def get_energies(rootdir, reanalyze, verbose, pretty):
if verbose:
FORMAT = "%(relativeCreated)d msecs : %(message)s"
logging.basicConfig(level=logging.INFO, format=FORMAT)
drone = GaussianToComputedEntryDrone(inc_structure=True,
parameters=['filename'])
ncpus = multiprocessing.cpu_count()
logging.info('Detected {} cpus'.format(ncpus))
queen = BorgQueen(drone, number_of_drones=ncpus)
if os.path.exists(save_file) and not reanalyze:
msg = 'Using previously assimilated data from {}. ' + \
'Use -f to force re-analysis'.format(save_file)
queen.load_data(save_file)
else:
queen.parallel_assimilate(rootdir)
msg = 'Results saved to {} for faster reloading.'.format(save_file)
queen.save_data(save_file)
entries = queen.get_data()
entries = sorted(entries, key=lambda x: x.parameters['filename'])
all_data = [(e.parameters['filename'].replace("./", ""),
re.sub("\s+", "", e.composition.formula),
"{}".format(e.parameters['charge']),
"{}".format(e.parameters['spin_mult']),
"{:.5f}".format(e.energy), "{:.5f}".format(e.energy_per_atom),
) for e in entries]
headers = ("Directory", "Formula", "Charge", "Spin Mult.", "Energy",
"E/Atom")
print(tabulate(all_data, headers=headers))
print("")
print(msg)
def test_get_data(self):
with warnings.catch_warnings():
warnings.simplefilter("ignore")
drone = VaspToComputedEntryDrone()
self.queen = BorgQueen(drone, test_dir, 1)
data = self.queen.get_data()
self.assertEqual(len(data), 7)
def test_load_data(self):
with warnings.catch_warnings():
warnings.simplefilter("ignore")
drone = VaspToComputedEntryDrone()
queen = BorgQueen(drone)
queen.load_data(os.path.join(test_dir, "assimilated.json"))
self.assertEqual(len(queen.get_data()), 1)
def get_energies(rootdir, reanalyze, verbose, pretty, detailed, sort):
if verbose:
FORMAT = "%(relativeCreated)d msecs : %(message)s"
logging.basicConfig(level=logging.INFO, format=FORMAT)
if not detailed:
drone = SimpleVaspToComputedEntryDrone(inc_structure=True)
else:
drone = VaspToComputedEntryDrone(
inc_structure=True, data=["filename", "initial_structure"])
ncpus = multiprocessing.cpu_count()
logging.info("Detected {} cpus".format(ncpus))
queen = BorgQueen(drone, number_of_drones=ncpus)
if os.path.exists(save_file) and not reanalyze:
msg = "Using previously assimilated data from {}.".format(save_file) \
+ " Use -f to force re-analysis."
queen.load_data(save_file)
else:
if ncpus > 1:
queen.parallel_assimilate(rootdir)
else:
queen.serial_assimilate(rootdir)
msg = "Analysis results saved to {} for faster ".format(save_file) + \
"subsequent loading."
queen.save_data(save_file)
entries = queen.get_data()
if sort == "energy_per_atom":
entries = sorted(entries, key=lambda x: x.energy_per_atom)
elif sort == "filename":
entries = sorted(entries, key=lambda x: x.data["filename"])
all_data = []
for e in entries:
if not detailed:
delta_vol = "{:.2f}".format(e.data["delta_volume"] * 100)
else:
delta_vol = e.structure.volume / \
e.data["initial_structure"].volume - 1
delta_vol = "{:.2f}".format(delta_vol * 100)
all_data.append(
(e.data["filename"].replace("./", ""),
re.sub("\s+", "",
e.composition.formula), "{:.5f}".format(e.energy),
"{:.5f}".format(e.energy_per_atom), delta_vol))
if len(all_data) > 0:
headers = ("Directory", "Formula", "Energy", "E/Atom", "% vol chg")
if pretty:
from prettytable import PrettyTable
t = PrettyTable(headers)
t.set_field_align("Directory", "l")
map(t.add_row, all_data)
print t
else:
print str_aligned(all_data, headers)
print msg
else:
print "No valid vasp run found."
def get_energies(rootdir, reanalyze, verbose, detailed, sort, fmt):
"""
Doc string.
