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 filter_unapplied_profiles(csv_data):
""" Filters unapplied profiles from csv_data. """
profile_tables = csv_tablenames('profiles')
element_tables = list(
pd.Series(profile_tables).str.split("Profile", expand=True)[0])
for prof_tab, elm_tab in zip(profile_tables, element_tables):
applied_profiles = list(csv_data[elm_tab].profile.dropna().unique())
if elm_tab == "Load" and len(applied_profiles):
applied_profiles_p = pd.Series(applied_profiles) + "_pload"
applied_profiles_q = pd.Series(applied_profiles) + "_qload"
applied_profiles = list(applied_profiles_p) + list(
applied_profiles_q)
applied_profiles.append("time")
unapplied_profiles = csv_data[prof_tab].columns.difference(
applied_profiles)
logger.debug("These %ss are dropped: " % prof_tab +
str(unapplied_profiles))
csv_data[prof_tab].drop(unapplied_profiles, axis=1, inplace=True)