def _get_parameters_to_rename_and_multiply():
""" Returns a dict of tuples and a dict of dataframes where csv column names are assigned to
pandapower columns names which differ. """
# --- create dummy_net to get pp columns
dummy_net = pp.create_empty_network()
_extend_pandapower_net_columns(dummy_net)
_prepare_res_bus_table(dummy_net)
for elm in ["dcline"]:
dummy_net[elm].rename(columns={"type": "std_type"}, inplace=True)
for elm in ["gen", "sgen"]:
dummy_net[elm]["vm_pu"] = np.nan
dummy_net[elm]["va_degree"] = np.nan
# --- get corresponding tables and dataframes
corr_strings = _csv_table_pp_dataframe_correspondings(str)
csv_tablenames_, pp_dfnames = _csv_table_pp_dataframe_correspondings(list)
# --- initialize tuples_dict
tuples_dict = dict.fromkeys(corr_strings, [("id", "name", None)])
tuples_dict['NodePFResult*res_bus'] = []
# --- determine tuples_dict
for corr_str, csv_tablename, pp_dfname in zip(corr_strings,
csv_tablenames_, pp_dfnames):
# adapt tuples_dict initialization of Type tables
if "Type" in csv_tablename:
tuples_dict[corr_str] = [("id", "std_type", None)]
# get all column correspodings
corr_col_tuples = _csv_pp_column_correspondings(csv_tablename)
# get csv and pp columns
csv_columns = get_columns(csv_tablename)
pp_columns = dummy_net[pp_dfname].columns if "std_types" not in pp_dfname else \
pd.DataFrame(dummy_net["std_types"][pp_dfname[10:]]).T.columns
# determine tuples_dict: all tuples which are in columns of both, csv and pp
tuples_dict[corr_str] = tuples_dict[corr_str] + [
corr_col_tuple
for corr_col_tuple in corr_col_tuples if corr_col_tuple[0] in
csv_columns and corr_col_tuple[1] in pp_columns
]
# unused:
# dfs_dict = {key: pd.DataFrame(data, columns=["csv_col", "pp_col", "factor"]) for key, data in
# tuples_dict.items()}
return tuples_dict
def _copy_data(input_data, output_data):
""" Copies the data from output_data[corr_strings] into input_data[element_table]. This function
handles that some corr_strings are not in output_data.keys() and copies all columns which
exists in both, output_data[corr_strings] and input_data[element_table]. """
corr_strings = _csv_table_pp_dataframe_correspondings(str)
output_names = _csv_table_pp_dataframe_correspondings(list)[int(
_is_pp_type(output_data))]
for corr_str, output_name in zip(corr_strings, output_names):
if corr_str in input_data.keys() and input_data[corr_str].shape[0]:
cols_to_copy = list(
set(output_data[output_name].columns)
& set(input_data[corr_str].columns))
if version.parse(pd.__version__) >= version.parse("0.23.0"):
output_data[output_name] = pd.concat(
[
output_data[output_name],
input_data[corr_str][cols_to_copy]
],
ignore_index=True,
sort=False).reindex(
columns=output_data[output_name].columns)
elif version.parse(pd.__version__) >= version.parse("0.21.0"):
output_data[output_name] = pd.concat(
[
output_data[output_name],
input_data[corr_str][cols_to_copy]
],
ignore_index=True).reindex(
columns=output_data[output_name].columns)
else:
output_data[output_name] = pd.concat(
[
output_data[output_name],
input_data[corr_str][cols_to_copy]
],
ignore_index=True).reindex_axis(
output_data[output_name].columns, axis=1)
if "std_types" in corr_str and _is_pp_type(output_data):
output_data[output_name].index = input_data[corr_str][
"std_type"]
_inscribe_fix_values(output_data, output_name)
def _rename_and_split_input_tables(data):
""" Splits the tables of ExternalNet, PowerPlant, RES, ext_grid, gen, sgen and name the df
according to _csv_table_pp_dataframe_correspondings(). """
# --- initilizations
split_gen = ["ext_grid", "gen", "sgen"] if _is_pp_type(data) else [
"ExternalNet", "PowerPlant", "RES"
]
split_gen_col = "phys_type" if _is_pp_type(data) else "calc_type"
split_Line = [] if _is_pp_type(data) else ["Line"]
split_ppelm_into_type_and_elm = ["dcline"] if _is_pp_type(data) else []
input_elm_col = "pp" if _is_pp_type(data) else "csv"
output_elm_col = "csv" if _is_pp_type(data) else "pp"
corr_df = _csv_table_pp_dataframe_correspondings(pd.DataFrame)
corr_df["comb_str"] = corr_df["csv"] + "*" + corr_df["pp"]
# all elements, which need to be converted to multiple output element tables, (dupl) need to be
# treated specially, thus must be in split lists
dupl = corr_df[input_elm_col][corr_df[input_elm_col].duplicated()]
assert dupl.isin(split_gen + split_Line +
split_ppelm_into_type_and_elm).all()
# -- start renaming and in case of dupl also splitting
for idx in corr_df.index:
# get actual element tablenames
input_elm = corr_df.loc[idx, input_elm_col]
output_elm = corr_df.loc[idx, output_elm_col]
corr_str = corr_df.loc[idx, "comb_str"]
if input_elm in split_gen:
data[corr_str] = data[input_elm][data[input_elm][split_gen_col] ==
_get_split_gen_val(output_elm)]
elif input_elm in split_Line:
continue # already done in _csv_types_to_pp1()
elif input_elm in split_ppelm_into_type_and_elm:
if "Type" in output_elm:
is_uniq = ~data[input_elm].std_type.duplicated()
data[corr_str] = data[input_elm].loc[is_uniq]
else:
data[corr_str] = data[input_elm]
else: # rename data.keys for all elements without special treatment
if corr_str not in data.keys():
if input_elm in data.keys():
data[corr_str] = data.pop(input_elm)
elif _is_pp_type(data) and "std_types" in input_elm:
data[corr_str] = pd.DataFrame(
data["std_types"][input_elm[10:]])
else:
data[corr_str] = pd.DataFrame()
def _replace_name_index(data):
""" While the simbench csv format assigns connected nodes via names, pandapower assigns via
indices. This function replaces the assignment of the input data. """
node_names = {"node", "nodeA", "nodeB", "nodeHV", "nodeMV", "nodeLV"}
bus_names = {"bus", "from_bus", "to_bus", "hv_bus", "mv_bus", "lv_bus"}
corr_strings = _csv_table_pp_dataframe_correspondings(str)
corr_strings.remove(
"Measurement*measurement") # already done in convert_measurement()
if _is_pp_type(data):
for corr_str in corr_strings:
for col in node_names & set(data[corr_str].columns):
data[corr_str][col] = data["Node*bus"]["id"].loc[data[corr_str]
[col]].values
else:
for corr_str in corr_strings:
for col in bus_names & set(data[corr_str].columns):
# each bus name must be unique
data[corr_str][col] = data["Node*bus"].index[idx_in_2nd_array(
data[corr_str][col].values,
data["Node*bus"]["name"].values)]