"""
if verbose:
logformat = "%(relativeCreated)d msecs : %(message)s"
logging.basicConfig(level=logging.INFO, format=logformat)
if not detailed:
drone = SimpleVaspToComputedEntryDrone(inc_structure=True)
else:
drone = VaspToComputedEntryDrone(
inc_structure=True, data=["filename", "initial_structure"])
ncpus = multiprocessing.cpu_count()
logging.info("Detected {} cpus".format(ncpus))
queen = BorgQueen(drone, number_of_drones=ncpus)
if os.path.exists(SAVE_FILE) and not reanalyze:
msg = "Using previously assimilated data from {}.".format(SAVE_FILE) \
+ " Use -r to force re-analysis."
queen.load_data(SAVE_FILE)
else:
if ncpus > 1:
queen.parallel_assimilate(rootdir)
else:
queen.serial_assimilate(rootdir)
msg = "Analysis results saved to {} for faster ".format(SAVE_FILE) + \
"subsequent loading."
queen.save_data(SAVE_FILE)
entries = queen.get_data()
if sort == "energy_per_atom":
entries = sorted(entries, key=lambda x: x.energy_per_atom)
elif sort == "filename":
entries = sorted(entries, key=lambda x: x.data["filename"])
all_data = []
for e in entries:
if not detailed:
delta_vol = "{:.2f}".format(e.data["delta_volume"] * 100)
else:
delta_vol = e.structure.volume / \
e.data["initial_structure"].volume - 1
delta_vol = "{:.2f}".format(delta_vol * 100)
all_data.append(
(e.data["filename"].replace("./", ""),
re.sub(r"\s+", "",
e.composition.formula), "{:.5f}".format(e.energy),
"{:.5f}".format(e.energy_per_atom), delta_vol))
if len(all_data) > 0:
headers = ("Directory", "Formula", "Energy", "E/Atom", "% vol chg")
print(tabulate(all_data, headers=headers, tablefmt=fmt))
print("")
print(msg)
else:
print("No valid vasp run found.")
os.unlink(SAVE_FILE)
def run_task(self, fw_spec):
"""
go through the measurement job dirs and
put the measurement jobs in the database
"""
drone = MPINTVaspToDbTaskDrone(**self.get("dbase_params", {}))
queen = BorgQueen(drone) # , number_of_drones=ncpus)
queen.serial_assimilate(self["measure_dir"])
return FWAction()
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 _extract_MP_data(self, MP_data_filename):
drone = VaspToComputedEntryDrone()
queen = BorgQueen(drone, "dummy", 1)
queen.load_data(MP_data_filename)
computed_entries = queen.get_data()
del drone
del queen
return computed_entries
def __init__(self, rootdir):
"""read vasp output via drone and extract all data
:param rootdir: root directory containing collection of VASP dirs
:type rootdir: str
"""
from pymatgen.apps.borg.hive import SimpleVaspToComputedEntryDrone
from pymatgen.apps.borg.queen import BorgQueen
self.rootdir = rootdir
self.drone = SimpleVaspToComputedEntryDrone(inc_structure=True)
self.queen = BorgQueen(
self.drone, rootdir,
1) # TODO: make sure uw2_si2 also works in parallel
self.data = self.queen.get_data()
def getEhull(new=''):
drone = VaspToComputedEntryDrone()
queen = BorgQueen(drone, './', 4)
entriesorig = queen.get_data()
queen.load_data(
os.path.join(os.path.dirname(__file__), '../ML/data/missingels.json'))
entriesextra = queen.get_data()
if new != '':
compat = MaterialsProjectCompatibility(check_potcar=False)
entriesorig = compat.process_entries(entriesorig)
for entry in entriesorig:
name = entry.name
line = re.findall('[A-Z][^A-Z]*',
name.replace('(', '').replace(')', ''))
searchset = set(re.sub('\d', ' ', ' '.join(line)).split())
entries = filter(
lambda e: set(
re.sub('\d', ' ',
str(e.composition).replace(' ', '')).split()) == searchset,
entriesorig)
entriesextra = filter(
lambda e: set(
re.sub('\d', ' ',
str(e.composition).replace(' ', '')).split()) & searchset,
entriesextra)
a = MPRester("s2vUo6mzETOHLdbu")
all_entries = a.get_entries_in_chemsys(
set(searchset)) + list(entries) + list(entriesextra)
pd = PhaseDiagram(all_entries) #,None
for e in pd.stable_entries:
if e.entry_id == None:
reaction = pd.get_equilibrium_reaction_energy(e)
return str(reaction) + ' None'
