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src/c/o/cogent-1.5.3/cogent/maths/stats/distribution.py   cogent(Download)
"""
from __future__ import division
from cogent.maths.stats.special import erf, erfc, igamc, igam, betai, log1p, \
    expm1, SQRTH, MACHEP, MAXNUM, PI, ndtri, incbi, igami, fix_rounding_error,\
    ln_binomial

src/p/y/pycogent-HEAD/cogent/maths/stats/distribution.py   pycogent(Download)
"""
from __future__ import division
from cogent.maths.stats.special import erf, erfc, igamc, igam, betai, log1p, \
    expm1, SQRTH, MACHEP, MAXNUM, PI, ndtri, incbi, igami, fix_rounding_error,\
    ln_binomial

src/p/i/picrust-HEAD/picrust/predict_traits.py   picrust(Download)
from numpy.random import normal
from cogent.maths.stats.distribution import z_high
from cogent.maths.stats.special import ndtri
from cogent import LoadTable
from warnings import warn
    #Start by calculating Z-scores using the inverse normal distribution,
    #starting with the given confidence value
    z = ndtri(confidence)
    #print "Z:",z
    #Now we need to fit a normal distribution given the confidence

src/c/o/cogent-1.5.3/tests/test_maths/test_stats/test_special.py   cogent(Download)
#!/usr/bin/env python
"""Unit tests for special functions used in statistics.
"""
from cogent.util.unit_test import TestCase, main
from cogent.maths.stats.special import permutations, permutations_exact, \
2.32634787404,
]
        obs = [ndtri(i/100.0) for i in range(100)]
        self.assertFloatEqual(obs, exp)
 

src/p/y/pycogent-HEAD/tests/test_maths/test_stats/test_special.py   pycogent(Download)
#!/usr/bin/env python
"""Unit tests for special functions used in statistics.
"""
from cogent.util.unit_test import TestCase, main
from cogent.maths.stats.special import permutations, permutations_exact, \
2.32634787404,
]
        obs = [ndtri(i/100.0) for i in range(100)]
        self.assertFloatEqual(obs, exp)
 

src/p/i/picrust-HEAD/tests/test_predict_traits.py   picrust(Download)
from cogent.app.util import get_tmp_filename
from cogent.util.misc import remove_files
from cogent.maths.stats.special import ndtri
from warnings import catch_warnings
from picrust.predict_traits  import assign_traits_to_tree,\
    def test_fit_normal_to_confidence_interval(self):
        """fit_normal_to_confidence_interval should return a mean and variance given CI"""
 
        #Lets use a normal distribution to generate test values
        normal_95 = ndtri(0.95)
 
        #An alternative normal:
        normal_99 = ndtri(0.99)
        mean = 5.0
        upper = mean + normal_99