, {0.0, 0.0, 0.0, 0.014426282505624435, -0.014467504896790148, -0.078722001062628819, 0.040367979030339923, 0.41784910915027457, -0.75890772945365415, 0.41784910915027457, 0.040367979030339923, -0.078722001062628819, -0.014467504896790148, 0.014426282505624435, 0.0, 0.0, 0.0, 0.0}, {0.0, 0.0, 0.0, 0.014426282505624435, 0.014467504896790148, -0.078722001062628819, -0.040367979030339923, 0.41784910915027457, 0.75890772945365415, 0.41784910915027457, -0.040367979030339923, -0.078722001062628819, 0.014467504896790148, 0.014426282505624435, 0.0, 0.0, 0.0, 0.0}, {0.0, -0.0019088317364812906, -0.0019142861290887667, 0.016990639867602342, 0.01193456527972926, -0.04973290349094079, -0.077263173167204144, 0.09405920349573646, 0.42079628460982682, -0.82592299745840225, 0.42079628460982682, 0.09405920349573646, -0.077263173167204144, -0.04973290349094079, 0.01193456527972926, 0.016990639867602342, -0.0019142861290887667, -0.0019088317364812906} }; static @type@ dmey_@type@[][62] = { {0.0, -1.0099999569414229e-012, 8.519459636796214e-009, -1.111944952595278e-008, -1.0798819539621958e-008, 6.0669757413511352e-008, -1.0866516536735883e-007, 8.2006806503864813e-008, 1.1783004497663934e-007, -5.5063405652522782e-007, 1.1307947017916706e-006, -1.4895492164971559e-006, 7.367572885903746e-007, 3.2054419133447798e-006, -1.6312699734552807e-005, 6.5543059305751491e-005, -0.00060115023435160925, -0.002704672124643725, 0.0022025341009110021, 0.006045814097323304, -0.0063877183184971563, -0.011061496392513451, 0.015270015130934803, 0.017423434103729693, -0.032130793990211758, -0.024348745906078023, 0.063739024322801596, 0.030655091960824263, -0.13284520043622938, -0.035087555656258346, 0.44459300275757724, 0.74458559231880628, 0.44459300275757724, -0.035087555656258346, -0.13284520043622938, 0.030655091960824263, 0.063739024322801596, -0.024348745906078023, -0.032130793990211758, 0.017423434103729693, 0.015270015130934803, -0.011061496392513451, -0.0063877183184971563, 0.006045814097323304, 0.0022025341009110021, -0.002704672124643725, -0.00060115023435160925, 6.5543059305751491e-005, -1.6312699734552807e-005, 3.2054419133447798e-006, 7.367572885903746e-007, -1.4895492164971559e-006, 1.1307947017916706e-006, -5.5063405652522782e-007, 1.1783004497663934e-007, 8.2006806503864813e-008, -1.0866516536735883e-007, 6.0669757413511352e-008, -1.0798819539621958e-008, -1.111944952595278e-008, 8.519459636796214e-009, -1.0099999569414229e-012}, {1.0099999569414229e-012, 8.519459636796214e-009, 1.111944952595278e-008, -1.0798819539621958e-008, -6.0669757413511352e-008, -1.0866516536735883e-007, -8.2006806503864813e-008, 1.1783004497663934e-007, 5.5063405652522782e-007, 1.1307947017916706e-006, 1.4895492164971559e-006, 7.367572885903746e-007, -3.2054419133447798e-006, -1.6312699734552807e-005, -6.5543059305751491e-005, -0.00060115023435160925, 0.002704672124643725, 0.0022025341009110021, -0.006045814097323304, -0.0063877183184971563, 0.011061496392513451, 0.015270015130934803, -0.017423434103729693, -0.032130793990211758, 0.024348745906078023, 0.063739024322801596, -0.030655091960824263, -0.13284520043622938, 0.035087555656258346, 0.44459300275757724, -0.74458559231880628, 0.44459300275757724, 0.035087555656258346, -0.13284520043622938, -0.030655091960824263, 0.063739024322801596, 0.024348745906078023, -0.032130793990211758, -0.017423434103729693, 0.015270015130934803, 0.011061496392513451, -0.0063877183184971563, -0.006045814097323304, 0.0022025341009110021, 0.002704672124643725, -0.00060115023435160925, -6.5543059305751491e-005, -1.6312699734552807e-005, -3.2054419133447798e-006, 7.367572885903746e-007, 1.4895492164971559e-006, 1.1307947017916706e-006, 5.5063405652522782e-007, 1.1783004497663934e-007, 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0.030655091960824263, 0.063739024322801596, -0.024348745906078023, -0.032130793990211758, 0.017423434103729693, 0.015270015130934803, -0.011061496392513451, -0.0063877183184971563, 0.006045814097323304, 0.0022025341009110021, -0.002704672124643725, -0.00060115023435160925, 6.5543059305751491e-005, -1.6312699734552807e-005, 3.2054419133447798e-006, 7.367572885903746e-007, -1.4895492164971559e-006, 1.1307947017916706e-006, -5.5063405652522782e-007, 1.1783004497663934e-007, 8.2006806503864813e-008, -1.0866516536735883e-007, 6.0669757413511352e-008, -1.0798819539621958e-008, -1.111944952595278e-008, 8.519459636796214e-009, -1.0099999569414229e-012, 0.0}, {0.0, 1.0099999569414229e-012, 8.519459636796214e-009, 1.111944952595278e-008, -1.0798819539621958e-008, -6.0669757413511352e-008, -1.0866516536735883e-007, -8.2006806503864813e-008, 1.1783004497663934e-007, 5.5063405652522782e-007, 1.1307947017916706e-006, 1.4895492164971559e-006, 7.367572885903746e-007, -3.2054419133447798e-006, -1.6312699734552807e-005, -6.5543059305751491e-005, -0.00060115023435160925, 0.002704672124643725, 0.0022025341009110021, -0.006045814097323304, -0.0063877183184971563, 0.011061496392513451, 0.015270015130934803, -0.017423434103729693, -0.032130793990211758, 0.024348745906078023, 0.063739024322801596, -0.030655091960824263, -0.13284520043622938, 0.035087555656258346, 0.44459300275757724, -0.74458559231880628, 0.44459300275757724, 0.035087555656258346, -0.13284520043622938, -0.030655091960824263, 0.063739024322801596, 0.024348745906078023, -0.032130793990211758, -0.017423434103729693, 0.015270015130934803, 0.011061496392513451, -0.0063877183184971563, -0.006045814097323304, 0.0022025341009110021, 0.002704672124643725, -0.00060115023435160925, -6.5543059305751491e-005, -1.6312699734552807e-005, -3.2054419133447798e-006, 7.367572885903746e-007, 1.4895492164971559e-006, 1.1307947017916706e-006, 5.5063405652522782e-007, 1.1783004497663934e-007, -8.2006806503864813e-008, -1.0866516536735883e-007, -6.0669757413511352e-008, -1.0798819539621958e-008, 1.111944952595278e-008, 8.519459636796214e-009, 1.0099999569414229e-012} }; /**end repeat**/ #endif PyWavelets-0.3.0/pywt/src/wt.h.src0000664000175000017500000000351012556460247020524 0ustar rgommersrgommers00000000000000/* Copyright (c) 2006-2012 Filip Wasilewski */ /* See COPYING for license details. */ /* Wavelet transforms using convolution functions defined in convolution.h */ #ifndef _WT_H_ #define _WT_H_ #include #include #include "common.h" #include "convolution.h" #include "wavelets.h" /* _a suffix - wavelet transform approximations */ /* _d suffix - wavelet transform details */ /**begin repeat * #type = double, float# */ /* Single level decomposition */ int @type@_dec_a(@type@ input[], index_t input_len, Wavelet* wavelet, @type@ output[], index_t output_len, MODE mode); int @type@_dec_d(@type@ input[], index_t input_len, Wavelet* wavelet, @type@ output[], index_t output_len, MODE mode); /* Single level reconstruction */ int @type@_rec_a(@type@ coeffs_a[], index_t coeffs_len, Wavelet* wavelet, @type@ output[], index_t output_len); int @type@_rec_d(@type@ coeffs_d[], index_t coeffs_len, Wavelet* wavelet, @type@ output[], index_t output_len); /* Single level IDWT reconstruction */ int @type@_idwt(@type@ coeffs_a[], index_t coeffs_a_len, @type@ coeffs_d[], index_t coeffs_d_len, Wavelet* wavelet, @type@ output[], index_t output_len, MODE mode, int fix_size_diff); /* SWT decomposition at given level */ int @type@_swt_a(@type@ input[], index_t input_len, Wavelet* wavelet, @type@ output[], index_t output_len, int level); int @type@_swt_d(@type@ input[], index_t input_len, Wavelet* wavelet, @type@ output[], index_t output_len, int level); /**end repeat**/ #endif PyWavelets-0.3.0/pywt/src/wavelets.c.src0000664000175000017500000003501712556460247021726 0ustar rgommersrgommers00000000000000/* Copyright (c) 2006-2012 Filip Wasilewski */ /* See COPYING for license details. */ /* Allocating, setting properties and destroying wavelet structs */ #include "wavelets.h" #include "wavelets_coeffs.h" Wavelet* wavelet(char name, int order) { Wavelet *w, *wtmp; index_t i; /* Haar wavelet */ if(name == 'h' || name == 'H'){ /* the same as db1 */ w = wavelet('d', 1); w->family_name = "Haar"; w->short_name = "haar"; return w; /* Reverse biorthogonal wavelets family */ } else if (name == 'r' || name == 'R') { /* rbio is like bior, only with switched filters */ wtmp = wavelet('b', order); w = copy_wavelet(wtmp); if(w == NULL) return NULL; w->dec_len = wtmp->rec_len; w->rec_len = wtmp->dec_len; for(i = 0; i < w->rec_len; ++i){ /**begin repeat * #type = double, float# */ w->rec_lo_@type@[i] = wtmp->dec_lo_@type@[wtmp->dec_len-1-i]; w->rec_hi_@type@[i] = wtmp->dec_hi_@type@[wtmp->dec_len-1-i]; /**end repeat**/ } for(i = 0; i < w->dec_len; ++i){ /**begin repeat * #type = double, float# */ w->dec_hi_@type@[i] = wtmp->rec_hi_@type@[wtmp->rec_len-1-i]; w->dec_lo_@type@[i] = wtmp->rec_lo_@type@[wtmp->rec_len-1-i]; /**end repeat**/ } w->vanishing_moments_psi = order / 10; /* 1st digit */ w->vanishing_moments_phi = -1; w->family_name = "Reverse biorthogonal"; w->short_name = "rbio"; free_wavelet(wtmp); return w; } w = wtmalloc(sizeof(Wavelet)); if(w == NULL) return NULL; w->_builtin = 1; switch(name){ /* Daubechies wavelets family */ case 'd': case 'D': w->dec_len = w->rec_len = 2*order; w->vanishing_moments_psi = order; w->vanishing_moments_phi = 0; w->support_width = 2*order - 1; w->orthogonal = 1; w->biorthogonal = 1; w->symmetry = ASYMMETRIC; w->compact_support = 1; w->family_name = "Daubechies"; w->short_name = "db"; switch (order) { /**begin repeat * #order = 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20# */ case @order@: /**begin repeat1 * #type = double, float# */ w->dec_lo_@type@ = db@order@_@type@[0]; w->dec_hi_@type@ = db@order@_@type@[1]; w->rec_lo_@type@ = db@order@_@type@[2]; w->rec_hi_@type@ = db@order@_@type@[3]; /**end repeat1**/ break; /**end repeat**/ default: wtfree(w); return NULL; } break; /* Symlets wavelets family */ case 's': case 'S': w->dec_len = w->rec_len = order << 1; w->vanishing_moments_psi = order; w->vanishing_moments_phi = 0; w->support_width = 2*order - 1; w->orthogonal = 1; w->biorthogonal = 1; w->symmetry = NEAR_SYMMETRIC; w->compact_support = 1; w->family_name = "Symlets"; w->short_name = "sym"; switch (order) { /**begin repeat * #order = 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20# */ case @order@: /**begin repeat1 * #type = double, float# */ w->dec_lo_@type@ = sym@order@_@type@[0]; w->dec_hi_@type@ = sym@order@_@type@[1]; w->rec_lo_@type@ = sym@order@_@type@[2]; w->rec_hi_@type@ = sym@order@_@type@[3]; /**end repeat1**/ break; /**end repeat**/ default: wtfree(w); return NULL; } break; /* Coiflets wavelets family */ case 'c': case 'C': w->dec_len = w->rec_len = order * 6; w->vanishing_moments_psi = 2*order; w->vanishing_moments_phi = 2*order -1; w->support_width = 6*order - 1; w->orthogonal = 1; w->biorthogonal = 1; w->symmetry = NEAR_SYMMETRIC; w->compact_support = 1; w->family_name = "Coiflets"; w->short_name = "coif"; switch (order) { /**begin repeat * #order = 1, 2, 3, 4, 5# */ case @order@: /**begin repeat1 * #type = double, float# */ w->dec_lo_@type@ = coif@order@_@type@[0]; w->dec_hi_@type@ = coif@order@_@type@[1]; w->rec_lo_@type@ = coif@order@_@type@[2]; w->rec_hi_@type@ = coif@order@_@type@[3]; /**end repeat1**/ break; /**end repeat**/ default: wtfree(w); return NULL; } break; /* Biorthogonal wavelets family */ case 'b': case 'B': w->vanishing_moments_psi = order/10; w->vanishing_moments_phi = -1; w->support_width = -1; w->orthogonal = 0; w->biorthogonal = 1; w->symmetry = SYMMETRIC; w->compact_support = 1; w->family_name = "Biorthogonal"; w->short_name = "bior"; switch (order) { /**begin repeat * #M = 1, 3, 5# */ case 1@M@: /**begin repeat1 * #type = double, float# */ w->dec_lo_@type@ = bior1_@M@_@type@[0]; w->dec_hi_@type@ = bior1_@M@_@type@[1]; w->rec_lo_@type@ = bior1_@M@_@type@[2]; w->rec_hi_@type@ = bior1_@M@_@type@[3]; /**end repeat1**/ w->dec_len = w->rec_len = 2 * @M@; break; /**end repeat**/ /**begin repeat * #M = 2, 4, 6, 8# */ case 2@M@: /**begin repeat1 * #type = double, float# */ w->dec_lo_@type@ = bior2_@M@_@type@[0]; w->dec_hi_@type@ = bior2_@M@_@type@[1]; w->rec_lo_@type@ = bior2_@M@_@type@[2]; w->rec_hi_@type@ = bior2_@M@_@type@[3]; /**end repeat1**/ w->dec_len = w->rec_len = 2 * @M@ + 2; break; /**end repeat**/ /**begin repeat * #M = 1, 3, 5, 7, 9# */ case 3@M@: /**begin repeat1 * #type = double, float# */ w->dec_lo_@type@ = bior3_@M@_@type@[0]; w->dec_hi_@type@ = bior3_@M@_@type@[1]; w->rec_lo_@type@ = bior3_@M@_@type@[2]; w->rec_hi_@type@ = bior3_@M@_@type@[3]; /**end repeat1**/ w->dec_len = w->rec_len = 2 * @M@ + 2; break; /**end repeat**/ /**begin repeat * #M = 4# */ case 4@M@: /**begin repeat1 * #type = double, float# */ w->dec_lo_@type@ = bior4_@M@_@type@[0]; w->dec_hi_@type@ = bior4_@M@_@type@[1]; w->rec_lo_@type@ = bior4_@M@_@type@[2]; w->rec_hi_@type@ = bior4_@M@_@type@[3]; /**end repeat1**/ w->dec_len = w->rec_len = 2 * @M@ + 2; break; /**end repeat**/ /**begin repeat * #M = 5# */ case 5@M@: /**begin repeat1 * #type = double, float# */ w->dec_lo_@type@ = bior5_@M@_@type@[0]; w->dec_hi_@type@ = bior5_@M@_@type@[1]; w->rec_lo_@type@ = bior5_@M@_@type@[2]; w->rec_hi_@type@ = bior5_@M@_@type@[3]; /**end repeat1**/ w->dec_len = w->rec_len = 2 * @M@ + 2; break; /**end repeat**/ /**begin repeat * #M = 8# */ case 6@M@: /**begin repeat1 * #type = double, float# */ w->dec_lo_@type@ = bior6_@M@_@type@[0]; w->dec_hi_@type@ = bior6_@M@_@type@[1]; w->rec_lo_@type@ = bior6_@M@_@type@[2]; w->rec_hi_@type@ = bior6_@M@_@type@[3]; /**end repeat1**/ w->dec_len = w->rec_len = 2 * @M@ + 2; break; /**end repeat**/ default: wtfree(w); return NULL; } break; /* Discrete FIR filter approximation of Meyer wavelet */ case 'm': case 'M': w->vanishing_moments_psi = -1; w->vanishing_moments_phi = -1; w->support_width = -1; w->orthogonal = 1; w->biorthogonal = 1; w->symmetry = SYMMETRIC; w->compact_support = 1; w->family_name = "Discrete Meyer (FIR Approximation)"; w->short_name = "dmey"; /**begin repeat * #type = double, float# */ w->dec_lo_@type@ = dmey_@type@[0]; w->dec_hi_@type@ = dmey_@type@[1]; w->rec_lo_@type@ = dmey_@type@[2]; w->rec_hi_@type@ = dmey_@type@[3]; /**end repeat**/ w->dec_len = w->rec_len = 62; return w; break; default: wtfree(w); return NULL; } return w; } Wavelet* blank_wavelet(index_t filters_length) { Wavelet* w; if(filters_length < 1) return NULL; /* pad to even length */ if(filters_length % 2) ++filters_length; w = wtmalloc(sizeof(Wavelet)); if(w == NULL) return NULL; /* * Important! * Otherwise filters arrays allocated here won't be deallocated by free_wavelet */ w->_builtin = 0; w->dec_len = w->rec_len = filters_length; /**begin repeat * #type = double, float# */ w->dec_lo_@type@ = wtcalloc(filters_length, sizeof(@type@)); w->dec_hi_@type@ = wtcalloc(filters_length, sizeof(@type@)); w->rec_lo_@type@ = wtcalloc(filters_length, sizeof(@type@)); w->rec_hi_@type@ = wtcalloc(filters_length, sizeof(@type@)); if(w->dec_lo_@type@ == NULL || w->dec_hi_@type@ == NULL || w->rec_lo_@type@ == NULL || w->rec_hi_@type@ == NULL){ free_wavelet(w); return NULL; } /**end repeat**/ /* set properties to "blank" values */ w->vanishing_moments_psi = 0; w->vanishing_moments_phi = 0; w->support_width = -1; w->orthogonal = 0; w->biorthogonal = 0; w->symmetry = UNKNOWN; w->compact_support = 0; w->family_name = ""; w->short_name = ""; return w; } Wavelet* copy_wavelet(Wavelet* base) { Wavelet* w; index_t i; if(base == NULL) return NULL; if(base->dec_len < 1 || base->rec_len < 1) return NULL; w = wtmalloc(sizeof(Wavelet)); if(w == NULL) return NULL; memcpy(w, base, sizeof(Wavelet)); w->_builtin = 0; /**begin repeat * #type = double, float# */ w->dec_lo_@type@ = wtcalloc(w->dec_len, sizeof(@type@)); w->dec_hi_@type@ = wtcalloc(w->dec_len, sizeof(@type@)); w->rec_lo_@type@ = wtcalloc(w->rec_len, sizeof(@type@)); w->rec_hi_@type@ = wtcalloc(w->rec_len, sizeof(@type@)); if(w->dec_lo_@type@ == NULL || w->dec_hi_@type@ == NULL || w->rec_lo_@type@ == NULL || w->rec_hi_@type@ == NULL){ free_wavelet(w); return NULL; } for(i=0; i< w->dec_len; ++i){ w->dec_lo_@type@[i] = base->dec_lo_@type@[i]; w->dec_hi_@type@[i] = base->dec_hi_@type@[i]; } for(i=0; i< w->rec_len; ++i){ w->rec_lo_@type@[i] = base->rec_lo_@type@[i]; w->rec_hi_@type@[i] = base->rec_hi_@type@[i]; } /**end repeat**/ return w; } void free_wavelet(Wavelet *w){ if(w->_builtin == 0){ /* deallocate filters */ /**begin repeat * #type = double, float# */ if(w->dec_lo_@type@ != NULL){ wtfree(w->dec_lo_@type@); w->dec_lo_@type@ = NULL; } if(w->dec_hi_@type@ != NULL){ wtfree(w->dec_hi_@type@); w->dec_hi_@type@ = NULL; } if(w->rec_lo_@type@ != NULL){ wtfree(w->rec_lo_@type@); w->rec_lo_@type@ = NULL; } if(w->rec_hi_@type@ != NULL){ wtfree(w->rec_hi_@type@); w->rec_hi_@type@ = NULL; } /**end repeat**/ } /* finally free struct */ wtfree(w); } PyWavelets-0.3.0/pywt/src/wt.c0000664000175000017500000003473212556460270017737 0ustar rgommersrgommers00000000000000 /* ***************************************************************************** ** This file was autogenerated from a template DO NOT EDIT!!!! ** ** Changes should be made to the original source (.src) file ** ***************************************************************************** */ #line 1 /* Copyright (c) 2006-2012 Filip Wasilewski */ /* See COPYING for license details. */ #include "wt.h" /* Decomposition of input with lowpass filter */ #line 11 int double_dec_a(double input[], index_t input_len, Wavelet* wavelet, double output[], index_t output_len, MODE mode){ /* check output length */ if(output_len != dwt_buffer_length(input_len, wavelet->dec_len, mode)){ return -1; } return double_downsampling_convolution(input, input_len, wavelet->dec_lo_double, wavelet->dec_len, output, 2, mode); } /* Decomposition of input with highpass filter */ int double_dec_d(double input[], index_t input_len, Wavelet* wavelet, double output[], index_t output_len, MODE mode){ /* check output length */ if(output_len != dwt_buffer_length(input_len, wavelet->dec_len, mode)) return -1; return double_downsampling_convolution(input, input_len, wavelet->dec_hi_double, wavelet->dec_len, output, 2, mode); } /* Direct reconstruction with lowpass reconstruction filter */ int double_rec_a(double coeffs_a[], index_t coeffs_len, Wavelet* wavelet, double output[], index_t output_len){ /* check output length */ if(output_len != reconstruction_buffer_length(coeffs_len, wavelet->rec_len)) return -1; return double_upsampling_convolution_full(coeffs_a, coeffs_len, wavelet->rec_lo_double, wavelet->rec_len, output, output_len); } /* Direct reconstruction with highpass reconstruction filter */ int double_rec_d(double coeffs_d[], index_t coeffs_len, Wavelet* wavelet, double output[], index_t output_len){ /* check for output length */ if(output_len != reconstruction_buffer_length(coeffs_len, wavelet->rec_len)) return -1; return double_upsampling_convolution_full(coeffs_d, coeffs_len, wavelet->rec_hi_double, wavelet->rec_len, output, output_len); } /* * IDWT reconstruction from approximation and detail coeffs * * If fix_size_diff is 1 then coeffs arrays can differ by one in length (this * is useful in multilevel decompositions and reconstructions of odd-length * signals). Requires zero-filled output buffer. */ int double_idwt(double coeffs_a[], index_t coeffs_a_len, double coeffs_d[], index_t coeffs_d_len, Wavelet* wavelet, double output[], index_t output_len, MODE mode, int fix_size_diff){ index_t input_len; /* * If one of coeffs array is NULL then the reconstruction will be performed * using the other one */ if(coeffs_a != NULL && coeffs_d != NULL){ if(fix_size_diff){ if( (coeffs_a_len > coeffs_d_len ? coeffs_a_len - coeffs_d_len : coeffs_d_len-coeffs_a_len) > 1){ /* abs(a-b) */ goto error; } input_len = coeffs_a_len>coeffs_d_len ? coeffs_d_len : coeffs_a_len; /* min */ } else { if(coeffs_a_len != coeffs_d_len) goto error; input_len = coeffs_a_len; } } else if(coeffs_a != NULL){ input_len = coeffs_a_len; } else if (coeffs_d != NULL){ input_len = coeffs_d_len; } else { goto error; } /* check output size */ if(output_len != idwt_buffer_length(input_len, wavelet->rec_len, mode)) goto error; /* * Set output to zero (this can be omitted if output array is already * cleared) memset(output, 0, output_len * sizeof(double)); */ /* reconstruct approximation coeffs with lowpass reconstruction filter */ if(coeffs_a){ if(double_upsampling_convolution_valid_sf(coeffs_a, input_len, wavelet->rec_lo_double, wavelet->rec_len, output, output_len, mode) < 0){ goto error; } } /* * Add reconstruction of details coeffs performed with highpass * reconstruction filter. */ if(coeffs_d){ if(double_upsampling_convolution_valid_sf(coeffs_d, input_len, wavelet->rec_hi_double, wavelet->rec_len, output, output_len, mode) < 0){ goto error; } } return 0; error: return -1; } /* basic SWT step (TODO: optimize) */ int double_swt_(double input[], index_t input_len, const double filter[], index_t filter_len, double output[], index_t output_len, int level){ double* e_filter; index_t i, e_filter_len; int ret; if(level < 1) return -1; if(level > swt_max_level(input_len)) return -2; if(output_len != swt_buffer_length(input_len)) return -1; /* TODO: quick hack, optimize */ if(level > 1){ /* allocate filter first */ e_filter_len = filter_len << (level-1); e_filter = wtcalloc(e_filter_len, sizeof(double)); if(e_filter == NULL) return -1; /* compute upsampled filter values */ for(i = 0; i < filter_len; ++i){ e_filter[i << (level-1)] = filter[i]; } ret = double_downsampling_convolution(input, input_len, e_filter, e_filter_len, output, 1, MODE_PERIODIZATION); wtfree(e_filter); return ret; } else { return double_downsampling_convolution(input, input_len, filter, filter_len, output, 1, MODE_PERIODIZATION); } } /* * Approximation at specified level * input - approximation coeffs from upper level or signal if level == 1 */ int double_swt_a(double input[], index_t input_len, Wavelet* wavelet, double output[], index_t output_len, int level){ return double_swt_(input, input_len, wavelet->dec_lo_double, wavelet->dec_len, output, output_len, level); } /* Details at specified level * input - approximation coeffs from upper level or signal if level == 1 */ int double_swt_d(double input[], index_t input_len, Wavelet* wavelet, double output[], index_t output_len, int level){ return double_swt_(input, input_len, wavelet->dec_hi_double, wavelet->dec_len, output, output_len, level); } #line 11 int float_dec_a(float input[], index_t input_len, Wavelet* wavelet, float output[], index_t output_len, MODE mode){ /* check output length */ if(output_len != dwt_buffer_length(input_len, wavelet->dec_len, mode)){ return -1; } return float_downsampling_convolution(input, input_len, wavelet->dec_lo_float, wavelet->dec_len, output, 2, mode); } /* Decomposition of input with highpass filter */ int float_dec_d(float input[], index_t input_len, Wavelet* wavelet, float output[], index_t output_len, MODE mode){ /* check output length */ if(output_len != dwt_buffer_length(input_len, wavelet->dec_len, mode)) return -1; return float_downsampling_convolution(input, input_len, wavelet->dec_hi_float, wavelet->dec_len, output, 2, mode); } /* Direct reconstruction with lowpass reconstruction filter */ int float_rec_a(float coeffs_a[], index_t coeffs_len, Wavelet* wavelet, float output[], index_t output_len){ /* check output length */ if(output_len != reconstruction_buffer_length(coeffs_len, wavelet->rec_len)) return -1; return float_upsampling_convolution_full(coeffs_a, coeffs_len, wavelet->rec_lo_float, wavelet->rec_len, output, output_len); } /* Direct reconstruction with highpass reconstruction filter */ int float_rec_d(float coeffs_d[], index_t coeffs_len, Wavelet* wavelet, float output[], index_t output_len){ /* check for output length */ if(output_len != reconstruction_buffer_length(coeffs_len, wavelet->rec_len)) return -1; return float_upsampling_convolution_full(coeffs_d, coeffs_len, wavelet->rec_hi_float, wavelet->rec_len, output, output_len); } /* * IDWT reconstruction from approximation and detail coeffs * * If fix_size_diff is 1 then coeffs arrays can differ by one in length (this * is useful in multilevel decompositions and reconstructions of odd-length * signals). Requires zero-filled output buffer. */ int float_idwt(float coeffs_a[], index_t coeffs_a_len, float coeffs_d[], index_t coeffs_d_len, Wavelet* wavelet, float output[], index_t output_len, MODE mode, int fix_size_diff){ index_t input_len; /* * If one of coeffs array is NULL then the reconstruction will be performed * using the other one */ if(coeffs_a != NULL && coeffs_d != NULL){ if(fix_size_diff){ if( (coeffs_a_len > coeffs_d_len ? coeffs_a_len - coeffs_d_len : coeffs_d_len-coeffs_a_len) > 1){ /* abs(a-b) */ goto error; } input_len = coeffs_a_len>coeffs_d_len ? coeffs_d_len : coeffs_a_len; /* min */ } else { if(coeffs_a_len != coeffs_d_len) goto error; input_len = coeffs_a_len; } } else if(coeffs_a != NULL){ input_len = coeffs_a_len; } else if (coeffs_d != NULL){ input_len = coeffs_d_len; } else { goto error; } /* check output size */ if(output_len != idwt_buffer_length(input_len, wavelet->rec_len, mode)) goto error; /* * Set output to zero (this can be omitted if output array is already * cleared) memset(output, 0, output_len * sizeof(float)); */ /* reconstruct approximation coeffs with lowpass reconstruction filter */ if(coeffs_a){ if(float_upsampling_convolution_valid_sf(coeffs_a, input_len, wavelet->rec_lo_float, wavelet->rec_len, output, output_len, mode) < 0){ goto error; } } /* * Add reconstruction of details coeffs performed with highpass * reconstruction filter. */ if(coeffs_d){ if(float_upsampling_convolution_valid_sf(coeffs_d, input_len, wavelet->rec_hi_float, wavelet->rec_len, output, output_len, mode) < 0){ goto error; } } return 0; error: return -1; } /* basic SWT step (TODO: optimize) */ int float_swt_(float input[], index_t input_len, const float filter[], index_t filter_len, float output[], index_t output_len, int level){ float* e_filter; index_t i, e_filter_len; int ret; if(level < 1) return -1; if(level > swt_max_level(input_len)) return -2; if(output_len != swt_buffer_length(input_len)) return -1; /* TODO: quick hack, optimize */ if(level > 1){ /* allocate filter first */ e_filter_len = filter_len << (level-1); e_filter = wtcalloc(e_filter_len, sizeof(float)); if(e_filter == NULL) return -1; /* compute upsampled filter values */ for(i = 0; i < filter_len; ++i){ e_filter[i << (level-1)] = filter[i]; } ret = float_downsampling_convolution(input, input_len, e_filter, e_filter_len, output, 1, MODE_PERIODIZATION); wtfree(e_filter); return ret; } else { return float_downsampling_convolution(input, input_len, filter, filter_len, output, 1, MODE_PERIODIZATION); } } /* * Approximation at specified level * input - approximation coeffs from upper level or signal if level == 1 */ int float_swt_a(float input[], index_t input_len, Wavelet* wavelet, float output[], index_t output_len, int level){ return float_swt_(input, input_len, wavelet->dec_lo_float, wavelet->dec_len, output, output_len, level); } /* Details at specified level * input - approximation coeffs from upper level or signal if level == 1 */ int float_swt_d(float input[], index_t input_len, Wavelet* wavelet, float output[], index_t output_len, int level){ return float_swt_(input, input_len, wavelet->dec_hi_float, wavelet->dec_len, output, output_len, level); } PyWavelets-0.3.0/pywt/src/wavelets.c0000664000175000017500000014047512556460270021141 0ustar rgommersrgommers00000000000000 /* ***************************************************************************** ** This file was autogenerated from a template DO NOT EDIT!!!! ** ** Changes should be made to the original source (.src) file ** ***************************************************************************** */ #line 1 /* Copyright (c) 2006-2012 Filip Wasilewski */ /* See COPYING for license details. */ /* Allocating, setting properties and destroying wavelet structs */ #include "wavelets.h" #include "wavelets_coeffs.h" Wavelet* wavelet(char name, int order) { Wavelet *w, *wtmp; index_t i; /* Haar wavelet */ if(name == 'h' || name == 'H'){ /* the same as db1 */ w = wavelet('d', 1); w->family_name = "Haar"; w->short_name = "haar"; return w; /* Reverse biorthogonal wavelets family */ } else if (name == 'r' || name == 'R') { /* rbio is like bior, only with switched filters */ wtmp = wavelet('b', order); w = copy_wavelet(wtmp); if(w == NULL) return NULL; w->dec_len = wtmp->rec_len; w->rec_len = wtmp->dec_len; for(i = 0; i < w->rec_len; ++i){ #line 39 w->rec_lo_double[i] = wtmp->dec_lo_double[wtmp->dec_len-1-i]; w->rec_hi_double[i] = wtmp->dec_hi_double[wtmp->dec_len-1-i]; #line 39 w->rec_lo_float[i] = wtmp->dec_lo_float[wtmp->dec_len-1-i]; w->rec_hi_float[i] = wtmp->dec_hi_float[wtmp->dec_len-1-i]; } for(i = 0; i < w->dec_len; ++i){ #line 48 w->dec_hi_double[i] = wtmp->rec_hi_double[wtmp->rec_len-1-i]; w->dec_lo_double[i] = wtmp->rec_lo_double[wtmp->rec_len-1-i]; #line 48 w->dec_hi_float[i] = wtmp->rec_hi_float[wtmp->rec_len-1-i]; w->dec_lo_float[i] = wtmp->rec_lo_float[wtmp->rec_len-1-i]; } w->vanishing_moments_psi = order / 10; /* 1st digit */ w->vanishing_moments_phi = -1; w->family_name = "Reverse biorthogonal"; w->short_name = "rbio"; free_wavelet(wtmp); return w; } w = wtmalloc(sizeof(Wavelet)); if(w == NULL) return NULL; w->_builtin = 1; switch(name){ /* Daubechies wavelets family */ case 'd': case 'D': w->dec_len = w->rec_len = 2*order; w->vanishing_moments_psi = order; w->vanishing_moments_phi = 0; w->support_width = 2*order - 1; w->orthogonal = 1; w->biorthogonal = 1; w->symmetry = ASYMMETRIC; w->compact_support = 1; w->family_name = "Daubechies"; w->short_name = "db"; switch (order) { #line 91 case 1: #line 95 w->dec_lo_double = db1_double[0]; w->dec_hi_double = db1_double[1]; w->rec_lo_double = db1_double[2]; w->rec_hi_double = db1_double[3]; #line 95 w->dec_lo_float = db1_float[0]; w->dec_hi_float = db1_float[1]; w->rec_lo_float = db1_float[2]; w->rec_hi_float = db1_float[3]; break; #line 91 case 2: #line 95 w->dec_lo_double = db2_double[0]; w->dec_hi_double = db2_double[1]; w->rec_lo_double = db2_double[2]; w->rec_hi_double = db2_double[3]; #line 95 w->dec_lo_float = db2_float[0]; w->dec_hi_float = db2_float[1]; w->rec_lo_float = db2_float[2]; w->rec_hi_float = db2_float[3]; break; #line 91 case 3: #line 95 w->dec_lo_double = db3_double[0]; w->dec_hi_double = db3_double[1]; w->rec_lo_double = db3_double[2]; w->rec_hi_double = db3_double[3]; #line 95 w->dec_lo_float = db3_float[0]; w->dec_hi_float = db3_float[1]; w->rec_lo_float = db3_float[2]; w->rec_hi_float = db3_float[3]; break; #line 91 case 4: #line 95 w->dec_lo_double = db4_double[0]; w->dec_hi_double = db4_double[1]; w->rec_lo_double = db4_double[2]; w->rec_hi_double = db4_double[3]; #line 95 w->dec_lo_float = db4_float[0]; w->dec_hi_float = db4_float[1]; w->rec_lo_float = db4_float[2]; w->rec_hi_float = db4_float[3]; break; #line 91 case 5: #line 95 w->dec_lo_double = db5_double[0]; w->dec_hi_double = db5_double[1]; w->rec_lo_double = db5_double[2]; w->rec_hi_double = db5_double[3]; #line 95 w->dec_lo_float = db5_float[0]; w->dec_hi_float = db5_float[1]; w->rec_lo_float = db5_float[2]; w->rec_hi_float = db5_float[3]; break; #line 91 case 6: #line 95 w->dec_lo_double = db6_double[0]; w->dec_hi_double = db6_double[1]; w->rec_lo_double = db6_double[2]; w->rec_hi_double = db6_double[3]; #line 95 w->dec_lo_float = db6_float[0]; w->dec_hi_float = db6_float[1]; w->rec_lo_float = db6_float[2]; w->rec_hi_float = db6_float[3]; break; #line 91 case 7: #line 95 w->dec_lo_double = db7_double[0]; w->dec_hi_double = db7_double[1]; w->rec_lo_double = db7_double[2]; w->rec_hi_double = db7_double[3]; #line 95 w->dec_lo_float = db7_float[0]; w->dec_hi_float = db7_float[1]; w->rec_lo_float = db7_float[2]; w->rec_hi_float = db7_float[3]; break; #line 91 case 8: #line 95 w->dec_lo_double = db8_double[0]; w->dec_hi_double = db8_double[1]; w->rec_lo_double = db8_double[2]; w->rec_hi_double = db8_double[3]; #line 95 w->dec_lo_float = db8_float[0]; w->dec_hi_float = db8_float[1]; w->rec_lo_float = db8_float[2]; w->rec_hi_float = db8_float[3]; break; #line 91 case 9: #line 95 w->dec_lo_double = db9_double[0]; w->dec_hi_double = db9_double[1]; w->rec_lo_double = db9_double[2]; w->rec_hi_double = db9_double[3]; #line 95 w->dec_lo_float = db9_float[0]; w->dec_hi_float = db9_float[1]; w->rec_lo_float = db9_float[2]; w->rec_hi_float = db9_float[3]; break; #line 91 case 10: #line 95 w->dec_lo_double = db10_double[0]; w->dec_hi_double = db10_double[1]; w->rec_lo_double = db10_double[2]; w->rec_hi_double = db10_double[3]; #line 95 w->dec_lo_float = db10_float[0]; w->dec_hi_float = db10_float[1]; w->rec_lo_float = db10_float[2]; w->rec_hi_float = db10_float[3]; break; #line 91 case 11: #line 95 w->dec_lo_double = db11_double[0]; w->dec_hi_double = db11_double[1]; w->rec_lo_double = db11_double[2]; w->rec_hi_double = db11_double[3]; #line 95 w->dec_lo_float = db11_float[0]; w->dec_hi_float = db11_float[1]; w->rec_lo_float = db11_float[2]; w->rec_hi_float = db11_float[3]; break; #line 91 case 12: #line 95 w->dec_lo_double = db12_double[0]; w->dec_hi_double = db12_double[1]; w->rec_lo_double = db12_double[2]; w->rec_hi_double = db12_double[3]; #line 95 w->dec_lo_float = db12_float[0]; w->dec_hi_float = db12_float[1]; w->rec_lo_float = db12_float[2]; w->rec_hi_float = db12_float[3]; break; #line 91 case 13: #line 95 w->dec_lo_double = db13_double[0]; w->dec_hi_double = db13_double[1]; w->rec_lo_double = db13_double[2]; w->rec_hi_double = db13_double[3]; #line 95 w->dec_lo_float = db13_float[0]; w->dec_hi_float = db13_float[1]; w->rec_lo_float = db13_float[2]; w->rec_hi_float = db13_float[3]; break; #line 91 case 14: #line 95 w->dec_lo_double = db14_double[0]; w->dec_hi_double = db14_double[1]; w->rec_lo_double = db14_double[2]; w->rec_hi_double = db14_double[3]; #line 95 w->dec_lo_float = db14_float[0]; w->dec_hi_float = db14_float[1]; w->rec_lo_float = db14_float[2]; w->rec_hi_float = db14_float[3]; break; #line 91 case 15: #line 95 w->dec_lo_double = db15_double[0]; w->dec_hi_double = db15_double[1]; w->rec_lo_double = db15_double[2]; w->rec_hi_double = db15_double[3]; #line 95 w->dec_lo_float = db15_float[0]; w->dec_hi_float = db15_float[1]; w->rec_lo_float = db15_float[2]; w->rec_hi_float = db15_float[3]; break; #line 91 case 16: #line 95 w->dec_lo_double = db16_double[0]; w->dec_hi_double = db16_double[1]; w->rec_lo_double = db16_double[2]; w->rec_hi_double = db16_double[3]; #line 95 w->dec_lo_float = db16_float[0]; w->dec_hi_float = db16_float[1]; w->rec_lo_float = db16_float[2]; w->rec_hi_float = db16_float[3]; break; #line 91 case 17: #line 95 w->dec_lo_double = db17_double[0]; w->dec_hi_double = db17_double[1]; w->rec_lo_double = db17_double[2]; w->rec_hi_double = db17_double[3]; #line 95 w->dec_lo_float = db17_float[0]; w->dec_hi_float = db17_float[1]; w->rec_lo_float = db17_float[2]; w->rec_hi_float = db17_float[3]; break; #line 91 case 18: #line 95 w->dec_lo_double = db18_double[0]; w->dec_hi_double = db18_double[1]; w->rec_lo_double = db18_double[2]; w->rec_hi_double = db18_double[3]; #line 95 w->dec_lo_float = db18_float[0]; w->dec_hi_float = db18_float[1]; w->rec_lo_float = db18_float[2]; w->rec_hi_float = db18_float[3]; break; #line 91 case 19: #line 95 w->dec_lo_double = db19_double[0]; w->dec_hi_double = db19_double[1]; w->rec_lo_double = db19_double[2]; w->rec_hi_double = db19_double[3]; #line 95 w->dec_lo_float = db19_float[0]; w->dec_hi_float = db19_float[1]; w->rec_lo_float = db19_float[2]; w->rec_hi_float = db19_float[3]; break; #line 91 case 20: #line 95 w->dec_lo_double = db20_double[0]; w->dec_hi_double = db20_double[1]; w->rec_lo_double = db20_double[2]; w->rec_hi_double = db20_double[3]; #line 95 w->dec_lo_float = db20_float[0]; w->dec_hi_float = db20_float[1]; w->rec_lo_float = db20_float[2]; w->rec_hi_float = db20_float[3]; break; default: wtfree(w); return NULL; } break; /* Symlets wavelets family */ case 's': case 'S': w->dec_len = w->rec_len = order << 1; w->vanishing_moments_psi = order; w->vanishing_moments_phi = 0; w->support_width = 2*order - 1; w->orthogonal = 1; w->biorthogonal = 1; w->symmetry = NEAR_SYMMETRIC; w->compact_support = 1; w->family_name = "Symlets"; w->short_name = "sym"; switch (order) { #line 127 case 2: #line 131 w->dec_lo_double = sym2_double[0]; w->dec_hi_double = sym2_double[1]; w->rec_lo_double = sym2_double[2]; w->rec_hi_double = sym2_double[3]; #line 131 w->dec_lo_float = sym2_float[0]; w->dec_hi_float = sym2_float[1]; w->rec_lo_float = sym2_float[2]; w->rec_hi_float = sym2_float[3]; break; #line 