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POPython: two-curve gaussian fitting with non-linear least-squares
 

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  1. POPython: two-curve gaussian fitting with non-linear least-squares
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    <p>My knowledge of maths is limited which is why I am probably stuck. I have a spectra to which I am trying to fit two Gaussian peaks. I can fit to the largest peak, but I cannot fit to the smallest peak. I understand that I need to sum the Gaussian function for the two peaks but I do not know where I have gone wrong. An image of my current output is shown:</p> <p><img src="https://i.stack.imgur.com/7JYwH.png" alt="Current Output"></p> <p>The blue line is my data and the green line is my current fit. There is a shoulder to the left of the main peak in my data which I am currently trying to fit, using the following code:</p> <pre><code>import matplotlib.pyplot as pt import numpy as np from scipy.optimize import leastsq from pylab import * time = [] counts = [] for i in open('/some/folder/to/file.txt', 'r'): segs = i.split() time.append(float(segs[0])) counts.append(segs[1]) time_array = arange(len(time), dtype=float) counts_array = arange(len(counts)) time_array[0:] = time counts_array[0:] = counts def model(time_array0, coeffs0): a = coeffs0[0] + coeffs0[1] * np.exp( - ((time_array0-coeffs0[2])/coeffs0[3])**2 ) b = coeffs0[4] + coeffs0[5] * np.exp( - ((time_array0-coeffs0[6])/coeffs0[7])**2 ) c = a+b return c def residuals(coeffs, counts_array, time_array): return counts_array - model(time_array, coeffs) # 0 = baseline, 1 = amplitude, 2 = centre, 3 = width peak1 = np.array([0,6337,16.2,4.47,0,2300,13.5,2], dtype=float) #peak2 = np.array([0,2300,13.5,2], dtype=float) x, flag = leastsq(residuals, peak1, args=(counts_array, time_array)) #z, flag = leastsq(residuals, peak2, args=(counts_array, time_array)) plt.plot(time_array, counts_array) plt.plot(time_array, model(time_array, x), color = 'g') #plt.plot(time_array, model(time_array, z), color = 'r') plt.show() </code></pre>
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    1. COThis would be quite difficult in this case, since the two peaks are rather close together - there is not a definite peak for the smaller 'gaussian'. Typically one would (I think) identify all the peaks of interest, then iterate over each peak masking out all the other peaks and fitting to each peak. The total fit is then the sum of all these fits. What it looks like you need to do is identify the large peak and it's extent and then mask that from the data before fitting to the smaller peak
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