In [10]:

    

import numpy as np
import matplotlib.pyplot as plt
import scipy.special as sps
shape, scale = 5., 12.  # mean=4, std=2*sqrt(2)
s1 = np.random.gamma(shape, scale, 1000)
s2 = np.random.gamma(shape*2, scale,1000)
count, bins, ignored = plt.hist(s1, 50)#, normed=True)
plt.hist(s2,50)
plt.hist(s1+s2,50)
#y = bins**(shape-1)*(np.exp(-bins/scale) / (sps.gamma(shape)*scale**shape))
#plt.plot(bins, y, linewidth=2, color='r')
plt.show()


    

In [27]:

    

import pandas


    

In [31]:

    

df = pandas.read_csv('C:/Users/sysl1_1704a/Documents/2017HISOL/gamma_out.txt',header=None)


    

In [44]:

    

plt.figure()
plt.show()
df.plot.hist(bins=150)
plt.show()


    

    






<matplotlib.figure.Figure at 0xba57da0>






    

In [45]:

    

df.min()


    

    
Out[45]:





0    1.834676
dtype: float64

In [46]:

    

df.max()


    

    
Out[46]:





0    871.50397
dtype: float64

In [19]:

    

y = bins**(shape-1)*(np.exp(-bins/scale) / (sps.gamma(shape)*scale**shape))
plt.plot(bins, y, linewidth=2, color='r')
shape2 = shape * 3
y2 = bins**(shape2-1)*(np.exp(-bins/scale) / (sps.gamma(shape2)*scale**shape2))
plt.plot(bins, y2, linewidth=1, color='b')
plt.plot(bins, y + y2, color='g')
plt.show()


    

In [ ]: