Python BorgQueen.save_data方法代码示例

# 需要导入模块: from pymatgen.apps.borg.queen import BorgQueen [as 别名]
# 或者: from pymatgen.apps.borg.queen.BorgQueen import save_data [as 别名]
def get_energies(rootdir, reanalyze, verbose, detailed, sort):
    """
    Doc string.
    """
    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")
        t = PrettyTable(headers)
        t.align["Directory"] = "l"
        for d in all_data:
            t.add_row(d)
        print(t)
        print(msg)
    else:
        print("No valid vasp run found.")

# 需要导入模块: from pymatgen.apps.borg.queen import BorgQueen [as 别名]
# 或者: from pymatgen.apps.borg.queen.BorgQueen import save_data [as 别名]
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)

# 需要导入模块: from pymatgen.apps.borg.queen import BorgQueen [as 别名]
# 或者: from pymatgen.apps.borg.queen.BorgQueen import save_data [as 别名]
def get_energies(rootdir, reanalyze, verbose, detailed,
                 sort, formulaunit, debug, hull, threshold, args, templatestructure):

    ion_list = 'Novalue'
    ave_key_list = 'Novalue'
    threscount = 0

    """
    Doc string.
    """
    if (verbose and not debug):
        FORMAT = "%(relativeCreated)d msecs : %(message)s"
        logging.basicConfig(level=logging.INFO, format=FORMAT)

    elif debug:
        logging.basicConfig(level=logging.DEBUG)

    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"])

    # logging.debug('First Energy entry is {}'.format(entries[0]))

    base_energy = entries[0].energy
    logging.debug('Type of entries is: {}'.format(type(entries)))
    logging.debug('First Element of Entries is:{}'.format(entries[0]))

    # logging.debug('First Energy entry structure is {}'.format(entries[0].structure))

    xy_direction = int(args.XYdirection)
    tolerance = float(args.tolerance)


    if args.template:

        logging.debug('Temp Structure site info is: {}'.format(Na12(['Co','Mn'],['Na'],templatestructure,templatestructure,XY_Direction=xy_direction,tol=tolerance)))
        template_site_info = Na12(['Co','Mn'],['Na'],templatestructure,templatestructure,XY_Direction=xy_direction,tol=tolerance)

    all_data = []
    energy_diff = []

    threshold=float(threshold)

    Structure_info_dict = {}
    check_ion_seq = [args.dupion]


    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)


        entry_path = e.data['filename'].rsplit('/',1)[0]

        entry_site_info = Na12(['Co','Mn'],['Na'],e.structure,e.structure,XY_Direction=xy_direction,tol=tolerance)

        logging.debug('Total Na site: {}'.format(entry_site_info['Total_Na_Site']))

        #Coordination extraction part
        # na_layer_site_fcoords = [site._fcoords for site in s if site.specie.symbol == "Na"]
        # if 'Cif_Structure' in e.data.keys():
        #     na_sites_fcoords = [site._fcoords for site in e.data['Cif_Structure'] if site.specie.symbol == 'Na']
        #     na_sites_fcoords_list_tuple = [tuple(coord) for coord in na_sites_fcoords]

        na_sites_fcoords = [site._fcoords for site in e.data['CONTCAR_Structure'] if site.specie.symbol == 'Na']
        na_sites_fcoords_list_tuple = [tuple(coord) for coord in na_sites_fcoords]
#.........这里部分代码省略.........