def from_files(cls, disp_dir):
"""
Args:
disp_dir:
directory corresponding to this displacement (usually written by caesar as atomn.dispm)
Returns:
CaesarDisplacement object
"""
amplitude = float(
np.genfromtxt(os.path.join(disp_dir, 'amplitude.dat')))
disp = np.genfromtxt(os.path.join(disp_dir, 'disp.dat')).tolist()
#drone = VaspToComputedEntryDrone(inc_structure=True)
#TODO: Possibly either find another drone or combine Vasprun and Outcar here
# because this drone will return a dict, not an object. Don't forget to modify from_dict and the docstring if you make this change.
drone = VaspToDbTaskDrone(simulate_mode=True)
print 'assimilating vaspruns in ', disp_dir
positive = drone.assimilate(os.path.join(disp_dir, 'positive'))
#returned dict has last_updated as datetime.datetime object which jsanitize complains about
if 'last_updated' in positive:
positive['last_updated'] = str(positive['last_updated'])
negative = drone.assimilate(os.path.join(disp_dir, 'negative'))
if 'last_updated' in negative:
negative['last_updated'] = str(negative['last_updated'])
return cls(amplitude, disp, positive, negative)
def run_task(self, fw_spec):
prev_dir = fw_spec['prev_vasp_dir']
# get the db credentials
db_dir = os.environ['DB_LOC']
db_path = os.path.join(db_dir, 'tasks_db.json')
# use MPDrone to put it in the database
with open(db_path) as f:
db_creds = json.load(f)
drone = VaspToDbTaskDrone(host=db_creds['host'],
port=db_creds['port'],
database=db_creds['database'],
user=db_creds['admin_user'],
password=db_creds['admin_password'],
collection=db_creds['collection'])
t_id = drone.assimilate(prev_dir)
if t_id:
print 'ENTERED task id:', t_id
stored_data = {'task_id': t_id}
update_spec = {
'prev_vasp_dir': prev_dir,
'prev_task_type': fw_spec['prev_task_type']
}
return FWAction(stored_data=stored_data, update_spec=update_spec)
else:
raise ValueError("Could not parse entry for database insertion!")
def setUpClass(cls):
try:
drone = VaspToDbTaskDrone(database="qetransmuter_unittest")
queen = BorgQueen(drone)
queen.serial_assimilate(
os.path.join(test_dir, 'db_test', 'success_mp_aflow'))
cls.conn = MongoClient()
cls.qe = QueryEngine(database="qetransmuter_unittest")
except ConnectionFailure:
cls.qe = None
cls.conn = None
def test_assimilate(self):
"""Borg assimilation code.
This takes too long for a unit test!
"""
simulate = True if VaspToDbTaskDroneTest.conn is None else False
drone = VaspToDbTaskDrone(database="creator_unittest",
simulate_mode=simulate,
parse_dos=True,
compress_dos=1)
queen = BorgQueen(drone)
queen.serial_assimilate(os.path.join(test_dir, 'db_test'))
data = queen.get_data()
self.assertEqual(len(data), 6)
if VaspToDbTaskDroneTest.conn:
db = VaspToDbTaskDroneTest.conn["creator_unittest"]
data = db.tasks.find()
self.assertEqual(data.count(), 6)
warnings.warn("Actual db insertion mode.")
for d in data:
dir_name = d['dir_name']
if dir_name.endswith("killed_mp_aflow"):
self.assertEqual(d['state'], "killed")
self.assertFalse(d['is_hubbard'])
self.assertEqual(d['pretty_formula'], "SiO2")
elif dir_name.endswith("stopped_mp_aflow"):
self.assertEqual(d['state'], "stopped")
self.assertEqual(d['pretty_formula'], "ThFe5P3")
elif dir_name.endswith("success_mp_aflow"):
self.assertEqual(d['state'], "successful")
self.assertEqual(d['pretty_formula'], "TbZn(BO2)5")
self.assertAlmostEqual(d['output']['final_energy'],
-526.66747274, 4)
elif dir_name.endswith("Li2O_aflow"):
self.assertEqual(d['state'], "successful")
self.assertEqual(d['pretty_formula'], "Li2O")
self.assertAlmostEqual(d['output']['final_energy'],
-14.31446494, 6)
self.assertEqual(len(d["calculations"]), 2)
self.assertEqual(d['input']['is_lasph'], False)
self.assertEqual(d['input']['xc_override'], None)
elif dir_name.endswith("Li2O"):
self.assertEqual(d['state'], "successful")
self.assertEqual(d['pretty_formula'], "Li2O")
self.assertAlmostEqual(d['output']['final_energy'],
-14.31337758, 6)
self.assertEqual(len(d["calculations"]), 1)
self.assertEqual(len(d["custodian"]), 1)
self.assertEqual(len(d["custodian"][0]["corrections"]), 1)
elif dir_name.endswith("Li2O_aflow_lasph"):
self.assertEqual(d['state'], "successful")
self.assertEqual(d['pretty_formula'], "Li2O")
self.assertAlmostEqual(d['output']['final_energy'], -13.998171,
6)
self.assertEqual(len(d["calculations"]), 2)
self.assertEqual(d['input']['is_lasph'], True)
self.assertEqual(d['input']['xc_override'], "PS")
if VaspToDbTaskDroneTest.conn:
warnings.warn("Testing query engine mode.")
qe = QueryEngine(database="creator_unittest")
self.assertEqual(qe.query().count(), 6)
#Test mappings by query engine.
