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 get_option_values(self, element_class, prop, element_name):
"""Return the option values for the element property.
element_class : str
prop : str
element_name : str
Returns
-------
list
"""
df = self.get_dataframe(element_class, prop, element_name)
return ValueStorageBase.get_option_values(df, element_name)
def get_full_dataframe(self,
element_class,
prop,
real_only=False,
abs_val=False,
**kwargs):
"""Return a dataframe containing all data. The dataframe is copied.
Parameters
----------
element_class : str
prop : str
real_only : bool
If dtype of any column is complex, drop the imaginary component.
abs_val : bool
If dtype of any column is complex, compute its absolute value.
kwargs
Filter on options; values can be strings or regular expressions.
Returns
-------
pd.DataFrame
"""
if prop not in self.list_element_properties(element_class):
raise InvalidParameter(f"property {prop} is not stored")
dataset = self._group[f"{element_class}/ElementProperties/{prop}"]
df = DatasetBuffer.to_dataframe(dataset)
if kwargs:
options = self._check_options(element_class, prop, **kwargs)
names = self._elems_by_class.get(element_class, set())
columns = ValueStorageBase.get_columns(df, names, options,
**kwargs)
columns = list(columns)
columns.sort()
df = df[columns]
self._finalize_dataframe(df,
dataset,
real_only=real_only,
abs_val=abs_val)
return df
def _get_elem_prop_dataframe(self,
elem_class,
prop,
name,
dataset,
real_only=False,
abs_val=False,
**kwargs):
col_range = self._get_element_column_range(elem_class, prop, name)
df = DatasetBuffer.to_dataframe(dataset, column_range=col_range)
if kwargs:
options = self._check_options(elem_class, prop, **kwargs)
columns = ValueStorageBase.get_columns(df, name, options, **kwargs)
df = df[columns]
self._finalize_dataframe(df,
dataset,
real_only=real_only,
abs_val=abs_val)
return df