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src/c/o/cogent-1.5.3/cogent/core/usage.py   cogent(Download)
#!/usr/bin/env python
"""Classes for dealing with base, codon, and amino acid usage.
"""
from __future__ import division
from cogent.maths.stats.util import Freqs, Numbers, UnsafeFreqs
            if not curr_codons:
                continue    #code might be missing some amino acids?
            curr_codon_freqs = Numbers([codon_usage[c] for c in curr_codons])
            curr_codon_freqs.normalize()
            for codon, c_freq in zip(curr_codons, curr_codon_freqs):

src/p/y/pycogent-HEAD/cogent/core/usage.py   pycogent(Download)
#!/usr/bin/env python
"""Classes for dealing with base, codon, and amino acid usage.
"""
from __future__ import division
from cogent.maths.stats.util import Freqs, Numbers, UnsafeFreqs
            if not curr_codons:
                continue    #code might be missing some amino acids?
            curr_codon_freqs = Numbers([codon_usage[c] for c in curr_codons])
            curr_codon_freqs.normalize()
            for codon, c_freq in zip(curr_codons, curr_codon_freqs):

src/c/o/cogent-1.5.3/cogent/phylo/distance.py   cogent(Download)
#!/usr/bin/env python
"""Estimating pairwise distances between sequences.
"""
from cogent.util import parallel, table, warning, progress_display as UI
from cogent.maths.stats.util import Numbers
            # get all the distances involving this pair
            for a, b in pairwise:
                values = Numbers()
                for comp_names, param_vals in self.__param_ests.items():
                    if a in comp_names and b in comp_names:
            - **kwargs: arguments passed to getPairwiseParam"""
        ests = self.getPairwiseParam(param, **kwargs)
        return Numbers(ests.values())
 
    def getTable(self,summary_function="mean", **kwargs):

src/p/y/pycogent-HEAD/cogent/phylo/distance.py   pycogent(Download)
 
from cogent.util import parallel, table, warning, progress_display as UI
from cogent.maths.stats.util import Numbers
from cogent import LoadSeqs, LoadTree
 
    # get all the distances involving this pair
    for pair in lengths:
        values = Numbers(lengths[pair])
        pairwise_stats[pair] = getattr(values, summary_func)
 
            - **kwargs: arguments passed to getPairwiseParam"""
        ests = self.getPairwiseParam(param, **kwargs)
        return Numbers(ests.values())
 
    def getAllParamValues(self):

src/q/i/qiime-1.8.0/qiime/make_3d_plots.py   qiime(Download)
from cogent.util.misc import flatten
from qiime.format import format_coords
from cogent.maths.stats.util import Numbers
from qiime.sort import natsort, signed_natsort
from qiime.pycogent_backports.test import ANOVA_one_way

src/c/o/cogent-1.5.3/tests/test_maths/test_stats/test_util.py   cogent(Download)
import numpy
from cogent.util.unit_test import TestCase, main
from cogent.maths.stats.util import SummaryStatistics, SummaryStatisticsError,\
        Numbers, UnsafeNumbers, Freqs, UnsafeFreqs, NumberFreqs, \
        UnsafeNumberFreqs
    def test_choice(self):
        """Freqs choice should work as expected"""
        self.Alphabetic.normalize()
        keys = self.Alphabetic.keys()
        vals = Numbers(self.Alphabetic.values())
    def test_choice(self):
        """Freqs choice should work as expected"""
        self.Alphabetic.normalize()
        keys = self.Alphabetic.keys()
        vals = Numbers(self.Alphabetic.values())
             31: 47L}
 
        nums = Numbers(NumberFreqs(data=data).expand())
        number_freqs = self.ClassToTest()
        number_freqs.update(data)

src/p/y/pycogent-HEAD/tests/test_maths/test_stats/test_util.py   pycogent(Download)
import numpy
from cogent.util.unit_test import TestCase, main
from cogent.maths.stats.util import SummaryStatistics, SummaryStatisticsError,\
        Numbers, UnsafeNumbers, Freqs, UnsafeFreqs, NumberFreqs, \
        UnsafeNumberFreqs
    def test_choice(self):
        """Freqs choice should work as expected"""
        self.Alphabetic.normalize()
        keys = self.Alphabetic.keys()
        vals = Numbers(self.Alphabetic.values())
    def test_choice(self):
        """Freqs choice should work as expected"""
        self.Alphabetic.normalize()
        keys = self.Alphabetic.keys()
        vals = Numbers(self.Alphabetic.values())
             31: 47L}
 
        nums = Numbers(NumberFreqs(data=data).expand())
        number_freqs = self.ClassToTest()
        number_freqs.update(data)

src/c/o/cogent-1.5.3/cogent/maths/stats/test.py   cogent(Download)
        #, std - currently incorrect
from numpy.random import permutation, randint
from cogent.maths.stats.util import Numbers
from operator import add
from random import choice
        group_variances.append(i.Variance * (len(i)-1))
        all_vals.extend(i)
    group_means = Numbers(group_means)
    #get within group variances (denominator)
    group_variances = Numbers(group_variances)

src/p/y/pycogent-HEAD/cogent/maths/stats/test.py   pycogent(Download)
        #, std - currently incorrect
from numpy.random import permutation, randint
from cogent.maths.stats.util import Numbers
from operator import add
from random import choice
        group_variances.append(i.Variance * (len(i)-1))
        all_vals.extend(i)
    group_means = Numbers(group_means)
    #get within group variances (denominator)
    group_variances = Numbers(group_variances)

src/p/y/pycogent-HEAD/tests/test_util/test_dict2d.py   pycogent(Download)
    average, largest, smallest, swap, nonzero, not_0, upper_to_lower, \
    lower_to_upper, Dict2DInitError, Dict2DError, Dict2DSparseError
from cogent.maths.stats.util import Numbers, Freqs
 
__author__ = "Greg Caporaso"

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