satlas.models.transformmodel.TransformHFSModel

class satlas.models.transformmodel.TransformHFSModel(*args, **kwargs)

Create an HFSModel that applies both a pre-processing transformation on the input data and a post-processing transformation on the output data. Mathematically:

Both transformations are initialized to the identical transformation for one input argument.

__init__(*args, **kwargs)

Passes all arguments on the HFSModel. See HFSModel for input information.

__call__(*args, **kwargs)

Methods

__init__(\*args, \*\*kwargs)	Passes all arguments on the HFSModel.
__call__(\*args, \*\*kwargs)
display_chisquare_fit([scaled])	Display all relevent info of the least-squares fitting routine, if this has been performed.
display_mle_fit([scaled])	Give a readable overview of the result of the MLE fitting routine.
fix_ratio(value[, target, parameter])	Fixes the ratio for a given hyperfine parameter to the given value.
get_chisquare_mapping()
get_lnprior_mapping(params)
get_result([selection])	Return the variable names, values and estimated error bars for the parameters as seperate lists.
get_result_dict([method, scaled])	Returns the fitted parameters in a dictionary of the form {name: [value, uncertainty]}.
get_result_frame([method, selected, bounds, ...])	Returns the data from the fit in a pandas DataFrame.
plot(\*args, \*\*kwargs)	Grants access to the HFSModel.plot method, passing all arguments.
plot_scheme([show, upper_color, ...])	Create a figure where both the splitting of the upper and lower state is drawn, and the hfs associated with this.
plot_spectroscopic(\*\*kwargs)	Plots the hfs on top of experimental data with errorbar given by the square root of the data.
save(path)	Saves the current spectrum, including the results of the fitting and the parameters, to the specified file.
seperate_response(x)	Wraps the output of the __call__ in a list, for ease of coding in the fitting routines.
set_boundaries(boundaryDict)	Sets the boundaries of the fitparameters as supplied in the dictionary.
set_chisquare_mapping(mappingDict)	Sets the prior mapping for the different parameters.
set_expr(exprDict)	Sets the expression of the selected parameters to the given expressions.
set_literature_values(literatureDict)	Sets the lnprior and chisquare mapping to handle the given literature values and uncertainties.
set_lnprior_mapping(mappingDict)	Sets the prior mapping for the different parameters.
set_value(valueDict)	Sets the value of the given parameters to the given values.
set_variation(varyDict)	Sets the variation of the fitparameters as supplied in the dictionary.

Attributes

ftof	List of transition labels, of the form Flow__Fhigh (half-integers have an underscore instead of a division sign), same ordering as given by the attribute locations.
locations	Contains the locations of the peaks.
params	Instance of lmfit.Parameters object characterizing the shape of the HFS.
post_transform	The transformation function to be applied to the output data.
pre_transform	The transformation function to be applied to the input data.
TransformHFSModel.roi
use_racah	Boolean to set the behaviour to Racah intensities (True) or to individual amplitudes (False).
use_saturation	Boolean to set the behaviour to the saturation model (True) or not (False).