Source code for fitter.histfit

import scipy.stats
import pylab
from pylab import mean, sqrt, std

__all__ = ["HistFit"]

[docs]class HistFit: """Plot the histogram of the data (barplot) and the fitted histogram. The input data can be a series. In this case, we compute the histogram. Then, we fit a curve on top on the histogram that best fit the histogram. If you already have the histogram, you can provide the arguments. In this case, X should be evenly spaced If you have some data, histogram is computed, then we add some noise during the fitting process and repeat the process Nfit=20 times. This gives us a better estimate of the underlying mu and sigma parameters of the distribution. .. plot:: from fitter import HistFit import scipy.stats data = [scipy.stats.norm.rvs(2,3.4) for x in range(10000)] hf = HistFit(data, bins=30), Nfit=20 ) print(, hf.sigma, hf.amplitude) You may already have your probability density function with the X and Y series. If so, just provide them; Note that the output of the hist function returns an X with N+1 values while Y has only N values. We take care of that. .. plot:: from fitter import HistFit from pylab import hist import scipy.stats data = [scipy.stats.norm.rvs(2,3.4) for x in range(10000)] Y, X, _ = hist(data, bins=30) hf = HistFit(X=X, Y=Y), Nfit=20) print(, hf.sigma, hf.amplitude) .. warning:: This is a draft class. It currently handles only gaussian distribution. The API is probably going to change in the close future. """ def __init__(self, data=None, X=None, Y=None, bins=None): """.. rubric:: **Constructor** One should provide either the parameter **data** alone, or the X and Y parameters, which are the histogram of some data sample. :param data: random data :param X: evenly spaced X data :param Y: probability density of the data :param bins: if data is providede, we will compute the probability using hist function and bins may be provided. """ = data if data: Y, X, _ = pylab.hist(, bins=bins, density=True) self.N = len(X) - 1 self.X = [(X[i] + X[i + 1]) / 2 for i in range(self.N)] self.Y = Y self.A = 1 self.guess_std = pylab.std( self.guess_mean = pylab.mean( self.guess_amp = 1 else: self.X = X self.Y = Y self.Y = self.Y / sum(self.Y) if len(self.X) == len(self.Y) + 1: self.X = [(X[i] + X[i + 1]) / 2 for i in range(len(X) - 1)] self.N = len(self.X) self.guess_mean = self.X[int(self.N / 2)] self.guess_std = sqrt(sum((self.X - mean(self.X)) ** 2) / self.N) / ( sqrt(2 * 3.14) ) self.guess_amp = 1.0 self.func = self._func_normal def fit( self, error_rate=0.05, semilogy=False, Nfit=100, error_kwargs={"lw": 1, "color": "black", "alpha": 0.2}, fit_kwargs={"lw": 2, "color": "red"}, ): self.mus = [] self.sigmas = [] self.amplitudes = [] self.fits = [] pylab.figure(1) pylab.clf(), self.Y, width=0.85, ec="k") for x in range(Nfit): # 5% error on the data to add errors self.E = [scipy.stats.norm.rvs(0, error_rate) for y in self.Y] # [scipy.stats.norm.rvs(0, self.std_data * error_rate) for x in range(self.N)] self.result = scipy.optimize.least_squares( self.func, (self.guess_mean, self.guess_std, self.guess_amp) ) mu, sigma, amplitude = self.result["x"] pylab.plot( self.X, amplitude * scipy.stats.norm.pdf(self.X, mu, sigma), **error_kwargs ) self.sigmas.append(sigma) self.amplitudes.append(amplitude) self.mus.append(mu) self.fits.append(amplitude * scipy.stats.norm.pdf(self.X, mu, sigma)) self.sigma = mean(self.sigmas) self.amplitude = mean(self.amplitudes) = mean(self.mus) pylab.plot( self.X, self.amplitude * scipy.stats.norm.pdf(self.X,, self.sigma), **fit_kwargs ) if semilogy: pylab.semilogy() pylab.grid() pylab.figure(2) pylab.clf() #, self.Y, width=0.85, ec="k", alpha=0.5) M = mean(self.fits, axis=0) S = pylab.std(self.fits, axis=0) pylab.fill_between(self.X, M - 3 * S, M + 3 * S, color="gray", alpha=0.5) pylab.fill_between(self.X, M - 2 * S, M + 2 * S, color="gray", alpha=0.5) pylab.fill_between(self.X, M - S, M + S, color="gray", alpha=0.5) # pylab.plot(self.X, M-S, color="k") # pylab.plot(self.X, M+S, color="k") pylab.plot( self.X, self.amplitude * scipy.stats.norm.pdf(self.X,, self.sigma), **fit_kwargs ) pylab.grid() return, self.sigma, self.amplitude def _func_normal(self, param): # amplitude is supposed to be 1./(np.sqrt(2*np.pi)*sigma)* if normalised mu, sigma, A = param return sum( (A * scipy.stats.norm.pdf(self.X, mu, sigma) - (self.Y + self.E)) ** 2 )