def get_dataframe(self,
element_class,
prop,
element_name,
real_only=False,
**kwargs):
"""Return the dataframe for an element.
Parameters
----------
element_class : str
prop : str
element_name : str
real_only : bool
If dtype of any column is complex, drop the imaginary component.
kwargs : **kwargs
Filter on options. Option values can be strings or regular expressions.
Returns
-------
pd.DataFrame
Raises
------
InvalidParameter
Raised if the element is not stored.
"""
if element_name not in self._elem_props:
raise InvalidParameter(f"element {element_name} is not stored")
elem_group = self._group[element_class][element_name]
dataset = elem_group[prop]
df = DatasetBuffer.to_dataframe(dataset)
if kwargs:
options = self._check_options(element_class, prop, **kwargs)
columns = ValueStorageBase.get_columns(df, element_name, options,
**kwargs)
df = df[columns]
if self._data_format_version == "1.0.0":
dataset_property_type = DatasetPropertyType.ELEMENT_PROPERTY
else:
dataset_property_type = get_dataset_property_type(dataset)
if dataset_property_type == DatasetPropertyType.FILTERED:
timestamp_path = get_timestamp_path(dataset)
timestamp_dataset = self._hdf_store[timestamp_path]
df["Timestamp"] = DatasetBuffer.to_datetime(timestamp_dataset)
df.set_index("Timestamp", inplace=True)
else:
self._add_indices_to_dataframe(df)
if real_only:
for column in df.columns:
if df[column].dtype == np.complex:
df[column] = [x.real for x in df[column]]
return df
def _finalize_dataframe(self, df, dataset, real_only=False, abs_val=False):
if df.empty:
return
dataset_property_type = get_dataset_property_type(dataset)
if dataset_property_type == DatasetPropertyType.FILTERED:
time_step_path = get_time_step_path(dataset)
time_step_dataset = self._hdf_store[time_step_path]
df["TimeStep"] = DatasetBuffer.to_datetime(time_step_dataset)
df.set_index("TimeStep", inplace=True)
else:
self._add_indices_to_dataframe(df)
if real_only:
for column in df.columns:
if df[column].dtype == complex:
df[column] = np.real(df[column])
elif abs_val:
for column in df.columns:
if df[column].dtype == complex:
df[column] = df[column].apply(np.absolute)