for e in pd.unstable_entries:
decomp, e_above_hull = pd.get_decomp_and_e_above_hull(e)
pretty_decomp = [("{}:{}".format(k.composition.reduced_formula,
k.entry_id), round(v, 2))
for k, v in decomp.items()]
if e.entry_id == None:
return str(e_above_hull) + ' ' + str(pretty_decomp)
def energy(self, spch_config):
""" Calculate total energy of the space charge model"""
structure = spch_config.structure.get_sorted_structure()
# if len(spinel_config.calc_history) >= 20:
# print("truncate calc_history")
# del spinel_config.calc_history[0:10]
# calc_history = spinel_config.calc_history
# if calc_history:
# # Try to avoid doing dft calculation for the same structure.
# # Assuming that calc_history is a list of ComputedStructureEntries
# for i in range(len(calc_history)):
# if self.matcher.fit(structure, calc_history[i].structure):
# print("match found in history")
# return calc_history[i].energy
# print("before poscar")
if self.selective_dynamics:
seldyn_arr = [[True, True, True] for i in range(len(structure))]
for specie in self.selective_dynamics:
indices = structure.indices_from_symbol(specie)
for i in indices:
seldyn_arr[i] = [False, False, False]
else:
seldyn_arr = None
poscar = Poscar(structure=structure, selective_dynamics=seldyn_arr)
# print("before vaspinput")
vaspinput = self.base_vaspinput
vaspinput.update({"POSCAR": poscar})
exitcode = self.vasp_run.submit(vaspinput, os.getcwd() + "/output")
# print("vasp exited with exit code", exitcode)
if exitcode != 0:
print("something went wrong")
sys.exit(1)
queen = BorgQueen(self.drone)
# print(os.getcwd())
queen.serial_assimilate("./output")
# print(queen.get_data())
# results = self.queen.get_data()[-1]
results = queen.get_data()[-1]
# calc_history.append(results)
spch_config.structure = results.structure
# print(results.energy)
# sys.stdout.flush()
return np.float64(results.energy)
def get_e_v(path):
"""
uses pymatgen drone and borgqueen classes to get energy and
volume data from the given directory path.
"""
volumes = []
energies = []
drone = MPINTVaspDrone(inc_structure=True)
bg = BorgQueen(drone)
# bg.parallel_assimilate(path)
bg.serial_assimilate(path)
allentries = bg.get_data()
for e in allentries:
if e:
energies.append(e.energy)
volumes.append(e.structure.lattice.volume)
return (volumes, energies)
def energy(self, HAp_config, save_history=False):
""" Calculate total energy of the space charge model"""
structure = HAp_config.structure.get_sorted_structure()
if save_history and self.matcher != None:
calc_history = HAp_config.calc_history
# Try to avoid doing dft calculation for the same structure.
# Assuming that calc_history is a list of ComputedStructureEntries
for i in range(len(calc_history)):
if self.matcher.fit(structure, calc_history[i].structure0):
print("match found in history")
return calc_history[i].energy
if self.selective_dynamics:
seldyn_arr = [[True, True, True] for i in range(len(structure))]
for specie in self.selective_dynamics:
indices = structure.indices_from_symbol(specie)
for i in indices:
seldyn_arr[i] = [False, False, False]
else:
seldyn_arr = None
poscar = Poscar(structure=structure, selective_dynamics=seldyn_arr)
vaspinput = self.base_vaspinput
vaspinput.update({"POSCAR": poscar})
exitcode = self.vasp_run.submit(vaspinput, os.getcwd() + "/output")
if exitcode != 0:
print("something went wrong")
sys.exit(1)
queen = BorgQueen(self.drone)
queen.serial_assimilate("./output")
results = queen.get_data()[-1]
if save_history:
results.structure0 = HAp_config.structure
HAp_config.calc_history.append(results)
HAp_config.structure = results.structure
return np.float64(results.energy)
def extract_json_data():
"""
Routine tries to read VASP data into pymatgen objects, and
then extracts only the relevant data. This is then written to json,
allowing the voluminous OUTCAR and vasprun.xml files to be discarded.
