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dftb_plus_output_analysis_mymac.py
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dftb_plus_output_analysis_mymac.py
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import numpy as np
import sys
import matplotlib
import matplotlib.pyplot as plt
from matplotlib.backends.backend_pdf import PdfPages
# Define some constant
Na = 6.022140e23 # (mol)
kb_jk = 1.380649e-23 # (J/K)
kb_evk = 8.617330e-5 # (eV/K)
# Unit converter functions
def au2ev(x):
# 1 au = 27.211324 (eV)
return x * 27.211324
def au2kj(x):
# 1 au = 4.359744e-21 (kJ)
return x * 4.359744e-21
def au2kcal(x):
# 1 au = 1.042003e-21 (kcal)
return x * 1.042003e-21
energies_au = np.loadtxt('extracted_energies.out')
energies_ev = au2ev(energies_au)
gradient_norm_au = np.loadtxt('extracted_gradient_norm.out')
def plot_energy_gradient(energy, gradient, label_e):
fig=plt.figure(figsize=(7,6), dpi=600)
ax1 = fig.add_subplot(111)
ax2 = ax1.twinx()
# Plot
ax1.plot(energy, linewidth=2, label='Energy', color='r')
ax2.plot(gradient, linewidth=2, label='Gradient', color='b')
# Parameters
# ax.set_xlim(0, 4)
# ax.set_ylim(2, 14)
# ax.xaxis.set_ticks(np.arange(0, 4.5, 0.5))
# ax.yaxis.set_ticks(np.arange(2, 16, 2))
ax2.set_yscale('log')
# ax1.set_xlim(0, 4.05)
# ax1.set_ylim(0, 6)
# ax2.set_ylim(0, max_y)
# ax1.xaxis.set_ticks(np.arange(0, 4.5, 0.5))
# ax1.yaxis.set_ticks(np.arange(0, 7, 1))
# ax2.yaxis.set_ticks(np.arange(0, max_y + 1, 1))
ax1.tick_params(axis='both', which='major', length=5, width=2)
ax1.tick_params(axis='both', which='minor', length=2.5, width=1)
ax2.tick_params(axis='both', which='major', length=5, width=2)
ax2.tick_params(axis='both', which='minor', length=2.5, width=1)
ax1.xaxis.set_ticks_position('both')
ax1.tick_params(axis='both', labelsize=16)
ax2.tick_params(axis='both', labelsize=16)
ax1.minorticks_on()
ax2.minorticks_on()
# ax.tick_params(axis='both', labelsize=16)
# ax.tick_params(axis='both', which='major', length=5, width=2)
# ax.tick_params(axis='both', which='minor', length=2.5, width=1)
# ax.xaxis.set_ticks_position('both')
# ax.yaxis.set_ticks_position('both')
# ax.minorticks_on()
ax1.set_xlabel('Nomber of step (count)', fontsize=20)
ax1.set_ylabel(label_e, fontsize=20)
ax2.set_ylabel('Gradient norm (H/au)', fontsize=20, rotation =-90, labelpad=25)
# ax.legend(fontsize=10, ncol=3, loc='upper center', bbox_to_anchor=(0.5, 1.15), frameon=False, labelspacing=0.2, handletextpad=0.2, columnspacing=1)
# ax1.legend(fontsize=10, loc='upper right', bbox_to_anchor=(0.9, 1), frameon=False)
l1, h1 = ax1.get_legend_handles_labels()
l2, h2 = ax2.get_legend_handles_labels()
label = l1 + l2
handle = h1 + h2
plt.legend(label, handle, fontsize=16, loc='upper right', bbox_to_anchor=(0.9, 1), frameon=False)
with PdfPages('dftb_plus_output_analysis.pdf') as pdf:
#!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
# Metadata informations
#!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
d = pdf.infodict()
d['Title'] = ''
d['Author'] = 'A. TETENOIRE'
'''
Plot
'''
plot_energy_gradient(energies_au, gradient_norm_au, label_e='Energy (au)')
pdf.savefig(bbox_inches='tight')
plt.close()
'''
Plot
'''
plot_energy_gradient(energies_ev, gradient_norm_au, label_e='Energy (eV)')
pdf.savefig(bbox_inches='tight')
plt.close()