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  • 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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tests/benchmark.py
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__copyright__ = "Copyright 2007-2009, The Cogent Project"
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__credits__ = ["Peter Maxwell", "Gavin Huttley"]
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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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def measure_evals_per_sec(pc, aln):
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    pc.setAlignment(aln)
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    lf = pc.real_par_controller.makeCalculator()
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    return lf.measureEvalsPerSecond(time_limit=2.0, wall=False)
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def profile(pc, aln):
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    pc.makeCalculator(aln)
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    p = hotshot.Profile('cogent.profile')
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    lf.measureEvalsPerSecond(time_limit=1.0, wall=False)
 
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    return pc.measureEvalsPerSecond(time_limit=2.0, wall=False)
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def makePC(modelClass, parameterisation, length, taxa, tree, opt_mprobs, **kw):
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    modelClass = eval(modelClass)
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        predicates = {}
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        par_rules = []
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    subMod = modelClass(equal_motif_probs=True, optimise_motif_probs=opt_mprobs,
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            predicates=predicates, recode_gaps=True)
 
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            predicates=predicates, recode_gaps=True, mprob_model="conditional")
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    (pc, aln) = brca_test(subMod, taxa, tree, length, par_rules, **kw)
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    return (pc, aln)
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parallel.inefficiency_forgiven = True
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if parallel.getCommunicator().rank > 0:
 
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if parallel.getCommunicator().Get_rank() > 0:
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    #benchmarks(test)
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    quiet(benchmarks, test)
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else: