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plotDeltaVsNf.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
import scipy.interpolate as interpolate
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_sc = "_diag_size21.hdf5"
extension_normal = "_diag_size21_normalState.hdf5"
figure_extension = "_diagDelta0_1_normalState"
sigma = 0.2
peak_height = 7.5
delta_bulk = 0.12
delta_at_imp = []
vz_points = []
text_pos = [1,1]
interesting_vz_values = [0, 0.5, 1.0, 1.5, 1.75, 2.0]#[0, 0.5, 0.75, 1.0, 1.25, 1.5, 1.75, 2.0]
#interesting_vz_values = np.arange(0.0, 10.1, 0.0625)
delta_fig_x, delta_ax_x = plt.subplots()
delta_fig_xy, delta_ax_xy = plt.subplots()
ldos_normal_fig, ldos_normal_ax = plt.subplots()
for vz in np.arange(0.0, 10.1, 0.0625):
vz_format = "{:.6f}".format(vz)
fName_sc = filenamebase + vz_format + extension_sc
fName_normal = filenamebase + vz_format + extension_normal
if os.path.exists(results_dir + fName_normal) and os.path.exists(results_dir + fName_sc) and \
vz in interesting_vz_values:
vz_points.append(vz)
filename_sc = results_dir + fName_sc
filename_normal = results_dir + fName_normal
file_sc = h5py.File(filename_sc, 'r');
file_normal = h5py.File(filename_normal, 'r');
dataset_sc = file_sc['LDOS']
dataset_normal = file_normal['LDOS']
dataset_charge_spin0 = file_normal['chargeDensity_spin_0Real']
dataset_charge_spin1 = file_normal['chargeDensity_spin_1Real']
dataset_charge_spin2 = file_normal['chargeDensity_spin_2Real']
dataset_charge_spin3 = file_normal['chargeDensity_spin_3Real']
vz_format += figure_extension
data_dimensions = dataset_sc.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_sc.attrs['UpLowLimits']
datasetDeltaReal = file_sc['deltaReal0']
datasetDeltaImag = file_sc['deltaImag0']
delta = np.array(datasetDeltaReal)
size_x = data_dimensions[0]
size_y = data_dimensions[1]
sigma_discrete_units = sigma*energy_resolution/(limits[0] - limits[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, x)
energy_range = delta_bulk *1.0
middle_energies = np.logical_and(energies>=-energy_range, energies<=energy_range)
dos_fermi = np.zeros((size_x, size_x))
for x_pos in x:
for y_pos in x:
dos_fermi[x_pos, y_pos] = np.sum(dataset_normal[x_pos, y_pos, :][middle_energies])/np.sum(middle_energies)
dos_fermi_normal_fig, dos_fermi_normal_ax = plt.subplots()
cs = dos_fermi_normal_ax.contourf(X.transpose(), Y.transpose(), dos_fermi) #, cmap=matplotlib.cm.coolwarm)
dos_fermi_normal_ax.contour(cs, colors='k', linewidths=0.2)
cbar = dos_fermi_normal_fig.colorbar(cs)
cbar.ax.set_ylabel(r'$N_0$')
dos_fermi_normal_ax.set_xlabel(r'$x$')
dos_fermi_normal_ax.set_ylabel(r'$y$')
dos_fermi_normal_ax.text(text_pos[0], text_pos[1], r'$V_Z =$ ' + str(vz))
dos_fermi_normal_fig.savefig("figures/dos" + vz_format + ".png")
plt.figure()
dos_xy = []
for i in range(len(x)):
dos_xy.append(dos_fermi[i,i])
plt.plot(x, dos_xy)
plt.savefig("figures/dos_xy_vz_" + vz_format + ".png")
