def load_hdf5_file(self, hdf5_file, encoding):
from pyNastran.utils.dict_to_h5py import _cast
keys = list(hdf5_file.keys())
for key in keys:
if key in ['_begin_count', 'debug', 'write_begin_bulk']: # scalars
value = _cast(hdf5_file[key])
setattr(self, key, value)
elif key in [
'reject_lines', 'begin_bulk', 'lines', 'output_lines'
]: # lists of strings
value_bytes = _cast(hdf5_file[key]).tolist()
value_str = [line.decode(encoding) for line in value_bytes]
elif key == 'subcases':
subcase_group = hdf5_file[key]
keys = list(subcase_group.keys())
keys.remove('keys')
subcases = {}
for key in keys:
sub_group = subcase_group[key]
ikey = int(key)
subcase = Subcase(id=ikey)
subcase.log = self.log
subcase.load_hdf5_file(sub_group, encoding)
subcases[ikey] = subcase
str(subcase)
self.subcases = subcases
#print(value_bytes)
else: # pragma: no cover
self.log.warning('skipping CaseControlDeck/%s' % key)
raise RuntimeError('error loading hdf5 CaseControlDeck/%s' %
key)
def load_hdf5_file(self, hdf5_file, encoding: str) -> None:
"""loads the case control deck section from a hdf5 file"""
from pyNastran.utils.dict_to_h5py import _cast
keys = list(hdf5_file.keys())
for key in keys:
if key in ['_begin_count', 'debug', 'write_begin_bulk']: # scalars
value = _cast(hdf5_file[key])
setattr(self, key, value)
elif key in [
'reject_lines', 'begin_bulk', 'lines', 'output_lines'
]: # lists of strings
unused_lines_str = decode_lines(_cast(hdf5_file[key]),
encoding)
elif key == 'subcases':
subcase_group = hdf5_file[key]
keys = list(subcase_group.keys())
keys.remove('keys')
subcases = {}
for key2 in keys:
sub_group = subcase_group[key2]
ikey2 = int(key2)
subcase = Subcase(id=ikey2)
subcase.log = self.log
subcase.load_hdf5_file(sub_group, encoding)
subcases[ikey2] = subcase
str(subcase)
self.subcases = subcases
#print(value_bytes)
else: # pragma: no cover
self.log.warning('skipping CaseControlDeck/%s' % key)
raise RuntimeError('error loading hdf5 CaseControlDeck/%s' %
key)
def load_hdf5(cls, h5_file, encoding):
from pyNastran.utils.dict_to_h5py import _cast
value = h5_file['value']
try:
value2 = _cast(value).decode(encoding)
except AttributeError:
print(cls.type, _cast(value))
raise
return cls(value2), []
def load_hdf5(cls, h5_file, encoding):
from pyNastran.utils.dict_to_h5py import _cast
value = h5_file['value']
casted_value = _cast(value)
if isinstance(casted_value, int):
value2 = casted_value
else:
value2 = casted_value.decode(
encoding) # if isinstance(value, bytes) else value
return cls(value2), []
def load_hdf5(self, subgroup, encoding):
"""loads EXTSEOUT from an h5py HDF5 file"""
from pyNastran.utils.dict_to_h5py import _cast
for key in subgroup.keys():
subgroupi = subgroup[key]
if key == 'data':
data_keys = _cast(subgroupi['keys'])
data_keys = decode_bytes_list(data_keys, encoding)
keys_none = subgroupi['keys_none']
keys_none = decode_bytes_list(keys_none, encoding)
data_values = _cast(subgroupi['values'])
data_values = decode_bytes_list(data_values, encoding)
keys = data_keys + keys_none
values = data_values + [None] * len(keys_none)
data = [(key, value) for (key, value) in zip(keys, values)]
else:
raise NotImplementedError(key)
return EXTSEOUT(data), []
def load_hdf5(cls, h5_file, encoding):
from pyNastran.utils.dict_to_h5py import _cast
value = h5_file['value']
value2 = _cast(value)
return cls(value2), []