#! /usr/bin/env python3
"""
A module for plotting and summarising segments information,
density-of-states information and effective mass analysis.
"""
import matplotlib
import matplotlib.pyplot as plt
import numpy as np
from adjustText import adjust_text
from prettytable import PrettyTable
from effmass import ev_to_hartree
from effmass.dos import _check_integrated_dos_loaded
from effmass.dos import _check_dos_loaded
from effmass.analysis import _check_poly_order
_default_figsize = (8, 8)
[docs]def plot_segments(Data, Settings, segments, savefig=False, random_int=None, figsize=_default_figsize):
"""Plots bandstructure overlaid with the DFT-calculated points for each Segment
instance. Each Segment is labelled with it's direction in reciprocal space
and index number from the segments argument.
Args:
Data (Data): instance of the :class:`Data` class.
Settings (Settings): instance of the :class:`Settings` class.
segments (list(Segment)): A list of instances of the :class:`Segment` class.
figsize (list): Size of matplotlib figure. Default: (8, 8)
Returns:
Figure, Axes: tuple containing instance of the `matplotlib.pyplot.figure <https://matplotlib.org/api/figure_api.html>`_ class and `matplotlib.pyplot.axes <https://matplotlib.org/api/axes_api.html>`_ class.
Notes:
The x-axis of the plot is not to scale.
"""
fig = plt.figure(figsize=figsize)
ax = fig.add_subplot(111)
[ax.plot(range(len(Data.energies[i])), Data.energies[i] - Data.VBM) for i in range(len(Data.energies))]
points = [ax.scatter(segments[i].kpoint_indices, segments[i].energies - Data.VBM) for i in range(len(segments))]
texts = [ax.text(segments[i].kpoint_indices[-1], segments[i].energies[-1] - Data.VBM, str(i)+", "+str(np.round(segments[i].direction,3))) for i in range(len(segments))]
adjust_text(texts, arrowprops=dict(arrowstyle='->', color='red'), autoalign='x', force_text=(0.01,0.025))
ax.set_ylim([
-(Settings.extrema_search_depth + Settings.energy_range + 1),
(Data.CBM - Data.VBM) +
(Settings.extrema_search_depth + Settings.energy_range + 1)
])
if (savefig is True) and (random_int is not None):
fig.savefig("effmass_{}.png".format(random_int))
return fig, ax
[docs]def plot_integrated_dos(DataVasp, figsize=_default_figsize):
"""Plots integrated density of states (states/unit-cell) against energy
(eV).
Args:
DataVasp (DataVasp): instance of the :class:`DataVasp` class.
figsize (list): Size of matplotlib figure. Default: (8, 8)
Returns:
Figure, Axes: tuple containing instance of the `matplotlib.pyplot.figure <https://matplotlib.org/api/figure_api.html>`_ class and `matplotlib.pyplot.axes <https://matplotlib.org/api/axes_api.html>`_ class.
Notes:
The valence band maximum is set to 0 eV.
"""
_check_integrated_dos_loaded(DataVasp)
fig = plt.figure(figsize=figsize)
ax = fig.add_subplot(111)
energy = [x[0] - DataVasp.VBM for x in DataVasp.integrated_dos]
dos_data = [x[1] for x in DataVasp.integrated_dos]
ax.plot(energy, dos_data)
ax.set_xlabel("Energy, eV")
ax.set_ylabel("Integrated DOS, states / unit cell")
ax.axvline(0, linestyle="--")
ax.axvline(DataVasp.CBM - DataVasp.VBM, linestyle="--")
return fig, ax
[docs]def plot_dos(DataVasp, figsize=_default_figsize):
"""Plots density of states (states/unit-cell) against energy (eV).
Args:
DataVasp (DataVasp): instance of the :class:`DataVasp` class.
figsize (list): Size of matplotlib figure. Default: (8, 8)
Returns:
Figure, Axes: tuple containing instance of the `matplotlib.pyplot.figure <https://matplotlib.org/api/figure_api.html>`_ class and `matplotlib.pyplot.axes <https://matplotlib.org/api/axes_api.html>`_ class.
Notes:
The valence band maximum is set to 0 eV.
"""
_check_dos_loaded(DataVasp)
fig = plt.figure(figsize=figsize)
ax = fig.add_subplot(111)
energy = [x[0] - DataVasp.VBM for x in DataVasp.dos]
dos = [x[1] for x in DataVasp.dos]
ax.plot(energy, dos)
ax.set_xlabel("Energy, eV")
ax.set_ylabel("DOS")
ax.axvline(0, linestyle="--")
ax.axvline(DataVasp.CBM - DataVasp.VBM, linestyle="--")
return fig, ax
[docs]def make_table(segments, which_values=None):
"""Prints table summary of segments data to terminal
Args:
segments (list): Which segments to use.
