def _from_file(self):
"""Create rudimentary dictionary of entries from a
file.
"""
kpoints = utils.read_from_file(self._file_path, self._file_handler)
kpoints_dict = self._from_list(kpoints)
return kpoints_dict
def _from_file(self):
"""
Create a dictionary of entries from a
file and store them in the this instance's data dictionary.
"""
doscar = utils.read_from_file(self._file_path, self._file_handler, encoding='utf8')
self._from_list(doscar)
def _from_file(self):
"""Create rudimentary dictionary of entries from a
file.
"""
incar = utils.read_from_file(self._file_path,
self._file_handler,
encoding='utf8')
return incar
def _from_file(self):
"""Create rudimentary dictionary of entries from a
file.
"""
poscar = utils.read_from_file(self._file_path,
self._file_handler,
encoding='utf8')
poscar_dict = self._from_list(poscar)
return poscar_dict
def _from_file(self):
"""
Load CHGCAR into NumPy arrays.
This method is presently not optimized to use as little memory as possible.
"""
content = utils.read_from_file(self._file_path,
self._file_handler,
lines=False)
# Extract header
temp = content.split('\n\n', 1)
header = temp[0]
content = temp[1]
header = header.split('\n')
# comment = header[0]
scaling = float(header[1])
lattice_vectors = np.zeros((3, 3))
for i in range(3):
# Read and scale lattice vectors
lattice_vectors[i] = scaling * np.array(
[float(item) for item in header[i + 2].split()])
# Calculate volume for later scaling
volume = 1.0
if volume:
volume = np.dot(lattice_vectors[0],
np.cross(lattice_vectors[1], lattice_vectors[2]))
# First line of content should now be NGXF, NGYF, NGZF
temp = content.split('\n', 1)
ngf_string = temp[0]
content = temp[1]
ngf = [int(item) for item in ngf_string.split()]
# Need to reverse as CHGCAR is x fastest, while we want
# to comply with z fastest (C order).
ngf.reverse()
# Check how many datasets we have
content = content.split(ngf_string)
num_datasets = len(content)
# First dataset is always there
self._data['total'] = np.fromstring(
content[0].split('augmentation occupancies')[0],
dtype=float,
sep=' ').reshape(ngf) / volume
if num_datasets == 2:
# Collinear spin
self._data['magnetization'] = np.fromstring(
content[1].split('augmentation occupancies')[0],
dtype=float,
sep=' ').reshape(ngf) / volume
elif num_datasets == 4:
# Non-collinear spin
self._data['magnetization'] = {}
self._data['magnetization']['x'] = np.fromstring(
content[1].split('augmentation occupancies')[0],
dtype=float,
sep=' ').reshape(ngf) / volume
self._data['magnetization']['y'] = np.fromstring(
content[2].split('augmentation occupancies')[0],
dtype=float,
sep=' ').reshape(ngf) / volume
self._data['magnetization']['z'] = np.fromstring(
content[3].split('augmentation occupancies')[0],
dtype=float,
sep=' ').reshape(ngf) / volume
