fit_logic.FitLogic Class Reference

UNSTABLE:Jochen Scheuer. More...

Inheritance diagram for fit_logic.FitLogic:

Collaboration diagram for fit_logic.FitLogic:

Public Member Functions
def	__init__ (self, kwargs)
def	on_activate (self)
	Initialisation performed during activation of the module.
def	on_deactivate (self)
def	validate_load_fits (self, fits)
	Take fit names and estimators from a dict and check if they are valid. More...
def	prepare_save_fits (self, fits)
	Convert fit dictionary into a storable form. More...
def	load_fits (self, filename)
	Load collection of fits from YAML file. More...
def	save_fits (self, filename, fits)
	Save a collection of configured fits to YAML file. More...
def	make_fit_container (self, container_name, dimension)
	Creare a fit container object. More...

Public Attributes
	lock
	fit_list

Detailed Description

UNSTABLE:Jochen Scheuer.

Documentation to add a new fit model/estimator/funciton can be found in documentation/how_to_use_fitting.md or in the online documentation at http://qosvn.physik.uni-ulm.de/qudi-docs/fit_logic.html

This is the fitting class where fit functions are defined and methods are implemented to process the data.

For clarity reasons the fit function are imported from different files seperated by function type, e.g. gaussianlikemethods, sinemethods, generalmethods

Member Function Documentation

load_fits()

def fit_logic.FitLogic.load_fits	(		self,
			filename
	)

Load collection of fits from YAML file.

Parameters

filename

str: path of file containing fits in YAML format

Returns

dict: validated fit dictionary with function references and parameter objects

make_fit_container()

def fit_logic.FitLogic.make_fit_container	(		self,
			container_name,
			dimension
	)

Creare a fit container object.

Parameters

container_name	str: user-fiendly name for configurable fit
dimension	str: dimension of fit input data ('1d', '2d' od '3d')

Returns

FitContainer: fit container object

This is a convenience function so you do not have to mess with an extra import in modules using FitLogic.

on_deactivate()

def fit_logic.FitLogic.on_deactivate

(

self

)

prepare_save_fits()

def fit_logic.FitLogic.prepare_save_fits	(		self,
			fits
	)

Convert fit dictionary into a storable form.

Parameters

fits	dict: fit dictionary with function references and parameter objects

Returns

dict: storable fits description dictionary

For the format of this dictionary, ess validate_load_fits.

save_fits()

def fit_logic.FitLogic.save_fits	(		self,
			filename,
			fits
	)

Save a collection of configured fits to YAML file.

Parameters

fits	dict: dictionay with fits, function references and parameter objects

Returns

dict: storable dictionary with fit description

validate_load_fits()

def fit_logic.FitLogic.validate_load_fits	(		self,
			fits
	)

Take fit names and estimators from a dict and check if they are valid.

Parameters

fits	dict: dictionary conatining fit and estimator description

Returns

dict: checked dictionary with references to fit, model and estimator

The stored dictionary must have the following format. There can be a parameter settings string included at the deepest level.

Example

'1d': 'Two Lorentzian dips': 'fit_function': 'doublelorentzoffset' 'estimator': 'dip'

The returned dictionary has the following format (example): '1d': 'Two Lorentzian dips': 'fit_name': 'doublelorentzoffset' 'est_name': 'dip' 'make_fit': function reference to fit function 'make_model': function reference to model function 'estimator': function reference to estimator function 'parameters': lmfit.parameter.Parameters object

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The documentation for this class was generated from the following file:

• logic/fit_logic.py