def _calculate_quasiharmonic_phonon(self):
energies = []
volumes = []
phonons = []
T = []
F = []
S = []
Cv = []
for i, task in enumerate(self._tasks):
energies.append(task.get_energy())
volumes.append(task.get_cell().get_volume())
if self._sampling_mesh is not None:
phonon = task.get_phonon()
phonon.set_mesh(self._sampling_mesh)
phonon.set_thermal_properties(
t_step=self._t_step,
t_max=self._t_max + self._t_step * 2.5,
t_min=self._t_min)
(temperatures,
free_energies,
entropies,
heat_capacities) = phonon.get_thermal_properties()
T.append(temperatures)
F.append(free_energies)
S.append(entropies)
Cv.append(heat_capacities)
phonon.write_yaml_thermal_properties(
"thermal_properties-%02d.yaml" % i)
if self._sampling_mesh:
qha = PhonopyQHA(volumes,
energies,
temperatures=T[0],
free_energy=np.transpose(F),
cv=np.transpose(Cv),
entropy=np.transpose(S),
t_max=self._t_max,
verbose=False)
qha.write_helmholtz_volume()
qha.write_volume_temperature()
qha.write_thermal_expansion()
qha.write_volume_expansion()
qha.write_gibbs_temperature()
qha.write_bulk_modulus_temperature()
qha.write_heat_capacity_P_numerical()
qha.write_heat_capacity_P_polyfit()
qha.write_gruneisen_temperature()
w = open("e-v.dat", 'w')
w.write("# cell volume energy of cell other than phonon\n")
for e, v in zip(energies, volumes):
w.write("%20.13f %20.13f\n" % (v, e))
self._quasiharmonic_phonon = None
def _calculate_quasiharmonic_phonon(self):
energies = []
volumes = []
phonons = []
T = []
F = []
S = []
Cv = []
for i, task in enumerate(self._tasks):
energies.append(task.get_energy())
volumes.append(task.get_cell().get_volume())
if self._sampling_mesh is not None:
phonon = task.get_phonon()
phonon.set_mesh(self._sampling_mesh)
phonon.set_thermal_properties(t_step=self._t_step,
t_max=self._t_max +
self._t_step * 2.5,
t_min=self._t_min)
(temperatures, free_energies, entropies,
heat_capacities) = phonon.get_thermal_properties()
T.append(temperatures)
F.append(free_energies)
S.append(entropies)
Cv.append(heat_capacities)
phonon.write_yaml_thermal_properties(
"thermal_properties-%02d.yaml" % i)
if self._sampling_mesh:
qha = PhonopyQHA(volumes,
energies,
temperatures=T[0],
free_energy=np.transpose(F),
cv=np.transpose(Cv),
entropy=np.transpose(S),
t_max=self._t_max,
verbose=False)
qha.write_helmholtz_volume()
qha.write_volume_temperature()
qha.write_thermal_expansion()
qha.write_volume_expansion()
qha.write_gibbs_temperature()
qha.write_bulk_modulus_temperature()
qha.write_heat_capacity_P_numerical()
qha.write_heat_capacity_P_polyfit()
qha.write_gruneisen_temperature()
w = open("e-v.dat", 'w')
w.write("# cell volume energy of cell other than phonon\n")
for e, v in zip(energies, volumes):
w.write("%20.13f %20.13f\n" % (v, e))
self._quasiharmonic_phonon = None
def collect_data(self):
energies = []
volumes = []
# Get the calculations from workflow
from itertools import chain
# wf_list = list(chain(self.get_step(self.volume_expansions).get_sub_workflows(),
# self.get_step(self.start).get_sub_workflows()))
wf_list = self.get_step(self.volume_expansions).get_sub_workflows()
for wf in wf_list:
dos = wf.get_result('dos').get_array('total_dos')
# self.append_to_report('DOS')
# self.append_to_report('{}'.format(dos.tolist()))
energy = wf.get_result(
'optimized_structure_data').get_dict()['energy']
volume = wf.get_result('final_structure').get_cell_volume()
self.append_to_report('{} {}'.format(volume, energy))
energies.append(energy)
volumes.append(volume)
import numpy as np
# data = ArrayData()
# data.set_array('energy', np.array(energies))
# data.set_array('volume', np.array(volumes))
# data.store()
# self.add_result('data', data)
volumes = []
electronic_energies = []
fe_phonon = []
entropy = []
cv = []
thermal_list = []
structures = []
optimized_data = []
inline_params = {}
wf_list = list(
chain(
self.get_step(self.volume_expansions).get_sub_workflows(),
self.get_step(self.start).get_sub_workflows()))
i = 0
for wf in wf_list:
# Check if suitable for qha (no imaginary frequencies in DOS)
try:
freq = wf.get_result('dos').get_array('frequency')
