rmd.filedump(run_specs, filename)
rmd.init_stdout()
# read settings
# POSCAR
if 'poscar' in run_specs:
structure = rmd.get_structure(run_specs)
elif os.path.isfile('../POSCAR'):
structure = mg.Structure.from_file('../POSCAR')
rmd.insert_elem_types(run_specs, structure)
# INCAR
incar = rmd.read_incar(run_specs)
if os.path.isfile('../properties.json'):
properties = rmd.fileload('../properties.json')
if 'ISPIN' not in incar:
if rmd.detect_is_mag(properties['mag']):
incar.update({'ISPIN': 2})
else:
incar.update({'ISPIN': 1})
# POTCAR dump
rmd.write_potcar(run_specs)
potcars = mg.io.vasp.Potcar.from_file('POTCAR')
incar['ENCUT'] = rmd.get_max_ENMAX(potcars)
NBANDS, basisfunctions_str = get_NBANDS_and_basisfunctions_str(potcars, structure)
incar['NBANDS'] = NBANDS
# KPOINTS
slice: [null, -4]
The slice is applied to the volume and structure list.
"""
run_specs, filename = rmd.get_run_specs_and_filename()
cwd = os.getcwd()
rmd.chdir(rmd.get_run_dir(run_specs))
rmd.filedump(run_specs, filename)
phonopy_specs = run_specs['phonopy']
phonopy_specs['dim'] = ' '.join(map(str, phonopy_specs['dim']))
incar = rmd.read_incar(run_specs)
if os.path.isfile('../properties.json'):
properties = rmd.fileload('../properties.json')
if 'ISPIN' not in incar:
if rmd.detect_is_mag(properties['mag']):
incar.update({'ISPIN': 2})
else:
incar.update({'ISPIN': 1})
# higher priority for run_specs
if 'poscar' in run_specs:
structure = rmd.get_structure(run_specs)
elif os.path.isfile('../POSCAR'):
structure = mg.Structure.from_file('../POSCAR')
rmd.insert_elem_types(run_specs, structure)
kpoints = rmd.read_kpoints(run_specs, structure)
is_well_fitted, V0, structure, is_mag = volume_fitting(structure, is_mag, fitting_results)
if 'volume' in run_specs and 'rerun' in run_specs['volume'] and run_specs['volume']['rerun']:
# possible next rounds
while not is_well_fitted:
V_begin = V0 * 9./10
V_end = V0 * 11./10
is_well_fitted, V0, structure, is_mag = volume_fitting(structure, is_mag, fitting_results)
# pressure runs
if 'pressure' in run_specs and run_specs['pressure']:
pressure_params = run_specs['pressure']
p_begin = pressure_params['begin']
p_end = pressure_params['end']
V_sample_point_num = pressure_params['sample_point_num']
params = rmd.fileload('fitting_params.json')
from scipy import optimize
def pressure_func(V):
return pv.fitting.vinet_p(V, params['V0'], params['B0'], params['B0_prime']) - p0
V_list = []
for p0 in [p_begin, p_end]:
V_list.append(optimize.fsolve(pressure_func, structure.volume)[0])
V_begin, V_end = V_list
is_well_fitted, V0, structure, is_mag = volume_fitting(structure, is_mag, fitting_results)
# equilibrium volume run
structure.scale_lattice(V0)
structure.to(filename='POSCAR')
incar.write_file('INCAR')
kpoints.write_file('KPOINTS')
rmd.write_potcar(run_specs)
run_specs, filename = rmd.get_run_specs_and_filename()
rmd.chdir(rmd.get_run_dir(run_specs))
phonopy_specs = run_specs['phonopy']
phonopy_specs['dim'] = ' '.join(map(str, phonopy_specs['dim']))
phonopy_specs['mp'] = ' '.join(map(str, phonopy_specs['mp']))
phonopy_specs['tmax'] = str(phonopy_specs['tmax'])
phonopy_specs['tstep'] = str(phonopy_specs['tstep'])
run_volume_dirname = phonopy_specs['volumes_and_structures']['from']\
if 'volumes_and_structures' in phonopy_specs and \
'from' in phonopy_specs['volumes_and_structures'] and \
phonopy_specs['volumes_and_structures']['from'] else 'run_volume'
if isinstance(run_volume_dirname, str):
fitting_results = rmd.fileload(os.path.join('..',
run_volume_dirname, 'fitting_results.json'))[-1]
volume = fitting_results['volume']
energy = fitting_results['energy']
elif isinstance(run_volume_dirname, list):
volume = []
energy = []
for dirname in run_volume_dirname:
fitting_results = rmd.fileload(os.path.join('..',
dirname, 'fitting_results.json'))[-1]
volume.extend(fitting_results['volume'])
energy.extend(fitting_results['energy'])
idx_slice = phonopy_specs['volumes_and_structures']['slice'] if \
'volumes_and_structures' in phonopy_specs and \
'slice' in phonopy_specs['volumes_and_structures'] and \
phonopy_specs['volumes_and_structures']['slice'] else [None] * 2
would use to actually run the routine script run_elastic_single.py before
this.