127 case 3: #line 131 w->dec_lo_double = sym3_double[0]; w->dec_hi_double = sym3_double[1]; w->rec_lo_double = sym3_double[2]; w->rec_hi_double = sym3_double[3]; #line 131 w->dec_lo_float = sym3_float[0]; w->dec_hi_float = sym3_float[1]; w->rec_lo_float = sym3_float[2]; w->rec_hi_float = sym3_float[3]; break; #line 127 case 4: #line 131 w->dec_lo_double = sym4_double[0]; w->dec_hi_double = sym4_double[1]; w->rec_lo_double = sym4_double[2]; w->rec_hi_double = sym4_double[3]; #line 131 w->dec_lo_float = sym4_float[0]; w->dec_hi_float = sym4_float[1]; w->rec_lo_float = sym4_float[2]; w->rec_hi_float = sym4_float[3]; break; #line 127 case 5: #line 131 w->dec_lo_double = sym5_double[0]; w->dec_hi_double = sym5_double[1]; w->rec_lo_double = sym5_double[2]; w->rec_hi_double = sym5_double[3]; #line 131 w->dec_lo_float = sym5_float[0]; w->dec_hi_float = sym5_float[1]; w->rec_lo_float = sym5_float[2]; w->rec_hi_float = sym5_float[3]; break; #line 127 case 6: #line 131 w->dec_lo_double = sym6_double[0]; w->dec_hi_double = sym6_double[1]; w->rec_lo_double = sym6_double[2]; w->rec_hi_double = sym6_double[3]; #line 131 w->dec_lo_float = sym6_float[0]; w->dec_hi_float = sym6_float[1]; w->rec_lo_float = sym6_float[2]; w->rec_hi_float = sym6_float[3]; break; #line 127 case 7: #line 131 w->dec_lo_double = sym7_double[0]; w->dec_hi_double = sym7_double[1]; w->rec_lo_double = sym7_double[2]; w->rec_hi_double = sym7_double[3]; #line 131 w->dec_lo_float = sym7_float[0]; w->dec_hi_float = sym7_float[1]; w->rec_lo_float = sym7_float[2]; w->rec_hi_float = sym7_float[3]; break; #line 127 case 8: #line 131 w->dec_lo_double = sym8_double[0]; w->dec_hi_double = sym8_double[1]; w->rec_lo_double = sym8_double[2]; w->rec_hi_double = sym8_double[3]; #line 131 w->dec_lo_float = sym8_float[0]; w->dec_hi_float = sym8_float[1]; w->rec_lo_float = sym8_float[2]; w->rec_hi_float = sym8_float[3]; break; #line 127 case 9: #line 131 w->dec_lo_double = sym9_double[0]; w->dec_hi_double = sym9_double[1]; w->rec_lo_double = sym9_double[2]; w->rec_hi_double = sym9_double[3]; #line 131 w->dec_lo_float = sym9_float[0]; w->dec_hi_float = sym9_float[1]; w->rec_lo_float = sym9_float[2]; w->rec_hi_float = sym9_float[3]; break; #line 127 case 10: #line 131 w->dec_lo_double = sym10_double[0]; w->dec_hi_double = sym10_double[1]; w->rec_lo_double = sym10_double[2]; w->rec_hi_double = sym10_double[3]; #line 131 w->dec_lo_float = sym10_float[0]; w->dec_hi_float = sym10_float[1]; w->rec_lo_float = sym10_float[2]; w->rec_hi_float = sym10_float[3]; break; #line 127 case 11: #line 131 w->dec_lo_double = sym11_double[0]; w->dec_hi_double = sym11_double[1]; w->rec_lo_double = sym11_double[2]; w->rec_hi_double = sym11_double[3]; #line 131 w->dec_lo_float = sym11_float[0]; w->dec_hi_float = sym11_float[1]; w->rec_lo_float = sym11_float[2]; w->rec_hi_float = sym11_float[3]; break; #line 127 case 12: #line 131 w->dec_lo_double = sym12_double[0]; w->dec_hi_double = sym12_double[1]; w->rec_lo_double = sym12_double[2]; w->rec_hi_double = sym12_double[3]; #line 131 w->dec_lo_float = sym12_float[0]; w->dec_hi_float = sym12_float[1]; w->rec_lo_float = sym12_float[2]; w->rec_hi_float = sym12_float[3]; break; #line 127 case 13: #line 131 w->dec_lo_double = sym13_double[0]; w->dec_hi_double = sym13_double[1]; w->rec_lo_double = sym13_double[2]; w->rec_hi_double = sym13_double[3]; #line 131 w->dec_lo_float = sym13_float[0]; w->dec_hi_float = sym13_float[1]; w->rec_lo_float = sym13_float[2]; w->rec_hi_float = sym13_float[3]; break; #line 127 case 14: #line 131 w->dec_lo_double = sym14_double[0]; w->dec_hi_double = sym14_double[1]; w->rec_lo_double = sym14_double[2]; w->rec_hi_double = sym14_double[3]; #line 131 w->dec_lo_float = sym14_float[0]; w->dec_hi_float = sym14_float[1]; w->rec_lo_float = sym14_float[2]; w->rec_hi_float = sym14_float[3]; break; #line 127 case 15: #line 131 w->dec_lo_double = sym15_double[0]; w->dec_hi_double = sym15_double[1]; w->rec_lo_double = sym15_double[2]; w->rec_hi_double = sym15_double[3]; #line 131 w->dec_lo_float = sym15_float[0]; w->dec_hi_float = sym15_float[1]; w->rec_lo_float = sym15_float[2]; w->rec_hi_float = sym15_float[3]; break; #line 127 case 16: #line 131 w->dec_lo_double = sym16_double[0]; w->dec_hi_double = sym16_double[1]; w->rec_lo_double = sym16_double[2]; w->rec_hi_double = sym16_double[3]; #line 131 w->dec_lo_float = sym16_float[0]; w->dec_hi_float = sym16_float[1]; w->rec_lo_float = sym16_float[2]; w->rec_hi_float = sym16_float[3]; break; #line 127 case 17: #line 131 w->dec_lo_double = sym17_double[0]; w->dec_hi_double = sym17_double[1]; w->rec_lo_double = sym17_double[2]; w->rec_hi_double = sym17_double[3]; #line 131 w->dec_lo_float = sym17_float[0]; w->dec_hi_float = sym17_float[1]; w->rec_lo_float = sym17_float[2]; w->rec_hi_float = sym17_float[3]; break; #line 127 case 18: #line 131 w->dec_lo_double = sym18_double[0]; w->dec_hi_double = sym18_double[1]; w->rec_lo_double = sym18_double[2]; w->rec_hi_double = sym18_double[3]; #line 131 w->dec_lo_float = sym18_float[0]; w->dec_hi_float = sym18_float[1]; w->rec_lo_float = sym18_float[2]; w->rec_hi_float = sym18_float[3]; break; #line 127 case 19: #line 131 w->dec_lo_double = sym19_double[0]; w->dec_hi_double = sym19_double[1]; w->rec_lo_double = sym19_double[2]; w->rec_hi_double = sym19_double[3]; #line 131 w->dec_lo_float = sym19_float[0]; w->dec_hi_float = sym19_float[1]; w->rec_lo_float = sym19_float[2]; w->rec_hi_float = sym19_float[3]; break; #line 127 case 20: #line 131 w->dec_lo_double = sym20_double[0]; w->dec_hi_double = sym20_double[1]; w->rec_lo_double = sym20_double[2]; w->rec_hi_double = sym20_double[3]; #line 131 w->dec_lo_float = sym20_float[0]; w->dec_hi_float = sym20_float[1]; w->rec_lo_float = sym20_float[2]; w->rec_hi_float = sym20_float[3]; break; default: wtfree(w); return NULL; } break; /* Coiflets wavelets family */ case 'c': case 'C': w->dec_len = w->rec_len = order * 6; w->vanishing_moments_psi = 2*order; w->vanishing_moments_phi = 2*order -1; w->support_width = 6*order - 1; w->orthogonal = 1; w->biorthogonal = 1; w->symmetry = NEAR_SYMMETRIC; w->compact_support = 1; w->family_name = "Coiflets"; w->short_name = "coif"; switch (order) { #line 164 case 1: #line 168 w->dec_lo_double = coif1_double[0]; w->dec_hi_double = coif1_double[1]; w->rec_lo_double = coif1_double[2]; w->rec_hi_double = coif1_double[3]; #line 168 w->dec_lo_float = coif1_float[0]; w->dec_hi_float = coif1_float[1]; w->rec_lo_float = coif1_float[2]; w->rec_hi_float = coif1_float[3]; break; #line 164 case 2: #line 168 w->dec_lo_double = coif2_double[0]; w->dec_hi_double = coif2_double[1]; w->rec_lo_double = coif2_double[2]; w->rec_hi_double = coif2_double[3]; #line 168 w->dec_lo_float = coif2_float[0]; w->dec_hi_float = coif2_float[1]; w->rec_lo_float = coif2_float[2]; w->rec_hi_float = coif2_float[3]; break; #line 164 case 3: #line 168 w->dec_lo_double = coif3_double[0]; w->dec_hi_double = coif3_double[1]; w->rec_lo_double = coif3_double[2]; w->rec_hi_double = coif3_double[3]; #line 168 w->dec_lo_float = coif3_float[0]; w->dec_hi_float = coif3_float[1]; w->rec_lo_float = coif3_float[2]; w->rec_hi_float = coif3_float[3]; break; #line 164 case 4: #line 168 w->dec_lo_double = coif4_double[0]; w->dec_hi_double = coif4_double[1]; w->rec_lo_double = coif4_double[2]; w->rec_hi_double = coif4_double[3]; #line 168 w->dec_lo_float = coif4_float[0]; w->dec_hi_float = coif4_float[1]; w->rec_lo_float = coif4_float[2]; w->rec_hi_float = coif4_float[3]; break; #line 164 case 5: #line 168 w->dec_lo_double = coif5_double[0]; w->dec_hi_double = coif5_double[1]; w->rec_lo_double = coif5_double[2]; w->rec_hi_double = coif5_double[3]; #line 168 w->dec_lo_float = coif5_float[0]; w->dec_hi_float = coif5_float[1]; w->rec_lo_float = coif5_float[2]; w->rec_hi_float = coif5_float[3]; break; default: wtfree(w); return NULL; } break; /* Biorthogonal wavelets family */ case 'b': case 'B': w->vanishing_moments_psi = order/10; w->vanishing_moments_phi = -1; w->support_width = -1; w->orthogonal = 0; w->biorthogonal = 1; w->symmetry = SYMMETRIC; w->compact_support = 1; w->family_name = "Biorthogonal"; w->short_name = "bior"; switch (order) { #line 200 case 11: #line 204 w->dec_lo_double = bior1_1_double[0]; w->dec_hi_double = bior1_1_double[1]; w->rec_lo_double = bior1_1_double[2]; w->rec_hi_double = bior1_1_double[3]; #line 204 w->dec_lo_float = bior1_1_float[0]; w->dec_hi_float = bior1_1_float[1]; w->rec_lo_float = bior1_1_float[2]; w->rec_hi_float = bior1_1_float[3]; w->dec_len = w->rec_len = 2 * 1; break; #line 200 case 13: #line 204 w->dec_lo_double = bior1_3_double[0]; w->dec_hi_double = bior1_3_double[1]; w->rec_lo_double = bior1_3_double[2]; w->rec_hi_double = bior1_3_double[3]; #line 204 w->dec_lo_float = bior1_3_float[0]; w->dec_hi_float = bior1_3_float[1]; w->rec_lo_float = bior1_3_float[2]; w->rec_hi_float = bior1_3_float[3]; w->dec_len = w->rec_len = 2 * 3; break; #line 200 case 15: #line 204 w->dec_lo_double = bior1_5_double[0]; w->dec_hi_double = bior1_5_double[1]; w->rec_lo_double = bior1_5_double[2]; w->rec_hi_double = bior1_5_double[3]; #line 204 w->dec_lo_float = bior1_5_float[0]; w->dec_hi_float = bior1_5_float[1]; w->rec_lo_float = bior1_5_float[2]; w->rec_hi_float = bior1_5_float[3]; w->dec_len = w->rec_len = 2 * 5; break; #line 216 case 22: #line 220 w->dec_lo_double = bior2_2_double[0]; w->dec_hi_double = bior2_2_double[1]; w->rec_lo_double = bior2_2_double[2]; w->rec_hi_double = bior2_2_double[3]; #line 220 w->dec_lo_float = bior2_2_float[0]; w->dec_hi_float = bior2_2_float[1]; w->rec_lo_float = bior2_2_float[2]; w->rec_hi_float = bior2_2_float[3]; w->dec_len = w->rec_len = 2 * 2 + 2; break; #line 216 case 24: #line 220 w->dec_lo_double = bior2_4_double[0]; w->dec_hi_double = bior2_4_double[1]; w->rec_lo_double = bior2_4_double[2]; w->rec_hi_double = bior2_4_double[3]; #line 220 w->dec_lo_float = bior2_4_float[0]; w->dec_hi_float = bior2_4_float[1]; w->rec_lo_float = bior2_4_float[2]; w->rec_hi_float = bior2_4_float[3]; w->dec_len = w->rec_len = 2 * 4 + 2; break; #line 216 case 26: #line 220 w->dec_lo_double = bior2_6_double[0]; w->dec_hi_double = bior2_6_double[1]; w->rec_lo_double = bior2_6_double[2]; w->rec_hi_double = bior2_6_double[3]; #line 220 w->dec_lo_float = bior2_6_float[0]; w->dec_hi_float = bior2_6_float[1]; w->rec_lo_float = bior2_6_float[2]; w->rec_hi_float = bior2_6_float[3]; w->dec_len = w->rec_len = 2 * 6 + 2; break; #line 216 case 28: #line 220 w->dec_lo_double = bior2_8_double[0]; w->dec_hi_double = bior2_8_double[1]; w->rec_lo_double = bior2_8_double[2]; w->rec_hi_double = bior2_8_double[3]; #line 220 w->dec_lo_float = bior2_8_float[0]; w->dec_hi_float = bior2_8_float[1]; w->rec_lo_float = bior2_8_float[2]; w->rec_hi_float = bior2_8_float[3]; w->dec_len = w->rec_len = 2 * 8 + 2; break; #line 232 case 31: #line 236 w->dec_lo_double = bior3_1_double[0]; w->dec_hi_double = bior3_1_double[1]; w->rec_lo_double = bior3_1_double[2]; w->rec_hi_double = bior3_1_double[3]; #line 236 w->dec_lo_float = bior3_1_float[0]; w->dec_hi_float = bior3_1_float[1]; w->rec_lo_float = bior3_1_float[2]; w->rec_hi_float = bior3_1_float[3]; w->dec_len = w->rec_len = 2 * 1 + 2; break; #line 232 case 33: #line 236 w->dec_lo_double = bior3_3_double[0]; w->dec_hi_double = bior3_3_double[1]; w->rec_lo_double = bior3_3_double[2]; w->rec_hi_double = bior3_3_double[3]; #line 236 w->dec_lo_float = bior3_3_float[0]; w->dec_hi_float = bior3_3_float[1]; w->rec_lo_float = bior3_3_float[2]; w->rec_hi_float = bior3_3_float[3]; w->dec_len = w->rec_len = 2 * 3 + 2; break; #line 232 case 35: #line 236 w->dec_lo_double = bior3_5_double[0]; w->dec_hi_double = bior3_5_double[1]; w->rec_lo_double = bior3_5_double[2]; w->rec_hi_double = bior3_5_double[3]; #line 236 w->dec_lo_float = bior3_5_float[0]; w->dec_hi_float = bior3_5_float[1]; w->rec_lo_float = bior3_5_float[2]; w->rec_hi_float = bior3_5_float[3]; w->dec_len = w->rec_len = 2 * 5 + 2; break; #line 232 case 37: #line 236 w->dec_lo_double = bior3_7_double[0]; w->dec_hi_double = bior3_7_double[1]; w->rec_lo_double = bior3_7_double[2]; w->rec_hi_double = bior3_7_double[3]; #line 236 w->dec_lo_float = bior3_7_float[0]; w->dec_hi_float = bior3_7_float[1]; w->rec_lo_float = bior3_7_float[2]; w->rec_hi_float = bior3_7_float[3]; w->dec_len = w->rec_len = 2 * 7 + 2; break; #line 232 case 39: #line 236 w->dec_lo_double = bior3_9_double[0]; w->dec_hi_double = bior3_9_double[1]; w->rec_lo_double = bior3_9_double[2]; w->rec_hi_double = bior3_9_double[3]; #line 236 w->dec_lo_float = bior3_9_float[0]; w->dec_hi_float = bior3_9_float[1]; w->rec_lo_float = bior3_9_float[2]; w->rec_hi_float = bior3_9_float[3]; w->dec_len = w->rec_len = 2 * 9 + 2; break; #line 248 case 44: #line 252 w->dec_lo_double = bior4_4_double[0]; w->dec_hi_double = bior4_4_double[1]; w->rec_lo_double = bior4_4_double[2]; w->rec_hi_double = bior4_4_double[3]; #line 252 w->dec_lo_float = bior4_4_float[0]; w->dec_hi_float = bior4_4_float[1]; w->rec_lo_float = bior4_4_float[2]; w->rec_hi_float = bior4_4_float[3]; w->dec_len = w->rec_len = 2 * 4 + 2; break; #line 264 case 55: #line 268 w->dec_lo_double = bior5_5_double[0]; w->dec_hi_double = bior5_5_double[1]; w->rec_lo_double = bior5_5_double[2]; w->rec_hi_double = bior5_5_double[3]; #line 268 w->dec_lo_float = bior5_5_float[0]; w->dec_hi_float = bior5_5_float[1]; w->rec_lo_float = bior5_5_float[2]; w->rec_hi_float = bior5_5_float[3]; w->dec_len = w->rec_len = 2 * 5 + 2; break; #line 280 case 68: #line 284 w->dec_lo_double = bior6_8_double[0]; w->dec_hi_double = bior6_8_double[1]; w->rec_lo_double = bior6_8_double[2]; w->rec_hi_double = bior6_8_double[3]; #line 284 w->dec_lo_float = bior6_8_float[0]; w->dec_hi_float = bior6_8_float[1]; w->rec_lo_float = bior6_8_float[2]; w->rec_hi_float = bior6_8_float[3]; w->dec_len = w->rec_len = 2 * 8 + 2; break; default: wtfree(w); return NULL; } break; /* Discrete FIR filter approximation of Meyer wavelet */ case 'm': case 'M': w->vanishing_moments_psi = -1; w->vanishing_moments_phi = -1; w->support_width = -1; w->orthogonal = 1; w->biorthogonal = 1; w->symmetry = SYMMETRIC; w->compact_support = 1; w->family_name = "Discrete Meyer (FIR Approximation)"; w->short_name = "dmey"; #line 316 w->dec_lo_double = dmey_double[0]; w->dec_hi_double = dmey_double[1]; w->rec_lo_double = dmey_double[2]; w->rec_hi_double = dmey_double[3]; #line 316 w->dec_lo_float = dmey_float[0]; w->dec_hi_float = dmey_float[1]; w->rec_lo_float = dmey_float[2]; w->rec_hi_float = dmey_float[3]; w->dec_len = w->rec_len = 62; return w; break; default: wtfree(w); return NULL; } return w; } Wavelet* blank_wavelet(index_t filters_length) { Wavelet* w; if(filters_length < 1) return NULL; /* pad to even length */ if(filters_length % 2) ++filters_length; w = wtmalloc(sizeof(Wavelet)); if(w == NULL) return NULL; /* * Important! * Otherwise filters arrays allocated here won't be deallocated by free_wavelet */ w->_builtin = 0; w->dec_len = w->rec_len = filters_length; #line 358 w->dec_lo_double = wtcalloc(filters_length, sizeof(double)); w->dec_hi_double = wtcalloc(filters_length, sizeof(double)); w->rec_lo_double = wtcalloc(filters_length, sizeof(double)); w->rec_hi_double = wtcalloc(filters_length, sizeof(double)); if(w->dec_lo_double == NULL || w->dec_hi_double == NULL || w->rec_lo_double == NULL || w->rec_hi_double == NULL){ free_wavelet(w); return NULL; } #line 358 w->dec_lo_float = wtcalloc(filters_length, sizeof(float)); w->dec_hi_float = wtcalloc(filters_length, sizeof(float)); w->rec_lo_float = wtcalloc(filters_length, sizeof(float)); w->rec_hi_float = wtcalloc(filters_length, sizeof(float)); if(w->dec_lo_float == NULL || w->dec_hi_float == NULL || w->rec_lo_float == NULL || w->rec_hi_float == NULL){ free_wavelet(w); return NULL; } /* set properties to "blank" values */ w->vanishing_moments_psi = 0; w->vanishing_moments_phi = 0; w->support_width = -1; w->orthogonal = 0; w->biorthogonal = 0; w->symmetry = UNKNOWN; w->compact_support = 0; w->family_name = ""; w->short_name = ""; return w; } Wavelet* copy_wavelet(Wavelet* base) { Wavelet* w; index_t i; if(base == NULL) return NULL; if(base->dec_len < 1 || base->rec_len < 1) return NULL; w = wtmalloc(sizeof(Wavelet)); if(w == NULL) return NULL; memcpy(w, base, sizeof(Wavelet)); w->_builtin = 0; #line 404 w->dec_lo_double = wtcalloc(w->dec_len, sizeof(double)); w->dec_hi_double = wtcalloc(w->dec_len, sizeof(double)); w->rec_lo_double = wtcalloc(w->rec_len, sizeof(double)); w->rec_hi_double = wtcalloc(w->rec_len, sizeof(double)); if(w->dec_lo_double == NULL || w->dec_hi_double == NULL || w->rec_lo_double == NULL || w->rec_hi_double == NULL){ free_wavelet(w); return NULL; } for(i=0; i< w->dec_len; ++i){ w->dec_lo_double[i] = base->dec_lo_double[i]; w->dec_hi_double[i] = base->dec_hi_double[i]; } for(i=0; i< w->rec_len; ++i){ w->rec_lo_double[i] = base->rec_lo_double[i]; w->rec_hi_double[i] = base->rec_hi_double[i]; } #line 404 w->dec_lo_float = wtcalloc(w->dec_len, sizeof(float)); w->dec_hi_float = wtcalloc(w->dec_len, sizeof(float)); w->rec_lo_float = wtcalloc(w->rec_len, sizeof(float)); w->rec_hi_float = wtcalloc(w->rec_len, sizeof(float)); if(w->dec_lo_float == NULL || w->dec_hi_float == NULL || w->rec_lo_float == NULL || w->rec_hi_float == NULL){ free_wavelet(w); return NULL; } for(i=0; i< w->dec_len; ++i){ w->dec_lo_float[i] = base->dec_lo_float[i]; w->dec_hi_float[i] = base->dec_hi_float[i]; } for(i=0; i< w->rec_len; ++i){ w->rec_lo_float[i] = base->rec_lo_float[i]; w->rec_hi_float[i] = base->rec_hi_float[i]; } return w; } void free_wavelet(Wavelet *w){ if(w->_builtin == 0){ /* deallocate filters */ #line 439 if(w->dec_lo_double != NULL){ wtfree(w->dec_lo_double); w->dec_lo_double = NULL; } if(w->dec_hi_double != NULL){ wtfree(w->dec_hi_double); w->dec_hi_double = NULL; } if(w->rec_lo_double != NULL){ wtfree(w->rec_lo_double); w->rec_lo_double = NULL; } if(w->rec_hi_double != NULL){ wtfree(w->rec_hi_double); w->rec_hi_double = NULL; } #line 439 if(w->dec_lo_float != NULL){ wtfree(w->dec_lo_float); w->dec_lo_float = NULL; } if(w->dec_hi_float != NULL){ wtfree(w->dec_hi_float); w->dec_hi_float = NULL; } if(w->rec_lo_float != NULL){ wtfree(w->rec_lo_float); w->rec_lo_float = NULL; } if(w->rec_hi_float != NULL){ wtfree(w->rec_hi_float); w->rec_hi_float = NULL; } } /* finally free struct */ wtfree(w); } PyWavelets-0.3.0/pywt/src/wt.h0000664000175000017500000000666212556460270017745 0ustar rgommersrgommers00000000000000 /* ***************************************************************************** ** This file was autogenerated from a template DO NOT EDIT!!!! ** ** Changes should be made to the original source (.src) file ** ***************************************************************************** */ #line 1 /* Copyright (c) 2006-2012 Filip Wasilewski */ /* See COPYING for license details. */ /* Wavelet transforms using convolution functions defined in convolution.h */ #ifndef _WT_H_ #define _WT_H_ #include #include #include "common.h" #include "convolution.h" #include "wavelets.h" /* _a suffix - wavelet transform approximations */ /* _d suffix - wavelet transform details */ #line 23 /* Single level decomposition */ int double_dec_a(double input[], index_t input_len, Wavelet* wavelet, double output[], index_t output_len, MODE mode); int double_dec_d(double input[], index_t input_len, Wavelet* wavelet, double output[], index_t output_len, MODE mode); /* Single level reconstruction */ int double_rec_a(double coeffs_a[], index_t coeffs_len, Wavelet* wavelet, double output[], index_t output_len); int double_rec_d(double coeffs_d[], index_t coeffs_len, Wavelet* wavelet, double output[], index_t output_len); /* Single level IDWT reconstruction */ int double_idwt(double coeffs_a[], index_t coeffs_a_len, double coeffs_d[], index_t coeffs_d_len, Wavelet* wavelet, double output[], index_t output_len, MODE mode, int fix_size_diff); /* SWT decomposition at given level */ int double_swt_a(double input[], index_t input_len, Wavelet* wavelet, double output[], index_t output_len, int level); int double_swt_d(double input[], index_t input_len, Wavelet* wavelet, double output[], index_t output_len, int level); #line 23 /* Single level decomposition */ int float_dec_a(float input[], index_t input_len, Wavelet* wavelet, float output[], index_t output_len, MODE mode); int float_dec_d(float input[], index_t input_len, Wavelet* wavelet, float output[], index_t output_len, MODE mode); /* Single level reconstruction */ int float_rec_a(float coeffs_a[], index_t coeffs_len, Wavelet* wavelet, float output[], index_t output_len); int float_rec_d(float coeffs_d[], index_t coeffs_len, Wavelet* wavelet, float output[], index_t output_len); /* Single level IDWT reconstruction */ int float_idwt(float coeffs_a[], index_t coeffs_a_len, float coeffs_d[], index_t coeffs_d_len, Wavelet* wavelet, float output[], index_t output_len, MODE mode, int fix_size_diff); /* SWT decomposition at given level */ int float_swt_a(float input[], index_t input_len, Wavelet* wavelet, float output[], index_t output_len, int level); int float_swt_d(float input[], index_t input_len, Wavelet* wavelet, float output[], index_t output_len, int level); #endif PyWavelets-0.3.0/pywt/src/wavelets_list.pxi0000664000175000017500000000455312556460247022552 0ustar rgommersrgommers00000000000000# Copyright (c) 2006-2012 Filip Wasilewski # See COPYING for license details. ## Mapping of wavelet names to the C backend codes cdef __wname_to_code __wname_to_code = { "haar": (c"h", 0), "db1": (c"d", 1), "db2": (c"d", 2), "db3": (c"d", 3), "db4": (c"d", 4), "db5": (c"d", 5), "db6": (c"d", 6), "db7": (c"d", 7), "db8": (c"d", 8), "db9": (c"d", 9), "db10": (c"d", 10), "db11": (c"d", 11), "db12": (c"d", 12), "db13": (c"d", 13), "db14": (c"d", 14), "db15": (c"d", 15), "db16": (c"d", 16), "db17": (c"d", 17), "db18": (c"d", 18), "db19": (c"d", 19), "db20": (c"d", 20), "sym2": (c"s", 2), "sym3": (c"s", 3), "sym4": (c"s", 4), "sym5": (c"s", 5), "sym6": (c"s", 6), "sym7": (c"s", 7), "sym8": (c"s", 8), "sym9": (c"s", 9), "sym10": (c"s", 10), "sym11": (c"s", 11), "sym12": (c"s", 12), "sym13": (c"s", 13), "sym14": (c"s", 14), "sym15": (c"s", 15), "sym16": (c"s", 16), "sym17": (c"s", 17), "sym18": (c"s", 18), "sym19": (c"s", 19), "sym20": (c"s", 20), "coif1": (c"c", 1), "coif2": (c"c", 2), "coif3": (c"c", 3), "coif4": (c"c", 4), "coif5": (c"c", 5), "bior1.1": (c"b", 11), "bior1.3": (c"b", 13), "bior1.5": (c"b", 15), "bior2.2": (c"b", 22), "bior2.4": (c"b", 24), "bior2.6": (c"b", 26), "bior2.8": (c"b", 28), "bior3.1": (c"b", 31), "bior3.3": (c"b", 33), "bior3.5": (c"b", 35), "bior3.7": (c"b", 37), "bior3.9": (c"b", 39), "bior4.4": (c"b", 44), "bior5.5": (c"b", 55), "bior6.8": (c"b", 68), "rbio1.1": (c"r", 11), "rbio1.3": (c"r", 13), "rbio1.5": (c"r", 15), "rbio2.2": (c"r", 22), "rbio2.4": (c"r", 24), "rbio2.6": (c"r", 26), "rbio2.8": (c"r", 28), "rbio3.1": (c"r", 31), "rbio3.3": (c"r", 33), "rbio3.5": (c"r", 35), "rbio3.7": (c"r", 37), "rbio3.9": (c"r", 39), "rbio4.4": (c"r", 44), "rbio5.5": (c"r", 55), "rbio6.8": (c"r", 68), "dmey": (c"m", 0), } ## Lists of family names cdef __wfamily_list_short, __wfamily_list_long __wfamily_list_short = ["haar", "db", "sym", "coif", "bior", "rbio", "dmey"] __wfamily_list_long = ["Haar", "Daubechies", "Symlets", "Coiflets", "Biorthogonal", "Reverse biorthogonal", "Discrete Meyer (FIR Approximation)"] PyWavelets-0.3.0/pywt/src/convolution.c0000664000175000017500000017614412556460270021670 0ustar rgommersrgommers00000000000000 /* ***************************************************************************** ** This file was autogenerated from a template DO NOT EDIT!!!! ** ** Changes should be made to the original source (.src) file ** ***************************************************************************** */ #line 1 /* Copyright (c) 2006-2012 Filip Wasilewski */ /* See COPYING for license details. */ #include "convolution.h" #line 9 int double_downsampling_convolution_periodization(const double* input, const_index_t N, const double* filter, const_index_t F, double* output, const_index_t step) { index_t i, j, k, F_2, corr; index_t start; double sum; double* ptr_w = output; i = step-1; /* first element taken from input is input[step-1] */ start = F_2 = F/2; /* extending by (F-2)/2 elements */ corr = 0; for(i=start; i < F; i+=step){ sum = 0; for(j = 0; j < i+1-corr; ++j) /* overlapping */ sum += filter[j]*input[i-j-corr]; if(N%2){ if(F-j){ /* if something to extend */ sum += filter[j] * input[N-1]; if(F-j){ for(k = 2-corr; k <= F-j; ++k) sum += filter[j-1+k] * input[N-k+1]; } } } else { /* extra element from input -> i0 i1 i2 [i2] */ for(k = 1; k <= F-j; ++k) sum += filter[j-1+k] * input[N-k]; } *(ptr_w++) = sum; } /* F - N-1 : filter in input range. Most time is spent in this loop */ for(; i < N; i+=step){ /* input elements, */ sum = 0; for(j = 0; j < F; ++j) sum += input[i-j]*filter[j]; *(ptr_w++) = sum; } for(; i < N-step + (F/2)+1 + N%2; i += step){ /* input elements */ sum = 0; k = i-N+1; for(j = k; j < F; ++j) /* overlapped elements */ sum += filter[j]*input[i-j]; if(N%2 == 0){ for(j = 0; j < k; ++j){ /* out of boundary (filter elements [0, k-1]) */ sum += filter[j]*input[k-1-j]; } } else { /* repeating extra element -> i0 i1 i2 [i2] */ for(j = 0; j < k-1; ++j) /* out of boundary (filter elements [0, k-1]) */ sum += filter[j]*input[k-2-j]; sum += filter[k-1] * input[N-1]; } *(ptr_w++) = sum; } return 0; } int double_downsampling_convolution(const double* input, const_index_t N, const double* filter, const_index_t F, double* output, const_index_t step, MODE mode) { /* * This convolution performs efficient downsampling by computing every * step'th element of normal convolution (currently tested only for step=1 * and step=2). * * It also implements several different strategies of dealing with border * distortion problem (the problem of computing convolution for not * existing elements of signal). To handle this the signal has to be * "extended" on both sides by computing the missing values. * * General schema is as follows: 1. Handle extended on the left, convolve * filter with samples computed for time < 0 2. Do the normal decimated * convolution of filter with signal samples 3. Handle extended on the * right, convolve filter with samples computed for time > n-1 */ index_t i, j, k; index_t start; double sum, tmp; #ifdef OPT_UNROLL2 double sum2; #endif #ifdef OPT_UNROLL4 #ifndef OPT_UNROLL2 double sum2; #endif double sum3, sum4; #endif double* ptr_w = output; i = start = step-1; /* first element taken from input is input[step-1] */ if(F <= N){ if(mode == MODE_PERIODIZATION){ return double_downsampling_convolution_periodization(input, N, filter, F, output, step); /* Other signal extension modes */ } else { /* 0 - F-1 : sliding in filter */ switch(mode) { case MODE_SYMMETRIC: for(i=start; i < F; i+=step){ sum = 0; for(j = 0; j <= i; ++j) sum += filter[j]*input[i-j]; k = i+1; for(j = i+1; j < F; ++j) sum += filter[j] * input[j-k]; *(ptr_w++) = sum; } break; case MODE_ASYMMETRIC: for(i=start; i < F; i+=step){ sum = 0; for(j = 0; j <= i; ++j) sum += filter[j]*input[i-j]; k = i+1; for(j = i+1; j < F; ++j) sum += filter[j] * (input[0] - input[j-k]); *(ptr_w++) = sum; } break; case MODE_CONSTANT_EDGE: for(i=start; i < F; i+=step){ sum = 0; for(j = 0; j <= i; ++j) sum += filter[j]*input[i-j]; k = i+1; for(j = i+1; j < F; ++j) sum += filter[j] * input[0]; *(ptr_w++) = sum; } break; case MODE_SMOOTH: for(i=start; i < F; i+=step){ sum = 0; for(j = 0; j <= i; ++j) sum += filter[j]*input[i-j]; tmp = input[0]-input[1]; for(j = i+1; j < F; ++j){ sum += filter[j] * (input[0] + tmp * (j-i)); } *(ptr_w++) = sum; } break; case MODE_PERIODIC: for(i=start; i < F; i+=step){ sum = 0; for(j = 0; j <= i; ++j) sum += filter[j]*input[i-j]; k = N+i; for(j = i+1; j < F; ++j) sum += filter[j] * input[k-j]; *(ptr_w++) = sum; } break; case MODE_ZEROPAD: default: for(i=start; i < F; i+=step){ sum = 0; for(j = 0; j <= i; ++j) sum += filter[j]*input[i-j]; *(ptr_w++) = sum; } break; } /* * F - N-1 : filter in input range - simple convolution * Most time is spent in this loop. */ #ifdef OPT_UNROLL4 /* manually unroll the loop a bit */ if((N - F)/step > 4) { for(; i < (N - (3*step)); i += 4*step){ /* input elements */ sum = input[i] * filter[0]; sum2 = input[i+step] * filter[0]; sum3 = input[i+(2*step)] * filter[0]; sum4 = input[i+(3*step)] * filter[0]; for(j = 1; j < F; ++j){ sum += input[i-j] * filter[j]; sum2 += input[(step+i)-j] * filter[j]; sum3 += input[(2*step+i)-j] * filter[j]; sum4 += input[(3*step+i)-j] * filter[j]; } *(ptr_w++) = sum; *(ptr_w++) = sum2; *(ptr_w++) = sum3; *(ptr_w++) = sum4; } } #endif #ifdef OPT_UNROLL2 if((N - F)/step > 2) { for(; i < (N - step); i += 2*step){ /* input elements, */ sum = input[i] * filter[0]; sum2 = input[i+step] * filter[0]; for(j = 1; j < F; ++j){ sum += input[i-j] * filter[j]; sum2 += input[(step+i)-j] * filter[j]; } *(ptr_w++) = sum; *(ptr_w++) = sum2; } } #endif for(; i < N; i+=step){ /* input elements, */ sum = input[i] * filter[0]; for(j = 1; j < F; ++j){ sum += input[i-j]*filter[j]; } *(ptr_w++) = sum; } /* N - N+F-1 : sliding out filter */ switch(mode) { case MODE_SYMMETRIC: for(; i < N+F-1; i += step){ /* input elements */ sum = 0; k = i-N+1; /* 1, 2, 3 : overlapped elements */ for(j = k; j < F; ++j) /*TODO: j < F-_offset */ sum += filter[j]*input[i-j]; for(j = 0; j < k; ++j) /* out of boundary (TODO: j = _offset) */ /* j-i-1 0*(N-1), 0*(N-2) 1*(N-1) */ sum += filter[j]*input[N-k+j]; *(ptr_w++) = sum; } break; case MODE_ASYMMETRIC: for(; i < N+F-1; i += step){ /* input elements */ sum = 0; k = i-N+1; for(j = k; j < F; ++j) /* overlapped elements */ sum += filter[j]*input[i-j]; for(j = 0; j < k; ++j) /* out of boundary */ sum += filter[j]*(input[N-1]-input[N-k-1+j]); /* -= j-i-1 */ *(ptr_w++) = sum; } break; case MODE_CONSTANT_EDGE: for(; i < N+F-1; i += step){ /* input elements */ sum = 0; k = i-N+1; for(j = k; j < F; ++j) /* overlapped elements */ sum += filter[j]*input[i-j]; for(j = 0; j < k; ++j) /* out of boundary (filter elements [0, k-1]) */ sum += filter[j]*input[N-1]; /* input[N-1] = const */ *(ptr_w++) = sum; } break; case MODE_SMOOTH: for(; i < N+F-1; i += step){ /* input elements */ sum = 0; k = i-N+1; for(j = k; j < F; ++j) /* overlapped elements */ sum += filter[j]*input[i-j]; tmp = input[N-1]-input[N-2]; for(j = 0; j < k; ++j) /* out of boundary (filter elements [0, k-1]) */ sum += filter[j] * (input[N-1] + tmp * (k-j)); *(ptr_w++) = sum; } break; case MODE_PERIODIC: for(; i < N+F-1; i += step){ /* input elements */ sum = 0; k = i-N+1; for(j = k; j < F; ++j) /* overlapped elements */ sum += filter[j]*input[i-j]; for(j = 0; j < k; ++j) /* out of boundary (filter elements [0, k-1]) */ sum += filter[j]*input[k-1-j]; *(ptr_w++) = sum; } break; case MODE_ZEROPAD: default: for(; i < N+F-1; i += step){ sum = 0; for(j = i-(N-1); j < F; ++j) sum += input[i-j]*filter[j]; *(ptr_w++) = sum; } break; } } return 0; } else { /* reallocating memory for short signals (shorter than filter) is cheap */ return double_allocating_downsampling_convolution(input, N, filter, F, output, step, mode); } } /* * ### like downsampling_convolution, but with memory allocation ### */ int double_allocating_downsampling_convolution(const double* input, const_index_t N, const double* filter, const_index_t F, double* output, const_index_t step, MODE mode) { index_t i, j, F_minus_1, N_extended_len, N_extended_right_start; index_t start, stop; double sum, tmp; double *buffer; double* ptr_w = output; F_minus_1 = F - 1; start = F_minus_1+step-1; /* allocate memory and copy input */ if(mode != MODE_PERIODIZATION){ N_extended_len = N + 2*F_minus_1; N_extended_right_start = N + F_minus_1; buffer = wtcalloc(N_extended_len, sizeof(double)); if(buffer == NULL) return -1; memcpy(buffer+F_minus_1, input, sizeof(double) * N); stop = N_extended_len; } else { N_extended_len = N + F-1; N_extended_right_start = N-1 + F/2; buffer = wtcalloc(N_extended_len, sizeof(double)); if(buffer == NULL) return -1; memcpy(buffer+F/2-1, input, sizeof(double) * N); start -= 1; if(step == 1) stop = N_extended_len-1; else /* step == 2 */ stop = N_extended_len; } /* copy extended signal elements */ switch(mode){ case MODE_PERIODIZATION: if(N%2){ /* odd - repeat last element */ buffer[N_extended_right_start] = input[N-1]; for(j = 1; j < F/2; ++j) buffer[N_extended_right_start+j] = buffer[F/2-2 + j]; /* copy from beginning of `input` to right */ for(j = 0; j < F/2-1; ++j) /* copy from 'buffer' to left */ buffer[F/2-2-j] = buffer[N_extended_right_start-j]; } else { for(j = 0; j < F/2; ++j) buffer[N_extended_right_start+j] = input[j%N]; /* copy from beginning of `input` to right */ for(j = 0; j < F/2-1; ++j) /* copy from 'buffer' to left */ buffer[F/2-2-j] = buffer[N_extended_right_start-1-j]; } break; case MODE_SYMMETRIC: for(j = 0; j < N; ++j){ buffer[F_minus_1-1-j] = input[j%N]; buffer[N_extended_right_start+j] = input[N-1-(j%N)]; } i=j; /* use `buffer` as source */ for(; j < F_minus_1; ++j){ buffer[F_minus_1-1-j] = buffer[N_extended_right_start-1+i-j]; buffer[N_extended_right_start+j] = buffer[F_minus_1+j-i]; } break; case MODE_ASYMMETRIC: for(j = 0; j < N; ++j){ buffer[F_minus_1-1-j] = input[0] - input[j%N]; buffer[N_extended_right_start+j] = (input[N-1] - input[N-1-(j%N)]); } i=j; /* use `buffer` as source */ for(; j < F_minus_1; ++j){ buffer[F_minus_1-1-j] = buffer[N_extended_right_start-1+i-j]; buffer[N_extended_right_start+j] = buffer[F_minus_1+j-i]; } break; case MODE_SMOOTH: if(N>1){ tmp = input[0]-input[1]; for(j = 0; j < F_minus_1; ++j) buffer[j] = input[0] + (tmp * (F_minus_1-j)); tmp = input[N-1]-input[N-2]; for(j = 0; j < F_minus_1; ++j) buffer[N_extended_right_start+j] = input[N-1] + (tmp*j); break; } case MODE_CONSTANT_EDGE: for(j = 0; j < F_minus_1; ++j){ buffer[j] = input[0]; buffer[N_extended_right_start+j] = input[N-1]; } break; case MODE_PERIODIC: for(j = 0; j < F_minus_1; ++j) buffer[N_extended_right_start+j] = input[j%N]; /* copy from beginning of `input` to right */ for(j = 0; j < F_minus_1; ++j) /* copy from 'buffer' to left */ buffer[F_minus_1-1-j] = buffer[N_extended_right_start-1-j]; break; case MODE_ZEROPAD: default: break; } /* * F - N-1 : filter in input range, perform convolution with decimation */ for(i=start; i < stop; i+=step){ /* input elements */ sum = 0; for(j = 0; j < F; ++j){ sum += buffer[i-j]*filter[j]; } *(ptr_w++) = sum; } /* free memory */ wtfree(buffer); return 0; } /* * Requires zero-filled output buffer output is larger than input * performs "normal" convolution of "upsampled" input coeffs array with filter */ int double_upsampling_convolution_full(const double* input, const_index_t N, const double* filter, const_index_t F, double* output, const_index_t O){ register index_t i; register index_t j; double *ptr_out; if(F<2) return -1; ptr_out = output + ((N-1) << 1); for(i = N-1; i >= 0; --i){ /* * sliding in filter from the right (end of input) * i0 0 i1 0 i2 0 * f1 -> o1 * f1 f2 -> o2 * f1 f2 f3 -> o3 */ for(j = 0; j < F; ++j) ptr_out[j] += input[i] * filter[j]; /* input[i] - const in loop */ ptr_out -= 2; } return 0; } /* * performs IDWT for PERIODIZATION mode only * (refactored from the upsampling_convolution_valid_sf function) * * The upsampling is performed by splitting filters to even and odd elements * and performing 2 convolutions * * The input data has to be periodically extended for this mode. */ int double_upsampling_convolution_valid_sf_periodization(const double* input, const_index_t N, const double* filter, const_index_t F, double* output, const_index_t O) { double *ptr_out = output; double *filter_even, *filter_odd; double *periodization_buf = NULL; double *periodization_buf_rear = NULL; double *ptr_base; double sum_even, sum_odd; index_t i, j, k, N_p = 0; index_t F_2 = F/2; if(F%2) return -3; /* Filter must have even-length. */ /* * Handle special situation when input coeff data is shorter than half of * the filter's length. The coeff array has to be extended periodically. * This can be only valid for PERIODIZATION_MODE */ if(N < F_2) { /* Input data for periodization mode has to be periodically extended */ /* New length for temporary input */ N_p = F_2-1 +N; /* periodization_buf will hold periodically copied input coeffs values */ periodization_buf = wtcalloc(N_p, sizeof(double)); if(periodization_buf == NULL) return -1; /* Copy input data to its place in the periodization_buf */ /* -> [0 0 0 i1 i2 i3 0 0 0] */ k = (F_2-1)/2; for(i=k; i < k+N; ++i) periodization_buf[i] = input[(i-k)%N]; /* if(N%2) * periodization_buf[i++] = input[N-1]; * * [0 0 0 i1 i2 i3 0 0 0] * points