def _get_extracted_csv_table(relevant_subnets, tablename, input_path, sep=";"):
""" Returns extracted csv data of the requested SimBench grid. """
csv_table = read_csv_data(input_path, sep=sep, tablename=tablename)
if tablename == "Switch":
node_table = read_csv_data(input_path, sep=sep, tablename="Node")
bus_bus_switches = set(get_bus_bus_switch_indices_from_csv(csv_table, node_table))
else:
bus_bus_switches = {}
extracted_csv_table = _extract_csv_table_by_subnet(csv_table, tablename, relevant_subnets,
bus_bus_switches=bus_bus_switches)
return extracted_csv_table
def get_all_simbench_profiles(scenario, input_path=None, sep=";"):
"""
Returns a dict of DataFrames with all simbench profiles of the given scenario.
These include all profiles with the net data received by
get_simbench_net("1-complete_data-mixed-all-%s-sw" % str(scenario)).
INPUT:
**scenario** (int) - defines to which scenario the requested profiles belong to. Should be
within [0, 1, 2].
OPTIONAL:
**input_path** (path) - option to change the path to all simbench grid csv files. However,
a change should not be necessary.
**sep** (str, ";") - seperator of the csv files which contain the profiles information.
OUTPUT:
**profiles** (dict) - dict of DataFrames with all simbench profiles of the given scenario
"""
input_path = input_path if input_path is not None else complete_data_path(scenario)
csvtablenames = csv_tablenames(['profiles'])
# read all profiles data
profiles = read_csv_data(input_path, sep=sep, tablename=csvtablenames)
# rename csv_tablenames by pandapower element names
for csv_name, pp_name in zip(csvtablenames, pp_profile_names()):
profiles[pp_name] = profiles.pop(csv_name)
return profiles
def get_extracted_csv_data(relevant_subnets, input_path, sep=";", **kwargs):
""" Returns extracted csv data of the requested SimBench grid
(per default from all SimBench grids csv data).
**kwargs are ignored.
"""
# --- import input data
if 'complete_data' in relevant_subnets[0]: # return complete data
return read_csv_data(input_path, sep=sep)
else:
csv_data = dict()
for tablename in csv_tablenames(['elements', 'profiles', 'types', 'cases']):
csv_data[tablename] = _get_extracted_csv_table(relevant_subnets, tablename,
input_path=input_path)
return csv_data
def csv_data_to_test_extracting():
test_network_path = os.path.join(sb_dir, "test", "converter", "test_network")
csv_data = sb.read_csv_data(test_network_path, sep=";")
tested_tables = ["Node", "Load", "ExternalNet", "Switch", "Coordinates", "Measurement"]
csv_data = {tt: csv_data[tt] for tt in tested_tables}
csv_data["Node"]["subnet"] = ["EHV1_Feeder1"]*2 + ["EHV2"]*2 + ["EHV1_HV1"]*3 + [
"HV1_Feder%i" % i for i in range(4)] + ["HV1_MV3.101"]*2 + ["MV3.101"] + ["EHV1_Feeder1"]*2
csv_data["Load"]["subnet"] = ["EHV1_Load1", "EHV2_Load6", "EHV1_HV1_eq", "EHV1_HV1_load",
"HV1_MV3.101_eq", "MV3.101_HV1_eq"]
csv_data["ExternalNet"]["subnet"] = ["EHV1_boundary", "HV1_EHV1_eq", "MV3.101_HV1_eq",
"MV3.101_bound", "EHV1_HV1_eq"]
csv_data["Switch"].loc[3] = csv_data["Switch"].loc[0]
csv_data["Switch"].loc[3, ["id", "nodeA", "nodeB"]] = ["BusBus2", "Bus 2", "Bus 4"]
csv_data["Switch"]["subnet"] = ["EHV1_HV1", "EHV1_HV1", "HV1_MV3.101", "HV1_MV3.101",
"EHV1_HV1"]
csv_data["Coordinates"]["subnet"] = list(csv_data["Node"]["subnet"][1:12]) + ["MV3.101"]
csv_data["Measurement"] = pd.concat([csv_data["Measurement"], csv_data["Measurement"],
csv_data["Measurement"]], ignore_index=True)
csv_data["Measurement"]["name"] = ["Messung %i" % i for i in range(1, 7)]
csv_data["Measurement"]["subnet"] = ["HV1", "HV1_MV3.101", "EHV1_HV1"]*2
return csv_data
def test_aux_nodes_without_multiple_connected_branches():
""" Test csv_data if there are auxiliary nodes with more than one connected branch element. """
for scenario in range(3):
csv_data = sb.read_csv_data(sb.complete_data_path(scenario), sep=";")
annwdb = aux_node_names_with_dupl_branches(csv_data)
assert not len(annwdb)