"""Created on Aug 10 23:08:38 2024"""
import itertools
from typing import Optional, Tuple, Union, List, Callable
import matplotlib.pyplot as plt
import numpy as np
from mpyez.backend.uPlotting import LinePlot
from mpyez.ezPlotting import plot_xy
from scipy.optimize import Bounds, curve_fit
# importing from files to avoid circular import
from .chiSquare_f import ChiSquareFitter
from .exponential_f import ExponentialFitter
from .foldedNormal_f import FoldedNormalFitter
from .gamma_f import GammaFitterSR, GammaFitterSS
from .gaussian_f import GaussianFitter
from .halfNormal_f import HalfNormalFitter
from .laplace_f import LaplaceFitter
from .logNormal_f import LogNormalFitter
from .others import LineFitter
from .skewNormal_f import SkewNormalFitter
from .utilities_f import sanity_check
from .. import (epsilon, GAUSSIAN, LAPLACE, LINE, LOG_NORMAL, SKEW_NORMAL, CHI_SQUARE, EXPONENTIAL, FOLDED_NORMAL, GAMMA_SR, GAMMA_SS, NORMAL,
HALF_NORMAL)
# mock initialize the internal classes for auto MixedDataFitter class
fitter_dict = {CHI_SQUARE: ChiSquareFitter,
EXPONENTIAL: ExponentialFitter,
FOLDED_NORMAL: FoldedNormalFitter,
GAMMA_SR: GammaFitterSR,
GAMMA_SS: GammaFitterSS,
GAUSSIAN: GaussianFitter,
NORMAL: GaussianFitter,
HALF_NORMAL: HalfNormalFitter,
LAPLACE: LaplaceFitter,
LOG_NORMAL: LogNormalFitter,
SKEW_NORMAL: SkewNormalFitter,
LINE: LineFitter}
[docs]
class MixedDataFitter:
r"""
Class to fit a mixture of different models to data.
:param x_values: The x-values for the data.
:param y_values: The y-values for the data.
:param model_list: List of models to fit (e.g., `LINE`, `GAUSSIAN`, `LOG_NORMAL`)
:param max_iterations: The maximum number of iterations for fitting procedure.
"""
def __init__(self, x_values: Union[List, np.ndarray], y_values: Union[List, np.ndarray],
model_list: List[str], fitter_dictionary=None, max_iterations: int = 1000):
x_values, y_values = sanity_check(x_values=x_values, y_values=y_values)
self.x_values = x_values
self.y_values = y_values
self.model_list = model_list
self.max_iterations = max_iterations
self.params = None
self.covariance = None
self.fitter_dict = fitter_dictionary or fitter_dict
# self._validate_models()
self.model_function = self._create_model_function()
def __repr__(self):
return (f"{self.__class__.__name__}(x_values={self.x_values}, y_values={self.y_values}, "
f"model_list={self.model_list}, max_iterations={self.max_iterations})")
[docs]
def _create_model_function(self) -> Callable:
"""
Creates a composite model function based on the specified models.
:return: A composite model for fitting.
"""
def _composite_model(x: np.ndarray, *params) -> np.ndarray:
"""
Compute the composite model.
Parameters
----------
x : np.ndarray
The x-values where the model is evaluated.
params : tuple
Parameters for the model components.
Returns
-------
y : np.ndarray
The computed y-values from the composite model.
"""
y = np.zeros_like(a=x, dtype=float)
param_index = 0
for model in self.model_list:
model_class, n_par = self._instantiate_fitter(model=model, return_values=['class', 'n_par'])
y += model_class.fitter(x=x, params=params[param_index:param_index + n_par])
param_index += n_par
return y
return _composite_model
[docs]
def _expected_param_count(self) -> int:
"""
Calculates the expected number of parameters based on the model list.
:return: The number of parameters.
"""
count = 0
for model in self.model_list:
n_par = self._instantiate_fitter(model=model, return_values='n_par')
count += n_par
return count
[docs]
def _get_bounds(self) -> Tuple[np.ndarray, np.ndarray]:
"""
Sets the bounds for each parameter based on the model list.