"""
run_specs, filename = rmd.get_run_specs_and_filename()
rmd.chdir(rmd.get_run_dir(run_specs))
cryst_sys = run_specs['elastic']['cryst_sys']
test_type_input = run_specs['elastic']['test_type']
incar = rmd.read_incar(run_specs)
if 'ISPIN' in incar:
is_mag = incar['ISPIN'] == 2
elif os.path.isfile(('../properties.json')):
properties = rmd.fileload('../properties.json')
is_mag = rmd.detect_is_mag(properties['mag'])
else:
is_mag = False
test_type_list, strain_list, delta_list = rmd_e.get_test_type_strain_delta_list(cryst_sys)
for test_type, strain, delta in zip(test_type_list, strain_list, delta_list):
if test_type == test_type_input:
rmd.chdir(test_type)
energy = np.zeros(len(delta))
mag = np.zeros(len(delta))
for ind, value in enumerate(delta):
rmd.chdir(str(value))
oszicar = mg.io.vasp.Oszicar('OSZICAR')
energy[ind] = oszicar.final_energy
if is_mag:
file you used to actually run the routine script
run_elastic_stress_strain.py before this.
"""
# pre-config
run_specs, filename = rmd.get_run_specs_and_filename()
rmd.chdir(rmd.get_run_dir(run_specs))
rmd.filedump(run_specs, filename)
rmd.init_stdout()
# read settings
structure = rmd.get_structure(run_specs)
# elastic
strain_list = [i for i in rmd.fileload('strain_list.json')]
stress_list = []
for idx, strain in enumerate(strain_list):
vasprun = mg.io.vasp.Vasprun(str(idx) + '/vasprun.xml')
stress_list.append(
elasticity.stress.Stress(vasprun.ionic_steps[-1]['stress']) / -10)
rmd.filedump([i.tolist() for i in stress_list], 'stress_list.json')
elastic_tensor = elasticity.elastic.ElasticTensor.from_strain_stress_list(
strain_list, stress_list).voigt
elastic_tensor_df = pd.DataFrame(elastic_tensor,
index=range(1, 7),
columns=range(1, 7))
elastic_tensor_df.to_csv('elastic_tensor.csv')
pd.set_option('display.float_format', lambda x: '%.6f' % x)
print(elastic_tensor_df)
range. A second run is done using those volume values. the tag
'skip_test_run' tells the code to just use the fitting_params.json in the
working directory without doing the test run.
