def get_dos_from_id(self, task_id):
"""
Overrides the get_dos_from_id for the MIT gridfs format.
"""
args = {'task_id': task_id}
fields = ['calculations']
structure = self.get_structure_from_id(task_id)
dosid = None
for r in self.query(fields, args):
dosid = r['calculations'][-1]['dos_fs_id']
if dosid is not None:
self._fs = gridfs.GridFS(self.db, 'dos_fs')
with self._fs.get(dosid) as dosfile:
s = dosfile.read()
try:
d = json.loads(s)
except:
s = zlib.decompress(s)
d = json.loads(s.decode("utf-8"))
tdos = Dos.from_dict(d)
pdoss = {}
for i in range(len(d['pdos'])):
ados = d['pdos'][i]
all_ados = {}
for j in range(len(ados)):
orb = Orbital(j)
odos = ados[str(orb)]
all_ados[orb] = {Spin(int(k)): v
for k, v
in odos['densities'].items()}
pdoss[structure[i]] = all_ados
return CompleteDos(structure, tdos, pdoss)
return None
def get_dos_from_id(self, task_id):
"""
Overrides the get_dos_from_id for the MIT gridfs format.
"""
args = {'task_id': task_id}
fields = ['calculations']
structure = self.get_structure_from_id(task_id)
dosid = None
for r in self.query(fields, args):
dosid = r['calculations'][-1]['dos_fs_id']
if dosid != None:
self._fs = gridfs.GridFS(self.db, 'dos_fs')
with self._fs.get(dosid) as dosfile:
s = dosfile.read()
try:
d = json.loads(s)
except:
s = zlib.decompress(s)
d = json.loads(s)
tdos = Dos.from_dict(d)
pdoss = {}
for i in range(len(d['pdos'])):
ados = d['pdos'][i]
all_ados = {}
for j in range(len(ados)):
orb = Orbital.from_vasp_index(j)
odos = ados[str(orb)]
all_ados[orb] = {Spin.from_int(int(k)): v
for k, v
in odos['densities'].items()}
pdoss[structure[i]] = all_ados
return CompleteDos(structure, tdos, pdoss)
return None
def get_dos(db_file):
""" return density of states object from the database """
d1, d2, d3, d4 = get_collections(db_file)
db = get_db(db_file)
fs = gridfs.GridFS(db, 'dos_fs')
dos_fs_id = d4["calcs_reversed"][0]["dos_fs_id"]
dos_json = zlib.decompress(fs.get(dos_fs_id).read())
dos_dict = json.loads(dos_json.decode())
return Dos.from_dict(dos_dict)
def from_dict(data):
def _make_float_array(a):
res = [[0.0, 0.0, 0.0], [0.0, 0.0, 0.0], [0.0, 0.0, 0.0]]
for i in range(3):
for j in range(3):
res[i][j] = float(a[i][j])
return res
def _make_float_hall(a):
return [i for i in a[:27]]
return BoltztrapAnalyzer(
float(data['gap']), [float(d) for d in data['mu_steps']],
{int(d): [_make_float_array(v) for v in data['cond'][d]]
for d in data['cond']},
{int(d): [_make_float_array(v) for v in data['seebeck'][d]]
for d in data['seebeck']},
{int(d): [_make_float_array(v) for v in data['kappa'][d]]
for d in data['kappa']},
{int(d): [_make_float_hall(v) for v in data['hall'][d]]
for d in data['hall']},
{'p': [float(d) for d in data['doping']['p']],
'n': [float(d) for d in data['doping']['n']]},
{'p': {int(d): [float(v) for v in data['mu_doping']['p'][d]]
for d in data['mu_doping']['p']},
'n': {int(d): [float(v) for v in data['mu_doping']['n'][d]]
for d in data['mu_doping']['n']}},
{'p': {int(d): [_make_float_array(v)
for v in data['seebeck_doping']['p'][d]]
for d in data['seebeck_doping']['p']},
'n': {int(d): [_make_float_array(v)
for v in data['seebeck_doping']['n'][d]]
for d in data['seebeck_doping']['n']}},
{'p': {int(d): [_make_float_array(v)
for v in data['cond_doping']['p'][d]]
for d in data['cond_doping']['p']},
'n': {int(d): [_make_float_array(v)
for v in data['cond_doping']['n'][d]]
for d in data['cond_doping']['n']}},
{'p': {int(d): [_make_float_array(v)
for v in data['kappa_doping']['p'][d]]
for d in data['kappa_doping']['p']},
'n': {int(d): [_make_float_array(v)
for v in data['kappa_doping']['n'][d]]
for d in data['kappa_doping']['n']}},
{'p': {int(d): [_make_float_hall(v)
for v in data['hall_doping']['p'][d]]
for d in data['hall_doping']['p']},
'n': {int(d): [_make_float_hall(v)
for v in data['hall_doping']['n'][d]]
for d in data['hall_doping']['n']}},
Dos.from_dict(data['dos']), data['carrier_conc'], data['dos_partial'],
data['vol'], str(data['warning']))
def from_dict(data):
def _make_float_array(a):