here ^^ */ periodization_buf_rear = periodization_buf+i-1; /* copy cyclically () to right [0 0 0 i1 i2 i3 i1 i2 ...] */ j = i-k; for(; i < N_p; ++i) periodization_buf[i] = periodization_buf[i-j]; /* copy cyclically () to left [... i2 i3 i1 i2 i3 i1 i2 i3] */ j = 0; for(i=k-1; i >= 0; --i){ periodization_buf[i] = periodization_buf_rear[j]; --j; } /* Now perform the valid convolution */ if(F_2%2){ double_upsampling_convolution_valid_sf(periodization_buf, N_p, filter, F, output, O, MODE_ZEROPAD); /* The F_2%2==0 case needs special result fix (oh my, another one..) */ } else { /* * Cheap result fix for short inputs * Memory allocation for temporary output is done. * Computed temporary result is copied to output* */ ptr_out = wtcalloc(idwt_buffer_length(N, F, MODE_PERIODIZATION), sizeof(double)); if(ptr_out == NULL){ wtfree(periodization_buf); return -1; } /* Convolve here as for (F_2%2) branch above */ double_upsampling_convolution_valid_sf(periodization_buf, N_p, filter, F, ptr_out, O, MODE_ZEROPAD); /* rewrite result to output */ for(i=2*N-1; i > 0; --i){ output[i] += ptr_out[i-1]; } /* and the first element */ output[0] += ptr_out[2*N-1]; wtfree(ptr_out); /* and voila!, ugh */ } } else { /* Otherwise (N >= F_2) */ /* Allocate memory for even and odd elements of the filter */ filter_even = wtmalloc(F_2 * sizeof(double)); filter_odd = wtmalloc(F_2 * sizeof(double)); if(filter_odd == NULL || filter_odd == NULL){ if(filter_odd == NULL) wtfree(filter_odd); if(filter_even == NULL) wtfree(filter_even); return -1; } /* split filter to even and odd values */ for(i = 0; i < F_2; ++i){ filter_even[i] = filter[i << 1]; filter_odd[i] = filter[(i << 1) + 1]; } /* * ############################################################ * This part is quite complicated and has some wild checking to * get results similar to those from Matlab(TM) Wavelet Toolbox */ k = F_2-1; /* Check if extending is really needed */ /* split filter len correct + extra samples*/ N_p = F_2-1 + (index_t) ceil(k/2.); /* * ok, if is then do: * 1. Allocate buffers for front and rear parts of extended input * 2. Copy periodically appropriate elements from input to the buffers * 3. Convolve front buffer, input and rear buffer with even and odd * elements of the filter (this results in upsampling) * 4. Free memory */ if(N_p > 0){ /* * Allocate memory only for the front and rear extension parts, not * the whole input */ periodization_buf = wtcalloc(N_p, sizeof(double)); periodization_buf_rear = wtcalloc(N_p, sizeof(double)); /* Memory checking */ if(periodization_buf == NULL || periodization_buf_rear == NULL){ if(periodization_buf == NULL) wtfree(periodization_buf); if(periodization_buf_rear == NULL) wtfree(periodization_buf_rear); wtfree(filter_odd); wtfree(filter_even); return -1; } /* Fill buffers with appropriate elements */ /* copy from beginning of input to end of buffer */ memcpy(periodization_buf + N_p - k, input, k * sizeof(double)); for(i = 1; i <= (N_p - k); ++i) periodization_buf[(N_p - k) - i] = input[N - (i%N)]; /* copy from end of input to beginning of buffer */ memcpy(periodization_buf_rear, input + N - k, k * sizeof(double)); for(i = 0; i < (N_p - k); ++i) periodization_buf_rear[k + i] = input[i%N]; /* * Convolve filters with the (front) periodization_buf and compute * the first part of output */ ptr_base = periodization_buf + F_2 - 1; if(k%2 == 1){ sum_odd = 0; for(j = 0; j < F_2; ++j) sum_odd += filter_odd[j] * ptr_base[-j]; *(ptr_out++) += sum_odd; --k; if(k) double_upsampling_convolution_valid_sf(periodization_buf + 1, N_p-1, filter, F, ptr_out, O-1, MODE_ZEROPAD); ptr_out += k; /* k0 - 1, really move backward by 1 */ } else if(k){ double_upsampling_convolution_valid_sf(periodization_buf, N_p, filter, F, ptr_out, O, MODE_ZEROPAD); ptr_out += k; } } /* * Perform _valid_ convolution (only when all filter_even and * filter_odd elements are in range of input data). * * This part is simple, no extra hacks, just two convolutions in one * loop */ ptr_base = (double*)input + F_2 - 1; for(i = 0; i < N-(F_2-1); ++i){ /* sliding over signal from left to right */ sum_even = 0; sum_odd = 0; for(j = 0; j < F_2; ++j){ sum_even += filter_even[j] * ptr_base[i-j]; sum_odd += filter_odd[j] * ptr_base[i-j]; } *(ptr_out++) += sum_even; *(ptr_out++) += sum_odd; } if(N_p > 0){ k = F_2-1; if(k%2 == 1){ if(F/2 <= N_p - 1){ /* k > 1 ? */ double_upsampling_convolution_valid_sf(periodization_buf_rear , N_p-1, filter, F, ptr_out, O-1, MODE_ZEROPAD); } ptr_out += k; /* move forward anyway -> see lower */ if(F_2%2 == 0){ /* remaining one element */ ptr_base = periodization_buf_rear + N_p - 1; sum_even = 0; for(j = 0; j < F_2; ++j){ sum_even += filter_even[j] * ptr_base[-j]; } *(--ptr_out) += sum_even; /* move backward first */ } } else { if(k){ double_upsampling_convolution_valid_sf(periodization_buf_rear, N_p, filter, F, ptr_out, O, MODE_ZEROPAD); } } } if(periodization_buf != NULL) wtfree(periodization_buf); if(periodization_buf_rear != NULL) wtfree(periodization_buf_rear); wtfree(filter_even); wtfree(filter_odd); } return 0; } /* * performs IDWT for all modes * * The upsampling is performed by splitting filters to even and odd elements * and performing 2 convolutions. After refactoring the PERIODIZATION mode * case to separate function this looks much clearer now. */ int double_upsampling_convolution_valid_sf(const double* input, const_index_t N, const double* filter, const_index_t F, double* output, const_index_t O, MODE mode){ double *ptr_out = output; double *filter_even, *filter_odd; double *ptr_base; double sum_even, sum_odd; #ifdef OPT_UNROLL2 double sum_even2, sum_odd2; #endif #ifdef OPT_UNROLL4 #ifndef OPT_UNROLL2 double sum_even2, sum_odd2; #endif double sum_even3, sum_odd3; double sum_even4, sum_odd4; #endif index_t i, j; index_t F_2 = F/2; if(mode == MODE_PERIODIZATION) /* Special case */ return double_upsampling_convolution_valid_sf_periodization(input, N, filter, F, output, O); if((F%2) || (N < F_2)) /* Filter must have even length. */ return -1; /* Allocate memory for even and odd elements of the filter */ filter_even = wtmalloc(F_2 * sizeof(double)); filter_odd = wtmalloc(F_2 * sizeof(double)); if(filter_odd == NULL || filter_odd == NULL){ if(filter_odd == NULL) wtfree(filter_odd); if(filter_even == NULL) wtfree(filter_even); return -1; } /* split filter to even and odd values */ for(i = 0; i < F_2; ++i){ filter_even[i] = filter[i << 1]; filter_odd[i] = filter[(i << 1) + 1]; } /* * Perform _valid_ convolution (only when all filter_even and filter_odd elements * are in range of input data). * * This part is simple, no extra hacks, just two convolutions in one loop */ ptr_base = (double*)input + F_2 - 1; i = 0; #ifdef OPT_UNROLL4 /* manually unroll the loop a bit */ for(; i < N-(F_2-1+8); i+=4){ /* sliding over signal from left to right */ sum_even = filter_even[0] * ptr_base[i]; sum_even2 = filter_even[0] * ptr_base[i+1]; sum_even3 = filter_even[0] * ptr_base[i+2]; sum_even4 = filter_even[0] * ptr_base[i+3]; sum_odd = filter_odd[0] * ptr_base[i]; sum_odd2 = filter_odd[0] * ptr_base[i+1]; sum_odd3 = filter_odd[0] * ptr_base[i+2]; sum_odd4 = filter_odd[0] * ptr_base[i+3]; for(j = 1; j < F_2; ++j){ sum_even += filter_even[j] * ptr_base[i-j]; sum_even2 += filter_even[j] * ptr_base[(i+1)-j]; sum_even3 += filter_even[j] * ptr_base[(i+2)-j]; sum_even4 += filter_even[j] * ptr_base[(i+3)-j]; sum_odd += filter_odd[j] * ptr_base[i-j]; sum_odd2 += filter_odd[j] * ptr_base[(i+1)-j]; sum_odd3 += filter_odd[j] * ptr_base[(i+2)-j]; sum_odd4 += filter_odd[j] * ptr_base[(i+3)-j]; } *(ptr_out++) += sum_even; *(ptr_out++) += sum_odd; *(ptr_out++) += sum_even2; *(ptr_out++) += sum_odd2; *(ptr_out++) += sum_even3; *(ptr_out++) += sum_odd3; *(ptr_out++) += sum_even4; *(ptr_out++) += sum_odd4; } #endif #ifdef OPT_UNROLL2 /* manually unroll the loop a bit */ for(; i < N-(F_2+1); i+=2){ /* sliding over signal from left to right */ sum_even = filter_even[0] * ptr_base[i]; sum_even2 = filter_even[0] * ptr_base[i+1]; sum_odd = filter_odd[0] * ptr_base[i]; sum_odd2 = filter_odd[0] * ptr_base[i+1]; for(j = 1; j < F_2; ++j){ sum_even += filter_even[j] * ptr_base[i-j]; sum_even2 += filter_even[j] * ptr_base[(i+1)-j]; sum_odd += filter_odd[j] * ptr_base[i-j]; sum_odd2 += filter_odd[j] * ptr_base[(i+1)-j]; } *(ptr_out++) += sum_even; *(ptr_out++) += sum_odd; *(ptr_out++) += sum_even2; *(ptr_out++) += sum_odd2; } #endif for(; i < N-(F_2-1); ++i){ /* sliding over signal from left to right */ sum_even = filter_even[0] * ptr_base[i]; sum_odd = filter_odd[0] * ptr_base[i]; for(j = 1; j < F_2; ++j){ sum_even += filter_even[j] * ptr_base[i-j]; sum_odd += filter_odd[j] * ptr_base[i-j]; } *(ptr_out++) += sum_even; *(ptr_out++) += sum_odd; } wtfree(filter_even); wtfree(filter_odd); return 0; } /* -> swt - todo */ int double_upsampled_filter_convolution(const double* input, const_index_t N, const double* filter, const_index_t F, double* output, const_index_t step, MODE mode) { return -1; } #line 9 int float_downsampling_convolution_periodization(const float* input, const_index_t N, const float* filter, const_index_t F, float* output, const_index_t step) { index_t i, j, k, F_2, corr; index_t start; float sum; float* ptr_w = output; i = step-1; /* first element taken from input is input[step-1] */ start = F_2 = F/2; /* extending by (F-2)/2 elements */ corr = 0; for(i=start; i < F; i+=step){ sum = 0; for(j = 0; j < i+1-corr; ++j) /* overlapping */ sum += filter[j]*input[i-j-corr]; if(N%2){ if(F-j){ /* if something to extend */ sum += filter[j] * input[N-1]; if(F-j){ for(k = 2-corr; k <= F-j; ++k) sum += filter[j-1+k] * input[N-k+1]; } } } else { /* extra element from input -> i0 i1 i2 [i2] */ for(k = 1; k <= F-j; ++k) sum += filter[j-1+k] * input[N-k]; } *(ptr_w++) = sum; } /* F - N-1 : filter in input range. Most time is spent in this loop */ for(; i < N; i+=step){ /* input elements, */ sum = 0; for(j = 0; j < F; ++j) sum += input[i-j]*filter[j]; *(ptr_w++) = sum; } for(; i < N-step + (F/2)+1 + N%2; i += step){ /* input elements */ sum = 0; k = i-N+1; for(j = k; j < F; ++j) /* overlapped elements */ sum += filter[j]*input[i-j]; if(N%2 == 0){ for(j = 0; j < k; ++j){ /* out of boundary (filter elements [0, k-1]) */ sum += filter[j]*input[k-1-j]; } } else { /* repeating extra element -> i0 i1 i2 [i2] */ for(j = 0; j < k-1; ++j) /* out of boundary (filter elements [0, k-1]) */ sum += filter[j]*input[k-2-j]; sum += filter[k-1] * input[N-1]; } *(ptr_w++) = sum; } return 0; } int float_downsampling_convolution(const float* input, const_index_t N, const float* filter, const_index_t F, float* output, const_index_t step, MODE mode) { /* * This convolution performs efficient downsampling by computing every * step'th element of normal convolution (currently tested only for step=1 * and step=2). * * It also implements several different strategies of dealing with border * distortion problem (the problem of computing convolution for not * existing elements of signal). To handle this the signal has to be * "extended" on both sides by computing the missing values. * * General schema is as follows: 1. Handle extended on the left, convolve * filter with samples computed for time < 0 2. Do the normal decimated * convolution of filter with signal samples 3. Handle extended on the * right, convolve filter with samples computed for time > n-1 */ index_t i, j, k; index_t start; float sum, tmp; #ifdef OPT_UNROLL2 float sum2; #endif #ifdef OPT_UNROLL4 #ifndef OPT_UNROLL2 float sum2; #endif float sum3, sum4; #endif float* ptr_w = output; i = start = step-1; /* first element taken from input is input[step-1] */ if(F <= N){ if(mode == MODE_PERIODIZATION){ return float_downsampling_convolution_periodization(input, N, filter, F, output, step); /* Other signal extension modes */ } else { /* 0 - F-1 : sliding in filter */ switch(mode) { case MODE_SYMMETRIC: for(i=start; i < F; i+=step){ sum = 0; for(j = 0; j <= i; ++j) sum += filter[j]*input[i-j]; k = i+1; for(j = i+1; j < F; ++j) sum += filter[j] * input[j-k]; *(ptr_w++) = sum; } break; case MODE_ASYMMETRIC: for(i=start; i < F; i+=step){ sum = 0; for(j = 0; j <= i; ++j) sum += filter[j]*input[i-j]; k = i+1; for(j = i+1; j < F; ++j) sum += filter[j] * (input[0] - input[j-k]); *(ptr_w++) = sum; } break; case MODE_CONSTANT_EDGE: for(i=start; i < F; i+=step){ sum = 0; for(j = 0; j <= i; ++j) sum += filter[j]*input[i-j]; k = i+1; for(j = i+1; j < F; ++j) sum += filter[j] * input[0]; *(ptr_w++) = sum; } break; case MODE_SMOOTH: for(i=start; i < F; i+=step){ sum = 0; for(j = 0; j <= i; ++j) sum += filter[j]*input[i-j]; tmp = input[0]-input[1]; for(j = i+1; j < F; ++j){ sum += filter[j] * (input[0] + tmp * (j-i)); } *(ptr_w++) = sum; } break; case MODE_PERIODIC: for(i=start; i < F; i+=step){ sum = 0; for(j = 0; j <= i; ++j) sum += filter[j]*input[i-j]; k = N+i; for(j = i+1; j < F; ++j) sum += filter[j] * input[k-j]; *(ptr_w++) = sum; } break; case MODE_ZEROPAD: default: for(i=start; i < F; i+=step){ sum = 0; for(j = 0; j <= i; ++j) sum += filter[j]*input[i-j]; *(ptr_w++) = sum; } break; } /* * F - N-1 : filter in input range - simple convolution * Most time is spent in this loop. */ #ifdef OPT_UNROLL4 /* manually unroll the loop a bit */ if((N - F)/step > 4) { for(; i < (N - (3*step)); i += 4*step){ /* input elements */ sum = input[i] * filter[0]; sum2 = input[i+step] * filter[0]; sum3 = input[i+(2*step)] * filter[0]; sum4 = input[i+(3*step)] * filter[0]; for(j = 1; j < F; ++j){ sum += input[i-j] * filter[j]; sum2 += input[(step+i)-j] * filter[j]; sum3 += input[(2*step+i)-j] * filter[j]; sum4 += input[(3*step+i)-j] * filter[j]; } *(ptr_w++) = sum; *(ptr_w++) = sum2; *(ptr_w++) = sum3; *(ptr_w++) = sum4; } } #endif #ifdef OPT_UNROLL2 if((N - F)/step > 2) { for(; i < (N - step); i += 2*step){ /* input elements, */ sum = input[i] * filter[0]; sum2 = input[i+step] * filter[0]; for(j = 1; j < F; ++j){ sum += input[i-j] * filter[j]; sum2 += input[(step+i)-j] * filter[j]; } *(ptr_w++) = sum; *(ptr_w++) = sum2; } } #endif for(; i < N; i+=step){ /* input elements, */ sum = input[i] * filter[0]; for(j = 1; j < F; ++j){ sum += input[i-j]*filter[j]; } *(ptr_w++) = sum; } /* N - N+F-1 : sliding out filter */ switch(mode) { case MODE_SYMMETRIC: for(; i < N+F-1; i += step){ /* input elements */ sum = 0; k = i-N+1; /* 1, 2, 3 : overlapped elements */ for(j = k; j < F; ++j) /*TODO: j < F-_offset */ sum += filter[j]*input[i-j]; for(j = 0; j < k; ++j) /* out of boundary (TODO: j = _offset) */ /* j-i-1 0*(N-1), 0*(N-2) 1*(N-1) */ sum += filter[j]*input[N-k+j]; *(ptr_w++) = sum; } break; case MODE_ASYMMETRIC: for(; i < N+F-1; i += step){ /* input elements */ sum = 0; k = i-N+1; for(j = k; j < F; ++j) /* overlapped elements */ sum += filter[j]*input[i-j]; for(j = 0; j < k; ++j) /* out of boundary */ sum += filter[j]*(input[N-1]-input[N-k-1+j]); /* -= j-i-1 */ *(ptr_w++) = sum; } break; case MODE_CONSTANT_EDGE: for(; i < N+F-1; i += step){ /* input elements */ sum = 0; k = i-N+1; for(j = k; j < F; ++j) /* overlapped elements */ sum += filter[j]*input[i-j]; for(j = 0; j < k; ++j) /* out of boundary (filter elements [0, k-1]) */ sum += filter[j]*input[N-1]; /* input[N-1] = const */ *(ptr_w++) = sum; } break; case MODE_SMOOTH: for(; i < N+F-1; i += step){ /* input elements */ sum = 0; k = i-N+1; for(j = k; j < F; ++j) /* overlapped elements */ sum += filter[j]*input[i-j]; tmp = input[N-1]-input[N-2]; for(j = 0; j < k; ++j) /* out of boundary (filter elements [0, k-1]) */ sum += filter[j] * (input[N-1] + tmp * (k-j)); *(ptr_w++) = sum; } break; case MODE_PERIODIC: for(; i < N+F-1; i += step){ /* input elements */ sum = 0; k = i-N+1; for(j = k; j < F; ++j) /* overlapped elements */ sum += filter[j]*input[i-j]; for(j = 0; j < k; ++j) /* out of boundary (filter elements [0, k-1]) */ sum += filter[j]*input[k-1-j]; *(ptr_w++) = sum; } break; case MODE_ZEROPAD: default: for(; i < N+F-1; i += step){ sum = 0; for(j = i-(N-1); j < F; ++j) sum += input[i-j]*filter[j]; *(ptr_w++) = sum; } break; } } return 0; } else { /* reallocating memory for short signals (shorter than filter) is cheap */ return float_allocating_downsampling_convolution(input, N, filter, F, output, step, mode); } } /* * ### like downsampling_convolution, but with memory allocation ### */ int float_allocating_downsampling_convolution(const float* input, const_index_t N, const float* filter, const_index_t F, float* output, const_index_t step, MODE mode) { index_t i, j, F_minus_1, N_extended_len, N_extended_right_start; index_t start, stop; float sum, tmp; float *buffer; float* ptr_w = output; F_minus_1 = F - 1; start = F_minus_1+step-1; /* allocate memory and copy input */ if(mode != MODE_PERIODIZATION){ N_extended_len = N + 2*F_minus_1; N_extended_right_start = N + F_minus_1; buffer = wtcalloc(N_extended_len, sizeof(float)); if(buffer == NULL) return -1; memcpy(buffer+F_minus_1, input, sizeof(float) * N); stop = N_extended_len; } else { N_extended_len = N + F-1; N_extended_right_start = N-1 + F/2; buffer = wtcalloc(N_extended_len, sizeof(float)); if(buffer == NULL) return -1; memcpy(buffer+F/2-1, input, sizeof(float) * N); start -= 1; if(step == 1) stop = N_extended_len-1; else /* step == 2 */ stop = N_extended_len; } /* copy extended signal elements */ switch(mode){ case MODE_PERIODIZATION: if(N%2){ /* odd - repeat last element */ buffer[N_extended_right_start] = input[N-1]; for(j = 1; j < F/2; ++j) buffer[N_extended_right_start+j] = buffer[F/2-2 + j]; /* copy from beginning of `input` to right */ for(j = 0; j < F/2-1; ++j) /* copy from 'buffer' to left */ buffer[F/2-2-j] = buffer[N_extended_right_start-j]; } else { for(j = 0; j < F/2; ++j) buffer[N_extended_right_start+j] = input[j%N]; /* copy from beginning of `input` to right */ for(j = 0; j < F/2-1; ++j) /* copy from 'buffer' to left */ buffer[F/2-2-j] = buffer[N_extended_right_start-1-j]; } break; case MODE_SYMMETRIC: for(j = 0; j < N; ++j){ buffer[F_minus_1-1-j] = input[j%N]; buffer[N_extended_right_start+j] = input[N-1-(j%N)]; } i=j; /* use `buffer` as source */ for(; j < F_minus_1; ++j){ buffer[F_minus_1-1-j] = buffer[N_extended_right_start-1+i-j]; buffer[N_extended_right_start+j] = buffer[F_minus_1+j-i]; } break; case MODE_ASYMMETRIC: for(j = 0; j < N; ++j){ buffer[F_minus_1-1-j] = input[0] - input[j%N]; buffer[N_extended_right_start+j] = (input[N-1] - input[N-1-(j%N)]); } i=j; /* use `buffer` as source */ for(; j < F_minus_1; ++j){ buffer[F_minus_1-1-j] = buffer[N_extended_right_start-1+i-j]; buffer[N_extended_right_start+j] = buffer[F_minus_1+j-i]; } break; case MODE_SMOOTH: if(N>1){ tmp = input[0]-input[1]; for(j = 0; j < F_minus_1; ++j) buffer[j] = input[0] + (tmp * (F_minus_1-j)); tmp = input[N-1]-input[N-2]; for(j = 0; j < F_minus_1; ++j) buffer[N_extended_right_start+j] = input[N-1] + (tmp*j); break; } case MODE_CONSTANT_EDGE: for(j = 0; j < F_minus_1; ++j){ buffer[j] = input[0]; buffer[N_extended_right_start+j] = input[N-1]; } break; case MODE_PERIODIC: for(j = 0; j < F_minus_1; ++j) buffer[N_extended_right_start+j] = input[j%N]; /* copy from beginning of `input` to right */ for(j = 0; j < F_minus_1; ++j) /* copy from 'buffer' to left */ buffer[F_minus_1-1-j] = buffer[N_extended_right_start-1-j]; break; case MODE_ZEROPAD: default: break; } /* * F - N-1 : filter in input range, perform convolution with decimation */ for(i=start; i < stop; i+=step){ /* input elements */ sum = 0; for(j = 0; j < F; ++j){ sum += buffer[i-j]*filter[j]; } *(ptr_w++) = sum; } /* free memory */ wtfree(buffer); return 0; } /* * Requires zero-filled output buffer output is larger than input * performs "normal" convolution of "upsampled" input coeffs array with filter */ int float_upsampling_convolution_full(const float* input, const_index_t N, const float* filter, const_index_t F, float* output, const_index_t O){ register index_t i; register index_t j; float *ptr_out; if(F<2) return -1; ptr_out = output + ((N-1) << 1); for(i = N-1; i >= 0; --i){ /* * sliding in filter from the right (end of input) * i0 0 i1 0 i2 0 * f1 -> o1 * f1 f2 -> o2 * f1 f2 f3 -> o3 */ for(j = 0; j < F; ++j) ptr_out[j] += input[i] * filter[j]; /* input[i] - const in loop */ ptr_out -= 2; } return 0; } /* * performs IDWT for PERIODIZATION mode only * (refactored from the upsampling_convolution_valid_sf function) * * The upsampling is performed by splitting filters to even and odd elements * and performing 2 convolutions * * The input data has to be periodically extended for this mode. */ int float_upsampling_convolution_valid_sf_periodization(const float* input, const_index_t N, const float* filter, const_index_t F, float* output, const_index_t O) { float *ptr_out = output; float *filter_even, *filter_odd; float *periodization_buf = NULL; float *periodization_buf_rear = NULL; float *ptr_base; float sum_even, sum_odd; index_t i, j, k, N_p = 0; index_t F_2 = F/2; if(F%2) return -3; /* Filter must have even-length. */ /* * Handle special situation when input coeff data is shorter than half of * the filter's length. The coeff array has to be extended periodically. * This can be only valid for PERIODIZATION_MODE */ if(N < F_2) { /* Input data for periodization mode has to be periodically extended */ /* New length for temporary input */ N_p = F_2-1 +N; /* periodization_buf will hold periodically copied input coeffs values */ periodization_buf = wtcalloc(N_p, sizeof(float)); if(periodization_buf == NULL) return -1; /* Copy input data to its place in the periodization_buf */ /* -> [0 0 0 i1 i2 i3 0 0 0] */ k = (F_2-1)/2; for(i=k; i < k+N; ++i) periodization_buf[i] = input[(i-k)%N]; /* if(N%2) * periodization_buf[i++] = input[N-1]; * * [0 0 0 i1 i2 i3 0 0 0] * points here ^^ */ periodization_buf_rear = periodization_buf+i-1; /* copy cyclically () to right [0 0 0 i1 i2 i3 i1 i2 ...] */ j = i-k; for(; i < N_p; ++i) periodization_buf[i] = periodization_buf[i-j]; /* copy cyclically () to left [... i2 i3 i1 i2 i3 i1 i2 i3] */ j = 0; for(i=k-1; i >= 0; --i){ periodization_buf[i] = periodization_buf_rear[j]; --j; } /* Now perform the valid convolution */ if(F_2%2){ float_upsampling_convolution_valid_sf(periodization_buf, N_p, filter, F, output, O, MODE_ZEROPAD); /* The F_2%2==0 case needs special result fix (oh my, another one..) */ } else { /* * Cheap result fix for short inputs * Memory allocation for temporary output is done. * Computed temporary result is copied to output* */ ptr_out = wtcalloc(idwt_buffer_length(N, F, MODE_PERIODIZATION), sizeof(float)); if(ptr_out == NULL){ wtfree(periodization_buf); return -1; } /* Convolve here as for (F_2%2) branch above */ float_upsampling_convolution_valid_sf(periodization_buf, N_p, filter, F, ptr_out, O, MODE_ZEROPAD); /* rewrite result to output */ for(i=2*N-1; i > 0; --i){ output[i] += ptr_out[i-1]; } /* and the first element */ output[0] += ptr_out[2*N-1]; wtfree(ptr_out); /* and voila!, ugh */ } } else { /* Otherwise (N >= F_2) */ /* Allocate memory for even and odd elements of the filter */ filter_even = wtmalloc(F_2 * sizeof(float)); filter_odd = wtmalloc(F_2 * sizeof(float)); if(filter_odd == NULL || filter_odd == NULL){ if(filter_odd == NULL) wtfree(filter_odd); if(filter_even == NULL) wtfree(filter_even); return -1; } /* split filter to even and odd values */ for(i = 0; i < F_2; ++i){ filter_even[i] = filter[i << 1]; filter_odd[i] = filter[(i << 1) + 1]; } /* * ############################################################ * This part is quite complicated and has some wild checking to * get results similar to those from Matlab(TM) Wavelet Toolbox */ k = F_2-1; /* Check if extending is really needed */ /* split filter len correct + extra samples*/ N_p = F_2-1 + (index_t) ceil(k/2.); /* * ok, if is then do: * 1. Allocate buffers for front and rear parts of extended input * 2. Copy periodically appropriate elements from input to the buffers * 3. Convolve front buffer, input and rear buffer with even and odd * elements of the filter (this results in upsampling) * 4. Free memory */ if(N_p > 0){ /* * Allocate memory only for the front and rear extension parts, not * the whole input */ periodization_buf = wtcalloc(N_p, sizeof(float)); periodization_buf_rear = wtcalloc(N_p, sizeof(float)); /* Memory checking */ if(periodization_buf == NULL || periodization_buf_rear == NULL){ if(periodization_buf == NULL) wtfree(periodization_buf); if(periodization_buf_rear == NULL) wtfree(periodization_buf_rear); wtfree(filter_odd); wtfree(filter_even); return -1; } /* Fill buffers with appropriate elements */ /* copy from beginning of input to end of buffer */ memcpy(periodization_buf + N_p - k, input, k * sizeof(float)); for(i = 1; i <= (N_p - k); ++i) periodization_buf[(N_p - k) - i] = input[N - (i%N)]; /* copy from end of input to beginning of buffer */ memcpy(periodization_buf_rear, input + N - k, k * sizeof(float)); for(i = 0; i < (N_p - k); ++i) periodization_buf_rear[k + i] = input[i%N]; /* * Convolve filters with the (front) periodization_buf and compute * the first part of output */ ptr_base = periodization_buf + F_2 - 1; if(k%2 == 1){ sum_odd = 0; for(j = 0; j < F_2; ++j) sum_odd += filter_odd[j] * ptr_base[-j]; *(ptr_out++) += sum_odd; --k; if(k) float_upsampling_convolution_valid_sf(periodization_buf + 1, N_p-1, filter, F, ptr_out, O-1, MODE_ZEROPAD); ptr_out += k; /* k0 - 1, really move backward by 1 */ } else if(k){ float_upsampling_convolution_valid_sf(periodization_buf, N_p, filter, F, ptr_out, O, MODE_ZEROPAD); ptr_out += k; } } /* * Perform _valid_ convolution (only when all filter_even and * filter_odd elements are in range of input data). * * This part is simple, no extra hacks, just two convolutions in one * loop */ ptr_base = (float*)input + F_2 - 1; for(i = 0; i < N-(F_2-1); ++i){ /* sliding over signal from left to right */ sum_even = 0; sum_odd = 0; for(j = 0; j < F_2; ++j){ sum_even += filter_even[j] * ptr_base[i-j]; sum_odd += filter_odd[j] * ptr_base[i-j]; } *(ptr_out++) += sum_even; *(ptr_out++) += sum_odd; } if(N_p > 0){ k = F_2-1; if(k%2 == 1){ if(F/2 <= N_p - 1){ /* k > 1 ? */ float_upsampling_convolution_valid_sf(periodization_buf_rear , N_p-1, filter, F, ptr_out, O-1, MODE_ZEROPAD); } ptr_out += k; /* move forward anyway -> see lower */ if(F_2%2 == 0){ /* remaining one element */ ptr_base = periodization_buf_rear + N_p - 1; sum_even = 0; for(j = 0; j < F_2; ++j){ sum_even += filter_even[j] * ptr_base[-j]; } *(--ptr_out) += sum_even; /* move backward first */ } } else { if(k){ float_upsampling_convolution_valid_sf(periodization_buf_rear, N_p, filter, F, ptr_out, O, MODE_ZEROPAD); } } } if(periodization_buf != NULL) wtfree(periodization_buf); if(periodization_buf_rear != NULL) wtfree(periodization_buf_rear); wtfree(filter_even); wtfree(filter_odd); } return 0; } /* * performs IDWT for all modes * * The upsampling is performed by splitting filters to even and odd elements * and performing 2 convolutions. After refactoring the PERIODIZATION mode * case to separate function this looks much clearer now. */ int float_upsampling_convolution_valid_sf(const float* input, const_index_t N, const float* filter, const_index_t F, float* output, const_index_t O, MODE mode){ float *ptr_out = output; float *filter_even, *filter_odd; float *ptr_base; float sum_even, sum_odd; #ifdef OPT_UNROLL2 float sum_even2, sum_odd2; #endif #ifdef OPT_UNROLL4 #ifndef OPT_UNROLL2 float sum_even2, sum_odd2; #endif float sum_even3, sum_odd3; float sum_even4, sum_odd4; #endif index_t i, j; index_t F_2 = F/2; if(mode == MODE_PERIODIZATION) /* Special case */ return float_upsampling_convolution_valid_sf_periodization(input, N, filter, F, output, O); if((F%2) || (N < F_2)) /* Filter must have even length. */ return -1; /* Allocate memory for even and odd elements of the filter */ filter_even = wtmalloc(F_2 * sizeof(float)); filter_odd = wtmalloc(F_2 * sizeof(float)); if(filter_odd == NULL || filter_odd == NULL){ if(filter_odd == NULL) wtfree(filter_odd); if(filter_even == NULL) wtfree(filter_even); return -1; } /* split filter to even and odd values */ for(i = 0; i < F_2; ++i){ filter_even[i] = filter[i << 1]; filter_odd[i] = filter[(i << 1) + 1]; } /* * Perform _valid_ convolution (only when all filter_even and filter_odd elements * are in range of input data). * * This part is simple, no extra hacks, just two convolutions in one loop */ ptr_base = (float*)input + F_2 - 1; i = 0; #ifdef OPT_UNROLL4 /* manually unroll the loop a bit */ for(; i < N-(F_2-1+8); i+=4){ /* sliding over signal from left to right */ sum_even = filter_even[0] * ptr_base[i]; sum_even2 = filter_even[0] * ptr_base[i+1]; sum_even3 = filter_even[0] * ptr_base[i+2]; sum_even4 = filter_even[0] * ptr_base[i+3]; sum_odd = filter_odd[0] * ptr_base[i]; sum_odd2 = filter_odd[0] * ptr_base[i+1]; sum_odd3 = filter_odd[0] * ptr_base[i+2]; sum_odd4 = filter_odd[0] * ptr_base[i+3]; for(j = 1; j < F_2; ++j){ sum_even += filter_even[j] * ptr_base[i-j]; sum_even2 += filter_even[j] * ptr_base[(i+1)-j]; sum_even3 += filter_even[j] * ptr_base[(i+2)-j]; sum_even4 += filter_even[j] * ptr_base[(i+3)-j]; sum_odd += filter_odd[j] * ptr_base[i-j]; sum_odd2 += filter_odd[j] * ptr_base[(i+1)-j]; sum_odd3 += filter_odd[j] * ptr_base[(i+2)-j]; sum_odd4 += filter_odd[j] * ptr_base[(i+3)-j]; } *(ptr_out++) += sum_even; *(ptr_out++) += sum_odd; *(ptr_out++) += sum_even2; *(ptr_out++) += sum_odd2; *(ptr_out++) += sum_even3; *(ptr_out++) += sum_odd3; *(ptr_out++) += sum_even4; *(ptr_out++) += sum_odd4; } #endif #ifdef OPT_UNROLL2 /* manually unroll the loop a bit */ for(; i < N-(F_2+1); i+=2){ /* sliding over signal from left to right */ sum_even = filter_even[0] * ptr_base[i]; sum_even2 = filter_even[0] * ptr_base[i+1]; sum_odd = filter_odd[0] * ptr_base[i]; sum_odd2 = filter_odd[0] * ptr_base[i+1]; for(j = 1; j < F_2; ++j){ sum_even += filter_even[j] * ptr_base[i-j]; sum_even2 += filter_even[j] * ptr_base[(i+1)-j]; sum_odd += filter_odd[j] * ptr_base[i-j]; sum_odd2 += filter_odd[j] * ptr_base[(i+1)-j]; } *(ptr_out++) += sum_even; *(ptr_out++) += sum_odd; *(ptr_out++) += sum_even2; *(ptr_out++) += sum_odd2; } #endif for(; i < N-(F_2-1); ++i){ /* sliding over signal from left to right */ sum_even = filter_even[0] * ptr_base[i]; sum_odd = filter_odd[0] * ptr_base[i]; for(j = 1; j < F_2; ++j){ sum_even += filter_even[j] * ptr_base[i-j]; sum_odd += filter_odd[j] * ptr_base[i-j]; } *(ptr_out++) += sum_even; *(ptr_out++) += sum_odd; } wtfree(filter_even); wtfree(filter_odd); return 0; } /* -> swt - todo */ int float_upsampled_filter_convolution(const float* input, const_index_t N, const float* filter, const_index_t F, float* output, const_index_t step, MODE mode) { return -1; } PyWavelets-0.3.0/pywt/src/_pywt.pyx0000664000175000017500000012154412556460247021047 0ustar rgommersrgommers00000000000000# Copyright (c) 2006-2012 Filip Wasilewski # See COPYING for license details. __doc__ = """Pyrex wrapper for low-level C wavelet transform implementation.""" __all__ = ['MODES', 'Wavelet', 'dwt', 'dwt_coeff_len', 'dwt_max_level', 'idwt', 'swt', 'swt_max_level', 'upcoef', 'downcoef', 'wavelist', 'families'] ############################################################################### # imports cimport c_wt from libc.math cimport pow, sqrt ctypedef Py_ssize_t index_t import warnings import numpy as np cimport numpy as np ctypedef fused data_t: np.float32_t np.float64_t ############################################################################### # MODES class _Modes(object): """ Because the most common and practical way of representing digital signals in computer science is with finite arrays of values, some extrapolation of the input data has to be performed in order to extend the signal before computing the :ref:`Discrete Wavelet Transform ` using the cascading filter banks algorithm. Depending on the extrapolation method, significant artifacts at the signal's borders can be introduced during that process, which in turn may lead to inaccurate computations of the :ref:`DWT ` at the signal's ends. PyWavelets provides several methods of signal extrapolation that can be used to minimize this negative effect: zpd - zero-padding 0 0 | x1 x2 ... xn | 0 0 cpd - constant-padding x1 x1 | x1 x2 ... xn | xn xn sym - symmetric-padding x2 x1 | x1 x2 ... xn | xn xn-1 ppd - periodic-padding xn-1 xn | x1 x2 ... xn | x1 x2 sp1 - smooth-padding (1st derivative interpolation) DWT performed for these extension modes is slightly redundant, but ensure a perfect reconstruction for IDWT. To receive the smallest possible number of coefficients, computations can be performed with the periodization mode: per - periodization - like periodic-padding but gives the smallest possible number of decomposition coefficients. IDWT must be performed with the same mode. Examples -------- >>> import pywt >>> pywt.MODES.modes ['zpd', 'cpd', 'sym', 'ppd', 'sp1', 'per'] >>> # The different ways of passing wavelet and mode parameters >>> (a, d) = pywt.dwt([1,2,3,4,5,6], 'db2', 'sp1') >>> (a, d) = pywt.dwt([1,2,3,4,5,6], pywt.Wavelet('db2'), pywt.MODES.sp1) Notes ----- Extending data in context of PyWavelets does not mean reallocation of the data in computer's physical memory and copying values, but rather computing the extra values only when they are needed. This feature saves extra memory and CPU resources and helps to avoid page swapping when handling relatively big data arrays on computers with low physical memory. """ zpd = c_wt.MODE_ZEROPAD cpd = c_wt.MODE_CONSTANT_EDGE sym = c_wt.MODE_SYMMETRIC ppd = c_wt.MODE_PERIODIC sp1 = c_wt.MODE_SMOOTH per = c_wt.MODE_PERIODIZATION _asym = c_wt.MODE_ASYMMETRIC modes = ["zpd", "cpd", "sym", "ppd", "sp1", "per"] def from_object(self, mode): if isinstance(mode, int): if mode <= c_wt.MODE_INVALID or mode >= c_wt.MODE_MAX: raise ValueError("Invalid mode.") m = mode else: try: m = getattr(MODES, mode) except AttributeError: raise ValueError("Unknown mode name '%s'." % mode) return m # All capitals for backwards compatibility MODES = _Modes() ############################################################################### # Wavelet include "wavelets_list.pxi" # __wname_to_code cdef object wname_to_code(name): cdef object code_number try: code_number = __wname_to_code[name] assert len(code_number) == 2 assert isinstance(code_number[0], int) assert isinstance(code_number[1], int) return code_number except KeyError: raise ValueError("Unknown wavelet name '%s', check wavelist() for the " "list of available builtin wavelets." % name) def wavelist(family=None): """ wavelist(family=None) Returns list of available wavelet names for the given family name. Parameters ---------- family : {'haar', 'db', 'sym', 'coif', 'bior', 'rbio', 'dmey'} Short family name. If the family name is None (default) then names of all the built-in wavelets are returned. Otherwise the function returns names of wavelets that belong to the given family. Returns ------- wavelist : list List of available wavelet names Examples -------- >>> import pywt >>> pywt.wavelist('coif') ['coif1', 'coif2', 'coif3', 'coif4', 'coif5'] """ cdef object wavelets, sorting_list sorting_list = [] # for natural sorting order wavelets = [] cdef object name if family is None: for name in __wname_to_code: sorting_list.append((name[:2], len(name), name)) elif family in __wfamily_list_short: for name in __wname_to_code: if name.startswith(family): sorting_list.append((name[:2], len(name), name)) else: raise ValueError("Invalid short family name '%s'." % family) sorting_list.sort() for x, x, name in sorting_list: wavelets.append(name) return wavelets def families(int short=True): """ families(short=True) Returns a list of available built-in wavelet families. Currently the built-in families are: * Haar (``haar``) * Daubechies (``db``) * Symlets (``sym``) * Coiflets (``coif``) * Biorthogonal (``bior``) * Reverse biorthogonal (``rbio``) * `"Discrete"` FIR approximation of Meyer wavelet (``dmey``) Parameters ---------- short : bool, optional Use short names (default: True). Returns ------- families : list List of available wavelet families. Examples -------- >>> import pywt >>> pywt.families() ['haar', 'db', 'sym', 'coif', 'bior', 'rbio', 'dmey'] >>> pywt.families(short=False) ['Haar', 'Daubechies', 'Symlets', 'Coiflets', 'Biorthogonal', 'Reverse biorthogonal', 'Discrete Meyer (FIR Approximation)'] """ if short: return __wfamily_list_short[:] return __wfamily_list_long[:] cdef public class Wavelet [type WaveletType, object WaveletObject]: """ Wavelet(name, filter_bank=None) object describe properties of a wavelet identified by name. In order to use a built-in wavelet the parameter name must be a valid name from the wavelist() list. To create a custom wavelet object, filter_bank parameter