def test_copyfilesfromcalcloc(self):
fw1 = Firework([CopyVaspOutputs(calc_dir=self.plain_outdir),
PassCalcLocs(name="fw1")], name="fw1")
fw2 = Firework([CopyVaspOutputs(calc_dir=self.relax2_outdir),
PassCalcLocs(name="fw2")], name="fw2")
fw3 = Firework([CopyFilesFromCalcLoc(calc_loc="fw1",
filenames=["POSCAR"],
name_prepend="",
name_append="_0"),
CopyFilesFromCalcLoc(calc_loc="fw2",
filenames=["POSCAR"],
name_prepend="",
name_append="_1"),
PassCalcLocs(name="fw3")],
name="fw3", parents=[fw1, fw2])
fw4 = Firework([PassCalcLocs(name="fw4")], name="fw4", parents=fw3)
wf = Workflow([fw1, fw2, fw3, fw4])
self.lp.add_wf(wf)
rapidfire(self.lp)
fw4 = self.lp.get_fw_by_id(self.lp.get_fw_ids({"name": "fw4"})[0])
calc_locs = fw4.spec["calc_locs"]
self.assertTrue(os.path.exists(get_calc_loc("fw3", calc_locs)["path"] +
"/POSCAR_0"))
self.assertTrue(os.path.exists(get_calc_loc("fw3", calc_locs)["path"] +
"/POSCAR_1"))
def test_copyfilesfromcalcloc(self):
fw1 = Firework(
[
CopyVaspOutputs(calc_dir=self.plain_outdir),
PassCalcLocs(name="fw1")
],
name="fw1",
)
fw2 = Firework(
[
CopyVaspOutputs(calc_dir=self.relax2_outdir),
PassCalcLocs(name="fw2")
],
name="fw2",
)
fw3 = Firework(
[
CopyFilesFromCalcLoc(
calc_loc="fw1",
filenames=["POSCAR"],
name_prepend="",
name_append="_0",
),
CopyFilesFromCalcLoc(
calc_loc="fw2",
filenames=["POSCAR"],
name_prepend="",
name_append="_1",
),
PassCalcLocs(name="fw3"),
],
name="fw3",
parents=[fw1, fw2],
)
fw4 = Firework([PassCalcLocs(name="fw4")], name="fw4", parents=fw3)
wf = Workflow([fw1, fw2, fw3, fw4])
self.lp.add_wf(wf)
rapidfire(self.lp)
fw4 = self.lp.get_fw_by_id(self.lp.get_fw_ids({"name": "fw4"})[0])
calc_locs = fw4.spec["calc_locs"]
self.assertTrue(
os.path.exists(
get_calc_loc("fw3", calc_locs)["path"] + "/POSCAR_0"))
self.assertTrue(
os.path.exists(
get_calc_loc("fw3", calc_locs)["path"] + "/POSCAR_1"))
def test_cleanupfiles_calc_loc(self):
# will test deleting files from previous folder
fw1 = Firework(
[
CreateFolder(folder_name="to_remove.relax0"),
CreateFolder(folder_name="to_remove.relax1"),
CreateFolder(folder_name="dont_remove.relax0"),
CreateFolder(folder_name="shouldnt_touch"),
PassCalcLocs(name="fw1"),
],
name="fw1",
)
fw2 = Firework(
[
DeleteFilesPrevFolder(files=["to_remove*", "dont_remove"],
calc_loc=True),
PassCalcLocs(name="fw2"),
],
name="fw2",
parents=fw1,
)
wf = Workflow([fw1, fw2])
self.lp.add_wf(wf)
rapidfire(self.lp)
fw2 = self.lp.get_fw_by_id(self.lp.get_fw_ids({"name": "fw2"})[0])
calc_locs = fw2.spec["calc_locs"]
self.assertTrue(
os.path.exists(
os.path.join(
get_calc_loc("fw1", calc_locs)["path"],
"dont_remove.relax0")))
self.assertTrue(
os.path.exists(
os.path.join(
get_calc_loc("fw1", calc_locs)["path"], "shouldnt_touch")))
self.assertFalse(
os.path.exists(
os.path.join(
get_calc_loc("fw1", calc_locs)["path"],
"to_remove.relax0")))
self.assertFalse(
os.path.exists(
os.path.join(
get_calc_loc("fw1", calc_locs)["path"],
"to_remove.relax1")))
def run_task(self, fw_spec):
# get the directory that contains the dir to parse
calc_dir = os.getcwd()
if "calc_dir" in self:
calc_dir = self["calc_dir"]
elif self.get("calc_loc"):
calc_dir = get_calc_loc(self["calc_loc"],
fw_spec["calc_locs"])["path"]
# parse the calc directory
logger.info("PARSING DIRECTORY: {} USING DRONE: {}".format(
calc_dir, self["drone"].__class__.__name__))
# get the database connection
db_file = env_chk(self.get("db_file"), fw_spec)
drone = self["drone"].__class__()
task_doc = drone.assimilate(calc_dir)
if not db_file:
with open("task.json", "w") as f:
f.write(json.dumps(task_doc, default=DATETIME_HANDLER))
else:
mmdb_str = self["mmdb"]
modname, classname = mmdb_str.strip().rsplit(".", 1)
cls_ = load_class(modname, classname)
db = cls_.from_db_file(db_file)
# insert the task document
t_id = db.insert(task_doc)
logger.info(f"Finished parsing with task_id: {t_id}")
return FWAction(
stored_data={"task_id": task_doc.get("task_id", None)},
defuse_children=(task_doc["state"] != "successful"),
)
def run_task(self, fw_spec):
calc_loc = get_calc_loc(
self["calc_loc"],
fw_spec["calc_locs"]) if self.get("calc_loc") else {}
# determine what files need to be copied
files_to_copy = None
if not "$ALL" in self.get("additional_files", []):
files_to_copy = [
'INCAR', 'POSCAR', 'KPOINTS', 'POTCAR', 'OUTCAR', 'vasprun.xml'
]
if self.get("additional_files"):
files_to_copy.extend(self["additional_files"])
# decide between poscar and contcar
contcar_to_poscar = self.get("contcar_to_poscar", True)
if contcar_to_poscar and "CONTCAR" not in files_to_copy:
files_to_copy.append("CONTCAR")
files_to_copy = [f for f in files_to_copy
if f != 'POSCAR'] # remove POSCAR
# setup the copy
self.setup_copy(self.get("calc_dir", None),
filesystem=self.get("filesystem", None),
files_to_copy=files_to_copy,
from_path_dict=calc_loc)
# do the copying
self.copy_files()
def run_task(self, fw_spec):
# get the directory that contains the dir to parse
calc_dir = os.getcwd()
if "calc_dir" in self:
calc_dir = self["calc_dir"]
elif self.get("calc_loc"):
calc_dir = get_calc_loc(self["calc_loc"], fw_spec["calc_locs"])["path"]
# parse the calc directory
logger.info("PARSING DIRECTORY: {} USING DRONE: {}".format(
calc_dir, self['drone'].__class__.__name__))
# get the database connection
db_file = env_chk(self.get('db_file'), fw_spec)
drone = self['drone'].__class__()
task_doc = drone.assimilate(calc_dir)
if not db_file:
with open("task.json", "w") as f:
f.write(json.dumps(task_doc, default=DATETIME_HANDLER))
else:
mmdb_str = self["mmdb"]
modname, classname = mmdb_str.strip().rsplit(".", 1)
cls_ = load_class(modname, classname)
db = cls_.from_db_file(db_file)
# insert the task document
t_id = db.insert(task_doc)
logger.info("Finished parsing with task_id: {}".format(t_id))
return FWAction(stored_data={"task_id": task_doc.get("task_id", None)},
defuse_children=(task_doc["state"] != "successful"))
def run_task(self, fw_spec):
calc_loc = get_calc_loc(self["calc_loc"],
fw_spec["calc_locs"]) if self.get(
"calc_loc") else {}
# determine what files need to be copied
files_to_copy = None
if not "$ALL" in self.get("additional_files", []):
files_to_copy = ['INCAR', 'POSCAR', 'KPOINTS', 'POTCAR', 'OUTCAR',
'vasprun.xml']
if self.get("additional_files"):
files_to_copy.extend(self["additional_files"])
# decide between poscar and contcar
contcar_to_poscar = self.get("contcar_to_poscar", True)
if contcar_to_poscar and "CONTCAR" not in files_to_copy:
files_to_copy.append("CONTCAR")
files_to_copy = [f for f in files_to_copy if
f != 'POSCAR'] # remove POSCAR
# setup the copy
self.setup_copy(self.get("calc_dir", None),
filesystem=self.get("filesystem", None),
files_to_copy=files_to_copy, from_path_dict=calc_loc)
# do the copying
self.copy_files()
def run_task(self, fw_spec):
calc_loc = get_calc_loc(self["calc_loc"], fw_spec["calc_locs"]) if self.get("calc_loc") else {}
exclude_files = self.get("exclude_files", [])
self.setup_copy(self.get("calc_dir", None), filesystem=self.get("filesystem", None),
exclude_files=exclude_files, from_path_dict=calc_loc)
self.copy_files()
def run_task(self, fw_spec):
# get the directory that contains the QChem dir to parse
calc_dir = os.getcwd()
if "calc_dir" in self:
calc_dir = self["calc_dir"]
elif self.get("calc_loc"):
calc_dir = get_calc_loc(self["calc_loc"],
fw_spec["calc_locs"])["path"]
input_file = self.get("input_file", "mol.qin")
output_file = self.get("output_file", "mol.qout")
multirun = self.get("multirun", False)
# parse the QChem directory
logger.info("PARSING DIRECTORY: {}".format(calc_dir))
additional_fields = self.get("additional_fields", [])
drone = QChemDrone(additional_fields=additional_fields)
# assimilate (i.e., parse)
task_doc = drone.assimilate(
path=calc_dir,
input_file=input_file,
output_file=output_file,
multirun=multirun)
if "tags" in fw_spec:
task_doc.update({"tags": fw_spec["tags"]})
# Check for additional keys to set based on the fw_spec
if self.get("fw_spec_field"):
task_doc.update({self.get("fw_spec_field"): fw_spec.get(self.get("fw_spec_field"))})
# Update fw_spec with final/optimized structure
update_spec = {}
if task_doc.get("output").get("optimized_molecule"):
update_spec["prev_calc_molecule"] = task_doc["output"]["optimized_molecule"]
update_spec["prev_calc_mulliken"] = task_doc["output"]["mulliken"]
if "RESP" in task_doc["output"]:
update_spec["prev_calc_resp"] = task_doc["output"]["RESP"]
elif "ESP" in task_doc["output"]:
update_spec["prev_calc_esp"] = task_doc["output"]["ESP"]
# get the database connection
db_file = env_chk(self.get("db_file"), fw_spec)
# db insertion or taskdoc dump
if not db_file:
with open(os.path.join(calc_dir, "task.json"), "w") as f:
f.write(json.dumps(task_doc, default=DATETIME_HANDLER))
else:
mmdb = QChemCalcDb.from_db_file(db_file, admin=True)
t_id = mmdb.insert(task_doc)
logger.info("Finished parsing with task_id: {}".format(t_id))
return FWAction(
stored_data={"task_id": task_doc.get("task_id", None)},
update_spec=update_spec)
def run_task(self, fw_spec):
files_to_copy = [
"POSCAR", "INCAR", "KPOINTS", "vasprun.xml", "OUTCAR", "POTCAR"
]
files_to_copy_add = [
"POSCAR", "INCAR", "KPOINTS", "vasprun.xml", "OUTCAR", "INPCAR",
"EIGENVAL"
]
path_dict = dict()
calc_loc_equi = get_calc_loc("equi nscf", fw_spec["calc_locs"])
path_dict["equi"] = calc_loc_equi["path"]
calc_loc_05 = get_calc_loc("0.5per nscf", fw_spec["calc_locs"])
path_dict["0.5per"] = calc_loc_05["path"]
calc_loc_10 = get_calc_loc("1.0per nscf", fw_spec["calc_locs"])
path_dict["1.0per"] = calc_loc_10["path"]
calc_loc_dielect = get_calc_loc("dielectric", fw_spec["calc_locs"])
path_dict["dielect"] = calc_loc_dielect["path"]
calc_loc_elastic = get_calc_loc("elastic", fw_spec["calc_locs"])
path_dict["elastic"] = calc_loc_elastic["path"]
calc_loc_CBM = get_calc_loc("CBM", fw_spec["calc_locs"])
path_dict["CBM"] = calc_loc_CBM["path"]
calc_loc_VBM = get_calc_loc("VBM", fw_spec["calc_locs"])
path_dict["VBM"] = calc_loc_VBM["path"]
try:
calc_loc_CSB = get_calc_loc("CSB", fw_spec["calc_locs"])
except ValueError as err:
print(err)
else:
path_dict["CSB"] = calc_loc_CSB["path"]
try:
calc_loc_VSB = get_calc_loc("VSB", fw_spec["calc_locs"])
except ValueError as err:
print(err)
else:
path_dict["VSB"] = calc_loc_VSB["path"]
curr_dir = os.getcwd()
for key, path in path_dict.items():
new_dir = os.path.join(curr_dir, key)
os.makedirs(new_dir)
if key not in ["CBM", "VBM", "CSB", "VSB"]:
copy = CopyFiles(from_dir=path,
to_dir=new_dir,
files_to_copy=files_to_copy)
copy.run_task(fw_spec)
else:
copy = CopyFiles(from_dir=path,
to_dir=new_dir,
files_to_copy=files_to_copy_add)
copy.run_task(fw_spec)
def run_task(self, fw_spec):
# get the directory that contains the VASP dir to parse
calc_dir = os.getcwd()
if "calc_dir" in self:
calc_dir = self["calc_dir"]
elif self.get("calc_loc"):
calc_dir = get_calc_loc(self["calc_loc"],
fw_spec["calc_locs"])["path"]
# parse the VASP directory
logger.info("PARSING DIRECTORY: {}".format(calc_dir))
drone = VaspDrone(additional_fields=self.get("additional_fields"),
parse_dos=self.get("parse_dos", False),
bandstructure_mode=self.get("bandstructure_mode",
False))
# assimilate (i.e., parse)
task_doc = drone.assimilate(calc_dir)
# Check for additional keys to set based on the fw_spec
if self.get("fw_spec_field"):
task_doc.update(fw_spec[self.get("fw_spec_field")])
# get the database connection
db_file = env_chk(self.get('db_file'), fw_spec)
# db insertion or taskdoc dump
if not db_file:
with open("task.json", "w") as f:
f.write(json.dumps(task_doc, default=DATETIME_HANDLER))
else:
mmdb = VaspMDCalcDb.from_db_file(db_file, admin=True)
# prevent duplicate insertion
mmdb.db.tasks.find_one_and_delete({
'formula_pretty':
task_doc['formula_pretty'],
'task_label':
task_doc['task_label']
})
t_id = mmdb.insert_task(
task_doc,
parse_dos=self.get("parse_dos", False),
parse_bs=bool(self.get("bandstructure_mode", False)),
md_structures=self.get("md_structures", True))
logger.info("Finished parsing with task_id: {}".format(t_id))
if self.get("defuse_unsuccessful", True):
defuse_children = (task_doc["state"] != "successful")
else:
defuse_children = False
return FWAction(stored_data={"task_id": task_doc.get("task_id", None)},
defuse_children=defuse_children)
def run_task(self, fw_spec):
# get the directory that contains the VASP dir to parse
calc_dir = os.getcwd()
if "calc_dir" in self:
calc_dir = self["calc_dir"]
elif self.get("calc_loc"):
calc_dir = get_calc_loc(self["calc_loc"],
fw_spec["calc_locs"])["path"]
# parse the VASP directory
logger.info("PARSING DIRECTORY: {}".format(calc_dir))
drone = VaspDrone(additional_fields=self.get("additional_fields"),
parse_dos=self.get("parse_dos", False),
compress_dos=1,
bandstructure_mode=self.get("bandstructure_mode",
False),
compress_bs=1)
# assimilate (i.e., parse)
task_doc = drone.assimilate(calc_dir)
# Check for additional keys to set based on the fw_spec
if self.get("fw_spec_field"):
task_doc.update(fw_spec[self.get("fw_spec_field")])
# get the database connection
db_file = env_chk(self.get('db_file'), fw_spec)
# db insertion or taskdoc dump
if not db_file:
with open("task.json", "w") as f:
f.write(json.dumps(task_doc, default=DATETIME_HANDLER))
else:
mmdb = VaspCalcDb.from_db_file(db_file, admin=True)
t_id = mmdb.insert_task(task_doc,
parse_dos=self.get("parse_dos", False),
parse_bs=bool(
self.get("bandstructure_mode", False)))
logger.info("Finished parsing with task_id: {}".format(t_id))
defuse_children = False
if task_doc["state"] != "successful":
if self.get("defuse_unsuccessful", DEFUSE_UNSUCCESSFUL) is True:
defuse_children = True
elif self.get("defuse_unsuccessful", DEFUSE_UNSUCCESSFUL).lower() \
== "fizzle":
raise RuntimeError(
"VaspToDb indicates that job is not successful "
"(perhaps your job did not converge within the "
"limit of electronic/ionic iterations)!")
return FWAction(stored_data={"task_id": task_doc.get("task_id", None)},
defuse_children=defuse_children)
def run_task(self, fw_spec):
files_to_copy = [
"POSCAR-unitcell", "INCAR", "vasprun.xml", "FORCE_CONSTANTS",
"band.yaml"
]
path_dict = dict()
calc_loc_orig = get_calc_loc("orig phonon band", fw_spec["calc_locs"])
path_dict["orig"] = calc_loc_orig["path"]
calc_loc_minus = get_calc_loc("minus phonon band",
fw_spec["calc_locs"])
path_dict["minus"] = calc_loc_minus["path"]
calc_loc_plus = get_calc_loc("plus phonon band", fw_spec["calc_locs"])
path_dict["plus"] = calc_loc_plus["path"]
curr_dir = os.getcwd()
for key, path in path_dict.items():
new_dir = os.path.join(curr_dir, key)
os.makedirs(new_dir)
copy = CopyFiles(from_dir=path,
to_dir=new_dir,
files_to_copy=files_to_copy)
copy.run_task(fw_spec)
phonons = {}
for vol in ("orig", "plus", "minus"):
(Keylist, Coordslist, prims,
transmat) = get_highsympath("%s/POSCAR-unitcell" % vol)
phonon = phonopy.load(
supercell_matrix=self.get("supercell"),
primitive_matrix=transmat,
unitcell_filename="%s/POSCAR-unitcell" % vol,
calculator="vasp",
is_nac=False,
force_constants_filename="%s/FORCE_CONSTANTS" % vol)
phonons[vol] = phonon
gruneisen = PhonopyGruneisen(phonons["orig"], phonons["plus"],
phonons["minus"])
bands = get_band_qpoints([np.array(Coordslist)], 51)
gruneisen.set_band_structure(bands)
gruneisen.write_yaml_band_structure()
def run_task(self, fw_spec):
calc_loc = get_calc_loc(
self["calc_loc"],
fw_spec["calc_locs"]) if self.get("calc_loc") else {}
exclude_files = self.get("exclude_files", [])
self.setup_copy(self.get("calc_dir", None),
filesystem=self.get("filesystem", None),
exclude_files=exclude_files,
from_path_dict=calc_loc)
self.copy_files()
def test_cleanupfiles(self):
fw1 = Firework([CreateFolder(folder_name="to_remove.relax0"),
CreateFolder(folder_name="to_remove.relax1"),
CreateFolder(folder_name="dont_remove.relax0"),
CreateFolder(folder_name="shouldnt_touch"),
DeleteFiles(files=["to_remove*", "dont_remove"]),
PassCalcLocs(name="fw1")], name="fw1")
fw2 = Firework([PassCalcLocs(name="fw2")], name="fw2", parents=fw1)
wf = Workflow([fw1, fw2])
self.lp.add_wf(wf)
rapidfire(self.lp)
fw2 = self.lp.get_fw_by_id(self.lp.get_fw_ids({"name": "fw2"})[0])
