BaseFitter1D¶
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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
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constraints
¶ 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
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label
= 'Fitter'¶ A short label for the fit, used by the GUI
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options
¶ 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}
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param_names
= []¶ list of parameter names that support restrictions
Methods Documentation
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build_and_fit
(x, y, dy=None)[source]¶ Method which builds the arguments to fit, and calls that method
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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.
- x (
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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
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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:
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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)
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