def snl_to_wf_phonon(snl, parameters):
# parameters["user_vasp_settings"] specifies user defined incar/kpoints parameters
fws = []
connections = defaultdict(list)
parameters = parameters if parameters else {}
snl_priority = parameters.get('priority', 1)
priority = snl_priority * 2 # once we start a job, keep going!
f = Composition(snl.structure.composition.reduced_formula).alphabetical_formula
# add the SNL to the SNL DB and figure out duplicate group
tasks = [AddSNLTask()]
spec = {'task_type': 'Add to SNL database', 'snl': snl.as_dict(),
'_queueadapter': QA_DB, '_priority': snl_priority}
if 'snlgroup_id' in parameters and isinstance(snl, MPStructureNL):
spec['force_mpsnl'] = snl.as_dict()
spec['force_snlgroup_id'] = parameters['snlgroup_id']
del spec['snl']
fws.append(Firework(tasks, spec,
name=get_slug(f + '--' + spec['task_type']), fw_id=0))
connections[0] = [1]
parameters["exact_structure"] = True
# run GGA structure optimization for force convergence
spec = snl_to_wf._snl_to_spec(snl, parameters=parameters)
user_vasp_settings = parameters.get("user_vasp_settings")
spec = update_spec_force_convergence(spec, user_vasp_settings)
spec['run_tags'].append("origin")
spec['_priority'] = priority
spec['_queueadapter'] = QA_VASP
del spec['_dupefinder']
spec['task_type'] = "Vasp force convergence optimize structure (2x)"
tasks = [VaspWriterTask(), get_custodian_task(spec)]
fws.append(Firework(tasks, spec,
name=get_slug(f + '--' + spec['task_type']), fw_id=1))
# insert into DB - GGA structure optimization
spec = {'task_type': 'VASP db insertion', '_priority': priority,
'_allow_fizzled_parents': True, '_queueadapter': QA_DB,
'clean_task_doc':True, 'elastic_constant':"force_convergence"}
fws.append(Firework([VaspToDBTask()], spec,
name=get_slug(f + '--' + spec['task_type']), fw_id=2))
connections[1] = [2]
spec = {'task_type': 'Setup Deformed Struct Task', '_priority': priority,
'_queueadapter': QA_CONTROL}
fws.append(Firework([SetupDeformedStructTask()], spec,
name=get_slug(f + '--' + spec['task_type']),fw_id=3))
connections[2] = [3]
wf_meta = get_meta_from_structure(snl.structure)
wf_meta['run_version'] = 'May 2013 (1)'
if '_materialsproject' in snl.data and 'submission_id' in snl.data['_materialsproject']:
wf_meta['submission_id'] = snl.data['_materialsproject']['submission_id']
return Workflow(fws, connections, name=Composition(
snl.structure.composition.reduced_formula).alphabetical_formula, metadata=wf_meta)
def snl_to_wf_elastic(snl, parameters):
# parameters["user_vasp_settings"] specifies user defined incar/kpoints parameters
fws = []
connections = defaultdict(list)
parameters = parameters if parameters else {}
snl_priority = parameters.get('priority', 1)
priority = snl_priority * 2 # once we start a job, keep going!
f = Composition(snl.structure.composition.reduced_formula).alphabetical_formula
# add the SNL to the SNL DB and figure out duplicate group
tasks = [AddSNLTask()]
spec = {'task_type': 'Add to SNL database', 'snl': snl.as_dict(),
'_queueadapter': QA_DB, '_priority': snl_priority}
if 'snlgroup_id' in parameters and isinstance(snl, MPStructureNL):
spec['force_mpsnl'] = snl.as_dict()
spec['force_snlgroup_id'] = parameters['snlgroup_id']
del spec['snl']
fws.append(Firework(tasks, spec,
name=get_slug(f + '--' + spec['task_type']), fw_id=0))
connections[0] = [1]
parameters["exact_structure"] = True
# run GGA structure optimization for force convergence
spec = snl_to_wf._snl_to_spec(snl, parameters=parameters)
user_vasp_settings = parameters.get("user_vasp_settings")
spec = update_spec_force_convergence(spec, user_vasp_settings)
spec['run_tags'].append("origin")
spec['_priority'] = priority
spec['_queueadapter'] = QA_VASP
del spec['_dupefinder']
spec['task_type'] = "Vasp force convergence optimize structure (2x)"
tasks = [VaspWriterTask(), get_custodian_task(spec)]
fws.append(Firework(tasks, spec,
name=get_slug(f + '--' + spec['task_type']), fw_id=1))
# insert into DB - GGA structure optimization
spec = {'task_type': 'VASP db insertion', '_priority': priority,
'_allow_fizzled_parents': True, '_queueadapter': QA_DB,
'clean_task_doc':True, 'elastic_constant':"force_convergence"}
fws.append(Firework([VaspToDBTask()], spec,
name=get_slug(f + '--' + spec['task_type']), fw_id=2))
connections[1] = [2]
spec = {'task_type': 'Setup Deformed Struct Task', '_priority': priority,
'_queueadapter': QA_CONTROL}
fws.append(Firework([SetupDeformedStructTask()], spec,
name=get_slug(f + '--' + spec['task_type']),fw_id=3))
connections[2] = [3]
wf_meta = get_meta_from_structure(snl.structure)
wf_meta['run_version'] = 'May 2013 (1)'
if '_materialsproject' in snl.data and 'submission_id' in snl.data['_materialsproject']:
wf_meta['submission_id'] = snl.data['_materialsproject']['submission_id']
return Workflow(fws, connections, name=Composition(
snl.structure.composition.reduced_formula).alphabetical_formula, metadata=wf_meta)
def submit_snl(self, snl, submitter_email, parameters=None):
parameters = parameters if parameters else {}
d = snl.to_dict
d['submitter_email'] = submitter_email
d['parameters'] = parameters
d['state'] = 'submitted'
d['state_details'] = {}
d['task_dict'] = {}
d['submission_id'] = self._get_next_submission_id()
d['submitted_at'] = datetime.datetime.utcnow().isoformat()
d.update(get_meta_from_structure(snl.structure))
self.jobs.insert(d)
return d['submission_id']
def task_dict_to_wf(task_dict, launchpad):
