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) Plot the result of a fit.
predict(fit_result, x) Evaulate 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 constraints
  • options – kwargs for model hyperparameters.
Returns:

An object representing the fit result.

plot(fit_result, axes, x)[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]

Evaulate 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