dos = wf.get_result('dos').get_array('total_dos')
except:
continue
mask_neg = np.ma.masked_less(freq, 0.0).mask
mask_pos = np.ma.masked_greater(freq, 0.0).mask
int_neg = -np.trapz(np.multiply(dos[mask_neg], freq[mask_neg]),
x=freq[mask_neg])
int_pos = np.trapz(np.multiply(dos[mask_pos], freq[mask_pos]),
x=freq[mask_pos])
if int_neg / int_pos > 1e-6:
volume = wf.get_result('final_structure').get_cell_volume()
self.append_to_report(
'Volume {} not suitable for QHA (imaginary frequencies in DOS), skipping it'
.format(volume))
continue
# print ('int_neg: {} int_pos: {} rel: {}'.format(int_neg, int_pos, int_neg/int_pos))
# Get necessary data
thermal_list.append(wf.get_result('thermal_properties'))
structures.append(wf.get_result('final_structure'))
optimized_data.append(wf.get_result('optimized_structure_data'))
electronic_energies.append(
wf.get_result('optimized_structure_data').get_dict()['energy'])
volumes.append(wf.get_result('final_structure').get_cell_volume())
# fe_phonon.append(wf.get_result('thermal_properties').get_array('free_energy'))
# entropy.append(wf.get_result('thermal_properties').get_array('entropy'))
# cv.append(wf.get_result('thermal_properties').get_array('cv'))
temperatures = wf.get_result('thermal_properties').get_array(
'temperature')
temperature_fit = np.arange(
0, 1001,
10) #[100, 200, 300, 400, 500, 600, 700, 800, 900, 1000]
fe_phonon.append(
np.interp(
temperature_fit, temperatures,
wf.get_result('thermal_properties').get_array(
'free_energy')))
entropy.append(
np.interp(
temperature_fit, temperatures,
wf.get_result('thermal_properties').get_array('entropy')))
cv.append(
np.interp(temperature_fit, temperatures,
wf.get_result('thermal_properties').get_array('cv')))
temperatures = temperature_fit
i += 1
# inline_params['structure_{}'.format(i)] = wf.get_result('final_structure')
# inline_params['optimized_data_{}'.format(i)] = wf.get_result('optimized_structure_data')
# inline_params['thermal_properties_{}'.format(i)] = wf.get_result('thermal_properties')
# self.append_to_report('accepted: {} {}'.format(i, wf.get_result('thermal_properties').pk))
# entropy = []
# cv = []
# fe_phonon = []
# temperatures = None
# volumes = [value.get_cell_volume() for key, value in inline_params.items() if 'structure' in key.lower()]
# electronic_energies = [value.get_dict()['energy'] for key, value in inline_params.items() if 'optimized_data' in key.lower()]
#
# thermal_properties_list = [key for key, value in inline_params.items() if 'thermal_properties' in key.lower()]
# for key in thermal_properties_list:
# thermal_properties = inline_params[key]
# fe_phonon.append(thermal_properties.get_array('free_energy'))
# entropy.append(thermal_properties.get_array('entropy'))
# cv.append(thermal_properties.get_array('cv'))
# temperatures = thermal_properties.get_array('temperature')
from phonopy import PhonopyQHA
import numpy as np
# Arrange data sorted by volume and transform them to numpy array
sort_index = np.argsort(volumes)
volumes = np.array(volumes)[sort_index]
electronic_energies = np.array(electronic_energies)[sort_index]
temperatures = np.array(temperatures)
fe_phonon = np.array(fe_phonon).T[:, sort_index]
entropy = np.array(entropy).T[:, sort_index]
cv = np.array(cv).T[:, sort_index]
opt = np.argmin(electronic_energies)
# Check minimum energy volume is within the data
if np.ma.masked_less_equal(volumes, volumes[opt]).mask.all():
self.append_to_report(
'higher volume structures are necessary to compute')
if np.ma.masked_greater_equal(volumes, volumes[opt]).mask.all():
self.append_to_report(
'Lower volume structures are necessary to compute')
self.append_to_report('Dimensions T{} : FE{}'.format(
len(temperatures), len(fe_phonon)))
# Calculate QHA
phonopy_qha = PhonopyQHA(
np.array(volumes),
np.array(electronic_energies),
eos="vinet",
temperatures=np.array(temperatures),
free_energy=np.array(fe_phonon),
cv=np.array(cv),
entropy=np.array(entropy),
# t_max=options.t_max,
verbose=False)
#Get data
qha_temperatures = phonopy_qha._qha._temperatures[:phonopy_qha._qha.