fw_id = launchpad.get_new_fw_id()
l_id = launchpad.get_new_launch_id()
spec = {'task_type': task_dict['task_type'], 'run_tags': task_dict['run_tags'],
'vaspinputset_name': None, 'vasp': None, 'mpsnl': task_dict['snl'],
'snlgroup_id': task_dict['snlgroup_id']}
tasks = [DummyLegacyTask()]
launch_dir = task_dict['dir_name_full']
stored_data = {'error_list': []}
update_spec = {'prev_vasp_dir': task_dict['dir_name'],
'prev_task_type': spec['task_type'],
'mpsnl': spec['mpsnl'], 'snlgroup_id': spec['snlgroup_id'],
'run_tags': spec['run_tags']}
fwaction = FWAction(stored_data=stored_data, update_spec=update_spec)
if task_dict['completed_at']:
complete_date = datetime.datetime.strptime(task_dict['completed_at'], "%Y-%m-%d %H:%M:%S")
state_history = [{"created_on": complete_date, 'state': 'COMPLETED'}]
else:
state_history = []
launches = [Launch('COMPLETED', launch_dir, fworker=None, host=None, ip=None, action=fwaction,
state_history=state_history, launch_id=l_id, fw_id=fw_id)]
f = Composition(task_dict['pretty_formula']).alphabetical_formula
fw = Firework(tasks, spec, name=get_slug(f + '--' + spec['task_type']), launches=launches, state='COMPLETED', created_on=None,
fw_id=fw_id)
wf_meta = get_meta_from_structure(Structure.from_dict(task_dict['snl']))
wf_meta['run_version'] = 'preproduction (0)'
wf = Workflow.from_FireWork(fw, name=f, metadata=wf_meta)
launchpad.add_wf(wf, reassign_all=False)
launchpad._upsert_launch(launches[0])
print 'ADDED', fw_id
# return fw_id
return fw_id
def task_dict_to_wf(task_dict, launchpad):
fw_id = launchpad.get_new_fw_id()
l_id = launchpad.get_new_launch_id()
spec = {'task_type': task_dict['task_type'], 'run_tags': task_dict['run_tags'],
'vaspinputset_name': None, 'vasp': None, 'mpsnl': task_dict['snl'],
'snlgroup_id': task_dict['snlgroup_id']}
tasks = [DummyLegacyTask()]
launch_dir = task_dict['dir_name_full']
stored_data = {'error_list': []}
update_spec = {'prev_vasp_dir': task_dict['dir_name'],
'prev_task_type': spec['task_type'],
'mpsnl': spec['mpsnl'], 'snlgroup_id': spec['snlgroup_id'],
'run_tags': spec['run_tags']}
fwaction = FWAction(stored_data=stored_data, update_spec=update_spec)
if task_dict['completed_at']:
complete_date = datetime.datetime.strptime(task_dict['completed_at'], "%Y-%m-%d %H:%M:%S")
state_history = [{"created_on": complete_date, 'state': 'COMPLETED'}]
else:
state_history = []
launches = [Launch('COMPLETED', launch_dir, fworker=None, host=None, ip=None, action=fwaction,
state_history=state_history, launch_id=l_id, fw_id=fw_id)]
f = Composition.from_formula(task_dict['pretty_formula']).alphabetical_formula
fw = FireWork(tasks, spec, name=get_slug(f + '--' + spec['task_type']), launches=launches, state='COMPLETED', created_on=None,
fw_id=fw_id)
wf_meta = get_meta_from_structure(Structure.from_dict(task_dict['snl']))
wf_meta['run_version'] = 'preproduction (0)'
wf = Workflow.from_FireWork(fw, name=f, metadata=wf_meta)
launchpad.add_wf(wf, reassign_all=False)
launchpad._upsert_launch(launches[0])
print 'ADDED', fw_id
# return fw_id
return fw_id
def run_task(self, fw_spec):
# get the band structure and nelect from files
"""
prev_dir = get_loc(fw_spec['prev_vasp_dir'])
vasprun_loc = zpath(os.path.join(prev_dir, 'vasprun.xml'))
kpoints_loc = zpath(os.path.join(prev_dir, 'KPOINTS'))
vr = Vasprun(vasprun_loc)
bs = vr.get_band_structure(kpoints_filename=kpoints_loc)
"""
# get the band structure and nelect from DB
block_part = get_block_part(fw_spec['prev_vasp_dir'])
db_dir = os.environ['DB_LOC']
assert isinstance(db_dir, object)
db_path = os.path.join(db_dir, 'tasks_db.json')
with open(db_path) as f:
creds = json.load(f)
connection = MongoClient(creds['host'], creds['port'])
tdb = connection[creds['database']]
tdb.authenticate(creds['admin_user'], creds['admin_password'])
m_task = tdb.tasks.find_one({"dir_name": block_part}, {"calculations": 1, "task_id": 1})
nelect = m_task['calculations'][0]['input']['parameters']['NELECT']
bs_id = m_task['calculations'][0]['band_structure_fs_id']
print bs_id, type(bs_id)
fs = gridfs.GridFS(tdb, 'band_structure_fs')
bs_dict = json.loads(fs.get(bs_id).read())
bs_dict['structure'] = m_task['calculations'][0]['output']['crystal']
bs = BandStructure.from_dict(bs_dict)
print 'Band Structure found:', bool(bs)
print nelect
# run Boltztrap
runner = BoltztrapRunner(bs, nelect)
dir = runner.run(path_dir=os.getcwd())
# put the data in the database
bta = BoltztrapAnalyzer.from_files(dir)
data = bta.to_dict
data.update(get_meta_from_structure(bs._structure))
data['snlgroup_id'] = fw_spec['snlgroup_id']
data['run_tags'] = fw_spec['run_tags']
data['snl'] = fw_spec['mpsnl']
data['dir_name_full'] = dir
data['dir_name'] = get_block_part(dir)
data['task_id'] = m_task['task_id']
data['hall'] = {} # remove because it is too large and not useful
data['hall_doping'] = {} # remove because it is too large and not useful
tdb.boltztrap.insert(clean_json(data))
update_spec = {'prev_vasp_dir': fw_spec['prev_vasp_dir'],
'boltztrap_dir': os.getcwd(),
'prev_task_type': fw_spec['task_type'],
'mpsnl': fw_spec['mpsnl'],
'snlgroup_id': fw_spec['snlgroup_id'],
'run_tags': fw_spec['run_tags'], 'parameters': fw_spec.get('parameters')}
return FWAction(update_spec=update_spec)
def snl_to_wf(snl, parameters=None):
fws = []
connections = defaultdict(list)
parameters = parameters if parameters else {}
snl_priority = parameters.get('priority', 1)
priority = snl_priority * 2 # once we start a job, keep going!