"""
try:
o = Outcar('OUTCAR')
found_outcar = True
except:
print('OUTCAR file missing or not readable')
found_outcar = False
try:
vr = Vasprun('vasprun.xml')
found_vasprun = True
except:
print('vasprun.xml file missing or not readable')
found_vasprun = False
dictionary_data = {}
if found_outcar:
dictionary_data['OUTCAR'] = o.as_dict()
if found_vasprun:
try:
# try to extract a Computed Entry object, using
# pymatgen technology
drone = VaspToComputedEntryDrone()
queen = BorgQueen(drone, './', 1)
entry = queen.get_data()[0]
dictionary_data['ComputedEntry'] = entry.as_dict()
except:
print('ComputedEntry COULD NOT BE EXTRACTED BY PYMATGEN...')
"""
# Do not extract DOS in run_data.json; this is too memory intensive!
try:
dictionary_data['DOS'] = vr.complete_dos.as_dict()
pymatgen_dos_success = True
except:
print('DOS COULD NOT BE EXTRACTED BY PYMATGEN...')
pymatgen_dos_success = False
"""
relaxation_data = []
for step in vr.ionic_steps:
data_dict = {}
for key in ['forces', 'structure', 'stress']:
if key in step:
data_dict[key] = step[key]
data_dict['electronic'] = step['electronic_steps'][-1]
relaxation_data.append(data_dict)
dictionary_data['relaxation'] = relaxation_data
if found_outcar or found_vasprun:
pmg_dump(dictionary_data, 'run_data.json')
return
def test_load_data(self):
drone = VaspToComputedEntryDrone()
queen = BorgQueen(drone)
queen.load_data(os.path.join(PymatgenTest.TEST_FILES_DIR, "assimilated.json"))
self.assertEqual(len(queen.get_data()), 1)
def test_get_data(self):
drone = VaspToComputedEntryDrone()
self.queen = BorgQueen(drone, PymatgenTest.TEST_FILES_DIR, 1)
data = self.queen.get_data()
self.assertEqual(len(data), 12)
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_get_data(self):
drone = VaspToComputedEntryDrone()
self.queen = BorgQueen(drone, test_dir, 1)
data = self.queen.get_data()
self.assertEqual(len(data), 12)
# coding: utf-8
# Copyright (c) Henniggroup.
# Distributed under the terms of the MIT License.
from __future__ import division, print_function, unicode_literals, \
absolute_import
from pymatgen.apps.borg.queen import BorgQueen
from mpinterfaces.database import MPINTVaspToDbTaskDrone
# import multiprocessing
additional_fields = {"author": "kiran"} # "doi":"10.1063/1.4865107"
drone = MPINTVaspToDbTaskDrone(host="127.0.0.1",
port=27017,
database="vasp",
collection="collection_name",
user="******",
password="******",
additional_fields=additional_fields)
ncpus = 4 # multiprocessing.cpu_count()
queen = BorgQueen(drone, number_of_drones=ncpus)
queen.parallel_assimilate('path_to_vasp_calculation_folders')
def get_phase_diagram_data(self):
"""
Returns grand potential phase diagram data to external plot
Assumes openelement specific element equals None
:return: Data to external plot
"""
open_elements_specific = None
open_element_all = Element(self.open_element)
mpr = MPRester(settings.apiKey)
drone = VaspToComputedEntryDrone()
queen = BorgQueen(drone, rootpath=".")
