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")