f = Composition(snl.structure.composition.reduced_formula).alphabetical_formula
snl_spec = {}
if 'snlgroup_id' in parameters:
if 'mpsnl' in parameters:
snl_spec['mpsnl'] = parameters['mpsnl']
elif isinstance(snl, MPStructureNL):
snl_spec['mpsnl'] = snl.as_dict()
else:
raise ValueError("improper use of force SNL")
snl_spec['snlgroup_id'] = parameters['snlgroup_id']
else:
# add the SNL to the SNL DB and figure out duplicate group
tasks = [AddSNLTask()]
spec = {'task_type': 'Add to SNL database', 'snl': snl.as_dict(), '_queueadapter': QA_DB, '_priority': snl_priority}
fws.append(Firework(tasks, spec, name=get_slug(f + '--' + spec['task_type']), fw_id=0))
connections[0] = [1]
trackers = [Tracker('FW_job.out'), Tracker('FW_job.error'), Tracker('vasp.out'), Tracker('OUTCAR'), Tracker('OSZICAR'), Tracker('OUTCAR.relax1'), Tracker('OUTCAR.relax2')]
trackers_db = [Tracker('FW_job.out'), Tracker('FW_job.error')]
# run GGA structure optimization
spec = _snl_to_spec(snl, enforce_gga=True, parameters=parameters)
spec.update(snl_spec)
spec['_priority'] = priority
spec['_queueadapter'] = QA_VASP
spec['_trackers'] = trackers
tasks = [VaspWriterTask(), get_custodian_task(spec)]
fws.append(Firework(tasks, spec, name=get_slug(f + '--' + spec['task_type']), fw_id=1))
# insert into DB - GGA structure optimization
spec = {'task_type': 'VASP db insertion', '_priority': priority*2,
'_allow_fizzled_parents': True, '_queueadapter': QA_DB, "_dupefinder": DupeFinderDB().to_dict(), '_trackers': trackers_db}
fws.append(
Firework([VaspToDBTask()], spec, name=get_slug(f + '--' + spec['task_type']), fw_id=2))
connections[1] = [2]
# determine if GGA+U FW is needed
incar = MPVaspInputSet().get_incar(snl.structure).as_dict()
ggau_compound = ('LDAU' in incar and incar['LDAU'])
if not parameters.get('skip_bandstructure', False) and (not ggau_compound or parameters.get('force_gga_bandstructure', False)):
spec = {'task_type': 'Controller: add Electronic Structure v2', '_priority': priority,
'_queueadapter': QA_CONTROL}
fws.append(
Firework([AddEStructureTask()], spec, name=get_slug(f + '--' + spec['task_type']),
fw_id=3))
connections[2] = [3]
if ggau_compound:
spec = _snl_to_spec(snl, enforce_gga=False, parameters=parameters)
del spec['vasp'] # we are stealing all VASP params and such from previous run
spec['_priority'] = priority
spec['_queueadapter'] = QA_VASP
spec['_trackers'] = trackers
fws.append(Firework(
[VaspCopyTask(), SetupGGAUTask(),
get_custodian_task(spec)], spec, name=get_slug(f + '--' + spec['task_type']),
fw_id=10))
connections[2].append(10)
spec = {'task_type': 'VASP db insertion', '_queueadapter': QA_DB,
'_allow_fizzled_parents': True, '_priority': priority, "_dupefinder": DupeFinderDB().to_dict(), '_trackers': trackers_db}
fws.append(
Firework([VaspToDBTask()], spec, name=get_slug(f + '--' + spec['task_type']), fw_id=11))
connections[10] = [11]
if not parameters.get('skip_bandstructure', False):
spec = {'task_type': 'Controller: add Electronic Structure v2', '_priority': priority,
'_queueadapter': QA_CONTROL}
fws.append(
Firework([AddEStructureTask()], spec, name=get_slug(f + '--' + spec['task_type']),
fw_id=12))
connections[11] = [12]
wf_meta = get_meta_from_structure(snl.structure)
wf_meta['run_version'] = 'May 2013 (1)' # not maintained
if '_materialsproject' in snl.data and 'submission_id' in snl.data['_materialsproject']:
wf_meta['submission_id'] = snl.data['_materialsproject']['submission_id']
return Workflow(fws, connections, name=Composition(
snl.structure.composition.reduced_formula).alphabetical_formula, metadata=wf_meta)
def task_dict_to_wf(task_dict, launchpad):
fw_id = launchpad.get_new_fw_id()
l_id = launchpad.get_new_launch_id()
spec = {
"task_type": task_dict["task_type"],
"run_tags": task_dict["run_tags"],
"vaspinputset_name": None,
"vasp": None,
"mpsnl": task_dict["snl"],
"snlgroup_id": task_dict["snlgroup_id"],
}
tasks = [DummyLegacyTask()]
launch_dir = task_dict["dir_name_full"]
stored_data = {"error_list": []}
update_spec = {
"prev_vasp_dir": task_dict["dir_name"],
"prev_task_type": spec["task_type"],
"mpsnl": spec["mpsnl"],
"snlgroup_id": spec["snlgroup_id"],
"run_tags": spec["run_tags"],
}
fwaction = FWAction(stored_data=stored_data, update_spec=update_spec)
if task_dict["completed_at"]:
complete_date = datetime.datetime.strptime(task_dict["completed_at"], "%Y-%m-%d %H:%M:%S")
state_history = [{"created_on": complete_date, "state": "COMPLETED"}]
else:
state_history = []
launches = [
Launch(
"COMPLETED",
launch_dir,
fworker=None,
host=None,
ip=None,
action=fwaction,
state_history=state_history,
launch_id=l_id,
fw_id=fw_id,
)
]
f = Composition(task_dict["pretty_formula"]).alphabetical_formula
fw = Firework(
tasks,
spec,
name=get_slug(f + "--" + spec["task_type"]),
launches=launches,
state="COMPLETED",
created_on=None,
fw_id=fw_id,
)
wf_meta = get_meta_from_structure(Structure.from_dict(task_dict["snl"]))
wf_meta["run_version"] = "preproduction (0)"
wf = Workflow.from_FireWork(fw, name=f, metadata=wf_meta)
launchpad.add_wf(wf, reassign_all=False)
launchpad._upsert_launch(launches[0])
print "ADDED", fw_id
# return fw_id
return fw_id
def run_task(self, fw_spec):
# import here to prevent import errors in bigger MPCollab
from mpcollab.thermoelectrics.boltztrap_TE import BoltztrapAnalyzerTE, BoltzSPB
# get the band structure and nelect from files
"""
prev_dir = get_loc(fw_spec['prev_vasp_dir'])
vasprun_loc = zpath(os.path.join(prev_dir, 'vasprun.xml'))
kpoints_loc = zpath(os.path.join(prev_dir, 'KPOINTS'))
vr = Vasprun(vasprun_loc)
bs = vr.get_band_structure(kpoints_filename=kpoints_loc)
"""
filename = get_slug('JOB--' +
fw_spec['mpsnl']['reduced_cell_formula_abc'] +
'--' + fw_spec['task_type'])
with open(filename, 'w+') as f:
f.write('')
# get the band structure and nelect from DB
block_part = get_block_part(fw_spec['prev_vasp_dir'])
db_dir = os.environ['DB_LOC']
assert isinstance(db_dir, object)
db_path = os.path.join(db_dir, 'tasks_db.json')
with open(db_path) as f:
creds = json.load(f)
connection = MongoClient(creds['host'], creds['port'])
tdb = connection[creds['database']]
tdb.authenticate(creds['admin_user'], creds['admin_password'])
props = {
"calculations": 1,
"task_id": 1,
"state": 1,
"pseudo_potential": 1,
"run_type": 1,
"is_hubbard": 1,
"hubbards": 1,
"unit_cell_formula": 1
}
m_task = tdb.tasks.find_one({"dir_name": block_part}, props)
if not m_task:
time.sleep(
60) # only thing to think of is wait for DB insertion(?)