_max_t_index]
helmholtz_volume = phonopy_qha.get_helmholtz_volume()
thermal_expansion = phonopy_qha.get_thermal_expansion()
volume_temperature = phonopy_qha.get_volume_temperature()
heat_capacity_P_numerical = phonopy_qha.get_heat_capacity_P_numerical()
volume_expansion = phonopy_qha.get_volume_expansion()
gibbs_temperature = phonopy_qha.get_gibbs_temperature()
qha_output = ArrayData()
qha_output.set_array('temperature', np.array(qha_temperatures))
qha_output.set_array('helmholtz_volume', np.array(helmholtz_volume))
qha_output.set_array('thermal_expansion', np.array(thermal_expansion))
qha_output.set_array('volume_temperature',
np.array(volume_temperature))
qha_output.set_array('heat_capacity_P_numerical',
np.array(heat_capacity_P_numerical))
qha_output.set_array('volume_expansion', np.array(volume_expansion))
qha_output.set_array('gibbs_temperature', np.array(gibbs_temperature))
qha_output.store()
# Test to leave something on folder
# phonopy_qha.plot_pdf_bulk_modulus_temperature()
# import matplotlib
# matplotlib.use('Agg')
repo_path = self._repo_folder.abspath
data_folder = self.current_folder.get_subfolder('DATA_FILES')
data_folder.create()
# phonopy_qha.plot_pdf_bulk_modulus_temperature(filename=repo_path + '/bulk_modulus-temperature.pdf')
phonopy_qha.write_bulk_modulus_temperature(filename='bm')
file = open('bm')
data_folder.create_file_from_filelike(file, 'bulk_modulus-temperature')
# qha_results = calculate_qha_inline(**inline_params)[1]
self.append_to_report('QHA properties calculated and retrieved')
self.add_result('qha', qha_output)
data = ArrayData()
data.set_array('energy', np.array(electronic_energies))
data.set_array('volume', np.array(volumes))
data.store()
self.add_result('data', data)
self.append_to_report('Finishing workflow_workflow')
self.next(self.exit)
class QuasiparticlesQHA():
def __init__(self, args, load_data=False, verbose=False, tmin=None):
input_parameters = reading.read_parameters_from_input_file(
args.input_file)
if 'structure_file_name_outcar' in input_parameters:
structure = reading.read_from_file_structure_outcar(
input_parameters['structure_file_name_outcar'])
else:
structure = reading.read_from_file_structure_poscar(
input_parameters['structure_file_name_poscar'])
structure.get_data_from_dict(input_parameters)
if 'supercell_phonon' in input_parameters:
supercell_phonon = input_parameters['supercell_phonon']
else:
supercell_phonon = np.identity(3)
structure.set_force_constants(
get_force_constants_from_file(
file_name=input_parameters['force_constants_file_name'],
fc_supercell=supercell_phonon))
if '_mesh_phonopy' in input_parameters:
mesh = input_parameters['_mesh_phonopy']
else:
mesh = [20, 20, 20] # Default
print('mesh set to: {}'.format(mesh))
if 'bands' in input_parameters is None:
self._bands = structure.get_path_using_seek_path()
else:
self._bands = input_parameters['_band_ranges']
volumes, energies = read_v_e(args.ev,
factor=1.0,
volume_factor=1.0,
pressure=args.pressure)
self._fc_fit = ForceConstantsFitting(
structure,
files_temperature=args.ct_data,
files_volume=args.cv_data,
temperatures=args.temperatures,
volumes=volumes,
mesh=mesh,
ref_temperature=args.ref_temperature,
fitting_order=args.order,
tmin=tmin,
use_NAC=True)
if not load_data:
temperatures = self._fc_fit.get_temperature_range()
free_energy = []
entropy = []
cv = []
for v, e in zip(volumes, energies):
print('Volume: {} Ang. Energy(U): {} eV'.format(v, e))
tp_data = self._fc_fit.get_thermal_properties(volume=v)
free_energy.append(tp_data[0])
entropy.append(tp_data[1])
cv.append(tp_data[2])
free_energy = np.array(free_energy).T
entropy = np.array(entropy).T
cv = np.array(cv).T
np.save('free_energy.npy', free_energy)
np.save('temperatures.npy', temperatures)
np.save('cv.npy', cv)
np.save('entropy.npy', entropy)
else:
free_energy = np.load('free_energy.npy')
temperatures = np.load('temperatures.npy')
cv = np.load('cv.npy')