:returns: Lower and upper bounds for the parameters.
"""
lower_bounds = []
upper_bounds = []
for model in self.model_list:
lb, ub = self._instantiate_fitter(model=model, return_values='bounds')
lower_bounds.extend(lb)
upper_bounds.extend(ub)
return np.array(lower_bounds), np.array(upper_bounds)
[docs]
def _instantiate_fitter(self, model: str, return_values: Union[str, List[str]] = 'class'):
"""
Instantiate the fitter for the specified model and return requested values.
:param model: The model name as a string.
:param return_values: The specific attribute(s) or instance to return.
Options are 'class', 'n_par', and 'bounds'.
Can be a string (for one value) or a list of strings.
:return: The requested values as a single value or a tuple.
:raises ValueError: If the model is not recognized or return_values are invalid.
"""
try:
fitter_instance = self.fitter_dict[model](x_values=[], y_values=[])
except KeyError:
raise ValueError(f"Model '{model}' not recognized. Ensure it is defined in the fitter dictionary.")
valid_options = {'class', 'n_par', 'bounds'}
if isinstance(return_values, str):
return_values = [return_values] # Convert to list for uniform processing
if not all(val in valid_options for val in return_values):
invalid_options = [val for val in return_values if val not in valid_options]
raise ValueError(f"Invalid return_values {invalid_options}. Expected values: {valid_options}")
result = []
for val in return_values:
if val == 'class':
result.append(fitter_instance)
elif val == 'n_par':
result.append(fitter_instance.n_par)
elif val == 'bounds':
try:
result.append(fitter_instance.fit_boundaries())
except NotImplementedError:
# in case the boundaries are not defined, put -np.inf, and np.inf to work with
n_par = fitter_instance.n_par
result.append([[-np.inf] * n_par, [np.inf] * n_par])
return result[0] if len(result) == 1 else tuple(result)
[docs]
def _params(self) -> np.ndarray:
r"""
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``.
"""
if self.params is None:
raise RuntimeError("Fit not performed yet. Call fit() first.")
return self.params
[docs]
def _plot_individual_fitter(self, plotter):
"""
Plot the individual fitters function.
:param plotter: The plotting axis object
"""
x = self.x_values
colors = plt.rcParams['axes.prop_cycle'].by_key()['color'][1:]
param_index = 0
for i, model in enumerate(self.model_list):
color = colors[i % len(colors)]
class_model, n_par = self._instantiate_fitter(model=model, return_values=['class', 'n_par'])
pars = self.params[param_index:param_index + n_par]
y_component = class_model.fitter(x=x, params=pars)
plot_xy(x_data=x, y_data=y_component,
x_label='', y_label='', plot_title='',
data_label=f'{model.capitalize()} {i + 1}({", ".join(self._format_param(i) for i in pars)})',
plot_dictionary=LinePlot(line_style='--', color=color),
axis=plotter)
param_index += n_par
[docs]
def _standard_errors(self) -> np.ndarray:
r"""
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``).
"""
if self.covariance is None:
raise RuntimeError("Fit not performed yet. Call fit() first.")
return np.sqrt(np.diag(self.covariance))
[docs]
def fit(self, p0: Union[List, np.ndarray], frozen: Union[int, List[int]] = None):
"""
Fit the data.
:param p0: Initial guess for the fitted parameters.
:type p0: Union[List, np.ndarray]
:param frozen: Parameter number of list of parameter numbers to freeze the value of.
:type frozen: Union[int, List[int]]
:raises ValueError: If the length of the initial guess is not equal to the expected parameter count.
"""
# flatten cannot always work here because the mixed fitter might contain variable number of parameters
p0 = list(itertools.chain.from_iterable(p0))
if len(p0) != self._expected_param_count():
raise ValueError(f"Initial parameters length {len(p0)} does not match expected count "
f"{self._expected_param_count()}.")
lb, ub = self._get_bounds()
if frozen:
if isinstance(frozen, int):
frozen = [frozen]
for par_num in frozen:
lb[par_num - 1] = p0[par_num - 1] - epsilon
ub[par_num - 1] = p0[par_num - 1] + epsilon
self.params, self.covariance, *_ = curve_fit(f=self.model_function, xdata=self.x_values, ydata=self.y_values,
p0=p0, maxfev=self.max_iterations, bounds=Bounds(lb=lb, ub=ub))
[docs]
def get_fit_values(self) -> np.ndarray:
"""
Gets the y-values from the fitted model.