m_task = tdb.tasks.find_one({"dir_name": block_part}, props)
if not m_task:
raise ValueError(
"Could not find task with dir_name: {}".format(block_part))
if m_task['state'] != 'successful':
raise ValueError(
"Cannot run Boltztrap; parent job unsuccessful")
nelect = m_task['calculations'][0]['input']['parameters']['NELECT']
bs_id = m_task['calculations'][0]['band_structure_fs_id']
print bs_id, type(bs_id)
fs = gridfs.GridFS(tdb, 'band_structure_fs')
bs_dict = json.loads(fs.get(bs_id).read())
bs_dict['structure'] = m_task['calculations'][0]['output'][
'crystal']
bs = BandStructure.from_dict(bs_dict)
print 'Band Structure found:', bool(bs)
print nelect
# run Boltztrap
runner = BoltztrapRunner(bs, nelect)
dir = runner.run(path_dir=os.getcwd())
# put the data in the database
bta = BoltztrapAnalyzer.from_files(dir)
data = bta.as_dict()
data.update(get_meta_from_structure(bs._structure))
data['snlgroup_id'] = fw_spec['snlgroup_id']
data['run_tags'] = fw_spec['run_tags']
data['snl'] = fw_spec['mpsnl']
data['dir_name_full'] = dir
data['dir_name'] = get_block_part(dir)
data['task_id'] = m_task['task_id']
del data['hall'] # remove because it is too large and not useful
fs = gridfs.GridFS(tdb, "boltztrap_full_fs")
btid = fs.put(json.dumps(jsanitize(data)))
# now for the "sanitized" data
te_analyzer = BoltztrapAnalyzerTE.from_BoltztrapAnalyzer(bta)
ted = te_analyzer.as_dict()
del ted['seebeck']
del ted['hall']
del ted['kappa']
del ted['cond']
ted['boltztrap_full_fs_id'] = btid
ted['snlgroup_id'] = fw_spec['snlgroup_id']
ted['run_tags'] = fw_spec['run_tags']
ted['snl'] = fw_spec['mpsnl']
ted['dir_name_full'] = dir
ted['dir_name'] = get_block_part(dir)
ted['task_id'] = m_task['task_id']
ted['pf_doping'] = te_analyzer.get_power_factor(
tau=self.TAU).as_dict()
ted['zt_doping'] = te_analyzer.get_ZT(kappal=self.KAPPAL,
tau=self.TAU).as_dict()
ted['pf_eigs'] = self.get_eigs(ted, 'pf_doping')
ted['pf_best'] = self.get_extreme(ted, 'pf_eigs')
ted['pf_best_dope18'] = self.get_extreme(ted,
'pf_eigs',
max_didx=3)
ted['pf_best_dope19'] = self.get_extreme(ted,
'pf_eigs',
max_didx=4)
ted['zt_eigs'] = self.get_eigs(ted, 'zt_doping')
ted['zt_best'] = self.get_extreme(ted, 'zt_eigs')
ted['zt_best_dope18'] = self.get_extreme(ted,
'zt_eigs',
max_didx=3)
ted['zt_best_dope19'] = self.get_extreme(ted,
'zt_eigs',
max_didx=4)
ted['seebeck_eigs'] = self.get_eigs(ted, 'seebeck_doping')
ted['seebeck_best'] = self.get_extreme(ted, 'seebeck_eigs')
ted['seebeck_best_dope18'] = self.get_extreme(ted,
'seebeck_eigs',
max_didx=3)
ted['seebeck_best_dope19'] = self.get_extreme(ted,
'seebeck_eigs',
max_didx=4)
ted['cond_eigs'] = self.get_eigs(ted, 'cond_doping')
ted['cond_best'] = self.get_extreme(ted, 'cond_eigs')
ted['cond_best_dope18'] = self.get_extreme(ted,
'cond_eigs',
max_didx=3)
ted['cond_best_dope19'] = self.get_extreme(ted,
'cond_eigs',
max_didx=4)
ted['kappa_eigs'] = self.get_eigs(ted, 'kappa_doping')
ted['kappa_best'] = self.get_extreme(ted,
'kappa_eigs',
maximize=False)
ted['kappa_best_dope18'] = self.get_extreme(ted,
'kappa_eigs',
maximize=False,
max_didx=3)
ted['kappa_best_dope19'] = self.get_extreme(ted,
'kappa_eigs',
maximize=False,
max_didx=4)
try:
bzspb = BoltzSPB(te_analyzer)
maxpf_p = bzspb.get_maximum_power_factor('p', temperature=0, tau=1E-14, ZT=False, kappal=0.5,\
otherprops=('get_seebeck_mu_eig', 'get_conductivity_mu_eig', \
'get_thermal_conductivity_mu_eig', 'get_average_eff_mass_tensor_mu'))
maxpf_n = bzspb.get_maximum_power_factor('n', temperature=0, tau=1E-14, ZT=False, kappal=0.5,\
otherprops=('get_seebeck_mu_eig', 'get_conductivity_mu_eig', \
'get_thermal_conductivity_mu_eig', 'get_average_eff_mass_tensor_mu'))
maxzt_p = bzspb.get_maximum_power_factor('p', temperature=0, tau=1E-14, ZT=True, kappal=0.5, otherprops=('get_seebeck_mu_eig', 'get_conductivity_mu_eig', \
'get_thermal_conductivity_mu_eig', 'get_average_eff_mass_tensor_mu'))
maxzt_n = bzspb.get_maximum_power_factor('n', temperature=0, tau=1E-14, ZT=True, kappal=0.5, otherprops=('get_seebeck_mu_eig', 'get_conductivity_mu_eig', \
'get_thermal_conductivity_mu_eig', 'get_average_eff_mass_tensor_mu'))
ted['zt_best_finemesh'] = {'p': maxzt_p, 'n': maxzt_n}
ted['pf_best_finemesh'] = {'p': maxpf_p, 'n': maxpf_n}
except:
import traceback
traceback.print_exc()
print 'COULD NOT GET FINE MESH DATA'
# add is_compatible
mpc = MaterialsProjectCompatibility("Advanced")
try:
func = m_task["pseudo_potential"]["functional"]
labels = m_task["pseudo_potential"]["labels"]
symbols = ["{} {}".format(func, label) for label in labels]
parameters = {
"run_type": m_task["run_type"],
"is_hubbard": m_task["is_hubbard"],
"hubbards": m_task["hubbards"],
"potcar_symbols": symbols
}
entry = ComputedEntry(Composition(m_task["unit_cell_formula"]),
0.0,
0.0,
parameters=parameters,
entry_id=m_task["task_id"])
ted["is_compatible"] = bool(mpc.process_entry(entry))
except:
traceback.print_exc()
print 'ERROR in getting compatibility, task_id: {}'.format(
m_task["task_id"])
ted["is_compatible"] = None
tdb.boltztrap.insert(jsanitize(ted))
update_spec = {
'prev_vasp_dir': fw_spec['prev_vasp_dir'],
'boltztrap_dir': os.getcwd(),
'prev_task_type': fw_spec['task_type'],
'mpsnl': fw_spec['mpsnl'],
'snlgroup_id': fw_spec['snlgroup_id'],
'run_tags': fw_spec['run_tags'],
'parameters': fw_spec.get('parameters')
}
return FWAction(update_spec=update_spec)
def snl_to_wf(snl, parameters=None):
fws = []
connections = defaultdict(list)
parameters = parameters if parameters else {}
snl_priority = parameters.get('priority', 1)
priority = snl_priority * 2 # once we start a job, keep going!
f = Composition(
snl.structure.composition.reduced_formula).alphabetical_formula
snl_spec = {}
if 'snlgroup_id' in parameters:
if 'mpsnl' in parameters:
snl_spec['mpsnl'] = parameters['mpsnl']
elif isinstance(snl, MPStructureNL):
snl_spec['mpsnl'] = snl.as_dict()
else:
raise ValueError("improper use of force SNL")
snl_spec['snlgroup_id'] = parameters['snlgroup_id']
else:
# add the SNL to the SNL DB and figure out duplicate group
tasks = [AddSNLTask()]
spec = {
'task_type': 'Add to SNL database',
'snl': snl.as_dict(),
'_queueadapter': QA_DB,
'_priority': snl_priority
}
fws.append(
Firework(tasks,
spec,
name=get_slug(f + '--' + spec['task_type']),
fw_id=0))
connections[0] = [1]
trackers = [
Tracker('FW_job.out'),
Tracker('FW_job.error'),
Tracker('vasp.out'),
Tracker('OUTCAR'),
Tracker('OSZICAR'),
Tracker('OUTCAR.relax1'),
Tracker('OUTCAR.relax2')
]
trackers_db = [Tracker('FW_job.out'), Tracker('FW_job.error')]
# run GGA structure optimization
spec = _snl_to_spec(snl, enforce_gga=True, parameters=parameters)
spec.update(snl_spec)
spec['_priority'] = priority
spec['_queueadapter'] = QA_VASP
spec['_trackers'] = trackers
tasks = [VaspWriterTask(), get_custodian_task(spec)]
fws.append(
Firework(tasks,
spec,
name=get_slug(f + '--' + spec['task_type']),
fw_id=1))
# insert into DB - GGA structure optimization
spec = {
'task_type': 'VASP db insertion',
'_priority': priority * 2,
'_allow_fizzled_parents': True,
'_queueadapter': QA_DB,
"_dupefinder": DupeFinderDB().to_dict(),
'_trackers': trackers_db
}
fws.append(
Firework([VaspToDBTask()],
spec,
name=get_slug(f + '--' + spec['task_type']),
fw_id=2))
connections[1] = [2]
# determine if GGA+U FW is needed
incar = MPVaspInputSet().get_incar(snl.structure).as_dict()
ggau_compound = ('LDAU' in incar and incar['LDAU'])
if not parameters.get('skip_bandstructure', False) and (
not ggau_compound
or parameters.get('force_gga_bandstructure', False)):
spec = {
'task_type': 'Controller: add Electronic Structure v2',
'_priority': priority,
'_queueadapter': QA_CONTROL
}
fws.append(
Firework([AddEStructureTask()],
spec,
name=get_slug(f + '--' + spec['task_type']),
fw_id=3))
connections[2] = [3]
if ggau_compound:
spec = _snl_to_spec(snl, enforce_gga=False, parameters=parameters)
del spec[
'vasp'] # we are stealing all VASP params and such from previous run
spec['_priority'] = priority
spec['_queueadapter'] = QA_VASP
spec['_trackers'] = trackers
fws.append(
Firework(
[VaspCopyTask(),
SetupGGAUTask(),
get_custodian_task(spec)],
spec,
name=get_slug(f + '--' + spec['task_type']),
fw_id=10))
connections[2].append(10)
spec = {
'task_type': 'VASP db insertion',
'_queueadapter': QA_DB,
'_allow_fizzled_parents': True,
'_priority': priority,
"_dupefinder": DupeFinderDB().to_dict(),
'_trackers': trackers_db
}
fws.append(
Firework([VaspToDBTask()],
spec,
name=get_slug(f + '--' + spec['task_type']),
fw_id=11))
connections[10] = [11]
if not parameters.get('skip_bandstructure', False):
spec = {
'task_type': 'Controller: add Electronic Structure v2',
'_priority': priority,
'_queueadapter': QA_CONTROL
}
fws.append(
Firework([AddEStructureTask()],
spec,
name=get_slug(f + '--' + spec['task_type']),
fw_id=12))
connections[11] = [12]
wf_meta = get_meta_from_structure(snl.structure)
wf_meta['run_version'] = 'May 2013 (1)'
if '_materialsproject' in snl.data and 'submission_id' in snl.data[
'_materialsproject']:
wf_meta['submission_id'] = snl.data['_materialsproject'][
'submission_id']
return Workflow(
fws,
connections,
name=Composition(
snl.structure.composition.reduced_formula).alphabetical_formula,
metadata=wf_meta)
def run_task(self, fw_spec):
# get the band structure and nelect from files
"""
prev_dir = get_loc(fw_spec['prev_vasp_dir'])
vasprun_loc = zpath(os.path.join(prev_dir, 'vasprun.xml'))
kpoints_loc = zpath(os.path.join(prev_dir, 'KPOINTS'))
vr = Vasprun(vasprun_loc)
bs = vr.get_band_structure(kpoints_filename=kpoints_loc)
"""
# get the band structure and nelect from DB
block_part = get_block_part(fw_spec['prev_vasp_dir'])
db_dir = os.environ['DB_LOC']
assert isinstance(db_dir, object)
db_path = os.path.join(db_dir, 'tasks_db.json')
with open(db_path) as f:
creds = json.load(f)