entropy = np.load('entropy.npy')
self.phonopy_qha = PhonopyQHA(
volumes,
energies,
eos="vinet", # options: 'vinet', 'murnaghan' or 'birch_murnaghan'
temperatures=temperatures,
free_energy=free_energy,
cv=cv,
entropy=entropy,
t_max=self.fc_fit.get_temperature_range()[-1],
verbose=False)
# Write data files to disk
self.phonopy_qha.write_bulk_modulus_temperature()
self.phonopy_qha.write_gibbs_temperature()
self.phonopy_qha.write_heat_capacity_P_numerical()
self.phonopy_qha.write_gruneisen_temperature()
self.phonopy_qha.write_thermal_expansion()
self.phonopy_qha.write_helmholtz_volume()
self.phonopy_qha.write_volume_expansion()
self.phonopy_qha.write_volume_temperature()
if verbose:
self.phonopy_qha.plot_qha().show()
# Designed for test only
def volume_shift(self, volume_range=np.arange(-2.0, 2.0, 0.1)):
import matplotlib.pyplot as plt
fig, ax = plt.subplots(1, 1)
volumes = self.phonopy_qha._qha._volumes
energies = self.phonopy_qha._qha._electronic_energies
free_energy = np.load('free_energy.npy')
temperatures = np.load('temperatures.npy')
cv = np.load('cv.npy')
entropy = np.load('entropy.npy')
for i in volume_range:
volumesi = np.array(volumes) + i
print volumesi
phonopy_qha = PhonopyQHA(
volumesi,
energies,
eos=
"vinet", # options: 'vinet', 'murnaghan' or 'birch_murnaghan'
temperatures=temperatures,
free_energy=free_energy,
cv=cv,
entropy=entropy,
t_max=self.fc_fit.get_temperature_range()[-1],
verbose=False)
cp = phonopy_qha.get_heat_capacity_P_numerical()
import matplotlib.pyplot as plt
import matplotlib.colors as colors
cNorm = colors.Normalize(vmin=volume_range[0],
vmax=volume_range[-1])
scalarMap = plt.cm.ScalarMappable(norm=cNorm,
cmap=plt.cm.get_cmap('plasma'))
ax.plot(phonopy_qha._qha._temperatures[:-3],
cp,
label='{}'.format(i),
color=scalarMap.to_rgba(i))
import matplotlib as mpl
ax2 = fig.add_axes([0.93, 0.1, 0.02, 0.8])
mpl.colorbar.ColorbarBase(ax2,
cmap=plt.cm.get_cmap('plasma'),
norm=cNorm,
spacing='proportional',
ticks=volume_range,
boundaries=None,
format='%1i')
plt.show()
def plot_dos_gradient(self):
import matplotlib.pyplot as plt
import matplotlib.colors as colors
import matplotlib.colorbar as colorbar
volumes = self.phonopy_qha.get_volume_temperature()
temperatures = self.fc_fit.get_temperature_range()
fig, ax = plt.subplots(1, 1)
for t, v in zip(temperatures[::40], volumes[::20]):
print('temperature: {} K'.format(t))
dos = self.fc_fit.get_dos(t, v)
cNorm = colors.Normalize(vmin=temperatures[0],
vmax=temperatures[-1])
scalarMap = plt.cm.ScalarMappable(norm=cNorm,
cmap=plt.cm.get_cmap('plasma'))
ax.plot(dos[0], dos[1], color=scalarMap.to_rgba(t))
plt.suptitle('Phonon density of states')
plt.xlabel('Frequency [THz]')
ax2 = fig.add_axes([0.93, 0.1, 0.02, 0.8])
colorbar.ColorbarBase(ax2,
cmap=plt.cm.get_cmap('plasma'),
norm=cNorm,
spacing='proportional',
ticks=temperatures[::40],
boundaries=None,
format='%1i')
plt.show()
def plot_band_structure_gradient(self, tmin=300, tmax=1600, tstep=100):
import matplotlib.pyplot as plt
import matplotlib.colors as colors
import matplotlib.colorbar as colorbar
def replace_list(text_string):
substitutions = {
'GAMMA': u'$\Gamma$',
}
for item in substitutions.iteritems():
text_string = text_string.replace(item[0], item[1])
return text_string
volumes = self.phonopy_qha.get_volume_temperature()
cNorm = colors.Normalize(vmin=tmin, vmax=tmax)
fig, ax = plt.subplots(1, 1)
for t in np.arange(tmin, tmax, tstep):
print('temperature: {} K'.format(t))
v = self.get_volume_at_temperature(t)
scalarMap = plt.cm.ScalarMappable(norm=cNorm,
cmap=plt.cm.get_cmap('plasma'))
band_data = self.fc_fit.get_band_structure(
t, v, band_ranges=self._bands['ranges'])
for i, freq in enumerate(band_data[1]):
ax.plot(band_data[1][i],
band_data[2][i],
color=scalarMap.to_rgba(t))
# plt.axes().get_xaxis().set_visible(False)
#plt.axes().get_xaxis().set_ticks([])
plt.ylabel('Frequency [THz]')
