UniformParameterSampleFromScaleVector

class sherpa.sim.sample.UniformParameterSampleFromScaleVector

Bases: sherpa.sim.sample.ParameterSampleFromScaleVector

Use a uniform distribution to sample 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 Summary

clip(fit, samples[, clip])	Clip the samples if out of bounds.
get_sample(fit[, factor, num])	Return the 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, factor=4, num=1)

Return the 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.

• factor (number, optional) – The half-width of the uniform distribution is factor times the one-sigma error.

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

Returns

samples – The array is num by npar size, where npar is the number of free parameters in the fit argument.

Return type

2D numpy array