ParameterHandlerBase

class pyMaCh3._pyMaCh3.parameters.ParameterHandlerBase

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_flat_prior`(self, index)	Is the parameter at index i flat?.
`get_internal_par_name`(self, index)	Get the internally used name of this parameter.
`get_lower_bound`(self, index)	Get the lower bound of parameter at index i.
`get_n_pars`(self)	Get the number of parameters that this ParameterHandler object knows about.
`get_par_error`(self, index)	The prior error on parameter at index i
`get_par_fixed`(self, index)	Is the parameter at index i fixed
`get_par_init`(self, index)	Get initial value of parameter at index i
`get_prior_cov`(self)	Get the prior covariance
`get_proposal_array`(self)	Get the parameter proposal values as a numpy array.
`get_upper_bound`(self, index)	Get the upper bound of parameter at index i.
`propose_step`(self)	Propose a step based on the covariances.
`set_parameters`(self[, pars])	Set parameter values using array.

Methods Documentation

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

Get the name of this parameter.

Is the parameter at index i flat?.

get_internal_par_name(self: pyMaCh3._pyMaCh3.parameters.ParameterHandlerBase, index: int) → str

Get the internally used name of this parameter.

Get the lower bound of parameter at index i.

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

The prior error on parameter at index i

Is the parameter at index i fixed

Get initial value of parameter at index i

get_prior_cov(self: pyMaCh3._pyMaCh3.parameters.ParameterHandlerBase) → numpy.ndarray[numpy.float32]: Get the prior covariance

get_proposal_array(self: pyMaCh3._pyMaCh3.parameters.ParameterHandlerBase) → numpy.ndarray[numpy.float64]

Get the parameter proposal values as a numpy array.: This returns a copy of the current proposal values. :return: A numpy array containing the proposal values for all parameters.

Get the upper bound of parameter at index i.

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

set_parameters(self: pyMaCh3._pyMaCh3.parameters.ParameterHandlerBase, pars: object = None) → None

Set parameter values using array.

Parameters:: pars (numpy.ndarray or list of float, optional) – Array holding new values for every parameter. Must have same size as the number of parameters in the covariance class. If not provided, parameters are set to their pre-fit values.

Examples

>>> import numpy as np
>>> handler.set_parameters(np.array([1.0, 2.0, 3.0]))
>>> handler.set_parameters([1.0, 2.0, 3.0])
>>> handler.set_parameters()