I need to iterate through the elements in a numpy array so I can treat any zero elements separately. The code below works for straighforward evaluations, but not when used with scipy.optimize.curve_fit(). Is there a way to make this work with the curve_fit fn?
import numpy as np
from matplotlib.pyplot import *
from scipy.optimize import curve_fit
def my_fn(x_array, b, a):
y = []
for x in np.nditer(x_array): #This doesn't work with curve_fit()
if x == 0:
y.append(0)
else:
y.append(b*(1/np.tanh(x/a) - a/x))
return np.array(y)
x_meas = [0, 5, 20, 50, 100, 200, 600]
y_meas = [0, 0.275, 1.22, 1.64, 1.77, 1.84, 1.9]
xfit = np.linspace(0,600,601)
yfit2 = my_fn(xfit, 1.95, 8.2) #manual fit
#Not working
#popt, pcov = curve_fit(my_fn, x_meas, y_meas, p0=[1.95, 8.2])
#yfit1 = my_fn(xfit, *popt) #auto fit
figure(1)
plot(x_meas, y_meas, 'o', xfit, yfit2)
show()
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