entries = queen.get_data()
# Get data to make phase diagram
mp_entries = mpr.get_entries_in_chemsys(self.system,
compatible_only=True)
entries.extend(mp_entries)
compat = MaterialsProjectCompatibility()
entries = compat.process_entries(entries)
#explanation_output = open("explain.txt",'w')
#entries_output = open("entries.txt", 'w')
compat.explain(entries[0])
#print(entries, file=entries_output)
if open_elements_specific:
gcpd = GrandPotentialPhaseDiagram(entries, open_elements_specific)
self.plot_phase_diagram(gcpd, False)
self.analyze_phase_diagram(gcpd)
if open_element_all:
pd = PhaseDiagram(entries)
chempots = pd.get_transition_chempots(open_element_all)
# print(chempots)
#all_gcpds = list()
toplot = []
# dic = {}
for idx in range(len(chempots)):
if idx == len(chempots) - 1:
avgchempot = chempots[idx] - 0.1
else:
avgchempot = 0.5 * (chempots[idx] + chempots[idx + 1])
gcpd = GrandPotentialPhaseDiagram(
entries, {open_element_all: avgchempot}, pd.elements)
# min_chempot = None if idx == len(
# chempots) - 1 else chempots[idx + 1]
# max_chempot = chempots[idx]
#gcpd = GrandPotentialPhaseDiagram(entries, {open_element_all: max_chempot}, pd.elements)
toplot.append(self.get_grand_potential_phase_diagram(gcpd))
# toplot.append(max_chempot)
#self.plot_phase_diagram(gcpd, False)
#print({open_element_all: max_chempot})
all_phase_diagrams = toplot
# print(all_phase_diagrams)
number_of_phase_diagrams = len(all_phase_diagrams)
#pd3 = PhaseDiagram(entries)
chempot_list = pd.get_transition_chempots(open_element_all)
pd_index = 0
chempots_range_of_each_phase = {}
for particular_phase_diagram in all_phase_diagrams:
chempot = chempot_list[pd_index]
if pd_index is not number_of_phase_diagrams - 1:
next_chempot = chempot_list[pd_index + 1]
else:
next_chempot = chempot_list[pd_index] - 2.0
chempot_range = [chempot, next_chempot]
phases_list = particular_phase_diagram[0]
for phase in phases_list:
if phase in chempots_range_of_each_phase.keys():
chempots_range_of_each_phase[phase][1] = next_chempot.copy(
)
else:
chempots_range_of_each_phase[phase] = chempot_range.copy()
pd_index = pd_index + 1
return chempots_range_of_each_phase
def test_load_data(self):
drone = VaspToComputedEntryDrone()
queen = BorgQueen(drone)
queen.load_data(os.path.join(test_dir, "assimilated.json"))
self.assertEqual(len(queen.get_data()), 1)
def setUp(self):
drone = VaspToComputedEntryDrone()
self.queen = BorgQueen(drone, test_dir, 1)
def get_phase_diagram_data(self):
"""
Returns grand potential phase diagram data to external plot
Assumes openelement specific element equals None
:return: Data to external plot
"""
open_elements_specific = None
open_element_all = Element(self.open_element)
mpr = MPRester("key")
# import do dados dos arquivos tipo vasp
drone = VaspToComputedEntryDrone()
queen = BorgQueen(drone, rootpath=".")