calc_locs = fw2.spec["calc_locs"]
self.assertTrue(os.path.exists(os.path.join(get_calc_loc("fw1", calc_locs)["path"], "dont_remove.relax0")))
self.assertTrue(os.path.exists(os.path.join(get_calc_loc("fw1", calc_locs)["path"], "shouldnt_touch")))
self.assertFalse(os.path.exists(os.path.join(get_calc_loc("fw1", calc_locs)["path"], "to_remove.relax0")))
self.assertFalse(os.path.exists(os.path.join(get_calc_loc("fw1", calc_locs)["path"], "to_remove.relax1")))
def test_passcalclocs(self):
fw1 = Firework([PassCalcLocs(name="fw1")], name="fw1")
fw2 = Firework([PassCalcLocs(name="fw2")], name="fw2", parents=fw1)
fw3 = Firework([PassCalcLocs(name="fw3")], name="fw3", parents=fw2)
wf = Workflow([fw1, fw2, fw3])
self.lp.add_wf(wf)
rapidfire(self.lp)
fw2 = self.lp.get_fw_by_id(self.lp.get_fw_ids({"name": "fw2"})[0])
fw3 = self.lp.get_fw_by_id(self.lp.get_fw_ids({"name": "fw3"})[0])
self.assertEqual(len(fw2.spec["calc_locs"]), 1)
self.assertEqual(len(fw3.spec["calc_locs"]), 2)
self.assertEqual(fw3.spec["calc_locs"][0]["name"], "fw1")
self.assertEqual(fw3.spec["calc_locs"][1]["name"], "fw2")
self.assertNotEqual(fw3.spec["calc_locs"][0]["path"],
fw3.spec["calc_locs"][1]["path"])
calc_locs = fw3.spec["calc_locs"]
self.assertEqual(get_calc_loc("fw1", calc_locs), calc_locs[0])
self.assertEqual(get_calc_loc("fw2", calc_locs), calc_locs[1])
self.assertEqual(get_calc_loc(True, calc_locs), calc_locs[1])
def run_task(self, fw_spec):
calc_dir = os.getcwd()
if "calc_dir" in self:
calc_dir = self["calc_dir"]
elif self.get("calc_loc"):
calc_dir = get_calc_loc(self["calc_loc"],
fw_spec["calc_locs"])["path"]
logger.info("PARSING DIRECTORY: {}".format(calc_dir))
db_file = env_chk(self.get('db_file'), fw_spec)
cluster_dict = None
tags = Tags.from_file(filename="feff.inp")
if "RECIPROCAL" not in tags:
cluster_dict = Atoms.cluster_from_file("feff.inp").as_dict()
doc = {
"input_parameters":
tags.as_dict(),
"cluster":
cluster_dict,
"structure":
self["structure"].as_dict(),
"absorbing_atom":
self["absorbing_atom"],
"spectrum_type":
self["spectrum_type"],
"spectrum":
np.loadtxt(os.path.join(calc_dir, self["output_file"])).tolist(),
"edge":
self.get("edge", None),
"metadata":
self.get("metadata", None),
"dir_name":
os.path.abspath(os.getcwd()),
"last_updated":
datetime.utcnow()
}
if not db_file:
with open("feff_task.json", "w") as f:
f.write(json.dumps(doc, default=DATETIME_HANDLER))
else:
db = FeffCalcDb.from_db_file(db_file, admin=True)
db.insert(doc)
logger.info("Finished parsing the spectrum")
return FWAction(stored_data={"task_id": doc.get("task_id", None)})
def test_passcalclocs(self):
fw1 = Firework([PassCalcLocs(name="fw1")], name="fw1")
fw2 = Firework([PassCalcLocs(name="fw2")], name="fw2", parents=fw1)
fw3 = Firework([PassCalcLocs(name="fw3")], name="fw3", parents=fw2)
wf = Workflow([fw1, fw2, fw3])
self.lp.add_wf(wf)
rapidfire(self.lp)
fw2 = self.lp.get_fw_by_id(self.lp.get_fw_ids({"name": "fw2"})[0])
fw3 = self.lp.get_fw_by_id(self.lp.get_fw_ids({"name": "fw3"})[0])
self.assertEqual(len(fw2.spec["calc_locs"]), 1)
self.assertEqual(len(fw3.spec["calc_locs"]), 2)
self.assertEqual(fw3.spec["calc_locs"][0]["name"], "fw1")
self.assertEqual(fw3.spec["calc_locs"][1]["name"], "fw2")
self.assertNotEqual(fw3.spec["calc_locs"][0]["path"],
fw3.spec["calc_locs"][1]["path"])
calc_locs = fw3.spec["calc_locs"]
self.assertEqual(get_calc_loc("fw1", calc_locs), calc_locs[0])
self.assertEqual(get_calc_loc("fw2", calc_locs), calc_locs[1])
self.assertEqual(get_calc_loc(True, calc_locs), calc_locs[1])
def run_task(self, fw_spec):
# get the directory that contains the QChem dir to parse
calc_dir = os.getcwd()
if "calc_dir" in self:
calc_dir = self["calc_dir"]
elif self.get("calc_loc"):
calc_dir = get_calc_loc(self["calc_loc"],
fw_spec["calc_locs"])["path"]
input_file = self.get("input_file", "mol.qin")
output_file = self.get("output_file", "mol.qout")
multirun = self.get("multirun", False)
# parse the QChem directory
logger.info("PARSING DIRECTORY: {}".format(calc_dir))
drone = QChemDrone(additional_fields=self.get("additional_fields"))
# assimilate (i.e., parse)
task_doc = drone.assimilate(
path=calc_dir,
input_file=input_file,
output_file=output_file,
multirun=multirun)
# Check for additional keys to set based on the fw_spec
if self.get("fw_spec_field"):
task_doc.update(fw_spec[self.get("fw_spec_field")])
# Update fw_spec with final/optimized structure
update_spec = {}
if task_doc.get("output").get("optimized_molecule"):
update_spec["prev_calc_molecule"] = task_doc["output"][
"optimized_molecule"]
# get the database connection
db_file = env_chk(self.get("db_file"), fw_spec)
# db insertion or taskdoc dump
if not db_file:
with open(os.path.join(calc_dir, "task.json"), "w") as f:
f.write(json.dumps(task_doc, default=DATETIME_HANDLER))
else:
mmdb = QChemCalcDb.from_db_file(db_file, admin=True)
t_id = mmdb.insert(task_doc)
logger.info("Finished parsing with task_id: {}".format(t_id))
return FWAction(
stored_data={"task_id": task_doc.get("task_id", None)},
update_spec=update_spec)
def test_getinterpolatedposcar(self):
nimages = 5
this_image = 1
autosort_tol = 0.5
fw1 = Firework([CopyVaspOutputs(calc_dir=self.static_outdir,
contcar_to_poscar=False,
additional_files=["CONTCAR"]),
PassCalcLocs(name="fw1")], name="fw1")
fw2 = Firework([CopyVaspOutputs(calc_dir=self.opt_outdir,