connection = MongoClient(creds['host'], creds['port'])
tdb = connection[creds['database']]
tdb.authenticate(creds['admin_user'], creds['admin_password'])
m_task = tdb.tasks.find_one({"dir_name": block_part}, {
"calculations": 1,
"task_id": 1
})
nelect = m_task['calculations'][0]['input']['parameters']['NELECT']
bs_id = m_task['calculations'][0]['band_structure_fs_id']
print bs_id, type(bs_id)
fs = gridfs.GridFS(tdb, 'band_structure_fs')
bs_dict = json.loads(fs.get(bs_id).read())
bs_dict['structure'] = m_task['calculations'][0]['output'][
'crystal']
bs = BandStructure.from_dict(bs_dict)
print 'Band Structure found:', bool(bs)
print nelect
# run Boltztrap
runner = BoltztrapRunner(bs, nelect)
dir = runner.run(path_dir=os.getcwd())
# put the data in the database
bta = BoltztrapAnalyzer.from_files(dir)
data = bta.to_dict
data.update(get_meta_from_structure(bs._structure))
data['snlgroup_id'] = fw_spec['snlgroup_id']
data['run_tags'] = fw_spec['run_tags']
data['snl'] = fw_spec['mpsnl']
data['dir_name_full'] = dir
data['dir_name'] = get_block_part(dir)
data['task_id'] = m_task['task_id']
data['hall'] = {} # remove because it is too large and not useful
data['hall_doping'] = {
} # remove because it is too large and not useful
tdb.boltztrap.insert(clean_json(data))
update_spec = {
'prev_vasp_dir': fw_spec['prev_vasp_dir'],
'boltztrap_dir': os.getcwd(),
'prev_task_type': fw_spec['task_type'],
'mpsnl': fw_spec['mpsnl'],
'snlgroup_id': fw_spec['snlgroup_id'],
'run_tags': fw_spec['run_tags'],
'parameters': fw_spec.get('parameters')
}
return FWAction(update_spec=update_spec)
def snl_to_wf(snl, parameters=None):
fws = []
connections = {}
parameters = parameters if parameters else {}
snl_priority = parameters.get('priority', 1)
priority = snl_priority * 2 # once we start a job, keep going!
f = Composition.from_formula(snl.structure.composition.reduced_formula).alphabetical_formula
# add the SNL to the SNL DB and figure out duplicate group
tasks = [AddSNLTask()]
spec = {'task_type': 'Add to SNL database', 'snl': snl.to_dict, '_queueadapter': QA_DB, '_priority': snl_priority}
if 'snlgroup_id' in parameters and isinstance(snl, MPStructureNL):
spec['force_mpsnl'] = snl.to_dict
spec['force_snlgroup_id'] = parameters['snlgroup_id']
del spec['snl']
fws.append(FireWork(tasks, spec, name=get_slug(f + '--' + spec['task_type']), fw_id=0))
connections[0] = [1]
# run GGA structure optimization
spec = _snl_to_spec(snl, enforce_gga=True)
spec['_priority'] = priority
spec['_queueadapter'] = QA_VASP
tasks = [VaspWriterTask(), get_custodian_task(spec)]
fws.append(FireWork(tasks, spec, name=get_slug(f + '--' + spec['task_type']), fw_id=1))
# insert into DB - GGA structure optimization
spec = {'task_type': 'VASP db insertion', '_priority': priority,
'_allow_fizzled_parents': True, '_queueadapter': QA_DB}
fws.append(
FireWork([VaspToDBTask()], spec, name=get_slug(f + '--' + spec['task_type']), fw_id=2))
connections[1] = [2]
if not parameters.get('skip_bandstructure', False):
spec = {'task_type': 'Controller: add Electronic Structure v2', '_priority': priority,
'_queueadapter': QA_CONTROL}
fws.append(
FireWork([AddEStructureTask()], spec, name=get_slug(f + '--' + spec['task_type']),
fw_id=3))
connections[2] = [3]
# determine if GGA+U FW is needed
incar = MPVaspInputSet().get_incar(snl.structure).to_dict
if 'LDAU' in incar and incar['LDAU']:
spec = _snl_to_spec(snl, enforce_gga=False)
del spec['vasp'] # we are stealing all VASP params and such from previous run
spec['_priority'] = priority
spec['_queueadapter'] = QA_VASP
fws.append(FireWork(
[VaspCopyTask(), SetupGGAUTask(),
get_custodian_task(spec)], spec, name=get_slug(f + '--' + spec['task_type']),
fw_id=10))
connections[2].append(10)
spec = {'task_type': 'VASP db insertion', '_queueadapter': QA_DB,
'_allow_fizzled_parents': True, '_priority': priority}
fws.append(
FireWork([VaspToDBTask()], spec, name=get_slug(f + '--' + spec['task_type']), fw_id=11))
connections[10] = [11]
if not parameters.get('skip_bandstructure', False):
spec = {'task_type': 'Controller: add Electronic Structure v2', '_priority': priority,
'_queueadapter': QA_CONTROL}
fws.append(
FireWork([AddEStructureTask()], spec, name=get_slug(f + '--' + spec['task_type']),
fw_id=12))
connections[11] = [12]
wf_meta = get_meta_from_structure(snl.structure)
wf_meta['run_version'] = 'May 2013 (1)'
if '_materialsproject' in snl.data and 'submission_id' in snl.data['_materialsproject']:
wf_meta['submission_id'] = snl.data['_materialsproject']['submission_id']
return Workflow(fws, connections, name=Composition.from_formula(
snl.structure.composition.reduced_formula).alphabetical_formula, metadata=wf_meta)
def snl_to_wf(snl, parameters=None):
fws = []
connections = {}
parameters = parameters if parameters else {}
snl_priority = parameters.get('priority', 1)
priority = snl_priority * 2 # once we start a job, keep going!
