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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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__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 test_simanneal_limited(self):
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        self._test_optimisation(SimulatedAnnealing,
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                {'max_evaluations': 50}, {'show_progress':True}, 0.5)
 
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                {'max_evaluations': 50}, {}, 0.5)
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    def test_powell(self):
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        self._test_optimisation(Powell, {}, {}, 0.0001)
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    def test_powell_limited(self):
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        self._test_optimisation(Powell,
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                {'max_evaluations': 10}, {'show_progress':True}, 0.1)
 
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                {'max_evaluations': 10}, {}, 0.1)
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    def test_checkpointing(self):
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        (f,x,b) = easilyOptimised(1)