must be specified. It can be either a list of four filters or an object that a `filter_bank` attribute which returns a list of four filters - just like the Wavelet instance itself. """ cdef c_wt.Wavelet* w cdef readonly name cdef readonly number #cdef readonly properties def __cinit__(self, name=u"", object filter_bank=None): cdef object family_code, family_number cdef object filters cdef index_t filter_length cdef object dec_lo, dec_hi, rec_lo, rec_hi if not name and filter_bank is None: raise TypeError("Wavelet name or filter bank must be specified.") if filter_bank is None: # builtin wavelet self.name = name.lower() family_code, family_number = wname_to_code(self.name) self.w = c_wt.wavelet(family_code, family_number) if self.w is NULL: raise ValueError("Invalid wavelet name.") self.number = family_number else: if hasattr(filter_bank, "filter_bank"): filters = filter_bank.filter_bank if len(filters) != 4: raise ValueError("Expected filter bank with 4 filters, " "got filter bank with %d filters." % len(filters)) elif hasattr(filter_bank, "get_filters_coeffs"): msg = ("Creating custom Wavelets using objects that define " "`get_filters_coeffs` method is deprecated. " "The `filter_bank` parameter should define a " "`filter_bank` attribute instead of " "`get_filters_coeffs` method.") warnings.warn(msg, DeprecationWarning) filters = filter_bank.get_filters_coeffs() if len(filters) != 4: msg = ("Expected filter bank with 4 filters, got filter " "bank with %d filters." % len(filters)) raise ValueError(msg) else: filters = filter_bank if len(filters) != 4: msg = ("Expected list of 4 filters coefficients, " "got %d filters." % len(filters)) raise ValueError(msg) try: dec_lo = np.asarray(filters[0], dtype=np.float64) dec_hi = np.asarray(filters[1], dtype=np.float64) rec_lo = np.asarray(filters[2], dtype=np.float64) rec_hi = np.asarray(filters[3], dtype=np.float64) except TypeError: raise ValueError("Filter bank with numeric values required.") if not (1 == dec_lo.ndim == dec_hi.ndim == rec_lo.ndim == rec_hi.ndim): raise ValueError("All filters in filter bank must be 1D.") filter_length = len(dec_lo) if not (0 < filter_length == len(dec_hi) == len(rec_lo) == len(rec_hi)) > 0: raise ValueError("All filters in filter bank must have " "length greater than 0.") self.w = c_wt.blank_wavelet(filter_length) if self.w is NULL: raise MemoryError("Could not allocate memory for given " "filter bank.") # copy values to struct copy_object_to_float32_array(dec_lo, self.w.dec_lo_float) copy_object_to_float32_array(dec_hi, self.w.dec_hi_float) copy_object_to_float32_array(rec_lo, self.w.rec_lo_float) copy_object_to_float32_array(rec_hi, self.w.rec_hi_float) copy_object_to_float64_array(dec_lo, self.w.dec_lo_double) copy_object_to_float64_array(dec_hi, self.w.dec_hi_double) copy_object_to_float64_array(rec_lo, self.w.rec_lo_double) copy_object_to_float64_array(rec_hi, self.w.rec_hi_double) self.name = name def __dealloc__(self): if self.w is not NULL: # if w._builtin is 0 then it frees the memory for the filter arrays c_wt.free_wavelet(self.w) self.w = NULL def __len__(self): return self.w.dec_len property dec_lo: "Lowpass decomposition filter" def __get__(self): return float64_array_to_list(self.w.dec_lo_double, self.w.dec_len) property dec_hi: "Highpass decomposition filter" def __get__(self): return float64_array_to_list(self.w.dec_hi_double, self.w.dec_len) property rec_lo: "Lowpass reconstruction filter" def __get__(self): return float64_array_to_list(self.w.rec_lo_double, self.w.rec_len) property rec_hi: "Highpass reconstruction filter" def __get__(self): return float64_array_to_list(self.w.rec_hi_double, self.w.rec_len) property rec_len: "Reconstruction filters length" def __get__(self): return self.w.rec_len property dec_len: "Decomposition filters length" def __get__(self): return self.w.dec_len property family_name: "Wavelet family name" def __get__(self): return self.w.family_name.decode('latin-1') property short_family_name: "Short wavelet family name" def __get__(self): return self.w.short_name.decode('latin-1') property orthogonal: "Is orthogonal" def __get__(self): return bool(self.w.orthogonal) def __set__(self, int value): self.w.orthogonal = (value != 0) property biorthogonal: "Is biorthogonal" def __get__(self): return bool(self.w.biorthogonal) def __set__(self, int value): self.w.biorthogonal = (value != 0) property symmetry: "Wavelet symmetry" def __get__(self): if self.w.symmetry == c_wt.ASYMMETRIC: return "asymmetric" elif self.w.symmetry == c_wt.NEAR_SYMMETRIC: return "near symmetric" elif self.w.symmetry == c_wt.SYMMETRIC: return "symmetric" else: return "unknown" property vanishing_moments_psi: "Number of vanishing moments for wavelet function" def __get__(self): if self.w.vanishing_moments_psi >= 0: return self.w.vanishing_moments_psi property vanishing_moments_phi: "Number of vanishing moments for scaling function" def __get__(self): if self.w.vanishing_moments_phi >= 0: return self.w.vanishing_moments_phi property _builtin: """Returns True if the wavelet is built-in one (not created with custom filter bank). """ def __get__(self): return bool(self.w._builtin) property filter_bank: """Returns tuple of wavelet filters coefficients (dec_lo, dec_hi, rec_lo, rec_hi) """ def __get__(self): return (self.dec_lo, self.dec_hi, self.rec_lo, self.rec_hi) def get_filters_coeffs(self): warnings.warn("The `get_filters_coeffs` method is deprecated. " "Use `filter_bank` attribute instead.", DeprecationWarning) return self.filter_bank property inverse_filter_bank: """Tuple of inverse wavelet filters coefficients (rec_lo[::-1], rec_hi[::-1], dec_lo[::-1], dec_hi[::-1]) """ def __get__(self): return (self.rec_lo[::-1], self.rec_hi[::-1], self.dec_lo[::-1], self.dec_hi[::-1]) def get_reverse_filters_coeffs(self): warnings.warn("The `get_reverse_filters_coeffs` method is deprecated. " "Use `inverse_filter_bank` attribute instead.", DeprecationWarning) return self.inverse_filter_bank def wavefun(self, int level=8): """ wavefun(self, level=8) Calculates approximations of scaling function (`phi`) and wavelet function (`psi`) on xgrid (`x`) at a given level of refinement. Parameters ---------- level : int, optional Level of refinement (default: 8). Returns ------- [phi, psi, x] : array_like For orthogonal wavelets returns scaling function, wavelet function and xgrid - [phi, psi, x]. [phi_d, psi_d, phi_r, psi_r, x] : array_like For biorthogonal wavelets returns scaling and wavelet function both for decomposition and reconstruction and xgrid Examples -------- >>> import pywt >>> # Orthogonal >>> wavelet = pywt.Wavelet('db2') >>> phi, psi, x = wavelet.wavefun(level=5) >>> # Biorthogonal >>> wavelet = pywt.Wavelet('bior3.5') >>> phi_d, psi_d, phi_r, psi_r, x = wavelet.wavefun(level=5) """ cdef index_t filter_length "filter_length" cdef index_t right_extent_length "right_extent_length" cdef index_t output_length "output_length" cdef index_t keep_length "keep_length" cdef double n "n" cdef double p "p" cdef double mul "mul" cdef Wavelet other "other" cdef phi_d, psi_d, phi_r, psi_r n = pow(sqrt(2.), level) p = (pow(2., level)) if self.w.orthogonal: filter_length = self.w.dec_len output_length = ((filter_length-1) * p + 1) keep_length = get_keep_length(output_length, level, filter_length) output_length = fix_output_length(output_length, keep_length) right_extent_length = get_right_extent_length(output_length, keep_length) # phi, psi, x return [np.concatenate(([0.], keep(upcoef('a', [n], self, level), keep_length), np.zeros(right_extent_length))), np.concatenate(([0.], keep(upcoef('d', [n], self, level), keep_length), np.zeros(right_extent_length))), np.linspace(0.0, (output_length-1)/p, output_length)] else: mul = 1 if self.w.biorthogonal: if (self.w.vanishing_moments_psi % 4) != 1: mul = -1 other = Wavelet(filter_bank=self.inverse_filter_bank) filter_length = other.w.dec_len output_length = ((filter_length-1) * p) keep_length = get_keep_length(output_length, level, filter_length) output_length = fix_output_length(output_length, keep_length) right_extent_length = get_right_extent_length(output_length, keep_length) phi_d = np.concatenate(([0.], keep(upcoef('a', [n], other, level), keep_length), np.zeros(right_extent_length))) psi_d = np.concatenate(([0.], keep(upcoef('d', [mul*n], other, level), keep_length), np.zeros(right_extent_length))) filter_length = self.w.dec_len output_length = ((filter_length-1) * p) keep_length = get_keep_length(output_length, level, filter_length) output_length = fix_output_length(output_length, keep_length) right_extent_length = get_right_extent_length(output_length, keep_length) phi_r = np.concatenate(([0.], keep(upcoef('a', [n], self, level), keep_length), np.zeros(right_extent_length))) psi_r = np.concatenate(([0.], keep(upcoef('d', [mul*n], self, level), keep_length), np.zeros(right_extent_length))) return [phi_d, psi_d, phi_r, psi_r, np.linspace(0.0, (output_length - 1) / p, output_length)] def __str__(self): s = [] for x in [ u"Wavelet %s" % self.name, u" Family name: %s" % self.family_name, u" Short name: %s" % self.short_family_name, u" Filters length: %d" % self.dec_len, u" Orthogonal: %s" % self.orthogonal, u" Biorthogonal: %s" % self.biorthogonal, u" Symmetry: %s" % self.symmetry ]: s.append(x.rstrip()) return u'\n'.join(s) cdef index_t get_keep_length(index_t output_length, int level, index_t filter_length): cdef index_t lplus "lplus" cdef index_t keep_length "keep_length" cdef int i "i" lplus = filter_length - 2 keep_length = 1 for i from 0 <= i < level: keep_length = 2*keep_length+lplus return keep_length cdef index_t fix_output_length(index_t output_length, index_t keep_length): if output_length-keep_length-2 < 0: output_length = keep_length+2 return output_length cdef index_t get_right_extent_length(index_t output_length, index_t keep_length): return output_length - keep_length - 1 def wavelet_from_object(wavelet): return c_wavelet_from_object(wavelet) cdef c_wavelet_from_object(wavelet): if isinstance(wavelet, Wavelet): return wavelet else: return Wavelet(wavelet) ############################################################################### # DWT def dwt_max_level(data_len, filter_len): """ dwt_max_level(data_len, filter_len) Compute the maximum useful level of decomposition. Parameters ---------- data_len : int Input data length. filter_len : int Wavelet filter length. Returns ------- max_level : int Maximum level. Examples -------- >>> import pywt >>> w = pywt.Wavelet('sym5') >>> pywt.dwt_max_level(data_len=1000, filter_len=w.dec_len) 6 >>> pywt.dwt_max_level(1000, w) 6 """ if isinstance(filter_len, Wavelet): return c_wt.dwt_max_level(data_len, filter_len.dec_len) else: return c_wt.dwt_max_level(data_len, filter_len) def dwt(object data, object wavelet, object mode='sym'): """ (cA, cD) = dwt(data, wavelet, mode='sym') Single level Discrete Wavelet Transform. Parameters ---------- data : array_like Input signal wavelet : Wavelet object or name Wavelet to use mode : str, optional (default: 'sym') Signal extension mode, see MODES Returns ------- (cA, cD) : tuple Approximation and detail coefficients. Notes ----- Length of coefficients arrays depends on the selected mode: for all modes except periodization: len(cA) == len(cD) == floor((len(data) + wavelet.dec_len - 1) / 2) for periodization mode ("per"): len(cA) == len(cD) == ceil(len(data) / 2) Examples -------- >>> import pywt >>> (cA, cD) = pywt.dwt([1, 2, 3, 4, 5, 6], 'db1') >>> cA [ 2.12132034 4.94974747 7.77817459] >>> cD [-0.70710678 -0.70710678 -0.70710678] """ # accept array_like input; make a copy to ensure a contiguous array dt = _check_dtype(data) data = np.array(data, dtype=dt) if data.ndim != 1: raise ValueError("dwt requires a 1D data array.") return _dwt(data, wavelet, mode) def _dwt(np.ndarray[data_t, ndim=1] data, object wavelet, object mode='sym'): """See `dwt` docstring for details.""" cdef np.ndarray[data_t, ndim=1, mode="c"] cA, cD cdef Wavelet w cdef c_wt.MODE mode_ w = c_wavelet_from_object(wavelet) mode_ = _try_mode(mode) data = np.array(data) output_len = c_wt.dwt_buffer_length(data.size, w.dec_len, mode_) if output_len < 1: raise RuntimeError("Invalid output length.") cA = np.zeros(output_len, data.dtype) cD = np.zeros(output_len, data.dtype) if data_t == np.float64_t: if (c_wt.double_dec_a(&data[0], data.size, w.w, &cA[0], cA.size, mode_) < 0 or c_wt.double_dec_d(&data[0], data.size, w.w, &cD[0], cD.size, mode_) < 0): raise RuntimeError("C dwt failed.") elif data_t == np.float32_t: if (c_wt.float_dec_a(&data[0], data.size, w.w, &cA[0], cA.size, mode_) < 0 or c_wt.float_dec_d(&data[0], data.size, w.w, &cD[0], cD.size, mode_) < 0): raise RuntimeError("C dwt failed.") else: raise RuntimeError("Invalid data type.") return (cA, cD) def dwt_coeff_len(data_len, filter_len, mode='sym'): """ dwt_coeff_len(data_len, filter_len, mode='sym') Returns length of dwt output for given data length, filter length and mode Parameters ---------- data_len : int Data length. filter_len : int Filter length. mode : str, optional (default: 'sym') Signal extension mode, see MODES Returns ------- len : int Length of dwt output. Notes ----- For all modes except periodization:: len(cA) == len(cD) == floor((len(data) + wavelet.dec_len - 1) / 2) for periodization mode ("per"):: len(cA) == len(cD) == ceil(len(data) / 2) """ cdef index_t filter_len_ if isinstance(filter_len, Wavelet): filter_len_ = filter_len.dec_len else: filter_len_ = filter_len if data_len < 1: raise ValueError("Value of data_len value must be greater than zero.") if filter_len_ < 1: raise ValueError("Value of filter_len must be greater than zero.") return c_wt.dwt_buffer_length(data_len, filter_len_, _try_mode(mode)) ############################################################################### # idwt def _try_mode(mode): try: return MODES.from_object(mode) except ValueError as e: if "Unknown mode name" in str(e): raise raise TypeError("Invalid mode: {0}".format(str(mode))) def _check_dtype(data): """Check for cA/cD input what (if any) the dtype is.""" try: dt = data.dtype if not dt == np.float32: # integer input was always accepted; convert to float64 dt = np.float64 except AttributeError: dt = np.float64 return dt def idwt(cA, cD, object wavelet, object mode='sym', int correct_size=0): """ idwt(cA, cD, wavelet, mode='sym', correct_size=0) Single level Inverse Discrete Wavelet Transform Parameters ---------- cA : array_like or None Approximation coefficients. If None, will be set to array of zeros with same shape as `cD`. cD : array_like or None Detail coefficients. If None, will be set to array of zeros with same shape as `cA`. wavelet : Wavelet object or name Wavelet to use mode : str, optional (default: 'sym') Signal extension mode, see MODES correct_size : int, optional (default: 0) Under normal conditions (all data lengths dyadic) `cA` and `cD` coefficients lists must have the same lengths. With `correct_size` set to True, length of `cA` may be greater by one than length of `cD`. Useful when doing multilevel decomposition and reconstruction of non-dyadic length signals. Returns ------- rec: array_like Single level reconstruction of signal from given coefficients. """ # accept array_like input; make a copy to ensure a contiguous array if cA is None and cD is None: raise ValueError("At least one coefficient parameter must be " "specified.") if cA is not None: dt = _check_dtype(cA) cA = np.array(cA, dtype=dt) if cA.ndim != 1: raise ValueError("idwt requires 1D coefficient arrays.") if cD is not None: dt = _check_dtype(cD) cD = np.array(cD, dtype=dt) if cD.ndim != 1: raise ValueError("idwt requires 1D coefficient arrays.") if cA is not None and cD is not None: if cA.dtype != cD.dtype: # need to upcast to common type cA = cA.astype(np.float64) cD = cD.astype(np.float64) elif cA is None: cA = np.zeros(cD.shape, dtype=cD.dtype) elif cD is None: cD = np.zeros(cA.shape, dtype=cA.dtype) return _idwt(cA, cD, wavelet, mode, correct_size) def _idwt(np.ndarray[data_t, ndim=1, mode="c"] cA, np.ndarray[data_t, ndim=1, mode="c"] cD, object wavelet, object mode='sym', int correct_size=0): """See `idwt` for details""" cdef index_t input_len cdef Wavelet w cdef c_wt.MODE mode_ w = c_wavelet_from_object(wavelet) mode_ = _try_mode(mode) cdef np.ndarray[data_t, ndim=1, mode="c"] rec cdef index_t rec_len cdef index_t size_diff # check for size difference between arrays size_diff = cA.size - cD.size if size_diff: if correct_size: if size_diff < 0 or size_diff > 1: msg = ("Coefficients arrays must satisfy " "(0 <= len(cA) - len(cD) <= 1).") raise ValueError(msg) input_len = cA.size - size_diff else: msg = "Coefficients arrays must have the same size." raise ValueError(msg) else: input_len = cA.size # find reconstruction buffer length rec_len = c_wt.idwt_buffer_length(input_len, w.rec_len, mode_) if rec_len < 1: msg = ("Invalid coefficient arrays length for specified wavelet. " "Wavelet and mode must be the same as used for decomposition.") raise ValueError(msg) # allocate buffer if cA is not None: rec = np.zeros(rec_len, dtype=cA.dtype) else: rec = np.zeros(rec_len, dtype=cD.dtype) # call idwt func. one of cA/cD can be None, then only # reconstruction of non-null part will be performed if data_t is np.float64_t: if c_wt.double_idwt(&cA[0], cA.size, &cD[0], cD.size, w.w, &rec[0], rec.size, mode_, correct_size) < 0: raise RuntimeError("C idwt failed.") elif data_t == np.float32_t: if c_wt.float_idwt(&cA[0], cA.size, &cD[0], cD.size, w.w, &rec[0], rec.size, mode_, correct_size) < 0: raise RuntimeError("C idwt failed.") else: raise RuntimeError("Invalid data type.") return rec ############################################################################### # upcoef & downcoef def upcoef(part, coeffs, wavelet, level=1, take=0): """ upcoef(part, coeffs, wavelet, level=1, take=0) Direct reconstruction from coefficients. Parameters ---------- part : str Coefficients type: * 'a' - approximations reconstruction is performed * 'd' - details reconstruction is performed coeffs : array_like Coefficients array to recontruct wavelet : Wavelet object or name Wavelet to use level : int, optional Multilevel reconstruction level. Default is 1. take : int, optional Take central part of length equal to 'take' from the result. Default is 0. Returns ------- rec : ndarray 1-D array with reconstructed data from coefficients. See Also -------- downcoef Examples -------- >>> import pywt >>> data = [1,2,3,4,5,6] >>> (cA, cD) = pywt.dwt(data, 'db2', 'sp1') >>> pywt.upcoef('a', cA, 'db2') + pywt.upcoef('d', cD, 'db2') [-0.25 -0.4330127 1. 2. 3. 4. 5. 6. 1.78589838 -1.03108891] >>> n = len(data) >>> pywt.upcoef('a', cA, 'db2', take=n) + pywt.upcoef('d', cD, 'db2', take=n) [ 1. 2. 3. 4. 5. 6.] """ # accept array_like input; make a copy to ensure a contiguous array dt = _check_dtype(coeffs) coeffs = np.array(coeffs, dtype=dt) return _upcoef(part, coeffs, wavelet, level, take) def _upcoef(part, np.ndarray[data_t, ndim=1, mode="c"] coeffs, wavelet, int level=1, int take=0): cdef Wavelet w cdef np.ndarray[data_t, ndim=1, mode="c"] rec cdef int i, do_rec_a cdef index_t rec_len, left_bound, right_bound rec_len = 0 if part not in ('a', 'd'): raise ValueError("Argument 1 must be 'a' or 'd', not '%s'." % part) do_rec_a = (part == 'a') w = c_wavelet_from_object(wavelet) if level < 1: raise ValueError("Value of level must be greater than 0.") for i from 0 <= i < level: # output len rec_len = c_wt.reconstruction_buffer_length(coeffs.size, w.dec_len) if rec_len < 1: raise RuntimeError("Invalid output length.") # reconstruct rec = np.zeros(rec_len, dtype=coeffs.dtype) if do_rec_a: if data_t is np.float64_t: if c_wt.double_rec_a(&coeffs[0], coeffs.size, w.w, &rec[0], rec.size) < 0: raise RuntimeError("C rec_a failed.") elif data_t is np.float32_t: if c_wt.float_rec_a(&coeffs[0], coeffs.size, w.w, &rec[0], rec.size) < 0: raise RuntimeError("C rec_a failed.") else: raise RuntimeError("Invalid data type.") else: if data_t is np.float64_t: if c_wt.double_rec_d(&coeffs[0], coeffs.size, w.w, &rec[0], rec.size) < 0: raise RuntimeError("C rec_a failed.") elif data_t is np.float32_t: if c_wt.float_rec_d(&coeffs[0], coeffs.size, w.w, &rec[0], rec.size) < 0: raise RuntimeError("C rec_a failed.") else: raise RuntimeError("Invalid data type.") do_rec_a = 1 # TODO: this algorithm needs some explaining coeffs = rec if take > 0 and take < rec_len: left_bound = right_bound = (rec_len-take) // 2 if (rec_len-take) % 2: # right_bound must never be zero for indexing to work right_bound = right_bound + 1 return rec[left_bound:-right_bound] return rec def downcoef(part, data, wavelet, mode='sym', level=1): """ downcoef(part, data, wavelet, mode='sym', level=1) Partial Discrete Wavelet Transform data decomposition. Similar to `pywt.dwt`, but computes only one set of coefficients. Useful when you need only approximation or only details at the given level. Parameters ---------- part : str Coefficients type: * 'a' - approximations reconstruction is performed * 'd' - details reconstruction is performed data : array_like Input signal. wavelet : Wavelet object or name Wavelet to use mode : str, optional Signal extension mode, see `MODES`. Default is 'sym'. level : int, optional Decomposition level. Default is 1. Returns ------- coeffs : ndarray 1-D array of coefficients. See Also -------- upcoef """ # accept array_like input; make a copy to ensure a contiguous array dt = _check_dtype(data) data = np.array(data, dtype=dt) return _downcoef(part, data, wavelet, mode, level) def _downcoef(part, np.ndarray[data_t, ndim=1, mode="c"] data, object wavelet, object mode='sym', int level=1): cdef np.ndarray[data_t, ndim=1, mode="c"] coeffs cdef int i, do_dec_a cdef index_t dec_len cdef Wavelet w cdef c_wt.MODE mode_ w = c_wavelet_from_object(wavelet) mode_ = _try_mode(mode) if part not in ('a', 'd'): raise ValueError("Argument 1 must be 'a' or 'd', not '%s'." % part) do_dec_a = (part == 'a') if level < 1: raise ValueError("Value of level must be greater than 0.") for i from 0 <= i < level: output_len = c_wt.dwt_buffer_length(data.size, w.dec_len, mode_) if output_len < 1: raise RuntimeError("Invalid output length.") coeffs = np.zeros(output_len, dtype=data.dtype) if do_dec_a: if data_t is np.float64_t: if c_wt.double_dec_a(&data[0], data.size, w.w, &coeffs[0], coeffs.size, mode_) < 0: raise RuntimeError("C dec_a failed.") elif data_t is np.float32_t: if c_wt.float_dec_a(&data[0], data.size, w.w, &coeffs[0], coeffs.size, mode_) < 0: raise RuntimeError("C dec_a failed.") else: raise RuntimeError("Invalid data type.") else: if data_t is np.float64_t: if c_wt.double_dec_d(&data[0], data.size, w.w, &coeffs[0], coeffs.size, mode_) < 0: raise RuntimeError("C dec_a failed.") elif data_t is np.float32_t: if c_wt.float_dec_d(&data[0], data.size, w.w, &coeffs[0], coeffs.size, mode_) < 0: raise RuntimeError("C dec_a failed.") else: raise RuntimeError("Invalid data type.") data = coeffs return coeffs ############################################################################### # swt def swt_max_level(input_len): """ swt_max_level(input_len) Calculates the maximum level of Stationary Wavelet Transform for data of given length. Parameters ---------- input_len : int Input data length. Returns ------- max_level : int Maximum level of Stationary Wavelet Transform for data of given length. """ return c_wt.swt_max_level(input_len) def swt(data, object wavelet, object level=None, int start_level=0): """ swt(data, wavelet, level=None, start_level=0) Performs multilevel Stationary Wavelet Transform. Parameters ---------- data : Input signal wavelet : Wavelet to use (Wavelet object or name) level : int, optional Transform level. start_level : int, optional The level at which the decomposition will begin (it allows to skip a given number of transform steps and compute coefficients starting from start_level) (default: 0) Returns ------- coeffs : list List of approximation and details coefficients pairs in order similar to wavedec function:: [(cAn, cDn), ..., (cA2, cD2), (cA1, cD1)] where ``n`` equals input parameter `level`. If *m* = *start_level* is given, then the beginning *m* steps are skipped:: [(cAm+n, cDm+n), ..., (cAm+1, cDm+1), (cAm, cDm)] """ # accept array_like input; make a copy to ensure a contiguous array dt = _check_dtype(data) data = np.array(data, dtype=dt) return _swt(data, wavelet, level, start_level) def _swt(np.ndarray[data_t, ndim=1, mode="c"] data, object wavelet, object level=None, int start_level=0): """See `swt` for details.""" cdef np.ndarray[data_t, ndim=1, mode="c"] cA, cD cdef Wavelet w cdef int i, end_level, level_ if data.size % 2: raise ValueError("Length of data must be even.") w = c_wavelet_from_object(wavelet) if level is None: level_ = c_wt.swt_max_level(data.size) else: level_ = level end_level = start_level + level_ if level_ < 1: raise ValueError("Level value must be greater than zero.") if start_level < 0: raise ValueError("start_level must be greater than zero.") if start_level >= c_wt.swt_max_level(data.size): raise ValueError("start_level must be less than %d." % c_wt.swt_max_level(data.size)) if end_level > c_wt.swt_max_level(data.size): msg = ("Level value too high (max level for current data size and " "start_level is %d)." % (c_wt.swt_max_level(data.size) - start_level)) raise ValueError(msg) # output length output_len = c_wt.swt_buffer_length(data.size) if output_len < 1: raise RuntimeError("Invalid output length.") ret = [] for i from start_level < i <= end_level: # alloc memory, decompose D cD = np.zeros(output_len, dtype=data.dtype) if data_t is np.float64_t: if c_wt.double_swt_d(&data[0], data.size, w.w, &cD[0], cD.size, i) < 0: raise RuntimeError("C swt failed.") elif data_t is np.float32_t: if c_wt.float_swt_d(&data[0], data.size, w.w, &cD[0], cD.size, i) < 0: raise RuntimeError("C swt failed.") else: raise RuntimeError("Invalid data type.") # alloc memory, decompose A cA = np.zeros(output_len, dtype=data.dtype) if data_t is np.float64_t: if c_wt.double_swt_a(&data[0], data.size, w.w, &cA[0], cA.size, i) < 0: raise RuntimeError("C swt failed.") elif data_t is np.float32_t: if c_wt.float_swt_a(&data[0], data.size, w.w, &cA[0], cA.size, i) < 0: raise RuntimeError("C swt failed.") else: raise RuntimeError("Invalid data type.") data = cA ret.append((cA, cD)) ret.reverse() return ret def keep(arr, keep_length): length = len(arr) if keep_length < length: left_bound = (length - keep_length) / 2 return arr[left_bound:left_bound + keep_length] return arr # Some utility functions cdef object float64_array_to_list(double* data, index_t n): cdef index_t i cdef object app cdef object ret ret = [] app = ret.append for i from 0 <= i < n: app(data[i]) return ret cdef void copy_object_to_float64_array(source, double* dest) except *: cdef index_t i cdef double x i = 0 for x in source: dest[i] = x i = i + 1 cdef void copy_object_to_float32_array(source, float* dest) except *: cdef index_t i cdef float x i = 0 for x in source: dest[i] = x i = i + 1 PyWavelets-0.3.0/pywt/src/convolution.h.src0000664000175000017500000000530712556460247022457 0ustar rgommersrgommers00000000000000/* Copyright (c) 2006-2012 Filip Wasilewski */ /* See COPYING for license details. */ #ifndef _CONVOLUTION_H_ #define _CONVOLUTION_H_ #include #include "common.h" /**begin repeat * #type = double, float# */ /* * Performs convolution of input with filter and downsamples by taking every * step-th element from the result. * * input - input data * N - input data length * filter - filter data * F - filter data length * output - output data * step - decimation step * mode - signal extension mode */ /* memory efficient version */ int @type@_downsampling_convolution(const @type@* input, const_index_t N, const @type@* filter, const_index_t F, @type@* output, const_index_t step, MODE mode); /* * Straightforward implementation with memory reallocation - for very short * signals (shorter than filter). This id called from downsampling_convolution */ int @type@_allocating_downsampling_convolution(const @type@* input, const_index_t N, const @type@* filter, const_index_t F, @type@* output, const_index_t step, MODE mode); /* * Performs normal (full) convolution of "upsampled" input coeffs array with * filter Requires zero-filled output buffer (adds values instead of * overwriting - can be called many times with the same output). * * input - input data * N - input data length * filter - filter data * F - filter data length * output - output data * O - output lenght (currently not used) * mode - signal extension mode */ int @type@_upsampling_convolution_full(const @type@* input, const_index_t N, const @type@* filter, const_index_t F, @type@* output, const_index_t O); /* * Performs valid convolution (signals must overlap) * Extends (virtually) input for MODE_PERIODIZATION. */ int @type@_upsampling_convolution_valid_sf(const @type@* input, const_index_t N, const @type@* filter, const_index_t F, @type@* output, const_index_t O, MODE mode); /* * TODO * for SWT * int upsampled_filter_convolution(const @type@* input, const int N, * const @type@* filter, const int F, * @type@* output, int step, int mode); */ /**end repeat**/ #endif PyWavelets-0.3.0/pywt/src/wt.c.src0000664000175000017500000001635112556460247020526 0ustar rgommersrgommers00000000000000/* Copyright (c) 2006-2012 Filip Wasilewski */ /* See COPYING for license details. */ #include "wt.h" /* Decomposition of input with lowpass filter */ /**begin repeat * #type = double, float# */ int @type@_dec_a(@type@ input[], index_t input_len, Wavelet* wavelet, @type@ output[], index_t output_len, MODE mode){ /* check output length */ if(output_len != dwt_buffer_length(input_len, wavelet->dec_len, mode)){ return -1; } return @type@_downsampling_convolution(input, input_len, wavelet->dec_lo_@type@, wavelet->dec_len, output, 2, mode); } /* Decomposition of input with highpass filter */ int @type@_dec_d(@type@ input[], index_t input_len, Wavelet* wavelet, @type@ output[], index_t output_len, MODE mode){ /* check output length */ if(output_len != dwt_buffer_length(input_len, wavelet->dec_len, mode)) return -1; return @type@_downsampling_convolution(input, input_len, wavelet->dec_hi_@type@, wavelet->dec_len, output, 2, mode); } /* Direct reconstruction with lowpass reconstruction filter */ int @type@_rec_a(@type@ coeffs_a[], index_t coeffs_len, Wavelet* wavelet, @type@ output[], index_t output_len){ /* check output length */ if(output_len != reconstruction_buffer_length(coeffs_len, wavelet->rec_len)) return -1; return @type@_upsampling_convolution_full(coeffs_a, coeffs_len, wavelet->rec_lo_@type@, wavelet->rec_len, output, output_len); } /* Direct reconstruction with highpass reconstruction filter */ int @type@_rec_d(@type@ coeffs_d[], index_t coeffs_len, Wavelet* wavelet, @type@ output[], index_t output_len){ /* check for output length */ if(output_len != reconstruction_buffer_length(coeffs_len, wavelet->rec_len)) return -1; return @type@_upsampling_convolution_full(coeffs_d, coeffs_len, wavelet->rec_hi_@type@, wavelet->rec_len, output, output_len); } /* * IDWT reconstruction from approximation and detail coeffs * * If fix_size_diff is 1 then coeffs arrays can differ by one in length (this * is useful in multilevel decompositions and reconstructions of odd-length * signals). Requires zero-filled output buffer. */ int @type@_idwt(@type@ coeffs_a[], index_t coeffs_a_len, @type@ coeffs_d[], index_t coeffs_d_len, Wavelet* wavelet, @type@ output[], index_t output_len, MODE mode, int fix_size_diff){ index_t input_len; /* * If one of coeffs array is NULL then the reconstruction will be performed * using the other one */ if(coeffs_a != NULL && coeffs_d != NULL){ if(fix_size_diff){ if( (coeffs_a_len > coeffs_d_len ? coeffs_a_len - coeffs_d_len : coeffs_d_len-coeffs_a_len) > 1){ /* abs(a-b) */ goto error; } input_len = coeffs_a_len>coeffs_d_len ? coeffs_d_len : coeffs_a_len; /* min */ } else { if(coeffs_a_len != coeffs_d_len) goto error; input_len = coeffs_a_len; } } else if(coeffs_a != NULL){ input_len = coeffs_a_len; } else if (coeffs_d != NULL){ input_len = coeffs_d_len; } else { goto error; } /* check output size */ if(output_len != idwt_buffer_length(input_len, wavelet->rec_len, mode)) goto error; /* * Set output to zero (this can be omitted if output array is already * cleared) memset(output, 0, output_len * sizeof(@type@)); */ /* reconstruct approximation coeffs with lowpass reconstruction filter */ if(coeffs_a){ if(@type@_upsampling_convolution_valid_sf(coeffs_a, input_len, wavelet->rec_lo_@type@, wavelet->rec_len, output, output_len, mode) < 0){ goto error; } } /* * Add reconstruction of details coeffs performed with highpass * reconstruction filter. */ if(coeffs_d){ if(@type@_upsampling_convolution_valid_sf(coeffs_d, input_len, wavelet->rec_hi_@type@, wavelet->rec_len, output, output_len, mode) < 0){ goto error; } } return 0; error: return -1; } /* basic SWT step (TODO: optimize) */ int @type@_swt_(@type@ input[], index_t input_len, const @type@ filter[], index_t filter_len, @type@ output[], index_t output_len, int level){ @type@* e_filter; index_t i, e_filter_len; int ret; if(level < 1) return -1; if(level > swt_max_level(input_len)) return -2; if(output_len != swt_buffer_length(input_len)) return -1; /* TODO: quick hack, optimize */ if(level > 1){ /* allocate filter first */ e_filter_len = filter_len << (level-1); e_filter = wtcalloc(e_filter_len, sizeof(@type@)); if(e_filter == NULL) return -1; /* compute upsampled filter values */ for(i = 0; i < filter_len; ++i){ e_filter[i << (level-1)] = filter[i]; } ret = @type@_downsampling_convolution(input, input_len, e_filter, e_filter_len, output, 1, MODE_PERIODIZATION); wtfree(e_filter); return ret; } else { return @type@_downsampling_convolution(input, input_len, filter, filter_len, output, 1, MODE_PERIODIZATION); } } /* * Approximation at specified level * input - approximation coeffs from upper level or signal if level == 1 */ int @type@_swt_a(@type@ input[], index_t input_len, Wavelet* wavelet, @type@ output[], index_t output_len, int level){ return @type@_swt_(input, input_len, wavelet->dec_lo_@type@, wavelet->dec_len, output, output_len, level); } /* Details at specified level * input - approximation coeffs from upper level or signal if level == 1 */ int @type@_swt_d(@type@ input[], index_t input_len, Wavelet* wavelet, @type@ output[], index_t output_len, int level){ return @type@_swt_(input, input_len, wavelet->dec_hi_@type@, wavelet->dec_len, output, output_len, level); } /**end repeat**/ PyWavelets-0.3.0/pywt/wavelet_packets.py0000664000175000017500000005705612556460247022115 0ustar rgommersrgommers00000000000000# -*- coding: utf-8 -*- # Copyright (c) 2006-2012 Filip Wasilewski # See COPYING for license details. """1D and 2D Wavelet packet transform module.""" from __future__ import division, print_function, absolute_import __all__ = ["BaseNode", "Node", "WaveletPacket", "Node2D", "WaveletPacket2D"] import numpy as np from ._pywt import Wavelet, dwt, idwt, dwt_max_level from .multidim import dwt2, idwt2 def get_graycode_order(level, x='a', y='d'): graycode_order = [x, y] for i in range(level - 1): graycode_order = [x + path for path in graycode_order] + \ [y + path for path in graycode_order[::-1]] return graycode_order class BaseNode(object): """ BaseNode for wavelet packet 1D and 2D tree nodes. The BaseNode is a base class for `Node` and `Node2D`. It should not be used directly unless creating a new transformation type. It is included here to document the common interface of 1D and 2D node and wavelet packet transform classes. Parameters ---------- parent : Parent node. If parent is None then the node is considered detached (ie root). data : 1D or 2D array Data associated with the node. 1D or 2D numeric array, depending on the transform type. node_name : A name identifying the coefficients type. See `Node.node_name` and `Node2D.node_name` for information on the accepted subnodes names. """ # PART_LEN and PARTS attributes that define path tokens for node[] lookup # must be defined in subclasses. PART_LEN = None PARTS = None def __init__(self, parent, data, node_name): self.parent = parent if parent is not None: self.wavelet = parent.wavelet self.mode = parent.mode self.level = parent.level + 1 self._maxlevel = parent.maxlevel self.path = parent.path + node_name else: self.wavelet = None self.mode = None self.path = "" self.level = 0 # data - signal on level 0, coeffs on higher levels self.data = data self._init_subnodes() def _init_subnodes(self): for part in self.PARTS: self._set_node(part, None) def _create_subnode(self, part, data=None, overwrite=True): raise