"""
run_specs, filename = rmd.get_run_specs_and_filename()
rmd.chdir(rmd.get_run_dir(run_specs))
rmd.filedump(run_specs, filename)
rmd.init_stdout()
incar = rmd.read_incar(run_specs)
is_properties = None
if os.path.isfile(('../properties.json')):
is_properties = True
properties = rmd.fileload('../properties.json')
if 'ISPIN' in incar:
is_mag = incar['ISPIN'] == 2
elif is_properties:
is_mag = rmd.detect_is_mag(properties['mag'])
if is_mag:
incar.update({'ISPIN': 2})
else:
incar.update({'ISPIN': 1})
else:
is_mag = False
# higher priority for run_specs
if 'poscar' in run_specs:
structure = rmd.get_structure(run_specs)
python INPUT/process_elastic_stress_strain.py INPUT/run_elastic_stress_strain.yaml
to read a specs file at INPUT/run_elastic_stress_strain.yaml, which is the
file you used to actually run the routine script
run_elastic_stress_strain.py before this.
"""
# pre-config
run_specs, filename = rmd.get_run_specs_and_filename()
rmd.chdir(rmd.get_run_dir(run_specs))
rmd.filedump(run_specs, filename)
rmd.init_stdout()
# read settings
structure = rmd.get_structure(run_specs)
# elastic
strain_list = [i for i in rmd.fileload('strain_list.json')]
stress_list = []
for idx, strain in enumerate(strain_list):
vasprun = mg.io.vasp.Vasprun(str(idx) + '/vasprun.xml')
stress_list.append(elasticity.stress.Stress(vasprun.ionic_steps[-1]['stress'])/-10)
rmd.filedump([i.tolist() for i in stress_list], 'stress_list.json')
elastic_tensor = elasticity.elastic.ElasticTensor.from_strain_stress_list(strain_list, stress_list)
elastic_tensor_df = pd.DataFrame(elastic_tensor, index=range(1, 7), columns=range(1, 7))
elastic_tensor_df.to_csv('elastic_tensor.csv')
pd.set_option('display.float_format', lambda x: '%.6f' % x)
print(elastic_tensor_df)
plt.savefig(test_type + '.pdf')
plt.close()
fitting_results['params'] = fitting_results['params'].tolist()
fitting_results.pop('fitted_data')
fitting_results.pop('ax')
fitting_results['delta'] = delta.tolist()
fitting_results['energy'] = energy.tolist()
fitting_results['mag'] = mag.tolist()
rmd.filedump(fitting_results, 'fitting_results.json')
# higher level fitting_results.json
fitting_results_summary[test_type] = fitting_results
rmd.filedump(fitting_results_summary, '../fitting_results.json')
shutil.copy(test_type + '.pdf', '..')
os.chdir('..')
# fitting_results_summary['c11+2c12'] = {}
# fitting_results_summary['c11+2c12']['params'] = [properties['B0'] * structure.volume / 160.2 * 9/2.]
combined_econst_array = [
fitting_results_summary[test_type]['params'][0]
for test_type in test_type_list
]
combined_econst_array = np.array(
combined_econst_array) * 160.2 / structure.volume
solved = rmd_e.solve(cryst_sys, combined_econst_array)
rmd.filedump(solved, 'elastic.json')
if os.path.isfile('../properties.json'):
properties = rmd.fileload('../properties.json')
properties['elastic'] = solved
rmd.filedump(properties, '../properties.json')
to read a specs file at INPUT/run_elastic_single.yaml, which is the file you
would use to actually run the routine script run_elastic_single.py and the
process script process_elastic_single.py for all the independent strain
sets before this.
"""
run_specs, filename = rmd.get_run_specs_and_filename()
rmd.chdir(rmd.get_run_dir(run_specs))
cryst_sys = run_specs['elastic']['cryst_sys']
is_properties = None
if os.path.isfile(('../properties.json')):
is_properties = True
properties = rmd.fileload('../properties.json')
# higher priority for run_specs
if 'poscar' in run_specs:
structure = rmd.get_structure(run_specs)
elif os.path.isfile('../POSCAR'):
structure = mg.Structure.from_file('../POSCAR')
rmd.insert_elem_types(run_specs, structure)
test_type_list, strain_list, delta_list = rmd_e.get_test_type_strain_delta_list(cryst_sys)
fitting_results_summary = rmd.fileload('fitting_results.json')
