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 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.")
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 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
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]
if args.nupdown:
entry_data= [rootdir,e.data["filename"].replace("./", ""),
re.sub("\s+", "", e.composition.formula),
"{:.5f}".format(e.energy),
"{:.5f}".format(1000*(e.energy-base_energy)/int(formulaunit)),
"{:.5f}".format(e.energy_per_atom),
delta_vol,e.parameters['run_type'],
e.data['NUPDOWN'],e.data['ISMEAR'],na_sites_fcoords_list_tuple]
else:
entry_data= [rootdir,e.data["filename"].replace("./", ""),
re.sub("\s+", "", e.composition.formula),
"{:.5f}".format(e.energy),
"{:.5f}".format(1000*(e.energy-base_energy)/int(formulaunit)),
"{:.5f}".format(e.energy_per_atom),
delta_vol,e.parameters['run_type'],na_sites_fcoords_list_tuple]
if args.structure:
entry_data.extend([entry_site_info['Total_Na_Site'],entry_site_info['Na2_Site'],entry_site_info['Na1_Mn_Site'],
entry_site_info['Na1_Co_Site'],entry_site_info['Na1_Mn_Co_Site']])
if args.template:
entry_data.extend([template_site_info['Total_Na_Site'],template_site_info['Na2_Site'],template_site_info['Na1_Mn_Site'],
template_site_info['Na1_Co_Site'],template_site_info['Na1_Mn_Co_Site']])
# sitelist = ['Existed','Duplicate_Entry']
logging.debug(e.data)
if args.duplicate:
# filename.rsplit('/',2)[-2]
Duplicate, Duplicat_Entry, Structure_info_dict = check_ex(check_ion_seq,Structure_info_dict,
e,args.tolerance)
entry_data.extend([Duplicate,Duplicat_Entry])
if args.ion_list:
if args.ion_list[0] == "All":
ion_list = None
else:
(start, end) = [int(i) for i in re.split("-", args.ion_list[0])]
ion_list = list(range(start, end + 1))
for d in entry_path:
magdata = get_magnetization(d, ion_list)
entry_data.extend(magdata)
if args.ion_avg_list:
ave_mag_data, ave_key_list = get_ave_magnetization(entry_path,args.ion_avg_list)
entry_data.extend(ave_mag_data)
if threshold != 0:
all_data.append(entry_data)
if float(entry_data[4])<threshold:
threscount +=1
elif threshold == 0:
all_data.append(entry_data)
energy_diff.append("{:.5f}".format(1000*(e.energy-base_energy)/int(formulaunit)))
# if len(all_data) > 0:
# headers = ("Directory", "Formula", "Energy", "Energy Diff (meV)/F.U.","E/Atom", "% vol chg")
# t = PrettyTable(headers)
# t.align["Directory"] = "l"
# for d in all_data:
# logging.debug('data row in all data is: \n {}'.format(d))
# t.add_row(d)
# print(t)
# print(msg)
# else:
# print("No valid vasp run found.")
if hull:
print 'Analyzing group: {}\n'.format(rootdir)
print 'Energy above hull is: \n'
print map(lambda x: x.encode('ascii'), energy_diff)
logging.info('In group: {}, number of entries fall in threshold is {}'.format(rootdir,threscount))
all_data.append([])
return all_data