NotImplementedError() def _create_subnode_base(self, node_cls, part, data=None, overwrite=True): self._validate_node_name(part) if not overwrite and self._get_node(part) is not None: return self._get_node(part) node = node_cls(self, data, part) self._set_node(part, node) return node def _get_node(self, part): return getattr(self, part) def _set_node(self, part, node): setattr(self, part, node) def _delete_node(self, part): self._set_node(part, None) def _validate_node_name(self, part): if part not in self.PARTS: raise ValueError("Subnode name must be in [%s], not '%s'." % (', '.join("'%s'" % p for p in self.PARTS), part)) def _evaluate_maxlevel(self, evaluate_from='parent'): """ Try to find the value of maximum decomposition level if it is not specified explicitly. Parameters ---------- evaluate_from : {'parent', 'subnodes'} """ assert evaluate_from in ('parent', 'subnodes') if self._maxlevel is not None: return self._maxlevel elif self.data is not None: return self.level + dwt_max_level( min(self.data.shape), self.wavelet) if evaluate_from == 'parent': if self.parent is not None: return self.parent._evaluate_maxlevel(evaluate_from) elif evaluate_from == 'subnodes': for node_name in self.PARTS: node = getattr(self, node_name, None) if node is not None: level = node._evaluate_maxlevel(evaluate_from) if level is not None: return level return None @property def maxlevel(self): if self._maxlevel is not None: return self._maxlevel # Try getting the maxlevel from parents first self._maxlevel = self._evaluate_maxlevel(evaluate_from='parent') # If not found, check whether it can be evaluated from subnodes if self._maxlevel is None: self._maxlevel = self._evaluate_maxlevel(evaluate_from='subnodes') return self._maxlevel @property def node_name(self): return self.path[-self.PART_LEN:] def decompose(self): """ Decompose node data creating DWT coefficients subnodes. Performs Discrete Wavelet Transform on the `~BaseNode.data` and returns transform coefficients. Note ---- Descends to subnodes and recursively calls `~BaseNode.reconstruct` on them. """ if self.level < self.maxlevel: return self._decompose() else: raise ValueError("Maximum decomposition level reached.") def _decompose(self): raise NotImplementedError() def reconstruct(self, update=False): """ Reconstruct node from subnodes. Parameters ---------- update : bool, optional If True, then reconstructed data replaces the current node data (default: False). Returns: - original node data if subnodes do not exist - IDWT of subnodes otherwise. """ if not self.has_any_subnode: return self.data return self._reconstruct(update) def _reconstruct(self): raise NotImplementedError() # override this in subclasses def get_subnode(self, part, decompose=True): """ Returns subnode or None (see `decomposition` flag description). Parameters ---------- part : Subnode name decompose : bool, optional If the param is True and corresponding subnode does not exist, the subnode will be created using coefficients from the DWT decomposition of the current node. (default: True) """ self._validate_node_name(part) subnode = self._get_node(part) if subnode is None and decompose and not self.is_empty: self.decompose() subnode = self._get_node(part) return subnode def __getitem__(self, path): """ Find node represented by the given path. Similar to `~BaseNode.get_subnode` method with `decompose=True`, but can access nodes on any level in the decomposition tree. Parameters ---------- path : str String composed of node names. See `Node.node_name` and `Node2D.node_name` for node naming convention. Notes ----- If node does not exist yet, it will be created by decomposition of its parent node. """ if isinstance(path, str): if (self.maxlevel is not None and len(path) > self.maxlevel * self.PART_LEN): raise IndexError("Path length is out of range.") if path: return self.get_subnode(path[0:self.PART_LEN], True)[ path[self.PART_LEN:]] else: return self else: raise TypeError("Invalid path parameter type - expected string but" " got %s." % type(path)) def __setitem__(self, path, data): """ Set node or node's data in the decomposition tree. Nodes are identified by string `path`. Parameters ---------- path : str String composed of node names. data : array or BaseNode subclass. """ if isinstance(path, str): if ( self.maxlevel is not None and len(self.path) + len(path) > self.maxlevel * self.PART_LEN ): raise IndexError("Path length out of range.") if path: subnode = self.get_subnode(path[0:self.PART_LEN], False) if subnode is None: self._create_subnode(path[0:self.PART_LEN], None) subnode = self.get_subnode(path[0:self.PART_LEN], False) subnode[path[self.PART_LEN:]] = data else: if isinstance(data, BaseNode): self.data = np.asarray(data.data, dtype=np.float64) else: self.data = np.asarray(data, dtype=np.float64) else: raise TypeError("Invalid path parameter type - expected string but" " got %s." % type(path)) def __delitem__(self, path): """ Remove node from the tree. Parameters ---------- path : str String composed of node names. """ node = self[path] # don't clear node value and subnodes (node may still exist outside # the tree) # # node._init_subnodes() # # node.data = None parent = node.parent node.parent = None # TODO if parent and node.node_name: parent._delete_node(node.node_name) def is_empty(self): return self.data is None is_empty = property(is_empty) def has_any_subnode(self): for part in self.PARTS: if self._get_node(part) is not None: # and not .is_empty return True return False has_any_subnode = property(has_any_subnode) def get_leaf_nodes(self, decompose=False): """ Returns leaf nodes. Parameters ---------- decompose : bool, optional (default: True) """ result = [] def collect(node): if node.level == node.maxlevel and not node.is_empty: result.append(node) return False if not decompose and not node.has_any_subnode: result.append(node) return False return True self.walk(collect, decompose=decompose) return result def walk(self, func, args=(), kwargs=None, decompose=True): """ Traverses the decomposition tree and calls ``func(node, *args, **kwargs)`` on every node. If `func` returns True, descending to subnodes will continue. Parameters ---------- func : callable Callable accepting `BaseNode` as the first param and optional positional and keyword arguments args : func params kwargs : func keyword params decompose : bool, optional If True (default), the method will also try to decompose the tree up to the `maximum level `. """ if kwargs is None: kwargs = {} if func(self, *args, **kwargs) and self.level < self.maxlevel: for part in self.PARTS: subnode = self.get_subnode(part, decompose) if subnode is not None: subnode.walk(func, args, kwargs, decompose) def walk_depth(self, func, args=(), kwargs=None, decompose=False): """ Walk tree and call func on every node starting from the bottom-most nodes. Parameters ---------- func : callable Callable accepting :class:`BaseNode` as the first param and optional positional and keyword arguments args : func params kwargs : func keyword params decompose : bool, optional (default: False) """ if kwargs is None: kwargs = {} if self.level < self.maxlevel: for part in self.PARTS: subnode = self.get_subnode(part, decompose) if subnode is not None: subnode.walk_depth(func, args, kwargs, decompose) func(self, *args, **kwargs) def __str__(self): return self.path + ": " + str(self.data) class Node(BaseNode): """ WaveletPacket tree node. Subnodes are called `a` and `d`, just like approximation and detail coefficients in the Discrete Wavelet Transform. """ A = 'a' D = 'd' PARTS = A, D PART_LEN = 1 def _create_subnode(self, part, data=None, overwrite=True): return self._create_subnode_base(node_cls=Node, part=part, data=data, overwrite=overwrite) def _decompose(self): """ See also -------- dwt : for 1D Discrete Wavelet Transform output coefficients. """ if self.is_empty: data_a, data_d = None, None if self._get_node(self.A) is None: self._create_subnode(self.A, data_a) if self._get_node(self.D) is None: self._create_subnode(self.D, data_d) else: data_a, data_d = dwt(self.data, self.wavelet, self.mode) self._create_subnode(self.A, data_a) self._create_subnode(self.D, data_d) return self._get_node(self.A), self._get_node(self.D) def _reconstruct(self, update): data_a, data_d = None, None node_a, node_d = self._get_node(self.A), self._get_node(self.D) if node_a is not None: data_a = node_a.reconstruct() # TODO: (update) ??? if node_d is not None: data_d = node_d.reconstruct() # TODO: (update) ??? if data_a is None and data_d is None: raise ValueError("Node is a leaf node and cannot be reconstructed" " from subnodes.") else: rec = idwt(data_a, data_d, self.wavelet, self.mode, correct_size=True) if update: self.data = rec return rec class Node2D(BaseNode): """ WaveletPacket tree node. Subnodes are called 'a' (LL), 'h' (LH), 'v' (HL) and 'd' (HH), like approximation and detail coefficients in the 2D Discrete Wavelet Transform """ LL = 'a' LH = 'h' HL = 'v' HH = 'd' PARTS = LL, LH, HL, HH PART_LEN = 1 def _create_subnode(self, part, data=None, overwrite=True): return self._create_subnode_base(node_cls=Node2D, part=part, data=data, overwrite=overwrite) def _decompose(self): """ See also -------- dwt2 : for 2D Discrete Wavelet Transform output coefficients. """ if self.is_empty: data_ll, data_lh, data_hl, data_hh = None, None, None, None else: data_ll, (data_lh, data_hl, data_hh) =\ dwt2(self.data, self.wavelet, self.mode) self._create_subnode(self.LL, data_ll) self._create_subnode(self.LH, data_lh) self._create_subnode(self.HL, data_hl) self._create_subnode(self.HH, data_hh) return (self._get_node(self.LL), self._get_node(self.LH), self._get_node(self.HL), self._get_node(self.HH)) def _reconstruct(self, update): data_ll, data_lh, data_hl, data_hh = None, None, None, None node_ll, node_lh, node_hl, node_hh =\ self._get_node(self.LL), self._get_node(self.LH),\ self._get_node(self.HL), self._get_node(self.HH) if node_ll is not None: data_ll = node_ll.reconstruct() if node_lh is not None: data_lh = node_lh.reconstruct() if node_hl is not None: data_hl = node_hl.reconstruct() if node_hh is not None: data_hh = node_hh.reconstruct() if (data_ll is None and data_lh is None and data_hl is None and data_hh is None): raise ValueError( "Tree is missing data - all subnodes of `%s` node " "are None. Cannot reconstruct node." % self.path ) else: coeffs = data_ll, (data_lh, data_hl, data_hh) rec = idwt2(coeffs, self.wavelet, self.mode) if update: self.data = rec return rec def expand_2d_path(self, path): expanded_paths = { self.HH: 'hh', self.HL: 'hl', self.LH: 'lh', self.LL: 'll' } return (''.join([expanded_paths[p][0] for p in path]), ''.join([expanded_paths[p][1] for p in path])) class WaveletPacket(Node): """ Data structure representing Wavelet Packet decomposition of signal. Parameters ---------- data : 1D ndarray Original data (signal) wavelet : Wavelet object or name string Wavelet used in DWT decomposition and reconstruction mode : str, optional Signal extension mode for the `dwt` and `idwt` decomposition and reconstruction functions. maxlevel : int, optional Maximum level of decomposition. If None, it will be calculated based on the `wavelet` and `data` length using `pywt.dwt_max_level`. """ def __init__(self, data, wavelet, mode='sym', maxlevel=None): super(WaveletPacket, self).__init__(None, data, "") if not isinstance(wavelet, Wavelet): wavelet = Wavelet(wavelet) self.wavelet = wavelet self.mode = mode if data is not None: data = np.asarray(data, dtype=np.float64) assert data.ndim == 1 self.data_size = data.shape[0] if maxlevel is None: maxlevel = dwt_max_level(self.data_size, self.wavelet) else: self.data_size = None self._maxlevel = maxlevel def reconstruct(self, update=True): """ Reconstruct data value using coefficients from subnodes. Parameters ---------- update : bool, optional If True (default), then data values will be replaced by reconstruction values, also in subnodes. """ if self.has_any_subnode: data = super(WaveletPacket, self).reconstruct(update) if self.data_size is not None and len(data) > self.data_size: data = data[:self.data_size] if update: self.data = data return data return self.data # return original data def get_level(self, level, order="natural", decompose=True): """ Returns all nodes on the specified level. Parameters ---------- level : int Specifies decomposition `level` from which the nodes will be collected. order : {'natural', 'freq'}, optional - "natural" - left to right in tree (default) - "freq" - band ordered decompose : bool, optional If set then the method will try to decompose the data up to the specified `level` (default: True). Notes ----- If nodes at the given level are missing (i.e. the tree is partially decomposed) and the `decompose` is set to False, only existing nodes will be returned. """ assert order in ["natural", "freq"] if level > self.maxlevel: raise ValueError("The level cannot be greater than the maximum" " decomposition level value (%d)" % self.maxlevel) result = [] def collect(node): if node.level == level: result.append(node) return False return True self.walk(collect, decompose=decompose) if order == "natural": return result elif order == "freq": result = dict((node.path, node) for node in result) graycode_order = get_graycode_order(level) return [result[path] for path in graycode_order if path in result] else: raise ValueError("Invalid order name - %s." % order) class WaveletPacket2D(Node2D): """ Data structure representing 2D Wavelet Packet decomposition of signal. Parameters ---------- data : 2D ndarray Data associated with the node. wavelet : Wavelet object or name string Wavelet used in DWT decomposition and reconstruction mode : str, optional Signal extension mode for the `dwt` and `idwt` decomposition and reconstruction functions. maxlevel : int Maximum level of decomposition. If None, it will be calculated based on the `wavelet` and `data` length using `pywt.dwt_max_level`. """ def __init__(self, data, wavelet, mode='sp1', maxlevel=None): super(WaveletPacket2D, self).__init__(None, data, "") if not isinstance(wavelet, Wavelet): wavelet = Wavelet(wavelet) self.wavelet = wavelet self.mode = mode if data is not None: data = np.asarray(data, dtype=np.float64) assert data.ndim == 2 self.data_size = data.shape if maxlevel is None: maxlevel = dwt_max_level(min(self.data_size), self.wavelet) else: self.data_size = None self._maxlevel = maxlevel def reconstruct(self, update=True): """ Reconstruct data using coefficients from subnodes. Parameters ---------- update : bool, optional If True (default) then the coefficients of the current node and its subnodes will be replaced with values from reconstruction. """ if self.has_any_subnode: data = super(WaveletPacket2D, self).reconstruct(update) if self.data_size is not None and (data.shape != self.data_size): data = data[:self.data_size[0], :self.data_size[1]] if update: self.data = data return data return self.data # return original data def get_level(self, level, order="natural", decompose=True): """ Returns all nodes from specified level. Parameters ---------- level : int Decomposition `level` from which the nodes will be collected. order : {'natural', 'freq'}, optional If `natural` (default) a flat list is returned. If `freq`, a 2d structure with rows and cols sorted by corresponding dimension frequency of 2d coefficient array (adapted from 1d case). decompose : bool, optional If set then the method will try to decompose the data up to the specified `level` (default: True). """ assert order in ["natural", "freq"] if level > self.maxlevel: raise ValueError("The level cannot be greater than the maximum" " decomposition level value (%d)" % self.maxlevel) result = [] def collect(node): if node.level == level: result.append(node) return False return True self.walk(collect, decompose=decompose) if order == "freq": nodes = {} for (row_path, col_path), node in [ (self.expand_2d_path(node.path), node) for node in result ]: nodes.setdefault(row_path, {})[col_path] = node graycode_order = get_graycode_order(level, x='l', y='h') nodes = [nodes[path] for path in graycode_order if path in nodes] result = [] for row in nodes: result.append( [row[path] for path in graycode_order if path in row] ) return result PyWavelets-0.3.0/CHANGES.txt0000664000175000017500000000344112556460247017155 0ustar rgommersrgommers00000000000000Changelog 0.3.0 A major refactoring, providing support for Python 3.x while maintaining full backwards compatiblity. Development has moved to https://github.com/PyWavelets/pywt 0.2.2 maintenance release: - resolved setup and build issues - support for compilation using MSVC compiler - updated documentation - moved main repository to GitHub (https://github.com/nigma/pywt) 0.2.0 changes: - 2D Wavelet Packet and Inverse Wavelet Packet Transforms - 2D Stationary Wavelet Transform - Single and double precision computations - DWT and IDWT optimizations - refactored Wavelet Packet code 0.1.6 changes: - argument order changed for wavedec to be more consistent with other functions. Now is (data, wavelet, *mode*, *level*). - added 2D DWT and IDWT (dwt2, idwt2) - added 2D multilevel transform - wavedec2 and waverec2 - added support for Python 2.5 (requires modified Pyrex, see the documentation) - using Python memory management functions instead of C stdlib ones fixes: - rbior wavelets filters corrected 0.1.4 changes: - Wavelet class can be subclassed - requires NumPy, edit numerix.py to use with other numeric modules, array.array is no more directly supported - code cleanup & comments - wavedec and waverec Pyrex code moved to pure Python multilevel.py module - doctesting doc examples fixes: - fixed swt for too high level value - fixed bug in upcoef wrapper code for some take values 0.1.2 changes: - support for custom filter banks - now compiles without numpy installed fixes: - fixed handling of non-contiguous arrays 0.1.0 initial release PyWavelets-0.3.0/setup.py0000775000175000017500000001613012556460247017060 0ustar rgommersrgommers00000000000000#!/usr/bin/env python #-*- coding: utf-8 -*- import os import sys import subprocess MAJOR = 0 MINOR = 3 MICRO = 0 ISRELEASED = True VERSION = '%d.%d.%d' % (MAJOR, MINOR, MICRO) # Return the git revision as a string def git_version(): def _minimal_ext_cmd(cmd): # construct minimal environment env = {} for k in ['SYSTEMROOT', 'PATH']: v = os.environ.get(k) if v is not None: env[k] = v # LANGUAGE is used on win32 env['LANGUAGE'] = 'C' env['LANG'] = 'C' env['LC_ALL'] = 'C' out = subprocess.Popen(cmd, stdout=subprocess.PIPE, env=env).communicate()[0] return out try: out = _minimal_ext_cmd(['git', 'rev-parse', 'HEAD']) GIT_REVISION = out.strip().decode('ascii') except OSError: GIT_REVISION = "Unknown" return GIT_REVISION def get_version_info(): # Adding the git rev number needs to be done inside # write_version_py(), otherwise the import of pywt.version messes # up the build under Python 3. FULLVERSION = VERSION if os.path.exists('.git'): GIT_REVISION = git_version() elif os.path.exists('pywt/version.py'): # must be a source distribution, use existing version file # load it as a separate module to not load pywt/__init__.py import imp version = imp.load_source('pywt.version', 'pywt/version.py') GIT_REVISION = version.git_revision else: GIT_REVISION = "Unknown" if not ISRELEASED: FULLVERSION += '.dev0+' + GIT_REVISION[:7] return FULLVERSION, GIT_REVISION def write_version_py(filename='pywt/version.py'): cnt = """ # THIS FILE IS GENERATED FROM PYWAVELETS SETUP.PY short_version = '%(version)s' version = '%(version)s' full_version = '%(full_version)s' git_revision = '%(git_revision)s' release = %(isrelease)s if not release: version = full_version """ FULLVERSION, GIT_REVISION = get_version_info() a = open(filename, 'w') try: a.write(cnt % {'version': VERSION, 'full_version': FULLVERSION, 'git_revision': GIT_REVISION, 'isrelease': str(ISRELEASED)}) finally: a.close() # BEFORE importing distutils, remove MANIFEST. distutils doesn't properly # update it when the contents of directories change. if os.path.exists('MANIFEST'): os.remove('MANIFEST') if sys.platform == "darwin": # Don't create resource files on OS X tar. os.environ["COPY_EXTENDED_ATTRIBUTES_DISABLE"] = "true" os.environ["COPYFILE_DISABLE"] = "true" setup_args = {} def expand_src_templates(): cwd = os.path.abspath(os.path.dirname(__file__)) print("Expanding templates") p = subprocess.call([sys.executable, os.path.join(cwd, 'util', 'templating_src.py'), 'pywt'], cwd=cwd) if p != 0: raise RuntimeError("Expanding templates failed!") def generate_cython(): cwd = os.path.abspath(os.path.dirname(__file__)) print("Cythonizing sources") p = subprocess.call([sys.executable, os.path.join(cwd, 'util', 'cythonize.py'), 'pywt'], cwd=cwd) if p != 0: raise RuntimeError("Running cythonize failed!") def configuration(parent_package='',top_path=None): from numpy.distutils.misc_util import Configuration config = Configuration(None, parent_package, top_path) config.set_options(ignore_setup_xxx_py=True, assume_default_configuration=True, delegate_options_to_subpackages=True, quiet=True) config.add_subpackage('pywt') config.get_version('pywt/version.py') return config def setup_package(): # Rewrite the version file everytime write_version_py() metadata = dict( name="PyWavelets", maintainer="The PyWavelets Developers", maintainer_email="http://groups.google.com/group/pywavelets", url="https://github.com/PyWavelets/pywt", download_url="https://github.com/PyWavelets/pywt/releases", license="MIT", description="PyWavelets, wavelet transform module", long_description="""\ PyWavelets is a Python wavelet transforms module that includes: * 1D and 2D Forward and Inverse Discrete Wavelet Transform (DWT and IDWT) * 1D and 2D Stationary Wavelet Transform (Undecimated Wavelet Transform) * 1D and 2D Wavelet Packet decomposition and reconstruction * Computing Approximations of wavelet and scaling functions * Over seventy built-in wavelet filters and support for custom wavelets * Single and double precision calculations * Results compatibility with Matlab Wavelet Toolbox (tm) """, keywords=["wavelets", "wavelet transform", "DWT", "SWT", "scientific"], classifiers=[ "Development Status :: 5 - Production/Stable", "Intended Audience :: Developers", "Intended Audience :: Education", "Intended Audience :: Science/Research", "License :: OSI Approved :: MIT License", "Operating System :: OS Independent", "Programming Language :: C", "Programming Language :: Python", "Programming Language :: Python :: 3", "Programming Language :: Python :: 2.6", "Programming Language :: Python :: 2.7", "Programming Language :: Python :: 3.3", "Programming Language :: Python :: 3.4", "Programming Language :: Python :: 3.5", "Topic :: Software Development :: Libraries :: Python Modules" ], platforms=["Windows", "Linux", "Solaris", "Mac OS-X", "Unix"], test_suite='nose.collector', cmdclass={}, **setup_args ) if len(sys.argv) >= 2 and ('--help' in sys.argv[1:] or sys.argv[1] in ('--help-commands', 'egg_info', '--version', 'clean')): # For these actions, NumPy is not required. # # They are required to succeed without Numpy for example when # pip is used to install PyWavelets when Numpy is not yet present in # the system. try: from setuptools import setup except ImportError: from distutils.core import setup FULLVERSION, GIT_REVISION = get_version_info() metadata['version'] = FULLVERSION else: if (len(sys.argv) >= 2 and sys.argv[1] == 'bdist_wheel') or ( 'develop' in sys.argv): # bdist_wheel needs setuptools import setuptools from numpy.distutils.core import setup cwd = os.path.abspath(os.path.dirname(__file__)) if not os.path.exists(os.path.join(cwd, 'PKG-INFO')): # Generate Cython sources, unless building from source release expand_src_templates() generate_cython() metadata['configuration'] = configuration setup(**metadata) if __name__ == '__main__': setup_package() PyWavelets-0.3.0/tox.ini0000664000175000017500000000240212556460247016653 0ustar rgommersrgommers00000000000000# Tox (http://tox.testrun.org/) is a tool for running tests # in multiple virtualenvs. This configuration file will run the # test suite on all supported python versions. To use it, "pip install tox" # and then run "tox" from this directory. # Running the command 'tox' while in the root of the pywt source # directory will: # - Create a pywt source distribution (setup.py sdist) # - Then for every supported version of Python: # - Create a virtualenv in {homedir}/.tox/pywt/py$VERSION and # install dependencies. (These virtualenvs are cached across # runs unless you use --recreate.) # - Use pip to install the pywt sdist into the virtualenv # - Run the pywt tests # To run against a specific subset of Python versions, use: # tox -e py26,py27 # Tox assumes that you have appropriate Python interpreters already # installed and that they can be run as 'python2.6', 'python2.7', etc. [tox] toxworkdir = {homedir}/.tox/pywt/ envlist = py26, py27, py33, py34, py35 [testenv] deps = flake8 nose cython numpy changedir = {envdir} commands = python {toxinidir}/runtests.py -n -m full {posargs:} # flake8 --exit-zero pywt [pep8] max_line_length = 79 statistics = True ignore = E121,E122,E123,E125,E126,E127,E128,E226,E231,E501,E712 PyWavelets-0.3.0/doc/0000775000175000017500000000000012556460303016100 5ustar rgommersrgommers00000000000000PyWavelets-0.3.0/doc/source/0000775000175000017500000000000012556460303017400 5ustar rgommersrgommers00000000000000PyWavelets-0.3.0/doc/source/conf.py0000664000175000017500000001544212556460247020714 0ustar rgommersrgommers00000000000000# -*- coding: utf-8 -*- # # PyWavelets documentation build configuration file, created by # sphinx-quickstart on Sun Mar 14 10:46:18 2010. # # This file is execfile()d with the current directory set to its containing dir. # # Note that not all possible configuration values are present in this # autogenerated file. # # All configuration values have a default; values that are commented out # serve to show the default. import re import datetime import jinja2.filters # If extensions (or modules to document with autodoc) are in another directory, # add these directories to sys.path here. If the directory is relative to the # documentation root, use os.path.abspath to make it absolute, like shown here. #sys.path.append(os.path.abspath('.')) # -- General configuration ----------------------------------------------------- # Add any Sphinx extension module names here, as strings. They can be extensions # coming with Sphinx (named 'sphinx.ext.*') or your custom ones. extensions = ['sphinx.ext.doctest', 'sphinx.ext.autodoc', 'sphinx.ext.todo', 'sphinx.ext.extlinks'] # Add any paths that contain templates here, relative to this directory. templates_path = ['_templates'] # The suffix of source filenames. source_suffix = '.rst' # The encoding of source files. #source_encoding = 'utf-8' # The master toctree document. master_doc = 'index' # General information about the project. project = 'PyWavelets' copyright = jinja2.filters.do_mark_safe('2006-%s, The PyWavelets Developers' % datetime.date.today().year) # The version info for the project you're documenting, acts as replacement for # |version| and |release|, also used in various other places throughout the # built documents. # The default replacements for |version| and |release|, also used in various # other places throughout the built documents. import pywt version = re.sub(r'\.dev0+.*$', r'.dev', pywt.__version__) release = pywt.__version__ print "PyWavelets (VERSION %s)" % (version,) # The language for content autogenerated by Sphinx. Refer to documentation # for a list of supported languages. #language = None # There are two options for replacing |today|: either, you set today to some # non-false value, then it is used: #today = '' # Else, today_fmt is used as the format for a strftime call. #today_fmt = '%B %d, %Y' # List of documents that shouldn't be included in the build. unused_docs = ['substitutions', 'overview'] # List of directories, relative to source directory, that shouldn't be searched # for source files. exclude_trees = ['_build'] # The reST default role (used for this markup: `text`) to use for all documents. #default_role = None # If true, '()' will be appended to :func: etc. cross-reference text. #add_function_parentheses = True # If true, the current module name will be prepended to all description # unit titles (such as .. function::). #add_module_names = True # If true, sectionauthor and moduleauthor directives will be shown in the # output. They are ignored by default. #show_authors = False # The name of the Pygments (syntax highlighting) style to use. pygments_style = 'sphinx' # A list of ignored prefixes for module index sorting. modindex_common_prefix = ['pywt.'] # -- Options for HTML output --------------------------------------------------- # The theme to use for HTML and HTML Help pages. Major themes that come with # Sphinx are currently 'default' and 'sphinxdoc'. html_theme = 'nature' # Theme options are theme-specific and customize the look and feel of a theme # further. For a list of options available for each theme, see the # documentation. #html_theme_options = {} # Add any paths that contain custom themes here, relative to this directory. #html_theme_path = [] # The name for this set of Sphinx documents. If None, it defaults to # " v documentation". html_title = 'PyWavelets Documentation' # A shorter title for the navigation bar. Default is the same as html_title. #html_short_title = None # The name of an image file (relative to this directory) to place at the top # of the sidebar. #html_logo = None # The name of an image file (within the static path) to use as favicon of the # docs. This file should be a Windows icon file (.ico) being 16x16 or 32x32 # pixels large. html_favicon = 'favicon.ico' # Add any paths that contain custom static files (such as style sheets) here, # relative to this directory. They are copied after the builtin static files, # so a file named "default.css" will overwrite the builtin "default.css". html_static_path = ['_static'] # If not '', a 'Last updated on:' timestamp is inserted at every page bottom, # using the given strftime format. html_last_updated_fmt = '%b %d, %Y' # If true, SmartyPants will be used to convert quotes and dashes to # typographically correct entities. #html_use_smartypants = True # Custom sidebar templates, maps document names to template names. #html_sidebars = {} html_sidebars = { '**': ['localtoc.html', "relations.html", 'quicklinks.html', 'searchbox.html', 'editdocument.html'], } # Additional templates that should be rendered to pages, maps page names to # template names. #html_additional_pages = {} # If false, no module index is generated. #html_use_modindex = True # If false, no index is generated. #html_use_index = True # If true, the index is split into individual pages for each letter. #html_split_index = False # If true, links to the reST sources are added to the pages. html_show_sourcelink = False # If true, an OpenSearch description file will be output, and all pages will # contain a tag referring to it. The value of this option must be the # base URL from which the finished HTML is served. html_use_opensearch = 'http://pywavelets.readthedocs.org' # If nonempty, this is the file name suffix for HTML files (e.g. ".xhtml"). #html_file_suffix = '' # Output file base name for HTML help builder. htmlhelp_basename = 'PyWaveletsdoc' # -- Options for LaTeX output -------------------------------------------------- # The paper size ('letter' or 'a4'). #latex_paper_size = 'letter' # The font size ('10pt', '11pt' or '12pt'). #latex_font_size = '10pt' # Grouping the document tree into LaTeX files. List of tuples # (source start file, target name, title, author, documentclass [howto/manual]). latex_documents = [ ('index', 'PyWavelets.tex', 'PyWavelets Documentation', 'The PyWavelets Developers', 'manual'), ] # The name of an image file (relative to this directory) to place at the top of # the title page. #latex_logo = None # For "manual" documents, if this is true, then toplevel headings are parts, # not chapters. #latex_use_parts = False # Additional stuff for the LaTeX preamble. #latex_preamble = '' # Documents to append as an appendix to all manuals. #latex_appendices = [] # If false, no module index is generated. #latex_use_modindex = True PyWavelets-0.3.0/doc/source/_static/0000775000175000017500000000000012556460303021026 5ustar rgommersrgommers00000000000000PyWavelets-0.3.0/doc/source/_static/twitter.png0000664000175000017500000000137012556460247023246 0ustar rgommersrgommers00000000000000PNG  IHDRaIDATxcdɓ'8 100~ٳgO€0`ٳfzAȑ#m Xl]dDx<4 AAߗ&O񨨬Oa͛=~t_$ȿ_>}:߿,?~V^P[[nƍ<=<}|hPJR2000\|yJGggҥK`p hPfFSM`03[UUu@KG o@;13DePbFfb#`&e_J+'Ng8028 320?+WWPXXۯXsus$#&ʄPT\q̙XXtWxhX@!LA |*O>`ee= n@QQrw{۾ @?+# C8fbc u)ffkg8 )&Mvefð`䔔EpTZW ~p?OA!C439ߵ M---(11Ѥng/;(v`xbHKIH@OO:}3WA0. ^AIENDB`PyWavelets-0.3.0/doc/source/_static/favicon.ico0000664000175000017500000000257612556460247023170 0ustar rgommersrgommers00000000000000h( B1_1`E}Xl/-P?pQcv1Qq,/KPhp1Qq/!P7pLcyϏ1Qq/Pp",6@J[1qQq/Pp  =1[Qyq/"P0p=LYgx1Qq&/@PZpt1Qq/&PAp[tϩ1Qq/P"p0>M[iy1Qqұ/Pp  >1\Qzq/Pp!+6@IZ1pQq/ P6pLbx1Qq,/KPip1Qq/-P?pRcv1Qqϑܱ/Pp!&,>X1qQq       *?PyWavelets-0.3.0/doc/source/_static/comments.png0000664000175000017500000000105512556460247023371 0ustar rgommersrgommers00000000000000PNG  IHDRagAMA7tEXtSoftwareAdobe ImageReadyqe<IDAT8ˍOPYML܌&.]$$κHA<–CXG((GIK^9Y:p V8y~h"+j).UP"K m:ruS( Qr?рOeb նUZ"[n_絖)TyXywe-~, BhX)7oȅ0i=] !B.m V}a~v$;Ř0!B.\]}ŕN.Z|*[\r> 0#B.8CQu]mKO vpAg\ƭIDAT(υѿgCQONdJNkn.!S]OdJY^J K.nBHNJw:9롢H낆 Ҋ?/}_'֏ jC E#m*Ưi)LYUھ}ظxq`u#RkRJj4$! dD!iOn9C/im&N{ A DJD)u\~'ו:nһaK A I"%wyĖU־zRWO؄wRD(SHIF^>> import pywt >>> cA, cD = pywt.dwt([1, 2, 3, 4], 'db1') Voilà! Computing wavelet transforms never before has been so simple :) Main features ------------- The main features of PyWavelets are: * 1D, 2D and nD Forward and Inverse Discrete Wavelet Transform (DWT and IDWT) * 1D and 2D Stationary Wavelet Transform (Undecimated Wavelet Transform) * 1D and 2D Wavelet Packet decomposition and reconstruction * Approximating wavelet and scaling functions * Over seventy `built-in wavelet filters`_ and custom wavelets supported * Single and double precision calculations * Results compatible with Matlab Wavelet Toolbox (TM) Requirements ------------ PyWavelets is a package for the Python programming language. It requires: - Python_ 2.6, 2.7 or >=3.3 - Numpy_ >= 1.6.2 Download -------- The most recent *development* version can be found on GitHub at https://github.com/PyWavelets/pywt. Latest release, including source and binary package for Windows, is available for download from the `Python Package Index`_ or on the `Releases Page`_. Install ------- In order to build PyWavelets from source, a working C compiler (GCC or MSVC) and a recent version of Cython_ is required. - Install PyWavelets with ``pip install PyWavelets``. - To build and install from source, navigate to downloaded PyWavelets source code directory and type ``python setup.py install``. Prebuilt Windows binaries and source code packages are also available from `Python Package Index`_. Binary packages for several Linux distributors are maintained by Open Source community contributors. Query your Linux package manager tool for `python-wavelets`, `python-pywt` or similar package name. .. seealso:: :ref:`Development notes ` section contains more information on building and installing from source code. Documentation ------------- Documentation with detailed examples and links to more resources is available online at http://pywavelets.readthedocs.org. For more usage examples see the `demo`_ directory in the source package. State of development & Contributing ----------------------------------- PyWavelets started in 2006 as an academic project for a master thesis on `Analysis and Classification of Medical Signals using Wavelet Transforms` and was maintained until 2012 by its `original developer`_. In 2013 maintenance was taken over in a `new repo `_) by a larger development team - a move supported by the original developer. The repo move doesn't mean that this is a fork - the package continues to be developed under the name "PyWavelets", and released on PyPi and Github (see `this issue `_ for the discussion where that was decided). All contributions including bug reports, bug fixes, new feature implementations and documentation improvements are welcome. Moreover, developers with an interest in PyWavelets are very welcome to join the development team! Python 3 -------- Python 3.x is fully supported from release v0.3.0 on. Contact ------- Use `GitHub Issues`_ or the `PyWavelets discussions group`_ to post your comments or questions. License ------- PyWavelets is a free Open Source software released under the MIT license. Contents -------- .. toctree:: :maxdepth: 1 ref/index regression/index dev/index resources contents .. _built-in wavelet filters: http://wavelets.pybytes.com/ .. _Cython: http://cython.org/ .. _demo: https://github.com/PyWavelets/pywt/tree/master/demo .. _GitHub: https://github.com/PyWavelets/pywt .. _GitHub Issues: https://github.com/PyWavelets/pywt/issues .. _Numpy: http://www.numpy.org .. _original developer: http://en.ig.ma .. _Python: http://python.org/ .. _Python Package Index: http://pypi.python.org/pypi/PyWavelets/ .. _PyWavelets discussions group: http://groups.google.com/group/pywavelets .. _Releases Page: https://github.com/PyWavelets/pywt/releases PyWavelets-0.3.0/doc/source/_templates/0000775000175000017500000000000012556460303021535 5ustar rgommersrgommers00000000000000PyWavelets-0.3.0/doc/source/_templates/page.html0000664000175000017500000000307512556460247023353 0ustar rgommersrgommers00000000000000{# Drop version number from the HTML documentation title #} {%- set docstitle = "PyWavelets Documentation" %} {% extends "!page.html" %} {% block extrahead %} {{ super() }} {% endblock %} {# Remove version number from the top and bottom path bars #} {%- block rootrellink %}
