NormalSampleFromScaleMatrix

class sherpa.sim.sample.NormalSampleFromScaleMatrix

Bases: sherpa.sim.sample.NormalParameterSampleFromScaleMatrix

Use a normal distribution to sample statistic and parameters (correlated),

The parameters are drawn from a normal distribution based on the parameter errors, and include the correlations between the parameters. The errors will be generated from the fit object or specified directly as a covariance matrix.

Methods Summary

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

Methods Documentation

clip(fit, samples, clip='none')

Clip the samples if out of bounds.

Parameters

• fit (sherpa.fit.Fit instance) – Contains the thawed parameters used to generate the samples.

• samples (2D 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

clipped – The clipped samples (may be unchanged) and a 1D boolean array indicating whether any sample in a row was clipped.

Return type

1D numpy array

get_sample(fit, num=1, numcores=None)

Return the statistic and parameter samples.

Parameters

• fit (sherpa.fit.Fit instance) – This defines the thawed parameters that are used to generate the samples, along with any possible error analysis.

• num (int, optional) – The number of samples to return.

• numcores (int 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.

Returns

samples – The array is num by (npar + 1) size, where npar is the number of free parameters in the fit argument. The first element in each row is the statistic value, and the remaining are the parameter values.

Return type

2D numpy array