Python - curve_fit of Plancks Law from data set

I have a large set of data that in terms of Intensity counts and Wavelength that I want fit with Planks Law to determine the guess parameter for Temperature. The Data set is imported as a text file import numpy as np import scipy as sp from scipy.optimize import curve_fit with open('Good_SolarRun2.txt') as g: data = g.read() data = data.split('n') Wavle2 = [float(row.split()[0]) for row in data] # Wavelength (nm) Int2 = [float(row.split()[1]) for row in data] # Intensity (counts) So I now define the fitting model for Planks Law (In terms of wavelength) https://en.wikipedia.org/wiki/Planck%27s_law from scipy.constants import h,k,c def Plancks_Law(lamb, T): a = np.float32( 2.0*h*c*c ) # J m^{-2} s^{-3} b = np.float32( (h*c)/(k) ) # K m s^{-2} return a/np.power(lamb,5.0) * 1/( np.exp( b/(lamb*T)) - 1 ) So now, I go about setting up the curve_fit configuration with my data set. # Convert Wavelength Arrays from nano-meters to meters x = [data*1e-9 for data in Wavle2] # This has the same shape as Wavle2, but these values are scaled by me. # Also the same shape as Int2 y = np.array(Intscale2p) p0_R = (5000.) optR, pcovR = curve_fit(Plancks_Law, x, y, p0_R) T_R = optR T_Rp = pcovR yM = Plancks_Law(x, T_R) This is not working for me. The parameter returns T_R = 5000, which is the value is et for the best guess. Am I doing something incorrect with the fits?