plt.xlabel('Wave vector')
plt.xlim([0, band_data[1][-1][-1]])
plt.axhline(y=0, color='k', ls='dashed')
plt.suptitle('Phonon dispersion')
if 'labels' in self._bands:
plt.rcParams.update({'mathtext.default': 'regular'})
labels = self._bands['labels']
labels_e = []
x_labels = []
for i in range(len(band_data[1])):
if labels[i][0] == labels[i - 1][1]:
labels_e.append(replace_list(labels[i][0]))
else:
labels_e.append(
replace_list(labels[i - 1][1]) + '/' +
replace_list(labels[i][0]))
x_labels.append(band_data[1][i][0])
x_labels.append(band_data[1][-1][-1])
labels_e.append(replace_list(labels[-1][1]))
labels_e[0] = replace_list(labels[0][0])
plt.xticks(x_labels, labels_e, rotation='horizontal')
ax2 = fig.add_axes([0.93, 0.1, 0.02, 0.8])
colorbar.ColorbarBase(ax2,
cmap=plt.cm.get_cmap('plasma'),
norm=cNorm,
spacing='proportional',
ticks=np.arange(tmin, tmax, tstep),
boundaries=None,
format='%1i')
plt.show()
def get_volume_at_temperature(self, temperature):
temperatures = self.get_qha_temperatures()
volumes = self.phonopy_qha.get_volume_temperature()
volume = np.interp(temperature, temperatures, volumes)
return volume
def plot_band_structure_constant_pressure(self,
temperature=300,
external_data=None):
import matplotlib.pyplot as plt
def replace_list(text_string):
substitutions = {
'GAMMA': u'$\Gamma$',
}
for item in substitutions.iteritems():
text_string = text_string.replace(item[0], item[1])
return text_string
volume = self.get_volume_at_temperature(temperature)
fig, ax = plt.subplots(1, 1)
band_data = self.fc_fit.get_band_structure(
temperature, volume, band_ranges=self._bands['ranges'])
for i, freq in enumerate(band_data[1]):
ax.plot(band_data[1][i], band_data[2][i], color='r')
#plt.axes().get_xaxis().set_ticks([])
plt.ylabel('Frequency [THz]')
plt.xlabel('Wave vector')
plt.xlim([0, band_data[1][-1][-1]])
plt.axhline(y=0, color='k', ls='dashed')
plt.suptitle('Phonon dispersion')
if 'labels' in self._bands:
plt.rcParams.update({'mathtext.default': 'regular'})
labels = self._bands['labels']
labels_e = []
x_labels = []
for i in range(len(band_data[1])):
if labels[i][0] == labels[i - 1][1]:
labels_e.append(replace_list(labels[i][0]))
else:
labels_e.append(
replace_list(labels[i - 1][1]) + '/' +
replace_list(labels[i][0]))
x_labels.append(band_data[1][i][0])
x_labels.append(band_data[1][-1][-1])
labels_e.append(replace_list(labels[-1][1]))
labels_e[0] = replace_list(labels[0][0])
plt.xticks(x_labels, labels_e, rotation='horizontal')
ax.plot(external_data[:, 0], external_data[:, 1], 'o', color='b')
plt.show()
def get_qha_temperatures(self):
max_t_index = self.phonopy_qha._qha._get_max_t_index(
self.phonopy_qha._qha._temperatures)
temperatures = self.phonopy_qha._qha._temperatures[:max_t_index]
return temperatures
def get_FC_constant_pressure(self):
temperatures = self.get_qha_temperatures()
volumes = self.phonopy_qha.get_volume_temperature()
for t, v in zip(temperatures[::20], volumes[::20]):
fc = self.fc_fit.get_total_force_constants(temperature=t, volume=v)
write_FORCE_CONSTANTS(fc.get_array(), filename='FC_{}'.format(t))
def get_total_shift_constant_pressure(self, qpoint=(0, 0, 0)):
qindex = self.fc_fit.qpoint_to_index(qpoint)
volumes = self.phonopy_qha.get_volume_temperature()
temperatures = self.get_qha_temperatures()
h_frequencies, ev = self.fc_fit.get_harmonic_frequencies_and_eigenvectors(
)
chk_shift_matrix = []
for v, t in zip(volumes, temperatures):
total_shifts = self.fc_fit.get_total_shifts(volume=v,
temperature=t)
chk_shift_matrix.append(total_shifts)
chk_shift_matrix = np.array(chk_shift_matrix).T
return chk_shift_matrix[:, qindex]
def plot_total_shift_constant_pressure(self,
qpoint=(0, 0, 0),
branch=None):
import matplotlib.pyplot as plt
temperatures = self.get_qha_temperatures()
chk_shift_matrix = self.get_total_shift_constant_pressure(
qpoint=qpoint)
plt.suptitle(
'Total frequency shift at wave vector={} (relative to {} K)'.
