In [13]:

    

import numpy as np
import scipy as sp
import scipy.misc as spm
import matplotlib.pyplot as plt
%matplotlib inline


    

In [14]:

    

#reading in file
x=[]
y=[]
with open("1CN.txt","r") as f:
        lines=f.readlines()
        for line in lines:
            if "#" not in line:
                x.append(float(line.split()[0]))
                y.append(float(line.split()[1]))
                
xwavenumber=np.array(x)
y=np.array(y)
xeV=xwavenumber*1.23984/10000.0
plt.plot(xeV,y,"--",linewidth=2)


    

    
Out[14]:





[<matplotlib.lines.Line2D at 0x7f22cad7d110>]






    

In [15]:

    

#Constant parameters to be entered into fit
i_max=5
m_max=4
mij=np.arange(m_max)
for i in range(i_max-1):
    mij=np.vstack((mij,np.arange(m_max)))
omegai=0.001*np.arange(1,i_max+1)
n=1.1
print mij
S=np.arange(i_max)
sigma=np.ones(m_max**2)
sigma


    

    




[[0 1 2 3]
 [0 1 2 3]
 [0 1 2 3]
 [0 1 2 3]
 [0 1 2 3]]






    
Out[15]:





array([ 1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.,
        1.,  1.,  1.])

In [16]:

    

def Io(S,m):
    return np.exp(-S)*np.power(S,mij.T)#/spm.factorial(mij.T)
    
print Io(S,mij)
print spm.factorial(mij)


    

    




[[ 1.          0.36787944  0.13533528  0.04978707  0.01831564]
 [ 0.          0.36787944  0.27067057  0.14936121  0.07326256]
 [ 0.          0.36787944  0.54134113  0.44808362  0.29305022]
 [ 0.          0.36787944  1.08268227  1.34425085  1.17220089]]
[[ 1.  1.  2.  6.]
 [ 1.  1.  2.  6.]
 [ 1.  1.  2.  6.]
 [ 1.  1.  2.  6.]
 [ 1.  1.  2.  6.]]

In [17]:

    

def Gamma(omega,omega0,mij,omegai,sigma):
    #print np.sum(mij.T*omegai,axis=0)        
    return 1/(sigma*np.sqrt(2*np.pi))*np.exp((-0.5*(omega-omega0-np.sum(mij.T*omegai,axis=0))[:,np.newaxis]/sigma)**2)
    
print Gamma(0.1,0.004,mij,omegai,sigma)


    

    




[[ 0.39975096  0.39975096  0.39975096  0.39975096  0.39975096  0.39975096
   0.39975096  0.39975096  0.39975096  0.39975096  0.39975096  0.39975096
   0.39975096  0.39975096  0.39975096  0.39975096]
 [ 0.39964664  0.39964664  0.39964664  0.39964664  0.39964664  0.39964664
   0.39964664  0.39964664  0.39964664  0.39964664  0.39964664  0.39964664
   0.39964664  0.39964664  0.39964664  0.39964664]
 [ 0.39954953  0.39954953  0.39954953  0.39954953  0.39954953  0.39954953
   0.39954953  0.39954953  0.39954953  0.39954953  0.39954953  0.39954953
   0.39954953  0.39954953  0.39954953  0.39954953]
 [ 0.39945964  0.39945964  0.39945964  0.39945964  0.39945964  0.39945964
   0.39945964  0.39945964  0.39945964  0.39945964  0.39945964  0.39945964
   0.39945964  0.39945964  0.39945964  0.39945964]
 [ 0.39937697  0.39937697  0.39937697  0.39937697  0.39937697  0.39937697
   0.39937697  0.39937697  0.39937697  0.39937697  0.39937697  0.39937697
   0.39937697  0.39937697  0.39937697  0.39937697]]

In [17]:

    




    

In [17]: