# -*- coding: utf-8 -*-
# Copyright (C) 2023-- Michał Kopczyński
#
# This file is part of MiniFit.
#
# MiniFit is free software; you can redistribute it and/or
# modify it under the terms of the GNU General Public License
# as published by the Free Software Foundation; either version 3
# of the License, or (at your option) any later version.
#
# MiniFit is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU General Public License for more details.
#
# You should have received a copy of the GNU General Public License
# along with this program; if not, see <http://www.gnu.org/licenses/>
#
"""
Module containing GaussFit class for curve fitting with normal distribution.
"""
import random
import numpy as np
from .fit_base import FitBase, log
[docs]
class GaussFit(FitBase):
"""
This class makes a curve fitting using the normal distribution
It reads data from a file given as the first argument.
The data file must contain at least two columns.
The first column represents x values,
the second and following columns are the y values.
"""
type_of_fit = "Normal distribution"
label_type = "gauss_fit"
[docs]
def __init__(self, filename, **kwargs):
"""
**Arguments:**
filename:
(string) Name of the file that contains data.
**Keyword arguments:**
guess:
(tuple) A guess of the optimal parameters for the model.
The number of guesses should match
the arguments that the model function expects.
The model is defined as:
``A * np.exp(-((x - mu) ** 2) / sig**2)``
it expects ``(val1, val2, val3)`` for A, mu, and sig, such as:
``(4.9, 20., 2.85)``
Otherwise, an exception will be raised. If not passed, a default guess adequate for
the model function will be used.
auto_guess:
(bool) If true, tries to fit until chosen ``precision`` is reached. Default false.
auto_range:
(tuple) Sets boundaries for each parameter of the guess.
If not set, the default range is used. Only used if auto_guess is True.
Instead of ``guess = (4.9, 20., 2.85)``
pass ``auto_range = ((0.,10.), (5., 35.), (1., 8.))``
If fitting takes a lot of time, giving a better range might be helpful.
precision:
(float) Used by the ``auto_guess`` feature to control the fitting accuracy.
The precision parameter represents the maximum allowable error
between the fitted curve and the actual data points.
During the fitting process, MiniFit continuously refines the parameters
until the absolute sum of differences and the square root error
between the model predictions and the data fall below this specified precision value.
If the precision is set to 0.4, for instance,
the fitted curve's error will be less than 0.4 for both the absolute sum of differences
and the square root error.
Default value is 0.4. Only used if ``auto_guess`` is True.
If ``auto_guess`` is True and fitting takes a lot of time,
lowering the ``precision`` may be necessary for convergence.
Lowering the precision will make the fitting process faster,
but the quality of the popt may be worse.
shift:
(bool) If true, shift the data. Default false.
Data can be shifted and it may
make the convergence of the least squares algorithm easier.
"""
super().__init__(filename, **kwargs)
[docs]
def model_function(self, x, *args, **kwargs):
"""Depends on the module."""
if len(args) != 3:
raise TypeError(
f"Passed a wrong number of guess parameters. Received {len(args)}, expected 3."
)
A, mu, sig = args
return A * np.exp(-((x - mu) ** 2) / sig**2)
[docs]
def default_guess(self, context):
"""Default guess adequate for the model function."""
guess = [2, 2, 3]
return guess
[docs]
def random_guess(self, context):
"""
Random guess adequate for the model function with constraints
chosen by the user.
"""
if self.auto_range is None:
guess = [
random.uniform(-60, 60),
random.uniform(-60, 60),
random.uniform(-60, 60),
]
else:
guess = [
random.uniform(min_val, max_val)
for min_val, max_val in self.auto_range
]
return guess
[docs]
def print_info(self, popt):
"""
Print info about popt in the format adequate for the model function.
"""
name_temp = ("A", "mu", "sigma")
for el, name_ in enumerate(name_temp):
log(f"\t {name_}: {popt[el]:2.8f}")
A, mu, sig = popt
log(" ")
log(" Resulting formula: ")
log("A * np.exp(-((x - mu) ** 2) / sig**2)")
log(f"{A} * np.exp(-((x - {mu}) ** 2) / {sig}**2)")
[docs]
def latex_text(self):
"""Returns a string with the formula used by the model in latex format."""
A, mu, sig = self.popt
n = "\n"
formula = (
f"$y = A \cdot e^{{- \\frac{{(x - \\mu)^2}}{{\\sigma^2}}}}${n}"
)
formula_instance = (
f"$y = {A:.3f} \cdot e^{{- \\frac{{(x - {mu:.3f})^2}}{{{sig:.3f}^2}}}}$, "
f"{n}where:{n}$A={A}$, $\\mu={mu}$ {n},$\\sigma={sig}$"
)
return formula + formula_instance