format(qpoint, self.fc_fit.ref_temperature))
plt.xlabel('Temperature [K]')
plt.ylabel('Frequency shift [THz]')
if branch is None:
plt.plot(temperatures, chk_shift_matrix.T, '-')
else:
plt.title('Branch {}'.format(branch))
plt.plot(temperatures, chk_shift_matrix[branch].T, '-')
plt.show()
@property
def fc_fit(self):
return self._fc_fit
class QuasiparticlesQHA():
def __init__(self, args, load_data=False, verbose=False, tmin=None):
input_parameters = reading.read_parameters_from_input_file(args.input_file)
if 'structure_file_name_outcar' in input_parameters:
structure = reading.read_from_file_structure_outcar(input_parameters['structure_file_name_outcar'])
else:
structure = reading.read_from_file_structure_poscar(input_parameters['structure_file_name_poscar'])
structure.get_data_from_dict(input_parameters)
if 'supercell_phonon' in input_parameters:
supercell_phonon = input_parameters['supercell_phonon']
else:
supercell_phonon = np.identity(3)
structure.set_force_constants(get_force_constants_from_file(file_name=input_parameters['force_constants_file_name'],
fc_supercell=supercell_phonon))
if '_mesh_phonopy' in input_parameters:
mesh = input_parameters['_mesh_phonopy']
else:
mesh = [20, 20, 20] # Default
print ('mesh set to: {}'.format(mesh))
if 'bands' in input_parameters is None:
self._bands = structure.get_path_using_seek_path()
else:
self._bands = input_parameters['_band_ranges']
volumes, energies = read_v_e(args.ev, factor=1.0, volume_factor=1.0, pressure=args.pressure)
self._fc_fit = ForceConstantsFitting(structure,
files_temperature=args.ct_data,
files_volume=args.cv_data,
temperatures=args.temperatures,
volumes=volumes,
mesh=mesh,
ref_temperature=args.ref_temperature,
fitting_order=args.order,
tmin=tmin,
use_NAC=True)
if not load_data:
temperatures = self._fc_fit.get_temperature_range()
free_energy = []
entropy = []
cv = []
for v, e in zip(volumes, energies):
print ('Volume: {} Ang. Energy(U): {} eV'.format(v, e))
tp_data = self._fc_fit.get_thermal_properties(volume=v)
free_energy.append(tp_data[0])
entropy.append(tp_data[1])
cv.append(tp_data[2])
free_energy = np.array(free_energy).T
entropy = np.array(entropy).T
cv = np.array(cv).T
np.save('free_energy.npy', free_energy)
np.save('temperatures.npy', temperatures)
np.save('cv.npy', cv)
np.save('entropy.npy', entropy)
else:
free_energy = np.load('free_energy.npy')
temperatures = np.load('temperatures.npy')
cv = np.load('cv.npy')
entropy = np.load('entropy.npy')
self.phonopy_qha = PhonopyQHA(volumes,
energies,
eos="vinet", # options: 'vinet', 'murnaghan' or 'birch_murnaghan'
temperatures=temperatures,
free_energy=free_energy,
cv=cv,
entropy=entropy,
t_max=self.fc_fit.get_temperature_range()[-1],
verbose=False)
# Write data files to disk
self.phonopy_qha.write_bulk_modulus_temperature()
self.phonopy_qha.write_gibbs_temperature()
self.phonopy_qha.write_heat_capacity_P_numerical()
self.phonopy_qha.write_gruneisen_temperature()
self.phonopy_qha.write_thermal_expansion()
self.phonopy_qha.write_helmholtz_volume()
self.phonopy_qha.write_volume_expansion()
self.phonopy_qha.write_volume_temperature()
if verbose:
self.phonopy_qha.plot_qha().show()
# Designed for test only
def volume_shift(self, volume_range=np.arange(-2.0, 2.0, 0.1)):
import matplotlib.pyplot as plt
fig, ax = plt.subplots(1, 1)
volumes = self.phonopy_qha._qha._volumes
energies = self.phonopy_qha._qha._electronic_energies
free_energy = np.load('free_energy.npy')
temperatures = np.load('temperatures.npy')
cv = np.load('cv.npy')
