def test_clean_json(self):
#clean_json should have no effect on None types.
d = {"hello": 1, "world": None}
clean = clean_json(d)
self.assertIsNone(clean["world"])
self.assertEqual(json.dumps(d), json.dumps(clean))
d = {"hello": self.get_test_structure()}
self.assertRaises(TypeError, json.dumps, d)
clean = clean_json(d)
self.assertIsInstance(clean["hello"], basestring)
clean_strict = clean_json(d, strict=True)
self.assertIsInstance(clean_strict["hello"], dict)
def test_clean_json(self):
#clean_json should have no effect on None types.
d = {"hello": 1, "world": None}
clean = clean_json(d)
self.assertIsNone(clean["world"])
self.assertEqual(json.dumps(d), json.dumps(clean))
d = {"hello": self.get_test_structure()}
self.assertRaises(TypeError, json.dumps, d)
clean = clean_json(d)
self.assertIsInstance(clean["hello"], basestring)
clean_strict = clean_json(d, strict=True)
self.assertIsInstance(clean_strict["hello"], dict)
def get_dos_dict(self):
"""
Returns the added doses as a json-serializable dict. Note that if you
have specified smearing for the DOS plot, the densities returned will
be the smeared densities, not the original densities.
Returns:
Dict of dos data. Generally of the form, {label: {'energies':..,
'densities': {'up':...}, 'efermi':efermi}}
"""
return clean_json(self._doses)
def get_dos_dict(self):
"""
Returns the added doses as a json-serializable dict. Note that if you
have specified smearing for the DOS plot, the densities returned will
be the smeared densities, not the original densities.
Returns:
Dict of dos data. Generally of the form, {label: {'energies':..,
'densities': {'up':...}, 'efermi':efermi}}
"""
return clean_json(self._doses)
def to_dict(self):
from pymatgen.util.io_utils import clean_json
results = {'gap': self.gap,
'mu_steps': self.mu_steps,
'cond': self.cond,
'seebeck': self.seebeck,
'kappa': self.kappa,
'hall': self.hall,
'warning': self.warning, 'doping': self.doping,
'mu_doping': self.mu_doping,
'seebeck_doping': self.seebeck_doping,
'cond_doping': self.cond_doping,
'kappa_doping': self.kappa_doping,
'hall_doping': self.hall_doping,
'dos': self.dos.to_dict,
'carrier_conc': self.carrier_conc,
'vol': self.vol}
return clean_json(results)
def to_dict(self):
from pymatgen.util.io_utils import clean_json
results = {'gap': self.gap,
'mu_steps': self.mu_steps,
'cond': self.cond,
'seebeck': self.seebeck,
'kappa': self.kappa,
'hall': self.hall,
'warning': self.warning, 'doping': self.doping,
'mu_doping': self.mu_doping,
'seebeck_doping': self.seebeck_doping,
'cond_doping': self.cond_doping,
'kappa_doping': self.kappa_doping,
'hall_doping': self.hall_doping,
'dos': self.dos.to_dict,
'dos_partial': self._dos_partial,
'carrier_conc': self.carrier_conc,
'vol': self.vol}
return clean_json(results)
def to_dict(self):
from pymatgen.util.io_utils import clean_json
results = {
"gap": self.gap,
"mu_steps": self.mu_steps,
"cond": self.cond,
"seebeck": self.seebeck,
"kappa": self.kappa,
"hall": self.hall,
"warning": self.warning,
"doping": self.doping,
"mu_doping": self.mu_doping,
"seebeck_doping": self.seebeck_doping,
"cond_doping": self.cond_doping,
"kappa_doping": self.kappa_doping,
"hall_doping": self.hall_doping,
"dos": self.dos.to_dict,
"dos_partial": self._dos_partial,
"carrier_conc": self.carrier_conc,
"vol": self.vol,
}
return clean_json(results)
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 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)