def test_atoms_from_string(self):
for poscar_file in self.file_list:
with open(poscar_file, "r") as f:
lines = f.readlines()
if poscar_file.split("/")[-1] == "POSCAR_spoilt":
self.assertRaises(AssertionError,
atoms_from_string,
string=lines)
else:
atoms = atoms_from_string(string=lines)
self.assertIsInstance(atoms, Atoms)
def _read_vol_data_old(filename, normalize=True):
"""
Convenience method to parse a generic volumetric static file in the vasp like format.
Used by subclasses for parsing the file. This routine is adapted from the pymatgen vasp VolumetricData
class with very minor modifications. The new parser is faster
http://pymatgen.org/_modules/pymatgen/io/vasp/outputs.html#VolumetricData.
Args:
filename (str): Path of file to parse
normalize (boolean): Flag to normalize by the volume of the cell
"""
if os.stat(filename).st_size == 0:
s = Settings()
s.logger.warning("File:" + filename +
"seems to be corrupted/empty")
return None, None
poscar_read = False
poscar_string = list()
dataset = list()
all_dataset = list()
dim = None
dimline = None
read_dataset = False
ngrid_pts = 0
data_count = 0
atoms = None
volume = None
with open(filename, "r") as f:
for line in f:
line = line.strip()
if read_dataset:
toks = line.split()
for tok in toks:
if data_count < ngrid_pts:
# This complicated procedure is necessary because
# vasp outputs x as the fastest index, followed by y
# then z.
x = data_count % dim[0]
y = int(math.floor(data_count / dim[0])) % dim[1]
z = int(math.floor(data_count / dim[0] / dim[1]))
dataset[x, y, z] = float(tok)
data_count += 1
if data_count >= ngrid_pts:
read_dataset = False
data_count = 0
all_dataset.append(dataset)
elif not poscar_read:
if line != "" or len(poscar_string) == 0:
poscar_string.append(line)
elif line == "":
try:
atoms = atoms_from_string(poscar_string)
except ValueError:
pot_str = filename.split("/")
pot_str[-1] = "POTCAR"
potcar_file = "/".join(pot_str)
species = get_species_list_from_potcar(potcar_file)
atoms = atoms_from_string(poscar_string,
species_list=species)
volume = atoms.get_volume()
poscar_read = True
elif not dim:
dim = [int(i) for i in line.split()]
ngrid_pts = dim[0] * dim[1] * dim[2]
dimline = line
read_dataset = True
dataset = np.zeros(dim)
elif line == dimline:
read_dataset = True
dataset = np.zeros(dim)
if not normalize:
volume = 1.0
if len(all_dataset) == 0:
s = Settings()
s.logger.warning("File:" + filename +
"seems to be corrupted/empty")
return None, None
if len(all_dataset) == 2:
data = {
"total": all_dataset[0] / volume,
"diff": all_dataset[1] / volume,
}
return atoms, [data["total"], data["diff"]]
else:
data = {"total": all_dataset[0] / volume}
return atoms, [data["total"]]
def _read_vol_data(self, filename, normalize=True):
"""
Parses the VASP volumetric type files (CHGCAR, LOCPOT, PARCHG etc). Rather than looping over individual values,
this function utilizes numpy indexing resulting in a parsing efficiency of at least 10%.
Args:
filename (str): File to be parsed
normalize (bool): Normalize the data with respect to the volume (Recommended for CHGCAR files)
Returns:
pyiron.atomistics.structure.atoms.Atoms: The structure of the volumetric snapshot
list: A list of the volumetric data (length >1 for CHGCAR files with spin)
"""
if not os.path.getsize(filename) > 0:
s = Settings()
s.logger.warning("File:" + filename + "seems to be empty! ")
return None, None
with open(filename, "r") as f:
struct_lines = list()
get_grid = False
n_x = 0
n_y = 0
n_z = 0
n_grid = 0
n_grid_str = None
total_data_list = list()
atoms = None
for line in f:
strip_line = line.strip()
if not get_grid:
if strip_line == "":
get_grid = True
struct_lines.append(strip_line)
elif n_grid_str is None:
n_x, n_y, n_z = [int(val) for val in strip_line.split()]
n_grid = n_x * n_y * n_z
n_grid_str = " ".join(
[str(val) for val in [n_x, n_y, n_z]])
load_txt = np.genfromtxt(f, max_rows=int(n_grid / 5))
load_txt = np.hstack(load_txt)
if n_grid % 5 != 0:
add_line = np.genfromtxt(f, max_rows=1)
load_txt = np.append(load_txt, np.hstack(add_line))
total_data = self._fastest_index_reshape(
load_txt, [n_x, n_y, n_z])
try:
atoms = atoms_from_string(struct_lines)
except ValueError:
pot_str = filename.split("/")
pot_str[-1] = "POTCAR"
potcar_file = "/".join(pot_str)
species = get_species_list_from_potcar(potcar_file)
atoms = atoms_from_string(struct_lines,
species_list=species)
if normalize:
total_data /= atoms.get_volume()
total_data_list.append(total_data)
elif atoms is not None:
grid_str = n_grid_str.replace(" ", "")
if grid_str == strip_line.replace(" ", ""):
load_txt = np.genfromtxt(f, max_rows=int(n_grid / 5))
load_txt = np.hstack(load_txt)
if n_grid % 5 != 0:
add_line = np.genfromtxt(f, max_rows=1)
load_txt = np.hstack(
np.append(load_txt, np.hstack(add_line)))
total_data = self._fastest_index_reshape(
load_txt, [n_x, n_y, n_z])
if normalize:
total_data /= atoms.get_volume()
total_data_list.append(total_data)
if len(total_data_list) == 0:
s = Settings()
s.logger.warning(
"File:" + filename +
"seems to be corrupted/empty even after parsing!")
return None, None
return atoms, total_data_list
def test_atoms_from_string(self):
for poscar_file in self.file_list:
with open(poscar_file, "r") as f:
lines = f.readlines()
atoms = atoms_from_string(string=lines)
self.assertIsInstance(atoms, Atoms)