BaseFitter#

class BaseFitter(x_values: List[int | float] | ndarray, y_values: List[int | float] | ndarray, max_iterations: int = 1000)[source]#

The base class for multi-fitting functionality.

Methods

`dry_run`([axis])	Plot the x and y data for a quick visual inspection of the data.
`fit`(p0[, frozen])	Fit the data.
`fit_boundaries`()	Defines the distribution boundaries to be used by fitter.
`fitter`(x, params)	Fitter function for multi-fitting.
`get_fitted_curve`()	Get the fitted values of the model.
`get_model_parameters`([select, errors])	Extract specific parameter values or their uncertainties from the fitting process.
`get_value_error_pair`([mean_values, std_values])	Retrieve the value/error pairs for the fitted parameters.
`plot_fit`([show_individuals, x_label, ...])	Plot the fitted models.

_adjust_parameters(p0: List[Tuple[int | float, ...]] | ndarray)[source]#

Adjust input parameters to include defaults for secondary parameters if missing.

Parameters:

p0: List[List[float]]: A list of initial guesses for the parameters.

Returns:

adjusted_p0: List[List[float]]: Adjusted parameter list with default values for missing secondary parameters.

_covariance()[source]#

Store the covariance matrix of the fitted model.

Returns:

np.ndarray: An array containing the covariance matrix of the fitted model.

_fit_boundaries() → Tuple[Sequence[float], Sequence[float]][source]#: Defines the internal distribution boundaries to be used by fitter.

_fit_preprocessing(p0, frozen)[source]#

Process frozen parameters and adjust bounds.

Parameters:

p0: Sequences_: A list of initial guesses for the parameters of the models. For example, [(1, 1, 0), (3, 3, 2)].
frozen: List[bool]: A list of booleans indicating which parameters are frozen. For example, [False, False, True] for 3 parameters.

Returns:

Tuple[np.ndarray, np.ndarray, np.ndarray]: Adjusted lower and upper bounds, and flattened initial guesses.

static _format_param(value, t_low: float = 0.001, t_high: float = 10000.0) → str[source]#

Formats the parameter value to scientific notation based on its magnitude.

Parameters:

value: float: The value of the parameter to be formatted.
t_low: float, optional: The lower bound below which the formatting should be applied to the value. Defaults to 0.001.
t_high: float, optional: The upper bound above which the formatting should be applied to the value. Defaults to 10,000.

Returns:

str:: A formatted string of the parameter value.

_n_fitter(x: ndarray[Any, dtype[_ScalarType_co]], *params: List[Tuple[int | float, ...]] | ndarray) → ndarray[source]#

Perform N-fitting by summing over multiple parameter sets.

Parameters:

xnp.ndarray: Input array of values for which the composite function is evaluated.
paramstuple: A tuple with all parameters to be fitted in an array of size (self.n_fits, self.n_par) where: - self.n_fits is the number of individual fits. - self.n_par is the number of parameters per fit.

Returns:

np.ndarray: An array containing the composite fitted values for the input x.

_params() → ndarray[source]#

Store the fitted parameters of the fitted model.

Returns:

np.ndarray: The parameters obtained after performing the fit.

Raises:

RuntimeError: If the fit has not been performed yet (i.e., self.params is None).

Notes

This method assumes that the fitting process assigns values to self.params.

_plot_individual_fitter(plotter)[source]#

Plot individual fits from the composite fitter.

Parameters:

plottermatplotlib.axes.Axes: The axis object where the plots will be rendered.

Notes

self.params must contain the fitted parameters reshaped into (self.n_fits, self.n_par).
Each plot will be labeled with the class name and the index of the fit, along with the formatted parameters.

_standard_errors() → ndarray[Any, dtype[_ScalarType_co]][source]#

Store the standard errors of the fitted parameters.

Returns:

np.ndarray: An array containing the standard errors of the fitted parameters.

Raises:

RuntimeError: If the fit has not been performed yet (i.e., self.covariance is None).

dry_run(axis=None)[source]#

Plot the x and y data for a quick visual inspection of the data.

Parameters:

axis: The axis to plot the data on.

fit(p0: List[Tuple[int | float, ...]] | ndarray, frozen: List[bool] | None = None)[source]#

Fit the data.

Parameters:

p0: Sequences_: A list of initial guesses for the parameters of the models. For example, [(1, 1, 0), (3, 3, 2)].
frozen: List[bool]: A list of booleans indicating whether each parameter is frozen. For example, [False, False, True] for 3 parameters.

fit_boundaries() → Tuple[Sequence[float], Sequence[float]][source]#: Defines the distribution boundaries to be used by fitter.

static fitter(x, params: List[Tuple[int | float, ...]] | ndarray) → ndarray[source]#

Fitter function for multi-fitting.

Parameters:

x: np.ndarray: The x-array on which the fitting is to be performed.
params: Params_: An array of parameters to fit.

get_fitted_curve() → ndarray[source]#

Get the fitted values of the model.

Returns:

np.ndarray: An array of fitted values.

get_model_parameters(select: Tuple[int, Any] | None = None, errors: bool = False)[source]#

Extract specific parameter values or their uncertainties from the fitting process.

This method allows for retrieving the fitted parameters or their corresponding standard errors for specific: submodels, or for all sub-models if no selection is provided.

Parameters:

selectlist of int or None, optional: A list of indices specifying which sub-models to extract parameters for. Indexing starts at 1. If None, parameters for all sub-models are returned. Defaults to None.
errorsbool, optional: If True, both the parameter values and their standard errors are returned. Defaults to False.

Returns:

np.ndarray or tuple of np.ndarray

If errors is False:
- A 2D array of shape (n_parameters, selected_models) with parameter values for the selected models.
If errors is True: A tuple of two 2D arrays:
- The first array contains the parameter values of shape (n_parameters, selected_models).
- The second array contains the standard errors of the parameters, with the same shape.

Raises:

ValueError: If the input select is not a valid list of indices or is incompatible with the model structure.

Notes

The select parameter allows filtering by specific sub-model indices. If None, use all sub-models.
When errors is True, both the parameter means and their uncertainties are returned as separate arrays.

get_value_error_pair(mean_values: bool = True, std_values: bool = False) → ndarray[source]#

Retrieve the value/error pairs for the fitted parameters.

This method provides the fitted parameter values and their corresponding standard errors as a combined array or individually based on the input flags.

Parameters:

mean_valuesbool, optional.: If True, return only the values of the fitted parameters. Defaults to True.
std_valuesbool, optional.: If True, return only the standard errors of the fitted parameters. Defaults to False.

Returns:

np.ndarray

If mean_values and std_values are both True: A 2D array of shape (n_parameters, 2),
where each row is [value, error].
If mean_values is True and std_values is False: A 1D array of parameter values.
If std_values is True and mean_values is False: A 1D array of standard errors.
If both flags are False: An error message.

Raises:

ValueError: If both mean_values and std_values are False.

Plot the fitted models.

Parameters:

show_individuals: bool, optional.: Whether to show individually fitted models or not.
x_label: str, optional: The label for the x-axis.
y_label: str, optional: The label for the y-axis.
title: str, optional: The title for the plot.
data_label: str, optional: The label for the data.
axis: Axes, optional: Axes to plot instead of the entire figure. Defaults to None.

Returns:

plotter: The plotter handle for the drawn plot.