git.net

[Date Prev][Date Next][Thread Prev][Thread Next][Date Index][Thread Index]# Levenberg-Marquardt non-linear least-squares fitting in Python [follow-on]

[Date Prev][Date Next][Thread Prev][Thread Next][Date Index][Thread Index]

Below I?ve included the code I ran, reasonably (I think) commented. Note the reference to the example. The data actually came from a pandas data frame that was in turn filled from a 100 MB data file that included lots of other data not needed for this, which was a curve fit to a calibration run. Bill PS: If you want, I can probably still find a couple of the plots of the raw data and fitted result. --------------------- import numpy as np, matplotlib.pyplot as plt # # Inverted exponential that axymptotically approaches "a" as x gets large # def func2fit(x,a,b,c): return a - b * np.exp(-c * x) # Curve fitting below from: https://docs.scipy.org/doc/scipy/reference/generated/scipy.optimize.curve_fit.html from scipy.optimize import curve_fit def fit(xdata, ydata, run_num): ll = len(xdata) # # The next four lines shift and scale the data so that the curve fit routine can # do its work without needing to use 8 or 16-byte precision. After fitting, we # will scale everything back. # ltemp = [ydata[i] - ydata[0] for i in range(ll)] ytemp = [ltemp[i] * .001 for i in range(ll)] ltemp = [xdata[i] - xdata[0] for i in range(ll)] xtemp = [ltemp[i] * .001 for i in range(ll)] # # popt is a list of the three optimized fittine parameters [a, b, c] # we are interested in the value of a. # cov is the 3 x 3 covariance matrix, the standard deviation (error) of the fit is # the square root of the diagonal. # popt,cov = curve_fit(func2fit, xtemp, ytemp) # # Here is what the fitted line looks like for plotting # fitted = [popt[0] - popt[1] * np.exp(-popt[2] * xtemp[i]) for i in range(ll)] # # And now plot the results to check the fit # fig1, ax1 = plt.subplots() plt.title('Normalized Data ' + str(run_num)) color_dic = {0: "red", 1: "green", 2: "blue", 3: "red", 4: "green", 5: "blue"} ax1.plot(xtemp, ytemp, marker = '.', linestyle = 'none', color = color_dic[run_num]) ax1.plot(xtemp, fitted, linestyle = '-', color = color_dic[run_num]) plt.savefig('Normalized ' + str(run_num)) perr = np.sqrt(np.diag(cov)) return popt, cov, xdata[0], ydata[0], fitted, perr[0]

- Prev by Date:
**Getting file extensions [linux fs]** - Next by Date:
**Prepare accented characters for HTML** - Previous by thread:
**Handy utilities = Friday Filosofical Finking** - Next by thread:
**Levenberg-Marquardt non-linear least-squares fitting in Python [follow-on]** - Index(es):