plt.close()
plt.figure()
plt.plot(x, dos_fermi[:,y_plane])
plt.savefig("figures/dos_x_vz_" + vz_format + ".png")
plt.close()
ratio = delta/dos_fermi
ratio_fig, ratio_ax = plt.subplots()
cs = ratio_ax.contourf(X.transpose(), Y.transpose(), ratio) #, cmap=matplotlib.cm.coolwarm)
ratio_ax.contour(cs, colors='k', linewidths=0.2)
cbar = ratio_fig.colorbar(cs)
cbar.ax.set_ylabel(r'$\frac{\Delta}{N_0}$')
ratio_ax.set_xlabel(r'$x$')
ratio_ax.set_ylabel(r'$y$')
ratio_ax.text(text_pos[0], text_pos[1], r'$V_Z =$ ' + str(vz))
ratio_fig.savefig("figures/ratio" + vz_format + ".png")
charge_density = np.array(dataset_charge_spin0) + np.array(dataset_charge_spin1)
charge_fermi_normal_fig, charge_fermi_normal_ax = plt.subplots()
cs = charge_fermi_normal_ax.contourf(X.transpose(), Y.transpose(), charge_density) #, cmap=matplotlib.cm.coolwarm)
charge_fermi_normal_ax.contour(cs, colors='k', linewidths=0.2)
cbar = charge_fermi_normal_fig.colorbar(cs)
cbar.ax.set_ylabel(r'$\rho_{el}$')
charge_fermi_normal_ax.set_xlabel(r'$x$')
charge_fermi_normal_ax.set_ylabel(r'$y$')
charge_fermi_normal_ax.text(text_pos[0], text_pos[1], r'$V_Z =$ ' + str(vz))
charge_fermi_normal_fig.savefig("figures/charge" + vz_format + ".png")
charge_fermi_normal_xy_fig, charge_fermi_xy_normal_ax = plt.subplots()
charge_xy = []
delta_xy = []
for i in range(len(x)):
charge_xy.append(charge_density[i,i])
delta_xy.append(delta[i,i])
charge_xy = np.array(charge_xy)
delta_xy = np.array(delta_xy)
charge_fermi_xy_normal_ax.plot(x, charge_xy, label=r'$\rho_{el}$')
charge_fermi_xy_normal_ax.set_xlabel(r'$xy$')
charge_fermi_xy_normal_ax.set_ylabel(r'$\rho_{el}$')
delta_xy_ax = charge_fermi_xy_normal_ax.twinx()
delta_xy_ax.plot(x, delta_xy, color="r", label=r'$\Delta$')
delta_xy_ax.set_ylabel(r'$\Delta$')
ratio_xy_ax = charge_fermi_xy_normal_ax.twinx()
ratio_xy_ax.plot(x, delta_xy/charge_xy, color="k", label=r'$\frac{\Delta}{\rho_{el}}$')
ratio_xy_ax.set_ylabel(r'$\frac{\Delta}{\rho_{el}}$')
prod_xy_ax = charge_fermi_xy_normal_ax.twinx()
prod_xy_ax.plot(x, delta_xy*charge_xy, color="g", label=r'$\Delta \rho_{el}$')
prod_xy_ax.set_ylabel(r'$\Delta \rho_{el}$')
charge_fermi_normal_xy_fig.legend()
charge_fermi_normal_xy_fig.savefig("figures/charge_xy_vz_" + vz_format + ".png")
charge_fermi_normal_x_fig, charge_fermi_x_normal_ax = plt.subplots()
charge_fermi_x_normal_ax.plot(x, charge_density[y_plane,:])
charge_fermi_x_normal_ax.set_xlabel(r'$x$')
charge_fermi_x_normal_ax.set_ylabel(r'$\rho_{el}$')
charge_fermi_normal_x_fig.savefig("figures/charge_x_vz_" + vz_format + ".png")
ratio = delta/dos_fermi
ratio_fig, ratio_ax = plt.subplots()
cs = ratio_ax.contourf(X.transpose(), Y.transpose(), ratio) #, cmap=matplotlib.cm.coolwarm)
ratio_ax.contour(cs, colors='k', linewidths=0.2)
cbar = ratio_fig.colorbar(cs)
cbar.ax.set_ylabel(r'$\frac{\Delta}{N_0}$')
ratio_ax.set_xlabel(r'$x$')
ratio_ax.set_ylabel(r'$y$')