entries = queen.get_data()
# Get data to make phase diagram
mp_entries = mpr.get_entries_in_chemsys(self.system,
compatible_only=True)
entries.extend(mp_entries)
compat = MaterialsProjectCompatibility()
entries = compat.process_entries(entries)
#explanation_output = open("explain.txt",'w')
entries_output = open("entries.txt", 'w')
compat.explain(entries[0])
print(entries, file=entries_output)
#print(entries)
if open_elements_specific:
gcpd = GrandPotentialPhaseDiagram(entries, open_elements_specific)
self.plot_phase_diagram(gcpd, False)
self.analyze_phase_diagram(gcpd)
if open_element_all:
pd = PhaseDiagram(entries)
chempots = pd.get_transition_chempots(open_element_all)
#print(chempots)
#all_gcpds = list()
toplot = []
# dic = {}
for idx in range(len(chempots)):
if idx == len(chempots) - 1:
avgchempot = chempots[idx] - 0.1
else:
avgchempot = 0.5 * (chempots[idx] + chempots[idx + 1])
gcpd = GrandPotentialPhaseDiagram(
entries, {open_element_all: avgchempot}, pd.elements)
# toplot.append(self.get_grand_potential_phase_diagram(gcpd))
min_chempot = None if idx == len(chempots) - 1 else chempots[
idx + 1]
max_chempot = chempots[idx]
#gcpd = GrandPotentialPhaseDiagram(entries, {open_element_all: max_chempot}, pd.elements)
toplot.append(self.get_grand_potential_phase_diagram(gcpd))
#toplot.append(max_chempot)
#self.plot_phase_diagram(gcpd, False)
#print({open_element_all: max_chempot})
# Data to plot phase diagram
return toplot
def __init__(self):
self.drone = SimpleVaspToComputedEntryDrone(inc_structure=True)
self.queen = BorgQueen(self.drone)
def submit_vasp_directory(self,
rootdir,
authors,
projects=None,
references='',
remarks=None,
master_data=None,
master_history=None,
created_at=None,
ncpus=None):
"""
Assimilates all vasp run directories beneath a particular
directory using BorgQueen to obtain structures, and then submits thhem
to the Materials Project as SNL files. VASP related meta data like
initial structure and final energies are automatically incorporated.
.. note::
As of now, this MP REST feature is open only to a select group of
users. Opening up submissions to all users is being planned for
the future.
Args:
rootdir (str): Rootdir to start assimilating VASP runs from.
authors: *List* of {"name":'', "email":''} dicts,
*list* of Strings as 'John Doe <*****@*****.**>',
or a single String with commas separating authors. The same
list of authors should apply to all runs.
projects ([str]): List of Strings ['Project A', 'Project B'].
This applies to all structures.
references (str): A String in BibTeX format. Again, this applies to
all structures.
remarks ([str]): List of Strings ['Remark A', 'Remark B']
master_data (dict): A free form dict. Namespaced at the root
level with an underscore, e.g. {"_materialsproject":<custom
data>}. This data is added to all structures detected in the
directory, in addition to other vasp data on a per structure
basis.
master_history: A master history to be added to all entries.
created_at (datetime): A datetime object
ncpus (int): Number of cpus to use in using BorgQueen to
assimilate. Defaults to None, which means serial.
"""
from pymatgen.apps.borg.hive import VaspToComputedEntryDrone
from pymatgen.apps.borg.queen import BorgQueen
drone = VaspToComputedEntryDrone(
inc_structure=True, data=["filename", "initial_structure"])
queen = BorgQueen(drone, number_of_drones=ncpus)
queen.parallel_assimilate(rootdir)
structures = []
metadata = []
histories = []
for e in queen.get_data():
structures.append(e.structure)
m = {
"_vasp": {
"parameters": e.parameters,
"final_energy": e.energy,
"final_energy_per_atom": e.energy_per_atom,
"initial_structure": e.data["initial_structure"].as_dict()
}
}
if "history" in e.parameters:
histories.append(e.parameters["history"])
if master_data is not None:
m.update(master_data)
metadata.append(m)
if master_history is not None:
histories = master_history * len(structures)
return self.submit_structures(structures,
authors,
projects=projects,
references=references,
remarks=remarks,
data=metadata,
histories=histories,
created_at=created_at)
def get_energies(rootdir, reanalyze, verbose, quick, sort, fmt):
"""
Get energies of all vaspruns in directory (nested).
Args:
rootdir (str): Root directory.
reanalyze (bool): Whether to ignore saved results and reanalyze
verbose (bool): Verbose mode or not.
quick (bool): Whether to perform a quick analysis (using OSZICAR instead
of vasprun.xml
sort (bool): Whether to sort the results in ascending order.
fmt (str): tablefmt passed to tabulate.
