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# cogent.maths.stats.distribution.ndtri

All Samples(18)  |  Call(10)  |  Derive(0)  |  Import(8)

```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)
```

```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)
```

```
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
```

```
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
```

```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

```

```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

```

```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:
```

```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:
```