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src/c/o/cogent-1.5.3/cogent/seqsim/microarray_normalize.py   cogent(Download)
11/10/05 Micah Hamady: merged w/my implementations
"""
from cogent.maths.stats.distribution import ndtri
from numpy import ceil, arange, argsort, sort, array, log2, zeros, ravel, \
                  transpose, take, mean, std
def make_normal_quantile_normalizer(mean, sd, bins=1000):
    """returns f(a) that converts a to the specified normal distribution."""
    dist = array([ndtri(i)*sd for i in arange(1.0/bins,1,1.0/bins)])
    dist = (dist * sd) + mean
    return make_quantile_normalizer(dist)

src/p/y/pycogent-HEAD/cogent/seqsim/microarray_normalize.py   pycogent(Download)
11/10/05 Micah Hamady: merged w/my implementations
"""
from cogent.maths.stats.distribution import ndtri
from numpy import ceil, arange, argsort, sort, array, log2, zeros, ravel, \
                  transpose, take, mean, std
def make_normal_quantile_normalizer(mean, sd, bins=1000):
    """returns f(a) that converts a to the specified normal distribution."""
    dist = array([ndtri(i)*sd for i in arange(1.0/bins,1,1.0/bins)])
    dist = (dist * sd) + mean
    return make_quantile_normalizer(dist)

src/q/i/qiime-1.8.0/qiime/estimate_observation_richness.py   qiime(Download)
 
from biom.util import compute_counts_per_sample_stats
from cogent.maths.stats.distribution import ndtri
from numpy import empty, ones, sqrt, tensordot
 
        if std_err is not None:
            # z_crit will be something like 1.96 for 95% CI.
            z_crit = abs(ndtri((1 - confidence_level) / 2))
            ci_bound = z_crit * std_err
            ci_low = estimate - ci_bound

src/q/i/qiime-HEAD/qiime/estimate_observation_richness.py   qiime(Download)
 
from biom.util import compute_counts_per_sample_stats
from cogent.maths.stats.distribution import ndtri
from numpy import empty, ones, sqrt, tensordot
 
        if std_err is not None:
            # z_crit will be something like 1.96 for 95% CI.
            z_crit = abs(ndtri((1 - confidence_level) / 2))
            ci_bound = z_crit * std_err
            ci_low = estimate - ci_bound

src/c/o/cogent-1.5.3/cogent/maths/stats/test.py   cogent(Download)
from __future__ import division
import warnings
from cogent.maths.stats.distribution import chi_high, z_low, z_high, zprob, \
    t_high, t_low, tprob, f_high, f_low, fprob, binomial_high, binomial_low, \
    ndtri
    # Compute the confidence interval for corr_coeff using Fisher's Z
    # transform.
    z_crit = abs(ndtri((1 - confidence_level) / 2))
    ci_low, ci_high = None, None
 

src/p/y/pycogent-HEAD/cogent/maths/stats/test.py   pycogent(Download)
from __future__ import division
import warnings
from cogent.maths.stats.distribution import chi_high, z_low, z_high, zprob, \
    t_high, t_low, tprob, f_high, f_low, fprob, binomial_high, binomial_low, \
    ndtri
    # Compute the confidence interval for corr_coeff using Fisher's Z
    # transform.
    z_crit = abs(ndtri((1 - confidence_level) / 2))
    ci_low, ci_high = None, None
 

src/q/i/qiime-1.8.0/qiime/pycogent_backports/test.py   qiime(Download)
from __future__ import division
import warnings
from cogent.maths.stats.distribution import (chi_high, z_low, z_high, zprob,
    t_high, t_low, tprob, f_high, f_low, fprob, binomial_high, binomial_low,
    ndtri)
    # Compute the confidence interval for corr_coeff using Fisher's Z
    # transform.
    z_crit = abs(ndtri((1 - confidence_level) / 2))
    ci_low, ci_high = None, None
 
    """
    # compute confidence intervals using fishers z transform
    z_crit = abs(ndtri((1 - confidence_level) / 2.))
    ci_low, ci_high = None, None
    if n > 3:

src/q/i/qiime-HEAD/qiime/pycogent_backports/test.py   qiime(Download)
from __future__ import division
import warnings
from cogent.maths.stats.distribution import (chi_high, z_low, z_high, zprob,
                                             t_high, t_low, tprob, f_high, f_low, fprob, binomial_high, binomial_low,
                                             ndtri)
    # Compute the confidence interval for corr_coeff using Fisher's Z
    # transform.
    z_crit = abs(ndtri((1 - confidence_level) / 2))
    ci_low, ci_high = None, None
 
    """
    # compute confidence intervals using fishers z transform
    z_crit = abs(ndtri((1 - confidence_level) / 2.))
    ci_low, ci_high = None, None
    if n > 3: