Distribution

class astropy.uncertainty.Distribution(samples)

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 NdarrayDistribution for numpy.ndarray input, and, e.g., QuantityDistribution for a subclass such as Quantity. But anything compatible with numpy.asanyarray is possible (generally producing NdarrayDistribution).

See also: https://docs.astropy.org/en/stable/uncertainty/

Parameters:

samplesarray_like

The distribution, with sampling along the trailing axis. If 1D, the sole dimension is used as the sampling axis (i.e., it is a scalar distribution). If an numpy.ndarray or subclass, the data will not be copied unless it is not possible to take a view (generally, only when the strides of the last axis are negative).

Attributes Summary

distribution
dtype
n_samples	The number of samples of this distribution.

Methods Summary

astype(dtype, *args, **kwargs)
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

dtype

n_samples

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

Methods Documentation

astype(dtype, *args, **kwargs)

pdf_histogram(**kwargs)

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:

histarray

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

bin_edgesarray of dtype float

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

pdf_mad(out=None)

The median absolute deviation of this distribution.

Parameters:

outarray, 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)

The mean of this distribution.

Arguments are as for numpy.mean.

pdf_median(out=None)

The median of this distribution.

Parameters:

outarray, 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)

Compute percentiles of this Distribution.

Parameters:

percentilefloat or array of float or Quantity

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:

percentilesQuantity [:ref: ‘dimensionless’]

The fracs percentiles of this distribution.

pdf_smad(out=None)

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

Parameters:

outarray, 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)

The standard deviation of this distribution.

Arguments are as for numpy.std.

pdf_var(dtype=None, out=None, ddof=0)

The variance of this distribution.

Arguments are as for numpy.var.