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. Copy this vector r times and combine the copies of v in a so-called score matrix M that has as many columns with normal scores as we want to. Divide the quantiles by the standard deviation of the vector elements so that their aggregate has standard deviation 1.0. X, M - S, M + S, color = "gray", alpha = 0.5 ) # ot(self.X, M-S, color="k") # ot(self.X, M+S, color="k") pylab. Create a vector v of standard normal quantiles, (i/(N+1). X, M - 2 * S, M + 2 * S, color = "gray", alpha = 0.5 ) pylab. X, M - 3 * S, M + 3 * S, color = "gray", alpha = 0.5 ) pylab. sigma ), ** fit_kwargs ) if semilogy : pylab. X, mu, sigma ), ** error_kwargs ) self. guess_amp ) ) mu, sigma, amplitude = self. Y, width = 0.85, ec = "k" ) for x in range ( Nfit ): # 5% error on the data to add errors self. _func_normal def fit ( self, error_rate = 0.05, semilogy = False, Nfit = 100, error_kwargs =, ): self. X = + X ) / 2 for i in range ( len ( X ) - 1 )] self. data, bins = bins, density = True ) self. :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. rubric:: **Constructor** One should provide either the parameter **data** alone, or the X and Y parameters, which are the histogram of some data sample. :from scipy import optimizeimport scipy.integrate as integratefrom math import exp,log,sqrtfrom scipy. In principle, this could be changed without too much work. """ def _init_ ( self, data = None, X = None, Y = None, bins = None ): """. A B + C > A numpy.sin(B) > A (B) These operations run sequentially, taking no advantage of multicore machines (but see below). The API is probably going to change in the close future. It currently handles only gaussian distribution. plot:: from fitter import HistFit from pylab import hist import scipy.stats data = Y, X, _ = hist(data, bins=30) hf = HistFit(X=X, Y=Y) hf.fit(error_rate=0.03, Nfit=20) print(hf.mu, hf.sigma, hf.amplitude). 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. plot:: from fitter import HistFit import scipy.stats data = hf = HistFit(data, bins=30) hf.fit(error_rate=0.03, Nfit=20 ) print(hf.mu, hf.sigma, hf.amplitude) You may already have your probability density function with the X and Y series. This gives us a better estimate of the underlying mu and sigma parameters of the distribution. 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. If you already have the histogram, you can provide the arguments. Then, we fit a curve on top on the histogram that best fit the histogram. Class HistFit : """Plot the histogram of the data (barplot) and the fitted histogram.