def phono3py(filename,
quantities=['kappa', 'temperature'],
write_lifetime=False,
write_mfp=False,
write_occupation=False):
"""Loads Phono3py data.
Can also calculate lifetimes, mean free paths and occupations, which
can be written to a file.
Includes unit conversions and outputs units for all the data (see
tp.settings). Also corrects mode_kappa for different phono3py
versions.
Arguments
---------
filename : str
filepath.
quantities : list, optional
values to extract. Accepts Phono3py keys, lifetime,
mean_free_path and occupation. Default: kappa, temperature.
write_lifetime : bool, optional
write lifetimes to a new hdf5 file if in quantites.
Default: False.
write_mfp : bool, optional
write mean free paths to a new hdf5 file if in quantities.
Default: False.
write_occupation : bool, optional
write occupations to a new hdf5 file if in quantities.
Default: False.
Returns
-------
dict
output data.
"""
import h5py
# name conversions and abbreviations
conversions = settings.phono3py_conversions()
pnames = settings.to_phono3py()
tnames = settings.to_tp()
units = settings.units()
if isinstance(quantities, str): quantities = quantities.split()
quantities = [pnames[q] if q in pnames else q for q in quantities]
subs = {
'dispersion': 'qpoint',
'waterfall': 'frequency',
'wideband': ['frequency', 'gamma', 'qpoint']
}
hast = [
'gamma', 'heat_capacity', 'kappa', 'lifetime', 'mean_free_path',
'mode_kappa', 'occupation'
]
for i in range(len(quantities)):
if quantities[i] in subs:
quantities[i] = subs[quantities[i]]
quantities = list(np.ravel(quantities))
# add dependant variables
if 'temperature' not in quantities:
for q in quantities:
if q in hast:
quantities.append('temperature')
break
# load and calculate data
data = h5py.File(filename, 'r')
data2 = {'meta': {'kappa_source': 'phono3py', 'units': {}}}
for q in quantities:
assert q in data or q in ['lifetime', 'mean_free_path', 'occupation'], \
'{} unrecognised. Quantity must be {}, lifetime, mean_free_path ' \
'or occupation'.format(q, ', '.join(data))
q2 = tnames[q] if q in tnames else q
if q in data:
data2[q2] = data[q][()]
elif q in ['lifetime', 'mean_free_path']:
data2['lifetime'] = np.reciprocal(np.multiply(
2, data['gamma'][()]))
data2['lifetime'] = np.where(np.isinf(data2['lifetime']), 0,
data2['lifetime'])
if q == 'mean_free_path':
data2[q] = np.multiply(
np.transpose([
data2['lifetime'],
] * 3, (1, 2, 3, 0)), data['group_velocity'][()])
elif q == 'occupation':
from tp.calculate import be_occupation as occupation
data2[q] = [
occupation(data['frequency'][()], t)
for t in data['temperature'][()]
]
if q2 in units:
data2['meta']['units'][q2] = units[q2]
# write calculated data (loath to mess with original file)
for write, q in zip([write_lifetime, write_mfp, write_occupation],
['lifetime', 'mean_free_path', 'occupation']):
if write and q in quantities:
data3 = h5py.File('{}-{}'.format(q, filename), 'w')
for q2 in [q, 'temperature', 'qpoint']:
data3.create_dataset(q2, np.shape(data2[q2]), data=data2[q2])
data3.close()
# check mode_kappa and correct for certain phono3py versions
if 'mode_kappa' in data2:
try:
k = round(data['kappa'][-1][0], 3)
mk = round(data['mode_kappa'][-1][:, :, 0].sum(axis=1).sum(), 3)
if k != mk:
raise Exception(
'The sum of mode_kappa does not equal kappa.\n '
'kappa={:.3f}; sum(mode_kappa)={:.3f}.'.format(k, mk))
except Exception:
mk2 = np.divide(data['mode_kappa'], np.prod(data['mesh'][:]))
k = round(data['kappa'][-1][0], 3)
mk = round(mk2[-1][:, :, 0].sum(axis=1).sum(), 3)
if k != mk:
raise Exception(
'Mode kappa has been divided by the mesh, but '
'the sum of mode_kappa does not equal kappa.\n '
'kappa={:.3f}; sum(mode_kappa)={:.3f}.'.format(k, mk))
else:
data2['mode_kappa'] = np.divide(data2['mode_kappa'],
np.prod(data['mesh'][()][:]))
data.close()
for c in conversions:
if c in data2:
data2[c] = np.multiply(data2[c], conversions[c])
return data2
def boltztrap(filename,
quantities=[
'temperature', 'doping', 'seebeck', 'conductivity',
'electronic_thermal_conductivity'
],
doping='n'):
"""Loads BoltzTraP data from the tp boltztrap.hdf5 file.
Includes unit conversion and outputs units (see tp.settings).
Arguments
---------
filename : str
filepath.
quantites : dict, optional
values to extract. Accepts boltztrap.hdf5 keys.
Default: temperature, doping, seebeck, conductivity,
electronic_thermal_conductivity.
doping : str, optional
doping. Default: n.