ratio_ax.text(text_pos[0], text_pos[1], r'$V_Z =$ ' + str(vz))
ratio_fig.savefig("figures/ratio" + vz_format + ".png")
product = delta*dos_fermi
product_fig, product_ax = plt.subplots()
cs = product_ax.contourf(X.transpose(), Y.transpose(), product) #, cmap=matplotlib.cm.coolwarm)
product_ax.contour(cs, colors='k', linewidths=0.2)
cbar = product_fig.colorbar(cs)
cbar.ax.set_ylabel(r'$\frac{\Delta}{N_0}$')
product_ax.set_xlabel(r'$x$')
product_ax.set_ylabel(r'$y$')
product_ax.text(text_pos[0], text_pos[1], r'$V_Z =$ ' + str(vz))
product_fig.savefig("figures/product" + vz_format + ".png")
ratio = delta/charge_density
ratio_fig, ratio_ax = plt.subplots()
cs = ratio_ax.contourf(X.transpose(), Y.transpose(), ratio) #, cmap=matplotlib.cm.coolwarm)
ratio_ax.contour(cs, colors='k', linewidths=0.2)
cbar = ratio_fig.colorbar(cs)
cbar.ax.set_ylabel(r'$\frac{\Delta}{\rho_{el}}$')
ratio_ax.set_xlabel(r'$x$')
ratio_ax.set_ylabel(r'$y$')
ratio_ax.text(text_pos[0], text_pos[1], r'$V_Z =$ ' + str(vz))
ratio_fig.savefig("figures/ratio_chargedensity" + vz_format + ".png")
product = delta*charge_density
product_fig, product_ax = plt.subplots()
cs = product_ax.contourf(X.transpose(), Y.transpose(), product) #, cmap=matplotlib.cm.coolwarm)
product_ax.contour(cs, colors='k', linewidths=0.2)
cbar = product_fig.colorbar(cs)
cbar.ax.set_ylabel(r'$\Delta \rho_{el}$')
product_ax.set_xlabel(r'$x$')
product_ax.set_ylabel(r'$y$')
product_ax.text(text_pos[0], text_pos[1], r'$V_Z =$ ' + str(vz))
product_fig.savefig("figures/product_charge" + vz_format + ".png")
delta_ax_x.plot(x, delta[:,y_plane], label=r'$V_Z = $' + str(vz) )
delta_xy = []
for i in range(len(x)):
delta_xy.append(delta[i,i])
delta_ax_xy.plot(x, delta_xy, label=r'$V_Z = $' + str(vz))
Z1 = dataset_normal[y_plane, y_plane, :]
signal = Z1[: int(data_dimensions[2]/2)]
# 300 represents number of points to make between T.min and T.max
xnew = np.linspace(y.min(), y.max(), 300000)
spline = interpolate.splrep(y, Z1, k=1)
ldos_new = interpolate.splev(xnew, spline)
ldos_new = scipy.ndimage.filters.gaussian_filter1d(ldos_new, sigma_discrete_units)
ldos_normal_ax.plot(xnew, ldos_new, label=r'$V_Z = $' + str(vz))
# plt.plot(y[peaks[-1]], Z1[peaks[-1]], 'x')
#
lim = np.max(Z1)*1.2
# plt.vlines([Eg[y_plane], -Eg[y_plane]], 0, lim)
delta_ax_x.set_xlabel(r'$x$')
delta_ax_x.set_ylabel(r'$\Delta$')
delta_ax_x.set_xlim([0,30])
delta_ax_x.legend(loc='lower left')
delta_fig_x.savefig("figures/delta_profile_compare_x.png")
delta_ax_xy.set_xlabel(r'$xy$')
delta_ax_xy.set_ylabel(r'$\Delta$')
delta_ax_xy.legend(loc='lower left')
delta_ax_xy.set_xlim([0,30])
delta_fig_xy.savefig("figures/delta_profile_compare_xy.png")
ldos_normal_ax.set_xlabel(r'$E$')
ldos_normal_ax.set_ylabel(r'LDOS')
ldos_normal_ax.legend(loc='upper left')
ldos_normal_ax.set_xlim([-2*delta_bulk, 2*delta_bulk])
ldos_normal_ax.set_ylim([-0.5, 10])
ldos_normal_fig.savefig("figures/LDOS_compare_normal.png")