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 beNdarrayDistribution
fornumpy.ndarray
input, and, e.g.,QuantityDistribution
for a subclass such asQuantity
. But anything compatible withnumpy.asanyarray
is possible (generally producingNdarrayDistribution
).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
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#
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:
- 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.
- out
- 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.
- out
- pdf_percentiles(percentile, **kwargs)[source]#
Compute percentiles of this Distribution.
- Parameters:
- Returns:
- percentiles
Quantity
[:ref: ‘dimensionless’] The
fracs
percentiles of this distribution.
- percentiles
- 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.
- out