contcar_to_poscar=False,
additional_files=["CONTCAR"]),
PassCalcLocs(name="fw2")], name="fw2")
fw3 = Firework([GetInterpolatedPOSCAR(start="fw1",
end="fw2",
this_image=this_image,
nimages=nimages,
autosort_tol=autosort_tol),
PassCalcLocs(name="fw3")],
name="fw3", parents=[fw1, fw2])
fw4 = Firework([PassCalcLocs(name="fw4")], name="fw4", parents=fw3)
wf = Workflow([fw1, fw2, fw3, fw4])
self.lp.add_wf(wf)
rapidfire(self.lp)
fw4 = self.lp.get_fw_by_id(self.lp.get_fw_ids({"name": "fw4"})[0])
calc_locs = fw4.spec["calc_locs"]
self.assertTrue(os.path.exists(get_calc_loc("fw3", calc_locs)["path"] +
"/POSCAR"))
self.assertTrue(os.path.exists(get_calc_loc("fw3", calc_locs)["path"] +
"/interpolate/CONTCAR_0"))
self.assertTrue(os.path.exists(get_calc_loc("fw3", calc_locs)["path"] +
"/interpolate/CONTCAR_1"))
struct_start = Structure.from_file(get_calc_loc("fw3", calc_locs)["path"] +
"/interpolate/CONTCAR_0")
struct_end = Structure.from_file(get_calc_loc("fw3", calc_locs)["path"] +
"/interpolate/CONTCAR_1")
struct_inter = Structure.from_file(get_calc_loc("fw3", calc_locs)["path"] +
"/POSCAR")
structs = struct_start.interpolate(struct_end,
nimages,
interpolate_lattices=True,
autosort_tol=autosort_tol)
# Check x of 1st site.
self.assertAlmostEqual(structs[this_image][1].coords[0],
struct_inter[1].coords[0])
# Check c lattice parameter
self.assertAlmostEqual(structs[this_image].lattice.abc[0],
struct_inter.lattice.abc[0])
def test_getinterpolatedposcar(self):
nimages = 5
this_image = 1
autosort_tol = 0.5
fw1 = Firework([CopyVaspOutputs(calc_dir=self.static_outdir,
contcar_to_poscar=False,
additional_files=["CONTCAR"]),
PassCalcLocs(name="fw1")], name="fw1")
fw2 = Firework([CopyVaspOutputs(calc_dir=self.opt_outdir,
contcar_to_poscar=False,
additional_files=["CONTCAR"]),
PassCalcLocs(name="fw2")], name="fw2")
fw3 = Firework([GetInterpolatedPOSCAR(start="fw1",
end="fw2",
this_image=this_image,
nimages=nimages,
autosort_tol=autosort_tol),
PassCalcLocs(name="fw3")],
name="fw3", parents=[fw1, fw2])
fw4 = Firework([PassCalcLocs(name="fw4")], name="fw4", parents=fw3)
wf = Workflow([fw1, fw2, fw3, fw4])
self.lp.add_wf(wf)
rapidfire(self.lp)
fw4 = self.lp.get_fw_by_id(self.lp.get_fw_ids({"name": "fw4"})[0])
calc_locs = fw4.spec["calc_locs"]
self.assertTrue(os.path.exists(get_calc_loc("fw3", calc_locs)["path"] +
"/POSCAR"))
self.assertTrue(os.path.exists(get_calc_loc("fw3", calc_locs)["path"] +
"/interpolate/CONTCAR_0"))
self.assertTrue(os.path.exists(get_calc_loc("fw3", calc_locs)["path"] +
"/interpolate/CONTCAR_1"))
struct_start = Structure.from_file(get_calc_loc("fw3", calc_locs)["path"] +
"/interpolate/CONTCAR_0")
struct_end = Structure.from_file(get_calc_loc("fw3", calc_locs)["path"] +
"/interpolate/CONTCAR_1")
struct_inter = Structure.from_file(get_calc_loc("fw3", calc_locs)["path"] +
"/POSCAR")
structs = struct_start.interpolate(struct_end,
nimages,
interpolate_lattices=True,
autosort_tol=autosort_tol)
# Check x of 1st site.
self.assertAlmostEqual(structs[this_image][1].coords[0],
struct_inter[1].coords[0])
# Check c lattice parameter
self.assertAlmostEqual(structs[this_image].lattice.abc[0],
struct_inter.lattice.abc[0])
def test_createfolder(self):
folder_name = "test_folder"
fw1 = Firework([CreateFolder(folder_name=folder_name, change_dir=False),
PassCalcLocs(name="fw1")],
name="fw3")
fw2 = Firework([PassCalcLocs(name="fw2")], name="fw2", parents=fw1)
wf = Workflow([fw1, fw2])
self.lp.add_wf(wf)
rapidfire(self.lp)
fw2 = self.lp.get_fw_by_id(self.lp.get_fw_ids({"name": "fw2"})[0])
calc_locs = fw2.spec["calc_locs"]
self.assertTrue(os.path.exists(get_calc_loc("fw1", calc_locs)["path"] +
"/" + folder_name))
def run_task(self, fw_spec):
lammps_input = self["lammps_input"]
diffusion_params = self.get("diffusion_params", {})
# get the directory that contains the LAMMPS dir to parse
calc_dir = os.getcwd()
if "calc_dir" in self:
calc_dir = self["calc_dir"]
elif self.get("calc_loc"):
calc_dir = get_calc_loc(self["calc_loc"], fw_spec["calc_locs"])["path"]
# parse the directory
logger.info("PARSING DIRECTORY: {}".format(calc_dir))
d = {}
d["dir_name"] = os.path.abspath(os.getcwd())
d["last_updated"] = datetime.today()
d["input"] = lammps_input.as_dict()
log_file = lammps_input.config_dict["log"]
if isinstance(lammps_input.config_dict["dump"], list):
dump_file = lammps_input.config_dict["dump"][0].split()[4]
else:
dump_file = lammps_input.config_dict["dump"].split()[4]
is_forcefield = hasattr(lammps_input.lammps_data, "bonds_data")
lammpsrun = LammpsRun(lammps_input.data_filename, dump_file, log_file,
is_forcefield=is_forcefield)
d["natoms"] = lammpsrun.natoms
d["nmols"] = lammpsrun.nmols
d["box_lengths"] = lammpsrun.box_lengths
d["mol_masses"] = lammpsrun.mol_masses
d["mol_config"] = lammpsrun.mol_config
if diffusion_params:
diffusion_analyzer = lammpsrun.get_diffusion_analyzer(**diffusion_params)
d["analysis"]["diffusion"] = diffusion_analyzer.get_summary_dict()
db_file = env_chk(self.get('db_file'), fw_spec)
# db insertion
if not db_file:
with open("task.json", "w") as f:
f.write(json.dumps(d, default=DATETIME_HANDLER))
else:
mmdb = LammpsCalcDb.from_db_file(db_file)
# insert the task document
t_id = mmdb.insert(d)
logger.info("Finished parsing with task_id: {}".format(t_id))
return FWAction(stored_data={"task_id": d.get("task_id", None)})
def run_task(self, fw_spec):