f = Composition.from_formula(
snl.structure.composition.reduced_formula).alphabetical_formula
# add the SNL to the SNL DB and figure out duplicate group
tasks = [AddSNLTask()]
spec = {
'task_type': 'Add to SNL database',
'snl': snl.to_dict,
'_queueadapter': QA_DB,
'_priority': snl_priority
}
if 'snlgroup_id' in parameters and isinstance(snl, MPStructureNL):
spec['force_mpsnl'] = snl.to_dict
spec['force_snlgroup_id'] = parameters['snlgroup_id']
del spec['snl']
fws.append(
FireWork(tasks,
spec,
name=get_slug(f + '--' + spec['task_type']),
fw_id=0))
connections[0] = [1]
# run GGA structure optimization
spec = _snl_to_spec(snl, enforce_gga=True)
spec['_priority'] = priority
spec['_queueadapter'] = QA_VASP
tasks = [VaspWriterTask(), get_custodian_task(spec)]
fws.append(
FireWork(tasks,
spec,
name=get_slug(f + '--' + spec['task_type']),
fw_id=1))
# insert into DB - GGA structure optimization
spec = {
'task_type': 'VASP db insertion',
'_priority': priority,
'_allow_fizzled_parents': True,
'_queueadapter': QA_DB
}
fws.append(
FireWork([VaspToDBTask()],
spec,
name=get_slug(f + '--' + spec['task_type']),
fw_id=2))
connections[1] = [2]
if not parameters.get('skip_bandstructure', False):
spec = {
'task_type': 'Controller: add Electronic Structure v2',
'_priority': priority,
'_queueadapter': QA_CONTROL
}
fws.append(
FireWork([AddEStructureTask()],
spec,
name=get_slug(f + '--' + spec['task_type']),
fw_id=3))
connections[2] = [3]
# determine if GGA+U FW is needed
incar = MPVaspInputSet().get_incar(snl.structure).to_dict
if 'LDAU' in incar and incar['LDAU']:
spec = _snl_to_spec(snl, enforce_gga=False)
del spec[
'vasp'] # we are stealing all VASP params and such from previous run
spec['_priority'] = priority
spec['_queueadapter'] = QA_VASP
fws.append(
FireWork(
[VaspCopyTask(),
SetupGGAUTask(),
get_custodian_task(spec)],
spec,
name=get_slug(f + '--' + spec['task_type']),
fw_id=10))
connections[2].append(10)
spec = {
'task_type': 'VASP db insertion',
'_queueadapter': QA_DB,
'_allow_fizzled_parents': True,
'_priority': priority
}
fws.append(
FireWork([VaspToDBTask()],
spec,
name=get_slug(f + '--' + spec['task_type']),
fw_id=11))
connections[10] = [11]
if not parameters.get('skip_bandstructure', False):
spec = {
'task_type': 'Controller: add Electronic Structure v2',
'_priority': priority,
'_queueadapter': QA_CONTROL
}
fws.append(
FireWork([AddEStructureTask()],
spec,
name=get_slug(f + '--' + spec['task_type']),
fw_id=12))
connections[11] = [12]
wf_meta = get_meta_from_structure(snl.structure)
wf_meta['run_version'] = 'May 2013 (1)'
if '_materialsproject' in snl.data and 'submission_id' in snl.data[
'_materialsproject']:
wf_meta['submission_id'] = snl.data['_materialsproject'][
'submission_id']
return Workflow(
fws,
connections,
name=Composition.from_formula(
snl.structure.composition.reduced_formula).alphabetical_formula,
metadata=wf_meta)
def run_task(self, fw_spec):
# import here to prevent import errors in bigger MPCollab
from mpcollab.thermoelectrics.boltztrap_TE import BoltztrapAnalyzerTE, BoltzSPB
# get the band structure and nelect from files
"""
prev_dir = get_loc(fw_spec['prev_vasp_dir'])
vasprun_loc = zpath(os.path.join(prev_dir, 'vasprun.xml'))
kpoints_loc = zpath(os.path.join(prev_dir, 'KPOINTS'))
vr = Vasprun(vasprun_loc)
bs = vr.get_band_structure(kpoints_filename=kpoints_loc)
"""
filename = get_slug(
'JOB--' + fw_spec['mpsnl']['reduced_cell_formula_abc'] + '--'
+ fw_spec['task_type'])
with open(filename, 'w+') as f:
f.write('')
# get the band structure and nelect from DB
block_part = get_block_part(fw_spec['prev_vasp_dir'])
db_dir = os.environ['DB_LOC']
assert isinstance(db_dir, object)
db_path = os.path.join(db_dir, 'tasks_db.json')
with open(db_path) as f:
creds = json.load(f)
connection = MongoClient(creds['host'], creds['port'])
tdb = connection[creds['database']]
tdb.authenticate(creds['admin_user'], creds['admin_password'])
props = {"calculations": 1, "task_id": 1, "state": 1, "pseudo_potential": 1, "run_type": 1, "is_hubbard": 1, "hubbards": 1, "unit_cell_formula": 1}
m_task = tdb.tasks.find_one({"dir_name": block_part}, props)
if not m_task:
time.sleep(60) # only thing to think of is wait for DB insertion(?)
