def setUp(self):
self.test_dir = os.path.join(
os.path.dirname(__file__), "..", "..", "..", "..", "test_files", "molecules"
)
self.drone = GaussianToComputedEntryDrone(data=["corrections"])
self.structure_drone = GaussianToComputedEntryDrone(True)
warnings.simplefilter("ignore")
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 setUp(self):
self.test_dir = os.path.join(os.path.dirname(__file__), "..", "..",
"..", "..", 'test_files', "molecules")
self.drone = GaussianToComputedEntryDrone(data=["corrections"])
self.structure_drone = GaussianToComputedEntryDrone(True)
def setUp(self):
self.drone = GaussianToComputedEntryDrone(data=["corrections"])
self.structure_drone = GaussianToComputedEntryDrone(True)
warnings.simplefilter("ignore")