# get the directory that contains the LAMMPS run parse.
calc_dir = os.getcwd()
if "calc_dir" in self:
calc_dir = self["calc_dir"]
elif self.get("calc_loc"):
calc_dir = get_calc_loc(self["calc_loc"],
fw_spec["calc_locs"])["path"]
# parse the directory
logger.info(f"PARSING DIRECTORY: {calc_dir}")
drone = LammpsDrone(
additional_fields=self.get("additional_fields"),
diffusion_params=self.get("diffusion_params", None),
)
task_doc = drone.assimilate(
calc_dir,
input_filename=self["input_filename"],
log_filename=self.get("log_filename", "log.lammps"),
is_forcefield=self.get("is_forcefield", False),
data_filename=self.get("data_filename", None),
dump_files=self.get("dump_filenames", None),
)
# Check for additional keys to set based on the fw_spec
if self.get("fw_spec_field"):
task_doc.update(fw_spec[self.get("fw_spec_field")])
db_file = env_chk(self.get("db_file"), fw_spec)
# db insertion
if not db_file:
with open("task.json", "w") as f:
f.write(json.dumps(task_doc, default=DATETIME_HANDLER))
else:
mmdb = LammpsCalcDb.from_db_file(db_file)
# insert the task document
t_id = mmdb.insert(task_doc)
logger.info(f"Finished parsing with task_id: {t_id}")
return FWAction(stored_data={"task_id": task_doc.get("task_id", None)})
def test_createfolder(self):
folder_name = "test_folder"
fw1 = Firework([
CreateFolder(folder_name=folder_name, change_dir=False),
PassCalcLocs(name="fw1")
],
name="fw3")
fw2 = Firework([PassCalcLocs(name="fw2")], name="fw2", parents=fw1)
wf = Workflow([fw1, fw2])
self.lp.add_wf(wf)
rapidfire(self.lp)
fw2 = self.lp.get_fw_by_id(self.lp.get_fw_ids({"name": "fw2"})[0])
calc_locs = fw2.spec["calc_locs"]
self.assertTrue(
os.path.exists(
get_calc_loc("fw1", calc_locs)["path"] + "/" + folder_name))
def run_task(self, fw_spec):
calc_loc = get_calc_loc(
self["calc_loc"],
fw_spec["calc_locs"]) if self.get("calc_loc") else {}
filepath = calc_loc['path']
C = Electron()
C.Get_bandstru(filepath)
if self["bnd_name"] == "CBM":
coord = C.engband.get_cbm()['kpoint'].frac_coords
bnd_num = np.min(C.engband.get_cbm()['band_index'][Spin.up]) + 1
elif self["bnd_name"] == "VBM":
coord = C.engband.get_vbm()['kpoint'].frac_coords
bnd_num = np.max(C.engband.get_vbm()['band_index'][Spin.up]) + 1
elif self["bnd_name"] == "CSB":
C.Get_SB()
if hasattr(C, 'CSB'):
coord = C.engband.kpoints[C.CSB.pos["kptindex"]].frac_coords
bnd_num = np.min(
C.engband.get_cbm()['band_index'][Spin.up]) + 1
else:
return FWAction(exit=True)
elif self["bnd_name"] == "VSB":
C.Get_SB()
if hasattr(C, 'VSB'):
coord = C.engband.kpoints[C.VSB.pos["kptindex"]].frac_coords
bnd_num = np.max(
C.engband.get_vbm()['band_index'][Spin.up]) + 1
else:
return FWAction(exit=True)
else:
raise ValueError("Must specify bnd_name")
lattice = C.engband.structure.lattice.matrix
Write_INPCAR(coord, self["step_size"], bnd_num, "V", lattice)
EMC = EffectMass()
inpcar_fh = open('INPCAR', 'r')
(kpt, stepsize, band, prg, basis) = EMC.parse_inpcar(inpcar_fh)
EMC.get_kpointsfile(kpt, stepsize, prg, basis)
def run_task(self, fw_spec):
