# Distribution¶

class `astropy.uncertainty.``Distribution`(samples)[source]

Bases: `object`

A scalar value or array values with associated uncertainty distribution.

This object will take its exact type from whatever the `samples` argument is. In general this is expected to be an `Quantity` or `numpy.ndarray`, although anything compatible with `numpy.asanyarray` is possible.

Parameters
samplesarray_like

The distribution, with sampling along the leading axis. If 1D, the sole dimension is used as the sampling axis (i.e., it is a scalar distribution).

Attributes Summary

 `distribution` `n_samples` The number of samples of this distribution.

Methods Summary

 `pdf_histogram`(**kwargs) Compute histogram over the samples in the distribution. `pdf_mad`([out]) The median absolute deviation of this distribution. `pdf_mean`([dtype, out]) The mean of this distribution. `pdf_median`([out]) The median of this distribution. `pdf_percentiles`(percentile, **kwargs) Compute percentiles of this Distribution. `pdf_smad`([out]) The median absolute deviation of this distribution rescaled to match the standard deviation for a normal distribution. `pdf_std`([dtype, out, ddof]) The standard deviation of this distribution. `pdf_var`([dtype, out, ddof]) The variance of this distribution.

Attributes Documentation

`distribution`
`n_samples`

The number of samples of this distribution. A single `int`.

Methods Documentation

`pdf_histogram`(**kwargs)[source]

Compute histogram over the samples in the distribution.

Parameters
All keyword arguments are passed into `astropy.stats.histogram`. Note
That some of these options may not be valid for some multidimensional
distributions.
Returns
hist`array`

The values of the histogram. Trailing dimension is the histogram dimension.

bin_edges`array` of `dtype` `float`

Return the bin edges `(length(hist)+1)`. Trailing dimension is the bin histogram dimension.

`pdf_mad`(out=None)[source]

The median absolute deviation of this distribution.

Parameters
out`array`, optional

Alternative output array in which to place the result. It must have the same shape and buffer length as the expected output, but the type (of the output) will be cast if necessary.

`pdf_mean`(dtype=None, out=None)[source]

The mean of this distribution.

Arguments are as for `numpy.mean`.

`pdf_median`(out=None)[source]

The median of this distribution.

Parameters
out`array`, optional

Alternative output array in which to place the result. It must have the same shape and buffer length as the expected output, but the type (of the output) will be cast if necessary.

`pdf_percentiles`(percentile, **kwargs)[source]

Compute percentiles of this Distribution.

Parameters
percentile

The desired percentiles of the distribution (i.e., on [0,100]). `Quantity` will be converted to percent, meaning that a `dimensionless_unscaled` `Quantity` will be interpreted as a quantile.

Additional keywords are passed into `numpy.percentile`.
Returns
percentiles

The `fracs` percentiles of this distribution.

`pdf_smad`(out=None)[source]

The median absolute deviation of this distribution rescaled to match the standard deviation for a normal distribution.

Parameters
out`array`, optional

Alternative output array in which to place the result. It must have the same shape and buffer length as the expected output, but the type (of the output) will be cast if necessary.

`pdf_std`(dtype=None, out=None, ddof=0)[source]

The standard deviation of this distribution.

Arguments are as for `numpy.std`.

`pdf_var`(dtype=None, out=None, ddof=0)[source]

The variance of this distribution.

Arguments are as for `numpy.var`.