which_values (list): use 'least squares' or 'finite differences' (default: both)
"""
table = PrettyTable()
column_names = ["particle", "band-index", "direction"]
least_squares, finite_differences = True, True
if which_values is not None:
if 'parabolic m* (least squares)' not in which_values:
least_squares = False
if 'parabolic m* (finite difference)' not in which_values:
finite_differences = False
if least_squares:
column_names.append("Least squares m* (m_e)")
if finite_differences:
column_names.append("Finite difference m* (m_e)")
table.field_names = column_names
for segment in segments:
if segment.band_type == "conduction_band":
particle = "electron"
if segment.band_type == "valence_band":
particle = "hole"
if segment.band_type == "unknown":
particle = "unknown"
segment_data = [
particle,
segment.band,
segment.direction
]
if least_squares:
segment_data.append("{:.4f}".format(segment.five_point_leastsq_effmass()))
if finite_differences:
segment_data.append("{:.4f}".format(segment.finite_difference_effmass()))
table.add_row(segment_data)
return table
def print_terminal_table(table):
print(table)
def print_summary_file(random_int, DFT_code, pathname, ignore, seedname,
fermi_level, extrema_search_depth, energy_range,
table):
with open("effmass_{}.txt".format(random_int), 'a') as out:
out.write(
"DFT code: "+DFT_code+'\n'+
"Path: "+pathname+'\n'
)
if ignore:
out.write("k-points to ignore: "+ignore+"\n")
if seedname:
out.write("File seedname: "+seedname+"\n")
if fermi_level:
out.write(
"User specified Fermi level: "+fermi_level+'\n'
)
out.write(
"Extrema search depth (eV): "+extrema_search_depth+"\n"+
"Energy range: "+energy_range+"\n"+'\n'+'\n'
)
out.write(table.get_string())
def print_results(segment, data, settings, polyfit_order=None, figsize=_default_figsize):
polyfit_order = settings.degree_bandfit if polyfit_order is None else polyfit_order
_check_poly_order(polyfit_order)
print(segment.band_type, segment.direction)
print("3-point finite difference mass is {:.2f}".format(
segment.finite_difference_effmass()))
print("5-point parabolic mass is {:.2f}".format(
segment.five_point_leastsq_effmass()))
try:
print("weighted parabolic mass is {:.2f}".format(
segment.weighted_leastsq_effmass()))
except AssertionError as e:
print ("----------\n")
print (e)
print ("\n-----------")
try:
print("alpha is {:.2f} 1/eV".format(
segment.alpha(polyfit_order=polyfit_order) * ev_to_hartree))
print("kane mass at bandedge is {:.2f}".format(
segment.kane_mass_band_edge(polyfit_order=polyfit_order)))
if segment.explosion_index(polyfit_order=polyfit_order) == len(
segment.dE_eV):
print(
"the Kane quasi linear approximation is valid for the whole segment"
)
else:
print("the Kane quasi-linear approximation is valid until {:.2f} eV".
format(segment.dE_eV[segment.explosion_index(
polyfit_order=polyfit_order)]))
except AssertionError as e:
print ("----------\n")
print (e)
print ("\n-----------")
try:
print("optical mass at band edge (assuming the Kane dispersion) is {:.2f}".
format(segment.optical_effmass_kane_dispersion()))
except AssertionError:
pass
plt.figure(figsize=figsize)
plt.plot(
np.linspace(segment.dk_angs[0], segment.dk_angs[-1], 100),
np.divide(
segment.poly_fit(
polyfit_order=polyfit_order, polyfit_weighting=False),
ev_to_hartree),
marker="x",
ms=5,
label="polynomial order {}".format(polyfit_order))
plt.plot(
np.linspace(segment.dk_angs[0], segment.dk_angs[-1], 100),
np.divide(segment.finite_difference_fit(), ev_to_hartree),
marker="<",
ms=5,
label="finite diff parabolic")
plt.plot(
np.linspace(segment.dk_angs[0], segment.dk_angs[-1], 100),
np.divide(segment.five_point_leastsq_fit(), ev_to_hartree),
marker="p",
ms=5,
label="five point parabolic")
try:
plt.plot(
np.linspace(segment.dk_angs[0], segment.dk_angs[-1], 100),
np.divide(segment.weighted_leastsq_fit(), ev_to_hartree),
marker=">",
ms=5,
label="weighted parabolic")
except AssertionError:
pass
try:
plt.plot(
np.linspace(segment.dk_angs[0], segment.dk_angs[-1], 100),
np.divide(
segment.kane_fit(polyfit_order=polyfit_order), ev_to_hartree),
marker="o",
ms=5,
label="Kane quasi-linear")
except AssertionError as e:
pass
plt.xlabel(r"k ($ \AA^{-1} $)")
plt.ylabel("energy (eV)")
plt.scatter(segment.dk_angs, segment.dE_eV, marker="x", s=200, label="DFT")
plt.legend()
plt.show()
fig, axes = plot_segments(data, settings, [segment], figsize=figsize)
plt.show()
plt.figure(figsize=figsize)
idx = segment.explosion_index(polyfit_order=polyfit_order)
plt.scatter(
segment.dE_hartree[1:idx + 1],
segment.transport_effmass(
polyfit_order=polyfit_order,
dk=segment.dk_bohr,
polyfit_weighting=False)[1:idx + 1])
plt.plot([0, segment.dE_hartree[idx]],
np.polyval(
np.polyfit(
segment.dE_hartree[1:idx + 1],
segment.transport_effmass(
polyfit_order=polyfit_order,
dk=segment.dk_bohr,
polyfit_weighting=False)[1:idx + 1], 1),
[0, segment.dE_hartree[idx]]))
plt.ylabel("transport mass")
plt.xlabel("energy (hartree)")
plt.xlim([0, segment.dE_hartree[idx]])
plt.show()