m_task = tdb.tasks.find_one({"dir_name": block_part}, props)
if not m_task:
raise ValueError("Could not find task with dir_name: {}".format(block_part))
if m_task['state'] != 'successful':
raise ValueError("Cannot run Boltztrap; parent job unsuccessful")
nelect = m_task['calculations'][0]['input']['parameters']['NELECT']
bs_id = m_task['calculations'][0]['band_structure_fs_id']
print bs_id, type(bs_id)
fs = gridfs.GridFS(tdb, 'band_structure_fs')
bs_dict = json.loads(fs.get(bs_id).read())
bs_dict['structure'] = m_task['calculations'][0]['output']['crystal']
bs = BandStructure.from_dict(bs_dict)
print 'Band Structure found:', bool(bs)
print nelect
# run Boltztrap
runner = BoltztrapRunner(bs, nelect)
dir = runner.run(path_dir=os.getcwd())
# put the data in the database
bta = BoltztrapAnalyzer.from_files(dir)
data = bta.as_dict()
data.update(get_meta_from_structure(bs._structure))
data['snlgroup_id'] = fw_spec['snlgroup_id']
data['run_tags'] = fw_spec['run_tags']
data['snl'] = fw_spec['mpsnl']
data['dir_name_full'] = dir
data['dir_name'] = get_block_part(dir)
data['task_id'] = m_task['task_id']
del data['hall'] # remove because it is too large and not useful
fs = gridfs.GridFS(tdb, "boltztrap_full_fs")
btid = fs.put(json.dumps(jsanitize(data)))
# now for the "sanitized" data
te_analyzer = BoltztrapAnalyzerTE.from_BoltztrapAnalyzer(bta)
ted = te_analyzer.as_dict()
del ted['seebeck']
del ted['hall']
del ted['kappa']
del ted['cond']
ted['boltztrap_full_fs_id'] = btid
ted['snlgroup_id'] = fw_spec['snlgroup_id']
ted['run_tags'] = fw_spec['run_tags']
ted['snl'] = fw_spec['mpsnl']
ted['dir_name_full'] = dir
ted['dir_name'] = get_block_part(dir)
ted['task_id'] = m_task['task_id']
ted['pf_doping'] = te_analyzer.get_power_factor(tau=self.TAU).as_dict()
ted['zt_doping'] = te_analyzer.get_ZT(kappal=self.KAPPAL, tau=self.TAU).as_dict()
ted['pf_eigs'] = self.get_eigs(ted, 'pf_doping')
ted['pf_best'] = self.get_extreme(ted, 'pf_eigs')
ted['pf_best_dope18'] = self.get_extreme(ted, 'pf_eigs', max_didx=3)
ted['pf_best_dope19'] = self.get_extreme(ted, 'pf_eigs', max_didx=4)
ted['zt_eigs'] = self.get_eigs(ted, 'zt_doping')
ted['zt_best'] = self.get_extreme(ted, 'zt_eigs')
ted['zt_best_dope18'] = self.get_extreme(ted, 'zt_eigs', max_didx=3)
ted['zt_best_dope19'] = self.get_extreme(ted, 'zt_eigs', max_didx=4)
ted['seebeck_eigs'] = self.get_eigs(ted, 'seebeck_doping')
ted['seebeck_best'] = self.get_extreme(ted, 'seebeck_eigs')
ted['seebeck_best_dope18'] = self.get_extreme(ted, 'seebeck_eigs', max_didx=3)
ted['seebeck_best_dope19'] = self.get_extreme(ted, 'seebeck_eigs', max_didx=4)
ted['cond_eigs'] = self.get_eigs(ted, 'cond_doping')
ted['cond_best'] = self.get_extreme(ted, 'cond_eigs')
ted['cond_best_dope18'] = self.get_extreme(ted, 'cond_eigs', max_didx=3)
ted['cond_best_dope19'] = self.get_extreme(ted, 'cond_eigs', max_didx=4)
ted['kappa_eigs'] = self.get_eigs(ted, 'kappa_doping')
ted['kappa_best'] = self.get_extreme(ted, 'kappa_eigs', maximize=False)
ted['kappa_best_dope18'] = self.get_extreme(ted, 'kappa_eigs', maximize=False, max_didx=3)
ted['kappa_best_dope19'] = self.get_extreme(ted, 'kappa_eigs', maximize=False, max_didx=4)
try:
bzspb = BoltzSPB(te_analyzer)
maxpf_p = bzspb.get_maximum_power_factor('p', temperature=0, tau=1E-14, ZT=False, kappal=0.5,\
otherprops=('get_seebeck_mu_eig', 'get_conductivity_mu_eig', \
'get_thermal_conductivity_mu_eig', 'get_average_eff_mass_tensor_mu'))
maxpf_n = bzspb.get_maximum_power_factor('n', temperature=0, tau=1E-14, ZT=False, kappal=0.5,\
otherprops=('get_seebeck_mu_eig', 'get_conductivity_mu_eig', \
'get_thermal_conductivity_mu_eig', 'get_average_eff_mass_tensor_mu'))
maxzt_p = bzspb.get_maximum_power_factor('p', temperature=0, tau=1E-14, ZT=True, kappal=0.5, otherprops=('get_seebeck_mu_eig', 'get_conductivity_mu_eig', \
'get_thermal_conductivity_mu_eig', 'get_average_eff_mass_tensor_mu'))
maxzt_n = bzspb.get_maximum_power_factor('n', temperature=0, tau=1E-14, ZT=True, kappal=0.5, otherprops=('get_seebeck_mu_eig', 'get_conductivity_mu_eig', \
'get_thermal_conductivity_mu_eig', 'get_average_eff_mass_tensor_mu'))
ted['zt_best_finemesh'] = {'p': maxzt_p, 'n': maxzt_n}
ted['pf_best_finemesh'] = {'p': maxpf_p, 'n': maxpf_n}
except:
import traceback
traceback.print_exc()
print 'COULD NOT GET FINE MESH DATA'
# add is_compatible
mpc = MaterialsProjectCompatibility("Advanced")
try:
func = m_task["pseudo_potential"]["functional"]
labels = m_task["pseudo_potential"]["labels"]
symbols = ["{} {}".format(func, label) for label in labels]
parameters = {"run_type": m_task["run_type"],
"is_hubbard": m_task["is_hubbard"],
"hubbards": m_task["hubbards"],
"potcar_symbols": symbols}
entry = ComputedEntry(Composition(m_task["unit_cell_formula"]),
0.0, 0.0, parameters=parameters,
entry_id=m_task["task_id"])
ted["is_compatible"] = bool(mpc.process_entry(entry))
except:
traceback.print_exc()
print 'ERROR in getting compatibility, task_id: {}'.format(m_task["task_id"])
ted["is_compatible"] = None
tdb.boltztrap.insert(jsanitize(ted))
update_spec = {'prev_vasp_dir': fw_spec['prev_vasp_dir'],
'boltztrap_dir': os.getcwd(),
'prev_task_type': fw_spec['task_type'],
'mpsnl': fw_spec['mpsnl'],
'snlgroup_id': fw_spec['snlgroup_id'],
'run_tags': fw_spec['run_tags'], 'parameters': fw_spec.get('parameters')}
return FWAction(update_spec=update_spec)
