UniformSampleFromScaleVector

class sherpa.sim.sample.UniformSampleFromScaleVector

Bases: UniformParameterSampleFromScaleVector

Use a uniform distribution to sample statistic and parameters.

The parameters are drawn from a uniform distribution which is set to factor times the parameter error (the lower bound is included but the upper bound is not).

Methods

clip(fit, samples[, clip])	Clip the samples if out of bounds.
get_sample(fit, *[, num, factor, numcores, ...])	Return the statistic and parameter samples.

Methods Summary

clip(fit, samples[, clip])	Clip the samples if out of bounds.
get_sample(fit, *[, num, factor, numcores, ...])	Return the statistic and parameter samples.

Methods Documentation

clip(fit: Fit, samples: ndarray, clip: Literal['none', 'hard', 'soft'] = 'none') → ndarray

Clip the samples if out of bounds.

Parameters:

fitsherpa.fit.Fit instance

Contains the thawed parameters used to generate the samples.

samples2D numpy array

The samples array, stored as a n by npar matrix. This array is changed in place.

clip{‘none’, ‘hard’, ‘soft’} optional

How should the values be clipped? The default (‘none’) has no clipping. The other methods restrict the values to lie within the hard or soft limits of the parameters.

Returns:

clipped1D numpy array

A 1D boolean array indicating whether any sample in a row was clipped. Note that the input samples array will have been updated if any element in clipped is True.

get_sample(fit: Fit, *, num: int = 1, factor: float = 4, numcores: int | None = None, rng: Generator | RandomState | None = None, clip: Literal['none', 'hard', 'soft'] = 'none') → ndarray

Return the statistic and parameter samples.

Changed in version 4.18.0: The clip argument has been added, and the return value now has an extra column, indicating if the row was clipped.

Changed in version 4.16.0: All arguments but the first one must be passed as a keyword argument. The rng parameter was added.

Parameters:

fitsherpa.fit.Fit instance

This defines the thawed parameters that are used to generate the samples, along with any possible error analysis.

numint, optional

The number of samples to return.

factornumber, optional

The half-width of the uniform distribution is factor times the one-sigma error.

numcoresint or None, optional

Should the calculation be done on multiple CPUs? The default (None) is to rely on the parallel.numcores setting of the configuration file.

rngnumpy.random.Generator, numpy.random.RandomState, or None, optional

Determines how random numbers are created. If set to None then the routines from numpy.random are used, and so can be controlled by calling numpy.random.seed.

clip{‘hard’, ‘soft’, ‘none’}, optional

What clipping strategy should be applied to the sampled parameters. The default (‘none’) applies no clipping, ‘hard’ uses the hard parameter limits, and ‘soft’ the soft limits.

Returns:

samples2D numpy array

The array is num by (npar + 2) size, where npar is the number of free parameters in the fit argument. The first element in each row is the statistic value, the remaining are the parameter values, and then the last column indicates whether any parameters were clipped.