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plotLDOSAndDelta_all.py
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# -*- encoding: utf-8 -*-
## @package TBTKview
# @file plotLDOS.py
# @brief Plot local density of states
#
# @author Kristofer Björnson
import h5py
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.axes
import matplotlib.cm
import scipy.ndimage.filters
import mpl_toolkits.mplot3d
import sys
from scipy.signal import find_peaks
import os
def findpeakdiscrete(input_array, peak_nr):
threshold = 0.1
counter = 0
length = len(input_array)
for i in range(int(length/2), length):
if input_array[i] > threshold:
counter +=1
if counter == peak_nr:
return i
raise Exception("No peak found")
#if(len(sys.argv) != 2):
# print( "Error, the following parameters are needed: .hdf5-file")
# exit(1)
filenamebase = "vz_"
#results_dir = ""
#extension = "_diag_size21.hdf5"
#figure_extension = "_diag"
results_dir = "" # "kebnekaiseResults/"
extension = "_diag_size21.hdf5" #"_chebychev_GPUsize20.hdf5"
figure_extension = "_diagDelta0_1"
sigma = 0.0001 #0.002
peak_height = 7.5
delta_at_imp = []
vz_points = []
text_pos = [1,1]
for vz in np.arange(0.0, 10.1, 0.0025):
vz_format = "{:.6f}".format(vz)
fName = filenamebase + vz_format + extension
if os.path.exists(results_dir + fName):
vz_points.append(vz)
filename = results_dir + fName
file = h5py.File(filename, 'r');
dataset = file['LDOS']
vz_format += figure_extension
eigenvals = file["EigenValues"][:]
positive_energies = np.sort(abs(eigenvals))
Eg = positive_energies[2]
data_dimensions = dataset.shape
y_plane = int(np.floor(data_dimensions[1]/2))
physical_dimensions = len(data_dimensions) - 1 #Last dimensions are for energy.
energy_resolution = data_dimensions[physical_dimensions];
limits = dataset.attrs['UpLowLimits']
datasetDeltaReal = file['deltaReal0']
datasetDeltaImag = file['deltaImag0']
delta = np.array(datasetDeltaReal)
size_x = data_dimensions[0]
size_y = data_dimensions[1]
x = np.arange(0, data_dimensions[0], 1)
y = np.arange(limits[1], limits[0], (limits[0] - limits[1])/energy_resolution)
energies = y
X, Y = np.meshgrid(x, y)
fig = matplotlib.pyplot.figure()
Z = dataset[:,y_plane,:]
sigma_discrete_units = sigma*energy_resolution/(limits[0] - limits[1])
for xp in range(0, size_x):
Z[xp,:] = scipy.ndimage.filters.gaussian_filter1d(Z[xp,:], sigma_discrete_units)
#Color map figure
ax = fig.gca()
im = ax.pcolormesh(X.transpose(), Y.transpose(), Z, cmap=matplotlib.cm.coolwarm)
plt.ylim([-0.2, 0.2])
fig.colorbar(im)
fig.savefig('figures/LDOS_vz_x_' + vz_format + '.png')
fig = matplotlib.pyplot.figure()
Z = np.zeros_like(Z)
for i in range(size_x):
Z[i, :] = dataset[i,i,:]
sigma_discrete_units = sigma*energy_resolution/(limits[0] - limits[1])
for xp in range(0, size_x):
Z[xp,:] = scipy.ndimage.filters.gaussian_filter1d(Z[xp,:], sigma_discrete_units)
#Color map figure
ax = fig.gca()
im = ax.pcolormesh(X.transpose(), Y.transpose(), Z, cmap=matplotlib.cm.coolwarm)
plt.ylim([-0.2, 0.2])
fig.colorbar(im)
fig.savefig('figures/LDOS_vz_xy_' + vz_format + '.png')
# Find peaks in the LDOS (Eg):
Eg = np.zeros_like(delta)
ratio = np.zeros_like(delta)
if vz != 0:
signal = dataset[y_plane, y_plane, :]
ysr_peak = findpeakdiscrete(signal, 1)
# print(ysr_peak)
else:
ysr_peak = 0
for i in range(data_dimensions[0]):
for j in range(data_dimensions[1]):
signal = dataset[i, j, : ]
peak_found = False
# print(signal)
for peak_counter in range(1,10):
peaks2 = findpeakdiscrete(signal, peak_counter)
if peaks2 != ysr_peak:
peak_found = True
if(peak_counter > 2):
print('oi')
break
if not peak_found:
raise Exception("peak not found")
# signal = scipy.ndimage.filters.gaussian_filter1d(signal, sigma_discrete_units)
# peaks, _ = find_peaks(signal) #, height=peak_height)
# print(energies[peaks[-2]], energies[peaks2])
Eg[i,j] = energies[peaks2]#energies[peaks[-2]]
ratio[i,j] = Eg[i,j]/delta[i,j]
sigma = 0.002
sigma_discrete_units = sigma*energy_resolution/(limits[0] - limits[1])
Z1 = dataset[y_plane, y_plane, :]
signal = Z1[: int(data_dimensions[2]/2)]
Z2 = dataset[0, 0, :]
plt.figure()
# Z1 = scipy.ndimage.filters.gaussian_filter1d(Z1, sigma_discrete_units)
# peaks, _ = find_peaks(signal, height=peak_height)
# Z2 = scipy.ndimage.filters.gaussian_filter1d(Z2, sigma_discrete_units)
plt.plot(y, Z2, '--', label="Bulk")
plt.plot(y, Z1, label=r'$x_0$')
# plt.plot(y[peaks[-1]], Z1[peaks[-1]], 'x')
#
lim = np.max(Z1)*1.2
# plt.vlines([Eg[y_plane], -Eg[y_plane]], 0, lim)
plt.xlim([-0.2, 0.2])
plt.legend()
plt.xlabel(r'$E$')
plt.ylabel(r'LDOS')
plt.savefig('figures/LDOS_middle_vz_' + vz_format + '.png')
plt.ylim([-0.01*lim, 0.05*lim ])
plt.savefig('figures/LDOS_middle_zoom_vz_' + vz_format + '.png')
plt.close()
fig, ax = plt.subplots()
x = np.arange(0, data_dimensions[0], 1)
y = np.arange(0, data_dimensions[1], 1)
X, Y = np.meshgrid(x, y)
Z = delta
cs = ax.contourf(X.transpose(), Y.transpose(), Z) #, cmap=matplotlib.cm.coolwarm)
ax.contour(cs, colors='k', linewidths=0.2)
ax.set_xlabel(r'$x$')
ax.set_ylabel(r'$y$')
cbar = fig.colorbar(cs)
cbar.ax.set_ylabel(r'$\Delta_{i}$')
ax.text(text_pos[0], text_pos[1], r'$V_Z =$ ' + str(vz))
fig.savefig("figures/delta_vz_" + vz_format + ".png")
plt.close()
plt.figure()
plt.plot(x, delta[:,y_plane])
plt.savefig("figures/delta_profile_x_vz_" + vz_format + ".png")
plt.close()
plt.figure()
delta_xy = []
for i in range(len(x)):
delta_xy.append(delta[i,i])
plt.plot(x, delta_xy)
plt.savefig("figures/delta_profile_xy_vz_" + vz_format + ".png")
plt.close()
delta_at_imp.append(delta[y_plane, y_plane])
fig, ax = plt.subplots()
x = np.arange(0, data_dimensions[0], 1)
y = np.arange(0, data_dimensions[1], 1)
X, Y = np.meshgrid(x, y)
Z = Eg
cs = ax.contourf(X.transpose(), Y.transpose(), Z) #, cmap=matplotlib.cm.coolwarm)
ax.contour(cs, colors='k', linewidths=0.2)
ax.set_xlabel(r'$x$')
ax.set_ylabel(r'$y$')
cbar = fig.colorbar(cs)
cbar.ax.set_ylabel(r'$E_{g}$')
ax.text(text_pos[0], text_pos[1], r'$V_Z =$ ' + str(vz))
fig.savefig("figures/Eg_vz_" + vz_format + ".png")
plt.close()
plt.figure()
Eg_xy = []
for i in range(len(x)):
Eg_xy.append(Eg[i,i])
plt.plot(x, Eg_xy)
plt.savefig("figures/Eg_xy_vz_" + vz_format + ".png")
plt.close()
plt.figure()
plt.plot(x, Eg[:,y_plane])
plt.savefig("figures/Eg_x_vz_" + vz_format + ".png")
plt.close()
fig, ax = plt.subplots()
x = np.arange(0, data_dimensions[0], 1)
y = np.arange(0, data_dimensions[1], 1)
X, Y = np.meshgrid(x, y)
Z = ratio
cs = ax.contourf(X.transpose(), Y.transpose(), Z) #, cmap=matplotlib.cm.coolwarm)
ax.contour(cs, colors='k', linewidths=0.2)
ax.set_xlabel(r'$x$')
ax.set_ylabel(r'$y$')
cbar = fig.colorbar(cs)
cbar.ax.set_ylabel(r'$\frac{E_{g}}{\Delta}$')
ax.text(text_pos[0], text_pos[1], r'$V_Z =$ ' + str(vz))
fig.savefig("figures/ration_vz_" + vz_format + ".png")
plt.close()
plt.figure()
ratio_xy = []
for i in range(len(x)):
ratio_xy.append(Eg[i,i])
plt.plot(x, ratio_xy)
plt.savefig("figures/ratio_xy_vz_" + vz_format + ".png")
plt.close()
plt.figure()
plt.plot(x, ratio[:,y_plane])
plt.savefig("figures/ratio_x_vz_" + vz_format + ".png")
plt.close()
plt.figure()
plt.plot(vz_points, delta_at_imp)
plt.savefig("figures/deltaAtImp.png")
plt.close()