# get the directory that contains the LAMMPS run parse.
calc_dir = os.getcwd()
if "calc_dir" in self:
calc_dir = self["calc_dir"]
elif self.get("calc_loc"):
calc_dir = get_calc_loc(self["calc_loc"], fw_spec["calc_locs"])["path"]
# parse the directory
logger.info("PARSING DIRECTORY: {}".format(calc_dir))
drone = LammpsDrone(additional_fields=self.get("additional_fields"),
diffusion_params=self.get("diffusion_params", None))
task_doc = drone.assimilate(calc_dir, input_filename=self["input_filename"],
log_filename=self.get("log_filename", "log.lammps"),
is_forcefield=self.get("is_forcefield", False),
data_filename=self.get("data_filename", None),
dump_files=self.get("dump_filenames", None))
# Check for additional keys to set based on the fw_spec
if self.get("fw_spec_field"):
task_doc.update(fw_spec[self.get("fw_spec_field")])
db_file = env_chk(self.get('db_file'), fw_spec)
# db insertion
if not db_file:
with open("task.json", "w") as f:
f.write(json.dumps(task_doc, default=DATETIME_HANDLER))
else:
mmdb = LammpsCalcDb.from_db_file(db_file)
# insert the task document
t_id = mmdb.insert(task_doc)
logger.info("Finished parsing with task_id: {}".format(t_id))
return FWAction(stored_data={"task_id": task_doc.get("task_id", None)})
def run_task(self, fw_spec):
calc_loc = (get_calc_loc(self["calc_loc"], fw_spec["calc_locs"])
if self.get("calc_loc") else {})
# determine what files need to be copied
files_to_copy = None
if "$ALL" not in self.get("additional_files", []):
files_to_copy = [
"INCAR",
"POSCAR",
"KPOINTS",
"POTCAR",
"OUTCAR",
"vasprun.xml",
]
if self.get("additional_files"):
files_to_copy.extend(self["additional_files"])
if self.get("potcar_spec", False):
files_to_copy.remove("POTCAR")
files_to_copy.append("POTCAR.spec")
# decide between poscar and contcar
contcar_to_poscar = self.get("contcar_to_poscar", True)
if contcar_to_poscar and "CONTCAR" not in files_to_copy:
files_to_copy.append("CONTCAR")
files_to_copy = [f for f in files_to_copy
if f != "POSCAR"] # remove POSCAR
# setup the copy
self.setup_copy(
self.get("calc_dir", None),
filesystem=self.get("filesystem", None),
files_to_copy=files_to_copy,
from_path_dict=calc_loc,
)
# do the copying
self.copy_files()
def run_task(self, fw_spec):
calc_dir = os.getcwd()
if "calc_dir" in self:
calc_dir = self["calc_dir"]
elif self.get("calc_loc"):
calc_dir = get_calc_loc(self["calc_loc"], fw_spec["calc_locs"])["path"]
logger.info("PARSING DIRECTORY: {}".format(calc_dir))
db_file = env_chk(self.get('db_file'), fw_spec)
cluster_dict = None
tags = Tags.from_file(filename="feff.inp")
if "RECIPROCAL" not in tags:
cluster_dict = Atoms.cluster_from_file("feff.inp").as_dict()
doc = {"input_parameters": tags.as_dict(),
"cluster": cluster_dict,
"structure": self["structure"].as_dict(),
"absorbing_atom": self["absorbing_atom"],
"spectrum_type": self["spectrum_type"],
"spectrum": np.loadtxt(os.path.join(calc_dir, self["output_file"])).tolist(),
"edge": self.get("edge", None),
"metadata": self.get("metadata", None),
"dir_name": os.path.abspath(os.getcwd()),
"last_updated": datetime.utcnow()}
if not db_file:
with open("feff_task.json", "w") as f:
f.write(json.dumps(doc, default=DATETIME_HANDLER))
else:
db = FeffCalcDb.from_db_file(db_file, admin=True)
db.insert(doc)
logger.info("Finished parsing the spectrum")
return FWAction(stored_data={"task_id": doc.get("task_id", None)})
def run_task(self, fw_spec):
# get the directory that contains the VASP dir to parse
calc_dir = os.getcwd()
if "calc_dir" in self:
calc_dir = self["calc_dir"]
elif self.get("calc_loc"):
calc_dir = get_calc_loc(self["calc_loc"],
fw_spec["calc_locs"])["path"]
# parse the VASP directory
logger.info("PARSING DIRECTORY: {}".format(calc_dir))
drone = VaspDrone(additional_fields=self.get("additional_fields"),
parse_dos=self.get("parse_dos", False),
bandstructure_mode=self.get("bandstructure_mode",
False),
parse_chgcar=self.get("parse_chgcar", False),
parse_aeccar=self.get("parse_aeccar", False))
# assimilate (i.e., parse)
task_doc = drone.assimilate(calc_dir)
# Check for additional keys to set based on the fw_spec
if self.get("fw_spec_field"):
task_doc.update(fw_spec[self.get("fw_spec_field")])
# get the database connection
db_file = env_chk(self.get('db_file'), fw_spec)
# db insertion or taskdoc dump
if not db_file:
with open("task.json", "w") as f:
f.write(json.dumps(task_doc, default=DATETIME_HANDLER))
else:
mmdb = VaspCalcDb.from_db_file(db_file, admin=True)
t_id = mmdb.insert_task(
task_doc,
use_gridfs=self.get("parse_dos", False)
or bool(self.get("bandstructure_mode", False))
or self.get("parse_chgcar", False)
or self.get("parse_aeccar", False))
logger.info("Finished parsing with task_id: {}".format(t_id))
defuse_children = False
if task_doc["state"] != "successful":
defuse_unsuccessful = self.get("defuse_unsuccessful",
DEFUSE_UNSUCCESSFUL)
if defuse_unsuccessful is True:
defuse_children = True
elif defuse_unsuccessful is False:
pass
elif defuse_unsuccessful == "fizzle":
raise RuntimeError(
"VaspToDb indicates that job is not successful "
"(perhaps your job did not converge within the "
"limit of electronic/ionic iterations)!")
else:
raise RuntimeError("Unknown option for defuse_unsuccessful: "
"{}".format(defuse_unsuccessful))
task_fields_to_push = self.get("task_fields_to_push", None)
update_spec = {}
if task_fields_to_push:
if isinstance(task_fields_to_push, dict):
for key, path_in_task_doc in task_fields_to_push.items():
if has(task_doc, path_in_task_doc):
update_spec[key] = get(task_doc, path_in_task_doc)
else:
logger.warn(
"Could not find {} in task document. Unable to push to next firetask/firework"
.format(path_in_task_doc))
else:
raise RuntimeError(
"Inappropriate type {} for task_fields_to_push. It must be a "
"dictionary of format: {key: path} where key refers to a field "
"in the spec and path is a full mongo-style path to a "
"field in the task document".format(
type(task_fields_to_push)))
return FWAction(stored_data={"task_id": task_doc.get("task_id", None)},
defuse_children=defuse_children,
update_spec=update_spec)
def run_task(self, fw_spec):
# get the directory that contains the QChem dir to parse
calc_dir = os.getcwd()
if "calc_dir" in self:
calc_dir = self["calc_dir"]
elif self.get("calc_loc"):
calc_dir = get_calc_loc(self["calc_loc"],
fw_spec["calc_locs"])["path"]
input_file = self.get("input_file", "mol.qin")
output_file = self.get("output_file", "mol.qout")
multirun = self.get("multirun", False)
# parse the QChem directory
logger.info("PARSING DIRECTORY: {}".format(calc_dir))
drone = QChemDrone(additional_fields=self.get("additional_fields"))
# assimilate (i.e., parse)
task_doc = drone.assimilate(path=calc_dir,
input_file=input_file,
output_file=output_file,
multirun=multirun)
# Check for additional keys to set based on the fw_spec
if self.get("fw_spec_field"):
task_doc.update(fw_spec[self.get("fw_spec_field")])
# Update fw_spec with final/optimized structure
update_spec = {}
if task_doc.get("output").get("optimized_molecule"):
update_spec["prev_calc_molecule"] = task_doc["output"][
"optimized_molecule"]
# get the database connection
db_file = env_chk(self.get("db_file"), fw_spec)
# db insertion or taskdoc dump
if not db_file:
with open(os.path.join(calc_dir, "task.json"), "w") as f:
f.write(json.dumps(task_doc, default=DATETIME_HANDLER))
else:
mmdb = QChemCalcDb.from_db_file(db_file, admin=True)
t_id = mmdb.insert(task_doc)
logger.info("Finished parsing with task_id: {}".format(t_id))
defuse_children = False
if task_doc["state"] != "successful":
defuse_unsuccessful = self.get("defuse_unsuccessful",
DEFUSE_UNSUCCESSFUL)
if defuse_unsuccessful is True:
defuse_children = True
elif defuse_unsuccessful is False:
pass
elif defuse_unsuccessful == "fizzle":
raise RuntimeError(
"QChemToDb indicates that job is not successful "
"(perhaps your job did not converge within the "
"limit of electronic iterations)!")