# fitting_results_summary['c11+2c12'] = {}
# fitting_results_summary['c11+2c12']['params'] = [properties['B0'] * structure.volume / 160.2 * 9/2.]
combined_econst_array = [fitting_results_summary[test_type]['params'][0] for test_type in test_type_list]
combined_econst_array = np.array(combined_econst_array) * 160.2 / structure.volume
structure_copy = structure.copy()
structure_copy.modify_lattice(lattice_modified)
structure_copy.to(filename='POSCAR')
rmd.write_potcar(run_specs)
rmd.run_vasp()
oszicar = mg.io.vasp.Oszicar('OSZICAR')
energy[ind] = oszicar.final_energy
if is_mag:
mag[ind] = oszicar.ionic_steps[-1]['mag']
os.chdir('..')
fitting_results = pydass_vasp.fitting.curve_fit(rmd_e.central_poly, delta, energy, plot=True)
plt.savefig(test_type + '.pdf')
plt.close()
fitting_results['params'] = fitting_results['params'].tolist()
fitting_results.pop('fitted_data')
fitting_results.pop('ax')
fitting_results['delta'] = delta.tolist()
fitting_results['energy'] = energy.tolist()
fitting_results['mag'] = mag.tolist()
rmd.filedump(fitting_results, 'fitting_results.json')
# higher level fitting_results.json
if os.path.isfile('../fitting_results.json'):
fitting_results_summary = rmd.fileload('../fitting_results.json')
else:
fitting_results_summary = {}
fitting_results_summary[test_type] = fitting_results
rmd.filedump(fitting_results_summary, '../fitting_results.json')
shutil.copy(test_type + '.pdf', '..')
os.chdir('..')
rmd.run_vasp()
oszicar = mg.io.vasp.Oszicar('OSZICAR')
energy[ind] = oszicar.final_energy
if is_mag:
mag[ind] = oszicar.ionic_steps[-1]['mag']
os.chdir('..')
fitting_results = pydass_vasp.fitting.curve_fit(rmd_e.central_poly,
delta,
energy,
plot=True)
plt.savefig(test_type + '.pdf')
plt.close()
fitting_results['params'] = fitting_results['params'].tolist()
fitting_results.pop('fitted_data')
fitting_results.pop('ax')
fitting_results['delta'] = delta.tolist()
fitting_results['energy'] = energy.tolist()
fitting_results['mag'] = mag.tolist()
rmd.filedump(fitting_results, 'fitting_results.json')
# higher level fitting_results.json
if os.path.isfile('../fitting_results.json'):
fitting_results_summary = rmd.fileload(
'../fitting_results.json')
else:
fitting_results_summary = {}
fitting_results_summary[test_type] = fitting_results
rmd.filedump(fitting_results_summary, '../fitting_results.json')
shutil.copy(test_type + '.pdf', '..')
os.chdir('..')
if 'volume' in run_specs and 'rerun' in run_specs['volume'] and run_specs[
'volume']['rerun']:
# possible next rounds
while not is_well_fitted:
V_begin = V0 * 9. / 10
V_end = V0 * 11. / 10
is_well_fitted, V0, structure, is_mag = volume_fitting(
structure, is_mag, fitting_results)
# pressure runs
if 'pressure' in run_specs and run_specs['pressure']:
pressure_params = run_specs['pressure']
p_begin = pressure_params['begin']
p_end = pressure_params['end']
V_sample_point_num = pressure_params['sample_point_num']
params = rmd.fileload('fitting_params.json')
from scipy import optimize
def pressure_func(V):
return pv.fitting.vinet_p(V, params['V0'], params['B0'],
params['B0_prime']) - p0
V_list = []
for p0 in [p_begin, p_end]:
V_list.append(optimize.fsolve(pressure_func, structure.volume)[0])
V_begin, V_end = V_list
is_well_fitted, V0, structure, is_mag = volume_fitting(
structure, is_mag, fitting_results)
# equilibrium volume run
structure.scale_lattice(V0)