res = [[0.0, 0.0, 0.0], [0.0, 0.0, 0.0], [0.0, 0.0, 0.0]]
for i in range(3):
for j in range(3):
res[i][j] = float(a[i][j])
return res
def _make_float_hall(a):
return [i for i in a[:27]]
return BoltztrapAnalyzer(
float(data['gap']), [float(d) for d in data['mu_steps']],
{int(d): [_make_float_array(v) for v in data['cond'][d]]
for d in data['cond']},
{int(d): [_make_float_array(v) for v in data['seebeck'][d]]
for d in data['seebeck']},
{int(d): [_make_float_array(v) for v in data['kappa'][d]]
for d in data['kappa']},
{int(d): [_make_float_hall(v) for v in data['hall'][d]]
for d in data['hall']},
{'p': [float(d) for d in data['doping']['p']],
'n': [float(d) for d in data['doping']['n']]},
{'p': {int(d): [float(v) for v in data['mu_doping']['p'][d]]
for d in data['mu_doping']['p']},
'n': {int(d): [float(v) for v in data['mu_doping']['n'][d]]
for d in data['mu_doping']['n']}},
{'p': {int(d): [_make_float_array(v)
for v in data['seebeck_doping']['p'][d]]
for d in data['seebeck_doping']['p']},
'n': {int(d): [_make_float_array(v)
for v in data['seebeck_doping']['n'][d]]
for d in data['seebeck_doping']['n']}},
{'p': {int(d): [_make_float_array(v)
for v in data['cond_doping']['p'][d]]
for d in data['cond_doping']['p']},
'n': {int(d): [_make_float_array(v)
for v in data['cond_doping']['n'][d]]
for d in data['cond_doping']['n']}},
{'p': {int(d): [_make_float_array(v)
for v in data['kappa_doping']['p'][d]]
for d in data['kappa_doping']['p']},
'n': {int(d): [_make_float_array(v)
for v in data['kappa_doping']['n'][d]]
for d in data['kappa_doping']['n']}},
{'p': {int(d): [_make_float_hall(v)
for v in data['hall_doping']['p'][d]]
for d in data['hall_doping']['p']},
'n': {int(d): [_make_float_hall(v)
for v in data['hall_doping']['n'][d]]
for d in data['hall_doping']['n']}},
Dos.from_dict(data['dos']), data['dos_partial'], data['carrier_conc'],
data['vol'], str(data['warning']))
def from_dict(data):
def _make_float_array(a):
res = [[0.0, 0.0, 0.0], [0.0, 0.0, 0.0], [0.0, 0.0, 0.0]]
for i in range(3):
for j in range(3):
res[i][j] = float(a[i][j])
return res
def _make_float_hall(a):
return [i for i in a[:27]]
return BoltztrapAnalyzer(
float(data["gap"]),
[float(d) for d in data["mu_steps"]],
{int(d): [_make_float_array(v) for v in data["cond"][d]] for d in data["cond"]},
{int(d): [_make_float_array(v) for v in data["seebeck"][d]] for d in data["seebeck"]},
{int(d): [_make_float_array(v) for v in data["kappa"][d]] for d in data["kappa"]},
{int(d): [_make_float_hall(v) for v in data["hall"][d]] for d in data["hall"]},
{"p": [float(d) for d in data["doping"]["p"]], "n": [float(d) for d in data["doping"]["n"]]},
{
"p": {int(d): [float(v) for v in data["mu_doping"]["p"][d]] for d in data["mu_doping"]["p"]},
"n": {int(d): [float(v) for v in data["mu_doping"]["n"][d]] for d in data["mu_doping"]["n"]},
},
{
"p": {
int(d): [_make_float_array(v) for v in data["seebeck_doping"]["p"][d]]
for d in data["seebeck_doping"]["p"]
},
"n": {
int(d): [_make_float_array(v) for v in data["seebeck_doping"]["n"][d]]
for d in data["seebeck_doping"]["n"]
},
},
{
"p": {
int(d): [_make_float_array(v) for v in data["cond_doping"]["p"][d]]
for d in data["cond_doping"]["p"]
},
"n": {
int(d): [_make_float_array(v) for v in data["cond_doping"]["n"][d]]
for d in data["cond_doping"]["n"]
},
},
{
"p": {
int(d): [_make_float_array(v) for v in data["kappa_doping"]["p"][d]]
for d in data["kappa_doping"]["p"]
},
"n": {
int(d): [_make_float_array(v) for v in data["kappa_doping"]["n"][d]]
for d in data["kappa_doping"]["n"]
},
},
{
"p": {
int(d): [_make_float_hall(v) for v in data["hall_doping"]["p"][d]] for d in data["hall_doping"]["p"]
},
"n": {
int(d): [_make_float_hall(v) for v in data["hall_doping"]["n"][d]] for d in data["hall_doping"]["n"]
},
},
Dos.from_dict(data["dos"]),
data["carrier_conc"],
data["dos_partial"],
data["vol"],
str(data["warning"]),
)
def setUp(self):
with open(os.path.join(test_dir, "complete_dos.json"), "r") as f:
self.dos = Dos.from_dict(json.load(f))
