# NormalParameterSampleFromScaleMatrix

class sherpa.sim.sample.NormalParameterSampleFromScaleMatrix[source] [edit on github]

Use a normal distribution to sample 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[, mycov, num]) Return the parameter samples.

Methods Documentation

clip(fit, samples, clip='none') [edit on github]

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, mycov=None, num=1)[source] [edit on github]

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.

• mycov (2D numpy array or None, optional) – The covariance matrix to use. If None then it is calculated from the fit object.

• 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