Returns
-------
dict
extracted values.
"""
import h5py
# name conversions and abbreviations
assert doping in ['n', 'p'], 'doping must be n or p'
conversions = settings.boltztrap_conversions()
bnames = settings.to_boltztrap()
tnames = settings.to_tp()
units = settings.units()
if isinstance(quantities, str): quantities = quantities.split()
quantities = [bnames[q] if q in bnames else q for q in quantities]
# list of quantities dependant on doping and temperature
hasdope = [
'average_eff_mass', 'conductivity', 'fermi_level', 'seebeck',
'power_factor', 'electronic_thermal_conductivity'
]
hastemp = [
'average_eff_mass', 'conductivity', 'fermi_level', 'seebeck',
'power_factor', 'electronic_thermal_conductivity'
]
# add dependant variables
if 'doping' not in quantities:
for q in quantities:
if q in hasdope:
quantities.append('doping')
break
if 'temperature' not in quantities:
for q in quantities:
if q in hastemp:
quantities.append('temperature')
break
# load data
data = h5py.File(filename, 'r')
data2 = {'meta': {'electronic_source': 'boltztrap', 'units': {}}}
for q in quantities:
assert q in data, '{} unrecognised. Quantity must be in {} or {}.'.format(
q, ', '.join(list(data)[:-1]),
list(data)[-1])
q2 = tnames[q] if q in tnames else q
data2[q2] = data[q]
if q in hasdope: data2[q2] = data2[q2][doping]
if q2 in units:
data2['meta']['units'][q2] = units[q2]
for c in conversions:
if c in data2:
data2[c] = np.multiply(data2[c], conversions[c])
return data2
def amset_mesh(filename,
quantities=[
'temperature', 'doping', 'scattering_rates',
'scattering_labels'
],
spin='avg'):
"""Loads AMSET mesh data.
Includes unit conversion and outputs units (see tp.settings).
Swaps temperature and doping indices so temperature is first, for
consistency with other codes.
Arguments
---------
filename : str
filepath.
quantites : dict, optional
values to extract. Accepts AMSET keys, without spin
channels, which are dealt with in the spin variable.
Default: temperature, doping, scattering_rates,
scattering_labels.
spin : str, optional
spin. Accepts up, down or avg. If avg and there is only one
spin channel, selects that, else averages both. Default: avg.
Returns
-------
dict
extracted values.
"""
import h5py
# name conversions and abbreviations
conversions = settings.amset_conversions()
anames = settings.to_amset()
tnames = settings.to_tp()
units = settings.units()
if isinstance(quantities, str): quantities = quantities.split()
quantities = [anames[q] if q in anames else q for q in quantities]
# list of quantities dependant on doping and temperature
hasdope = ['fermi_levels', 'scattering_rates']
hastemp = ['fermi_levels', 'scattering_rates']
hastype = ['scattering_rates']
hasspin = ['energies', 'vb_index', 'scattering_rates', 'velocities']
# add dependant variables
if 'doping' not in quantities:
for q in quantities:
if q in hasdope:
quantities.append('doping')
break
if 'temperatures' not in quantities:
for q in quantities:
if q in hastemp:
quantities.append('temperatures')
break
if 'scattering_labels' not in quantities:
for q in quantities:
if q in hastype:
quantities.append('scattering_labels')
break
# load data
data = h5py.File(filename, 'r')
if spin in ['avg', 'average']:
if 'energies_up' in data and 'energies_down' in data:
spin = 'avg'
elif 'energies_up' in data:
spin = 'up'
elif 'energies_down' in data:
spin = 'down'
data2 = {'meta': {'electronic_source': 'amset', 'spin': spin, 'units': {}}}
for q in quantities:
q2 = tnames[q] if q in tnames else q
if q in hasspin:
if spin == 'avg':
data2[q2] = np.average(
[data['{}_up'.format(q)], data['{}_down'.format(q)]],
axis=0)
elif spin in ['up', 'down']:
data2[q2] = data['{}_{}'.format(q, spin)]
else:
raise Exception('spin must be up or down or avg')
else:
data2[q2] = data[q]
if q in hasdope and q in hastemp:
# temperature in first index for consistency with other codes
if q in hastype:
data2[q2] = np.swapaxes(data2[q2], 1, 2)
else:
data2[q2] = np.swapaxes(data2[q2], 0, 1)
if q2 in units:
data2['meta']['units'][q2] = units[q2]
for c in conversions:
if c in data2:
data2[c] = np.multiply(data2[c], conversions[c])
return data2
def amset(filename,
quantities=[
'temperature', 'doping', 'seebeck', 'conductivity',
'electronic_thermal_conductivity'
]):
"""Loads AMSET transport data.
Includes unit conversion and outputs units (see tp.settings).
Swaps temperature and doping indices so temperature is first, for
consistency with other codes, and mobility is formatted as an array,
like scattering_rates is in the mesh.h5 file. Maintains basic
compatibility with amset 0.1.
Arguments
---------
filename : str
filepath.
quantites : dict, optional
values to extract. Default: temperature, doping, seebeck,
conductivity, electronic_thermal_conductivity.
