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copied!<p>This code worked for me providing that you are only fitting a function that is a combination of two Gaussian distributions.</p> <p>I just made a residuals function that adds two Gaussian functions and then subtracts them from the real data. </p> <p>The parameters (p) that I passed to Numpy's least squares function include: the mean of the first Gaussian function (m), the difference in the mean from the first and second Gaussian functions (dm, i.e. the horizontal shift), the standard deviation of the first (sd1), and the standard deviation of the second (sd2).</p> <pre><code>import numpy as np from scipy.optimize import leastsq import matplotlib.pyplot as plt ###################################### # Setting up test data def norm(x, mean, sd): norm = [] for i in range(x.size): norm += [1.0/(sd*np.sqrt(2*np.pi))*np.exp(-(x[i] - mean)**2/(2*sd**2))] return np.array(norm) mean1, mean2 = 0, -2 std1, std2 = 0.5, 1 x = np.linspace(-20, 20, 500) y_real = norm(x, mean1, std1) + norm(x, mean2, std2) ###################################### # Solving m, dm, sd1, sd2 = [5, 10, 1, 1] p = [m, dm, sd1, sd2] # Initial guesses for leastsq y_init = norm(x, m, sd1) + norm(x, m + dm, sd2) # For final comparison plot def res(p, y, x): m, dm, sd1, sd2 = p m1 = m m2 = m1 + dm y_fit = norm(x, m1, sd1) + norm(x, m2, sd2) err = y - y_fit return err plsq = leastsq(res, p, args = (y_real, x)) y_est = norm(x, plsq[0][0], plsq[0][2]) + norm(x, plsq[0][0] + plsq[0][1], plsq[0][3]) plt.plot(x, y_real, label='Real Data') plt.plot(x, y_init, 'r.', label='Starting Guess') plt.plot(x, y_est, 'g.', label='Fitted') plt.legend() plt.show() </code></pre> <p><img src="https://i.stack.imgur.com/pDnAE.png" alt="Results of the code."></p>

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