BaseFitter1D#

class glue.core.fitters.BaseFitter1D(**params)[source]#

Bases: object

Base class for 1D fitters.

This abstract class must be overwritten.

Attributes Summary

`constraints`	A dict of the constraints on each parameter in `param_names`.
`label`	A short label for the fit, used by the GUI
`options`	A dictionary of the current setting of each model hyperparameter.
`param_names`	list of parameter names that support restrictions

Methods Summary

`build_and_fit`(x, y[, dy])	Method which builds the arguments to fit, and calls that method
`fit`(x, y, dy, constraints, **options)	Fit the model to data.
`plot`(fit_result, axes, x[, linewidth, ...])	Plot the result of a fit.
`predict`(fit_result, x)	Evaluate the model at a set of locations.
`set_constraint`(parameter_name[, value, ...])	Update a constraint.
`summarize`(fit_result, x, y[, dy])	Return a textual summary of the fit.

Attributes Documentation

constraints[source]#

A dict of the constraints on each parameter in param_names. Each value is itself a dict with 3 items:

Key value:: The default value
Key fixed:: True / False, indicating whether the parameter is fixed
Key bounds:: [min, max] or None, indicating lower/upper limits

label = 'Fitter'#: A short label for the fit, used by the GUI

options[source]#

A dictionary of the current setting of each model hyperparameter.

Hyperparameters are defined in subclasses by creating class-level Option attributes. This attribute dict maps {hyperparameter_name: current_value}

param_names = []#: list of parameter names that support restrictions

Methods Documentation

build_and_fit(x, y, dy=None)[source]#: Method which builds the arguments to fit, and calls that method

fit(x, y, dy, constraints, **options)[source]#

Fit the model to data.

This must be overriden by a subclass.

Parameters:

x (numpy.ndarray) – The x values of the data
y (numpy.ndarray) – The y values of the data
dy (numpy.ndarray) – 1 sigma uncertainties on each datum (optional)
constraints – The current value of the constraints property
options – kwargs for model hyperparameters.

Returns:

An object representing the fit result.

plot(fit_result, axes, x, linewidth=None, alpha=None, color=None, normalize=None)[source]#

Plot the result of a fit.

Parameters:

fit_result – The output from fit
axes – The Matplotlib axes to add the fit to
x – The values of X at which to visualize the model

Returns:

A list of matplotlib artists. This is important: plots will not be properly cleared if this isn’t provided

predict(fit_result, x)[source]#

Evaluate the model at a set of locations.

This must be overridden in a subclass.

Parameters:

fit_result – The result from the fit method
x (numpy.ndarray) – Locations to evaluate model at

Returns:

model(x)

Return type:

numpy.ndarray

set_constraint(parameter_name, value=None, fixed=None, limits=None)[source]#

Update a constraint.

Parameters:

parameter_name (str) – name of the parameter to update
value – Set the default value (optional)
limits – Set the limits to[min, max] (optional)
fixed – Set whether the parameter is fixed (optional)

summarize(fit_result, x, y, dy=None)[source]#

Return a textual summary of the fit.

Parameters:

fit_result – The return value from fit()
x – The x values passed to fit()

Returns:

A description of the fit result

Return type:

str

BaseFitter1D

Contents

BaseFitter1D#