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    PyWavelets-0.3.0/doc/source/_templates/quicklinks.html0000664000175000017500000000132212556460247024605 0ustar rgommersrgommers00000000000000 PyWavelets-0.3.0/doc/source/releasenotes.rst0000664000175000017500000000011412556460247022626 0ustar rgommersrgommers00000000000000Release Notes ============= .. toctree:: :maxdepth: 1 release.0.3.0 PyWavelets-0.3.0/doc/source/substitutions.rst0000664000175000017500000000122212556460247023075 0ustar rgommersrgommers00000000000000.. |mode| replace:: Signal extension mode to deal with the border distortion problem. See :ref:`MODES ` for details. .. |data| replace:: Input signal can be NumPy array, Python list or other iterable object. Both *single* and *double* precision floating-point data types are supported and the output type depends on the input type. If the input data is not in one of these types it will be converted to the default *double* precision data format before performing computations. .. |wavelet| replace:: Wavelet to use in the transform. This can be a name of the wavelet from the :func:`wavelist` list or a :class:`Wavelet` object instance. PyWavelets-0.3.0/doc/source/dev/0000775000175000017500000000000012556460303020156 5ustar rgommersrgommers00000000000000PyWavelets-0.3.0/doc/source/dev/testing.rst0000664000175000017500000000175012556460247022377 0ustar rgommersrgommers00000000000000.. _dev-testing: Testing ======= Continous integration with Travis-CI ------------------------------------ The project is using `Travis-CI `_ service for continous integration and testing. Current build status is: .. image:: https://secure.travis-ci.org/PyWavelets/pywt.png?branch=master :alt: Build Status :target: https://secure.travis-ci.org/PyWavelets/pywt If you are submitting a patch or pull request please make sure it does not break the build. Running tests locally --------------------- Tests are implemented with `nose`_, so use one of: $ nosetests pywt >>> pywt.test() Running tests with Tox ---------------------- There's also a config file for running tests with `Tox`_ (``pip install tox``). To for example run tests for Python 2.7 and Python 3.4 use:: tox -e py27,py34 For more information see the `Tox`_ documentation. .. _nose: http://nose.readthedocs.org/en/latest/ .. _Tox: http://tox.testrun.org/ PyWavelets-0.3.0/doc/source/dev/preparing_windows_build_environment.rst0000664000175000017500000000720712556460247030271 0ustar rgommersrgommers00000000000000.. _dev-building-on-windows: Preparing Windows build environment =================================== To start developing PyWavelets code on Windows you will have to install a C compiler and prepare the build environment. Installing Windows SDK C/C++ compiler ------------------------------------- Microsoft Visual C++ 2008 (Microsoft Visual Studio 9.0) is the compiler that is suitable for building extensions for Python 2.6, 2.7, 3.0, 3.1 and 3.2 (both 32 and 64 bit). .. note:: For reference: - the *MSC v.1500* in the Python version string is Microsoft Visual C++ 2008 (Microsoft Visual Studio 9.0 with msvcr90.dll runtime) - *MSC v.1600* is MSVC 2010 (10.0 with msvcr100.dll runtime) - *MSC v.1700* is MSVC 2011 (11.0) :: Python 2.7.3 (default, Apr 10 2012, 23:31:26) [MSC v.1500 32 bit (Intel)] on win32 Python 3.2 (r32:88445, Feb 20 2011, 21:30:00) [MSC v.1500 64 bit (AMD64)] on win32 To get started first download, extract and install *Microsoft Windows SDK for Windows 7 and .NET Framework 3.5 SP1* from http://www.microsoft.com/downloads/en/details.aspx?familyid=71DEB800-C591-4F97-A900-BEA146E4FAE1&displaylang=en. There are several ISO images on the site, so just grab the one that is suitable for your platform: - ``GRMSDK_EN_DVD.iso`` for 32-bit x86 platform - ``GRMSDKX_EN_DVD.iso`` for 64-bit AMD64 platform (AMD64 is the codename for 64-bit CPU architecture, not the processor manufacturer) After installing the SDK and before compiling the extension you have to configure some environment variables. For 32-bit build execute the ``util/setenv_build32.bat`` script in the cmd window: .. sourcecode:: bat rem Configure the environment for 32-bit builds. rem Use "vcvars32.bat" for a 32-bit build. "C:\Program Files (x86)\Microsoft Visual Studio 9.0\VC\bin\vcvars32.bat" rem Convince setup.py to use the SDK tools. set MSSdk=1 setenv /x86 /release set DISTUTILS_USE_SDK=1 For 64-bit use ``util/setenv_build64.bat``: .. sourcecode:: bat rem Configure the environment for 64-bit builds. rem Use "vcvars32.bat" for a 32-bit build. "C:\Program Files (x86)\Microsoft Visual Studio 9.0\VC\bin\vcvars64.bat" rem Convince setup.py to use the SDK tools. set MSSdk=1 setenv /x64 /release set DISTUTILS_USE_SDK=1 See also http://wiki.cython.org/64BitCythonExtensionsOnWindows. MinGW C/C++ compiler -------------------- MinGW distribution can be downloaded from http://sourceforge.net/projects/mingwbuilds/. In order to change the settings and use MinGW as the default compiler, edit or create a Distutils configuration file ``c:\Python2*\Lib\distutils\distutils.cfg`` and place the following entry in it:: [build] compiler = mingw32 You can also take a look at Cython's "Installing MinGW on Windows" page at http://wiki.cython.org/InstallingOnWindows for more info. .. note:: Python 2.7/3.2 distutils package is incompatible with the current version (4.7+) of MinGW (MinGW dropped the ``-mno-cygwin`` flag, which is still passed by distutils). To use MinGW to compile Python extensions you have to patch the ``distutils/cygwinccompiler.py`` library module and remove every occurrence of ``-mno-cygwin``. See http://bugs.python.org/issue12641 bug report for more information on the issue. Next steps ---------- After completing these steps continue with :ref:`Installing build dependencies `. .. _Python: http://python.org/ .. _numpy: http://numpy.scipy.org/ .. _Cython: http://cython.org/ .. _Sphinx: http://sphinx.pocoo.org/ .. _MinGW C compiler: http://sourceforge.net/projects/mingwbuilds/ PyWavelets-0.3.0/doc/source/dev/index.rst0000664000175000017500000000130512556460247022025 0ustar rgommersrgommers00000000000000.. _dev-index: Development notes ================= This section contains information on building and installing PyWavelets from source code as well as instructions for preparing the build environment on Windows and Linux. .. toctree:: :maxdepth: 2 preparing_windows_build_environment preparing_linux_build_environment installing_build_dependencies building_extension testing Something not working? ---------------------- If these instructions are not clear or you need help setting up your development environment, go ahead and ask on the PyWavelets discussion group at http://groups.google.com/group/pywavelets or open a ticket on GitHub_. .. _GitHub: https://github.com/nigma/pywt PyWavelets-0.3.0/doc/source/dev/building_extension.rst0000664000175000017500000000210712556460247024610 0ustar rgommersrgommers00000000000000.. _dev-building-extension: Building and installing PyWavelets ================================== Installing from source code --------------------------- Go to https://github.com/nigma/pywt GitHub project page, fork and clone the repository or use the upstream repository to get the source code:: git clone https://github.com/nigma/pywt.git PyWavelets Activate your Python virtual environment, go to the cloned source directory and type the following commands to build and install the package:: python setup.py build python setup.py install To verify the installation run the following command:: python setup.py test To build docs:: cd doc make html Installing a development version -------------------------------- You can also install directly from the source repository:: pip install -e git+https://github.com/nigma/pywt.git#egg=PyWavelets or:: pip install PyWavelets==dev Installing a regular release from PyPi -------------------------------------- A regular release can be installed with pip or easy_install:: pip install PyWavelets PyWavelets-0.3.0/doc/source/dev/preparing_linux_build_environment.rst0000664000175000017500000000127512556460247027735 0ustar rgommersrgommers00000000000000.. _dev-preparing-linux-build-environment: Preparing Linux build environment ================================= There is a good chance that you already have a working build environment. Just skip steps that you don't need to execute. Installing basic build tools ---------------------------- Note that the example below uses ``aptitude`` package manager, which is specific to Debian and Ubuntu Linux distributions. Use your favourite package manager to install these packages on your OS. :: aptitude install build-essential gcc python-dev git-core Next steps ---------- After completing these steps continue with :ref:`Installing build dependencies `. PyWavelets-0.3.0/doc/source/dev/installing_build_dependencies.rst0000664000175000017500000000305112556460247026747 0ustar rgommersrgommers00000000000000.. _dev-installing-build-dependencies: Installing build dependencies ============================= Setting up Python virtual environment ------------------------------------- A good practice is to create a separate Python virtual environment for each project. If you don't have `virtualenv`_ yet, install and activate it using:: curl -O https://raw.github.com/pypa/virtualenv/master/virtualenv.py python virtualenv.py . /bin/activate Installing Cython ----------------- Use ``pip`` (http://pypi.python.org/pypi/pip) to install Cython_:: pip install Cython>=0.16 Installing numpy ---------------- Use ``pip`` to install numpy_:: pip install numpy It takes some time to compile numpy, so it might be more convenient to install it from a binary release. .. note:: Installing numpy in a virtual environment on Windows is not straightforward. It is recommended to download a suitable binary ``.exe`` release from http://www.scipy.org/Download/ and install it using ``easy_install`` (i.e. ``easy_install numpy-1.6.2-win32-superpack-python2.7.exe``). .. note:: You can find binaries for 64-bit Windows on http://www.lfd.uci.edu/~gohlke/pythonlibs/. Installing Sphinx ----------------- Sphinx_ is a documentation tool that converts reStructuredText files into nicely looking html documentation. Install it with:: pip install Sphinx .. _virtualenv: http://pypi.python.org/pypi/virtualenv .. _numpy: http://numpy.scipy.org/ .. _Cython: http://cython.org/ .. _Sphinx: http://sphinx.pocoo.org PyWavelets-0.3.0/doc/source/ref/0000775000175000017500000000000012556460303020154 5ustar rgommersrgommers00000000000000PyWavelets-0.3.0/doc/source/ref/swt-stationary-wavelet-transform.rst0000664000175000017500000000462112556460247027406 0ustar rgommersrgommers00000000000000.. _ref-swt: .. currentmodule:: pywt .. include:: ../substitutions.rst Stationary Wavelet Transform ---------------------------- `Stationary Wavelet Transform (SWT) `_, also known as *Undecimated wavelet transform* or *Algorithme à trous* is a translation-invariance modification of the *Discrete Wavelet Transform* that does not decimate coefficients at every transformation level. Multilevel ``swt`` ~~~~~~~~~~~~~~~~~~ .. function:: swt(data, wavelet, level[, start_level=0]) Performs multilevel Stationary Wavelet Transform. :param data: |data| :param wavelet: |wavelet| :param int level: Required transform level. See the :func:`swt_max_level` function. :param int start_level: The level at which the decomposition will begin (it allows to skip a given number of transform steps and compute coefficients starting directly from the *start_level*) .. compound:: Returns list of coefficient pairs in the form:: [(cAn, cDn), ..., (cA2, cD2), (cA1, cD1)] where *n* is the *level* value. If *m* = *start_level* is given, then the beginning *m* steps are skipped:: [(cAm+n, cDm+n), ..., (cAm+1, cDm+1), (cAm, cDm)] Multilevel ``swt2`` ~~~~~~~~~~~~~~~~~~~~~ .. function:: swt2(data, wavelet, level[, start_level=0]) Performs multilevel 2D Stationary Wavelet Transform. :param data: 2D array with input data. :param wavelet: |wavelet| :param level: Number of decomposition steps to perform. :param start_level: The level at which the decomposition will begin. .. compound:: The result is a set of coefficients arrays over the range of decomposition levels:: [ (cA_n, (cH_n, cV_n, cD_n) ), (cA_n+1, (cH_n+1, cV_n+1, cD_n+1) ), ..., (cA_n+level, (cH_n+level, cV_n+level, cD_n+level) ) ] where *cA* is approximation, *cH* is horizontal details, *cV* is vertical details, *cD* is diagonal details, *n* is *start_level* and *m* equals *n+level*. Maximum decomposition level - ``swt_max_level`` ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ .. function:: swt_max_level(input_len) Calculates the maximum level of Stationary Wavelet Transform for data of given length. :param input_len: Input data length. PyWavelets-0.3.0/doc/source/ref/index.rst0000664000175000017500000000046512556460247022031 0ustar rgommersrgommers00000000000000.. _ref-index: API Reference ============= .. toctree:: :maxdepth: 2 wavelets signal-extension-modes dwt-discrete-wavelet-transform idwt-inverse-discrete-wavelet-transform 2d-dwt-and-idwt swt-stationary-wavelet-transform wavelet-packets thresholding-functions other-functions PyWavelets-0.3.0/doc/source/ref/dwt-discrete-wavelet-transform.rst0000664000175000017500000001205412556460247026773 0ustar rgommersrgommers00000000000000.. _ref-dwt: .. currentmodule:: pywt .. include:: ../substitutions.rst ================================ Discrete Wavelet Transform (DWT) ================================ Wavelet transform has recently become a very popular when it comes to analysis, de-noising and compression of signals and images. This section describes functions used to perform single- and multilevel Discrete Wavelet Transforms. Single level ``dwt`` -------------------- .. function:: dwt(data, wavelet[, mode='sym']) The :func:`dwt` function is used to perform single level, one dimensional Discrete Wavelet Transform. :: (cA, cD) = dwt(data, wavelet, mode='sym') :param data: |data| :param wavelet: |wavelet| :param mode: |mode| The transform coefficients are returned as two arrays containing approximation (*cA*) and detail (*cD*) coefficients respectively. Length of returned arrays depends on the selected signal extension *mode* - see the :ref:`signal extension modes ` section for the list of available options and the :func:`dwt_coeff_len` function for information on getting the expected result length: * for all :ref:`modes ` except :ref:`periodization `:: len(cA) == len(cD) == floor((len(data) + wavelet.dec_len - 1) / 2) * for :ref:`periodization ` mode (``"per"``):: len(cA) == len(cD) == ceil(len(data) / 2) **Example:** .. sourcecode:: python >>> import pywt >>> (cA, cD) = pywt.dwt([1,2,3,4,5,6], 'db1') >>> print cA [ 2.12132034 4.94974747 7.77817459] >>> print cD [-0.70710678 -0.70710678 -0.70710678] Multilevel decomposition using ``wavedec`` ------------------------------------------ .. function:: wavedec(data, wavelet, mode='sym', level=None) .. compound:: The :func:`wavedec` function performs 1D multilevel Discrete Wavelet Transform decomposition of given signal and returns ordered list of coefficients arrays in the form: :: [cA_n, cD_n, cD_n-1, ..., cD2, cD1], where *n* denotes the level of decomposition. The first element (*cA_n*) of the result is approximation coefficients array and the following elements (*cD_n* - *cD_1*) are details coefficients arrays. :param data: |data| :param wavelet: |wavelet| :param mode: |mode| :param level: Number of decomposition steps to perform. If the level is ``None``, then the full decomposition up to the level computed with :func:`dwt_max_level` function for the given data and wavelet lengths is performed. **Example:** .. sourcecode:: python >>> import pywt >>> coeffs = pywt.wavedec([1,2,3,4,5,6,7,8], 'db1', level=2) >>> cA2, cD2, cD1 = coeffs >>> print cD1 [-0.70710678 -0.70710678 -0.70710678 -0.70710678] >>> print cD2 [-2. -2.] >>> print cA2 [ 5. 13.] Partial Discrete Wavelet Transform data decomposition ``downcoef`` ------------------------------------------------------------------ .. function:: downcoef(part, data, wavelet[, mode='sym'[, level=1]]) Similar to :func:`~pywt.dwt`, but computes only one set of coefficients. Useful when you need only approximation or only details at the given level. :param part: decomposition type. For ``a`` computes approximation coefficients, for ``d`` - details coefficients. :param data: |data| :param wavelet: |wavelet| :param mode: |mode| :param level: Number of decomposition steps to perform. Maximum decomposition level - ``dwt_max_level`` ----------------------------------------------- .. function:: dwt_max_level(data_len, filter_len) The :func:`~pywt.dwt_max_level` function can be used to compute the maximum *useful* level of decomposition for the given *input data length* and *wavelet filter length*. The returned value equals to:: floor( log(data_len/(filter_len-1)) / log(2) ) Although the maximum decomposition level can be quite high for long signals, usually smaller values are chosen depending on the application. The *filter_len* can be either an ``int`` or :class:`Wavelet` object for convenience. **Example:** .. sourcecode:: python >>> import pywt >>> w = pywt.Wavelet('sym5') >>> print pywt.dwt_max_level(data_len=1000, filter_len=w.dec_len) 6 >>> print pywt.dwt_max_level(1000, w) 6 .. _`dwt_coeff_len`: Result coefficients length - ``dwt_coeff_len`` ---------------------------------------------- .. function:: dwt_coeff_len(data_len, filter_len, mode) Based on the given *input data length*, Wavelet *decomposition filter length* and :ref:`signal extension mode `, the :func:`dwt_coeff_len` function calculates length of resulting coefficients arrays that would be created while performing :func:`dwt` transform. For :ref:`periodization ` mode this equals:: ceil(data_len / 2) which is the lowest possible length guaranteeing perfect reconstruction. For other :ref:`modes `:: floor((data_len + filter_len - 1) / 2) The *filter_len* can be either an *int* or :class:`Wavelet` object for convenience. PyWavelets-0.3.0/doc/source/ref/idwt-inverse-discrete-wavelet-transform.rst0000664000175000017500000000766012556460247030624 0ustar rgommersrgommers00000000000000.. _ref-idwt: .. currentmodule:: pywt .. include:: ../substitutions.rst ========================================= Inverse Discrete Wavelet Transform (IDWT) ========================================= Single level ``idwt`` --------------------- .. function:: idwt(cA, cD, wavelet[, mode='sym'[, correct_size=0]]) The :func:`idwt` function reconstructs data from the given coefficients by performing single level Inverse Discrete Wavelet Transform. :param cA: Approximation coefficients. :param cD: Detail coefficients. :param wavelet: |wavelet| :param mode: |mode| This is only important when DWT was performed in :ref:`periodization ` mode. :param correct_size: Typically, *cA* and *cD* coefficients lists must have equal lengths in order to perform IDWT. Setting *correct_size* to `True` allows *cA* to be greater in size by one element compared to the *cD* size. This option is very useful when doing multilevel decomposition and reconstruction (as for example with the :func:`wavedec` function) of non-dyadic length signals when such minor differences can occur at various levels of IDWT. **Example:** .. sourcecode:: python >>> import pywt >>> (cA, cD) = pywt.dwt([1,2,3,4,5,6], 'db2', 'sp1') >>> print pywt.idwt(cA, cD, 'db2', 'sp1') [ 1. 2. 3. 4. 5. 6.] One of the neat features of :func:`idwt` is that one of the *cA* and *cD* arguments can be set to ``None``. In that situation the reconstruction will be performed using only the other one. Mathematically speaking, this is equivalent to passing a zero-filled array as one of the arguments. **Example:** .. sourcecode:: python >>> import pywt >>> (cA, cD) = pywt.dwt([1,2,3,4,5,6], 'db2', 'sp1') >>> A = pywt.idwt(cA, None, 'db2', 'sp1') >>> D = pywt.idwt(None, cD, 'db2', 'sp1') >>> print A + D [ 1. 2. 3. 4. 5. 6.] Multilevel reconstruction using ``waverec`` ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ .. function:: waverec(coeffs, wavelet[, mode='sym']) Performs multilevel reconstruction of signal from the given list of coefficients. :param coeffs: Coefficients list must be in the form like returned by :func:`wavedec` decomposition function, which is:: [cAn, cDn, cDn-1, ..., cD2, cD1] :param wavelet: |wavelet| :param mode: |mode| **Example:** .. sourcecode:: python >>> import pywt >>> coeffs = pywt.wavedec([1,2,3,4,5,6,7,8], 'db2', level=2) >>> print pywt.waverec(coeffs, 'db2') [ 1. 2. 3. 4. 5. 6. 7. 8.] Direct reconstruction with ``upcoef`` ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ .. function:: upcoef(part, coeffs, wavelet[, level=1[, take=0]]) Direct reconstruction from coefficients. :param part: Defines the input coefficients type: - **'a'** - approximations reconstruction is performed - **'d'** - details reconstruction is performed :param coeffs: Coefficients array to reconstruct. :param wavelet: |wavelet| :param level: If *level* value is specified then a multilevel reconstruction is performed (first reconstruction is of type specified by *part* and all the following ones with *part* type ``a``) :param take: If *take* is specified then only the central part of length equal to the *take* parameter value is returned. **Example:** .. sourcecode:: python >>> import pywt >>> data = [1,2,3,4,5,6] >>> (cA, cD) = pywt.dwt(data, 'db2', 'sp1') >>> print pywt.upcoef('a', cA, 'db2') + pywt.upcoef('d', cD, 'db2') [-0.25 -0.4330127 1. 2. 3. 4. 5. 6. 1.78589838 -1.03108891] >>> n = len(data) >>> print pywt.upcoef('a',cA,'db2',take=n) + pywt.upcoef('d',cD,'db2',take=n) [ 1. 2. 3. 4. 5. 6.] PyWavelets-0.3.0/doc/source/ref/other-functions.rst0000664000175000017500000000510312556460247024043 0ustar rgommersrgommers00000000000000.. _ref-other: .. currentmodule:: pywt .. include:: ../substitutions.rst =============== Other functions =============== Single-level n-dimensional Discrete Wavelet Transform. ------------------------------------------------------ .. function:: dwtn(data, wavelet[, mode='sym']) Performs single-level n-dimensional Discrete Wavelet Transform. :param data: n-dimensional array :param wavelet: |wavelet| :param mode: |mode| Results are arranged in a dictionary, where key specifies the transform type on each dimension and value is a n-dimensional coefficients array. For example, for a 2D case the result will look something like this:: { 'aa': # A(LL) - approx. on 1st dim, approx. on 2nd dim 'ad': # H(LH) - approx. on 1st dim, det. on 2nd dim 'da': # V(HL) - det. on 1st dim, approx. on 2nd dim 'dd': # D(HH) - det. on 1st dim, det. on 2nd dim } Integrating wavelet functions - :func:`intwave` ----------------------------------------------- .. function:: intwave(wavelet[, precision=8]) Integration of wavelet function approximations as well as any other signals can be performed using the :func:`pywt.intwave` function. The result of the call depends on the *wavelet* argument: * for orthogonal wavelets - an integral of the wavelet function specified on an x-grid:: [int_psi, x] = intwave(wavelet, precision) * for other wavelets - integrals of decomposition and reconstruction wavelet functions and a corresponding x-grid:: [int_psi_d, int_psi_r, x] = intwave(wavelet, precision) * for a tuple of coefficients data and a x-grid - an integral of function and the given x-grid is returned (the x-grid is used for computations).:: [int_function, x] = intwave((data, x), precision) **Example:** .. sourcecode:: python >>> import pywt >>> wavelet1 = pywt.Wavelet('db2') >>> [int_psi, x] = pywt.intwave(wavelet1, precision=5) >>> wavelet2 = pywt.Wavelet('bior1.3') >>> [int_psi_d, int_psi_r, x] = pywt.intwave(wavelet2, precision=5) Central frequency of *psi* wavelet function ------------------------------------------- .. function:: centfrq(wavelet[, precision=8]) centfrq((function_approx, x)) :param wavelet: :class:`Wavelet`, wavelet name string or `(wavelet function approx., x grid)` pair :param precision: Precision that will be used for wavelet function approximation computed with the :meth:`Wavelet.wavefun` method. PyWavelets-0.3.0/doc/source/ref/wavelet-packets.rst0000664000175000017500000002601012556460247024013 0ustar rgommersrgommers00000000000000.. _ref-wp: .. currentmodule:: pywt .. include:: ../substitutions.rst =============== Wavelet Packets =============== .. versionadded:: 0.2 Version `0.2` of PyWavelets includes many new features and improvements. One of such new feature is a two-dimensional wavelet packet transform structure that is almost completely sharing programming interface with the one-dimensional tree structure. In order to achieve this simplification, a new inheritance scheme was used in which a :class:`~pywt.BaseNode` base node class is a superclass for both :class:`~pywt.Node` and :class:`~pywt.Node2D` node classes. The node classes are used as data wrappers and can be organized in trees (binary trees for 1D transform case and quad-trees for the 2D one). They are also superclasses to the :class:`~pywt.WaveletPacket` class and :class:`~pywt.WaveletPacket2D` class that are used as the decomposition tree roots and contain a couple additional methods. The below diagram illustrates the inheritance tree: - :class:`~pywt.BaseNode` - common interface for 1D and 2D nodes: - :class:`~pywt.Node` - data carrier node in a 1D decomposition tree - :class:`~pywt.WaveletPacket` - 1D decomposition tree root node - :class:`~pywt.Node2D` - data carrier node in a 2D decomposition tree - :class:`~pywt.WaveletPacket2D` - 2D decomposition tree root node BaseNode - a common interface of WaveletPacket and WaveletPacket2D ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ .. class:: BaseNode Node(BaseNode) WaveletPacket(Node) Node2D(BaseNode) WaveletPacket2D(Node2D) .. note:: The BaseNode is a base class for :class:`Node` and :class:`Node2D`. It should not be used directly unless creating a new transformation type. It is included here to document the common interface of 1D and 2D node an wavelet packet transform classes. .. method:: __init__(parent, data, node_name) :param parent: parent node. If parent is ``None`` then the node is considered detached. :param data: data associated with the node. 1D or 2D numeric array, depending on the transform type. :param node_name: a name identifying the coefficients type. See :attr:`Node.node_name` and :attr:`Node2D.node_name` for information on the accepted subnodes names. .. attribute:: data Data associated with the node. 1D or 2D numeric array (depends on the transform type). .. attribute:: parent Parent node. Used in tree navigation. ``None`` for root node. .. attribute:: wavelet :class:`~pywt.Wavelet` used for decomposition and reconstruction. Inherited from parent node. .. attribute:: mode Signal extension :ref:`mode ` for the :func:`dwt` (:func:`dwt2`) and :func:`idwt` (:func:`idwt2`) decomposition and reconstruction functions. Inherited from parent node. .. attribute:: level Decomposition level of the current node. ``0`` for root (original data), ``1`` for the first decomposition level, etc. .. attribute:: path Path string defining position of the node in the decomposition tree. .. attribute:: node_name Node name describing :attr:`~BaseNode.data` coefficients type of the current subnode. See :attr:`Node.node_name` and :attr:`Node2D.node_name`. .. attribute:: maxlevel Maximum allowed level of decomposition. Evaluated from parent or child nodes. .. attribute:: is_empty Checks if :attr:`~BaseNode.data` attribute is ``None``. .. attribute:: has_any_subnode Checks if node has any subnodes (is not a leaf node). .. method:: decompose() Performs Discrete Wavelet Transform on the :attr:`~BaseNode.data` and returns transform coefficients. .. method:: reconstruct([update=False]) Performs Inverse Discrete Wavelet Transform on subnodes coefficients and returns reconstructed data for the current level. :param update: If set, the :attr:`~BaseNode.data` attribute will be updated with the reconstructed value. .. note:: Descends to subnodes and recursively calls :meth:`~BaseNode.reconstruct` on them. .. method:: get_subnode(part[, decompose=True]) Returns subnode or None (see *decomposition* flag description). :param part: Subnode name :param decompose: If True and subnode does not exist, it will be created using coefficients from the DWT decomposition of the current node. .. method:: __getitem__(path) Used to access nodes in the decomposition tree by string *path*. :param path: Path string composed from valid node names. See :attr:`Node.node_name` and :attr:`Node2D.node_name` for node naming convention. Similar to :meth:`~BaseNode.get_subnode` method with `decompose=True`, but can access nodes on any level in the decomposition tree. If node does not exist yet, it will be created by decomposition of its parent node. .. method:: __setitem__(path, data) Used to set node or node's data in the decomposition tree. Nodes are identified by string *path*. :param path: Path string composed from valid node names. See :attr:`Node.node_name` and :attr:`Node2D.node_name` for node naming convention. :param data: numeric array or :class:`~BaseNode` subclass. .. method:: __delitem__(path) Used to delete node from the decomposition tree. :param path: Path string composed from valid node names. See :attr:`Node.node_name` and :attr:`Node2D.node_name` for node naming convention. .. method:: get_leaf_nodes([decompose=False]) Traverses through the decomposition tree and collects leaf nodes (nodes without any subnodes). :param decompose: If *decompose* is ``True``, the method will try to decompose the tree up to the :attr:`maximum level `. .. method:: walk(self, func, [args=(), [kwargs={}, [decompose=True]]]) Traverses the decomposition tree and calls ``func(node, *args, **kwargs)`` on every node. If `func` returns ``True``, descending to subnodes will continue. :param func: callable accepting :class:`BaseNode` as the first param and optional positional and keyword arguments:: func(node, *args, **kwargs) :args: arguments to pass to the *func* :kwargs: keyword arguments to pass to the *func* :param decompose: If *decompose* is ``True`` (default), the method will also try to decompose the tree up to the :attr:`maximum level `. .. method:: walk_depth(self, func, [args=(), [kwargs={}, [decompose=False]]]) Similar to :meth:`~BaseNode.walk` but traverses the tree in depth-first order. :param func: callable accepting :class:`BaseNode` as the first param and optional positional and keyword arguments:: func(node, *args, **kwargs) :args: arguments to pass to the *func* :kwargs: keyword arguments to pass to the *func* :param decompose: If *decompose* is ``True``, the method will also try to decompose the tree up to the :attr:`maximum level `. WaveletPacket and WaveletPacket tree Node ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ .. class:: Node(BaseNode) WaveletPacket(Node) .. attribute:: node_name Node name describing :attr:`~BaseNode.data` coefficients type of the current subnode. For :class:`WaveletPacket` case it is just as in :func:`dwt`: - ``a`` - approximation coefficients - ``d`` - details coefficients .. method:: decompose() .. seealso:: - :func:`dwt` for 1D Discrete Wavelet Transform output coefficients. .. class:: WaveletPacket(Node) .. method:: __init__(data, wavelet, [mode='sym', [maxlevel=None]]) :param data: data associated with the node. 1D numeric array. :param wavelet: |wavelet| :param mode: Signal extension :ref:`mode ` for the :func:`dwt` and :func:`idwt` decomposition and reconstruction functions. :param maxlevel: Maximum allowed level of decomposition. If not specified it will be calculated based on the *wavelet* and *data* length using :func:`pywt.dwt_max_level`. .. method:: get_level(level, [order="natural", [decompose=True]]) Collects nodes from the given level of decomposition. :param level: Specifies decomposition *level* from which the nodes will be collected. :param order: Specifies nodes order - natural (``natural``) or frequency (``freq``). :param decompose: If set then the method will try to decompose the data up to the specified *level*. If nodes at the given level are missing (i.e. the tree is partially decomposed) and the *decompose* is set to ``False``, only existing nodes will be returned. WaveletPacket2D and WaveletPacket2D tree Node2D ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ .. class:: Node2D(BaseNode) WaveletPacket2D(Node2D) .. attribute:: node_name For :class:`WaveletPacket2D` case it is just as in :func:`dwt2`: - ``a`` - approximation coefficients (`LL`) - ``h`` - horizontal detail coefficients (`LH`) - ``v`` - vertical detail coefficients (`HL`) - ``d`` - diagonal detail coefficients (`HH`) .. method:: decompose() .. seealso:: :func:`dwt2` for 2D Discrete Wavelet Transform output coefficients. .. method:: expand_2d_path(self, path): .. class:: WaveletPacket2D(Node2D) .. method:: __init__(data, wavelet, [mode='sym', [maxlevel=None]]) :param data: data associated with the node. 2D numeric array. :param wavelet: |wavelet| :param mode: Signal extension :ref:`mode ` for the :func:`dwt` and :func:`idwt` decomposition and reconstruction functions. :param maxlevel: Maximum allowed level of decomposition. If not specified it will be calculated based on the *wavelet* and *data* length using :func:`pywt.dwt_max_level`. .. method:: get_level(level, [order="natural", [decompose=True]]) Collects nodes from the given level of decomposition. :param level: Specifies decomposition *level* from which the nodes will be collected. :param order: Specifies nodes order - natural (``natural``) or frequency (``freq``). :param decompose: If set then the method will try to decompose the data up to the specified *level*. If nodes at the given level are missing (i.e. the tree is partially decomposed) and the *decompose* is set to ``False``, only existing nodes will be returned. PyWavelets-0.3.0/doc/source/ref/2d-dwt-and-idwt.rst0000664000175000017500000001112012556460247023516 0ustar rgommersrgommers00000000000000.. _ref-dwt2: .. currentmodule:: pywt .. include:: ../substitutions.rst ================================================= 2D Forward and Inverse Discrete Wavelet Transform ================================================= Single level ``dwt2`` ~~~~~~~~~~~~~~~~~~~~~ .. function:: dwt2(data, wavelet[, mode='sym']) The :func:`dwt2` function performs single level 2D Discrete Wavelet Transform. :param data: 2D input data. :param wavelet: |wavelet| :param mode: |mode| This is only important when DWT was performed in :ref:`periodization ` mode. .. compound:: Returns one average and three details 2D coefficients arrays. The coefficients arrays are organized in tuples in the following form: :: (cA, (cH, cV, cD)) where *cA*, *cH*, *cV*, *cD* denote approximation, horizontal detail, vertical detail and diagonal detail coefficients respectively. The relation to the other common data layout where all the approximation and details coefficients are stored in one big 2D array is as follows: :: ------------------- | | | | cA(LL) | cH(LH) | | | | (cA, (cH, cV, cD)) <---> ------------------- | | | | cV(HL) | cD(HH) | | | | ------------------- PyWavelets does not follow this pattern because of pure practical reasons of simple access to particular type of the output coefficients. **Example:** .. sourcecode:: python >>> import pywt, numpy >>> data = numpy.ones((4,4), dtype=numpy.float64) >>> coeffs = pywt.dwt2(data, 'haar') >>> cA, (cH, cV, cD) = coeffs >>> print cA [[ 2. 