def from_hdf(self, hdf=None, group_name=None):
super(TableJob, self).from_hdf(hdf=hdf, group_name=group_name)
with self.project_hdf5.open("input") as hdf5_input:
if 'project' in hdf5_input.list_nodes():
project_dict = hdf5_input['project']
project = self.project.__class__(
path=project_dict['path'],
user=project_dict['user'],
sql_query=project_dict['sql_query'])
project._filter = project_dict['filter']
project._inspect_mode = project_dict['inspect_mode']
self.analysis_project = project
if 'filter' in hdf5_input.list_nodes():
self._filter_function_str = hdf5_input['filter']
self.pyiron_table.filter_function = get_function_from_string(
hdf5_input['filter'])
bool_dict = hdf5_input['bool_dict']
self._enforce_update = bool_dict['enforce_update']
self._pyiron_table.convert_to_object = bool_dict[
'convert_to_object']
self._pyiron_table.add._from_hdf(hdf5_input)
pyiron_table = os.path.join(self.working_directory, 'pyirontable.csv')
if os.path.exists(pyiron_table):
try:
self._pyiron_table._df = pandas.read_csv(pyiron_table)
self._pyiron_table._csv_file = pyiron_table
except EmptyDataError:
pass
else:
with self.project_hdf5.open("output") as hdf5_output:
if 'table' in hdf5_output.list_nodes():
self._pyiron_table._df = pandas.DataFrame(
json.loads(hdf5_output['table']))
def from_hdf(self, hdf=None, group_name=None):
"""
Restore pyiron table job from HDF5
Args:
hdf:
group_name:
"""
super(TableJob, self).from_hdf(hdf=hdf, group_name=group_name)
with self.project_hdf5.open("input") as hdf5_input:
if "project" in hdf5_input.list_nodes():
project_dict = hdf5_input["project"]
project = self.project.__class__(
path=project_dict["path"],
user=project_dict["user"],
sql_query=project_dict["sql_query"],
)
project._filter = project_dict["filter"]
project._inspect_mode = project_dict["inspect_mode"]
self.analysis_project = project
if "filter" in hdf5_input.list_nodes():
self.pyiron_table._filter_function_str = hdf5_input["filter"]
self.pyiron_table.filter_function = get_function_from_string(
hdf5_input["filter"]
)
if "db_filter" in hdf5_input.list_nodes():
self.pyiron_table._db_filter_function_str = hdf5_input["db_filter"]
self.pyiron_table.db_filter_function = get_function_from_string(
hdf5_input["db_filter"]
)
bool_dict = hdf5_input["bool_dict"]
self._enforce_update = bool_dict["enforce_update"]
self._pyiron_table.convert_to_object = bool_dict["convert_to_object"]
self._pyiron_table.add._from_hdf(hdf5_input)
pyiron_table = os.path.join(self.working_directory, "pyirontable.csv")
if os.path.exists(pyiron_table):
try:
self._pyiron_table._df = pandas.read_csv(pyiron_table)
self._pyiron_table._csv_file = pyiron_table
except EmptyDataError:
pass
else:
with self.project_hdf5.open("output") as hdf5_output:
if "table" in hdf5_output.list_nodes():
self._pyiron_table._df = pandas.DataFrame(
json.loads(hdf5_output["table"])
)
def from_hdf(self, hdf=None, group_name=None):
"""
Restore the SerialMaster from an HDF5 file
Args:
hdf (ProjectHDFio): HDF5 group object - optional
group_name (str): HDF5 subgroup name - optional
"""
super(SerialMasterBase, self).from_hdf(hdf=hdf, group_name=group_name)
with self.project_hdf5.open("input") as hdf5_input:
self._input.from_hdf(hdf5_input)
convergence_goal_str = hdf5_input["convergence_goal"]
if convergence_goal_str == "None":
self._convergence_goal = None
else:
self._convergence_goal_str = convergence_goal_str
self._convergence_goal = get_function_from_string(convergence_goal_str)
self._convergence_goal_qwargs = hdf5_input["convergence_goal_qwargs"]
def from_hdf(self, hdf=None, group_name=None):
"""
Restore the ParameterMaster from an HDF5 file
Args:
hdf (ProjectHDFio): HDF5 group object - optional
group_name (str): HDF5 subgroup name - optional
"""
super(MapMaster, self).from_hdf(hdf=hdf, group_name=group_name)
with self.project_hdf5.open("input") as hdf5_input:
if "structures" in hdf5_input.list_groups():
with hdf5_input.open("structures") as hdf5_input_str:
self.parameter_list = [
Atoms().from_hdf(hdf5_input_str, group_name)
for group_name in sorted(hdf5_input_str.list_groups())
]
else:
self.parameter_list = hdf5_input["parameters_list"]
function_str = hdf5_input["map_function"]
if function_str == "None":
self._map_function = None
else:
self._map_function = get_function_from_string(function_str)