plot_eigenvector_im.py

import sys
import numpy as np
import matplotlib.pyplot as plt

filename = sys.argv[1]
with open(filename, 'rt') as f:
	dim = str(sys.argv[2])
	EV = 0	
	if sys.argv[3]:	
		EV = int(sys.argv[3])	
	dim_list = ['p', 'v1', 'v2', 'v3', 'T', 'A1', 'A2', 'A3']
	dataset = [[str(entry).strip() for entry in line.split()] for line in f.readlines()]
	[NEV, NR, MANZ, MDIF] = [int(str(entry).strip()) for entry in dataset[0]]
	dataset.pop(0);
	NEV_list = [dataset[i:i+NR] for i in range(0, len(dataset), NR)]
	NEV_list = [[[NRx[i:i+16] for i in range(0, len(NRx), 16)] for NRx in N] for N in NEV_list]
	NEV_list = [[[[entry[i:i+2] for i in range(0, len(entry), 2)] for entry in NRx] for NRx in N] for N in NEV_list]
	NEV_list = [[[[[float(x) for x in re_im] for re_im in dims] for dims in M] for M in N] for N in NEV_list]
	# Now we have a dataset of the form [re,im] for each [p,v1,v2,...] for each [1,...,MANZ] for each [1,...,NR] for each [1,...,NEV]
	# As a nested list	
	var = [[NEV_list[EV][i][M][dim_list.index(dim)][1] for i in range(0, len(NEV_list[EV]))] for M in range(0,MANZ,1)]
	fig, ax = plt.subplots()
	for N in range(0,len(var)):	
		plt.plot(np.linspace(0, 1, num=len(var[N])), var[N], label='M='+str(N+startingharmonic-np.floor((len(var)-1)/2)), marker=".", markersize=3, ls="")
	plt.xlabel('S')
	plt.ylabel('{}'.format(dim))
	plt.title('Im {} component for eigenvalue {}'.format(dim, EV))
	plt.legend(loc='best')
	plt.autoscale(enable=True, axis="y", tight = False)
	#plt.ylim(-0.0005, 0.0015)
	plt.show()