mirror of
https://github.com/slsdetectorgroup/aare.git
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7309cff47c
- added stand alone fitting using: https://jugit.fz-juelich.de/mlz/lmfit.git - fit_gaus, fit_pol1 with and without errors - multi threaded fitting --------- Co-authored-by: JulianHeymes <julian.heymes@psi.ch>
80 lines
2.6 KiB
Python
80 lines
2.6 KiB
Python
import matplotlib.pyplot as plt
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import numpy as np
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from aare import fit_gaus, fit_pol1
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from aare import gaus, pol1
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textpm = f"±" #
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textmu = f"μ" #
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textsigma = f"σ" #
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# ================================= Gauss fit =================================
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# Parameters
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mu = np.random.uniform(1, 100) # Mean of Gaussian
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sigma = np.random.uniform(4, 20) # Standard deviation
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num_points = 10000 # Number of points for smooth distribution
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noise_sigma = 100
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# Generate Gaussian distribution
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data = np.random.normal(mu, sigma, num_points)
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# Generate errors for each point
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errors = np.abs(np.random.normal(0, sigma, num_points)) # Errors with mean 0, std 0.5
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# Create subplot
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fig0, ax0 = plt.subplots(1, 1, num=0, figsize=(12, 8))
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x = np.histogram(data, bins=30)[1][:-1] + 0.05
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y = np.histogram(data, bins=30)[0]
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yerr = errors[:30]
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# Add the errors as error bars in the step plot
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ax0.errorbar(x, y, yerr=yerr, fmt=". ", capsize=5)
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ax0.grid()
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par, err = fit_gaus(x, y, yerr)
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print(par, err)
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x = np.linspace(x[0], x[-1], 1000)
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ax0.plot(x, gaus(x, par), marker="")
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ax0.set(xlabel="x", ylabel="Counts", title=f"A0 = {par[0]:0.2f}{textpm}{err[0]:0.2f}\n"
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f"{textmu} = {par[1]:0.2f}{textpm}{err[1]:0.2f}\n"
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f"{textsigma} = {par[2]:0.2f}{textpm}{err[2]:0.2f}\n"
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f"(init: {textmu}: {mu:0.2f}, {textsigma}: {sigma:0.2f})")
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fig0.tight_layout()
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# ================================= pol1 fit =================================
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# Parameters
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n_points = 40
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# Generate random slope and intercept (origin)
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slope = np.random.uniform(-10, 10) # Random slope between 0.5 and 2.0
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intercept = np.random.uniform(-10, 10) # Random intercept between -10 and 10
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# Generate random x values
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x_values = np.random.uniform(-10, 10, n_points)
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# Calculate y values based on the linear function y = mx + b + error
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errors = np.abs(np.random.normal(0, np.random.uniform(1, 5), n_points))
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var_points = np.random.normal(0, np.random.uniform(0.1, 2), n_points)
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y_values = slope * x_values + intercept + var_points
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fig1, ax1 = plt.subplots(1, 1, num=1, figsize=(12, 8))
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ax1.errorbar(x_values, y_values, yerr=errors, fmt=". ", capsize=5)
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par, err = fit_pol1(x_values, y_values, errors)
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x = np.linspace(np.min(x_values), np.max(x_values), 1000)
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ax1.plot(x, pol1(x, par), marker="")
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ax1.set(xlabel="x", ylabel="y", title=f"a = {par[0]:0.2f}{textpm}{err[0]:0.2f}\n"
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f"b = {par[1]:0.2f}{textpm}{err[1]:0.2f}\n"
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f"(init: {slope:0.2f}, {intercept:0.2f})")
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fig1.tight_layout()
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plt.show()
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