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Viewing changes to cogent/evolve/likelihood_function.py

  • Committer: Bazaar Package Importer
  • Author(s): Steffen Moeller
  • Date: 2010-12-04 22:30:35 UTC
  • mfrom: (1.1.1 upstream)
  • Revision ID: james.westby@ubuntu.com-20101204223035-j11kinhcrrdgg2p2
Tags: 1.5-1
* Bumped standard to 3.9.1, no changes required.
* New upstream version.
  - major additions to Cookbook
  - added AlleleFreqs attribute to ensembl Variation objects.
  - added getGeneByStableId method to genome objects.
  - added Introns attribute to Transcript objects and an Intron class.
  - added Mann-Whitney test and a Monte-Carlo version
  - exploratory and confirmatory period estimation techniques (suitable for
    symbolic and continuous data)
  - Information theoretic measures (AIC and BIC) added
  - drawing of trees with collapsed nodes
  - progress display indicator support for terminal and GUI apps
  - added parser for illumina HiSeq2000 and GAiix sequence files as 
    cogent.parse.illumina_sequence.MinimalIlluminaSequenceParser.
  - added parser to FASTQ files, one of the output options for illumina's
    workflow, also added cookbook demo.
  - added functionality for parsing of SFF files without the Roche tools in
    cogent.parse.binary_sff
  - thousand fold performance improvement to nmds
  - >10-fold performance improvements to some Table operations

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cogent/evolve/likelihood_function.py
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from cogent.util.dict_array import DictArrayTemplate
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from cogent.evolve.simulate import AlignmentEvolver, randomSequence
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from cogent.util import parallel, table
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__author__ = "Gavin Huttley"
 
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from cogent.recalculation.definition import ParameterController
 
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from cogent.util.warning import discontinued, deprecated
 
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__author__ = "Peter Maxwell"
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__copyright__ = "Copyright 2007-2009, The Cogent Project"
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__credits__ = ["Gavin Huttley", "Andrew Butterfield", "Peter Maxwell",
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                    "Matthew Wakefield", "Rob Knight", "Brett Easton"]
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__license__ = "GPL"
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__version__ = "1.4.1"
 
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__version__ = "1.5.0"
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__maintainer__ = "Gavin Huttley"
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__email__ = "gavin.huttley@anu.edu.au"
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__status__ = "Production"
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# the parameter, psub, mprob and likelihood values after the optimisation is
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# complete.
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class LikelihoodFunction(object):
 
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class LikelihoodFunction(ParameterController):
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    def setpar(self, param_name, value, edge=None, **scope):
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        # for tests only
 
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        deprecated('method', 'setpar','setParamRule', '1.6')
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        return self.setParamRule(param_name, edge=edge, value=value, is_const=True, **scope)
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    def testfunction(self):
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        # for tests only
 
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        deprecated('method', 'testfunction','getLogLikelihood', '1.6')
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        return self.getLogLikelihood()
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    def getParamValue(self, *args, **kw):
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        return self.real_par_controller.getParamValue(*args, **kw)
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    def getParamInterval(self, *args, **kw):
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        return self.real_par_controller.getParamInterval(*args, **kw)
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    def getParamValueDict(self, *args, **kw):
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        return self.real_par_controller.getParamValueDict(*args, **kw)
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    def getParamNames(self, *args, **kw):
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        return self.real_par_controller.getParamNames(*args, **kw)
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    def getUsedDimensions(self, param_name, **kw):
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        return self.real_par_controller.getUsedDimensions(param_name, **kw)
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    def getLogLikelihood(self):
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        return self.real_par_controller.getFinalResult()
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    def getNumFreeParams(self):
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        """returns the number of free parameters."""
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        return self.real_par_controller.getNumFreeParams()
 
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        return self.getFinalResult()
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    def getPsubForEdge(self, name):
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        array = self.getParamValue('psubs', edge=name)
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        return DictArrayTemplate(self._motifs, self._motifs).wrap(array)
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    def getFullLengthLikelihoods(self, locus=None):
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        # XXX These will not really be full length if MPI is
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        # being used!
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        if self.bin_names and len(self.bin_names) > 1:
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            root_lh = self.getParamValue('bindex', locus=locus)
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            root_lhs = [self.getParamValue('lh', locus=locus, bin=bin) for
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        return '\n'.join(map(str, result))
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    def getAnnotatedTree(self):
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        d = self.getStatisticsAsDict(with_parent_names=False)
 
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        d = self.getParamValueDict(['edge'])
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        tree = self._tree.deepcopy()
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        for edge in tree.getEdgeVector():
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            if edge.Name == 'root':
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              False.
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        """
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        discontinued('method', "'getStatisticsAsDict' "
 
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                "use 'getParamValueDict(['edge'])' is nearly equivalent", 
 
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                '1.6')
 
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        stats_dict = self.getParamValueDict(['edge'])
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        if hasattr(self.model, 'scale_masks'):