After great help from @Brenlla the code was modified to: def sigmoid(x, L ,x0, k, b): y = L / (1 + np.exp(-k*(x-x0))) + b return (y) p0 = [max(ydata), np.median(xdata),1,min(ydata)] # this is an mandatory initial guess popt, pcov = curve_fit(sigmoid, xdata, ydata,p0, method=’dogbox’) The parameters optimized are L, x0, k, b, who are … Read more
Thanks David, I converted your pseudocode to the following C# code: public static void CalcParabolaVertex(int x1, int y1, int x2, int y2, int x3, int y3, out double xv, out double yv) { double denom = (x1 – x2) * (x1 – x3) * (x2 – x3); double A = (x3 * (y2 – y1) … Read more
Instead of plotting datenums, use the associated datetimes. import numpy as np import matplotlib.pyplot as plt import matplotlib.dates as mdates import datetime as DT import time dates = [DT.datetime(1978, 7, 7), DT.datetime(1980, 9, 26), DT.datetime(1983, 8, 1), DT.datetime(1985, 8, 8)] y = [0.00134328779552718, 0.00155187668863844, 0.0039431374327427, 0.00780037563783297] yerr = [0.0000137547160254577, 0.0000225670232594083, 0.000105623642510075, 0.00011343121508] x = mdates.date2num(dates) … Read more
Take a look at this answer for fitting arbitrary curves to data. Basically you can use scipy.optimize.curve_fit to fit any function you want to your data. The code below shows how you can fit a Gaussian to some random data (credit to this SciPy-User mailing list post). import numpy from scipy.optimize import curve_fit import matplotlib.pyplot … Read more
I think you’re most of the way there. You need to put the data sets into an array or structure that can be used in a single, global objective function that you give to minimize() and fits all data sets with a single set of Parameters for all the data sets. You can share this … Read more
Scipy doesn’t produce multiple lines, the strange output is caused by the way you present your unsorted data to matplotlib. Sort your x-values and you get the desired output: from scipy.optimize import curve_fit from matplotlib import pyplot as plt def func(x, a, b, c): return a * x** b + c # my data pred_data … Read more
Unfortunately, np.polynomial.polynomial.polyfit returns the coefficients in the opposite order of that for np.polyfit and np.polyval (or, as you used np.poly1d). To illustrate: In [40]: np.polynomial.polynomial.polyfit(x, y, 4) Out[40]: array([ 84.29340848, -100.53595376, 44.83281408, -8.85931101, 0.65459882]) In [41]: np.polyfit(x, y, 4) Out[41]: array([ 0.65459882, -8.859311 , 44.83281407, -100.53595375, 84.29340846]) In general: np.polynomial.polynomial.polyfit returns coefficients [A, B, C] … Read more
You can define your own residuals function, including a penalization parameter, like detailed in the code below, where it is known beforehand that the integral along the interval must be 2.. If you test without the penalization you will see that what your are getting is the conventional curve_fit: import matplotlib.pyplot as plt import scipy … Read more