2.] [ 2. 2.]] >>> print cV [[ 0. 0.] [ 0. 0.]] Single level ``idwt2`` ~~~~~~~~~~~~~~~~~~~~~~ .. function:: idwt2(coeffs, wavelet[, mode='sym']) The :func:`idwt2` function reconstructs data from the given coefficients set by performing single level 2D Inverse Discrete Wavelet Transform. :param coeffs: A tuple with approximation coefficients and three details coefficients 2D arrays like from :func:`dwt2`:: (cA, (cH, cV, cD)) :param wavelet: |wavelet| :param mode: |mode| This is only important when the :func:`dwt` was performed in the :ref:`periodization ` mode. **Example:** .. sourcecode:: python >>> import pywt, numpy >>> data = numpy.array([[1,2], [3,4]], dtype=numpy.float64) >>> coeffs = pywt.dwt2(data, 'haar') >>> print pywt.idwt2(coeffs, 'haar') [[ 1. 2.] [ 3. 4.]] 2D multilevel decomposition using ``wavedec2`` ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ .. function:: wavedec2(data, wavelet[, mode='sym'[, level=None]]) .. compound:: Performs multilevel 2D Discrete Wavelet Transform decomposition and returns coefficients list:: [cAn, (cHn, cVn, cDn), ..., (cH1, cV1, cD1)] where *n* denotes the level of decomposition and *cA*, *cH*, *cV* and *cD* are approximation, horizontal detail, vertical detail and diagonal detail coefficients arrays respectively. :param data: |data| :param wavelet: |wavelet| :param mode: |mode| :param level: Decomposition level. This should not be greater than the reasonable maximum value computed with the :func:`dwt_max_level` function for the smaller dimension of the input data. **Example:** .. sourcecode:: python >>> import pywt, numpy >>> coeffs = pywt.wavedec2(numpy.ones((8,8)), 'db1', level=2) >>> cA2, (cH2, cV2, cD2), (cH1, cV1, cD1) = coeffs >>> print cA2 [[ 4. 4.] [ 4. 4.]] 2D multilevel reconstruction using ``waverec2`` ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ .. function:: waverec2(coeffs, wavelet[, mode='sym']) Performs multilevel reconstruction from the given coefficients set. :param coeffs: Coefficients set must be in the form like that from :func:`wavedec2` decomposition:: [cAn, (cHn, cVn, cDn), ..., (cH1, cV1, cD1)] :param wavelet: |wavelet| :param mode: |mode| **Example:** .. sourcecode:: python >>> import pywt, numpy >>> coeffs = pywt.wavedec2(numpy.ones((4,4)), 'db1') >>> print "levels:", len(coeffs)-1 levels: 2 >>> print pywt.waverec2(coeffs, 'db1') [[ 1. 1. 1. 1.] [ 1. 1. 1. 1.] [ 1. 1. 1. 1.] [ 1. 1. 1. 1.]] PyWavelets-0.3.0/doc/source/ref/signal-extension-modes.rst0000664000175000017500000000537212556460247025320 0ustar rgommersrgommers00000000000000.. _ref-modes: .. currentmodule:: pywt ====================== Signal extension modes ====================== .. _MODES: Because the most common and practical way of representing digital signals in computer science is with finite arrays of values, some extrapolation of the input data has to be performed in order to extend the signal before computing the :ref:`Discrete Wavelet Transform ` using the cascading filter banks algorithm. Depending on the extrapolation method, significant artifacts at the signal's borders can be introduced during that process, which in turn may lead to inaccurate computations of the :ref:`DWT ` at the signal's ends. PyWavelets provides several methods of signal extrapolation that can be used to minimize this negative effect: .. _`MODES.zpd`: * ``zpd`` - **zero-padding** - signal is extended by adding zero samples:: ... 0 0 | x1 x2 ... xn | 0 0 ... .. _`MODES.cpd`: * ``cpd`` - **constant-padding** - border values are replicated:: ... x1 x1 | x1 x2 ... xn | xn xn ... .. _`MODES.sym`: * ``sym`` - **symmetric-padding** - signal is extended by *mirroring* samples:: ... x2 x1 | x1 x2 ... xn | xn xn-1 ... .. _`MODES.ppd`: .. _`periodic-padding`: * ``ppd`` - **periodic-padding** - signal is treated as a periodic one:: ... xn-1 xn | x1 x2 ... xn | x1 x2 ... .. _`MODES.sp1`: * ``sp1`` - **smooth-padding** - signal is extended according to the first derivatives calculated on the edges (straight line) :ref:`DWT ` performed for these extension modes is slightly redundant, but ensures perfect reconstruction. To receive the smallest possible number of coefficients, computations can be performed with the `periodization`_ mode: .. _`periodization`: .. _`MODES.per`: * ``per`` - **periodization** - is like `periodic-padding`_ but gives the smallest possible number of decomposition coefficients. :ref:`IDWT ` must be performed with the same mode. **Example:** .. sourcecode:: python >>> import pywt >>> print pywt.MODES.modes ['zpd', 'cpd', 'sym', 'ppd', 'sp1', 'per'] Notice that you can use any of the following ways of passing wavelet and mode parameters: .. sourcecode:: python >>> import pywt >>> (a, d) = pywt.dwt([1,2,3,4,5,6], 'db2', 'sp1') >>> (a, d) = pywt.dwt([1,2,3,4,5,6], pywt.Wavelet('db2'), pywt.MODES.sp1) .. note:: Extending data in context of PyWavelets does not mean reallocation of the data in computer's physical memory and copying values, but rather computing the extra values only when they are needed. This feature saves extra memory and CPU resources and helps to avoid page swapping when handling relatively big data arrays on computers with low physical memory. PyWavelets-0.3.0/doc/source/ref/thresholding-functions.rst0000664000175000017500000000271712556460247025424 0ustar rgommersrgommers00000000000000.. _ref-thresholding: Thresholding functions ====================== The :mod:`~pywt.thresholding` helper module implements the most popular signal thresholding functions. Hard thresholding ----------------- .. function:: hard(data, value[, substitute=0]) Hard thresholding. Replace all *data* values with *substitute* where their absolute value is less than the *value* param. *Data* values with absolute value greater or equal to the thresholding *value* stay untouched. :param data: numeric data :param value: thresholding value :param substitute: substitute value :returns: array Soft thresholding ----------------- .. function:: soft(data, value[, substitute=0]) Soft thresholding. :param data: numeric data :param value: thresholding value :param substitute: substitute value :returns: array Greater ------- .. function:: greater(data, value[, substitute=0]) Replace *data* with *substitute* where *data* is below the thresholding *value*. `Greater` *data* values pass untouched. :param data: numeric data :param value: thresholding value :param substitute: substitute value :returns: array Less ---- .. function:: less(data, value[, substitute=0]) Replace *data* with *substitute* where *data* is above the thresholding *value*. `Less` *data* values pass untouched. :param data: numeric data :param value: thresholding value :param substitute: substitute value :returns: array PyWavelets-0.3.0/doc/source/ref/wavelets.rst0000664000175000017500000001726612556460247022563 0ustar rgommersrgommers00000000000000.. _ref-wavelets: .. currentmodule:: pywt ======== Wavelets ======== Wavelet ``families()`` ---------------------- .. function:: families() Returns a list of available built-in wavelet families. Currently the built-in families are: * Haar (``haar``) * Daubechies (``db``) * Symlets (``sym``) * Coiflets (``coif``) * Biorthogonal (``bior``) * Reverse biorthogonal (``rbio``) * `"Discrete"` FIR approximation of Meyer wavelet (``dmey``) **Example:** .. sourcecode:: python >>> import pywt >>> print pywt.families() ['haar', 'db', 'sym', 'coif', 'bior', 'rbio', 'dmey'] Built-in wavelets - ``wavelist()`` ---------------------------------- .. function:: wavelist([family]) The :func:`wavelist` function returns a list of names of the built-in wavelets. If the *family* name is ``None`` then names of all the built-in wavelets are returned. Otherwise the function returns names of wavelets that belong to the given family. **Example:** .. sourcecode:: python >>> import pywt >>> print pywt.wavelist('coif') ['coif1', 'coif2', 'coif3', 'coif4', 'coif5'] Custom user wavelets are also supported through the :class:`Wavelet` object constructor as described below. ``Wavelet`` object ------------------ .. class:: Wavelet(name[, filter_bank=None]) Describes properties of a wavelet identified by the specified wavelet *name*. In order to use a built-in wavelet the *name* parameter must be a valid wavelet name from the :func:`pywt.wavelist` list. Custom Wavelet objects can be created by passing a user-defined filters set with the *filter_bank* parameter. :param name: Wavelet name :param filter_bank: Use a user supplied filter bank instead of a built-in :class:`Wavelet`. The filter bank object can be a list of four filters coefficients or an object with :attr:`~Wavelet.filter_bank` attribute, which returns a list of such filters in the following order:: [dec_lo, dec_hi, rec_lo, rec_hi] Wavelet objects can also be used as a base filter banks. See section on :ref:`using custom wavelets ` for more information. **Example:** .. sourcecode:: python >>> import pywt >>> wavelet = pywt.Wavelet('db1') .. attribute:: name Wavelet name. .. attribute:: short_name Short wavelet name. .. attribute:: dec_lo Decomposition filter values. .. attribute:: dec_hi Decomposition filter values. .. attribute:: rec_lo Reconstruction filter values. .. attribute:: rec_hi Reconstruction filter values. .. attribute:: dec_len Decomposition filter length. .. attribute:: rec_len Reconstruction filter length. .. attribute:: filter_bank Returns filters list for the current wavelet in the following order:: [dec_lo, dec_hi, rec_lo, rec_hi] .. attribute:: inverse_filter_bank Returns list of reverse wavelet filters coefficients. The mapping from the `filter_coeffs` list is as follows:: [rec_lo[::-1], rec_hi[::-1], dec_lo[::-1], dec_hi[::-1]] .. attribute:: short_family_name Wavelet short family name .. attribute:: family_name Wavelet family name .. attribute:: orthogonal Set if wavelet is orthogonal .. attribute:: biorthogonal Set if wavelet is biorthogonal .. attribute:: symmetry ``asymmetric``, ``near symmetric``, ``symmetric`` .. attribute:: vanishing_moments_psi Number of vanishing moments for the wavelet function .. attribute:: vanishing_moments_phi Number of vanishing moments for the scaling function **Example:** .. sourcecode:: python >>> def format_array(arr): ... return "[%s]" % ", ".join(["%.14f" % x for x in arr]) >>> import pywt >>> wavelet = pywt.Wavelet('db1') >>> print wavelet Wavelet db1 Family name: Daubechies Short name: db Filters length: 2 Orthogonal: True Biorthogonal: True Symmetry: asymmetric >>> print format_array(wavelet.dec_lo), format_array(wavelet.dec_hi) [0.70710678118655, 0.70710678118655] [-0.70710678118655, 0.70710678118655] >>> print format_array(wavelet.rec_lo), format_array(wavelet.rec_hi) [0.70710678118655, 0.70710678118655] [0.70710678118655, -0.70710678118655] Approximating wavelet and scaling functions - ``Wavelet.wavefun()`` ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ .. method:: Wavelet.wavefun(level) .. versionchanged:: 0.2 The time (space) localisation of approximation function points was added. The :meth:`~Wavelet.wavefun` method can be used to calculate approximations of scaling function (*phi*) and wavelet function (*psi*) at the given level of refinement. For :attr:`orthogonal ` wavelets returns approximations of scaling function and wavelet function with corresponding x-grid coordinates:: [phi, psi, x] = wavelet.wavefun(level) **Example:** .. sourcecode:: python >>> import pywt >>> wavelet = pywt.Wavelet('db2') >>> phi, psi, x = wavelet.wavefun(level=5) For other (:attr:`biorthogonal ` but not :attr:`orthogonal `) wavelets returns approximations of scaling and wavelet function both for decomposition and reconstruction and corresponding x-grid coordinates:: [phi_d, psi_d, phi_r, psi_r, x] = wavelet.wavefun(level) **Example:** .. sourcecode:: python >>> import pywt >>> wavelet = pywt.Wavelet('bior3.5') >>> phi_d, psi_d, phi_r, psi_r, x = wavelet.wavefun(level=5) .. See also plots of Daubechies and Symlets wavelet families generated using the :meth:`~Wavelet.wavefun` function: - `db.png`_ - `sym.png`_ .. seealso:: You can find live examples of :meth:`~Wavelet.wavefun` usage and images of all the built-in wavelets on the `Wavelet Properties Browser `_ page. .. _using-custom-wavelets: .. _custom-wavelets: Using custom wavelets --------------------- PyWavelets comes with a :func:`long list ` of the most popular wavelets built-in and ready to use. If you need to use a specific wavelet which is not included in the list it is very easy to do so. Just pass a list of four filters or an object with a :attr:`~Wavelet.filter_bank` attribute as a *filter_bank* argument to the :class:`Wavelet` constructor. .. compound:: The filters list, either in a form of a simple Python list or returned via the :attr:`~Wavelet.filter_bank` attribute, must be in the following order: * lowpass decomposition filter * highpass decomposition filter * lowpass reconstruction filter * highpass reconstruction filter just as for the :attr:`~Wavelet.filter_bank` attribute of the :class:`Wavelet` class. The Wavelet object created in this way is a standard :class:`Wavelet` instance. The following example illustrates the way of creating custom Wavelet objects from plain Python lists of filter coefficients and a *filter bank-like* objects. **Example:** .. sourcecode:: python >>> import pywt, math >>> c = math.sqrt(2)/2 >>> dec_lo, dec_hi, rec_lo, rec_hi = [c, c], [-c, c], [c, c], [c, -c] >>> filter_bank = [dec_lo, dec_hi, rec_lo, rec_hi] >>> myWavelet = pywt.Wavelet(name="myHaarWavelet", filter_bank=filter_bank) >>> >>> class HaarFilterBank(object): ... @property ... def filter_bank(self): ... c = math.sqrt(2)/2 ... dec_lo, dec_hi, rec_lo, rec_hi = [c, c], [-c, c], [c, c], [c, -c] ... return [dec_lo, dec_hi, rec_lo, rec_hi] >>> filter_bank = HaarFilterBank() >>> myOtherWavelet = pywt.Wavelet(name="myHaarWavelet", filter_bank=filter_bank) PyWavelets-0.3.0/doc/source/regression/0000775000175000017500000000000012556460303021560 5ustar rgommersrgommers00000000000000PyWavelets-0.3.0/doc/source/regression/wavelet.rst0000664000175000017500000001743712556460247024004 0ustar rgommersrgommers00000000000000.. _reg-wavelet: .. currentmodule:: pywt The Wavelet object ================== Wavelet families and builtin Wavelets names ------------------------------------------- :class:`Wavelet` objects are really a handy carriers of a bunch of DWT-specific data like *quadrature mirror filters* and some general properties associated with them. At first let's go through the methods of creating a :class:`Wavelet` object. The easiest and the most convenient way is to use builtin named Wavelets. These wavelets are organized into groups called wavelet families. The most commonly used families are: >>> import pywt >>> pywt.families() ['haar', 'db', 'sym', 'coif', 'bior', 'rbio', 'dmey'] The :func:`wavelist` function with family name passed as an argument is used to obtain the list of wavelet names in each family. >>> for family in pywt.families(): ... print "%s family:" % family, ', '.join(pywt.wavelist(family)) haar family: haar db family: db1, db2, db3, db4, db5, db6, db7, db8, db9, db10, db11, db12, db13, db14, db15, db16, db17, db18, db19, db20 sym family: sym2, sym3, sym4, sym5, sym6, sym7, sym8, sym9, sym10, sym11, sym12, sym13, sym14, sym15, sym16, sym17, sym18, sym19, sym20 coif family: coif1, coif2, coif3, coif4, coif5 bior family: bior1.1, bior1.3, bior1.5, bior2.2, bior2.4, bior2.6, bior2.8, bior3.1, bior3.3, bior3.5, bior3.7, bior3.9, bior4.4, bior5.5, bior6.8 rbio family: rbio1.1, rbio1.3, rbio1.5, rbio2.2, rbio2.4, rbio2.6, rbio2.8, rbio3.1, rbio3.3, rbio3.5, rbio3.7, rbio3.9, rbio4.4, rbio5.5, rbio6.8 dmey family: dmey To get the full list of builtin wavelets' names just use the :func:`wavelist` with no argument. As you can see currently there are 76 builtin wavelets. >>> len(pywt.wavelist()) 76 Creating Wavelet objects ------------------------ Now when we know all the names let's finally create a :class:`Wavelet` object: >>> w = pywt.Wavelet('db3') So.. that's it. Wavelet properties ------------------ But what can we do with :class:`Wavelet` objects? Well, they carry some interesting information. First, let's try printing a :class:`Wavelet` object. This shows a brief information about its name, its family name and some properties like orthogonality and symmetry. >>> print w Wavelet db3 Family name: Daubechies Short name: db Filters length: 6 Orthogonal: True Biorthogonal: True Symmetry: asymmetric But the most important information are the wavelet filters coefficients, which are used in :ref:`Discrete Wavelet Transform `. These coefficients can be obtained via the :attr:`~Wavelet.dec_lo`, :attr:`Wavelet.dec_hi`, :attr:`~Wavelet.rec_lo` and :attr:`~Wavelet.rec_hi` attributes, which corresponds to lowpass and highpass decomposition filters and lowpass and highpass reconstruction filters respectively: >>> def print_array(arr): ... print "[%s]" % ", ".join(["%.14f" % x for x in arr]) >>> print_array(w.dec_lo) [0.03522629188210, -0.08544127388224, -0.13501102001039, 0.45987750211933, 0.80689150931334, 0.33267055295096] >>> print_array(w.dec_hi) [-0.33267055295096, 0.80689150931334, -0.45987750211933, -0.13501102001039, 0.08544127388224, 0.03522629188210] >>> print_array(w.rec_lo) [0.33267055295096, 0.80689150931334, 0.45987750211933, -0.13501102001039, -0.08544127388224, 0.03522629188210] >>> print_array(w.rec_hi) [0.03522629188210, 0.08544127388224, -0.13501102001039, -0.45987750211933, 0.80689150931334, -0.33267055295096] Another way to get the filters data is to use the :attr:`~Wavelet.filter_bank` attribute, which returns all four filters in a tuple: >>> w.filter_bank == (w.dec_lo, w.dec_hi, w.rec_lo, w.rec_hi) True Other Wavelet's properties are: Wavelet :attr:`~Wavelet.name`, :attr:`~Wavelet.short_family_name` and :attr:`~Wavelet.family_name`: >>> print w.name db3 >>> print w.short_family_name db >>> print w.family_name Daubechies - Decomposition (:attr:`~Wavelet.dec_len`) and reconstruction (:attr:`~.Wavelet.rec_len`) filter lengths: >>> int(w.dec_len) # int() is for normalizing longs and ints for doctest 6 >>> int(w.rec_len) 6 - Orthogonality (:attr:`~Wavelet.orthogonal`) and biorthogonality (:attr:`~Wavelet.biorthogonal`): >>> w.orthogonal True >>> w.biorthogonal True - Symmetry (:attr:`~Wavelet.symmetry`): >>> print w.symmetry asymmetric - Number of vanishing moments for the scaling function *phi* (:attr:`~Wavelet.vanishing_moments_phi`) and the wavelet function *psi* (:attr:`~Wavelet.vanishing_moments_psi`) associated with the filters: >>> w.vanishing_moments_phi 0 >>> w.vanishing_moments_psi 3 Now when we know a bit about the builtin Wavelets, let's see how to create :ref:`custom Wavelets ` objects. These can be done in two ways: 1) Passing the filter bank object that implements the `filter_bank` attribute. The attribute must return four filters coefficients. >>> class MyHaarFilterBank(object): ... @property ... def filter_bank(self): ... from math import sqrt ... return ([sqrt(2)/2, sqrt(2)/2], [-sqrt(2)/2, sqrt(2)/2], ... [sqrt(2)/2, sqrt(2)/2], [sqrt(2)/2, -sqrt(2)/2]) >>> my_wavelet = pywt.Wavelet('My Haar Wavelet', filter_bank=MyHaarFilterBank()) 2) Passing the filters coefficients directly as the *filter_bank* parameter. >>> from math import sqrt >>> my_filter_bank = ([sqrt(2)/2, sqrt(2)/2], [-sqrt(2)/2, sqrt(2)/2], ... [sqrt(2)/2, sqrt(2)/2], [sqrt(2)/2, -sqrt(2)/2]) >>> my_wavelet = pywt.Wavelet('My Haar Wavelet', filter_bank=my_filter_bank) Note that such custom wavelets **will not** have all the properties set to correct values: >>> print my_wavelet Wavelet My Haar Wavelet Family name: Short name: Filters length: 2 Orthogonal: False Biorthogonal: False Symmetry: unknown You can however set a few of them on your own: >>> my_wavelet.orthogonal = True >>> my_wavelet.biorthogonal = True >>> print my_wavelet Wavelet My Haar Wavelet Family name: Short name: Filters length: 2 Orthogonal: True Biorthogonal: True Symmetry: unknown And now... the `wavefun`! ------------------------- We all know that the fun with wavelets is in wavelet functions. Now what would be this package without a tool to compute wavelet and scaling functions approximations? This is the purpose of the :meth:`~Wavelet.wavefun` method, which is used to approximate scaling function (*phi*) and wavelet function (*psi*) at the given level of refinement, based on the filters coefficients. The number of returned values varies depending on the wavelet's orthogonality property. For orthogonal wavelets the result is tuple with scaling function, wavelet function and xgrid coordinates. >>> w = pywt.Wavelet('sym3') >>> w.orthogonal True >>> (phi, psi, x) = w.wavefun(level=5) For biorthogonal (non-orthogonal) wavelets different scaling and wavelet functions are used for decomposition and reconstruction, and thus five elements are returned: decomposition scaling and wavelet functions approximations, reconstruction scaling and wavelet functions approximations, and the xgrid. >>> w = pywt.Wavelet('bior1.3') >>> w.orthogonal False >>> (phi_d, psi_d, phi_r, psi_r, x) = w.wavefun(level=5) .. seealso:: You can find live examples of :meth:`~Wavelet.wavefun` usage and images of all the built-in wavelets on the `Wavelet Properties Browser `_ page. PyWavelets-0.3.0/doc/source/regression/dwt-idwt.rst0000664000175000017500000001363212556460247024071 0ustar rgommersrgommers00000000000000.. _reg-dwt-idwt: .. currentmodule:: pywt DWT and IDWT ============ Discrete Wavelet Transform -------------------------- Let's do a :func:`Discrete Wavelet Transform ` of a sample data *x* using the ``db2`` wavelet. It's simple.. >>> import pywt >>> x = [3, 7, 1, 1, -2, 5, 4, 6] >>> cA, cD = pywt.dwt(x, 'db2') And the approximation and details coefficients are in ``cA`` and ``cD`` respectively: >>> print cA [ 5.65685425 7.39923721 0.22414387 3.33677403 7.77817459] >>> print cD [-2.44948974 -1.60368225 -4.44140056 -0.41361256 1.22474487] Inverse Discrete Wavelet Transform ---------------------------------- Now let's do an opposite operation - :func:`Inverse Discrete Wavelet Transform `: >>> print pywt.idwt(cA, cD, 'db2') [ 3. 7. 1. 1. -2. 5. 4. 6.] Voilà! That's it! More Examples ------------- Now let's experiment with the :func:`dwt` some more. For example let's pass a :class:`Wavelet` object instead of the wavelet name and specify signal extension mode (the default is :ref:`sym `) for the border effect handling: >>> w = pywt.Wavelet('sym3') >>> cA, cD = pywt.dwt(x, wavelet=w, mode='cpd') >>> print cA [ 4.38354585 3.80302657 7.31813271 -0.58565539 4.09727044 7.81994027] >>> print cD [-1.33068221 -2.78795192 -3.16825651 -0.67715519 -0.09722957 -0.07045258] Note that the output coefficients arrays length depends not only on the input data length but also on the :class:Wavelet type (particularly on its :attr:`filters lenght <~Wavelet.dec_len>` that are used in the transformation). To find out what will be the output data size use the :func:`dwt_coeff_len` function: >>> # int() is for normalizing Python integers and long integers for documentation tests >>> int(pywt.dwt_coeff_len(data_len=len(x), filter_len=w.dec_len, mode='sym')) 6 >>> int(pywt.dwt_coeff_len(len(x), w, 'sym')) 6 >>> len(cA) 6 Looks fine. (And if you expected that the output length would be a half of the input data length, well, that's the trade-off that allows for the perfect reconstruction...). The third argument of the :func:`dwt_coeff_len` is the already mentioned signal extension mode (please refer to the PyWavelets' documentation for the :ref:`modes ` description). Currently there are six :ref:`extension modes ` available: >>> pywt.MODES.modes ['zpd', 'cpd', 'sym', 'ppd', 'sp1', 'per'] >>> [int(pywt.dwt_coeff_len(len(x), w.dec_len, mode)) for mode in pywt.MODES.modes] [6, 6, 6, 6, 6, 4] As you see in the above example, the :ref:`per ` (periodization) mode is slightly different from the others. It's aim when doing the :func:`DWT ` transform is to output coefficients arrays that are half of the length of the input data. Knowing that, you should never mix the periodization mode with other modes when doing :func:`DWT ` and :func:`IDWT `. Otherwise, it will produce **invalid results**: >>> x [3, 7, 1, 1, -2, 5, 4, 6] >>> cA, cD = pywt.dwt(x, wavelet=w, mode='per') >>> print pywt.idwt(cA, cD, 'sym3', 'sym') # invalid mode [ 1. 1. -2. 5.] >>> print pywt.idwt(cA, cD, 'sym3', 'per') [ 3. 7. 1. 1. -2. 5. 4. 6.] Tips & tricks ------------- Passing ``None`` instead of coefficients data to :func:`idwt` ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ Now some tips & tricks. Passing ``None`` as one of the coefficient arrays parameters is similar to passing a *zero-filled* array. The results are simply the same: >>> print pywt.idwt([1,2,0,1], None, 'db2', 'sym') [ 1.19006969 1.54362308 0.44828774 -0.25881905 0.48296291 0.8365163 ] >>> print pywt.idwt([1, 2, 0, 1], [0, 0, 0, 0], 'db2', 'sym') [ 1.19006969 1.54362308 0.44828774 -0.25881905 0.48296291 0.8365163 ] >>> print pywt.idwt(None, [1, 2, 0, 1], 'db2', 'sym') [ 0.57769726 -0.93125065 1.67303261 -0.96592583 -0.12940952 -0.22414387] >>> print pywt.idwt([0, 0, 0, 0], [1, 2, 0, 1], 'db2', 'sym') [ 0.57769726 -0.93125065 1.67303261 -0.96592583 -0.12940952 -0.22414387] Remember that only one argument at a time can be ``None``: >>> print pywt.idwt(None, None, 'db2', 'sym') Traceback (most recent call last): ... ValueError: At least one coefficient parameter must be specified. Coefficients data size in :attr:`idwt` ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ When doing the :func:`IDWT ` transform, usually the coefficient arrays must have the same size. >>> print pywt.idwt([1, 2, 3, 4, 5], [1, 2, 3, 4], 'db2', 'sym') Traceback (most recent call last): ... ValueError: Coefficients arrays must have the same size. But for some applications like multilevel DWT and IDWT it is sometimes convenient to allow for a small departure from this behaviour. When the *correct_size* flag is set, the approximation coefficients array can be larger from the details coefficient array by one element: >>> print pywt.idwt([1, 2, 3, 4, 5], [1, 2, 3, 4], 'db2', 'sym', correct_size=True) [ 1.76776695 0.61237244 3.18198052 0.61237244 4.59619408 0.61237244] >>> print pywt.idwt([1, 2, 3, 4], [1, 2, 3, 4, 5], 'db2', 'sym', correct_size=True) Traceback (most recent call last): ... ValueError: Coefficients arrays must satisfy (0 <= len(cA) - len(cD) <= 1). Not every coefficient array can be used in :func:`IDWT `. In the following example the :func:`idwt` will fail because the input arrays are invalid - they couldn't be created as a result of :func:`DWT `, because the minimal output length for dwt using ``db4`` wavelet and the :ref:`sym ` mode is ``4``, not ``3``: >>> pywt.idwt([1,2,4], [4,1,3], 'db4', 'sym') Traceback (most recent call last): ... ValueError: Invalid coefficient arrays length for specified wavelet. Wavelet and mode must be the same as used for decomposition. >>> int(pywt.dwt_coeff_len(1, pywt.Wavelet('db4').dec_len, 'sym')) 4 PyWavelets-0.3.0/doc/source/regression/index.rst0000664000175000017500000000061612556460247023433 0ustar rgommersrgommers00000000000000.. _reg-index: .. currentmodule:: pywt Usage examples ============== The following examples are used as doctest regression tests written using reST markup. They are included in the documentation since they contain various useful examples illustrating how to use and how not to use PyWavelets. .. toctree:: :maxdepth: 1 wavelet modes dwt-idwt multilevel wp wp2d gotchas PyWavelets-0.3.0/doc/source/regression/wp2d.rst0000664000175000017500000003050312556460247023176 0ustar rgommersrgommers00000000000000.. _reg-wp2d: .. currentmodule:: pywt 2D Wavelet Packets ================== Import pywt ----------- >>> import pywt >>> import numpy Create 2D Wavelet Packet structure ---------------------------------- Start with preparing test data: >>> x = numpy.array([[1, 2, 3, 4, 5, 6, 7, 8]] * 8, 'd') >>> print x [[ 1. 2. 3. 4. 5. 6. 7. 8.] [ 1. 2. 3. 4. 5. 6. 7. 8.] [ 1. 2. 3. 4. 5. 6. 7. 8.] [ 1. 2. 3. 4. 5. 6. 7. 8.] [ 1. 2. 3. 4. 5. 6. 7. 8.] [ 1. 2. 3. 4. 5. 6. 7. 8.] [ 1. 2. 3. 4. 5. 6. 7. 8.] [ 1. 2. 3. 4. 5. 6. 7. 8.]] Now create a :class:`2D Wavelet Packet ` object: >>> wp = pywt.WaveletPacket2D(data=x, wavelet='db1', mode='sym') The input *data* and decomposition coefficients are stored in the :attr:`WaveletPacket2D.data` attribute: >>> print wp.data [[ 1. 2. 3. 4. 5. 6. 7. 8.] [ 1. 2. 3. 4. 5. 6. 7. 8.] [ 1. 2. 3. 4. 5. 6. 7. 8.] [ 1. 2. 3. 4. 5. 6. 7. 8.] [ 1. 2. 3. 4. 5. 6. 7. 8.] [ 1. 2. 3. 4. 5. 6. 7. 8.] [ 1. 2. 3. 4. 5. 6. 7. 8.] [ 1. 2. 3. 4. 5. 6. 7. 8.]] :class:`Nodes ` are identified by paths. For the root node the path is ``''`` and the decomposition level is ``0``. >>> print repr(wp.path) '' >>> print wp.level 0 The :attr:`WaveletPacket2D.maxlevel`, if not given in the constructor, is automatically computed based on the data size: >>> print wp.maxlevel 3 Traversing WP tree: ------------------- Wavelet Packet :class:`nodes ` are arranged in a tree. Each node in a WP tree is uniquely identified and addressed by a :attr:`~Node2D.path` string. In the 1D :class:`WaveletPacket` case nodes were accessed using ``'a'`` (approximation) and ``'d'`` (details) path names (each node has two 1D children). Because now we deal with a bit more complex structure (each node has four children), we have four basic path names based on the dwt 2D output convention to address the WP2D structure: * ``a`` - LL, low-low coefficients * ``h`` - LH, low-high coefficients * ``v`` - HL, high-low coefficients * ``d`` - HH, high-high coefficients In other words, subnode naming corresponds to the :func:`dwt2` function output naming convention (as wavelet packet transform is based on the dwt2 transform):: ------------------- | | | | cA(LL) | cH(LH) | | | | (cA, (cH, cV, cD)) <---> ------------------- | | | | cV(HL) | cD(HH) | | | | ------------------- (fig.1: DWT 2D output and interpretation) Knowing what the nodes names are, we can now access them using the indexing operator `obj[x]` (:meth:`WaveletPacket2D.__getitem__`): >>> print wp['a'].data [[ 3. 7. 11. 15.] [ 3. 7. 11. 15.] [ 3. 7. 11. 15.] [ 3. 7. 11. 15.]] >>> print wp['h'].data [[ 0. 0. 0. 0.] [ 0. 0. 0. 0.] [ 0. 0. 0. 0.] [ 0. 0. 0. 0.]] >>> print wp['v'].data [[-1. -1. -1. -1.] [-1. -1. -1. -1.] [-1. -1. -1. -1.] [-1. -1. -1. -1.]] >>> print wp['d'].data [[ 0. 0. 0. 0.] [ 0. 0. 0. 0.] [ 0. 0. 0. 0.] [ 0. 0. 0. 0.]] Similarly, a subnode of a subnode can be accessed by: >>> print wp['aa'].data [[ 10. 26.] [ 10. 26.]] Indexing base :class:`WaveletPacket2D` (as well as 1D :class:`WaveletPacket`) using compound path is just the same as indexing WP subnode: >>> node = wp['a'] >>> print node['a'].data [[ 10. 26.] [ 10. 26.]] >>> print wp['a']['a'].data is wp['aa'].data True Following down the decomposition path: >>> print wp['aaa'].data [[ 36.]] >>> print wp['aaaa'].data Traceback (most recent call last): ... IndexError: Path length is out of range. Ups, we have reached the maximum level of decomposition for the ``'aaaa'`` path, which btw. was: >>> print wp.maxlevel 3 Now try some invalid path: >>> print wp['f'] Traceback (most recent call last): ... ValueError: Subnode name must be in ['a', 'h', 'v', 'd'], not 'f'. Accessing Node2D's attributes: ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ :class:`WaveletPacket2D` is a tree data structure, which evaluates to a set of :class:`Node2D` objects. :class:`WaveletPacket2D` is just a special subclass of the :class:`Node2D` class (which in turn inherits from a :class:`BaseNode`, just like with :class:`Node` and :class:`WaveletPacket` for the 1D case.). >>> print wp['av'].data [[-4. -4.] [-4. -4.]] >>> print wp['av'].path av >>> print wp['av'].node_name v >>> print wp['av'].parent.path a >>> print wp['av'].parent.data [[ 3. 7. 11. 15.] [ 3. 7. 11. 15.] [ 3. 7. 11. 15.] [ 3. 7. 11. 15.]] >>> print wp['av'].level 2 >>> print wp['av'].maxlevel 3 >>> print wp['av'].mode sym Collecting nodes ~~~~~~~~~~~~~~~~ We can get all nodes on the particular level using the :meth:`WaveletPacket2D.get_level` method: * 0 level - the root `wp` node: >>> len(wp.get_level(0)) 1 >>> print [node.path for node in wp.get_level(0)] [''] * 1st level of decomposition: >>> len(wp.get_level(1)) 4 >>> print [node.path for node in wp.get_level(1)] ['a', 'h', 'v', 'd'] * 2nd level of decomposition: >>> len(wp.get_level(2)) 16 >>> paths = [node.path for node in wp.get_level(2)] >>> for i, path in enumerate(paths): ... print path, ... if (i+1) % 4 == 0: print aa ah av ad ha hh hv hd va vh vv vd da dh dv dd * 3rd level of decomposition: >>> print len(wp.get_level(3)) 64 >>> paths = [node.path for node in wp.get_level(3)] >>> for i, path in enumerate(paths): ... print path, ... if (i+1) % 8 == 0: print aaa aah aav aad aha ahh ahv ahd ava avh avv avd ada adh adv add haa hah hav had hha hhh hhv hhd hva hvh hvv hvd hda hdh hdv hdd vaa vah vav vad vha vhh vhv vhd vva vvh vvv vvd vda vdh vdv vdd daa dah dav dad dha dhh dhv dhd dva dvh dvv dvd dda ddh ddv ddd Note that :meth:`WaveletPacket2D.get_level` performs automatic decomposition until it reaches the given level. Reconstructing data from Wavelet Packets: ----------------------------------------- Let's create a new empty 2D Wavelet Packet structure and set its nodes values with known data from the previous examples: >>> new_wp = pywt.WaveletPacket2D(data=None, wavelet='db1', mode='sym') >>> new_wp['vh'] = wp['vh'].data # [[0.0, 0.0], [0.0, 0.0]] >>> new_wp['vv'] = wp['vh'].data # [[0.0, 0.0], [0.0, 0.0]] >>> new_wp['vd'] = [[0.0, 0.0], [0.0, 0.0]] >>> new_wp['a'] = [[3.0, 7.0, 11.0, 15.0], [3.0, 7.0, 11.0, 15.0], ... [3.0, 7.0, 11.0, 15.0], [3.0, 7.0, 11.0, 15.0]] >>> new_wp['d'] = [[0.0, 0.0, 0.0, 0.0], [0.0, 0.0, 0.0, 0.0], ... [0.0, 0.0, 0.0, 0.0], [0.0, 0.0, 0.0, 0.0]] For convenience, :attr:`Node2D.data` gets automatically extracted from the base :class:`Node2D` object: >>> new_wp['h'] = wp['h'] # all zeros Note: just remember to not assign to the node.data parameter directly (todo). And reconstruct the data from the ``a``, ``d``, ``vh``, ``vv``, ``vd`` and ``h`` packets (Note that ``va`` node was not set and the WP tree is "not complete" - the ``va`` branch will be treated as *zero-array*): >>> print new_wp.reconstruct(update=False) [[ 1.5 1.5 3.5 3.5 5.5 5.5 7.5 7.5] [ 1.5 1.5 3.5 3.5 5.5 5.5 7.5 7.5] [ 1.5 1.5 3.5 3.5 5.5 5.5 7.5 7.5] [ 1.5 1.5 3.5 3.5 5.5 5.5 7.5 7.5] [ 1.5 1.5 3.5 3.5 5.5 5.5 7.5 7.5] [ 1.5 1.5 3.5 3.5 5.5 5.5 7.5 7.5] [ 1.5 1.5 3.5 3.5 5.5 5.5 7.5 7.5] [ 1.5 1.5 3.5 3.5 5.5 5.5 7.5 7.5]] Now set the ``va`` node with the known values and do the reconstruction again: >>> new_wp['va'] = wp['va'].data # [[-2.0, -2.0], [-2.0, -2.0]] >>> print new_wp.reconstruct(update=False) [[ 1. 2. 3. 4. 5. 6. 7. 8.] [ 1. 2. 3. 4. 5. 6. 7. 8.] [ 1. 2. 3. 4. 5. 6. 7. 8.] [ 1. 2. 3. 4. 5. 6. 7. 8.] [ 1. 2. 3. 4. 5. 6. 7. 8.] [ 1. 2. 3. 4. 5. 6. 7. 8.] [ 1. 2. 3. 4. 5. 6. 7. 8.] [ 1. 2. 3. 4. 5. 6. 7. 8.]] which is just the same as the base sample data *x*. Of course we can go the other way and remove nodes from the tree. If we delete the ``va`` node, again, we get the "not complete" tree from one of the previous examples: >>> del new_wp['va'] >>> print new_wp.reconstruct(update=False) [[ 1.5 1.5 3.5 3.5 5.5 5.5 7.5 7.5] [ 1.5 1.5 3.5 3.5 5.5 5.5 7.5 7.5] [ 1.5 1.5 3.5 3.5 5.5 5.5 7.5 7.5] [ 1.5 1.5 3.5 3.5 5.5 5.5 7.5 7.5] [ 1.5 1.5 3.5 3.5 5.5 5.5 7.5 7.5] [ 1.5 1.5 3.5 3.5 5.5 5.5 7.5 7.5] [ 1.5 1.5 3.5 3.5 5.5 5.5 7.5 7.5] [ 1.5 1.5 3.5 3.5 5.5 5.5 7.5 7.5]] Just restore the node before next examples. >>> new_wp['va'] = wp['va'].data If the *update* param in the :meth:`WaveletPacket2D.reconstruct` method is set to ``False``, the node's :attr:`Node2D.data` attribute will not be updated. >>> print new_wp.data None Otherwise, the :attr:`WaveletPacket2D.data` attribute will be set to the reconstructed value. >>> print new_wp.reconstruct(update=True) [[ 1. 2. 3. 4. 5. 6. 7. 8.] [ 1. 2. 3. 4. 5. 6. 7. 8.] [ 1. 2. 3. 4. 5. 6. 7. 8.] [ 1. 2. 3. 4. 5. 6. 7. 8.] [ 1. 2. 3. 4. 5. 6. 7. 8.] [ 1. 2. 3. 4. 5. 6. 7. 8.] [ 1. 2. 3. 4. 5. 6. 7. 8.] [ 1. 2. 3. 4. 5. 6. 7. 8.]] >>> print new_wp.data [[ 1. 2. 3. 4. 5. 6. 7. 8.] [ 1. 2. 3. 4. 5. 6. 7. 8.] [ 1. 