Read hessian matrix from VASP output file and calculate Eigenvalues and Eigenvectors

Read_hessian_from_vasp.py

#!/usr/bin/env python3

import numpy as np
import os, sys, subprocess
from scipy import linalg as LA
import matplotlib.pyplot as plt

''' The complication that arises by parsing the data from the file is 
the trailing empty lines and tabs that need to be deleted before it 
can be read. In the case of a simple file format, there is no need for 
it. But if the file contains irregular data entry such as empty lines 
with commas and spaces then it needs to be formatted. 
In my case, I have only leading empty lines and spaces and in between 
columns there are white spaces of different sizes.
'''

filename = sys.argv[1]
#########################################################################
#os.system(' grep "\S" input.txt')
#os.system(" sed -e '/^\s*$/d' input.txt ")
#os.system(" sed -i '/^[[:space:]]*$/d' $filename ")
subprocess.call(['sed','-i','/^[[:space:]]*$/d',sys.argv[1]], shell = False)
#########################################################################

#---------------------------------'''METHOD 1'''  

#a = np.loadtxt("new.dat")
#print(a)

#---------------------------------'''METHOD 2'''  

a = [] ; l=0
f = open(filename, 'r')

for line in f.readlines():
	a.append([])
	print (line[0])
	if (line[0] == '#'):
		continue
	for i in line.strip().split():
		a[-1].append(float(i))
	l+=1		
#print (a)

f.close()
#------------------------------------------------------------------------
#a = np.random.random((900, 900))	
s = np.shape(a); print('size of a Matrix', s)
a = np.matrix(a)
#print(a)

print ('--------------------------------EIG VAL AND EIG VECTORS ARE:')

w, v = LA.eigh(a)

print (w);
print (v)