CovarianceBase

class pyMaCh3._pyMaCh3.covariance.CovarianceBase

Bases: pybind11_object

Methods Summary

`calculate_likelihood`(self)	Calculate penalty term based on inverted covariance matrix.
`get_fancy_par_name`(self, index)	Get the name of this parameter.
`get_internal_par_name`(self, index)	Get the internally used name of this parameter.
`get_n_pars`(self)	Get the number of parameters that this covariance object knows about.
`get_proposal_array`(self)	Bind a python array to the parameter proposal values for this covariance object.
`propose_step`(self)	Propose a step based on the covariances.
`throw_par_prop`(self[, mag])	Throw the proposed parameter by magnitude mag X sigma.

Methods Documentation

calculate_likelihood(self: pyMaCh3._pyMaCh3.covariance.CovarianceBase) → float: Calculate penalty term based on inverted covariance matrix.

get_fancy_par_name(self: pyMaCh3._pyMaCh3.covariance.CovarianceBase, index: int) → str

Get the name of this parameter.

get_internal_par_name(self: pyMaCh3._pyMaCh3.covariance.CovarianceBase, index: int) → str

Get the internally used name of this parameter.

get_n_pars(self: pyMaCh3._pyMaCh3.covariance.CovarianceBase) → int: Get the number of parameters that this covariance object knows about.

Bind a python array to the parameter proposal values for this covariance object.: This allows you to set e.g. a numpy array to ‘track’ the parameter proposal values. You could either use this to directly set the proposals, or to just read the values proposed by e.g. throw_par_prop() :warning: This should be set AFTER all of the parameters have been read in from the config file as it resizes the array to fit the number of parameters. :param array: This is the array that will be set. Size and contents don’t matter as it will be changed to fit the parameters.

propose_step(self: pyMaCh3._pyMaCh3.covariance.CovarianceBase) → None: Propose a step based on the covariances. Also feel free to overwrite if you want something more funky.

throw_par_prop(self: pyMaCh3._pyMaCh3.covariance.CovarianceBase, mag: float = 1.0) → None

Throw the proposed parameter by magnitude mag X sigma.

param mag:: This value multiplied by the prior value of each parameter will be the width of the distribution that the parameter values are drawn from.