How can I better fit double Gauss? I care about small y-data. I need to very accurate result.
import pandas as pd
import numpy as np
import matplotlib
import matplotlib.pyplot as plt
from sklearn import mixture
from pylab import *
import matplotlib.mlab
from scipy.optimize import leastsq
data2 = pd.read_csv('Downloads/dose_r2.dat',names=
['Promien','Dawka','odchylenie'],sep=' ')
x = data2["Promien"]
y = data2["Dawka"]
%matplotlib inline
def double_gaussian( x, params ):
(c1, mu1, sigma1, c2, mu2, sigma2) = params
res = c1 * np.exp( - (x - mu1)**2.0 / (2.0 * sigma1**2.0) ) \
+ c2 * np.exp( - (x - mu2)**2.0 / (2.0 * sigma2**2.0) )
return res
def double_gaussian_fit( params ):
fit = double_gaussian( x, params )
return (fit - y)
# Least squares fit. Starting values found by inspection.
fit = leastsq( double_gaussian_fit, [1.5,0,0.7,0.00005,10.5,3.0] )
plt.scatter( x, y,s=0.7)
plt.xlabel("zasieg [cm]")
plt.ylabel("dawka [Gy]")
plt.grid(True)
#plt.yscale('log')
#plt.ylim(((0,0.0004)))
plot( x, double_gaussian( x, fit[0] ) )
Gauss from afar looks great, but when I enlargements ...
