exciton-FMO-.py

# import numpy as np
# import math
# from openpyxl import Workbook
# from openpyxl import load_workbook
# from openpyxl.chart import (ScatterChart, Reference, Series)
import matplotlib.pyplot as plt
import numpy as np

from ClassExciton import Exciton


nam = "HamiltonBrixner"
N = 8
# nam = "HamiltonKell"; N=9

ex = np.ndarray(N, Exciton)
namin = nam + ".xlsx"
for i in range(0, N):  # knockout pigment i, i=0 - full hamiltonian
    namout = nam + str(i) + ".xlsx"
    ex[i] = Exciton(namin, namout, i)  # knockout pigment i
    ex[i].bandwidth = 200  # overwrite spectral bandwidth before calculating
    ex[i].calculate()
    if i == 0:
        x = np.zeros(len(ex[0].x))
        x = 1.0e7 / ex[0].x  # convert wavenumbers into nanometers
    namabs = (
        nam + "ABS" + str(i) + ".xy"
    )  # names of xy files for spectra, easier to load into SpectraSolve
    namcd = nam + "CD" + str(i) + ".xy"
    file_abs = open(namabs, "w")
    file_cd = open(namcd, "w")
    for j in range(0, len(x)):
        file_abs.write("%g,%g\r\n" % (x[j], ex[i].abs[j]))
        file_cd.write("%g,%g\r\n" % (x[j], ex[i].CD[j]))
    file_abs.close()
    file_cd.close()

absd = np.zeros((N, len(x)))
cdd = np.zeros((N, len(x)))
absd[0] = ex[0].abs
cdd[0] = ex[0].CD
# make difference spectra for knockout pigments
for i in range(1, N):
    absd[i] = ex[i].abs - ex[0].abs
    cdd[i] = ex[i].CD - ex[0].CD

# next will plot whatever we set a and c to be (combinations of knockouts, for example)
a = absd[2] + 0.0 * absd[2]
c = cdd[2] - 0.0 * cdd[2]
# ex.wb.save('test.xlsx')
fig, (ax1, ax2) = plt.subplots(2, 1, sharex=True)
ax1.plot(x, a)
ax2.plot(x, c)

ax1.set(xlabel="", ylabel="absorbance", title="Exciton spectrum")
ax2.set(xlabel="wavenumers", ylabel="CD")
ax1.grid()
ax2.grid()

# fig.savefig("test.png")
plt.show()