Distribution¶

class
astropy.uncertainty.
Distribution
[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 anQuantity
ornumpy.ndarray
, although anything compatible withnumpy.asanyarray
is possible.See also: http://docs.astropy.org/en/stable/uncertainty/
Parameters:  samples : array_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
(self, \*\*kwargs)Compute histogram over the samples in the distribution. pdf_mad
(self[, out])The median absolute deviation of this distribution. pdf_mean
(self[, dtype, out])The mean of this distribution. pdf_median
(self[, out])The median of this distribution. pdf_percentiles
(self, percentile, \*\*kwargs)Compute percentiles of this Distribution. pdf_smad
(self[, out])The median absolute deviation of this distribution rescaled to match the standard deviation for a normal distribution. pdf_std
(self[, dtype, out, ddof])The standard deviation of this distribution. pdf_var
(self[, dtype, out, ddof])The variance of this distribution. Attributes Documentation

distribution
¶
Methods Documentation

pdf_histogram
(self, **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
(self, 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
(self, dtype=None, out=None)[source]¶ The mean of this distribution.
Arguments are as for
numpy.mean
.

pdf_median
(self, 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
(self, percentile, **kwargs)[source]¶ Compute percentiles of this Distribution.
Parameters: Returns:  percentiles :
Quantity
The
fracs
percentiles of this distribution.
 percentiles :

pdf_smad
(self, 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.