Returns
-------
dict
extracted values.
"""
import json
# name conversions and abbreviations
conversions = settings.amset_conversions()
anames = settings.to_amset()
tnames = settings.to_tp()
units = settings.units()
if isinstance(quantities, str): quantities = quantities.split()
quantities = [anames[q] if q in anames else q for q in quantities]
# list of quantities dependant on doping, temperature and scattering
hasdope = [
'fermi_levels', 'conductivity', 'seebeck',
'electronic_thermal_conductivity', 'mobility'
]
hastemp = [
'fermi_levels', 'conductivity', 'seebeck',
'electronic_thermal_conductivity', 'mobility'
]
hastype = ['mobility']
# add dependant variables
if 'doping' not in quantities:
for q in quantities:
if q in hasdope:
quantities.append('doping')
break
if 'temperatures' not in quantities:
for q in quantities:
if q in hastemp:
quantities.append('temperatures')
break
# load data
with open(filename) as f:
data = json.load(f)
data2 = {'meta': {'electronic_source': 'amset', 'units': {}}}
for q in quantities:
assert q in data, \
'{} unrecognised. Quantity must be in {} or {}.'.format(q,
', '.join(list(data)[:-1]), list(data)[-1])
q2 = tnames[q] if q in tnames else q
# compatibility with previous version
if isinstance(data[q], dict) and 'data' in data[q]:
data2[q2] = data[q]['data']
else:
data2[q2] = data[q]
if q in hasdope and q in hastemp:
# temperature index first for consistency with other codes
if q in hastype:
for t in data2[q2]:
data2[q2][t] = np.swapaxes(data2[q2][t], 0, 1)
else:
data2[q2] = np.swapaxes(data2[q2], 0, 1)
if q in hastype:
if 'scattering_labels' not in data2:
data2['scattering_labels'] = data[q].keys()
# for consistency with the format in the mesh data
data2[q2] = [data2[q2][l] for l in data2['scattering_labels']]
if q2 in units:
data2['meta']['units'][q2] = units[q2]
for c in conversions:
if c in data2:
data2[c] = np.multiply(data2[c], conversions[c])
return data2
def amset(filename,
quantities=[
'temperatures', 'doping', 'seebeck', 'conductivity',
'electronic_thermal_conductivity'
],
spin='up'):
"""Loads AMSET data.
Includes unit conversion and outputs units (see tp.settings).
Arguments:
filename : str
filepath.
quantites : dict, optional
values to extract. Accepts AMSET keys
Default: temperatures, doping, seebeck, conductivity,
electronic_thermal_conductivity.
spin : str, optional
spin. Default: up.
Returns:
dict
extracted values.
"""
import json
# name conversions and abbreviations
conversions = settings.amset_conversions()
anames = settings.to_amset()
tnames = settings.to_tp()
units = settings.units()
if isinstance(quantities, str): quantities = quantities.split()
quantities = [anames[q] if q in anames else q for q in quantities]
# list of quantities requiring 'data' key, scattering type and spin
hasdata = [
'doping',
'temperatures',
'fermi_levels',
'conductivity',
'seebeck',
'electronic_thermal_conductivity', # 'mobility',
'kpoints',
'ir_kpoints',
'ir_to_full_kpoint_mapping'
]
hastype = ['mobility', 'scattering_rates', 'scattering_labels']
hasspin = ['energies', 'vb_idx',
'velocities_product'] #, 'scattering_rates']
# list of quantities dependant on doping and temperature
hasdope = [
'fermi_levels', 'conductivity', 'seebeck',
'electronic_thermal_conductivity', 'mobility', 'scattering_rates'
]
hastemp = [
'fermi_levels', 'conductivity', 'seebeck',
'electronic_thermal_conductivity', 'mobility', 'energies',
'velocities_product', 'scattering_rates'
]
# add dependant variables
if 'doping' not in quantities:
for q in quantities:
if q in hasdope:
quantities.append('doping')
break
if 'temperatures' not in quantities:
for q in quantities:
if q in hastemp:
quantities.append('temperatures')
break
# load data
with open(filename) as f:
data = json.load(f)
assert spin in data['scattering_rates'].keys(), \
'spin must be in {}'.format(' or '.join(data['scattering_rates'].keys()))
data2 = {'meta': {'electronic_source': 'amset', 'units': {}}}
for q in quantities:
assert q in data, '{} unrecognised. Quantity must be in {} or {}.'.format(
q, ', '.join(list(data)[:-1]),
list(data)[-1])
q2 = tnames[q] if q in tnames else q
data2[q2] = data[q]
if q in hasspin: data2[q2] = data2[q2][spin]
if q in hasdata: data2[q2] = data2[q2]['data']
if q in hasdope and q not in hastype:
# for consistency with other codes
data2[q2] = np.swapaxes(data2[q2], 0, 1)
if q2 in units:
data2['meta']['units'][q2] = units[q2]
for c in conversions:
if c in data2:
data2[c] = np.multiply(data2[c], conversions[c])
return data2