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POPython: 4-curve gaussian fitting with non-linear least-squares with multiple spectra
 

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  1. POPython: 4-curve gaussian fitting with non-linear least-squares with multiple spectra
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    <p>I'm using the top rated code from <a href="https://stackoverflow.com/questions/10143905/python-two-curve-gaussian-fitting-with-non-linear-least-squares">here</a></p> <p>and modified it to fit 4 gaussian gives for 3 spectra. The code works when I'm just doing working one spectra at a time but I want to automate the code so that I do curve fitting on multiple spectra. Here is the code I have so far just for 3 spectra but I plan to do more. Note that my y_real is a list for 3 different fluxes while my xfit notes the wavelength which is the same range for all 3 spectra. I'm just using a few data points just for this example. My problem arises for res statement(ValueError: operands could not be broadcast together with shapes) which I do not know how to fix before I start plotting. </p> <pre><code>import numpy as np import matplotlib.pyplot as plt from scipy.optimize import leastsq xfit = np.array([6520.0, 6545.0, 6570.0, 6595.0, 6620.0]) y_real = np.array([[0.0, 8.9013813844724581, 33.114149624958607, 13.297472921668451,3.8540928722192049], [0.0, 11.491419225489725, 18.866478352891686, 9.0782365151426738, 1.8757625220616632], [0.0, 10.680394752562883, 21.394692831684814, 11.661265465293802, 0.9362924799953376]]) Lamda = np.array([ 6564.61389433, 6564.61389433, 6564.61389433]) delLamda1 = np.array([ 14.75496508, 14.75496508, 14.75496508]) delLamda2 = np.array([ 20.65455091, 20.65455091, 20.65455091]) def norm(xfit, mean, sd, a): norm = [] for i in range(xfit.size): norm += [a*np.exp(-(xfit[i] - mean)**2/(2*sd**2))] return np.array(norm) mean1, mean2 = 0, -2 std1, std2 = 0.5, 1 m, dm1, dm2, sd1, sd2, sd3, sd4, a, a2, a3, ab= [Lamda, -delLamda1, delLamda2, 1.0,1.0, 1.0, 2.0, 20.0, 20.0, 20.0, 10.0] p = [m, dm1, dm2, sd1, sd2, sd3, sd4, a, a2, a3, ab] # Initial guesses for leastsq y_init = norm(xfit, m, sd1, a) + norm(xfit, m + dm1, sd2, a2) + norm(xfit, m+dm2, sd3, a3) + norm(xfit, m, sd4, ab) # For final comparison plot \ def res(p, y_real, xfit): m, dm1, dm2, sd1, sd2, sd3, sd4, a, a2, a3, ab= p m1 = m m2 = m1 + dm1 m3 = m1 +dm2 y_fit = norm(xfit, m1, sd1, a) + norm(xfit, m2, sd2, a2) + norm(xfit, m3, sd3, a3) + norm(xfit, m1, sd4, ab) err = y_real - y_fit return err plsq = leastsq(res, p, args = (y_real, xfit), ftol=1.0e-09, gtol=1.0e-09, xtol=1.0e-09, maxfev=2000) </code></pre>
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    1. USuser1821176
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    1. COI would suggest to try first to fit the spectra consecutively in a loop. The error comes from the fact tat y_real and y_fit in res() do not have the same shape. You can fix this by transposing one of them, e.g. y_fit.T but then you run into other problems which seems to be related to the fact that you use less data points than fit parameters, but to be honest, I do not really understand your code.
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      1. POPython: 4-curve gaussian fitting with non-linear least-squares with multiple spectra
      1. USChristian K.
    2. COYou might also want to have a look at peak-o-mat (http://lorentz.sf.net) which is a peak fitting application (on top of scipy) and which can be scripted entirely. It would take just a few lines to solve your fitting task. I can tell you how, if you like.
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      1. POPython: 4-curve gaussian fitting with non-linear least-squares with multiple spectra
      1. USChristian K.
    3. COI am wondering how do you correctly tranpose y_fit in my code though?
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      1. POPython: 4-curve gaussian fitting with non-linear least-squares with multiple spectra
      1. USuser1821176
 

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