entropy = np.load('entropy.npy')
for i in volume_range:
volumesi = np.array(volumes) + i
print volumesi
phonopy_qha = PhonopyQHA(volumesi,
energies,
eos="vinet", # options: 'vinet', 'murnaghan' or 'birch_murnaghan'
temperatures=temperatures,
free_energy=free_energy,
cv=cv,
entropy=entropy,
t_max=self.fc_fit.get_temperature_range()[-1],
verbose=False)
cp = phonopy_qha.get_heat_capacity_P_numerical()
import matplotlib.pyplot as plt
import matplotlib.colors as colors
cNorm = colors.Normalize(vmin=volume_range[0], vmax=volume_range[-1])
scalarMap = plt.cm.ScalarMappable(norm=cNorm, cmap=plt.cm.get_cmap('plasma'))
ax.plot(phonopy_qha._qha._temperatures[:-3], cp, label='{}'.format(i), color=scalarMap.to_rgba(i))
import matplotlib as mpl
ax2 = fig.add_axes([0.93, 0.1, 0.02, 0.8])
mpl.colorbar.ColorbarBase(ax2, cmap=plt.cm.get_cmap('plasma'), norm=cNorm,
spacing='proportional', ticks=volume_range,
boundaries=None, format='%1i')
plt.show()
def plot_dos_gradient(self):
import matplotlib.pyplot as plt
import matplotlib.colors as colors
import matplotlib.colorbar as colorbar
volumes = self.phonopy_qha.get_volume_temperature()
temperatures = self.fc_fit.get_temperature_range()
fig, ax = plt.subplots(1,1)
for t, v in zip(temperatures[::40], volumes[::20]):
print ('temperature: {} K'.format(t))
dos = self.fc_fit.get_dos(t, v)
cNorm = colors.Normalize(vmin=temperatures[0], vmax=temperatures[-1])
scalarMap = plt.cm.ScalarMappable(norm=cNorm, cmap=plt.cm.get_cmap('plasma'))
ax.plot(dos[0], dos[1], color=scalarMap.to_rgba(t))
plt.suptitle('Phonon density of states')
plt.xlabel('Frequency [THz]')
ax2 = fig.add_axes([0.93, 0.1, 0.02, 0.8])
colorbar.ColorbarBase(ax2, cmap=plt.cm.get_cmap('plasma'), norm=cNorm,
spacing='proportional', ticks=temperatures[::40],
boundaries=None, format='%1i')
plt.show()
def plot_band_structure_gradient(self, tmin=300, tmax=1600, tstep=100):
import matplotlib.pyplot as plt
import matplotlib.colors as colors
import matplotlib.colorbar as colorbar
def replace_list(text_string):
substitutions = {'GAMMA': u'$\Gamma$',
}
for item in substitutions.iteritems():
text_string = text_string.replace(item[0], item[1])
return text_string
volumes = self.phonopy_qha.get_volume_temperature()
cNorm = colors.Normalize(vmin=tmin, vmax=tmax)
fig, ax = plt.subplots(1,1)
for t in np.arange(tmin, tmax, tstep):
print ('temperature: {} K'.format(t))
v = self.get_volume_at_temperature(t)
scalarMap = plt.cm.ScalarMappable(norm=cNorm, cmap=plt.cm.get_cmap('plasma'))
band_data = self.fc_fit.get_band_structure(t, v, band_ranges=self._bands['ranges'])
for i, freq in enumerate(band_data[1]):
ax.plot(band_data[1][i], band_data[2][i], color=scalarMap.to_rgba(t))
# plt.axes().get_xaxis().set_visible(False)
#plt.axes().get_xaxis().set_ticks([])
plt.ylabel('Frequency [THz]')
plt.xlabel('Wave vector')
plt.xlim([0, band_data[1][-1][-1]])
plt.axhline(y=0, color='k', ls='dashed')
plt.suptitle('Phonon dispersion')
if 'labels' in self._bands:
plt.rcParams.update({'mathtext.default': 'regular'})
labels = self._bands['labels']
labels_e = []
x_labels = []
for i in range(len(band_data[1])):
if labels[i][0] == labels[i - 1][1]:
labels_e.append(replace_list(labels[i][0]))
else:
labels_e.append(
replace_list(labels[i - 1][1]) + '/' + replace_list(labels[i][0]))
x_labels.append(band_data[1][i][0])
x_labels.append(band_data[1][-1][-1])
labels_e.append(replace_list(labels[-1][1]))
labels_e[0] = replace_list(labels[0][0])
plt.xticks(x_labels, labels_e, rotation='horizontal')
ax2 = fig.add_axes([0.93, 0.1, 0.02, 0.8])