:return: The y-values from the fitted model
:raises ValueError: If the model has not been fitted yet.
"""
if self.params is None:
raise RuntimeError("Fit not performed yet. Call fit() first.")
return self.model_function(self.x_values, *self.params)
[docs]
def get_model_parameters(self, model: Optional[str] = None, errors: bool = False):
"""
Extracts parameters (and error) values for a specific model, or for all models if no model is specified.
:param model: Model name to extract parameters for. If unspecified, extracts parameters for all models.
Defaults to ``None``.
:param errors: If ``True``, includes the errors in the returned output. Defaults to ``False``.
:return: A dictionary containing:
- "parameters": Nested dictionary of parameter values for each model if `get_errors` is True.
- "errors": Nested dictionary of errors for each model (if `get_errors=True`).
Otherwise, returns just the parameters directly.
"""
parameters = self._parameter_extractor(self.params)
errs = self._parameter_extractor(np.sqrt(np.diag(self.covariance)))
if not errors:
return parameters if model is None else parameters.get(model, [])
if model is None:
# Return combined dictionary for all models
return {"parameters": parameters, "errors": errs}
# Prepare output for a specific model
output = {"parameters": {}, "errors": {}}
keys = ["parameters", "errors"]
n_pars = self._instantiate_fitter(model=model, return_values='n_par')
for temp_, key in zip([parameters, errs], keys):
par_dict = temp_.get(model, [])
if n_pars == 2:
output[key] = par_dict
else:
output[key] = np.array_split(ary=np.array(par_dict).flatten(), indices_or_sections=n_pars)
return output
[docs]
def get_value_error_pair(self, mean_values: bool = True, std_values: bool = False) -> np.ndarray:
r"""
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_values : bool, optional
If ``True``, return only the values of the fitted parameters.
Defaults to ``True``.
std_values : bool, 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``.
"""
pairs = np.column_stack([self._params(), self._standard_errors()])
if mean_values and std_values:
return pairs
elif mean_values:
return pairs[:, 0]
elif std_values:
return pairs[:, 1]
else:
raise ValueError("Either 'mean_values' or 'std_values' must be True.")
[docs]
def plot_fit(self, show_individuals: bool = False,
x_label: Optional[str] = None, y_label: Optional[str] = None, title: Optional[str] = None,
data_label: Optional[str] = None, figure_size: tuple = (12, 6)) -> tuple:
"""
Plots the original data, fitted model, and optionally individual components.
:param show_individuals: Whether to plot individual fitted functions, by default False.
:param x_label: The label for the x-axis of the plot.
:param y_label: The label for the y-axis of the plot.
:param title: The title for the plot.
:param data_label: The label for the data to be plotted.
:param figure_size: The size of the figure. Default is (12,6).
:return: A tuple of figure and axes object for the drawn plot
:raises ValueError: Raised if the plotting function is called before the fitting is done.
"""
if self.y_values is None or self.params is None:
raise ValueError("Data must be fitted before plotting.")
fig, ax = plt.subplots(figsize=figure_size)
plotter = plot_xy(x_data=self.x_values, y_data=self.y_values,
data_label=data_label if data_label else 'Data', axis=ax)
plot_xy(x_data=self.x_values, y_data=self.model_function(self.x_values, *self.params),
data_label='Total Fit', plot_dictionary=LinePlot(color='k'), axis=plotter)
if show_individuals:
self._plot_individual_fitter(plotter=plotter)
plotter.set_xlabel(x_label if x_label else 'X')
plotter.set_ylabel(y_label if y_label else 'Y')
plotter.set_title(title if title else f'{self.__class__.__name__} fit')
plotter.legend(loc='best')
fig.tight_layout()
return fig, plotter