"""
if verbose:
logformat = "%(relativeCreated)d msecs : %(message)s"
logging.basicConfig(level=logging.INFO, format=logformat)
if quick:
drone = SimpleVaspToComputedEntryDrone(inc_structure=True)
else:
drone = VaspToComputedEntryDrone(
inc_structure=True, data=["filename", "initial_structure"])
ncpus = multiprocessing.cpu_count()
logging.info("Detected {} cpus".format(ncpus))
queen = BorgQueen(drone, number_of_drones=ncpus)
if os.path.exists(SAVE_FILE) and not reanalyze:
msg = ("Using previously assimilated data from {}.".format(SAVE_FILE) +
" Use -r to force re-analysis.")
queen.load_data(SAVE_FILE)
else:
if ncpus > 1:
queen.parallel_assimilate(rootdir)
else:
queen.serial_assimilate(rootdir)
msg = ("Analysis results saved to {} for faster ".format(SAVE_FILE) +
"subsequent loading.")
queen.save_data(SAVE_FILE)
entries = queen.get_data()
if sort == "energy_per_atom":
entries = sorted(entries, key=lambda x: x.energy_per_atom)
elif sort == "filename":
entries = sorted(entries, key=lambda x: x.data["filename"])
all_data = []
for e in entries:
if quick:
delta_vol = "NA"
else:
delta_vol = e.structure.volume / e.data[
"initial_structure"].volume - 1
delta_vol = "{:.2f}".format(delta_vol * 100)
all_data.append((
e.data["filename"].replace("./", ""),
re.sub(r"\s+", "", e.composition.formula),
"{:.5f}".format(e.energy),
"{:.5f}".format(e.energy_per_atom),
delta_vol,
))
if len(all_data) > 0:
headers = ("Directory", "Formula", "Energy", "E/Atom", "% vol chg")
print(tabulate(all_data, headers=headers, tablefmt=fmt))
print("")
print(msg)
else:
print("No valid vasp run found.")
os.unlink(SAVE_FILE)
return 0
vasprun = runner(
Solver=solver,
nprocs=nprocs_per_vasp,
comm=MPI.COMM_SELF,
)
baseinput = VaspInput.from_directory(
"baseinput") # (os.path.join(os.path.dirname(__file__), "baseinput"))
ltol = 0.1
matcher = StructureMatcher(ltol=ltol, primitive_cell=False)
matcher_base = StructureMatcher(ltol=ltol,
primitive_cell=False,
stol=0.5,
allow_subset=True) # ,
# comparator=FrameworkComparator(), ignored_species=["Pt","Zr"])
drone = SimpleVaspToComputedEntryDrone(inc_structure=True)
queen = BorgQueen(drone)
model = dft_HAp(
calcode="VASP",
vasp_run=vasprun,
base_vaspinput=baseinput,
matcher_base=matcher_base,
queen=queen,
matcher=matcher,
)
# matcher=matcher, matcher_site=matcher_site, queen=queen, selective_dynamics=["Pt"])
myrank = comm.Get_rank()
os.mkdir(str(myrank))
os.chdir(str(myrank))
for i in range(len(reps)):
if i % nreplicas == myrank:
from pymatgen.ext.matproj import MPRester
from pymatgen.apps.borg.hive import VaspToComputedEntryDrone
from pymatgen.apps.borg.queen import BorgQueen
from pymatgen.entries.compatibility import MaterialsProjectCompatibility
from pymatgen.analysis.phase_diagram import PhaseDiagram
from pymatgen.analysis.phase_diagram import PDPlotter
# Assimilate VASP calculations into ComputedEntry object. Let's assume that
# the calculations are for a series of new LixFeyOz phases that we want to
# know the phase stability.
drone = VaspToComputedEntryDrone()
queen = BorgQueen(drone, rootpath=".")
entries = queen.get_data()
# Obtain all existing Li-Fe-O phases using the Materials Project REST API
with MPRester("key") as m:
mp_entries = m.get_entries_in_chemsys(["Li", "Sn", "S"])
# Combined entry from calculated run with Materials Project entries
entries.extend(mp_entries)
# Process entries using the MaterialsProjectCompatibility
compat = MaterialsProjectCompatibility()
entries = compat.process_entries(entries)
# Generate and plot Li-Fe-O phase diagram
pd = PhaseDiagram(entries)
plotter = PDPlotter(pd)
plotter.show()
