hyperbola.py

import matplotlib.pyplot as plt
from scipy.optimize import curve_fit
import numpy as np
import sys
import math

def exp_func(x, *p):
    A = p
    return A/x


if len(sys.argv) != 4:
    print "Usage is: python process.py [file to be processed] [start] [end]"
    exit()
    
fname = sys.argv[1]
start = int(sys.argv[2])
end = int(sys.argv[3])

hist_temp= np.loadtxt(fname, dtype='uint16', usecols=[3], delimiter=',')

a_max = np.amax(hist_temp)
print 'max = ', a_max
hist, bin_edges = np.histogram( hist_temp, bins=np.arange(1,a_max+2) )

bin_centers = (bin_edges[:-1] + bin_edges[1:])/2

p0 = [100.]
coeff, var_matrix = curve_fit(exp_func, bin_centers[start:end], hist[start:end], p0=p0)

print 'Fitted Amplitude = ', coeff[0]

hist_fit = exp_func(bin_centers, *coeff)

error = math.sqrt(np.mean((hist_fit - hist)**2))

print np.mean((hist_fit - hist)**2)/hist_fit.size
print 'RMSE = ', error

plt.plot(bin_centers, hist, label='Test data')
plt.plot(bin_centers, hist_fit, label='Fitted data')
plt.show()