colorbar.ColorbarBase(ax2, cmap=plt.cm.get_cmap('plasma'), norm=cNorm,
spacing='proportional', ticks=np.arange(tmin, tmax, tstep),
boundaries=None, format='%1i')
plt.show()
def get_volume_at_temperature(self, temperature):
temperatures = self.get_qha_temperatures()
volumes = self.phonopy_qha.get_volume_temperature()
volume = np.interp(temperature, temperatures, volumes)
return volume
def plot_band_structure_constant_pressure(self, temperature=300, external_data=None):
import matplotlib.pyplot as plt
def replace_list(text_string):
substitutions = {'GAMMA': u'$\Gamma$',
}
for item in substitutions.iteritems():
text_string = text_string.replace(item[0], item[1])
return text_string
volume = self.get_volume_at_temperature(temperature)
fig, ax = plt.subplots(1,1)
band_data = self.fc_fit.get_band_structure(temperature, volume, band_ranges=self._bands['ranges'])
for i, freq in enumerate(band_data[1]):
ax.plot(band_data[1][i], band_data[2][i], color='r')
#plt.axes().get_xaxis().set_ticks([])
plt.ylabel('Frequency [THz]')
plt.xlabel('Wave vector')
plt.xlim([0, band_data[1][-1][-1]])
plt.axhline(y=0, color='k', ls='dashed')
plt.suptitle('Phonon dispersion')
if 'labels' in self._bands:
plt.rcParams.update({'mathtext.default': 'regular'})
labels = self._bands['labels']
labels_e = []
x_labels = []
for i in range(len(band_data[1])):
if labels[i][0] == labels[i - 1][1]:
labels_e.append(replace_list(labels[i][0]))
else:
labels_e.append(
replace_list(labels[i - 1][1]) + '/' + replace_list(labels[i][0]))
x_labels.append(band_data[1][i][0])
x_labels.append(band_data[1][-1][-1])
labels_e.append(replace_list(labels[-1][1]))
labels_e[0] = replace_list(labels[0][0])
plt.xticks(x_labels, labels_e, rotation='horizontal')
ax.plot(external_data[:, 0], external_data[:, 1], 'o', color='b')
plt.show()
def get_qha_temperatures(self):
max_t_index = self.phonopy_qha._qha._get_max_t_index(self.phonopy_qha._qha._temperatures)
temperatures = self.phonopy_qha._qha._temperatures[:max_t_index]
return temperatures
def get_FC_constant_pressure(self):
temperatures = self.get_qha_temperatures()
volumes = self.phonopy_qha.get_volume_temperature()
for t, v in zip(temperatures[::20], volumes[::20]):
fc = self.fc_fit.get_total_force_constants(temperature=t, volume=v)
write_FORCE_CONSTANTS(fc.get_array(), filename='FC_{}'.format(t))
def get_total_shift_constant_pressure(self, qpoint=(0, 0, 0)):
qindex = self.fc_fit.qpoint_to_index(qpoint)
volumes = self.phonopy_qha.get_volume_temperature()
temperatures = self.get_qha_temperatures()
h_frequencies, ev = self.fc_fit.get_harmonic_frequencies_and_eigenvectors()
chk_shift_matrix = []
for v, t in zip(volumes, temperatures):
total_shifts = self.fc_fit.get_total_shifts(volume=v, temperature=t)
chk_shift_matrix.append(total_shifts)
chk_shift_matrix = np.array(chk_shift_matrix).T
return chk_shift_matrix[:, qindex]
def plot_total_shift_constant_pressure(self, qpoint=(0, 0, 0), branch=None):
import matplotlib.pyplot as plt
temperatures = self.get_qha_temperatures()
chk_shift_matrix = self.get_total_shift_constant_pressure(qpoint=qpoint)
plt.suptitle('Total frequency shift at wave vector={} (relative to {} K)'.format(qpoint, self.fc_fit.ref_temperature))
plt.xlabel('Temperature [K]')
plt.ylabel('Frequency shift [THz]')
if branch is None:
plt.plot(temperatures, chk_shift_matrix.T, '-')
else:
plt.title('Branch {}'.format(branch))
plt.plot(temperatures, chk_shift_matrix[branch].T, '-')
plt.show()
@property
def fc_fit(self):
return self._fc_fit