2. 3. 4. 5. 6. 7. 8.] [ 1. 2. 3. 4. 5. 6. 7. 8.] [ 1. 2. 3. 4. 5. 6. 7. 8.] [ 1. 2. 3. 4. 5. 6. 7. 8.] [ 1. 2. 3. 4. 5. 6. 7. 8.] [ 1. 2. 3. 4. 5. 6. 7. 8.]] Since we have an interesting WP structure built, it is a good occasion to present the :meth:`WaveletPacket2D.get_leaf_nodes` method, which collects non-zero leaf nodes from the WP tree: >>> print [n.path for n in new_wp.get_leaf_nodes()] ['a', 'h', 'va', 'vh', 'vv', 'vd', 'd'] Passing the *decompose=True* parameter to the method will force the WP object to do a full decomposition up to the *maximum level* of decomposition: >>> paths = [n.path for n in new_wp.get_leaf_nodes(decompose=True)] >>> len(paths) 64 >>> for i, path in enumerate(paths): ... print path, ... if (i+1) % 8 == 0: print aaa aah aav aad aha ahh ahv ahd ava avh avv avd ada adh adv add haa hah hav had hha hhh hhv hhd hva hvh hvv hvd hda hdh hdv hdd vaa vah vav vad vha vhh vhv vhd vva vvh vvv vvd vda vdh vdv vdd daa dah dav dad dha dhh dhv dhd dva dvh dvv dvd dda ddh ddv ddd Lazy evaluation: ---------------- .. note:: This section is for demonstration of pywt internals purposes only. Do not rely on the attribute access to nodes as presented in this example. >>> x = numpy.array([[1, 2, 3, 4, 5, 6, 7, 8]] * 8) >>> wp = pywt.WaveletPacket2D(data=x, wavelet='db1', mode='sym') 1) At first the wp's attribute `a` is ``None`` >>> print wp.a None **Remember that you should not rely on the attribute access.** 2) During the first attempt to access the node it is computed via decomposition of its parent node (the wp object itself). >>> print wp['a'] a: [[ 3. 7. 11. 15.] [ 3. 7. 11. 15.] [ 3. 7. 11. 15.] [ 3. 7. 11. 15.]] 3) Now the `a` is set to the newly created node: >>> print wp.a a: [[ 3. 7. 11. 15.] [ 3. 7. 11. 15.] [ 3. 7. 11. 15.] [ 3. 7. 11. 15.]] And so is `wp.d`: >>> print wp.d d: [[ 0. 0. 0. 0.] [ 0. 0. 0. 0.] [ 0. 0. 0. 0.] [ 0. 0. 0. 0.]] PyWavelets-0.3.0/doc/source/regression/wp.rst0000664000175000017500000001614112556460247022752 0ustar rgommersrgommers00000000000000.. _reg-wp: .. currentmodule:: pywt Wavelet Packets =============== Import pywt ----------- >>> import pywt >>> def format_array(a): ... """Consistent array representation across different systems""" ... import numpy ... a = numpy.where(numpy.abs(a) < 1e-5, 0, a) ... return numpy.array2string(a, precision=5, separator=' ', suppress_small=True) Create Wavelet Packet structure ------------------------------- Ok, let's create a sample :class:`WaveletPacket`: >>> x = [1, 2, 3, 4, 5, 6, 7, 8] >>> wp = pywt.WaveletPacket(data=x, wavelet='db1', mode='sym') The input *data* and decomposition coefficients are stored in the :attr:`WaveletPacket.data` attribute: >>> print wp.data [1, 2, 3, 4, 5, 6, 7, 8] :class:`Nodes ` are identified by :attr:`paths <~Node.path>`. For the root node the path is ``''`` and the decomposition level is ``0``. >>> print repr(wp.path) '' >>> print wp.level 0 The *maxlevel*, if not given as param in the constructor, is automatically computed: >>> print wp['ad'].maxlevel 3 Traversing WP tree: ------------------- Accessing subnodes: ~~~~~~~~~~~~~~~~~~~ >>> x = [1, 2, 3, 4, 5, 6, 7, 8] >>> wp = pywt.WaveletPacket(data=x, wavelet='db1', mode='sym') First check what is the maximum level of decomposition: >>> print wp.maxlevel 3 and try accessing subnodes of the WP tree: * 1st level: >>> print wp['a'].data [ 2.12132034 4.94974747 7.77817459 10.60660172] >>> print wp['a'].path a * 2nd level: >>> print wp['aa'].data [ 5. 13.] >>> print wp['aa'].path aa * 3rd level: >>> print wp['aaa'].data [ 12.72792206] >>> print wp['aaa'].path aaa Ups, we have reached the maximum level of decomposition and got an :exc:`IndexError`: >>> print wp['aaaa'].data Traceback (most recent call last): ... IndexError: Path length is out of range. Now try some invalid path: >>> print wp['ac'] Traceback (most recent call last): ... ValueError: Subnode name must be in ['a', 'd'], not 'c'. which just yielded a :exc:`ValueError`. Accessing Node's attributes: ~~~~~~~~~~~~~~~~~~~~~~~~~~~~ :class:`WaveletPacket` object is a tree data structure, which evaluates to a set of :class:`Node` objects. :class:`WaveletPacket` is just a special subclass of the :class:`Node` class (which in turn inherits from the :class:`BaseNode`). Tree nodes can be accessed using the *obj[x]* (:meth:`Node.__getitem__`) operator. Each tree node has a set of attributes: :attr:`~Node.data`, :attr:`~Node.path`, :attr:`~Node.node_name`, :attr:`~Node.parent`, :attr:`~Node.level`, :attr:`~Node.maxlevel` and :attr:`~Node.mode`. >>> x = [1, 2, 3, 4, 5, 6, 7, 8] >>> wp = pywt.WaveletPacket(data=x, wavelet='db1', mode='sym') >>> print wp['ad'].data [-2. -2.] >>> print wp['ad'].path ad >>> print wp['ad'].node_name d >>> print wp['ad'].parent.path a >>> print wp['ad'].level 2 >>> print wp['ad'].maxlevel 3 >>> print wp['ad'].mode sym Collecting nodes ~~~~~~~~~~~~~~~~ >>> x = [1, 2, 3, 4, 5, 6, 7, 8] >>> wp = pywt.WaveletPacket(data=x, wavelet='db1', mode='sym') We can get all nodes on the particular level either in ``natural`` order: >>> print [node.path for node in wp.get_level(3, 'natural')] ['aaa', 'aad', 'ada', 'add', 'daa', 'dad', 'dda', 'ddd'] or sorted based on the band frequency (``freq``): >>> print [node.path for node in wp.get_level(3, 'freq')] ['aaa', 'aad', 'add', 'ada', 'dda', 'ddd', 'dad', 'daa'] Note that :meth:`WaveletPacket.get_level` also performs automatic decomposition until it reaches the specified *level*. Reconstructing data from Wavelet Packets: ----------------------------------------- >>> x = [1, 2, 3, 4, 5, 6, 7, 8] >>> wp = pywt.WaveletPacket(data=x, wavelet='db1', mode='sym') Now create a new :class:`Wavelet Packet ` and set its nodes with some data. >>> new_wp = pywt.WaveletPacket(data=None, wavelet='db1', mode='sym') >>> new_wp['aa'] = wp['aa'].data >>> new_wp['ad'] = [-2., -2.] For convenience, :attr:`Node.data` gets automatically extracted from the :class:`Node` object: >>> new_wp['d'] = wp['d'] And reconstruct the data from the ``aa``, ``ad`` and ``d`` packets. >>> print new_wp.reconstruct(update=False) [ 1. 2. 3. 4. 5. 6. 7. 8.] If the *update* param in the reconstruct method is set to ``False``, the node's :attr:`~Node.data` will not be updated. >>> print new_wp.data None Otherwise, the :attr:`~Node.data` attribute will be set to the reconstructed value. >>> print new_wp.reconstruct(update=True) [ 1. 2. 3. 4. 5. 6. 7. 8.] >>> print new_wp.data [ 1. 2. 3. 4. 5. 6. 7. 8.] >>> print [n.path for n in new_wp.get_leaf_nodes(False)] ['aa', 'ad', 'd'] >>> print [n.path for n in new_wp.get_leaf_nodes(True)] ['aaa', 'aad', 'ada', 'add', 'daa', 'dad', 'dda', 'ddd'] Removing nodes from Wavelet Packet tree: ---------------------------------------- Let's create a sample data: >>> x = [1, 2, 3, 4, 5, 6, 7, 8] >>> wp = pywt.WaveletPacket(data=x, wavelet='db1', mode='sym') First, start with a tree decomposition at level 2. Leaf nodes in the tree are: >>> dummy = wp.get_level(2) >>> for n in wp.get_leaf_nodes(False): ... print n.path, format_array(n.data) aa [ 5. 13.] ad [-2. -2.] da [-1. -1.] dd [ 0. 0.] >>> node = wp['ad'] >>> print node ad: [-2. -2.] To remove a node from the WP tree, use Python's `del obj[x]` (:class:`Node.__delitem__`): >>> del wp['ad'] The leaf nodes that left in the tree are: >>> for n in wp.get_leaf_nodes(): ... print n.path, format_array(n.data) aa [ 5. 13.] da [-1. -1.] dd [ 0. 0.] And the reconstruction is: >>> print wp.reconstruct() [ 2. 3. 2. 3. 6. 7. 6. 7.] Now restore the deleted node value. >>> wp['ad'].data = node.data Printing leaf nodes and tree reconstruction confirms the original state of the tree: >>> for n in wp.get_leaf_nodes(False): ... print n.path, format_array(n.data) aa [ 5. 13.] ad [-2. -2.] da [-1. -1.] dd [ 0. 0.] >>> print wp.reconstruct() [ 1. 2. 3. 4. 5. 6. 7. 8.] Lazy evaluation: ---------------- .. note:: This section is for demonstration of pywt internals purposes only. Do not rely on the attribute access to nodes as presented in this example. >>> x = [1, 2, 3, 4, 5, 6, 7, 8] >>> wp = pywt.WaveletPacket(data=x, wavelet='db1', mode='sym') 1) At first the wp's attribute `a` is None >>> print wp.a None **Remember that you should not rely on the attribute access.** 2) At first attempt to access the node it is computed via decomposition of its parent node (the wp object itself). >>> print wp['a'] a: [ 2.12132034 4.94974747 7.77817459 10.60660172] 3) Now the `wp.a` is set to the newly created node: >>> print wp.a a: [ 2.12132034 4.94974747 7.77817459 10.60660172] And so is `wp.d`: >>> print wp.d d: [-0.70710678 -0.70710678 -0.70710678 -0.70710678] PyWavelets-0.3.0/doc/source/regression/gotchas.rst0000664000175000017500000000111012556460247023742 0ustar rgommersrgommers00000000000000.. _reg-gotchas: .. currentmodule:: pywt ======= Gotchas ======= PyWavelets utilizes ``NumPy`` under the hood. That's why handling the data containing ``None`` values can be surprising. ``None`` values are converted to 'not a number' (``numpy.NaN``) values: >>> import numpy, pywt >>> x = [None, None] >>> mode = 'sym' >>> wavelet = 'db1' >>> cA, cD = pywt.dwt(x, wavelet, mode) >>> numpy.all(numpy.isnan(cA)) True >>> numpy.all(numpy.isnan(cD)) True >>> rec = pywt.idwt(cA, cD, wavelet, mode) >>> numpy.all(numpy.isnan(rec)) True PyWavelets-0.3.0/doc/source/regression/multilevel.rst0000664000175000017500000000267412556460247024514 0ustar rgommersrgommers00000000000000.. _reg-multilevel: .. currentmodule:: pywt Multilevel DWT, IDWT and SWT ============================ Multilevel DWT decomposition ---------------------------- >>> import pywt >>> x = [3, 7, 1, 1, -2, 5, 4, 6] >>> db1 = pywt.Wavelet('db1') >>> cA3, cD3, cD2, cD1 = pywt.wavedec(x, db1) >>> print cA3 [ 8.83883476] >>> print cD3 [-0.35355339] >>> print cD2 [ 4. -3.5] >>> print cD1 [-2.82842712 0. -4.94974747 -1.41421356] >>> pywt.dwt_max_level(len(x), db1) 3 >>> cA2, cD2, cD1 = pywt.wavedec(x, db1, mode='cpd', level=2) Multilevel IDWT reconstruction ------------------------------ >>> coeffs = pywt.wavedec(x, db1) >>> print pywt.waverec(coeffs, db1) [ 3. 7. 1. 1. -2. 5. 4. 6.] Multilevel SWT decomposition ---------------------------- >>> x = [3, 7, 1, 3, -2, 6, 4, 6] >>> (cA2, cD2), (cA1, cD1) = pywt.swt(x, db1, level=2) >>> print cA1 [ 7.07106781 5.65685425 2.82842712 0.70710678 2.82842712 7.07106781 7.07106781 6.36396103] >>> print cD1 [-2.82842712 4.24264069 -1.41421356 3.53553391 -5.65685425 1.41421356 -1.41421356 2.12132034] >>> print cA2 [ 7. 4.5 4. 5.5 7. 9.5 10. 8.5] >>> print cD2 [ 3. 3.5 0. -4.5 -3. 0.5 0. 0.5] >>> [(cA2, cD2)] = pywt.swt(cA1, db1, level=1, start_level=1) >>> print cA2 [ 7. 4.5 4. 5.5 7. 9.5 10. 8.5] >>> print cD2 [ 3. 3.5 0. -4.5 -3. 0.5 0. 0.5] >>> coeffs = pywt.swt(x, db1) >>> len(coeffs) 3 >>> pywt.swt_max_level(len(x)) 3 PyWavelets-0.3.0/doc/source/regression/modes.rst0000664000175000017500000001037512556460247023436 0ustar rgommersrgommers00000000000000.. _reg-modes: .. currentmodule:: pywt Signal Extension Modes ====================== Import :mod:`pywt` first >>> import pywt >>> def format_array(a): ... """Consistent array representation across different systems""" ... import numpy ... a = numpy.where(numpy.abs(a) < 1e-5, 0, a) ... return numpy.array2string(a, precision=5, separator=' ', suppress_small=True) List of available signal extension :ref:`modes `: >>> print pywt.MODES.modes ['zpd', 'cpd', 'sym', 'ppd', 'sp1', 'per'] Test that :func:`dwt` and :func:`idwt` can be performed using every mode: >>> x = [1,2,1,5,-1,8,4,6] >>> for mode in pywt.MODES.modes: ... cA, cD = pywt.dwt(x, 'db2', mode) ... print "Mode:", mode ... print "cA:", format_array(cA) ... print "cD:", format_array(cD) ... print "Reconstruction:", pywt.idwt(cA, cD, 'db2', mode) Mode: zpd cA: [-0.03468 1.73309 3.40612 6.32929 6.95095] cD: [-0.12941 -2.156 -5.95035 -1.21545 -1.8625 ] Reconstruction: [ 1. 2. 1. 5. -1. 8. 4. 6.] Mode: cpd cA: [ 1.2848 1.73309 3.40612 6.32929 7.51936] cD: [-0.48296 -2.156 -5.95035 -1.21545 0.25882] Reconstruction: [ 1. 2. 1. 5. -1. 8. 4. 6.] Mode: sym cA: [ 1.76777 1.73309 3.40612 6.32929 7.77817] cD: [-0.61237 -2.156 -5.95035 -1.21545 1.22474] Reconstruction: [ 1. 2. 1. 5. -1. 8. 4. 6.] Mode: ppd cA: [ 6.91627 1.73309 3.40612 6.32929 6.91627] cD: [-1.99191 -2.156 -5.95035 -1.21545 -1.99191] Reconstruction: [ 1. 2. 1. 5. -1. 8. 4. 6.] Mode: sp1 cA: [-0.51764 1.73309 3.40612 6.32929 7.45001] cD: [ 0. -2.156 -5.95035 -1.21545 0. ] Reconstruction: [ 1. 2. 1. 5. -1. 8. 4. 6.] Mode: per cA: [ 4.05317 3.05257 2.85381 8.42522] cD: [ 0.18947 4.18258 4.33738 2.60428] Reconstruction: [ 1. 2. 1. 5. -1. 8. 4. 6.] Invalid mode name should rise a :exc:`ValueError`: >>> pywt.dwt([1,2,3,4], 'db2', 'invalid') Traceback (most recent call last): ... ValueError: Unknown mode name 'invalid'. You can also refer to modes via :ref:`MODES ` class attributes: >>> for mode_name in ['zpd', 'cpd', 'sym', 'ppd', 'sp1', 'per']: ... mode = getattr(pywt.MODES, mode_name) ... cA, cD = pywt.dwt([1,2,1,5,-1,8,4,6], 'db2', mode) ... print "Mode:", mode, "(%s)" % mode_name ... print "cA:", format_array(cA) ... print "cD:", format_array(cD) ... print "Reconstruction:", pywt.idwt(cA, cD, 'db2', mode) Mode: 0 (zpd) cA: [-0.03468 1.73309 3.40612 6.32929 6.95095] cD: [-0.12941 -2.156 -5.95035 -1.21545 -1.8625 ] Reconstruction: [ 1. 2. 1. 5. -1. 8. 4. 6.] Mode: 2 (cpd) cA: [ 1.2848 1.73309 3.40612 6.32929 7.51936] cD: [-0.48296 -2.156 -5.95035 -1.21545 0.25882] Reconstruction: [ 1. 2. 1. 5. -1. 8. 4. 6.] Mode: 1 (sym) cA: [ 1.76777 1.73309 3.40612 6.32929 7.77817] cD: [-0.61237 -2.156 -5.95035 -1.21545 1.22474] Reconstruction: [ 1. 2. 1. 5. -1. 8. 4. 6.] Mode: 4 (ppd) cA: [ 6.91627 1.73309 3.40612 6.32929 6.91627] cD: [-1.99191 -2.156 -5.95035 -1.21545 -1.99191] Reconstruction: [ 1. 2. 1. 5. -1. 8. 4. 6.] Mode: 3 (sp1) cA: [-0.51764 1.73309 3.40612 6.32929 7.45001] cD: [ 0. -2.156 -5.95035 -1.21545 0. ] Reconstruction: [ 1. 2. 1. 5. -1. 8. 4. 6.] Mode: 5 (per) cA: [ 4.05317 3.05257 2.85381 8.42522] cD: [ 0.18947 4.18258 4.33738 2.60428] Reconstruction: [ 1. 2. 1. 5. -1. 8. 4. 6.] The default mode is :ref:`sym `: >>> cA, cD = pywt.dwt(x, 'db2') >>> print cA [ 1.76776695 1.73309178 3.40612438 6.32928585 7.77817459] >>> print cD [-0.61237244 -2.15599552 -5.95034847 -1.21545369 1.22474487] >>> print pywt.idwt(cA, cD, 'db2') [ 1. 2. 1. 5. -1. 8. 4. 6.] And using a keyword argument: >>> cA, cD = pywt.dwt(x, 'db2', mode='sym') >>> print cA [ 1.76776695 1.73309178 3.40612438 6.32928585 7.77817459] >>> print cD [-0.61237244 -2.15599552 -5.95034847 -1.21545369 1.22474487] >>> print pywt.idwt(cA, cD, 'db2') [ 1. 2. 1. 5. -1. 8. 4. 6.] PyWavelets-0.3.0/doc/source/contents.rst0000664000175000017500000000033112556460247021773 0ustar rgommersrgommers00000000000000.. _contents: PyWavelets ========== .. toctree:: :maxdepth: 2 ref/index regression/index dev/index resources releasenotes Indices and tables ================== * :ref:`genindex` * :ref:`search` PyWavelets-0.3.0/doc/source/release.0.3.0.rst0000664000175000017500000000005012556460247022211 0ustar rgommersrgommers00000000000000.. include:: ../release/0.3.0-notes.rst PyWavelets-0.3.0/doc/source/overview.rst0000664000175000017500000000004212556460247022003 0ustar rgommersrgommers00000000000000Moved to :ref:`index `.PyWavelets-0.3.0/doc/source/resources.rst0000664000175000017500000000203212556460247022150 0ustar rgommersrgommers00000000000000.. _ref-resources: ========= Resources ========= Code ---- The `GitHub repository`_ is now the main code repository. If you are using the Mercurial repository at Bitbucket, please switch to Git/GitHub and follow for development updates. Questions and bug reports ------------------------- Use `GitHub Issues`_ or `PyWavelets discussions group`_ to post questions and open tickets. Wavelet Properties Browser -------------------------- Browse properties and graphs of wavelets included in PyWavelets on `wavelets.pybytes.com`_. Articles -------- - `Denoising: wavelet thresholding `_ - `Wavelet Regression in Python `_ .. _GitHub repository: https://github.com/PyWavelets/pywt .. _GitHub Issues: https://github.com/PyWavelets/pywt/issues .. _PyWavelets discussions group: http://groups.google.com/group/pywavelets .. _wavelets.pybytes.com: http://wavelets.pybytes.com/ PyWavelets-0.3.0/doc/source/COPYING.txt0000664000175000017500000000207412556460247021263 0ustar rgommersrgommers00000000000000Copyright (c) 2006-2012 Filip Wasilewski Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. 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PyWavelets-0.3.0/doc/release/0000775000175000017500000000000012556460303017520 5ustar rgommersrgommers00000000000000PyWavelets-0.3.0/doc/release/0.3.0-notes.rst0000664000175000017500000001451312556460247022051 0ustar rgommersrgommers00000000000000============================== PyWavelets 0.3.0 Release Notes ============================== .. contents:: PyWavelets 0.3.0 is the first release of the package in 3 years. It is the result of a significant effort of a growing development team to modernize the package, to provide Python 3.x support and to make a start with providing new features as well as improved performance. A 0.4.0 release will follow shortly, and will contain more significant new features as well as changes/deprecations to streamline the API. This release requires Python 2.6, 2.7 or 3.3-3.5 and NumPy 1.6.2 or greater. Highlights of this release include: - Support for Python 3.x (>=3.3) - Added a test suite (based on nose, coverage up to 61% so far) - Maintenance work: C style complying to the Numpy style guide, improved templating system, more complete docstrings, pep8/pyflakes compliance, and more. New features ============ Test suite ---------- The test suite can be run with ``nosetests pywt`` or with:: >>> import pywt >>> pywt.test() n-D Inverse Discrete Wavelet Transform -------------------------------------- The function ``pywt.idwtn``, which provides n-dimensional inverse DWT, has been added. It complements ``idwt``, ``idwt2`` and ``dwtn``. Thresholding ------------ The function `pywt.threshold` has been added. It unifies the four thresholding functions that are still provided in the ``pywt.thresholding`` namespace. Backwards incompatible changes ============================== None in this release. Other changes ============= Development has moved to `a new repo `_. Everyone with an interest in wavelets is welcome to contribute! Building wheels, building with ``python setup.py develop`` and many other standard ways to build and install PyWavelets are supported now. Authors ======= * Ankit Agrawal + * François Boulogne + * Ralf Gommers + * David Menéndez Hurtado + * Gregory R. Lee + * David McInnis + * Helder Oliveira + * Filip Wasilewski * Kai Wohlfahrt + A total of 9 people contributed to this release. People with a "+" by their names contributed a patch for the first time. This list of names is automatically generated, and may not be fully complete. Issues closed for v0.3.0 ------------------------ - `#3 `__: Remove numerix compat layer - `#4 `__: Add single code base Python 3 support - `#5 `__: PEP8 issues - `#6 `__: Migrate tests to nose - `#7 `__: Expand test coverage without Matlab to a reasonable level - `#8 `__: Replace custom C templates by Numpy's templating system - `#9 `__: Replace Cython templates by fused types - `#10 `__: Replace use of __array_interface__ with Cython's memoryviews - `#11 `__: Format existing docstrings in numpydoc format. - `#12 `__: Complete docstrings, they're quite sparse right now - `#13 `__: Reorganize source tree - `#24 `__: doc/source/regression should be moved - `#27 `__: Broken test: test_swt_decomposition - `#28 `__: Install issue, no module tools.six - `#29 `__: wp.update fails after removal of nodes - `#32 `__: wp.update fails on 2D - `#34 `__: Wavelet string attributes shouldn't be bytes in Python 3 - `#35 `__: Re-enable float32 support - `#36 `__: wavelet instance vs string - `#40 `__: Test with Numpy 1.8rc1 - `#45 `__: demos should be updated and integrated in docs - `#60 `__: Moving pywt forward faster - `#61 `__: issues to address in moving towards 0.3.0 - `#71 `__: BUG: _pywt.downcoef always returns level=1 result Pull requests for v0.3.0 ------------------------ - `#1 `__: travis: check all branches + fix URL - `#17 `__: [DOC] doctrings for multilevel functions - `#18 `__: DOC: format -> functions.py - `#20 `__: MAINT: remove unnecessary zero() copy() - `#21 `__: Doc wavelet_packets - `#22 `__: Minor doc fixes - `#25 `__: TEST: remove useless functions and use numpy instead - `#26 `__: Merge most recent work - `#30 `__: Adding test for wp.rst - `#41 `__: Change to Numpy templating system - `#43 `__: MAINT: update six.py to not use lazy loading. - `#49 `__: Taking on API Issues - `#50 `__: Add idwtn - `#53 `__: readme updated with info related to Py3 version - `#63 `__: Remove six - `#65 `__: Thresholding - `#70 `__: MAINT: PEP8 fixes - `#72 `__: BUG: fix _downcoef for level > 1 - `#73 `__: MAINT: documentation and metadata update for repo fork - `#74 `__: STY: fix pep8/pyflakes issues - `#77 `__: MAINT: raise ValueError if data given to dwt or idwt is not 1D... 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It is written in Python, Cython and C for a mix of easy and powerful high-level interface and the best performance. PyWavelets is very easy to start with and use. Just install the package, open the Python interactive shell and type: .. sourcecode:: python >>> import pywt >>> cA, cD = pywt.dwt([1, 2, 3, 4], 'db1') Voilà! Computing wavelet transforms never before has been so simple :) Main features ------------- The main features of PyWavelets are: * 1D, 2D and nD Forward and Inverse Discrete Wavelet Transform (DWT and IDWT) * 1D and 2D Stationary Wavelet Transform (Undecimated Wavelet Transform) * 1D and 2D Wavelet Packet decomposition and reconstruction * Approximating wavelet and scaling functions * Over seventy `built-in wavelet filters`_ and custom wavelets supported * Single and double precision calculations * Results compatible with Matlab Wavelet Toolbox (TM) Requirements ------------ PyWavelets is a package for the Python programming language. It requires: - Python_ 2.6, 2.7 or >=3.3 - Numpy_ >= 1.6.2 Download -------- The most recent *development* version can be found on GitHub at https://github.com/PyWavelets/pywt. Latest release, including source and binary package for Windows, is available for download from the `Python Package Index`_ or on the `Releases Page`_. Install ------- In order to build PyWavelets from source, a working C compiler (GCC or MSVC) and a recent version of Cython_ is required. - Install PyWavelets with ``pip install PyWavelets``. - To build and install from source, navigate to downloaded PyWavelets source code directory and type ``python setup.py install``. Prebuilt Windows binaries and source code packages are also available from `Python Package Index`_. Binary packages for several Linux distributors are maintained by Open Source community contributors. Query your Linux package manager tool for `python-wavelets`, `python-pywt` or similar package name. .. seealso:: :ref:`Development notes ` section contains more information on building and installing from source code. Documentation ------------- Documentation with detailed examples and links to more resources is available online at http://pywavelets.readthedocs.org. For more usage examples see the `demo`_ directory in the source package. State of development & Contributing ----------------------------------- PyWavelets started in 2006 as an academic project for a master thesis on `Analysis and Classification of Medical Signals using Wavelet Transforms` and was maintained until 2012 by its `original developer`_. In 2013 maintenance was taken over in a `new repo `_) by a larger development team - a move supported by the original developer. The repo move doesn't mean that this is a fork - the package continues to be developed under the name "PyWavelets", and released on PyPi and Github (see `this issue `_ for the discussion where that was decided). All contributions including bug reports, bug fixes, new feature implementations and documentation improvements are welcome. Moreover, developers with an interest in PyWavelets are very welcome to join the development team! Python 3 -------- Python 3.x is fully supported from release v0.3.0 on. Contact ------- Use `GitHub Issues`_ or the `PyWavelets discussions group`_ to post your comments or questions. License ------- PyWavelets is a free Open Source software released under the MIT license. Contents -------- .. toctree:: :maxdepth: 1 ref/index regression/index dev/index resources contents .. _built-in wavelet filters: http://wavelets.pybytes.com/ .. _Cython: http://cython.org/ .. _demo: https://github.com/PyWavelets/pywt/tree/master/demo .. _GitHub: https://github.com/PyWavelets/pywt .. _GitHub Issues: https://github.com/PyWavelets/pywt/issues .. _Numpy: http://www.numpy.org .. _original developer: http://en.ig.ma .. _Python: http://python.org/ .. _Python Package Index: http://pypi.python.org/pypi/PyWavelets/ .. _PyWavelets discussions group: http://groups.google.com/group/pywavelets .. _Releases Page: https://github.com/PyWavelets/pywt/releases PyWavelets-0.3.0/THANKS.txt0000664000175000017500000000020112556460247017064 0ustar rgommersrgommers00000000000000A special thanks goes to: * Fernando Perez and people behind scipy.org for help with wavelets.scipy.org wiki and SVN hosting PyWavelets-0.3.0/MANIFEST.in0000664000175000017500000000074012556460247017101 0ustar rgommersrgommers00000000000000include setup.py runtests.py include README.rst include *.txt include MANIFEST.in # All source files recursive-include pywt * # All documentation recursive-include doc * recursive-include demo * # Cached Cython signatures include cythonize.dat # Add build and testing tools include tox.ini recursive-include util * # Exclude what we don't want to include prune build prune doc/build prune */__pycache__ global-exclude *.py[cod] *.egg *.egg-info global-exclude *~ *.bak *.swp PyWavelets-0.3.0/demo/0000775000175000017500000000000012556460303016257 5ustar rgommersrgommers00000000000000PyWavelets-0.3.0/demo/swt2.py0000664000175000017500000000150712556460247017542 0ustar rgommersrgommers00000000000000#!/usr/bin/env python # -*- coding: utf-8 -*- import numpy as np import matplotlib.pyplot as plt from PIL import Image import pywt im = Image.open("data/aero.png").convert('L') arr = np.fromstring(im.tostring(), np.uint8) arr.shape = (im.size[1], im.size[0]) plt.imshow(arr, interpolation="nearest", cmap=plt.cm.gray) level = 0 titles = ['Approximation', ' Horizontal detail', 'Vertical detail', 'Diagonal detail'] for LL, (LH, HL, HH) in pywt.swt2(arr, 'bior1.3', level=3, start_level=0): fig = plt.figure() for i, a in enumerate([LL, LH, HL, HH]): ax = fig.add_subplot(2, 2, i + 1) ax.imshow(a, origin='image', interpolation="nearest", cmap=plt.cm.gray) ax.set_title(titles[i], fontsize=12) fig.suptitle("SWT2 coefficients, level %s" % level, fontsize=14) level += 1 plt.show() PyWavelets-0.3.0/demo/dwt2_dwtn_image.py0000664000175000017500000000334612556460247021724 0ustar rgommersrgommers00000000000000#!/usr/bin/env python # -*- coding: utf-8 -*- import numpy as np import matplotlib.pyplot as plt from scipy import ndimage import pywt # Load image original = ndimage.imread('data/aero.png', mode='L') # Wavelet transform of image, and plot approximation and details titles = ['Approximation', ' Horizontal detail', 'Vertical detail', 'Diagonal detail'] coeffs2 = pywt.dwt2(original, 'bior1.3') LL, (LH, HL, HH) = coeffs2 fig = plt.figure() for i, a in enumerate([LL, LH, HL, HH]): ax = fig.add_subplot(2, 2, i + 1) ax.imshow(a, origin='image', interpolation="nearest", cmap=plt.cm.gray) ax.set_title(titles[i], fontsize=12) fig.suptitle("dwt2 coefficients", fontsize=14) # Now reconstruct and plot the original image reconstructed = pywt.idwt2(coeffs2, 'bior1.3') fig = plt.figure() plt.imshow(reconstructed, interpolation="nearest", cmap=plt.cm.gray) # Check that reconstructed image is close to the original np.testing.assert_allclose(original, reconstructed, atol=1e-13, rtol=1e-13) # Now do the same with dwtn/idwtn, to show the difference in their signatures coeffsn = pywt.dwtn(original, 'bior1.3') fig = plt.figure() for i, key in enumerate(['aa', 'ad', 'da', 'dd']): ax = fig.add_subplot(2, 2, i + 1) ax.imshow(coeffsn[key], origin='image', interpolation="nearest", cmap=plt.cm.gray) ax.set_title(titles[i], fontsize=12) fig.suptitle("dwtn coefficients", fontsize=14) # Now reconstruct and plot the original image reconstructed = pywt.idwtn(coeffsn, 'bior1.3') fig = plt.figure() plt.imshow(reconstructed, interpolation="nearest", cmap=plt.cm.gray) # Check that reconstructed image is close to the original np.testing.assert_allclose(original, reconstructed, atol=1e-13, rtol=1e-13) plt.show() PyWavelets-0.3.0/demo/wp_scalogram.py0000664000175000017500000000262112556460247021317 0ustar rgommersrgommers00000000000000#!/usr/bin/env python # -*- coding: utf-8 -*- import numpy as np import matplotlib.pyplot as plt import pywt x = np.linspace(0, 1, num=512) data = np.sin(250 * np.pi * x**2) wavelet = 'db2' level = 4 order = "freq" # other option is "normal" interpolation = 'nearest' cmap = plt.cm.cool # Construct wavelet packet wp = pywt.WaveletPacket(data, wavelet, 'sym', maxlevel=level) nodes = wp.get_level(level, order=order) labels = [n.path for n in nodes] values = np.array([n.data for n in nodes], 'd') values = abs(values) # Show signal and wavelet packet coefficients fig = plt.figure() fig.subplots_adjust(hspace=0.2, bottom=.03, left=.07, right=.97, top=.92) ax = fig.add_subplot(2, 1, 1) ax.set_title("linchirp signal") ax.plot(x, data, 'b') ax.set_xlim(0, x[-1]) ax = fig.add_subplot(2, 1, 2) ax.set_title("Wavelet packet coefficients at level %d" % level) ax.imshow(values, interpolation=interpolation, cmap=cmap, aspect="auto", origin="lower", extent=[0, 1, 0, len(values)]) ax.set_yticks(np.arange(0.5, len(labels) + 0.5), labels) # Show spectrogram and wavelet packet coefficients fig2 = plt.figure() ax2 = fig2.add_subplot(211) ax2.specgram(data, NFFT=64, noverlap=32, cmap=cmap) ax2.set_title("Spectrogram of signal") ax3 = fig2.add_subplot(212) ax3.imshow(values, origin='upper', extent=[-1, 1, -1, 1], interpolation='nearest') ax3.set_title("Wavelet packet coefficients") plt.show() PyWavelets-0.3.0/demo/image_blender.py0000664000175000017500000001534612556460247021426 0ustar rgommersrgommers00000000000000#!/usr/bin/env python # -*- coding: utf-8 -*- # Copyright (c) 2006-2012 Filip Wasilewski # See COPYING for license details. """ Wavelet Image Blender. Blend image A with texture extracted from image B by selecting detail coefficients: ----------------- ----------------- | | | | | | | | | | | | | A | | B | | | | | | | | | | | | | ----------------- ----------------- | | 2D DWT | 2D DWT | V V ----------------- --------- ----------------- | | | | | | | | A(LL) | H(LH) | | H(LH) | | | | | | | | IDWT | | ----------------- + ----------------- -----> | C | | | | | | | | | | V(HL) | D(HH) | | V(HL) | D(HH) | | | | | | | | | | | ----------------- ----------------- ----------------- (details only) """ import optparse import os import sys if os.name == 'nt': from time import clock # noqa else: from time import time as clock # noqa from PIL import Image # PIL import numpy # http://www.scipy.org import pywt def image2array(image): """PIL Image to NumPy array""" assert image.mode in ('L', 'RGB', 'CMYK') arr = numpy.fromstring(image.tostring(), numpy.uint8) arr.shape = (image.size[1], image.size[0], len(image.getbands())) return arr.swapaxes(0, 2).swapaxes(1, 2).astype(numpy.float32) def array2image(arr, mode): """NumPy array to PIL Image""" arr = arr.swapaxes(1, 2).swapaxes(0, 2) arr[arr < 0] = 0 arr[arr > 255] = 255 arr = numpy.fix(arr).astype(numpy.uint8) return Image.fromstring(mode, arr.shape[1::-1], arr.tostring()) def load_image(path, mode=None, size=None): """Load image""" im = Image.open(path) if im.mode not in ('L', 'P', 'RGB', 'CMYK'): raise TypeError("Image mode must be 'L', 'P', 'RGB' or 'CMYK'") if mode is not None: if mode == 'P': raise ValueError("Mode must be 'L', 'RGB' or 'CMYK'") im = im.convert(mode) elif im.mode == 'P': im = im.convert('RGB') if size is not None and im.size != size: im = im.resize(size, Image.ANTIALIAS) return im def blend_images(base, texture, wavelet, level, mode='sp1', base_gain=None, texture_gain=None): """Blend loaded images at `level` of granularity using `wavelet`""" base_data = image2array(base) texture_data = image2array(texture) output_data = [] # process color bands for base_band, texture_band in zip(base_data, texture_data): # multilevel dwt base_band_coeffs = pywt.wavedec2(base_band, wavelet, mode, level) texture_band_coeffs = pywt.wavedec2(texture_band, wavelet, mode, level) # average coefficients of base image output_band_coeffs = [base_band_coeffs[0]] # cA del base_band_coeffs[0], texture_band_coeffs[0] # blend details coefficients for n, (base_band_details, texture_band_details) in enumerate( zip(base_band_coeffs, texture_band_coeffs)): blended_details = [] for (base_detail, texture_detail) in zip(base_band_details, texture_band_details): if base_gain is not None: base_detail *= base_gain if texture_gain is not None: texture_detail *= texture_gain # select coeffs with greater energy blended = numpy.where(abs(base_detail) > abs(texture_detail), base_detail, texture_detail) blended_details.append(blended) base_band_coeffs[n] = texture_band_coeffs[n] = None output_band_coeffs.append(blended_details) # multilevel idwt new_band = pywt.waverec2(output_band_coeffs, wavelet, mode) output_data.append(new_band) del new_band, base_band_coeffs, texture_band_coeffs del base_data, texture_data output_data = numpy.array(output_data) return array2image(output_data, base.mode) def main(): usage = "usage: %prog -b BASE -t TEXTURE -o OUTPUT "\ "[-w WAVELET] [-l LEVEL] [-m MODE]" parser = optparse.OptionParser(usage=usage) parser.add_option("-b", "--base", dest="base", metavar="BASE", help="base image name") parser.add_option("-t", "--texture", dest="texture", metavar="TEXTURE", help="texture image name") parser.add_option("-o", "--output", dest="output", metavar="OUTPUT", help="output image name") parser.add_option("-w", "--wavelet", dest="wavelet", metavar="WAVELET", default='db2', help="wavelet name [default: %default]") parser.add_option("-l", "--level", dest="level", metavar="LEVEL", type="int", default=4, help="decomposition level [default: %default]") parser.add_option("-m", "--mode", dest="mode", metavar="MODE", default='sym', help="decomposition mode. Adjust this if" " getting edge artifacts [default: %default]") parser.add_option("-x", "--base_gain", dest="base_gain", metavar="BG", type="float", default=None, help="Base image gain [default: %default]") parser.add_option("-y", "--texture_gain", dest="texture_gain", metavar="TG", type="float", default=None, help="Texture image gain [default: %default]") parser.add_option("--timeit", dest="timeit", action="store_true", default=False, help="time blending operations") (options, args) = parser.parse_args() if None in (options.base, options.texture, options.output): parser.print_help() sys.exit(-1) base = load_image(options.base) texture = load_image(options.texture, base.mode, base.size) if options.timeit: t = clock() im = blend_images(base, texture, options.wavelet, options.level, options.mode, options.base_gain, options.texture_gain) if options.timeit: print("%.3fs" % (clock() - t)) im.save(options.output) if __name__ == '__main__': main() PyWavelets-0.3.0/demo/data/0000775000175000017500000000000012556460303017170 5ustar rgommersrgommers00000000000000PyWavelets-0.3.0/demo/data/aero.png0000664000175000017500000070015212556460247020640 0ustar rgommersrgommers00000000000000PNG  IHDRæ$PLTE  !!!"""###$$$%%%&&&'''((()))***+++,,,---...///000111222333444555666777888999:::;;;<<<===>>>???@@@AAABBBCCCDDDEEEFFFGGGHHHIIIJJJKKKLLLMMMNNNOOOPPPQQQRRRSSSTTTUUUVVVWWWXXXYYYZZZ[[[\\\]]]^^^___```aaabbbcccdddeeefffggghhhiiijjjkkklllmmmnnnooopppqqqrrrssstttuuuvvvwwwxxxyyyzzz{{{|||}}}~~~]}tIME 1W IDATxUtgڦ}gO N'&۱8q8bf33VIJRI*fffq=3kޯfEYˑmsUUI$R>,PIBH5XM:HjjÎRU8tY ,N!W/}~2E%%B6@fpXB#Q8Ɏ;Yf37o˯QNvr!}=ýAj6Z,ImHzhqŗS^{5V[\_hzY__qoݺqڷ'N}yיdrz04&0l.äSph*%$e %ٝfJU*NdrAf 9$X@2dv6mbVV6֗e L`dJ9,) BZUFI-uZ+ZΦv6g8`Ҙ~X\r;=+tz7\㩯?>ftA'a `L C3$*9ΠP\&Gdl1ˠq-Oᴙ-ZR ؑkzSW,H$g&z:zPX(lriessy6/,tfDL2ϗJe2nLfHNhu Lp?؝:h3ۭN'29k_{nF/VH7pY#a. 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THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.