python - Scipy curvefit RuntimeError:Optimal parameters not found: Number of calls to function has reached maxfev = 1000 -
i want make logharitmic fit. keep getting runtime error:
optimal parameters not found: number of calls function has reached maxfev = 1000
i use following script. can tell me go wrong? use spyder still beginner.
import math import matplotlib mpl scipy.optimize import curve_fit import numpy np #data f1=[735.0,696.0,690.0,683.0,680.0,678.0,679.0,675.0,671.0,669.0,668.0,664.0,664.0] t1=[1,90000.0,178200.0,421200.0,505800.0,592200.0,768600.0,1036800.0,1371600.0,1630800.0,1715400.0,2345400.0,2409012.0] f1n=np.array(f1) t1n=np.array(t1) plt.plot(t1,f1,'ro',label="original data") # curvefit def func(t,a,b): return a+b*np.log(t) t=np.linspace(0,3600*24*28,13) popt, pcov = curve_fit(func, t, f1n, maxfev=1000) plt.plot(t, func(t, *popt), label="fitted curve") plt.legend(loc='upper left') plt.show()
your original data t1 , f1. therefore curve_fit should given t1 second argument, not t.
popt, pcov = curve_fit(func, t1, f1, maxfev=1000) now once obtain fitted parameters, popt, can evaluate func @ points in t obtain fitted curve:
t = np.linspace(1, 3600 * 24 * 28, 13) plt.plot(t, func(t, *popt), label="fitted curve") (i removed 0 t (per stugrey's answer) avoid warning: divide 0 encountered in log.)
import matplotlib.pyplot plt import scipy.optimize optimize import numpy np # data f1 = np.array([ 735.0, 696.0, 690.0, 683.0, 680.0, 678.0, 679.0, 675.0, 671.0, 669.0, 668.0, 664.0, 664.0]) t1 = np.array([ 1, 90000.0, 178200.0, 421200.0, 505800.0, 592200.0, 768600.0, 1036800.0, 1371600.0, 1630800.0, 1715400.0, 2345400.0, 2409012.0]) plt.plot(t1, f1, 'ro', label="original data") # curvefit def func(t, a, b): return + b * np.log(t) popt, pcov = optimize.curve_fit(func, t1, f1, maxfev=1000) t = np.linspace(1, 3600 * 24 * 28, 13) plt.plot(t, func(t, *popt), label="fitted curve") plt.legend(loc='upper left') plt.show() 
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