else:
raise RuntimeError("Unknown option for defuse_unsuccessful: "
"{}".format(defuse_unsuccessful))
return FWAction(stored_data={"task_id": task_doc.get("task_id", None)},
defuse_children=defuse_children,
update_spec=update_spec)
def run_task(self, fw_spec):
# get the directory that contains the VASP dir to parse
calc_dir = os.getcwd()
if "calc_dir" in self:
calc_dir = self["calc_dir"]
elif self.get("calc_loc"):
calc_dir = get_calc_loc(self["calc_loc"], fw_spec["calc_locs"])["path"]
# parse the VASP directory
logger.info("PARSING DIRECTORY: {}".format(calc_dir))
drone = VaspDrone(additional_fields=self.get("additional_fields"),
parse_dos=self.get("parse_dos", False),
bandstructure_mode=self.get("bandstructure_mode", False),
parse_chgcar=self.get("parse_chgcar", False),
parse_aeccar=self.get("parse_aeccar", False))
# assimilate (i.e., parse)
task_doc = drone.assimilate(calc_dir)
# Check for additional keys to set based on the fw_spec
if self.get("fw_spec_field"):
task_doc.update(fw_spec[self.get("fw_spec_field")])
# get the database connection
db_file = env_chk(self.get('db_file'), fw_spec)
# db insertion or taskdoc dump
if not db_file:
with open("task.json", "w") as f:
f.write(json.dumps(task_doc, default=DATETIME_HANDLER))
else:
mmdb = VaspCalcDb.from_db_file(db_file, admin=True)
t_id = mmdb.insert_task(
task_doc, use_gridfs=self.get("parse_dos", False)
or bool(self.get("bandstructure_mode", False))
or self.get("parse_chgcar", False)
or self.get("parse_aeccar", False))
logger.info("Finished parsing with task_id: {}".format(t_id))
defuse_children = False
if task_doc["state"] != "successful":
defuse_unsuccessful = self.get("defuse_unsuccessful",
DEFUSE_UNSUCCESSFUL)
if defuse_unsuccessful is True:
defuse_children = True
elif defuse_unsuccessful is False:
pass
elif defuse_unsuccessful == "fizzle":
raise RuntimeError(
"VaspToDb indicates that job is not successful "
"(perhaps your job did not converge within the "
"limit of electronic/ionic iterations)!")
else:
raise RuntimeError("Unknown option for defuse_unsuccessful: "
"{}".format(defuse_unsuccessful))
task_fields_to_push = self.get("task_fields_to_push", None)
update_spec = {}
if task_fields_to_push:
if isinstance(task_fields_to_push, dict):
for key, path_in_task_doc in task_fields_to_push.items():
if has(task_doc, path_in_task_doc):
update_spec[key] = get(task_doc, path_in_task_doc)
else:
logger.warn("Could not find {} in task document. Unable to push to next firetask/firework".format(path_in_task_doc))
else:
raise RuntimeError("Inappropriate type {} for task_fields_to_push. It must be a "
"dictionary of format: {key: path} where key refers to a field "
"in the spec and path is a full mongo-style path to a "
"field in the task document".format(type(task_fields_to_push)))
return FWAction(stored_data={"task_id": task_doc.get("task_id", None)},
defuse_children=defuse_children, update_spec=update_spec)
def run_task(self, fw_spec):
vasp_calc_dir = self.get("calc_dir", None)
vasp_calc_loc = (
get_calc_loc(self["calc_loc"], fw_spec["calc_locs"])
if self.get("calc_loc")
else {}
)
# get the directory that contains the Lobster dir to parse
current_dir = os.getcwd()
# parse the Lobster directory
logger.info("PARSING DIRECTORY: {}".format(current_dir))
task_doc = {}
struct = Structure.from_file(self._find_gz_file("POSCAR"))
Lobsterout_here = Lobsterout(self._find_gz_file("lobsterout"))
task_doc["output"] = Lobsterout_here.get_doc()
Lobsterin_here = Lobsterin.from_file(self._find_gz_file("lobsterin"))
task_doc["input"] = Lobsterin_here
try:
Lobsterin_orig = Lobsterin.from_file(self._find_gz_file("lobsterin.orig"))
task_doc["orig_input"] = Lobsterin_orig
except ValueError:
pass
# save custodian details
if os.path.exists("custodian.json"):
task_doc["custodian"] = loadfn("custodian.json")
additional_fields = self.get("additional_fields", {})
if additional_fields:
task_doc.update(additional_fields)
task_doc.update(get_meta_from_structure(struct))
if vasp_calc_dir != None:
task_doc["vasp_dir_name"] = vasp_calc_dir
else:
task_doc["vasp_dir_name"] = vasp_calc_loc["path"]
task_doc["dir_name"] = current_dir
# Check for additional keys to set based on the fw_spec
if self.get("fw_spec_field"):
task_doc.update(fw_spec[self.get("fw_spec_field")])
task_doc["state"] = "successful"
task_doc = jsanitize(task_doc)
# get the database connection
db_file = env_chk(self.get("db_file"), fw_spec)
# db insertion or taskdoc dump
if not db_file:
with open("task_lobster.json", "w") as f:
f.write(json.dumps(task_doc, default=DATETIME_HANDLER))
else:
db = VaspCalcDb.from_db_file(db_file, admin=True)
db.collection = db.db["lobster"]
additional_outputs = self.get("additional_outputs", None)
if additional_outputs:
for filename in additional_outputs:
fs_id = None
if os.path.isfile(filename):
fs_id = put_file_in_gridfs(
filename, db, collection_name="lobster_files", compress=True
)
elif os.path.isfile(filename + ".gz"):
fs_id = put_file_in_gridfs(
filename + ".gz",
db,
collection_name="lobster_files",
compress=False,
compression_type="zlib",
)
if fs_id:
key_name = filename.split(".")[0].lower() + "_id"
task_doc[key_name] = fs_id
db.insert(task_doc)
return FWAction()