for r in qe.query(
criteria={"pretty_formula": "Li2O"},
properties=["dir_name", "energy", "calculations",
"input"]):
if r["dir_name"].endswith("Li2O_aflow"):
self.assertAlmostEqual(r['energy'], -14.31446494, 4)
self.assertEqual(len(r["calculations"]), 2)
self.assertEqual(r["input"]["is_lasph"], False)
self.assertEqual(r['input']['xc_override'], None)
elif r["dir_name"].endswith("Li2O"):
self.assertAlmostEqual(r['energy'], -14.31337758, 4)
self.assertEqual(len(r["calculations"]), 1)
self.assertEqual(r["input"]["is_lasph"], False)
self.assertEqual(r['input']['xc_override'], None)
#Test lasph
e = qe.get_entries({"dir_name": {"$regex": "lasph"}})
self.assertEqual(len(e), 1)
self.assertEqual(e[0].parameters["is_lasph"], True)
self.assertEqual(e[0].parameters["xc_override"], "PS")
# Test query one.
d = qe.query_one(criteria={"pretty_formula": "TbZn(BO2)5"},
properties=["energy"])
self.assertAlmostEqual(d['energy'], -526.66747274, 4)
d = qe.get_entries_in_system(["Li", "O"])
self.assertEqual(len(d), 3)
self.assertIsInstance(d[0], ComputedEntry)
s = qe.get_structure_from_id(d[0].entry_id)
self.assertIsInstance(s, Structure)
self.assertEqual(s.formula, "Li2 O1")
self.assertIsInstance(qe.get_dos_from_id(d[0].entry_id),
CompleteDos)
def test_to_from_dict(self):
drone = VaspToDbTaskDrone(database="wacky", simulate_mode=True)
d = drone.as_dict()
drone = VaspToDbTaskDrone.from_dict(d)
self.assertTrue(drone.simulate)
self.assertEqual(drone.database, "wacky")
def test_get_valid_paths(self):
drone = VaspToDbTaskDrone(simulate_mode=True)
all_paths = []
for path in os.walk(os.path.join(test_dir, 'db_test')):
all_paths.extend(drone.get_valid_paths(path))
self.assertEqual(len(all_paths), 6)
"""
#%%
import json
from pynter.data.datasets import Dataset
from pymatgen.io.vasp.inputs import VaspInput, Incar, Kpoints, Poscar, Potcar
from pymatgen.core.structure import Structure
ds = Dataset.from_directory(
'/home/lorenzo/tests/project-test/tutorials/Si-BS-dataset')
#%%
from matgendb.creator import VaspToDbTaskDrone
drone = VaspToDbTaskDrone(collection='test')
for j in ds:
drone.assimilate(j.path)
#%%
from matgendb.query_engine import QueryEngine
qe = QueryEngine(collection='test')
# entries = qe.get_entries({'dir_name':'localhost:/home/lorenzo/tests/project-test/tutorials/Si-BS-dataset/3-PBE-BS'})
entries = qe.get_entries({'chemsys': 'Si'},
optional_data=['calculations'],
inc_structure=True)
def run_task(self, fw_spec):
"""
Required Parameters:
host (str): See SurfaceWorkflowManager in surface_wf.py
port (int): See SurfaceWorkflowManager in surface_wf.py
user (str): See SurfaceWorkflowManager in surface_wf.py
password (str): See SurfaceWorkflowManager in surface_wf.py
database (str): See SurfaceWorkflowManager in surface_wf.py
collection (str): See SurfaceWorkflowManager in surface_wf.py
struct_type (str): either oriented_unit_cell or slab_cell
miller_index (list): Miller Index of the oriented
unit cell or slab
loc (str path): Location of the outputs of
the vasp calculations
Optional Parameters:
surface_area (float): surface area of the slab, obtained
from slab object before relaxation
shift (float): A shift value in Angstrom that determines how
much a slab should be shifted. For determining number of
terminations, obtained from slab object before relaxation
vsize (float): Size of vacuum layer of slab in Angstroms,
obtained from slab object before relaxation
ssize (float): Size of slab layer of slab in Angstroms,
obtained from slab object before relaxation
"""
dec = MontyDecoder()
struct_type = dec.process_decoded(self.get("struct_type"))
loc = dec.process_decoded(self.get("loc"))
cwd = dec.process_decoded(self.get("cwd"))
surface_area = dec.process_decoded(self.get("surface_area", None))
shift = dec.process_decoded(self.get("shift", None))
vsize = dec.process_decoded(self.get("vsize", None))
ssize = dec.process_decoded(self.get("ssize", None))
miller_index = dec.process_decoded(self.get("miller_index"))
# Sets default for DB parameters
if not self["host"]:
self["host"] = "127.0.0.1"
if not self["port"]:
self["port"] = 27017
if not self["database"]:
self["database"] = "vasp"
if not self["collection"]:
self["collection"] = "tasks"
# Addtional info relating to slabs
additional_fields={"author": os.environ.get("USER"),
"structure_type": struct_type,
"miller_index": miller_index,
"surface_area": surface_area, "shift": shift,
"vac_size": vsize, "slab_size": ssize}
drone = VaspToDbTaskDrone(host=self["host"], port=self["port"],
user=self["user"],
password=self["password"],
database=self["database"],
use_full_uri=False,
additional_fields=additional_fields,
collection=self["collection"])
drone.assimilate(cwd+loc)